Although the green revolution adapted a handful of crops to homogeneous and high-input industrialized agriculture, much of the global population still relies on the local production of variable crop cultivars by low-inpu...Although the green revolution adapted a handful of crops to homogeneous and high-input industrialized agriculture, much of the global population still relies on the local production of variable crop cultivars by low-input smallholder farms. This diversity of unhomogenized crops, like that of the grain and bioenergy crop sorghum, offers raw materials for genetic gain and cultivar improvement. However, breeding efforts can be constrained by highly specialized traits and breeding targets. Here, to bridge this diversity, we constructed a 33-member pangenome reference and a diversity panel across 1,984 cultivars and landraces. We leveraged these resources to explore the complex interplay among historical contingency, ongoing adaptation and previously uncharacterized structural diversity. Specifically, our analyses conclusively demonstrated multiple nested and deeply diverged structural variants in the domestication gene SHATTERING1, which distinguish the previously established multicentric origin of sorghum. We then applied landscape genomics to reveal how gene flow and secondary contact created the complex genetic mosaic in contemporary breeding networks. As proof of concept for pangenome-accelerated trait discovery, we connected biosynthetic gene cluster structural variation to phenotypic leaf concentration of the cyanogenic glucoside dhurrin. Combined, these approaches will accelerate breeding and trait discovery and provide a framework for similar applications in other crops.
Brixi G, Durrant MG, Ku J
… +59 more, Naghipourfar M, Poli M, Sun G, Brockman G, Chang D, Fanton A, Gonzalez GA, King SH, Li DB, Merchant AT, Nguyen E, Ricci-Tam C, Romero DW, Schmok JC, Taghibakhshi A, Vorontsov A, Yang B, Deng M, Gorton L, Nguyen N, Wang NK, Pearce MT, Simon E, Adams E, Amador ZJ, Ashley EA, Baccus SA, Dai H, Dillmann S, Ermon S, Guo D, Herschl MH, Ilango R, Janik K, Lu AX, Mehta R, Mofrad MRK, Ng MY, Pannu J, Ré C, St John J, Sullivan J, Tey J, Viggiano B, Zhu K, Zynda G, Balsam D, Collison P, Costa AB, Hernandez-Boussard T, Ho E, Liu MY, McGrath T, Powell K, Pinglay S, Burke DP, Goodarzi H, Hsu PD, Hie BL
All of life encodes information with DNA. Although tools for genome sequencing, synthesis and editing have transformed biological research, we still lack sufficient understanding of the immense complexity encoded by geno...All of life encodes information with DNA. Although tools for genome sequencing, synthesis and editing have transformed biological research, we still lack sufficient understanding of the immense complexity encoded by genomes to predict the effects of many classes of genomic changes or to intelligently compose new biological systems. Artificial intelligence models that learn information from genomic sequences across diverse organisms have increasingly advanced prediction and design capabilities. Here we introduce Evo 2, a biological foundation model trained on 9 trillion DNA base pairs from a highly curated genomic atlas spanning all domains of life to have a 1 million token context window with single-nucleotide resolution. Evo 2 learns to accurately predict the functional impacts of genetic variation-from noncoding pathogenic mutations to clinically significant BRCA1 variants-without task-specific fine-tuning. Mechanistic interpretability analyses reveal that Evo 2 learns representations associated with biological features, including exon-intron boundaries, transcription factor binding sites, protein structural elements and prophage genomic regions. The generative abilities of Evo 2 produce mitochondrial, prokaryotic and eukaryotic sequences at genome scale with greater naturalness and coherence than previous methods. Evo 2 also generates experimentally validated chromatin accessibility patterns when guided by predictive models and inference-time search. We have made Evo 2 fully open, including model parameters, training code, inference code and the OpenGenome2 dataset, to accelerate the exploration and design of biological complexity.
