Biology subjects

McLaughlin, R.

Publications and source records attributed to McLaughlin, R..

11 recordsLinked to original sources

bioRxiv2024

Sex-specific DNA methylation differences in Amyotrophic lateral sclerosis

Sex is an important covariate in all genetic and epigenetic research due to its role in the incidence, progression and outcome of many phenotypic characteristics and human diseases. Amyotrophic lateral sclerosis (ALS) is a motor neuron disease with a sex bias towards higher incidence in males. Here, we report for the first time a blood-based epigenome-wide association study meta-analysis in 9274 individuals after stringent quality control (5529 males and 3975 females). We identified a total of 226 ALS saDMPs (sex-associated DMPs) annotated to a total of 159 unique genes. These ALS saDMPs were depleted at transposable elements yet significantly enriched at enhancers and slightly enriched at 3UTRs. These ALS saDMPs were enriched for transcription factor motifs such as ESR1 and REST. Moreover, we identified an additional 10 genes associated with ALS saDMPs through chromatin loop interactions, suggesting a potential regulatory role for these saDMPs on distant genes. Furthermore, we investigated the relationship between DNA methylation at specific CpG sites and overall survival in ALS using Cox proportional hazards models. We identified two ALS saDMPs, cg14380013 and cg06729676, that showed significant associations with survival. Overall, our study reports a reliable catalogue of sex-associated ALS saDMPs in ALS and elucidates several characteristics of these sites using a large-scale dataset. This resource will benefit future studies aiming to investigate the role of sex in the incidence, progression and risk for ALS.

genomics↗

bioRxiv2024

A Metagenomic Analysis of the Fecal Microbiota of the Critically Endangered Eastern Bongo

The Eastern Bongo (Tragelaphus eurycerus isaaci) is a critically endangered mammal. In zoos, this animal has been known to have a sensitive gastrointestinal tract. Using a metagenomic approach the purpose of this study was to determine the microbiota of two adult (n=2) Eastern Bongos living in a zoo in South Dakota, as well as a juvenile (n=6) over a six-week period to see the microbial succession, and to learn if there were pathogenic microorganisms present which are capable of causing gastrointestinal disease. The dominant phylum in seven samples was Bacillota with Pseudomonadota dominant in only one of the juvenile samples. Functional classifications showed Protein Synthesis was the most predominant (11.36-35.71%). Almost equally predominant were Energy and Precursor Metabolites Generation (7.00-13.27%) and Stress Response, Defense and Virulence (8.44-12.90%). Finally, we also determined the resistomes which will contribute to the One Health approach.

microbiology↗

bioRxiv2024

MetaPathways v3.5: Modularity and Scalability Improvements for Pathway Inference from Environmental Genomes

Over the past decade MO_SCPLOWETAC_SCPLOWPO_SCPLOWATHWAYSC_SCPLOW has advanced as a modular pipeline for constructing environmental pathway genome databases (ePGDBs), increasing our understanding of microbial metabolism at the individual, population and community levels of biological organization. With this release, we have addressed several user experience issues related to installation, module integration, and database management. With a refactored code base, MO_SCPLOWETAC_SCPLOWPO_SCPLOWATHWAYSC_SCPLOW v3.5 enhances the user experience through streamlined installation via package indexes or containers, refined modules, and interface upgrades. It boasts updated algorithm support for sequence feature prediction, annotation, metabolic inference, and coverage metrics including genome resolved metagenomes. Tested and refined on synthetic datasets, MO_SCPLOWETAC_SCPLOWPO_SCPLOWATHWAYSC_SCPLOW v3.5 demonstrates improved performance and usability; facilitating more in-depth exploration of microbial interactions and metabolic functions in environmental genomes that scales with con-temporary sequencing throughput. Availability and ImplementationMO_SCPLOWETAC_SCPLOWPO_SCPLOWATHWAYSC_SCPLOW v3.5 is available via AO_SCPLOWNACONDAC_SCPLOW, DO_SCPLOWOCKERC_SCPLOW, and AO_SCPLOWPPTAINERC_SCPLOW. The source code is available on BO_SCPLOWITC_SCPLOWBO_SCPLOWUCKETC_SCPLOW:https://bitbucket.org/BCB2/metapathways/ The documentation is available via RO_SCPLOWEADC_SCPLOWTO_SCPLOWHEC_SCPLOWDO_SCPLOWOCSC_SCPLOW:https://metapathways.readthedocs.io Contactshallam@mail.ubc.ca

bioinformatics↗

bioRxiv2022

Molecular dynamics analysis of Superoxide Dismutase 1 mutations suggests decoupling between mechanisms underlying ALS onset and progression

