In silico analysis of promoter regions to identify regulatory elements in TetR family transcriptional regulatory genes of Mycobacterium colombiense CECT 3035

Abstract

Background
Mycobacterium colombiense is an acid-fast, non-motile, rod-shaped mycobacterium confirmed to cause respiratory disease and disseminated infection in immune-compromised patients, and lymphadenopathy in immune-competent children. It has virulence mechanisms that allow them to adapt, survive, replicate, and produce diseases in the host. To tackle the diseases caused by M . colombiense , understanding of the regulation mechanisms of its genes is important. This paper, therefore, analyzes transcription start sites, promoter regions, motifs, transcription factors, and CpG islands in TetR family transcriptional regulatory (TFTR) genes of M . colombiense CECT 3035 using neural network promoter prediction, MEME, TOMTOM algorithms, and evolutionary analysis with the help of MEGA-X.

Results
The analysis of 22 protein coding TFTR genes of M . colombiense CECT 3035 showed that 86.36% and 13.64% of the gene sequences had one and two TSSs, respectively. Using MEME, we identified five motifs (MTF1, MTF2, MTF3, MTF4, and MTF5) and MTF1 was revealed as the common promoter motif for 100% TFTR genes of M . colombiense CECT 3035 which may serve as binding site for transcription factors that shared a minimum homology of 95.45%. MTF1 was compared to the registered prokaryotic motifs and found to match with 15 of them. MTF1 serves as the binding site mainly for AraC, LexA, and Bacterial histone-like protein families. Other protein families such as MATP, RR, σ-70 factor, TetR, LytTR, LuxR, and NAP also appear to be the binding candidates for MTF1. These families are known to have functions in virulence mechanisms, metabolism, quorum sensing, cell division, and antibiotic resistance. Furthermore, it was found that TFTR genes of M . colombiense CECT 3035 have many CpG islands with several fragments in their CpG islands. Molecular evolutionary genetic analysis showed close relationship among the genes.

Conclusion
We believe these findings will provide a better understanding of the regulation of TFTR genes in M . colombiense CECT 3035 involved in vital processes such as cell division, pathogenesis, and drug resistance and are likely to provide insights for drug development important to tackle the diseases caused by this mycobacterium. We believe this is the first report of in silico analyses of the transcriptional regulation of M . colombiense TFTR genes.

Full text

Hamdeetal.
Journal of Genetic Engineering and Biotechnology (2022) 20:53
https://doi.org/10.1186/s43141-022-00331-6
RESEARCH
In silico analysis ofpromoter regions
toidentify regulatory elements inTetR
family transcriptional regulatory genes
ofMycobacterium colombiense CECT 3035
Feyissa Hamde*, Hunduma Dinka and Mohammed Naimuddin*
Abstract
Background: Mycobacterium colombiense is an acid-fast, non-motile, rod-shaped mycobacterium confirmed to cause
respiratory disease and disseminated infection in immune-compromised patients, and lymphadenopathy in immune-
competent children. It has virulence mechanisms that allow them to adapt, survive, replicate, and produce diseases in
the host. To tackle the diseases caused by M. colombiense, understanding of the regulation mechanisms of its genes is
important. This paper, therefore, analyzes transcription start sites, promoter regions, motifs, transcription factors, and
CpG islands in TetR family transcriptional regulatory (TFTR) genes of M. colombiense CECT 3035 using neural network
promoter prediction, MEME, TOMTOM algorithms, and evolutionary analysis with the help of MEGA-X.
Results: The analysis of 22 protein coding TFTR genes of M. colombiense CECT 3035 showed that 86.36% and 13.64%
of the gene sequences had one and two TSSs, respectively. Using MEME, we identified five motifs (MTF1, MTF2, MTF3,
MTF4, and MTF5) and MTF1 was revealed as the common promoter motif for 100% TFTR genes of M. colombiense
CECT 3035 which may serve as binding site for transcription factors that shared a minimum homology of 95.45%.
MTF1 was compared to the registered prokaryotic motifs and found to match with 15 of them. MTF1 serves as the
binding site mainly for AraC, LexA, and Bacterial histone-like protein families. Other protein families such as MATP, RR,
σ-70 factor, TetR, LytTR, LuxR, and NAP also appear to be the binding candidates for MTF1. These families are known to
have functions in virulence mechanisms, metabolism, quorum sensing, cell division, and antibiotic resistance. Further-
more, it was found that TFTR genes of M. colombiense CECT 3035 have many CpG islands with several fragments in
their CpG islands. Molecular evolutionary genetic analysis showed close relationship among the genes.
Conclusion: We believe these findings will provide a better understanding of the regulation of TFTR genes in M.
colombiense CECT 3035 involved in vital processes such as cell division, pathogenesis, and drug resistance and are
likely to provide insights for drug development important to tackle the diseases caused by this mycobacterium. We
believe this is the first report of in silico analyses of the transcriptional regulation of M. colombiense TFTR genes.
Keywords: M. colombiense, Transcription start site, Promoter, Motif, CpG islands, Antibiotic resistance
© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the
original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or
other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this
licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.
Background
Mycobacterium colombiense is an acid-fast, non-motile,
rod-shaped mycobacterium that belongs to the Myco-
bacterium avium complex (MAC) [1]. MAC contains
clinically important non-tuberculous mycobacteria
(NTM) and is the second largest medical complex in the
Open Access
Journal of Genetic Engineering
and Biotechnology
*Correspondence: feyissahamde@yahoo.com; mnaimuddin@gmail.com
Department of Applied Biology, School of Applied Natural Science,
Adama Science and Technology University, P.O. Box 1888, Adama,
Ethiopia

