A method for predicting drugs that can boost the efficacy of immune checkpoint blockade.

Combination therapy is a promising therapeutic strategy to enhance the efficacy of immune checkpoint blockade (ICB); however, predicting drugs for effective combination is challenging. Here we developed a general data-driven method called CM-Drug for screening compounds that can boost ICB treatment efficacy based on core and minor gene sets identified between responsive and nonresponsive samples in ICB therapy. The CM-Drug method was validated using melanoma and lung cancer mouse models, with combined therapeutic efficacy demonstrated in eight of nine predicted compounds. Among these compounds, taltirelin had the strongest synergistic effect. Mechanistic analysis and experimental verification demonstrated that taltirelin can stimulate CD8+ T cells and is mediated by the induction of thyroid-stimulating hormone. This study provides an effective and general method for predicting and evaluating drugs for combination therapy and identifies candidate compounds for future ICB combination therapy.

Pan-Genome of Wild and Cultivated Soybeans.

Soybean is one of the most important vegetable oil and protein feed crops. To capture the entire genomic diversity, it is needed to construct a complete high-quality pan-genome from diverse soybean accessions. In this study, we performed individual de novo genome assemblies for 26 representative soybeans that were selected from 2,898 deeply sequenced accessions. Using these assembled genomes together with three previously reported genomes, we constructed a graph-based genome and performed pan-genome analysis, which identified numerous genetic variations that cannot be detected by direct mapping of short sequence reads onto a single reference genome. The structural variations from the 2,898 accessions that were genotyped based on the graph-based genome and the RNA sequencing (RNA-seq) data from the representative 26 accessions helped to link genetic variations to candidate genes that are responsible for important traits. This pan-genome resource will promote evolutionary and functional genomics studies in soybean.

ncRNADrug: a database for validated and predicted ncRNAs associated with drug resistance and targeted by drugs.

Drug resistance is a major barrier in cancer treatment and anticancer drug development. Growing evidence indicates that non-coding RNAs (ncRNAs), especially microRNAs (miRNAs), long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs), play pivotal roles in cancer progression, therapy, and drug resistance. Furthermore, ncRNAs have been proven to be promising novel therapeutic targets for cancer treatment. Reversing dysregulated ncRNAs by drugs holds significant potential as an effective therapeutic strategy for overcoming drug resistance. Therefore, we developed ncRNADrug, an integrated and comprehensive resource that records manually curated and computationally predicted ncRNAs associated with drug resistance, ncRNAs targeted by drugs, as well as potential drug combinations for the treatment of resistant cancer. Currently, ncRNADrug collects 29 551 experimentally validated entries involving 9195 ncRNAs (2248 miRNAs, 4145 lncRNAs and 2802 circRNAs) associated with the drug resistance of 266 drugs, and 32 969 entries involving 10 480 ncRNAs (4338 miRNAs, 6087 lncRNAs and 55 circRNAs) targeted by 965 drugs. In addition, ncRNADrug also contains associations between ncRNAs and drugs predicted from ncRNA expression profiles by differential expression analysis. Altogether, ncRNADrug surpasses the existing related databases in both data volume and functionality. It will be a useful resource for drug development and cancer treatment. ncRNADrug is available at http://www.jianglab.cn/ncRNADrug.

GSCA: an integrated platform for gene set cancer analysis at genomic, pharmacogenomic and immunogenomic levels.

