Integrating multiomics and prior knowledge: a study of the Graphnet penalty impact

MOTIVATION

In the field of oncology, statistical models are used for the discovery of candidate factors that influence the development of the pathology or its outcome. These statistical models can be designed in a multiblock framework to study the relationship between different multiomic data, and variable selection is often achieved by imposing constraints on the model parameters. A priori graph constraints have been used in the literature as a way to improve feature selection in the model, yielding more interpretability. However, it is still unclear how these graphs interact with the models and how they impact the feature selection. Additionally, with the availability of different graphs encoding different information, one can wonder how the choice of the graph meaningfully impacts the results obtained.

RESULTS

We proposed to study the graph penalty impact on a multiblock model. Specifically, we used the SGCCA as the multiblock framework. We studied the effect of the penalty on the model using the TCGA-LGG dataset. Our findings are 3-fold. We showed that the graph penalty increases the number of selected genes from this dataset, while selecting genes already identified in other works as pertinent biomarkers in the pathology. We demonstrated that using different graphs leads to different though consistent results, but that graph density is the main factor influencing the obtained results. Finally, we showed that the graph penalty increases the performance of the survival prediction from the model-derived components and the interpretability of the results.

AVAILABILITY AND IMPLEMENTATION

Source code is freely available at https://github.com/neurospin/netSGCCA.

Behavioural disinhibition in frontotemporal dementia investigated within an ecological framework

Disinhibition is a core symptom in behavioural variant frontotemporal dementia (bvFTD) particularly affecting the daily lives of both patients and caregivers. Yet, characterisation of inhibition disorders is still unclear and management options of these disorders are limited. Questionnaires currently used to investigate behavioural disinhibition do not differentiate between several subtypes of disinhibition, encompass observation biases and lack of ecological validity. In the present work, we explored disinhibition in an original semi-ecological situation, by distinguishing three categories of disinhibition: compulsivity, impulsivity and social disinhibition. First, we measured prevalence and frequency of these disorders in 23 bvFTD patients and 24 healthy controls (HC) in order to identify the phenotypical heterogeneity of disinhibition. Then, we examined the relationships between these metrics, the neuropsychological scores and the behavioural states to propose a more comprehensive view of these neuropsychiatric manifestations. Finally, we studied the context of occurrence of these disorders by investigating environmental factors potentially promoting or reducing them. As expected, we found that patients were more compulsive, impulsive and socially disinhibited than HC. We found that 48% of patients presented compulsivity (e.g., repetitive actions), 48% impulsivity (e.g., oral production) and 100% of the patients group showed social disinhibition (e.g., disregards for rules or investigator). Compulsivity was negatively related with emotions recognition. BvFTD patients were less active if not encouraged in an activity, and their social disinhibition decreased as activity increased. Finally, impulsivity and social disinhibition decreased when patients were asked to focus on a task. Summarising, this study underlines the importance to differentiate subtypes of disinhibition as well as the setting in which they are exhibited, and points to stimulating area for non-pharmacological management.

Susceptibilities of CNS Cells towards Rabies Virus Infection Is Linked to Cellular Innate Immune Responses

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 (IFNB1, CCL5, CXCL10, IL1B, IL6, and LIF), and microglia (CCL5, CXCL10, ISG15, MX1, and IL6) 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.

Alterations of the axon initial segment in multiple sclerosis grey matter

Grey matter damage has been established as a key contributor to disability progression in multiple sclerosis. Aside from neuronal loss and axonal transections, which predominate in cortical demyelinated lesions, synaptic alterations have been detected in both demyelinated plaques and normal-appearing grey matter, resulting in functional neuronal damage. The axon initial segment is a key element of neuronal function, responsible for action potential initiation and maintenance of neuronal polarity. Despite several reports of profound axon initial segment alterations in different pathological models, among which experimental auto-immune encephalomyelitis, whether the axon initial segment is affected in multiple sclerosis is still unknown. Using immunohistochemistry, we analysed axon initial segments from control and multiple sclerosis tissue, focusing on layer 5/6 pyramidal neurons in the neocortex and Purkinje cells in the cerebellum and performed analysis on the parameters known to control neuronal excitability, i.e. axon initial segment length and position. We found that the axon initial segment length was increased only in pyramidal neurons of inactive demyelinated lesions, compared with normal appearing grey matter tissue. In contrast, in both cell types, the axon initial segment position was altered, with an increased soma-axon initial segment gap, in both active and inactive demyelinated lesions. In addition, using a computational model, we show that this increased gap between soma and axon initial segment might increase neuronal excitability. Taken together, these results show, for the first time, changes of axon initial segments in multiple sclerosis, in active as well as inactive grey matter lesions in both neocortex and cerebellum, which might alter neuronal function.

