Molecular characterization of five porcine candidate genes for drip loss in pork

Drip loss is the loss of fluid from a piece of meat without mechanical force and represents an important meat quality trait. Previous work revealed a quantitative trait locus (QTL) for drip loss in pork in an experimental Duroc x Pietrain (DUPI) F2 family on SSC 5. Based on functional data indicating their possible involvement in water holding capacity and their expression in skeletal muscle, we selected five positional candidates (ACO2, ADSL, CBY1, KCNJ4, PLA2AG6) out of 130 predicted genes in the QTL interval for further analysis. We performed a mutation analysis of all coding exons and discovered 204 polymorphisms. We genotyped 39 single nucleotide polymorphisms (SNPs) in 192 Pietrain pigs with extreme drip loss phenotypes and detected a possible association with drip loss for one non-coding SNP in the ADSL gene (ss107793818, p(raw) = 0.021). Correspondingly, ADSL diplotypes were associated with drip loss and pH1 of M. longissimus dorsi. However, after correction for multiple testing, none of the tested SNPs were significantly associated with drip loss. One possible explanation for these results is that one of the QTL-alleles from the experimental DUPI family may be fixed or nearly fixed in the tested Pietrain population.

Eosinophil extracellular DNA traps: molecular mechanisms and potential roles in disease

Eosinophil extracellular traps (EETs) are part of the innate immune response and are seen in multiple infectious, allergic, and autoimmune eosinophilic diseases. EETs are composed of a meshwork of DNA fibers and eosinophil granule proteins, such as major basic protein (MBP) and eosinophil cationic protein (ECP). Interestingly, the DNA within the EETs appears to have its origin in the mitochondria of eosinophils, which had released most their mitochondrial DNA, but were still viable, exhibiting no evidence of a reduced life span. Multiple eosinophil activation mechanisms are represented, whereby toll-like, cytokine, chemokine, and adhesion receptors can all initiate transmembrane signal transduction processes leading to the formation of EETs. One of the key signaling events required for DNA release is the activation of the NADPH oxidase. Here, we review recent progress made in the understanding the molecular mechanisms involved in DNA and granule protein release, discuss the presence of EETs in disease, speculate on their potential role(s) in pathogenesis, and compare available data on other DNA-releasing cells, particularly neutrophils.

The intracellular Ig fold: a robust protein scaffold for the engineering of molecular recognition

Protein scaffolds that support molecular recognition have multiple applications in biotechnology. Thus, protein frames with robust structural cores but adaptable surface loops are in continued demand. Recently, notable progress has been made in the characterization of Ig domains of intracellular origin--in particular, modular components of the titin myofilament. These Ig belong to the I(intermediate)-type, are remarkably stable, highly soluble and undemanding to produce in the cytoplasm of Escherichia coli. Using the Z1 domain from titin as representative, we show that the I-Ig fold tolerates the drastic diversification of its CD loop, constituting an effective peptide display system. We examine the stability of CD-loop-grafted Z1-peptide chimeras using differential scanning fluorimetry, Fourier transform infrared spectroscopy and nuclear magnetic resonance and demonstrate that the introduction of bioreactive affinity binders in this position does not compromise the structural integrity of the domain. Further, the binding efficiency of the exogenous peptide sequences in Z1 is analyzed using pull-down assays and isothermal titration calorimetry. We show that an internally grafted, affinity FLAG tag is functional within the context of the fold, interacting with the anti-FLAG M2 antibody in solution and in affinity gel. Together, these data reveal the potential of the intracellular Ig scaffold for targeted functionalization.

Clinical and molecular characterization of the potential CF disease modifier syntaxin 1A

