Utilisation du microbiome intestinal dans la prédiction de l'état de santé de l'hôte

Durant les dernières décennies, la recherche sur le microbiome intestinal a positionné ce dernier comme important régulateur de nombreux processus physiologiques chez l'humain. Propulsée par les technologies de séquençage à haut débit, la recherche sur l'écologie microbienne a connu un important changement de paradigme. Les méthodes d'isolation et de mise en culture de bactéries d'intérêt sont maintenant, de manière générale, remplacées par le séquençage génétique de communautés microbiennes complètes directement dans leur environnement. Ce type d'analyse, la métagénomique, a révélé l'immense catalogue de gènes bactériens présents dans l'environnement intestinal et a levé le voile sur la majorité silencieuse du microbiote : les microorganismes non-cultivables. Ce vaste catalogue de gènes microbiens représente une véritable mine d'information dans un contexte où la recherche tente de trouver des mécanismes moléculaires expliquant la relation entre le microbiome et la santé des individus. Dans ce contexte, l'apprentissage automatique, qui permet l'analyse de données complexes, peut être utilisé pour pointer vers des effecteurs microbiens d'intérêt. L'objectif du projet est d'utiliser les données métagénomiques d'individus malades et en santé dans une tâche de classification. Plus précisément, notre but est de comparer le pouvoir prédictif de différentes représentations du microbiome, toutes dérivées des données de séquençage en métagénomique non-ciblée. Notre étude a démontré que dans un contexte de classification de phénotype de l'hôte, les méthodes de représentation qui utilisent toute l'information génique séquencée permettent de meilleures performances de prédiction que celles qui utilisent exclusivement l'information contenue dans les banques de données de référence, comme les profils taxonomique et fonctionnel. Nos résultats suggèrent que l'utilisation exclusive de l'information a priori dans un contexte d'apprentissage automatique limite, d'une certaine façon, la possibilité de trouver de nouveaux effecteurs microbiens inconnus des banques de données.During the last decades, research positioned the gut microbiome as a major regulator of numerous physiological processes in humans. Propelled by next-generation sequencing technologies, the research on microbial ecology has undergone a significant paradigm shift; generally, bacteria isolation and cultivation are now being replaced by genetic sequencing of whole bacterial communities directly from their environment. This type of analysis, referred as metagenomics, revealed the large catalog of microbial genes comprised in the gut environment and lifted the veil on the microbiota's silent majority: non-cultivable microorganisms. This vast catalog of genes represents a real mine of information in a context where research aims at finding molecular mechanisms to explain the relation between microbiome and host health. In this context, machine learning, which allows the analysis of complex data, can be used to point toward promising microbial features. The objective of this project is to use metagenomics data from healthy and diseased individuals in a classification task. More precisely, our goal is to compare the predictive power of different microbiome representations, all derived from untargeted metagenomics data. Our study has shown that in a context of host phenotype classification, representation methods that use all the available sequenced information allow better prediction performances than those that are based on reference databases, like the taxonomic and functional profiles. Our results suggest that the exclusive use of a priori information, in a machine learning context, limits, in a way, the possibility of finding new microbial effectors unknown from reference databases

Dust-Gas Scaling Relations and OH Abundance in the Galactic ISM

Observations of interstellar dust are often used as a proxy for total gas column density $N_\mathrm{H}$. By comparing $\textit{Planck}$ thermal dust data (Release 1.2) and new dust reddening maps from Pan-STARRS 1 and 2MASS (Green et al. 2018), with accurate (opacity-corrected) HI column densities and newly-published OH data from the Arecibo Millennium survey and 21-SPONGE, we confirm linear correlations between dust optical depth $\tau_{353}$, reddening $E(B{-}V)$ and the total proton column density $N_\mathrm{H}$ in the range (1$-$30)$\times$10$^{20}$cm$^{-2}$, along sightlines with no molecular gas detections in emission. We derive an $N_\mathrm{H}$/$E(B{-}V)$ ratio of (9.4$\pm$1.6)$\times$10$^{21}$cm$^{-2}$mag$^{-1}$ for purely atomic sightlines at $|b|$$>$5$^{\circ}$, which is 60$\%$ higher than the canonical value of Bohlin et al. (1978). We report a $\sim$40$\%$ increase in opacity $\sigma_{353}$=$\tau_{353}$/$N_\mathrm{H}$, when moving from the low column density ($N_\mathrm{H}$$<$5$\times$10$^{20}$cm$^{-2}$) to moderate column density ($N_\mathrm{H}$$>$5$\times$10$^{20}$cm$^{-2}$) regime, and suggest that this rise is due to the evolution of dust grains in the atomic ISM. Failure to account for HI opacity can cause an additional apparent rise in $\sigma_{353}$, of the order of a further $\sim$20$\%$. We estimate molecular hydrogen column densities $N_{\mathrm{H}_{2}}$ from our derived linear relations, and hence derive the OH/H$_2$ abundance ratio of $X_\mathrm{OH}$$\sim$1$\times$10$^{-7}$ for all molecular sightlines. Our results show no evidence of systematic trends in OH abundance with $N_{\mathrm{H}_{2}}$ in the range $N_{\mathrm{H}_{2}}$$\sim$(0.1$-$10)$\times$10$^{21}$cm$^{-2}$. This suggests that OH may be used as a reliable proxy for H$_2$ in this range, which includes sightlines with both CO-dark and CO-bright gas.Comment: The revised manuscript is accepted for publication in The Astrophysical Journa

