Biology subjects

Jiao, N.

Publications and source records attributed to Jiao, N..

At least 19 recordsLinked to original sources

bioRxiv2026

A multidimensional benchmark of confounder-aware differential abundance strategies for single- and multi-cohort microbiome studies

Confounding is a pervasive challenge in microbiome differential abundance analysis (DAA), yet systematic benchmarks of confounder-aware DAA remain lacking. Existing benchmarks have also largely evaluated statistical methods or analytical components in isolation rather than the complete analytical strategies used in practice. We evaluated 71 single-cohort strategies spanning default and extended input configurations using 2,500 real-data-informed simulations, reference-based recovery of established biological signals and resampling-based within-dataset consistency; benchmark-prioritized strategies were further assessed for stability under systematic perturbations. We further evaluated 63 multi-cohort extensions using simulations with cohort heterogeneity and empirical datasets. Covariate adjustment improved false-discovery control at the cost of reduced sensitivity, while performance also depended strongly on the specific combination of preprocessing procedure and statistical model. Different strategies were favored across benchmark dimensions and analytical objectives. ANCOM-BC2_CSS and edgeR_CSS were favored for high-confidence, perturbation-stable inference, whereas ANCOM-BC2_count and ANCOM-BC2_TMM provided more balanced performance. In multi-cohort settings, mega-analysis provided the strongest overall balance when individual-level data were available: ANCOM-BC2_CSS_Mega and edgeR_CSS_Mega showed stronger accuracy performance, whereas ANCOM-BC2_TMM_Mega and ANCOM-BC2_count_Mega showed greater cross-cohort consistency. We implemented these findings in xConDA, an interactive web server linking analytical objectives and study designs to benchmark-prioritized, confounder-adjusted microbiome DAA workflows.

bioinformatics↗

bioRxiv2026

Diatoms maintain metabolic activity in deep ocean twilight zones

Phytoplankton are primarily confined to the sunlit epipelagic zone, yet intact algal cells persist in the oceans mesopelagic twilight zone where light is minimal. However, their survival strategies in the deeper ocean remain unknown. Here, we isolate Chaetoceros sp. DS1 from 1000 m depth and reveal its survival mechanisms under prolonged darkness and high pressure, including membrane lipid remodeling and enhanced antioxidant defenses. Importantly, DS1 maintains a latent photosynthetic reserve that activates upon exposure to dim blue light and higher pressure typical of the twilight zone, enabling photosynthesis and nutrient uptake. It exhibits metabolic flexibility by utilizing stored carbon in darkness and assimilating organic carbon under dim light. Global meta-omics data show widespread, transcriptionally active Chaetoceros-like diatoms in the twilight zone, expressing blue-light sensors and protein synthesis genes. These findings identify the ocean twilight zone as a previously overlooked niche for metabolically active phytoplankton, expanding understanding of deep-ocean microbial ecology and carbon cycling.

ecology↗

bioRxiv2026

Active non-redundancy and viral orchestration sustain diel microbial successions in the coastal ocean

Ecosystem resilience in dynamic coastal oceans is conventionally ascribed to functional redundancy, where taxonomically distinct microbes buffer environmental fluctuations through interchangeable metabolic roles. In this study, we reveal a deterministic succession architecture sustained by active non redundancy and temporal metabolic coupling, uncovered via autonomous drone array metatranscriptomic sampling in Daya Bay, China. By resolving the transcriptional landscape into six chronometrically phased modules rather than simple time points, we demonstrate a near total renewal of the active gene pool, with greater than 90% of transcribed clusters showing phase exclusivity within approximatively a two-hour window. This radical functional reshaping exposes a tight, scale dependent taxonomic functional coupling where community composition is a strong, linear predictor of metabolic output, peaking at the genus level (p < 0.0001). Functional continuity is maintained not by redundant generalists, but by a precisely sequential relay of specialists, each optimized for transient microniches with minimal overlap between successive phases. Deep learning structural adjudication of the psbA gene pool further reveals viral orchestration of this temporal coupling, where incoming cyanophages introduce structurally distinct protein variants that sustain photosynthetic electron flow under peak irradiance. These findings redefine coastal microbiome stability as a fine-tuned rapid succession of temporal specialists rather than a redundant backdrop. We propose a fundamental revision of marine ecosystem models, shifting from passive buffering frameworks to deterministic, clock driven architectures, which may prove to be critical for forecasting microbiome responses to accelerating short term climate variability.

