Current insights into cow's milk allergy in children: Microbiome, metabolome, and immune response-A systematic review.

The increasing prevalence of IgE-mediated cow's milk allergy (CMA) in childhood is a worldwide health concern. There is a growing awareness that the gut microbiome (GM) might play an important role in CMA development. Therefore, treatment with probiotics and prebiotics has gained popularity. This systematic review provides an overview of the alterations of the GM, metabolome, and immune response in CMA children and animal models, including post-treatment modifications. MEDLINE, PubMed, Scopus, and Web of Science were searched for studies on GM in CMA-diagnosed children, published before 1 March 2023. A total of 21 articles (13 on children and 8 on animal models) were included. The studies suggest that the GM, characterized by an enrichment of the Clostridia class and reductions in the Lactobacillales order and Bifidobacterium genus, is associated with CMA in early life. Additionally, reduced levels of short-chain fatty acids (SCFAs) and altered amino acid metabolism were reported in CMA children. Commonly used probiotic strains belong to the Bifidobacterium and Lactobacillus genera. However, only Bifidobacterium levels were consistently upregulated after the intervention, while alterations of other bacteria taxa remain inconclusive. These interventions appear to contribute to the restoration of SCFAs and amino acid metabolism balance. Mouse models indicate that these interventions tend to restore the Th 2/Th 1 balance, increase the Treg response, and/or silence the overall pro- and anti-inflammatory cytokine response. Overall, this systematic review highlights the need for multi-omics-related research in CMA children to gain a mechanistic understanding of this disease and to develop effective treatments and preventive strategies.

Assessment of infant outgrowth of cow's milk allergy in relation to the faecal microbiome and metaproteome.

Previous studies provide evidence for an association between modifications of the gut microbiota in early life and the development of food allergies. We studied the faecal microbiota composition (16S rRNA gene amplicon sequencing) and faecal microbiome functionality (metaproteomics) in a cohort of 40 infants diagnosed with cow's milk allergy (CMA) when entering the study. Some of the infants showed outgrowth of CMA after 12 months, while others did not. Faecal microbiota composition of infants was analysed directly after CMA diagnosis (baseline) as well as 6 and 12 months after entering the study. The aim was to gain insight on gut microbiome parameters in relation to outgrowth of CMA. The results of this study show that microbiome differences related to outgrowth of CMA can be mainly identified at the taxonomic level of the 16S rRNA gene, and to a lesser extent at the protein-based microbial taxonomy and functional protein level. At the 16S rRNA gene level outgrowth of CMA is characterized by lower relative abundance of Lachnospiraceae at baseline and lower Bacteroidaceae at visit 12 months.

Comparison of two simulators for individual based models in HIV epidemiology in a population with HSV 2 in Yaoundé (Cameroon).

Model comparisons have been widely used to guide intervention strategies to control infectious diseases. Agreement between different models is crucial for providing robust evidence for policy-makers because differences in model properties can influence their predictions. In this study, we compared models implemented by two individual-based model simulators for HIV epidemiology in a heterosexual population with Herpes simplex virus type-2 (HSV-2). For each model simulator, we constructed four models, starting from a simplified basic model and stepwise including more model complexity. For the resulting eight models, the predictions of the impact of behavioural interventions on the HIV epidemic in Yaoundé-Cameroon were compared. The results show that differences in model assumptions and model complexity can influence the size of the predicted impact of the intervention, as well as the predicted qualitative behaviour of the HIV epidemic after the intervention. These differences in predictions of an intervention were also observed for two models that agreed in their predictions of the HIV epidemic in the absence of that intervention. Without additional data, it is impossible to determine which of these two models is the most reliable. These findings highlight the importance of making more data available for the calibration and validation of epidemiological models.

Influence of sexual risk behaviour and STI co-infection dynamics on the evolution of HIV set point viral load in MSM.

