Effect of CAR activation on selected metabolic pathways in normal and hyperlipidemic mouse livers

<p>Abstract</p> <p>Background</p> <p>Detoxification in the liver involves activation of nuclear receptors, such as the constitutive androstane receptor (CAR), which regulate downstream genes of xenobiotic metabolism. Frequently, the metabolism of endobiotics is also modulated, resulting in potentially harmful effects. We therefore used 1,4-Bis [2-(3,5-dichloropyridyloxy)] benzene (TCPOBOP) to study the effect of CAR activation on mouse hepatic transcriptome and lipid metabolome under conditions of diet-induced hyperlipidemia.</p> <p>Results</p> <p>Using gene expression profiling with a dedicated microarray, we show that xenobiotic metabolism, PPARα and adipocytokine signaling, and steroid synthesis are the pathways most affected by TCPOBOP in normal and hyperlipidemic mice. TCPOBOP-induced CAR activation prevented the increased hepatic and serum cholesterol caused by feeding mice a diet containing 1% cholesterol. We show that this is due to increased bile acid metabolism and up-regulated removal of LDL, even though TCPOBOP increased cholesterol synthesis under conditions of hyperlipidemia. Up-regulation of cholesterol synthesis was not accompanied by an increase in mature SREBP2 protein. As determined by studies in CAR -/- mice, up-regulation of cholesterol synthesis is however CAR-dependent; and no obvious CAR binding sites were detected in promoters of cholesterogenic genes. TCPOBOP also affected serum glucose and triglyceride levels and other metabolic processes in the liver, irrespective of the diet.</p> <p>Conclusion</p> <p>Our data show that CAR activation modulates hepatic metabolism by lowering cholesterol and glucose levels, through effects on PPARα and adiponectin signaling pathways, and by compromising liver adaptations to hyperlipidemia.</p

Possible role of estrogen metabolism and aldo-keto reductase activity in chemoresistance of ovarian cancer

High-grade serous ovarian cancer (HGSOC) is the most aggressive and chemoresistant form of epithelial ovarian cancer (OC) and is responsible for ~80% of OC-related deaths. OC is associated with disturbed estrogen action. In postmenopausal patients, estrogens are formed locally from steroid precursors. Enzymes of the AKR1C subfamily are associated with resistance to chemotherapeutic agents and are involved in the biosynthesis and metabolism of steroid hormones, thus may contribute to the growth of hormone-dependent tumors. To date, the interplay of estrogen synthesis and aldo-keto reductase activity in HGSOC chemoresistance remains unclear. The aim of this study was to investigate the differences in targeted transcriptomics of HGSOC cell lines with different sensitivity to carboplatin: OVSAHO, OVCAR-3, Kuramochi, OVCAR-4, Caov- 3, and COV362, and to evaluate the differences in correlation patterns between targeted gene expression profiles in platinum-sensitive and -resistant patients using publicly available data (PAD) (cBioPortal). We first determined the expression of genes involved in estrogen biosynthesis/metabolism (STS, SULT1E1, HSD17B1, HSD17B2, HSD17B14, PAPSS1, PAPSS2), steroid transport (SLCO1A2, SLCO1B3, SLCO2B1, SLCO4A1, SLCO4C1, ABCC1, ABCC4, ABCC11, ABCG2, SLC51A, SLC51B), estrogen action (ESR1, ESR2, GPER) and oxidative metabolism (CYP1A1, CYP1A2, CYP1B1, SULT1A1, SULT2B1, SULT1E1, UGTB7, COMT, NOQ1, NOQ2, GSTP1), NFE2L2 and AKR1C1-3 by qPCR. Next, by using PAD we conducted a correlation analysis using the Pearson correlation coefficient for gene expression data of targeted genes in OC patients. The patients were classified into two groups based on their response to platinum treatment: sensitive and resistant. The correlation matrix was computed independently for each group. Expression analysis revealed that the estrogen receptor ESR2, the efflux transporter ABCG2 and aldo-keto reductase AKR1C1 were highly expressed in the most resistant cell lines COV362 and Caov-3. The mRNA levels of estrogen biosynthesis and oxidative metabolism genes STS, HSD17B14, NOQ1, and GSTP1 increased with carboplatin resistance in the HGSOC cell lines. These results indicate the potential of ESR2, STS, HSD17B14, NOQ1, GSTP1, and ABCG2 as predictive markers for HGSOC chemoresistance. Furthermore, analysis of PAD revealed different correlation profiles between genes in sensitive and resistant patients. In chemoresistant were found a moderately to strong positive correlations (p<0.001) between gene pairs including AKR1C1– AKR1C3, AKR1C1 – NFE2L2, AKR1C1 – SULT1E1, NOQ1 – HSD17B14, COMT – SULT1A1, ABCG2 – SLC515. In chemosensitive patients was found a strong positive correlation (p<0.001) between gene pair CYP1B1 – SULT1E1. The correlation differences between sensitive and resistant OC patients suggest possible gene regulatory networks or molecular interactions contributing to the heterogeneity of response to platinum in OC. Further studies are ongoing to elucidate the mechanism of the interplay between local estrogen metabolism and aldo-keto reductase activity in HGSOC chemoresistanceBook of abstract: 4th Belgrade Bioinformatics Conference, June 19-23, 202

