Metabolic diversity in commensal protists regulates intestinal immunity and trans-kingdom competition.

The microbiota influences intestinal health and physiology, yet the contributions of commensal protists to the gut environment have been largely overlooked. Here, we discover human- and rodent-associated parabasalid protists, revealing substantial diversity and prevalence in nonindustrialized human populations. Genomic and metabolomic analyses of murine parabasalids from the genus Tritrichomonas revealed species-level differences in excretion of the metabolite succinate, which results in distinct small intestinal immune responses. Metabolic differences between Tritrichomonas species also determine their ecological niche within the microbiota. By manipulating dietary fibers and developing in vitro protist culture, we show that different Tritrichomonas species prefer dietary polysaccharides or mucus glycans. These polysaccharide preferences drive trans-kingdom competition with specific commensal bacteria, which affects intestinal immunity in a diet-dependent manner. Our findings reveal unappreciated diversity in commensal parabasalids, elucidate differences in commensal protist metabolism, and suggest how dietary interventions could regulate their impact on gut health.

Loss of epigenetic information as a cause of mammalian aging.

All living things experience an increase in entropy, manifested as a loss of genetic and epigenetic information. In yeast, epigenetic information is lost over time due to the relocalization of chromatin-modifying proteins to DNA breaks, causing cells to lose their identity, a hallmark of yeast aging. Using a system called "ICE" (inducible changes to the epigenome), we find that the act of faithful DNA repair advances aging at physiological, cognitive, and molecular levels, including erosion of the epigenetic landscape, cellular exdifferentiation, senescence, and advancement of the DNA methylation clock, which can be reversed by OSK-mediated rejuvenation. These data are consistent with the information theory of aging, which states that a loss of epigenetic information is a reversible cause of aging.

GPC3-Unc5 receptor complex structure and role in cell migration.

Neural migration is a critical step during brain development that requires the interactions of cell-surface guidance receptors. Cancer cells often hijack these mechanisms to disseminate. Here, we reveal crystal structures of Uncoordinated-5 receptor D (Unc5D) in complex with morphogen receptor glypican-3 (GPC3), forming an octameric glycoprotein complex. In the complex, four Unc5D molecules pack into an antiparallel bundle, flanked by four GPC3 molecules. Central glycan-glycan interactions are formed by N-linked glycans emanating from GPC3 (N241 in human) and C-mannosylated tryptophans of the Unc5D thrombospondin-like domains. MD simulations, mass spectrometry and structure-based mutants validate the crystallographic data. Anti-GPC3 nanobodies enhance or weaken Unc5-GPC3 binding and, together with mutant proteins, show that Unc5/GPC3 guide migrating pyramidal neurons in the mouse cortex, and cancer cells in an embryonic xenograft neuroblastoma model. The results demonstrate a conserved structural mechanism of cell guidance, where finely balanced Unc5-GPC3 interactions regulate cell migration.

The NK cell granule protein NKG7 regulates cytotoxic granule exocytosis and inflammation.

Immune-modulating therapies have revolutionized the treatment of chronic diseases, particularly cancer. However, their success is restricted and there is a need to identify new therapeutic targets. Here, we show that natural killer cell granule protein 7 (NKG7) is a regulator of lymphocyte granule exocytosis and downstream inflammation in a broad range of diseases. NKG7 expressed by CD4+ and CD8+ T cells played key roles in promoting inflammation during visceral leishmaniasis and malaria-two important parasitic diseases. Additionally, NKG7 expressed by natural killer cells was critical for controlling cancer initiation, growth and metastasis. NKG7 function in natural killer and CD8+ T cells was linked with their ability to regulate the translocation of CD107a to the cell surface and kill cellular targets, while NKG7 also had a major impact on CD4+ T cell activation following infection. Thus, we report a novel therapeutic target expressed on a range of immune cells with functions in different immune responses.

Macrophages Facilitate Electrical Conduction in the Heart.

Organ-specific functions of tissue-resident macrophages in the steady-state heart are unknown. Here, we show that cardiac macrophages facilitate electrical conduction through the distal atrioventricular node, where conducting cells densely intersperse with elongated macrophages expressing connexin 43. When coupled to spontaneously beating cardiomyocytes via connexin-43-containing gap junctions, cardiac macrophages have a negative resting membrane potential and depolarize in synchrony with cardiomyocytes. Conversely, macrophages render the resting membrane potential of cardiomyocytes more positive and, according to computational modeling, accelerate their repolarization. Photostimulation of channelrhodopsin-2-expressing macrophages improves atrioventricular conduction, whereas conditional deletion of connexin 43 in macrophages and congenital lack of macrophages delay atrioventricular conduction. In the Cd11bDTR mouse, macrophage ablation induces progressive atrioventricular block. These observations implicate macrophages in normal and aberrant cardiac conduction.

