Genetic Profiling of Sodium Channels in Diabetic Painful and Painless and Idiopathic Painful and Painless Neuropathies

Neuropathic pain is a frequent feature of diabetic peripheral neuropathy (DPN) and small fiber neuropathy (SFN). Resolving the genetic architecture of these painful neuropathies will lead to better disease management strategies, counselling and intervention. Our aims were to profile ten sodium channel genes (SCG) expressed in a nociceptive pathway in painful and painless DPN and painful and painless SFN patients, and to provide a perspective for clinicians who assess patients with painful peripheral neuropathy. Between June 2014 and September 2016, 1125 patients with painful-DPN (n = 237), painless-DPN (n = 309), painful-SFN (n = 547) and painless-SFN (n = 32), recruited in four different centers, were analyzed for SCN3A, SCN7A-SCN11A and SCN1B-SCN4B variants by single molecule Molecular inversion probes-Next Generation Sequence. Patients were grouped based on phenotype and the presence of SCG variants. Screening of SCN3A, SCN7A-SCN11A, and SCN1B-SCN4B revealed 125 different (potential) pathogenic variants in 194 patients (17.2%, n = 194/1125). A potential pathogenic variant was present in 18.1% (n = 142/784) of painful neuropathy patients vs. 15.2% (n = 52/341) of painless neuropathy patients (17.3% (n = 41/237) for painful-DPN patients, 14.9% (n = 46/309) for painless-DPN patients, 18.5% (n = 101/547) for painful-SFN patients, and 18.8% (n = 6/32) for painless-SFN patients). Of the variants detected, 70% were in SCN7A, SCN9A, SCN10A and SCN11A. The frequency of SCN9A and SCN11A variants was the highest in painful-SFN patients, SCN7A variants in painful-DPN patients, and SCN10A variants in painless-DPN patients. Our findings suggest that rare SCG genetic variants may contribute to the development of painful neuropathy. Genetic profiling and SCG variant identification should aid in a better understanding of the genetic variability in patients with painful and painless neuropathy, and may lead to better risk stratification and the development of more targeted and personalized pain treatments.

Genome-wide RNA polymerase stalling shapes the transcriptome during aging

Gene expression profiling has identified numerous processes altered in aging, but how these changes arise is largely unknown. Here we combined nascent RNA sequencing and RNA polymerase II chromatin immunoprecipitation followed by sequencing to elucidate the underlying mechanisms triggering gene expression changes in wild-type aged mice. We found that in 2-year-old liver, 40% of elongating RNA polymerases are stalled, lowering productive transcription and skewing transcriptional output in a gene-length-dependent fashion. We demonstrate that this transcriptional stress is caused by endogenous DNA damage and explains the majority of gene expression changes in aging in most mainly postmitotic organs, specifically affecting aging hallmark pathways such as nutrient sensing, autophagy, proteostasis, energy metabolism, immune function and cellular stress resilience. Age-related transcriptional stress is evolutionary conserved from nematodes to humans. Thus, accumulation of stochastic endogenous DNA damage during aging deteriorates basal transcription, which establishes the age-related transcriptome and causes dysfunction of key aging hallmark pathways, disclosing how DNA damage functionally underlies major aspects of normal aging.

Phenotypic clustering of dilated cardiomyopathy patients highlights important pathophysiological differences

AIMS

The dilated cardiomyopathy (DCM) phenotype is the result of combined genetic and acquired triggers. Until now, clinical decision-making in DCM has mainly been based on ejection fraction (EF) and NYHA classification, not considering the DCM heterogenicity. The present study aimed to identify patient subgroups by phenotypic clustering integrating aetiologies, comorbidities, and cardiac function along cardiac transcript levels, to unveil pathophysiological differences between DCM subgroups.

METHODS AND RESULTS

We included 795 consecutive DCM patients from the Maastricht Cardiomyopathy Registry who underwent in-depth phenotyping, comprising extensive clinical data on aetiology and comorbodities, imaging and endomyocardial biopsies. Four mutually exclusive and clinically distinct phenogroups (PG) were identified based upon unsupervised hierarchical clustering of principal components: [PG1] mild systolic dysfunction, [PG2] auto-immune, [PG3] genetic and arrhythmias, and [PG4] severe systolic dysfunction. RNA-sequencing of cardiac samples (n = 91) revealed a distinct underlying molecular profile per PG: pro-inflammatory (PG2, auto-immune), pro-fibrotic (PG3; arrhythmia), and metabolic (PG4, low EF) gene expression. Furthermore, event-free survival differed among the four phenogroups, also when corrected for well-known clinical predictors. Decision tree modelling identified four clinical parameters (auto-immune disease, EF, atrial fibrillation, and kidney function) by which every DCM patient from two independent DCM cohorts could be placed in one of the four phenogroups with corresponding outcome (n = 789; Spain, n = 352 and Italy, n = 437), showing a feasible applicability of the phenogrouping.

