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McDonald, D.

Publications and source records attributed to McDonald, D..

At least 19 recordsLinked to original sources

Genome-resolved ecology and evolution of the microbiome enabled by quantitative long-read metagenomics

Standard metagenomics resolves microbial communities only in relative terms and at coarse taxonomic resolution, obscuring how individual strains change in absolute abundance and evolve within a host. We present long-read quantiomics (LRQ), a quantitative metagenomics platform coupling long-read sequencing with plasmid-based internal standards to recover high-quality genomes, including from degraded or low-input stool, and measure absolute, genome-resolved strain abundances. We validate LRQ using large plasmid standards and bacterial spike-ins, then apply it to eight years of dense sampling from an individual with colonic Crohn's disease. LRQ resolves co-resident strains of the same species following opposing trajectories with inflammation. Escherichia coli, long treated as a single pathobiont, separates into two sub-populations with opposing inflammation responses; the inflammation-associated lineage carries an immune-evasion module of virulence-associated metal-acquisition, biofilm, capsule and toxin genes. We observe genome-wide adaptive sweeps on timescales matched to disease incidence. LRQ therefore converts long-read metagenomics from a genome-recovery tool into a quantitative framework providing absolute, strain-level measurement of microbial ecology and evolution.

microbiology↗

An expansive animal gut microbiome dataset elucidates major compositional shifts across bilaterian evolution

Animal gut microbiomes provide key physiological functions and are critical for host health. They vary dramatically across the animal kingdom, and are shaped by factors including host diet, evolutionary history and environment. However, analyses of gut microbiomes spanning the entire metazoan clade are lacking, limiting our understanding of the fundamental principles governing gut microbiomes. Here we present the Gut Microbiome Tree of Life (GMToL), a curated 16S amplicon dataset of 17,366 samples from 1,553 host species across 26 host classes from 284 studies, enabling analysis of large-scale evolutionary trends. Using ancestral state reconstruction, we provide a critical baseline calculation of major compositional shifts in gut microbiomes throughout animal evolution. We show that the ancestral animal gut was likely dominated by Pseudomonadota. A major shift to Bacteroidota occurred during the evolution of tetrapods, followed by the emergence of Bacillota-dominated guts in mammals and birds. We identify conserved core gut microbes and demonstrate how GMToL can be leveraged to contextualize the evolutionary history of specific microbial taxa. Ultimately, this framework enables the predictive mapping of microbial symbionts across uncharacterized host lineages, and establishes a quantitative baseline for comparative microbiome research at scale.

microbiology↗

Farnesoid X receptor-dependent microbiome-bile acid signaling mediates obstructive sleep apnea-induced atherosclerosis

Intermittent hypoxia and hypercapnia (IHC), a hallmark of obstructive sleep apnea (OSA), accelerates atherosclerosis, yet the underlying mechanisms remain unclear. The gut microbiota and metabolites, specifically bile acids, change with IHC and thus the bile acid receptor farnesoid X receptor (FXR) might mediate IHC-induced atherosclerosis. In this study, ApoE-/- and ApoE-/- FXR-/- mice were exposed to IHC or room air and fed with a high-fat, high-cholesterol diet for 10 weeks. Markers of atherosclerosis, fecal microbiome, and metabolome were then examined via Sudan IV staining, absolute abundance shotgun metagenomics, and untargeted liquid chromatography tandem mass spectrometry (LC-MS/MS). IHC markedly increased aortic atherosclerosis in ApoE-/-mice, an increase that was abolished by FXR deficiency. In addition, IHC reshaped gut microbial composition, promoting enrichment of bile acid-modifying taxa and increasing levels of microbial hydroxysteroid dehydrogenase (hsdh). The bile acid pool was also remodeled and associated with aortic atherosclerosis via FXR-dependent metabolic signals in ApoE-/- mice. Knockout of FXR disrupted microbiome shift under IHC and uncoupled microbial bile acid metabolism from vascular lesion development, thereby protecting against aortic atherosclerosis. These findings show that FXR has a central role in linking IHC, microbial bile acid metabolism, and cardiovascular pathology.

microbiology↗

A Multi-Omics Processing Pipeline (MOPP) for Extracting Taxonomic and Functional Insights from Metaribosome Profiling (metaRibo-Seq) data

