Abstract

The innate immune protection provided by cationic antimicrobial peptides (CAMPs) has been shown to extend to antiviral activity, with putative mechanisms of action including direct interaction with host cells or pathogen membranes. The lack of therapeutics available for the treatment of viruses such as Venezuelan equine encephalitis virus (VEEV) underscores the urgency of novel strategies for antiviral discovery. American alligator plasma has been shown to exhibit strong in vitro antibacterial activity, and functionalized hydrogel particles have been successfully employed for the identification of specific CAMPs from alligator plasma. Here, a novel bait strategy in which particles were encapsulated in membranes from either healthy or VEEV-infected cells was implemented to identify peptides preferentially targeting infected cells for subsequent evaluation of antiviral activity. Statistical analysis of peptide identification results was used to select five candidate peptides for testing, of which one exhibited a dose-dependent inhibition of VEEV and also significantly inhibited infectious titers. Results suggest our bioprospecting strategy provides a versatile platform that may be adapted for antiviral peptide identification from complex biological samples.

Abstract

The evolution of omics and computational competency has accelerated discoveries of the underlying biological processes in an unprecedented way. High throughput methodologies, such as flow cytometry, can reveal deeper insights into cell processes, thereby allowing opportunities for accelerated discoveries related to health and diseases. However, working with cytometry data often imposes complex computational challenges due to high-dimensionality, large size, and non-linearity of the data structure. In addition, cytometry data frequently exhibit diverse patterns across biomarkers and suffer from substantial class imbalances which can further complicate the problem. The existing methods of cytometry data analysis either predict cell population or perform feature selection. Through this study, we propose a “wisdom of the crowd” approach to simultaneously predict rare cell populations and perform feature selection by integrating a pool of modern machine learning algorithms. Given that our approach integrates superior performing machine learning models across different normalization techniques based on entropy and rank, our method can detect diverse patterns existing across the model features. Furthermore, the method identifies a dynamic biomarker structure that divides the features into persistently selected, unselected, and fluctuating assemblies indicating the role of each biomarker in rare cell prediction, which can subsequently aid in studies of disease progression.

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Abstract

Target identification by modification-free proteomic approaches can potentially reveal the pharmacological mechanism of small molecular compounds. By combining the recent solvent-induced protein precipitation (SIP) method with TMT-labeling quantitative proteomics, we propose solvent-induced proteome profiling (SIPP) approach to identify small molecule-protein interactions. The SIPP approach enables to depict a denaturation curves of the target protein by varying concentrations of organic solvents to induce unfolding and precipitation of the cellular proteome. By using this approach, we have successfully identified the known targets of market drugs and natural products and extended the proteome information of SIP for target identification.

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Abstract

Lumen formation and inflation are crucial steps for tubular organ morphogenesis, yet the underling mechanism remains largely unrevealed. Here, we applied 4D proteomics to screen the lumenogenesis-related proteins and revealed the biological pathways potentially that are involved in lumen inflation during notochord lumen formation in the ascidian Ciona savignyi. In total, 910 differentiated expressed proteins (DEPs) were identified before and after notochord lumen formation utilizing Mfuzz analysis. Those DEPs were grouped into four upregulated clusters based on their quantitative expression patterns; the functions of these proteins were enriched in protein metabolic and biosynthetic process, the establishment of localization, and vesicle-mediated transport. We analyzed the vesicle trafficking cluster and focused on several vesicle transport hub proteins. In vivo function-deficient experiments showed that mutation of vesicle transport proteins resulted in an abnormal lumen in notochord development, demonstrating the crucial role of intracellular trafficking for lumen formation. Moreover, abundant extracellular matrix proteins were identified, the majority of which were predicted to be glycosylated proteins. Inhibition of glycosylation markedly reduced the lumen expansion rate in notochord cells, suggesting that protein glycosylation is essential for lumenogenesis. Overall, our study provides an invaluable resource and reveals the crucial mechanisms in lumen formation and expansion.

Abstract

Genetically and phenotypically identical immune cell populations can be highly heterogenous in terms of their immune functions and protein secretion profiles. The microfluidic chip-based single-cell highly multiplexed secretome proteomics enables characterization of cellular heterogeneity of immune responses at different cellular and molecular layers. Increasing evidence has demonstrated that polyfunctional T cells that simultaneously produce 2+ proteins per cell at the single-cell level are key effector cells that contribute to the development of potent and durable cellular immunity against pathogens and cancers. The functional proteomic technology offers a wide spectrum of cellular function assessment and can uniquely define highly polyfunctional cell subsets with cytokine signatures from live individual cells. This high-dimensional single-cell analysis provides deep dissection into functional heterogeneity and helps identify predictive biomarkers and potential correlates that are crucial for immunotherapeutic product design optimization and personalized immunotherapy development to achieve better clinical outcomes.