Biology subjects

Parkinson, J.

Publications and source records attributed to Parkinson, J..

At least 19 recordsLinked to original sources

bioRxiv2026

The gut microbiome of infants with hypoplastic left heart syndrome is enriched in pathobionts and exhibits altered responses to nutritional intervention

Congenital heart disease (CHD) can expose infants to chronic cyanosis, altered intestinal perfusion, and prolonged hospitalization. Altered gut microbiota has been linked to inflammation and gut barrier dysfunction in CHD cohorts, but few studies have examined whether cardiac severity and oxygenation shape microbial function, or whether this can be leveraged nutritionally, particularly for hypoplastic left heart syndrome (HLHS), the most hemodynamically severe common CHD lesion. We performed shotgun metagenomics on stool from 28 infants spanning the CHD severity spectrum, including HLHS (n=7), ventricular septal defect (n=8), tetralogy of Fallot (n=9), atrioventricular septal defect (n=2), and other lesions (n=2). We reconstructed 609 metagenome-assembled genomes, used for taxonomic and functional profiling, differential abundance testing, and oxygenation-associated modeling. Cardiac disease group was the strongest driver of taxonomic composition, with enrichment of the opportunistic Klebsiella specific to HLHS over contributions of feeding modality, birth type, or baseline oxygen saturation alone. HLHS communities showed a dual functional signature: depletion of core biosynthetic pathways, including aminoacyl-tRNA and peptidoglycan biosynthesis, alongside expansion of Enterobacteriaceae-driven aromatic amino acid catabolism and ABC transporters; this dual signature varied continuously with oxygenation. We then built infant-specific community metabolic models curated for human milk oligosaccharide metabolism and simulated a panel of feeding regimens. Individual microbiome identity, rather than diet, was the dominant determinant of predicted short-chain fatty acid (SCFA) production. In silico screening of dietary supplements identified threonine, methionine, and mucin-derived sugars as the strongest candidates for enhancing SCFA output, concentrated largely in formula-fed communities. These findings describe a coordinated shift in gut microbial function linked to CHD types and oxygenation, and provide a hypothesis-generating framework for microbiome-informed nutritional interventions in infants with severe CHD.

bioinformatics↗

bioRxiv2026

What colour are Actinomycetes? We have a tool for that! PhylaChrome - A systematic and deterministic tool for assigning colours that capture taxonomic relationships in microbiome datasets

Visual representations of data generated from complex microbial communities (i.e. microbiomes) can provide intuitive insights into their structure, function, and dynamics. However, current approaches typically rely on ad hoc selection of colour palettes that fail to capture taxonomic relationships and limit cross-study comparisons. PhylaChrome has been developed to systematically and deterministically assign colours to enhance visual separation of taxa, capture taxonomic relationships and facilitate intuitive comparisons across datasets.

microbiology↗

bioRxiv2026

Multi-compartment spatiotemporal metabolic modeling of the chicken gut guides dietary intervention design

Understanding the interactions between diet and the gut microbiome is critical for identifying dietary interventions that support gut health. This is of particular importance for poultry where the elimination of antibiotic growth promoters has resulted in an alarming rise in enteric infections with significant economic consequences. While the application of computational models capable of dissecting the metabolic interactions supporting gut communities has shown promise, they remain limited, largely ignoring the physiological and geographical considerations of the poultry gastrointestinal tract. To address these limitations, we developed the first multi-compartment, spatiotemporally-resolved metabolic model of the chicken gastrointestinal tract. Our six-compartment framework integrates avian-specific physiological features including bidirectional flow, feeding-fasting cycles, and compartment-specific environmental parameters. The model captured distinct metabolic specialization along the gut, with upper compartments enriched for biosynthetic pathways and lower compartments specialized for fermentation. In silico screening of 34 dietary supplements revealed context-dependent metabolic responses and predicted cellulose, starch, and L-threonine as robust enhancers of short-chain fatty acid production. A controlled feeding trial validated key predictions, particularly for butyrate, with prediction accuracy further improved through integration of trial-specific microbial community data. Our findings demonstrate that community composition is a major driver of metabolic outcomes and underscore the need for context-specific modeling. Our framework provides a mechanistic platform for rational dietary intervention design and is broadly adaptable to other animal or human gastrointestinal systems.

