Integrating transcriptomic and proteomic data for accurate assembly and annotation of genomes

Integrative In-Silico Analysis of Retroperitoneal Tumors in Colorectal Surgery: Advancements and Implications

Retroperitoneal tumors pose significant challenges in colorectal surgery due to their complex anatomical location, aggressive behavior, and heterogeneous nature. Traditional diagnostic and treatment methods often fall short in effectively managing these tumors. This study leverages advanced in-silico methodologies to perform a comprehensive analysis of retroperitoneal tumors associated with colorectal conditions. By integrating computational modeling and cutting-edge bioinformatics tools, we aim to enhance the understanding of tumor biology, improve diagnostic precision, and optimize surgical outcomes. Our integrative approach combines transcriptomic, and proteomic data from publicly available databases such as The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Transcriptomic analysis reveals differentially expressed genes (DEGs) that serve as potential biomarkers for early diagnosis and prognosis. Proteomic analysis highlights critical protein interaction networks and pathways involved in tumorigenesis and metastasis. Our integrative approach identifies key DEGs and constructs protein-protein interaction (PPI) networks to pinpoint critical regulatory genes, such as VWF, PF4, ITGA2B, CXCL8, and GP9, that may serve as potential biomarkers or therapeutic targets. Functional enrichment analysis reveals significant pathways involved in tumorigenesis, including cell proliferation, immune response, and DNA repair. Additionally, immune cell infiltration analysis using the CIBERSORT algorithm demonstrates an immunosuppressive tumor microenvironment characterized by increased regulatory T cells (Tregs) and M2 macrophages, which could contribute to tumor immune evasion.Future studies should focus on clinical validation of these findings and the expansion of computational models to include diverse patient populations. Through these efforts, we aim to revolutionize the management of retroperitoneal tumors in colorectal surgery, ultimately improving patient care and survival rates.

Draft genome of the endangered visayan spotted deer (Rusa alfredi), a Philippine endemic species

The Visayan Spotted Deer (VSD), or Rusa alfredi, is an endangered and endemic species in the Philippines. Despite its status, genomic information on R. alfredi, and the genus Rusa in general, is missing. This study presents the first draft genome assembly of the VSD using the Illumina short-read sequencing technology. The resulting RusAlf_1.1 assembly has a 2.52 Gb total length, with a contig N50 of 46 Kb and scaffold N50 size of 75 Mb. The assembly has a BUSCO complete score of 95.5%, demonstrating the genome's completeness, and includes the annotation of 24,531 genes. Our phylogenetic analysis based on single-copy orthologs revealed a close evolutionary relationship between R. alfredi and the genus Cervus. RusAlf_1.1 represents a significant advancement in our understanding of the VSD. It opens opportunities for further research in population genetics and evolutionary biology, potentially contributing to more effective conservation and management strategies for this endangered species.

How to overcome constraints imposed by microsporidian genome features to ensure gene prediction?

Since the advent of sequencing techniques and due to their continuous evolution, it has become easier and less expensive to obtain the complete genome sequence of any organism. Nevertheless, to elucidate all biological processes governing organism development, quality annotation is essential. In genome annotation, predicting gene structure is one of the most important and captivating challenges for computational biology. This aspect of annotation requires continual optimization, particularly for genomes as unusual as those of microsporidia. Indeed, this group of fungal-related parasites exhibits specific features (highly reduced gene sizes, sequences with high rate of evolution) linked to their evolution as intracellular parasites, requiring the implementation of specific annotation approaches to consider all these features. This review aimed to outline these characteristics and to assess the increasingly efficient approaches and tools that have enhanced the accuracy of gene prediction for microsporidia, both in terms of sensitivity and specificity. Subsequently, a final part will be dedicated to postgenomic approaches aimed at reinforcing the annotation data generated by prediction software. These approaches include the characterization of other understudied genes, such as those encoding regulatory noncoding RNAs or very small proteins, which also play crucial roles in the life cycle of these microorganisms.

Proteogenomic Gene Structure Validation in the Pineapple Genome

MD2 pineapple (Ananas comosus) is the second most important tropical crop that preserves crassulacean acid metabolism (CAM), which has high water-use efficiency and is fast becoming the most consumed fresh fruit worldwide. Despite the significance of environmental efficiency and popularity, until very recently, its genome sequence has not been determined and a high-quality annotated proteome has not been available. Here, we have undertaken a pilot proteogenomic study, analyzing the proteome of MD2 pineapple leaves using liquid chromatography-mass spectrometry (LC-MS/MS), which validates 1781 predicted proteins in the annotated F153 (V3) genome. In addition, a further 603 peptide identifications are found that map exclusively to an independent MD2 transcriptome-derived database but are not found in the standard F153 (V3) annotated proteome. Peptide identifications derived from these MD2 transcripts are also cross-referenced to a more recent and complete MD2 genome annotation, resulting in 402 nonoverlapping peptides, which in turn support 30 high-quality gene candidates novel to both pineapple genomes. Many of the validated F153 (V3) genes are also supported by an independent proteomics data set collected for an ornamental pineapple variety. The contigs and peptides have been mapped to the current F153 genome build and are available as bed files to display a custom gene track on the Ensembl Plants region viewer. These analyses add to the knowledge of experimentally validated pineapple genes and demonstrate the utility of transcript-derived proteomics to discover both novel genes and genetic structure in a plant genome, adding value to its annotation.