Mutations in the superoxide dismutase 1 (SOD1) gene are the second most common known cause of ALS. SOD1 variants express high phenotypic variability and over 200 have been reported in people with ALS. Investigating how different SOD1 variants affect the protein dynamics might help in understanding their pathogenic mechanism and explaining their heterogeneous clinical presentation. It was previously proposed that variants can be broadly classified in two groups, wild-type like (WTL) and metal binding region (MBR) variants, based on their structural location and biophysical properties. MBR variants are associated with a loss of SOD1 enzymatic activity. In this study we used molecular dynamics and large clinical datasets to characterise the differences in the structural and dynamic behaviour of WTL and MBR variants with respect to the wild-type SOD1, and how such differences influence the ALS clinical phenotype. Our study identified marked structural differences, some of which are observed in both variant groups, while others are group specific. Moreover, applying graph theory to a network representation of the proteins, we identified differences in the intramolecular contacts of the two classes of variants. Finally, collecting clinical data of approximately 500 SOD1 ALS patients carrying variants from both classes, we showed that the survival time of patients carrying an MBR variant is generally longer (~6 years median difference, p < 0.001) with respect to patients with a WTL variant. In conclusion, our study highlights key differences in the dynamic behaviour of the WTL and MBR SOD1 variants, and wild-type SOD1 at an atomic and molecular level. We identified interesting structural features that could be further investigated to explain the associated phenotypic variability. Our results support the hypothesis of a decoupling between mechanisms of onset and progression of SOD1 ALS, and an involvement of loss-of-function of SOD1 with the disease progression.

neuroscience↗

bioRxiv2021

Characterization of striatal dopamine projections across striatal subregions in behavioral flexibility

Behavioral flexibility is key to survival in a dynamic environment. While flexible, goal-directed behaviors are initially dependent on dorsomedial striatum, they become dependent on lateral striatum with extended training as behaviors become inflexible. Similarly, dopamine release shifts from ventromedial to lateral striatum across learning, and impairment of lateral dopamine release disrupts habitual, inflexible responding. This raises the possibility that lateral dopamine release is a causative mechanism in establishing inflexible behaviors late in training, though this has not been directly tested. Here, we utilized optogenetics to activate dopamine terminals in dorsal medial (DMS), dorsal lateral (DLS), and ventral (NAc) striatum in DATcre mice to determine how specific dopamine subpopulations impact behavioral flexibility. Mice performed a reversal task in which they self-stimulated DMS, DLS, or NAc dopamine terminals by pressing one of two levers before action-outcome lever contingencies were reversed. Consistent with presumed ventromedial/lateral striatal function, we found that mice self-stimulating ventromedial dopamine terminals rapidly reversed lever preference following contingency reversal, while mice self-stimulating dopamine terminals in DLS showed impaired reversal learning. These impairments were characterized by more regressive errors and reliance on lose-stay strategies following reversal, suggesting reward insensitivity and overreliance on previously learned actions. This study supports a model of striatal function in which dorsomedial dopamine facilitates goal-directed responding, and dorsolateral dopamine release is a key mechanism in supporting the transition toward inflexible behaviors.

neuroscience↗

bioRxiv2020

Optogenetic interrogation of the role of striatal patches in habit formation and inhibition of striatal dopamine

Habits are inflexible behaviors that can be maladaptive in diseases including drug addiction. The striatum is integral to habit formation, and interspersed throughout the striatum are patches, or striosomes, which are characterized by unique gene expression relative to the surrounding matrix. Recent work has indicated that patches are necessary for habit formation, but how patches contribute to habits remains partially understood. Here, using optogenetics, we modulated striatal patches in Sepw1-NP67 mice during habit formation. We find that patch activation during operant training impairs habit formation, and conversely, that acute patch stimulation after reward devaluation can drive habitual reward seeking. Patch stimulation invigorates general locomotion but is not inherently rewarding. Finally, we use fast-scan cyclic voltammetry to demonstrate that patch stimulation suppresses dopamine release in dorsal striatum in vivo. Overall, this work provides novel insight into the role of the patch compartment in habit formation, and potential interactions with dopamine signaling.

