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Reflow soldering

Reflow soldering is a process in which a solder paste (a sticky mixture of powdered solder and flux) is used to temporarily attach one or thousands of tiny electrical components to their contact pads, after which the entire assembly is subjected to controlled heat. The solder paste reflows in a molten state, creating permanent solder joints. Heating may be accomplished by passing the assembly through a reflow oven, under an infrared lamp, or (unconventionally) by soldering individual joints with a desoldering hot air pencil.

Reflow soldering with long industrial convection ovens is the preferred method of soldering surface mount technology components or SMT to a printed circuit board or PCB. Each segment of the oven has a regulated temperature, according to the specific thermal requirements of each assembly. Reflow ovens meant specifically for the soldering of surface mount components may also be used for through-hole components by filling the holes with solder paste and inserting the component leads through the paste. Wave soldering however, has been the common method of soldering multi-leaded through-hole components onto a circuit board designed for surface-mount components.

When used on boards containing a mix of SMT and plated through-hole (PTH) components, through-hole reflow, when achievable by specifically modified paste stencils, may allow for the wave soldering step to be eliminated from the assembly process, potentially reducing assembly costs. While this may be said of lead-tin solder pastes used previously, lead-free solder alloys such as SAC present a challenge in terms of the limits of oven temperature profile adjustment and requirements of specialized through-hole components that must be hand soldered with solder wire or cannot reasonably withstand the high temperatures directed at circuit boards as they travel on the conveyor of the reflow oven. The reflow soldering of through-hole components using solder paste in a convection oven process is called intrusive soldering.

The goal of the reflow process is for the solder paste to reach the eutectic temperature at which the particular solder alloy undergoes a phase change to a liquid or molten state. At this specific temperature range, the molten alloy demonstrates properties of adhesion. Molten solder alloy behaves much as water, with properties of cohesion and adhesion. With sufficient flux, in the state of liquidus, molten solder alloys will exhibit a characteristic called "wetting."

Wetting is a property of the alloy when within its specific eutectic temperature range. Wetting is a necessary condition for the formation of solder joints that meet the criteria as "acceptable" or "target" conditions, while "non-conforming" is considered defective according to IPC.

The reflow oven temperature profile is suited for characteristics of a particular circuit board assembly, the size and depth of the ground plane layer within the board, the number of layers within the board, the number and size of the components, for example. The temperature profile for a particular circuit board will allow for reflow of solder onto the adjoining surfaces, without overheating and damaging the electrical components beyond their temperature tolerance. In the conventional reflow soldering process, there are usually four stages, called "zones", each having a distinct thermal profile: preheat, thermal soak (often shortened to just soak), reflow, and cooling.

Preheat zone[edit]

Preheat is the first stage of the reflow process. During this reflow phase, the entire board assembly climbs towards a target soak or dwell temperature. The main goal of the preheat phase is to get the entire assembly safely and consistently to a soak or pre-reflow temperature. Preheat is also an opportunity for volatile solvents in the solder paste to outgas. For paste solvents to be properly expelled and the assembly to safely reach pre-reflow temperatures the PCB must be heated in a consistent, linear manner. An important metric for the first phase of the reflow process is the temperature slope rate or rise vs time. This is often measured in degrees Celsius per second, C/s. Many variables factor into a manufacturer's target slope rate. These include: target processing time, solder paste volatility, and component considerations. It is important to account for all these process variables, but in most cases sensitive component considerations are paramount. “Many components will crack if their temperature is changed too quickly. The maximum rate of thermal change that the most sensitive components can withstand becomes the maximum allowable slope”. However, if thermally sensitive components are not in use and maximizing throughput is of great concern, aggressive slope rates may be tailored to improve processing time. For this reason, many manufacturers push these slope rates up to the maximum common allowable rate of 3.0°C/Second. Conversely, if a solder paste containing particularly strong solvents is being used, heating the assembly too fast can easily create an out of control process. As the volatile solvents outgas they may splatter solder off the pads and onto the board. Solder-balling is the main concern of violent outgassing during the preheat phase. Once a board has been ramped up to temperature in the preheat phase it is time to enter the soak or pre-reflow phase.

Thermal soak zone[edit]

The second section, thermal soak, is typically a 60 to 120 second exposure for removal of solder paste volatiles and activation of the fluxes, where the flux components begin oxide reduction on component leads and pads. Too high a temperature can lead to solder spattering or balling as well as oxidation of the paste, the attachment pads and the component terminations. Similarly, fluxes may not fully activate if the temperature is too low. At the end of the soak zone a thermal equilibrium of the entire assembly is desired just before the reflow zone. A soak profile is suggested to decrease any delta T between components of varying sizes or if the PCB assembly is very large. A soak profile is also recommended to diminish voiding in area array type packages.[1]

Reflow zone[edit]

The third section, the reflow zone, is also referred to as the “time above reflow” or “temperature above liquidus” (TAL), and is the part of the process where the maximum temperature is reached. An important consideration is peak temperature, which is the maximum allowable temperature of the entire process. A common peak temperature is 20–40 °C above liquidus.[1] This limit is determined by the component on the assembly with the lowest tolerance for high temperatures (the component most susceptible to thermal damage). A standard guideline is to subtract 5 °C from the maximum temperature that the most vulnerable component can sustain to arrive at the maximum temperature for process. It is important to monitor the process temperature to keep it from exceeding this limit. Additionally, high temperatures (beyond 260 °C) may cause damage to the internal dies of SMT components as well as foster intermetallic growth. Conversely, a temperature that isn’t hot enough may prevent the paste from reflowing adequately.

An example of a commercial reflow oven.

[2]

Example of a modern thermal profiler

Time above liquidus (TAL), or time above reflow, measures how long the solder is a liquid. The flux reduces surface tension at the juncture of the metals to accomplish metallurgical bonding, allowing the individual solder powder spheres to combine. If the profile time exceeds the manufacturer’s specification, the result may be premature flux activation or consumption, effectively “drying” the paste before formation of the solder joint. An insufficient time/temperature relationship causes a decrease in the flux’s cleaning action, resulting in poor wetting, inadequate removal of the solvent and flux, and possibly defective solder joints. Experts usually recommend the shortest TAL possible, however, most pastes specify a minimum TAL of 30 seconds, although there appears to be no clear reason for that specific time. One possibility is that there are places on the PCB that are not measured during profiling, and therefore, setting the minimum allowable time to 30 seconds reduces the chances of an unmeasured area not reflowing. A high minimum reflow time also provides a margin of safety against oven temperature changes. The wetting time ideally stays below 60 seconds above liquidus. Additional time above liquidus may cause excessive intermetallic growth, which can lead to joint brittleness. The board and components may also be damaged at extended temperature over liquidus, and most components have a well-defined time limit for how long they may be exposed to temperatures over a given maximum. Too little time above liquidus may trap solvents and flux and create the potential for cold or dull joints as well as solder voids.

Cooling zone[edit]

The last zone is a cooling zone to gradually cool the processed board and solidify the solder joints. Proper cooling inhibits excess intermetallic formation or thermal shock to the components. Typical temperatures in the cooling zone range from 30–100 °C (86–212 °F). A fast cooling rate is chosen to create a fine grain structure that is most mechanically sound.[1] Unlike the maximum ramp-up rate, the ramp–down rate is often ignored. It may be that the ramp rate is less critical above certain temperatures, however, the maximum allowable slope for any component should apply whether the component is heating up or cooling down. A cooling rate of 4°C/s is commonly suggested. It is a parameter to consider when analyzing process results.

Etymology[edit]

The term "reflow" is used to refer to the temperature above which a solid mass of solder alloy is certain to melt (as opposed to merely soften). If cooled below this temperature, the solder will not flow. Warmed above it once more, the solder will flow again—hence "re-flow".

Modern circuit assembly techniques that use reflow soldering do not necessarily allow the solder to flow more than once. They guarantee that the granulated solder contained in the solder paste surpasses the reflow temperature of the solder involved.

Thermal profiling[edit]

Thermal profiling is the act of measuring several points on a circuit board to determine the thermal excursion it takes through the soldering process. In the electronics manufacturing industry, SPC (Statistical Process Control) helps determine if the process is in control, measured against the reflow parameters defined by the soldering technologies and component requirements. [3][4] Modern software tools allow a profile to be captured, then automatically optimized using a mathematical simulation, which greatly reduces the time needed to establish optimal settings for the process. [5]

See also[edit]

References[edit]

Источник: https://en.wikipedia.org/wiki/Reflow_soldering

LncRNA MALAT1 promotes gastric cancer progression via inhibiting autophagic flux and inducing fibroblast activation

Abstract

Autophagy defection contributes to inflammation dysregulation, which plays an important role in gastric cancer (GC) progression. Various studies have demonstrated that long noncoding RNA could function as novel regulators of autophagy. Previously, long noncoding RNA MALAT1 was reported upregulated in GC cells and could positively regulate autophagy in various cancers. Here, we for the first time found that MALAT1 could promote interleukin-6 (IL-6) secretion in GC cells by blocking autophagic flux. Moreover, IL-6 induced by MALAT1 could activate normal to cancer-associated fibroblast conversion. The interaction between GC cells and cancer-associated fibroblasts in the tumour microenvironment could facilitate cancer progression. Mechanistically, MALAT1 overexpression destabilized the PTEN mRNA in GC cells by competitively interacting with the RNA-binding protein ELAVL1 to activate the AKT/mTOR pathway for impairing autophagic flux. As a consequence of autophagy inhibition, SQSTM1 accumulation promotes NF-κB translocation to elevate IL-6 expression. Overall, these results demonstrated that intercellular interaction between GC cells and fibroblasts was mediated by autophagy inhibition caused by increased MALAT1 that promotes GC progression, providing novel prevention and therapeutic strategies for GC.

Introduction

Inflammatory mediators within the tumour microenvironment (TME) play important roles in promoting gastric cancer (GC) progression. The various cytokines within the GC TME are secreted from inflammatory cells, fibroblasts, and GC cells1. Moreover, GC cells could receive extracellular signals, which could further modulate TME via paracrine secretion of cytokine. The cross-talk between GC cells and stroma cells facilitate cancer progression. Cancer-associated fibroblasts (CAFs), a major component of the tumour stroma, are a critical source of various molecules secreted in TME, which stimulate cancer cells progression. Similarly, the fluctuation of inflammatory mediators (growth factors, interleukin) by cancer cells in TME also altered resident fibroblast phenotypes and lead to normal fibroblast (NF) activation, considered as the main CAF source2,3. Increasing evidence demonstrated interleukin-6 (IL-6) was abundant in GC TME, facilitating GC progression4. Most studies have reported that IL-6 could be released from CAFs and promote GC cells proliferation or metastasis in a paracrine way. However, IL-6 secretion from GC and its effect on modulating TME has not been studied in detail. Here, we have found that autophagy inhibition in GC could upregulate IL-6 expression and secretion.

Autophagy is an important biological process that appears to be a double-edged sword with respect to cytokine signalling and modulating tumour progression in certain instances5. Activated autophagy could protect cells from inflammatory damage by inhibiting autophagy and aggravating inflammatory responses in many tissues5,6. It is widely accepted that autophagy defects contribute to inflammation and autophagy inhibition under the condition of chronic inflammation devoted to oncogenesis7,8. Several reports have suggested that long noncoding RNA (lncRNA) could function as novel autophagy regulators. Silencing lncRNA-FA2H-2 facilitates impairment of oxidized low-density lipoprotein-induced autophagy flux to activate inflammation for increased IL-6 and other cytokine production9. Defective autophagy increases inflammatory mediator (such as TNF-α and HGF) production to promote hepatocellular carcinoma7. Impaired autophagy could promote chemoresistance in GC via lncRNA ARHGAP5-AS1 accumulation10. Although recent studies have shed light on some autophagy impairment mechanisms in GC, the molecular components that mediate the process are yet to be fully identified.

LncRNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) has been reported to activate autophagy in pancreatic ductal adenocarcinoma11, retinoblastoma12, and multiple myeloma13 to promote tumour progression. However, the effect of MALAT1 on autophagy in GC has not well reported. In this study, we found that MALAT1 upregulation in GC could inhibit autophagic flux, which led to sequestosome1(SQSTM1) protein accumulation and IL-6 overexpression. SQSTM1 is a scaffold and stress-inducible protein with multiple domains (such as ZZ, LIR, and PBI), which not only acts as an indicator of autophagy flux but also mediates inflammation response14. Hence, SQSTM1 protein accumulation might be responsible for IL-6 overexpression in GC cells. The majority of studies have revealed the effect of CAFs on GC cell growth or metastasis within the interaction between CAFs and cancer cells. However, the influence on CAFs exerted by GC cells has not been studied in detail. Autophagy inhibition in cancer cells led to the expansion and release of cytokines. The dysregulated cytokines could activate the transition from NFs to CAFs via paracrine signalling15. Here, we found that impairment of autophagy caused by increased MALAT1 could activate NF to CAF conversion through expansion and secretion of IL-6. These data suggest a critical role for MALAT1 in the interaction between CAFs and GCs cells. Furthermore, the underlying mechanisms were investigated to identify potential therapeutic strategies targeting GC.

Results

MALAT1 blocks autophagic flux in GC cells

A previous study documented that MALAT1 could function as an oncogene to promote GC cell proliferation and positively correlated with TNM stages in GC. However, the effect of aberrant MALAT1 expression on autophagic flux in GC was rarely investigated. Anomalous autophagy activity led to a variation of inflammation process16. Here, we found that MALAT1 overexpression (Supplementary Fig. 1A) could enhance LC3-1 conversion to LC3-II and SQSTM1 protein accumulation in both MKN-45 and MGC-803 cells. (Fig. 1A, P < 0.05). Contrarily, silencing MALAT1 by transducing siMALAT1 (Supplementary Fig. 1B) could inhibit LC3-II and SQSTM1 accumulation (Fig. 1A, P < 0.05). Furthermore, MALAT1 had no influence on the expression of SQSTM1 mRNA level (Supplementary Fig. 1C). LC3-II cloud accumulation results from autophagy activation or reduced turnover from autophagosome to autolysosomes. Moreover, the accumulation of SQSTM1 was an indicator of autophagy impairment. Therefore, autophagy inhibitors, 3-methyladenine (3-MA) and bafilomycin A1 (BafA1) were used to treat cells to block autophagy initiation and maturation, respectively. MALAT1 effect on LC3-II and SQSTM1 accumulation were not compromised by 3-MA treatment (Fig. 1B, P < 0.05). In contrast, LC3-II and SQSTM1 accumulation was not affected by BafA1 treatment in the MALAT1 overexpression group compared to the negative group (Fig. 1C, P < 0.05). Subsequently, an mRFP-GFP-LC3 lentivirus vector was introduced to determine the MALAT1 effect on autophagy flux. When autolysosomes formed, green fluorescence faded, leaving only the RFP signal as the RFP signal is more stable than green fluorescence in acidic conditions. MALAT1 overexpression in MKN-45 and MGC-803 led to yellow puncta enrichment rather than red ones, indicating autolysosome maturation blockage (Fig. 1D, E, P < 0.05). In addition, we used TEM to evaluate autophagosomes and found out that the number of autophagic vesicles increased in MKN-45/MALAT1 and MGC-803/MALAT1 cells compared to that in control cells (Fig. 1F, G). Taken together, these results suggested that increased MATLA1 in GC cells could impair autophagy flux.

A The LC3 and SQSTM1 protein levels in MKN-45/MALAT1, MGC-803/MALAT1, and their parental cells were determined by western blot assay; B, C The LC3 and SQSTM1 protein levels in MKN-45/MALAT1 and MGC-803/MALAT1 cells with 3-MA (10 mM) and baf-A1 (10 mM) were determined by western blot assay; D, E mRFP-GFP-LC3 distribution in MKN-45/MALAT1, MGC-803/MALAT1, and their parental cells were analysed by fluorescence microscopy (MKN-45/MALAT1 vs MKN-45/NC: 50.3 ± 4.5 vs 11.3 ± 2.6; MGC-803/MALAT1 vs MGC-803/NC: 28.3 ± 6.2 vs 2.6 ± 1.6, P < 0.01); F, G The number of autophagic vesicles was increased in MKN-45/MALAT1 and MGC-803/MALAT1 group as seen by TEM.

Full size image

MALAT1 activates AKT/mTOR pathway to inhibit autophagy in GC cells

Activation of mTOR is crucial to inhibit autophagy flux, which led to substantial autophagosome–lysosome fusion inhibition and lysosome dysfunction so that autophagy degradation was impaired17,18. Hence, both phosphorylated-mTOR (p-mTOR) and its key substrate, phosphorylated-p70 S6 kinase (p-p70S6K), were detected to assess the MALAT1 effect on mTOR pathway activation. As shown in Fig. 2A, a significant p-mTOR and p-p70S6K level increase was observed in MKN-45 and MGC-803 cells transfected with MALAT1 overexpression vectors. Silencing MALAT1 resulted in p-mTOR and p-p70S6K level reduction, which indicated that MALAT1 could activate the mTOR pathway (Fig. 2A, P < 0.05). Autophagy flux impaired by MALAT1 in GC led to SQSTM1 accumulation and rapamycin was used to inhibit mTOR activation to better understand whether MALAT1 impaired autophagy degradation to elevate SQSTM1 accumulation via the mTOR pathway. Rapamycin could promote SQSTM1 reduction through silencing and reversing mTOR activation induced by MALAT1 in MKN-45 and MGC-803 cells (Fig. 2B, P < 0.05). Since the canonical PTEN/AKT pathway could regulate mTOR activity, phosphorylated AKT and PTEN expressions were also detected. MALAT1 overexpression could obviously downregulate PTEN protein level and upregulate phosphorylated AKT levels in MKN-45 and MGC-803 cells (Fig. 2C, P < 0.05). In addition, we found that MALAT1 not only inhibited PTEN protein level but also negatively regulated PTEN mRNA expression in MKN-45 and MGC-803 cells (Fig. 2D, E, P < 0.05). Analysis of the GSE dataset (GSE26942) also indicated a strong negative correlation between MALAT1 and PTEN mRNA (Fig. 2F, P < 0.05). Furthermore, GESA dataset analysis was also performed to suggest that autophagy was negatively associated with MALAT1 expression (Fig. 2G, NES =−1.459, FDR q-value = 0.06). Taken together, increased MALAT1 could negatively regulate PTEN expression to activate AKT/mTOR pathway, thus impairing autophagy flux and further elevating SQSTM1 accumulation in GC cells.

A The p-mTOR and p-p70S6K protein levels were increased in MKN-45 and MGC-803 cells transfected with MALAT1 overexpression vectors. Silencing MALAT1 resulted in p-mTOR and p-p70S6K level reduction; B The p-mTOR, p-p70S6K, and SQSTM1 protein levels were detected in MKN-45/MALAT1 and MGC-803/MALAT1 in presence of rapamycin; C The p-AKT and PTEN protein levels were detected in MKN-45 and MGC-803 cells transfected with MALAT1 overexpression vectors. Silencing MALAT1 resulted in PTEN overexpression and p-AKT downregulation; D, E The PTEN mRNA levels were detected in MKN-45/MALAT1 and MGC-803/MALAT1 cells. Silencing MALAT1 led to PTEN mRNA upregulation (MKN-45/MALAT1 vs MKN-45/NC: 0.66 ± 0.03 vs 1, MGC-803/MALAT1 vs MGC-803/NC: 0.53 ± 0.04 vs 1, P < 0.01; MKN-45/siMALAT1 vs MKN-45/siNC: 1.27 ± 0.04 vs 1 ± 0.01, MGC-803/siMALAT1 vs MGC-803/siNC: 1.53 ± 0.04 vs 1 ± 0.01, P < 0.01); F GEO dataset analysis showed a negative correlation between MALAT1 and PTEN. G GESA dataset analysis showed a negative correlation between MALAT1 and autophagy pathway (NES = −1.459, FDR q-value=0.06); Bars, S.D.; *P < 0.05; **P < 0.01; ***P < 0.001.

