Impaired Function of Solute Carrier Family 19 Leads to Low Folate Levels and Lipid Droplet Accumulation in Hepatocytes.

Low serum folate levels are inversely related to metabolic associated fatty liver disease (MAFLD). The role of the folate transporter gene (SLC19A1) was assessed to clarify its involvement in lipid accumulation during the onset of MAFLD in humans and in liver cells by genomic, transcriptomic, and metabolomic techniques. Genotypes of 3 SNPs in a case-control cohort were initially correlated to clinical and serum MAFLD markers. Subsequently, the expression of 84 key genes in response to the loss of SLC19A1 was evaluated with the aid of an RT2 profiler-array. After shRNA-silencing of SLC19A1 in THLE2 cells, folate and lipid levels were measured by ELISA and staining techniques, respectively. In addition, up to 482 amino acids and lipid metabolites were semi-quantified in SLC19A1-knockdown (KD) cells through ultra-high-performance liquid chromatography coupled with mass spectrometry. SNPs, rs1051266 and rs3788200, were significantly associated with the development of fatty liver for the single-marker allelic test. The minor alleles of these SNPs were associated with a 0.6/-1.67-fold decreased risk of developing MAFLD. When SLC19A1 was KD in THLE2 cells, intracellular folate content was four times lower than in wild-type cells. The lack of functional SLC19A1 provoked significant changes in the regulation of genes associated with lipid droplet accumulation within the cell and the onset of NAFLD. Metabolomic analyses showed a highly altered profile, where most of the species that accumulated in SLC19A1-KD-cells belong to the chemical groups of triacylglycerols, diacylglycerols, polyunsaturated fatty acids, and long chain, highly unsaturated cholesterol esters. In conclusion, the lack of SLC19A1 gene expression in hepatocytes affects the regulation of key genes for normal liver function, reduces intracellular folate levels, and impairs lipid metabolism, which entails lipid droplet accumulation in hepatocytes.

Genetic correlation and causal relationships between cardio-metabolic traits and lung function impairment.

BACKGROUND: Associations of low lung function with features of poor cardio-metabolic health have been reported. It is, however, unclear whether these co-morbidities reflect causal associations, shared genetic heritability or are confounded by environmental factors. METHODS: We performed three analyses: (1) cardio-metabolic health to lung function association tests in Northern Finland Birth cohort 1966, (2) cross-trait linkage disequilibrium score regression (LDSC) to compare genetic backgrounds and (3) Mendelian randomisation (MR) analysis to assess the causal effect of cardio-metabolic traits and disease on lung function, and vice versa (bidirectional MR). Genetic associations were obtained from the UK Biobank data or published large-scale genome-wide association studies (N > 82,000). RESULTS: We observed a negative genetic correlation between lung function and cardio-metabolic traits and diseases. In Mendelian Randomisation analysis (MR), we found associations between type 2 diabetes (T2D) instruments and forced vital capacity (FVC) as well as FEV1/FVC. Body mass index (BMI) instruments were associated to all lung function traits and C-reactive protein (CRP) instruments to FVC. These genetic associations provide evidence for a causal effect of cardio-metabolic traits on lung function. Multivariable MR suggested independence of these causal effects from other tested cardio-metabolic traits and diseases. Analysis of lung function specific SNPs revealed a potential causal effect of FEV1/FVC on blood pressure. CONCLUSIONS: The present study overcomes many limitations of observational studies by using Mendelian Randomisation. We provide evidence for an independent causal effect of T2D, CRP and BMI on lung function with some of the T2D effect on lung function being attributed to inflammatory mechanisms. Furthermore, this analysis suggests a potential causal effect of FEV1/FVC on blood pressure. Our detailed analysis of the interplay between cardio-metabolic traits and impaired lung function provides the opportunity to improve the quality of existing intervention strategies.

HuR/ELAVL1 drives malignant peripheral nerve sheath tumor growth and metastasis.

