Correction: Human fetal neuroblast and neuroblastoma transcriptome analysis confirms neuroblast origin and highlights neuroblastoma candidate genes
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ABSTRACT:
SUBMITTER: De Preter K
PROVIDER: S-EPMC1839119 | biostudies-literature | 2007 Jan
REPOSITORIES: biostudies-literature
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Project description:BackgroundNeuroblastoma tumor cells are assumed to originate from primitive neuroblasts giving rise to the sympathetic nervous system. Because these precursor cells are not detectable in postnatal life, their transcription profile has remained inaccessible for comparative data mining strategies in neuroblastoma. This study provides the first genome-wide mRNA expression profile of these human fetal sympathetic neuroblasts. To this purpose, small islets of normal neuroblasts were isolated by laser microdissection from human fetal adrenal glands.ResultsExpression of catecholamine metabolism genes, and neuronal and neuroendocrine markers in the neuroblasts indicated that the proper cells were microdissected. The similarities in expression profile between normal neuroblasts and malignant neuroblastomas provided strong evidence for the neuroblast origin hypothesis of neuroblastoma. Next, supervised feature selection was used to identify the genes that are differentially expressed in normal neuroblasts versus neuroblastoma tumors. This approach efficiently sifted out genes previously reported in neuroblastoma expression profiling studies; most importantly, it also highlighted a series of genes and pathways previously not mentioned in neuroblastoma biology but that were assumed to be involved in neuroblastoma pathogenesis.ConclusionThis unique dataset adds power to ongoing and future gene expression studies in neuroblastoma and will facilitate the identification of molecular targets for novel therapies. In addition, this neuroblast transcriptome resource could prove useful for the further study of human sympathoadrenal biogenesis.
| S-EPMC1794547 | biostudies-literature
Project description:Inherited myopathies are a heterogeneous group of disabling disorders with still barely understood pathological mechanisms. Around 40% of afflicted patients remain without a molecular diagnosis after exclusion of known genes. The advent of high-throughput sequencing has opened avenues to the discovery of new implicated genes, but a working list of prioritized candidate genes is necessary to deal with the complexity of analyzing large-scale sequencing data. Here we used an integrative data mining strategy to analyze the genetic network linked to myopathies, derive specific signatures for inherited myopathy and related disorders, and identify and rank candidate genes for these groups. Training sets of genes were selected after literature review and used in Manteia, a public web-based data mining system, to extract disease group signatures in the form of enriched descriptor terms, which include functional annotation, human and mouse phenotypes, as well as biological pathways and protein interactions. These specific signatures were then used as an input to mine and rank candidate genes, followed by filtration against skeletal muscle expression and association with known diseases. Signatures and identified candidate genes highlight both potential common pathological mechanisms and allelic disease groups. Recent discoveries of gene associations to diseases, like B3GALNT2, GMPPB and B3GNT1 to congenital muscular dystrophies, were prioritized in the ranked lists, suggesting a posteriori validation of our approach and predictions. We show an example of how the ranked lists can be used to help analyze high-throughput sequencing data to identify candidate genes, and highlight the best candidate genes matching genomic regions linked to myopathies without known causative genes. This strategy can be automatized to generate fresh candidate gene lists, which help cope with database annotation updates as new knowledge is incorporated.
| S-EPMC4213015 | biostudies-literature
Project description:[This corrects the article DOI: 10.1371/journal.pone.0192730.].
| S-EPMC6428262 | biostudies-literature
Project description:Brassica napus, an allotetraploid crop, is hypothesized to be a hybrid from unknown varieties of Brassica rapa and Brassica oleracea. Despite the economic importance of B. napus, much is unresolved regarding its phylogenomic relationships, genetic structure, and diversification. Here we conduct a comprehensive study among diverse accessions from 183 B. napus (including rapeseed, rutabaga, and Siberian kale), 112 B. rapa, and 62 B. oleracea and its wild relatives. Using RNA-seq of B. napus accessions, we define the genetic diversity and sub-genome variance of six genetic clusters. Nuclear and organellar phylogenies for B. napus and its progenitors reveal varying patterns of inheritance and post-formation introgression. We discern regions with signatures of selective sweeps and detect 8,187 differentially expressed genes with implications for B. napus diversification. This study highlights the complex origin and evolution of B. napus providing insights that can further facilitate B. napus breeding and germplasm preservation.