Metaribosome profiling (metaRibo-Seq) enables genome-wide measurement of translation across complex microbial communities by sequencing ribosome-protected mRNA fragments, but the short length of these footprints creates substantial nonspecific mapping against large reference genome collections, leading to spurious taxonomic and functional assignments. Here we present MOPP (Multi-Omics Processing Pipeline), a modular reference-based workflow that denoises metaRibo-Seq data by leveraging matched metagenomic coverage breadth to identify genomes likely to be truly present in a sample before aligning metatranslatomic and optional metatranscriptomic reads. MOPP generates taxon-by-gene count tables across genomic, transcriptional and translational layers, enabling integrated downstream analyses of microbial function. We evaluated MOPP using a defined 79-member synthetic human gut community profiled by metagenomics and metaRibo-Seq. Coverage breadth filtering markedly improved detection accuracy relative to a standard baseline workflow, with performance remaining robust across a broad intermediate threshold range and peaking at 92-95% coverage breadth. At a 92% threshold, MOPP reduced the number of distinct detected operational genomic units by 99.4% while retaining 87.8% of aligned metaRibo-Seq reads on average, and increased the F1 score from 0.02 to 0.61. Residual false positives were predominantly attributable to genomes with extremely high nucleotide similarity to true community members, whereas false negatives were enriched among low-abundance taxa, indicating that remaining errors are driven primarily by biological similarity and detection limits rather than widespread nonspecific mapping. Together, these results establish MOPP as a high-throughput workflow for robust processing of metaRibo-Seq in the context of matched metagenomics and position it as a scalable framework for integrated taxonomic and functional analysis of microbial communities across genomic, transcriptional and translational layers.

bioinformatics↗

Enabling Megascale Microbiome Analysis with DartUniFrac

We introduce a new algorithm, DartUniFrac, and a near-optimal implementation with GPU acceleration, up to three orders of magnitude faster than the state of the art and scaling to millions of samples (pairwise) and billions of taxa. DartUniFrac connects UniFrac with weighted Jaccard similarity and exploits sketching algorithms for fast computation. We benchmark DartUniFrac against exact UniFrac implementations, demonstrating that DartUniFrac is statistically indistinguishable from them on real-world microbiome and metagenomic datasets.

bioinformatics↗

Multi-omics Analyses of Facial Skin in Acne Identify Distinct Microbial and Metabolic Features at Lesional and Non-lesional Sites

The microbial and biochemical landscape of clinically normal-appearing skin in individuals with acne remains uncharacterized. Here, we performed longitudinal multi-omics profiling of facial skin from 10 individuals with moderate acne and 10 healthy controls, integrating 16S rRNA gene sequencing, shotgun metagenomics, and untargeted metabolomics across lesional and non-lesional sites. Compositional tensor factorization revealed that non-lesional acne skin occupies a distinct intermediate state between healthy and lesional skin in both the microbiome and the metabolome. Machine learning models distinguished healthy from non-lesional acne skin with 70% accuracy, demonstrating that molecular dysbiosis occurs in areas of the skin without visible lesions. Non-lesional sites exhibited reduced microbial diversity, strain-level shifts in Corynebacterium and Lawsonella correlating with disease severity, and metabolic alterations, including elevated lipids and perturbed amino acid and dipeptide profiles. Microbe-metabolite co-occurrence network analyses revealed that healthy skin is enriched for protective metabolites such as urocanic acid, while acne-associated skin shows distinct co-occurrence patterns. These findings establish that acne represents a field effect disorder, with molecular alterations extending beyond visible lesions to encompass the entire facial skin ecosystem. This molecular signature of pre-lesional skin provides potential biomarkers for early intervention and suggests that effective acne treatment may require holistic approaches targeting the broader skin environment rather than individual lesions alone.

microbiology↗

Integrated multi-omics analysis identifies microbial and metabolic signatures and drivers of CNS autoimmunity