Full size image

MALAT1 inhibits PTEN expression at the post-transcriptional level

Although it has been demonstrated that MALAT1 could inhibit PTEN mRNA expression, the underlying mechanism has not been reported. Several studies have addressed the interaction between MALAT1 and the RNA binding protein (RBP) ELAVL1 to suppress target gene expression via modifying mRNA stability or mRNA initiation19. This inspired us to investigate whether MALAT1 regulates PTEN mRNA expression at the post-transcriptional level. The transcription inhibitor actinomycin D (Act D) was added in MKN-45 and MGC-803 cells transfected with or without MALAT1 plasmids for different times ranging from 0 to 6 h. The levels of remaining mRNAs were determined, and the PTEN mRNA half-lives decreased from 5.62 ± 0.21 to 1.54 ± 0.12 h and from 5.79 ± 0.18 to 2.47 ± 0.17 h (P < 0.01) in MKN-45 and MGC-803 cells, respectively, in response to increased MALAT1 (Fig. 3A, P < 0.01). AU-rich elements (AREs) usually exist in the 3′-UTR of mRNA, which could interact with RBPs to modulate mRNA stability. RBPmap database was used to analyse the ARE regions in PTEN 3′-UTR and predict the potential RBPs, which revealed that ARE regions were abundant in PTEN 3′-UTR and most possibly in ARE–ELAVL1 binding regions (Fig. 3B). Meanwhile, a significant positive correlation between ELAVL1 and PTEN mRNA expression was observed via analysing GEO datasets (GSE63048) (Fig. 3C, P < 0.001). Moreover, we found that ELAVL1 upregulation could increase PTEN mRNA expression in both MKN-45 and MGC-803 cells (Fig. 3D, P < 0.05). Similarly, after ELAVL1 overexpression, the PTEN mRNA half-lives increased from 6.16 ± 0.20 to 8.13 ± 0.28 h and from 5.29 ± 0.18 to 8.92 ± 0.35 h in MKN-45 and MGC-803 cells, respectively (Fig. 3E, P < 0.01). These results indicated that ELAVL1 could stabilize the PTEN mRNA. In addition, increased MALAT1 had no influence on ELAVL1 expression in GC cells (Supplementary Fig. 2A). Based on the current evidence on the opposite effects of MALAT1 and ELAVL1 on PTEN mRNA expression and the reported correlation between MALAT1 and ELAVL1, we assumed that increased MALAT1 could competitively interact with ELAVL1 to expose PTEN 3′-UTR such that PTEN mRNA destabilization was augmented. To determine the above assumption better, a rescue assay was carried out. As shown in Fig. 3F, ELAVL1-induced PTEN mRNA levels were significantly abolished by increased MALAT1 in both MKN-45 and MGC-803 cells (Fig. 3F, P < 0.05). Subsequently, RIP-PCR assay was performed to determine PTEN 3′-UTR enrichment bound by ELAVL1 with or without MALAT1 transfection in MGC-803 cells (Fig. 3G, H, P < 0.05) and MKN-45(Supplementary Fig. 2B, D, P < 0.05). These results showed that ELAVL1 could bind more MALAT1 mRNA fractions than PTEN 3′-UTR enrichments under MALAT1 overexpression condition. Additionally, we found that increased MALAT1 led to more ELAVL1 protein being distributed within the nucleus where MALAT1 was located (Fig. 3I), indicating that ELAVL1 nucleocytoplasmic shuttling was abrogated and resulted in PTEN mRNA destabilization. The combined data implied that MALAT1 could competitively interact with ELAVL1 to destabilize PTEN mRNA.

A PTEN mRNA expression in MKN-45 and MGC-803 transfected with MALAT1 overexpression vectors and the control after treatment with 5 μg/ml actinomycin D for 0, 2, 4, and 6 h. The PTEN transcript half-life was down-regulated by MALAT1; B Potential bindings sites of AU-rich elements on PTEN 3′-UTR; C GEO database analysis showed a positive correlation between ELAVL1 and PTEN; D ELAVL1 upregulation increase PTEN mRNA in MKN-45 and MGC-803 cells (MKN-45/ELAVL1 vs MKN-45/NC: 2.96 ± 0.18 vs 1 ± 0.01, MGC-803/ELAVL1 vs MGC-803/NC: 4.14 ± 0.35 vs 1 ± 0.01, P < 0.01); E PTEN mRNA expression in MKN-45 and MGC-803 transfected with ELAVL1 overexpression vectors and the control after treatment with 5 μg/ml actinomycin D for 0, 2, 4, and 6 h. The PTEN transcript half-life was upregulated by ELAVL1 (MKN-45/ELAVL1 vs MKN-45/MALAT1 + ELAVL1: 1 vs 3.2 ± 0.02, MGC-803/ELAVL1 vs MGC-803/MALAT1 + ELAVL1: 1 vs 1.80 ± 0.02, P < 0.05); F The remainder of ELAVL1-induced PTEN mRNA levels were abolished by increased MALAT1 in both MKN-45 and MGC-803 cells; G, H ELAVL1 captured more MALAT1 mRNA fractions than PTEN 3′-UTR enrichments under MALAT1 overexpression condition through performing RIP-PCR (MALAT1/Anti-ELAVL1 vs NC/Anti-ELAVL1: 4.0 ± 0.08 vs 2.13 ± 0.12 (MALAT1%), MALAT1/Anti-ELAVL1 vs NC/Anti-ELAVL1: 0.15 ± 0.04 vs 4.3 ± 0.21 (PTEN 3′-UTR%), P < 0.01); I MKN-45 and MGC-803 cells were transfected with MALAT1 overexpression vectors and the subcellular locations of HuR were determined by immunocytochemistry. Bars, SD; *P < 0.05; **P < 0.01; ***P < 0.001.

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Inhibition of autophagy promotes IL-6 secretion via accumulation of SQSTM1

Increasing evidence showed that autophagy flux inhibition aggravates the inflammatory response20. To investigate whether inhibition of autophagic flux inhibited by MALAT1 in GC cells could increase inflammatory cytokine release, a human cytokine antibody array was used to compare the conditioned media of MKN-45/MALAT1 and MGC-803/MALAT1 cells and those of MKN-45/NC and MGC-803/NC cells (Supplementary Table 1). The significant IL-6 increase was detected in cultured media (CM) of MKN-45/MALAT1 and MGC-803/MALAT1 cells (Fig. 4A), which was further confirmed by ELISA assay (Fig. 4B, P < 0.05). Next, we investigated the IL-6 expression in mRNA and protein level within GC cells transfected with MALAT1 plasmids, which demonstrated that increased MALAT1, could promote both IL-6 protein and mRNA expressions in MKN-45 and MGC-803 cells (Fig. 4C, D, P < 0.05). IL-6 expression is regulated by a wide range of transcription factors and NF-κB plays a crucial role. As expected, NF-κB activation and nuclear translocation were observed in MKN-45 and MGC-803 cells transfected with MALAT1 (Fig. 4E, F, P < 0.05). In addition, increased MALAT1 had no effect on SQSTM1 mRNA expression, which had been shown in the first part of the results. As mentioned above, increased MALAT1 impaired autophagic flux, resulting in elevated SQSTM1 accumulation within GC cells, with SQSTM1 being involved in both autophagy and inflammation response. Therefore, we assumed that MALAT1 might activate the NF-κB pathway to increase IL-6 expression via SQSTM1. SQSTM1 siRNA-treated MKN-45/MALAT1 and MGC-803/MALAT1 cells abrogated the enhanced phosphorylated-NF-κB and IL-6 expressions. Similarly, increased SQSTM1 via transfection with SQSTM1 plasmid could reverse the NF-κB/IL-6 inactivation pathway caused by MALAT1 siRNAs (Fig. 4G, P < 0.05). Taken together, increased MALAT1 could elevate SQSTM1 accumulation to activate NF-κB so that IL-6 expression could be increased.

A Human cytokine antibody arrays were used to screen the difference of conditioned medium between GC cells transfected with MALAT1 overexpression vectors and NC vectors; B IL-6 protein expression level in the MKN-45/MALAT1, MGC-803/MALAT1, and compared groups was quantified 24 h after changing the culture medium as measured by ELISA (MKN-45/MALAT1 vs MKN-45/NC: 39.24 ± 1.24 vs 28.62 ± 0.17; MGC-803/MALAT1 vs MGC-803/NC: 40.6 ± 0.47 vs 35.79 ± 0.08, P < 0.05); C, D The mRNA and protein levels of p-IL-6 were detected in MKN-45 and MGC-803 cells transfected with MALAT1 overexpression vectors. Silencing MALAT1 resulted in IL-6 protein level downregulation (MKN-45/MALAT1 vs MKN-45/NC: 1.61 ± 0.2 vs 1 ± 0.07; MGC-803/MALAT1 vs MGC-803/NC: 2.35 ± 0.2 vs 1 ± 0.07, P < 0.01); E The NF-κB and p-NF-κB protein levels were increased in MKN-45 and MGC-803 cells transfected with MALAT1 overexpression vectors. Silencing MALAT1 resulted in p-NF-κB level reduction; F MKN-45 and MGC-803 cells were transfected with MALAT1 overexpression vectors, and the NF-κB subcellular locations were determined by immunofluorescence assay; G P-NF-κB and IL-6 expressions were abrogated in MKN-45/MALAT1 and MGC-803/MALAT1 cells with SQSTM1 siRNA treatment. Transfected SQSTM1 plasmid into MKN-45 and MGC-803 cells reversed NF-κB/IL-6 pathway inactivation caused by MALAT1 siRNAs. Three biological replicates were performed for in vitro assays. Bars, S.D.; *P < 0.05; **P < 0.01; ***P < 0.001.

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Impairment of autophagy in GC cells facilitates the transition from NFs to CAFs

Existing evidence has revealed that inflammatory cytokines in TME can induce conversion of NFs to CAFs. Thus, autophagic flux impairment-induced inflammatory cytokine effect on NF activation was investigated. We found that the CM collected from MKN-45/MALAT1 and MGC-803/MALAT1 cells markedly induced NFs to acquire myofibroblast phenotype characterized by a-SMA and FAP expression (Fig. 5A–C). Furthermore, the fibroblast contraction abilities were markedly enhanced after treatment with CM derived from GC cells with increased MALAT1 (Fig. 5D, P < 0.05). To determine whether IL-6 was the dominant driver of this effect, NFs were treated with rIL-6, and the results demonstrated a dose-dependent FAP and a-SMA expression increase (Fig. 5E, P < 0.05). To better understand the paracrine effect of MKN-45/MALAT1- and MGC-803/MALAT1-secreted IL-6 on fibroblasts, the anti-IL-6 neutralizing antibody was used within rescue assay, which could weaken FAP and a-SMA expression in NFs treated with CM from GC cells with increased MALAT1 (Fig. 5F, P < 0.05). These results demonstrated that autophagy impairment-induced IL-6 from GC cells could activate NF to CAF conversion in a paracrine manner.

AC Cultured medium collected from MKN-45/MALAT1 and MGC-803/MALAT1 cells induced NFs to acquire myofibroblast phenotype characterized by a-SMA and FAP expression as detected by Immunofluorescence and western blot assays; D NFs treated with cultured medium released by different GC cells or blank control were assessed for their ability to contract collagen; E The a-SMA and FAP protein levels were detected in NFs treated with different concentrations of reIL-6; F Protein levels of a-SMA and FAP in NFs co-cultured with cultured medium collected from MKN-45/MALAT1 and MGC-803/MALAT1 in the presence of IL-6 neutralizing antibody were analysed by western blot. Bars, S.D.; *P < 0.05; **P < 0.01; ***P < 0.001.

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To demonstrate the function of activated fibroblast converted from NFs (Activated-NFs), NFs treated with CM derived from GC cells with increased MALAT1 were prepared to perform proliferation assay and EdU dye assay. Then, MKN-45 and MGC-803 cells were incubated by CM collected from fibroblasts pre-co-cultured with MKN-45/MALAT1 and MGC-803/MALAT1 cells, respectively. As shown in Fig. 6A–C, GC cells exhibited proliferation and colony formation enhancement after treatment (Fig. 6A, C, P < 0.05). To further determine whether IL-6 from activated-NFs play a crucial role in promoting GC cells proliferation, and IL-6 blocking antibody was used to treat Activated-NF/MKN-45 and Activated-NF/MGC-803 group. Then we found that impairment of IL-6 with blocking antibody could significantly attenuate GC cells proliferation (Fig. 6D, P < 0.05). In addition, we further investigated whether activated-NFs could promote tumour growth in vivo. Co-injection of activated-NFs or NFs cells with MGC-803 was performed in nude mice. MGC-803 treated activated-NFs cells generated tumours with larger volume and weight than those generated by MGC-803 treated with NFs (Fig. 6E, F, P < 0.05). Furthermore, immunohistochemistry staining results showed that the FAP and a-SMA (CAF activation markers), SQSTM1 (autophagy marker) and IL-6 expressions were highly increased in MGC-803/activated-NF group (Fig. 6G, P < 0.01), which is consistent with in vitro experiment results.

AC Proliferation of MKN-45 and MGC-803 cells treated with activated-NF was determined by CCK8 (Activated-NF-CM/MKN-45 vs NF-CM/MKN-45: 0.93 ± 0.01 vs 0.85 ± 0.02; Activated-NF-CM/MGC-803 vs NF-CM/MGC-803: 0.74 ± 0.04 Vs 0.65 ± 0.01, P < 0.05), colony-formation (Activated-NF-CM/MKN-45 Vs NF-CM/MKN-45: 53.5 ± 11.8 Vs 28 ± 8.8; Activated-NF-CM/MGC-803 Vs NF-CM/MGC-803: 37.25 ± 8.8 Vs 7.25 ± 1.9, P < 0.05) and EdU (Activated-NF-CM/MKN-45 vs NF-CM/MKN-45: 35.3 ± 3.8 vs 12.6 ± 1.67; Activated-NF-CM/MGC-803 vs NF-CM/MGC-803: 33.3 ± 5.4 vs 18 ± 2.44, P < 0.05) assays; D Proliferation of MKN-45 and MGC-803 cells treated with activated-NF-CM and IL-6 blocking antibody was determined by CCK8 (Activated-NF-CM + IL-6 antibody/MKN-45 vs Activated-NF-CM/MKN-45: 0.79 ± 0.01 vs 0.65 ± 0.02; Activated-NF-CM + IL-6 antibody /MGC-803 vs NF-CM/MGC-803: 0.703 ± 0.01 Vs 0.59 ± 0.04, P < 0.01): E Photographs of tumours in nude mice derived from MGC-803 co-injected with activated-NFs and NFs; F MGC-803 mixed with activated-NFs generated tumours of larger volume and weight than those generated by MGC-803 mixed with NFs (MGC-803/activated-NFs vs MGC-803/NFs: 0.80 ± 0.5 vs 0.12 ± 0.34 cm^3; MGC-803/activated-NFs vs MGC-803/NFs: 248 ± 103 vs 124 ± 88 mg, P < 0.05); G FAP, a-SMA, SQSTM1 and IL-6 expressions were examined by IHC in tumours resulting from MGC-803/activated-NFs and MGC-803/NFs group. Bars, SD; *P < 0.05; **P < 0.01; ***P < 0.001.

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IL-6 derived from CAFs promote MALAT1 expression in GC cells

Increasing evidence showed that MALAT1 were aberrantly overexpressed and could act as an oncogene in GC21. The Cancer Genome Atlas (TCGA) data demonstrated that MALAT1 was highly expressed in GC (Supplementary Fig. 3A), and survival curve analysis with GEO dataset showed MALAT1 expression was negatively correlated with post-progression survival time of GC patients (Supplementary Fig. 3B). The cross-talk between CAFs and GC cells could aggravate the dysregulation of gene expression22,23. However, the mechanism of upregulation of MALAT1 within TME was rarely reported. Therefore, co-culture CAFs or NFs with GC cells were performed to determine whether CAFs could upregulate MALAT1 expression in GC cells via paracrine signalling. As shown in Fig. 7A, relative expression of MALAT1 was significantly higher in MKN-45 or MGC-803 co-cultured with CAFs group than that co-cultured with NFs group (Fig. 7A, P < 0.05), which means cytokine from CAFs might induce MALAT1 expression in GC. GESA dataset analysis was performed to suggest that IL-6/STAT3 pathway signalling was a positive association with MALAT1 expression (Fig. 7B, NES = 1.459, FDR q-value=0.26). Then the expression of IL-6 in CAFs, NFs and GC cells were determined by ELISA, which showed that IL-6 was dominantly overexpressed in CAFs (Fig. 7C, P < 0.01). Furthermore, higher expression of MALTA1 was detected in MKN-45 and MGC-803 cells treated with recombinant IL-6 protein (rIL-6) than that in MKN-45 and MGC-803 cells alone (Fig. 7D, P < 0.05). Blocking IL-6 activity with neutralizing IL-6 antibody of the co-culture system of CAFs and GC cells led to obvious impairment of MALAT1 expression (Fig. 7E, P < 0.05), indicating IL-6 derived from CAFs could promote MALAT1 expression in GC cells. GEO dataset (GSE60839) analysis showed overexpression of MALAT1 was significantly positive with the expression of IL-6 and STAT3 in GC samples (Fig. 7F, G, P < 0.05), suggesting STAT3 might be responsible for high expression of MALAT1 in GC.

A Relative expression of MALAT1 was significantly higher in MKN-45 or MGC-803 co-cultured with CAFs than those co-cultured with NFs (CAF/MKN-45vs NF/MKN-45: 1.51 ± 0.13 vs 0.99 ± 0.01; CAF/MGC-803 vs NF/MGC-803: 1.95 ± 0.23 vs 0.99 ± 0.01, *P < 0.05, **P < 0.01); B GESA dataset analysis showed that IL-6/STAT3 pathway signalling had a positive association with MALAT1 expression(NES = 1.459, FDR q-value=0.26); C IL-6 was highly expressed in CAFs compared to NFs and GC cells(*P < 0.05, **P < 0.01); D The effect of reIL-6 (100 ng/mL) on MALAT1 expression in MKN-45 and MGC-803 were measured by qRT-PCR (MKN-45/IL-6 vs MKN-45/PBS: 1.86 ± 0.27 vs 1.03 ± 0.01: MGC-803/IL-6 vs MGC-803/PBS: 2.15 ± 0.03 vs 1.03 ± 0.01, *P < 0.05, **P < 0.01); E The effect of CAFs on MALAT1 expression in MKN-45 and MGC-803 cells was determined with the presence of IL-6 neutralizing antibody or IgG isotype control antibody (CAF + Anti-IgG/MKN-45 vs CAF + Anti-IL-6/MKN-45: 1.38 ± 0.04 vs. 1.02 ± 0.03; CAF + Anti-IgG/MGC-803 vs CAF + Anti-IL-6/MGC-803: 1.51 ± 0.04 vs 1.02 ± 0.03, **P < 0.01); F, G GEO dataset analysis suggested MALAT1 expression was positively associated with IL-6 and STAT3 expressions, respectively; H STAT3 upregulation promoted MALAT1 expression in MKN-45 and MGC-803 cells (STAT3/MKN-45 vs NC/MKN-45: 2.16 ± 0.16 vs 1: STAT3/MGC-803 vs NC/MGC-803: 3.46 ± 0.27 vs 1.1 ± 0.01, P < 0.01); I WP1066, a selective STAT3 inhibitor, could attenuate MALAT1 expression induced by recombinant IL-6 protein, which was measured by qRT-PCR (IL-6 + WP1066/MKN-45 vs IL-6+DMSO/MKN-45: 0.60 ± 0.07 vs 0.99 ± 0.03: IL-6 + WP1066/MGC-803 vs IL-6+DMSO/MGC-803: 0.24 ± 0.01 vs 0.99 ± 0.03, P < 0.01); J Potential STAT3 bindings sites on MALAT1 promoter; K Luciferase activity was measured after transfecting with MALAT1 promoter truncations, indicating that MALAT1 promoter site#3 contains binding sites (STAT3 vs NC: 3.91 ± 0.33 Vs 1.13 ± 0.17, P < 0.01); L Chip assay was performed to show that STAT3 could physically bind to MALAT1 promoter site#3 in MKN-45 and MGC-803 cells. Bars, S.D.; *P < 0.05; **P < 0.01; ***P < 0.001.

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Subsequently, upregulation of STAT3 expression via transfected STAT3 overexpression plasmid led to increasement of MALAT1 in both MKN-45 and MGC-803(Fig. 7H, P < 0.05). Furthermore, WP1066, a selective STAT3 inhibitor (inhibition of efficiency was shown in Supplementary Fig. 4C, D), could attenuate MALAT1 expression induced by recombinant IL-6 protein (Fig. 7I, P < 0.05), which suggested that IL-6 could increase MALAT1 expression via stimulating STAT3 activation. With help of the JASPAR database, we found that there are four most potential binding sites of STAT3 on MALAT1’s promoter accounting for the upregulation of MALAT1 in GC (Fig. 7J). To better understand whether STAT3 could interact with the MALAT1 promoter, a dual-luciferase reporter assay was carried out to measure luciferase activity after transfecting of truncations of the MALAT1 promoter. The results showed that site#3(-618bp~-200bp) of the MALAT1 promoter contains binding sites which mediated MALAT1 transcription activation induced by STAT3 (Fig. 7K, P < 0.01). Moreover, a CHIP assay was performed to show that STAT3 could physically bind to site#3(-618bp~-200bp) of the MALAT1 promoter in MKN-45 and MGC-803 cells (Fig. 7L, P < 0.01). Taken together, aberrant MALAT1 expression was partly attributed to IL-6 derived from CAFs via activation of the STAT3 pathway within GC TME. Additionally, we also found that overexpression of IL-6 was detected in MKN-45 and MGC-803 cells treated with rIL-6 (Supplementary Fig. 4E).

Discussion

In the present study, we showed, for the first time, that increased MALAT1 in GC cells could impair autophagic flux to aggravate IL-6 secretion to activate NF to CAF conversion via paracrine signalling, which resulted in GC cell progression. Increased MALAT1 could destabilize PTEN mRNA to activate AKT/mTOR pathway for blocking autophagic flux, leading to IL-6 overexpression induced by SQSTM1/NF-κB pathway, and the secreted IL-6 from GC cells stimulate NF to CAF conversion (Fig. 8). The interaction between GC and stromal cells could cause positive feedback to foster an inflammatory microenvironment and promote GC progression.

Increased MALAT1 in GC cells could impair autophagic flux to aggravate IL-6 secretion to activate converts NFs to CAFs via paracrine signalling, which resulted in GC cell progression. Increased MALAT1 could destabilize PTEN mRNA stability to activate AKT/mTOR pathway, which blocked autophagic flux leading to IL-6 overexpression induced by SQSTM1/NF-κB pathway. In addition, IL-6 secretion from GC cells stimulates NF conversion to CAFs.

