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Abstract Immune checkpoint blockade (ICB) has become a standard anti-cancer treatment, offering durable clinical benefits. However, the limited response rate of ICB necessitates biomarkers to predict and modulate the efficacy of the therapy. The gut microbiome’s influence on ICB efficacy is of particular interest due to its modifiability through various interventions. However, gut microbiome biomarkers for ICB response have been inconsistent across different studies. Here, we identify TANB77, an uncultured and distinct bacterial clade, as the most consistent responder-enriched taxon through meta-analysis of ten independent ICB recipient cohorts. Traditional taxonomy fails to distinguish TANB77 from unrelated taxa, leading to its oversight. Mice with higher gut TANB77 abundance, either naturally or through transplantation, show improved response to anti-PD-1 therapy. Additionally, mice injected with TANB77-derived pilin-like protein exhibit improved anti-PD-1 therapy response, providing in vivo evidence for the beneficial role of the pilin-like protein. These findings suggest that pilins from the TANB77 order may enhance responses to ICB therapy across diverse cohorts of cancer patients.

Cell type-specific actions of disease genes add a significant layer of complexity to the genetic architecture underlying diseases, obscuring our understanding of disease mechanisms. Single-cell omics have revealed the functional roles of genes at the cellular level, identifying cell types critical for disease progression. Often, a gene impact on disease through its altered network within specific cell types, rather than mere changes in expression levels. To explore the cell type-specific roles of disease genes, we developed HCNetlas (human cell network atlas), a resource cataloging cell type-specific gene networks (CGNs) for various healthy tissue cells. We also devised 3 network analysis methods to investigate cell type-specific functions of disease genes. These methods involve comparing HCNetlas CGNs with those derived from disease-affected tissue samples. These methods find that systemic lupus erythematosus genes predominantly function in myeloid cells, and Alzheimer's disease genes mainly play roles in inhibitory and excitatory neurons. Additionally, they suggest that many lung cancer-related genes may exert their roles in immune cells. These findings suggest that HCNetlas has the potential to link disease-associated genes to cell types of action, facilitating development of cell type-resolved diagnostics and therapeutic strategies for complex human diseases.

Abstract Recent advancements in translational gut microbiome research have revealed its crucial role in shaping predictive healthcare applications. Herein, we introduce the Gut Microbiome Wellness Index 2 (GMWI2), an enhanced version of our original GMWI prototype, designed as a standardized disease-agnostic health status indicator based on gut microbiome taxonomic profiles. Our analysis involves pooling existing 8069 stool shotgun metagenomes from 54 published studies across a global demographic landscape (spanning 26 countries and six continents) to identify gut taxonomic signals linked to disease presence or absence. GMWI2 achieves a cross-validation balanced accuracy of 80% in distinguishing healthy (no disease) from non-healthy (diseased) individuals and surpasses 90% accuracy for samples with higher confidence (i.e., outside the “reject option”). This performance exceeds that of the original GMWI model and traditional species-level α-diversity indices, indicating a more robust gut microbiome signature for differentiating between healthy and non-healthy phenotypes across multiple diseases. When assessed through inter-study validation and external validation cohorts, GMWI2 maintains an average accuracy of nearly 75%. Furthermore, by reevaluating previously published datasets, GMWI2 offers new insights into the effects of diet, antibiotic exposure, and fecal microbiota transplantation on gut health. Available as an open-source command-line tool, GMWI2 represents a timely, pivotal resource for evaluating health using an individual’s unique gut microbial composition.

Abstract Recent substantial evidence implicating commensal bacteria in human diseases has given rise to a new domain in biomedical research: microbiome medicine. This emerging field aims to understand and leverage the human microbiota and derivative molecules for disease prevention and treatment. Despite the complex and hierarchical organization of this ecosystem, most research over the years has relied on 16S amplicon sequencing, a legacy of bacterial phylogeny and taxonomy. Although advanced sequencing technologies have enabled cost-effective analysis of entire microbiota, translating the relatively short nucleotide information into the functional and taxonomic organization of the microbiome has posed challenges until recently. In the last decade, genome-resolved metagenomics, which aims to reconstruct microbial genomes directly from whole-metagenome sequencing data, has made significant strides and continues to unveil the mysteries of various human-associated microbial communities. There has been a rapid increase in the volume of whole metagenome sequencing data and in the compilation of novel metagenome-assembled genomes and protein sequences in public depositories. This review provides an overview of the capabilities and methods of genome-resolved metagenomics for studying the human microbiome, with a focus on investigating the prokaryotic microbiota of the human gut. Just as decoding the human genome and its variations marked the beginning of the genomic medicine era, unraveling the genomes of commensal microbes and their sequence variations is ushering us into the era of microbiome medicine. Genome-resolved metagenomics stands as a pivotal tool in this transition and can accelerate our journey toward achieving these scientific and medical milestones.

