Language is a unique trait of the human species, of which the genetic architecture remains largely unknown. Through language disorders studies, many candidate genes were identified. However, such ...complex and multifactorial trait is unlikely to be driven by only few genes and case-control studies, suffering from a lack of power, struggle to uncover significant variants. In parallel, neuroimaging has significantly contributed to the understanding of structural and functional aspects of language in the human brain and the recent availability of large scale cohorts like UK Biobank have made possible to study language via image-derived endophenotypes in the general population. Because of its strong relationship with task-based fMRI (tbfMRI) activations and its easiness of acquisition, resting-state functional MRI (rsfMRI) have been more popularised, making it a good surrogate of functional neuronal processes. Taking advantage of such a synergistic system by aggregating effects across spatially distributed traits, we performed a multivariate genome-wide association study (mvGWAS) between genetic variations and resting-state functional connectivity (FC) of classical brain language areas in the inferior frontal (pars opercularis, triangularis and orbitalis), temporal and inferior parietal lobes (angular and supramarginal gyri), in 32,186 participants from UK Biobank. Twenty genomic loci were found associated with language FCs, out of which three were replicated in an independent replication sample. A locus in 3p11.1, regulating EPHA3 gene expression, is found associated with FCs of the semantic component of the language network, while a locus in 15q14, regulating THBS1 gene expression is found associated with FCs of the perceptual-motor language processing, bringing novel insights into the neurobiology of language.
We propose a new sparsification method for the singular value decomposition-called the constrained singular value decomposition (CSVD)-that can incorporate multiple constraints such as sparsification ...and orthogonality for the left and right singular vectors. The CSVD can combine different constraints because it implements each constraint as a projection onto a convex set, and because it integrates these constraints as projections onto the intersection of multiple convex sets. We show that, with appropriate sparsification constants, the algorithm is guaranteed to converge to a stable point. We also propose and analyze the convergence of an efficient algorithm for the specific case of the projection onto the balls defined by the norms L1 and L2. We illustrate the CSVD and compare it to the standard singular value decomposition and to a non-orthogonal related sparsification method with: 1) a simulated example, 2) a small set of face images (corresponding to a configuration with a number of variables much larger than the number of observations), and 3) a psychometric application with a large number of observations and a small number of variables. The companion R-package, csvd, that implements the algorithms described in this paper, along with reproducible examples, are available for download from https://github.com/vguillemot/csvd.
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Genome-wide association studies (GWASs) have uncovered a wealth of associations between common variants and human phenotypes. Here, we present an integrative analysis of GWAS summary statistics from ...36 phenotypes to decipher multitrait genetic architecture and its link with biological mechanisms. Our framework incorporates multitrait association mapping along with an investigation of the breakdown of genetic associations into clusters of variants harboring similar multitrait association profiles. Focusing on two subsets of immunity and metabolism phenotypes, we then demonstrate how genetic variants within clusters can be mapped to biological pathways and disease mechanisms. Finally, for the metabolism set, we investigate the link between gene cluster assignment and the success of drug targets in randomized controlled trials.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Rabies is caused by neurotropic rabies virus (RABV), contributing to 60,000 human deaths annually. Even though rabies leads to major public health concerns worldwide, we still do not fully understand ...factors determining RABV tropism and why glial cells are unable to clear RABV from the infected brain. Here, we compare susceptibilities and immune responses of CNS cell types to infection with two RABV strains, Tha and its attenuated variant Th2P-4M, mutated on phospho- (P-protein) and matrix protein (M-protein). We demonstrate that RABV replicates in human stem cell-derived neurons and astrocytes but fails to infect human iPSC-derived microglia. Additionally, we observed major differences in transcription profiles and quantification of intracellular protein levels between antiviral immune responses mediated by neurons, astrocytes (
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) upon Tha infection. We also show that P- and M-proteins of Tha mediate evasion of NF-κB- and JAK-STAT-controlled antiviral host responses in neuronal cell types in contrast to glial cells, potentially explaining the strong neuron-specific tropism of RABV. Further, Tha-infected astrocytes and microglia protect neurons from Tha infection via a filtrable and transferable agent. Overall, our study provides novel insights into RABV tropism, showing the interest in studying the interplay of CNS cell types during RABV infection.
Environmental Enteric Dysfunction (EED) refers to an incompletely defined syndrome of inflammation, reduced absorptive capacity, and reduced barrier function in the small intestine. It is widespread ...among children and adults in low- and middle-income countries and is also associated with poor sanitation and certain gut infections possibly resulting in an abnormal gut microbiota, small intestinal bacterial overgrowth (SIBO) and stunting. We investigated bacterial pathogen exposure in stunted and non-stunted children in Antananarivo, Madagascar by collecting fecal samples from 464 children (96 severely stunted, 104 moderately stunted and 264 non-stunted) and the prevalence of SIBO in 109 duodenal aspirates from stunted children (61 from severely stunted and 48 from moderately stunted children). SIBO assessed by both aerobic and anaerobic plating techniques was very high: 85.3% when selecting a threshold of ≥105 CFU/ml of bacteria in the upper intestinal aspirates. Moreover, 58.7% of the children showed more than 106 bacteria/ml in these aspirates. The most prevalent cultivated genera recovered were Streptococcus, Neisseria, Staphylococcus, Rothia, Haemophilus, Pantoea and Branhamella. Feces screening by qPCR showed a high prevalence of bacterial enteropathogens, especially those categorized as being enteroinvasive or causing mucosal disruption, such as Shigella spp., enterotoxigenic Escherichia coli, enteropathogenic E. coli and enteroaggregative E. coli. These pathogens were detected at a similar rate in stunted children and controls, all showing no sign of severe diarrhea the day of inclusion but both living in a highly contaminated environment (slum-dwelling). Interestingly Shigella spp. was the most prevalent enteropathogen found in this study (83.3%) without overrepresentation in stunted children.
