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Transcriptional Regulatory Networks (TRNs)

Regulatory proteins, also called Transcription Factors (TF) interact with the DNA through specific binding sites called Transcription Factor Binding Sites (TFBS) in order to activate or repress the expression of specific genes or operons. Transcriptional Regulatory Networks (TRN) are usually represented as directed graphs where the TFs and their Target Genes (TGs) are the nodes and the criterion to create an edge is the regulatory interaction between the TF and TG.

Further information about Transcription regulation and Transcriptional/Gene Regulatory Networks can be found in the following materials:

CoryneRegNet experimental collection:

Corynebacterium glutamicum ATCC 13032:

Ecoli coli K-12:

Mycobacterium tuberculosis H37Rv:

Bacillus subtilis 168:

Information about Corynebacterial Transcriptional/Gene Regulatory Networks found in the literature:

CoryneRegNet transfer methodology is based on four steps: all-vs-all protein BLAST, operon prediction, identification of upstream regions and predictions of conserved TFBSs using nhmmer from HMMER package. The following paragraphs summarize these steps:

  • First, we downloaded all fully sequenced and annotated corynebacterial genomes available on NCBI web site (June 2019) and of our four model genomes (Corynebacterium glutamicum ATCC 13032, Escherichia coli K12, Bacillus subtilis 168 and Mycobacterium tuberculosis H37Rv). Then we performed all-vs-all protein BLAST and selected the best blast hits (BBHs) with a cutoff of 10-10 to predict the homologous proteins, among all 228 organisms.

  • In the second step we predicted operons for all target organisms by considering genes with an intergenic distance less than 50 base pairs as to part of the same operon. Operons of template organisms they were available in the respective TRN database.

  • Then, upstream regions (-560,+20) were identified.

  • To identify TFBSs we scanned the upstream regions using nhmmer from HMMER package in order to predict conserved TFBSs. In this step we just scanned the upstream region when the TF and TG (or first gene of operon) were pointed as homologous in the first step. The profile HMMs of the conserved TFs were applied to the upstream regions of the potentially regulated TGs by using HMMER’s default parameters, which corresponds to a p-value of ~10 -5. When a TF, a TG and a TFBS are conserved we consider this as a conserved regulatory interaction. Regulatory interactions predicted to the first gene of an operon were extended to the entire operon. The role of a predicted regulatory interaction is inherited from the model regulatory interaction. Finally, the predicted binding sites are used to create HMM profiles for TFs of the target organisms with hmmbuild from HMMER package. The interaction p-value was obtained by applying Tippet's method. The R package Metap was used to calculate the joint p-value of the p-values obtained in the homology and motif searches.

Software used in the methodology

Software Description Publication
BLAST "BLAST finds regions of similarity between biological sequences. The program compares nucleotide or protein sequences to sequence databases and calculates the statistical significance." Altschul, S.F., Madden, T.L., Schäffer, A.A., Zhang, J., Zhang, Z., Miller, W. and Lipman, D.J., 1997. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic acids research, 25(17), pp.3389-3402.
Clustal Omega "Clustal Omega can align virtually any number of protein sequences quickly and that delivers accurate alignments." Sievers, F. and Higgins, D.G., 2018. Clustal Omega for making accurate alignments of many protein sequences. Protein Science, 27(1), pp.135-145.
nhmmer from HMMER package "nhmmer is a tool for DNA/DNA sequence comparison that is built on the HMMER framework, which applies probabilistic inference methods based on hidden Markov models to the problem of homology search." Wheeler, T.J. and Eddy, S.R., 2013. nhmmer: DNA homology search with profile HMMs. Bioinformatics, 29(19), pp.2487-2489.
Metap Metap is an R package that implements methods for the meta–analysis of p–values (significance values). Dewey, M., 2019. Introduction to the metap package.
Skylign "Skylign is a tool for creating logos representing both sequence alignments and profile hidden Markov models". Wheeler, T.J., Clements, J. and Finn, R.D., 2014. Skylign: a tool for creating informative, interactive logos representing sequence alignments and profile hidden Markov models. BMC bioinformatics, 15(1), p.7.

