Keloid disease (KD) is a common connective tissue disorder of unknown aetiopathogenesis with ill-defined treatment. Keloid scars present as exophytic fibroproliferative reticular lesions postcutaneous injury, and even though KD remains neoplastically benign, keloid lesions behave locally aggressive, invasive and expansive. To date, there is limited understanding and validation of biomarkers identified through combined proteomic and genomic evaluation of KD. Therefore, the aim in this study was to identify putative causative candidates in KD by performing a comprehensive proteomics analysis of subcellular fractions as well as the whole cell, coupled with transcriptomics data analysis of normal compared with KD fibroblasts. We then applied novel integrative bioinformatics analysis to demonstrate that NF-kB-p65 (RELA) from the cytosolic fraction and CAPN2 from the whole-cell lysate were statistically significantly upregulated in KD and associated with alterations in relevant key signaling pathways, including apoptosis. Our findings were further confirmed by showing upregulation of both RELA and CAPN2 in KD using flow cytometry and immunohistochemistry. Moreover, functional evaluation using real-time cell analysis and flow cytometry demonstrated that both omeprazole and dexamethasone inhibited the growth of KD fibroblasts by enhancing the rate of apoptosis. In conclusion, subcellular fractionation and metaproteogenomic analyses have identified, to our knowledge, 2 previously unreported biomarkers of significant relevance to keloid diagnostics and therapeutics.

Drought and heat stress significantly limit crop growth and productivity. Their simultaneous occurrence, as often observed in summer crops, leads to larger yield losses. Sorghum is well adapted to dry and hot conditions. Despite the progress that has been made in determining proteomic responses to water deficit or heat stress in crops, such information remains limited for crops subjected to combined water deficit and heat stress. This study presents quantitative proteomics analyses of leaf and root tissues of two contrasting sorghum genotypes grown under normal conditions, water deficit stress, heat stress, and water deficit combined with heat stress. We identified differentially expressed proteins between the two genotypes under these different treatments. GO and KEGG annotation revealed biological processes and molecular pathways associated with sorghum responses to these treatments. Interpretation as well as integration of these proteomics data with other ‘omics’ signatures may highlight key mechanisms involved in sorghum adaptations to these stresses.

Generating top-down tandem mass spectra (MS/MS) for complex mixtures of proteoforms has become possible through improvements in fractionation, on-line separation, dissociation, and mass analysis. The algorithms to match MS/MS to sequences have undergone a parallel evolution, with both spectral alignment and peak matching being paired with diverse methods for scoring proteoform-spectral matches (PrSMs). This study assesses state-of-the-art algorithms for top-down identification through three distinct challenges. The first is identifying a large yield of PrSMs while controlling false discovery rate (FDR) in identifying thousands of proteoforms from complex cell lysates via four software workflows: ProSight Proteome Discoverer, TopPIC, Informed Proteomics, and pTop. The second is the deconvolution of data from both Thermo Orbitrap-class and Bruker maXis Q-TOF instruments to produce consistent precursor charge and mass determinations while generating fragment mass lists to optimize identification. The third attempts to detect diverse post-translational modifications (PTMs) in proteoforms from bovine milk and human ovarian tissue. The data demonstrate that existing software suites produce admirable sensitivity, in some cases identifying a third of collected MS/MS with FDR controlled below 2%; the overlap in these PrSMs, however, illustrates real value in searching data with multiple search engines. Differences among identification workflows seem to result from each search algorithm incorporating its own deconvolution algorithm. By transmitting deconvolution data from multiple deconvolution routes (Thermo Xtract, Bruker Auto MSn, Mascot Distiller, TopFD, and FLASHDeconv) to the downstream TopPIC search algorithm, we were able to detect common causes of deconvolution disagreement. The detection of PTMs was very inconsistent among search algorithms, with some workflows suggesting as little as 1% of PrSMs from bovine milk were singly-phosphorylated while other workflows found that 18% of PrSMs were singly-phosphorylated. Taken together, these results make a strong argument for top-down researchers to adopt a standard practice of analyzing each MS/MS experiment with at least two different search engines.

