Biology subjects

McWeeney, S.

Publications and source records attributed to McWeeney, S..

12 recordsLinked to original sources

bioRxiv2025

A Rapid Gene Expression Profiler Classifies AML Tumor Responsiveness to Standard Therapies

The emergence of transcriptional signatures that define cell types and pathways has made it possible to guide cancer therapy selection through gene expression profiling. We developed a rapid qPCR-based platform to profile cell state, stemness, and BCL2 family gene expression as a companion diagnostic test for acute myeloid leukemia (AML). We validated the stability and utility of the signatures across multiple measurement platforms and using patient samples from two centers. Integrating these signatures with clinical features enables an expedient means to predict the likelihood of patient responses to two standard-of-care therapies: intensive chemotherapy and hypomethylating agent plus venetoclax (HMA+Ven). For patients treated with HMA+Ven, expression levels of the promonocyte-like signature and BCL2 add predictive value for response and overall survival in multivariable models that include genetic features. The incorporation of the rapid profiler into the prospective evaluation of newly diagnosed AML patients may enhance treatment stratification and improve outcomes.

cancer biology↗

bioRxiv2024

Multi-omics analysis in mouse primary cortical neurons reveals complex positive and negative biological interactions between constituent compounds in Centella asiatica

BackgroundA water extract of the Ayurvedic plant Centella asiatica (CAW) improves cognitive function in mouse models of aging and Alzheimers disease, and affects dendritic arborization, mitochondrial activity and oxidative stress in mouse primary neurons. Triterpenes (TT) and caffeoylquinic acids (CQA) are constituents associated with these bioactivities of CAW although little is known about how interactions between these compounds contribute to the plants therapeutic benefit. MethodsMouse primary cortical neurons were treated with CAW, or equivalent concentrations of four TT combined, eight CQA combined, or these twelve compounds combined (TTCQA). Treatment effects on the cell transcriptome (18,491 genes) and metabolome (192 metabolites) relative to vehicle control were evaluated using RNAseq and metabolomic analyses respectively. ResultsExtensive differentially expressed genes (DEGs) were seen with all treatments, as well as evidence of interactions between compounds. Notably many DEGs seen with TT treatment were not observed in the TTCQA condition, possibly suggesting CQA reduced the effects of TT. Moreover, additional gene activity seen with CAW as compared to TTCQA indicate the presence of additional compounds in CAW that further modulate TTCQA interactions. Weighted Gene Correlation Network Analysis (WGCNA) identified 4 gene co-expression modules altered by treatments that were associated with extracellular matrix organization, fatty acid metabolism, cellular response to stress and stimuli, and immune function. Compound interaction patterns were seen at the eigengene level in these modules. Interestingly, in metabolomics analysis, the TTCQA treatment saw the highest number of changes in individual metabolites (20), followed by CQA (15), then TT (8) and finally CAW (3). WGCNA analysis found two metabolomics modules with significant eigenmetabolite differences for TT and CQA, and possible compound interactions at this level. ConclusionsFour gene expression modules and two metabolite modules were altered by the four types of treatments applied. This methodology demonstrated the existence of both negative and positive interactions between TT, CQA and additional compounds found in CAW on the transcriptome and metabolome of mouse primary cortical neurons.

neuroscience↗

bioRxiv2024

Graph Structured Neural Networks for Perturbation Biology

1Computational modeling of perturbation biology identifies relationships between molecular elements and cellular response, and an accurate understanding of these systems will support the full realization of precision medicine. Traditional deep learning, while often accurate in predicting response, is unlikely to capture the true sequence of involved molecular interactions. Our work is motivated by two assumptions: 1) Methods that encourage mechanistic prediction logic are likely to be more trustworthy, and 2) problem-specific algorithms are likely to outperform generic algorithms. We present an alternative to Graph Neural Networks (GNNs) termed Graph Structured Neural Networks (GSNN), which uses cell signaling knowledge, encoded as a graph data structure, to add inductive biases to deep learning. We apply our method to perturbation biology using the LINCS L1000 dataset and literature-curated molecular interactions. We demonstrate that GSNNs outperform baseline algorithms in several prediction tasks, including 1) perturbed expression, 2) cell viability of drug combinations, and 3) disease-specific drug prioritization. We also present a method called GSNNExplainer to explain GSNN predictions in a biologically interpretable form. This work has broad application in basic biological research and pre-clincal drug repurposing. Further refinement of these methods may produce trustworthy models of drug response suitable for use as clinical decision aids. Availability and implementationOur implementation of the GSNN method is available at https://github.com/nathanieljevans/GSNN. All data used in this work is publicly available.

