Functional Gene Networks Research Articles

DriverRWH: discovering cancer driver genes by random walk on a gene mutation hypergraph

BackgroundRecent advances in next-generation sequencing technologies have helped investigators generate massive amounts of cancer genomic data. A critical challenge in cancer genomics is identification of a few cancer driver genes whose mutations cause tumor growth. However, the majority of existing computational approaches underuse the co-occurrence mutation information of the individuals, which are deemed to be important in tumorigenesis and tumor progression, resulting in high rate of false positive.ResultsTo make full use of co-mutation information, we present a random walk algorithm referred to as DriverRWH on a weighted gene mutation hypergraph model, using somatic mutation data and molecular interaction network data to prioritize candidate driver genes. Applied to tumor samples of different cancer types from The Cancer Genome Atlas, DriverRWH shows significantly better performance than state-of-art prioritization methods in terms of the area under the curve scores and the cumulative number of known driver genes recovered in top-ranked candidate genes. Besides, DriverRWH discovers several potential drivers, which are enriched in cancer-related pathways. DriverRWH recovers approximately 50% known driver genes in the top 30 ranked candidate genes for more than half of the cancer types. In addition, DriverRWH is also highly robust to perturbations in the mutation data and gene functional network data.ConclusionDriverRWH is effective among various cancer types in prioritizes cancer driver genes and provides considerable improvement over other tools with a better balance of precision and sensitivity. It can be a useful tool for detecting potential driver genes and facilitate targeted cancer therapies.

Dietary Curcuma, a Powerful Epigenome Modulator in Breast Cancer: an In Silico Study

The inhibition of DNA methyltransferase-1 enzyme can strongly decrease the capacity of cells to enhance the tumour-genesis process. Members of the Estrogen-Related Receptors family regulate several elements of cellular metabolism. These are orphan nuclear receptors that regulate a wide range of functional gene networks involved in breast carcinogenesis and the regulation of associated methionine and folate cycles, providing a proven direct relationship to DNA methylation as a result. Moreover, dietary phytochemicals, such as Curcumin, can involve epigenetic modification, which may decrease the development of many types of cancer, especially breast cancer in women. We conducted this study to investigate the effect of Curcuma (PubChem ID: 969516) on the epigenetic modification and inhibition of the DNA methyltransferase-1 (PDB ID: 3PTA) activity and Estrogen-Related Receptors (PDB ID: 1XB7) using Molecular docking approach and computational tools that may inform whether the Curcuma could provide this protective anticancer effect or not. Interestingly, the DNA methyltrasferase1-Curcumin and Estrogen-Related Receptors-Curcumin complexes display a docking score of -6.9 and -7.1 kcal/mol, respectively. Furthermore, Curcumin displays hydrogen, Pi-Cation, Pi-Anion and Van der Waals bonds with active site residues of the targeted molecules. By targeting DNA methylation via the combined inhibition of estrogen-related receptors and DNMT1, our research opens up a new therapeutic path for breast cancer treatment.Keywords: curcumin, breast cancer, epigenetic, molecular docking, treatment.

Alternative splicing diversifies the skeletal muscle transcriptome during prolonged spaceflight

BackgroundAs the interest in manned spaceflight increases, so does the requirement to understand the transcriptomic mechanisms that underlay the detrimental physiological adaptations of skeletal muscle to microgravity. While microgravity-induced differential gene expression (DGE) has been extensively investigated, the contribution of differential alternative splicing (DAS) to the plasticity and functional status of the skeletal muscle transcriptome has not been studied in an animal model. Therefore, by evaluating both DGE and DAS across spaceflight, we set out to provide the first comprehensive characterization of the transcriptomic landscape of skeletal muscle during exposure to microgravity.MethodsRNA-sequencing, immunohistochemistry, and morphological analyses were conducted utilizing total RNA and tissue sections isolated from the gastrocnemius and quadriceps muscles of 30-week-old female BALB/c mice exposed to microgravity or ground control conditions for 9 weeks.ResultsIn response to microgravity, the skeletal muscle transcriptome was remodeled via both DGE and DAS. Importantly, while DGE showed variable gene network enrichment, DAS was enriched in structural and functional gene networks of skeletal muscle, resulting in the expression of alternatively spliced transcript isoforms that have been associated with the physiological changes to skeletal muscle in microgravity, including muscle atrophy and altered fiber type function. Finally, RNA-binding proteins, which are required for regulation of pre-mRNA splicing, were themselves differentially spliced but not differentially expressed, an upstream event that is speculated to account for the downstream splicing changes identified in target skeletal muscle genes.ConclusionsOur work serves as the first investigation of coordinate changes in DGE and DAS in large limb muscles across spaceflight. It opens up a new opportunity to understand (i) the molecular mechanisms by which splice variants of skeletal muscle genes regulate the physiological adaptations of skeletal muscle to microgravity and (ii) how small molecule splicing regulator therapies might thwart muscle atrophy and alterations to fiber type function during prolonged spaceflight.

