OMiR: Identification of associations between OMIM diseases and microRNAs

AbstractA large number of loci for genetic diseases have been mapped on the human genome and a group of hereditary diseases among them have thus far proven unsuccessful to clone. It is conceivable that such "unclonable" diseases are not linked to abnormalities of protein coding genes (PCGs), but of non-coding RNAs (ncRNAs). We developed a novel approach termed OMiR (OMIM and miRNAs), to test whether microRNAs (miRNAs) exhibit any associations with mapped genetic diseases not yet associated with a PCG. We found that "orphan" genetic disease loci were proximal to miRNA loci more frequently than to loci for which the responsible protein coding gene is known, thus suggesting that miRNAs might be the elusive culprits. Our findings indicate that inclusion of miRNAs among the candidate genes to be considered could assist geneticists in their hunt for disease genes, particularly in the case of rare diseases

Optimal transport on wireless networks

We present a study of the application of a variant of a recently introduced heuristic algorithm for the optimization of transport routes on complex networks to the problem of finding the optimal routes of communication between nodes on wireless networks. Our algorithm iteratively balances network traffic by minimizing the maximum node betweenness on the network. The variant we consider specifically accounts for the broadcast restrictions imposed by wireless communication by using a different betweenness measure. We compare the performance of our algorithm to two other known algorithms and find that our algorithm achieves the highest transport capacity both for minimum node degree geometric networks, which are directed geometric networks that model wireless communication networks, and for configuration model networks that are uncorrelated scale-free networks.Comment: 5 pages, 4 figure

LncRNA RP11-19E11 is an E2F1 target required for proliferation and survival of basal breast cancer

Long non-coding RNAs (lncRNAs) play key roles in the regulation of breast cancer initiation and progression. LncRNAs are differentially expressed in breast cancer subtypes. Basal-like breast cancers are generally poorly differentiated tumors, are enriched in embryonic stem cell signatures, lack expression of estrogen receptor, progesterone receptor, and HER2 (triple-negative breast cancer), and show activation of proliferation-associated factors. We hypothesized that lncRNAs are key regulators of basal breast cancers. Using The Cancer Genome Atlas, we identified lncRNAs that are overexpressed in basal tumors compared to other breast cancer subtypes and expressed in at least 10% of patients. Remarkably, we identified lncRNAs whose expression correlated with patient prognosis. We then evaluated the function of a subset of lncRNA candidates in the oncogenic process in vitro. Here, we report the identification and characterization of the chromatin-associated lncRNA, RP11-19E11.1, which is upregulated in 40% of basal primary breast cancers. Gene set enrichment analysis in primary tumors and in cell lines uncovered a correlation between RP11-19E11.1 expression level and the E2F oncogenic pathway. We show that this lncRNA is chromatin-associated and an E2F1 target, and its expression is necessary for cancer cell proliferation and survival. Finally, we used lncRNA expression levels as a tool for drug discovery in vitro, identifying protein kinase C (PKC) as a potential therapeutic target for a subset of basal-like breast cancers. Our findings suggest that lncRNA overexpression is clinically relevant. Understanding deregulated lncRNA expression in basal-like breast cancer may lead to potential prognostic and therapeutic applications

Dietary metabolite profiling brings new insight into the relationship between nutrition and metabolic risk: An IMI Direct study

Background: Dietary advice remains the cornerstone of prevention and management of type 2 diabetes (T2D). However, understanding the efficacy of dietary interventions is confounded by the challenges inherent in assessing free living diet. Here we profiled dietary metabolites to investigate glycaemic deterioration and cardiometabolic risk in people at risk of or living with T2D. Methods: We analysed data from plasma collected at baseline and 18-month follow-up in individuals from the Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) cohort 1 n=403 individuals with normal or impaired glucose regulation (prediabetic) and cohort 2 n=458 individuals with new onset of T2D. A dietary metabolite profile model (Tpred) was constructed using multivariate regression of 113 plasma metabolites obtained from targeted metabolomics assays. The continuous Tpred score was used to explore the relationships between diet, glycaemic deterioration and cardio-metabolic risk via multiple linear regression models. Findings: A higher Tpred was associated with healthier diets high in wholegrain (β=0.004 g, p=0.02 and β=0.003 g, p=0.03) and lower energy intake (β=-0.0002 kcal, p=0.04 and β=-0.0002 kcal, p=0.003), and saturated fat (β=-0.03 g, p<.0001 and β=-0.03 g, p<.0001), respectively for cohort 1 and 2. In both cohorts a higher Tpred score was also associated with lower total body adiposity and improved lipid profiles HDL-cholesterol (β=0.07 mmol/L, p<.0001), (β=0.08 mmol/L, p=0.0002), and triglycerides (β=-0.1 mmol/L, p=0.003), (β=-0.2 mmol/L, p=0.0002), respectively for cohort 1 and 2. In cohort 2, the Tpred score was negatively associated with liver fat content (β=-0.74 %, p<.0001), and lower fasting concentrations of HbA1c (β=-0.9mmol/mol, p=0.02), glucose (β=-0.2 mmol/L, p=0.01) and insulin (β=-11.0 pmol/mol, p=0.01). Longitudinal analysis showed at 18-month follow up a higher Tpred score was also associated lower total body adiposity in both cohorts and lower fasting glucose (β=-0.2 mmol/L, p=0.03) and insulin (β=-9.2 pmol/mol, p=0.04) concentrations in cohort 2. Interpretation: Plasma dietary metabolite profiling provides objective measures of diet intake, showing a relationship to glycaemic deterioration and cardiometabolic health

