Search

As systems biology approaches to virology have become more tractable, highly studied viruses such as HIV can now be analyzed in new unbiased ways, including spatial proteomics. We employed here a differential centrifugation protocol to fractionate Jurkat T cells for proteomic analysis by mass spectrometry; these cells contain inducible HIV-1 genomes, enabling us to look for changes in the spatial proteome induced by viral gene expression. Using these proteomics data, we evaluated the merits of several reported machine learning pipelines for classification of the spatial proteome and identification of protein translocations. From these analyses, we found that classifier performance in this system was organelle dependent, with Bayesian t-augmented Gaussian mixture modeling outperforming support vector machine learning for mitochondrial and endoplasmic reticulum proteins but underperforming on cytosolic, nuclear, and plasma membrane proteins by QSep analysis. We also observed a generally higher performance for protein translocation identification using a Bayesian model, Bayesian analysis of differential localization experiments, on row-normalized data. Comparative Bayesian analysis of differential localization experiment analysis of cells induced to express the WT viral genome versus cells induced to express a genome unable to express the accessory protein Nef identified known Nef-dependent interactors such as T-cell receptor signaling components and coatomer complex. Finally, we found that support vector machine classification showed higher consistency and was less sensitive to HIV-dependent noise. These findings illustrate important considerations for studies of the spatial proteome following viral infection or viral gene expression and provide a reference for future studies of HIV-gene-dropout viruses., Competing Interests: Conflict of interest The Krogan Laboratory has received research support from Vir Biotechnology and F. Hoffmann-La Roche. N. J. K. has consulting agreements with the Icahn School of Medicine at Mount Sinai, New York, Maze Therapeutics, and Interline Therapeutics. He is a shareholder in Tenaya Therapeutics, Maze Therapeutics, and Interline Therapeutics, and a financially compensated Scientific Advisory Board Member for GEn1E Lifesciences, Inc. All other authors declare no competing interests., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Consumption of refined high-fat, low-fiber diets promotes development of obesity and its associated consequences. Although genetics play an important role in dictating susceptibility to such obesogenic diets, mice with nearly uniform genetics exhibit marked heterogeneity in their extent of obesity in response to such diets. This suggests non-genetic determinants play a role in diet-induced obesity. Hence, we sought to identify parameters that predict, and/or correlate with, development of obesity in response to an obesogenic diet. We assayed behavior, metabolic parameters, inflammatory markers/cytokines, microbiota composition, and the fecal metaproteome, in a cohort of mice ( n = 50) prior to, and the 8 weeks following, administration of an obesogenic high-fat low-fiber diet. Neither behavioral testing nor quantitation of inflammatory markers broadly predicted severity of diet-induced obesity. Although, the small subset of mice that exhibited basal elevations in serum IL-6 ( n = 5) were among the more obese mice in the cohort. While fecal microbiota composition changed markedly in response to the obesogenic diet, it lacked the ability to predict which mice were relative prone or resistant to obesity. In contrast, fecal metaproteome analysis revealed functional and taxonomic differences among the proteins associated with proneness to obesity. Targeted interrogation of microbiota composition data successfully validated the taxonomic differences seen in the metaproteome. Although future work will be needed to determine the breadth of applicability of these associations to other cohorts of animals and humans, this study nonetheless highlights the potential power of gut microbial proteins to predict and perhaps impact development of obesity., (© 2019 Tran et al.)

