A De Novo Whole Genome Assembly and Annotation of Parelaphostrongylus tenuis.

Parelaphostrongylus tenuis causes ungulate morbidity and mortality in eastern and central North America, but no reference genome sequence exists to facilitate research. Here, we present a P. tenuis genome assembly and annotation, generated with PacBio and Illumina technologies. The assembly is 491 Mbp, with 7285 scaffolds and 185 kb N50.

Development of an exosomal gene signature to detect residual disease in dogs with osteosarcoma using a novel xenograft platform and machine learning.

Osteosarcoma has a guarded prognosis. A major hurdle in developing more effective osteosarcoma therapies is the lack of disease-specific biomarkers to predict risk, prognosis, or therapeutic response. Exosomes are secreted extracellular microvesicles emerging as powerful diagnostic tools. However, their clinical application is precluded by challenges in identifying disease-associated cargo from the vastly larger background of normal exosome cargo. We developed a method using canine osteosarcoma in mouse xenografts to distinguish tumor-derived from host-response exosomal messenger RNAs (mRNAs). The model allows for the identification of canine osteosarcoma-specific gene signatures by RNA sequencing and a species-differentiating bioinformatics pipeline. An osteosarcoma-associated signature consisting of five gene transcripts (SKA2, NEU1, PAF1, PSMG2, and NOB1) was validated in dogs with spontaneous osteosarcoma by real-time quantitative reverse transcription PCR (qRT-PCR), while a machine learning model assigned dogs into healthy or disease groups. Serum/plasma exosomes were isolated from 53 dogs in distinct clinical groups ("healthy", "osteosarcoma", "other bone tumor", or "non-neoplastic disease"). Pre-treatment samples from osteosarcoma cases were used as the training set, and a validation set from post-treatment samples was used for testing, classifying as "osteosarcoma detected" or "osteosarcoma-NOT detected". Dogs in a validation set whose post-treatment samples were classified as "osteosarcoma-NOT detected" had longer remissions, up to 15 months after treatment. In conclusion, we identified a gene signature predictive of molecular remissions with potential applications in the early detection and minimal residual disease settings. These results provide proof of concept for our discovery platform and its utilization in future studies to inform cancer risk, diagnosis, prognosis, and therapeutic response.

A multitask clustering approach for single-cell RNA-seq analysis in Recessive Dystrophic Epidermolysis Bullosa.

Single-cell RNA sequencing (scRNA-seq) has been widely applied to discover new cell types by detecting sub-populations in a heterogeneous group of cells. Since scRNA-seq experiments have lower read coverage/tag counts and introduce more technical biases compared to bulk RNA-seq experiments, the limited number of sampled cells combined with the experimental biases and other dataset specific variations presents a challenge to cross-dataset analysis and discovery of relevant biological variations across multiple cell populations. In this paper, we introduce a method of variance-driven multitask clustering of single-cell RNA-seq data (scVDMC) that utilizes multiple single-cell populations from biological replicates or different samples. scVDMC clusters single cells in multiple scRNA-seq experiments of similar cell types and markers but varying expression patterns such that the scRNA-seq data are better integrated than typical pooled analyses which only increase the sample size. By controlling the variance among the cell clusters within each dataset and across all the datasets, scVDMC detects cell sub-populations in each individual experiment with shared cell-type markers but varying cluster centers among all the experiments. Applied to two real scRNA-seq datasets with several replicates and one large-scale droplet-based dataset on three patient samples, scVDMC more accurately detected cell populations and known cell markers than pooled clustering and other recently proposed scRNA-seq clustering methods. In the case study applied to in-house Recessive Dystrophic Epidermolysis Bullosa (RDEB) scRNA-seq data, scVDMC revealed several new cell types and unknown markers validated by flow cytometry. MATLAB/Octave code available at https://github.com/kuanglab/scVDMC.

EditR: A Method to Quantify Base Editing from Sanger Sequencing.

