Next Article in Journal
A Complex Intrachromosomal Rearrangement Disrupting IRF6 in a Family with Popliteal Pterygium and Van der Woude Syndromes
Previous Article in Journal
Characteristics of a Three-Generation Family with Stickler Syndrome Type I Carrying Two Different COL2A1 Mutations
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Genes
Volume 14
Issue 4
10.3390/genes14040848
genes-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

An in Silico Approach to Identifying TF Binding Sites: Analysis of the Regulatory Regions of BUSCO Genes from Fungal Species in the Ceratocystidaceae Family

by
Nomaswazi N. Maseko
,
Emma T. Steenkamp
,
Brenda D. Wingfield
* and
P. Markus Wilken
Department of Biochemistry, Genetics and Microbiology, Forestry and Agricultural Biotechnology Institute (FABI), University of Pretoria, Pretoria 0083, South Africa
*
Author to whom correspondence should be addressed.
Genes 2023, 14(4), 848; https://doi.org/10.3390/genes14040848
Submission received: 27 February 2023 / Revised: 26 March 2023 / Accepted: 27 March 2023 / Published: 31 March 2023
(This article belongs to the Section Microbial Genetics and Genomics)
Browse Figures
Review Reports Versions Notes

Abstract

:
Transcriptional regulation controls gene expression through regulatory promoter regions that contain conserved sequence motifs. These motifs, also known as regulatory elements, are critically important to expression, which is driving research efforts to identify and characterize them. Yeasts have been the focus of such studies in fungi, including in several in silico approaches. This study aimed to determine whether in silico approaches could be used to identify motifs in the Ceratocystidaceae family, and if present, to evaluate whether these correspond to known transcription factors. This study targeted the 1000 base-pair region upstream of the start codon of 20 single-copy genes from the BUSCO dataset for motif discovery. Using the MEME and Tomtom analysis tools, conserved motifs at the family level were identified. The results show that such in silico approaches could identify known regulatory motifs in the Ceratocystidaceae and other unrelated species. This study provides support to ongoing efforts to use in silico analyses for motif discovery.

1. Introduction

The initiation of transcription is an integral part of gene expression and occurs within the promoter, the region directly upstream of genes [1,2]. The functional units of the promoter are called regulatory elements, which are conserved sequence motifs that act as binding sites for regulatory proteins such as transcription factors (TFs) [2,3]. These regulatory elements have been the focus of numerous studies, and efforts to understand gene regulation at the transcriptional level have focused on identifying and characterizing these DNA binding motifs.
The identification of motifs (commonly referred to as ‘motif discovery’) can be performed either in vitro or in silico. Initially, in vitro motif discovery relied on techniques exploiting physical protein–DNA interactions such as the electrophoretic mobility shift assay (EMSA) [4], DNase I protection (footprinting) assay [5] and South Western blotting [6]. However, with the increasing availability of whole genome sequences, motif discovery has moved to the realm of bioinformatics. In silico methods for motif discovery are based on creating sequence alignment profiles, after which the occurrence of each nucleotide is quantified to identify and further characterize any motifs that may be present [7]. Many tools to simplify motif discovery have been developed, including platforms such as TRANSFAC, MEME, Motif-Sampler and CONSENSUS [8]. These tools search for statistically overrepresented motifs that occur more often than expected by chance alone [9].
Motif discovery algorithms can either use de novo (or unbiased) searches or the searches that are informed by known regulatory motifs [10]. De novo motif discovery algorithms take advantage of the fact that regulatory elements tend to occur in non-coding regions, which show more sequence divergence than coding regions [11]. Among these divergent regions, regulatory elements would show higher sequence conservation due to a slower divergence rate in the regulatory regions [11]. These evolutionary patterns prompted the use of motif discovery strategies such as the ‘single gene, multiple species’ approach where the same gene is compared among multiple species, and the ‘multiple genes, single species’ approach assuming gene regulation is conserved in a single organism [12]. For example, the ‘multiple genes, single species’ approach was used to detect over-represented motifs in non-coding sequences from co-regulated genes in the yeast genome [13].
De novo motif discovery in fungi has mostly been successful in the model yeast species [3,14,15], although some work has been performed in non-model species as well. De novo TF motif discovery were reported in Fusarium graminearum, F. verticillioides, F. oxysporum and F. solani [16], all non-model filamentous ascomycetes. However, for many filamentous fungi, little to nothing is known about regulatory regions for transcription. This knowledge gap includes information about the architecture and regulatory element presence, as well as how these compare to previously characterized fungal regulatory regions and elements.
The current study aimed to determine whether in silico approaches could be applied to discover putative TF motifs from the Ceratocystidaceae, for which very little is known about transcriptional regulatory regions. This fungal family was chosen for its economic importance, which is underpinned by the large number of plant pathogenic fungi that belong to this group [17,18]. Additionally, full genome sequences are available for many species, lending itself to an in silico analyses. A total of 25 Ceratocystidaceae genomes across the family were used. A 1000 bp region upstream of the start codon was targeted as the putative regulatory region for 20 candidate genes. This region was analysed for the presence of conserved sequence motifs that could represent putative regulatory regions.

2. Materials and Methods

2.1. Gene Selection and Annotation for Motif Discovery

A selection of genomes representing 25 species across seven genera of the Ceratocystidaceae was used to identify unique motifs that could be potential transcriptional regulators. All genome sequences were obtained from the NCBI database (www.ncbi.nlm.nih.gov, [19]) (Table 1).
Genes were selected for analysis based on the results of an analysis using the Benchmarking Universal Single-Copy Orthologs (BUSCO) (Felipe A Simão, version 2.0.1, California, USA) tool [39]. To do this, each genome was subjected to a BUSCO analysis using genome mode and the Ascomycota_odb9 dataset, with F. graminearum as a reference species. The output included a table that stipulates whether each BUSCO ortholog (represented by the BUSCO ID) are complete, duplicated, fragmented, or missing in the analysed genome. Only complete genes that were present across all 25 genomes were chosen for further analysis. These were sorted alphabetically by BUSCO ID, and the first 20 genes were selected (Table 2).
To identify regions that would putatively contain promoter regions, the chosen genes were annotated in each genome. To do this, the GFF files generated by BUSCO were used to annotate the genes using the “Annotate with GFF file” plugin in CLC Genomics Workbench version 11.0 (Qiagen, Aarhus, Denmark). Following the annotation of the 20 selected genes, the first 1000 base pair (bp) upstream of the ATG start codon of each gene in each species was selected as the probable range for regulatory region presence. These 1000 bp sequences were grouped by gene and exported as a single FASTA files. In total, 20 such FASTA files were generated, with each FASTA file containing 25 different 1000 bp sequences, one from each of the 25 species. All these regions were subjects to a BLASTn search against the nt/nr nucleotide database [19] to evaluate whether any of these regions formed part of a different gene region, which could influence motif discovery. BLASTn parameters were adjusted to only use the “ascomycete fungi” subset of the dataset, to report a maximum of 10 target sequences, and to not report matches with an e-value of more than 0.01.

