HumanNet v3: an improved database of human gene networks for disease research.

Network medicine has proven useful for dissecting genetic organization of complex human diseases. We have previously published HumanNet, an integrated network of human genes for disease studies. Since the release of the last version of HumanNet, many large-scale protein-protein interaction datasets have accumulated in public depositories. Additionally, the numbers of research papers and functional annotations for gene-phenotype associations have increased significantly. Therefore, updating HumanNet is a timely task for further improvement of network-based research into diseases. Here, we present HumanNet v3 (https://www.inetbio.org/humannet/, covering 99.8% of human protein coding genes) constructed by means of the expanded data with improved network inference algorithms. HumanNet v3 supports a three-tier model: HumanNet-PI (a protein-protein physical interaction network), HumanNet-FN (a functional gene network), and HumanNet-XC (a functional network extended by co-citation). Users can select a suitable tier of HumanNet for their study purpose. We showed that on disease gene predictions, HumanNet v3 outperforms both the previous HumanNet version and other integrated human gene networks. Furthermore, we demonstrated that HumanNet provides a feasible approach for selecting host genes likely to be associated with COVID-19.

Pore-Scale Investigation of Dynamic Immiscible Displacement in Layered Media using Synchrotron X-ray Microtomography.

Understanding the dynamics of immiscible fluid in a porous media is critical in many chemical and environmental engineering processes. However, the geological heterogeneity effect on multiphase flow behavior remains unclear. Here, the dynamics of immiscible fluid displacement and entrapment were experimentally demonstrated at pore-level using time-lapse synchrotron X-ray microtomography. A drainage-imbibition experiment was designed using an unconsolidated layered sand pack that comprised coarse sand and fine sand zones. There were significant differences between the two zones, with regard to the temporal variations in fluid saturation and morphological evolution of nonwetting fluid (oil) during imbibition. Highly connected oil clusters in the coarse zone broke up into many small fragments, whereas the cluster in the fine zone remained connected while spanning multiple pores. To further understand the impacts of pore size and connectivity on multiphase fluid dynamics, a new approach that tracks the temporal variation of immiscible fluid in individual pores was conducted. The surface area at the oil-water interface increased during imbibition, which is expected to facilitate mass transfer and surface interactions. Understanding immiscible fluid displacement in layered porous media at the pore-level could lead to more effective environmental remediation.

HumanNet v2: human gene networks for disease research.

Human gene networks have proven useful in many aspects of disease research, with numerous network-based strategies developed for generating hypotheses about gene-disease-drug associations. The ability to predict and organize genes most relevant to a specific disease has proven especially important. We previously developed a human functional gene network, HumanNet, by integrating diverse types of omics data using Bayesian statistics framework and demonstrated its ability to retrieve disease genes. Here, we present HumanNet v2 (http://www.inetbio.org/humannet), a database of human gene networks, which was updated by incorporating new data types, extending data sources and improving network inference algorithms. HumanNet now comprises a hierarchy of human gene networks, allowing for more flexible incorporation of network information into studies. HumanNet performs well in ranking disease-linked gene sets with minimal literature-dependent biases. We observe that incorporating model organisms' protein-protein interactions does not markedly improve disease gene predictions, suggesting that many of the disease gene associations are now captured directly in human-derived datasets. With an improved interactive user interface for disease network analysis, we expect HumanNet will be a useful resource for network medicine.

TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions.

