scholarly journals PAREameters: computational inference of plant microRNA-mRNA targeting rules using RNA sequencing data

2019 ◽  
Author(s):  
Joshua Thody ◽  
Vincent Moulton ◽  
Irina Mohorianu
Keyword(s):  
Rna Sequencing ◽  
Target Prediction ◽  
Model Organisms ◽  
Sequencing Data ◽  
Translation Rate ◽  
Input Size ◽  
Specific Subset ◽  
Mirna Pathway ◽  
The Stability ◽  
Species Specific

ABSTRACTMicroRNAs (miRNAs) are short, non-coding RNAs that influence the translation-rate of mRNAs by directing the RNA-induced silencing complex to sequence-specific targets. In plants, this typically results in cleavage and subsequent degradation of the mRNA. This can be captured on a high-throughput scale using degradome sequencing, which supports miRNA target prediction by aligning degradation fragments to reference mRNAs enabling the identification of causal miRNA(s). The current criteria used for target prediction were inferred on experimentally validated A. thaliana interactions, which were adapted to fit that specific subset of miRNA interactions. In addition, the miRNA pathway in other organisms may have acquired specific changes, e.g. lineage-specific miRNAs or new miRNA-mRNA interactions, thus previous criteria may not be optimal. We present a new tool, PAREameters, for inferring targeting criteria from RNA sequencing datasets; the stability of inferred criteria under subsampling and the effect of input-size are discussed. We first evaluate its performance using experimentally validated miRNA-mRNA interactions in multiple A. thaliana datasets, including conserved and species-specific miRNAs. We then perform comprehensive analyses on the differences in flower miRNA-mRNA interactions in several non-model organisms and quantify the observed variations. PAREameters highlights an increase in sensitivity on most tested datasets when data-inferred criteria are used.

scholarly journals PAREameters: a tool for computational inference of plant miRNA–mRNA targeting rules using small RNA and degradome sequencing data

2020 ◽  
Vol 48 (5) ◽  
pp. 2258-2270 ◽  
Author(s):  
Joshua Thody ◽  
Vincent Moulton ◽  
Irina Mohorianu
Keyword(s):  
Small Rna ◽  
Mirna Target ◽  
Target Prediction ◽  
Model Organisms ◽  
Messenger Rnas ◽  
Mirna Target Prediction ◽  
Sequencing Data ◽  
Translation Rate ◽  
Degradome Sequencing ◽  
Using Data

Abstract MicroRNAs (miRNAs) are short, non-coding RNAs that modulate the translation-rate of messenger RNAs (mRNAs) by directing the RNA-induced silencing complex to sequence-specific targets. In plants, this typically results in cleavage and subsequent degradation of the mRNA. Degradome sequencing is a high-throughput technique developed to capture cleaved mRNA fragments and thus can be used to support miRNA target prediction. The current criteria used for miRNA target prediction were inferred on a limited number of experimentally validated A. thaliana interactions and were adapted to fit these specific interactions; thus, these fixed criteria may not be optimal across all datasets (organisms, tissues or treatments). We present a new tool, PAREameters, for inferring targeting criteria from small RNA and degradome sequencing datasets. We evaluate its performance using a more extensive set of experimentally validated interactions in multiple A. thaliana datasets. We also perform comprehensive analyses to highlight and quantify the differences between subsets of miRNA–mRNA interactions in model and non-model organisms. Our results show increased sensitivity in A. thaliana when using the PAREameters inferred criteria and that using data-driven criteria enables the identification of additional interactions that further our understanding of the RNA silencing pathway in both model and non-model organisms.

scholarly journals Molecular and phenotypic analysis of rodent models reveals conserved and species-specific modulators of human sarcopenia

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Anastasiya Börsch ◽  
Daniel J. Ham ◽  
Nitish Mittal ◽  
Lionel A. Tintignac ◽  
Eugenia Migliavacca ◽  
...  
Keyword(s):  
Muscle Mass ◽  
Inflammatory Responses ◽  
Molecular Data ◽  
Model Organisms ◽  
Sequencing Data ◽  
Phenotypic Analysis ◽  
Age Related ◽  
Analogous Data ◽  
Species Specific ◽  
And Function