2.2. Motif Discovery

The Multiple Em for Motif Elicitation (MEME) tool [40] from the online interface of MEME suite (Timothy L. Bailey, version 5.2.0, Reno, NV, USA) [41] was used for the de novo prediction of motifs. The exported FASTA files were individually used as input files in MEME, and the number of motifs to identify were limited to 30 motifs per gene for each dataset. This number was chosen based on initial trial runs that limited the motif finder to 30 motifs and minimized the overlaps between different motifs. The motif discovery mode was set to classic; the sequence alphabet was set to DNA, RNA, or protein and the site distribution was set to zero or one occurrence per sequence (zoops). An additional parameter of motif presence in at least 80% of the species was also set. Only motifs that met all these parameters were retained for subsequent analyses.
To determine whether any motifs were repeated within the 1000 bp target regions, or were present in the 1000 bp regions of different genes, a Tomtom analysis was used. This analysis was performed from the online MEME interface [41]. The Tomtom tool compares a set of input sequences to a defined target set, and scores matches based on the p-value and E-value [42]. Here, the p-value is the probability that a random motif of the same width as the target would have an optimal alignment with a match score as good or better than the target’s, while the E-value is the expected number of false positives in the identified matches [42]. A database was constructed by including all motifs retained from the original MEME analysis, and was used to determine whether any of the motifs were unique. To do this, the individual motifs were combined into a single text document, and this combined dataset was used as both the query and a user-specified database in an “all-vs.-all” comparison in TOMTOM. Motifs were only considered significantly similar when both the reported E-value and p-value was ≤0.01.
The identified motifs were also mapped onto the corresponding 1000 bp sequences using the MAST tool [43] available in the web-based version of the MEME suite [41]. The MAST tool searches for given motifs in a set of sequences and provides a graphical map of the position of each motif. The maps were then analysed to evaluate the positional conservation of the motifs. The positional conservation of each motif was evaluated using two criteria: spatial position and order pattern. The spatial position indicates the distance between the motifs and the ATG start codon, while the order pattern referred to the order in which the motifs occurred in the 1000 bp region.

2.3. Motif Comparison

The resulting motifs from the MEME analysis were compared to known and characterised TF binding sites using the Tomtom tool set to use the JASPAR CORE (2018) and the JASPAR CORE fungi (2018) databases under the JASPAR non-redundant DNA category [44]. For each motif, the cut-off parameters were set at both an E-value and p-value of ≤0.01. Again, the p-value is the probability that a random motif of the same width as the target would have an optimal alignment with a match score as good or better than that of the target, while the E-value is the expected number of false positives in the identified matches [42].

2.4. Novel Motif Identification in Non-Ceratocystidaceae Species

To evaluate whether any motifs identified in the Ceratocystidaceae dataset might also be present in other fungi, four Sordariomycetes genomes outside of the Ceratocystidaceae were obtained from the NCBI Genbank database (www.ncbi.nlm.nih.gov/genbank, [45]) for analysis. These were the genome sequences of Neurospora crassa, Sordaria macrospora, Ophiostoma novo-ulmi and Fusarium circinatum (Table 1). To identify the homologs of the 20 genes used in this study from these four genomes, the relevant gene models from Ambrosiella beaveri were translated and used in a tBLASTn search against the target genomes. Regions producing significant BLAST hits (E ≤ 0.01) were used for de novo annotation on the online interface of the Augustus gene prediction software [46] using F. graminearum gene models as reference. Predicted genes were annotated using the “annotate with GFF file” plugin of the CLC Genomics Workbench. The predicted genes were then translated and subjected to a BLASTp search against the NCBI database to confirm the identity of each gene.
Following annotation, a 1000 bp sequence upstream of the ATG start codon of each of the 20 selected genes was isolated for each of the four genomes. These were then grouped according to gene and exported as single FASTA files. Each dataset was used as an input file in MAST and searched against the relevant set of motifs discovered in the Ceratocystidaceae genomes.

3. Results

3.1. Gene Selection for Motif Discovery

The BUSCO analysis of the 25 Ceratocystidaceae genomes identified between 574 and 1287 of the 1315 orthologs present in the Ascomycota_odb9 database as complete. Ceratocystis albifundus had the most complete orthologs (1287), while only 547 complete orthologs were identified from Davidsoniella neocalidoniae. A total of 454 orthologs were shared across all 25 genomes.
The 20 genes that were selected are involved in many different biological processes, including protein turnover (EOG092D01ZK), cell division (EOG092D03RY, EOG092D0454, EOG092D01QP), transcription and mRNA processing (EOG092D0AI2, EOG092D05X9, EOG092D05RI, EOG092D0564, EOG092D02YC, EOG092D00LL, EOG092D0124), translation and rRNA processing (EOG092D01J4, EOG092D0ACX), intracellular vesicle processing and trafficking (EOG092D03RC, EOG092D01YA, EOG092D01WX), DNA repair (EOG092D042R), chromatin remodelling (EOG092D01IY), cell signalling (EOG092D01MX) and cytoskeletal transport (EOG092D0072) (Table S1).
The BLASTn analysis of the 1000 bp upstream region of all the genes of all species mostly did not show similarity to any other known sequence in the database (did not produce a BLAST hit, or the BLAST hit had an e-value of more than 0.01). In some cases, a BLAST hit was produced, but this was mostly to a chromosome region that was not associated with any coding region. In a minority of the regions, some blast hits were produced that matched a gene sequence. However, this was always for a single gene in a maximum of two species (of the 20 analysed). As the 1000 bp target regions were compared between all 20 species per gene, such a small overlap (in only 2 of the 20 species) would not significantly influence the identification of conserved motifs. The full dataset was used for subsequent analysis.

3.2. Motif Discovery in the Ceratocystidaceae Species

The MEME analysis identified motifs within the provided cut-off parameters for most, but not all the 1000 bp target regions. No motifs conforming to the target parameters were identified in the 1000 bp upstream regions for genes EOG092D01QP, EOG092D042R and EOG092D05RI. A total of 61 motifs were discovered across the 1000 bp target region for the remaining 17 genes. Although the minimum representation parameter for a motif was set as present in least 80% of the genomes, this value was higher in most cases, with 42 of the 61 discovered motifs present in at least 23 of the 25 genomes. Furthermore, despite the e-value and p-value cut-offs being set to <0.01, the resulting motifs had e-values and p-values well under this parameter, with the highest recorded e-value being 3.6 × 10−12 and the highest p-value being 8.47 × 10−6 (Table S2). The number of motifs identified per gene varied from one motif per gene to ten motifs per gene, with a median of three motifs. These motifs ranged in length from 15 bp to 50 bp, with an average length of 43 bp and median of 49 bp (Table S3).
The all-vs.-all Tomtom analysis produced 4 motif groups that contained between two and 13 motifs per group (Table 3). In total, 22 of the motifs were split into these groups, with 1 motif (EOG092D01YA_motif 8) present in two groups. A single gene could have motifs classified under different groups, such as motifs for gene EOG092D0124 present in groups 3 and 4, and motifs for gene EOG092D00LL present in groups 1, 2 and 3. Furthermore, for one gene (EOG092D00LL) multiple motifs were grouped into a single group and might be indicative of repeated copies of that motif in the target region. Ten of the 13 motifs from group 4 were enriched in adenine bases (Figure 1). These motifs were present in 6 of the 17 genes from group 4, with the genes EOG092D01YA, EOG092D01IY, EOG092D0072 and EOG092D03RC each having two adenine-rich motifs (Table 3). The remaining 39 motifs that were not matched to any groups were considered unique.
Mapping the motifs onto the relevant 1000 bp upstream region identified three recognizable distribution patterns (Figure 2). In pattern 1, the motif distribution showed a similar spatial position (the position from the ATG start codon) and the pattern (the order in which the motifs occurred) in all analysed regions, while pattern 2 was defined by good conservation in terms of the pattern but lacked spatial positional conservation. Lastly, pattern 3 was characterised by a lack of spatial and positional conservation. The distribution of conserved elements mostly followed pattern 3 (9 of the 17 genes), although two genes (EOG092D02YC and EOG092D00LL) matched pattern 1, and four genes (EOG092D0072, EOG092D01ZK, EOG092D01WX, EOG092D01J4) matched pattern 2. Genes EOG092D0454 and EOG092D01MX only had a single motif each, precluding the identification of any complex distribution patterns for these genes.
Genes 14 00848 g001 550
Figure 1. The nucleotide sequence logos of the A-rich motifs, with Adenine in red, Guanine in yellow, Thymine in green and Cytosine in blue. The genes are represented by their BUSCO IDs. Individual motifs are also shown in higher resolution in Figure S1.
Figure 1. The nucleotide sequence logos of the A-rich motifs, with Adenine in red, Guanine in yellow, Thymine in green and Cytosine in blue. The genes are represented by their BUSCO IDs. Individual motifs are also shown in higher resolution in Figure S1.
Genes 14 00848 g001