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It has been widely reported that MALAT1 could function as versatile regulators by modulating transcription and post-transcriptional processes. Previously, we had found that MALAT1 could function as an oncogene to promote the proliferation of GC cells24. MALAT1 was overexpressed in GC and associated with TNM stages. Cancer cells play a vital role in tumour progression with the help of stromal cells within TME. However, the MALAT1 effect on the interaction between stromal and cancer cells have been rarely studied. Autophagy is a biological process involved with interaction between different types of cells through the production of inflammatory mediators6,20, which can regulate complex multicellular interactions within TME. Several studies have reported that MALAT1 could promote autophagy in various cancers, including retinoblastoma13, lung cancer25, and pancreatic cancer11. For GC, autophagy inhibition caused by increased MALAT1 has been rarely reported and investigated. For autophagy regulation, it is widely accepted that the mammalian target of rapamycin complex 1 (mTORC1) from the autophagy-inhibiting PI3k–Akt pathway26 and increased MALAT1 could activate PI3K–AKT pathway in numerous cancers including GC27. In this study, we demonstrated that increased MALAT1 could inhibit autophagy flux through activating AKT/mTOR pathway. Not only LC3-I to LC3-II protein conversion was increased along with MALAT1 augmentation in GC cells, SQSTM1 protein accumulation was also detected, suggesting LC3 protein conversion resulted from autophagy impairment rather than autophagy induction. In addition, rescue assays were performed to further confirm that increased MALAT1 could inhibit autophagy flux with 3-MA and BafA1 treatment. Besides the AKT/mTOR pathway being activated by increased MALAT1, expression of PTEN, the negative regulator of AKT/mTOR signalling, was also changed. Our study found that increased MALAT1 could destabilize PTEN mRNA to shorten its half-life in GC. AREs were rich in the PTEN 3′-UTR, to which RBP could bind to modulate mRNA stability. ELAVL1 is a ubiquitously expressed RBP that regulates many post-transcriptional steps including mRNA stability and translation. ELAVL1 has been reported to stabilize COX-2, β-catenin and BECN1 mRNA via binding to target AREs of 3′-UTR28,29. ELAVL1 not only could bind to 3′-UTR but also interact with lncRNA to form a functional complex. ELAVL1/MALAT1 complex was found to repress CD133 expression and suppress epithelial-mesenchymal transition in breast cancer19. However, whether ELAVL1 could bind to PTEN 3′-UTR regulating mRNA stability had not been reported, and whether MALAT1 could modulate PTEN mRNA expression via competitive interfering with the interaction between ELAVL1 and 3′-UTR was not investigated. In the present study, we found that MALAT1 could interact with ELAVL1 directly and restrain ELAVL1 in the nucleus away from the cytoplasm, where it could stabilize PTEN mRNA, as shown by RIP and IF assays. Based on collected evidence, we confirmed that increased MALAT1 could impair autophagy flux in GC via stimulating PTEN/AKT/mTOR signalling pathway. As a consequence of autophagy impairment caused by MALAT1, SQSTM1 accumulation was increased. Although expression of SQSTM1 was not investigated, several studies reported that SQSTM1 protein levels were more significantly upregulated in GC samples than in normal gastric mucosae30,31. SQSTM1 has been reported to be a significant activate factor in inflammatory responses32,33 through many signalling pathways including stimulating the NF-κB activation34,35. Therefore, we observed whether SQSTM1/NF-κB activation was responsible for IL-6 upregulation induced by increased MALAT1 in GC. From the results of rescue assays, we clearly found that SQSTM1 knockdown could reverse NF-κB activation and IL-6 upregulation caused by MALAT1, and restored SQSTM1 could reverse the NF-κB/IL-6 inhibition induced by silencing MALAT1 in GC cells.

CAFs secret inflammatory mediators to modulate components in TME and changes in TME can also regulate CAF function. We have shown previously that miR-149 can inhibit CAF activation via targeting IL-6 expression, which indicated that IL-6 has an important role in the CAF activation process36. In this study, we found that increased MALAT1 in GC cells results in IL-6 expression and secretion, and IL-6 augmentation activates NF to CAF conversion. The IL-6 effect on activating NFs was found in GC. IL-6 could also mediate the interaction between cancer cells and CAFs not only by supporting tumour cell growth but also by promoting fibroblast activation in oesophageal cancer37. Although IL-6 could stimulate NF to CAF conversion, the underlying molecular mechanisms were rarely known. Most studies attributed that IL-6 mediate the microRNA-dependent pathway to CAF activation38,39,40, which could not fully describe the underlying mechanisms. The mechanism of cytokines, like IL-6, on stimulating CAF activation should be further investigated. Chronic inflammation leads to NF activation and their conversion into CAFs, producing pro-tumorigenic cytokines, interacting with the cancer cells, and altering their gene expression profile, which results in cancer progression. In this study, activated CAFs induced by IL-6 could express α-SMA, acquire a highly contractile phenotype, and functionally, activated CAFs could facilitate GC cell proliferation, which resulted in co-evolution of CAFs with cancer cells. Additionally, MALAT1 has been reported to be aberrantly overexpressed in GC samples; however, the mechanism of upregulation of MALAT1 within TME was rarely reported. The interaction between CAFs and GC cells could aggravate the dysregulation of gene expression. We found that CAFs could upregulate MALAT1 expression in GC cells via paracrine signalling. Moreover, IL-6 derived from CAFs might be responsible for the high expression of MALAT1 in GC via promoting STAT3 binding to the MALAT1 promoter. In this way, the positive feedback loop contributed to positive feedback to foster an inflammatory microenvironment and promote GC progression.

In summary, our results indicate that MALAT1 could inhibit autophagic flux and instigate IL-6 via regulating PTEN/AKT/mTOR and SQSTM1/NF-κB pathways, which convert fibroblasts to CAFs to promote GC progression. (Fig. 8). However, the mechanism for CAF activation induced by IL-6 needs to be further investigated. Our study illustrated a new molecular mechanism underlying the interaction between cancer cells and fibroblasts, which may contribute to provide novel prevention and therapeutic strategies for GC.

Materials and methods

Cell lines

Human GC cell lines MKN-45, MGC-803, and GES-1 were purchased from the Shanghai Institute for Biological Sciences of the Chinese Academy of Sciences. They have been authenticated by an STR DNA profiling analysis and routinely examined for Mycoplasma contamination. GC cells were cultured in RPMI 1640 medium supplemented with 10% foetal bovine serum (FBS) and penicillin (100 μ/mL)/streptomycin (100 μg/mL) at 37 °C in 5% CO2 in air at saturation humidity.

Isolation and culture of fibroblasts

CAFs and adjacent NFs were isolated from resected tissues from GC patients at the Department of Surgery, Ruijin hospital affiliated with Shanghai JiaoTong University, School of Medicine. The tissues were well cultured in Dulbecco’s modified Eagle’s medium (DMEM) with 10% FBS, 100 μ/mL penicillin and 100 ug/mL streptomycin. A homogeneous group of fibroblasts were developed after two weeks of culture, which were cultured >10 times so that the minimum number of clones could be selected. Identification test for CAFs and NFs were performed as described previously (Supplementary Fig. 4A, B). All patient samples were obtained with informed consent from Ruijin Hospital, Shanghai Jiao Tong University School of Medicine.

RNA interference and plasmids

Small interfering RNAs (siRNAs) that specifically target human MALAT1 and SQSTM1 were purchased from Ribobio Technology (Guangzhou, China) and GenePharma (Shanghai, China), respectively. The siRNAs (100 nM siMALAT1, 100 nM siSQSTM1) were transfected into cells using the RNAi-MAX reagent (Life Technologies, CA, USA) according to the manufacturer’s instructions. The pcDNA-MALAT1 plasmid was kindly gifted by Prof. Huating Wang (The Chinese University of Hong Kong, China). Human ELAVL1 expression plasmids were purchased from Sangon Biotech (Shanghai, China). Plasmids (4 mg/ml) were transfected into cells using Lipofectamine 3000 (Life Technologies). Stably transfected cells (MGC-803/MALAT1, MGC-803/NC) were selected by using puromycin (1 mg/ml; InvivoGen). The RNA interference sequences are listed in Supplementary Table 1.

Quantitative reverse transcription PCR (qRT-PCR)

Total RNA was extracted with TRIzol® reagent (Invitrogen, Austin, TX, USA), and real-time PCR analysis was conducted according to the manufacturer’s instructions (Life Technologies). The mRNA level was measured using the SYBR Green PCR Master Mix (Applied Biosystems, Waltham, MA, USA) and normalized to glyceraldehyde 3-phosphate dehydrogenase (GAPDH) mRNA level. The primer sequences used are listed in Supplementary Table 1.

Western blot

Cells were lysed in RIPA buffer containing complete protease and phosphatase inhibitor cocktail (Sigma, USA). The protein concentration of the cell lysates was quantified by a BCA Protein Assay Kit (Pierce, Rockford). The same amount of protein samples was resolved onto 10% SDS-PAGE and then transferred to PVDF membranes. After blocking with 5% non-fat milk at 37 °C for 2 h, the membranes were incubated with the primary antibodies (1: 1000) diluted in TBST buffer overnight at 4 °C, followed by incubation with the HRP-conjugated secondary antibody for 2 h at room temperature. GAPDH antibody was used to verify equal protein loading. The protein band images were captured and analysed by a Tanon detection system with ECL reagent (Thermo) and the antigen-antibody reaction was visualized by enhanced chemiluminescence (ECL, Thermo, USA). The antibodies used in this study were obtained from Cell Signaling Technology.

Transfection of mRFP-GFP-LC3 lentivirus vector

The mRFP-GFP-LC3 lentivirus vector was purchased from Genechem (Shanghai, China), which was transfected to GC cells according to the manual. Puromycin (1 μg/ml) was used to select stably expressing mRFP-GFP-LC3 cells. GC cells treated with different plasmids were fixed and analysed using fluorescence microscopy.

Transmission electron microscopy (TEM)

GC cells were fixed in 2% glutaraldehyde containing 0.1 mol/l phosphate-buffered saline at 4 °C for 2 h, incubated in 1% osmium tetroxide containing 0.1 mol/l phosphate-buffered saline for 1.5 h at 4 °C, dehydrated in graded ethanol, saturated in graded ethanol, embedded, cut into ultrathin sections, stained with lead citrate, and finally viewed using Philip CM-120 TEM (Philips, Netherlands).

RNA stability assay

Transcription inhibitor Actinomycin D (Sigma-Aldrich, USA) was added to the culture medium of GC cells transfected with different plasmids for 0, 2, 4, and 6 h. Individual total RNA was harvested for qRT-PCR analysis. The relative mRNA decay rate was measured and fit into an exponential curve.

RNA immunoprecipitation-quantitative PCR (RIP-PCR)

RIP assays were performed by using the Magna RIP RNA-Binding Protein Immunoprecipitation Kit (Millipore, USA) according to the manufacturer’s instructions. Briefly, cells were lysed in lysis buffer and the cleared lysates were immunoprecipitated with the indicated anti-ELAVL1 and anti-IgG antibodies (Cell Signaling Technology). Immunoprecipitated and input RNA was isolated and reverse transcribed for qRT-PCR amplifications with PTEN 3′-UTR-specific primers. mRNA relative expression level was normalized to input mRNA expression. The primers used for amplification are listed in Supplementary Table 1.

Enzyme-linked immunosorbent assay (ELISA)

The human angiogenesis array (Raybiotech, USA) was used to analyse the soluble mediators according to the manufacturer’s protocol. A human IL-6 ELISA kit (Raybiotech) was used to determine the concentration of human IL-6 in the medium of different treatments according to the manufacturer’s instructions.

Immunofluorescence (IF)/Immunohistochemistry (IHC)

For IF assay, GC cells were fixed with 4% paraformaldehyde for 15 min at room temperature, permeabilized with 0.5% Triton X-100, and blocked with 5% BSA for 2 h before incubation with primary antibodies including anti-ELAV1, anti-DAPI, anti-NF-KB, anti-FAP (1: 500, Cell Signaling Technology), and anti-α-SMA (1: 500, Abcam, USA) overnight at 4 °C. After incubation with fluorescent secondary antibody for 2 h, images were acquired by fluorescence microscope.

Collagen contraction assays

A total of 1 × 105 NFs were suspended in 100 μl DMEM, which was mixed with 100 μl of collagen mix containing 68.75 μl DMEM and 31.25 μl Type 1 Rat tail collagen (Solarbio, China), and added to one well of a 96-well plate at 37 °C for 30 min. After incubation with media derived from different treatments for 24 h, the gels were photographed and the contractions were evaluated by using the Image J program.

Cell-proliferation/EdU assay

Cells were seeded into 96-well plates (1.0×105cells/well) and cell proliferation was documented every 24 h for 4 days. Cell proliferation was assessed in triplicates by using the Cell Counting Kit-8 (Dojindo, Kumamoto, Japan) following the manufacturer’s instructions. EdU assay was performed using Cell-Light EdU Apollo 567 In Vitro Imaging Kit (Ribobio, Guangzhou, China) according to the manufacturer’s instructions.

Xenograft assay

All the experiments were performed in accordance with the official recommendations of the Chinese animal community. Four-week-old male BALB/C nude mice were purchased from the Institute of Zoology, Chinese Academy of Sciences of Shanghai. All nude mice were randomized allocated into two groups, in which NFs and MGC-803/MALAT1 or MGC-803/NC cells mixed at the ratio of 1:4 in 100 lL PBS were injected subcutaneously. During the experiment, the tumour volume was measured weekly using the formula V = (length × width2)/2.

Statistical methods

Student’s t-test or one-way ANOVA were used for statistical analysis when appropriate. All statistical analyses were performed using SPSS 19.0 (SPSS Inc., Chicago, IL, USA). A two-tailed value of P < 0.05 was considered statistically significant. Gene set enrichment analysis (GSEA) was performed using GSEA v3.0 software.

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Acknowledgements

Thanks to Prof. Hua-ting Wang (The Chinese University of Hong Kong) and Prof. Kannanganattu V. Prasanth (University of Illinois) for providing the Human MALAT1 expression vector as a kind gift.

Funding

This work was supported by the National Natural Science Foundation of China Grant NO.81772518, No. 81871904 (ZG Zhu) and No. 81902944(ZQ Wang); and Interdisciplinary Program of Shanghai Jiao Tong University Grant No. YG2017MS58 (C Li), No. ZH2018QNA51(ZQ Wang) and Multicenter Clinical Trial of Shanghai JiaoTong University of medicine NO.DLY201602(ZG Zhu).

Author information

Author notes
  1. These authors contributed equally: Zhenqiang Wang, Xinjing Wang

Affiliations

  1. Department of General Surgery, Shanghai Key Laboratory of Gastric Neoplasms, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China

    Zhenqiang Wang, Tianqi Zhang, Liping Su, Bingya Liu, Zhenggang Zhu & Chen Li

  2. Shanghai Institute of Digestive Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China

    Zhenqiang Wang, Xinjing Wang, Tianqi Zhang, Liping Su, Bingya Liu, Zhenggang Zhu & Chen Li

Contributions

W.Z.Q and W.X.J. carried out the molecular lab work, participated in data analysis, carried out sequence alignments, participated in the design of the study and drafted the manuscript; Z.T.Q. carried out the statistical analyses; S.L.P., L.B.Y., Z.Z.G. and C.L. conceived of the study, designed the study, coordinated the study and helped draft the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Zhenggang Zhu or Chen Li.

Ethics declarations

Ethical statement

All patient samples were obtained with informed consent from Ruijin Hospital, Shanghai Jiao Tong University School of Medicine. All the experiments were performed in accordance with the official recommendations of the Chinese animal community.

Conflict of interest

The authors declare no competing interests.

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Wang, Z., Wang, X., Zhang, T. et al. LncRNA MALAT1 promotes gastric cancer progression via inhibiting autophagic flux and inducing fibroblast activation. Cell Death Dis12, 368 (2021). https://doi.org/10.1038/s41419-021-03645-4

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U2AF1 mutation promotes tumorigenicity through facilitating autophagy flux mediated by FOXO3a activation in myelodysplastic syndromes

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33. Vardiman JW, Harris NL, Brunning RD. The World Health Organization (WHO) classification of the myeloid neoplasms. Blood. 2002;100:2292–302. doi: 10.1182/blood-2002-04-1199. [PubMed] [CrossRef] [Google Scholar]

34. Valent P, Horny HP, Bennett JM, Fonatsch C, Germing U, Greenberg P, et al. Definitions and standards in the diagnosis and treatment of the myelodysplastic syndromes: Consensus statements and report from a working conference. Leuk Res. 2007;31:727–36. doi: 10.1016/j.leukres.2006.11.009. [PubMed] [CrossRef] [Google Scholar]

Источник: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8238956/

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Источник: https://proserialkeys.com/f-lux-crack-license-key

U2AF1 mutation promotes tumorigenicity through facilitating autophagy flux mediated by FOXO3a activation in myelodysplastic syndromes

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33. Vardiman JW, Harris NL, Brunning RD. The World Health Organization (WHO) classification of the myeloid neoplasms. Blood. 2002;100:2292–302. doi: 10.1182/blood-2002-04-1199. [PubMed] [CrossRef] [Google Scholar]

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Источник: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8238956/

Open Access

Peer-reviewed

  • Irene Rodríguez-Sánchez,
  • Xenia L. Schafer,
  • Morgan Monaghan,
  • Joshua Munger

    Roles Conceptualization, Formal analysis, Funding acquisition, Methodology, Project administration, Supervision, Visualization, Writing – original draft, Writing – review & editing

    * E-mail:josh.munger@rochester.edu

    Affiliations Department of Microbiology and Immunology, School of Medicine and Dentistry, University of Rochester, Rochester, New York, United States of America, Department of Biochemistry and Biophysics, School of Medicine and Dentistry, University of Rochester, Rochester, New York, United States of America

  • Irene Rodríguez-Sánchez, 
  • Xenia L. Schafer, 
  • Morgan Monaghan, 
  • Joshua Munger
PLOS

x

Abstract

Human Cytomegalovirus (HCMV) infection induces several metabolic activities that are essential for viral replication. Despite the important role that this metabolic modulation plays during infection, the viral mechanisms involved are largely unclear. We find that the HCMV UL38 protein is responsible for many aspects of HCMV-mediated metabolic activation, with UL38 being necessary and sufficient to drive glycolytic activation and induce the catabolism of specific amino acids. UL38’s metabolic reprogramming role is dependent on its interaction with TSC2, a tumor suppressor that inhibits mTOR signaling. Further, shRNA-mediated knockdown of TSC2 recapitulates the metabolic phenotypes associated with UL38 expression. Notably, we find that in many cases the metabolic flux activation associated with UL38 expression is largely independent of mTOR activity, as broad spectrum mTOR inhibition does not impact UL38-mediated induction of glycolysis, glutamine consumption, or the secretion of proline or alanine. In contrast, the induction of metabolite concentrations observed with UL38 expression are largely dependent on active mTOR. Collectively, our results indicate that the HCMV UL38 protein induces a pro-viral metabolic environment via inhibition of TSC2.

Author summary

Viruses are parasites that usurp the energy and molecular building blocks of their hosts to support their replication. In the past few years, numerous studies have shown that a wide variety of viruses induce cellular metabolic activities that are essential for successful infection. However, the viral mechanisms responsible for these metabolic alterations have remained unclear. Here, we find that the Human Cytomegalovirus (HCMV) UL38 gene is responsible for inducing many of the metabolic activities that are critical for successful HCMV infection. HCMV is a herpes virus that causes severe disease in newborns, as well as in those with weakened immune systems including transplant recipients and patients with common blood-based cancers. Our work shows that the UL38 protein drives cells to substantially increase the consumption of glucose and specific amino acids, which provide the energy and building blocks necessary to create new viral particles. Mechanistically, we find that UL38 triggers these metabolic changes through inhibition of a cellular tumor suppressor protein, TSC2. Collectively, our data provide substantial insight into how a viral pathogen reprograms cellular metabolism to support infection.

Citation: Rodríguez-Sánchez I, Schafer XL, Monaghan M, Munger J (2019) The Human Cytomegalovirus UL38 protein drives mTOR-independent metabolic flux reprogramming by inhibiting TSC2. PLoS Pathog 15(1): e1007569. https://doi.org/10.1371/journal.ppat.1007569

Editor: Dirk P. Dittmer, University of North Carolina at Chapel Hill, UNITED STATES

Received: September 13, 2018; Accepted: January 7, 2019; Published: January 24, 2019

Copyright: © 2019 Rodríguez-Sánchez et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: The work was supported by NIH grant AI127370 and by a Research Scholar Grant from the American Cancer Society (RSG-15-049-01-MPC) to JM. IRS is supported by a pre-doctoral fellowship from the American Heart Association. MM is supported by a post-doctoral fellowship from the American Heart Association. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Viruses depend on cellular energy and macromolecules to support their replication. Several studies have identified specific virally-induced metabolic activities that are important for the production of viral progeny [1–9]. Further, many successful anti-viral treatments target virally-induced metabolic activities, e.g., those that target aberrant nucleotide metabolism during viral infection [10, 11]. Despite these successes, very little is known regarding the mechanisms through which viruses manipulate cellular metabolic activity. Given their importance to viral infection, identification of these mechanisms could provide novel targets for therapeutic intervention.

Human Cytomegalovirus (HCMV) is a widespread opportunistic pathogen that causes severe disease in neonates and immunosuppressed patients, such as cancer patients undergoing immunosuppressive treatment, transplant recipients and HIV positive patients [12]. HCMV infection is also associated with increased incidence and mortality of cardiovascular disease [13–15]. HCMV is a betaherpes virus with a double-stranded DNA genome of ∼240 kb that encodes for over 200 open reading frames (ORF)[12]. We and others have previously found that HCMV infection induces dramatic changes to the host cell metabolic network. These changes include the induction of central carbon metabolism, including glycolysis [1–3, 16, 17], glutaminolysis [18], tricarboxylic acid (TCA) cycle [1, 2], fatty acid biosynthesis [1, 5] and pyrimidine biosynthesis [2, 4]. However, HCMV’s impact on amino acid metabolism is much less clear. Further, the viral mechanisms responsible for metabolic manipulations are largely unknown, an important consideration given that inhibition of these metabolic changes attenuates HCMV infection [1–5, 16].

