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doi:10.1111/j.1745-7270.2005.00075.x |
Identification and Categorization of Horizontally Transferred Genes
in Prokaryotic Genomes
Shuo-Yong SHI1, Xiao-Hui CAI1,
and Da-fu DING1,2*
1 Key Laboratory of Proteomics,
Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological
Sciences, Chinese Academy of Sciences, Graduate School of the Chinese Academy
of Sciences, Shanghai 200031, China;
2
School
of Life Science & Technology, Shanghai Jiaotong University, Shanghai
200030, China
Received: February
3, 2005
Accepted: May 7,
2005
This work was
supported by the grants from the National High Technology Research and Development
Program of China (No. 2002AA234021) and the Knowledge Innovation Program of the
Chinese Academy of Sciences (KJCX1-08, KSCX2-2-07)
*Corresponding
author: Tel, 86-21-54921254; Fax, 86-21-54921011; E-mail,
[email protected]
Abstract Horizontal gene transfer (HGT), a
process through which genomes acquire genetic materials from distantly related
organisms, is believed to be one of the major forces in prokaryotic genome
evolution. However, systematic investigation is still scarce to clarify two
basic issues about HGT: (1) what types of genes are transferred; and (2) what
influence HGT events over the organization and evolution of biological
pathways. Genome-scale investigations of these two issues will advance the
systematical understanding of HGT in the context of prokaryotic genome
evolution. Having investigated 82 genomes, we constructed an HGT database
across broad evolutionary timescales. We identified four function categories
containing a high proportion of horizontally transferred genes: cell envelope,
energy metabolism, regulatory functions, and transport/binding proteins. Such
biased function distribution indicates that HGT is not completely random;
instead, it is under high selective pressure, required by function
restraints in organisms. Furthermore, we mapped the transferred genes onto the
connectivity structure map of organism-specific pathways listed in Kyoto
Encyclopedia of Genes and Genomes (KEGG). Our results suggest that recruitment
of transferred genes into pathways is also selectively constrained because of
the tuned interaction between original pathway members. Pathway organization
structures still conserve well through evolution even with the recruitment of
horizontally transferred genes. Interestingly, in pathways whose organization
were significantly affected by HGT events, the operon-like arrangement of
transferred genes was found to be prevalent. Such results suggest that operon
plays an essential and directional role in the integration of alien genes into
pathways.
Key words genome evolution; horizontal gene transfer; function and
pathway analysis
Horizontal gene transfer, the transfer of genes between different
species, has been widely recognized as one of the major driving forces in prokaryotic
genome evolution [1,2]. However, it has been pointed out that most analyses of
HGT have focused on either studying the phylogenetic relations of individual
gene families, which may be actively transferred, or estimating the global
fraction of individual genomes where genes are introduced by HGT [3]. To date,
there are inadequate data to answer two important issues: what types of genes
are subject to be transferred and to what extent the transferred genes have
been successfully integrated into existing biological pathways.
As to the first question, Nakamura et al. recently reported
that biological functions of transferred genes are biased to three categories:
cell envelope, cellular process, and regulatory process, suggesting that the
transferability of genes depends heavily on their functions [4]. However, their
work was based on a set of horizontally transferred (HT) genes detected by DNA
composition analysis, which is limited in identifying recent HGT events due to
the amelioration processes (adaptation to host genome features) [5].
Therefore, their conclusion may not be generalized to all HGT genes.
Pathway organization and evolution is a long-standing, interesting
and important question. Schmidt et al. investigated the enzyme structure
distribution and concluded that the overall topology of metabolic networks is
preserved through the evolution process [6]. It is interesting to measure
whether HGT, a force different from other vertical evolutionary events, will
also comply with pathway organization rules. Some previous individual pathway
analyses indicated that HGT might improve pathway capacities and thus provide
recipient species readily-made responses to environmental challenge [3,7,8].
All of those results imply the active role of HGT in pathway evolution. In
contrast, some other reports argued that selective barriers exist and preclude
the recruitment of transferred genes. As a result, in most cases, alien
homologues will have little chance to improve the fitness of networks [9,10]. A
systematic investigation is necessary to measure the impact of HGT on pathway
organization and evolution.
We emphasized that the reliable HGT dataset across broad
evolutionary timescales was preliminary for all above analyses. Various methods
based on different models have been designed to detect a specific HGT dataset
with different types and ages [11]. The gene transfer events will be more
robustly delineated when all of the available methods are used; application of
a variety of methods will provide the best information about the scope of gene
transfer across broad timescale [12,13].
In this article, we attempt
for the first time to design a stringent procedure in combination with
different methods to address two basic issues mentioned above.
Experimental Procedures
We retrieved the complete sequences of 82 prokaryote genomes from
the National Center for Biotechnology Information ftp site (ftp://www.ncbi.nih.gov/genomes/bacteria/
). We parsed the downloaded GenBank files and then constructed a
non-redundant protein database.