In Silico Integration of Transcriptome and Interactome Predicts an ETP-ALL-Specific Transcriptional Footprint that Decodes its Developmental Propensity.

Early T precursor acute lymphoblastic leukemia (ETP-ALL) exhibits poor clinical outcomes and high relapse rates following conventional chemotherapeutic protocols. Extensive developmental flexibility of the multipotent ETP-ALL blasts with considerable intra-population heterogeneity in terms of immunophenotype and prognostic parameters might be a target for novel therapeutic interventions. Using a public gene expression dataset (GSE28703) from NCBI GEO DataSets with 12 ETP-ALL and 40 non-ETP-ALL samples, such heterogeneity was found to be reflected in their transcriptome as well. Hub genes were identified from the STRING-derived functional interaction network of genes showing differential expression between ETP-ALL and non-ETP-ALL as well as variable expression across ETP-ALL. Nine genes (KIT, HGF, NT5E, PROM1, CD33, ANPEP, CDH2, IL1B, and CXCL2) among the hubs were further validated as possible diagnostic ETP-ALL markers using another gene expression dataset (GSE78132) with 17 ETP-ALL and 27 non-ETP-ALL samples. Linear dimensionality reduction analysis with the expression levels of the hub genes in ETP-ALL revealed their divergent inclinations towards different hematopoietic lineages, proposing them as novel indicators of lineage specification in the incompletely differentiated ETP-ALL blasts. This further led to the formulation of a personalized lineage score calculation algorithm, which uncovered a considerable B-lineage-bias in a substantial fraction of ETP-ALL subjects from the GSE28703 and GSE78132 cohorts. In addition, STRING-derived physical interactome of the potential biomarkers displayed complete segregation of the B-lineage-skewed markers from other lineage-associated factors, highlighting their distinct functionality and possible druggability in ETP-ALL. A panel of these biomarkers might be useful in pinpointing the dominant lineage specification programmes in the ETP-ALL blasts on a personalized level, urging the development of novel lineage-directed precision therapies as well as repurposing of existing therapies against leukemia of different hematopoietic lineages; which might overcome the drawbacks of conventional chemotherapy.

A Bayesian framework to integrate multi-level genome-scale data for Autism risk gene prioritization

BackgroundAutism spectrum disorder (ASD) is a group of complex neurodevelopment disorders with a strong genetic basis. Large scale sequencing studies have identified over one hundred ASD risk genes. Nevertheless, the vast majority of ASD risk genes remain to be discovered, as it is estimated that more than 1000 genes are likely to be involved in ASD risk. Prioritization of risk genes is an effective strategy to increase the power of identifying novel risk genes in genetics studies of ASD. As ASD risk genes are likely to exhibit distinct properties from multiple angles, we reason that integrating multiple levels of genomic data is a powerful approach to pinpoint genuine ASD risk genes.ResultsWe present BNScore, a Bayesian model selection framework to probabilistically prioritize ASD risk genes through explicitly integrating evidence from sequencing-identified ASD genes, biological annotations, and gene functional network. We demonstrate the validity of our approach and its improved performance over existing methods by examining the resulting top candidate ASD risk genes against sets of high-confidence benchmark genes and large-scale ASD genome-wide association studies. We assess the tissue-, cell type- and development stage-specific expression properties of top prioritized genes, and find strong expression specificity in brain tissues, striatal medium spiny neurons, and fetal developmental stages.ConclusionsIn summary, we show that by integrating sequencing findings, functional annotation profiles, and gene-gene functional network, our proposed BNScore provides competitive performance compared to current state-of-the-art methods in prioritizing ASD genes. Our method offers a general and flexible strategy to risk gene prioritization that can potentially be applied to other complex traits as well.

