BackgroundHuman aneuploidy is the leading cause of early pregnancy loss, mental retardation, and multiple congenital anomalies. Due to the high mortality associated with aneuploidy, the pathophysiological mechanisms of aneuploidy syndrome remain largely unknown. Previous studies focused mostly on whether dosage compensation occurs, and the next generation transcriptomics sequencing technology RNA-seq is expected to eventually uncover the mechanisms of gene expression regulation and the related pathological phenotypes in human aneuploidy.ResultsUsing next generation transcriptomics sequencing technology RNA-seq, we profiled the transcriptomes of four human aneuploid induced pluripotent stem cell (iPSC) lines generated from monosomy × (Turner syndrome), trisomy 8 (Warkany syndrome 2), trisomy 13 (Patau syndrome), and partial trisomy 11:22 (Emanuel syndrome) as well as two umbilical cord matrix iPSC lines as euploid controls to examine how phenotypic abnormalities develop with aberrant karyotype. A total of 466 M (50-bp) reads were obtained from the six iPSC lines, and over 13,000 mRNAs were identified by gene annotation. Global analysis of gene expression profiles and functional analysis of differentially expressed (DE) genes were implemented. Over 5000 DE genes are determined between aneuploidy and euploid iPSCs respectively while 9 KEGG pathways are overlapped enriched in four aneuploidy samples.ConclusionsOur results demonstrate that the extra or missing chromosome has extensive effects on the whole transcriptome. Functional analysis of differentially expressed genes reveals that the genes most affected in aneuploid individuals are related to central nervous system development and tumorigenesis.
All eukaryotic genomes have genes with introns in variable sizes. As far as spliceosomal introns are concerned, there are at least three basic parameters to stratify introns across diverse eukaryotic taxa: size, number, and sequence context. The number parameter is highly variable in lower eukaryotes, especially among protozoan and fungal species, which ranges from less than 4% to 78% of the genes. Over greater evolutionary time scales, the number parameter undoubtedly increases as observed in higher plants and higher vertebrates, reaching greater than 12.5 exons per gene in average among mammalian genomes. The size parameter is more complex, where multiple modes appear at work. Aside from intronless genes, there are three other types of intron-containing genes: half-sized, minimal, and size-expandable introns. The half-sized introns have only been found in a limited number of genomes among protozoan and fungal lineages and the other two types are prevalent in all animal and plant genomes. Among the size-expandable introns, the sizes of plant introns are expansion-limited in that the large introns exceeding 1000 bp are fewer in numbers and transposon-free as compared to the large introns among animals, where the larger introns are filled with transposable elements and appear expansion-flexible, reaching several kilobasepairs (kbp) and even thousands of kbp in size. Most of the intron parameters can be studied as signatures of the specific splicing machineries of different eukaryotic lineages and are highly relevant to the regulation of gene expression and functionality. In particular, the transcription-splicing-export coupling of eukaryotic intron dispensing leads to a working hypothesis that all intron parameters are evolved to be efficient and function-related in processing and routing the spliced transcripts. Eukaryotic genes have introns with variable size, number, and sequence context [1][2][3][4]. The number and size parameters by and large reflect the nature and efficiency of the intron splicing machinery of particular species or lineages [5][6][7][8][9]. The context parameters are most complicated, concerning the sequence content and context of nucleotide composition (such as GC and purine contents), transposable elements, and functional elements (such as splicing enhancers and the branch point) [10][11][12]. There are at least three possibilities for the existence and the absence of introns in eukaryotic genes: intronless (no intron), small introns in fixed sizes, and large introns in variable sizes. However, the rules of these intron parameters across diverse taxa have yet to be thoroughly summarized. The spliceosomal machinery is very complex, containing different molecular complexes of proteins and RNAs, which are partitioned into both the nucleus and the cytosol [13,14].
