BACKGROUND: Naïve and primed pluripotent stem cells (PSCs) represent two different pluripotent states. Primed PSCs following in vitro culture exhibit lower developmental potency as evidenced by failure in germline chimera assays, unlike mouse naïve PSCs. However, the molecular mechanisms underlying the lower developmental competency of primed PSCs remain elusive. RESULTS: We examine the regulation of telomere maintenance, retrotransposon activity, and genomic stability of primed PSCs and compare them with naïve PSCs. Surprisingly, primed PSCs only minimally maintain telomeres and show fragile telomeres, associated with declined DNA recombination and repair activity, in contrast to naïve PSCs that robustly elongate telomeres. Also, we identify LINE1 family integrant L1Md_T as naïve-specific retrotransposon and ERVK family integrant IAPEz to define primed PSCs, and their transcription is differentially regulated by heterochromatic histones and Dnmt3b. Notably, genomic instability of primed PSCs is increased, in association with aberrant retrotransposon activity. CONCLUSIONS: Our data suggest that fragile telomere, retrotransposon-associated genomic instability, and declined DNA recombination repair, together with reduced function of cell cycle and mitochondria, increased apoptosis, and differentiation properties may link to compromised developmental potency of primed PSCs, noticeably distinguishable from naïve PSCs.

BACKGROUND: Recent studies highlight the role of metabolites in immune diseases, but it remains unknown how much of this effect is driven by genetic and non-genetic host factors. RESULT: We systematically investigate circulating metabolites in a cohort of 500 healthy subjects (500FG) in whom immune function and activity are deeply measured and whose genetics are profiled. Our data reveal that several major metabolic pathways, including the alanine/glutamate pathway and the arachidonic acid pathway, have a strong impact on cytokine production in response to ex vivo stimulation. We also examine the genetic regulation of metabolites associated with immune phenotypes through genome-wide association analysis and identify 29 significant loci, including eight novel independent loci. Of these, one locus (rs174584-FADS2) associated with arachidonic acid metabolism is causally associated with Crohn's disease, suggesting it is a potential therapeutic target. CONCLUSION: This study provides a comprehensive map of the integration between the blood metabolome and immune phenotypes, reveals novel genetic factors that regulate blood metabolite concentrations, and proposes an integrative approach for identifying new disease treatment targets.

BACKGROUND: Single-cell technologies are transforming biomedical research, including the recent demonstration that unspliced pre-mRNA present in single-cell RNA-Seq permits prediction of future expression states. Here we apply this RNA velocity concept to an extended timecourse dataset covering mouse gastrulation and early organogenesis. RESULTS: Intriguingly, RNA velocity correctly identifies epiblast cells as the starting point, but several trajectory predictions at later stages are inconsistent with both real-time ordering and existing knowledge. The most striking discrepancy concerns red blood cell maturation, with velocity-inferred trajectories opposing the true differentiation path. Investigating the underlying causes reveals a group of genes with a coordinated step-change in transcription, thus violating the assumptions behind current velocity analysis suites, which do not accommodate time-dependent changes in expression dynamics. Using scRNA-Seq analysis of chimeric mouse embryos lacking the major erythroid regulator Gata1, we show that genes with the step-changes in expression dynamics during erythroid differentiation fail to be upregulated in the mutant cells, thus underscoring the coordination of modulating transcription rate along a differentiation trajectory. In addition to the expected block in erythroid maturation, the Gata1-chimera dataset reveals induction of PU.1 and expansion of megakaryocyte progenitors. Finally, we show that erythropoiesis in human fetal liver is similarly characterized by a coordinated step-change in gene expression. CONCLUSIONS: By identifying a limitation of the current velocity framework coupled with in vivo analysis of mutant cells, we reveal a coordinated step-change in gene expression kinetics during erythropoiesis, with likely implications for many other differentiation processes.

