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Exosomes and other small extracellular vesicles (sEVs) provide a unique mode of cell-to-cell communication in which microRNAs (miRNAs) produced and released from one cell are taken up by cells at a distance where they can enact changes in gene expression 1 – 3 . However, the mechanism by which miRNAs are sorted into exosomes/sEVs or retained in cells remains largely unknown. Here we demonstrate that miRNAs possess sorting sequences that determine their secretion in sEVs (EXOmotifs) or cellular retention (CELLmotifs) and that different cell types, including white and brown adipocytes, endothelium, liver and muscle, make preferential use of specific sorting sequences, thus defining the sEV miRNA profile of that cell type. Insertion or deletion of these CELLmotifs or EXOmotifs in a miRNA increases or decreases retention in the cell of production or secretion into exosomes/sEVs. Two RNA-binding proteins, Alyref and Fus, are involved in the export of miRNAs carrying one of the strongest EXOmotifs, CGGGAG. Increased miRNA delivery mediated by EXOmotifs leads to enhanced inhibition of target genes in distant cells. Thus, this miRNA code not only provides important insights that link circulating exosomal miRNAs to tissues of origin, but also provides an approach for improved targeting in RNA-mediated therapies. MicroRNAs encode sorting sequences that determine whether they are secreted in exosomal vesicles to regulate gene expression in distant cells or retained in cells that produced them, with different sequences used by individual cell types.

Tissue fibrosis and extracellular matrix (ECM) stiffening promote tumour progression. The mechanisms by which ECM regulates its contacting cells have been extensively studied. However, how stiffness influences intercellular communications in the microenvironment for tumour progression remains unknown. Here we report that stiff ECM stimulates the release of exosomes from cancer cells. We delineate a molecular pathway that links stiff ECM to activation of Akt, which in turn promotes GTP loading to Rab8 that drives exosome secretion. We further show that exosomes generated from cells grown on stiff ECM effectively promote tumour growth. Proteomic analysis revealed that the Notch signalling pathway is activated in cells treated with exosomes derived from tumour cells grown on stiff ECM, consistent with our gene expression analysis of liver tissues from patients. Our study reveals a molecular mechanism that regulates exosome secretion and provides insight into how mechanical properties of the ECM control the tumour microenvironment for tumour growth. Wu et al. report that a stiff extracellular matrix stimulates the release of exosomes from cancer cells under the control of Akt and Rab8. These exosomes in turn promote tumour growth.

Detailed knowledge of how diversity in the sequence of the human genome affects phenotypic diversity depends on a comprehensive and reliable characterization of both sequences and phenotypic variation. Over the past decade, insights into this relationship have been obtained from whole-exome sequencing or whole-genome sequencing of large cohorts with rich phenotypic data 1 , 2 . Here we describe the analysis of whole-genome sequencing of 150,119 individuals from the UK Biobank 3 . This constitutes a set of high-quality variants, including 585,040,410 single-nucleotide polymorphisms, representing 7.0% of all possible human single-nucleotide polymorphisms, and 58,707,036 indels. This large set of variants allows us to characterize selection based on sequence variation within a population through a depletion rank score of windows along the genome. Depletion rank analysis shows that coding exons represent a small fraction of regions in the genome subject to strong sequence conservation. We define three cohorts within the UK Biobank: a large British Irish cohort, a smaller African cohort and a South Asian cohort. A haplotype reference panel is provided that allows reliable imputation of most variants carried by three or more sequenced individuals. We identified 895,055 structural variants and 2,536,688 microsatellites, groups of variants typically excluded from large-scale whole-genome sequencing studies. Using this formidable new resource, we provide several examples of trait associations for rare variants with large effects not found previously through studies based on whole-exome sequencing and/or imputation. To measure selection on variants, whole-genome sequencing of approximately 150,000 individuals from the UK Biobank is used to rank sequence variants by their level of depletion.

