Biology subjects

Walsh, B.

Publications and source records attributed to Walsh, B..

10 recordsLinked to original sources

bioRxiv2026

cGMP signaling regulates context-dependent sensory valence in C. elegans

An animals response to chemosensory cues depends on the animals prior experience, internal state, or life stage. However, the molecular mechanisms that regulate sensory valence (i.e., whether a chemosensory cue is attractive or repulsive) remain poorly understood. We investigated the mechanisms that specify sensory valence using the responses of the free-living nematode Caenorhabditis elegans to carbon dioxide (CO2). C. elegans exhibits highly flexible responses to CO2: well-fed animals are repelled by CO2, while both starved animals and well-fed animals raised under high CO2 conditions are attracted to CO2. Here, we show that CO2 attraction in animals raised at high CO2 requires a cGMP signaling pathway that involves the cGMP-dependent protein kinase EGL-4. This pathway does not regulate CO2 response in starved animals, indicating that the role of EGL-4 in mediating CO2 attraction depends on satiety state. Cultivation under high CO2 conditions leads to increased cGMP levels in the CO2-detecting BAG neurons, consistent with a specific requirement for EGL-4 in high-CO2-cultivated animals. We also show that EGL-4 regulates CO2 valence by altering neuropeptide expression in BAG. Our results indicate that sensory valence is established in a context-dependent manner at the level of the primary sensory neuron.

neuroscience↗

bioRxiv2026

Neuronally sensed oxygen drives behavior and development in human-infective, skin-penetrating nematodes

Parasitic nematodes infect over a billion people worldwide and cause some of the most prevalent neglected tropical diseases1-5. Many of these parasites are skin penetrating and have both extra-host life stages that inhabit host feces and surrounding soil, and intra-host life stages that inhabit host niches such as skin, vasculature, and intestine2,6-8. Across life stages, these parasites encounter oxygen (O2) levels that range from ~21% at the soil-air interface to near-anaerobic levels in the host intestine9-12. However, whether parasitic nematodes detect and respond to changes in O2 levels was unknown. Here, we examine O2 sensation in skin-penetrating parasitic nematodes and find that they show robust responses to changes in O2 levels. Moreover, their O2-evoked behaviors differ from those of the free-living nematode Caenorhabditis elegans. We then investigate the molecular and neural mechanisms of O2 sensing in Strongyloides stercoralis, a genetically tractable human-infective nematode, and find that parasite-specific behavioral responses to O2 arise in part from evolutionary changes in their soluble guanylate cyclase repertoire. Finally, we find that neuronal O2 sensing regulates intra-host development in S. stercoralis. Our results demonstrate that skin-penetrating nematodes exhibit neuronally mediated O2 responses that are critical for multiple steps of their parasitic life cycle.

neuroscience↗

bioRxiv2025

FHIR as a Unifying Format for Genomic Research Data Tracking, Aggregation, and Integration

The increasing complexity of genomic research demands standardized data sharing and integration. The Fast Healthcare Interoperability Resources (FHIR) specification has become a well-established standard for exchanging data among health data systems. While designed primarily for clinical and patient data in health care environments, it also has applicability to represent genomic research data and offers a path for aggregating and integrating extremely rich datasets that have traditionally remained siloed and disparate. To study this potential, we developed FHIR Aggregator, an integration of seven major biomedical repositories, including the Genomic Data Commons, GTEx, HTAN, and DepMap, that covered 142334 patients, 819251 specimens, 1096491 observations and 711166 documents. We explore the various ways the FHIR standard can be applied to structure genomic research data and enable new possibilities. We demonstrate how FHIR can be used, where it succeeds or falls short, and which concepts must be extended to better support large-scale clinical and genomics research projects.

