Biology subjects

Bradley, G.

Publications and source records attributed to Bradley, G..

6 recordsLinked to original sources

bioRxiv2026

Spatial transcriptomic analysis of progressing oral epithelial dysplasia reveals unique differentially expressed genes and microenvironmental changes.

Oral squamous cell carcinoma (OSCC) often arises from oral epithelial dysplasia (OED); however, the gene expression changes during OED progression and its microenvironment are not fully understood. This study used spatial transcriptomics to identify differentially expressed genes and microenvironmental alterations associated with OEDs malignant transformation of OED. A ten-year retrospective analysis of paired OSCC and prior OED samples was conducted at the University of Toronto Oral Pathology Laboratory. A total of 24 paired progressing OED cases and 23 matched non-progressing OED cases were examined using spatial transcriptomics in PanCK+ (dysplastic epithelium or OSCC) and PanCK- (stroma) regions. The analysis included differential gene expression, pathway analysis and spatial deconvolution. Three genes (STOM, KIF26A, and CDKN2A) showed increased expression in the epithelial component of progressing OED compared with non-progressing OED, whereas 41 genes were differentially expressed in OSCC versus the precursor samples. Ubiquitination-related pathways were enriched during OED progression. Functional validation identified TNFRSF12A (Fn14) as a potential regulator of OED progression to OSCC. The OSCC microenvironment displayed increased numbers of fibroblasts, neutrophils, monocytes, and mast cells compared with that of the precursor samples. Our findings suggest that spatial profiling of OED can help identify unique gene signatures and microenvironmental changes that occur before the malignant transformation.

cancer biology↗

bioRxiv2024

An atlas of protein-protein associations of human tissues prioritizes candidate disease genes

Proteins that interact together participate in the same cellular process and influence the same organismal traits. Despite the progress in mapping protein-protein interactions we lack knowledge of how they differ between tissues. Due to coordinated (post)transcriptional control, protein complex members have highly correlated abundances that are predictive of functional association. Here, we have compiled 7873 proteomic samples measuring protein levels in 11 human tissues and use these to define an atlas with tissue-specific protein associations. This method recapitulates known protein complexes and the larger structural organization of the cell. Interactions of stable protein complexes are well preserved across tissues, while signaling and metabolic interactions show larger variation. Further, we find that less than 18% of differences between tissues are estimated to be due to differences in gene expression while cell-type specific cellular structures, such as synaptic components, represent a significant driver of differences between tissues. We further supported the brain protein association network through co-fractionation experiments in synaptosomes, curation of brain derived pull-down data and AlphaFold2 models. Together these results illustrate how this brain specific protein interaction network can functionally prioritize candidate genes within loci linked to brain disorders.

systems biology↗

bioRxiv2023

Simple Causal Relationships in Gene Expression Discovered through Deep Learned Collective Variables

Developments in high-content phenotypic screening with single-cell read-out hold the promise of revealing interactions and functional relationships between genes at the genomic scale scale. However, the high-dimensionality and noisiness of gene expression makes this endeavor highly challenging when treated as a conventional problem in causal machine learning, both because of the statistical power required and because of the limits on computational tractability. Here we take different tack, and propose a deep-learning approach that finds low-dimensional representations of gene expression in which the response to genetic perturbation is highly predictable. We demonstrate that the interactions between genes that are cooperative in these representations are highly consistent with known ground-truth in terms of causal ordering, functional relatedness, and synergistic impact on cell growth and death. Our novel, statistical physics-inspired approach provides a tractable means through which to examine the response the living cell to perturbation, employing coarse graining that reduces data requirements and focuses on identifying simple relationships between groups of genes. Author summaryUnderstanding the causal relationships between genes and the functions of a cells molecular components has long been a challenge in biology and biomedicine. With recent advancements in technologies that manipulate and measure the activity of thousands of genes at once at the single-cell level, scientists are now afforded with the opportunity to interrogate such relationships at scale. However, extracting useful information from the vast readouts of these technologies is non-trivial, in part due to their many-dimensional and noisy nature. Here we develop a machine learning model that allows for the interpretation of complex genetic perturbations in terms of a simple set of causal relations. By analyzing cooperative groups of genes identified by our model, we demonstrate the model can group genes accurately based on their biological function, their relative ordering up- or downstream in the flow of causation, and how their activities combine to affect cell growth and death. Our approach complements existing machine learning methods in providing a simple way to interpret causal mechanism governing genetic interactions and functional states of cells.