bioinformatics↗

bioRxiv2025

Network-based population analysis of 31,595 gonococcal genomes reveals phase variability, genetic diversity and mobile element dynamics drive antimicrobial resistance and phenotypic diversity

With a highly plastic genome allowing the accumulation of antimicrobial resistance (AMR) determinants, Neisseria gonorrhoeae (Ngo) is an urgent public health threat. To better understand the spread of AMR in Ngo and its ability to persist in humans, we performed a systematic analysis of its global population structure. From 31,595 publicly available genomes, we curated a maximally diverse representative set of 485 gonococcal genomes, which were defined into 12 distinct clades on the basis of shared patterns of single nucleotide polymorphisms. Mapping AMR determinants and mobile elements onto these clades revealed significant correlation with gonococcal population structure. Notably, several variants associated with AMR were identified in genes not previously linked with resistance, suggesting roles as new determinants of resistance or compensatory mutations in resistant strains. Analysis of phase variable motifs, a significant mechanism driving regulation of expression in Ngo, identified 57 genes not previously identified as phase variable; in vivo and in vitro passaging validated our sequence-based definition of phase variability and revealed a strong association of phase variation of the surface proteins Opa and LgtG with improved colonization during infection. Together these findings highlight the ability of systematic comparative genomic analyses to shed new light on the drivers of Ngo population structure and identify new AMR determinants.

microbiology↗

bioRxiv2024

Metabolic modeling of microbial communities in the chicken ceca reveals a landscape of competition and co-operation

With their ability to degrade dietary fibers to liberate otherwise unavailable substrates, members of the Bacteroidales exert a substantial influence on the microbiome of the lower intestine. Currently our knowledge of how this influence translates to the metabolic interactions that support community structure is limited. Here we applied constraints-based modeling to chicken cecal communities to investigate metabolic interactions in the presence and absence of Bacteroides. From metagenomic datasets previously generated from 33 chicken ceca, we constructed 237 metagenome-assembled genomes. Metabolic modeling of communities built from these genomes generated profiles of short chain fatty acids largely consistent with experimental assays and confirmed the role of B. fragilis as a metabolic hub, central to the production of metabolites consumed by other taxa. In its absence, communities undergo significant functional reconfiguration, with metabolic roles typically fulfilled by B. fragilis assumed by multiple taxa. Beyond B. fragilis, we found Escherichia coli and Lactobacillus crispatus also mediate influential metabolic roles that vary in the presence or absence of B. fragilis. Compensatory adaptations adopted by the microbiome in the absence of B. fragilis resulted in metabolic profiles previously associated with inflammatory bowel disease in humans, including energy deficiency, increased lactate production and altered amino acid metabolism. This work demonstrates the potential of chicken cecal microbiomes to investigate the complex metabolic interactions and key contributions that drive community dynamics.

systems biology↗

bioRxiv2024

Phenotypic diversity and shared genomic determinants among isolates causing a large incidence of disseminated gonococcal infections in Canada

The incidence of disseminated gonococcal infection (DGI) has remained low since the advent of antibiotics, however recent surge in DGI have inexplicably emerged within several regions during the past decade. In an effort to understand whether Neisseria gonorrhoeae that cause disseminated disease can be differentiated from non-invasive strains, we have performed a phenotypic and genotypic analysis on a selection of isolates obtained from invasive and uncomplicated infections in Canada. Phenotypic analysis of a matched subset of 19 isolates obtained since 2013 found that these varied in their capacity to aggregate in suspension and in their association with serum complement proteins, however these interactions did not discriminate between the invasive and mucosal isolates. Sequence typing of 360 Canadian isolates revealed that two porB alleles are significantly associated with the DGI strains, one of these being present throughout the past decade whereas the other became associated more recently. A PopNet-based population dynamics analysis, which instead establishes relationships based upon variance among discrete chromosomal segments, found that DGI isolates were restricted in their phylogenetic distribution. While this implies a genetically-linked potential to cause invasive disease, it cannot distinguish between an inherent difference in the phenotype of these populations or the horizontal exchange of some virulence factor among closely related strains. Regardless, a large number of genetic determinants are enriched in the DGI strains, making these enticing candidates for future work to understand how they might either promote the gonococcal capacity to cause systemic infection or reduce the presentation of clinical symptoms from localized infection so that it remains untreated. AUTHOR SUMMARYNeisseria gonorrhoeae is a sexually transmitted bacteria that causes over 82 million cases of gonorrhea each year. With its re-emergence, rising incidence rates and the prevalence of multidrug resistant strains increasing, the bacteria is considered to be a high burden threat to global public health. While disseminated gonococcal disease arising from untreated infections are uncommon, there have recently been regions with high incidence invasive disease. Here, we take advantage of the active ongoing collection of gonococcal isolates in Canada to perform a combined phenotypic and genotypic analysis that aims to understand whether certain strains are more often linked to invasive infections. We found that all disseminated isolates bound to the complement regulatory factors factor H and/or C4 binding protein to facilitate their resistance to the bactericidal activity of serum, but this was not sufficient to explain the heightened instance of invasive disease. While classical genome-based phylogenetic analysis displayed little association between invasive strains, PopNet-based analysis revealed that invasive isolates fell within defined sub-populations and indicated variant alleles enriched among the disseminating bacteria, and a broader pan-genome approach revealed genes more likely to be present in invasive strains. Our study thereby provides support for a genetic contribution to the invasive potential of gonococcal isolates and provides candidate drivers of this virulent outcome.

