Identification of Novel Seroreactive Antigens in Johne’s Disease Cattle by Using the Mycobacterium tuberculosis Protein Array

Johne’s disease, a chronic gastrointestinal inflammatory disease caused by Mycobacterium avium subspecies paratuberculosis, is endemic in dairy cattle and other ruminants worldwide and remains a challenge to diagnose using traditional serological methods. Given the close phylogenetic relationship between M. aviumsubsp. paratuberculosis and the human pathogen Mycobacterium tuberculosis, here, we applied a whole-proteome M. tuberculosis protein array to identify seroreactive and diagnostic M. avium subsp. paratuberculosis antigens. A genome-scale pairwise analysis of amino acid identity levels between orthologous proteins in M. avium subsp. paratuberculosis and M. tuberculosis showed an average of 62% identity, with more than half the orthologous proteins sharing 75% identity. Analysis of the M. tuberculosis protein array probed with sera from M. avium subsp. paratuberculosis- infected cattle showed antibody binding to 729 M. tuberculosis proteins, with 58% of them having 70% identity to M. avium subsp. paratuberculosis orthologs. The results showed that only 4 of the top 40 seroreactive M. tuberculosis antigens were orthologs of previously reported M. avium subsp. paratuberculosis antigens, revealing the existence of a large number of previously unrecognized candidate diagnostic antigens. Enzyme-linked immunosorbent assay (ELISA) testing of 20 M. avium subsp. paratuberculosis recombinant proteins, representing reactive and nonreactive M. tuberculosis orthologs, further confirmed that the M. tuberculosis array has utility as a screening tool for identifying candidate antigens for Johne’s disease diagnostics. Additional ELISA testing of field serum samples collected from dairy herds around the United States revealed that MAP2942c had the strongest seroreactivity with Johne’s disease-positive samples. Collectively, our studies have considerably expanded the number of candidate M. avium subsp. paratuberculosis proteins with potential utility in the next generation of rationally designed Johne’s disease diagnostic assays

Metformin Affects the Transcriptomic Profile of Chicken Ovarian Cancer Cells

Ovarian cancer is the most lethal gynecological malignancy in women. Metformin intake is associated with a reduced incidence of ovarian cancer and increased overall survival rate. We determined the effect of metformin on sphere formation, extracellular matrix invasion, and transcriptome profile of ovarian cancer cells (COVCAR) isolated from ascites of chickens that naturally developed ovarian cancer. We found that metformin treatment significantly decreased sphere formation and invasiveness of COVCAR cells. RNA-Seq data analysis revealed 0, 4, 365 differentially expressed genes in cells treated with 0.5, 1, 2 mM metformin, respectively compared to controls. Transcriptomic and ingenuity pathway analysis (IPA) revealed significant downregulation of MMP7, AICDA, GDPD2, APOC3, APOA1 and predicted inhibition of upstream regulators NFKB, STAT3, TP53 that are involved in epithelial–mesenchymal transition, DNA repair, and lipid metabolism. The analysis revealed significant upregulation of RASD2, IHH, CRABP-1 and predicted activation of upstream regulators VEGF and E2F1 that are associated with angiogenesis and cell cycle. Causal network analysis revealed novel pathways suggesting predicted inhibition of ovarian cancer through master regulator ASCL1 and dataset genes DCX, SEMA6B, HEY2, and KCNIP2. In summary, advanced pathway analysis in IPA revealed novel target genes, upstream regulators, and pathways affected by metformin treatment of COVCAR cells

Giraffe genome sequence reveals clues to its unique morphology and physiology

Research Article published by Nature CommunicationsThe origins of giraffe’s imposing stature and associated cardiovascular adaptations are unknown. Okapi, which lacks these unique features, is giraffe’s closest relative and provides a useful comparison, to identify genetic variation underlying giraffe’s long neck and cardiovascular system. The genomes of giraffe and okapi were sequenced, and through comparative analyses genes and pathways were identified that exhibit unique genetic changes and likely contribute to giraffe’s unique features. Some of these genes are in the HOX, NOTCH and FGF signalling pathways, which regulate both skeletal and cardiovascular development, suggesting that giraffe’s stature and cardiovascular adaptations evolved in parallel through changes in a small number of genes. Mitochondrial metabolism and volatile fatty acids transport genes are also evolutionarily diverged in giraffe and may be related to its unusual diet that includes toxic plants. Unexpectedly, substantial evolutionary changes have occurred in giraffe and okapi in double-strand break repair and centrosome functions

A combination of conserved and diverged responses underlies Theobroma cacao’s defense response to Phytophthora palmivora

Abstract Background Plants have complex and dynamic immune systems that have evolved to resist pathogens. Humans have worked to enhance these defenses in crops through breeding. However, many crops harbor only a fraction of the genetic diversity present in wild relatives. Increased utilization of diverse germplasm to search for desirable traits, such as disease resistance, is therefore a valuable step towards breeding crops that are adapted to both current and emerging threats. Here, we examine diversity of defense responses across four populations of the long-generation tree crop Theobroma cacao L., as well as four non-cacao Theobroma species, with the goal of identifying genetic elements essential for protection against the oomycete pathogen Phytophthora palmivora. Results We began by creating a new, highly contiguous genome assembly for the P. palmivora-resistant genotype SCA 6 (Additional file 1: Tables S1-S5), deposited in GenBank under accessions CP139290-CP139299. We then used this high-quality assembly to combine RNA and whole-genome sequencing data to discover several genes and pathways associated with resistance. Many of these are unique, i.e., differentially regulated in only one of the four populations (diverged 40 k–900 k generations). Among the pathways shared across all populations is phenylpropanoid biosynthesis, a metabolic pathway with well-documented roles in plant defense. One gene in this pathway, caffeoyl shikimate esterase (CSE), was upregulated across all four populations following pathogen treatment, indicating its broad importance for cacao’s defense response. Further experimental evidence suggests this gene hydrolyzes caffeoyl shikimate to create caffeic acid, an antimicrobial compound and known inhibitor of Phytophthora spp. Conclusions Our results indicate most expression variation associated with resistance is unique to populations. Moreover, our findings demonstrate the value of using a broad sample of evolutionarily diverged populations for revealing the genetic bases of cacao resistance to P. palmivora. This approach has promise for further revealing and harnessing valuable genetic resources in this and other long-generation plants

A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium

We present primary results from the Sequencing Quality Control (SEQC) project, coordinated by the US Food and Drug Administration. Examining Illumina HiSeq, Life Technologies SOLiD and Roche 454 platforms at multiple laboratory sites using reference RNA samples with built-in controls, we assess RNA sequencing (RNA-seq) performance for junction discovery and differential expression profiling and compare it to microarray and quantitative PCR (qPCR) data using complementary metrics. At all sequencing depths, we discover unannotated exon-exon junctions, with >80% validated by qPCR. We find that measurements of relative expression are accurate and reproducible across sites and platforms if specific filters are used. In contrast, RNA-seq and microarrays do not provide accurate absolute measurements, and gene-specific biases are observed for all examined platforms, including qPCR. Measurement performance depends on the platform and data analysis pipeline, and variation is large for transcript-level profiling. The complete SEQC data sets, comprising >100 billion reads (10Tb), provide unique resources for evaluating RNA-seq analyses for clinical and regulatory settings