Multi-tissue proteogenomic analysis for mechanistic toxicology studies in non-model species

New approach methodologies (NAM), including omics and in vitro approaches, are contributing to the implementation of 3R (reduction, refinement and replacement) strategies in regulatory science and risk assessment. In this study, we present an integrative transcriptomics and proteomics analysis workflow for the validation and revision of complex fish genomes and demonstrate how proteogenomics expression matrices can be used to support multi-level omics data integration in non-model species in vivo and in vitro. Using Atlantic salmon as an example, we constructed proteogenomic databases from publicly available transcriptomic data and in-house generated RNA-Seq and LC-MS/MS data. Our analysis identified ∼80,000 peptides, providing direct evidence of translation for over 40,000 RefSeq structures. The data also highlighted 183 co-located peptide groups that supported a single transcript each, and in each case, either corrected a previous annotation, supported Ensembl annotations not present in RefSeq, or identified novel previously unannotated genes. Proteogenomics data-derived expression matrices revealed distinct profiles for the different tissue types analyzed. Focusing on proteins involved in defense against xenobiotics, we detected distinct expression patterns across different salmon tissues and observed homology in the expression of chemical defense proteins between in vivo and in vitro liver systems. Our study demonstrates the potential of proteogenomic analyses in extending our understanding of complex fish genomes and provides an advanced bioinformatic toolkit to support the further development of NAMs and their application in regulatory science and (eco)toxicological studies of non-model species.

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.

Nematode gene annotation by machine-learning-assisted proteotranscriptomics enables proteome-wide evolutionary analysis

Nematodes encompass more than 24,000 described species, which were discovered in almost every ecological habitat, and make up >80% of metazoan taxonomic diversity in soils. The last common ancestor of nematodes is believed to date back to ∼650-750 million years, generating a large and phylogenetically diverse group to be explored. However, for most species high-quality gene annotations are incomprehensive or missing. Combining short-read RNA sequencing with mass spectrometry-based proteomics and machine-learning quality control in an approach called proteotranscriptomics, we improve gene annotations for nine genome-sequenced nematode species and provide new gene annotations for three additional species without genome assemblies. Emphasizing the sensitivity of our methodology, we provide evidence for two hitherto undescribed genes in the model organism Caenorhabditis elegans Extensive phylogenetic systems analysis using this comprehensive proteome annotation provides new insights into evolutionary processes of this metazoan group.

Comprehensive Proteomic Analysis of Brucella melitensis ATCC23457 Strain Reveals Metabolic Adaptations in Response to Nutrient Stress

In the present study, a comprehensive proteomic analysis of Brucella melitensis (B. melitensis) strain ATCC23457 was carried out to investigate proteome alterations in response to in vitro-induced nutrient stress. Our analysis resulted in the identification of 2440 proteins, including 365 hypothetical proteins and 850 potentially secretory proteins representing ~77.8% of the B. melitensis proteome. Utilizing a proteogenomics approach, we provide translational evidence for eight novel putative protein-coding genes and confirmed the coding potential of 31 putatively annotated pseudogenes, thus refining the existing genome annotation. Further, using a label-free quantitative proteomic approach, new insights into the cellular processes governed by nutrient stress, including enrichment of amino acid metabolism (E), transcription (K), energy production and conversion (C), and biogenesis (J) processes were obtained. Pathway analysis revealed the enrichment of survival and homeostasis maintenance pathways, including type IV secretion system, nitrogen metabolism, and urease pathways in response to nutrient limitation. To conclude, our analysis demonstrates the utility of in-depth proteomic analysis in enabling improved annotation of the B. melitensis genome. Further, our results indicate that B. melitensis undergoes metabolic adaptations during nutrient stress similar to other Brucella. sp, and adapts itself for long-term persistence and survival.

Microbes of traditional fermentation processes as synthetic biology chassis to tackle future food challenges

Microbial diversity is magnificent and essential to almost all life on Earth. Microbes are an essential part of every human, allowing us to utilize otherwise inaccessible resources. It is no surprise that humans started, initially unconsciously, domesticating microbes for food production: one may call this microbial domestication 1.0. Sourdough bread is just one of the miracles performed by microbial fermentation, allowing extraction of more nutrients from flour and at the same time creating a fluffy and delicious loaf. There are a broad range of products the production of which requires fermentation such as chocolate, cheese, coffee and vinegar. Eventually, with the rise of microscopy, humans became aware of microbial life. Today our knowledge and technological advances allow us to genetically engineer microbes - one may call this microbial domestication 2.0. Synthetic biology and microbial chassis adaptation allow us to tackle current and future food challenges. One of the most apparent challenges is the limited space on Earth available for agriculture and its major tolls on the environment through use of pesticides and the replacement of ecosystems with monocultures. Further challenges include transport and packaging, exacerbated by the 24/7 on-demand mentality of many customers. Synthetic biology already tackles multiple food challenges and will be able to tackle many future food challenges. In this perspective article, we highlight recent microbial synthetic biology research to address future food challenges. We further give a perspective on how synthetic biology tools may teach old microbes new tricks, and what standardized microbial domestication could look like.

Proteotranscriptomics - A facilitator in omics research

Applications in omics research, such as comparative transcriptomics and proteomics, require the knowledge of the species-specific gene sequence and benefit from a comprehensive high-quality annotation of the coding genes to achieve high coverage. While protein-coding genes can in simple cases be detected by scanning the genome for open reading frames, in more complex genomes exonic sequences are separated by introns. Despite advances in sequencing technologies that allow for ever-growing numbers of genomes, the quality of many of the provided genome assemblies do not reach reference quality. These non-contiguous assemblies with gaps and the necessity to predict splice sites limit accurate gene annotation from solely genomic data. In contrast, the transcriptome only contains transcribed gene regions, is devoid of introns and thus provides the optimal basis for the identification of open reading frames. The additional integration of proteomics data to validate predicted protein-coding genes further enriches for accurate gene models. This review outlines the principles of the proteotranscriptomics approach, discusses common challenges and suggests methods for improvement.

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.

