scholarly journals Regulatory Potential of Competing Endogenous RNAs in Myotonic Dystrophies

2021 ◽  
Vol 22 (11) ◽  
pp. 6089
Author(s):  
Edyta Koscianska ◽  
Emilia Kozlowska ◽  
Agnieszka Fiszer
Keyword(s):  
Cell Fate ◽  
Regulatory Networks ◽  
Large Scale ◽  
Neuromuscular Diseases ◽  
Protein Coding ◽  
Protein Levels ◽  
Cellular Processes ◽  
General Rules ◽  
Competing Endogenous Rnas ◽  
Mirna Sponges

Non-coding RNAs (ncRNAs) have been reported to be implicated in cell fate determination and various human diseases. All ncRNA molecules are emerging as key regulators of diverse cellular processes; however, little is known about the regulatory interaction among these various classes of RNAs. It has been proposed that the large-scale regulatory network across the whole transcriptome is mediated by competing endogenous RNA (ceRNA) activity attributed to both protein-coding and ncRNAs. ceRNAs are considered to be natural sponges of miRNAs that can influence the expression and availability of multiple miRNAs and, consequently, the global mRNA and protein levels. In this review, we summarize the current understanding of the role of ncRNAs in two neuromuscular diseases, myotonic dystrophy type 1 and 2 (DM1 and DM2), and the involvement of expanded CUG and CCUG repeat-containing transcripts in miRNA-mediated RNA crosstalk. More specifically, we discuss the possibility that long repeat tracts present in mutant transcripts can be potent miRNA sponges and may affect ceRNA crosstalk in these diseases. Moreover, we highlight practical information related to innovative disease modelling and studying RNA regulatory networks in cells. Extending knowledge of gene regulation by ncRNAs, and of complex regulatory ceRNA networks in DM1 and DM2, will help to address many questions pertinent to pathogenesis and treatment of these disorders; it may also help to better understand general rules of gene expression and to discover new rules of gene control.

scholarly journals Leveraging high-powered RNA-Seq datasets to improve inference of regulatory activity in single-cell RNA-Seq data

2019 ◽  
Author(s):  
Ning Wang ◽  
Andrew E. Teschendorff
Keyword(s):  
Transcription Factors ◽  
Single Cell ◽  
Cell Fate ◽  
Regulatory Networks ◽  
Large Scale ◽  
Single Cells ◽  
Differential Expression Analysis ◽  
Dropout Rate ◽  
Rna Seq ◽  
Regulatory Activity

AbstractInferring the activity of transcription factors in single cells is a key task to improve our understanding of development and complex genetic diseases. This task is, however, challenging due to the relatively large dropout rate and noisy nature of single-cell RNA-Seq data. Here we present a novel statistical inference framework called SCIRA (Single Cell Inference of Regulatory Activity), which leverages the power of large-scale bulk RNA-Seq datasets to infer high-quality tissue-specific regulatory networks, from which regulatory activity estimates in single cells can be subsequently obtained. We show that SCIRA can correctly infer regulatory activity of transcription factors affected by high technical dropouts. In particular, SCIRA can improve sensitivity by as much as 70% compared to differential expression analysis and current state-of-the-art methods. Importantly, SCIRA can reveal novel regulators of cell-fate in tissue-development, even for cell-types that only make up 5% of the tissue, and can identify key novel tumor suppressor genes in cancer at single cell resolution. In summary, SCIRA will be an invaluable tool for single-cell studies aiming to accurately map activity patterns of key transcription factors during development, and how these are altered in disease.

scholarly journals MON-719 The Cellular and Molecular Landscape of Hypothalamic Patterning and Differentiation

2020 ◽  
Vol 4 (Supplement_1) ◽  
Author(s):  
Thomas Kim
Keyword(s):  
Cell Fate ◽  
Regulatory Networks ◽  
Large Scale ◽  
Molecular Mechanisms ◽  
Brain Atlas ◽  
Specific Gene ◽  
Cell Fate Specification ◽  
Human Hypothalamus ◽  
Fate Specification ◽  
Time Points

