Intra-Individual Paired Mass Spectrometry Dataset for Decoding Solar-Induced Proteomic Changes in Facial Skin.

Photoaging is the premature aging of the skin caused by prolonged exposure to solar radiation. The visual alterations manifest as wrinkles, reduced skin elasticity, uneven skin tone, as well as other signs that surpass the expected outcomes of natural aging. Beyond these surface changes, there is a complex interplay of molecular alterations, encompassing shifts in cellular function, DNA damage, and protein composition disruptions. This data descriptor introduces a unique dataset derived from ten individuals, each with a minimum of 18 years of professional experience as a driver, who are asymmetrically and chronically exposed to solar radiation due to their driving orientation. Skin samples were independently collected from each side of the face using a microdermabrasion-like procedure and analyzed on an Exploris 240 mass spectrometer. Our adapted proteomic statistical framework leverages the sample pairing to provide robust insights. This dataset delves into the molecular differences in exposed skin and serves as a foundational resource for interdisciplinary research in photodermatology, targeted skincare treatments, and computational modelling of skin health.

Corrected and republished from: "Extracellular Vesicle Formation in Cryptococcus deuterogattii Impacts Fungal Virulence".

Small molecules are components of fungal extracellular vesicles (EVs), but their biological roles are only superficially known. NOP16 is a eukaryotic gene that is required for the activity of benzimidazoles against Cryptococcus deuterogattii. In this study, during the phenotypic characterization of C. deuterogattii mutants expected to lack NOP16 expression, we observed a reduced EV production. Whole-genome sequencing, RNA-Seq, and cellular proteomics revealed that, contrary to our initial findings, these mutants expressed Nop16 but exhibited altered expression of 14 genes potentially involved in sugar transport. Based on this observation, we designated these mutant strains as Past1 and Past2, representing potentially altered sugar transport. Analysis of the small molecule composition of EVs produced by wild-type cells and the Past1 and Past2 mutant strains revealed not only a reduced number of EVs but also an altered small molecule composition. In a Galleria mellonella model of infection, the Past1 and Past2 mutant strains were hypovirulent. The hypovirulent phenotype was reverted when EVs produced by wild-type cells, but not mutant EVs, were co-injected with the mutant cells in G. mellonella. These results connect EV biogenesis, cargo, and cryptococcal virulence.

Proteomics reveals that the antifungal activity of fenbendazole against Cryptococcus neoformans requires protein kinases.

Cryptococcus neoformans is responsible for over 100 000 deaths annually, and the treatment of this fungal disease is expensive and not consistently effective. Unveiling new therapeutic avenues is crucial. Previous studies have suggested that the anthelmintic drug fenbendazole is an affordable and nontoxic candidate to combat cryptococcosis. However, its mechanism of anticryptococcal activity has been only superficially investigated. In this study, we examined the global cellular response of C. neoformans to fenbendazole using a proteomic approach (data are available via ProteomeXchange with identifier PXD047041). Fenbendazole treatment mostly impacted the abundance of proteins related to metabolic pathways, RNA processing, and intracellular traffic. Protein kinases, in particular, were significantly affected by fenbendazole treatment. Experimental validation of the proteomics data using a collection of C. neoformans mutants led to the identification of critical roles of five protein kinases in fenbendazole's antifungal activity. In fact, mutants lacking the expression of genes encoding Chk1, Tco2, Tco3, Bub1, and Sch9 kinases demonstrated greater resistance to fenbendazole compared to wild-type cells. In combination with the standard antifungal drug amphotericin B, fenbendazole reduced the cryptococcal burden in mice. These findings not only contribute to the elucidation of fenbendazole's mode of action but also support its use in combination therapy with amphotericin B. In conclusion, our data suggest that fenbendazole holds promise for further development as an anticryptococcal agent.

Proteomics reveals differentially regulated pathways when comparing grade 2 and 4 astrocytomas.

