Biology subjects

He, P.

Publications and source records attributed to He, P..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

A Spatial and Temporal Transcriptomic Atlas of Mouse Intestinal Regeneration

Background & AimsThe intestinal epithelium exhibits a remarkable capacity for regeneration following injury. However, the spatial and temporal dynamics of the injury-repair cycle remain incompletely understood. MethodsWe employ spatial transcriptomics to create an atlas of the damage and repair response to ionizing radiation in the mouse intestine. We map molecular events driving epithelial recovery over a six-day period and 23 biological samples, spanning the early apoptotic response to tissue remodeling and repair. ResultsThe datasets capture mRNA of 19,042 genes in [~]26 million bins at 2{micro}m resolution. Analysis revealed transcriptional patterns and niche signals that would remain undetected in bulk or single-cell approaches, including a non-random activation of interferon-target genes. Temporal shifts in cytokine and growth factor gene expression, particularly in the crypt and lower villus regions, corroborate published studies and reveal new predictions of the mechanisms governing intestinal healing. Global transcriptional upregulation was observed in the regenerating epithelium, suggesting hypertranscription is a hallmark of intestinal repair. Furthermore, we observe altered cellular differentiation trajectories and villus patterning at the early stages of regeneration. ConclusionsTogether, our work provides a detailed spatiotemporal map of intestinal regeneration at subcellular resolution and nearly whole-genome scale. These data lay the groundwork for future discoveries and therapeutic strategies to enhance epithelial repair in inflammatory bowel diseases and other gastrointestinal pathologies or in response to side-effects of cancer therapies.

molecular biology↗

bioRxiv2025

Clade V MLO proteins are bona fide host susceptibility factors required for powdery mildew pathogenesis in Arabidopsis

Obligate biotrophic powdery mildew (PM) fungi strictly require living host to survive. To search for host factors or processes essential for PM pathogenesis, a tailored genetic screen was conducted with the immuno-compromised eds1-2/pad4-1/sid2-2 (eps) triple Arabidopsis mutant. This led to the identification of five allelic disruptive mutations in Mildew Locus O 2 (MLO2) to be responsible for the compromised-immunity-yet-poor infection (cipi) mutant phenotype upon challenge from an adapted PM isolate. Moreover, the eds1/pad4/sid2/mlo2/mlo6/mlo12 (eps3m) sextuple mutant display near complete immunity to the adapted PM fungus without sign of defense activation, demonstrating that these three clade V MLOs in Arabidopsis are bona fide host susceptibility factors of PM fungi. Confocal imaging revealed focal accumulation of MLO2-GFP in the peri-penetration peg membranous space, implicating MLO2 in repairing and stabilizing the damaged host plasma membrane, which may be co-opted by PM fungi for haustorium differentiation. Results from domain-swapping analysis between MLO1 and MLO2 suggest a bipartite functional configuration for MLO2: its C-terminus determines where and when MLO2 functions, while its N-terminal seven transmembrane domain region executes the cellular function that is critical for PM pathogenesis. Genetic studies further demonstrate that, unlike MLO7 in synergids, focal accumulation of MLO2 does not depend on FERONIA (FER) and its five other family members, nor does it require phosphatidylinositol 4,5-bisphosphate produced from phosphatidylinositol 4-phosphate 5-kinase 1 (PIP5K1) and PIP5K2. Together, these findings define clade V MLOs as host factors co-opted by obligate biotrophic PM fungi for successful host colonization.

plant biology↗

bioRxiv2025

Geno-pheno characterization of crop rhizospheres: An integrated Raman spectroscopy and microbiome approach in conventional and organic agriculture

