The SNPcurator: literature mining of enriched SNP-disease associations

DEBBIE: The Open Access Database of Experimental Scaffolds and Biomaterials Built Using an Automated Text Mining Pipeline

Biomaterials research output has experienced an exponential increase over the last three decades. The majority of research is published in the form of scientific articles and is therefore available as unstructured text, making it a challenging input for computational processing. Computational tools are becoming essential to overcome this information overload. Among them, text mining systems present an attractive option for the automated extraction of information from text documents into structured datasets. This work presents the first automated system for biomaterial related information extraction from the National Library of Medicine's premier bibliographic database (MEDLINE) research abstracts into a searchable database. The system is a text mining pipeline that periodically retrieves abstracts from PubMed and identifies research and clinical studies of biomaterials. Thereafter, the pipeline identifies sixteen concept types of interest in the abstract using the Biomaterials Annotator, a tool for biomaterials Named Entity Recognition (NER). These concepts of interest, along with the abstract and relevant metadata are then deposited in DEBBIE, the Database of Experimental Biomaterials and their Biological Effect. DEBBIE is accessible through a web application that provides keyword searches and displays results in an intuitive and meaningful manner, aiming to facilitate an efficient mapping and organization of biomaterials information.

Common genetic variants and pathways in diabetes and associated complications and vulnerability of populations with different ethnic origins

Diabetes mellitus is a complex and heterogeneous metabolic disorder which is often pre- or post-existent with complications such as cardiovascular disease, hypertension, inflammation, chronic kidney disease, diabetic retino- and nephropathies. However, the frequencies of these co-morbidities vary among individuals and across populations. It is, therefore, not unlikely that certain genetic variants might commonly contribute to these conditions. Here, we identified four single nucleotide polymorphisms (rs5186, rs1800795, rs1799983 and rs1800629 in AGTR1, IL6, NOS3 and TNFA genes, respectively) to be commonly associated with each of these conditions. We explored their possible interplay in diabetes and associated complications. The variant allele and haplotype frequencies at these polymorphic loci vary among different super-populations (African, European, admixed Americans, South and East Asians). The variant alleles are particularly highly prevalent in different European and admixed American populations. Differential distribution of these variants in different ethnic groups suggests that certain drugs might be more effective in selective populations rather than all. Therefore, population specific genetic architectures should be considered before considering a drug for these conditions.

Use of Electrocatalysis for Differentiating DNA Polymorphisms and Enhancing the Sensitivity of Electrochemical Nucleic Acid-Based Sensors with Covalent Redox Tags-Part II

Single-nucleotide polymorphisms (SNPs), insertion/deletion (indel) polymorphisms, and DNA methylation are the most frequent types of genetic variations. As such, DNA polymorphisms play significant roles in genetic mapping and diagnostics. Thus, analytical methods enabling DNA polymorphism detection will provide an invaluable means for early disease diagnosis. However, no single electrochemical nucleic acid-based sensor has achieved the detection of the three major polymorphisms (SNPs, indel polymorphisms, and DNA methylation) with sufficient specificity and sensitivity. In response, we explore the utilization of a catalytic reaction between methylene blue (MB) covalently linked to surface-bound nucleic acid and freely diffusing ferricyanide (Fe(CN)₆^3-) to improve specificity and sensitivity of DNA polymorphism detection. We find that the dynamics of the nucleic acid tether is an additional rate-limiting factor for the electrocatalytic reaction, in addition to the more traditional kinetic and excess factors. Our proof-of-concept experiments demonstrate that the use of electrocatalysis enables differentiation of the three polymorphisms when target sequences are present at 10 nM. We hypothesize that this ability is a result of the distinct dynamics of the DNA probe with each respective polymorphism. In addition to the specificity the sensor displays, the sensor achieves a 20 pM limit of detection. We believe that the electrocatalysis between nucleic acid-tethered MB and Fe(CN)₆^3- is highly promising for electrochemical nucleic acid-based sensors to achieve better specificity and sensitivity.

Common genetic variation in obesity, lipid transfer genes and risk of Metabolic Syndrome: Results from IDEFICS/I.Family study and meta-analysis

As the prevalence of metabolic syndrome (MetS) in children and young adults is increasing, a better understanding of genetics that underlie MetS will provide critical insights into the origin of the disease. We examined associations of common genetic variants and repeated MetS score from early childhood to adolescence in a pan-European, prospective IDEFICS/I.Family cohort study with baseline survey and follow-up examinations after two and six years. We tested associations in 3067 children using a linear mixed model and confirmed the results with meta-analysis of identified SNPs. With a stringent Bonferroni adjustment for multiple comparisons we obtained significant associations(p < 1.4 × 10⁻⁴) for 5 SNPs, which were in high LD (r² > 0.85) in the 16q12.2 non-coding intronic chromosomal region of FTO gene with strongest association observed for rs8050136 (effect size(β) = 0.31, p_Wald = 1.52 × 10⁻⁵). We also observed a strong association of rs708272 in CETP with increased HDL (p = 5.63 × 10⁻⁴⁰) and decreased TRG (p = 9.60 × 10⁻⁵) levels. These findings along with meta-analysis advance etiologic understanding of childhood MetS, highlighting that genetic predisposition to MetS is largely driven by genes of obesity and lipid metabolism. Inclusion of the associated genetic variants in polygenic scores for MetS may prove to be fundamental for identifying children and subsequently adults of the high-risk group to allow earlier targeted interventions.