Biology subjects

Waldron, L. D.

Publications and source records attributed to Waldron, L. D..

12 recordsLinked to original sources

bioRxiv2026

Elucidating the dysbiotic features of gut microbiome, interaction with the human genome, and utility as a biomarker for treatment of Parkinson disease.

Parkinsons disease (PD) is the fastest-growing neurologic disease and a leading cause of disability worldwide. PD affects the body and mind, is progressive, and there is no prevention or cure. Gut microbiome, a recently recognized contributing factor in PD, offers new leads for understanding the underlying pathobiology and devising new treatments. Here, we present the most comprehensive study of the PD gut microbiome to date, comprising three large datasets with a sample size of 1,006 PD and 544 neurologically healthy controls, generated with uniform methodology from subject recruitment to data analysis, and characterized using deep shotgun metagenome sequencing, genome-wide genotypes, and metadata. We begin by describing the gut dysbiosis at the species, gene, pathway, and functional level. Next, we find that PD-associated genetic variants at the SNCA gene region are associated with increased abundance of opportunistic pathogens and depletion of fiber degraders in the PD gut. We show that the presence of opportunistic pathogens at high levels in the gut increases the penetrance of SNCA variants for PD risk, raising the GWAS-derived odds ratio from less than 1.5 to over 8. The genetic variants identified here control splicing of the SNCA transcripts into alpha-synuclein isoforms with varying affinity for pathological aggregation. These data suggest pathogens are triggers for disease in the setting of genetic susceptibility, and the link to the genome implicates the microbes in the causation of PD. Finally, shifting focus to translation, we show that not all PD patients have the same dysbiotic features, and propose a conceptual framework to identify microbiome-based biomarkers to select appropriate patients for targeted microbiome-based clinical trials and personalized treatment.

molecular biology↗

bioRxiv2025

Faecalibacterium prausnitzii, depleted in the Parkinson's disease microbiome, improves motor deficits in α-synuclein overexpressing mice

Gut microbiome composition is altered in Parkinsons disease (PD), a neurodegenerative disorder characterized by motor dysfunction and frequently accompanied by gastrointestinal (GI) symptoms. Notably, microbial taxa with anti-inflammatory properties are consistently depleted in PD patients compared to controls. To explore whether specific gut bacteria may be disease-protective, we assembled a microbial consortium of 8 human-associated taxa that are reduced in individuals with PD across multiple cohorts and geographies. Treatment of -synuclein overexpressing (Thy1-ASO) mice, an animal model of PD, with this consortium improved motor and GI deficits. A single bacterial species from this consortium, Faecalibacterium prausnitzii, was sufficient to correct gut microbiome deviations in Thy1-ASO mice, induce anti-inflammatory immune responses, and promote protective colonic gene expression profiles. Accordingly, oral treatment with F. prausnitzii robustly ameliorated motor and GI symptoms and reduced -synuclein aggregates in the brain. These findings support the emerging hypothesis of functional contributions by the microbiome to PD and embolden development of potential probiotic therapies.

physiology↗

bioRxiv2025

Commonly used compositional data analysis implementations are not advantageous in microbial differential abundance analyses benchmarked against biological ground truth

Previous benchmarking of differential abundance (DA) analysis methods in microbiome studies have employed synthetic data, simulations, and "real data" examples, but to the best of our knowledge, none have yet employed experimental data with known "ground truth" differential abundance. A key debate in the field centers on whether compositional methods are necessary for DA analysis, which is challenging to answer due to the lack of ground truth data. To address this gap, we created the Bioconductor data package MicrobiomeBenchmarkData, featuring three microbiome datasets with established biological ground truths: 1) diverse oral microbiomes from supragingival and subgingival plaques, expected to favor aerobic and anaerobic bacteria, respectively, 2) low-diversity microbiomes from healthy vaginas and bacterial vaginosis, conditions that have been well-characterized through cell culture and microscopy, and 3) a spike-in dataset with constant, known absolute abundances of three bacteria. We benchmarked 17 DA approaches and demonstrated that compositional DA methods are not beneficial but rather lack sensitivity, show increased variability in constant-abundance spike-ins, and, most surprisingly, more frequently produce paradoxical results with DA in the wrong direction for the low-diversity microbiome. Conversely, commonly used methods in microbiome literature, such as LEfSe, the Wilcoxon test, and RNA-seq-derived methods, performed best. We conclude that researchers continue using widely adopted non-parametric or RNA-seq DA methods and that further development of compositional methods includes benchmarking against datasets with known biological ground truth.