Topologically associating domains (TAD) are a key structure of the 3D mammalian genomes. However, the prevalence and dynamics of TAD-like domains in single cells remain elusive. Here we develop a new algorithm, named deTOKI, to decode TAD-like domains with single-cell Hi-C data. By non-negative matrix factorization, deTOKI seeks regions that insulate the genome into blocks with minimal chance of clustering. deTOKI outperforms competing tools and reliably identifies TAD-like domains in single cells. Finally, we find that TAD-like domains are not only prevalent, but also subject to tight regulation in single cells.

We introduce STrain Resolution ON assembly Graphs (STRONG), which identifies strains de novo, from multiple metagenome samples. STRONG performs coassembly, and binning into metagenome assembled genomes (MAGs), and stores the coassembly graph prior to variant simplification. This enables the subgraphs and their unitig per-sample coverages, for individual single-copy core genes (SCGs) in each MAG, to be extracted. A Bayesian algorithm, BayesPaths, determines the number of strains present, their haplotypes or sequences on the SCGs, and abundances. STRONG is validated using synthetic communities and for a real anaerobic digestor time series generates haplotypes that match those observed from long Nanopore reads.

BACKGROUND: The phenotype of an individual can be affected not only by the individual's own genotypes, known as direct genetic effects (DGE), but also by genotypes of interacting partners, indirect genetic effects (IGE). IGE have been detected using polygenic models in multiple species, including laboratory mice and humans. However, the underlying mechanisms remain largely unknown. Genome-wide association studies of IGE (igeGWAS) can point to IGE genes, but have not yet been applied to non-familial IGE arising from "peers" and affecting biomedical phenotypes. In addition, the extent to which igeGWAS will identify loci not identified by dgeGWAS remains an open question. Finally, findings from igeGWAS have not been confirmed by experimental manipulation. RESULTS: We leverage a dataset of 170 behavioral, physiological, and morphological phenotypes measured in 1812 genetically heterogeneous laboratory mice to study IGE arising between same-sex, adult, unrelated mice housed in the same cage. We develop and apply methods for igeGWAS in this context and identify 24 significant IGE loci for 17 phenotypes (FDR < 10%). We observe no overlap between IGE loci and DGE loci for the same phenotype, which is consistent with the moderate genetic correlations between DGE and IGE for the same phenotype estimated using polygenic models. Finally, we fine-map seven significant IGE loci to individual genes and find supportive evidence in an experiment with a knockout model that Epha4 gives rise to IGE on stress-coping strategy and wound healing. CONCLUSIONS: Our results demonstrate the potential for igeGWAS to identify IGE genes and shed light into the mechanisms of peer influence.

Large-scale phenotype data can enhance the power of genomic prediction in plant and animal breeding, as well as human genetics. However, the statistical foundation of multi-trait genomic prediction is based on the multivariate linear mixed effect model, a tool notorious for its fragility when applied to more than a handful of traits. We present MegaLMM, a statistical framework and associated software package for mixed model analyses of a virtually unlimited number of traits. Using three examples with real plant data, we show that MegaLMM can leverage thousands of traits at once to significantly improve genetic value prediction accuracy.

Update of bioRxiv. 2021 Jan 26;:

BACKGROUND: Understanding the genetic architecture of temperature adaptation is key for characterizing and predicting the effect of climate change on natural populations. One particularly promising approach is Evolve and Resequence, which combines advantages of experimental evolution such as time series, replicate populations, and controlled environmental conditions, with whole genome sequencing. Recent analysis of replicate populations from two different Drosophila simulans founder populations, which were adapting to the same novel hot environment, uncovered very different architectures-either many selection targets with large heterogeneity among replicates or fewer selection targets with a consistent response among replicates. RESULTS: Here, we expose the founder population from Portugal to a cold temperature regime. Although almost no selection targets are shared between the hot and cold selection regime, the adaptive architecture was similar. We identify a moderate number of targets under strong selection (19 selection targets, mean selection coefficient = 0.072) and parallel responses in the cold evolved replicates. This similarity across different environments indicates that the adaptive architecture depends more on the ancestry of the founder population than the specific selection regime. CONCLUSIONS: These observations will have broad implications for the correct interpretation of the genomic responses to a changing climate in natural populations.

