Biology subjects

Satagopam, V. P.

Publications and source records attributed to Satagopam, V. P..

7 recordsLinked to original sources

bioRxiv2025

Sex-specific microRNA regulators of Parkinson disease: insights from cohort-stratified simulations of compensatory pathway dynamics

Parkinsons disease (PD) exhibits sex differences in prevalence, symptom severity, and progression, suggesting distinct underlying molecular mechanisms. However, the pathophysiological mechanisms remain largely obscure, particularly in the context of the post-transcriptional regulations, where microRNAs (miRNAs) suppress the expression of multiple genes. Bulk transcriptomic data often blur these effects, especially when regulatory patterns vary by sex or cell type. miRNAs have emerged as key regulators of PD-related processes, but their complexity demands computational methods that enable capturing the functional impact at the pathway level. In this study, we investigated how sex may affect miRNA-regulated PD pathways using Boolean modeling across two large PD cohorts, the Parkinsons Progression Markers Initiative (PPMI) and the Luxembourg Parkinsons Study (LuxPark). First, differential expression analysis identified significant variations in miRNA expression between the sexes across both cohorts. These miRNAs were analysed to identify molecular pathways that are over-represented among the predicted targets of the dysregulated microRNAs (i.e. enrichment analysis). The enriched pathways were used to build Boolean models to simulate the effects of sex-specific miRNA dysregulation. These simulations showed consistent male-specific impairment in mitochondrial biogenesis, and respiratory chain activity. Mitophagy and oxidative stress response pathways were also disrupted, alongside dysregulation of autophagy-related protein-folding mechanisms. Our findings suggest that sex-specific miRNA dysregulation contributes to differences in molecular patterns in PD by influencing compensatory related pathways and responses. These results highlight the need for sex-stratified approaches in modeling, translational research, and precision medicine strategies for disease-modifying treatments.

systems biology↗

bioRxiv2025

FAIRification of computational models in biology

Computational models are essential for studying complex systems which, particularly in clinical settings, need to be quality-approved and transparent. To enhance the communication of a models features and capabilities, we propose an adaptation of the Findability, Accessibility, Interoperability and Reusability (FAIR) indicators published by the Research Data Alliance to assess models encoded in domain-specific standards, such as those established by COMBINE. The assessments guide FAIRification and add value to models.

systems biology↗

bioRxiv2024

Deciphering shared molecular dysregulation across Parkinson's disease variants using a multi-modal network-based data integration and analysis

Parkinsons disease (PD) is a progressive neurodegenerative disorder with no effective treatment. Advances in neuroscience and systems biomedicine now enable the use of complex patient-specific in vitro disease models and cutting-edge computational tools for data integration, enhancing our understanding of complex PD mechanisms. To explore common biomedical features across monogenic PD forms, we developed a knowledge graph (KG) by integrating previously published high-content imaging and RNA sequencing data of PD patient-specific midbrain organoids harbouring LRRK2-G2019S, SNCA triplication, GBA-N370S or MIRO1-R272Q mutations with publicly available biological data. Furthermore, we generated a single-cell RNA sequencing dataset of midbrain organoids derived fromidiopathic PD patients (IPD) to stratify IPD patients towards genetic forms of PD. Despite high PD heterogeneity, we found that common transcriptomic dysregulation in monogenic PD forms is reflected in IPD glial cells. In addition, dysregulation in ROBO signalling might be involved in shared pathophysiology between monogenic PD and IPD cases.

systems biology↗

bioRxiv2024

FROG Analysis Ensures the Reproducibility of Genome Scale Metabolic Models

Genome-scale metabolic models (GEMs) and other constraint-based models (CBMs) play a pivotal role in understanding biological phenotypes and advancing research in areas like metabolic engineering, human disease modelling, drug discovery, and personalized medicine. Despite their growing application, a significant challenge remains in ensuring the reproducibility of GEMs, primarily due to inconsistent reporting and inadequate model documentation of model results. Addressing this gap, we introduce FROG analysis, a community-driven initiative aimed at standardizing reproducibility assessments of CBMs and GEMs. The FROG framework encompasses four key analyses--Flux variability, Reaction deletion, Objective function, and Gene deletion--to produce standardized, numerically reproducible FROG reports. These reports serve as reference datasets, enabling model evaluators, curators, and independent researchers to verify the reproducibility of GEMs systematically. BioModels, a leading repository of systems biology models, has integrated FROG analysis into its curation workflow, enhancing the reproducibility and reusability of submitted GEMs. In our study evaluating 65 GEM submissions from the community, approximately 40% reproduced without intervention, 28% requiring minor adjustments, and 32% needing input from authors. The standardization introduced by FROG analysis facilitated the detection and resolution of issues, ultimately leading to the successful reproduction of all models. By establishing a standardized and comprehensive approach to evaluating GEM reproducibility, FROG analysis significantly contributes to making CBMs and GEMs more transparent, reusable, and reliable for the broader scientific community.