1In this study, we examined phenotypic and compositional patterns in rhizosphere microbial communities across conventionally and organically managed farms to assess impacts on soil microbiomes. We employed newly developed single-cell Raman microspectroscopy (SCRS)-based community phenotypic profiling analysis with microbiome 16S rRNA gene amplicon sequencing to compare the soil microbial communities of alfalfa, carrot, corn, lettuce, potato, soybean, squash, tomato, triticale, wheat, oat, and pea grown under either conventional or organic agriculture across farms in New York State (USA). Distinct microbiome clustering patterns indicated that organic and conventional production methods imposed strong selective pressures, shaping microbial assemblages within each group more distinctly than site or plant species variations. Using SCRS-based microbial phenotyping, we identified distinct microbial adaptations in agricultural soils, with organic systems favoring lipid-accumulating phenotypes for energy storage and stress resilience in low-input environments, while higher nutrient availability in conventional systems promoted carbon-rich phenotypes, enhancing rapid carbon assimilation and biomass production. Through network analysis of ecological hub species, we identified Pseudomonas, a plant growth-promoting rhizobacteria (PGPR), along with several nitrogen-fixing prokaryotes as core members within conventional agricultural systems. In contrast, organically managed soils featured PGPR taxa from the Bacilli class and contained microorganisms carrying antibiotic resistance genes, potentially indicating the presence of antibiotic resistance genes within organic agricultural environments. Overall, we found that the novel inclusion of microbial phenotyping methods, such as SCRS, can describe unique linkages between microbiome structure and their physiology that are distinctive between conventional and organic agricultural systems. 2 ImportanceOur study successfully integrated single-cell Raman microspectroscopy and amplicon sequencing, two established techniques for analyzing microbial communities and their functions, enabling a link between genotype and phenotype to better characterize ecosystem dynamics. While few studies have explored microbial phenotypes alongside community composition to infer agricultural management differences, our research offered key insights into functional relevance of microbial communities to agricultural practices, demonstrating how management strategies influenced microbial adaptation. These findings advance microbial ecology research, demonstrating how agricultural management strategies influence microbiome structure and function, reinforcing the importance of phenotypic profiling in sustainable agriculture.

ecology↗

bioRxiv2025

New alleles of Arabidopsis BIK1 reinforce its predominant role in pattern-triggered immunity and caution interpretations of other reported functions

The receptor-like cytoplasmic kinase BIK1 and its close homolog PBL1 have been widely recognized as central components of plant immunity. However, most genetic studies of BIK1 and PBL1 functions were carried out with single T-DNA insertional mutant alleles. Some phenotypes observed in these mutants, e.g. autoimmunity, have been difficult to reconcile with the proposed role of BIK1 and PBL1 in pattern-triggered immunity. In this study, we generated multiple new alleles of bik1 and pbl1 by CRISPR-Cas9-based gene editing and systematically analyzed these mutants alongside existing T-DNA insertional lines. These analyses reinforced the central role of BIK1 and PBL1 in pattern-triggered immunity mediated by both receptor kinases and receptor-like proteins. At the same time, however, we revealed several pleiotropic phenotypes associated with T-DNA insertions that are not necessarily linked to loss of BIK1 or PBL1 function. Further analyses of newly generated bik1 pbl1 double mutants uncovered an even greater contribution of these kinases to immune signaling and disease resistance than previously appreciated. These findings clarify longstanding ambiguities surrounding BIK1 and PBL1 functions.

plant biology↗

bioRxiv2025

Biomni: A General-Purpose Biomedical AI Agent

Biomedical research underpins progress in our understanding of human health and disease, drug discovery, and clinical care. However, with the growth of complex lab experiments, large datasets, many analytical tools, and expansive literature, biomedical research is increasingly constrained by repetitive and fragmented workflows that slow discovery and limit innovation, underscoring the need for a fundamentally new way to scale scientific expertise. Here, we introduce Biomni, a general-purpose biomedical AI agent designed to autonomously execute a wide spectrum of research tasks across diverse biomedical subfields. To systematically map the biomedical action space, Biomni first employs an action discovery agent to create the first unified agentic environment - mining essential tools, databases, and protocols from tens of thousands of publications across 25 biomedical domains. Built on this foundation, Biomni features a generalist agentic architecture that integrates large language model (LLM) reasoning with retrieval-augmented planning and code-based execution, enabling it to dynamically compose and carry out complex biomedical workflows - entirely without relying on predefined templates or rigid task flows. Systematic benchmarking demonstrates that Biomni achieves strong generalization across heterogeneous biomedical tasks - including causal gene prioritization, drug repurposing, rare disease diagnosis, micro-biome analysis, and molecular cloning - without any task-specific prompt tuning. Real-world case studies further showcase Biomnis ability to interpret complex, multi-modal biomedical datasets and autonomously generate experimentally testable protocols. Biomni envisions a future where virtual AI biologists operate alongside and augment human scientists to dramatically enhance research productivity, clinical insight, and healthcare. Biomni is ready to use at https://biomni.stanford.edu, and we invite scientists to explore its capabilities, stress-test its limits, and co-create the next era of biomedical discoveries.