HIV viral load (VL) is an important predictor of HIV progression and transmission. Anti-retroviral therapy (ART) has been reported to reduce HIV transmission by lowering VL. However, apart from this beneficial effect, increased levels of population mean set-point viral load (SPVL), an estimator for HIV virulence, have been observed in men who have sex with men (MSM) in the decade following the introduction of ART in The Netherlands. Several studies have been devoted to explain these counter-intuitive trends in SPVL. However, to our knowledge, none of these studies has investigated an explanation in which it arises as the result of a sexually transmitted infection (STI) co-factor in detail. In this study, we adapted an event-based, individual-based model to investigate how STI co-infection and sexual risk behaviour affect the evolution of HIV SPVL in MSM before and after the introduction of ART. The results suggest that sexual risk behaviour has an effect on SPVL and indicate that more data are needed to test the effect of STI co-factors on SPVL. Furthermore, the observed trends in SPVL cannot be explained by sexual risk behaviour and STI co-factors only. We recommend to develop mathematical models including also factors related to viral evolution as reported earlier in the literature. However, this requires more complex models, and the collection of more data for parameter estimation than what is currently available.

A New Synuclein-Transgenic Mouse Model for Early Parkinson's Reveals Molecular Features of Preclinical Disease.

Understanding Parkinson's disease (PD), in particular in its earliest phases, is important for diagnosis and treatment. However, human brain samples are collected post-mortem, reflecting mainly end-stage disease. Because brain samples of mouse models can be collected at any stage of the disease process, they are useful in investigating PD progression. Here, we compare ventral midbrain transcriptomics profiles from α-synuclein transgenic mice with a progressive, early PD-like striatal neurodegeneration across different ages using pathway, gene set, and network analysis methods. Our study uncovers statistically significant altered genes across ages and between genotypes with known, suspected, or unknown function in PD pathogenesis and key pathways associated with disease progression. Among those are genotype-dependent alterations associated with synaptic plasticity and neurotransmission, as well as mitochondria-related genes and dysregulation of lipid metabolism. Age-dependent changes were among others observed in neuronal and synaptic activity, calcium homeostasis, and membrane receptor signaling pathways, many of which linked to G-protein coupled receptors. Most importantly, most changes occurred before neurodegeneration was detected in this model, which points to a sequence of gene expression events that may be relevant for disease initiation and progression. It is tempting to speculate that molecular changes similar to those changes observed in our model happen in midbrain dopaminergic neurons before they start to degenerate. In other words, we believe we have uncovered molecular changes that accompany the progression from preclinical to early PD.

Comparative transcriptome analysis of Parkinson's disease and Hutchinson-Gilford progeria syndrome reveals shared susceptible cellular network processes.

BACKGROUND: Parkinson's Disease (PD) and Hutchinson-Gilford Progeria Syndrome (HGPS) are two heterogeneous disorders, which both display molecular and clinical alterations associated with the aging process. However, similarities and differences between molecular changes in these two disorders have not yet been investigated systematically at the level of individual biomolecules and shared molecular network alterations. METHODS: Here, we perform a comparative meta-analysis and network analysis of human transcriptomics data from case-control studies for both diseases to investigate common susceptibility genes and sub-networks in PD and HGPS. Alzheimer's disease (AD) and primary melanoma (PM) were included as controls to confirm that the identified overlapping susceptibility genes for PD and HGPS are non-generic. RESULTS: We find statistically significant, overlapping genes and cellular processes with significant alterations in both diseases. Interestingly, the majority of these shared affected genes display changes with opposite directionality, indicating that shared susceptible cellular processes undergo different mechanistic changes in PD and HGPS. A complementary regulatory network analysis also reveals that the altered genes in PD and HGPS both contain targets controlled by the upstream regulator CDC5L. CONCLUSIONS: Overall, our analyses reveal a significant overlap of affected cellular processes and molecular sub-networks in PD and HGPS, including changes in aging-related processes that may reflect key susceptibility factors associated with age-related risk for PD.

SimpactCyan 1.0: An Open-source Simulator for Individual-Based Models in HIV Epidemiology with R and Python Interfaces.

