@article { ISI:000287627200012, title = {Single nucleotide polymorphism in transcobalamin II (15V) induces structural changes in the protein as revealed by molecular modeling studies}, journal = {Biochemistry}, volume = {50}, number = {8}, year = {2011}, month = {MAR}, pages = {1396-1402}, publisher = {AMER CHEMICAL SOC}, address = {1155 16TH ST, NW, WASHINGTON, DC 20036 USA}, abstract = {

Cobalamin is an essential micronutrient in mammals. Deficiencies of this micronutrient have been implicated as risk factors for various complex diseases. Cobalamin is transported to the cells by the transport protein transcobalamin II (TCII), and hence genetic variations (like single nucleotide polymorphisms) in TCII could be perceived to affect the binding of cobalamin to TCII, thereby modulating the intracellular concentrations of cobalamin. To understand whether three nonsynonymous mutations in TCII (I5V, P241R, and R381Q) alter the structure of the protein which could potentially affect cobalamin binding, we performed molecular dynamics simulation in silico. Superimposition of active sites of the four simulated models (wild type and three variants) with the human TCII crystal structure revealed that the distance between the N epsilon nitrogen atom of His-173 and the cobalt ion of cobalamin deviated considerably in the I5V model as compared to wild type and other variants. His-173 directly coordinates with the cobalt ion of cobalamin. Further, from our dynamic cross-correlation and principal component analysis it appears that in the I5V model the beta-domain moves apart from the alpha-domain creating a wide gap between the two domains. This might facilitate the initial binding of cobalamin in the I5V model as cobalamin enters the binding site through the gap between the two domains. These observations were not found in the other variants. We thus speculate that binding of cobalamin will be more facile in the I5V variant.

}, issn = {0006-2960}, doi = {10.1021/bi101095g}, author = {Silla, Yumnam and Chandamouli, Balasubramanian and Maiti, Souvik and Sengupta, Shantanu} } @article { ISI:000337763200018, title = {Matrix-assisted laser desorption/ionization mass spectrometry analysis of dimethyl arginine isomers from urine}, journal = {Analytical Methods}, volume = {6}, number = {13}, year = {2014}, month = {APR}, pages = {4602-4609}, publisher = {ROYAL SOC CHEMISTRY}, address = {THOMAS GRAHAM HOUSE, SCIENCE PARK, MILTON RD, CAMBRIDGE CB4 0WF, CAMBS, ENGLAND}, abstract = {

Isomeric asymmetric and symmetric dimethyl arginine (ADMA and SDMA respectively) residues are excreted in urine and are putative markers of cardiovascular and chronic kidney diseases. In this work, we demonstrate simultaneous and quantitative detection of endogeneous ADMA and SDMA from urine samples of healthy subjects using MALDI-TOF MS without any chromatographic separation. The DMA isomers yielded [M + H](+) ions along with their product ions formed due to MALDI in-source fragmentation. The precursor ions were validated using MALDI-TOF MS/MS as well as direct injection ESI-Q-TOF MS/MS. ADMA and SDMA generated unique product ions at similar to m/z 46 and similar to m/z 172 respectively in the MS-mode itself. These were advantageously used for full scan-mode absolute quantification of the isomeric metabolites. The m/z observed for all the ions was within 10 ppm mass accuracy. The calibration method was established by generating internal standard normalized peak area-based concentration response curves using synthetic standards. Good linearities (R-2 \> 0.95) with acceptable intra-assay, inter-assay variation (within 15\% RSD) and excellent recoveries were observed for quality control samples. Finally, endogeneous concentrations of the metabolites were determined in urine from healthy subjects (n = 11). ADMA and SDMA were found to be in the range of 1.6-8 mu M and 2.9-9.1 mu M in urine and were in agreement with previously reported physiological levels.

}, issn = {1759-9660}, doi = {10.1039/c4ay00309h}, author = {Bhattacharya, Nivedita and Singh, Ajeet and Ghanate, Avinash and Phadke, Gayatri and Parmar, Dharmesh and Dhaware, Deepika G. and Basak, Trayambak and Sengupta, Shantanu and Panchagnula, Venkateswarlu} } @article {47373, title = {Chronic systemic exposure to IL6 leads to deregulation of glycolysis and fat accumulation in the zebrafish liver}, journal = {Biochimica ET Biophysica Acta-Molecular and Cell Biology of Lipids}, volume = {1866}, year = {2021}, month = {MAY}, pages = {158905}, type = {Article}, abstract = {

