High serum sodium predicts immunotherapy response in metastatic renal cell and urothelial carcinoma.

OBJECTIVES: The development of reliable biomarkers for the prediction of immune checkpoint inhibition (ICI) response in patients with metastatic renal cell carcinoma (mRCC) and urothelial carcinoma (mUC) remains an unresolved challenge. Conventional ICI biomarkers typically focus on tumor-related factors such as PD-L1 expression. However, a comprehensive evaluation of the predictive value of serum electrolyte levels, a so far widely unexplored area, is still pending. METHODS: We conducted a post-hoc analysis of baseline sodium, potassium, chloride, magnesium and calcium levels in two independent phase 3 clinical trials: IMvigor211 for mUC comparing atezolizumab to chemotherapy, and IMmotion151 for mRCC comparing atezolizumab+bevacizumab to sunitinib. This analysis aimed to evaluate the prognostic and predictive value of these electrolyte levels in these clinical settings. A total of 1787 patients (IMvigor211 n = 901; IMmotion151 n = 886) were analyzed. RESULTS: We found a linear correlation of baseline serum sodium and chloride with prognosis across both trials, which was not found for potassium, magnesium and calcium. In multivariate analysis, the prognostic capacity of sodium was limited to patients receiving ICI as compared to the control group. Interestingly, in both studies, the chance of achieving an objective response was highest in the patient subgroup with high baseline serum sodium levels of > 140 mmol/L (IMmotion151: Complete response in 17.9% versus 2.0% in patients with mRCC with baseline sodium < 135 mmol/L). Serum sodium outperformed tumor PD-L1 expression as a predictor for immunotherapy efficacy. CONCLUSIONS: Patients exhibiting elevated serum sodium levels derive the greatest benefit from immunotherapy, suggesting that baseline serum concentration could serve as a valuable and cost-effective predictive biomarker for immunotherapy across entities.

NECTIN4 Amplification Is Frequent in Solid Tumors and Predicts Enfortumab Vedotin Response in Metastatic Urothelial Cancer.

PURPOSE: The anti-NECTIN4 antibody-drug conjugate enfortumab vedotin (EV) is approved for patients with metastatic urothelial cancer (mUC). However, durable benefit is only achieved in a small, yet uncharacterized patient subset. NECTIN4 is located on chromosome 1q23.3, and 1q23.3 gains represent frequent copy number variations (CNVs) in urothelial cancer. Here, we aimed to evaluate NECTIN4 amplifications as a genomic biomarker to predict EV response in patients with mUC. MATERIALS AND METHODS: We established a NECTIN4-specific fluorescence in situ hybridization (FISH) assay to assess the predictive value of NECTIN4 CNVs in a multicenter EV-treated mUC patient cohort (mUC-EV, n = 108). CNVs were correlated with membranous NECTIN4 protein expression, EV treatment responses, and outcomes. We also assessed the prognostic value of NECTIN4 CNVs measured in metastatic biopsies of non-EV-treated mUC (mUC-non-EV, n = 103). Furthermore, we queried The Cancer Genome Atlas (TCGA) data sets (10,712 patients across 32 cancer types) for NECTIN4 CNVs. RESULTS: NECTIN4 amplifications are frequent genomic events in muscle-invasive bladder cancer (TCGA bladder cancer data set: approximately 17%) and mUC (approximately 26% in our mUC cohorts). In mUC-EV, NECTIN4 amplification represents a stable genomic alteration during metastatic progression and associates with enhanced membranous NECTIN4 protein expression. Ninety-six percent (27 of 28) of patients with NECTIN4 amplifications demonstrated objective responses to EV compared with 32% (24 of 74) in the nonamplified subgroup (P < .001). In multivariable Cox analysis adjusted for age, sex, and Bellmunt risk factors, NECTIN4 amplifications led to a 92% risk reduction for death (hazard ratio, 0.08 [95% CI, 0.02 to 0.34]; P < .001). In the mUC-non-EV, NECTIN4 amplifications were not associated with outcomes. TCGA Pan-Cancer analysis demonstrated that NECTIN4 amplifications occur frequently in other cancers, for example, in 5%-10% of breast and lung cancers. CONCLUSION: NECTIN4 amplifications are genomic predictors of EV responses and long-term survival in patients with mUC.