High prevalence of small intestine bacteria overgrowth and asymptomatic carriage of enteric pathogens in stunted children in Antananarivo, Madagascar

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.

About 2 million children under the age of 5 suffer from stunted growth in Madagascar. Although deficient diet is the major cause of undernutrition, impaired absorption or assimilation caused by Environmental Enteric dysfunction (EED) has been proposed to play an important role in stunting. EED is widespread among children and adults in low- and middle-income countries (LMIC) and is also associated with undernutrition, poor sanitation, certain gut infections resulting in an abnormal gut microbiota and small intestinal bacterial overgrowth (SIBO) although the role of SIBO in EED remains unclear. The current study highlights the presence at high concentrations of bacterial taxa usually found in the oro-pharyngeal sphere in a high number of duodenal fluids of stunted children. This uncommon presence suggests a decompartmentalization of the gastrointestinal tract and a possible pro-inflammatory effect due to the ectopic presence of some of these bacteria in the duodenum.

The study also points to a high prevalence of enteropathogens (especially Shigella spp.) in the feces of both stunted and control children, hence preventing from proposing a direct association with stunting. This suggests that, beside combatting poverty and improving diet, environmental sanitation, quality of water sources, hygiene promotion and health education are key points to mitigate stunting and restore nutritional benefits.

The genetic architecture of language functional connectivity

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.

Bedside assessment of dependence as an independent correlate of mortality in elderly patients admitted for Acute Coronary Syndromes

Background

Elderly patients are at high risk of dependence and mortality after acute coronary syndrome (ACS)

Purpose

To assess the impact of dependence detected by a simple bedside test in elderly patients admitted for ACS on 1-year mortality.

Methods

We compared mortality between patients with or without dependence based on an Activities of Daily Living (ADL) index <6 in a prospective, cohort of patients ≥75 years old admitted for an ACS to the cardiology department of our center using a Cox proportional survival model adjusted on pre-defined confounding variables (age, gender, revascularization, heart failure, left ventricular ejection fraction and admission GRACE and CRUSADE scores).

Results

In a cohort of 1011 consecutive patients, 946 underwent ADL assessment and completed one-year follow-up. Dependence was identified in 190 (20%). At 1 year follow-up 112 patients died, 52 (27.4%) in the dependent and 60 (7.9%) in the independent groups. Dependence was associated with higher rates of 1 year mortality both on undajusted (HR 3.79; 95% CI 2.62; 5.50], p<0.0001) and adjusted models (adj-HR 2.85; 95% CI 1.89; 4.33], p<0.0001). Other independent correlates of mortality were female gender (p<0.0001), CRUSADE score (p<0.0001) and coronary revascularization (p<0.001).

Conclusions

Dependence detected by a simple bedside test in patients ≥75 admitted for ACS is associated with a 2 to 3-fold increase of the risk of mortality independent of other predictors of poor outcome. Assessment of dependence should be performed in all elderly patients as a risk stratification tool and considered for the management of such patients.

Funding Acknowledgement

Type of funding sources: None.

Multitrait GWAS to connect disease variants and biological mechanisms

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.

Dichotomous metabolic networks govern human ILC2 proliferation and function

Group 2 innate lymphoid cells (ILC2s) represent innate homologs of type 2 helper T cells (T_H2) that participate in immune defense and tissue homeostasis through production of type 2 cytokines. While T lymphocytes metabolically adapt to microenvironmental changes, knowledge of human ILC2 metabolism is limited, and its key regulators are unknown. Here, we show that circulating 'naive' ILC2s have an unexpected metabolic profile with a higher level of oxidative phosphorylation (OXPHOS) than natural killer (NK) cells. Accordingly, ILC2s are severely reduced in individuals with mitochondrial disease (MD) and impaired OXPHOS. Metabolomic and nutrient receptor analysis revealed ILC2 uptake of amino acids to sustain OXPHOS at steady state. Following activation with interleukin-33 (IL-33), ILC2s became highly proliferative, relying on glycolysis and mammalian target of rapamycin (mTOR) to produce IL-13 while continuing to fuel OXPHOS with amino acids to maintain cellular fitness and proliferation. Our results suggest that proliferation and function are metabolically uncoupled in human ILC2s, offering new strategies to target ILC2s in disease settings.

