scholarly journals isomiRs-specific differential expression is the rule, not the exception: Are we missing hundreds of species in microRNA analysis?

2021 ◽  
Author(s):  
Eloi Schmauch ◽  
Pia Laitinen ◽  
Tiia A Turunen ◽  
Mari-Anna Vaananen ◽  
Tarja Malm ◽  
...  
Keyword(s):  
Differential Expression ◽  
Expression Analysis ◽  
Umbilical Vein ◽  
Expression Profiles ◽  
Differential Expression Analysis ◽  
Biological Significance ◽  
Complex Signal ◽  
Reference Sequences ◽  
Specific Regulation ◽  
The Impact

MicroRNAs (miRNAs) are small RNA molecules that act as regulators of gene expression through targeted mRNA degradation. They are involved in many biological and pathophysiological processes and are widely studied as potential biomarkers and therapeutics agents for human diseases, including cardiovascular disorders. Recently discovered isoforms of miRNAs (isomiRs) exist in high quantities and are very diverse. Despite having few differences with their corresponding reference miRNAs, they display specific functions and expression profiles, across tissues and conditions. However, they are still overlooked and understudied, as we lack a comprehensive view on their condition-specific regulation and impact on differential expression analysis. Here, we show that isomiRs can have major effects on differential expression analysis results, as their expression is independent of their host miRNA genes or reference sequences. We present two miRNA-seq datasets from human umbilical vein endothelial cells, and assess isomiR expression in response to senescence and compartment-specificity (nuclear/cytosolic) under hypoxia. We compare three different methods for miRNA analysis, including isomiR-specific analysis, and show that ignoring isomiRs induces major biases in differential expression. Moreover, isomiR analysis permits higher resolution of complex signal dissection, such as the impact of hypoxia on compartment localization, and differential isomiR type enrichments between compartments. Finally, we show important distribution differences across conditions, independently of global miRNA expression signals. Our results raise concerns over the quasi exclusive use of miRNA reference sequences in miRNA-seq processing and experimental assays. We hope that our work will guide future isomiR expression studies, which will correct some biases introduced by golden standard analysis, improving the resolution of such assays and the biological significance of their downstream studies.

Proper imputation of missing values in proteomics datasets for differential expression analysis

2020 ◽  
Author(s):  
Mingyi Liu ◽  
Ashok Dongre
Keyword(s):  
Differential Expression ◽  
Expression Analysis ◽  
Protein Identification ◽  
Missing Values ◽  
Tissue Sample ◽  
Differential Expression Analysis ◽  
Real Life ◽  
Label Free ◽  
Imputation Methods ◽  
The Impact

Abstract Label-free shotgun proteomics is an important tool in biomedical research, where tandem mass spectrometry with data-dependent acquisition (DDA) is frequently used for protein identification and quantification. However, the DDA datasets contain a significant number of missing values (MVs) that severely hinders proper analysis. Existing literature suggests that different imputation methods should be used for the two types of MVs: missing completely at random or missing not at random. However, the simulated or biased datasets utilized by most of such studies offer few clues about the composition and thus proper imputation of MVs in real-life proteomic datasets. Moreover, the impact of imputation methods on downstream differential expression analysis—a critical goal for many biomedical projects—is largely undetermined. In this study, we investigated public DDA datasets of various tissue/sample types to determine the composition of MVs in them. We then developed simulated datasets that imitate the MV profile of real-life datasets. Using such datasets, we compared the impact of various popular imputation methods on the analysis of differentially expressed proteins. Finally, we make recommendations on which imputation method(s) to use for proteomic data beyond just DDA datasets.