microbiology↗

bioRxiv2024

Phylogeny-based selection of representative variants with Navargator: Proof of principle using humoral cross-reactivity data from two immunization studies

BackgroundThe accessibility to the immune system of bacterial surface proteins makes them attractive targets for subunit vaccines. However, this same property also means they tend to exhibit high sequence variability. Achieving broad cross-protection usually necessitates that antigens from multiple isolates are included, but the choice of sequence variant is a non-trivial problem. Visual inspection of phylogenetic trees is the norm, but this is subjective and can be greatly influenced by the choice of viewing software. This has real-world implications, as groups have shown that the selection of non-optimal antigens likely led to lower cross-protection in the commercially available vaccines against Neisseria meningitidis serogroup B. Aim / MethodsTo address this problem, we have developed Navargator, bioinformatics software that takes a phylogenetic tree as input and identifies the variants that are the most similar to the greatest number of other sequences. The underlying premise is that cross-reactivity will be correlated with phylogenetic distances extracted from the tree; this was validated by several rodent immunization studies with the proteins transferrin-binding protein B and factor H binding protein from N. meningitidis and N. gonorrhoeae, measuring antibody-based cross-reactivity between an antigen panel using a custom high-throughput ELISA. ResultsNavargator has been made freely available both as an online tool and as source code for local installation. We implemented several different clustering methods, with exact algorithms for smaller datasets, and heuristics suitable for large trees of thousands of sequences. Our immunization studies have shown that this approach is sound, and that cross-reactivity is predicted well by phylogenetic distances in a sigmoidal manner. ConclusionsThe complexity of vaccine development rises sharply with each additional antigen included, so using the minimal number required is an important consideration. Navargator attempts to facilitate this in a systematic and generalizable manner. The user can run the analysis by selecting their desired number of representatives, or they can provide any form of cross-reactivity data and have the program identify a minimum reactivity threshold via correlation with the phylogenetic tree. The program will then identify the smallest number of representatives required to satisfy this threshold.

bioinformatics↗

bioRxiv2024

Benchmarking text-integrated protein language model embeddings and embedding fusion on diverse downstream tasks

Protein language models (pLMs) have become essential tools in computational biology, powering diverse applications from variant effect prediction to protein engineering. Central to their success is the use of pretrained embeddings-contextualized representations of amino acid sequences-which enable effective transfer learning, especially in data-scarce settings. However, recent studies have revealed that standard masked language modeling objectives often produce representations that are misaligned with the needs of downstream tasks. While scaling up model size improves performance in some cases, it does not universally yield better representations. In this study, we investigate two complementary strategies for improving pLM representations: (1) integrating text annotations through contrastive learning, and (2) combining multiple embeddings via embedding fusion. We benchmark six text-integrated pLMs (tpLMs) and three large-scale pLMs across six biologically diverse tasks, showing that no single model dominates across settings. Fusion of multiple tpLMs embeddings improves performance on most tasks but presents a computational bottleneck due to the combinatorial number of possible combinations. To overcome this, we introduce greedier forward selection, a linear-time algorithm that efficiently identifies near-optimal embedding subsets. We validate its utility through two case studies, homologous sequence recovery and protein-protein interaction prediction, demonstrating new state-of-the-art results in both. Our work highlights embedding fusion as a practical and scalable strategy for improving protein representations.