Aβ42 Expressing Drosophila melanogaster Model for Alzheimer's Disease: Quantitative Proteomics Identifies Altered Protein Dynamics of Relevance to Neurodegeneration

Production and deposition of β-amyloid peptides (Aβ) are among the major hallmarks of the pathogenesis of Alzheimer's disease (AD). Mapping the altered protein dynamics associated with Aβ accumulation and neuronal damage may open up new avenues to innovation for drug target discovery in AD. Using quantitative proteomics, we report new findings from the amyloid beta-peptide with 42 amino acids (Aβ42) expressing Drosophila melanogaster model for AD compared to that of the wild-type flies. We identified 302,241 peptide-spectrum matches with 25,641 nonredundant peptides corresponding to 7959 D. melanogaster proteins. Furthermore, we unraveled 538 significantly altered proteins in Aβ42 expressing flies. These differentially expressed proteins were enriched for biological processes associated with neuronal damage leading to AD progression. We also identified 463 unique post-translational modification events mapping to 202 proteins from the same dataset. Among these, 303 modified peptides corresponding to 246 proteins were also altered in the AD model. These modified proteins are known to be involved in the disruption of molecular functions maintaining neuronal plasticity. This study provides new molecular leads on altered protein dynamics relevant to neurodegeneration, neuroplasticity, and AD progression induced by Aβ42 toxicity. These proteins may prove useful to discover new drugs in an AD model of D. melanogaster and evaluate their efficacy and mode of molecular action in the future.

The role of proteomics in the multiplexed analysis of gene alterations in human cancer

INTRODUCTION

Proteomics has played a pivotal role in identifying proteins perturbed in disease conditions when compared with healthy samples. Study of dysregulated proteins aids in identifying diagnostic markers and potential therapeutic targets. Cancer is an outcome of interplay of several such disarrayed proteins and molecular pathways which perturb cellular homeostasis, resulting in transformation. In this review, we discuss various facets of proteomic approaches, including tools and technological advancements, aiding in understanding differentially expressed molecules and signaling mechanisms.

AREAS COVERED

In this review, we have taken the approach of documenting the different methods of proteomic studies, ranging from labeling techniques, data analysis methods, and the nature of molecule detected. We summarize each technique and provide a glimpse of cancer research carried out using them, highlighting the advantages and drawbacks in comparison with others. Literature search using online resources, such as PubMed and Google Scholar were carried out for this approach.

EXPERT OPINION

Technological advancements in proteomics studies have come a long way from the study of two-dimensional mapping of proteins separated on gels in the early 1970s. Higher precision in molecular identification and quantification (high throughput), and greater number of samples analyzed have been the focus of researchers.

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.

Advances in Understanding Leishmania Pathobiology: What Does RNA-Seq Tell Us?

Leishmaniasis is a vector-borne disease caused by a protozoa parasite from over 20 Leishmania species. The clinical manifestations and the outcome of the disease vary greatly. Global RNA sequencing (RNA-Seq) analyses emerged as a powerful technique to profile the changes in the transcriptome that occur in the Leishmania parasites and their infected host cells as the parasites progresses through their life cycle. Following the bite of a sandfly vector, Leishmania are transmitted to a mammalian host where neutrophils and macrophages are key cells mediating the interactions with the parasites and result in either the elimination the infection or contributing to its proliferation. This review focuses on RNA-Seq based transcriptomics analyses and summarizes the main findings derived from this technology. In doing so, we will highlight caveats in our understanding of the parasite's pathobiology and suggest novel directions for research, including integrating more recent data highlighting the role of the bacterial members of the sandfly gut microbiota and the mammalian host skin microbiota in their potential role in influencing the quantitative and qualitative aspects of leishmaniasis pathology.

Novel Technologies in Studying Brain Immune Response

Over the past few decades, the immune system, including both the adaptive and innate immune systems, proved to be essential and critical to brain damage and recovery in the pathogenesis of several diseases, opening a new avenue for developing new immunomodulatory therapies and novel treatments for many neurological diseases. However, due to the specificity and structural complexity of the central nervous system (CNS), and the limit of the related technologies, the biology of the immune response in the brain is still poorly understood. Here, we discuss the application of novel technologies in studying the brain immune response, including single-cell RNA analysis, cytometry by time-of-flight, and whole-genome transcriptomic and proteomic analysis. We believe that advancements in technology related to immune research will provide an optimistic future for brain repair.

Proteomics dataset of adult Anopheles Stephensi female brain

Mosquitoes with their ability to transmit several pathogens of human disease pose a serious threat to healthcare worldwide. Although much has been done to prevent the disease transmission by mosqitos. The rising rate of resistance in mosquitos towards conventionally used control strategies necessitates developing of novel strategies to counter disease transmission. The mosquito brain plays a key role in host-seeking, finding mates and selection of oviposition sites. However, not much is know about the underlying physiological processes in mosquito brain. The data presented in this study describes the proteins that have been identified in the brain tissue of adult female Anopheles stephensi and their associated processes. Interpretation of the data can be related to the previously published article “Integrating transcriptomics and proteomics data for accurate assembly and annotation of genomes” [1].

Recent advances and future perspectives in vector-omics

We have reviewed recent progress and the remaining challenges in vector-omics. We have highlighted several technologies and applications that facilitate novel biological insights beyond achieving a reference-quality genome assembly. Among other topics, we have discussed the applications of chromatin conformation capture, chromatin accessibility assays, optical mapping, full-length RNA sequencing, single cell RNA analysis, proteomics, and population genomics. We anticipate that we will witness a great expansion in vector-omics research not only in its application in a broad range of species, but also its ability to uncover novel genetic elements and tackle previously inaccessible regions of the genome. It is our hope that the continued innovation in device portability, cost reduction, and informatics support will in the foreseeable future bring vector-omics to every vector laboratory and field station in the world, which will have an unparalleled impact on basic research and the control of vector-borne infectious diseases.

Potential mechanisms of action of celastrol against rheumatoid arthritis: Transcriptomic and proteomic analysis

The clinical efficacy for treating of celastrol rheumatoid arthritis (RA) has been well-documented, but its mechanism of action remains unclear. Here we explored through what proteins and processes celastrol may act in activated fibroblast-like synoviocytes (FLS) from RA patients. Differential expression of genes and proteins after celastrol treatment of FLS was examined using RNA sequencing, label-free relatively quantitative proteomics and molecular docking. In this paper, expression of 26,565 genes and 3,372 proteins was analyzed. Celastrol was associated with significant changes in genes that respond to oxidative stress and oxygen levels, as well as genes that stabilize or synthesize components of the extracellular matrix. These results identify several potential mechanisms through which celastrol may inhibit inflammation in RA.

