Ran is known to be expressed in the mouse brain at early embryonic stages , and is thus a good gene candidate to characterize in mouse brain development usingRNAi. We immunolabeled Ran in dissociated cortical neurons and also found high levels of expression in the nuclei of these cells (Figure S4). Furthermore, Ran immunolabeling can be detected in the processes (Figure S4), suggesting a role for Ran in neurite outgrowth, as well as in nuclear import. To analyze the role of Ran in mouse development, we transfected Ran RNAi constructs into the lateral ventricles of the embryonic day 14 (E14) mouse brains using microinjection and electroporation techniques. The transfected cortices were dissected and cultured as explants or dissociated cultures. To test the efficacy of the Ran RNAi constructs (1 and 2) in reducing the levels of Ran protein, nih- 3T3 cells were transfected with RNAi constructs at 70% transfection efficency. Western blot analysis of total protein from transfected and untransfected cells showed a 64% knockdown of Ran in the presence of Ran RNAi construct number 2 (Figure 5B). Ran RNAi electroporated neurons showed processes with abnor- mal blebbing (arrow in Figure 5A right panel) compared to the normal appearing processes in the vector control (Figure 5A left panel). We observed that only 0.7% of control neurons presented blebs while 65.6% of the Ran RNAi neurons showed blebs (Figure 5C). The blebbing phenotypes in the mutant compared to wild type was statistically significant (P,0.02). To ensure that the blebs present in Ran RNAi neurons were not due to the cell death we analyzed the explants with an apoptosis marker, anti-Cleaved Caspase3. We found that GFP-labeled neurons in the Ran RNAi explants did not colocalize with Cleaved Caspase3 (Figure 5J). Thus, the blebbing phenotype was probably due to defects in neurite outgrowth. In addition to the blebbing phenotype, Ran- deficient neurons showed an increase in branch arborization (Figure 5A
To identify Populus invertase genes, we conducted a BLASTP search against the Populus genome database (Phytozome v. 9.1) using known protein sequences of invertase genes from Arabidopsis as queries; the resulting sequences were used as secondary queries. By removing redundant sequences, 24 putative invertase genes (8 from the acid invertase sub-family and 16 from the neutral/alkaline invertase sub-family) were identified in the Populus genome. After manual reannotation and confirmation of the protein characteristic domain, the 24 Populus invertase genes were designated PtrCWINV1-5, PtrVINV1-3, and PtrNINV1-16 following the nomenclature proposed in a previous study . The information on poplar invertase genes in the latest database (Phytozome v. 9.1) varies considerably from that in previous genome data- base and assembly. Based on the transcript number ofgenes from this study, we increased the total number to 45 (20 in the acid invertase sub-family and 25 in the neutral/alkaline invertase subfamily). PtrCWINV1 and PtrNINV 2–6 had two transcripts, PtrCWINV4, PtrVINV3, PtrNINV1 and PtrNINV8 had three transcripts, while PtrVINV2 had eight transcripts. It is worth noting that the sizes of the genomic DNA, transcripts, CDS, and the numbers of peptide residues, have also been updated, along with the theoretical Mw and pI and the location of the functional domains (S1 and S2 Tables). Furthermore, we identified invertase genes from 10 other plant species, including the dicotyledonous angiosperms Medicago truncatula, V. vinif- era, Malus × domestica, Glycine max and Citrus sinensis, and the monocotyledonous angio- sperms O. sativa, Brachypodium distachyon, Sorghum bicolor and Zea mays. All angiosperm genomes, as well as the Physcomitrella patens genome, contain invertase genes. The numbers of invertase genes identified in the 10 other plant species are shown in Table 1.