Holzmann KL, Schmitzer T, Abels A
… +15 more, Čorkalo M, Mitesser O, Kortmann M, Alonso-Alonso P, Correa-Carmona Y, Pinos A, Yon F, Alvarado M, Forsyth A, Lopera-Toro A, Brehm G, Keller A, Otieno M, Steffan-Dewenter I, Peters MK
Insects make up the majority of all animal species, with 70% occurring in the tropics, yet the impacts of warming on tropical insects remain highly uncertain. This stems from sparse, taxonomically biased data on thermal...Insects make up the majority of all animal species, with 70% occurring in the tropics, yet the impacts of warming on tropical insects remain highly uncertain. This stems from sparse, taxonomically biased data on thermal tolerance of tropical insects and an incomplete understanding of the underlying physiological mechanisms. Here we compared environmental temperatures with field-measured upper and lower thermal tolerance limits of around 2,300 insect species along Afrotropical and Neotropical elevational gradients and identified genomic signatures of thermal tolerance across the insect tree of life. We show that thermal tolerances do not proportionally track environmental temperatures but approach an asymptote in tropical lowlands. Insects at high elevations utilize plasticity to cope with rising temperatures, whereas lowland species have limited plastic abilities. Heat tolerance showed strong differences among insect orders and families, reflected in the thermal stability of proteins, suggesting that variation in thermal tolerance is founded in the fundamental protein architecture. Up to 52% of future surface temperatures and 38% of air temperatures in the Amazonian lowlands can cause heat mortality in half of the studied community. Our data suggest a limited capacity of insects in the Earth's most biodiverse regions to buffer future warming.
Coral reefs are marine biodiversity hotspots that provide a wide range of ecosystem services. They are reservoirs of bioactive metabolites, many produced by microorganisms associated with reef invertebrate hosts. However...Coral reefs are marine biodiversity hotspots that provide a wide range of ecosystem services. They are reservoirs of bioactive metabolites, many produced by microorganisms associated with reef invertebrate hosts. However, for the keystone species of coral reefs-the reef-building corals-we still lack a systematic assessment of their microbially encoded biosynthetic potential and the molecular resources at stake due to the alarming decline in reef biodiversity. Here we analysed microbial genomes reconstructed from 820 reef-building coral samples of three representative coral genera collected at 99 reefs across 32 islands throughout the Pacific Ocean (Tara Pacific expedition). By contextualizing our analyses with the microbiomes of other reef species, we found that only 10% of the 4,224 microbial species and less than 1% of the 645 species exclusively identified in Tara Pacific samples had genomic information available. Furthermore, the biosynthetic potential of reef-building coral microbiomes rivalled or surpassed that of traditional natural product sources such as sponges. Among the biosynthetically rich bacteria in the reef microbiome, we identified new groups of Acidobacteriota that encode previously unknown enzymology, in turn opening promising avenues for functional protein engineering. Together, this study underscores the importance of conserving coral reefs as vital reservoirs of molecular diversity.
Intrinsically disordered proteins and regions (collectively IDRs) are found across all kingdoms of life and have critical roles in virtually every eukaryotic cellular process. IDRs exist in a broad ensemble of structural...Intrinsically disordered proteins and regions (collectively IDRs) are found across all kingdoms of life and have critical roles in virtually every eukaryotic cellular process. IDRs exist in a broad ensemble of structurally distinct conformations. This structural plasticity facilitates diverse molecular recognition and function. Here we combine advances in physics-based force fields with the power of multi-modal generative deep learning to develop STARLING, a framework for rapid generation of accurate IDR ensembles and ensemble-aware representations from sequence. STARLING supports environmental conditioning across ionic strengths and demonstrates proof of concept for the interpolative ability of generative models beyond their training domain. Moreover, we enable ensemble refinement under experimental constraints using a Bayesian maximum-entropy reweighting scheme. Beyond ensemble characterization, STARLING sequence representations can be used in multiple ways. We showcase two examples: first, STARLING lets us perform ensemble-based search for 'biophysical look-alikes'. Second, we demonstrate how these latent representations can be used to accelerate ensemble-first sequence design from weeks or hours per candidate to seconds, enabling library-scale designs. Together, STARLING dramatically lowers the barrier to the computational interrogation of IDR function through the lens of emergent biophysical properties, complementing bioinformatic protein sequence analysis. We evaluate the accuracy of STARLING against extant experimental data and offer a series of vignettes illustrating how STARLING can enable rapid hypothesis generation for IDR function and aid the interpretation of experimental data.