Here, we find that HCMV targets many aspects of amino acid metabolism, and that the HCMV UL38 protein is necessary and sufficient to drive many features of the HCMV-induced metabolic program. UL38 is an HCMV immediate early gene, conserved among beta-herpesviruses that is important for viral replication [19, 20], and has been found to induce mTORC1 activation [21, 22]. Our data suggest that UL38 reprograms cellular metabolic activities through its interaction with the tuberous sclerosis complex 2 protein (TSC2). TSC2 is a negative regulator of mTORC1 activity, but we find that UL38-mediated activation of various metabolic fluxes is largely independent of mTOR. Collectively, we propose that the HCMV UL38 protein is an important metabolic regulator that induces metabolic reprogramming through its inhibition of TSC2 but is largely independent of mTOR.

Results

HCMV infection reprograms cellular amino acid metabolism

As previously reported, HCMV infection increases glycolysis, inducing both glucose uptake and lactate secretion (Fig 1A) [1]. Much less is known regarding how HCMV infection affects cellular amino acid dynamics. To explore this issue, we measured how HCMV infection affected the intake and secretion of amino acids in the media. HCMV infection broadly increased the consumption of several amino acids, with the consumption of leucine/isoleucine and arginine increasing the most (Fig 1B and 1C). In contrast, infection did not detectably impact the consumption of others, such as lysine and phenylalanine (Fig 1C). HCMV infection also increased the excretion of several amino acids, most notably alanine, proline and ornithine (Fig 1B and 1C). These results demonstrate that HCMV infection modulates the metabolic dynamics of several amino acids to varying extents.

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Fig 1. HCMV-induced metabolic reprogramming of amino acid metabolism.

(A- C) MRC5 cells were mock or HCMV-infected (MOI = 3). At 36hpi, cellular medium was renewed, harvested 24h later (60hpi), and analyzed for changes in metabolite levels. Values are means ± SE (n = 6). (D, E, G & H) MRC5 cells were infected as in (A-C). At 36hpi, fresh medium containing DMSO (+DMSO) or 100 nm of rapamycin (+Rap) was added to the plates and conditioned medium and cells were harvested after 24h (60hpi). (D) Western blot analysis of drug treated mock or HCMV-infected cells (E, G & H) Changes in metabolic intermediates present in the conditioned medium were measured. Values are means ± SE (n = 3) (*p<0.05, **p<0.01). (F) Schematic of central carbon metabolism. (I) MRC5 cells were mock-infected (Mock) or infected with HCMV (HCMV) (MOI = 3) and 24h after, fresh medium containing DMSO (+DMSO) or 100 nm of rapamycin (+Rap) was added. At 48hpi cells were quenched and extracted. Absolute intracellular metabolite concentrations were determined by LC-MS/MS and normalized to protein levels. Values are means ± SE (n = 4). (*p<0.05, **p<0.01).

https://doi.org/10.1371/journal.ppat.1007569.g001

The mammalian target of rapamycin complex 1 (mTORC1) coordinates cell growth, proliferation and metabolism by controlling the balance between anabolic and catabolic processes in response to environmental cues, such as nutrients or growth factors [23, 24]. Previous work has demonstrated that HCMV infection activates mTORC1 and that maintenance of this activity is required for high-titer viral replication [25, 26]. mTORC1 has been shown to regulate glycolysis, glutaminolysis, fatty acid biosynthesis, and nucleotide biosynthesis [23, 24], metabolic processes previously described to be induced during HCMV infection [1, 4, 5]. To test the role that mTOR plays in HCMV-induced modulation of central carbon metabolism, we assessed the impact of rapamycin treatment, an FDA approved mTORC1 inhibitor, on amino acid levels during HCMV infection [27, 28]. As previously reported [25, 26], HCMV infection increases the phosphorylation of S6K, a canonical mTORC1 phospho-substrate (Fig 1D). Rapamycin treatment prevented this accumulation of pS6K in both mock and HCMV-infected cells, and reduced the levels of phosphorylated 4E-BP, consistent with its inhibition of mTORC1 activity (Fig 1D and S1D Fig). Rapamycin treatment appeared to block some HCMV-induced metabolic changes, while leaving others largely unaffected. For example, rapamycin had a minimal impact on HCMV-induced glucose consumption, yet reduced lactate secretion to nearly uninfected levels (Fig 1E). This suggests that in HCMV-infected cells, mTORC1 activity preferentially drives glycolytic carbon towards lactate production and away from other glycolytic branch points, e.g., the TCA cycle (see metabolic branch point at pyruvate in Fig 1F).

Rapamycin appeared to attenuate HCMV-induced glutamine consumption (Fig 1G), a TCA cycle carbon source important for HCMV replication [18], although the changes did not reach the level of statistical significance. This rapamycin-induced decrease in glutamine consumption could potentially be playing a role in the observed reduction of lactate secretion, as a reduction in glutamine carbon suppling the TCA cycle could be compensated for by directing pyruvate into the TCA cycle and away from lactate (see branch point at pyruvate in Fig 1F). Rapamycin had little impact on HCMV-induced serine or leucine/isoleucine consumption (Fig 1G) and had no impact on HCMV-induced proline secretion, yet significantly reduced ornithine secretion (Fig 1H).

HCMV infection induces the abundance of several intracellular glycolytic, TCA cycle, and nucleotide metabolites [1, 2]. To analyze the impact of mTORC1 inhibition on these metabolic changes, we utilized LC-MS/MS to profile the impact of rapamycin treatment on intracellular metabolites pools during HCMV infection (S1A Fig). Based on these data, we subsequently constructed a partial least-squares discriminant analysis-based (PLS-DA) model (S1B and S1C Fig). HCMV and mock-infected samples segregated along the top principal component (S1B Fig), with several glycolytic, TCA cycle and nucleotide metabolites contributing most to this separation (S1C Fig). Rapamycin treatment shifted the concentrations of metabolite pools closer to those of uninfected cells (S1B Fig), including reversing HCMV-induced increases in glycolytic and nucleotide biosynthetic intermediates, e.g., dihdroxyacetone-phosphate/glyceraldehyde 3-phosphate (DHAP/G3P), hexose-phosphate, N-carbamoyl-aspartate and phosphoribosyl pyrophosphate (PRPP) (Fig 1I). In contrast, other metabolic changes induced by HCMV infection were largely rapamycin insensitive, including the increases in TCA cycle pools, e.g., citrate/isocitrate and malate (Fig 1I). Collectively, our data indicate that the relationship between mTORC1 activity and virally-induced metabolic reprogramming is complex, likely reflecting the nuances associated with mTOR-mediated metabolic regulation.

The HCMV UL38 protein is necessary for HCMV-induced metabolic reprogramming

The HCMV UL38 protein has been reported to modulate mTORC1 activation [21, 22], and since we have shown that mTORC1 is important for some metabolic changes during infection, we therefore hypothesized that the UL38 protein might be important for HCMV-induced metabolic reprogramming. To explore this possibility, we analyzed the impact of UL38 deletion on host cell metabolism during HCMV infection with a previously described UL38 deletion mutant (ΔUL38) [20]. As expected, infection with the ΔUL38 mutant did not accumulate UL38, but expressed IE1 to similar levels as WT HCMV (Fig 2A). Infection with the ΔUL38 mutant significantly attenuated the increases in glucose consumption and lactate secretion observed during WT HCMV infection (Fig 2B). Additionally, deletion of UL38 inhibited HCMV-mediated induction of serine and glutamine consumption, as well as ornithine, alanine and glutamate secretion (Fig 2C and 2D). The lack of UL38 during infection did not impact the consumption of phenylalanine or lysine, nor the secretion of proline or tyrosine (Fig 2C and 2D). The absence of UL38 also attenuated the HCMV-induced increases to several intracellular metabolite pools (S2A–S2C Fig). Both hierarchical clustering and PLS-DA-based modeling of their intracellular pool sizes suggested that mock, WT, and F.lux License Key - Crack Key For U cells largely segregate into distinct groups (S2A–S2C Fig). The metabolite pools that were significantly decreased during ΔUL38 infection relative to WT included N-carbamoyl-asparate, the product of the rate-determining step of pyrimidine biosynthesis, as well as pyrimidine end-products including, CDP and dTTP (Fig 2E), the production of which we have previously found to be important for HCMV infection [4]. Deletion of UL38 also decreased the glycolytically related metabolites NADH and phosphoenolpyruvate (PEP), as well as malonyl-CoA, the product of the rate-determining step of fatty acid biosynthesis, whose production is also important for HCMV infection [5] (Fig 2E). Collectively, the disruption of metabolic reprogramming observed during infection with the ΔUL38 virus indicates that the UL38 is important for the induction of the pro-HCMV metabolic program.

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Fig 2. UL38 is important for HCMV-induced metabolic reprogramming.

MRC5 cells were mock-infected, infected with a defective UL38 HCMV virus (ΔUL38) or infected with WT HCMV (WT) (MOI = 3). At 36hpi, medium was renewed, harvested 24h later (60hpi), and analyzed for changes in metabolite levels. (A) Western blot analysis of mock, ΔUL38- and HCMV-infected cells. (B-D) Changes in metabolic intermediates present in the conditioned medium were measured. Values are means ± SE (n = 4) (*p<0.05, **p<0.01). (E) MRC5 cells were mock-infected (Mock), infected with a defective UL38 HCMV virus (ΔUL38) or infected with WT HCMV (WT) (MOI = 3) and 24h after fresh medium was added. At 48hpi cells were quenched and extracted. Absolute intracellular metabolite concentrations were determined by LC-MS/MS and normalized to protein levels. Values are means ± SE (n = 8). (*p<0.05, **p<0.01).

https://doi.org/10.1371/journal.ppat.1007569.g002

UL38 is sufficient to drive metabolic reprogramming

The UL38 protein is expressed at the earliest time of HCMV infection [20], and has been reported to be important for attenuating apoptosis during infection [20–22]. These findings raise the possibility that UL38’s contributions to metabolic reprogramming during infection could be an indirect consequence of other functions during viral infection. To determine if UL38 alone is sufficient to drive metabolic reprogramming, we expressed UL38 via lentiviral transduction (UL38) and found that it accumulated to approximately equivalent levels as during WT HCMV infection (Fig 3A). UL38 expression induced glucose consumption and lactate secretion (Fig 3B). UL38 expression also increased the influx of several amino acids including serine, valine, leucine/isoleucine and glutamine (Fig 3C and 3D), while also inducing the excretion of proline, alanine, ornithine and glutamate (Fig 3C and 3D). Expression of UL38 also induced increases to several intracellular metabolic pools, including citrate/isocitrate, and several key nucleotide biosynthetic intermediates such as N-carbamoyl-asparate and PRPP (Fig 3E and S3A Fig). These data suggest that UL38 is sufficient to drive many of the metabolic changes associated with HCMV infection in the absence of other HCMV proteins.

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Fig 3. UL38 expression is sufficient to drive metabolic activation.

After 24h incubation, conditioned serum free medium from confluent MRC5 cells transduced with an empty vector or UL38 was harvested for analysis. (A) Western blot analysis of UL38 expression in EV and UL38 transduced cells compared to mock and HCMV infected cells (MRC5 cells infected at MOI = 3 and harvested at 53hpi). (B-D) Changes in metabolic intermediates present in the conditioned medium were measured. Values are means ± SE (n = 3). (*p<0.05, **p<0.01). (E) Confluent MRC5 cells expressing an empty vector control (EV) or UL38 protein (UL38) were cultured in serum free media for 24h. Cells were then quenched and extracted for analysis. Absolute intracellular metabolite concentrations were determined by LC-MS/MS and normalized to protein levels. Values are means ± SE (n = 6). (*p<0.05, **p<0.01).

https://doi.org/10.1371/journal.ppat.1007569.g003

UL38-mediated reprogramming of core central carbon metabolic fluxes is mTOR independent

Given that the UL38 protein has been reported to modulate mTORC1 activation [21, 22], and mTORC1 activity is important for some metabolic changes during HCMV infection, we sought to determine if UL38’s metabolic reprogramming role is dependent on mTOR activation. To that end, we treated control or UL38-expressing cells with rapamycin and assessed the metabolic impact. As previously reported, UL38 protein expression induces the activation of mTORC1 [29], as demonstrated by an increase in the abundance of phosphorylated S6K and 4EBP (Fig 4A and S4F Fig). Rapamycin treatment attenuated this activation, as indicated by the reduction in phosphorylated S6K and 4EBP levels (Fig 4A and S4F Fig). However, rapamycin treatment had little impact on UL38-induced glucose consumption or lactate secretion (Fig 4B). Further, rapamycin had little effect on UL38-mediated changes to amino acid metabolism. Alanine and proline secretion, as well as valine and lysine consumption were largely unaffected by rapamycin treatment (Fig 4C and 4D). Rapamycin did appear to reduce glutamine and leucine/isoleucine consumption to a small extent, although these changes were not statistically significant (Fig 4C and 4D). In contrast, rapamycin treatment did impact the intracellular levels of several metabolites in UL38-expressing cells, including several glycolytic metabolites (S4A–S4D Fig). Hierarchical clustering and PLS-DA-based modeling separated UL38-expressing cells from empty vector control cells regardless of rapamycin treatment (S4A–S4C Fig), suggesting that their metabolic states were distinct. However, while some metabolic pools were insensitive to rapamycin treatment, e.g., PRPP, CDP and glycerol phosphate (S4D Fig), many of the greatest UL38-induced increases to metabolite concentrations were reversed, including PEP, 3PG, G3P/DHAP, and malate, among others (S4D Fig). To further explore the role of mTOR in UL38-mediated metabolic reprogramming, we examined the impact of torin-1 treatment, which is an mTOR inhibitor that blocks both the mTORC1 and mTORC2 complexes [30]. As expected, torin-1 treatment blocked the phosphorylation of S6K, AKT and 4EBP (Fig 4E and S4F Fig). Upon torin-1 treatment, UL38 still induced glucose and glutamine consumption, as well as lactate, alanine and proline secretion (Fig 4F and 4G). Torin-1 treatment did reduce the UL38-associated increased consumption of a few amino acids, e.g., phenylalanine and arginine, but the metabolism of many were not affected, e.g., threonine, valine, glutamate, and ornithine (S4E Fig). Collectively, these data indicate that UL38-mediated activation of key central carbon fluxes is largely independent of mTOR activity. Further, the observation that rapamycin did not significantly impact UL38-induced changes to glycolysis and amino acid consumption (Fig 4B–4D), but attenuated increased metabolite concentrations, highlights that metabolite concentrations and metabolic molecular fluxes can be independently regulated. Additionally, in the context of UL38-mediated metabolic activation, these results suggest that mTOR is playing a larger role in the increased metabolite concentrations as compared to the increased molecular metabolic fluxes.

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Fig 4. UL38-induced metabolic flux activation is mTOR independent.

After 24h incubation, conditioned serum free medium from confluent MRC5 cells transduced with an empty vector or UL38 was harvested for analysis. Media contained DMSO (+DMSO), 100 nm of rapamycin (+Rap) or 250nM of Torin-1 (TOR or +Torin1) as indicated. (A, E) Western blot analysis of treated EV and UL38 cells. (B, C, D, F & G) Changes in metabolic intermediates present in the conditioned medium were measured. Values are means ± SE (B-D n = 9, F-G n = 8). (*p<0.05, **p<0.01).

https://doi.org/10.1371/journal.ppat.1007569.g004

A mutant UL38 allele (T23A/Q24A) with reduced TSC2 interaction fails to activate central carbon metabolic fluxes

Previously, UL38 was found to bind and inhibit TSC2 [21, 22], a tumor suppressor that inhibits mTORC1 [31]. TSC2, in conjunction with TSC1, is a GTPase activating protein (GAP) for the Rheb (Ras homolog enriched in brain) GTPase [23, 24]. GTP-bound Rheb directly activates mTORC1, thus TSC2’s GAP activity inhibits mTORC1 [23, 24]. With respect to UL38-mediated inhibition of TSC2, previous work identified a TQ motif at amino acid residues 23 and 24 to be important for its interaction with TSC2, yet dispensable for maintaining mTORC1 activity [22]. We assessed the effects of these mutations on UL38-mediated metabolic modulation. Cells expressing wildtype or mutant UL38 exhibited similar amounts of UL38 protein expression (Fig 5A), and further, as previously described, wildtype UL38 protein interacts with TSC2 (Fig 5B), and this interaction is significantly reduced by the T23A/Q24A substitutions in UL38 (Fig 5B). We next tested how this mutation affected UL38’s metabolic reprogramming ability. Expression of UL38T23A/Q24A (mUL38) failed to induce many of the metabolic phenotypes associated with wildtype UL38 (Fig 5C–5E). Transduction with mUL38 failed to activate glycolysis (Fig 5C) and did not induce UL38-mediated changes to amino acid consumption and secretion (Fig 5D and 5E), highlighting that the mutations that significantly reduce TSC2 interaction also strongly attenuate UL38’s ability to activate central carbon metabolic flux.

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Fig 5. A mutant UL38 allele (T23A/Q24A) that shows reduced binding to TSC2 fails to activate metabolic flux.

(A, C, D & E) Confluent MRC5 cells expressing an empty vector control, mutant UL38 T23A/Q24A (mUL38) or WT UL38 (UL38) were cultured in serum free media for 24h, after which conditioned medium and cells were harvested for analysis. (A) Western blot analysis of EV, mUL38 and UL38 cells. (B) 293T cells were transfected with expressing vectors for UL38, mUL38 or FLAG-TSC2 (TSC2-F) proteins, harvested 48h later and immunoprecipitated with a Flag-specific antibody. Lysate (Lys) represents 10% of the IP input. Protein band intensities are shown relative to the FLAG-TSC2+UL38 value. (C-E) Changes in metabolic intermediates present in the conditioned medium were measured. Values are means ± SE (n = 3). (F) Confluent MRC5 cells expressing EV, mUL38 or UL38 protein were cultured in serum free media for 24h. Cells were then quenched and extracted for LC-MS/MS analysis. Values are means ± SE (n = 9, except n-carb-asp, where n = 3). (*p<0.05, **p<0.01). (G) Confluent MRC5 cells expressing EV, mUL38 or UL38 protein were cultured in serum free media containing DMSO (DMSO) or 100 nm of rapamycin (RAP) for 24h and harvested for western blot analysis.

https://doi.org/10.1371/journal.ppat.1007569.g005

In contrast to the impact on glycolytic and amino acid fluxes, cells expressing mUL38 still exhibited increased levels of several intracellular metabolites, including central carbon metabolites, such as PEP and citrate/isocitrate, various UDP-sugar intermediates including UDP-glucose and UDP-N-acetylglucosamine, and core pyrimidine metabolites, such as N-carbamoyl-asparate and UTP (Fig 5F and S5 Fig). Further highlighting the similarity in metabolite abundances between WT UL38 and mUL38 expressing cells, there was extensive overlap between these cells with respect to hierarchical clustering and a PLS-DA model of metabolite concentrations (S5A–S5C Fig). This disconnect between metabolic flux and metabolite concentrations was observed earlier with rapamycin treatment of UL38-expressing cells (Fig 4 and S4 Fig), i.e. rapamycin treatment did not substantially change UL38-induced molecular flux rates, but did reduce intracellular metabolite pools (Fig 4 and S4 Fig). The current mUL38 results underscore the importance of mTORC1 in increasing metabolite pool sizes, as mutant UL38 expression maintains mTORC1 activation as analyzed by S6K phosphorylation (Fig 5G). Given that mUL38 has been demonstrated to maintain mTORC1 activation [22], our collective data suggest that mTORC1 activity is not required for UL38-mediated induction of metabolic flux, e.g., consumption keyshot + crack glucose or specific amino acids, but is important for increasing the concentrations of specific metabolite pools. In total, our results suggest that the UL38 TQ motif, which is important for TSC2 binding, is necessary for metabolic flux remodeling, but does not affect the mTORC1-mediated increases to specific metabolic pools.

TSC2 knock-down phenocopies UL38-mediated metabolic activation

Our results suggest that UL38’s role in metabolic activation may be dependent on its inhibition of TSC2. This would suggest that TSC2 knockdown should result in similar metabolic phenotypes as UL38 expression. To test this prediction, we measured the metabolic impact of targeting TSC2 with shRNA. Lentiviral-delivered TSC2-specfic shRNA resulted in ~50% reduction in TSC2 protein abundance relative to vector control cells (Fig 6A). TSC2 knockdown also increased the accumulation of phosphorylated S6K, indicative of active mTORC1 (Fig 6A). Similar to UL38 expression, knockdown of TSC2 substantially increased glycolysis and lactate secretion (Fig 6B). Also analogous to expression of UL38, TSC2 knockdown increased glutamine, serine and valine consumption, and elevated the secretion of alanine, proline and glutamate (Fig 6C and 6D). Further, knockdown of TSC2 also induced changes to several intracellular metabolic pools, including glycolytic metabolites, UDP-sugars and nucleotide intermediates/end products such as G3P/DHAP, PEP, UDP-glucose, ADP, NADH and NADPH (S6A and S6B Fig). These results are consistent with UL38 modulating cellular metabolism via inhibition of TSC2.

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Fig 6. TSC2 knock-down induces activation of metabolic fluxes independent of mTORC1.

