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During the neonatal period, the ability to generate immune effector and memory responses to vaccines or pathogens is often questioned. This study was undertaken to obtain a global view of the natural differences in the expression of immune genes early in life. Our hypothesis was that transcriptome analyses of peripheral blood mononuclear cells (PBMCs) of foals (on day 1 and day 42 after birth) and adult horses would show differential gene expression profiles that characterize natural immune processes. Gene ontology enrichment analysis provided assessment of biological processes affected by age, and a list of 897 genes with ≥2 fold higher (p<0.01) expression in day 42 when compared to day 1 foal samples. Up-regulated genes included B cell and T cell receptor diversity genes; DNA replication enzymes; natural killer cell receptors; granzyme B and perforin; complement receptors; immunomodulatory receptors; cell adhesion molecules; and cytokines/chemokines and their receptors. The list of 1,383 genes that had higher (p<0.01) expression on day 1 when compared to day 42 foal samples was populated by genes with roles in innate immunity such as antimicrobial proteins; pathogen recognition receptors; cytokines/chemokines and their receptors; cell adhesion molecules; co-stimulatory molecules; and T cell receptor delta chain. Within the 742 genes with increased expression between day 42 foal and adult samples, B cell immunity was the main biological process (p = 2.4E-04). Novel data on markedly low (p<0.0001) TLR3 gene expression, and high (p≤0.01) expression of IL27, IL13RA1, IREM-1, SIRL-1, and SIRPα on day 1 compared to day 42 foal samples point out potential mechanisms of increased susceptibility to pathogens in early life. The results portray a progression from innate immune gene expression predominance early in life to adaptive immune gene expression increasing with age with a putative overlay of immune suppressing genes in the neonatal phase. These results provide insight to the unique attributes of the equine neonatal and young immune system, and offer many avenues of future investigation. [ABSTRACT FROM AUTHOR]

Background: As one of the most common types of co-regulatory motifs, feed-forward loops (FFLs) control many cell functions and play an important role in human cancers. Therefore, it is crucial to reconstruct and analyze cancer-related FFLs that are controlled by transcription factor (TF) and microRNA (miRNA) simultaneously, in order to find out how miRNAs and TFs cooperate with each other in cancer cells and how they contribute to carcinogenesis. Current FFL studies rely on predicted regulation information and therefore suffer the false positive issue in prediction results. More critically, FFLs generated by existing approaches cannot represent the dynamic and conditional regulation relationship under different experimental conditions. Methodology/Principal Findings: In this study, we proposed a novel filter-wrapper feature selection method to accurately identify co-regulatory mechanism by incorporating prior information from predicted regulatory interactions with parallel miRNA/mRNA expression datasets. By applying this method, we reconstructed 208 and 110 TF-miRNA co-regulatory FFLs from human pan-cancer and prostate datasets, respectively. Further analysis of these cancer-related FFLs showed that the top-ranking TF STAT3 and miRNA hsa-let-7e are key regulators implicated in human cancers, which have regulated targets significantly enriched in cellular process regulations and signaling pathways that are involved in carcinogenesis. Conclusions/Significance: In this study, we introduced an efficient computational approach to reconstruct co-regulatory FFLs by accurately identifying gene co-regulatory interactions. The strength of the proposed feature selection method lies in the fact it can precisely filter out false positives in predicted regulatory interactions by quantitatively modeling the complex co-regulation of target genes mediated by TFs and miRNAs simultaneously. Moreover, the proposed feature selection method can be generally applied to other gene regulation studies using parallel expression data with respect to different biological contexts. [ABSTRACT FROM AUTHOR]

Leaves of C4 crops usually have higher radiation, water and nitrogen use efficiencies compared to the C3 species. Engineering C4 traits into C3 crops has been proposed as one of the most promising ways to repeal the biomass yield ceiling. To better understand the function of C4 photosynthesis, and to identify candidate genes that are associated with the C4 pathways, a comparative transcription network analysis was conducted on leaf developmental gradients of three C4 species including maize, green foxtail and sorghum and one C3 species, rice. By combining the methods of gene co-expression and differentially co-expression networks, we identified a total of 128 C4 specific genes. Besides the classic C4 shuttle genes, a new set of genes associated with light reaction, starch and sucrose metabolism, metabolites transportation, as well as transcription regulation, were identified as involved in C4 photosynthesis. These findings will provide important insights into the differential gene regulation between C3 and C4 species, and a good genetic resource for establishing C4 pathways in C3 crops. [ABSTRACT FROM AUTHOR]

Genotyping by sequencing (GBS) provides opportunities to generate high-resolution genetic maps at a low genotyping cost, but for highly heterozygous species, missing data and heterozygote undercalling complicate the creation of GBS genetic maps. To overcome these issues, we developed a publicly available, modular approach called HetMappS, which functions independently of parental genotypes and corrects for genotyping errors associated with heterozygosity. For linkage group formation, HetMappS includes both a reference-guided synteny pipeline and a reference-independent de novo pipeline. The de novo pipeline can be utilized for under-characterized or high diversity families that lack an appropriate reference. We applied both HetMappS pipelines in five half-sib F1 families involving genetically diverse Vitis spp. Starting with at least 116,466 putative SNPs per family, the HetMappS pipelines identified 10,440 to 17,267 phased pseudo-testcross (Pt) markers and generated high-confidence maps. Pt marker density exceeded crossover resolution in all cases; up to 5,560 non-redundant markers were used to generate parental maps ranging from 1,047 cM to 1,696 cM. The number of markers used was strongly correlated with family size in both de novo and synteny maps (r = 0.92 and 0.91, respectively). Comparisons between allele and tag frequencies suggested that many markers were in tandem repeats and mapped as single loci, while markers in regions of more than two repeats were removed during map curation. Both pipelines generated similar genetic maps, and genetic order was strongly correlated with the reference genome physical order in all cases. Independently created genetic maps from shared parents exhibited nearly identical results. Flower sex was mapped in three families and correctly localized to the known sex locus in all cases. The HetMappS pipeline could have wide application for genetic mapping in highly heterozygous species, and its modularity provides opportunities to adapt portions of the pipeline to other family types, genotyping technologies or applications. [ABSTRACT FROM AUTHOR]