bioinformatics↗

bioRxiv2025

An atlas of TF driven gene programs across human cells

Combinations of transcription factors (TFs) regulate gene expression and determine cell fate. Much effort has been devoted to understanding TF activity in different tissues and how tissue-specificity is achieved. However, ultimately gene regulation occurs at the single cell level and the recent explosion in the availability of single cell gene expression data now makes it possible to understand TF activity at this granular level of resolution. Here, we leverage a large collection of Human Cell Atlas (HCA) single cell data to explore TF activity by examining cell-type and tissue-specific sets of target genes, or regulons. We compile a regulon atlas, CellRegulon, and map the activity of TFs in an extensive set of healthy adult and foetal tissues spanning hundreds of cell types. Using CellRegulon, we describe dynamic patterns of co-regulation, associate TF-modules with different cellular functions and characterise the distribution of active TFs and TF families across cell types. We show that CellRegulon can link disease gene expression signatures to cell types and TFs relevant to the disease. Finally, using a newly generated multiome dataset of the adult lung, we show how CellRegulon can be extended into an enhancer-gene regulatory network (eGRN) to improve cell-type associations with genetic risk loci for diseases, such as childhood onset asthma, COPD and IPF, and to identify high risk gene modules. Our database for easy download and interactive exploration allows researchers to understand key gene modules activated at cell type transitions and will therefore be valuable for tasks such as cell type engineering (https://www.cellregulondb.org).

genetics↗

bioRxiv2025

Predicting the patterns of kinship dynamics in human societies

By shaping the inclusive fitness effects of behaviours, kinship environments have important implications for social evolution, and studies across non-human animal societies have shown that the patterns of kinship dynamics individuals experience as they age can be predicted by simple demographic parameters relating to sex-specific dispersal patterns, the extent of extra-group mating, and the age-specific patterns of mortality and fertility. However, it remains unclear whether these insights apply within our own species, where we see both extended lifespans in which kinship dynamics can play out, and remarkable variations in cultural practices relating to marriage and residence that influence the patterns of inter-individual relatedness. Here, using a newly developed model of kinship dynamics, we explore how marriage practices and post-marital residence rules, along with the typical survival and fertility schedules of our own species, can predict the emergent patterns of female and male kinship dynamics in human societies. By showing evidence that the model successfully captures the patterns of female and male kinship dynamics underlying human societies, we demonstrate that, despite the diversity in human social organisation, a relatively simple set of parameters relating to male and female rates of dispersal and the prevalence of extra-group mating suffices to capture the qualitative patterns of human kinship dynamics. Given the inclusive fitness implications kinship dynamics have for sex- and age-linked variations in behaviours, our study thus generates new insights into human social evolution.

