Stratification of Kidney Transplant Recipients Into Five Subgroups Based on Temporal Disease Trajectories

Background

Kidney transplantation is the treatment of choice for patients with end-stage renal disease. Considerable clinical research has focused on improving graft survival and an increasing number of kidney recipients die with a functioning graft. There is a need to improve patient survival and to better understand the individualized risk of comorbidities and complications. Here, we developed a method to stratify recipients into similar subgroups based on previous comorbidities and subsequently identify complications and for a subpopulation, laboratory test values associated with survival.

Methods

First, we identified significant disease patterns based on all hospital diagnoses from the Danish National Patient Registry for 5752 kidney transplant recipients from 1977 to 2018. Using hierarchical clustering, these longitudinal patterns of diseases segregate into 3 main clusters of glomerulonephritis, hypertension, and diabetes. As some recipients are diagnosed with diseases from >1 cluster, recipients are further stratified into 5 more fine-grained trajectory subgroups for which survival, stratified complication patterns as well as laboratory test values are analyzed.

Results

The study replicated known associations indicating that diabetes and low levels of albumin are associated with worse survival when investigating all recipients. However, stratification of recipients by trajectory subgroup showed additional associations. For recipients with glomerulonephritis, higher levels of basophils are significantly associated with poor survival, and these patients are more often diagnosed with bacterial infections. Additional associations were also found.

Conclusions

This study demonstrates that disease trajectories can confirm known comorbidities and furthermore stratify kidney transplant recipients into clinical subgroups in which we can characterize stratified risk factors. We hope to motivate future studies to stratify recipients into more fine-grained, homogenous subgroups to better discover associations relevant for the individual patient and thereby enable more personalized disease-management and improve long-term outcomes and survival.

Population-wide analysis of hospital laboratory tests to assess seasonal variation and temporal reference interval modification

We identified mortality-, age-, and sex-associated differences in relation to reference intervals (RIs) for laboratory tests in population-wide data from nearly 2 million hospital patients in Denmark and comprising more than 300 million measurements. A low-parameter mathematical wave-based modification method was developed to adjust for dietary and environment influences during the year. The resulting mathematical fit allowed for improved association rates between re-classified abnormal laboratory tests, patient diagnoses, and mortality. The study highlights the need for seasonally modified RIs and presents an approach that has the potential to reduce over- and underdiagnosis, affecting both physician-patient interactions and electronic health record research as a whole.

Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models

The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. We developed a deep-learning-based framework, multi-omics variational autoencoders (MOVE), to integrate such data and applied it to a cohort of 789 people with newly diagnosed type 2 diabetes with deep multi-omics phenotyping from the DIRECT consortium. Using in silico perturbations, we identified drug-omics associations across the multi-modal datasets for the 20 most prevalent drugs given to people with type 2 diabetes with substantially higher sensitivity than univariate statistical tests. From these, we among others, identified novel associations between metformin and the gut microbiota as well as opposite molecular responses for the two statins, simvastatin and atorvastatin. We used the associations to quantify drug-drug similarities, assess the degree of polypharmacy and conclude that drug effects are distributed across the multi-omics modalities.

A read-across case study on chronic toxicity of branched carboxylic acids (1): Integration of mechanistic evidence from new approach methodologies (NAMs) to explore a common mode of action

This read-across case study characterises thirteen, structurally similar carboxylic acids demonstrating the application of in vitro and in silico human-based new approach methods, to determine biological similarity. Based on data from in vivo animal studies, the read-across hypothesis is that all analogues are steatotic and so should be considered hazardous. Transcriptomic analysis to determine differentially expressed genes (DEGs) in hepatocytes served as first tier testing to confirm a common mode-of-action and identify differences in the potency of the analogues. An adverse outcome pathway (AOP) network for hepatic steatosis, informed the design of an in vitro testing battery, targeting AOP relevant MIEs and KEs, and Dempster-Shafer decision theory was used to systematically quantify uncertainty and to define the minimal testing scope. The case study shows that the read-across hypothesis is the critical core to designing a robust, NAM-based testing strategy. By summarising the current mechanistic understanding, an AOP enables the selection of NAMs covering MIEs, early KEs, and late KEs. Experimental coverage of the AOP in this way is vital since MIEs and early KEs alone are not confirmatory of progression to the AO. This strategy exemplifies the workflow previously published by the EUTOXRISK project driving a paradigm shift towards NAM-based NGRA.

Impaired Vitamin D Signaling in T Cells From a Family With Hereditary Vitamin D Resistant Rickets

The active form of vitamin D, 1,25-dihydroxyvitamin D₃ (1,25(OH)₂D₃), mediates its immunomodulatory effects by binding to the vitamin D receptor (VDR). Here, we describe a new point mutation in the DNA-binding domain of the VDR and its consequences for 1,25(OH)₂D₃ signaling in T cells from heterozygous and homozygous carriers of the mutation. The mutation did not affect the overall structure or the ability of the VDR to bind 1,25(OH)₂D₃ and the retinoid X receptor. However, the subcellular localization of the VDR was strongly affected and the transcriptional activity was abolished by the mutation. In heterozygous carriers of the mutation, 1,25(OH)₂D₃-induced gene regulation was reduced by ~ 50% indicating that the expression level of wild-type VDR determines 1,25(OH)₂D₃ responsiveness in T cells. We show that vitamin D-mediated suppression of vitamin A-induced gene regulation depends on an intact ability of the VDR to bind DNA. Furthermore, we demonstrate that vitamin A inhibits 1,25(OH)₂D₃-induced translocation of the VDR to the nucleus and 1,25(OH)₂D₃-induced up-regulation of CYP24A1. Taken together, this study unravels novel aspects of vitamin D signaling and function of the VDR in human T cells.