Further information about CoryneRegNet Transcriptional Regulatory Networks transfer methodology:

CoryneRegNet's small RNA methodology is based on seven steps: sRNA detection, homology detection, alignment , sRNA classification, filter, structure prediction and target prediction. The following paragraphs summarize these steps:

  • sRNA detection: We recovered 805 experimentally verified small RNAs from (Mentz et al., 2013) and 70 predicted small RNAS from BSRD (Li et al.,2013). We ran cmsearch with the genomes of C. efficiens, C. jeikeium, C. pseudotuberculosis, C. ulcerans and C. diphtheriae.

  • Homology detection: We detected homologous small RNAs for every gene in the analysis. Homologous sRNAs belonging to the genomes of interest were considered as potential functional sRNAs and, thus, added to the analysis.

  • Alignment: we aligned each sRNA with two selected homologous with more than 80% of similarity.

  • sRNA classification: the aligned sequences were given to both RNAz and RNAdetect, which classified the sRNAs as functional RNAs or other.

  • Filter: Predicted sRNAs classified that were not classified as functional RNAs were removed from the analysis.

  • Structure prediction: secondary structure was predicted using RNAalifold for every sRNA in the analysis.

  • Target prediction: sRNA targets are predicted with CopraRNA using default settings. We selected the best 15 predictions with pvalue < 0.01 as suggested in (Wright and Georg, 2018).

Software used in the methodology

Software Description Publication
cmsearch "Infernal (INFERence of RNA ALignment) uses an implementation of a special case of profile stochastic context-free grammars called covariance models (CMs). for searching DNA sequence databases for RNA structure and sequence similarities." S. R. Eddy, Infernal 1.1: 100-fold faster RNA homology searches, Bioinformatics 29:2933-2935 (2013).
GLASSgo "GLASSgo (GLobal Automated sRNA Search go) combines iterative BLAST searches, pairwise identity filtering, and structure based clustering in an automated prediction pipeline to find sRNA homologs from scratch." Steffen C. Lott, Richard A Schäfer, Martin Mann, Rolf Backofen, Wolfgang R Hess, Bjoern Voss, Jens Georg GLASSgo - Automated and reliable detection of sRNA homologs from a single input sequences Frontiers in Genetics, 9, 124, 2018.
clustalo "Clustal Omega is a new multiple sequence alignment program that uses seeded guide trees and HMM profile-profile techniques to generate alignments between three or more sequences." Sievers F, Wilm A, Dineen D, Gibson TJ, Karplus K, Li W, Lopez R, McWilliam H, Remmert M, Söding J, Thompson JD, Higgins DG. Fast, Scalable Generation of High-Quality Protein Multiple Sequence Alignments Using Clustal Omega. Mol Syst Biol. 2011 Oct 11;7:539
RNAdetect "RNAdetect can accurately detect the presence of functional ncRNAs by incorporating novel predictive features based on the concept of generalized ensemble defect (GED), which assesses the degree of structure conservation across multiple related sequences and the conformation of the individual folding structures to a common consensus structure." Chen CC, Qian X, Yoon BJ. RNAdetect: efficient computational detection of novel non-coding RNAs. Bioinformatics. 2019;35(7):1133‐1141.
RNAalifold "RNAalifold calculates secondary structures for a set of aligned RNAs taking into account both thermodynamic stability and sequence covariation. It calculates their minimum free energy (mfe) structure, partition function (pf) and base pairing probability matrix." Bernhart SH, Hofacker IL, Will S, Gruber AR, & Stadler PF RNAalifold: Improved consensus structure prediction for RNA alignments BMC Bioinformatics: 9, pp 474, 2008.
RNAz "RNAz is a program for predicting structurally conserved and thermodynamically stable RNA secondary structures in multiple sequence alignments. It can be used in genome wide screens to detect functional RNA structures, as found in noncoding RNAs and cis-acting regulatory elements of mRNAs." Gruber AR, Findeiß S, Washietl S, Hofacker IL, Stadler PF. RNAz 2.0: Improved noncoding RNA detection Pac Symp Biocomput. 15:69-79, 2010.
CopraRNA "CopraRNA is a tool for sRNA target prediction. It computes whole genome predictions by combination of distinct whole genome IntaRNA predictions." Wright PR, Richter AS, Papenfort K, Mann M, Vogel J, Hess WR, Backofen R, Georg J. Comparative genomics boosts target prediction for bacterial small RNAs Proceedings of the National Academy of Sciences, 110(37):E3487-96, 2013.