evolutionary biology↗

bioRxiv2025

Predicting kinship dynamics during pre- and post reproductive life stages

Individuals relatedness to their groups often changes with age, potentially favouring age-linked trends in social behaviours, and these kinship dynamics have been invoked to explain the evolution of life history traits such as early reproductive cessation and post-reproductive helping. While models showed that simple demographic parameters (e.g., the rates of male and female philopatry vs dispersal, and of local vs non-local mating) suffice to predict the patterns of kinship dynamics breeders experience, they make the unrealistic assumptions that survival and fecundity are age-independent, thus fail to capture the full complexity of kinship dynamics. In particular, they cannot consistently predict how individuals relatedness to their groups should change during their pre- and post-reproductive phases. Here, we extend existing models of kinship dynamics to allow any age-linked changes in mortality and fecundity of both sexes. We describe the new model, and demonstrate its improved predictive power, by comparing the observed kinship dynamics of the killer whales to the predictions of the previously available models (assuming constant mortality and fecundity) and of our new model (considering any observed age-specific survival and fecundity). We show the new model better predicts the data, successfully capturing the observed three-stage female kinship dynamics: an individuals relatedness to her group decreases initially while juvenile, increases across reproductive lifespan, and decreases again during post-reproductive life. These predictions demonstrate the power of our model in generating new insights into the theory of social life history evolution (e.g., explaining why post-reproductive lifespan evolved yet are constrained).

evolutionary biology↗

bioRxiv2025

Discovering Polyphosphate and Polyhydroxyalkanoate-Accumulating Organisms Across Ecosystems: Phenotype-targeted Genotyping via FACS-sequencing

Intracellular biopolymers serve versatile functions that allow microbes to adapt to fluctuating environmental conditions. The metabolic interdependence of dual intracellular polymers, namely polyphosphate (polyP) and polyhydroxyalkanoates (PHA), is a defining feature of functionally polyphosphate-accumulating organisms (PAOs), the key agents enabling enhanced biological phosphorus removal (EBPR) for wastewater treatment. However, beyond EBPR systems, the presence and identities of PAOs that possess both polyP and PHA in natural environments such as soil have never been examined due to a lack of available detection tools, despite their potential roles in carbon and phosphorus cycling. This study presents a novel phenotype-targeted approach integrating triple-stained fluorescence-activated cell sorting (FACS) with 16S rRNA gene amplicon sequencing (termed TriFlow-Seq) to simultaneously detect, quantify, and phylogenetically characterize PAOs accumulating both polyP and PHA (referred to as PHA- PAOs). TriFlow-Seq was validated using polymer staining image analysis and Single-Cell Raman micro-spectroscopy. Application to EBPR systems successfully enriched known PHA- PAOs, including Candidatus Accumulibacter, Tetrasphaera, Dechloromonas, Pseudomonas. It also revealed novel candidate PHA-PAOs, particularly within the Rhodobacteraceae family. When applied to soil samples, TriFlow-Seq led to the first discovery of diverse PHA-PAOs dominated by Pseudomonas, Halomonas, and Nannocystis in maize rhizosphere soils. These predominant genera are known rhizosphere inhabitants of essential crops with key plant growth- promoting functions including phosphate solubilization, biofilm formation, and phytohormone production, yet simultaneous polyP and PHA accumulation has not been directly reported in the maize rhizosphere. Our findings revealed unexpectedly high PHA-PAO prevalence and distinct phylogenetic patterns associated with different maize genotypes, suggesting a potentially overlooked role for dual polymer storage in microbial rhizosphere dynamics and function. This study establishes a pioneering approach to investigate dual polyP and PHA-containing PAO identities and their important roles in rhizosphere structure and plant health. SynopsisNovel method reveals dual-polymer bacteria in activated sludge and crop rhizospheres, suggesting new candidates supporting wastewater treatment and agricultural sustainability.

microbiology↗

bioRxiv2025

Endocytosis of the damage-associated molecular pattern receptor PEPR1 is BAK1-dependent.

After cellular damage caused by wounding or pathogens, Arabidopsis thaliana endogenous elicitor peptides (Peps) are released into the apoplast, enhancing innate immunity by directly binding to the membrane-localized leucine-rich repeat receptor kinase PEP RECEPTOR1 (PEPR1). Ligand binding induces PEPR1 heterodimerization with the co-receptor BRASSINOSTEROID INSENSITIVE1-ASSOCIATED KINASE1 (BAK1), followed by PEPR1 internalization, both essential for a subset of Pep1-induced responses. However, the role of BAK1 in Pep1-triggered PEPR1 endocytosis remains unclear. Here, we show that the ligand-induced PEPR1 endocytosis depends on its kinase activity and requires BAK1 C-terminal tail phosphorylation, which is equally indispensable for immune signaling and BAK1 internalization. Using a GFP insertional mutagenesis approach, we generated a partially functional GFP-tagged BAK1 to demonstrate that, following Pep1 elicitation, BAK1 and PEPR1 are endocytosed together with similar dynamics. Our findings identify the BAK1 function as a prerequisite for PEPR1 internalization.