Cancer initiation and progression are likely caused by the dysregulation of biological pathways. Gene set analysis (GSA) could improve the signal-to-noise ratio and identify potential biological insights on the gene set level. However, platforms exploring cancer multi-omics data using GSA methods are lacking. In this study, we upgraded our GSCALite to GSCA (gene set cancer analysis, http://bioinfo.life.hust.edu.cn/GSCA) for cancer GSA at genomic, pharmacogenomic and immunogenomic levels. In this improved GSCA, we integrated expression, mutation, drug sensitivity and clinical data from four public data sources for 33 cancer types. We introduced useful features to GSCA, including associations between immune infiltration with gene expression and genomic variations, and associations between gene set expression/mutation and clinical outcomes. GSCA has four main functional modules for cancer GSA to explore, analyze and visualize expression, genomic variations, tumor immune infiltration, drug sensitivity and their associations with clinical outcomes. We used case studies of three gene sets: (i) seven cell cycle genes, (ii) tumor suppressor genes of PI3K pathway and (iii) oncogenes of PI3K pathway to prove the advantage of GSCA over single gene analysis. We found novel associations of gene set expression and mutation with clinical outcomes in different cancer types on gene set level, while on single gene analysis level, they are not significant associations. In conclusion, GSCA is a user-friendly web server and a useful resource for conducting hypothesis tests by using GSA methods at genomic, pharmacogenomic and immunogenomic levels.

ICBcomb: a comprehensive expression database for immune checkpoint blockade combination therapy.

The success of immune checkpoint blockade (ICB) promotes the immunotherapy to be a new pillar in cancer treatment. However, the low response rate of the ICB therapy limits its application. To increase the response rate and enhance efficacy, the ICB combination therapy has emerged and its clinical trials are increasing. Nevertheless, the gene expression profile and its pattern of ICB combination were not comprehensively studied, which limits the understanding of the ICB combination therapy and the identification of new drugs. Here, we constructed ICBcomb (http://bioinfo.life.hust.edu.cn/ICBcomb/), a comprehensive database, by analyzing the human and mouse expression data of the ICB combination therapy and comparing them between groups treated with ICB, other drugs or their combinations. ICBcomb contains 1399 samples across 29 cancer types involving 52 drugs. It provides a user-friendly web interface for demonstrating the results of the available comparisons in the ICB combination therapy datasets with five functional modules: [1, 2] the 'Dataset/Disease' modules for browsing the expression, enrichment and comparison results in each dataset or disease; [3] the 'Gene' module for inputting a gene symbol and displaying its expression and comparison results across datasets/diseases; [4] the 'Gene Set' module for GSVA/GSEA enrichment analysis on the built-in gene sets and the user-input gene sets in different comparisons; [5] the 'Immune Cell' module for immune cell infiltration comparison between different groups by immune cell abundance analysis. The ICBcomb database provides the first resource for gene expression profile and comparison in ICB combination therapy, which may provide clues for discovering the mechanism of effective combination strategies and new combinatory drugs.

AnimalTFDB 4.0: a comprehensive animal transcription factor database updated with variation and expression annotations.

Transcription factors (TFs) are proteins that interact with specific DNA sequences to regulate gene expression and play crucial roles in all kinds of biological processes. To keep up with new data and provide a more comprehensive resource for TF research, we updated the Animal Transcription Factor Database (AnimalTFDB) to version 4.0 (http://bioinfo.life.hust.edu.cn/AnimalTFDB4/) with up-to-date data and functions. We refined the TF family rules and prediction pipeline to predict TFs in genome-wide protein sequences from Ensembl. As a result, we predicted 274 633 TF genes and 150 726 transcription cofactor genes in AnimalTFDB 4.0 in 183 animal genomes, which are 86 more species than AnimalTFDB 3.0. Besides double data volume, we also added the following new annotations and functions to the database: (i) variations (including mutations) on TF genes in various human cancers and other diseases; (ii) predicted post-translational modification sites (including phosphorylation, acetylation, methylation and ubiquitination sites) on TFs in 8 species; (iii) TF regulation in autophagy; (iv) comprehensive TF expression annotation for 38 species; (v) exact and batch search functions allow users to search AnimalTFDB flexibly. AnimalTFDB 4.0 is a useful resource for studying TF and transcription regulation, which contains comprehensive annotation and classification of TFs and transcription cofactors.

lncRNASNP v3: an updated database for functional variants in long non-coding RNAs.

