Unassigned MS/MS Spectra: Who Am I?

Mining proteomics data to extract post-translational modifications associated with gastric cancer

Gastric cancers are highly heterogeneous, deep-seated tumours associated with late diagnosis and poor prognosis. Post-translational modifications (PTMs) of proteins are known to be well-associated with oncogenesis and metastasis in most cancers. Several enzymes which drive PTMs have also been used as theranostics in cancers of the breast, ovary, prostate and bladder. However, there is limited data on PTMs in gastric cancers. Considering that experimental protocols for simultaneous analysis of multiple PTMs are being explored, a data-driven approach involving reanalysis of mass spectrometry-derived data is useful in cataloguing altered PTMs. We subjected publicly available mass spectrometry data on gastric cancer to an iterative searching strategy for fetching PTMs including phosphorylation, acetylation, citrullination, methylation and crotonylation. These PTMs were catalogued and further analyzed for their functional enrichment through motif analysis. This value-added approach delivered identification of 21,710 unique modification sites on 16,364 modified peptides. Interestingly, we observed 278 peptides corresponding to 184 proteins to be differentially abundant. Using bioinformatics approaches, we observed that majority of these altered PTMs/proteins belonged to cytoskeletal and extracellular matrix proteins, which are known to be perturbed in gastric cancer. The dataset derived by this mutiPTM investigation can provide leads to further investigate the potential role of altered PTMs in gastric cancer management.

Development of a spectral library for the discovery of altered genomic events in Mycobacterium avium associated with virulence using mass spectrometry-based proteogenomic analysis

Mycobacterium avium is one of the prominent disease-causing bacteria in humans. It causes lymphadenitis, chronic and extrapulmonary, and disseminated infections in adults, children, and immunocompromised patients. M. avium has ∼4,500 predicted protein-coding regions on average, which can help discover several variants at the proteome level. Many of them are potentially associated with virulence; thus, identifying such proteins can be a helpful feature in developing panel-based theranostics. In line with such a long-term goal, we carried out an in-depth proteomic analysis of M. avium with both data-dependent and data-independent acquisition methods. Further, a set of proteogenomic investigations were carried out using i) a protein database for Mycobacterium tuberculosis, ii) a M. avium genome six-frame translated database, and iii) a variant protein database of M. avium. A search of mass spectrometry data against M. avium protein database resulted in identifying 2,954 proteins. Further, proteogenomic analyses aided in identifying 1,301 novel peptide sequences and correcting translation start sites for 15 proteins. Ultimately, we created a spectral library of M. avium proteins, including novel genome search-specific peptides and variant peptides detected in this study. We validated the spectral library by a data-independent acquisition of the M. avium proteome. Thus, we present an M. avium spectral library of 29,033 peptide precursors supported by 0.4 million fragment ions for further use by the biomedical community.

Peptidomics Methods Applied to the Study of Flower Development

Understanding the global and dynamic nature of plant developmental processes requires not only the study of the transcriptome, but also of the proteome, including its largely uncharacterized peptidome fraction. Recent advances in proteomics and high-throughput analyses of translating RNAs (ribosome profiling) have begun to address this issue, evidencing the existence of novel, uncharacterized, and possibly functional peptides. To validate the accumulation in tissues of sORF-encoded polypeptides (SEPs), the basic setup of proteomic analyses (i.e., LC-MS/MS) can be followed. However, the detection of peptides that are small (up to ~100 aa, 6-7 kDa) and novel (i.e., not annotated in reference databases) presents specific challenges that need to be addressed both experimentally and with computational biology resources. Several methods have been developed in recent years to isolate and identify peptides from plant tissues. In this chapter, we outline two different peptide extraction protocols and the subsequent peptide identification by mass spectrometry using the database search or the de novo identification methods.

Mosaic translation hypothesis: chimeric polypeptides produced via multiple ribosomal frameshifting as a basis for adaptability

How many different proteins can be produced from a single spliced transcript? Genome annotation projects overlook the coding potential of reading frames other than that of the reference open reading frames (refORFs). Recently, alternative open reading frames (altORFs) and their translational products, alternative proteins, have been shown to carry out important functions in various organisms. AltORFs overlapping refORFs or other altORFs in a different reading frame may be involved in one fundamental mechanism so far overlooked. A few years ago, it was proposed that altORFs may act as building blocks for chimeric (mosaic) polypeptides, which are produced via multiple ribosomal frameshifting events from a single mature transcript. We adopt terminology from that earlier discussion and call this mechanism mosaic translation. This way of extracting and combining genetic information may significantly increase proteome diversity. Thus, we hypothesize that this mechanism may have contributed to the flexibility and adaptability of organisms to a variety of environmental conditions. Specialized ribosomes acting as sensors probably played a central role in this process. Importantly, mosaic translation may be the main source of protein diversity in genomes that lack alternative splicing. The idea of mosaic translation is a testable hypothesis, although its direct demonstration is challenging. Should mosaic translation occur, we would currently highly underestimate the complexity of translation mechanisms and thus the proteome.

Non-lysine ubiquitylation: Doing things differently

The post-translational modification of proteins with ubiquitin plays a central role in nearly all aspects of eukaryotic biology. Historically, studies have focused on the conjugation of ubiquitin to lysine residues in substrates, but it is now clear that ubiquitylation can also occur on cysteine, serine, and threonine residues, as well as on the N-terminal amino group of proteins. Paradigm-shifting reports of non-proteinaceous substrates have further extended the reach of ubiquitylation beyond the proteome to include intracellular lipids and sugars. Additionally, results from bacteria have revealed novel ways to ubiquitylate (and deubiquitylate) substrates without the need for any of the enzymatic components of the canonical ubiquitylation cascade. Focusing mainly upon recent findings, this review aims to outline the current understanding of non-lysine ubiquitylation and speculate upon the molecular mechanisms and physiological importance of this non-canonical modification.

