Transcriptional signature of durable effector T cells elicited by a replication defective HCMV vaccine.

Human cytomegalovirus (HCMV) is a leading infectious cause of birth defects and the most common opportunistic infection that causes life-threatening diseases post-transplantation; however, an effective vaccine remains elusive. V160 is a live-attenuated replication defective HCMV vaccine that showed a 42.4% efficacy against primary HCMV infection among seronegative women in a phase 2b clinical trial. Here, we integrated the multicolor flow cytometry, longitudinal T cell receptor (TCR) sequencing, and single-cell RNA/TCR sequencing approaches to characterize the magnitude, phenotype, and functional quality of human T cell responses to V160. We demonstrated that V160 de novo induces IE-1 and pp65 specific durable polyfunctional effector CD8 T cells that are comparable to those induced by natural HCMV infection. We identified a variety of V160-responsive T cell clones which exhibit distinctive "transient" and "durable" expansion kinetics, and revealed a transcriptional signature that marks durable CD8 T cells post-vaccination. Our study enhances the understanding of human T-cell immune responses to V160 vaccination.

Developing deep learning-based strategies to predict the risk of hepatocellular carcinoma among patients with nonalcoholic fatty liver disease from electronic health records.

OBJECTIVE: The accuracy of deep learning models for many disease prediction problems is affected by time-varying covariates, rare incidence, covariate imbalance and delayed diagnosis when using structured electronic health records data. The situation is further exasperated when predicting the risk of one disease on condition of another disease, such as the hepatocellular carcinoma risk among patients with nonalcoholic fatty liver disease due to slow, chronic progression, the scarce of data with both disease conditions and the sex bias of the diseases. The goal of this study is to investigate the extent to which the aforementioned issues influence deep learning performance, and then devised strategies to tackle these challenges. These strategies were applied to improve hepatocellular carcinoma risk prediction among patients with nonalcoholic fatty liver disease. METHODS: We evaluated two representative deep learning models in the task of predicting the occurrence of hepatocellular carcinoma in a cohort of patients with nonalcoholic fatty liver disease (n = 220,838) from a national EHR database. The disease prediction task was carefully formulated as a classification problem while taking censorship and the length of follow-up into consideration. RESULTS: We developed a novel backward masking scheme to deal with the issue of delayed diagnosis which is very common in EHR data analysis and evaluate how the length of longitudinal information after the index date affects disease prediction. We observed that modeling time-varying covariates improved the performance of the algorithms and transfer learning mitigated reduced performance caused by the lack of data. In addition, covariate imbalance, such as sex bias in data impaired performance. Deep learning models trained on one sex and evaluated in the other sex showed reduced performance, indicating the importance of assessing covariate imbalance while preparing data for model training. CONCLUSIONS: The strategies developed in this work can significantly improve the performance of hepatocellular carcinoma risk prediction among patients with nonalcoholic fatty liver disease. Furthermore, our novel strategies can be generalized to apply to other disease risk predictions using structured electronic health records, especially for disease risks on condition of another disease.

The interplay of machine learning-based resonant anomaly detection methods.

Machine learning-based anomaly detection (AD) methods are promising tools for extending the coverage of searches for physics beyond the Standard Model (BSM). One class of AD methods that has received significant attention is resonant anomaly detection, where the BSM physics is assumed to be localized in at least one known variable. While there have been many methods proposed to identify such a BSM signal that make use of simulated or detected data in different ways, there has not yet been a study of the methods' complementarity. To this end, we address two questions. First, in the absence of any signal, do different methods pick the same events as signal-like? If not, then we can significantly reduce the false-positive rate by comparing different methods on the same dataset. Second, if there is a signal, are different methods fully correlated? Even if their maximum performance is the same, since we do not know how much signal is present, it may be beneficial to combine approaches. Using the Large Hadron Collider (LHC) Olympics dataset, we provide quantitative answers to these questions. We find that there are significant gains possible by combining multiple methods, which will strengthen the search program at the LHC and beyond.

