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

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.

Download Full-text

Genome-wide identification of long non-coding RNAs and their regulatory networks involved in Apis mellifera ligustica response to Nosema ceranae infection

10.1101/643627 ◽

2019 ◽

Author(s):

Rui Guo ◽

Huazhi Chen ◽

Yu Du ◽

Dingding Zhou ◽

Sihai Geng ◽

...

Keyword(s):

Apis Mellifera ◽

Expression Pattern ◽

Structural Features ◽

Nosema Ceranae ◽

Post Inoculation ◽

Sequencing Data ◽

Form Complex ◽

Apis Mellifera Ligustica ◽

Wide Range ◽

Non Coding Rnas

AbstractLong non-coding RNAs (lncRNAs) are a diverse class of transcripts that structurally resemble mRNAs but do not encode proteins, and lncRNAs have been proved to play pivotal roles in a wide range of biological processes in animals and plants. However, knowledge of expression pattern and potential role of honeybee lncRNAs 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 low sequence similarities with other known lncRNAs in other species; however, their structural features were similar with 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 day post inoculation (dpi) and 10 dpi compared with control midguts. 12 differentially expressed lncRNAs (DElncRNAs) were shared by Am7CK vs Am7T and Am10CK vs Am10T comparison groups, while the numbers of unique ones were 99 and 134, respectively. Functional annotation and pathway analysis showed the DElncRNAs may regulate the expression of neighboring genes by acting in cis. 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 these DElncRNAs may act as miRNA sponges. Furthermore, DElncRNA-miRNA-mRNA networks were constructed and investigated, the result demonstrated that part of DElncRNAs were likely to participate in regulating the material and energy metabolism as well as cellular and humoral immune during host responses to N. ceranae invasion. Finally, the expression pattern of 10 DElncRNAs was validated using RT-qPCR, confirming the reliability of our sequencing data. Our findings revealed here offer not only a rich genetic resource for further investigation of the functional roles of lncRNAs involved in A. m. ligustica response to N. ceranae infection, but also a novel insight into understanding host-pathogen interaction during microsporidiosis of honeybee.

Download Full-text

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

International Journal of Molecular Sciences ◽

10.3390/ijms21186513 ◽

2020 ◽

Vol 21 (18) ◽

pp. 6513 ◽

Cited By ~ 1

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.

Download Full-text

CURVE-BASED CLUSTERING OF TIME COURSE GENE EXPRESSION DATA USING SELF-ORGANIZING MAPS

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720009004291 ◽

2009 ◽

Vol 07 (04) ◽

pp. 645-661 ◽

Cited By ~ 11

Author(s):

XIN CHEN

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Regulatory Networks ◽

Time Course ◽

Clustering Algorithm ◽

Expression Patterns ◽

Self Organizing Map ◽

Expression Data ◽

Wide Range ◽

Self Organizing

There is an increasing interest in clustering time course gene expression data to investigate a wide range of biological processes. However, developing a clustering algorithm ideal for time course gene express data is still challenging. As timing is an important factor in defining true clusters, a clustering algorithm shall explore expression correlations between time points in order to achieve a high clustering accuracy. Moreover, inter-cluster gene relationships are often desired in order to facilitate the computational inference of biological pathways and regulatory networks. In this paper, a new clustering algorithm called CurveSOM is developed to offer both features above. It first presents each gene by a cubic smoothing spline fitted to the time course expression profile, and then groups genes into clusters by applying a self-organizing map-based clustering on the resulting splines. CurveSOM has been tested on three well-studied yeast cell cycle datasets, and compared with four popular programs including Cluster 3.0, GENECLUSTER, MCLUST, and SSClust. The results show that CurveSOM is a very promising tool for the exploratory analysis of time course expression data, as it is not only able to group genes into clusters with high accuracy but also able to find true time-shifted correlations of expression patterns across clusters.

