Biology subjects

Manohar, M.

Publications and source records attributed to Manohar, M..

3 recordsLinked to original sources

bioRxiv2021

Integrated plasma proteomic and single-cell immune signaling network signatures demarcate mild, moderate, and severe COVID-19

The biological determinants of the wide spectrum of COVID-19 clinical manifestations are not fully understood. Here, over 1400 plasma proteins and 2600 single-cell immune features comprising cell phenotype, basal signaling activity, and signaling responses to inflammatory ligands were assessed in peripheral blood from patients with mild, moderate, and severe COVID-19, at the time of diagnosis. Using an integrated computational approach to analyze the combined plasma and single-cell proteomic data, we identified and independently validated a multivariate model classifying COVID-19 severity (multi-class AUCtraining = 0.799, p-value = 4.2e-6; multi-class AUCvalidation = 0.773, p-value = 7.7e-6). Features of this high-dimensional model recapitulated recent COVID-19 related observations of immune perturbations, and revealed novel biological signatures of severity, including the mobilization of elements of the renin-angiotensin system and primary hemostasis, as well as dysregulation of JAK/STAT, MAPK/mTOR, and NF-{kappa}B immune signaling networks. These results provide a set of early determinants of COVID-19 severity that may point to therapeutic targets for the prevention of COVID-19 progression. SummaryFeyaerts et al. demonstrate that an integrated analysis of plasma and single-cell proteomics differentiates COVID-19 severity and reveals severity-specific biological signatures associated with the dysregulation of the JAK/STAT, MAPK/mTOR, and NF-{kappa}B immune signaling networks and the mobilization of the renin-angiotensin and hemostasis systems.

immunology↗

bioRxiv2020

CyAnno: A semi-automated approach for cell type annotation of mass cytometry datasets

For immune system monitoring in large-scale studies at the single-cell resolution using CyTOF, (semi-)automated computational methods are applied for annotating live cells of mixed cell types. Here, we show that the live cell pool can be highly enriched with undefined heterogeneous cells, i.e. ungated cells, and that current (semi-)automated approaches ignore their modeling resulting in misclassified annotations. Therefore, we introduce CyAnno, a novel semi-automated approach for deconvoluting the unlabeled cytometry dataset based on a machine learning framework utilizing manually gated training data that allows the integrative modeling of gated cell types and the ungated cells. By applying this framework on several CyTOF datasets, we demonstrated that including the ungated cells can lead to a significant increase in the prediction accuracy of the gated cell types. CyAnno can be used to identify even a single cell type, including rare cells, with higher efficacy than current state-of-the-art semi-automated approaches.

bioinformatics↗

bioRxiv2017

Members of the abscisic acid co-receptor PP2C protein family mediate salicylic acid-abscisic acid crosstalk

The interplay between abscisic acid (ABA) and salicylic acid (SA) influences plant responses to various (a)biotic stresses; however, the underlying mechanism(s) for this crosstalk is largely unknown. Here we report that type 2C protein phosphatases (PP2Cs), some of which are negative regulators of ABA signaling, bind SA. SA binding suppressed the ABA-enhanced interaction between these PP2Cs and various ABA receptors belonging to the PYR/PYL/RCAR protein family. Additionally, SA suppressed ABA-enhanced degradation of PP2Cs and ABA-induced stabilization of SnRK2s. Supporting SAs role as a negative regulator of ABA signaling, exogenous SA suppressed ABA-induced gene expression, whereas SA-deficient sid2-1 mutants displayed heightened PP2C degradation and hypersensitivity to ABA-induced suppression of seed germination. Together, these results suggest a new molecular mechanism through which SA antagonizes ABA signaling. A better understanding of the crosstalk between these hormones is important for improving the sustainability of agriculture in the face of climate change.

plant biology↗