Intragenic DNA inversions expand bacterial coding capacity.

Bacterial populations that originate from a single bacterium are not strictly clonal and often contain subgroups with distinct phenotypes1. Bacteria can generate heterogeneity through phase variation-a preprogrammed, reversible mechanism that alters gene expression levels across a population1. One well-studied type of phase variation involves enzyme-mediated inversion of specific regions of genomic DNA2. Frequently, these DNA inversions flip the orientation of promoters, turning transcription of adjacent coding regions on or off2. Through this mechanism, inversion can affect fitness, survival or group dynamics3,4. Here, we describe the development of PhaVa, a computational tool that identifies DNA inversions using long-read datasets. We also identify 372 'intragenic invertons', a novel class of DNA inversions found entirely within genes, in genomes of bacterial and archaeal isolates. Intragenic invertons allow a gene to encode two or more versions of a protein by flipping a DNA sequence within the coding region, thereby increasing coding capacity without increasing genome size. We validate ten intragenic invertons in the gut commensal Bacteroides thetaiotaomicron, and experimentally characterize an intragenic inverton in the thiamine biosynthesis gene thiC.

Climate warming increases extreme daily wildfire growth risk in California.

California has experienced enhanced extreme wildfire behaviour in recent years1-3, leading to substantial loss of life and property4,5. Some portion of the change in wildfire behaviour is attributable to anthropogenic climate warming, but formally quantifying this contribution is difficult because of numerous confounding factors6,7 and because wildfires are below the grid scale of global climate models. Here we use machine learning to quantify empirical relationships between temperature (as well as the influence of temperature on aridity) and the risk of extreme daily wildfire growth (>10,000 acres) in California and find that the influence of temperature on the risk is primarily mediated through its influence on fuel moisture. We use the uncovered relationships to estimate the changes in extreme daily wildfire growth risk under anthropogenic warming by subjecting historical fires from 2003 to 2020 to differing background climatological temperatures and aridity conditions. We find that the influence of anthropogenic warming on the risk of extreme daily wildfire growth varies appreciably on a fire-by-fire and day-by-day basis, depending on whether or not climate warming pushes conditions over certain thresholds of aridity, such as 1.5 kPa of vapour-pressure deficit and 10% dead fuel moisture. So far, anthropogenic warming has enhanced the aggregate expected frequency of extreme daily wildfire growth by 25% (5-95 range of 14-36%), on average, relative to preindustrial conditions. But for some fires, there was approximately no change, and for other fires, the enhancement has been as much as 461%. When historical fires are subjected to a range of projected end-of-century conditions, the aggregate expected frequency of extreme daily wildfire growth events increases by 59% (5-95 range of 47-71%) under a low SSP1-2.6 emissions scenario compared with an increase of 172% (5-95 range of 156-188%) under a very high SSP5-8.5 emissions scenario, relative to preindustrial conditions.

Transfer learning enables predictions in network biology.

Mapping gene networks requires large amounts of transcriptomic data to learn the connections between genes, which impedes discoveries in settings with limited data, including rare diseases and diseases affecting clinically inaccessible tissues. Recently, transfer learning has revolutionized fields such as natural language understanding1,2 and computer vision3 by leveraging deep learning models pretrained on large-scale general datasets that can then be fine-tuned towards a vast array of downstream tasks with limited task-specific data. Here, we developed a context-aware, attention-based deep learning model, Geneformer, pretrained on a large-scale corpus of about 30 million single-cell transcriptomes to enable context-specific predictions in settings with limited data in network biology. During pretraining, Geneformer gained a fundamental understanding of network dynamics, encoding network hierarchy in the attention weights of the model in a completely self-supervised manner. Fine-tuning towards a diverse panel of downstream tasks relevant to chromatin and network dynamics using limited task-specific data demonstrated that Geneformer consistently boosted predictive accuracy. Applied to disease modelling with limited patient data, Geneformer identified candidate therapeutic targets for cardiomyopathy. Overall, Geneformer represents a pretrained deep learning model from which fine-tuning towards a broad range of downstream applications can be pursued to accelerate discovery of key network regulators and candidate therapeutic targets.

Tissue CD14+CD8+ T cells reprogrammed by myeloid cells and modulated by LPS.

