Biology subjects

Patil, S. S.

Publications and source records attributed to Patil, S. S..

8 recordsLinked to original sources

bioRxiv2026

DNA Damage and Repair Mechanisms in Duckweed (Spirodela polyrhiza) Under Ultraviolet-B (UV-B) Light Stress

Exposure to Ultraviolet-B (UV-B) light can adversely affect plant growth and cellular integrity by inducing oxidative stress and DNA damage. In this study, we investigated UV-B-induced DNA damage and repair responses in the aquatic monocotyledonous plant species Spirodela polyrhiza (duckweed). We exposed 13-day-old duckweed plantlets to broadband UV-B light for 1-10 min, followed by recovery periods of up to 24 h under normal growth conditions. We observed progressive chlorosis, wilting, and diminished plant vigor with longer durations of UV-B light exposure. Agarose gel electrophoresis demonstrated compromised genomic DNA integrity immediately after UV-B light treatment, with partial restoration of DNA quality during recovery. Immuno-slot blot assays established the accumulation of two major UV light-induced photoproducts, cyclobutane pyrimidine dimers (CPDs) and 6-4 pyrimidine-pyrimidone photoproducts [(6-4)PPs], in a dose-dependent manner following UV-B light exposure. Notably, the abundance of these DNA lesions declined substantially after recovery, indicating activation of endogenous DNA repair mechanisms. Staining with 3,3-diaminobenzidine revealed elevated accumulation of hydrogen peroxide immediately following UV-B exposure, suggesting enhanced oxidative stress. Collectively, these findings demonstrate that S. polyrhiza possesses efficient mechanisms for sensing, repairing, and mitigating DNA damage induced by oxidative stress resulting from UV-B light exposure. This study highlights the potential of duckweed as an effective model system for investigating DNA damage and repair pathways under UV-B light stress in plants.

plant biology↗

bioRxiv2026

An Integrated Computational Approach to Predict and Characterize Emerging Mutations in the Japanese Encephalitis Virus Envelope Protein

Japanese encephalitis virus (JEV) causes significant encephalitis across the Asia-Pacific region. Current vaccines target historical genotype III strains, but emerging genotypes,potentially driven by vaccine-mediated selective pressure, threaten vaccine effectiveness through altered envelope protein sequences that may reduce antibody cross-neutralisation. This study employed integrated sequence and structural analyses to identify E protein mutations affecting neutralising antibody binding and protein stability. The study curated JEV polyprotein sequences from NCBI, performed multiple sequence alignment, and used Shannon entropy to pinpoint highly variable positions. Mutations occurring at [≥]1% frequency within high-entropy regions were selected for analysis. From 34 initially identified mutations, four candidates were prioritized based on structural stabilization potential. Mutations were evaluated through FoldX stability predictions, molecular docking with antibody 2H4 using HADDOCK3, and molecular dynamics simulations. Binding energies were calculated using MM-GBSA analysis. Results demonstrated that all mutant E-2H4 complexes remained stable during simulations, with root-mean-square deviation plateauing after equilibration and minimal localized changes in root-mean-square fluctuation. These findings suggest that EDIII substitutions represent important candidates for further investigation to understand genotype-specific variations and inform next-generation vaccine development strategies against emerging JEV strains.

bioinformatics↗

bioRxiv2026

Conservation of Long G4-rich (LG4) genomic enhancer regulations

Long G4-rich regions (LG4s) are defined as DNA sequences containing a high density of guanine triplets capable of forming non-B DNA structures called G-quadruplexes (G4s). These regions frequently overlap with enhancers, which are regulatory DNA elements that modulate gene expression by interacting with DNA regions that dictate where transcription is initiated known as promoters. While LG4s have now been well-characterized in the human genome, neither LG4 occurrence, nor the ability of LG4s to function as enhancers, in other species has been described. To address this, we screened the genomes of 16 different species from various taxa to identify LG4s and then determined if they were conserved, and if so, if their regulatory capacity was similarly conserved. Our analyses characterized a number of previously unreported LG4s in the human genome as well as LG4s in 13 additional species. Of note, we identified a highly conserved LG4 enhancer predicted to regulate over 40 genes. This LG4 is embedded in the MAZ (Myc-Associated Zinc finger protein) locus, and we find this LG4 possesses the ability to directly interact with the same target promoter in both human and mouse. In summary, this work describes LG4s in the genomes of both unicellular and multicellular species including vertebrates, invertebrates, plants, and fungi. Furthermore, many of these LG4 sequences are highly conserved as is their regulatory capacity.

