Biomedical subjects

Marina Bakay

Publications and source records attributed to Marina Bakay.

7 recordsLinked to original sources

PubMed2006

Nuclear envelope dystrophies show a transcriptional fingerprint suggesting disruption of Rb-MyoD pathways in muscle regeneration.

Mutations of lamin A/C (LMNA) cause a wide range of human disorders, including progeria, lipodystrophy, neuropathies and autosomal dominant Emery-Dreifuss muscular dystrophy (EDMD). EDMD is also caused by X-linked recessive loss-of-function mutations of emerin, another component of the inner nuclear lamina that directly interacts with LMNA. One model for disease pathogenesis of LMNA and emerin mutations is cell-specific perturbations of the mRNA transcriptome in terminally differentiated cells. To test this model, we studied 125 human muscle biopsies from 13 diagnostic groups (125 U133A, 125 U133B microarrays), including EDMD patients with LMNA and emerin mutations. A Visual and Statistical Data Analyzer (VISDA) algorithm was used to statistically model cluster hierarchy, resulting in a tree of phenotypic classifications. Validations of the diagnostic tree included permutations of U133A and U133B arrays, and use of two probe set algorithms (MAS5.0 and MBEI). This showed that the two nuclear envelope defects (EDMD LMNA, EDMD emerin) were highly related disorders and were also related to fascioscapulohumeral muscular dystrophy (FSHD). FSHD has recently been hypothesized to involve abnormal interactions of chromatin with the nuclear envelope. To identify disease-specific transcripts for EDMD, we applied a leave-one-out (LOO) cross-validation approach using LMNA patient muscle as a test data set, with reverse transcription-polymerase chain reaction (RT-PCR) validations in both LMNA and emerin patient muscle. A high proportion of top-ranked and validated transcripts were components of the same transcriptional regulatory pathway involving Rb1 and MyoD during muscle regeneration (CRI-1, CREBBP, Nap1L1, ECREBBP/p300), where each was specifically upregulated in EDMD. Using a muscle regeneration time series (27 time points) we develop a transcriptional model for downstream consequences of LMNA and emerin mutations. We propose that key interactions between the nuclear envelope and Rb and MyoD fail in EDMD at the point of myoblast exit from the cell cycle, leading to poorly coordinated phosphorylation and acetylation steps. Our data is consistent with mutations of nuclear lamina components leading to destabilization of the transcriptome in differentiated cells.

Biopsy↗

PubMed2006

Optimized multilayer perceptrons for molecular classification and diagnosis using genomic data.

MOTIVATION: Multilayer perceptrons (MLP) represent one of the widely used and effective machine learning methods currently applied to diagnostic classification based on high-dimensional genomic data. Since the dimensionalities of the existing genomic data often exceed the available sample sizes by orders of magnitude, the MLP performance may degrade owing to the curse of dimensionality and over-fitting, and may not provide acceptable prediction accuracy. RESULTS: Based on Fisher linear discriminant analysis, we designed and implemented an MLP optimization scheme for a two-layer MLP that effectively optimizes the initialization of MLP parameters and MLP architecture. The optimized MLP consistently demonstrated its ability in easing the curse of dimensionality in large microarray datasets. In comparison with a conventional MLP using random initialization, we obtained significant improvements in major performance measures including Bayes classification accuracy, convergence properties and area under the receiver operating characteristic curve (A(z)). SUPPLEMENTARY INFORMATION: The Supplementary information is available on http://www.cbil.ece.vt.edu/publications.htm

Biomarkers, Tumor↗

PubMed2006

Loss of emerin at the nuclear envelope disrupts the Rb1/E2F and MyoD pathways during muscle regeneration.

Emery-Dreifuss muscular dystrophy (EDMD1) is caused by mutations in either the X-linked gene emerin (EMD) or the autosomal lamin A/C (LMNA) gene. Here, we describe the derivation of mice lacking emerin in an attempt to derive a mouse model for EDMD1. Although mice lacking emerin show no overt pathology, muscle regeneration in these mice revealed defects. A bioinformatic array analysis of regenerating Emd null muscle revealed abnormalities in cell-cycle parameters and delayed myogenic differentiation, which were associated with perturbations to transcriptional pathways regulated by the retinoblastoma (Rb1) and MyoD genes. Temporal activation of MyoD transcriptional targets was significantly delayed, whereas targets of the Rb1/E2F transcriptional repressor complex remained inappropriately active. The inappropriate modulation of Rb1/MyoD transcriptional targets was associated with up-regulation of Rb1, MyoD and their co-activators/repressors transcripts, suggesting a compensatory effort to overcome a molecular block to differentiation at the myoblast/myotube transition during regeneration. This compensation appeared to be effective for MyoD transcriptional targets, although was less effective for Rb1 targets. Analysis of Rb1 phosphorylation states showed prolonged hyper-phosphorylation at key developmental stages in Emd null myogenic cells, both in vivo and in vitro. We also analyzed the same pathways in Lmna null muscle, which shows extensive dystrophy. Surprisingly, Lmna null muscle did not show the same perturbations to Rb- and MyoD-dependent pathways. We did observe increased transcriptional expression of Lap2alpha and delayed expression of Rb1, which may regulate alternative transcriptional pathways in the Lmna null myoblasts. We suggest that the dominant LMNA mutations seen in many clinically disparate laminopathies may similarly alter Rb function, with regard to either the timing of exit from the cell cycle or terminal differentiation programs or both.

