Diabetic modifier QTLs in F(2) intercrosses carrying homozygous transgene of TGF-beta

Influence of a growth hormone transgene on the genetic architecture of growth-related traits: A comparative analysis between transgenic and wild-type coho salmon

Genetic engineering has been increasingly applied to many commercially important plant and animal species, generating phenotypic changes that are not observed in natural populations and creating genetic interactions that have not experienced natural selection. The degree to and way in which such human-induced genetic variation interacts with the rest of the genome is currently largely unknown. Integrating such information into ecological and risk assessment frameworks is crucial to understand the potential effects of genetically modified organisms in natural environments. Here, we performed QTL mapping to investigate the genetic architecture of growth-related traits in nontransgenic (NT) and growth hormone transgenic (T) coho salmon with large changes in growth and related physiology, with the aim of identifying how an inserted transgene might influence the opportunity for selection. These fish shared the same parental genetic background, thus allowing us to determine whether the same or different loci influence these traits within the two groups. The use of over 1,700 loci, derived from restriction site-associated DNA sequencing, revealed that different genomic regions were linked with growth over time between the two groups. Additionally, the effect sizes of detected QTL appear to have been influenced by the transgene. Direct comparison of QTL between the T and NT fish during two size-matched periods identified little overlap in their location. Taken together, the results showed that the transgene altered the genetic basis of growth-related traits in this species. The study has important implications for effective conservation and management of wild populations experiencing introduction of transgenes. Evolutionary changes and their ecological consequences may occur at different rates and in different directions in NT versus T individuals in response to selection. Thus, assessments of phenotypic change, and hence ecological risk, should be determined periodically to evaluate whether initial estimates made with founder strains remain valid.

Genetic architecture of early pre-inflammatory stage transcription signatures of autoimmune diabetes in the pancreatic lymph nodes of the NOD mouse reveals significant gene enrichment on chromosomes 6 and 7

Autoimmune diseases are characterized by the stimulation of an excessive immune response to self-tissues by inner and/or outer organism factors. Common characteristics in their etiology include a complex genetic predisposition and environmental triggers as well as the implication of the major histocompatibility (MHC) locus on human chromosome 6p21. A restraint number of non-MHC susceptibility genes, part of the genetic component of type 1 diabetes have been identified in human and in animal models, while the complete spectrum of genes involved remains unknown. We elaborate herein patterns of chromosomal organization of 162 genes differentially expressed in the pancreatic lymph nodes of Non-Obese Diabetic mice, carefully selected by early sub-phenotypic evaluation (presence or absence of insulin autoantibodies). Chromosomal assignment of these genes revealed a non-random distribution on five chromosomes (47%). Significant gene enrichment was observed in particular for two chromosomes, 6 and 7. While a subset of these genes coding for secreted proteins showed significant enrichment on both chromosomes, the overall pool of genes was significantly enriched on chromosome 7. The significance of this unexpected gene distribution on the mouse genome is discussed in the light of novel findings indicating that genes affecting common diseases map to recombination “hotspot” regions of mammalian genomes. The genetic architecture of transcripts differentially expressed in specific stages of autoimmune diabetes offers novel venues towards our understanding of patterns of inheritance potentially affecting the pathological disease mechanisms.

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Chromosomal organization of differentially expressed genes in early autoimmunity

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Non-random distribution of type 1 diabetes-related transcripts on the mouse genome

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High density of genetic polymorphisms on specific loci of chromosomes 6 and 7

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Correlation of mapping positions on chromosomes 6 and 7 with known T1D loci

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Our study offers candidate genes and loci drawn in the genomics of T1D initiation.

Study on Certain Biomarkers of Inflammation in Psoriasis Through "OMICS" Platforms

Background:

In recent years, research on psoriasis has focused on the identification of biomarkers for the diagnosis, pathogenesis, prognosis, or therapeutic response of the disease. These studies could provide insights into the susceptibility and natural history of psoriasis. The identification of biomarkers related to comorbidities in psoriasis, such as arthritis, cardiovascular disease, and the metabolic syndrome, is of special clinical interest.

Materials and Methods:

We performed an extensive review on psoriasis biomarkers, including cytokine and growth factors, in the literature published between 1997 and 2013, including cross-references of any retrieved articles. We also included some data from our own studies.

Results:

This review presents current knowledge of soluble biomarkers in psoriasis, including cytokines, chemokines, proangiogenic mediators, growth factors, antimicrobial proteins, neuropeptides, and oxidative stress markers.

Conclusion:

In conclusion, a number of studies have been conducted with the aim of establishing soluble biomarkers for psoriasis. Most of the biomarkers that have been studied do not meet the criteria for a clinically useful biomarker. Further work is needed to establish a role for soluble biomarkers in the diagnosis and treatment of psoriasis, with a special focus on biomarkers for psoriasis comorbidities, such as arthritis, cardiovascular disease, and the metabolic syndrome.

Meta-analysis derived (MAD) transcriptome of psoriasis defines the "core" pathogenesis of disease

The cause of psoriasis, a common chronic inflammatory skin disease, is not fully understood. Microarray experiments have been widely used in recent years to identify genes associated with psoriasis pathology, by comparing expression levels of lesional (LS) with adjacent non-lesional (NL) skin. It is commonly observed that the differentially expressed genes (DEGs) differ greatly across experiments, due to variations introduced in the microarray experiment pipeline. Therefore, a statistically based meta-analytic approach, which combines the results of individual studies, is warranted. In this study, a meta-analysis was conducted on 5 microarray data sets, including 193 LS and NL pairs. We termed this the Meta-Analysis Derived (MAD) transcriptome. In “MAD-5” transcriptome, 677 genes were up-regulated and 443 were down-regulated in LS skin compared to NL skin. This represents a much larger set than the intersection of DEGs of these 5 studies, which consisted of 100 DEGs. We also analyzed 3 of the studies conducted on the Affymetrix hgu133plus2 chips and found a greater number of DEGs (1084 up- and 748 down-regulated). Top canonical pathways over-represented in the MAD transcriptome include Atherosclerosis Signaling and Fatty Acid Metabolism, while several “new” genes identified are involved in Cardiovascular Development and Lipid Metabolism. These findings highlight the relationship between psoriasis and systemic manifestations such as the metabolic syndrome and cardiovascular disease. Then, the Meta Threshold Gradient Descent Regularization (MTGDR) algorithm was used to select potential markers distinguishing LS and NL skin. The resulting set (20 genes) contained many genes that were part of the residual disease genomic profile (RDGP) or “molecular scar” after successful treatment, and also genes subject to differential methylation in LS tissues. To conclude, this MAD transcriptome yielded a reference list of reliable psoriasis DEGs, and represents a robust pool of candidates for further discovery of pathogenesis and treatment evaluation.