bioinformatics↗

bioRxiv2023

Identification of Cellular Interactions in the Tumor Immune Microenvironment Underlying CD8 T Cell Exhaustion

While immune checkpoint inhibitors show success in treating a subset of patients with certain late-stage cancers, these treatments fail in many other patients as a result of mechanisms that have yet to be fully characterized. The process of CD8 T cell exhaustion, by which T cells become dysfunctional in response to prolonged antigen exposure, has been implicated in immunotherapy resistance. Single-cell RNA sequencing (scRNA-seq) produces an abundance of data to analyze this process; however, due to the complexity of the process, contributions of other cell types to a process within a single cell type cannot be simply inferred. We constructed an analysis framework to first rank human skin tumor samples by degree of exhaustion in tumor-infiltrating CD8 T cells and then identify immune cell type-specific gene-regulatory network patterns significantly associated with T cell exhaustion. Using this framework, we further analyzed scRNA-seq data from human tumor and chronic viral infection samples to compare the T cell exhaustion process between these two contexts. In doing so, we identified transcription factor activity in the macrophages of both tissue types associated with this process. Our framework can be applied beyond the tumor immune microenvironment to any system involving cell-cell communication, facilitating insights into key biological processes that underpin the effective treatment of cancer and other complicated diseases.

bioinformatics↗

bioRxiv2021

A noncanonical FLT3 gatekeeper mutation disrupts gilteritinib binding and confers resistance

The recent FDA approval of the FLT3 inhibitor, gilteritinib, for AML represents a major breakthrough for treatment of FLT3 mutated AML. However, patients only respond to gilteritinib for 6-7 months due to the emergence of drug resistance. Clinical resistance to gilteritinib is often associated with expansion of NRAS mutations, and less commonly via gatekeeper mutations in FLT3, with F691L being the most common. We developed an in vitro model that charts the temporal evolution of resistance to gilteritinib from early microenvironmental-mediated resistance to late intrinsic resistance mutations. Our model system accurately recapitulates the expansion of NRAS mutations and the F691L gatekeeper mutations found in AML patients. As part of this study, we also identified a novel FLT3N701K mutation that also appeared to promote resistance to gilteritinib. Using the Ba/F3 system, we demonstrate that N701K mutations effectively act like a gatekeeper mutation and block gilteritinib from binding to FLT3, thereby promoting resistance. Structural modeling of FLT3 reveals how N701K, and other reported gilteritinib resistance mutations, obstruct the gilteritinib binding pocket on FLT3. Interestingly, FLT3N701K does not block quizartinib binding, suggesting that FLT3N701K mutations are more specific for type 1 FLT3 inhibitors (gilteritinib, midostaurin, and crenolanib). Thus, our data suggests that for the FLT3N701K mutation, switching classes of FLT3 inhibitors may restore clinical response. As the use of gilteritinib expands in the clinic, this information will become critical to define clinical strategies to manage gilteritinib resistance.

cancer biology↗

bioRxiv2020

Baseline T cell immune phenotypes predict virologic and disease control upon SARS-CoV infection

The COVID-19 pandemic has revealed that infection with SARS-CoV-2 can result in a wide range of clinical outcomes in humans, from asymptomatic or mild disease to severe disease that can require mechanical ventilation. An incomplete understanding of immune correlates of protection represents a major barrier to the design of vaccines and therapeutic approaches to prevent infection or limit disease. This deficit is largely due to the lack of prospectively collected, pre-infection samples from indiviuals that go on to become infected with SARS-CoV-2. Here, we utilized data from a screen of genetically diverse mice from the Collaborative Cross (CC) infected with SARS-CoV to determine whether circulating baseline T cell signatures are associated with a lack of viral control and severe disease upon infection. SARS-CoV infection of CC mice results in a variety of viral load trajectories and disease outcomes. Further, early control of virus in the lung correlates with an increased abundance of activated CD4 and CD8 T cells and regulatory T cells prior to infections across strains. A basal propensity of T cells to express IFNg and IL17 over TNFa also correlated with early viral control. Overall, a dysregulated, pro-inflammatory signature of circulating T cells at baseline was associated with severe disease upon infection. While future studies of human samples prior to infection with SARS-CoV-2 are required, our studies in mice with SARS-CoV serve as proof of concept that circulating T cell signatures at baseline can predict clinical and virologic outcomes upon SARS-CoV infection. Identification of basal immune predictors in humans could allow for identification of individuals at highest risk of severe clinical and virologic outcomes upon infection, who may thus most benefit from available clinical interventions to restrict infection and disease. SummaryWe used a screen of genetically diverse mice from the Collaborative Cross infected with mouse-adapted SARS-CoV in combination with comprehensive pre-infection immunophenotyping to identify baseline circulating immune correlates of severe virologic and clinical outcomes upon SARS-CoV infection.

