PANGEA: a new gene set enrichment tool for Drosophila and common research organisms.

Gene set enrichment analysis (GSEA) plays an important role in large-scale data analysis, helping scientists discover the underlying biological patterns over-represented in a gene list resulting from, for example, an 'omics' study. Gene Ontology (GO) annotation is the most frequently used classification mechanism for gene set definition. Here we present a new GSEA tool, PANGEA (PAthway, Network and Gene-set Enrichment Analysis; https://www.flyrnai.org/tools/pangea/), developed to allow a more flexible and configurable approach to data analysis using a variety of classification sets. PANGEA allows GO analysis to be performed on different sets of GO annotations, for example excluding high-throughput studies. Beyond GO, gene sets for pathway annotation and protein complex data from various resources as well as expression and disease annotation from the Alliance of Genome Resources (Alliance). In addition, visualizations of results are enhanced by providing an option to view network of gene set to gene relationships. The tool also allows comparison of multiple input gene lists and accompanying visualisation tools for quick and easy comparison. This new tool will facilitate GSEA for Drosophila and other major model organisms based on high-quality annotated information available for these species.

A cell atlas of the adult Drosophila midgut.

Studies of the adult Drosophila midgut have led to many insights in our understanding of cell-type diversity, stem cell regeneration, tissue homeostasis, and cell fate decision. Advances in single-cell RNA sequencing provide opportunities to identify new cell types and molecular features. We used single-cell RNA sequencing to characterize the transcriptome of midgut epithelial cells and identified 22 distinct clusters representing intestinal stem cells, enteroblasts, enteroendocrine cells (EEs), and enterocytes. This unbiased approach recovered most of the known intestinal stem cells/enteroblast and EE markers, highlighting the high quality of the dataset, and led to insights on intestinal stem cell biology, cell type-specific organelle features, the roles of new transcription factors in progenitors and regional variation along the gut, 5 additional EE gut hormones, EE hormonal expression diversity, and paracrine function of EEs. To facilitate mining of this rich dataset, we provide a web-based resource for visualization of gene expression in single cells. Altogether, our study provides a comprehensive resource for addressing functions of genes in the midgut epithelium.

A time-dependent genome-wide SNP-SNP interaction analysis of chicken body weight.

BACKGROUND: The important property of the quantitative traits of model organisms is time-dependent. However, the methodology for investigating the genetic interaction network over time is still lacking. Our study aims to provide insights into the mechanistic basis of epistatic interactions affecting the phenotypes of model organisms. RESULTS: We performed an exhaustive genome-wide search for significant SNP-SNP interactions associated with male birds' body weight (BW) (n = 475) at multiple time points (day of hatch (BW0) and 1, 3, 5, and 7 weeks (BW1, BW3, BW5, and BW7)). Statistical analysis detected 67, four, and two significant SNP pairs associated with BW0, BW1, and BW3, respectively, with a significance threshold at 8.67 × 10- 12 (Bonferroni-adjusted: 1%). Meanwhile, no significant SNP pairs associated with BW5 and BW7 were found. The SNP-SNP interaction networks of BW0, BW1, and BW3 were built and annotated. CONCLUSIONS: With strong annotated information and a strict significant threshold, SNP-SNP interactions underpinned the gene-gene interactions that might occur between chromosomes or within the same chromosome. Comparing and combing the networks, the results indicated that the genetic network for chicken body weight was dynamic and time-dependent.

PANGEA: A New Gene Set Enrichment Tool for Drosophila and Common Research Organisms.

Gene set enrichment analysis (GSEA) plays an important role in large-scale data analysis, helping scientists discover the underlying biological patterns over-represented in a gene list resulting from, for example, an 'omics' study. Gene Ontology (GO) annotation is the most frequently used classification mechanism for gene set definition. Here we present a new GSEA tool, PANGEA (PAthway, Network and Gene-set Enrichment Analysis; https://www.flyrnai.org/tools/pangea/ ), developed to allow a more flexible and configurable approach to data analysis using a variety of classification sets. PANGEA allows GO analysis to be performed on different sets of GO annotations, for example excluding high-throughput studies. Beyond GO, gene sets for pathway annotation and protein complex data from various resources as well as expression and disease annotation from the Alliance of Genome Resources (Alliance). In addition, visualisations of results are enhanced by providing an option to view network of gene set to gene relationships. The tool also allows comparison of multiple input gene lists and accompanying visualisation tools for quick and easy comparison. This new tool will facilitate GSEA for Drosophila and other major model organisms based on high-quality annotated information available for these species.

[Current status of SNPs interaction in genome-wide association study].

