Porcine germline genome engineering.

Germline editing, the process by which the genome of an individual is edited in such a way that the change is heritable, has been applied to a wide variety of animals [D. A. Sorrell, A. F. Kolb, Biotechnol. Adv. 23, 431-469 (2005); D. Baltimore et al., Science 348, 36-38 (2015)]. Because of its relevancy in agricultural and biomedical research, the pig genome has been extensively modified using a multitude of technologies [K. Lee, K. Farrell, K. Uh, Reprod. Fertil. Dev. 32, 40-49 (2019); C. Proudfoot, S. Lillico, C. Tait-Burkard, Anim. Front. 9, 6-12 (2019)]. In this perspective, we will focus on using pigs as the model system to review the current methodologies, applications, and challenges of mammalian germline genome editing. We will also discuss the broad implications of animal germline editing and its clinical potential.

microRNA and inflammatory gene expression as prognostic marker for overall survival in esophageal squamous cell carcinoma.

MicroRNAs (miRNAs) and inflammatory genes have a role in the initiation and development of esophageal squamous cell carcinoma (ESCC). In our study, we examined the potential of using miRNA and inflammatory gene expression patterns as prognostic classifiers for ESCC. Five miRNAs and 25 inflammatory-related genes were measured by quantitative reverse transcriptase PCR in tumor tissues and adjacent noncancerous tissues from 178 Chinese patients with ESCC. The expression levels of miR-21 (p = 0.027), miR-181b (p = 0.002) and miR-146b (p = 0.021) in tumor tissue and miR-21 (p = 0.003) in noncancerous tissue were associated with overall survival of patients. These data were combined to generate a miRNA risk score that was significantly associated with worse prognosis (p = 0.0001), suggesting that these miRNAs may be useful prognostic classifiers for ESCC. To construct an inflammatory gene prognostic classifier, we divided the population into training (n = 124) and test cohorts (n = 54). The expression levels of CRY61, CTGF and IL-18 in tumor tissue and VEGF in adjacent noncancerous tissue were modestly associated with prognosis in the training cohort |Z-score| > 1.5 and were subsequently used to construct a Cox regression-based inflammatory risk score (IRS). IRS was significantly associated with survival in both the training cohort (p = 0.002) and the test cohort (p = 0.005). Furthermore, Cox regression models combining both miRNA risk score and IRS performed significantly better than models with either alone (p < 0.001 likelihood ratio test). Therefore, miRNA and inflammatory gene expression patterns, alone or in combination, have potential as prognostic classifiers for ESCC and may help to guide therapeutic decisions.

Large-scale genotyping of complex DNA.

Genetic studies aimed at understanding the molecular basis of complex human phenotypes require the genotyping of many thousands of single-nucleotide polymorphisms (SNPs) across large numbers of individuals. Public efforts have so far identified over two million common human SNPs; however, the scoring of these SNPs is labor-intensive and requires a substantial amount of automation. Here we describe a simple but effective approach, termed whole-genome sampling analysis (WGSA), for genotyping thousands of SNPs simultaneously in a complex DNA sample without locus-specific primers or automation. Our method amplifies highly reproducible fractions of the genome across multiple DNA samples and calls genotypes at >99% accuracy. We rapidly genotyped 14,548 SNPs in three different human populations and identified a subset of them with significant allele frequency differences between groups. We also determined the ancestral allele for 8,386 SNPs by genotyping chimpanzee and gorilla DNA. WGSA is highly scaleable and enables the creation of ultrahigh density SNP maps for use in genetic studies.

Risk and survival of cutaneous melanoma diagnosed subsequent to a previous cancer.