Page 2 of 12
Hamdeetal. Journal of Genetic Engineering and Biotechnology (2022) 20:53
Mycobacterium genus after the Mycobacterium tubercu-
losis complex [2]. MAC comprises species that include
M. colombiense, M. avium, M. intracellulare, M. chi-
maera, M. marseillense, M. timonense, M. boucher-
durhonense, M. vulneris, M. arosiense, four subspecies
of M. avium, and “MAC-other” species [3]. NTM are
believed to be natural inhabitants of the environment,
found as saprophytes, commensals, and symbionts in the
ecosystem. Since their clinical relevance was unknown,
these bacteria have been neglected for many years as
they have always been recognized as just environmen-
tal contaminants or colonizers [4]. Although, they are
not considered as a public health problem, their impor-
tance is increasing due to their frequent association with
immune-suppression, especially in HIV/AIDS patients,
which is highly fatal [5].
NTM are generally acquired from the environment via
ingestion, inhalation, and dermal contact [6]. ey are
opportunistic pathogens that cause lymphadenitis, lung
infections, skin, and soft tissue infections mostly affect-
ing patients with preexisting pulmonary disease such as
chronic obstructive pulmonary disease or tuberculosis
(TB), or those with systemic impairment of immunity
(i.e., patients with HIV infection, leukemia, and those
using immunosuppressive drugs) [1, 7]. ere are more
than 150 non-tuberculous mycobacterial species listed
in public databases and about a third of them have been
implicated in diseases of humans [4]. NTM has been
observed for 100 years, but the trend of increasing prev-
alence of NTM is of great concern for clinicians as well
as microbiologists. In some areas, NTM-associated dis-
ease is more abundant than previously believed and is a
quietly unfolding disease epidemic, even overtaking TB
prevalence which results in an increase in the medical
costs [8]. NTM are an important cause of morbidity and
mortality in the progressive lung diseases [9] where they
are also important pathogens because of their high level
of antitubercular drug resistance [10].
Among NTM, M. colombiense has been confirmed to
cause respiratory disease and disseminated infection in
immune-compromised HIV patients, as well as lymphad-
enopathy in immune-competent children. Nevertheless,
very little is known about the molecular mechanisms
that underlie M. colombiense gene expression regula-
tion that play a great role in infection and pathogenesis
[11]. Mycobateria are known to display differential drug
susceptibility and strong drug resistance to several anti-
biotics by various mechanisms [12]. Understanding the
regulatory pathways involved in drug resistance would
aid the drug development process against this pathogen
and possibly NTM [13]. TetR family transcription regula-
tors (TFTRs) play a significant role in conferring antibi-
otic resistance and also control expression of biosynthesis
of antibiotics, pathogenicity, biofilm formation, quorum
sensing, cytokinesis, morphogenesis, osmotic stress, and
various metabolic pathways [14, 15]. A recent report
indicated that TFTRs represent the most abundant class
of regulators in mycobacteria [16]. However, there are
no reports about the regulatory analysis of TFTRs of M.
colombiense CECT 3035 in silico predictions of tran-
scriptome data could provide key information on the
molecular details of regulatory mechanisms including
promoter sequences, type of sigma factors associated to
the RNA polymerase (RNAP) involved in the initiation of
transcription, as well as other regulatory elements [17].
e objective of this study was therefore, to analyze tran-
scription start site (TSS), promoter regions, transcrip-
tion factors (TF), and cytosine-phosphate-guanine (CpG)
islands in TFTR genes of M. colombiense CECT 3035 to
gain insights into the regulation of gene expression. We
also discuss about the role of drug resistance and possible
directions for drug development.
Materials andmethods
Identication oftranscription start site andpromoter
region
Twenty two encoding genome sequences of TFTR genes
of M. colombiense CECT 3035 starting with prokary-
otic start codons (ATG, GTG, and TTG) were identified
from National Center for Biotechnology Information
(NCBI) database. First, the sequences with start codons
were identified and used to determine their TSS. To
find TSS, 1 kb sequences upstream of prokaryotic start
codon were excised from each gene sequence. In most
of the TFTR genes of M. colombiense CECT 3035, since
the TSS regions are confined beyond 1 kb upstream of a
start codon, an additional 1 kb, 2 kb, or more sequences
from prokaryotic start codons were excised from each
gene sequence. Promoter regions for the anticipated gene
in M. colombiense were defined as 1 kb length upstream
of each TSSs. For this purpose, the sequences were pre-
pared in the Fasta format and entered into neural net-
work promoter prediction (NNPP version 2.2) tool.
NNPP tool was set with minimum standard predictive
score (between 0 and 1) cutoff value of 0.8 for prokary-
otes [18]. In order to have more accurate prediction
value, the highest value of prediction score was consid-
ered for regions containing more than one TSS on NNPP
output.
Identication ofmotifs andtranscription factors
To identify motifs and transcription factors, 22 sequence
encoding genes of M. colombiense were downloaded
from GenBank of NCBI database in their Fasta format.
After the collection of genes, the whole gene promot-
ers were identified for each gene using NNPP algorithm

Page 3 of 12
Hamdeetal. Journal of Genetic Engineering and Biotechnology (2022) 20:53
to find possible transcription promoters in prokary-
otic organisms. All identified M. colombiense pro-
moter sequences were analyzed using Multiple Em for
Motif Elicitation (MEME version 5.3.3) search tool/web
server hosted by the National Biomedical Computation
Resource to look for motifs and transcription factors
that regulate the expression of genes [19]. In addition to
motif and transcription factor discovery, MEME is also
important in carrying out motif scanning, motif enrich-
ment, motif comparison, and gene regulation [20]. From
the optional inputs in MEME, Classic mode (for motif
discovery), DNA (for sequence alphabet), zero or one
occurrence per sequence (for site distribution) were kept
as a default, while five (for the number of motifs MEME
should find) were set prior to start searching. Since zero
occurrence per sequence or one occurrence per sequence
models are sufficient for most motif finding [21], zero or
one occurrence per sequence was applied for motif dis-
tribution. MEME outputs the result as MEME HTML
(High Pretext Markup Programming Language), MEME
XML, MEME text output, MAST HTML, MAST XML,
and MAST text. We used MEME HTML to discover
motifs and motif locations. e discovered motifs were
displayed as a request and the motif locations were dis-
played in the form of block diagrams.
Following MEME results, one of the discovered motifs
with the smallest e value was forwarded to other web-
based program (TOMTOM) that compares one or more
motifs against a database of known motifs for further
investigation. e output of TOMTOM includes LOGOS
representing the alignment of two motifs, the p value and
q value (a measure of false discovery rate) of the match
and links back to the parent motif database for more
detailed information about the target motif. TOMTOM
shows the query motif closely resembles the binding
motif (transcription factor) in the set of M. colombiense
CECT 3035 gene promoter regions [22].
Identication ofCpG islands
To find the CpG islands in TFTR genes of M. colom-
biense CECT 3035, both promoter regions and body
regions were used. Accordingly, two algorithms were
used. e first algorithm was Takai and Jones’ stringent
algorithm—CpG island finder (Database of CpG Islands–
http://dbcat.cgm.ntu.edu.tw/). is algorithm was used
since it outperforms the others in excluding the short
interspersed elements and can identify CpGs that are
more likely associated with the 5′ regions of genes [23].
e second was CLC searching genomics Workbench
ver. 3.6.5 (http:// clcbio. com, CLC Bio, Aarhus, Denmark).
It was used for searching CpG islands using the restric-
tion enzyme MspI cutting sites (fragment sizes between
40 and 220 bp).
Phylogenetic tree
Phylogenetic tree was constructed using molecular evo-
lutionary genetics analysis X (MEGA-X version 10.2.6)
using neighbor-joining method [24] with the use of
aligned protein sequences from TFTR genes of M.
colombiense CECT 3035. e tree was drawn to scale
with branch lengths showing the evolutionary distances
those infer phylogenetic tree. e evolutionary dis-
tances were computed using the maximum composite
likelihood method [25] and are in the units of the num-
ber of base substitutions per site. All ambiguous posi-
tions were removed for each sequence pair. Evolutionary
analyses were conducted in MEGA X [26]. Bootstrap
tests were also performed to estimate the phylogeny of
the sequences. All ambiguous positions were removed
for each sequence pair (pairwise deletion option). ere
were a total of 1000 positions in the final dataset.
Results
Transcription start sites identication
From the 22 encoding genome sequences of TFTR genes
of M. colombiense CECT 3035, 17 (77.27%), 4 (18.18%),
and 1 (4.55%) genes start with ATG, GTG, and TTG,
respectively. TSS of all the 22 genes were identified using
NNPP. Accordingly, the highest prediction score was
considered to determine the promoter regions for genes
containing more than one transcription start sites. e
results show that most genes (19 genes, 86.36%) contain
single TSS and only 3 genes (13.64%) contain two TSSs
using predictive score at the cutoff value of 0.8. Look-
ing at their distance from the start codon, the farthest
gene was found 11,105 bp away from the start codon at
92% predictive score and the closest gene was found 24
bp away from the start codon at 83% predictive score
(Table1).
Common motifs andtranscription factors
After the identification of TSS, promoter regions were
identified for each gene and loaded to MEME. Accord-
ingly, significant motifs in the input sequence set was
searched using MEME through web server and the E
value which is the probability of finding well conserved
pattern in random sequences. MEME output revealed
five motifs (MTF1, MTF2, MTF3, MTF4, and MTF5).
MTF1 was found to be the common motif for 100%
with the lowest E value (1.1e-013) and a motif width of
29 bp which serve as binding sites for transcription fac-
tors sharing a minimum of (95.45%) (Table2). MTF1 was
found to serve as binding sites for transcription factors in
the expression and regulation of the genes. Of the total
108 motifs, slightly higher distributions were found in
positive strands (56) than negative strands (52) of TFTR

