Mapping the phylogeny and lineage history of geographically distinct BCG vaccine strains.

The bacillus Calmette-Guérin (BCG) vaccine has been in use for prevention of tuberculosis for over a century. It remains the only widely available tuberculosis vaccine and its protective efficacy has varied across geographical regions. Since it was developed, the BCG vaccine strain has been shared across different laboratories around the world, where use of differing culture methods has resulted in genetically distinct strains over time. Whilst differing BCG vaccine efficacy around the world is well documented, and the reasons for this may be multifactorial, it has been hypothesized that genetic differences in BCG vaccine strains contribute to this variation. Isolates from an historic archive of lyophilized BCG strains were regrown, DNA was extracted and then whole-genome sequenced using Oxford Nanopore Technologies. The resulting whole-genome data were plotted on a phylogenetic tree and analysed to identify the presence or absence of regions of difference (RDs) and single-nucleotide polymorphisms (SNPs) relating to virulence, growth and cell wall structure. Of 50 strains available, 36 were revived in culture and 39 were sequenced. Morphology differed between the strains distributed before and after 1934. There was phylogenetic association amongst certain geographically classified strains, most notably BCG-Russia, BCG-Japan and BCG-Danish. RD2, RD171 and RD713 deletions were associated with late strains (seeded after 1927). When mapped to BCG-Pasteur 1172, the SNPs in sigK, plaA, mmaA3 and eccC5 were associated with early strains. Whilst BCG-Russia, BCG-Japan and BCG-Danish showed strong geographical isolate clustering, the late strains, including BCG-Pasteur, showed more variation. A wide range of SNPs were seen within geographically classified strains, and as much intra-strain variation as between-strain variation was seen. The date of distribution from the original Pasteur laboratory (early pre-1927 or late post-1927) gave the strongest association with genetic differences in regions of difference and virulence-related SNPs, which agrees with the previous literature.

Development of a Spectral Library for the Discovery of Altered Genomic Events in Mycobacterium avium Associated With Virulence Using Mass Spectrometry-Based Proteogenomic Analysis.

Mycobacterium avium is one of the prominent disease-causing bacteria in humans. It causes lymphadenitis, chronic and extrapulmonary, and disseminated infections in adults, children, and immunocompromised patients. M. avium has ∼4500 predicted protein-coding regions on average, which can help discover several variants at the proteome level. Many of them are potentially associated with virulence; thus, identifying such proteins can be a helpful feature in developing panel-based theranostics. In line with such a long-term goal, we carried out an in-depth proteomic analysis of M. avium with both data-dependent and data-independent acquisition methods. Further, a set of proteogenomic investigations were carried out using (i) a protein database for Mycobacterium tuberculosis, (ii) an M. avium genome six-frame-translated database, and (iii) a variant protein database of M. avium. A search of mass spectrometry data against M. avium protein database resulted in identifying 2954 proteins. Further, proteogenomic analyses aided in identifying 1301 novel peptide sequences and correcting translation start sites for 15 proteins. Ultimately, we created a spectral library of M. avium proteins, including novel genome search-specific peptides and variant peptides detected in this study. We validated the spectral library by a data-independent acquisition of the M. avium proteome. Thus, we present an M. avium spectral library of 29,033 peptide precursors supported by 0.4 million fragment ions for further use by the biomedical community.

A computational workflow for predicting cancer neo-antigens.

Neo-antigens presented on cell surface play a pivotal role in the success of immunotherapies. Peptides derived from mutant proteins are thought to be the primary source of neo-antigens presented on the surface of cancer cells. Mutation data from cancer genome sequencing is often used to predict cancer neo-antigens. However, this strategy is associated with significant false positives as many coding mutations may not be expressed at the protein level. Hence, we describe a computational workflow to integrate genomic and proteomic data to predictpotential neo-antigens.

Dissecting Plasmodium yoelii Pathobiology: Proteomic Approaches for Decoding Novel Translational and Post-Translational Modifications.

Malaria is a vector-borne disease. It is caused by Plasmodium parasites. Plasmodium yoelii is a rodent model parasite, primarily used for studying parasite development in liver cells and vectors. To better understand parasite biology, we carried out a high-throughput-based proteomic analysis of P. yoelii. From the same mass spectrometry (MS)/MS data set, we also captured several post-translational modified peptides by following a bioinformatics analysis without any prior enrichment. Further, we carried out a proteogenomic analysis, which resulted in improvements to some of the existing gene models along with the identification of several novel genes. Analysis of proteome and post-translational modifications (PTMs) together resulted in the identification of 3124 proteins. The identified PTMs were found to be enriched in mitochondrial metabolic pathways. Subsequent bioinformatics analysis provided an insight into proteins associated with metabolic regulatory mechanisms. Among these, the tricarboxylic acid (TCA) cycle and the isoprenoid synthesis pathway are found to be essential for parasite survival and drug resistance. The proteogenomic analysis discovered 43 novel protein-coding genes. The availability of an in-depth proteomic landscape of a malaria pathogen model will likely facilitate further molecular-level investigations on pre-erythrocytic stages of malaria.