SimpactCyan is an open-source simulator for individual-based models in HIV epidemiology. Its core algorithm is written in C++ for computational efficiency, while the R and Python interfaces aim to make the tool accessible to the fast-growing community of R and Python users. Transmission, treatment and prevention of HIV infections in dynamic sexual networks are simulated by discrete events. A generic "intervention" event allows model parameters to be changed over time, and can be used to model medical and behavioural HIV prevention programmes. First, we describe a more efficient variant of the modified Next Reaction Method that drives our continuous-time simulator. Next, we outline key built-in features and assumptions of individual-based models formulated in SimpactCyan, and provide code snippets for how to formulate, execute and analyse models in SimpactCyan through its R and Python interfaces. Lastly, we give two examples of applications in HIV epidemiology: the first demonstrates how the software can be used to estimate the impact of progressive changes to the eligibility criteria for HIV treatment on HIV incidence. The second example illustrates the use of SimpactCyan as a data-generating tool for assessing the performance of a phylodynamic inference framework.

The impact of behavioral interventions on co-infection dynamics: An exploration of the effects of home isolation.

Behavioral epidemiology, the field aiming to determine the impact of individual behavior on the spread of an epidemic, has gained increased recognition during the last few decades. Behavioral changes due to the development of symptoms have been studied in mono-infections. However, in reality, multiple infections are circulating within the same time period and behavioral changes resulting from contraction of one of the diseases affect the dynamics of the other. The present study aims at assessing the effect of home isolation on the joint dynamics of two infectious diseases, including co-infection, assuming that the two diseases do not confer cross-immunity. We use an age- and time- structured co-infection model based on partial differential equations. Social contact matrices, describing different mixing patterns of symptomatic and asymptomatic individuals are incorporated into the calculation of the age- and time-specific marginal forces of infection. Two scenarios are simulated, assuming that one of the diseases has more severe symptoms than the other. In the first scenario, people stay only at home when having symptoms of the most severe disease. In the second scenario, twice as many people stay at home when having symptoms of the most severe disease than when having symptoms of the other disease. The results show that the impact of home isolation on the joint dynamics of two infectious diseases depends on the epidemiological parameters and properties of the diseases (e.g., basic reproduction number, symptom severity). In case both diseases have a low to moderate basic reproduction number, and there is no home isolation for the less severe disease, the final size of the less severe disease increases with the proportion of symptomatic cases of the most severe disease staying at home, after an initial decrease. This counterintuitive result could be explained by a shift in the peak time of infection of the disease with the most severe symptoms, resulting in a smaller number of people with less contacts at the peak time of the other infection. When twice as many people stay at home when having symptoms of the most severe disease than when having symptoms of the other disease, increasing the proportion staying at home always reduces the final size of both diseases, and the number of co-infections. In conclusion, when providing advise if people should stay at home in the context of two or more co-circulating diseases, one has to take into account epidemiological parameters and symptom severity.

Screening for hepatitis C at the emergency department: Should babyboomers also be screened in Belgium?

BACKGROUND & AIMS: Patients are not screened adequately for hepatitis C virus infection in Belgium. In the USA, the Center for Disease Control recommends screening for patients born in the babyboom period (1945-1965). In Europe, the babyboom cohort was born between 1955 and 1974, but no screening policy has been targeted to this group. We aimed to study the prevalence of hepatitis C virus in an emergency department population in Belgium and the risk factors associated with hepatitis C virus infection. METHOD: We performed a monocentric, cross-sectional seroprevalence study between January and November 2017 in a large Belgian non-university hospital. Patients aged 18-70 years presenting at the emergency department were eligible. Patients completed a risk assessment questionnaire and were screened for hepatitis C virus antibodies (Ab) with reflex hepatitis C virus ribonucleic acid testing. RESULTS: Of 2970 patients, 2366 (79.7%) agreed to participate. hepatitis C virus Ab prevalence was 1.31%. Twenty-one (67.7%) hepatitis C virus Ab-positive patients were born between 1955 and 1974. With a previous treatment uptake of 54.5%, the prevalence of viremia was 0.9% in retrospect; 0.2% were newly diagnosed. The weighted multiple logistic regression model identified males born in the 1955-1974 cohort, intravenous drug use and high endemic birth country as significant risk factors for hepatitis C virus infection (P < 0.05). CONCLUSION: Although the prevalence of hepatitis C virus Ab at the emergency department was higher than previously estimated for the general population in Belgium, the number of newly diagnosed patients with viremia was low. To optimize screening strategies, screening should be offered to males born in the 1955-1974 cohort, but especially in drug users, the prison population and immigrants from high endemic countries.

