Influence of batch effect correction methods on drug induced differential gene expression profiles

A pipeline for the development and analysis of extracellular vesicle-based transcriptomic biomarkers in molecular diagnostics

Extracellular vesicles are shed by every cell type and can be found in any biofluid. They contain different molecules that can be utilized as biomarkers, including several RNA species which they protect from degradation. Here, we present a pipeline for the development and analysis of extracellular vesicle-associated transcriptomic biomarkers that our group has successfully applied multiple times. We highlight the key steps of the pipeline and give particular emphasis to the necessary quality control checkpoints, which are linked to numerous available guidelines that should be considered along the workflow. Our pipeline starts with patient recruitment and continues with blood sampling and processing. The purification and characterization of extracellular vesicles is explained in detail, as well as the isolation and quality control of extracellular vesicle-associated RNA. We point out the possible pitfalls during library preparation and RNA sequencing and present multiple bioinformatic tools to pinpoint biomarker signature candidates from the sequencing data. Finally, considerations and pitfalls during the validation of the biomarker signature using RT-qPCR will be elaborated.

Using Transcriptomics and Cell Morphology Data in Drug Discovery: The Long Road to Practice

Gene expression and cell morphology data are high-dimensional biological readouts of much recent interest for drug discovery. They are able to describe biological systems in different states (e.g., healthy and diseased), as well as biological systems before and after compound treatment, and they are hence useful for matching both spaces (e.g., for drug repurposing) as well as for characterizing compounds with respect to efficacy and safety endpoints. This Microperspective describes recent advances in this direction with a focus on applied drug discovery and drug repurposing, as well as outlining what else is needed to advance further, with a particular focus on better understanding the applicability domain of readouts and their relevance for decision making, which is currently often still unclear.

Identification of Critical Genes and Pathways for Influenza A Virus Infections via Bioinformatics Analysis

Influenza A virus (IAV) requires the host cellular machinery for many aspects of its life cycle. Knowledge of these host cell requirements not only reveals molecular pathways exploited by the virus or triggered by the immune system but also provides further targets for antiviral drug development. To uncover critical pathways and potential targets of influenza infection, we assembled a large amount of data from 8 RNA sequencing studies of IAV infection for integrative network analysis. Weighted gene co-expression network analysis (WGCNA) was performed to investigate modules and genes correlated with the time course of infection and/or multiplicity of infection (MOI). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to explore the biological functions and pathways of the genes in 5 significant modules. Top hub genes were identified using the cytoHubba plugin in the protein interaction network. The correlation between expression levels of 7 top hub genes and time course or MOI was displayed and validated, including BCL2L13, PLSCR1, ARID5A, LMO2, NDRG4, HAP1, and CARD10. Dysregulated expression of these genes potently impacted the development of IAV infection through modulating IAV-related biological processes and pathways. This study provides further insights into the underlying molecular mechanisms and potential targets in IAV infection.

BatchDTA: implicit batch alignment enhances deep learning-based drug-target affinity estimation

Candidate compounds with high binding affinities toward a target protein are likely to be developed as drugs. Deep neural networks (DNNs) have attracted increasing attention for drug-target affinity (DTA) estimation owning to their efficiency. However, the negative impact of batch effects caused by measure metrics, system technologies and other assay information is seldom discussed when training a DNN model for DTA. Suffering from the data deviation caused by batch effects, the DNN models can only be trained on a small amount of 'clean' data. Thus, it is challenging for them to provide precise and consistent estimations. We design a batch-sensitive training framework, namely BatchDTA, to train the DNN models. BatchDTA implicitly aligns multiple batches toward the same protein through learning the orders of candidate compounds with respect to the batches, alleviating the impact of the batch effects on the DNN models. Extensive experiments demonstrate that BatchDTA facilitates four mainstream DNN models to enhance the ability and robustness on multiple DTA datasets (BindingDB, Davis and KIBA). The average concordance index of the DNN models achieves a relative improvement of 4.0%. The case study reveals that BatchDTA can successfully learn the ranking orders of the compounds from multiple batches. In addition, BatchDTA can also be applied to the fused data collected from multiple sources to achieve further improvement.

Expression Analysis in a Dispersal-Fecundity Polyphenism Identifies Growth Regulators and Effectors

Polyphenism allows organisms to respond to varying environmental conditions by adopting alternative collections of morphological traits, often leading to different reproductive strategies. In many insects, polyphenism affecting the development of flight trades dispersal ability for increased fecundity. The soapberry bug Jadera haematoloma (Hemiptera: Rhopalidae) exhibits wing polyphenism in response to juvenile nutritional resources and cohort density. Development of full-length wings and flight-capable thoracic muscles occurs more frequently in cohorts raised under low food density conditions, and these features are correlated to reduced female fecundity. Soapberry bugs represent an example of polyphenic dispersal-fecundity trade-off. Short-wing development is not sex-limited, and morphs can also differ in male fertility. We have previously shown, via a candidate gene approach, that manipulation of insulin signaling can alter the threshold for nutritional response and that changes in the activity of this pathway underlie, at least in part, differences in the polyphenic thresholds in different host-adapted populations of J. haematoloma. We now expand the examination of this system using transcriptome sequencing across a multidimensional matrix of life stage, tissue, sex, food density and host population. We also examine the use of wing and thorax shape as factors modeling gene expression. In addition to insulin signaling, we find that components of the TOR, Hippo, Toll and estrogen-related receptor pathways are differentially expressed in the thorax of polyphenic morphs. The transcription factor Sox14 was one of the few genes differentially expressed in the gonads of morphs, being up-regulated in ovaries. We identify two transcription factors as potential mediators of morph-specific male fertility differences. We also find that bugs respond to nutrient limitation with expression of genes linked to cuticle structure and spermatogenesis. These findings provide a broad perspective from which to view this nutrition-dependent polyphenism.