The liver is bathed in bacterial products, including lipopolysaccharide transported from the intestinal portal vasculature, but maintains a state of tolerance that is exploited by persistent pathogens and tumours1-4. The cellular basis mediating this tolerance, yet allowing a switch to immunity or immunopathology, needs to be better understood for successful immunotherapy of liver diseases. Here we show that a variable proportion of CD8+ T cells compartmentalized in the human liver co-stain for CD14 and other prototypic myeloid membrane proteins and are enriched in close proximity to CD14high myeloid cells in hepatic zone 2. CD14+CD8+ T cells preferentially accumulate within the donor pool in liver allografts, among hepatic virus-specific and tumour-infiltrating responses, and in cirrhotic ascites. CD14+CD8+ T cells exhibit increased turnover, activation and constitutive immunomodulatory features with high homeostatic IL-10 and IL-2 production ex vivo, and enhanced antiviral/anti-tumour effector function after TCR engagement. This CD14+CD8+ T cell profile can be recapitulated by the acquisition of membrane proteins-including the lipopolysaccharide receptor complex-from mononuclear phagocytes, resulting in augmented tumour killing by TCR-redirected T cells in vitro. CD14+CD8+ T cells express integrins and chemokine receptors that favour interactions with the local stroma, which can promote their induction through CXCL12. Lipopolysaccharide can also increase the frequency of CD14+CD8+ T cells in vitro and in vivo, and skew their function towards the production of chemotactic and regenerative cytokines. Thus, bacterial products in the gut-liver axis and tissue stromal factors can tune liver immunity by driving myeloid instruction of CD8+ T cells with immunomodulatory ability.

Single-nucleus profiling of human dilated and hypertrophic cardiomyopathy.

Heart failure encompasses a heterogeneous set of clinical features that converge on impaired cardiac contractile function1,2 and presents a growing public health concern. Previous work has highlighted changes in both transcription and protein expression in failing hearts3,4, but may overlook molecular changes in less prevalent cell types. Here we identify extensive molecular alterations in failing hearts at single-cell resolution by performing single-nucleus RNA sequencing of nearly 600,000 nuclei in left ventricle samples from 11 hearts with dilated cardiomyopathy and 15 hearts with hypertrophic cardiomyopathy as well as 16 non-failing hearts. The transcriptional profiles of dilated or hypertrophic cardiomyopathy hearts broadly converged at the tissue and cell-type level. Further, a subset of hearts from patients with cardiomyopathy harbour a unique population of activated fibroblasts that is almost entirely absent from non-failing samples. We performed a CRISPR-knockout screen in primary human cardiac fibroblasts to evaluate this fibrotic cell state transition; knockout of genes associated with fibroblast transition resulted in a reduction of myofibroblast cell-state transition upon TGFß1 stimulation for a subset of genes. Our results provide insights into the transcriptional diversity of the human heart in health and disease as well as new potential therapeutic targets and biomarkers for heart failure.

Transcriptome variation in human tissues revealed by long-read sequencing.

Regulation of transcript structure generates transcript diversity and plays an important role in human disease1-7. The advent of long-read sequencing technologies offers the opportunity to study the role of genetic variation in transcript structure8-16. In this Article, we present a large human long-read RNA-seq dataset using the Oxford Nanopore Technologies platform from 88 samples from Genotype-Tissue Expression (GTEx) tissues and cell lines, complementing the GTEx resource. We identified just over 70,000 novel transcripts for annotated genes, and validated the protein expression of 10% of novel transcripts. We developed a new computational package, LORALS, to analyse the genetic effects of rare and common variants on the transcriptome by allele-specific analysis of long reads. We characterized allele-specific expression and transcript structure events, providing new insights into the specific transcript alterations caused by common and rare genetic variants and highlighting the resolution gained from long-read data. We were able to perturb the transcript structure upon knockdown of PTBP1, an RNA binding protein that mediates splicing, thereby finding genetic regulatory effects that are modified by the cellular environment. Finally, we used this dataset to enhance variant interpretation and study rare variants leading to aberrant splicing patterns.

Differentiation Drives Widespread Rewiring of the Neural Stem Cell Chaperone Network.

