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Abstract This manuscript describes the development of a resource module that is part of a learning platform named ‘NIGMS Sandbox for Cloud-based Learning’ (https://github.com/NIGMS/NIGMS-Sandbox). The module delivers learning materials on Cloud-based Consensus Pathway Analysis in an interactive format that uses appropriate cloud resources for data access and analyses. Pathway analysis is important because it allows us to gain insights into biological mechanisms underlying conditions. But the availability of many pathway analysis methods, the requirement of coding skills, and the focus of current tools on only a few species all make it very difficult for biomedical researchers to self-learn and perform pathway analysis efficiently. Furthermore, there is a lack of tools that allow researchers to compare analysis results obtained from different experiments and different analysis methods to find consensus results. To address these challenges, we have designed a cloud-based, self-learning module that provides consensus results among established, state-of-the-art pathway analysis techniques to provide students and researchers with necessary training and example materials. The training module consists of five Jupyter Notebooks that provide complete tutorials for the following tasks: (i) process expression data, (ii) perform differential analysis, visualize and compare the results obtained from four differential analysis methods (limma, t-test, edgeR, DESeq2), (iii) process three pathway databases (GO, KEGG and Reactome), (iv) perform pathway analysis using eight methods (ORA, CAMERA, KS test, Wilcoxon test, FGSEA, GSA, SAFE and PADOG) and (v) combine results of multiple analyses. We also provide examples, source code, explanations and instructional videos for trainees to complete each Jupyter Notebook. The module supports the analysis for many model (e.g. human, mouse, fruit fly, zebra fish) and non-model species. The module is publicly available at https://github.com/NIGMS/Consensus-Pathway-Analysis-in-the-Cloud. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses. 

Starvation is a complex physiological state that induces changes in protein expression to ensure survival. The insect midgut is sensitive to changes in dietary content as it is at the forefront of communicating information about incoming nutrients to the body via hormones. Therefore, a DIA proteomics approach was used to examine starvation physiology and, specifically, the role of midgut neuropeptide hormones in a representative lepidopteran, Manduca sexta. Proteomes were generated from midguts of M. sexta fourth-instar caterpillars, starved for 24 h and 48 h, and compared to fed controls. A total of 3047 proteins were identified, and 854 of these were significantly different in abundance. KEGG analysis revealed that metabolism pathways were less abundant in starved caterpillars, but oxidative phosphorylation proteins were more abundant. In addition, six neuropeptides or related signaling cascade proteins were detected. Particularly, neuropeptide F1 (NPF1) was significantly higher in abundance in starved larvae. A change in juvenile hormone-degrading enzymes was also detected during starvation. Overall, our results provide an exploration of the midgut response to starvation in M. sexta and validate DIA proteomics as a useful tool for quantifying insect midgut neuropeptide hormones. 

ABSTRACT Neurons are almost exclusively cultured in media containing glucose at much higher concentrations than found in the brain. To test whether these “standard” hyperglycemic culture conditions affect neuronal respiration relative to near‐euglycemic conditions, we compared neuronal cultures grown with minimal glial contamination from the hippocampus and cortex of neonatal C57BL/6NCrl mice in standard commercially available media (25 mM Glucose) and in identical media with 5 mM glucose. Neuronal growth in both glucose concentrations proceeded until at least 14 days in vitro, with similar morphology and synaptogenesis. Neurons grown in high glucose were highly dependent on glycolysis as their primary source of ATP, measured using ATP luminescence and cellular respirometry assays. In contrast, neurons grown in 5 mM glucose showed a more balanced dependence on glycolysis and mitochondrial oxidative phosphorylation (OXPHOS), greater reserve mitochondrial respiration capacity, and increased mitochondrial population relative to standard media. Our results show that neurons cultured in artificially high glucose‐containing media preferentially use glycolysis, opposite to what is known for neurons in vivo as the primary pathway for ATP maintenance. Changes in gene and protein expression levels corroborate these changes in function and additionally suggest that high glucose culture media increases neuronal inflammation. We suggest using neuronal culture systems in 5 mM glucose to better represent physiologically relevant neuronal respiration.image 

The immune equilibrium model suggests that exposure to microbes during early life primes immune responses for pathogen exposure later in life. While recent studies using a range of gnotobiotic (germ-free) model organisms offer support for this theory, we currently lack a tractable model system for investigating the influence of the microbiome on immune system development. Here, we used an amphibian species ( Xenopus laevis ) to investigate the importance of the microbiome in larval development and susceptibility to infectious disease later in life. We found that experimental reductions of the microbiome during embryonic and larval stages effectively reduced microbial richness, diversity and altered community composition in tadpoles prior to metamorphosis. In addition, our antimicrobial treatments resulted in few negative effects on larval development, body condition, or survival to metamorphosis. However, contrary to our predictions, our antimicrobial treatments did not alter susceptibility to the lethal fungal pathogen Batrachochytrium dendrobatidis ( Bd ) in the adult life stage. While our treatments to reduce the microbiome during early development did not play a critical role in determining susceptibility to disease caused by Bd in X. laevis , they nevertheless indicate that developing a gnotobiotic amphibian model system may be highly useful for future immunological investigations. This article is part of the theme issue ‘Amphibian immunity: stress, disease and ecoimmunology’. 

Abstract In molecular biology and genetics, there is a large gap between the ease of data collection and our ability to extract knowledge from these data. Contributing to this gap is the fact that living organisms are complex systems whose emerging phenotypes are the results of multiple complex interactions taking place on various pathways. This demands powerful yet user-friendly pathway analysis tools to translate the now abundant high-throughput data into a better understanding of the underlying biological phenomena. Here we introduce Consensus Pathway Analysis (CPA), a web-based platform that allows researchers to (i) perform pathway analysis using eight established methods (GSEA, GSA, FGSEA, PADOG, Impact Analysis, ORA/Webgestalt, KS-test, Wilcox-test), (ii) perform meta-analysis of multiple datasets, (iii) combine methods and datasets to accurately identify the impacted pathways underlying the studied condition and (iv) interactively explore impacted pathways, and browse relationships between pathways and genes. The platform supports three types of input: (i) a list of differentially expressed genes, (ii) genes and fold changes and (iii) an expression matrix. It also allows users to import data from NCBI GEO. The CPA platform currently supports the analysis of multiple organisms using KEGG and Gene Ontology, and it is freely available at http://cpa.tinnguyen-lab.com.