Beyond the identifiable proteome: Delving into the proteomics of polymyxin-resistant and non-resistant Acinetobacter baumannii from Brazilian hospitals.

This work discloses a unique, comprehensive proteomic dataset of Acinetobacter baumannii strains, both resistant and non-resistant to polymyxin B, isolated in Brazil generated using Orbitrap Fusion Lumos. From nearly 4 million tandem mass spectra, the software DiagnoMass produced 240,685 quality-filtered mass spectral clusters, of which PatternLab for proteomics identified 44,553 peptides mapping to 3479 proteins. Crucially, DiagnoMass shortlisted 3550 and 1408 unique mass spectral clusters for the resistant and non-resistant strains, respectively, with only about a third with sequences (and PTMs) identified by PatternLab. Further open-search attempts via FragPipe yielded an additional ∼20% identifications, suggesting the remaining unidentified spectra likely arise from complex combinations of post-translational modifications and amino-acid substitutions. This highlights the untapped potential of the dataset for future discoveries, particularly given the importance of PTMs, which remain elusive to nucleotide sequencing approaches but are crucial for understanding biological mechanisms. Our innovative approach extends beyond the identifications that are typically subjected to the bias of a search engine; we discern which spectral clusters are differential and subject them to increased scrutiny, akin to spectral library matching by comparing captured spectra to themselves. Our analysis reveals adaptations in the resistant strain, including enhanced detoxification, altered protein synthesis, and metabolic adjustments. SIGNIFICANCE: We present comprehensive proteomic profiles of non-resistant and resistant Acinetobacter baumannii from Brazilian Hospitals strains, and highlight the presence of discriminative and yet unidentified mass spectral clusters. Our work emphasizes the importance of exploring this overlooked data, as it could hold the key to understanding the complex dynamics of antibiotic resistance. This approach not only informs antimicrobial stewardship efforts but also paves the way for the development of innovative diagnostic tools. Thus, our findings have profound implications for the field, as far as methods for providing a new perspective on diagnosing antibiotic resistance as well as classifying proteomes in general.

DiagnoMass: A proteomics hub for pinpointing discriminative spectral clusters.

MOTIVATION: There are several well-established paradigms for identifying and pinpointing discriminative peptides/proteins using shotgun proteomic data; examples are peptide-spectrum matching, de novo sequencing, open searches, and even hybrid approaches. Such an arsenal of complementary paradigms can provide deep data coverage, albeit some unidentified discriminative peptides remain. RESULTS: We present DiagnoMass, software tool that groups similar spectra into spectral clusters and then shortlists those clusters that are discriminative for biological conditions. DiagnoMass then communicates with proteomic tools to attempt the identification of such clusters. We demonstrate the effectiveness of DiagnoMass by analyzing proteomic data from Escherichia coli, Salmonella, and Shigella, listing many high-quality discriminative spectral clusters that had thus far remained unidentified by widely adopted proteomic tools. DiagnoMass can also classify proteomic profiles. We anticipate the use of DiagnoMass as a vital tool for pinpointing biomarkers. AVAILABILITY: DiagnoMass and related documentation, including a usage protocol, are available at http://www.diagnomass.com.

Proteogenomic Analysis Reveals Proteins Involved in the First Step of Adipogenesis in Human Adipose-Derived Stem Cells.

BACKGROUND: Obesity is characterized as a disease that directly affects the whole-body metabolism and is associated with excess fat mass and several related comorbidities. Dynamics of adipocyte hypertrophy and hyperplasia play an important role in health and disease, especially in obesity. Human adipose-derived stem cells (hASC) represent an important source for understanding the entire adipogenic differentiation process. However, little is known about the triggering step of adipogenesis in hASC. Here, we performed a proteogenomic approach for understanding the protein abundance alterations during the initiation of the adipogenic differentiation process. METHODS: hASC were isolated from adipose tissue of three donors and were then characterized and expanded. Cells were cultured for 24 hours in adipogenic differentiation medium followed by protein extraction. We used shotgun proteomics to compare the proteomic profile of 24 h-adipogenic, differentiated, and undifferentiated hASC. We also used our previous next-generation sequencing data (RNA-seq) of the total and polysomal mRNA fractions of hASC to study posttranscriptional regulation during the initial steps of adipogenesis. RESULTS: We identified 3420 proteins out of 48,336 peptides, of which 92 proteins were exclusively identified in undifferentiated hASC and 53 proteins were exclusively found in 24 h-differentiated cells. Using a stringent criterion, we identified 33 differentially abundant proteins when comparing 24 h-differentiated and undifferentiated hASC (14 upregulated and 19 downregulated, respectively). Among the upregulated proteins, we shortlisted several adipogenesis-related proteins. A combined analysis of the proteome and the transcriptome allowed the identification of positive correlation coefficients between proteins and mRNAs. CONCLUSIONS: These results demonstrate a specific proteome profile related to adipogenesis at the beginning (24 hours) of the differentiation process in hASC, which advances the understanding of human adipogenesis and obesity. Adipogenic differentiation is finely regulated at the transcriptional, posttranscriptional, and posttranslational levels.

The impact of blood-processing time on the proteome of human peripheral blood mononuclear cells.