CONCLUSION

The present study identified four different DCM phenogroups associated with significant differences in clinical presentation, underlying molecular profiles and outcome, paving the way for a more personalized treatment approach.

Cardiac Inflammation Impedes Response to Cardiac Resynchronization Therapy in Patients With Idiopathic Dilated Cardiomyopathy

BACKGROUND

Cardiac resynchronization therapy (CRT) is an established therapy in patients with dilated cardiomyopathy (DCM) and conduction disorders. Still, one-third of the patients with DCM do not respond to CRT. This study aims to depict the underlying cardiac pathophysiological processes of nonresponse to CRT in patients with DCM using endomyocardial biopsies.

METHODS

Within the Maastricht and Innsbruck registries of patients with DCM, 99 patients underwent endomyocardial biopsies before CRT implantation, with histological quantification of fibrosis and inflammation, where inflammation was defined as >14 infiltrating cells/mm². Echocardiographic left ventricular end-systolic volume reduction ≥15% after 6 months was defined as response to CRT. RNA was isolated from cardiac biopsies of a representative subset of responders and nonresponders.

RESULTS

Sixty-seven patients responded (68%), whereas 32 (32%) did not respond to CRT. Cardiac inflammation before implantation was negatively associated with response to CRT (25% of responders, 47% of nonresponders; odds ratio 0.3 [0.12-0.76]; P=0.01). Endomyocardial biopsies fibrosis did not relate to CRT response. Cardiac inflammation improved the robustness of prediction beyond well-known clinical predictors of CRT response (likelihood ratio test P<0.001). Cardiac transcriptomic profiling of endomyocardial biopsies reveals a strong proinflammatory and profibrotic signature in the hearts of nonresponders compared with responders. In particular, COL1A1, COL1A2, COL3A1, COL5A1, POSTN, CTGF, LOX, TGFβ1, PDGFRA, TNC, BGN, and TSP2 were significantly higher expressed in the hearts of nonresponders.

CONCLUSIONS

Cardiac inflammation along with a transcriptomic profile of high expression of combined proinflammatory and profibrotic genes are associated with a poor response to CRT in patients with DCM.

Targeted Apoptosis of Senescent Cells Restores Tissue Homeostasis in Response to Chemotoxicity and Aging

The accumulation of irreparable cellular damage restricts healthspan after acute stress or natural aging. Senescent cells are thought to impair tissue function, and their genetic clearance can delay features of aging. Identifying how senescent cells avoid apoptosis allows for the prospective design of anti-senescence compounds to address whether homeostasis can also be restored. Here, we identify FOXO4 as a pivot in senescent cell viability. We designed a FOXO4 peptide that perturbs the FOXO4 interaction with p53. In senescent cells, this selectively causes p53 nuclear exclusion and cell-intrinsic apoptosis. Under conditions where it was well tolerated in vivo, this FOXO4 peptide neutralized doxorubicin-induced chemotoxicity. Moreover, it restored fitness, fur density, and renal function in both fast aging Xpd^TTD/TTD and naturally aged mice. Thus, therapeutic targeting of senescent cells is feasible under conditions where loss of health has already occurred, and in doing so tissue homeostasis can effectively be restored.

Inefficient DNA Repair Is an Aging-Related Modifier of Parkinson's Disease

The underlying relation between Parkinson’s disease (PD) etiopathology and its major risk factor, aging, is largely unknown. In light of the causative link between genome stability and aging, we investigate a possible nexus between DNA damage accumulation, aging, and PD by assessing aging-related DNA repair pathways in laboratory animal models and humans. We demonstrate that dermal fibroblasts from PD patients display flawed nucleotide excision repair (NER) capacity and that Ercc1 mutant mice with mildly compromised NER exhibit typical PD-like pathological alterations, including decreased striatal dopaminergic innervation, increased phospho-synuclein levels, and defects in mitochondrial respiration. Ercc1 mouse mutants are also more sensitive to the prototypical PD toxin MPTP, and their transcriptomic landscape shares important similarities with that of PD patients. Our results demonstrate that specific defects in DNA repair impact the dopaminergic system and are associated with human PD pathology and might therefore constitute an age-related risk factor for PD.

