Abstract
Background
Tuberculosis (TB) is an infectious disease caused by Mycobacterium
tuberculosis (M. tuberculosis). The annotation of functional genome and
signaling network in M. tuberculosis are still not systematic.
Essential gene modules are a collection of functionally related
essential genes in the same signaling or metabolic pathway. The
determination of essential genes and essential gene modules at genomic
level may be important for better understanding of the physiology and
pathology of M. tuberculosis, and also helpful for the development of
drugs against this pathogen. The establishment of genomic operon
database (DOOR) and the annotation of gene pathways have felicitated
the genomic analysis of the essential gene modules of M. tuberculosis.
Method
Bibliometric approach has been used to perform a High-throughput screen
for essential genes of M. tuberculosis strain H37Rv. Ant colony
algorithm were used to identify the essential genes in other M.
tuberculosis reference strains. Essential gene modules were analyzed by
operon database DOOR. The pathways of essential genes were assessed by
Biocarta, KEGG, NCI-PID, HumanCyc and Reactome. The function prediction
of essential genes was analyzed by Pfam.
Results
A total approximately 700 essential genes were identified in M.
tuberculosis genome. 40% of operons are consisted of two or more
essential genes. The essential genes were distributed in 92 pathways in
M. tuberculosis. In function prediction, 61.79% of essential genes were
categorized into virulence, intermediary metabolism/respiration,cell
wall related and lipid metabolism, which are fundamental functions that
exist in most bacteria species.
Conclusion
We have identified the essential genes of M. tuberculosis using
bibliometric approach at genomic level. The essential gene modules were
further identified and analyzed.
Keywords: Mycobacterium tuberculosis, Essential gene modules, Operon,
Pathway
Introduction
Tuberculosis (TB) is an infectious disease caused by Mycobacterium
tuberculosis (M. tuberculosis) [[38]1,[39]2]. In recent years, the
prevention and treatment of TB have become difficult due to the
prevalence of co-infection with HIV, drug resistance and uncertainty of
Bacillus Calmette-Guérin (BCG) prevention [[40]3-[41]5]. Essential
genes are those genes required for cell growth and survival
[[42]6,[43]7]. Previous studies on the essential genes of M.
tuberculosis pathogenesis primarily using gene knockout or RNA
interference [[44]8]. This approach is expensive and inefficient, and
due to limitations of experimental techniques, no experimental method
can achieve an essential gene screen at a High-throughput level
[[45]9,[46]10]. Essential gene modules are a collection of functionally
related essential genes in the same signaling or metabolic pathway
[[47]11]. The determination of essential genes and essential gene
modules at genomic level may be important for better understanding of
the physiology and pathology of M. tuberculosis, and also helpful for
the development of drugs against this pathogen.
To date, more than 31 genomes of Mycobacterium spp. have been sequenced
including nine M. tuberculosis strains [[48]12]. However, the
systematic analysis of functional genomics and metabolic regulation
were not established in M. tuberculosis. In this study, we used a
bibliometric approach and performed a High-throughput screening of five
M. tuberculosis strains to identify the essential genes. We further
analyzed the essential operons and pathways, based on early-established
genomic operon database and annotation of gene locus [[49]13-[50]15].
Material and methods
Bibliometric method
The bibliometric was used as previously described [[51]16]. The
keywords “Mycobacterium tuberculosis” “H37Rv” “essential gene” have
been used to search the publications from 2002 to 2011 in PubMed,
MEDLINE, BiosisPreview, EMbase and SciFinder. Using Epidata3.1, the
duplications of literatures and unrelated literatures were deleted by
parallel entry and logical error test. A total of 819 literatures were
retrieved and 112 literatures were used to analysis the essential gene
modules.
Ant colony algorithm
Multiple sequence alignment bases on BLAST algorithm was restricted by
the length and number of the sequences. So, in this study, we used ant
colony algorithm to optimize it [[52]17,[53]18]. First, we divided all
sequences into several parts and gain an initial population of K as a
column, N as a row. K is the number of individuals of the
K-substituting groups; N is the number of sequences. d[ij] indicates
the i^th individual dividing positions on the j^th sequence. For the
array, the calculation formula of fitness(r) is as follow:
[MATH: fitnessr=∑i=1<
mi>N-1∑j=i+
1NSESipdri,Sjpdri+∑i=1<
mi>N-1∑j=i+
1NSESisdri,Sjsdri :MATH]
The following parameters were used for analysis: the Initial = 5,
d[1] = 2, d[2] = 2, d[3] = 3, NCmax (The maximum number of
iterations) = 100, m(number of ants) = 100; Parameters for information
volatile degree are p = 0.05, q = 0.03, q[1] = 0.6, q[2] = 0.35,
q3 = 0.2, a = 5, b = 3, c = 2, T[1] = 50 , T[2] = 79 , T[3] = 99,
Q[1] = 0.1, Q[2] = 0.2.