Apple belongs to the Pyreae (Maleae), a subtribe in the family Rosaceae which shares duplication event with other eudicots . During evolution, gene duplication has contributed to the expansion of gene families and establishment of new gene functions underlying the origins of evolutionary novelty [15,41]. Sequencing and analysis of apple genome revealed that it has undergone a relatively recent (.50 million years ago) genome- wide duplication (GWD) event which results in the transition from nine ancestral chromosomes to 17 chromosomes . The large size of MdNBS family suggests that it has evolved through a large number of duplication events in apple. Thus, in order to study the contribution of gene duplication events in expansion of MdNBS family, we analyzed whole genome duplication events in apple genomeusing MCScanX software. In whole genomeof apple, we found 15,465 (24.35%) genes as segmentally duplicated and 10,812 (17.02%) as tandem duplicated genes. Among MdNBSs, 132 were found to be segmentally duplicated, which are located on duplicated segments on all chromosome except 14 and 16 (Figure 1 and Table S2). Maximum nineteen MdNBSs are located in duplicated segments on chromosome 11, followed by seventeen on chromosome 15, sixteen on chromosome 2, fifteen on chromosome 7, thirteen on chromosome 8, eleven on chromo- some 9, nine on chromosome 1, four each on chromosome 17 and 10, three on chromosome 5 and two on chromosome 4. Duplicated segments on chromosome 6, 12 and 13 each contains one MdNBS gene. Remaining 7 segmentally duplicated MdNBSs were found to be unanchored. The McScanX analysis has also shown 254 MdNBS genes that were generated through tandem duplication which are present on all 17 chromosomes with the highest number (40) on chromosome 2.
homogeneous as possible, eliminating the physical separations present in microplate-based assays and taking advantage of the great number of experiments performed simultaneously in a single array. This primary screening using the microarray methodology served as a first filter to provide us with a smaller number of hits, and it can be used successfully in the future to understand other mechanisms and host-parasite interactions in different organisms. In a secondary screening, we confirmed the inhibitory effects of 15 genes on the infection ratio among 162 genes that showed a strong inhibitory effect. The fact that only about 10% of the hits were confirmed in the secondary screening must be a consequence of subtle differences that can affect T. cruzi infection. In the primary screening, the intricate combination of a sensitive microarray format with this complex parasite, and the lack of a positive control as a direct phenotype during analysis, led us to use simple parameters to extract the hits and therefore increase the number of artifacts. By contrast, the microplate format used for the secondary screening likely offered higher assay stringency in comparison with the microarray format and consequently restricted the number of hits obtained. This higher stringency could be explained by factors such as more homogeneous distribution of cells and different transfection conditions. Never- Figure 5. Confirming specificity of siRNA silencing. To confirm specificity and exclude off-target effects of the siRNA pools used in the primary and secondary screens, the 4 different siRNA duplexes from the pool targeting each gene were separated and individually transfected to U2OS cells in 96-well plates following the same experimental conditions used for secondary screen. (A) Schematic representation of the four siRNAs targeting the calcium binding protein 2 (CABP2) mRNA sequence. bp = base pairs. (B) Pictures showing CABP2 gene silencing using the four different siRNAs depicted in (A). U2OS cells were transfected with each one of the siRNAs and then infected with T. cruzi parasites. Pictures a, b, c and d show a decrease in infection when compared to scrambled siRNA (bottom picture). All 13 genes tested were confirmed by at least two individual siRNAs, demonstrating that the infection inhibition seen in primary and secondary screens was not due to an off target effect. Scale bar represents 80 mm.
Three out of the 8 successfully validated CNVRs contain functionally important genes. Two qPCR assays with primers located in two olfactory receptor 1J4-like genes (LOC100623462 and LOC100157267) were used for CNVR22 validation. The olfactory receptor gene superfamily is the largest in vertebrate genomes, which function in the reception of innumerable odour molecules in the environment. Previous studies have showed that CNVs are highly prevalent among human and other vertebrate OR genes [40,41,42]. Two qPCR assays with primers located in two defensins genes (BD114 and DEFB110) were used for CNVR#50 validation. Defensins are one of the largest and most studied families of antimicrobial peptides. In addition to their antimicrobial activity, they are also thought to play fundamental roles in both innate and adaptive immunity in higher organisms. A qPCR assay with primers located in the SERPINA3 gene was used for validation of CNVR#53. SERPINA3 also known as a1- antichymotrypsin, is a typical acute-phase protein secreted into the circulation during acute and chronic inflammation. Variations in this protein’s sequence have been implicated in Alzheimer’s disease, and deficiency of this protein has been associated with liver disease [43,44]. By impacting the gene product amount or the regulation of these genes, copy number change of these immune- related genes, such as defensins and SERPINA3, may have a great influence on pathogen monitoring and disease resistance of diverse pigs.