microbiology↗

bioRxiv2026

IBDkb: an AI-enhanced integrative knowledge base for inflammatory bowel disease research and drug discovery

IBDkb (Inflammatory Bowel Disease Knowledge Base; https://www.biosino.org/ibdkb) is a freely accessible, integrated web-based platform that systematically curates and harmonizes multi-source data related to inflammatory bowel disease (IBD). To address the fragmentation and therapeutic gaps in existing specialized resources, IBDkb establishes a unified framework featuring advanced full-text search, interactive visualizations, cross-module knowledge graphs, and AI-powered utilities for real-time literature retrieval, trend analysis, text/PDF interpretation, and domain-specific conversational assistance. The platform currently integrates 98,453 research articles, 3,390 clinical trials, 200 investigational drugs, 200,606 bioactive compounds, 103 therapeutic targets, 77 experimental models, 12 pathogenesis summaries, and 15 treatment strategies. These integrated tools facilitate efficient exploration of complex associations among drugs, targets, trials, and mechanisms, thereby accelerating hypothesis generation and translational research in IBD. The platform is openly available without registration and supports data downloads. A case study on structure-aware drug comparison further demonstrates its utility in facilitating cross-disease drug repositioning hypotheses.

bioinformatics↗

bioRxiv2025

DeepSeMS: a large language model reveals hidden biosynthetic potential of the global ocean microbiome

Microbial biosynthetic diversity holds immense potential for discovering natural products with therapeutic applications, yet a substantial quantity of natural products derived from uncultivated microorganisms remains uncharacterized. The intricate nature of biosynthetic enzymes poses a major challenge in accurately predicting the chemical structures of secondary metabolites solely based on genome sequences using current rule-based methods. Here, we present DeepSeMS, a large language model designed to predict the chemical structures of secondary metabolites from various microbial biosynthetic gene clusters. Built on the Transformer architecture, DeepSeMS innovatively identifies sequence features using functional domains of biosynthetic enzymes, and incorporates feature-aligned chemical structure enumeration for training data augmentation. External evaluation results show that DeepSeMS predicts more accurate chemical structures of secondary metabolites with a Tanimoto coefficient up to 0.6 compared with the ground truth, significantly outperforming antiSMASH and PRISM with coefficients of only 0.14 and 0.45 respectively. Moreover, DeepSeMS successfully predicted secondary metabolites for 96.60% of cryptic biosynthetic gene clusters, surpassing existing methods with success rates less than 50%. Leveraging DeepSeMS, we characterized over 65,000 novel secondary metabolites from the global ocean microbiome with previously undocumented structural types, ecological distribution, and biomedical applications especially antibiotics. A login-free and user-friendly web server for DeepSeMS (https://biochemai.cstspace.cn/deepsems/) has been launched, featuring an integrated global ocean microbial secondary metabolites repository to expedite the discovery of novel natural products. Collectively, this study underscores the great capacity of a large language model-driven method in revealing hidden biosynthetic potential of the global ocean microbiome.

synthetic biology↗

bioRxiv2024

Photochemical processes drive thermal responses of dissolved organic matter in the dark ocean

How dissolved organic matter (DOM) responds to climate warming is critical for understanding its role in future ocean carbon cycling. Here, we use a highly resolved dataset of over 800 DOM samples covering the surface waters to the deep Atlantic, Southern, and Pacific oceans to examine the changes in DOM molecular composition in response to warming water temperatures, referred to as DOM thermal responses. Towards the deep waters, the strength (i.e., overall magnitude) and diversity (i.e., variation among molecules) of these thermal responses both decline. However, these responses show opposite trends with the concentration of more recalcitrant molecules, decreasing and increasing, respectively. These contrasting trends concur with the observation that, compared to the strength of thermal responses, their diversity is more strongly explained by photochemical processes of DOM. By projecting global ocean thermal responses from 1950 to 2020 using environmental temperature, salinity and radiation, we predict that increases in the diversity of thermal responses are unexpectedly largest at deeper depths (> 1,000 m). Such increases could elevate the recalcitrant deep-ocean carbon sink by approximately 10 Tg C yr-1, which accounts for > 5% of the carbon flux reaching and persisting in the deep ocean. Our findings highlight the role of photochemical processes in imprinting DOM thermal responses, offering new insights into the future capacity of the oceanic carbon sink under global climate change.

ecology↗

bioRxiv2024

Diversity and ecological roles of RNA viral communities in the cryosphere of the Tibetan Plateau