Inflammation is a constant in Non-Alcoholic Fatty Liver Disease (NAFLD), although their relationship is unclear. In a transgenic zebrafish system with chronic systemic overexpression of human IL6 (IL6-OE) we show that inflammation can cause intra-hepatic accumulation of triglycerides. Transcriptomics and proteomics analysis of the IL6-OE liver revealed a deregulation of glycolysis/gluconeogenesis pathway, especially a striking down regulation of the glycolytic enzyme aldolase b. Metabolomics analysis by mass spectrometry showed accumulation of hexose monophosphates and their derivatives, which can act as precursors for triglyceride synthesis. Our results suggest that IL6-driven repression of glycolysis/gluconeogenesis, specifically aldolase b, may be a novel mechanism for fatty liver. This mechanism may be relevant for NAFLD in lean individuals, an emerging class of NAFLD prevalent more in Asian Indian populations.

}, keywords = {Aldolase b, DHAP, Inflammation, Interleukin 6, Lean NAFLD, Non-alcoholic fatty liver}, issn = {1388-1981}, doi = {10.1016/j.bbalip.2021.158905}, author = {Singh, Manoj K. and Jayarajan, Rijith and Varshney, Swati and Upadrasta, Sindhuri and Singh, Archana and Yadav, Rajni and Scaria, Vinod and Sengupta, Shantanu and Shanmugam, Dhanasekaran and Shalimar and Sivasubbu, Sridhar and Gandotra, Sheetal and Sachidanandan, Chetana} } @article {47390, title = {Insights from a pan India Sero-epidemiological survey (Phenome-India Cohort) for SARS-CoV2}, journal = {eLife}, volume = {10}, year = {2021}, month = {APR }, pages = {e66537}, type = {Article}, abstract = {

To understand the spread of SARS-CoV2, in August and September 2020, the Council of Scientific and Industrial Research (India) conducted a serosurvey across its constituent laboratories and centers across India. Of 10,427 volunteers, 1058 (10.14\%) tested positive for SARS-CoV2 anti-nucleocapsid (anti-NC) antibodies, 95\% of which had surrogate neutralization activity. Three-fourth of these recalled no symptoms. Repeat serology tests at 3 (n = 607) and 6 (n = 175) months showed stable anti-NC antibodies but declining neutralization activity. Local seropositivity was higher in densely populated cities and was inversely correlated with a 30-day change in regional test positivity rates (TPRs). Regional seropositivity above 10\% was associated with declining TPR. Personal factors associated with higher odds of seropositivity were high-exposure work (odds ratio, 95\% confidence interval, p value: 2.23, 1.92-2.59, \<0.0001), use of public transport (1.79, 1.43-2.24, \<0.0001), not smoking (1.52, 1.16-1.99, 0.0257), non-vegetarian diet (1.67, 1.41-1.99, \<0.0001), and B blood group (1.36, 1.15-1.61, 0.001).