Optimal consistency in microRNA expression analysis using reference-gene-based normalization

2015 ◽  
Vol 11 (5) ◽  
pp. 1235-1240 ◽  
Author(s):  
Xi Wang ◽  
Erin J. Gardiner ◽  
Murray J. Cairns
Keyword(s):  
Differential Expression ◽  
Reference Gene ◽  
Expression Analysis ◽  
Expression Profiles ◽  
Differential Expression Analysis ◽  
Microrna Expression

Reference gene-based normalization of expression profiles secures consistent differential expression analysis between samples of different phenotypes or biological conditions, and facilitates comparison between experimental batches.

scholarly journals Cellular Model of CEBPA N321D Captures Gene Expression Profile in the Transition to Pre-Leukaemic Status

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 3922-3922
Author(s):  
Moosa Qureshi ◽  
Wajid Jawaid ◽  
Fernando J Calero-Nieto ◽  
Rebecca Hannah ◽  
Sarah J Kinston ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Expression Analysis ◽  
Transcriptional Regulatory Network ◽  
Expression Profiles ◽  
Expression System ◽  
Differential Expression Analysis ◽  
Inducible Expression ◽  
Cellular Model ◽  
Inducible Expression System

Abstract Background C/EBPα plays a pivotal role in myeloid differentiation at the CMP to GMP transition point, where it interacts with other transcription factors (TFs) implicated in haematopoiesis. CEBPA mutations are common in acute myeloid leukaemia (AML), predominantly in patients with M1 and M2 French-American-British (FAB) morphological classifications, but relatively little is understood about the pre-leukaemic alterations caused by mutated CEBPA. Murine models have established N321D as a particularly potent CEBPA mutation which causes AML with high mortality (Togami et al, Experimental Hematology, 2015). We aimed to develop an inducible expression system for CEBPA N321D in a cellular model which replicates early haematopoietic progenitors, to study the effects of this mutation on gene expression profiles relevant for malignant haematopoiesis. Methods We constructed a Piggy-bac Tet-on inducible expression system which has a 2A peptide mechanism enabling simultaneous expression of both N321D and mCherry fluorescent protein from the same transcript (Fig. 1). We also constructed a control with inducible expression of mCherry. These two plasmids were then transfected into the mouse progenitor cell line Hoxb8-FL (Redeckeet al, Nature Methods, 2013), which is conditionally immortalized and models multipotent myelo-lymphoid progenitors. Single cell clones were established and selected for analysis on the basis of cell growth and mCherry fluorescence on induction. RNA was collected post-induction and without induction at 24, 48 and 72 hours in two replicates each from the N321D clone and from the empty control vector. RNA-seq data was aligned to the mouse genome using STAR aligner, processed to generate high throughput sequencing counts, and finally differential expression analysis was performed between N321D and the control. Results Differential expression analysis identified 172 downregulated and 60 upregulated genes after N321D induction. Further analysis of the 172 downregulated genes against online published datasets of gene expression (Gene Expression Commons, https://gexc.stanford.edu), revealed that 19 of these genes are normally upregulated at the CMP to GMP transition. These include genes such as Hck, Met, Hdac8 and Kdm7a which have been previously implicated in haematological malignancy and which may provide novel insights into the leukaemic process fostered by the CEBPA N321D mutation. To further validate our data, we performed unsupervised hierarchical clustering of previously published microarray data from a large collection of over 400 AML expression profiles (Verhaaket al, Haematologica, 2009) using the genes identified in our study, and found that patient samples who had predominantly FAB classifications M1 and M2 clustered together (Fig. 2A,B), as would be expected in CEBPA-mutated AML. Conclusions Our inducible expression system has the potential to provide novel insights into altered gene expression caused by induction of mutated CEBPA. In particular, our cellular model replicates an early stage of haematopoiesis, and implicates genes which were not previously known to interact with CEBPA. The importance of these genes in CEBPA N321D-mediated re-configuration of the myeloid transcriptional regulatory network requires further analysis. Disclosures No relevant conflicts of interest to declare.

scholarly journals Expression profiling of ileal mucosa in asthma reveals upregulation of innate immunity and genes characteristic of Paneth and goblet cells

2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Jan K. Nowak ◽  
Marzena Dworacka ◽  
Nazgul Gubaj ◽  
Arystan Dossimov ◽  
Zhumabek Dossimov ◽  
...  
Keyword(s):  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Differential Expression ◽  
Expression Profiles ◽  
Differential Expression Analysis ◽  
Goblet Cells ◽  
Ileal Mucosa ◽  
Significant Differential Expression ◽  
Immune Genes ◽  
Asthma Patients