Immune response profiling of patients with spondyloarthritis reveals signalling networks mediating TNF-blocker function in vivo

Objectives

Antitumour necrosis factor (TNF) therapy has revolutionised treatment of several chronic inflammatory diseases, including spondyloarthritis (SpA). However, TNF inhibitors (TNFi) are not effective in all patients and the biological basis for treatment failure remains unknown. We have analysed induced immune responses to define the mechanism of action of TNF blockers in SpA and to identify immunological correlates of responsiveness to TNFi.

Methods

Immune responses to microbial and pathway-specific stimuli were analysed in peripheral blood samples from 80 patients with axial SpA before and after TNFi treatment, using highly standardised whole-blood stimulation assays. Cytokines and chemokines were measured in a Clinical Laboratory Improvement Amendments (CLIA)-certified laboratory, and gene expression was monitored using nCounter assays.

Results

Anti-TNF therapy induced profound changes in patients’ innate immune responses. TNFi action was selective, and had only minor effects on Th1/Th17 immunity. Modular transcriptional repertoire analysis identified prostaglandin E₂ synthesis and signalling, leucocyte recirculation, macrophage polarisation, dectin and interleukin (IL)-1 signalling, as well as the nuclear factor kappa B (NF-kB) transcription factor family as key pathways targeted by TNF blockers in vivo. Analysis of induced immune responses before treatment initiation revealed that expression of molecules associated with leucocyte adhesion and invasion, chemotaxis and IL-1 signalling are correlated with therapeutic responses to anti-TNF.

Conclusions

We show that TNFi target multiple immune cell pathways that cooperate to resolve inflammation. We propose that immune response profiling provides new insight into the biology of TNF-blocker action in patients and can identify signalling pathways associated with therapeutic responses to biological therapies.

JASS: command line and web interface for the joint analysis of GWAS results

Genome-wide association study (GWAS) has been the driving force for identifying association between genetic variants and human phenotypes. Thousands of GWAS summary statistics covering a broad range of human traits and diseases are now publicly available. These GWAS have proven their utility for a range of secondary analyses, including in particular the joint analysis of multiple phenotypes to identify new associated genetic variants. However, although several methods have been proposed, there are very few large-scale applications published so far because of challenges in implementing these methods on real data. Here, we present JASS (Joint Analysis of Summary Statistics), a polyvalent Python package that addresses this need. Our package incorporates recently developed joint tests such as the omnibus approach and various weighted sum of Z-score tests while solving all practical and computational barriers for large-scale multivariate analysis of GWAS summary statistics. This includes data cleaning and harmonization tools, an efficient algorithm for fast derivation of joint statistics, an optimized data management process and a web interface for exploration purposes. Both benchmark analyses and real data applications demonstrated the robustness and strong potential of JASS for the detection of new associated genetic variants. Our package is freely available at https://gitlab.pasteur.fr/statistical-genetics/jass.

Exploration Deficits Under Ecological Conditions as a Marker of Apathy in Frontotemporal Dementia

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.

Adjusting for Principal Components of Molecular Phenotypes Induces Replicating False Positives

High-throughput measurements of molecular phenotypes provide an unprecedented opportunity to model cellular processes and their impact on disease. These highly structured datasets are usually strongly confounded, creating false positives and reducing power. This has motivated many approaches based on principal components analysis (PCA) to estimate and correct for confounders, which have become indispensable elements of association tests between molecular phenotypes and both genetic and nongenetic factors. Here, we show that these correction approaches induce a bias, and that it persists for large sample sizes and replicates out-of-sample. We prove this theoretically for PCA by deriving an analytic, deterministic, and intuitive bias approximation. We assess other methods with realistic simulations, which show that perturbing any of several basic parameters can cause false positive rate (FPR) inflation. Our experiments show the bias depends on covariate and confounder sparsity, effect sizes, and their correlation. Surprisingly, when the covariate and confounder have [Formula: see text], standard two-step methods all have [Formula: see text]-fold FPR inflation. Our analysis informs best practices for confounder correction in genomic studies, and suggests many false discoveries have been made and replicated in some differential expression analyses.