Dust–Gas Scaling Relations and OH Abundance in the Galactic ISM

Observations of interstellar dust are often used as a proxy for total gas column density NH. By comparing Planck thermal dust data (Release 1.2) and new dust reddening maps from Pan-STARRS 1 and 2MASS, with accurate (opacity-corrected) H I column densities and newly published OH data from the Arecibo Millennium survey and 21-SPONGE, we confirm linear correlations between dust optical depth τ353, reddening E(B − V), and the total proton column density NH in the range (1–30) × 1020 cm−2, along sightlines with no molecular gas detections in emission. We derive an NH/E(B − V) ratio of (9.4 ± 1.6) × 1021 cm−2 mag−1 for purely atomic sightlines at |b| \u3e 5°, which is 60% higher than the canonical value of Bohlin et al. We report a ~40% increase in opacity σ353 = τ 353/NH, when moving from the low column density (NH \u3c 5 × 1020 cm−2) to the moderate column density (NH \u3e 5 × 1020 cm−2) regime, and suggest that this rise is due to the evolution of dust grains in the atomic interstellar medium. Failure to account for H I opacity can cause an additional apparent rise in σ353 of the order of a further ~20%. We estimate molecular hydrogen column densities NH2 from our derived linear relations, and hence derive the OH/H2 abundance ratio of XOH ~ 1 × 10−7 for all molecular sightlines. Our results show no evidence of systematic trends in OH abundance with NH2 in the range NH2 ~ (0.1−10) × 1021 cm−2. This suggests that OH may be used as a reliable proxy for H2 in this range, which includes sightlines with both CO-dark and CO-bright gas

BLAST: Correlations in the Cosmic Far-Infrared Background at 250, 350, and 500 microns Reveal Clustering of Star-Forming Galaxies

We detect correlations in the cosmic far-infrared background due to the clustering of star-forming galaxies in observations made with the Balloon-borne Large Aperture Submillimeter Telescope, BLAST, at 250, 350, and 500 microns. We perform jackknife and other tests to confirm the reality of the signal. The measured correlations are well fit by a power law over scales of 5-25 arcminutes, with Delta I/I = 15.1 +/- 1.7%. We adopt a specific model for submillimeter sources in which the contribution to clustering comes from sources in the redshift ranges 1.3 <= z <= 2.2, 1.5 <= z <= 2.7, and 1.7 <= z <= 3.2, at 250, 350, and 500 microns, respectively. With these distributions, our measurement of the power spectrum, P(k_theta), corresponds to linear bias parameters, b = 3.8 +/- 0.6, 3.9 +/- 0.6 and 4.4 +/- 0.7, respectively. We further interpret the results in terms of the halo model, and find that at the smaller scales, the simplest halo model fails to fit our results. One way to improve the fit is to increase the radius at which dark matter halos are artificially truncated in the model, which is equivalent to having some star-forming galaxies at z >= 1 located in the outskirts of groups and clusters. In the context of this model we find a minimum halo mass required to host a galaxy is log (M_min / M_sun) = 11.5 (+0.4/-0.1), and we derive effective biases $b_eff = 2.2 +/- 0.2, 2.4 +/- 0.2, and 2.6 +/- 0.2, and effective masses log (M_eff / M_sun) = 12.9 +/- 0.3, 12.8 +/- 0.2, and 12.7 +/- 0.2, at 250, 350, and 500 microns, corresponding to spatial correlation lengths of r_0 = 4.9, 5.0, and 5.2 +/- 0.7 h^-1 Mpc, respectively. Finally, we discuss implications for clustering measurement strategies with Herschel and Planck.Comment: Accepted for publication in the Astrophysical Journal. Maps and other results available at http://blastexperiment.info

More anxious than depressed: prevalence and correlates in a 15-nation study of anxiety disorders in people with type 2 diabetes mellitus

