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Accurately identifying genes responsible for specific functions is a cornerstone of biological research, but current methods are often limited to single-species analyses. Here, we present a novel method, called Genomic and Phenotype-based machine learning for Gene Identification (GPGI), that leverages large-scale, cross-species genomic and phenotypic data for functional gene discovery. Using bacterial rod-shape determination as a case study, we demonstrate GPGI’s ability to rapidly identify key genes. Our approach uses machine learning to predict bacterial shape from protein structural domain profiles, identifying influential domains whose corresponding genes are selected for experimental validation. Focused gene knockouts in Escherichia coli confirmed the critical roles of two genes, pal and mreB , in maintaining rod-shaped morphology. We further validated GPGI’s robustness by demonstrating its consistent performance even with reduced datasets. GPGI thus offers a rapid, accurate, and efficient way to identify multiple key genes associated with complex traits across diverse organisms.

Schizophrenia has a heritability of 60-80%1, much of which is attributable to common risk alleles. Here, in a two-stage genome-wide association study of up to 76,755 individuals with schizophrenia and 243,649 control individuals, we report common variant associations at 287 distinct genomic loci. Associations were concentrated in genes that are expressed in excitatory and inhibitory neurons of the central nervous system, but not in other tissues or cell types. Using fine-mapping and functional genomic data, we identify 120 genes (106 protein-coding) that are likely to underpin associations at some of these loci, including 16 genes with credible causal non-synonymous or untranslated region variation. We also implicate fundamental processes related to neuronal function, including synaptic organization, differentiation and transmission. Fine-mapped candidates were enriched for genes associated with rare disruptive coding variants in people with schizophrenia, including the glutamate receptor subunit GRIN2A and transcription factor SP4, and were also enriched for genes implicated by such variants in neurodevelopmental disorders. We identify biological processes relevant to schizophrenia pathophysiology; show convergence of common and rare variant associations in schizophrenia and neurodevelopmental disorders; and provide a resource of prioritized genes and variants to advance mechanistic studies.

The adzuki bean Vigna angularis (Wild.) is an important leguminous crop cultivated mainly for food purposes in Asian countries; it represents a source of carbohydrates, digestible proteins, minerals, and vitamins. Aquaporins (AQPs) are crucial membrane proteins involved in the transmembrane diffusion of water and small solutes in all living organisms, including plants. In this study, we used the whole genome sequence of the adzuki bean for in silico analysis to comprehensively identify 40 Vigna angularis aquaporin (VaAQP) genes and reveal how these plants react to drought stress. VaAQPs were compared with AQPs from other closely-related leguminous plants, and the results showed that mustard (Brassica rapa) (59), barrel medic (Medicago truncatula) (46), soybean (Glycine max) (66), and common bean (Phaseolus vulgaris L.) (41) had more AQP genes. Phylogenetic analysis revealed that forty VaAQPs belong to five subfamilies, with the VaPIPs (fifteen) subfamily the largest, followed by the VaNIPs (ten), VaTIPs (ten), VaSIPs (three), and VaXIPs (two) subfamilies. Furthermore, all AQP subcellular locations were found at the plasma membrane, and intron–exon analysis revealed a relationship between the intron number and gene expression, duplication, evolution, and diversity. Among the six motifs identified, motifs one, two, five, and six were prevalent in VaTIP, VaNIP, VaPIP, and VaXIP, while motifs one, three, and four were not observed in VaPIP1-3 and VaPIP1-4. Under drought stress, two of the VaAQPs (VaPIP2-1 and VaPIP2-5) showed significantly higher expression in the root tissue while the other two genes (VaPIP1-1 and VaPIP1-7) displayed variable expression in leaf tissue. This finding revealed that the selected VaAQPs might have unique molecular functions linked with the uptake of water under drought stress or in the exertion of osmoregulation to transport particular substrates rather than water to protect plants from drought. This study presents the first thorough investigation of VaAQPs in adzuki beans, and it reveals the transport mechanisms and related physiological processes that may be utilized for the development of drought-tolerant adzuki bean cultivars.

The full complement of homeobox transcription factor sequences, including genes and pseudogenes, was determined from the analysis of 10 complete genomes from flowering plants, moss, Selaginella, unicellular green algae, and red algae. Our exhaustive genome-wide searches resulted in the discovery in each class of a greater number of homeobox genes than previously reported. All homeobox genes can be unambiguously classified by sequence evolutionary analysis into 14 distinct classes also characterized by conserved intron–exon structure and by unique codomain architectures. We identified many new genes belonging to previously defined classes (HD-ZIP I to IV, BEL, KNOX, PLINC, WOX). Other newly identified genes allowed us to characterize PHD, DDT, NDX, and LD genes as members of four new evolutionary classes and to define two additional classes, which we named SAWADEE and PINTOX. Our comprehensive analysis allowed us to identify several newly characterized conserved motifs, including novel zinc finger motifs in SAWADEE and DDT. Members of the BEL and KNOX classes were found in Chlorobionta (green plants) and in Rhodophyta. We found representatives of the DDT, WOX, and PINTOX classes only in green plants, including unicellular green algae, moss, and vascular plants. All 14 homeobox gene classes were represented in flowering plants, Selaginella, and moss, suggesting that they had already differentiated in the last common ancestor of moss and vascular plants.

