Social patterning of childhood overweight in the French national ELFE cohort.

An inverse social gradient in early childhood overweight has been consistently described in high-income countries; however, less is known about the role of migration status. We studied the social patterning of overweight in preschool children according to the mother's socio-economic and migration background. For 9250 children of the French ELFE birth cohort with body mass index collected at age 3.5 years, we used nested logistic regression to investigate the association of overweight status in children with maternal educational level, occupation, household income and migration status. Overall, 8.3% (95%CI [7.7-9.0]) of children were classified as overweight. The odds of overweight was increased for children from immigrant mothers (OR 2.22 [95% CI 1.75-2.78]) and descendants of immigrant mothers (OR 1.35 [1.04-2.78]) versus non-immigrant mothers. The highest odds of overweight was also observed in children whose mothers had low education, were unemployed or students, or were from households in the lowest income quintile. Our findings confirm that socio-economic disadvantage and migration status are risk factors for childhood overweight. However, the social patterning of overweight did not apply uniformly to all variables. These new and comprehensive insights should inform future public health interventions aimed at tackling social inequalities in childhood overweight.

SARS-CoV-2 seroprevalence in French 9-year-old children and their parents after the first lockdown in 2020.

Introduction: Children have been significantly less affected by COVID-19 than adults and presented with milder and less symptomatic forms of the disease. However, there has been suggestion that children older than 10 years and adolescents exhibits features closer to that of young adults. Most studies combine children in different age-groups and lack sufficient numbers to explore in detail age specificities. We report data on a population-based sample of 2,555 children at the pivotal age of 9 years. Methods: In April 2020, the participants in two French nationwide cohorts of children, Elfe and Epipage2, were invited to take part into an online survey about Covid related symptoms and family life during the lockdown. A second questionnaire was sent on May 5. This questionnaire also proposed to the child included in the cohort and to one of his/her parents to take part into a capillary blood collection for Covid serology. Families who agreed to the serological survey were sent kits for dried blood spots self-sampling (DBS) with instructions. Samples were processed with a commercial Elisa test (Euroimmun®, Lübeck, Germany) to detect anti-SARS-CoV-2 antibodies (IgG) directed against the S1 domain of the spike protein of the virus. Results: Children's acceptance rate for the serological survey was around 60%. 2,555 serological results were analyzed. The weighted prevalence of a positive Elisa Spike serology was 2.8% in 9 yr-old children (95% CI: 1.7%-4.0%). Positive serology was found in 8.6% (7.4%-9.7%) of parents who provided blood. There was a significant association (p < 0.001) between serology of the child and parent from the same household with an odds ratio of 13.8 (7.9-24.2). Discussion: We have shown that 9-yr old children had a lower susceptibility to SARS-Cov2 infection than adults with the initial Chinese strain, similar to younger children and estimated that around 3% of them have developed antibodies against SARS-Cov2 in France after the first wave of the Covid-19 epidemics.

Parents' depression and anxiety associated with hyperactivity-inattention and emotional symptoms in children during school closure due to COVID-19 in France.

Several risk factors of children's mental health issues have been identified during the pandemic of COronaVIrus Disease first appeared in 2019 (COVID-19). This study aims to fill the knowledge gap regarding the association between parents' and children's mental health issues during the COVID-19 school closure in France. We conducted a cross-sectional analysis of data collected in the SAPRIS-ELFE study during the COVID-19 pandemic in France. Using multinomial logistic regressions, we estimated associations between parents' and children's mental health issues. Symptoms of anxiety were assessed by the General Anxiety Disorder-7 (GAD-7) and depression by the Patient Health Questionnaire-9 (PHQ-9) for the parents. Hyperactivity/inattention and emotional symptoms in children were assessed by the Strengths and Difficulties Questionnaire (SDQ). The sample included 3496 children aged 8 to 9 years, of whom 50.0% were girls. During the school closure, 7.1% of responding parents had moderate to severe levels of anxiety and 6.7% had moderate to severe levels of depression. A total of 11.8% of the children had an abnormal hyperactivity/inattention score and 6.6% had an abnormal emotional symptoms score. In multivariate regression models, parental moderate to severe level of anxiety and moderate to severe level of depression were associated with abnormal hyperactivity-inattention score (adjusted Odds Ratio (aOR) 3.31; 95% Confidence Interval (CI) 2.33-4.70 and aOR 4.65; 95% CI 3.27-6.59, respectively) and abnormal emotional symptoms score in children (aOR 3.58; 95% CI 2.33-5.49 and aOR 3.78; 95 CI 2.47-5.78 respectively). Children whose parents have symptoms of anxiety and/or depression have an increased likelihood of symptoms of hyperactivity/inattention and emotional symptoms during school closures in France due to COVID-19. Our findings suggest that public health initiatives should target parents and children to limit the impact of such crises on their mental health issues.