A constrained singular value decomposition method that integrates sparsity and orthogonality

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.

Continuation of Nesterov's Smoothing for Regression With Structured Sparsity in High-Dimensional Neuroimaging

Predictive models can be used on high-dimensional brain images to decode cognitive states or diagnosis/prognosis of a clinical condition/evolution. Spatial regularization through structured sparsity offers new perspectives in this context and reduces the risk of overfitting the model while providing interpretable neuroimaging signatures by forcing the solution to adhere to domain-specific constraints. Total variation (TV) is a promising candidate for structured penalization: it enforces spatial smoothness of the solution while segmenting predictive regions from the background. We consider the problem of minimizing the sum of a smooth convex loss, a non-smooth convex penalty (whose proximal operator is known) and a wide range of possible complex, non-smooth convex structured penalties such as TV or overlapping group Lasso. Existing solvers are either limited in the functions they can minimize or in their practical capacity to scale to high-dimensional imaging data. Nesterov's smoothing technique can be used to minimize a large number of non-smooth convex structured penalties. However, reasonable precision requires a small smoothing parameter, which slows down the convergence speed to unacceptable levels. To benefit from the versatility of Nesterov's smoothing technique, we propose a first order continuation algorithm, CONESTA, which automatically generates a sequence of decreasing smoothing parameters. The generated sequence maintains the optimal convergence speed toward any globally desired precision. Our main contributions are: gap to probe the current distance to the global optimum in order to adapt the smoothing parameter and the To propose an expression of the duality convergence speed. This expression is applicable to many penalties and can be used with other solvers than CONESTA. We also propose an expression for the particular smoothing parameter that minimizes the number of iterations required to reach a given precision. Furthermore, we provide a convergence proof and its rate, which is an improvement over classical proximal gradient smoothing methods. We demonstrate on both simulated and high-dimensional structural neuroimaging data that CONESTA significantly outperforms many state-of-the-art solvers in regard to convergence speed and precision.

Covariate selection for association screening in multiphenotype genetic studies

Testing for associations in big data faces the problem of multiple comparisons, wherein true signals are difficult to detect on the background of all associations queried. This difficulty is particularly salient in human genetic association studies, in which phenotypic variation is often driven by numerous variants of small effect. The current strategy to improve power to identify these weak associations consists of applying standard marginal statistical approaches and increasing study sample sizes. Although successful, this approach does not leverage the environmental and genetic factors shared among the multiple phenotypes collected in contemporary cohorts. Here we developed covariates for multiphenotype studies (CMS), an approach that improves power when correlated phenotypes are measured on the same samples. Our analyses of real and simulated data provide direct evidence that correlated phenotypes can be used to achieve increases in power to levels often surpassing the power gained by a twofold increase in sample size.

Modeling a linkage between blood transcriptional expression and activity in brain regions to infer the phenotype of schizophrenia patients

Hundreds of genetic loci participate to schizophrenia liability. It is also known that impaired cerebral connectivity is directly related to the cognitive and affective disturbances in schizophrenia. How genetic susceptibility and brain neural networks interact to specify a pathological phenotype in schizophrenia remains elusive. Imaging genetics, highlighting brain variations, has proven effective to establish links between vulnerability loci and associated clinical traits. As previous imaging genetics works in schizophrenia have essentially focused on structural DNA variants, these findings could be blurred by epigenetic mechanisms taking place during gene expression. We explored the meaningful links between genetic data from peripheral blood tissues on one hand, and regional brain reactivity to emotion task assayed by blood oxygen level-dependent functional magnetic resonance imaging on the other hand, in schizophrenia patients and matched healthy volunteers. We applied Sparse Generalized Canonical Correlation Analysis to identify joint signals between two blocks of variables: (i) the transcriptional expression of 33 candidate genes, and (ii) the blood oxygen level-dependent activity in 16 region of interest. Results suggested that peripheral transcriptional expression is related to brain imaging variations through a sequential pathway, ending with the schizophrenia phenotype. Generalization of such an approach to larger data sets should thus help in outlining the pathways involved in psychiatric illnesses such as schizophrenia.

