Biology subjects

So, E.

Publications and source records attributed to So, E..

6 recordsLinked to original sources

bioRxiv2023

Reusability Report: Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients

Machine learning (ML) and artificial intelligence (AI) methods are increasingly used in personalized medicine, including precision oncology. Ma et al. (Nature Cancer 2021) developed a new method c alled "Transfer of Cell Line Response Prediction" (TCRP) to train predictors of drug response in cancer cell lines and optimize their performance in higher complex cancer model systems via few-shot learning. TCRP was presented as a successful modeling approach in multiple case studies. Given the importance of this approach to assist clinicians in their treatment decision process, we sought to reproduce independently the authors findings and improve the reusability of TCRP in new case studies, including validation in clinical trial datasets, a high bar for drug response prediction. Our results support the superiority of TCRP over established statistical and machine learning approaches in preclinical and clinical settings. We developed new resources to increase the reusability of the TCRP model for future improvements and validation studies.

bioinformatics↗

bioRxiv2023

GraphComm: A Graph-based Deep Learning Method to Predict Cell-Cell Communication in single-cell RNAseq data

Interactions between cells coordinate various functions across cell-types in health and disease states. Novel single-cell techniques enable deep investigation of cellular crosstalk at single-cell resolution. Cell-cell communication (CCC) is mediated by underlying gene-gene networks, however most current methods are unable to account for complex interactions within the cell as well as incorporate the effect of pathway and protein complexes on interactions. This results in the inability to infer overarching signalling patterns within a dataset as well as limit the ability to successfully explore other data types such as spatial cell dimension. Therefore, to represent transcriptomic data as intricate networks, complementing gene expression with information from cells to ligands and receptors for relevant cell-cell communication inference, we present GraphComm - a new graph-based deep learning method for predicting cell-cell communication in single-cell RNAseq datasets. GraphComm improves CCC inference by capturing detailed information such as cell location and intracellular signalling patterns from a database of more than 30,000 protein interaction pairs. With this framework, GraphComm is able to predict biologically relevant results in datasets previously validated for CCC, datasets that have undergone chemical or genetic perturbations and datasets with spatial cell information.

cell biology↗

bioRxiv2023

Detection of circular RNAs and their potential as biomarkers predictive of drug response

The introduction of high-throughput sequencing technologies has allowed for comprehensive RNA species detection, both coding and non-coding, which opened new avenues for the discovery of predictive and prognostic biomarkers. However the consistency of the detection of different RNA species depends on the RNA selection protocol used for RNA-sequencing. While preliminary reports indicated that non-coding RNAs, in particular circular RNAs, constitute a rich source of biomarkers predictive of drug response, the reproducibility of this novel class of biomarkers has not been rigorously investigated. To address this issue, we assessed the inter- lab consistency of circular RNA expression in cell lines profiled in large pharmacogenomic datasets. We found that circular RNA expression quantified from rRNA-depleted RNA-seq data is stable and yields robust prognostic markers in cancer. On the other hand, quantification of the expression of circular RNA from poly(A)-selected RNA-seq data yields highly inconsistent results, calling into question results from previous studies reporting their potential as predictive biomarkers in cancer. We have also identified median expression of transcripts and transcript length as potential factors influencing the consistency of RNA detection. Our study provides a framework to quantitatively assess the stability of coding and non-coding RNA expression through the analysis of biological replicates within and across independent studies.

bioinformatics↗

bioRxiv2021

Wear and Tear of the Intestinal Visceral Musculature by Intrinsic and Extrinsic Factors

The gut visceral musculature plays essential roles in not only moving substances through the lumen but also maintaining the function and physiology of the gut. Although the development of the visceral musculature has been studied in multiple model organisms, how it degenerates is poorly understood. Here, we employ the Drosophila midgut as a model to demonstrate that the visceral musculature is disrupted by intrinsic and extrinsic factors, such as aging, feeding, chemical-induced tissue damage, and oncogenic transformation in the epithelium. Notably, we define four prominent visceral musculature disruption phenotypes, which we refer as sprout, discontinuity, furcation, and crossover of the longitudinal muscle. Given that the occurrence of these phenotypes is increased during aging and under various stresses, we propose that these phenotypes can be used as quantitative readouts of deterioration of the visceral musculature. Intriguingly, administration of a tissue-damaging chemical dextran sulfate sodium (DSS) induced similar visceral musculature disruption phenotypes in zebrafish larvae, indicating that ingestion of a tissue-damaging chemical can disrupt the visceral musculature in a vertebrate as well. Our study provides insights into the deterioration of the gut visceral musculature and lays a groundwork for investigating the underlying mechanisms in Drosophila as well as other animals.

developmental biology↗

bioRxiv2019

Single-cell transcriptomics identifies an effectorness gradient shaping the response of CD4+ T cells to cytokines

Naive CD4+ T cells coordinate the immune response by acquiring an effector phenotype in response to cytokines. However, the cytokine responses in memory T cells remain largely understudied. We used quantitative proteomics, bulk RNA-seq and single-cell RNA-seq of over 40,000 human naive and memory CD4+ T cells to generate a detailed map of cytokine-regulated gene expression programs. We demonstrated that cytokine response differs substantially between naive and memory T cells and showed that memory cells are unable to differentiate into the Th2 phenotype. Moreover, memory T cells acquire a Th17-like phenotype in response to iTreg polarization. At the single-cell level, we demonstrated that T cells form a continuum which progresses from naive to effector memory T cells. This continuum is accompanied by a gradual increase in the expression levels of chemokines and cytokines and thus represents an effectorness gradient. Finally, we found that T cell cytokine responses are determined by where the cells lie in the effectorness gradient and identified genes whose expression is controlled by cytokines in an effectorness-dependent manner. Our results shed light on the heterogeneity of T cells and their responses to cytokines, provide insight into immune disease inflammation and could inform drug development.

immunology↗

bioRxiv2019

Identification of drugs for leukaemia differentiation therapy by network pharmacology

Acute leukaemias differ from their normal haematopoietic counterparts in their inability to differentiate. This phenomenon is thought to be the result of aberrant cellular reprogramming involving transcription factors (TFs). Here we leveraged on Mogrify, a network-based algorithm, to identify TFs and their gene regulatory networks that drive differentiation of the acute promyelocytic leukaemia (APL) cell line NB4 in response to ATRA (all-trans retinoic acid). We further integrated the detected TF regulatory networks with the Connectivity Map (CMAP) repository and recovered small molecule drugs which induce similar transcriptional changes. Our method outperformed standard approaches, retrieving ATRA as the top hit. Of the other drug hits, dimaprit and mebendazole enhanced ATRA-mediated differentiation in both parental NB4 and ATRA-resistant NB4-MR2 cells. Thus, we provide proof-of-principle of our network-based computational platform for drug discovery and repositioning in leukaemia differentiation therapy, which can be extended to other dysregulated disease states.

systems biology↗