Cancer cells can develop a strong addiction to discrete molecular regulators, which control the aberrant gene expression programs that drive and maintain the cancer phenotype. Here, we report the identification of the RNA-binding protein HuR/ELAVL1 as a central oncogenic driver for malignant peripheral nerve sheath tumors (MPNSTs), which are highly aggressive sarcomas that originate from cells of the Schwann cell lineage. HuR was found to be highly elevated and bound to a multitude of cancer-associated transcripts in human MPNST samples. Accordingly, genetic and pharmacological inhibition of HuR had potent cytostatic and cytotoxic effects on tumor growth, and strongly suppressed metastatic capacity in vivo. Importantly, we linked the profound tumorigenic function of HuR to its ability to simultaneously regulate multiple essential oncogenic pathways in MPNST cells, including the Wnt/ß-catenin, YAP/TAZ, RB/E2F, and BET pathways, which converge on key transcriptional networks. Given the exceptional dependency of MPNST cells on HuR for survival, proliferation, and dissemination, we propose that HuR represents a promising therapeutic target for MPNST treatment.

GWAS for urinary sodium and potassium excretion highlights pathways shared with cardiovascular traits.

Urinary sodium and potassium excretion are associated with blood pressure (BP) and cardiovascular disease (CVD). The exact biological link between these traits is yet to be elucidated. Here, we identify 50 loci for sodium and 13 for potassium excretion in a large-scale genome-wide association study (GWAS) on urinary sodium and potassium excretion using data from 446,237 individuals of European descent from the UK Biobank study. We extensively interrogate the results using multiple analyses such as Mendelian randomization, functional assessment, co localization, genetic risk score, and pathway analyses. We identify a shared genetic component between urinary sodium and potassium expression and cardiovascular traits. Ingenuity pathway analysis shows that urinary sodium and potassium excretion loci are over-represented in behavioural response to stimuli. Our study highlights pathways that are shared between urinary sodium and potassium excretion and cardiovascular traits.

Identification of Mutated Cancer Driver Genes in Unpaired RNA-Seq Samples.

The identification of cancer driver genes through the analysis of mutations detected with high-throughput sequencing is a useful tool and a key challenge in cancer genomics. The workflow presented here relies on unpaired RNA-seq tumoral samples, thus leveraging already available RNA-seq data and providing the intrinsical benefits of directly targeting the transcriptome. Based on well-established methods for variant detection, this workflow also involves thorough data cleaning and extensive annotation, which enable the selection for somatic mutations with functional impact and the prioritization of genes relevant to the carcinogenic processes in the input samples.

Publisher Correction: Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits.

In the version of this article originally published, the name of author Martin H. de Borst was coded incorrectly in the XML. The error has now been corrected in the HTML version of the paper.

Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits.

High blood pressure is a highly heritable and modifiable risk factor for cardiovascular disease. We report the largest genetic association study of blood pressure traits (systolic, diastolic and pulse pressure) to date in over 1 million people of European ancestry. We identify 535 novel blood pressure loci that not only offer new biological insights into blood pressure regulation but also highlight shared genetic architecture between blood pressure and lifestyle exposures. Our findings identify new biological pathways for blood pressure regulation with potential for improved cardiovascular disease prevention in the future.

Epigenetic Networks Regulate the Transcriptional Program in Memory and Terminally Differentiated CD8+ T Cells.

Epigenetic mechanisms play a critical role during differentiation of T cells by contributing to the formation of stable and heritable transcriptional patterns. To better understand the mechanisms of memory maintenance in CD8+ T cells, we performed genome-wide analysis of DNA methylation, histone marking (acetylated lysine 9 in histone H3 and trimethylated lysine 9 in histone), and gene-expression profiles in naive, effector memory (EM), and terminally differentiated EM (TEMRA) cells. Our results indicate that DNA demethylation and histone acetylation are coordinated to generate the transcriptional program associated with memory cells. Conversely, EM and TEMRA cells share a very similar epigenetic landscape. Nonetheless, the TEMRA transcriptional program predicts an innate immunity phenotype associated with genes never reported in these cells, including several mediators of NK cell activation (VAV3 and LYN) and a large array of NK receptors (e.g., KIR2DL3, KIR2DL4, KIR2DL1, KIR3DL1, KIR2DS5). In addition, we identified up to 161 genes that encode transcriptional regulators, some of unknown function in CD8+ T cells, and that were differentially expressed in the course of differentiation. Overall, these results provide new insights into the regulatory networks involved in memory CD8+ T cell maintenance and T cell terminal differentiation.