Cystic fibrosis (CF) is caused by mutations in the CF transmembrane conductance regulator gene (CFTR). Disease severity in CF varies greatly, and sibling studies strongly indicate that genes other than CFTR modify disease outcome. Syntaxin 1A (STX1A) has been reported as a negative regulator of CFTR and other ion channels. We hypothesized that STX1A variants act as a CF modifier by influencing the remaining function of mutated CFTR. We identified STX1A variants by genomic resequencing patients from the Bernese CF Patient Data Registry and applied linear mixed model analysis to establish genotype-phenotype correlations, revealing STX1A rs4363087 (c.467-38A>G) to significantly influence lung function. The same STX1A risk allele was recognized in the European CF Twin and Sibling Study (P=0.0027), demonstrating that the genotype-phenotype association of STX1A to CF disease severity is robust enough to allow replication in two independent CF populations. rs4363087 is in linkage disequilibrium to the exonic variant rs2228607 (c.204C>T). Considering that neither rs4363087 nor rs2228607 changes the amino-acid sequence of STX1A, we investigated their effects on mRNA level. We show that rs2228607 reinforces aberrant splicing of STX1A mRNA, leading to nonsense-mediated mRNA decay. In conclusion, we demonstrate the clinical relevance of STX1A variants in CF, and evidence the functional relevance of STX1A variant rs2228607 at molecular level. Our findings show that genes interacting with CFTR can modify CF disease progression.European Journal of Human Genetics advance online publication, 10 April 2013; doi:10.1038/ejhg.2013.57.

Molecular characterization of the porcine deleted in malignant brain tumors 1 gene (DMBT1)

The human gene deleted in malignant brain tumors 1 (DMBT1) is considered to play a role in tumorigenesis and pathogen defense. It encodes a protein with multiple scavenger receptor cysteine-rich (SRCR) domains, which are involved in recognition and binding of a broad spectrum of bacterial pathogens. The SRCR domains are encoded by highly homologous repetitive exons, whose number in humans may vary from 8 to 13 due to genetic polymorphism. Here, we characterized the porcine DMBT1 gene on the mRNA and genomic level. We assembled a 4.5 kb porcine DMBT1 cDNA sequence from RT-PCR amplified seminal vesicle RNA. The porcine DMBT1 cDNA contains an open reading frame of 4050 nt. The transcript gives rise to a putative polypeptide of 1349 amino acids with a calculated mass of 147.9 kDa. Compared to human DMBT1, it contains only four N-terminal SRCR domains. Northern blotting revealed transcripts of approximately 4.7 kb in size in the tissues analyzed. Analysis of ESTs suggested the existence of secreted and transmembrane variants. The porcine DMBT1 gene spans about 54 kb on chromosome 14q28-q29. In contrast to the characterized cDNA, the genomic BAC clone only contained 3 exons coding for N-terminal SRCR domains. In different mammalian DMBT1 orthologs large interspecific differences in the number of SRCR exons and utilization of the transmembrane exon exist. Our data suggest that the porcine DMBT1 gene may share with the human DMBT1 gene additional intraspecific variations in the number of SRCR-coding exons.

Analysis of pedigree and conformation data to explain genetic variability of the horse breed Franches-Montagnes

Franches-Montagnes is the only native horse breed in Switzerland, therefore special efforts should be made for ensuring its survival. The objectives of this study were to characterize the structure of this population as well as genetic variability with pedigree data, conformation traits and molecular markers. Studies were focused to clarify if this population is composed of a heavy- and a light-type subpopulation. Extended pedigree records of 3-year-old stallions (n = 68) and mares (n = 108) were available. Evaluations of body conformation traits as well as pedigree data and molecular markers did not support the two-subpopulation hypothesis. The generation interval ranged from 7.8 to 9.3 years. The complete generation equivalent was high (>12). The number of effective ancestors varied between 18.9 and 20.1, whereof 50% of the genetic variability was attributed to seven of them. Genetic contribution of Warmblood horses ranged from 36% to 42% and that of Coldblood horses from 4% to 6%. The average inbreeding coefficient reached 6%. Inbreeding effective population size was 114.5 when the average increase of the inbreeding coefficient per year since 1910 was taken. Our results suggest that bottleneck situations occurred because of selection of a small number of sire lines. Promotion of planned matings between parents that are less related is recommended in order to avoid a reduction of the genetic diversity.

A Data-Analytic Strategy for Protein-Biomarker Discovery: Profiling of High-Dimensional Proteomic Data for Cancer Detection