Mouse gut microbiome research is pivotal for understanding the human gut microbiome, providing insights into disease modeling, host-microbe interactions, and the dietary influence on the gut microbiome. To enhance the translational value of mouse gut microbiome studies, we need detailed and high-quality catalogs of mouse gut microbial genomes. We introduce the Mouse Reference Gut Microbiome (MRGM), a comprehensive catalog with 42,245 non-redundant mouse gut bacterial genomes across 1,524 species. MRGM marks a 40% increase in the known taxonomic diversity of mouse gut microbes, capturing previously underrepresented lineages through refined genome quality assessment techniques. MRGM not only broadens the taxonomic landscape but also enriches the functional landscape of the mouse gut microbiome. Using deep learning, we have elevated the Gene Ontology annotation rate for mouse gut microbial proteins from 3.2% with orthology to 60%, marking an over 18-fold increase. MRGM supports both DNA- and marker-based taxonomic profiling by providing custom databases, surpassing previous catalogs in performance. Finally, taxonomic and functional comparisons between human and mouse gut microbiota reveal diet-driven divergences in their taxonomic composition and functional enrichment. Overall, our study highlights the value of high-quality microbial genome catalogs in advancing our understanding of the co-evolution between gut microbes and their host.

The gut microbiome, linked significantly to host diseases, offers potential for disease diagnosis through machine learning (ML) pipelines. These pipelines, crucial in modeling diseases using high-dimensional microbiome data, involve selecting profile modalities, data preprocessing techniques, and classification algorithms, each impacting the model accuracy and generalizability. Despite whole metagenome shotgun sequencing (WMS) gaining popularity for human gut microbiome profiling, a consensus on the optimal methods for ML pipelines in disease diagnosis using WMS data remains elusive. Addressing this gap, we comprehensively evaluated ML methods for diagnosing Crohn’s disease and colorectal cancer, using 2,553 fecal WMS samples from 21 case-control studies. Our study uncovered crucial insights: gut-specific, species-level taxonomic features proved to be the most effective for profiling; batch correction was not consistently beneficial for model performance; compositional data transformations markedly improved the models; and while nonlinear ensemble classification algorithms typically offered superior performance, linear models with proper regularization were found to be more effective for diseases that are linearly separable based on microbiome data. An optimal ML pipeline, integrating the most effective methods, was validated for generalizability using holdout data. This research offers practical guidelines for constructing reliable disease diagnostic ML models with fecal WMS data.

Abstract Frugivory evolved multiple times in mammals, including bats. However, the cellular and molecular components driving it remain largely unknown. Here, we use integrative single-cell sequencing (scRNA-seq and scATAC-seq) on insectivorous (Eptesicus fuscus; big brown bat) and frugivorous (Artibeus jamaicensis; Jamaican fruit bat) bat kidneys and pancreases and identify key cell population, gene expression and regulatory differences associated with the Jamaican fruit bat that also relate to human disease, particularly diabetes. We find a decrease in loop of Henle and an increase in collecting duct cells, and differentially active genes and regulatory elements involved in fluid and electrolyte balance in the Jamaican fruit bat kidney. The Jamaican fruit bat pancreas shows an increase in endocrine and a decrease in exocrine cells, and differences in genes and regulatory elements involved in insulin regulation. We also find that these frugivorous bats share several molecular characteristics with human diabetes. Combined, our work provides insights from a frugivorous mammal that could be leveraged for therapeutic purposes.

Background Oropharyngeal squamous cell carcinoma (OPSCC) induced by human papillomavirus (HPV-positive) is associated with better clinical outcomes than HPV-negative OPSCC. However, the clinical benefits of immunotherapy in patients with HPV-positive OPSCC remain unclear.Methods To identify the cellular and molecular factors that limited the benefits associated with HPV in OPSCC immunotherapy, we performed single-cell RNA (n=20) and T-cell receptor sequencing (n=10) analyses of tonsil or base of tongue tumor biopsies prior to immunotherapy. Primary findings from our single-cell analysis were confirmed through immunofluorescence experiments, and secondary validation analysis were performed via publicly available transcriptomics data sets.Results We found significantly higher transcriptional diversity of malignant cells among non-responders to immunotherapy, regardless of HPV infection status. We also observed a significantly larger proportion of CD4+ follicular helper T cells (Tfh) in HPV-positive tumors, potentially due to enhanced Tfh differentiation. Most importantly, CD8+ resident memory T cells (Trm) with elevated KLRB1 (encoding CD161) expression showed an association with dampened antitumor activity in patients with HPV-positive OPSCC, which may explain their heterogeneous clinical outcomes. Notably, all HPV-positive patients, whose Trm presented elevated KLRB1 levels, showed low expression of CLEC2D (encoding the CD161 ligand) in B cells, which may reduce tertiary lymphoid structure activity. Immunofluorescence of HPV-positive tumors treated with immune checkpoint blockade showed an inverse correlation between the density of CD161+ Trm and changes in tumor size.Conclusions We found that CD161+ Trm counteracts clinical benefits associated with HPV in OPSCC immunotherapy. This suggests that targeted inhibition of CD161 in Trm could enhance the efficacy of immunotherapy in HPV-positive oropharyngeal cancers.Trial registration number NCT03737968.