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A currently very active field of research is how to incorporate structure and prior knowledge in machine learning methods. It has lead to numerous developments in the field of non-smooth convex ...minimization. With recently developed methods it is possible to perform an analysis in which the computed model can be linked to a given structure of the data and simultaneously do variable selection to find a few important features in the data. However, there is still no way to unambiguously simulate data to test proposed algorithms, since the exact solutions to such problems are unknown.
The main aim of this paper is to present a theoretical framework for generating simulated data. These simulated data are appropriate when comparing optimization algorithms in the context of linear regression problems with sparse and structured penalties. Additionally, this approach allows the user to control the signal-to-noise ratio, the correlation structure of the data and the optimization problem to which they are the solution.
The traditional approach is to simulate random data without taking into account the actual model that will be fit to the data. But when using such an approach it is not possible to know the exact solution of the underlying optimization problem. With our contribution, it is possible to know the exact theoretical solution of a penalized linear regression problem, and it is thus possible to compare algorithms without the need to use, e.g., cross-validation.
We also present our implementation, the Python package pylearn-simulate , available at https://github.com/neurospin/pylearn-simulate and released under the BSD 3clause license. We describe the package and give examples at the end of the paper.
The organization of chromosomes is important for various biological processes and is involved in the formation of rearrangements often observed in cancer. In mammals, chromosomes are organized in ...territories that are radially positioned in the nucleus. However, it remains unclear whether chromosomes are organized relative to each other. Here, we examine the nuclear arrangement of 10 chromosomes in human epithelial cancer cells by three-dimensional FISH analysis. We show that their radial position correlates with the ratio of their gene density to chromosome size. We also observe that inter-homologue distances are generally larger than inter-heterologue distances. Using numerical simulations taking radial position constraints into account, we demonstrate that, for some chromosomes, radial position is enough to justify the inter-homologue distance, whereas for others additional constraints are involved. Among these constraints, we propose that nucleolar organizer regions participate in the internal positioning of the acrocentric chromosome HSA21, possibly through interactions with nucleoli. Maintaining distance between homologous chromosomes in human cells could participate in regulating genome stability and gene expression, both mechanisms that are key players in tumorigenesis.
Apathy is one of the six clinical criteria for the behavioral variant of frontotemporal dementia (bvFTD), and it is almost universal in this disease. Although its consequences in everyday life are ...debilitating, its underlying mechanisms are poorly known, its assessment is biased by subjectivity and its care management is very limited. In this context, we have developed “ECOCAPTURE,” a method aimed at providing quantifiable and objective signature(s) of apathy in order to assess it and identify its precise underlying mechanisms. ECOCAPTURE consists of the observation and recording of the patient's behavior when the participant is being submitted to a multiple-phase scenario reproducing a brief real-life situation. It is performed in a functional exploration platform transformed into a fully furnished waiting room equipped with a video and sensor-based data acquisition system. This multimodal method allowed video-based behavior analyses according to predefined behavioral categories (exploration behavior, sustained activities or inactivity) and actigraphy analyses from a 3D accelerometer. The data obtained were also correlated with behavioral/cognitive tests and scales assessing global cognitive efficiency, apathy, cognitive disinhibition, frontal syndrome, depression and anxiety. Here, bvFTD patients (n = 14) were compared to healthy participants (n = 14) during the very first minutes of the scenario, when the participants discovered the room and were encouraged to explore it. We showed that, in the context of facing a new environment, healthy participants first explored it and then engaged in sustained activities. By contrast, bvFTD patients were mostly inactive and eventually explored this new place, but in a more irregular and less efficient mode than normal subjects. This exploration deficit was correlated with apathy, disinhibition and cognitive and behavioral dysexecutive syndromes. These findings led us to discuss the presumed underlying mechanisms responsible for the exploration deficit (an inability to self-initiate actions, to integrate reward valuation and to inhibit involuntary behavior). Altogether, these results pave the way for simple and objective assessment of behavioral changes that represents a critical step for the evaluation of disease progression and efficacy of treatment in bvFTD.
In the context of Gaussian Graphical Models (GGMs) with high-dimensional small sample data, we present a simple procedure, called PACOSE - standing for PArtial COrrelation SElection - to estimate ...partial correlations under the constraint that some of them are strictly zero. This method can also be extended to covariance selection. If the goal is to estimate a GGM, our new procedure can be applied to re-estimate the partial correlations after a first graph has been estimated in the hope to improve the estimation of non-zero coefficients. This iterated version of PACOSE is called iPACOSE. In a simulation study, we compare PACOSE to existing methods and show that the re-estimated partial correlation coefficients may be closer to the real values in important cases. Plus, we show on simulated and real data that iPACOSE shows very interesting properties with regards to sensitivity, positive predictive value and stability.
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Integrating gene regulatory networks (GRNs) into the classification process of DNA microarrays is an important issue in bioinformatics, both because this information has a true biological interest ...and because it helps in the interpretation of the final classifier. We present a method called graph-constrained discriminant analysis (gCDA), which aims to integrate the information contained in one or several GRNs into a classification procedure. We show that when the integrated graph includes erroneous information, gCDA's performance is only slightly worse, thus showing robustness to misspecifications in the given GRNs. The gCDA framework also allows the classification process to take into account as many a priori graphs as there are classes in the dataset. The gCDA procedure was applied to simulated data and to three publicly available microarray datasets. gCDA shows very interesting performance when compared to state-of-the-art classification methods. The software package gcda, along with the real datasets that were used in this study, are available online: http://biodev.cea.fr/gcda/.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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