VtaA8 and VtaA9 from Haemophilus parasuis delay phagocytosis by alveolar macrophages

Haemophilus parasuis, a member of the family Pasteurellaceae, is a common inhabitant of the upper respiratory tract of healthy pigs and the etiological agent of Glässer's disease. As other virulent Pasteurellaceae, H. parasuis can prevent phagocytosis, but the bacterial factors involved in this virulence mechanism are not known. In order to identify genes involved in phagocytosis resistance, we constructed a genomic library of the highly virulent reference strain Nagasaki and clones were selected by increased survival after incubation with porcine alveolar macrophages (PAM). Two clones containing two virulent-associated trimeric autotransporter (VtaA) genes, vtaA8 and vtaA9, respectively, were selected by this method. A reduction in the interaction of the two clones with the macrophages was detected by flow cytometry. Monoclonal antibodies were produced and used to demonstrate the presence of these proteins on the bacterial surface of the corresponding clone, and on the H. parasuis phagocytosis-resistant strain PC4-6P. The effect of VtaA8 and VtaA9 in the trafficking of the bacteria through the endocytic pathway was examined by fluorescence microscopy and a delay was detected in the localization of the vtaA8 and vtaA9 clones in acidic compartments. These results are compatible with a partial inhibition of the routing of the bacteria via the degradative phagosome. Finally, antibodies against a common epitope in VtaA8 and VtaA9 were opsonic and promoted phagocytosis of the phagocytosis-resistant strain PC4-6P by PAM. Taken together, these results indicate that VtaA8 and VtaA9 are surface proteins that play a role in phagocytosis resistance of H. parasuis

Heterogeneity of Moraxella isolates found in the nasal cavities of piglets

Background Previous studies have shown that the genus Moraxella is commonly present in the nasal microbiota of swine. Results In this study, 51 isolates of Moraxella were obtained from nasal swabs from 3 to 4 week old piglets, which represented 26 different fingerprintings by enterobacterial repetitive intergenic consensus (ERIC)-PCR. Whole 16S rRNA gene sequencing allowed the identification at species level of the Moraxella spp. isolates. The majority of the field strains were identified as Moraxella pluranimalium, but Moraxella porci was also detected. In addition, a cluster of 7 strains did not group with any described Moraxella species, probably representing a new species. Subsequent phenotypic characterization indicated that strains of Moraxella pluranimalium were mainly sensitive to serum complement, while the cluster representing the putative new species was highly resistant. Biofilm formation capacity was very variable among the Moraxella spp. isolates, while adherence to epithelial cell lines was similar among selected strains. Additionally, variability was also observed in the association of selected strains to porcine alveolar macrophages. Antimicrobial tests evidenced the existence of multidrug-resistance in the strains. Conclusions In summary, phenotypic characterization revealed heterogeneity among Moraxella strains from the nasal cavity of piglets. Strains with pathogenic potential were detected as well as those that may be commensal members of the nasal microbiota. However, the role of Moraxella in porcine diseases and health should be further evaluated.info:eu-repo/semantics/publishedVersio

BECOME: A Modular Recommender System for Coaching and Promoting Empowerment in Healthcare

In this chapter, we present BECOME (Behavior Change recOMender systEm), a modular Recommender System built to cope with issues like personalization, adaptation, and delivery of contents pertinently designed to solve idiosyncrasies of various topics in the healthcare field. The main objective is to empower citizens or patients to make informed decisions to improve their health condition. It deals with a double-edged personalization process as one of the key aspects to fostering self-empowerment: content dynamically personalized and adapted as new information is gathered and flexibility in the strategies and timings of the delivery. Thus, we take personalization one step further by not only tailoring the content, which is the standard customization strategy, but also adapting its timings and complexity in a dynamic manner while dealing with the feeling of having an entity (the coach) behind, ready to help. To show the modularity of the system and the diverse ways of interaction, different studies representing various use cases are presented

Alterations in Metabolome and Microbiome Associated with an Early Stress Stage in Male Wistar Rats: A Multi-Omics Approach

Stress disorders have dramatically increased in recent decades becoming the most prevalent psychiatric disorder in the United States and Europe. However, the diagnosis of stress disorders is currently based on symptom checklist and psychological questionnaires, thus making the identification of candidate biomarkers necessary to gain better insights into this pathology and its related metabolic alterations. Regarding the identification of potential biomarkers, omic profiling and metabolic footprint arise as promising approaches to recognize early biochemical changes in such disease and provide opportunities for the development of integrative candidate biomarkers. Here, we studied plasma and urine metabolites together with metagenomics in a 3 days Chronic Unpredictable Mild Stress (3d CUMS) animal approach that aims to focus on the early stress period of a well-established depression model. The multi-omics integration showed a profile composed by a signature of eight plasma metabolites, six urine metabolites and five microbes. Specifically, threonic acid, malic acid, alpha-ketoglutarate, succinic acid and cholesterol were proposed as key metabolites that could serve as key potential biomarkers in plasma metabolome of early stages of stress. Such findings targeted the threonic acid metabolism and the tricarboxylic acid (TCA) cycle as important pathways in early stress. Additionally, an increase in opportunistic microbes as virus of the Herpesvirales was observed in the microbiota as an effect of the primary stress stages. Our results provide an experimental biochemical characterization of the early stage of CUMS accompanied by a subsequent omic profiling and a metabolic footprinting that provide potential candidate biomarkers