RNA viruses play crucial roles in modulating host communities, but their diversity and ecological roles in the cryosphere are poorly understood. We investigated the RNA virome from the cryosphere of the Tibetan Plateau (TPC), the largest area of the cryosphere in the mid-low latitude regions, which includes 79 metatranscriptomes from ecosystems including glaciers, proglacial lake sediments, permafrost wetland and upland soils. We obtained 8,799 RNA-dependent RNA polymerase encoding contigs, which clustered into 5,333 viral species, adding more than 4,900 new RNA viral species to the global RNA virome database. The recovered RNA viruses primarily infect prokaryotes, distinctively different from the dominance of eukaryotic RNA viruses in oceanic ecosystems. In addition to the known taxa such as Leviviricetes, we identified novel clades of putative prokaryotic RNA viruses, which greatly extended our understanding of their diversity. We further expanded the host of RNA virus by 20 additional prokaryotic phylum- or class-level taxonomic groups. Moreover, the TPC RNA viruses encoded dozens of auxiliary metabolic genes that could reprogram the metabolisms of diverse hosts, including photosynthesis in glacier eukaryotic algae and cyanobacteria, which may exert great impacts on glacier dynamics through modulating host adaptation. Furthermore, we identified potential pathogenic RNA viruses in various cryosphere environments, though their overall risks are low. This study provides the first comprehensive view of cryosphere RNA virome, highlighting their high diversity, vital ecological functions, and potential health impacts.

ecology↗

bioRxiv2024

Alpine lakes harbor diverse, endemic and structurally unique RNA viruses

Alpine lakes, characterized by isolation, low temperatures, oligotrophic conditions, and intense ultraviolet radiation, remain a poorly explored ecosystem for RNA viruses. Here, we present the first comprehensive metatranscriptomic study of RNA viruses in Lake Nam Co, a high-altitude alkaline saline lake on the Tibetan Plateau. Using a combination of sequence- and structure-based homology searches, we identified 742 RNA virus species, including 383 novel genus-level groups and 84 novel family-level groups exclusively found in Lake Nam Co. These findings significantly expand the known diversity of the Orthornavirae, uncovering evolutionary adaptations such as permutated RNA-dependent RNA polymerase (RdRP) motifs and distinct RNA secondary structures. Notably, 14 previously unknown RNA virus families potentially infecting prokaryotes were predicted, broadening the known host range of RNA viruses and questioning the traditional assumption that RNA viruses predominantly target eukaryotes. The presence of auxiliary metabolic genes (AMGs) in viral genomes suggested that RNA viruses (families f.0102 and Nam-Co_family_51) exploit host energy production mechanisms in energy-limited alpine lakes. Low nucleotide diversity, SNP frequencies, and pN/pS ratios indicate strong purifying selection in Nam Co viral populations. Our findings offer insights into RNA virus evolution and ecology, highlighting the importance of extreme environments in uncovering hidden viral diversity, and further shed light into their potential ecological implications, particularly in the context of climate change. Significance StatementThis study unveils the hidden diversity and complexity of RNA viruses in the high-altitude ecosystem of Lake Nam Co, significantly expanding the known RNA virome. The discovery of novel viral clades, expanded potential host ranges, and unique evolutionary adaptations emphasizes the importance of exploring extreme environments to uncover viral diversity and evolutionary pathways. Our findings offer critical insights into viral evolution, host-virus interactions, and the ecological roles of RNA viruses, particularly in the context of climate change and environmental resilience.

microbiology↗

bioRxiv2024

Systematic identification of secondary bile acid production genes in global microbiome

Microbial metabolism of bile acids (BAs) is crucial for maintaining homeostasis in vertebrate hosts and environments. Although certain organisms involved in BA metabolism have been identified, a global, comprehensive elucidation of the microbes, metabolic enzymes, and BA remains incomplete. To bridge this gap, we employed hidden Markov models to systematically search in a large-scale and high-quality search library comprising 28,813 RefSeq multi-kingdom microbial complete genomes, which enabled us to construct a secondary bile acid (SBA)-production gene catalog. This catalog greatly expanded the distribution of SBA-production genes across 11 phyla, encompassing bacteria, archaea and fungi, and extended to 14 habitats spanning hosts and environmental contexts. Furthermore, we highlighted the associations between SBAs and gastrointestinal and hepatic disorders, including inflammatory bowel disease, colorectal cancer and nonalcoholic fatty liver disease, further elucidating disease-specific alterations in SBA-production genes. Additionally, we proposed the pig as a particularly suitable animal model for investigating SBA production in humans, given its closely aligned SBA-production gene composition. This gene catalog provides a comprehensive and reliable foundation on microbial BA metabolism for future studies, offering new insights into the microbial contributions to health and disease.

