Quantitative imaging and semiotic phenotyping of mitochondrial network morphology in live human cells.

The importance of mitochondria in tissue homeostasis, stress responses and human diseases, combined to their ability to transition between various structural and functional states, makes them excellent organelles for monitoring cell health. There is therefore a need for technologies to accurately analyze and quantify changes in mitochondrial organization in a variety of cells and cellular contexts. Here we present an innovative computerized method that enables accurate, multiscale, fast and cost-effective analysis of mitochondrial shape and network architecture from confocal fluorescence images by providing more than thirty features. In order to facilitate interpretation of the quantitative results, we introduced two innovations: the use of Kiviat-graphs (herein named MitoSpider plots) to present highly multidimensional data and visualization of the various mito-cellular configurations in the form of morphospace diagrams (called MitoSigils). We tested our fully automated image analysis tool on rich datasets gathered from live normal human skin cells cultured under basal conditions or exposed to specific stress including UVB irradiation and pesticide exposure. We demonstrated the ability of our proprietary software (named MitoTouch) to sensitively discriminate between control and stressed dermal fibroblasts, and between normal fibroblasts and other cell types (including cancer tissue-derived fibroblasts and primary keratinocytes), showing that our automated analysis captures subtle differences in morphology. Based on this novel algorithm, we report the identification of a protective natural ingredient that mitigates the deleterious impact of hydrogen peroxide (H2O2) on mitochondrial organization. Hence we conceived a novel wet-plus-dry pipeline combining cell cultures, quantitative imaging and semiotic analysis for exhaustive analysis of mitochondrial morphology in living adherent cells. Our tool has potential for broader applications in other research areas such as cell biology and medicine, high-throughput drug screening as well as predictive and environmental toxicology.

Immune profiles of pre-frail people living with HIV-1: a prospective longitudinal study.

BACKGROUND: People living with HIV (PLWH) are at risk of frailty, which is predictive for death. As an overactivity of the immune system is thought to fuel frailty, we characterized the immune activation profiles linked to frailty. METHODS: We quantified twenty-seven activation markers in forty-six virological responders (four females and forty-two males; median age, 74 years; median duration of infection, 24 years; median duration of undetectability, 13 years), whose frailty was determined according to the Fried criteria. T cell and NK cell activation was evaluated by flow cytometry, using a panel of cell surface markers. Soluble markers of inflammation, and monocyte activation and endothelial activation were measured by ELISA. The participants' immune activation was profiled by an unsupervised double hierarchical clustering analysis. We used ANOVA p-values to rank immunomarkers most related to Fried score. A Linear Discriminant Analysis (LDA) was performed to link immune activation markers to frailty. RESULTS: 41% of the participants were pre-frail, including 24% with a Fried score of 1, and 17% with a Fried score of 2. ANOVA identified the 14 markers of T cell, monocyte, NK cell, endothelial activation, and inflammation the most linked to Fried 3 classes. The LDA performed with these 14 markers was capable of discriminating volunteers according to their Fried score. Two out of the 5 immune activation profiles revealed by the hierarchical clustering were linked to and predictive of pre-frailty. These two profiles were characterized by a low percentage of CD4 T cells and a high percentage of CD8 T cells, activated CD4 T cells, CD8 T cells, and NK cells, and inflammation. CONCLUSIONS: We identified a particular immune activation profile associated with pre-frailty in PLWH. Profiling participants at risk of developing frailty might help to tailor the screening and prevention of medical complications fueled by loss of robustness. Further studies will indicate whether this frailty signature is specific or not of HIV infection, and whether it also precedes frailty in the general population.

Quantitative assessment of mitochondrial morphology relevant for studies on cellular health and environmental toxicity.

Mitochondria are essential organelles that play crucial roles in cellular energy metabolism, calcium signaling and apoptosis. Their importance in tissue homeostasis and stress responses, combined to their ability to transition between various structural and functional states, make them excellent organelles for monitoring cellular health. Quantitative assessment of mitochondrial morphology can therefore provide valuable insights into environmentally-induced cell damage. High-content screening (HCS) provides a powerful tool for analyzing organelles and cellular substructures. We developed a fully automated and miniaturized HCS wet-plus-dry pipeline (MITOMATICS) exploiting mitochondrial morphology as a marker for monitoring cellular health or damage. MITOMATICS uses an in-house, proprietary software (MitoRadar) to enable fast, exhaustive and cost-effective analysis of mitochondrial morphology and its inherent diversity in live cells. We applied our pipeline and big data analytics software to assess the mitotoxicity of selected chemicals, using the mitochondrial uncoupler CCCP as an internal control. Six different pesticides (inhibiting complexes I, II and III of the mitochondrial respiratory chain) were tested as individual compounds and five other pesticides present locally in Occitanie (Southern France) were assessed in combination to determine acute mitotoxicity. Our results show that the assayed pesticides exhibit specific signatures when used as single compounds or chemical mixtures and that they function synergistically to impact mitochondrial architecture. Study of environment-induced mitochondrial damage has the potential to open new fields in mechanistic toxicology, currently underexplored by regulatory toxicology and exposome research. Such exploration could inform health policy guidelines and foster pharmacological intervention, water, air and soil pollution control and food safety.