The Sterolgene v0 cDNA microarray: a systemic approach to studies of cholesterol homeostasis and drug metabolism

<p>Abstract</p> <p>Background</p> <p>Cholesterol homeostasis and xenobiotic metabolism are complex biological processes, which are difficult to study with traditional methods. Deciphering complex regulation and response of these two processes to different factors is crucial also for understanding of disease development. Systems biology tools as are microarrays can importantly contribute to this knowledge and can also discover novel interactions between the two processes.</p> <p>Results</p> <p>We have developed a low density Sterolgene v0 cDNA microarray dedicated to studies of cholesterol homeostasis and drug metabolism in the mouse. To illustrate its performance, we have analyzed mouse liver samples from studies focused on regulation of cholesterol homeostasis and drug metabolism by diet, drugs and inflammation. We observed down-regulation of cholesterol biosynthesis during fasting and high-cholesterol diet and subsequent up-regulation by inflammation. Drug metabolism was down-regulated by fasting and inflammation, but up-regulated by phenobarbital treatment and high-cholesterol diet. Additionally, the performance of the Sterolgene v0 was compared to the two commercial high density microarray platforms: the Agilent cDNA (G4104A) and the Affymetrix MOE430A GeneChip. We hybridized identical RNA samples to the commercial microarrays and showed that the performance of Sterolgene is comparable to commercial arrays in terms of detection of changes in cholesterol homeostasis and drug metabolism.</p> <p>Conclusion</p> <p>Using the Sterolgene v0 microarray we were able to detect important changes in cholesterol homeostasis and drug metabolism caused by diet, drugs and inflammation. Together with its next generations the Sterolgene microarrays represent original and dedicated tools enabling focused and cost effective studies of cholesterol homeostasis and drug metabolism. These microarrays have the potential of being further developed into screening or diagnostic tools.</p

Obesity resistant mechanisms in the Lean polygenic mouse model as indicated by liver transcriptome and expression of selected genes in skeletal muscle

