Ultraconserved element (UCE) probe set design: Base genome and initial design parameters critical for optimization

Targeted capture and enrichment approaches have proven effective for phylogenetic study. Ultraconserved elements (UCEs) in particular have exhibited great utility for phylogenomic analyses, with the software package phyluce being among the most utilized pipelines for UCE phylogenomics, including probe design. Despite the success of UCEs, it is becoming increasing apparent that diverse lineages require probe sets tailored to focal taxa in order to improve locus recovery. However, factors affecting probe design and methods for optimizing probe sets to focal taxa remain underexplored. Here, we use newly available beetle (Coleoptera) genomic resources to investigate factors affecting UCE probe set design using phyluce . In particular, we explore the effects of stringency during initial design steps, as well as base genome choice on resulting probe sets and locus recovery. We found that both base genome choice and initial bait design stringency parameters greatly alter the number of resultant probes included in final probe sets and strongly affect the number of loci detected and recovered during in silico testing of these probe sets. In addition, we identify attributes of base genomes that correlated with high performance in probe design. Ultimately, we provide a recommended workflow for using Phyluce to design an optimized UCE probe set that will work across a targeted lineage, and use our findings to develop a new, open‐source UCE probe set for beetles of the suborder Adephaga.     

Chromosomal assignment of porcine pregnancy-associated glycoprotein gene family

This study presents the chromosomal assignment of a multiple pregnancy-associated glycoprotein (PAG) gene family in the domestic pig (pPAG). The pPAG locus was identified by physical mapping (fluorescent in situ hybridisation—FISH; with various probes), and additionally confirmed by Southern hybridisation of pPAG amplicons using laser microdissected Sus scrofa chromosome 1 (SSC1), as genomic templates. Various pPAG probes were produced with the use of diverse identified templates: pPAG1-6, -8, -10 cDNAs (GenBank: L34360–1, AF315377, AF272734, AY188554, AF272735, AY373029 and AY775784, respectively), or genomic DNA (gDNA) probes of pPAG2 gene and its promoter (GenBank: U39198–9, U39762–3, U41421–4). All probes, including long gDNA probes (～9.2 kbp GpPAG2 gene; ～2.8 kbp GpPAG2 promoter), a shorter cDNA probe (PlpPAG4, 1385 bp) and amplified pPAG2-like probes (ApPAG2L) specific for cDNA inserts of pPAG2-like gene subfamily (pPAG2, -4, -6, -8 and -10; 1283–1385 bp) were produced by random priming using biotin-labelled deoxynucleotides (16-dUTP). Numerous FISH mappings with various pPAG probes revealed the chromosomal assignment of the pPAG gene family to the long arm of porcine chromosome 1 (SSC1q16–q24 region). This cytogenetic assignment was confirmed by Southern hybridisation (with ³²P-labelled pPAG10 probe) of multiple distinct pPAG amplicons (603–3943 bp) produced with the use of 25 laser microdissected SSC1, as gDNA templates. This is the first study identifying the chromosomal locus of the pPAG gene family in the pig.     

Genomic prediction ability for feed efficiency traits using different models and pseudo-phenotypes under several validation strategies in Nelore cattle

There is a growing interest to improve feed efficiency (FE) traits in cattle. The genomic selection was proposed to improve these traits since they are difficult and expensive to measure. Up to date, there are scarce studies about the implementation of genomic selection for FE traits in indicine cattle under different scenarios of pseudo-phenotypes, models, and validation strategies on a commercial large scale. Thus, the aim was to evaluate the feasibility of genomic selection implementation for FE traits in Nelore cattle applying different models and pseudo-phenotypes under validation strategies. Phenotypic and genotypic information from 4 329 and 3 467 animals were used, respectively, which were tested for residual feed intake, DM intake, feed efficiency, feed conversion ratio, residual BW gain, and residual intake and BW gain. Six prediction methods were used: single-step genomic best linear unbiased prediction, Bayes A, Bayes B, Bayes Cπ, Bayesian least absolute shrinkage and selection operator (BLASSO), and Bayes R. Phenotypes adjusted for fixed effects (Y*), estimated breeding value (EBV), and EBV deregressed (DEBV) were used as pseudo-phenotypes. The validation approaches used were: (1) random: the data was randomly divided into ten subsets and the validation was done in each subset at a time; (2) age: the partition into training and testing sets was based on year of birth and testing animals were born after 2016; and (3) EBV accuracy: the data was split into two groups, being animals with accuracy above 0.45 the training set; and below 0.45 the validation set. In the analyses that used the Y* as pseudo-phenotype, prediction ability (PA) was obtained by dividing the correlation between pseudo-phenotype and genomic EBV (GEBV) by the square root of the heritability of the trait. When EBV and DEBV were used as the pseudo-phenotype, the simple correlation of this quantity with the GEBV was considered as PA. The prediction methods show similar results for PA and bias. The random cross-validation presented higher PA (0.17) than EBV accuracy (0.14) and age (0.13). The PA was higher for Y* than for EBV and DEBV (30.0 and 34.3%, respectively). Random validation presented the highest PA, being indicated for use in populations composed mainly of young animals and traits with few generations of data recording. For high heritability traits, the validation can be done by age, enabling the prediction of the next-generation genetic merit. These results would support breeders to identify genomic approaches that are more viable for genomic prediction for FE-related traits.     