BACKGROUND: Akkermansia muciniphila is a human gut microbe with a key role in the physiology of the intestinal mucus layer and reported associations with decreased body mass and increased gut barrier function and health. Despite its biomedical relevance, the genomic diversity of A. muciniphila remains understudied and that of closely related species, except for A. glycaniphila, unexplored. RESULTS: We present a large-scale population genomics analysis of the Akkermansia genus using 188 isolate genomes and 2226 genomes assembled from 18,600 metagenomes from humans and other animals. While we do not detect A. glycaniphila, the Akkermansia strains in the human gut can be grouped into five distinct candidate species, including A. muciniphila, that show remarkable whole-genome divergence despite surprisingly similar 16S rRNA gene sequences. These candidate species are likely human-specific, as they are detected in mice and non-human primates almost exclusively when kept in captivity. In humans, Akkermansia candidate species display ecological co-exclusion, diversified functional capabilities, and distinct patterns of associations with host body mass. Analysis of CRISPR-Cas loci reveals new variants and spacers targeting newly discovered putative bacteriophages. Remarkably, we observe an increased relative abundance of Akkermansia when cognate predicted bacteriophages are present, suggesting ecological interactions. A. muciniphila further exhibits subspecies-level genetic stratification with associated functional differences such as a putative exo/lipopolysaccharide operon. CONCLUSIONS: We uncover a large phylogenetic and functional diversity of the Akkermansia genus in humans. This variability should be considered in the ongoing experimental and metagenomic efforts to characterize the health-associated properties of A. muciniphila and related bacteria.

One challenge facing omics association studies is the loss of statistical power when adjusting for confounders and multiple testing. The traditional statistical procedure involves fitting a confounder-adjusted regression model for each omics feature, followed by multiple testing correction. Here we show that the traditional procedure is not optimal and present a new approach, 2dFDR, a two-dimensional false discovery rate control procedure, for powerful confounder adjustment in multiple testing. Through extensive evaluation, we demonstrate that 2dFDR is more powerful than the traditional procedure, and in the presence of strong confounding and weak signals, the power improvement could be more than 100%.

BACKGROUND: The full biosphere structure and functional exploration of the microbial communities of the Challenger Deep of the Mariana Trench, the deepest known hadal zone on Earth, lag far behind that of other marine realms. RESULTS: We adopt a deep metagenomics approach to investigate the microbiome in the sediment of Challenger Deep, Mariana Trench. We construct 178 metagenome-assembled genomes (MAGs) representing 26 phyla, 16 of which are reported from hadal sediment for the first time. Based on the MAGs, we find the microbial community functions are marked by enrichment and prevalence of mixotrophy and facultative anaerobic metabolism. The microeukaryotic community is found to be dominated by six fungal groups that are characterized for the first time in hadal sediment to possess the assimilatory and dissimilatory nitrate/sulfate reduction, and hydrogen sulfide oxidation pathways. By metaviromic analysis, we reveal novel hadal Caudovirales clades, distinctive virus-host interactions, and specialized auxiliary metabolic genes for modulating hosts' nitrogen/sulfur metabolism. The hadal microbiome is further investigated by large-scale cultivation that cataloged 1070 bacterial and 19 fungal isolates from the Challenger Deep sediment, many of which are found to be new species specialized in the hadal habitat. CONCLUSION: Our hadal MAGs and isolates increase the diversity of the Challenger Deep sediment microbial genomes and isolates present in the public. The deep metagenomics approach fills the knowledge gaps in structure and diversity of the hadal microbiome, and provides novel insight into the ecology and metabolism of eukaryotic and viral components in the deepest biosphere on earth.

