Biology subjects

Hallam, S. J.

Publications and source records attributed to Hallam, S. J..

12 recordsLinked to original sources

bioRxiv2026

ASPIRE: the Amplicon Sequencing Profiler for Investigating Respiratory Ecosystems

Microbial communities inhabiting the respiratory tract contribute to health status through interactions with host physiology, immune function, and local environmental conditions. Advances in small subunit ribosomal RNA (SSU or 16S rRNA) gene amplicon sequencing enable culture-independent profiling of microbial communities as amplicon sequence variants (ASVs), revealing links between microbial dysbiosis and respiratory diseases, and the use of mass spectrometry to measure volatile organic compounds (VOCs) in exhaled breath shows emerging promise for biomarker discovery. Here we present ASPIRE, the Amplicon Sequencing Profiler for Investigating Respiratory Ecosystems, an accessible Nextflow workflow for processing, analyzing, and interpreting linked ASV-VOC data from respiratory microbiome studies. ASPIRE is designed to support scalable comparative analysis across respiratory sample types while preserving intermediate file outputs for inspection and reuse within a standardized file structure.

bioinformatics↗

bioRxiv2025

Cellulase catalysis on cell surfaces using Caulobacter S-layer display

Sustainable bioprocesses for energy and materials production and waste resource recovery are needed to support circular economic development. Enzyme surface display on cells or functionalized materials has emerged as a promising paradigm for sustainable bioprocess innovation. Surface (S)-layers are geometric, monomolecular, highly stable crystalline protein lattices encasing the outside of many bacteria and archaea. Several S-layer genes have been shown to tolerate heterologous insertions, thereby enabling high-density display of peptides of interest on cell surfaces without additional costly immobilisation or conjugation steps. Here, we employ an S-layer display platform in Caulobacter vibrioides CB2A JS4038 to express functional cellulases up to 445 amino acids in length. We explore critical design considerations needed for successful catalytic display and demonstrate synergistic activities between differentially expressed cellulases relevant to combinatorial lignocellulosic biomass conversion. Functionalised S-layers capable of transforming lignocellulosic biomass could have useful applications in engineering whole-cell biocatalysts and synthetic microbial consortia tuned for different bioprocess applications.

bioengineering↗

bioRxiv2025

Activity-targeted metaproteomics enhances the ecophysiological characterization of cryptic syntrophic metabolisms

Syntrophic microbial consortia can contribute significantly to the activity and function of anoxic ecosystems, yet are often too rare to study their in situ physiologies using traditional molecular methods. Here, we combined bioorthogonal non-canonical amino acid tagging (BONCAT), stable isotope probing, and metaproteomics to improve the recovery of proteins from active members and track isotope incorporation in an anaerobic digestion community. Both click chemistry-enabled cell-sorting and direct protein pulldown coupled to metaproteomics improved recovery of isotopically labeled proteins during anaerobic acetate oxidation. Resulting labeled protein expression profiles revealed elevated activity of a rare and so-far uncharacterized syntrophic bacterium belonging to the family Natronincolaceae. BONCAT-based capture of newly translated proteins provided direct molecular evidence for the expression of a previously hypothesized oxidative glycine pathway for syntrophic acetate oxidation by this microorganism, showcasing the potential of targeted metaproteomics to characterize rare and active cells central to community metabolism in natural and engineered ecosystems.

microbiology↗

bioRxiv2025

Planetary-scale marine community modeling predicts metabolic synergy and viral impacts