AbstractSarcopenia, the age-related loss of skeletal muscle mass and function, affects 5–13% of individuals aged over 60 years. While rodents are widely-used model organisms, which aspects of sarcopenia are recapitulated in different animal models is unknown. Here we generated a time series of phenotypic measurements and RNA sequencing data in mouse gastrocnemius muscle and analyzed them alongside analogous data from rats and humans. We found that rodents recapitulate mitochondrial changes observed in human sarcopenia, while inflammatory responses are conserved at pathway but not gene level. Perturbations in the extracellular matrix are shared by rats, while mice recapitulate changes in RNA processing and autophagy. We inferred transcription regulators of early and late transcriptome changes, which could be targeted therapeutically. Our study demonstrates that phenotypic measurements, such as muscle mass, are better indicators of muscle health than chronological age and should be considered when analyzing aging-related molecular data.

scholarly journals Genome-scale metabolic network reconstruction of model animals as a platform for translational research

2021 ◽  
Vol 118 (30) ◽  
pp. e2102344118
Author(s):  
Hao Wang ◽  
Jonathan L. Robinson ◽  
Pinar Kocabas ◽  
Johan Gustafsson ◽  
Mihail Anton ◽  
...  
Keyword(s):  
Transgenic Mice ◽  
Metabolic Network ◽  
Model Organisms ◽  
Protein Overexpression ◽  
Sequencing Data ◽  
Proteomics Data ◽  
Gm2 Ganglioside ◽  
Species Specific ◽  
Specific Reactions ◽  
Genome Scale

Genome-scale metabolic models (GEMs) are used extensively for analysis of mechanisms underlying human diseases and metabolic malfunctions. However, the lack of comprehensive and high-quality GEMs for model organisms restricts translational utilization of omics data accumulating from the use of various disease models. Here we present a unified platform of GEMs that covers five major model animals, including Mouse1 (Mus musculus), Rat1 (Rattus norvegicus), Zebrafish1 (Danio rerio), Fruitfly1 (Drosophila melanogaster), and Worm1 (Caenorhabditis elegans). These GEMs represent the most comprehensive coverage of the metabolic network by considering both orthology-based pathways and species-specific reactions. All GEMs can be interactively queried via the accompanying web portal Metabolic Atlas. Specifically, through integrative analysis of Mouse1 with RNA-sequencing data from brain tissues of transgenic mice we identified a coordinated up-regulation of lysosomal GM2 ganglioside and peptide degradation pathways which appears to be a signature metabolic alteration in Alzheimer’s disease (AD) mouse models with a phenotype of amyloid precursor protein overexpression. This metabolic shift was further validated with proteomics data from transgenic mice and cerebrospinal fluid samples from human patients. The elevated lysosomal enzymes thus hold potential to be used as a biomarker for early diagnosis of AD. Taken together, we foresee that this evolving open-source platform will serve as an important resource to facilitate the development of systems medicines and translational biomedical applications.

scholarly journals hnRNPK-regulated LINC00263 promotes malignant phenotypes through miR-147a/CAPN2

2021 ◽  
Vol 12 (4) ◽  
Author(s):  
Woo Joo Lee ◽  
Chang Hoon Shin ◽  
Haein Ji ◽  
Seong Dong Jeong ◽  
Mi-So Park ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Cancer Progression ◽  
Rna Binding ◽  
Critical Role ◽  
Target Prediction ◽  
Direct Interaction ◽  
Metastatic Potential ◽  
Luciferase Reporter ◽  
Small Rna Sequencing ◽  
Sequencing Data

AbstractMalignant characteristics of cancers, represented by rapid cell proliferation and high metastatic potential, are a major cause of high cancer-related mortality. As a multifunctional RNA-binding protein, heterogeneous nuclear ribonucleoprotein K (hnRNPK) is closely associated with cancer progression in various types of cancers. In this study, we sought to identify hnRNPK-regulated long intergenic non-coding RNAs (lincRNAs) that play a critical role in the regulation of cancer malignancy. We found that hnRNPK controlled malignant phenotypes including invasiveness, proliferation, and clonogenicity. RNA sequencing and functional studies revealed that LINC00263, a novel target of hnRNPK, is involved in the oncogenic functions of hnRNPK. Knockdown of LINC00263 mitigated the malignant capabilities. Conversely, increased malignant phenotypes were observed in LINC00263-overexpressing cells. Since LINC00263 was mainly localized in the cytosol and highly enriched in Argonaute 2-immunoprecipitation (Ago2-IP), we hypothesized that LINC00263 acts as a competitive endogenous RNA (ceRNA), and thus sought to identify LINC00263-associated microRNAs. Using small RNA sequencing followed by antisense oligonucleotide pull-down, miR-147a was selected for further study. We found that miR-147a negatively regulates LINC00263 via direct interaction, thus suppressing malignant capabilities. Moreover, knockdown of hnRNPK and LINC00263 upregulated miR-147a, indicating that LINC00263 serves as a ceRNA for miR-147a. By analyzing RNA sequencing data and miRNA target prediction, calpain 2 (CAPN2) was identified as a putative target of miR-147a. Ago2-IP and luciferase reporter assay revealed that miR-147a suppressed CAPN2 expression by directly binding to the 3′UTR of CAPN2 mRNA. In addition, we found that the weakened malignant capabilities following knockdown of hnRNPK or LINC00263 were restored by miR-147a inhibition or CAPN2 overexpression. Furthermore, our findings were validated in various other types of cancer cells including lung cancer, colorectal cancer, neuroblastoma, and melanoma. Collectively, we demonstrate that hnRNPK-regulated LINC00263 plays an important role in cancer malignancy by acting as a miR-147a decoy and thus upregulating CAPN2.

