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The genetic control of many plant traits can be highly complex. Both allelic variation (sequence change) and dosage variation (copy number change) contribute to a plants phenotype. While numerous studies have investigated the effect of allelic or dosage variation, very few have documented both within the same system, leaving their relative contribution to phenotypic effects unclear. The Populus genome is highly polymorphic, and poplars are fairly tolerant of gene dosage variation. Here, using a previously established Populus hybrid F1 population, we assessed and compared the effect of natural allelic variation and induced dosage variation on biomass, phenology, and leaf morphology traits. We identified QTLs for many of these traits, but our results indicate limited overlap between the QTLs associated with natural allelic variation and induced dosage variation. Additionally, the integration of data from both allelic and dosage variation identifies a larger set of QTLs that together explain a larger percentage of the phenotypic variance. Finally, our results suggest that the effect of the large indels might mask that of allelic QTLs. Our study helps clarify the relationship between allelic and dosage variation and their effects on quantitative traits.

Transcriptional control is fundamental to cellular function. However, despite knowing that transcription factors can repress or activate specific genes, how these functions are implemented at the molecular level has remained elusive, particularly in the endogenous context of developing animals. Here, we combine optogenetics, single-cell live-imaging, and mathematical modeling to study how a zinc-finger repressor, Knirps, induces switch-like transitions into long-lived quiescent states. Using optogenetics, we demonstrate that repression is rapidly reversible (~1 min) and memoryless. Furthermore, we show that the repressor acts by decreasing the frequency of transcriptional bursts in a manner consistent with an equilibrium binding model. Our results provide a quantitative framework for dissecting the in vivo biochemistry of eukaryotic transcriptional regulation.

The dog, Canis lupus familiaris, is an important model for studying human diseases. Unlike many model organisms, the dog genome has a comparatively poor functional annotation, which hampers gene discovery for development, morphology, disease, and behavior. To fill this gap, we established a comprehensive tissue biobank for both the dog and wolf samples. The biobank consists of 5485 samples representing 132 tissues from 13 dogs, 12 dog embryos, and 24 wolves. In a subset of 100 tissues from nine dogs and 12 embryos, we characterized gene expression activity for each promoter, including alternative and novel, i.e., previously not annotated, promoter regions, using the 5 targeting RNA sequencing technology STRT2-seq. We identified over 100,000 promoter region candidates in the recent canine genome assembly, CanFam4, including over 45,000 highly reproducible sites with gene expression and respective tissue enrichment levels. We provide a promoter and gene expression atlas with interactive, open data resources, including a data coordination center and genome browser track hubs. We demonstrated the applicability of Dog Genome Annotation (DoGA) data and resources using multiple examples spanning canine embryonic development, morphology and behavior, and diseases across species.

Energy and its dissipation are fundamental to all living systems, including cells. Insufficient abundance of energy carriers─as caused by the additional burden of artificial genetic circuits─shifts a cell's priority to survival, also impairing the functionality of the genetic circuit. Moreover, recent works have shown the importance of energy expenditure in information transmission. Despite living organisms being non-equilibrium systems, non-equilibrium models capable of accounting for energy dissipation and non-equilibrium response curves are not yet employed in genetic design automation (GDA) software. To this end, we introduce Energy Aware Technology Mapping, the automated design of genetic logic circuits with respect to energy efficiency and functionality. The basis for this is an energy aware non-equilibrium steady state model of gene expression, capturing characteristics like energy dissipation─which we link to the entropy production rate─and transcriptional bursting, relevant to eukaryotes as well as prokaryotes. Our evaluation shows that a genetic logic circuit's functional performance and energy efficiency are disjoint optimization goals. For our benchmark, energy efficiency improves by 37.2% on average when comparing to functionally optimized variants. We discover a linear increase in energy expenditure and overall protein expression with the circuit size, where Energy Aware Technology Mapping allows for designing genetic logic circuits with the energetic costs of circuits that are one to two gates smaller. Structural variants improve this further, while results show the Pareto dominance among structures of a single Boolean function. By incorporating energy demand into the design, Energy Aware Technology Mapping enables energy efficiency by design. This extends current GDA tools and complements approaches coping with burden in vivo.

Biallelic loss of cyclin-dependent kinase 12 (CDK12) defines a metastatic castration-resistant prostate cancer (mCRPC) subtype. It remains unclear, however, whether CDK12 loss drives prostate cancer (PCa) development or uncovers pharmacologic vulnerabilities. Here, we show Cdk12 ablation in murine prostate epithelium is sufficient to induce preneoplastic lesions with lymphocytic infiltration. In allograft-based CRISPR screening, Cdk12 loss associates positively with Trp53 inactivation but negatively with Pten inactivation. Moreover, concurrent Cdk12/Trp53 ablation promotes proliferation of prostate-derived organoids, while Cdk12 knockout in Pten-null mice abrogates prostate tumor growth. In syngeneic systems, Cdk12/Trp53-null allografts exhibit luminal morphology and immune checkpoint blockade sensitivity. Mechanistically, Cdk12 inactivation mediates genomic instability by inducing transcription-replication conflicts. Strikingly, CDK12-mutant organoids and patient-derived xenografts are sensitive to inhibition or degradation of the paralog kinase, CDK13. We therein establish CDK12 as a bona fide tumor suppressor, mechanistically define how CDK12 inactivation causes genomic instability, and advance a therapeutic strategy for CDK12-mutant mCRPC.

Our knowledge of the contribution of genetic interactions (epistasis) to variation in human complex traits remains limited, partly due to the lack of efficient, powerful, and interpretable algorithms to detect interactions. Recently proposed approaches for set-based association tests show promise in improving the power to detect epistasis by examining the aggregated effects of multiple variants. Nevertheless, these methods either do not scale to large Biobank data sets or lack interpretability. We propose QuadKAST, a scalable algorithm focused on testing pairwise interaction effects (quadratic effects) within small to medium-sized sets of genetic variants (window size ≤100) on a trait and provide quantified interpretation of these effects. Comprehensive simulations show that QuadKAST is well-calibrated. Additionally, QuadKAST is highly sensitive in detecting loci with epistatic signals and accurate in its estimation of quadratic effects. We applied QuadKAST to 52 quantitative phenotypes measured in ≈300,000 unrelated white British individuals in the UK Biobank to test for quadratic effects within each of 9515 protein-coding genes. We detect 32 trait-gene pairs across 17 traits and 29 genes that demonstrate statistically significant signals of quadratic effects (accounting for the number of genes and traits tested). Across these trait-gene pairs, the proportion of trait variance explained by quadratic effects is comparable to additive effects, with five pairs having a ratio >1. Our method enables the detailed investigation of epistasis on a large scale, offering new insights into its role and importance.

SNP heritability, the proportion of phenotypic variation explained by genotyped SNPs, is an important parameter in understanding the genetic architecture underlying various diseases and traits. Methods that aim to estimate SNP heritability from individual genotype and phenotype data are limited by their ability to scale to Biobank-scale data sets and by the restrictions in access to individual-level data. These limitations have motivated the development of methods that only require summary statistics. Although the availability of publicly accessible summary statistics makes them widely applicable, these methods lack the accuracy of methods that utilize individual genotypes. Here we present a SUMmary-statistics-based Randomized Haseman-Elston regression (SUM-RHE), a method that can estimate the SNP heritability of complex phenotypes with accuracies comparable to approaches that require individual genotypes, while exclusively relying on summary statistics. SUM-RHE employs Genome-Wide Association Study (GWAS) summary statistics and statistics obtained on a reference population, which can be efficiently estimated and readily shared for public use. Our results demonstrate that SUM-RHE obtains estimates of SNP heritability that are substantially more accurate compared with other summary statistic methods and on par with methods that rely on individual-level data.

