Nucleotide sequence of the luxA gene of Vibrio harveyi and the complete amino acid sequence of the alpha subunit of bacterial luciferase

The nucleotide sequence of the 1.85-kilobase EcoRI fragment from Vibrio harveyi that was cloned using a mixed-sequence synthetic oligonucleotide probe (Cohn, D. H., Ogden, R. C., Abelson, J. N., Baldwin, T. O., Nealson, K. H., Simon, M. I., and Mileham, A. J. (1983) Proc. Natl. Acad. Sci. U.S.A. 80, 120-123) has been determined. The alpha subunit-coding region (luxA) was found to begin at base number 707 and end at base number 1771. The alpha subunit has a calculated molecular weight of 40,108 and comprises a total of 355 amino acid residues. There are 34 base pairs separating the start of the alpha subunit structural gene and a 669-base open reading frame extending from the proximal EcoRI site. At the 3' end of the luxA coding region there are 26 bases between the end of the structural gene and the start of the luxB structural gene. Approximately two-thirds of the alpha subunit was sequenced by protein chemical techniques. The amino acid sequence implied by the DNA sequence, with few exceptions, confirmed the chemically determined sequence. Regions of the alpha subunit thought to comprise the active center were found to reside in two discrete and relatively basic regions, one from around residues 100-115 and the second from around residues 280-295

Evaluation of sequencing strategies for whole-genome imputation with hybrid peeling

Background: For assembling large whole-genome sequence datasets for routine use in research and breeding, the sequencing strategy should be adapted to the methods that will be used later for variant discovery and imputation. In this study, we used simulation to explore the impact that the sequencing strategy and level of sequencing investment have on the overall accuracy of imputation using hybrid peeling, a pedigree-based imputation method that is well suited for large livestock populations. Methods: We simulated marker array and whole-genome sequence data for 15 populations with simulated or real pedigrees that had different structures. In these populations, we evaluated the effect on imputation accuracy of seven methods for selecting which individuals to sequence, the generation of the pedigree to which the sequenced individuals belonged, the use of variable or uniform coverage, and the trade-off between the number of sequenced individuals and their sequencing coverage. For each population, we considered four levels of investment in sequencing that were proportional to the size of the population. Results: Imputation accuracy depended greatly on pedigree depth. The distribution of the sequenced individuals across the generations of the pedigree underlay the performance of the different methods used to select individuals to sequence and it was critical for achieving high imputation accuracy in both early and late generations. Imputation accuracy was highest with a uniform coverage across the sequenced individuals of 2× rather than variable coverage. An investment equivalent to the cost of sequencing 2% of the population at 2× provided high imputation accuracy. The gain in imputation accuracy from additional investment decreased with larger populations and higher levels of investment. However, to achieve the same imputation accuracy, a proportionally greater investment must be used in the smaller populations compared to the larger ones. Conclusions: Suitable sequencing strategies for subsequent imputation with hybrid peeling involve sequencing ~2% of the population at a uniform coverage 2×, distributed preferably across all generations of the pedigree, except for the few earliest generations that lack genotyped ancestors. Such sequencing strategies are beneficial for generating whole-genome sequence data in populations with deep pedigrees of closely related individuals.The authors acknowledge the financial support from the BBSRC ISPG to The Roslin Institute (BBS/E/D/30002275), from Genus plc, Innovate UK (grant 102271), and from grant numbers BB/N004736/1, BB/N015339/1, BB/L020467/1, and BB/M009254/1

Accuracy of whole-genome sequence imputation using hybrid peeling in large pedigreed livestock populations