IMAGING

SEARCHING FOR LINKS TO AID DIAGNOSIS: Researchers explore links between the expression of genes associated with schizophrenia in blood cells and variations in brain activity during emotion processing. El Chérif Ibrahim and Eric Fakra at Aix-Marseille Université, France, and colleagues have developed a method to relate the expression levels of 33 schizophrenia susceptibility genes in blood cells and functional magnetic resonance imaging (fMRI) data obtained as individuals carry out a task that triggers emotional responses. Although they found no significant differences in the expression of genes between the 26 patients with schizophrenia and 26 healthy controls they examined, variations in activity in the superior temporal gyrus were strongly linked to schizophrenia-associated gene expression and presence of disease. Similar analyses of larger data sets will shed further light on the relationship between peripheral molecular changes and disease-related behaviors and ultimately, aid the diagnosis of neuropsychiatric disease.

Adaptive human immunity drives remyelination in a mouse model of demyelination

One major challenge in multiple sclerosis is to understand the cellular and molecular mechanisms leading to disease severity progression. The recently demonstrated correlation between disease severity and remyelination emphasizes the importance of identifying factors leading to a favourable outcome. Why remyelination fails or succeeds in multiple sclerosis patients remains largely unknown, mainly because remyelination has never been studied within a humanized pathological context that would recapitulate major events in plaque formation such as infiltration of inflammatory cells. Therefore, we developed a new paradigm by grafting healthy donor or multiple sclerosis patient lymphocytes in the demyelinated lesion of nude mice spinal cord. We show that lymphocytes play a major role in remyelination whose efficacy is significantly decreased in mice grafted with multiple sclerosis lymphocytes compared to those grafted with healthy donors lymphocytes. Mechanistically, we demonstrated in vitro that lymphocyte-derived mediators influenced differentiation of oligodendrocyte precursor cells through a crosstalk with microglial cells. Among mice grafted with lymphocytes from different patients, we observed diverse remyelination patterns reproducing for the first time the heterogeneity observed in multiple sclerosis patients. Comparing lymphocyte secretory profile from patients exhibiting high and low remyelination ability, we identified novel molecules involved in oligodendrocyte precursor cell differentiation and validated CCL19 as a target to improve remyelination. Specifically, exogenous CCL19 abolished oligodendrocyte precursor cell differentiation observed in patients with high remyelination pattern. Multiple sclerosis lymphocytes exhibit intrinsic capacities to coordinate myelin repair and further investigation on patients with high remyelination capacities will provide new pro-regenerative strategies.

Clinical-genetic model predicts incident impulse control disorders in Parkinson's disease

OBJECTIVES

Impulse control disorders (ICD) are commonly associated with dopamine replacement therapy (DRT) in patients with Parkinson's disease (PD). Our aims were to estimate ICD heritability and to predict ICD by a candidate genetic multivariable panel in patients with PD.

METHODS

Data from de novo patients with PD, drug-naïve and free of ICD behaviour at baseline, were obtained from the Parkinson's Progression Markers Initiative cohort. Incident ICD behaviour was defined as positive score on the Questionnaire for Impulsive-Compulsive Disorders in PD. ICD heritability was estimated by restricted maximum likelihood analysis on whole exome sequencing data. 13 candidate variants were selected from the DRD2, DRD3, DAT1, COMT, DDC, GRIN2B, ADRA2C, SERT, TPH2, HTR2A, OPRK1 and OPRM1 genes. ICD prediction was evaluated by the area under the curve (AUC) of receiver operating characteristic (ROC) curves.

RESULTS

Among 276 patients with PD included in the analysis, 86% started DRT, 40% were on dopamine agonists (DA), 19% reported incident ICD behaviour during follow-up. We found heritability of this symptom to be 57%. Adding genotypes from the 13 candidate variants significantly increased ICD predictability (AUC=76%, 95% CI (70% to 83%)) compared to prediction based on clinical variables only (AUC=65%, 95% CI (58% to 73%), p=0.002). The clinical-genetic prediction model reached highest accuracy in patients initiating DA therapy (AUC=87%, 95% CI (80% to 93%)). OPRK1, HTR2A and DDC genotypes were the strongest genetic predictive factors.

CONCLUSIONS

Our results show that adding a candidate genetic panel increases ICD predictability, suggesting potential for developing clinical-genetic models to identify patients with PD at increased risk of ICD development and guide DRT management.