The genomes of living lungfishes can inform on the molecular-developmental basis of the Devonian sarcopterygian fish-tetrapod transition. We de novo sequenced the genomes of the African (Protopterus annectens) and South American lungfishes (Lepidosiren paradoxa). The Lepidosiren genome (about 91 Gb, roughly 30 times the human genome) is the largest animal genome sequenced so far and more than twice the size of the Australian (Neoceratodus forsteri)1 and African2 lungfishes owing to enlarged intergenic regions and introns with high repeat content (about 90%). All lungfish genomes continue to expand as some transposable elements (TEs) are still active today. In particular, Lepidosiren's genome grew extremely fast during the past 100 million years (Myr), adding the equivalent of one human genome every 10 Myr. This massive genome expansion seems to be related to a reduction of PIWI-interacting RNAs and C2H2 zinc-finger and Krüppel-associated box (KRAB)-domain protein genes that suppress TE expansions. Although TE abundance facilitates chromosomal rearrangements, lungfish chromosomes still conservatively reflect the ur-tetrapod karyotype. Neoceratodus' limb-like fins still resemble those of their extinct relatives and remained phenotypically static for about 100 Myr. We show that the secondary loss of limb-like appendages in the Lepidosiren-Protopterus ancestor was probably due to loss of sonic hedgehog limb-specific enhancers.

Living cells regulate the dynamics of developmental events through interconnected signaling systems that activate and deactivate inert precursors. This suggests that similarly, synthetic biomaterials could be designed to develop over time by using chemical reaction networks to regulate the availability of assembling components. Here we demonstrate how the sequential activation or deactivation of distinct DNA building blocks can be modularly coordinated to form distinct populations of self-assembling polymers using a transcriptional signaling cascade of synthetic genes. Our building blocks are DNA tiles that polymerize into nanotubes, and whose assembly can be controlled by RNA molecules produced by synthetic genes that target the tile interaction domains. To achieve different RNA production rates, we use a strategy based on promoter nicking and strand displacement. By changing the way the genes are cascaded and the RNA levels, we demonstrate that we can obtain spatially and temporally different outcomes in nanotube assembly, including random DNA polymers, block polymers, and as well as distinct autonomous formation and dissolution of distinct polymer populations. Our work demonstrates a way to construct autonomous supramolecular materials whose properties depend on the timing of molecular instructions for self-assembly, and can be immediately extended to a variety of other nucleic acid circuits and assemblies.

The adoption of agriculture triggered a rapid shift towards starch-rich diets in human populations1. Amylase genes facilitate starch digestion, and increased amylase copy number has been observed in some modern human populations with high-starch intake2, although evidence of recent selection is lacking3,4. Here, using 94 long-read haplotype-resolved assemblies and short-read data from approximately 5,600 contemporary and ancient humans, we resolve the diversity and evolutionary history of structural variation at the amylase locus. We find that amylase genes have higher copy numbers in agricultural populations than in fishing, hunting and pastoral populations. We identify 28 distinct amylase structural architectures and demonstrate that nearly identical structures have arisen recurrently on different haplotype backgrounds throughout recent human history. AMY1 and AMY2A genes each underwent multiple duplication/deletion events with mutation rates up to more than 10,000-fold the single-nucleotide polymorphism mutation rate, whereas AMY2B gene duplications share a single origin. Using a pangenome-based approach, we infer structural haplotypes across thousands of humans identifying extensively duplicated haplotypes at higher frequency in modern agricultural populations. Leveraging 533 ancient human genomes, we find that duplication-containing haplotypes (with more gene copies than the ancestral haplotype) have rapidly increased in frequency over the past 12,000 years in West Eurasians, suggestive of positive selection. Together, our study highlights the potential effects of the agricultural revolution on human genomes and the importance of structural variation in human adaptation.

There is evidence that transcription factor (TF) encoding genes, which temporally control development in multiple cell types, can have tens of enhancers that regulate their expression. The NR2F1 TF developmentally promotes caudal and ventral cortical regional fates. Here, we epigenomically compared the activity of Nr2f1's enhancers during mouse cortical development with their activity in a transgenic assay. We identified at least six that are likely to be important in prenatal cortical development, with three harboring de novo mutants identified in ASD individuals. We chose to study the function of two of the most robust enhancers by deleting them singly or together. We found that they have distinct and overlapping functions in driving Nr2f1's regional and laminar expression in the developing cortex. Thus, these two enhancers, probably in combination with the others that we defined epigenetically, precisely tune Nr2f1's regional, cell type, and temporal expression during corticogenesis.

The ability to label proteins by fusion with genetically encoded fluorescent proteins is a powerful tool for understanding dynamic biological processes. However, current approaches for expressing fluorescent protein fusions possess drawbacks, especially at the whole organism level. Expression by transgenesis risks potential overexpression artifacts while fluorescent protein insertion at endogenous loci is technically difficult and, more importantly, does not allow for tissue-specific study of broadly expressed proteins. To overcome these limitations, we have adopted the split fluorescent protein system mNeonGreen21-10/11 (split-mNG2) to achieve tissue-specific and endogenous protein labeling in zebrafish. In our approach, mNG21-10 is expressed under a tissue-specific promoter using standard transgenesis while mNG211 is inserted into protein-coding genes of interest using CRISPR/Cas-directed gene editing. Each mNG2 fragment on its own is not fluorescent, but when co-expressed the fragments self-assemble into a fluorescent complex. Here, we report successful use of split-mNG2 to achieve differential labeling of the cytoskeleton genes tubb4b and krt8 in various tissues. We also demonstrate that by anchoring the mNG21-10 component to specific cellular compartments, the split-mNG2 system can be used to manipulate protein localization. Our approach should be broadly useful for a wide range of applications.

Unsolved Mendelian cases often lack obvious pathogenic coding variants, suggesting potential non-coding etiologies. Here, we present a single cell multi-omic framework integrating embryonic mouse chromatin accessibility, histone modification, and gene expression assays to discover cranial motor neuron (cMN) cis-regulatory elements and subsequently nominate candidate non-coding variants in the congenital cranial dysinnervation disorders (CCDDs), a set of Mendelian disorders altering cMN development. We generate single cell epigenomic profiles for ~86,000 cMNs and related cell types, identifying ~250,000 accessible regulatory elements with cognate gene predictions for ~145,000 putative enhancers. We evaluate enhancer activity for 59 elements using an in vivo transgenic assay and validate 44 (75%), demonstrating that single cell accessibility can be a strong predictor of enhancer activity. Applying our cMN atlas to 899 whole genome sequences from 270 genetically unsolved CCDD pedigrees, we achieve significant reduction in our variant search space and nominate candidate variants predicted to regulate known CCDD disease genes MAFB, PHOX2A, CHN1, and EBF3 - as well as candidates in recurrently mutated enhancers through peak- and gene-centric allelic aggregation. This work delivers non-coding variant discoveries of relevance to CCDDs and a generalizable framework for nominating non-coding variants of potentially high functional impact in other Mendelian disorders.

