Highly porous novel chondro-instructive bioactive glass scaffolds tailored for cartilage tissue engineering.

Abstract Cartilage injuries remain challenging since the regenerative capacity of cartilage is extremely low. The aim was to design a novel type of bioactive glass (BG) scaffold with suitable topology that allows the formation of cartilage-specific extracellular matrix (ECM) after colonization with chondrogenic cells for cartilage repair. Highly porous scaffolds with interconnecting pores consisting of 100 % BG were manufactured using a melting, milling, sintering and leaching technique. Scaffolds were colonized with porcine articular chondrocytes (pAC) and undifferentiated human mesenchymal stromal cells (hMSC) for up to 35 days. Scaffolds displayed high cytocompatibility with no major pH shift. Scanning electron microscopy revealed the intimate pAC-scaffold interaction with typical cell morphology. After 14 days MSCs formed cell clusters but still expressed cartilage markers. Both cell types showed aggrecan, SOX9 gene and protein expression, cartilage proteoglycan and sulfated glycosaminoglycan synthesis for the whole culture time. Despite type II collagen gene expression could not anymore be detected at day 35, protein synthesis was visualized for both cell types during the whole culturing period, increasing in pAC and declining after day 14 in hMSC cultures. The novel BG scaffold was stable, cytocompatible and cartilage-specific protein synthesis indicated maintenance of pAC's differentiated phenotype and chondro-instructive effects on hMSCs

Genetic determinants of complement activation in the general population

Complement is a fundamental innate immune response component. Its alterations are associated with severe systemic diseases. To illuminate the complement's genetic underpinnings, we conduct genome-wide association studies of the functional activity of the classical (CP), lectin (LP), and alternative (AP) complement pathways in the Cooperative Health Research in South Tyrol study (n = 4,990). We identify seven loci, encompassing 13 independent, pathway-specific variants located in or near complement genes (CFHR4, C7, C2, MBL2) and non-complement genes (PDE3A, TNXB, ABO), explaining up to 74% of complement pathways' genetic heritability and implicating long-range haplotypes associated with LP at MBL2. Two-sample Mendelian randomization analyses, supported by transcriptome- and proteome-wide colocalization, confirm known causal pathways, establish within-complement feedback loops, and implicate causality of ABO on LP and of CFHR2 and C7 on AP. LP causally influences collectin-11 and KAAG1 levels and the risk of mouth ulcers. These results build a comprehensive resource to investigate the role of complement in human health

SNP Prioritization Using a B ayesian Probability of Association

Prioritization is the process whereby a set of possible candidate genes or SNP s is ranked so that the most promising can be taken forward into further studies. In a genome‐wide association study, prioritization is usually based on the P ‐values alone, but researchers sometimes take account of external annotation information about the SNP s such as whether the SNP lies close to a good candidate gene. Using external information in this way is inherently subjective and is often not formalized, making the analysis difficult to reproduce. Building on previous work that has identified 14 important types of external information, we present an approximate B ayesian analysis that produces an estimate of the probability of association. The calculation combines four sources of information: the genome‐wide data, SNP information derived from bioinformatics databases, empirical SNP weights, and the researchers’ subjective prior opinions. The calculation is fast enough that it can be applied to millions of SNPS and although it does rely on subjective judgments, those judgments are made explicit so that the final SNP selection can be reproduced. We show that the resulting probability of association is intuitively more appealing than the P ‐value because it is easier to interpret and it makes allowance for the power of the study. We illustrate the use of the probability of association for SNP prioritization by applying it to a meta‐analysis of kidney function genome‐wide association studies and demonstrate that SNP selection performs better using the probability of association compared with P ‐values alone.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/96317/1/gepi21704.pd

Importance of Different Types of Prior Knowledge in Selecting Genome‐Wide Findings for Follow‐Up