Multiple sclerosis (MS) is an autoimmune disease of the central nervous system (CNS) driven by genetic and environmental determinants. The gut microbiome of people with MS (pwMS) is distinct and influences disease through immunomodulatory metabolite production. Circulating metabolites are altered in pwMS, but identifying microbial-metabolic drivers remains challenging. We previously showed that colonization by the gut commensal Limosilactobacillus reuteri (L. reuteri) exacerbates disease in the experimental autoimmune encephalomyelitis (EAE) model of MS, in a tryptophan-dependent manner. Here, we integrated microbiomic and metabolomic datasets from a longitudinal EAE study utilizing high and low tryptophan diets in mice colonized or not with L. reuteri. Gut microbiome dynamics under short- and long-term alterations in tryptophan bioavailability, were affected by diet, microbiome context, or disease. During short-term dietary intervention, L. reuteri colonization exerted a greater impact on microbiome composition than tryptophan bioavailability. With longer dietary exposure and EAE progression, high dietary tryptophan and L. reuteri colonization synergized to elicit profound microbiota changes, including alterations in Lachnospiraceae, Blautia, and Akkermansia. Integration of metabolomic and microbiomic datasets using joint Robust Aitchison PCA revealed clusters of associated metabolites and microbiota enriched for functional pathways, including bile acid and tryptophan metabolism. Metabolites outperformed microbiota in predicting EAE severity, identifying p-cresols and indoles as top disease-associated metabolites. Treatment with p-cresol or 3-indoleglyoxylic acid exacerbated EAE, enhanced proinflammatory T cell responses, and increased cerebellar pathology. These data demonstrate that dietary responses are shaped by gut microbiome composition and that integrated microbiomic-metabolomic analyses can identify drivers of disease worsening in MS. IMPORTANCEMS is a multifactorial disease influenced not only by genetics but also by environmental factors, potentially including diet and the composition of the gut microbiome. We show that interactions between diet and commensal gut microbiota profoundly impact levels of immunomodulatory systemic metabolites, including several that are associated with disease in pwMS. Importantly, we demonstrate that individual gut microbiota produced metabolites are sufficient to worsen disease in a mouse model of MS. Integration of gut microbiome and blood metabolite datasets combined with subsequent predictive modeling, may bolster biomarker identification and the capacity to predict disease severity in pwMS, as compared to performance of individual datasets alone. These findings highlight metabolites as key mediators linking diet and the gut microbiota to neuroinflammation. Importantly, this work suggests that targeting microbial metabolites or modifying diet-microbiome interactions may represent new strategies to reduce disease activity in MS and related autoimmune disorders.

bioinformatics↗

Personalised whole-body modelling links gut microbiota to metabolic perturbations in Alzheimer's disease

The human gut microbiome has been linked to metabolic disturbances in Alzheimers disease (AD). However, the mechanisms by which gut microbes might influence metabolic dysfunction in AD remain poorly understood. Previously, we used constraint-based metabolic modelling to associate an increased risk of AD with altered production of microbiome-derived metabolites. In this study, we investigated whether these previous results can also be identified in AD patients. Therefore, we created personalised whole-body metabolic models from gut metagenomics samples from 34 AD dementia patients, 51 individuals with mild cognitive impairments, and 298 healthy controls. These in silico models were profiled to predict the metabolic influences of gut microbiomes on blood metabolites with previously reported alterations in AD. We found an increased capacity of AD host-microbiome co-metabolism to produce S-adenosyl-L-methionine, L-arginine, creatine, taurine, and formate in the blood of AD dementia patients and patients with mild cognitive impairments. The metabolic predictions were then mechanistically linked to gut microbial changes in AD. This analysis identified that increased relative abundances of Bacteroides uniformis and Bacteroides thetaiotamicron were key factors driving the predicted metabolic changes. Furthermore, the predicted altered microbial influences on blood metabolites were also associated with allelic variations in the APOE risk gene in healthy individuals, which confirmed our previous findings. In conclusion, we identified blood metabolites whose perturbations in AD may be influenced by gut microbiota and predicted the key microbial drivers for these metabolic influences. These findings may facilitate the development of microbiome-informed treatments of AD.

systems biology↗

Industrialization drives convergent microbial and physiological shifts in the human metaorganism

Understanding how host lifestyle and industrialization shape the human gut microbiome and intestinal physiology requires multimodal analyses across diverse global host contexts. Here, we generate multivariate data from the Global Microbiome Conservancy cohort, including gut microbiome, IgA-sequencing, host genotyping, diet, lifestyle and fecal biomarker profiles, to investigate host-microbiome interactions across gradients of industrialization and geography. We show that industrialization is associated with homogenized microbial compositions, reduced microbial diversity, and lower community stability, independent of host confounders. We further show that industrialization is linked to elevated markers of gut stress, increased IgA secretion, and altered patterns of IgA-bacteria interactions. Finally, we show that microbiome-based disease predictors trained on industrialized populations lose accuracy in less industrialized cohorts, highlighting limited cross-population transferability. Together, our results suggest profound restructuring of host-microbiome interactions due to industrialized lifestyles, and emphasize the need for inclusive, globally representative data to improve translational microbiome applications across diverse human populations.