Towards Phytopathogen Diagnostics? Coconut Bud Rot Pathogen Phytophthora palmivora Mycelial Proteome Analysis Informs Genome Annotation.

Planetary agriculture stands to benefit immensely from phytopathogen diagnostics, which would enable early detection of pathogens with harmful effects on crops. For example, Phytophthora palmivora is one of the most destructive phytopathogens affecting many economically important tropical crops such as coconut. P. palmivora causes diseases in over 200 host plants, and notably, the bud rot disease in coconut and oil palm, which is often lethal because it is usually detected at advanced stages of infection. Limited availability of large-scale omics datasets for P. palmivora is an important barrier for progress toward phytopathogen diagnostics. We report here the mycelial proteome of P. palmivora using high-resolution mass spectrometry analysis. We identified 8073 proteins in the mycelium. Gene Ontology-based functional classification of detected proteins revealed 4884, 4981, and 3044 proteins, respectively, with roles in biological processes, molecular functions, and cellular components. Proteins such as P-loop, NTPase, and WD40 domains with key roles in signal transduction pathways were identified. KEGG pathway analysis annotated 2467 proteins to various signaling pathways, such as phosphatidylinositol, Ca2+, and mitogen-activated protein kinase, and autophagy and cell cycle. These molecular substrates might possess vital roles in filamentous growth, sporangia formation, degradation of damaged cellular content, and recycling of nutrients in P. palmivora. This large-scale proteomics data and analyses pave the way for new insights on biology, genome annotation, and vegetative growth of the important plant pathogen P. palmivora. They also can help accelerate research on future phytopathogen diagnostics and preventive interventions.

MS2Compound: A User-Friendly Compound Identification Tool for LC-MS/MS-Based Metabolomics Data.

Metabolomics is a leading frontier of systems science and biomedical innovation. However, metabolite identification in mass spectrometry (MS)-based global metabolomics investigations remains a formidable challenge. Moreover, lack of comprehensive spectral databases hinders accurate identification of compounds in global MS-based metabolomics. Creating experiment-derived metabolite spectral libraries tailored to each experiment is labor-intensive. Therefore, predicted spectral libraries could serve as a better alternative. User-friendly tools are much needed, as the currently available metabolomic analysis tools do not offer adequate provision for users to create or choose context-specific databases. Here, we introduce the MS2Compound, a metabolite identification tool, which can be used to generate a custom database of predicted spectra using the Competitive Fragmentation Modeling-ID (CFM-ID) algorithm, and identify metabolites or compounds from the generated database. The database generator can create databases of the model/context/species used in the metabolomics study. The MS2Compound is also powered with mS-score, a scoring function for matching raw fragment spectra to a predicted spectra database. We demonstrated that mS-score is robust in par with dot product and hypergeometric score in identifying metabolites using benchmarking datasets. We evaluated and highlight here the unique features of the MS2Compound by a re-analysis of a publicly available metabolomic dataset (MassIVE id: MSV000086784) for a complex traditional drug formulation called Triphala. In conclusion, we believe that the omics systems science and biomedical research and innovation community in the field of metabolomics will find the MS2Compound as a user-friendly analysis tool of choice to accelerate future metabolomic analyses.

CusVarDB: A tool for building customized sample-specific variant protein database from next-generation sequencing datasets.

Cancer genome sequencing studies have revealed a number of variants in coding regions of several genes. Some of these coding variants play an important role in activating specific pathways that drive proliferation. Coding variants present on cancer cell surfaces by the major histocompatibility complex serve as neo-antigens and result in immune activation. The success of immune therapy in patients is attributed to neo-antigen load on cancer cell surfaces. However, which coding variants are expressed at the protein level can't be predicted based on genomic data. Complementing genomic data with proteomic data can potentially reveal coding variants that are expressed at the protein level. However, identification of variant peptides using mass spectrometry data is still a challenging task due to the lack of an appropriate tool that integrates genomic and proteomic data analysis pipelines. To overcome this problem, and for the ease of the biologists, we have developed a graphical user interface (GUI)-based tool called CusVarDB. We integrated variant calling pipeline to generate sample-specific variant protein database from next-generation sequencing datasets. We validated the tool with triple negative breast cancer cell line datasets and identified 423, 408, 386 and 361 variant peptides from BT474, MDMAB157, MFM223 and HCC38 datasets, respectively.