Zou XZ, Hu F, Lou H
… +31 more, Burren OS, Li X, Megy K, Wheeler E, Wu Q, Atanur SS, Karpinski M, Loesch D, Fairhurst-Hunter Z, Deevi SVV, Oerton E, Wen S, Jiang X, Salvoro C, Mitchell J, Nag A, Hollis B, O'Neill A, AstraZeneca Genomics Initiative, Harrow J, MacArthur S, Wasilewski S, O'Dell S, Tian L, Smith KR, Del Angel G, Fabre M, Dhindsa RS, Wang Q, Petrovski S, Carss K
Copy number variants (CNVs) are key drivers of human diversity and disease risk. Here we evaluate the role of CNVs across a broad range of human phenotypes and diseases by analysing CNVs from 470,727 UK Biobank whole-gen...Copy number variants (CNVs) are key drivers of human diversity and disease risk. Here we evaluate the role of CNVs across a broad range of human phenotypes and diseases by analysing CNVs from 470,727 UK Biobank whole-genome sequences and conducting a variant- and gene-level phenome-wide association study (PheWAS) with 2,941 plasma protein abundance measurements, 13,336 binary clinical phenotypes and 1,911 quantitative traits. Proteomic analyses validated functional associations of CNVs with nearby genes (cis-protein quantitative trait loci; cis-pQTLs)-with deletions and duplications typically associated with reduced and increased protein levels, respectively-and uncovered previously unknown protein-protein interactions (trans-pQTLs). Our PheWAS recapitulated known associations and uncovered associations in both coding and non-coding regions. Notably, we identified a rare deletion in ZNF451 associated with increased leukocyte telomere length and a non-coding deletion of a SLC2A9 enhancer associated with reduced gout risk. In addition, by combining CNVs with protein-coding single nucleotide variants and indels, we enhanced the power of our study to detect gene-disease associations. Finally, we leveraged this multiomics dataset to identify several pQTLs that constitute candidate biomarkers, including TMPRSS5 for Charcot-Marie-Tooth disease type 1A. This multiancestry whole-genome-sequence CNV PheWAS offers insights into the roles of CNVs in human health outcomes and could serve as a valuable resource for therapeutic development.
Namba S, Sonehara K, Koyanagi YN
… +36 more, Kikuchi T, Ojima T, Edahiro R, Sato G, Yamaji T, Tomofuji Y, Ueda H, Yamamoto K, Ogawa Y, Suzuki K, Kanai A, Higashiue S, Kobayashi S, Yamaguchi H, Nagata Y, Okazaki Y, Matsumoto N, Motomura K, Koga H, Hishida A, Ikezaki H, Hara M, Nagayoshi M, Oze I, Nakano S, BioBank Japan Project, Oda Y, Suzuki Y, Iwasaki M, Sawada N, Matsuo K, Morisaki T, Yamauchi T, Kadowaki T, Matsuda K, Okada Y
Environmental differences in genetic effect sizes, namely, gene-environment interactions, may uncover the genetic encoding of phenotypic plasticity. We provide a cross-population atlas of gene-environment interactions co...Environmental differences in genetic effect sizes, namely, gene-environment interactions, may uncover the genetic encoding of phenotypic plasticity. We provide a cross-population atlas of gene-environment interactions comprising 440,210 individuals from European and Japanese populations, with replication in 539,794 individuals from diverse populations. By decomposing the contributions from age, sex and lifestyles, we delineate the aetiology of these gene-environment interactions, including a reverse-causality from a disease-related dietary change. Genome-wide analyses uncovered missing heritability and trait-trait relationships connected by the synergistic effects of genome and environments, which systematically affected polygenic prediction accuracy and cross-population portability. Single-cell projection revealed aging shift of pathways and cell types responsible for genetic regulation. Omics-level gene-environment analyses identified multiple sex-discordant genetic effects in lipid metabolism, informing clinical trial failures for genetically supported drug development. Our comprehensive gene-environment study decodes the dynamics of genetic associations, offering insights into complex trait biology, personalized medicine and drug development.
Köninger J, Labouyrie M, Ballabio C
… +11 more, Dulya O, Mikryukov V, Romero F, Franco A, Bahram M, Panagos P, Jones A, Tedersoo L, Orgiazzi A, Briones MJI, van der Heijden MGA
Pesticides are widely distributed in soils, yet their effects on soil biodiversity remain poorly understood. Here we examined the effects of 63 pesticides on soil archaea, bacteria, fungi, protists, nematodes, arthropods...Pesticides are widely distributed in soils, yet their effects on soil biodiversity remain poorly understood. Here we examined the effects of 63 pesticides on soil archaea, bacteria, fungi, protists, nematodes, arthropods and key functional gene groups across 373 sites spanning woodlands, grasslands and croplands in 26 European countries. Pesticide residues were detected in 70% of sites and emerged as the second strongest driver of soil biodiversity patterns after soil properties. Our analysis further revealed organism- and function-specific patterns, emphasizing complex and widespread non-target effects on soil biodiversity. Pesticides altered microbial functions, including phosphorus and nitrogen cycling, and suppressed beneficial taxa, including arbuscular mycorrhizal fungi and bacterivore nematodes. Our findings highlight the need to integrate functional and taxonomic characteristics into future risk assessment methodology to safeguard soil biodiversity, a cornerstone of ecosystem functioning.