plant biology↗

bioRxiv2025

Mutant IDH impairs chromatin binding by PDGFB to promote chromosome instability

Non-canonical roles for growth factors in the nucleus have been previously described, but their mechanism of action and biological roles remain enigmatic. Platelet-derived growth factor B (PDGFB) can drive formation of low-grade glioma and here we show that it localizes to the nucleus of human glioma cells where it binds chromatin to preserve genome stability and cell lineage. Failure of PDGFB to localize to the nucleus leads to chromosomal abnormalities, aberrant heterochromatin architecture and accelerated tumorigenesis. Furthermore, nuclear localization of PDGFB is reliant upon the expression levels and mutation status of isocitrate dehydrogenase (IDH). Unexpectedly, we identified macrophages as the predominant source of PDGFB in human, finding that immune-derived PDGFB can localize to the nucleus of glioma cells. Collectively, these studies show that immune derived PDGFB enters the nucleus of glioma cells to maintain genomic stability, while identifying a new mechanism by which IDH mutations promote gliomagenesis.

cancer biology↗

bioRxiv2025

Dynamic Reprogramming of Stromal Pdgfra-expressing cells during WNT-Mediated Transformation of the Intestinal Epithelium

Stromal fibroblasts regulate critical signaling gradients along the intestinal crypt-villus axis1 and provide a niche that supports adjacent epithelial stem cells. Here we report that Pdgfra-expressing fibroblasts secrete ligands that promote a regenerative-like state in the intestinal mucosa during early WNT-mediated tumorigenesis. Using a mouse model of WNT-driven oncogenesis and single-cell RNA sequencing (RNA-seq) of mesenchyme cell populations, we revealed a dynamic reprogramming of Pdgfra+ fibroblasts that facilitates WNT-mediated tissue transformation. Functional assays of potential mediators of cell-to-cell communication between these fibroblasts and the oncogenic epithelium revealed that TGFB signaling is notably induced in Pdgfra+ fibroblasts in the presence of oncogenic epithelium, and TGFB was essential to sustain regenerative-like growth of organoids ex vivo. Genetic reduction of Cdx2 in the {beta}-catenin mutant epithelium elevated the fetal-like/regenerative transcriptome and accelerated WNT-dependent onset of oncogenic transformation of the tissue in vivo. These results demonstrate that Pdgfra+ fibroblasts are activated during WNT-driven oncogenesis to promote a regenerative state in the epithelium that precedes and facilitates formation of tumors.

cancer biology↗

bioRxiv2024

MAPK signaling modulates the partition of DCP1 between P-bodies and stress granules in plant cells

Processing bodies (PBs) and stress granules (SGs) are membrane-less cellular compartments consisting of ribonucleoprotein complexes. Whereas PBs are more ubiquitous, SGs are assembled mainly in response to stress. PBs and SGs are known to physically interact and molecules exchange between the two have been documented in mammals. However, the molecular mechanisms underpinning these processes are virtually unknown in plants. We have reported recently that tandem CCCH zinc finger 1 (TZF1) protein can recruit MAPK signaling components to SGs. Here we have found that TZF1-MPK3/6-MKK4/5 form a protein-protein interacting network in SGs. The mRNA decapping factor 1 (DCP1) is a core component of PBs. MAPK signaling mediated phosphorylation triggers a rapid reduction of DCP1 partition into PBs, concomitantly associated with an increase of DCP1 assembly into SGs. Furthermore, we have found that plant SG marker protein UBP1b (oligouridylate binding protein 1b) plays a role in maintaining DCP1 in PBs by suppressing the accumulation of MAPK signaling components. Together, we propose that MAPK signaling and UBP1b mediate the dynamics of PBs and SGs in plant cells.

