Proteogenomic Analysis of Candida glabrata using High Resolution Mass Spectrometry

Discovery of Species-Specific Proteotypic Peptides To Establish a Spectral Library Platform for Identification of Nontuberculosis Mycobacteria from Mass Spectrometry-Based Proteomics

Nontuberculous mycobacteria are opportunistic bacteria pulmonary and extra-pulmonary infections in humans that closely resemble Mycobacterium tuberculosis. Although genome sequencing strategies helped determine NTMs, a common assay for the detection of coinfection by multiple NTMs with M. tuberculosis in the primary attempt of diagnosis is still elusive. Such a lack of efficiency leads to delayed therapy, an inappropriate choice of drugs, drug resistance, disease complications, morbidity, and mortality. Although a high-resolution LC-MS/MS-based multiprotein panel assay can be developed due to its specificity and sensitivity, it needs a library of species-specific peptides as a platform. Toward this, we performed an analysis of proteomes of 9 NTM species with more than 20 million peptide spectrum matches gathered from 26 proteome data sets. Our metaproteomic analyses determined 48,172 species-specific proteotypic peptides across 9 NTMs. Notably, M. smegmatis (26,008), M. abscessus (12,442), M. vaccae (6487), M. fortuitum (1623), M. avium subsp. paratuberculosis (844), M. avium subsp. hominissuis (580), and M. marinum (112) displayed >100 species-specific proteotypic peptides. Finally, these peptides and corresponding spectra have been compiled into a spectral library, FASTA, and JSON formats for future reference and validation in clinical cohorts by the biomedical community for further translation.

A proteomic approach to identifying spermatozoa proteins in Indonesian native Madura bulls

Proteins assist sperm mature, transit the female reproductive tract, and recognise sperm oocytes. Indigenous Indonesian bulls, Madura bulls, have not been studied for reproductive proteomics. As local Indonesian beef livestock, Madura cattle assist in achieving food security; hence, their number must be improved. Thus, the identification of molecular proteomics-based bull fertility biomarkers is needed. This study aimed to characterise the sperm fertility function of the superior Madura bull (Bos indicus × Bos Javanicus) spermatozoa proteome. Frozen semen from eight Madura superior bulls (Bos indicus × Bos javanicus) aged 4-8 years was obtained from the artificial insemination centre (AIC) in Singosari and Lembang. Madura superior bulls are those that have passed the bull breeding soundness evaluation. Frozen sperm were thawed and centrifuged at 3000 × g for 30 min. Proteins in sperm were characterised through proteomic analysis using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The resulting gene symbols for each protein were then subjected to bioinformatics tools, including UniProt, DAVID, and STRING databases. Regarding sperm fertility, the analysis revealed that 15 proteins were identified in the sperm of Madura bulls. Amongst the identified proteins, the superior Madura bull sperm contained several motilities, energy-related proteins, and chaperone proteins. A substantial portion of characterised proteins are linked to metabolic pathways and the tricarboxylic acid (TCA) cycle, contributing to sperm energy production. In conclusion, the first in-depth proteome identification of sperm related to sperm quality and bull fertility of a unique indigenous Madura breed of Indonesia was performed using the LC-MS/MS proteomic method. These findings may serve as a reference point for further studies related to the functions of bovine sperm and biomarkers of fertility and sperm quality.

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 ∼4,500 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) a 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 2,954 proteins. Further, proteogenomic analyses aided in identifying 1,301 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.

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.

Unraveling Toxoplasma gondii GT1 Strain Virulence and New Protein-Coding Genes with Proteogenomic Analyses

Toxoplasma gondii is one of the most widespread parasites of great relevance to planetary health. It infects approximately one-third of the world population. T. gondii establishes itself in warm-blooded animals and causes adverse health outcomes, particularly in immunocompromised patients. T. gondii is also widely used as a model organism to study other related apicomplexan parasites, which requires a deeper understanding of its molecular biology. Type I strains (GT1 and RH) of T. gondii are considered the most virulent forms. The whole-genome sequencing of T. gondii annotated 8460 predicted gene models in the parasite. To this end, the proteogenomics technology allows harnessing of mass spectrometry (MS)-derived proteomic data to unravel new protein-coding genes, not to mention validation and correction of the existing gene models. In this study using the proteogenomic approach, we report the identification of 31 novel protein-coding genes while reannotating 88 existing gene models. Notably, the genome annotations were corrected for genes, such as SAG5C, GRA6, ROP4, ROP5, and ROP26. The associated proteins are known to play important roles in host-parasite interactions, particularly in relation to parasite virulence, suppression of host immune response, and distinctively pertinent for the survival of the parasite inside the host system. These new findings offer new insights, informing planetary health broadly and the knowledge base on T. gondii virulence specifically. The proteogenomics approach also provides a concrete example to study related apicomplexan organisms of relevance to planetary health, and so as to develop new diagnostics and therapeutics against toxoplasmosis and related diseases.