Mario Falchi, Philippe Froguel and colleagues report association of a multi-allelic copy number variant encompassing the salivary amylase gene AMY1 with body mass index and risk of obesity. Common multi-allelic copy number variants (CNVs) appear enriched for phenotypic associations compared to their biallelic counterparts 1 , 2 , 3 , 4 . Here we investigated the influence of gene dosage effects on adiposity through a CNV association study of gene expression levels in adipose tissue. We identified significant association of a multi-allelic CNV encompassing the salivary amylase gene ( AMY1 ) with body mass index (BMI) and obesity, and we replicated this finding in 6,200 subjects. Increased AMY1 copy number was positively associated with both amylase gene expression ( P = 2.31 × 10 −14 ) and serum enzyme levels ( P < 2.20 × 10 −16 ), whereas reduced AMY1 copy number was associated with increased BMI (change in BMI per estimated copy = −0.15 (0.02) kg/m 2 ; P = 6.93 × 10 −10 ) and obesity risk (odds ratio (OR) per estimated copy = 1.19, 95% confidence interval (CI) = 1.13–1.26; P = 1.46 × 10 −10 ). The OR value of 1.19 per copy of AMY1 translates into about an eightfold difference in risk of obesity between subjects in the top (copy number > 9) and bottom (copy number < 4) 10% of the copy number distribution. Our study provides a first genetic link between carbohydrate metabolism and BMI and demonstrates the power of integrated genomic approaches beyond genome-wide association studies.

Cellulose is the most abundant biopolymer on Earth and an important component of bacterial biofilms. Thongsomboon et al. used solid-state nuclear magnetic resonance spectroscopy to identify a naturally derived, chemically modified cellulose, phosphoethanolamine cellulose (see the Perspective by Galperin and Shalaeva). They went on to identify the genetic basis and molecular signaling involved in introducing this modification in bacteria, which regulates biofilm matrix architecture and function. This discovery has implications for understanding bacterial biofilms and for the generation of new cellulosic materials. Science , this issue p. 334 ; see also p. 276 Solid-state nuclear magnetic resonance spectroscopy identifies naturally produced, chemically modified cellulose crucial for bacterial biofilm architecture. Cellulose is a major contributor to the chemical and mechanical properties of plants and assumes structural roles in bacterial communities termed biofilms. We find that Escherichia coli produces chemically modified cellulose that is required for extracellular matrix assembly and biofilm architecture. Solid-state nuclear magnetic resonance spectroscopy of the intact and insoluble material elucidates the zwitterionic phosphoethanolamine modification that had evaded detection by conventional methods. Installation of the phosphoethanolamine group requires BcsG, a proposed phosphoethanolamine transferase, with biofilm-promoting cyclic diguanylate monophosphate input through a BcsE-BcsF-BcsG transmembrane signaling pathway. The bcsEFG operon is present in many bacteria, including Salmonella species, that also produce the modified cellulose. The discovery of phosphoethanolamine cellulose and the genetic and molecular basis for its production offers opportunities to modulate its production in bacteria and inspires efforts to biosynthetically engineer alternatively modified cellulosic materials.

Two distinct Polycomb complexes, PRC1 and PRC2, collaborate to maintain epigenetic repression of key developmental loci in embryonic stem cells (ESCs). PRC1 and PRC2 have histone modifying activities, catalyzing mono-ubiquitination of histone H2A (H2AK119u1) and trimethylation of H3 lysine 27 (H3K27me3), respectively. Compared to H3K27me3, localization and the role of H2AK119u1 are not fully understood in ESCs. Here we present genome-wide H2AK119u1 maps in ESCs and identify a group of genes at which H2AK119u1 is deposited in a Ring1-dependent manner. These genes are a distinctive subset of genes with H3K27me3 enrichment and are the central targets of Polycomb silencing that are required to maintain ESC identity. We further show that the H2A ubiquitination activity of PRC1 is dispensable for its target binding and its activity to compact chromatin at Hox loci, but is indispensable for efficient repression of target genes and thereby ESC maintenance. These data demonstrate that multiple effector mechanisms including H2A ubiquitination and chromatin compaction combine to mediate PRC1-dependent repression of genes that are crucial for the maintenance of ESC identity. Utilization of these diverse effector mechanisms might provide a means to maintain a repressive state that is robust yet highly responsive to developmental cues during ES cell self-renewal and differentiation.

Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation. Deep learning can predict spatial variations in gene expression from haematoxylin-and-eosin-stained histopathology images of patients with cancer.