Comprehensive Mass Spectrometric Survey of Streptococcus pyogenes Subcellular Proteomes

Multienzyme deep learning models improve peptide de novo sequencing by mass spectrometry proteomics

Generating and analyzing overlapping peptides through multienzymatic digestion is an efficient procedure for de novo protein using from bottom-up mass spectrometry (MS). Despite improved instrumentation and software, de novo MS data analysis remains challenging. In recent years, deep learning models have represented a performance breakthrough. Incorporating that technology into de novo protein sequencing workflows require machine-learning models capable of handling highly diverse MS data. In this study, we analyzed the requirements for assembling such generalizable deep learning models by systemcally varying the composition and size of the training set. We assessed the generated models' performances using two test sets composed of peptides originating from the multienzyme digestion of samples from various species. The peptide recall values on the test sets showed that the deep learning models generated from a collection of highly N- and C-termini diverse peptides generalized 76% more over the termini-restricted ones. Moreover, expanding the training set's size by adding peptides from the multienzymatic digestion with five proteases of several species samples led to a 2-3 fold generalizability gain. Furthermore, we tested the applicability of these multienzyme deep learning (MEM) models by fully de novo sequencing the heavy and light monomeric chains of five commercial antibodies (mAbs). MEMs extracted over 10000 matching and overlapped peptides across six different proteases mAb samples, achieving a 100% sequence coverage for 8 of the ten polypeptide chains. We foretell that the MEMs' proven improvements to de novo analysis will positively impact several applications, such as analyzing samples of high complexity, unknown nature, or the peptidomics field.

The Proteomic and Transcriptomic Landscapes Altered by Rgg2/3 Activity in Streptococcus pyogenes

Streptococcus pyogenes, otherwise known as Group A Streptococcus (GAS), is an important and highly adaptable human pathogen with the ability to cause both superficial and severe diseases. Understanding how S. pyogenes senses and responds to its environment will likely aid in determining how it causes a breadth of diseases. One regulatory network involved in GAS's ability to sense and respond to the changing environment is the Rgg2/3 quorum sensing (QS) system, which responds to metal and carbohydrate availability and regulates changes to the bacterial surface. To better understand the impact of Rgg2/3 QS on S. pyogenes physiology, we performed RNA-seq and tandem mass tag (TMT)-LC-MS/MS analysis on cells in which this system was induced with short hydrophobic peptide (SHP) pheromone or disrupted. Primary findings confirmed that pheromone stimulation in wild-type cultures is limited to the induction of operons whose promoters contain previously determined Rgg2/3 binding sequences. However, a deletion mutant of rgg3, a strain that endogenously produces elevated amounts of pheromone, led to extended alterations of the transcriptome and proteome, ostensibly by stress-induced pathways. Under such exaggerated pheromone conditions, a connection was identified between Rgg2/3 and the stringent response. Mutation of relA, the bifunctional guanosine tetra- and penta-phosphate nucleoside synthetase/hydrolase, and alarmone synthase genes sasA and sasB, impacted culture doubling times and disabled induction of Rgg2/3 in response to mannose, while manipulation of Rgg2/3 signaling modestly altered nucleotide levels. Our findings indicate that excessive pheromone production or exposure places stress on GAS resulting in an indirect altered proteome and transcriptome beyond primary pheromone signaling. IMPORTANCE Streptococcus pyogenes causes several important human diseases. This study evaluates how the induction or disruption of a cell-cell communication system alters S. pyogenes's gene expression and, in extreme conditions, its physiology. Using transcriptomic and proteomic approaches, the results define the pheromone-dependent regulon of the Rgg2/3 quorum sensing system. In addition, we find that excessive pheromone stimulation, generated by genetic disruption of the Rgg2/3 system, leads to stress responses that are associated with the stringent response. Disruption of stringent response affects the ability of the cell-cell communication system to respond under normally inducing conditions. These findings assist in the determination of how S. pyogenes is impacted by and responds to nontraditional sources of stress.

Protocol for Proteome Analysis of Group A Streptococcus

This chapter describes a protocol for the separation of different cell fractions from Streptococcus pyogenes and sample preparation for analysis by mass spectrometry. The presented approach can be used for the analysis of all subcellular proteomes from S. pyogenes and enables identification of drug or vaccine targets within a certain cellular fraction. Here, proteins integral to the plasma membrane are of particular interest for the development of new antimicrobial therapies.

Genome-Wide Screens Identify Group A Streptococcus Surface Proteins Promoting Female Genital Tract Colonization and Virulence

Group A streptococcus (GAS) is a major pathogen that impacts health and economic affairs worldwide. Although the oropharynx is the primary site of infection, GAS can colonize the female genital tract and cause severe diseases, such as puerperal sepsis, neonatal infections, and necrotizing myometritis. Our understanding of how GAS genes contribute to interaction with the primate female genital tract is limited by the lack of relevant animal models. Using two genome-wide transposon mutagenesis screens, we identified 69 GAS genes required for colonization of the primate vaginal mucosa in vivo and 96 genes required for infection of the uterine wall ex vivo. We discovered a common set of 39 genes important for GAS fitness in both environments. They include genes encoding transporters, surface proteins, transcriptional regulators, and metabolic pathways. Notably, the genes that encode the surface-exclusion protein (SpyAD) and the immunogenic secreted protein 2 (Isp2) were found to be crucial for GAS fitness in the female primate genital tract. Targeted gene deletion confirmed that isogenic mutant strains ΔspyAD and Δisp2 are significantly impaired in ability to colonize the primate genital tract and cause uterine wall pathologic findings. Our studies identified novel GAS genes that contribute to female reproductive tract interaction that warrant translational research investigation.

An overview on enrichment methods for cell surface proteome profiling

Cell surface proteins are essential for many important biological processes, including cell-cell interactions, signal transduction, and molecular transportation. With the characteristics of low abundance, high hydrophobicity, and high heterogeneity, it is difficult to get a comprehensive view of cell surface proteome by direct analysis. Thus, it is important to selectively enrich the cell surface proteins before liquid chromatography with mass spectrometry analysis. In recent years, a variety of enrichment methods have been developed. Based on the separation mechanism, these methods could be mainly classified into three types. The first type is based on their difference in the physicochemical property, such as size, density, charge, and hydrophobicity. The second one is based on the bimolecular affinity interaction with lectin or antibody. And the third type is based on the chemical covalent coupling to free side groups of surface-exposed proteins or carbohydrate chains, such as primary amines, carboxyl groups, glycan side chains. In addition, metabolic labeling and enzymatic reaction-based methods have also been employed to selectively isolate cell surface proteins. In this review, we will provide a comprehensive overview of the enrichment methods for cell surface proteome profiling.