Microarray characterization of gene expression changes in blood during acute ethanol exposure

In silico based analysis to explore genetic linkage between atherosclerosis and its potential risk factors

Atherosclerosis (ATH) is a chronic cardiovascular disease characterized by plaque formation in arteries, and it is a major cause of illness and death. Although therapeutic advances have significantly improved the prognosis of ATH, missing therapeutic targets pose a significant residual threat. This research used a systems biology approach to identify the molecular biomarkers involved in the onset and progression of ATH, analysing microarray gene expression datasets from ATH and tissues impacted by risk factors such as high cholesterol, adipose tissue, smoking, obesity, sedentary lifestyle, stress, alcohol consumption, hypertension, hyperlipidaemia, high fat, diabetes to find the differentially expressed genes (DEGs). Bioinformatic analyses of Protein-Protein Interaction (PPI), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) were conducted on differentially expressed genes, revealing metabolic and signaling pathways (the chemokine signaling pathway, cytokine-cytokine receptor interaction, the cytosolic DNA-sensing pathway, the peroxisome proliferator-activated receptors signaling pathway, and the nuclear factor-kappa B signaling pathway), ten hubs proteins (CCL5, CCR1, TLR1, CCR2, FCGR2A, IL1B, CD163, AIF1, CXCL-1 and TNF), five transcription factors (YY1, FOXL1, FOXC1, SRF, and GATA2), and five miRNAs (mir-27a-3p, mir-124-3p, mir-16-5p, mir-129-2-3p, mir-1-3p). These findings identify potential biomarkers that may increase knowledge of the mechanisms underlying ATH and their connection to risk factors, aiding in the development of new therapies.

RNA biomarkers for alcohol use disorder

Alcohol use disorder (AUD) is highly prevalent and one of the leading causes of disability in the US and around the world. There are some molecular biomarkers of heavy alcohol use and liver damage which can suggest AUD, but these are lacking in sensitivity and specificity. AUD treatment involves psychosocial interventions and medications for managing alcohol withdrawal, assisting in abstinence and reduced drinking (naltrexone, acamprosate, disulfiram, and some off-label medications), and treating comorbid psychiatric conditions (e.g., depression and anxiety). It has been suggested that various patient groups within the heterogeneous AUD population would respond more favorably to specific treatment approaches. For example, there is some evidence that so-called reward-drinkers respond better to naltrexone than acamprosate. However, there are currently no objective molecular markers to separate patients into optimal treatment groups or any markers of treatment response. Objective molecular biomarkers could aid in AUD diagnosis and patient stratification, which could personalize treatment and improve outcomes through more targeted interventions. Biomarkers of treatment response could also improve AUD management and treatment development. Systems biology considers complex diseases and emergent behaviors as the outcome of interactions and crosstalk between biomolecular networks. A systems approach that uses transcriptomic (or other -omic data, e.g., methylome, proteome, metabolome) can capture genetic and environmental factors associated with AUD and potentially provide sensitive, specific, and objective biomarkers to guide patient stratification, prognosis of treatment response or relapse, and predict optimal treatments. This Review describes and highlights state-of-the-art research on employing transcriptomic data and artificial intelligence (AI) methods to serve as molecular biomarkers with the goal of improving the clinical management of AUD. Considerations about future directions are also discussed.

The Proteasome Activator PA200/PSME4: An Emerging New Player in Health and Disease

Proteasomes comprise a family of proteasomal complexes essential for maintaining protein homeostasis. Accordingly, proteasomes represent promising therapeutic targets in multiple human diseases. Several proteasome inhibitors are approved for treating hematological cancers. However, their side effects impede their efficacy and broader therapeutic applications. Therefore, understanding the biology of the different proteasome complexes present in the cell is crucial for developing tailor-made inhibitors against specific proteasome complexes. Here, we will discuss the structure, biology, and function of the alternative Proteasome Activator 200 (PA200), also known as PSME4, and summarize the current evidence for its dysregulation in different human diseases. We hereby aim to stimulate research on this enigmatic proteasome regulator that has the potential to serve as a therapeutic target in cancer.