Determinants of incomplete vaccination in children at age two in France: results from the nationwide ELFE birth cohort.

Incomplete vaccination in the pediatric population is a growing public health issue in high-income countries, but its determinants are poorly understood. Their identification is necessary to design target actions that can improve vaccination uptake. Our aim was to assess the determinants of incomplete vaccination in two-year-old children in France. Among the 18,329 children included in the 2011 ELFE French nationwide population-based birth cohort, we selected those for whom vaccination status was available at age two years. Incomplete vaccination was defined as ≥ 1 missing dose of recommended vaccines. Potential determinants of incomplete vaccination were identified by using logistic regression, taking into account attrition and missing data. Of the 5,740 (31.3%) children analyzed, 46.5% (95% confidence interval [CI] 44.7-48.0) were incompletely vaccinated. Factors independently associated with incomplete vaccination were having older siblings (adjusted odds ratio 1.18, 95% CI [1.03-1.34] and 1.28 [1.06-1.54] for one and ≥ 2 siblings, respectively, vs. 0), residing in an isolated area (1.92 [1.36-2.75] vs. an urban area), parents not following health recommendations or using alternative medicines (1.81 [1.41-2.34] and 1.23 [1.04-1.46], respectively, vs. parents confident in institutions and following heath recommendations), not being visited by a maternal and child protection service nurse during the child's first two months (1.19 [1.03-1.38] vs. ≥ 1 visit), and being followed by a general practitioner (2.87 [2.52-3.26] vs. a pediatrician). CONCLUSIONS: Incomplete vaccination was highly prevalent in the studied pediatric population and was associated with several socio-demographic, parental, and healthcare service characteristics. These findings may help in designing targeted corrective actions. WHAT IS KNOWN: • Incomplete vaccination in the pediatric population is a growing public health issue in high-income countries. • The partial understanding of the determinants of incomplete vaccination precludes the design of effective targeted corrective actions. WHAT IS NEW: • High prevalence of incomplete vaccination at age two years in France. • Incomplete vaccination was independently associated with several socio-demographic, parental, and healthcare service characteristics.

Longitudinal impact of psychosocial status on children's mental health in the context of COVID-19 pandemic restrictions.

Emerging research suggests that the prevalence of child and adolescent mental health problems has increased considerably during the COVID-19 crisis. However, there have been few longitudinal studies on children's mental health issues according to their social determinants in this context, especially in Europe. Our aim was to investigate the association between family socioeconomic status (SES) and children' mental health during the period of school closure due to COVID-19. Longitudinal data came from 4575 children aged 8-9 years old in 2020 and participating in the ELFE population-based birth cohort that focuses on children's health, development and socialization. Parents completed the Strengths and Difficulties Questionnaire (SDQ) when children were (a) 5 years of age and (b) 9 years of age, which corresponded to the period of school closure due to the COVID-19 pandemic in France. We retrieved data from the ELFE cohort collected on children from birth to age 5 years (birth, 1 year, 2 years, 3,5 years and 5 years). Socioeconomic status (SES) was measured based on information obtained when the child was 5 years old. Data were analyzed using multinomial logistic regression models. Children's elevated levels of symptoms of Attention-deficit/Hyperactivity disorder (ADHD) during the period of school closure were significantly associated with prior low family SES (aOR 1.26, 95% CI 1.08-1.48). Children's elevated symptoms of hyperactivity/inattention and of emotional symptoms were associated with decline in income during the COVID crisis (respectively, aOR 1.38, 95% CI 1.16-1.63 and aOR 1.23, 95% CI 1.01-1.51). Moreover, when testing interactions, a low prior SES was significantly associated with a higher risk of emotional symptoms aOR 1.54 (1.07-2.21), only for children whose families experienced a decline in income, while gender, parental separation and prior mental health difficulties were not associated. This study underlines the impact of the financial crisis related to the COVID-19 epidemic on children's mental health. Both pre-existing family SES before lockdown and more proximal financial difficulties during the COVID crisis were negatively associated with children's psychological difficulties during the period of school closure. The pandemic appears to exacerbate mental health problems in deprived children whose families suffer from financial difficulties.

