Biology subjects

Bhosale, S.

Publications and source records attributed to Bhosale, S..

5 recordsLinked to original sources

bioRxiv2026

The serum from critical COVID-19 patients induces proteomic changes in olfactory neuroepithelial cells that resemble post-covid neurological complications

Post-acute sequelae of SARS-CoV-2 infection (PASC), commonly referred to as Long COVID, comprise a constellation of persistent, recurrent, or newly emerging symptoms that may endure for months or years following acute infection. Beyond respiratory impairment, PASC is characterized by a wide spectrum of extrapulmonary manifestations, among which neurological and neuropsychiatric symptoms are highly prevalent. Reported features include olfactory dysfunction with loss of smell and taste, fatigue, neuroinflammation, cognitive and memory impairment, depression, and anxiety, with some symptoms persisting up to one year post-infection. Despite increasing recognition of these complications, the molecular mechanisms underlying post-COVID neurological sequelae remain poorly defined. In this study, we employed a label-free quantitative (LFQ) proteomics approach to investigate protein alterations in olfactory neuroepithelium-derived stem cells (ONEs), a unique population of neural progenitors located in the olfactory mucosa at the interface between the respiratory system and both the peripheral and central nervous systems. Due to their anatomical exposure and susceptibility to SARS-CoV-2, ONEs represent a highly relevant translational model for exploring virus-associated neurobiological processes. ONEs derived from healthy donors were incubated with serum from either asymptomatic PCR-positive individuals (AS; n=4) or critically ill hospitalized patients (CR; n=6). Proteomic profiling revealed a distinct differential protein expression pattern in ONEs exposed to CR serum compared with AS serum. Altered pathways were associated with viral infection responses, respiratory and cardiovascular dysfunction, and notably, cerebrovascular and nervous system disorders. These findings highlight the vulnerability of ONEs to systemic factors associated with severe COVID-19 and provide molecular insight into mechanisms potentially contributing to persistent neurological sequelae in PASC. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/710460v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@12cfda5org.highwire.dtl.DTLVardef@c0636borg.highwire.dtl.DTLVardef@bf303eorg.highwire.dtl.DTLVardef@1f861e9_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2025

Future flooding tolerant rice germplasm: resilience afforded beyond Sub1A gene

Developing high-yielding, flood-tolerant rice varieties is essential for enhancing productivity and livelihoods in flood-prone ecologies. We explored genetic avenues beyond the well-known SUB1A gene to improve flood resilience in rice. We screened a collection of 6,274 elite genotypes from IRRIs germplasm repository for submergence and stagnant flooding tolerance over multiple seasons and years. This rigorous screening identified 89 outstanding elite genotypes, among which thirty-seven exhibited high submergence tolerance, surpassing the survival rate of SUB1A introgression genotypes by 40-50%. Thirty-five genotypes showed significant tolerance to stagnant flooding, and 17 demonstrated dual tolerance capabilities, highlighting their adaptability to varying flood conditions. The genotypes identified have a broader genetic diversity and harbor 86 key QTLs and genes related to traits such as grain quality, grain yield, herbicide resistance, and various biotic and abiotic traits, highlighting the richness of the identified elite collection. Besides germplasm, we introduce an innovative breeding approach called Transition from Trait to Environment (TTE). TTE leverages a parental pool of high-performing genotypes with complete submergence tolerance to drive population improvement and enable genomic selection in the flood breeding program. Our approach of TTE achieved a remarkable 65% increase in genetic gain for submergence tolerance, with the resulting fixed breeding genotypes demonstrating exceptional performance in flood-prone environments of India and Bangladesh. The elite genotypes identified herein represent invaluable genetic resources for the global rice research community. By adopting the TTE approach, which is trait agonistic, we establish a robust framework for developing more resilient genotypes using advanced breeding tools. Plain Language SummaryTo address climate challenges, an urgent focus is necessary to identify and develop flood-tolerant rice varieties, particularly for flood-prone ecosystems across Asia and Africa. We screened 6,274 elite genotypes from IRRIs germplasm and identified 89 promising lines with improved tolerance to submergence and stagnant flooding. Among these, 37 demonstrated 40-50% greater submergence tolerance than SUB1A introgression lines, 35 exhibited stagnant flooding tolerance, and 17 showed dual tolerance. These genotypes contain 86 key QTLs and genes associated with yield, grain quality, and biotic and abiotic tolerance traits. A new breeding strategy, the Transition from Trait to Environment (TTE) approach, was developed. We achieved a genetic gain of 65% for submergence tolerance in rice using this method. The newly identified germplasm provides invaluable genetic resources for the global rice research community to develop flood-tolerant rice genotypes. Core ideas The SUB1A gene, enabling rice to survive underwater for 14 days, marked a significant breakthrough. We have identified elite genotypes with submergence tolerance significantly surpassing the SUB1A gene-mediated tolerance. The diverse elite genotypes identified harbor 86 key genes and QTLs that affect various traits positively. Developed a unique breeding strategy for implementing population improvement in challenging environments. The new breeding strategy demonstrated a genetic gain of 65% for submergence tolerance.

