In vitro models of the human colon have been used extensively in understanding the human gut microbiome (GM) and evaluating how internal and external factors affect the residing bacterial populations. Such models have been shown to be highly predictive of in vivo outcomes and have a number of advantages over animal models. The complexity required by in vitro models to closely mimic the physiology of the colon poses practical limits on their scalability. The scalable Mini Gut (MiGut) platform presented in this paper allows considerable expansion of model replicates and enables complex study design, without compromising on in vivo reflectiveness as is often the case with other model systems. MiGut has been benchmarked against a validated gut model in a demanding 9‐week study. MiGut showed excellent repeatability between model replicates and results were consistent with those of the benchmark system. The novel technology presented in this paper makes it conceivable that tens of models could be run simultaneously, allowing complex microbiome‐xenobiotic interactions to be explored in far greater detail, with minimal added resources or complexity. This platform expands the capacity to generate clinically relevant data to support our understanding of the cause‐effect relationships that govern the GM. In vitro models of the human colon have been used extensively in developing an understanding of the human gut microbiome, but current technologies are extremely complex and have limited throughput. MiGut is a novel platform which addresses these shortcomings, allowing for considerable expansion of model runs without compromising on in vivo reflectiveness. The technology has been validated in a demanding 9‐week study where dysbiosis was simulated in vitro and has been benchmarked against a previously validated and extremely well understood triple‐stage gut model system. The gut microbiome (GM) is one of the most populous and diverse ecosystems of microorganisms found in the human body  platform builds upon current in vitro technologies to create a scalable colonic simulator with increased experimental throughput. Unlike other scalable systems, MiGut remains clinically reflective by retaining all the features of a standard triple‐stage system. It also takes advantage of high‐throughput molecular analysis techniques such as real‐time PCR (rtPCR) to overcome time‐consuming culture‐based methods which have been historically used with triple‐stage models  considering the reduction in volume from 300 to 45 mL per vessel. Growth media consists of magnesium sulphate (0.01 g/L), calcium chloride (0.01 g/L), sodium chloride (0.1 g/L), di‐potassium monohydrogen phosphate (0.04 g/L) potassium di‐hydrogen phosphate (0.04 g/L), sodium hydrogen carbonate (2.0 g/L), haemin (0.005 g/L), cysteine HCL (0.5 g/L), bile salts (0.5 g/L), arabinogalactan (1.0 g/L), tween 80 (2 mL/L), pectin (2.0 g/L), starch (3.0 g/L), vitamin K1 (10 μL/L), peptone water (2.0 g/L), yeast extract (2.0 g/L), chenodeoxycholic acid (0.25 mg/L), lithocholic acid (0.017 mg/L), mucin (2 g/L), glucose (0.4 g/L) and resazurin anaerobic indicator (0.005 g/L), as previously described in the literature  and subsequently exposed to antibiotics. Each model was dosed with amoxicillin , ciprofloxacin  and piperacillin/tazobactam . Antibiotics were instilled only in V1 of each model and in the order described. Each antibiotic treatment was performed for 5 days, with a 2‐day recovery/washout period between them. These drugs were chosen based on an internal analysis of clinical patient data at the Leeds Teaching Hospital Trust showing these were some of the most frequently prescribed antibiotics. Furthermore, the antibiotic concentrations used are reflective of the drug values found in human colon when a patient is prescribed each antibiotic. Bacterial populations were left undisturbed for a further 4 weeks after antibiotic dosing. This experimental timeline allowed for performance under steady state to be evaluated, as well as the response to antibiotics.Data for the PCoA plot were created with 16S rRNA gene sequencing of four testing runs. DNA was extracted from 1 mL of bioreactor fluid from V3. Briefly, samples were centrifuged , supernatants were discarded, and cell pellets were stored at −80°C for up to 48 h until processing. Following cell disruption using a tissue lyser, DNA extraction was performed using the DNeasy 96 PowerSoil Pro QIAcube HT Kit (Qiagen).Briefly, 16S rRNA V4 fragments were amplified, cleaned and checked using gel electrophoresis before running a PCR for addition of the index sequences. The fragments were then cleaned, quantified, normalised and sequenced on a MiSeq (Illumina) using a 2 × 250 bp paired‐end reads cycle. Demultiplexed FASTQ files were trimmed using cutadapt Martin,  and filtTaxonomic analysis is represented as mean per cent abundance from four technical replicates. The distance matrix based on thetayc approach was used for PCoAs analysis and visualisation for each group of samples, based on four technical replicates. Full details can be found in the Bacteroides spp., Bifidobacteria, Clostridium coccoides group, Clostridium leptum group, Enterobacteriaceae, Lactobacillus spp. and Prevotella spp.), as previously described  cannot be rejected. Note that when R is close to zero, p is often greater than 0.05. Both these results have the same outcome (populations are similar); thus, in this analysis, the R statistic is often more important that the p value. Finally, BC dissimilarity is an ecological measure of dissimilarity between two groups ranging from 0  to 1 (completely dissimilar) . Heatmaps  and PMCCs were calculated with Microsoft Excel .Non‐metric multidimensional scaling of the Bray–Curtis dissimilarity matrix (NMDS)  being continually streamed to a database (Microsoft Azure) for processing and visualisation, allowing for real‐time monitoring. Vessels are periodically sampled and key bacterial populations are monitored by rtPCR, which has been shown to correlate well with bacterial enumeration data   was performed using 16S rRNA sequencing data Figure . The resThe MiGut platform was then tested over a 9‐week period alongside a HGM under identical conditions, allowing for direct comparisons. Initially, the microbial populations within the models were allowed to equilibrate for 2 weeks. Then, they were exposed to a series of three antibiotics for 1 week each and monitored for a further 4 weeks after dosing was finished. Due to a technical fault in the initial week, one MiGut model was stopped; thus, the data reported are from the remaining three models.Bacterial populations in all models were monitored thrice weekly by quantitative (rtPCR) of key communities. These groups were specifically selected as key populations representative of microbial homeostasis in human gut, and they are therefore a good indicator of antibiotic impact on the GM  while the V1 equilibrium had a greater difference from V2 and V3 . These differences can be visualised by using non‐metric multidimensional scaling , while V3 was highly similar (R < 0.1). Overall, V3 showed the greatest similarity between MiGut and the HGM.The same techniques were used to define equilibrium in the HGM As discussed previously, V3 represents the distal colon, and thus, its content is the most closely related to human faecal samples. Consequently, we also focused on this vessel when reporting the effects of antibiotic dosing on the residing populations.Bifidobacteria, where the abundance in MiGut was 1.54 log10copies/μL higher than in the HGM. However, this difference subsequently reduced to only 0.31 log10copies/μL as the experiment proceeded. Similar trends were observed in other populations: C. leptum, Enterobacteriaceae, and Lactobacillus spp., for example, all show increased dissimilarity during the antibiotic dosing stages. Overall, post‐antibiotic Week 4 is the stage where MiGut and the HGM are most similar, with a maximum deviation of 0.65 log10copies/μL, also reflected in Figure Prevotella spp., which had a higher difference in concentration between the models throughout, had the highest PMCC (0.99).BC dissimilarities were calculated relative to the faecal slurry across all stages of the experiment using data from rtPCR. The results, shown in Figure R = 0.92, p = 0.0001), indicating that even 4 weeks after antibiotic withdrawal the bacterial populations did not return to their original state. Within the stage groupings, points corresponding to MiGut and the HGM occupied the same space, showing that there was no difference between the two sets of models, further supporting the equivalence of MiGut to the HGM.Plotting the data on an NMDS plot and grouping by stage Figure  shows thThe use of in vitro models that aim to simulate the human GM offers a valuable insight into how multiple internal and external factors can interact to affect the microbial populations. However, the inherent complexity of these models required to closely mimic the colonic environment has previously meant that their scale‐up is impractical  and 8 (V2 and V3) days of inoculation, which is consistent with reports from other gut model systems  showed more variation. However, by the final week of the study, differences in concentrations for each bacterial population had reduced to at most 0.65 log10copies/μL, indicating that the microbiomes in different models equilibrate to the same levels if undisturbed. The PMCC gives additional insight into how well population changes in the HGM were reflected in MiGut: overall, populations correlated extremely well, with six out of the eight reported populations having PMCC > 0.9. Bifidobacteria and Enterobacteriaceae showed some differences , mostly during the antibiotic dosing. A greater degree of variability is expected during dosing as the populations are subject to external stressors. Interestingly, Prevotella spp., which had a relatively high concentration difference between MiGut and the HGM throughout, had a PMCC of 0.99: indicating that although there were absolute differences in concentration, the relative changes from one stage to another in the HGM were precisely replicated within MiGut. However, our data suggest that even with a prolonged recovery period, the bacterial populations fail to return to their original equilibrium in both the MiGut and the HGM  offer a very useful tool for simulating the GM. However, a criticism of these systems is that they can be over‐simplistic and fail to adequately simulate some of the complexity of the in vivo environment  was consistent across all replicates, correlating extremely well with the benchmark system and demonstrating the suitability of MiGut to simulate both healthy and perturbed states of the human GM.In summary, the MiGut platform has miniaturised and improved current triple‐stage gut modelling methods, offering far greater scalability. This technology has great potential to improve the reliability of results and allow for far more complex studies. The composition of the human GM can be affected by multiple factors  which, in turn, affect clinical outcomes ; formal analysis (lead); investigation (lead); visualization (lead); writing—original draft (lead). Ines B. Moura: Conceptualization ; data curation ; investigation ; methodology ; supervision ; writing—review and editing . Duncan J. Ewin: Investigation ; methodology ; writing—review and editing (supporting). Mark H. Wilcox: Conceptualization (supporting); writing—review and editing (supporting). Anthony M. Buckley: Conceptualization ; supervision ; writing—review and editing . Peter R. Culmer: Conceptualization ; supervision ; writing—review and editing . Nikil Kapur: Conceptualization ; project administration ; supervision ; writing—review and editing .No funding information provided.IM has received funding to attend conferences from TECHLAB Inc. MW has received honoraria for consultancy work, financial support to attend meetings and research funding from Astellas, AstraZeneca, Abbott, Actelion, Alere, Bayer, bioMérieux, Cerexa, Cubist, Da Volterra, Durata, European Tissue Symposium, Merck, Nabriva Therapeutics plc, Pfizer, Qiagen, Roche, Seres Therapeutics Inc., Synthetic Biologics, Summit, and The Medicines Company. AB has received financial support to attend meetings and research funding from Seres Therapeutics Inc., Motif Biosciences plc., Nabriva Therapeutics plc, Tetraphase Pharmaceuticals, Almirall SA, GlaxoSmithKline plc, and Hayashibara Co. Ltd. All other authors state no conflict of interest.The collection and use of human faeces in our gut model have been approved by the School of Medicine Research Ethics Committee, University of Leeds . Participants were provided with a “Participant Information Sheet” (PIS) detailing a lay summary of the in vitro gut model and the scientific work they were contributing to by providing a faecal donation. Within this PIS, it is explained that by providing the sample, the participant is giving informed consent for that sample to be used in the gut model.Supporting Information S1.Click here for additional data file.