Single-cell transcriptome data provide a unique opportunity to explore the gene networks of a particular cell type. However, insufficient capture rate and high dimensionality of single-cell RNA sequencing (scRNA-seq) data challenge cell-type-specific gene network (CGN) reconstruction. Here, we demonstrated that the imputation of scRNA-seq data enables reconstruction of CGNs by effective retrieval of gene functional associations. We reconstructed CGNs for seven primary and nine metastatic breast cancer cell lines using scRNA-seq data with imputation. Key genes for primary or metastatic cell lines were prioritized based on network centrality measures and CGN hub genes that were presumed to be the major determinant of cell type characteristics. To identify novel genes in breast cancer metastasis, we used the average rank difference of centrality between the primary and metastatic cell lines. Genes predicted using CGN centrality analysis were more enriched for known breast cancer metastatic genes than those predicted using differential expression. The molecular chaperone CCT2 was identified as a novel gene for breast metastasis during knockdown assays of several candidate genes. Overall, our study demonstrated an effective CGN reconstruction technique with imputation of scRNA-seq data and the feasibility of identifying key genes for particular cell subsets using single-cell network analysis.

Here, the authors construct 102 complete metagenome-assembled genomes (cMAGs) from Pacific Biosciences (PacBio) high-accuracy long-read (HiFi) metagenomic sequencing, showing as high nucleotide accuracy as reference genomes and revealing that regions hard to assemble by short-read sequencing comprise mostly of genomic islands and rRNAs.

The nucleolus is the traditional site for ribosomal RNA biogenesis. Here, the authors find that the nucleolus is a site of inflammatory pre-mRNA turnover and elucidated how immune homeostasis can be maintained by controlling inflammatory gene expression.

Viral coinfection alongside mycobacterium tuberculosis (Mtb) infection may lead to immune complications or interference with immune responses. Here the authors show that in mice infected with Mtb and LCMV virus the specific TH1 response to MTb is reduced through a type I IFN response to the infecting virus.

Abstract Background The prevalence of alcohol use disorder (AUD) among women in South Korea has been rising, causing public health problems. Yet women’s treatment needs are mostly unmet in South Korea due to the lack of women-focused treatment programs. This study evaluated the feasibility, acceptability, and clinical outcomes of a therapeutic community (TC)-oriented day treatment program for Korean women with AUD on alcohol abstinence self-efficacy, forgiveness, and spirituality. Methods The current study employed a quasi-experimental, non-equivalent control group design with a pretest and posttest. Participants were assigned to 6-month TC-oriented day treatment program (n = 19) or usual treatment (n = 21). Feasibility (treatment completion rate) and acceptability (overall program satisfaction) were assessed. Alcohol abstinence was measured as a clinical outcome at baseline, 3 months, and 6 months. Repeated measures using Alcohol Abstinence Self-Efficacy (AASE), Enright Forgiveness Inventory-Korea (EFI-K), and Spiritual Assessment Scale (SAS) were also obtained from both conditions at those three time points. Results Fifteen participants (78.9%) in the intervention group successfully completed the program. The overall program satisfaction ratings were very high (4.9 ± 0.2). Continuous abstinence rates at 6 months were significantly higher in the treatment group (78.9%) than in the control group (9.5%). Results of the two-way repeated measures ANOVA indicated that statistically significant two-way (group × time) interaction effects were found for the intervention group on AASE, EFI-K, and SAS but not for the control group on any of the outcomes. Conclusion The current study demonstrated the feasibility and acceptability of implementing a TC-oriented intensive day treatment program to promote recovery in Korean women with AUD. This intervention merits further investigation as a potential strategy to help address alcohol abstinence self-efficacy, forgiveness and spirituality. Trial registration: KCT0006386 (Cris.nih.go.kr).

EGFR mutant lung tumours do not respond favourably to immunotherapy. Here, using single cell sequencing, the authors find that tissue resident memory CD8+ T like cells are reduced in the immune landscape of EGFR mutant tumours in comparison to wild type tumours and the presence of these cells may predict response to immunotherapy.