| S-EPMC6599199 | biostudies-literature
Project description:Unravelling the biosynthetic pathway of quinolizidine alkaloids (QAs), regarded as antinutritional compounds of narrow-leafed lupin (NLL) seeds, is fundamental to best exploit NLL as food or feed. We investigated 12 candidate genes connected to QA biosynthesis, selecting them by transcriptomic and genomic approaches, from the landscape of genes differentially expressed in leaves of the high- and low-alkaloid NLL accessions. Linkage analysis enabled the assessment of the location of the candidate genes in relation to iucundus, a major locus of unknown identity, that confers reduced QA content in seeds. The key finding was the identification of APETALA2/ethylene response transcription factor, RAP2-7, cosegregating with the iucundus locus and located within a region with highly significant QTLs that affect QA composition. We additionally identified a 4-hydroxy-tetrahydrodipicolinate synthase (DHDPS) gene involved in L-lysine biosynthesis as being closely linked to iucundus. The distributed location of other remaining candidates (including previously known QA genes) across different linkage groups, also indirectly supports the transcription factor as a possible regulator of lupin alkaloid biosynthesis. Our findings provide crucial insight into QA biosynthesis in NLL. Additionally, we evaluated and selected appropriate reference genes for qRT-PCRs to analyse the expression levels of QA genes in NLL.
| S-EPMC6381137 | biostudies-literature
Project description:We recently identified pathogenic KIF1Bβ mutations in sympathetic nervous system malignancies that are defective in developmental apoptosis. Here we deleted KIF1Bβ in the mouse sympathetic nervous system and observed impaired sympathetic nervous function and misexpression of genes required for sympathoadrenal lineage differentiation. We discovered that KIF1Bβ is required for nerve growth factor (NGF)-dependent neuronal differentiation through anterograde transport of the NGF receptor TRKA. Moreover, pathogenic KIF1Bβ mutations identified in neuroblastoma impair TRKA transport. Expression of neuronal differentiation markers is ablated in both KIF1Bβ-deficient mouse neuroblasts and human neuroblastomas that lack KIF1Bβ. Transcriptomic analyses show that unfavorable neuroblastomas resemble mouse sympathetic neuroblasts lacking KIF1Bβ independent of MYCN amplification and the loss of genes neighboring KIF1B on chromosome 1p36. Thus, defective precursor cell differentiation, a common trait of aggressive childhood malignancies, is a pathogenic effect of KIF1Bβ loss in neuroblastomas. Furthermore, neuropathy-associated KIF1Bβ mutations impede cargo transport, providing a direct link between neuroblastomas and neurodegeneration.
| S-EPMC5495120 | biostudies-literature
Project description:BackgroundFetal alcohol syndrome (FAS) is a serious global health problem and is observed at high frequencies in certain South African communities. Although in utero alcohol exposure is the primary trigger, there is evidence for genetic- and other susceptibility factors in FAS development. No genome-wide association or linkage studies have been performed for FAS, making computational selection and -prioritization of candidate disease genes an attractive approach.Results10174 Candidate genes were initially selected from the whole genome using a previously described method, which selects candidate genes according to their expression in disease-affected tissues. Hereafter candidates were prioritized for experimental investigation by investigating criteria pertinent to FAS and binary filtering. 29 Criteria were assessed by mining various database sources to populate criteria-specific gene lists. Candidate genes were then prioritized for experimental investigation using a binary system that assessed the criteria gene lists against the candidate list, and candidate genes were scored accordingly. A group of 87 genes was prioritized as candidates and for future experimental validation. The validity of the binary prioritization method was assessed by investigating the protein-protein interactions, functional enrichment and common promoter element binding sites of the top-ranked genes.ConclusionThis analysis highlighted a list of strong candidate genes from the TGF-beta, MAPK and Hedgehog signalling pathways, which are all integral to fetal development and potential targets for alcohol's teratogenic effect. We conclude that this novel bioinformatics approach effectively prioritizes credible candidate genes for further experimental analysis.