Deciphering carbohydrate metabolism during wheat grain development via integrated transcriptome and proteome dynamics

Grain development of Triticum aestivum is being studied extensively using individual OMICS tools. However, integrated transcriptome and proteome studies are limited mainly due to complexity of genome. Current study focused to unravel the transcriptome-proteome coordination of key mechanisms underlying carbohydrate metabolism during whole wheat grain development. Wheat grains were manually dissected to obtain grain tissues for proteomics and transcriptomics analyses. Differentially expressed proteins and transcripts at the 11 stages of grain development were compared. Computational workflow for integration of two datasets related to carbohydrate metabolism was designed. For CM proteins, output peptide sequences of proteomic analyses (via LC-MS/MS) were used as source to search corresponding transcripts. The transcript that turned out with higher number of peptides was selected as bona fide ribonucleotide sequence for respective protein synthesis. More than 90% of hits resulted in successful identification of respective transcripts. Comparative analysis of protein and transcript expression profiles resulted in overall 32% concordance between these two series of data. However, during grain development correlation of two datasets gradually increased up to ~ tenfold from 152 to 655 °Cd and then dropped down. Proteins involved in carbohydrate metabolism were divided in five categories in accordance with their functions. Enzymes involved in starch and sucrose biosynthesis showed the highest correlations between proteome-transcriptome profiles. High percentage of identification and validation of protein-transcript hits highlighted the power of omics data integration approach over existing gene functional annotation tools. We found that correlation of two datasets is highly influenced by stage of grain development. Further, gene regulatory networks would be helpful in unraveling the mechanisms underlying the complex and significant traits such as grain weight and yield.

Unexpected predicted length variation for the coding sequence of the sleep related gene, BHLHE41 in gorilla amidst strong purifying selection across mammals

There is a molecular basis for many sleep patterns and disorders involving circadian clock genes. In humans, "short-sleeper" behavior has been linked to specific amino acid substitutions in BHLHE41 (DEC2), yet little is known about variation at these sites and across this gene in mammals. We compare BHLHE41 coding sequences for 27 mammals. Approximately half of the coding sequence was invariable at the nucleotide level and close to three-quarters of the amino acid alignment was identical. No other mammals had the same "short-sleeper" amino acid substitutions previously described from humans. Phylogenetic analyses based on the nucleotides of the coding sequence alignment are consistent with established mammalian relationships confirming orthology among the sampled sequences. Significant purifying selection was detected in about two-thirds of the variable codons and no codons exhibited significant signs of positive selection. Unexpectedly, the gorilla BHLHE41 sequence has a 318 bp insertion at the 5' end of the coding sequence and a deletion of 195 bp near the 3' end of the coding sequence (including the two short sleeper variable sites). Given the strong signal of purifying selection across this gene, phylogenetic congruence with expected relationships and generally conserved function among mammals investigated thus far, we suggest the indels predicted in the gorilla BHLHE41 may represent an annotation error and warrant experimental validation.

The Cannabis Proteome Draft Map Project

Recently we have seen a relaxation of the historic restrictions on the use and subsequent research on the Cannabis plants, generally classified as Cannabis sativa and Cannabis indica. What research has been performed to date has centered on chemical analysis of plant flower products, namely cannabinoids and various terpenes that directly contribute to phenotypic characteristics of the female flowers. In addition, we have seen many groups recently completing genetic profiles of various plants of commercial value. To date, no comprehensive attempt has been made to profile the proteomes of these plants. We report herein our progress on constructing a comprehensive draft map of the Cannabis proteome. To date we have identified over 17,000 potential protein sequences. Unfortunately, no annotated genome of Cannabis plants currently exists. We present a method by which “next generation” DNA sequencing output and shotgun proteomics data can be combined to produce annotated FASTA files, bypassing the need for annotated genetic information altogether in traditional proteomics workflows. The resulting material represents the first comprehensive annotated protein FASTA for any Cannabis plant. Using this annotated database as reference we can refine our protein identifications, resulting in the confident identification of 13,000 proteins with putative function. Furthermore, we demonstrate that post-translational modifications play an important role in the proteomes of Cannabis flower, particularly lysine acetylation and protein glycosylation. To facilitate the evolution of analytical investigations into these plant materials, we have created a portal to host resources developed from our proteomic and metabolomic analysis of Cannabis plant material as well as our results integrating these resources.

Combination of Proteogenomics with Peptide De Novo Sequencing Identifies New Genes and Hidden Posttranscriptional Modifications

Next-generation sequencing techniques have considerably increased the number of completely sequenced eukaryotic genomes. These genomes are mostly automatically annotated, and ab initio gene prediction is commonly combined with homology-based search approaches and often supported by transcriptomic data. The latter in particular improve the prediction of intron splice sites and untranslated regions. However, correct prediction of translation initiation sites (TIS), alternative splice junctions, and protein-coding potential remains challenging. Here, we present an advanced proteogenomics approach, namely, the combination of proteogenomics and de novo peptide sequencing analysis, in conjunction with Blast2GO and phylostratigraphy. Using the model fungus Sordaria macrospora as an example, we provide a comprehensive view of the proteome that not only increases the functional understanding of this multicellular organism at different developmental stages but also immensely enhances the genome annotation quality.

Proteogenomics combines proteomics, genomics, and transcriptomics and has considerably improved genome annotation in poorly investigated phylogenetic groups for which homology information is lacking. Furthermore, it can be advantageous when reinvestigating well-annotated genomes. Here, we applied an advanced proteogenomics approach, combining standard proteogenomics with peptide de novo sequencing, to refine annotation of the well-studied model fungus Sordaria macrospora. We investigated samples from different developmental and physiological conditions, resulting in the detection of 104 so-far hidden proteins and annotation changes in 575 genes, including 389 splice site refinements. Significantly, our approach provides peptide-level evidence for 113 single-amino-acid variations and 15 C-terminal protein elongations originating from A-to-I RNA editing, a phenomenon recently detected in fungi. Coexpression and phylostratigraphic analysis of the refined proteome suggest that new functions in evolutionarily young genes correlate with distinct developmental stages. In conclusion, our advanced proteogenomics approach supports and promotes functional studies of fungal model systems.

