Response to anti-IL17 therapy in inflammatory disease is not strongly impacted by genetic background

Response to the anti-IL17 monoclonal antibody secukinumab is heterogeneous, and not all participants respond to treatment. Understanding whether this heterogeneity is driven by genetic variation is a key aim of pharmacogenetics and could influence precision medicine approaches in inflammatory diseases. Using changes in disease activity scores across 5,218 genotyped individuals from 19 clinical trials across four indications (psoriatic arthritis, psoriasis, ankylosing spondylitis, and rheumatoid arthritis), we tested whether genetics predicted response to secukinumab. We did not find any evidence of association between treatment response and common variants, imputed HLA alleles, polygenic risk scores of disease susceptibility, or cross-disease components of shared genetic risk. This suggests that anti-IL17 therapy is equally effective regardless of an individual's genetic background, a finding that has important implications for future genetic studies of biological therapy response in inflammatory diseases.

Investigating Molecular Signatures Underlying Trapeziometacarpal Osteoarthritis Through the Evaluation of Systemic Cytokine Expression

Purpose

Non-operative management of trapeziometacarpal osteoarthritis (TMOA) demonstrates only short-term symptomatic alleviation, and no approved disease modifying drugs exist to treat this condition. A key issue in these patients is that radiographic disease severity can be discordant with patient reported pain, illustrating the need to identify molecular mediators of disease. This study characterizes the biochemical profile of TMOA patients to elucidate molecular mechanisms driving TMOA progression.

Methods

Plasma from patients with symptomatic TMOA undergoing surgical (n=39) or non-surgical management (n=44) with 1-year post-surgical follow-up were compared using a targeted panel of 27 cytokines. Radiographic (Eaton-Littler), anthropometric, longitudinal pain (VAS, TASD, quick DASH) and functional (key pinch, grip strength) data were used to evaluate relationships between structure, pain, and systemic cytokine expression. Principal Component Analysis was used to identify clusters of patients.

Results

Patients undergoing surgery had greater BMI as well as higher baseline quick DASH, TASD scores. Systemically, these patients could only be distinguished by differing levels of Interleukin-7 (IL-7), with an adjusted odds ratio of 0.22 for surgery for those with increased levels of this cytokine. Interestingly, PCA analysis of all patients (regardless of surgical status) identified a subset of patients with an “inflammatory” phenotype, as defined by a unique molecular signature consisting of thirteen cytokines.

Conclusion

Overall, this study demonstrated that circulating cytokines are capable of distinguishing TMOA disease severity, and identified IL-7 as a target capable of differentiating disease severity with higher levels associated with a decreased likelihood of TMOA needing surgical intervention. It also identified a cluster of patients who segregate based on a molecular signature of select cytokines.

DCMD: Distance-based classification using mixture distributions on microbiome data

Current advances in next-generation sequencing techniques have allowed researchers to conduct comprehensive research on the microbiome and human diseases, with recent studies identifying associations between the human microbiome and health outcomes for a number of chronic conditions. However, microbiome data structure, characterized by sparsity and skewness, presents challenges to building effective classifiers. To address this, we present an innovative approach for distance-based classification using mixture distributions (DCMD). The method aims to improve classification performance using microbiome community data, where the predictors are composed of sparse and heterogeneous count data. This approach models the inherent uncertainty in sparse counts by estimating a mixture distribution for the sample data and representing each observation as a distribution, conditional on observed counts and the estimated mixture, which are then used as inputs for distance-based classification. The method is implemented into a k-means classification and k-nearest neighbours framework. We develop two distance metrics that produce optimal results. The performance of the model is assessed using simulated and human microbiome study data, with results compared against a number of existing machine learning and distance-based classification approaches. The proposed method is competitive when compared to the other machine learning approaches, and shows a clear improvement over commonly used distance-based classifiers, underscoring the importance of modelling sparsity for achieving optimal results. The range of applicability and robustness make the proposed method a viable alternative for classification using sparse microbiome count data. The source code is available at https://github.com/kshestop/DCMD for academic use.

