Outcomes of two different unbalanced segregations from a maternal t(4;10)(q33;p15.1) translocation.

BACKGROUND: Unbalanced translocations can cause developmental delay (DD), intellectual disability (ID), growth problems, dysmorphic features, and congenital anomalies. They may arise de novo or may be inherited from a parent carrying a balanced rearrangement. It is estimated that 1/500 people is a balanced translocation carrier. The outcomes of different chromosomal rearrangements have the potential to reveal the functional consequences of partial trisomy or partial monosomy and can help guide genetic counseling for balanced carriers, and other young patients diagnosed with similar imbalances. METHODS: We performed clinical phenotyping and cytogenetic analyses of two siblings with a history of developmental delay (DD), intellectual disability (ID) and dysmorphic features. RESULTS: The proband, a 38-year-old female, has a history of short stature, dysmorphic features and aortic coarctation. She underwent chromosomal microarray analysis, which identified partial monosomy of 4q and partial trisomy of 10p. Her brother, a 37-year-old male, has a history of more severe DD, behavioral problems, dysmorphic features, and congenital anomalies. Subsequently, karyotype confirmed two different unbalanced translocations in the siblings: 46,XX,der(4)t(4;10)(q33;p15.1) and 46,XY,der(10)t(4;10)(q33;p15.1), respectively. These chromosomal rearrangements represent two possible outcomes from a parent who is a carrier for a balanced translocation 46,XX,t(4;10)(q33;p15.1). CONCLUSION: To our knowledge, this 4q and 10p translocation has not been described in literature. In this report we compare clinical features due to the composite effects of partial monosomy 4q with partial trisomy 10p and partial trisomy 4q with partial monosomy 10p. These findings speak to the relevance of old and new genomic testing, the viability of these segregation outcomes, and need for genetic counseling.

Case series: clinical outcomes of the transthyretin valine-to-isoleucine mutation in a brother-sister pair.

BACKGROUND: Approximately 4% of the African-American population possess a valine-to-isoleucine (V122I) substitution within the transthyretin protein that results in a tendency for a normally tetrameric protein to dissociate into misfolded, monomeric subunits. These misfolded proteins can then accumulate pathologically and cause an autosomal dominant amyloid cardiomyopathy. Homozygous patients are infrequently documented in case reports, and though there are larger studies among heterozygous patients, there is a lack of studies or reports comparing disease within a family. CASE SUMMARY: In this case series, we discuss a 61-year-old African-American male who succumbed to heart failure secondary to cardiac amyloidosis while awaiting orthotopic heart transplantation. We compare his case with that of his sister, a 65-year-old African-American woman with a history of recurrent supraventricular tachycardia requiring radiofrequency ablation, and intermittent chest pain with chronically elevated troponin despite no evidence of coronary artery disease. The sister in question was found to be homozygous for the transthyretin (TTR) V122I mutation with evidence of infiltrative process on cardiac magnetic resonance imaging, while clinical testing verified a heterozygous genotype in the brother. Here, we compare the clinical course and imaging data for the aforementioned brother-sister pair in the context of the amyloidogenic transthyretin V122I gene variant. DISCUSSION: Through this familial report, we aim to highlight the variations in expression both within this family and in comparison, to the population. We also hope to emphasize the importance of genetic testing of families at risk for this specific transthyretin variant within the African-American community especially as novel therapies begin to emerge.

PE-Swab Direct STR Amplification of Forensic Touch DNA Samples.

The PE-Swab direct STR amplification workflow was developed to process low-level "touch DNA" samples. In this workflow, a forensic sample is first collected on a 4-mm PE-Swab (a novel sample collection device); two 2-mm punches containing collected samples are then generated from the PE-Swab and directly amplified for STR typing. Compared to the conventional STR workflow, which involves DNA extraction, purification, and elution volume reduction, the PE-Swab direct STR amplification workflow does not require sample preparation and takes <60 sec before a touch sample is ready for STR amplification. Because there is no DNA loss due to sample preparation, the PE-Swab workflow is more sensitive than the conventional STR workflow. The average peak height per sample obtained by the PE-swab workflow is 3 times higher than that from the conventional workflow with both low-level single source and two-contributor mixture samples tested in this study.

Candidate disease gene prediction using Gentrepid: application to a genome-wide association study on coronary artery disease.

Current single-locus-based analyses and candidate disease gene prediction methodologies used in genome-wide association studies (GWAS) do not capitalize on the wealth of the underlying genetic data, nor functional data available from molecular biology. Here, we analyzed GWAS data from the Wellcome Trust Case Control Consortium (WTCCC) on coronary artery disease (CAD). Gentrepid uses a multiple-locus-based approach, drawing on protein pathway- or domain-based data to make predictions. Known disease genes may be used as additional information (seeded method) or predictions can be based entirely on GWAS single nucleotide polymorphisms (SNPs) (ab initio method). We looked in detail at specific predictions made by Gentrepid for CAD and compared these with known genetic data and the scientific literature. Gentrepid was able to extract known disease genes from the candidate search space and predict plausible novel disease genes from both known and novel WTCCC-implicated loci. The disease gene candidates are consistent with known biological information. The results demonstrate that this computational approach is feasible and a valuable discovery tool for geneticists.

