Biology subjects

Vizcarra, P.

Publications and source records attributed to Vizcarra, P..

2 recordsLinked to original sources

bioRxiv2021

Metabolic snapshot of plasma samples reveals new pathways implicated in SARS-CoV-2 pathogenesis

Despite of the scientific and human efforts to understand COVID-19, there are questions still unanswered. Variations in the metabolic reaction to SARS-CoV-2 infection could explain the striking differences in the susceptibility to infection and the risk of severe disease. Here, we used untargeted metabolomics to examine novel metabolic pathways related to SARS-CoV-2 susceptibility and COVID-19 clinical severity using capillary electrophoresis coupled to a time-of-flight mass spectrometer (CE-TOF-MS) in plasma samples. We included 27 patients with confirmed COVID-19 early after symptom onset who were prospectively followed and 29 healthcare workers heavily exposed to SARS-CoV-2 but with low susceptibility to infection ( nonsusceptible). We found that the metabolite profile was predictive of the study group. We identified a total of 55 metabolites as biomarkers of SARS-CoV-2 susceptibility or COVID-19 clinical severity. We report the discovery of new plasma biomarkers for COVID-19 that provide mechanistic explanations for the clinical consequences of SARS-CoV-2, including mitochondrial and liver dysfunction as a consequence of hypoxemia (citrulline, citrate, and BAIBA), energy production and amino acid catabolism (L-glycine, L-alanine, L-serine, L-proline, L-aspartic acid and L-histidine), endothelial dysfunction and thrombosis (citrulline, L-ADMA, 2-AB, and Neu5Ac), and we found interconnections between these pathways. In summary, in this first report of the metabolomic profile of individuals with severe COVID-19 and SARS-CoV-2 susceptibility by CE-MS, we define several metabolic pathways implicated in SARS-CoV-2 susceptibility and COVID-19 clinical progression that could be developed as biomarkers of COVID-19.

biochemistry↗

bioRxiv2021

Pixelwise H-score: a novel digital image analysis based-metric to quantify membrane biomarker expression from immunohistochemistry images

Immunohistochemistry (IHC) assays play a central role in evaluating biomarker expression in tissue sections for diagnostic and research applications. Manual scoring of IHC images, which is the current standard of practice, is known to have several shortcomings in terms of reproducibility and scalability to large scale studies. Here, by using a digital image analysis-based approach, we introduce a new metric called the pixelwise H-score (pix H-score) that quantifies biomarker expression from whole-slide scanned IHC images. The pix H-score is an unsupervised algorithm that only requires the specification of intensity thresholds for the biomarker and the nuclear-counterstain channels. We present the detailed implementation of the pix H-score in two different whole-slide image analysis software packages Visiopharm and HALO. We consider three biomarkers P-cadherin, PD-L1, and 5T4, and show how the pix H-score exhibits tight concordance to multiple orthogonal measurements of biomarker abundance such as the biomarker mRNA transcript and the pathologist H-score. We also compare the pix H-score to existing automated image analysis algorithms and demonstrate that the pix H-score provides either comparable or significantly better performance over these methodologies. We also present results of an empirical resampling approach to assess the performance of the pix H-score in estimating biomarker abundance from select regions within the tumor tissue relative to the whole tumor resection. We anticipate that the new metric will be broadly applicable to quantify biomarker expression from a wide variety of IHC images. Moreover, these results underscore the benefit of digital image analysis-based approaches which offer an objective, reproducible, and highly scalable strategy to quantitatively analyze IHC images.

pathology↗