Biology subjects

Heath, L.

Publications and source records attributed to Heath, L..

8 recordsLinked to original sources

bioRxiv2026

The SEA-AD DREAM Challenge: Community benchmarking human and AI agent solutions for Alzheimer's disease neuropathology prediction from single-nucleus transcriptomics

Single-nucleus transcriptomic atlases offer an unprecedented opportunity to connect cellular molecular states with Alzheimer's disease (AD) neuropathology, but whether these profiles encode reproducible, predictive information about pathological burden remains unclear. We present the SEA-AD DREAM Challenge, an open, international, model-to-data competition built on the Seattle Alzheimer's Disease Brain Cell Atlas to predict Alzheimer's disease neuropathological severity from single-nucleus RNA-sequencing data. Participants developed containerized models to predict categorical neuropathological staging, including overall Alzheimer's disease neuropathologic change, Braak stage, Thal phase, and CERAD score, as well as quantitative amyloid-{beta} and phospho-tau burden measured by 6E10 and AT8 immunohistochemistry. Across 17 eligible teams from 15 countries, the crowdsourcing framework enabled systematic comparison of diverse computational approaches and surfaced a broad landscape of modeling strategies and candidate predictive features. Top-performing methods achieved near-perfect prediction of categorical staging, with the best submission reaching a quadratic weighted kappa of 1.0 for the Overall AD Neuropathological Change score (ADNC), and competitive prediction of quantitative pathological burden in held-out data, with a best concordance correlation coefficient of 0.48. Post hoc perturbation analyses revealed that top categorical-stage predictions relied heavily on donor-level metadata-driven signals rather than transcriptomic features, whereas quantitative pathology prediction was more robust and supported by transcriptomic and cell-type-associated features with potential biological relevance to AD progression. The challenge also introduced the first AI Agent Track in a DREAM Challenge, providing an early benchmark for autonomous and human-guided agentic model development in single-cell neuroscience. This work demonstrates that single-nucleus transcriptomes encode substantial information about Alzheimer's disease pathology, establishes a reproducible benchmark for molecular neuropathology prediction, and highlights critical principles for designing privacy-preserving, leakage-aware community challenges using deeply phenotyped human brain data.

neuroscience↗

bioRxiv2025

Gut microbe-derived short-chain fatty acids regulate joint inflammation and activation of infiltrating and resident myeloid cells after alphavirus infection

Although oral antibiotics can predispose to joint inflammation, this phenomenon remains poorly understood. Here, we leverage mouse models of alphavirus-induced arthritis to investigate the roles of gut commensals, metabolites, and host immune mechanisms in promoting musculoskeletal inflammation. Mice treated with a short course of oral antibiotics exhibited worsened arthritis after chikungunya (CHIKV) or Mayaro virus infections. This phenotype was associated with loss of short chain fatty acids (SCFA), greater intestinal permeability, and activation of gut-associated immune cells, and required TLR4 signaling, MyD88 expression, monocytes, antigen-specific and bystander CD4+ T cells, and pro-inflammatory cytokines. Administration of exogenous SCFA or colonization of mice with bacterial species that generate SCFA mitigated CHIKV-induced joint inflammation. Single-cell RNA sequencing revealed that gut-derived SCFA ameliorate the inflammatory phenotype of synovial CD4+ T cells, infiltrating monocytes, and resident osteoclast-like cells. Thus, antibiotic-triggered gut dysbiosis exacerbates alphavirus arthritis by shaping the inflammatory profile of both infiltrating and resident immune cells in joint tissues.

immunology↗

bioRxiv2024

Large- Scale Deep Proteomic Analysis in Alzheimer's Disease Brain Regions Across Race and Ethnicity

