Biology subjects

Jain, G.

Publications and source records attributed to Jain, G..

9 recordsLinked to original sources

bioRxiv2025

Non-Invasive Diagnostic Evaluation of Urinary Exosomal Let-7c Cluster Expression in Bladder Cancer Using Machine Learning Approaches

BackgroundBladder cancer (BCa) diagnosis typically relies on invasive cystoscopy, which is effective but costly and uncomfortable. Urinary microRNAs (miRNAs), especially exosomal ones, are promising non-invasive biomarkers due to their stability in biological fluids and disease specificity. However, challenges such as population variability, methodological inconsistencies and normalization issues hinder their clinical translation, emphasizing the need for innovative approaches to enhance diagnostic performance. ObjectiveTo evaluate the diagnostic potential of urinary exosomal let-7c cluster (let-7c-5p, miR-99a-5p and miR-125b-5p) in BCa patients by integrating miRNA expression data with Machine Learning (ML) models. MethodsUrine samples were collected from 66 participants, including 50 BCa patients and 16 healthy controls (HC). Exosomal miRNAs were isolated and quantified using Quantitative Real-Time-Polymerase-Chain-Reaction (qRT-PCR). Statistical analysis and hypothesis tests were conducted to explore the nature and diagnostic relevance of individual biomarkers. A logistic regression classifier was applied to evaluate both the combined and differential diagnostic capabilities of the selected biomarkers. Accuracy, precision, recall and AU-ROC scores were used to assess model performance. Bioinformatics analysis was performed to identify pathways associated with the features prioritized by the ML models, ensuring their relevance to BCa. ResultsThe result revealed significant differentiation between BCa patients and HC, with miR-99a-5p (p=0.013, AU-ROC=0.71) and miR-125b-5p (p=0.047, AU-ROC=0.64) demonstrating reliable diagnostic performance (let-7c-5p showed weaker discrimination, AU-ROC=0.65, p>0.1). The logistic regression ML model achieved an accuracy of 80.0% (AU-ROC=0.86, recall=100%) in distinguishing cancer from HC and 53.3% (AU-ROC=0.63) when applied to miRNA-only grade classification. When clinical variables were integrated with miRNA expression, performance improved to 73.3% accuracy (AU-ROC=0.61) for high-versus low-grade differentiation. Across Ta-T2, miR-99a-5p displayed relatively better separation, whereas let-7c-5p and miR-125b-5p showed weak stage-related differences. The integration of bioinformatics analysis confirmed the biological relevance of these miRNAs in BCa-related pathways, including PI3K-Akt, p53, NF-{kappa}B and RAS/MAPK signaling, with hub genes such as TP53, MYC, EGFR, and CCND1 identified, further validating the diagnostic utility of the selected biomarkers. ConclusionUrinary let-7c cluster miRNAs demonstrate promising diagnostic potential when analyzed with ML models, offering a non-invasive alternative to conventional methods. These findings highlight the promise of ML-based approaches alongside molecular markers for advancing clinical diagnostics in BCa.

molecular biology↗

bioRxiv2025

Probing How Anti-Huntingtin Antibodies Bind The Fibrillar Fuzzy Coat Using Solid-State NMR

Antibodies are critical for the immune response and serve as important tools due to their ability to recognize specific amino acid sequences, or epitopes. Based on the latter, they are utilized as diagnostic tools in biological and biomedical research. Huntingtons disease (HD) is a neurodegenerative condition caused by CAG repeat expansions in the huntingtin (HTT) gene and characterized by amyloid-like protein deposits in patients. Multiple anti-HTT antibodies are used in HD research for their ability to recognize specific HTT inclusions in both post-mortem tissue and in laboratory conditions. Some of the antibodies are seen as detectors of distinct structural motifs. However, most knowledge of their binding mechanism stems from studies of soluble monomers or short fragments of the epitopes, rather than the aggregated, misfolded target protein. Here, we investigate how MW8 antibodies interact with HTT exon 1 (HTTex1) fibrils, using solid-state NMR, electron microscopy, and complementary techniques. Magic angle spinning NMR revealed localized impacts of the antibody on exposed parts of the HTTex1 fibrils: the flanking segments that form its fuzzy coat. Antibody binding affected the structure and dynamics of the fuzzy coat, but also modulated the propensity for forming supramolecular fibril clusters, which has important implications for (reducing) cytotoxicity.