The IL6/JAK/STAT3 signaling axis is a therapeutic vulnerability in SMARCB1-deficient bladder cancer.

SMARCB1 loss has long been observed in many solid tumors. However, there is a need to elucidate targetable pathways driving growth and metastasis in SMARCB1-deficient tumors. Here, we demonstrate that SMARCB1 deficiency, defined as genomic SMARCB1 copy number loss associated with reduced mRNA, drives disease progression in patients with bladder cancer by engaging STAT3. SMARCB1 loss increases the chromatin accessibility of the STAT3 locus in vitro. Orthotopically implanted SMARCB1 knockout (KO) cell lines exhibit increased tumor growth and metastasis. SMARCB1-deficient tumors show an increased IL6/JAK/STAT3 signaling axis in in vivo models and patients. Furthermore, a pSTAT3 selective inhibitor, TTI-101, reduces tumor growth in SMARCB1 KO orthotopic cell line-derived xenografts and a SMARCB1-deficient patient derived xenograft model. We have identified a gene signature generated from SMARCB1 KO tumors that predicts SMARCB1 deficiency in patients. Overall, these findings support the clinical evaluation of STAT3 inhibitors for the treatment of SMARCB1-deficient bladder cancer.

Developing deep learning-based strategies to predict the risk of hepatocellular carcinoma among patients with nonalcoholic fatty liver disease from electronic health records.

Background: Deep learning models showed great success and potential when applied to many biomedical problems. However, the accuracy of deep learning models for many disease prediction problems is affected by time-varying covariates, rare incidence, and covariate imbalance when using structured electronic health records data. The situation is further exasperated when predicting the risk of one disease on condition of another disease, such as the hepatocellular carcinoma risk among patients with nonalcoholic fatty liver disease due to slow, chronic progression, the scarce of data with both disease conditions and the sex bias of the diseases. Objective: The goal of this study is to investigate the extent to which time-varying covariates, rare incidence, and covariate imbalance influence deep learning performance, and then devised strategies to tackle these challenges. These strategies were applied to improve hepatocellular carcinoma risk prediction among patients with nonalcoholic fatty liver disease. Methods: We evaluated two representative deep learning models in the task of predicting the occurrence of hepatocellular carcinoma in a cohort of patients with nonalcoholic fatty liver disease (n = 220,838) from a national EHR database. The disease prediction task was carefully formulated as a classification problem while taking censorship and the length of follow-up into consideration. Results: We developed a novel backward masking scheme to evaluate how the length of longitudinal information after the index date affects disease prediction. We observed that modeling time-varying covariates improved the performance of the algorithms and transfer learning mitigated reduced performance caused by the lack of data. In addition, covariate imbalance, such as sex bias in data impaired performance. Deep learning models trained on one sex and evaluated in the other sex showed reduced performance, indicating the importance of assessing covariate imbalance while preparing data for model training. Conclusions: Devising proper strategies to address challenges from time-varying covariates, lack of data, and covariate imbalance can be key to counteracting data bias and accurately predicting disease occurrence using deep learning models. The novel strategies developed in this work can significantly improve the performance of hepatocellular carcinoma risk prediction among patients with nonalcoholic fatty liver disease. Furthermore, our novel strategies can be generalized to apply to other disease risk predictions using structured electronic health records, especially for disease risks on condition of another disease.

The impact of the multi-disciplinary molecular tumour board and integrative next generation sequencing on clinical outcomes in advanced solid tumours.