Bioinformatics for Marine Products: An Overview of Resources, Bottlenecks, and Perspectives

The sea represents a major source of biodiversity. It exhibits many different ecosystems in a huge variety of environmental conditions where marine organisms have evolved with extensive diversification of structures and functions, making the marine environment a treasure trove of molecules with potential for biotechnological applications and innovation in many different areas. Rapid progress of the omics sciences has revealed novel opportunities to advance the knowledge of biological systems, paving the way for an unprecedented revolution in the field and expanding marine research from model organisms to an increasing number of marine species. Multi-level approaches based on molecular investigations at genomic, metagenomic, transcriptomic, metatranscriptomic, proteomic, and metabolomic levels are essential to discover marine resources and further explore key molecular processes involved in their production and action. As a consequence, omics approaches, accompanied by the associated bioinformatic resources and computational tools for molecular analyses and modeling, are boosting the rapid advancement of biotechnologies. In this review, we provide an overview of the most relevant bioinformatic resources and major approaches, highlighting perspectives and bottlenecks for an appropriate exploitation of these opportunities for biotechnology applications from marine resources.

Integrated Transcriptomic and Proteomic Analysis of Primary Human Umbilical Vein Endothelial Cells

Understanding the molecular profile of every human cell type is essential for understanding its role in normal physiology and disease. Technological advancements in DNA sequencing, mass spectrometry, and computational methods allow us to carry out multiomics analyses although such approaches are not routine yet. Human umbilical vein endothelial cells (HUVECs) are a widely used model system to study pathological and physiological processes associated with the cardiovascular system. In this study, next-generation sequencing and high-resolution mass spectrometry to profile the transcriptome and proteome of primary HUVECs is employed. Analysis of 145 million paired-end reads from next-generation sequencing confirmed expression of 12 186 protein-coding genes (FPKM ≥0.1), 439 novel long non-coding RNAs, and revealed 6089 novel isoforms that were not annotated in GENCODE. Proteomics analysis identifies 6477 proteins including confirmation of N-termini for 1091 proteins, isoforms for 149 proteins, and 1034 phosphosites. A database search to specifically identify other post-translational modifications provide evidence for a number of modification sites on 117 proteins which include ubiquitylation, lysine acetylation, and mono-, di- and tri-methylation events. Evidence for 11 "missing proteins," which are proteins for which there was insufficient or no protein level evidence, is provided. Peptides supporting missing protein and novel events are validated by comparison of MS/MS fragmentation patterns with synthetic peptides. Finally, 245 variant peptides derived from 207 expressed proteins in addition to alternate translational start sites for seven proteins and evidence for novel proteoforms for five proteins resulting from alternative splicing are identified. Overall, it is believed that the integrated approach employed in this study is widely applicable to study any primary cell type for deeper molecular characterization.

Genome annotation improvements from cross-phyla proteogenomics and time-of-day differences in malaria mosquito proteins using untargeted quantitative proteomics

The malaria mosquito, Anopheles stephensi, and other mosquitoes modulate their biology to match the time-of-day. In the present work, we used a non-hypothesis driven approach (untargeted proteomics) to identify proteins in mosquito tissue, and then quantified the relative abundance of the identified proteins from An. stephensi bodies. Using these quantified protein levels, we then analyzed the data for proteins that were only detectable at certain times-of-the day, highlighting the need to consider time-of-day in experimental design. Further, we extended our time-of-day analysis to look for proteins which cycle in a rhythmic 24-hour ("circadian") manner, identifying 31 rhythmic proteins. Finally, to maximize the utility of our data, we performed a proteogenomic analysis to improve the genome annotation of An. stephensi. We compare peptides that were detected using mass spectrometry but are 'missing' from the An. stephensi predicted proteome, to reference proteomes from 38 other primarily human disease vector species. We found 239 such peptide matches and reveal that genome annotation can be improved using proteogenomic analysis from taxonomically diverse reference proteomes. Examination of 'missing' peptides revealed reading frame errors, errors in gene-calling, overlapping gene models, and suspected gaps in the genome assembly.

Large Scale Profiling of Protein Isoforms Using Label-Free Quantitative Proteomics Revealed the Regulation of Nonsense-Mediated Decay in Moso Bamboo (Phyllostachys edulis)

Moso bamboo is an important forest species with a variety of ecological, economic, and cultural values. However, the gene annotation information of moso bamboo is only based on the transcriptome sequencing, lacking the evidence of proteome. The lignification and fiber in moso bamboo leads to a difficulty in the extraction of protein using conventional methods, which seriously hinders research on the proteomics of moso bamboo. The purpose of this study is to establish efficient methods for extracting the total proteins from moso bamboo for following mass spectrometry-based quantitative proteome identification. Here, we have successfully established a set of efficient methods for extracting total proteins of moso bamboo followed by mass spectrometry-based label-free quantitative proteome identification, which further improved the protein annotation of moso bamboo genes. In this study, 10,376 predicted coding genes were confirmed by quantitative proteomics, accounting for 35.8% of all annotated protein-coding genes. Proteome analysis also revealed the protein-coding potential of 1015 predicted long noncoding RNA (lncRNA), accounting for 51.03% of annotated lncRNAs. Thus, mass spectrometry-based proteomics provides a reliable method for gene annotation. Especially, quantitative proteomics revealed the translation patterns of proteins in moso bamboo. In addition, the 3284 transcript isoforms from 2663 genes identified by Pacific BioSciences (PacBio) single-molecule real-time long-read isoform sequencing (Iso-Seq) was confirmed on the protein level by mass spectrometry. Furthermore, domain analysis of mass spectrometry-identified proteins encoded in the same genomic locus revealed variations in domain composition pointing towards a functional diversification of protein isoform. Finally, we found that part transcripts targeted by nonsense-mediated mRNA decay (NMD) could also be translated into proteins. In summary, proteomic analysis in this study improves the proteomics-assisted genome annotation of moso bamboo and is valuable to the large-scale research of functional genomics in moso bamboo. In summary, this study provided a theoretical basis and technical support for directional gene function analysis at the proteomics level in moso bamboo.

