Reduced neurite density in the brain and cervical spinal cord in relapsing–remitting multiple sclerosis: A NODDI study

Background: Multiple sclerosis (MS) affects both brain and spinal cord. However, studies of the neuraxis with advanced magnetic resonance imaging (MRI) are rare because of long acquisition times. We investigated neurodegeneration in MS brain and cervical spinal cord using neurite orientation dispersion and density imaging (NODDI). / Objective: The aim of this study was to investigate possible alterations, and their clinical relevance, in neurite morphology along the brain and cervical spinal cord of relapsing–remitting MS (RRMS) patients. / Methods: In total, 28 RRMS patients and 20 healthy controls (HCs) underwent brain and spinal cord NODDI at 3T. Physical and cognitive disability was assessed. Individual maps of orientation dispersion index (ODI) and neurite density index (NDI) in brain and spinal cord were obtained. We examined differences in NODDI measures between groups and the relationships between NODDI metrics and clinical scores using linear regression models adjusted for age, sex and brain tissue volumes or cord cross-sectional area (CSA). / Results: Patients showed lower NDI in the brain normal-appearing white matter (WM) and spinal cord WM than HCs. In patients, a lower NDI in the spinal cord WM was associated with higher disability. / Conclusion: Reduced neurite density occurs in the neuraxis but, especially when affecting the spinal cord, it may represent a mechanism of disability in MS

Parameter estimation for robust HMM analysis of ChIP-chip data

Tiling arrays are an important tool for the study of transcriptional activity, protein-DNA interactions and chromatin structure on a genome-wide scale at high resolution. Although hidden Markov models have been used successfully to analyse tiling array data, parameter estimation for these models is typically ad hoc. Especially in the context of ChIP-chip experiments, no standard procedures exist to obtain parameter estimates from the data. Common methods for the calculation of maximum likelihood estimates such as the Baum-Welch algorithm or Viterbi training are rarely applied in the context of tiling array analysis. Results: Here we develop a hidden Markov model for the analysis of chromatin structure ChIP-chip tiling array data, using t emission distributions to increase robustness towards outliers. Maximum likelihood estimates are used for all model parameters. Two different approaches to parameter estimation are investigated and combined into an efficient procedure. Conclusion: We illustrate an efficient parameter estimation procedure that can be used for HMM based methods in general and leads to a clear increase in performance when compared to the use of ad hoc estimates. The resulting hidden Markov model outperforms established methods like TileMap in the context of histone modification studies.13 page(s

Candidate gene prioritization by network analysis of differential expression using machine learning approaches

<p>Abstract</p> <p>Background</p> <p>Discovering novel disease genes is still challenging for diseases for which no prior knowledge - such as known disease genes or disease-related pathways - is available. Performing genetic studies frequently results in large lists of candidate genes of which only few can be followed up for further investigation. We have recently developed a computational method for constitutional genetic disorders that identifies the most promising candidate genes by replacing prior knowledge by experimental data of differential gene expression between affected and healthy individuals.</p> <p>To improve the performance of our prioritization strategy, we have extended our previous work by applying different machine learning approaches that identify promising candidate genes by determining whether a gene is surrounded by highly differentially expressed genes in a functional association or protein-protein interaction network.</p> <p>Results</p> <p>We have proposed three strategies scoring disease candidate genes relying on network-based machine learning approaches, such as kernel ridge regression, heat kernel, and Arnoldi kernel approximation. For comparison purposes, a local measure based on the expression of the direct neighbors is also computed. We have benchmarked these strategies on 40 publicly available knockout experiments in mice, and performance was assessed against results obtained using a standard procedure in genetics that ranks candidate genes based solely on their differential expression levels (<it>Simple Expression Ranking</it>). Our results showed that our four strategies could outperform this standard procedure and that the best results were obtained using the <it>Heat Kernel Diffusion Ranking </it>leading to an average ranking position of 8 out of 100 genes, an AUC value of 92.3% and an error reduction of 52.8% relative to the standard procedure approach which ranked the knockout gene on average at position 17 with an AUC value of 83.7%.</p> <p>Conclusion</p> <p>In this study we could identify promising candidate genes using network based machine learning approaches even if no knowledge is available about the disease or phenotype.</p

