scholarly journals Maturation State-Specific Alternative Splicing in FLT3-ITD and NPM1 Mutated AML

Cancers ◽  
2021 ◽  
Vol 13 (16) ◽  
pp. 3929
Author(s):  
Anna Wojtuszkiewicz ◽  
Inge van der Werf ◽  
Stephan Hutter ◽  
Wencke Walter ◽  
Constance Baer ◽  
...  
Keyword(s):  
Functional Divergence ◽  
Cell Cycle Control ◽  
Specific Gene ◽  
Differential Splicing ◽  
Sequencing Data ◽  
Gene Sets ◽  
Substantial Progress ◽  
Alternatively Spliced ◽  
Specific Alternative ◽  
Maturation State

Despite substantial progress achieved in unraveling the genetics of AML in the past decade, its treatment outcome has not substantially improved. Therefore, it is important to better understand how genetic mutations translate to phenotypic features of AML cells to further improve response predictions and to find innovative therapeutic approaches. In this respect, aberrant splicing is a crucial contributor to the pathogenesis of hematological malignancies. Thus far, altered splicing is well characterized in relation to splicing factor mutations in AML. However, splicing profiles associated with mutations in other genes remain largely unexplored. In this study, we explored differential splicing profiles associated with two of the most common aberrations in AML: FLT3-ITD and NPM1 mutations. Using RNA-sequencing data of a total of 382 primary AML samples, we found that the co-occurrence of FLT3-ITD and mutated NPM1 is associated with differential splicing of FAB-type specific gene sets. Despite the FAB-type specificity of particular gene sets, the primary functions perturbed by differential splicing in all three FAB types include cell cycle control and DNA damage response. Interestingly, we observed functional divergence between alternatively spliced and differentially expressed genes in FLT3-ITD+/NPM1+ samples in all analyzed FAB types, with differential expression affecting genes involved in hematopoietic differentiation. Altogether, these observations indicate that concomitant FLT3-ITD and mutated NPM1 are associated with the maturation state-specific differential splicing of genes with potential oncogenic relevance.

scholarly journals Unsupervised cell functional annotation for single-cell RNA-Seq

2021 ◽  
Author(s):  
Dongshunyi Li ◽  
Jun Ding ◽  
Ziv Bar-Joseph
Keyword(s):  
Single Cell ◽  
Dimensional Space ◽  
Cell Types ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Sequencing Data ◽  
Gene Sets ◽  
Supervised Methods ◽  
Low Dimensional

One of the first steps in the analysis of single cell RNA-Sequencing data (scRNA-Seq) is the assignment of cell types. While a number of supervised methods have been developed for this, in most cases such assignment is performed by first clustering cells in low-dimensional space and then assigning cell types to different clusters. To overcome noise and to improve cell type assignments we developed UNIFAN, a neural network method that simultaneously clusters and annotates cells using known gene sets. UNIFAN combines both, low dimension representation for all genes and cell specific gene set activity scores to determine the clustering. We applied UNIFAN to human and mouse scRNA-Seq datasets from several different organs. As we show, by using knowledge on gene sets, UNIFAN greatly outperforms prior methods developed for clustering scRNA-Seq data. The gene sets assigned by UNIFAN to different clusters provide strong evidence for the cell type that is represented by this cluster making annotations easier.

scholarly journals A primer for generating and using transcriptome data and gene sets

Development ◽  
2020 ◽  
Vol 147 (24) ◽  
pp. dev193854
Author(s):  
Chad Cockrum ◽  
Kiyomi R. Kaneshiro ◽  
Andreas Rechtsteiner ◽  
Tomoko M. Tabuchi ◽  
Susan Strome
Keyword(s):  
Gene Expression ◽  
Rna Sequencing ◽  
High Throughput ◽  
Single Cells ◽  
Transcript Abundance ◽  
Specific Gene ◽  
Transcriptome Data ◽  
Sequencing Data ◽  
Gene Sets ◽  
Genome Wide

