scholarly journals Inference of bacterial small RNA regulatory networks and integration with transcription factor driven regulatory networks

2019 ◽  
Author(s):  
Mario L. Arrieta-Ortiz ◽  
Christoph Hafemeister ◽  
Bentley Shuster ◽  
Nitin S. Baliga ◽  
Richard Bonneau ◽  
...  
Keyword(s):  
Messenger Rna ◽  
Regulatory Networks ◽  
Network Inference ◽  
Bacterial Species ◽  
Data Driven ◽  
Translation Efficiency ◽  
General Applicability ◽  
Bacterial Gene ◽  
Wide Range ◽  
Non Coding Rnas

ABSTRACTSmall non-coding RNAs (sRNAs) are key regulators of bacterial gene expression. Through complementary base pairing, sRNAs affect messenger RNA stability and translation efficiency. Here, we describe a network inference approach designed to identify sRNA-mediated regulation of transcript levels. We use existing transcriptional datasets and prior knowledge to infer sRNA regulons using our network inference tool, theInferelator. This approach produces genome-wide gene regulatory networks that include contributions by both transcription factors and sRNAs. We show the benefits of estimating and incorporating sRNA activities into network inference pipelines. We comprehensively assess the accuracy of inferred sRNA regulons using available experimental data. We uncover 30 novel experimentally supported sRNA-mRNA interactions inEscherichia coli, outperforming previous network-based efforts. Our findings expand the role of sRNAs in the regulation of chemotaxis, oxidation-reduction processes, galactose intake, and generation of pyruvate. Additionally, our pipeline complements sequence-based sRNA-mRNA interaction prediction methods by adding a data-driven filtering step. Finally, we show the general applicability of our approach by identifying novel, experimentally supported, sRNA-mRNA interactions inPseudomonas aeruginosaandBacillus subtilis. Overall, our strategy generates novel insights into the functional implications of sRNA regulation in multiple bacterial species.IMPORTANCEIndividual bacterial genomes can have dozens of small non-coding RNAs with largely unexplored regulatory functions. Although bacterial sRNAs influence a wide range of biological processes, including antibiotic resistance and pathogenicity, our current understanding of sRNA-mediated regulation is far from complete. Most of the available information is restricted to a few well-studied bacterial species; and even in those species, only partial sets of sRNA targets have been characterized in detail. To close this information gap, we developed a computational strategy that takes advantage of available transcriptional data and knowledge about validated and putative sRNA-mRNA interactions. Our approach facilitates the identification of experimentally supported novel interactions while filtering out false positives. Due to its data-driven nature, our method emerges as an ideal strategy to identify biologically relevant interactions among lists of candidate sRNA-target pairs predictedin silicofrom sequence analysis or derived from sRNA-mRNA binding experiments.

scholarly journals Inference of Bacterial Small RNA Regulatory Networks and Integration with Transcription Factor-Driven Regulatory Networks

mSystems ◽  
2020 ◽  
Vol 5 (3) ◽  
Author(s):  
Mario L. Arrieta-Ortiz ◽  
Christoph Hafemeister ◽  
Bentley Shuster ◽  
Nitin S. Baliga ◽  
Richard Bonneau ◽  
...  
Keyword(s):  
Regulatory Networks ◽  
Network Inference ◽  
Bacterial Species ◽  
Noncoding Rnas ◽  
Data Driven ◽  
Translation Efficiency ◽  
Bacterial Gene ◽  
Content Type ◽  
Small Noncoding Rnas ◽  
Wide Range