Harnessing Artificial Intelligence for Enhanced Renal Analysis: Automated Detection of Hydronephrosis and Precise Kidney Segmentation.

Background and objective: Hydronephrosis is essential in the diagnosis of renal colic. We automated the detection of hydronephrosis from ultrasound images to standardize the therapy and reduce the misdiagnosis of renal colic. Methods: Anonymously collected ultrasound images of human kidneys, both normal and hydronephrotic, were preprocessed for neural networks. Six "state of the art" models were trained and cross-validated for the detection of hydronephrosis, and two convolutional networks were used for kidney segmentation. In the testing phase, performance metrics included true positives, true negatives, false positives, false negatives, accuracy, and F1 score, while the evaluation of the segmentation task involved accuracy, precision, dice, jaccard, recall, and ASSD. Key findings and limitations: A total of 523 sonographic kidney images (423 nonhydronephrotic and 100 hydronephrotic) were collected from three different ultrasound devices. After training on this dataset, all models were used to evaluate 200 new ultrasound kidney images (142 nonhydronephrotic and 58 hydronephrotic kidneys). The highest validation accuracy (98.5%) was achieved by the AlexNet model (GoogLeNet 97%, AlexNet_v2 96%, ResNet50 96%, ResNet101 97.5%, and ResNet152 95%). The deeplabv3_resnet50 and deeplabv3_resnet101 reached a dice coefficient of 94.74% and 94.48%, respectively, on the task of automated kidney segmentation. The study is limited by analyzing only hydronephrosis, but this specific focus enabled high detection accuracy. Conclusions and clinical implications: We show that our automated ultrasound deep learning model can be trained and used to interpret and segmentate ultrasound images from different sources with high accuracy. This method will serve as an automated tool in the diagnostic algorithm of acute renal failure in the future. Patient summary: Hydronephrosis is crucial in the diagnosis of renal colic. Recent advances in artificial intelligence allow automated detection of hydronephrosis in ultrasound images with high accuracy. These methods will help standardize the diagnosis and treatment renal colic.

New tools to study renal fibrogenesis.

PURPOSE OF THE REVIEW: Kidney fibrosis is a key pathological aspect and outcome of chronic kidney disease (CKD). The advent of multiomic analyses using human kidney tissue, enabled by technological advances, marks a new chapter of discovery in fibrosis research of the kidney. This review highlights the rapid advancements of single-cell and spatial multiomic techniques that offer new avenues for exploring research questions related to human kidney fibrosis development. RECENT FINDINGS: We recently focused on understanding the origin and transition of myofibroblasts in kidney fibrosis using single-cell RNA sequencing (scRNA-seq) [1]. We analysed cells from healthy human kidneys and compared them to patient samples with CKD. We identified PDGFRα+/PDGFRß+ mesenchymal cells as the primary cellular source of extracellular matrix (ECM) in human kidney fibrosis. We found several commonly shared cell states of fibroblasts and myofibroblasts and provided insights into molecular regulators. Novel single-cell and spatial multiomics tools are now available to shed light on cell lineages, the plasticity of kidney cells and cell-cell communication in fibrosis. SUMMARY: As further single-cell and spatial multiomic approaches are being developed, opportunities to apply these methods to human kidney tissues expand similarly. Careful design and optimisation of the multiomic experiments are needed to answer questions related to cell lineages, plasticity and cell-cell communication in kidney fibrosis.

Increased levels of a mycophenolic acid metabolite in patients with kidney failure negatively affect cardiomyocyte health.

Chronic kidney disease (CKD) significantly increases cardiovascular risk and mortality, and the accumulation of uremic toxins in the circulation upon kidney failure contributes to this increased risk. We thus performed a screening for potential novel mediators of reduced cardiovascular health starting from dialysate obtained after hemodialysis of patients with CKD. The dialysate was gradually fractionated to increased purity using orthogonal chromatography steps, with each fraction screened for a potential negative impact on the metabolic activity of cardiomyocytes using a high-throughput MTT-assay, until ultimately a highly purified fraction with strong effects on cardiomyocyte health was retained. Mass spectrometry and nuclear magnetic resonance identified the metabolite mycophenolic acid-ß-glucuronide (MPA-G) as a responsible substance. MPA-G is the main metabolite from the immunosuppressive agent MPA that is supplied in the form of mycophenolate mofetil (MMF) to patients in preparation for and after transplantation or for treatment of autoimmune and non-transplant kidney diseases. The adverse effect of MPA-G on cardiomyocytes was confirmed in vitro, reducing the overall metabolic activity and cellular respiration while increasing mitochondrial reactive oxygen species production in cardiomyocytes at concentrations detected in MMF-treated patients with failing kidney function. This study draws attention to the potential adverse effects of long-term high MMF dosing, specifically in patients with severely reduced kidney function already displaying a highly increased cardiovascular risk.