Proteogenomics Study of Blastobotrys adeninivorans TMCC 70007-A Dominant Yeast in the Fermentation Process of Pu-erh Tea

Blastobotrys adeninivorans plays an essential role in pile-fermenting of Pu-erh tea. Its ability to assimilate various carbon and nitrogen sources makes it available for application in a wide range of industry sectors. The genome of B. adeninivorans TMCC 70007 isolated from pile-fermented Pu-erh tea was sequenced and assembled. Proteomics analysis indicated that 4900 proteins in TMCC 70007 were expressed under various culture conditions. Proteogenomics mapping revealed 48 previously unknown genes and corrected 118 gene models predicted by GeneMark-ES. Ortho-proteogenomics analysis identified 17 previously unidentified genes in B. adeninivorans LS3, the first strain with a sequenced genome among the genus Blastobotrys as well. More importantly, five species specific genes were identified from TMCC 70007, which could serve as a barcode for strain typing and were applicable for fermentation process protection of this industrial species. The datasets generated from tea aqueous extract culture not only increased the proteome coverage and accuracy but also contributed to the identification of proteins related to polyphenols and caffeine, which were considered to change greatly during the microbial fermentation of Pu-erh tea. This study provides a proteome perspective on TMCC 70007, which was considered to be an important strain in the production of Pu-erh tea. The systematic proteogenomics analysis not only made a better annotation on the genome of B. adeninivorans TMCC 70007 as previous proteogenomics study but also provided solution for fermentation process protection on valuable industrial species with species specific genes uniquely identified from proteogenomics study.

Triton X-114 Fractionated Subcellular Proteome of Leptospira interrogans Shows Selective Enrichment of Pathogenic and Outer Membrane Proteins in the Detergent Fraction

The Triton X-114-based solubilization and temperature-dependent phase separation of proteins is used for subcellular fractionation where, aqueous, detergent, and pellet fractions represents cytoplasmic, outer membrane (OM), and inner membrane proteins, respectively. Mass spectrometry-based proteomic analysis of Triton X-114 fractions of proteomic analysis of Leptospira interrogans identified 2957 unique proteins distributed across the fractions. The results are compared with bioinformatics predictions on their subcellular localization and pathogenic nature. Analysis of the distribution of proteins across the Triton X-114 fractions with the predicted characteristics is performed based on "number" of unique type of proteins, and "quantity" which represents the amount of unique protein. The highest number of predicted outer membrane proteins (OMPs) and pathogenic proteins are found in aqueous and pellet fractions, whereas detergent fraction representing the OM has the highest quantity of OMPs and pathogenic proteins though lower in number than the aqueous and pellet fractions. This leaves the possibility of an upsurge in pathogenic proteins and OMPs on the OM under pathogenic conditions suggesting their potential use to combat leptospirosis. Further, the Triton X-114 subcellular fractions are more correlated to enrichment of pathogenic proteins predicted by MP3 software than predicted localization.

Global Secretome Characterization of the Pathogenic Yeast Candida glabrata

Secretory proteins are key modulators of host–pathogen interaction. The human opportunistic fungal pathogen Candida glabrata lacks secreted proteolytic activity but possesses 11 glycosylphosphatidylinositol-anchored aspartyl proteases, also referred to as Yapsins (CgYps1–11), that are essential for its virulence. To delineate the role of CgYapsins in interaction with host cells, we have profiled, through liquid chromatography-tandem mass spectrometry (LC-MS/MS) approach, the total secretome of wild-type and Cgyps1-11Δ mutant. The wild-type secretome consisted of 119 proteins which were primarily involved in cell wall organization, carbohydrate metabolism, proteolysis, and translation processes. Of eight CgYapsins identified in the secretome, the release of two major CgYapsins, CgYps1 and CgYps7, to the medium was confirmed by Western analysis. Further, comparative analysis revealed 20 common proteins, probably signifying the core fungal secretome, among C. glabrata, Saccharomyces cerevisiae, and Candida albicans secretomes. Strikingly, the Cgyps1-11Δ secretome was 4.6-fold larger, and contained 65 differentially abundant proteins, as revealed by label-free quantitative profiling, with 49 and 16 being high- and low-abundant proteins, respectively, compared to the wild-type secretome. Importantly, the CgMsb2 mucin, a putative CgYapsins’ substrate, was six-fold underrepresented in the mutant secretome. Altogether, we demonstrate for the first time that CgYapsins are both bona fide constituents and key modulators of the C. glabrata secretome.