Regulation of the transcriptional program by DNA methylation during human αß T-cell development.

Thymocyte differentiation is a complex process involving well-defined sequential developmental stages that ultimately result in the generation of mature T-cells. In this study, we analyzed DNA methylation and gene expression profiles at successive human thymus developmental stages. Gain and loss of methylation occurred during thymocyte differentiation, but DNA demethylation was much more frequent than de novo methylation and more strongly correlated with gene expression. These changes took place in CpG-poor regions and were closely associated with T-cell differentiation and TCR function. Up to 88 genes that encode transcriptional regulators, some of whose functions in T-cell development are as yet unknown, were differentially methylated during differentiation. Interestingly, no reversion of accumulated DNA methylation changes was observed as differentiation progressed, except in a very small subset of key genes (RAG1, RAG2, CD8A, PTCRA, etc.), indicating that methylation changes are mostly unique and irreversible events. Our study explores the contribution of DNA methylation to T-cell lymphopoiesis and provides a fine-scale map of differentially methylated regions associated with gene expression changes. These can lay the molecular foundations for a better interpretation of the regulatory networks driving human thymopoiesis.

TRIB3 suppresses tumorigenesis by controlling mTORC2/AKT/FOXO signaling.

In a recent article, we found that Tribbles pseudokinase 3 (TRIB3) plays a tumor suppressor role and that this effect relies on the dysregulation of the phosphorylation of v-akt murine thymoma viral oncogene homolog (AKT) by the mammalian target of rapamycin complex 2 (mTORC2 complex), and the subsequent hyperphosphorylation and inactivation of the transcription factor Forkhead box O3 (FOXO3).

S-adenosylmethionine levels regulate the schwann cell DNA methylome.

Axonal myelination is essential for rapid saltatory impulse conduction in the nervous system, and malformation or destruction of myelin sheaths leads to motor and sensory disabilities. DNA methylation is an essential epigenetic modification during mammalian development, yet its role in myelination remains obscure. Here, using high-resolution methylome maps, we show that DNA methylation could play a key gene regulatory role in peripheral nerve myelination and that S-adenosylmethionine (SAMe), the principal methyl donor in cytosine methylation, regulates the methylome dynamics during this process. Our studies also point to a possible role of SAMe in establishing the aberrant DNA methylation patterns in a mouse model of diabetic neuropathy, implicating SAMe in the pathogenesis of this disease. These critical observations establish a link between SAMe and DNA methylation status in a defined biological system, providing a mechanism that could direct methylation changes during cellular differentiation and in diverse pathological situations.

seqCNA: an R package for DNA copy number analysis in cancer using high-throughput sequencing.

BACKGROUND: Deviations in the amount of genomic content that arise during tumorigenesis, called copy number alterations, are structural rearrangements that can critically affect gene expression patterns. Additionally, copy number alteration profiles allow insight into cancer discrimination, progression and complexity. On data obtained from high-throughput sequencing, improving quality through GC bias correction and keeping false positives to a minimum help build reliable copy number alteration profiles. RESULTS: We introduce seqCNA, a parallelized R package for an integral copy number analysis of high-throughput sequencing cancer data. The package includes novel methodology on (i) filtering, reducing false positives, and (ii) GC content correction, improving copy number profile quality, especially under great read coverage and high correlation between GC content and copy number. Adequate analysis steps are automatically chosen based on availability of paired-end mapping, matched normal samples and genome annotation. CONCLUSIONS: seqCNA, available through Bioconductor, provides accurate copy number predictions in tumoural data, thanks to the extensive filtering and better GC bias correction, while providing an integrated and parallelized workflow.