Neural stem and progenitor cells (NSPCs) are critical for continued cellular replacement in the adult brain. Lifelong maintenance of a functional NSPC pool necessitates stringent mechanisms to preserve a pristine proteome. We find that the NSPC chaperone network robustly maintains misfolded protein solubility and stress resilience through high levels of the ATP-dependent chaperonin TRiC/CCT. Strikingly, NSPC differentiation rewires the cellular chaperone network, reducing TRiC/CCT levels and inducing those of the ATP-independent small heat shock proteins (sHSPs). This switches the proteostasis strategy in neural progeny cells to promote sequestration of misfolded proteins into protective inclusions. The chaperone network of NSPCs is more effective than that of differentiated cells, leading to improved management of proteotoxic stress and amyloidogenic proteins. However, NSPC proteostasis is impaired by brain aging. The less efficient chaperone network of differentiated neural progeny may contribute to their enhanced susceptibility to neurodegenerative diseases characterized by aberrant protein misfolding and aggregation.

Enhancement of LIN28B-induced hematopoietic reprogramming by IGF2BP3.

Fetal hematopoietic stem and progenitor cells (HSPCs) hold promise to cure a wide array of hematological diseases, and we previously found a role for the RNA-binding protein (RBP) Lin28b in respecifying adult HSPCs to resemble their fetal counterparts. Here we show by single-cell RNA sequencing that Lin28b alone was insufficient for complete reprogramming of gene expression from the adult toward the fetal pattern. Using proteomics and in situ analyses, we found that Lin28b (and its closely related paralog, Lin28a) directly interacted with Igf2bp3, another RBP, and their enforced co-expression in adult HSPCs reactivated fetal-like B-cell development in vivo more efficiently than either factor alone. In B-cell progenitors, Lin28b and Igf2bp3 jointly stabilized thousands of mRNAs by binding at the same sites, including those of the B-cell regulators Pax5 and Arid3a as well as Igf2bp3 mRNA itself, forming an autoregulatory loop. Our results suggest that Lin28b and Igf2bp3 are at the center of a gene regulatory network that mediates the fetal-adult hematopoietic switch. A method to efficiently generate induced fetal-like hematopoietic stem cells (ifHSCs) will facilitate basic studies of their biology and possibly pave a path toward their clinical application.

Rare variants in long non-coding RNAs are associated with blood lipid levels in the TOPMed whole-genome sequencing study.

Long non-coding RNAs (lncRNAs) are known to perform important regulatory functions in lipid metabolism. Large-scale whole-genome sequencing (WGS) studies and new statistical methods for variant set tests now provide an opportunity to assess more associations between rare variants in lncRNA genes and complex traits across the genome. In this study, we used high-coverage WGS from 66,329 participants of diverse ancestries with measurement of blood lipids and lipoproteins (LDL-C, HDL-C, TC, and TG) in the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) program to investigate the role of lncRNAs in lipid variability. We aggregated rare variants for 165,375 lncRNA genes based on their genomic locations and conducted rare-variant aggregate association tests using the STAAR (variant-set test for association using annotation information) framework. We performed STAAR conditional analysis adjusting for common variants in known lipid GWAS loci and rare-coding variants in nearby protein-coding genes. Our analyses revealed 83 rare lncRNA variant sets significantly associated with blood lipid levels, all of which were located in known lipid GWAS loci (in a ±500-kb window of a Global Lipids Genetics Consortium index variant). Notably, 61 out of 83 signals (73%) were conditionally independent of common regulatory variation and rare protein-coding variation at the same loci. We replicated 34 out of 61 (56%) conditionally independent associations using the independent UK Biobank WGS data. Our results expand the genetic architecture of blood lipids to rare variants in lncRNAs.

Unsupervised removal of systematic background noise from droplet-based single-cell experiments using CellBender.

Droplet-based single-cell assays, including single-cell RNA sequencing (scRNA-seq), single-nucleus RNA sequencing (snRNA-seq) and cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq), generate considerable background noise counts, the hallmark of which is nonzero counts in cell-free droplets and off-target gene expression in unexpected cell types. Such systematic background noise can lead to batch effects and spurious differential gene expression results. Here we develop a deep generative model based on the phenomenology of noise generation in droplet-based assays. The proposed model accurately distinguishes cell-containing droplets from cell-free droplets, learns the background noise profile and provides noise-free quantification in an end-to-end fashion. We implement this approach in the scalable and robust open-source software package CellBender. Analysis of simulated data demonstrates that CellBender operates near the theoretically optimal denoising limit. Extensive evaluations using real datasets and experimental benchmarks highlight enhanced concordance between droplet-based single-cell data and established gene expression patterns, while the learned background noise profile provides evidence of degraded or uncaptured cell types.

Repeat modules and N-linked glycans define structure and antigenicity of a critical enterotoxigenic E. coli adhesin.