Disease trajectory browser for exploring temporal, population-wide disease progression patterns in 7.2 million Danish patients

We present the Danish Disease Trajectory Browser (DTB), a tool for exploring almost 25 years of data from the Danish National Patient Register. In the dataset comprising 7.2 million patients and 122 million admissions, users can identify diagnosis pairs with statistically significant directionality and combine them to linear disease trajectories. Users can search for one or more disease codes (ICD-10 classification) and explore disease progression patterns via an array of functionalities. For example, a set of linear trajectories can be merged into a disease trajectory network displaying the entire multimorbidity spectrum of a disease in a single connected graph. Using data from the Danish Register for Causes of Death mortality is also included. The tool is disease-agnostic across both rare and common diseases and is showcased by exploring multimorbidity in Down syndrome (ICD-10 code Q90) and hypertension (ICD-10 code I10). Finally, we show how search results can be customized and exported from the browser in a format of choice (i.e. JSON, PNG, JPEG and CSV).

A Comparative Assessment Study of Known Small-Molecule Keap1-Nrf2 Protein-Protein Interaction Inhibitors: Chemical Synthesis, Binding Properties, and Cellular Activity

Inhibiting the protein-protein interaction (PPI) between the transcription factor Nrf2 and its repressor protein Keap1 has emerged as a promising strategy to target oxidative stress in diseases, including central nervous system (CNS) disorders. Numerous non-covalent small-molecule Keap1-Nrf2 PPI inhibitors have been reported to date, but many feature suboptimal physicochemical properties for permeating the blood-brain barrier, while others contain problematic structural moieties. Here, we present the first side-by-side assessment of all reported Keap1-Nrf2 PPI inhibitor classes using fluorescence polarization, thermal shift assay, and surface plasmon resonance-and further evaluate the compounds in an NQO1 induction cell assay and in counter tests for nonspecific activities. Surprisingly, half of the compounds were inactive or deviated substantially from reported activities, while we confirm the cross-assay activities for others. Through this study, we have identified the most promising Keap1-Nrf2 inhibitors that can serve as pharmacological probes or starting points for developing CNS-active Keap1 inhibitors.

sAOP: linking chemical stressors to adverse outcomes pathway networks

MOTIVATION

Adverse outcome pathway (AOP) is a toxicological concept proposed to provide a mechanistic representation of biological perturbation over different layers of biological organization. Although AOPs are by definition chemical-agnostic, many chemical stressors can putatively interfere with one or several AOPs and such information would be relevant for regulatory decision-making.

RESULTS

With the recent development of AOPs networks aiming to facilitate the identification of interactions among AOPs, we developed a stressor-AOP network (sAOP). Using the 'cytotoxitiy burst' (CTB) approach, we mapped bioactive compounds from the ToxCast data to a list of AOPs reported in AOP-Wiki database. With this analysis, a variety of relevant connections between chemicals and AOP components can be identified suggesting multiple effects not observed in the simplified 'one-biological perturbation to one-adverse outcome' model. The results may assist in the prioritization of chemicals to assess risk-based evaluations in the context of human health.

AVAILABILITY AND IMPLEMENTATION

sAOP is available at http://saop.cpr.ku.dk.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Analysis of Time-Series Gene Expression Data to Explore Mechanisms of Chemical-Induced Hepatic Steatosis Toxicity

Non-alcoholic fatty liver disease (NAFLD) represents a wide spectrum of disease, ranging from simple fatty liver through steatosis with inflammation and necrosis to cirrhosis. One of the most challenging problems in biomedical research and within the chemical industry is to understand the underlying mechanisms of complex disease, and complex adverse outcome pathways (AOPs). Based on a set of 28 steatotic chemicals with gene expression data measured on primary hepatocytes at three times (2, 8, and 24 h) and three doses (low, medium, and high), we identified genes and pathways, defined as molecular initiating events (MIEs) and key events (KEs) of steatosis using a combination of a time series and pathway analyses. Among the genes deregulated by these compounds, the study highlighted OSBPL9, ALDH7A1, MYADM, SLC51B, PRDX6, GPAT3, TMEM135, DLGDA5, BCO2, APO10LA, TSPAN6, NEURL1B, and DUSP1. Furthermore, pathway analysis indicated deregulation of pathways related to lipid accumulation, such as fat digestion and absorption, linoleic and linolenic acid metabolism, calcium signaling pathway, fatty acid metabolism, peroxisome, retinol metabolism, and steroid metabolic pathways in a time dependent manner. Such transcription profile analysis can help in the understanding of the steatosis evolution over time generated by chemical exposure.

In Silico Systems Pharmacology to Assess Drug's Therapeutic and Toxic Effects

For many years, the "one target, one drug" paradigm has been the driving force behind developments in pharmaceutical research. With the recent advances in molecular biology and genomics technologies, the focus is shifting toward "drug-holistic" systems based approaches (i.e. systems pharmacology). The integration of large and diverse amount of data from chemistry and biology coupled with the development and the application of network-based approaches to cope with these data is the next paradigm of drug discovery. Systems pharmacology offers a novel way of approaching drug discovery by developing models that consider the global physiological environment of protein targets and their modification by drugs. Studying drug action across multiple scales of complexity from molecular and cellular to tissue and organism levels may help identify new druggable disease genes and to design new drugs with a better efficacy and clinical safety.