Long non-coding RNAs (lncRNAs) act as versatile regulators of many biological processes and play vital roles in various diseases. lncRNASNP is dedicated to providing a comprehensive repository of single nucleotide polymorphisms (SNPs) and somatic mutations in lncRNAs and their impacts on lncRNA structure and function. Since the last release in 2018, there has been a huge increase in the number of variants and lncRNAs. Thus, we updated the lncRNASNP to version 3 by expanding the species to eight eukaryotic species (human, chimpanzee, pig, mouse, rat, chicken, zebrafish, and fruitfly), updating the data and adding several new features. SNPs in lncRNASNP have increased from 11 181 387 to 67 513 785. The human mutations have increased from 1 174 768 to 2 387 685, including 1 031 639 TCGA mutations and 1 356 046 CosmicNCVs. Compared with the last release, updated and new features in lncRNASNP v3 include (i) SNPs in lncRNAs and their impacts on lncRNAs for eight species, (ii) SNP effects on miRNA-lncRNA interactions for eight species, (iii) lncRNA expression profiles for six species, (iv) disease & GWAS-associated lncRNAs and variants, (v) experimental & predicted lncRNAs and drug target associations and (vi) SNP effects on lncRNA expression (eQTL) across tumor & normal tissues. The lncRNASNP v3 is freely available at http://gong_lab.hzau.edu.cn/lncRNASNP3/.

Acylcarnitines: Nomenclature, Biomarkers, Therapeutic Potential, Drug Targets, and Clinical Trials.

Acylcarnitines are fatty acid metabolites that play important roles in many cellular energy metabolism pathways. They have historically been used as important diagnostic markers for inborn errors of fatty acid oxidation and are being intensively studied as markers of energy metabolism, deficits in mitochondrial and peroxisomal ß -oxidation activity, insulin resistance, and physical activity. Acylcarnitines are increasingly being identified as important indicators in metabolic studies of many diseases, including metabolic disorders, cardiovascular diseases, diabetes, depression, neurologic disorders, and certain cancers. The US Food and Drug Administration-approved drug L-carnitine, along with short-chain acylcarnitines (acetylcarnitine and propionylcarnitine), is now widely used as a dietary supplement. In light of their growing importance, we have undertaken an extensive review of acylcarnitines and provided a detailed description of their identity, nomenclature, classification, biochemistry, pathophysiology, supplementary use, potential drug targets, and clinical trials. We also summarize these updates in the Human Metabolome Database, which now includes information on the structures, chemical formulae, chemical/spectral properties, descriptions, and pathways for 1240 acylcarnitines. This work lays a solid foundation for identifying, characterizing, and understanding acylcarnitines in human biosamples. We also discuss the emerging opportunities for using acylcarnitines as biomarkers and as dietary interventions or supplements for many wide-ranging indications. The opportunity to identify new drug targets involved in controlling acylcarnitine levels is also discussed. SIGNIFICANCE STATEMENT: This review provides a comprehensive overview of acylcarnitines, including their nomenclature, structure and biochemistry, and use as disease biomarkers and pharmaceutical agents. We present updated information contained in the Human Metabolome Database website as well as substantial mapping of the known biochemical pathways associated with acylcarnitines, thereby providing a strong foundation for further clarification of their physiological roles.

AnnoSM: An Automated Annotation Tool for Determining the Substituent Modes on the Parent Skeleton Based on a Characteristic MS/MS Fragment Ion Library.

Mass spectrometry (MS) is a powerful technology for the structural elucidation of known or unknown small molecules. However, the accuracy of MS-based structure annotation is still limited due to the presence of numerous isomers in complex matrices. There are still challenges in automatically interpreting the fine structure of molecules, such as the types and positions of substituents (substituent modes, SMs) in the structure. In this study, we employed flavones, flavonols, and isoflavones as examples to develop an automated annotation method for identifying the SMs on the parent molecular skeleton based on a characteristic MS/MS fragment ion library. Importantly, user-friendly software AnnoSM was built for the convenience of researchers with limited computational backgrounds. It achieved 76.87% top-1 accuracy on the 148 authentic standards. Among them, 22 sets of flavonoid isomers were successfully differentiated. Moreover, the developed method was successfully applied to complex matrices. One such example is the extract of Ginkgo biloba L. (EGB), in which 331 possible flavonoids with SM candidates were annotated. Among them, 23 flavonoids were verified by authentic standards. The correct SMs of 13 flavonoids were ranked first on the candidate list. In the future, this software can also be extrapolated to other classes of compounds.