PASS: A Proteomics Alternative Splicing Screening Pipeline

Alternative splicing (AS) has been well-investigated at the trancriptome level by the application of RNA-seq technology. There is an ongoing debate on the biological importance of AS to proteome complexity. A toolkit for accurately identifying AS from proteome data is urgently needed. Here, a software called PASS is developed to comprehensively detect AS events for the proteomics mass spectrometry (MS) data. Moreover, PASS is well compatible with MS identification by the proteogenomics approach, which provides novel AS candidates for proteome identification. The workflow of PASS mainly contains five core steps: transcripts reconstruction from RNA-Seq data, novel protein sequence generation, MS data searching, proSAM file formatting, and AS detection. Access to the program from either step is supported. PASS is successfully applied to proteome data of mouse hepatocytes and 407 AS events are first identified with proteomics MS evidences. PASS is expected to be widely used to identify AS events on proteome data and provide a deeper understanding of the proteome isoforms. The PASS software is freely available at https://github.com/wupengomics/PASS.

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.

Proteome data of female Anopheles stephensi antennae

Antennae of female Anopheles stephensi mosquitoes were dissected and lysed with 1% SDS. Proteins were extracted using ultra sonication and analyzed on high resolution mass spectrometer. Proteomic data was analyzed using two search algorithms SEQUEST and Mascot, resulting in the identification of 22,729 peptides corresponding to 3262 proteins. These proteins were characterized using different bioinformatics tools. VectorBase resource was used to assign Gene Ontology (GO) terms. Using Biomart tool ortholog information was fetched from the VectorBase database. Raw mass spectrometric data was deposited in ProteomeXchange Consortium via PRIDE partner repository in the public dataset PXD001128. Proteins involved in insecticide resistance and odorant binding were the most abundant in the antennae. The proteins identified in this study could be targeted for developing novel vector control strategy.

Proteome data of Anopheles stephensi salivary glands using high-resolution mass spectrometry analysis

The data article reports data of the proteins expressed in female Anopheles stephensi salivary glands. Proteomic data were acquired using high-resolution mass spectrometers - Orbitrap-Velos and Orbitrap-Elite. Samples derived from adult female A. stephensi salivary glands led to the identification of 4390 proteins. Mass spectrometry data were analyzed on Proteome Discoverer (Version 2.1) platform with Sequest and Mascot search engines. The identified proteins were analyzed for their Gene Ontology annotation, interaction network and their possible roles in vector-parasite interaction. The data provided here are related to our published article "Integrating transcriptomics and proteomics data for accurate assembly and annotation of genomes" (Prasad et al., 2017) [1].

Dataset on fat body proteome of Anopheles stephensi Liston

Fat body from Anopheles stephensi female mosquitoes were dissected and processed for proteomic analysis. Both SDS-PAGE and basic Reverse Phase Liquid Chromatography-based fractionation strategies were used to achieve a broad coverage of protein identification. The fractionated peptides were then analyzed on a high-resolution mass spectrometer. Searching the raw data against the protein database of An. stephensi resulted in identification of 4535 proteins, which is, to our knowledge, the largest catalog of fat body proteome in any mosquito vector species reported so far. Bioinformatics analysis on these fat body proteins suggested the enrichment of biological processes including carbon and lipid metabolism, amino acid metabolism, signal peptide processing and oxidation-reduction. In addition, using proteogenomic approaches, 43 novel proteins were identified, which were not listed in the annotated gene annotations of An. stephensi. The data used in the analysis are related to the article ‘Integrating transcriptomic and proteomic data for accurate assembly and annotation of genomes’ (Prasad et al., 2017).

Proteome analysis of Phytomonas serpens, a phytoparasite of medical interest

The protozoan Phytomonas serpens (class Kinetoplastea) is an important phytoparasite that has gained medical importance due to its similarities to Trypanosoma cruzi, the etiological agent of Chagas disease. The present work describes the first proteome analysis of P. serpens. The parasite was separated into cytosolic and high density organelle fractions, which, together with total cell extract, were subjected to LC-MS/MS analyses. Protein identification was conducted using a comprehensive database composed of genome sequences of other related kinetoplastids. A total of 1,540 protein groups were identified among the three sample fractions. Sequences from Phytomonas sp. in the database allowed the highest number of identifications, with T. cruzi and T. brucei the human pathogens providing the greatest contribution to the identifications. Based on the proteomics data obtained, we proposed a central metabolic map of P. serpens, which includes all enzymes of the citric acid cycle. Data also revealed a new range of proteins possibly responsible for immunological cross-reactivity between P. serpens and T. cruzi.

Quantitative proteome of midgut, Malpighian tubules, ovaries and fat body from sugar-fed adult An. stephensi mosquitoes

The data presented in this article is associated with the quantitative proteomic analysis of four mosquito tissues – midgut, Malpighian tubules, ovaries and fat body from female Anopheles stephensi mosquitoes. To identify the proteins that were expressed in a tissue-specific manner, the four mosquito tissues were labelled with iTRAQ labels and analyzed using a high-resolution mass spectrometer. Database searches of the 1,10,616 raw spectra from 23 peptide fractions resulted in the identification of 84,733 peptide spectrum matches corresponding to 16,278 peptides and 3372 proteins. Of these, 959 proteins were found to be differentially expressed across the tissues. Gene ontology-based bioinformatic analysis of the differentially expressed proteins are also provided in the article. The data in this article has been deposited in the (ProteomeXchange) Consortium via the PRIDE repository and can be accessed through the accession ID, PXD001128.

