Mahendra Mariadassou

Bio: Mahendra Mariadassou is an academic researcher from Université Paris-Saclay. The author has contributed to research in topics: Gut flora & Multivariate statistics. The author has an hindex of 22, co-authored 84 publications receiving 1780 citations. Previous affiliations of Mahendra Mariadassou include University of Basel & Paris Descartes University.

FROGS: Find, Rapidly, OTUs with Galaxy Solution.

Abstract: Motivation Metagenomics leads to major advances in microbial ecology and biologists need user friendly tools to analyze their data on their own. Results This Galaxy-supported pipeline, called FROGS, is designed to analyze large sets of amplicon sequences and produce abundance tables of Operational Taxonomic Units (OTUs) and their taxonomic affiliation. The clustering uses Swarm. The chimera removal uses VSEARCH, combined with original cross-sample validation. The taxonomic affiliation returns an innovative multi-affiliation output to highlight databases conflicts and uncertainties. Statistical results and numerous graphical illustrations are produced along the way to monitor the pipeline. FROGS was tested for the detection and quantification of OTUs on real and in silico datasets and proved to be rapid, robust and highly sensitive. It compares favorably with the widespread mothur, UPARSE and QIIME. Availability and implementation Source code and instructions for installation: https://github.com/geraldinepascal/FROGS.git. A companion website: http://frogs.toulouse.inra.fr. Contact geraldine.pascal@inra.fr. Supplementary information Supplementary data are available at Bioinformatics online.

Uncovering latent structure in valued graphs: A variational approach

Abstract: As more and more network-structured data sets are available, the statistical analysis of valued graphs has become common place Looking for a latent structure is one of the many strategies used to better understand the behavior of a network Several methods already exist for the binary case We present a model-based strategy to uncover groups of nodes in valued graphs This framework can be used for a wide span of parametric random graphs models and allows to include covariates Variational tools allow us to achieve approximate maximum likelihood estimation of the parameters of these models We provide a simulation study showing that our estimation method performs well over a broad range of situations We apply this method to analyze host parasite interaction networks in forest ecosystems

Beneficial metabolic effects of selected probiotics on diet-induced obesity and insulin resistance in mice are associated with improvement of dysbiotic gut microbiota

Abstract: Alterations in gut microbiota composition and diversity were suggested to play a role in the development of obesity, a chronic subclinical inflammatory condition. We here evaluated the impact of oral consumption of a monostrain or multi-strain probiotic preparation in high-fat diet-induced obese mice. We observed a strain-specific effect and reported dissociation between the capacity of probiotics to dampen adipose tissue inflammation and to limit body weight gain. A multi-strain mixture was able to improve adiposity, insulin resistance and dyslipidemia through adipose tissue immune cell-remodelling, mainly affecting macrophages. At the gut level, the mixture modified the uptake of fatty acids and restored the expression level of the short-chain fatty acid receptor GPR43. These beneficial effects were associated with changes in the microbiota composition, such as the restoration of the abundance of Akkermansia muciniphila and Rikenellaceae and the decrease of other taxa like Lactobacillaceae. Using an in vitro gut model, we further showed that the probiotic mixture favours the production of butyrate and propionate. Our findings provide crucial clues for the design and use of more efficient probiotic preparations in obesity management and may bring new insights into the mechanisms by which host-microbe interactions govern such protective effects.

Gut bacteria are critical for optimal muscle function: a potential link with glucose homeostasis.

Abstract: Gut microbiota is involved in the development of several chronic diseases, including diabetes, obesity, and cancer, through its interactions with the host organs. It has been suggested that the cross talk between gut microbiota and skeletal muscle plays a role in different pathological conditions, such as intestinal chronic inflammation and cachexia. However, it remains unclear whether gut microbiota directly influences skeletal muscle function. In this work, we studied the impact of gut microbiota modulation on mice skeletal muscle function and investigated the underlying mechanisms. We determined the consequences of gut microbiota depletion after treatment with a mixture of a broad spectrum of antibiotics for 21 days and after 10 days of natural reseeding. We found that, in gut microbiota-depleted mice, running endurance was decreased, as well as the extensor digitorum longus muscle fatigue index in an ex vivo contractile test. Importantly, the muscle endurance capacity was efficiently normalized by natural reseeding. These endurance changes were not related to variation in muscle mass, fiber typology, or mitochondrial function. However, several pertinent glucose metabolism markers, such as ileum gene expression of short fatty acid chain and glucose transporters G protein-coupled receptor 41 and sodium-glucose cotransporter 1 and muscle glycogen level, paralleled the muscle endurance changes observed after treatment with antibiotics for 21 days and reseeding. Because glycogen is a key energetic substrate for prolonged exercise, modulating its muscle availability via gut microbiota represents one potent mechanism that can contribute to the gut microbiota-skeletal muscle axis. Taken together, our results strongly support the hypothesis that gut bacteria are required for host optimal skeletal muscle function.

SPAdes, a new genome assembly algorithm and its applications to single-cell sequencing ( 7th Annual SFAF Meeting, 2012)

Abstract: The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.

Random graphs

Abstract: We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Univariate Discrete Distributions

Abstract: Here are two books on a topic new to Technometrics: statistical and mathematical demography. The first author of Applied Mathematical Demography wrote the first two editions of this book alone. The second edition was published in 1985. Professor Keyfritz noted in the Preface (p. vii) that at age 90 he had no interest in doing another edition; however, the publisher encouraged him to find a coauthor. The result is an additional focus for the book in the world of biology that makes it much more relevant for the sciences. The book is now part of the publisher’s series on Statistics for Biology and Health. Much of it, of course, focuses on the many aspects of human populations. The new material focuses on mature population models, the particular focus of the new author (see, e.g., Caswell 2000). As one might expect from a book that was originally written in the 1970s, it does not include a lot of information on statistical computing. The new book by Alho and Spencer is focused on putting a better emphasis on statistics in the discipline of demography (Preface, p. vii). It is part of the publisher’s Series in Statistics. The authors are both statisticians, so the focus is on statistics as used for demographic problems. The authors are targeting human applications, so their perspective on science does not extend any further than epidemiology. The book actually strikes a good balance between statistical tools and demographic applications. The authors use the first two chapters to teach statisticians about the concepts of demography. The next four chapters are very similar to the statistics content found in introductory books on survival analysis, such as the recent book by Kleinbaum and Klein (2005), reported by Ziegel (2006). The next three chapters are focused on various aspects of forecasting demographic rates. The book concludes with chapters focusing on three areas of applications: errors in census numbers, financial applications, and small-area estimates.