Avsec Ž, Latysheva N, Cheng J
… +24 more, Novati G, Taylor KR, Ward T, Bycroft C, Nicolaisen L, Arvaniti E, Pan J, Thomas R, Dutordoir V, Perino M, De S, Karollus A, Gayoso A, Sargeant T, Mottram A, Wong LH, Drotár P, Kosiorek A, Senior A, Tanburn R, Applebaum T, Basu S, Hassabis D, Kohli P
Deep learning models that predict functional genomic measurements from DNA sequences are powerful tools for deciphering the genetic regulatory code. Existing methods involve a trade-off between input sequence length and...Deep learning models that predict functional genomic measurements from DNA sequences are powerful tools for deciphering the genetic regulatory code. Existing methods involve a trade-off between input sequence length and prediction resolution, thereby limiting their modality scope and performance. We present AlphaGenome, a unified DNA sequence model, which takes as input 1 Mb of DNA sequence and predicts thousands of functional genomic tracks up to single-base-pair resolution across diverse modalities. The modalities include gene expression, transcription initiation, chromatin accessibility, histone modifications, transcription factor binding, chromatin contact maps, splice site usage and splice junction coordinates and strength. Trained on human and mouse genomes, AlphaGenome matches or exceeds the strongest available external models in 25 of 26 evaluations of variant effect prediction. The ability of AlphaGenome to simultaneously score variant effects across all modalities accurately recapitulates the mechanisms of clinically relevant variants near the TAL1 oncogene. To facilitate broader use, we provide tools for making genome track and variant effect predictions from sequence.
Ricci L, Heidrich V, Punčochář M
… +10 more, Armanini F, Ciciani M, Nabinejad A, Fazaeli F, Piperni E, Servais C, Pinto F, Valles-Colomer M, Asnicar F, Segata N
The early infant microbiome is largely primed by microbial transmission from the mother between birth and the first few weeks of life, but how interpersonal transmission further shapes the developing microbiome in the fi...The early infant microbiome is largely primed by microbial transmission from the mother between birth and the first few weeks of life, but how interpersonal transmission further shapes the developing microbiome in the first year remains unexplored. Here we report a metagenomic survey to model microbiome transmission in the nursery setting among babies attending the first year, their educators and their families (n = 134 individuals). We performed dense longitudinal microbiome sampling (n = 1,013 faecal samples) during the first year of nursery and tracked microbial strain transmission within and between nursery groups across 3 different facilities. We detected extensive baby-to-baby microbiome transmission within nursery groups even after only 1 month of nursery attendance, with nursery-acquired strains accounting for a proportion of the infant gut microbiome comparable to that from family by the end of the first term. Baby-to-baby transmission continued to grow over the nursery year, in an increasingly intricate transmission network with single strains spreading in some classes, and with multiple baby-acquisition and species-transmissibility patterns. Having siblings was associated with higher microbiome diversity and reduced strain acquisition from nursery peers, while antibiotic treatment was the condition that most accounted for the increased influx of strains. This study shows that microbiome transmission between babies is extensive during the first year of nursery, and points to social interactions in infancy as crucial drivers of infant microbiome development.
The ability to construct entirely new synthetic DNA sequences de novo is essential to engineering and studying biology. However, the ability to produce long complex synthetic DNA sequences and libraries currently lags be...The ability to construct entirely new synthetic DNA sequences de novo is essential to engineering and studying biology. However, the ability to produce long complex synthetic DNA sequences and libraries currently lags behind the ability to sequence and edit DNA. All existing DNA-assembly technologies rely on DNA sequence information found within the final construct to direct assembly between DNA molecules. As a result of this paradigm, these sequences cannot be extensively optimized specifically for assembly without affecting the final sequence. To fundamentally address this challenge, here we show the development of a new DNA assembly technique named Sidewinder that separates the information that guides assembly from the final assembled sequence using DNA three-way junctions. We demonstrate the transformative nature of the Sidewinder technique with highly robust and accurate construction of a 40-piece multifragment assembly, complex DNA sequences of both high GC content and high repeats, parallel assembly of multiple distinct genes in the same reaction and a combinatorial library with a large number of diversified positions across the entire length of the gene for high coverage of a library of 442,368 variants. This technology enables high-fidelity DNA assembly with a misconnection rate at the three-way junction of approximately 1 in 1,000,000.