Global Proteome Profiling Reveals Drug-Resistant Traits in Elizabethkingia meningoseptica: An Opportunistic Nosocomial Pathogen

Elizabethkingia meningoseptica is Gram-negative, rod-shaped opportunistic bacterial pathogen increasingly reported in hospital-acquired outbreaks. This bacterium is well known to thrive in the hospital environment. One of the leading causes of meningitis in pediatric and immune-compromised patients, E. meningoseptica has been noted as a "pathogen of interest" in the context of nosocomial diseases associated with device-related infections in particular. This pathogen's multidrug-resistant phenotype and attendant lack of adequate molecular mechanistic data limit the current approaches for its effective management in hospitals and public health settings. This study provides the global proteome of E. meningoseptica. The reference strain E. meningoseptica ATCC 13253 was used for proteomic analysis using high-resolution Fourier transform mass spectrometry. The study provided translational evidence for 2506 proteins of E. meningoseptica. We identified multiple metallo-β-lactamases, transcriptional regulators, and efflux transporter proteins associated with multidrug resistance. A protein Car D, which is an enzyme of the carbapenem synthesis pathway, was also discovered in E. meningoseptica. Further, the proteomics data were harnessed for refining the genome annotation. We discovered 39 novel protein-coding genes and corrected four existing translations using proteogenomic workflow. Novel translations reported in this study enhance the molecular data on this organism, thus improving current databases. We believe that the in-depth proteomic data presented in this study offer a platform for accelerated research on this pathogen. The identification of multiple proteins, particularly those involved in drug resistance, offers new future opportunities to design novel and specific antibiotics against infections caused by E. meningoseptica.

Proteogenomics of Candida tropicalis--An Opportunistic Pathogen with Importance for Global Health

The frequency of Candida infections is currently rising, and thus adversely impacting global health. The situation is exacerbated by azole resistance developed by fungal pathogens. Candida tropicalis is an opportunistic pathogen that causes candidiasis, for example, in immune-compromised individuals, cancer patients, and those who undergo organ transplantation. It is a member of the non-albicans group of Candida that are known to be azole-resistant, and is frequently seen in individuals being treated for cancers, HIV-infection, and those who underwent bone marrow transplantation. Although the genome of C. tropicalis was sequenced in 2009, the genome annotation has not been supported by experimental validation. In the present study, we have carried out proteomics profiling of C. tropicalis using high-resolution Fourier transform mass spectrometry. We identified 2743 proteins, thus mapping nearly 44% of the computationally predicted protein-coding genes with peptide level evidence. In addition to identifying 2591 proteins in the cell lysate of this yeast, we also analyzed the proteome of the conditioned media of C. tropicalis culture and identified several unique secreted proteins among a total of 780 proteins. By subjecting the mass spectrometry data derived from cell lysate and conditioned media to proteogenomic analysis, we identified 86 novel genes, 12 novel exons, and corrected 49 computationally-predicted gene models. To our knowledge, this is the first high-throughput proteomics study of C. tropicalis validating predicted protein coding genes and refining the current genome annotation. The findings may prove useful in future global health efforts to fight against Candida infections.

Unassigned MS/MS Spectra: Who Am I?

Recent advances in high resolution tandem mass spectrometry (MS) has resulted in the accumulation of high quality data. Paralleled with these advances in instrumentation, bioinformatics software have been developed to analyze such quality datasets. In spite of these advances, data analysis in mass spectrometry still remains critical for protein identification. In addition, the complexity of the generated MS/MS spectra, unpredictable nature of peptide fragmentation, sequence annotation errors, and posttranslational modifications has impeded the protein identification process. In a typical MS data analysis, about 60 % of the MS/MS spectra remains unassigned. While some of these could attribute to the low quality of the MS/MS spectra, a proportion can be classified as high quality. Further analysis may reveal how much of the unassigned MS spectra attribute to search space, sequence annotation errors, mutations, and/or posttranslational modifications. In this chapter, the tools used to identify proteins and ways to assign unassigned tandem MS spectra are discussed.

Tissue-specific Proteogenomic Analysis of Plutella xylostella Larval Midgut Using a Multialgorithm Pipeline

The diamondback moth, Plutella xylostella (L.), is the major cosmopolitan pest of brassica and other cruciferous crops. Its larval midgut is a dynamic tissue that interfaces with a wide variety of toxicological and physiological processes. The draft sequence of the P. xylostella genome was recently released, but its annotation remains challenging because of the low sequence coverage of this branch of life and the poor description of exon/intron splicing rules for these insects. Peptide sequencing by computational assignment of tandem mass spectra to genome sequence information provides an experimental independent approach for confirming or refuting protein predictions, a concept that has been termed proteogenomics. In this study, we carried out an in-depth proteogenomic analysis to complement genome annotation of P. xylostella larval midgut based on shotgun HPLC-ESI-MS/MS data by means of a multialgorithm pipeline. A total of 876,341 tandem mass spectra were searched against the predicted P. xylostella protein sequences and a whole-genome six-frame translation database. Based on a data set comprising 2694 novel genome search specific peptides, we discovered 439 novel protein-coding genes and corrected 128 existing gene models. To get the most accurate data to seed further insect genome annotation, more than half of the novel protein-coding genes, i.e. 235 over 439, were further validated after RT-PCR amplification and sequencing of the corresponding transcripts. Furthermore, we validated 53 novel alternative splicings. Finally, a total of 6764 proteins were identified, resulting in one of the most comprehensive proteogenomic study of a nonmodel animal. As the first tissue-specific proteogenomics analysis of P. xylostella, this study provides the fundamental basis for high-throughput proteomics and functional genomics approaches aimed at deciphering the molecular mechanisms of resistance and controlling this pest.