bioinformatics↗

bioRxiv2024

RESP2: An uncertainty aware multi-target multi-property optimization AI pipeline for antibody discovery

Discovery of therapeutic antibodies against infectious disease pathogens presents distinct challenges. Ideal candidates must possess not only the properties required for any therapeutic antibody (e.g. specificity, low immunogenicity) but also high affinity to many mutants of the target antigen. Here we present RESP2, an enhanced version of our RESP pipeline, designed for the discovery of antibodies against one or multiple antigens with simultaneously optimized developability properties. We first evaluate this pipeline in silico using the Absolut! database of scores for antibodies docked to target antigens. We show that RESP2 consistently identifies sequences that bind more tightly to a group of target antigens than any sequence present in the training set with success rates >= 85%. Popular generative AI techniques evaluated on the same datasets achieve success rates of 1.5% or less by comparison. Next we use the receptor binding domain (RBD) of the COVID-19 spike protein as a case study, and discover a highly human antibody with broad (mid to high-affinity) binding to at least 8 different variants of the RBD. These results illustrate the advantages of this pipeline for antibody discovery against a challenging target. A Python package that enables users to utilize the RESP pipeline on their own targets is available at https://github.com/Wang-lab-UCSD/RESP2, together with code needed to reproduce the experiments in this paper.

bioinformatics↗

bioRxiv2024

TUnA: An uncertainty aware transformer model for sequence-based protein-protein interaction prediction

Protein-protein interactions (PPIs) are important for many biological processes, but predicting them from sequence data remains challenging. Existing deep learning models often cannot generalize to proteins not present in the training set, and do not provide uncertainty estimates for their predictions. To address these limitations, we present TUnA, a Transformer-based uncertainty aware model for PPI prediction. TUnA uses ESM-2 embeddings with Transformer encoders and incorporates a Spectral-normalized Neural Gaussian Process. TUnA achieves state-of-the-art performance and, importantly, evaluates uncertainty for unseen sequences. We demonstrate that TUnAs uncertainty estimates can effectively identify the most reliable predictions, significantly reducing false positives. This capability is crucial in bridging the gap between computational predictions and experimental validation.

bioinformatics↗

bioRxiv2024

For antibody sequence generative modeling, mixture models may be all you need

Antibody therapeutic candidates must exhibit not only tight binding to their target but also good developability properties, especially low risk of immunogenicity. In this work, we fit a simple generative model, SAM, to sixty million human heavy and seventy million human light chains. We show that the probability of a sequence calculated by the model distinguishes human sequences from other species with the same or better accuracy on a variety of benchmark datasets containing >400 million sequences than any other model in the literature, outperforming large language models (LLMs) by large margins. SAM can humanize sequences, generate new sequences, and score sequences for humanness. It is both fast and fully interpretable. Our results highlight the importance of using simple models as baselines for protein engineering tasks. We additionally introduce a new tool for numbering antibody sequences which is orders of magnitude faster than existing tools in the literature. Both these tools are available at https://github.com/Wang-lab-UCSD/AntPack.

bioinformatics↗

bioRxiv2023

The impact of gestational diabetes on functional capacity of the infant gut microbiome is modest and transient

Gestational diabetes mellitus (GDM) is a metabolic complication that manifests as hyperglycemia during the later stages of pregnancy. In high resource settings, careful management of GDM limits risk to the pregnancy, and hyperglycemia typically resolves after birth. At the same time, previous studies have revealed that the gut microbiome of infants born to mothers who experienced GDM exhibit reduced diversity and reduction in the abundance of several key taxa, including Lactobacillus. What is not known is what the functional consequences of these changes might be. In this case control study, we applied 16S rRNA sequence surveys and metatranscriptomics to profile the gut microbiome of 30 twelve-month old infants to examine the impact of GDM during pregnancy. Relative to mode of delivery and sex of the infant, maternal GDM status had only a limited impact on the structure and function of the developing microbiome. While GDM samples were associated with a decrease in alpha diversity, we observed no effect on beta diversity and no differentially abundant taxa. Further, while mode of delivery and sex of infant affected the expression of multiple bacterial pathways, most of the impact of GDM status on the function of the infant microbiome appears to be lost by twelve months of age. These data serve to reassure parents and paediatricians that, at least in high resource settings, while mode of delivery appears to impact function and diversity for longer than anticipated, GDM may not have persistent effects on the function nor composition of the infant gut microbiome.