plant biology↗

bioRxiv2025

Isolation and characterization of synaptic structures from human neural organoids

Human neural organoids (NOs) provide a powerful platform for investigating synaptic development and dysfunction during early neurodevelopment. However, methodologies for isolating functional synaptic structures from these models remain limited. Here, we present a differential centrifugation protocol enabling the enrichment of growth cone particles (GCPs) and immature synaptosomes from air-liquid interface cerebral organoids (ALI-COs) at distinct developmental stages (day 90 and 150). Notably, the method avoids density gradients, requires minimal starting material while maintaining reproducibility across human and murine tissues. Quantitative proteomic profiling revealed significant enrichment of growth cone markers (e.g. GAP43) and classical synaptosomal proteins (e.g. PCLO, BSN, SYN1). Transmission electron microscopy (TEM) confirmed the presence of membrane-enclosed GCPs with fibrous content and mitochondria in day 90 isolates, and immature synaptosomes containing synaptic vesicles on day 150. Functional viability of both types of synaptic structures was demonstrated through KCl-induced depolarization, which triggered phosphorylation changes in growth cone proteins (GAP43, MARCKS, MARCKSL1), cytoskeletal regulators (DCLK1, SHTN1, MARK4, MAP1B) and protein kinases (CAMK2G, PRKCE) in day 90 GCPs, as well as classical synaptic vesicle cycle proteins (SYN1, DNM1, RPH3A) at day 150. Overall, this study establishes a centrifugation-based protocol for isolating growth cones and immature synapses from human organoids, capturing key stages of synaptic development and enabling scalable, patient-compatible models to study synaptic function and dysfunction in neurodevelopmental and neurodegenerative disorders.

neuroscience↗

bioRxiv2023

DeepMap: A deep learning-based model with four-line code for prediction-based breeding in crops

Prediction of phenotype through genotyping data using the emerging machine or deep learning technology has been proven successful in genomic prediction. We present here a graphical processing unit (GPU) enabled DeepMap configurable deep learning-based python package for the genomic prediction of quantitative phenotype traits. We found that deep learning captures non-linear patterns more efficiently than conventional statistical methods. Furthermore, we suggest an additional module inclusion of epistasis interactions and training of the model on Graphical Processing Units (GPUs) in addition to Central Processing Unit (CPU) to enhance efficiency and increase the models performance. We developed and demonstrated the application of DeepMap using a 3K rice genome panel and 1K-Rice Custom Amplicon (1kRiCA) data for several phenotypic traits including days to 50% flowering (DTF), number of productive tillers (NPT), panicle length (PL), plant height (PH), and plot yield (PY). We have found that DeepMap outperformed the best existing state-of-the-art models by giving higher predictive correlation and low mean squared error for the datasets studied. This prediction performance was higher than other compared models in the range of 13-31%. Similarly for Dataset-2, significantly higher predictions were observed than the compared models (16-20% higher prediction ability). On Dataset-3, we have also shown the better and versatile performance of our model across crops (wheat, maize, and soybean) for yield and yield-related traits. This demonstrates the potentiality of the framework and ease of use for future research in crop improvement. The DeepMap is accessible at https://test.pypi.org/project/DeepMap-1.0/. Short SummaryDeepMap is a deep learning-based breeder-friendly python package to perform genomic prediction. It utilizes epistatic interactions for data augmentation and outperforms the existing state-of-the-art machine/deep learning models such as Bayesian LASSO, GBLUP, DeepGS, and dualCNN. DeepMap developed for rice and tested across crops such as maize, wheat, soybean etc.

genomics↗

bioRxiv2023

Genetic Gains in IRRIs Rice Salinity Breeding and Elite Panel Development as a Future Breeding Resource

Genetic gain is a crucial parameter to check the breeding programs success and help optimize future breeding strategies for enhanced genetic gains. In this work, IRRIs historical data from the Philippines and Bangladesh of the salinity breeding program was used to estimate the genetic gains and identify the best lines based on higher breeding values for yield as a future genetic resource. Two-stage mixed-model approach accounting for experimental design factors and pedigrees was adopted to obtain the breeding values for yield and estimate genetic trends under the salinity conditions. A positive genetic trend of 0.1% per annum with a yield advantage of 1.52 kg/ha for the Philippines and 0.31% per annum with a yield advantage of 14.02 kg/ha for Bangladesh datasets was observed. For the released varieties, genetic gain was 0.12% per annum with a yield advantage of 2.2 kg/ha/year and 0.14% per annum with a yield advantage of 5.9 kg/ha/year, respectively. Further, based on higher breeding values for grain yield, a core set of the top 145 genotypes with higher breeding values of >2400 kg/ha in the Philippines and >3500 kg/ha in Bangladesh with a selection accuracy >0.4 were selected for formulating the elite breeding panel as a future breeding resource. Conclusively, higher genetic gains are pivotal in IRRIs rice salinity breeding program, which requires a holistic breeding approach with a major paradigm shift in breeding strategies to enhance genetic gains. Key MessageEstimating genetic gains and formulating a future salinity elite breeding panel for rice pave the way for developing better high-yielding salinity tolerant lines with enhanced genetic gains.

genetics↗