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McGuire, M.

Publications and source records attributed to McGuire, M..

3 recordsLinked to original sources

Pan-cancer Analysis of Homologous Recombination Deficiency in Cell Lines

Homologous Recombination Deficiency (HRD) drives genomic instability in multiple cancer types and renders tumors vulnerable to certain DNA damaging agents such as PARP inhibitors. Thus, HRD is emerging as an attractive biomarker in oncology. A variety of in silico methods are available for predicting HRD; however, few of these methods have been applied to cell lines in a comprehensive manner. Here we utilized two of these methods, "CHORD" and "HRDsum" scores, to predict HRD for 1,332 cancer cell lines and 84 non-cancerous cell lines. Cell lines with biallelic mutations in BRCA1 or BRCA2, which encode key components of the homologous recombination pathway, showed the strongest HRD predictions, validating the two methods in cell lines. A small subset of BRCA1/2-wildtype cell lines were also classified as HRD, several of which showed evidence of epigenetic BRCA1 silencing. Similar to HRD in patient samples, HRD in cell lines was associated with p53 loss, was mutually exclusive with microsatellite instability and occurred most frequently in breast and ovarian cancer types. In addition to validating previously identified associations with HRD, we leveraged cell line-specific datasets to gain new insights into HRD and its relation to various genetic dependency and drug sensitivity profiles. We found that in cell lines, HRD was associated with sensitivity to PARP inhibition in breast cancer, but not at a pan-cancer level. By generating large-scale, pan-cancer datasets on HRD predictions in cell lines, we aim to facilitate efforts to improve our understanding of HRD and its utility as a biomarker. SIGNIFICANCE STATEMENTHomologous Recombination Deficiency (HRD) is common in cancer and can be exploited therapeutically, as it sensitizes cells to DNA damaging agents. Here we scored over 1,300 cancer cell lines for HRD using two different bioinformatic approaches, thereby enabling large-scale analyses that provide insight into the etiology and features of HRD.

cancer biology↗

Associations between SARS-CoV-2 Infection or COVID-19 Vaccination and Human Milk Composition: A Multi-Omics Approach

The risk of contracting SARS-CoV-2 via human milk-feeding is virtually non-existent. Adverse effects of COVID-19 vaccination for lactating individuals are not different from the general population, and no evidence has been found that their infants exhibit adverse effects. Yet, there remains substantial hesitation among this population globally regarding the safety of these vaccines. Herein we aimed to determine if compositional changes in milk occur following infection or vaccination, including any evidence of vaccine components. Using an extensive multi-omics approach, we found that compared to unvaccinated individuals SARS-CoV-2 infection was associated with significant compositional differences in 67 proteins, 385 lipids, and 13 metabolites. In contrast, COVID-19 vaccination was not associated with any changes in lipids or metabolites, although it was associated with changes in 13 or fewer proteins. Compositional changes in milk differed by vaccine. Changes following vaccination were greatest after 1-6 hours for the mRNA-based Moderna vaccine (8 changed proteins), 3 days for the mRNA-based Pfizer (4 changed proteins), and adenovirus-based Johnson and Johnson (13 changed proteins) vaccines. Proteins that changed after both natural infection and Johnson and Johnson vaccine were associated mainly with systemic inflammatory responses. In addition, no vaccine components were detected in any milk sample. Together, our data provide evidence of only minimal changes in milk composition due to COVID-19 vaccination, with much greater changes after natural SARS-CoV-2 infection. IMPORTANCEThe impact of the observed changes in global milk composition on infant health remain unknown. These findings emphasize the importance of vaccinating the lactating population against COVID-19, as compositional changes in milk were found to be far less evident after vaccination compared to SARS-CoV-2 infection. Importantly, vaccine components were not detected in milk after vaccination.

molecular biology↗

Differential patterns of change in brain connectivity resulting from traumatic brain injury

BackgroundTraumatic brain injury (TBI) damages white matter tracts, disrupting brain network structure and communication. There exists a wide heterogeneity in the pattern of structural damage associated with injury, as well as a large heterogeneity in behavioral outcomes. However, little is known about the relationship between changes in network connectivity and clinical outcomes. MethodsWe utilize the rat lateral fluid-percussion injury (FPI) model of severe TBI to study differences in brain connectivity in 8 animals that received the insult and 11 animals that received only a craniectomy. Diffusion Tensor Imaging (DTI) is performed 5 weeks after the injury and network theory is used to investigate changes in white matter connectivity. ResultsWe find that 1) global network measures are not able to distinguish between healthy and injured animals; 2) injury induced alterations predominantly exist in a subset of connections (subnetworks) distributed throughout the brain; and 3) injured animals can be divided into subgroups based on changes in network motifs - measures of local structural connectivity. Additionally, alterations in predicted functional connectivity indicate that the subgroups have different propensities to synchronize brain activity, which could relate to the heterogeneity of clinical outcomes. DiscussionThese results suggest that network measures can be used to quantify progressive changes in brain connectivity due to injury and differentiate among subpopulations with similar injuries but different pathological trajectories. Impact StatementWhite matter tracts are important for efficient communication between brain regions and their connectivity pattern underlies proper brain function. Traumatic brain injury (TBI) damages white matter tracts and changes brain connectivity, but how specific changes relate to differences in clinical/behavioral outcomes is not known. Using network theory to study injury related changes in structural connectivity, we find that local measures of network structure can identify subgroups of injured rats with different types of changes in brain structure. Our results suggest that these different patterns of change could relate to differences in clinical outcomes.

neuroscience↗