A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical models.

Idiopathic pulmonary fibrosis (IPF) is an aggressive interstitial lung disease with a high mortality rate. Putative drug targets in IPF have failed to translate into effective therapies at the clinical level. We identify TRAF2- and NCK-interacting kinase (TNIK) as an anti-fibrotic target using a predictive artificial intelligence (AI) approach. Using AI-driven methodology, we generated INS018_055, a small-molecule TNIK inhibitor, which exhibits desirable drug-like properties and anti-fibrotic activity across different organs in vivo through oral, inhaled or topical administration. INS018_055 possesses anti-inflammatory effects in addition to its anti-fibrotic profile, validated in multiple in vivo studies. Its safety and tolerability as well as pharmacokinetics were validated in a randomized, double-blinded, placebo-controlled phase I clinical trial (NCT05154240) involving 78 healthy participants. A separate phase I trial in China, CTR20221542, also demonstrated comparable safety and pharmacokinetic profiles. This work was completed in roughly 18 months from target discovery to preclinical candidate nomination and demonstrates the capabilities of our generative AI-driven drug-discovery pipeline.

Spatial multi-omics: novel tools to study the complexity of cardiovascular diseases.

Spatial multi-omic studies have emerged as a promising approach to comprehensively analyze cells in tissues, enabling the joint analysis of multiple data modalities like transcriptome, epigenome, proteome, and metabolome in parallel or even the same tissue section. This review focuses on the recent advancements in spatial multi-omics technologies, including novel data modalities and computational approaches. We discuss the advancements in low-resolution and high-resolution spatial multi-omics methods which can resolve up to 10,000 of individual molecules at subcellular level. By applying and integrating these techniques, researchers have recently gained valuable insights into the molecular circuits and mechanisms which govern cell biology along the cardiovascular disease spectrum. We provide an overview of current data analysis approaches, with a focus on data integration of multi-omic datasets, highlighting strengths and weaknesses of various computational pipelines. These tools play a crucial role in analyzing and interpreting spatial multi-omics datasets, facilitating the discovery of new findings, and enhancing translational cardiovascular research. Despite nontrivial challenges, such as the need for standardization of experimental setups, data analysis, and improved computational tools, the application of spatial multi-omics holds tremendous potential in revolutionizing our understanding of human disease processes and the identification of novel biomarkers and therapeutic targets. Exciting opportunities lie ahead for the spatial multi-omics field and will likely contribute to the advancement of personalized medicine for cardiovascular diseases.

Detection of PatIent-Level distances from single cell genomics and pathomics data with Optimal Transport (PILOT).

Although clinical applications represent the next challenge in single-cell genomics and digital pathology, we still lack computational methods to analyze single-cell or pathomics data to find sample-level trajectories or clusters associated with diseases. This remains challenging as single-cell/pathomics data are multi-scale, i.e., a sample is represented by clusters of cells/structures, and samples cannot be easily compared with each other. Here we propose PatIent Level analysis with Optimal Transport (PILOT). PILOT uses optimal transport to compute the Wasserstein distance between two individual single-cell samples. This allows us to perform unsupervised analysis at the sample level and uncover trajectories or cellular clusters associated with disease progression. We evaluate PILOT and competing approaches in single-cell genomics or pathomics studies involving various human diseases with up to 600 samples/patients and millions of cells or tissue structures. Our results demonstrate that PILOT detects disease-associated samples from large and complex single-cell or pathomics data. Moreover, PILOT provides a statistical approach to find changes in cell populations, gene expression, and tissue structures related to the trajectories or clusters supporting interpretation of predictions.

Inhibition of the glucocorticoid receptor attenuates proteinuric kidney diseases in multiple species.