biochemistry↗

bioRxiv2024

Direct observation of secondary nucleation in huntingtin amyloid formation by High-Speed Atomic Force Microscopy

Amyloid fibril formation is a hallmark of various neurodegenerative diseases such as Huntingtons (HD), Alzheimers and Parkinsons disease. The protein aggregation process involves slow nucleation events followed by rapid growth and elongation of formed fibrils. Understanding the pathways of amyloid formation is key to development of novel therapeutic agents that can interfere with the pathogenic protein misfolding events. Recent studies of aggregation by polypeptides from Alzheimers and Huntingtons disease have identified the importance of a poorly understood secondary nucleation process that may even be the dominant source of protein aggregate formation. Here we focus on the polyglutamine-expansion disorder HD and employ mechanistic and structural studies to study different aspects of secondary nucleation in the aggregation of huntingtin Exon 1 (HttEx1). Notably, we apply high-speed atomic force microscopy (HS-AFM) to directly observe the process on the single-particle level and in real time. Our observations show unique features of the amyloid formation dynamics in real time, including secondary nucleation, elongation and the formation of large bundles of fibrils as a result of nucleated branching. We examine the role of HttEx1 flanking segments during the aggregation process, revealing that the N-terminal HttNT segment exhibits a clear primary nucleation-aggregation-enhancing ability, however, that it does not seem to induce or affect the secondary nucleation process. The obtained results illuminate the complex aggregation process of HttEx1 and have implications for attempts to inhibit or modulate it for therapeutic purposes.

molecular biology↗

bioRxiv2024

miRNA Biomarkers in Prostate Cancer: Leveraging Machine Learning for Improved Diagnostic Accuracy

Prostate cancer (PCa) diagnosis often relies on prostate-specific antigen (PSA) testing, but its high false-positive rates often lead to unnecessary biopsies. MicroRNAs (miRNAs) have emerged as promising non-invasive biomarkers for cancer detection due to their stability in biological fluid and disease specificity. Despite their potential, the clinical translation of miRNAs as non-invasive cancer biomarkers is hindered by several challenges - population-based variability, environmental Factors, methodological Inconsistencies, lack of standardization, normalization Issues, and complexity of the biological System. These factors significantly impact the consistency of miRNA expression readouts, particularly in terms of Ct-values, across different studies, which in turn affects the determination of cutoff values that are crucial in a diagnostic setup. This preliminary study offers a pilot demonstration for integrating miRNA biomarker expression with machine learning (ML), which can help identify patterns and improve classification, potentially reducing the reliance on fixed cutoff values in certain contexts and pave the path to wider clinical translation. We analyzed the expression of key miRNAs (miR-21-5p, miR-221-3p, and miR-141-3p) in blood samples from patients with PCa and benign prostatic hyperplasia (BPH). Utilizing a Random Forest classifier, we achieved an accuracy of 77.42%, a precision of 86.21%, a recall of 71.43%, and an AUC-ROC score of 0.78. The application of ML enabled us to leverage complex features, such as combinations and ratios of miRNA expression data, which enhanced the robustness and reliability of the diagnostic model. Additionally, bioinformatics analysis of the preferential features identified by the ML model confirmed the biological relevance of these miRNAs in PCa-related pathways, further supporting their potential as clinical biomarkers. In the future, ML is poised to significantly enhance diagnostic performance compared to traditional linear analyses of a limited set of biomarkers. While our study did not explore multiple populations or the effects of methodological variables, it highlights the potential of ML by demonstrating improved accuracy and eliminating the need for cutoff values. This capability could broaden the applicability of miRNA-based diagnostics, making them more reliable and actionable in clinical settings. However, to fully realize this potential, further validation with larger and more diverse cohorts is essential. Overall, this study lays the groundwork for utilizing ML-enhanced miRNA panels as powerful tools for the early and non-invasive diagnosis of PCa in future clinical practice. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=130 SRC="FIGDIR/small/618146v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@2eddd9org.highwire.dtl.DTLVardef@e6d6c8org.highwire.dtl.DTLVardef@11eea0forg.highwire.dtl.DTLVardef@989c15_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2023