Assembly and Annotation of the Nuclear and Organellar Genomes of a Dwarf Coconut (Chowghat Green Dwarf) Possessing Enhanced Disease Resistance.

Coconut (Cocos nucifera L.), an important source of vegetable oil, nutraceuticals, functional foods, and housing materials, provides raw materials for a repertoire of industries engaged in the manufacture of cosmetics, soaps, detergents, paints, varnishes, and emulsifiers, among other products. The palm plays a vital role in maintaining and promoting the sustainability of farming systems of the fragile ecosystems of islands and coastal regions of the tropics. In this study, we present the genome of a dwarf coconut variety "Chowghat Green Dwarf" (CGD) from India, possessing enhanced resistance to root (wilt) disease. Utilizing short reads from the Illumina HiSeq 4000 platform and long reads from the Pacific Biosciences RSII platform, we have assembled the draft genome assembly of 1.93 Gb. The genome is distributed over 26,855 scaffolds, with ∼81.56% of the assembled genome present in scaffolds of lengths longer than 50 kb. About 77.29% of the genome was composed of transposable elements and repeats. Gene prediction yielded 51,953 genes, which upon stringent filtering, based on Annotation Edit Distance, resulted in 13,707 genes, which coded for 11,181 proteins. Among these, we gathered transcript level evidence for a total of 6828 predicted genes based on the RNA-Seq data from different coconut tissues, since they presented assembled transcripts within the genome annotation coordinates. A total of 112 nucleotide-binding and leucine-rich repeat loci, belonging to six classes, were detected. We have also undertaken the assembly and annotation of the CGD chloroplast and mitochondrial genomes. The availability of the dwarf coconut genome shall prove invaluable for deducing the origin of dwarf coconut cultivars, dissection of genes controlling plant habit and fruit color, and accelerated breeding for improved agronomic traits.

Proteomics and Visual Health Research: Proteome of the Human Sclera Using High-Resolution Mass Spectrometry.

Eye disorders and resulting visual loss are major public health problems affecting millions of people worldwide. In this context, the sclera is an opaque, thick outer coat covering more than 80% of the eye, and essential in maintaining the shape of the eye and protecting the intraocular contents against infection and the external environment. Despite efforts undertaken to decipher the scleral proteome, the functional and structural picture of the sclera still remain elusive. Recently, proteomics has arisen as a powerful tool that enables identification of proteins playing a critical role in health and disease. Therefore, we carried out an in-depth proteomic analysis of the human scleral tissue using a high-resolution Orbitrap Fusion Tribrid mass spectrometer. We identified 4493 proteins using SequestHT and Mascot as search algorithms in Proteome Discoverer 2.1. Importantly, the proteins, including radixin, synaptopodin, paladin, netrin 1, and kelch-like family member 41, were identified for the first time in human sclera. Gene ontology analysis unveiled that the majority of proteins were localized to the cytoplasm and involved in cell communication and metabolism. In sum, this study offers the largest catalog of proteins identified in sclera with the aim of facilitating their contribution to diagnostics and therapeutics innovation in visual health and autoimmune disorders. This study also provides a valuable baseline for future investigations so as to map the dynamic changes that occur in sclera in various pathological conditions.

Dissecting Candida Pathobiology: Post-Translational Modifications on the Candida tropicalis Proteome.

Candida tropicalis belongs to the non-albicans group of Candida, and causes epidermal, mucosal, or systemic candidiasis in immunocompromised individuals. Although the prevalence of candidiasis has increased worldwide and non-albicans Candida (NAC) are becoming more significant, there are very few studies that focus on the NAC biology. Proteins and their post-translational modifications (PTMs) are an integral aspect in the pathobiology of such medically important fungi. Previously, we had reported the largest proteomic catalog of C. tropicalis. Notably, PTMs can be identified from proteomics data without a priori enrichment for a particular PTM, thus allowing broad-scale omics analyses. In this study, we developed the "PTM-Pro," a graphical user interface-based tool for identification and summary of high-confidence PTM sites based on statistical threshold of users' choice. We mined available proteomic data of C. tropicalis, and using PTM-Pro identified nearly 600 high-confidence PTM sites. The PTMs identified include phosphorylation of serine, threonine, and tyrosine; acetylation, crotonylation, methylation, and succinylation of lysine. These PTMs reside on biologically significant molecules, including histones, enzymes, and transcription factors. To our knowledge, this is the first report of PTMs in C. tropicalis and lays a foundation for future investigations of C. tropicalis PTMs. In addition, the PTM-Pro offers a graphical user interface tool for research on PTM sites in the field of proteomics.