CyclicPepedia: a knowledge base of natural and synthetic cyclic peptides.

Cyclic peptides offer a range of notable advantages, including potent antibacterial properties, high binding affinity and specificity to target molecules, and minimal toxicity, making them highly promising candidates for drug development. However, a comprehensive database that consolidates both synthetically derived and naturally occurring cyclic peptides is conspicuously absent. To address this void, we introduce CyclicPepedia (https://www.biosino.org/iMAC/cyclicpepedia/), a pioneering database that encompasses 8744 known cyclic peptides. This repository, structured as a composite knowledge network, offers a wealth of information encompassing various aspects of cyclic peptides, such as cyclic peptides' sources, categorizations, structural characteristics, pharmacokinetic profiles, physicochemical properties, patented drug applications, and a collection of crucial publications. Supported by a user-friendly knowledge retrieval system and calculation tools specifically designed for cyclic peptides, CyclicPepedia will be able to facilitate advancements in cyclic peptide drug development.

Host-microbiota interactions contributing to the heterogeneous tumor microenvironment in colorectal cancer.

Colorectal cancer (CRC) exhibits pronounced heterogeneity and is categorized into four widely accepted consensus molecular subtypes (CMSs) with unique tumor microenvironments (TMEs). However, the intricate landscape of the microbiota and host-microbiota interactions within these TMEs remains elusive. Using RNA-sequencing data from The Cancer Genome Atlas, we analyzed the host transcriptomes and intratumoral microbiome profiles of CRC samples. Distinct host genes and microbial genera were identified among the CMSs. Immune microenvironments were evaluated using CIBERSORTx and ESTIMATE, and microbial coabundance patterns were assessed with FastSpar. Through LASSO penalized regression, we explored host-microbiota associations for each CMS. Our analysis revealed distinct host gene signatures within the CMSs, which encompassed ferroptosis-related genes and specific immune microenvironments. Moreover, we identified 293, 153, 66, and 109 intratumoral microbial genera with differential abundance, and host-microbiota associations contributed to distinct TMEs, characterized by 829, 1,270, 634, and 1,882 robust gene-microbe associations for each CMS in CMS1-CMS4, respectively. CMS1 featured inflammation-related HSF1 activation and gene interactions within the endothelin pathway and Flammeovirga. Integrin-related genes displayed positive correlations with Sutterella in CMS2, whereas CMS3 spotlighted microbial associations with biosynthetic and metabolic pathways. In CMS4, genes involved in collagen biosynthesis showed positive associations with Sutterella, contributing to disruptions in homeostasis. Notably, immune-rich subtypes exhibited pronounced ferroptosis dysregulation, potentially linked to tissue microbial colonization. This comprehensive investigation delineates the diverse landscapes of the TME within each CMS, incorporating host genes, intratumoral microbiota, and their complex interactions. These findings shed light on previously uncharted mechanisms underpinning CRC heterogeneity and suggest potential therapeutic targets.NEW & NOTEWORTHY This study determined the following: 1) providing a comprehensive landscape of consensus molecular subtype (CMS)-specific tumor microenvironments (TMEs); 2) constructing CMS-specific networks, including host genes, intratumoral microbiota, and enriched pathways, analyzing their associations to uncover unique patterns that demonstrate the intricate interplay within the TME; and 3) revealing a connection between immune-rich subtypes and ferroptosis activation, suggesting a potential regulatory role of the microbiota in ferroptosis dysregulation of the colorectal cancer TME.

NAFLDkb: A Knowledge Base and Platform for Drug Development against Nonalcoholic Fatty Liver Disease.

Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease with a broad spectrum of histologic manifestations. The rapidly growing prevalence and the complex pathologic mechanisms of NAFLD pose great challenges for treatment development. Despite tremendous efforts devoted to drug development, there are no FDA-approved medicines yet. Here, we present NAFLDkb, a specialized knowledge base and platform for computer-aided drug design against NAFLD. With multiperspective information curated from diverse source materials and public databases, NAFLDkb presents the associations of drug-related entities as individual knowledge graphs. Practical drug discovery tools that facilitate the utilization and expansion of NAFLDkb have also been implemented in the web interface, including chemical structure search, drug-likeness screening, knowledge-based repositioning, and research article annotation. Moreover, case studies of a knowledge graph repositioning model and a generative neural network model are presented herein, where three repositioning drug candidates and 137 novel lead-like compounds were newly established as NAFLD pharmacotherapy options reusing data records and machine learning tools in NAFLDkb, suggesting its clinical reliability and great potential in identifying novel drug-disease associations of NAFLD and generating new insights to accelerate NAFLD drug development. NAFLDkb is freely accessible at https://www.biosino.org/nafldkb and will be updated periodically with the latest findings.