ANKRD1 and SPP1 as diagnostic markers and correlated with immune infiltration in biliary atresia

The diagnosis of biliary atresia (BA) remains a clinical challenge, reliable biomarkers that can easily distinguish BA and other forms of intrahepatic cholestasis (IC) are urgently needed.Differentially expressed genes were identified by R software. The least absolute shrinkage and selection operator regression and support vector machine algorithms were used to filter the diagnostic biomarkers of BA. The candidate biomarkers were further validated in another independent cohort of patients with BA and IC. Then CIBERSORT was used for estimating the fractions of immune cell types in BA. Gene set enrichment analyses were conducted and the correlation between diagnostic genes and immune cells was analyzed.A total of 419 differentially expressed genes in BA were detected and 2 genes (secreted phosphoprotein 1 [SPP1] and ankyrin repeat domain [ANKRD1]) among them were selected as diagnostic biomarkers. The SPP1 yielded an area under the curve (AUC) value of 0.798 (95% confidence interval [CI]: 0.742-0.854) to distinguish patients with BA from those with IC, and ANKRD1 exhibited AUC values of 0.686 (95% CI: 0.616-0.754) in discriminating BA patients and those with IC. Further integrating them into one variable resulted in a higher AUC of 0.830 (95% CI: 0.777-0.879). The regulatory T cells, M2 macrophages cells, CD4 memory T cells, and dendritic cells may be involved in the BA process. The ANKRD1 and SPP1 was negatively correlated with regulatory T cells.In conclusion, the ANKRD1 and SPP1 could potentially provide extra guidance in discriminating BA and IC. The immune cell infiltration of BA gives us new insight to explore its pathogenesis.

Integrative Pan-Cancer Analysis Reveals Decreased Melatonergic Gene Expression in Carcinogenesis and RORA as a Prognostic Marker for Hepatocellular Carcinoma

Background

Melatonin has been shown to play a protective role in the development and progression of cancer. However, the relationship between alterations in the melatonergic microenvironment and cancer development has remained unclear.

Methods

We performed a comprehensive investigation on 12 melatonergic genes and their relevance to cancer occurrence, progression and survival by integrating multi-omics data from microarray analysis and RNA sequencing across 11 cancer types. Specifically, the 12 melatonergic genes that we investigated, which reflect the melatonergic microenvironment, included three membrane receptor genes, three nuclear receptor genes, two intracellular receptor genes, one synthetic gene, and three metabolic genes.

Results

Widely coherent underexpression of nuclear receptor genes, intracellular receptor genes, and metabolic genes was observed in cancerous samples from multiple cancer types compared to that in normal samples. Furthermore, genomic and/or epigenetic alterations partially contributed to these abnormal expression patterns in cancerous samples. Moreover, the majority of melatonergic genes had significant prognostic effects in predicting overall survival. Nevertheless, few corresponding alterations in expression were observed during cancer progression, and alterations in expression patterns varied greatly across cancer types. However, the association of melatonergic genes with one specific cancer type, hepatocellular carcinoma, identified RORA as a tumor suppressor and a prognostic marker for patients with hepatocellular carcinoma.

Conclusions

Overall, our study revealed decreased melatonergic gene expression in various cancers, which may help to better elucidate the relationship between melatonin and cancer development. Taken together, our findings highlight the potential prognostic significance of melatonergic genes in various cancers.

Predicting mechanism of action of cellular perturbations with pathway activity signatures

MOTIVATION

Misregulation of signaling pathway activity is etiologic for many human diseases, and modulating activity of signaling pathways is often the preferred therapeutic strategy. Understanding the mechanism of action (MOA) of bioactive chemicals in terms of targeted signaling pathways is the essential first step in evaluating their therapeutic potential. Changes in signaling pathway activity are often not reflected in changes in expression of pathway genes which makes MOA inferences from transcriptional signatures (TSeses) a difficult problem.

RESULTS

We developed a new computational method for implicating pathway targets of bioactive chemicals and other cellular perturbations by integrated analysis of pathway network topology, the Library of Integrated Network-based Cellular Signature TSes of genetic perturbations of pathway genes and the TS of the perturbation. Our methodology accurately predicts signaling pathways targeted by the perturbation when current pathway analysis approaches utilizing only the TS of the perturbation fail.

AVAILABILITY AND IMPLEMENTATION

Open source R package paslincs is available at https://github.com/uc-bd2k/paslincs.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Knowledge Generation with Rule Induction in Cancer Omics

The explosion of omics data availability in cancer research has boosted the knowledge of the molecular basis of cancer, although the strategies for its definitive resolution are still not well established. The complexity of cancer biology, given by the high heterogeneity of cancer cells, leads to the development of pharmacoresistance for many patients, hampering the efficacy of therapeutic approaches. Machine learning techniques have been implemented to extract knowledge from cancer omics data in order to address fundamental issues in cancer research, as well as the classification of clinically relevant sub-groups of patients and for the identification of biomarkers for disease risk and prognosis. Rule induction algorithms are a group of pattern discovery approaches that represents discovered relationships in the form of human readable associative rules. The application of such techniques to the modern plethora of collected cancer omics data can effectively boost our understanding of cancer-related mechanisms. In fact, the capability of these methods to extract a huge amount of human readable knowledge will eventually help to uncover unknown relationships between molecular attributes and the malignant phenotype. In this review, we describe applications and strategies for the usage of rule induction approaches in cancer omics data analysis. In particular, we explore the canonical applications and the future challenges and opportunities posed by multi-omics integration problems.