Inflammation subtypes in psychosis and their relationships with genetic risk for psychiatric and cardiometabolic disorders

Cardiometabolic disorders have known inflammatory implications, and peripheral measures of inflammation and cardiometabolic disorders are common in persons with psychotic disorders. Inflammatory signatures are also related to neurobiological and behavioral changes in psychosis. Relationships between systemic inflammation and cardiometabolic genetic risk in persons with psychosis have not been examined. Thirteen peripheral inflammatory markers and genome-wide genotyping were assessed in 122 participants (n ​= ​86 psychosis, n ​= ​36 healthy controls) of European ancestry. Cluster analyses of inflammatory markers classified higher and lower inflammation subgroups. Single-trait genetic risk scores (GRS) were constructed for each participant using previously reported GWAS summary statistics for the following traits: schizophrenia, bipolar disorder, major depressive disorder, coronary artery disease, type-2 diabetes, low-density lipoprotein, high-density lipoprotein, triglycerides, and waist-to-hip ratio. Genetic correlations across traits were quantified. Principal component (PC) analysis of the cardiometabolic GRSs generated six PC loadings used in regression models to examine associations with inflammation markers. Functional module discovery explored biological mechanisms of the inflammation association of cardiometabolic GRS genes. A subgroup of 38% persons with psychotic disorders was characterized with higher inflammation status. These higher inflammation individuals had lower BACS scores (p ​= ​0.038) compared to those with lower inflammation. The first PC of the cardiometabolic GRS matrix was related to higher inflammation status in persons with psychotic disorders (OR ​= ​2.037, p ​= ​0.001). Two of eight modules within the functional interaction network of cardiometabolic GRS genes were enriched for immune processes. Cardiometabolic genetic risk may predispose some individuals with psychosis to elevated inflammation which adversely impacts cognition associated with illness.

Fine-mapping of intracranial aneurysm susceptibility based on a genome-wide association study

In addition to conventional genome-wide association studies (GWAS), a fine-mapping analysis is increasingly used to identify the genetic function of variants associated with disease susceptibilities. Here, we used a fine-mapping approach to evaluate candidate variants based on a previous GWAS involving patients with intracranial aneurysm (IA). A fine-mapping analysis was conducted based on the chromosomal data provided by a GWAS of 250 patients diagnosed with IA and 296 controls using posterior inclusion probability (PIP) and log10 transformed Bayes factor (log10BF). The narrow sense of heritability (h2) explained by each candidate variant was estimated. Subsequent gene expression and functional network analyses of candidate genes were used to calculate transcripts per million (TPM) values. Twenty single-nucleotide polymorphisms (SNPs) surpassed a genome-wide significance threshold for creditable evidence (log10BF > 6.1). Among them, four SNPs, rs75822236 (GBA; log10BF = 15.06), rs112859779 (TCF24; log10BF = 12.12), rs79134766 (OLFML2A; log10BF = 14.92), and rs371331393 (ARHGAP32; log10BF = 20.88) showed a completed PIP value in each chromosomal region, suggesting a higher probability of functional candidate variants associated with IA. On the contrary, these associations were not shown clearly under different replication sets. Our fine-mapping analysis suggested that four functional candidate variants of GBA, TCF24, OLFML2A, and ARHGAP32 were linked to IA susceptibility and pathogenesis. However, this approach could not completely replace replication sets based on large-scale data. Thus, caution is required when interpreting results of fine-mapping analysis.

AlzCode: a platform for multiview analysis of genes related to Alzheimer's disease.

Alzheimer's disease (AD) is a complex brain disorder with risk genes incompletely identified. The candidate genes are dominantly obtained by computational approaches. In order to obtain biological insights of candidate genes or screen genes for experimental testing, it is essential to assess their relevance to AD. A platform that integrates different types of omics data and approaches would facilitate the analysis of candidate genes and is in great need. We report AlzCode, a platform for multiview analysis of genes related to AD. First, this platform integrates a rich collection of functional genomic data, including expression data of AD samples (gene expression, single-cell RNA-seq data and protein expression), AD-specific biological networks (co-expression networks and functional gene networks), neuropathological and clinical traits (CERAD score, Braak staging score, Clinical Dementia Rating, cognitive function and clinical severity) and general data such as protein-protein interaction, regulatory networks, sequence similarity and miRNA-target interactions. These data provide basis for analyzing genes from different views. Second, the platform integrates multiple approaches designed for the various types of data. We implement functions to analyze both individual genes and gene sets. We also compare AlzCode with two existing platforms for AD analysis, which are Agora and AD Atlas. We pinpoint the features of each platform and highlight their differences. This platform would be valuable to the understanding of AD genetics and pathological mechanisms. AlzCode is freely available at: http://www.alzcode.xyz. Supplementary data are available at Bioinformatics online.