Genome-enabled prediction of meat and carcass traits using Bayesian regression,single-step genomic best linear unbiased prediction and blending methods in Nelore cattle

Several methods have been used for genome-enabled prediction (or genomic selection) of complex traits, for example, multiple regression models describing a target trait with a linear function of a set of genetic markers. Genomic selection studies have been focused mostly on single-trait analyses. However, most profitability traits are genetically correlated, and an increase in prediction accuracy of genomic breeding values for genetically correlated traits is expected when using multiple-trait models. Thus, this study was carried out to assess the accuracy of genomic prediction for carcass and meat quality traits in Nelore cattle, using single- and multiple-trait approaches. The study considered 15 780, 15 784, 15 742 and 526 records of rib eye area (REA, cm²), back fat thickness (BF, mm), rump fat (RF, mm) and Warner–Bratzler shear force (WBSF, kg), respectively, in Nelore cattle, from the Nelore Brazil Breeding Program. Animals were genotyped with a low-density single nucleotide polymorphism (SNP) panel and subsequently imputed to arrays with 54 and 777 k SNPs. Four Bayesian specifications of genomic regression models, namely, Bayes A, Bayes B, Bayes Cπ and Bayesian Ridge Regression; blending methods, BLUP; and single-step genomic best linear unbiased prediction (ssGBLUP) methods were compared in terms of prediction accuracy using a fivefold cross-validation. Estimates of heritability ranged from 0.20 to 0.35 and from 0.21 to 0.46 for RF and WBSF on single- and multiple-trait analyses, respectively. Prediction accuracies for REA, BF, RF and WBSF were all similar using the different specifications of regression models. In addition, this study has shown the impact of genomic information upon genetic evaluations in beef cattle using the multiple-trait model, which was also advantageous compared to the single-trait model because it accounted for the selection process using multiple traits at the same time. The advantage of multi-trait analyses is attributed to the consideration of correlations and genetic influences between the traits, in addition to the non-random association of alleles.     

Interspecific Chromosome-Wide Transcription Profiles Reveal the Existence of Mammalian-Specific and Species-Specific Chromosome Domains

A long-range exploration of expression levels through wide chromosome territories was carried out in three species (pig, cattle, and chicken) by aligning EST counts against the human genome. This strategy made it possible to produce expression profiles that were very similar between pig and cattle and that were significantly correlated with chicken levels of expression. In parallel with these alignments, we developed a statistical approach enabling us to screen genomic regions for both underexpression and overexpression at the chromosome level within a given species, as well as interspecifically. The observed correlations are indicative of the existence of interspecifically conserved domains of gene expression, not only for housekeeping genes (which are highly expressed), but also for regions where genes are significantly underexpressed. Furthermore, our strategy made it possible to point out regions that are differentially regulated between species. These expression data were crossed with available comparative mapping information for pigs and cattle, suggesting that coregulated regions are syntenic in various mammals.     