Transcription factors (TFs) are major trans-acting factors in transcriptional regulation. Therefore, elucidating TF-target interactions is a key step toward understanding the regulatory circuitry underlying complex traits such as human diseases. We previously published a reference TF-target interaction database for humans-TRRUST (Transcriptional Regulatory Relationships Unraveled by Sentence-based Text mining)-which was constructed using sentence-based text mining, followed by manual curation. Here, we present TRRUST v2 (www.grnpedia.org/trrust) with a significant improvement from the previous version, including a significantly increased size of the database consisting of 8444 regulatory interactions for 800 TFs in humans. More importantly, TRRUST v2 also contains a database for TF-target interactions in mice, including 6552 TF-target interactions for 828 mouse TFs. TRRUST v2 is also substantially more comprehensive and less biased than other TF-target interaction databases. We also improved the web interface, which now enables prioritization of key TFs for a physiological condition depicted by a set of user-input transcriptional responsive genes. With the significant expansion in the database size and inclusion of the new web tool for TF prioritization, we believe that TRRUST v2 will be a versatile database for the study of the transcriptional regulation involved in human diseases.

COEXPEDIA: exploring biomedical hypotheses via co-expressions associated with medical subject headings (MeSH).

The use of high-throughput array and sequencing technologies has produced unprecedented amounts of gene expression data in central public depositories, including the Gene Expression Omnibus (GEO). The immense amount of expression data in GEO provides both vast research opportunities and data analysis challenges. Co-expression analysis of high-dimensional expression data has proven effective for the study of gene functions, and several co-expression databases have been developed. Here, we present a new co-expression database, COEXPEDIA (www.coexpedia.org), which is distinctive from other co-expression databases in three aspects: (i) it contains only co-functional co-expressions that passed a rigorous statistical assessment for functional association, (ii) the co-expressions were inferred from individual studies, each of which was designed to investigate gene functions with respect to a particular biomedical context such as a disease and (iii) the co-expressions are associated with medical subject headings (MeSH) that provide biomedical information for anatomical, disease, and chemical relevance. COEXPEDIA currently contains approximately eight million co-expressions inferred from 384 and 248 GEO series for humans and mice, respectively. We describe how these MeSH-associated co-expressions enable the identification of diseases and drugs previously unknown to be related to a gene or a gene group of interest.

GWAB: a web server for the network-based boosting of human genome-wide association data.

During the last decade, genome-wide association studies (GWAS) have represented a major approach to dissect complex human genetic diseases. Due in part to limited statistical power, most studies identify only small numbers of candidate genes that pass the conventional significance thresholds (e.g. P ≤ 5 × 10-8). This limitation can be partly overcome by increasing the sample size, but this comes at a higher cost. Alternatively, weak association signals can be boosted by incorporating independent data. Previously, we demonstrated the feasibility of boosting GWAS disease associations using gene networks. Here, we present a web server, GWAB (www.inetbio.org/gwab), for the network-based boosting of human GWAS data. Using GWAS summary statistics (P-values) for SNPs along with reference genes for a disease of interest, GWAB reprioritizes candidate disease genes by integrating the GWAS and network data. We found that GWAB could more effectively retrieve disease-associated reference genes than GWAS could alone. As an example, we describe GWAB-boosted candidate genes for coronary artery disease and supporting data in the literature. These results highlight the inherent value in sub-threshold GWAS associations, which are often not publicly released. GWAB offers a feasible general approach to boost such associations for human disease genetics.

Function-driven discovery of disease genes in zebrafish using an integrated genomics big data resource.

Whole exome sequencing (WES) accelerates disease gene discovery using rare genetic variants, but further statistical and functional evidence is required to avoid false-discovery. To complement variant-driven disease gene discovery, here we present function-driven disease gene discovery in zebrafish (Danio rerio), a promising human disease model owing to its high anatomical and genomic similarity to humans. To facilitate zebrafish-based function-driven disease gene discovery, we developed a genome-scale co-functional network of zebrafish genes, DanioNet (www.inetbio.org/danionet), which was constructed by Bayesian integration of genomics big data. Rigorous statistical assessment confirmed the high prediction capacity of DanioNet for a wide variety of human diseases. To demonstrate the feasibility of the function-driven disease gene discovery using DanioNet, we predicted genes for ciliopathies and performed experimental validation for eight candidate genes. We also validated the existence of heterozygous rare variants in the candidate genes of individuals with ciliopathies yet not in controls derived from the UK10K consortium, suggesting that these variants are potentially involved in enhancing the risk of ciliopathies. These results showed that an integrated genomics big data for a model animal of diseases can expand our opportunity for harnessing WES data in disease gene discovery.