}, issn = {2050-084X}, doi = {10.7554/eLife.66537}, author = {Naushin, Salwa and Sardana, Viren and Ujjainiya, Rajat and Bhatheja, Nitin and Kutum, Rintu and Bhaskar, Akash Kumar and Pradhan, Shalini and Prakash, Satyartha and Khan, Raju and Rawat, Birendra Singh and Tallapaka, Karthik Bharadwaj and Anumalla, Mahesh and Chandak, Giriraj Ratan and Lahiri, Amit and Kar, Susanta and Mulay, Shrikant Ramesh and Mugale, Madhav Nilakanth and Srivastava, Mrigank and Khan, Shaziya and Srivastava, Anjali and Tomar, Bhawana and Veerapandian, Murugan and Venkatachalam, Ganesh and Vijayakumar, Selvamani Raja and Agarwal, Ajay and Gupta, Dinesh and Halami, Prakash M. and Peddha, Muthukumar Serva and Sundaram, Gopinath M. and Veeranna, Ravindra P. and Pal, Anirban and Agarwal, Vinay Kumar and Maurya, Anil Ku and Singh, Ranvijay Kumar and Raman, Ashok Kumar and Anandasadagopan, Suresh Kumar and Karuppanan, Parimala and Venkatesan, Subramanian and Sardana, Harish Kumar and Kothari, Anamika and Jain, Rishabh and Thakur, Anupama and Parihar, Devendra Singh and Saifi, Anas and Kaur, Jasleen and Kumar, Virendra and Mishra, Avinash and Gogeri, Iranna and Rayasam, Geethavani and Singh, Praveen and Chakraborty, Rahul and Chaturvedi, Gaura and Karunakar, Pinreddy and Yadav, Rohit and Singhmar, Sunanda and Singh, Dayanidhi and Sarkar, Sharmistha and Bhattacharya, Purbasha and Acharya, Sundaram and Singh, Vandana and Verma, Shweta and Soni, Drishti and Seth, Surabhi and Vashisht, Sakshi and Thakran, Sarita and Fatima, Firdaus and Singh, Akash Pratap and Sharma, Akanksha and Sharma, Babita and Subramanian, Manikandan and Padwad, Yogendra S. and Hallan, Vipin and Patial, Vikram and Singh, Damanpreet and Tripude, Narendra Vijay and Chakrabarti, Partha and Maity, Sujay Krishna and Ganguly, Dipyaman and Sarkar, Jit and Ramakrishna, Sistla and Kumar, Balthu Narender and Kumar, Kiran A. and Gandhi, Sumit G. and Jamwal, Piyush Singh and Chouhan, Rekha and Jamwal, Vijay Lakshmi and Kapoor, Nitika and Ghosh, Debashish and Thakkar, Ghanshyam and Subudhi, Umakanta and Sen, Pradip and Chaudhury, Saumya Ray and Kumar, Rashmi and Gupta, Pawan and Tuli, Amit and Sharma, Deepak and Ringe, Rajesh P. and Amarnarayan, D. and Kulkarni, Mahesh and Shanmugam, Dhansekaran and Dharne, Mahesh S. and Dastager, Sayed G. and Joshi, Rakesh and Patil, Amita P. and Mahajan, Sachin N. and Khan, Abujunaid Habib and Wagh, Vasudev and Yadav, Rakesh Kumar and Khilari, Ajinkya and Bhadange, Mayuri and Chaurasiya, Arvindkumar H. and Kulsange, Shabda E. and Khairnar, Krishna and Paranjape, Shilpa and Kalita, Jatin and Sastry, Narahari G. and Phukan, Tridip and Manna, Prasenjit and Romi, Wahengbam and Bharali, Pankaj and Ozah, Dibyajyoti and Sahu, Ravi Kumar and Babu, Elapavalooru Vssk and Sukumaran, Rajeev and Nair, Aiswarya R. and Valappil, Prajeesh Kooloth and Puthiyamadam, Anoop and Velayudhanpillai, Adarsh and Chodankar, Kalpana and Damare, Samir and Madhavi, Yennapu and Aggarwal, Ved Varun and Dahiya, Sumit and Agrawal, Anurag and Dash, Debasis and Sengupta, Shantanu} } @article {48236, title = {Machine learning-based approach to determine infection status in recipients of BBV152 (Covaxin) whole-virion inactivated SARS-CoV-2 vaccine for serological surveys}, journal = {Computers in Biology and Medicine}, volume = {146}, year = {2022}, month = {JUL}, pages = {105419}, type = {Article}, abstract = {

Data science has been an invaluable part of the COVID-19 pandemic response with multiple applications, ranging from tracking viral evolution to understanding the vaccine effectiveness. Asymptomatic breakthrough infections have been a major problem in assessing vaccine effectiveness in populations globally. Serological discrimination of vaccine response from infection has so far been limited to Spike protein vaccines since whole virion vaccines generate antibodies against all the viral proteins. Here, we show how a statistical and machine learning (ML) based approach can be used to discriminate between SARS-CoV-2 infection and immune response to an inactivated whole virion vaccine (BBV152, Covaxin). For this, we assessed serial data on antibodies against Spike and Nucleocapsid antigens, along with age, sex, number of doses taken, and days since last dose, for 1823 Covaxin recipients. An ensemble ML model, incorporating a consensus clustering approach alongside the support vector machine model, was built on 1063 samples where reliable qualifying data existed, and then applied to the entire dataset. Of 1448 self-reported negative subjects, our ensemble ML model classified 724 to be infected. For method validation, we determined the relative ability of a random subset of samples to neutralize Delta versus wild-type strain using a surrogate neutralization assay. We worked on the premise that antibodies generated by a whole virion vaccine would neutralize wild type more efficiently than delta strain. In 100 of 156 samples, where ML prediction differed from self-reported uninfected status, neutralization against Delta strain was more effective, indicating infection. We found 71.8\% subjects predicted to be infected during the surge, which is concordant with the percentage of sequences classified as Delta (75.6\%-80.2\%) over the same period. Our approach will help in real-world vaccine effectiveness assessments where whole virion vaccines are commonly used.