Large-scale sequencing efforts have been undertaken to understand the mutational landscape of the coding genome. However, the vast majority of variants occur within non-coding genomic regions. We designed an integrative computational and experimental framework to identify recurrently mutated non-coding regulatory regions that drive tumor progression. Applying this framework to sequencing data from a large prostate cancer patient cohort revealed a large set of candidate drivers. We used (1) in silico analyses, (2) massively parallel reporter assays, and (3) in vivo CRISPR interference screens to systematically validate metastatic castration-resistant prostate cancer (mCRPC) drivers. One identified enhancer region, GH22I030351, acts on a bidirectional promoter to simultaneously modulate expression of the U2-associated splicing factor SF3A1 and chromosomal protein CCDC157. SF3A1 and CCDC157 promote tumor growth in vivo. We nominated a number of transcription factors, notably SOX6, to regulate expression of SF3A1 and CCDC157. Our integrative approach enables the systematic detection of non-coding regulatory regions that drive human cancers.

Enhancers, critical regulatory elements within the human genome, are often transcribed into enhancer RNAs. The dysregulation of enhancers leads to diseases collectively termed enhanceropathies. While it is known that enhancers play a role in diseases by regulating gene expression, the specific mechanisms by which individual enhancers cause diseases are not well understood. Studies of individual enhancers are needed to fill this gap. This study delves into the role of APOE-activating noncoding RNA, AANCR, in the central nervous system, elucidating its function as a genetic modifier in Alzheimers Disease. We employed RNA interference, RNaseH-mediated degradation, and single-molecule RNA fluorescence in situ hybridization to demonstrate that mere transcription of AANCR is insufficient; rather, its transcripts are crucial for promoting APOE expression. Our findings revealed that AANCR is induced by ATM-mediated ERK phosphorylation and subsequent AP-1 transcription factor activation. Once activated, AANCR enhances APOE expression, which in turn imparts an inflammatory phenotype to astrocytes. These findings demonstrate that AANCR is a key enhancer RNA in some cell types within the nervous system, pivotal for regulating APOE expression and influencing inflammatory responses, underscoring its potential as a therapeutic target in neurodegenerative diseases.

During human development, a temporary organ is formed, the placenta, which invades the uterine wall to support nutrient, oxygen, and waste exchange between the mother and fetus until birth. Most of the human placenta is formed by a syncytial villous structure lined by syncytialized trophoblasts, a specialized cell type that forms via cell-cell fusion of underlying progenitor cells. Genetic and functional studies have characterized the membrane protein fusogens Syncytin-1 and Syncytin-2, both of which are necessary and sufficient for human trophoblast cell-cell fusion. However, identification and characterization of upstream transcriptional regulators regulating their expression have been limited. Here, using CRISPR knockout in an in vitro cellular model of syncytiotrophoblast development (BeWo cells), we found that the transcription factor TFEB, mainly known as a regulator of autophagy and lysosomal biogenesis, is required for cell-cell fusion of syncytiotrophoblasts. TFEB translocates to the nucleus, exhibits increased chromatin interactions, and directly binds the Syncytin-1 and Syncytin-2 promoters to control their expression during differentiation. Although TFEB appears to play a critical role in syncytiotrophoblast differentiation, ablation of TFEB largely does not affect lysosomal gene expression or lysosomal biogenesis in differentiating BeWo cells, suggesting a previously uncharacterized role for TFEB in controlling the expression of human syncytins.

We describe a framework that addresses concern that the rate of change in any aging biomarker displays a trivial inverse relation with maximum lifespan. We apply this framework to methylation data from the Mammalian Methylation Consortium. We study the relationship of lifespan with the average rate of change in methylation (AROCM) from two datasets: one with 90 dog breeds and the other with 125 mammalian species. After examining 54 chromatin states, we conclude three key findings: First, a reciprocal relationship exists between the AROCM in bivalent promoter regions and maximum mammalian lifespan: AROCM ∝ 1/MaxLifespan. Second, the correlation between average methylation and age bears no relation to maximum lifespan, Cor(Methyl,Age) ⊥ MaxLifespan. Third, the rate of methylation change in young animals is related to that in old animals: Young animals AROCM ∝ Old AROCM. These findings critically hinge on the chromatin context, as different results emerge in other chromatin contexts.

Transcriptional silencing in Saccharomyces cerevisiae is a persistent and highly stable form of gene repression. It involves DNA silencers and repressor proteins that bind nucleosomes. The silenced state is influenced by numerous factors including the concentration of repressors, nature of activators, architecture of regulatory elements, modifying enzymes and the dynamics of chromatin.Silencers function to increase the residence time of repressor Sir proteins at silenced domains while clustering of silenced domains enables increased concentrations of repressors and helps facilitate long-range interactions. The presence of an accessible NDR at the regulatory regions of silenced genes, the cycling of chromatin configurations at regulatory sites, the mobility of Sir proteins, and the non-uniform distribution of the Sir proteins across the silenced domain, all result in silenced chromatin that only stably silences weak promoters and enhancers via changes in transcription burst duration and frequency.These data collectively suggest that silencing is probabilistic and the robustness of silencing is achieved through sub-optimization of many different nodes of action such that a stable expression state is generated and maintained even though individual constituents are in constant flux.

Alu retrotransposons, which form the largest family of mobile DNA elements in the human genome, have recently come to attention as a potential source of regulatory novelties, most notably by participating in enhancer function. Even though Alu transcription by RNA polymerase III is subjected to tight epigenetic silencing, their expression has long been known to increase in response to various types of stress, including viral infection. Here we show that, in primary human fibroblasts, adenovirus small e1a triggered derepression of hundreds of individual Alus by promoting TFIIIB recruitment by Alu-bound TFIIIC. Epigenome profiling revealed an e1a-induced decrease of H3K27 acetylation and increase of H3K4 monomethylation at derepressed Alus, making them resemble poised enhancers. The enhancer nature of e1a-targeted Alus was confirmed by the enrichment, in their upstream regions, of the EP300/CBP acetyltransferase, EP400 chromatin remodeler and YAP1 and FOS transcription factors. The physical interaction of e1a with EP400 was critical for Alu derepression, which was abrogated upon EP400 ablation. Our data suggest that e1a targets a subset of enhancer Alus whose transcriptional activation, which requires EP400 and is mediated by the e1a-EP400 interaction, may participate in the manipulation of enhancer activity by adenoviruses.

BACKGROUND: The incidence of breast and colorectal cancer (CRC) in younger-than-average-age patients is rising and poorly understood. This is the largest study on patients with both cancers who are less than 60 years old and aims to characterize demographic, clinicopathologic, and genetic features and describe therapeutic dilemmas and management strategies. MATERIALS AND METHODS: This is a retrospective medical records review of patients at the University of California San Francisco with both primary breast and CRC before age 60. RESULTS: Fifty-one patients were identified; 41 had detailed medical records. Median age of diagnosis with breast cancer was 43 (range 27-59) and CRC was 50 (28-59). Most were Caucasian (38, 74.5%) and never smokers (23, 56.1%); about half were current alcohol consumers (20, 48.8%) and about one-third had sedentary jobs (14, 34.1%). Average BMI was 25.8 (range: 14-49), and 30% were overweight or obese. Breast was the first cancer diagnosed in 36 patients (70.6%) and 44 (86.3%) had a metachronous CRC diagnosis. Breast cancer was early stage (0-2) in 32 (78.0%) patients whereas CRC was split between early stage (1-2) in 14 (34.1%) and later stage (3-4) in 19 (46.2%). Ten patients (24.3%) had a known germline mutation, although 23 (56.1%) had a family history of cancer in a first-degree relative. CONCLUSION: Younger patients with both breast and CRC are a unique cohort, often without known risk factors. Alcohol consumption and sedentary jobs were the most common risk factors, and about one-quarter had a known genetic predisposition. Comanagement of both cancers requires individualized, multidisciplinary care.