Astrocytic tumors are known for their high progression capacity and high mortality rates; in this regard, proteins correlated to prognosis can aid medical conduct. Although several genetic changes related to progression from grade 2 to grade 4 astrocytoma are already known, mRNA copies do not necessarily correlate with protein abundance and therefore could shadow further comprehension about this tumor's biology. This motivates us to seek for complementary strategies to study tumor progression at the protein level. Here we compare the proteomic profile of biopsies from patients with grade 2 (diffuse, n = 6) versus grade 4 astrocytomas (glioblastomas, n = 10) using shotgun proteomics. Data analysis performed with PatternLab for proteomics identified 5,206 and 6,004 proteins in the 2- and 4-grade groups, respectively. Our results revealed seventy-four differentially abundant proteins (p < 0.01); we then shortlist those related to greater malignancy. We also describe molecular pathways distinctly activated in the two groups, such as differences in the organization of the extracellular matrix, decisive both in tumor invasiveness and in signaling for cell division, which, together with marked contrasts in energy metabolism, are determining factors in the speed of growth and dissemination of these neoplasms. The degradation pathways of GABA, enriched in the grade 2 group, is consistent with a favorable prognosis. Other functions such as platelet degranulation, apoptosis, and activation of the MAPK pathway were correlated to grade 4 tumors and, consequently, unfavorable prognoses. Our results provide an important survey of molecular pathways involved in glioma pathogenesis for these histopathological groups.

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.

PatternLab V Handles Multiplex Spectra in Shotgun Proteomic Searches and Increases Identification.

Complex protein mixtures typically generate many tandem mass spectra produced by different peptides coisolated in the gas phase. Widely adopted proteomic data analysis environments usually fail to identify most of these spectra, succeeding at best in identifying only one of the multiple cofragmenting peptides. We present PatternLab V (PLV), an updated version of PatternLab that integrates the YADA 3 deconvolution algorithm to handle such cases efficiently. In general, we expect an increase of 10% in spectral identifications when dealing with complex proteomic samples. PLV is freely available at http://patternlabforproteomics.org.

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.

Increasing confidence in proteomic spectral deconvolution through mass defect.

MOTIVATION: Confident deconvolution of proteomic spectra is critical for several applications such as de novo sequencing, cross-linking mass spectrometry and handling chimeric mass spectra. RESULTS: In general, all deconvolution algorithms may eventually report mass peaks that are not compatible with the chemical formula of any peptide. We show how to remove these artifacts by considering their mass defects. We introduce Y.A.D.A. 3.0, a fast deconvolution algorithm that can remove peaks with unacceptable mass defects. Our approach is effective for polypeptides with less than 10 kDa, and its essence can be easily incorporated into any deconvolution algorithm. AVAILABILITY AND IMPLEMENTATION: Y.A.D.A. 3.0 is freely available for academic use at http://patternlabforproteomics.org/yada3. SUPPLEMENTARY INFORMATION: Supplementary information is available at Bioinformatics online.

Platelet proteome reveals features of cell death, antiviral response and viral replication in covid-19.

Coronavirus disease 2019 (COVID-19) has affected over 400 million people worldwide, leading to 6 million deaths. Among the complex symptomatology of COVID-19, hypercoagulation and thrombosis have been described to directly contribute to lethality, pointing out platelets as an important SARS-CoV-2 target. In this work, we explored the platelet proteome of COVID-19 patients through a label-free shotgun proteomics approach to identify platelet responses to infection, as well as validation experiments in a larger patient cohort. Exclusively detected proteins (EPs) and differentially expressed proteins (DEPs) were identified in the proteomic dataset and thus classified into biological processes to map pathways correlated with pathogenesis. Significant changes in the expression of proteins related to platelet activation, cell death, and antiviral response through interferon type-I were found in all patients. Since the outcome of COVID-19 varies highly among individuals, we also performed a cross-comparison of proteins found in survivors and nonsurvivors. Proteins belonging to the translation pathway were strongly highlighted in the nonsurvivor group. Moreover, the SARS-CoV-2 genome was fully sequenced in platelets from five patients, indicating viral internalization and preprocessing, with CD147 as a potential entry route. In summary, platelets play a significant role in COVID-19 pathogenesis via platelet activation, antiviral response, and disease severity.

Simple, efficient and thorough shotgun proteomic analysis with PatternLab V.