Low levels of peripheral blood activated and senescent T cells characterize people with HIV-1-associated neurocognitive disorders.

Background: HIV infection induces a 75% increase in the risk of developing neurocognitive impairment (NCI), which has been linked to immune activation. We therefore looked for immune activation markers correlating with NCI. Method: Sixty-five people aged 55-70 years living with controlled HIV-1 infection were enrolled in the study and their neurocognitive ability was assessed according to the Frascati criteria. Fifty-nine markers of T4 cell, T8 cell, NK cell, and monocyte activation, inflammation and endothelial activation were measured in their peripheral blood. White matter hyperintensities (WMH) were identified by magnetic resonance imaging. Double hierarchical clustering was performed for the activation markers and 240 patients including the 65 whose neurocognitive performance had been evaluated. Results: Thirty-eight percent of volunteers presented NCI. Twenty-four percent of them were asymptomatic and fourteen percent had a mild disorder. Strikingly, activated (HLA-DR+) as well as senescent (CD57+CD28-CD27±) T4 cells and T8 cells were less prevalent in the peripheral blood of participants with NCI than in participants without the disorder. Accordingly, the percentage of HLA-DR+ T4 cells was lower in volunteers with periventricular and deep WMH. The double hierarchical clustering unveiled six different immune activation profiles. The neurocognitive performances of participants with two of these six profiles were poor. Here again, these two profiles were characterized by a low level of T4 and T8 cell activation and senescence. Conclusion: Our observation of low circulating levels of activated and senescent T cells in HIV-1 patients with NCI raises the interesting hypothesis that these lymphocytes may be recruited into the central nervous system.

Comparative analysis of transcriptome remodeling in plaque-associated and plaque-distant microglia during amyloid-ß pathology progression in mice.

BACKGROUND: Research in recent years firmly established that microglial cells play an important role in the pathogenesis of Alzheimer's disease (AD). In parallel, a series of studies showed that, under both homeostatic and pathological conditions, microglia are a heterogeneous cell population. In AD, amyloid-ß (Aß) plaque-associated microglia (PAM) display a clearly distinct phenotype compared to plaque-distant microglia (PCM), suggesting that these two microglia subtypes likely differently contribute to disease progression. So far, molecular characterization of PAM was performed indirectly using single cell RNA sequencing (scRNA-seq) approaches or based on markers that are supposedly up-regulated in this microglia subpopulation. METHODS: In this study based on a well-characterized AD mouse model, we combined cell-specific laser capture microdissection and RNA-seq analysis to i) identify, without preconceived notions of the molecular and/or functional changes that would affect these cells, the genes and gene networks that are dysregulated in PAM or PCM at three critical stages of the disease, and ii) to investigate the potential contribution of both plaque-associated and plaque-distant microglia. RESULTS: First, we established that our approach allows selective isolation of microglia, while preserving spatial information and preventing transcriptome changes induced by classical purification approaches. Then, we identified, in PAM and PCM subpopulations, networks of co-deregulated genes and analyzed their potential functional roles in AD. Finally, we investigated the dynamics of microglia transcriptomic remodeling at early, intermediate and late stages of the disease and validated select findings in postmortem human AD brain. CONCLUSIONS: Our comprehensive study provides useful transcriptomic information regarding the respective contribution of PAM and PCM across the Aß pathology progression. It highlights specific pathways that would require further study to decipher their roles across disease progression. It demonstrates that the proximity of microglia to Aß-plaques dramatically alters the microglial transcriptome and reveals that these changes can have both positive and negative impacts on the surrounding cells. These opposing effects may be driven by local microglia heterogeneity also demonstrated by this study. Our approach leads to molecularly define the less well studied plaque-distant microglia. We show that plaque-distant microglia are not bystanders of the disease, although the transcriptomic changes are far less striking compared to what is observed in plaque-associated microglia. In particular, our results suggest they may be involved in Aß oligomer detection and in Aß-plaque initiation, with increased contribution as the disease progresses.

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.