}, keywords = {BBV152, Covaxin, COVID-19, Ensemble methods, Infection, machine learning, SARS-CoV-2}, issn = {0010-4825}, doi = {10.1016/j.compbiomed.2022.105419}, author = {Singh, Prateek and Ujjainiya, Rajat and Prakash, Satyartha and Naushin, Salwa and Sardana, Viren and Bhatheja, Nitin and Singh, Ajay Pratap and Barman, Joydeb and Kumar, Kartik and Gayali, Saurabh and Khan, Raju and Rawat, Birendra Singh and Tallapaka, Karthik Bharadwaj and Anumalla, Mahesh and Lahiri, Amit and Kar, Susanta and Bhosale, Vivek and Srivastava, Mrigank and Mugale, Madhav Nilakanth and Pandey, C. P. and Khan, Shaziya and Katiyar, Shivani and Raj, Desh and Ishteyaque, Sharmeen and Khanka, Sonu and Rani, Ankita and Promila and Sharma, Jyotsna and Seth, Anuradha and Dutta, Mukul and Saurabh, Nishant and Veerapandian, Murugan and Venkatachalam, Ganesh and Bansal, Deepak and Gupta, Dinesh and Halami, Prakash M. and Peddha, Muthukumar Serva and Veeranna, Ravindra P. and Pal, Anirban and Singh, Ranvijay Kumar and Anandasadagopan, Suresh Kumar and Karuppanan, Parimala and Rahman, Syed Nasar and Selvakumar, Gopika and Venkatesan, Subramanian and Karmakar, Malay Kumar and Sardana, Harish Kumar and Kothari, Anamika and Parihar, Devendra Singh and Thakur, Anupma and Saifi, Anas and Gupta, Naman and Singh, Yogita and Reddu, Ritu and Gautam, Rizul and Mishra, Anuj and Mishra, Avinash and Gogeri, Iranna and Rayasam, Geethavani and Padwad, Yogendra and Patial, Vikram and Hallan, Vipin and Singh, Damanpreet and Tirpude, Narendra and Chakrabarti, Partha and Maity, Sujay Krishna and Ganguly, Dipyaman and Sistla, Ramakrishna and Balthu, Narender Kumar and Kumar, Kiran A. and Ranjith, Siva and Kumar, B. Vijay and Jamwal, Piyush Singh and Wali, Anshu and Ahmed, Sajad and Chouhan, Rekha and Gandhi, Sumit G. and Sharma, Nancy and Rai, Garima and Irshad, Faisal and Jamwal, Vijay Lakshmi and Paddar, Masroor Ahmad and Khan, Sameer Ullah and Malik, Fayaz and Ghosh, Debashish and Thakkar, Ghanshyam and Barik, S. K. and Tripathi, Prabhanshu and Satija, Yatendra Kumar and Mohanty, Sneha and Khan, Md Tauseef and Subudhi, Umakanta and Sen, Pradip and Kumar, Rashmi and Bhardwaj, Anshu and Gupta, Pawan and Sharma, Deepak and Tuli, Amit and Chaudhuri, Saumya Ray and Krishnamurthi, Srinivasan and Prakash, L. and Rao, V. Ch and Singh, B. N. and Chaurasiya, Arvindkumar and Chaurasiya, Meera and Bhadange, Mayuri and Likhitkar, Bhagyashree and Mohite, Sharada and Patil, Yogita and Kulkarni, Mahesh and Joshi, Rakesh and Pandya, Vaibhav and Mahajan, Sachin and Patil, Amita and Samson, Rachel and Vare, Tejas and Dharne, Mahesh and Giri, Ashok and Mahajan, Sachin and Paranjape, Shilpa and Sastry, G. Narahari and Kalita, Jatin and Phukan, Tridip and Manna, Prasenjit and Romi, Wahengbam and Bharali, Pankaj and Ozah, Dibyajyoti and Sahu, RaviKumar and Dutta, Prachurjya and Singh, Moirangthem Goutam and Gogoi, Gayatri and Tapadar, Yasmin Begam and Babu, Elapavalooru V. S. S. K. and Sukumaran, Rajeev K. and Nair, Aishwarya R. and Puthiyamadam, Anoop and Valappil, Prajeesh Kooloth and Prasannakumari, Adrash Velayudhan Pillai and Chodankar, Kalpana and Damare, Samir and Agrawal, Ved Varun and Chaudhary, Kumardeep and Agrawal, Anurag and Sengupta, Shantanu and Dash, Debasis} }