Leptospira and Leptospirosis: New Systems Science Insights on Proteome, Posttranslational Modifications, and Pathogen-Host Interaction

Leptospirosis is one of the most important zoonotic diseases for planetary health. It is caused by Leptospira spp., which poses a formidable challenge in both rural and urban geographies. Discovery of molecular targets is crucial for developing interventions, including vaccines, against leptospirosis. We report here novel systems science insights on Leptospira proteome, posttranslational modifications (PTMs), and pathogen-host interactions, with an eye to bacterial pathophysiology from a functional standpoint. A systematic reanalysis of unassigned spectra from our previous total proteome identification was used for a multi-PTM search. Notably, we identified 3693 unique high-confidence PTM sites corresponding to 1266 proteins (PTM-profiling probability cutoff value ≥75%). The majority of the phosphorylated peptides were found to be GroEL molecular chaperones. Notably, the molecular docking of PTM-GroEL with STAT3, an important signaling protein in cytokine production, resulted in the prediction of druggable "hotspots." These energetically significant smaller subsets of amino acids (hotspot residues) offer promise for practical applications in planetary health, rational drug design, and peptide engineering. Furthermore, the prediction strategies described here could serve as a starting point for narrowing down the more extensive interface in protein-protein interactions that currently exist. Going forward, systems science approaches and the new insights reported here offer veritable prospects for innovation in preventing and treating leptospirosis.

Proteomic Database Search Engine for Two-Dimensional Partial Covariance Mass Spectrometry

We present a protein database search engine for the automatic identification of peptide and protein sequences using the recently introduced method of two-dimensional partial covariance mass spectrometry (2D-PC-MS). Because the 2D-PC-MS measurement reveals correlations between fragments stemming from the same or consecutive decomposition processes, the first-of-its-kind 2D-PC-MS search engine is based entirely on the direct matching of the pairs of theoretical and the experimentally detected correlating fragments, rather than of individual fragment signals or their series. We demonstrate that the high structural specificity afforded by 2D-PC-MS fragment correlations enables our search engine to reliably identify the correct peptide sequence, even from a spectrum with a large proportion of contaminant signals. While for peptides, the 2D-PC-MS correlation-matching procedure is based on complementary and internal ion correlations, the identification of intact proteins is entirely based on the ability of 2D-PC-MS to spatially separate and resolve the experimental correlations between complementary fragment ions.

Novel Post-Translational Modifications and Molecular Substrates in Glioma Identified by Bioinformatics

Glioma is the most common type of brain cancer that originates from the glial cells. It constitutes about one-third of all brain cancers. Recently, transcriptomics, proteomics, and multiomics approaches have been harnessed to discover potential biomarkers and therapeutic targets in glioma. Moreover, post-translational modifications (PTMs) of proteins play a major role in cell biology and function and offer new avenues of research in cancer. Using unbiased multi-PTM bioinformatics analyses of two proteomic datasets of glioma available in the public domain, we identified 866 proteins with common PTMs from both studies. Out of these 866 proteins, 19 proteins were identified with the common PTMs, with the same site modifications pertaining to glioma. Importantly, the identified PTMs belonged to proteins involved in integrin PI3K/Akt/mTOR, JAK/STAT, and Ras/Raf/MAPK pathways. These pathways are essential for cell proliferation in tumor cells and thus involved in glioma progression. Taken together, these findings call for validation in larger datasets in glioma and brain cancers and with an eye to future drug discovery and diagnostic innovation. Bioinformatics-guided discovery of novel PTMs from the publicly available proteomic data can offer new avenues for innovation in cancer research.

Proteogenomics Reveals Orthologous Alternatively Spliced Proteoforms in the Same Human and Mouse Brain Regions with Differential Abundance in an Alzheimer's Disease Mouse Model

Alternative splicing (AS) may increase the number of proteoforms produced by a gene. Alzheimer's disease (AD) is a neurodegenerative disease with well-characterized AS proteoforms. In this study, we used a proteogenomics strategy to build a customized protein sequence database and identify orthologous AS proteoforms between humans and mice on publicly available shotgun proteomics (MS/MS) data of the corpus callosum (CC) and olfactory bulb (OB). Identical proteotypic peptides of six orthologous AS proteoforms were found in both species: PKM1 (gene PKM/Pkm), STXBP1a (gene STXBP1/Stxbp1), Isoform 3 (gene HNRNPK/Hnrnpk), LCRMP-1 (gene CRMP1/Crmp1), SP3 (gene CADM1/Cadm1), and PKCβII (gene PRKCB/Prkcb). These AS variants were also detected at the transcript level by publicly available RNA-Seq data and experimentally validated by RT-qPCR. Additionally, PKM1 and STXBP1a were detected at higher abundances in a publicly available MS/MS dataset of the AD mouse model APP/PS1 than its wild type. These data corroborate other reports, which suggest that PKM1 and STXBP1a AS proteoforms might play a role in amyloid-like aggregate formation. To the best of our knowledge, this report is the first to describe PKM1 and STXBP1a overexpression in the OB of an AD mouse model. We hope that our strategy may be of use in future human neurodegenerative studies using mouse models.