bioinformatics↗

bioRxiv2024

Ecological Determinants of Altruism in Prokaryote Antivirus Defense

Prokaryote evolution is driven in large part by the incessant arms race with viruses. Genomic investments in antivirus defense can be coarsely classified into two categories, immune systems that abrogate virus reproduction resulting in clearance, and altruistic programmed cell death (PCD) systems. Prokaryotic defense systems are enormously diverse, as revealed by an avalanche of recent discoveries, but the basic ecological determinants of defense strategy remain poorly understood. Through mathematical modeling of defense against lytic virus infection, we identify two principal determinants of optimal defense strategy and, through comparative genomics, we test this model by measuring the genomic investment into immunity vs PCD among diverse bacteria and archaea. First, as viral pressure grows, immunity becomes the preferred defense strategy. Second, as host population size grows, PCD becomes the preferred strategy. We additionally predict that, although optimal strategy typically involves investment in both PCD and immunity, overinvestment in immunity can result in system antagonism, increasing the probability a PCD-competent cell will lyse due to infection. Together these findings indicate that, generally, PCD is preferred at low multiplicity of infection (MOI) and immunity is preferred at high MOI, and that the landscape of prokaryotic antivirus defense is substantially more complex than previously suspected.

microbiology↗

bioRxiv2021

Curated Single Cell Multimodal Landmark Datasets for R/Bioconductor

BackgroundThe majority of high-throughput single-cell molecular profiling methods quantify RNA expression; however, recent multimodal profiling methods add simultaneous measurement of genomic, proteomic, epigenetic, and/or spatial information on the same cells. The development of new statistical and computational methods in Bioconductor for such data will be facilitated by easy availability of landmark datasets using standard data classes. ResultsWe collected, processed, and packaged publicly available landmark datasets from important single-cell multimodal protocols, including CITE-Seq, ECCITE-Seq, SCoPE2, scNMT, 10X Multiome, seqFISH, and G&T. We integrate data modalities via the MultiAssayExperiment Bioconductor class, document and re-distribute datasets as the SingleCellMultiModal package in Bioconductors Cloud-based ExperimentHub. The result is single-command actualization of landmark datasets from seven single-cell multimodal data generation technologies, without need for further data processing or wrangling in order to analyze and develop methods within Bioconductors ecosystem of hundreds of packages for single-cell and multimodal data. ConclusionsWe provide two examples of integrative analyses that are greatly simplified by SingleCellMultiModal. The package will facilitate development of bioinformatic and statistical methods in Bioconductor to meet the challenges of integrating molecular layers and analyzing phenotypic outputs including cell differentiation, activity, and disease. Author SummaryExperimental data packages that provide landmark datasets have historically played an important role in the development of new statistical methods in Bioconductor by lowering the barrier of access to relevant data, providing a common testing ground for software development and benchmarking, and encouraging interoperability around common data structures. In this manuscript, we review major classes of technologies for collecting multimodal data including genomics, transcriptomics, epigenetics, proteomics, and spatial information at the level of single cells. We present the SingleCellMultiModal R/Bioconductor package that provides single-command access to landmark datasets from seven different technologies, storing datasets using HDF5 and sparse arrays for memory efficiency and integrating data modalities via the MultiAssayExperiment class. We demonstrate two integrative analyses that are greatly simplified by SingleCellMultiModal. The package facilitates development and benchmarking of bioinformatic and statistical methods to integrate molecular layers at the level of single cells with phenotypic outputs including cell differentiation, activity, and disease, within Bioconductors ecosystem of hundreds of packages for single-cell and multimodal data.

bioinformatics↗

bioRxiv2021

Inverting the model of genomics data sharing with the NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL)

The traditional model of genomic data analysis - downloading data from centralized warehouses for analysis with local computing resources - is increasingly unsustainable. Not only are transfers slow and cost prohibitive, but this approach also leads to redundant and siloed compute infrastructure that makes it difficult to ensure security and compliance of protected data. The NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL; https://anvilproject.org) inverts this model, providing a unified cloud computing environment for data storage, management, and analysis. AnVIL eliminates the need for data movement, allows for active threat detection and monitoring, and provides scalable, shared computing resources that can be acquired by researchers as needed. This presents many new opportunities for collaboration and data sharing that will ultimately lead to scientific discoveries at scales not previously possible.

bioinformatics↗

bioRxiv2020

HGNChelper: Identification and correction of invalid gene symbols for human and mouse

Gene symbols are recognizable identifiers for gene names but are unstable and error-prone due to aliasing, manual entry, and unintentional conversion by spreadsheets to date format. Official gene symbol resources such as HUGO Gene Nomenclature Committee (HGNC) for human genes and the Mouse Genome Informatics project (MGI) for mouse genes provide authoritative sources of valid, aliased, and outdated symbols, but lack a programmatic interface and correction of symbols converted by spreadsheets. We present HGNChelper, an R package that identifies known aliases and outdated gene symbols based on the HGNC human and MGI mouse gene symbol databases, in addition to common mislabeling introduced by spreadsheets, and provides corrections where possible. HGNChelper identified invalid gene symbols in the most recent Molecular Signatures Database (mSigDB 7.0) and in platform annotation files of the Gene Expression Omnibus, with prevalence ranging from [~]3% in recent platforms to 30-40% in the earliest platforms from 2002-03. HGNChelper is installable from CRAN, with open development and issue tracking on GitHub and an associated pkgdown site https://waldronlab.io/HGNChelper/.