OBJECTIVE: To understand the risk of cutaneous melanoma (CM) following a previous cancer. DESIGN: Using the Surveillance, Epidemiology, and End Results database (1988-2007), we compared a cohort of patients diagnosed as having CM as a first cancer with a cohort of patients diagnosed as having CM following a previous cancer. PARTICIPANTS: We included 70,819 patients with CM as a first primary cancer and 6353 patients with CM following a previous cancer. MAIN OUTCOME MEASURES: We calculated the relative risk (RR) for development of primary CM following a previous cancer and used Cox modeling to examine survival characteristics of the 2 cohorts. RESULTS: Patients younger than 45 years at first cancer diagnosis had significantly higher risk of CM following cutaneous melanoma (RR, 11.89; 95% CI, 10.83-13.03), other nonepithelial skin cancer (RR, 2.81; 95% CI, 1.13-5.79), Kaposi sarcoma (RR, 3.26; 95% CI 1.41-6.42), female breast cancer (RR, 1.38; (1.11-1.70), and lymphoma (RR, 1.79; (95% CI, 1.30-2.41). Patients 45 years or older at first cancer diagnosis had significantly higher risk of CM following cutaneous melanoma (RR, 8.36; 95% CI, 7.93-8.81), other nonepithelial skin cancer (RR, 2.00; 95% CI 1.35-2.86), ocular melanoma (RR, 5.34; 95% CI 3.42-7.94), female breast cancer (RR, 1.12; 95% CI, 1.03-1.21), prostate cancer (RR, 1.08; 95% CI, 1.03-1.13), thyroid cancer (RR, 1.40; 95% CI, 1.06-1.82), lymphoma (RR, 1.34; 95% CI, 1.16-1.55), and leukemia (RR, 1.79; 95% CI, 1.49-2.13). Characteristics associated with better survival in both cohorts included female sex, age younger than 45 years at melanoma diagnosis, being married, being white vs black, decreasing Breslow depth, lack of tumor ulceration, no nodal involvement, and absence of metastases. CONCLUSION: Given that cutaneous melanoma is the most common second primary cancer in patients with a first CM (a risk that remains elevated for over 15 years), our results suggest the need for continued skin surveillance in melanoma survivors.

Modeling inspiratory and expiratory steady-state velocity fields in the Sprague-Dawley rat nasal cavity.

Distribution patterns of odorant molecules in the rat nasal olfactory region depend in large part on the detailed airflow patterns in the nasal cavity, which in turn depend on the anatomical structure. To investigate these flow patterns, we constructed an anatomically accurate finite element model of the right nasal cavity of the Sprague-Dawley rat based on horizontal (anterior-posterior) nasal cast cross sections. By numerically solving the fluid mechanical momentum and continuity equations using the finite element method, we studied the flow distribution and the complete velocity field for both inspiration and expiration throughout the nasal cavity under physiological flow rates of resting breathing and sniffing. Detailed velocity profiles, volumetric flow distributions, and streamline patterns for quasi-steady airflow are presented. S-shaped streamlines passing through the olfactory region are found to be less prevalent during expiratory than inspiratory flow leading to trapping and an increase in odorant molecule retention in the olfactory region during sniffing. The rat nasal velocity calculations will be used to study the distribution of odorant uptake onto the rat olfactory mucosa and compare it with the known anatomic location of some types of rat olfactory receptors.

Numerical modeling of odorant uptake in the rat nasal cavity.

An anatomically accurate 3-dimensional numerical model of the right rat nasal cavity was developed and used to compute low, medium, and high flow rate inspiratory and expiratory mucosal odorant uptake (imposed patterning) for 3 odorants with different mucus solubilities. The computed surface mass flux distributions were compared with anatomic receptor gene expression zones identified in the literature. In general, simulations predicted that odorants that were highly soluble in mucus were absorbed dorsally and medially, corresponding roughly to receptors from one of the gene expression zones. Insoluble odorants tended to be absorbed more peripherally in the rat olfactory region corresponding to the other 2 zones. These findings also agreed in general with the electroolfactogram measurements and the voltage-sensitive dye measurements reported in the literature. This numerical approach is the first to predict detailed odorant flux information across the olfactory mucosa in the rat nasal cavity during inspiratory and expiratory flow and to relate it to anatomic olfactory receptor location, physiological function, and biochemical experiment. This numerical technique can allow us to separate the contributions of imposed and inherent patterning mechanisms on the rat olfactory mucosa.

Dynamic model based algorithms for screening and genotyping over 100 K SNPs on oligonucleotide microarrays.