ABSTRACTTranscriptomic approaches have provided a growing set of powerful tools with which to study genome-wide patterns of gene expression. Rapidly evolving technologies enable analysis of transcript abundance data from particular tissues and even single cells. This Primer discusses methods that can be used to collect and profile RNAs from specific tissues or cells, process and analyze high-throughput RNA-sequencing data, and define sets of genes that accurately represent a category, such as tissue-enriched or tissue-specific gene expression.

scholarly journals Effect of Different Expression of Immune-Related lncRNA on Colon Adenocarcinoma and Its Relation to Prognosis

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Meiwei Mu ◽  
Yi Tang ◽  
Zheng Yang ◽  
Yuling Qiu ◽  
Xiaohong Li ◽  
...  
Keyword(s):  
Cox Regression ◽  
Colon Adenocarcinoma ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Future Research ◽  
Sequencing Data ◽  
Tissue Samples ◽  
Gene Sets ◽  
Cancer Genome Atlas ◽  
Immune Related Genes

Objective. To explore the expression of immune-related lncRNAs in colon adenocarcinoma and find out the effect on how these lncRNAs influence the development and prognosis of colon adenocarcinoma. Method. Transcriptome data of colon adenocarcinoma from The Cancer Genome Atlas (TCGA) were downloaded, and gene sets “IMMUNE RESPONSE” and “IMMUNE SYSTEM PROCESS” were sought from the Molecular Signatures Database (MSigDB). The expression of immune-related genes was extracted that were immune-related mRNAs. Then, the immune-related lncRNAs were sought out by utilizing of the above data. Clinical traits were combined with immune-related lncRNAs, so that prognostic-related lncRNAs were identified by Cox regression. Multivariate Cox regression was built to calculate risk scores. Relationships between clinical traits and immune-related lncRNAs were also calculated. Result. A total of 480 colorectal adenocarcinoma patients and 41 normal control patients’ transcriptome sequencing data of tissue samples were obtained from TCGA database. 918 immune-related lncRNAs were screened. Cox regression showed that 34 immune-related lncRNAs were associated with colon adenocarcinoma prognosis. Seven lncRNAs were independent risk factors. Conclusion. This study revealed that some lncRNAs can affect the development and prognosis of colon adenocarcinoma. It may provide new theory evidence of molecular mechanism for the future research and molecular targeted therapy of colon adenocarcinoma.

scholarly journals A survey of computational methods in transcriptome-wide alternative splicing analysis

2015 ◽  
Vol 6 (1) ◽  
pp. 59-66 ◽  
Author(s):  
Jianbo Wang ◽  
Zhenqing Ye ◽  
Tim H.-M. Huang ◽  
Huidong Shi ◽  
Victor Jin
Keyword(s):  
Alternative Splicing ◽  
Experimental Validation ◽  
Gene Diversity ◽  
Exon Skipping ◽  
Differential Splicing ◽  
Sequencing Data ◽  
Future Directions ◽  
Specific Analysis ◽  
Software Implementations ◽  
Expected Outcomes

AbstractAlternative splicing is widely recognized for its roles in regulating genes and creating gene diversity. Consequently the identification and quantification of differentially spliced transcripts is pivotal for transcriptome analysis. Here, we review the currently available computational approaches for the analysis of RNA-sequencing data with a focus on exon-skipping events of alternative splicing and discuss the novelties as well as challenges faced to perform differential splicing analyses. In accordance with operational needs we have classified the software tools, which may be instrumental for a specific analysis based on the experimental objectives and expected outcomes. In addition, we also propose a framework for future directions by pinpointing more extensive experimental validation to assess the accuracy of the software predictions and improvements that would facilitate visualizations, data processing, and downstream analyses along with their associated software implementations.

scholarly journals Improving gene function predictions using independent transcriptional components

