Integrative analysis of -omics data and histologic scoring in renal disease and transplantation: renal histogenomics

Disruptive innovations in the clinical laboratory: catching the wave of precision diagnostics

Disruptive innovation is an invention that disrupts an existing market and creates a new one by providing a different set of values, which ultimately overtakes the existing market. Typically, when disruptive innovations are introduced, their performance is initially less than existing standard technologies, but because of their ability to bring the cost down, and with gradual improvement, they end up replacing established service standards.Disruptive technologies have their fingerprints in health care. Pathology and laboratory medicine are fertile soils for disruptive innovations because they are heavily reliant on technology. Disruptive innovations have resulted in a revolution of our diagnostic ability and will take laboratory medicine to the next level of patient care. There are several examples of disruptive innovations in the clinical laboratory. Digitizing pathology practice is an example of disruptive technology, with many advantages and an extended scope of applications. Next-generation sequencing can be disruptive in two ways. The first is by replacing an array of laboratory tests, which each requires expensive and specialized instruments and expertise, with a single cost-effective technology. The second is by disrupting the current paradigm of the clinical laboratory as a diagnostic service by taking it into a new era of preventive or primary care pathology. Other disruptive innovations include the use of dry chemistry reagents in chemistry analyzers and also point of care testing. The use of artificial intelligence is another promising disruptive innovation that can transform the future of pathology and laboratory medicine. Another emerging disruptive concept is the integration of two fields of medicine to create an interrelated discipline such as "histogenomics and radiohistomics." Another recent disruptive innovation in laboratory medicine is the use of social media in clinical practice, education, and publication.There are multiple reasons to encourage disruptive innovations in the clinical laboratory, including the escalating cost of health care, the need for better accessibility of diagnostic care, and the increased demand on the laboratory in the era of precision diagnostics. There are, however, a number of challenges that need to be overcome such as the significant resistance to disruptive innovations by current technology providers and governmental regulatory bodies. The hesitance from health care providers and insurance companies must also be addressed.Adoption of disruptive innovations requires a multifaceted approach that involves orchestrated solutions to key aspects of the process, including creating successful business models, multidisciplinary collaborations, and innovative accreditation and regulatory oversight. It also must be coupled with successful commercialization plans and modernization of health care structure. Fostering a culture of disruptive innovation requires establishing unique collaborative models between academia and industry. It also requires uncovering new sources of unconventional funding that are open to high-risk high-reward projects. It should also be matched with innovative thinking, including new approaches for delivery of care and identifying novel cohorts of patients who can benefit from disruptive technology.

Influence of polymorphisms in the vascular endothelial growth factor gene on allograft rejection after kidney transplantation: a meta-analysis

Background: Reported associations of allograft rejection in kidney transplant patients with VEGF single nucleotide polymorphisms (SNPs) have been inconsistent between studies, which prompted a meta-analysis to obtain more precise estimates. Methods: Using the PICO elements, kidney transplant patients (P) were compared by genotype data between rejectors (I) and non-rejectors (C) in order to determine the risk of allograft rejection (O) attributed to the VEGF SNPs. Literature search of four databases yielded seven articles. To calculate risks for allograft rejection, four SNPs were examined. Using the allele-genotype model we compared the variant ( var) with the wild-type ( wt) and heterozygous ( var- wt) alleles. Meta-analysis treatments included outlier and subgroup analyses, the latter was based on ethnicity (Indians/Caucasians) and rejection type (acute/chronic). Multiple comparisons were corrected with the Bonferroni test. Results: Five highly significant outcomes (P a < 0.01) survived Bonferroni correction, one of which showed reduced risk for the var allele (OR 0.61, 95% CI 0.45-0.82). The remaining four indicated increased risk for the wt allele where the chronic rejection (OR 2.10, 95% CI 1.36-3.24) and Indian (OR 1.44, 95% CI 1.13-1.84) subgroups were accorded susceptibility status. Conclusions: Risk associations for renal allograft rejection were increased and reduced on account of the wt and var alleles, respectively. These findings could render the VEGF polymorphisms useful in the clinical genetics of kidney transplantation.

