Complex network-based approaches to biomarker discovery

CDK5R1, GSE1, HSPG2 and WDFY3 as indirect epigenetic-sensitive genes in atrial fibrillation

BACKGROUND

Although mounting evidence supports that aberrant DNA methylation occurs in the hearts of patients with atrial fibrillation (AF), noninvasive epigenetic characterization of AF has not yet been defined.

METHODS

We investigated DNA methylome changes in peripheral blood CD4+ T cells isolated from 10 patients with AF relative to 11 healthy subjects (HS) who were enrolled in the DIANA clinical trial (NCT04371809) via reduced-representation bisulfite sequencing (RRBS).

RESULTS

An atrial-specific PPI network revealed 18 hub differentially methylated genes (DMGs), wherein ROC curve analysis revealed reasonable diagnostic performance of DNA methylation levels found within CDK5R1 (AUC = 0.76; p = 0.049), HSPG2 (AUC = 0.77; p = 0.038), WDFY3 (AUC = 0.78; p = 0.029), USP49 (AUC = 0.76; p = 0.049), GSE1 (AUC = 0.76; p = 0.049), AIFM1 (AUC = 0.76; p = 0.041), CDK5RAP2 (AUC = 0.81; p = 0.017), COL4A1 (AUC = 0.86; p < 0.001), SEPT8 (AUC = 0.90; p < 0.001), PFDN1 (AUC = 0.90; p < 0.01) and ACOT7 (AUC = 0.78; p = 0.032). Transcriptional profiling of the hub DMGs provided a significant overexpression of PSDM6 (p = 0.004), TFRC (p = 0.01), CDK5R1 (p < 0.001), HSPG2 (p = 0.01), WDFY3 (p < 0.001), USP49 (p = 0.004) and GSE1 (p = 0.021) in AF patients vs HS.

CONCLUSIONS

CDK5R1, GSE1, HSPG2 and WDFY3 resulted the best discriminatory genes both at methylation and gene expression level. Our results provide several candidate diagnostic biomarkers with the potential to advance precision medicine in AF.

Identification of marker genes for spinal cord injury

Introduction

Spinal cord injury (SCI) is a profoundly disabling and devastating neurological condition, significantly impacting patients' quality of life. It imposes unbearable psychological and economic pressure on both patients and their families, as well as placing a heavy burden on society.

Methods

In this study, we integrated datasets GSE5296 and GSE47681 as training groups, analyzed gene variances between sham group and SCI group mice, and conducted Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis based on the differentially expressed genes. Subsequently, we performed Weighted Gene Correlation Network Analysis (WGCNA) and Lasso regression analyses.

Results

We identified four characteristic disease genes: Icam1, Ch25h, Plaur and Tm4sf1. We examined the relationship between SCI and immune cells, and validated the expression of the identified disease-related genes in SCI rats using PCR and immunohistochemistry experiments.

Discussion

In conclusion, we have identified and verified four genes related to SCI: Icam1, Ch25h, Plaur and Tm4sf1, which could offer insights for SCI treatment.

Application of machine learning and complex network measures to an EEG dataset from ayahuasca experiments

Ayahuasca is a blend of Amazonian plants that has been used for traditional medicine by the inhabitants of this region for hundreds of years. Furthermore, this plant has been demonstrated to be a viable therapy for a variety of neurological and mental diseases. EEG experiments have found specific brain regions that changed significantly due to ayahuasca. Here, we used an EEG dataset to investigate the ability to automatically detect changes in brain activity using machine learning and complex networks. Machine learning was applied at three different levels of data abstraction: (A) the raw EEG time series, (B) the correlation of the EEG time series, and (C) the complex network measures calculated from (B). Further, at the abstraction level of (C), we developed new measures of complex networks relating to community detection. As a result, the machine learning method was able to automatically detect changes in brain activity, with case (B) showing the highest accuracy (92%), followed by (A) (88%) and (C) (83%), indicating that connectivity changes between brain regions are more important for the detection of ayahuasca. The most activated areas were the frontal and temporal lobe, which is consistent with the literature. F3 and PO4 were the most important brain connections, a significant new discovery for psychedelic literature. This connection may point to a cognitive process akin to face recognition in individuals during ayahuasca-mediated visual hallucinations. Furthermore, closeness centrality and assortativity were the most important complex network measures. These two measures are also associated with diseases such as Alzheimer's disease, indicating a possible therapeutic mechanism. Moreover, the new measures were crucial to the predictive model and suggested larger brain communities associated with the use of ayahuasca. This suggests that the dissemination of information in functional brain networks is slower when this drug is present. Overall, our methodology was able to automatically detect changes in brain activity during ayahuasca consumption and interpret how these psychedelics alter brain networks, as well as provide insights into their mechanisms of action.

Genomic, proteomic, and systems biology approaches in biomarker discovery for multiple sclerosis

Multiple sclerosis (MS) is a neuroinflammatory disorder characterized by autoimmune-mediated inflammatory lesions in CNS leading to myelin damage and axonal loss. MS is a heterogenous disease with variable and unpredictable disease course. Due to its complex nature, MS is difficult to diagnose and responses to specific treatments may vary between individuals. Therefore, there is an indisputable need for biomarkers for early diagnosis, prediction of disease exacerbations, monitoring the progression of disease, and for measuring responses to therapy. Genomic and proteomic studies have sought to understand the molecular basis of MS and find biomarker candidates. Advances in next-generation sequencing and mass-spectrometry techniques have yielded an unprecedented amount of genomic and proteomic data; yet, translation of the results into the clinic has been underwhelming. This has prompted the development of novel data science techniques for exploring these large datasets to identify biologically relevant relationships and ultimately point towards useful biomarkers. Herein we discuss optimization of omics study designs, advances in the generation of omics data, and systems biology approaches aimed at improving biomarker discovery and translation to the clinic for MS.