Download Full-text

Retroviral Transcriptional Regulation and Embryonic Stem Cells: War and Peace

Molecular and Cellular Biology ◽

10.1128/mcb.01293-14 ◽

2014 ◽

Vol 35 (5) ◽

pp. 770-777 ◽

Cited By ~ 63

Author(s):

Sharon Schlesinger ◽

Stephen P. Goff

Keyword(s):

Dna Sequences ◽

Regulatory Networks ◽

Es Cells ◽

Expression Patterns ◽

Embryonic Stem ◽

Cell Types ◽

Endogenous Retroviruses ◽

Regulatory Sequences ◽

Cellular Genes ◽

Wide Range

Retroviruses have evolved complex transcriptional enhancers and promoters that allow their replication in a wide range of tissue and cell types. Embryonic stem (ES) cells, however, characteristically suppress transcription of proviruses formed after infection by exogenous retroviruses and also of most members of the vast array of endogenous retroviruses in the genome. These cells have unusual profiles of transcribed genes and are poised to make rapid changes in those profiles upon induction of differentiation. Many of the transcription factors in ES cells control both host and retroviral genes coordinately, such that retroviral expression patterns can serve as markers of ES cell pluripotency. This overlap is not coincidental; retrovirus-derived regulatory sequences are often used to control cellular genes important for pluripotency. These sequences specify the temporal control and perhaps “noisy” control of cellular genes that direct proper cell gene expression in primitive cells and their differentiating progeny. The evidence suggests that the viral elements have been domesticated for host needs, reflecting the wide-ranging exploitation of any and all available DNA sequences in assembling regulatory networks.

Download Full-text

Whole transcriptome data of uninfected and Nosema ceranae-infected midguts of eastern honeybee workers

10.1101/2020.03.13.990697 ◽

2020 ◽

Author(s):

Huazhi Chen ◽

Dingding Zhou ◽

Yu Du ◽

Cuiling Xiong ◽

Yanzhen Zheng ◽

...

Keyword(s):

Apis Cerana ◽

Gc Content ◽

Nosema Ceranae ◽

Post Inoculation ◽

List Type ◽

Valuable Resource ◽

Fungal Parasite ◽

Differential Expression Pattern ◽

Non Coding Rnas ◽

Whole Transcriptome

ABSTRACTApis cerana cerana is a subspecies of eastern honeybee, Apis cerana. Nosema ceranae is a widespread fungal parasite of honeybee, causing heavy losses for beekeeping industry all over the world. In this article, total RNA of normal midguts (AcCK1, AcCK2) and N. ceranae-infected midguts of A. c. cerana workers at 7 d and 10 d post inoculation (AcT1, AcT2) were respectively isolated followed by strand-specific cDNA library construction and next-generation RNA sequencing. In tolal, 56270223688, 44860946964, 78991623806, and 92712308296 raw reads were derived from AcCK1, AcCK2, AcT1 and AcT2, respectively. Following strict quality control, 54495191388, 43570608753, 76708161525, and 89467858351 clean reads were obtained, with Q30 value of 95.80%, 95.99%, 96.07% and 96.04%, and GC content of 44.20%, 43.44%, 44.83% and 43.63%, respectively. The raw data were submitted to the NCBI Sequence Read Archive database and connected to BioProject PRJNA562784. These data offers a valuable resource for deep investigation of mechanisms underlying eastern honeybee responding to N. ceranae infection and host-fungal parasite interaction during microsporidiosis.Value of the DataCurrent dataset offers a valuable resource for exploring mRNAs, lncRNAs and circRNAs involved in response of A. c. cerana worker to N. ceranae infection.The accessible data can be used to investigate differential expression pattern and regulatory network of non-coding RNAs in A. c. cerana workers’ midguts responding to N. ceranae challenge.This data will enable a better understanding of the molecular mechanism regulating eastern honeybee-N. ceranae interaction.

Download Full-text

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

10.1101/657478 ◽

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.

Download Full-text

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

Molecular BioSystems ◽

10.1039/c4mb00511b ◽

2015 ◽

Vol 11 (3) ◽

pp. 760-769 ◽

Cited By ~ 109

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.

Download Full-text

A comprehensive transcriptome data of normal and Nosema ceranae-stressed midguts of Apis mellifera ligustica workers

Data in Brief ◽

10.1016/j.dib.2019.104349 ◽

2019 ◽

Vol 26 ◽

pp. 104349 ◽

Cited By ~ 6

Author(s):

Huazhi Chen ◽

Yu Du ◽

Cuiling Xiong ◽

Yanzhen Zheng ◽

Dafu Chen ◽

...

