Andreas Heinzel

Concept and application of a computational vaccinology workflow.

BackgroundThe last years have seen a renaissance of the vaccine area, driven by clinical needs in infectious diseases but also chronic diseases such as cancer and autoimmune disorders. Equally important are technological improvements involving nano-scale delivery platforms as well as third generation adjuvants. In parallel immunoinformatics routines have reached essential maturity for supporting central aspects in vaccinology going beyond prediction of antigenic determinants. On this basis computational vaccinology has emerged as a discipline aimed at ab-initio rational vaccine design.Here we present a computational workflow for implementing computational vaccinology covering aspects from vaccine target identification to functional characterization and epitope selection supported by a Systems Biology assessment of central aspects in host-pathogen interaction. We exemplify the procedures for Epstein Barr Virus (EBV), a clinically relevant pathogen causing chronic infection and suspected of triggering malignancies and autoimmune disorders.ResultsWe introduce pBone/pView as a computational workflow supporting design and execution of immunoinformatics workflow modules, additionally involving aspects of results visualization, knowledge sharing and re-use. Specific elements of the workflow involve identification of vaccine targets in the realm of a Systems Biology assessment of host-pathogen interaction for identifying functionally relevant targets, as well as various methodologies for delineating B- and T-cell epitopes with particular emphasis on broad coverage of viral isolates as well as MHC alleles.Applying the workflow on EBV specifically proposes sequences from the viral proteins LMP2, EBNA2 and BALF4 as vaccine targets holding specific B- and T-cell epitopes promising broad strain and allele coverage.ConclusionBased on advancements in the experimental assessment of genomes, transcriptomes and proteomes for both, pathogen and (human) host, the fundaments for rational design of vaccines have been laid out. In parallel, immunoinformatics modules have been designed and successfully applied for supporting specific aspects in vaccine design. Joining these advancements, further complemented by novel vaccine formulation and delivery aspects, have paved the way for implementing computational vaccinology for rational vaccine design tackling presently unmet vaccine challenges.

Mapping of molecular pathways, biomarkers and drug targets for diabetic nephropathy

Purpose: For diseases with complex phenotype such as diabetic nephropathy (DN), integration of multiple Omics sources promises an improved description of the disease pathophysiology, being the basis for novel diagnostics and therapy, but equally important personalization aspects.

A panel of novel biomarkers representing different disease pathways improves prediction of renal function decline in type 2 diabetes.

Objective We aimed to identify a novel panel of biomarkers predicting renal function decline in type 2 diabetes, using biomarkers representing different disease pathways speculated to contribute to the progression of diabetic nephropathy. Research Design and Methods A systematic data integration approach was used to select biomarkers representing different disease pathways. Twenty-eight biomarkers were measured in 82 patients seen at an outpatient diabetes center in The Netherlands. Median follow-up was 4.0 years. We compared the cross-validated explained variation (R2) of two models to predict eGFR decline, one including only established risk markers, the other adding a novel panel of biomarkers. Least absolute shrinkage and selection operator (LASSO) was used for model estimation. The C-index was calculated to assess improvement in prediction of accelerated eGFR decline defined as <-3.0 mL/min/1.73m2/year. Results Patients’ average age was 63.5 years and baseline eGFR was 77.9 mL/min/1.73m2. The average rate of eGFR decline was -2.0 ± 4.7 mL/min/1.73m2/year. When modeled on top of established risk markers, the biomarker panel including matrix metallopeptidases, tyrosine kinase, podocin, CTGF, TNF-receptor-1, sclerostin, CCL2, YKL-40, and NT-proCNP improved the explained variability of eGFR decline (R2 increase from 37.7% to 54.6%; p=0.018) and improved prediction of accelerated eGFR decline (C-index increase from 0.835 to 0.896; p=0.008). Conclusions A novel panel of biomarkers representing different pathways of renal disease progression including inflammation, fibrosis, angiogenesis, and endothelial function improved prediction of eGFR decline on top of established risk markers in type 2 diabetes. These results need to be confirmed in a large prospective cohort.