bioinformatics↗

bioRxiv2024

The Plasmepsin-Piperaquine Paradox Persists

Malaria is still a major health issue in many parts of the world, particularly in tropical and subtropical regions of Africa, Asia, and Latin America. Despite significant efforts to control and eliminate the disease, malaria remains a leading cause of illness and death, mainly due to the occurrence of drug-resistant parasites to the frontline antimalarials such as dihydroartemisinin-piperaquine (DHA-PPQ). Artemisinin resistance has been linked to kelch13 mutations, while decreased PPQ sensitivity has been associated with higher plasmepsin II and III gene copies and mutations in the chloroquine resistance transporter. In this study, we demonstrate the effective use of CRISPR/Cas9 technology to generate single knockouts (KO) of plasmepsin II and plasmepsin III, as well as a double KOs of both genes, in two isogenic lines of Cambodian parasites with varying numbers of plasmepsin gene copies. The deletion of plasmepsin II and/or III increased the parasites sensitivity to PPQ, evaluated by the area under the curve. We explored several hypotheses to understand how an increased plasmepsin gene copy number might influence parasite survival under high PPQ pressure. Our findings indicate that protease inhibitors have a minimal impact on parasite susceptibility to PPQ. Additionally, parasites with higher plasmepsin gene copy numbers did not exhibit significantly increased hemoglobin digestion, nor did they produce different amounts of free heme following PPQ treatment compared to wildtype parasites. Interestingly, hemoglobin digestion was slowed in parasites with plasmepsin II deletions. By treating parasites with digestive vacuole (DV) function modulators, we found that changes in DV pH potentially affect their response to PPQ. Our research highlights the crucial role of increased plasmepsin II and III gene copy numbers in modulating response to PPQ and begins to uncover the molecular and physiological mechanisms underlying PPQ resistance in Cambodian parasites. Author SummaryGlobal malaria control has plateaued, with drug-resistant Plasmodium falciparum posing a significant challenge. Artemisinin-based combination therapies (ACTs) are becoming less effective, especially in South-East Asia, where resistance to dihydroartemisinin-piperaquine (DHA-PPQ) is leading to treatment failures, notably in Cambodia. Genome-wide studies link artemisinin resistance to kelch13 mutations, while decreased PPQ sensitivity is tied to higher plasmepsin II and III gene copies and mutations in chloroquine resistance transporter. We previously showed a connection between increased plasmepsin gene copies and reduced PPQ sensitivity. In this study we try to understand the biological role of the plasmepsins in PPQ sensitivity. Therefore, we knocked out plasmepsin II and III genes in Cambodian strains using CRISPR/Cas9, and found increased PPQ sensitivity, confirming these genes roles in resistance. Plasmepsins are proteases that participate in the hemoglobin degradation cascade in the digestive vacuole of the parasites. Protease inhibitor experiments and hemoglobin digestion studies indicate that digestive vacuole pH fluctuations affect PPQ response, highlighting the need for further research into PPQ resistance mechanisms.

molecular biology↗

bioRxiv2022

recountmethylation enables flexible analysis ofpublic blood DNA methylation array data

Thousands of DNA methylation (DNAm) array samples from human blood are publicly available on the Gene Expression Omnibus (GEO), but they remain underutilized for experiment planning, replication, and cross-study and cross-platform analyses. To facilitate these tasks, we augmented our recountmethylation R/Bioconductor package with 12,537 uniformly processed EPIC and HM450K blood samples on GEO as well as several new features. We subsequently used our updated package in several illustrative analyses, finding (1) study ID bias adjustment increased variation explained by biological and demographic variables, (2) most variation in autosomal DNAm was explained by genetic ancestry and CD4+ T-cell fractions, and (3) the dependence of power to detect differential methylation on sample size was similar for each of peripheral blood mononuclear cells (PBMC), whole blood, and umbilical cord blood. Finally, we used PBMC and whole blood to perform independent validations, and we recovered 40-46% of differentially methylated probes (DMPs) between sexes from two previously published epigenome-wide association studies (EWAS).

bioinformatics↗

bioRxiv2021

The GA4GH Variation Representation Specification (VRS): a Computational Framework for the Precise Representation and Federated Identification of Molecular Variation

Maximizing the personal, public, research, and clinical value of genomic information will require that clinicians, researchers, and testing laboratories exchange genetic variation data reliably. Developed by a partnership among national information resource providers, public initiatives, and diagnostic testing laboratories under the auspices of the Global Alliance for Genomics and Health (GA4GH), the Variation Representation Specification (VRS, pronounced "verse") is an extensible framework for the semantically precise and computable representation of variation that complements contemporary human-readable and flat file standards for variation representation. VRS objects are designed to be semantically precise representations of variation, and leverage this design to enable unique, federated identification of molecular variation. We describe the components of this framework, including the terminology and information model, schema, data sharing conventions, and a reference implementation, each of which is intended to be broadly useful and freely available for community use. The specification, documentation, examples, and community links are available at https://vrs.ga4gh.org/.

bioinformatics↗

bioRxiv2020

Temperatures that sterilise males better predict global distributions of species than lethal temperatures

Predicting how biodiversity will respond to increased temperatures caused by climate change is vital. However, our understanding of the traits that determine species response to thermal stress remains incomplete. Laboratory measurements of lethal temperatures have successfully been used to predict global species distributions and the vulnerability of species to future climate change. However, although it has long been known that fertility is sensitive to heat stress, temperatures that cause sterility have not been incorporated into predictions about how climate change will affect biodiversity. Here we show that male sterility temperatures predict the global distributions of 43 species of Drosophila substantially better than their lethal temperatures. This strongly suggests that thermal limits to reproduction can underpin how temperature affects species distributions. High temperatures impair male fertility across a broad range of animals and plants, so many organisms may be more vulnerable to high temperatures than currently expected.