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Ercc1-mediated DNA repair is necessary for preservation of dopaminergic neurons

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Mouse mutants with mild Ercc1 defects display signs of dopaminergic pathology

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Mild Ercc1 dysfunction is sensitized to the prototypical PD neurotoxin MPTP

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PD patients’ peripheral cells exhibit inefficient nucleotide excision repair

Deciphering the RNA landscape by RNAome sequencing

Current RNA expression profiling methods rely on enrichment steps for specific RNA classes, thereby not detecting all RNA species in an unperturbed manner. We report strand-specific RNAome sequencing that determines expression of small and large RNAs from rRNA-depleted total RNA in a single sequence run. Since current analysis pipelines cannot reliably analyze small and large RNAs simultaneously, we developed TRAP, Total Rna Analysis Pipeline, a robust interface that is also compatible with existing RNA sequencing protocols. RNAome sequencing quantitatively preserved all RNA classes, allowing cross-class comparisons that facilitates the identification of relationships between different RNA classes. We demonstrate the strength of RNAome sequencing in mouse embryonic stem cells treated with cisplatin. MicroRNA and mRNA expression in RNAome sequencing significantly correlated between replicates and was in concordance with both existing RNA sequencing methods and gene expression arrays generated from the same samples. Moreover, RNAome sequencing also detected additional RNA classes such as enhancer RNAs, anti-sense RNAs, novel RNA species and numerous differentially expressed RNAs undetectable by other methods. At the level of complete RNA classes, RNAome sequencing also identified a specific global repression of the microRNA and microRNA isoform classes after cisplatin treatment whereas all other classes such as mRNAs were unchanged. These characteristics of RNAome sequencing will significantly improve expression analysis as well as studies on RNA biology not covered by existing methods.

RNAome sequencing delineates the complete RNA landscape

Standard RNA expression profiling methods rely on enrichment steps for specific RNA classes, thereby not detecting all RNA species. For example, small and large RNAs from the same sample cannot be sequenced in a single sequence run. We designed RNAome sequencing, which is a strand-specific method to determine the expression of small and large RNAs from ribosomal RNA-depleted total RNA in a single sequence run. RNAome sequencing quantitatively preserves all RNA classes. This characteristic allows comparisons between RNA classes, thereby facilitating relationships between different RNA classes. Here, we describe in detail the experimental procedure associated with RNAome sequencing published by Derks and colleagues in RNA Biology (2015) [1]. We also provide the R code for the developed Total Rna Analysis Pipeline (TRAP), an algorithm to analyze RNAome sequencing datasets (deposited at the Gene Expression Omnibus data repository, accession number GSE48084).

The DNA damage response: the omics era and its impact

The emergence of high density technologies monitoring the genome, transcriptome and proteome in relation to genotoxic stress have tremendously enhanced our knowledge on global responses and dynamics in the DNA damage response, including its relation with cancer and aging. Moreover, '-omics' technologies identified many novel factors, their post-translational modifications, pathways and global responses in the cellular response to DNA damage. Based on omics, it is currently estimated that thousands of gene(product)s participate in the DNA damage response, recognizing complex networks that determine cell fate after damage to the most precious cellular molecule, DNA. The development of next generation sequencing technology and associated specialized protocols can quantitatively monitor RNA and DNA at unprecedented single nucleotide resolution. In this review we will discuss the contribution of omics technologies and in particular next generation sequencing to our understanding of the DNA damage response and the future prospective of next generation sequencing, its single cell application and omics dataset integration in unraveling intricate DNA damage signaling networks.

In vivo murine hepatic microRNA and mRNA expression signatures predicting the (non-)genotoxic carcinogenic potential of chemicals

There is a high need to improve the assessment of, especially non-genotoxic, carcinogenic features of chemicals. We therefore explored a toxicogenomics-based approach using genome-wide microRNA and mRNA expression profiles upon short-term exposure in mice. For this, wild-type mice were exposed for seven days to three different classes of chemicals, i.e., four genotoxic carcinogens (GTXC), seven non-genotoxic carcinogens (NGTXC), and five toxic non-carcinogens. Hepatic expression patterns of mRNA and microRNA transcripts were determined after exposure and used to assess the discriminative power of the in vivo transcriptome for GTXC and NGTXC. A final classifier set, discriminative for GTXC and NGTXC, was generated from the transcriptomic data using a tiered approach. This appeared to be a valid approach, since the predictive power of the final classifier set in three different classifier algorithms was very high for the original training set of chemicals. Subsequent validation in an additional set of chemicals revealed that the predictive power for GTXC remained high, in contrast to NGTXC, which appeared to be more troublesome. Our study demonstrated that the in vivo microRNA-ome has less discriminative power to correctly identify (non-)genotoxic carcinogen classes. The results generally indicate that single mRNA transcripts do have the potential to be applied in risk assessment, but that additional (genomic) strategies are necessary to correctly predict the non-genotoxic carcinogenic potential of a chemical.