3.3. Motif Comparison to Known TF Binding Sites

When the 61 motifs discovered from the Ceratocystidaceae genomes were compared to known transcription factor binding sites from both the JASPAR core 2018 database and the JASPAR fungi 2018 sub-collection, 43 of the motifs showed matches while only 18 motifs did not have any matches. Among the matches, 25 returned hits to both databases, eight motifs returned hits to the JASPAR core 2018 database only and 10 motifs returned hits to the JASPAR fungi 2018 sub-collection only. The motifs were matched to multiple known TF binding sites, with most matches to the JASPAR core 2018 database (1 to 25 hits per motif) and less to the JASPAR fungi 2018 sub-collection (1 to 6 hits per motif) (Table S4). All of the TF binding sites that matched in the JASPAR fungi 2018 sub-collection had Saccharomyces cerevisiae as the species of origin for the motif, while this varied in the JASPAR core 2018 dataset between S. cerevisiae, Homo sapiens and Arabidopsis thaliana which dominated the matches.
The identified Ceratocystidaceae motifs matched to 97 unique known TF binding sites belonging to 20 different TF classes in the JASPAR core 2018 database. The most represented class was the C2H2 zinc finger factors, comprising 66.2% of the alignment-matched motifs (Table 4). This is seven times more than the next most prevalent protein class, the MADS box factors with 9.6% of the known motifs aligning to the discovered motifs. The remaining 18 protein classes make up 24.1% of the aligned known motifs (Table 4).
A total of 61 unique TF binding sites matched from the JASPAR fungi 2018 sub-collection, belonging to 15 different TF classes. The C2H2 zinc finger factors were again the most represented class with 33.3% of the known motifs that aligned to the discovered motifs falling under this group (Table 5). The remaining 14 classes were more evenly distributed than those from the JASPAR core 2018 set, with the second most prevalent classes being fork head/winged helix factors and heat shock factors at 8.3% each, followed by other C4 zinc finger-type factors and tryptophan cluster factors at 6.9% each, and basic helix–loop–helix factors (bHLH), AT hook factors and C6 zinc factors all at 5.6%.

3.4. Identifying Novel Motifs in Non-Ceratocystidaceae

The 17 genes from which motifs were identified in the Ceratocystidaceae genomes were also successfully annotated in the genomes of O. novo-ulmi, F. circinatum, S. macrospora and N. crassa. Of the 17 genes investigated, MAST searches identified motifs corresponding to those discovered from the Ceratocystidaceae genomes in the 1000 bp upstream region of only 10 genes (Table 6). These included 17 motifs in the O. novo-ulmi genome, 11 motifs in the F. circinatum genome, 8 motifs in the S. macrospora genome and 6 motifs in the N. crassa genome (Figure 3). Despite F. graminearum being most closely related to Figure 4, most matches to the Ceratocystidaceae motifs were identified in O. novo-ulmi. The low number of hits per genes precluded the investigation for similarities in the distribution pattern of the motifs.
Only five (EOG092D0454_Motif 22, EOG092D0072_Motif 1, EOG092D0072_Motif 4, EOG092D01YA_Motif 8 and EOG092D01YA_Motif 18) of the 10 A-rich motifs identified from the Ceratocystidaceae genomes were found in the four non-Ceratocystidaceae genomes. These A-rich motifs were identified in the 1000 bp region of only three genes from the non-Ceratocystidaceae genomes as opposed to 10 genes from the Ceratocystidaceae.

4. Discussion

In this study, an in silico approach was used to identify potential promoter elements from the genomes of 25 Ceratocystidaceae species. This study is the first to attempt motif discovery using an in silico analysis at the genus level. The analysis identified 39 different conserved motifs present in the putative promoter region of 17 core BUSCO genes. Many of these motifs (70.5%) matched known TF binding sites associated with Eukaryotic promoter regions. The results of this study provide a framework for in silico motif discovery at a higher taxonomic rank, while also adding to the limited information currently available on regulatory motifs in the Ascomycota [3,16].
Although many motifs corresponding to known TF binding sequences were found, the C2H2 zinc finger factor motifs were the most abundant. The classical C2H2 domain is 28–30 aa in length and includes a β-hairpin stabilized by a zinc atom which allows C2H2 containing TFs to bind to long stretches of DNA [47]. The abundance of this group is not surprising considering that C2H2 zinc finger factors are one of the largest TF families [47,48]. In addition, an evaluation of TF prevalence in fungal genomes found that the C2H2 zinc finger factor superfamily was one of only 12 (out of 37) TF super families that were considered abundant in fungi [49].
A set of A-rich motifs were identified across many of the target regions, and these motifs shared sequence logo similarity when aligned in the Tomtom analysis. These sequence motifs also matched to the same known TF motifs, with 70% of these present in the non-Ceratocystidaceae genomes analysed in this study. A-rich sequences have long been associated with regulatory regions. This can be attributed to the ability of A-rich regions to easily bend, facilitating unwinding and allowing strand separation needed to initiate transcription [50].
It has been well documented that, for many genes, transcription initiation requires the sequential interaction of transcription factors (TFs) with their relevant transcription factor binding sites (TFBSs) [51]. The need for such a sequential interaction is thought to impose positional constrains on TFBSs, resulting in the positional conservation of these motifs [51,52]. Several motifs identified in this study did show positional conservation across the Ceratocystidaceae genomes, adding weight to the assumption that these are likely functional TFBs. For example, the pattern designated as pattern 1 in this study (similar spatial position and pattern in all analysed regions) is reminiscent of cis-regulatory modules (CRMs) which are clusters of motifs that co-occur on regulatory regions [53].
The in silico approach used in this study did not identify motifs in all 20 genes that were targeted, with no motifs identified in any representative species for three of the genes. One reason for this could be the methodology employed here. The approach relied on identifying overrepresented motifs in the aligned sequence set for each gene, making it possible that more divergent regulatory elements such as low-affinity binding sites and non-conserved functional sites could have been missed [54]. Another possibility is that the regulatory elements of these genes might be present outside of the 1000 bp regions used in this study [55,56]. Transcription can be regulated by gene-distal enhancers, and examples exist where enhancers can drive transcription independent of promoters. This has led to calls for a unified description of the promoters and enhancers [57,58,59]. This would make sense as enhancers and promoters share many characteristics such as similarities in their local structure (the presence of DNase hypersensitivity), they are often functionally interchangeable, rely on similar mechanisms for transcription initiation (both act as assembly points for RNA pol II and general transcription factors), both drive bidirectional transcription, and they display consistent histone modification patterns [58,60,61,62,63,64,65,66]. Disappointingly, only a small number of the discovered motifs were identified in the genomes of the four non-Ceratocystidaceae species, and might indicate that these elements are not true regulatory regions, but are regions of high-sequence conservation produced by common descent [67]. This can be dismissed for motifs that showed strong matches to known TF binding sites, and it might therefore be possible that other motifs might be novel conserved elements.
By making use of both the JASPAR (2018) and JASPAR fungi (2018) sub-collections for identifying known transcription factor binding sequences, some differences in the efficacy of these two databases were identified. The fungal sub-collection is largely biased towards S. cerevisiae, with 179 of the 184 TFs in the JASPAR (2018) fungi sub-collection originating from this species (Khan et al., 2018). Unsurprisingly, all of the matched TFs originated from this species, but the majority had no matches. This could indicate that the TF specific to these biding sites might not be present in S. cerevisiae, or that there is a high degree of sequence divergence due to the large evolutionary distance between S. cerevisiae and the Ceratocystidaceae species [68]. The JASPAR core (2018) database returned more hits per motif, and could be attributed to this subset having a higher level of cross-kingdom representation (Vertebrata, Nematoda, Insecta, Plantae, Fungi, and Urochordata) and more TF entries (1964 compared to 184 in the JASPAR fungi (2018) sub collection). Although S. cerevisiae was represented in both datasets, there were several discrepancies between the results from these two analyses, including variation in the identified TFs or matched in only one of the sub collections. Supplementing the JASPAR core 2018 database analysis with the JASPAR fungi (2018) sub collection produced the best results, but there is a clear need for updating and revising both datasets.
A de novo motif discovery approach such as that reported herein has a number of inherent risks that cannot easily be addressed. Identified similarities could be present due to chance alone, or due to sequence conservation unrelated to function. In this study, attempts were made to mitigate this risk by conducting a trial run of the MEME analysis to determine the optimal settings preventing overlaps in the identified motifs. In addition, only motifs that had wide representation among the Ceratocystidaceae genera were retained. Despite this, the functional analysis of the identified motifs would be needed to confirm their role (if any) in transcription [69,70,71]. Approaches for the genetic manipulation of Ceratocystidaceae species are actively being developed [72,73,74] and would be useful for future studies addressing these questions.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/genes14040848/s1, Table S1: The BUSCO genes used in this study; Table S2: The highest p-value per motif; Table S3: Number of identified motifs per gene; Table S4: Discovered motifs that matched to known TFs; and Figure S1: The sequence logos of the A-rich motifs. Figure S2: Positional distribution maps of motifs that met the cut-off requirements.