Shotgun proteomics aims to identify and quantify the thousands of proteins in complex mixtures such as cell and tissue lysates and biological fluids. This approach uses liquid chromatography coupled with tandem mass spectrometry and typically generates hundreds of thousands of mass spectra that require specialized computational environments for data analysis. PatternLab for proteomics is a unified computational environment for analyzing shotgun proteomic data. PatternLab V (PLV) is the most comprehensive and crucial update so far, the result of intensive interaction with the proteomics community over several years. All PLV modules have been optimized and its graphical user interface has been completely updated for improved user experience. Major improvements were made to all aspects of the software, ranging from boosting the number of protein identifications to faster extraction of ion chromatograms. PLV provides modules for preparing sequence databases, protein identification, statistical filtering and in-depth result browsing for both labeled and label-free quantitation. The PepExplorer module can even pinpoint de novo sequenced peptides not already present in the database. PLV is of broad applicability and therefore suitable for challenging experimental setups, such as time-course experiments and data handling from unsequenced organisms. PLV interfaces with widely adopted software and community initiatives, e.g., Comet, Skyline, PEAKS and PRIDE. It is freely available at http://www.patternlabforproteomics.org .

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.

Leveraging the partition selection bias to achieve a high-quality clustering of mass spectra.

In proteomics, the identification of peptides from mass spectral data can be mathematically described as the partitioning of mass spectra into clusters (i.e., groups of spectra derived from the same peptide). The way partitions are validated is just as important, having evolved side by side with the clustering algorithms themselves and given rise to many partition assessment measures. An assessment measure is said to have a selection bias if, and only if, the probability that a randomly chosen partition scoring a high value depends on the number of clusters in the partition. In the context of clustering mass spectra, this might mislead the validation process to favor clustering algorithms that generate too many (or few) spectral clusters, regardless of the underlying peptide sequence. A selection bias toward the number of peptides is desirable for proteomics as it estimates the number of peptides in a complex protein mixture. Here, we introduce an assessment measure that is purposely biased toward the number of peptide ion species. We also introduce a partition assessment framework for proteomics, called the Partition Assessment Tool, and demonstrate its importance by evaluating the performance of eight clustering algorithms on seven proteomics datasets while discussing the trade-offs involved. SIGNIFICANCE: Clustering algorithms are widely adopted in proteomics for undertaking several tasks such as speeding up search engines, generating consensus mass spectra, and to aid in the classification of proteomic profiles. Choosing which algorithm is most fit for the task at hand is not simple as each algorithm has advantages and disadvantages; furthermore, specifying clustering parameters is also a necessary and fundamental step. For example, deciding on whether to generate "pure clusters" or fewer clusters but accepting noise. With this as motivation, we verify the performance of several widely adopted algorithms on proteomic datasets and introduce a theoretical framework for drawing conclusions on which approach is suitable for the task at hand.

Characterizing protein conformers by cross-linking mass spectrometry and pattern recognition.

MOTIVATION: Chemical cross-linking coupled to mass spectrometry (XLMS) emerged as a powerful technique for studying protein structures and large-scale protein-protein interactions. Nonetheless, XLMS lacks software tailored toward dealing with multiple conformers; this scenario can lead to high-quality identifications that are mutually exclusive. This limitation hampers the applicability of XLMS in structural experiments of dynamic protein systems, where less abundant conformers of the target protein are expected in the sample. RESULTS: We present QUIN-XL, a software that uses unsupervised clustering to group cross-link identifications by their quantitative profile across multiple samples. QUIN-XL highlights regions of the protein or system presenting changes in its conformation when comparing different biological conditions. We demonstrate our software's usefulness by revisiting the HSP90 protein, comparing three of its different conformers. QUIN-XL's clusters correlate directly to known protein 3D structures of the conformers and therefore validates our software. AVAILABILITYAND IMPLEMENTATION: QUIN-XL and a user tutorial are freely available at http://patternlabforproteomics.org/quinxl for academic users. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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 reveals that quinoa bioester promotes replenishing effects in epidermal tissue.

The continuous search for natural products that attenuate age-related losses has increasingly gained notice; among them, those applicable for skin care have drawn significant attention. The bioester generated from the Chenopodium quinoa's oil is a natural-origin ingredient described to produce replenishing skin effects. With this as motivation, we used shotgun proteomics to study the effects of quinoa bioester on human reconstructed epidermis tridimensional cell cultures after 0, 3, 6, 12, 24, and 48 h of exposure. Our experimental setup employed reversed-phase nano-chromatography coupled online with an Orbitrap-XL and PatternLab for proteomics as the data analysis tool. Extracted ion chromatograms were obtained as surrogates for relative peptide quantitation. Our findings spotlight proteins with increased abundance, as compared to the untreated cell culture counterparts at the same timepoints, that were related to preventing premature aging, homeostasis, tissue regeneration, protection against ultraviolet radiation and oxidative damage.