Emotional and attention-deficit/hyperactivity disorder symptoms of preterm vs. full-term children during COVID-19 pandemic restrictions.

BACKGROUND: Preterm children are at higher risk of developing mental health problems than full-term children. Deterioration of children's mental health was observed during COVID-19 pandemic restrictive measures. Our study compared emotional and attention-deficit/hyperactivity disorder (ADHD) symptoms during school closure between preterm and full-term children. METHODS: Data from two French birth cohorts-ELFE and EPIPAGE-2-were used. In 2011, infants born ≥22 weeks' gestation were recruited. Parents completed the Strengths and Difficulties Questionnaire when the children were 9 years old and experiencing school closure. Multivariate multinomial logistic regression models were used. RESULTS: Subjects included 4164 full-term and 1119 preterm children. In univariate analyses, compared to full-term children: extremely and very preterm children more frequently had abnormal and borderline ADHD scores (odds ratio [OR] 1.86, 95% confidence interval [CI] 1.50-2.30, OR 1.42, 95% CI 1.08-1.85, respectively) and abnormal emotional scores (OR 1.86, 95% CI 1.43-2.40); moderate to late preterm children more often had abnormal ADHD scores (OR 1.33, 95% CI 1.01-1.78). The associations did not remain when previous symptoms at 5 years old were considered. CONCLUSIONS: School closure during lockdown did not appear to increase the risk of mental health problems in preterm compared to full-term children. IMPACT STATEMENT: Preterm children are at higher risk of developing mental health problems than full-term children. Deterioration in children's mental health was observed during COVID-19 pandemic restrictions. However, whether preterm children were a particularly vulnerable subgroup during school closure is unclear. In univariate analyses, extremely and very preterm children more often had abnormal and borderline ADHD symptoms and abnormal emotional symptom scores than full-term children. The associations did not remain significantly associated when previous symptoms were considered. Preterm compared to full-term children more often suffer from ADHD and emotional symptoms, but school closure during lockdown did not appear to increase this risk.

MoMA-LoopSampler: a web server to exhaustively sample protein loop conformations.

SUMMARY: MoMA-LoopSampler is a sampling method that globally explores the conformational space of flexible protein loops. It combines a large structural library of three-residue fragments and a novel reinforcement-learning-based approach to accelerate the sampling process while maintaining diversity. The method generates a set of statistically likely loop states satisfying geometric constraints, and its ability to sample experimentally observed conformations has been demonstrated. This paper presents a web user interface to MoMA-LoopSampler through the illustration of a typical use-case. AVAILABILITY AND IMPLEMENTATION: MoMA-LoopSampler is freely available at: https://moma.laas.fr/applications/LoopSampler/. We recommend users to create an account, but anonymous access is possible. In most cases, jobs are completed within a few minutes. The waiting time may increase depending on the server load, but it very rarely exceeds an hour. For users requiring more intensive use, binaries can be provided upon request. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Children's mental and behavioral health, schooling, and socioeconomic characteristics during school closure in France due to COVID-19: the SAPRIS project.