neuroscience↗

bioRxiv2020

Metabolic pathway inference using non-negative matrix factorization with community detection

Machine learning provides a probabilistic framework for metabolic pathway inference from genomic sequence information at different levels of complexity and completion. However, several challenges including pathway features engineering, multiple mapping of enzymatic reactions and emergent or distributed metabolism within populations or communities of cells can limit prediction performance. In this paper, we present triUMPF, triple non-negative matrix factorization (NMF) with community detection for metabolic pathway inference, that combines three stages of NMF to capture myriad relationships between enzymes and pathways within a graph network. This is followed by community detection to extract higher order structure based on the clustering of vertices which share similar statistical properties. We evaluated triUMPF performance using experimental datasets manifesting diverse multi-label properties, including Tier 1 genomes from the BioCyc collection of organismal Pathway/Genome Databases and low complexity microbial communities. Resulting performance metrics equaled or exceeded other prediction methods on organismal genomes with improved precision on multi-organismal datasets. Availability and implementationThe software package, and installation instructions are published on github.com/triUMPF

bioinformatics↗

bioRxiv2020

Metabolic pathway inference using multi-label classification with rich pathway features

Metabolic inference from genomic sequence information is a necessary step in determining the capacity of cells to make a living in the world at different levels of biological organization. A common method for determining the metabolic potential encoded in genomes is to map conceptually translated open reading frames onto a database containing known product descriptions. Such gene-centric methods are limited in their capacity to predict pathway presence or absence and do not support standardized rule-sets for automated and reproducible research. Pathway-centric methods based on defined rule sets or machine learning algorithms provide an adjunct or alternative inference method that supports hypothesis generation and testing of metabaolic relationships within and between cells. Here, we present mlLGPR, multi-label based on logistic regression for pathway prediction, a software package that uses supervised multi-label classification and rich pathway features to infer metabolic networks at the individual, population and community levels of organization. We evaluated mlLGPR performance using a corpora of 12 experimental datasets manifesting diverse multi-label properties, including manually curated organismal genomes, synthetic microbial communities and low complexity microbial communities. Resulting performance metrics equaled or exceeded previous reports for organismal genomes and identify specific challenges associated with features engineering and training data for community-level metabolic inference. Author summaryPredicting the complex series of metabolic interactions e.g. pathways, within and between cells from genomic sequence information is an integral problem in biology linking genotype to phenotype. This is a prerequisite to both understanding fundamental life processes and ultimately engineering these processes for specific biotechnological applications. A pathway prediction problem exists because we have limited knowledge of the reactions and pathways operating in cells even in model organisms like Esherichia coli where the majority of protein functions are determined. To improve pathway prediction outcomes for genomes at different levels of complexity and completion we have developed mlLGPR, multi-label based on logistic regression for pathway prediction, a scalable open source software package that uses supervised multi-label classification and rich pathway features to infer metabolic networks. We benchmark mlLGPR performance against other inference methods providing a code base and metrics for continued application of machine learning methods to the pathway prediction problem at the individual, population and community levels of biological organization.

bioinformatics↗

bioRxiv2019

Evolution-guided design of super-restrictor antiviral proteins reveals a breadth-versus-specificity tradeoff

Antagonistic interactions drive host-virus evolutionary arms-races, which often manifest as recurrent amino acid changes (i.e., positive selection) at their protein-protein interaction interfaces. Here, we investigated whether combinatorial mutagenesis of positions under positive selection in a host antiviral protein could enhance its restrictive properties. We tested ~700 variants of human MxA, generated by combinatorial mutagenesis, for their ability to restrict Thogoto orthomyxovirus (THOV). We identified MxA super-restrictors with increased binding to THOV NP target protein and 10-fold higher anti-THOV restriction relative to wild-type human MxA, the most potent naturally-occurring anti-THOV restrictor identified. Our findings reveal a means to elicit super-restrictor antiviral proteins by leveraging signatures of positive selection. Although some MxA super-restrictors of THOV were impaired in their restriction of H5N1 influenza A virus (IAV), other super-restrictor variants increased THOV restriction without impairment of IAV restriction. Thus, broadly acting antiviral proteins such as MxA mitigate breadth-versus-specificity tradeoffs that could otherwise constrain their adaptive landscape.