RNA interference (RNAi) is targeted gene silencing via double-stranded RNA (dsRNA); a gene is inactivated by speciﬁc breakdown of the mRNA (Fire et al. 1998; Mont- gomery et al. 1998). It is an ideal method for rapid identiﬁcation of in vivo gene function. Initial studies on RNAi used microinjection to deliver dsRNA (Fire et al. 1998), but it was subsequently shown that dsRNA can be introduced very easily by feeding worms with bacteria that express dsRNA (Timmons and Fire 1998). Using this technique on a global scale, an RNAi feeding library consisting of 16,757 bacterial clones that correspond to 87% of the predicted genes in Caenorhabditis elegans was constructed (Fraser et al. 2000; Kamath et al. 2003). Upon feeding to worms, these clones will give transient loss-of-function phenotypes for many genes by inactivating the target genes via RNAi. By feeding the clones in this library to wild-type Bristol N2 worms, loss-of-function phenotypes were assigned to about 10% ofgenes. However, RNAi phenotypes were missed for about 30% of essential genes and 60% ofgenes required for postembryonic development, probably because RNAi is not completely effective (Kamath et al. 2003). Other global RNAi screens have been recently performed in C. elegans using this RNAi library or other techniques (Go¨nczy et al. 2000; Maeda et al. 2001; Dillin et al. 2002; Piano et al. 2002; Ashraﬁ et al. 2003; Lee et al. 2003; Pothof et al. 2003). These screens were done using wild-type worms.
All these results are fundamentally important; they have provided direct evidence that a microRNA gene can be transcribed by pol II. However, a few critical questions remain unanswered. One of them is whether all known microRNA genesof different species are class-II genes. Although more than 50 A. thaliana microRNA genes have been shown to be transcribed by pol II, our knowledge of the transcription of microRNA genes in animals is still limited. We consider this important issue through a genome-wide computational analysis on four model species, C. elegans, H. sapiens, A. thaliana, and O. sativa. Our overall strategy is based on the following perspective on transcriptional regulation. Class-II genes and class-III genes (genes transcribed by RNA polymerase III) must have distinctive features in their promoter regions, including transcription factor binding motifs, to recruit the right transcriptional machineries to initiate their transcription. Based on this perspective and supported in part by the results in [20–22], we first assume that the core promoters of intergenic microRNA genes share common sequence features with the core promoters of the known class-II or class-III genes. We then build computational models to separate the core promoters of class-II and class-III genes as well as random sequences. Using these models, we test all known intergenic microRNA genes in the four species to determine what types of promoters they have. We subsequently answer the question: which RNA polymerase is responsible for the transcription of these microRNA genes?
intensity (Fig. 1A) was stronger using a hrpA labeled DNA probe than when the same amount (200 fmol) of hrpN labeled DNA probe was used (Fig. 1B). Interestingly, we observed a correlation between the binding affinity of HrpL and the expression level of HrpL-induced genes: the relatively higher affinity of HrpL for the hrpA promoter (Fig. 1A) renders hrpA expression over four-fold higher than that of hrpN based on the microarray data (Table 1). The calmodulin-dependent Cya protein is not secreted or translocated by the T3SS, , and is inactive inside the bacterial cytosol due to the absence of calmodulin. These properties make Cya a good reporter for T3SS translocation research in bacterial pathogens [28–31]. To avoid false positive results during our analysis of T3SS translocation as a consequence of plant tissue maceration by pectinases secreted from 3937, an outC mutant was used. This mutant is unable to secrete pectinases through the T2SS . hrcV encodes an inner membrane structural compo- nent of the T3SS apparatus and is essential for secretion of effectors . We used single mutants of either outC, or hrcV and a double mutant outC/hrcV to demonstrate that DspA/E from 3937 is delivered into the plant cytosol in a T3SS-dependent manner. In addition, a plasmid containing only the first 315 amino acids of DspA/E was used to show that the T3SS secretion signal is located in the first one-fifth of the N-terminus of the DspA/E protein. This latter result is consistent with other reports, where T3SS secretion signals have been found within the first 20–150 amino acids of T3SS substrates [42–45].