Gentrepid V2.0: a web server for candidate disease gene prediction.

BACKGROUND: Candidate disease gene prediction is a rapidly developing area of bioinformatics research with the potential to deliver great benefits to human health. As experimental studies detecting associations between genetic intervals and disease proliferate, better bioinformatic techniques that can expand and exploit the data are required. DESCRIPTION: Gentrepid is a web resource which predicts and prioritizes candidate disease genes for both Mendelian and complex diseases. The system can take input from linkage analysis of single genetic intervals or multiple marker loci from genome-wide association studies. The underlying database of the Gentrepid tool sources data from numerous gene and protein resources, taking advantage of the wealth of biological information available. Using known disease gene information from OMIM, the system predicts and prioritizes disease gene candidates that participate in the same protein pathways or share similar protein domains. Alternatively, using an ab initio approach, the system can detect enrichment of these protein annotations without prior knowledge of the phenotype. CONCLUSIONS: The system aims to integrate the wealth of protein information currently available with known and novel phenotype/genotype information to acquire knowledge of biological mechanisms underpinning disease. We have updated the system to facilitate analysis of GWAS data and the study of complex diseases. Application of the system to GWAS data on hypertension using the ICBP data is provided as an example. An interesting prediction is a ZIP transporter additional to the one found by the ICBP analysis. The webserver URL is https://www.gentrepid.org/.

AutoMate Express™ forensic DNA extraction system for the extraction of genomic DNA from biological samples.

The AutoMate Express™ Forensic DNA Extraction System was developed for automatic isolation of DNA from a variety of forensic biological samples. The performance of the system was investigated using a wide range of biological samples. Depending on the sample type, either PrepFiler™ lysis buffer or PrepFiler BTA™ lysis buffer was used to lyse the samples. After lysis and removal of the substrate using LySep™ column, the lysate in the sample tubes were loaded onto AutoMate Express™ instrument and DNA was extracted using one of the two instrument extraction protocols. Our study showed that DNA was recovered from as little as 0.025 µL of blood. DNA extracted from casework-type samples was free of detectable PCR inhibitors and the short tandem repeat profiles were complete, conclusive, and devoid of any PCR artifacts. The system also showed consistent performance from day-to-day operation.

Analysis of genome-wide association study data using the protein knowledge base.

BACKGROUND: Genome-wide association studies (GWAS) aim to identify causal variants and genes for complex disease by independently testing a large number of SNP markers for disease association. Although genes have been implicated in these studies, few utilise the multiple-hit model of complex disease to identify causal candidates. A major benefit of multi-locus comparison is that it compensates for some shortcomings of current statistical analyses that test the frequency of each SNP in isolation for the phenotype population versus control. RESULTS: Here we developed and benchmarked several protocols for GWAS data analysis using different in-silico gene prediction and prioritisation methodologies. We adopted a high sensitivity approach to the data, using less conservative statistical SNP associations. Multiple gene search spaces, either of fixed-widths or proximity-based, were generated around each SNP marker. We used the candidate disease gene prediction system Gentrepid to identify candidates based on shared biomolecular pathways or domain-based protein homology. Predictions were made either with phenotype-specific known disease genes as input; or without a priori knowledge, by exhaustive comparison of genes in distinct loci. Because Gentrepid uses biomolecular data to find interactions and common features between genes in distinct loci of the search spaces, it takes advantage of the multi-locus aspect of the data. CONCLUSIONS: Results suggest testing multiple SNP-to-gene search spaces compensates for differences in phenotypes, populations and SNP platforms. Surprisingly, domain-based homology information was more informative when benchmarked against gene candidates reported by GWA studies compared to previously determined disease genes, possibly suggesting a larger contribution of gene homologs to complex diseases than Mendelian diseases.

Multiple targets of miR-302 and miR-372 promote reprogramming of human fibroblasts to induced pluripotent stem cells.

The embryonic stem cell-specific cell cycle-regulating (ESCC) family of microRNAs (miRNAs) enhances reprogramming of mouse embryonic fibroblasts to induced pluripotent stem cells. Here we show that the human ESCC miRNA orthologs hsa-miR-302b and hsa-miR-372 promote human somatic cell reprogramming. Furthermore, these miRNAs repress multiple target genes, with downregulation of individual targets only partially recapitulating the total miRNA effects. These targets regulate various cellular processes, including cell cycle, epithelial-mesenchymal transition (EMT), epigenetic regulation and vesicular transport. ESCC miRNAs have a known role in regulating the unique embryonic stem cell cycle. We show that they also increase the kinetics of mesenchymal-epithelial transition during reprogramming and block TGFß-induced EMT of human epithelial cells. These results demonstrate that the ESCC miRNAs promote dedifferentiation by acting on multiple downstream pathways. We propose that individual miRNAs generally act through numerous pathways that synergize to regulate and enforce cell fate decisions.

Conformational changes in redox pairs of protein structures.

