Glucometabolic Changes Are Associated with Structural Gray Matter Alterations in Prodromal Dementia.

BACKGROUND: Glucometabolic changes, such as high glycemic load (GL) diet and insulin resistance (IR), are potential risk factor of Alzheimer's disease (AD). Yet, the effect of these factors on brain alterations that contribute to AD pathology has not been clearly demonstrated. OBJECTIVE: We aimed to assess the relationship of GL and IR with gray matter volumes involved in prodromal dementia. METHODS: GL and Triglyceride-Glucose (TyG) index, an IR surrogate marker, were calculated in 497 participants who underwent magnetic resonance imaging (MRI). The gray matter volumes most related to prodromal dementia/mild cognitive impairment (diagnosed in 18/158 participants during the 7-year follow-up) were identified using a data-driven machine learning algorithm. RESULTS: Higher GL diet was associated with reduced amygdala volume. The TyG index was negatively associated with the hippocampus, amygdala, and putamen volumes. CONCLUSION: These results suggest that GL and IR are associated with lower gray matter volumes in brain regions involved in AD pathology.

Prediction of Alexandrium and Dinophysis algal blooms and shellfish contamination in French Mediterranean Lagoons using decision trees and linear regression: a result of 10 years of sanitary monitoring.

French Mediterranean lagoons are frequently subject to shellfish contamination by Diarrheic Shellfish Toxins (DSTs) and Paralytic Shellfish Toxins (PSTs). To predict the effect of various environmental factors (temperature, salinity and turbidity) on the abundance of the major toxins producing genera, Dinophysis and Alexandrium, and the link with shellfish contamination, we analysed a 10-year dataset collected from 2010 to 2019 in two major shellfish farming lagoons, Thau and Leucate, using two methods: decision trees and Zero Inflated Negative Binomial (ZINB) linear regression models. Analysis of these decision trees revealed that the highest risk of Dinophysis bloom events occurred at temperature <16.3°C and salinity <27.8, and of Alexandrium at temperature ranging from 10.4 to 21.5°C and salinity >39.2. The highest risk of shellfish contaminations by DSTs and PSTs occurred during the set of conditions associated with high risk of bloom events. Linear regression prediction enables us to understand whether temperature and salinity influence the presence of Alexandrium and affect its abundance. However, Dinophysis linear regression could not be validated due to overdispersion issues. This work demonstrates the tools which could help sanitary management of shellfish rearing areas.

TopoFun: a machine learning method to improve the functional similarity of gene co-expression modules.

A comprehensive, accurate functional annotation of genes is key to systems-level approaches. As functionally related genes tend to be co-expressed, one possible approach to identify functional modules or supplement existing gene annotations is to analyse gene co-expression. We describe TopoFun, a machine learning method that combines topological and functional information to improve the functional similarity of gene co-expression modules. Using LASSO, we selected topological descriptors that discriminated modules made of functionally related genes and random modules. Using the selected topological descriptors, we performed linear discriminant analysis to construct a topological score that predicted the type of a module, random-like or functional-like. We combined the topological score with a functional similarity score in a fitness function that we used in a genetic algorithm to explore the co-expression network. To illustrate the use of TopoFun, we started from a subset of the Gene Ontology Biological Processes (GO-BPs) and showed that TopoFun efficiently retrieved genes that we omitted, and aggregated a number of novel genes to the initial GO-BP while improving module topology and functional similarity. Using an independent protein-protein interaction database, we confirmed that the novel genes gathered by TopoFun were functionally related to the original gene set.

Insulin resistance is linked to a specific profile of immune activation in human subjects.

We tested the hypothesis that a particular immune activation profile might be correlated with insulin resistance in a general population. By measuring 43 markers of immune, endothelial, and coagulation activation, we have previously shown that five different immune activation profiles may be distinguished in 150 volunteers. One of these profiles, Profile 2, characterized by CD4+ T cell senescence, inflammation, monocyte, B cell, and endothelial activation, presented elevated insulinemia, glycemia, triglyceridemia, and γ-glutamyl transferase, a marker of liver injury, in comparison with other profiles. Our data are compatible with a model in which a particular immune activation profile might favor the development of insulin resistance and metabolic syndrome. In this hypothesis, identification of this profile, that is feasible with only 3 markers with an error rate of 5%, might allow to personalize the screening and prevention of metabolic syndrome-driven morbidities as liver steatosis.