The oceans buffer against climate change via biogeochemical cycles underpinned by microbial metabolic activities. While planetary-scale surveys provide baseline microbiome data, inferring metabolic and biogeochemical impacts remains challenging. Genome-scale modeling has addressed analogous issues at the cellular level, highlighting key metabolic reactions contingent upon specific environmental conditions. Here we adapt this mechanistic modeling framework towards analyzing global ocean microbial communities to reveal metabolic processes predicted to maintain ecosystem functioning. To achieve this, we developed a genome-scale superorganism metabolic model for each TARA Ocean metagenome or metatranscriptome (i.e., limited to reactions known from heterotrophic prokaryotes and viruses), and evaluated these models to establish a community-wide metabolic phenotype for each sample. To validate, we showed that even with reaction-mappable genes only ([~]1/4 of the total genes), model composition revealed metabolism-inferred ecological zones that matched taxonomy-inferred zones. Model inferred metabolic phenotypes revealed reaction cooperation associated with microbial metabolism and organism diversity. These phenotypes also suggest elevated ecological roles for viruses as model predictions suggest they genomically target community-critical metabolic reactions that underpin metabolic phenotype stability, and also demonstrate that, as metabolites are better understood, immediate estimates could be made for where viruses remineralize versus sink carbon. While this new constraints-based, agile, and mechanistic modeling framework is highly upgradable, it already begins to convert molecular-scale environmental omics data to ecological and even planetary-scale biogeochemical features that will better bring microbes and their viruses into Earth system and climate models.

systems biology↗

bioRxiv2023

Self-Assembly of a Repeatable DNA Nanohinge System Supporting Higher Order Structure Formation

DNA base pairs can both encode biological information and be used as a programmable material to build nanostructures with potential application in nanofabrication, data processing and storage, biosensing and drug delivery. Over several decades development of these DNA origami nanostructures has led to increasingly advanced self-assembling nanostructures and molecular machines actuated by various mechanisms such as toehold-mediated strand displacement (TMSD), magnetism and even light. However, scalability remains challenging as using larger scaffold strands can increase the likelihood of kinetic traps and misfolded conformations. Here we describe a repeatable DNA nanohinge system to increase the scalability of existing nanohinge designs for hierarchical assembly of more complex structures with greater degrees of mobility and functionality. The components of this system, comprising two distinct nanohinges, were designed in caDNAno. Structure conformation and stability were simulated using CanDo and MrDNA, and hinge assembly was validated by TEM. Electron micrographs revealed hinge-shaped nanostructures capable of self-assembly into more complex structures, as well as actuation using TMSD through a reversible locking mechanism incorporated into the design. Our work expands the existing utility of DNA nanohinges as building blocks for scalable DNA nanostructures and demonstrates the feasibility of polymerizing hinges in a novel manner for higher order assembly. The enhanced functionality of our dual hinge systems can be employed in future applications requiring greater control and mobility of DNA nanostructures.

bioengineering↗

bioRxiv2021

Modeling the trajectory of SARS-CoV-2 spike protein evolution in continuous latent space using a neural network and Gaussian process

Viral vaccines can lose their efficacy as the genomes of targeted viruses rapidly evolve, resulting in new variants that may evade vaccine-induced immunity. This process is apparent in the emergence of new SARS-CoV-2 variants which have the potential to undermine vaccination efforts and cause further outbreaks. Predictive vaccinology points to a future of pandemic preparedness in which vaccines can be developed preemptively based in part on predictive models of viral evolution. Thus, modeling the trajectory of SARS-CoV-2 spike protein evolution could have value for mRNA vaccine development. Traditionally, in silico sequence evolution has been modeled discretely, while there has been limited investigation into continuous models. Here we present the Viral Predictor for mRNA Evolution (VPRE), an open-source software tool which learns from mutational patterns in viral proteins and models their most statistically likely evolutionary trajectories. We trained a variational autoencoder with real-time and simulated SARS-CoV-2 genome data from Australia to encode discrete spike protein sequences into continuous numerical variables. To simulate evolution along a phylogenetic path, we trained a Gaussian process model with the numerical variables to project spike protein evolution up to five months in advance. Our predictions mapped primarily to a sequence that differed by a single amino acid from the most reported spike protein in Australia within the prediction timeframe, indicating the utility of deep learning and continuous latent spaces for modeling viral protein evolution. VPRE can be readily adapted to investigate and predict the evolution of viruses other than SARS-CoV-2 in temporal, geographic, and lineage-specific pathways.