As these findings suggested a more general role for these genes in protein/membrane traffic, we examined the effect of the depletion of the four candidate genes on ER-Golgi trafficking and Golgi structure using immunofluorescence and electron micros- copy (EM). Under normal conditions, the transmembrane MannosidaseII-GFP (MannII-GFP) traffics via the ER to the medial-Golgi [18,26], and localizes immediately adjacent to the cis-Golgi marker GMAP (Figure S5A and S5D) . GMAP is a peripheral Golgi protein that does not traffic via the ER to reach the Golgi. When ER-Golgi traffic is compromised, MannII-GFP is retained in the ER and is dissociated from GMAP, which appears as diffuse staining likely representing GMAP targeted to fragmented Golgi membranes (Figure S5B and S5E). We also observed cells displaying fragmented Golgi, where numerous MannII-GFP/GMAP-positive structures are observed. We quan- tified the effect of depletion of the 4 candidate genes on MannII- GFP staining by counting the percentage of cells displaying a normal, diffuse, or fragmented MannII-GFP pattern (Figure 4C). Depletion of CG5964 and CG3305 caused predominantly fused MannII-GFP staining, similar to the effect of betaCOP dsRNA, suggesting that ER-Golgi traffic was compromised. Depletion of CG12693, on the other hand, resulted in highly fragmented MannII-GFP distribution. CG8441 depletion resulted in cells displaying either fused or fragmented MannII-GFP staining. Thus, Figure 2. A genome-wideRNAi screen for regulators of Hh secretion and release. (A) RNAi screening procedure. Cells stably expressing Hh- Ren and a cytoplasmic firefly luciferase were treated with dsRNA for 5 days, at which point the culture medium was replaced. After 24 h, we measured the Renilla activity in the culture medium and both the Renilla and firefly activities in the cell lysates. (B–D) Scatter plots representing the duplicate z- scores for (B) medium Renilla/firefly (C) medium Renilla/lysate Renilla, and (D
Wells of interest from the initial screen were selected by analysis of the robust z-score and of the controls on each plate. Using CellHTS2 (software package implemented in Bioconductor/R), the raw data were first transformed in log-2 space and normalized to the per-plate median to remove systematic plate-to-plate variation. The robust z-score of each well was then calculated based on the median and median absolute deviation . We also compared the experimental wells to the NT and clathrin siRNA controls on each plate, selecting those genes with lower luciferase activity than clathrin siRNA in both replicates, thereby reducing the likelihood of false positives. The ranked list resulting from these two criteria was manually curated to remove genes in which knockdown was previously reported to cause cell death, or in which luciferase levels were at background, indicative of cell death. Figure 6. TSPAN9 depletion inhibits alphavirus membrane fusion. U-2 OS cells were transfected as in Fig. 2 A and tested as follows: A. Assay of the low pH-triggered E1 conformational change. Cells with pre-bound SFV were incubated at 37uC for 20 min to permit endocytosis. Cells were fixed and permeabilized, and the low pH conformation of viral E1 was quantitated in cells by staining with mAb E1a-1. Control shows cells in which the 37uC incubation was carried out in medium containing 20 mM NH 4 Cl to block endosome acidification. B. Virus fusion assay. Cells with pre-bound
Taken together, our study demonstrates the potency ofRNAi combined with systematic follow-up analyses to identify and profile functionally relevant effector genes within GWAS loci in an objective and unbiased manner. Several independent studies on individual candidate genes are well in line with some of the findings described here. For instance, it was recently shown that overexpression and knockdown of TBL2 inversely modulates cellular cholesterol in HEK293 and bladder cancer cells . Likewise, SIK3-deficient mice have low levels of serum HDL and total cholesterol, but under a lipid-rich diet cholesterol accumu- lates in mouse livers . Although systematic studies in mammalian cells have contributed significantly to our under- standing of human lipid biology and disease , future work using e.g. suitable animal models will be necessary to test the in vivo roles in cholesterol metabolism of the candidate genes Figure 4. Impact on FC levels and subcellular localization of GFP–tagged candidate genes. (A) cDNAs encoding for indicated candidate genes linked to GFP were transiently expressed in Hela-Kyoto cells and impact on cellular FC levels was analyzed (see Materials and Methods, Figure S6 and Table S8 for comprehensive datasets). Arrows denote ‘‘transfected’’, arrowheads ‘‘untransfected’’ cells. See Materials and Methods for definition of threshholds (dashed lines in graphs).Graphs depict total segmental filipin signal plotted against total cellular intensities in the GFP- channel. Each dot reflects one individual cell, trend lines are given in red. Numbers indicate mean ratios of FC in GFP-positive relative to non- expressing cells within the identical dish (n = 3–4 experiments). (B) Maximal projections of confocal stacks showing representative GFP-cDNA expressing cells under control and sterol-depleted conditions (see Materials and Methods). Arrows denote cellular compartments with increased signals upon sterol-depletion. Bars = 10 mm.
