Recent publications
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Welcome to Alan!

Alan Lihic is a Master student at the Unil in medical biology. He is doing a Master thesis in Prof. Thorens’s group, under the supervision of Ms. Clara Roujeau on glucose-stimulated insulin secretion in pancreatic beta cells.

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Canceled Mon June 25, 2018 CIG Sem. J.-Y. Masson

CIG Seminars Spring 2018

Monday 12:15, Génopode, auditorium A, followed by sandwiches

Download the poster

Monday June 25, 2018 – Canceled and postponed to December 17, 2018

Jean-Yves Masson, Université Laval, Québec, CA
«Charting the roles of Fanconi anemia/breast cancer genes in DNA double-strand break repair and synthetic lethal strategies»
Host: Vincent Dion

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PLoS Comput Biol.: auth.: C.Dessimoz

 2018 May 31;14(5):e1006137. doi: 10.1371/journal.pcbi.1006137. eCollection 2018 May.

Submit a Topic Page to PLOS Computational Biology and Wikipedia.

Mietchen D1Wodak S2Wasik S3,4,5Szostak N3,4,5Dessimoz C6,7,8.
PMID: 29851950

 

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Fri June 22, 2018 Sem. CHUV L. Francioli

Speaker: Dr Laurent Francioli (Massachusetts General Hospital, MA / Broad Institute, Cambridge MA)

Title: “Large-scale genomics for clinical and research genome interpretation”

Friday June 22, 2018 Auditoire Jequier-Doge (CHUV BL08), 10h30

To make sense of the genetic changes in a person’s genome – for instance, to determine which of them is responsible for a severe disease – we need to be able to compare that person’s sequence against the genomes of thousands of other individuals. However, getting access to very large collections of human genomes is challenging, and extracting useful information from such enormous data sets requires substantial computational resources and bioinformatics knowledge. In this talk I will describe the development of the Genome Aggregation Database (gnomAD), which encompasses variants derived from over 123,000 exomes and 15,000 whole genomes of unrelated individuals of diverse ancestries. This resource is accessible freely and without restrictions on a website (http://gnomad.broadinstitute.org) that is used worldwide by clinicians, genomics researchers and clinical labs, with over twelve million page views to date. I will describe how this resource was created and the tools that were engineered in order to robustly identify genetic variants from more than two petabytes of raw sequencing data. Finally, I will discuss how we can leverage these data to guide the interpretation of individual genomes in the context of rare diseases.

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Mol Cell Endocrinol.: co-auth.: MEF

 2018 May 3. pii: S0303-7207(18)30134-5. doi: 10.1016/j.mce.2018.04.015.

Involvement of glucocorticoid prereceptor metabolism and signaling in rat visceral adipose tissue lipid metabolism after chronic stress combined with high-fructose diet.

Abstract

Both fructose overconsumption and increased glucocorticoids secondary to chronic stress may contribute to overall dyslipidemia. In this study we specifically assessed the effects and interactions of dietary fructose and chronic stress on lipid metabolism in the visceral adipose tissue(VAT) of male Wistar rats. We analyzed the effects of 9-week 20% high fructose diet and 4-week chronic unpredictable stress, separately and in combination, on VAT histology, glucocorticoid prereceptor metabolismglucocorticoid receptor subcellular redistribution and expression of major metabolic genes. Blood triglycerides and fatty acid composition were also measured to assess hepatic Δ9 desaturase activity. The results showed that fructose diet increased blood triglycerides and Δ9 desaturase activity. On the other hand, stress led to corticosterone elevation, glucocorticoid receptor activation and decrease in adipocyte size, while phosphoenolpyruvate carboxykinase, adipose tissuetriglyceride lipase, FAT/CD36 and sterol regulatory element binding protein-1c (SREBP-1c) were increased, pointing to VAT lipolysis and glyceroneogenesis. The combination of stress and fructose diet was associated with marked stimulation of fatty acid synthase and acetyl-CoA carboxylase mRNA level and with increased 11β-hydroxysteroid dehydrogenase type 1 and hexose-6-phosphate dehydrogenase protein levels, suggesting a coordinated increase in hexose monophosphate shunt and de novo lipogenesis. It however did not influence the level of peroxisome proliferator-activated receptor-gamma, SREBP-1c and carbohydrate responsive element-binding protein. In conclusion, our results showed that only combination of dietary fructose and stress increase glucocorticoid prereceptor metabolism and stimulates lipogenic enzyme expression suggesting that interaction between stress and fructose may be instrumental in promoting VAT expansion and dysfunction.

KEYWORDS:

Chronic unpredictable stress; Fructose; Glucocorticoids; Lipid metabolismVisceral adipose tissue

PMID: 29729371
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Laurent Francioli will give a seminar – 10:30 am, Aud. Jequier-Doge (CHUV BL08), June 22, 2018

Séminaire de l’unité de médecine de précision

Dr Laurent Francioli (Massachusetts General Hospital, MA / Broad Institute, Cambridge MA).
Auditoire Jequier-Doge (CHUV BL08), 10h30
“Large-scale genomics for clinical and research genome interpretation”

To make sense of the genetic changes in a person’s genome – for instance, to determine which of them is responsible for a severe disease – we need to be able to compare that person’s sequence against the genomes of thousands of other individuals. However, getting access to very large collections of human genomes is challenging, and extracting useful information from such enormous data sets requires substantial computational resources and bioinformatics knowledge. In this talk I will describe the development of the Genome Aggregation Database (gnomAD), which encompasses variants derived from over 123,000 exomes and 15,000 whole genomes of unrelated individuals of diverse ancestries. This resource is accessible freely and without restrictions on a website (http://gnomad.broadinstitute.org) that is used worldwide by clinicians, genomics researchers and clinical labs, with over twelve million page views to date. I will describe how this resource was created and the tools that were engineered in order to robustly identify genetic variants from more than two petabytes of raw sequencing data. Finally, I will discuss how we can leverage these data to guide the interpretation of individual genomes in the context of rare diseases.