A First Generation Comparative Chromosome Map between Guinea Pig (Cavia porcellus) and Humans

The domesticated guinea pig, Cavia porcellus (Hystricomorpha, Rodentia), is an important laboratory species and a model for a number of human diseases. Nevertheless, genomic tools for this species are lacking; even its karyotype is poorly characterized. The guinea pig belongs to Hystricomorpha, a widespread and important group of rodents; so far the chromosomes of guinea pigs have not been compared with that of other hystricomorph species or with any other mammals. We generated full sets of chromosome-specific painting probes for the guinea pig by flow sorting and microdissection, and for the first time, mapped the chromosomal homologies between guinea pig and human by reciprocal chromosome painting. Our data demonstrate that the guinea pig karyotype has undergone extensive rearrangements: 78 synteny-conserved human autosomal segments were delimited in the guinea pig genome. The high rate of genome evolution in the guinea pig may explain why the HSA7/16 and HSA16/19 associations presumed ancestral for eutherians and the three syntenic associations (HSA1/10, 3/19, and 9/11) considered ancestral for rodents were not found in C. porcellus. The comparative chromosome map presented here is a starting point for further development of physical and genetic maps of the guinea pig as well as an aid for genome assembly assignment to specific chromosomes. Furthermore, the comparative mapping will allow a transfer of gene map data from other species. The probes developed here provide a genomic toolkit, which will make the guinea pig a key species to unravel the evolutionary biology of the Hystricomorph rodents.     

Chromosome reshuffling in birds of prey: the karyotype of the world's largest eagle (Harpy eagle, Harpia harpyja) compared to that of the chicken (Gallus gallus)

Like various other diurnal birds of prey, the world's largest eagle, the Harpy (Harpia harpyja), presents an atypical bird karyotype with 2n=58 chromosomes. There is little knowledge about the dramatic changes in the genomic reorganization of these species compared to other birds. Since recently, the chicken provides a “default map” for various birds including the first genomic DNA sequence of a bird species. Obviously, the gross division of the chicken genome into relatively gene-poor macrochromosomes and predominantly gene-rich microchromosomes has been conserved for more than 150 million years in most bird species. Here, we present classical features of the Harpy eagle karyotype but also chromosomal homologies between H. harpyja and the chicken by chromosome painting and comparison to the chicken genome map. We used two different sets of painting probes: (1) chicken chromosomes were divided into three size categories: (a) macrochromosomes 1–5 and Z, (b) medium-sized chromosomes 6–10, and (c) 19 microchromosomes; (2) combinatorially labeled chicken chromosome paints 1–6 and Z. Both probe sets were visualized on H. harpyja chromosomes by multicolor fluorescence in situ hybridization (FISH). Our data show how the organization into micro- and macrochromosomes has been lost in the Harpy eagle, seemingly without any preference or constraints.     

Equalizer reduces SNP bias in Affymetrix microarrays

Background

Gene expression microarrays measure the levels of messenger ribonucleic acid (mRNA) in a sample using probe sequences that hybridize with transcribed regions. These probe sequences are designed using a reference genome for the relevant species. However, most model organisms and all humans have genomes that deviate from their reference. These variations, which include single nucleotide polymorphisms, insertions of additional nucleotides, and nucleotide deletions, can affect the microarray’s performance. Genetic experiments comparing individuals bearing different population-associated single nucleotide polymorphisms that intersect microarray probes are therefore subject to systemic bias, as the reduction in binding efficiency due to a technical artifact is confounded with genetic differences between parental strains. This problem has been recognized for some time, and earlier methods of compensation have attempted to identify probes affected by genome variants using statistical models. These methods may require replicate microarray measurement of gene expression in the relevant tissue in inbred parental samples, which are not always available in model organisms and are never available in humans.

Results

By using sequence information for the genomes of organisms under investigation, potentially problematic probes can now be identified a priori. However, there is no published software tool that makes it easy to eliminate these probes from an annotation. I present equalizer, a software package that uses genome variant data to modify annotation files for the commonly used Affymetrix IVT and Gene/Exon platforms. These files can be used by any microarray normalization method for subsequent analysis. I demonstrate how use of equalizer on experiments mapping germline influence on gene expression in a genetic cross between two divergent mouse species and in human samples significantly reduces probe hybridization-induced bias, reducing false positive and false negative findings.

Conclusions

The equalizer package reduces probe hybridization bias from experiments performed on the Affymetrix microarray platform, allowing accurate assessment of germline influence on gene expression.     