BACKGROUND AND HYPOTHESIS: Glucocorticoids are the treatment of choice for proteinuric patients with minimal-change disease (MCD) and primary focal and segmental glomerulosclerosis (FSGS). Immunosuppressive as well as direct effects on podocytes are believed to mediate their actions. In this study, we analyzed the anti-proteinuric effects of inhibition of the glucocorticoid receptor (GR) in glomerular epithelial cells, including podocytes. METHODS: We employed genetic and pharmacological approaches to inhibit the GR. Genetically, we used Pax8-Cre/GRfl/fl mice to specifically inactivate the GR in kidney epithelial cells. Pharmacologically, we utilized a glucocorticoid antagonist called mifepristone. RESULTS: Genetic inactivation of GR, specifically in kidney epithelial cells, using Pax8-Cre/GRfl/fl mice, ameliorated proteinuria following protein overload. We further tested the effects of pharmacological GR inhibition in three models and species: the puromycin-aminonucleoside-induced nephrosis model in rats, the protein overload model in mice and the inducible transgenic NTR/MTZ zebrafish larvae with specific and reversible podocyte injury. In all three models, both pharmacological GR activation and inhibition consistently and significantly ameliorated proteinuria. Additionally, we translated our findings to humans, where three nephrotic adult patients with MCD or primary FSGS with contraindications or insufficient responses to corticosteroids, were treated with mifepristone. This treatment resulted in a clinically relevant reduction of proteinuria. CONCLUSIONS: Thus, across multiple species and proteinuria models, both genetic and pharmacological GR inhibition was at least as effective as pronounced GR activation. While, the mechanism remains perplexing, GR inhibition may be a novel and targeted therapeutic approach to treat glomerular proteinuria potentially bypassing adverse actions of steroids.

Exploring the genetic and functional diversity of Porphyromonas gingivalis long fimbriae.

Porphyromonas gingivalis is a key pathobiont in periodontitis. Its long fimbriae consist of a single anchor (FimB), a varying number of stalk (FimA), and three accessory (tip-related) proteins (FimC, FimD, and FimE). Based on 133 strains/genomes available, it was our aim to investigate the diversity within FimA and FimB and explain the variety of long fimbriae (super-)structures. Combining the new forward primer fimAnewF with the established fimAunivR, we were able to amplify and sequence fimA including its leader region covering all genotypes and serotypes for phylogenetic analysis. We designed two primer pairs sensing the presence of an internal stop codon in fimB with an impact on fimbrial length. Finally, we examined fimbrial secondary structures by transmission electron microscopy (TEM) and scanning electron microscopy (SEM). The phylogeny of fimA/FimA revealed two new subtypes (IIa and IIb) with specific changes in functional domains and thus adding to the current classification scheme (I, Ib, and II-V). Regarding evolution, we confirm that Porphyromonas gulae fimA-type A is closely related to human P. gingivalis strains of cluster Ib and might be its ancestor genotype. A fimB internal stop codon is rare and was found in ATCC 33277 only. Comparing P. gingivalis TEM/SEM pictures of type I ATCC 33277 with type V OMI622 revealed a broad spectrum of fimbrial structures including bundling, cell-cell knotting, and brick-wall formation. In conclusion, FimA forms more distinct subtypes than previously known. The bundling of long fimbriae, a mechanism known from EPEC/EHEC and Salmonella, is proposed and supported by TEM/SEM pictures for the first time here. The role and variations of terminal accessory FimC-E in superstructure formation and/or (co-) adhesion should be investigated more closely next.

Defining cardiac functional recovery in end-stage heart failure at single-cell resolution.

Recovery of cardiac function is the holy grail of heart failure therapy yet is infrequently observed and remains poorly understood. In this study, we performed single-nucleus RNA sequencing from patients with heart failure who recovered left ventricular systolic function after left ventricular assist device implantation, patients who did not recover and non-diseased donors. We identified cell-specific transcriptional signatures of recovery, most prominently in macrophages and fibroblasts. Within these cell types, inflammatory signatures were negative predictors of recovery, and downregulation of RUNX1 was associated with recovery. In silico perturbation of RUNX1 in macrophages and fibroblasts recapitulated the transcriptional state of recovery. Cardiac recovery mediated by BET inhibition in mice led to decreased macrophage and fibroblast Runx1 expression and diminished chromatin accessibility within a Runx1 intronic peak and acquisition of human recovery signatures. These findings suggest that cardiac recovery is a unique biological state and identify RUNX1 as a possible therapeutic target to facilitate cardiac recovery.

