Uncovering metabolic pathways relevant to phenotypic traits of microbial genomes

A new machine learning-based method is presented here for the identification of metabolic pathways related to specific phenotypes in multiple microbial genomes

Cohort profile: biological pathways of risk and resilience in Syrian refugee children (BIOPATH)

The BIOPATH cohort was established to explore the interplay of psychosocial and biological factors in the development of resilience and mental health problems in Syrian refugee children. Based in Lebanon, a middle-income country significantly impacted by the refugee crisis, it is the first such cohort of refugees in the Middle East. Families were recruited from informal tented settlements in the Beqaa region using purposive cluster sampling. At baseline (October 2017–January 2018), N = 3188 individuals participated [n = 1594 child–caregiver dyads; child gender, 52.6% female; mean (SD) age = 11.44 (2.44) years, range = 6–19]. Re-participation rate at 1-year follow-up was 62.8%. Individual interviews were conducted with children and primary caregivers and biological samples collected from children. Measures include: (1) children’s well-being and mental health problems (using tools validated against clinical interviews in a subsample of the cohort); (2) psychosocial risk and protective factors at the level of the individual (e.g. coping strategies), family (e.g. parent–child relationship), community (e.g. collective efficacy), and wider context (e.g. services); (3) saliva samples for genetic and epigenetic (methylation) analyses; (4) hair samples to measure cortisol, dehydroepiandrosterone (DHEA) and testosterone. This cohort profile provides details about sampling and recruitment, data collection and measures, demographic data, attrition and potential bias, key findings on resilience and mental health problems in children and strengths and limitations of the cohort. Researchers interested in accessing data should contact Professor Michael Pluess at Queen Mary University of London, UK (e-mail: [email protected]). SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00127-022-02228-8

A new dynamical layout algorithm for complex biochemical reaction networks

BACKGROUND: To study complex biochemical reaction networks in living cells researchers more and more rely on databases and computational methods. In order to facilitate computational approaches, visualisation techniques are highly important. Biochemical reaction networks, e.g. metabolic pathways are often depicted as graphs and these graphs should be drawn dynamically to provide flexibility in the context of different data. Conventional layout algorithms are not sufficient for every kind of pathway in biochemical research. This is mainly due to certain conventions to which biochemists/biologists are used to and which are not in accordance to conventional layout algorithms. A number of approaches has been developed to improve this situation. Some of these are used in the context of biochemical databases and make more or less use of the information in these databases to aid the layout process. However, visualisation becomes also more and more important in modelling and simulation tools which mostly do not offer additional connections to databases. Therefore, layout algorithms used in these tools have to work independently of any databases. In addition, all of the existing algorithms face some limitations with respect to the number of edge crossings when it comes to larger biochemical systems due to the interconnectivity of these. Last but not least, in some cases, biochemical conventions are not met properly. RESULTS: Out of these reasons we have developed a new algorithm which tackles these problems by reducing the number of edge crossings in complex systems, taking further biological conventions into account to identify and visualise cycles. Furthermore the algorithm is independent from database information in order to be easily adopted in any application. It can also be tested as part of the SimWiz package (free to download for academic users at [1]). CONCLUSION: The new algorithm reduces the complexity of pathways, as well as edge crossings and edge length in the resulting graphical representation. It also considers existing and further biological conventions to create a drawing most biochemists are familiar with. A lot of examples can be found on [2]

An extensive (co-)expression analysis tool for the cytochrome P450 superfamily in Arabidopsis thaliana

<p>Abstract</p> <p>Background</p> <p>Sequencing of the first plant genomes has revealed that cytochromes P450 have evolved to become the largest family of enzymes in secondary metabolism. The proportion of P450 enzymes with characterized biochemical function(s) is however very small. If P450 diversification mirrors evolution of chemical diversity, this points to an unexpectedly poor understanding of plant metabolism. We assumed that extensive analysis of gene expression might guide towards the function of P450 enzymes, and highlight overlooked aspects of plant metabolism.</p> <p>Results</p> <p>We have created a comprehensive database, 'CYPedia', describing P450 gene expression in four data sets: organs and tissues, stress response, hormone response, and mutants of <it>Arabidopsis thaliana</it>, based on public Affymetrix ATH1 microarray expression data. P450 expression was then combined with the expression of 4,130 re-annotated genes, predicted to act in plant metabolism, for co-expression analyses. Based on the annotation of co-expressed genes from diverse pathway annotation databases, co-expressed pathways were identified. Predictions were validated for most P450s with known functions. As examples, co-expression results for P450s related to plastidial functions/photosynthesis, and to phenylpropanoid, triterpenoid and jasmonate metabolism are highlighted here.</p> <p>Conclusion</p> <p>The large scale hypothesis generation tools presented here provide leads to new pathways, unexpected functions, and regulatory networks for many P450s in plant metabolism. These can now be exploited by the community to validate the proposed functions experimentally using reverse genetics, biochemistry, and metabolic profiling.</p