microbiology↗

Environmental and Maternal Imprints on Infant Gut Metabolic Programming

Early life is a critical period for immune and metabolic programming, but developmental patterns remain underexplored in populations from low- and middle-income countries. Here, we profiled the microbiome and metabolome of 55 Bangladeshi mother-infant dyads over the first six months of life. Importantly, we observed an increase in microbially-derived bile amidates and N-acyl lipids with age in conjunction with reads matching the bile salt hydrolase/transferase (bsh) gene. While microbial source tracking confirmed maternal fecal seeding, a substantial environmental contribution was also highlighted. Differences in infant fecal metabolic profiles were associated with delivery mode, maternal milk composition, household assets, and household-level water treatment. C-section delivery and untreated drinking water were linked to transient metabolic differences, including increases in bile amidates, N-acyl lipids, and other host-microbe co-metabolic products, including acylcarnitines. Multi-omics analysis revealed specific microbial-metabolite relationships, highlighting how early environmental and maternal living circumstances shape metabolic gut programming through the microbiome.

microbiology↗

Spatial profiling of longitudinal glioblastoma reveals consistent changes in cellular architecture, post-treatment

Glioblastoma (GBM), the most aggressive adult brain cancer, comprises a complex tumour microenvironment (TME) with diverse cellular interactions driving progression and pathobiology. How these spatial patterns and interactions evolve with treatment remains unclear. Here, we apply imaging mass cytometry to analyse protein-level changes in paired pre- and post-treatment GBM samples from five patients. We find a significant post-treatment increase in normal brain cells alongside a reduction in vascular cells. Moreover, despite minimal overall change in cellular diversity, interactions among astrocytes, oligodendrocytes, and vascular cells increase post-treatment, suggesting reorganisation of the TME. The GBM TME cells form spatially organized layers driven by hypoxia pre-treatment, but this influence diminishes post-treatment, giving way to less organised layers with organisation driven by reactive astrocytes and lymphocytes. These findings provide insight into treatment-induced shifts in GBMs cellular landscape, highlighting aspects of the evolving TME that appear to facilitate recurrence and are, therefore, potential therapeutic targets. Key pointsO_LISpatial organisation in primary GBM consist of layers driven by the presence of hypoxia C_LIO_LIThe layers in recurrent GBM appear are driven more by the presence of reactive astrocytes C_LIO_LIIncreased cellular cross-talk in recurrent GBM presents novel therapeutic targets C_LI

cancer biology↗

Community assembly modeling of microbial evolution within Barrett's esophagus and esophageal adenocarcinoma

Mathematical modeling of somatic evolution, a process impacting both host cells and microbial communities in the human body, can capture important dynamics driving carcinogenesis. Here we considered models for esophageal adenocarcinoma (EAC), a cancer that has dramatically increased in incidence over the past few decades in Western populations, with high case fatality rates due to late-stage diagnoses. Despite advancements in genomic analyses of the precursor Barretts esophagus (BE), prevention of late-stage EAC remains a significant clinical challenge. Previous microbiome studies in BE and EAC have focused on quantifying static microbial abundance differences rather than evolutionary dynamics. Using whole genome sequencing data from esophageal tissues, we first applied a robust bioinformatics pipeline to extract non-host DNA reads, mapped these putative reads to microbial taxa, and retained those taxa with high genomic coverage. When applying mathematical models of microbial evolution to sequential stages of progression to EAC, we observed evidence of neutral dynamics in community assembly within normal esophageal tissue and BE, but not EAC. In a case-control study of BE patients who progressed to EAC cancer outcomes (CO) versus those who had non-cancer outcomes (NCO) during follow-up (mean=10.5 years), we found that Helicobacter pylori deviated significantly from the neutral expectation in BE NCO, suggesting that factors related to H. pylori or H. pylori infection itself may influence EAC risk. Additionally, simulations incorporating selection recapitulated non-neutral behaviors observed in the datasets. Formally modeling dynamics during progression holds promise in clinical applications by offering a deeper understanding of microbial involvement in cancer development.

cancer biology↗

Empirically establishing drug exposure records directly from untargeted metabolomics data