Causal relationships between atopic dermatitis and psychiatric disorders: a bidirectional two-sample Mendelian randomization study.

BACKGROUND: Observational studies have suggested the potential associations between atopic dermatitis (AD) and psychiatric disorders. However, the causal relationship between them remains uncertain. This study aimed to evaluate the potential bidirectional causal relationship between AD and psychiatric disorders, including autism spectrum disorder (ASD), major depressive disorder (MDD), attention deficit hyperactivity disorder (ADHD), bipolar disorder (BD), anorexia nervosa (AN), Tourette syndrome (TS), schizophrenia, and anxiety. METHODS: Bidirectional two-sample Mendelian randomization (MR) was employed to elucidate the causality between AD and psychiatric disorders, using summary statistics from the most comprehensive genome-wide association studies conducted on AD (Ncases = 60,653, Ncontrols = 804,329). Psychiatric disorders were derived from the Psychiatric Genomics Consortium and were independent of AD data sources. The MR analysis entailed the implementation of multiple methods, including the inverse variance weighted method, MR-Egger regression method, weighted median method, simple mode method, and weighted mode method. RESULTS: Bidirectional two-sample MR analysis uncovered significant causal associations between AD and severe psychiatric disorders. Specifically, liability to AD was associated with increased risk of ADHD (OR = 1.116; 95% CI: [1.009, 1.234]; P = 0.033) and ASD (OR = 1.131; 95% CI: [1.023, 1.251]; P = 0.016). Additionally, evidence suggested that liability to ADHD (OR = 1.112; 95% CI: [1.094, 1.130]; P = 9.20e-40), liability to AN (OR = 1.1; 95% CI: [1.068, 1.134]; P = 4.45e-10) and liability to BD (OR = 1.067; 95% CI: [1.009, 1.128]; P = 0.023) were associated with an increased risk of AD. Only the causal association between AD and ASD was independent of the reverse effect bias. These causal associations were robust and not affected by biases of heterogeneity and horizontal pleiotropy. CONCLUSIONS: Our study emphasizes the significant causal association between AD and an increased risk of ASD, and also identifying BD and AN as risk factors for AD.

Dr AFC: drug repositioning through anti-fibrosis characteristic.

Fibrosis is a key component in the pathogenic mechanism of a variety of diseases. These diseases involving fibrosis may share common mechanisms and therapeutic targets, and therefore common intervention strategies and medicines may be applicable for these diseases. For this reason, deliberately introducing anti-fibrosis characteristics into predictive modeling may lead to more success in drug repositioning. In this study, anti-fibrosis knowledge base was first built by collecting data from multiple resources. Both structural and biological profiles were then derived from the knowledge base and used for constructing machine learning models including Structural Profile Prediction Model (SPPM) and Biological Profile Prediction Model (BPPM). Three external public data sets were employed for validation purpose and further exploration of potential repositioning drugs in wider chemical space. The resulting SPPM and BPPM models achieve area under the receiver operating characteristic curve (area under the curve) of 0.879 and 0.972 in the training set, and 0.814 and 0.874 in the testing set. Additionally, our results also demonstrate that substantial amount of multi-targeting natural products possess notable anti-fibrosis characteristics and might serve as encouraging candidates in fibrosis treatment and drug repositioning. To leverage our methodology and findings, we developed repositioning prediction platform, drug repositioning based on anti-fibrosis characteristic that is freely accessible via https://www.biosino.org/drafc.

Alterations in bile acid metabolizing gut microbiota and specific bile acid genes as a precision medicine to subclassify NAFLD.