<p>Abstract</p> <p>Background</p> <p>Divergently selected Lean and Fat mouse lines represent unique models for a polygenic form of resistance and susceptibility to obesity development. Previous research on these lines focused mainly on obesity-susceptible factors in the Fat line. This study aimed to examine the molecular basis of obesity-resistant mechanisms in the Lean line by analyzing various fat depots and organs, the liver transcriptome of selected metabolic pathways, plasma and lipid homeostasis and expression of selected skeletal muscle genes.</p> <p>Results</p> <p>Expression profiling using our custom Steroltalk v2 microarray demonstrated that Lean mice exhibit a higher hepatic expression of cholesterol biosynthesis genes compared to the Fat line, although this was not reflected in elevation of total plasma or liver cholesterol. However, FPLC analysis showed that protective HDL cholesterol was elevated in Lean mice. A significant difference between the strains was also found in bile acid metabolism. Lean mice had a higher expression of <it>Cyp8b1</it>, a regulatory enzyme of bile acid synthesis, and the <it>Abcb11 </it>bile acid transporter gene responsible for export of acids to the bile. Additionally, a higher content of blood circulating bile acids was observed in Lean mice. Elevated HDL and upregulation of some bile acids synthesis and transport genes suggests enhanced reverse cholesterol transport in the Lean line - the flux of cholesterol out of the body is higher which is compensated by upregulation of endogenous cholesterol biosynthesis. Increased skeletal muscle <it>Il6 </it>and <it>Dio2 </it>mRNA levels as well as increased activity of muscle succinic acid dehydrogenase (SDH) in the Lean mice demonstrates for the first time that changes in muscle energy metabolism play important role in the Lean line phenotype determination and corroborate our previous findings of increased physical activity and thermogenesis in this line. Finally, differential expression of <it>Abcb11 </it>and <it>Dio2 </it>identifies novel strong positional candidate genes as they map within the quantitative trait loci (QTL) regions detected previously in crosses between the Lean and Fat mice.</p> <p>Conclusion</p> <p>We identified novel candidate molecular targets and metabolic changes which can at least in part explain resistance to obesity development in the Lean line. The major difference between the Lean and Fat mice was in increased liver cholesterol biosynthesis gene mRNA expression, bile acid metabolism and changes in selected muscle genes' expression in the Lean line. The liver <it>Abcb11 </it>and muscle <it>Dio2 </it>were identified as novel positional candidate genes to explain part of the phenotypic difference between the Lean and Fat lines.</p

Context-Specific Genome-Scale Metabolic Modelling and Its Application to the Analysis of COVID-19 Metabolic Signatures

Genome-scale metabolic models (GEMs) have found numerous applications in different domains, ranging from biotechnology to systems medicine. Herein, we overview the most popular algorithms for the automated reconstruction of context-specific GEMs using high-throughput experimental data. Moreover, we describe different datasets applied in the process, and protocols that can be used to further automate the model reconstruction and validation. Finally, we describe recent COVID-19 applications of context-specific GEMs, focusing on the analysis of metabolic implications, identification of biomarkers and potential drug targets

Integrative analysis of rhythmicity

From biological to socio-technical systems, rhythmic processes are pervasive in our environment. However, methods for their comprehensive analysis are prevalent only in specific fields that limit the transfer of knowledge across scientific disciplines. This hinders interdisciplinary research and integrative analyses of rhythms across different domains and datasets. In this paper, we review recent developments in cross-disciplinary rhythmicity research, with a focus on the importance of rhythmic analyses in urban planning and biomedical research. Furthermore, we describe the current state of the art of (integrative) computational methods for the investigation of rhythmic data. Finally, we discuss the further potential and propose necessary future developments for cross-disciplinary rhythmicity analysis to foster integration of heterogeneous datasets across different domains, as well as guide data-driven decision making beyond the boundaries of traditional intradisciplinary research, especially in the context of sustainable and healthy cities

Integrative Analysis of Rhythmicity: From Biology to Urban Environments and Sustainability

From biological to socio-technical systems, rhythmic processes are pervasive in our environment. However, methods for their comprehensive analysis are prevalent only in specific fields that limit the transfer of knowledge across scientific disciplines. This hinders interdisciplinary research and integrative analyses of rhythms across different domains and datasets. In this paper, we review recent developments in cross-disciplinary rhythmicity research, with a focus on the importance of rhythmic analyses in urban planning and biomedical research. Furthermore, we describe the current state of the art of (integrative) computational methods for the investigation of rhythmic data. Finally, we discuss the further potential and propose necessary future developments for cross-disciplinary rhythmicity analysis to foster integration of heterogeneous datasets across different domains, as well as guide data-driven decision making beyond the boundaries of traditional intradisciplinary research, especially in the context of sustainable and healthy cities