Significant, replicable, and functional associations between KTN1 variants and alcohol and drug codependence

The gray matter volume (GMV) of the putamen has been reported to be regulated by kinectin 1 gene (KTN1). As a hub of the dopaminergic circuit, the putamen is widely implicated in the etiological processes of substance use disorders (SUD). Here, we aimed to identify robust and reliable associations between KTN1 SNPs and SUD across multiple samples. We examined the associations between SUD and KTN1 SNPs in four independent population-based or family-based samples (n = 10,209). The potential regulatory effects of the risk alleles on the putamen GMVs, the effects of alcohol, nicotine, marijuana and cocaine on KTN1 mRNA expression, and the relationship between KTN1 mRNA expression and SUD were explored. We found that a total of 23 SNPs were associated with SUD across at least two independent samples (1.4 × 10-4 ≤ p ≤ 0.049), including one SNP (rs12895072) across three samples (8.8 × 10-3 ≤ p ≤ 0.049). Four other SNPs were significantly or suggestively associated with SUD only in European-Australians (4.8 × 10-4 ≤ p ≤ 0.058). All of the SUD-risk alleles of these 27 SNPs increased (β > 0) the putamen GMVs and represented major alleles (f > 0.5) in Europeans. Twenty-two SNPs were potentially biologically functional. Alcohol, nicotine and cocaine significantly affected the KTN1 mRNA expression, and the KTN1 mRNA was differentially expressed between nicotine or cocaine dependent and control subjects. We concluded that there was a replicable and robust relationship among the KTN1 variants, KTN1 mRNA expression, putamen GMVs, molecular effects of substances, and SUD, suggesting that some risk KTN1 alleles might increase kinectin 1 expression in the putamen, altering putamen structures and functions, and leading to SUD.

Ethanol exposure drives colon location specific cell composition changes in a normal colon crypt 3D organoid model

Alcohol is a consistently identified risk factor for colon cancer. However, the molecular mechanism underlying its effect on normal colon crypt cells remains poorly understood. We employed RNA-sequencing to asses transcriptomic response to ethanol exposure (0.2% vol:vol) in 3D organoid lines derived from healthy colon (n = 34). Paired regression analysis identified 2,162 differentially expressed genes in response to ethanol. When stratified by colon location, a far greater number of differentially expressed genes were identified in organoids derived from the left versus right colon, many of which corresponded to cell-type specific markers. To test the hypothesis that the effects of ethanol treatment on colon organoid populations were in part due to differential cell composition, we incorporated external single cell RNA-sequencing data from normal colon biopsies to estimate cellular proportions following single cell deconvolution. We inferred cell-type-specific changes, and observed an increase in transit amplifying cells following ethanol exposure that was greater in organoids from the left than right colon, with a concomitant decrease in more differentiated cells. If this occurs in the colon following alcohol consumption, this would lead to an increased zone of cells in the lower crypt where conditions are optimal for cell division and the potential to develop mutations.

SOS GEFs in health and disease

SOS1 and SOS2 are the most universal and widely expressed family of guanine exchange factors (GEFs) capable or activating RAS or RAC1 proteins in metazoan cells. SOS proteins contain a sequence of modular domains that are responsible for different intramolecular and intermolecular interactions modulating mechanisms of self-inhibition, allosteric activation and intracellular homeostasis. Despite their homology, analyses of SOS1/2-KO mice demonstrate functional prevalence of SOS1 over SOS2 in cellular processes including proliferation, migration, inflammation or maintenance of intracellular redox homeostasis, although some functional redundancy cannot be excluded, particularly at the organismal level. Specific SOS1 gain-of-function mutations have been identified in inherited RASopathies and various sporadic human cancers. SOS1 depletion reduces tumorigenesis mediated by RAS or RAC1 in mouse models and is associated with increased intracellular oxidative stress and mitochondrial dysfunction. Since WT RAS is essential for development of RAS-mutant tumors, the SOS GEFs may be considered as relevant biomarkers or therapy targets in RAS-dependent cancers. Inhibitors blocking SOS expression, intrinsic GEF activity, or productive SOS protein-protein interactions with cellular regulators and/or RAS/RAC targets have been recently developed and shown preclinical and clinical effectiveness blocking aberrant RAS signaling in RAS-driven and RTK-driven tumors.