EVAtool: an optimized reads assignment tool for small ncRNA quantification and its application in extracellular vesicle datasets.

Extracellular vesicles (EVs) carrying various small non-coding RNAs (sncRNAs) play a vital roles in cell communication and diseases. Correct quantification of multiple sncRNA biotypes simultaneously in EVs is a challenge due to the short reads (<30 bp) could be mapped to multiple sncRNA types. To address this question, we developed an optimized reads assignment algorithm (ORAA) to dynamically map multi-mapping reads to the sncRNA type with a higher proportion. We integrated ORAA with reads processing steps into EVAtool Python-package (http://bioinfo.life.hust.edu.cn/EVAtool) to quantify sncRNAs, especially for sncRNA-seq from EV samples. EVAtool allows users to specify interested sncRNA types in advanced mode or use default seven sncRNAs (microRNA, small nucleolar RNA, PIWI-interacting RNAs, small nuclear RNA, ribosomal RNA, transfer RNA and Y RNA). To prove the utilities of EVAtool, we quantified the sncRNA expression profiles for 200 samples from cognitive decline and multiple sclerosis. We found that more than 20% of short reads on average were mapped to multiple sncRNA biotypes in multiple sclerosis. In cognitive decline, the proportion of Y RNA is significantly higher than other sncRNA types. EVAtool is a flexible and extensible tool that would benefit to mine potential biomarkers and functional molecules in EVs.

EVAtlas: a comprehensive database for ncRNA expression in human extracellular vesicles.

Extracellular vesicles (EVs) packing various molecules play vital roles in intercellular communication. Non-coding RNAs (ncRNAs) are important functional molecules and biomarkers in EVs. A comprehensive investigation of ncRNAs expression in EVs under different conditions is a fundamental step for functional discovery and application of EVs. Here, we curated 2030 small RNA-seq datasets for human EVs (1506 sEV and 524 lEV) in 24 conditions and over 40 diseases. We performed a unified reads dynamic assignment algorithm (RDAA) considering mismatch and multi-mapping reads to quantify the expression profiles of seven ncRNA types (miRNA, snoRNA, piRNA, snRNA, rRNA, tRNA and Y RNA). We constructed EVAtlas (http://bioinfo.life.hust.edu.cn/EVAtlas), a comprehensive database for ncRNA expression in EVs with four functional modules: (i) browse and compare the distribution of ncRNAs in EVs from 24 conditions and eight sources (plasma, serum, saliva, urine, sperm, breast milk, primary cell and cell line); (ii) prioritize candidate ncRNAs in condition related tissues based on their expression; (iii) explore the specifically expressed ncRNAs in EVs from 24 conditions; (iv) investigate ncRNA functions, related drugs, target genes and EVs isolation methods. EVAtlas contains the most comprehensive ncRNA expression in EVs and will be a key resource in this field.

N-acetylcysteine alleviated tris(2-chloroisopropyl) phosphate-induced sperm motility decline and functional dysfunction in mice through reversing oxidative stress and DNA damage.

The decline in male fertility caused by environmental pollutants has attracted worldwide attention nowadays. Tris(2-chloroisopropyl) phosphate (TCPP) is a chlorine-containing organophosphorus flame retardant applied in many consumer products and has multiple side effects on health. However, whether TCPP impairs spermatogenesis remains unclear. In this study, we found that TCPP reduced the sperm motility and blastocyst formation, inhibited proliferation and induced apoptosis in mice testes and spermatocyte cell line GC-2. Moreover, TCPP induced imbalance of oxidant and anti-oxidant, DNA damage and mitochondrial dysfunction, thus induced abnormal spermatogenesis. In this process, p53 signaling pathway was activated and N-acetylcysteine treatment partially alleviated the side effects of TCPP, including decrease of sperm motility, activation of p53 signaling pathway and DNA damage. Finally, our study verified that TCPP elevated reactive oxygen species (ROS), decreased mitochondrial membrane potential and induced apoptosis in human semen samples. Overall, ROS mediated TCPP-induced germ cell proliferation inhibition and apoptosis, which finally led to the decline of sperm motility.