ABSTRACT Small noncoding RNAs (sRNAs) are key regulators of bacterial gene expression. Through complementary base pairing, sRNAs affect mRNA stability and translation efficiency. Here, we describe a network inference approach designed to identify sRNA-mediated regulation of transcript levels. We use existing transcriptional data sets and prior knowledge to infer sRNA regulons using our network inference tool, the Inferelator. This approach produces genome-wide gene regulatory networks that include contributions by both transcription factors and sRNAs. We show the benefits of estimating and incorporating sRNA activities into network inference pipelines using available experimental data. We also demonstrate how these estimated sRNA regulatory activities can be mined to identify the experimental conditions where sRNAs are most active. We uncover 45 novel experimentally supported sRNA-mRNA interactions in Escherichia coli, outperforming previous network-based efforts. Additionally, our pipeline complements sequence-based sRNA-mRNA interaction prediction methods by adding a data-driven filtering step. Finally, we show the general applicability of our approach by identifying 24 novel, experimentally supported, sRNA-mRNA interactions in Pseudomonas aeruginosa, Staphylococcus aureus, and Bacillus subtilis. Overall, our strategy generates novel insights into the functional context of sRNA regulation in multiple bacterial species. IMPORTANCE Individual bacterial genomes can have dozens of small noncoding RNAs with largely unexplored regulatory functions. Although bacterial sRNAs influence a wide range of biological processes, including antibiotic resistance and pathogenicity, our current understanding of sRNA-mediated regulation is far from complete. Most of the available information is restricted to a few well-studied bacterial species; and even in those species, only partial sets of sRNA targets have been characterized in detail. To close this information gap, we developed a computational strategy that takes advantage of available transcriptional data and knowledge about validated and putative sRNA-mRNA interactions for inferring expanded sRNA regulons. Our approach facilitates the identification of experimentally supported novel interactions while filtering out false-positive results. Due to its data-driven nature, our method prioritizes biologically relevant interactions among lists of candidate sRNA-target pairs predicted in silico from sequence analysis or derived from sRNA-mRNA binding experiments.

scholarly journals Non-Coding RNAs in the Brain-Heart Axis: The Case of Parkinson’s Disease

2020 ◽  
Vol 21 (18) ◽  
pp. 6513 ◽  
Author(s):  
Shubhra Acharya ◽  
Antonio Salgado-Somoza ◽  
Francesca Maria Stefanizzi ◽  
Andrew I. Lumley ◽  
Lu Zhang ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
High Throughput ◽  
Regulatory Networks ◽  
Current Knowledge ◽  
Circular Rnas ◽  
Clinical Markers ◽  
Wide Range ◽  
Non Coding Rnas ◽  
The Brain

Parkinson’s disease (PD) is a complex and heterogeneous disorder involving multiple genetic and environmental influences. Although a wide range of PD risk factors and clinical markers for the symptomatic motor stage of the disease have been identified, there are still no reliable biomarkers available for the early pre-motor phase of PD and for predicting disease progression. High-throughput RNA-based biomarker profiling and modeling may provide a means to exploit the joint information content from a multitude of markers to derive diagnostic and prognostic signatures. In the field of PD biomarker research, currently, no clinically validated RNA-based biomarker models are available, but previous studies reported several significantly disease-associated changes in RNA abundances and activities in multiple human tissues and body fluids. Here, we review the current knowledge of the regulation and function of non-coding RNAs in PD, focusing on microRNAs, long non-coding RNAs, and circular RNAs. Since there is growing evidence for functional interactions between the heart and the brain, we discuss the benefits of studying the role of non-coding RNAs in organ interactions when deciphering the complex regulatory networks involved in PD progression. We finally review important concepts of harmonization and curation of high throughput datasets, and we discuss the potential of systems biomedicine to derive and evaluate RNA biomarker signatures from high-throughput expression data.

scholarly journals Genome-Wide Identification of Long Non-Coding RNAs and Their Regulatory Networks Involved in Apis mellifera ligustica Response to Nosema ceranae Infection

Insects ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 245 ◽  
Author(s):  
Dafu Chen ◽  
Huazhi Chen ◽  
Yu Du ◽  
Dingding Zhou ◽  
Sihai Geng ◽  
...  
Keyword(s):  
Apis Mellifera ◽  
Regulatory Networks ◽  
Expression Patterns ◽  
Structural Features ◽  
Nosema Ceranae ◽  
Post Inoculation ◽  
Form Complex ◽  
Apis Mellifera Ligustica ◽  
Wide Range ◽  
Non Coding Rnas

