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.

Proteomic Analysis of a Rat Streptozotocin Model Shows Dysregulated Biological Pathways Implicated in Alzheimer's Disease.

Alzheimer's Disease (AD) is an age-related neurodegenerative disorder characterized by progressive memory loss and cognitive impairment, affecting 35 million individuals worldwide. Intracerebroventricular (ICV) injection of low to moderate doses of streptozotocin (STZ) in adult male Wistar rats can reproduce classical physiopathological hallmarks of AD. This biological model is known as ICV-STZ. Most studies are focused on the description of behavioral and morphological aspects of the ICV-STZ model. However, knowledge regarding the molecular aspects of the ICV-STZ model is still incipient. Therefore, this work is a first attempt to provide a wide proteome description of the ICV-STZ model based on mass spectrometry (MS). To achieve that, samples from the pre-frontal cortex (PFC) and hippocampus (HPC) of the ICV-STZ model and control (wild-type) were used. Differential protein abundance, pathway, and network analysis were performed based on the protein identification and quantification of the samples. Our analysis revealed dysregulated biological pathways implicated in the early stages of late-onset Alzheimer's disease (LOAD), based on differentially abundant proteins (DAPs). Some of these DAPs had their mRNA expression further investigated through qRT-PCR. Our results shed light on the AD onset and demonstrate the ICV-STZ as a valid model for LOAD proteome description.

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.

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.

Improving the accuracy of beef cattle methane inventories in Latin America and Caribbean countries.

On-farm methane (CH4) emissions need to be estimated accurately so that the mitigation effect of recommended practices can be accounted for. In the present study prediction equations for enteric CH4 have been developed in lieu of expensive animal measurement approaches. Our objectives were to: (1) compile a dataset from individual beef cattle data for the Latin America and Caribbean (LAC) region; (2) determine main predictors of CH4 emission variables; (3) develop and cross-validate prediction models according to dietary forage content (DFC); and (4) compare the predictive ability of these newly-developed models with extant equations reported in literature, including those currently used for CH4 inventories in LAC countries. After outlier's screening, 1100 beef cattle observations from 55 studies were kept in the final dataset (∼ 50 % of the original dataset). Mixed-effects models were fitted with a random effect of study. The whole dataset was split according to DFC into a subset for all-forage (DFC = 100 %), high-forage (94 % ≥ DFC ≥ 54 %), and low-forage (50 % ≥ DFC) diets. Feed intake and average daily gain (ADG) were the main predictors of CH4 emission (g d-1), whereas this was feeding level [dry matter intake (DMI) as % of body weight] for CH4 yield (g kg-1 DMI). The newly-developed models were more accurate than IPCC Tier 2 equations for all subsets. Simple and multiple regression models including ADG were accurate and a feasible option to predict CH4 emission when data on feed intake are not available. Methane yield was not well predicted by any extant equation in contrast to the newly-developed models. The present study delivered new models that may be alternatives for the IPCC Tier 2 equations to improve CH4 prediction for beef cattle in inventories of LAC countries based either on more or less readily available data.

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.

Advances in knowledge of Manihot (Euphorbiaceae) from Brazil.

Herbarium-based studies and extensive field work revealed the existence of a new species in the genus Manihot which is restricted to the municipality of Itirapina, in the state of São Paulo, Brazil. Characteristics such as the size and morphology of the leaves and bracts were important to recognize M. irregularis as a new taxon. Description, illustration, as well as comments on its morphology, distribution, conservation status, and a key for the identification of Manihot species for the state of São Paulo are presented.

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 .

CIGB-300 Anticancer Peptide Differentially Interacts with CK2 Subunits and Regulates Specific Signaling Mediators in a Highly Sensitive Large Cell Lung Carcinoma Cell Model.

