Abstract:Background
We describe a patient presenting with pachygyria, epilepsy, developmental delay, short stature, failure to thrive, facial dysmorphisms, and multiple osteochondromas.
Methods
The patient underwent extensive genetic testing and analysis in an attempt to diagnose the cause of his condition. Clinical testing included metaphase karyotyping, array comparative genomic hybridization, direct sequencing and multiplex ligation‐dependent probe amplification and trio‐base… Show more
“…In fact, RNA-Seq of human colon cancer LSC confirmed a non-sense mutation in N-terminal area of C1GALT1C1 (COSMC) as we previously reported [(35), data not shown]. This suggests that RNA-Seq can avoid errors in counting transcripts with mutations, which causes no or low enzymatic activity that is consistent with the recent reports (36,37).…”
Glycans are primarily generated by "glycogenes," which consist of more than 200 genes for glycosynthesis, including sugar-nucleotide synthases, sugar-nucleotide transporters, and glycosyltransferases. Measuring the expression level of glycogenes is one of the approaches to analyze the glycomes of particular biological and clinical samples. To develop an effective strategy for identifying the glycosylated biomarkers, we performed transcriptome analyses using quantitative real-time polymerase chain reaction (qRT-PCR) arrays and RNA sequencing (RNA-Seq). First, we measured and analyzed the transcriptome from the primary culture of human liver cells and hepatocarcinoma cells using RNA-Seq. This analysis revealed similar but distinctive expression profiles of glycogenes among hepatic cells as indicated by the qRT-PCR arrays, which determined a copy number of 186 glycogenes. Both data sets indicated that altered expression of glycosyltransferases affect the glycosylation of particular glycoproteins, which is consistent with the mass analysis data. Moreover, RNA-Seq analysis can uncover mutations in glycogenes and search differently expressed genes out of more than 50,000 distinct human gene transcripts including candidate biomarkers that were previously reported for hepatocarcinoma cells. Identification of candidate glyco-biomarkers from the expression profile of the glycogenes and proteins from liver cancer tissues available from public database emphasized the possibility that even though the expression level of biomarkers might not be altered, the expression of the glycogenes modifying biomarkers, generating glyco-biomarkers, might be different. Pathway analysis revealed that ∼20% of the glycogenes exhibited different expression levels in normal and cancer cells. Thus, transcriptome analyses using both qRT-PCR array and RNA-Seq in combination with glycome and glycoproteome analyses can be advantageous to identify "glyco-biomarkers" by reinforcing information at the expression levels of both glycogenes and proteins.
“…In fact, RNA-Seq of human colon cancer LSC confirmed a non-sense mutation in N-terminal area of C1GALT1C1 (COSMC) as we previously reported [(35), data not shown]. This suggests that RNA-Seq can avoid errors in counting transcripts with mutations, which causes no or low enzymatic activity that is consistent with the recent reports (36,37).…”
Glycans are primarily generated by "glycogenes," which consist of more than 200 genes for glycosynthesis, including sugar-nucleotide synthases, sugar-nucleotide transporters, and glycosyltransferases. Measuring the expression level of glycogenes is one of the approaches to analyze the glycomes of particular biological and clinical samples. To develop an effective strategy for identifying the glycosylated biomarkers, we performed transcriptome analyses using quantitative real-time polymerase chain reaction (qRT-PCR) arrays and RNA sequencing (RNA-Seq). First, we measured and analyzed the transcriptome from the primary culture of human liver cells and hepatocarcinoma cells using RNA-Seq. This analysis revealed similar but distinctive expression profiles of glycogenes among hepatic cells as indicated by the qRT-PCR arrays, which determined a copy number of 186 glycogenes. Both data sets indicated that altered expression of glycosyltransferases affect the glycosylation of particular glycoproteins, which is consistent with the mass analysis data. Moreover, RNA-Seq analysis can uncover mutations in glycogenes and search differently expressed genes out of more than 50,000 distinct human gene transcripts including candidate biomarkers that were previously reported for hepatocarcinoma cells. Identification of candidate glyco-biomarkers from the expression profile of the glycogenes and proteins from liver cancer tissues available from public database emphasized the possibility that even though the expression level of biomarkers might not be altered, the expression of the glycogenes modifying biomarkers, generating glyco-biomarkers, might be different. Pathway analysis revealed that ∼20% of the glycogenes exhibited different expression levels in normal and cancer cells. Thus, transcriptome analyses using both qRT-PCR array and RNA-Seq in combination with glycome and glycoproteome analyses can be advantageous to identify "glyco-biomarkers" by reinforcing information at the expression levels of both glycogenes and proteins.