CRISPR-Cas9-Cytidine deaminase fusion enzymes-termed "base editors"-allow targeted editing of genomic deoxycytidine to deoxythymidine (C:G→T:A) without the need for double-stranded break induction. Base editors represent a paradigm shift in gene editing technology due to their unprecedented efficiency to mediate targeted, single-base conversion. However, current analysis of base editing outcomes rely on methods that are either imprecise or expensive and time-consuming. To overcome these limitations, we developed a simple, cost-effective, and accurate program to measure base editing efficiency from fluorescence-based Sanger sequencing, termed "EditR." We provide EditR as a free online tool or downloadable desktop application requiring a single Sanger sequencing file and guide RNA sequence. EditR is more accurate than enzymatic assays, and provides added insight to the position, type, and efficiency of base editing. Furthermore, EditR is likely amenable to quantify base editing from the recently developed adenosine deaminase base editors that act on either DNA (adenosine deaminase base editors [ABEs]) or RNA (REPAIRs) (catalyzes A:T→G:C). Collectively, we demonstrate that EditR is a robust, inexpensive tool that will facilitate the broad application of base editing technology, thereby fostering further innovation in this burgeoning field.

Heterotypic mouse models of canine osteosarcoma recapitulate tumor heterogeneity and biological behavior.

Osteosarcoma (OS) is a heterogeneous and rare disease with a disproportionate impact because it mainly affects children and adolescents. Lamentably, more than half of patients with OS succumb to metastatic disease. Clarification of the etiology of the disease, development of better strategies to manage progression, and methods to guide personalized treatments are among the unmet health needs for OS patients. Progress in managing the disease has been hindered by the extreme heterogeneity of OS; thus, better models that accurately recapitulate the natural heterogeneity of the disease are needed. For this study, we used cell lines derived from two spontaneous canine OS tumors with distinctly different biological behavior (OS-1 and OS-2) for heterotypic in vivo modeling that recapitulates the heterogeneous biology and behavior of this disease. Both cell lines demonstrated stability of the transcriptome when grown as orthotopic xenografts in athymic nude mice. Consistent with the behavior of the original tumors, OS-2 xenografts grew more rapidly at the primary site and had greater propensity to disseminate to lung and establish microscopic metastasis. Moreover, OS-2 promoted formation of a different tumor-associated stromal environment than OS-1 xenografts. OS-2-derived tumors comprised a larger percentage of the xenograft tumors than OS-1-derived tumors. In addition, a robust pro-inflammatory population dominated the stromal cell infiltrates in OS-2 xenografts, whereas a mesenchymal population with a gene signature reflecting myogenic signaling dominated those in the OS-1 xenografts. Our studies show that canine OS cell lines maintain intrinsic features of the tumors from which they were derived and recapitulate the heterogeneous biology and behavior of bone cancer in mouse models. This system provides a resource to understand essential interactions between tumor cells and the stromal environment that drive the progression and metastatic propensity of OS.

Genomic Inbreeding and Relatedness in Wild Panda Populations.

Inbreeding and relatedness in wild panda populations are important parameters for panda conservation. Habitat loss and fragmentation are expected to increase inbreeding but the actual inbreeding levels in natural panda habitats were unknown. Using 150,025 SNPs and 14,926 SNPs selected from published whole-genome sequences, we estimated genomic inbreeding coefficients and relatedness of 49 pandas including 34 wild pandas sampled from six habitats. Qinling and Liangshan pandas had the highest levels of inbreeding and relatedness measured by genomic inbreeding and coancestry coefficients, whereas the inbreeding levels in Qionglai and Minshan were 28-45% of those in Qinling and Liangshan. Genomic coancestry coefficients between pandas from different habitats showed that panda populations from the four largest habitats, Minshan, Qionglai, Qinling and Liangshan, were genetically unrelated. Pandas between these four habitats on average shared 66.0-69.1% common alleles and 45.6-48.6% common genotypes, whereas pandas within each habitat shared 71.8-77.0% common alleles and 51.7-60.4% common genotypes. Pandas in the smaller populations of Qinling and Liangshan were more similarly to each other than pandas in the larger populations of Qionglai and Minshan according to three genomic similarity measures. Panda genetic differentiation between these habitats was positively related to their geographical distances. Most pandas separated by 200 kilometers or more shared no common ancestral alleles. The results provided a genomic quantification of the actual levels of inbreeding and relatedness among pandas in their natural habitats, provided genomic confirmation of the relationship between genetic diversity and geographical distances, and provided genomic evidence to the urgency of habitat protection.