Page 4 of 12
Hamdeetal. Journal of Genetic Engineering and Biotechnology (2022) 20:53
genes of M. colombiense CECT 3035. e location and
distribution of the motifs were found between − 994 and
− 9 bp of the transcription start sites (Fig.1).
To analyze the information content, sequence logos for
the common promoter motif (MTF1) was generated by
MEME (Fig. 2). is resulted in different characters of
motif alignment columns, where the height of the letter
represents how frequently that nucleotide is expected to
be observed at the defined position.
Furthermore, MTF1 was compared to the registered
motifs in publicly available databases so as to check if
there are similarities to known regulatory motifs using
TOMTOM web application. In a similar manner, TOM-
TOM provides LOGOS that represent the alignment of
two motifs and a numeric score for the match between
two motifs. e output from TOMTOM also links back
to the parent motif database for detailed information on
the biological functions of the matched motif. e results
show that MTF1 matched with 15 out of 84 known
motifs found in Prokaryotic DNA databases. Looking at
the ratio, MTF1 matched with 4 AraC families, 2 LexA
families, 2 Bacterial histone-like protein families, and one
Table 1 Identified TSSs, predictive score value, and their distances from start codon
Gene name Gene ID Number of TSS identied Predictive score at cut value
of 0.8 Distance from start codon
MCOL_RS11660 31527733 1 0.94 2390
MCOL_RS11090 31527620 2 0.91, 0.92 − 11,928, − 11,105
MCOL_RS08825 31527172 1 0.99 − 943
MCOL_RS08145 31527038 1 0.89 1150
MCOL_RS06375 31526691 1 0.82 3946
MCOL_RS04605 31526350 1 0.89 478
MCOL_RS03660 31526168 1 0.83 24
MCOL_RS02905 31526017 1 0.93 5711
MCOL_RS02170 31525872 1 0.91 − 2877
MCOL_RS26110 31530593 2 0.97, 0.99 1780, 952
MCOL_RS07580 31526928 1 0.93 − 52
MCOL_RS05990 31526617 1 0.9 7888
MCOL_RS23260 31530032 1 0.82 6063
MCOL_RS22650 31529910 1 0.85 − 677
MCOL_RS17010 31528795 1 0.82 10425
MCOL_RS03070 31526050 1 0.95 − 1426
MCOL_RS04305 31526295 1 0.9 − 2778
MCOL_RS22305 31529842 2 0.82, 0.91 4568, 4044
MCOL_RS20235 31529434 1 0.90 1645
MCOL_RS25615 31530496 1 0.90 1468
MCOL_RS15085 31528411 1 0.85 7727
MCOL_RS13375 31528074 1 0.82 113
Table 2 Identified common motifs in gene promoter regions and number of binding sites
MTF motif
a Probability of nding an equally well conserved motif in random sequences
Discovered motif Number (%) of CECT 3035 promoters
containing the motifs E valueaMotif width Total no.
of binding
sites
MTF 1 22 (100%) 1.1e-013 29 22
MTF 2 21 (95.45%) 3.0e-007 29 21
MTF 3 22 (100%) 6.8e-006 21 22
MTF 4 21 (95.45%) 1.5e-003 15 21
MTF 5 22 (100%) 1.8e-001 15 22

Page 5 of 12
Hamdeetal. Journal of Genetic Engineering and Biotechnology (2022) 20:53
Fig. 1 Positions of motifs relative to TSSs. The nucleotide positions are specified at the bottom of the graph from + 1 (beginning of TSSs) to the
upstream 1 kb (− 1 kb) bp
Fig. 2 Sequence logos for the identified common promoter motif, MTF1 of M. colombiense CECT 3035 genes. The analysis was carried out using the
MEME Suite

Page 6 of 12
Hamdeetal. Journal of Genetic Engineering and Biotechnology (2022) 20:53
family of each SLC45 (MATP) family, RR family, σ-70
factor family, TetR family, LytTR family, LuxR family,
and NAP family. e matched motifs and their biologi-
cal roles are shown in Table3. Based on the functions,
we categorized the TFs into different groups. e major-
ity of the matched TFs (7/15) are found to be involved in
pathogenesis by the generation of different virulence fac-
tors and antibiotic resistance. We grouped other TFs by
functions related to Information storage and replication
(2/15), metabolism (3/15), and stress survival (3/15).
Determination ofCpG islands
To further explore the regulatory elements that are
involved in TFTR genes of M. colombiense CECT 3035,
CpG islands were investigated in its promoter and gene
body regions using CpG island finder (http:// dbcat. cgm.
ntu. edu. tw/) and CLC searching genomics Workbench
ver. 3.6.5 (http:// clcbio. com, CLC bio, Aarhus, Den-
mark). Accordingly, only 4 (MCOL_RS08145, MCOL_
RS05990, MCOL_RS17010, and MCOL_RS04305) of 22
genes of TFTR genes of M. colombiense CECT 3035 lack
CpG islands in their promoter regions with GC content
greater than 50% in all genes as parameter set of Obs/Exp
greater than 0.65. Similarly, only 1 (MCOL_RS22650) of
the 22 genes lack CpG islands in their body regions while
all the remaining genes contain one possible CpG island
with GC content greater than 61.1%.
On the other hand, digestion of TFTR genes of M.
colombiense CECT 3035 using CLC genomics workbench
ver 3.6.1 with MspI restriction enzyme showed a single
CpG island in one gene, and all the remaining 21 genes
have multiple CpG islands in their promoter regions
(Table4).
Likewise, digestion of the body regions of TFTR
genes of M. colombiense CECT 3035 by MspI restric-
tion enzyme showed 1 gene lacking CpG islands, 5 genes
with single CpG islands, 4 genes with two CpG islands,
and all the remaining 12 genes with multiple CpG islands
(Table5).
Analysis ofphylogenetic tree
In recent years, the purpose of phylogenetic trees was
expanded to include understanding the relationships
among the sequences without regard to the host spe-
cies, inferring the functions of genes that have not been
studied experimentally and elucidating mechanisms that
lead to microbial outbreaks [27]. Here, a phylogenetic
tree was constructed using the neighbor-joining method
and minimum-evolution method of MEGA-X as shown
in Fig.3. Even though homologous evolutionary ances-
tor supported with 100% bootstrap was shown in the
Table 3 Transcription factor families binding to MTF1 motif of the promoter regions from prokaryotic database and their roles
TF families Candidate
transcription factors
and species
E value Function
AraC family VqsM, P. aeruginosa 2.46e-02 Control the production of virulence factors and quorum-sensing signaling mol-
ecules
AraC family AmrZ, P. aeruginosa 3.37e-01 Associated with biofilms and quorum sensing
SLC45 (MATP) family MatP, E. coli 3.51e-01 Involved in cross-linking of DNA in the Ter MD and linking the chromosome to the
divisome together with ZapA and ZapB
RR family CtrA, C. crescentus 6.12e-01 Regulate morphogenesis, DNA replication initiation, DNA methylation, cell division,
and cell wall metabolism.
σ-70 factor family PvdS, P. aeruginosa 1.70e+00 Activate transcription of genes for the biosynthesis or the uptake of siderophores.
TetR family RutR, E. coli 1.95e+00 Regulate genes (rutABCDEFG operon) involved in degradation and synthesis of
pyrimidine, degradation of purines, glutamine supply and pH homeostasis.
LytTR family AlgR, P. aeruginosa 3.05e+00 Control alginate production, type IV pilus function and virulence
NAP family EspR, M. tuberculosis 4.24e+00 controls the virulence of Mtb by regulating expression of EspA
Bacterial histone-like proteins IHF, P. putida 4.46e+00 Downregulated genes encoding ribosomal proteins, the alpha subunit of RNA
polymerase and components of the ATP synthase.
AraC family ArgR, P. aeruginosa 4.91e+00 Have major role in the control of certain biosynthetic and catabolic arginine genes
LexA families LexA, S. aureus 6.28e+00 Govern Salt overly sensitive (SOS) response
Bacterial histone-like proteins IHF, P. putida 7.34e+00 Essential for xyl gene expression from the TOL plasmid and for the biodegradation
of benzyl alcohol
LuxR protein family LasR, P. aeruginosa 7.52e+00 Involved in transcription, enhances exotoxin A production, plays role in pathogen-
esis
LexA families LexA, C. glutamicum 8.07e+00 Represses a number of genes involved in the response to DNA damage
AraC family HrpX, X. oryzae 8.36e+00 Regulation of virulence and motility required for pilus assembly