Results
Operons and pathways of screened essential genes in M. tuberculosis strains
The genome of the highly pathogenic M. tuberculosis strain H37Rv has
been sequenced [[54]19]. Using bibliometric analysis, 684 essential
genes were identified in H37Rv strain, 617 genes were proved by
experiments as well as 67 genes were identified using an in silico
approach. (Related literature of these genes listed in Additional file
[55]1: Table S1). These genes were evenly distributed in the genome,
consistent with the previous study [[56]20]. The genomes of M.
tuberculosis among the five strains (H37Rv, H37Ra, CDC1511, F11, and
KZN1435) were highly conserved. Therefore, we searched the Gene Bank
([57]http://www.ncbi.nlm.nih.gov/genbank) using ant colony algorithm to
look for essential genes of other reference strains (H37Ra, CDC1511,
F11, and KZN1435). The essential genes of these strains were 702, 665,
699 and 697, respectively (Table [58]1). It was worth noting that the
number of essential genes in the different strains are varies, although
the genomes of M. tuberculosis are highly conserved. This phenomenon is
mainly caused by the total number of genes in each stain are different,
and some essential gene will divide into two genes in another genome,
which caused the differences of essential genes.
Table 1.
Essential genes, operons and pathway in reference strains H37Rv, H37Ra,
CDC1511, F11, KZN1435
__________________________________________________________________
__________________________________________________________________
Operons
__________________________________________________________________
Pathways
__________________________________________________________________
Strains Essential genes Contain 1 essential gene Contain more than 2
essential genes Contain 1 essential gene Contain less than 50 essential
genes Contain more than 50 essential genes
H37Rv
__________________________________________________________________
684
__________________________________________________________________
185(60%)
__________________________________________________________________
122(40%)
__________________________________________________________________
7(8%)
__________________________________________________________________
82(89%)
__________________________________________________________________
3(3%)
__________________________________________________________________
H37Ra
__________________________________________________________________
702
__________________________________________________________________
192(59%)
__________________________________________________________________
134(41%)
__________________________________________________________________
7(8%)
__________________________________________________________________
82(89%)
__________________________________________________________________
3(3%)
__________________________________________________________________
CDC1551
__________________________________________________________________
665
__________________________________________________________________
178(60%)
__________________________________________________________________
119(40%)
__________________________________________________________________
7(8%)
__________________________________________________________________
82(89%)
__________________________________________________________________
3(3%)
__________________________________________________________________
F11
__________________________________________________________________
699
__________________________________________________________________
186(60%)
__________________________________________________________________
124(40%)
__________________________________________________________________
7(8%)
__________________________________________________________________
82(89%)
__________________________________________________________________
3(3%)
__________________________________________________________________
KZN1435 697 185(59%) 131(41%) 7(8%) 82(89%) 3(3%)
[59]Open in a new tab
We used operon database (DOOR) to assess the essential gene modules.
These essential genes were fallen in 307, 326, 297, 310 and 316 operons
in H37Rv, H37Ra, CDC1511, F11 and KZN1435, respectively (Table [60]1).
Statistical analysis showed that there is no relationship between the
size of the operon and the number of essential genes identified. In all
strains, approximately 40% of operons are consisted of two or more
essential genes and some operons controlled more than ten genes,
suggesting that these operons may play an important role in physiology
or pathogenesis. For example, the operon ID7843 controls eleven genes,
nine of which are essential genes (Figure [61]1A). Of them, five genes
are related to cell membrane, two genes encode protein related to
PE/PPE family and another two genes encode hypothetical proteins.
Figure 1.
[62]Figure 1
[63]Open in a new tab
Essential gene modules operons and pathways. (A) An illustration for
operon 7843. Non-essential genes are marked in red; PE/PPE family
related genes are marked in green; possible conserved membrance genes
are marked in yellow; hypothetical protein are marked in blue. (B) An
illustration for histidine metabolism pathway. (C) An illustration for
petidoglycan biosynthesis pathway.