genomics↗

bioRxiv2024

Annexin A2 and lamin B join membrane recycling compartments for the assembly of biomolecular condensates operating in mitotic partitioning

Localization of the actin-, lipid- and mRNA-binding protein Annexin A2 (AnxA2) in dividing cells revealed its presence in large spherical structures which are confined to the cell periphery and frequently co-align with astral microtubules. These structures emerge at prometaphase and disappear at telophase, coinciding with the mitotic breakdown and reformation of the nuclear envelope, respectively. Their size increases towards anaphase, while their number decreases, indicating that they are capable of fusion. Treatment of cells with propylene glycol caused rapid and reversible disassembly of the structures, providing additional evidence that they correspond to biomolecular condensates. Interestingly, the condensates contain compartments operating in biosynthetic or endocytic membrane recycling - defined by Rab1, Rab11 or endocytosed transferrin - but are devoid of other membrane organelles, implying that they constitute a mitotic reservoir for selected endomembranes. They also contain lamin B, which connects with the pericentrosomal membrane recycling compartments at prometaphase, when the nuclear lamina breaks down in parallel with centrosome separation. These results suggest that the peripheral condensates correspond to the enigmatic membranous spindle matrix implicated in spindle organization and organelle inheritance. The separating daughter cells at late anaphase contain equal numbers of the condensates, in accordance with their role in mitotic partitioning of endomembranes and other cytoplasmic components.

cell biology↗

bioRxiv2023

Transkingdom Network Analysis (TkNA): a systems approach inferring causal factors underlying host-microbiota interactions.

Technological advances have generated tremendous amounts of high-throughput omics data. Integrating data from multiple cohorts and diverse omics types from new and previously published studies can offer a holistic view of a biological system and aid in deciphering its critical players and key mechanisms. In this protocol, we describe how to use Transkingdom Network Analysis (TkNA), a unique causal-inference analytical framework that can perform meta-analysis of cohorts and detect master regulators among measured parameters that govern pathological or physiological responses of host-microbiota (or any multi-omic data) interactions in a particular condition or disease. TkNA first reconstructs the network that represents a statistical model capturing the complex relationships between the different omics of the biological system. Here, it selects differential features and their per-group correlations by identifying robust and reproducible patterns of fold change direction and sign of correlation across several cohorts. Next, a causality-sensitive metric, statistical thresholds, and a set of topological criteria are used to select the final edges that form the transkingdom network. The second part of the analysis involves interrogating the network. Using the networks local and global topology metrics, it detects nodes that are responsible for control of given subnetwork or control of communication between kingdoms and/or subnetworks. The underlying basis of the TkNA approach involves fundamental principles including laws of causality, graph theory and information theory. Hence, TkNA can be used for causal inference via network analysis of any host and/or microbiota multi-omics data. This quick and easy-to-run protocol requires very basic familiarity with the Unix command-line environment.

systems biology↗

bioRxiv2021

Targeting the translational machinery in gastrointestinal stromal tumors (GIST) - a new therapeutic vulnerability