Author Contributions

Conceptualization, N.N.M., E.T.S., B.D.W. and P.M.W.; methodology, N.N.M., E.T.S., B.D.W. and P.M.W.; formal analysis, N.N.M.; investigation, N.N.M.; data curation, N.N.M.; writing—original draft preparation, N.N.M.; writing—review and editing, E.T.S., B.D.W. and P.M.W.; visualization, N.N.M. and P.M.W.; supervision, E.T.S., B.D.W. and P.M.W.; project administration, N.N.M. and P.M.W.; funding acquisition, E.T.S. and B.D.W. All authors have read and agreed to the published version of the manuscript.

Funding

This project was supported by the University of Pretoria, the Department of Science and Technology (DST)/National Research Foundation (NRF) Centre of Excellence in Tree Health Biotechnology (CTHB). The project was additionally supported by B. D. Wingfield’s DST/NRF SARChI chair in Fungal Genomics (grant number 98353). The grant holders acknowledge that opinions, findings and conclusions, or recommendations expressed in this piece of work are those of the researchers and that the funding bodies accept no liability whatsoever in this regard.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Publicly available datasets were analysed in this study. These data can be found at https://www.ncbi.nlm.nih.gov/ using the information provided in the manuscript.

Acknowledgments

The authors acknowledge the use of the genome resource for Ambrosiella beaveri provided by Tuan Duong.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Latchman, D.S. Transcription factors: An overview. Int. J. Biochem. Cell Biol. 1997, 29, 1305–1312. [Google Scholar] [CrossRef] [Green Version]
  2. Mitsis, T.; Efthimiadou, A.; Bacopoulou, F.; Vlachakis, D.; Chrousos, G.P.; Eliopoulos, E. Transcription factors and evolution: An integral part of gene expression. World Acad. Sci. J. 2020, 2, 3–8. [Google Scholar] [CrossRef] [Green Version]
  3. Chen, M.-J.M.; Chou, L.-C.; Hsieh, T.-T.; Lee, D.-D.; Liu, K.-W.; Yu, C.-Y.; Oyang, Y.-J.; Tsai, H.-K.; Chen, C.-Y. De novo motif discovery facilitates identification of interactions between transcription factors in Saccharomyces cerevisiae. Bioinformatics 2012, 28, 701–708. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Garner, M.M.; Revzin, A. A gel electrophoresis method for quantifying the binding of proteins to specific DNA regions: Application to components of the Escherichia coli lactose operon regulatory system. Nucleic Acids Res. 1981, 9, 3047–3060. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Brenowitz, M.; Senear, D.F.; Shea, M.A.; Ackers, G.K. Quantitative DNase footprint titration: A method for studying protein-DNA interactions. Methods Enzymol. 1986, 130, 132–181. [Google Scholar] [CrossRef]
  6. Yang, V.W. Eukaryotic transcription factors: Identification, characterization and functions. J. Nutr. 1998, 128, 2045–2051. [Google Scholar] [CrossRef] [Green Version]
  7. Zambelli, F.; Pesole, G.; Pavesi, G. Motif discovery and transcription factor binding sites before and after the next-generation sequencing era. Brief. Bioinform. 2013, 14, 225–237. [Google Scholar] [CrossRef]
  8. Hashim, F.A.; Mabrouk, M.S.; Al-Atabany, W. Review of different sequence motif finding algorithms. Avicenna J. Med. Biotechnol. 2019, 11, 130. [Google Scholar]
  9. Das, M.K.; Dai, H.-K. A survey of DNA motif finding algorithms. BMC Bioinf. 2007, 8, 1–13. [Google Scholar] [CrossRef] [Green Version]
  10. Simcha, D.; Price, N.D.; Geman, D. The limits of de novo DNA motif discovery. PLoS ONE 2012, 7, e47836. [Google Scholar] [CrossRef] [Green Version]
  11. Gasch, A.P.; Moses, A.M.; Chiang, D.Y.; Fraser, H.B.; Berardini, M.; Eisen, M.B. Conservation and evolution of cis-regulatory systems in ascomycete fungi. PLoS Biol. 2004, 2, e398. [Google Scholar] [CrossRef] [PubMed]
  12. Wang, T.; Stormo, G.D. Combining phylogenetic data with co-regulated genes to identify regulatory motifs. Bioinformatics 2003, 19, 2369–2380. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. van Helden, J.; André, B.; Collado-Vides, J. Extracting regulatory sites from the upstream region of yeast genes by computational analysis of oligonucleotide frequencies. J. Mol. Biol. 1998, 281, 827–842. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Sandve, G.K.; Drabløs, F. A survey of motif discovery methods in an integrated framework. Biol. Direct 2006, 1, 1–16. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  15. MacIsaac, K.D.; Wang, T.; Gordon, D.B.; Gifford, D.K.; Stormo, G.D.; Fraenkel, E. An improved map of conserved regulatory sites for Saccharomyces cerevisiae. BMC Bioinf. 2006, 7, 1–14. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Kumar, L.; Breakspear, A.; Kistler, C.; Ma, L.-J.; Xie, X. Systematic discovery of regulatory motifs in Fusarium graminearum by comparing four Fusarium genomes. BMC Genom. 2010, 11, 1–13. [Google Scholar] [CrossRef] [Green Version]
  17. Roux, J.; Wingfield, M.J. Ceratocystis species: Emerging pathogens of non-native plantation Eucalyptus and Acacia species. South. For. 2009, 71, 115. [Google Scholar] [CrossRef]
  18. De Beer, Z.W.; Duong, T.A.; Barnes, I.; Wingfield, B.D.; Wingfield, M.J. Redefining Ceratocystis and allied genera. Stud. Mycol. 2014, 79, 187–219. [Google Scholar] [CrossRef] [Green Version]
  19. Clark, K.; Karsch-Mizrachi, I.; Lipman, D.J.; Ostell, J.; Sayers, E.W. GenBank. Nucleic Acids Res. 2016, 44, D67–D72. [Google Scholar] [CrossRef] [Green Version]
  20. Wilken, P.M.; Aylward, J.; Chand, R.; Grewe, F.; Lane, F.A.; Sinha, S.; Ametrano, C.; Distefano, I.; Divakar, P.K.; Duong, T.A.; et al. IMA Genome—F13: Draft genome sequences of Ambrosiella cleistominuta, Cercospora brassicicola, C. citrullina, Physcia stellaris, and Teratosphaeria pseudoeucalypti. IMA Fungus 2020, 11, 19. [Google Scholar] [CrossRef]
  21. Vanderpool, D.; Bracewell, R.R.; McCutcheon, J.P. Know your farmer: Ancient origins and multiple independent domestications of ambrosia beetle fungal cultivars. Mol. Ecol. 2017, 27, 2077–2094. [Google Scholar] [CrossRef] [PubMed]
  22. Nel, W.J.; Duong, T.A.; Wingfield, B.D.; Wingfield, M.J.; de Beer, Z.W. A new genus and species for the globally important, multihost root pathogen Thielaviopsis basicola. Plant Pathol. 2018, 67, 871–882. [Google Scholar] [CrossRef]