Rhea—a manually curated resource of biochemical reactions

Rhea (http://www.ebi.ac.uk/rhea) is a comprehensive resource of expert-curated biochemical reactions. Rhea provides a non-redundant set of chemical transformations for use in a broad spectrum of applications, including metabolic network reconstruction and pathway inference. Rhea includes enzyme-catalyzed reactions (covering the IUBMB Enzyme Nomenclature list), transport reactions and spontaneously occurring reactions. Rhea reactions are described using chemical species from the Chemical Entities of Biological Interest ontology (ChEBI) and are stoichiometrically balanced for mass and charge. They are extensively manually curated with links to source literature and other public resources on metabolism including enzyme and pathway databases. This cross-referencing facilitates the mapping and reconciliation of common reactions and compounds between distinct resources, which is a common first step in the reconstruction of genome scale metabolic networks and models

Different Effects of BORIS/CTCFL on Stemness Gene Expression, Sphere Formation and Cell Survival in Epithelial Cancer Stem Cells

Cancer stem cells are cancer cells characterized by stem cell properties and represent a small population of tumor cells that drives tumor development, progression, metastasis and drug resistance. To date, the molecular mechanisms that generate and regulate cancer stem cells are not well defined. BORIS (Brother of Regulator of Imprinted Sites) or CTCFL (CTCF-like) is a DNA-binding protein that is expressed in normal tissues only in germ cells and is re-activated in tumors. Recent evidences have highlighted the correlation of BORIS/CTCFL expression with poor overall survival of different cancer patients. We have previously shown an association of BORIS-expressing cells with stemness gene expression in embryonic cancer cells. Here, we studied the role of BORIS in epithelial tumor cells. Using BORIS-molecular beacon that was already validated, we were able to show the presence of BORIS mRNA in cancer stem cell-enriched populations (side population and spheres) of cervical, colon and breast tumor cells. BORIS silencing studies showed a decrease of sphere formation capacity in breast and colon tumor cells. Importantly, BORIS-silencing led to down-regulation of hTERT, stem cell (NANOG, OCT4, SOX2 and BMI1) and cancer stem cell markers (ABCG2, CD44 and ALDH1) genes. Conversely, BORIS-induction led to up-regulation of the same genes. These phenotypes were observed in cervical, colon and invasive breast tumor cells. However, a completely different behavior was observed in the non-invasive breast tumor cells (MCF7). Indeed, these cells acquired an epithelial mesenchymal transition phenotype after BORIS silencing. Our results demonstrate that BORIS is associated with cancer stem cell-enriched populations of several epithelial tumor cells and the different phenotypes depend on the origin of tumor cells

A generic algorithm for layout of biological networks

BackgroundBiological networks are widely used to represent processes in biological systems and to capture interactions and dependencies between biological entities. Their size and complexity is steadily increasing due to the ongoing growth of knowledge in the life sciences. To aid understanding of biological networks several algorithms for laying out and graphically representing networks and network analysis results have been developed. However, current algorithms are specialized to particular layout styles and therefore different algorithms are required for each kind of network and/or style of layout. This increases implementation effort and means that new algorithms must be developed for new layout styles. Furthermore, additional effort is necessary to compose different layout conventions in the same diagram. Also the user cannot usually customize the placement of nodes to tailor the layout to their particular need or task and there is little support for interactive network exploration.ResultsWe present a novel algorithm to visualize different biological networks and network analysis results in meaningful ways depending on network types and analysis outcome. Our method is based on constrained graph layout and we demonstrate how it can handle the drawing conventions used in biological networks.ConclusionThe presented algorithm offers the ability to produce many of the fundamental popular drawing styles while allowing the exibility of constraints to further tailor these layouts.publishe

