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Objective: The unprecedented events of 2020 required a pivot in scientific training to better prepare the biomedical research workforce to address global pandemics, structural racism, and social inequities that devastate human health individually and erode it collectively. Furthermore, this pivot had to be accomplished in the virtual environment given the nation-wide lockdown., Methods: These needs and context led to leveraging of the San Francisco Building Infrastructure Leading to Diversity (SF BUILD) theories of change to innovate a Virtual BUILD Research Collaboratory (VBRC). The purpose of VBRC was to train Black, Indigenous, and people of color (BIPOC) students to apply their unique perspectives to biomedical research. These training activities were evaluated using a pre-post survey design that included both validated and new psychosocial scales. A new scale was piloted to measure culturally relevant pedagogy., Results: VBRC scholars increased science identity on two items: thinking of myself as a scientist (+1point, p = 0.006) and belonging to a community of scientists (+1point, p = 0.069). Overall, scholars perceived stress also decreased over VBRC (-2.35 points, p = 0.02). Post VBRC, scholars had high agency scores (μ = 11.02, Md = 12, range = 6-12, σ = 1.62) and cultural humility scores (μ = 22.11, Md = 23, range = 12-24, σ = 2.71). No notable race/ethnic differences were found in any measures., Conclusions: Taken together, our innovative approach to data science training for BIPOC in unprecedented times shows promise for better preparing the workforce critically needed to address the fundamental gaps in knowledge at the intersection of public health, structural racism, and biomedical sciences., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Ceberio et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

To provide a detailed analysis of the molecular components and underlying mechanisms associated with ovarian cancer, we performed a comprehensive mass-spectrometry-based proteomic characterization of 174 ovarian tumors previously analyzed by The Cancer Genome Atlas (TCGA), of which 169 were high-grade serous carcinomas (HGSCs). Integrating our proteomic measurements with the genomic data yielded a number of insights into disease, such as how different copy-number alternations influence the proteome, the proteins associated with chromosomal instability, the sets of signaling pathways that diverse genome rearrangements converge on, and the ones most associated with short overall survival. Specific protein acetylations associated with homologous recombination deficiency suggest a potential means for stratifying patients for therapy. In addition to providing a valuable resource, these findings provide a view of how the somatic genome drives the cancer proteome and associations between protein and post-translational modification levels and clinical outcomes in HGSC. VIDEO ABSTRACT., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Advances in both targeted and unbiased MS-based proteomics are now at a mature stage for comprehensively and reproducibly characterizing a large part of the cancer proteome. These developments combined with the extensive genomic characterization of several cancer types by large-scale initiatives such as the International Cancer Genome Consortium and Cancer Genome Atlas Project have paved the way for proteogenomic analysis of omics datasets and integration methods. The advances serve as the basis for the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium and this article highlights its current work and future steps in the area of proteogenomics., (© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

α-Synuclein is an intrinsically disordered protein whose aggregation is implicated in Parkinson's disease. A second member of the synuclein family, β-synuclein, shares significant sequence similarity with α-synuclein but is much more resistant to aggregation. β-Synuclein is missing an 11-residue stretch in the central non-β-amyloid component region that forms the core of α-synuclein amyloid fibrils, yet insertion of these residues into β-synuclein to produce the βSHC construct does not markedly increase the aggregation propensity. To investigate the structural basis of these different behaviors, quantitative nuclear magnetic resonance data, in the form of paramagnetic relaxation enhancement-derived interatomic distances, are combined with molecular dynamics simulations to generate ensembles of structures representative of the solution states of α-synuclein, β-synuclein, and βSHC. Comparison of these ensembles reveals that the differing aggregation propensities of α-synuclein and β-synuclein are associated with differences in the degree of residual structure in the C-terminus coupled to the shorter separation between the N- and C-termini in β-synuclein and βSHC, making protective intramolecular contacts more likely.

Extensive genomic characterization of human cancers presents the problem of inference from genomic abnormalities to cancer phenotypes. To address this problem, we analysed proteomes of colon and rectal tumours characterized previously by The Cancer Genome Atlas (TCGA) and perform integrated proteogenomic analyses. Somatic variants displayed reduced protein abundance compared to germline variants. Messenger RNA transcript abundance did not reliably predict protein abundance differences between tumours. Proteomics identified five proteomic subtypes in the TCGA cohort, two of which overlapped with the TCGA 'microsatellite instability/CpG island methylation phenotype' transcriptomic subtype, but had distinct mutation, methylation and protein expression patterns associated with different clinical outcomes. Although copy number alterations showed strong cis- and trans-effects on mRNA abundance, relatively few of these extend to the protein level. Thus, proteomics data enabled prioritization of candidate driver genes. The chromosome 20q amplicon was associated with the largest global changes at both mRNA and protein levels; proteomics data highlighted potential 20q candidates, including HNF4A (hepatocyte nuclear factor 4, alpha), TOMM34 (translocase of outer mitochondrial membrane 34) and SRC (SRC proto-oncogene, non-receptor tyrosine kinase). Integrated proteogenomic analysis provides functional context to interpret genomic abnormalities and affords a new paradigm for understanding cancer biology.

Background: Clinical proteomics presents great promise in biology and medicine because of its potential for improving our understanding of diseases at the molecular level and for detecting disease-related biomarkers for diagnosis, prognosis, and prediction of therapeutic responses. To realize its full potential to improve clinical outcome for patients, proteomic studies have to be well designed, from biosample cohorts to data and statistical analyses. One key component in the biomarker development pipeline is the understanding of the regulatory science that evaluates diagnostic assay performance through rigorous analytical and clinical review criteria., Content: The National Cancer Institute's Clinical Proteomic Technologies for Cancer (CPTC) initiative has proposed an intermediate preclinical "verification" step to close the gap between protein-based biomarker discovery and clinical qualification. In collaboration with the US Food and Drug Administration (FDA), the CPTC network investigators recently published 2 mock submission review documents, first-of-their-kind educational materials that may help the scientific community interested in developing products for the clinic in understanding the likely analytical evaluation requirements for multiplex protein technology-based diagnostic tests., Conclusions: Building on this momentum, the CPTC continues with this report its collaboration with the FDA, as well as its interactions with the AACC and the Centers for Medicare and Medicaid Services, to further the understanding of regulatory requirements for approving multiplex proteomic platform-based tests and analytically validating multiple analytes.

Alpha- and beta-synuclein are closely related proteins, the first of which is associated with deposits formed in neurodegenerative conditions such as Parkinson's disease while the second appears to have no relationship to any such disorders. The aggregation behavior of alpha- and beta-synuclein as well as a series of chimeric variants were compared by exploring the structural transitions that occur in the presence of a widely used lipid mimetic, sodium dodecyl sulfate (SDS). We found that the aggregation rates of all these protein variants are significantly enhanced by low concentrations of SDS. In particular, we inserted the 11-residue sequence of mainly hydrophobic residues from the non-amyloid-beta-component (NAC) region of alpha-synuclein into beta-synuclein and show that the fibril formation rate of this chimeric protein is only weakly altered from that of beta-synuclein. These intrinsic propensities to aggregate are rationalized to a very high degree of accuracy by analysis of the sequences in terms of their associated physicochemical properties. The results begin to reveal that the differences in behavior are primarily associated with a delicate balance between the positions of a range of charged and hydrophobic residues rather than the commonly assumed presence or absence of the highly aggregation-prone region of the NAC region of alpha-synuclein. This conclusion provides new insights into the role of alpha-synuclein in disease and into the factors that regulate the balance between solubility and aggregation of a natively unfolded protein.