Fertility Relevance Probability Analysis Shortlists Genetic Markers for Male Fertility Impairment

Impairment of male fertility is one of the major public health issues worldwide. Nevertheless, genetic causes of male sub- and infertility can often only be suspected due to the lack of reliable and easy-to-use routine tests. Yet, the development of a marker panel is complicated by the large quantity of potentially predictive markers. Actually, hundreds or even thousands of genes could have fertility relevance. Thus, a systematic method enabling a selection of the most predictive markers out of the many candidates is required. As a criterion for marker selection, we derived a gene-specific score, which we refer to as fertility relevance probability (FRP). For this purpose, we first categorized 2,753 testis-expressed genes as either candidate markers or non-candidates, according to phenotypes in male knockout mice. In a parallel approach, 2,502 genes were classified as candidate markers or non-candidates based on phenotypes in men. Subsequently, we conducted logistic regression analyses with evolutionary rates of genes (dN/dS), transcription levels in testis relative to other organs, and connectivity of the encoded proteins in a protein-protein interaction network as covariates. In confirmation of the procedure, FRP values showed the expected pattern, thus being overall higher in genes with known relevance for fertility than in their counterparts without corresponding evidence. In addition, higher FRP values corresponded with an increased dysregulation of protein abundance in spermatozoa of 37 men with normal and 38 men with impaired fertility. Present analyses resulted in a ranking of genes according to their probable predictive power as candidate markers for male fertility impairment. Thus, AKAP4, TNP1, DAZL, BRDT, DMRT1, SPO11, ZPBP, HORMAD1, and SMC1B are prime candidates toward a marker panel for male fertility impairment. Additional candidate markers are DDX4, SHCBP1L, CCDC155, ODF1, DMRTB1, ASZ1, BOLL, FKBP6, SLC25A31, PRSS21, and RNF17. FRP inference additionally provides clues for potential new markers, thereunder TEX37 and POU4F2. The results of our logistic regression analyses are freely available at the PreFer Genes website (https://prefer-genes.uni-mainz.de/).

Integrative Network Analysis of Predicted miRNA-Targets Regulating Expression of Immune Response Genes in Bovine Coronavirus Infection

Bovine coronavirus (BCoV) infection that causes disease outbreaks among farm animals, resulting in significant economic losses particularly in the cattle industry, has the potential to become zoonotic. miRNAs, which are short non-coding segments of RNA that inhibits the expression of their target genes, have been identified as potential biomarkers and drug targets, though this potential in BCoV remains largely unknown. We hypothesize that certain miRNAs could simultaneously target multiple genes, are significantly conserved across many species, thereby demonstrating the potential to serve as diagnostic or therapeutic tools for bovine coronavirus infection. To this end, we utilized different existing and publicly available computational tools to conduct system analysis predicting important miRNAs that could affect BCoV pathogenesis. Eleven genes including CEBPD, IRF1, TLR9, SRC, and RHOA, significantly indicated in immune-related pathways, were identified to be associated with BCoV, and implicated in other coronaviruses. Of the 70 miRNAs predicted to target the identified genes, four concomitant miRNAs (bta-miR-11975, bta-miR-11976, bta-miR-22-3p, and bta-miR-2325c) were found. Examining the gene interaction network suggests IL-6, IRF1, and TP53 as key drivers. Phylogenetic analysis revealed that miR-22 was completely conserved across all 14 species it was searched against, suggesting a shared and important functional role. Functional annotation and associated pathways of target genes, such as positive regulation of cytokine production, IL-6 signaling pathway, and regulation of leukocyte differentiation, indicate the miRNAs are major participants in multiple aspects of both innate and adaptive immune response. Examination of variants evinced a potentially deleterious SNP in bta-miR-22-3p and an advantageous SNP in bta-miR-2325c. Conclusively, this study provides new insight into miRNAs regulating genes responding to BCoV infection, with bta-miR-22-3p particularly indicated as a potential drug target or diagnostic marker for bovine coronavirus.

STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein-protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein-protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.

STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein-protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein-protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.

Key genes and regulatory networks involved in the initiation, progression and invasion of colorectal cancer

Aim

Until now, identification of drug targets for treatment of patients with specific stages of colorectal cancer (CRC) has remained a challenging field of research. Herein, we aimed to identify the key genes and regulatory networks involved in each stage of CRC.

Results

The results of gene expression profiles were integrated with protein-protein interaction networks, and topologically analyzed. The most important regulatory genes (e.g., CDK1, UBC, ESR1 and ATXN1) and signaling pathways (e.g., Wnt, MAPK and JAK-STAT) in CRC initiation, progression and metastasis were identified. In vitro analysis confirmed some in silico findings.

Conclusion

Our study introduces functional hub genes, subnetworks, prioritizes signaling pathways and novel biomarkers in CRC that may guide further development of targeted therapy programs.