Carrizo GE, Lin P, Lee SH
… +18 more, Shenderov K, Blériot C, Cha M, Schimmelpfennig L, Shen Z, van Teijlingen Bakker N, Grzes KM, Kelly B, Safinia N, Schole KL, Musa Y, Mittler G, Zen Y, Pearce EJ, Ginhoux F, Sanin DE, Puleston DJ, Pearce EL
Tissue-resident macrophages (RTMs) form during embryogenesis, self-renew locally, and regulate tissue homeostasis by clearing dead cells and debris. During tissue damage, however, bone-marrow-derived monocytes enter tiss...Tissue-resident macrophages (RTMs) form during embryogenesis, self-renew locally, and regulate tissue homeostasis by clearing dead cells and debris. During tissue damage, however, bone-marrow-derived monocytes enter tissues and differentiate into RTMs, repairing the tissue and replenishing macrophages in the niche. The universal cell-intrinsic mechanisms that control the monocyte-to-RTM transition and the maintenance of mature RTMs across tissues remain elusive. Here we show that deoxyhypusine synthase (DHPS), an enzyme that mediates spermidine-dependent hypusine modification of translation factor eIF5A, is required for RTM differentiation and maintenance. Mice with myeloid cell lack of DHPS (Dhps-ΔM mice) had a global defect in RTMs across tissues, resulting in persistent but ultimately futile monocyte influx. Transcriptional analyses of DHPS-deficient macrophages indicated a block in their ability to differentiate into mature RTMs, whereas proteomics revealed defects in cell adhesion and signalling pathways. Sequencing of ribosome-engaged transcripts identified a subset of mRNAs involved in cell adhesion and signalling that rely on DHPS for efficient translation. Imaging of DHPS-deficient macrophages in tissues showed differences in morphology and tissue interactions, which were correlated with their failed RTM differentiation. DHPS-deficient macrophages were also defective in critical homeostatic RTM functions including efferocytosis and tissue maintenance. Together, our results demonstrate a cell-intrinsic, tissue-agnostic pathway that drives differentiation of monocyte-derived macrophages into RTMs.
Physiological and pathological processes such as inflammation and cancer emerge from interactions between cells over time. However, methods to follow cell populations over time within the native context of a human tissue...Physiological and pathological processes such as inflammation and cancer emerge from interactions between cells over time. However, methods to follow cell populations over time within the native context of a human tissue are lacking because a biopsy offers only a single snapshot. Here we present one-shot tissue dynamics reconstruction (OSDR), an approach to estimate a dynamical model of cell populations based on a single tissue sample. OSDR uses spatial proteomics to learn how the composition of cellular neighbourhoods influences division rate, providing a dynamical model of cell population change over time. We apply OSDR to human breast cancer data, and reconstruct two fixed points of fibroblasts and macrophage interactions. These fixed points correspond to hot and cold fibrosis, in agreement with co-culture experiments that measured these dynamics directly. We then use OSDR to discover a pulse-generating excitable circuit of T and B cells in the tumour microenvironment, suggesting temporal flares of anticancer immune responses. Finally, we study longitudinal biopsies from a triple-negative breast cancer clinical trial, in which OSDR predicts the collapse of the tumour cell population in responders but not in non-responders, based on early-treatment biopsies. OSDR can be applied to a wide range of spatial proteomics assays to enable analysis of tissue dynamics based on patient biopsies.
Ramsay M, Etheredge H, Tluway F
… +59 more, D'Amato ME, Chikwambi Z, Hamdi Y, Alhudiri I, Fakim Y, Ahmad KM, Belguith N, Bentley D, Boujemaa M, Calumbuana N, Chaouch M, Charfeddine C, Chinien G, Dukuze N, Eljilani M, Elzagheid A, Ferraz N, Ghoorah A, Goorah S, Gribaa M, Guidara S, Guirat M, Hazelhurst S, Jallul M, Kasu M, Kharrat N, Khumalo U, Kingsbury Z, Kisiangani I, Lopes-Cendes I, Lukusa P, Makay P, Makulo J, Mubungu G, Muhinda C, Mukhongo DM, Murwira A, Mustafa A, Ndinkabandi J, Ngole M, Nlandu Y, Nyathi M, Pereira L, Rejeb I, Santos LL, Sengupta D, Shebani A, Smyth N, Souissi A, Trabelsi M, Rebai A, Chimpolo MM, Lumaka A, Masimirembwa C, Mohamed SF, Mulder N, Mutesa L, Hanchard NA, Choudhury A
African populations remain substantially under-represented in research studies and global genomic databases. As the ancestral home of anatomically modern humans, Africa holds pride of place regarding human genetic divers...African populations remain substantially under-represented in research studies and global genomic databases. As the ancestral home of anatomically modern humans, Africa holds pride of place regarding human genetic diversity, with a deep and complex evolution over hundreds of thousands of years of human migration, admixture, and exposure to climate changes and infectious agents. Yet our present view of genomic diversity in Africa is sparse and poorly captures the rich variation across its more than 2,000 ethnolinguistic groups. To enhance representation, the Assessing Genetic Diversity in Africa (AGenDA) project, under the umbrella of the Human Heredity and Health in Africa (H3Africa) consortium, identified under-represented groups across nine different African countries for human whole-genome sequencing, with a view to enriching global datasets. Here we share our processes, including community engagement, obtaining ethics approvals, navigating legal compliance and developing a common governance framework. AGenDA is a testament to the determination of the scientific community to undertake research in challenging environments. It is led from Africa by African investigators who are the decision-makers in data-sharing processes. AGenDA is a step towards greater African representation in global genomic datasets to advance genomic research towards enabling precision medicine for Africa and the world.