Multiple mechanisms for the gut microbiome contributing to the pathogenesis of nonalcoholic fatty liver disease (NAFLD) have been implicated. Here, we aim to investigate the contribution and potential application for altered bile acids (BA) metabolizing microbes in NAFLD by post hoc analysis of whole metagenome sequencing (WMS) data. The discovery cohort consisted of 86 well-characterized patients with biopsy-proven NAFLD and 38 healthy controls. Assembly-based analysis was performed to identify BA-metabolizing microbes. Statistical tests, feature selection, and microbial coabundance analysis were integrated to identify microbial alterations and markers in NAFLD. An independent validation cohort was subjected to similar analyses. NAFLD microbiota exhibited decreased diversity and microbial associations. We established a classifier model with 53 differential species exhibiting a robust diagnostic accuracy [area under the receiver-operator curve (AUC) = 0.97] for detecting NAFLD. Next, eight important differential pathway markers including secondary BA biosynthesis were identified. Specifically, increased abundance of 7α-hydroxysteroid dehydrogenase (7α-HSDH), 3α-hydroxysteroid dehydrogenase (baiA), and bile acid-coenzyme A ligase (baiB) was detected in NAFLD. Furthermore, 10 of 50 BA-metabolizing metagenome-assembled genomes (MAGs) from Bacteroides ovatus and Eubacterium biforme were dominant in NAFLD and interplayed as a synergetic ecological guild. Importantly, two subtypes of patients with NAFLD were observed according to secondary BA metabolism potentials. Elevated capability for secondary BA biosynthesis was also observed in the validation cohort. These bacterial BA-metabolizing genes and microbes identified in this study may serve as disease markers. Microbial differences in BA-metabolism and strain-specific differences among patients highlight the potential for precision medicine in NAFLD treatment.

Risk factors for the critical illness in SARS-CoV-2 infection: a multicenter retrospective cohort study.

BACKGROUND: Prior studies reported that 5 ~ 32% COVID-19 patients were critically ill, a situation that poses great challenge for the management of the patients and ICU resources. We aim to identify independent risk factors to serve as prediction markers for critical illness of SARS-CoV-2 infection. METHODS: Fifty-two critical and 200 non-critical SARS-CoV-2 nucleic acid positive patients hospitalized in 15 hospitals outside Wuhan from January 19 to March 6, 2020 were enrolled in this study. Multivariable logistic regression and LASSO logistic regression were performed to identify independent risk factors for critical illness. RESULTS: Age older than 60 years, dyspnea, respiratory rate > 24 breaths per min, leukocytosis > 9.5 × 109/L, neutrophilia > 6.3 × 109/L, lymphopenia < 1.1 × 109/L, neutrophil-to-lymphocyte ratio > 3.53, fibrinogen > 4 g/L, d-dimer > 0.55 µg/mL, blood urea nitrogen > 7.1 mM, elevated aspartate transaminase, elevated alanine aminotransferase, total bilirubin > 21 µM, and Sequential Organ Failure Assessment (SOFA) score ≥ 2 were identified as risk factors for critical illness. LASSO logistic regression identified the best combination of risk factors as SOFA score, age, dyspnea, and leukocytosis. The Area Under the Receiver-Operator Curve values for the risk factors in predicting critical illness were 0.921 for SOFA score, 0.776 for age, 0.764 for dyspnea, 0.658 for leukocytosis, and 0.960 for the combination of the four risk factors. CONCLUSIONS: Our findings advocate the use of risk factors SOFA score ≥ 2, age > 60, dyspnea and leukocytosis > 9.5 × 109/L on admission, alone or in combination, to determine the optimal management of the patients and health care resources.

DSab-origin: a novel IGHD sensitive VDJ mapping method and its application on antibody response after influenza vaccination.

BACKGROUND: Functional antibody genes are often assembled by VDJ recombination and then diversified by somatic hypermutation. Identifying the combination of sourcing germline genes is critical to understand the process of antibody maturation, which may facilitate the diagnostics and rapid generation of human monoclonal antibodies in therapeutics. Despite of successful efforts in V and J fragment assignment, method in D segment tracing remains weak for immunoglobulin heavy diversity (IGHD). RESULTS: In this paper, we presented a D-sensitive mapping method called DSab-origin with accuracies around 90% in human monoclonal antibody data and average 95.8% in mouse data. Besides, DSab-origin achieved the best performance in holistic prediction of VDJ segments assignment comparing with other methods commonly used in simulation data. After that, an application example was explored on the antibody response based on a time-series antibody sequencing data after influenza vaccination. The result indicated that, despite the personal response among different donors, IGHV3-7 and IGHD4-17 were likely to be dominated gene segments in these three donors. CONCLUSIONS: This work filled in a computational gap in D segment assignment for VDJ germline gene identification in antibody research. And it offered an application example of DSab-origin for studying the antibody maturation process after influenza vaccination.