Enterotoxigenic Escherichia coli (ETEC) cause hundreds of millions of cases of infectious diarrhea annually, predominantly in children from low-middle income regions. Notably, in children, as well as volunteers challenged with ETEC, diarrheal severity is significantly increased in blood group A (bgA) individuals. EtpA, is a secreted glycoprotein adhesin that functions as a blood group A lectin to promote critical interactions between ETEC and blood group A glycans on intestinal epithelia for effective bacterial adhesion and toxin delivery. EtpA is highly immunogenic resulting in robust antibody responses following natural infection and experimental challenge of volunteers with ETEC. To understand how EtpA directs ETEC-blood group A interactions and stimulates adaptive immunity, we mutated EtpA, mapped its glycosylation by mass-spectrometry (MS), isolated polyclonal (pAbs) and monoclonal antibodies (mAbs) from vaccinated mice and ETEC-infected volunteers, and determined structures of antibody-EtpA complexes by cryo-electron microscopy. Both bgA and mAbs that inhibited EtpA-bgA interactions and ETEC adhesion, bound to the C-terminal repeat domain highlighting this region as crucial for ETEC pathogen-host interaction. MS analysis uncovered extensive and heterogeneous N-linked glycosylation of EtpA and cryo-EM structures revealed that mAbs directly engage these unique glycan containing epitopes. Finally, electron microscopy-based polyclonal epitope mapping revealed antibodies targeting numerous distinct epitopes on N and C-terminal domains, suggesting that EtpA vaccination generates responses against neutralizing and decoy regions of the molecule. Collectively, we anticipate that these data will inform our general understanding of pathogen-host glycan interactions and adaptive immunity relevant to rational vaccine subunit design.

Thrombosis risk in single- and double-heterozygous carriers of factor V Leiden and prothrombin G20210A in FinnGen and the UK Biobank.

ABSTRACT: The factor V Leiden (FVL; rs6025) and prothrombin G20210A (PTGM; rs1799963) polymorphisms are 2 of the most well-studied genetic risk factors for venous thromboembolism (VTE). However, double heterozygosity (DH) for FVL and PTGM remains poorly understood, with previous studies showing marked disagreement regarding thrombosis risk conferred by the DH genotype. Using multidimensional data from the UK Biobank (UKB) and FinnGen biorepositories, we evaluated the clinical impact of DH carrier status across 937 939 individuals. We found that 662 participants (0.07%) were DH carriers. After adjustment for age, sex, and ancestry, DH individuals experienced a markedly elevated risk of VTE compared with wild-type individuals (odds ratio [OR] = 5.24; 95% confidence interval [CI], 4.01-6.84; P = 4.8 × 10-34), which approximated the risk conferred by FVL homozygosity. A secondary analysis restricted to UKB participants (N = 445 144) found that effect size estimates for the DH genotype remained largely unchanged (OR = 4.53; 95% CI, 3.42-5.90; P < 1 × 10-16) after adjustment for commonly cited VTE risk factors, such as body mass index, blood type, and markers of inflammation. In contrast, the DH genotype was not associated with a significantly higher risk of any arterial thrombosis phenotype, including stroke, myocardial infarction, and peripheral artery disease. In summary, we leveraged population-scale genomic data sets to conduct, to our knowledge, the largest study to date on the DH genotype and were able to establish far more precise effect size estimates than previously possible. Our findings indicate that the DH genotype may occur as frequently as FVL homozygosity and may confer a similarly increased risk of VTE.

MMCT-Loop: a mix model-based pipeline for calling targeted 3D chromatin loops.

Protein-specific Chromatin Conformation Capture (3C)-based technologies have become essential for identifying distal genomic interactions with critical roles in gene regulation. The standard techniques include Chromatin Interaction Analysis by Paired-End Tag (ChIA-PET), in situ Hi-C followed by chromatin immunoprecipitation (HiChIP) also known as PLAC-seq. To identify chromatin interactions from these data, a variety of computational methods have emerged. Although these state-of-art methods address many issues with loop calling, only few methods can fit different data types simultaneously, and the accuracy as well as the efficiency these approaches remains limited. Here we have generated a pipeline, MMCT-Loop, which ensures the accurate identification of strong loops as well as dynamic or weak loops through a mixed model. MMCT-Loop outperforms existing methods in accuracy, and the detected loops show higher activation functionality. To highlight the utility of MMCT-Loop, we applied it to conformational data derived from neural stem cell (NSCs) and uncovered several previously unidentified regulatory regions for key master regulators of stem cell identity. MMCT-Loop is an accurate and efficient loop caller for targeted conformation capture data, which supports raw data or pre-processed valid pairs as input, the output interactions are formatted and easily uploaded to a genome browser for visualization.