Pathway is a signal transduction network that involves in multiple gene
interaction. We analyzed the essential genes using pathway databases
Biocarta, KEGG, NCI-PID, HumanCyc and Reactome. Although the numbers of
essential genes in the five strains are slightly different, these
essential genes have the same number of pathways (Table [64]1). The 684
essential genes of the H37Rv strain were distributed in 92 pathways. Of
them, seven pathways only have one essential gene; 82 pathways have
less than 50 essential genes; three pathways have more than 50
essential genes. It is interesting to note that in a portion of the
pathway is entirely constituted by essential genes, which adjacent to
each other in the genome. Histidine metabolism pathway, which is
related to intermediary metabolism and respiratory, involves in ten
essential genes. Seven of them are adjacent to each other and these
clustered genes (Rv1599-1606) are required for L-histidine synthesis
[[65]21] (Figure [66]1B). Peptidoglycan synthesis pathway, which is
related to cell wall and membrane formation, involves in ten essential
genes (Figure [67]1C). Five essential genes (Rv2152-2157) are clustered
together in the genome. Two of them are involved in N-acetyl muramic
acid synthesis and the others for uridine monophosphate (UMP) synthesis
[[68]22]. We speculated that the linked genes are required for proper
function and play crucial roles in pathways.
Function prediction of essential genes in M. tuberculosis
We used Pfam ([69]http://pfam.sanger.ac.uk/) to predict the potential
function of the essential genes by analyzing the functional domains of
encoded proteins. The functions of the essential genes are categorized
into replication, regulatory proteins, virulence, intermediary
metabolism and respiration, cell wall related, signal pathways, lipid
metabolism, PE/PPE family, insertion sequences/phages and unknown
(Table [70]2). 61.79% of essential genes are fallen into virulence,
intermediary metabolism/respiration, cell wall related, signal pathways
and lipid metabolism. 0.06% of essential genes are fallen into
replication, regulatory proteins, PE/PPE family, and insertion
sequences/phages; 38.15% of essential genes into unknown.
Table 2.
Function predictions of essential genes for reference strains H37Rv,
H37Ra, CDC1511, F11, KZN1435
H37Rv H37Ra CDC1551 F11 KZN1435
Replication
__________________________________________________________________
10
__________________________________________________________________
10
__________________________________________________________________
10
__________________________________________________________________
10
__________________________________________________________________
10
__________________________________________________________________
(0.01%)
__________________________________________________________________
(0.01%)
__________________________________________________________________
(0.02%)
__________________________________________________________________
(0.01%)
__________________________________________________________________
(0.01%)
__________________________________________________________________
Regulatory proteins
__________________________________________________________________
10
__________________________________________________________________
10
__________________________________________________________________
10
__________________________________________________________________
10
__________________________________________________________________
10
__________________________________________________________________
(0.01%)
__________________________________________________________________
(0.01%)
__________________________________________________________________
(0.02%)
__________________________________________________________________
(0.01%)
__________________________________________________________________
(0.01%)
__________________________________________________________________
Virulence
__________________________________________________________________
288
__________________________________________________________________
293
__________________________________________________________________
284
__________________________________________________________________
292
__________________________________________________________________
291
__________________________________________________________________
(42.10%)
__________________________________________________________________
(41.74%)
__________________________________________________________________
(42.71%)
__________________________________________________________________
(41.77%)
__________________________________________________________________
(41.75%)
__________________________________________________________________
Intermediary metabolism and respiration
__________________________________________________________________
265
__________________________________________________________________
270
__________________________________________________________________
260
__________________________________________________________________
269
__________________________________________________________________
266
__________________________________________________________________
(38.74%)
__________________________________________________________________
(38.46%)
__________________________________________________________________
(39.10%)
__________________________________________________________________
(38.48%)
__________________________________________________________________
(38.16%)
__________________________________________________________________
Cell wall related
__________________________________________________________________
297
__________________________________________________________________
302
__________________________________________________________________
295
__________________________________________________________________
302
__________________________________________________________________
299
__________________________________________________________________
(43.42%)
__________________________________________________________________
(43.021%)
__________________________________________________________________
(44.36%)
__________________________________________________________________
(43.20%)
__________________________________________________________________
(42.90%)
__________________________________________________________________
information pathway
__________________________________________________________________
251
__________________________________________________________________
255
__________________________________________________________________
248
__________________________________________________________________
256
__________________________________________________________________
253
__________________________________________________________________
(36.70%)
__________________________________________________________________
(36.32%)
__________________________________________________________________
(37.29%)
__________________________________________________________________
(36.62%)
__________________________________________________________________
(36.30%)
__________________________________________________________________
Lipid metabolism
__________________________________________________________________
257
__________________________________________________________________
263
__________________________________________________________________
254
__________________________________________________________________
263
__________________________________________________________________
260
__________________________________________________________________
(37.57%)
__________________________________________________________________
(37.46%)
__________________________________________________________________
(38.20%)
__________________________________________________________________
(37.63%)
__________________________________________________________________
(37.30%)
__________________________________________________________________
PE/PPE family
__________________________________________________________________
11
__________________________________________________________________
14
__________________________________________________________________
8
__________________________________________________________________
14
__________________________________________________________________
13
__________________________________________________________________
(0.02%)
__________________________________________________________________
(0.02%)
__________________________________________________________________
(0.01%)
__________________________________________________________________
(0.02%)
__________________________________________________________________
(0.02%)
__________________________________________________________________
Insertion seqs and phages
__________________________________________________________________
12
__________________________________________________________________
13
__________________________________________________________________
10
__________________________________________________________________
13
__________________________________________________________________
12
__________________________________________________________________
(0.02%)
__________________________________________________________________
(0.02%)
__________________________________________________________________
(0.02%)
__________________________________________________________________
(0.02%)
__________________________________________________________________
(0.02%)
__________________________________________________________________
Hypothetical/unknown 261
__________________________________________________________________
269
__________________________________________________________________
255
__________________________________________________________________
269
__________________________________________________________________
268
__________________________________________________________________
(38.15%) (38.31%) (38.35%) (38.48%) (38.45%)
[71]Open in a new tab
Discussion
In the current study, we have done a High-throughput screen for
essential genes of M. tuberculosis. A total approximately 700 essential
genes are identified in the genome, some genes were proved by
experiments as well as some genes were identified using an in silico
approach. We further identified the operons and pathways of these
essential genes and predicted the functions of these genes.