The uneven performance of conventional distanced-based classifiers when using microbiome profiles to predict disease status has motivated us to develop a novel distance-based method that accounts for uncertainty when modeling sparse counts. We propose a classification algorithm that uses mixture distributions to measure normed distances between microbiome distributions, which better models the underlying structure by handling excess zeros and sparsity inherent in microbial abundance counts. Applications of DCMD have shown improved classification performance and robustness, making the proposed method an improved alternative for classification using microbiome data.

MicroRNA-34a-5p Promotes Joint Destruction During Osteoarthritis

Objective

MicroRNA‐34a‐5p (miR‐34a‐5p) expression is elevated in the synovial fluid of patients with late‐stage knee osteoarthritis (OA); however, its exact role and therapeutic potential in OA remain to be fully elucidated. This study was undertaken to examine the role of miR‐34a‐5p in OA pathogenesis.

Methods

Expression of miR‐34a‐5p was determined in joint tissues and human plasma (n = 71). Experiments using miR‐34a‐5p mimic or antisense oligonucleotide (ASO) treatment were performed in human OA chondrocytes, fibroblast‐like synoviocytes (FLS) (n = 7–9), and mouse OA models, including destabilization of the medial meniscus (DMM; n = 22) and the accelerated, more severe model of mice fed a high‐fat diet and subjected to DMM (n = 11). Wild‐type (WT) mice (n = 9) and miR‐34a–knockout (KO) mice (n = 11) were subjected to DMM. Results were expressed as the mean ± SEM and analyzed by t‐test or analysis of variance, with appropriate post hoc tests. P values less than 0.05 were considered significant. RNA sequencing was performed on WT and KO mouse chondrocytes.

Results

Expression of miR‐34a‐5p was significantly increased in the plasma, cartilage, and synovium of patients with late‐stage OA and in the cartilage and synovium of mice subjected to DMM. Plasma miR‐34a‐5p expression was significantly increased in obese patients with late‐stage OA, and in the plasma and knee joints of mice fed a high‐fat diet. In human OA chondrocytes and FLS, miR‐34a‐5p mimic increased key OA pathology markers, while miR‐34a‐5p ASO improved cellular gene expression. Intraarticular miR‐34a‐5p mimic injection induced an OA‐like phenotype. Conversely, miR‐34a‐5p ASO injection imparted cartilage‐protective effects in the DMM and high‐fat diet/DMM models. The miR‐34a–KO mice exhibited protection against DMM‐induced cartilage damage. RNA sequencing of WT and KO chondrocytes revealed a putative miR‐34a‐5p signaling network.

Conclusion

Our findings provide comprehensive evidence of the role and therapeutic potential of miR‐34a‐5p in OA.

Sequencing identifies a distinct signature of circulating microRNAs in early radiographic knee osteoarthritis

OBJECTIVE

MicroRNAs act locally and systemically to impact osteoarthritis (OA) pathophysiology, but comprehensive profiling of the circulating miRNome in early vs late stages of OA has yet to be conducted. Sequencing has emerged as the preferred method for microRNA profiling since it offers high sensitivity and specificity. Our objective was to sequence the miRNome in plasma from 91 patients with early [Kellgren-Lawrence (KL) grade 0 or 1 (n = 41)] or late [KL grade 3 or 4 (n = 50)] symptomatic radiographic knee OA to identify unique microRNA signatures in each disease state.

DESIGN

MicroRNA libraries were prepared using the QIAseq miRNA Library Kit and sequenced on the Illumina NextSeq 550. Counts were produced for microRNAs captured in miRBase and for novel microRNAs. Statistical, bioinformatics, and computational biology approaches were used to refine and interpret the final list of microRNAs.