Animals↗

PubMed2005

Definition of the unique human extraocular muscle allotype by expression profiling.

The extraocular muscles (EOMs) are a unique group of specialized muscles that are anatomically and physiologically distinct from other skeletal muscles. Perhaps the most striking characteristic of the EOMs is their differential sensitivity to disease. EOMs are spared in Duchenne's muscular dystrophy (DMD) despite widespread involvement of other skeletal muscles. Conversely, they are early and prominent targets in myasthenia gravis and mitochondrial myopathies. It is unclear how EOMs achieve such specialization or a differential response to diseases; however, this has been attributed to a unique, group-specific pattern of gene expression or "allotype." To begin to address these issues as well as define the human EOM allotype, we analyzed the human EOM transcriptome using oligonucleotide-based expression profiling. Three hundred thirty-eight genes were found to be differentially expressed in EOM compared with quadriceps femoris limb muscle, using a twofold cutoff. Functional characterization revealed expression patterns corresponding to known metabolic and structural properties of EOMs such as expression of EOM-specific myosin heavy chain (MYH13) and high neural, vascular, and mitochondrial content, suggesting that the profiling was sensitive and specific. Genes related to myogenesis, stem cells, and apoptosis were detected at high levels in normal human EOMs, suggesting that efficient and continuous regeneration and/or myogenesis may be a mechanism by which the EOMs remain clinically and pathologically spared in diseases such as DMD. Taken together, this study provides insight into how human EOMs achieve their unique structural, metabolic, and pathophysiological properties.

Adolescent↗

PubMed2004

Interactively optimizing signal-to-noise ratios in expression profiling: project-specific algorithm selection and detection p-value weighting in Affymetrix microarrays.

MOTIVATION: The most commonly utilized microarrays for mRNA profiling (Affymetrix) include 'probe sets' of a series of perfect match and mismatch probes (typically 22 oligonucleotides per probe set). There are an increasing number of reported 'probe set algorithms' that differ in their interpretation of a probe set to derive a single normalized 'signal' representative of expression of each mRNA. These algorithms are known to differ in accuracy and sensitivity, and optimization has been done using a small set of standardized control microarray data. We hypothesized that different mRNA profiling projects have varying sources and degrees of confounding noise, and that these should alter the choice of a specific probe set algorithm. Also, we hypothesized that use of the Microarray Suite (MAS) 5.0 probe set detection p-value as a weighting function would improve the performance of all probe set algorithms. RESULTS: We built an interactive visual analysis software tool (HCE2W) to test and define parameters in Affymetrix analyses that optimize the ratio of signal (desired biological variable) versus noise (confounding uncontrolled variables). Five probe set algorithms were studied with and without statistical weighting of probe sets using the MAS 5.0 probe set detection p-values. The signal-to-noise ratio optimization method was tested in two large novel microarray datasets with different levels of confounding noise, a 105 sample U133A human muscle biopsy dataset (11 groups: mutation-defined, extensive noise), and a 40 sample U74A inbred mouse lung dataset (8 groups: little noise). Performance was measured by the ability of the specific probe set algorithm, with and without detection p-value weighting, to cluster samples into the appropriate biological groups (unsupervised agglomerative clustering with F-measure values). Of the total random sampling analyses, 50% showed a highly statistically significant difference between probe set algorithms by ANOVA [F(4,10) > 14, p < 0.0001], with weighting by MAS 5.0 detection p-value showing significance in the mouse data by ANOVA [F(1,10) > 9, p < 0.013] and paired t-test [t(9) = -3.675, p = 0.005]. Probe set detection p-value weighting had the greatest positive effect on performance of dChip difference model, ProbeProfiler and RMA algorithms. Importantly, probe set algorithms did indeed perform differently depending on the specific project, most probably due to the degree of confounding noise. Our data indicate that significantly improved data analysis of mRNA profile projects can be achieved by optimizing the choice of probe set algorithm with the noise levels intrinsic to a project, with dChip difference model with MAS 5.0 detection p-value continuous weighting showing the best overall performance in both projects. Furthermore, both existing and newly developed probe set algorithms should incorporate a detection p-value weighting to improve performance. AVAILABILITY: The Hierarchical Clustering Explorer 2.0 is available at http://www.cs.umd.edu/hcil/hce/ Murine arrays (40 samples) are publicly available at the PEPR resource (http://microarray.cnmcresearch.org/pgadatatable.asp http://pepr.cnmcresearch.org Chen et al., 2004).

