Using genome-wide expression profiling to define gene networks relevant to the study of complex traits: from RNA integrity to network topology

Gene expression networks regulated by human personality

Genome-wide association studies of human personality have been carried out, but transcription of the whole genome has not been studied in relation to personality in humans. We collected genome-wide expression profiles of adults to characterize the regulation of expression and function in genes related to human personality. We devised an innovative multi-omic approach to network analysis to identify the key control elements and interactions in multi-modular networks. We identified sets of transcribed genes that were co-expressed in specific brain regions with genes known to be associated with personality. Then we identified the minimum networks for the co-localized genes using bioinformatic resources. Subjects were 459 adults from the Young Finns Study who completed the Temperament and Character Inventory and provided peripheral blood for genomic and transcriptomic analysis. We identified an extrinsic network of 45 regulatory genes from seed genes in brain regions involved in self-regulation of emotional reactivity to extracellular stimuli (e.g., self-regulation of anxiety) and an intrinsic network of 43 regulatory genes from seed genes in brain regions involved in self-regulation of interpretations of meaning (e.g., production of concepts and language). We discovered that interactions between the two networks were coordinated by a control hub of 3 miRNAs and 3 protein-coding genes shared by both. Interactions of the control hub with proteins and ncRNAs identified more than 100 genes that overlap directly with known personality-related genes and more than another 4000 genes that interact indirectly. We conclude that the six-gene hub is the crux of an integrative network that orchestrates information-transfer throughout a multi-modular system of over 4000 genes enriched in liquid-liquid-phase-separation (LLPS)-related RNAs, diverse transcription factors, and hominid-specific miRNAs and lncRNAs. Gene expression networks associated with human personality regulate neuronal plasticity, epigenesis, and adaptive functioning by the interactions of salience and meaning in self-awareness.

DDK-Linker: a network-based strategy identifies disease signals by linking high-throughput omics datasets to disease knowledge

The high-throughput genomic and proteomic scanning approaches allow investigators to measure the quantification of genome-wide genes (or gene products) for certain disease conditions, which plays an essential role in promoting the discovery of disease mechanisms. The high-throughput approaches often generate a large gene list of interest (GOIs), such as differentially expressed genes/proteins. However, researchers have to perform manual triage and validation to explore the most promising, biologically plausible linkages between the known disease genes and GOIs (disease signals) for further study. Here, to address this challenge, we proposed a network-based strategy DDK-Linker to facilitate the exploration of disease signals hidden in omics data by linking GOIs to disease knowns genes. Specifically, it reconstructed gene distances in the protein-protein interaction (PPI) network through six network methods (random walk with restart, Deepwalk, Node2Vec, LINE, HOPE, Laplacian) to discover disease signals in omics data that have shorter distances to disease genes. Furthermore, benefiting from the establishment of knowledge base we established, the abundant bioinformatics annotations were provided for each candidate disease signal. To assist in omics data interpretation and facilitate the usage, we have developed this strategy into an application that users can access through a website or download the R package. We believe DDK-Linker will accelerate the exploring of disease genes and drug targets in a variety of omics data, such as genomics, transcriptomics and proteomics data, and provide clues for complex disease mechanism and pharmacological research. DDK-Linker is freely accessible at http://ddklinker.ncpsb.org.cn/.

High-Intensity interval training reduces transcriptomic age: A randomized controlled trial

While the relationship between exercise and life span is well-documented, little is known about the effects of specific exercise protocols on modern measures of biological age. Transcriptomic age (TA) predictors provide an opportunity to test the effects of high-intensity interval training (HIIT) on biological age utilizing whole-genome expression data. A single-site, single-blinded, randomized controlled clinical trial design was utilized. Thirty sedentary participants (aged 40-65) were assigned to either a HIIT group or a no-exercise control group. After collecting baseline measures, HIIT participants performed three 10 × 1 HIIT sessions per week for 4 weeks. Each session lasted 23 min, and total exercise duration was 276 min over the course of the 1-month exercise protocol. TA, PSS-10 score, PSQI score, PHQ-9 score, and various measures of body composition were all measured at baseline and again following the conclusion of exercise/control protocols. Transcriptomic age reduction of 3.59 years was observed in the exercise group while a 3.29-years increase was observed in the control group. Also, PHQ-9, PSQI, BMI, body fat mass, and visceral fat measures were all improved in the exercise group. A hypothesis-generation gene expression analysis suggested exercise may modify autophagy, mTOR, AMPK, PI3K, neurotrophin signaling, insulin signaling, and other age-related pathways. A low dose of HIIT can reduce an mRNA-based measure of biological age in sedentary adults between the ages of 40 and 65 years old. Other changes in gene expression were relatively modest, which may indicate a focal effect of exercise on age-related biological processes.