Guggulsterone Enhances NKX3.1 Expression and Induces Apoptosis in Prostate Cancer Cells: Implications for Chemoprevention

AimProstate cancer is a leading cause of cancer-related mortality, necessitating novel therapeutic and preventive strategies. This study aimed to investigate the anti-cancer properties of Guggulsterone (GS) on prostate cancer cells, specifically focusing on its effects on homeobox protein NKX3.1, apoptosis, and mitochondrial function. MethodThe study utilized the LNCaP prostate cancer cell line and involved cell culture, dose-response analysis, flow cytometry for apoptosis quantification, siRNA-mediated knockdown of NKX3.1, mitochondrial membrane potential assessment using TMRM staining, qRT-PCR analysis, and Western blotting. The stability of NKX3.1 mRNA and protein in response to GS was also evaluated. ResultGS treatment induced apoptosis in LNCaP cells in a dose and time-dependent manner, with an impact on early and late-stage apoptosis. This effect was mediated, at least in part, through the upregulation of NKX3.1. GS enhanced NKX3.1 expression at both the protein and mRNA levels, even in the absence of androgens. Furthermore, GS treatment extended the half-life of NKX3.1 mRNA and protein, suggesting an effect on their stability. ConclusionThis study provides valuable insights into the anti-cancer mechanisms of GS in prostate cancer cells. The upregulation of NKX3.1 by GS and its role in mediating apoptosis offers NKX3.1 as a potential prevention target for PCa. The findings open new research avenues for the development of targeted therapies that stabilize NKX3.1 expression and protect mitochondrial function. Further investigations are needed to understand the intricate molecular mechanisms underlying GSs effects fully, potentially improving prostate cancer management and outcomes.

cancer biology↗

bioRxiv2023

gQuant: A Robust and Generalizable Algorithm for Identifying Normalizer Genes in qRT-PCR Data: A Case Study on Urinary Exosomal miRNAs

The emergent role of nucleic acid-based biomarkers, such as microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and messenger RNAs (mRNAs), is becoming increasingly prominent in the realms of disease diagnostics and risk assessment. Quantitative reverse transcription PCR (qRT-PCR) is the primary analytical method for quantitative measurement of biomarkers. Yet, the relative infancy of non-coding RNAs (ncRNAs) recognition as biomarkers poses a challenge due to the absence of a consensus on a universally accepted normalizer gene, which is pivotal for accurate quantification. Current tools for selecting normalizer genes in qRT-PCR are fraught with limitations, including inadequate handling of null values, reliance on elementary statistical tools, use of a biased integrated approach, outlier sensitivity, and suboptimal graphical user interface for data visualization. These deficiencies underscore the necessity for a more nuanced and algorithmically balanced tool tailored to handle qRT-PCR datasets and facilitate the discernment of the most appropriate normalizer gene for specific datasets. Addressing the identified challenges, we have developed gQuant, a tool crafted to address the limitations present in existing methods. In gQuant we employed voting classifiers as an ensemble technique that combines predictions from multiple statistical methods to make more accurate rankings than any individual statistical measures. The tools efficacy was substantiated through rigorous validation against datasets from the Gene Expression Omnibus (GEO) database and corroborated with experimental data derived from urinary exosomal miRNAs. Comparative analysis with existing tools revealed that their integrated methodologies could skew the ranking of normalizer genes, whereas gQuant consistently yielded rankings characterized by lower standard deviation, reduced covariance, and enhanced kernel density estimation (KDE) values. Given gQuants promising performance, normalizer gene identification will be greatly improved, improving the precision of gene expression quantification in a variety of research scenarios.

