A gene expression system offering multiple levels of regulation: the Dual Drug Control (DDC) system

BACKGROUND: Whether for cell culture studies of protein function, construction of mouse models to enable in vivo analysis of disease epidemiology, or ultimately gene therapy of human diseases, a critical enabling step is the ability to achieve finely controlled regulation of gene expression. Previous efforts to achieve this goal have explored inducible drug regulation of gene expression, and construction of synthetic promoters based on two-hybrid paradigms, among others. RESULTS: In this report, we describe the combination of dimerizer-regulated two-hybrid and tetracycline regulatory elements in an ordered cascade, placing expression of endpoint reporters under the control of two distinct drugs. In this Dual Drug Control (DDC) system, a first plasmid expresses fusion proteins to DBD and AD, which interact only in the presence of a small molecule dimerizer; a second plasmid encodes a cassette transcriptionally responsive to the first DBD, directing expression of the Tet-OFF protein; and a third plasmid encodes a reporter gene transcriptionally responsive to binding by Tet-OFF. We evaluate the dynamic range and specificity of this system in comparison to other available systems. CONCLUSION: This study demonstrates the feasibility of combining two discrete drug-regulated expression systems in a temporally sequential cascade, without loss of dynamic range of signal induction. The efficient layering of control levels allowed by this combination of elements provides the potential for the generation of complex control circuitry that may advance ability to regulate gene expression in vivo

Three allele combinations associated with Multiple Sclerosis

BACKGROUND: Multiple sclerosis (MS) is an immune-mediated disease of polygenic etiology. Dissection of its genetic background is a complex problem, because of the combinatorial possibilities of gene-gene interactions. As genotyping methods improve throughput, approaches that can explore multigene interactions appropriately should lead to improved understanding of MS. METHODS: 286 unrelated patients with definite MS and 362 unrelated healthy controls of Russian descent were genotyped at polymorphic loci (including SNPs, repeat polymorphisms, and an insertion/deletion) of the DRB1, TNF, LT, TGFβ1, CCR5 and CTLA4 genes and TNFa and TNFb microsatellites. Each allele carriership in patients and controls was compared by Fisher's exact test, and disease-associated combinations of alleles in the data set were sought using a Bayesian Markov chain Monte Carlo-based method recently developed by our group. RESULTS: We identified two previously unknown MS-associated tri-allelic combinations: -509TGFβ1*C, DRB1*18(3), CTLA4*G and -238TNF*B1,-308TNF*A2, CTLA4*G, which perfectly separate MS cases from controls, at least in the present sample. The previously described DRB1*15(2) allele, the microsatellite TNFa9 allele and the biallelic combination CCR5Δ32, DRB1*04 were also reidentified as MS-associated. CONCLUSION: These results represent an independent validation of MS association with DRB1*15(2) and TNFa9 in Russians and are the first to find the interplay of three loci in conferring susceptibility to MS. They demonstrate the efficacy of our approach for the identification of complex-disease-associated combinations of alleles

Genome-wide significant association with seven novel multiple sclerosis risk loci

Objective: A recent large-scale study in multiple sclerosis (MS) using the ImmunoChip platform reported on 11 loci that showed suggestive genetic association with MS. Additional data in sufficiently sized and independent data sets are needed to assess whether these loci represent genuine MS risk factors. Methods: The lead SNPs of all 11 loci were genotyped in 10 796 MS cases and 10 793 controls from Germany, Spain, France, the Netherlands, Austria and Russia, that were independent from the previously reported cohorts. Association analyses were performed using logistic regression based on an additive model. Summary effect size estimates were calculated using fixed-effect meta-analysis. Results: Seven of the 11 tested SNPs showed significant association with MS susceptibility in the 21 589 individuals analysed here. Meta-analysis across our and previously published MS case-control data (total sample size n=101 683) revealed novel genome-wide significant association with MS susceptibility (p<5×10−8) for all seven variants. This included SNPs in or near LOC100506457 (rs1534422, p=4.03×10−12), CD28 (rs6435203, p=1.35×10−9), LPP (rs4686953, p=3.35×10−8), ETS1 (rs3809006, p=7.74×10−9), DLEU1 (rs806349, p=8.14×10−12), LPIN3 (rs6072343, p=7.16×10−12) and IFNGR2 (rs9808753, p=4.40×10−10). Cis expression quantitative locus effects were observed in silico for rs6435203 on CD28 and for rs9808753 on several immunologically relevant genes in the IFNGR2 locus. Conclusions: This study adds seven loci to the list of genuine MS genetic risk factors and further extends the list of established loci shared across autoimmune diseases