The recent progress in proteochemometric modelling: focusing on target descriptors, cross-term descriptors and application scope.

As an extension of the conventional quantitative structure activity relationship models, proteochemometric (PCM) modelling is a computational method that can predict the bioactivity relations between multiple ligands and multiple targets. Traditional PCM modelling includes three essential elements: descriptors (including target descriptors, ligand descriptors and cross-term descriptors), bioactivity data and appropriate learning functions that link the descriptors to the bioactivity data. Since its appearance, PCM modelling has developed rapidly over the past decade by taking advantage of the progress of different descriptors and machine learning techniques, along with the increasing amounts of available bioactivity data. Specifically, the new emerging target descriptors and cross-term descriptors not only significantly increased the performance of PCM modelling but also expanded its application scope from traditional protein-ligand interaction to more abundant interactions, including protein-peptide, protein-DNA and even protein-protein interactions. In this review, target descriptors and cross-term descriptors, as well as the corresponding application scope, are intensively summarized. Additionally, we look forward to seeing PCM modelling extend into new application scopes, such as Target-Catalyst-Ligand systems, with the further development of descriptors, machine learning techniques and increasing amounts of available bioactivity data.

Systematic transcriptome analysis reveals elevated expression of alcohol-metabolizing genes in NAFLD livers.

Obese animals and non-alcoholic fatty liver disease (NAFLD) patients exhibit elevated blood alcohol, suggesting potential contributions of alcohol metabolism to the development of NAFLD. Liver gene expression in patients with biopsy-proven mild (N = 40) and severe (N = 32) NAFLD were compared to that in healthy liver donors (N = 7) and alcoholic hepatitis (AH; N = 15) using microarrays. Principal components analyses (PCA) revealed similar gene expression patterns between mild and severe NAFLD which clustered with those of AH but were distinct from those of healthy livers. Differential gene expression between NAFLD and healthy livers was consistent with established NAFLD-associated genes and NAFLD pathophysiology. Alcohol-metabolizing enzymes including ADH, ALDH, CYP2E1, and CAT were up-regulated in NAFLD livers. The expression level of alcohol-metabolizing genes in severe NAFLD was similar to that in AH. The NAFLD gene expression profiles provide new directions for future investigations to identify disease markers and targets for prevention and treatment, as well as to foster our understanding of NAFLD pathogenesis and pathophysiology. Particularly, increased expression of alcohol-metabolizing genes in NAFLD livers supports a role for endogenous alcohol metabolism in NAFLD pathology and provides further support for gut microbiome therapy in NAFLD management. Copyright © 2015 Pathological Society of Great Britain and Ireland. Published by John Wiley © Sons, Ltd.

Connection Map for Compounds (CMC): A Server for Combinatorial Drug Toxicity and Efficacy Analysis.

Drug discovery and development is a costly and time-consuming process with a high risk for failure resulting primarily from a drug's associated clinical safety and efficacy potential. Identifying and eliminating inapt candidate drugs as early as possible is an effective way for reducing unnecessary costs, but limited analytical tools are currently available for this purpose. Recent growth in the area of toxicogenomics and pharmacogenomics has provided with a vast amount of drug expression microarray data. Web servers such as CMap and LTMap have used this information to evaluate drug toxicity and mechanisms of action independently; however, their wider applicability has been limited by the lack of a combinatorial drug-safety type of analysis. Using available genome-wide drug transcriptional expression profiles, we developed the first web server for combinatorial evaluation of toxicity and efficacy of candidate drugs named "Connection Map for Compounds" (CMC). Using CMC, researchers can initially compare their query drug gene signatures with prebuilt gene profiles generated from two large-scale toxicogenomics databases, and subsequently perform a drug efficacy analysis for identification of known mechanisms of drug action or generation of new predictions. CMC provides a novel approach for drug repositioning and early evaluation in drug discovery with its unique combination of toxicity and efficacy analyses, expansibility of data and algorithms, and customization of reference gene profiles. CMC can be freely accessed at http://cadd.tongji.edu.cn/webserver/CMCbp.jsp .