Efficient nonmeiotic allele introgression in livestock using custom endonucleases.

We have expanded the livestock gene editing toolbox to include transcription activator-like (TAL) effector nuclease (TALEN)- and clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9-stimulated homology-directed repair (HDR) using plasmid, rAAV, and oligonucleotide templates. Toward the genetic dehorning of dairy cattle, we introgressed a bovine POLLED allele into horned bull fibroblasts. Single nucleotide alterations or small indels were introduced into 14 additional genes in pig, goat, and cattle fibroblasts using TALEN mRNA and oligonucleotide transfection with efficiencies of 10-50% in populations. Several of the chosen edits mimic naturally occurring performance-enhancing or disease- resistance alleles, including alteration of single base pairs. Up to 70% of the fibroblast colonies propagated without selection harbored the intended edits, of which more than one-half were homozygous. Edited fibroblasts were used to generate pigs with knockout alleles in the DAZL and APC genes to model infertility and colon cancer. Our methods enable unprecedented meiosis-free intraspecific and interspecific introgression of select alleles in livestock for agricultural and biomedical applications.

Strategies for selection marker-free swine transgenesis using the Sleeping Beauty transposon system.

Swine transgenesis by pronuclear injection or cloning has traditionally relied on illegitimate recombination of DNA into the pig genome. This often results in animals containing concatemeric arrays of transgenes that complicate characterization and can impair long-term transgene stability and expression. This is inconsistent with regulatory guidance for transgenic livestock, which also discourages the use of selection markers, particularly antibiotic resistance genes. We demonstrate that the Sleeping Beauty (SB) transposon system effectively delivers monomeric, multi-copy transgenes to the pig embryo genome by pronuclear injection without markers, as well as to donor cells for founder generation by cloning. Here we show that our method of transposon-mediated transgenesis yielded 38 cloned founder pigs that altogether harbored 100 integrants for five distinct transposons encoding either human APOBEC3G or YFP-Cre. Two strategies were employed to facilitate elimination of antibiotic genes from transgenic pigs, one based on Cre-recombinase and the other by segregation of independently transposed transgenes upon breeding.

Efficient mammalian germline transgenesis by cis-enhanced Sleeping Beauty transposition.

Heightened interest in relevant models for human disease increases the need for improved methods for germline transgenesis. We describe a significant improvement in the creation of transgenic laboratory mice and rats by chemical modification of Sleeping Beauty transposons. Germline transgenesis in mice and rats was significantly enhanced by in vitro cytosine-phosphodiester-guanine methylation of transposons prior to injection. Heritability of transgene alleles was also greater from founder mice generated with methylated versus non-methylated transposon. The artificial methylation was reprogrammed in the early embryo, leading to founders that express the transgenes. We also noted differences in transgene insertion number and structure (single-insert versus concatemer) based on the influence of methylation and plasmid conformation (linear versus supercoiled), with supercoiled substrate resulting in efficient transpositional transgenesis (TnT) with near elimination of concatemer insertion. Combined, these substrate modifications resulted in increases in both the frequency of transgenic founders and the number of transgenes per founder, significantly elevating the number of potential transgenic lines. Given its simplicity, versatility and high efficiency, TnT with enhanced Sleeping Beauty components represents a compelling non-viral approach to modifying the mammalian germline.

Development and application of bovine and porcine oligonucleotide arrays with protein-based annotation.

The design of oligonucleotide sequences for the detection of gene expression in species with disparate volumes of genome and EST sequence information has been broadly studied. However, a congruous strategy has yet to emerge to allow the design of sensitive and specific gene expression detection probes. This study explores the use of a phylogenomic approach to align transcribed sequences to vertebrate protein sequences for the detection of gene families to design genomewide 70-mer oligonucleotide probe sequences for bovine and porcine. The bovine array contains 23,580 probes that target the transcripts of 16,341 genes, about 72% of the total number of bovine genes. The porcine array contains 19,980 probes targeting 15,204 genes, about 76% of the genes in the Ensembl annotation of the pig genome. An initial experiment using the bovine array demonstrates the specificity and sensitivity of the array.