Background Anxiety disorder, one of the highly disabling, prevalent and common mental disorders, is known to be more prevalent in persons with type 2 diabetes mellitus (T2DM) than the general population, and the comorbid presence of anxiety disorders is known to have an impact on the diabetes outcome and the quality of life. However, the information on the type of anxiety disorder and its prevalence in persons with T2DM is limited. Aims To assess the prevalence and correlates of anxiety disorder in people with type 2 diabetes in different countries. Methods People aged 18–65 years with diabetes and treated in outpatient settings were recruited in 15 countries and underwent a psychiatric interview with the Mini-International Neuropsychiatric Interview. Demographic and medical record data were collected. Results A total of 3170 people with type 2 diabetes (56.2% women; with mean (SD) duration of diabetes 10.01 (7.0) years) participated. The overall prevalence of anxiety disorders in type 2 diabetic persons was 18%; however, 2.8% of the study population had more than one type of anxiety disorder. The most prevalent anxiety disorders were generalised anxiety disorder (8.1%) and panic disorder (5.1%). Female gender, presence of diabetic complications, longer duration of diabetes and poorer glycaemic control (HbA1c levels) were significantly associated with comorbid anxiety disorder. A higher prevalence of anxiety disorders was observed in Ukraine, Saudi Arabia and Argentina with a lower prevalence in Bangladesh and India. Conclusions Our international study shows that people with type 2 diabetes have a high prevalence of anxiety disorders, especially women, those with diabetic complications, those with a longer duration of diabetes and poorer glycaemic control. Early identification and appropriate timely care of psychiatric problems of people with type 2 diabetes is warranted

Rapid on-site detection of harmful algal blooms: real-time cyanobacteria identification using Oxford Nanopore sequencing

With the increasing occurrence and severity of cyanobacterial harmful algal blooms (cHAB) at the global scale, there is an urgent need for rapid, accurate, accessible, and cost-effective detection tools. Here, we detail the RosHAB workflow, an innovative, in-the-field applicable genomics approach for real-time, early detection of cHAB outbreaks. We present how the proposed workflow offers consistent taxonomic identification of water samples in comparison to traditional microscopic analyses in a few hours and discuss how the generated data can be used to deepen our understanding on cyanobacteria ecology and forecast HABs events. In parallel, processed water samples will be used to iteratively build the International cyanobacterial toxin database (ICYATOX; http://icyatox.ibis.ulaval.ca) containing the analysis of novel cyanobacterial genomes, including phenomics and genomics metadata. Ultimately, RosHAB will (1) improve the accuracy of on-site rapid diagnostics, (2) standardize genomic procedures in the field, (3) facilitate these genomics procedures for non-scientific personnel, and (4) identify prognostic markers for evidence-based decisions in HABs surveillance

Correlations in the (Sub)millimeter background from ACTxBLAST

We present measurements of the auto- and cross-frequency correlation power spectra of the cosmic (sub)millimeter background at: 250, 350, and 500 um (1200, 860, and 600 GHz) from observations made with the Balloon-borne Large Aperture Submillimeter Telescope, BLAST; and at 1380 and 2030 um (218 and 148 GHz) from observations made with the Atacama Cosmology Telescope, ACT. The overlapping observations cover 8.6 deg^2 in an area relatively free of Galactic dust near the south ecliptic pole (SEP). The ACT bands are sensitive to radiation from the CMB, the Sunyaev-Zel'dovich (SZ) effect from galaxy clusters, and to emission by radio and dusty star-forming galaxies (DSFGs), while the dominant contribution to the BLAST bands is from DSFGs. We confirm and extend the BLAST analysis of clustering with an independent pipeline, and also detect correlations between the ACT and BLAST maps at over 25sigma significance, which we interpret as a detection of the DSFGs in the ACT maps. In addition to a Poisson component in the cross-frequency power spectra, we detect a clustered signal at >4sigma, and using a model for the DSFG evolution and number counts, we successfully fit all our spectra with a linear clustering model and a bias that depends only on redshift and not on scale. Finally, the data are compared to, and generally agree with, phenomenological models for the DSFG population. This study represents a first of its kind, and demonstrates the constraining power of the cross-frequency correlation technique to constrain models for the DSFGs. Similar analyses with more data will impose tight constraints on future models.Comment: 17 pages, 11 figure

Planck 2013 results. XIII. Galactic CO emission

Peer reviewe

First Observation of the Submillimeter Polarization Spectrum in a Translucent Molecular Cloud

Polarized emission from aligned dust is a crucial tool for studies of magnetism in the ISM, but a troublesome contaminant for studies of cosmic microwave background polarization. In each case, an understanding of the significance of the polarization signal requires well-calibrated physical models of dust grains. Despite decades of progress in theory and observation, polarized dust models remain largely underconstrained. During its 2012 flight, the balloon-borne telescope BLASTPol obtained simultaneous broadband polarimetric maps of a translucent molecular cloud at 250, 350, and 500 μm. Combining these data with polarimetry from the Planck 850 μm band, we have produced a submillimeter polarization spectrum, the first for a cloud of this type. We find the polarization degree to be largely constant across the four bands. This result introduces a new observable with the potential to place strong empirical constraints on ISM dust polarization models in a previously inaccessible density regime. Compared to models by Draine & Fraisse, our result disfavors two of their models for which all polarization arises due only to aligned silicate grains. By creating simple models for polarized emission in a translucent cloud, we verify that extinction within the cloud should have only a small effect on the polarization spectrum shape, compared to the diffuse ISM. Thus, we expect the measured polarization spectrum to be a valid check on diffuse ISM dust models. The general flatness of the observed polarization spectrum suggests a challenge to models where temperature and alignment degree are strongly correlated across major dust components