Heterografting has long been used to enhance the chilling tolerance of temperature-sensitive crops, including watermelon, whose mechanism is known to involve bidirectional long-distance mRNA movements. Despite several studies reporting on mobile mRNA (mb-mRNA) profiles in plants, accurate identification of mb-mRNAs is challenging owing to an array of technical problems. Here, we developed a bioinformatical pipeline that took most of the known technical concerns into consideration and is considered to be a universal tool for mb-mRNA detection in heterografts. By applying this pipeline to a commercial watermelon-bottle gourd heterografting system, we detected 130 and 1144 mb-mRNAs upwardly and 167 and 1051 mb-mRNAs downwardly transmitted under normal and chilling-stress conditions, respectively. Quantitative real-time PCR indicated a high accuracy rate (88.2%) of mb-mRNA prediction with our pipeline. We further revealed that the mobility of mRNAs was not associated with their abundance. Functional annotation and classification implied that scions may convey the stress signal to the rootstock, subsequently triggering energy metabolism reprogramming and abscisic acid-mediated stress responses by upward movement of effective mRNAs, ultimately leading to enhanced chilling tolerance. This study provides a universal tool for mb-mRNA detection in plant heterografting systems and novel insights into heterografting advantages under chilling stress.
BackgroundSince PGAP (pan-genome analysis pipeline) was published in 2012, it has been widely employed in bacterial genomics research. Though PGAP has integrated several modules for pan-genomics analysis, how to properly and effectively interpret and visualize the results data is still a challenge.ResultTo well present bacterial genomic characteristics, a novel cross-platform software was developed, named PGAP-X. Four kinds of data analysis modules were developed and integrated: whole genome sequences alignment, orthologous genes clustering, pan-genome profile analysis, and genetic variants analysis. The results from these analyses can be directly visualized in PGAP-X. The modules for data visualization in PGAP-X include: comparison of genome structure, gene distribution by conservation, pan-genome profile curve and variation on genic and genomic region. Meanwhile, result data produced by other programs with similar function can be imported to be further analyzed and visualized in PGAP-X. To test the performance of PGAP-X, we comprehensively analyzed 14 Streptococcus pneumonia strains and 14 Chlamydia trachomatis. The results show that, S. pneumonia strains have higher diversity on genome structure and gene contents than C. trachomatis strains. In addition, S. pneumonia strains might have suffered many evolutionary events, such genomic rearrangements, frequent horizontal gene transfer, homologous recombination, and other evolutionary process.ConclusionBriefly, PGAP-X directly presents the characteristics of bacterial genomic diversity with different visualization methods, which could help us to intuitively understand dynamics and evolution in bacterial genomes. The source code and the pre-complied executable programs are freely available from http://pgapx.ybzhao.com.Electronic supplementary materialThe online version of this article (doi: 10.1186/s12864-017-4337-7) contains supplementary material, which is available to authorized users.
Background Ovarian cancer (OC) is a highly lethal gynecologic cancer, and it is hard to diagnose at an early stage. Clinically, there are no ovarian cancer-specific markers for early detection. Here, we demonstrate the use of cell-free DNA (cfDNA) methylomes to detect ovarian cancer, especially the early-stage OC. Experimental design Plasma from 74 epithelial ovarian cancer patients, 86 healthy volunteers, and 20 patients with benign pelvic masses was collected. The cfDNA methylomes of these samples were generated by cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq). The differentially methylated regions (DMRs) were identified by the contrasts between tumor and non-tumor groups, and the discrimination performance was evaluated with the iterative training and testing method. Results The DMRs identified for cfDNA methylomes can well discriminate tumor groups and non-tumor groups (ROC values from 0.86 to 0.98). The late-stage top 300 DMRs are more late-stage-specific and failed to detect early-stage OC. However, the early-stage markers have the potential to discriminate all-stage OCs from non-tumor samples. Conclusions This study demonstrates that cfDNA methylomes generated with cfMeDIP-seq could be used to identify OC-specific biomarkers for OC, especially early OC detection. To detect early-stage OC, the biomarkers should be directly identified from early OC plasma samples rather than mix-stage ones. Further exploration of DMRs from a k larger early-stage OC cohort is warranted.
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