| S-EPMC2194724 | biostudies-literature
Project description:Intrauterine Growth Restriction (IUGR) affects 8% of newborns and increases morbidity and mortality for the offspring even during later stages of life. Single omics studies have evidenced epigenetic, genetic, and metabolic alterations in IUGR, but pathogenic mechanisms as a whole are not being fully understood. An in-depth strategy combining methylomics and transcriptomics analyses was performed on 36 placenta samples in a case-control study. Data-mining algorithms were used to combine the analysis of more than 1,200 genes found to be significantly expressed and/or methylated. We used an automated text-mining approach, using the bulk textual gene annotations of the discriminant genes. Machine learning models were then used to explore the phenotypic subgroups (premature birth, birth weight, and head circumference) associated with IUGR. Gene annotation clustering highlighted the alteration of cell signaling and proliferation, cytoskeleton and cellular structures, oxidative stress, protein turnover, muscle development, energy, and lipid metabolism with insulin resistance. Machine learning models showed a high capacity for predicting the sub-phenotypes associated with IUGR, allowing a better description of the IUGR pathophysiology as well as key genes involved.
| S-EPMC6962302 | biostudies-literature
Project description:BackgroundDefining the transcriptome and the genetic pathways of pancreatic cells is of great interest for elucidating the molecular attributes of pancreas disorders such as diabetes and cancer. As the function of the different pancreatic cell types has been maintained during vertebrate evolution, the comparison of their transcriptomes across distant vertebrate species is a means to pinpoint genes under strong evolutionary constraints due to their crucial function, which have therefore preserved their selective expression in these pancreatic cell types.ResultsIn this study, RNA-sequencing was performed on pancreatic alpha, beta, and delta endocrine cells as well as the acinar and ductal exocrine cells isolated from adult zebrafish transgenic lines. Comparison of these transcriptomes identified many novel markers, including transcription factors and signaling pathway components, specific for each cell type. By performing interspecies comparisons, we identified hundreds of genes with conserved enriched expression in endocrine and exocrine cells among human, mouse, and zebrafish. This list includes many genes known as crucial for pancreatic cell formation or function, but also pinpoints many factors whose pancreatic function is still unknown. A large set of endocrine-enriched genes can already be detected at early developmental stages as revealed by the transcriptomic profiling of embryonic endocrine cells, indicating a potential role in cell differentiation. The actual involvement of conserved endocrine genes in pancreatic cell differentiation was demonstrated in zebrafish for myt1b, whose invalidation leads to a reduction of alpha cells, and for cdx4, selectively expressed in endocrine delta cells and crucial for their specification. Intriguingly, comparison of the endocrine alpha and beta cell subtypes from human, mouse, and zebrafish reveals a much lower conservation of the transcriptomic signatures for these two endocrine cell subtypes compared to the signatures of pan-endocrine and exocrine cells. These data suggest that the identity of the alpha and beta cells relies on a few key factors, corroborating numerous examples of inter-conversion between these two endocrine cell subtypes.ConclusionThis study highlights both evolutionary conserved and species-specific features that will help to unveil universal and fundamental regulatory pathways as well as pathways specific to human and laboratory animal models such as mouse and zebrafish.
| S-EPMC5360028 | biostudies-literature
Project description: Not available
| S-EPMC9205880 | biostudies-literature