DTNI: a novel toxicogenomics data analysis tool for identifying the molecular mechanisms underlying the adverse effects of toxic compounds.

Unravelling gene regulatory networks (GRNs) influenced by chemicals is a major challenge in systems toxicology. Because toxicant-induced GRNs evolve over time and dose, the analysis of global gene expression data measured at multiple time points and doses will provide insight in the adverse effects of compounds. Therefore, there is a need for mathematical methods for GRN identification from time-over-dose-dependent data. One of the current approaches for GRN inference is Time Series Network Identification (TSNI). TSNI is based on ordinary differential equations (ODE), describing the time evolution of the expression of each gene, which is assumed to be dependent on the expression of other genes and an external perturbation (i.e. chemical exposure). Here, we present Dose-Time Network Identification (DTNI), a method extending TSNI by including ODE describing how the expression of each gene evolves with dose, which is supposed to depend on the expression of other genes and the exposure time. We also adapted TSNI in order to enable inclusion of time-over-dose-dependent data from multiple compounds. Here, we show that DTNI outperforms TSNI in inferring a toxicant-induced GRN. Moreover, we show that DTNI is a suitable method to infer a GRN dose- and time-dependently induced by a group of compounds influencing a common biological process. Applying DTNI on experimental data from TG-GATEs, we demonstrate that DTNI provides in-depth information on the mode of action of compounds, in particular key events and potential molecular initiating events. Furthermore, DTNI also discloses several unknown interactions which have to be verified experimentally.

Bayesian Network Inference Enables Unbiased Phenotypic Anchoring of Transcriptomic Responses to Cigarette Smoke in Humans.

Microarray-based transcriptomic analysis has been demonstrated to hold the opportunity to study the effects of human exposure to, e.g., chemical carcinogens at the whole genome level, thus yielding broad-ranging molecular information on possible carcinogenic effects. Since genes do not operate individually but rather through concerted interactions, analyzing and visualizing networks of genes should provide important mechanistic information, especially upon connecting them to functional parameters, such as those derived from measurements of biomarkers for exposure and carcinogenic risk. Conventional methods such as hierarchical clustering and correlation analyses are frequently used to address these complex interactions but are limited as they do not provide directional causal dependence relationships. Therefore, our aim was to apply Bayesian network inference with the purpose of phenotypic anchoring of modified gene expressions. We investigated a use case on transcriptomic responses to cigarette smoking in humans, in association with plasma cotinine levels as biomarkers of exposure and aromatic DNA-adducts in blood cells as biomarkers of carcinogenic risk. Many of the genes that appear in the Bayesian networks surrounding plasma cotinine, and to a lesser extent around aromatic DNA-adducts, hold biologically relevant functions in inducing severe adverse effects of smoking. In conclusion, this study shows that Bayesian network inference enables unbiased phenotypic anchoring of transcriptomics responses. Furthermore, in all inferred Bayesian networks several dependencies are found which point to known but also to new relationships between the expression of specific genes, cigarette smoke exposure, DNA damaging-effects, and smoking-related diseases, in particular associated with apoptosis, DNA repair, and tumor suppression, as well as with autoimmunity.

Distinct genotype-dependent differences in transcriptome responses in humans exposed to environmental carcinogens.

Considering genetic variability in population studies focusing on the health risk assessment of exposure to environmental carcinogens may provide improved insights in individual environmental cancer risks. Therefore, the current study aims to determine the impact of genetic polymorphisms on the relationship between exposure and gene expression, by identifying exposure-dependently coregulated genes and genetic pathways. Statistical analysis based on mixed models, was performed to relate gene expression data from 134 subjects to exposure measurements of multiple carcinogens, 28 polymorphisms, age, sex and biomarkers of cancer risk. We evaluated the combined exposure to cadmium, lead, polychlorinated biphenyls, p,p'-dichlorodiphenyldichloroethylene, hexachlorobenzene and 1-OH-pyrene, and the outcome was biologically interpreted by using ConsensusPathDB, thereby focusing on carcinogenesis-related pathways. We found generic and carcinogenesis-related pathways deregulated in both sexes, but males showed a stronger transcriptome response than females. We highlighted NOTCH1, CBR1, ITGB3, ITGA4, ADI1, HES1, NCOA2 and SMARCA2 in view of their direct link with cancer development. Two of these, NOTCH1 and ITGB3, are also known to respond to PCBs and cadmium chloride exposure in rodents and to lead in humans. Subjects carrying a high number of risk alleles appear more responsive to combined carcinogen exposure with respect to the induced expression of some of these cancer-related genes, which may be indicative of increased cancer risk as a consequence of environmental factors.