Identification of novel RNA binding proteins influencing circular RNA expression in hepatocellular carcinoma

Circular RNAs (circRNAs) are increasingly recognized as having a role in cancer development. Their expression is modified in numerous cancers, including hepatocellular carcinoma (HCC)however, little is known about the mechanisms of their regulation. The aim of this study was to identify regulators of circRNAome expression in HCC. Using publicly available datasets, we identified RNA binding proteins (RBPs) with enriched motifs around the splice sites of differentially expressed circRNAs in HCC. We confirmed the binding of some of the candidate RBPs using ChIP-seq and eCLIP datasets in the ENCODE database. Several of the identified RBPs were found to be differentially expressed in HCC and/or correlated with the overall survival of HCC patients. According to our bioinformatics analyses and published evidence, we propose that NONO, PCPB2, PCPB1, ESRP2, and HNRNPK are candidate regulators of circRNA expression in HCC. We confirmed that the knocking down the epithelial splicing regulatory protein 2 (ESRP2), known to be involved in the maintenance of the adult liver phenotype, significantly changed the expression of candidate circRNAs in a model HCC cell line. By understanding the systemic changes in transcriptome splicing, we can identify new proteins involved in the molecular pathways leading to HCC development and progression

Circular RNA hsa_circ_0062682 Binds to YBX1 and Promotes Oncogenesis in Hepatocellular Carcinoma

Circular RNAs (circRNAs) have been shown to play an important role in the pathogenesis of hepatocellular carcinoma (HCC). By implementing available transcriptomic analyses of HCC patients, we identified an upregulated circRNA hsa_circ_0062682. Stable perturbations of hsa_circ_0062682 in Huh-7 and SNU-449 cell lines influenced colony formation, migration, cell proliferation, sorafenib sensitivity, and additionally induced morphological changes in cell lines, indicating an important role of hsa_circ_0062682 in oncogenesis. Pathway enrichment analysis and gene set enrichment analysis of the transcriptome data from hsa_circ_0062682 knockdown explained the observed phenotypes and exposed transcription factors E2F1, Sp1, HIF-1α, and NFκB1 as potential downstream targets. Biotinylated oligonucleotide pulldown combined with proteomic analyses identified protein interaction partners of which YBX1, a known oncogene, was confirmed by RNA immunoprecipitation. Furthermore, we discovered a complex cell-type-specific phenotype in response to the oncogenic potential of hsa_circ_0062682. This finding is in line with different classes of HCC tumours, and more studies are needed to shed a light on the molecular complexity of liver cancer

Guided extraction of genome-scale metabolic models for the integration and analysis of omics data

Omics data can be integrated into a reference model using various model extraction methods (MEMs) to yield context-specific genome-scale metabolic models (GEMs). How to chose the appropriate MEM, thresholding rule and threshold remains a challenge. We integrated mouse transcriptomic data from a Cyp51 knockout mice diet experiment (GSE58271) using five MEMs (GIMME, iMAT, FASTCORE, INIT an tINIT) in a combination with a recently published mouse GEM iMM1865. Except for INIT and tINIT, the size of extracted models varied with the MEM used (t-test: p-value 90%) in PC1 for FASTCORE. In iMAT, each of the three factors explained less than 40% of the variability within PC1, PC2 and PC3. Among all the MEMs, FASTCORE captured the most of the true variability in the data by clustering samples by gender. Our results show that for the efficient use of MEMs in the context of omics data integration and analysis, one should apply various MEMs, thresholding rules, and thresholding values to select the MEM and its configuration that best captures the true variability in the data. This selection can be guided by the methodology as proposed and used in this paper. Moreover, we describe certain approaches that can be used to analyse the results obtained with the selected MEM and to put these results in a biological context