Proteogenomics for understanding oncology: recent advances and future prospects

The concept of proteogenomics has emerged rapidly as a valuable approach to integrate mass spectrometry-derived proteomic data with genomic and transcriptomic data. It is used to harness the full potential of the former dataset in the discovery of potential biomarkers, therapeutic targets and novel proteins associated with various biological processes including diseases. Proteogenomic strategies have been successfully utilized to identify novel genes and redefine annotation of existing gene models in various genomes. In recent years, this approach has been extended to the field of cancer biology to unravel complexities in the tumor genomes and proteomes. Standard proteomics workflows employing translated cancer genomes and transcriptomes can potentially identify peptides from mutant proteins, splice variants and fusion proteins in the tumor proteome, which in addition to the currently available biomarker panels can serve as potential diagnostic and prognostic biomarkers, besides having therapeutic utility. This review focuses on the role of proteogenomics to understand cancer biology.

Proteomic Analysis of Pathogenic Fungi Reveals Highly Expressed Conserved Cell Wall Proteins

We are presenting a quantitative proteomics tally of the most commonly expressed conserved fungal proteins of the cytosol, the cell wall, and the secretome. It was our goal to identify fungi-typical proteins that do not share significant homology with human proteins. Such fungal proteins are of interest to the development of vaccines or drug targets. Protein samples were derived from 13 fungal species, cultured in rich or in minimal media; these included clinical isolates of Aspergillus, Candida, Mucor, Cryptococcus, and Coccidioides species. Proteomes were analyzed by quantitative MS^E (Mass Spectrometry-Elevated Collision Energy). Several thousand proteins were identified and quantified in total across all fractions and culture conditions. The 42 most abundant proteins identified in fungal cell walls or supernatants shared no to very little homology with human proteins. In contrast, all but five of the 50 most abundant cytosolic proteins had human homologs with sequence identity averaging 59%. Proteomic comparisons of the secreted or surface localized fungal proteins highlighted conserved homologs of the Aspergillus fumigatus proteins 1,3-β-glucanosyltransferases (Bgt1, Gel1-4), Crf1, Ecm33, EglC, and others. The fact that Crf1 and Gel1 were previously shown to be promising vaccine candidates, underlines the value of the proteomics data presented here.

Proteogenomic Methods to Improve Genome Annotation

Annotation of protein coding genes in sequenced genomes has been routinely carried out using gene prediction programs guided by available transcript data. The advent of mass spectrometry has enabled the identification of proteins in a high-throughput manner. In addition to searching proteins annotated in public databases, mass spectrometry data can also be searched against conceptually translated genome as well as transcriptome to identify novel protein coding regions. This proteogenomics approach has resulted in the identification of novel protein coding regions in both prokaryotic and eukaryotic genomes. These studies have also revealed that some of the annotated noncoding RNAs and pseudogenes code for proteins. This approach is likely to become a part of most genome annotation workflows in the future. Here we describe a general methodology and approach that can be used for proteogenomics.

A proteomic map of the unsequenced kala-azar vector Phlebotomus papatasi using cell line

The debilitating disease kala-azar or visceral leishmaniasis is caused by the kinetoplastid protozoan parasite Leishmania donovani. The parasite is transmitted by the hematophagous sand fly vector of the genus Phlebotomus in the old world and Lutzomyia in the new world. The predominant Phlebotomine species associated with the transmission of kala-azar are Phlebotomus papatasi and Phlebotomus argentipes. Understanding the molecular interaction of the sand fly and Leishmania, during the development of parasite within the sand fly gut is crucial to the understanding of the parasite life cycle. The complete genome sequences of sand flies (Phlebotomus and Lutzomyia) are currently not available and this hinders identification of proteins in the sand fly vector. The current study utilizes a three frame translated transcriptomic data of P. papatasi in the absence of genomic sequences to analyze the mass spectrometry data of P. papatasi cell line using a proteogenomic approach. Additionally, we have carried out the proteogenomic analysis of P. papatasi by comparative homology-based searches using related sequenced dipteran protein data. This study resulted in the identification of 1313 proteins from P. papatasi based on homology. Our study demonstrates the power of proteogenomic approaches in mapping the proteomes of unsequenced organisms.