genomics↗

bioRxiv2024

Enhanced microbiota profiling in patients with quiescent Crohn's disease through comparison with paired healthy first-degree relatives using fecal metagenomics and metabolomics

Prior studies indicate no correlation between gut microbiota of healthy first-degree relatives (HFDRs) of Crohns disease (CD) patients and development of CD. Here, we utilized 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. Compared to non-relative controls, the use of HFDR controls identified fewer differential taxa. Faecalibacterium, Dorea, and Fusicatenibacter showed decreased abundances in CD-R, independent of inflammation, and correlated with fecal SCFAs. Validation with a large multi-center cohort confirmed decreased abundances in Faecalibacterium and other SCFA-producing genera in CD-R. Classification models based on these genera distinguished CD-R and CD-A from healthy individuals, in both the discovery and validation cohorts. Thus, the decreased presence of Faecalibacterium, Dorea, and Fusicatenibacter in CD-R likely contributed to disease relapse through reduced SCFA production, highlighting their potential as diagnostic markers and therapeutic targets for CD.

pathology↗

bioRxiv2023

The diversity and ecological significance of microbial traits potentially involved in B12 biosynthesis in the global ocean

Cobalamin (B12), an essential nutrient and growth cofactor for many living organisms on the Earth, can be fully synthesized only by selected prokaryotes in nature. Therefore, microbial communities related with B12 biosynthesis could serve as an example subsystem to disentangle the underlying ecological mechanisms balancing the function and taxonomy of complex functional assemblages. By anchoring microbial traits potentially involved in B12 biosynthesis, we depict the biogeographic patterns of B12 biosynthesis genes and their carrying taxa in the global ocean, in light of the limitation to detect de novo B12 synthesizers via metagenomes alone. Both the taxonomic and functional composition of B12 biosynthesis genes were strongly shaped by depth, differentiating epipelagic from mesopelagic zones. The functional genes were relatively stably distributed across different oceans, but their carrying taxa varied considerably, showing clear functional redundancy of microbial systems. Microbial taxa carrying B12 biosynthesis genes in the surface water were influenced by environmental factors such as temperature, oxygen and nitrate. However, the composition of functional genes was weakly associated with these environmental factors. Null model analyses demonstrated that determinism governed the compositional variation of B12 biosynthesis genes, whereas a higher degree of stochasticity was associated with taxonomic variations. Significant associations were observed between chlorophyll a concentration and B12 biosynthesis traits, indicating their importance in global ocean primary production. Conclusively, this study revealed an essential ecological mechanism governing the assembly of microbes in nature: the environment selects function rather than taxonomy; functional redundancy underlies stochastic community assembly. Impact StatementA central question in ecology is how a galaxy of microbial taxa is assembled and distributed across space and through time, executing essential ecosystem functions. By anchoring microbial functional traits potentially involved in B12 biosynthesis and their carrying microbial taxa in the global ocean, this study addresses essential ecological questions from functional and taxonomic angles. Integrating multiple lines of evidence, we show that the ecosystem selects functional traits rather than taxonomic groups, and functional redundancy underlies stochastic taxonomic community assembly. Also, microbial communities potentially involved in B12 biosynthesis are significantly associated with chlorophyll a concentration, demonstrating their importance in global ocean primary production. This study provides valuable mechanistic insights into the complex microbial community assembly in natural ecosystems.