Molecular pathogenesis of post-transplant acute kidney injury: Assessment of whole-genome mRNA and miRNA profiles

Acute kidney injury (AKI) affects roughly 25% of all recipients of deceased donor organs. The prevention of post-transplant AKI is still an unmet clinical need. We prospectively collected zero-hour, indication as well as protocol kidney biopsies from 166 allografts between 2011 and 2013. In this cohort eight cases with AKI and ten matched allografts without pathology serving as control group were identified with a follow-up biopsy within the first twelve days after engraftment. For this set the zero-hour and follow-up biopsies were subjected to genome wide microRNA and mRNA profiling and analysis, followed by validation in independent expression profiles of 42 AKI and 21 protocol biopsies for strictly controlling the false discovery rate. Follow-up biopsies of AKI allografts compared to time-matched protocol biopsies, further baseline adjustment for zero-hour biopsy expression level and validation in independent datasets, revealed a molecular AKI signature holding 20 mRNAs and two miRNAs (miR-182-5p and miR-21-3p). Next to several established biomarkers such as lipocalin-2 also novel candidates of interest were identified in the signature. In further experimental evaluation the elevated transcript expression level of the secretory leukocyte peptidase inhibitor (SLPI) in AKI allografts was confirmed in plasma and urine on the protein level (p<0.001 and p = 0.003, respectively). miR-182-5p was identified as a molecular regulator of post-transplant AKI, strongly correlated with global gene expression changes during AKI. In summary, we identified an AKI-specific molecular signature providing the ground for novel biomarkers and target candidates such as SLPI and miR-182-5p in addressing AKI.

Renal microRNA‐ and RNA‐profiles in progressive chronic kidney disease

MicroRNAs (miRNAs) contribute to chronic kidney disease (CKD) progression via regulating mRNAs involved in renal homeostasis. However, their association with clinical outcome remains poorly understood.

From molecular signatures to predictive biomarkers: modeling disease pathophysiology and drug mechanism of action

Omics profiling significantly expanded the molecular landscape describing clinical phenotypes. Association analysis resulted in first diagnostic and prognostic biomarker signatures entering clinical utility. However, utilizing Omics for deepening our understanding of disease pathophysiology, and further including specific interference with drug mechanism of action on a molecular process level still sees limited added value in the clinical setting. We exemplify a computational workflow for expanding from statistics-based association analysis toward deriving molecular pathway and process models for characterizing phenotypes and drug mechanism of action. Interference analysis on the molecular model level allows identification of predictive biomarker candidates for testing drug response. We discuss this strategy on diabetic nephropathy (DN), a complex clinical phenotype triggered by diabetes and presenting with renal as well as cardiovascular endpoints. A molecular pathway map indicates involvement of multiple molecular mechanisms, and selected biomarker candidates reported as associated with disease progression are identified for specific molecular processes. Selective interference of drug mechanism of action and disease-associated processes is identified for drug classes in clinical use, in turn providing precision medicine hypotheses utilizing predictive biomarkers.

MicroRNAs in kidney transplantation.

The discovery of novel classes of non-coding RNAs (ncRNAs) has revolutionized medicine. Long thought to be a mere cellular housekeeper, surprising functions have recently been uncovered. MicroRNAs (miRNAs), are a representative of the class of short ncRNAs, play a fundamental role in the control of DNA and protein biosynthesis and activity as well as pathology. Currently, miRNAs are being investigated as diagnostic and prognostic markers and potential therapeutic targets in kidney transplantation for such indolent processes as ischaemia-reperfusion injury, humoral rejection or viral infections. It is realistic to believe that monitoring of renal allograft recipients in the future will include genome-wide miRNA profiling of biological fluids. Based on these individual profiles, an informed decision on therapeutic consequences will be possible. A first success with a specific suppression of miRNAs by antisense oligonucleotides was achieved in experimental studies of reperfusion injury and humoral rejection. Proof of this concept in men comes from studies in such indolent viral infections as Ebola and hepatitis C, where anti-miR therapy led to sustained viral clearance. In this review, we summarize the basis of the recent ncRNA revolution and its implication for kidney transplantation.