Proteogenomic Analysis of Trichophyton rubrum Aided by RNA Sequencing

Infections caused by dermatophytes, Trichophyton rubrum in particular, are among the most common diseases in humans. In this study, we present a proteogenomic analysis of T. rubrum based on whole-genome proteomics and RNA-Seq studies. We confirmed 4291 expressed proteins in T. rubrum and validated their annotated gene structures based on 35 874 supporting peptides. In addition, we identified 323 novel peptides (not present in the current annotated protein database of T. rubrum) that can be used to enhance current T. rubrum annotations. A total of 104 predicted genes supported by novel peptides were identified, and 127 gene models suggested by the novel peptides that conflicted with existing annotations were manually assigned based on transcriptomic evidence. RNA-Seq confirmed the validity of 95% of the total peptides. Our study provides evidence that confirms and improves the genome annotation of T. rubrum and represents the first survey of T. rubrum genome annotations based on experimental evidence. Additionally, our integrated proteomics and multisourced transcriptomics approach provides stronger evidence for annotation refinement than proteomic data alone, which helps to address the dilemma of one-hit wonders (uncertainties supported by only one peptide).

Proteomics research in India: an update

After a successful completion of the Human Genome Project, deciphering the mystery surrounding the human proteome posed a major challenge. Despite not being largely involved in the Human Genome Project, the Indian scientific community contributed towards proteomic research along with the global community. Currently, more than 76 research/academic institutes and nearly 145 research labs are involved in core proteomic research across India. The Indian researchers have been major contributors in drafting the "human proteome map" along with international efforts. In addition to this, virtual proteomics labs, proteomics courses and remote triggered proteomics labs have helped to overcome the limitations of proteomics education posed due to expensive lab infrastructure. The establishment of Proteomics Society, India (PSI) has created a platform for the Indian proteomic researchers to share ideas, research collaborations and conduct annual conferences and workshops. Indian proteomic research is really moving forward with the global proteomics community in a quest to solve the mysteries of proteomics. A draft map of the human proteome enhances the enthusiasm among intellectuals to promote proteomic research in India to the world.This article is part of a Special Issue entitled: Proteomics in India.

Proteogenomics in microbiology: taking the right turn at the junction of genomics and proteomics

High-accuracy and high-throughput proteomic methods have completely changed the way we can identify and characterize proteins. MS-based proteomics can now provide a unique supplement to genomic data and add a new level of information to the interpretation of genomic sequences. Proteomics-driven genome annotation has become especially relevant in microbiology where genomes are sequenced on a daily basis and limitations of an in silico driven annotation process are well recognized. In this review paper, we outline different strategies on how one can design a proteogenomic experiment, for example on genome-sequenced (synonymous proteogenomics) versus unsequenced organisms (ortho-proteogenomics) or with the aid of other "omic" data such as RNA-seq. We touch upon many challenges that are encountered during a typical proteogenomic study, mostly concerning bioinformatics methods and downstream data analysis, but also related to creation and use of sequence databases. A large list of proteogenomic case studies of different microorganisms is provided to illustrate the mapping of MS/MS-derived peptide spectra to genomic DNA sequences. These investigations have led to accurate determination of translational initiation sites, pointed out eventual read-throughs or programmed frameshifts, detected signal peptide processing or other protein maturation events, removed questionable annotation assignments, and provided evidence for predicted hypothetical proteins.

Annotation of the zebrafish genome through an integrated transcriptomic and proteomic analysis

Accurate annotation of protein-coding genes is one of the primary tasks upon the completion of whole genome sequencing of any organism. In this study, we used an integrated transcriptomic and proteomic strategy to validate and improve the existing zebrafish genome annotation. We undertook high-resolution mass-spectrometry-based proteomic profiling of 10 adult organs, whole adult fish body, and two developmental stages of zebrafish (SAT line), in addition to transcriptomic profiling of six organs. More than 7,000 proteins were identified from proteomic analyses, and ∼ 69,000 high-confidence transcripts were assembled from the RNA sequencing data. Approximately 15% of the transcripts mapped to intergenic regions, the majority of which are likely long non-coding RNAs. These high-quality transcriptomic and proteomic data were used to manually reannotate the zebrafish genome. We report the identification of 157 novel protein-coding genes. In addition, our data led to modification of existing gene structures including novel exons, changes in exon coordinates, changes in frame of translation, translation in annotated UTRs, and joining of genes. Finally, we discovered four instances of genome assembly errors that were supported by both proteomic and transcriptomic data. Our study shows how an integrative analysis of the transcriptome and the proteome can extend our understanding of even well-annotated genomes.