The numbers of essential genes in the different strains are distinct
suggesting that although the genome of M. tuberculosis is highly
conserved, variations exist among different strains. The differences
lead to the various capacities of virulence, evolution, and immunogenic
among M. tuberculosis strains. Therefore, the investigations on the
difference among essential genes in different strains probably gain
insight the new mechanism of pathogenesis, especially between the
virulent stain (H37Rv) and avirulent stain (H37Ra).
In our study, there were about 40% operons having two or more essential
genes. Some operons have as much as ten essential genes. In the pathway
analysis, some pathways are consisted of as much as 50 essential genes.
At present, there is no any experimental methods can perform the
scanning of essential genes aspect for M. tuberculosis. In order to
further verify whether these identified genes are essential genes, we
used pathway analysis to found that if multiple essential genes are
adjacent to each other and constitute known essential pathway, we
highly suspected these genes identified are essential, which is
critical for drug or vaccine development. Histidine metabolism pathway
and peptidoglycan synthesis pathway were found in this study base on
pathway enrichment analysis,most genes in these two pathways were
essential genes and adjacent to each other. In this case, in-depth
studies of above two pathways maybe provide more broad perspective for
the new drug development.
Function analysis revealed that 61.79% of essential genes were
categorized into virulence,intermediary metabolism/respiration,cell
wall related and lipid metabolism, which are fundamental functions that
exist in most bacteria species [[72]23,[73]24], however, insertion
sequences, phages and horizontal transfer genes (HTG) are also founded.
The function of insertion sequence in Mycobacterium tuberculosis are
till obscure, and several literatures report that insertion sequences
plays a vital role in the growth cycle, which are essential for the
bacteria [[74]25,[75]26]. The PE/PPE family is M. tuberculosis-specific
and is involved in M. tuberculosis infection and virulence. PE/PPE
genes accounted for 10% of M. tuberculois genome. Several essential
genes that are related to PE/PPE family were also identified in this
study, which plays an important role in cell wall synthesis [[76]27].
Conclusion
In current study, we have identified the essential genes of M.
tuberculosis using bibliometric approach at genomic level. The
essential gene modules were further identified and analyzed.
Abbreviations
DOOR: Database of prOkaryotic OpeRons; KEGG: Kyoto Encyclopedia of
Genes and Genome; UMP: Uridine monophosphate
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
Conceived and design: GW, FL. Data collection: JS, FW. Analyzed the
data: CW, BL, YZ. Wrote the paper: GX, GW. All authors read and
approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
[77]http://www.biomedcentral.com/1471-2334/13/227/prepub
Supplementary Material
Additional file 1: Table S1
All essential genes collected with Bioliometric approach.
[78]Click here for file^ (195KB, xls)
Contributor Information
Guangyu Xu, Email: xuguangyu2005@163.com.
Bin Liu, Email: aass9454@yahoo.com.cn.
Fang Wang, Email: wf@jlu.edu.cn.
Chengguo Wei, Email: hbuwei@126.com.
Ying Zhang, Email: zhangying3032008@163.com.
Jiyao Sheng, Email: kakashisheng@foxmail.com.
Guoqing Wang, Email: qing@jlu.edu.cn.
Fan Li, Email: lifan@jlu.edu.cn.
Acknowledgements