Systematic Identification of Placental Epigenetic Signatures for the Noninvasive Prenatal Detection of Edwards Syndrome

Background: Noninvasive prenatal diagnosis of fetal aneuploidy by maternal plasma analysis is challenging owing to the low fractional and absolute concentrations of fetal DNA in maternal plasma. Previously, we demonstrated for the first time that fetal DNA in maternal plasma could be specifically targeted by epigenetic (DNA methylation) signatures in the placenta. By comparing one such methylated fetal epigenetic marker located on chromosome 21 with another fetal genetic marker located on a reference chromosome in maternal plasma, we could infer the relative dosage of fetal chromosome 21 and noninvasively detect fetal trisomy 21. Here we apply this epigenetic-genetic (EGG) chromosome dosage approach to detect Edwards syndrome (trisomy 18) in the fetus noninvasively. Principal Findings: We have systematically identified methylated fetal epigenetic markers on chromosome 18 by methylated DNA immunoprecipitation (MeDIP) and tiling array analysis with confirmation using quantitative DNA methylation assays. Methylated DNA sequences from an intergenic region between the VAPA and APCDD1 genes (the VAPAAPCDD1 DNA) were detected in pre-delivery, but not post-delivery, maternal plasma samples. The concentrations correlated positively with those of an established fetal genetic marker, ZFY, in pre-delivery maternal plasma. The ratios of methylated VAPA-APCDD1(chr18) to ZFY(chrY) were higher in maternal plasma samples of 9 male trisomy 18 fetuses than those of 27 male euploid fetuses (Mann-Whitney test, P = 0.029). We defined the cutoff value for detecting trisomy 18 fetuses as mean+1.96 SD of the EGG ratios of the euploid cases. Eight of 9 trisomy 18 and 1 of 27 euploid cases showed EGG ratios higher than the cutoff value, giving a sensitivity of 88.9% and a specificity of 96.3%. Conclusions: Our data have shown that the methylated VAPA-APCDD1 DNA in maternal plasma is redominantly derived from the fetus. We have demonstrated that this novel fetal epigenetic marker in maternal plasma is useful for the noninvasive detection of fetal trisomy 18. © Tsui et al.published_or_final_versio

Generative Embedding for Model-Based Classification of fMRI Data

Decoding models, such as those underlying multivariate classification algorithms, have been increasingly used to infer cognitive or clinical brain states from measures of brain activity obtained by functional magnetic resonance imaging (fMRI). The practicality of current classifiers, however, is restricted by two major challenges. First, due to the high data dimensionality and low sample size, algorithms struggle to separate informative from uninformative features, resulting in poor generalization performance. Second, popular discriminative methods such as support vector machines (SVMs) rarely afford mechanistic interpretability. In this paper, we address these issues by proposing a novel generative-embedding approach that incorporates neurobiologically interpretable generative models into discriminative classifiers. Our approach extends previous work on trial-by-trial classification for electrophysiological recordings to subject-by-subject classification for fMRI and offers two key advantages over conventional methods: it may provide more accurate predictions by exploiting discriminative information encoded in ‘hidden’ physiological quantities such as synaptic connection strengths; and it affords mechanistic interpretability of clinical classifications. Here, we introduce generative embedding for fMRI using a combination of dynamic causal models (DCMs) and SVMs. We propose a general procedure of DCM-based generative embedding for subject-wise classification, provide a concrete implementation, and suggest good-practice guidelines for unbiased application of generative embedding in the context of fMRI. We illustrate the utility of our approach by a clinical example in which we classify moderately aphasic patients and healthy controls using a DCM of thalamo-temporal regions during speech processing. Generative embedding achieves a near-perfect balanced classification accuracy of 98% and significantly outperforms conventional activation-based and correlation-based methods. This example demonstrates how disease states can be detected with very high accuracy and, at the same time, be interpreted mechanistically in terms of abnormalities in connectivity. We envisage that future applications of generative embedding may provide crucial advances in dissecting spectrum disorders into physiologically more well-defined subgroups