Biological plausibility and other prior information could help select genome‐wide association ( GWA ) findings for further follow‐up, but there is no consensus on which types of knowledge should be considered or how to weight them. We used experts’ opinions and empirical evidence to estimate the relative importance of 15 types of information at the single‐nucleotide polymorphism ( SNP ) and gene levels. Opinions were elicited from 10 experts using a two‐round Delphi survey. Empirical evidence was obtained by comparing the frequency of each type of characteristic in SNP s established as being associated with seven disease traits through GWA meta‐analysis and independent replication, with the corresponding frequency in a randomly selected set of SNP s. SNP and gene characteristics were retrieved using a specially developed bioinformatics tool. Both the expert and the empirical evidence rated previous association in a meta‐analysis or more than one study as conferring the highest relative probability of true association, whereas previous association in a single study ranked much lower. High relative probabilities were also observed for location in a functional protein domain, although location in a region evolutionarily conserved in vertebrates was ranked high by the data but not by the experts. Our empirical evidence did not support the importance attributed by the experts to whether the gene encodes a protein in a pathway or shows interactions relevant to the trait. Our findings provide insight into the selection and weighting of different types of knowledge in SNP or gene prioritization, and point to areas requiring further research.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/96262/1/gepi21705.pd

Cluster analysis of angiotensin biomarkers to identify antihypertensive drug treatment in population studies

Background: The recent progress in molecular biology generates an increasing interest in investigating molecular biomarkers as markers of response to treatments. The present work is motivated by a study, where the objective was to explore the potential of the molecular biomarkers of renin-angiotensin-aldosterone system (RAAS) to identify the undertaken antihypertensive treatments in the general population. Population-based studies offer an opportunity to assess the effectiveness of treatments in real-world scenarios. However, lack of quality documentation, especially when electronic health record linkage is unavailable, leads to inaccurate reporting and classification bias. Method: We present a machine learning clustering technique to determine the potential of measured RAAS biomarkers for the identification of undertaken treatments in the general population. The biomarkers were simultaneously determined through a novel mass-spectrometry analysis in 800 participants of the Cooperative Health Research In South Tyrol (CHRIS) study with documented antihypertensive treatments. We assessed the agreement, sensitivity and specificity of the resulting clusters against known treatment types. Through the lasso penalized regression, we identified clinical characteristics associated with the biomarkers, accounting for the effects of cluster and treatment classifications. Results: We identified three well-separated clusters: cluster 1 (n = 444) preferentially including individuals not receiving RAAS-targeting drugs; cluster 2 (n = 235) identifying angiotensin type 1 receptor blockers (ARB) users (weighted kappa κw = 74%; sensitivity = 73%; specificity = 83%); and cluster 3 (n = 121) well discriminating angiotensin-converting enzyme inhibitors (ACEi) users (κw = 81%; sensitivity = 55%; specificity = 90%). Individuals in clusters 2 and 3 had higher frequency of diabetes as well as higher fasting glucose and BMI levels. Age, sex and kidney function were strong predictors of the RAAS biomarkers independently of the cluster structure. Conclusions: Unsupervised clustering of angiotensin-based biomarkers is a viable technique to identify individuals on specific antihypertensive treatments, pointing to a potential application of the biomarkers as useful clinical diagnostic tools even outside of a controlled clinical setting

Novel loci affecting iron homeostasis and their effects in individuals at risk for hemochromatosis.

Variation in body iron is associated with or causes diseases, including anaemia and iron overload. Here, we analyse genetic association data on biochemical markers of iron status from 11 European-population studies, with replication in eight additional cohorts (total up to 48,972 subjects). We find 11 genome-wide-significant (P<5 × 10(-8)) loci, some including known iron-related genes (HFE, SLC40A1, TF, TFR2, TFRC, TMPRSS6) and others novel (ABO, ARNTL, FADS2, NAT2, TEX14). SNPs at ARNTL, TF, and TFR2 affect iron markers in HFE C282Y homozygotes at risk for hemochromatosis. There is substantial overlap between our iron loci and loci affecting erythrocyte and lipid phenotypes. These results will facilitate investigation of the roles of iron in disease

Novel loci affecting iron homeostasis and their effects in individuals at risk for hemochromatosis

Variation in body iron is associated with or causes diseases, including anaemia and iron overload. Here, we analyse genetic association data on biochemical markers of iron status from 11 European-population studies, with replication in eight additional cohorts (total up to 48,972 subjects). We find 11 genome-wide-significant (P&lt;5 × 10(-8)) loci, some including known iron-related genes (HFE, SLC40A1, TF, TFR2, TFRC, TMPRSS6) and others novel (ABO, ARNTL, FADS2, NAT2, TEX14). SNPs at ARNTL, TF, and TFR2 affect iron markers in HFE C282Y homozygotes at risk for hemochromatosis. There is substantial overlap between our iron loci and loci affecting erythrocyte and lipid phenotypes. These results will facilitate investigation of the roles of iron in disease