Abstract Background Metagenome sampling bias for geographical location and lifestyle is partially responsible for the incomplete catalog of reference genomes of gut microbial species. Thus, genome assembly from currently under-represented populations may effectively expand the reference gut microbiome and improve taxonomic and functional profiling. Methods We assembled genomes using public whole-metagenomic shotgun sequencing (WMS) data for 110 and 645 fecal samples from India and Japan, respectively. In addition, we assembled genomes from newly generated WMS data for 90 fecal samples collected from Korea. Expecting genome assembly for low-abundance species may require a much deeper sequencing than that usually employed, so we performed ultra-deep WMS (> 30 Gbp or > 100 million read pairs) for the fecal samples from Korea. We consequently assembled 29,082 prokaryotic genomes from 845 fecal metagenomes for the three under-represented Asian countries and combined them with the Unified Human Gastrointestinal Genome (UHGG) to generate an expanded catalog, the Human Reference Gut Microbiome (HRGM). Results HRGM contains 232,098 non-redundant genomes for 5414 representative prokaryotic species including 780 that are novel, > 103 million unique proteins, and > 274 million single-nucleotide variants. This is an over 10% increase from the UHGG. The new 780 species were enriched for the Bacteroidaceae family, including species associated with high-fiber and seaweed-rich diets. Single-nucleotide variant density was positively associated with the speciation rate of gut commensals. We found that ultra-deep sequencing facilitated the assembly of genomes for low-abundance taxa, and deep sequencing (e.g., > 20 million read pairs) may be needed for the profiling of low-abundance taxa. Importantly, the HRGM significantly improved the taxonomic and functional classification of sequencing reads from fecal samples. Finally, analysis of human self-antigen homologs on the HRGM species genomes suggested that bacterial taxa with high cross-reactivity potential may contribute more to the pathogenesis of gut microbiome-associated diseases than those with low cross-reactivity potential by promoting inflammatory condition. Conclusions By including gut metagenomes from previously under-represented Asian countries, Korea, India, and Japan, we developed a substantially expanded microbiome catalog, HRGM. Information of the microbial genomes and coding genes is publicly available ( www.mbiomenet.org/HRGM/ ). HRGM will facilitate the identification and functional analysis of disease-associated gut microbiota.

Chloroplast development and brassinosteroid (BR) signaling both regulate plant photomorphogenesis. Here the authors show that DAY, a DnaJ-like domain-containing membrane protein, regulates both processes by binding and stabilizing both BRI1, the BR receptor, and POR, a key enzyme in chlorophyll biosynthesis.

Regulatory T cells (Tregs) are enriched in the tumor microenvironment and play key roles in immune evasion of cancer cells. Cell surface markers specific for tumor-infiltrating Tregs (TI-Tregs) can be effectively targeted to enhance antitumor immunity and used for stratification of immunotherapy outcomes. Here, we present a systems biology approach to identify functional cell surface markers for TI-Tregs. We selected differentially expressed genes for surface proteins of TI-Tregs and compared these with other CD4+ T cells using bulk RNA-sequencing data from murine lung cancer models. Thereafter, we filtered for human orthologues with conserved expression in TI-Tregs using single-cell transcriptome data from patients with non-small cell lung cancer (NSCLC). To evaluate the functional importance of expression-based markers of TI-Tregs, we utilized network-based measure of context-associated centrality in a Treg-specific coregulatory network. We identified TNFRSF9 (also known as 4-1BB or CD137), a previously reported target for enhancing antitumor immunity, among the final candidates for TI-Treg markers with high functional importance score. We found that the low TNFRSF9 expression level in Tregs was associated with enhanced overall survival rate and response to anti-PD-1 immunotherapy in patients with NSCLC, proposing that TNFRSF9 promotes immune suppressive activity of Tregs in tumor. Collectively, these results demonstrated that integrative transcriptome and network analysis can facilitate the discovery of functional markers of tumor-specific immune cells to develop novel therapeutic targets and biomarkers for boosting cancer immunotherapy.

Cancer: DNA modifications affecting response to treatment A study into natural regulatory DNA modifications that influence patient responses could guide strategies to help the roughly two-thirds of patients who respond poorly to treatment with anticancer drugs called PD-1 inhibitors. Researchers at Yonsei University in South Korea, led by Hye Ryun Kim and Insuk Lee, investigated the significance in lung cancer patients of pre-treatment levels of DNA methylation, in which methyl (CH3) groups are added to DNA. They identified > 1000 regions of DNA in which varying methylation levels were associated with differing responses to PD-1 inhibitors. These differences affected regions of DNA called enhancers and promoters, which have been implicated in controlling the activity of more than 1000 identified genes. The research could help predict response outcomes to potential treatments and suggest possibilities for new interventions that might improve responses.