Background: The coupling of appropriate sequencing strategies and imputation methods is critical for assembling large whole-genome sequence datasets from livestock populations for research and breeding. In this paper, we describe and validate the coupling of a sequencing strategy with the imputation method hybrid peeling in real animal breeding settings. Methods: We used data from four pig populations of different size (18,349 to 107,815 individuals) that were widely genotyped at densities between 15,000 and 75,000 markers genome-wide. Around 2% of the individuals in each population were sequenced (most of them at 1× or 2× and 37-92 individuals per population, totalling 284, at 15-30×). We imputed whole-genome sequence data with hybrid peeling. We evaluated the imputation accuracy by removing the sequence data of the 284 individuals with high coverage, using a leave-one-out design. We simulated data that mimicked the sequencing strategy used in the real populations to quantify the factors that affected the individual-wise and variant-wise imputation accuracies using regression trees. Results: Imputation accuracy was high for the majority of individuals in all four populations (median individual-wise dosage correlation: 0.97). Imputation accuracy was lower for individuals in the earliest generations of each population than for the rest, due to the lack of marker array data for themselves and their ancestors. The main factors that determined the individual-wise imputation accuracy were the genotyping status, the availability of marker array data for immediate ancestors, and the degree of connectedness to the rest of the population, but sequencing coverage of the relatives had no effect. The main factors that determined variant-wise imputation accuracy were the minor allele frequency and the number of individuals with sequencing coverage at each variant site. Results were validated with the empirical observations. Conclusions: We demonstrate that the coupling of an appropriate sequencing strategy and hybrid peeling is a powerful strategy for generating whole-genome sequence data with high accuracy in large pedigreed populations where only a small fraction of individuals (2%) had been sequenced, mostly at low coverage. This is a critical step for the successful implementation of whole-genome sequence data for genomic prediction and fine-mapping of causal variantsThe authors acknowledge the financial support from the BBSRC ISPG to The Roslin Institute (BBS/E/D/30002275), from Genus plc, Innovate UK (Grant 102271), and from Grant numbers BB/N004736/1, BB/N015339/1, BB/L020467/1, and BB/M009254/1

Impact of index hopping and bias towards the reference allele on accuracy of genotype calls from low-coverage sequencing

Abstract Background Inherent sources of error and bias that affect the quality of sequence data include index hopping and bias towards the reference allele. The impact of these artefacts is likely greater for low-coverage data than for high-coverage data because low-coverage data has scant information and many standard tools for processing sequence data were designed for high-coverage data. With the proliferation of cost-effective low-coverage sequencing, there is a need to understand the impact of these errors and bias on resulting genotype calls from low-coverage sequencing. Results We used a dataset of 26 pigs sequenced both at 2× with multiplexing and at 30× without multiplexing to show that index hopping and bias towards the reference allele due to alignment had little impact on genotype calls. However, pruning of alternative haplotypes supported by a number of reads below a predefined threshold, which is a default and desired step of some variant callers for removing potential sequencing errors in high-coverage data, introduced an unexpected bias towards the reference allele when applied to low-coverage sequence data. This bias reduced best-guess genotype concordance of low-coverage sequence data by 19.0 absolute percentage points. Conclusions We propose a simple pipeline to correct the preferential bias towards the reference allele that can occur during variant discovery and we recommend that users of low-coverage sequence data be wary of unexpected biases that may be produced by bioinformatic tools that were designed for high-coverage sequence data

Potential of gene drives with genome editing to increase genetic gain in livestock breeding programs

