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Background The risk of hospitalization or death after influenza infection is higher at the extremes of age and in individuals with comorbidities. We estimated the number of hospitalizations with influenza and characterized the cumulative risk of comorbidities and age on severe outcomes in Mexico and Brazil. Methods We used national hospital discharge data from Brazil (SIH/SUS) from 2010–2018 and Mexico (SAEH) from 2010–2017 to estimate the number of influenza admissions using ICD-10 discharge codes, stratified by age (0–4, 5–17, 18–49, 50–64, and ≥65 years). Duration of hospital stay, admission to the intensive care unit (ICU), and in-hospital case fatality rates (CFRs) defined the severe outcomes. Rates were compared between patients with or without pre-specified comorbidities and by age. Results A total of 327,572 admissions with influenza were recorded in Brazil and 20,613 in Mexico, with peaks period most years. In Brazil, the median hospital stay duration was 3.0 days (interquartile range, 2.0–5.0), ICU admission rate was 3.3% (95% CI, 3.2–3.3%), and in-hospital CFR was 4.6% (95% CI, 4.5–4.7). In Mexico, the median duration of stay was 5.0 days (interquartile range, 3.0–7.0), ICU admission rate was 1.8% (95% CI, 1.6–2.0%), and in-hospital CFR was 6.9% (95% CI, 6.5–7.2). In Brazil, ICU admission and in-hospital CFR were higher in adults aged ≥50 years and increased in the presence of comorbidities, especially cardiovascular disease. In Mexico, comorbidities increased the risk of ICU admission by 1.9 (95% CI, 1.0–3.5) and in-hospital CFR by 13.9 (95% CI, 8.4–22.9) in children 0–4 years. Conclusion The SIH/SUS and SAEH databases can be used to estimate hospital admissions with influenza, and the disease severity. Age and comorbidities, especially cardiovascular disease, are cumulatively associated with more severe outcomes, with differences between countries. This association should be further analyzed in prospective surveillance studies designed to support influenza vaccination strategy decisions.

Background and Objectives A clinical trial was conducted to measure and analyse the pharmacokinetic parameters of a lipid formulation of risperidone, VAL401. The VAL401 formulation is designed to repurpose risperidone from an antipsychotic to an adenocarcinoma treatment, with the lipid formulation altering the cellular uptake of risperidone, thus enabling anticancer biology to be exhibited in preclinical testing. Methods This first human trial of VAL401 measured the concentrations of risperidone and its primary metabolite, 9-hydroxyrisperidone, in the blood of patients after treatment with a single 2-mg dose of VAL401. Results The trial provided information on differences in the pharmacokinetic profile of risperidone in VAL401 that may be caused by the formulation and/or the nature of the cancer patient population. VAL401 provided the following key pharmacokinetic parameters for the risperidone plasma concentration after a single 2-mg dose of VAL401, with results normalised to a dosage of 1 mg for comparison with literature values: Tmax, 2 h; Cmax, 8 ng/ml; half-life, 3.5 h; area under the plasma concentration–time curve from time zero to infinity (AUC0–∞), 58.2 ng h2/mL. Conclusions Further comparisons of the pharmacokinetic parameters of risperidone and 9-hydroxyrisperidone in plasma of patients administered VAL401 and the corresponding parameters obtained from published data for conventionally formulated risperidone provide evidence for altered biological processing of VAL401 as compared to risperidone. The absolute values obtained provide support for future studies of VAL401 as a cancer treatment, as the Cmax demonstrates sufficient exposure to reach the concentrations seen during preclinical anticancer testing, yet the overall exposure to the active moiety supports the use of the safety and tolerability data from conventional risperidone during future clinical trials. Electronic supplementary material The online version of this article (10.1007/s13318-018-00538-4) contains supplementary material, which is available to authorized users.