Abstract Background T cells exhibit heterogeneous functional states in the tumor microenvironment. Immune checkpoint inhibitors (ICIs) can reinvigorate only the stem cell-like progenitor exhausted T cells, which suggests that inhibiting the exhaustion progress will improve the efficacy of immunotherapy. Thus, regulatory factors promoting T cell exhaustion could serve as potential targets for delaying the process and improving ICI efficacy. Methods We analyzed the single-cell transcriptome data derived from human melanoma and non-small cell lung cancer (NSCLC) samples and classified the tumor-infiltrating (TI) CD8+ T cell population based on PDCD1 (PD-1) levels, i.e., PDCD1-high and PDCD1-low cells. Additionally, we identified differentially expressed genes as candidate factors regulating intra-tumoral T cell exhaustion. The co-expression of candidate genes with immune checkpoint (IC) molecules in the TI CD8+ T cells was confirmed by single-cell trajectory and flow cytometry analyses. The loss-of-function effect of the candidate regulator was examined by a cell-based knockdown assay. The clinical effect of the candidate regulator was evaluated based on the overall survival and anti-PD-1 responses. Results We retrieved many known factors for regulating T cell exhaustion among the differentially expressed genes between PDCD1-high and PDCD1-low subsets of the TI CD8+ T cells in human melanoma and NSCLC. TOX was the only transcription factor (TF) predicted in both tumor types. TOX levels tend to increase as CD8+ T cells become more exhausted. Flow cytometry analysis revealed a correlation between TOX expression and severity of intra-tumoral T cell exhaustion. TOX knockdown in the human TI CD8+ T cells resulted in downregulation of PD-1, TIM-3, TIGIT, and CTLA-4, which suggests that TOX promotes intra-tumoral T cell exhaustion by upregulating IC proteins in cancer. Finally, the TOX level in the TI T cells was found to be highly predictive of overall survival and anti-PD-1 efficacy in melanoma and NSCLC. Conclusions We predicted the regulatory factors involved in T cell exhaustion using single-cell transcriptome profiles of human TI lymphocytes. TOX promoted intra-tumoral CD8+ T cell exhaustion via upregulation of IC molecules. This suggested that TOX inhibition can potentially impede T cell exhaustion and improve ICI efficacy. Additionally, TOX expression in the TI T cells can be used for patient stratification during anti-tumor treatments, including anti-PD-1 immunotherapy.

Most genetic variations associated with human complex traits are located in non-coding genomic regions. Therefore, understanding the genotype-to-phenotype axis requires a comprehensive catalog of functional non-coding genomic elements, most of which are involved in epigenetic regulation of gene expression. Genome-wide maps of open chromatin regions can facilitate functional analysis of cis- and trans-regulatory elements via their connections with trait-associated sequence variants. Currently, Assay for Transposase Accessible Chromatin with high-throughput sequencing (ATAC-seq) is considered the most accessible and cost-effective strategy for genome-wide profiling of chromatin accessibility. Single-cell ATAC-seq (scATAC-seq) technology has also been developed to study cell type-specific chromatin accessibility in tissue samples containing a heterogeneous cellular population. However, due to the intrinsic nature of scATAC-seq data, which are highly noisy and sparse, accurate extraction of biological signals and devising effective biological hypothesis are difficult. To overcome such limitations in scATAC-seq data analysis, new methods and software tools have been developed over the past few years. Nevertheless, there is no consensus for the best practice of scATAC-seq data analysis yet. In this review, we discuss scATAC-seq technology and data analysis methods, ranging from preprocessing to downstream analysis, along with an up-to-date list of published studies that involved the application of this method. We expect this review will provide a guideline for successful data generation and analysis methods using appropriate software tools and databases for the study of chromatin accessibility at single-cell resolution.

Host genetics affect both the susceptibility and response to viral infection. Searching for host genes that contribute to COVID-19, the Host Genetics Initiative (HGI) was formed to investigate the genetic factors involved in COVID-19 via genome-wide association studies (GWAS). The GWAS suffer from limited statistical power and in general, only a few genes can pass the conventional significance thresholds. This statistical limitation may be overcome by boosting weak association signals through integrating independent functional information such as molecular interactions. Additionally, the boosted results can be evaluated by various independent data for further connections to COVID-19. We present COVID-GWAB, a web-based tool to boost original GWAS signals from COVID-19 patients by taking the signals of the interactome neighbors. COVID-GWAB takes summary statistics from the COVID-19 HGI or user input data and reprioritizes candidate host genes for COVID-19 using HumanNet, a co-functional human gene network. The current version of COVID-GWAB provides the pre-processed data of releases 5, 6, and 7 of the HGI. Additionally, COVID-GWAB provides web interfaces for a summary of augmented GWAS signals, prediction evaluations by appearance frequency in COVID-19 literature, single-cell transcriptome data, and associated pathways. The web server also enables browsing the candidate gene networks.

Summary: Disorders of the biliary epithelium, known as cholangiopathies, cause severe and irreversible liver diseases. The limited accessibility of bile duct precludes modeling of several cholangiocyte-mediated diseases. Therefore, novel approaches for obtaining functional cholangiocytes with high purity are needed. Previous work has shown that the combination of Hnf1β and Foxa3 could directly convert mouse fibroblasts into bipotential hepatic stem cell-like cells, termed iHepSCs. However, the efficiency of converting fibroblasts into iHepSCs is low, and these iHepSCs exhibit extremely low differentiation potential into cholangiocytes, thus hindering the translation of iHepSCs to the clinic. Here, we describe that the expression of Hnf1α and Foxa3 dramatically facilitates the robust generation of iHepSCs. Notably, prolonged in vitro culture of Hnf1α- and Foxa3-derived iHepSCs induces a Notch signaling-mediated secondary conversion into cholangiocyte progenitor-like cells that display dramatically enhanced differentiation capacity into mature cholangiocytes. Our study provides a robust two-step approach for obtaining cholangiocyte progenitor-like cells using defined factors. : In this article, Han and colleagues revisited the roles of HepSC-specific factors and found that Hnf1α and Foxa3 facilitate the robust conversion into iHepSCs displaying a relatively closer transcriptional pattern with LEPCs. The prolonged in vitro culture of iHepSCs induces Notch-mediated secondary conversion into iCPCs, which could efficiently be differentiated into mature cholangiocytes. Keywords: direct conversion, cholangiocyte progenitor cells, hepatic stem cells, Notch signal