Abstract Background This paper uses simulation to explore how gene drives can increase genetic gain in livestock breeding programs. Gene drives are naturally occurring phenomena that cause a mutation on one chromosome to copy itself onto its homologous chromosome. Methods We simulated nine different breeding and editing scenarios with a common overall structure. Each scenario began with 21 generations of selection, followed by 20 generations of selection based on true breeding values where the breeder used selection alone, selection in combination with genome editing, or selection with genome editing and gene drives. In the scenarios that used gene drives, we varied the probability of successfully incorporating the gene drive. For each scenario, we evaluated genetic gain, genetic variance ( \u3c3 A 2 ) , rate of change in inbreeding ( \u394 F ), number of distinct quantitative trait nucleotides (QTN) edited, rate of increase in favourable allele frequencies of edited QTN and the time to fix favourable alleles. Results Gene drives enhanced the benefits of genome editing in seven ways: (1) they amplified the increase in genetic gain brought about by genome editing; (2) they amplified the rate of increase in the frequency of favourable alleles and reduced the time it took to fix them; (3) they enabled more rapid targeting of QTN with lesser effect for genome editing; (4) they distributed fixed editing resources across a larger number of distinct QTN across generations; (5) they focussed editing on a smaller number of QTN within a given generation; (6) they reduced the level of inbreeding when editing a subset of the sires; and (7) they increased the efficiency of converting genetic variation into genetic gain. Conclusions Genome editing in livestock breeding results in short-, medium- and long-term increases in genetic gain. The increase in genetic gain occurs because editing increases the frequency of favourable alleles in the population. Gene drives accelerate the increase in allele frequency caused by editing, which results in even higher genetic gain over a shorter period of time with no impact on inbreeding

Precision engineering for PRRSV resistance in pigs: Macrophages from genome edited pigs lacking CD163 SRCR5 domain are fully resistant to both PRRSV genotypes while maintaining biological function

Porcine Reproductive and Respiratory Syndrome (PRRS) is a panzootic infectious disease of pigs, causing major economic losses to the world-wide pig industry. PRRS manifests differently in pigs of all ages but primarily causes late-term abortions and stillbirths in sows and respiratory disease in piglets. The causative agent of the disease is the positive-strand RNA PRRS virus (PRRSV). PRRSV has a narrow host cell tropism, limited to cells of the monocyte/macrophage lineage. CD163 has been described as a fusion receptor for PRRSV, whereby the scavenger receptor cysteine-rich domain 5 (SRCR5) region was shown to be an interaction site for the virus in vitro. CD163 is expressed at high levels on the surface of macrophages, particularly in the respiratory system. Here we describe the application of CRISPR/Cas9 to pig zygotes, resulting in the generation of pigs with a deletion of Exon 7 of the CD163 gene, encoding SRCR5. Deletion of SRCR5 showed no adverse effects in pigs maintained under standard husbandry conditions with normal growth rates and complete blood counts observed. Pulmonary alveolar macrophages (PAMs) and peripheral blood monocytes (PBMCs) were isolated from the animals and assessed in vitro. Both PAMs and macrophages obtained from PBMCs by CSF1 stimulation (PMMs) show the characteristic differentiation and cell surface marker expression of macrophages of the respective origin. Expression and correct folding of the SRCR5 deletion CD163 on the surface of macrophages and biological activity of the protein as hemoglobin-haptoglobin scavenger was confirmed. Challenge of both PAMs and PMMs with PRRSV genotype 1, subtypes 1, 2, and 3 and PMMs with PRRSV genotype 2 showed complete resistance to viral infections assessed by replication. Confocal microscopy revealed the absence of replication structures in the SRCR5 CD163 deletion macrophages, indicating an inhibition of infection prior to gene expression, i.e. at entry/fusion or unpacking stages

Genome edited sheep and cattle

Genome editing tools enable efficient and accurate genome manipulation. An enhanced ability to modify the genomes of livestock species could be utilized to improve disease resistance, productivity or breeding capability as well as the generation of new biomedical models. To date, with respect to the direct injection of genome editor mRNA into livestock zygotes, this technology has been limited to the generation of pigs with edited genomes. To capture the far-reaching applications of gene-editing, from disease modelling to agricultural improvement, the technology must be easily applied to a number of species using a variety of approaches. In this study, we demonstrate zygote injection of TALEN mRNA can also produce gene-edited cattle and sheep. In both species we have targeted the myostatin (MSTN) gene. In addition, we report a critical innovation for application of gene-editing to the cattle industry whereby gene-edited calves can be produced with specified genetics by ovum pickup, in vitro fertilization and zygote microinjection (OPU-IVF-ZM). This provides a practical alternative to somatic cell nuclear transfer for gene knockout or introgression of desirable alleles into a target breed/genetic line