Unpacking Immune Modulation as a Site of Therapeutics Innovation for Nematode Parasite Wuchereria bancrofti: A Temporal Quantitative Phosphoproteomics Profiling of Macrophage Migration Inhibitory Factor 2.

Nematode infections are common in both humans and livestock, with major adverse planetary health and economic impacts. Wuchereria bancrofti is a parasitic nematode that causes lymphatic filariasis, a neglected tropical disease that can lead to severe disability and deformity worldwide. For the long-term survival of the bancroftian parasites in the host, a complex immune invasion strategy is involved through immunomodulation. Therefore, immunomodulation can serve as a site of research and innovation for molecular targets. Macrophage migration inhibitory factor (MIF) is a pleiotropic cytokine crucial to the host antimicrobial alarm system and stress response. Interestingly, the nematode parasite W. bancrofti also produces two homologs of MIF (Wba-MIF1 and 2). Using a mass spectrometry-based phosphoproteomics approach, we report new findings on the immunomodulatory effect and signaling mechanism of Wba-MIF2 in macrophage cells. Accordingly, we observed 1201 phosphorylated sites on 467 proteins. Out of the 1201 phosphorylated sites, 1075, 117, and 9 were found on serine (S), threonine (T), and tyrosine (Y) residues, respectively. Our bioinformatics analysis led to identification of major pathways, including spliceosomes, T cell receptor signaling pathway, Th17 differentiation pathway, interleukin-17 signaling pathway, and insulin signaling pathway upon Wba-MIF2 treatment. Wba-MIF2 treatment also enriched CDK4, CDK1, and DNAPK kinases. The comparison of the signaling pathway of Wba-MIF2 with that of human-MIF suggests both share similar signaling pathways. These findings collectively offer new insights into the role and mechanism of Wba-MIF2 as an immunomodulator and inform future diagnostics and drug discovery research for W. bancrofti.

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 comparative proteomic analysis for non-invasive early prediction of hypoxic-ischemic injury in asphyxiated neonates.

AIM: Hypoxic Ischemic Encephalopathy (HIE) is one of the principal causes of neonatal mortality and long-term morbidity worldwide. The neonatal signs of mild cerebral injury are subtle, making an early precise diagnosis difficult. Delayed detection, poor prognosis, and lack of specific biomarkers for the disease are increasing mortality rates. In this study, we intended to identify specific biomarkers using comparative proteomic analysis to predict the severity of perinatal asphyxia so that its outcome can also be prevented. EXPERIMENTAL DESIGN: A case-control study was conducted on 38 neonates, and urine samples were collected within 24 and 72 h of life. A tandem mass spectrometry-based quantitative proteomics approach, followed by validation via sandwich ELISA, was performed. RESULTS: The LC-MS/MS-based proteomics analysis resulted in the identification of 1201 proteins in urine, with 229, 244, and 426 being differentially expressed in HIE-1, HIE-2, and HIE-3, respectively. Axon guidance, Diseases of programmed cell death, and Detoxification of reactive oxygen species pathways were significantly enriched in mild HIE versus severe HIE. Among the differentially expressed proteins in various stages of HIE, we chose to validate four proteins - APP, AGT, FABP1, and FN1 - via sandwich ELISA. Individual and cumulative ROC curves were plotted. AGT and FABP1 together showed high sensitivity, specificity, and accuracy as potential biomarkers for early diagnosis of HIE. CONCLUSION: Establishing putative urinary biomarkers will facilitate clinicians to more accurately screen neonates for brain injury and monitor the disease progression. Prompt treatment of neonates may reduce mortality and neurodevelopmental impairment.

Mining proteomics data to extract post-translational modifications associated with gastric cancer.

Gastric cancers are highly heterogeneous, deep-seated tumours associated with late diagnosis and poor prognosis. Post-translational modifications (PTMs) of proteins are known to be well-associated with oncogenesis and metastasis in most cancers. Several enzymes which drive PTMs have also been used as theranostics in cancers of the breast, ovary, prostate and bladder. However, there is limited data on PTMs in gastric cancers. Considering that experimental protocols for simultaneous analysis of multiple PTMs are being explored, a data-driven approach involving reanalysis of mass spectrometry-derived data is useful in cataloguing altered PTMs. We subjected publicly available mass spectrometry data on gastric cancer to an iterative searching strategy for fetching PTMs including phosphorylation, acetylation, citrullination, methylation and crotonylation. These PTMs were catalogued and further analyzed for their functional enrichment through motif analysis. This value-added approach delivered identification of 21,710 unique modification sites on 16,364 modified peptides. Interestingly, we observed 278 peptides corresponding to 184 proteins to be differentially abundant. Using bioinformatics approaches, we observed that majority of these altered PTMs/proteins belonged to cytoskeletal and extracellular matrix proteins, which are known to be perturbed in gastric cancer. The dataset derived by this mutiPTM investigation can provide leads to further investigate the potential role of altered PTMs in gastric cancer management.

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

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

A computational workflow for predicting cancer neo-antigens.

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

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

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

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.

Novel Post-Translational Modifications and Molecular Substrates in Glioma Identified by Bioinformatics.

Glioma is the most common type of brain cancer that originates from the glial cells. It constitutes about one-third of all brain cancers. Recently, transcriptomics, proteomics, and multiomics approaches have been harnessed to discover potential biomarkers and therapeutic targets in glioma. Moreover, post-translational modifications (PTMs) of proteins play a major role in cell biology and function and offer new avenues of research in cancer. Using unbiased multi-PTM bioinformatics analyses of two proteomic datasets of glioma available in the public domain, we identified 866 proteins with common PTMs from both studies. Out of these 866 proteins, 19 proteins were identified with the common PTMs, with the same site modifications pertaining to glioma. Importantly, the identified PTMs belonged to proteins involved in integrin PI3K/Akt/mTOR, JAK/STAT, and Ras/Raf/MAPK pathways. These pathways are essential for cell proliferation in tumor cells and thus involved in glioma progression. Taken together, these findings call for validation in larger datasets in glioma and brain cancers and with an eye to future drug discovery and diagnostic innovation. Bioinformatics-guided discovery of novel PTMs from the publicly available proteomic data can offer new avenues for innovation in cancer research.

Omics Data Mining for multiPTMs in Oral Cancer: Cellular Proteome and Secretome of Chronic Tobacco-Treated Oral Keratinocytes.

Oral cancer is common worldwide but lacks robust diagnostics and therapeutics. Lifestyle factors, such as tobacco chewing and smoking, are significantly associated with oral cancers. Mapping the changes in the global proteome, secretome and post-translational modifications (PTMs) during tobacco exposure of oral keratinocytes hold great potential for understanding the mechanisms of oral carcinogenesis, not to mention for innovation toward clinical interventions in the future. On the other hand, although advances in mass spectrometry (MS)-based techniques have enabled the deep mining of complex proteomes, a large portion of the mass spectrometric data remains unassigned. These unassigned spectral data can be researched for multiple post-translational modifications (multiPTMs). Using data mining of publicly available proteomics data, we report, in this study, a multiPTM analysis of high-resolution MS-derived datasets on cellular proteome and secretome of chronic tobacco-treated oral keratinocytes. We identified 800 PTM sites in 496 proteins. Among them, 43 PTM sites in 37 proteins were found to be differentially expressed, accounting for their protein-level expression. Enrichment analysis of the proteins with altered phosphosite expression and the known kinases of these phosphosites discovered the overrepresentation of certain biological processes such as splicing and hemidesmosome assembly. These findings contribute to a deeper understanding of omics level changes in chronic tobacco-treated oral keratinocytes, and by extension, pathophysiology of oral cancers.

Identification of potential salivary biomarker panels for oral squamous cell carcinoma.

Oral squamous cell carcinoma (OSCC) is one of the most prevalent cancers worldwide with the maximum number of incidences and deaths reported from India. One of the major causes of poor survival rate associated with OSCC has been attributed to late presentation due to non-availability of a biomarker. Identification of early diagnostic biomarker will help in reducing the disease morbidity and mortality. We validated 12 salivary proteins using targeted proteomics, identified initially by relative quantification of salivary proteins on LC-MS, in OSCC patients and controls. Salivary AHSG (p = 0.0041**) and KRT6C (p = 0.002**) were upregulated in OSCC cases and AZGP1 (p ≤ 0.0001***), KLK1 (p = 0.006**) and BPIFB2 (p = 0.0061**) were downregulated. Regression modelling resulted in a significant risk prediction model (p < 0.0001***) consisting of AZGP1, AHSG and KRT6C for which ROC curve had AUC, sensitivity and specificity of 82.4%, 78% and 73.5% respectively for all OSCC cases and 87.9%, 87.5% and 73.5% respectively for late stage (T3/T4) OSCC. AZGP1, AHSG, KRT6C and BPIFB2 together resulted in ROC curve (p < 0.0001***) with AUC, sensitivity and specificity of 94%, 100% and 77.6% respectively for N0 cases while KRT6C and AZGP1 for N+ cases with ROC curve (p < 0.0001***) having AUC sensitivity and specificity of 76.8%, 73% and 69.4%. Our data aids in the identification of biomarker panels for the diagnosis of OSCC cases with a differential diagnosis between early and late-stage cases.

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

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

Stability behaviour of antiretroviral drugs and their combinations. 10: LC-HRMS, LC-MSn, LC-NMR and NMR characterization of fosamprenavir degradation products and in silico determination of their ADMET properties.

The present study focused upon the forced degradation behaviour of fosamprenavir (FPV), an antiretroviral drug. A total of six degradation products (DPs) were separated on a non-polar stationary phase by high performance liquid chromatography (HPLC). For the characterization, comprehensive mass fragmentation pathway of the drug was initially established using high resolution mass spectrometry (HRMS) and multi-stage tandem mass spectrometry (MSn) data. Subsequently, LC-HRMS and LC-MSn studies were carried out on the forced degraded samples containing the DPs. Five DPs were isolated and subjected to extensive 1D (1H, 13C, and DEPT-135 (distortionless enhancement by polarization)) and 2D (COSY (correlation spectroscopy), TOCSY (total correlation spectroscopy), HSQC (heteronuclear single quantum coherence) and HMBC (heteronuclear multiple bond correlation)) nuclear magnetic resonance (NMR) studies to ascertain their structures, while one degradation product was subjected to LC-NMR studies, as it could not be isolated. The collated information was helpful in characterization of all the DPs, and to delineate the degradation pathway of the drug. Additionally, physicochemical, as well as absorption, distribution, metabolism, excretion and toxicity (ADMET) properties of the drug and its DPs were evaluated in silico by ADMET Predictor™ software.