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.
In the past two decades, longitudinal personal health record (LPHR) adoption rate has been low in the United States. Patients' privacy and security concerns was the primary negative factor impacting LPHR adoption. Patients desire to control the privacy of their own LPHR in multiple information systems at multiple facilities. However, little is known how to model and construct a scalable and interoperable LPHR with patient-controlled privacy and confidentiality that preserves patients' health information integrity and availability. Understanding this problem and proposing a practical solution are considered important to increase LPHR adoption rate and improve the efficiency as well as the quality of care. Even though having the state-of-the-art encryption methodologies being applied to patients' data, without a set of secure access control policies being implemented, LPHR patient data privacy is not guaranteed due to insider threats. We proposed a definition of "secure LPHR" and argued LPHR is secure when the security and privacy requirements are fulfilled through adopting an access control security model. In searching for an access control model, we enhanced the National Institute of Standards and Technology (NIST) next generation access control (NGAC) model by replacing the centralized access control policy database with a permissioned blockchain peer-to-peer database, which not only eases the race condition in NGAC, but also provides patient-managed access control policy update capability. We proposed a novel blockchain-enabled next generation access control (BeNGAC) model to protect security and privacy of LPHR. We sketched BeNGAC and LPHR architectures and identified limitations of the design.
The Supplementary information is available on http://www.cbil.ece.vt.edu/publications.htm
Gene expression profiling has been widely used to study molecular signatures of many diseases and to develop molecular diagnostics for disease prediction. Gene selection, as an important step for improved diagnostics, screens tens of thousands of genes and identifies a small subset that discriminates between disease types. A two-step gene selection method is proposed to identify informative gene subsets for accurate classification of multiclass phenotypes. In the first step, individually discriminatory genes (IDGs) are identified by using one-dimensional weighted Fisher criterion (wFC). In the second step, jointly discriminatory genes (JDGs) are selected by sequential search methods, based on their joint class separability measured by multidimensional weighted Fisher criterion (wFC). The performance of the selected gene subsets for multiclass prediction is evaluated by artificial neural networks (ANNs) and/or support vector machines (SVMs). By applying the proposed IDG/JDG approach to two microarray studies, that is, small round blue cell tumors (SRBCTs) and muscular dystrophies (MDs), we successfully identified a much smaller yet efficient set of JDGs for diagnosing SRBCTs and MDs with high prediction accuracies (96.9% for SRBCTs and 92.3% for MDs, resp.). These experimental results demonstrated that the two-step gene selection method is able to identify a subset of highly discriminative genes for improved multiclass prediction.
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