Long non-coding RNAs (lncRNAs) are a diverse class of transcripts that structurally resemble mRNAs but do not encode proteins, and lncRNAs have been proven to play pivotal roles in a wide range of biological processes in animals and plants. However, knowledge of expression patterns and potential roles of honeybee lncRNA response to Nosema ceranae infection is completely unknown. Here, we performed whole transcriptome strand-specific RNA sequencing of normal midguts of Apis mellifera ligustica workers (Am7CK, Am10CK) and N. ceranae-inoculated midguts (Am7T, Am10T), followed by comprehensive analyses using bioinformatic and molecular approaches. A total of 6353 A. m. ligustica lncRNAs were identified, including 4749 conserved lncRNAs and 1604 novel lncRNAs. These lncRNAs had minimal sequence similarities with other known lncRNAs in other species; however, their structural features were similar to counterparts in mammals and plants, including shorter exon and intron length, lower exon number, and lower expression level, compared with protein-coding transcripts. Further, 111 and 146 N. ceranae-responsive lncRNAs were identified from midguts at 7-days post-inoculation (dpi) and 10 dpi compared with control midguts. Twelve differentially expressed lncRNAs (DElncRNAs) were shared by Am7CK vs. Am7T and Am10CK vs. Am10T comparison groups, while the numbers of unique DElncRNAs were 99 and 134, respectively. Functional annotation and pathway analysis showed that the DElncRNAs may regulate the expression of neighboring genes by acting in cis and trans fashion. Moreover, we discovered 27 lncRNAs harboring eight known miRNA precursors and 513 lncRNAs harboring 2257 novel miRNA precursors. Additionally, hundreds of DElncRNAs and their target miRNAs were found to form complex competitive endogenous RNA (ceRNA) networks, suggesting that these DElncRNAs may act as miRNA sponges. Furthermore, DElncRNA-miRNA-mRNA networks were constructed and investigated, the results demonstrated that a portion of the DElncRNAs were likely to participate in regulating the host material and energy metabolism as well as cellular and humoral immune host responses to N. ceranae invasion. Our findings revealed here offer not only a rich genetic resource for further investigation of the functional roles of lncRNAs involved in the A. m. ligustica response to N. ceranae infection, but also a novel insight into understanding the host-pathogen interaction during honeybee microsporidiosis.

Prioritizing candidate disease-related long non-coding RNAs by walking on the heterogeneous lncRNA and disease network

2015 ◽  
Vol 11 (3) ◽  
pp. 760-769 ◽  
Author(s):  
Meng Zhou ◽  
Xiaojun Wang ◽  
Jiawei Li ◽  
Dapeng Hao ◽  
Zhenzhen Wang ◽  
...  
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Biological Processes ◽  
Disease Network ◽  
Wide Range ◽  
Gene Regulatory ◽  
Non Coding Rnas

Accumulated evidence has shown that long non-coding RNAs (lncRNA) act as a widespread layer in gene regulatory networks and are involved in a wide range of biological processes.

Overview of Gene Regulatory Network Inference Based on Differential Equation Models

2020 ◽  
Vol 21 (11) ◽  
pp. 1054-1059
Author(s):  
Bin Yang ◽  
Yuehui Chen
Keyword(s):  
Differential Equation ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Network Inference ◽  
New Drugs ◽  
Gene Regulatory Network Inference ◽  
Ode Models ◽  
Differential Equation Models ◽  
Linear Ode ◽  
Gene Regulatory

: Reconstruction of gene regulatory networks (GRN) plays an important role in understanding the complexity, functionality and pathways of biological systems, which could support the design of new drugs for diseases. Because differential equation models are flexible androbust, these models have been utilized to identify biochemical reactions and gene regulatory networks. This paper investigates the differential equation models for reverse engineering gene regulatory networks. We introduce three kinds of differential equation models, including ordinary differential equation (ODE), time-delayed differential equation (TDDE) and stochastic differential equation (SDE). ODE models include linear ODE, nonlinear ODE and S-system model. We also discuss the evolutionary algorithms, which are utilized to search the optimal structures and parameters of differential equation models. This investigation could provide a comprehensive understanding of differential equation models, and lead to the discovery of novel differential equation models.

Radio-Susceptibility of Nasopharyngeal Carcinoma: Focus on Epstein- Barr Virus, MicroRNAs, Long Non-Coding RNAs and Circular RNAs

2020 ◽  
Vol 13 (3) ◽  
pp. 192-205 ◽  
Author(s):  
Fanghong Lei ◽  
Tongda Lei ◽  
Yun Huang ◽  
Mingxiu Yang ◽  
Mingchu Liao ◽  
...  
Keyword(s):  
Nasopharyngeal Carcinoma ◽  
Regulatory Networks ◽  
Epstein Barr Virus ◽  
Molecular Mechanisms ◽  
Acquired Resistance ◽  
Treatment Strategies ◽  
Circular Rnas ◽  
Barr Virus ◽  
Non Coding Rnas ◽  
Epstein Barr