Phosphorylation is catalyzed by protein kinases and is irreplaceable in regulating biological processes. Identification of phosphorylation sites with their corresponding kinases contributes to the understanding of molecular mechanisms. Mass spectrometry analysis of phosphor-proteomes generates a large number of phosphorylated sites. However, experimental methods are costly and time-consuming, and most phosphorylation sites determined by experimental methods lack kinase information. Therefore, computational methods are urgently needed to address the kinase identification problem. To this end, we propose a new kernel-based machine learning method called Supervised Laplacian Regularized Least Squares (SLapRLS), which adopts a new method to construct kernels based on the similarity matrix and minimizes both structure risk and overall inconsistency between labels and similarities. The results predicted using both Phospho.ELM and an additional independent test dataset indicate that SLapRLS can more effectively identify kinases compared to other existing algorithms. [ABSTRACT FROM AUTHOR]

Background: Tumor single nucleotide polymorphism (SNP) array is a common platform for investigating the cancer genomic aberration and the functionally important altered genes. Original SNP array signals are usually corrupted by noise, and need to be de-convoluted into absolute copy number profile by analytical methods. Unfortunately, in contrast with the popularity of tumor Affymetrix SNP array, the methods that are specifically designed for this platform are still limited. The complicated characteristics of noise in signals is one of the difficulties for dissecting tumor Affymetrix SNP array data, as they inevitably blur the distinction between aberrations and create an obstacle for the copy number aberration (CNA) identification. Results: We propose a tool named TAFFYS for comprehensive analysis of tumor Affymetrix SNP array data. TAFFYS introduce a wavelet-based de-noising approach and copy number-specific signal variance model for suppressing and modelling the noise in signals. Then a hidden Markov model is employed for copy number inference. Finally, by using the absolute copy number profile, statistical significance of each aberration region is calculated in term of different aberration types, including amplification, deletion and loss of heterozygosity (LOH). The result shows that copy number specific-variance model and wavelet de-noising algorithm fits well with the Affymetrix SNP array signals, leading to more accurate estimation for diluted tumor sample (even with only 30% of cancer cells) than other existed methods. Results of examinations also demonstrate a good compatibility and extensibility for different Affymetrix SNP array platforms. Application on the 35 breast tumor samples shows that TAFFYS can automatically dissect the tumor samples and reveal statistically significant aberration regions where cancer-related genes locate. Conclusions: TAFFYS provide an efficient and convenient tool for identifying the copy number alteration and allelic imbalance and assessing the recurrent aberrations for the tumor Affymetrix SNP array data. [ABSTRACT FROM AUTHOR]

The aim of this study was to evaluate the relationship between enhanced intensity of contrast enhanced ultrasound and microvessel density of aortic atherosclerotic plaque in rabbit model. The abdominal aortas of thirty-six male New Zealand rabbits were damaged by balloon expansion and the animals were then fed a high fat diet for 12 weeks. Twenty-seven plaques on the near aortic wall were detected using conventional ultrasound examination. The maximum thickness of each plaque was recorded. CEUS was performed on these 27 plaques and the time-intensity curves (TICs) were analyzed offline. Using the quantitative ACQ software, features such as the arrival time (AT), time to peak (TTP), baseline intensity (BI), peak intensity (PI) and enhanced intensity (EI) (EI = PI-BI) were assessed. Inter- and intra-observer agreement of EI were assessed using the Bland-Altman test. After CEUS examination, the rabbits were sacrificed for pathological examination and CD34 monoclonal antibody immunohistochemical detection. Microvessel density (MVD) was counted under the microscope. The relationship between indexes of CEUS and the level of MVD was analyzed. There was a good positive linear correlation between EI and MVD (γ = 0. 854, P<0. 001), the intraclass correlations for inter- and intra-observer agreement for EI were 0.73 and 0.82 respectively, suggesting that EI may be act as a useful index for plaque risk stratification in animal models. [ABSTRACT FROM AUTHOR]

Genomic copy number alteration and allelic imbalance are distinct features of cancer cells, and recent advances in the genotyping technology have greatly boosted the research in the cancer genome. However, the complicated nature of tumor usually hampers the dissection of the SNP arrays. In this study, we describe a bioinformatic tool, named GIANT, for genome-wide identification of somatic aberrations from paired normal-tumor samples measured with SNP arrays. By efficiently incorporating genotype information of matched normal sample, it accurately detects different types of aberrations in cancer genome, even for aneuploid tumor samples with severe normal cell contamination. Furthermore, it allows for discovery of recurrent aberrations with critical biological properties in tumorigenesis by using statistical significance test. We demonstrate the superior performance of the proposed method on various datasets including tumor replicate pairs, simulated SNP arrays and dilution series of normal-cancer cell lines. Results show that GIANT has the potential to detect the genomic aberration even when the cancer cell proportion is as low as 5∼10%. Application on a large number of paired tumor samples delivers a genome-wide profile of the statistical significance of the various aberrations, including amplification, deletion and LOH. We believe that GIANT represents a powerful bioinformatic tool for interpreting the complex genomic aberration, and thus assisting both academic study and the clinical treatment of cancer. [ABSTRACT FROM AUTHOR]