Cultivated barley (Hordeum vulgare L.) is one of the most produced cereal crops worldwide after maize, bread wheat, and rice. Barley is an important crop species not only as a food source, but also in plant genetics because it harbors numerous stress response alleles in its genome that can be exploited for crop engineering. However, the functional annotation of its genome is relatively poor compared with other major crops. Moreover, bioinformatics tools for system-wide analyses of omics data from barley are not yet available. We have thus developed BarleyNet, a co-functional network of 26,145 barley genes, along with a web server for network-based predictions (http://www.inetbio.org/barleynet). We demonstrated that BarleyNet's prediction of biological processes is more accurate than that of an existing barley gene network. We implemented three complementary network-based algorithms for prioritizing genes or functional concepts to study genetic components of complex traits such as environmental stress responses: (i) a pathway-centric search for candidate genes of pathways or complex traits; (ii) a gene-centric search to infer novel functional concepts for genes; and (iii) a context-centric search for novel genes associated with stress response. We demonstrated the usefulness of these network analysis tools in the study of stress response using proteomics and transcriptomics data from barley leaves and roots upon drought or heat stresses. These results suggest that BarleyNet will facilitate our understanding of the underlying genetic components of complex traits in barley.

ABSTRACT Global increases in the use of carbapenems have resulted in several strains of Gram-negative bacteria acquiring carbapenem resistance, thereby limiting treatment options. Klebsiella pneumoniae is a common carbapenem-resistant pathogenic bacterium that is widely studied to identify novel antibiotic resistance mechanisms and drug targets. Antibiotic-resistant clinical isolates generally harbor many genetic alterations, and the identification of responsible mutations would provide insights into the molecular mechanisms of antibiotic resistance. We propose a method to prioritize mutated genes responsible for antibiotic resistance on the basis of expression changes in their local subnetworks, hypothesizing that mutated genes that show significant expression changes among the corresponding functionally associated genes are more likely to be involved in the carbapenem resistance. For network-based gene prioritization, we developed KlebNet (www.inetbio.org/klebnet), a genome-scale cofunctional network of K. pneumoniae genes. Using KlebNet, we reconstructed the functional modules for carbapenem resistance and virulence and identified the functional association between antibiotic resistance and virulence. Using complementation assays with the top candidate genes, we were able to validate a novel gene that negatively regulated carbapenem resistance and four novel genes that positively regulated virulence in Galleria mellonella larvae. Therefore, our study demonstrated the feasibility of network-based identification of genes required for antibiotic resistance and virulence of human-pathogenic bacteria. IMPORTANCE Klebsiella pneumoniae is a major bacterial pathogen that causes pneumonia and urinary tract infections in human. K. pneumoniae infections are treated with carbapenem, but carbapenem-resistant K. pneumoniae has been spreading worldwide. We are able to identify antimicrobial-resistant genes among mutated genes of the antibiotic-resistant clinical isolates. However, they usually harbor many mutated genes, including those that cause weak or neutral functional effects. Therefore, we need to prioritize the mutated genes to identify the more likely candidates for the follow-up functional analysis. For this study, we present a functional network of K. pneumoniae genes and propose a network-based method of prioritizing the mutated genes of the resistant clinical isolates. We also reconstructed the network-based functional modules for carbapenem resistance and virulence and retrieved the functional association between antibiotic resistance and virulence. This study demonstrated the feasibility of network-based analysis of clinical genomics data for the study of K. pneumoniae infection.

Protein domains are basic functional units of proteins. Many protein domains are pervasive among diverse biological processes, yet some are associated with specific pathways. Human complex diseases are generally viewed as pathway-level disorders. Therefore, we hypothesized that pathway-specific domains could be highly informative for human diseases. To test the hypothesis, we developed a network-based scoring scheme to quantify specificity of domain-pathway associations. We first generated domain profiles for human proteins, then constructed a co-pathway protein network based on the associations between domain profiles. Based on the score, we classified human protein domains into pathway-specific domains (PSDs) and non-specific domains (NSDs). We found that PSDs contained more pathogenic variants than NSDs. PSDs were also enriched for disease-associated mutations that disrupt protein-protein interactions (PPIs) and tend to have a moderate number of domain interactions. These results suggest that mutations in PSDs are likely to disrupt within-pathway PPIs, resulting in functional failure of pathways. Finally, we demonstrated the prediction capacity of PSDs for disease-associated genes with experimental validations in zebrafish. Taken together, the network-based quantitative method of modeling domain-pathway associations presented herein suggested underlying mechanisms of how protein domains associated with specific pathways influence mutational impacts on diseases via perturbations in within-pathway PPIs, and provided a novel genomic feature for interpreting genetic variants to facilitate the discovery of human disease genes.