Together, ER‐targeting therapy benefits from the autophagy‐enhancing strategy more than the autophagy‐inhibiting strategy for antitumor and antimetastasis treatment.Strategies that induce dysfunction in the endoplasmic reticulum (ER) hold great promise for anticancer therapy, but remain unsatisfactory due to the compensatory autophagy induction after ER disruption. Moreover, as autophagy can either promote or suppress cell survival, which direction of autophagy better suits ER‐targeting therapy remains controversial. Here, a targeted nanosystem is constructed, which efficiently escorts anticancer therapeutics into the ER, triggering substantial ER stress and autophagy. Concurrently, an autophagy enhancer or inhibitor is combined into the same nanoparticle, and their impacts on ER‐related activities are compared. In the orthotopic breast cancer mouse model, the autophagy enhancer increases the antimetastasis effect of ER‐targeting therapy and suppresses over 90% of cancer metastasis, while the autophagy inhibitor has a bare effect. Mechanism studies reveal that further enhancing autophagy accelerates central protein snail family transcriptional repressor 1 (SNAI1) degradation, suppressing downstream epithelial–mesenchymal transition, while inhibiting autophagy does the opposite. With the same trend, ER‐targeting therapy combined with an autophagy enhancer provokes stronger immune response and tumor inhibition than the autophagy inhibitor. Mechanism studies reveal that the autophagy enhancer elevates Ca Therapeutic effects of endoplasmic reticulum (ER)‐targeted therapies are hindered by compensatory autophagy induced by ER disruption. Since autophagy plays dual roles in cell survival, which direction of autophagy better suits ER‐targeted therapy remains unknown. Here, two distinct drug delivery systems are developed to promote (or inhibit) autophagy to investigate potential synergistic effects of autophagy regulators with ER‐targeted therapies. Our study found that the ER‐targeting efficiency of DOX positively correlated with the capability to induce immunogenic cell death (ICD). Ultimately, we screened out p‐toluene sulfonyl‐modified DOX (ED) as the most effective compound, with the highest ER localization and ICD‐inducing ability. However, even though ED causes severe ER dysfunction, the cytotoxicity of ED was significantly attenuated. The underlying mechanism could be attributed to our findings that in response to ED‐induced ER stress, autophagy was generated as compensatory and protective mechanism against ED treatment. Therefore, the modulation of autophagy is imperative after effective ER‐targeting therapy.Endoplasmic reticulum (ER) is a vital organelle responsible for protein folding, intracellular homeostasis regulation, and cell proliferation in eukaryotic cells, which establishes ER as a potentially effective target for anticancer therapy.2, 3 On the one hand, autophagy could promote cell survival by recycling damaged organelles and misfolded proteins. Inhibiting autophagy has been shown to activated apoptosis, and restraining the disassembly of metastatic focal adhesion. For instance, He and co‐workers combined an autophagy inhibitor, chloroquine, with chemotherapeutics to treat breast cancer, and achieved significant antimetastatic effects. On the other hand, overactivation of autophagy could trigger the autophagic cell death pathway, which reduces the secretion of metastasis‐related factors and increases the release of ICD‐associated factors in dying cells. Wei and co‐workers, co‐delivering autophagy promoter (rapamycin) and photosensitizer  to the tumor, initiated the autophagy process and significantly enhanced the efficacy of photodynamic therapy. Nevertheless, on the basis of ER stress‐induced autophagy, which direction to modulate (enhance or inhibit) autophagy better suits ER‐targeting therapy remains unknown.A new problem has arisen; autophagy is a double‐edged sword playing a dual role in regulating cellular homeostasis.5, 6p‐toluene sulfonyl guided ED to sulfonamide receptors on ER and was decomposed by carboxylesterases, resulting in the release of DOX from ED. It was reported that 3‐methyladenine could inhibit the formation of a double membrane, named phagophore, thereby suppressing the formation of autophagosome. Thus, we denoted 3‐methyladenine as the autophagy inhibitor (API). Rapamycin was reported to induce autophagosome formation and the subsequent fusion of lysosomes and autophagosomes. so that we denoted rapamycin as an autophagy enhancer (APE). Hence, API or APE were co‐loaded with ED in Poly‐lactic‐co‐glycolic acid (PLGA) nanoparticle (NP), respectively. ICD inducing and immune activating performance of different autophagy modulators with ED was compared, and the antitumor effects were subsequently investigated. In parallel, the metastasis suppression effects and the underlying mechanisms of the two different strategies were elucidated. Collectively, our research provided a systematic study of the impact of regulating autophagy on ER‐targeted therapies, including differences in their antitumor and antimetastasis effects, which might shed light on further studies on efficient antitumor and antimetastasis therapies.In this study, two distinct drug delivery systems were developed to either promote or inhibit autophagy in order to investigate the potential synergistic effects of autophagy regulators with ER‐targeted therapies. For this purpose, ED was selected as an ER stress inducer. The  from ED.17 It 22.1Figure1H‐NMR and mass spectrum  were selected to assess the autophagy induction ability in 4T1 cells. As shown in Figure  In addition, ED promoted p62 degradation, suggesting that autophagy process was activated.First, the ER stress inducer ED was synthesized Figure1A and cE (rapamycin) or the autophagy inhibitor API (3‐methyladenine) was selected at the concentrations of 10 and 70 µm, respectively , the cytotoxicity of both ED+APE and ED+API was strongly enhanced in 4T1 cells compared to ED with the half maximal inhibitory concentration (IC50) of 13.76 µg mL−1. Nevertheless, the two combination strategies both increased the cytotoxicity of ED, with the IC50 of ED+APE and ED+API at the same order of magnitude (IC50 = 7.36 and 9.19 µg mL−1). In terms of the ability to modulate autophagy, the downregulated LC3‐II/ LC3‐I ratio and upregulated p62 expression suggested that ED+API inhibited cellular autophagy ‐treated cells presented more green punctate dots, indicating that more LC3 proteins were accumulated on autophagosome membranes and autophagy process was stimulated. In contrast, barely no obvious green punctate dots were observed in (ED+API)‐treated cells, confirming its autophagy inhibiting ability. The above results indicated that the combination of two autophagy modulators with ED had comparable cytotoxicity. Therefore, subsequent differences in the results obtained from using the two combined therapies would only be attributed to the different directions of regulating autophagy.After determining the appropriate concentrations of autophagic modulators, the combined effects of ED with different autophagy modulators were evaluated. As shown in Figure y Figure , wherease Figure . FurtherE Figure . ‐treated cells markedly downregulated, demonstrating that API blocked the autophagy process by PI3K/AKT/mTOR pathway. Moreover, the upregulated ratio of p‐AMPK/AMPK and p‐mTOR/mTOR in APE‐ and (ED+APE)‐treated cells relative to the ED‐treated group suggested APE promoting the autophagy process by AMPK/mTOR pathway. The above results demonstrated that the two compensatory prosurvival pathways were activated in cells after ED treatment, while both autophagy enhancement and inhibition disrupted these compensatory pathways and increased cell apoptosis.The intrinsic mechanism regulating autophagy was then investigated. The available research suggests that PI3K/AKT/mTOR  and AMPK/mTOR  pathways both modulate autophagy via mTOR signaling, and these two pathways are also essential pro‐survival mechanisms when cells respond to stress.24 AMP2.2E)@NPs and (ED+API)@NPs were spherical in shape  were ED:API = 1:0.75 and ED:APE = 1:0.457, respectively –PLGA nanoparticles containing both ED and autophagy regulators were manufactured using the nanoprecipitation method, and the transmission electron microscopy images showed that both  also exhibited stronger ability to inhibit tumor cell invasion with the relative invasion rate decreasing to 41.98% , with equivalent drug concentrations of 5 µmol kg−1 DOX, 122 µmol kg−1 API, and 17.4 µmol kg−1 APE, respectively. After 7 days of tumor inoculation, each group of mice received different nanoparticles via the tail vain every 3 days @NPs exhibited the most potent antitumor ability @NPs group had minimal metastatic nodules and the highest antimetastatic efficacy. On the contrary, the (ED+API)@NPs group was less effective in metastasis than the ED@NPs group, suggesting that suppression of autophagy might diminish the in vivo antimetastatic efficacy of ED. These findings indicated that autophagy activation reinforced ED in suppressing tumor metastasis.We next investigated in vivo antimetastasis effects of both combination strategies. The administrated drugs were kept the same mass ratios as in vitro experiments  and metastasis‐related proteins under ER‐targeted treatment was investigated. It was discovered that the levels of snail family transcriptional repressor 1 (SNAI1), an EMT‐inducing transcription factor controlling the initiation of EMT, were significantly decreased when autophagy was induced by ED+APE in 4T1 cells @NPs group (vs ED@NPs group), and the proportion of LC3B‐II was significantly higher, indicating that autophagy was greatly stimulated in these two groups and verifying the selective autophagic degradation of SNAI1. In addition, only the treatment of ED@NPs and (ED+APE)@NPs led to an increase in E‐cadherin, indicating that tumor metastasis was suppressed. In contrast, the outcomes of API@NPs and (ED+API)@NPs were significantly different. In these two groups, reduction in E‐cadherin, accumulation of p62, and degradation of LC3B‐II were discovered, suggesting a situation that was more conducive to tumor metastasis.Moreover, the expressions of metastasis‐related proteins and autophagy‐associated proteins in tumor tissues were also examined by western blotting and immunohistochemistry Figure . SNAI1 aE had a stronger ability to dampen tumor cell migration and invasion (vs ED+ API) both in vitro and in vivo, revealing that autophagy activation reinforced the antimetastatic capacity of ED. ED+APE also increased autophagy‐dependent SNAI1 degradation, which acts as a major driver of EMT and subsequently regulated the expression of EMT‐related proteins such as E‐cadherin and vimentin, indicating the blocked EMT process. In contrast, API and ED+ API did not affect SNAI1 degradation. These results indicated that for ER stress‐induced autophagy, enhancing autophagy accelerated SNAI1 degradation, resulting in the downregulation of vimentin and the overexpression of E‐cadherin, suggesting the termination of the EMT process @NPs‐ and (ED+APE)@NPs‐treated groups showed potent antitumor ability with prolonged median survival of mice to 29 and 34 days, respectively @NPs with CD8 depletion antibody‐treated group and the (ED+API)@NPs group shared the same trend, so as the number of pulmonary metastatic nodules. In contrast, the CD8 depletion antibody in (ED+APE)@NPs group potently accelerated tumor growth and obviously increased numbers of metastatic lung nodules compared with (ED+APE)@NPs group @NPs strongly depend on tumor immune response.In light of the observation that both autophagy modulators comparably enhanced the in vitro cytotoxicity of ED, further investigation was conducted to evaluate the in vivo tumor‐killing efficacy of the two combined groups. The balb/c