Automated annotations of protein functions are error-prone because of our lack of knowledge of protein functions. For example, it is often impossible to predict the correct substrate for an enzyme or a transporter. Furthermore, much of the knowledge that we do have about the functions of proteins is missing from the underlying databases. We discuss how to use interactive tools to quickly find different kinds of information relevant to a protein's function. Many of these tools are available via PaperBLAST (http://papers.genomics.lbl.gov). Combining these tools often allows us to infer a protein's function. Ideally, accurate annotations would allow us to predict a bacterium's capabilities from its genome sequence, but in practice, this remains challenging. We describe interactive tools that infer potential capabilities from a genome sequence or that search a genome to find proteins that might perform a specific function of interest. Database URL: http://papers.genomics.lbl.gov.

Phylogenetic and discrete-trait evolutionary inference depend heavily on an appropriate characterization of the underlying character substitution process. In this paper, we present random-effects substitution models that extend common continuous-time Markov chain models into a richer class of processes capable of capturing a wider variety of substitution dynamics. As these random-effects substitution models often require many more parameters than their usual counterparts, inference can be both statistically and computationally challenging. Thus, we also propose an efficient approach to compute an approximation to the gradient of the data likelihood with respect to all unknown substitution model parameters. We demonstrate that this approximate gradient enables scaling of sampling-based inference, namely Bayesian inference via Hamiltonian Monte Carlo, under random-effects substitution models across large trees and state-spaces. Applied to a dataset of 583 SARS-CoV-2 sequences, an HKY model with random-effects shows strong signals of nonreversibility in the substitution process, and posterior predictive model checks clearly show that it is a more adequate model than a reversible model. When analyzing the pattern of phylogeographic spread of 1441 influenza A virus (H3N2) sequences between 14 regions, a random-effects phylogeographic substitution model infers that air travel volume adequately predicts almost all dispersal rates. A random-effects state-dependent substitution model reveals no evidence for an effect of arboreality on the swimming mode in the tree frog subfamily Hylinae. Simulations reveal that random-effects substitution models can accommodate both negligible and radical departures from the underlying base substitution model. We show that our gradient-based inference approach is over an order of magnitude more time efficient than conventional approaches.

Epistasis is caused by genetic interactions among mutations that affect fitness. To characterize properties and potential mechanisms of epistasis, we engineered eight double mutants that combined mutations from the rho and rpoB genes of Escherichia coli. The two genes encode essential functions for transcription, and the mutations in each gene were chosen because they were beneficial for adaptation to thermal stress (42.2 °C). The double mutants exhibited patterns of fitness epistasis that included diminishing returns epistasis at 42.2 °C, stronger diminishing returns between mutations with larger beneficial effects and both negative and positive (sign) epistasis across environments (20.0 °C and 37.0 °C). By assessing gene expression between single and double mutants, we detected hundreds of genes with gene expression epistasis. Previous work postulated that highly connected hub genes in coexpression networks have low epistasis, but we found the opposite: hub genes had high epistasis values in both coexpression and protein-protein interaction networks. We hypothesized that elevated epistasis in hub genes reflected that they were enriched for targets of Rho termination but that was not the case. Altogether, gene expression and coexpression analyses revealed that thermal adaptation occurred in modules, through modulation of ribonucleotide biosynthetic processes and ribosome assembly, the attenuation of expression in genes related to heat shock and stress responses, and with an overall trend toward restoring gene expression toward the unstressed state.

BACKGROUND: Traditional recombinant inbred lines (RILs) are generated from repeated self-fertilization or brother-sister mating from the F1 hybrid of two inbred parents. Compared with the F2 population, RILs cumulate more crossovers between loci and thus increase the number of recombinants, resulting in an increased resolution of genetic mapping. Since they are inbred to the isogenic stage, another consequence of the heterozygosity reduction is the increased genetic variance and thus the increased power of QTL detection. Self-fertilization is the primary form of developing RILs in plants. Brother-sister mating is another way to develop RILs but in small laboratory animals. To ensure that the RILs have at least 98% of homozygosity, we need about seven generations of self-fertilization or 20 generations of brother-sister mating. Prior to homozygosity, these lines are called pre-recombinant inbred lines (PRERIL). Phenotypic values of traits in PRERILs are often collected but not used in QTL mapping. To perform QTL mapping in PRERILs, we need the recombination fraction between two markers at generation t for t

Selfish genetic elements contribute to hybrid incompatibility and bias or drive their own transmission1,2. Chromosomal drive typically functions in asymmetric female meiosis, whereas gene drive is normally post-meiotic and typically found in males. Here, using single-molecule and single-pollen genome sequencing, we describe Teosinte Pollen Drive, an instance of gene drive in hybrids between maize (Zea mays ssp. mays) and teosinte mexicana (Z. mays ssp. mexicana) that depends on RNA interference (RNAi). 22-nucleotide small RNAs from a non-coding RNA hairpin in mexicana depend on Dicer-like 2 (Dcl2) and target Teosinte Drive Responder 1 (Tdr1), which encodes a lipase required for pollen viability. Dcl2, Tdr1 and the hairpin are in tight pseudolinkage on chromosome 5, but only when transmitted through the male. Introgression of mexicana into early cultivated maize is thought to have been critical to its geographical dispersal throughout the Americas3, and a tightly linked inversion in mexicana spans a major domestication sweep in modern maize4. A survey of maize traditional varieties and sympatric populations of teosinte mexicana reveals correlated patterns of admixture among unlinked genes required for RNAi on at least four chromosomes that are also subject to gene drive in pollen from synthetic hybrids. Teosinte Pollen Drive probably had a major role in maize domestication and diversification, and offers an explanation for the widespread abundance of self small RNAs in the germ lines of plants and animals.

Fapy•dG (N6-(2-deoxy-α,β-D-erythro-pentofuranosyl)-2,6-diamino-4-hydroxy-5-formamidopyrimidine) and 8-OxodGuo (8-oxo-7,8-dihydro-2-deoxyguanosine) are major products of 2-deoxyguanosine oxidation. Fapy•dG is unusual in that it exists as a dynamic mixture of anomers. Much less is known about the effects of Fapy•dG than 8-OxodGuo on transcriptional bypass. The data presented here indicate that T7 RNA polymerase (T7 RNAP) bypass of Fapy•dG is more complex than that of 8-OxodGuo. Primer-dependent transcriptional bypass of Fapy•dG by T7 RNAP is hindered compared to 2-deoxyguanosine. T7 RNAP incorporates cytidine opposite Fapy•dG in a miniscaffold at least 13-fold more rapidly than A, G, or U. Fitting of reaction data indicates that Fapy•dG anomers are kinetically distinguishable. Extension of a nascent transcript past Fapy•dG is weakly dependent on the nucleotide opposite the lesion. The rate constants describing extension past fast- or slow-reacting base pairs vary less than twofold as a function of the nucleotide opposite the lesion. Promoter-dependent T7 RNAP bypass of Fapy•dG and 8-OxodGuo was carried out side by side. 8-OxodGuo bypass results in >55% A opposite it. When the shuttle vector contains a Fapy•dG:dA base pair, as high as 20% point mutations and 9% single-nucleotide deletions are produced upon Fapy•dG bypass. Error-prone bypass of a Fapy•dG:dC base pair accounts for ∼9% of the transcripts. Transcriptional bypass mutation frequencies of Fapy•dG and 8-OxodGuo measured in RNA products are comparable to or greater than replication errors, suggesting that these lesions could contribute to mutations significantly through transcription.