RESULTS

From 215 differentially expressed microRNAs (FDR < 0.01), 97 microRNAs showed an increase or decrease in expression in ≥85% of samples in the early OA group as compared to the median expression in the late OA group. Increasing this threshold to ≥95%, seven microRNAs were identified: hsa-miR-335-3p, hsa-miR-199a-5p, hsa-miR-671-3p, hsa-miR-1260b, hsa-miR-191-3p, hsa-miR-335-5p, and hsa-miR-543. Four novel microRNAs were present in ≥50% of early OA samples and had 27 predicted gene targets in common with the prioritized set of predicted gene targets from the 97 microRNAs, suggesting common underlying mechanisms.

CONCLUSION

Sequencing of well-characterized patient cohorts produced unbiased profiling of the circulating miRNome and identified a unique panel of 11 microRNAs in early radiographic knee OA.

A221 CROHN’S DISEASE POLYGENIC RISK SCORE IS ASSOCIATED WITH FECAL CALPROTECTIN CONCENTRATION IN ASYMPTOMATIC FIRST-DEGREE RELATIVES OF CROHN’S DISEASE PATIENTS

Background

Fecal calprotectin concentration (FC), a measure of gut inflammation is reported to be significantly higher in healthy first-degree relatives (FDR) of Crohn’s disease (CD) patients compared to healthy controls. In contrast, FC in spouses of CD patients was not significantly different from controls, suggesting that a genetic predisposition rather than a shared environmental factor affects FC.

Aims

We investigated the genetic association with FC in healthy FDRs of CD patients. Notably, these subjects are known to be enriched with CD risk alleles.

Methods

We investigated 1455 healthy Caucasian FDRs of CD patients from the GEM Project. Subjects were genotyped by HumanCoreEXOME chip and ImmunoChip platforms and then imputed by the Haplotype Reference Consortium v1.1 panel (Michigan Imputation Server). SNPs with a minor allele frequency&lt;5% were removed. FC was measured using BUHLMANN ELISA kit. Heritability was estimated using a pedigree based SOLAR program and a SNP-based GCTA software. Genome wide association of FC was tested using the GEE framework that accounts for family clusters, age, sex, first 3 genetic principal components and multiplex family status (≥2 FDRs diagnosed with CD). In addition, CD-polygenic risk scores were derived based on summary statistics and imputed SNPs from a recent GWAS by pruning and thresholding (P+T) and LDPred algorithm (PMID:31002795).

Results

Among 1455 subjects, 45.2% were male, median age was 19 years (IQR 13–26), 8.8% were from multiplex families, and median FC was 52 mg/kg (IQR 31–87; 20.8% had FC&gt;100). We estimated the heritability of FC to be 27% (27.1%, standard error=9%, p&lt;0.001 by pedigree approach; 27.9%, SE=12%, p&lt;0.001 by SNP approach). An untargeted GWAS failed to show any significant association with FC (i.e. p&lt;5x10-8). The lowest p value was obtained for rs224631 (p=5x10-7). Strikingly, an increase in CD polygenic risk scores was significantly associated with an increase of FC (p=5.2x10-5 with P+T method).

Conclusions

We demonstrate that FC concentration is a heritable trait in unaffected FDRs of CD patients. Although the association between genetic variants with FC did not reach GWAS significance, CD-polygenic risk score, which incorporates small effect size CD-associated SNPs, was significantly associated with FC concentrationin this cohort. Our results suggest that FC concentration is influenced genetically with contributions from CD-associated SNPs in unaffected FDRs of CD probands. It remains to be determined if the genetic influence to FC concentration is dependent/independent with the future development of CD.