mice were subcutaneously implanted with a 4T1 cell to evaluate the in vivo antitumor efficacy of different nanoparticles Figure5A. The y Figure . Besidesy Figure  suggestey Figure . As showp Figure , which m2.6E exhibited superior efficacy in inhibiting tumor growth. As a result, further investigation was conducted to elucidate the underlying mechanisms responsible for the enhanced antitumor ability conferred by the autophagy enhancer. Initially, the accumulation of ED in ER was nearly the same across ED and ED‐coupled autophagy regulator groups, indicating that the levels of ED accumulated in ER were insusceptible by autophagy regulators  was examined. As shown in Figure E+ED treatment, implying stronger ER stress. The above results suggested that APE elevated Ca2+ release from ER and increased ER stress.In vivo studies have revealed that ED+APrs Figure6A. TherE group significantly increased the CRT  exposure of 4T1 cells, as well as extracellular ATP level and HMGB1 (High mobility group protein) level, implying the strongest ICD induction ability of the ED+APE group. In contrast, the ICD induction ability of the ED+API group had no significant difference compared to that of the ED group, indicating that the ICD induction ability of ED could be greatly improved only with the combination of the autophagy enhancer. In vivo ICD‐inducing capacity of the nanoparticles was subsequently evaluated, displaying a similar trend. ED@NPs and (ED+APE)@NPs were able to obviously stimulate CRT expression from ER to the cell membrane surface, as shown in Figure E)@NPs group than in the ED@NPs group, which was presumably the consequence of the autophagy‐dependent ATP release based on ED treatment. Also, the HMGB1 level in tumor interstitial fluid @NPs group was 5‐fold higher than the saline group and 2.7‐fold greater than the ED@NPs group. The aforementioned results demonstrated that the increased levels of Ca2+ and ER stress resulting from enhanced autophagy further amplified ED‐induced ICD.Our previous research demonstrated that ED may cause cellular ICD; however, the ICD‐inducing potential of ED after modulating autophagy remains unknown. The results in Figure reatment.38 Alsd Figure  revealed Thereby, ED+APE might contribute to mounting antigen‐specific T‐cell responses we speculated. The role of T lymphocytes was investigated. Notably, the infiltration of CD3+ T cells in tumors and the proportion of CD8+ T cells among them were elevated after treatment with NPs @NPs resulted in a significant increase in the proportion of cytotoxic CD3+CD8+ T cells, 8.47 times more than saline. These results indicated that (ED+APE)@NPs could boost intense ICD, thereby leading to a robust tumor immune response.Considering that autophagy mediates the release of tumor antigens and danger‐associated molecular patterns (DAMP), enhancing autophagy might restrain advanced tumors from escaping immunosurveillance.41 Thes Figure . HoweverE did not affect the accumulation of ED in ER. Then we were surprised to discover that the levels of ER stress‐related proteins were considerably upregulated by ED+APE, demonstrating APE could magnify ED‐induced ER stress. Moreover, the Ca2+ released in the cytoplasm was dramatically elevated by ED+APE, suggesting that APE might increase the release of Ca2+ via amplifying ER stress. Correspondingly, our results showed that only ED+APE was capable of augmenting ED‐induced ICD. Besides, only ED+APE intensively increased the tumor infiltration of CD8+ T cells and led to robust immune response, whereas the antitumor process of ED+API had little involvement of CD8+ T cells. Consequently, enhancing autophagy‐elevated intracellular Ca2+ release from ER, and functioned as an amplifier of ER dysfunction→Ca2+ release → ICD induction → immune response cascade, eventually eliciting powerful antitumor effect ‐treated group, ED upregulated Ca2+ level and activated autophagy while APE could increase Ca2+ release via enhancing autophagy. In this way, APE functioned as an amplifier in ER stress‐Ca2+ release network, eventually resulting in robust ER stress and extremely elevated Ca2+ release, thus triggering robust ICD. In contrast, inhibiting autophagy did not affect intracellular Ca2+ levels, so it failed to affect ER stress‐Ca2+ release network  the main ER‐associated metastasis process was tightly tied to SNAI1‐related autophagy pathway, and enhancing autophagy consecutively activated SNAI1 degradation, and the blocked EMT process, leading to much higher metastasis suppression as compared with ED; and 2) enhancing autophagy‐elevated Ca4The full materials and experimental methods can be found in the Supporting Information. All of the animal experiments were approved by the Medical Ethics Committee of Sichuan University, and the animal experiments were performed in the Animal Laboratory of West China School of Pharmacy in Sichuan University (accreditation number: SYXK(Chuan)2018‐113).The authors declare no conflict of interest.Supporting InformationClick here for additional data file.


Carbohydrate antigen (CA) 19–9 is a known pancreatic cancer (PC) biomarker, but is not commonly used for general screening due to its low sensitivity and specificity. This study aimed to develop a serum metabolites‐based diagnostic calculator for detecting PC with high accuracy.A targeted quantitative approach of direct flow injection‐tandem mass spectrometry combined with liquid chromatography–tandem mass spectrometry was employed for metabolomic analysis of serum samples using an Absolute IDQ™ p180 kit. Integrated metabolomic analysis was performed on 241 pooled or individual serum samples collected from healthy donors and patients from nine disease groups, including chronic pancreatitis, PC, other cancers, and benign diseases. Orthogonal partial least squares discriminant analysis (OPLS‐DA) based on characteristics of 116 serum metabolites distinguished patients with PC from those with other diseases. Sparse partial least squares discriminant analysis (SPLS‐DA) was also performed, incorporating simultaneous dimension reduction and variable selection. Predictive performance between discrimination models was compared using a 2‐by‐2 contingency table of predicted probabilities obtained from the models and actual diagnoses.Predictive values obtained through OPLS‐DA for accuracy, sensitivity, specificity, balanced accuracy, and area under the receiver operating characteristic curve (AUC) were 0.9825, 0.9916, 0.9870, 0.9866, and 0.9870, respectively. The number of metabolite candidates was narrowed to 76 for SPLS‐DA. The SPLS‐DA‐obtained predictive values for accuracy, sensitivity, specificity, balanced accuracy, and AUC were 0.9773, 0.9649, 0.9832, 0.9741, and 0.9741, respectively.We successfully developed a 76 metabolome‐based diagnostic panel for detecting PC that demonstrated high diagnostic performance in differentiating PC from other diseases. We found that among 157 serum metabolites detected preoperatively in patients with resected PC, serum carbohydrate antigen (CA) 19–9 and three phosphatidyl choline derivatives  can be used preoperatively to estimate 1‐year disease‐free survival.In the current study, we aimed to develop a highly accurate diagnostic serum metabolomic panel for detection of PC with high accuracy. Such a preoperative serum metabolite‐based diagnostic tool could provide all‐in‐one clinical advantages to both the detection of PC and in predicting early recurrence of resected PC. This would support the potential of providing tailored treatment approaches for patients with resectable PC.22.1Two separate clinical enrollments were used in the study to obtain blood samples for metabolomic analysis. The first enrollment was the source of blood samples from a development cohort of 186 individuals that were collected by the Biobank, Severance Hospital, Seoul, South Korea. Among the first enrollment participants, ten patients with PC had missing values and were excluded from the study. Thus, the development cohort used for model building included blood samples from 176 individuals of which 57 were patients with PC and 119 were patients with other diseases  within 40 min after sample collection. Then 1 mL serum was aliquoted into the pre‐cooled and labeled storage vials. The serum aliquots were frozen immediately and stored below −80 °C until use.2.3PC.Aa.Cx:y or PC.ae.Cx:y]), 15 sphingolipids, and 1 hexose. Cx:y denotes the lipid side chain configuration, where x indicates the number of carbons in the side chain and y indicates the number of unsaturated chains. Among these 188 metabolites, 21 amino acids and 21 biogenic amines were analyzed by LC–MS/MS while the other 146 metabolites were analyzed by FIA‐MS/MS. The serum samples were processed in strict accordance with the manufacturer's instructions. Briefly, 10 μL of the supplied internal standard solution was added to each well of a 96‐well extraction plate, followed by 10 μL of each serum sample being added to the appropriate wells. The plate was dried under a gentle stream of nitrogen. The samples were derivatized with phenylisothiocyanate and eluted with 5 mM ammonium acetate in methanol. The samples were diluted with 40% methanol in water (15:1) for LC–MS/MS analysis or a proprietary running solvent provided by BIOCRATES Life Sciences AG (20:1) for FIA‐MS/MS. The samples were analyzed in 96‐well plates using a QTRAP 5500 mass spectrometer  coupled with an Agilent 1290 series HPLC system. For LC–MS/MS analysis in positive mode, 5 μL of the sample extract were injected onto an Agilent Zorbax Eclipse XDB C18, 3.0 × 100 mm, 3.5 μm protected by an SecurityGuard pre‐column C18, 4 × 3 mm (Phenomenex) at 50°C using a 9.5 min solvent gradient employing 0.2% formic acid in water (mobile phase A) and 0.2% formic acid in acetonitrile (mobile phase B). LC–MS/MS data were imported into the SCIEX application Analyst™ for peak integration, calibration, and concentration calculations. Twenty microliters of the sample extract were used in the FIA‐MS/MS in positive mode to measure acylcarnitines, glycerophospholipids, and sphingolipids, while hexoses were monitored in a subsequent run in negative mode. All FIA injections were carried out using the mobile phase prepared by Biocrates Solvent I in isocratic mode. The LC and MS settings for LC–MS/MS and FIA‐MS/MS mode are described in Tables A targeted quantitative approach was used to analyze the serum samples for metabolomic analysis. Specifically, direct flow injection‐tandem mass spectrometry (FIA‐MS/MS) combined with liquid chromatography–tandem mass spectrometry (LC–MS/MS) was performed using an Absolute IDQ™ p180 Kit  as described previously.2.4Linear discriminant analysis was used for classification, and a five‐fold cross‐validation method was used to select the tuning parameters of the model. This approach was used to separate the datasets into training and test datasets, to prevent overestimation, and estimate some parameters of the model. Discriminant models were developed and internally validated using the development cohort. These models were then applied to the validation cohort for external validation.The types of diseases of the patients and the corresponding number of patients are shown in Table p‐value of the correlation coefficient was adjusted using Bonferroni's correction. Default 7‐cross validation and response permutation testing with 200 permutations were conducted to evaluate the quality and validity of the OPLS‐DA model. Moreover, the OPLS‐DA models were assessed for predictability through p‐values obtained from analysis of variance of cross‐validated residuals (CV‐ANOVA) implemented by SIMCA (version 17.0)Continuous variables are presented as median values with interquartile ranges (IQR) and were compared using the Mann–Whitney U‐test. Because serum metabolites are highly correlated with each other, orthogonal partial least squares