Inferring the driving regulatory programs from comparative analysis of gene expression data is a cornerstone of systems biology. Many computational frameworks were developed to address this problem, including our iPAGE (information-theoretic Pathway Analysis of Gene Expression) toolset that uses information theory to detect non-random patterns of expression associated with given pathways or regulons. Our recent observations, however, indicate that existing approaches are susceptible to the technical biases that are inherent to most real world annotations. To address this, we have extended our information-theoretic framework to account for specific biases and artifacts in biological networks using the concept of conditional information. To showcase pyPAGE, we performed a comprehensive analysis of regulatory perturbations that underlie the molecular etiology of Alzheimers disease (AD). pyPAGE successfully recapitulated several known AD-associated gene expression programs. We also discovered several additional regulons whose differential activity is significantly associated with AD. We further explored how these regulators relate to pathological processes in AD through cell-type specific analysis of single cell and spatial gene expression datasets. Our findings showcase the utility of pyPAGE as a precise and reliable biomarker discovery in complex diseases such as Alzheimers disease.

Systemic low-grade inflammation is a feature of chronic disease. C-reactive protein (CRP) is a common biomarker of inflammation and used as an indicator of disease risk; however, the role of inflammation in disease is not completely understood. Methylation is an epigenetic modification in the DNA which plays a pivotal role in gene expression. In this study we evaluated differential DNA methylation patterns associated with blood CRP level to elucidate biological pathways and genetic regulatory mechanisms to improve the understanding of chronic inflammation. The racially and ethnically diverse participants in this study were included as 50% White, 41% Black or African American, 7% Hispanic or Latino/a, and 2% Native Hawaiian, Asian American, American Indian, or Alaska Native (total n = 13,433) individuals. We replicated 113 CpG sites from 87 unique loci, of which five were novel (CADM3, NALCN, NLRC5, ZNF792, and cg03282312), across a discovery set of 1,150 CpG sites associated with CRP level (p

Long-lived PFKFB3-expressing β-cells are dysfunctional partly because of prevailing glycolysis that compromises metabolic coupling of insulin secretion. Their accumulation in type 2 diabetes (T2D) appears to be related to the loss of apoptotic competency of cell fitness competition that maintains islet function by favoring constant selection of healthy winner cells. To investigate how PFKFB3 can disguise the competitive traits of dysfunctional loser β-cells, we analyzed the overlap between human β-cells with bona fide loser signature across diabetes pathologies using the HPAP scRNA-seq and spatial transcriptomics of PFKFB3-positive β-cells from nPOD T2D pancreata. The overlapping transcriptional profile of loser β-cells was represented by down-regulated ribosomal biosynthesis and genes encoding for mitochondrial respiration. PFKFB3-positive loser β-cells had the reduced expression of HLA class I and II genes. Gene-gene interaction analysis revealed that PFKFB3 rs1983890 can interact with the anti-apoptotic gene MAIP1 implicating positive epistasis as a mechanism for prolonged survival of loser β-cells in T2D. Inhibition of PFKFB3 resulted in the clearance of dysfunctional loser β-cells leading to restored glucose tolerance in the mouse model of T2D.

Cytomegalovirus (CMV) infection and reactivation in solid organ transplant (SOT) recipients increases the risk of viremia, graft failure and death. Clinical studies of CMV serostatus indicate that donor positive recipient negative (D+/R-) patients have greater viremia risk than D-/R-. The majority of patients are R+ having intermediate serologic risk. To characterize the long-term impact of CMV infection and assess viremia risk, we sought to measure the effects of CMV on the recipient immune epigenome. Specifically, we profiled DNA methylation in 156 individuals before lung or kidney transplant. We found that the methylome of CMV positive SOT recipients is hyper-methylated at loci associated with neural development and Polycomb group (PcG) protein binding, and hypo-methylated at regions critical for the maturation of lymphocytes. In addition, we developed a machine learning-based model to predict the recipient CMV serostatus after correcting for cell type composition and ancestry. This CMV episcore measured at baseline in R+ individual stratifies viremia risk accurately in the lung transplant cohort, and along with serostatus the CMV episcore could be a potential biomarker for identifying R+ patients at high viremia risk.

Stroke causes death of brain tissue leading to long-term deficits. Behavioral evidence from neurorehabilitative therapies suggest learning-induced neuroplasticity can lead to beneficial outcomes. However, molecular and cellular mechanisms that link learning and stroke recovery are unknown. We show that in a mouse model of stroke, which exhibits enhanced recovery of function due to genetic perturbations of learning and memory genes, animals display activity-dependent transcriptional programs that are normally active during formation or storage of new memories. The expression of neuronal activity-dependent genes are predictive of recovery and occupy a molecular latent space unique to motor recovery. With motor recovery, networks of activity-dependent genes are co-expressed with their transcription factor targets forming gene regulatory networks that support activity-dependent transcription, that are normally diminished after stroke. Neuronal activity-dependent changes at the circuit level are influenced by interactions with microglia. At the molecular level, we show that enrichment of activity-dependent programs in neurons lead to transcriptional changes in microglia where they differentially interact to support intercellular signaling pathways for axon guidance, growth and synaptogenesis. Together, these studies identify activity-dependent transcriptional programs as a fundamental mechanism for neural repair post-stroke.

Resistance to the current Androgen Receptor Signaling Inhibitor (ARSI) therapies has led to higher incidences of therapy-induced neuroendocrine-like prostate cancer (t-NEPC). This highly aggressive subtype with predominant small-cell-like characteristics is resistant to taxane chemotherapies and has a dismal overall survival. t-NEPCs are mostly treated with platinum-based drugs with a combination of etoposide or taxane and have less selectivity and high systemic toxicity, which often limit their clinical potential. During t-NEPC transformation, adenocarcinomas lose their luminal features and adopt neuro-basal characteristics. Whether the adaptive neuronal characteristics of t-NEPC are responsible for such taxane resistance remains unknown. Pathway analysis from patient gene-expression databases indicates that t-NEPC upregulates various neuronal pathways associated with enhanced cellular networks. To identify transcription factor(s) (TF) that could be important for promoting the gene expression for neuronal characters in t-NEPC, we performed ATAC-Seq, acetylated-histone ChIP-seq, and RNA-seq in our NE-like cell line models and analyzed the promoters of transcriptionally active and significantly enriched neuroendocrine-like (NE-like) cancer-specific genes. Our results indicate that Pax5 could be an important transcription factor for neuronal gene expression and specific to t-NEPC. Pathway analysis revealed that Pax5 expression is involved in axonal guidance, neurotransmitter regulation, and neuronal adhesion, which are critical for strong cellular communications. Further results suggest that depletion of Pax5 disrupts neurite-mediated cellular communication in NE-like cells and reduces surface growth factor receptor activation, thereby, sensitizing them to docetaxel therapies. Moreover, t-NEPC-specific hydroxymethylation of Pax5 promoter CpG islands favors Pbx1 binding to induce Pax5 expression. Based on our study, we concluded that continuous exposure to ARSI therapies leads to epigenetic modifications and Pax5 activation in t-NEPC, which promotes the expression of genes necessary to adopt taxane-resistant NE-like cancer. Thus, targeting the Pax5 axis can be beneficial for reverting their taxane sensitivity.