Despite extensive efforts, extracting information on medication exposure from clinical records remains challenging. To complement this approach, we developed the tandem mass spectrometry (MS/MS) based GNPS Drug Library. This resource integrates MS/MS data for drugs and their metabolites/analogs with controlled vocabularies on exposure sources, pharmacologic classes, therapeutic indications, and mechanisms of action. It enables direct analysis of drug exposure and metabolism from untargeted metabolomics data independent of clinical records. Our library facilitates stratification of individuals in clinical studies based on the empirically detected medications, exemplified by drug-dependent microbiota-derived N-acyl lipid changes in a cohort with human immunodeficiency virus. The GNPS Drug Library holds potential for broader applications in drug discovery and precision medicine.

bioinformatics↗

A three-country analysis of the gut microbiome indicates taxon associations with diet vary by location and strain

Emerging research suggests that diet plays a vital role in shaping the composition and function of the gut microbiota. While significant efforts have been made to identify general patterns linking diet to the gut microbiome, much of this research lacks representation from low- and middle-income countries such as Mexico. Additionally, both diet and the gut microbiome have highly complex and individualized configurations, and there is growing evidence that tailoring diets to individual gut microbiota profiles may optimize the path toward improving or maintaining health and preventing disease. Using fecal metagenomic data from 1,291 individuals across three countries, we examine two bacterial genera prevalent in the human gut, Prevotella and Faecalibacterium, which have gained significant attention due to their potential roles in human health. We find that they show significant associations with many aspects of diet, but that these associations vary in scale and direction, depending on the level of metagenomic resolution and the contextual population. These results highlight the growing importance of assembling metagenomic datasets that are standardized, comprehensive, and representative of diverse populations to increase our ability to tease apart the complex relationship between diet and the microbiome.

microbiology↗

Ex-vivo mouse precision cut tumour slices for modelling hepatocellular carcinoma; A 3Rs solution for at-scale drug screening

Disease modelling is vital for improving knowledge of disease mechanisms and for development of new therapeutic molecules and strategies. Modelling the intact living tumour microenvironment (TME) is increasingly considered to be vital not only for gaining a better understanding of the biology of cancer but for examining the efficacy of novel oncology drugs. To date, pre-clinical mouse models of cancer have represented the mainstay methodology for studying the evolving TME and for determining the effects of potential therapeutic molecules on tumour evolution and growth. Regarding drug screening, in vivo mouse models are expensive, require the use of large cohorts of mice and involve the administration of drugs with unknown toxicities to animals which often result in adverse effects that can cause animal suffering and the discontinuation of drug investigations. Hepatocellular carcinoma (HCC) is a primary cancer of the liver for which there is an urgent need for improved systemic treatments due to the disease usually being diagnosed at an advanced stage and current treatments having limited efficacy. To provide a practical solution to the screening of drugs for their likely efficacy in HCC we have developed an ex-vivo model in which orthotopic tumours are excised from the liver and subsequently processed to generate precision-cut tumour slices (PCTS) which provide an intact culture model of the HCC-TME. We describe simplified culture conditions that maintain the viability and metabolic activity of live PCTS which maintain the architecture, cellular complexity, drug sensitivity and responsiveness to immunotherapy of the original tumour. Importantly, we show that HCC derived PCTS can be miniaturised to 96-well scale and modified to express soluble luciferase, which in combination enabled non-destructive screening of a library of 26 drugs at two doses using just 5 tumours as the source for PCTS. This screen identified two small molecules, salinomycin and rottlerin, that have potent anti-tumour activities in HCC-PCTS and subsequently validated salinomycin as effective in vivo. In summary, we report a 3Rs (reduction, refinement and replacement) solution for study of HCC biology and for 96-well-scale screening of potential therapeutic agents in the context of an intact, metabolically active TME.

cancer biology↗

Distinct tissue-niche localization and function of synovial tissue myeloid DC subsets in health, and in active and remission Rheumatoid Arthritis