Background: The integration of next-generation sequencing (NGS) comprehensive gene profiling (CGP) into clinical practice is playing an increasingly important role in oncology. Therefore, the HKU-HKSH Multi-disciplinary Molecular Tumour Board (MTB) was established to advance precision oncology in Hong Kong. A multicenter retrospective study investigated the feasibility of the HKU-HKSH MTB in determining genome-guided therapy for treatment-refractory solid cancers in Hong Kong. Methods: Patients who were presented at the HKU-HKSH MTB between August 2018 and June 2022 were included in this study. The primary study endpoints were the proportion of patients who receive MTB-guided therapy based on genomic analysis and overall survival (OS). Secondary endpoints included the proportion of patients with actionable genomic alterations, objective response rate (ORR), and disease control rate (DCR). The Kaplan-Meier method was used in the survival analyses, and hazard ratios were calculated using univariate Cox regression. Findings: 122 patients were reviewed at the HKU-HKSH MTB, and 63% (n = 77) adopted treatment per the MTB recommendations. These patients achieved a significantly longer median OS than those who did not receive MTB-guided therapy (12.7 months vs. 5.2 months, P = 0.0073). Their ORR and DCR were 29% and 65%, respectively. Interpretation: Our study demonstrated that among patients with heavily pre-treated advanced solid cancers, MTB-guided treatment could positively impact survival outcomes, thus illustrating the applicability of NGS CGPs in real-world clinical practice. Funding: The study was supported by the Li Shu Pui Medical Foundation. Dr Aya El Helali was supported by the Li Shu Pui Medical Foundation Fellowship grant from the Li Shu Pui Medical Foundation. Funders had no role in study design, data collection, data analysis, interpretation, or writing of the report.

DR3 Regulates Intestinal Epithelial Homeostasis and Regeneration After Intestinal Barrier Injury.

BACKGROUND & AIMS: Tumor necrosis factor (TNF) superfamily member tumor necrosis factor-like protein 1A (TL1A) has been associated with the susceptibility and severity of inflammatory bowel diseases. However, the function of the tumor necrosis factor-like protein 1A and its receptor death receptor 3 (DR3) in the development of intestinal inflammation is incompletely understood. We investigated the role of DR3 expressed by intestinal epithelial cells (IECs) during intestinal homeostasis, tissue injury, and regeneration. METHODS: Clinical phenotype and histologic inflammation were assessed in C57BL/6 (wild-type), Tl1a-/- and Dr3-/- mice in dextran sulfate sodium (DSS)-induced colitis. We generated mice with an IEC-specific deletion of DR3 (Dr3ΔIEC) and assessed intestinal inflammation and epithelial barrier repair. In vivo intestinal permeability was assessed by fluorescein isothiocyanate dextran uptake. Proliferation of IECs was analyzed by bromodeoxyuridine incorporation. Expression of DR3 messenger RNA was assessed by fluorescent in situ hybridization. Small intestinal organoids were used to determine ex vivo regenerative potential. RESULTS: Dr3-/- mice developed more severe colonic inflammation than wild-type mice in DSS-induced colitis with significantly impaired IEC regeneration. Homeostatic proliferation of IECs was increased in Dr3-/- mice, but blunted during regeneration. Cellular localization and expression of the tight junction proteins Claudin-1 and zonula occludens-1 were altered, leading to increased homeostatic intestinal permeability. Dr3ΔIEC mice recapitulated the phenotype observed in Dr3-/- mice with increased intestinal permeability and IEC proliferation under homeostatic conditions and impaired tissue repair and increased bacterial translocation during DSS-induced colitis. Impaired regenerative potential and altered zonula occludens-1 localization also were observed in Dr3ΔIEC enteroids. CONCLUSIONS: Our findings establish a novel function of DR3 in IEC homeostasis and postinjury regeneration independent of its established role in innate lymphoid cells and T-helper cells.

HSC-independent definitive hematopoiesis persists into adult life.

