Genome Report: Identification and Validation of Antigenic Proteins from Pajaroellobacter abortibovis Using De Novo Genome Sequence Assembly and Reverse Vaccinology.

Epizootic bovine abortion (EBA), or "foothill abortion," is the leading cause of beef cattle abortion in California and has also been reported in Nevada and Oregon. In the 1970s, the soft-shelled tick Ornithodoros coriaceus, or "pajaroello tick," was confirmed as the disease-transmitting vector. In 2005, a novel Deltaproteobacterium was discovered as the etiologic agent of EBA (aoEBA), recently named Pajaroellobacter abortibovis This organism cannot be grown in culture using traditional microbiological techniques; it can only be grown in experimentally-infected severe combined immunodeficient (SCID) mice. The objectives of this study were to perform a de novo genome assembly for P. abortibovis and identify and validate potential antigenic proteins as candidates for future recombinant vaccine development. DNA and RNA were extracted from spleen tissue collected from experimentally-infected SCID mice following exposure to P. abortibovis This combination of mouse and bacterial DNA was sequenced and aligned to the mouse genome. Mouse sequences were subtracted from the sequence pool and the remaining sequences were de novo assembled at 50x coverage into a 1.82 Mbp complete closed circular Deltaproteobacterial genome containing 2250 putative protein-coding sequences. Phylogenetic analysis of P. abortibovis predicts that this bacterium is most closely related to the organisms of the order Myxococcales, referred to as Myxobacteria. In silico prediction of vaccine candidates was performed using a reverse vaccinology approach resulting in the identification and ranking of the top 10 candidate proteins that are likely to be antigenic. Immunologic testing of these candidate proteins confirmed antigenicity of seven of the nine expressed protein candidates using serum from P. abortibovis immunized mice.

Identification of Gene Networks for Residual Feed Intake in Angus Cattle Using Genomic Prediction and RNA-seq.

Improvement in feed conversion efficiency can improve the sustainability of beef cattle production, but genomic selection for feed efficiency affects many underlying molecular networks and physiological traits. This study describes the differences between steer progeny of two influential Angus bulls with divergent genomic predictions for residual feed intake (RFI). Eight steer progeny of each sire were phenotyped for growth and feed intake from 8 mo. of age (average BW 254 kg, with a mean difference between sire groups of 4.8 kg) until slaughter at 14-16 mo. of age (average BW 534 kg, sire group difference of 28.8 kg). Terminal samples from pituitary gland, skeletal muscle, liver, adipose, and duodenum were collected from each steer for transcriptome sequencing. Gene expression networks were derived using partial correlation and information theory (PCIT), including differentially expressed (DE) genes, tissue specific (TS) genes, transcription factors (TF), and genes associated with RFI from a genome-wide association study (GWAS). Relative to progeny of the high RFI sire, progeny of the low RFI sire had -0.56 kg/d finishing period RFI (P = 0.05), -1.08 finishing period feed conversion ratio (P = 0.01), +3.3 kg^0.75 finishing period metabolic mid-weight (MMW; P = 0.04), +28.8 kg final body weight (P = 0.01), -12.9 feed bunk visits per day (P = 0.02) with +0.60 min/visit duration (P = 0.01), and +0.0045 carcass specific gravity (weight in air/weight in air-weight in water, a predictor of carcass fat content; P = 0.03). RNA-seq identified 633 DE genes between sire groups among 17,016 expressed genes. PCIT analysis identified >115,000 significant co-expression correlations between genes and 25 TF hubs, i.e. controllers of clusters of DE, TS, and GWAS SNP genes. Pathway analysis suggests low RFI bull progeny possess heightened gut inflammation and reduced fat deposition. This multi-omics analysis shows how differences in RFI genomic breeding values can impact other traits and gene co-expression networks.