Advances in mass spectrometry (MS) instrumentation, including higher resolution, faster scan speeds, and improved sensitivity, have dramatically increased the data volume and complexity. The adoption of imaging and ion mobility further amplifies these challenges in proteomics, metabolomics, and lipidomics. Current open formats such as mzML and imzML struggle to keep pace due to large file sizes, slow data access, and limited metadata support. Vendor-specific formats offer faster access but lack interoperability and long-term archival guarantees. We here lay the groundwork for mzPeak, a next-generation community data format designed to address these challenges and support high-throughput, multidimensional MS workflows. By adopting a hybrid model that combines efficient binary storage for numerical data and both human- and machine-readable metadata storage, mzPeak will reduce file sizes, accelerate data access, and offer a scalable, adaptable solution for evolving MS technologies. For researchers, mzPeak will support complex workflows and regulatory compliance through faster access, improved metadata, and interoperability. For vendors, it offers a streamlined, open alternative to proprietary formats. mzPeak aims to become a cornerstone of MS data management, enabling sustainable, high-performance solutions for future data types and fostering collaboration across the mass spectrometry community.

To provide a detailed analysis of the molecular components and underlying mechanisms associated with ovarian cancer, we performed a comprehensive mass-spectrometry-based proteomic characterization of 174 ovarian tumors previously analyzed by The Cancer Genome Atlas (TCGA), of which 169 were high-grade serous carcinomas (HGSCs). Integrating our proteomic measurements with the genomic data yielded a number of insights into disease, such as how different copy-number alternations influence the proteome, the proteins associated with chromosomal instability, the sets of signaling pathways that diverse genome rearrangements converge on, and the ones most associated with short overall survival. Specific protein acetylations associated with homologous recombination deficiency suggest a potential means for stratifying patients for therapy. In addition to providing a valuable resource, these findings provide a view of how the somatic genome drives the cancer proteome and associations between protein and post-translational modification levels and clinical outcomes in HGSC.Video Abstract

Female genital tract (FGT) diseases such as bacterial vaginosis (BV) and sexually transmitted infections are prevalent in South Africa, with young women being at an increased risk. Since imbalances in the FGT microbiome are associated with FGT diseases, it is vital to investigate the factors that influence FGT health. The mycobiome plays an important role in regulating mucosal health, especially when the bacterial component is disturbed. However, we have a limited understanding of the FGT mycobiome since many studies have focused on bacterial communities and have neglected low-abundance taxonomic groups, such as fungi. To reduce this knowledge deficit, we present the first large-scale metaproteomic study to define the taxonomic composition and potential functional processes of the FGT mycobiome in South African reproductive-age women. We examined FGT fungal communities present in 123 women by collecting lateral vaginal wall swabs for liquid chromatography-tandem mass spectrometry. From this, 39 different fungal genera were identified, with Candida dominating the mycobiome (53.2

Mass spectrometry is a powerful technique for analyzing molecules in complex biological samples. However, inter- and intralaboratory variability and bias can affect the data due to various factors, including sample handling and preparation, instrument calibration and performance, and data acquisition and processing. To address this issue, the Quality Control (QC) working group of the Human Proteome Organization's Proteomics Standards Initiative has established the standard mzQC file format for reporting and exchanging information relating to data quality. mzQC is based on the JavaScript Object Notation (JSON) format and provides a lightweight yet versatile file format that can be easily implemented in software. Here, we present open-source software libraries to process mzQC data in three programming languages: Python, using pymzqc; R, using rmzqc; and Java, using jmzqc. The libraries follow a common data model and provide shared functionalities, including the (de)serialization and validation of mzQC files. We demonstrate use of the software libraries in a workflow for extracting, analyzing, and visualizing QC metrics from different sources. Additionally, we show how these libraries can be integrated with each other, with existing software tools, and in automated workflows for the QC of mass spectrometry data. All software libraries are available as open source under the MS-Quality-Hub organization on GitHub (https://github.com/MS-Quality-Hub).