Abstract Background The expression profiles of the intestinal mucosa have not been comprehensively investigated in asthma. We aimed to explore this in the Correlated Expression and Disease Association Research (CEDAR) patient cohort. Methods Differential expression analysis of ileal, transverse colon, and rectal biopsies were supplemented by a comparison of transcriptomes from platelets and leukocytes subsets, including CD4+, CD8+, CD14+, CD15+, and CD19+ cells. Asthma patients (n = 15) and controls (n = 15) had similar age (p = 0.967), body mass index (p = 0.870), similar numbers of females (80%) and smoking rates (13.3%). Results Significant differential expression was found in the ileum alone, and not in any other cell/tissue types. More genes were found to be overexpressed (1,150) than under-expressed (380). The most overexpressed genes included Fc Fragment of IgG Binding Protein (FCGBP, logFC = 3.01, pFDR = 0.015), Mucin 2 (MUC2, logFC = 2.78, pFDR = 0.015), and Alpha 1B Defensin (DEFA1B, logFC = 2.73, pFDR = 0.024). Gene ontology implicated the immune system, including interleukins 4 and 13, as well as antimicrobial peptides in this overexpression. There was concordance of gene over- (STAT1, XBP1) and underexpression (NELF, RARA) in asthma and Crohn’s disease ileum when our results were compared to another dataset (p = 3.66 × 10–7). Conclusion Ileal mucosa in asthma exhibits a specific transcriptomic profile, which includes the overexpression of innate immune genes, mostly characteristic of Paneth and goblet cells, in addition to other changes that may resemble Crohn’s disease.

scholarly journals Best practices on the differential expression analysis of multi-species RNA-seq

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Matthew Chung ◽  
Vincent M. Bruno ◽  
David A. Rasko ◽  
Christina A. Cuomo ◽  
José F. Muñoz ◽  
...  
Keyword(s):  
Best Practices ◽  
Differential Expression ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Single Species ◽  
Rna Seq ◽  
Species Analysis ◽  
Differential Gene ◽  
Multiple Species ◽  
Downstream Analysis

AbstractAdvances in transcriptome sequencing allow for simultaneous interrogation of differentially expressed genes from multiple species originating from a single RNA sample, termed dual or multi-species transcriptomics. Compared to single-species differential expression analysis, the design of multi-species differential expression experiments must account for the relative abundances of each organism of interest within the sample, often requiring enrichment methods and yielding differences in total read counts across samples. The analysis of multi-species transcriptomics datasets requires modifications to the alignment, quantification, and downstream analysis steps compared to the single-species analysis pipelines. We describe best practices for multi-species transcriptomics and differential gene expression.

scholarly journals Characterization of the nuclear and cytosolic transcriptomes in human brain tissue reveals new insights into the subcellular distribution of RNA transcripts

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ammar Zaghlool ◽  
Adnan Niazi ◽  
Åsa K. Björklund ◽  
Jakub Orzechowski Westholm ◽  
Adam Ameur ◽  
...  
Keyword(s):  
Human Brain ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Adult Brain ◽  
Sequencing Data ◽  
Human Brain Tissue ◽  
Protein Coding ◽  
Rna Transcripts ◽  
Nuclear Rna ◽  
The Impact

AbstractTranscriptome analysis has mainly relied on analyzing RNA sequencing data from whole cells, overlooking the impact of subcellular RNA localization and its influence on our understanding of gene function, and interpretation of gene expression signatures in cells. Here, we separated cytosolic and nuclear RNA from human fetal and adult brain samples and performed a comprehensive analysis of cytosolic and nuclear transcriptomes. There are significant differences in RNA expression for protein-coding and lncRNA genes between cytosol and nucleus. We show that transcripts encoding the nuclear-encoded mitochondrial proteins are significantly enriched in the cytosol compared to the rest of protein-coding genes. Differential expression analysis between fetal and adult frontal cortex show that results obtained from the cytosolic RNA differ from results using nuclear RNA both at the level of transcript types and the number of differentially expressed genes. Our data provide a resource for the subcellular localization of thousands of RNA transcripts in the human brain and highlight differences in using the cytosolic or the nuclear transcriptomes for expression analysis.

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