Ankit P. Jain

Bio: Ankit P. Jain is an academic researcher from KIIT University. The author has contributed to research in topics: Head and neck squamous-cell carcinoma & Quantitative proteomics. The author has an hindex of 10, co-authored 25 publications receiving 1098 citations.

Correction: Corrigendum: A dual specificity kinase, DYRK1A, as a potential therapeutic target for head and neck squamous cell carcinoma

Abstract: Scientific Reports 6: Article number: 36132; published online: 31 October 2016; updated: 26 June 2017. This Article contains errors. The Acknowledgements section is incomplete. “We thank the Department of Biotechnology (DBT), Government of India for research support to the Institute of Bioinformatics (IOB), Bangalore.

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

Abstract: Complementing genome sequence with deep transcriptome and proteome data could enable more accurate assembly and annotation of newly sequenced genomes. Here, we provide a proof-of-concept of an integrated approach for analysis of the genome and proteome of Anopheles stephensi, which is one of the most important vectors of the malaria parasite. To achieve broad coverage of genes, we carried out transcriptome sequencing and deep proteome profiling of multiple anatomically distinct sites. Based on transcriptomic data alone, we identified and corrected 535 events of incomplete genome assembly involving 1196 scaffolds and 868 protein-coding gene models. This proteogenomic approach enabled us to add 365 genes that were missed during genome annotation and identify 917 gene correction events through discovery of 151 novel exons, 297 protein extensions, 231 exon extensions, 192 novel protein start sites, 19 novel translational frames, 28 events of joining of exons, and 76 events of joining of adjacent genes as a single gene. Incorporation of proteomic evidence allowed us to change the designation of more than 87 predicted "noncoding RNAs" to conventional mRNAs coded by protein-coding genes. Importantly, extension of the newly corrected genome assemblies and gene models to 15 other newly assembled Anopheline genomes led to the discovery of a large number of apparent discrepancies in assembly and annotation of these genomes. Our data provide a framework for how future genome sequencing efforts should incorporate transcriptomic and proteomic analysis in combination with simultaneous manual curation to achieve near complete assembly and accurate annotation of genomes.

A dual specificity kinase, DYRK1A, as a potential therapeutic target for head and neck squamous cell carcinoma.

Abstract: Despite advances in clinical management, 5-year survival rate in patients with late-stage head and neck squamous cell carcinoma (HNSCC) has not improved significantly over the past decade Targeted therapies have emerged as one of the most promising approaches to treat several malignancies Though tyrosine phosphorylation accounts for a minority of total phosphorylation, it is critical for activation of signaling pathways and plays a significant role in driving cancers To identify activated tyrosine kinase signaling pathways in HNSCC, we compared the phosphotyrosine profiles of a panel of HNSCC cell lines to a normal oral keratinocyte cell line Dual-specificity tyrosine-(Y)-phosphorylation regulated kinase 1A (DYRK1A) was one of the kinases hyperphosphorylated at Tyr-321 in all HNSCC cell lines Inhibition of DYRK1A resulted in an increased apoptosis and decrease in invasion and colony formation ability of HNSCC cell lines Further, administration of the small molecular inhibitor against DYRK1A in mice bearing HNSCC xenograft tumors induced regression of tumor growth Immunohistochemical labeling of DYRK1A in primary tumor tissues using tissue microarrays revealed strong to moderate staining of DYRK1A in 975% (39/40) of HNSCC tissues analyzed Taken together our results suggest that DYRK1A could be a novel therapeutic target in HNSCC

Long-Term Cigarette Smoke Exposure and Changes in MiRNA Expression and Proteome in Non-Small-Cell Lung Cancer