Revealing the impact of CD70 expression on the manufacture and functions of CAR-70 T-cells based on single-cell transcriptomics.

BACKGROUND: Chimeric antigen receptor-modified T cells (CAR T-cells) have shown exhilarative clinical efficacy for hematological malignancies. However, a shared antigen pool between healthy and malignant T-cells remains a concept to be technically and clinically explored for CAR T-cell therapy in T-cell cancers. No guidelines for engineering CAR T-cells targeting self-expressed antigens are currently available. METHOD: Based on anti-CD70 CAR (CAR-70) T-cells, we constructed CD70 knock-out and wild-type CAR (CAR-70KO and CAR-70WT) T-cells and evaluated their manufacturing and anti-tumor capability. Single-cell RNA sequencing and TCR sequencing were performed to further reveal the underlying differences between the two groups of CAR T-cells. RESULTS: Our data showed that the disruption of target genes in T-cells before CAR transduction advantaged the expansion and cell viability of CAR T-cells during manufacturing periods, as well as the degranulation, anti-tumor efficacy, and proliferation potency in response to tumor cells. Meanwhile, more naïve and central memory phenotype CAR+ T-cells, with higher TCR clonal diversity, remained in the final products in KO samples. Gene expression profiles revealed a higher activation and exhaustion level of CAR-70WT T-cells, while signaling transduction pathway analysis identified a higher level of the phosphorylation-related pathway in CAR-70KO T-cells. CONCLUSION: This study evidenced that CD70 stimulation during manufacturing process induced early exhaustion of CAR-70 T-cells. Knocking-out CD70 in T-cells prevented the exhaustion and led to a better-quality CAR-70 T-cell product. Our research will contribute to good engineering CAR T-cells targeting self-expressed antigens.

Palaeobotanical evidence reveals the living conditions of Miocene Lufengpithecus in East Asia.

BACKGROUND: Understanding the relationship between human evolution and environmental changes is the key to lifting the veil on human origin. The hypothesis that environmental changes triggered the divergence of humans from apes (ca. 9.3-6.5 million years ago, Ma) has been poorly tested because of limited continuous environmental data from fossil localities. Lufengpithecus (12.5-6.0 Ma) found on the southeastern margin of the Tibetan Plateau (SEMTP) across the ape-human split provides a good chance for testing this hypothesis. RESULTS: Here, we reconstructed the habitats of L. keiyuanensis (12.5-11.6 Ma) with comprehensive vegetation, climate, and potential food web data by palaeobotanical evidence, together with other multidisciplinary data and partly tested the environment-driven hypothesis by revealing the living conditions of Lufengpithecus. CONCLUSION: A detailed comparison of hominoids on different continents reveals their behaviour and fate divergence across the ape-human split against the background of global climate change, i.e., the stable living conditions of SEMTP not only provided a so-called 'refuge' for arboreal Lufengpithecus but also acted as a 'double-edged sword', preventing their further evolution while vegetation shifts in East Africa probably stimulated the emergence of human bipedalism, and the intense climatic changes in Europe possibly prevented those hominoids from surviving that time interval. Our findings provide interesting insight into the environmental impacts on the behavioural evolution of hominoids.

A comprehensive survey for human transcription factors on expression, regulation, interaction, phenotype and cancer survival.