Recently there is an increase interest to use nonparametric methods, such as artificial neural networks (ANN). In animal breeding, a especial class of ANN called Bayesian Regularized Neural Network (BRNN) has been preferable since it does not demand a priori knowledge of the genetic architecture of the characteristic (assumption about markers effects) as assumed by the most used parametric methods (RR-BLUP, Bayes A, B, C , BLASSO, and many others). Although BRNN has been shown to be effective for genomic enable prediction, its results approaching marker effects and genetic parameter estimates are still been scarcely used. The aim of the present study was to apply the ANN based on Bayesian regularization to genome-enable prediction regarding simulated data sets, to select the most relevant SNP markers by using two proposed methods, to estimate heritabilities for the considered traits, and to compare the results with two traditional methods (RR-BLUP and BLASSO). The simplest Bayesian Regularized Neural Network (BRNN) model gave consistent predictions for both traits, which were similar to the results obtained from the traditional RR-BLUP and BLASSO methods. The SNP importance identification methods based on BRNN proposed here showed correlation values (0.61 and 0.81 for traits 1 and 2, respectively) between true and estimated marker effects higher than the traditional BLASSO (0.55 and 0.71, respectively for traits 1 and 2) method. With respect to h 2 estimates (assuming 0.35 as true value), the simplest BRNN found 0.33 for both traits, thus outperforming the RR- BLUP and BLASSO, that in average estimated h 2 equal to 0.215.
Comparison of the 170 P. aeruginosa putative virulence genes identified in C. elegans with genes required for virulence in a rat chronic infection model. In order to evaluate the degree to which virulence genes from our screen represent putative conserved virulence determinants, we com- pared the 170 genes identified in our secondary screen with the largest available set ofgenes identified in another unbiased screen, a negative signature tagged mutagenesis (STM) selection for P. aeruginosa mutants defective in virulence in a rat chronic respiratory infection model . It is notable that like the 170 genes identified in our screen, and in contrast with the VFDB set, the 148 P. aeruginosa genes identified by Potvin et al (2003) by STM also appear as a group to possess a broad distribution across all functional classes. The P. aeruginosa mutant set identified in the rat chronic infection model exhibits an underrepresentation of secreted proteins and secretion systems and includes a number of auxotrophic mutants and many mutants in genes with enzymatic functions not previously linked to pathogenesis, reminiscent of the mutants identified in C. elegans. Only five genes identified in the rat infection model were also found in the 170 gene set from our secondary screen and only one of these was also found in the VFDB (Figure S4). A number of well-studied P. aeruginosa virulence factors, required for slow killing in C. elegans and in some mammalian infection models, including gacA, lasR, rhlR, ptsP, mucD, and rpoN, are absent from the rat chronic infection set. We do not know if this reflects a difference between chronic and acute infections, nor whether the C. elegans model is more analogous to acute or chronic infection in mammals. These data suggest that genes required for virulence under a particular set of circumstances are highly dependent on host model, infection site, and most likely phase of infection.
The canonical plant miRNAs were incorporated into AGO- associated (AGO1 normally) miRISCs to guide the silencing complexes to the target transcripts for cleavage-based post- transcriptional regulation [2–4]. Here, the highly accumulated RC-miRNAs (with total read counts more than 20 RPM) of Arabidopsis, rice and soybean were recruited for degradome sequencing data-based target identification. First, according to the gene model annotations from TAIR (Release 10) , TIGR rice (Release 6.1) , and GmGDB belonging to PlantGDB (version Glyma1) , transcriptome-wide target prediction was performed for the three plants by using miRU algorithm [21,22]. Then, the predicted targets were subjected to degradome data- based (see Table S2 for data sources) validation by generating t- plots (target plots) [23,24] for manual check following the rules proposed previously . Strikingly, only a few targets were confirmed to be cleaved at the target binding sites in Arabidopsis Figure 2. Sequence characteristics of the reverse complementary microRNAs (RC-miRNAs) identified in nine plant species. (A) Sequence length distribution patterns. (B) 5’ terminal nucleotide compositions. For both (A) and (B), all the identified RC-miRNAs (black bars) and the ones with normalized total read counts larger than 20 RPM (reads per million) (gray bars) were analyzed separately.