Although KIT-mutant GISTs can be effectively treated with tyrosine kinase inhibitors (TKIs), many patients develop resistance to imatinib mesylate (IM) as well as the FDA-approved later-line agents sunitinib, regorafenib and ripretinib. Resistance mechanisms mainly involve secondary mutations in the KIT receptor tyrosine kinase gene indicating continued dependency on the KIT signaling pathway. The fact that the type of secondary mutation confers either sensitivity or resistance towards TKIs and the notion that secondary mutations exhibit intra- and intertumoral heterogeneity complicates the optimal choice of treatment in the imatinib-resistant setting. Therefore, new strategies that target KIT independently of its underlying mutations are urgently needed. Homoharringtonine (HHT) is a first-in-class inhibitor of protein biosynthesis and is FDA-approved for the treatment of chronic myeloid leukemia (CML) that is resistant to at least two TKIs. HHT has also shown activity in KIT-mutant mastocytosis models, which are intrinsically resistant to imatinib and most other TKIs. We hypothesized that HHT could be effective in GIST through downregulation of KIT expression and subsequent decrease of KIT activation and downstream signaling. Testing several GIST cell line models, HHT led to a significant reduction in nascent protein synthesis and was highly effective in the nanomolar range in IM-sensitive and IM-resistant GIST cell lines. HHT treatment resulted in a rapid and complete abolishment of KIT expression and activation, while KIT mRNA levels were minimally affected. The response to HHT involved induction of apoptosis as well as cell cycle arrest. The antitumor activity of HHT was confirmed in a GIST xenograft model. Taken together, inhibition of protein biosynthesis is a promising strategy to overcome TKI resistance in GIST.

cancer biology↗

bioRxiv2021

Multi-Trait Machine and Deep Learning Models for Genomic Selection using Spectral Information in a Wheat Breeding Program

Prediction of breeding values and phenotypes is central to plant breeding and has been revolutionized by the adoption of genomic selection (GS). Use of machine and deep learning algorithms applied to complex traits in plants can improve prediction accuracies in the context of GS. Spectral reflectance indices further provide information about various physiological parameters previously undetectable in plants. This research explores the potential of multi-trait (MT) machine and deep learning models for predicting grain yield and grain protein content in wheat using spectral information in GS models. This study compares the performance of four machine and deep learning-based uni-trait (UT) and MT models with traditional GBLUP and Bayesian models. The dataset consisted of 650 recombinant inbred lines from a spring wheat breeding program, grown for three years (2014-2016), and spectral data were collected at heading and grain filling stages. MT-GS models performed 0-28.5% and -0.04-15% superior to the UT-GS models for predicting grain yield and grain protein content. Random forest and multilayer perceptron were the best performing machine and deep learning models to predict both traits. These two models performed similarly under UT and MT-GS models. Four explored Bayesian models gave similar accuracies, which were less than machine and deep learning-based models, and required increased computational time. Green normalized difference vegetation index best predicted grain protein content in seven out of the nine MT-GS models. Overall, this study concluded that machine and deep learning-based MT-GS models increased prediction accuracy and should be employed in large-scale breeding programs. Core IdeasO_LIPotential for combining high throughput phenotyping, machine and deep learning in breeding. C_LIO_LIMulti-trait models exploit information from secondary correlated traits efficiently. C_LIO_LISpectral information improves genomic selection models. C_LIO_LIDeep learning can aid plant breeders owing to increased data generated in breeding programs C_LI

genetics↗

bioRxiv2019

Differential antitumor activity of compounds targeting the ubiquitin-proteasome machinery in gastrointestinal stromal tumors (GIST)

The majority of gastrointestinal stromal tumors (GISTs) are driven by oncogenic KIT signaling and can therefore be effectively treated with the tyrosine kinase inhibitor (TKI) imatinib mesylate. However, most GISTs develop imatinib resistance through secondary KIT mutations. The type of resistance mutation determines sensitivity to approved second-/third-line TKIs but shows high inter- and intratumoral heterogeneity. Therefore, therapeutic strategies that target KIT independently of the mutational status are intriguing. Inhibiting the ubiquitin-proteasome machinery with bortezomib is effective in GIST cells through a dual mechanism of KIT transcriptional downregulation and upregulation of the pro-apoptotic histone H2AX but clinically problematic due to the drugs adverse effects. We therefore tested second-generation inhibitors of the 20S proteasome (delanzomib, carfilzomib and ixazomib) with better pharmacologic profiles as well as compounds targeting regulators of ubiquitination (b-AP15, MLN4924) for their effectiveness and mechanism of action in GIST. All three 20S proteasome inhibitors were highly effective in vitro and in vivo, including in imatinib-resistant models. In contrast, b-AP15 and MLN4924 were only effective at high concentrations or had mostly cytostatic effects, respectively. Our results confirm 20S proteasome inhibitors as promising strategy to overcome TKI resistance in GIST, while highlighting the complexity of the ubiquitin-proteasome machinery as a therapeutic target.

cancer biology↗