Qiang H, Wang F, Lu W
… +28 more, Xing X, Kim H, Mérette SAM, Ayres LB, Oler E, AbuSalim JE, Roichman A, Neinast M, Cordova RA, Lee WD, Herbst E, Gupta V, Neff SL, Hiebert-Giesbrecht M, Young A, Gautam V, Tian S, Wang B, Röst H, Baidwan J, Greiner R, Chen L, Johnston CW, Foster LJ, Shapiro AM, Wishart DS, Rabinowitz JD, Skinnider MA
Despite decades of study, large parts of the mammalian metabolome remain unexplored. Mass spectrometry-based metabolomics routinely detects thousands of small molecule-associated peaks in human tissues and biofluids, but...Despite decades of study, large parts of the mammalian metabolome remain unexplored. Mass spectrometry-based metabolomics routinely detects thousands of small molecule-associated peaks in human tissues and biofluids, but typically only a small fraction of these can be identified, and structure elucidation of novel metabolites remains challenging. Biochemical language models have transformed the interpretation of DNA, RNA and protein sequences, but have not yet had a comparable impact on understanding small molecule metabolism. Here we present an approach that leverages chemical language models to anticipate the existence of previously uncharacterized metabolites. We introduce DeepMet, a chemical language model that learns from the structures of known metabolites to anticipate the existence of previously unrecognized metabolites. Integration of DeepMet with mass spectrometry-based metabolomics data facilitates metabolite discovery. We harness DeepMet to reveal several dozen structurally diverse mammalian metabolites. Our work demonstrates the potential for language models to advance the mapping of the mammalian metabolome.
Lipid transfer proteins (LTPs) maintain the specialized lipid compositions of organellar membranes. In humans, many LTPs are implicated in diseases, but the cargo and auxiliary lipids that facilitate the transfer of the...Lipid transfer proteins (LTPs) maintain the specialized lipid compositions of organellar membranes. In humans, many LTPs are implicated in diseases, but the cargo and auxiliary lipids that facilitate the transfer of the majority of LTPs remain unknown. Here we combined biochemical, lipidomic and computational methods to systematically characterize LTP-lipid complexes and measure how LTP gains of function affect cellular lipidomes. We identified bound lipids for around half of the hundreds of LTPs that we analysed, confirming known ligands and identifying new ones across most LTP families. Gains in LTP function affected the cellular abundance of both their known and newly identified lipid ligands, indicating comparable functional relevance of the two ligand sets. Using structural bioinformatics, we characterized mechanisms that contribute to lipid selectivity and identified preferences based on headgroup or acyl chain. We demonstrate some basic principles of how LTPs mobilize their ligands. They commonly interact with several classes of lipids and exhibit broad but selective preference for particular headgroups and for lipid species with shorter acyl chains that contain one or two unsaturated carbons, suggesting that only subsets of lipid species are efficiently mobilized. The datasets represent a resource for further analysis in different cell types and states, such as those associated with pathologies.