AbstractO_ST_ABSIntroductionC_ST_ABSAlzheimers disease (AD) is the most prevalent neurodegenerative disease, yet our comprehension predominantly relies on studies within the non-Hispanic White (NHW) population. Here we aimed to provide comprehensive insights into the proteomic landscape of AD across diverse racial and ethnic groups. MethodsDorsolateral prefrontal cortex (DLPFC) and superior temporal gyrus (STG) brain tissues were donated from multiple centers (Mayo Clinic, Emory University, Rush University, Mt. Sinai School of Medicine) and were harmonized through neuropathological evaluation, specifically adhering to the Braak staging and CERAD criteria. Among 1105 DLPFC tissue samples (998 unique individuals), 333 were from African American donors, 223 from Latino Americans, 529 from NHW donors, and the rest were from a mixed or unknown racial background. Among 280 STG tissue samples (244 unique individuals), 86 were African American, 76 Latino American, 116 NHW and the rest were mixed or unknown ethnicity. All tissues were uniformly homogenized and analyzed by tandem mass tag mass spectrometry (TMT-MS). ResultsAs a Quality control (QC) measure, proteins with more than 50% missing values were removed and iterative principal component analysis was conducted to remove outliers within brain regions. After QC, 9,180 and 9,734 proteins remained in the DLPC and STG proteome, respectively, of which approximately 9,000 proteins were shared between regions. Protein levels of microtubule-associated protein tau (MAPT) and amyloid-precursor protein (APP) demonstrated AD-related elevations in DLPFC tissues with a strong association with CERAD and Braak across racial groups. APOE4 protein levels in brain were highly concordant with APOE genotype of the individuals. DiscussionThis comprehensive region resolved large-scale proteomic dataset provides a resource for the understanding of ethnoracial-specific protein differences in AD brain.

neuroscience↗

bioRxiv2024

Bridging the Gap: Multi-Omics Profiling of Brain Tissue in Alzheimer's Disease and Older Controls in Multi-Ethnic Populations

INTRODUCTIONMulti-omics studies in Alzheimers disease (AD) revealed many potential disease pathways and therapeutic targets. Despite their promise of precision medicine, these studies lacked African Americans (AA) and Latin Americans (LA), who are disproportionately affected by AD. METHODSTo bridge this gap, Accelerating Medicines Partnership in AD (AMP-AD) expanded brain multi-omics profiling to multi-ethnic donors. RESULTSWe generated multi-omics data and curated and harmonized phenotypic data from AA (n=306), LA (n=326), or AA and LA (n=4) brain donors plus Non-Hispanic White (n=252) and other (n=20) ethnic groups, to establish a foundational dataset enriched for AA and LA participants. This study describes the data available to the research community, including transcriptome from three brain regions, whole genome sequence, and proteome measures. DISCUSSIONInclusion of traditionally underrepresented groups in multi-omics studies is essential to discover the full spectrum of precision medicine targets that will be pertinent to all populations affected with AD.

neuroscience↗

bioRxiv2021

Meta Analysis of the Ralstonia solanacearum species complex (RSSC) based on comparative evolutionary genomics and reverse ecology

Ralstonia solanacearum species complex (RSSC) strains are bacteria that colonize plant xylem and cause vascular wilt diseases. However, individual strains vary in host range, optimal disease temperatures, and physiological traits. To increase our understanding of the evolution, diversity, and biology of the RSSC, we performed a meta-analysis of 100 representative RSSC genomes. These 100 RSSC genomes contain 4,940 genes on average, and a pangenome analysis found that there are 3,262 genes in the core genome ([~]60% of the mean RSSC genome) with 13,128 genes in the extensive flexible genome. Although a core genome phylogenetic tree and a genome similarity matrix aligned with the previously named species (R. solanacearum, R. pseudosolanacearum, R. syzygii) and phylotypes (I-IV), these analyses also highlighted an unrecognized sub-clade of phylotype II. Additionally, we identified differences between phylotypes with respect to gene content and recombination rate, and we delineated population clusters based on the extent of horizontal gene transfer. Multiple analyses indicate that phylotype II is the most diverse phylotype, and it may thus represent the ancestral group of the RSSC. Additionally, we also used our genome-based framework to test whether the RSSC sequence variant (sequevar) taxonomy is a robust method to define within-species relationships of strains. The sequevar taxonomy is based on alignments of a single conserved gene (egl). Although sequevars in phylotype II describe monophyletic groups, the sequevar system breaks down in the highly recombinogenic phylotype I, which highlights the need for an improved cost-effective method for genotyping strains in phylotype I. Finally, we enabled quick and precise genome-based identification of newly sequenced Ralstonia strains by assigning Life Identification Numbers (LINs) to the 100 strains and by circumscribing the RSSC and its sub-groups in the LINbase Web service. IMPACT STATEMENTThe Ralstonia solanacearum species complex (RSSC) includes dozens of economically important pathogens of many cultivated and wild plants. The extensive genetic and phenotypic diversity that exists within the RSSC has made it challenging to subdivide this group into meaningful subgroups with relevance to plant disease control and plant biosecurity. This study provides a solid genome-based framework for improved classification and identification of the RSSC by analyzing one hundred representative RSSC genome sequences with a suite of comparative evolutionary genomic tools. The results also lay the foundation for additional in-depth studies to gain further insights into evolution and biology of this heterogeneous complex of destructive plant pathogens. DATA SUMMARYThe authors confirm that all raw data and code and protocols have been provided within the manuscript. All publicly available sequencing data used for analysis have been supplemented with accession numbers to access the data. The assembled genome of strain 19-3PR_UW348 was submitted to NCBI under Bioproject PRJNA775652 Biosample SAMN22612291. This Whole Genome Shotgun project has been deposited at GenBank under the accession JAJMMU000000000. The version described in this paper is version JAJMMU010000000.