  23. Wingfield, B.D.; Ambler, J.M.; Coetzee, M.; De Beer, Z.W.; Duong, T.A.; Joubert, F.; Hammerbacher, A.; McTaggart, A.R.; Naidoo, K.; Nguyen, H.D.; et al. IMA Genome-F 6: Draft genome sequences of Armillaria fuscipes, Ceratocystiopsis minuta, Ceratocystis adiposa, Endoconidiophora laricicola, E. polonica and Penicillium freii DAOMC 242723. IMA Fungus 2016, 7, 217–227. [Google Scholar] [CrossRef] [PubMed]
  24. van der Nest, M.A.; Beirn, L.A.; Crouch, J.A.; Demers, J.E.; de Beer, Z.W.; De Vos, L.; Gordon, T.R.; Moncalvo, J.-M.; Naidoo, K.; Sanchez-Ramirez, S.; et al. IMA Genome-F 3: Draft genomes of Amanita jacksonii, Ceratocystis albifundus, Fusarium circinatum, Huntiella omanensis, Leptographium procerum, Rutstroemia sydowiana, and Sclerotinia echinophila. IMA Fungus 2014, 5, 472–485. [Google Scholar] [CrossRef] [Green Version]
  25. Wilken, P.M.; Steenkamp, E.T.; De Beer, Z.W.; Wingfield, M.J.; Wingfield, B.D. IMA Genome-F1: Draft nuclear genome sequence for the plant pathogen, Ceratocystis fimbriata. IMA Fungus 2013, 4, 357–358. [Google Scholar] [CrossRef]
  26. Wingfield, B.D.; Barnes, I.; De Beer, Z.W.; De Vos, L.; Duong, T.A.; Kanzi, A.M.; Naidoo, K.; Nguyen, H.D.; Santana, Q.C.; Sayari, M.; et al. IMA Genome-F 5: Draft genome sequences of Ceratocystis eucalypticola, Chrysoporthe cubensis, C. deuterocubensis, Davidsoniella virescens, Fusarium temperatum, Graphilbum fragrans, Penicillium nordicum, and Thielaviopsis musarum. IMA Fungus 2015, 6, 493–506. [Google Scholar] [CrossRef]
  27. Wingfield, B.D.; Duong, T.A.; Hammerbacher, A.; van der Nest, M.A.; Wilson, A.; Chang, R.; de Beer, Z.W.; Steenkamp, E.T.; Wilken, P.M.; Naidoo, K.; et al. IMA Genome-F 7: Draft genome sequences for Ceratocystis fagacearum, C. harringtonii, Grosmannia penicillata, and Huntiella bhutanensis. IMA Fungus 2016, 7, 317–323. [Google Scholar] [CrossRef] [Green Version]
  28. Van der Nest, M.A.; Bihon, W.; De Vos, L.; Naidoo, K.; Roodt, D.; Rubagotti, E.; Slippers, B.; Steenkamp, E.T.; Wilken, P.M.; Wilson, A.; et al. IMA Genome-F 2: Draft genome sequences of Diplodia sapinea, Ceratocystis manginecans, and Ceratocystis moniliformis. IMA Fungus 2014, 5, 135–140. [Google Scholar] [CrossRef] [Green Version]
  29. Wingfield, B.D.; Bills, G.F.; Dong, Y.; Huang, W.; Nel, W.J.; Swalarsk-Parry, B.S.; Vaghefi, N.; Wilken, P.M.; An, Z.; de Beer, Z.W.; et al. IMA Genome-F 9: Draft genome sequence of Annulohypoxylon stygium, Aspergillus mulundensis, Berkeleyomyces basicola (syn. Thielaviopsis basicola), Ceratocystis smalleyi, two Cercospora beticola strains, Coleophoma cylindrospora, Fusarium fracticaudum, Phialophora cf. hyalina, and Morchella septimelata. IMA Fungus 2018, 9, 199–223. [Google Scholar] [CrossRef] [Green Version]
  30. Sayari, M.; van der Nest, M.A.; Steenkamp, E.T.; Soal, N.C.; Wilken, P.M.; Wingfield, B.D. Distribution and evolution of nonribosomal peptide synthetase gene clusters in the Ceratocystidaceae. Genes 2019, 10, 328. [Google Scholar] [CrossRef] [Green Version]
  31. Wingfield, B.D.; Liu, M.; Nguyen, H.D.; Lane, F.A.; Morgan, S.W.; De Vos, L.; Wilken, P.M.; Duong, T.A.; Aylward, J.; Coetzee, M.; et al. IMA Genome-F 10: Nine draft genome sequences of Claviceps purpurea s. lat., including C. arundinis, C. humidiphila, and C. cf. spartinae, pseudomolecules for the pitch canker pathogen Fusarium circinatum, draft genome of Davidsoniella eucalypti, Grosmannia galeiformis, Quambalaria eucalypti, and Teratosphaeria destructans. IMA Fungus 2018, 9, 401–418. [Google Scholar] [CrossRef] [PubMed]
  32. Wingfield, B.D.; Berger, D.K.; Steenkamp, E.T.; Lim, H.J.; Duong, T.A.; Bluhm, B.H.; de Beer, Z.W.; De Vos, L.; Fourie, G.; Naidoo, K.; et al. IMA Genome-F 8: Draft genome of Cercospora zeina, Fusarium pininemorale, Hawksworthiomyces lignivorus, Huntiella decipiens and Ophiostoma ips. IMA Fungus 2017, 8, 385–396. [Google Scholar] [CrossRef] [PubMed]
  33. Van der Nest, M.A.; Steenkamp, E.T.; McTaggart, A.R.; Trollip, C.; Godlonton, T.; Sauerman, E.; Roodt, D.; Naidoo, K.; Coetzee, M.P.A.; Wilken, P.M.; et al. Saprophytic and pathogenic fungi in the Ceratocystidaceae differ in their ability to metabolize plant-derived sucrose. BMC Evol. Biol. 2015, 15, 1–20. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  34. Wilken, P.M.; Steenkamp, E.T.; Van der Nest, M.A.; Wingfield, M.J.; De Beer, Z.W.; Wingfield, B.D. Unexpected placement of the MAT1-1-2 gene in the MAT1-2 idiomorph of Thielaviopsis. Fungal Genet. Biol. 2018, 113, 32–41. [Google Scholar] [CrossRef] [Green Version]
  35. Galagan, J.E.; Calvo, S.E.; Borkovich, K.A.; Selker, E.U.; Read, N.D.; Jaffe, D.; FitzHugh, W.; Ma, L.-J.; Smirnov, S.; Purcell, S.; et al. The genome sequence of the filamentous fungus Neurospora crassa. Nature 2003, 422, 859–868. [Google Scholar] [CrossRef] [Green Version]
  36. Nowrousian, M.; Stajich, J.E.; Chu, M.; Engh, I.; Espagne, E.; Halliday, K.; Kamerewerd, J.; Kempken, F.; Knab, B.; Kuo, H.-C.; et al. De novo assembly of a 40 Mb eukaryotic genome from short sequence reads: Sordaria macrospora, a model organism for fungal morphogenesis. PLoS Genet. 2010, 6, e1000891. [Google Scholar] [CrossRef] [Green Version]
  37. Wingfield, B.D.; Steenkamp, E.T.; Santana, Q.C.; Coetzee, M.P.A.; Bam, S.; Barnes, I.; Beukes, C.W.; Chan, W.Y.; de Vos, L.; Fourie, G.; et al. First fungal genome sequence from Africa: A preliminary analysis. S. Afr. J. Sci. 2012, 108, 104–112. [Google Scholar] [CrossRef] [Green Version]
  38. Forgetta, V.; Leveque, G.; Dias, J.; Grove, D.; Lyons, R., Jr.; Genik, S.; Wright, C.; Singh, S.; Peterson, N.; Zianni, M.; et al. Sequencing of the Dutch elm disease fungus genome using the Roche/454 GS-FLX Titanium System in a comparison of multiple genomics core facilities. J. Biomol. Tech. 2013, 24, 39. [Google Scholar] [CrossRef]
  39. Simão, F.A.; Waterhouse, R.M.; Ioannidis, P.; Kriventseva, E.V.; Zdobnov, E.M. BUSCO: Assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics 2015, 31, 3210–3212. [Google Scholar] [CrossRef] [Green Version]
  40. Bailey, T.L.; Elkan, C. Fitting a mixture model by expectation maximization to discover motifs in biopolymers. Proc. Int. Conf. Intell. Syst. Mol. Biol. 1994, 2, 28–36. [Google Scholar]
  41. Bailey, T.; Boden, M.; Buske, F.; Frith, M.; Grant, C.; Clementi, L.; Ren, J.L. MEME suite: Tools for motif discovery and searching. Nucleic Acids Res. 2009, 37, 202–208. [Google Scholar] [CrossRef]