With recent advances in mass spectrometry techniques, it is now possible to investigate proteins over a wide range of molecular weights in small biological specimens. This advance has generated data-analytic challenges in proteomics, similar to those created by microarray technologies in genetics, namely, discovery of "signature" protein profiles specific to each pathologic state (e.g., normal vs. cancer) or differential profiles between experimental conditions (e.g., treated by a drug of interest vs. untreated) from high-dimensional data. We propose a data analytic strategy for discovering protein biomarkers based on such high-dimensional mass-spectrometry data. A real biomarker-discovery project on prostate cancer is taken as a concrete example throughout the paper: the project aims to identify proteins in serum that distinguish cancer, benign hyperplasia, and normal states of prostate using the Surface Enhanced Laser Desorption/Ionization (SELDI) technology, a recently developed mass spectrometry technique. Our data analytic strategy takes properties of the SELDI mass-spectrometer into account: the SELDI output of a specimen contains about 48,000 (x, y) points where x is the protein mass divided by the number of charges introduced by ionization and y is the protein intensity of the corresponding mass per charge value, x, in that specimen. Given high coefficients of variation and other characteristics of protein intensity measures (y values), we reduce the measures of protein intensities to a set of binary variables that indicate peaks in the y-axis direction in the nearest neighborhoods of each mass per charge point in the x-axis direction. We then account for a shifting (measurement error) problem of the x-axis in SELDI output. After these pre-analysis processing of data, we combine the binary predictors to generate classification rules for cancer, benign hyperplasia, and normal states of prostate. Our approach is to apply the boosting algorithm to select binary predictors and construct a summary classifier. We empirically evaluate sensitivity and specificity of the resulting summary classifiers with a test dataset that is independent from the training dataset used to construct the summary classifiers. The proposed method performed nearly perfectly in distinguishing cancer and benign hyperplasia from normal. In the classification of cancer vs. benign hyperplasia, however, an appreciable proportion of the benign specimens were classified incorrectly as cancer. We discuss practical issues associated with our proposed approach to the analysis of SELDI output and its application in cancer biomarker discovery.

Visualizing Genomic Data

The advent of experimental techniques capable of probing biomolecules and cells at high levels of resolution has led to a rapid change in the methods used for the analysis of experimental molecular biology data. In this article we give an overview over visualization techniques and methods that can be used to assess various aspects of genomic data.

Immune cell migration across the blood–brain barrier: molecular mechanisms and therapeutic targeting

The endothelial blood–brain barrier (BBB) and the epithelial blood–cerebrospinal fluid barrier protect the CNS from the constantly changing milieu within the bloodstream. The BBB strictly controls immune cell entry into the CNS, which is rare under physiological conditions. During a variety of pathological conditions of the CNS, such as viral or bacterial infections, or during inflammatory diseases, such as multiple sclerosis, immunocompetent cells readily traverse the BBB and subsequently enter the CNS parenchyma. Most of the available information on immune cell entry into the CNS is derived from studying experimental autoimmune encephalomyelitis (EAE), an animal model for multiple sclerosis. Consequently, our current knowledge on traffic signals mediating immune cell entry across the BBB during immunosurveillance and disease results mainly from experimental data in the EAE model. Therefore, a large part of this review summarizes these findings. Similarly, the potential benefits and risks associated with therapeutic targeting of immune cell trafficking across the BBB will be discussed in the context of multiple sclerosis, since elucidation of the molecular mechanisms relevant to this disease have largely relied on the use of its in vivo model, EAE.

Stochastic Models Based on Molecular Hybridization Theory for Short Oligonucleotide Microarrays

High density oligonucleotide expression arrays are a widely used tool for the measurement of gene expression on a large scale. Affymetrix GeneChip arrays appear to dominate this market. These arrays use short oligonucleotides to probe for genes in an RNA sample. Due to optical noise, non-specific hybridization, probe-specific effects, and measurement error, ad-hoc measures of expression, that summarize probe intensities, can lead to imprecise and inaccurate results. Various researchers have demonstrated that expression measures based on simple statistical models can provide great improvements over the ad-hoc procedure offered by Affymetrix. Recently, physical models based on molecular hybridization theory, have been proposed as useful tools for prediction of, for example, non-specific hybridization. These physical models show great potential in terms of improving existing expression measures. In this paper we demonstrate that the system producing the measured intensities is too complex to be fully described with these relatively simple physical models and we propose empirically motivated stochastic models that compliment the above mentioned molecular hybridization theory to provide a comprehensive description of the data. We discuss how the proposed model can be used to obtain improved measures of expression useful for the data analysts.