Abstract: Chronic exposure to cigarette smoke markedly increases the risk for lung cancer. Regulation of gene expression at the post-transcriptional level by miRNAs influences a variety of cancer-related interactomes. Yet, relatively little is known on the effects of long-term cigarette smoke exposure on miRNA expression and gene regulation. NCI-H292 (H292) is a cell line sensitive to cigarette smoke with mucoepidermoid characteristics in culture. We report, in this study, original observations on long-term (12 months) cigarette smoke effects in the H292 cell line, using microarray-based miRNA expression profiling, and stable isotopic labeling with amino acids in cell culture-based quantitative proteomic analysis. We identified 112 upregulated and 147 downregulated miRNAs (by twofold) in cigarette smoke-treated H292 cells. The liquid chromatography-tandem mass spectrometry analysis identified 3,959 proteins, of which, 303 proteins were overexpressed and 112 proteins downregulated (by twofold). We observed 39 miRNA target pairs (proven targets) that were differentially expressed in response to chronic cigarette smoke exposure. Gene ontology analysis of the target proteins revealed enrichment of proteins in biological processes driving metabolism, cell communication, and nucleic acid metabolism. Pathway analysis revealed the enrichment of phagosome maturation, antigen presentation pathway, nuclear factor erythroid 2-related factor 2-mediated oxidative stress response, and cholesterol biosynthesis pathways in cigarette smoke-exposed cells. In conclusion, this report makes an important contribution to knowledge on molecular changes in a lung cell line in response to long term cigarette smoke exposure. The findings might inform future strategies for drug target, biomarker and diagnostics innovation in lung cancer, and clinical oncology. These observations also call for further research on the extent to which continuing or stopping cigarette smoking in patients diagnosed with lung cancer translates into molecular and clinical outcomes.

Green synthesis, biomedical and biotechnological applications of carbon and graphene quantum dots. A review

Abstract: Carbon and graphene quantum dots are prepared using top-down and bottom-up methods. Sustainable synthesis of quantum dots has several advantages such as the use of low-cost and non-toxic raw materials, simple operations, expeditious reactions, renewable resources and straightforward post-processing steps. These nanomaterials are promising for clinical and biomedical sciences, especially in bioimaging, diagnosis, bioanalytical assays and biosensors. Here we review green methods for the fabrication of quantum dots, and biomedical and biotechnological applications.

Artificial intelligence to deep learning: machine intelligence approach for drug discovery.

Abstract: Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.

Mechanisms of Action for Antimicrobial Peptides With Antibacterial and Antibiofilm Functions.

Abstract: The antibiotic crisis has led to a pressing need for alternatives such as antimicrobial peptides (AMPs). Recent work has shown that these molecules have great potential not only as antimicrobials, but also as antibiofilm agents, immune modulators, anti-cancer agents and anti-inflammatories. A better understanding of the mechanism of action (MOA) of AMPs is an important part of the discovery of more potent and less toxic AMPs. Many models and techniques have been utilized to describe the MOA. This review will examine how biological assays and biophysical methods can be utilized in the context of the specific antibacterial and antibiofilm functions of AMPs.

Understanding and Designing the Strategies for the Microbe-Mediated Remediation of Environmental Contaminants Using Omics Approaches.

Abstract: Rapid industrialization and population explosion has resulted in the generation and dumping of various contaminants into the environment. These harmful compounds deteriorate the human health as well as the surrounding environments. Current research aims to harness and enhance the natural ability of different microbes to metabolize these toxic compounds. Microbial-mediated bioremediation offers great potential to reinstate the contaminated environments in an ecologically acceptable approach. However, the lack of the knowledge regarding the factors controlling and regulating the growth, metabolism, and dynamics of diverse microbial communities in the contaminated environments often limits its execution. In recent years the importance of advanced tools such as genomics, proteomics, transcriptomics, metabolomics, and fluxomics has increased to design the strategies to treat these contaminants in ecofriendly manner. Previously researchers has largely focused on the environmental remediation using single omics-approach, however the present review specifically addresses the integrative role of the multi-omics approaches in microbial-mediated bioremediation. Additionally, we discussed how the multi-omics approaches help to comprehend and explore the structural and functional aspects of the microbial consortia in response to the different environmental pollutants and presented some success stories by using these approaches.