discriminant analysis (OPLS‐DA) was used to distinguish patients with PC from those with other diseases based on characteristics of the serum metabolites.p < 0.05. All statistical analyses were performed using the R package, version 4.1.1 . All patients provided written informed consent prior to surgery, and the study was approved by the Institutional Review Board of Yonsei University College of Medicine .Performance of the predicted probability obtained from the model was compared with the actual diagnoses using two‐by‐two contingency tables to evaluate sensitivity, specificity, accuracy, balanced accuracy, and the area under the receiver operating characteristic curve (AUC) for the development cohort and external validation cohort datasets. In addition, a 1000 bootstrap procedure was employed for internal validation. Significance was confirmed according to whether the 95% confidence interval for the differences between the two measurements included zero. Statistical significance was set at 33.12Y = 0.769 and Q2Y = 0.708, and the p‐value for CV‐ANOVA was <0.001. SPLS‐DA was then performed to highlight the metabolites that best discriminated cases of patients with PC from the controls. Seventy‐six metabolites with coefficients in SPLS‐DA are listed in Table Serum samples from 176 individuals in the development cohort set were evaluated. Of the 188 metabolites measured, 72 were excluded based on the data quality criteria. As a result, a total of 116 endogenous metabolites were included in the statistical analysis. A representative OPLS‐DA scatterplot of this classification model based on the development set is shown in Figure 3.2OPLS‐DA was performed to identify metabolites responsible for the discrimination between patients with PC and those with other diseases. As shown in Figure 2X cum) of the total explained variation in the dataset accounted for 76.9% of the variance in group separation (R2Y cum). The cross‐validated predictability of the discrimination analysis model was 70.8% (Q2Y cum). Based on the positive R2Y and negative Q2Y values of the permutation test, the performance of the OPLS‐DA model was satisfactory. In addition, the OPLS‐DA loading plot in Figure p‐values for the Spearman's correlation coefficients and VIP values were evaluated  and poor specificity (33%–100%).Metabolites are thought to represent the final status of functional responses of the body to environmental stimuli, thereby providing a functional signal derived from the genome‐based proteome that closely reflects the current phenotypic state of an individual. Therefore, the distribution of data regarding serum metabolites in a patient may potentially be used to detect PC.p < 0.000025).Based on this theory, Yun et al. and Kang et al. attempted to modulate PC‐related metabolites in patients with PC.Based on a better understanding of metabolic dysregulation in PC, recent studies have been conducted to diagnose PC with metabolomics. Xie et al. found that a panel of six metabolites  could discriminate PC from chronic pancreatitis or healthy controls with high accuracy.The current study had several strengths. Unlike other previous studies that considered only healthy donors, or chronic pancreatitis as a controlNo single metabolite has been previously shown to be promising enough for the detection and discrimination of patients with PC. Kobayshi et al. constructed an effective diagnostic model for PC using four serum metabolites, xylitol, 1,5‐anhydro‐D‐glucitol, histidine, and inositol, which were selected from 45 potentially altered metabolites in patients with PC.Bathe et al. attempted to identify potential serum metabolites to differentiate between benign and malignant pancreatic diseases, and demonstrated a good discrimination power with an AUC of 0.8308.We recently reported the potential role of serum complement factor B in detecting PC.The current study had several limitations. For instance, the study had a retrospective design and was based on data from a single ethnicity population with a limited number of study samples. External validation using geographically and demographically different cohorts should be performed. In addition, evaluating 76 metabolites may be too substantial to be practical for clinical oncology. Accordingly, an external validation study using a larger sample size should be performed to in effort to support or improve the diagnostic performance of the current diagnostic panel. Lastly, our study relied on a commercial metabolomics kit. Although other techniques could also quantify these metabolites, the development and validation of robust quantification methods for about hundreds of metabolites will be hard works for individual researchers or clinical laboratory. We selected this commercial kit since it has been analytically validated in the previous study.In conclusion, we developed a 76‐metabolite‐based diagnostic panel for detecting PC and demonstrated its high diagnostic performance in differentiating patients with PC from those with other diseases.Munseok Choi: Investigation ; methodology ; resources ; writing – original draft . Minsu Park: Funding acquisition (supporting); investigation ; methodology ; writing – original draft . Sung Hwan Lee: Methodology (supporting). Min Jung Lee: Methodology (supporting). Young‐Ki Paik: Funding acquisition . Sung Ill Jang: Resources (supporting). Dong Ki Lee: Resources (supporting). Sang‐Guk Lee: Conceptualization ; investigation ; supervision ; writing – review and editing . Chang Moo Kang: Conceptualization ; funding acquisition ; supervision ; writing – review and editing .This research was partly supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), which was funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI16C0257), and a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (grant number: 2021R1C1C1009976 and 2022M3J6A1084843).The authors have no conflict of interest.The study was approved by the Institutional Review Board of Yonsei University College of Medicine .Data S1: SPLS calculator.Click here for additional data file.Data S2: Supplementary figure.Click here for additional data file.Data S3: Supplementary table.Click here for additional data file.


The study aimed to investigate the comprehensive characteristics of brain functional activity and integration in patients with subcortical stroke using dynamic and static analysis methods and to examine whether alterations in brain functional activity and integration were associated with clinical symptoms of patients.Dynamic amplitude of low-frequency fluctuation (dALFF), static amplitude of low-frequency fluctuation (sALFF), dynamic degree centrality (dDC), and static degree centrality (sDC) were calculated for 19 patients with right subcortical stroke, 16 patients with left subcortical stroke, and 25 healthy controls (HC). Furthermore, correlation analysis was performed to investigate the relationships between changes in brain functional measurements of patients and clinical variables.Group comparison results showed that significantly decreased dALFF in the left angular (ANG_L) and right inferior parietal gyrus (IPG_R), decreased sALFF in the left precuneus (PCUN_L), and decreased sDC in the left crus II of cerebellar hemisphere (CERCRU2_L) and IPG_R, while significantly increased sDC in the right lobule X of cerebellar hemisphere (CER10_R) were detected in patients with right subcortical stroke relative to HC. Patients with left subcortical stroke showed significantly decreased sALFF in the left precuneus (PCUN_L) but increased sDC in the right hippocampus (HIP_R) compared with HC. Additionally, the altered sDC values in the CER10_R of patients with right subcortical stroke and in the HIP_R of patients with left subcortical stroke were associated with the severity of stroke and lower extremities motor function. A correlation was also found between the altered sALFF values in the PCUN_L of patients with left subcortical stroke and lower extremities motor function.These findings suggest that time-varying brain activity analysis may supply complementary information for static brain activity analysis. Dynamic and static brain functional activity and integration analysis may contribute to a more comprehensive understanding of the underlying neuropathology of dysfunction in stroke patients. Stroke is a neurological disease with high incidence and is the second leading cause of death and disability in the world . Motor iIt has been well documented that the brain is a dynamic system. In recent years, an increasing number of researchers have focused on exploring time-varying intrinsic brain activity or functional connectivity . The sliTherefore, the major aim of the present study was to explore the comprehensive characteristics of brain functional activity and integration in patients with subcortical stroke by calculating dALFF, sALFF, dynamic DC (dDC), and static DC (sDC). Stroke patients were divided into the right subcortical stroke group and left subcortical stroke group, and the differences in brain metrics between the two groups of patients and healthy control groups (HC) were compared to investigate the pattern of specific changes in brain function caused by different hemisphere lesions. We further evaluated the relationships between neuroimaging measurement changes in the two groups of patients and the severity of stroke, cognitive impairment, and upper and lower extremities motor deficits to investigate whether dysfunctions in stroke patients were associated with changes in brain function. We sought to explore whether dynamic analysis shows similar or complementary information to static analysis. These results may contribute to a more comprehensive understanding of the underlying neuropathology of dysfunction in stroke patients.A total of 38 patients with subcortical stroke  were recruited from the Department of Rehabilitation Medicine, the Affiliated Hospital of Yunnan University . The inclusion criteria were as follows: (1) the first-onset stroke (ischemic or hemorrhagic stroke confirmed by computed tomography or MRI), (2) course of stroke between 2 weeks and 3 months, (3) right-handedness before stroke, (4) people aged 18 to 75 years, and (5) manifesting motor or cognitive deficit evaluated by clinical scales. The exclusion criteria were as follows: (1) a history of other neurological or psychiatric disorders and (2) any contraindications for MRI scanning, such as intracranial metal clips, cardiac pacemakers, defibrillators, or aneurysm clips. The severity of stroke, cognitive function, and upper and lower extremities motor functions of all patients were assessed on the same day as MRI scan acquisition by the National Institutes of Health Stroke Scale (NIHSS), the Chinese version of Montreal Cognitive Assessment Basic (MoCA-BC), and the Fugl–Meyer Upper and Lower Extremities assessment , respectively. The NIHSS assesses the level of consciousness, eye movements, integrity of visual fields, facial movements, upper and lower limb muscle strength, sensory function, body coordination, language, speech, and neglect of patients . The cum2, matrix = 228 × 228, and voxel = 1.1 × 1.1 × 1.2 mm3, no gap. The parameters for obtaining resting-state BOLD-fMRI data were as follows: TR = 2,000 ms, TE = 30 ms, FA = 90°, FOV = 240 × 240 mm2, matrix = 64 × 64, voxel = 3.75 × 3.75 × 4 mm3, no gap, 200 volumes, and the entire scanning procedure lasted for 6 min and 46 s. T2-weighted images were also obtained to identify the location of stroke lesions: TR = 3,500 ms, TE = 108 ms, FA = 90°, FOV = 230 × 230 mm2, matrix = 384 × 384, voxel = 0.6 × 0.6 × 6 mm3, no gap. The whole MRI scanning process was completed in 13 min and 9 s. All participants were instructed to keep their heads motionless, close their eyes, not think about anything, and avoid falling asleep during the scanning. Additionally, comfortable foam padding and earplugs