REMOVING TECHNICAL VARIABILITY IN RNA-SEQ DATA USING CONDITIONAL QUANTILE NORMALIZATION

The ability to measure gene expression on a genome-wide scale is one of the most promising accomplishments in molecular biology. Microarrays, the technology that first permitted this, were riddled with problems due to unwanted sources of variability. Many of these problems are now mitigated, after a decade’s worth of statistical methodology development. The recently developed RNA sequencing (RNA-seq) technology has generated much excitement in part due to claims of reduced variability in comparison to microarrays. However, we show RNA-seq data demonstrates unwanted and obscuring variability similar to what was first observed in microarrays. In particular, we find GC-content has a strong sample specific effect on gene expression measurements that, if left uncorrected, leads to false positives in downstream results. We also report on commonly observed data distortions that demonstrate the need for data normalization. Here we describe statistical methodology that improves precision by 42% without loss of accuracy. Our resulting conditional quantile normalization (CQN) algorithm combines robust generalized regression to remove systematic bias introduced by deterministic features such as GC-content, and quantile normalization to correct for global distortions.

Biological mediators and periodontal regeneration: a review of enamel matrix proteins at the cellular and molecular levels

BACKGROUND: Despite a large body of clinical and histological data demonstrating beneficial effects of enamel matrix proteins (EMPs) for regenerative periodontal therapy, it is less clear how the available biological data can explain the mechanisms underlying the supportive effects of EMPs. OBJECTIVE: To analyse all available biological data of EMPs at the cellular and molecular levels that are relevant in the context of periodontal wound healing and tissue formation. METHODS: A stringent systematic approach was applied using the key words "enamel matrix proteins" OR "enamel matrix derivative" OR "emdogain" OR "amelogenin". The literature search was performed separately for epithelial cells, gingival fibroblasts, periodontal ligament cells, cementoblasts, osteogenic/chondrogenic/bone marrow cells, wound healing, and bacteria. RESULTS: A total of 103 papers met the inclusion criteria. EMPs affect many different cell types. Overall, the available data show that EMPs have effects on: (1) cell attachment, spreading, and chemotaxis; (2) cell proliferation and survival; (3) expression of transcription factors; (4) expression of growth factors, cytokines, extracellular matrix constituents, and other macromolecules; and (5) expression of molecules involved in the regulation of bone remodelling. CONCLUSION: All together, the data analysis provides strong evidence for EMPs to support wound healing and new periodontal tissue formation.

Progression of atherosclerosis in ApoE-deficient mice that express distinct molecular forms of TNF-alpha

TNFalpha (TNF) critically regulates inflammation-driven atherosclerosis. Because the transmembrane (tmTNF) and soluble (sTNF) forms of TNF possess distinct immuno-modulatory properties, we hypothesized that they might differentially regulate atherosclerosis progression. Three groups of male ApoE(-/-) mice were studied: one expressing wild-type TNF (WT-TNF); one expressing exclusively a mutated non-cleavable form of TNF (KI-TNF); and one deficient in TNF (KO-TNF). Mice aged 5 weeks were fed the high-fat diet for 5 (T5) and 15 weeks (T15) or a standard chow diet for 15 weeks. At T5, in mice fed the high-fat diet, no significant differences in lesion area were observed among the three groups, either in valves or in aortas. At T15, lesion areas in valves were significantly lower in KO-TNF mice compared with those in WT-TNF mice, whereas in KI-TNF mice, they were intermediate between KO- and WT-TNF mice but not significantly different from these two groups. In aortas, lesions in KI-TNF were comparable to those of KO-TNF, both being significantly lower than those in WT-TNF. Theses differences were not linked to circulating lipids, or to macrophage, actin, and collagen contents of lesions. At T15, in mice fed the chow diet, lesion areas in valves and the aortic arch were not significantly different between the three groups. Levels of IL-6, IFNgamma, IL-10, and Foxp3 mRNAs in spleens and production of IL-6, IL-10, MCP-1, RANTES, and TNFR-2 by peritoneal macrophages at T15 of the high-fat diet showed a decrease in pro-inflammatory status, more marked in KO-TNF than in KI-TNF mice. Apoptosis was reduced only in KO-TNF mice. In conclusion, these data show that TNF effects on atherosclerosis development are detectable at stages succeeding fatty streaks and that wild-type TNF is superior to tmTNF alone in promoting atherosclerosis. TNF-dependent progression of atherosclerosis is probably linked to the differential production of pro-inflammatory mediators whether tmTNF is preponderant or essentially cleaved. Copyright (c) 2008 Pathological Society of Great Britain and Ireland. Published by John Wiley ; Sons, Ltd.