Differential Expression and Functional Analysis of High-Throughput -Omics Data Using Open Source Tools

Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review

In recent years, the role of bioinformatics and computational biology together with omics techniques and transcriptomics has gained tremendous importance in biomedicine and healthcare, particularly for the identification of biomarkers for precision medicine and drug discovery. Differential gene expression (DGE) analysis is one of the most used techniques for RNA-sequencing (RNA-seq) data analysis. This tool, which is typically used in various RNA-seq data processing applications, allows the identification of differentially expressed genes across two or more sample sets. Functional enrichment analyses can then be performed to annotate and contextualize the resulting gene lists. These studies provide valuable information about disease-causing biological processes and can help in identifying molecular targets for novel therapies. This review focuses on differential gene expression (DGE) analysis pipelines and bioinformatic techniques commonly used to identify specific biomarkers and discuss the advantages and disadvantages of these techniques.

Crosstalk Between Polygonatum kingianum, the miRNA, and Gut Microbiota in the Regulation of Lipid Metabolism

Objectives: Polygonatum kingianum is a medicinal herb used in various traditional Chinese medicine formulations. The polysaccharide fraction of P. kingianum can reduce insulin resistance and restore the gut microbiota in a rat model of aberrant lipid metabolism by down regulating miR-122. The aim of this study was to further elucidate the effect of P. kingianum on lipid metabolism, and the roles of specific miRNAs and the gut microbiota. Key findings: P. kingianum administration significantly altered the abundance of 29 gut microbes and 27 differentially expressed miRNAs (DEMs). Several aberrantly expressed miRNAs closely related to lipid metabolism were identified, of which some were associated with specific gut microbiota. MiR-484 in particular was identified as the core factor involved in the therapeutic effects of P. kingianum. We hypothesize that the miR-484-Bacteroides/Roseburia axis acts as an important bridge hub that connects the entire miRNA-gut microbiota network. In addition, we observed that Parabacteroides and Bacillus correlated significantly with several miRNAs, including miR-484, miR-122-5p, miR-184 and miR-378b. Summary: P. kingianum alleviates lipid metabolism disorder by targeting the network of key miRNAs and the gut microbiota.

Spectral quality overrides software score-A brief tutorial on the analysis of peptide fragmentation data for mass spectrometry laymen

The use of mass spectrometry has dramatically increased the research pace in the life sciences. The influence of the technique is enormous and its results can have far-reaching consequences such as jail time when applied in forensics. Therefore, analytical chemists trained in proper procedure know that they must validate their experiments. However, those quality measures have not been adopted in a similar manner in the omics technologies even though the stakes are equally high. Reasons are, among others, the segregation of the data generation and data mining functions and an undue belief in software capabilities. In this article, problematic issues such as false or overinterpretation of data are discussed, and assistance is provided for mass spectrometry laymen to evaluate the quality of their results; a quick guide to mass spectral data interpretation of peptide fragmentation experiments, the basis of bottom-up proteomics, is offered. Good science can only be generated in tight collaboration of principal investigator, analytical chemist, and bioinformatician so that the limits and the potential of each method and approach can be responsibly communicated.

Epigenetic differences at the HTR2A locus in progressive multiple sclerosis patients

The pathology of progressive multiple sclerosis (MS) is poorly understood. We have previously assessed DNA methylation in the CD4⁺ T cells of relapsing–remitting (RR) MS patients compared to healthy controls and identified differentially methylated regions (DMRs) in HLA-DRB1 and RNF39. This study aimed to investigate the DNA methylation profiles of the CD4⁺ T cells of progressive MS patients. DNA methylation was measured in two separate case/control cohorts using the Illumina 450K/EPIC arrays and data was analysed with the Chip Analysis Methylation Pipeline (ChAMP). Single nucleotide polymorphisms (SNPs) were assessed using the Illumina Human OmniExpress24 arrays and analysed using PLINK. Expression was assessed using the Illumina HT12 array and analysed in R using a combination of Limma and Illuminaio. We identified three DMRs at HTR2A, SLC17A9 and HDAC4 that were consistent across both cohorts. The DMR at HTR2A is located within the bounds of a haplotype block; however, the DMR remained significant after accounting for SNPs in the region. No expression changes were detected in any DMRs. HTR2A is differentially methylated in progressive MS independent of genotype. This differential methylation is not evident in RRMS, making it a potential biomarker of progressive disease.

Transcriptome profile of highly osteoblastic/cementoblastic periodontal ligament cell clones

Heterogeneous cell populations of osteo/cementoblastic (O/C) or fibroblastic phenotypes constitute the periodontal dental ligament (PDL). A better understanding of these PDL cell subpopulations is essential to propose regenerative approaches based on a sound biological rationale.

Towards Differential Connectomics with NeuroVIISAS

The comparison of connectomes is an essential step to identify changes in structural and functional neuronal networks. However, the connectomes themselves as well as the comparisons of connectomes could be manifold. In most applications, comparisons of connectomes are applied to specific sets of data. In many studies collections of scripts are applied optimized for certain species (non-generic approaches) or diseases (control versus disease group connectomes). These collections of scripts have a limited functionality which do not support functional and topographic mappings of connectomes (hemispherical asymmetries, peripheral nervous system). The platform-independent and generic neuroVIISAS framework is built to circumvent limitations that come with variants of nomenclatures, connectivity lists and connectional hierarchies as well as restrictions to structural connectome analyses. A new analytical module is introduced into the framework to compare different types of connectomes and different representations of the same connectome within a unique software environment. As an example a differential analysis of the partial connectome of the laboratory rat that is based on virus tract tracing with the same regions of non-virus tract tracing has been performed. A relatively large connectional coherence between the two different techniques was found. However, some detected connections are described by virus tract-tracing only.