were used to reduce head motion and scanner noise.All participants were examined on the Philips Achieva 3.0 T MR scanner  with the 32-channel head–neck coil. High-resolution 3D T1-weighted images were acquired with the following parameters: repetition time (TR) =7.5 ms, echo time (TE) = 3.5 ms, flip angle (FA) = 8°, field of view (FOV) = 250 × 250 mmLesion masks of all patients were first manually delineated on the native 3D T1-weighted MRI images using the software ITK-SNAP .1 Then, 3; (d) detrending; (e) Friston-24 head motion parameters, white matter, cerebrospinal fluid, and whole brain signals were regressed out; (f) filtered with a temporal bandpass of 0.01–0.1 Hz; (g) scrubbing to remove the “bad” time points and their 1 forward and 2 back time points on the basis of FD threshold of 0.5 mm. Three patients with maximal head motion that exceeded 2.5 mm displacement or 2.5° of rotation were excluded after image preprocessing. Nineteen patients with right subcortical stroke and 16 patients with left subcortical stroke were left to calculate brain functional measurements.Resting-state BOLD-fMRI data were preprocessed with the Data Processing and Analysis of Brain Imaging toolkit  is appropriate to capture dynamic brain activity . Therefot-test or Wilcoxon rank sum test. The chi-square test was performed to detect the sex differences. A value of p of <0.05 was considered to be statistically significant. In terms of the group differences in imaging indices, the two-sample t-test was performed with the age, gender, education level, and head motion parameters of the mean FD values as covariates. AlphaSim correction was employed for multiple testing  and the automated anatomical labeling 3 (AAL3) template was applied as the brain mask to obtain brain region with significant differences in each brain functional measurement between the stroke patient group and HC group. Brain regions were excluded if they overlapped with the patient’s lesions to avoid the influence of lesions.The demographic characteristics of the stroke patient group and HC group were statistically analyzed using Graphpad Prism (software version 9.5.1).value of p of <0.05 with false discovery rate (FDR) correction.To better investigate the associations between brain functional measurements and clinical symptoms of patients, the mean brain functional measurements within each brain region showing significant differences between the stroke patient group and HC group were calculated. All functional measurements of the two groups of stroke patients were tested for normal distribution by the Shapiro–Wilk test. Then, Pearson correlation analysis or Spearman correlation analysis was used to determine the relationships between brain functional measurements and NIHSS, MoCA-BC, FMA-UE, and FMA-LE scores of the two groups of stroke patients. The significance level was set at a p > 0.05). Significantly decreased MoCA-BC scores were found in stroke patients compared to HC (p < 0.0001).Three stroke patients were excluded from further analysis owing to excessive head motion after the resting-state BOLD fMRI data head motion check. The demographic characteristics for the final 35 stroke patients and 25 HC are summarized in Significant differences in dALFF, sALFF, and sDC between stroke patients and HC were found , 3. Signr = −0.604, p = 0.011) but a significant positive correlation with FMA-LE scores  but a significant negative correlation with FMA-LE scores  for the publication of any potentially identifiable images or data included in this article.JW, DQ, and YY designed this study. LG, ZZ, and XY participated into the participants recruitment and acquired the clinical assessments data. LG, ZZ, and PW acquired the MRI data. LG, WS, and JW analyzed the MRI data. LG wrote the manuscript’s first draft. ZZ, XY, PW, JW, DQ, and YY revised the manuscript and provided the critical comments. All authors contributed to the article and approved the submitted version.


Autoimmune glial fibrillary acidic protein (GFAP) astrocytopathy is a novel autoimmune disease of central nervous system (CNS). It is unclear whether Epstein–Barr virus (EBV) is related to autoimmune GFAP astrocytopathy.To describe the clinical, laboratory, and imaging characteristics of patients with autoimmune GFAP astrocytopathy.The clinical, laboratory, and imaging findings of patients are presented. The levels of GFAP in CSF were detected by ELISA. T and B cell subsets in CSF were detected by flow cytometry. GFAP‐IgG in serum and cerebrospinal fluid (CSF) were tested by cell‐based assay (CBA) and tissue‐based assay (TBA).All three patients had fever, cognitive dysfunction, limb weakness, and positive GFAP‐IgG with EBV infection in CSF. Enteric glia cells may involve in this disease. Typical imaging findings include the gadolinium enhancement of linear perivascular radial perpendicular to the ventricle, meningeal enhancement , longitudinally extensive lesions involving spindle cords, and more T2/Flair‐hyperintense lesions in the periventricular white matter at late stage. The patients had poor response to antiviral treatment and strong response to steroid pulse therapy.EBV could induce CNS autoimmune response in autoimmune GFAP astrocytopathy. The detection of GFAP‐IgG and EBV may facilitate the early diagnosis in these patients. + T cell mediated damage, infection‐mediated immune response, and EBV reactivation.Epstein–Barr virus (EBV) could induce central nervous system (CNS) autoimmune response in autoimmune glial fibrillary acidic protein (GFAP) astrocytopathy. The potential pathogenic mechanisms including: directly invade CNS , CD8 Before the procedure and during follow‐up, all the patients were evaluated by a routine brain and spinal cord magnetic resonance imaging (MRI) study at 1.5T (GE signa) or 3.0T (uMR 790) magnetic field strength. The scan include different sequences . Blood and cerebrospinal fluid (CSF) samples were collected. Ethical approval was granted by the ethics committee of the First Affiliated Hospital of Shandong First Medical University. Written informed consents were obtained from patients before the investigation.2.2CSF of patient 2 and patient 3 at different time points were evaluated for the presence of GFAP. CSF (1:5) samples were added to GFAP‐IgG (immunoglobulin G)‐precoated 96‐well plates. Then 50‐μL horseradish peroxidase (HRP)‐conjugated agent was added to each well except blank control well and incubated for 30 min. After washing five times, 50 μL of Chromogen Solution A and 50 μL of Chromogen Solution B were added to each well and incubate at 37°C for 10 min. The reaction was stopped by sulfuric acid solution. Then the absorbance was measured at 450 nm. All reagents are provided by Wuhan ColorfulGene Biological Technology Co., LTD .2.3CSF was centrifuged, and cell counts were performed. The cell suspension was incubated with APC‐CD3 (Biolegend), Percp‐CD19 (Biolegend), PE‐Cy7‐CD4 (Invitrogen), and FITC‐CD8 (eBioscience) antibodies for 30 min at 4°C. Then, a flow cytometer (Becton Dickinson) was used for detection.2.4HEK‐293 cell lines were transfected with plasmids encoding GFAP homosapiens transcript variant 1 (NM_002055.5). Transfection cells were fixed with 4% paraformaldehyde and permeabilized with 0.2% Triton X‐100 and incubated at 4°C with patients' serum (1:10 dilution), CSF (1:1 dilution), and rabbit GFAP polyclonal antibody  (1:500). After washing with phosphate‐buffered saline (PBS), cells were incubated with secondary antibodies (1:200) for 30 min. Then images were captured by fluorescence microscopy.2.5Rat frozen sections (10 μm) of hippocampus, cerebellar, and small intestine were fixed with acetone for 30 min. Sections were incubated with patients' serum (1:10 dilution) and CSF for 60 min. After washing with PBS, sections were incubated with FITC‐conjugated goat anti‐human IgG antibody (1:200) for 30 min. After washing, slides were labeled with DAPI  (Solarbio) and observed by fluorescence microscopy.3Anti‐nuclear antibodies, anti‐double stranded DNA IgG antibodies, anti‐Smith antibodies, anti‐SSA antibodies, anti‐SSB antibodies, anti‐neutrophil cytoplasmic antibodies, anti‐aquaporin 4 (AQP4) antibodies, and anti‐myelin oligodendrocyte glycoprotein (MOG) antibodies were negative in the serum of three patients. Tests for serum EBV, herpes simplex virus, influenza virus, hepatitis, and mycobacterium tuberculosis were all negative. All patients showed no evidence of tumor.3.1A 33‐year‐old woman initially presented with fever, headache. Gradually, she presented with cognitive dysfunction, dysphoria, and limb weakness. She had no coexisting autoimmune diseases. Reduced muscle strength of bilateral limbs, weakened tendon reflexes of bilateral lower limbs, stiff neck, and bilateral Babinski signs were found in the patient.6/L) with 70% monocytes and protein of 40.7 mg/dL on day 22, while showed normal leukocytes (1*106/L) and protein of 28.9 mg/dL on day 28 . Repeated detection of GFAP‐IgG was positive in CSF  and negative in serum. NMDAR‐IgG was also negative in CSF. The patient was diagnosed as autoimmune GFAP astrocytopathy and received low dose of corticosteroid and mycophenolate mofetil. The clinical symptoms were significantly improved and no relapse after 3 months of clinical follow‐up.During follow‐up, repeated brain MRI showed less meningeal enhancement , linear enhancement oriented radially to the ventricles, and more lesions in the periventricular white matter on day 42 Figure . Spinal 3.2A 60‐year‐old man developed fever, abdominal pain, ventosity, limb shaking, and limb weakness. After 10 days, he presented with cognitive dysfunction and urine retention. He had hypertension and coronary atherosclerotic cardiopathy. Upon neurological examination, he presented with disorientations of place and time, impaired memory, and comprehension. Bilateral Babinski signs were positive.6/L) with 90% monocytes and elevated protein (193.6 mg/dL)  Figure . mNGS sh) Figure . Repeate) Figure . Spinal ) Figure . GFAP‐Ig) Figure . CSF rev6/L) and protein level of 77.8 mg/dL  with 70% monocytes and protein level of 127.5 mg/dL . The levels of GFAP Figure  and the s Figure  in CSF ws Figure . The pat4+ T cells, CD8+ T cells, and B cells were detected in CSF. The patient had poor response to antiviral treatment and clinical symptom and pleocytosis were improved quickly after steroid pulse therapy, which also support the notion of EBV‐induced CNS autoimmune response. Variable numbers of EBV from one to thousands of sequence reads were reported by CSF mNGS in EBV encephalitis.GFAP uniquely presents in astrocytes of CNS, non‐myelinated Schwann cells and satellite glia cells of the peripheral nervous system, and enteric glia cells (EGC). GFAP participates in the formation of skeleton structure of astrocytes and blood–brain barrier (BBB), maintaining morphological stability of astrocytes.+ T cell responses is regarded as the main determinant of CNS tissue damage.+ T cell might be an important pathological and diagnostic feature in autoimmune GFAP astrocytopathy.+ T cells, CD8+ T cells, B cells, plasma cells, macrophages, and neutrophils in autoimmune GFAP astrocytopathy.There are some potential pathogenic mechanisms of EBV that may lead to CNS disorders: ① The virus directly invades nervous system. EBV may enter the brain via normal B cell trafficking or through infection of brain microvasculature endothelial cells.+ T cell‐mediated cytotoxic autoimmunity might involve in EGC abnormalities, which were associated with massive intestinal inflammation.EGC is an important component of enteric nervous system (ENS) that reside within the smooth muscle wall, submucosa, and lamina propria. EGC shares morphological features and antigenic markers with the astrocytes of the CNS.Based on our data, we propose that EBV infection in brain microvasculature endothelial cells and B cells could lead to not only neuronal damage but also a breakdown of the BBB, which can attract autoreactive lymphocytes into the CNS and further result in perivascular inflammation. In our patients, typical imaging findings include the gadolinium enhancement of linear perivascular radial perpendicular to the ventricle, meningeal enhancement , longitudinally extensive lesions involving spindle cords, and more T2/Flair‐hyperintense lesions in the periventricular white matter at late stage. The gadolinium enhancement of linear perivascular radial perpendicular to the ventricle and more T2/Flair‐hyperintense lesions in the periventricular white matter at late stage may be related to the perivascular inflammation induced by EBV and higher expression of GFAP in white matter compared to gray matter astrocytes.RSD, BY, and XLL contributed to the study concept and design. RSD, XLL, LKL, JYW, CLY, XLZ, PZ, BL, and YBL participated in the data the data acquisition and analysis. RSD, XLL, LKL, JYW, and CLY contributed to the drafting of the manuscript. XLL, CLY, and ZXZ contributed to drafting the text and preparing the figures. All authors read and approved the final manuscript.This work was supported by grants from Academic promotion program of Shandong First Medical University (2019QL013), National Natural Science Foundation of China , and Cultivation Fund for the First Affiliated Hospital of Shandong First Medical University (QYPY2020NSFC0610).The authors declare that they have no competing interests.