bioinformatics↗

bioRxiv2020

Strengthening The Organizing and Reporting of Microbiome Studies (STORMS)

BackgroundHuman microbiome research is a growing field with the potential for improving our understanding and treatment of diseases and other conditions. The field is interdisciplinary, making concise organization and reporting of results across different styles of epidemiology, biology, bioinformatics, translational medicine, and statistics a challenge. Commonly used reporting guidelines for observational or genetic epidemiology studies lack key features specific to microbiome studies. MethodsA multidisciplinary group of microbiome epidemiology researchers reviewed elements of available reporting guidelines for observational and genetic studies and adapted these for application to culture-independent human microbiome studies. New reporting elements were developed for laboratory, bioinformatic, and statistical analyses tailored to microbiome studies, and other parts of these checklists were streamlined to keep reporting manageable. ResultsSTORMS is a 17-item checklist for reporting on human microbiome studies, organized into six sections covering typical sections of a scientific publication, presented as a table with space for author-provided details and intended for inclusion in supplementary materials. ConclusionsSTORMS provides guidance for authors and standardization for interdisciplinary microbiome studies, facilitating complete and concise reporting and augments information extraction for downstream applications. AvailabilityThe STORMS checklist is available as a versioned spreadsheet from https://www.stormsmicrobiome.org/.

genomics↗

bioRxiv2020

Assessment of single cell RNA-seq statistical methods on microbiome data

BackgroundThe correct identification of differentially abundant microbial taxa between experimental conditions is a methodological and computational challenge. Recent work has produced methods to deal with the high sparsity and compositionality characteristic of microbiome data, but independent benchmarks comparing these to alternatives developed for RNA-seq data analysis are lacking. ResultsHere, we compare methods developed for single cell, bulk RNA-seq, and microbiome data, in terms of suitability of distributional assumptions, ability to control false discoveries, concordance, and power. We benchmark these methods using 100 manually curated datasets from 16S and whole metagenome shotgun sequencing. ConclusionsThe multivariate and compositional methods developed specifically for microbiome analysis did not outperform univariate methods developed for differential expression analysis of RNA-seq data. We recommend a careful exploratory data analysis prior to application of any inferential model and we present a framework to help scientists make an informed choice of analysis methods in a dataset-specific manner.

bioinformatics↗

bioRxiv2018

Tobacco exposure associated with oral microbiota oxygen utilization in the New York City Health and Nutrition Examination Study

PurposeThe effect of tobacco exposure on the oral microbiome has not been established. MethodsWe performed amplicon sequencing of the 16S ribosomal RNA gene V4 variable region to estimate bacterial community characteristics in 259 oral rinse samples, selected based on self-reported smoking and serum cotinine levels, from the 2013-14 New York City Health and Nutrition Examination Study. We identified differentially abundant operational taxonomic units (OTUs) by primary and secondhand tobacco exposure, and employed "microbe set enrichment analysis" to assess shifts in microbial oxygen utilization. ResultsCigarette smoking was associated with depletion of aerobic OTUs (Enrichment Score test statistic ES = -0.75, p = 0.002) with a minority (29%) of aerobic OTUs enriched in current smokers compared to never smokers. Consistent shifts in the microbiota were observed for current cigarette smokers as for non-smokers with secondhand exposure as measured by serum cotinine levels. Differential abundance findings were similar in crude and adjusted analyses. ConclusionResults support a plausible link between tobacco exposure and shifts in the oral microbiome at the population level through three lines of evidence: 1) a shift in microbiota oxygen utilization associated with primary tobacco smoke exposure, 2) consistency of abundance fold-changes associated with current smoking and shifts along the gradient of secondhand smoke exposure among non-smokers, and 3) consistency after adjusting for a priori hypothesized confounders. HighlightsO_LICigarette smoke was associated with microbial anaerobiosis in oral rinse specimens C_LIO_LIThe microbiome shifts associated with smoking and secondhand exposure were correlated C_LIO_LIShifts in oral bacterial oxygen utilization may mediate smoking and health outcomes C_LIO_LIWe propose "microbe set enrichment analysis" for interpreting shifts in the microbiome C_LI

epidemiology↗

bioRxiv2017

Sociodemographic patterning in the oral microbiome of a diverse sample of New Yorkers