microbiology↗

bioRxiv2023

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

BackgroundColorectal cancer (CRC) is a highly heterogeneous cancer with four consensus molecular subtypes (CMSs) characterized by distinct tumor microenvironment (TME). We aimed to depict the characteristics of host-microbiota interactions and their contributions to TME in each CMS. MethodsHost transcriptome and intratumoral microbiome profiles of 594 CRC samples were derived from RNA-Seq data from TCGA. Differential host genes and microbes among CMSs were identified. Immune microenvironments were assessed by CIBERSORTx and ESTIMATE and microbial co-abundance analyses were performed by FastSpar. Host-microbiota associations were evaluated by LASSO penalized regression in each CMS. ResultsAlong with distinct host gene signatures, including ferroptosis-related genes and immune microenvironments, 293, 153, 66 and 109 intratumoral differential microbial genera were identified within each of the four CMSs, respectively. Furthermore, the host-microbiota interactions contributed to distinct TME in each CMS, represented by 829, 1,270, 634 and 1,882 robust gene-microbe associations, respectively. The TME in CMS1 was featured with inflammation-related HSF1 activation and interactions between genes of endothelin pathway and Flammeovirga. Integrins-related genes positively correlated with Sutterella in CMS2 while CMS3 displayed microbial associations with biosynthetic and metabolic pathways. Genes in collagens biosynthesis positively correlated with Sutterella, contributing to homeostasis disturbance in CMS4. Besides, ferroptosis dysregulation was more remarkable in immune-high subtypes, which might partly result from the colonization of tissue microbes. ConclusionsWe systematically profiled the landscapes of TME, of each CMS in CRC, encompassing host genes, intratumoral microbiome and their interactions, which could illuminate novel mechanisms for the heterogeneity in CRC and potential therapeutic targets.

microbiology↗

bioRxiv2023

Multimodal meta-analysis of fecal metagenomes 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 pre-cancerous 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, while classifier based on SNVs of bacterial species displayed the strongest diagnostic capability (AUC=0.89). SNV biomarkers also exhibited outstanding performances in three independent validation cohorts (AUCs = 0.83, 0.82, and 0.76, respectively) and were highly specific to 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 further 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.

cancer biology↗

bioRxiv2021

Unbalanced dietary patterns contribute to the pathogenesis of precocious puberty by affecting gut microbiota and host metabolites

Precocious puberty (PP) mostly stems from endocrine disorders. However, its triggering factors, especially for the early onset of partial PP, and the associated pathogenic mechanisms remain ambiguous. In this study, a systematic analysis in the form of a questionnaire of lifestyles, gut microbiome, and serum metabolome data was carried out to examine the pathogenesis of PP in a cohort comprised of 200 girls, with or without PP. The analysis revealed substantial alterations in gut microbiota, serum metabolites, as well as lifestyle patterns in the PP group, which were characterized by an elevated abundance of {beta}-glucuronidase-producing and butyrate-producing bacteria, and excessive lipid concentration with decreased levels of organic nitrogen compounds in the serum of the participants. These differential microbes and metabolites tend to be reliable non-invasive diagnostic biomarkers aiding the early diagnosis of PP and exhibit a strong discriminative power (AUC = 0.93 and AUC = 0.97, respectively). Furthermore, the microbial biomarkers were confirmed in an independent validation cohort (n = 83, AUC = 0.85). Moreover, structural equation modeling revealed that unhealthy dietary habits were the primary contributors for the alteration of gut microbiota and serum metabolites, triggering the imbalance in the host hormones that leads to premature physical development. Our study determines a causal relationship among the gut microbiota, host metabolites, diet, and clinical characteristics of preadolescent girls who experienced early onset of PP, and formulates non-invasive diagnostic tools demonstrating excellent performance for the early detection of PP.

microbiology↗

bioRxiv2020

Identification of microbial markers across populations in early detection of colorectal cancer

Several studies have investigated the association between microbial and colorectal cancer (CRC). However, the replicable markers for early stage adenoma diagnosis across multiple populations remain elusive. Here, a meta-analysis of six studies, comprising a total of 1057 fecal samples, was performed to identify candidate markers. By adjusting the potential confounders, 11 and 26 markers (P<0.05) were identified and separately applied into constructing Random Forest classifier models to discriminate adenoma from control, and adenoma from CRC, achieving robust diagnostic accuracy with AUC = 0.80 and 0.89, respectively. Moreover, these markers demonstrated high diagnostic accuracy in independent validation cohorts. Pooled functional analysis and targeted qRT-PCR based genetic profiles reveal that the altered microbiome triggers different pathways of ADP-heptose and menaquinone biosynthesis (P<0.05) in adenoma vs. control and adenoma vs. CRC sequences respectively. The combined analysis of heterogeneous studies confirm adenoma-specific but universal markers across multi-populations, which improves early diagnosis and prompt treatment of CRC.