Genome-wide expression analysis reveals different heat shock responses in indigenous (Bos indicus) and crossbred (Bos indicus X Bos taurus) cattle

Environmental heat stress in dairy cattle leads to poor health, reduced milk production and decreased reproductive efficiency. Multiple genes interact and coordinate the response to overcome the impact of heat stress. The present study identified heat shock regulated genes in the peripheral blood mononuclear cells (PBMC). Genome-wide expression patterns for cellular stress response were compared between two genetically distinct groups of cattle viz., Hariana (B. indicus) and Vrindavani (B. indicus X B. taurus). In addition to major heat shock response genes, oxidative stress and immune response genes were also found to be affected by heat stress. Heat shock proteins such as HSPH1, HSPB8, FKB4, DNAJ4 and SERPINH1 were up-regulated at higher fold change in Vrindavani compared to Hariana cattle. The oxidative stress response genes (HMOX1, BNIP3, RHOB and VEGFA) and immune response genes (FSOB, GADD45B and JUN) were up-regulated in Vrindavani whereas the same were down-regulated in Hariana cattle. The enrichment analysis of dysregulated genes revealed the biological functions and signaling pathways that were affected by heat stress. Overall, these results show distinct cellular responses to heat stress in two different genetic groups of cattle. This also highlight the long-term adaptation of B. indicus (Hariana) to tropical climate as compared to the crossbred (Vrindavani) with mixed genetic makeup (B. indicus X B. taurus).

Predicting Key Genes and Therapeutic Molecular Modelling to Explain the Association between Porphyromonas gingivalis (P. gingivalis) and Alzheimer’s Disease (AD)

The association between Porphyromonas gingivalis (P. gingivalis) and Alzheimer’s disease (AD) remains unclear. The major aim of this study was to elucidate the role of genes and molecular targets in P. gingivalis-associated AD. Two Gene Expression Omnibus (GEO) datasets, GSE5281 for AD (n = 84 Alzheimer’s, n = 74 control) and GSE9723 (n = 4 P. gingivalis, n = 4 control), were downloaded from the GEO database. Differentially expressed genes (DEGs) were obtained, and genes common to both diseases were drawn. Additionally, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis was performed from the top 100 genes (50 upregulated and 50 downregulated genes). We then proceeded with CMap analysis to screen for possible small drug molecules targeting these genes. Subsequently, we performed molecular dynamics simulations. A total of 10 common genes (CALD1, HES1, ID3, PLK2, PPP2R2D, RASGRF1, SUN1, VPS33B, WTH3DI/RAB6A, and ZFP36L1) were identified with a p-value < 0.05. The PPI network of the top 100 genes showed UCHL1, SST, CHGB, CALY, and INA to be common in the MCC, DMNC, and MNC domains. Out of the 10 common genes identified, only 1 was mapped in CMap. We found three candidate small drug molecules to be a fit for PLK2, namely PubChem ID: 24971422, 11364421, and 49792852. We then performed molecular docking of PLK2 with PubChem ID: 24971422, 11364421, and 49792852. The best target, 11364421, was used to conduct the molecular dynamics simulations. The results of this study unravel novel genes to P. gingivalis-associated AD that warrant further validation.

A Nutrigenomics Approach Using RNA Sequencing Technology to Study Nutrient-Gene Interactions in Agricultural Animals

Thorough understanding of animal gene expression driven by dietary nutrients can be regarded as a bottom line of advanced animal nutrition research. Nutrigenomics (including transcriptomics) studies the effects of dietary nutrients on cellular gene expression and, ultimately, phenotypic changes in living organisms. Transcriptomics can be applied to investigate animal tissue transcriptomes at a defined nutritional state, which can provide a holistic view of intracellular RNA expression. As a novel transcriptomics approach, RNA sequencing (RNA-Seq) technology can monitor all gene expressions simultaneously in response to dietary intervention. The principle and history of RNA-Seq are briefly reviewed, and its 3 principal steps are described in this article. Application of RNA-Seq in different areas of animal nutrition research is summarized. Lastly, the application of RNA-Seq in swine science and nutrition is also reviewed. In short, RNA-Seq holds significant potential to be employed for better understanding the nutrient-gene interactions in agricultural animals.

