REGNET: mining context-specific human transcription networks using composite genomic information

Target repositioning using multi-layer networks and machine learning: The case of prostate cancer

The discovery of novel therapeutic targets, defined as proteins which drugs can interact with to induce therapeutic benefits, typically represent the first and most important step of drug discovery. One solution for target discovery is target repositioning, a strategy which relies on the repurposing of known targets for new diseases, leading to new treatments, less side effects and potential drug synergies. Biological networks have emerged as powerful tools for integrating heterogeneous data and facilitating the prediction of biological or therapeutic properties. Consequently, they are widely employed to predict new therapeutic targets by characterizing potential candidates, often based on their interactions within a Protein-Protein Interaction (PPI) network, and their proximity to genes associated with the disease. However, over-reliance on PPI networks and the assumption that potential targets are necessarily near known genes can introduce biases that may limit the effectiveness of these methods. This study addresses these limitations in two ways. First, by exploiting a multi-layer network which incorporates additional information such as gene regulation, metabolite interactions, metabolic pathways, and several disease signatures such as Differentially Expressed Genes, mutated genes, Copy Number Alteration, and structural variants. Second, by extracting relevant features from the network using several approaches including proximity to disease-associated genes, but also unbiased approaches such as propagation-based methods, topological metrics, and module detection algorithms. Using prostate cancer as a case study, the best features were identified and utilized to train machine learning algorithms to predict 5 novel promising therapeutic targets for prostate cancer: IGF2R, C5AR, RAB7, SETD2 and NPBWR1.

Bronchial epithelium epithelial-mesenchymal plasticity forms aberrant basaloid-like cells in vitro

Although epithelial-mesenchymal transition (EMT) is a common feature of fibrotic lung disease, its role in fibrogenesis is controversial. Recently, aberrant basaloid cells were identified in fibrotic lung tissue as a novel epithelial cell type displaying a partial EMT phenotype. The developmental origin of these cells remains unknown. To elucidate the role of EMT in the development of aberrant basaloid cells from the bronchial epithelium, we mapped EMT-induced transcriptional changes at the population and single-cell levels. Human bronchial epithelial cells grown as submerged or air-liquid interface (ALI) cultures with or without EMT induction were analyzed by bulk and single-cell RNA-Sequencing. Comparison of submerged and ALI cultures revealed differential expression of 8,247 protein coding (PC) and 1,621 long noncoding RNA (lncRNA) genes and revealed epithelial cell-type-specific lncRNAs. Similarly, EMT induction in ALI cultures resulted in robust transcriptional reprogramming of 6,020 PC and 907 lncRNA genes. Although there was no evidence for fibroblast/myofibroblast conversion following EMT induction, cells displayed a partial EMT gene signature and an aberrant basaloid-like cell phenotype. The substantial transcriptional differences between submerged and ALI cultures highlight that care must be taken when interpreting data from submerged cultures. This work supports that lung epithelial EMT does not generate fibroblasts/myofibroblasts and confirms ALI cultures provide a physiologically relevant system to study aberrant basaloid-like cells and mechanisms of EMT. We provide a catalog of PC and lncRNA genes and an interactive browser (https://bronc-epi-in-vitro.cells.ucsc.edu/) of single-cell RNA-Seq data for further exploration of potential roles in the lung epithelium in health and lung disease.

Gene X environment: the cellular environment governs the transcriptional response to environmental chemicals

Background

An individual’s response to environmental exposures varies depending on their genotype, which has been termed the gene-environment interaction. The phenotype of cell exposed can also be a key determinant in the response to physiological cues, indicating that a cell-gene-environment interaction may exist. We investigated whether the cellular environment could alter the transcriptional response to environmental chemicals. Publicly available gene expression array data permitted a targeted comparison of the transcriptional response to a unique subclass of environmental chemicals that alter the activity of the estrogen receptor, xenoestrogens.

Results

Thirty xenoestrogens were included in the analysis, for which 426 human gene expression studies were identified. Comparisons were made for studies that met the predefined criteria for exposure length, concentration, and experimental replicates. The cellular response to the phytoestrogen genistein resulted in remarkably unique transcriptional profiles in breast, liver, and uterine cell-types. Analysis of gene regulatory networks and molecular pathways revealed that the cellular context mediated the activation or repression of functions important to cellular organization and survival, including opposing effects by genistein in breast vs. liver and uterine cell-types. When controlling for cell-type, xenoestrogens regulate unique gene networks and biological functions, despite belonging to the same class of environmental chemicals. Interestingly, the genetic sex of the cell-type also strongly influenced the transcriptional response to xenoestrogens in the liver, with only 22% of the genes significantly regulated by genistein common between male and female cells.

Conclusions

Our results demonstrate that the transcriptional response to environmental chemicals depends on a variety of factors, including the cellular context, the genetic sex of a cell, and the individual chemical. These findings highlight the importance of evaluating the impact of exposure across cell-types, as the effect is responsive to the cellular environment. These comparative genetic results support the concept of a cell-gene-environment interaction.

Effect of the absolute statistic on gene-sampling gene-set analysis methods

Gene-set enrichment analysis and its modified versions have commonly been used for identifying altered functions or pathways in disease from microarray data. In particular, the simple gene-sampling gene-set analysis methods have been heavily used for datasets with only a few sample replicates. The biggest problem with this approach is the highly inflated false-positive rate. In this paper, the effect of absolute gene statistic on gene-sampling gene-set analysis methods is systematically investigated. Thus far, the absolute gene statistic has merely been regarded as a supplementary method for capturing the bidirectional changes in each gene set. Here, it is shown that incorporating the absolute gene statistic in gene-sampling gene-set analysis substantially reduces the false-positive rate and improves the overall discriminatory ability. Its effect was investigated by power, false-positive rate, and receiver operating curve for a number of simulated and real datasets. The performances of gene-set analysis methods in one-tailed (genome-wide association study) and two-tailed (gene expression data) tests were also compared and discussed.