Residual Viremia Is Linked to a Specific Immune Activation Profile in HIV-1-Infected Adults Under Efficient Antiretroviral Therapy.

Chronic immune activation persists in persons living with HIV-1 even though they are aviremic under antiretroviral therapy, and fuels comorbidities. In previous studies, we have revealed that virologic responders present distinct profiles of immune activation, and that one of these profiles is related to microbial translocation. In the present work, we tested in 140 HIV-1-infected adults under efficient treatment for a mean duration of eight years whether low-level viremia might be another cause of immune activation. We observed that the frequency of viremia between 1 and 20 HIV-1 RNA copies/mL (39.5 ± 24.7% versus 21.1 ± 22.5%, p = 0.033) and transient viremia above 20 HIV-1 RNA copies/mL (15.1 ± 16.9% versus 3.3 ± 7.2%, p = 0.005) over the 2 last years was higher in patients with one profile of immune activation, Profile E, than in the other patients. Profile E, which is different from the profile related to microbial translocation with frequent CD38+ CD8+ T cells, is characterized by a high level of CD4+ T cell (cell surface expression of CD38), monocyte (plasma concentration of soluble CD14), and endothelium (plasma concentration of soluble Endothelial Protein C Receptor) activation, whereas the other profiles presented low CD4:CD8 ratio, elevated proportions of central memory CD8+ T cells or HLA-DR+ CD4+ T cells, respectively. Our data reinforce the hypothesis that various etiological factors shape the form of the immune activation in virologic responders, resulting in specific profiles. Given the type of immune activation of Profile E, a potential causal link between low-level viremia and atherosclerosis should be investigated.

Two Metabolomics Phenotypes of Human Hepatocellular Carcinoma in Non-Alcoholic Fatty Liver Disease According to Fibrosis Severity.

Non-Alcoholic Fatty Liver Disease (NAFLD) is considered as the forthcoming predominant cause for hepatocellular carcinoma (HCC). NAFLD-HCC may rise in non-cirrhotic livers in 40 to 50% of patients. The aim of this study was to identify different metabolic pathways of HCC according to fibrosis level (F0F1 vs. F3F4). A non-targeted metabolomics strategy was applied. We analyzed 52 pairs of human HCC and adjacent non-tumoral tissues which included 26 HCC developed in severe fibrosis or cirrhosis (F3F4) and 26 in no or mild fibrosis (F0F1). Tissue extracts were analyzed using 1H-Nuclear Magnetic Resonance spectroscopy. An optimization evolutionary method based on genetic algorithm was used to identify discriminant metabolites. We identified 34 metabolites differentiating the two groups of NAFLD-HCC according to fibrosis level, allowing us to propose two metabolomics phenotypes of NAFLD-HCC. We showed that HCC-F0F1 mainly overexpressed choline derivatives and glutamine, whereas HCC-F3F4 were characterized by a decreased content of monounsaturated fatty acids (FA), an increase of saturated FA and an accumulation of branched amino acids. Comparing HCC-F0F1 and HCC-F3F4, differential expression levels of glucose, choline derivatives and phosphoethanolamine, monounsaturated FA, triacylglycerides were identified as specific signatures. Our metabolomics analysis of HCC tissues revealed for the first time two phenotypes of HCC developed in NAFLD according to fibrosis level. This study highlighted the impact of the underlying liver disease on metabolic reprogramming of the tumor.

Identification of distinct immune activation profiles in adult humans.