immunology↗

bioRxiv2020

The serine-threonine kinase TAO3 promotes cancer invasion and tumor growth by facilitating trafficking of endosomes containing the invadopodia scaffold TKS5α

Invadopodia are actin-based proteolytic membrane protrusions required for invasive behavior and tumor growth. We used our high-content screening assay to identify kinases impacting invadopodia formation. Among the top hits we selected TAO3, a STE20-like kinase of the GCK subfamily, for further analysis. TAO3 was over-expressed in many human cancers, and regulated invadopodia formation in melanoma, breast and bladder cancers. Furthermore, TAO3 catalytic activity facilitated melanoma growth in 3-dimensional matrices and in vivo. We developed potent catalytic inhibitors of TAO3 that inhibited invadopodia formation and function, and tumor cell extravasation and growth. Using these inhibitors, we determined that TAO3 activity was required for endosomal trafficking of TKS5, an obligate invadopodia scaffold protein. A phosphoproteomics screen for TAO3 substrates revealed the dynein subunit protein LIC2 as a relevant substrate. Knockdown of LIC2 or expression of a phosphomimetic form promoted invadopodia formation. Thus, TAO3 is a new therapeutic target with a distinct mechanism of action. SIGNIFICANCETargeting tumor invasive behavior represents an understudied opportunity. We used an unbiased screening approach to identify kinases required for invadopodia formation and function. We validated TAO3, both genetically and with a novel inhibitor, and determined TAO3 function. Our data support clinical development of this class of target.

cancer biology↗

bioRxiv2020

Mucin 4 Protects Female Mice from Coronavirus Pathogenesis

Using incipient lines of the Collaborative Cross (CC), a murine genetic reference population, we previously identified a quantitative trait loci (QTL) associated with low SARS-CoV titer. In this study, we integrated sequence information and RNA expression of genes within the QTL to identify mucin 4 (Muc4) as a high priority candidate for controlling SARS-CoV titer in the lung. To test this hypothesis, we infected Muc4-/- mice and found that female, but not male, Muc4-/- mice developed more weight loss and disease following infection with SARS-CoV. Female Muc4-/- mice also had more difficulty breathing despite reduced lung pathology; however, no change in viral titers was observed. Comparing across viral families, studies with chikungunya virus, a mosquito-borne arthralgic virus, suggests that Muc4s impact on viral pathogenesis may be widespread. Although not confirming the original titer QTL, our data identifies a role for Muc4 in the SARS-CoV disease and viral pathogenesis. ImportanceGiven the recent emergence of SARS-CoV-2, this work suggest that Muc4 expression plays a protective role in female mice not conserved in male mice following SARS-CoV infection. With the SARS-CoV-2 outbreak continuing, treatments that modulate or enhance Muc4 activity may provide an avenue for treatment and improved outcomes. In addition, the work highlights the importance of studying host factors including host genetics and biological sex as key parameters influencing infection and disease outcomes.

microbiology↗

bioRxiv2019

A distinct neutrophil population invades the central nervous system during pancreatic cancer

Weight loss, fatigue, and cognitive dysfunction are common symptoms in cancer patients that occur prior to initiation of cancer therapy. Inflammation in the brain is a driver of these symptoms, yet cellular sources of neuroinflammation during malignancy are unknown. In a mouse model of pancreatic ductal adenocarcinoma (PDAC), we observed early and robust myeloid cell infiltration into the brain. Infiltrating immune cells were predominately neutrophils, which accumulated at a unique central nervous system entry portal called the velum interpositum, where they expressed CCR2. CCR2 knockout mice had significantly decreased brain-infiltrating neutrophils as well as attenuated anorexia and muscle catabolism during PDAC, without any changes in neutrophils in other organs. Lastly, intracerebroventricular blockade of the purinergic receptor P2RX7 during PDAC abolished neutrophil recruitment to the brain and attenuated anorexia. Our data demonstrate a novel function for the CCR2/CCL2 axis in recruiting neutrophils to the brain, which drives anorexia and muscle catabolism.

