Negative Binomial mixed models estimated with the maximum likelihood method can be used for longitudinal RNAseq data

Time-course RNAseq experiments, where tissues are repeatedly collected from the same subjects, e.g. humans or animals over time or under several different experimental conditions, are becoming more popular due to the reducing sequencing costs. Such designs offer the great potential to identify genes that change over time or progress differently in time across experimental groups. Modelling of the longitudinal gene expression in such time-course RNAseq data is complicated by the serial correlations, missing values due to subject dropout or sequencing errors, long follow up with potentially non-linear progression in time and low number of subjects. Negative Binomial mixed models can address all these issues. However, such models under the maximum likelihood (ML) approach are less popular for RNAseq data due to convergence issues (see, e.g. [1]). We argue in this paper that it is the use of an inaccurate numerical integration method in combination with the typically small sample sizes which causes such mixed models to fail for a great portion of tested genes. We show that when we use the accurate adaptive Gaussian quadrature approach to approximate the integrals over the random-effects terms, we can successfully estimate the model parameters with the maximum likelihood method. Moreover, we show that the boostrap method can be used to preserve the type I error rate in small sample settings. We evaluate empirically the small sample properties of the test statistics and compare with state-of-the-art approaches. The method is applied on a longitudinal mice experiment to study the dynamics in Duchenne Muscular Dystrophy. Contact:[email protected] Tsonaka is an assistant professor at the Medical Statistics, Department of Biomedical Data Sciences, Leiden University Medical Center. Her research focuses on statistical methods for longitudinal omics data. Pietro Spitali is an assistant professor at the Department of Human Genetics, Leiden University Medical Center. His research focuses on the identification of biomarkers for neuromuscular disorders.Development and application of statistical models for medical scientific researc

Corrigendum to: Poisson-Tweedie mixed-effects model: a flexible approach for the analysis of longitudinal RNA-seq data (Aug, 10.1177/1471082X20936017, 2020)

Functional Genomics of Muscle, Nerve and Brain Disorder

Pathway testing for longitudinal metabolomics

We propose a top-down approach for pathway analysis of longitudinal metabolite data. We apply a score test based on a shared latent process mixed model which can identify pathways with differentially progressing metabolites. The strength of our approach is that it can handle unbalanced designs, deals with potential missing values in the longitudinal markers, and gives valid results even with small sample sizes. Contrary to bottom-up approaches, correlations between metabolites are explicitly modeled leveraging power gains. For large pathway sizes, a computationally efficient solution is proposed based on pseudo-likelihood methodology. We demonstrate the advantages of the proposed method in identification of differentially expressed pathways through simulation studies. Finally, longitudinal metabolite data from a mice experiment is analyzed to demonstrate our methodology.Functional Genomics of Muscle, Nerve and Brain Disorder

Plasma lipidomic analysis shows a disease progression signature in mdx mice

Duchenne muscular dystrophy (DMD) is a rare genetic disorder affecting paediatric patients. The disease course is characterized by loss of muscle mass, which is rapidly substituted by fibrotic and adipose tissue. Clinical and preclinical models have clarified the processes leading to muscle damage and myofiber degeneration. Analysis of the fat component is however emerging as more evidence shows how muscle fat fraction is associated with patient performance and prognosis. In this article we aimed to study whether alterations exist in the composition of lipids in plasma samples obtained from mouse models. Analysis of plasma samples was performed in 4 mouse models of DMD and wild-type mice by LC-MS. Longitudinal samplings of individual mice covering an observational period of 7 months were obtained to cover the different phases of the disease. We report clear elevation of glycerolipids and glycerophospholipids families in dystrophic mice compared to healthy mice. Triacylglycerols were the strongest contributors to the signatures in mice. Annotation of individual lipids confirmed the elevation of lipids belonging to these families as strongest discriminants between healthy and dystrophic mice. A few sphingolipids (such as ganglioside GM2, sphingomyelin and ceramide), sterol lipids (such as cholesteryl oleate and cholesteryl arachidonate) and a fatty acyl (stearic acid) were also found to be affected in dystrophic mice. Analysis of serum and plasma samples show how several lipids are affected in dystrophic mice affected by muscular dystrophy. This study sets the basis to further investigations to understand how the lipid signature relates to the disease biology and muscle performance.Development and application of statistical models for medical scientific researc

Penalized regression calibration: a method for the prediction of survival outcomes using complex longitudinal and high-dimensional data