GRIDSS2 is the first structural variant caller to explicitly report single breakends-breakpoints in which only one side can be unambiguously determined. By treating single breakends as a fundamental genomic rearrangement signal on par with breakpoints, GRIDSS2 can explain 47% of somatic centromere copy number changes using single breakends to non-centromere sequence. On a cohort of 3782 deeply sequenced metastatic cancers, GRIDSS2 achieves an unprecedented 3.1% false negative rate and 3.3% false discovery rate and identifies a novel 32-100 bp duplication signature. GRIDSS2 simplifies complex rearrangement interpretation through phasing of structural variants with 16% of somatic calls phasable using paired-end sequencing.

BACKGROUND: Metazoan cells only utilize a small subset of the potential DNA replication origins to duplicate the whole genome in each cell cycle. Origin choice is linked to cell growth, differentiation, and replication stress. Although various genetic and epigenetic signatures have been linked to the replication efficiency of origins, there is no consensus on how the selection of origins is determined. RESULTS: We apply dual-color stochastic optical reconstruction microscopy (STORM) super-resolution imaging to map the spatial distribution of origins within individual topologically associating domains (TADs). We find that multiple replication origins initiate separately at the spatial boundary of a TAD at the beginning of the S phase. Intriguingly, while both high-efficiency and low-efficiency origins are distributed homogeneously in the TAD during the G1 phase, high-efficiency origins relocate to the TAD periphery before the S phase. Origin relocalization is dependent on both transcription and CTCF-mediated chromatin structure. Further, we observe that the replication machinery protein PCNA forms immobile clusters around TADs at the G1/S transition, explaining why origins at the TAD periphery are preferentially fired. CONCLUSION: Our work reveals a new origin selection mechanism that the replication efficiency of origins is determined by their physical distribution in the chromatin domain, which undergoes a transcription-dependent structural re-organization process. Our model explains the complex links between replication origin efficiency and many genetic and epigenetic signatures that mark active transcription. The coordination between DNA replication, transcription, and chromatin organization inside individual TADs also provides new insights into the biological functions of sub-domain chromatin structural dynamics.

BACKGROUND: The origin of sex chromosomes requires the establishment of recombination suppression between the proto-sex chromosomes. In many fish species, the sex chromosome pair is homomorphic with a recent origin, providing species for studying how and why recombination suppression evolved in the initial stages of sex chromosome differentiation, but this requires accurate sequence assembly of the X and Y (or Z and W) chromosomes, which may be difficult if they are recently diverged. RESULTS: Here we produce a haplotype-resolved genome assembly of zig-zag eel (Mastacembelus armatus), an aquaculture fish, at the chromosomal scale. The diploid assembly is nearly gap-free, and in most chromosomes, we resolve the centromeric and subtelomeric heterochromatic sequences. In particular, the Y chromosome, including its highly repetitive short arm, has zero gaps. Using resequencing data, we identify a ~7 Mb fully sex-linked region (SLR), spanning the sex chromosome centromere and almost entirely embedded in the pericentromeric heterochromatin. The SLRs on the X and Y chromosomes are almost identical in sequence and gene content, but both are repetitive and heterochromatic, consistent with zero or low recombination. We further identify an HMG-domain containing gene HMGN6 in the SLR as a candidate sex-determining gene that is expressed at the onset of testis development. CONCLUSIONS: Our study supports the idea that preexisting regions of low recombination, such as pericentromeric regions, can give rise to SLR in the absence of structural variations between the proto-sex chromosomes.

In response to the ongoing SARS-CoV-2 pandemic in the UK, the COVID-19 Genomics UK (COG-UK) consortium was formed to rapidly sequence SARS-CoV-2 genomes as part of a national-scale genomic surveillance strategy. The network consists of universities, academic institutes, regional sequencing centres and the four UK Public Health Agencies. We describe the development and deployment of CLIMB-COVID, an encompassing digital infrastructure to address the challenge of collecting and integrating both genomic sequencing data and sample-associated metadata produced across the COG-UK network.