bioinformatics↗

bioRxiv2021

Comparative genomics reveals electron transfer and syntrophic mechanisms differentiating methanotrophic and methanogenic archaea

The anaerobic oxidation of methane coupled to sulfate reduction is a microbially mediated process requiring a syntrophic partnership between anaerobic methanotrophic (ANME) archaea and sulfate reducing bacteria (SRB). Based on genome taxonomy, ANME lineages are polyphyletic within the phylum Halobacterota, none of which have been isolated in pure culture. Here we reconstruct 28 ANME genomes from environmental metagenomes and flow sorted syntrophic consortia. Together with a reanalysis of previously published datasets, these genomes enable a comparative analysis of all marine ANME clades. We review the genomic features which separate ANME from their methanogenic relatives and identify what differentiates ANME clades. Large multiheme cytochromes and bioenergetic complexes predicted to be involved in novel electron bifurcation reactions are well-distributed and conserved in the ANME archaea, while significant variations in the anabolic C1 pathways exists between clades. Our analysis raises the possibility that methylotrophic methanogenesis may have evolved from a methanotrophic ancestor.

microbiology↗

bioRxiv2021

Insights into the controls on metabolite distributions along a latitudinal transect of the western Atlantic Ocean

Metabolites, or the small organic molecules that are synthesized by cells during metabolism, comprise a complex and dynamic pool of carbon in the ocean. They are an essential form of information, linking genotype to phenotype at the individual, population and community levels of biological organization. Characterizing metabolite distributions inside microbial cells and dissolved in seawater is essential to understanding the controls on their production and fate, as well as their roles in shaping marine microbial food webs. Here, we apply a targeted metabolomics method to quantify particulate and dissolved distributions of a suite of biologically relevant metabolites including vitamins, amino acids, nucleic acids, osmolytes, and intermediates in biosynthetic pathways along a latitudinal transect in the western Atlantic Ocean. We find that, in the euphotic zone, most particulate or intracellular metabolites positively co-vary with the most abundant microbial taxa. In contrast, dissolved metabolites exhibited greater variability with differences in distribution between ocean regions. Although fewer particulate metabolites were detected below the euphotic zone, molecules identified in the deep ocean may be linked to preservation of organic matter or adaptive physiological strategies of deep-sea microbes. Based on the identified metabolite distributions, we propose relationships between certain metabolites and microbial populations, and find that dissolved metabolite distributions are not directly related to their particulate abundances.

microbiology↗

bioRxiv2020

reMap: Relabeling Multi-label Pathway Data with Bags to Enhance Predictive Performance

Metabolic pathway inference from genomic sequence information is an integral scientific problem with wide ranging applications in the life sciences. As sequencing throughput increases, scalable and performative methods for pathway prediction at different levels of genome complexity and completion become compulsory. In this paper, we present reMap (relabeling metabolic pathway data with groups) a simple, and yet, generic framework, that performs relabeling examples to a different set of labels, characterized as groups. A pathway group is comprised of a subset of statistically correlated pathways that can be further distributed between multiple pathway groups. This has important implications for pathway prediction, where a learning algorithm can revisit a pathway multiple times across groups to improve sensitivity. The relabeling process in reMap is achieved through an alternating feedback process. In the first feed-forward phase, a minimal subset of pathway groups is picked to label each example. In the second feed-backward phase, reMaps internal parameters are updated to increase the accuracy of mapping examples to pathway groups. The resulting pathway group dataset is then be used to train a multi-label learning algorithm. reMaps effectiveness was evaluated on metabolic pathway prediction where resulting performance metrics equaled or exceeded other prediction methods on organismal genomes with improved predictive performance.

