Chemical-induced Gene Expression Ranking and its Application to Pancreatic Cancer Drug Repurposing

Chemical-induced gene expression profiles provide critical information on the mode of action, off-target effect, and cellar heterogeneity of chemical actions in a biological system, thus offer new opportunities for drug discovery, system pharmacology, and precision medicine. Despite their successful applications in drug repurposing, large-scale analysis that leverages these profiles is limited by sparseness and low throughput of the data. Several methods have been proposed to predict missing values in gene expression data. However, most of them focused on imputation and classification settings which have limited applications to real-world scenarios of drug discovery. Therefore, a new deep learning framework named chemical-induced gene expression ranking (CIGER) is proposed to target a more realistic but more challenging setting in which the model predicts the rankings of genes in the whole gene expression profiles induced by de novo chemicals. The experimental results show that CIGER significantly outperforms existing methods in both ranking and classification metrics for this prediction task. Furthermore, a new drug screening pipeline based on CIGER is proposed to select approved or investigational drugs for the potential treatments of pancreatic cancer. Our predictions have been validated by experiments, thereby showing the effectiveness of CIGER for phenotypic compound screening of precision drug discovery in practice.

Download Full-text

A deep learning framework for high-throughput mechanism-driven phenotype compound screening

10.1101/2020.07.19.211235 ◽

2020 ◽

Author(s):

Thai-Hoang Pham ◽

Yue Qiu ◽

Jucheng Zeng ◽

Lei Xie ◽

Ping Zhang

Keyword(s):

Neural Network ◽

Gene Expression ◽

High Throughput ◽

Missing Values ◽

Data Augmentation ◽

De Novo ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Patient Specific ◽

Compound Screening

ABSTRACTTarget-based high-throughput compound screening dominates conventional one-drug-one-gene drug discovery process. However, the readout from the chemical modulation of a single protein is poorly correlated with phenotypic response of organism, leading to high failure rate in drug development. Chemical-induced gene expression profile provides an attractive solution to phenotype-based screening. However, the use of such data is currently limited by their sparseness, unreliability, and relatively low throughput. Several methods have been proposed to impute missing values for gene expression datasets. However, few existing methods can perform de novo chemical compound screening. In this study, we propose a mechanism-driven neural network-based method named DeepCE (Deep Chemical Expression) which utilizes graph convolutional neural network to learn chemical representation and multi-head attention mechanism to model chemical substructure-gene and gene-gene feature associations. In addition, we propose a novel data augmentation method which extracts useful information from unreliable experiments in L1000 dataset. The experimental results show that DeepCE achieves the superior performances not only in de novo chemical setting but also in traditional imputation setting compared to state-of-the-art baselines for the prediction of chemical-induced gene expression. We further verify the effectiveness of gene expression profiles generated from DeepCE by comparing them with gene expression profiles in L1000 dataset for downstream classification tasks including drug-target and disease predictions. To demonstrate the value of DeepCE, we apply it to patient-specific drug repurposing of COVID-19 for the first time, and generate novel lead compounds consistent with clinical evidences. Thus, DeepCE provides a potentially powerful framework for robust predictive modeling by utilizing noisy omics data as well as screening novel chemicals for the modulation of systemic response to disease.

Download Full-text

LncGSEA: a versatile tool to infer lncRNA associated pathways from large-scale cancer transcriptome sequencing data

BMC Genomics ◽

10.1186/s12864-021-07900-y ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Yanan Ren ◽

Ting-You Wang ◽

Leah C. Anderton ◽

Qi Cao ◽

Rendong Yang

Keyword(s):

Gene Expression ◽

Large Scale ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Clinical Samples ◽

Sequencing Data ◽

Multiple Cancer ◽

Regulatory Pathways ◽

Cancer Transcriptome ◽

Versatile Tool

Abstract Background Long non-coding RNAs (lncRNAs) are a growing focus in cancer research. Deciphering pathways influenced by lncRNAs is important to understand their role in cancer. Although knock-down or overexpression of lncRNAs followed by gene expression profiling in cancer cell lines are established approaches to address this problem, these experimental data are not available for a majority of the annotated lncRNAs. Results As a surrogate, we present lncGSEA, a convenient tool to predict the lncRNA associated pathways through Gene Set Enrichment Analysis of gene expression profiles from large-scale cancer patient samples. We demonstrate that lncGSEA is able to recapitulate lncRNA associated pathways supported by literature and experimental validations in multiple cancer types. Conclusions LncGSEA allows researchers to infer lncRNA regulatory pathways directly from clinical samples in oncology. LncGSEA is written in R, and is freely accessible at https://github.com/ylab-hi/lncGSEA.