Identifying genetic variants associated with complex diseases/traits via genome-wide single nucleotide polymorphisms (SNPs) has proved to be a new and efficient method for studying genetics. With a large number of achievements of genome-wide association study (GWAS), researchers have focused on performing genome-wide SNPs interaction analysis. The search for interaction effects is marked by an exponential growth, not only in terms of methodological development, practical applications and translation of statistical interaction to biological interaction, but also in terms of integration of omics information sources. Many strategies and methods have been applied in detecting interaction analysis, which provides new insights into genetics basis of complex diseases/traits. In this review based on the theory and algorithm realizations, the statistical methods have been sorted into regression, machine learning, Bayesian model, SNP filtering methods and parallel processing methods. Especially, the principle, efficiency and difference of the methods are summarized to offer references to the researchers in this field.

Intestinal response to dietary manganese depletion in Drosophila.

Manganese is considered essential for animal growth. Manganese ions serve as cofactors to three mitochondrial enzymes: superoxide dismutase (Sod2), arginase and glutamine synthase, and to glycosyltransferases residing in the Golgi. In Drosophila melanogaster, manganese has also been implicated in the formation of ceramide phosphoethanolamine, the insect's sphingomyelin analogue, a structural component of cellular membranes. Manganese overload leads to neurodegeneration and toxicity in both humans and Drosophila. Here, we report specific absorption and accumulation of manganese during the first week of adulthood in flies, which correlates with an increase in Sod2 activity during the same period. To test the requirement of dietary manganese for this accumulation, we generated a Drosophila model of manganese deficiency. Due to the lack of manganese-specific chelators, we used chemically defined media to grow the flies and deplete them of the metal. Dietary manganese depletion reduced Sod2 activity. We then examined gene and protein expression changes in the intestines of manganese depleted flies. We found adaptive responses to the presumed loss of known manganese-dependent enzymatic activities: less glutamine synthase activity (amination of glutamate to glutamine) was compensated by 50% reduction in glutaminase (deamination of glutamine to glutamate); less glycosyltransferase activity, predicted to reduce protein glycosylation, was compensated by 30% reduction in lysosomal mannosidases (protein deglycosylating enzymes); less ceramide phosphoethanolamine synthase activity was compensated by 30% reduction in the Drosophila sphingomyeline phospodiesterase, which could catabolize ceramide phosphoethanolamine in flies. Reduced Sod2 activity, predicted to cause superoxide-dependent iron-sulphur cluster damage, resulted in cellular iron misregulation.

A single-cell survey of Drosophila blood.

Drosophila blood cells, called hemocytes, are classified into plasmatocytes, crystal cells, and lamellocytes based on the expression of a few marker genes and cell morphologies, which are inadequate to classify the complete hemocyte repertoire. Here, we used single-cell RNA sequencing (scRNA-seq) to map hemocytes across different inflammatory conditions in larvae. We resolved plasmatocytes into different states based on the expression of genes involved in cell cycle, antimicrobial response, and metabolism together with the identification of intermediate states. Further, we discovered rare subsets within crystal cells and lamellocytes that express fibroblast growth factor (FGF) ligand branchless and receptor breathless, respectively. We demonstrate that these FGF components are required for mediating effective immune responses against parasitoid wasp eggs, highlighting a novel role for FGF signaling in inter-hemocyte crosstalk. Our scRNA-seq analysis reveals the diversity of hemocytes and provides a rich resource of gene expression profiles for a systems-level understanding of their functions.

Epistatic effects on abdominal fat content in chickens: results from a genome-wide SNP-SNP interaction analysis.

We performed a pairwise epistatic interaction test using the chicken 60 K single nucleotide polymorphism (SNP) chip for the 11(th) generation of the Northeast Agricultural University broiler lines divergently selected for abdominal fat content. A linear mixed model was used to test two dimensions of SNP interactions affecting abdominal fat weight. With a threshold of P<1.2×10(-11) by a Bonferroni 5% correction, 52 pairs of SNPs were detected, comprising 45 pairs showing an Additive×Additive and seven pairs showing an Additive×Dominance epistatic effect. The contribution rates of significant epistatic interactive SNPs ranged from 0.62% to 1.54%, with 47 pairs contributing more than 1%. The SNP-SNP network affecting abdominal fat weight constructed using the significant SNP pairs was analyzed, estimated and annotated. On the basis of the network's features, SNPs Gga_rs14303341 and Gga_rs14988623 at the center of the subnet should be important nodes, and an interaction between GGAZ and GGA8 was suggested. Twenty-two quantitative trait loci, 97 genes (including nine non-coding genes), and 50 pathways were annotated on the epistatic interactive SNP-SNP network. The results of the present study provide insights into the genetic architecture underlying broiler chicken abdominal fat weight.