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Carlos G. Urzúa-Traslaviña ◽  
Vincent C. Leeuwenburgh ◽  
Arkajyoti Bhattacharya ◽  
Stefan Loipfinger ◽  
Marcel A. T. M. van Vugt ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
High Throughput Sequencing ◽  
Principal Component ◽  
Component Analysis ◽  
Independent Component ◽  
Sequencing Data ◽  
New Members ◽  
High Throughput Sequencing Data ◽  
Gene Sets ◽  
Functional Understanding

AbstractThe interpretation of high throughput sequencing data is limited by our incomplete functional understanding of coding and non-coding transcripts. Reliably predicting the function of such transcripts can overcome this limitation. Here we report the use of a consensus independent component analysis and guilt-by-association approach to predict over 23,000 functional groups comprised of over 55,000 coding and non-coding transcripts using publicly available transcriptomic profiles. We show that, compared to using Principal Component Analysis, Independent Component Analysis-derived transcriptional components enable more confident functionality predictions, improve predictions when new members are added to the gene sets, and are less affected by gene multi-functionality. Predictions generated using human or mouse transcriptomic data are made available for exploration in a publicly available web portal.

FC 011KIDNEYNETWORK: USING KIDNEY DERIVED GENE EXPRESSION DATA TO PREDICT AND PRIORITIZE NOVEL GENES INVOLVED IN KIDNEY DISEASE

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
Floranne Boulogne ◽  
Laura Claus ◽  
Henry Wiersma ◽  
Roy Oelen ◽  
Floor Schukking ◽  
...  
Keyword(s):  
Gene Expression ◽  
Kidney Disease ◽  
Candidate Gene ◽  
Exome Sequencing ◽  
Rna Sequencing ◽  
Expression Patterns ◽  
Genetic Diagnosis ◽  
Specific Gene ◽  
Sequencing Data ◽  
Exome Sequencing Data

Abstract Background and Aims Genetic testing in patients with suspected hereditary kidney disease does not always reveal the genetic cause for the patient's disorder. Potentially pathogenic variants can reside in genes that are not known to be involved in kidney disease, which makes it difficult to prioritize and interpret the relevance of these variants. As such, there is a clear need for methods that predict the phenotypic consequences of gene expression in a way that is as unbiased as possible. To help identify candidate genes we have developed KidneyNetwork, in which tissue-specific expression is utilized to predict kidney-specific gene functions. Method We combined gene co-expression in 878 publicly available kidney RNA-sequencing samples with the co-expression of a multi-tissue RNA-sequencing dataset of 31,499 samples to build KidneyNetwork. The expression patterns were used to predict which genes have a kidney-related function, and which (disease) phenotypes might be caused when these genes are mutated. By integrating the information from the HPO database, in which known phenotypic consequences of disease genes are annotated, with the gene co-expression network we obtained prediction scores for each gene per HPO term. As proof of principle, we applied KidneyNetwork to prioritize variants in exome-sequencing data from 13 kidney disease patients without a genetic diagnosis. Results We assessed the prediction performance of KidneyNetwork by comparing it to GeneNetwork, a multi-tissue co-expression network we previously developed. In KidneyNetwork, we observe a significantly improved prediction accuracy of kidney-related HPO-terms, as well as an increase in the total number of significantly predicted kidney-related HPO-terms (figure 1). To examine its clinical utility, we applied KidneyNetwork to 13 patients with a suspected hereditary kidney disease without a genetic diagnosis. Based on the HPO terms “Renal cyst” and “Hepatic cysts”, combined with a list of potentially damaging variants in one of the undiagnosed patients with mild ADPKD/PCLD, we identified ALG6 as a new candidate gene. ALG6 bears a high resemblance to other genes implicated in this phenotype in recent years. Through the 100,000 Genomes Project and collaborators we identified three additional patients with kidney and/or liver cysts carrying a suspected deleterious variant in ALG6. Conclusion We present KidneyNetwork, a kidney specific co-expression network that accurately predicts what genes have kidney-specific functions and may result in kidney disease. Gene-phenotype associations of genes unknown for kidney-related phenotypes can be predicted by KidneyNetwork. We show the added value of KidneyNetwork by applying it to exome sequencing data of kidney disease patients without a molecular diagnosis and consequently we propose ALG6 as a promising candidate gene. KidneyNetwork can be applied to clinically unsolved kidney disease cases, but it can also be used by researchers to gain insight into individual genes to better understand kidney physiology and pathophysiology. Acknowledgments This research was made possible through access to the data and findings generated by the 100,000 Genomes Project; http://www.genomicsengland.co.uk.