systems biology↗

bioRxiv2023

HiTaxon: A hierarchical ensemble framework for taxonomic classification of short reads

Whole microbiome DNA and RNA sequencing (metagenomics and metatranscriptomics) are pivotal to determining functional roles within microbial communities. A key challenge in analysing these complex datasets, typically composed of tens of millions of short reads, is accurately classifying reads to their taxon of origin. Traditional reference-based short-read classification tools are compromised by reference database biases, leading to interest in classifiers leveraging machine learning (ML) algorithms. While ML classifiers have shown promise, they still lag reference-based tools in species-level classification. To address this performance gap, attention has turned to approaches that incorporate the hierarchical structure of taxonomic classifications, albeit with limited results. Here we introduce HiTaxon, a hierarchical framework for creating ensembles of reference-dependent and ML classifiers. HiTaxon facilitates data collection and processing, reference database construction and model training to streamline ensemble creation. We show that databases created by HiTaxon improve the species-level performance of reference-dependent classifiers, while reducing their computational overhead. Additionally, through exploring hierarchical methods for HiTaxon, we highlight that our custom hierarchical ML approach improves species-level classification relative to traditional strategies. Finally, we demonstrate the improved performance of our hierarchical ensemble over current state-of-the-art classifiers in species classification using datasets comprised of either simulated or experimentally-derived reads.

bioinformatics↗

bioRxiv2023

Cell4D: A general purpose spatial stochastic simulator for cellular pathways

STRUCTURED ABSTRACTO_ST_ABSMotivationC_ST_ABSWith the generation of vast compendia of omics datasets, the challenge is how best to interpret these datasets to gain meaningful biological insights. Key to this challenge are computational methods that enable domain-users to generate novel hypotheses that can be used to guide future experiments. Of particular interest are flexible modeling platforms, capable of simulating a diverse range of biological systems with low barriers of adoption to those with limited computational expertise. ResultsWe introduce Cell4D, a spatial-temporal modeling platform combining a robust simulation engine with integrated graphics visualization, a model design editor, and an underlying XML data model capable of capturing a variety of cellular functions. Cell4D provides an interactive visualization mode, allowing intuitive feedback on model behaviour and exploration of novel hypotheses, together with a non-graphics mode, compatible with high performance cloud compute solutions, to facilitate generation of statistical data. To demonstrate the flexibility and effectiveness of Cell4D, we investigate the dynamics of CEACAM1 localization in T-cell activation. We confirm the importance of Ca++ microdomains in activating calmodulin and highlight a key role of activated calmodulin on the surface expression of CEACAM1. We further show how lymphocyte-specific protein tyrosine kinase can help regulate this cell surface expression and exploit spatial modeling features of Cell4D to test the hypothesis that lipid rafts regulate clustering of CEACAM1 to promote trans-binding to neighbouring cells. Through demonstrating its ability to test and generate hypotheses, Cell4D represents an effective tool to help interpret complex omics datasets. Availability and Implementationhttps://github.com/ParkinsonLab/cell4d Contactjparkin@sickkids.ca

bioinformatics↗

bioRxiv2023

The commensal protist Tritrichomonas musculus exhibits a dynamic life cycle that induces B cell-modulated remodeling of the gut microbiota

Commensal protists and gut bacterial communities exhibit complex relationships, mediated at least in part through host immunity. To improve our understanding of this tripartite interplay, we investigated community and functional dynamics between the murine protist Tritrichomonas musculus (T. mu) and intestinal bacteria in healthy and B cell-deficient mice. We identified dramatic, protist-driven remodeling of resident microbiome growth and activities, in parallel with T. mu functional changes, accelerated in the absence of B cells. Metatranscriptomic data revealed nutrient-based competition between bacteria and the protist. Single cell transcriptomics identified distinct T. mu life stages, providing new evidence for trichomonad sexual replication and the formation of pseudocysts. Unique cell states were validated in situ through microscopy and flow cytometry. Our results reveal complex microbial dynamics during the establishment of a commensal protist in the gut, and provide valuable datasets to drive future mechanistic studies.