A Comparative Study of Pygmy Hippopotamus (<Emphasis Type="Italic">Choeropsis liberiensis</Emphasis>) Karyotype by Cross-Species Chromosome Painting

The family Hippopotamidae is comprised of two genera with two living species, the common hippo (Hippopotamus amphibius) and the pygmy hippo (Choeropsis liberiensis). Unlike the common hippo, the karyotype of C. liberiensis has not yet been investigated via cross-species chromosome painting methods. We established chromosomal homologies between the pygmy hippo, pig, and cattle by fluorescence in situ hybridization using whole chromosome, arm-specific, region specific, and bacterial artificial chromosome (BAC) probes. Probes from the 18 pig autosomes painted 45 conserved chromosomal segments in the pygmy hippo genome. The pygmy hippo and cattle homology map was deduced from our hybridization results of painting probes to pygmy hippo chromosomes with a combination of previously published dromedary hybridization data. On the pygmy hippo and cattle homology map, 29 cattle autosomes revealed 39 conservative segments on pygmy hippo chromosomes. For a more detailed structural analysis of genome rearrangements and X chromosome structure, we used cattle region specific and BAC probes. Our report demonstrates that cattle probes are useful not only in comparative studies within Ruminantia, but also in more phylogenetically distant Artiodactyla species.     

In Situ Detection of Freshwater Fungi in an Alpine Stream by New Taxon-Specific Fluorescence In Situ Hybridization Probes

New rRNA-targeting oligonucleotide probes permitted the fluorescence in situ hybridization (FISH) identification of freshwater fungi in an Austrian second-order alpine stream. Based on computer-assisted comparative sequence analysis, nine taxon-specific probes were designed and evaluated by whole-fungus hybridizations. Oligonucleotide probe MY1574, specific for a wide range of Eumycota, and the genus (Tetracladium)-specific probe TCLAD1395, as well as the species-specific probes ALacumi1698 (Alatospora acuminata), TRIang322 (Tricladium angulatum), and Alongi340 (Anguillospora longissima), are targeted against 18S rRNA, whereas probes TmarchB10, TmarchC1_1, TmarchC1_2, and AlongiB16 are targeted against the 28S rRNA of Tetracladium marchalianum and Anguillospora longissima, respectively. After 2 weeks and 3 months of exposure of polyethylene slides in the stream, attached germinating conidia and growing hyphae of freshwater fungi were accessible for FISH. Growing hyphae and germinating conidia on leaves and in membrane cages were also visualized by the new FISH probes.     

BGVD:An Integrated Database for Bovine Sequencing Variations and Selective Signatures

Next-generation sequencing has yielded a vast amount of cattle genomic data for global characterization of population genetic diversity and identification of genomic regions under natural and artificial selection. However, efficient storage, querying, and visualization of such large datasets remain challenging. Here, we developed a comprehensive database, the Bovine Genome Variation Database (BGVD). It provides six main functionalities: gene search, variation search, genomic signature search, Genome Browser, alignment search tools, and the genome coordinate conversion tool. BGVD contains information on genomic variations comprising ~60.44 M SNPs, ~6.86 M indels, 76,634 CNV regions, and signatures of selective sweeps in 432 samples from modern cattle worldwide. Users can quickly retrieve distribution patterns of these variations for 54 cattle breeds through an interactive source of breed origin map, using a given gene symbol or genomic region for any of the three versions of the bovine reference genomes (ARS-UCD1.2, UMD3.1.1, and Btau 5.0.1). Signals of selection sweep are displayed as Manhattan plots and Genome Browser tracks. To further investigate and visualize the relationships between variants and signatures of selection, the Genome Browser integrates all variations, selection data, and resources, from NCBI, the UCSC Genome Browser, and Animal QTLdb. Collectively, all these features make the BGVD a useful archive for in-depth data mining and analyses of cattle biology and cattle breeding on a global scale. BGVD is publicly available at http://animal.nwsuaf.edu.cn/BosVar.     