Page 7 of 12
Hamdeetal. Journal of Genetic Engineering and Biotechnology (2022) 20:53
following phylogenetic tree, different closely related clus-
ters and sister groups were observed.
Discussion
e DNA sequences around TSSs are important for
gene regulation in bacteria. Pinpointing of these TSS
permits the identification of potential binding sites for
transcriptional regulators those may inhibit or promote
translation [28]. In this study, 86.36% and 13.64% genes
were found to have a single and two TSSs, respectively.
For those genes with two TSSs, TSS with a higher value
was considered. is result is in agreement with Bou-
tard etal. [29] where most genes were expressed from
a single TSS. Identification of transcription start sites
enables identification of promoter regions [30]. Hence,
using the identified TSSs of each gene the promoter
region was identified for every gene. e promoter ele-
ment defines the DNA site directing the RNA polymer-
ase for transcription initiation, and it is a crucial element
to understand gene expression in bacteria [31]. Accurate
prediction of promoters is fundamental for interpret-
ing gene expression patterns, and for constructing and
understanding genetic regulatory networks [32]. After
the discovery of promoter regions for each gene, we used
each promoter sequence to identify motifs and transcrip-
tion factors using MEME. From the five identified motifs,
motif 1 (MTF1) was found as the most common regula-
tory motif for the TFTR genes of M. colombiense CECT
3035 to regulate expression of genes (Table2). e motif
width of MTF1 was found to be 29 bp which is in agree-
ment with a recent report which found a motif length of
27 bp represented DNA binding site for a TetR-depend-
ent regulation of a drug efflux pump in Mycobacterium
abscessus [33]. In addition, results of MEME also indi-
cated the particular location and distributions of motifs
to largely occur between − 974 bp and − 9 bp from tran-
scription start site. is confirms the location of motif to
be upstream, neighborhood of the TSS in corresponding
with other transcription factors [34].
Additionally, the comparison of query motif (MTF1)
with registered motifs in publicly available database of M.
colombiense CECT 3035 genes using TOMTOM web
application showed that MTF1 matched with 15 out of 84
known motifs found in prokaryotic DNA databases
Table 4 Determination of MspI sites and fragment sizes for promoter regions
Names of
corresponding
promoter regions
Nucleotide positions of MspI sites Fragment sizes (between 40 and 220 bp)
Pro-MCOL_RS11660 Multiple (17, 64, 163, 223, 336, 476, 484, 692, 706, 742, 827, 838, 886, 937) 47, 99, 60, 113, 140, 208, 85, 48, 51
Pro-MCOL_RS11090 Multiple (108, 130, 446, 504, 586, 656, 665, 707, 839, 867, 995) 58, 82, 70, 42, 132, 128
Pro-MCOL_RS08825 Multiple (21, 143, 163, 258, 343, 362, 379, 439, 653, 691, 752, 768, 953, 995) 122, 95, 85, 60, 214, 61, 185, 42
Pro-MCOL_RS08145 Multiple (27, 107, 206, 269, 388, 475, 499, 727, 764, 774, 849, 885, 962) 80, 99, 63, 119, 87, 75, 77
Pro-MCOL_RS06375 Multiple (50, 175, 239, 299, 428, 460, 557, 591, 595, 618, 735, 739, 750, 769, 866,
982, 994) 125, 64, 60, 129, 97, 117, 97, 116
Pro-MCOL_RS04605 Multiple (126, 157, 180, 329, 574, 613, 634, 837, 907, 985) 149, 203, 70, 78
Pro-MCOL_RS03660 Multiple (12, 116, 173, 213, 253, 426, 473, 540, 648, 654, 707, 857) 104, 57, 40, 173, 47, 67, 108, 53, 150
Pro-MCOL_RS02905 Multiple (21, 92, 137, 251, 610, 818, 828, 931) 71, 45, 114, 208, 103
Pro-MCOL_RS02170 Multiple (57, 204, 224, 261, 308, 416, 435, 471, 563, 592, 604, 733, 874, 965) 147, 47, 108, 92, 129, 141, 91
Pro-MCOL_RS26110 Multiple (14, 140, 168, 241, 545, 571, 653, 665, 868) 126, 73, 82, 203
Pro-MCOL_RS07580 Multiple (66, 165, 204, 255, 273, 461, 489, 516, 594, 642, 724, 758) 99, 51, 188, 78, 48, 82
Pro-MCOL_RS05990 Multiple (295, 312, 327, 347, 906, 968, 985) 62
Pro-MCOL_RS23260 Multiple (91, 97, 115, 295, 302, 391, 450, 550, 557, 627, 687, 709, 745, 821, 953) 180, 89, 59, 100, 70, 60, 76, 132
Pro-MCOL_RS22650 Multiple (5, 79, 86, 225, 239, 318, 330, 348, 435, 457, 553, 624, 640, 716, 765, 844,
888) 74, 139, 79, 87, 96, 71, 76, 49, 44
Pro-MCOL_RS17010 Multiple (5, 23, 33, 40, 130, 195, 247, 403, 543, 585, 680, 688, 716, 724, 851, 910) 90, 65, 52, 156, 140, 42, 95, 127, 59
Pro-MCOL_RS03070 Multiple (80, 158, 170, 209, 222, 341, 388, 415, 509, 577, 604, 706, 785, 923, 955,
965) 78, 119, 47, 94, 68, 102, 79, 138
Pro-MCOL_RS04305 Multiple (11, 149, 165, 170, 409, 804, 886, 906, 922, 964) 138, 82, 42
Pro-MCOL_RS22305 Multiple (175, 236, 248, 302, 554, 616, 636, 729, 782, 946) 61, 54, 62, 93, 53, 164
Pro-MCOL_RS20235 Multiple (10, 14, 20, 47, 380, 402, 571, 641, 820, 979) 169, 70, 179, 159
Pro-MCOL_RS25615 Multiple (0, 22, 92, 142, 148, 184, 254, 290, 310, 377, 851, 876) 70, 50, 67
Pro-MCOL_RS15085 Multiple (13, 18, 105, 127, 200, 223, 306, 369, 497, 553, 565, 992) 87, 73, 83, 63, 128, 56
Pro-MCOL_RS13375 Multiple (58, 83, 227, 242, 295, 329, 393, 422, 553, 618, 652, 861, 884, 893) 144, 53, 64, 131, 65, 209