Proteome data of Anopheles stephensi ovary using high-resolution mass spectrometry

This article contains data on the proteins expressed in the ovaries of Anopheles stephensi, a major vector of malaria in India. Data acquisition was performed using a high-resolution Orbitrap-Velos mass spectrometer. The acquired MS/MS data was searched against An. stephensi protein database comprising of 11,789 sequences. Overall, 4407 proteins were identified, functional analysis was performed for the identified proteins and a protein-protein interaction map predicted. The data provided here is also related to a published article - “Integrating transcriptomics and proteomics data for accurate assembly and annotation of genomes” (Prasad et al., 2017) [1].

Integration of large-scale multi-omic datasets: a protein-centric view

Innovative mass spectrometry-based proteomics has enabled routine measurements of protein abundance, localization, interactions, and modifications, covering unique aspects of gene expression regulation and function. It is now time to move from isolated analyses of these datasets toward true integration of proteomics with other data types to gain insights from the interactions and interdependencies of biomolecules. When combined with genomic or transcriptomic data, proteomics expands genome annotation to identify variant or missing genes. Dynamic proteomic measurements can move analysis from predominantly concentration-based framework to that of synthesis and degradation of proteins. Proteomic data from thousands of cancer patients can foster identification of novel pathogenic mutations via detection of protein sequence changes that lead to dysregulated pathways in various tumors. Such comprehensive efforts can exploit the synergy arising from large and complex datasets to advance virtually every field of biology.

Integrated Multi-Omic Analysis of Mycobacterium tuberculosis H37Ra Redefines Virulence Attributes

H37Ra is a virulence attenuated strain of Mycobacterium tuberculosis widely employed as a model to investigate virulence mechanisms. Comparative high-throughput studies have earlier correlated its avirulence to the presence of specific mutations or absence of certain proteins. However, a recent sequencing study of H37Ra, has disproved several genomic differences earlier reported to be associated with virulence. This warrants further investigations on the H37Ra proteome as well. In this study, we carried out an integrated analysis of the genome, transcriptome, and proteome of H37Ra. In addition to confirming single nucleotide variations (SNVs) and insertion-deletions that were reported earlier, our study provides novel insights into the mutation spectrum in the promoter regions of 7 genes. We also provide transcriptional and proteomic evidence for 3,900 genes representing ~80% of the total predicted gene count including 408 proteins that have not been identified previously. We identified 9 genes whose coding potential was hitherto reported to be absent in H37Ra. These include 2 putative virulence factors belonging to ESAT-6 like family of proteins. Furthermore, proteogenomic analysis enabled us to identify 63 novel proteins coding genes and correct 25 existing gene models in H37Ra genome. A majority of these were found to be conserved in the virulent strain H37Rv as well as in other mycobacterial species suggesting that the differences in the virulent and avirulent strains of M. tuberculosis are not entirely dependent on the expression of certain proteins or their absence but may possibly be ascertained to functional changes.

Mapping Anopheles stephensi midgut proteome using high-resolution mass spectrometry

Anopheles stephensi Liston is one of the major vectors of malaria in urban areas of India. Midgut plays a central role in the vector life cycle and transmission of malaria. Because gene expression of An. stephensi midgut has not been investigated at protein level, an unbiased mass spectrometry-based proteomic analysis of midgut tissue was carried out. Midgut tissue proteins from female An. stephensi mosquitoes were extracted using 0.5% SDS and digested with trypsin using two complementary approaches, in-gel and in-solution digestion. Fractions were analysed on high-resolution mass spectrometer, which resulted in acquisition of 494,960 MS/MS spectra. The MS/MS spectra were searched against protein database comprising of known and predicted proteins reported in An. stephensi using Sequest and Mascot software. In all, 47,438 peptides were identified corresponding to 5,709 An. stephensi proteins. The identified proteins were functionally categorized based on their cellular localization, biological processes and molecular functions using Gene Ontology (GO) annotation. Several proteins identified in this data are known to mediate the interaction of the Plasmodium with vector midgut and also regulate parasite maturation inside the vector host. This study provides information about the protein composition in midgut tissue of female An. stephensi, which would be useful in understanding vector parasite interaction at molecular level and besides being useful in devising malaria transmission blocking strategies. The data of this study is related to the research article “Integrating transcriptomics and proteomics data for accurate assembly and annotation of genomes”.

Proteome data of Anopheles stephensi hemolymph using high resolution mass spectrometry

The article provides insights into the protein expression in Anopheles stephensi hemolymph. We carried out data acquisition using a high-resolution LTQ-Orbitrap Velos mass spectrometer to identify the hemolymph proteins of An. stephensi. Experimentally derived mass spectrometry data was analyzed using Proteome Discoverer 2.1 software using two different search algorithms SEQUEST and MASCOT. A total of 1091 proteins were identified from the hemolymph. The identified proteins were categorized for their role in biological processes and molecular functions. The interactions between these proteins were predicted using STRING online tool. Relation can be drawn between the data provided in this study to the already published article “Integrating transcriptomics and proteomics data for accurate assembly and annotation of genomes” (Prasad et al., 2017) [1].

Multi-Omics Driven Assembly and Annotation of the Sandalwood (Santalum album) Genome

Indian sandalwood (Santalum album) is an important tropical evergreen tree known for its fragrant heartwood-derived essential oil and its valuable carving wood. Here, we applied an integrated genomic, transcriptomic, and proteomic approach to assemble and annotate the Indian sandalwood genome. Our genome sequencing resulted in the establishment of a draft map of the smallest genome for any woody tree species to date (221 Mb). The genome annotation predicted 38,119 protein-coding genes and 27.42% repetitive DNA elements. In-depth proteome analysis revealed the identities of 72,325 unique peptides, which confirmed 10,076 of the predicted genes. The addition of transcriptomic and proteogenomic approaches resulted in the identification of 53 novel proteins and 34 gene-correction events that were missed by genomic approaches. Proteogenomic analysis also helped in reassigning 1,348 potential noncoding RNAs as bona fide protein-coding messenger RNAs. Gene expression patterns at the RNA and protein levels indicated that peptide sequencing was useful in capturing proteins encoded by nuclear and organellar genomes alike. Mass spectrometry-based proteomic evidence provided an unbiased approach toward the identification of proteins encoded by organellar genomes. Such proteins are often missed in transcriptome data sets due to the enrichment of only messenger RNAs that contain poly(A) tails. Overall, the use of integrated omic approaches enhanced the quality of the assembly and annotation of this nonmodel plant genome. The availability of genomic, transcriptomic, and proteomic data will enhance genomics-assisted breeding, germplasm characterization, and conservation of sandalwood trees.