Latent infectious agents, microbial translocation, some metabolites and immune cell subpopulations, as well as senescence modulate the level and quality of activation of our immune system. Here, we tested whether various in vivo immune activation profiles may be distinguished in a general population. We measured 43 markers of immune activation by 8-color flow cytometry and ELISA in 150 adults, and performed a double hierarchical clustering of biomarkers and volunteers. We identified five different immune activation profiles. Profile 1 had a high proportion of naïve T cells. By contrast, Profiles 2 and 3 had an elevated percentage of terminally differentiated and of senescent CD4+ T cells and CD8+ T cells, respectively. The fourth profile was characterized by NK cell activation, and the last profile, Profile 5, by a high proportion of monocytes. In search for etiologic factors that could determine these profiles, we observed a high frequency of naïve Treg cells in Profile 1, contrasting with a tendency to a low percentage of Treg cells in Profiles 2 and 3. Moreover, Profile 5 tended to have a high level of 16s ribosomal DNA, a direct marker of microbial translocation. These data are compatible with a model in which specific causes, as the frequency of Treg or the level of microbial translocation, shape specific profiles of immune activation. It will be of interest to analyze whether some of these profiles drive preferentially some morbidities known to be fueled by immune activation, as insulin resistance, atherothrombosis or liver steatosis.

Discrimination of rosé wines using shotgun metabolomics with a genetic algorithm and MS ion intensity ratios.

A rapid Ultra Performance Liquid Chromatography coupled with Quadrupole/Time Of Flight Mass Spectrometry (UPLC-QTOF-MS) method was designed to quickly acquire high-resolution mass spectra metabolomics fingerprints for rosé wines. An original statistical analysis involving ion ratios, discriminant analysis, and genetic algorithm (GA) was then applied to study the discrimination of rosé wines according to their origins. After noise reduction and ion peak alignments on the mass spectra, about 14 000 different signals were detected. The use of an in-house mass spectrometry database allowed us to assign 72 molecules. Then, a genetic algorithm was applied on two series of samples (learning and validation sets), each composed of 30 commercial wines from three different wine producing regions of France. Excellent results were obtained with only four diagnostic peaks and two ion ratios. This new approach could be applied to other aspects of wine production but also to other metabolomics studies.

ISoLDE: a data-driven statistical method for the inference of allelic imbalance in datasets with reciprocal crosses.

MOTIVATION: Allelic imbalance (AI), i.e. the unequal expression of the alleles of the same gene in a single cell, affects a subset of genes in diploid organisms. One prominent example of AI is parental genomic imprinting, which results in parent-of-origin-dependent, mono-allelic expression of a limited number of genes in metatherian and eutherian mammals and in angiosperms. Currently available methods for identifying AI rely on data modeling and come with the associated limitations. RESULTS: We have designed ISoLDE (Integrative Statistics of alleLe Dependent Expression), a novel nonparametric statistical method that takes into account both AI and the characteristics of RNA-seq data to infer allelic expression bias when at least two biological replicates are available for reciprocal crosses. ISoLDE learns the distribution of a specific test statistic from the data and calls genes 'allelically imbalanced', 'bi-allelically expressed' or 'undetermined'. Depending on the number of replicates, predefined thresholds or permutations are used to make calls. We benchmarked ISoLDE against published methods, and showed that ISoLDE compared favorably with respect to sensitivity, specificity and robustness to the number of replicates. Using ISoLDE on different RNA-seq datasets generated from hybrid mouse tissues, we did not discover novel imprinted genes (IGs), confirming the most conservative estimations of IG number. AVAILABILITY AND IMPLEMENTATION: ISoLDE has been implemented as a Bioconductor package available at http://bioconductor.org/packages/ISoLDE/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Microbial Translocation Is Linked to a Specific Immune Activation Profile in HIV-1-Infected Adults With Suppressed Viremia.

Persistent immune activation in virologically suppressed HIV-1 patients, which may be the consequence of various factors including microbial translocation, is a major cause of comorbidities. We have previously shown that different profiles of immune activation may be distinguished in virological responders. Here, we tested the hypothesis that a particular profile might be the consequence of microbial translocation. To this aim, we measured 64 soluble and cell surface markers of inflammation and CD4+ and CD8+ T-cell, B cell, monocyte, NK cell, and endothelial activation in 140 adults under efficient antiretroviral therapy, and classified patients and markers using a double hierarchical clustering analysis. We also measured the plasma levels of the microbial translocation markers bacterial DNA, lipopolysaccharide binding protein (LBP), intestinal-fatty acid binding protein, and soluble CD14. We identified five different immune activation profiles. Patients with an immune activation profile characterized by a high percentage of CD38+CD8+ T-cells and a high level of the endothelial activation marker soluble Thrombomodulin, presented with higher LBP mean (± SEM) concentrations (33.3 ± 1.7 vs. 28.7 ± 0.9 µg/mL, p = 0.025) than patients with other profiles. Our data are consistent with the hypothesis that the immune activation profiles we described are the result of different etiological factors. We propose a model, where particular causes of immune activation, as microbial translocation, drive particular immune activation profiles responsible for particular comorbidities.