Brain proteomics of Anopheles gambiae

Anopheles gambiae has a well-adapted system for host localization, feeding, and mating behavior, which are all governed by neuronal processes in the brain. However, there are no published reports characterizing the brain proteome to elucidate neuronal signaling mechanisms in the vector. To this end, a large-scale mapping of the brain proteome of An. gambiae was carried out using high resolution tandem mass spectrometry, revealing a repertoire of >1800 proteins, of which 15% could not be assigned any function. A large proportion of the identified proteins were predicted to be involved in diverse biological processes including metabolism, transport, protein synthesis, and olfaction. This study also led to the identification of 10 GPCR classes of proteins, which could govern sensory pathways in mosquitoes. Proteins involved in metabolic and neural processes, chromatin modeling, and synaptic vesicle transport associated with neuronal transmission were predominantly expressed in the brain. Proteogenomic analysis expanded our findings with the identification of 15 novel genes and 71 cases of gene refinements, a subset of which were validated by RT-PCR and sequencing. Overall, our study offers valuable insights into the brain physiology of the vector that could possibly open avenues for intervention strategies for malaria in the future.

Proteogenomic convergence for understanding cancer pathways and networks

During the past several decades, the understanding of cancer at the molecular level has been primarily focused on mechanisms on how signaling molecules transform homeostatically balanced cells into malignant ones within an individual pathway. However, it is becoming more apparent that pathways are dynamic and crosstalk at different control points of the signaling cascades, making the traditional linear signaling models inadequate to interpret complex biological systems. Recent technological advances in high throughput, deep sequencing for the human genomes and proteomic technologies to comprehensively characterize the human proteomes in conjunction with multiplexed targeted proteomic assays to measure panels of proteins involved in biologically relevant pathways have made significant progress in understanding cancer at the molecular level. It is undeniable that proteomic profiling of differentially expressed proteins under many perturbation conditions, or between normal and "diseased" states is important to capture a first glance at the overall proteomic landscape, which has been a main focus of proteomics research during the past 15-20 years. However, the research community is gradually shifting its heavy focus from that initial discovery step to protein target verification using multiplexed quantitative proteomic assays, capable of measuring changes in proteins and their interacting partners, isoforms, and post-translational modifications (PTMs) in response to stimuli in the context of signaling pathways and protein networks. With a critical link to genotypes (i.e., high throughput genomics and transcriptomics data), new and complementary information can be gleaned from multi-dimensional omics data to (1) assess the effect of genomic and transcriptomic aberrations on such complex molecular machinery in the context of cell signaling architectures associated with pathological diseases such as cancer (i.e., from genotype to proteotype to phenotype); and (2) target pathway- and network-driven changes and map the fluctuations of these functional units (proteins) responsible for cellular activities in response to perturbation in a spatiotemporal fashion to better understand cancer biology as a whole system.

Genome structure and dynamics of the yeast pathogen Candida glabrata

The yeast pathogen Candida glabrata is the second most frequent cause of Candida infections. However, from the phylogenetic point of view, C. glabrata is much closer to Saccharomyces cerevisiae than to Candida albicans. Apparently, this yeast has relatively recently changed its life style and become a successful opportunistic pathogen. Recently, several C. glabrata sister species, among them clinical and environmental isolates, have had their genomes characterized. Also, hundreds of C. glabrata clinical isolates have been characterized for their genomes. These isolates display enormous genomic plasticity. The number and size of chromosomes vary drastically, as well as intra- and interchromosomal segmental duplications occur frequently. The observed genome alterations could affect phenotypic properties and thus help to adapt to the highly variable and harsh habitats this yeast finds in different human patients and their tissues. Further genome sequencing of pathogenic isolates will provide a valuable tool to understand the mechanisms behind genome dynamics and help to elucidate the genes contributing to the virulence potential.

N-Terminal-oriented proteogenomics of the marine bacterium roseobacter denitrificans Och114 using N-Succinimidyloxycarbonylmethyl)tris(2,4,6-trimethoxyphenyl)phosphonium bromide (TMPP) labeling and diagonal chromatography

Given the ease of whole genome sequencing with next-generation sequencers, structural and functional gene annotation is now purely based on automated prediction. However, errors in gene structure are frequent, the correct determination of start codons being one of the main concerns. Here, we combine protein N termini derivatization using (N-Succinimidyloxycarbonylmethyl)tris(2,4,6-trimethoxyphenyl)phosphonium bromide (TMPP Ac-OSu) as a labeling reagent with the COmbined FRActional DIagonal Chromatography (COFRADIC) sorting method to enrich labeled N-terminal peptides for mass spectrometry detection. Protein digestion was performed in parallel with three proteases to obtain a reliable automatic validation of protein N termini. The analysis of these N-terminal enriched fractions by high-resolution tandem mass spectrometry allowed the annotation refinement of 534 proteins of the model marine bacterium Roseobacter denitrificans OCh114. This study is especially efficient regarding mass spectrometry analytical time. From the 534 validated N termini, 480 confirmed existing gene annotations, 41 highlighted erroneous start codon annotations, five revealed totally new mis-annotated genes; the mass spectrometry data also suggested the existence of multiple start sites for eight different genes, a result that challenges the current view of protein translation initiation. Finally, we identified several proteins for which classical genome homology-driven annotation was inconsistent, questioning the validity of automatic annotation pipelines and emphasizing the need for complementary proteomic data. All data have been deposited to the ProteomeXchange with identifier PXD000337.