Reconstruction of human protein-coding gene functional association network based on machine learning.

Networks consisting of molecular interactions are intrinsically dynamical systems of an organism. These interactions curated in molecular interaction databases are still not complete and contain false positives introduced by high-throughput screening experiments. In this study, we propose a framework to integrate interactions of functional associated protein-coding genes from 31 data sources to reconstruct a network with high coverage and quality. For each interaction, 369 features were constructed including properties of both the interaction and the involved genes. The training and validation sets were built on the pathway interactions as positives and the potential negative instances resulting from our proposed semi-supervised strategy. Random forest classification method was then applied to train and predict multiple times to give a score for each interaction. After setting a threshold estimated by a Binomial distribution, a Human protein-coding Gene Functional Association Network (HuGFAN) was reconstructed with 20 383 genes and 1185 429 high confidence interactions. Then, HuGFAN was compared with other networks from data sources with respect to network properties, suggesting that HuGFAN is more function and pathway related. Finally, HuGFAN was applied to identify cancer driver through two famous network-based methods (DriverNet and HotNet2) to show its outstanding performance compared with other networks. HuGFAN and other supplementary files are freely available at https://github.com/xthuang226/HuGFAN.

A novel graph-based k-partitioning approach improves the detection of gene-gene correlations by single-cell RNA sequencing

BackgroundGene expression is regulated by transcription factors, cofactors, and epigenetic mechanisms. Coexpressed genes indicate similar functional categories and gene networks. Detecting gene-gene coexpression is important for understanding the underlying mechanisms of cellular function and human diseases. A common practice of identifying coexpressed genes is to test the correlation of expression in a set of genes. In single-cell RNA-seq data, an important challenge is the abundance of zero values, so-called “dropout”, which results in biased estimation of gene-gene correlations for downstream analyses. In recent years, efforts have been made to recover coexpressed genes in scRNA-seq data. Here, our goal is to detect coexpressed gene pairs to reduce the “dropout” effect in scRNA-seq data using a novel graph-based k-partitioning method by merging transcriptomically similar cells.ResultsWe observed that the number of zero values was reduced among the merged transcriptomically similar cell clusters. Motivated by this observation, we leveraged a graph-based algorithm and develop an R package, scCorr, to recover the missing gene-gene correlation in scRNA-seq data that enables the reliable acquisition of cluster-based gene-gene correlations in three independent scRNA-seq datasets. The graphically partitioned cell clusters did not change the local cell community. For example, in scRNA-seq data from peripheral blood mononuclear cells (PBMCs), the gene-gene correlation estimated by scCorr outperformed the correlation estimated by the nonclustering method. Among 85 correlated gene pairs in a set of 100 clusters, scCorr detected 71 gene pairs, while the nonclustering method detected only 4 pairs of a dataset from PBMCs. The performance of scCorr was comparable to those of three previously published methods. As an example of downstream analysis using scCorr, we show that scCorr accurately identified a known cell type (i.e., CD4+ T cells) in PBMCs with a receiver operating characteristic area under the curve of 0.96.ConclusionsOur results demonstrate that scCorr is a robust and reliable graph-based method for identifying correlated gene pairs, which is fundamental to network construction, gene-gene interaction, and cellular omic analyses. scCorr can be quickly and easily implemented to minimize zero values in scRNA-seq analysis and is freely available at https://github.com/CBIIT-CGBB/scCorr.

Reprogramming CRISPR-Mediated RNA Interference for Silencing of Essential Genes in Sulfolobales.

The manipulation of gene expression levels in vivo is often key to elucidating gene function and regulatory network interactions, especially when it comes to the investigation of essential genes that cannot be deleted from the model organism's genome. Several techniques have been developed for prokaryotes that allow to interfere with transcription initiation of specific genes by blocking or modifying promoter regions. However, a tool functionally similar to RNAi used in eukaryotes to efficiently degrade mRNA posttranscriptionally did not exist until recently. Type III CRISPR-Cas systems use small RNAs (crRNAs) that guide effector complexes (encoded by cas genes) which act as site-specific RNA endonuclease and can thus be harnessed for targeted posttranscriptional gene silencing. Guide RNAs complementary to the desired target mRNA that, in addition, exhibit complementarity to repeat sequences found in the CRISPR arrays, effectively suppress unspecific DNA and RNA activities of the CRISPR-Cas complexes. Here we describe the use of endogenous type III CRISPR-Cas systems in two model organisms of Crenarchaeota, Saccharolobus solfataricus and Sulfolobus acidocaldarius.

