Management of severe malaria remains a critical global challenge. In this study, using a multiplexed quantitative proteomics pipeline we systematically investigated the plasma proteome alterations in non-severe and severe malaria patients. We identified a few parasite proteins in severe malaria patients, which could be promising from a diagnostic perspective. Further, from host proteome analysis we observed substantial modulations in many crucial physiological pathways, including lipid metabolism, cytokine signaling, complement, and coagulation cascades in severe malaria. We propose that severe manifestations of malaria are possibly underpinned by modulations of the host physiology and defense machinery, which is evidently reflected in the plasma proteome alterations. Importantly, we identified multiple blood markers that can effectively define different complications of severe falciparum malaria, including cerebral syndromes and severe anemia. The ability of our identified blood markers to distinguish different severe complications of malaria may aid in developing new clinical tests for monitoring malaria severity.

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.

The GeneCaRNA human gene database is a member of the GeneCards Suite. It presents ~280,000 human non-coding RNA genes, identified algorithmically from ~690,000 RNAcentral transcripts. This expands by ~tenfold the ncRNA gene count relative to other sources. GeneCaRNA thus contains ~120,000 long non-coding RNAs (LncRNAs, >200 bases long), including ~100,000 novel genes. The latter have sparse functional information, a vast terra incognita for future research. LncRNA genes are uniformly represented on all nuclear chromosomes, with 10 genes on mitochondrial DNA. Data obtained from MalaCards, another GeneCards Suite member, finds 1547 genes associated with 1 to 50 diseases. About 15% of the associations portray experimental evidence, with cancers tending to be multigenic. Preliminary text mining within GeneCaRNA discovers interactions of lncRNA transcripts with target gene products, with 25% being ncRNAs and 75% proteins. GeneCaRNA has a biological pathways section, which at present shows 131 pathways for 38 lncRNA genes, a basis for future expansion. Finally, our GeneHancer database provides regulatory elements for ~110,000 lncRNA genes, offering pointers for co-regulated genes and genetic linkages from enhancers to diseases. We anticipate that the broad vista provided by GeneCaRNA will serve as an essential guide for further lncRNA research in disease decipherment.

Background: In recent times,Plasmodium vivax (P. vivax)has become a serious threat to public health due to its ability to cause severe infection with fatal outcomes. Its unique biology makes it resilient to control measures that are otherwise effective againstP. falciparum. A deeper understanding ofP. vivaxbiology and pathogenesis is, therefore, essential for developing the right control strategies. Proteomics ofP. falciparumhas been helpful in studying disease biology and elucidating molecular mechanisms involved in the development of disease. However, unlikeP. falciparum, proteomics data forP. vivaxinfection is minimal due to the absence of a continuous culture system. The dependence on clinical samples and animal models has drastically limitedP. vivaxresearch, creating critical knowledge gaps in our understanding of the disease. This study describes an in-depth proteomics analysis ofP. vivax-infected human plasma and parasite isolates, to understand parasite biology, pathogenesis, and to identify new diagnostic targets forP. vivaxmalaria.Methods: A mass-spectrometry- (MS) based proteomics approach (Q Exactive) was applied to analyze human plasma and parasite isolates from vivax malaria patients visiting a primary health centre in India. Additionally, a targeted proteomics assay was standardized for validating unique peptides of most recurring parasite proteins.Results: Thirty-eightP. vivaxproteins were detected in human plasma with high confidence. Several glycolytic enzymes were found along with hypothetical, cytoskeletal, ribosomal, and nuclear proteins. Additionally, 103 highly abundantP. vivaxproteins were detected in parasite isolates. This represents the highest number of parasite proteins to be reported from clinical samples so far. Interestingly, five of these; threePlasmodiumexported proteins (PVX_003545, PVX_003555 and PVX_121935), a hypothetical protein (PVX_083555) and Pvstp1 (subtelomeric transmembrane protein 1, PVX_094303) were found in both plasma and parasite isolates.Conclusions: A parasite proteomics investigation is essential to understand disease pathobiology and design novel interventions. Control strategies againstP. vivaxalso depend on early diagnosis. This work provides deeper insights into the biology ofP. vivaxby identifying proteins expressed by the parasite during its complex life-cycle within the human host. The study also reports antigens that may be explored as diagnostic candidates.

Malaria is one of the neglected infectious diseases, and drugs are the first line of action taken against the onset of malaria as therapeutics. The drugs can be of either natural or artificial origin. Drug development has multiple impediments grouped under three categories, a. drug discovery and screening, b. the drug's action on the host and the pathogen, and c. clinical trials. Drug development takes coon’s age from discovery to the market after FDA approval. At the same time, targeted organisms develop drug resistance quicker than drug approval, raising the requirement for advancement in drug development. The approach to explore drug candidates using the classical methods from natural sources, computation-based docking, mathematical and machine learningbased high throughput in silico models or drug repurposing has been investigated and developed. Also, drug development with information about the interaction between Plasmodium species and its host, humans, may facilitate obtaining an efficient drug cohort for further drug discovery or repurposing expedition. However, drugs may have side effects on the host system. Hence, machine learning and systems-based approaches may provide a holistic view of genomic, proteomic, and transcriptomic data and their interaction with the selected drug candidates. This review comprehensively describes the drug discovery workflows using drug and target screening methodologies, followed by possible ways to check the binding affinity of the drug and targets using various docking software.