Proteogenomic analysis of pathogenic yeast Cryptococcus neoformans using high resolution mass spectrometry

Background

Cryptococcus neoformans, a basidiomycetous fungus of universal occurrence, is a significant opportunistic human pathogen causing meningitis. Owing to an increase in the number of immunosuppressed individuals along with emergence of drug-resistant strains, C. neoformans is gaining importance as a pathogen. Although, whole genome sequencing of three varieties of C. neoformans has been completed recently, no global proteomic studies have yet been reported.

Results

We performed a comprehensive proteomic analysis of C. neoformans var. grubii (Serotype A), which is the most virulent variety, in order to provide protein-level evidence for computationally predicted gene models and to refine the existing annotations. We confirmed the protein-coding potential of 3,674 genes from a total of 6,980 predicted protein-coding genes. We also identified 4 novel genes and corrected 104 predicted gene models. In addition, our studies led to the correction of translational start site, splice junctions and reading frame used for translation in a number of proteins. Finally, we validated a subset of our novel findings by RT-PCR and sequencing.

Conclusions

Proteogenomic investigation described here facilitated the validation and refinement of computationally derived gene models in the intron-rich genome of C. neoformans, an important fungal pathogen in humans.

Moving from unsequenced to sequenced genome: reanalysis of the proteome of Leishmania donovani

The kinetoplastid protozoan parasite, Leishmania donovani, is the causative agent of kala azar or visceral leishmaniasis. Kala azar is a severe form of leishmaniasis that is fatal in the majority of untreated cases. Studies on proteomic analysis of L. donovani thus far have been carried out using homology-based identification based on related Leishmania species (L. infantum, L. major and L. braziliensis) whose genomes have been sequenced. Recently, the genome of L. donovani was fully sequenced and the data became publicly available. We took advantage of the availability of its genomic sequence to carry out a more accurate proteogenomic analysis of L. donovani proteome using our previously generated dataset. This resulted in identification of 17,504 unique peptides upon database-dependent search against the annotated proteins in L. donovani. These peptides were assigned to 3999 unique proteins in L. donovani. 2296 proteins were identified in both the life stages of L. donovani, while 613 and 1090 proteins were identified only from amastigote and promastigote stages, respectively. The proteomic data was also searched against six-frame translated L. donovani genome, which led to 255 genome search-specific peptides (GSSPs) resulting in identification of 20 novel genes and correction of 40 existing gene models in L. donovani.

BIOLOGICAL SIGNIFICANCE

Leishmania donovani genome sequencing was recently completed, which permitted us to use a proteogenomic approach to map its proteome and to carry out annotation of it genome. This resulted in mapping of 50% (3999 proteins) of L. donovani proteome. Our study identified 20 novel genes previously not predicted from the L. donovani genome in addition to correcting annotations of 40 existing gene models. The identified proteins may help in better understanding of stage-specific protein expression profiles in L. donovani and to identify novel stage-specific drug targets in L. donovani which could be used in the treatment of leishmaniasis. This article is part of a Special Issue entitled: Trends in Microbial Proteomics.

Evaluating the impact of different sequence databases on metaproteome analysis: insights from a lab-assembled microbial mixture

Metaproteomics enables the investigation of the protein repertoire expressed by complex microbial communities. However, to unleash its full potential, refinements in bioinformatic approaches for data analysis are still needed. In this context, sequence databases selection represents a major challenge. This work assessed the impact of different databases in metaproteomic investigations by using a mock microbial mixture including nine diverse bacterial and eukaryotic species, which was subjected to shotgun metaproteomic analysis. Then, both the microbial mixture and the single microorganisms were subjected to next generation sequencing to obtain experimental metagenomic- and genomic-derived databases, which were used along with public databases (namely, NCBI, UniProtKB/SwissProt and UniProtKB/TrEMBL, parsed at different taxonomic levels) to analyze the metaproteomic dataset. First, a quantitative comparison in terms of number and overlap of peptide identifications was carried out among all databases. As a result, only 35% of peptides were common to all database classes; moreover, genus/species-specific databases provided up to 17% more identifications compared to databases with generic taxonomy, while the metagenomic database enabled a slight increment in respect to public databases. Then, database behavior in terms of false discovery rate and peptide degeneracy was critically evaluated. Public databases with generic taxonomy exhibited a markedly different trend compared to the counterparts. Finally, the reliability of taxonomic attribution according to the lowest common ancestor approach (using MEGAN and Unipept software) was assessed. The level of misassignments varied among the different databases, and specific thresholds based on the number of taxon-specific peptides were established to minimize false positives. This study confirms that database selection has a significant impact in metaproteomics, and provides critical indications for improving depth and reliability of metaproteomic results. Specifically, the use of iterative searches and of suitable filters for taxonomic assignments is proposed with the aim of increasing coverage and trustworthiness of metaproteomic data.