Image analysis and statistical analysis. To identify proteins that give centrosome defects after depletion with dsRNA, we scored each well by three different methods. First, each well was inspected manually on the wideﬁeld microscope system described above, and given a numerical score (from 3 to 3) for the severity of any defect in cell number, mitotic index, centrosome number, and centrosome size. Second, the pictures taken with the automated microscope were manually scored using the same criteria. All of these analyses were performed ‘‘blind,’’ so that we did not know which genes were being analysed. Finally, the pictures were analysed with CellProﬁler (http:// www.cellproﬁler.org)  using a self-made pipeline (See Text S1). This resulted in a numerical value for the number of Cnn dots per mitotic cell. The inverse of this numerical dataset was normalised (plate average was set to zero) and corrected for plate-by-plate variations and possible edge effects using the CellHTS software (, using the B-score method) (See Figure S3). The Z9-score was calculated using Cnn and Polo as positive controls, and all empty and DsRed wells as negative controls. This analysis enabled us to give a statistical signiﬁcance to each potential hit. A total of 108 genes were excluded from both the manual and the automated analysis because of the lack of cells or lack of mitotic cells in the well (Table S2); 119 genes were selected for secondary analysis as they were scored as hits with at least two of these three methods. From these 119 genes, only 79 were selected for a more detailed secondary analysis, as we eliminated genes that were commonly identiﬁed in previous screens (indicating they are likely false positives), were known components of the ribosome or transcription machinery, or were the result of clear off-target effects (Table S1).
Rice restorer lines play an important role in three-line hybrid rice production. Previous research based on molecular tagging has suggested that the restorer lines used widely today have narrow genetic backgrounds. However, patterns of genetic variation at a genome-wide scale in these restorer lines remain largely unknown. The present study performed re- sequencing and genome-wide variation analysis of three important representative restorer lines, namely, IR24, MH63, and SH527, using the Solexa sequencing technology. With the genomic sequence of the Indica cultivar 9311 as the reference, the following genetic features were identified: 267,383 single-nucleotide polymorphisms (SNPs), 52,847 insertion/deletion polymorphisms (InDels), and 3,286 structural variations (SVs) in the genomeof IR24; 288,764 SNPs, 59,658 InDels, and 3,226 SVs in MH63; and 259,862 SNPs, 55,500 InDels, and 3,127 SVs in SH527. Variations between samples were also determined by comparative analysis of authentic collections of SNPs, InDels, and SVs, and were functionally annotated. Furthermore, variations in several important genes were also surveyed by alignment analysis in these lines. Our results suggest that genetic variations among these lines, although far lower than those reported in the landrace population, are greater than expected, indicating a complicated genetic basis for the phenotypic diversity of the restorer lines. Identificationofgenome- wide variation and pattern analysis among the restorer lines will facilitate future genetic studies and the molecular improvement of hybrid rice.
into the pathophysiology of aneurysm formation. In this angiographic case series, 169 patients diagnosed with intracranial aneurysms were compared with 256 patients re- ferred for multiple reasons to catheter angiography without intracranial aneurysms. A series of common anatomic variants of the circle of Willis were found to be relat- ed to different aneurysm locations. Aneurysms in the posterior communicating ar- tery (PComA) were associated with fetal-type PComA; and anterior communicating artery (AComA) aneurysms were associated with A1 hypoplasia. Both variants increase flow to arterial bifurcations, theoretically increasing the odds of aneurysm formation. Moreover, a careful analysis of the carotid syphon was performed, measuring the angle between the intracavernous and supraclinoid segments of the carotid artery. A lower angle (which also increased local blood flow distally) was associated with higher occur- rence of aneurysm.