Pattern recognition methods to relate time profiles of gene expression with phenotypic data: a comparative study.

MOTIVATION: Comparing time courses of gene expression with time courses of phenotypic data may provide new insights in cellular mechanisms. In this study, we compared the performance of five pattern recognition methods with respect to their ability to relate genes and phenotypic data: one classical method (k-means) and four methods especially developed for time series [Short Time-series Expression Miner (STEM), Linear Mixed Model mixtures, Dynamic Time Warping for -Omics and linear modeling with R/Bioconductor limma package]. The methods were evaluated using data available from toxicological studies that had the aim to relate gene expression with phenotypic endpoints (i.e. to develop biomarkers for adverse outcomes). Additionally, technical aspects (influence of noise, number of time points and number of replicates) were evaluated on simulated data. RESULTS: None of the methods outperforms the others in terms of biology. Linear modeling with limma is mostly influenced by noise. STEM is mostly influenced by the number of biological replicates in the dataset, whereas k-means and linear modeling with limma are mostly influenced by the number of time points. In most cases, the results of the methods complement each other. We therefore provide recommendations to integrate the five methods. AVAILABILITY: The Matlab code for the simulations performed in this research is available in the Supplementary Data (Word file). The microarray data analysed in this paper are available at ArrayExpress (E-TOXM-22 and E-TOXM-23) and Gene Expression Omnibus (GSE39291). The phenotypic data are available in the Supplementary Data (Excel file). Links to the pattern recognition tools compared in this paper are provided in the main text. CONTACT: d.hendrickx@maastrichtuniversity.nl SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MetDFBA: incorporating time-resolved metabolomics measurements into dynamic flux balance analysis.

Understanding cellular adaptation to environmental changes is one of the major challenges in systems biology. To understand how cellular systems react towards perturbations of their steady state, the metabolic dynamics have to be described. Dynamic properties can be studied with kinetic models but development of such models is hampered by limited in vivo information, especially kinetic parameters. Therefore, there is a need for mathematical frameworks that use a minimal amount of kinetic information. One of these frameworks is dynamic flux balance analysis (DFBA), a method based on the assumption that cellular metabolism has evolved towards optimal changes to perturbations. However, DFBA has some limitations. It is less suitable for larger systems because of the high number of parameters to estimate and the computational complexity. In this paper, we propose MetDFBA, a modification of DFBA, that incorporates measured time series of both intracellular and extracellular metabolite concentrations, in order to reduce both the number of parameters to estimate and the computational complexity. MetDFBA can be used to estimate dynamic flux profiles and, in addition, test hypotheses about metabolic regulation. In a first case study, we demonstrate the validity of our method by comparing our results to flux estimations based on dynamic 13C MFA measurements, which we considered as experimental reference. For these estimations time-resolved metabolomics data from a feast-famine experiment with Penicillium chrysogenum was used. In a second case study, we used time-resolved metabolomics data from glucose pulse experiments during aerobic growth of Saccharomyces cerevisiae to test various metabolic objectives.

diXa: a data infrastructure for chemical safety assessment.