Cartilage preservation by inhibition of Janus kinase 3 in two rodent models of rheumatoid arthritis

10.1186/ar2365Arthritis Research and Therapy101R1

Exploratory Visual Analysis of Statistical Results from Microarray Experiments Comparing High and Low Grade Glioma

The biological interpretation of gene expression microarray results is a daunting challenge. For complex diseases such as cancer, wherein the body of published research is extensive, the incorporation of expert knowledge provides a useful analytical framework. We have previously developed the Exploratory Visual Analysis (EVA) software for exploring data analysis results in the context of annotation information about each gene, as well as biologically relevant groups of genes. We present EVA as a flexible combination of statistics and biological annotation that provides a straightforward visual interface for the interpretation of microarray analyses of gene expression in the most commonly occuring class of brain tumors, glioma. We demonstrate the utility of EVA for the biological interpretation of statistical results by analyzing publicly available gene expression profiles of two important glial tumors. The results of a statistical comparison between 21 malignant, high-grade glioblastoma multiforme (GBM) tumors and 19 indolent, low-grade pilocytic astrocytomas were analyzed using EVA. By using EVA to examine the results of a relatively simple statistical analysis, we were able to identify tumor class-specific gene expression patterns having both statistical and biological significance. Our interactive analysis highlighted the potential importance of genes involved in cell cycle progression, proliferation, signaling, adhesion, migration, motility, and structure, as well as candidate gene loci on a region of Chromosome 7 that has been implicated in glioma. Because EVA does not require statistical or computational expertise and has the flexibility to accommodate any type of statistical analysis, we anticipate EVA will prove a useful addition to the repertoire of computational methods used for microarray data analysis. EVA is available at no charge to academic users and can be found at http://www.epistasis.org

The dynamic nature of refugee children's resilience: a cohort study of Syrian refugees in Lebanon.

AIMS: Children's responses to war and displacement are varied; many struggle, while others appear resilient. However, research into these outcomes disproportionately focuses on cross-sectional data in high-income countries. We aimed to (1) investigate change in resilience across two timepoints in a highly vulnerable sample of Syrian refugee children in Lebanon, and (2) explore predictors of their mental health problems across time. METHODS: In total, 982 Syrian child-caregiver dyads living in refugee settlements in Lebanon completed questionnaires via interview at baseline and follow-up one year later. We categorised children into groups based on their risk for mental health problems across both timepoints (stable high risk/SHR, deteriorating, improving, stable low risk) according to locally validated cut-offs on measures of post-traumatic stress disorder (PTSD), depression and behavioural problems. Analyses of covariance identified how the groups differed on a range of individual and socio-environmental predictors, followed up by cross-lagged panel models (CLPMs) to investigate the directionality of the relationships between significantly related predictors and symptoms. RESULTS: The sample showed a meaningful amount of change in mental health symptoms from baseline to follow-up. Over half (56.3%) of children met SHR criteria and 10.3% deteriorated over time, but almost one-quarter (24.2%) showed meaningful improvement, and 9.2% were consistently at low risk for mental health problems at both timepoints. Several predictors differentiated the groups, particularly social measures. According to CLPMs, maternal acceptance (β = -0.07) predicted child mental health symptoms over time. Self-esteem (β = -0.08), maternal psychological control (β = 0.10), child maltreatment (β = 0.09) and caregiver depression (β = 0.08) predicted child symptoms and vice versa (βse = -0.11, βb = 0.07, βmpc = 0.08, βcm = 0.1, βcd = 0.11). Finally, child symptoms predicted loneliness (β = 0.12), bullying (β = 0.07), perceived social support (β = -0.12), parent-child conflict (β = 0.13), caregiver PTSD (β = 0.07), caregiver anxiety (β = 0.08) and the perceived refugee environment (β = -0.09). CONCLUSIONS: Our results show risk and resilience are dynamic, and the family environment plays a key role in children's response to war and displacement. Conversely, children also have a significant impact on the family environment and caregiver's own mental health. Interventions to promote resilience in refugee children should therefore consider family-wide mechanisms