  42. Gupta, S.; Stamatoyannopoulos, J.A.; Bailey, T.L.; Noble, W.S. Quantifying similarity between motifs. Genome Biol. 2007, 8, 1–9. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  43. Bailey, T.L.; Gribskov, M. Combining evidence using p-values: Application to sequence homology searches. Bioinformatics 1998, 14, 48–54. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Khan, A.; Fornes, O.; Stigliani, A.; Gheorghe, M.; Castro-Mondragon, J.A.; Van Der Lee, R.; Bessy, A.; Cheneby, J.; Kulkarni, S.R.; Tan, G.; et al. JASPAR 2018: Update of the open-access database of transcription factor binding profiles and its web framework. Nucleic Acids Res. 2018, 46, D260–D266. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  45. Benson, D.A.; Cavanaugh, M.; Clark, K.; Karsch-Mizrachi, I.; Lipman, D.J.; Ostell, J.; Sayers, E.W. GenBank. Nucleic Acids Res. 2013, 41, D36–D42. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  46. Stanke, M.; Morgenstern, B. AUGUSTUS: A web server for gene prediction in eukaryotes that allows user-defined constraints. Nucleic Acids Res. 2005, 33, W465–W467. [Google Scholar] [CrossRef] [Green Version]
  47. Fedotova, A.; Bonchuk, A.; Mogila, V.; Georgiev, P. C2H2 zinc finger proteins: The largest but poorly explored family of higher eukaryotic transcription factors. Acta Nat. 2017, 9, 47–58. [Google Scholar] [CrossRef] [Green Version]
  48. Han, G.; Lu, C.; Guo, J.; Qiao, Z.; Sui, N.; Qiu, N.; Wang, B. C2H2 zinc finger proteins: Master regulators of abiotic stress responses in plants. Front. Plant Sci. 2020, 11, 115. [Google Scholar] [CrossRef] [Green Version]
  49. Shelest, E. Transcription factors in fungi: TFome dynamics, three major families, and dual-specificity TFs. FEMS Microbiol. Lett. 2008, 286, 145–151. [Google Scholar] [CrossRef] [Green Version]
  50. Forde, B.G. AT-rich elements (ATREs) in the promoter regions of nodulin and other higher plant genes: A novel class of cis-acting regulatory element? Results Probl. Cell Differ. 1994, 20, 87–103. [Google Scholar] [CrossRef]
  51. Vardhanabhuti, S.; Wang, J.; Hannenhalli, S. Position and distance specificity are important determinants of cis-regulatory motifs in addition to evolutionary conservation. Nucleic Acids Res. 2007, 35, 3203–3213. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  52. Iengar, P.; Joshi, N. Identification of putative regulatory motifs in the upstream regions of co-expressed functional groups of genes in Plasmodium falciparum. BMC Genom. 2009, 10, 1–21. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  53. Cai, X.; Hou, L.; Su, N.; Hu, H.; Deng, M.; Li, X. Systematic identification of conserved motif modules in the human genome. BMC Genom. 2010, 11, 1–10. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  54. Magasanik, B.; Kaiser, C.A. Nitrogen regulation in Saccharomyces cerevisiae. Gene 2002, 290, 1–18. [Google Scholar] [CrossRef] [PubMed]
  55. Kageyama-Yahara, N.; Yamamichi, N.; Takahashi, Y.; Takeuchi, C.; Matsumoto, Y.; Sakaguchi, Y.; Koike, K. Tandem repeats of the 5′ flanking region of human MUC5AC have a role as a novel enhancer in MUC5AC gene expression. Biochem. Biophys. Rep. 2019, 18, 100632. [Google Scholar] [CrossRef] [PubMed]
  56. Vatov, E.; Ludewig, U.; Zentgraf, U. Disparate dynamics of gene body and cis-regulatory element evolution illustrated for the senescence-associated cysteine protease gene SAG12 of plants. Plants 2021, 10, 1380. [Google Scholar] [CrossRef] [PubMed]
  57. Anderson, J.; Gibson, S.; Williamson, D.; Rampling, D.; Austin, C.; Shipley, J.; Sebire, N.; Brock, P. Rapid and accurate determination of MYCN copy number and 1p deletion in neuroblastoma by quantitative PCR. Pediatr. Blood Cancer 2006, 46, 820. [Google Scholar] [CrossRef]
  58. Kim, T.-K.; Shiekhattar, R. Architectural and functional commonalities between enhancers and promoters. Cell 2015, 162, 948–959. [Google Scholar] [CrossRef] [Green Version]
  59. Tippens, N.D.; Vihervaara, A.; Lis, J.T. Enhancer transcription: What, where, when, and why? Genes Dev. 2018, 32, 1–3. [Google Scholar] [CrossRef]
  60. Serfling, E.; Lübbe, A.; Dorsch-Häsler, K.; Schaffner, W. Metal-dependent SV40 viruses containing inducible enhancers from the upstream region of metallothionein genes. EMBO J. 1985, 4, 3851–3859. [Google Scholar] [CrossRef]
  61. Goppelt, A.; Stelzer, G.; Lottspeich, F.; Meisterernst, M. A mechanism for repression of class II gene transcription through specific binding of NC2 to TBP-promoter complexes via heterodimeric histone fold domains. EMBO J. 1996, 15, 3105–3116. [Google Scholar] [CrossRef] [PubMed]
  62. Flynn, R.A.; Almada, A.E.; Zamudio, J.R.; Sharp, P.A. Antisense RNA polymerase II divergent transcripts are P-TEFb dependent and substrates for the RNA exosome. Proc. Natl. Acad. Sci. USA 2011, 108, 10460–10465. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  63. Koch, F.; Fenouil, R.; Gut, M.; Cauchy, P.; Albert, T.K.; Zacarias-Cabeza, J.; Spicuglia, S.; De La Chapelle, A.L.; Heidemann, M.; Hintermair, C.; et al. Transcription initiation platforms and GTF recruitment at tissue-specific enhancers and promoters. Nat. Struct. Mol. Biol. 2011, 18, 956–963. [Google Scholar] [CrossRef]
  64. Natoli, G.; Andrau, J.-C. Noncoding transcription at enhancers: General principles and functional models. Annu. Rev. Genet. 2012, 46, 1–19. [Google Scholar] [CrossRef] [PubMed]
  65. Young, R.S.; Kumar, Y.; Bickmore, W.A.; Taylor, M.S. Bidirectional transcription initiation marks accessible chromatin and is not specific to enhancers. Genome Biol. 2017, 18, 242. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  66. Scruggs, B.S.; Gilchrist, D.A.; Nechaev, S.; Muse, G.W.; Burkholder, A.; Fargo, D.C.; Adelman, K. Bidirectional transcription arises from two distinct hubs of transcription factor binding and active chromatin. Mol. Cell 2015, 58, 1101–1112. [Google Scholar] [CrossRef] [Green Version]
  67. Cooper, G.M.; Brown, C.D. Qualifying the relationship between sequence conservation and molecular function. Genome Res. 2008, 18, 201–205. [Google Scholar] [CrossRef] [Green Version]
  68. Schoch, C.L.; Sung, G.H.; López-Giráldez, F.; Townsend, J.P.; Miadlikowska, J.; Hofstetter, V.; Robbertse, B.; Matheny, P.B.; Kauff, F.; Wang, Z.; et al. The Ascomycota tree of life: A phylum-wide phylogeny clarifies the origin and evolution of fundamental reproductive and ecological traits. Syst. Biol. 2009, 58, 224–239. [Google Scholar] [CrossRef]