microbiology↗

bioRxiv2017

The Population Genomics Of Archaeological Transition In West Iberia

We analyse new genomic data (0.05-2.95x) from 14 ancient individuals from Portugal distributed from the Middle Neolithic (4200-3500 BC) to the Middle Bronze Age (1740-1430 BC) and impute genomewide diploid genotypes in these together with published ancient Eurasians. While discontinuity is evident in the transition to agriculture across the region, sensitive haplotype-based analyses suggest a significant degree of local hunter-gatherer contribution to later Iberian Neolithic populations. A more subtle genetic influx is also apparent in the Bronze Age, detectable from analyses including haplotype sharing with both ancient and modern genomes, D-statistics and Y-chromosome lineages. However, the limited nature of this introgression contrasts with the major Steppe migration turnovers within third Millennium northern Europe and echoes the survival of non-Indo-European language in Iberia. Changes in genomic estimates of individual height across Europe are also associated with these major cultural transitions, and ancestral components continue to correlate with modern differences in stature.\n\nAuthor SummaryRecent ancient DNA work has demonstrated the significant genetic impact of mass migrations from the Steppe into Central and Northern Europe during the transition from the Neolithic to the Bronze Age. In Iberia, archaeological change at the level of material culture and funerary rituals has been reported during this period, however, the genetic impact associated with this cultural transformation has not yet been estimated. In order to investigate this, we sequence Neolithic and Bronze Age samples from Portugal, which we compare to other ancient and present-day individuals. Genome-wide imputation of a large dataset of ancient samples enabled sensitive methods for detecting population structure and selection in ancient samples. We revealed subtle genetic differentiation between the Portuguese Neolithic and Bronze Age samples suggesting a markedly reduced influx in Iberia compared to other European regions. Furthermore, we predict individual height in ancients, suggesting that stature was reduced in the Neolithic and affected by subsequent admixtures. Lastly, we examine signatures of strong selection in important traits and the timing of their origins.

genomics↗

bioRxiv2016

Detection of long repeat expansions from PCR-free whole-genome sequence data

Identifying large repeat expansions such as those that cause amyotrophic lateral sclerosis (ALS) and Fragile X syndrome is challenging for short-read (100-150 bp) whole genome sequencing (WGS) data. A solution to this problem is an important step towards integrating WGS into precision medicine. We have developed a software tool called ExpansionHunter that, using PCR-free WGS short-read data, can genotype repeats at the locus of interest, even if the expanded repeat is larger than the read length. We applied our algorithm to WGS data from 3,001 ALS patients who have been tested for the presence of the C9orf72 repeat expansion with repeat-primed PCR (RP-PCR). Taking the RP-PCR calls as the ground truth, our WGS-based method identified pathogenic repeat expansions with 98.1% sensitivity and 99.7% specificity. Further inspection identified that all 11 conflicts were resolved as errors in the original RP-PCR results. Compared against this updated result, ExpansionHunter correctly classified all (212/212) of the expanded samples as either expansions (208) or potential expansions (4). Additionally, 99.9% (2,786/2,789) of the wild type samples were correctly classified as wild type by this method with the remaining two identified as possible expansions. We further applied our algorithm to a set of 144 samples where every sample had one of eight different pathogenic repeat expansions including examples associated with fragile X syndrome, Friedreichs ataxia and Huntingtons disease and correctly flagged all of the known repeat expansions. Finally, we tested the accuracy of our method for short repeats by comparing our genotypes with results from 860 samples sized using fragment length analysis and determined that our calls were >95% accurate. ExpansionHunter can be used to accurately detect known pathogenic repeat expansions and provides researchers with a tool that can be used to identify new pathogenic repeat expansions.

bioinformatics↗