Within the ECDC‐VEBIS project, we prospectively monitored vaccine effectiveness (VE) against COVID‐19 hospitalisation and COVID‐19‐related death using electronic health registries (EHR), between October 2021 and November 2022, in community‐dwelling residents aged 65–79 and ≥80 years in six European countries.EHR linkage was used to construct population cohorts in Belgium, Denmark, Luxembourg, Navarre (Spain), Norway and Portugal. Using a common protocol, for each outcome, VE was estimated monthly over 8‐week follow‐up periods, allowing 1 month‐lag for data consolidation. Cox proportional‐hazards models were used to estimate adjusted hazard ratios (aHR) and VE = (1 − aHR) × 100%. Site‐specific estimates were pooled using random‐effects meta‐analysis.For ≥80 years, considering unvaccinated as the reference, VE against COVID‐19 hospitalisation decreased from 66.9%  to 36.1%  for the primary vaccination and from 95.6%  to 67.7%  for the first booster. Similar trends were observed for 65–79 years. The second booster VE against hospitalisation ranged between 82.0%  and 83.9%  for the ≥80 years and between 39.3%  and 80.6%  for 65–79 years. The first booster VE against COVID‐19‐related death declined over time for both age groups, while the second booster VE against death remained above 80% for the ≥80 years.Successive vaccine boosters played a relevant role in maintaining protection against COVID‐19 hospitalisation and death, in the context of decreasing VE over time. Multicountry data from EHR facilitate robust near‐real‐time VE monitoring in the EU/EEA and support public health decision‐making. One component of VEBIS is based on estimating VE using routinely collected vaccination and outcome data from EHR.The overall aim of this component of the VEBIS project is to expand the use of electronic health registries across EU/EEA and to establish robust statistical methods to monitor COVID‐19 VE over time as well as to improve the timeliness of reporting of VE estimates across EU/EEA. With this aim, we carried out a multi‐country study based on EHR to prospectively provide monthly VE estimates of the complete primary vaccination series, the first, the second, and the third booster dose against COVID‐19 hospitalisation and COVID‐19‐related death, in community‐dwelling resident population aged 65 years and over.22.1Using a common protocol,We estimated COVID‐19 VE with monthly frequency. For each month, the observation period covered 8 weeks to allow sufficient events to provide precise estimates and to be sensitive to changes in VE over time. Overall, the observation period was between October 2021 and November 2022. Between October 2021 and March 2022, we piloted a common protocol for an outcome of COVID‐19 hospitalisation in four study sites: Denmark, Navarre (Spain), Norway, and Portugal.2.2We included individuals aged between 65 and 110 years (inclusive). We excluded residents in long‐term‐care facilities (using the last available information) and early vaccinees (defined as either being vaccinated before recommended for their age group or the first 5% vaccinated of each 5‐year age band) as these were considered especially vulnerable groups with different probabilities of vaccination and developing severe COVID‐19. In addition, we excluded those with inconsistent data on vaccination . Individuals with a previously recorded positive SARS‐CoV‐2 test (previous infection) were excluded until April 2022, but not thereafter. During the Omicron wave, there was a very high incidence, which was largely under‐reported due to the discontinuation of systematic testing in most countries and the widespread use of self‐tests. In order to reduce misclassification and increase the generalisability of results, the restriction to individuals without documented infection was removed for estimates from April–May 2022 onwards.Outcomes of interest were (1) hospitalisation due to COVID‐19, defined as admission to a hospital with a SARS‐CoV‐2 infection laboratory‐confirmed from 14 days before to 1 day after admission, in which admission criteria are compatible with a severe acute respiratory infection,We assumed as the date of the outcome the minimum between the date of the positive test result  and the date of hospital admission or death (respectively for each outcome).The vaccination status was defined as a time‐varying variable within each observation period and classified as follows: (1) unvaccinated (no record of COVID‐19 vaccine administration); (2) complete primary vaccination, if received one dose of Jcovden® or two doses of any combination of Comirnaty®, Spikevax® or Vaxzevria®; (3) vaccination with the first booster, if received an additional dose of Comirnaty®, Spikevax®  at least 90 days after the complete primary vaccination; (4) vaccination with the second booster, if received a first booster and an additional dose of Comirnaty®, Spikevax®  at least 90 days later; (5) vaccination with the third booster, defined in the same way as the second booster.2.3We used a survival analysis framework with calendar time as the underlying time scale, assigning time zero to the first day of each observation period. We excluded person‐time at‐risk from the date of receipt of any vaccine dose until 13 days after. Follow‐up started at the beginning of the observation period and ended at the earliest occurrence of any of the following events: (1) outcome of interest, (2) death of any cause, (3) discontinuation in the administrative database , or (4) administrative censoring (8 weeks after time zero).We estimated confounder adjusted hazard ratios (aHR) of each outcome and 95% confidence intervals (CI) with Cox proportional hazards models. The adjustment variables included sex, age group (in 5‐year age bands), previous SARS‐CoV‐2 infection, comorbidities (with the exception of Luxembourg) and other variables relevant at each study site Appendix . We compFor data protection reasons, sites reported aHR estimates only when at least five events per vaccination status category were observed. All sites fulfilled ethical and data protection requirements according to their national legislation Appendix .2.4We used a random‐effects meta‐analysis (Paule‐Mandel method)33.1Pooling together the data from the four participating study‐sites up to June–July 2022 and from the six participating study‐sites thereon, the distribution of study participants by vaccination status in each observation period is shown in Figure The number of observed COVID‐19 hospitalisations ranged between 1045 in the first observation period (October–November 2021) and 475 in the last observation period (October–November 2022) among 65–79 years, and between 820 and 687 among ≥80 years Appendix . The num3.2For the 65–79 years, VE of complete primary vaccination against COVID‐19 hospitalisation declined from 86.8%  in October–November 2021 to 31.5%  in October–November 2022. For the ≥80 years, a similar trend was observed VE of complete primary vaccination against COVID‐19 hospitalisation decreased from 66.9%  to 36.1%   in November–December 2021 to 52.1%  in October–November 2022. VE of the first booster for the ≥80 years peaked at 95.6%  in October–November 2021 declining to 54.6%  by May 2022 with little variation afterwards  in August–September 2022 but increased to 80.6%  in October–November 2022. For the ≥80 years, VE started at 82.0%  in June–July 2022 and was 83.9%  in October–November 2022  but was only available for ≥80 years in October–November 2022 in Portugal.3.3Relative to complete primary vaccination ≥169 days ago, the first booster rVE for the 65–79 years varied between 64.2%  in April–May 2022 and 30.4%  in October–November 2022. The corresponding estimates for the ≥80 years were 52.2%  and 45.5% , respectively. Table .For the 65–79 years, relative to the first booster vaccination ≥90 days ago, the incremental protection conferred by the second booster varied, from −39.9%  in August–September 2022 to 57.4%  in October–November 2022. For the ≥80 years rVE of the second booster, introduced in late spring of 2022 ranged between 54.0%  in June–July 2022 and 34.2%  in September–October 2022., Table .3.4For the 65–79 years, complete primary vaccination VE against COVID‐19‐related death varied between 21.3%  in March–April 2022 and 45.0%  in October–November 2022. The corresponding figures for ≥80 years were 41.4%  and 45.1%  Figure .We observed a decrease in the first booster VE estimates between March–April 2022 and October–November 2022 from 85.4%  to 43.1%  for the 65–79 years and from 83.5%  to 64.4%  for the ≥80 years Figure .For the 65–79 years, second booster VE was 77.0%  in the last observation period (October–November 2022); for ≥80 years, it remained above 80% between June–July 2022 and October–November 2022, showing no evident trends  was 74.0%  in October–November 2022 Appendix . For the4The prospective production of VE estimates using population‐based EHR with short time lag between data consolidation and data analysis is an added value to provide necessary evidence to adapt vaccine policies in the different target groups in a timely way.Our results showed a decrease in complete primary vaccination VE against hospitalisation in both age groups (65–79 and ≥80 years) from 87%–67% in October–November 2021 to 32%–36% in October–November 2022. While the first booster initially restored immunity to similar levels to the ones observed at the beginning of the vaccination programme (≥95% by the end of 2021), its VE also decreased to approximately 50%–68% by May 2022, around 6–7 months after first booster vaccination campaign and after the emergence of Omicron and its subvariants. VE estimates against COVID‐19‐related deaths of the first booster, available since March–April 2022, showed a similar trend, although less pronounced among ≥80 years, compared to 65–79 years.The significant decline in VE following the emergence of SARS‐CoV‐2 Omicron in December 2021 is in line with neutralisation studies indicating vaccine escape by Omicron.Other factors could also contribute to the observed decrease in VE. The Omicron BA.1 wave in early 2022 resulted in the highest SARS‐CoV‐2 incidence observed throughout the pandemic in Europe, with an estimated 48% of the European population infected.The administration of a second booster for the ≥80 years and other vulnerable population groups in the Spring of 2022 In addition, adapted