Download Full-text

Analysis of blood-based gene expression in idiopathic Parkinson disease

Neurology ◽

10.1212/wnl.0000000000004516 ◽

2017 ◽

Vol 89 (16) ◽

pp. 1676-1683 ◽

Cited By ~ 36

Author(s):

Ron Shamir ◽

Christine Klein ◽

David Amar ◽

Eva-Juliane Vollstedt ◽

Michael Bonin ◽

...

Keyword(s):

Gene Expression ◽

Parkinson Disease ◽

Gene Networks ◽

Large Scale ◽

Expression Profiles ◽

Area Under The Curve ◽

Gene Expression Profiles ◽

Gene Signature ◽

Gene Profiles ◽

Independent Test

Objective:To examine whether gene expression analysis of a large-scale Parkinson disease (PD) patient cohort produces a robust blood-based PD gene signature compared to previous studies that have used relatively small cohorts (≤220 samples).Methods:Whole-blood gene expression profiles were collected from a total of 523 individuals. After preprocessing, the data contained 486 gene profiles (n = 205 PD, n = 233 controls, n = 48 other neurodegenerative diseases) that were partitioned into training, validation, and independent test cohorts to identify and validate a gene signature. Batch-effect reduction and cross-validation were performed to ensure signature reliability. Finally, functional and pathway enrichment analyses were applied to the signature to identify PD-associated gene networks.Results:A gene signature of 100 probes that mapped to 87 genes, corresponding to 64 upregulated and 23 downregulated genes differentiating between patients with idiopathic PD and controls, was identified with the training cohort and successfully replicated in both an independent validation cohort (area under the curve [AUC] = 0.79, p = 7.13E–6) and a subsequent independent test cohort (AUC = 0.74, p = 4.2E–4). Network analysis of the signature revealed gene enrichment in pathways, including metabolism, oxidation, and ubiquitination/proteasomal activity, and misregulation of mitochondria-localized genes, including downregulation of COX4I1, ATP5A1, and VDAC3.Conclusions:We present a large-scale study of PD gene expression profiling. This work identifies a reliable blood-based PD signature and highlights the importance of large-scale patient cohorts in developing potential PD biomarkers.

Download Full-text

Discovering Distinct Patterns in Gene Expression Profiles

Journal of Integrative Bioinformatics ◽

10.1515/jib-2008-105 ◽

2008 ◽

Vol 5 (2) ◽

Cited By ~ 1

Author(s):

Li Teng ◽

Laiwan Chan

Keyword(s):

Gene Expression ◽

Large Scale ◽

Expression Profiles ◽

Expression Patterns ◽

Gene Expression Profiles ◽

Clustering Methods ◽

Gene Expressions ◽

Real Gene ◽

Large Scale Dataset ◽

Coexpressed Genes

SummaryTraditional analysis of gene expression profiles use clustering to find groups of coexpressed genes which have similar expression patterns. However clustering is time consuming and could be diffcult for very large scale dataset. We proposed the idea of Discovering Distinct Patterns (DDP) in gene expression profiles. Since patterns showing by the gene expressions reveal their regulate mechanisms. It is significant to find all different patterns existing in the dataset when there is little prior knowledge. It is also a helpful start before taking on further analysis. We propose an algorithm for DDP by iteratively picking out pairs of gene expression patterns which have the largest dissimilarities. This method can also be used as preprocessing to initialize centers for clustering methods, like K-means. Experiments on both synthetic dataset and real gene expression datasets show our method is very effective in finding distinct patterns which have gene functional significance and is also effcient.

Download Full-text

CFTR ΔF508 mutation has minimal effect on the gene expression profile of differentiated human airway epithelia

AJP Lung Cellular and Molecular Physiology ◽

10.1152/ajplung.00065.2005 ◽

2005 ◽

Vol 289 (4) ◽

pp. L545-L553 ◽

Cited By ~ 29

Author(s):

Joseph Zabner ◽

Todd E. Scheetz ◽

Hakeem G. Almabrazi ◽

Thomas L. Casavant ◽

Jian Huang ◽

...