[Urothelial carcinoma of the upper urinary tract]. / Urothelkarzinom des oberen Harntrakts.

Urothelial carcinoma of the upper urinary tract (upper tract urothelial carcinoma, UTUC) is less common than bladder carcinoma with nearly identical risk factors and has a poorer prognosis. The standard diagnostic procedure is imaging of the upper urinary tract by computed tomography urography. In cases of diagnostic uncertainty, a diagnostic ureterorenoscopy with biopsy sampling can be performed in addition to urine cytology. Treatment depends primarily on the stage and grading of the tumor. Depending on the extent and localization, organ-preserving treatment or radical nephroureterectomy is indicated. Perioperative systemic treatment in high-risk UTUC can be performed in both neoadjuvant and adjuvant settings, although the current data on neoadjuvant chemo- and immunotherapy do not yet allow standard application. For metastatic disease, a multimodal treatment approach consisting of cisplatin-based or carboplatin-based chemotherapy, immunotherapy, and treatment with enfortumab vedotin can be considered. Salvage surgery, radiotherapy and metastasectomy are available for rare individual cases.

A previously uncharacterized Factor Associated with Metabolism and Energy (FAME/C14orf105/CCDC198/1700011H14Rik) is related to evolutionary adaptation, energy balance, and kidney physiology.

In this study we use comparative genomics to uncover a gene with uncharacterized function (1700011H14Rik/C14orf105/CCDC198), which we hereby name FAME (Factor Associated with Metabolism and Energy). We observe that FAME shows an unusually high evolutionary divergence in birds and mammals. Through the comparison of single nucleotide polymorphisms, we identify gene flow of FAME from Neandertals into modern humans. We conduct knockout experiments on animals and observe altered body weight and decreased energy expenditure in Fame knockout animals, corresponding to genome-wide association studies linking FAME with higher body mass index in humans. Gene expression and subcellular localization analyses reveal that FAME is a membrane-bound protein enriched in the kidneys. Although the gene knockout results in structurally normal kidneys, we detect higher albumin in urine and lowered ferritin in the blood. Through experimental validation, we confirm interactions between FAME and ferritin and show co-localization in vesicular and plasma membranes.

Expansion-enhanced super-resolution radial fluctuations enable nanoscale molecular profiling of pathology specimens.

Expansion microscopy physically enlarges biological specimens to achieve nanoscale resolution using diffraction-limited microscopy systems1. However, optimal performance is usually reached using laser-based systems (for example, confocal microscopy), restricting its broad applicability in clinical pathology, as most centres have access only to light-emitting diode (LED)-based widefield systems. As a possible alternative, a computational method for image resolution enhancement, namely, super-resolution radial fluctuations (SRRF)2,3, has recently been developed. However, this method has not been explored in pathology specimens to date, because on its own, it does not achieve sufficient resolution for routine clinical use. Here, we report expansion-enhanced super-resolution radial fluctuations (ExSRRF), a simple, robust, scalable and accessible workflow that provides a resolution of up to 25 nm using LED-based widefield microscopy. ExSRRF enables molecular profiling of subcellular structures from archival formalin-fixed paraffin-embedded tissues in complex clinical and experimental specimens, including ischaemic, degenerative, neoplastic, genetic and immune-mediated disorders. Furthermore, as examples of its potential application to experimental and clinical pathology, we show that ExSRRF can be used to identify and quantify classical features of endoplasmic reticulum stress in the murine ischaemic kidney and diagnostic ultrastructural features in human kidney biopsies.

Targeting Immune-Fibroblast Crosstalk in Myocardial Infarction and Cardiac Fibrosis.