Disulfides are conventionally viewed as structurally stabilizing elements in proteins but emerging evidence suggests two disulfide subproteomes exist. One group mediates the well known role of structural stabilization. A second redox-active group are best known for their catalytic functions but are increasingly being recognized for their roles in regulation of protein function. Redox-active disulfides are, by their very nature, more susceptible to reduction than structural disulfides; and conversely, the Cys pairs that form them are more susceptible to oxidation. In this study, we searched for potentially redox-active Cys Pairs by scanning the Protein Data Bank for structures of proteins in alternate redox states. The PDB contains over 1134 unique redox pairs of proteins, many of which exhibit conformational differences between alternate redox states. Several classes of structural changes were observed, proteins that exhibit: disulfide oxidation following expulsion of metals such as zinc; major reorganisation of the polypeptide backbone in association with disulfide redox-activity; order/disorder transitions; and changes in quaternary structure. Based on evidence gathered supporting disulfide redox activity, we propose disulfides present in alternate redox states are likely to have physiologically relevant redox activity.

Molecular properties of CD133+ glioblastoma stem cells derived from treatment-refractory recurrent brain tumors.

Glioblastoma multiforme (GBM) remains refractory to conventional therapy. CD133+ GBM cells have been recently isolated and characterized as chemo-/radio-resistant tumor-initiating cells and are hypothesized to be responsible for post-treatment recurrence. In order to explore the molecular properties of tumorigenic CD133+ GBM cells that resist treatment, we isolated CD133+ GBM cells from tumors that are recurrent and have previously received chemo-/radio-therapy. We found that the purified CD133+ GBM cells sorted from the CD133+ GBM spheres express SOX2 and CD44 and are capable of clonal self-renewal and dividing to produce fast-growing CD133- progeny, which form the major cell population within GBM spheres. Intracranial injection of purified CD133+, not CD133- GBM daughter cells, can lead to the development of YKL-40+ infiltrating tumors that display hypervascularity and pseudopalisading necrosis-like features in mouse brain. The molecular profile of purified CD133+ GBM cells revealed characteristics of neuroectoderm-like cells, expressing both radial glial and neural crest cell developmental genes, and portraying a slow-growing, non-differentiated, polarized/migratory, astrogliogenic, and chondrogenic phenotype. These data suggest that at least a subset of treated and recurrent GBM tumors may be seeded by CD133+ GBM cells with neural and mesenchymal properties. The data also imply that CD133+ GBM cells may be clinically indolent/quiescent prior to undergoing proliferative cell division (PCD) to produce CD133- GBM effector progeny. Identifying intrinsic and extrinsic cues, which promote CD133+ GBM cell self-renewal and PCD to support ongoing tumor regeneration may highlight novel therapeutic strategies to greatly diminish the recurrence rate of GBM.

Comparison of automated candidate gene prediction systems using genes implicated in type 2 diabetes by genome-wide association studies.

BACKGROUND: Automated candidate gene prediction systems allow geneticists to hone in on disease genes more rapidly by identifying the most probable candidate genes linked to the disease phenotypes under investigation. Here we assessed the ability of eight different candidate gene prediction systems to predict disease genes in intervals previously associated with type 2 diabetes by benchmarking their performance against genes implicated by recent genome-wide association studies. RESULTS: Using a search space of 9556 genes, all but one of the systems pruned the genome in favour of genes associated with moderate to highly significant SNPs. Of the 11 genes associated with highly significant SNPs identified by the genome-wide association studies, eight were flagged as likely candidates by at least one of the prediction systems. A list of candidates produced by a previous consensus approach did not match any of the genes implicated by 706 moderate to highly significant SNPs flagged by the genome-wide association studies. We prioritized genes associated with medium significance SNPs. CONCLUSION: The study appraises the relative success of several candidate gene prediction systems against independent genetic data. Even when confronted with challengingly large intervals, the candidate gene prediction systems can successfully select likely disease genes. Furthermore, they can be used to filter statistically less-well-supported genetic data to select more likely candidates. We suggest consensus approaches fail because they penalize novel predictions made from independent underlying databases. To realize their full potential further work needs to be done on prioritization and annotation of genes.

Analysis of protein sequence and interaction data for candidate disease gene prediction.

Linkage analysis is a successful procedure to associate diseases with specific genomic regions. These regions are often large, containing hundreds of genes, which make experimental methods employed to identify the disease gene arduous and expensive. We present two methods to prioritize candidates for further experimental study: Common Pathway Scanning (CPS) and Common Module Profiling (CMP). CPS is based on the assumption that common phenotypes are associated with dysfunction in proteins that participate in the same complex or pathway. CPS applies network data derived from protein-protein interaction (PPI) and pathway databases to identify relationships between genes. CMP identifies likely candidates using a domain-dependent sequence similarity approach, based on the hypothesis that disruption of genes of similar function will lead to the same phenotype. Both algorithms use two forms of input data: known disease genes or multiple disease loci. When using known disease genes as input, our combined methods have a sensitivity of 0.52 and a specificity of 0.97 and reduce the candidate list by 13-fold. Using multiple loci, our methods successfully identify disease genes for all benchmark diseases with a sensitivity of 0.84 and a specificity of 0.63. Our combined approach prioritizes good candidates and will accelerate the disease gene discovery process.