Drought severely affects crop yield and yield stability. Maize and sorghum are major crops in Africa and globally, and both are negatively impacted by drought. However, sorghum has a better ability to withstand drought than maize. Consequently, this study identifies differences between maize and sorghum grown in water deficit conditions, and identifies proteins associated with drought tolerance in these plant species. Leaf relative water content and proline content were measured, and label-free proteomics analysis was carried out to identify differences in protein expression in the two species in response to water deficit. Water deficit enhanced the proline accumulation in sorghum roots to a higher degree than in maize, and this higher accumulation was associated with enhanced water retention in sorghum. Proteomic analyses identified proteins with differing expression patterns between the two species, revealing key metabolic pathways that explain the better drought tolerance of sorghum than maize. These proteins include phenylalanine/tyrosine ammonia-lyases, indole-3-acetaldehyde oxidase, sucrose synthase and phenol/catechol oxidase. This study highlights the importance of phenylpropanoids, sucrose, melanin-related metabolites and indole acetic acid (auxin) as determinants of the differences in drought stress tolerance between maize and sorghum. The selection of maize and sorghum genotypes with enhanced expression of the genes encoding these differentially expressed proteins, or genetically engineering maize and sorghum to increase the expression of such genes, can be used as strategies for the production of maize and sorghum varieties with improved drought tolerance.

Although Top-down (TD) proteomics techniques, aimed at the analysis of intact proteins and proteoforms, are becoming increasingly popular, efforts are needed at different levels to generalise its adoption. In this context, there are numerous improvements that are possible in the area of open science including the FAIR (Findability, Accessibility, Interoperability and Reusability) data principles. These include e.g. increased data sharing practices and availability of tailored open data standards. Additionally, the field would benefit from the development of open analysis workflows that can enable e.g. data reuse of public datasets, something that is increasingly common in other proteomics fields. We present an open and modular platform for the analysis and visualisation of TD proteomics data called TopDownApp. It can be used as a flexible analysis platform, through the use of a common workflow engine, common data formats for input/output, and software containerisation. It can also serve as a tool for visual inspection through its simple setup. As a key point, it can also be used as a development platform for new tools through the use of Python, a modular design, software containerisation and common data formats. TopDownApp is open source and freely available at: https://github.com/mwalzer/TopDownApp.

BackgroundMicrobiome research is providing important new insights into the metabolic interactions of complex microbial ecosystems involved in fields as diverse as the pathogenesis of human diseases, agriculture and climate change. Poor correlations typically observed between RNA and protein expression datasets make it hard to accurately infer microbial protein synthesis from metagenomic data. Additionally, mass spectrometry-based metaproteomic analyses typically rely on focused search sequence databases based on prior knowledge for protein identification that may not represent all the proteins present in a set of samples. Metagenomic 16S rRNA sequencing only targets the bacterial component, while whole genome sequencing is at best an indirect measure of expressed proteomes. Here we describe a novel approach, MetaNovo, that combines existing open-source software tools to perform scalable de novo sequence tag matching with a novel algorithm for probabilistic optimization of the entire UniProt knowledgebase to create tailored sequence databases for target-decoy searches directly at the proteome level, enabling metaproteomic analyses without prior expectation of sample composition or metagenomic data generation and compatible with standard downstream analysis pipelines.ResultsWe compared MetaNovo to published results from the MetaPro-IQ pipeline on 8 human mucosal-luminal interface samples, with comparable numbers of peptide and protein identifications, many shared peptide sequences and a similar bacterial taxonomic distribution compared to that found using a matched metagenome sequence database-but simultaneously identified many more non-bacterial peptides than the previous approaches. MetaNovo was also benchmarked on samples of known microbial composition against matched metagenomic and whole genomic sequence database workflows, yielding many more MS/MS identifications for the expected taxa, with improved taxonomic representation, while also highlighting previously described genome sequencing quality concerns for one of the organisms, and identifying an experimental sample contaminant without prior expectation.ConclusionsBy estimating taxonomic and peptide level information directly on microbiome samples from tandem mass spectrometry data, MetaNovo enables the simultaneous identification of peptides from all domains of life in metaproteome samples, bypassing the need for curated sequence databases to search. We show that the MetaNovo approach to mass spectrometry metaproteomics is more accurate than current gold standard approaches of tailored or matched genomic sequence database searches, can identify sample contaminants without prior expectation and yields insights into previously unidentified metaproteomic signals, building on the potential for complex mass spectrometry metaproteomic data to speak for itself.