Page 8 of 12
Hamdeetal. Journal of Genetic Engineering and Biotechnology (2022) 20:53
(Table3). MTF1 matched with 4 AraC families, involved
in pathogenesis by the production of virulence factors,
dormancy survival and drug resistance by the formation
of biofilms, cell-to-cell communication, and arginine
metabolism; 2 LexA families, involved in survival by
inducing SOS response upon DNA damage and salt
stress management; 2 bacterial histone-like protein fami-
lies, involved in metabolism of aromatic compounds and
Table 5 MspI cutting sites and fragment sizes for gene body regions
Name of corresponding body region Nucleotide positions of MspI sites Fragment sizes
MCOL_RS11660 Multiple (23, 41, 53, 127, 152, 162, 189, 241, 374, 378, 432, 450, 517) 74, 52, 133, 54, 67
MCOL_RS11090 Multiple (124, 193, 231, 340) 69, 109
MCOL_RS08825 Multiple (9, 130, 155, 240, 361, 395, 529) 121, 85, 134
MCOL_RS08145 Multiple (130, 243, 360) 113, 117
MCOL_RS06375 Multiple (4, 35, 182, 540) 147
MCOL_RS04605 Multiple (121, 267, 542) 146
MCOL_RS03660 Multiple (109, 152, 227, 448, 557, 566) 43, 75, 109
MCOL_RS02905 Multiple (98, 275, 286, 303) 177
MCOL_RS02170 Multiple (306, 431, 464, 565) 125, 101
MCOL_RS26110 Multiple (288, 420, 446, 458) 132
MCOL_RS07580 Multiple (74, 133, 158, 200, 459, 554, 586, 616) 59, 42, 95
MCOL_RS05990 Multiple (185, 274, 539) 89
MCOL_RS23260 Multiple (4, 118, 214, 225, 263, 276, 280, 320, 369, 519, 541) 114, 96, 40, 49, 150
MCOL_RS22650 Multiple (16, 23, 39, 43, 261, 361, 442, 592, 603) 218, 55, 81, 150
MCOL_RS17010 Multiple (29, 231, 310, 388, 445, 524, 540) 202, 79, 78, 57, 79
MCOL_RS03070 Multiple (95, 189, 203, 292, 311, 440, 484, 608, 643, 652) 94, 89, 129, 44, 124
MCOL_RS04305 Multiple (20, 278, 377, 394, 485, 489) 99, 91
MCOL_RS22305 Multiple (243, 540) -
MCOL_RS20235 Multiple (27, 52, 140, 149, 274, 382, 459, 493, 518, 558, 613, 624) 88, 125, 108, 77, 40, 55
MCOL_RS25615 Multiple (14, 113, 133, 387, 480, 607) 99, 93, 127
MCOL_RS15085 Multiple (68, 127, 246, 310, 367, 401, 459, 505, 529, 566) 59, 119, 64, 57, 58, 46
MCOL_RS13375 Multiple (49, 57, 106, 183, 291, 296, 396, 440, 466, 598, 604) 49, 77, 108, 100, 132
Fig. 3 Phylogenetic tree of M. colombiense CECT 3035 genes using neighbor-joining method

Page 9 of 12
Hamdeetal. Journal of Genetic Engineering and Biotechnology (2022) 20:53
downregulation of genes for entering into stationary
phase; and one family of each SLC45 (MATP) family,
involved in replication; RR family, involved in cell cycle
programs of chromosome replication and genetic tran-
scription; σ-70 factor family, for virulence and mobiliza-
tion of metals by siderophores; TetR family, for cell
growth by pyrimidine catabolism; LytTR family, for viru-
lence by control of alginate production and type IV pilus
function; LuxR family, for pathogenesis by production of
endotoxin A; and NAP family, controls the virulence of
M. tuberculosis by regulating expression of EspA. ese
findings match with the functions of TFTR that play a
significant role in conferring antibiotic resistance and
also control the expression of biosynthesis of antibiotics,
pathogenicity, biofilm formation, quorum sensing,
cytokinesis, morphogenesis, osmotic stress, and various
metabolic pathways [14, 15]. Formation of biofilms is an
important strategy in bacteria for survival, pathogenesis,
and antibiotic resistance [3, 35]. e presence of glyco-
phospholipids on the outermost portions of the cell enve-
lope enables formation of biofilms on the hyrdrophobic
surfaces. Biofilms allow communication and exchange of
materials between the closely associated cells and has
been linked to confer antibiotic resistance [13, 35, 36].
Antibiotic resistance by biofilms is a complex process
which has various modes of action such as the formation
of barrier where the exopolysaccharide component
greatly reduces permeability to antibiotics, detoxification
mechanism which produces enzymes to disrupt or alter
the structure of antibiotics that render them inactive,
drug efflux pumps that reduces the intracellular concen-
tration of antibiotics by transporting antibiotics outside
of the cell, and drug sequestration where specific proteins
prevent binding of antibiotics to the targets [35, 37, 38].
e potentially increased horizontal gene transfer
between the closely interacting bacterial cells in the bio-
films may also contribute to the spread of antibiotic
resistance [37, 38]. Effective anitmicrobials can be
designed based on this knowledge against bacterial bio-
films [39]. Quorum sensing (QS) is an important cell-cell
communication process that play significant roles in reg-
ulation of a variety of biological processes such as viru-
lence gene expression, biofilm formation, drug efflux
pumps, and plasmid transfer [37, 40]. In QS regulatory
systems, microorganisms produce and release a diffusible
autoinducer or QS signal to the surrounding environ-
ment, which accumulates along with bacterial growth
and induces target gene transcriptional expression upon
interaction with the respective signal receptor. In this
study, we found MTF1 binds to two TFs, VqsM and LasR,
from P. aeruginosa that have been reported to play a role
in virulence and QS modulation that positively regulates
the QS systems [40, 41]. QS signals could offer an
important possible direction for the development of anti-
microbials by the design of antagonists based on enzymes
that can abolish the QS signals or QS inhibitors that can
interfere with the signaling process. Based on the type of
signals used by the microbes, i.e., conserved or unique
signals, the choice of the design of broad-specificity or
narrow-specificity antimicrobials could possibly be facili-
tated [37, 42]. Iron is an essential element required for
microbial growth and virulence. Siderophore molecules
(also called mycobactins) are sophisticated iron-acquisi-
tion systems to overcome iron deficiency imposed by the
host defensive mechanism. ese small molecules are
secreted into the extracellular space, tightly bind availa-
ble iron, and then are reinternalized with their bound
iron through specific cell surface receptors [43]. Antimi-
crobial susceptibility with respect to iron metabolism in
MAC has been shown to be dependent on mycobactins.
Under iron-restricted conditions, the susceptibility to
antibiotics such as ethambutol, isoniazid, and -cycloser-
ine that target cell wall synthesis increased [44]. In this
study, MTF1 was found to match with the TF, PvdS from
P. aeruginosa involved in the biosynthesis or the uptake
of siderophores [45] and may be a potential target for the
development of antimicrobials. e other matched TFs
for virulence by various mechanisms include AlgR, EspR
(regulates gene expression of EspA) and HrpX [2, 46, 47].
Interestingly, EspR has been found to be conserved in M.
tuberculosis and M. colombiense [2]. One of the most
important aspect of survival of living organisms is metab-
olism that determines growth or dormancy, several bio-
synthetic processes including DNA replication and
division of cells. Several TFs were found to match with
the binding motif MTF1 revealed in this study. RutR,
belonging to TetR family, is involved in the regulation of
degradation and synthesis of pyrimidines, degradation of
purines, glutamine supply, and pH homeostasis by mech-
anisms that both stimulates and inhibits gene expression
at different promoters [48]. ArgR has been found to play
a major role in the control of certain biosynthetic and
catabolic arginine genes [49]. Integration host factor
(IHF) is known to be involved in a large number of cellu-
lar functions; however, it plays a major regulatory role
during transition from exponential to stationary phase by
controlling various cell surface-related functions and
downregulating genes encoding ribosomal proteins, the
alpha subunit of RNA polymerase, and components of
ATP synthase. IHF also controls xylR, which is the mas-
ter transcriptional factor of the TOL pathway for biodeg-
radation of m-xylene [50]. Mycobacterial infections are
known to be difficult to treat due to this switchover from
growth to stationary phases. Cell division is a vital pro-
cess for survival and propagation for living organisms. In
this study, we have found two transcription factors MatP

