Background: Rheumatoid arthritis (RA) is a heterogeneous disease with unknown cause. Aim: To identify peripheral blood (PB) gene expression profiles that may distinguish RA subtypes. Methods: Large-scale expression profiling by cDNA microarrays was performed on PB from 35 patients and 15 healthy individuals. Differential gene expression was analysed by significance analysis of microarrays (SAM), followed by gene ontology analysis of the significant genes. Gene set enrichment analysis was applied to identify pathways relevant to disease. Results: A substantially raised expression of a spectrum of genes involved in immune defence was found in the PB of patients with RA compared with healthy individuals. SAM analysis revealed a highly significant elevated expression of interferon (IFN) type I regulated genes in patients with RA compared with healthy individuals, which was confirmed by gene ontology and pathway analysis, suggesting that this pathway was activated systemically in RA. A quantitative analysis revealed that increased expression of IFN-response genes was characteristic of approximately half of the patients (IFN^high patients). Application of pathway analysis revealed that the IFN^high group was largely different from the controls, with evidence for upregulated pathways involved in coagulation and complement cascades, and fatty acid metabolism, while the IFN^low group was similar to the controls. Conclusion: The IFN type I signature defines a subgroup of patients with RA, with a distinct biomolecular phenotype, characterised by increased activity of the innate defence system, coagulation and complement cascades, and fatty acid metabolism.

The intestinal microbiota is involved in many physiological processes and it is increasingly recognized that differences in community composition can influence the outcome of a variety of murine models used in biomedical research. In an effort to describe and account for the variation in intestinal microbiota composition across the animal facilities of participating members of the DFG Priority Program 1656 "Intestinal Microbiota", we performed a survey of C57BL/6J mice from 21 different mouse rooms/facilities located at 13 different institutions across Germany. Fresh feces was sampled from five mice per room/facility using standardized procedures, followed by extraction and 16S rRNA gene profiling (V1-V2 region, Illumina MiSeq) at both the DNA and RNA (reverse transcribed to cDNA) level. In order to determine the variables contributing to bacterial community differences, we collected detailed questionnaires of animal husbandry practices and incorporated this information into our analyses. We identified considerable variation in a number of descriptive aspects including the proportions of major phyla, alpha- and beta diversity, all of which displayed significant associations to specific aspects of husbandry. Salient findings include a reduction in alpha diversity with the use of irradiated chow, an increase in inter-individual variability (beta diversity) with respect to barrier access and open cages and an increase in bacterial community divergence with time since importing from a vendor. We further observe a high degree of facility-level individuality, which is likely due to each facility harboring its own unique combination of multiple varying attributes of animal husbandry. While it is important to account and control for such differences between facilities, the documentation of such diversity may also serve as a valuable future resource for investigating the origins of microbial-driven host phenotypes.

Purpose: To better understand the molecular mechanisms of malignant melanoma progression and metastasis, gene expression profiling was done of primary melanomas and melanoma metastases. Experimental Design: Tumor cell–specific gene expression in 19 primary melanomas and 22 melanoma metastases was analyzed using oligonucleotide microarrays after laser-capture microdissection of melanoma cells. Statistical analysis was done by random permutation analysis and support vector machines. Microarray data were further validated by immunohistochemistry and immunoblotting. Results: Overall, 308 genes were identified that showed significant differential expression between primary melanomas and melanoma metastases (false discovery rate ≤ 0.05). Significantly overrepresented gene ontology categories in the list of 308 genes were cell cycle regulation, mitosis, cell communication, and cell adhesion. Overall, 47 genes showed up-regulation in metastases. These included Cdc6, Cdk1, septin 6, mitosin, kinesin family member 2C, osteopontin, and fibronectin. Down-regulated genes included E-cadherin, fibroblast growth factor binding protein, and desmocollin 1 and desmocollin 3, stratifin/14-3-3σ, and the chemokine CCL27. Using support vector machine analysis of gene expression data, a performance of >85% correct classifications for primary melanomas and metastases was reached. Further analysis showed that subtypes of primary melanomas displayed characteristic gene expression patterns, as do thin tumors (≤1.0 mm Breslow thickness) compared with intermediate and thick tumors (>2.0 mm Breslow thickness). Conclusions: Taken together, this large-scale gene expression study of malignant melanoma identified molecular signatures related to metastasis, melanoma subtypes, and tumor thickness. These findings not only provide deeper insights into the pathogenesis of melanoma progression but may also guide future research on innovative treatments.

In 1970, before any antigen-bound immunoglobulin structure had been solved, Elvin Kabat proposed that regions of high amino acid diversity would be the antigen binding sites of immunoglobulin (Kabat, 1970). Conversely, sites of low variability were proposed to be structural, framework regions. This variability was defined by Wu and Kabat as the number of different amino acids found at a site divided by the relative frequency of the most common amino acid at that site (Wu and Kabat, 1970). Several groups have subsequently devised improvements of Kabat-Wu variability analysis (Litwin and Jores, 1992). While these methods are somewhat better than Kabat-Wu, they still suffer from Kabat-Wu's basic limitation: they account for only the most common one or two amino acids in estimating diversity. This leads to underestimates of low diversities and exaggerations of high diversities. Shannon information analysis eliminates serious bias and is more stable than Kabat-Wu and second generation measures of diversity (Jores et al. 1990; Wu and Kabat, 1970). Statistical reliability can be measured using Shannon analysis, and Shannon measurements can be provided with error estimates. Here we use Shannon's method to analyze the amino acid diversity at each site of T cell receptor Valpha and Vbeta to identify complementarity determining regions and framework sites. Our results reveal that the T cell receptor is significantly more diverse than immunoglobulin-suggesting T cell receptor has more than the previously-discovered four complementarity determining regions. These new complementarity determining regions may represent a larger antigen combining site, additional combining sites, or an evolutionary strategy to avoid inappropriate interaction with other molecules.