genomics↗

bioRxiv2021

Network expansion of genetic associations defines a pleiotropy map of human cell biology

Proteins that interact within molecular networks tend to have similar functions and when perturbed influence the same organismal traits. Interaction networks can be used to expand the list of likely trait associated genes from genome-wide association studies (GWAS). Here, we used improvements in SNP-to-gene mapping to perform network based expansion of trait associated genes for 1,002 human traits showing that this recovers known disease genes or drug targets. The similarity of network expansion scores identifies groups of traits likely to share a common genetic basis as well as the biological processes underlying this. We identified 73 pleiotropic gene modules linked to multiple traits that are enriched in genes involved in processes such as protein ubiquitination and RNA processing. We show examples of modules linked to human diseases enriched in genes with pathogenic variants found in patients or relevant mouse knock-out phenotypes and can be used to map targets of approved drugs for repurposing opportunities. Finally, we illustrate the use of the network expansion scores to study genes at inflammatory bowel disease (IBD) GWAS loci, and implicate IBD-relevant genes with strong functional and genetic support.

genetics↗

bioRxiv2018

CausalTab: PSI-MITAB 2.8 updated format for signaling data representation and dissemination

Combining multiple layers of information underlying biological complexity into a structured framework, and in particular deciphering the molecular mechanisms behind cellular phenotypes, represent two challenges in systems biology. A key task is the formalisation of such information in models describing how biological entities interact to mediate the response to external and internal signals. Several databases with signaling information, such as SIGNOR, SignaLink and IntAct, focus on capturing, organising and displaying signaling interactions by representing them as binary, causal relationships between biological entities. The curation efforts that build these individual databases demand a concerted effort to ensure interoperability among resources, through the development of a standardized exchange format, ontologies and controlled vocabularies supporting the domain of causal interactions. Aware of the enormous benefits of standardization efforts in the molecular interaction research field, representatives of the signalling network community agreed to extend the PSI-MI controlled vocabulary to include additional terms representing aspects of causal interactions. Here, we present a common standard for the representation and dissemination of signaling information: the PSI Causal Interaction tabular format (CausalTAB) which is an extension of the existing PSI-MI tab-delimited format, now designated MITAB2.8. We define the new term \"causal interaction\", and related child terms, which are children of the PSI-MI \"molecular interaction\" term. The new vocabulary terms in this extended PSI-MI format will enable systems biologists to model large-scale signaling networks more precisely and with higher coverage than before.

systems biology↗

bioRxiv2018

PI3Kδ hyper-activation promotes the development of B cells that exacerbate Streptococcus pneumoniae infection in an antibody-independent manner

Streptococcus pneumoniae is a major cause of pneumonia and a leading cause of death world-wide. Antibody-mediated immune responses can offer protection against repeated exposure to S. pneumoniae, yet vaccines only offer partial protection. Patients with Activated PI3K{delta} Syndrome (APDS) are highly susceptible to S. pneumoniae. We generated a conditional knockin mouse model of this disease and identified a CD19+B220- B cell subset that is induced by PI3K{delta} signaling, is resident in the lungs, and which promotes increased susceptibility to S. pneumoniae during the early phase of infection via an antibody-independent mechanism. We show that an inhaled PI3K{delta} inhibitor improves survival rates following S. pneumoniae infection in wild-type mice and in mice with activated PI3K{delta}. These results suggest that a subset of B cells in the lung can promote the severity of S. pneumoniae infection, representing a novel therapeutic target.

immunology↗