Introduction The search for drugs to treat Alzheimer's disease (AD) has failed to yield effective therapies. Here we report the first genome‐wide search for biomarkers associated with therapeutic response in AD. Blarcamesine (ANAVEX2‐73), a selective sigma‐1 receptor (SIGMAR1) agonist, was studied in a 57‐week Phase 2a trial (NCT02244541). The study was extended for a further 208 weeks (NCT02756858) after meeting its primary safety endpoint. Methods Safety, clinical features, pharmacokinetic, and efficacy, measured by changes in the Mini‐Mental State Examination (MMSE) and the Alzheimer's Disease Cooperative Study‐Activities of Daily Living scale (ADCS‐ADL), were recorded. Whole exome and transcriptome sequences were obtained for 21 patients. The relationship between all available patient data and efficacy outcome measures was analyzed with unsupervised formal concept analysis (FCA), integrated in the Knowledge Extraction and Management (KEM) environment. Results Biomarkers with a significant impact on clinical outcomes were identified at week 57: mean plasma concentration of blarcamesine (slope MMSE:P

Gene deregulation has been associated with several neurodegenerative diseases (NDs) and modeling genome-wide data such as transcriptomic data has provided holistic models of ND progression on a gene expression level. However, the consequences of gene deregulation on the temporal dynamics of gene-gene interaction systems (genetic cooperativity) is poorly understood in NDs. Using a multi-layer network approach, we cross-integrated three families of networks describing RNA-seq time series data in Huntington’s disease (HD) knock-in mice for reconstructing the dynamics of genetic cooperativity in the brain of these mice based on the consistency and complementarity of edge information in source networks. The resulting model suggests that the HD process may develop as two critical phases of genetic cooperativity, pre-symptomatically in the cortex, involving a neurotransmission response, and symptomatically in the striatum, involving cell survival responses intertwined with cellular senescence and DNA damage responses. These data highlight a 2-step logic for injury/adaptation of the HD mouse brain in which a cortical response for modulating neurotransmission may precede a striatal response for regulating cellular homeostasis synchronous to symptoms.

Chantier qualité GA; International audience; Objective: Since a reduction of the insulin/IGF-1 signaling cascade extends life span in many species and IGF-1 signaling might partly mediate the effects of caloric restriction (CR), an experimental intervention for increasing longevity, the purpose of the present study was to use quantitative trait loci (QTL) analysis, an unbiased genetic approach, to identify particular regions of the genome influencing plasma IGF-1 levels in an F2 intercross between F344 and LOU/C rats; the latter being an inbred strain of Wistar origin, considered as a model of healthy aging since it resists to age (and diet)-induced obesity. Design: F1 hybrids were obtained by crossbreeding LOU/C with F344 rats, and then F1 were bred inter se to obtain the F2 population, of which 93 males and 94 females were studied. Total plasma IGF-1 levels were determined by radioimmunoassay. A genome scan of the F2 population was made with 100 microsatellite markers) selected for their polymorphism between LOU/C and F344 strains (and by covering evenly the whole genome. Results: By simple interval mapping sex-dependent QTLs were found on chromosome 17 in males and on chromosome 18 in females. By multiple interval mapping, additional QTLs were found on chromosomes 1, 4, 5, 6, 12, 15 and 19 in males and on chromosomes 3, 5, 6, 12 and 17 in females. Only the markers D1Rat196 and D12Mgh5 were found in both males and females. The majority of QTLs corresponded to metabolic syndrome (cardiac function: n = 45 (30%), obesity/diabetes: n = 22 (15%), inflammation: n = 19 (13%) and only a limited number to body weight: n = 13 (9%), proliferation (n = 10 (7%) or ossification: n = 7 (5%). Ninety-six candidate genes were located on the different QTLs. A significant proportion of these genes are connected to IGF-1 production and receptor pathways (n = 18) or metabolic syndrome (n = 11). Conclusions: Subsequent studies are necessary to determine whether the genetic networks underscored are also involved in age-associated obesity, diabetes and inflammation as well as cardiovascular impairments.