Generating top-down tandem mass spectra (MS/MS) from complex mixtures of proteoforms benefits from improvements in fractionation, separation, fragmentation, and mass analysis. The algorithms to match MS/MS to sequences have undergone a parallel evolution, with both spectral alignment and match-counting approaches producing high-quality proteoform-spectrum matches (PrSMs). This study assesses state-of-the-art algorithms for top-down identification (ProSight PD, TopPIC, MSPathFinderT, and pTop) in their yield of PrSMs while controlling false discovery rate. We evaluated deconvolution engines (ThermoFisher Xtract, Bruker AutoMSn, Matrix Science Mascot Distiller, TopFD, and FLASHDeconv) in both ThermoFisher Orbitrap-class and Bruker maXis Q-TOF data (PXD033208) to produce consistent precursor charges and mass determinations. Finally, we sought post-translational modifications (PTMs) in proteoforms from bovine milk (PXD031744) and human ovarian tissue. Contemporary identification workflows produce excellent PrSM yields, although approximately half of all identified proteoforms from these four pipelines were specific to only one workflow. Deconvolution algorithms disagree on precursor masses and charges, contributing to identification variability. Detection of PTMs is inconsistent among algorithms. In bovine milk, 18% of PrSMs produced by pTop and TopMG were singly phosphorylated, but this percentage fell to 1% for one algorithm. Applying multiple search engines produces more comprehensive assessments of experiments. Top-down algorithms would benefit from greater interoperability.

The Human Proteome Organization (HUPO) Proteomics Standards Initiative (PSI) has been successfully developing guidelines, data formats, and controlled vocabularies (CVs) for the proteomics community and other fields supported by mass spectrometry since its inception 20 years ago. Here we describe the general operation of the PSI, including its leadership, working groups, yearly workshops, and the document process by which proposals are thoroughly and publicly reviewed in order to be ratified as PSI standards. We briefly describe the current state of the many existing PSI standards, some of which remain the same as when originally developed, some of which have undergone subsequent revisions, and some of which have become obsolete. Then the set of proposals currently being developed are described, with an open call to the community for participation in the forging of the next generation of standards. Finally, we describe some synergies and collaborations with other organizations and look to the future in how the PSI will continue to promote the open sharing of data and thus accelerate the progress of the field of proteomics.