Current rheumatoid arthritis (RA) treatments do not restore immune tolerance. Investigating dendritic cell (DC) populations in human synovial tissue (ST) may reveal pathways to re-instate tolerance in RA. With single-cell and spatial-transcriptomics of synovial tissue biopsies, validated by micro co-culture systems, we identified condition and niche-specific myeloid DC clusters with distinct differentiation trajectories and functions. Healthy synovium contains a unique tolerogenic AXLpos DC2 cluster in the superficial sublining layer. In active RA, a macrophage-rich lining-layer niche becomes populated with inflammatory DC3 clusters that specifically activate memory CCL5pos TEM and CCL5posCXCL13pos TPH, promoting synovitis. In the sublining lymphoid niche, CCR7pos DC2 mReg specifically interact with naive-T-cells, potentially driving the local expansion of new effector T-cells. Sustained remission sees the resolution of these niches but lacks the recovery of tolerogenic AXLpos DC2, indicating latent potential for disease flare. A human RA disease-flare model showed that the activation of blood predecessor of ST-DC3 clusters precedes the onset of inflammation in joints. Therapeutic strategies targeting pathogenic ST-DC3 clusters, or reinstating tolerogenic AXLpos DC2, may restore immune homeostasis in RA. In briefDeconstruction of human RA synovium, using single-cell spatial transcriptomics and micro-culture systems, reveals distinct neighbourhoods within the synovial architecture across health, and RA patients with active disease or sustained remission. Discrete niches are identified that contain distinct myeloid DC clusters that differ in frequency, differentiation trajectories, and effector functions. HighlightsO_LIHuman RA synovium exhibits condition and niche specific myeloid DC clusters that vary in their tissue differentiation trajectories and functions. C_LIO_LIST-CD14pos DC3 (iDC3) support inflammatory CCL5pos TEM and CCL5pos TPH cell activation in the hyperplastic lining layer. C_LIO_LIST-CCR7pos DC2 (mReg), driven by MIR155, interact with naive-T-cells in sublining lymphoid niches. C_LIO_LIA specific inflammatory signature of blood predecessors of ST-DC3s predict flare in RA. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=199 HEIGHT=200 SRC="FIGDIR/small/600758v1_ufig1.gif" ALT="Figure 1"> View larger version (74K): org.highwire.dtl.DTLVardef@27ab01org.highwire.dtl.DTLVardef@4be238org.highwire.dtl.DTLVardef@1f10734org.highwire.dtl.DTLVardef@1102056_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

Medication Use is Associated with Distinct Microbial Features in Anxiety and Depression

This study investigated the relationship between gut microbiota and neuropsychiatric disorders (NPDs), specifically anxiety disorder (ANXD) and/or major depressive disorder (MDD), as defined by DSM-IV or V criteria. The study also examined the influence of medication use, particularly antidepressants and/or anxiolytics, classified through the Anatomical Therapeutic Chemical (ATC) Classification System, on the gut microbiota. Both 16S rRNA gene amplicon sequencing and shallow shotgun sequencing were performed on DNA extracted from 666 fecal samples from the Tulsa-1000 and NeuroMAP CoBRE cohorts. The results highlight the significant influence of medication use; antidepressant use is associated with significant differences in gut microbiota beta diversity and has a larger effect size than NPD diagnosis. Next, specific microbes were associated with ANXD and MDD, highlighting their potential for non-pharmacological intervention. Finally, the study demonstrated the capability of Random Forest classifiers to predict diagnoses of NPD and medication use from microbial profiles, suggesting a promising direction for the use of gut microbiota as biomarkers for NPD. The findings suggest that future research on the gut microbiotas role in NPD and its interactions with pharmacological treatments are needed.

microbiology↗

Arctos: Community-driven innovations for managing biodiversity and cultural collections

Museum collections house millions of objects and associated data records that document biological and cultural diversity. In recent decades, digitization efforts have greatly increased accessibility to these data, thereby revolutionizing interdisciplinary studies in evolutionary biology, biogeography, epidemiology, cultural change, and human-mediated environmental impacts. Curators and collection managers can make museum data as accessible as possible to scientists and learners by using a collection management system. However, selecting a system can be a challenging task. Here, we describe Arctos, a community solution for managing and accessing collections data for research and education. Specific goals are to: (1) Describe the core elements of Arctos for a broad audience with respect to the biodiversity informatics principles that enable high quality research; (2) Highlight the unique aspects of Arctos; (3) Illustrate Arctos as a model for supporting and enhancing the Digital Extended Specimen; and (4) Emphasize the role of the Arctos community for improving data discovery and enabling cross-disciplinary, integrative studies within a sustainable governance model. In addition to detailing Arctos as both a community of museum professionals and a collection database platform, we discuss how Arctos achieves its richly annotated data by creating a web of knowledge with deep connections between catalog records and derived or associated data. We also highlight the value of Arctos as an educational resource. Finally, we present a financial model of fiscal sponsorship by a non-profit organization, implemented in 2022, to ensure the long-term success and sustainability of Arctos. We attribute Arctos longevity of nearly three decades to its core development principles of standardization, flexibility, interdisciplinarity, and connectivity within a nimble development model for addressing novel needs and information types in response to changing technology, workflows, ethical considerations, and regulations.

bioinformatics↗