Longitudinal and high-dimensional measurements have become increasingly common in biomedical research. However, methods to predict survival outcomes using covariates that are both longitudinal and high-dimensional are currently missing. In this article, we propose penalized regression calibration (PRC), a method that can be employed to predict survival in such situations. PRC comprises three modeling steps: First, the trajectories described by the longitudinal predictors are flexibly modeled through the specification of multivariate mixed effects models. Second, subject-specific summaries of the longitudinal trajectories are derived from the fitted mixed models. Third, the time to event outcome is predicted using the subject-specific summaries as covariates in a penalized Cox model. To ensure a proper internal validation of the fitted PRC models, we furthermore develop a cluster bootstrap optimism correction procedure that allows to correct for the optimistic bias of apparent measures of predictiveness. PRC and the CBOCP are implemented in the R package pencal, available from CRAN. After studying the behavior of PRC via simulations, we conclude by illustrating an application of PRC to data from an observational study that involved patients affected by Duchenne muscular dystrophy, where the goal is predict time to loss of ambulation using longitudinal blood biomarkers.Development and application of statistical models for medical scientific researc

226th ENMC International Workshop: Towards validated and qualified biomarkers for therapy development for Duchenne muscular dystrophy 20-22 January 2017, Heemskerk, The Netherlands

Functional Genomics of Muscle, Nerve and Brain Disorder

Longitudinal metabolomic analysis of plasma enables modeling disease progression in Duchenne muscular dystrophy mouse models

Duchenne muscular dystrophy is a severe pediatric neuromuscular disorder caused by the lack of dystrophin. Identification of biomarkers is needed to support and accelerate drug development. Alterations of metabolites levels in muscle and plasma have been reported in pre-clinical and clinical cross-sectional comparisons. We present here a 7-month longitudinal study comparing plasma metabolomic data in wild-type and mdx mice. A mass spectrometry approach was used to study metabolites in up to five time points per mouse at 6, 12, 18, 24 and 30 weeks of age, providing an unprecedented in depth view of disease trajectories. A total of 106 metabolites were studied. We report a signature of 31 metabolites able to discriminate between healthy and disease at various stages of the disease, covering the acute phase of muscle degeneration and regeneration up to the deteriorating phase. We show how metabolites related to energy production and chachexia (e.g. glutamine) are affected in mdx mice plasma over time. We further show how the signature is connected to molecular targets of nutraceuticals and pharmaceutical compounds currently in development as well as to the nitric oxide synthase pathway (e.g. arginine and citrulline). Finally, we evaluate the signature in a second longitudinal study in three independent mouse models carrying 0, 1 or 2 functional copies of the dystrophin paralog utrophin. In conclusion, we report an in-depth metabolomic signature covering previously identified associations and new associations, which enables drug developers to peripherally assess the effect of drugs on the metabolic status of dystrophic mice.Development and application of statistical models for medical scientific researc

Rimeporide as a first- in-class NHE-1 inhibitor: Results of a phase Ib trial in young patients with Duchenne Muscular Dystrophy

Rimeporide, a first-in-class sodium/proton exchanger Type 1 inhibitor (NHE-1 inhibitor) is repositioned by EspeRare for patients with Duchenne Muscular Dystrophy (DMD). Historically, NHE-1 inhibitors were developed for cardiac therapeutic interventions. There is considerable overlap in the pathophysiological mechanisms in Congestive Heart Failure (CHF) and in cardiomyopathy in DMD, therefore NHE-1 inhibition could be a promising pharmacological approach to the cardiac dysfunctions observed in DMD. Extensive preclinical data was collected in various animal models including dystrophin-deficient (mdx) mice to characterise Rimeporide’s anti-fibrotic and anti-inflammatory properties and there is evidence that NHE-1 inhibitors could play a significant role in modifying DMD cardiac and also skeletal pathologies, as the NHE-1 isoform is ubiquitous. We report here the first study with Rimeporide in DMD patients. This 4-week treatment, open label phase Ib, multiple oral ascending dose study, enrolled 20 ambulant boys with DMD (6–11 years), with outcomes including safety, pharmacokinetic (PK) and pharmacodynamic (PD) biomarkers. Rimeporide was safe and well-tolerated at all doses. PK evaluations showed that Rimeporide was well absorbed orally reaching pharmacological concentrations from the lowest dose, with exposure increasing linearly with dose and with no evidence of accumulation upon repeated dosing. Exploratory PD biomarkers showed positive effect upon a 4-week treatment, supporting its therapeutic potential in patients with DMD, primarily as a cardioprotective treatment, and provide rationale for further efficacy studies

Cross-sectional serum metabolomic study of multiple forms of muscular dystrophy

Development and application of statistical models for medical scientific researc