Transcription factors (TFs) act as key regulators in biological processes through controlling gene expression. Here, we conducted a systematic study for all human TFs on the expression, regulation, interaction, mutation, phenotype and cancer survival. We revealed that the average expression levels of TFs in normal tissues were lower than 50% expression of non-TFs, whereas TF expression was increased in cancers. TFs that are specifically expressed in an individual tissue or cancer may be potential marker genes. For instance, TGIF2LX/Y were preferentially expressed in testis and NEUROG1, PRDM14, SRY, ZNF705A and ZNF716 were specifically highly expressed in germ cell tumors. We found different distributions of target genes and TF co-regulations in different TF families. Some small TF families have huge protein interaction pairs, suggesting their central roles in transcriptional regulation. The bZIP family is a small family involving many signaling pathways. Survival analysis indicated that most TFs significantly affect survival of one or more cancers. Some survival-related TFs were also specifically highly expressed in the corresponding cancer types, which may be potential targets for cancer therapy. Finally, we identified 43 TFs whose mutations were closely correlated to survival, suggesting their cancer-driven roles. The systematic analysis of TFs provides useful clues for further investigation of TF regulatory mechanisms and the role of TFs in diseases.

CCLA: an accurate method and web server for cancer cell line authentication using gene expression profiles.

Cancer cell lines (CCLs) as important model systems play critical roles in cancer research. The misidentification and contamination of CCLs are serious problems, leading to unreliable results and waste of resources. Current methods for CCL authentication are mainly based on the CCL-specific genetic polymorphism, whereas no method is available for CCL authentication using gene expression profiles. Here, we developed a novel method and homonymic web server (CCLA, Cancer Cell Line Authentication, http://bioinfo.life.hust.edu.cn/web/CCLA/) to authenticate 1291 human CCLs of 28 tissues using gene expression profiles. CCLA showed an excellent speed advantage and high accuracy for CCL authentication, a top 1 accuracy of 96.58 or 92.15% (top 3 accuracy of 100 or 95.11%) for microarray or RNA-Seq validation data (719 samples, 461 CCLs), respectively. To the best of our knowledge, CCLA is the first approach to authenticate CCLs using gene expression data. Users can freely and conveniently authenticate CCLs using gene expression profiles or NCBI GEO accession on CCLA website.

Expression profile of immune checkpoint genes and their roles in predicting immunotherapy response.

Immune checkpoint genes (ICGs) play critical roles in circumventing self-reactivity and represent a novel target to develop treatments for cancers. However, a comprehensive analysis for the expression profile of ICGs at a pan-cancer level and their correlation with patient response to immune checkpoint blockade (ICB) based therapy is still lacking. In this study, we defined three expression patterns of ICGs using a comprehensive survey of RNA-seq data of tumor and immune cells from the functional annotation of the mammalian genome (FANTOM5) project. The correlation between the expression patterns of ICGs and patients survival and response to ICB therapy was investigated. The expression patterns of ICGs were robust across cancers, and upregulation of ICGs was positively correlated with high lymphocyte infiltration and good prognosis. Furthermore, we built a model (ICGe) to predict the response of patients to ICB therapy using five features of ICG expression. A validation scenario of six independent datasets containing data of 261 patients with CTLA-4 and PD-1 blockade immunotherapies demonstrated that ICGe achieved area under the curves of 0.64-0.82 and showed a robust performance and outperformed other mRNA-based predictors. In conclusion, this work revealed expression patterns of ICGs and underlying correlations between ICGs and response to ICB, which helps to understand the mechanisms of ICGs in ICB signal pathways and other anticancer treatments.

ImmuCellAI-mouse: a tool for comprehensive prediction of mouse immune cell abundance and immune microenvironment depiction.