Immune gene expression and functional networks in distinct lupus nephritis classes

ObjectiveTo explore the utility of the NanoString platform in elucidating kidney immune transcripts for class III, IV and V lupus nephritis (LN) using a retrospective cohort of formalin-fixed paraffin-embedded (FFPE)...

Experimental Verification of Inferred Regulatory Interactions of EARLY FLOWERING 3 ( GmELF3-1 ) in Glycine max.

Understanding the roles of evening complex (EC) genes in the circadian clock of plants can inform how diurnal transcriptional loops in the clock gene network function to regulate key physiological and developmental events, including flowering transition. Gene regulatory interactions among soybean's circadian clock and flowering genes were inferred using time-series RNA-seq data and the network inference algorithmic package CausNet. In this study, we seek to clarify the inferred regulatory interactions of the EC gene GmELF3-1. A gene expression analysis using soybean protoplasts as a transient model indicated regulatory roles of GmELF3-1 in expression of selected flowering genes.

Regulation of Swine Growth by Backfat Tissue during Growing and Finishing Stages

Simple SummarySwine have a large influence on livestock animals. In particular, Korean native pigs (KNPs) have unique traits in their body composition including lipids and proteins. In this study, we performed RNA-sequencing analysis to identify porcine transcriptomic changes during growing and finishing stages in the backfat tissue of KNP and Yorkshire pig crossbreeds. Enrichment analysis revealed that differentially expressed genes (DEGs) were significantly influenced by lipid metabolism and hormonal changes. Network analysis showed that the LEP and ACTC1 genes were connected with significant terminologies which resulted from up- and down-regulated DEGs. The results of our analysis indicate that backfat tissue could regulate swine biology during stages of growth. Consequently, our analysis provided comprehensive understanding for transcriptomic changes during growth periods.Recently, interest in the function of pig backfat (BF) has increased in the field of livestock animals, and many transcriptome-based studies using commercial pig breeds have been conducted. However, there is a lack of comprehensive studies regarding the biological mechanisms of Korean native pigs (KNPs) and Yorkshire pig crossbreeds. In this study, therefore, BF samples of F1 crossbreeds of KNPs and Yorkshire pigs were investigated to identify differentially expressed genes (DEGs) and their related terms using RNA-sequencing analysis. DEG analysis identified 611 DEGs, of which 182 were up-regulated and 429 were down-regulated. Lipid metabolism was identified in the up-regulated genes, whereas growth and maturation-related terminologies were identified in the down-regulated genes. LEP and ACTC1 were identified as highly connected core genes during functional gene network analysis. Fat tissue was observed to affect lipid metabolism and organ development due to hormonal changes driven by transcriptional alteration. This study provides a comprehensive understanding of BF contribution to crossbreeds of KNPs and Yorkshire pigs during growth periods.

Identification of Novel Genomic Variations in Susceptibility to Nonsyndromic Cleft Lip and Palate Patients

Background: Nonsyndromic cleft lip with or without palate (NSCL/P) is a multifactorial and common birth malformation caused by genetic and environmental factors, as well as by teratogens. Genome-wide association studies found genetic variations with modulatory effects of NSCL/P formation in Chinese and Iranian populations. We aimed to identify the susceptibility of single-nucleotide polymorphisms (SNPs) to nonsyndromic cleft lip with or without palate in the Indian population. Material and Methods: The present study was conducted on NSCL/P cases and controls. Genomic DNA was extracted from peripheral blood and Axiom- Precision Medicine Research Array (PMRA) was performed. The Axiom-PMRA covers 902,527 markers and several thousand novel risk variants. Quality control-passed samples were included for candidate genetic variation identification, gene functional enrichment, and pathway and network analysis. Results: The genome-wide association study identified fourteen novel candidate gene SNPs that showed the most significant association with the risk of NSCL/P, and eight were predicted to have regulatory sequences. Conclusion: The GWAS study showed novel candidate genetic variations in NSCL/P formations. These findings contribute to the understanding of genetic predisposition to nonsyndromic cleft lip with or without palate.