Keyword(s):

Gene Expression ◽

Cystic Fibrosis ◽

Large Scale ◽

Expression Profiles ◽

Expression Patterns ◽

Primary Cultures ◽

Gene Expression Profiles ◽

Filter Method ◽

Tissue Destruction ◽

Airway Epithelia

Cystic fibrosis (CF) is caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR), an epithelial chloride channel regulated by phosphorylation. Most of the disease-associated morbidity is the consequence of chronic lung infection with progressive tissue destruction. As an approach to investigate the cellular effects of CFTR mutations, we used large-scale microarray hybridization to contrast the gene expression profiles of well-differentiated primary cultures of human CF and non-CF airway epithelia grown under resting culture conditions. We surveyed the expression profiles for 10 non-CF and 10 ΔF508 homozygote samples. Of the 22,283 genes represented on the Affymetrix U133A GeneChip, we found evidence of significant changes in expression in 24 genes by two-sample t-test ( P < 0.00001). A second, three-filter method of comparative analysis found no significant differences between the groups. The levels of CFTR mRNA were comparable in both groups. There were no significant differences in the gene expression patterns between male and female CF specimens. There were 18 genes with significant increases and 6 genes with decreases in CF relative to non-CF samples. Although the function of many of the differentially expressed genes is unknown, one transcript that was elevated in CF, the KCl cotransporter (KCC4), is a candidate for further study. Overall, the results indicate that CFTR dysfunction has little direct impact on airway epithelial gene expression in samples grown under these conditions.

Download Full-text

Dynamical consequences of regional heterogeneity in the brain’s transcriptional landscape

10.1101/2020.10.28.359943 ◽

2020 ◽

Cited By ~ 1

Author(s):

Gustavo Deco ◽

Kevin Aquino ◽

Aurina Arnatkevičiūtė ◽

Stuart Oldham ◽

Kristina Sabaroedin ◽

...

Keyword(s):

Gene Expression ◽

Large Scale ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Global Gene Expression ◽

Brain Regions ◽

Biophysical Model ◽

Neuronal Dynamics ◽

Regional Heterogeneity ◽

Magnetic Resonance Imaging Mri

AbstractBrain regions vary in their molecular and cellular composition, but how this heterogeneity shapes neuronal dynamics is unclear. Here, we investigate the dynamical consequences of regional heterogeneity using a biophysical model of whole-brain functional magnetic resonance imaging (MRI) dynamics in humans. We show that models in which transcriptional variations in excitatory and inhibitory receptor (E:I) gene expression constrain regional heterogeneity more accurately reproduce the spatiotemporal structure of empirical functional connectivity estimates than do models constrained by global gene expression profiles and MRI-derived estimates of myeloarchitecture. We further show that regional heterogeneity is essential for yielding both ignition-like dynamics, which are thought to support conscious processing, and a wide variance of regional activity timescales, which supports a broad dynamical range. We thus identify a key role for E:I heterogeneity in generating complex neuronal dynamics and demonstrate the viability of using transcriptional data to constrain models of large-scale brain function.

Download Full-text

Sex differences in viral entry protein expression and host transcript responses to SARS-CoV-2

10.21203/rs.3.rs-100914/v2 ◽

2020 ◽

Author(s):

Mengying Sun ◽

Rama Shankar ◽

Meehyun Ko ◽

Christopher Daniel Chang ◽

Shan-Ju Yeh ◽

...

Keyword(s):

Gene Expression ◽

Sex Differences ◽

Viral Entry ◽

Large Scale ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Scale Analysis ◽

Transcriptional Responses ◽

Deconvolution Analysis ◽

Large Scale Analysis

Abstract Epidemiological studies suggest that men exhibit a higher mortality rate to COVID-19 than women, yet the underlying biology is largely unknown. Here, we seek to delineate sex differences in the gene expression of viral entry proteins ACE2 and TMPRSS2, and host transcriptional responses to SARS-CoV-2 through large-scale analysis of genomic and clinical data. We first compiled 220,000 human gene expression profiles from three databases and completed the meta-information through machine learning and manual annotation. Large scale analysis of these profiles indicated that male samples show higher expression levels of ACE2 and TMPRSS2 than female samples, especially in the older group (>60 years) and in the kidney. Subsequent analysis of 6,031 COVID-19 patients at Mount Sinai Health System revealed that men have significantly higher creatinine levels, an indicator of impaired kidney function. Further analysis of 782 COVID-19 patient gene expression profiles taken from upper airway and blood suggested men and women present distinct expression changes. Computational deconvolution analysis of these profiles revealed male COVID-19 patients have enriched kidney-specific mesangial cells in blood compared to healthy patients. Together, this study suggests biological differences in the kidney between sexes may contribute to sex disparity in COVID-19.

Download Full-text