Validation of systems biology derived molecular markers of renal donor organ status associated with long term allograft function

Donor organ quality affects long term outcome after renal transplantation. A variety of prognostic molecular markers is available, yet their validity often remains undetermined. A network-based molecular model reflecting donor kidney status based on transcriptomics data and molecular features reported in scientific literature to be associated with chronic allograft nephropathy was created. Significantly enriched biological processes were identified and representative markers were selected. An independent kidney pre-implantation transcriptomics dataset of 76 organs was used to predict estimated glomerular filtration rate (eGFR) values twelve months after transplantation using available clinical data and marker expression values. The best-performing regression model solely based on the clinical parameters donor age, donor gender, and recipient gender explained 17% of variance in post-transplant eGFR values. The five molecular markers EGF, CD2BP2, RALBP1, SF3B1, and DDX19B representing key molecular processes of the constructed renal donor organ status molecular model in addition to the clinical parameters significantly improved model performance (p-value = 0.0007) explaining around 33% of the variability of eGFR values twelve months after transplantation. Collectively, molecular markers reflecting donor organ status significantly add to prediction of post-transplant renal function when added to the clinical parameters donor age and gender.

Integrative biology identifies shared transcriptional networks in CKD

A previous meta-analysis of genome-wide association data by the Cohorts for Heart and Aging Research in Genomic Epidemiology and CKDGen consortia identified 16 loci associated with eGFR. To define how each of these single-nucleotide polymorphisms (SNPs) could affect renal function, we integrated GFR-associated loci with regulatory pathways, producing a molecular map of CKD. In kidney biopsy specimens from 157 European subjects representing nine different CKDs, renal transcript levels for 18 genes in proximity to the SNPs significantly correlated with GFR. These 18 genes were mapped into their biologic context by testing coregulated transcripts for enriched pathways. A network of 97 pathways linked by shared genes was constructed and characterized. Of these pathways, 56 pathways were reported previously to be associated with CKD; 41 pathways without prior association with CKD were ranked on the basis of the number of candidate genes connected to the respective pathways. All pathways aggregated into a network of two main clusters comprising inflammation- and metabolism-related pathways, with the NRF2-mediated oxidative stress response pathway serving as the hub between the two clusters. In all, 78 pathways and 95% of the connections among those pathways were verified in an independent North American biopsy cohort. Disease-specific analyses showed that most pathways are shared between sets of three diseases, with closest interconnection between lupus nephritis, IgA nephritis, and diabetic nephropathy. Taken together, the network integrates candidate genes from genome-wide association studies into their functional context, revealing interactions and defining established and novel biologic mechanisms of renal impairment in renal diseases.

Design of the Nephrotic Syndrome Study Network (NEPTUNE) to evaluate primary glomerular nephropathy by a multidisciplinary approach

The Nephrotic Syndrome Study Network (NEPTUNE) is a North American multicenter collaborative consortium established to develop a translational research infrastructure for nephrotic syndrome. This includes a longitudinal observational cohort study, a pilot and ancillary study program, a training program, and a patient contact registry. NEPTUNE will enroll 450 adults and children with minimal change disease, focal segmental glomerulosclerosis, and membranous nephropathy for detailed clinical, histopathological, and molecular phenotyping at the time of clinically indicated renal biopsy. Initial visits will include an extensive clinical history, physical examination, collection of urine, blood and renal tissue samples, and assessments of quality of life and patient-reported outcomes. Follow-up history, physical measures, urine and blood samples, and questionnaires will be obtained every 4 months in the first year and biannually, thereafter. Molecular profiles and gene expression data will be linked to phenotypic, genetic, and digitalized histological data for comprehensive analyses using systems biology approaches. Analytical strategies were designed to transform descriptive information to mechanistic disease classification for nephrotic syndrome and to identify clinical, histological, and genomic disease predictors. Thus, understanding the complexity of the disease pathogenesis will guide further investigation for targeted therapeutic strategies.