systems biology↗

bioRxiv2024

Cohort-specific Boolean models highlight different regulatory modules during Parkinson's disease progression

1Parkinsons Disease (PD) is a multifaceted neurodegenerative disorder characterised by complex molecular dysregulations and diverse comorbidities. It is critical to decode the molecular pathophysiology of PD, which involves complex molecular interactions and their changes over time. Systems medicine approaches can help with this by a) encoding knowledge about the mechanisms into computational models b) simulating these models using patient-specific omics data. This study employs the PD map, a detailed repository of PD-related molecular interactions, as a comprehensive knowledge resource. We aim to dissect and understand the intricate molecular pathways implicated in PD by using logical modelling. This approach is essential for capturing the dynamic interplay of molecular components that contribute to the disease. We incorporate cohort-level and real-world patient data to ensure our models accurately reflect PDs subtype-specific pathway deregulations. This integration is crucial for addressing the heterogeneity observed in PD manifestations and responses to treatment. To combine logical modelling with empirical data, we rely on Probabilistic Boolean Networks (PBNs).These networks provide a robust framework, capturing the stochastic nature of molecular interactions and offering insights into the variable progression of the disease. By combining logical modelling with empirical data through PBNs, we achieve a more refined and realistic representation of PDs molecular landscape. The findings provide insights into the molecular mechanisms of PD. We identify key regulatory biomolecules and pathways that differ significantly across PD subtypes. These discoveries have substantial implications for the development of precise medical treatments. The study provides hypothesis for targeted therapeutic interventions by linking molecular dysregulation patterns to clinical phenotypes and advancing our understanding of PD progression and patient stratification.

systems biology↗

bioRxiv2021

COBREXA.jl: constraint-based reconstruction and exascale analysis

COBREXA.jl is a Julia package for scalable, high-performance constraint-based reconstruction and analysis of very large-scale biological models. Its primary purpose is to facilitate the integration of modern high performance computing environments with the processing and analysis of large-scale metabolic models of challenging complexity. We report the architecture of the package, and demonstrate how the design promotes analysis scalability on several use-cases with multi-organism community models. Availability and implementationhttps://doi.org/10.17881/ZKCR-BT30. Contactchristophe.trefois@uni.lu, wei.gu@uni.lu

systems biology↗

bioRxiv2020

GigaSOM.jl: High-perfomance clustering and visualization of huge cytometry datasets

BackgroundThe amount of data generated in large clinical and phenotyping studies that use single-cell cytometry is constantly growing. Recent technological advances allow to easily generate data with hundreds of millions of single-cell data points with more than 40 parameters, originating from thousands of individual samples. The analysis of that amount of high-dimensional data becomes demanding in both hardware and software of high-performance computational resources. Current software tools often do not scale to the datasets of such size; users are thus forced to down-sample the data to bearable sizes, in turn losing accuracy and ability to detect many underlying complex phenomena. ResultsWe present GigaSOM.jl, a fast and scalable implementation of clustering and dimensionality-reduction for flow and mass cytometry data. The implementation of GigaSOM.jl in the high-level and high-performance programming language Julia makes it accessible to the scientific community, and allows for efficient handling and processing of datasets with billions of data points using distributed computing infrastructures. We describe the design of GigaSOM.jl, measure its performance and horizontal scaling capability, and showcase the functionality on a large dataset from a recent study. ConclusionsGigaSOM.jl facilitates utilization of the commonly available high-performance computing resources to process the largest available datasets within minutes, while producing results of the same quality as the current state-of-art software. Measurements indicate that the performance scales to much larger datasets. The example use on the data from an massive mouse phenotyping effort confirms the applicability of GigaSOM.jl to huge-scale studies. Key pointsO_LIGigaSOM.jl improves the applicability of FlowSOM-style single-cell cytometry data analysis by increasing the acceptable dataset size to billions of single cells. C_LIO_LISignificant speedup over current methods is achieved by distributed processing and utilization of efficient algorithms. C_LIO_LIGigaSOM.jl package includes support for fast visualization of multidimensional data. C_LI

bioinformatics↗