cell biology↗

bioRxiv2024

Understanding the epidemiology and pathogenesis of Mycobacterium tuberculosis with non-redundant pangenome and population genetics

Tuberculosis is a major public health threat demanding more than one million lives every year. Many challenges exist to defeat this deadly infectious disease which address the importance of a thorough understanding of the biology of the causative agent Mycobacterium tuberculosis (MTB). We generated a non-redundant pangenome of 420 epidemic MTB strains from China. We estimate that MTB strains have a pangenome of 4,278 genes encoding 4,183 proteins, of which 3,438 of which are core genes. However, due to 99,694 interruptions in 2,447 coding genes, only 1,651 may be translated in all samples, which dramatically reduces the number of active core genes. Of these interruptions, 67,315 (67.52%) could be classified by various genetic variations detected by currently available tools, and more than half of them are due to structure variations, mostly small indels. We further describe differential evolutionary patterns of genes under the influences of selective pressure, population structure and background selection. While selective pressure is ubiquitous among these coding genes, evolutionary adaptations primarily occur in 1,313 genes. Genes located in the cell wall and membrane region are under the strongest selective pressure, while biological processes including regulation of transcription, translation and regulation of growth are under strongest background selection in MTB. The metabolism of fatty acids may be an outstanding example of evolutionary adaption for MTB under current selective pressure. This study provides a comprehensive view on the genetic diversity and evolution patterns of coding genes in MTB which may deepen our understanding of its epidemiology and pathogenicity.

microbiology↗

bioRxiv2024

Discovery of novel quinoline papain-like protease inhibitors for COVID-19 through topology constrained molecular generative model

The rapid emergence of drug-resistant SARS-CoV-2 variants poses a persistent challenge to current antiviral strategies. Mutations in key viral targets, including RNA-dependent RNA polymerase (RdRp), main protease (3CLpro), and papain-like protease (PLpro), have been shown to markedly reduce the efficacy of approved therapeutics, highlighting the urgent need for next-generation antivirals capable of overcoming resistance. Here, we report the discovery of a novel class of PLpro inhibitors through an AIDD strategy based on a topology-constrained molecular generative model (Tree-Invent) integrated with structure-guided optimization. Scaffold hopping from a previously reported lead enabled the identification of a quinoline-based chemical series with substantially improved metabolic stability and antiviral potency. Structure-guided optimization yielded compound GZNL-2016, which exhibited potent enzymatic inhibition of PLpro (IC50 = 10.5 nM), robust antiviral activity against multiple SARS-CoV-2 variants, including Omicron BA.5 and XBB.1, and favorable pharmacokinetic properties following oral administration. Notably, GZNL-2016 retained substantial inhibitory activity against the clinically relevant drug-resistant mutant PLpro E167K (IC50 = 480.2 nM; Ki = 439.3 nM), in contrast to previously reported inhibitors that exhibit markedly reduced potency. In a SARS-CoV-2 infection mouse model, oral administration of GZNL-2016 significantly reduced pulmonary viral titers, demonstrating in vivo antiviral efficacy. Collectively, this study establishes an AI-enabled strategy for rapid antiviral discovery and identifies GZNL-2016 as a promising lead compound to address the threat of coronavirus infections caused by drug-resistant mutant SARS-CoV-2 variants. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=134 SRC="FIGDIR/small/611841v3_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@ecbd3eorg.highwire.dtl.DTLVardef@7c663corg.highwire.dtl.DTLVardef@11c0e37org.highwire.dtl.DTLVardef@e950f0_HPS_FORMAT_FIGEXP M_FIG C_FIG We leveraged an AI generative model to discover a novel PLpro inhibitor with excellent liver stability, low CYP, hERG inhibition and reasonable oral PK properties. At the same time, the compound 16 exhibits high efficacy for the resistance mutation E167K, which resulted in severe resistance to the previously reported inhibitor Jun12682 and PF-07957472.

microbiology↗

bioRxiv2024

PerturbAtlas: A Comprehensive Atlas of Public Genetic Perturbation Bulk RNA-seq Datasets