Cluster Analysis↗

PubMed2002

Sources of variability and effect of experimental approach on expression profiling data interpretation.

BACKGROUND: We provide a systematic study of the sources of variability in expression profiling data using 56 RNAs isolated from human muscle biopsies (34 Affymetrix MuscleChip arrays), and 36 murine cell culture and tissue RNAs (42 Affymetrix U74Av2 arrays). RESULTS: We studied muscle biopsies from 28 human subjects as well as murine myogenic cell cultures, muscle, and spleens. Human MuscleChip arrays (4,601 probe sets) and murine U74Av2 Affymetrix microarrays were used for expression profiling. RNAs were profiled both singly, and as mixed groups. Variables studied included tissue heterogeneity, cRNA probe production, patient diagnosis, and GeneChip hybridizations. We found that the greatest source of variability was often different regions of the same patient muscle biopsy, reflecting variation in cell type content even in a relatively homogeneous tissue such as muscle. Inter-patient variation was also very high (SNP noise). Experimental variation (RNA, cDNA, cRNA, or GeneChip) was minor. Pre-profile mixing of patient cRNA samples effectively normalized both intra- and inter-patient sources of variation, while retaining a high degree of specificity of the individual profiles (86% of statistically significant differences detected by absolute analysis; and 85% by a 4-pairwise comparison survival method). CONCLUSIONS: Using unsupervised cluster analysis and correlation coefficients of 92 RNA samples on 76 oligonucleotide microarrays, we found that experimental error was not a significant source of unwanted variability in expression profiling experiments. Major sources of variability were from use of small tissue biopsies, particularly in humans where there is substantial inter-patient variability (SNP noise).

Animals↗

PubMed2002

A web-accessible complete transcriptome of normal human and DMD muscle.

We present an assessment of the complete transcriptome of human skeletal muscle in Duchenne muscular dystrophy patient muscle and non-dystrophic controls (36 RNAs analyzed from ten Duchenne dystrophy and eight controls; approximately 65,000 gene/expressed sequence tag/probe sets queried on U95 five-GeneChip series and MuscleChip). The use of the multiple chip types allowed us to compare results from different probe sets for the same gene: we found excellent concordance between different probe sets on different microarrays. We found 30% of human genes expressed in muscle at detectable levels. Three percent of these showed differential regulation in dystrophin deficiency. Among 1,882 dysregulated probe sets, 1,324 corresponded to characterized genes/proteins (891 non-redundant transcript units), and 588 to expressed sequence tags or predicted genes. Data interpretation was limited to the insulin-like growth factor pathway members, an investigation of possible de-regulation towards a cardiac lineage, and identification of male- and female-specific transcripts. We found transcriptional upregulation of both IGF-I and IGF-II in dystrophic muscle, however the possible beneficial effects of the growth factors appear offset by transcriptional upregulation of inhibitory IGF-binding proteins and regulators (IGFBP-2, -4, -6 and -7; and PRSS11 [IGFBP-5 protease]). We hypothesize that the beneficial effects of IGF-I or IGF-II supplementation in dystrophic muscle may be the result of dose-dependent sequestration of inhibitory IGF-binding proteins. We also focused on six 'cardiac' genes expressed in muscle (alpha-cardiac actin, CARP, CASQ2, troponin T2 cardiac [TNNT2], CUGBP2, and connexin 43). Comparison to a 27 time point murine muscle regeneration series and mdx muscle profiles showed that CARP and Cx43 were macrophage-associated, and TNNT2 activated-myoblast-associated. Upregulation of cardiac actin and CUGBP2 was not associated with muscle regeneration profiles, suggesting a more specific dysregulation induced by dystrophin deficiency. We found two Y-linked genes expressed solely in male muscle (RPS4Y, DDX3Y), and two autosomal genes expressed much more highly in female muscle (GRO2, ZNF91) (all comparisons P<0.01). Finally, we present the first web-accessible expression profiling database for all data, including image files (.dat), processed image files (.cel), and complete comparison files which are publicly available through a novel queriable web site, that permits query-by-gene across all profiles (http://microarray.cnmcresearch.org/pga). These data enumerate the full range of molecular changes associated downstream of dystrophin deficiency, and provide a web-accessible platform to study the specificity of transcriptional pathway alterations in muscle disease.

Animals↗