COVID-19 limitation strategies have led to widespread school closures around the world. The present study reports children's mental health and associated factors during the COVID-19 school closure in France in the spring of 2020. We conducted a cross-sectional analysis using data from the SAPRIS project set up during the COVID-19 pandemic in France. Using multinomial logistic regression models, we estimated associations between children's mental health, children's health behaviors, schooling, and socioeconomic characteristics of the children's families. The sample consisted of 5702 children aged 8-9 years, including 50.2% girls. In multivariate logistic regression models, children's sleeping difficulties were associated with children's abnormal symptoms of both hyperactivity-inattention (adjusted Odds Ratio (aOR) 2.05; 95% Confidence Interval 1.70-2.47) and emotional symptoms (aOR 5.34; 95% CI 4.16-6.86). Factors specifically associated with abnormal hyperactivity/inattention were: male sex (aOR 2.29; 95% CI 1.90-2.76), access to specialized care prior to the pandemic and its suspension during school closure (aOR 1.51; 95% CI 1.21-1.88), abnormal emotional symptoms (aOR 4.06; 95% CI 3.11-5.29), being unschooled or schooled with assistance before lockdown (aOR 2.13; 95% CI 1.43-3.17), and tutoring with difficulties or absence of a tutor (aOR 3.25; 95% CI 2.64-3.99; aOR 2.47; 95% CI 1.48-4.11, respectively). Factors associated with children's emotional symptoms were the following: being born pre-term (aOR 1.34; 95% CI 1.03-1.73), COVID-19 cases among household members (aOR 1.72; 95% CI 1.08-2.73), abnormal symptoms of hyperactivity/inattention (aOR 4.18; 95% CI 3.27-5.34) and modest income (aOR 1.45; 95% CI 1.07-1.96; aOR 1.36; 95% CI 1.01-1.84). Multiple characteristics were associated with elevated levels of symptoms of hyperactivity-inattention and emotional symptoms in children during the period of school closure due to COVID-19. Further studies are needed to help policymakers to balance the pros and cons of closing schools, taking into consideration the educational and psychological consequences for children.

Protein loops with multiple meta-stable conformations: A challenge for sampling and scoring methods.

Flexible regions in proteins, such as loops, cannot be represented by a single conformation. Instead, conformational ensembles are needed to provide a more global picture. In this context, identifying statistically meaningful conformations within an ensemble generated by loop sampling techniques remains an open problem. The difficulty is primarily related to the lack of structural data about these flexible regions. With the majority of structural data coming from x-ray crystallography and ignoring plasticity, the conception and evaluation of loop scoring methods is challenging. In this work, we compare the performance of various scoring methods on a set of eight protein loops that are known to be flexible. The ability of each method to identify and select all of the known conformations is assessed, and the underlying energy landscapes are produced and projected to visualize the qualitative differences obtained when using the methods. Statistical potentials are found to provide considerable reliability despite their being designed to tradeoff accuracy for lower computational cost. On a large pool of loop models, they are capable of filtering out statistically improbable states while retaining those that resemble known (and thus likely) conformations. However, computationally expensive methods are still required for more precise assessment and structural refinement. The results also highlight the importance of employing several scaffolds for the protein, due to the high influence of small structural rearrangements in the rest of the protein over the modeled energy landscape for the loop.

A reinforcement-learning-based approach to enhance exhaustive protein loop sampling.

MOTIVATION: Loop portions in proteins are involved in many molecular interaction processes. They often exhibit a high degree of flexibility, which can be essential for their function. However, molecular modeling approaches usually represent loops using a single conformation. Although this conformation may correspond to a (meta-)stable state, it does not always provide a realistic representation. RESULTS: In this paper, we propose a method to exhaustively sample the conformational space of protein loops. It exploits structural information encoded in a large library of three-residue fragments, and enforces loop-closure using a closed-form inverse kinematics solver. A novel reinforcement-learning-based approach is applied to accelerate sampling while preserving diversity. The performance of our method is showcased on benchmark datasets involving 9-, 12- and 15-residue loops. In addition, more detailed results presented for streptavidin illustrate the ability of the method to exhaustively sample the conformational space of loops presenting several meta-stable conformations. AVAILABILITY AND IMPLEMENTATION: We are developing a software package called MoMA (for Molecular Motion Algorithms), which includes modeling tools and algorithms to sample conformations and transition paths of biomolecules, including the application described in this work. The binaries can be provided upon request and a web application will also be implemented in the short future. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Conformational changes in antibody Fab fragments upon binding and their consequences on the performance of docking algorithms.