The genetic study of three population microisolates in South Tyrol (MICROS): study design and epidemiological perspectives

<p>Abstract</p> <p>Background</p> <p>There is increasing evidence of the important role that small, isolated populations could play in finding genes involved in the etiology of diseases. For historical and political reasons, South Tyrol, the northern most Italian region, includes several villages of small dimensions which remained isolated over the centuries.</p> <p>Methods</p> <p>The MICROS study is a population-based survey on three small, isolated villages, characterized by: old settlement; small number of founders; high endogamy rates; slow/null population expansion. During the stage-1 (2002/03) genealogical data, screening questionnaires, clinical measurements, blood and urine samples, and DNA were collected for 1175 adult volunteers. Stage-2, concerning trait diagnoses, linkage analysis and association studies, is ongoing. The selection of the traits is being driven by expert clinicians. Preliminary, descriptive statistics were obtained. Power simulations for finding linkage on a quantitative trait locus (QTL) were undertaken.</p> <p>Results</p> <p>Starting from participants, genealogies were reconstructed for 50,037 subjects, going back to the early 1600s. Within the last five generations, subjects were clustered in one pedigree of 7049 subjects plus 178 smaller pedigrees (3 to 85 subjects each). A significant probability of familial clustering was assessed for many traits, especially among the cardiovascular, neurological and respiratory traits. Simulations showed that the MICROS pedigree has a substantial power to detect a LOD score ≥ 3 when the QTL specific heritability is ≥ 20%.</p> <p>Conclusion</p> <p>The MICROS study is an extensive, ongoing, two-stage survey aimed at characterizing the genetic epidemiology of Mendelian and complex diseases. Our approach, involving different scientific disciplines, is an advantageous strategy to define and to study population isolates. The isolation of the Alpine populations, together with the extensive data collected so far, make the MICROS study a powerful resource for the study of diseases in many fields of medicine. Recent successes and simulation studies give us confidence that our pedigrees can be valuable both in finding new candidates loci and to confirm existing candidate genes.</p

Genetic Associations for Activated Partial Thromboplastin Time and Prothrombin Time, their Gene Expression Profiles, and Risk of Coronary Artery Disease

Activatedpartialthromboplastintime (aPTT) and prothrombintime (PT) are clinical tests commonly used to screen for coagulation-factor deficiencies. One genome-wide association study (GWAS) has been reported previously for aPTT, but no GWAS has been reported for PT. We conducted a GWAS and meta-analysis to identify genetic loci for aPTT and PT. The GWAS for aPTT was conducted in 9,240 individuals of European ancestry from the Atherosclerosis Risk in Communities (ARIC) study, and the GWAS for PT was conducted in 2,583 participants from the Genetic Study of Three Population Microisolates in South Tyrol (MICROS) and the Lothian Birth Cohorts (LBC) of 1921 and 1936. Replication was assessed in 1,041 to 3,467 individuals. For aPTT, previously reported associations with KNG1, HRG, F11, F12, and ABO were confirmed. A second independent association in ABO was identified and replicated (rs8176704, p = 4.26 × 10−24). Pooling the ARIC and replication data yielded two additional loci in F5 (rs6028, p = 3.22 × 10−9) and AGBL1 (rs2469184, p = 3.61 × 10−8). For PT, significant associations were identified and confirmed in F7 (rs561241, p = 3.71 × 10−56) and PROCR/EDEM2 (rs2295888, p = 5.25 × 10−13). Assessment of existing geneexpression and coronaryarterydisease (CAD) databases identified associations of five of the GWAS loci with altered geneexpression and two with CAD. In summary, eight genetic loci that account for ∼29% of the variance in aPTT and two loci that account for ∼14% of the variance in PT were detected and supported by functional data

Genome-wide meta-analysis associates HLA-DQA1/DRB1 and LPA and lifestyle factors with human longevity

Genomic analysis of longevity offers the potential to illuminate the biology of human aging. Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA). We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity. Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated. We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD. Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan