04148nas a2200589   4500008004100000022001400041245012400055210006900179260000800248300001400256490000700270520236200277653001302639653001002652653001202662653003602674653002302710653003402733100002102767700001702788700002102805700002102826700002102847700001902868700001602887700002302903700001702926700002702943700001902970700001902989700002103008700001803029700002003047700001903067700001903086700002103105700002203126700001903148700002203167700001903189700001803208700001903226700002303245700001803268700002003286700002403306700002603330700002803356700002103384700002603405856012703431       2023                        eng d  a1876-034100aTale of two waves: Delineating diverse genomic and transmission landscapes driving the COVID-19 pandemic in Pune, India0 aTale of two waves Delineating diverse genomic and transmission l  cAUG  a1290-13000 v163 a<p>
	Background: Modern response to pandemics, critical for effective public health measures, is shaped by the availability and integration of diverse epidemiological outbreak data. Tracking variants of concern (VOC) is integral to understanding the evolution of SARS-CoV-2 in space and time, both at the local level and global context. This potentially generates actionable information when integrated with epidemiological outbreak data.Methods: A city-wide network of researchers, clinicians, and pathology diagnostic laboratories was formed for genome surveillance of COVID-19 in Pune, India. The genomic landscapes of 10,496 sequenced samples of SARS-CoV-2 driving peaks of infection in Pune between December-2020 to March-2022, were determined. As a modern response to the pandemic, a ``band of five'' outbreak data analytics approach was used. This integrated the genomic data (Band 1) of the virus through molecular phylogenetics with key outbreak data including sample collection dates and case numbers (Band 2), demographics like age and gender (Band 3-4), and geospatial mapping (Band 5).Results: The transmission dynamics of VOCs in 10,496 sequenced samples identified B.1.617.2 (Delta) and BA(x) (Omicron formerly known as B.1.1.529) variants as drivers of the second and third peaks of infection in Pune. Spike Protein mutational profiling during pre and post-Omicron VOCs indicated differential rank ordering of high-frequency mutations in specific domains that increased the charge and binding properties of the protein. Time-resolved phylogenetic analysis of Omicron sub-lineages identified a highly divergent BA.1 from Pune in addition to recombinant X lineages, XZ, XQ, and XM. Conclusions: The band of five outbreak data analytics approach, which integrates five different types of data, highlights the importance of a strong surveillance system with high-quality meta-data for understanding the spatiotemporal evolution of the SARS-CoV-2 genome in Pune. These findings have important implica-tions for pandemic preparedness and could be critical tools for understanding and responding to future outbreaks.&amp; COPY; 2023 Published by Elsevier Ltd on behalf of King Saud Bin Abdulaziz University for Health Sciences. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).</p>
10aCOVID-1910aDelta10aOmicron10aSARS-CoV-2 genomic surveillance10aVariant of concern10aWhole Genome Sequencing (WGS)1 aNiveditha, Divya1 aKhan, Soumen1 aKhilari, Ajinkya1 aNadkarni, Sanica1 aBhalerao, Unnati1 aKadam, Pradnya1 aYadav, Ritu1 aKanekar, Jugal, B.1 aShah, Nikita1 aLikhitkar, Bhagyashree1 aSawant, Rutuja1 aThakur, Shikha1 aTupekar, Manisha1 aNagar, Dhriti1 aRao, Anjani, G.1 aJagtap, Rutuja1 aJogi, Shraddha1 aBelekar, Madhuri1 aPathak, Maitreyee1 aShah, Priyanki1 aRanade, Shatakshi1 aPhadke, Nikhil1 aDas, Rashmita1 aJoshi, Suvarna1 aKaryakarte, Rajesh1 aGhose, Aurnab1 aKadoo, Narendra1 aShashidhara, L., S.1 aMonteiro, Joy, Merwin1 aShanmugam, Dhanasekaran1 aRaghunathan, Anu1 aKarmodiya, Krishanpal  uhttps://library.ncl.res.in/content/tale-two-waves-delineating-diverse-genomic-and-transmission-landscapes-driving-covid-1902423nas a2200217   4500008004100000022001400041245013900055210006900194260000900263490000800272520162400280653002501904653002001929653002301949653001801972653002101990653002202011100002002033700002002053856013202073       2024                        eng d  a0167-732200aTranscriptome analysis and Structure-Based drug discovery identifies potential biofungicides for controlling Fusarium wilt in chickpea0 aTranscriptome analysis and StructureBased drug discovery identif  cAPR 0 v3993 a<p>
	Fusarium wilt caused by the fungal pathogen Fusarium oxysporum f.sp. ciceri (Foc) is a devastating chickpea disease. Foc is a soil-borne pathogen that invades plants through roots and can kill them in three to four weeks. Although Fusarium wilt can be controlled by soil solarization or fumigation with chemical fungicides, these are not always effective. Soil fumigation is also hazardous to the beneficial soil microflora, deteriorates soil health, and causes pollution. Hence, there is an urgent need to identify potent and environment-friendly biofungicides to control fungal pathogens. We employed transcriptome analysis and structure-based drug discovery approaches to identify potential biofungicides from four widely used medicinal plants: Lantana camara, Piper betel, Ricinus communis, and Azadirachta indica. Fusarium wilt-resistant and susceptible chickpea varieties were pathogeninoculated and grown under controlled conditions in a greenhouse. Transcriptome analysis was performed to identify pathogenicity-related differentially expressed genes (DEGs). Over 600 phytochemicals from the four medicinal plants and four chemical fungicides were considered for molecular docking against the predicted protein structures of the four most expressed pathogen DEGs. The phytochemicals with the best docking scores and the lowest predicted toxicity risk were considered for molecular dynamics (MD) simulation at 100 ns timescale, and 15 potential biofungicides were identified. This study paves the way for developing biofungicides with enhanced efficacy and safety to manage Fusarium wilt in chickpea.</p>
10aAntifungal molecules10aCicer arietinum10afusarium oxysporum10aMD Simulation10amedicinal plants10aMolecular docking1 aSahoo, Rosaleen1 aKadoo, Narendra  uhttps://library.ncl.res.in/content/transcriptome-analysis-and-structure-based-drug-discovery-identifies-potential-biofungicides