BACKGROUND: The existence of conformational changes in antibodies upon binding has been previously established. However, existing analyses focus on individual cases and no quantitative study provides a more global view of potential moves and repacking, especially on recent data. The present study focuses on analyzing the conformational changes in various antibodies upon binding, providing quantitative observations to be exploited for antibody-related modeling. METHODS: Cartesian and dihedral Root-Mean-Squared Deviations were calculated for different subparts of 27 different antibodies, for which X-ray structures in the bound and unbound states are available. Elbow angle variations were also calculated. Previously reported results of four docking algorithms were condensed into one score giving overall docking success for each of 16 antibody-antigen cases. RESULTS: Very diverse movements are observed upon binding. While many loops stay very rigid, several others display side-chain repacking or backbone rearrangements, or both, at many different levels. Large conformational changes restricted to one or more antibody hypervariable loops were found to be a better indicator of docking difficulty than overall conformational variation at the antibody-antigen interface. However, the failure of docking algorithms on some almost-rigid cases shows that scoring is still a major bottleneck in docking pose prediction. CONCLUSIONS: This study is aimed to help scientists working on antibody analysis and design by giving insights into the nature and the extent of conformational changes at different levels upon antigen binding.

Segmenting Proteins into Tripeptides to Enhance Conformational Sampling with Monte Carlo Methods.

This paper presents an approach to enhance conformational sampling of proteins employing stochastic algorithms such as Monte Carlo (MC) methods. The approach is based on a mechanistic representation of proteins and on the application of methods originating from robotics. We outline the general ideas of our approach and detail how it can be applied to construct several MC move classes, all operating on a shared representation of the molecule and using a single mathematical solver. We showcase these sampling techniques on several types of proteins. Results show that combining several move classes, which can be easily implemented thanks to the proposed approach, significantly improves sampling efficiency.

Characterizing Energy Landscapes of Peptides Using a Combination of Stochastic Algorithms.

Obtaining accurate representations of energy landscapes of biomolecules such as proteins and peptides is central to the study of their physicochemical properties and biological functions. Peptides are particularly interesting, as they exploit structural flexibility to modulate their biological function. Despite their small size, peptide modeling remains challenging due to the complexity of the energy landscape of such highly-flexible dynamic systems. Currently, only stochastic sampling-based methods can efficiently explore the conformational space of a peptide. In this paper, we suggest to combine two such methods to obtain a full characterization of energy landscapes of small yet flexible peptides. First, we propose a simplified version of the classical Basin Hopping algorithm to reveal low-energy regions in the landscape, and thus to identify the corresponding meta-stable structural states of a peptide. Then, we present several variants of a robotics-inspired algorithm, the Transition-based Rapidly-exploring Random Tree, to quickly determine transition path ensembles, as well as transition probabilities between meta-stable states. We demonstrate this combined approach on met-enkephalin.

MoMA-LigPath: a web server to simulate protein-ligand unbinding.

Protein-ligand interactions taking place far away from the active site, during ligand binding or release, may determine molecular specificity and activity. However, obtaining information about these interactions with experimental or computational methods remains difficult. The computational tool presented in this article, MoMA-LigPath, is based on a mechanistic representation of the molecular system, considering partial flexibility, and on the application of a robotics-inspired algorithm to explore the conformational space. Such a purely geometric approach, together with the efficiency of the exploration algorithm, enables the simulation of ligand unbinding within short computing time. Ligand unbinding pathways generated by MoMA-LigPath are a first approximation that can provide useful information about protein-ligand interactions. When needed, this approximation can be subsequently refined and analyzed using state-of-the-art energy models and molecular modeling methods. MoMA-LigPath is available at http://moma.laas.fr. The web server is free and open to all users, with no login requirement.

Modeling protein conformational transitions by a combination of coarse-grained normal mode analysis and robotics-inspired methods.

BACKGROUND: Obtaining atomic-scale information about large-amplitude conformational transitions in proteins is a challenging problem for both experimental and computational methods. Such information is, however, important for understanding the mechanisms of interaction of many proteins. METHODS: This paper presents a computationally efficient approach, combining methods originating from robotics and computational biophysics, to model protein conformational transitions. The ability of normal mode analysis to predict directions of collective, large-amplitude motions is applied to bias the conformational exploration performed by a motion planning algorithm. To reduce the dimension of the problem, normal modes are computed for a coarse-grained elastic network model built on short fragments of three residues. Nevertheless, the validity of intermediate conformations is checked using the all-atom model, which is accurately reconstructed from the coarse-grained one using closed-form inverse kinematics. RESULTS: Tests on a set of ten proteins demonstrate the ability of the method to model conformational transitions of proteins within a few hours of computing time on a single processor. These results also show that the computing time scales linearly with the protein size, independently of the protein topology. Further experiments on adenylate kinase show that main features of the transition between the open and closed conformations of this protein are well captured in the computed path. CONCLUSIONS: The proposed method enables the simulation of large-amplitude conformational transitions in proteins using very few computational resources. The resulting paths are a first approximation that can directly provide important information on the molecular mechanisms involved in the conformational transition. This approximation can be subsequently refined and analyzed using state-of-the-art energy models and molecular modeling methods.