Transcriptome Analysis of Peripheral Blood in Chronic Inflammatory Demyelinating Polyradiculoneuropathy Patients Identifies TNFR1 and TLR Pathways in the IVIg Response

We have studied the response to intravenous immunoglobulins (IVIg) by a transcriptomic approach in 11 chronic inflammatory demyelinating polyradiculoneuropathy (CIDP) patients (CIDP duration = 6 [0.83-6.5] years). RNA was extracted from cells in whole blood collected before and 3 weeks after IVIg treatment, and hybridized on Illumina chips. After RNA quality controls, gene expression was analyzed using statistical tests fitted for microarrays (R software, limma package), and a pathway analysis was performed using DAVID software. We identified 52 genes with expression that varied significantly after IVIg (fold change [FC] > 1.2, P < 0.001, false discovery rate [FDR] <0.05). Among these 52 genes, 7 were related to immunity, 3 were related to the tumor necrosis factor (TNF)-α receptor 1 (TNFR1) pathway (inhibitor of caspase-activated DNase (ICAD): FC = 1.8, P = 1.7E-7, FDR = 0.004; p21 protein-activated kinase 2 [PAK2]: FC = 1.66, P = 2.6E-5, FDR = 0.03; TNF-α-induced protein 8-like protein 1 [TNFAIP8L1]: P = 1.00E-05, FDR = 0.026), and 2 were related to Toll-like receptors (TLRs), especially TLRs 7 and 9, and were implicated in autoimmunity. These genes were UNC93B1 (FC = 1.6, P = 2E-5, FDR = 0.03), which transports TLRs 7 and 9 to the endolysosomes, and RNF216 (FC = 1.5, P = 1E-05, FDR = 0.03), which promotes TLR 9 degradation. Pathway analysis showed that the TNFR1 pathway was significantly lessened by IVIg (enrichment score = 24, Fischer exact test = 0.003). TNF-α gene expression was higher in responder patients than in nonresponders; however, it decreased after IVIg in responders (P = 0.04), but remained stable in nonresponders. Our data suggest the actions of IVIg on the TNFR1 pathway and an original mechanism involving innate immunity through TLRs in CIDP pathophysiology and the response to IVIg. We conclude that responder patients have stronger inflammatory activity that is lessened by IVIg.

Single-voxel (1)H spectroscopy in the human hippocampus at 3 T using the LASER sequence: characterization of neurochemical profile and reproducibility

The hippocampus is crucial for long-term episodic memory and learning. It undergoes structural change in aging and is sensitive to neurodegenerative and psychiatric diseases. MRS studies have seldom been performed in the hippocampus due to technical challenges. The reproducibility of MRS in the hippocampus has not been evaluated at 3 T. The purpose of the present study was to quantify the concentration of metabolites in a small voxel placed in the hippocampus and evaluate the reproducibility of the quantification. Spectra were measured in a 2.4 mL voxel placed in the left hippocampus covering the body and most of the tail of the structure in 10 healthy subjects across three different sessions and quantified using LCModel. High-quality spectra were obtained, which allowed a reliable quantification of 10 metabolites including glutamate and glutamine. Reproducibility of MRS was evaluated with coefficient of variation, standard errors of measurement, and intraclass correlation coefficients. All of these measures showed improvement with increased number of averages. Changes of less than 5% in concentration of N-acetylaspartate, choline-containing compounds, and total creatine and of less than 10% in concentration of myo-inositol and the sum of glutamate and glutamine can be confidently detected between two measurements in a group of 20 subjects. A reliable and reproducible neurochemical profile of the human hippocampus was obtained using MRS at 3 T in a small hippocampal volume.

Hypomorphic variants of cationic amino acid transporter 3 in males with autism spectrum disorders

Cationic amino acid transporters (CATs) mediate the entry of L-type cationic amino acids (arginine, ornithine and lysine) into the cells including neurons. CAT-3, encoded by the SLC7A3 gene on chromosome X, is one of the three CATs present in the human genome, with selective expression in brain. SLC7A3 is highly intolerant to variation in humans, as attested by the low frequency of deleterious variants in available databases, but the impact on variants in this gene in humans remains undefined. In this study, we identified a missense variant in SLC7A3, encoding the CAT-3 cationic amino acid transporter, on chromosome X by exome sequencing in two brothers with autism spectrum disorder (ASD). We then sequenced the SLC7A3 coding sequence in 148 male patients with ASD and identified three additional rare missense variants in unrelated patients. Functional analyses of the mutant transporters showed that two of the four identified variants cause severe or moderate loss of CAT-3 function due to altered protein stability or abnormal trafficking to the plasma membrane. The patient with the most deleterious SLC7A3 variant had high-functioning autism and epilepsy, and also carries a de novo 16p11.2 duplication possibly contributing to his phenotype. This study shows that rare hypomorphic variants of SLC7A3 exist in male individuals and suggest that SLC7A3 variants possibly contribute to the etiology of ASD in male subjects in association with other genetic factors.