Manipulating gene expression is crucial for understanding gene function, with high-throughput sequencing techniques such as RNA-seq elucidating the downstream mechanisms involved. However, the lack of a standardized metadata format for small-scale perturbation expression datasets in public repositories hinders their reuse. To address this issue, we developed PerturbAtlas, an add-value resource that re-analyzes publicly archived RNA-seq libraries to provide quantitative data on gene expression, transcript profiles, and alternative splicing events following genetic perturbation. PerturbAtlas assists users in identifying trends at the gene and isoform levels in perturbation assays by re-analyzing a curated set of 122,801 RNA-seq libraries across 13 species. This resource is freely available at https://perturbatlas.kratoss.site as both raw data tables and an interactive browser, allowing searches by species, tissue, or genomic features. The results provide detailed information on alterations following perturbations, accessible through both forward and reverse approaches, thereby enabling the exploration of perturbation consequences and the identification of potential causal perturbations. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=199 SRC="FIGDIR/small/605482v1_ufig1.gif" ALT="Figure 1"> View larger version (76K): org.highwire.dtl.DTLVardef@150012org.highwire.dtl.DTLVardef@b7ac25org.highwire.dtl.DTLVardef@86c4feorg.highwire.dtl.DTLVardef@1ef3341_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

A multiomic atlas of human early skeletal development

Bone and joint formation in the developing skeleton rely on co-ordinated differentiation of progenitors in the nascent developing limbs and joints. The cell states, epigenetic processes and key regulatory factors underlying their lineage commitment to osteogenic and other mesenchymal populations during ossification and joint formation remain poorly understood and are largely unexplored in human studies. Here, we apply paired single-nuclei transcriptional and epigenetic profiling of 336,000 droplets, in addition to spatial transcriptomics, to construct a comprehensive atlas of human bone, cartilage and joint development in the shoulder, hip, knee and cranium from 5 to 11 post-conception weeks. Spatial mapping of cell clusters to our highly multiplexed in situ sequencing (ISS) data using our newly developed tool ISS-Patcher revealed new cellular mechanisms of zonation during bone and joint formation. Combined modelling of chromatin accessibility and RNA expression allowed the identification of the transcriptional and epigenetic regulatory landscapes that drive differentiation of mesenchymal lineages including osteogenic and chondrogenic lineages, and novel chondrocyte cell states. In particular, we define regionally distinct limb and cranial osteoprogenitor populations and trajectories across the fetal skeleton and characterise differential regulatory networks that govern intramembranous and endochondral ossification. We also introduce SNP2Cell, a tool to link cell-type specific regulatory networks to numerous polygenic traits such as osteoarthritis. We also conduct in silico perturbations of genes that cause monogenic craniosynostosis and implicate potential pathogenic cell states and disease mechanisms involved. This work forms a detailed and dynamic regulatory atlas of human fetal skeletal maturation and advances our fundamental understanding of cell fate determination in human skeletal development.

developmental biology↗

bioRxiv2024

MetaLATTE: Metal Binding Prediction via Multi-Task Learning on Protein Language Model Latents

The bioremediation of environments contaminated with heavy metals is an important challenge in environmental biotechnology, which may benefit from the identification of proteins that bind and neutralize these metals. Here, we introduce a novel predictive algorithm that conducts Metal binding prediction via LAnguage model laTenT Embeddings using a multi-task learning approach to accurately classify the metal-binding properties of input protein sequences. Our MetaLATTE model utilizes the state-of-the-art ESM-2 protein language model (pLM) embeddings and a position-sensitive attention mechanism to predict the likelihood of binding to specific metals, such as zinc, lead, and mercury. Importantly, our approach addresses the challenges posed by proteins from understudied organisms, which are often absent in traditional metal-binding databases, without the requirement of an input structure. By providing a probability distribution over potential binding metals, our classifier elucidates specific interactions of proteins with diverse metal ions. We envision that MetaLATTE will serve as a powerful tool for rapidly screening and identifying new metal-binding proteins, from metagenomic discovery or de novo design efforts, which can later be employed in targeted bioremediation campaigns.

bioinformatics↗