Visualization of Cancer and Cardiovascular Disease Co-Occurrence With Network Methods

PURPOSE

Cancer and cardiovascular disease (CVD) are common causes of morbidity and mortality, and measurement and interpretation of their co-occurrence rate have important implications for public health and patient care. Here, we present the raw and adjusted co-occurrence rates of cancer and CVD in the overall population by using a visually intuitive network approach.

METHODS

By using baseline survey and linked health outcome data from 490,842 individuals age 40 to 69 years from the UK Biobank, we recorded diagnoses between 1997 and 2014 of specific cancers and specific CVDs ascertained through hospital claims. We measured raw and adjusted rates of CVD for the following groups: individuals with Hodgkin or non-Hodgkin lymphoma, lung and trachea cancer, uterus cancer, colorectal cancer, prostate cancer, breast cancer, or no recorded diagnosed cancer during this time period. Analysis accounted for age, sex, and behavioral risk factors, without regard to the order of occurrence of cancer and CVD.

RESULTS

A significantly increased rate of CVD was found in patients with multiple types of cancers, including Hodgkin and non-Hodgkin lymphoma and lung and trachea, uterus, colorectal, and breast cancer, compared with patients without cancer by using age and sex-adjusted models. Increased co-occurrence for many CVD categories remained after correction for behavioral risk factors. Construction of co-occurrence networks highlighted heart failure as a shared CVD diagnosis across multiple cancer types, including breast cancer, lung cancer, non-Hodgkin lymphoma, and colorectal cancer. Smoking, physical activity, and other lifestyle factors accounted for some but not all of the increased co-occurrence for many of the CVD diagnoses.

CONCLUSION

Increased co-occurrence of several common CVD conditions is seen widely across multiple malignancies, and shared diagnoses, such as heart failure, were highlighted by using network methods.

Systems Genomics of Thigh Adipose Tissue From Asian Indian Type-2 Diabetics Revealed Distinct Protein Interaction Hubs

We performed a systematic analysis of genes implicated in thigh subcutaneous adipose tissue of Asian Indian Type 2 Diabetes Mellitus (AIT2DM) and created a phenome-interactome network. This analysis was performed on 60 subjects specific to limb thigh fat by integrating phenotypic traits and similarity scores associated with AIT2DM. Using a phenotypic attribute, a contextual neighbor was identified across all the traits, viz. body mass index (BMI) statistics, adipocyte size, lipid parameters, homeostatic model assessment- insulin resistance (HOMA-IR), HOMA-ß. In this work, we have attempted to characterize transcription signatures using the phenome-interactome maps where each of the traits under study including the intermediary phenotypes has a distinct set of genes forming the hubs. Furthermore, we have identified various clinical, biochemical, and radiological parameters which show significant correlation with distinct hubs. We observed a number of novel pathways and genes including those that are non-coding RNAs implicated in AIT2DM.We showed that they appear to be associated with pathways, viz. tyrosine kinase JAK2, NOTCH thereby recruiting signaling molecules such as STAT5 and Src family kinases on the cell surface regulated them and our analyses comprising significant hubs suggest that thigh subcutaneous adipose tissue plays a role in pathophysiology of AIT2DM.

Identification of Biomarkers Based on Differentially Expressed Genes in Papillary Thyroid Carcinoma

The incidence of papillary thyroid carcinoma (PTC) is increasing rapidly throughout the world. Hence, there is an urgent need for identifying more specific and sensitive biomarkers to explorate the pathogenesis of PTC. In this study, three pairs of stage I PTC tissues and matched normal adjacent tissues were sequenced by RNA-Seq, and 719 differentially expressed genes (DEGs) were screened. KEGG pathway enrichment analyses indicated that the DEGs were significantly enriched in 28 pathways. A total of 18 nodes consisting of 20 DEGs were identified in the top 10% of KEGG integrated networks. The functions of DEGs were further analysed by GO. The 13 selected genes were confirmed by qRT-PCR in 16 stage I PTC patients and by The Cancer Genome Atlas (TCGA) database. The relationship interactions between DEGs were analysed by protein-protein interaction networks and chromosome localizations. Finally, four newly discovered genes, COMP, COL3A1, ZAP70, and CD247, were found to be related with PTC clinical phenotypes, and were confirmed by Spearman’s correlation analyses in TCGA database. These four DEGs might be promising biomarkers for early-stage PTC, and provide an experimental foundation for further exploration of the pathogenesis of early-stage PTC.