Iterative reconstruction of high-dimensional Gaussian Graphical Models based on a new method to estimate partial correlations under constraints

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.

Significant correlation between a set of genetic polymorphisms and a functional brain network revealed by feature selection and sparse Partial Least Squares

Brain imaging is increasingly recognised as an intermediate phenotype to understand the complex path between genetics and behavioural or clinical phenotypes. In this context, a first goal is to propose methods to identify the part of genetic variability that explains some neuroimaging variability. Classical univariate approaches often ignore the potential joint effects that may exist between genes or the potential covariations between brain regions. In this paper, we propose instead to investigate an exploratory multivariate method in order to identify a set of Single Nucleotide Polymorphisms (SNPs) covarying with a set of neuroimaging phenotypes derived from functional Magnetic Resonance Imaging (fMRI). Recently, Partial Least Squares (PLS) regression or Canonical Correlation Analysis (CCA) have been proposed to analyse DNA and transcriptomics. Here, we propose to transpose this idea to the DNA vs. imaging context. However, in very high-dimensional settings like in imaging genetics studies, such multivariate methods may encounter overfitting issues. Thus we investigate the use of different strategies of regularisation and dimension reduction techniques combined with PLS or CCA to face the very high dimensionality of imaging genetics studies. We propose a comparison study of the different strategies on a simulated dataset first and then on a real dataset composed of 94 subjects, around 600,000 SNPs and 34 functional MRI lateralisation indexes computed from reading and speech comprehension contrast maps. We estimate the generalisability of the multivariate association with a cross-validation scheme and demonstrate the significance of this link, using a permutation procedure. Univariate selection appears to be necessary to reduce the dimensionality. However, the significant association uncovered by this two-step approach combining univariate filtering and L1-regularised PLS suggests that discovering meaningful genetic associations calls for a multivariate approach.

Graph constrained discriminant analysis: a new method for the integration of a graph into a classification process

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/.

Distance between homologous chromosomes results from chromosome positioning constraints

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.

Over-optimism in bioinformatics: an illustration

MOTIVATION

In statistical bioinformatics research, different optimization mechanisms potentially lead to 'over-optimism' in published papers. So far, however, a systematic critical study concerning the various sources underlying this over-optimism is lacking.

RESULTS

We present an empirical study on over-optimism using high-dimensional classification as example. Specifically, we consider a 'promising' new classification algorithm, namely linear discriminant analysis incorporating prior knowledge on gene functional groups through an appropriate shrinkage of the within-group covariance matrix. While this approach yields poor results in terms of error rate, we quantitatively demonstrate that it can artificially seem superior to existing approaches if we 'fish for significance'. The investigated sources of over-optimism include the optimization of datasets, of settings, of competing methods and, most importantly, of the method's characteristics. We conclude that, if the improvement of a quantitative criterion such as the error rate is the main contribution of a paper, the superiority of new algorithms should always be demonstrated on independent validation data.

AVAILABILITY

The R codes and relevant data can be downloaded from http://www.ibe.med.uni-muenchen.de/organisation/mitarbeiter/020_professuren/boulesteix/overoptimism/, such that the study is completely reproducible.

Gene association networks from microarray data using a regularized estimation of partial correlation based on PLS regression

Reconstruction of gene-gene interactions from large-scale data such as microarrays is a first step toward better understanding the mechanisms at work in the cell. Two main issues have to be managed in such a context: 1) choosing which measures have to be used to distinguish between direct and indirect interactions from high-dimensional microarray data and 2) constructing networks with a low proportion of false-positive edges. We present an efficient methodology for the reconstruction of gene interaction networks in a small-sample-size setting. The strength of independence of any two genes is measured, in such "high-dimensional network," by a regularized estimation of partial correlation based on Partial Least Squares Regression. We finally emphasize specific properties of the proposed method. To assess the sensitivity and specificity of the method, we carried out the reconstruction of networks from simulated data. We also tested PLS-based partial correlation network on static and dynamic real microarray data. An R implementation of the proposed algorithm is available from http://biodev.extra.cea.fr/plspcnetwork/.