MOTIVATION: A high density of single nucleotide polymorphism (SNP) coverage on the genome is desirable and often an essential requirement for population genetics studies. Region-specific or chromosome-specific linkage studies also benefit from the availability of as many high quality SNPs as possible. The availability of millions of SNPs from both Perlegen and the public domain and the development of an efficient microarray-based assay for genotyping SNPs has brought up some interesting analytical challenges. Effective methods for the selection of optimal subsets of SNPs spanning the genome and methods for accurately calling genotypes from probe hybridization patterns have enabled the development of a new microarray-based system for robustly genotyping over 100,000 SNPs per sample. RESULTS: We introduce a new dynamic model-based algorithm (DM) for screening over 3 million SNPs and genotyping over 100,000 SNPs. The model is based on four possible underlying states: Null, A, AB and B for each probe quartet. We calculate a probe-level log likelihood for each model and then select between the four competing models with an SNP-level statistical aggregation across multiple probe quartets to provide a high-quality genotype call along with a quality measure of the call. We assess performance with HapMap reference genotypes, informative Mendelian inheritance relationship in families, and consistency between DM and another genotype classification method. At a call rate of 95.91% the concordance with reference genotypes from the HapMap Project is 99.81% based on over 1.5 million genotypes, the Mendelian error rate is 0.018% based on 10 trios, and the consistency between DM and MPAM is 99.90% at a comparable rate of 97.18%. We also develop methods for SNP selection and optimal probe selection. AVAILABILITY: The DM algorithm is available in Affymetrix's Genotyping Tools software package and in Affymetrix's GDAS software package. See http://www.affymetrix.com for further information. 10 K and 100 K mapping array data are available on the Affymetrix website.

Algorithms for large-scale genotyping microarrays.

MOTIVATION: Analysis of many thousands of single nucleotide polymorphisms (SNPs) across whole genome is crucial to efficiently map disease genes and understanding susceptibility to diseases, drug efficacy and side effects for different populations and individuals. High density oligonucleotide microarrays provide the possibility for such analysis with reasonable cost. Such analysis requires accurate, reliable methods for feature extraction, classification, statistical modeling and filtering. RESULTS: We propose the modified partitioning around medoids as a classification method for relative allele signals. We use the average silhouette width, separation and other quantities as quality measures for genotyping classification. We form robust statistical models based on the classification results and use these models to make genotype calls and calculate quality measures of calls. We apply our algorithms to several different genotyping microarrays. We use reference types, informative Mendelian relationship in families, and leave-one-out cross validation to verify our results. The concordance rates with the single base extension reference types are 99.36% for the SNPs on autosomes and 99.64% for the SNPs on sex chromosomes. The concordance of the leave-one-out test is over 99.5% and is 99.9% higher for AA, AB and BB cells. We also provide a method to determine the gender of a sample based on the heterozygous call rate of SNPs on the X chromosome. See http://www.affymetrix.com for further information. The microarray data will also be available from the Affymetrix web site. AVAILABILITY: The algorithms will be available commercially in the Affymetrix software package.

Parallel genotyping of over 10,000 SNPs using a one-primer assay on a high-density oligonucleotide array.

The analysis of single nucleotide polymorphisms (SNPs) is increasingly utilized to investigate the genetic causes of complex human diseases. Here we present a high-throughput genotyping platform that uses a one-primer assay to genotype over 10,000 SNPs per individual on a single oligonucleotide array. This approach uses restriction digestion to fractionate the genome, followed by amplification of a specific fractionated subset of the genome. The resulting reduction in genome complexity enables allele-specific hybridization to the array. The selection of SNPs was primarily determined by computer-predicted lengths of restriction fragments containing the SNPs, and was further driven by strict empirical measurements of accuracy, reproducibility, and average call rate, which we estimate to be >99.5%, >99.9%, and>95%, respectively [corrected]. With average heterozygosity of 0.38 and genome scan resolution of 0.31 cM, the SNP array is a viable alternative to panels of microsatellites (STRs). As a demonstration of the utility of the genotyping platform in whole-genome scans, we have replicated and refined a linkage region on chromosome 2p for chronic mucocutaneous candidiasis and thyroid disease, previously identified using a panel of microsatellite (STR) markers.

Genotyping over 100,000 SNPs on a pair of oligonucleotide arrays.

We present a genotyping method for simultaneously scoring 116,204 SNPs using oligonucleotide arrays. At call rates >99%, reproducibility is >99.97% and accuracy, as measured by inheritance in trios and concordance with the HapMap Project, is >99.7%. Average intermarker distance is 23.6 kb, and 92% of the genome is within 100 kb of a SNP marker. Average heterozygosity is 0.30, with 105,511 SNPs having minor allele frequencies >5%.