FlyNet: a versatile network prioritization server for the Drosophila community.

Drosophila melanogaster (fruit fly) has been a popular model organism in animal genetics due to the high accessibility of reverse-genetics tools. In addition, the close relationship between the Drosophila and human genomes rationalizes the use of Drosophila as an invertebrate model for human neurobiology and disease research. A platform technology for predicting candidate genes or functions would further enhance the usefulness of this long-established model organism for gene-to-phenotype mapping. Recently, the power of network prioritization for gene-to-phenotype mapping has been demonstrated in many organisms. Here we present a network prioritization server dedicated to Drosophila that covers ∼95% of the coding genome. This server, dubbed FlyNet, has several distinctive features, including (i) prioritization for both genes and functions; (ii) two complementary network algorithms: direct neighborhood and network diffusion; (iii) spatiotemporal-specific networks as an additional prioritization strategy for traits associated with a specific developmental stage or tissue and (iv) prioritization for human disease genes. FlyNet is expected to serve as a versatile hypothesis-generation platform for genes and functions in the study of basic animal genetics, developmental biology and human disease. FlyNet is available for free at http://www.inetbio.org/flynet.

RiceNet v2: an improved network prioritization server for rice genes.

Rice is the most important staple food crop and a model grass for studies of bioenergy crops. We previously published a genome-scale functional network server called RiceNet, constructed by integrating diverse genomics data and demonstrated the use of the network in genetic dissection of rice biotic stress responses and its usefulness for other grass species. Since the initial construction of the network, there has been a significant increase in the amount of publicly available rice genomics data. Here, we present an updated network prioritization server for Oryza sativa ssp. japonica, RiceNet v2 (http://www.inetbio.org/ricenet), which provides a network of 25 765 genes (70.1% of the coding genome) and 1 775 000 co-functional links. Ricenet v2 also provides two complementary methods for network prioritization based on: (i) network direct neighborhood and (ii) context-associated hubs. RiceNet v2 can use genes of the related subspecies O. sativa ssp. indica and the reference plant Arabidopsis for versatility in generating hypotheses. We demonstrate that RiceNet v2 effectively identifies candidate genes involved in rice root/shoot development and defense responses, demonstrating its usefulness for the grass research community.

Social context modulates multibrain broadband dynamics and functional brain-to-brain coupling in the group of mice.

Although mice are social, multiple animals' neural activities are rarely explored. To characterise the neural activities during multi-brain interaction, we simultaneously recorded local field potentials (LFP) in the prefrontal cortex of four mice. The social context and locomotive states predominately modulated the entire LFP structure. The power of lower frequency bands-delta to alpha-were correlated with each other and anti-correlated with gamma power. The high-to-low-power ratio (HLR) provided a useful measure to understand LFP changes along the change of behavioural and locomotive states. The HLR during huddled conditions was lower than that during non-huddled conditions, dividing the social context into two. Multi-brain analyses of HLR indicated that the mice in the group displayed high cross-correlation. The mice in the group often showed unilateral precedence of HLR by Granger causality analysis, possibly comprising a hierarchical social structure. Overall, this study shows the importance of the social environment in brain dynamics and emphasises the simultaneous multi-brain recordings in social neuroscience.

Effects of natural non-volcanic CO2 leakage on soil microbial community composition and diversity.