Parallel and serial computing tools for testing single-locus and epistatic SNP effects of quantitative traits in genome-wide association studies.

BACKGROUND: Genome-wide association studies (GWAS) using single nucleotide polymorphism (SNP) markers provide opportunities to detect epistatic SNPs associated with quantitative traits and to detect the exact mode of an epistasis effect. Computational difficulty is the main bottleneck for epistasis testing in large scale GWAS. RESULTS: The EPISNPmpi and EPISNP computer programs were developed for testing single-locus and epistatic SNP effects on quantitative traits in GWAS, including tests of three single-locus effects for each SNP (SNP genotypic effect, additive and dominance effects) and five epistasis effects for each pair of SNPs (two-locus interaction, additive x additive, additive x dominance, dominance x additive, and dominance x dominance) based on the extended Kempthorne model. EPISNPmpi is the parallel computing program for epistasis testing in large scale GWAS and achieved excellent scalability for large scale analysis and portability for various parallel computing platforms. EPISNP is the serial computing program based on the EPISNPmpi code for epistasis testing in small scale GWAS using commonly available operating systems and computer hardware. Three serial computing utility programs were developed for graphical viewing of test results and epistasis networks, and for estimating CPU time and disk space requirements. CONCLUSION: The EPISNPmpi parallel computing program provides an effective computing tool for epistasis testing in large scale GWAS, and the epiSNP serial computing programs are convenient tools for epistasis analysis in small scale GWAS using commonly available computer hardware.

Genome-wide analysis of single-locus and epistasis single-nucleotide polymorphism effects on anti-cyclic citrullinated peptide as a measure of rheumatoid arthritis.

The goal of this study was to identify single-locus and epistasis effects of SNP markers on anti-cyclic citrullinated peptide (anti-CCP) that is associated with rheumatoid arthritis, using the North American Rheumatoid Arthritis Consortium data. A square root transformation of the phenotypic values of anti-CCP with sex, smoking status, and a selected subset of 20 single-nucleotide polymorphism (SNP) markers in the model achieved residual normality (p > 0.05). Three single-locus effects of two SNPs were significant (p < 10-4). The epistasis analysis tested five effects of each pair of SNPs, the two-locus interaction, additive x additive, additive x dominance, dominance x additive, and dominance x dominance effects. A total of ten epistasis effects of eight pairs of SNPs on 11 autosomes and the X chromosome had significant epistasis effects (p < 10-7). Three of these epistasis effects reached significance levels of p < 10-8, p < 10-9, and p < 10-10, respectively. Two potential SNP epistasis networks were identified. The results indicate that the genetic factors underlying anti-CCP may include single-gene action and gene interactions and that the gene-interaction mechanism underlying anti-CCP could be a complex mechanism involving pairwise epistasis effects and multiple SNPs.

Linkage analysis for mapping genes of sex-influenced traits.

Statistical methods are developed to estimate gender-specific and gender-average recombination frequencies between a biallelic or multiallelic marker and a sex-influenced gene. Iterative solutions are developed for intercross (or F-2 design). For biallelic markers, two iterative solutions are required, one for coupling and repulsion parental linkage phases and one for mixed parental linkage phases. For multiallelic markers, one set of iterative equations applies to all possible parental linkage phases. The resulting formulae for estimating recombination frequency use the full data set and yield estimates that are exactly the same as the true parameters if the observed and expected phenotypic distributions are equal. When one parent is homozygous for the sex-influenced gene as is expected with the backcross design, simple solutions exist for estimating recombination frequencies. However, offspring of one gender (male or female) do not have linkage information depending on whether the homozygous parent has two male-dominant or male-recessive alleles. Consequently, an F-2 design is more efficient than a backcross design for mapping a sex-influenced gene. Knowing each parental linkage phase is important to apply the methods developed in this article. It is shown that an individual's linkage phase of the sex-influenced locus can be determined based on allele transmission from the parents for all crosses except under the mating between an expressed male and an unexpressed female.