Large cell lung carcinoma (LCLC) is one form of NSCLC that spreads more aggressively than some other forms, and it represents an unmet medical need. Here, we investigated for the first time the effect of the anti-CK2 CIGB-300 peptide in NCI-H460 cells as an LCLC model. NCI-H460 cells were highly sensitive toward CIGB-300 cytotoxicity, reaching a peak of apoptosis at 6 h. Moreover, CIGB-300 slightly impaired the cell cycle of NCI-H460 cells. The CIGB-300 interactomics profile revealed in more than 300 proteins that many of them participated in biological processes relevant in cancer. Interrogation of the CK2 subunits targeting by CIGB-300 indicated the higher binding of the peptide to the CK2α' catalytic subunit by in vivo pull-down assays plus immunoblotting analysis and confocal microscopy. The down-regulation of both phosphorylation and protein levels of the ribonuclear protein S6 (RPS6) was observed 48 h post treatment. Altogether, we have found that NCI-H460 cells are the most CIGB-300-sensitive solid tumor cell line described so far, and also, the findings we provide here uncover novel features linked to CK2 targeting by the CIGB-300 anticancer peptide.

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.

Synthesis, LC-MS/MS analysis, and biological evaluation of two vaccine candidates against ticks based on the antigenic P0 peptide from R. sanguineus linked to the p64K carrier protein from Neisseria meningitidis.

A peptide from the P0 acidic ribosomal protein (pP0) of ticks conjugated to keyhole limpet hemocyanin from Megathura crenulata has shown to be effective against different tick species when used in host vaccination. Turning this peptide into a commercial anti-tick vaccine will depend on finding the appropriate, technically and economically feasible way to present it to the host immune system. Two conjugates (p64K-Cys1pP0 and p64K-ßAla1pP0) were synthesized using the p64K carrier protein from Neisseria meningitidis produced in Escherichia coli, the same cross-linking reagent, and two analogues of pP0. The SDS-PAGE analysis of p64K-Cys1pP0 showed a heterogeneous conjugate compared to p64K-ßAla1pP0 that was detected as a protein band at 91kDa. The pP0/p64K ratio determined by MALDI-MS for p64K-Cys1pP0 ranged from 1 to 8, being 3-5 the predominant ratio, while in the case of p64K-ßAla1pP0 this ratio was 5-7. Cys1pP0 was partially linked to 35 out of 39 Lys residues and the N-terminal end, while ßAla1pP0 was mostly linked to the six free cysteine residues, to the N-terminal end, and, in a lesser extent, to Lys residues. The assignment of the conjugation sites and side reactions were based on the identification of type 2 peptides. Rabbit immunizations showed the best anti-pP0 titers and the highest efficacy against Rhipicephalus sanguineus ticks when the p64K-Cys1pP0 was used as vaccine antigen. The presence of high molecular mass aggregates observed in the SDS-PAGE analysis of p64K-Cys1pP0 could be responsible for a better immune response against pP0 and consequently for its better efficacy as an anti-tick vaccine. Graphical abstract.

Low-Intensity, High-Frequency Grazing Positively Affects Defoliating Behavior, Nutrient Intake and Blood Indicators of Nutrition and Stress in Sheep.

The intensity and frequency of grazing affect the defoliating strategy of ruminants, their daily nutrient intake, thus nutrition and physiological status. Italian ryegrass (Lolium multiflorum Lam.) pastures were grazed by sheep either under a low-intensity/high-frequency grazing strategy (Rotatinuous stocking; RN) with nominal pre- and post-grazing sward heights of 18 and 11 cm, respectively, or under a high-intensity/low-frequency strategy (traditional rotational stocking; RT) with nominal pre- and post-grazing sward heights of 25 and 5 cm, respectively. Treatments were arranged under a complete randomized design and evaluated over two periods, in different years. In 2017, the aim was to depict the type of bites that sheep perform during the grazing-down and associate them to the grazing management strategy according to their relative contribution to the diet ingested. In 2018 we estimated the total nutrient intake and evaluated blood indicators of the nutritional status and immune response to stress of sheep. The bite types accounting the most for the diet ingested by RN sheep were those performed on the "top stratum" of plants with around 20, 15, and 25 cm, whereas the type of bites accounting the most for the diet of RT sheep were those performed on "grazed plants" with around 10, 5, and ≤ 3 cm. In 2018, the RN sheep increased by 18% the total organic matter (OM) intake and by 20-25% the intake of soluble nutrients (i.e., crude protein, total soluble sugars, crude fat), digestible OM and of metabolizable energy, and had 17.5, 18, and 6.1% greater blood concentration of glucose, urea nitrogen (BUN) and albumin, respectively, but 17% lower blood neutrophil-to-lymphocyte (N:L) ratio. Sheep grazing vegetative Italian ryegrass pastures under the low-intensity/high-frequency grazing strategy (RN) ingested a diet of better quality from bites allocated on the top stratum of plants, had greater intake of soluble nutrients and blood parameters positively associated with nutritional status and immune response to stress.