ecology↗

bioRxiv2019

Exploring Integrative Analysis using the BioMedical Evidence Graph

The analysis of cancer biology data involves extremely heterogeneous datasets including information from RNA sequencing, genome-wide copy number, DNA methylation data reporting on epigenomic regulation, somatic mutations from whole-exome or whole-genome analyses, pathology estimates from imaging sections or subtyping, drug response or other treatment outcomes, and various other clinical and phenotypic measurements. Bringing these different resources into a common framework, with a data model that allows for complex relationships as well as dense vectors of features, will unlock integrative analysis. We introduce a graph database and query engine for discovery and analysis of cancer biology, called the BioMedical Evidence Graph (BMEG). The BMEG is unique from other biological data graphs in that sample level molecular information is connected to reference knowledge bases. It combines gene expression and mutation data, with drug response experiments, pathway information databases and literature derived associations. The construction of the BMEG has resulted in a graph containing over 36M vertices and 29M edges. The BMEG system provides a graph query based API to enable analysis, with client code available for Python, Javascript and R, and a server online at bmeg.io. Using this system we have developed several forms of integrated analysis to demonstrate the utility of the system. The BMEG is an evolving resource dedicated to enabling integrative analysis. We have demonstrated queries on the system that illustrate mutation significance analysis, drug response machine learning, patient level knowledge base queries and pathway level analysis. We have compared the resulting graph to other available integrated graph systems, and demonstrated that it is unique in the scale of the graph and the type of data it makes available.\n\nHighlightsO_LIData resource connected extremely diverse set of cancer data sets\nC_LIO_LIGraph query engine that can be easily deployed and used on new datasets\nC_LIO_LIEasily installed python client\nC_LIO_LIServer online at bmeg.io\nC_LI\n\nSummaryThe analysis of cancer biology data involves extremely heterogeneous datasets including information. Bringing these different resources into a common framework, with a data model that allows for complex relationships as well as dense vectors of features, will unlock integrative analysis. We introduce a graph database and query engine for discovery and analysis of cancer biology, called the BioMedical Evidence Graph (BMEG). The construction of the BMEG has resulted in a graph containing over 36M vertices and 29M edges. The BMEG system provides a graph query based API to enable analysis, with client code available for Python, Javascript and R, and a server online at bmeg.io. Using this system we have developed several forms of integrated analysis to demonstrate the utility of the system.

cancer biology↗

bioRxiv2018

A harmonized meta-knowledgebase of clinical interpretations of cancer genomic variants

Precision oncology relies on the accurate discovery and interpretation of genomic variants to enable individualized diagnosis, prognosis, and therapy selection. We found that knowledgebases containing clinical interpretations of somatic cancer variants are highly disparate in interpretation content, structure, and supporting primary literature, impeding consensus when evaluating variants and their relevance in a clinical setting. With the cooperation of experts of the Global Alliance for Genomics and Health (GA4GH) and six prominent cancer variant knowledgebases, we developed a framework for aggregating and harmonizing variant interpretations to produce a meta-knowledgebase of 12,856 aggregate interpretations covering 3,437 unique variants in 415 genes, 357 diseases, and 791 drugs. We demonstrated large gains in overlap between resources across variants, diseases, and drugs as a result of this harmonization. We subsequently demonstrated improved matching between a patient cohort and harmonized interpretations of potential clinical significance, observing an increase from an average of 33% per individual knowledgebase to 56% in aggregate. Our analyses illuminate the need for open, interoperable sharing of variant interpretation data. We also provide an open and freely available web interface (search.cancervariants.org) for exploring the harmonized interpretations from these six knowledgebases.

bioinformatics↗

bioRxiv2017

Functional significance of spectrotemporal response functions obtained using magnetoencephalography

The spectrotemporal response function (STRF) model of neural encoding quantitatively associates dynamic auditory neural (output) responses to a spectrogram-like representation of a dynamic (input) stimulus. STRFs were experimentally obtained via whole-head human cortical responses to dynamic auditory stimuli using magnetoencephalography (MEG). The stimuli employed consisted of unpredictable pure tones presented at a range of rates. The predictive power of the estimated STRFs was found to be comparable to those obtained from the cortical single and multiunit activity literature. The STRFs were also qualitatively consistent with those obtained from electrophysiological studies in animal models; in particular their local-field-potential-generated spectral distributions and multiunit-activity-generated temporal distributions. Comparison of these MEG STRFs with others obtained using natural speech and music stimuli reveal a general structure consistent with common baseline auditory processing, including evidence for a transition in low-level neural representations of natural speech by 100 ms, when an appropriately chosen stimulus representation was used. It is also demonstrated that MEG-based STRFs contain information similar to that obtained using classic auditory evoked potential based approaches, but with extended applications to long-duration, non-repeated stimuli.\n\nAuthor summaryThe spectrotemporal response function (STRF) model of linking dynamic acoustic stimuli to dynamic neural responses is applied to whole-head non-invasive magnetoencephalography (MEG) recordings of the human auditory cortex. MEG STRFs were consistent predictors of neural activity, quantitatively and qualitatively, by comparison to those obtained from animal models using local field potential or multiunit activity as neural responses. Comparison of STRFs using stimuli as diverse as tone clouds, natural speech, and music revealed a common structure consistent with shared baseline auditory processing, when an appropriately chosen stimulus representation was used.

neuroscience↗