Background: Machine learning is used to process big data volumes with complex non-linear relationships between predictive variables and predictions. Research into the usefulness of machine learning in small data volumes remains limited.Aim: To compare conventional statistical methods and machine learning to predict angiogram outcomes in a small cohort of South African cardiac patients.Methods: This is a retrospective study on patients with cardiac risk factors at Inkosi Albert Luthuli Central Hospital, Durban, South Africa, from 2002 to 2008. Models were designed using predictive risk factors to forecast a binary angiogram outcome (normal or abnormal) by applying conventional statistical models (binary logistic and log binomial) and stacking ensemble machine learning.Results: The outcome prevalence of abnormal angiograms was 99/173 (57%). Predictive data was used to model this outcome. The binary logistic regression model, which estimates odds ratio, was unsuitable. The log binomial model, which estimates relative risk, did not converge after various stepwise modelling attempts. Thereafter, machine learning models were used. These included logistic regression, k-nearest neighbour, decision tree, support vector machine, and naïve Bayes. The ensemble model amalgamated all algorithms and showed accuracy >70% and excellent performance at different thresholds with an area under the curve (AUC) >80%.Discussion: The logistic regression model was unsuitable because an odds ratio would have been unreliable and overestimated the true effect since the outcome prevalence was >10%. A log binomial model with relative risk estimates did not converge, possibly owing to the multiple predictive variables. Overall, conventional statistical models were unsuccessful in this instance. Machine learning models had limitations from a small dataset. However, the combined modelling with the stacking ensemble method produced good results in the small, homogenous database by exploiting the strengths of each contributing algorithm.Conclusions: Researchers may apply machine learning when conventional statistical models are inconclusive in homogenous small databases with multiple variables and a complex relationship to the outcome. Machine learning is a viable option even with relatively small cohorts if the number of predictive variables is also small.

Acquisition and homeostasis of essential metals during host colonization by bacterial pathogens rely on metal uptake, trafficking, and storage proteins. How these factors have evolved within bacterial pathogens is poorly defined. Urease, a nickel enzyme, is essential for Helicobacter pylori to colonize the acidic stomach. Our previous data suggest that acquisition of nickel transporters and a histidine-rich protein (HRP) involved in nickel storage in H. pylori and gastric Helicobacter spp. have been essential evolutionary events for gastric colonization. Using bioinformatics, proteomics, and phylogenetics, we extended this analysis to determine how evolution has framed the repertoire of HRPs among 39 Epsilonproteobacteria; 18 gastric and 11 non-gastric enterohepatic (EH) Helicobacter spp., as well as 10 other Epsilonproteobacteria. We identified a total of 213 HRPs distributed in 22 protein families named orthologous groups (OGs) with His-rich domains, including 15 newly described OGs. Gastric Helicobacter spp. are enriched in HRPs (7.7 +/- 1.9 HRPs/strain) as compared to EH Helicobacter spp. (1.9 +/- 1.0 HRPs/strain) with a particular prevalence of HRPs with C-terminal histidine-rich domains in gastric species. The expression and nickel-binding capacity of several HRPs was validated in five gastric Helicobacter spp. We established the evolutionary history of new HRP families, such as the periplasmic HP0721-like proteins and the HugZ-type heme oxygenases. The expansion of histidine-rich extensions in gastric Helicobacter spp. proteins is intriguing but can tentatively be associated with the presence of the urease nickel enzyme. We conclude that this HRP expansion is associated with unique properties of organisms that rely on large intracellular nickel amounts for their survival.

The introduction of "open search" algorithms has enabled many researchers to discern which biological posttranslational modifications are hiding in their LC-MS/MS data. For chemical biologists, these tools are even more essential, since unanticipated side reactions and redox chemistry can dramatically expand the diversity of mass shifts present in these experiments. This guide walks through the steps necessary to employ the MSFragger and Philosopher tool sets and, more importantly, it assists readers in interpreting the results from open search for subsequent database search in MS-GF+. Four different chemical biology investigations are profiled from SCIEX TripleTOF and Thermo Orbitrap-class instruments: an investigation of organophosphate pesticides by Bui-Nguyen et al., an examination of alkylating reagents by Hains and Robinson, a model of acrylamide neurotoxicity by Prats et al., and a test of formalin fixation and paraffin embedding by Buthelezi et al.