TissueNexus: a database of human tissue functional gene networks built with a large compendium of curated RNA-seq data.

Mapping gene interactions within tissues/cell types plays a crucial role in understanding the genetic basis of human physiology and disease. Tissue functional gene networks (FGNs) are essential models for mapping complex gene interactions. We present TissueNexus, a database of 49 human tissue/cell line FGNs constructed by integrating heterogeneous genomic data. We adopted an advanced machine learning approach for data integration because Bayesian classifiers, which is the main approach used for constructing existing tissue gene networks, cannot capture the interaction and nonlinearity of genomic features well. A total of 1,341 RNA-seq datasets containing 52,087 samples were integrated for all of these networks. Because the tissue label for RNA-seq data may be annotated with different names or be missing, we performed intensive hand-curation to improve quality. We further developed a user-friendly database for network search, visualization, and functional analysis. We illustrate the application of TissueNexus in prioritizing disease genes. The database is publicly available at https://www.diseaselinks.com/TissueNexus/.

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.

Identification of New Tumor-Related Gene Mutations in Chinese Gastrointestinal Stromal Tumors.

Gastrointestinal stromal tumors (GISTs) are the most common mesenchymal tumors of the gastrointestinal tract. As the main GIST drivers, gain-of-function mutations in KIT or PDGFRA are closely associated with not only tumor development and progression but also therapeutic response. In addition to the status of KIT and PDGFRA, little is known about other potential GIST-related genes. In this study, we identified the mutation profiles in 49 KIT-mutated GIST tumors using the whole exome sequencing (WES) method. Furthermore, some representative mutations were further validated in an independent GIST cohort using the SNaPshot SNP assay. We identified extensive and diverse mutations of KIT in GIST, including many undescribed variants. In addition, we revealed some new tumor-related gene mutations with unknown pathogenicity. By enrichment analyses of gene function and protein-protein interaction network construction, we showed that these genes were enriched in several important cancer- or metabolism-related signaling pathways, including PI3K-AKT,RTK-RAS, Notch, Wnt, Hippo, mTOR, AMPK, and insulin signaling. In particular, DNA repair-related genes, including MLH1, MSH6, BRCA1, BRCA2, and POLE, are frequently mutated in GISTs, suggesting that immune checkpoint blockade may have promising clinical applications for these GIST subpopulations. In conclusion, in addition to extensive and diverse mutations of KIT, some genes related to DNA-repair and cell metabolism may play important roles in the development, progression and therapeutic response of GIST.

Identifying essential genes across eukaryotes by machine learning.

Identifying essential genes on a genome scale is resource intensive and has been performed for only a few eukaryotes. For less studied organisms essentiality might be predicted by gene homology. However, this approach cannot be applied to non-conserved genes. Additionally, divergent essentiality information is obtained from studying single cells or whole, multi-cellular organisms, and particularly when derived from human cell line screens and human population studies. We employed machine learning across six model eukaryotes and 60 381 genes, using 41 635 features derived from the sequence, gene function information and network topology. Within a leave-one-organism-out cross-validation, the classifiers showed high generalizability with an average accuracy close to 80% in the left-out species. As a case study, we applied the method to Tribolium castaneum and Bombyx mori and validated predictions experimentally yielding similar performances. Finally, using the classifier based on the studied model organisms enabled linking the essentiality information of human cell line screens and population studies.

Spatial transcriptional mapping of the human nephrogenic program.

Congenital abnormalities of the kidney and urinary tract are among the most common birth defects, affecting 3% of newborns. The human kidney forms around a million nephrons from a pool of nephron progenitors over a 30-week period of development. To establish a framework for human nephrogenesis, we spatially resolved a stereotypical process by which equipotent nephron progenitors generate a nephron anlage, then applied data-driven approaches to construct three-dimensional protein maps on anatomical models of the nephrogenic program. Single-cell RNA sequencing identified progenitor states, which were spatially mapped to the nephron anatomy, enabling the generation of functional gene networks predicting interactions within and between nephron cell types. Network mining identified known developmental disease genes and predicted targets of interest. The spatially resolved nephrogenic program made available through the Human Nephrogenesis Atlas (https://sckidney.flatironinstitute.org/) will facilitate an understanding of kidney development and disease and enhance efforts to generate new kidney structures.