SEPPA 2.0--more refined server to predict spatial epitope considering species of immune host and subcellular localization of protein antigen.

Spatial Epitope Prediction server for Protein Antigens (SEPPA) has received lots of feedback since being published in 2009. In this improved version, relative ASA preference of unit patch and consolidated amino acid index were added as further classification parameters in addition to unit-triangle propensity and clustering coefficient which were previously reported. Then logistic regression model was adopted instead of the previous simple additive one. Most importantly, subcellular localization of protein antigen and species of immune host were fully taken account to improve prediction. The result shows that AUC of 0.745 (5-fold cross-validation) is almost the baseline performance with no differentiation like all the other tools. Specifying subcellular localization of protein antigen and species of immune host will generally push the AUC up. Secretory protein immunized to mouse can push AUC to 0.823. In this version, the false positive rate has been largely decreased as well. As the first method which has considered the subcellular localization of protein antigen and species of immune host, SEPPA 2.0 shows obvious advantages over the other popular servers like SEPPA, PEPITO, DiscoTope-2, B-pred, Bpredictor and Epitopia in supporting more specific biological needs. SEPPA 2.0 can be accessed at http://badd.tongji.edu.cn/seppa/. Batch query is also supported.

Systematic toxicity mechanism analysis of proton pump inhibitors: an in silico study.

Proton pump inhibitors (PPIs) are extensively used for the treatment of gastric acid-related disorders. PPIs appear to be well tolerated and almost have no short-term side effects. However, the clinical adverse reactions of long-term PPI usage are increasingly reported in recent years. So far, there is no study that elucidates the side effect mechanisms of PPIs comprehensively and systematically. In this study, a well-defined small molecule perturbed microarray data set of 344 compounds and 1695 samples was analyzed. With this high-throughput data set, a new index (Identity, I) was designed to identify PPI-specific differentially expressed genes. Results indicated that (1) up-regulated genes, such as RETSAT, CYP1A1, CYP1A2, and UGT, enhanced vitamin A's metabolism processes in the cellular retinol metabolism pathway; and that (2) down-regulated genes, such as C1QA, C1QC, C4BPA, C4BPB, CFI, and SERPING1, enriched in the complement and coagulation cascades pathway. In addition, strong association was observed between these PPI-specific differentially expressed genes and the reported side effects of PPIs by the gene-disease association network analysis. One potential toxicity mechanism of PPIs as suggested from this systematic PPI-specific gene expression analysis is that PPIs are enriched in acidic organelles where they are activated and inhibit V-ATPases and acid hydrolases, and consequently block the pathways of antigen presentation, the synthesis and secretion of cytokines, and complement component proteins and coagulation factors. The strategies developed in this work could be extended to studies on other drugs.

Enhanced microbiota profiling in patients with quiescent Crohn's disease through comparison with paired healthy first-degree relatives.

Prior studies indicate no correlation between the gut microbes of healthy first-degree relatives (HFDRs) of patients with Crohn's disease (CD) and the development of CD. Here, we utilize HFDRs as controls to examine the microbiota and metabolome in individuals with active (CD-A) and quiescent (CD-R) CD, thereby minimizing the influence of genetic and environmental factors. When compared to non-relative controls, the use of HFDR controls identifies fewer differential taxa. Faecalibacterium, Dorea, and Fusicatenibacter are decreased in CD-R, independent of inflammation, and correlated with fecal short-chain fatty acids (SCFAs). Validation with a large multi-center cohort confirms decreased Faecalibacterium and other SCFA-producing genera in CD-R. Classification models based on these genera distinguish CD from healthy individuals and demonstrate superior diagnostic power than models constructed with markers identified using unrelated controls. Furthermore, these markers exhibited limited discriminatory capabilities for other diseases. Finally, our results are validated across multiple cohorts, underscoring their robustness and potential for diagnostic and therapeutic applications.

Identification and validation of microbial biomarkers from cross-cohort datasets using xMarkerFinder.