From -omics to personalized medicine in nephrology: integration is the key

Large-scale gene, protein and metabolite measurements ('omics') have driven the resolution of biology to an unprecedented high definition. Passing from reductionism to a system-oriented perspective, medical research will take advantage of these high-throughput technologies unveiling their full potential. Integration is the key to decoding the underlying principles that govern the complex functions of living systems. Extensive computational support and statistical modelling is needed to manage and connect the -omic data sets but this, in turn, is speeding up the hypothesis generation in biology enormously and yielding a deep insight into the pathophysiology. This systems biology approach will transform diagnostic and therapeutic strategies with the discovery of novel biomarkers that will enable a predictive and preventive medicine leading to personalized medicine.

Protein and peptide biomarkers in organ transplantation

Organ transplantation is the optimal treatment choice for end-stage organ failure in pediatric patients. The ideal maintenance of a transplanted organ requires efficient monitoring tools and an effective individualized post-transplant treatment plan. Currently available post-transplant monitoring options are not ideal because of their invasiveness or their lack of sensitivity and specificity when providing an accurate assessment of transplant injury. Current research on proteins and peptides, including mass spectrometry-based proteomics, can identify novel surrogate protein and peptide biomarkers that can assist in monitoring the graft in order to correctly assess the status of the transplanted organ. In this article, we have critically reviewed current relevant literature to highlight the importance of protein and peptide biomarkers in the field of pediatric organ transplantation, the status of research findings in the field of protein and peptide biomarkers in different organ transplantation and factors that impact and inhibit the progression of protein biomarker discovery in the field of solid-organ transplantation in pediatrics.

Genetic polymorphisms located in TGFB1, AGTR1, and VEGFA genes are associated to chronic renal allograft dysfunction

BACKGROUND

Persistent inflammation and fibrosis have been related to active progression of renal deterioration and reduced survival of kidney transplant. The aim of this study was to determine the impact of single-nucleotide polymorphisms (SNPs) located in regions related to inflammatory and immune processes on the development of chronic renal allograft dysfunction (CRAD).

METHODS

A retrospective study was carried out on 276 patients who received kidney transplant (KT). SNPs were genotyped via the SNPlex platform. Statistical analysis was performed with SNPstat and regression logistic analyses were adjusted by age and gender of recipients and donors, cold ischemia time and the number of human leukocyte antigen (HLA) mismatches.

RESULTS

From 276 patients with KT, 118 were non-CRAD and 158 were CRAD. Three SNPs showed significant associations with CRAD development: rs1800471 in transforming growth factor beta 1 (TGFB1), rs5186 in angiotensin II receptor type 1 (AGTR1), and rs699947 in vascular endothelial growth factor A (VEGFA). GC genotype of rs1800471 was associated with increased odds of CRAD compared to GG genotype (OR=2.65 (95% confidence interval (CI)=1.09; 6.47), p=0.025), as well as AC and AA genotype of rs699947 assuming a dominant model (OR=1.80 (95% CI=1.02; 3.20), p=0.044). Besides, AC and CC genotypes of rs5186 were associated with reduced odds of CRAD assuming a dominant model (OR=0.56 (95% CI=0.33; 0.96), p=0.033).

CONCLUSION

Our findings suggest that three genes related to immunity and inflammation (rs1800471, rs5186 and rs699947) are associated to susceptibility or protection to CRAD, and might have diagnostic utility in predicting the likelihood of developing CRAD.

A systems view of genetics in chronic kidney disease

A tight interplay of genetic predisposition and environmental factors define the onset and the rate of progression of chronic renal disease. We are seeing a rapid expansion of information about genetic loci associated with kidney function and complex renal disease. However, discovering the functional links that bridge the gap from genetic risk loci to disease phenotype is one of the main challenges ahead. Risk loci are currently assigned to a putative context using the functional annotation of the closest genes via a guilt-by-proximity approach. These approaches can be extended by strategies integrating genetic risk loci with kidney-specific, genome-wide gene expression. Risk loci-associated transcripts can be assigned a putative disease-specific function using gene expression coregulation networks. Ultimately, genotype-phenotype dependencies postulated from these associative approaches in humans need to be tested via genetic modification in model organisms. In this review, we survey strategies that employ human tissue-specific expression and the use of model organisms to identify and validate the functional relationship between genotype and phenotype in renal disease. Strategies to unravel how genetic risk and environmental factors orchestrate renal disease manifestation can be the first steps toward a more integrated, holistic approach urgently needed for chronic renal diseases.