Inflammation and tissue fibrosis co-exist and are causally linked to organ dysfunction. However, the molecular mechanisms driving immune-fibroblast crosstalk in human cardiac disease remains unexplored and there are currently no therapeutics to target fibrosis. Here, we performed multi-omic single-cell gene expression, epitope mapping, and chromatin accessibility profiling in 38 donors, acutely infarcted, and chronically failing human hearts. We identified a disease-associated fibroblast trajectory marked by cell surface expression of fibroblast activator protein (FAP), which diverged into distinct myofibroblasts and pro-fibrotic fibroblast populations, the latter resembling matrifibrocytes. Pro-fibrotic fibroblasts were transcriptionally similar to cancer associated fibroblasts and expressed high levels of collagens and periostin (POSTN), thymocyte differentiation antigen 1 (THY-1), and endothelin receptor A (EDNRA) predicted to be driven by a RUNX1 gene regulatory network. We assessed the applicability of experimental systems to model tissue fibrosis and demonstrated that 3 different in vivo mouse models of cardiac injury were superior compared to cultured human heart and dermal fibroblasts in recapitulating the human disease phenotype. Ligand-receptor analysis and spatial transcriptomics predicted that interactions between C-C chemokine receptor type 2 (CCR2) macrophages and fibroblasts mediated by interleukin 1 beta (IL-1ß) signaling drove the emergence of pro-fibrotic fibroblasts within spatially defined niches. This concept was validated through in silico transcription factor perturbation and in vivo inhibition of IL-1ß signaling in fibroblasts where we observed reduced pro-fibrotic fibroblasts, preferential differentiation of fibroblasts towards myofibroblasts, and reduced cardiac fibrosis. Herein, we show a subset of macrophages signal to fibroblasts via IL-1ß and rewire their gene regulatory network and differentiation trajectory towards a pro-fibrotic fibroblast phenotype. These findings highlight the broader therapeutic potential of targeting inflammation to treat tissue fibrosis and restore organ function.

Platelet-instructed SPP1+ macrophages drive myofibroblast activation in fibrosis in a CXCL4-dependent manner.

Fibrosis represents the common end stage of chronic organ injury independent of the initial insult, destroying tissue architecture and driving organ failure. Here we discover a population of profibrotic macrophages marked by expression of Spp1, Fn1, and Arg1 (termed Spp1 macrophages), which expands after organ injury. Using an unbiased approach, we identify the chemokine (C-X-C motif) ligand 4 (CXCL4) to be among the top upregulated genes during profibrotic Spp1 macrophage differentiation. In vitro and in vivo studies show that loss of Cxcl4 abrogates profibrotic Spp1 macrophage differentiation and ameliorates fibrosis after both heart and kidney injury. Moreover, we find that platelets, the most abundant source of CXCL4 in vivo, drive profibrotic Spp1 macrophage differentiation. Single nuclear RNA sequencing with ligand-receptor interaction analysis reveals that macrophages orchestrate fibroblast activation via Spp1, Fn1, and Sema3 crosstalk. Finally, we confirm that Spp1 macrophages expand in both human chronic kidney disease and heart failure.

[Artificial intelligence in urology-opportunities and possibilities]. / Künstliche Intelligenz in der Urologie ­ Chancen und Möglichkeiten.

The use of artificial intelligence (AI) in urology can contribute to a significant improvement with regard to individualization of diagnostics and therapy as well as healthcare cost reduction. The potential applications and advantages of AI in medicine are often underestimated or incompletely understood. This makes it difficult to conceptually solve relevant medical problems using AI. With current advances in computer science, multiple, highly complex nonmedical processes have already been studied and optimized in an automated fashion. The development of AI models, if applied correctly, can lead to more effective processing and analysis of patient-related data and correspondingly optimized diagnosis and therapy of urological patients. In this review, the current status on the application of AI in medicine and its opportunities and possibilities in urology are presented from a conceptual perspective using practical examples.

scMEGA: single-cell multi-omic enhancer-based gene regulatory network inference.

Summary: The increasing availability of single-cell multi-omics data allows to quantitatively characterize gene regulation. We here describe scMEGA (Single-cell Multiomic Enhancer-based Gene Regulatory Network Inference) that enables an end-to-end analysis of multi-omics data for gene regulatory network inference including modalities integration, trajectory analysis, enhancer-to-promoter association, network analysis and visualization. This enables to study the complex gene regulation mechanisms for dynamic biological processes, such as cellular differentiation and disease-driven cellular remodeling. We provide a case study on gene regulatory networks controlling myofibroblast activation in human myocardial infarction. Availability and implementation: scMEGA is implemented in R, released under the MIT license and available from https://github.com/CostaLab/scMEGA. Tutorials are available from https://costalab.github.io/scMEGA. Supplementary information: Supplementary data are available at Bioinformatics Advances online.