International audience; Huntington's disease (HD) is a neurodegenerative disease associated with extensive down-regulation of genes controlling neuronal function, particularly in the striatum. Whether altered epigenetic regulation underlies transcriptional defects in HD is unclear. Integrating RNA-sequencing (RNA-seq) and chromatin-immunoprecipitation followed by massively parallel sequencing (ChIP-seq), we show that down-regulated genes in HD mouse striatum associate with selective decrease in H3K27ac, a mark of active enhancers, and RNA Polymerase II (RNAPII). In addition, we reveal that decreased genes in HD mouse striatum display a specific epigenetic signature, characterized by high levels and broad patterns of H3K27ac and RNAPII. Our results indicate that this signature is that of super-enhancers, a category of broad enhancers regulating genes defining tissue identity and function. Specifically, we reveal that striatal super-enhancers display extensive H3K27 acetylation within gene bodies, drive transcription characterized by low levels of paused RNAPII, regulate neuronal function genes and are enriched in binding motifs for Gata transcription factors, such as Gata2 regulating striatal identity genes. Together, our results provide evidence for preferential down-regulation of genes controlled by super-enhancers in HD striatum and indicate that enhancer topography is a major parameter determining the propensity of a gene to be deregulated in a neurodegenerative disease.

The FOXO family of transcription factors is central to the regulation of organismal longevity and cellular survival. Several studies have indicated that FOXO factors lie at the center of a complex network of upstream pathways, cofactors and downstream targets (FOXO-interaction networks), which may have developmental and post-developmental roles in the regulation of chronic-stress response in normal and diseased cells. Noticeably, FOXO factors are important for the regulation of proteotoxicity and neuron survival in several models of neurodegenerative disease, suggesting that FOXO-interaction networks may have therapeutic potential. However, the status of FOXO-interaction networks in neurodegenerative disease remains largely unknown. Systems modeling is anticipated to provide a comprehensive assessment of this question. In particular, interrogating the context-dependent variability of FOXO-interaction networks could predict the clinical potential of cellular-stress response genes and aging regulators for tackling brain and peripheral pathology in neurodegenerative disease. Using published transcriptomic data obtained from murine models of Huntington’s disease and post-mortem brains, blood samples and induced-pluripotent-stem cells from Huntington’s disease carriers as a case example, this review briefly highlights how the biological status and clinical potential of FOXO-interaction networks for Huntington’s disease may be decoded by developing network and entropy based feature selection across heterogeneous datasets.

Intercellular communication mediated by extracellular vesicles (EV) is emerging as a mechanism that is important to neuronal homeostasis and integrity. However, there is little information available on the importance of EV signalling in response to proteotoxic stress in Huntington’s disease (HD). Using network methods to integrate HD gene expression datasets across model species, we developed a computational model of the transition from the early (cell differentiation) to intermediate (dysfunctional striatum) and late (advanced neurodegeneration) phases of HD. This model indicates that genes associated to extracellular vesicular exosomes are impacted across biological phases, raising the possibility that EV signalling may be altered in cells expressing mutant huntingtin. To test for this, we analysed EVs in mouse striatal cells derived from the HdhQ111 knock-in mice. In studies of EVs, it is important to discriminate different subtypes of EVs based for example on size and intracellular origin as this may determine function. In these experiments, we used protocols and EV markers that allow for the differential analysis of EV subtypes to be performed, testing for changes in protein content and level of secretion. The results suggest that specific EV populations may be altered in cells expressing mutant huntingtin.

Network methods are useful for reducing data complexity and modelling biological and pathogenic processes on a system level, thus having strong potential for prioritising testable hypotheses in Huntington’s disease (HD) research. Several types of mathematical formalisms are being used for network analysis of signal variability in HD datasets, each of them shedding particular light on transcriptional codes and network dynamics in HD. We tested for HD sub-graphs that may be consistently highlighted by several of these mathematical formalisms and whether this may be linked to specific features of the HD process and translate into a more selective level of gene prioritisation. We will present data based on integrating two types of HD networks and discuss how network comparison methods may add value to systems modelling in HD.