Geological carbon capture and storage (CCS) can reduce anthropogenic CO2 emissions, but questions exist about impacts at the surface if CO2 leaks from deep storage reservoirs. To examine potential impacts on soils, previous studies have investigated the geochemistry and microbiology of volcanic soils hosting high fluxes of CO2 rich gas. This study builds on those previous investigations by considering impacts of CO2 leakage at a non-volcanic site, where deep geogenic CO2 leaks from a cracked well casing. At the site, we collected 26 soil cores adjacent to soil gas monitoring wells. Based on measured CO2 fluxes, the soil samples fall into two groups 1) high CO2 (flux = 304.6 ± 272.1 g m-2 d-1, conc. = 29.1 ± 34 %) and 2) low CO2 (flux = 15.8 ± 6.1 g m-2 d-1, conc. = 0.8 ± 0.9 %). Soil pH was significantly lower (p < 0.05) in high flux group samples (4.6 ± 0.3) than the low flux ones (5.3 ± 0.7). Beta diversity calculations using 16S rRNA gene sequences and redundancy analysis (RDA) revealed clear clustering of microbial communities relative to CO2 flux and significant correlations of community composition with pH and organic carbon content. In the high flux soils, abundant microbial groups included Acidobacteriota, Ktedonobacteria, and SC-I-84 in the phylum Proteobacteria, as well as Nitrososphaeria, a genus of ammonia oxidizing archaea. Compared to volcanic sites described previously, our non-volcanic site had slight differences in soil geochemical properties and gradual shifts in community compositions between CO2 hotspots and background locations. Moreover, the elevated abundance of SC-I-84 has not been reported in studies of volcanic sites. This study improves our ability to predict potential environmental impacts of geological CCS by expanding the range of conditions over which existing CO2 leakage has been observed.

HiFi metagenomic sequencing enables assembly of accurate and complete genomes from human gut microbiota.

Advances in metagenomic assembly have led to the discovery of genomes belonging to uncultured microorganisms. Metagenome-assembled genomes (MAGs) often suffer from fragmentation and chimerism. Recently, 20 complete MAGs (cMAGs) have been assembled from Oxford Nanopore long-read sequencing of 13 human fecal samples, but with low nucleotide accuracy. Here, we report 102 cMAGs obtained by Pacific Biosciences (PacBio) high-accuracy long-read (HiFi) metagenomic sequencing of five human fecal samples, whose initial circular contigs were selected for complete prokaryotic genomes using our bioinformatics workflow. Nucleotide accuracy of the final cMAGs was as high as that of Illumina sequencing. The cMAGs could exceed 6 Mbp and included complete genomes of diverse taxa, including entirely uncultured RF39 and TANB77 orders. Moreover, cMAGs revealed that regions hard to assemble by short-read sequencing comprised mostly genomic islands and rRNAs. HiFi metagenomic sequencing will facilitate cataloging accurate and complete genomes from complex microbial communities, including uncultured species.

Human reference gut microbiome catalog including newly assembled genomes from under-represented Asian metagenomes.

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.

Network Integrative Genomic and Transcriptomic Analysis of Carbapenem-Resistant Klebsiella pneumoniae Strains Identifies Genes for Antibiotic Resistance and Virulence.

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.

Network-based genetic investigation of virulence-associated phenotypes in methicillin-resistant Staphylococcus aureus.

Staphylococcus aureus is a gram-positive bacterium that causes a wide range of infections. Recently, the spread of methicillin-resistant S. aureus (MRSA) strains has seriously reduced antibiotic treatment options. Anti-virulence strategies, the objective of which is to target the virulence instead of the viability of the pathogen, have become widely accepted as a means of avoiding the emergence of new antibiotic-resistant strains. To increase the number of anti-virulence therapeutic options, it is necessary to identify as many novel virulence-associated genes as possible in MRSA. Co-functional networks have proved useful for mapping gene-to-phenotype associations in various organisms. Herein, we present StaphNet (www.inetbio.org/staphnet), a genome-scale co-functional network for an MRSA strain, S. aureus subsp. USA300_FPR3757. StaphNet, which was constructed by the integration of seven distinct types of genomics data within a Bayesian statistics framework, covers approximately 94% of the coding genome with a high degree of accuracy. We implemented a companion web server for network-based gene prioritization of the phenotypes of 31 different S. aureus strains. We demonstrated that StaphNet can effectively identify genes for virulence-associated phenotypes in MRSA. These results suggest that StaphNet can facilitate target discovery for the development of anti-virulence drugs to treat MRSA infection.