Submitted on behalf of The CCC-GEM Project research team

Funding Agencies

CCCHelmsley Charitable Trust/ Mount Sinai Hospital Fellowship Award

Bone Marrow Mesenchymal Stromal Cell Treatment in Patients with Osteoarthritis Results in Overall Improvement in Pain and Symptoms and Reduces Synovial Inflammation

Patients with late‐stage Kellgren‐Lawrence knee osteoarthritis received a single intra‐articular injection of 1, 10, or 50 million bone marrow mesenchymal stromal cells (BM‐MSCs) in a phase I/IIa trial to assess safety and efficacy using a broad toolset of analytical methods. Besides safety, outcomes included patient‐reported outcome measures (PROMs): Knee Injury and Osteoarthritis Outcome Score (KOOS) and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC); contrast‐enhanced magnetic resonance imaging (MRI) for cartilage morphology (Whole Organ MRI Scores [WORMS]), collagen content (T2 scores), and synovitis; and inflammation and cartilage turnover biomarkers, all over 12 months. BM‐MSCs were characterized by a panel of anti‐inflammatory markers to predict clinical efficacy. There were no serious adverse events, although four patients had minor, transient adverse events. There were significant overall improvements in KOOS pain, symptoms, quality of life, and WOMAC stiffness relative to baseline; the 50 million dose achieved clinically relevant improvements across most PROMs. WORMS and T2 scores did not change relative to baseline. However, cartilage catabolic biomarkers and MRI synovitis were significantly lower at higher doses. Pro‐inflammatory monocytes/macrophages and interleukin 12 levels decreased in the synovial fluid after MSC injection. The panel of BM‐MSC anti‐inflammatory markers was strongly predictive of PROMs over 12 months. Autologous BM‐MSCs are safe and result in significant improvements in PROMs at 12 months. Our analytical tools provide important insights into BM‐MSC dosing and BM‐MSC reduction of synovial inflammation and cartilage degradation and provide a highly predictive donor selection criterion that will be critical in translating MSC therapy for osteoarthritis. stem cells translational medicine2019;8:746&757

Analyzing differences between microbiome communities using mixture distributions

In this paper, we present a method to assess differences between microbiome communities that effectively models sparse count data and accounts for presence-absence bias frequently encountered when zeros are present. We assume that the observed data for each operational taxonomic unit is Poisson generated with the rate for each sample originating from an underlying rate distribution. We propose to model this distribution using a mixture model, specifying the components based on the posterior rate distribution of a count and estimating the optimal weights using a least squares objective function. The distribution incorporates varying resolutions of samples, a point mass for differentiating structural and nonstructural zeros, and a truncation point mass to account for high values that are too sparse to model. As mixture component specification is not always straightforward, a method to estimate a joint model from several mixture distributions using minimum distances of bootstrap iterates is proposed. Once the population rate distribution is approximated, we obtain sample-specific distributions by conditioning on the observed operational taxonomic unit count, resolution, and estimated mixture distribution and then use these to estimate pairwise distances for a permutation test. The method gives an accurate estimate of the true proportion of zeros for presence-absence, effectively models the distribution of low counts using the mixture distribution, and achieves good power for detecting differences in a variety of scenarios. The method is tested using a simulation study and applied to two microbiome datasets. In each case, the results are compared with a number of existing methods.

FUT2 genotype and secretory status are not associated with fecal microbial composition and inferred function in healthy subjects

Heritability analysis of the microbiota has demonstrated the importance of host genotype in defining the human microbiota. The alpha (1,2)-fucosyltransferase 2 encoded by FUT2 is involved in the formation of the H antigen and the SNP, rs601338 is associated with ABO histo-blood group antigen secretion in the intestinal mucosa. Previous studies have provided non replicated results for the association of this polymorphism with the composition and inferred function of intestinal microbiota. We aimed to assess this relationship in a large cohort of 1,190 healthy individuals. Genotyping was performed using the HumanCoreEXOME chip, microbial composition was addressed by 16S rRNA gene sequencing. Firmicutes, Bacteroidetes, and Actinobacteria were the dominant phyla in this cohort. Although we have sufficient power to detect significant associations of FUT2 genotype/ inferred phenotype with the microbiota, our data demonstrate that FUT2 genotype and secretor status is not associated with microbial alpha diversity, microbial composition or inferred microbial function after correction for multiple testing. Thus, FUT2 genotype and inferred phenotype are not associated with human fecal microbial composition and imputed function.