The interactions between gut bacteria and enteric pathogens are poorly understood. Here, Yoon et al. show that subinhibitory antibiotic treatment in a mouse model leads to overgrowth of an E. coli strain carrying a catalase-encoding gene that enhances infection with the human pathogen Vibrio cholerae.

Cancer driving genes have been identified as recurrently affected by variants that alter protein-coding sequences. However, a majority of cancer variants arise in noncoding regions, and some of them are thought to play a critical role through transcriptional perturbation. Here we identified putative transcriptional driver genes based on combinatorial variant recurrence in cis-regulatory regions. The identified genes showed high connectivity in the cancer type-specific transcription regulatory network, with high outdegree and many downstream genes, highlighting their causative role during tumorigenesis. In the protein interactome, the identified transcriptional drivers were not as highly connected as coding driver genes but appeared to form a network module centered on the coding drivers. The coding and regulatory variants associated via these interactions between the coding and transcriptional drivers showed exclusive and complementary occurrence patterns across tumor samples. Transcriptional cancer drivers may act through an extensive perturbation of the regulatory network and by altering protein network modules through interactions with coding driver genes.

Phylogenetic profiling, a network inference method based on gene inheritance profiles, has been widely used to construct functional gene networks in microbes. However, its utility for network inference in higher eukaryotes has been limited. An improved algorithm with an in-depth understanding of pathway evolution may overcome this limitation. In this study, we investigated the effects of taxonomic structures on co-inheritance analysis using 2,144 reference species in four query species: Escherichia coli, Saccharomyces cerevisiae, Arabidopsis thaliana, and Homo sapiens. We observed three clusters of reference species based on a principal component analysis of the phylogenetic profiles, which correspond to the three domains of life-Archaea, Bacteria, and Eukaryota-suggesting that pathways inherit primarily within specific domains or lower-ranked taxonomic groups during speciation. Hence, the co-inheritance pattern within a taxonomic group may be eroded by confounding inheritance patterns from irrelevant taxonomic groups. We demonstrated that co-inheritance analysis within domains substantially improved network inference not only in microbe species but also in the higher eukaryotes, including humans. Although we observed two sub-domain clusters of reference species within Eukaryota, co-inheritance analysis within these sub-domain taxonomic groups only marginally improved network inference. Therefore, we conclude that co-inheritance analysis within domains is the optimal approach to network inference with the given reference species. The construction of a series of human gene networks with increasing sample sizes of the reference species for each domain revealed that the size of the high-accuracy networks increased as additional reference species genomes were included, suggesting that within-domain co-inheritance analysis will continue to expand human gene networks as genomes of additional species are sequenced. Taken together, we propose that co-inheritance analysis within the domains of life will greatly potentiate the use of the expected onslaught of sequenced genomes in the study of molecular pathways in higher eukaryotes.

A network-based approach has proven useful for the identification of novel genes associated with complex phenotypes, including human diseases. Because network-based gene prioritization algorithms are based on propagating information of known phenotype-associated genes through networks, the pathway structure of each phenotype might significantly affect the effectiveness of algorithms. We systematically compared two popular network algorithms with distinct mechanisms--direct neighborhood which propagates information to only direct network neighbors, and network diffusion which diffuses information throughout the entire network--in prioritization of genes for worm and human phenotypes. Previous studies reported that network diffusion generally outperforms direct neighborhood for human diseases. Although prioritization power is generally measured for all ranked genes, only the top candidates are significant for subsequent functional analysis. We found that high prioritizing power of a network algorithm for all genes cannot guarantee successful prioritization of top ranked candidates for a given phenotype. Indeed, the majority of the phenotypes that were more efficiently prioritized by network diffusion showed higher prioritizing power for top candidates by direct neighborhood. We also found that connectivity among pathway genes for each phenotype largely determines which network algorithm is more effective, suggesting that the network algorithm used for each phenotype should be chosen with consideration of pathway gene connectivity.

We find that the topologies of real world networks, such as those formed within human societies, by the Internet, or among cellular proteins, are dominated by the mode of the interactions considered among the individuals. Specifically, a major dichotomy in previously studied networks arises from modeling networks in terms of pairwise versus group tasks. The former often intrinsically give rise to scale-free, disassortative, hierarchical networks, whereas the latter often give rise to single- or broad-scale, assortative, nonhierarchical networks. These dependencies explain contrasting observations among previous topological analyses of real world complex systems. We also observe this trend in systems with natural hierarchies, in which alternate representations of the same networks, but which capture different levels of the hierarchy, manifest these signature topological differences. For example, in both the Internet and cellular proteomes, networks of lower-level system components (routers within domains or proteins within biological processes) are assortative and nonhierarchical, whereas networks of upper-level system components (internet domains or biological processes) are disassortative and hierarchical. Our results demonstrate that network topologies of complex systems must be interpreted in light of their hierarchical natures and interaction types.