molecular biology↗

bioRxiv2023

Integrative determination of the atomic structure of mutant huntingtin exon 1 fibrils from Huntington's disease

Neurodegeneration in Huntingtons disease (HD) is accompanied by the aggregation of fragments of the mutant huntingtin protein, a biomarker of disease progression. A particular pathogenic role has been attributed to the aggregation-prone huntingtin exon 1 (HTTex1), generated by aberrant splicing or proteolysis, and containing the expanded polyglutamine (polyQ) segment. Unlike amyloid fibrils from Parkinsons and Alzheimers diseases, the atomic-level structure of HTTex1 fibrils has remained unknown, limiting diagnostic and treatment efforts. We present and analyze the structure of fibrils formed by polyQ peptides and polyQ-expanded HTTex1 in vitro. Atomic-resolution perspectives are enabled by an integrative analysis and unrestrained all-atom molecular dynamics (MD) simulations incorporating experimental data from electron microscopy (EM), solid-state NMR, and other techniques. Alongside the use of prior data, we report new magic angle spinning NMR studies of glutamine residues of the polyQ fibril core and surface, distinguished via hydrogen-deuterium exchange (HDX). Our study provides a new understanding of the structure of the core as well as surface of aggregated HTTex1, including the fuzzy coat and polyQ-water interface. The obtained data are discussed in context of their implications for understanding the detection of such aggregates (diagnostics) as well as known biological properties of the fibrils.

molecular biology↗

bioRxiv2023

Inhibitor-based modulation of huntingtin aggregation reduces fibril toxicity

Huntingtons disease (HD) is a neurodegenerative disorder in which mutated fragments of the huntingtin protein (Htt) undergo misfolding and aggregation. Since misfolded and aggregated proteins can cause cellular stress and cytotoxicity, there is an interest in the development of small molecule aggregation inhibitors as potential modulators of HD pathogenesis. Here, we study how a polyphenol modulates the aggregation mechanism of huntingtin exon 1 (HttEx1) even at sub-stoichiometric ratios. Sub-stoichiometric amounts of curcumin impacted the primary and/or secondary nucleation events, extending the pre-aggregation lag phase. Remarkably, the disrupted aggregation process changed both the aggregate structure and its cell metabolic properties. When administered to neuronal cells, the break-through protein aggregates induced a significantly reduced cellular stress compared to aggregates formed in absence of inhibitors. Structural analysis by electron microscopy, small angle X-ray scattering (SAXS), and solid-state NMR spectroscopy identified changes in the fibril structures, probing both the flanking domains that form the fuzzy coat, as well as the fibril core. The biggest changes affected the latter, which we propose to relate to the presence or absence of polyglutamine (polyQ) {beta}-hairpin structures. Our findings highlight multifaceted consequences of small molecule inhibitors that modulate the protein misfolding landscape, with potential implications for treatment strategies in HD and other amyloid disorders.

biophysics↗

bioRxiv2017

RNA-dependent intergenerational inheritance of enhanced synaptic plasticity after environmental enrichment

Physical exercise in combination with cognitive training is known to enhance synaptic plasticity and lower the risk for various complex diseases including Alzheimers disease. Here we show that exposure of adult male mice to an environmental enrichment paradigm leads to long-lasting enhancement of synaptic plasticity that is also passed on to the next generation. We show that the effect is mediated through sperm RNA and is explained by microRNAs 212/132. In conclusion, our study reports intergenerational inheritance of an acquired cognitive benefit and points to specific microRNAs as candidates mechanistically involved in this type of transmission.

neuroscience↗