BACKGROUND: The effect of vaccination on the epigenome remains poorly characterized. In previous research, we identified an association between seroprotection against influenza and DNA methylation at sites associated with the RIG-1 signaling pathway, which recognizes viral double-stranded RNA and leads to a type I interferon response. However, these studies did not fully account for confounding factors including age, gender, and BMI, along with changes in cell-type composition. RESULTS: Here, we studied the influenza vaccine response in a longitudinal cohort vaccinated over two consecutive years (2019-2020 and 2020-2021), using peripheral blood mononuclear cells and a targeted DNA methylation approach. To address the effects of multiple factors on the epigenome, we designed a multivariate multiple regression model that included seroprotection levels as quantified by the hemagglutination-inhibition (HAI) assay test. CONCLUSIONS: Our findings indicate that 179 methylation sites can be combined as potential signatures to predict seroprotection. These sites were not only enriched for genes involved in the regulation of the RIG-I signaling pathway, as found previously, but also enriched for other genes associated with innate immunity to viruses and the transcription factor binding sites of BRD4, which is known to impact T cell memory. We propose a model to suggest that the RIG-I pathway and BRD4 could potentially be modulated to improve immunization strategies.

Drugs that inhibit HIV transcription and/or reactivation of latent HIV have been proposed as a strategy to reduce HIV-associated immune activation or to achieve a functional cure, yet comparative studies are lacking. We evaluated 26 drugs, including drugs previously reported to inhibit HIV transcription (inhibitors of Tat-dependent HIV transcription, Rev, HSF-1/PTEF-b, HSP90, Jak/Stat, or SIRT1/Tat deacetylation) and other agents that were not tested before (inhibitors of PKC, NF-κB, SP-1, or histone acetyltransferase; NR2F1 agonists), elongation (inhibitors of CDK9/ PTEF-b), completion (inhibitors of PolyA-polymerase), or splicing (inhibitors of human splice factors). To investigate if those drugs would vary in their ability to affect different blocks to HIV transcription, we measured levels of initiated, elongated, midtranscribed, completed, and multiply spliced HIV RNA in PBMCs from antiretroviral therapy-suppressed individuals following ex vivo treatment with each drug and subsequent T cell activation. We identified new drugs that prevent HIV reactivation, including CDK and splicing inhibitors. While some drugs inhibited 1 or 2 steps, other drugs (CDK inhibitors, splicing inhibitors, tanespimycin, and triptolide) inhibited multiple stages of HIV transcription and blocked the production of supernatant viral RNA. These drugs and targets deserve further study in strategies aimed at reducing HIV-associated immune activation or achieving a functional cure.

Transcription-blocking lesions (TBLs) stall elongating RNA polymerase II (Pol II), which then initiates transcription-coupled repair (TCR) to remove TBLs and allow transcription recovery. In the absence of TCR, eviction of lesion-stalled Pol II is required for alternative pathways to address the damage, but the mechanism is unclear. Using Protein-Associated DNA Damage Sequencing (PADD-seq), this study reveals that the p97-proteasome pathway can evict lesion-stalled Pol II independently of repair. Both TCR and repair-independent eviction require CSA and ubiquitination. However, p97 is dispensable for TCR and Pol II eviction in TCR-proficient cells, highlighting repairs prioritization over repair-independent eviction. Moreover, ubiquitination of RPB1-K1268 is important for both pathways, with USP7s deubiquitinase activity promoting TCR without abolishing repair-independent Pol II release. In summary, this study elucidates the fate of lesion-stalled Pol II, and may shed light on the molecular basis of genetic diseases caused by the defects of TCR genes.

Papua New Guinea (PNG) hosts distinct environments mainly represented by the ecoregions of the Highlands and Lowlands that display increased altitude and a predominance of pathogens, respectively. Since its initial peopling approximately 50,000 years ago, inhabitants of these ecoregions might have differentially adapted to the environmental pressures exerted by each of them. However, the genetic basis of adaptation in populations from these areas remains understudied. Here, we investigated signals of positive selection in 62 highlanders and 43 lowlanders across 14 locations in the main island of PNG using whole-genome genotype data from the Oceanian Genome Variation Project (OGVP) and searched for signals of positive selection through population differentiation and haplotype-based selection scans. Additionally, we performed archaic ancestry estimation to detect selection signals in highlanders within introgressed regions of the genome. Among highland populations we identified candidate genes representing known biomarkers for mountain sickness (SAA4, SAA1, PRDX1, LDHA) as well as candidate genes of the Notch signaling pathway (PSEN1, NUMB, RBPJ, MAML3), a novel proposed pathway for high altitude adaptation in multiple organisms. We also identified candidate genes involved in oxidative stress, inflammation, and angiogenesis, processes inducible by hypoxia, as well as in components of the eye lens and the immune response. In contrast, candidate genes in the lowlands are mainly related to the immune response (HLA-DQB1, HLA-DQA2, TAAR6, TAAR9, TAAR8, RNASE4, RNASE6, ANG). Moreover, we find two candidate regions to be also enriched with archaic introgressed segments, suggesting that archaic admixture has played a role in the local adaptation of PNG populations.

Broadening gene therapy applications requires manufacturable vectors that efficiently transduce target cells in humans and preclinical models. Conventional selections of adeno-associated virus (AAV) capsid libraries are inefficient at searching the vast sequence space for the small fraction of vectors possessing multiple traits essential for clinical translation. Here, we present Fit4Function, a generalizable machine learning (ML) approach for systematically engineering multi-trait AAV capsids. By leveraging a capsid library that uniformly samples the manufacturable sequence space, reproducible screening data are generated to train accurate sequence-to-function models. Combining six models, we designed a multi-trait (liver-targeted, manufacturable) capsid library and validated 88% of library variants on all six predetermined criteria. Furthermore, the models, trained only on mouse in vivo and human in vitro Fit4Function data, accurately predicted AAV capsid variant biodistribution in macaque. Top candidates exhibited production yields comparable to AAV9, efficient murine liver transduction, up to 1000-fold greater human hepatocyte transduction, and increased enrichment relative to AAV9 in a screen for liver transduction in macaques. The Fit4Function strategy ultimately makes it possible to predict cross-species traits of peptide-modified AAV capsids and is a critical step toward assembling an ML atlas that predicts AAV capsid performance across dozens of traits.

SUMMARY: Deep mutational scanning (DMS) experiments provide a powerful method to measure the functional effects of genetic mutations at massive scales. However, the data generated from these experiments can be difficult to analyze, with significant variation between experimental replicates. To overcome this challenge, we developed popDMS, a computational method based on population genetics theory, to infer the functional effects of mutations from DMS data. Through extensive tests, we found that the functional effects of single mutations and epistasis inferred by popDMS are highly consistent across replicates, comparing favorably with existing methods. Our approach is flexible and can be widely applied to DMS data that includes multiple time points, multiple replicates, and different experimental conditions. AVAILABILITY AND IMPLEMENTATION: popDMS is implemented in Python and Julia, and is freely available on GitHub at https://github.com/bartonlab/popDMS.