immunology↗

bioRxiv2019

Myeloid Lineage Enhancers Drive Oncogene Synergy in CEBPA/CSF3R Mutant Acute Myeloid Leukemia

Acute Myeloid Leukemia (AML) develops due to the acquisition of mutations from multiple functional classes. Here, we demonstrate that activating mutations in the granulocyte colony stimulating factor receptor (CSF3R), cooperate with loss of function mutations in the transcription factor CEBPA to promote acute leukemia development. This finding of mutation-synergy is broadly applicable other mutations that activate the JAK/STAT pathway or disrupt CEBPA function (i.e. activating mutations in JAK3 and Core Binding Factor translocations). The interaction between these distinct classes of mutations occurs at the level of myeloid lineage enhancers where mutant CEBPA prevents activation of subset of differentiation associated enhancers. To confirm this enhancer-dependent mechanism, we demonstrate that CEBPA mutations must occur as the initial event in AML initiation, confirming predictions from clinical sequencing data. This improved mechanistic understanding will facilitate therapeutic development targeting the intersection of oncogene cooperativity.

cancer biology↗

bioRxiv2018

Controlling for contaminants in low biomass 16S rRNA gene sequencing experiments

BackgroundMicrobial communities are commonly studied using culture-independent methods such as 16S rRNA gene sequencing. However, one challenge in accurately characterizing microbial communities is exogenous bacterial DNA contamination. This is particularly problematic for sites of low microbial biomass such as the urinary tract, placenta, and lower airway. Computational approaches have been proposed as a post-processing step to identify and remove potential contaminants, but their performance has not been independently evaluated.\n\nTo identify the impact of decreasing microbial biomass on polymicrobial 16S rRNA gene sequencing experiments, we used a serial dilution of a mock microbial community. We evaluated two computational approaches to identify and remove contaminants: 1) identifying sequences that have an inverse correlation with DNA concentration implemented in Decontam and 2) predicting the proportion of experimental sample arising from defined contaminant sources implemented in SourceTracker.\n\nResultsAs expected, the proportion of contaminant bacterial DNA increased with decreasing starting microbial biomass, with 79.12% of the most dilute sample arising from contaminant sequences. Inclusion of contaminant sequences in analyses leads to overinflated diversity estimates (up to 12 times greater than the expected values) and distorts microbiome composition. SourceTracker successfully removed over 98% of contaminants when the experimental environments are well defined. However, SourceTracker performed poorly when the experimental environment is unknown, failing to remove the majority of contaminants. Decontam successfully removed 74-91% of contaminants regardless of prior knowledge of the experimental environment.\n\nConclusionsOur study indicates that computational methods can reduce the amount of contaminants in 16S rRNA gene sequencing experiments. The appropriate computational approach for removing contaminant sequences from an experiment depends on the prior knowledge about the microbial environment under investigation and can be evaluated with a dilution series of a mock microbial community.

bioinformatics↗

bioRxiv2017

Teaching data science fundamentals through realistic synthetic clinical cardiovascular data

ObjectiveOur goal was to create a synthetic dataset and curricular materials to assist in teaching fundamentals of translational data science.\n\nMaterials and MethodsA literature review was conducted to extract current cardiovascular risk score logic, data elements, and population characteristics. Then, clinical data elements in the models were pulled from clinical data and transformed to the Observational Medical Outcomes Partnership (OMOP) common data model; genetic data elements were added based on population rates. A hybrid Bayesian network was used to create synthetic data from the logical elements of the risk scores and the underlying population frequencies of the clinical data.\n\nResultsA synthetic dataset of 446,000 patients was created. A two-day curriculum was created based on this synthetic data with exploratory data analysis and machine learning components. The curriculum was offered on two separate occasions; the two groups of learners were given the curriculum and data, and results were tallied, summarized, and compared. Students ability to complete the challenge was mixed; more experienced students achieved a range of 70%-85% in balanced accuracy, but many others did not perform better than the baseline model.\n\nDiscussionOverall, students enjoyed the course and dataset, but some struggled to consistently apply machine learning techniques. The curriculum, data set, techniques for generation, and results are available for others to use for their own training.\n\nConclusionA realistic synthetic data with clinical and genetic components helps students learn issues in cardiovascular risk scoring, practice data science skills, and compete in a challenge to improve identification of risk.

scientific communication and education↗