genomics↗

bioRxiv2019

Comparing Time Series Transcriptome Data Between Plants Using A Network Module Finding Algorithm

Comparative transcriptome analysis is the comparison of expression patterns between homologous genes in different species. Since most molecular mechanistic studies in plants have been performed in model species including Arabidopsis and rice, comparative transcriptome analysis is particularly important for functional annotation of genes in other plant species. Many biological processes, such as embryo development, are highly conserved between different plant species. The challenge is to establish one-to-one mapping of the developmental stages between two species. In this protocol, we solve this problem by converting the gene expression patterns into a co-expression network and then apply network module-finding algorithms to the cross-species co-expression network. We describe how to perform such analysis using bash scripts for preliminary data processing and R programming language, which implemented simulated annealing method for module finding. We also provide instructions on how to visualize the resulting co-expression networks across species.

bioinformatics↗

bioRxiv2018

ARG-miner: A web platform for crowdsourcing-based curation of antibiotic resistance genes

Curation of antibiotic resistance gene (ARG) databases is a labor-intensive process that requires expert knowledge to manually collect, correct, and/or annotate individual genes. Correspondingly, updates to existing databases tend to be infrequent, commonly requiring years for completion and often containing inconsistences. Further, because of limitations of manual curation, most existing ARG databases contain only a small proportion of known ARGs (~5k genes). A new approach is needed to achieve a truly comprehensive ARG database, while also maintaining a high level of accuracy. Here we propose a new web-based curation system, ARG-miner, which supports annotation of ARGs at multiple levels, including: gene name, antibiotic category, resistance mechanism, and evidence for mobility and occurrence in clinically-important bacterial strains. To overcome limitations of manual curation, we employ crowdsourcing as a novel strategy for expanding curation capacity towards achieving a truly comprehensive, up-to-date database. We develop and validate the approach by comparing performance of multiple cohorts of curators with varying levels of expertise, demonstrating that ARG-miner is more cost effective and less time-consuming relative to traditional expert curation. We further demonstrate the reliability of a trust validation filter for rejecting confounding input generated by spammers. Crowdsourcing was found to be as accurate as expert annotation, with an accuracy >90% for the annotation of a diverse test set of ARGs. ARG-miner provides a public API and database available at http://bench.cs.vt.edu/argminer.

bioinformatics↗

bioRxiv2017

Panaconda: Application of pan-synteny graph models to genome content analysis

MotivationWhole-genome alignment and pan-genome analysis are useful tools in understanding the similarities and differences of many genomes in an evolutionary context. Here we introduce the concept of pan-synteny graphs, an analysis method that combines elements of both to represent conservation and change of multiple prokaryotic genomes at an architectural level. Pan-synteny graphs represent a reference free approach for the comparison of many genomes and allows for the identification of synteny, insertion, deletion, replacement, inversion, recombination, missed assembly joins, evolutionary hotspots, and reference based scaffolding.\n\nResultsWe present an algorithm for creating whole genome multiple sequence comparisons and a model for representing the similarities and differences among sequences as a graph of syntenic gene families. As part of the pan-synteny graph creation, we first create a de Bruijn graph. Instead of the alphabet of nucleotides commonly used in genome assembly, we use an alphabet of gene families. This de Bruijn graph is then processed to create the pan-synteny graph. Our approach is novel in that it explicitly controls how regions from the same sequence and genome are aligned and generates a graph in which all sequences are fully represented as paths. This method harnesses previous computation involved in protein family calculation to speed up the creation of whole genome alignment for many genomes. We provide the software suite Panaconda, for the calculation of pan-synteny graphs given annotation input, and an implementation of methods for their layout and visualization.\n\nAvailabilityPanaconda is available at https://github.com/aswarren/pangenome_graphs and datasets used in examples are available at https://github.com/aswarren/pangenome_examples\n\nContactAndrew Warren anwarren@vt.edu

bioinformatics↗