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Microbiome 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. We 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. By 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.

A newly developed algorithm enabled clustering of all 256 million (66 million identified and 190 million unidentified) peptide MS/MS spectra available in the PRIDE Archive database, allowing the detection of millions of consistently unidentified spectra across different data sets, of which roughly 20% could be identified using multiple complementary analysis tools. Mass spectrometry (MS) is the main technology used in proteomics approaches. However, on average, 75% of spectra analyzed in an MS experiment remain unidentified. We propose to use spectrum clustering at a large scale to shed light on these unidentified spectra. The Proteomics Identifications (PRIDE) Database Archive is one of the largest MS proteomics public data repositories worldwide. By clustering all tandem MS spectra publicly available in the PRIDE Archive, coming from hundreds of data sets, we were able to consistently characterize spectra into three distinct groups: (1) incorrectly identified, (2) correctly identified but below the set scoring threshold, and (3) truly unidentified. Using multiple complementary analysis approaches, we were able to identify ∼20% of the consistently unidentified spectra. The complete spectrum-clustering results are available through the new version of the PRIDE Cluster resource ( http://www.ebi.ac.uk/pride/cluster ). This resource is intended, among other aims, to encourage and simplify further investigation into these unidentified spectra.

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.

Pancreatic cancer accounts for 2.8% of new cancer cases worldwide and is projected to become the second leading cause of cancer-related deaths by 2030. Patients of African ancestry appear to be at an increased risk for pancreatic ductal adenocarcinoma (PDAC), with more severe disease and outcomes. The purpose of this study was to map the proteomic and genomic landscape of a cohort of PDAC patients of African ancestry. Thirty tissues (15 tumours and 15 normal adjacent tissues) were obtained from consenting South African PDAC patients. Optimisation of the sample preparation method allowed for the simultaneous extraction of high-purity protein and DNA for SWATH-MS and OncoArray SNV analyses. We quantified 3402 proteins with 49 upregulated and 35 downregulated proteins at a minimum 2.1 fold change and FDR adjusted p-value (q-value) ≤ 0.01 when comparing tumour to normal adjacent tissue. Many of the upregulated proteins in the tumour samples are involved in extracellular matrix formation (ECM) and related intracellular pathways. In addition, proteins such as EMIL1, KBTB2, and ZCCHV involved in the regulation of ECM proteins were observed to be dysregulated in pancreatic tumours. Downregulation of pathways involved in oxygen and carbon dioxide transport were observed. Genotype data showed missense mutations in some upregulated proteins, such as MYPN, ESTY2 and SERPINB8. Approximately 11% of the dysregulated proteins, including ISLR, BP1, PTK7 and OLFL3, were predicted to be secretory proteins. These findings help in further elucidating the biology of PDAC and may aid in identifying future plausible markers for the disease.

Since its introduction in 1994, SEQUEST has gained many important new capabilities, and a host of successor algorithms have built upon its successes. This Account and Perspective maps the evolution of this important tool and charts the relationships among contributions to the SEQUEST legacy. Many of the changes represented improvements in computing speed by clusters and graphics cards. Mass spectrometry innovations in mass accuracy and activation methods led to shifts in fragment modeling and scoring strategies. These changes, as well as the movement of laboratories and lab members, have led to great diversity among the members of the SEQUEST family. Graphical Abstract ᅟ

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.

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.

Background 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. Results 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. Conclusions 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/ . DwvYvmP_Lqo1TfCUgVQTWu Video Abstract

Background 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. Results 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% relative abundance). We observed changes in relative abundance at the protein, genus, and functional (gene ontology biological processes) level between BV states. In women with BV, Malassezia and Conidiobolus proteins were more abundant, while Candida proteins were less abundant compared to BV-negative women. Correspondingly, Nugent scores were negatively associated with total fungal protein abundance. The clinical variables, Nugent score, pro-inflammatory cytokines, chemokines, vaginal pH, Chlamydia trachomatis , and the presence of clue cells were associated with fungal community composition. Conclusions The results of this study revealed the diversity of FGT fungal communities, setting the groundwork for understanding the FGT mycobiome. -WgVkTUVMn1xysf-XxHxoj Video Abstract

Background The prevalence of Parkinson’s disease (PD) is increasing in sub-Saharan Africa, but little is known about the genetics of PD in these populations. Due to their unique ancestry and diversity, sub-Saharan African populations have the potential to reveal novel insights into the pathobiology of PD. In this study, we aimed to characterise the genetic variation in known and novel PD genes in a group of Black South African and Nigerian patients. Methods We recruited 33 Black South African and 14 Nigerian PD patients, and screened them for sequence variants in 751 genes using an Ion AmpliSeq™ Neurological Research panel. We used bcftools to filter variants and annovar software for the annotation. Rare variants were prioritised using MetaLR and MetaSVM prediction scores. The effect of a variant on ATP13A2’s protein structure was investigated by molecular modelling. Results We identified 14,655 rare variants with a minor allele frequency ≤ 0.01, which included 2448 missense variants. Notably, no common pathogenic mutations were identified in these patients. Also, none of the known PD-associated mutations were found highlighting the need for more studies in African populations. Altogether, 54 rare variants in 42 genes were considered deleterious and were prioritized, based on MetaLR and MetaSVM scores, for follow-up studies. Protein modelling showed that the S1004R variant in ATP13A2 possibly alters the conformation of the protein. Conclusions We identified several rare variants predicted to be deleterious in sub-Saharan Africa PD patients; however, further studies are required to determine the biological effects of these variants and their possible role in PD. Studies such as these are important to elucidate the genetic aetiology of this disorder in patients of African ancestry.