Thumb-loops up for catalysis: a structure/function investigation of a functional loop movement in a GH11 xylanase.

Dynamics is a key feature of enzyme catalysis. Unfortunately, current experimental and computational techniques do not yet provide a comprehensive understanding and description of functional macromolecular motions. In this work, we have extended a novel computational technique, which combines molecular modeling methods and robotics algorithms, to investigate functional motions of protein loops. This new approach has been applied to study the functional importance of the so-called thumb-loop in the glycoside hydrolase family 11 xylanase from Thermobacillus xylanilyticus (Tx-xyl). The results obtained provide new insight into the role of the loop in the glycosylation/deglycosylation catalytic cycle, and underline the key importance of the nature of the residue located at the tip of the thumb-loop. The effect of mutations predicted in silico has been validated by in vitro site-directed mutagenesis experiments. Overall, we propose a comprehensive model of Tx-xyl catalysis in terms of substrate and product dynamics by identifying the action of the thumb-loop motion during catalysis.

Enhancing systematic protein-protein docking methods using ray casting: application to ATTRACT.

Systematic protein-protein docking methods need to evaluate a huge number of different probe configurations, thus leading to high computational cost. We present an efficient filter-ray casting filter (RCF)-that enables a notable speed-up of systematic protein-protein docking. The high efficiency of RCF is the outcome of the following factors: (i) extracting of pockets and protrusions on the surfaces of the proteins using visibilities; (ii) a ray casting method that finds aligned receptor pocket/probe protrusion pairs without explicit similarity computations. The RCF method enables the integration of systematic methods and local shape feature matching methods. To verify the efficiency and the accuracy of RCF, we integrated it with a systematic protein-protein docking approach (ATTRACT) based on a reduced protein representation. The test results show that the integrated docking approach is much faster. At the same time, it ranks the lowest ligand root-mean-square deviation (RMSD) (L_rms) solutions higher when docking enzyme-enzyme inhibitor complexes. Consequently, RCF not only enables much faster execution of systematic docking runs but also improves the qualities of docking predictions.

A mixed molecular modeling-robotics approach to investigate lipase large molecular motions.

Large-scale conformational rearrangement of a lid subdomain is a key event in the interfacial activation of many lipases. We present herein a study in which the large-scale "open-to-closed" movement of Burkholderia cepacia lipase lid has been simulated at the atomic level using a hybrid computational method. The two-stage approach combines path-planning algorithms originating from robotics and molecular mechanics methods. In the first stage, a path-planning approach is used to compute continuous and geometrically feasible pathways between two protein conformational states. Then, an energy minimization procedure using classical molecular mechanics is applied to intermediate conformations in the path. The main advantage of such a combination of methods is that only geometrically feasible solutions are prompted for energy calculation in explicit solvent, which allows the atomic-scale description of the transition pathway between two extreme conformations of B. cepacia lipase (BCL; open and closed states) within very short computing times (a few hours on a desktop computer). Of interest, computed pathways enable the description of intermediate conformations along the "open-to-closed" conformational transition of BCL lid and the identification of bottlenecks during the lid closing. Furthermore, consideration of the solvent effect when computing the transition energy profiles provides valuable information regarding the feasibility and the spontaneity of the movement under the influence of the solvent environment. This new hybrid computational method turned out to be well-suited for investigating at an atomistic level large-scale conformational motion and at a qualitative level, the solvent effect on the energy profiles associated with the global motion.

Encoding molecular motions in voxel maps.

This paper builds on the combination of robotic path planning algorithms and molecular modeling methods for computing large-amplitude molecular motions, and introduces voxel maps as a computational tool to encode and to represent such motions. We investigate several applications and show results that illustrate the interest of such representation.