A Novel Prioritization Method in Identifying Recurrent Venous Thromboembolism-Related Genes

Identifying the genes involved in venous thromboembolism (VTE) recurrence is important not only for understanding the pathogenesis but also for discovering the therapeutic targets. We proposed a novel prioritization method called Function-Interaction-Pearson (FIP) by creating gene-disease similarity scores to prioritize candidate genes underling VTE. The scores were calculated by integrating and optimizing three types of resources including gene expression, gene ontology and protein-protein interaction. As a result, 124 out of top 200 prioritized candidate genes had been confirmed in literature, among which there were 34 antithrombotic drug targets. Compared with two well-known gene prioritization tools Endeavour and ToppNet, FIP was shown to have better performance. The approach provides a valuable alternative for drug targets discovery and disease therapy.

The Dlx5 and Foxg1 transcription factors, linked via miRNA-9 and -200, are required for the development of the olfactory and GnRH system

During neuronal development and maturation, microRNAs (miRs) play diverse functions ranging from early patterning, proliferation and commitment to differentiation, survival, homeostasis, activity and plasticity of more mature and adult neurons. The role of miRs in the differentiation of olfactory receptor neurons (ORNs) is emerging from the conditional inactivation of Dicer in immature ORN, and the depletion of all mature miRs in this system. Here, we identify specific miRs involved in olfactory development, by focusing on mice null for Dlx5, a homeogene essential for both ORN differentiation and axon guidance and connectivity. Analysis of miR expression in Dlx5^−/− olfactory epithelium pointed to reduced levels of miR-9, miR-376a and four miRs of the -200 class in the absence of Dlx5. To functionally examine the role of these miRs, we depleted miR-9 and miR-200 class in reporter zebrafish embryos and observed delayed ORN differentiation, altered axonal trajectory/targeting, and altered genesis and position of olfactory-associated GnRH neurons, i.e. a phenotype known as Kallmann syndrome in humans. miR-9 and miR-200-class negatively control Foxg1 mRNA, a fork-head transcription factor essential for development of the olfactory epithelium and of the forebrain, known to maintain progenitors in a stem state. Increased levels of z-foxg1 mRNA resulted in delayed ORN differentiation and altered axon trajectory, in zebrafish embryos. This work describes for the first time the role of specific miR (-9 and -200) in olfactory/GnRH development, and uncovers a Dlx5–Foxg1 regulation whose alteration affects receptor neuron differentiation, axonal targeting, GnRH neuron development, the hallmarks of the Kallmann syndrome.

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Dlx5 controls the expressions of miR9 and miR-200, which target the Foxg1 mRNA

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miR-9 and -200 are needed for olfactory neurons differentiation and axon extension

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miR-9 and -200 are required for the genesis and position of GnRH neurons.

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Altered expression of miR-9 and -200 might contribute to the Kallmann disease.

Simultaneous separation of five major ribonucleic acids by capillary electrophoresis with laser-induced fluorescence in the presence of electroosmotic flow: application to the rapid screening of 5S rRNA from ovarian cancer cells

RNA integrity is important in RNA studies because poor RNA quality may impact downstream methodologies. This study proposes a rapid and cost-effective method for the determination of RNA integrity based on CE-LIF in the presence of electroosmotic flow. The proposed method uses poly(ethylene) oxide (Mavg=4,000,000 Da) as a sieving matrix for total RNA separation. Ethidium bromide (μg mL(-1)) was dissolved in a polymer solution as an interchelating dye for on-column fluorescent labeling. The 28S rRNA, 18S rRNA, 5.8S rRNA, 5S rRNA and tRNA from the total human RNA extracted from the cells were fully separated using the proposed method. The lowest detectable concentration of total RNA achieved was 100 pg μL(-1) with a 6 min sample injection followed by on-column concentration. In addition, the temperature-induced degradation of total RNA was observed by CE-LIF. The electropherograms revealed more fragmentation of 28S and 18S rRNAs by temperature-induced hydrolysis compared with the 5.8S rRNA, 5S rRNA and tRNA. Therefore, the results indicated that RNA degradation should be considered for long-term, high-temperature incubations in RNA-related experiments involving RNA hybridization. The proposed method is furthermore, applied to the determination of 5S rRNA overexpressed in ovarian cancer cells as compared to the cervical cancer cells. Overall, CE-LIF is highly promising for rapid screening of ovarian cancers without tedious pre-amplification steps.