Genome-wide significant association with seven novel multiple sclerosis risk loci

Objective: A recent large-scale study in multiple sclerosis (MS) using the ImmunoChip platform reported on 11 loci that showed suggestive genetic association with MS. Additional data in sufficiently sized and independent data sets are needed to assess whether these loci represent genuine MS risk factors. Methods: The lead SNPs of all 11 loci were genotyped in 10 796 MS cases and 10 793 controls from Germany, Spain, France, the Netherlands, Austria and Russia, that were independent from the previously reported cohorts. Association analyses were performed using logistic regression based on an additive model. Summary effect size estimates were calculated using fixed-effect meta-analysis. Results: Seven of the 11 tested SNPs showed significant association with MS susceptibility in the 21 589 individuals analysed here. Meta-analysis across our and previously published MS case-control data (total sample size n=101 683) revealed novel genome-wide significant association with MS susceptibility (p<5×10−8) for all seven variants. This included SNPs in or near LOC100506457 (rs1534422, p=4.03×10−12), CD28 (rs6435203, p=1.35×10−9), LPP (rs4686953, p=3.35×10−8), ETS1 (rs3809006, p=7.74×10−9), DLEU1 (rs806349, p=8.14×10−12), LPIN3 (rs6072343, p=7.16×10−12) and IFNGR2 (rs9808753, p=4.40×10−10). Cis expression quantitative locus effects were observed in silico for rs6435203 on CD28 and for rs9808753 on several immunologically relevant genes in the IFNGR2 locus. Conclusions: This study adds seven loci to the list of genuine MS genetic risk factors and further extends the list of established loci shared across autoimmune diseases

A Markov Chain Monte Carlo Technique for Identification of Combinations of Allelic Variants Underlying Complex Diseases in Humans

In recent years, the number of studies focusing on the genetic basis of common disorders with a complex mode of inheritance, in which multiple genes of small effect are involved, has been steadily increasing. An improved methodology to identify the cumulative contribution of several polymorphous genes would accelerate our understanding of their importance in disease susceptibility and our ability to develop new treatments. A critical bottleneck is the inability of standard statistical approaches, developed for relatively modest predictor sets, to achieve power in the face of the enormous growth in our knowledge of genomics. The inability is due to the combinatorial complexity arising in searches for multiple interacting genes. Similar “curse of dimensionality” problems have arisen in other fields, and Bayesian statistical approaches coupled to Markov chain Monte Carlo (MCMC) techniques have led to significant improvements in understanding. We present here an algorithm, APSampler, for the exploration of potential combinations of allelic variations positively or negatively associated with a disease or with a phenotype. The algorithm relies on the rank comparison of phenotype for individuals with and without specific patterns (i.e., combinations of allelic variants) isolated in genetic backgrounds matched for the remaining significant patterns. It constructs a Markov chain to sample only potentially significant variants, minimizing the potential of large data sets to overwhelm the search. We tested APSampler on a simulated data set and on a case-control MS (multiple sclerosis) study for ethnic Russians. For the simulated data, the algorithm identified all the phenotype-associated allele combinations coded into the data and, for the MS data, it replicated the previously known findings

A gene expression system offering multiple levels of regulation: the Dual Drug Control (DDC) system-1