pathology↗

bioRxiv2020

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

Background & AimsMultiple mechanisms for the gut microbiome contributing to the pathogenesis of non-alcoholic fatty liver disease (NAFLD) have been implicated. Here, we aim to investigate the contribution and potential application for altered bile acid (BA) metabolizing microbe in NAFLD using whole metagenome sequencing (WMS) data. Methods86 well-characterized biopsy-proven NAFLD patients and 38 healthy controls were included in the discovery cohort. Assembly-based analysis was performed to identify BA-metabolizing microbes. Statistical tests, feature selection and microbial interaction analysis were integrated to identify microbial alterations and markers in NAFLD. An independent validation cohort was subjected to similar analyses. ResultsNAFLD microbiota exhibited decreased diversity and microbial interactions. We established a classifier model with 53 differential species exhibiting a robust diagnostic accuracy (AUC=0.97) for dectecting NAFLD. Next, 8 important differential pathway markers including secondary BA biosynthesis were identified. Specifically, increased abundance of 7-HSDH, baiA and baiB were detected in NAFLD. Further, 10 of 50 BA-metabolizing metagenome-assembled genomes (MAG)s, from Bacteroides ovatus and Eubacterium biforme, were dominant in NAFLD and interplayed as a synergetic ecological guild. Importantly, two subtypes of NAFLD patients were observed according to secondary BA metabolism potentials. Elevated capability for secondary BA biosynthesis was also observed in the validation cohort. ConclusionsWe identified novel bacterial BA-metabolizing genes and microbes that may contribute to NAFLD pathogenesis and serve as disease markers. Microbial differences in BA-metabolism and strain-specific differences among patients highlight the potential for precision medicine in NAFLD treatment.

pathology↗

bioRxiv2020

Identification of the keystone species in non-alcoholic fatty liver disease by causal inference and dynamic intervention modeling

ObjectiveKeystone species are required for the integrity and stability of an ecological community, and therefore, are potential intervention targets for microbiome related diseases. DesignHere we describe an algorithm for the identification of keystone species from cross-sectional microbiome data of non-alcoholic fatty liver disease (NAFLD) based on causal inference theories and dynamic intervention modeling (DIM). ResultsEight keystone species in the gut of NAFLD, represented by P. loveana, A. indistinctus and D. pneumosintes, were identified by our algorithm, which could efficiently restore the microbial composition of the NAFLD toward a normal gut microbiome with 92.3% recovery. These keystone species regulate intestinal amino acids metabolism and acid-base environment to promote the growth of the butyrate-producing Lachnospiraceae and Ruminococcaceae species. ConclusionOur method may benefit microbiome studies in the broad fields of medicine, environmental science and microbiology. SUMMARYWhat is already known about this subject? O_LINon-alcoholic fatty liver disease (NAFLD) is a complex multifactorial disease whose pathogenesis remains unclear. C_LIO_LIDysbiosis in the gut microbiota affects the initiation and development of NAFLD, but the mechanisms is yet to be established. C_LIO_LIKeystone species represent excellent candidate targets for gut microbiome-based interventions, as they are defined as the species required for the integrity and stability of the ecological system. C_LI What are the new findings? O_LINAFLD showed significant dysbiosis in butyrate-producing Lachnospiraceae and Ruminococcaceae species. C_LIO_LIMicrobial interaction networks were constructed by the novel algorithm with causal inference. C_LIO_LIKeystone species were identified form microbial interaction networks through dynamic intervention modeling based on generalized Lotka-Volterra model. C_LIO_LIEight keystone species of NAFLD with the highest potential for restoring the microbial composition were identified. C_LI How might it impact on clinical practice in the foreseeable future? O_LIAn algorithm for the identification of keystone species from cross-sectional microbiome data based on causal inference theories and dynamic intervention modeling. C_LIO_LIEight keystone species in the gut of NAFLD, represented by P. loveana, A. indistinctus and D. pneumosintes, which could efficiently restore the microbial composition of the NAFLD toward a normal gut microbiome. C_LIO_LIOur method may benefit microbiome studies in the broad fields of medicine, environmental science and microbiology. C_LI

bioinformatics↗

bioRxiv2020

Dr AFC: Drug Repositioning Through Anti-Fibrosis Characteristic

Fibrosis is a key component in the pathogenic mechanism of many diseases. These diseases involving fibrosis may share common mechanisms, therapeutic targets and therefore, common intervention strategies and medicines may be applicable for these diseases. For this reason, deliberately introducing anti-fibrosis characteristics into modelling 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 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 (AUC) 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 (Dr AFC) that is freely accessible via https://www.biosino.org/drafc.

bioinformatics↗