Rice (Oryza sativa) is a staple food for more than half the world and a model for studies of monocotyledonous species, which include cereal crops and candidate bioenergy grasses. A major limitation of crop production is imposed by a suite of abiotic and biotic stresses resulting in 30%-60% yield losses globally each year. To elucidate stress response signaling networks, we constructed an interactome of 100 proteins by yeast two-hybrid (Y2H) assays around key regulators of the rice biotic and abiotic stress responses. We validated the interactome using protein-protein interaction (PPI) assays, co-expression of transcripts, and phenotypic analyses. Using this interactome-guided prediction and phenotype validation, we identified ten novel regulators of stress tolerance, including two from protein classes not previously known to function in stress responses. Several lines of evidence support cross-talk between biotic and abiotic stress responses. The combination of focused interactome and systems analyses described here represents significant progress toward elucidating the molecular basis of traits of agronomic importance.

Haploinsufficiency, wherein a single functional copy of a gene is insufficient to maintain normal function, is a major cause of dominant disease. Human disease studies have identified several hundred haploinsufficient (HI) genes. We have compiled a map of 1,079 haplosufficient (HS) genes by systematic identification of genes unambiguously and repeatedly compromised by copy number variation among 8,458 apparently healthy individuals and contrasted the genomic, evolutionary, functional, and network properties between these HS genes and known HI genes. We found that HI genes are typically longer and have more conserved coding sequences and promoters than HS genes. HI genes exhibit higher levels of expression during early development and greater tissue specificity. Moreover, within a probabilistic human functional interaction network HI genes have more interaction partners and greater network proximity to other known HI genes. We built a predictive model on the basis of these differences and annotated 12,443 genes with their predicted probability of being haploinsufficient. We validated these predictions of haploinsufficiency by demonstrating that genes with a high predicted probability of exhibiting haploinsufficiency are enriched among genes implicated in human dominant diseases and among genes causing abnormal phenotypes in heterozygous knockout mice. We have transformed these gene-based haploinsufficiency predictions into haploinsufficiency scores for genic deletions, which we demonstrate to better discriminate between pathogenic and benign deletions than consideration of the deletion size or numbers of genes deleted. These robust predictions of haploinsufficiency support clinical interpretation of novel loss-of-function variants and prioritization of variants and genes for follow-up studies.

Biogenesis of ribosomes is an essential cellular process conserved across all eukaryotes and is known to require >170 genes for the assembly, modification, and trafficking of ribosome components through multiple cellular compartments. Despite intensive study, this pathway likely involves many additional genes. Here, we employ network-guided genetics-an approach for associating candidate genes with biological processes that capitalizes on recent advances in functional genomic and proteomic studies-to computationally identify additional ribosomal biogenesis genes. We experimentally evaluated >100 candidate yeast genes in a battery of assays, confirming involvement of at least 15 new genes, including previously uncharacterized genes (YDL063C, YIL091C, YOR287C, YOR006C/TSR3, YOL022C/TSR4). We associate the new genes with specific aspects of ribosomal subunit maturation, ribosomal particle association, and ribosomal subunit nuclear export, and we identify genes specifically required for the processing of 5S, 7S, 20S, 27S, and 35S rRNAs. These results reveal new connections between ribosome biogenesis and mRNA splicing and add >10% new genes-most with human orthologs-to the biogenesis pathway, significantly extending our understanding of a universally conserved eukaryotic process.

Probabilistic functional gene networks are powerful theoretical frameworks for integrating heterogeneous functional genomics and proteomics data into objective models of cellular systems. Such networks provide syntheses of millions of discrete experimental observations, spanning DNA microarray experiments, physical protein interactions, genetic interactions, and comparative genomics; the resulting networks can then be easily applied to generate testable hypotheses regarding specific gene functions and associations.We report a significantly improved version (v. 2) of a probabilistic functional gene network of the baker's yeast, Saccharomyces cerevisiae. We describe our optimization methods and illustrate their effects in three major areas: the reduction of functional bias in network training reference sets, the application of a probabilistic model for calculating confidences in pair-wise protein physical or genetic interactions, and the introduction of simple thresholds that eliminate many false positive mRNA co-expression relationships. Using the network, we predict and experimentally verify the function of the yeast RNA binding protein Puf6 in 60S ribosomal subunit biogenesis.YeastNet v. 2, constructed using these optimizations together with additional data, shows significant reduction in bias and improvements in precision and recall, in total covering 102,803 linkages among 5,483 yeast proteins (95% of the validated proteome). YeastNet is available from http://www.yeastnet.org.