Genome-wide association studies uncover genes associated with litter traits in the pig

Litter traits are critical economic variables in the pig industry as they represent a production indicator that can serve to determine sow fertility. In this study, a genome-wide association study on litter traits, including total number born (TNB), number born alive (NBA), litter birth weight (LBW), average birth weight (ABW), and piglet uniformity (PU), was carried out on two pig breeds (Yorkshire and Landrace). A total of 3 637 pigs of both breeds were genotyped using the GeneSeek GGP Porcine 50K SNP BeadChip. A mixed linear model (MLM) and fixed and random model circulating probability unification (FarmCPU) were employed in the genome-wide association studies for litter traits using combined data from the two pig breeds and data from each breed separately. Additionally, the heritability of traits was estimated using three methods—pedigree-based best linear unbiased prediction (PBLUP), genomic best linear unbiased prediction (GBLUP), and single-step best linear unbiased prediction (ssGBLUP)—and was found to lie between 0.065 and 0.1289, 0.0478 and 0.0938, 0.0793 and 0.0935, 0.1862 and 0.2163, and 0.0327 and 0.0419 for TNB, NBA, LBW, ABW, and PU, respectively. We also compared the genomic prediction accuracies and unbiasedness for litter traits of the three BLUP models. Our results indicated that the ssGBLUP method provided higher predictive accuracies and more rational unbiasedness compared with the PBLUP and GBLUP methodologies. Furthermore, based on their possible roles, eight candidate genes (INHBA, LEPR, HDHD2, CTNND2, RNF216, HMX1, PAPPA2, and NTN1) were identified as being linked with litter traits. In the middle of the test, these genes were found to be connected with pig metabolism and ovulation rate. Our results provide the insights into the genetic architecture of litter traits in pigs, and the potential single nucleotide polymorphisms (SNPs) and candidate genes identified may benefit economic profits in pig-breeding industry and contribute to improve litter traits.     

Fitting hidden Markov models of protein domains to a target species: application to Plasmodium falciparum

Background

The identification of gene sets that are significantly impacted in a given condition based on microarray data is a crucial step in current life science research. Most gene set analysis methods treat genes equally, regardless how specific they are to a given gene set.

Results

In this work we propose a new gene set analysis method that computes a gene set score as the mean of absolute values of weighted moderated gene t-scores. The gene weights are designed to emphasize the genes appearing in few gene sets, versus genes that appear in many gene sets. We demonstrate the usefulness of the method when analyzing gene sets that correspond to the KEGG pathways, and hence we called our method P athway A nalysis with D own-weighting of O verlapping G enes (PADOG). Unlike most gene set analysis methods which are validated through the analysis of 2-3 data sets followed by a human interpretation of the results, the validation employed here uses 24 different data sets and a completely objective assessment scheme that makes minimal assumptions and eliminates the need for possibly biased human assessments of the analysis results.

Conclusions

PADOG significantly improves gene set ranking and boosts sensitivity of analysis using information already available in the gene expression profiles and the collection of gene sets to be analyzed. The advantages of PADOG over other existing approaches are shown to be stable to changes in the database of gene sets to be analyzed. PADOG was implemented as an R package available at: http://bioinformaticsprb.med.wayne.edu/PADOG/or http://www.bioconductor.org.     

Syntheses and evaluation of a homologous series of aza-vesamicol as improved radioiodine-labeled probes for sigma-1 receptor imaging

Sigma-1 receptor imaging probes for determining the expression levels are desirable for diagnoses of various diseases and companion diagnoses of therapeutic agents targeting the sigma-1 receptor. In this study, we aimed to develop probes with higher affinity for the sigma-1 receptor. For this purpose, we synthesized and evaluated compounds, namely, vesamicol derivatives, in which alkyl chains of varying chain length were introduced between a piperazine ring and a benzene ring. The binding affinity of the vesamicol derivatives for the sigma-1 receptor tended to increase depending on the length of the alkyl chain between the benzene ring and the piperazine ring. The sigma-1 receptor of 2-(4-(3-phenylpropyl)piperazin-1-yl)cyclohexan-1-ol (5) (K_i?=?5.8?nM) exhibited the highest binding affinity; therefore, we introduced radioiodine into the benzene ring in 5. The radioiodine labeled probe [¹²⁵I]2-(4-(3-(4-iodophenyl)propyl)piperazin-1-yl)cyclohexan-1-ol ([¹²⁵I]10) showed high accumulation in the sigma-1 receptor expressing DU-145 cells both in vitro and in vivo. Co-injection of [¹²⁵I]10 with an excess level of a sigma receptor ligand, haloperidol, resulted in a significant decrease in the tumor accumulation in vitro and in vivo, indicating sigma receptor-mediated tumor uptake. These results provide useful information for developing sigma-1 receptor imaging probes.