Noncanonical DNA Motifs as Transactivation Targets by Wild Type and Mutant p53

Sequence-specific binding by the human p53 master regulator is critical to its tumor suppressor activity in response to environmental stresses. p53 binds as a tetramer to two decameric half-sites separated by 0–13 nucleotides (nt), originally defined by the consensus RRRCWWGYYY (n = 0–13) RRRCWWGYYY. To better understand the role of sequence, organization, and level of p53 on transactivation at target response elements (REs) by wild type (WT) and mutant p53, we deconstructed the functional p53 canonical consensus sequence using budding yeast and human cell systems. Contrary to early reports on binding in vitro, small increases in distance between decamer half-sites greatly reduces p53 transactivation, as demonstrated for the natural TIGER RE. This was confirmed with human cell extracts using a newly developed, semi–in vitro microsphere binding assay. These results contrast with the synergistic increase in transactivation from a pair of weak, full-site REs in the MDM2 promoter that are separated by an evolutionary conserved 17 bp spacer. Surprisingly, there can be substantial transactivation at noncanonical ½-(a single decamer) and ¾-sites, some of which were originally classified as biologically relevant canonical consensus sequences including PIDD and Apaf-1. p53 family members p63 and p73 yielded similar results. Efficient transactivation from noncanonical elements requires tetrameric p53, and the presence of the carboxy terminal, non-specific DNA binding domain enhanced transactivation from noncanonical sequences. Our findings demonstrate that RE sequence, organization, and level of p53 can strongly impact p53-mediated transactivation, thereby changing the view of what constitutes a functional p53 target. Importantly, inclusion of ½- and ¾-site REs greatly expands the p53 master regulatory network

Deep grey matter volume loss drives disability worsening in multiple sclerosis

Gray matter (GM) atrophy occurs in all multiple sclerosis (MS) phenotypes. We investigated whether there is a spatiotemporal pattern of GM atrophy that is associated with faster disability accumulation in MS.We analyzed 3,604 brain high-resolution T1-weighted magnetic resonance imaging scans from 1,417 participants: 1,214 MS patients (253 clinically isolated syndrome [CIS], 708 relapsing-remitting [RRMS], 128 secondary-progressive [SPMS], and 125 primary-progressive [PPMS]), over an average follow-up of 2.41 years (standard deviation [SD] = 1.97), and 203 healthy controls (HCs; average follow-up = 1.83 year; SD = 1.77), attending seven European centers. Disability was assessed with the Expanded Disability Status Scale (EDSS). We obtained volumes of the deep GM (DGM), temporal, frontal, parietal, occipital and cerebellar GM, brainstem, and cerebral white matter. Hierarchical mixed models assessed annual percentage rate of regional tissue loss and identified regional volumes associated with time-to-EDSS progression.SPMS showed the lowest baseline volumes of cortical GM and DGM. Of all baseline regional volumes, only that of the DGM predicted time-to-EDSS progression (hazard ratio = 0.73; 95% confidence interval, 0.65, 0.82; p < 0.001): for every standard deviation decrease in baseline DGM volume, the risk of presenting a shorter time to EDSS worsening during follow-up increased by 27%. Of all longitudinal measures, DGM showed the fastest annual rate of atrophy, which was faster in SPMS (-1.45%), PPMS (-1.66%), and RRMS (-1.34%) than CIS (-0.88%) and HCs (-0.94%; p < 0.01). The rate of temporal GM atrophy in SPMS (-1.21%) was significantly faster than RRMS (-0.76%), CIS (-0.75%), and HCs (-0.51%). Similarly, the rate of parietal GM atrophy in SPMS (-1.24-%) was faster than CIS (-0.63%) and HCs (-0.23%; all p values <0.05). Only the atrophy rate in DGM in patients was significantly associated with disability accumulation (beta = 0.04; p < 0.001).This large, multicenter and longitudinal study shows that DGM volume loss drives disability accumulation in MS, and that temporal cortical GM shows accelerated atrophy in SPMS than RRMS. The difference in regional GM atrophy development between phenotypes needs to be taken into account when evaluating treatment effect of therapeutic interventions