We here present CLUES2, a full-likelihood method to infer natural selection from sequence data that is an extension of the method CLUES. We make several substantial improvements to the CLUES method that greatly increases both its applicability and its speed. We add the ability to use ancestral recombination graphs on ancient data as emissions to the underlying hidden Markov model, which enables CLUES2 to use both temporal and linkage information to make estimates of selection coefficients. We also fully implement the ability to estimate distinct selection coefficients in different epochs, which allows for the analysis of changes in selective pressures through time, as well as selection with dominance. In addition, we greatly increase the computational efficiency of CLUES2 over CLUES using several approximations to the forward-backward algorithms and develop a new way to reconstruct historic allele frequencies by integrating over the uncertainty in the estimation of the selection coefficients. We illustrate the accuracy of CLUES2 through extensive simulations and validate the importance sampling framework for integrating over the uncertainty in the inference of gene trees. We also show that CLUES2 is well-calibrated by showing that under the null hypothesis, the distribution of log-likelihood ratios follows a χ2 distribution with the appropriate degrees of freedom. We run CLUES2 on a set of recently published ancient human data from Western Eurasia and test for evidence of changing selection coefficients through time. We find significant evidence of changing selective pressures in several genes correlated with the introduction of agriculture to Europe and the ensuing dietary and demographic shifts of that time. In particular, our analysis supports previous hypotheses of strong selection on lactase persistence during periods of ancient famines and attenuated selection in more modern periods.

SummaryHarmonizing variant indexing and allele assignments across datasets is crucial for data integrity in cross-dataset studies such as multi-cohort genome-wide association studies, meta-analyses, and the development, validation, and application of polygenic risk scores. Ensuring this indexing and allele consistency is a laborious, time-consuming, and error-prone process requiring a certain degree of computational proficiency. Here, we introduce GRIEVOUS, a command-line tool for cross-dataset variant homogenization. By means of an internal database and a custom indexing methodology, GRIEVOUS identifies, formats, and aligns all biallelic single nucleotide polymorphisms (SNPs) across all summary statistic and genotype files of interest. Upon completion of dataset harmonization, GRIEVOUS can also be used to extract the maximal set of biallelic SNPs common to all datasets.Availability and implementationGRIEVOUS and all supporting documentation and tutorials can be found at https://github.com/jvtalwar/GRIEVOUS. It is freely and publicly available under the MIT license and can be installed via pip.

Genome-wide association studies (GWAS) implicate broad genomic loci containing clusters of highly correlated genetic variants. Finemapping techniques can select and prioritize variants within each GWAS locus which are more likely to have a functional influence on the trait. Here, we present a novel method, Finemap-MiXeR, for finemapping causal variants from GWAS summary statistics, controlling for correlation among variants due to linkage disequilibrium. Our method is based on a variational Bayesian approach and direct optimization of the Evidence Lower Bound (ELBO) of the likelihood function derived from the MiXeR model. After obtaining the analytical expression for ELBOs gradient, we apply Adaptive Moment Estimation (ADAM) algorithm for optimization, allowing us to obtain the posterior causal probability of each variant. Using these posterior causal probabilities, we validated Finemap-MiXeR across a wide range of scenarios using both synthetic data, and real data on height from the UK Biobank. Comparison of Finemap-MiXeR with two existing methods, FINEMAP and SuSiE RSS, demonstrated similar or improved accuracy. Furthermore, our method is computationally efficient in several aspects. For example, unlike many other methods in the literature, its computational complexity does not increase with the number of true causal variants in a locus and it does not require any matrix inversion operation. The mathematical framework of Finemap-MiXeR is flexible and may also be applied to other problems including cross-trait and cross-ancestry finemapping.

The ENIGMA research consortium develops and applies methods to determine clinical significance of variants in hereditary breast and ovarian cancer genes. An ENIGMA BRCA1/2 classification sub-group, formed in 2015 as a ClinGen external expert panel, evolved into a ClinGen internal Variant Curation Expert Panel (VCEP) to align with Food and Drug Administration recognized processes for ClinVar contributions. The VCEP reviewed American College of Medical Genetics and Genomics/Association of Molecular Pathology (ACMG/AMP) classification criteria for relevance to interpreting BRCA1 and BRCA2 variants. Statistical methods were used to calibrate evidence strength for different data types. Pilot specifications were tested on 40 variants and documentation revised for clarity and ease of use. The original criterion descriptions for 13 evidence codes were considered non-applicable or overlapping with other criteria. Scenario of use was extended or re-purposed for eight codes. Extensive analysis and/or data review informed specification descriptions and weights for all codes. Specifications were applied to pilot variants with pre-existing ClinVar classification as follows: 13 uncertain significance or conflicting, 14 pathogenic and/or likely pathogenic, and 13 benign and/or likely benign. Review resolved classification for 11/13 uncertain significance or conflicting variants and retained or improved confidence in classification for the remaining variants. Alignment of pre-existing ENIGMA research classification processes with ACMG/AMP classification guidelines highlighted several gaps in the research processes and the baseline ACMG/AMP criteria. Calibration of evidence strength was key to justify utility and strength of different data types for gene-specific application. The gene-specific criteria demonstrated value for improving ACMG/AMP-aligned classification of BRCA1 and BRCA2 variants.

The impact of variations in the three-dimensional structure of the genome has been recognized, but solid cancer tissue studies are limited. Here, we performed integrated deep Hi-C sequencing with matched whole-genome sequencing, whole-genome bisulfite sequencing, 5-hydroxymethylcytosine (5hmC) sequencing and RNA sequencing across a cohort of 80 biopsy samples from patients with metastatic castration-resistant prostate cancer. Dramatic differences were present in gene expression, 5-methylcytosine/5hmC methylation and in structural variation versus mutation rate between A and B (open and closed) chromatin compartments. A subset of tumors exhibited depleted regional chromatin contacts at the AR locus, linked to extrachromosomal circular DNA (ecDNA) and worse response to AR signaling inhibitors. We also identified topological subtypes associated with stark differences in methylation structure, gene expression and prognosis. Our data suggested that DNA interactions may predispose to structural variant formation, exemplified by the recurrent TMPRSS2-ERG fusion. This comprehensive integrated sequencing effort represents a unique clinical tumor resource.

The androgen receptor (AR) is the main driver in the development of castration-resistant prostate cancer, where the emergence of AR splice variants leads to treatment-resistant disease. Through detailed molecular studies of the marine alkaloid manzamine A (MA), we identified transcription factor E2F8 as a previously unknown regulator of AR transcription that prevents AR synthesis in prostate cancer cells. MA significantly inhibited the growth of various prostate cancer cell lines and was highly effective in inhibiting xenograft tumor growth in mice without any pathophysiological perturbations in major organs. MA suppressed the full-length AR (AR-FL), its spliced variant AR-V7, and the AR-regulated prostate-specific antigen (PSA; also known as KLK3) and human kallikrein 2 (hK2; also known as KLK2) genes. RNA sequencing (RNA-seq) analysis and protein modeling studies revealed E2F8 interactions with DNA as a potential novel target of MA, suppressing AR transcription and its synthesis. This novel mechanism of blocking AR biogenesis via E2F8 may provide an opportunity to control therapy-resistant prostate cancer over the currently used AR antagonists designed to target different parts of the AR gene.

The contrast between the disruption of genome topology after cohesin loss and the lack of downstream gene expression changes instigates intense debates regarding the structure-function relationship between genome and gene regulation. Here, by analyzing transcriptome and chromatin accessibility at the single-cell level, we discover that, instead of dictating population-wide gene expression levels, cohesin supplies a general function to neutralize stochastic coexpression tendencies of cis-linked genes in single cells. Notably, cohesin loss induces widespread gene coactivation and chromatin co-opening tens of million bases apart in cis. Spatial genome and protein imaging reveals that cohesin prevents gene co-bursting along the chromosome and blocks spatial mixing of transcriptional hubs. Single-molecule imaging shows that cohesin confines the exploration of diverse enhancer and core promoter binding transcriptional regulators. Together, these results support that cohesin arranges nuclear topology to control gene coexpression in single cells.