Further information about sRNA and target prediction:

Home Page

In the home page the user can choose which evidence level he/she is interested in.

  • Experimental: all analysis and searches are performed on our four model organisms which have TRNs reconstructed experimentally.

  • Predicted: all analysis and searches are performed on all 228 organisms (templates and targets).

Search Experimental/Predicted Database

In this page the user can search regulations or see the TRN of a gene or an organism. He/she can also see a brief a statistics overview in the bottom of the page.

  • Search by organism: allows the user to search for all organisms or for a specific organism accordingly with the evidence level (See: Home Page Section). If a gene is not provided, choosing an organism is mandatory.

  • Search by gene: allows the user to search for gene id (locus tag from NCBI RefSeq or GenBank version) or gene name.

  • Search button: search for genomic and regulatory information based on organism and gene options and present it in a table format. If the option ALL is selected a gene is mandatory.

  • Network Visualization button: selecting an organism is mandatory to activate this button. It draws a dynamic TRN of the selected organism. It is also possible to visualize the regulations of a gene of the selected organism by entering the geneId or gene name in gene option.

  • Statistics Overview: presents an overview of the database content accordingly with the evidence level (experimental or predicted).

Search Results Page

In this page genomic and regulatory information of an organism or gene of interest is presented in a table format. Some fields contain links to a respective page information or to NCBI (protein id).

Network Visualization

This page presents a dynamic TRN of the selected organism or gene. In this page, it is possible to download the regulatory network in a SIF file format by clicking on the download network file button in the top-left of the screen. Below the download network file button there is a question mark button explaining how to explore the network and two network layout options (Genes and Operons). "Genes" is the default network layout where each gene is presented as an individual node (red for repressor, green for activator, blue for dual regulator, light blue for sigma factor and gray for target gene) and the regulatory interactions are represented by the edges (red for repressions, green for activations, blue for dual regulations, light blue for sigma factors and regulatory interactions with an unknown role). "Operons" is an alternative visualization in which genes that are part of operons that are grouped and represented as unique nodes in yellow. In both layouts, if the user clicks on the nodes or edges it presents further information about the selected element. Additionally, user is also able to visualize the regulatory network of that gene (or operon) in a new tab by clicking on the Network Visualization button inside the gene information box. Moving a node is possible when dragging the node to a desired position. Besides that, the Improve Layout button below network layout options restart the automatic layouting of the network (it is especially useful for big networks), it can be stopped by clicking on the same button (now called “Stop Layouting”).

Gene Information Page

In this page genome and regulatory information are presented in the respective tabs. In the binding site prediction tab the user can search for binding sites in the upstream sequence of the gene of interest (Figure 2a) or, if it is a regulator, use its profile HMM to search possible binding sites in other upstream sequences (Figure 2b).

pHMM
pHMM2

  • a) In the upstream sequence of this gene: it searches for possible binding sites in the upstream sequencing of the gene using the profile HMMs of all or a specific organism

  • b) For this regulator in other upstream sequences: it searches for possible binding sites in the upstream sequencing of all genes of an organism using the profile HMM of the gene of interest in case it is known to code for a TF.