scholarly journals Two Alternatively Spliced Transcripts from the Drosophila period Gene Rescue Rhythms Having Different Molecular and Behavioral Characteristics

1998 ◽  
Vol 18 (11) ◽  
pp. 6505-6514 ◽  
Author(s):  
Yuzhong Cheng ◽  
Barbara Gvakharia ◽  
Paul E. Hardin
Keyword(s):  
Type A ◽  
Behavioral Characteristics ◽  
Cdna Clones ◽  
Type B ◽  
Differential Splicing ◽  
Rnase Protection ◽  
Period Gene ◽  
Alternatively Spliced ◽  
Transgenic Flies

ABSTRACT The period (per) and timeless(tim) genes encode key components of the circadian oscillator in Drosophila melanogaster. The pergene is thought to encode three transcripts via differential splicing (types A, B, and C) that give rise to three proteins. Since the threeper mRNA types were based on the analysis of cDNA clones, we tested whether these mRNA types were present in vivo by RNase protection assays and reverse transcriptase-mediated PCR. The results show that per generates two transcript types that differ only by the presence (type A) or absence (type B′) of an alternative intron in the 3′ untranslated region. Transgenic flies containing transgenes that produce only type B′ transcripts (perB′ ), type A transcripts (perA ), or both transcripts (perG ) rescue locomotor activity rhythms with average periods of 24.7, 25.4, and 24.4 h, respectively. Although no appreciable differences in type A and type B′ mRNA cycling were observed, a slower accumulation of PER in flies making only type A transcripts suggests that the intron affects the translation ofper mRNA.

scholarly journals Genome Assemblies of the Warthog and Kenyan Domestic Pig Provide Insights into Suidae Evolution and Candidate Genes for African Swine Fever Tolerance

2021 ◽  
Author(s):  
Wen Feng ◽  
Lei Zhou ◽  
Pengju Zhao ◽  
Heng Du ◽  
Chenguang Diao ◽  
...  
Keyword(s):  
Large Scale ◽  
Genetic Resistance ◽  
African Swine Fever ◽  
Gene Families ◽  
Specific Gene ◽  
Chromosome 2 ◽  
Sequencing Data ◽  
Domestic Pig ◽  
Phacochoerus Africanus ◽  
Contraction And Expansion

As warthog (Phacochoerus africanus) has innate immunity against African swine fever (ASF), it is critical to understanding the evolutionary novelty of warthog to explain its specific ASF resistance. Here, we present two completed new genomes of one warthog and one Kenyan domestic pig, as the fundamental genomic references to decode the genetic mechanism on ASF tolerance. Our results indicated, multiple genomic variations, including gene losses, independent contraction and expansion of specific gene families, likely moulded warthog's genome to adapt the environment. Importantly, the analysis of presence and absence of genomic sequences revealed that, the warthog genome had a DNA sequence absence of the lactate dehydrogenase B (LDHB) gene on chromosome 2 compared to the reference genome. The overexpression and siRNA of LDHB indicated that its inhibition on the replication of ASFV. The Combining with large scale sequencing data of 123 pigs from all over world, contraction and expansion of TRIM genes families revealed that TRIM family genes in the warthog genome were potentially responsible for its tolerance to ASF. Our results will help further improve the understanding of genetic resistance ASF in pigs.

