Biology subjects

Hallam, S.

Publications and source records attributed to Hallam, S..

8 recordsLinked to original sources

bioRxiv2026

Methodological Impacts on Microbiome Structure and Indicator Status in the Human Lower Respiratory Tract

RationaleLung cancer is the leading cause of cancer-related death globally, and rising incidence among traditionally low-risk individuals intensifies the need for improved early-detection methods that the lower-airway microbiome may inform. ObjectivesTo evaluate how respiratory tract sampling method shapes inferred microbiome structure, and whether bronchial brushing recovers a microbial community ecologically distinct from BAL and oral rinse. MethodsProspective cohort of 33 participants (8 lung cancer, 25 non-cancer controls) underwent oral rinse, bilateral bronchoalveolar lavage (BAL), and bronchial brushing. Microbiome structure was characterized by small subunit ribosomal rRNA gene amplicon sequence variant (ASV) profiling, with indicator-species analysis and SPIEC-EASI correlation-network mapping used to identify ASVs associated with sample type or cancer status. Measurements and Main ResultsSampling method was the dominant axis of variation. BAL communities closely resembled oral rinse (65 jointly indicative ASVs; none shared with brushing), whereas bronchial brushing yielded a distinct but low-biomass signal. After excluding host-derived sequences, two brush-specific indicator ASVs affiliated with Sphingomonadaceae and an uncultured Steroidobacteraceae were identified that co-localized within a single co-occurrence module. Cancer-status effects were not detectable in this pilot cohort, consistent with limited statistical power. ConclusionsSampling method is the primary determinant of inferred respiratory microbiome structure. Bronchial brushing recovers a distinct but low-biomass signal that is obscured when BAL is used in isolation. However, overlap between this low-biomass signal with host and contaminant sequences indicates that confidently resolving a discrete lower-airway community will require deeper sequencing and dedicated contamination controls. These methodological findings directly inform the design of future cancer-and disease-association studies.

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↗

bioRxiv2020

Multi-label pathway prediction based on active dataset subsampling

Metabolic pathways are composed of reaction sequences catalyzed by enzymes. The set of reactions within and between cells comprises a reactome. Pathways and reactomes can be predicted from organismal or multi-organismal genomes using rule-based or machine learning methods. While machine learning methods overcome issues of probability and scale associated with rule-based methods, several complications remain that can degrade performance including inadequately labeled training data, missing feature information, and inherent imbalances in the distribution of pathways within a dataset. Here, we present leADS (multi-label learning based on active dataset subsampling), a machine learning method, that uses subsampling to reduce the negative impact of training loss due to class imbalance. We demonstrate leADs performance using organismal and multi-organismal datasets in relation to other machine learning pathway prediction methods. Availability and implementationleADS is available under the GNU license at github.com/hallamlab/leADS. A wiki, including a tutorial, is available at github.com//hallamlab/leADS/wiki Contactshallam@mail.ubc.ca

bioinformatics↗

bioRxiv2020

Ecology of inorganic sulfur auxiliary metabolism in widespread bacteriophages

Microbial sulfur metabolism contributes to biogeochemical cycling on global scales. Sulfur metabolizing microbes are infected by phages that can encode auxiliary metabolic genes (AMGs) to alter sulfur metabolism within host cells but remain poorly characterized. Here we identified 191 phages derived from twelve environments that encoded 227 AMGs for oxidation of sulfur and thiosulfate (dsrA, dsrC/tusE, soxC, soxD and soxYZ). Evidence for retention of AMGs during niche-differentiation of diverse phage populations provided evidence that auxiliary metabolism imparts measurable fitness benefits to phages with ramifications for ecosystem biogeochemistry. Gene abundance and expression profiles of AMGs suggested significant contributions by phages to sulfur and thiosulfate oxidation in freshwater lakes and oceans, and a sensitive response to changing sulfur concentrations in hydrothermal environments. Overall, our study provides novel insights on the distribution, diversity and ecology of phage auxiliary metabolism associated with sulfur and reinforces the necessity of incorporating viral contributions into biogeochemical configurations.

microbiology↗

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

Ecology and molecular targets of hypermutation in the global microbiome.

Changes in the sequence of an organisms genome, i.e. mutations, are the raw material of evolution1. The frequency and location of mutations can be constrained by specific molecular mechanisms, such as Diversity-generating retroelements (DGRs)2-4. DGRs introduce mutations in specific target genes, and were characterized from several cultivated bacteria and bacteriophages2. Whilst a larger diversity of DGR loci has been identified in genomic data from environmental samples, i.e. metagenomes, the ecological role of these DGRs and their associated evolutionary drivers remain poorly understood5-7. Here we built and analyzed an extensive dataset of >30,000 metagenome-derived DGRs, and determine that DGRs have a single evolutionary origin and a universal bias towards adenine mutations. We further identified six major lineages of DGRs, each associated with a specific ecological niche defined as a genome type, i.e. whether the DGR is encoded on a viral or cellular genome, a limited set of taxa and environments, and a distinct type of target. Finally, we leverage read mapping and metagenomic time series to demonstrate that DGRs are consistently and broadly active, and responsible for >10% of all amino acid changes in some organisms at a conservative estimate. Overall, these results highlight the strong constraints under which DGRs diversify and expand, and elucidate several distinct roles these elements play in natural communities and in shaping microbial community structure and function in our environment.

microbiology↗

bioRxiv2020

Leveraging Heterogeneous Network Embedding for Metabolic Pathway Prediction

Metabolic pathway reconstruction from genomic sequence information is a key step in predicting regulatory and functional potential of cells at the individual, population and community levels of organization. Although the most common methods for metabolic pathway reconstruction are gene-centric e.g. mapping annotated proteins onto known pathways using a reference database, pathway-centric methods based on heuristics or machine learning to infer pathway presence provide a powerful engine for hypothesis generation in biological systems. Such methods rely on rule sets or rich feature information that may not be known or readily accessible. Here, we present pathway2vec, a software package consisting of six representational learning based modules used to automatically generate features for pathway inference. Specifically, we build a three layered network composed of compounds, enzymes, and pathways, where nodes within a layer manifest inter-interactions and nodes between layers manifest betweenness interactions. This layered architecture captures relevant relationships used to learn a neural embedding-based low-dimensional space of metabolic features. We benchmark pathway2vec performance based on node-clustering, embedding visualization and pathway prediction using MetaCyc as a trusted source. In the pathway prediction task, results indicate that it is possible to leverage embeddings to improve pathway prediction outcomes. Availability and implementationThe software package, and installation instructions are published on github.com/pathway2vec Contactshallam@mail.ubc.ca

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↗