when Texel individuals were compared with all other breeds (Kijas et al., 2012). In addition, Clop et al. (2006) showed a reduction in the variability of microsatellites sur- rounding this gene upon comparing hyper-muscled Texels with other sheep breeds. The region surrounding GDF-8 was associated with QTLs for carcass traits in the Texel breed (Johnson et al., 2005) and a point in the 3’ UTR of this gene was suggested to be the causal mutation affecting extreme muscling in Texel individuals (Clop et al., 2006). Moradi et al. (2012) performed a genome scan with approximately 50K SNPs to search for signatures of diver- gent selection in a comparison between fat and thin-tailed sheep breeds; their study identified at least three regions (OAR5, OAR7 and OARX chromosomes) that have under- gone selection. Interestingly, most of the regions identified by Moradi et al. (2012) intersect with QTLs for carcass traits. Improvement in the sheep genome annotation will fa- cilitate the search for and validation of candidate genes re- lated to these traits.
Specific selection genes in CMF breed Production traits. TECRL is associated with with- ers height in racing quarter horse . SLC27A6 is part of the peroxisome proliferator- activated receptor (PPAR) signaling pathway, which is associated with carcass conformation in cattle . Meat trait: FAM190A is a QTL associated with weight after slaughter in Hanwoo cattle . CRADD is associated with muscle compactness . PHKG1 causes high glycogen content and low meat quality in pig skeletal muscle . CAPN3 is related to meat quality traits in chickens . Reproduction traits: TYRO3 modulates female reproduction by influencing gonadotropin-releasing hormone . SLC16A1 plays an important role in the transport of mevalonate and ketone bodies  and may be involved in differences in efficiency of repro- duction in cattle. Health traits: SOCS3 is associated with somatic cell score trait in cattle and is expressed in goat milk fat globules in response to experimental intramammary infection with Staphylococcus aureus . Other traits: In milk traits, PRKAA1 is associated with fat percentage and may have effects on fat metabolism affecting milk production traits in cattle . DERA is a positional candidate gene for milk fat percentage in the German Holstein-
Raw data of 10 pairs of in-house samples was preprocessed using GC-RMA method to conduct background correction and normalization. Summarization was done with median polish, using the hgu133plus2entrezg.13.0.0 CDF  and the prepro- cessed expression dataset (eSet) were filtered on I/NI calls . All informative genesof 20 cases of experimental hepatic samples (10 cancer tissue samples and 10 adjacent normal tissue samples) were collected to generate corresponding spectral maps to distinguish the most significant diversity of gene expression profiles among samples. Spectral Map Analysis (SMA) is a multivariate projection method that allows reduction of the complexity of high dimensional data, in which a bi-plot is created by the first two principal components displays the maximal separation of both the transcripts and the samples which provides means to visually Figure 2. A subgroup of NTS high expressing HCC is identified from the gene expression profiling data of 10 pairs of in-house HCC tissues and corresponding adjacent normal tissues. Different subgroups of HCC samples were separated by the unsupervised filtering analysis SMA algorithm. In SMA, the sample names were shown in colorful squares. The genes were depicted in black circles and the size of the circle reflected the mean expression level of the gene over all samples. The subgroups of samples were separated using rectangles in different color, and the characteristic gene signature of certain subgroup was included in the corresponding rectangle as well. A). In Figure 2A, 3 subgroups of 20 cases of samples with distinct expression profiling features are separated along the X(PC1) axis and Y(PC2) axis. PC1 contributes 47% of variability and PC2 contributed 18% of variability in the gene expression profiling data of all 20 samples. The most significantly differentially expressed genes in each subgroup were listed in the same direction and included in the corresponding rectangle as well. Two HCC samples (c-13426 and c-13732, red rectangle) were distinguished from the others 8 cases of HCC samples (blue rectangle) and 10 cases of corresponding adjacent normal tissues (green rectangle) in the spectral map. NTS (red arrow), KRT19 and MMP12 were screened out as highly expressed genes in these 2 samples. B). The SMA was repeated in 10 cases of HCC samples which were divided into 2 subgroups scattering around the central marker ‘‘+’’. Two HCC samples (c-13426 and c-13732, red rectangle) were separated out of the others 8 cases of HCC samples (blue rectangle), in which the NTS gene showed relatively higher expression in these 2 samples (red arrow). C). Quantity of NTS expression at mRNA level in the HCC tissues and adjacent normal tissues. The green plots represent cancer tissue samples, and the red plots represent normal adjacent tissue samples.