A classification modeling approach for determining metabolite signatures in osteoarthritis

Multiple factors can help predict knee osteoarthritis (OA) patients from healthy individuals, including age, sex, and BMI, and possibly metabolite levels. Using plasma from individuals with primary OA undergoing total knee replacement and healthy volunteers, we measured lysophosphatidylcholine (lysoPC) and phosphatidylcholine (PC) analogues by metabolomics. Populations were stratified on demographic factors and lysoPC and PC analogue signatures were determined by univariate receiver-operator curve (AUC) analysis. Using signatures, multivariate classification modeling was performed using various algorithms to select the most consistent method as measured by AUC differences between resampled training and test sets. Lists of metabolites indicative of OA [AUC > 0.5] were identified for each stratum. The signature from males age > 50 years old encompassed the majority of identified metabolites, suggesting lysoPCs and PCs are dominant indicators of OA in older males. Principal component regression with logistic regression was the most consistent multivariate classification algorithm tested. Using this algorithm, classification of older males had fair power to classify OA patients from healthy individuals. Thus, individual levels of lysoPC and PC analogues may be indicative of individuals with OA in older populations, particularly males. Our metabolite signature modeling method is likely to increase classification power in validation cohorts.

Germline INDELs and CNVs in a cohort of colorectal cancer patients: their characteristics, associations with relapse-free survival time, and potential time-varying effects on the risk of relapse

INDELs and CNVs are structural variations that may play roles in cancer susceptibility and patient outcomes. Our objectives were a) to computationally detect and examine the genome‐wide INDEL/CNV profiles in a cohort of colorectal cancer patients, and b) to examine the associations of frequent INDELs/CNVs with relapse‐free survival time. We also identified unique variants in 13 Familial Colorectal Cancer Type X (FCCX) cases. The study cohort consisted of 495 colorectal cancer patients. QuantiSNP and PennCNV algorithms were utilized to predict the INDELs/CNVs using genome‐wide signal intensity data. Duplex PCR was used to validate predictions for 10 variants. Multivariable Cox regression models were used to test the associations of 106 common variants with relapse‐free survival time. Score test and the multivariable Cox proportional hazards models with time‐varying coefficients were applied to identify the variants with time‐varying effects on the relapse‐free survival time. A total of 3486 distinct INDELs/CNVs were identified in the patient cohort. The majority of these variants were rare (83%) and deletion variants (81%). The results of the computational predictions and duplex PCR results were highly concordant (93–100%). We identified four promising variants significantly associated with relapse‐free survival time (P < 0.05) in the multivariable Cox proportional hazards regression models after adjustment for clinical factors. More importantly, two additional variants were identified to have time‐varying effects on the risk of relapse. Finally, 58 rare variants were identified unique to the FCCX cases; none of them were detected in more than one patient. This is one of the first genome‐wide analyses that identified the germline INDEL/CNV profiles in colorectal cancer patients. Our analyses identified novel variants and genes that can biologically affect the risk of relapse in colorectal cancer patients. Additionally, for the first time, we identified germline variants that can potentially be early‐relapse markers in colorectal cancer.