Abstract The hypothalamus is a central regulator of physiological homeostasis. During development, multiple transcription factors coordinate the patterning and specification of hypothalamic nuclei. However, the molecular mechanisms controlling hypothalamic patterning and cell fate specification are poorly understood. To identify genes that control these processes, we have used single-cell RNA sequencing (scRNA-Seq) to profile mouse hypothalamic gene expression across multiple developmental time points. We have further utilised scRNA-Seq to phenotype mutations in genes that play major roles in early hypothalamic patterning. To first understand hypothalamic development, hypothalami were collected at both embryonic (E10-E16, E18) and postnatal (PN4, PN8, PN14, PN45) time points. At early stages, when the bulk of hypothalamic patterning occurs (E11-E13), we observe a clear separation between mitotic progenitors and postmitotic neural precursor cells. We likewise observed clean segregation among cells expressing regional hypothalamic markers identified in previous large-scale analysis of hypothalamic development. This analysis reveals new region-specific markers and identifies candidate genes for selectively regulating patterning and cell fate specification in individual hypothalamic regions. With our rich dataset of developing mouse hypothalamus, we integrated our dataset with the Allen Brain Atlas in situ data, publicly available adult hypothalamic scRNA-Seq dataset to understand hierarchy of hypothalamic cell differentiation, as well as re-defining cell types of the hypothalamus. We next used scRNA-Seq to phenotype multiple mutant lines, including a line that has been extensively characterised as a proof of concept (Ctnnb1 overexpression), and lines that have not been characterised (Nkx2.1, Nkx2.2, Dlx1/2 deletion). We show that this approach can rapidly and comprehensively characterize mutants that have altered hypothalamic patterning, and in doing so, have identified multiple genes that simultaneously repress posterior hypothalamic identity while promoting prethalamic identity. This result supports a modified columnar model of organization for the diencephalon, where prethalamus and hypothalamus are situated in adjacent dorsal and ventral domains of the anterior diencephalon. These data serve as a resource for further studies of hypothalamic development and dysfunction, and able to delineate transcriptional regulatory networks of hypothalamic formation. Lastly, using our mouse hypothalamus as a guideline, we are comparing dataset of developing chicken, zebrafish and human hypothalamus, to identify evolutionarily conserved and divergent region-specific gene regulatory networks. We aim to use this knowledge and information of key molecular pathways of human hypothalamic development and produce human hypothalamus organoids.

scholarly journals Accurate Prediction of Kinase-Substrate Networks Using Knowledge Graphs

2019 ◽  
Author(s):  
Vít Nováček ◽  
Gavin McGauran ◽  
David Matallanas ◽  
Adrián Vallejo Blanco ◽  
Piero Conca ◽  
...  
Keyword(s):  
Cell Fate ◽  
Large Scale ◽  
Conceptual Approach ◽  
Kinase Substrate ◽  
Biochemical Processes ◽  
Cell Fate Decisions ◽  
Cellular Processes ◽  
Robust Model ◽  
Vital Cell ◽  
Knowledge Graphs

AbstractPhosphorylation of specific substrates by protein kinases is a key control mechanism for vital cell-fate decisions and other cellular processes. However, discovering specific kinase-substrate relationships is timeconsuming and often rather serendipitous. Computational predictions alleviate these challenges, but the current approaches suffer from limitations like restricted kinome coverage and inaccuracy. They also typically utilise only local features without reflecting broader interaction context. To address these limitations, we have developed an alternative predictive model. It uses statistical relational learning on top of phosphorylation networks interpreted as knowledge graphs, a simple yet robust model for representing networked knowledge. Compared to a representative selection of six existing systems, our model has the highest kinome coverage and produces biologically valid high-confidence predictions not possible with the other tools. Specifically, we have experimentally validated predictions of previously unknown phosphorylations by the LATS1, AKT1, PKA and MST2 kinases in human. Thus, our tool is useful for focusing phosphoproteomic experiments, and facilitates the discovery of new phosphorylation reactions. Our model can be accessed publicly via an easy-to-use web interface (LinkPhinder).Author SummaryLinkPhinder is a new approach to prediction of protein signalling networks based on kinase-substrate relationships that outperforms existing approaches. Phosphorylation networks govern virtually all fundamental biochemical processes in cells, and thus have moved into the centre of interest in biology, medicine and drug development. Fundamentally different from current approaches, LinkPhinder is inherently network-based and makes use of the most recent AI de-velopments. We represent existing phosphorylation data as knowledge graphs, a format for large-scale and robust knowledge representation. Training a link prediction model on such a structure leads to novel, biologically valid phosphorylation network predictions that cannot be made with competing tools. Thus our new conceptual approach can lead to establishing a new niche of AI applications in computational biology.