Addressing statistical biases in nucleotide-derived protein databases for proteogenomic search strategies

Proteogenomics has the potential to advance genome annotation through high quality peptide identifications derived from mass spectrometry experiments, which demonstrate a given gene or isoform is expressed and translated at the protein level. This can advance our understanding of genome function, discovering novel genes and gene structure that have not yet been identified or validated. Because of the high-throughput shotgun nature of most proteomics experiments, it is essential to carefully control for false positives and prevent any potential misannotation. A number of statistical procedures to deal with this are in wide use in proteomics, calculating false discovery rate (FDR) and posterior error probability (PEP) values for groups and individual peptide spectrum matches (PSMs). These methods control for multiple testing and exploit decoy databases to estimate statistical significance. Here, we show that database choice has a major effect on these confidence estimates leading to significant differences in the number of PSMs reported. We note that standard target:decoy approaches using six-frame translations of nucleotide sequences, such as assembled transcriptome data, apparently underestimate the confidence assigned to the PSMs. The source of this error stems from the inflated and unusual nature of the six-frame database, where for every target sequence there exists five "incorrect" targets that are unlikely to code for protein. The attendant FDR and PEP estimates lead to fewer accepted PSMs at fixed thresholds, and we show that this effect is a product of the database and statistical modeling and not the search engine. A variety of approaches to limit database size and remove noncoding target sequences are examined and discussed in terms of the altered statistical estimates generated and PSMs reported. These results are of importance to groups carrying out proteogenomics, aiming to maximize the validation and discovery of gene structure in sequenced genomes, while still controlling for false positives.

Microbiology and proteomics, getting the best of both worlds!

High-throughput identification of proteins with the latest generation of hybrid high-resolution mass spectrometers is opening new perspectives in microbiology. I present, here, an overview of tandem mass spectrometry technology and bioinformatics for shotgun proteomics that make 2D-PAGE approaches obsolete. Non-labelling quantitative approaches have become more popular than labelling techniques on most proteomic platforms because they are easier to carry out while their quantitative outcome is rather robust. Parameters for recording mass spectrometry data, however, need to be chosen carefully and statistics to assess the confidence of the results should not be neglected. Interestingly, next-generation sequencing methodologies make any microbial model quickly amenable to proteomics, leading to the documentation of a wide range of organisms from diverse environments. Some recent discoveries made using microbial proteomics have challenged some biological dogma, such as: (i) initiation of the translation does not occur predominantly from ATG codons in some microorganisms, (ii) non-canonical initiation codons are used to regulate the production of specific but important proteins and (iii) a gene may code for multiple polypeptide species, heterogeneous in terms of sequences. Microbial diversity and microbial physiology can now be revisited by means of exhaustive comparative proteomic surveys where thousands of proteins are detected and quantified. Proteogenomics, consisting of better annotating of genomes with the help of proteomic evidence, is paving the way for integrated multi-omic approaches in microbiology. Finally, meta-proteomic tools and approaches are emerging for tackling the high complexity of the microbial world as a whole, opening new perspectives for assessing how microbial communities function.

Comparative omics-driven genome annotation refinement: application across Yersiniae

Genome sequencing continues to be a rapidly evolving technology, yet most downstream aspects of genome annotation pipelines remain relatively stable or are even being abandoned. The annotation process is now performed almost exclusively in an automated fashion to balance the large number of sequences generated. One possible way of reducing errors inherent to automated computational annotations is to apply data from omics measurements (i.e. transcriptional and proteomic) to the un-annotated genome with a proteogenomic-based approach. Here, the concept of annotation refinement has been extended to include a comparative assessment of genomes across closely related species. Transcriptomic and proteomic data derived from highly similar pathogenic Yersiniae (Y. pestis CO92, Y. pestis Pestoides F, and Y. pseudotuberculosis PB1/+) was used to demonstrate a comprehensive comparative omic-based annotation methodology. Peptide and oligo measurements experimentally validated the expression of nearly 40% of each strain's predicted proteome and revealed the identification of 28 novel and 68 incorrect (i.e., observed frameshifts, extended start sites, and translated pseudogenes) protein-coding sequences within the three current genome annotations. Gene loss is presumed to play a major role in Y. pestis acquiring its niche as a virulent pathogen, thus the discovery of many translated pseudogenes, including the insertion-ablated argD, underscores a need for functional analyses to investigate hypotheses related to divergence. Refinements included the discovery of a seemingly essential ribosomal protein, several virulence-associated factors, a transcriptional regulator, and many hypothetical proteins that were missed during annotation.