Microbial signatures have emerged as promising biomarkers for disease diagnostics and prognostics, yet their variability across different studies calls for a standardized approach to biomarker research. Therefore, we introduce xMarkerFinder, a four-stage computational framework for microbial biomarker identification with comprehensive validations from cross-cohort datasets, including differential signature identification, model construction, model validation and biomarker interpretation. xMarkerFinder enables the identification and validation of reproducible biomarkers for cross-cohort studies, along with the establishment of classification models and potential microbiome-induced mechanisms. Originally developed for gut microbiome research, xMarkerFinder's adaptable design makes it applicable to various microbial habitats and data types. Distinct from existing biomarker research tools that typically concentrate on a singular aspect, xMarkerFinder uniquely incorporates a sophisticated feature selection process, specifically designed to address the heterogeneity between different cohorts, extensive internal and external validations, and detailed specificity assessments. Execution time varies depending on the sample size, selected algorithm and computational resource. Accessible via GitHub ( https://github.com/tjcadd2020/xMarkerFinder ), xMarkerFinder supports users with diverse expertise levels through different execution options, including step-to-step scripts with detailed tutorials and frequently asked questions, a single-command execution script, a ready-to-use Docker image and a user-friendly web server ( https://www.biosino.org/xmarkerfinder ).

Misuse of reporter score in microbial enrichment analysis.

A modified new method for microbial enrichment analysis, reporter score was incorrectly used in many articles due to a lack of comprehensive and systematic understanding of the original method by the researchers, leading to a serious snowball effect. Here we describe the reasons for the misuse of reporter score and its negative impact on microbial research and hope this comment will facilitate community discussion on the importance of statistical rigor, informing future efforts to enhance reliable and reproducible research.

Evaluation of Chinese healthcare organizations' innovative performance in the digital health era.

Background: Healthcare workers' relationship with industry is not merely an agent mediating between consumer and vendor, but they are also inventors of the interventions they exist to deliver. Driven by the background of the digital health era, scientific research and technological (Sci-tech) innovation in the medical field are becoming more and more closely integrated. However, scholars shed little light on Sci-tech relevance to evaluate the innovation performance of healthcare organizations, a distinctive feature of healthcare organizations' innovation in the digital health era. Methods: Academic publications and patents are the manifestations of scientific research outputs and technological innovation outcomes, respectively. The study extracted data from publications and patents of 159 hospitals in China to evaluate their innovation performance. A total of 18 indicators were constructed, four of which were based on text similarity match and represented the Sci-tech relevance. We then applied factor analyses, analytical hierarchy process, and logistic regression to construct an evaluation model. We also examined the relationship between hospitals' innovation performance and their geographical locations. Finally, we implemented a mediation analysis to show the influence of digital health on hospital innovation performance. Results: A total of 16 indicators were involved, four of which represented the Sci-tech including the number of articles matched per patent (NAMP), the number of patents matched per article (NPMA), the proportion of highly matched patents (HMP), and the proportion of highly matched articles (HMA). Indicators of HMP (r = 0.52, P = 2.40 × 10-12), NAMP (r = 0.52, P = 2.54 × 10-12), and NPMA (r = 0.51, P = 5.53 × 10-12) showed a strong positive correlation with hospital innovation performance score. The evaluation model in this study was different from other Chinese existing hospital ranking systems. The regional innovation performance index (RIP) of healthcare organizations is highly correlated with per capita disposable income (r = 0.58) and regional GDP (r = 0.60). There was a positive correlation between digital health innovation performance scores and overall hospital innovation performance scores (r = 0.20). In addition, the hospitals' digital health innovation performance affected the hospital's overall innovation score with the mediation of Sci-tech relevance indicators (NPMA and HMA). The hospitals' digital health innovation performance score showed a significant correlation with the number of healthcare workers (r = 0.44). Conclusion: This study constructed an assessment model with four invented indicators focusing on Sci-tech relevance to provide a novel tool for researchers to evaluate the innovation performance of healthcare organizations in the digital health era. The regions with high RIP were concentrated on the eastern coastal areas with a higher level of economic development. Therefore, the promotion of scientific and technological innovation policies could be carried out in advance in areas with better economic development. The innovations in the digital health field by healthcare workers enhance the Sci-tech relevance in hospitals and boost their innovation performance. The development of digital health in hospitals depends on the input of medical personnel.

Multimodal metagenomic analysis reveals microbial single nucleotide variants as superior biomarkers for early detection of colorectal cancer.