Background A central goal in Huntington's disease (HD) research is to identify and prioritize candidate targets for neuroprotective intervention, which requires genome-scale information on the modifiers of early-stage neuron injury in HD. Results Here, we performed a large-scale RNA interference screen in C. elegans strains that express N-terminal huntingtin (htt) in touch receptor neurons. These neurons control the response to light touch. Their function is strongly impaired by expanded polyglutamines (128Q) as shown by the nearly complete loss of touch response in adult animals, providing an in vivo model in which to manipulate the early phases of expanded-polyQ neurotoxicity. In total, 6034 genes were examined, revealing 662 gene inactivations that either reduce or aggravate defective touch response in 128Q animals. Several genes were previously implicated in HD or neurodegenerative disease, suggesting that this screen has effectively identified candidate targets for HD. Network-based analysis emphasized a subset of high-confidence modifier genes in pathways of interest in HD including metabolic, neurodevelopmental and pro-survival pathways. Finally, 49 modifiers of 128Q-neuron dysfunction that are dysregulated in the striatum of either R/2 or CHL2 HD mice, or both, were identified. Conclusions Collectively, these results highlight the relevance to HD pathogenesis, providing novel information on the potential therapeutic targets for neuroprotection in HD.

Background As a growing and more diverse amount of genetic and molecular profile data are generated in several models of Huntington’s disease (HD), the potential of these datasets for enhancing HD research has significantly increased. Network-based analysis has the capacity to identify robust HD-associated rules and signatures from complex datasets. These rules and signatures can in turn be exploited for better understanding HD biology on a global scale as well as for prioritising models and genes around questions of specific interest, thereby supporting decision making and biomedical intelligence in HD research. Aim and method To support biomedical intelligence in HD, and as part of the project and activities of the EHDN’s working group ‘Biological modifiers’, we have developed Biogemix, a network-based data integration framework for precise rule/pattern extraction across models and species. We used this framework for integrating greater than 16 publically available datasets including transcriptomic and gene perturbation data. Outcome We found previously suspected as well as novel HD-associated features, the latter providing new insights into the global effects of mutant huntingtin pathogenicity. Additionally, we managed to determine the functional distances between experimental models of HD and assess the biological relevance of individual models of HD, functional profiles of biological modifiers and shared/unshared properties between pathological and compensatory/survival genes. Conclusion Our results validate the value and precision of the Biogemix framework for actionable knowledge in HD. As larger HD datasets become available, our results suggest that applying the Biogemix approach to the analysis of these datasets will provide deep insights into the essential features of mutant huntingtin pathogenicity while providing strong guidance to translational and pharmacological research in HD. Support CHDI Foundation, ANR, EHDN, ANRT/GSK.

Background The generation and large-scale molecular analysis of multiple models of Huntington9s disease (HD) and the integration of genome-scale information across species provide a framework with strong potential for best understanding HD biology and prioritising candidate targets. However, on a global scale level, we know little about the variation in pathophysiological patterns across the species in which HD modelling is performed and how this may impact on the target prioritisation and validation process. An important issue in this regard is about the biological significance that may be associated with specific combinations of HD models across species, and the type of HD mechanism/target documented. Aims To provide a solution for decision-making in HD research, we developed the Biogemix procedure, a comprehensive cross-species network-based method for data integration, model sensitivity analysis and target prioritisation. Methods The Biogemix procedure is data-driven and unbiased, and it takes advantage of the information contained in probabilistic functional gene networks for pattern discovery in HD. The Biogemix tool chain comprises several features for the optimal use of biologically- and disease-relevant information in large bionetworks. The resulting knowledge is stored into the Biogemix-HD knowledge base prototype, allowing the users to select specific data integration schemes and visualise the results of their queries in a comprehensive fashion thanks to a rich variety of biological- and HD-relevant annotations. Results and Conclusions Our results highlight similarities and differences between model species in which HD modelling is performed, providing guidance for the selection of individual models/combinations-of-models to prosecute specific targets. We will show the latest developments obtained with the Biogemix-HD knowledge base project and discuss their implications for target validation in HD.