RawVegetable - A data assessment tool for proteomics and cross-linking mass spectrometry experiments.

We present RawVegetable, a software for mass spectrometry data assessment and quality control tailored toward shotgun proteomics and cross-linking experiments. RawVegetable provides four main modules with distinct features: (A) The charge state chromatogram that independently displays the ion current for each charge state; useful for optimizing the chromatography for highly charged ions and with lower XIC values such as those typically found in cross-linking experiments. (B) The XL-Artefact determination, which flags possible noncovalently associated peptides. (C) The TopN density estimation, for detecting retention time intervals of under or over-sampling, and (D) The chromatography reproducibility module, which provides pairwise comparisons between multiple experiments. RawVegetable, a tutorial, and the example data are freely available for academic use at: http://patternlabforproteomics.org/rawvegetable. SIGNIFICANCE: Chromatography optimization is a critical step for any shotgun proteomic or cross-linking mass spectrometry experiment. Here, we present a nifty solution with several key features, such as displaying individual charge state chromatograms, highlighting chromatographic regions of under- or over-sampling and checking for reproducibility.

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.

Mixed-Data Acquisition: Next-Generation Quantitative Proteomics Data Acquisition.

We present the Mixed-Data Acquisition (MDA) strategy for mass spectrometry data acquisition. MDA combines Data-Dependent Acquisition (DDA) and Data-Independent Acquisition (DIA) in the same run, thus doing away with the requirements for separate DDA spectral libraries. MDA is a natural result from advances in mass spectrometry, such as high scan rates and multiple analyzers, and is tailored toward exploiting these features. We demonstrate MDA's effectiveness on a yeast proteome analysis by overcoming a common bottleneck for XIC-based label-free quantitation; namely, the coelution of precursors when m/z values cannot be distinguished. We anticipate that MDA will become the next mainstream data generation approach for proteomics. MDA can also serve as an orthogonal validation approach for DDA experiments. Specialized software for MDA data analysis is made available on the project's website.

Mixed-data acquisition: next-generation quantitative proteomics data acquisition

We present the Mixed-Data Acquisition (MDA) strategy for mass spectrometry data acquisition. MDA combines Data-Dependent Acquisition (DDA) and Data-Independent Acquisition (DIA) in the same run, thus doing away with the requirements for separate DDA spectral libraries. MDA is a natural result from advances in mass spectrometry, such as high scan rates and multiple analyzers, and is tailored toward exploiting these features. We demonstrate MDA's effectiveness on a yeast proteome analysis by overcoming a common bottleneck for XIC-based label-free quantitation; namely, the coelution of precursors when m/z values cannot be distinguished. We anticipate that MDA will become the next mainstream data generation approach for proteomics. MDA can also serve as an orthogonal validation approach for DDA experiments. Specialized software for MDA data analysis is made available on the project's website.

The ribosome assembly factor Nop53 controls association of the RNA exosome with pre-60S particles in yeast.

Eukaryotic ribosomal biogenesis is a high-energy-demanding and complex process that requires hundreds of trans-acting factors to dynamically build the highly-organized 40S and 60S subunits. Each ribonucleoprotein complex comprises specific rRNAs and ribosomal proteins that are organized into functional domains. The RNA exosome complex plays a crucial role as one of the pre-60S-processing factors, because it is the RNase responsible for processing the 7S pre-rRNA to the mature 5.8S rRNA. The yeast pre-60S assembly factor Nop53 has previously been shown to associate with the nucleoplasmic pre-60S in a region containing the "foot" structure assembled around the 3' end of the 7S pre-rRNA. Nop53 interacts with 25S rRNA and with several 60S assembly factors, including the RNA exosome, specifically, with its catalytic subunit Rrp6 and with the exosome-associated RNA helicase Mtr4. Nop53 is therefore considered the adaptor responsible for recruiting the exosome complex for 7S processing. Here, using proteomics-based approaches in budding yeast to analyze the effects of Nop53 on the exosome interactome, we found that the exosome binds pre-ribosomal complexes early during the ribosome maturation pathway. We also identified interactions through which Nop53 modulates exosome activity in the context of 60S maturation and provide evidence that in addition to recruiting the exosome, Nop53 may also be important for positioning the exosome during 7S processing. On the basis of these findings, we propose that the exosome is recruited much earlier during ribosome assembly than previously thought, suggesting the existence of additional interactions that remain to be described.