It is widely believed that hematopoiesis after birth is established by hematopoietic stem cells (HSCs) in the bone marrow and that HSC-independent hematopoiesis is limited only to primitive erythro-myeloid cells and tissue-resident innate immune cells arising in the embryo. Here, surprisingly, we find that significant percentages of lymphocytes are not derived from HSCs, even in 1-year-old mice. Instead, multiple waves of hematopoiesis occur from embryonic day 7.5 (E7.5) to E11.5 endothelial cells, which simultaneously produce HSCs and lymphoid progenitors that constitute many layers of adaptive T and B lymphocytes in adult mice. Additionally, HSC lineage tracing reveals that the contribution of fetal liver HSCs to peritoneal B-1a cells is minimal and that the majority of B-1a cells are HSC independent. Our discovery of extensive HSC-independent lymphocytes in adult mice attests to the complex blood developmental dynamics spanning the embryo-to-adult transition and challenges the paradigm of HSCs exclusively underpinning the postnatal immune system.

Incorporating COVID-19 into Acute Febrile Illness Surveillance Systems, Belize, Kenya, Ethiopia, Peru, and Liberia, 2020-2021.

Existing acute febrile illness (AFI) surveillance systems can be leveraged to identify and characterize emerging pathogens, such as SARS-CoV-2, which causes COVID-19. The US Centers for Disease Control and Prevention collaborated with ministries of health and implementing partners in Belize, Ethiopia, Kenya, Liberia, and Peru to adapt AFI surveillance systems to generate COVID-19 response information. Staff at sentinel sites collected epidemiologic data from persons meeting AFI criteria and specimens for SARS-CoV-2 testing. A total of 5,501 patients with AFI were enrolled during March 2020-October 2021; >69% underwent SARS-CoV-2 testing. Percentage positivity for SARS-CoV-2 ranged from 4% (87/2,151, Kenya) to 19% (22/115, Ethiopia). We show SARS-CoV-2 testing was successfully integrated into AFI surveillance in 5 low- to middle-income countries to detect COVID-19 within AFI care-seeking populations. AFI surveillance systems can be used to build capacity to detect and respond to both emerging and endemic infectious disease threats.

Crohn's disease in endoscopic remission, obesity, and cases of high genetic risk demonstrates overlapping shifts in the colonic mucosal-luminal interface microbiome.

BACKGROUND: Crohn's disease (CD) patients demonstrate distinct intestinal microbial compositions and metabolic characteristics compared to unaffected controls. However, the impact of inflammation and underlying genetic risk on these microbial profiles and their relationship to disease phenotype are unclear. We used lavage sampling to characterize the colonic mucosal-luminal interface (MLI) microbiome of CD patients in endoscopic remission and unaffected controls relative to obesity, disease genetics, and phenotype. METHODS: Cecum and sigmoid colon were sampled from 110 non-CD controls undergoing screening colonoscopy who were stratified by body mass index and 88 CD patients in endoscopic remission (396 total samples). CD polygenic risk score (GRS) was calculated using 186 known CD variants. MLI pellets were analyzed by 16S ribosomal RNA gene sequencing, and supernatants by untargeted liquid chromatography-mass spectrometry. RESULTS: CD and obesity were each associated with decreased cecal and sigmoid MLI bacterial diversity and distinct bacterial composition compared to controls, including expansion of Escherichia/Shigella. Cecal and sigmoid dysbiosis indices for CD were significantly greater in obese controls than non-overweight controls. CD, but not obesity, was characterized by altered biogeographic relationship between the sigmoid and cecum. GRS was associated with select taxonomic shifts that overlapped with changes seen in CD compared to controls including Fusobacterium enrichment. Stricturing or penetrating Crohn's disease behavior was characterized by lower MLI bacterial diversity and altered composition, including reduced Faecalibacterium, compared to uncomplicated CD. Taxonomic profiles including reduced Parasutterella were associated with clinical disease progression over a mean follow-up of 3.7 years. Random forest classifiers using MLI bacterial abundances could distinguish disease state (area under the curve (AUC) 0.93), stricturing or penetrating Crohn's disease behavior (AUC 0.82), and future clinical disease progression (AUC 0.74). CD patients showed alterations in the MLI metabolome including increased cholate:deoxycholate ratio compared to controls. CONCLUSIONS: Obesity, CD in endoscopic remission, and high CD genetic risk have overlapping colonic mucosal-luminal interface (MLI) microbiome features, suggesting a shared microbiome contribution to CD and obesity which may be influenced by genetic factors. Microbial profiling during endoscopic remission predicted Crohn's disease behavior and progression, supporting that MLI sampling could offer unique insight into CD pathogenesis and provide novel prognostic biomarkers.