scholarly journals Analysis of differences in the stability of proteins encoded in mitochondrial DNA of model organisms

2020 ◽  
Vol 76 (01) ◽  
pp. 6340-2020
Author(s):  
KRZYSZTOF KOWAL ◽  
ADAM BOWNIK ◽  
ANGELIKA TKACZYK ◽  
BRYGIDA ŚLASKA
Keyword(s):  
Mitochondrial Dna ◽  
Specific Protein ◽  
Amino Acid Sequences ◽  
Mitochondrial Proteins ◽  
Model Organisms ◽  
Free Oxygen Radicals ◽  
Phylogenetic Groups ◽  
Protein Instability ◽  
The Stability ◽  
Species Specific

The aim of this study was to describe the stability of proteins encoded in mtDNA, which are part of the OXPHOS system, in different model organisms and to define why certain proteins are more prone to be unstable than others. The in silico analyses involved 155 reference sequences of all proteins encoded in the mitochondrial DNA in twelve model organisms representing different phylogenetic groups. The amino acid sequences of the proteins were taken from the GenPept database. The bioinformatic analyses were performed in the ProtParam program. Thirty-eight of the 155 analyzed proteins exhibited instability. The greatest numbers of unstable mitochondrial proteins were detected in H. sapiens and A. mexicanum and the lowest levels were found in C. elegans. ND1 and ATP8 were the most unstable mitochondrial proteins. Proteins COX1 and COX3 did not exhibit instability in the examined group of organisms. The highest instability index values were recorded in the case of protein ATP8. Protein ND1 turned out to be stable in the representatives of the class invertebrates. The preliminary results of the pioneer investigations indicate that the type and number of unstable proteins encoded in mtDNA was species specific. Protein instability in lower organisms may be associated with resistance to oxidative stress. In higher organisms, in turn, protein instability may be related to the physiological production of free oxygen radicals, which play multiple roles in metabolic processes. The phenomenon of instability in the respiratory chain proteins may have a strategic function although it appears to be detrimental to the stability of the protein structure per se.

scholarly journals HEDGEHOG/GLI Modulates the PRR11-SKA2 Bidirectional Transcription Unit in Lung Squamous Cell Carcinomas

Genes ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 120
Author(s):  
Yiyun Sun ◽  
Dandan Xu ◽  
Chundong Zhang ◽  
Yitao Wang ◽  
Lian Zhang ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Squamous Cell ◽  
Gene Pair ◽  
Gene List ◽  
Ectopic Expression ◽  
Lung Squamous Cell Carcinoma ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Sequencing Data ◽  
Gene Set

We previously demonstrated that proline-rich protein 11 (PRR11) and spindle and kinetochore associated 2 (SKA2) constituted a head-to-head gene pair driven by a prototypical bidirectional promoter. This gene pair synergistically promoted the development of non-small cell lung cancer. However, the signaling pathways leading to the ectopic expression of this gene pair remains obscure. In the present study, we first analyzed the lung squamous cell carcinoma (LSCC) relevant RNA sequencing data from The Cancer Genome Atlas (TCGA) database using the correlation analysis of gene expression and gene set enrichment analysis (GSEA), which revealed that the PRR11-SKA2 correlated gene list highly resembled the Hedgehog (Hh) pathway activation-related gene set. Subsequently, GLI1/2 inhibitor GANT-61 or GLI1/2-siRNA inhibited the Hh pathway of LSCC cells, concomitantly decreasing the expression levels of PRR11 and SKA2. Furthermore, the mRNA expression profile of LSCC cells treated with GANT-61 was detected using RNA sequencing, displaying 397 differentially expressed genes (203 upregulated genes and 194 downregulated genes). Out of them, one gene set, including BIRC5, NCAPG, CCNB2, and BUB1, was involved in cell division and interacted with both PRR11 and SKA2. These genes were verified as the downregulated genes via RT-PCR and their high expression significantly correlated with the shorter overall survival of LSCC patients. Taken together, our results indicate that GLI1/2 mediates the expression of the PRR11-SKA2-centric gene set that serves as an unfavorable prognostic indicator for LSCC patients, potentializing new combinatorial diagnostic and therapeutic strategies in LSCC.