What, how, and how much do herbivores eat? The Continuous Bite Monitoring method for assessing forage intake of grazing animals.

Determining herbage intake is pivotal for studies on grazing ecology. Direct observation of animals allows describing the interactions of animals with the pastoral environment along the complex grazing process. The objectives of the study were to evaluate the reliability of the continuous bite monitoring (CBM) method in determining herbage intake in grazing sheep compared to the standard double-weighing technique method during 45-min feeding bouts; evaluate the degree of agreement between the two techniques; and to test the effect of different potential sources of variation on the reliability of the CBM. The CBM method has been used to describe the intake behavior of grazing herbivores. In this study, we evaluated a new approach to this method, that is, whether it is a good proxy for determining the intake of grazing animals. Three experiments with grazing sheep were carried out in which we tested for different sources of variations, such as the number of observers, level of detail of bite coding grid, forage species, forage allowance, sward surface height heterogeneity, experiment site, and animal weight, to determine the short-term intake rate (45 min). Observer (Pexp1  = 0.018, Pexp2  = 0.078, and Pexp3  = 0.006), sward surface height (Pexp2  < 0.001), total number of bites observed per grazing session (Pexp2  < 0.001 and Pexp3  < 0.001), and sward depletion (Pexp3  < 0.001) were found to affect the absolute error of intake estimation. The results showed a high correlation and agreement between the two methods in the three experiments, although intake was overestimation by CBM on experiments 2 and 3 (181.38 and 214.24 units, respectively). This outcome indicates the potential of CBM to determining forage intake with the benefit of a greater level of detail on foraging patterns and components of the diet. Furthermore, direct observation is not invasive nor disrupts natural animal behavior.

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.

Proteome remodeling in the Mycobacterium tuberculosis PknG knockout: Molecular evidence for the role of this kinase in cell envelope biogenesis and hypoxia response.

Mycobacterium tuberculosis, the etiological agent of tuberculosis, is among the deadliest human pathogens. One of M. tuberculosis's pathogenic hallmarks is its ability to persist in a dormant state in the host. Thus, this pathogen has developed mechanisms to withstand stressful conditions found in the human host. Particularly, the Ser/Thr-protein kinase PknG has gained relevance since it regulates nitrogen metabolism and facilitates bacterial survival inside macrophages. Nevertheless, the molecular mechanisms underlying these effects are far from being elucidated. To further investigate these issues, we performed quantitative proteomic analyses of protein extracts from M. tuberculosis H37Rv and a mutant lacking pknG. We found that in the absence of PknG the mycobacterial proteome was remodeled since 5.7% of the proteins encoded by M. tuberculosis presented significant changes in its relative abundance compared with the wild-type. The main biological processes affected by pknG deletion were cell envelope components biosynthesis and response to hypoxia. Thirteen DosR-regulated proteins were underrepresented in the pknG deletion mutant, including Hrp-1, which was 12.5-fold decreased according to Parallel Reaction Monitoring experiments. Altogether, our results allow us to postulate that PknG regulation of bacterial adaptation to stress conditions might be an important mechanism underlying its reported effect on intracellular bacterial survival. SIGNIFICANCE: PknG is a Ser/Thr kinase from Mycobacterium tuberculosis with key roles in bacterial metabolism and bacterial survival within the host. However, at present the molecular mechanisms underlying these functions remain largely unknown. In this work, we evaluate the effect of pknG deletion on M. tuberculosis proteome using different approaches. Our results clearly show that the global proteome was remodeled in the absence of PknG and shed light on new molecular mechanism underlying PknG role. Altogether, this work contributes to a better understanding of the molecular bases of the adaptation of M. tuberculosis, one of the most deadly human pathogens, to its host.

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.