scholarly journals Controversial T1G3 bladder cancer is the key to revealing the changes in the biological functions of bladder cancer cells

2020 ◽  
Author(s):  
Shen Pan ◽  
Yunhong Zhan ◽  
Xiaonan Chen ◽  
Bin Wu ◽  
Bitian Liu
Keyword(s):  
Gene Expression ◽  
Bladder Cancer ◽  
Cancer Cells ◽  
Interaction Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Specific Gene ◽  
Gene Sets ◽  
Bladder Cancer Cells

Abstract Background T1G3 shows a higher chance of recurrence and progression among early bladder cancer types and the available treatment option is controversial. High recurrence and progression are the problems that need to be explored and solved. Changes in the internal signals of bladder cancer cells and differential genes may be the root cause of these problems. Methods GSE120736, GSE19915, GSE19423, GSE32548 and GSE37815 datasets were obtained from Gene Expression Omnibus (GEO ) to identify differentially expressed genes (DEGs). Bladder cancer transcript data from The Cancer Genome Atlas (TCGA) were clustered into different cell-specific gene sets according to weighted gene co-expression network analysis (WGCNA). Multiple sets of databases were used for gene expression comparison, functional enrichment, and protein interaction analysis, including The Human Protein Atlas, Cancer Dependency Map, Metascape, Gene set enrichment analysis, and DisNor. Results DEGs were obtained through GEO data comparison and intersection. After WGCNA was proven to recognise cell-specific gene sets, candidate DEGs were selected and shown to be specifically expressed in cancer cells. Candidate DEGs were related to mitosis and cell cycle. Further, 12 functional candidate markers were identified from the sequencing data of 30 bladder cancer cell lines. These genes were all up-regulated and previously shown to be closely related to bladder cancer progression. Conclusions Twelve functional genes with specific differential expression in bladder cancer cells were identified. WGCNA can identify the relatively specific expression sets of different cells in bladder cancer with greater tumour heterogeneity, which provides new perspectives for future cancer research.

scholarly journals FACS-array–based cell purification yields a specific transcriptome of striatal medium spiny neurons in a murine Huntington disease model

2020 ◽  
Vol 295 (29) ◽  
pp. 9768-9785 ◽  
Author(s):  
Haruko Miyazaki ◽  
Tomoyuki Yamanaka ◽  
Fumitaka Oyama ◽  
Yoshihiro Kino ◽  
Masaru Kurosawa ◽  
...  
Keyword(s):  
Gene Expression ◽  
Huntington Disease ◽  
Fluorescent Protein ◽  
Neurodegenerative Disorder ◽  
Disease Model ◽  
Medium Spiny Neuron ◽  
Cag Repeats ◽  
Specific Gene ◽  
Protein Marker ◽  
Gene Sets

Huntington disease (HD) is a neurodegenerative disorder caused by expanded CAG repeats in the Huntingtin gene. Results from previous studies have suggested that transcriptional dysregulation is one of the key mechanisms underlying striatal medium spiny neuron (MSN) degeneration in HD. However, some of the critical genes involved in HD etiology or pathology could be masked in a common expression profiling assay because of contamination with non-MSN cells. To gain insight into the MSN-specific gene expression changes in presymptomatic R6/2 mice, a common HD mouse model, here we used a transgenic fluorescent protein marker of MSNs for purification via FACS before profiling gene expression with gene microarrays and compared the results of this “FACS-array” with those obtained with homogenized striatal samples (STR-array). We identified hundreds of differentially expressed genes (DEGs) and enhanced detection of MSN-specific DEGs by comparing the results of the FACS-array with those of the STR-array. The gene sets obtained included genes ubiquitously expressed in both MSNs and non-MSN cells of the brain and associated with transcriptional regulation and DNA damage responses. We proposed that the comparative gene expression approach using the FACS-array may be useful for uncovering the gene cascades affected in MSNs during HD pathogenesis.

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