  69. Spokevicius, A.; Taylor, L.; Melder, E.; Van Beveren, K.; Tibbits, J.; Creux, N.; Bossinger, G. The use of Induced Somatic Sector Analysis (ISSA) for studying genes and promoters involved in wood formation and secondary stem development. J. Vis. Exp. 2016, 116, e54553. [Google Scholar] [CrossRef]
  70. Duester, G. Knocking out enhancers to enhance epigenetic research. Trends Genet. 2019, 35, 89. [Google Scholar] [CrossRef]
  71. Borys, S.M.; Younger, S.T. Identification of functional regulatory elements in the human genome using pooled CRISPR screens. BMC Genom. 2020, 21, 1–15. [Google Scholar] [CrossRef] [PubMed]
  72. Sayari, M.; Van der Nest, M.A.; Steenkamp, E.T.; Adegeye, O.; Marincowitz, S.; Wingfield, B.D. Agrobacterium-mediated transformation of Ceratocystis albifundus. Microbiol. Res. 2019, 226, 55–64. [Google Scholar] [CrossRef] [PubMed]
  73. Wilson, A.M.; Wingfield, B.D. CRISPR-cas9-mediated genome editing in the filamentous ascomycete Huntiella omanensis. J. Vis. Exp. 2020, 160, e61367. [Google Scholar] [CrossRef]
  74. Lane, F.A.; Du Plessis, D.; Wingfield, B.D.; Wilken, P.M. Transferring an Agrobacterium-mediated transformation protocol across eight genera in the Ceratocystidaceae. For. Pathol. 2021, 51, e12688. [Google Scholar] [CrossRef]
Genes 14 00848 g002 550
Figure 2. A diagrammatic illustration of the three different distribution patterns of the motifs across the different genes. Each black line represents a 1000 bp sequence upstream of the start codon for a gene, while coloured blocks represent different motifs present in this region. In Pattern 1, there is clear conservation in the spatial position and order of the motifs across the different genes. In Pattern 2, there was conservation in the order of the motifs only but conservation in the spatial position was lacking. There was no observed conservation in either motif order, or motif positioning in those motifs grouped in Pattern 3. This figure was modelled after the distribution patterns observed in this study, with full patterns provided in Figure S2.
Figure 2. A diagrammatic illustration of the three different distribution patterns of the motifs across the different genes. Each black line represents a 1000 bp sequence upstream of the start codon for a gene, while coloured blocks represent different motifs present in this region. In Pattern 1, there is clear conservation in the spatial position and order of the motifs across the different genes. In Pattern 2, there was conservation in the order of the motifs only but conservation in the spatial position was lacking. There was no observed conservation in either motif order, or motif positioning in those motifs grouped in Pattern 3. This figure was modelled after the distribution patterns observed in this study, with full patterns provided in Figure S2.
Genes 14 00848 g002
Genes 14 00848 g003 550
Figure 3. The distribution of motifs from the corresponding Ceratocystidaceae genes identified in non-Ceratocystidaceae species. The graph shows the number of motifs found in the 1000 bp region upstream of the ATG start codon in non-Ceratocystidaceae genomes that matched to corresponding motifs from Ceratocystidaceae genomes. The Ceratocystidaceae column represents the total number of motifs found in Ceratocystidaceae genomes per gene, and therefore represents the total number of possible matches.
Figure 3. The distribution of motifs from the corresponding Ceratocystidaceae genes identified in non-Ceratocystidaceae species. The graph shows the number of motifs found in the 1000 bp region upstream of the ATG start codon in non-Ceratocystidaceae genomes that matched to corresponding motifs from Ceratocystidaceae genomes. The Ceratocystidaceae column represents the total number of motifs found in Ceratocystidaceae genomes per gene, and therefore represents the total number of possible matches.
Genes 14 00848 g003
Genes 14 00848 g004 550
Figure 4. A schematic representation showing the phylogenetic relationship between the non-Ceratocystidaceae species and the Ceratocystidaceae family, rooted to Saccharomyces. The tree was constructed using the online interface phyloT based on the current taxonomic structure of the NCBI taxonomic database. Subsequent edits were made using iTol v6 (https://itol.embl.de/). The numbers in brackets indicate the number of motifs (from the Ceratocystidaceae genomes) that were found in each of the non-Ceratocystidaceae genomes.
Figure 4. A schematic representation showing the phylogenetic relationship between the non-Ceratocystidaceae species and the Ceratocystidaceae family, rooted to Saccharomyces. The tree was constructed using the online interface phyloT based on the current taxonomic structure of the NCBI taxonomic database. Subsequent edits were made using iTol v6 (https://itol.embl.de/). The numbers in brackets indicate the number of motifs (from the Ceratocystidaceae genomes) that were found in each of the non-Ceratocystidaceae genomes.
Genes 14 00848 g004
Table
Table 1. Fungal genomes with references used in this study.
Table 1. Fungal genomes with references used in this study.
SpeciesStrain 1Whole Genome Accession NumberReferences 2
Ceratocystidaceae
Ambrosiella beaveriCMW 26179 *JARQWA010000000Unpublished
Ambrosiella cleistominutaCBS 141682 *JABFIG010000000[20]
Ambrosiella xyleboriCBS 110.61PCDO01000000[21]
Berkeleyomyces basicolaCMW 25440 *PJAC01000000[22]
Ceratocystis adiposaCMW 2574 *LXGU01000000[23]
Ceratocystis albifundusCMW 17620JSSU01000000[24]
Ceratocystis fimbriataCBS 114723APWK03000000[25]
Ceratocystis eucalypticolaCMW 9998LJOA01000000[26]
Ceratocystis harringtoniiCMW 14789MKGM01000000[27]
Ceratocystis manginecansCMW 17570JJRZ01000000[28]
Ceratocystis smalleyiCMW 14800NETT01000000[29]
Davidsoniella australisCMW 2333 *RHLR01000000[30]
Davidsoniella eucalyptiCMW 3254 *RMBW01000000[31]
Davidsoniella neocaledoniaeCMW 26392RHDR01000000[30]
Davidsoniella virescensCMW 17339LJZU01000000[26]
Endoconidiophora laricicolaCBS 100207LXGT01000000[23]
Endoconidiophora polonicaCBS 100205LXKZ01000000[23]
Huntiella bhutanensisCMW 8217MJMS01000000[27]
Huntiella decipiensCMW 30855NETU01000000[32]
Huntiella moniliformisCBS 118127JMSH01000000[28]
Huntiella omanensisCMW 11056JSUI01000000[24]
Huntiella savannaeCMW 17300LCZG01000000[33]
Thielaviopsis ethaceticaJCM 6961BCFY01000000[34]