scholarly journals LncRNAs Regulatory Networks in Cellular Senescence

2019 ◽  
Vol 20 (11) ◽  
pp. 2615 ◽  
Author(s):  
Pavan Kumar Puvvula
Keyword(s):  
Signaling Pathways ◽  
Cell Fate ◽  
Regulatory Networks ◽  
Translation Regulation ◽  
Reading Frame ◽  
Downstream Signaling ◽  
Cellular Processes ◽  
Fate Decision ◽  
Functional Mechanisms

Long noncoding RNAs (lncRNAs) are a class of transcripts longer than 200 nucleotides with no open reading frame. They play a key role in the regulation of cellular processes such as genome integrity, chromatin organization, gene expression, translation regulation, and signal transduction. Recent studies indicated that lncRNAs are not only dysregulated in different types of diseases but also function as direct effectors or mediators for many pathological symptoms. This review focuses on the current findings of the lncRNAs and their dysregulated signaling pathways in senescence. Different functional mechanisms of lncRNAs and their downstream signaling pathways are integrated to provide a bird’s-eye view of lncRNA networks in senescence. This review not only highlights the role of lncRNAs in cell fate decision but also discusses how several feedback loops are interconnected to execute persistent senescence response. Finally, the significance of lncRNAs in senescence-associated diseases and their therapeutic and diagnostic potentials are highlighted.

scholarly journals Non-Coding RNAs and their Integrated Networks

2019 ◽  
Vol 16 (3) ◽  
Author(s):  
Peijing Zhang ◽  
Wenyi Wu ◽  
Qi Chen ◽  
Ming Chen
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Protein Coding ◽  
Integrated Networks ◽  
Cellular Processes ◽  
Sequencing Technologies ◽  
Different Types ◽  
Gene Regulatory ◽  
Non Coding Rnas ◽  
Eukaryotic Genomes

AbstractEukaryotic genomes are pervasively transcribed. Besides protein-coding RNAs, there are different types of non-coding RNAs that modulate complex molecular and cellular processes. RNA sequencing technologies and bioinformatics methods greatly promoted the study of ncRNAs, which revealed ncRNAs’ essential roles in diverse aspects of biological functions. As important key players in gene regulatory networks, ncRNAs work with other biomolecules, including coding and non-coding RNAs, DNAs and proteins. In this review, we discuss the distinct types of ncRNAs, including housekeeping ncRNAs and regulatory ncRNAs, their versatile functions and interactions, transcription, translation, and modification. Moreover, we summarize the integrated networks of ncRNA interactions, providing a comprehensive landscape of ncRNAs regulatory roles.

scholarly journals Predicting Functions of Uncharacterized Human Proteins: From Canonical to Proteoforms

Genes ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 677
Author(s):  
Ekaterina Poverennaya ◽  
Olga Kiseleva ◽  
Anastasia Romanova ◽  
Mikhail Pyatnitskiy
Keyword(s):  
Large Scale ◽  
Interaction Network ◽  
Full Potential ◽  
Protein Coding ◽  
Protein Protein Interaction ◽  
Cellular Processes ◽  
Functional Differences ◽  
Human Proteins ◽  
Alternatively Spliced ◽  
Splice Forms