BACKGROUND: A number of deep learning models have been developed to predict epigenetic features such as chromatin accessibility from DNA sequence. Model evaluations commonly report performance genome-wide; however, cis regulatory elements (CREs), which play critical roles in gene regulation, make up only a small fraction of the genome. Furthermore, cell type-specific CREs contain a large proportion of complex disease heritability. RESULTS: We evaluate genomic deep learning models in chromatin accessibility regions with varying degrees of cell type specificity. We assess two modeling directions in the field: general purpose models trained across thousands of outputs (cell types and epigenetic marks) and models tailored to specific tissues and tasks. We find that the accuracy of genomic deep learning models, including two state-of-the-art general purpose models-Enformer and Sei-varies across the genome and is reduced in cell type-specific accessible regions. Using accessibility models trained on cell types from specific tissues, we find that increasing model capacity to learn cell type-specific regulatory syntax-through single-task learning or high capacity multi-task models-can improve performance in cell type-specific accessible regions. We also observe that improving reference sequence predictions does not consistently improve variant effect predictions, indicating that novel strategies are needed to improve performance on variants. CONCLUSIONS: Our results provide a new perspective on the performance of genomic deep learning models, showing that performance varies across the genome and is particularly reduced in cell type-specific accessible regions. We also identify strategies to maximize performance in cell type-specific accessible regions.

Identifying and characterizing mobile genetic elements in sequencing data is essential for understanding their diversity, ecology, biotechnological applications and impact on public health. Here we introduce geNomad, a classification and annotation framework that combines information from gene content and a deep neural network to identify sequences of plasmids and viruses. geNomad uses a dataset of more than 200,000 marker protein profiles to provide functional gene annotation and taxonomic assignment of viral genomes. Using a conditional random field model, geNomad also detects proviruses integrated into host genomes with high precision. In benchmarks, geNomad achieved high classification performance for diverse plasmids and viruses (Matthews correlation coefficient of 77.8% and 95.3%, respectively), substantially outperforming other tools. Leveraging geNomad's speed and scalability, we processed over 2.7 trillion base pairs of sequencing data, leading to the discovery of millions of viruses and plasmids that are available through the IMG/VR and IMG/PR databases. geNomad is available at https://portal.nersc.gov/genomad .

UNLABELLED: Vibrio cholerae, the causative agent of the diarrheal disease cholera, poses an ongoing health threat due to its wide repertoire of horizontally acquired elements (HAEs) and virulence factors. New clinical isolates of the bacterium with improved fitness abilities, often associated with HAEs, frequently emerge. The appropriate control and expression of such genetic elements is critical for the bacteria to thrive in the different environmental niches they occupy. H-NS, the histone-like nucleoid structuring protein, is the best-studied xenogeneic silencer of HAEs in gamma-proteobacteria. Although H-NS and other highly abundant nucleoid-associated proteins (NAPs) have been shown to play important roles in regulating HAEs and virulence in model bacteria, we still lack a comprehensive understanding of how different NAPs modulate transcription in V. cholerae. By obtaining genome-wide measurements of protein occupancy and active transcription in a clinical isolate of V. cholerae, harboring recently discovered HAEs encoding for phage defense systems, we show that a lack of H-NS causes a robust increase in the expression of genes found in many HAEs. We further found that TsrA, a protein with partial homology to H-NS, regulates virulence genes primarily through modulation of H-NS activity. We also identified few sites that are affected by TsrA independently of H-NS, suggesting TsrA may act with diverse regulatory mechanisms. Our results demonstrate how the combinatorial activity of NAPs is employed by a clinical isolate of an important pathogen to regulate recently discovered HAEs. IMPORTANCE: New strains of the bacterial pathogen Vibrio cholerae, bearing novel horizontally acquired elements (HAEs), frequently emerge. HAEs provide beneficial traits to the bacterium, such as antibiotic resistance and defense against invading bacteriophages. Xenogeneic silencers are proteins that help bacteria harness new HAEs and silence those HAEs until they are needed. H-NS is the best-studied xenogeneic silencer; it is one of the nucleoid-associated proteins (NAPs) in gamma-proteobacteria and is responsible for the proper regulation of HAEs within the bacterial transcriptional network. We studied the effects of H-NS and other NAPs on the HAEs of a clinical isolate of V. cholerae. Importantly, we found that H-NS partners with a small and poorly characterized protein, TsrA, to help domesticate new HAEs involved in bacterial survival and in causing disease. A proper understanding of the regulatory state in emerging isolates of V. cholerae will provide improved therapies against new isolates of the pathogen.

BACKGROUND: The obligate intracellular bacterial family Chlamydiaceae comprises a number of different species that cause disease in various vertebrate hosts including humans. Chlamydia suis, primarily found in the gastrointestinal tract of pigs, is the only species of the Chlamydiaceae family to have naturally gained tetracycline resistance (TetR), through a genomic island (Tet-island), integrated into the middle of chromosomal invasin-like gene inv. Previous studies have hypothesised that the uptake of the Tet-island from a host outside the Chlamydiaceae family was a unique event, followed by spread among C. suis through homologous recombination. In vitro recombination studies have shown that Tet-island exchange between C. suis strains is possible. Our aim in this study was to gain a deeper understanding of the interclade recombination of the Tet-island, among currently circulating C. suis field strains compared to in vitro-generated recombinants, using published whole genome sequences of C. suis field strains (n = 35) and in vitro-generated recombinants (n = 63). RESULTS: We found that the phylogeny of inv better reflected the phylogeny of the Tet-island than that of the whole genome, supporting recombination rather than site-specific insertion as the means of transfer. There were considerable differences between the distribution of recombinations within in vitro-generated strains compared to that within the field strains. These differences are likely because in vitro-generated recombinants were selected for a tetracycline and rifamycin/rifampicin resistant background, leading to the largest peak of recombination across the Tet-island. Finally, we found that interclade recombinations across the Tet-island were more variable in length downstream of the Tet-island than upstream. CONCLUSIONS: Our study supports the hypothesis that the occurrence of TetR strains in both clades of C. suis came about through interclade recombination after a single ancestral horizontal gene transfer event.

Patterns of transcriptional activity are encoded in our genome through regulatory elements such as promoters or enhancers that, paradoxically, contain similar assortments of sequence-specific transcription factor (TF) binding sites1-3. Knowledge of how these sequence motifs encode multiple, often overlapping, gene expression programs is central to understanding gene regulation and how mutations in non-coding DNA manifest in disease4,5. Here, by studying gene regulation from the perspective of individual transcription start sites (TSSs), using natural genetic variation, perturbation of endogenous TF protein levels and massively parallel analysis of natural and synthetic regulatory elements, we show that the effect of TF binding on transcription initiation is position dependent. Analysing TF-binding-site occurrences relative to the TSS, we identified several motifs with highly preferential positioning. We show that these patterns are a combination of a TF's distinct functional profiles-many TFs, including canonical activators such as NRF1, NFY and Sp1, activate or repress transcription initiation depending on their precise position relative to the TSS. As such, TFs and their spacing collectively guide the site and frequency of transcription initiation. More broadly, these findings reveal how similar assortments of TF binding sites can generate distinct gene regulatory outcomes depending on their spatial configuration and how DNA sequence polymorphisms may contribute to transcription variation and disease and underscore a critical role for TSS data in decoding the regulatory information of our genome.