An algorithm for wake and sleep detection

Curs 2018-2019Sleep is increasingly recognized as a critical component of healthy development. To better understand the impact of daily physical activity on sleep behaviour, new tools are needed. This project aims to develop an algorithm capable of detecting sleep and wake parts using a wearable device. To this end, exploration of state-of-the-art techniques was needed as well as data collection to verify the techniques and develop new algorithms. The research phase consisted of the identification of existing techniques and algorithm performances that had a direct impact on the developing phase. The development phase started obtaining the data for the study. Because of that, a protocol was defined and launched in IMEC. For three weeks, 6 subjects wore a Chill+ watch for 7 days in a row, day and night. The dataset was evaluated with several acceleration and gyroscope data processing techniques to develop an algorithm to accomplish the objective of the project. To check whether these signals can be used to assess the sleep of the subject, self-assessment methods were used. Sensitivity (84.62 19.85 %) and specificity (90.78 14.73 %) of the results proved that our new technique can strongly detect the sleep and wake phases. Actigraphy and gyroscope represent a useful diagnostic tool for the sleep medicine practitioner. The performance of the algorithm is high and makes it suitable to measure sleep. In conclusion, a new method is presented for sleep analysis which is accurate and follows the design criteria guidelines. This algorithm proves its validity by being faster and accurate while improving the detection of sleep phases. Sleep analysis has a wide range of opportunities to discover due to the health importance which it involves. New signals, tools and devices need to be explored to better understand all the aspects which influence the sleep and thus improve its quality

VtaA8 and VtaA9 from Haemophilus parasuis delay phagocytosis by alveolar macrophages

Haemophilus parasuis, a member of the family Pasteurellaceae, is a common inhabitant of the upper respiratory tract of healthy pigs and the etiological agent of Glässer's disease. As other virulent Pasteurellaceae, H. parasuis can prevent phagocytosis, but the bacterial factors involved in this virulence mechanism are not known. In order to identify genes involved in phagocytosis resistance, we constructed a genomic library of the highly virulent reference strain Nagasaki and clones were selected by increased survival after incubation with porcine alveolar macrophages (PAM). Two clones containing two virulent-associated trimeric autotransporter (VtaA) genes, vtaA8 and vtaA9, respectively, were selected by this method. A reduction in the interaction of the two clones with the macrophages was detected by flow cytometry. Monoclonal antibodies were produced and used to demonstrate the presence of these proteins on the bacterial surface of the corresponding clone, and on the H. parasuis phagocytosis-resistant strain PC4-6P. The effect of VtaA8 and VtaA9 in the trafficking of the bacteria through the endocytic pathway was examined by fluorescence microscopy and a delay was detected in the localization of the vtaA8 and vtaA9 clones in acidic compartments. These results are compatible with a partial inhibition of the routing of the bacteria via the degradative phagosome. Finally, antibodies against a common epitope in VtaA8 and VtaA9 were opsonic and promoted phagocytosis of the phagocytosis-resistant strain PC4-6P by PAM. Taken together, these results indicate that VtaA8 and VtaA9 are surface proteins that play a role in phagocytosis resistance of H. parasuis

Alterations in Metabolome and Microbiome Associated with an Early Stress Stage in Male Wistar Rats: A Multi-Omics Approach

Stress disorders have dramatically increased in recent decades becoming the most prevalent psychiatric disorder in the United States and Europe. However, the diagnosis of stress disorders is currently based on symptom checklist and psychological questionnaires, thus making the identification of candidate biomarkers necessary to gain better insights into this pathology and its related metabolic alterations. Regarding the identification of potential biomarkers, omic profiling and metabolic footprint arise as promising approaches to recognize early biochemical changes in such disease and provide opportunities for the development of integrative candidate biomarkers. Here, we studied plasma and urine metabolites together with metagenomics in a 3 days Chronic Unpredictable Mild Stress (3d CUMS) animal approach that aims to focus on the early stress period of a well-established depression model. The multi-omics integration showed a profile composed by a signature of eight plasma metabolites, six urine metabolites and five microbes. Specifically, threonic acid, malic acid, alpha-ketoglutarate, succinic acid and cholesterol were proposed as key metabolites that could serve as key potential biomarkers in plasma metabolome of early stages of stress. Such findings targeted the threonic acid metabolism and the tricarboxylic acid (TCA) cycle as important pathways in early stress. Additionally, an increase in opportunistic microbes as virus of the Herpesvirales was observed in the microbiota as an effect of the primary stress stages. Our results provide an experimental biochemical characterization of the early stage of CUMS accompanied by a subsequent omic profiling and a metabolic footprinting that provide potential candidate biomarkers