Page 10 of 12
Hamdeetal. Journal of Genetic Engineering and Biotechnology (2022) 20:53
(Escherichia coli) and CtrA (Caulobacter crescentus)
involved in cell division process taking part in mecha-
nisms such as linking chromosome to the divisome along
with ZapA and ZapB, initiation of DNA replication, mor-
phogenesis, DNA methylation, and cell wall metabolism
among other functions [51, 52]. In recent years, bacterial
cell division has been recognized as a promising new
direction for the discovery of antibiotics. Filamenting
temperature-sensitive mutant Z (FtsZ) protein has
emerged as a promising target for drug discovery. FtsZ is
an essential and central protein that has the ability to
organize into dynamic polymers at the cell membrane to
form a “divisome.” Most cell division inhibitors act via
FtsZ, either by interfering with GTPase activity or the
assembly/disassembly of the Z-ring, as well as by destabi-
lizing the structure of FtsZ [53]. LexA family transcrip-
tion factor is involved in transcriptional repressor [54].
erefore, targeting SOS response might play a central
role in promoting survival and the evolution of resistance
under antibiotic stress [55]. Identification and under-
standing of the transcriptional regulatory process by TFs
revealed in this study could provide important insights
into the development of antimicrobials against M. colom-
biense CECT 3035 and possibly other NTMs and open
gates for further research.
CpG Island is a pattern that plays a crucial role in the
analysis of genomes. It consist high-frequency of CpG
dinucleotides [56]. CpG islands are DNA methylations
regions in promoters known to regulate gene expres-
sion through transcriptional silencing of the correspond-
ing gene. DNA methylation at CpG islands is crucial for
gene expression and tissue-specific processes [57]. In the
present study, an investigation of the CpG islands was
performed for both promoter regions and body regions
of M. colombiense CECT 3035 genes using CpG island
finder and MspI restriction enzyme digestion. Only 5 of
22 genes of M. colombiense CECT 3035 genes lack CpG
islands in their promoter regions and only 2 of them
lack CpG islands in their body regions with GC content
greater than 50% and 61.1% in promoter regions and body
regions respectively, while all the rest (17 genes, 77.27%
promoter gene and 20 genes, 90.91% body regions) con-
tain one possible CpG island. On the other hand, diges-
tion of the promoter regions of M. colombiense CECT
3035 genes with MspI showed 1 gene with single frag-
ment and all the remaining 21 genes with multiple frag-
ments in their promoter regions, whereas CpG islands
of 1 genes lacking fragment, 5 genes contain single frag-
ment, CpG islands of 4 genes contain two fragments, and
the remaining 12 body region genes were found to con-
tain multiple fragments, respectively. is result is com-
parable with a recent report with regard to digestion by
MspI enzyme (the existence of several fragments (28/29)
in promoter regions and several fragments (26/29) in
body regions of Herbaspirillum seropedicae genes) [58].
is result implies that the promoter region of M. colom-
biense CECT 3035 genes have rich CpG islands that can
play a crucial role in gene regulation.
e phylogenetic tree generated in this study showed
22 different branches representing different genes. e
branching patterns of the tree indicated that a shared
evolutionary history existed among the genes with 100%
bootstrap. In addition, it is clear that there were differ-
ent clusters and sister groups those may differ from each
other due to base substitutions in the sequences. Hence,
knowing these features can contribute significantly to our
knowledge on molecular evolution, species phylogeny,
and biotechnology [59, 60] which may help in tackling the
spread of the bacteria. Furthermore, the close relatedness
of the genes is also the characteristics of M. colombiense
since study of DNA–DNA relatedness clearly differenti-
ates M. colombiense as separate species within the MAC
[61].
Conclusion
M. colombiense is a member of MAC responsible to
cause respiratory disease and disseminated infection in
immune-compromised patients and lymphadenopathy
in immune-competent children. erefore, understand-
ing of the mechanisms and its components that regu-
late gene expression is very important in order to tackle
the infection of this mycobacterium. TFTRs are known
to play diverse regulatory functions including antibiotic
resistance, pathogenicity, biofilm formation, quorum
sensing, cytokinesis, morphogenesis, osmotic stress, and
various metabolic pathways. In this paper, transcription
start site, promoter region, binding motifs, and CpG
islands of TFTR genes of M. colombiense CECT 3035
were analyzed. Accordingly, TSSs of 22 genes were iden-
tified and five motifs were found to be shared by at least
95.45% genes of M. colombiense CECT 3035 promoter
input sequences. Among the five motifs, MTF1 was iden-
tified as a common promoter motif shared by (100%)
TFTR genes of M. colombiense CECT 3035 promoters.
MTF1 was compared to the known Prokaryotic DNA
motif databases and identified to match with 15 out of
84 known motifs. e matched TFs were found to be in
good agreement with the regulatory functions of TFTRs
and indicated good candidates for the development
of antimicrobials including biofilms, quorum signals,
siderophores, biosynthesis of cell wall, metabolic states,
and cell division and may help to design a combination of
therapeutic molecules. ese findings are anticipated to
provide knowledge for the discovery and development of
antimicrobials and possibly next-generation antimicrobi-
als against M. colombiense CECT 3035 and other NTMs

Page 11 of 12
Hamdeetal. Journal of Genetic Engineering and Biotechnology (2022) 20:53
as well. Furthermore, analysis of CpG islands showed
the existence of a high frequency of CpG islands in both
promoter and body regions of genes of M. colombiense
CECT 3035 that can have epigenetic regulatory impli-
cations while molecular evolutionary genetic analysis
showed close relationships among the genes.
Abbreviations
MAC: Mycobacterium avium complex; NTM: Non-tuberculous mycobacteria;
RNAP: RNA polymerase; TFs: transcription factors; TFTR: TetR family transcrip-
tion regulator; CpG: Cytosine-phosphate-guanine; NCBI: National Center for
Biotechnology Information; TSS: Transcription start site; NNPP: Neural network
promoter prediction; MEME: Multiple Em for Motif Elicitation; MEGA-X:
Molecular evolutionary genetics analysis X; MTF: Motif.
Acknowledgements
We would like to acknowledge School of Applied Natural Science, Adama
Science and Technology University.
Authors’ contributions
All the authors designed and performed the study. FH analyzed the data and
wrote the manuscript. HD initiated the study. HD and MN supervised the
research. All the authors read and approved the manuscript.
Funding
School of Applied Natural Science, Adama Science and Technology University
financially supported the authors only during the study, not in other activities.
Availability of data and materials
Data analyzed in current study were taken from NCBI database of CECT 3035
genes of M. colombiense.
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Received: 30 November 2021 Accepted: 9 March 2022
References
1. Lahiri A, Sanchini A, Semmler T, Schafer H, Lewin A (2014) Identification
and comparative analysis of a genomic island in Mycobacterium avium
subsp. hominissuis. FEBS Let 588(21):3906–3911. https:// doi. org/ 10.
1016/j. febsl et. 2014. 08. 037
2. Gonzalez-Perez MN, Murcia MI, Parra-Lopez C, Blom J, Tauch A (2016)
Deciphering the virulence factors of the opportunistic pathogen Myco-
bacterium colombiense. New Microbe New Infect 14:98–105. https:// doi.
org/ 10. 1016/j. nmni. 2016. 09. 007
3. Maya-Hoyos M, Leguizamon J, Marino-Ramirez L, Soto CY (2015) Sliding
motility, biofilm formation, and Glycopeptidolipid production in Myco-
bacterium colombiense strains. Biomed Res Int 2015:419549. https:// doi.
org/ 10. 1155/ 2015/ 419549
4. Gcebe N, Hlokwe TM (2017) Non-tuberculous mycobacteria in south Afri-
can wildlife: neglected pathogens and potential impediments for bovine
tuberculosis diagnosis. Front Cell Infect Microbiol 7:15. https:// doi. org/ 10.
3389/ fcimb. 2017. 00015
5. Gonzalez-Perez M, Marino-Ramirez L, Parra-Lopez CA, Murcia MI,
Marquina B, Mata-Espinoza D (2013) Virulence and immune response
induced by Mycobacterium avium complex strains in a model of progres-
sive pulmonary tuberculosis and subcutaneous infection in BALB/c mice.
Infect Immun 81(11):4001–4012. https:// doi. org/ 10. 1128/ IAI. 00150- 13
6. Nishiuchi Y, Iwamoto T, Maruyama F (2017) Infection sources of a com-
mon non-tuberculous mycobacterial pathogen, Mycobacterium avium
complex. Front Med 4:27. https:// doi. org/ 10. 3389/ fmed. 2017. 00027
7. Al-Mahruqi SH, van Ingen J, Al Busaidy S, Boeree MJ, Al Zadjali S, Patel
A, Richard Dekhuijzen PN, van Soolingen D (2009) Clinical relevance of
nontuberculous mycobacteria, Oman. Emerg Infect Dis 15(2):292–294.
https:// doi. org/ 10. 3201/ eid15 02. 080977
8. Baldwin SL, Larsen SE, Ordway D, Cassell G, Coler RN (2019) The complexi-
ties and challenges of preventing and treating nontuberculous myco-
bacterial diseases. PLoS Negl Trop Dis 13(2):e0007083. https:// doi. org/ 10.
1371/ journ al. pntd. 00070 83
9. Maurya AK, Nag VL, Kant S, Kushwaha RA, Kumar M, Singh AK, Dhole TN
(2015) Prevalence of nontuberculous mycobacteria among extrapulmo-
nary tuberculosis cases in tertiary care centers in northern India. Biomed
Res Int 2015:465403. https:// doi. org/ 10. 1155/ 2015/ 465403
10. Sharma P, Singh D, Sharma K, Verma S, Mahajan S, Kanga A (2018) Are we
neglecting nontuberculous mycobacteria just as laboratory contami-
nants? Time to reevaluate things. J Pathog 2018:8907629. https:// doi. org/
10. 1155/ 2018/ 89076 29
11. Gonzalez-Perez M, Murcia MI, Landsman D, Jordan IK, Marino-Ramírez L
(2011) Genome sequence of the Mycobacterium colombiense type strain,
CECT 3035. J Bacteriol 193(20):5866–5867. https:// doi. org/ 10. 1128/ JB.
05928- 11
12. Maurer FP, Pohle P, Kernbach M et al (2019) Differential drug susceptibil-
ity patterns of Mycobacterium chimaera and other members of the
Mycobacterium avium-intracellulare complex. Clin Microbiol Infect
25(3):371–379. https:// doi. org/ 10. 1016/j. cmi. 2018. 06. 0108
13. Saxena S, Spaink HP, Forn-Cuni G (2021) Drug resistance in nontubercu-
lous mycobacteria: mechanisms and models. Biology 10:96. https:// doi.
org/ 10. 3390/ biolo gy100 20096
14. Cuthbertson L, Nodwell JR (2013) The TetR family of regulators. Microbiol
Mol Biol Rev 77(3):440–475. https:// doi. org/ 10. 1128/ MMBR. 00018- 13
15. Colclough AL, Scadden J, Blair JMA (2019) TetR-family transcription factors
in gram-negative bacteria: conservation, variation and implications for
efflux-mediated antimicrobial resistance. BMC Genomics 20:731. https://
doi. org/ 10. 1186/ s12864- 019- 6075-5
16. Balhana RJ, Singla A, Sikder MH, Withers M, Kendall SL (2015) Global
analyses of TetR family transcriptional regulators in mycobacteria indi-
cates conservation across species and diversity in regulated functions.
BMC Genomics 16(1):479. https:// doi. org/ 10. 1186/ s12864- 015- 1696-9
17. Soutourina O, Dubois T, Monot M, Shelyakin PV, Saujet L, Boudry P, Gel-
fand MS, Dupuy B, Martin-Verstraete I (2020) Genome-wide transcription
start site mapping and promoter assignments to a sigma factor in the
human enteropathogen Clostridioides difficile. Front Microbiol 11(1939):1–
24. https:// doi. org/ 10. 3389/ fmicb. 2020. 01939
18. Reese MG, Harris NL, Eeckman FH (1996) Large scale sequencing specific
neural networks for promoter and splice site recognition. In: Bio - com-
puting: proceedings of the 1996 Pacific symposium, Singapore http://
www. fruit fly. org/ seq_ tools/ promo ter. html
19. Bailey TL, Elkan C (1994) Fitting a mixture model by expectation maximi-
zation to discover motifs in biopolymers. Proc Int Conf Intell Syst Mol Biol
2:28–36 https:// www. osti. gov/ biblio/ 377124
20. Bailey TL, Johnson J, Grant CE, Noble WS (2015) The MEME suite. Nucleic
Acids Res 43(W1):39–49. https:// doi. org/ 10. 1093/ nar/ gkv416
21. Peng S, Cheng M, Huang K (2018) Efficient computation of motif discov-
ery on Intel many integrated Core (MIC) architecture. BMC Bioinformatics
19(282):102–121. https:// doi. org/ 10. 1186/ s12859- 018- 2276-1
22. Gupta S, Stamatoyannopoulos JA, Bailey TL, Noble WS (2007) Quantifying
similarity between motifs. Genome Biol 8:R24. https:// doi. org/ 10. 1186/
gb- 2007-8- 2- r24
23. Takai D, Jones PA (2002) Comprehensive analysis of CpG islands in human
chromosomes 21 and 22. Proc Natl Acad Sci U S A 99:3740–3745. https://
doi. org/ 10. 1073/ pnas. 05241 0099
24. Saitou N, Nei M (1987) The neighbor-joining method: a new method for
reconstructing phylogenetic trees. Mol Biol Evol 4:406–425. https:// doi.
org/ 10. 1093/ oxfor djour nals. molbev. a0404 54

