Biology subjects

Russell, J. C.

Publications and source records attributed to Russell, J. C..

4 recordsLinked to original sources

bioRxiv2025

Foraging ecology drives viral community structure in New Zealand's aquatic birds

Wild migratory birds play a major role in the global spread of viruses, yet the diversity, host range and transmission patterns of viruses harboured by migratory species in Aotearoa/New Zealand remain largely unknown. This knowledge gap is critical given New Zealands position along major migratory flyways spanning Oceania, Antarctica and east Asia, where understanding viral diversity is key to assessing the risk of viral introductions such as highly pathogenic avian influenza virus and viral dispersal across these regions. To address this, we conducted the first large-scale metatranscriptomic survey of wild birds from New Zealand and its subantarctic islands, collecting 1,348 samples from 31 host species spanning four avian orders. We identified 118 avian viruses from 17 families, including 107 novel species, greatly expanding our knowledge of avian viral diversity. Viral communities differed significantly by host order and foraging behaviour, with scavenger birds harbouring more diverse viromes than non-scavengers. Although no HPAI subtypes were detected, we recovered a low-pathogenic avian influenza A/H1N9 virus from red knots (Calidris canutus) and a divergent tobanivirus from Auckland Island teal (Anas aucklandica), the first putative avian member of the Tobaniviridae. Notably, we detected 12 mammalian-associated viruses, primarily in scavenger birds, including Hedgehog hepatovirus, Rabbit haemorrhagic disease virus 2, and sea lion astroviruses, with mammalian host reads confirming their dietary origin. This study establishes the first virome baseline for New Zealands migratory birds, highlighting the ecological role of foraging in shaping viral communities and improving regional preparedness for HPAI and other emerging avian pathogens.

microbiology↗

bioRxiv2022

Principled Feature Attribution for Unsupervised Gene Expression Analysis

As interest in unsupervised deep learning models for the analysis of gene expression data has grown, an increasing number of methods have been developed to make these deep learning models more interpretable. These methods can be separated into two groups: (1) post hoc analyses of black box models through feature attribution methods and (2) approaches to build inherently interpretable models through biologically-constrained architectures. In this work, we argue that these approaches are not mutually exclusive, but can in fact be usefully combined. We propose a novel unsupervised pathway attribution method, which better identifies major sources of transcriptomic variation than prior methods when combined with biologically-constrained neural network models. We demonstrate how principled feature attributions aid in the analysis of a variety of single cell datasets. Finally, we apply our approach to a large dataset of post-mortem brain samples from patients with Alzheimers disease, and show that it identifies Mitochondrial Respiratory Complex I as an important factor in this disease.

bioinformatics↗

bioRxiv2018

Isolation and characterization of extracellular vesicles from Caenorhabditis elegans for multi-omic analysis.

Cells from bacteria to human release vesicles into their extracellular environment. These extracellular vesicles (EVs) contain multiple classesof molecules, including nucleic acids, proteins, and lipids. The isolation and analysis of EV cargos from mammalian cell culture and liquid biopsysamples has become a powerful approach for uncovering the messages that are packaged into these organelles. However, this approach has not been tenable in invertebrate model systems due to lack of sufficient amounts of pure EVs. Here we report a robust and reproducible procedure to isolateEVs from Caenorhabditis elegans with yields similar to those obtained from human cell culture. Through nanoparticle tracking, transmission electron microscopy, flow cytometry, mass spectrometry, RNAseq, and immunoaffinity analysis we provide the first ever detailed characterization of C. elegans EV composition and demonstrate that C. elegans EVs share fundamentally similar properties with their mammalian counterparts. These include vesicle size, enrichment for lipid rafts, and similar types of RNA and protein cargos. This ability of isolate pure EVs on ascale amenable to multiple types of downstream analyses permits, multi-omics characterization of EV cargos in an invertebrate model system.

cell biology↗

bioRxiv2018

A computational framework identifying concordant gene expression-neuropathology associations reveals Complex I as a potential Alzheimer’s disease therapeutic target

Identifying gene expression markers for Alzheimers disease (AD) neuropathology through meta-analysis is a complex undertaking because available data are often from different studies and/or brain regions involving study-specific confounders and/or region-specific biological processes. Here we introduce a novel probabilistic model-based framework, DECODER, leveraging these discrepancies to identify robust biomarkers for complex phenotypes. Our experiments present: (1) DECODERs potential as a general meta-analysis framework widely applicable to various diseases (e.g., AD and cancer) and phenotypes (e.g., Amyloid-{beta} (A{beta}) pathology, tau pathology, and survival), (2) our results from a meta-analysis using 1,746 human brain tissue samples from nine brain regions in three studies -- the largest expression meta-analysis for AD, to our knowledge --, and (3) in vivo validation of identified modifiers of A{beta} toxicity in a transgenic Caenorhabditis elegans model expressing AD-associated A{beta}, which pinpoints mitochondrial Complex I as a critical mediator of proteostasis and a promising pharmacological avenue toward treating AD.

systems biology↗