@article {46005, title = {Metagenomic insights to understand transient influence of Yamuna River on taxonomic and functional aspects of bacterial and archaeal communities of River Ganges}, journal = {Science of the Total Environment}, volume = {674}, year = {2019}, month = {JUL}, pages = {288-299}, type = {Article}, abstract = {

River confluences are interesting ecosystems to investigate for their microbial community structure and functional potentials. River Ganges is one of the most important and holy river of India with great mythological history and religious significance. The Yamuna River meets Ganges at the Prayagraj (formerly known as Allahabad), India to form a unique confluence. The influence of Yamuna River on taxonomic and functional aspects of microbiome at this confluence and its downstream, remains unexplored. To unveil this dearth, whole metagenome sequencing of the microbial (bacterial and archaeal) community from the sediment samples of December 2017 sampling expedition was executed using high throughput MinION technology. Results revealed differences in the relative abundance of bacterial and archaeal communities across the confluence. Grouped by the confluence, a higher abundance of Proteobacteria and lower abundance of Bacteroidetes and Firnacutes was observed for Yamuna River (G15Y) and at immediate downstream of confluence of Ganges (G15DS), as compared to the upstream, confluence, and farther downstream of confluence. A similar trend was observed for archaeal communities with a higher abundance of Euryarchaeoto in G15Y and G15DS, indicating Yamuna River{\textquoteright}s influence. Functional gene(s) analysis revealed the influence of Yamuna River on xenobiotic degradation, resistance to toxic compounds, and antibiotic resistance interceded by the autochthonous microbes at the confluence and succeeding downstream locations. Overall, similar taxonomic and functional profiles of microbial communities before confluence (upstream of Ganges) and farther downstream of confluence, suggested a transient influence of Yamuna River. Our study is significant since it may be foundational basis to understand impact of Yamuna River and also rare event of mass bathing on the microbiome of River Ganges. Further investigation would be required to understand, the underlying cause behind the restoration of microbial profiles post-confluence farther zone, to unravel the rejuvenation aspects of this unique ecosystem. (C) 2019 Elsevier B.A. All rights reserved.

}, keywords = {Confluence zone, Ganges, Metagenomics, Transient influence, Yamuna}, issn = {0048-9697}, doi = {10.1016/j.scitotenv.2019.04.166}, author = {Samson, Rachel and Shah, Manan and Yadav, Rakeshkumar and Sarode, Priyanka and Rajput, Vinay and Dastager, Syed G. and Dharne, Mahesh S. and Khairnar, Krishna} } @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} } @article {48127, title = {Metagenomic mining of Indian river confluence reveal functional microbial community with lignocelluloytic potential}, journal = {3 Biotech}, volume = {12}, year = {2022}, month = {JUN}, pages = {132}, type = {Article}, abstract = {

Microbial carbohydrate-active enzymes (CAZyme) can be harnessed for valorization of Lignocellulosic biomass (LCB) to value-added chemicals/products. The two Indian Rivers Ganges and the Yamuna having different origins and flow, face accumulation of carbon-rich substrates due to the discharge of wastewater from adjoining paper and pulp industries, which could potentially contribute to the natural enrichment of LCB utilizing genes, especially at their confluence. We analyzed CAZyme diversity in metagenomic datasets across the sacred confluence of the Rivers Ganges and Yamuna. Functional annotation using CAZyme database identified a total of 77,815 putative genes with functional domains involved in the catalysis of carbohydrate degradation or synthesis of glycosidic bonds. The metagenomic analysis detected similar to 41\% CAZymes catalyzing the hydrolysis of lignocellulosic biomass polymers- cellulose, hemicellulose, lignin, and pectin. The Beta diversity analysis suggested higher CAZyme diversity at downstream region of the river confluence, which could be useful niche for culture-based studies. Taxonomic origin for CAZymes revealed the predominance of bacteria (97\%), followed by archaea (1.67\%), Eukaryota (0.63\%), and viruses (0.7\%). Metagenome guided CAZyme diversity of the microflora spanning across the confluence of Ganges-Yamuna River, could be harnessed for biomass and bioenergy applications.

}, keywords = {CAZymes, Confluence (Sangam), Lignocellulosic, River Ganges, River Yamuna}, issn = {2190-572X}, doi = {10.1007/s13205-022-03190-7}, author = {Rajput, Vinay and Samson, Rachel and Yadav, Rakeshkumar and Dastager, Syed and Khairnar, Krishna and Dharne, Mahesh} }