Ubiquitin and SUMO conjugation as biomarkers of acute myeloid leukemias response to chemotherapies.

Ubiquitin and the ubiquitin-like SUMO are covalently conjugated to thousands of proteins to modulate their function and fate. Many of the enzymes involved in their conjugation are dysregulated in cancers and involved in cancer cell response to therapies. We describe here the identification of biomarkers of the activity of these enzymes and their use to predict acute myeloid leukemias (AML) response to standard chemotherapy (daunorubicin-DNR and cytarabine-Ara-C). We compared the ability of extracts from chemosensitive and chemoresistant AML cells to conjugate ubiquitin or SUMO-1 on 9,000 proteins spotted on protein arrays. We identified 122 proteins whose conjugation by these posttranslational modifiers marks AML resistance to DNR and/or Ara-C. Based on this signature, we defined a statistical score predicting AML patient response to standard chemotherapy. We finally developed a miniaturized assay allowing for easy assessment of modification levels of the selected biomarkers and validated it in patient cell extracts. Thus, our work identifies a new type of ubiquitin-based biomarkers that could be used to predict cancer patient response to treatments.

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.

THETA: a new genotypic approach for predicting HIV-1 CRF02-AG coreceptor usage.

MOTIVATION: The circulating recombinant form of HIV-1 CRF02-AG is the most frequent non-B subtype in Europe. Anti-HIV therapy and pathophysiological studies on the impact of HIV-1 tropism require genotypic determination of HIV-1 tropism for non-B subtypes. But genotypic approaches based on analysis of the V3 envelope region perform poorly when used to determine the tropism of CRF02-AG. We, therefore, designed an algorithm based on information from the gp120 and gp41 ectodomain that better predicts the tropism of HIV-1 subtype CRF02-AG. RESULTS: We used a bio-statistical method to identify the genotypic determinants of CRF02-AG coreceptor use. Toulouse HIV Extended Tropism Algorithm (THETA), based on a Least Absolute Shrinkage and Selection Operator method, uses HIV envelope sequence from phenotypically characterized clones. Prediction of R5X4/X4 viruses was 86% sensitive and that of R5 viruses was 89% specific with our model. The overall accuracy of THETA was 88%, making it sufficiently reliable for predicting the tropism of subtype CRF02-AG sequences. AVAILABILITY AND IMPLEMENTATION: Binaries are freely available for download at https://github.com/viro-tls/THETA. It was implemented in Matlab and supported on MS Windows platform. The sequence data used in this work are available from GenBank under the accession numbers MK618182-MK618417.

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.

What makes a bad egg? Egg transcriptome reveals dysregulation of translational machinery and novel fertility genes important for fertilization.

BACKGROUND: Egg quality can be defined as the egg ability to be fertilized and subsequently develop into a normal embryo. Previous research has shed light on factors that can influence egg quality. Large gaps however remain including a comprehensive view of what makes a bad egg. Initial development of the embryo relies on maternally-inherited molecules, such as transcripts, deposited in the egg during its formation. Bad egg quality is therefore susceptible to be associated with alteration or dysregulation of maternally-inherited transcripts. We performed transcriptome analysis on a large number (N = 136) of zebrafish egg clutches, each clutch being split to monitor developmental success and perform transcriptome analysis in parallel. We aimed at drawing a molecular portrait of the egg in order to characterize the relation between egg transcriptome and developmental success and to subsequently identify new candidate genes involved in fertility. RESULTS: We identified 66 transcript that were differentially abundant in eggs of contrasted phenotype (low or high developmental success). Statistical modeling using partial least squares regression and genetics algorithm demonstrated that gene signatures from transcriptomic data can be used to predict developmental success. The identity and function of differentially expressed genes indicate a major dysregulation of genes of the translational machinery in poor quality eggs. Two genes, otulina and slc29a1a, predominantly expressed in the ovary and dysregulated in poor quality eggs were further investigated using CRISPR/Cas9 mediated genome editing. Mutants of each gene revealed remarkable subfertility whereby the majority of their eggs were unfertilizable. The Wnt pathway appeared to be dysregulated in the otulina mutant-derived eggs. CONCLUSIONS: Here we show that egg transcriptome contains molecular signatures, which can be used to predict developmental success. Our results also indicate that poor egg quality in zebrafish is associated with a dysregulation of (i) the translational machinery genes and (ii) novel fertility genes, otulina and slc29a1a, playing an important role for fertilization. Together, our observations highlight the diversity of the possible causes of egg quality defects and reveal mechanisms of maternal origin behind the lack of fertilization and early embryonic failures that can occur under normal reproduction conditions.

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.