Plasma proteomics classifiers improve risk prediction for renal disease in patients with hypertension or type 2 diabetes

Objective: Micro and macroalbuminuria are strong risk factors for progression of nephropathy in patients with hypertension or type 2 diabetes. Early detection of progression to micro and macroalbuminuria may facilitate prevention and treatment of renal diseases. We aimed to develop plasma proteomics classifiers to predict the development of micro or macroalbuminuria in hypertension or type 2 diabetes. Methods: Patients with hypertension (n = 125) and type 2 diabetes (n = 82) were selected for this case-control study from the Prevention of REnal and Vascular ENd-stage Disease cohort and the Steno Diabetes Center. Cases transitioned from normo to microalbuminuria, or from micro to macroalbuminuria. Controls, matched for age, sex, and baseline albuminuria stage, did not transition. Follow-up was 3.0 ± 0.9 years. Plasma proteomics profiles were measured by liquid chromatography-electrospray-trap mass-spectrometry. Classifiers were developed and cross-validated for prediction of transition in albuminuria stage. Improvement in risk prediction was tested on top of a reference model of baseline albuminuria, estimated glomerular filtration rate, and renin–angiotensin–aldosterone system intervention. Results: In hypertensive patients, the classifier improved risk prediction for transition in albuminuria stage on top of the reference model (C-index from 0.69 to 0.78; P < 0.01). In type 2 diabetes, the classifier improved risk prediction for transition from micro to macroalbuminuria (C-index from 0.73 to 0.80; P = 0.04). In both diseases, the identified peptides were linked to pathways recognized to contribute to nephropathy, including fibrosis, inflammation, angiogenesis, and mineral metabolism. Conclusions: Plasma proteomics predict the transition in albuminuria stage beyond established renal risk markers in hypertension or type 2 diabetes. External validation is needed to assess reproducibility.

Drugs meeting the molecular basis of diabetic kidney disease: bridging from molecular mechanism to personalized medicine

Diabetic kidney disease (DKD) is a complex, multifactorial disease and is associated with a high risk of renal and cardiovascular morbidity and mortality. Clinical practice guidelines for diabetes recommend essentially identical treatments for all patients without taking into account how the individual responds to the instituted therapy. Yet, individuals vary widely in how they respond to medications and therefore optimal therapy differs between individuals. Understanding the underlying molecular mechanisms of variability in drug response will help tailor optimal therapy. Polymorphisms in genes related to drug pharmacokinetics have been used to explore mechanisms of response variability in DKD, but with limited success. The complex interaction between genetic make-up and environmental factors on the abundance of proteins and metabolites renders pharmacogenomics alone insufficient to fully capture response variability. A complementary approach is to attribute drug response variability to individual variability in underlying molecular mechanisms involved in the progression of disease. The interplay of different processes (e.g. inflammation, fibrosis, angiogenesis, oxidative stress) appears to drive disease progression, but the individual contribution of each process varies. Drugs at the other hand address specific targets and thereby interfere in certain disease-associated processes. At this level, biomarkers may help to gain insight into which specific pathophysiological processes are involved in an individual followed by a rational assessment whether a specific drugs mode of action indeed targets the relevant process at hand. This article describes the conceptual background and data-driven workflow developed by the SysKid consortium aimed at improving characterization of the molecular mechanisms underlying DKD at the interference of the molecular impact of individual drugs in order to tailor optimal therapy to individual patients.

Functional molecular units for guiding biomarker panel design.

The field of biomarker research has experienced a major boost in recent years, and the number of publications on biomarker studies evaluating given, but also proposing novel biomarker candidates is increasing rapidly for numerous clinically relevant disease areas. However, individual markers often lack sensitivity and specificity in the clinical context, resting essentially on the intra-individual phenotype variability hampering sensitivity, or on assessing more general processes downstream of the causative molecular events characterizing a disease term, in consequence impairing disease specificity. The trend to circumvent these shortcomings goes towards utilizing multimarker panels, thus combining the strength of individual markers to further enhance performance regarding both sensitivity and specificity. A way of identifying the optimal composition of individual markers in a panel approach is to pick each marker as representative for a specific pathophysiological (mechanistic) process relevant for the disease under investigation, hence resulting in a multimarker panel for covering the set of pathophysiological processes underlying the frequently multifactorial composition of a clinical phenotype.Here we outline a procedure of identifying such sets of disease-specific pathophysiological processes (units) delineated on the basis of disease-associated molecular feature lists derived from literature mining as well as aggregated, publicly available Omics profiling experiments. With such molecular units in hand, providing an improved reflection of a specific clinical phenotype, biomarker candidates can then be assigned to or novel candidates are to be selected from these units, subsequently resulting in a multimarker panel promising improved accuracy in disease diagnosis as well as prognosis.