Female genital tract (FGT) inflammation is an important risk factor for HIV acquisition. The FGT microbiome is closely associated with inflammatory profile; however, the relative importance of microbial activities has not been established. Since proteins are key elements representing actual microbial functions, this study utilized metaproteomics to evaluate the relationship between FGT microbial function and inflammation in 113 young and adolescent South African women at high risk of HIV infection. Women were grouped as having low, medium, or high FGT inflammation by K-means clustering according to pro-inflammatory cytokine concentrations. A total of 3186 microbial and human proteins were identified in lateral vaginal wall swabs using liquid chromatography-tandem mass spectrometry, while 94 microbial taxa were included in the taxonomic analysis. Both metaproteomics and 16S rRNA gene sequencing analyses showed increased non-optimal bacteria and decreased lactobacilli in women with FGT inflammatory profiles. However, differences in the predicted relative abundance of most bacteria were observed between 16S rRNA gene sequencing and metaproteomics analyses. Bacterial protein functional annotations (gene ontology) predicted inflammatory cytokine profiles more accurately than bacterial relative abundance determined by 16S rRNA gene sequence analysis, as well as functional predictions based on 16S rRNA gene sequence data (p < 0.0001). The majority of microbial biological processes were underrepresented in women with high inflammation compared to those with low inflammation, including a Lactobacillus-associated signature of reduced cell wall organization and peptidoglycan biosynthesis. This signature remained associated with high FGT inflammation in a subset of 74 women 9 weeks later, was upheld after adjusting for Lactobacillus relative abundance, and was associated with in vitro inflammatory cytokine responses to Lactobacillus isolates from the same women. Reduced cell wall organization and peptidoglycan biosynthesis were also associated with high FGT inflammation in an independent sample of ten women. Both the presence of specific microbial taxa in the FGT and their properties and activities are critical determinants of FGT inflammation. Our findings support those of previous studies suggesting that peptidoglycan is directly immunosuppressive, and identify a possible avenue for biotherapeutic development to reduce inflammation in the FGT. To facilitate further investigations of microbial activities, we have developed the FGT-DB application that is available at http://fgtdb.org/ .

While proteomics has demonstrated its value for model organisms and for organisms with mature genome sequence annotations, proteomics has been of less value in nonmodel organisms that are unaccompanied by genome sequence annotations. This project sought to determine the value of RNA-Seq experiments as a basis for establishing a set of protein sequences to represent a nonmodel organism, in this case, the pseudocereal chia. Assembling four publicly available chia RNA-Seq datasets produced transcript sequence sets with a high BUSCO completeness, though the number of transcript sequences and Trinity "genes" varied considerably among them. After six-frame translation, ProteinOrtho detected substantial numbers of orthologs among other species within the taxonomic order Lamiales. These protein sequence databases demonstrated a good identification efficiency for three different LC-MS/MS proteomics experiments, though a seed proteome showed considerable variability in the identification of peptides based on seed protein sequence inclusion. If a proteomics experiment emphasizes a particular tissue, an RNA-Seq experiment incorporating that same tissue is more likely to support a database search identification of that proteome.

Pancreatic ductal adenocarcinoma (PDAC) accounts for 2.8% of new cancer cases worldwide and is projected to become the 2nd leading cause of cancer-related deaths by 2030. Patients of African ancestry appear to be at increased risk for PDAC with the worst severity and outcome.This proposed study sought to determine and integrate proteomic and genomic profiles of PDAC patients of African ancestry to identify potential markers and better understand molecular mechanisms of the disease, especially in our population cohort. Thirty tissues (15 tumours and 15 corresponding normal tissues) were obtained from consenting South African PDAC patients undergoing Whipple procedure at Chris Hani Baragwanath Hospital in Johannesburg, South Africa (HREC-M150778). Protein and DNA were extracted from tissue samples and SWATH Mass Spectrometry and OncoArray anlaysis performed. Network and functional analysis were conducted using STRINGv11.0 and REACTOMEv70. We also used the Variant effect predictor (VEPv98) tool to predict consequences of the SNPs observed. We found 55 upregulated and 36 downregulated proteins in tumour samples which were mostly involved in key biological processes, including haemostasis, signal transduction, neuronal system and developmental biology. These aberrant processes are known to exacerbate tumour aggressiveness, invasion and metastasis. Furthermore, we observed 219 SNPs covering key gene regions such as those observed to be upregulated by SWATH-MS analysis. They include genes such as PALLD, AVL9, BPGM, SERPINB8 and MYPN. We have shown the dysregulation and simultaneous mutations of several key genes/proteins highlighting their roles as plausible biomarkers and therapeutic targets. Validation studies are required to confirm these results.