bivalent vaccines were introduced and used as boosters  from September 2022 onwards,There are several limitations to be flagged. Even though all the sites followed a common protocol, there were some differences in the information available at each site and the outcomes definitions allow a small degree of flexibility. Also, because variables for adjustment collected by study sites were limited by the information available within the respective EHR, there might be some residual confounding in the estimates. The VE monitoring system was implemented in highly vaccinated populations. By October 2021, the primary series vaccination coverage was already >90% in all participating study sites and continued to increase,Sites contributed to the pooled estimates only if a general recommendation of respective doses was in place and if they had registered more than five events in the vaccination groups for each comparison. This results in some estimates excluding particular countries, which could affect the comparability across time. To exclude vulnerable groups with different probabilities of vaccination and developing severe COVID‐19, we excluded those living in long‐term care facilities (if information was available in EHR) and early vaccinees. Our approach might bias VE estimates since vulnerable individuals who remained unvaccinated were not excluded, although primary vaccination coverage in this population was high.Heterogeneity in VE estimates between study sites was variable, but was often high Appendix . AlthougWhile most of the vaccines administered as first, second and third boosters were mRNA vaccines in the EU/EEA, it would be of importance to get brand‐specific estimates. Unfortunately, there was not sufficient information in some registries to provide vaccine brand‐specific estimates. Last but not least, the project aims to expand to additional countries in order to have better geographical representativeness across the EU/EEA.In conclusion, according to our results, successive COVID‐19 vaccine booster doses have been key to maintaining protection against severe form of the disease over time. Despite the reduction in VE, booster vaccination continues to substantially reduce the risk of hospitalisation and death due to COVID‐19 in older individuals. Overall, this study demonstrated the feasibility of real‐world prospective monitoring of COVID‐19 VE in real time using EHR with the application of a common protocol across six EU/EEA countries. Although it comes with some methodological challenges, the use of population‐based EHR across several sites provide a robust estimate at the EU level and should be maintained to continue with near‐real‐time VE estimates in a changing landscape of COVID‐19 vaccine recommendations.The common protocol, including the design of the statistical analysis, was elaborated by SM, IK, BN, AS, JS, MV, AN and NN, with active contributions from IMB, KFN, HM, JC, HDE, CHH, AA, TB, SS, IVE. BS, JAFL, AM, PVB, CD, PH, IC, AE, CB, and AL contributed to common protocol adaptation and site‐specific study implementation. BN, JS, IMB, HM, SS, IK, IVE, and CHH contributed to the data management and analysis at the site level. MM, LA, MD, EK, MFV, and SM were in charge of pooling site estimates. IK and SM drafted the first version of the manuscript. BN, AS, JS, MV, AN, SB, NN, IMB, KFN, HM, JC, HDE, CHH, AA, TB, SS, IVE, BS, JAFL, AM, PVB, CD, PH, IC, AE, CB, AL, MM, LA, MD, EK, and MFV contributed to the interpretation of results and critically reviewed manuscript. All the authors approved the final version of this manuscript.The authors declare no conflict of interest.https://www.webofscience.com/api/gateway/wos/peer-review/10.1111/irv.13195.The peer review history for this article is available at All participating study sites conformed with their respective national and EU ethical and data protection requirements. Ethical statements for each of the participating study sites are reported in Supporting Information Appendix .Appendix S1. Data sources used in the six study sites to extract the study variables.Appendix S2. Methodological details in the different study sites.Appendix S3. Ethical statements for six study sites.Appendix S4. Age‐specific rollout of the COVID‐19 vaccination campaign for the 65–79 year‐olds and ≥80‐year‐olds by vaccine dose and study site.Appendix S5. Number of events and person‐months for VE estimates against hospitalisation and death due to COVID‐19.Appendix S6. Detailed results of vaccine effectiveness against hospitalisation due to COVID‐19.Appendix S7. Detailed results of vaccine effectiveness against death due to COVID‐19.Appendix S8. Measures of heterogeneity from the random‐effects meta‐analysis pooling site‐specific estimates from six EU/EEA countries.Click here for additional data file.


GRIA1–4 have been described in patients with developmental delay, intellectual disability, autism spectrum disorder, and seizures, with GRIA2 variants typically causing AMPAR loss of function. Here, we identify a novel, heterozygous de novo pathogenic missense mutation in GRIA2  in a 1‐year‐old boy with epilepsy, developmental delay, and failure to thrive. We made patch‐clamp recordings to compare the functional and pharmacological properties of variant and wild‐type receptors expressed in HEK293 cells, with and without the transmembrane AMPAR regulatory protein γ2. This showed GluA2 A643V‐containing AMPARs to exhibit a novel gain of function, with greatly slowed deactivation, markedly reduced desensitization, and increased glutamate sensitivity. Perampanel, an antiseizure AMPAR negative allosteric modulator, was able to fully block GluA2 A643V/γ2 currents, suggesting potential therapeutic efficacy. The subsequent introduction of perampanel to the patient's treatment regimen was associated with a marked reduction in seizure burden, a resolution of failure to thrive, and clear developmental gains. Our study reveals that GRIA2 disorder can be caused by a gain‐of‐function variant, and both predicts and suggests the therapeutic efficacy of perampanel. Perampanel may prove beneficial for patients with other gain‐of‐function GRIA variants.α‐Amino‐3‐hydroxy‐5‐methyl‐4‐isoxazolepropionic acid‐type glutamate receptors (AMPARs) are ligand‐gated cationic channels formed from combinations of GluA1‐4 subunits. Pathogenic variants of  Developmental progress was ascertained by chart review of clinical notes (produced by the patient's neurologist and therapists) and family reports (age = 7–33 months). Seizure burden (from ages 20 to 33 months) was evaluated from daily seizure diaries kept by the patient's family. Concurrent antiseizure medication doses were kept stable during perampanel initiation. Levetiracetam was weaned after 2 months, following improved seizure control. Electroencephalographic (EEG) evaluations were performed as clinically indicated. Recording of glutamate‐evoked currents from heterologously expressed AMPARs in outside‐out membrane patches was performed as previously described  of the University of Michigan (UoM); publishing clinical findings from a single individual does not fit the definition of human‐subjects research requiring IRB approval . Written informed parental consent for genetic testing and publication was obtained. Trio genetic analysis (EpiXpanded Panel) was performed by GeneDX , but maintained growth along the 50th percentile while treated with ranitidine and omeprazole. At 6 months, he was evaluated by an ophthalmologist for intermittent exotropia and poor visual tracking, and at 7 months by a pediatric neurologist for episodes of back arching and vomiting after feeds. His examination at that time showed decreased muscle tone and mild developmental delay. He was able to tripod sit, but unable to sit unsupported. He babbled appropriately, but was not clearly responsive to his name. A routine 30‐min EEG was normal Figure .GRIA2  located at the lurcher siteAF sequence , the weighted time constant of deactivation  was approximately sevenfold greater with A643V compared to wild type  was almost double that of wild type and the residual current  was increased ~50‐fold  wild‐type or A643V subunits in HEK293 cells and compared outside‐out patch responses to glutamate Figure . For cure Figure ; Table 1d Figure , Table 1d Figure , whereasw, deact was increased nearly 10‐fold, τw, des more than fourfold, and Iss/Ipeak more than 10‐fold .3.3As the A643V variant caused gain of function, we tested the AMPAR NAM perampanelAdjunctive treatment with perampanel was initiated at 27 months of age . Prior to this, at 24 months, introduction of MAD resulted in an initial drop in seizure number and frequency, although occasional (1–3/month) clusters of 6+ seizures persisted. Perampanel introduction was associated with a further reduction in seizure frequency Figure . In the Perampanel introduction coincided with an acceleration in developmental gains; the patient gained the ability to sit independently, weight‐bear in quadrupedal position, and displayed improved fine motor, visual, and communication skills. Constipation, vomiting, and failure to thrive resolved, with weight (at 34 months) recovering to the 15.8th percentile. His family reports a significant improvement in baseline irritability, and, within the first 2 h of his perampanel dose, he displays consistent smiling and purposeful laughing, behaviors not previously observed. Nonetheless, the EEG at 3 months postperampanel remained slow and disorganized, with multifocal spikes predominantly over the bilateral posterior and left temporal regions, suggestive of an epileptic encephalopathy with ongoing risk for recurrent seizures Figure .44.1GRIA disorder is an emerging neurological disease with 100+ variants identified across GRIA1–4.GRIA2 disorder, including the onset of neurological symptoms at ~6 months, epilepsy, and developmental delay.GRIA2 A643V as a gain‐of‐function variant, a previously unreported molecular phenotype that could be expected to account for the neurological symptoms observed.AF motif, the most highly conserved sequence in the ionotropic glutamate receptor superfamily. This forms the upper channel gate with all four Ala643 residues of the tetramer in close proximity.The A643V variant modifies the SYTANLA4.2We found perampanel to be much less potent on GluA2 A643V than on wild‐type receptors, likely due to the Val643‐induced rearrangements displacing the adjacent Phe644, which is indispensable for perampanel binding.Commonly reported perampanel side effects include dizziness and sedation, as well as negative mood alteration and aggression.GRIA2 gain‐of‐function variants, and potentially those with other GRIA gain‐of‐function variants.It is important to note that clinical management decisions for our patient were not made as part of a placebo‐controlled, double‐blinded study. Perampanel was added once in vitro results confirmed gain of function and probable safety. The lack of a placebo control and the use of parent reporting may lead to significant ascertainment bias in noting the overall effect of perampanel. Future follow‐up will provide continued insight as to the benefit of perampanel for patients with Conceptualization: Ian D. Coombs, Stuart G. Cull‐Candy, Mark Farrant. Formal analysis: Ian D. Coombs, Volodymyr Krotov, Mark Farrant. Funding acquisition: Stuart G. Cull‐Candy, Mark Farrant. Investigation: Ian D. Coombs, Volodymyr Krotov, Julie Ziobro. Project administration: Ian D. Coombs, Julie Ziobro, Mark Farrant. Writing–original draft: Ian D. Coombs, Stuart G. Cull‐Candy, Mark Farrant. Writing–review & editing: Julie Ziobro, Taryn‐Leigh Surtees, Ian D. Coombs, Stuart G. Cull‐Candy, Mark Farrant.None of the authors has any conflict of interest to disclose.Written informed consent for genetic testing and publication was obtained from the patient's parents.APPENDIX S1Click here for additional data file.