Despite tremendous efforts in genomics, transcriptomics, and proteomics communities, there is still no comprehensive data about the exact number of protein-coding genes, translated proteoforms, and their function. In addition, by now, we lack functional annotation for 1193 genes, where expression was confirmed at the proteomic level (uPE1 proteins). We re-analyzed results of AP-MS experiments from the BioPlex 2.0 database to predict functions of uPE1 proteins and their splice forms. By building a protein–protein interaction network for 12 ths. identified proteins encoded by 11 ths. genes, we were able to predict Gene Ontology categories for a total of 387 uPE1 genes. We predicted different functions for canonical and alternatively spliced forms for four uPE1 genes. In total, functional differences were revealed for 62 proteoforms encoded by 31 genes. Based on these results, it can be carefully concluded that the dynamics and versatility of the interactome is ensured by changing the dominant splice form. Overall, we propose that analysis of large-scale AP-MS experiments performed for various cell lines and under various conditions is a key to understanding the full potential of genes role in cellular processes.

scholarly journals Large-scale inference of competing endogenous RNA networks with sparse partial correlation

2019 ◽  
Vol 35 (14) ◽  
pp. i596-i604 ◽  
Author(s):  
Markus List ◽  
Azim Dehghani Amirabad ◽  
Dennis Kostka ◽  
Marcel H Schulz
Keyword(s):  
Large Scale ◽  
The Cancer Genome Atlas ◽  
Supplementary Information ◽  
P Value ◽  
Protein Coding ◽  
Individual Mirnas ◽  
Competing Endogenous Rnas ◽  
Cancer Genome Atlas ◽  
Gene Regulatory ◽  
Novel Protein

AbstractMotivationMicroRNAs (miRNAs) are important non-coding post-transcriptional regulators that are involved in many biological processes and human diseases. Individual miRNAs may regulate hundreds of genes, giving rise to a complex gene regulatory network in which transcripts carrying miRNA binding sites act as competing endogenous RNAs (ceRNAs). Several methods for the analysis of ceRNA interactions exist, but these do often not adjust for statistical confounders or address the problem that more than one miRNA interacts with a target transcript.ResultsWe present SPONGE, a method for the fast construction of ceRNA networks. SPONGE uses ’multiple sensitivity correlation’, a newly defined measure for which we can estimate a distribution under a null hypothesis. SPONGE can accurately quantify the contribution of multiple miRNAs to a ceRNA interaction with a probabilistic model that addresses previously neglected confounding factors and allows fast P-value calculation, thus outperforming existing approaches. We applied SPONGE to paired miRNA and gene expression data from The Cancer Genome Atlas for studying global effects of miRNA-mediated cross-talk. Our results highlight already established and novel protein-coding and non-coding ceRNAs which could serve as biomarkers in cancer.Availability and implementationSPONGE is available as an R/Bioconductor package (doi: 10.18129/B9.bioc.SPONGE).Supplementary informationSupplementary data are available at Bioinformatics online.

scholarly journals Mutational patterns and clonal evolution from diagnosis to relapse in pediatric acute lymphoblastic leukemia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shumaila Sayyab ◽  
Anders Lundmark ◽  
Malin Larsson ◽  
Markus Ringnér ◽  
Sara Nystedt ◽  
...  
Keyword(s):  
Acute Lymphoblastic Leukemia ◽  
Large Scale ◽  
Somatic Mutations ◽  
Lymphoblastic Leukemia ◽  
Clonal Evolution ◽  
Point Mutations ◽  
Driver Genes ◽  
Protein Coding ◽  
Pediatric Acute Lymphoblastic Leukemia ◽  
Evolutionary Trajectories

AbstractThe mechanisms driving clonal heterogeneity and evolution in relapsed pediatric acute lymphoblastic leukemia (ALL) are not fully understood. We performed whole genome sequencing of samples collected at diagnosis, relapse(s) and remission from 29 Nordic patients. Somatic point mutations and large-scale structural variants were called using individually matched remission samples as controls, and allelic expression of the mutations was assessed in ALL cells using RNA-sequencing. We observed an increased burden of somatic mutations at relapse, compared to diagnosis, and at second relapse compared to first relapse. In addition to 29 known ALL driver genes, of which nine genes carried recurrent protein-coding mutations in our sample set, we identified putative non-protein coding mutations in regulatory regions of seven additional genes that have not previously been described in ALL. Cluster analysis of hundreds of somatic mutations per sample revealed three distinct evolutionary trajectories during ALL progression from diagnosis to relapse. The evolutionary trajectories provide insight into the mutational mechanisms leading relapse in ALL and could offer biomarkers for improved risk prediction in individual patients.