Dekker J, Oksuz BA, Zhang Y
… +87 more, Wang Y, Minsk MK, Kuang S, Yang L, Gibcus JH, Krietenstein N, Rando OJ, Xu J, Janssens DH, Henikoff S, Kukalev A, Andréa W, Winick-Ng W, Kempfer R, Pombo A, Yu M, Kumar P, Zhang L, Belmont AS, Sasaki T, van Schaik T, Brueckner L, Peric-Hupkes D, van Steensel B, Wang P, Chai H, Kim M, Ruan Y, Zhang R, Quinodoz SA, Bhat P, Guttman M, Zhao W, Chien S, Liu Y, Venev SV, Plewczynski D, Azcarate II, Szabó D, Thieme CJ, Szczepińska T, Chiliński M, Sengupta K, Conte M, Esposito A, Abraham A, Zhang R, Wang Y, Wen X, Wu Q, Yang Y, Liu J, Boninsegna L, Yildirim A, Zhan Y, Chiariello AM, Bianco S, Lee L, Hu M, Li Y, Barnett RJ, Cook AL, Emerson DJ, Marchal C, Zhao P, Park PJ, Alver BH, Schroeder AJ, Navelkar R, Bakker C, Ronchetti W, Ehmsen S, Veit AD, Gehlenborg N, Wang T, Li D, Wang X, Nicodemi M, Ren B, Zhong S, Phillips-Cremins JE, Gilbert DM, Pollard KS, Alber F, Ma J, Noble WS, Yue F
The dynamic three-dimensional (3D) organization of the human genome (the 4D nucleome) is linked to genome function. Here we describe efforts by the 4D Nucleome Project to map and analyse the 4D nucleome in widely used H1...The dynamic three-dimensional (3D) organization of the human genome (the 4D nucleome) is linked to genome function. Here we describe efforts by the 4D Nucleome Project to map and analyse the 4D nucleome in widely used H1 human embryonic stem cells and immortalized fibroblasts (HFFc6). We produced and integrated diverse genomic datasets of the 4D nucleome, each contributing unique observations, which enabled us to assemble extensive catalogues of more than 140,000 looping interactions per cell type, to generate detailed classifications and annotations of chromosomal domain types and their subnuclear positions, and to obtain single-cell 3D models of the nuclear environment of all genes including their long-range interactions with distal elements. Through extensive benchmarking, we describe the unique strengths of different genomic assays for studying the 4D nucleome, providing guidelines for future studies. Three-dimensional models of population-based and individual cell-to-cell variation in genome structure showed connections between chromosome folding, nuclear organization, chromatin looping, gene transcription and DNA replication. Finally, we demonstrate the use of computational methods to predict genome folding from DNA sequence, which will facilitate the discovery of potential effects of genetic variants, including variants associated with disease, on genome structure and function.
Bromage TG, Denys C, De Jesus CL
… +16 more, Erdjument-Bromage H, Kullmer O, Sandrock O, Schrenk F, McKee MD, Reznikov N, Ashley GM, Hu B, Poudel SB, Souron A, Buss DJ, Ittah E, Kubat J, Rabieh S, Yakar S, Neubert TA
The science of metabolic profiling exploits chemical compound byproducts of metabolism called metabolites that explain internal biological functions, physiological health and disease, and provide evidence of external inf...The science of metabolic profiling exploits chemical compound byproducts of metabolism called metabolites that explain internal biological functions, physiological health and disease, and provide evidence of external influences specific to an organism's habitat. Here we assess palaeometabolomes from fossilized mammalian hard tissues as a molecular ecological strategy to provide evidence of an ancient organism's relationship with its environment. From eastern, central and southern African Plio-Pleistocene localities of palaeoanthropological significance, we study six fossils from Olduvai Gorge, Tanzania, one from the Chiwondo Beds, Malawi, and one from Makapansgat, South Africa. We perform endogeneity assessments by analysing palaeometabolomes of palaeosols and the effects of owl digestion on rodent bones to enable prudent ecological inferences. Diagenesis is indicated by metabolites of collagenase-producing bacteria, whereas the preservation of peptides including those of collagen are identified by proteomics. Endogenous metabolites document biological functions and exogenous metabolites render environmental details including soil characteristics and woody cover, and enable annual minimum and maximum rainfall and temperature reconstructions at Olduvai Gorge, supporting the freshwater woodland and grasslands of Olduvai Gorge Bed I, and the dry woodlands and marsh of Olduvai Gorge Upper Bed II. All sites denote wetter and/or warmer conditions than today. We infer that metabolites preserved in hard tissues derive from an extravasated vasculature serum filtrate that becomes entombed within developing mineralized matrices, and most probably survive palaeontological timeframes in the nanoscopic 'pool' of structural-bound water that occurs in hard tissue niches.