Fully moderated t-statistic in linear modeling of mixed effects for differential expression analysis

BACKGROUND

Gene expression profiling experiments with few replicates lead to great variability in the estimates of gene variances. Toward this end, several moderated t-test methods have been developed to reduce this variability and to increase power for testing differential expression. Most of these moderated methods are based on linear models with fixed effects where residual variances are smoothed under a hierarchical Bayes framework. However, they are inadequate for designs with complex correlation structures, therefore application of moderated methods to linear models with mixed effects are needed for differential expression analysis.

RESULTS

We demonstrated the implementation of the fully moderated t-statistic method for linear models with mixed effects, where both residual variances and variance estimates of random effects are smoothed under a hierarchical Bayes framework. We compared the proposed method with two current moderated methods and show that the proposed method can control the expected number of false positives at the nominal level, while the two current moderated methods fail.

CONCLUSIONS

We proposed an approach for testing differential expression under complex correlation structures while providing variance shrinkage. The proposed method is able to improve power by moderation and controls the expected number of false positives properly at the nominal level.

Review and Meta-Analyses of TAAR1 Expression in the Immune System and Cancers

Since its discovery in 2001, the major focus of TAAR1 research has been on its role in monoaminergic regulation, drug-induced reward and psychiatric conditions. More recently, TAAR1 expression and functionality in immune system regulation and immune cell activation has become a topic of emerging interest. Here, we review the immunologically-relevant TAAR1 literature and incorporate open-source expression and cancer survival data meta-analyses. We provide strong evidence for TAAR1 expression in the immune system and cancers revealed through NCBI GEO datamining and discuss its regulation in a spectrum of immune cell types as well as in numerous cancers. We discuss connections and logical directions for further study of TAAR1 in immunological function, and its potential role as a mediator or modulator of immune dysregulation, immunological effects of psychostimulant drugs of abuse, and cancer progression.

Regulation of Chemokine Signal Integration by Activator of G-Protein Signaling 4 (AGS4)

Activator of G-protein signaling 4 (AGS4)/G-protein signaling modulator 3 (Gpsm3) contains three G-protein regulatory (GPR) motifs, each of which can bind Gαi-GDP free of Gβγ We previously demonstrated that the AGS4-Gαi interaction is regulated by seven transmembrane-spanning receptors (7-TMR), which may reflect direct coupling of the GPR-Gαi module to the receptor analogous to canonical Gαβγ heterotrimer. We have demonstrated that the AGS4-Gαi complex is regulated by chemokine receptors in an agonist-dependent manner that is receptor-proximal. As an initial approach to investigate the functional role(s) of this regulated interaction in vivo, we analyzed leukocytes, in which AGS4/Gpsm3 is predominantly expressed, from AGS4/Gpsm3-null mice. Loss of AGS4/Gpsm3 resulted in mild but significant neutropenia and leukocytosis. Dendritic cells, T lymphocytes, and neutrophils from AGS4/Gpsm3-null mice also exhibited significant defects in chemoattractant-directed chemotaxis and extracellular signal-regulated kinase activation. An in vivo peritonitis model revealed a dramatic reduction in the ability of AGS4/Gpsm3-null neutrophils to migrate to primary sites of inflammation. Taken together, these data suggest that AGS4/Gpsm3 is required for proper chemokine signal processing in leukocytes and provide further evidence for the importance of the GPR-Gαi module in the regulation of leukocyte function.