Network-assisted investigation of virulence and antibiotic-resistance systems in Pseudomonas aeruginosa.

Pseudomonas aeruginosa is a Gram-negative bacterium of clinical significance. Although the genome of PAO1, a prototype strain of P. aeruginosa, has been extensively studied, approximately one-third of the functional genome remains unknown. With the emergence of antibiotic-resistant strains of P. aeruginosa, there is an urgent need to develop novel antibiotic and anti-virulence strategies, which may be facilitated by an approach that explores P. aeruginosa gene function in systems-level models. Here, we present a genome-wide functional network of P. aeruginosa genes, PseudomonasNet, which covers 98% of the coding genome, and a companion web server to generate functional hypotheses using various network-search algorithms. We demonstrate that PseudomonasNet-assisted predictions can effectively identify novel genes involved in virulence and antibiotic resistance. Moreover, an antibiotic-resistance network based on PseudomonasNet reveals that P. aeruginosa has common modular genetic organisations that confer increased or decreased resistance to diverse antibiotics, which accounts for the pervasiveness of cross-resistance across multiple drugs. The same network also suggests that P. aeruginosa has developed mechanism of trade-off in resistance across drugs by altering genetic interactions. Taken together, these results clearly demonstrate the usefulness of a genome-scale functional network to investigate pathogenic systems in P. aeruginosa.

A single gene of a commensal microbe affects host susceptibility to enteric infection.

Indigenous microbes inside the host intestine maintain a complex self-regulating community. The mechanisms by which gut microbes interact with intestinal pathogens remain largely unknown. Here we identify a commensal Escherichia coli strain whose expansion predisposes mice to infection by Vibrio cholerae, a human pathogen. We refer to this strain as 'atypical' E. coli (atEc) because of its inability to ferment lactose. The atEc strain is resistant to reactive oxygen species (ROS) and proliferates extensively in antibiotic-treated adult mice. V. cholerae infection is more severe in neonatal mice transplanted with atEc compared with those transplanted with a typical E. coli strain. Intestinal ROS levels are decreased in atEc-transplanted mice, favouring proliferation of ROS-sensitive V. cholerae. An atEc mutant defective in ROS degradation fails to facilitate V. cholerae infection when transplanted, suggesting that host infection susceptibility can be regulated by a single gene product of one particular commensal species.

TRRUST: a reference database of human transcriptional regulatory interactions.

The reconstruction of transcriptional regulatory networks (TRNs) is a long-standing challenge in human genetics. Numerous computational methods have been developed to infer regulatory interactions between human transcriptional factors (TFs) and target genes from high-throughput data, and their performance evaluation requires gold-standard interactions. Here we present a database of literature-curated human TF-target interactions, TRRUST (transcriptional regulatory relationships unravelled by sentence-based text-mining, http://www.grnpedia.org/trrust), which currently contains 8,015 interactions between 748 TF genes and 1,975 non-TF genes. A sentence-based text-mining approach was employed for efficient manual curation of regulatory interactions from approximately 20 million Medline abstracts. To the best of our knowledge, TRRUST is the largest publicly available database of literature-curated human TF-target interactions to date. TRRUST also has several useful features: i) information about the mode-of-regulation; ii) tests for target modularity of a query TF; iii) tests for TF cooperativity of a query target; iv) inferences about cooperating TFs of a query TF; and v) prioritizing associated pathways and diseases with a query TF. We observed high enrichment of TF-target pairs in TRRUST for top-scored interactions inferred from high-throughput data, which suggests that TRRUST provides a reliable benchmark for the computational reconstruction of human TRNs.