11.1 PurposeVariations in the oral microbiome are potentially implicated in social inequalities in oral disease, cancers, and metabolic disease. We describe sociodemographic variation of oral microbiomes in a diverse sample.\n\n1.2 MethodsWe performed 16S rRNA sequencing on mouthwash specimens in a subsample (n=282) of the 2013-14 population-based New York City Health and Nutrition Examination Study (NYC-HANES). We examined differential abundance of 216 operational taxonomic units (OTUs), and alpha and beta diversity by age, sex, income, education, nativity, and race/ethnicity. For comparison, we also examined differential abundance by diet, smoking status, and oral health behaviors.\n\n1.3 Results69 OTUs were differentially abundant by any sociodemographic variable (false discovery rate < 0.01), including 27 by race/ethnicity, 21 by family income, 19 by education, three by sex. We also found 49 differentially abundant by smoking status, 23 by diet, 12 by oral health behaviors. Genera differing for multiple sociodemographic characteristics included Lactobacillus, Prevotella, Porphyromonas, Fusobacterium.\n\n1.4 ConclusionsWe identified oral microbiome variation consistent with health inequalities, with more taxa differing by race/ethnicity than diet, and more by SES variables than oral health behaviors. Investigation is warranted into possible mediating effects of the oral microbiome in social disparities in oral, metabolic and cancers.\n\nHighlightsO_LIMost microbiome studies to date have had minimal sociodemographic variability, limiting what is known about associations of social factors and the microbiome.\nC_LIO_LIWe examined the oral microbiome in a population-based sample of New Yorkers with wide sociodemographic variation.\nC_LIO_LINumerous taxa were differentially abundant by race/ethnicity, income, education, marital status, and nativity.\nC_LIO_LIFrequently differentially abundant taxa include Porphyromonas, Fusobacterium, Streptococcus, and Prevotella, which are associated with oral and systemic disease.\nC_LIO_LIMediation of health disparities by microbial factors may represent an important intervention site to reduce health disparities, and should be explored in prospective studies.\nC_LI

epidemiology↗

bioRxiv2017

Meta-analysis and Commentary: Preemptive Correction of Arteriovenous Access Stenosis

BackgroundA recent meta-analysis by Ravani and colleagues (Ravani, P., et al., Am J Kidney Dis, 2016. 67(3): p. 446-60.) studied the effect of pre-emptive correction of arterio-venous dialysis vascular access versus deferred care, based on data from 11 trials. The authors reported a non-significant protective treatment effect of pre-emptive correction on access loss, while showing a significant protective effect on thrombosis rates conferred by pre-emptive correction. We revisit this analysis, including data extraction and effects of a heterogenous study population.\n\nMethodsWe repeated data extraction from all referenced publications in the meta-analysis by Ravani et al. and corrected event counts where applicable. We repeated the meta-analyses with access loss as the outcome for studies that recruited patients with arterio-venous fistulae (AVF) and grafts (AVG), respectively, using a random effects model with relative risk (RR) and risk difference (RD) of access loss as the outcomes of interest. We repeated data extraction from all referenced publications, and corrected event counts where applicable.\n\nResultsOur conclusions differ from the original findings in two ways. First, after some amendment of the event counts extracted from Mayer et al. (Vascular and Endovascular Surgery 1993), we find a significant overall positive effect of pre-emptive correction on arterio-venous access loss in the overall study population [RR 0.80 (95% CI 0.64 to 0.99), RD -0.07 (95% CI -0.12 to -0.02); Figure 1]. Secondly, we highlight the impact of heterogeneous study populations on the meta-analysis. Whereas the data do not conclusively show a benefit of pre-emptive correction for arteriovenous grafts (AVG; RR = 0.87, 95% CI: 0.69 - 1.11), they show a strong protective effect for arteriovenous fistulae (AVF; RR = 0.5, 95% CI: 0.29 to 0.86).\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=128 SRC=\"FIGDIR/small/179580_fig1.gif\" ALT=\"Figure 1\">\nView larger version (41K):\norg.highwire.dtl.DTLVardef@af3c13org.highwire.dtl.DTLVardef@911a50org.highwire.dtl.DTLVardef@568cf1org.highwire.dtl.DTLVardef@3f3c08_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1:C_FLOATNO Meta-analysis of access loss, overall and by access type using risk ratio (RR) as the measure of association.\n\nC_FIG\n\nDiscussion and ConclusionThese findings corroborate clinical arguments such as superior long-term patency of AVF and the nature of AVG failure that often involve infectious causes. The available data indicate mild or no benefit of pre-emptive correction for AVG, but strongly support tight monitoring of dialysis accesses and preemptive intervention and correction upon the slightest suspicion of access stenosis for AVF.

epidemiology↗