Evaluating the Stability of RNA-Seq Transcriptome Profiles and Drug-Induced Immune-Related Expression Changes in Whole Blood

Methods were developed to evaluate the stability of rat whole blood expression obtained from RNA sequencing (RNA-seq) and assess changes in whole blood transcriptome profiles in experiments replicated over time. Expression was measured in globin-depleted RNA extracted from the whole blood of Sprague-Dawley rats, given either saline (control) or neurotoxic doses of amphetamine (AMPH). The experiment was repeated four times (paired control and AMPH groups) over a 2-year span. The transcriptome of the control and AMPH-treated groups was evaluated on: 1) transcript levels for ribosomal protein subunits; 2) relative expression of immune-related genes; 3) stability of the control transcriptome over 2 years; and 4) stability of the effects of AMPH on immune-related genes over 2 years. All, except one, of the 70 genes that encode the 80s ribosome had levels that ranked in the top 5% of all mean expression levels. Deviations in sequencing performance led to significant changes in the ribosomal transcripts. The overall expression profile of immune-related genes and genes specific to monocytes, T-cells or B-cells were well represented and consistent within treatment groups. There were no differences between the levels of ribosomal transcripts in time-matched control and AMPH groups but significant differences in the expression of immune-related genes between control and AMPH groups. AMPH significantly increased expression of some genes related to monocytes but down-regulated those specific to T-cells. These changes were partially due to changes in the two types of leukocytes present in blood, which indicate an activation of the innate immune system by AMPH. Thus, the stability of RNA-seq whole blood transcriptome can be verified by assessing ribosomal protein subunits and immune-related gene expression. Such stability enables the pooling of samples from replicate experiments to carry out differential expression analysis with acceptable power.

Long term intake of 0.1% ethanol decreases serum adiponectin by suppressing PPARγ expression via p38 MAPK pathway

Light alcohol consumption was reported to be negatively associated with insulin resistance and risk of cardiovascular diseases; however, the results were inconsistent. We here investigate whether long term intake of low-concentration ethanol can affect adiponectin levels. Male Wistar rats were exposed to 0.1% ethanol in drinking water for 26weeks. Visceral adipose tissue (VAT) was cultured and treated with ethanol, SB203580, GW9662, or rosiglitazone. Adiponectin in serum and culture supernatant were measured by ELISA, mRNA levels of adiponectin and PPARγ were determined by RT-PCR, and protein expressions of PPARγ, p38 MAPK and phospho-p38 MAPK were determined by Western blot. In vivo, ethanol decreased the mRNA of adiponectin in VAT and serum adiponectin significantly. Decreased PPARγ and increased activation of p38 MAPK were observed in ethanol treated group. In vitro, SB203580 increased the adiponectin and PPARγ levels in normal DMEM cultured VAT and ameliorated ethanol-induced decrease of adiponectin and PPARγ expressions. GW9662 also decreased the adiponectin levels; Both ethanol and GW9662 weakened the rosiglitazone-induced elevation of adiponectin levels in cultured VAT. These data suggest that long term intake of 0.1% ethanol down-regulated adiponectin levels, and the regulation of PPARγ via p38 MAPK pathway plays an important role in the mechanism underneath.

Feasibility of whole RNA sequencing from single-cell mRNA amplification

Single-cell sampling with RNA-seq analysis plays an important role in reference laboratory; cytogenomic diagnosis for specimens on glass-slides or rare cells in circulating blood for tumor and genetic diseases; measurement of sensitivity and specificity in tumor-tissue genomic analysis with mixed-cells; mechanism analysis of differentiation and proliferation of cancer stem cell for academic purpose. Our single- cell RNA-seq technique shows that fragments were 250-450 bp after fragmentation, amplification, and adapter addition. There were 11.6 million reads mapped in raw sequencing reads (19.6 million). The numbers of mapped genes, mapped transcripts, and mapped exons were 31,332, 41,210, and 85,786, respectively. All QC results demonstrated that RNA-seq techniques could be used for single-cell genomic performance. Analysis of the mapped genes showed that the number of genes mapped by RNA-seq (6767 genes) was much higher than that of differential display (288 libraries) among similar specimens which we have developed and published. The single-cell RNA-seq can detect gene splicing using different subtype TGF-beta analysis. The results from using Q-rtPCR tests demonstrated that sensitivity is 76% and specificity is 55% from single-cell RNA-seq technique with some gene expression missing (2/8 genes). However, it will be feasible to use RNA-seq techniques to contribute to genomic medicine at single-cell level.