Pathogenic mycobacteria, such as Mycobacterium tuberculosis , modulate the host immune system to evade clearance and promote long-term persistence, resulting in disease progression or latent infection. Understanding the mechanisms pathogenic mycobacteria use to escape elimination by the host immune system is critical to better understanding the molecular mechanisms of mycobacterial infection. Protein kinase G (PknG) in pathogenic mycobacteria has been shown to play an important role in avoiding clearance by macrophages through blocking phagosome-lysosome fusion; however, the exact mechanism is not completely understood. Here, to further investigate the role of mycobacterial PknG during early events of macrophage infection, RAW 264.7 macrophage cell lines were infected with M. bovis BCG wild-type and PknG knock-out mutant strains. After proteolysis, phosphopeptides were enriched via TiO2 columns and subjected to LC-MS/MS to identify differentially phosphorylated peptides between the wild-type and PknG mutant infected macrophages. A total of 1401 phosphosites on 914 unique proteins were identified. Following phosphoproteome normalisation and differential expression analysis, a total of 149 phosphosites were differentially phosphorylated in the wild-type infected RAW 264.7 macrophages versus the PknG knock-out mutant. A subset of 95 phosphosites was differentially up-regulated in the presence of PknG. Functional analysis of our data revealed that PknG kinase activity reprograms normal macrophage function through interfering with host cytoskeletal organisation, spliceosomal machinery, translational initiation, and programmed cell death. Differentially phosphorylated proteins in this study serve as a foundation for further validation and PknG host substrate assignment. Importance Tuberculosis (TB) remains one of the leading causes of death from infection worldwide, due to the ability of Mycobacterium tuberculosis ( Mtb ) to survive and replicate within the host, establishing reservoirs of live bacteria that promote persistence and recurrence of disease. Understanding the mechanisms that Mtb uses to evade the host immune system is thus a major goal in the TB field. Protein kinase G is thought to play an important role in Mtb avoiding clearance by the host through disruption of macrophage function, but the underlying molecular mechanisms of this are not well understood. Here, our new phosphoproteomic data reveals that mycobacterial PknG substantially reprograms normal macrophage function through extensive PknG-mediated post-translational control of critical host cellular processes. These novel findings therefore considerably increase our knowledge of mycobacterial pathogenicity, including specific host cellular pathways that might be re-activatable through host-directed therapy, thereby restoring macrophage ability to eliminate Mtb .

Vegetative desiccation tolerance, or the ability to survive the loss of ~95% relative water content (RWC), is rare in angiosperms, with these being commonly called resurrection plants. It is a complex multigenic and multi-factorial trait, with its understanding requiring a comprehensive systems biology approach. The aim of the current study was to conduct a label-free proteomic analysis of leaves of the resurrection plant Xerophyta schlechteri in response to desiccation. A targeted metabolomics approach was validated and correlated to the proteomics, contributing the missing link in studies on this species. Three physiological stages were identified: an early response to drying, during which the leaf tissues declined from full turgor to a RWC of ~80–70%, a mid-response in which the RWC declined to 40% and a late response where the tissues declined to 10% RWC. We identified 517 distinct proteins that were differentially expressed, of which 253 proteins were upregulated and 264 were downregulated in response to the three drying stages. Metabolomics analyses, which included monitoring the levels of a selection of phytohormones, amino acids, sugars, sugar alcohols, fatty acids and organic acids in response to dehydration, correlated with some of the proteomic differences, giving insight into the biological processes apparently involved in desiccation tolerance in this species.