Dectin-2 recognises mannosylated O-antigens of human opportunistic pathogens and augments lipopolysaccharide activation of myeloid cells

Lipopolysaccharide (LPS) consists of a relatively conserved region of lipid A and core-oligosaccharide, and a highly variable region of O-antigen polysaccharide. While lipid A is known to bind to the toll-like receptor 4 (TLR4)-myeloid differentiation factor 2 (MD2) complex, the role of the O-antigen remains unclear. Here we report a novel molecular interaction between dendritic cell-associated C-type lectin-2 (Dectin-2) and the mannosylated O-antigen found in a human opportunistic pathogen Hafnia alvei PCM 1223, which has a repeating unit of [-Man-α1,3-Man-α1,2-Man-α1,2-Man-α1,2-Man-α1,3-]. H. alvei LPS induced higher levels of TNFα and IL-10 from mouse bone marrow-derived dendritic cells (BM-DCs), when compared to Salmonella enterica O66 LPS which has a repeat of [-Gal-α1,6-Gal-α1,4-[Glc-β1,3]GalNAc-α1,3-GalNAc-β1,3-]. In a cell-based reporter assay, Dectin-2 was shown to recognise H. alvei LPS. This binding was inhibited by mannosidase treatment of H. alvei LPS and by mutations in the carbohydrate-binding domain of Dectin-2, demonstrating that H. alvei LPS is a novel glycan ligand of Dectin-2. The enhanced cytokine production by H. alvei LPS was Dectin-2 dependent, as Dectin-2 knockout BM-DCs failed to do so. This receptor crosstalk between Dectin-2 and TLR4 involved events including spleen tyrosine kinase (Syk) activation and receptor juxtaposition. Furthermore, another mannosylated LPS from Escherichia coli O9a, also bound to Dectin-2 and augmented TLR4 activation of BM-DCs. Taken together, these data indicate that mannosylated O-antigens from several gram-negative bacteria augment TLR4 responses through interaction with Dectin-2

Predicting Diabetic Nephropathy Using a Multifactorial Genetic Model

AIMS: The tendency to develop diabetic nephropathy is, in part, genetically determined, however this genetic risk is largely undefined. In this proof-of-concept study, we tested the hypothesis that combined analysis of multiple genetic variants can improve prediction. METHODS: Based on previous reports, we selected 27 SNPs in 15 genes from metabolic pathways involved in the pathogenesis of diabetic nephropathy and genotyped them in 1274 Ashkenazi or Sephardic Jewish patients with Type 1 or Type 2 diabetes of >10 years duration. A logistic regression model was built using a backward selection algorithm and SNPs nominally associated with nephropathy in our population. The model was validated by using random "training" (75%) and "test" (25%) subgroups of the original population and by applying the model to an independent dataset of 848 Ashkenazi patients. RESULTS: The logistic model based on 5 SNPs in 5 genes (HSPG2, NOS3, ADIPOR2, AGER, and CCL5) and 5 conventional variables (age, sex, ethnicity, diabetes type and duration), and allowing for all possible two-way interactions, predicted nephropathy in our initial population (C-statistic = 0.672) better than a model based on conventional variables only (C = 0.569). In the independent replication dataset, although the C-statistic of the genetic model decreased (0.576), it remained highly associated with diabetic nephropathy (χ(2) = 17.79, p<0.0001). In the replication dataset, the model based on conventional variables only was not associated with nephropathy (χ(2) = 3.2673, p = 0.07). CONCLUSION: In this proof-of-concept study, we developed and validated a genetic model in the Ashkenazi/Sephardic population predicting nephropathy more effectively than a similarly constructed non-genetic model. Further testing is required to determine if this modeling approach, using an optimally selected panel of genetic markers, can provide clinically useful prediction and if generic models can be developed for use across multiple ethnic groups or if population-specific models are required

The multifactorial model: ORs and 95% CI for different SNPs and interactions in the model (expressed in logarithmic form).

<p>For the exact values see estimates in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0018743#pone-0018743-t003" target="_blank">Table 3</a>. All variables, single or interactions, contribute to the model significantly, but in different ways.</p

ROC Curve and area under the curve (C Statistic) for the “full” model in the replication dataset (dotted line; C = 0.576).

<p>The ROC curve and C statistic for the same model in the original population (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0018743#pone-0018743-g001" target="_blank">Figure 1A</a>) is shown for comparison (solid line).</p

Model parameters with and without genetic factors.

<p>1– The intercept and the predictor variables in the model. – see <i>Statistical Analysis and Modeling</i> section for description of how the variables were coded.</p><p>2– Binary logit regression estimates for the parameters in the model. In the logistic regression equation log[p/(1-p)] =  <i>a+βx</i> where p is the probability that nephropathy  =  1, the estimate of each variable contributes to β.</p><p>3– Standard errors of the individual regression coefficients.</p><p>4– Test statistic; the squared ratio of the Estimate to the SE of the respective predictor.</p><p>5- The probability that a particular Chi-Square test statistic (1 df) is as extreme as, or more so, than what has been observed under the null hypothesis; the null hypothesis is that all of the regression coefficients in the model are equal to zero. The numbers in the column are the associated p-values.</p><p>6– The logistic regression estimate when all variables in the model are evaluated at zero. In the above equation intercept contributes to the <i>α</i>-coefficient.</p

Receiver Operating Characteristic (ROC) curves in the original population.

<p>A. Predictive ability of the full and “conventional” models in the original population. ROC Curve and area under the curve (C Statistic) for “full” model (solid line; C = 0.672) and for the “conventional” model (dotted line; C = 0.569). B. Validation of the model on original population. The ROC Curve and area under the curve (C Statistic) for the model built on 75% of the original population (solid line; C = 0.678) and applied to the remaining 25% of the population (dotted line; C = 0.630). The diagonal line indicates zero predictive value of model.</p

Graph of type I vs type II error.

<p>The solid line indicates the false positive rate (FP, error type I), the dashed line the false negative rate (FN, error type II) and the dotted line represents the sum of false positive and false negative rates at each probability level. The minimal errors sum is 0.7427 with probability of 0.3368.</p

Clinical and demographic characteristics of the subjects meeting all inclusion criteria and having DNA available for genotyping.

<p>1. Nephropathy  =  microalbinuria or proteinuria or end-stage renal disease (dialysis) due to diabetic nephropathy.</p><p>2. p value comparing Nephropathy and No-Nephropathy subsets of same population.</p><p>3. p value comparing total primary population to total Replication Population.</p><p>4. p value comparing prevalence of nephropathy in the 2 populations.</p><p>5. Age, age at DM diagnosis, years of DM, HbA1c, BMI are expressed in mean ± SD.</p><p>6. Retinopathy  =  For primary population retinopathy defined as proliferative retinopathy or macular edema; For replication population retinopathy defined as background or proliferative retinopathy or macular edema.</p><p>7. CHD  =  coronary heart disease, CABG  =  coronary artery bypass graft, PCI  =  percutaneous coronary intervention, MI  =  Myocardial infarction.</p

Genes/Pathways/SNPs studied.

<p>1. MAF  =  Minor allele frequency determined in this dataset.</p><p>2. p values for unadjusted association with nephropathy.</p><p>3. Odds ratios are given for the comparison between the rare and common alleles. CI denotes confidence interval.</p><p>4. p value for logistic regression analysis adjusting for age, sex, duration of diabetes and type of diabetes.</p><p>5. SNPs included in the model are shown in bold.</p

Model prediction based on minimum Alpha+Beta error.

<p>Model prediction based on minimum Alpha+Beta error.</p