Biomolecular Condensation: A New Phase in Cancer Research.

Multicellularity was a watershed development in evolution. However, it also meant that individual cells could escape regulatory mechanisms that restrict proliferation at a severe cost to the organism: cancer. From the standpoint of cellular organization, evolutionary complexity scales to organize different molecules within the intracellular milieu. The recent realization that many biomolecules can "phase-separate" into membraneless organelles, reorganizing cellular biochemistry in space and time, has led to an explosion of research activity in this area. In this review, we explore mechanistic connections between phase separation and cancer-associated processes and emerging examples of how these become deranged in malignancy. SIGNIFICANCE: One of the fundamental functions of phase separation is to rapidly and dynamically respond to environmental perturbations. Importantly, these changes often lead to alterations in cancer-relevant pathways and processes. This review covers recent advances in the field, including emerging principles and mechanisms of phase separation in cancer.

Elafin Reverses Intestinal Fibrosis by Inhibiting Cathepsin S-Mediated Protease-Activated Receptor 2.

BACKGROUND & AIMS: More than half of Crohn's disease patients develop intestinal fibrosis-induced intestinal strictures. Elafin is a human protease inhibitor that is down-regulated in the stricturing intestine of Crohn's disease patients. We investigated the efficacy of elafin in reversing intestinal fibrosis and elucidated its mechanism of action. METHODS: We developed a new method to mimic a stricturing Crohn's disease environment and induce fibrogenesis using stricturing Crohn's disease patient-derived serum exosomes to condition fresh human intestinal tissues and primary stricturing Crohn's disease patient-derived intestinal fibroblasts. Three mouse models of intestinal fibrosis, including SAMP1/YitFc mice, Salmonella-infected mice, and trinitrobenzene sulfonic acid-treated mice, were also studied. Elafin-Eudragit FS30D formulation and elafin-overexpressing construct and lentivirus were used. RESULTS: Elafin reversed collagen synthesis in human intestinal tissues and fibroblasts pretreated with Crohn's disease patient-derived serum exosomes. Proteome arrays identified cathepsin S as a novel fibroblast-derived pro-fibrogenic protease. Elafin directly suppressed cathepsin S activity to inhibit protease-activated receptor 2 activity and Zinc finger E-box-binding homeobox 1 expression, leading to reduced collagen expression in intestinal fibroblasts. Elafin overexpression reversed ileal fibrosis in SAMP1/YitFc mice, cecal fibrosis in Salmonella-infected mice, and colonic fibrosis in trinitrobenzene sulfonic acid-treated mice. Cathepsin S, protease-activated receptor 2 agonist, and zinc finger E-box-binding homeobox 1 overexpression abolished the anti-fibrogenic effect of elafin in fibroblasts and all 3 mouse models of intestinal fibrosis. Oral elafin-Eudragit FS30D treatment abolished colonic fibrosis in trinitrobenzene sulfonic acid-treated mice. CONCLUSIONS: Elafin suppresses collagen synthesis in intestinal fibroblasts via cathepsin S-dependent protease-activated receptor 2 inhibition and decreases zinc finger E-box-binding homeobox 1 expression. The reduced collagen synthesis leads to the reversal of intestinal fibrosis. Thus, modified elafin may be a therapeutic approach for intestinal fibrosis.

CNGPLD: case-control copy-number analysis using Gaussian process latent difference.

