Time-dependent specific molecular signatures of inflammation and remodelling are associated with trimethylamine-N-oxide (TMAO)-induced endothelial cell dysfunction.

Endothelial dysfunction is a critical initiating factor contributing to cardiovascular diseases, involving the gut microbiome-derived metabolite trimethylamine N-oxide (TMAO). This study aims to clarify the time-dependent molecular pathways by which TMAO mediates endothelial dysfunction through transcriptomics and metabolomics analyses in human microvascular endothelial cells (HMEC-1). Cell viability and reactive oxygen species (ROS) generation were also evaluated. TMAO treatment for either 24H or 48H induces reduced cell viability and enhanced oxidative stress. Interestingly, the molecular signatures were distinct between the two time-points. Specifically, few Gene Ontology biological processes (BPs) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were modulated after a short (24H) compared to a long (48H) treatment. However, the KEGG signalling pathways namely "tumour necrosis factor (TNF)" and "cytokine-cytokine receptor interaction" were downregulated at 24H but activated at 48H. In addition, at 48H, BPs linked to inflammatory phenotypes were activated (confirming KEGG results), while BPs linked to extracellular matrix (ECM) structural organisation, endothelial cell proliferation, and collagen metabolism were repressed. Lastly, metabolic profiling showed that arachidonic acid, prostaglandins, and palmitic acid were enriched at 48H. This study demonstrates that TMAO induces distinct time-dependent molecular signatures involving inflammation and remodelling pathways, while pathways such as oxidative stress are also modulated, but in a non-time-dependent manner.

Singapore Undiagnosed Disease Program: Genomic Analysis aids Diagnosis and Clinical Management.

OBJECTIVE: Use next-generation sequencing (NGS) technology to improve our diagnostic yield in patients with suspected genetic disorders in the Asian setting. DESIGN: A diagnostic study conducted between 2014 and 2019 (and ongoing) under the Singapore Undiagnosed Disease Program. Date of last analysis was 1 July 2019. SETTING: Inpatient and outpatient genetics service at two large academic centres in Singapore. PATIENTS: Inclusion criteria: patients suspected of genetic disorders, based on abnormal antenatal ultrasound, multiple congenital anomalies and developmental delay. EXCLUSION CRITERIA: patients with known genetic disorders, either after clinical assessment or investigations (such as karyotype or chromosomal microarray). INTERVENTIONS: Use of NGS technology-whole exome sequencing (WES) or whole genome sequencing (WGS). MAIN OUTCOME MEASURES: (1) Diagnostic yield by sequencing type, (2) diagnostic yield by phenotypical categories, (3) reduction in time to diagnosis and (4) change in clinical outcomes and management. RESULTS: We demonstrate a 37.8% diagnostic yield for WES (n=172) and a 33.3% yield for WGS (n=24). The yield was higher when sequencing was conducted on trios (40.2%), as well as for certain phenotypes (neuromuscular, 54%, and skeletal dysplasia, 50%). In addition to aiding genetic counselling in 100% of the families, a positive result led to a change in treatment in 27% of patients. CONCLUSION: Genomic sequencing is an effective method for diagnosing rare disease or previous 'undiagnosed' disease. The clinical utility of WES/WGS is seen in the shortened time to diagnosis and the discovery of novel variants. Additionally, reaching a diagnosis significantly impacts families and leads to alteration in management of these patients.

ChronQC: a quality control monitoring system for clinical next generation sequencing.

Summary: ChronQC is a quality control (QC) tracking system for clinical implementation of next-generation sequencing (NGS). ChronQC generates time series plots for various QC metrics to allow comparison of current runs to historical runs. ChronQC has multiple features for tracking QC data including Westgard rules for clinical validity, laboratory-defined thresholds and historical observations within a specified time period. Users can record their notes and corrective actions directly onto the plots for long-term recordkeeping. ChronQC facilitates regular monitoring of clinical NGS to enable adherence to high quality clinical standards. Availability and implementation: ChronQC is freely available on GitHub (https://github.com/nilesh-tawari/ChronQC), Docker (https://hub.docker.com/r/nileshtawari/chronqc/) and the Python Package Index. ChronQC is implemented in Python and runs on all common operating systems (Windows, Linux and Mac OS X). Contact: tawari.nilesh@gmail.com or pauline.c.ng@gmail.com. Supplementary information: Supplementary data are available at Bioinformatics online.

Selective influence of Sox2 on POU transcription factor binding in embryonic and neural stem cells.

Embryonic stem cell (ESC) identity is orchestrated by co-operativity between the transcription factors (TFs) Sox2 and the class V POU-TF Oct4 at composite Sox/Oct motifs. Neural stem cells (NSCs) lack Oct4 but express Sox2 and class III POU-TFs Oct6, Brn1 and Brn2. This raises the question of how Sox2 interacts with POU-TFs to transcriptionally specify ESCs versus NSCs. Here, we show that Oct4 alone binds the Sox/Oct motif and the octamer-containing palindromic MORE equally well. Sox2 binding selectively increases the affinity of Oct4 for the Sox/Oct motif. In contrast, Oct6 binds preferentially to MORE and is unaffected by Sox2. ChIP-Seq in NSCs shows the MORE to be the most enriched motif for class III POU-TFs, including MORE subtypes, and that the Sox/Oct motif is not enriched. These results suggest that in NSCs, co-operativity between Sox2 and class III POU-TFs may not occur and that POU-TF-driven transcription uses predominantly the MORE cis architecture. Thus, distinct interactions between Sox2 and POU-TF subclasses distinguish pluripotent ESCs from multipotent NSCs, providing molecular insight into how Oct4 alone can convert NSCs to pluripotency.

Comprehensive analysis of transcriptome variation uncovers known and novel driver events in T-cell acute lymphoblastic leukemia.

RNA-seq is a promising technology to re-sequence protein coding genes for the identification of single nucleotide variants (SNV), while simultaneously obtaining information on structural variations and gene expression perturbations. We asked whether RNA-seq is suitable for the detection of driver mutations in T-cell acute lymphoblastic leukemia (T-ALL). These leukemias are caused by a combination of gene fusions, over-expression of transcription factors and cooperative point mutations in oncogenes and tumor suppressor genes. We analyzed 31 T-ALL patient samples and 18 T-ALL cell lines by high-coverage paired-end RNA-seq. First, we optimized the detection of SNVs in RNA-seq data by comparing the results with exome re-sequencing data. We identified known driver genes with recurrent protein altering variations, as well as several new candidates including H3F3A, PTK2B, and STAT5B. Next, we determined accurate gene expression levels from the RNA-seq data through normalizations and batch effect removal, and used these to classify patients into T-ALL subtypes. Finally, we detected gene fusions, of which several can explain the over-expression of key driver genes such as TLX1, PLAG1, LMO1, or NKX2-1; and others result in novel fusion transcripts encoding activated kinases (SSBP2-FER and TPM3-JAK2) or involving MLLT10. In conclusion, we present novel analysis pipelines for variant calling, variant filtering, and expression normalization on RNA-seq data, and successfully applied these for the detection of translocations, point mutations, INDELs, exon-skipping events, and expression perturbations in T-ALL.