MOTIVATION: Immune cells are important components of the immune system and are crucial for disease initiation, progression, prognosis and survival. Although several computational methods have been designed for predicting the abundance of immune cells, very few tools are applicable to mouse. Given that, mouse is the most widely used animal model in biomedical research, there is an urgent need to develop a precise algorithm for predicting mouse immune cells. RESULTS: We developed a tool named Immune Cell Abundance Identifier for mouse (ImmuCellAI-mouse), for estimating the abundance of 36 immune cell (sub)types from gene expression data in a hierarchical strategy of three layers. Reference expression profiles and robust marker gene sets of immune cell types were curated. The abundance of cells in three layers was predicted separately by calculating the ssGSEA enrichment score of the expression deviation profile per cell type. Benchmark results showed high accuracy of ImmuCellAI-mouse in predicting most immune cell types, with correlation coefficients between predicted value and real cell proportion of most cell types being larger than 0.8. We applied ImmuCellAI-mouse to a mouse breast tumor dataset and revealed the dynamic change of immune cell infiltration during treatment, which is consistent with the findings of the original study but with more details. We also constructed an online server for ImmuCellAI-mouse, on which users can upload expression matrices for analysis. ImmuCellAI-mouse will be a useful tool for studying the immune microenvironment, cancer immunology and immunotherapy in mouse models, providing an indispensable supplement for human disease studies. AVAILABILITY AND IMPLEMENTATION: Software is available at http://bioinfo.life.hust.edu.cn/ImmuCellAI-mouse/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Salidroside Regulates Mitochondrial Homeostasis After Polarization of RAW264.7 Macrophages.

ABSTRACT: Salidroside has anti-inflammatory and antiatherosclerotic effects, and mitochondrial homeostasis imbalance is closely related to cardiovascular disease. The aim of this study was to investigate the effect of salidroside on mitochondrial homeostasis after macrophage polarization and elucidate its possible mechanism against atherosclerosis. RAW264.7 cells were stimulated with 1 µg·mL -1 Lipopolysaccharide and 50 ng·mL -1 IFN-γ establish M1 polarization and were also pretreated with 400 µM salidroside. The relative expression of proinflammatory genes was detected by RT-PCR whereas that of mitochondrial homeostasis-related proteins and nuclear factor kappa-B (NF-κB) was detected by WB. Levels of intracellular reactive oxygen species (ROS), mitochondrial membrane potential, and mass were measured by chemifluorescence whereas that of NF-κB nuclear translocation was detected by immunofluorescence. Compared with the Mφ group, the M1 group demonstrated increased mRNA expression of interleukin-1ß , inductible nitric oxide synthase (iNOS), and tumor necrosis factor-α ; increased protein expression of iNOS, NOD-like receptor protein 3, putative kinase 1 , and NF-κB p65 but decreased protein expression of MFN2, Tom20, and PGC-1α; decreased mitochondrial membrane potential and mass; and increased ROS levels and NF-κB p65 nuclear translocation. Salidroside intervention decreased mRNA expression of interleukin-1ß and tumor necrosis factor-α compared with the M1 group but did not affect that of iNOS. Furthermore, salidroside intervention prevented the changes in protein expression, mitochondrial membrane potential and mass, ROS levels, and NF-κB p65 nuclear translocation observed in the M1 group. In summary, salidroside ultimately inhibits M1 macrophage polarization and maintains mitochondrial homeostasis after macrophage polarization by increasing mitochondrial membrane potential, decreasing ROS levels, inhibiting NF-κB activation, and in turn regulating the expression of proinflammatory factors and mitochondrial homeostasis-associated proteins.

TCRdb: a comprehensive database for T-cell receptor sequences with powerful search function.

T cells and the T-cell receptor (TCR) repertoire play pivotal roles in immune response and immunotherapy. TCR sequencing (TCR-Seq) technology has enabled accurate profiling TCR repertoire and currently a large number of TCR-Seq data are available in public. Based on the urgent need to effectively re-use these data, we developed TCRdb, a comprehensive human TCR sequences database, by a uniform pipeline to characterize TCR sequences on TCR-Seq data. TCRdb contains more than 277 million highly reliable TCR sequences from over 8265 TCR-Seq samples across hundreds of tissues/clinical conditions/cell types. The unique features of TCRdb include: (i) comprehensive and reliable sequences for TCR repertoire in different samples generated by a strict and uniform pipeline of TCRdb; (ii) powerful search function, allowing users to identify their interested TCR sequences in different conditions; (iii) categorized sample metadata, enabling comparison of TCRs in different sample types; (iv) interactive data visualization charts, describing the TCR repertoire in TCR diversity, length distribution and V-J gene utilization. The TCRdb database is freely available at http://bioinfo.life.hust.edu.cn/TCRdb/ and will be a useful resource in the research and application community of T cell immunology.