If I have a genome of interest, how can I see genomic and regulatory information? How can I later check for information of a specific regulator? E.g. Corynebacterium glutamicum ATCC 13032 and gene cg2502 (fur).

TF_of_interest

1. Go to the CoryneRegNet home page and click on the "Experimental" button to search for organisms in the experimental database or on the "Predicted button" to search for organisms in the predicted database. In case you do not know which button to choose, please click on the "Predicted" button. It will show experimental and predicted databases.

2. In the "Search by organism" dropdown menu choose Corynebacterium glutamicum DSM 20300 = ATCC 13032 and click on the "Search" button.

3. In the results table, you will see genomic and regulatory information of the selected genome. Scroll down to find cg2502 (fur) in the "Gene ID" column or in the "Regulation" column and click on it.

4. The next page will present genomic and regulatory information of the selected gene. The first tab will present gene information.

5. If you click on "Homologous proteins", it will present found homologous candidates of the selected gene.

6. Clicking on the "Regulated by" tab will present the transcription factors that regulate the selected gene. This tab will only appear if the gene of interest is regulated by a transcription factor.

7. The "Regulates" tab presents all genes that are regulated by the selected gene. This tab will only appear if the gene of interest regulates at least one gene.

8. The "Gene position and HMM logo" tab presents the start and end positions of the gene. In case the selected gene is a transcription factor, it will show if it is an auto-regulator or not, and will present the HMM logo, as well as a button to download the profile HMM.

If I have a genome of interest, how can I visualize its network in a graph-based layout? Can I select a gene of interest from the network and visualize its gene information of it? Furthermore, can I visualize its regulated and regulating genes in a graph based layout ? E.g. Corynebacterium diphtheriae 31A and gene CD31A_RS034300.

organism_of_interest

1. Go to CoryneRegNet home page and click on the "Predicted" button.

2. In "Search by organism", choose Corynebacterium diphtheriae 31A and click on the "Network Visualization" button.

3. It will automatically draw a dynamic network in a new page. Before you continue following these instructions, read "How to navigate in CoryneRegNet" in the above section to understand the options of the network visualization.

4. Once you are in the network, scroll up the mouse to zoom in, go to gene (node) CD31A_RS034300 and click on it.

5. Now, you will see a modal with some information about the gene and some links.

6. Click on CD31A_RS034300 in order to open its gene information page in a new tab.

7. Click on the "Network Visualization" button to open the network with the regulated and regulating genes of gene CD31A_RS034300 in a new tab - if the selected gene is regulated by other genes, it will also appear in the new network.

If I want to see the sRNA information of a genome of interest, how can I see this information? How can I later check for information of a specific sRNA? E.g. Corynebacterium glutamicum ATCC 13032 and sRNA cgb_07555.

sRNA_of_interest

1. Go to the CoryneRegNet home page and click on the "Predicted" button to search for organisms in the predicted database.

2. In Search for choose Small RNAs. In the "Search by organism" dropdown menu choose Corynebacterium glutamicum DSM 20300 = ATCC 13032 and click on the "Search" button.

3. In the results table, you will see sRNAs and sRNAs regulatory information of the selected genome. Scroll down (or in Search field type cgb_07555) to find the cgb_07555 sRNA in the "RNA ID" column and click on it.

4. The next page will present genomic and regulatory information of the selected sRNA. The first tab will present sRNA information.

5. If you click on "See dot plot graph", it will present the dot plot graph in a pop-up.

6. If you click on "See alignment graph", it will present the alignment graph in a pop-up.

7. The "Regulates" tab presents all genes that are regulated by the selected sRNA.

Developed by: Mariana Parise, Doglas Parise, Josch Pauling, Vasco Azevedo and Jan Baumbach (2020) - CoryneRegNet 7.0