A double-blinded, randomized, parallel intervention to evaluate biomarker-based nutrition plans for weight loss: The PREVENTOMICS study

BACKGROUND & AIMS: Growing evidence suggests that biomarker-guided dietary interventions can optimize response to treatment. In this study, we evaluated the efficacy of the PREVENTOMCIS platform-which uses metabolomic and genetic information to classify individuals into different 'metabolic clusters' and create personalized dietary plans-for improving health outcomes in subjects with overweight or obesity. METHODS: A 10-week parallel, double-blinded, randomized intervention was conducted in 100 adults (82 completers) aged 18-65 years, with body mass index ≥27 but <40 kg/m 2, who were allocated into either a personalized diet group (n = 49) or a control diet group (n = 51). About 60% of all food was provided free-of-charge. No specific instruction to restrict energy intake was given. The primary outcome was change in fat mass from baseline, evaluated by dual energy X-ray absorptiometry. Other endpoints included body weight, waist circumference, lipid profile, glucose homeostasis markers, inflammatory markers, blood pressure, physical activity, stress and eating behavior. RESULTS: There were significant main effects of time (P < 0.01), but no group main effects, or time-by-group interactions, for the change in fat mass (personalized: -2.1 [95% CI -2.9, -1.4] kg; control: -2.0 [95% CI -2.7, -1.3] kg) and body weight (personalized: -3.1 [95% CI -4.1, -2.1] kg; control: -3.3 [95% CI -4.2, -2.4] kg). The difference between groups in fat mass change was -0.1 kg (95% CI -1.2, 0.9 kg, P = 0.77). Both diets resulted in significant improvements in insulin resistance and lipid profile, but there were no significant differences between groups. CONCLUSION: Personalized dietary plans did not result in greater benefits over a generic, but generally healthy diet, in this 10-week clinical trial. Further studies are required to establish the soundness of different precision nutrition approaches, and translate this science into clinically relevant dietary advice to reduce the burden of obesity and its comorbidities. CLINICAL TRIAL REGISTRY: ClinicalTrials.gov registry (NCT04590989)

Changes in Macrophage Phenotype after Infection of Pigs with Haemophilus parasuis Strains with Different Levels of Virulence

Haemophilus parasuis is a colonizer of healthy piglets and the etiological agent of Glässer's disease. Differences in virulence among strains of H. parasuis have been widely observed. In order to explore the host-pathogen interaction, snatch-farrowed colostrum-deprived piglets were intranasally infected with 4 strains of H. parasuis: reference virulent strain Nagasaki, reference nonvirulent strain SW114, field strain IT29205 (from a systemic lesion and virulent in a previous challenge), and field strain F9 (from the nasal cavity of a healthy piglet). At different times after infection, two animals of each group were euthanized and alveolar macrophages were analyzed for the expression of CD163, CD172a, SLA I (swine histocompatibility leukocyte antigen I), SLA II, sialoadhesin (or CD169), and CD14. At 1 day postinfection (dpi), virulent strains induced reduced expression of CD163, SLA II, and CD172a on the surfaces of the macrophages, while nonvirulent strains induced increased expression of CD163, both compared to noninfected controls. At 2 dpi, the pattern switched into a strong expression of CD172a, CD163, and sialoadhesin by the virulent strains, which was followed by a steep increase in interleukin 8 (IL-8) and soluble CD163 in serum at 3 to 4 dpi. The early increase in surface expression of CD163 induced by nonvirulent strains went along with higher levels of IL-8 in serum than those induced by virulent strains in the first 2 days of infection. Alpha interferon (IFN-α) induction was observed only in animals infected with nonvirulent strains. Overall, these results are compatible with a delay in macrophage activation by virulent strains, which may be critical for disease production

Omics biomarkers and an approach for their practical implementation to delineate health status for personalized nutrition strategies

Personalized nutrition (PN) has gained much attention as a tool for empowerment of consumers to promote changes in dietary behavior, optimizing health status and preventing diet related diseases. Generalized implementation of PN faces different obstacles, one of the most relevant being metabolic characterization of the individual. Although omics technologies allow for assessment the dynamics of metabolism with unprecedented detail, its translatability as affordable and simple PN protocols is still difficult due to the complexity of metabolic regulation and to different technical and economical constrains. In this work, we propose a conceptual framework that considers the dysregulation of a few overarching processes, namely Carbohydrate metabolism, lipid metabolism, inflammation, oxidative stress and microbiota-derived metabolites, as the basis of the onset of several non-communicable diseases. These processes can be assessed and characterized by specific sets of proteomic, metabolomic and genetic markers that minimize operational constrains and maximize the information obtained at the individual level. Current machine learning and data analysis methodologies allow the development of algorithms to integrate omics and genetic markers. Reduction of dimensionality of variables facilitates the implementation of omics and genetic information in digital tools. This framework is exemplified by presenting the EU-Funded project PREVENTOMICS as a use case