Effects of prenatal exposure to particulate matter air pollution on corpus callosum and behavioral problems in children.

OBJECTIVE: Air pollution (AP) may affect neurodevelopment, but studies about the effects of AP on the growing human brain are still scarce. We aimed to investigate the effects of prenatal exposure to AP on lateral ventricles (LV) and corpus callosum (CC) volumes in children and to determine whether the induced brain changes are associated with behavioral problems. METHODS: Among the children recruited through a set of representative schools of the city of Barcelona, (Spain) in the Brain Development and Air Pollution Ultrafine Particles in School Children (BREATHE) study, 186 typically developing participants aged 8-12 years underwent brain MRI on the same 1.5 T MR unit over a 1.5-year period (October 2012-April 2014). Brain volumes were derived from structural MRI scans using automated tissue segmentation. Behavioral problems were assessed using the Strengths and Difficulties Questionnaire (SDQ) and the criteria of the Attention Deficit Hyperactivity Disorder DSM-IV list. Prenatal fine particle (PM2.5) levels were retrospectively estimated at the mothers' residential addresses during pregnancy with land use regression (LUR) models. To determine whether brain structures might be affected by prenatal PM2.5 exposure, linear regression models were run and adjusted for age, sex, intracranial volume (ICV), maternal education, home socioeconomic vulnerability index, birthweight and mothers' smoking status during pregnancy. To test for associations between brain changes and behavioral outcomes, negative binomial regressions were performed and adjusted for age, sex, ICV. RESULTS: Prenatal PM2.5 levels ranged from 11.8 to 39.5 µg/m3 during the third trimester of pregnancy. An interquartile range increase in PM2.5 level (7 µg/m3) was significantly linked to a decrease in the body CC volume (mm3) (ß = -53.7, 95%CI [-92.0, -15.5] corresponding to a 5% decrease of the mean body CC volume) independently of ICV, age, sex, maternal education, socioeconomic vulnerability index at home, birthweight and mothers' smoking status during the third trimester of pregnancy. A 50 mm3 decrease in the body CC was associated with a significant higher hyperactivity subscore (Rate Ratio (RR) = 1.09, 95%CI [1.01, 1.17) independently of age, sex and ICV. The statistical significance of these results did not survive to False Discovery Rate correction for multiple comparisons. CONCLUSIONS: Prenatal exposure to PM2.5 may be associated with CC volume decrease in children. The consequences might be an increase in behavioral problems.

GECKO is a genetic algorithm to classify and explore high throughput sequencing data.

Comparative analysis of high throughput sequencing data between multiple conditions often involves mapping of sequencing reads to a reference and downstream bioinformatics analyses. Both of these steps may introduce heavy bias and potential data loss. This is especially true in studies where patient transcriptomes or genomes may vary from their references, such as in cancer. Here we describe a novel approach and associated software that makes use of advances in genetic algorithms and feature selection to comprehensively explore massive volumes of sequencing data to classify and discover new sequences of interest without a mapping step and without intensive use of specialized bioinformatics pipelines. We demonstrate that our approach called GECKO for GEnetic Classification using k-mer Optimization is effective at classifying and extracting meaningful sequences from multiple types of sequencing approaches including mRNA, microRNA, and DNA methylome data.

High-salt-recovered sequences are associated with the active chromosomal compartment and with large ribonucleoprotein complexes including nuclear bodies.

The mammalian cell nucleus contains numerous discrete suborganelles named nuclear bodies. While recruitment of specific genomic regions into these large ribonucleoprotein (RNP) complexes critically contributes to higher-order functional chromatin organization, such regions remain ill-defined. We have developed the high-salt-recovered sequences-sequencing (HRS-seq) method, a straightforward genome-wide approach whereby we isolated and sequenced genomic regions associated with large high-salt insoluble RNP complexes. By using mouse embryonic stem cells (ESCs), we showed that these regions essentially correspond to the most highly expressed genes, and to cis-regulatory sequences like super-enhancers, that belong to the active A chromosomal compartment. They include both cell-type-specific genes, such as pluripotency genes in ESCs, and housekeeping genes associated with nuclear bodies, such as histone and snRNA genes that are central components of Histone Locus Bodies and Cajal bodies. We conclude that HRSs are associated with the active chromosomal compartment and with large RNP complexes including nuclear bodies. Association of such chromosomal regions with nuclear bodies is in agreement with the recently proposed phase separation model for transcription control and might thus play a central role in organizing the active chromosomal compartment in mammals.

Pilot Study of Whole Blood MicroRNAs as Potential Tools for Diffuse Low-Grade Gliomas Detection.

Earlier diagnosis and longitudinal monitoring of diffuse low-grade gliomas (DLGG) increase overall survival by maximizing surgery efficacy and optimizing time for an adjuvant treatment when resection is incomplete. Presently, only imaging permits the non-invasive detection and monitoring of DLGG, but it lacks sensitivity. Measure of circulating microRNAs levels could represent a non-invasive alternative. We hypothesized that slow-growing DLGG induce overtime a systemic reaction impacting blood cells microRNA profiles, while the intact blood-brain barrier restricts the passage of tumor microRNAs into bloodstream. In 15 DLGG patients and 15 healthy controls, expression levels of 758 microRNAs were measured by the TaqMan OpenArray RT-qPCR platform, on preoperative whole blood, containing both cell-free and blood cells microRNAs. Normalized data were computed by a Student t test with a p value threshold allowing a 10% rate of false positive. Statistical analysis retained fifteen microRNAs, all overexpressed in patients. MiR-20a, miR-106a, miR-20b, and miR-93 belong to clusters genetically related. As miR-223 and miR-let7e, they target the transcription factor STAT3. MicroRNA expression levels were not correlated to preoperative tumor volume. A signature composed of miR-93, miR-590-3p, and miR-454 enabled to nearly perfectly separate patients from controls. Our study performed on a homogeneous cohort was designed accordingly to DLGG particularities and provided the first microRNAs signature proposal. Functional convergence on STAT3 and overexpression of miR-223, factors respectively involved in myeloid-derived suppressor cells and granulocytes, argued for a systemic peripheral response. Overexpressed microRNAs and tumor volume were uncorrelated, making a tumor origin elusive.

Effect of exposure to polycyclic aromatic hydrocarbons on basal ganglia and attention-deficit hyperactivity disorder symptoms in primary school children.

BACKGROUND: Polycyclic aromatic hydrocarbons (PAHs) have been proposed as environmental risk factors for attention deficit hyperactivity disorder (ADHD). The effects of these pollutants on brain structures potentially involved in the pathophysiology of ADHD are unknown. OBJECTIVE: The aim of this study was to investigate the effects of PAHs on basal ganglia volumes and ADHD symptoms in school children. METHODS: We conducted an imaging study in 242 children aged 8-12years, recruited through a set of representative schools of the city of Barcelona, Spain. Indoor and outdoor PAHs and benzo[a]pyrene (BPA) levels were assessed in the school environment, one year before the MRI assessment. Whole-brain volumes and basal ganglia volumes (caudate nucleus, globus pallidus, putamen) were derived from structural MRI scans using automated tissue segmentation. ADHD symptoms (ADHD/DSM-IV Scales, American Psychiatric Association 2002) were reported by teachers, and inattentiveness was evaluated with standard error of hit reaction time in the attention network computer-based test. RESULTS: Total PAHs and BPA were associated with caudate nucleus volume (CNV) (i.e., an interquartile range increase in BPA outdoor level (67pg/m3) and indoor level (76pg/m3) was significantly linked to a decrease in CNV (mm3) (ß=-150.6, 95% CI [-259.1, -42.1], p=0.007, and ß=-122.4, 95% CI [-232.9, -11.8], p=0.030 respectively) independently of intracranial volume, age, sex, maternal education and socioeconomic vulnerability index at home). ADHD symptoms and inattentiveness increased in children with higher exposure to BPA, but these associations were not statistically significant. CONCLUSIONS: Exposure to PAHs, and in particular to BPA, is associated with subclinical changes on the caudate nucleus, even below the legislated annual target levels established in the European Union. The behavioral consequences of this induced brain change were not identified in this study, but given the caudate nucleus involvement in many crucial cognitive and behavior processes, this volume reduction is concerning for the children's neurodevelopment.