“…40 Additionally, the identification of gene fusions has recently been applied to constitutional diseases, specifically in a variety of rare, undiagnosed phenotypes, which was found to result in improved diagnoses as well. 41–42 As ALS is a multifactorial and heterogenous neurodegenerative disease arising from a combination of genetic and environmental factors, the enrichment of gene fusions we have identified here may suggest a role for structural genomic anomalies in ALS risk, onset or progression.…”
Genetics is an import risk factor for amyotrophic lateral sclerosis (ALS), a devastating neurodegenerative disease affecting motor neurons. Recent findings demonstrate that, in addition to specific genetic mutations, structural variants caused by genetic instability can also play a causative role in ALS. Genomic instability can lead to deletions, duplications, insertions, inversions, and translocations in the genome, and these changes can sometimes lead to fusion of distinct genes into a single transcript. While such gene fusion events have been studied extensively in cancer, they have not been thoroughly investigated in ALS. We leveraged bulk RNA-Seq data from human post-mortem samples to determine whether fusion events occur in ALS. We report for the first time the presence of gene fusion events in several brain regions as well as in spinal cord samples in ALS. Although most gene fusions were intra-chromosomal events between neighboring genes and present in both ALS and control samples, there was a significant increase in the number of unique gene fusion in ALS compared to controls. Lastly, we have identified specific gene fusions with a significant burden in ALS, that were absent from both control samples and known cancer gene fusion databases. Collectively, our findings reveal an enrichment of gene fusion in ALS and suggest that these events may be an additional genetic cause linked to ALS pathogenesis.
“…Patient 37 is a male child who presented with a phenotype including pachygyria, epilepsy, developmental delay, short stature, failure to thrive, facial dysmorphisms, and multiple exostoses [45]. Trio-based clinical exome sequencing identified a maternally inherited, X-linked loss-of-function variant in Doublecortin ( DCX ), which was classified as pathogenic and diagnostic of the patient’s neurological phenotype.…”
Section: Resultsmentioning
confidence: 99%
“…Agilent 44k and 180k array comparative genome hybridization (aCGH), fluorescence in-situ hybridization (FISH), multiplex-ligation probe analysis (MLPA) and Molecular Inversion Probe (MIP) Analysis were performed as previously described by Oliver et al . [45]. Flow cytometry, long range PCR, Pacific Biosciences (PacBio) sequencing, targeted PCR and Sanger sequencing were performed as previously described by Cousin et al .…”
BackgroundRNA sequencing has been proposed as a means of increasing diagnostic rates in studies of undiagnosed rare inherited disease. Recent studies have reported diagnostic improvements in the range of 7.5–35% by profiling splicing, gene expression quantification and allele specific expression. To-date however, no study has systematically assessed the presence of gene-fusion transcripts in cases of germline disease. Fusion transcripts are routinely identified in cancer studies and are increasingly recognized as having diagnostic, prognostic or therapeutic relevance. Isolated reports exist of fusion transcripts being detected in cases of developmental and neurological phenotypes, and thus, systematic application of fusion detection to germline conditions may further increase diagnostic rates. However, current fusion detection methods are unsuited to the investigation of germline disease due to performance biases arising from their development using tumor, cell-line or in-silico data.MethodsWe describe a tailored approach to fusion candidate identification and prioritization in a cohort of 47 undiagnosed, suspected inherited disease patients. We modify an existing fusion transcript detection algorithm by eliminating its cell line-derived filtering steps, and instead, prioritize candidates using a custom workflow that integrates genomic and transcriptomic sequence alignment, biological and technical annotations, customized categorization logic, and phenotypic prioritization.ResultsWe demonstrate that our approach to fusion transcript identification and prioritization detects genuine fusion events excluded by standard analyses and efficiently removes phenotypically unimportant candidates and false positive events, resulting in a reduced candidate list enriched for events with potential phenotypic relevance. We describe the successful genetic resolution of two previously undiagnosed disease cases through the detection of pathogenic fusion transcripts. Furthermore, we report the experimental validation of five additional cases of fusion transcripts with potential phenotypic relevance.ConclusionsThe approach we describe can be implemented to enable the detection of phenotypically relevant fusion transcripts in studies of rare inherited disease. Fusion transcript detection has the potential to increase diagnostic rates in rare inherited disease and should be included in RNA-based analytical pipelines aimed at genetic diagnosis.
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