Biology subjects

Hadizadeh Esfahani, A.

Publications and source records attributed to Hadizadeh Esfahani, A..

3 recordsLinked to original sources

bioRxiv2020

Plant PhysioSpace: a robust tool to compare stress response across plant species

Generalization of transcriptomics results can be achieved by comparison across experiments, which is based on integration of interrelated transcriptomics studies into a compendium. Both characterization of the fate of the organism as well as distinguishing between generic and specific responses can be gained in such a broader context. There are numerous methods for analyzing such data sets, most focusing on gene-wise dimension reduction to obtain marker genes and gene sets, e.g. for pathway analysis. Relying only on isolated biological modules might lead to missing of important confounders and relevant context. We have developed a novel method called Plant PhysioSpace, which provides the ability to compute experimental conditions across species and platforms without a priori reducing the reference information to specific gene-sets. It extracts physiologically relevant signatures from a reference data set, a collection of public data sets, by integrating and transforming heterogeneous reference gene expression data into a set of physiology-specific patterns. New experimental data can be mapped to these patterns, resulting in similarity scores which provide quantitative likeness of the new experiment to the a priori compendium. Because of its robustness against noise and platform bias, Plant PhysioSpace can be used as an inter-species or cross-platform similarity measure. We have demonstrated its success in translating stress responses between different species and platforms (including single-cell technologies). We also report the implementation of two R packages, one software and one data package, and a shiny web application, which provides plant biologists convenient ways to access our method and precomputed models from four different species.

plant biology↗

bioRxiv2019

Methodological Challenges in Translational Drug Response Modeling in Cancer

Translational models directly relating drug response-specific processes observed in vitro to their in vivo role in cancer patients constitute a crucial part of the development of personalized medication. Unfortunately, ongoing research is often confined by the irreproducibility of the results in other contexts. While the inconsistency of pharmacological data has received great attention recently, the computational aspect of this crisis still deserves closer examination. Notably, studies often focus only on isolated model characteristics instead of examining the overall workflow and the interplay of individual model components. Here, we present a systematic investigation of translational models using the R-package FORESEE. Our findings confirm that with the current exploitation of the available data and the prevailing trend of optimizing methods to only one specific use case, modeling solutions will continue to suffer from non-transferability. Instead, the conduct of developing translational approaches urgently needs to change to retrieve clinical relevance in the future.

bioinformatics↗

bioRxiv2017

A Systematic Atlas of Chaperome Deregulation Topologies Across the Human Cancer Landscape

Proteome balance is safeguarded by the proteostasis network (PN), an intricately regulated network of conserved processes that evolved to maintain native function of the diverse ensemble of protein species, ensuring cellular and organismal health. Proteostasis imbalances and collapse are implicated in a spectrum of human diseases, from neurodegeneration to cancer. The characteristics of PN disease alterations however have not been assessed in a systematic way. Zooming in on the chaperome as a central PN component we turned to a curated functional ontology of the human chaperome that we connect in a high-confidence physical protein-protein interaction network. Challenged by the lack of a systems-level understanding of proteostasis alterations in the heterogeneous spectrum of human cancers, we assessed gene expression across more than 10,000 patient biopsies covering 22 solid cancers. We derived a novel customized Meta-PCA dimension reduction approach yielding M-scores as quantitative indicators of disease expression changes to condense the complexity of cancer transcriptomics datasets into quantitative functional network topographies. We confirm upregulation of the HSP90 family and also highlight HSP60s, Prefoldins, HSP100s, ER- and mitochondria-specific chaperones as pan-cancer enriched. Our analysis also reveals a surprisingly consistent strong downregulation of small heat shock proteins (sHSPs) and we stratify two cancer groups based on the preferential upregulation of ATP-dependent chaperones. Strikingly, our analysis highlight similarities between stem cell and cancer proteostasis, and diametrically opposed chaperome deregulation between cancers and neurodegenerative diseases. We developed a web-based Proteostasis Profiler tool (Pro2) enabling intuitive analysis and visual exploration of proteostasis disease alterations using gene expression data. Our study showcases a comprehensive profiling of chaperome shifts in human cancers and sets the stage for a systematic global analysis of PN alterations across the human diseasome towards novel hypotheses for therapeutic network re-adjustment in proteostasis disorders.

systems biology↗