Human peripheral blood mononuclear cells (PBMC) are key to several diagnostics assays and basic science research. Blood pre-analytical variations that occur before obtaining the PBMC fraction can significantly impact the assays results, including viability, composition, integrity, and gene expression changes of immune cells. With this as motivation, we performed a quantitative shotgun proteomics analysis using Isobaric Tag for Relative and Absolute Quantitation (iTRAQ 8plex) labeling to compare PBMC obtained from 24 h-stored blood at room temperature versus freshly isolated. We identified a total of 3195 proteins, of which 245 were differentially abundant (101 upregulated and 144 downregulated). Our results revealed enriched pathways of downregulated proteins related to exocytosis, localization, vesicle-mediated transport, cell activation, and secretion. In contrast, pathways related to exocytosis, neutrophil degranulation and activation, granulocyte activation, leukocyte degranulation, and myeloid leukocyte activation involved in immune response were enriched in upregulated proteins, which may indicate probable granulocyte contamination and activation due to blood storage time and temperature. Examples of upregulated proteins in the 24 h-PBMC samples are CAMP, S100A8, LTA4H, RASAL3, and S100A6, which are involved in an adaptive immune system and antimicrobial activity, proinflammatory mediation, aminopeptidase activities, and naïve T cells survival. Moreover, examples of downregulated proteins are NDUFA5, TAGLN2, H3C1, TUBA8, and CCT2 that are related to the cytoskeleton, cell junction, mitochondrial respiratory chain. In conclusion, the delay in blood-processing time directly impacts the proteomic profile of human PBMC, possibly through granulocyte contamination and activation.

Proteomics pinpoints alterations in grade I meningiomas of male versus female patients.

Meningiomas are among the most common primary tumors of the central nervous system (CNS) and originate from the arachnoid or meningothelial cells of the meninges. Surgery is the first option of treatment, but depending on the location and invasion patterns, complete removal of the tumor is not always feasible. Reports indicate many differences in meningiomas from male versus female patients; for example, incidence is higher in females, whereas males usually develop the malignant and more aggressive type. With this as motivation, we used shotgun proteomics to compare the proteomic profile of grade I meningioma biopsies of male and female patients. Our results listed several differentially abundant proteins between the two groups; some examples are S100-A4 and proteins involved in RNA splicing events. For males, we identified enriched pathways for cell-matrix organization and for females, pathways related to RNA transporting and processing. We believe our findings contribute to the understanding of the molecular differences between grade I meningiomas of female and male patients.

A scoring model for phosphopeptide site localization and its impact on the question of whether to use MSA.

The production of structurally significant product ions during the dissociation of phosphopeptides is a key to the successful determination of phosphorylation sites. These diagnostic ions can be generated using the widely adopted MS/MS approach, MS3 (Data Dependent Neutral Loss - DDNL), or by multistage activation (MSA). The main purpose of this work is to introduce a false-localization rate (FLR) probabilistic model to enable unbiased phosphoproteomics studies. Briefly, our algorithm infers a probabilistic function from the distribution of the identified phosphopeptides' XCorr Delta scores (XD-Scores) in the current experiment. Our module infers p-values by relying on Gaussian mixture models and a logistic function. We demonstrate the usefulness of our probabilistic model by revisiting the "to MSA, or not to MSA" dilemma. For this, we use human leukemia-derived cells (K562) as a study model and enriched for phosphopeptides using the hydroxyapatite (HAP) chromatography. The aliquots were analyzed with and without MSA on an Orbitrap-XL. Our XD-Scoring analysis revealed that the MS/MS approach provides more identifications because of its faster scan rate, but that for the same given scan rate higher-confidence spectra can be achieved with MSA. Our software is integrated into the PatternLab for proteomics freely available for academic community at http://www.patternlabforproteomics.org. Biological significance Assigning statistical confidence to phosphorylation sites is necessary for proper phosphoproteomic assessment. Here we present a rigorous statistical model, based on Gaussian mixture models and a logistic function, which overcomes shortcomings of previous tools. The algorithm described herein is made readily available to the scientific community by integrating it into the widely adopted PatternLab for proteomics. This article is part of a Special Issue entitled: Computational Proteomics.

Proteome analysis of formalin-fixed paraffin-embedded tissues from a primary gastric melanoma and its meningeal metastasis: a case report.

Melanoma is the third most common brain metastasis cause in the United States as it has a relatively high susceptibility to metastasize to the central nervous system. Among the different origins for brain metastasis, those originating from primary gastric melanomas are extremely rare. Here, we compare protein profiles obtained from formalin-fixed paraffin- embedded (FFPE) tissues of a primary gastric melanoma with its meningeal metastasis. For this, the contents of a microscope slide were scraped and ultimately analyzed by nano-chromatography coupled online with tandem mass spectrometry using an Orbitrap XL. Our results disclose 184 proteins uniquely identified in the primary gastric melanoma, 304 in the meningeal metastasis, and 177 in common. Notably, we identified several enzymes related to changes in the metabolism that are linked to producing energy by elevated rates of glycolysis in a process called the Warburg effect. Moreover, we show that our FFPE proteomic approach allowed identification of key biological markers such as the S100 protein that we further validated by immunohistochemistry for both, the primary and metastatic tumor samples. That said, we demonstrated a powerful strategy to retrospectively mine data for aiding in the understanding of metastasis, biomarker discovery, and ultimately, diseases. To our knowledge, these results disclose for the first time a comparison of the proteomic profiles of gastric melanoma and its corresponding meningeal metastasis.