<p><b>Copyright information:</b></p><p>Taken from "A gene expression system offering multiple levels of regulation: the Dual Drug Control (DDC) system"</p><p>BMC Biotechnology 2004;4():9-9.</p><p>Published online 29 Apr 2004</p><p>PMCID:PMC420247.</p><p>Copyright © 2004 Sudomoina et al; licensee BioMed Central Ltd. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original URL.</p>; the total amount of plasmid DNA was constant at 0.5 μg per test point. As graphically represented, at each apex of the triangle shown, only one of the test plasmids is present (100% of mix), while opposite the matrix this plasmid is absent (0% of mix), with the two other plasmids present in equal proportions. White circles indicate ratios of plasmids evaluated for degree of induction of SEAP activity. Degree of induction reflects difference between growth without dimerizer and with tetracycline (OFF), versus with dimerizer and without tetracycline (ON). Based on the ON:OFF ratios obtained, the software program "Statistica" modeled the optimal response surface, shown as a contour plot on the triangle. Faded edges of the triangle represent limitations of the model, because it is meaningless whenever one of the components is absent. The optimal ratio for plasmid transfection with the DDC system was 1.5:1.5:1 (pCN-RS/ZF3:pZBS-Tet-OFF:pBI-EGFP-SEAP). This contrasted with a ratio of 3:1 (activator proteins:reporter) used for the original ARIAD system

A gene expression system offering multiple levels of regulation: the Dual Drug Control (DDC) system-3

<p><b>Copyright information:</b></p><p>Taken from "A gene expression system offering multiple levels of regulation: the Dual Drug Control (DDC) system"</p><p>BMC Biotechnology 2004;4():9-9.</p><p>Published online 29 Apr 2004</p><p>PMCID:PMC420247.</p><p>Copyright © 2004 Sudomoina et al; licensee BioMed Central Ltd. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original URL.</p>imerizer only; scale is on the right Y-axis) and the SEAP gene (tTA-dependent transcription expression responsive to dimerizer AND tetracycline; scale is on the left Y-axis). All cells contain pCN-RS/ZF3, pZI-hGH-2, pZBS-Tet-OFF, and pBI-EGFP-SEAP. See Results for details of manipulations A-D; briefly, A was grown in continuous dimerizer-/tetracycline+; B-D were initially in dimerizer+/tetracycline+. At 48 hours after transfection with plasmids, A and B were harvested, while C and D were grown for 48 more hours in dimerizer+/tetracycline+ (C) or dimerizer+/tetracycline- (D) medium before harvesting. Results of one typical experiment, with four parallel transfections for each data point, are shown. Each experiment was repeated at least three times

Multiplex Analysis of Serum Cytokine Profiles in Systemic Lupus Erythematosus and Multiple Sclerosis

Changes in cytokine profiles and cytokine networks are known to be a hallmark of autoimmune diseases, including systemic lupus erythematosus (SLE) and multiple sclerosis (MS). However, cytokine profiles research studies are usually based on the analysis of a small number of cytokines and give conflicting results. In this work, we analyzed cytokine profiles of 41 analytes in patients with SLE and MS compared with healthy donors using multiplex immunoassay. The SLE group included treated patients, while the MS patients were drug-free. Levels of 11 cytokines, IL-1b, IL-1RA, IL-6, IL-9, IL-10, IL-15, MCP-1/CCL2, Fractalkine/CX3CL1, MIP-1a/CCL3, MIP-1b/CCL4, and TNFa, were increased, but sCD40L, PDGF-AA, and MDC/CCL22 levels were decreased in SLE patients. Thus, changes in the cytokine profile in SLE have been associated with the dysregulation of interleukins, TNF superfamily members, and chemokines. In the case of MS, levels of 10 cytokines, sCD40L, CCL2, CCL3, CCL22, PDGF-AA, PDGF-AB/BB, EGF, IL-8, TGF-a, and VEGF, decreased significantly compared to the control group. Therefore, cytokine network dysregulation in MS is characterized by abnormal levels of growth factors and chemokines. Cross-disorder analysis of cytokine levels in MS and SLE showed significant differences between 22 cytokines. Protein interaction network analysis showed that all significantly altered cytokines in both SLE and MS are functionally interconnected. Thus, MS and SLE may be associated with impaired functional relationships in the cytokine network. A cytokine correlation networks analysis revealed changes in correlation clusters in SLE and MS. These data expand the understanding of abnormal regulatory interactions in cytokine profiles associated with autoimmune diseases