MOTIVATION: Cross-sectional analyses of primary cancer genomes have identified regions of recurrent somatic copy-number alteration, many of which result from positive selection during cancer formation and contain driver genes. However, no effective approach exists for identifying genomic loci under significantly different degrees of selection in cancers of different subtypes, anatomic sites or disease stages. RESULTS: CNGPLD is a new tool for performing case-control somatic copy-number analysis that facilitates the discovery of differentially amplified or deleted copy-number aberrations in a case group of cancer compared with a control group of cancer. This tool uses a Gaussian process statistical framework in order to account for the covariance structure of copy-number data along genomic coordinates and to control the false discovery rate at the region level. AVAILABILITY AND IMPLEMENTATION: CNGPLD is freely available at https://bitbucket.org/djhshih/cngpld as an R package. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Exploiting induced vulnerability to overcome PARPi resistance and clonal heterogeneity in BRCA mutant triple-negative inflammatory breast cancer.

Acquired resistance and clonal heterogeneity are critical challenges in cancer treatment, and the lack of effective computational tools hampers the discovery of new treatments to overcome resistance. Using high-throughput transcriptomic databases of compound perturbation profiles, we have developed a bioinformatic strategy for identifying candidate drugs to overcome resistance with combinatorial therapy. We devised this strategy during an investigation into the acquired resistance against PARP inhibitors (PARPi) in a triple-negative inflammatory breast cancer cell line. In this study, we derived multiple PARPi-resistant clones and characterized their transcriptomic adaptations compared to the parental clone. The transcriptomes of the resistant clones showed substantial heterogeneity, highlighting the importance of characterizing multiple clones from the same tumour. Surprisingly, we found that these transcriptomic changes may not actually confer PARPi resistance, but they may nevertheless induce a shared secondary vulnerability. By modeling our data in relation to transcriptomic perturbation profiles of compounds, we uncovered deficiencies in Ras signaling that resulted from transcriptional adaptation to long-term PARPi treatment across multiple resistant clones. Due to these induced deficiencies, we predicted that the resistant clones would be sensitive to pharmacological reinforcement of PARPi-induced transcriptional adaptation. We then experimentally validated this predicted vulnerability that is shared by multiple resistant clones. Our results thus provide a promising paradigm for integrating transcriptomic data with compound perturbation profiles in order to identify drugs that can exploit an induced vulnerability and overcome therapeutic resistance, thus providing another strategy towards precision oncology.

The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics.

A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. In order to develop and benchmark new anomaly detection methods within this framework, it is essential to have standard datasets. To this end, we have created the LHC Olympics 2020, a community challenge accompanied by a set of simulated collider events. Participants in these Olympics have developed their methods using an R&D dataset and then tested them on black boxes: datasets with an unknown anomaly (or not). Methods made use of modern machine learning tools and were based on unsupervised learning (autoencoders, generative adversarial networks, normalizing flows), weakly supervised learning, and semi-supervised learning. This paper will review the LHC Olympics 2020 challenge, including an overview of the competition, a description of methods deployed in the competition, lessons learned from the experience, and implications for data analyses with future datasets as well as future colliders.

Effect and Mechanism of TL1A Expression on Epithelial-Mesenchymal Transition during Chronic Colitis-Related Intestinal Fibrosis.