Page 12 of 12
Hamdeetal. Journal of Genetic Engineering and Biotechnology (2022) 20:53
25. Tamura K, Nei M, Kumar S (2004) Prospects for inferring very large phy-
logenies by using the neighbor-joining method. Proc Natl Acad Sci U S A
101:11030–11035. https:// doi. org/ 10. 1073/ pnas. 04042 06101
26. Kumar S, Stecher G, Li M, Knyaz C, Tamura K (2018) MEGA X: molecular
evolutionary genetics analysis across computing platforms. Mol Biol Evol
35(6):1547–1549. https:// doi. org/ 10. 1093/ molbev/ msy096
27. Hall BG (2013) Building phylogenetic trees from molecular data with
MEGA. Mol Biol Evol 30(5):1229–1235. https:// doi. org/ 10. 1093/ molbev/
mst012
28. Prados J, Linder P, Redder P (2016) TSS-EMOTE, a refined protocol for a
more complete and less biased global mapping of transcription start
sites in bacterial pathogens. BMC Genomics 17(1):849. https:// doi. org/ 10.
1186/ s12864- 016- 3211-3
29. Boutard M, Ettwiller L, Cerisy T, Alberti A, Labadie K, Salanoubat M, Schild-
kraut I, Tolonean AC (2016) Global repositioning of transcription start sites
in a plant-fermenting bacterium. Nat Commun 7(13783):1–9. https:// doi.
org/ 10. 1038/ ncomm s13783
30. Jorjani H, Zavolan M (2014) TSSer: an automated method to identify
transcription start sites in prokaryotic genomes from differential RNA
sequencing data. Bioinformatics 30(7):971–974. https:// doi. org/ 10. 1093/
bioin forma tics/ btt752
31. Mendoza-Vargas A, Olvera L, Olvera M, Grande R, Vega-Alvarado L,
Taboada B et al (2009) Genome-wide identification of transcription start
sites, promoters and transcription factor binding sites in E. coli. PLoS One
4(10):e7526. https:// doi. org/ 10. 1371/ journ al. pone. 00075 26
32. Umarov V, Solovyev R (2017) Prediction of prokaryotic and eukaryotic
promoters using convolutional deep learning neural networks. PLoS One
12:2. https:// doi. org/ 10. 1371/ journ al. pone. 01714 10
33. Richard M, Gutiérrez AV, Viljoen AJ, Ghigo E, Blaise M, Kremer L (2018)
(2018) mechanistic and structural insights into the unique TetR-depend-
ent regulation of a drug efflux pump in Mycobacterium abscessus. Front
Microbiol 9:649. https:// doi. org/ 10. 3389/ fmicb. 2018. 00649
34. Gordon JJ, Towsey MW, Hogan JM, Mathews SA, Timms P (2006)
Improved prediction of bacterial transcription start sites. Bioinformatics
22(2):142–148. https:// doi. org/ 10. 1093/ bioin forma tics/ bti771
35. Shin MK, Shin SJ (2021) Genetic involvement of Mycobacterium avium
complex in the regulation and manipulation of innate immune functions
of host cells. Int J Mol Sci 22:3011. https:// doi. org/ 10. 3390/ ijms2 20630 11
36. Falkinham JO III (2018) Challenges of NTM drug development. Front
Microbiol 9:1613. https:// doi. org/ 10. 3389/ fmicb. 2018. 01613
37. Huang Y, Chen Y, Zhang LH (2020) The roles of microbial cell-cell chemical
communication systems in the modulation of antimicrobial resistance.
Antibiotics (Basel) 9(11):779. https:// doi. org/ 10. 3390/ antib iotic s9110 779
38. Faria S, Joao I, Jordao L (2015) General overview on nontuberculous
mycobacteria, biofilms, and human infection. J Pathog 2015:809014.
https:// doi. org/ 10. 1155/ 2015/ 809014
39. Simoes M (2011) Antimicrobial strategies effective against infectious
bacterial biofilms. Curr Med Chem 18(14):2129–2145. https:// doi. org/ 10.
2174/ 09298 67117 95656 216
40. Dong YH, Zhang XF, Xu JL, Tan AT, Zhang LH (2005) VqsM, a novel AraC-
type global regulator of quorum-sensing signalling and virulence in
Pseudomonas aeruginosa. Mol Microbiol 58(2):552–564. https:// doi. org/
10. 1111/j. 1365- 2958. 2005. 04851.x
41. Wang Y, Gao L, Rao X, Wang J, Yu H, Jiang J, Zhou W, Wang J, Xiao Y, Li M,
Zhang Y, Zhang K, Shen L, Hua Z (2018) Characterization of lasR-deficient
clinical isolates of Pseudomonas aeruginosa. Sci Rep 8(1):13344. https://
doi. org/ 10. 1038/ s41598- 018- 30813-y
42. Lade H, Paul D, Kweon JH (2014) Quorum quenching mediated
approaches for control of membrane biofouling Int. J Biol Sci 10(5):550–
565. https:// doi. org/ 10. 7150/ ijbs. 9028
43. De Voss JJ, Rutter K, Schroeder BG, Su H, Zhu Y, Barry CE 3rd (2000) The
salicylate-derived mycobactin siderophores of Mycobacterium tubercu-
losis are essential for growth in macrophages. Proc Natl Acad Sci U S A
97(3):1252–1257. https:// doi. org/ 10. 1073/ pnas. 97.3. 1252
44. Kopinˇc R, Lapanje A (2012) Antibiotic susceptibility profile of Mycobac-
terium avium subspecies hominissuis is altered in low-iron conditions. J
Antimicrob Chemother 67(12):2903–2907. https:// doi. org/ 10. 1093/ jac/
dks313
45. Leoni L, Orsi N, Lorenzo V, Visca P (2000) Functional analysis of PvdS, an
iron starvation sigma factor of Pseudomonas aeruginosa. J Bacteriol
182(6):1481–1491. https:// doi. org/ 10. 1128/ JB. 182.6. 1481- 1491. 2000
46. Lizewski SE, Lundberg DS, Schurr MJ (2002) The transcriptional regulator
AlgR is essential for Pseudomonas aeruginosa pathogenesis. Infect Immun
70(11):6083–6093. https:// doi. org/ 10. 1128/ IAI. 70. 11. 6083- 6093. 2002
47. Li Y, Xiao Y, Zou L, Chen G (2012) Identification of HrpX regulon
genes in Xanthomonas oryzae pv. Oryzicola using a GFP visualization
technique. Arch Microbiol 194(4):281–291. https:// doi. org/ 10. 1007/
s00203- 011- 0758-x
48. Nguyen Le Minh P, de Cima S, Bervoets I, Maes D, Rubio V, Charlier D
(2015) Ligand binding specificity of RutR, a member of the TetR family of
transcription regulators in Escherichia coli. FEBS Open Bio 5:76–84. https://
doi. org/ 10. 1016/j. fob. 2015. 01. 002
49. Lu CD, Yang Z, Li W (2004) Transcriptome analysis of the ArgR regulon in
Pseudomanas aeruginosa. J Bacteriol 186(12):3855–3861. https:// doi. org/
10. 1128/ JB. 186. 12. 3855- 3861. 2004
50. Silva-Rocha R, Chavarría M, Kleijn RJ, Sauer U, de Lorenzo V (2013) The IHF
regulon of exponentially growing pseudomonas putida cells. Environ
Microbiol 15(1):49–63. https:// doi. org/ 10. 1111/j. 1462- 2920. 2012. 02750.x
51. Mercier R, Petit MA, Schbath S, Karoui ME, Boccard F, Espeli O (2008) The
MatP/mats site-specific system organizes the terminus region of the
E. coli chromosome into a macrodomain. Cell 135(3):475–485. https:// doi.
org/ 10. 1016/j. cell. 2008. 08. 031
52. Spencer W, Siam R, Ouimet MC, Bastedo DP, Marczynski GT (2009) CtrA, a
global response regulator, uses a distinct second category of weak DNA
binding sites for cell cycle transcription control in Caulobacter crescentus.
J Bacteriol 191(17):5458–5470. https:// doi. org/ 10. 1128/ JB. 00355- 09
53. Silber N, de Opitz CLM, Mayer C, Sass P (2020) Cell division protein Ftsz:
from structure and mechanism to antibiotic target. Future Microbiol
15(9):348. https:// doi. org/ 10. 2217/ fmb- 2019- 0348
54. Adikesavan AK, Katsonis P, Marciano DC, Lua R, Herman C, Lichtarge O
(2011) Separation of recombination and SOS response in Escherichia coli
RecA suggests LexA interaction sites. PLoS Genet 7(9):1–14. https:// doi.
org/ 10. 1371/ journ al. pgen. 10022 44
55. Mo CY, Manning SA, Roggiani M, Culyba MJ, Samuels AN, Sriegowski PD,
Goulian M, Kohli RM (2016) Systematically altering bacterial SOS activity
under stress reveals therapeutic strategies for potentiating antibiot-
ics. mSphere 1(4):e00163–e00116. https:// doi. org/ 10. 1128/ mSphe re.
00163- 16
56. Kakumani R, Ahmad O, Devabhaktuni V (2012) Identification of
CpG islands in DNA sequences using statistically optimal null filters.
EURASTP J Bioinform Syst Biol 2012(1):12. https:// doi. org/ 10. 1186/
1687- 4153- 2012- 12
57. Lim WJ, Kim KH, Kim JY, Jeong S, Kim N (2019) Identification of DNA-
methylated CpG islands associated with gene silencing in the adult body
tissues of the Ogye chicken using RNA-Seq and reduced representation
bisulfite sequencing. Front Genet 10:346. https:// doi. org/ 10. 3389/ fgene.
2019. 00346
58. Yirgu M, Kebede M (2019) Analysis of the promoter region, motif and
CpG islands in AraC family transcriptional regulator ACP92 genes of
Herbaspirillum seropedicae. Adv Biosci Biotechnol 10:150–164. https://
doi. org/ 10. 4236/ abb. 2019. 106011
59. Hershberg R, Petrov DA (2008) Selection on codon bias. Annu Rev Genet
42:287–299. https:// doi. org/ 10. 1146/ annur ev. genet. 42. 110807. 091442
60. Plotkin JB, Kudla G (2011) Synonymous but not the same: the causes and
consequences of codon bias. Nat Rev Genet 12:32–42. https:// doi. org/ 10.
1038/ nrg28 99
61. Murcia MI, Tortoli E, Menendez C, Palenque E, Garcia MJ (2006) Myco-
bacterium colombiense sp. nov., a novel member of the Mycobacterium
avium complex and description of MAC-X as a new ITS genetic variant.
Int J Syst Evol Microbiol 56(9):2049–2054. https:// doi. org/ 10. 1099/ ijs.0.
64190-0
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in pub-
lished maps and institutional affiliations.