Association of host genome with intestinal microbial composition in a large healthy cohort

Intestinal microbiota is known to be important in health and disease. Its composition is influenced by both environmental and host factors. Few large-scale studies have evaluated the association between host genetic variation and the composition of microbiota. We recruited a cohort of 1,561 healthy individuals, of whom 270 belong in 123 families, and found that almost one-third of fecal bacterial taxa were heritable. In addition, we identified 58 SNPs associated with the relative abundance of 33 taxa in 1,098 discovery subjects. Among these, four loci were replicated in a second cohort of 463 subjects: rs62171178 (nearest gene UBR3) associated with Rikenellaceae, rs1394174 (CNTN6) associated with Faecalibacterium, rs59846192 (DMRTB1) associated with Lachnospira, and rs28473221 (SALL3) associated with Eubacterium. After correction for multiple testing, 6 of the 58 associations remained significant, one of which replicated. These results identify associations between specific genetic variants and the gut microbiome.

No associations of a set of SNPs in the Vascular Endothelial Growth Factor (VEGF) and Matrix Metalloproteinase (MMP) genes with survival of colorectal cancer patients

In this study, we aimed to investigate the associations of genetic variations within select genes functioning in angiogenesis, lymph‐angiogenesis, and metastasis pathways and the risk of outcome in colorectal cancer patients. We followed a two‐stage analysis: First, 381 polymorphisms from 30 genes (eight Vascular Endothelial Growth Factor (VEGF) and 22 Matrix Metalloproteinase [MMP] genes) were investigated in the discovery cohort (n = 505). Then, 16 polymorphisms with the lowest P‐value in this analysis were investigated in a separate replication cohort (n = 247). Genotypes were obtained using the Illumina^® HumanOmni‐1‐Quad (discovery cohort) and Sequenom MassArray^® (replication cohort) platforms. The primary outcome measure was overall survival (OS). Kaplan–Meier, univariate and multivariable Cox regression methods were used to test the associations between genotypes and OS. Four SNPs (rs12365082, rs11225389, rs11225388, and rs2846707) had the univariate analysis P < 0.05 in both the discovery and replication cohorts. These SNPs are in linkage disequilibrium with each other to varying extent and are located in the MMP8 and MMP27 genes. In the multivariable analysis adjusting for age, stage, and microsatellite instability status, three of these SNPs (rs12365082, rs11225389, rs11225388) were independent predictors of OS (P < 0.05) in the discovery cohort. However, the same analysis in the replication cohort did not yield statistically significant results. Overall, while the genetic variations in the VEGF and MMP genes are attractive candidates as prognostic markers, our study showed no evidence of associations of a large set of SNPs in these genes and overall survival of colorectal cancer patients in our study.

A genome wide association study on Newfoundland colorectal cancer patients' survival outcomes

Background

In this study we performed genome-wide association studies to identify candidate SNPs that may predict the risk of disease outcome in colorectal cancer.

Methods

Patient cohort consisted of 505 unrelated patients with Caucasian ancestry. Germline DNA samples were genotyped using the Illumina® human Omni-1quad SNP chip. Associations of SNPs with overall and disease free survivals were examined primarily for 431 patients with microsatellite instability-low (MSI-L) or stable (MSS) colorectal tumors using Cox proportional hazards method adjusting for clinical covariates. Bootstrap method was applied for internal validation of results. As exploratory analyses, association analyses for the colon (n = 334) and rectal (n = 171) cancer patients were also performed.

Results

As a result, there was no SNP that reached the genomewide significance levels (p < 5x10⁻⁸) in any of the analyses. A small number of genetic markers (n = 10) showed nominal associations (p <10⁻⁶) for MSS/MSI-L, colon, or rectal cancer patient groups. These markers were located in two non-coding RNA genes or intergenic regions and none were amino acid substituting polymorphisms. Bootstrap analysis for the MSS/MSI-L cohort data suggested the robustness of the observed nominal associations.

Conclusions

Likely due to small number of patients, our study did not identify an acceptable level of association of SNPs with outcome in MSS/MSI-L, colon, or rectal cancer patients. A number of SNPs with sub-optimal p-values were, however, identified; these loci may be promising and examined in other larger-sized patient cohorts.

Electronic supplementary material

The online version of this article (doi:10.1186/s40364-015-0031-6) contains supplementary material, which is available to authorized users.