BACKGROUND AND AIMS: Recent evidences reveal that epithelial to mesenchymal transition (EMT) exacerbates the process of intestinal fibrosis. Tumor necrosis factor-like ligand 1A (TL1A) is a member of the tumor necrosis family (TNF), which can take part in the development of colonic inflammation and fibrosis by regulating immune response or inflammatory factors. The purpose of this study was to elucidate the possible contribution of TL1A in onset and progression of intestinal inflammation and fibrosis through EMT. METHODS: Colonic specimens were obtained from patients with inflammatory bowel disease (IBD) and control individuals. The expression levels of TL1A and EMT-related markers in intestinal tissues were evaluated. Furthermore, the human colorectal adenocarcinoma cell line, HT-29, was stimulated with TL1A, anti-TL1A antibody, or BMP-7 to assess EMT process. In addition, transgenic mice expressing high levels of TL1A in lymphoid cells were used to further investigate the mechanism of TL1A in intestinal fibrosis. RESULTS: High levels of TL1A expression were detected in the intestinal specimens of patients with ulcerative colitis and Crohn's disease and were negatively associated with the expression of an epithelial marker (E-cadherin), while it was positively associated with the expression of interstitial markers (FSP1 and α-SMA). Transgenic mice with high expression of TL1A were more sensitive to dextran sodium sulfate and exhibited severe intestinal inflammation and fibrosis. Additionally, the TGF-ß1/Smad3 pathway may be involved in TL1A-induced EMT, and the expression of IL-13 and EMT-related transcriptional molecules (e.g., ZEB1 and Snail1) was increased in the intestinal specimens of the transgenic mice. Furthermore, TL1A-induced EMT can be influenced by anti-TL1A antibody or BMP-7 in vitro. CONCLUSIONS: TL1A participates in the formation and process of EMT in intestinal fibrosis. This new knowledge enables us to better understand the pathogenesis of intestinal fibrosis and identify new therapeutic targets for its treatment.

A Gene Expression Signature to Predict Nucleotide Excision Repair Defects and Novel Therapeutic Approaches.

Nucleotide excision repair (NER) resolves DNA adducts, such as those caused by ultraviolet light. Deficient NER (dNER) results in a higher mutation rate that can predispose to cancer development and premature ageing phenotypes. Here, we used isogenic dNER model cell lines to establish a gene expression signature that can accurately predict functional NER capacity in both cell lines and patient samples. Critically, none of the identified NER deficient cell lines harbored mutations in any NER genes, suggesting that the prevalence of NER defects may currently be underestimated. Identification of compounds that induce the dNER gene expression signature led to the discovery that NER can be functionally impaired by GSK3 inhibition, leading to synergy when combined with cisplatin treatment. Furthermore, we predicted and validated multiple novel drugs that are synthetically lethal with NER defects using the dNER gene signature as a drug discovery platform. Taken together, our work provides a dynamic predictor of NER function that may be applied for therapeutic stratification as well as development of novel biological insights in human tumors.

TNF-Like Ligand 1 Aberrance Aggravates Nonalcoholic Steatohepatitis via M1 Macrophage Polarization.

Nonalcoholic steatohepatitis (NASH) is a progressive, chronic liver disease worldwide which imposes a large economic burden on society. M1/M2 macrophage balance destruction and recruitment of mononuclear immune cells to the liver play critical roles in NASH. Several studies have shown that the expression of TNF-like ligand 1 aberrance (TL1A) increased in macrophages associated with many inflammatory diseases, for example, inflammatory bowel disease, primary biliary cholangitis, and liver fibrosis. One recent research showed that weight, abdominal adipose, and liver leptin, one of the critical fat cytokines, were reduced in TL1A knockout mice. However, the functional and molecular regulatory mechanisms of TL1A on macrophage polarization and recruitment in NASH have yet to be clarified. The authors found that high fructose high fat diet and methionine-choline deficiency diet induced the expression of TL1A in macrophages of liver tissue from murine NASH models. Myeloid-specific TL1A overexpressed mice showed exacerbated steatohepatitis with increased hepatic lipid accumulation, inflammation, liver injury, and apoptosis. M1 macrophages' infiltration and the production of proinflammatory and chemotactic cytokines increased in liver of NASH mouse models with myeloid-specific TL1A overexpressed. Furthermore, this paper revealed that bone marrow-derived macrophages and Kupffer cells with overexpression of TL1A exacerbated the lipid accumulation and expression of proinflammatory factors in the murine primary hepatocytes after free fatty acid treatment in vitro. In conclusion, TL1A-mediated M1-type macrophage polarization and recruitment into the liver promoted steatohepatitis in murine NASH.