Proteolysis is an integral component of life and has been implicated in many disease processes. To improve our understanding of peptidase function, it is imperative to develop tools to uncover substrate specificity and cleavage efficiency. Here, we combine the quantitative power of tandem mass tags (TMTs) with an established peptide cleavage assay to yield quantitative Multiplex Substrate Profiling by Mass Spectrometry (qMSP-MS). This assay was validated with papain, a well-characterized cysteine peptidase, to generate cleavage efficiency values for hydrolysis of 275 unique peptide bonds in parallel. To demonstrate the breath of this assay, we show that qMSP-MS can uncover the substrate specificity of minimally characterized intramembrane rhomboid peptidases, as well as define hundreds of proteolytic activities in complex biological samples, including secretions from lung cancer cell lines. Importantly, our qMSP-MS library uses synthetic peptides whose termini are unmodified, allowing us to characterize not only endo- but also exo-peptidase activity. Each cleaved peptide sequence can be ranked by turnover rate, and the amino acid sequence of the best substrates can be used for designing fluorescent reporter substrates. Discovery of peptide substrates that are selectively cleaved by peptidases which are active at the site of disease highlights the potential for qMSP-MS to guide the development of peptidase-activating drugs for cancer and infectious disease., (© 2019 Lapek et al.)

The mechanisms by which human immunodeficiency virus (HIV) circumvents and coopts cellular machinery to replicate and persist in cells are not fully understood. HIV accessory proteins play key roles in the HIV life cycle by altering host pathways that are often dependent on post-translational modifications (PTMs). Thus, the identification of HIV accessory protein host targets and their PTM status is critical to fully understand how HIV invades, avoids detection and replicates to spread infection. To date, a comprehensive characterization of HIV accessory protein host targets and modulation of their PTM status does not exist. The significant gap in knowledge regarding the identity and PTMs of HIV host targets is due, in part, to technological limitations. Here, we applied current mass spectrometry techniques to define mechanisms of viral protein action by identifying host proteins whose abundance is affected by the accessory protein Vpr and the corresponding modulation of down-stream signaling pathways, specifically those regulated by phosphorylation. By utilizing a novel, inducible HIV-1 CD4+ T-cell model system expressing either the wild type or a vpr- negative viral genome, we overcame challenges associated with synchronization and infection-levels present in other models. We report identification and abundance dynamics of over 7000 proteins and 28,000 phospho-peptides. Consistent with Vpr's ability to impair cell-cycle progression, we observed Vpr-mediated modulation of spindle and centromere proteins, as well as Aurora kinase A and cyclin-dependent kinase 4 (CDK4). Unexpectedly, we observed evidence of Vpr-mediated modulation of the activity of serine/arginine-rich protein-specific kinases (SRPKs), suggesting a possible role for Vpr in the regulation of RNA splicing. This study presents a new experimental system and provides a data-resource that lays the foundation for validating host proteins and phosphorylation-pathways affected by HIV-1 and its accessory protein Vpr., (© 2017 by The American Society for Biochemistry and Molecular Biology, Inc.)

Molecular genetic analysis indicates that the problematic human bacterial pathogen methicillin-resistant Staphylococcus aureus possesses more than 2000 open reading frames in its genome. This number of potential gene products, coupled with intrinsic mechanisms of posttranslational modification, endows methicillin-resistant Staphylococcus aureus with a highly complex biochemical repertoire. Recent proteomic and metabolomic advances have provided methodologies to better understand and characterize the biosynthetic factors released by microbial organisms. Here, the emerging tool of mass spectrometry-based molecular networking was used to visualize and map the repertoire of biosynthetic factors produced by a community-associated methicillin-resistant Staphylococcus aureus strain representative of the epidemic USA300 clone. In particular, the study focused on elucidating the complexity of the recently discovered phenol soluble modulin family of peptides when placed under various antibiotic treatment stresses. Novel PSM truncated variant peptides were captured, and the type of variants that were clustered by the molecular networks platform changed in response to the different antibiotic treatment conditions. After discovery, a group of the peptides were selected for functional analysis in vitro. The peptides displayed bioactive properties including the ability to induce proinflammatory responses in human THP-1 monocytes. Additionally, the tested peptides did not display antimicrobial activity as previously reported for other phenol soluble modulin truncated variants. Our findings reveal that the PSM family of peptides are quite structurally diverse, and suggest a single phenol soluble modulin parent peptide can functionally spawn differential bioactivities in response to various external stimuli.