(A-D) HFF cells were transduced with control (pLKO) or TSC2-specific shRNA (TSC2 KD) -expressing lentiviruses and selected. Confluent cells were cultured in serum free media for 24h, at which time the conditioned medium and cells were harvested for analysis. (A) Western blot analysis of pLKO and TSC2 KD cells. Protein band intensities are shown relative to the pLKO control value. Arrows indicate both p70 and p85 isoform of S6K (B-D). Changes in metabolic intermediates present in the conditioned medium were measured. Values are means ± SE (n = 4). (E-H) Confluent HFF cells expressing pLKO or TSC2-specific shRNA were cultured in serum free media containing DMSO (+DMSO) or 100 nm of rapamycin (+Rap) for 24h, at which time the conditioned medium and cells were harvested for analysis. (E) Western blot analysis of pLKO and TSC2 KD drug treated cells. Protein band intensities are shown relative to the pLKO+DMSO control value. (F-H) Changes in metabolic intermediates present in the conditioned medium were measured by LC-MS/MS. Values are means ± SE (n = 4). (*p<0.05, **p<0.01).

https://doi.org/10.1371/journal.ppat.1007569.g006

Given that UL38-mediated activation of glycolytic and amino acid fluxes is rapamycin insensitive, if UL38 is mediating metabolic remodeling via inhibition of TSC2, we hypothesized that the metabolic flux remodeling associated with TSC2 knockdown should also be rapamycin insensitive. We assessed the effect of rapamycin treatment on the metabolic impact of TSC knockdown to test this prediction. As hypothesized, rapamycin treatment inhibited mTORC1 as demonstrated by the depletion of phosphorylated S6K (Fig 6E). Rapamycin treatment had little effect on the TSC2-knockdown-mediated induction of glucose and glutamine consumption or the excretion of lactate, alanine or glutamate (Fig 6F–6H). These results largely mirror the observations that UL38-mediated remodeling of many metabolic fluxes are TSC2 dependent but mTORC1 independent.

Discussion

Viruses are obligate parasites that depend on cellular metabolic resources for their replication. Increasingly, viruses are being found to induce specific metabolic activities that are important for infection [5, 16, 32–35]. However, the mechanisms through which viruses modulate host cell metabolism have largely remained a mystery. Here we show that the HCMV UL38 protein is a key virally-encoded metabolic regulator. We find that UL38 expression is necessary and sufficient to drive multiple aspects of HCMV-mediated metabolic reprogramming, including activation of glycolytic and amino acid catabolic fluxes, activities that have been previously shown to be critical for high-titer HCMV infection [16, 18, 36]. Given the viral dependence on these metabolic activities, the mechanisms responsible may represent therapeutic vulnerabilities that could be exploited to attenuate infection.

We find that the HCMV UL38 protein is necessary for many HCMV-induced metabolic alterations, e.g., induction of glucose and glutamine consumption as well as lactate secretion (Figs 2 and 7). Further, expression of UL38 is sufficient to drive many of these activities, including glucose consumption and lactate secretion, and the consumption and secretion of a number of different amino acids (Figs 3 and 7). While there was extensive overlap between the metabolic phenotypes induced by HCMV infection and those induced by UL38 expression, they were not identical. Several metabolic activities were induced by UL38 expression but not impacted by HCMV infection. For example, UL38 expression induced lysine consumption and tyrosine secretion, but these fluxes where not affected in the context of HCMV infection. We speculate that these changes may reflect the anabolic differences between HCMV-infected cells and uninfected cells expressing UL38. Specifically, virally directed biosynthetic activities such as viral protein synthesis, viral DNA replication, and envelope biogenesis, likely impact the requirements for specific amino acids, and thereby impact nutrient uptake and waste excretion.

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Fig 7. Model of UL38-mediated metabolic remodeling.

The HCMV UL38 protein interacts with and inhibits TSC2, a key mTOR regulator. UL38 expression or TSC2 knock-down activates glycolytic and specific amino acid turnover rates, and increases metabolite concentrations. The induction of specific metabolic fluxes, e.g., glucose and glutamine consumption, and lactate, alanine and proline secretion are independent of mTOR activation, whereas the increases in metabolite pools sizes are largely mTOR dependent.

https://doi.org/10.1371/journal.ppat.1007569.g007

Our results also highlight novel metabolic activities induced by HCMV infection. For example, we find that HCMV induces the secretion of ornithine, an arginine and polyamine biosynthetic intermediate, as well as proline (Fig 1). These increases were largely independent of the presence of UL38 (Fig 2C), suggesting that other viral factors are responsible for driving the bulk of ornithine and proline secretion. The mechanisms involved in their activation, and how these virally-induced metabolic phenotypes contribute to infection remains to be elucidated. Additionally, it is important to note that our analysis of these metabolic changes occurred over a specific time frame of infection, 36-60hpi for analysis metabolic fluxes and 48hpi for analysis of intracellular metabolic concentrations, respectively. This time frame represents a metabolically active stage in the viral life cycle, with robust viral DNA synthesis occurring. However, the metabolic consequences of infection could be substantially different at other time points of the viral life cycle. Further, the viral requirements for specific metabolic activities could change over the course of infection.

Several UL38-activated metabolic fluxes were largely resistant to torin-1-mediated mTOR inhibition, e.g., glucose and glutamine consumption, as well as lactate, proline and alanine secretion (Figs 4G and 7). Other UL38-activated fluxes were more sensitive to mTOR inhibition, most notably arginine and phenylalanine consumption (S4E Fig), indicating that mTOR plays different roles in the regulation of these metabolic pathways. In contrast to the resistance of certain metabolic fluxes to mTOR inhibition, rapamycin treatment largely reversed most of the increases in metabolite concentrations associated with UL38 expression. Analogously, relative to wildtype UL38, expression of mUL38 resulted in reduced metabolic fluxes, but maintained mTORC1 activity, as measured by S6K phosphorylation, and largely increased metabolite pool concentrations (Fig 5 and S5 Fig). These data indicate that metabolite concentrations and molecular flux rates can be independently regulated. Further, they suggest that in the current context, mTOR has a nuanced regulatory role in mediating metabolite concentrations and flux rates, the specific mechanisms of which require further elucidation. Given the generalized importance of metabolic regulation in a number of disease pathologies, e.g., cancer formation and metabolic syndrome, further elucidation of the mechanisms of metabolic flux control should be a high priority.

In contrast to certain core metabolic fluxes, rapamycin treatment completely reversed the HCMV-induced changes to N-carbamoyl-aspartate and PRPP (Fig 1I), key pyrimidine biosynthetic intermediates. Similar decreases in pyrimidine biosynthetic intermediates were observed in rapamycin treated UL38-expressing cells (S4 Fig). These results likely reflect the described roles that mTORC1 and S6K play in regulating pyrimidine metabolism [37]. Indeed, treatment with rapamycin analogs has resulted in clinical benefits with respect to HCMV infection [38]. Given that pyrimidine metabolism is important for HCMV infection [4], a possible link between the anti-HCMV effect of rapamycin and rapamycin’s impact on HCMV-induced changes to pyrimidine metabolism is worthy of further examination.

The relative insensitivity of HCMV and UL38-mediated metabolic activation to mTOR inhibition was similar for several metabolic fluxes, including glucose and serine consumption, as well as proline secretion (Figs 1 and 4). However, in other cases, e.g., glutamate and lactate secretion, the HCMV-induced metabolic changes appeared to be more sensitive to mTOR inhibition relative to their induction by UL38 expression in isolation. We speculate that the increased sensitivity to mTOR inhibition during viral infection reflects the pleotropic effects of mTOR inhibition during the viral life cycle. Numerous HCMV gene products alter various signaling pathways including, NFκB, PI3K, and various cell cycle pathways [39–41], all of which have functional links to mTOR and metabolism [42–44]. Given the role of mTOR in translational regulation, the delicate balance of viral gene interactions with these pathways would likely be dramatically affected by mTOR inhibition. We speculate that in uninfected cells expressing UL38, the absence of these confounding virus-host signaling interactions likely differentially impact the metabolic response to mTOR inhibition.

Our results strongly suggest that UL38 mediates metabolic reprogramming via inhibition of the cellular TSC2 protein. A mutant UL38T23A/Q24A protein, which exhibits significantly reduced TSC2 binding, does not induce the activation of central carbon fluxes (Figs 5 and 7). Further, TSC2 knockdown largely phenocopies the metabolic phenotypes associated with UL38 expression (Fig 6). It is possible that the UL38T23A/Q24A mutation impacts a non-TSC2-related function of UL38 that is important for metabolic remodeling, and therefore, UL38 could be inducing metabolic remodeling through a non-TSC2 mechanism. We think this is very unlikely. For one, the UL38T23A/Q24A allele accumulates to wildtype levels and retains several functions ascribed to wildtype UL38 (Fig 5A and 5G and [22]). This suggests that the UL38T23A/Q24A is not grossly defective. Further, the extent of overlap in the metabolic phenotypes associated with UL38 expression and TSC2 knockdown is large and unlikely to be coincidental, e.g., induction of glycolysis, glutaminolysis, as well as the consumption and secretion of several other amino acids. Collectively, these data support the model that UL38’s metabolic manipulation is largely due to TSC2 inhibition.

TSC2 is a tumor suppressor and well-known inhibitor of mTORC1, which globally regulates cellular metabolism in many contexts, e.g., fluctuations in nutrient availability or in response to various signal transduction pathways [45]. Surprisingly, as noted above, UL38’s role in inducing many metabolic fluxes appears to be independent of activated mTOR. UL38-mediated activation of glycolysis, glutamine consumption, and secretion of proline and alanine were resistant to mTOR inhibition (Figs 4, 6 and 7). Our results indicating that UL38-mediated metabolic activation depends on its interaction with TSC2 suggests that additional mTOR-independent roles for TSC2 contribute to metabolic regulation. While the vast majority of research on TSC2 focuses on its mTOR related activities, a few manuscripts describe mTOR independent activities. For example, TSC2 has been implicated in mTOR-independent vascular endothelial growth factor (VEGF) signaling, as well as in mTOR-independent stem cell self-renewal and differentiation [46, 47]. The F.lux License Key - Crack Key For U complex has also been shown to regulate PAK2 activity independently of mTOR [48]. Aside from the aforementioned mTOR-independent TSC2 phenotypes, to our knowledge, prior to this study, there is no evidence that TSC2 can regulate metabolism independent of its effects on mTOR. Further work will elucidate how these mTOR-independent activities of the TSC complex contribute to overall cellular metabolic regulation and tumor formation. With respect to HCMV infection, TSC2 inactivation and mTOR signaling can modulate diverse signaling processes including metabolism, translation and autophagy [49], and it remains to be determined how the different facets of these two important regulatory signaling components contribute to successful HCMV infection.

The UL38 protein is critical for successful HCMV infection [20], and has been strongly implicated in a number of diverse cellular f.lux License Key - Crack Key For U. UL38 was first found to block ER stress-induced apoptosis [20, 50], and was subsequently found to increase mTORC1 activity [21]. Further, UL38 increases the expression of fatty acid elongases that are important for infection [51]. Likely as part of its role in modulating mTORC1 activity, UL38 also enhances the polysome association and thereby the translational efficiency of specific mRNAs [52]. These UL38-associated activities could be independent from one another; there are multiple examples of viral proteins with independent functional roles (reviewed in [53]). Supporting this view, mutational analysis of UL38 suggests that the inhibition of cell death and mTORC1 activation are separable [29]. However, functional overlap between various UL38 phenotypes could still exist. Numerous links exist between cellular metabolism and both translation and apoptosis. For example, amino acids levels are actively sensed by GCN2, and if amino acid levels are insufficient, translation is inhibited [54]. Further, translational regulatory controls drive the expression of rate-determining nucleotide biosynthetic enzymes to coordinate nucleotide and protein biosynthesis [55]. Similarly, glucose is actively sensed through multiple mechanisms, that ultimately induce apoptosis if concentrations are insufficient [56, 57], and activation of glycolysis has more recently been found to actively inhibit apoptotic signaling [58, 59]. Similar functional links exist between glycolysis, glycosylation and ER stress [60, 61]. While the inhibition of TSC2 appears to be critical for UL38-mediated metabolic modulation, the exact mechanisms through which UL38 modulates apoptosis and translation still require significant elucidation.

It has become clear that viruses actively modulate metabolism to support infection (reviewed in [8]). Viral metabolic modulation could be contributing to the production of energy and biomolecular subunits necessary for virion production. Other contributions include induction of lipid metabolic enzymes critical for the organization of viral maturation compartments [33, 62] or the production of specialized virion components [51]. In addition, increasing evidence suggests that metabolic signaling is playing a deterministic role in various cell fate decisions, including modulating cell death [63], immune responses [64] and stem cell differentiation [65]. Collectively, these findings raise the possibility that viral metabolic manipulation could potentially be more complex than providing a single metabolic activity to support infection, but rather, could be responsible for inducing a broader pro-viral cellular state. In this regard, it remains to be determined whether evolutionarily divergent viruses induce similar metabolic states to support infection. If so, the mechanisms to do so would likely diverge, e.g., UL38 is only conserved among beta herpesviruses. Regardless, it seems very likely that host cells do not simply cede the metabolic controls to viral pathogens, but rather that these controls serve as a core host-pathogen interaction. Here, we find that the HCMV UL38 protein is a major viral player in this interaction, driving a large portion of the HCMV-induced metabolic program through targeting the cellular TSC2 metabolic regulator.

Materials and methods

Cell culture and viral infections

Human 293T cells (ATC CCRL-3216), MRC5 human fibroblasts (ATCC CCL-171), telomerase-immortalized HFF fibroblasts (HFF), telomerase-immortalized MRC5 fibroblasts and their derived recombinant cells lines (see below) were cultured in Dulbecco's modified Eagle medium (DMEM; Invitrogen) supplemented with 10% fetal bovine serum, 4.5 g/liter glucose, and 1% penicillin-streptomycin (Pen-Strep; Life Technologies) at 37°C in a 5% (vol/vol) CO2 atmosphere. All experiments involving MRC5 cells utilized MRC5 cells that express hTERT, with the exception of the experiments in S1 Fig, which were performed using non-hTERT expressing MRC5 cells. Before HCMV infection, MRC5 cells were grown to confluence, resulting in ∼3.2 × 104 cells per cm2. Once confluent, medium was removed, and serum-free medium was added. Cells were maintained in serum-free medium for 24h before infection at which point they were mock infected or infected at a multiplicity of infection of 3.0 pfu/ cell. After a 2h adsorption period, the inoculum was aspirated and fresh serum-free medium was added. Conditioned medium and cells were harvested for metabolic, transcriptional, or total protein analysis at various times after the initiation of infection. Unless indicated otherwise, the strain utilized for viral infections was BADwt derived from a bacterial artificial chromosome (BAC) clone of the HCMV AD169 laboratory strain [66]. The recombinant HCMV-ΔUL38 BAC derived virus which lacks the entire UL38 allele, was courteously provided by Thomas Shenk, Princeton University (ADdlUL38) [20]. For counting cells, adherent cells were washed with phosphate-buffered saline (PBS), trypsinized and homogenized in supplemented DMEM medium. An aliquot of the cell suspension was mixed 1:1 with 0.4% trypan blue solution and counted using a TC10 automated cell counter (Bio-Rad), following the manufacturer's instructions. Live cell counts, i.e. trypan blue excluding cells, were used for normalizations.

Compounds

Rapamycin (Sigma-Aldrich) and Torin-1 (ApexBio) were prepared at 100uM and 250uM respectively in dimethyl sulfoxide (DMSO). Standards for LC-MS flux analysis that were not present in DMEM include: Lactic acid (Acros Organics), L-glutamic acid (Sigma-Aldrich), L-alanine (VWR), L-ornithine (Alfa Aesar) and L-proline (Alfa Aesar), which were prepared in OmniSolv Water (MilliporeSigma) at 710 mM, 16 mM, 16 mM, 0.5mM and 8mM respectively.

Cloning

The human telomerase (hTERT) cDNA was amplified by PCR from pWZL-Blast-Flag-HA-hTERT (Addgene plasmid 22396) using the following primers: forward primer 5′-GGAACCAATTCAGTCGACTGGGATCCCGTCCTGCTGCGCACGTG-3′ and reverse primer 5′-TTTGTACAAGAAAGCTGGGTTCTAGATCAGTCCAGGATGGTCTTGAAGTCTG-3′. hTERT cDNA was then cloned via Gibson assembly into the BamHI and XbaI sites of pLenti CMV/TO/Hygro (Addgene plasmid 17484) [67]. Wild type TB40/e UL38 allele (UL38) was amplified by PCR from the TB40/e BAC clone (EF999921.1) using the following primers: forward primer 5’- CTTTAAAGGAACCAATTCAGTCGACTGGATCATGACTACGACCACGCATAGCACCGCCGC-3’ and reverse primer 5’- AACCACTTTGTACAAGAAAGCTGGGTCTAGCTAGACCACGACCACCATCTGTACCACGTC-3’.

A TB40/e mutant UL38 allele-T23A/Q24A (mUL38) was synthetized as a 996bp gBlocks Gene Fragment (IDT) using the TB40/e UL38 sequence (EF999921.1) and mutating the sequence corresponding to the 23 and 24 amino acids [22]. This construct was amplified by PCR using the same primers described above for the wild type UL38 allele. Both UL38 and mUL38 constructs were then cloned via Gibson assembly into a BamHI and XbaI digested pLenti CMV/TO Puro plasmid (Addgene plasmid 22262). pLenti CMV/TO/Puro/empty (EV) was provided by Hartmut Land, University of Rochester.

Lentiviral transfection and transduction

293T cells were seeded at 2 × 106 cells per 10-cm dish and grown for 24h. For the generation of pseudotyped lentivirus, each 10-cm dish of 293T cells was transfected with 2.6 μg lentiviral vector, 2.4 μg PAX2, and 0.25 μg vesicular stomatitis virus G glycoprotein using the Fugene 6 reagent (Promega). Twenty-four hours later, the medium was removed and replaced with 4 ml of fresh medium. Lentivirus–containing medium was collected after an additional 24 h and filtered through a 0.45μm pore-size filter prior to transduction. The fibroblasts were transduced with lentivirus in the presence of 5 μg/ml Polybrene (Millipore Sigma) and incubated overnight. The lentivirus-containing medium was then removed and replaced with fresh DMEM. At 72 h after transduction, the cells were placed under selection with antibiotics. Cells transduced with pLenti CMV/TO/Hygro/hTERT were grown in 200 μg/ml Hygromycin B (Invitrogen) for 1 week, and the expression of hTERT was confirmed by quantitative PCR (qPCR). Cells transduced with pLenti CMV/TO/Puro/empty, pLenti CMV/TO/Puro/UL38 or pLenti CMV/TO/Puro/mUL38 were selected in 10 μg/ml Puromycin (MilliporeSigma) for 4 days. At the time of antibiotic selection of transduced cells, non-transduced control cells were also treated with Puromycin or Hygromycin as appropriate to ensure killing of non-transduced cells and ubiquitous transduction efficiencies. The expression of UL38 in these cells was confirmed by Western blot (WB). Empty vector or UL38-expressing cells were cultured in serum free media for 24 h prior to analysis.

Immunoprecipitation

293T cells (~60% confluent) grown in 10-cm dishes were transiently transfected with pRK7-FLAG-TSC2 (Addgene plasmid 8996), CMV/TO/Puro/UL38 or pLenti CMV/TO/Puro/mUL38 using the Fugene 6 reagent (Promega) according to the manufacturer's instructions. Twenty-four hours later, the medium was removed and replaced with fresh medium. Forty-eight hours post-transfection, cells were scraped and harvested in 750 ul of RIPA buffer (Tris-HCl, 50 mM, pH 7.4; 1% Triton X-100; 0.25% Na-deoxycholate; 150 mM NaCl; 1 mM EDTA) supplemented with Pierce Protease Inhibitor tablets (PI; Thermo Scientific). Lysates were sonicated and incubated on ice for 30 min with vortexing for 5 sec every 5 min. Insoluble material was pelleted by centrifugation at 16,000 x g for 5 min at 4o C. ANTI-FLAG M2 Affinity Gel (Sigma-Aldrich) in RIPA+PI buffer was added and the sample was incubated for 2h at 4o C with rotation. The agarose beads were pelleted and washed 5 times with RIPA+PI buffer. Following the final wash, residual buffer was removed and the beads were resuspended in disruption buffer (see below), boiled at 100°C for 5 min, and insoluble material pelleted by spinning for 3 min at room temperature at 16,000 x g. Samples were resolved on 10% SDS-containing polyacrylamide gels, and proteins were identified by Western blot [68].