In Vitro Corticogenesis from Embryonic Stem Cells Recapitulates the In Vivo Epigenetic Control of Imprinted Gene Expression.

In vitro corticogenesis from embryonic stem cells (ESCs) is an attractive model of cortical development and a promising tool for cortical therapy. It is unknown to which extent epigenetic mechanisms crucial for cortex development and function, such as parental genomic imprinting, are recapitulated by in vitro corticogenesis. Here, using genome-wide transcriptomic and methylation analyses on hybrid mouse tissues and cells, we find a high concordance of imprinting status between in vivo and ESC-derived cortices. Notably, in vitro corticogenesis strictly reproduced the in vivo parent-of-origin-dependent expression of 41 imprinted genes (IGs), including Mest and Cdkn1c known to control corticogenesis. Parent-of-origin-dependent DNA methylation was also conserved at 14 of 18 imprinted differentially methylated regions. The least concordant imprinted locus was Gpr1-Zdbf2, where the aberrant bi-allelic expression of Zdbf2 and Adam23 was concomitant with a gain of methylation on the maternal allele in vitro. Combined, our data argue for a broad conservation of the epigenetic mechanisms at imprinted loci in cortical cells derived from ESCs. We propose that in vitro corticogenesis helps to define the still poorly understood mechanisms that regulate imprinting in the brain and the roles of IGs in cortical development.

Statistical study on bracket debonding rate with the win lingual technique.

The aim of this study is to present the results of a statistical inquiry measuring the bracket debonding rate using the WIN lingual technique. This inquiry was performed in the Odontology Unit at the Rothschild Hospital in the framework of the Lingual Orthodontics University Diploma at Paris VII university. Results were gathered by 8 orthodontic practitioners during two years of training from all their patients treated with the WIN lingual technique. The sample study comprised 33 patients treated exclusively with the WIN lingual technique. The 8 practitioners filled in a questionnaire relating to bracket bond failure in their patients in the course of treatment. In all, sixty-two questionnaires were analyzed. The data were then computed using the statistical tool of R software version 3.1.3. The results of this analysis demonstrate the reliability of the WIN system evidenced by an average of 2.1 bracket bond failures over the two-year treatment period, i.e. approximately 1 bracket per patient per year. Given the right conditions, (precise prescription, compliance with bonding protocols), the bond failure rate can be lower still, even compared with the buccal technique or relative to other lingual techniques. The benefits of lingual appliances, particularly in the WIN system, are essential to clinicians in their daily practice in order to optimize the quality and duration of their treatments with a view to ensuring patient satisfaction.

One of the immune activation profiles observed in HIV-1-infected adults with suppressed viremia is linked to metabolic syndrome: The ACTIVIH study.

Immune activation in HIV-1-infected individuals is reduced under antiretroviral therapies, but persists, resulting in various morbidities. To better characterize this phenomenon, using a panel of 68 soluble and cell surface markers, we measured the level of activation in circulating CD4+ and CD8+ T cells, B cells, monocytes, NK cells, polynuclear and endothelial cells as well as of inflammation and fibrinolysis in 120 virologic responders over 45years of age. As compared with age- and sex-matched uninfected individuals, we observed a persistence of activation in all the cell subpopulations analyzed, together with marks of inflammation and fibrinolysis. Two independent hierarchical clustering analyses allowed us to identify five clusters of markers that varied concurrently, and five patient groups, each with the same activation profile. The five groups of patients could be characterized by a marker of CD4+ T cell, CD8+ T cell, NK cell, monocyte activation or of inflammation, respectively. One of these profiles was strongly associated with marks of metabolic syndrome, particularly with hyperinsulinemia (OR 12.17 [95% CI 1.79-82.86], p=0.011). In conclusion, our study unveils biomarkers linked to metabolic syndrome that could be tested as predictive markers, and opens the way to new therapeutic approaches tailored to each patient group.