Sequencing of LRP2 Reveals Multiple Rare Variants Associated with Urinary Trefoil Factor-3

Novel biomarkers are being investigated to identify patients with kidney disease. We measured a panel of 13 urinary biomarkers in participants from the Offspring Cohort of the Framingham Heart Study. Using an Affymetrix chip with imputation to 2.5 M single-nucleotide polymorphisms (SNPs), we conducted a GWAS of these biomarkers (n=2640) followed by exonic sequencing and genotyping. Functional studies in zebrafish were used to investigate histologic correlation with renal function. Across all 13 biomarkers, there were 97 significant SNPs at three loci. Lead SNPs at each locus were rs6555820 (P=6.7×10⁻⁴⁹; minor allele frequency [MAF]=0.49) in HAVCR1 (associated with kidney injury molecule-1), rs7565788 (P=2.15×10⁻¹⁶; MAF=0.22) in LRP2 (associated with trefoil factor 3 [TFF3]), and rs11048230 (P=4.77×10⁻⁸; MAF=0.10) in an intergenic region near RASSF8 (associated with vascular endothelial growth factor). Validation in the CKDGen Consortium (n=67,093) showed that only rs7565788 at LRP2, which encodes megalin, was associated with eGFR (P=0.003). Sequencing of exons 16–72 of LRP2 in 200 unrelated individuals at extremes of urinary TFF3 levels identified 197 variants (152 rare; MAF<0.05), 31 of which (27 rare) were nonsynonymous. In aggregate testing, rare variants were associated with urinary TFF3 levels (P=0.003), and the lead GWAS signal was not explained by these variants. Knockdown of LRP2 in zebrafish did not alter the renal phenotype in static or kidney injury models. In conclusion, this study revealed common variants associated with urinary levels of TFF3, kidney injury molecule-1, and vascular endothelial growth factor and identified a cluster of rare variants independently associated with TFF3.Serum creatinine, as used in most GFR estimating equations, is the primary biomarker of CKD.¹ Creatinine has significant limitations as a biomarker. It is insensitive to early declines in kidney function, and there are important extrarenal factors that influence its concentration, thus increasing the potential for misclassification when it is used to diagnose renal disease.² As a result, novel biomarkers are being investigated that could allow earlier and more accurate diagnosis of CKD.³ It is likely, however, that these biomarkers also have non-GFR determinants, including genetic factors. For example, levels of cystatin C, a novel GFR biomarker, are known to be influenced by both nongenetic factors, such as adiposity and the metabolic syndrome,⁴ and the presence of specific genetic variants in the cystatin C-gene cluster.⁵Genome-wide association studies (GWAS) allow for the evaluation of genetic associations of biomarkers in an unbiased manner. These studies could illuminate previously unrecognized pathways for CKD pathogenesis, thus identifying new potential molecular targets for therapeutic intervention. Multiple variants have been identified in association with kidney function through GWAS,⁶ whereas a variant in CUBN encoding cubilin, a proximal tubular transport protein, has been associated with albuminuria.⁷ Recently, a GWAS identified a locus at PTGDS in association with another kidney biomarker, β-trace protein.⁸To gain additional insight into biologic pathways of renal function and kidney injury, we measured a panel of 13 urinary biomarkers in participants from the Framingham Heart Study (FHS) and performed a GWAS of their levels. Here, we report the primary results from these studies as well as follow-up work to identify whether rare variants in LRP2 are associated with levels of trefoil factor 3 (TFF3).     

Genetic Biomarkers in Epilepsy

The identification of valid biomarkers for outcome prediction of diseases and improvement of drug response, as well as avoidance of side effects is an emerging field of interest in medicine. The concept of individualized therapy is becoming increasingly important in the treatment of patients with epilepsy, as predictive markers for disease prognosis and treatment outcome are still limited. Currently, the clinical decision process for selection of an antiepileptic drug (AED) is predominately based on the patient’s epileptic syndrome and side effect profiles of the AEDs, but not on effectiveness data. Although standard dosages of AEDs are used, supplemented, in part, by therapeutic monitoring, the response of an individual patient to a specific AED is generally unpredictable, and the standard care of patients in antiepileptic treatment is more or less based on trial and error. Therefore, there is an urgent need for valid predictive biomarkers to guide patient-tailored individualized treatment strategies in epilepsy, a research area that is still in its infancy. This review focuses on genomic factors as part of an individual concept for AED therapy summarizing examples that influence the prognosis of the disease and the response to AEDs, including side effects.     

Concurrent stable and unstable cortical correlates of human wrist movements

Cortical activity has been shown to correlate with different parameters of movement. However, the dynamic properties of cortico‐motor mappings still remain unexplored in humans. Here, we show that during the repetition of simple stereotyped wrist movements both stable and unstable correlates simultaneously emerge in human sensorimotor cortex. Using visual feedback of wrist movement target inferred online from MEG, we assessed the dynamics of the tuning properties of two neuronal signals: the MEG signal below 1.6 Hz and within the 4 to 6 Hz range. We found that both components are modulated by wrist movement allowing for closed‐loop inference of movement targets. Interestingly, while tuning of 4 to 6 Hz signals remained stable over time leading to stable inference of movement target using a static classifier, the tuning of cortical signals below 1.6 Hz significantly changed resulting in steadily decreasing inference accuracy. Our findings demonstrate that non‐invasive neuronal population signals in human sensorimotor cortex can reflect a stable correlate of voluntary movements. Hence, we provide first evidence for a stable control signal in non‐invasive human brain‐machine interface research. However, as not all neuronal signals initially tuned to movement were stable across days, a careful selection of features for real‐life applications seems to be mandatory. Hum Brain Mapp 35:3867–3879, 2014. © 2014 Wiley Periodicals, Inc .