Protein analysis

For Western blot assays [69] cells were scraped and solubilized in disruption buffer containing 50 mM Tris (pH 7.0), 2% SDS, 5% 2-mercaptoethanol, and 2.75% sucrose. The resulting extracts were sonicated, boiled for 5 min, and centrifuged at 14,000 × g for 5 min to pellet insoluble material. The extracts were then subjected to electrophoresis in an 8 or 10% SDS polyacrylamide gel and transferred to a nitrocellulose sheet. The blots were then stained with Ponceau S to ensure equivalent protein loading and transfer, blocked by incubation in 5% milk in TBST (50 mM Tris-HCl, pH 7.6, 150 wondershare filmora 32 bit NaCl, 0.1% Tween 20), and reacted with primary and, subsequently, secondary antibodies. Protein bands were visualized using an enhanced chemiluminescence (ECL) system (Bio-Rad) and by using the Molecular Imager Gel Doc XR+ system (Bio-Rad). For protein band quantifications, the Molecular Imager Gel Doc was used and band intensities were integrated by using ImageJ software. The antibodies used were specific for p70 S6 Kinase (S6K; Cell Signaling), phospho-p70 S6 Kinase (Thr389) (pS6K; Cell Signaling), tuberin (TSC2; Santa Cruz Biotechnology), glyceraldehyde-3-phosphate dehydrogenase (GAPDH; Cell Signaling Technology) anti-UL38 (8D6) [20], anti-IE1 [70] and ANTI-FLAG M2 (Sigma-Aldrich). For total protein analysis, cells were washed with PBS, scraped in 1ml of RIPA buffer supplemented with Pierce Protease Inhibitor tablets (Thermo Scientific) and vortexed. After 10 min on wet ice, lysates were centrifuged at 14,000 × g for 10 min. The protein concentration of supernatants was determined by using the Bradford assay (Bio-Rad).

shRNA knockdown

Human TSC2 mRNA expression was targeted by using a TSC2-specific MISSION shRNA construct (#TRCN0000010454, Sigma-Aldrich) selected after a screening process in which TSC2-knockdown was assessed by qPCR and WB. For the shRNA transductions, pseudotyped lentiviruses were generated using the previously mentioned lentiviral transfection protocol using #TRCN0000010454 vector and non-target control MISSION pLKO.1-puro (SHC001; Sigma-Aldrich). HFF fibroblasts at 30% confluence were transduced with half of the filtered lentivirus-containing medium supplemented with 5ug/ml of polybrene. Cells were incubated overnight and the lentivirus-containing medium was then removed and replaced with fresh DMEM. At 72 h after transduction, the cells were placed under 10 μg/ml Puromycin selection for 4 days. The knockdown of TSC2 in these cells was confirmed by Western blot (WB) for all subsequent experiments.

Measurement of metabolic fluxes and concentrations

For quantification of metabolic consumptions and secretions, cells were plated in 10-cm dishes. Once confluent, medium was removed and serum-free medium was added with or without chemical inhibitors as indicated. An aliquot of this virgin medium was saved to be used as t = 0 control. Cells were maintained in this serum-free medium for 24h, at which time conditioned medium was collected for glucose measurement or LC-MS/MS analysis, and cells were harvested for qPCR, WB or cell counts.

Glucose consumption rates were quantified using the HemoCue Glucose 201 System (HemoCue). A glucose standard curve was utilized for each experiment using the t = 0 virgin DMEM medium (4.5 g/liter glucose) serially diluted in PBS. Conditioned medium samples were then diluted serially 1/4 in PBS to ensure signal linearity. The glucose present in each sample was measured using the HemoCue System and normalized using the generated standard curve. To obtain consumption values, the glucose value measured for normalized virgin DMEM medium was subtracted from the result of each normalized conditioned medium value. These values were then normalized to the number of live cells counted in each plate. A negative f.lux License Key - Crack Key For U indicates glucose has been consumed (less glucose in the conditioned medium than in the virgin medium).

For quantification of metabolic fluxes, serially diluted supplemented t = 0 virgin DMEM (see compounds section) and conditioned medium samples diluted 1/2 in OmniSolv Water were diluted 1/100 in 80% methanol. Samples were then centrifuged at 4°C for 5 minutes at full speed to pellet insoluble material. For amino acid quantification, 100 μl of the above methanol dilutions were derivatized with 1 μl benzyl chloroformate and 5 μl trimethylamine. The samples were then centrifuged at 4°C for 5 minutes at full speed to pellet insoluble material and subsequently analyzed by LC-MS/MS as indicated below. For lactate quantification, 100ul of the underivatized methanol dilutions were centrifuged at 4°C for 5 minutes at full speed to pellet insoluble and analyzed by LC-MS/MS as indicated below.

For quantification of intracellular metabolite concentrations, cells were plated in 10-cm dishes and once confluent, medium was removed and changed to serum-free medium supplemented with 10mM HEPES, 1% penicillin-streptomycin and chemical inhibitors as indicated. Cells were maintained in this serum-free medium for 24h, and one hour prior to metabolite extraction medium was once again changed. Medium was aspirated and 80:20 OmniSolv Methanol: OmniSolv Water (80% methanol) at −80°C was immediately added to quench metabolic activity and extract metabolites. Cells were then incubated at −80°C for 10 min. Following cell quenching, cells were scraped in the dish and kept on dry ice, and the resulting cell suspension vortexed, centrifuged at 3,000 × g for 5 min, and reextracted twice more with 80% methanol at −80°C. After pooling the three extractions, the samples were completely dried under N2 gas, dissolved in 175 μl 50:50 OmniSolv Methanol: OmniSolv Water methanol, and centrifuged at 13,000 × g for 5 min to remove debris. Samples were loaded in the LC-MS/MS for analysis as indicated below.

LC-MS/MS analysis and normalization

Metabolites were analyzed using reverse phase chromatography with an ion-paring reagent in a Shimadzu HPLC coupled to a Thermo Quantum triple quadrupole mass spectrometer running in negative mode with selected-reaction monitoring (SRM) specific scans as previously described [4, 71]. LC-MS/MS data were then analyzed using the publicly available mzRock machine learning toolkit (http://code.google.com/p/mzrock/), which automates SRM/HPLC feature detection, grouping, signal to noise classification, and comparison to known metabolite retention times [72].

For relative quantification of intracellular metabolite levels, protein-normalized peak heights were normalized by the maximum value for a specific metabolite measured across the samples run on a given day. This normalization serves to reduce the impact of inter-day mass spectrometry variability, i.e. batch effects, while preserving relative differences between samples.

For quantification of metabolite consumption and secretion, the concentrations of control cell (either Mock or EV cells) media metabolites were estimated by comparing the extracted ion chromatograms of metabolite-specific SRM peak heights to those of metabolite standard dilution curves. Extracts of control cell f.lux License Key - Crack Key For U were subsequently used as standards to estimate the absolute media metabolite abundances for the other samples. Consumption and secretion rates were obtained by subtracting the concentration of virgin medium metabolites from the conditioned media metabolite concentrations. The resulting values were then normalized to the number of live cells counted for each sample. A negative rate indicates the compound has been consumed (less of that compound in the conditioned medium than in the virgin medium) and a positive rate indicates that the metabolite has been secreted into the medium.

Statistics

Statistical analysis of the reported metabolic data were performed using JMP Statistical Analysis Software (https://www.jmp.com/). Response Screening was performed using one-way ANOVA, with False Discovery Rate (FDR) correction as described [73]. Robust Estimation, i.e. a Huber M-estimation, was employed to limit the sensitivity of outliers. Data were judged significantly different if the robust estimated FDR-corrected value p-value was <0.05. Although plotted separately, for the most accurate statistical modeling, and to increase the associated statistical power, the data comparing EV versus UL38 in Fig 3 and Fig 5 were combined for statistical comparisons. Statistics for all figures are available in S1 File.

Protein normalized concentration data were utilized for PLS-DA modeling and hierarchical clustering, both of which were performed using the publicly available software MetaboAnalyst 3.0 (http://www.metaboanalyst.ca) [74]. PLS-DA regression was performed using the plsr function provided by the R pls package [75]. Model classification and cross-validation were performed using the corresponding wrapper function in the R caret package [76]. Permutation testing was performed on the PLS-DA model class assignments, with 1,000 permutations, yielding a p-value less than 10−3. Agglomerative hierarchical clustering was performed with the hclust function in R stat package, using Euclidean distance as the similarity measure, and Ward’s linkage as the clustering f.lux License Key - Crack Key For U information

S1 Fig. The impact of rapamycin treatment on HCMV-induced metabolite pools.

MRC5 cells were mock-infected (Mock) or infected with HCMV (HCMV) (MOI = 3) and 24h after, fresh medium containing DMSO (+DMSO) or 100 nm of rapamycin (+Rap) was added. At 48hpi cells were quenched and extracted. Absolute intracellular metabolite concentrations were determined by LC-MS/MS and normalized to protein levels. (A) Heatmap of clustered metabolite pools. (B) Partial least-squares discriminant analysis (PLS-DA) of metabolic concentrations. (C) Loading plot for PLS-DA model. Values are means ± SE (n = 4). (*p<0.05, **p<0.01). (D) Western blot analysis of mock and HCMV-infected drug treated cells. MRC5 cells were mock or HCMV-infected (MOI = 3). At 36hpi, fresh medium containing DMSO (DMSO), 100 nm of rapamycin (Rap) or 250nM of Torin-1 (Torin1) were added to the plates and cells were harvested after 24h (60hpi).

https://doi.org/10.1371/journal.ppat.1007569.s001

(TIF)

S2 Fig. UL38 protein is important for the induction of several intracellular metabolic pools during HCMV infection.

MRC5 cells were mock-infected (Mock), infected with a defective UL38 HCMV virus (ΔUL38) or infected with WT HCMV (WT) (MOI = 3) and 24h after fresh medium was added. At 48hpi cells were quenched and extracted. Absolute intracellular metabolite concentrations were determined by LC-MS/MS and normalized to protein levels. (A) Heatmap of clustered metabolite pools. (B) Partial least-squares discriminant analysis (PLS-DA) of metabolic concentrations. (C) Loading plot for PLS-DA model. Values are means ± SE (n = 8). (*p<0.05, **p<0.01).

https://doi.org/10.1371/journal.ppat.1007569.s002

(TIF)

S3 Fig. UL38 expression is sufficient to induce several intracellular metabolic pools.

Confluent MRC5 cells expressing an empty vector control (EV) or UL38 protein (UL38) were cultured in serum free media for 24h. Cells were then quenched and extracted for analysis. Absolute intracellular metabolite concentrations were determined by LC-MS/MS and normalized to protein levels. (A) Heatmap of clustered metabolite pools. Values are means ± SE (n = 6). (*p<0.05, **p<0.01).

https://doi.org/10.1371/journal.ppat.1007569.s003

(TIF)

S4 Fig. Impact of mTOR inhibitors on UL38-induced metabolic reprogramming.

(A-D) Confluent MRC5 cells expressing an empty vector control (EV) or UL38 protein (UL38) were cultured in serum free media containing DMSO (+DMSO) or 100 nm of rapamycin (+Rap) for 24h. Cells were then quenched and extracted. Absolute intracellular metabolite concentrations were determined by LC-MS/MS and normalized to protein levels. (A) Heatmap of clustered metabolite pools. (B) Partial least-squares discriminant analysis (PLS-DA) of metabolic concentrations. (C) Loading plot for PLS-DA model. (D) Plotted selected metabolites. Values are means ± SE (n = 8). (E) Confluent MRC5 cells expressing EV or UL38 protein were cultured for 24h in serum free media containing DMSO (+DMSO) or Torin-1 (+Torin1). Conditioned medium and cells were harvested after 24h for analysis. Values are means ± SE. (n = 8) (*p<0.05, **p<0.01). (F) Western blot analysis of drug treated EV and UL38 cells (D = DMSO; R = Rapamycin; T = Torin1). Samples correspond to experiments described in Fig 4.

https://doi.org/10.1371/journal.ppat.1007569.s004

(TIF)

S5 Fig. The mutant UL38 allele (T23A/Q24A) maintains the induction of intracellular metabolic pools.

Confluent MRC5 cells expressing an empty vector control (EV), mutant UL38 T23A/Q24A (mUL38) or WT UL38 (UL38) were cultured in serum free media for 24h prior to metabolic quenching and extraction. Cellular absolute intracellular metabolite concentrations were determined by LC-MS/MS and normalized to protein levels. (A) Heatmap of clustered metabolite pools. (B) Partial least-squares discriminant analysis (PLS-DA) of metabolic concentrations. (C) Loading plot for PLS-DA model. (D) Plotted selected metabolites. Values are means ± SE (n = 9). (*p<0.05, **p<0.01).

https://doi.org/10.1371/journal.ppat.1007569.s005

(TIF)

S6 Fig. Impact of TSC2 knockdown on cellular metabolite pool concentrations.

HFF cells were transduced with control (pLKO) or TSC2-specific shRNA (TSC2 KD)-expressing lentiviruses and selected. Confluent cells were cultured in serum free media for 24h before quenching and extraction. Absolute intracellular metabolite concentrations were determined by LC-MS/MS and normalized to protein levels. (A) Heatmap of clustered metabolite pools. (B) Plotted selected metabolites. Values are means ± SE (n = 3).

https://doi.org/10.1371/journal.ppat.1007569.s006

(TIF)

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  56. 57. Danial NN, Gramm CF, Scorrano L, Zhang CY, Krauss S, Ranger AM, et al. BAD and glucokinase reside in a mitochondrial complex that integrates glycolysis and apoptosis. Nature. 2003;424(6951):952–6. Epub 2003/08/22. pmid:12931191.
  57. 58. Li FL, Liu JP, Bao RX, Yan G, Feng X, Xu YP, et al. Acetylation accumulates PFKFB3 in cytoplasm to promote glycolysis and protects cells from cisplatin-induced apoptosis. Nat Commun. 2018;9(1):508. Epub 2018/02/08. pmid:29410405; PubMed Central PMCID: PMCPMC5802808.
  58. 59. Abu-Hamad S, Zaid H, Israelson A, Nahon E, Shoshan-Barmatz V. Hexokinase-I protection against apoptotic cell death is mediated via interaction with the voltage-dependent anion channel-1: mapping the site of binding. J Biol Chem. 2008;283(19):13482–90. Epub 2008/03/01. pmid:18308720.
  59. 60. Xi H, Kurtoglu M, Liu H, Wangpaichitr M, You M, Liu X, et al. 2-Deoxy-D-glucose activates autophagy via endoplasmic reticulum stress rather than ATP depletion. Cancer chemotherapy and pharmacology. 2011;67(4):899–910. Epub 2010/07/02. pmid:20593179; PubMed Central PMCID: PMCPMC3093301.
  60. 61. Yu SM, Kim SJ. Endoplasmic reticulum stress (ER-stress) by 2-deoxy-D-glucose (2DG) reduces cyclooxygenase-2 (COX-2) expression and N-glycosylation and induces a loss of COX-2 activity via a Src kinase-dependent pathway in rabbit articular chondrocytes. Experimental & molecular medicine. 2010;42(11):777–86. Epub 2010/10/12. pmid:20926918; PubMed Central PMCID: PMCPMC2992857.
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Источник: https://journals.plos.org/plospathogens/article?id=10.1371/journal.ppat.1007569

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LncRNA MALAT1 promotes gastric cancer progression via inhibiting autophagic flux and inducing fibroblast activation

Abstract

Autophagy defection contributes to inflammation dysregulation, which plays an important role in gastric cancer (GC) progression. Various studies have demonstrated that long noncoding RNA could function as novel regulators of autophagy. Previously, long noncoding RNA MALAT1 was reported upregulated in GC cells and could positively regulate autophagy in various cancers. Here, we for the first time found that MALAT1 could promote interleukin-6 (IL-6) secretion in GC cells by blocking autophagic flux. Moreover, IL-6 induced by MALAT1 could activate normal to cancer-associated fibroblast conversion. The interaction between GC cells and cancer-associated fibroblasts in the tumour microenvironment could facilitate cancer progression. Mechanistically, MALAT1 overexpression destabilized the PTEN mRNA in GC cells by competitively interacting with the RNA-binding protein ELAVL1 to activate the AKT/mTOR pathway for impairing autophagic flux. As a consequence of autophagy inhibition, SQSTM1 accumulation promotes NF-κB translocation to elevate IL-6 expression. Overall, these results demonstrated that intercellular interaction between GC cells and fibroblasts was mediated by autophagy inhibition caused by increased MALAT1 that promotes GC progression, providing novel prevention and therapeutic strategies for GC.

Introduction

Inflammatory mediators within the tumour microenvironment (TME) play important roles in promoting gastric cancer (GC) progression. The various cytokines within the GC TME are secreted from inflammatory cells, fibroblasts, and GC cells1. Moreover, GC cells could receive extracellular signals, which could further modulate TME via paracrine secretion of cytokine. The cross-talk between GC cells and stroma cells facilitate cancer progression. Cancer-associated fibroblasts (CAFs), a major component of the tumour stroma, are a critical source of various molecules secreted in TME, which stimulate cancer cells progression. Similarly, the fluctuation of inflammatory mediators (growth factors, interleukin) by cancer cells in TME also altered resident fibroblast phenotypes and lead to normal fibroblast (NF) activation, considered as the main CAF source2,3. Increasing evidence demonstrated interleukin-6 (IL-6) was abundant in GC TME, facilitating GC progression4. Most studies have reported that IL-6 could be released from CAFs and promote GC cells proliferation or metastasis in a paracrine way. However, IL-6 secretion from GC and its effect on modulating TME has not been studied in detail. Here, we have found that autophagy inhibition in GC could upregulate IL-6 expression and secretion.

Autophagy is an important biological process that appears to be a double-edged sword with respect to cytokine signalling and modulating tumour progression in certain instances5. Activated autophagy could protect cells from inflammatory damage by inhibiting autophagy and aggravating inflammatory responses in many tissues5,6. It is widely accepted that autophagy defects contribute to inflammation and autophagy inhibition under the condition of chronic inflammation devoted to oncogenesis7,8. Several reports have suggested that long noncoding RNA (lncRNA) could function as novel autophagy regulators. Silencing lncRNA-FA2H-2 facilitates impairment of oxidized low-density lipoprotein-induced autophagy flux to activate inflammation for increased IL-6 and other cytokine production9. Defective autophagy increases inflammatory mediator (such as TNF-α and HGF) production to promote hepatocellular carcinoma7. Impaired autophagy could promote chemoresistance in GC via lncRNA ARHGAP5-AS1 accumulation10. Although recent studies have shed light on some autophagy impairment mechanisms in GC, the molecular components that mediate the process are yet to be fully identified.

LncRNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) has been reported to activate autophagy in pancreatic ductal adenocarcinoma11, retinoblastoma12, and multiple myeloma13 to promote tumour progression. However, the effect of MALAT1 on autophagy in GC has not well reported. In this study, we found that MALAT1 upregulation in GC could inhibit autophagic flux, which led to sequestosome1(SQSTM1) protein accumulation and IL-6 overexpression. SQSTM1 is a scaffold and stress-inducible protein with multiple domains (such as ZZ, LIR, and PBI), which not only acts as an indicator of autophagy flux but also mediates inflammation response14. Hence, SQSTM1 protein accumulation might be responsible for IL-6 overexpression in GC cells. The majority of studies have revealed the effect of CAFs on GC cell growth or metastasis within the interaction between CAFs and cancer cells. However, the influence on CAFs exerted by GC cells has not been studied in detail. Autophagy inhibition in cancer cells led to the expansion and release of cytokines. The dysregulated cytokines could activate the transition from NFs to CAFs via paracrine signalling15. Here, we found that impairment of autophagy caused by increased MALAT1 could activate NF to CAF conversion through expansion and secretion of IL-6. These data suggest a critical role for MALAT1 in the interaction between CAFs and GCs cells. Furthermore, the underlying mechanisms were investigated to identify potential therapeutic strategies targeting GC.

Results

MALAT1 blocks autophagic flux in GC cells

A previous study documented that MALAT1 could function as an oncogene to promote GC cell proliferation and positively correlated with TNM stages in GC. However, the effect of aberrant MALAT1 expression on autophagic flux in GC was rarely investigated. Anomalous autophagy activity led to a variation of inflammation process16. Here, we found that MALAT1 overexpression (Supplementary Fig. 1A) could enhance LC3-1 conversion to LC3-II and SQSTM1 protein accumulation in both MKN-45 and MGC-803 cells. (Fig. 1A, P < 0.05). Contrarily, silencing MALAT1 by transducing siMALAT1 (Supplementary Fig. 1B) could inhibit LC3-II and SQSTM1 accumulation (Fig. 1A, P < 0.05). Furthermore, MALAT1 had no influence on the expression of SQSTM1 mRNA level (Supplementary Fig. 1C). LC3-II cloud accumulation results from autophagy activation or reduced turnover from autophagosome to autolysosomes. Moreover, the accumulation of SQSTM1 was an indicator of autophagy impairment. Therefore, autophagy inhibitors, 3-methyladenine (3-MA) and bafilomycin A1 (BafA1) were used to treat cells to block autophagy initiation and maturation, respectively. MALAT1 effect on LC3-II and SQSTM1 accumulation were not compromised by 3-MA treatment (Fig. 1B, P < 0.05). In contrast, LC3-II and SQSTM1 accumulation was not affected by BafA1 treatment in the MALAT1 overexpression group compared to the negative group (Fig. 1C, P < 0.05). Subsequently, an mRFP-GFP-LC3 lentivirus vector was introduced to determine the MALAT1 effect on autophagy flux. When autolysosomes formed, green fluorescence faded, leaving only the RFP signal as the RFP signal is more stable than green fluorescence in acidic conditions. MALAT1 overexpression in MKN-45 and MGC-803 led to yellow puncta enrichment rather than red ones, indicating autolysosome maturation blockage (Fig. 1D, E, P < 0.05). In addition, we used TEM to evaluate autophagosomes and found out that the number of autophagic vesicles increased in MKN-45/MALAT1 and MGC-803/MALAT1 cells compared to that in control cells (Fig. 1F, G). Taken together, these results suggested that increased MATLA1 in GC cells could impair autophagy flux.

A The LC3 and SQSTM1 protein levels in MKN-45/MALAT1, MGC-803/MALAT1, and their parental cells were determined by western blot assay; B, C The LC3 and SQSTM1 protein levels in MKN-45/MALAT1 and MGC-803/MALAT1 cells with 3-MA (10 mM) and baf-A1 (10 mM) were determined by western blot assay; D, E mRFP-GFP-LC3 distribution in MKN-45/MALAT1, MGC-803/MALAT1, and their parental cells were analysed by fluorescence microscopy (MKN-45/MALAT1 vs MKN-45/NC: 50.3 ± 4.5 vs 11.3 ± 2.6; MGC-803/MALAT1 vs MGC-803/NC: 28.3 ± 6.2 vs 2.6 ± 1.6, P < 0.01); F, G The number of autophagic vesicles was increased in MKN-45/MALAT1 and MGC-803/MALAT1 group as seen by TEM.

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MALAT1 activates AKT/mTOR pathway to inhibit autophagy in GC cells

Activation of mTOR is crucial to inhibit autophagy flux, which led to substantial autophagosome–lysosome fusion inhibition and lysosome dysfunction so that autophagy degradation was impaired17,18. Hence, both phosphorylated-mTOR (p-mTOR) and its key substrate, phosphorylated-p70 S6 kinase (p-p70S6K), were detected to assess the MALAT1 effect on mTOR pathway activation. As shown in Fig. 2A, a significant p-mTOR and p-p70S6K level increase was observed in MKN-45 and MGC-803 cells transfected with MALAT1 overexpression vectors. Silencing MALAT1 resulted in p-mTOR and p-p70S6K level reduction, which indicated that MALAT1 could activate the mTOR pathway (Fig. 2A, P < 0.05). Autophagy flux impaired by MALAT1 in GC led to SQSTM1 accumulation and rapamycin was used to inhibit mTOR activation to better understand whether MALAT1 impaired autophagy degradation to elevate SQSTM1 accumulation via the mTOR pathway. Rapamycin could promote SQSTM1 reduction through silencing and reversing mTOR activation induced by MALAT1 in MKN-45 and MGC-803 cells (Fig. 2B, P < 0.05). Since the canonical PTEN/AKT pathway could regulate mTOR activity, phosphorylated AKT and PTEN expressions were also detected. MALAT1 overexpression could obviously downregulate PTEN protein level and upregulate phosphorylated AKT levels in MKN-45 and MGC-803 cells (Fig. 2C, P < 0.05). In addition, we found that MALAT1 not only inhibited PTEN f.lux License Key - Crack Key For U level but also negatively regulated PTEN mRNA expression in MKN-45 and MGC-803 cells (Fig. 2D, E, P < 0.05). Analysis of the GSE dataset (GSE26942) also indicated a strong negative correlation between MALAT1 and PTEN mRNA (Fig. 2F, P < 0.05). Furthermore, GESA dataset analysis was also performed to suggest that autophagy was negatively associated with MALAT1 expression (Fig. 2G, NES =−1.459, FDR q-value = 0.06). Taken together, increased MALAT1 could negatively regulate PTEN expression to activate AKT/mTOR pathway, thus impairing autophagy flux and further elevating SQSTM1 accumulation in GC cells.

A The p-mTOR and p-p70S6K protein levels were increased in MKN-45 and MGC-803 cells transfected with MALAT1 overexpression vectors. Silencing MALAT1 resulted in p-mTOR and p-p70S6K level reduction; B The p-mTOR, p-p70S6K, and SQSTM1 protein levels were detected in MKN-45/MALAT1 and MGC-803/MALAT1 in presence of rapamycin; C The p-AKT and PTEN protein levels were detected in MKN-45 and MGC-803 cells transfected with MALAT1 overexpression vectors. Silencing MALAT1 resulted in PTEN overexpression and p-AKT downregulation; D, E The PTEN mRNA levels were detected in MKN-45/MALAT1 and MGC-803/MALAT1 cells. Silencing MALAT1 led to PTEN mRNA upregulation (MKN-45/MALAT1 vs MKN-45/NC: 0.66 ± 0.03 vs 1, MGC-803/MALAT1 vs MGC-803/NC: 0.53 ± 0.04 vs 1, P < 0.01; MKN-45/siMALAT1 vs MKN-45/siNC: 1.27 ± 0.04 vs 1 ± 0.01, MGC-803/siMALAT1 vs MGC-803/siNC: 1.53 ± 0.04 vs 1 ± 0.01, P < 0.01); F GEO dataset analysis showed a negative correlation between MALAT1 and PTEN. G GESA dataset analysis showed a negative correlation between MALAT1 and autophagy pathway (NES = −1.459, FDR q-value=0.06); Bars, S.D.; *P < 0.05; **P < 0.01; ***P < 0.001.

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MALAT1 inhibits PTEN expression at the post-transcriptional level

Although it has been demonstrated that MALAT1 could inhibit PTEN mRNA expression, the underlying mechanism has not been reported. Several studies have addressed the interaction between MALAT1 and the RNA binding protein (RBP) ELAVL1 to suppress target gene expression via modifying mRNA stability or mRNA initiation19. This inspired us to investigate whether MALAT1 regulates PTEN mRNA expression at the post-transcriptional level. The transcription inhibitor actinomycin D (Act D) was added in MKN-45 and MGC-803 cells transfected with or without MALAT1 plasmids for different times ranging from 0 to 6 h. The levels of remaining mRNAs were determined, and the PTEN mRNA half-lives decreased from 5.62 ± 0.21 to 1.54 ± 0.12 h and from 5.79 ± 0.18 to 2.47 ± 0.17 h (P < 0.01) in MKN-45 and MGC-803 cells, respectively, in response to increased MALAT1 (Fig. 3A, P < 0.01). AU-rich elements (AREs) usually exist in the 3′-UTR of mRNA, which could interact with RBPs to modulate mRNA stability. RBPmap database was used to analyse the ARE regions in PTEN 3′-UTR and predict the potential RBPs, which revealed that ARE regions were abundant in PTEN 3′-UTR and most possibly in ARE–ELAVL1 binding regions (Fig. 3B). Meanwhile, a significant positive correlation between ELAVL1 and PTEN mRNA expression was observed via analysing GEO datasets (GSE63048) (Fig. 3C, P < 0.001). Moreover, we found that ELAVL1 upregulation could increase PTEN mRNA expression in both MKN-45 and MGC-803 cells (Fig. 3D, P < 0.05). Similarly, after ELAVL1 overexpression, the PTEN mRNA half-lives increased from 6.16 ± 0.20 to 8.13 ± 0.28 h and from 5.29 ± 0.18 to 8.92 ± 0.35 h in MKN-45 and MGC-803 cells, respectively (Fig. 3E, P < 0.01). These results indicated that ELAVL1 could stabilize the PTEN mRNA. In addition, increased MALAT1 had no influence on ELAVL1 expression in GC cells (Supplementary Fig. 2A). Based on the current evidence on the opposite effects of MALAT1 and ELAVL1 on PTEN mRNA expression and the reported correlation between MALAT1 and ELAVL1, we assumed that increased MALAT1 could competitively interact with ELAVL1 to expose PTEN 3′-UTR such that PTEN mRNA destabilization was augmented. To determine the above assumption better, a rescue assay was carried out. As shown in Fig. 3F, ELAVL1-induced PTEN mRNA levels were significantly abolished by increased MALAT1 in both MKN-45 and MGC-803 cells (Fig. 3F, P < 0.05). Subsequently, RIP-PCR assay was performed to determine PTEN 3′-UTR enrichment bound by ELAVL1 with or without MALAT1 transfection in MGC-803 cells (Fig. 3G, H, P < 0.05) and MKN-45(Supplementary Fig. 2B, D, P < 0.05). These results showed that ELAVL1 could bind more MALAT1 mRNA fractions than PTEN 3′-UTR enrichments under MALAT1 overexpression condition. Additionally, we found that increased MALAT1 led to more ELAVL1 protein being distributed within the nucleus where MALAT1 was located (Fig. 3I), indicating that ELAVL1 nucleocytoplasmic shuttling was abrogated and resulted in PTEN mRNA destabilization. The combined data implied that MALAT1 could competitively interact with ELAVL1 to destabilize PTEN mRNA.

A PTEN mRNA expression in MKN-45 and MGC-803 transfected with MALAT1 overexpression vectors and the control after treatment with 5 μg/ml actinomycin D for 0, 2, 4, and 6 h. The PTEN transcript half-life was down-regulated by MALAT1; B Potential bindings sites of AU-rich elements on PTEN 3′-UTR; C GEO database analysis showed a positive correlation between ELAVL1 and PTEN; D ELAVL1 upregulation increase PTEN mRNA in MKN-45 and MGC-803 cells (MKN-45/ELAVL1 vs MKN-45/NC: 2.96 ± 0.18 vs 1 ± 0.01, MGC-803/ELAVL1 vs MGC-803/NC: 4.14 ± 0.35 vs 1 ± 0.01, P < 0.01); E PTEN mRNA expression in MKN-45 and MGC-803 transfected with ELAVL1 overexpression vectors and the control after treatment with 5 μg/ml actinomycin D for 0, 2, 4, and 6 h. The PTEN transcript half-life was upregulated by ELAVL1 (MKN-45/ELAVL1 vs MKN-45/MALAT1 + ELAVL1: 1 vs 3.2 ± 0.02, MGC-803/ELAVL1 vs MGC-803/MALAT1 + ELAVL1: 1 vs 1.80 ± 0.02, P < 0.05); F The remainder of ELAVL1-induced PTEN mRNA levels were abolished by increased MALAT1 in both MKN-45 and MGC-803 cells; G, H ELAVL1 captured more MALAT1 mRNA fractions than PTEN 3′-UTR enrichments under MALAT1 overexpression condition through performing RIP-PCR (MALAT1/Anti-ELAVL1 vs NC/Anti-ELAVL1: 4.0 ± 0.08 vs 2.13 ± 0.12 (MALAT1%), MALAT1/Anti-ELAVL1 vs NC/Anti-ELAVL1: 0.15 ± 0.04 vs 4.3 ± 0.21 (PTEN 3′-UTR%), P < 0.01); I MKN-45 and MGC-803 cells were transfected with MALAT1 overexpression vectors and the subcellular locations of HuR were determined by immunocytochemistry. Bars, SD; *P < 0.05; **P < 0.01; ***P < 0.001.

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Inhibition of autophagy promotes IL-6 secretion via accumulation of SQSTM1

Increasing evidence showed that autophagy flux inhibition aggravates the inflammatory response20. To investigate MAGIX ACID Pro Suite 10.0.4.29 Free Download with Crack inhibition of autophagic flux inhibited by MALAT1 in GC cells could increase inflammatory cytokine release, a human cytokine antibody array was used to compare the conditioned media of MKN-45/MALAT1 and MGC-803/MALAT1 cells and those of MKN-45/NC and MGC-803/NC cells (Supplementary Table 1). The significant IL-6 increase was detected in cultured media (CM) of MKN-45/MALAT1 dc unlocker crack 2018 download MGC-803/MALAT1 cells (Fig. 4A), which was further confirmed by ELISA assay (Fig. 4B, P < 0.05). Next, we investigated the IL-6 expression in mRNA and protein level within GC cells transfected with MALAT1 plasmids, which demonstrated that increased MALAT1, could promote both IL-6 protein and mRNA expressions in MKN-45 and MGC-803 cells (Fig. 4C, D, P < 0.05). IL-6 expression is regulated by a wide range of transcription factors and NF-κB plays a crucial role. As expected, NF-κB activation and nuclear translocation were observed in MKN-45 and MGC-803 cells transfected with MALAT1 (Fig. 4E, F, P < 0.05). In addition, increased MALAT1 had no effect on SQSTM1 mRNA expression, which had been shown in the first part of the results. As mentioned above, increased MALAT1 impaired autophagic flux, resulting in elevated SQSTM1 accumulation within GC cells, with SQSTM1 being involved in both autophagy and inflammation response. Therefore, we assumed that MALAT1 might activate the NF-κB pathway to increase IL-6 expression via SQSTM1. SQSTM1 siRNA-treated MKN-45/MALAT1 and MGC-803/MALAT1 cells abrogated the enhanced phosphorylated-NF-κB and IL-6 expressions. Similarly, increased SQSTM1 via transfection with SQSTM1 plasmid could reverse the NF-κB/IL-6 inactivation pathway caused by MALAT1 siRNAs (Fig. 4G, P < 0.05). Taken together, increased MALAT1 could elevate SQSTM1 accumulation to activate NF-κB so that IL-6 expression could be increased.

A Human cytokine antibody arrays were used to screen the difference of conditioned medium between GC cells transfected with MALAT1 overexpression vectors and NC vectors; B IL-6 protein expression level in the MKN-45/MALAT1, MGC-803/MALAT1, and compared groups was quantified 24 h after changing the culture medium as measured by ELISA (MKN-45/MALAT1 vs MKN-45/NC: 39.24 ± 1.24 vs 28.62 ± 0.17; MGC-803/MALAT1 vs MGC-803/NC: 40.6 ± 0.47 vs 35.79 ± 0.08, P < 0.05); C, D The mRNA and protein levels of p-IL-6 were detected in MKN-45 and MGC-803 cells transfected with MALAT1 overexpression vectors. Silencing MALAT1 resulted in IL-6 protein level downregulation (MKN-45/MALAT1 vs MKN-45/NC: 1.61 ± 0.2 vs 1 ± 0.07; MGC-803/MALAT1 vs MGC-803/NC: 2.35 ± 0.2 vs 1 ± 0.07, P < 0.01); E The NF-κB and p-NF-κB protein levels were increased in MKN-45 and MGC-803 cells transfected with MALAT1 overexpression vectors. Silencing MALAT1 resulted in p-NF-κB level reduction; F MKN-45 and MGC-803 cells were transfected with MALAT1 overexpression vectors, and the NF-κB subcellular locations were determined by immunofluorescence assay; G P-NF-κB and IL-6 expressions were abrogated in MKN-45/MALAT1 and MGC-803/MALAT1 cells with SQSTM1 siRNA treatment. Transfected SQSTM1 plasmid into MKN-45 and MGC-803 cells reversed NF-κB/IL-6 pathway inactivation caused by MALAT1 siRNAs. Three biological replicates were performed for in vitro assays. Bars, S.D.; *P < 0.05; **P < 0.01; ***P < 0.001.

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Impairment of autophagy in GC cells facilitates the transition from NFs to CAFs

Existing evidence has revealed that inflammatory cytokines in TME can induce conversion of NFs to CAFs. Thus, autophagic flux impairment-induced inflammatory cytokine effect on NF activation was investigated. We found that the CM collected from MKN-45/MALAT1 and MGC-803/MALAT1 cells markedly induced NFs to acquire myofibroblast phenotype characterized by a-SMA and FAP expression (Fig. 5A–C). Furthermore, the fibroblast contraction abilities were markedly enhanced after treatment with CM derived from GC cells with increased MALAT1 (Fig. 5D, P < 0.05). To determine whether IL-6 was the dominant driver of this effect, NFs were treated with rIL-6, and the results demonstrated a dose-dependent FAP and a-SMA expression increase (Fig. 5E, P < 0.05). To better understand the paracrine effect of MKN-45/MALAT1- and MGC-803/MALAT1-secreted IL-6 on fibroblasts, the anti-IL-6 neutralizing antibody was used within rescue assay, which could weaken FAP and a-SMA expression in NFs treated with CM from GC cells with increased MALAT1 (Fig. 5F, P < 0.05). These results demonstrated that autophagy impairment-induced IL-6 from GC cells could activate NF to CAF conversion in a paracrine manner.

AC Cultured medium collected from MKN-45/MALAT1 and MGC-803/MALAT1 cells induced NFs to acquire myofibroblast phenotype characterized by a-SMA and FAP expression as detected by Immunofluorescence and western blot assays; D NFs treated with cultured medium released by different GC cells or blank control were assessed for their ability to contract collagen; E The a-SMA and FAP protein levels were detected in NFs treated with different concentrations of reIL-6; F Protein levels of a-SMA and FAP in NFs co-cultured with cultured medium collected from MKN-45/MALAT1 and MGC-803/MALAT1 in the presence of IL-6 neutralizing antibody were analysed by western blot. Bars, S.D.; *P < 0.05; **P < 0.01; ***P < 0.001.

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To demonstrate the function of activated fibroblast converted from NFs (Activated-NFs), NFs treated with CM derived from GC cells with increased MALAT1 were prepared to perform proliferation assay and EdU dye assay. Then, MKN-45 and MGC-803 cells were incubated by CM collected from fibroblasts pre-co-cultured with MKN-45/MALAT1 and MGC-803/MALAT1 cells, respectively. As shown in Fig. 6A–C, GC cells exhibited proliferation and colony formation enhancement after treatment (Fig. 6A, C, P < 0.05). To further determine whether IL-6 from activated-NFs play a crucial role in promoting GC cells proliferation, and IL-6 blocking antibody was used to treat Activated-NF/MKN-45 and Activated-NF/MGC-803 group. Then we found that impairment of IL-6 with blocking antibody could significantly attenuate GC cells proliferation (Fig. 6D, P < 0.05). In addition, we further investigated whether activated-NFs could promote tumour growth in vivo. Co-injection of activated-NFs or NFs cells with MGC-803 was performed in nude mice. MGC-803 treated activated-NFs cells generated tumours with larger volume and weight than those generated by MGC-803 treated with NFs (Fig. 6E, F, P < 0.05). Furthermore, immunohistochemistry staining results showed that the FAP and a-SMA (CAF activation markers), SQSTM1 (autophagy marker) and IL-6 expressions were highly increased in MGC-803/activated-NF group (Fig. 6G, P < 0.01), which is consistent with in vitro experiment results.

AC Proliferation of MKN-45 and MGC-803 cells treated with activated-NF was determined by CCK8 (Activated-NF-CM/MKN-45 vs NF-CM/MKN-45: 0.93 ± 0.01 vs 0.85 ± 0.02; Activated-NF-CM/MGC-803 vs NF-CM/MGC-803: 0.74 ± 0.04 Vs 0.65 ± 0.01, P < 0.05), colony-formation (Activated-NF-CM/MKN-45 Vs NF-CM/MKN-45: 53.5 ± 11.8 Vs 28 ± 8.8; Activated-NF-CM/MGC-803 Vs NF-CM/MGC-803: 37.25 ± 8.8 Vs 7.25 ± 1.9, P < 0.05) and EdU (Activated-NF-CM/MKN-45 vs NF-CM/MKN-45: 35.3 ± 3.8 vs 12.6 ± 1.67; Activated-NF-CM/MGC-803 vs NF-CM/MGC-803: 33.3 ± 5.4 vs 18 ± 2.44, P < 0.05) assays; D Proliferation of MKN-45 and MGC-803 cells treated with activated-NF-CM and IL-6 blocking antibody was determined by CCK8 (Activated-NF-CM + IL-6 antibody/MKN-45 vs Activated-NF-CM/MKN-45: 0.79 ± 0.01 vs 0.65 ± 0.02; Activated-NF-CM + IL-6 antibody /MGC-803 vs NF-CM/MGC-803: 0.703 ± 0.01 Vs 0.59 ± 0.04, P < 0.01): E Photographs of tumours in nude mice derived from MGC-803 co-injected with activated-NFs and NFs; F MGC-803 mixed with activated-NFs generated tumours of larger volume and weight than those generated by MGC-803 mixed with NFs (MGC-803/activated-NFs vs MGC-803/NFs: 0.80 ± 0.5 vs 0.12 ± 0.34 cm^3; MGC-803/activated-NFs vs MGC-803/NFs: 248 ± 103 vs 124 ± 88 mg, P < 0.05); G FAP, a-SMA, SQSTM1 and IL-6 expressions were examined by IHC in tumours resulting from MGC-803/activated-NFs and MGC-803/NFs group. Bars, SD; *P < 0.05; **P < 0.01; ***P < 0.001.

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IL-6 derived from CAFs promote MALAT1 expression in GC cells

Increasing evidence showed that MALAT1 were aberrantly overexpressed and could act as an oncogene in GC21. The Cancer Genome Atlas (TCGA) data demonstrated that MALAT1 was highly expressed in GC (Supplementary Fig. 3A), and survival curve analysis with GEO dataset showed MALAT1 expression was negatively correlated with post-progression survival time of GC patients (Supplementary Fig. 3B). The cross-talk between CAFs and GC cells could aggravate the dysregulation of gene expression22,23. However, the f.lux License Key - Crack Key For U of upregulation of MALAT1 within TME was rarely reported. Therefore, co-culture CAFs or NFs with GC cells were performed to determine whether CAFs could upregulate MALAT1 expression in GC cells via paracrine signalling. As shown in Fig. 7A, relative expression of MALAT1 was significantly higher in MKN-45 or MGC-803 co-cultured with CAFs group than that co-cultured with NFs group (Fig. 7A, P < 0.05), which means cytokine from CAFs might induce MALAT1 expression in GC. GESA dataset analysis was performed to suggest that IL-6/STAT3 pathway signalling was a positive association with MALAT1 expression (Fig. 7B, NES = 1.459, FDR q-value=0.26). Then the expression of IL-6 in CAFs, NFs and GC cells were determined by ELISA, which showed that IL-6 was dominantly overexpressed in CAFs (Fig. 7C, P < 0.01). Furthermore, higher expression of MALTA1 was detected in MKN-45 and MGC-803 cells treated with recombinant IL-6 protein (rIL-6) than that in MKN-45 and MGC-803 cells alone (Fig. 7D, P < 0.05). Blocking IL-6 activity with neutralizing IL-6 antibody of the co-culture system of CAFs and GC cells led to obvious impairment of MALAT1 expression (Fig. 7E, P < 0.05), indicating IL-6 derived from CAFs could promote MALAT1 expression in GC cells. GEO dataset (GSE60839) analysis showed overexpression of MALAT1 was significantly positive with the expression of IL-6 and STAT3 in GC samples (Fig. 7F, G, P < 0.05), suggesting STAT3 might be responsible for high expression of MALAT1 in GC.

A Relative expression of MALAT1 was significantly higher in MKN-45 or MGC-803 co-cultured with CAFs than those co-cultured with NFs (CAF/MKN-45vs NF/MKN-45: 1.51 ± 0.13 vs 0.99 ± 0.01; CAF/MGC-803 vs NF/MGC-803: 1.95 ± 0.23 vs 0.99 ± 0.01, *P < 0.05, **P < 0.01); B GESA dataset analysis showed that IL-6/STAT3 pathway signalling had a positive association with MALAT1 expression(NES = 1.459, FDR q-value=0.26); C IL-6 was highly expressed in CAFs compared to NFs and GC cells(*P < 0.05, **P < 0.01); D The effect of reIL-6 (100 ng/mL) on MALAT1 expression in MKN-45 and MGC-803 were measured by qRT-PCR (MKN-45/IL-6 vs MKN-45/PBS: 1.86 ± 0.27 vs 1.03 ± 0.01: MGC-803/IL-6 vs MGC-803/PBS: 2.15 ± 0.03 vs 1.03 ± 0.01, *P < 0.05, **P < 0.01); E The effect of CAFs on MALAT1 expression in MKN-45 and MGC-803 cells was determined with the presence of IL-6 neutralizing antibody or IgG isotype control antibody (CAF + Anti-IgG/MKN-45 vs CAF + Anti-IL-6/MKN-45: 1.38 ± 0.04 vs. 1.02 ± 0.03; CAF + Anti-IgG/MGC-803 vs CAF + Anti-IL-6/MGC-803: 1.51 ± 0.04 vs 1.02 ± 0.03, **P < 0.01); F, G GEO dataset analysis suggested MALAT1 expression was positively associated with IL-6 and STAT3 expressions, respectively; H STAT3 upregulation promoted MALAT1 expression in MKN-45 and MGC-803 cells (STAT3/MKN-45 vs NC/MKN-45: 2.16 ± 0.16 vs 1: STAT3/MGC-803 vs NC/MGC-803: 3.46 ± 0.27 vs 1.1 ± 0.01, Recovery Explorer Professional License key I WP1066, a selective STAT3 inhibitor, could attenuate MALAT1 expression induced by recombinant IL-6 protein, which was measured by qRT-PCR (IL-6 + WP1066/MKN-45 vs IL-6+DMSO/MKN-45: 0.60 ± 0.07 vs 0.99 ± 0.03: IL-6 + WP1066/MGC-803 vs IL-6+DMSO/MGC-803: 0.24 ± 0.01 vs 0.99 ± 0.03, P < 0.01); J Potential STAT3 bindings sites on MALAT1 promoter; K Luciferase activity was measured after transfecting with MALAT1 promoter truncations, indicating that MALAT1 promoter site#3 contains binding sites (STAT3 vs NC: 3.91 ± 0.33 Vs 1.13 ± 0.17, P < 0.01); L Chip assay was performed to show that STAT3 could physically bind to MALAT1 promoter site#3 in MKN-45 and MGC-803 cells. Bars, S.D.; *P < 0.05; **P < 0.01; ***P < 0.001.

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Subsequently, upregulation of STAT3 expression via transfected STAT3 overexpression plasmid led to increasement of MALAT1 in both MKN-45 and MGC-803(Fig. 7H, P < 0.05). Furthermore, WP1066, a selective STAT3 inhibitor (inhibition of efficiency was shown in Supplementary Fig. 4C, D), could attenuate MALAT1 expression induced by recombinant IL-6 protein (Fig. 7I, P < 0.05), which suggested that IL-6 could increase MALAT1 expression via stimulating STAT3 activation. With help of the JASPAR database, we found that there are four most potential binding sites of STAT3 on MALAT1’s promoter accounting for the upregulation of MALAT1 in GC (Fig. 7J). To better understand whether STAT3 could interact with the MALAT1 promoter, a dual-luciferase reporter assay was carried out to measure luciferase activity after transfecting of truncations of the MALAT1 promoter. The results showed that site#3(-618bp~-200bp) of the MALAT1 promoter contains binding sites which mediated MALAT1 transcription activation induced by STAT3 (Fig. 7K, P < 0.01). Moreover, a CHIP assay was performed to show that STAT3 could physically bind to site#3(-618bp~-200bp) of the MALAT1 promoter in MKN-45 and MGC-803 cells (Fig. 7L, P < 0.01). Taken together, aberrant MALAT1 expression was partly attributed to IL-6 derived from CAFs via activation of the STAT3 pathway within GC TME. Additionally, we also found that overexpression of IL-6 was detected in MKN-45 and MGC-803 cells treated with rIL-6 (Supplementary Fig. 4E).

Discussion

In the present study, we showed, for the first time, that increased MALAT1 in GC cells could impair autophagic flux to aggravate IL-6 secretion to activate NF to CAF conversion via paracrine signalling, which resulted in GC cell progression. Increased MALAT1 could destabilize PTEN mRNA to activate AKT/mTOR pathway for blocking autophagic flux, leading to IL-6 overexpression induced by SQSTM1/NF-κB pathway, and the secreted IL-6 from GC cells stimulate NF to CAF conversion (Fig. 8). The interaction between GC and stromal cells could cause positive feedback to foster an inflammatory microenvironment and promote GC progression.

Increased MALAT1 in GC cells could impair autophagic flux to aggravate IL-6 secretion to activate converts NFs to CAFs via paracrine signalling, which resulted in GC cell progression. Increased MALAT1 could destabilize PTEN mRNA stability to activate AKT/mTOR pathway, which blocked autophagic flux leading to IL-6 overexpression induced by SQSTM1/NF-κB pathway. In addition, IL-6 secretion from GC cells stimulates NF conversion to CAFs.

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It has been widely reported that MALAT1 could function as versatile regulators by modulating transcription and post-transcriptional processes. Previously, we had found that MALAT1 could function as an oncogene to promote the proliferation of GC cells24. MALAT1 was overexpressed in GC and associated with TNM stages. Cancer cells play a vital role in tumour progression with the help of stromal cells within TME. However, the MALAT1 effect on the interaction between stromal and cancer cells have been rarely studied. Autophagy is a biological process involved with interaction between different types of cells through the production of inflammatory mediators6,20, which can regulate complex multicellular interactions within TME. Several studies have reported that MALAT1 could promote autophagy in various cancers, including retinoblastoma13, lung cancer25, and pancreatic cancer11. For GC, autophagy inhibition caused by increased MALAT1 has been rarely reported and investigated. For autophagy regulation, it is widely accepted that the mammalian target of rapamycin complex 1 (mTORC1) from the autophagy-inhibiting PI3k–Akt pathway26 and increased MALAT1 could activate PI3K–AKT pathway in numerous cancers including GC27. In this study, we demonstrated that increased MALAT1 could inhibit autophagy flux through activating AKT/mTOR pathway. Not only LC3-I to LC3-II protein conversion was increased along with MALAT1 augmentation in GC cells, SQSTM1 protein accumulation was also detected, suggesting LC3 protein conversion resulted from autophagy impairment rather than autophagy induction. In addition, rescue assays were performed to further confirm that increased MALAT1 could inhibit autophagy flux with 3-MA and BafA1 treatment. Besides the AKT/mTOR pathway being activated by increased MALAT1, expression of PTEN, the negative regulator of AKT/mTOR signalling, was also changed. Our study found that increased MALAT1 could destabilize PTEN mRNA to shorten its half-life in GC. AREs were rich in the PTEN 3′-UTR, to which RBP could bind to modulate mRNA stability. ELAVL1 is a ubiquitously expressed RBP that regulates many post-transcriptional steps including mRNA stability and translation. ELAVL1 has been reported to stabilize COX-2, β-catenin and BECN1 mRNA via binding to target AREs of 3′-UTR28,29. ELAVL1 not only could bind to 3′-UTR but also interact with lncRNA to form a functional complex. ELAVL1/MALAT1 complex was found to repress CD133 expression and suppress epithelial-mesenchymal transition in breast cancer19. However, whether ELAVL1 could bind to PTEN 3′-UTR regulating mRNA stability had not been reported, and whether MALAT1 could modulate PTEN mRNA expression via competitive interfering with the interaction between ELAVL1 and 3′-UTR was not investigated. In the present study, we found that MALAT1 could interact with ELAVL1 directly and restrain ELAVL1 in the nucleus away from the cytoplasm, where it could stabilize PTEN mRNA, as shown by RIP and IF assays. Based on collected evidence, we confirmed that increased MALAT1 could impair autophagy flux in GC via stimulating PTEN/AKT/mTOR signalling pathway. As a consequence of autophagy impairment caused by MALAT1, SQSTM1 accumulation was increased. Although expression of SQSTM1 was not investigated, several studies reported that SQSTM1 protein levels were more significantly upregulated in GC samples than in normal gastric mucosae30,31. SQSTM1 has been reported to be a significant activate factor in inflammatory responses32,33 through many signalling pathways including stimulating the NF-κB activation34,35. Therefore, we observed whether SQSTM1/NF-κB activation was responsible for IL-6 upregulation induced by increased MALAT1 in GC. From the results of rescue assays, we clearly found that SQSTM1 knockdown could reverse NF-κB activation and IL-6 upregulation caused by MALAT1, and restored SQSTM1 could reverse the NF-κB/IL-6 inhibition induced by silencing MALAT1 in GC cells.

CAFs secret inflammatory mediators to modulate components in TME and changes in TME can also regulate CAF function. We have shown previously that miR-149 can inhibit CAF activation via targeting IL-6 expression, which indicated that IL-6 has an important role in the CAF activation process36. In this study, we found that increased MALAT1 in GC cells results in IL-6 expression and secretion, and IL-6 augmentation activates NF to CAF conversion. The IL-6 effect on activating NFs was found in GC. IL-6 could also mediate the interaction between cancer cells and CAFs not only by supporting tumour cell growth but also by promoting fibroblast activation in oesophageal cancer37. Although IL-6 could stimulate NF to CAF conversion, the underlying molecular mechanisms were rarely known. Most studies attributed that IL-6 mediate the microRNA-dependent pathway to CAF activation38,39,40, which could not fully describe the underlying mechanisms. The mechanism of cytokines, like IL-6, on stimulating CAF activation should be further investigated. Chronic inflammation leads to NF activation and their conversion into CAFs, producing pro-tumorigenic cytokines, interacting with the cancer cells, and altering their gene expression profile, which results in cancer progression. In this study, activated CAFs induced by IL-6 could express α-SMA, acquire a highly contractile phenotype, and functionally, activated CAFs could facilitate GC cell proliferation, which resulted in co-evolution of CAFs with cancer cells. Additionally, MALAT1 has been reported to be aberrantly overexpressed in GC samples; however, the mechanism of upregulation of MALAT1 within TME was rarely reported. The interaction between CAFs and GC cells could aggravate the dysregulation of gene expression. We found that CAFs could upregulate MALAT1 expression in GC cells via paracrine signalling. Moreover, IL-6 derived from CAFs might be responsible for the high expression of MALAT1 in GC via promoting STAT3 binding to the MALAT1 promoter. In this way, the positive feedback loop contributed to positive feedback to foster an inflammatory microenvironment and promote GC progression.

In summary, our results indicate that MALAT1 could inhibit autophagic flux and instigate IL-6 via regulating PTEN/AKT/mTOR and SQSTM1/NF-κB pathways, which convert fibroblasts to CAFs to promote GC progression. (Fig. 8). However, the mechanism for CAF activation induced by IL-6 needs to be further investigated. Our study illustrated a new molecular mechanism underlying the interaction between cancer cells and fibroblasts, which may contribute to provide novel prevention and therapeutic strategies for GC.

Materials and methods

Cell lines

Human GC cell lines MKN-45, MGC-803, and GES-1 were purchased from the Shanghai Institute for Biological Sciences of the Chinese Academy of Sciences. They have been authenticated by an STR DNA profiling analysis and routinely examined for Mycoplasma contamination. GC cells were cultured in RPMI 1640 medium supplemented with 10% foetal bovine serum (FBS) and penicillin (100 μ/mL)/streptomycin (100 μg/mL) at 37 °C in 5% CO2 in air at saturation humidity.

Isolation and culture of fibroblasts

CAFs and adjacent NFs were isolated from resected tissues from GC patients at the Department of Surgery, Ruijin hospital affiliated with Shanghai JiaoTong University, School of Medicine. The tissues were well cultured in Dulbecco’s modified Eagle’s medium (DMEM) with 10% FBS, 100 μ/mL penicillin and 100 ug/mL streptomycin. A homogeneous group of fibroblasts were developed after two weeks of culture, which were cultured >10 times so that the minimum number of clones could be selected. Identification test for CAFs and NFs were performed as described previously (Supplementary Fig. 4A, B). All patient samples were obtained with informed consent from Ruijin Hospital, Shanghai Jiao Tong University School of Medicine.

RNA interference and plasmids

Small interfering RNAs (siRNAs) that specifically target human MALAT1 and SQSTM1 were purchased from Ribobio Technology (Guangzhou, China) and GenePharma (Shanghai, China), respectively. The siRNAs (100 nM siMALAT1, 100 nM siSQSTM1) were transfected into cells using the RNAi-MAX reagent (Life Technologies, CA, USA) according to the manufacturer’s instructions. The pcDNA-MALAT1 plasmid was kindly gifted by Prof. Huating Wang (The Chinese University of Hong Kong, China). Human ELAVL1 expression plasmids were purchased from Sangon Biotech (Shanghai, China). Plasmids (4 mg/ml) were transfected into cells using Lipofectamine 3000 (Life Technologies). Stably transfected cells (MGC-803/MALAT1, MGC-803/NC) were selected by using puromycin (1 mg/ml; InvivoGen). The RNA interference sequences are listed in Supplementary Table 1.

Quantitative reverse transcription PCR (qRT-PCR)

Total RNA was extracted with TRIzol® reagent (Invitrogen, Austin, TX, USA), and real-time PCR analysis was conducted according to the manufacturer’s instructions (Life Technologies). The mRNA level was measured using the SYBR Green PCR Master Mix (Applied Biosystems, Waltham, MA, USA) and normalized to glyceraldehyde 3-phosphate dehydrogenase (GAPDH) mRNA level. The primer sequences used are listed in Supplementary Table 1.

Western blot

Cells were lysed in RIPA buffer containing complete protease and phosphatase inhibitor cocktail (Sigma, USA). The protein concentration of the cell lysates was quantified by a BCA Protein Assay Kit (Pierce, Rockford). The same amount of protein samples was resolved onto 10% SDS-PAGE and then transferred to PVDF membranes. After blocking with 5% non-fat milk at 37 °C for 2 h, the membranes were incubated with the primary antibodies (1: 1000) diluted in TBST buffer overnight at 4 °C, followed by incubation with the HRP-conjugated secondary antibody for 2 h at room temperature. GAPDH antibody was used to verify equal protein loading. The protein band images were captured and analysed by a Tanon detection system with ECL reagent (Thermo) and the antigen-antibody reaction was visualized by enhanced chemiluminescence (ECL, Thermo, USA). The antibodies used in this study were obtained from Cell Signaling Technology.

Transfection of mRFP-GFP-LC3 lentivirus vector

The mRFP-GFP-LC3 lentivirus vector was purchased from Genechem (Shanghai, China), which was transfected to GC cells according to the manual. Puromycin (1 μg/ml) was used to select stably expressing mRFP-GFP-LC3 cells. GC cells treated with different plasmids were fixed and analysed using fluorescence microscopy.

Transmission electron microscopy (TEM)

GC cells were fixed in 2% glutaraldehyde containing 0.1 mol/l phosphate-buffered saline at 4 °C for 2 h, incubated in 1% osmium tetroxide containing 0.1 mol/l phosphate-buffered saline for 1.5 h at 4 °C, dehydrated in graded ethanol, saturated in graded ethanol, embedded, cut into ultrathin sections, stained with lead citrate, and finally viewed using Philip CM-120 TEM (Philips, Netherlands).

RNA stability assay

Transcription inhibitor Actinomycin D (Sigma-Aldrich, USA) was added to the culture medium of GC cells transfected with different plasmids for 0, 2, 4, and 6 h. Individual total RNA was harvested for qRT-PCR analysis. The relative mRNA decay rate was measured and fit into an exponential curve.

RNA immunoprecipitation-quantitative PCR (RIP-PCR)

RIP assays were performed by using the Magna RIP RNA-Binding Protein Immunoprecipitation Kit (Millipore, USA) according to the manufacturer’s instructions. Briefly, cells were lysed in lysis buffer and the cleared lysates were immunoprecipitated with the indicated anti-ELAVL1 and anti-IgG antibodies (Cell Signaling Technology). Immunoprecipitated and input RNA was isolated and reverse transcribed for qRT-PCR amplifications with PTEN 3′-UTR-specific primers. mRNA relative expression level was normalized to input mRNA expression. The primers used for amplification are listed in Supplementary Table 1.

Enzyme-linked immunosorbent assay (ELISA)

The human angiogenesis array (Raybiotech, USA) was used to analyse the soluble mediators according to the manufacturer’s protocol. A human IL-6 ELISA kit (Raybiotech) was used to determine the concentration of human IL-6 in the medium of different treatments according to the manufacturer’s instructions.

Immunofluorescence (IF)/Immunohistochemistry (IHC)

For IF assay, GC cells were fixed with Image Converters - Crack Key For U paraformaldehyde for 15 min at room temperature, permeabilized with 0.5% Triton X-100, and blocked with 5% BSA for 2 h before incubation with primary antibodies including anti-ELAV1, anti-DAPI, anti-NF-KB, anti-FAP (1: 500, Cell Signaling Technology), and anti-α-SMA (1: 500, Abcam, USA) overnight at 4 °C. After incubation with fluorescent secondary antibody for 2 h, images were acquired by fluorescence microscope.

Collagen contraction assays

A total of 1 × 105 NFs were suspended in 100 μl DMEM, which was mixed with 100 μl of collagen mix containing 68.75 μl DMEM and 31.25 μl Type 1 Rat tail collagen (Solarbio, China), and added to one well of a 96-well plate at 37 °C for 30 min. After incubation with media derived from different treatments for 24 h, the gels were photographed and the contractions were evaluated by using the Image J program.

Cell-proliferation/EdU assay

Cells were seeded into 96-well plates (1.0×105cells/well) and cell proliferation was documented every 24 h for 4 days. Cell proliferation was assessed in triplicates by using the Cell Counting Kit-8 (Dojindo, Kumamoto, Japan) following the manufacturer’s instructions. EdU assay was performed using Cell-Light EdU Apollo 567 In Vitro Imaging Kit (Ribobio, Guangzhou, China) according to the manufacturer’s instructions.

Xenograft assay

All the experiments were performed in accordance with the official recommendations of the Chinese animal community. Four-week-old male BALB/C nude mice were purchased from the Institute of Zoology, Chinese Academy of Sciences of Shanghai. All nude mice were randomized allocated into two groups, in which NFs and MGC-803/MALAT1 or MGC-803/NC cells mixed at the ratio of 1:4 in 100 lL PBS were injected subcutaneously. During the experiment, the tumour volume was measured weekly using the formula V = (length × width2)/2.

Statistical methods

Student’s t-test or one-way ANOVA were used for statistical analysis when appropriate. All statistical analyses were performed using SPSS 19.0 (SPSS Inc., Chicago, IL, USA). A two-tailed value of P < 0.05 was considered statistically significant. Gene set enrichment analysis (GSEA) was performed using GSEA v3.0 software.

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Acknowledgements

Thanks to Prof. Hua-ting Wang (The Chinese University of Hong Kong) and Prof. Kannanganattu V. Prasanth (University of Illinois) for providing the Human MALAT1 expression vector as a kind gift.

Funding

This work was supported by the National Natural Science Foundation of China Grant NO.81772518, No. 81871904 (ZG Zhu) and No. 81902944(ZQ Wang); and Interdisciplinary Program of Shanghai Jiao Tong University Grant No. YG2017MS58 (C Li), No. ZH2018QNA51(ZQ Wang) and Multicenter Clinical Trial of Shanghai JiaoTong University of medicine NO.DLY201602(ZG Zhu).

Author information

Author notes
  1. These authors contributed equally: Zhenqiang Wang, Xinjing Wang

Affiliations

  1. Department of General Surgery, Shanghai Key Laboratory of Gastric Neoplasms, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China

    Zhenqiang Wang, Tianqi Zhang, Liping Su, Bingya Liu, Zhenggang Zhu & Chen Li

  2. Shanghai Institute of Digestive Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China

    Zhenqiang Wang, Xinjing Wang, Tianqi Zhang, Liping Su, Bingya Liu, Zhenggang Zhu & Chen Li

Contributions

W.Z.Q and W.X.J. carried out the molecular lab work, participated in data analysis, carried out sequence alignments, participated in the design of the study and drafted the manuscript; Z.T.Q. carried out the statistical analyses; S.L.P., L.B.Y., Z.Z.G. and C.L. conceived of the study, designed the study, coordinated the study and helped draft the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Zhenggang Zhu or Chen Li.

Ethics declarations

Ethical statement

All patient samples were obtained with informed consent from Ruijin Hospital, Shanghai Jiao Tong University School of Medicine. All the experiments were performed in accordance with the official recommendations of the Chinese animal community.

Conflict of interest

The authors declare no competing interests.

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Edited by G. Calin

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Wang, Z., Wang, X., Zhang, T. et al. LncRNA MALAT1 promotes gastric cancer progression via inhibiting autophagic flux and inducing fibroblast activation. Cell Death Dis12, 368 (2021). https://doi.org/10.1038/s41419-021-03645-4

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