Thielaviopsis musarumCMW 1546LKBB01000000[26]
Thielaviopsis euricoiJCM 6020BCHJ01000000[34]
non-Ceratocystidaceae
Neurospora crassaOR74AAABX03000000[35]
Sordaria macrosporak-hellCABT02000000[36]
Fusarium circinatumFSP 34AYJV02000000[37]
Ophiostoma novo-ulmiH327AMZD01000000[38]
1 Genome not obtained from NCBI indicated with asterisk. 2 Genomes not published indicated with hyphen.
Table
Table 2. BUSCO IDs and corresponding gene names.
Table 2. BUSCO IDs and corresponding gene names.
BUSCO IDGene Name
EOG092D0072Dynein heavy chain (Penicillium sp. 2HH)
EOG092D00LLPre-mRNA-processing-splicing factor 8 (Penicillium sp. 2HH)
EOG092D0124General negative regulator of transcription subunit 1 (Penicillium sp. 2HH)
EOG092D01IYSNF2-related protein (Penicillium sp. 2HH)
EOG092D01J4U3 snoRNP protein (Arthrobotrys oligospora)
EOG092D01MXPhospholipase D family protein (Penicillium sp. 2HH)
EOG092D01QPCell morphogenesis protein PAG1 (Penicillium sp. 2HH)
EOG092D01WXphosphatidylinositol-4- kinase (Arthrobotrys oligospora)
EOG092D01YAClathrin, heavy chain (Penicillium sp. 2HH)
EOG092D01ZKE3 ubiquitin-protein ligase listerin (Penicillium sp. 2HH)
EOG092D02YCTranscriptional regulatory protein sin3 (Arthrobotrys oligospora)
EOG092D03RCUDP-glucose:glycoprotein glucosyltransferase (Penicillium sp. 2HH)
EOG092D03RYSister chromatid cohesion protein 2 (Arthrobotrys oligospora)
EOG092D042RDNA repair protein rad50 (Arthrobotrys oligospora)
EOG092D0454Anaphase-promoting complex subunit 1 (Arthrobotrys oligospora)
EOG092D0564THO complex subunit 2 (Arthrobotrys oligospora)
EOG092D05RIPAN2-PAN3 deadenylation complex catalytic subunit PAN2 (Penicillium sp. 2HH)
EOG092D05X9Transcription elongation factor spt6 (Helotiales sp. DMI_Dod_QoI)
EOG092D0ACXElongation factor EF-Tu (Penicillium sp. 2HH)
EOG092D0AI2MIFG and Upf2 domain-containing protein (Penicillium sp. 2HH)
Table
Table 3. Results of the all-vs.-all comparison using Tomtom, which attempted to group the different motifs. Among the motifs, 4 groups were identified that shared motif identity. The motifs and genes in bold are present across multiple groups.
Table 3. Results of the all-vs.-all comparison using Tomtom, which attempted to group the different motifs. Among the motifs, 4 groups were identified that shared motif identity. The motifs and genes in bold are present across multiple groups.
Group NumberGenes (BUSCO ID)Matched Motifs 1
1EOG092D01MXMotif 28
EOG092D00LLMotif 1
EOG092D01YAMotif 8 2
2EOG092D00LLMotif 2, Motif 3, Motif 5, Motif 6
EOG092D03RYMotif 18
3EOG092D0124Motif 10
EOG092D00LLMotif 4
4EOG092D0454Motif 22 2
EOG092D0564Motif 2 2, Motif 3 2
EOG092D01YAMotif 8 2, Motif 18 2
EOG092D01IYMotif 9 2, Motif 4 2
EOG092D01WXMotif 7 2
EOG092D0072Motif 1 2, Motif 4 2
EOG092D0124Motif 12
EOG092D03RCMotif 9, Motif 14
1 Motif numbers retained from the original MEME analysis for motifs that met the cut-off parameters. 2 Adenine rich motifs (see Figure 1).
Table
Table 4. The distribution of TF classes for motifs that returned matches to the JASPAR core 2018 database.
Table 4. The distribution of TF classes for motifs that returned matches to the JASPAR core 2018 database.
Known TF ClassesNumber of Matched Motifs
C2H2 zinc finger factors151
MADS box factors22
Fork head/winged helix factors11
Tryptophan cluster factors8
Other7
Helix–turn–helix6
AP2/ERF domain5
B3 domain3
A.T hook factors2
Heat shock factors2
Basic helix–loop–helix factors (bHLH)2
APSES-type DNA-binding domain1
C6 zinc cluster factors1
Other C4 zinc finger-type factors1
Other α1
Homeo domain factors1
High-mobility group (HMG) domain factors1
Basic leucine zipper (bZIP)1
Copper-fist DNA-binding domain1
Rel homology region (RHR) factors1
Table
Table 5. The distribution of TF classes for motifs that matched to the JASPAR fungi 2018 sub-collection.
Table 5. The distribution of TF classes for motifs that matched to the JASPAR fungi 2018 sub-collection.
Known TF ClassesNumber of Matched Motifs
C2H2 zinc finger factors24
Heat shock factors6
Fork head/winged helix factors6
Tryptophan cluster factor5
Other C4 zinc finger-type factors5
Basic helix–loop–helix factors (bHLH)4
A.T Hook factors4
C6 zinc cluster factors4
Copper-first DNA-binding domain3
High-mobility group (HMG) domain factors3
Heteromeric CCAAT-binding2
Basic leucine zipper factors (bZIP)2
Homeo domain factors2
MADS box factors1
APSES-type DNA-binding domain1
Table
Table 6. Motifs discovered from the Ceratocystidaceae that were present in non-Ceratocystidaceae species.
Table 6. Motifs discovered from the Ceratocystidaceae that were present in non-Ceratocystidaceae species.
SpeciesMotif
EOG092D0072
N. crassaMotif 1
F. circinatumMotif 4
O. novo-ulmiMotif 1, Motif 4
S. macrosporaMotif 1
EOG092D01ZK
F. circinatumMotif 3
O. novo-ulmiMotif 2, Motif 3
EOG092D01J4
F. circinatumMotif 6
EOG092D01YA
N. crassaMotif 1, Motif 2
F. circinatumMotif 2, Motif 5, Motif 9, Motif 18
O. novo-ulmiMotif 2, Motif 5, Motif 8, Motif 18
S. macrosporaMotif 3, Motif 5, Motif 8
EOG092D01WX
F. circinatumMotif 1, Motif 2, Motif 4
O. novo-ulmiMotif 1, Motif 2, Motif 4, Motif 7
S. macrosporaMotif 4, Motif 9
EOG092D01MX
N. crassaMotif 28
EOG092D01IY
N. crassaMotif 4
O. novo-ulmiMotif 4, Motif 9
S. macrosporaMotif 4
EOG092D0124
N. crassaMotif 1
O. novo-ulmiMotif 1
S. macrosporaMotif 1
EOG092D0454
F. circinatumMotif 22
O. novo-ulmiMotif 22
EOG092D03RY
O. novo-ulmiMotif 5
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Maseko, N.N.; Steenkamp, E.T.; Wingfield, B.D.; Wilken, P.M. An in Silico Approach to Identifying TF Binding Sites: Analysis of the Regulatory Regions of BUSCO Genes from Fungal Species in the Ceratocystidaceae Family. Genes 2023, 14, 848. https://doi.org/10.3390/genes14040848

AMA Style

Maseko NN, Steenkamp ET, Wingfield BD, Wilken PM. An in Silico Approach to Identifying TF Binding Sites: Analysis of the Regulatory Regions of BUSCO Genes from Fungal Species in the Ceratocystidaceae Family. Genes. 2023; 14(4):848. https://doi.org/10.3390/genes14040848

Chicago/Turabian Style

Maseko, Nomaswazi N., Emma T. Steenkamp, Brenda D. Wingfield, and P. Markus Wilken. 2023. "An in Silico Approach to Identifying TF Binding Sites: Analysis of the Regulatory Regions of BUSCO Genes from Fungal Species in the Ceratocystidaceae Family" Genes 14, no. 4: 848. https://doi.org/10.3390/genes14040848

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop