A Proteogenomic Analysis of Haptoglobin in Malaria

Awasthi, Gauri; Tyagi, Suchi; Kumar, Vipin; Patel, Shaunak; Rojh, Dharmendar; Sakrappanavar, Vijeth; Kochar, Sanjay Kumar; Talukdar, Arunansu; Samanta, Biaus; Das, Ashim; Srivastava, Sanjeeva; Patankar, Swati

doi:10.1002/prca.201700077

Cited by 8 publications

(6 citation statements)

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“…Of P. falciparum and P. vivax, P. falciparum is well studied, resulting in integrated omics, transcriptomics, and proteomics analysis to understand different stages in infected hosts [ 112 ]. Proteogenomics of haptoglobin in infected host blood aided understanding of the correlation between the gene and protein of polymorph in haptoglobin co-dominant alleles to understand the malaria progression [ 32 ]. Transcriptomic and proteomic study of the sporozoites and oocytes of P. falciparum extracted from the salivary gland of mosquitos exhibited the role of a set of genes that help in parasite maturation and are capable of infecting the liver in humans [ 33 ].…”

Section: Approaches To Integrative Multi-omicsmentioning

confidence: 99%

“…In short, we will discuss the recent development of omics-based malaria biomarkers [ 31 , 32 , 33 , 34 ] ( Figure 1 , Table 2 ) and the emergence of malaria diagnosis technologies targeting the inorganic biocrystal (hemozoin) as a marker. Hemozoin is a by-product of heme utilisation through various redox transitioning for the parasite’s survival during the intra-erythrocyte cycle [ 2 , 3 , 12 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

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Multi-Omics Advancements towards Plasmodium vivax Malaria Diagnosis

2021

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Plasmodium vivax malaria is one of the most lethal infectious diseases, with 7 million infections annually. One of the roadblocks to global malaria elimination is the lack of highly sensitive, specific, and accurate diagnostic tools. The absence of diagnostic tools in particular has led to poor differentiation among parasite species, poor prognosis, and delayed treatment. The improvement necessary in diagnostic tools can be broadly grouped into two categories: technologies-driven and omics-driven progress over time. This article discusses the recent advancement in omics-based malaria for identifying the next generation biomarkers for a highly sensitive and specific assay with a rapid and antecedent prognosis of the disease. We summarize the state-of-the-art diagnostic technologies, the key challenges, opportunities, and emerging prospects of multi-omics-based sensors.

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Section: Approaches To Integrative Multi-omicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Omics Advancements towards Plasmodium vivax Malaria Diagnosis

2021

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“…AI mediated data integration obtained from different “-omics” platforms such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics enables the understanding of complex biological systems by describing nearly all biomolecules ranging from DNA to metabolites. Multi-omics researches have diverse applications in veterinary medicine ( Li Q. et al, 2015 ), microbiology ( Zhang et al, 2010 ), agriculture science ( Van Emon, 2016 ), biofuel ( Rai et al, 2016 ), and biomedical sciences ( More et al, 2015 ; Hasin et al, 2017 ; Awasthi et al, 2018 ; Patel et al, 2019 ) including oncology (see Table 1 ).…”

Section: Implications Of Artificial Intelligence In Cancer Multi-omicmentioning

confidence: 99%

Artificial Intelligence to Decode Cancer Mechanism: Beyond Patient Stratification for Precision Oncology

2020

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The multitude of multi-omics data generated cost-effectively using advanced high-throughput technologies has imposed challenging domain for research in Artificial Intelligence (AI). Data curation poses a significant challenge as different parameters, instruments, and sample preparations approaches are employed for generating these big data sets. AI could reduce the fuzziness and randomness in data handling and build a platform for the data ecosystem, and thus serve as the primary choice for data mining and big data analysis to make informed decisions. However, AI implication remains intricate for researchers/clinicians lacking specific training in computational tools and informatics. Cancer is a major cause of death worldwide, accounting for an estimated 9.6 million deaths in 2018. Certain cancers, such as pancreatic and gastric cancers, are detected only after they have reached their advanced stages with frequent relapses. Cancer is one of the most complex diseases affecting a range of organs with diverse disease progression mechanisms and the effectors ranging from gene-epigenetics to a wide array of metabolites. Hence a comprehensive study, including genomics, epi-genomics, transcriptomics, proteomics, and metabolomics, along with the medical/mass-spectrometry imaging, patient clinical history, treatments provided, genetics, and disease endemicity, is essential. Cancer Moonshot℠ Research Initiatives by NIH National Cancer Institute aims to collect as much information as possible from different regions of the world and make a cancer data repository. AI could play an immense role in (a) analysis of complex and heterogeneous data sets (multi-omics and/or inter-omics), (b) data integration to provide a holistic disease molecular mechanism, (c) identification of diagnostic and prognostic markers, and (d) monitor patient’s response to drugs/treatments and recovery. AI enables precision disease management well beyond the prevalent disease stratification patterns, such as differential expression and supervised classification. This review highlights critical advances and challenges in omics data analysis, dealing with data variability from lab-to-lab, and data integration. We also describe methods used in data mining and AI methods to obtain robust results for precision medicine from “big” data. In the future, AI could be expanded to achieve ground-breaking progress in disease management.

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“…Further, proteomic profiling using label-free quantitation (LFQ) is another attractive method for cancer biomarker quantification ( 23 , 31 ). In recent years, we have reported targeted quantitation of proteins by Multiple Reaction Monitoring (MRM) mass spectrometry ( 18 , 32 ). Here, we have described an amalgamated analysis pipeline for plasma biomarker analysis by integrating the know-how of different quantitative & targeted proteomics methods ( Figures 1A–D ).…”

Section: Introductionmentioning

confidence: 99%

An Integrated Quantitative Proteomics Workflow for Cancer Biomarker Discovery and Validation in Plasma

Kumar

Ghantasala

et al. 2020

Front. Oncol.

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Blood plasma is one of the most widely used samples for cancer biomarker discovery research as well as clinical investigations for diagnostic and therapeutic purposes. However, the plasma proteome is extremely complex due to its wide dynamic range of protein concentrations and the presence of high-abundance proteins. Here we have described an optimized, integrated quantitative proteomics pipeline combining the label-free and multiplexed-labeling-based (iTRAQ and TMT) plasma proteome profiling methods for biomarker discovery, followed by the targeted approaches for validation of the identified potential marker proteins. In this workflow, the targeted quantitation of proteins is carried out by multiple-reaction monitoring (MRM) and parallel-reaction monitoring (PRM) mass spectrometry. Thus, our approach enables both unbiased screenings of biomarkers and their subsequent selective validation in human plasma. The overall procedure takes only ~2 days to complete, including the time for data acquisition (excluding database searching). This protocol is quick, flexible, and eliminates the need for a separate immunoassay-based validation workflow in blood cancer biomarker investigations. We anticipate that this plasma proteomics workflow will help to accelerate the cancer biomarker discovery program and provide a valuable resource to the cancer research community.

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A Proteogenomic Analysis of Haptoglobin in Malaria

Abstract: This proteo-genomic study focuses on the correlation of the Hp protein and gene with malaria, thus highlighting the pivotal role of this acute phase immune gene in malaria pathogenesis.

Cited by 8 publications

References 24 publications

Multi-Omics Advancements towards Plasmodium vivax Malaria Diagnosis

Multi-Omics Advancements towards Plasmodium vivax Malaria Diagnosis

Artificial Intelligence to Decode Cancer Mechanism: Beyond Patient Stratification for Precision Oncology

An Integrated Quantitative Proteomics Workflow for Cancer Biomarker Discovery and Validation in Plasma

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