Microbial signatures show remarkable potentials in predicting colorectal cancer (CRC). This study aimed to evaluate the diagnostic powers of multimodal microbial signatures, multi-kingdom species, genes, and single-nucleotide variants (SNVs) for detecting precancerous adenomas. We performed cross-cohort analyses on whole metagenome sequencing data of 750 samples via xMarkerFinder to identify adenoma-associated microbial multimodal signatures. Our data revealed that fungal species outperformed species from other kingdoms with an area under the ROC curve (AUC) of 0.71 in distinguishing adenomas from controls. The microbial SNVs, including dark SNVs with synonymous mutations, displayed the strongest diagnostic capability with an AUC value of 0.89, sensitivity of 0.79, specificity of 0.85, and Matthews correlation coefficient (MCC) of 0.74. SNV biomarkers also exhibited outstanding performances in three independent validation cohorts (AUCs = 0.83, 0.82, 0.76; sensitivity = 1.0, 0.72, 0.93; specificity = 0.67, 0.81, 0.67, MCCs = 0.69, 0.83, 0.72) with high disease specificity for adenoma. In further support of the above results, functional analyses revealed more frequent inter-kingdom associations between bacteria and fungi, and abnormalities in quorum sensing, purine and butanoate metabolism in adenoma, which were validated in a newly recruited cohort via qRT-PCR. Therefore, these data extend our understanding of adenoma-associated multimodal alterations in the gut microbiome and provide a rationale of microbial SNVs for the early detection of CRC.

Microbial genes outperform species and SNVs as diagnostic markers for Crohn's disease on multicohort fecal metagenomes empowered by artificial intelligence.

Dysbiosis of gut microbial community is associated with the pathogenesis of CD and may serve as a promising noninvasive diagnostic tool. We aimed to compare the performances of the microbial markers of different biological levels by conducting a multidimensional analysis on the microbial metagenomes of CD. We collected fecal metagenomic datasets generated from eight cohorts that altogether include 870 CD patients and 548 healthy controls. Microbial alterations in CD patients were assessed at multidimensional levels including species, gene, and SNV level, and then diagnostic models were constructed using artificial intelligence algorithm. A total of 227 species, 1047 microbial genes, and 21,877 microbial SNVs were identified that differed between CD and controls. The species, gene, and SNV models achieved an average AUC of 0.97, 0.95, and 0.77, respectively. Notably, the gene model exhibited superior diagnostic capability, achieving an average AUC of 0.89 and 0.91 for internal and external validations, respectively. Moreover, the gene model was specific for CD against other microbiome-related diseases. Furthermore, we found that phosphotransferase system (PTS) contributed substantially to the diagnostic capability of the gene model. The outstanding performance of PTS was mainly explained by genes celB and manY, which demonstrated high predictabilities for CD with metagenomic datasets and was validated in an independent cohort by qRT-PCR analysis. Our global metagenomic analysis unravels the multidimensional alterations of the microbial communities in CD and identifies microbial genes as robust diagnostic biomarkers across geographically and culturally distinct cohorts.

Screening of selective histone deacetylase inhibitors by proteochemometric modeling.

BACKGROUND: Histone deacetylase (HDAC) is a novel target for the treatment of cancer and it can be classified into three classes, i.e., classes I, II, and IV. The inhibitors selectively targeting individual HDAC have been proved to be the better candidate antitumor drugs. To screen selective HDAC inhibitors, several proteochemometric (PCM) models based on different combinations of three kinds of protein descriptors, two kinds of ligand descriptors and multiplication cross-terms were constructed in our study. RESULTS: The results show that structure similarity descriptors are better than sequence similarity descriptors and geometry descriptors in the leftacterization of HDACs. Furthermore, the predictive ability was not improved by introducing the cross-terms in our models. Finally, a best PCM model based on protein structure similarity descriptors and 32-dimensional general descriptors was derived (R2 = 0.9897, Qtest2 = 0.7542), which shows a powerful ability to screen selective HDAC inhibitors. CONCLUSIONS: Our best model not only predict the activities of inhibitors for each HDAC isoform, but also screen and distinguish class-selective inhibitors and even more isoform-selective inhibitors, thus it provides a potential way to discover or design novel candidate antitumor drugs with reduced side effect.