Biology subjects

Pattanayak, A.

Publications and source records attributed to Pattanayak, A..

3 recordsLinked to original sources

bioRxiv2025

Chromosome-Scale, Telomere-to-Telomere Assembly of Winged Bean (Psophocarpus tetragonolobus (L.) DC.) Genome Reveals Lipid Biosynthetic Hotspots and Gene Family Dynamics

Winged bean (Psophocarpus tetragonolobus) is a nutritionally rich but cytologically and genomically underexplored legume with potential for climate-resilient agriculture. We report a telomere-to-telomere, chromosome-scale genome assembly ([~]697.69 Mb; N50 = 85.98 Mb; [~]98.25% of the cytometric estimate) of the cultivar AKWB1 using a combination of PacBio HiFi, Illumina, BioNano, and Hi-C technologies. Cytogenetics revealed a diploid karyotype (2n = 18) with exclusively metacentric chromosomes, establishing the first chromosomal ideotype map for the species. The high-quality assembly anchors 98.28% of bases to nine pseudochromosomes and includes 53,745 annotated protein-coding genes, with 97.6% BUSCO completeness. Repeat elements comprise [~]60% of the genome, dominated by LINEs, LTRs, and DNA transposons. KEGG and GO annotations reveal diverse biosynthetic capabilities across metabolic and stress-response pathways. Comparative genomics with nine legumes demonstrates conserved chromosomal synteny with Glycine max, absence of recent whole-genome duplication, and a contraction-dominated gene family history. Lipid metabolism analysis identified 750+ genes across 12 KEGG pathways, including fatty acid biosynthesis, elongation, and triacylglycerol assembly. Chromosomes 2, 3, and 5 showed enrichment of lipid biosynthetic loci, suggesting evolutionary hotspots. This reference genome positions P. tetragonolobus as a valuable model for studying genome evolution and engineering underutilized legumes for oil yield, protein enrichment, and climate adaptability.

genomics↗

bioRxiv2025

A New Class of Precision Therapeutics that Inhibit Prostate Cancer Mediated Bone Destruction

Cancer mediated bone destruction remains a common cause of morbidity and mortality. Here we couple an inhibitor of cell movement to bone trophic bisphosphonates, forming heterobifunctional compounds termed Dual Acting Bone Defender agents (DABDs). DABDs inhibit prostate cancer cell motility and osteoclast mediated bone destruction in vitro. Our lead compound, BTE-EN1, inhibits circulating human prostate cancer cells from forming bone destructive lesions in mice and prolongs life in a dose-dependent fashion. In mice with established metastatic lesions, BTE-EN1 significantly decreases bone destruction by over 60% compared to the control. Mechanistically, BTE-EN1 binds hydroxyapatite, inhibits prenylation of Rap1A, induces apoptosis in osteoclasts, and inhibits Raf1 activation and formation of bone resorptive cavities in osteoclasts, thus retaining the function of its constituent chemicals. BTE-EN1 is not toxic to mice at doses 4,000-fold over those required for efficacy, exhibits anti-cell motility activity across other bone-destructive cancer cell types, and does not interfere with standard-of-care hormone therapy or chemotherapy. BTE-EN1 is a new class of highly active therapeutics that inhibit cancer mediated bone destruction and holds high potential for translation into humans.

cancer biology↗

bioRxiv2017

From Neural Network To Psychophysics Of Time: Exploring Emergent Properties Of RNNs Using Novel Hamiltonian Formalism

The stability analysis of dynamical neural network systems generally follows the route of finding a suitable Liapunov function after the fashion Hopfields famous paper on content addressable memory network or by finding conditions that make divergent solutions impossible. For the current work we focused on biological recurrent neural networks (bRNNs) that require transient external inputs (Cohen-Grossberg networks). In the current work we have proposed a general method to construct Liapunov functions for recurrent neural network with the help of a physically meaningful Hamiltonian function. This construct allows us to explore the emergent properties of the recurrent network (e.g., parameter configuration needed for winner-take-all competition in a leaky accumulator design) beyond that available in standard stability analysis, while also comparing well with standard stability analysis (ordinary differential equation approach) as a special case of the general stability constraint derived from the Hamiltonian formulation. We also show that the Cohen-Grossberg Liapunov function can be derived naturally from the Hamiltonian formalism. A strength of the construct comes from its usability as a predictor for behavior in psychophysical experiments involving numerosity and temporal duration judgements.

neuroscience↗