MOTIVATION: The field of toxicogenomics (the application of '-omics' technologies to risk assessment of compound toxicities) has expanded in the last decade, partly driven by new legislation, aimed at reducing animal testing in chemical risk assessment but mainly as a result of a paradigm change in toxicology towards the use and integration of genome wide data. Many research groups worldwide have generated large amounts of such toxicogenomics data. However, there is no centralized repository for archiving and making these data and associated tools for their analysis easily available. RESULTS: The Data Infrastructure for Chemical Safety Assessment (diXa) is a robust and sustainable infrastructure storing toxicogenomics data. A central data warehouse is connected to a portal with links to chemical information and molecular and phenotype data. diXa is publicly available through a user-friendly web interface. New data can be readily deposited into diXa using guidelines and templates available online. Analysis descriptions and tools for interrogating the data are available via the diXa portal. AVAILABILITY AND IMPLEMENTATION: http://www.dixa-fp7.eu CONTACT: d.hendrickx@maastrichtuniversity.nl; info@dixa-fp7.eu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Workshop report: Identifying opportunities for global integration of toxicogenomics databases, 26-27 June 2013, Research Triangle Park, NC, USA.

A joint US-EU workshop on enhancing data sharing and exchange in toxicogenomics was held at the National Institute for Environmental Health Sciences. Currently, efficient reuse of data is hampered by problems related to public data availability, data quality, database interoperability (the ability to exchange information), standardization and sustainability. At the workshop, experts from universities and research institutes presented databases, studies, organizations and tools that attempt to deal with these problems. Furthermore, a case study showing that combining toxicogenomics data from multiple resources leads to more accurate predictions in risk assessment was presented. All participants agreed that there is a need for a web portal describing the diverse, heterogeneous data resources relevant for toxicogenomics research. Furthermore, there was agreement that linking more data resources would improve toxicogenomics data analysis. To outline a roadmap to enhance interoperability between data resources, the participants recommend collecting user stories from the toxicogenomics research community on barriers in data sharing and exchange currently hampering answering to certain research questions. These user stories may guide the prioritization of steps to be taken for enhancing integration of toxicogenomics databases.

Inferring differences in the distribution of reaction rates across conditions.

Elucidating changes in the distribution of reaction rates in metabolic pathways under different conditions is a central challenge in systems biology. Here we present a method for inferring regulation mechanisms responsible for changes in the distribution of reaction rates across conditions from correlations in time-resolved data. A reversal of correlations between conditions reveals information about regulation mechanisms. With the use of a small in silico hypothetical network, based on only the topology and directionality of a known pathway, several regulation scenarios can be formulated. Confronting these scenarios with experimental data results in a short list of possible pathway regulation mechanisms associated with the reversal of correlations between conditions. This procedure allows for the formulation of regulation scenarios without detailed prior knowledge of kinetics and for the inference of reaction rate changes without rate information. The method was applied to experimental time-resolved metabolomics data from multiple short-term perturbation-response experiments in S. cerevisiae across aerobic and anaerobic conditions. The method's output was validated against a detailed kinetic model of glycolysis in S. cerevisiae, which showed that the method can indeed infer the correct regulation scenario.

Global test for metabolic pathway differences between conditions.

In many metabolomics applications there is a need to compare metabolite levels between different conditions, e.g., case versus control. There exist many statistical methods to perform such comparisons but only few of these explicitly take into account the fact that metabolites are connected in pathways or modules. Such a priori information on pathway structure can alleviate problems in, e.g., testing on individual metabolite level. In gene-expression analysis, Goeman's global test is used to this extent to determine whether a group of genes has a different expression pattern under changed conditions. We examined if this test can be generalized to metabolomics data. The goal is to determine if the behavior of a group of metabolites, belonging to the same pathway, is significantly related to a particular outcome of interest, e.g., case/control or environmental conditions. The results show that the global test can indeed be used in such situations. This is illustrated with extensive intracellular metabolomics data from Escherichia coli and Saccharomyces cerevisiae under different environmental conditions.

Reverse engineering of metabolic networks, a critical assessment.

Inferring metabolic networks from metabolite concentration data is a central topic in systems biology. Mathematical techniques to extract information about the network from data have been proposed in the literature. This paper presents a critical assessment of the feasibility of reverse engineering of metabolic networks, illustrated with a selection of methods. Appropriate data are simulated to study the performance of four representative methods. An overview of sampling and measurement methods currently in use for generating time-resolved metabolomics data is given and contrasted with the needs of the discussed reverse engineering methods. The results of this assessment show that if full inference of a real-world metabolic network is the goal there is a large discrepancy between the requirements of reverse engineering of metabolic networks and contemporary measurement practice. Recommendations for improved time-resolved experimental designs are given.