Although diabetes mellitus (DM) is regarded as a risk factor of colorectal cancer (CRC), the impacts of pre‐existing DM on CRC without drug intervention remain unknown. The purpose of this study was to investigate and analyze the effects of diabetes mellitus (DM) on colorectal cancer (CRC). And, to further explore the influencing factors and the mechanisms of DM affects CRC progression.In this study, we investigated the effects of DM on CRC progression in a streptozotocin‐induced DM mice model. Furthermore, we evaluated the change of T cells levels using flow cytometry and indirect immunofluorescence. We assessed the alternation of gut microbiome and the transcriptional response using 16s rRNA sequencing and RNA‐seq.+ T cells, CD8+ T cells and mucosal‐associated invariant T cell (MAIT) in the CRC progression. In addition, DM could induce gut microbiome dysbiosis and change the transcriptional response in CRC complicated with DM.Results showed that the mice survival time was significantly decreased in CRC complicated with DM group (DM‐CRC), compared with only tumor bearing mice (CRC group). Furthermore, we found that DM could affect the immune response by changing the infiltration of CD4For the first time, the effects of DM on CRC were systematically characterized in a mice model. Our findings highlight the effects of pre‐existing DM on CRC, and these findings should facilitate further studies in exploring and developing potentially targeted therapy for CRC in diabetic patients. Our results suggest that the effects induced by DM should be considered in the treatment for CRC complicated with DM patients. Graphical abstract exhibition of the effects of DM on CRC. Thus, in the present study, we evaluated systematically the effects of preexisting DM on CRC progression using a streptozotocin‐induced DM mice model. We further evaluated the alternation of gut microbiota, T‐cell response, and the changes in transcript levels from tumor tissues in CRC complicated with DM and without DM. Our results help address the challenge of developing CRC complicated with DM treatment plan.22.12 at 37 °C. The antibodies for FCM analysis including anti‐CD4, anti‐CD8, anti‐CD3, anti‐TCR beta, and MR1 were bought from Becton Dickinson (BD). The antibodies  for indirect immunofluorescence (IF) analysis were bought from Thermo Fisher Scientific .The mouse CRC cell lines CT26 were obtained from the American Type Culture Collection (ATCC), and cultured in RPMI 1640 (Gibco) with 10% FBS  and 1% penicillin/streptomycin (Gibco) in a humidified atmosphere with 5% CO2.26 CT26 cells or phosphate‐buffered saline (PBS) buffer.Female BALB/c mice (4–6 weeks old) were bought from SPF Biotechnology Co., LTD (Beijing). Mice received an intraperitoneal injection of streptozotocin or citrate buffer (Sigma‐Aldrich) with 130 mg/kg body using a 29‐G needles.2.32)/2.3. Imaging of CT26‐bearing mouse was detected using the IVIS Spectrum imaging system (IVIS Spectrum).The tumor size was tested using a digital caliper, and tumor volume is calculated as  cells were detected by flow cytometry (FCM). The samples were prepared as previously reported.The levels of CD42.5The tumor tissue samples collected from different groups were fixed. IF was used to detect T‐cell infiltration in the tumor microenvironment. Both primary and secondary antibodies associated with T‐cell detection were bought from Thermo Fisher Scientific. The 4′, 6‐diamidino‐2‐phenylindole (DAPI) was bought from (Beyotime Biotechnology). Finally, the slides were washed with PBST and imaged under a fluorescence microscope.2.6The fecal samples from the DM, DM‐CRC, and CRC groups were collected, and DNA were extracted using a Microbiome DNA Purification Kit (Thermo Fisher Scientific) according to the manual protocols. A NanoDrop ND‐1000 spectrophotometer (Thermo Fisher Scientific) was used to test the concentration of DNA. The DNA was stored at −80°C until further experiments.2.7https://www.genescloud.cn).The 16S rRNA gene was sequenced using the Illumina MiSeq platform (Illumina). 16S rRNA gene (hypervariable V3–V4 region) were amplified with the primer pair F (ACTCCTACGGGAGGCAGCA) and R (GGACTACHVGGGTWTCTAAT). The raw data were processed as previously reported.2.8Total RNA from tumor tissues in different groups was extracted and purified RNA was used for cDNA library construction according to the Collibri Stranded RNA Library Prep Kit manufacturer's instructions (Thermo Fisher Scientific). RNA‐seq used the next‐generation sequencing to analyze gene expression. The data were processed and analyzed using HISAT2 software, DESeq package, topGO, and clusterProfiler.2.9−ΔΔCt method. All of the qRT‐PCR amplifications were performed in triplicate and were repeated three times. The primers used are listed in Table Total RNA was extracted from tissues, and cDNA was synthesized using PrimeScript™ II 1st Strand cDNA Synthesis Kit (Takara). The quantitative real‐time PCR (qPCR) was carried out using TB Green® Fast qPCR Mix (Takara) for 30 s at 95°C, followed by 40 cycles at 95°C for 5 s, 60°C for 10 s. The data were analyzed using the 22.10t‐test was used to compare tumor burden. The log‐rank (Mantel‐Cox) test was used to analyze survival difference. GraphPad Prism 8 (GraphPad Software) was used to create chart and common data analysis. A value of p < 0.05 was considered significant.Student's 33.1We evaluated the effect of DM in CRC progression using a streptozotocin‐induced DM mice model.The results indicated that there was a higher ratio of mouse tumor volume/body weight in the DM‐CRC group compared to the CRC group Figure . Further3.2+CD4+ T cells, CD3+CD8+ T cells, and MAIT cells are related to CRC complicated with DM. Here, we analyzed the proportions of CD3+CD4+ T cells, CD3+CD8+ T cells, and MAIT cells from different groups in the tumor tissues and in the peripheral blood mononuclear cell (PBMC) using FCM. Our result showed that the proportions of CD3+CD4+, CD3+CD8+, and MAIT cells in the DM‐CRC group in tumor significantly decreased compared with that in the CRC group  family, which is heparin‐binding growth factors, it has been confirmed to be involved in the invasion and metastasis of tumor and also has a great potential for the treatment of DM.5In conclusion, we found the pre‐existing DM promotes CRC progression in a mice model. Furthermore, we found that DM changed tumor microenvironment and the gut microbiome. Our findings highlight the effects of pre‐existing DM on CRC, and these findings should facilitate further studies in exploring and developing potentially targeted therapy for CRC in diabetic patientsYangbo Lv: Conceptualization (lead); data curation (lead); formal analysis ; writing – original draft (lead); writing – review and editing . Shuiquan Lin: Data curation ; formal analysis ; methodology . Mingsheng Liu: Data curation ; formal analysis ; investigation ; methodology . Lihui Wang: Data curation ; formal analysis ; investigation ; methodology . Xiaoyu Wang: Data curation ; formal analysis ; methodology . Li Cui: Conceptualization (lead); funding acquisition (lead); supervision . Jianguang Xu: Conceptualization (lead); investigation ; supervision (lead).This work was sponsored by grants from the Shanghai Science and Technology Innovation Action Plan (21140901400).The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported.The animal experiments in our study were approved by the local Animal Ethical Committee (SV20210316‐01). After approval, the experiments were performed according to the Guidelines on the Humane Treatment of Laboratory Animals. The study did not involve human trials, so no informed consent was required for the study.Data S1. Supporting Information.Click here for additional data file.