microbiology↗

bioRxiv2022

IL-17A promotes epithelial cell IL-33 production during nematode lung migration

The early migratory phase of pulmonary helminth infections is characterized by tissue injury leading to the release of the alarmin IL-33 and subsequent induction of type 2 immune responses. We recently described a role for IL-17A, through regulation of IFN{gamma}, as an important inducer of type 2 responses during infection with the lung-migrating rodent nematode Nippostrongylus brasiliensis. Here, we aimed to investigate the interaction between IL-17A and IL-33 during the early lung migratory stages of N. brasiliensis infection. In this brief report, we demonstrate that deficiency of IL-17A leads to impaired IL-33 expression and secretion early in infection, independent of IL-17A suppression of IFN{gamma}. Impaired IL-33 production was evident in lung epithelial cells, but not innate immune cells. Therefore, our results demonstrate that IL-17A can drive IL-33 during helminth infection, highlighting an additional mechanism through which IL-17A can regulate pulmonary type 2 immunity.

immunology↗

bioRxiv2022

The protozoan commensal Tritrichomonas musculis is a natural adjuvant for mucosal IgA.

Immunoglobulin(Ig) A antibodies are the most abundant antibodies supporting mucosal immune homeostasis and host-microbiota interactions. Driven by gut commensal microbes, IgA-secreting plasma cells (PC) differentiate through T cell-dependent (Td) or T cell independent (Ti) mechanisms. While commensal bacteria within the microbiota are known for their ability to promote IgA, the role of non-bacterial commensal microbes on the induction of IgA remains elusive. Here, we demonstrate that permanent colonization with the protozoan commensal Tritrichomonas musculis (T.mu) promotes T-cell dependent, IgA class-switch recombination and intestinal accumulation of IgA-secreting PC. T.mu colonization specifically drives the expansion of T follicular helper cells and a unique ICOS+ non-Tfh cell population, accompanied by an increase in germinal center B cells. Blockade of ICOS:ICOSL co-stimulation or MHCII-expression on B cells are central for the induction of IgA following colonization by T.mu, implicating a previously underappreciated mode of IgA induction following protozoan commensal colonization. Finally, the commensal T.mu further improves the induction of IgA-secreting plasma cells and their peripheral dissemination, even against non-protozoan, orally ingested antigens, identifying T.mu as natural adjuvant for IgA. Collectively, these findings propose a previously unknown, protozoa-driven mode of IgA induction that supports intestinal immune homeostasis even against non-microbial antigens.

immunology↗

bioRxiv2022

The faecal metabolome and its determinants in inflammatory bowel disease

ObjectiveInflammatory bowel disease (IBD) is a multifactorial immune-mediated inflammatory disease of the intestine, comprising Crohns disease and ulcerative colitis. By characterising metabolites in faeces, combined with faecal metagenomics, host genetics and clinical characteristics, we aimed to unravel metabolic alterations in IBD. DesignWe measured 1,684 different faecal metabolites and 8 short-chain and branched-chain fatty acids in stool samples of 424 IBD patients and 255 non-IBD controls. Regression analyses were used to compare concentrations of metabolites between cases and controls and determine the relationship between metabolites and each participants lifestyle, clinical characteristics and gut microbiota composition. Moreover, genome-wide association analysis was conducted on faecal metabolite levels. ResultsWe identified over 300 molecules that were differentially abundant in the faeces of patients with IBD. The ratio between a sphingolipid and L-urobilin could discriminate between IBD and non-IBD samples (AUC = 0.85). We found changes in the bile acid pool in patients with dysbiotic microbial communities and a strong association between faecal metabolome and gut microbiota. For example, the abundance of Ruminococcus gnavus was positively associated with tryptamine levels. In addition, we found 158 associations between metabolites and dietary patterns, and polymorphisms near NAT2 strongly associated with coffee metabolism. ConclusionIn this large-scale analysis, we identified alterations in the metabolome of patients with IBD that are independent of commonly overlooked confounders such as diet and surgical history. Considering the influence of the microbiome on faecal metabolites, our results pave the way for future interventions targeting intestinal inflammation.

microbiology↗