P_RNA_scaffolder: a fast and accurate genome scaffolder using paired-end RNA-sequencing reads

BACKGROUND

Obtaining complete gene structures is one major goal of genome assembly. Some gene regions are fragmented in low quality and high-quality assemblies. Therefore, new approaches are needed to recover gene regions. Genomes are widely transcribed, generating messenger and non-coding RNAs. These widespread transcripts can be used to scaffold genomes and complete transcribed regions.

RESULTS

We present P_RNA_scaffolder, a fast and accurate tool using paired-end RNA-sequencing reads to scaffold genomes. This tool aims to improve the completeness of both protein-coding and non-coding genes. After this tool was applied to scaffolding human contigs, the structures of both protein-coding genes and circular RNAs were almost completely recovered and equivalent to those in a complete genome, especially for long proteins and long circular RNAs. Tested in various species, P_RNA_scaffolder exhibited higher speed and efficiency than the existing state-of-the-art scaffolders. This tool also improved the contiguity of genome assemblies generated by current mate-pair scaffolding and third-generation single-molecule sequencing assembly.

CONCLUSIONS

The P_RNA_scaffolder can improve the contiguity of genome assembly and benefit gene prediction. This tool is available at http://www.fishbrowser.org/software/P_RNA_scaffolder .

Identification of Host-Response in Cerebral Malaria Patients Using Quantitative Proteomic Analysis

PURPOSE

The objective of this study was to study the altered proteome in the frontal lobe of patients with CM. Unbiased analysis of differentially abundant proteins could lead to identification of host responses against Plasmodium falciparum infection, which will aid in better understanding of the molecular mechanism of pathophysiology in CM.

EXPERIMENTAL DESIGN

TMT-based quantitative proteomic analysis using high-resolution mass spectrometry is employed. In brief, proteins are isolated from frontal lobe samples, which are collected at autopsy from three cases of CM and three control subjects. Equal amounts of protein from each case are digested using trypsin and labeled with different TMT reagents. The pooled sample is fractionated using strong cation exchange chromatography and analyzed on Orbitrap Fusion in triplicates. For accurate quantitation of peptides, the samples are analyzed in MS3 mode. The data is searched against a combined database of human and P. falciparum proteins using Sequest and Mascot search engines.

RESULTS

A total of 4174 proteins are identified, of which, 107 are found to be differentially abundant in the test samples with significant p-value (<0.05). Proteins associated with biological processes such as innate immune response, complement system, coagulation, and platelet activation are found to be elevated in CM cases. In contrast, proteins associated with myelination, oxidative phosphorylation, regulation of reactive oxygen species, and sodium and calcium ions transport are found to be depleted in response to CM. In addition, three P. falciparum proteins exclusively in CM brain samples are also identified.

CONCLUSIONS AND CLINICAL RELEVANCE

The study signifies neuronal assault due to axonal injury, altered sodium and calcium ion channels, deregulated inflammation and demyelination as a part of host response to CM. Enhanced oxidative stress, repressed oxidative phosphorylation, and demyelination of axons may contribute to the severity of the disease. Further validation of these results on a large cohort can provide leads in the development of neuroprotective therapies for CM.

Response to Blood Meal in the Fat Body of Anopheles stephensi Using Quantitative Proteomics: Toward New Vector Control Strategies Against Malaria

Malaria remains a grand challenge for disruptive innovation in global health therapeutics and diagnostics. Anopheles stephensi is one of the major vectors of malaria in Asia. Vector and transmission control are key focus areas in the fight against malaria, a field of postgenomics research where proteomics can play a substantive role. Moreover, to identify novel strategies to control the vector population, it is necessary to understand the vector life processes at a global and molecular scale. In this context, fat body is a vital organ required for vitellogenesis, vector immunity, vector physiology, and vector-parasite interaction. Given its central role in energy metabolism, vitellogenesis, and immune function, the proteome profile of the fat body and the impact of blood meal (BM) ingestion on the protein abundances of this vital organ have not been investigated so far. Therefore, using a proteomics approach, we identified the proteins expressed in the fat body of An. stephensi and their differential expression in response to BM ingestion. In all, we identified 3,218 proteins in the fat body using high-resolution mass spectrometry, of which 483 were found to be differentially expressed in response to the BM ingestion. Bioinformatics analysis of these proteins underscored their role in amino acid metabolism, vitellogenesis, lipid transport, signal peptide processing, mosquito immunity, and oxidation-reduction processes. Interestingly, we identified five novel genes, which were found to be differentially expressed upon BM ingestion. Proteins that exhibited altered expression in the present study are potential targets for vector control strategies and development of transmission blocking vaccines in the fight against malaria.

Identification of the actual coding region for polyamine oxidase 6 from rice (OsPAO6) and its partial characterization

Polyamines (PA) in plant play roles in growth and development and in responses to environmental stresses. The family of polyamine oxidases (PAO) contributes to a balanced homeostasis of PAs catalyzing two different reactions, terminal catabolic (TC) and back-conversion (BC) pathway, in PA catabolism. From the seven PAOs encoded by the rice genome (OsPAO1 - OsPAO7) OsPAO6 could so far not be characterized due to failure in obtaining the coding cDNA based on accessions in the genomic databases. We report cloning and characterization of the correct OsPAO6 cDNA with a length of 1,742 bp. The 1,491 bp long open reading frame codes for a 497-amino acid protein from nine exons. The protein which has 92% identity to OsPAO7 localizes to plasma membrane.