A Pilot Study on the Potential of RNA-Associated to Urinary Vesicles as a Suitable Non-Invasive Source for Diagnostic Purposes in Bladder Cancer.

Bladder cancer is one of the most common cancers and, together with prostate carcinoma, accounts for the majority of the malignancies of the genitourinary tract. Since prognosis ameliorates with early detection, it will be beneficial to have a repertoire of diagnostic markers that could complement the current diagnosis protocols. Recently, cell-secreted extracellular vesicles have received great interest as a source of low invasive disease biomarkers because they are found in many body fluids, including urine. The current work describes a pilot study to generate an array-based catalogue of mRNA associated to urinary vesicles, and also a comparison with samples obtained from bladder cancer patients. After an analysis of presence/absence of transcripts in bladder cancer EVs, a list of genes was selected for further validation using PCR technique. We found four genes differentially expressed in cancer samples. LASS2 and GALNT1 were present in cancer patients, while ARHGEF39 and FOXO3 were found only in non-cancer urinary vesicles. Previous studies have pointed to the involvement of those genes in tumour progression and metastasis.

Bivariate segmentation of SNP-array data for allele-specific copy number analysis in tumour samples.

BACKGROUND: SNP arrays output two signals that reflect the total genomic copy number (LRR) and the allelic ratio (BAF), which in combination allow the characterisation of allele-specific copy numbers (ASCNs). While methods based on hidden Markov models (HMMs) have been extended from array comparative genomic hybridisation (aCGH) to jointly handle the two signals, only one method based on change-point detection, ASCAT, performs bivariate segmentation. RESULTS: In the present work, we introduce a generic framework for bivariate segmentation of SNP array data for ASCN analysis. For the matter, we discuss the characteristics of the typically applied BAF transformation and how they affect segmentation, introduce concepts of multivariate time series analysis that are of concern in this field and discuss the appropriate formulation of the problem. The framework is implemented in a method named CnaStruct, the bivariate form of the structural change model (SCM), which has been successfully applied to transcriptome mapping and aCGH. CONCLUSIONS: On a comprehensive synthetic dataset, we show that CnaStruct outperforms the segmentation of existing ASCN analysis methods. Furthermore, CnaStruct can be integrated into the workflows of several ASCN analysis tools in order to improve their performance, specially on tumour samples highly contaminated by normal cells.

Comparison of methods to detect copy number alterations in cancer using simulated and real genotyping data.

BACKGROUND: The detection of genomic copy number alterations (CNA) in cancer based on SNP arrays requires methods that take into account tumour specific factors such as normal cell contamination and tumour heterogeneity. A number of tools have been recently developed but their performance needs yet to be thoroughly assessed. To this aim, a comprehensive model that integrates the factors of normal cell contamination and intra-tumour heterogeneity and that can be translated to synthetic data on which to perform benchmarks is indispensable. RESULTS: We propose such model and implement it in an R package called CnaGen to synthetically generate a wide range of alterations under different normal cell contamination levels. Six recently published methods for CNA and loss of heterozygosity (LOH) detection on tumour samples were assessed on this synthetic data and on a dilution series of a breast cancer cell-line: ASCAT, GAP, GenoCNA, GPHMM, MixHMM and OncoSNP. We report the recall rates in terms of normal cell contamination levels and alteration characteristics: length, copy number and LOH state, as well as the false discovery rate distribution for each copy number under different normal cell contamination levels.Assessed methods are in general better at detecting alterations with low copy number and under a little normal cell contamination levels. All methods except GPHMM, which failed to recognize the alteration pattern in the cell-line samples, provided similar results for the synthetic and cell-line sample sets. MixHMM and GenoCNA are the poorliest performing methods, while GAP generally performed better. This supports the viability of approaches other than the common hidden Markov model (HMM)-based. CONCLUSIONS: We devised and implemented a comprehensive model to generate data that simulate tumoural samples genotyped using SNP arrays. The validity of the model is supported by the similarity of the results obtained with synthetic and real data. Based on these results and on the software implementation of the methods, we recommend GAP for advanced users and GPHMM for a fully driven analysis.