Keyword(s):

Apis Mellifera ◽

Nosema Ceranae ◽

Transcriptome Data ◽

Apis Mellifera Ligustica

Download Full-text

Nanopore-based long-read transcriptome data of Nosema ceranae-infected and un-infected western honeybee workers’ midguts

10.1101/2020.03.21.001958 ◽

2020 ◽

Author(s):

Huazhi Chen ◽

Xiaoxue Fan ◽

Yu Du ◽

Yuanchan Fan ◽

Jie Wang ◽

...

Keyword(s):

Apis Mellifera ◽

Length Distribution ◽

Quality Score ◽

Nosema Ceranae ◽

Full Length ◽

Post Inoculation ◽

Transcriptome Data ◽

Fungal Parasite ◽

Long Read ◽

Colony Productivity

ABSTRACTApis mellifera ligustica is a subspecies of western honeybee, Apis mellifera. Nosema ceranae is known to cause bee microspodiosis, which seriously affects bee survival and colony productivity. In this article, Nanopore long-read sequencing was used to sequence N. ceranae-infected and un-infected midguts of A. m. ligustica workers at 7 d and 10 d post inoculation (dpi). In total, 5942745, 6664923, 7100161 and 6506665 raw reads were respectively yielded from AmT1, AmT2, AmCK1 and AmCK2, with average lengths of 1148, 1196, 1178 and 1201 bp, and N50 of 1328, 1394, 1347 and 1388 bp. The length distribution of raw reads from AmT1, AmT2, AmCK1 and AmCK2 was ranged from 1 kb to more than 10 kb. Additionally, the distribution of quality score of raw reads from AmT1 and AmT2 was among Q6∼Q12, while that from AmCK1 and AmCK2 was among Q6∼Q16. Further, 5745048, 6416987, 6928170, 6353066 clean reads were respectively gained from AmT1, AmT2, AmCK1 and AmCK2, and among them 4172542, 4638289, 5068270 and 4857960 were identified as being full-length. After removing redundant reads, the length distribution of remaining full-length transcripts was among 1 kb∼8 kb, with the most abundant length of 2 kb. The long-read transcriptome data reported here contributes to a deeper understanding of the molecular regulating N. ceranae-response of A. m. ligustica and host-fungal parasite interaction during microsporidiosis.

Download Full-text

pH-Dependent Thermal Stability of Vibrio cholerae L-asparaginase

Protein and Peptide Letters ◽

10.2174/0929866526666190617092944 ◽

2019 ◽

Vol 26 (10) ◽

pp. 743-750 ◽

Cited By ~ 1

Author(s):

Remya Radha ◽

Sathyanarayana N. Gummadi

Keyword(s):

Vibrio Cholerae ◽

Conformational Changes ◽

Kinetic Studies ◽

Structural Features ◽

Structure Stability ◽

Kcal Mole ◽

Scanning Calorimetry ◽

Wide Range ◽

Ph Dependent ◽

Ph Range

Background:pH is one of the decisive macromolecular properties of proteins that significantly affects enzyme structure, stability and reaction rate. Change in pH may protonate or deprotonate the side group of aminoacid residues in the protein, thereby resulting in changes in chemical and structural features. Hence studies on the kinetics of enzyme deactivation by pH are important for assessing the bio-functionality of industrial enzymes. L-asparaginase is one such important enzyme that has potent applications in cancer therapy and food industry.Objective:The objective of the study is to understand and analyze the influence of pH on deactivation and stability of Vibrio cholerae L-asparaginase.Methods:Kinetic studies were conducted to analyze the effect of pH on stability and deactivation of Vibrio cholerae L-asparaginase. Circular Dichroism (CD) and Differential Scanning Calorimetry (DSC) studies have been carried out to understand the pH-dependent conformational changes in the secondary structure of V. cholerae L-asparaginase.Results:The enzyme was found to be least stable at extreme acidic conditions (pH< 4.5) and exhibited a gradual increase in melting temperature from 40 to 81 °C within pH range of 4.0 to 7.0. Thermodynamic properties of protein were estimated and at pH 7.0 the protein exhibited ΔG37of 26.31 kcal mole-1, ΔH of 204.27 kcal mole-1 and ΔS of 574.06 cal mole-1 K-1.Conclusion:The stability and thermodynamic analysis revealed that V. cholerae L-asparaginase was highly stable over a wide range of pH, with the highest stability in the pH range of 5.0–7.0.

Download Full-text