Grotzinger AD, Werme J, Peyrot WJ
… +64 more, Frei O, de Leeuw C, Bicks LK, Guo Q, Margolis MP, Coombes BJ, Batzler A, Pazdernik V, Biernacka JM, Andreassen OA, Anttila V, Børglum AD, Breen G, Cai N, Demontis D, Edenberg HJ, Faraone SV, Franke B, Gandal MJ, Gelernter J, Hatoum AS, Hettema JM, Johnson EC, Jonas KG, Knowles JA, Koenen KC, Maihofer AX, Mallard TT, Mattheisen M, Mitchell KS, Neale BM, Nievergelt CM, Nurnberger JI, O'Connell KS, Peterson RE, Robinson EB, Sanchez-Roige SS, Santangelo SL, Scharf JM, Stefansson H, Stefansson K, Stein MB, Strom NI, Thornton LM, Tucker-Drob EM, Verhulst B, Waldman ID, Walters GB, Wray NR, Yu D, Anxiety Disorders Working Group of the Psychiatric Genomics Consortium, Attention-Deficit/Hyperactivity Disorder (ADHD) Working Group of the Psychiatric Genomics Consortium, Autism Spectrum Disorders Working Group of the Psychiatric Genomics Consortium, Bipolar Disorder Working Group of the Psychiatric Genomics Consortium, Eating Disorders Working Group of the Psychiatric Genomics Consortium, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Nicotine Dependence GenOmics (iNDiGO) Consortium, Obsessive-Compulsive Disorder and Tourette Syndrome Working Group of the Psychiatric Genomics Consortium, Post-Traumatic Stress Disorder Working Group of the Psychiatric Genomics Consortium, Schizophrenia Working Group of the Psychiatric Genomics Consortium, Substance Use Disorders Working Group of the Psychiatric Genomics Consortium, Lee PH, Kendler KS, Smoller JW
Psychiatric disorders display high levels of comorbidity and genetic overlap, challenging current diagnostic boundaries. For disorders for which diagnostic separation has been most debated, such as schizophrenia and bipo...Psychiatric disorders display high levels of comorbidity and genetic overlap, challenging current diagnostic boundaries. For disorders for which diagnostic separation has been most debated, such as schizophrenia and bipolar disorder, genomic methods have revealed that the majority of genetic signal is shared. While over a hundred pleiotropic loci have been identified by recent cross-disorder analyses, the full scope of shared and disorder-specific genetic influences remains poorly defined. Here we addressed this gap by triangulating across a suite of cutting-edge statistical and functional genomic analyses applied to 14 childhood- and adult-onset psychiatric disorders (1,056,201 cases). Using genetic association data from common variants, we identified and characterized five underlying genomic factors that explained the majority of the genetic variance of the individual disorders (around 66% on average) and were associated with 238 pleiotropic loci. The two factors defined by (1) Schizophrenia and bipolar disorders (SB factor); and (2) major depression, PTSD and anxiety (Internalizing factor) showed high levels of polygenic overlap and local genetic correlation and very few disorder-specific loci. The genetic signal shared across all 14 disorders was enriched for broad biological processes (for example, transcriptional regulation), while more specific pathways were shared at the level of the individual factors. The shared genetic signal across the SB factor was substantially enriched in genes expressed in excitatory neurons, whereas the Internalizing factor was associated with oligodendrocyte biology. These observations may inform a more neurobiologically valid psychiatric nosology and implicate targets for therapeutic development designed to treat commonly occurring comorbid presentations.
The incidence of cardiometabolic diseases is increasing globally, and both poor diet and the human gut microbiome have been implicated. However, the field lacks large-scale, comprehensive studies exploring these links in...The incidence of cardiometabolic diseases is increasing globally, and both poor diet and the human gut microbiome have been implicated. However, the field lacks large-scale, comprehensive studies exploring these links in diverse populations. Here, in over 34,000 US and UK participants with metagenomic, diet, anthropometric and host health data, we identified known and yet-to-be-cultured gut microbiome species associated significantly with different diets and risk factors. We developed a ranking of species most favourably and unfavourably associated with human health markers, called the 'ZOE Microbiome Health Ranking 2025'. This system showed strong and reproducible associations between the ranking of microbial species and both body mass index and host disease conditions on more than 7,800 additional public samples. In an additional 746 people from two dietary interventional clinical trials, favourably ranked species increased in abundance and prevalence, and unfavourably ranked species reduced over time. In conclusion, these analyses provide strong support for the association of both diet and microbiome with health markers, and the summary system can be used to inform the basis for future causal and mechanistic studies. It should be emphasized, however, that causal inference is not possible without prospective cohort studies and interventional clinical trials.