RNA Sequencing Data from Human Intracranial Aneurysm Tissue Reveals a Complex Inflammatory Environment Associated with Rupture

2021 ◽  
Author(s):  
Vincent M. Tutino ◽  
Haley R. Zebraski ◽  
Hamidreza Rajabzadeh-Oghaz ◽  
Lee Chaves ◽  
Adam A. Dmytriw ◽  
...  
Keyword(s):  
Intracranial Aneurysm ◽  
Rna Sequencing ◽  
Sequencing Data

scholarly journals Improvement, identification, and target prediction for miRNAs in the porcine genome by using massive, public high-throughput sequencing data

2021 ◽  
Vol 99 (2) ◽  
Author(s):  
Yuhua Fu ◽  
Pengyu Fan ◽  
Lu Wang ◽  
Ziqiang Shu ◽  
Shilin Zhu ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
Target Genes ◽  
Target Prediction ◽  
Large Data ◽  
Sequencing Data ◽  
Regulate Gene Expression ◽  
High Throughput Sequencing Data ◽  
Annotation Information ◽  
Public Data ◽  
Broad Variety

Abstract Despite the broad variety of available microRNA (miRNA) research tools and methods, their application to the identification, annotation, and target prediction of miRNAs in nonmodel organisms is still limited. In this study, we collected nearly all public sRNA-seq data to improve the annotation for known miRNAs and identify novel miRNAs that have not been annotated in pigs (Sus scrofa). We newly annotated 210 mature sequences in known miRNAs and found that 43 of the known miRNA precursors were problematic due to redundant/missing annotations or incorrect sequences. We also predicted 811 novel miRNAs with high confidence, which was twice the current number of known miRNAs for pigs in miRBase. In addition, we proposed a correlation-based strategy to predict target genes for miRNAs by using a large amount of sRNA-seq and RNA-seq data. We found that the correlation-based strategy provided additional evidence of expression compared with traditional target prediction methods. The correlation-based strategy also identified the regulatory pairs that were controlled by nonbinding sites with a particular pattern, which provided abundant complementarity for studying the mechanism of miRNAs that regulate gene expression. In summary, our study improved the annotation of known miRNAs, identified a large number of novel miRNAs, and predicted target genes for all pig miRNAs by using massive public data. This large data-based strategy is also applicable for other nonmodel organisms with incomplete annotation information.

scholarly journals In-depth transcriptomic analysis of human retina reveals molecular mechanisms underlying diabetic retinopathy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kolja Becker ◽  
Holger Klein ◽  
Eric Simon ◽  
Coralie Viollet ◽  
Christian Haslinger ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Rna Sequencing ◽  
Molecular Mechanisms ◽  
Vision Loss ◽  
Ganglion Cells ◽  
Expression Profiles ◽  
Cell Types ◽  
Sequencing Data ◽  
Disease Stages ◽  
Post Transcriptional Regulation

AbstractDiabetic Retinopathy (DR) is among the major global causes for vision loss. With the rise in diabetes prevalence, an increase in DR incidence is expected. Current understanding of both the molecular etiology and pathways involved in the initiation and progression of DR is limited. Via RNA-Sequencing, we analyzed mRNA and miRNA expression profiles of 80 human post-mortem retinal samples from 43 patients diagnosed with various stages of DR. We found differentially expressed transcripts to be predominantly associated with late stage DR and pathways such as hippo and gap junction signaling. A multivariate regression model identified transcripts with progressive changes throughout disease stages, which in turn displayed significant overlap with sphingolipid and cGMP–PKG signaling. Combined analysis of miRNA and mRNA expression further uncovered disease-relevant miRNA/mRNA associations as potential mechanisms of post-transcriptional regulation. Finally, integrating human retinal single cell RNA-Sequencing data revealed a continuous loss of retinal ganglion cells, and Müller cell mediated changes in histidine and β-alanine signaling. While previously considered primarily a vascular disease, attention in DR has shifted to additional mechanisms and cell-types. Our findings offer an unprecedented and unbiased insight into molecular pathways and cell-specific changes in the development of DR, and provide potential avenues for future therapeutic intervention.

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