Contrasting roles of histone 3 lysine 27 demethylases in acute lymphoblastic leukaemia

T-cell acute lymphoblastic leukaemia (T-ALL) is a haematological malignancy with a dismal overall prognosis, including a relapse rate of up to 25%, mainly because of the lack of non-cytotoxic targeted therapy options. Drugs that target the function of key epigenetic factors have been approved in the context of haematopoietic disorders, and mutations that affect chromatin modulators in a variety of leukaemias have recently been identified; however, ‘epigenetic’ drugs are not currently used for T-ALL treatment. Recently, we described that the polycomb repressive complex 2 (PRC2) has a tumour-suppressor role in T-ALL. Here we delineated the role of the histone 3 lysine 27 (H3K27) demethylases JMJD3 and UTX in T-ALL. We show that JMJD3 is essential for the initiation and maintenance of T-ALL, as it controls important oncogenic gene targets by modulating H3K27 methylation. By contrast, we found that UTX functions as a tumour suppressor and is frequently genetically inactivated in T-ALL. Moreover, we demonstrated that the small molecule inhibitor GSKJ4 (ref. 5) affects T-ALL growth, by targeting JMJD3 activity. These findings show that two proteins with a similar enzymatic function can have opposing roles in the context of the same disease, paving the way for treating haematopoietic malignancies with a new category of epigenetic inhibitors.National Institutes of Health (U.S.) (Grant R37-HD04502

Four groups of type 2 diabetes contribute to the etiological and clinical heterogeneity in newly diagnosed individuals: An IMI DIRECT study

The presentation and underlying pathophysiology of type 2 diabetes (T2D) is complex and heterogeneous. Recent studies attempted to stratify T2D into distinct subgroups using data-driven approaches, but their clinical utility may be limited if categorical representations of complex phenotypes are suboptimal. We apply a soft-clustering (archetype) method to characterize newly diagnosed T2D based on 32 clinical variables. We assign quantitative clustering scores for individuals and investigate the associations with glycemic deterioration, genetic risk scores, circulating omics biomarkers, and phenotypic stability over 36 months. Four archetype profiles represent dysfunction patterns across combinations of T2D etiological processes and correlate with multiple circulating biomarkers. One archetype associated with obesity, insulin resistance, dyslipidemia, and impaired β cell glucose sensitivity corresponds with the fastest disease progression and highest demand for anti-diabetic treatment. We demonstrate that clinical heterogeneity in T2D can be mapped to heterogeneity in individual etiological processes, providing a potential route to personalized treatments

Membrane insertion and topology of the translocon-associated protein (TRAP) gamma subunit

Translocon-associated protein (TRAP) complex is intimately associated with the ER translocon for the insertion or translocation of newly synthesised proteins in eukaryotic cells. The TRAP complex is comprised of three single-spanning and one multiple-spanning subunits. We have investigated the membrane insertion and topology of the multiple-spanning TRAP-γ subunit by glycosylation mapping and green fluorescent protein fusions both in vitro and in cell cultures. Results demonstrate that TRAP-γ has four transmembrane (TM) segments, an Nt/Ct cytosolic orientation and that the less hydrophobic TM segment inserts efficiently into the membrane only in the cellular context of full-length protein

Four groups of type 2 diabetes contribute to the etiological and clinical heterogeneity in newly diagnosed individuals: An IMI DIRECT study

The presentation and underlying pathophysiology of type 2 diabetes (T2D) is complex and heterogeneous. Recent studies attempted to stratify T2D into distinct subgroups using data-driven approaches, but their clinical utility may be limited if categorical representations of complex phenotypes are suboptimal. We apply a soft-clustering (archetype) method to characterize newly diagnosed T2D based on 32 clinical variables. We assign quantitative clustering scores for individuals and investigate the associations with glycemic deterioration, genetic risk scores, circulating omics biomarkers, and phenotypic stability over 36 months. Four archetype profiles represent dysfunction patterns across combinations of T2D etiological processes and correlate with multiple circulating biomarkers. One archetype associated with obesity, insulin resistance, dyslipidemia, and impaired β cell glucose sensitivity corresponds with the fastest disease progression and highest demand for anti-diabetic treatment. We demonstrate that clinical heterogeneity in T2D can be mapped to heterogeneity in individual etiological processes, providing a potential route to personalized treatments