scholarly journals Genetic and Molecular Analysis of Region 88E9;88F2 in Drosophila melanogaster, Including the ear Gene Related to Human Factors Involved in Lineage-Specific Leukemias

Genetics ◽  
2002 ◽  
Vol 160 (3) ◽  
pp. 1051-1065
Author(s):  
Claudia B Zraly ◽  
Yun Feng ◽  
Andrew K Dingwall
Keyword(s):  
Large Scale ◽  
Drosophila Genome ◽  
Embryonic Cells ◽  
Ems Mutagenesis ◽  
Recessive Lethal ◽  
Protein Levels ◽  
Late Embryogenesis ◽  
Complementation Groups ◽  
Mammalian Genes ◽  
Map Location

Abstract We identified and characterized the Drosophila gene ear (ENL/AF9-related), which is closely related to mammalian genes that have been implicated in the onset of acute lymphoblastic and myelogenous leukemias when their products are fused as chimeras with those of human HRX, a homolog of Drosophila trithorax. The ear gene product is present in all early embryonic cells, but becomes restricted to specific tissues in late embryogenesis. We mapped the ear gene to cytological region 88E11-13, near easter, and showed that it is deleted by Df(3R)ea5022rx1, a small, cytologically invisible deletion. Annotation of the completed Drosophila genome sequence suggests that this region might contain as many as 26 genes, most of which, including ear, are not represented by mutant alleles. We carried out a large-scale noncom-plementation screen using Df(3R)ea5022rx1 and chemical (EMS) mutagenesis from which we identified sevenc novel multi-allele recessive lethal complementation groups in this region. An overlapping deficiency, Df(3R)Po4, allowed us to map several of these groups to either the proximal or the distal regions of Df(3R)ea5022rx1. One of these complementation groups likely corresponds to the ear gene as judged by map location, terminal phenotype, and reduction of EAR protein levels.

scholarly journals Integrated whole transcriptome and small RNA analysis revealed multiple regulatory networks in colorectal cancer

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hibah Shaath ◽  
Salman M. Toor ◽  
Mohamed Abu Nada ◽  
Eyad Elkord ◽  
Nehad M. Alajez
Keyword(s):  
Colorectal Cancer ◽  
Messenger Rna ◽  
Regulatory Networks ◽  
Potential Interaction ◽  
Therapeutic Interventions ◽  
Signaling Networks ◽  
Protein Coding ◽  
Paired Samples ◽  
And Migration ◽  
Whole Transcriptome

AbstractColorectal cancer (CRC) remains a global disease burden and a leading cause of cancer related deaths worldwide. The identification of aberrantly expressed messenger RNA (mRNA), long non-coding RNA (lncRNA), and microRNA (miRNA), and the resulting molecular interactions and signaling networks is essential for better understanding of CRC, identification of novel diagnostic biomarkers and potential development of therapeutic interventions. Herein, we performed microRNA (miRNA) sequencing on fifteen CRC and their non-tumor adjacent tissues and whole transcriptome RNA-Seq on six paired samples from the same cohort and identified alterations in miRNA, mRNA, and lncRNA expression. Computational analyses using Ingenuity Pathway Analysis (IPA) identified multiple activated signaling networks in CRC, including ERBB2, RABL6, FOXM1, and NFKB networks, while functional annotation highlighted activation of cell proliferation and migration as the hallmark of CRC. IPA in combination with in silico prediction algorithms and experimentally validated databases gave insight into the complex associations and interactions between downregulated miRNAs and upregulated mRNAs in CRC and vice versa. Additionally, potential interaction between differentially expressed lncRNAs such as H19, SNHG5, and GATA2-AS1 with multiple miRNAs has been revealed. Taken together, our data provides thorough analysis of dysregulated protein-coding and non-coding RNAs in CRC highlighting numerous associations and regulatory networks thus providing better understanding of CRC.

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