bioinformatics↗

bioRxiv2020

An integrated, modular approach to data science education in the life sciences

We live in an increasingly data-driven world, where high-throughput sequencing and mass spectrometry platforms are transforming biology into an information science. This has shifted major challenges in biological research from data generation and processing to interpretation and knowledge translation. However, post-secondary training in bioinformatics, or more generally data science for life scientists, lags behind current demand. In particular, development of accessible, undergraduate data science curricula has potential to improve research and learning outcomes and better prepare students in the life sciences to thrive in public and private sector careers. Here, we describe the Experiential Data science for Undergraduate Cross-Disciplinary Education (EDUCE) initiative, which aims to progressively build data science competency across several years of integrated practice. Through EDUCE, students complete data science modules integrated into required and elective courses augmented with coordinated co-curricular activities. The EDUCE initiative draws on a community of practice consisting of teaching assistants, postdocs, instructors and research faculty from multiple disciplines to overcome several reported barriers to data science for life scientists, including instructor capacity, student prior knowledge, and relevance to discipline-specific problems. Preliminary survey results indicate that even a single module improves student self-reported interest and/or experience in bioinformatics and computer science. Thus, EDUCE provides a flexible and extensible active learning framework for integration of data science curriculum into undergraduate courses and programs across the life sciences. Availability and implementationThe EDUCE teaching and learning framework is accessible at educe-ubc.github.io

scientific communication and education↗

bioRxiv2020

Mercury methylation by metabolically versatile and cosmopolitan marine bacteria

Microbes transform aqueous mercury (Hg) into methylmercury (MeHg), a potent neurotoxin in terrestrial and marine food webs. This process requires the gene pair hgcAB, which encodes for proteins that actuate Hg methylation, and has been well described for anoxic environments. However, recent studies report potential MeHg formation in suboxic seawater, although the microorganisms involved remain poorly understood. In this study, we conducted large-scale multi-omic analyses to search for putative microbial Hg methylators along defined redox gradients in Saanich Inlet (SI), British Columbia, a model natural ecosystem with previously measured Hg and MeHg concentration profiles. Analysis of gene expression profiles along the redoxcline identified several putative Hg methylating microbial groups, including Calditrichaeota, SAR324 and Marinimicrobia, with the last by far the most active based on hgc transcription levels. Marinimicrobia hgc genes were identified from multiple publicly available marine metagenomes, consistent with a potential key role in marine Hg methylation. Computational homology modelling predicted that Marinimicrobia HgcAB proteins contain the highly conserved structures required for functional Hg methylation. Furthermore, a number of terminal oxidases from aerobic respiratory chains were associated with several SI putative novel Hg methylators. Our findings thus reveal potential novel marine Hg-methylating microorganisms with a greater oxygen tolerance and broader habitat range than previously recognised.

microbiology↗

bioRxiv2018

Metabolite composition of sinking particles reflects a changing microbial community and differential metabolite degradation

Marine sinking particles transport carbon from the surface and bury it in deep sea sediments where it can be sequestered on geologic time scales. The combination of the surface ocean food web that produces these particles and the particle-associated microbial community that degrades these particles, creates a complex set of variables that control organic matter cycling. We use targeted metabolomics to characterize a suite of small biomolecules, or metabolites, in sinking particles and compare their metabolite composition to that of the suspended particles in the euphotic zone from which they are likely derived. These samples were collected in the South Atlantic subtropical gyre, as well as in the equatorial Atlantic region and the Amazon River plume. The composition of targeted metabolites in the sinking particles was relatively similar throughout the transect, despite the distinct oceanic regions in which they were generated. Metabolites possibly derived from the degradation of nucleic acids and lipids, such as xanthine and glycine betaine, were an increased mole fraction of the targeted metabolites in the sinking particles relative to surface suspended particles, while algal-derived metabolites like the osmolyte dimethylsulfoniopropionate were a smaller fraction of the observed metabolites on the sinking particles. These compositional changes are shaped both by the removal of metabolites associated with detritus delivered from the surface ocean and by production of metabolites by the sinking particle-associated microbial communities. Further, they provide a basis for examining the types and quantities of metabolites that may be delivered to the deep sea by sinking particles.

microbiology↗