Upland rice is a distinct ecotype that grows in aerobic environments and tolerates drought stress. However, the genetic basis of its drought resistance is unclear. Here, using an integrative approach combining a genome-wide association study with analyses of introgression lines and transcriptomic profiles, we identify a gene, DROUGHT1 ( DROT1 ), encoding a COBRA-like protein that confers drought resistance in rice. DROT1 is specifically expressed in vascular bundles and is directly repressed by ERF3 and activated by ERF71, both drought-responsive transcription factors. DROT1 improves drought resistance by adjusting cell wall structure by increasing cellulose content and maintaining cellulose crystallinity. A C-to-T single-nucleotide variation in the promoter increases DROT1 expression and drought resistance in upland rice. The potential elite haplotype of DROT1 in upland rice could originate in wild rice ( O. rufipogon ) and may be beneficial for breeding upland rice varieties. Genetic basis of the drought tolerance of upland rice is unclear. Here, the authors report the cloning of a COBRA-like protein encoding gene DROT1 and reveal that it is repressed by ERF3 and activated by ERF71 to help control the balance between growth and drought tolerance in upland rice.

The arrangement (syntax) of transcription factor (TF) binding motifs is an important part of the cis-regulatory code, yet remains elusive. We introduce a deep learning model, BPNet, that uses DNA sequence to predict base-resolution chromatin immunoprecipitation (ChIP)–nexus binding profiles of pluripotency TFs. We develop interpretation tools to learn predictive motif representations and identify soft syntax rules for cooperative TF binding interactions. Strikingly, Nanog preferentially binds with helical periodicity, and TFs often cooperate in a directional manner, which we validate using clustered regularly interspaced short palindromic repeat (CRISPR)-induced point mutations. Our model represents a powerful general approach to uncover the motifs and syntax of cis-regulatory sequences in genomics data. BPNet is an interpretable deep learning tool that predicts transcription-factor binding profiles from DNA sequence at base-pair resolution, enabling the identification of motifs and the regulatory syntax underlying transcription-factor binding.

Genome editing has emerged as a technology with a potential to revolutionize plant breeding. In this study, we report on generating, in less than ten months, Tomelo, a non-transgenic tomato variety resistant to the powdery mildew fungal pathogen using the CRISPR/Cas9 technology. We used whole-genome sequencing to show that Tomelo does not carry any foreign DNA sequences but only carries a deletion that is indistinguishable from naturally occurring mutations. We also present evidence for CRISPR/Cas9 being a highly precise tool, as we did not detect off-target mutations in Tomelo. Using our pipeline, mutations can be readily introduced into elite or locally adapted tomato varieties in less than a year with relatively minimal effort and investment.

CRISPR-Cas9 viability screens are increasingly performed at a genome-wide scale across large panels of cell lines to identify new therapeutic targets for precision cancer therapy. Integrating the datasets resulting from these studies is necessary to adequately represent the heterogeneity of human cancers and to assemble a comprehensive map of cancer genetic vulnerabilities. Here, we integrated the two largest public independent CRISPR-Cas9 screens performed to date (at the Broad and Sanger institutes) by assessing, comparing, and selecting methods for correcting biases due to heterogeneous single-guide RNA efficiency, gene-independent responses to CRISPR-Cas9 targeting originated from copy number alterations, and experimental batch effects. Our integrated datasets recapitulate findings from the individual datasets, provide greater statistical power to cancer- and subtype-specific analyses, unveil additional biomarkers of gene dependency, and improve the detection of common essential genes. We provide the largest integrated resources of CRISPR-Cas9 screens to date and the basis for harmonizing existing and future functional genetics datasets. The integration of independent pan-cancer CRISPR-Cas9 datasets allows better representation of genomic heterogeneity across different cancer types. Here, the authors propose a strategy for the integration of two large CRISPR-Cas9 screens and report increased coverage of molecular diversity and genetic dependencies.

The fungal microbiota (mycobiota) is an integral part of the complex multikingdom microbial community colonizing the mammalian gastrointestinal tract and has an important role in immune regulation 1 – 6 . Although aberrant changes in the mycobiota have been linked to several diseases, including inflammatory bowel disease 3 – 9 , it is currently unknown whether fungal species captured by deep sequencing represent living organisms and whether specific fungi have functional consequences for disease development in affected individuals. Here we developed a translational platform for the functional analysis of the mycobiome at the fungal-strain- and patient-specific level. Combining high-resolution mycobiota sequencing, fungal culturomics and genomics, a CRISPR–Cas9-based fungal strain editing system, in vitro functional immunoreactivity assays and in vivo models, this platform enables the examination of host–fungal crosstalk in the human gut. We discovered a rich genetic diversity of opportunistic Candida albicans strains that dominate the colonic mucosa of patients with inflammatory bowel disease. Among these human-gut-derived isolates, strains with high immune-cell-damaging capacity (HD strains) reflect the disease features of individual patients with ulcerative colitis and aggravated intestinal inflammation in vivo through IL-1β-dependent mechanisms. Niche-specific inflammatory immunity and interleukin-17A-producing T helper cell (T H 17 cell) antifungal responses by HD strains in the gut were dependent on the C. albicans -secreted peptide toxin candidalysin during the transition from a benign commensal to a pathobiont state. These findings reveal the strain-specific nature of host–fungal interactions in the human gut and highlight new diagnostic and therapeutic targets for diseases of inflammatory origin. Genetically diverse Candida albicans strains in patients with inflammatory bowel disease secrete a toxin and aggravate IL-1β-dependent intestinal inflammation.

Although circumstantial evidence supports enhanced Toll-like receptor 7 (TLR7) signalling as a mechanism of human systemic autoimmune disease 1 – 7 , evidence of lupus-causing TLR7 gene variants is lacking. Here we describe human systemic lupus erythematosus caused by a TLR7 gain-of-function variant. TLR7 is a sensor of viral RNA 8 , 9 and binds to guanosine 10 – 12 . We identified a de novo, previously undescribed missense TLR7 Y264H variant in a child with severe lupus and additional variants in other patients with lupus. The TLR7 Y264H variant selectively increased sensing of guanosine and 2',3'-cGMP 10 – 12 , and was sufficient to cause lupus when introduced into mice. We show that enhanced TLR7 signalling drives aberrant survival of B cell receptor (BCR)-activated B cells, and in a cell-intrinsic manner, accumulation of CD11c + age-associated B cells and germinal centre B cells. Follicular and extrafollicular helper T cells were also increased but these phenotypes were cell-extrinsic. Deficiency of MyD88 (an adaptor protein downstream of TLR7) rescued autoimmunity, aberrant B cell survival, and all cellular and serological phenotypes. Despite prominent spontaneous germinal-centre formation in Tlr7 Y264H mice, autoimmunity was not ameliorated by germinal-centre deficiency, suggesting an extrafollicular origin of pathogenic B cells. We establish the importance of TLR7 and guanosine-containing self-ligands for human lupus pathogenesis, which paves the way for therapeutic TLR7 or MyD88 inhibition. The missense TLR7 Y264H gain-of-function genetic variation causes systemic lupus erythematosus in humans and mice.

Mutations in non-coding regulatory DNA sequences can alter gene expression, organismal phenotype and fitness 1 – 3 . Constructing complete fitness landscapes, in which DNA sequences are mapped to fitness, is a long-standing goal in biology, but has remained elusive because it is challenging to generalize reliably to vast sequence spaces 4 – 6 . Here we build sequence-to-expression models that capture fitness landscapes and use them to decipher principles of regulatory evolution. Using millions of randomly sampled promoter DNA sequences and their measured expression levels in the yeast Saccharomyces cerevisiae , we learn deep neural network models that generalize with excellent prediction performance, and enable sequence design for expression engineering. Using our models, we study expression divergence under genetic drift and strong-selection weak-mutation regimes to find that regulatory evolution is rapid and subject to diminishing returns epistasis; that conflicting expression objectives in different environments constrain expression adaptation; and that stabilizing selection on gene expression leads to the moderation of regulatory complexity. We present an approach for using such models to detect signatures of selection on expression from natural variation in regulatory sequences and use it to discover an instance of convergent regulatory evolution. We assess mutational robustness, finding that regulatory mutation effect sizes follow a power law, characterize regulatory evolvability, visualize promoter fitness landscapes, discover evolvability archetypes and illustrate the mutational robustness of natural regulatory sequence populations. Our work provides a general framework for designing regulatory sequences and addressing fundamental questions in regulatory evolution. A framework for studying and engineering gene regulatory DNA sequences, based on deep neural sequence-to-expression models trained on large-scale libraries of random DNA, provides insight into the evolution, evolvability and fitness landscapes of regulatory DNA.