Nasopharyngeal carcinoma (NPC) is a type of head and neck cancer. As a neoplastic disorder, NPC is a highly malignant squamous cell carcinoma that is derived from the nasopharyngeal epithelium. NPC is radiosensitive; radiotherapy or radiotherapy combining with chemotherapy are the main treatment strategies. However, both modalities are usually accompanied by complications and acquired resistance to radiotherapy is a significant impediment to effective NPC therapy. Therefore, there is an urgent need to discover effective radio-sensitization and radio-resistance biomarkers for NPC. Recent studies have shown that Epstein-Barr virus (EBV)-encoded products, microRNAs (miRNAs), long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs), which share several common signaling pathways, can function in radio-related NPC cells or tissues. Understanding these interconnected regulatory networks will reveal the details of NPC radiation sensitivity and resistance. In this review, we discuss and summarize the specific molecular mechanisms of NPC radio-sensitization and radio-resistance, focusing on EBV-encoded products, miRNAs, lncRNAs and circRNAs. This will provide a foundation for the discovery of more accurate, effective and specific markers related to NPC radiotherapy. EBVencoded products, miRNAs, lncRNAs and circRNAs have emerged as crucial molecules mediating the radio-susceptibility of NPC. This understanding will improve the clinical application of markers and inform the development of novel therapeutics for NPC.

scholarly journals Modes of Interaction in Naturally Occurring Medical Encounters With General Practitioners: The “One in a Million” Study

2021 ◽  
pp. 104973232199379
Author(s):  
Olaug S. Lian ◽  
Sarah Nettleton ◽  
Åge Wifstad ◽  
Christopher Dowrick
Keyword(s):  
General Practitioners ◽  
Narrative Analysis ◽  
Mode Competition ◽  
General Applicability ◽  
Naturally Occurring ◽  
Wide Range ◽  
The One ◽  
Narrative Mode ◽  
Modes Of Interaction

In this article, we qualitatively explore the manner and style in which medical encounters between patients and general practitioners (GPs) are mutually conducted, as exhibited in situ in 10 consultations sourced from the One in a Million: Primary Care Consultations Archive in England. Our main objectives are to identify interactional modes, to develop a classification of these modes, and to uncover how modes emerge and shift both within and between consultations. Deploying an interactional perspective and a thematic and narrative analysis of consultation transcripts, we identified five distinctive interactional modes: question and answer (Q&A) mode, lecture mode, probabilistic mode, competition mode, and narrative mode. Most modes are GP-led. Mode shifts within consultations generally map on to the chronology of the medical encounter. Patient-led narrative modes are initiated by patients themselves, which demonstrates agency. Our classification of modes derives from complete naturally occurring consultations, covering a wide range of symptoms, and may have general applicability.

scholarly journals Trends in Molecular Diagnosis and Diversity Studies for Phytosanitary Regulated Xanthomonas

2021 ◽  
Vol 9 (4) ◽  
pp. 862
Author(s):  
Vittoria Catara ◽  
Jaime Cubero ◽  
Joël F. Pothier ◽  
Eran Bosis ◽  
Claude Bragard ◽  
...  
Keyword(s):  
Molecular Diagnosis ◽  
Bacterial Species ◽  
Management Strategies ◽  
Diagnostic Methods ◽  
Plant Diseases ◽  
Wild Plants ◽  
Future Climate Change ◽  
Climate Change Scenarios ◽  
Wide Range ◽  
Diversity Studies

Bacteria in the genus Xanthomonas infect a wide range of crops and wild plants, with most species responsible for plant diseases that have a global economic and environmental impact on the seed, plant, and food trade. Infections by Xanthomonas spp. cause a wide variety of non-specific symptoms, making their identification difficult. The coexistence of phylogenetically close strains, but drastically different in their phenotype, poses an added challenge to diagnosis. Data on future climate change scenarios predict an increase in the severity of epidemics and a geographical expansion of pathogens, increasing pressure on plant health services. In this context, the effectiveness of integrated disease management strategies strongly depends on the availability of rapid, sensitive, and specific diagnostic methods. The accumulation of genomic information in recent years has facilitated the identification of new DNA markers, a cornerstone for the development of more sensitive and specific methods. Nevertheless, the challenges that the taxonomic complexity of this genus represents in terms of diagnosis together with the fact that within the same bacterial species, groups of strains may interact with distinct host species demonstrate that there is still a long way to go. In this review, we describe and discuss the current molecular-based methods for the diagnosis and detection of regulated Xanthomonas, taxonomic and diversity studies in Xanthomonas and genomic approaches for molecular diagnosis.

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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