In general, there are three types of approaches for HGT
identification: abnormally high BLAST hits to genes of distant taxa or
anomalous phylogenetic profile distribution; phylogenetic analysis; and
atypical DNA composition analysis. Each method has its advantages and caveats
[11]. To achieve a more conservative HGT set across broad timescales in
evolution, we designed a combined method for HGT identification. The scheme of
pipeline is available in supplement file (https://www.abbs.info/sd/pipeline.pdf
).
The first method we used draws on unexpectedly high BLAST hits to a
distant taxon. The suspicion of HGT usually emerges when a protein/gene
sequence from a particular organism shows stronger similarity to homologs from
distant taxa. However, the simple best-match method is prone to erroneous
results [14,15], so we designed a more rigorous approach to avoid false
positives. All protein sequences from each genome were compared with our
protein database using the BLASTP program [16] (E-value cut off 1e–10) and the results were parsed to search for paradoxical phyletic
distribution of homologs. We considered an open-reading frame (ORF) to be
horizontally derived if all of the following conditions were met.
(1) The E-value of the best BLAST hit in H should be
less than 1e–20 and the first five BLAST
hits must be homologs from a distant taxon. We set the distant taxon on two
levels: non-self phylum or non-self superkingdom.
(2) All of its homologs are
from a distant taxon or the E-value of the closest homolog from a
distant taxon (E1) is significantly lower than the E-value
of the closest homolog not from the distant taxon (End).
We set the cutoff as Equation (1):
[log(E1)/log(End)]³2 (1)
(3) In the taxonomy tree constructed from our 82 species, there are
10 different phylums which include less than five species members. Therefore,
only 23 species from those phylums will be suitable for identifying the candidates
of HT genes originated from non-self superkingdoms to avoid sampling bias [15].
Orthologs are genes in different species that evolved from a common
ancestral gene by speciation [17]. When the orthologs of one gene are mainly
distributed in a non-self superkingdom or a non-self phylum, it is evident
that HGT occurred. In this article, we used the operational definition of
ortholog as follows. Given a protein P, its ortholog in another genome
must be the best hit of P in this genome by BLAST search (E-value
cut off 1e–10 and more than 60% of
residues should be included in BLAST alignment), and vice versa. If the
corresponding phylogenetic orthologs distribution of P is unusual, the
gene encoding protein P may be horizontally transferred in evolution.
Both of the two methods described above are based on BLAST search.
Although a stringent cutoff was set to remove some false positives, these
methods are still approximate approaches and need more phylogenetic
validation. Recently, Frickey et
al. developed a suite of programs, PhyloGenie (http://protevo.eb.tuebingen.mpg.de/miscpages/phylogenie/
), which provides a novel alignment routine to
achieve good alignment and also automates the steps from seed sequence to
phylogenetic inference [18]. HGT candidates identified by the two methods
mentioned above were re-checked by PhyloGenie.
As stated before, phylogenetic analysis does not work well in
identifying transferred genes among close species [11]. Thus, there are
legitimate reasons for using atypical DNA composition analysis as a alternative
approach to infer HGT. Garcia-Vallve et al. constructed an HGT database
by analyzing G+C content, codon and amino acid usage of 82 genomes [19].
Recently, Tsirigos et al. also developed a new composition analysis
method for HGT identification [20]. Previous research has pointed out that
different methods based on DNA composition analysis have different advantages
and caveats [21]. To achieve a more conservative HGT set, the agreement results
of the two methods were taken as HT candidates.
KEGG protein pathways provide organism-specific molecular
interaction network information including metabolic pathways, regulatory
pathways, and molecular complexes. We mapped the protein dataset to the KEGG
organism-specific protein pathways according to the pathway information
relating to each record in the KEGG protein database. What should be noted is
that the so-called “organism-specific pathways” are computer-driven to some
extent. Therefore, certain pathways represented both in isolation and as a
subgraph of larger pathways were merged by recursive procedures to avoid
partial pathway duplication. All the proteins in the 82 genomes were mapped to
the pathway database KEGG. In one genome containing N genes, there are M
genes that are horizontally transferred. If we consider a pathway containing K
members, any of these members either belongs to HT genes or does not; for
example, H of the A genes are horizontally transferred and K–H are not. It is important to establish the
probability of this happening by chance. This probability is appropriately
modeled by hypergeometric distribution with parameters N, M and K
[22]. Using the cumulative hypergeometric probability distribution, we can
calculate the chance of finding at least H horizontally transferred
genes in this specific pathway by chance, so that the p-value is given
by Equation (2).
(2)
This test measures whether a pathway is enriched with HT genes to a
greater extent than expected by chance. When the p-value is less than
0.05 (significant level 0.05) and that pathway has more than three transferred
genes, we considered that the organization of this pathway was significantly affected
by HGT.
Results and Discussion
We applied our combined method to examine 82 completely sequenced
genomes. The statistics of HGT number and proportion in each genome is
available (https://www.abbs.info/sd/hgtstatistictable.htm
). In this article, different methods were combined to collect an HGT set
across broad timescales in evolution. The outputs of different methods are
compared in Table 1.
As expected, it is clear that one individual method failed to detect
all the candidates of HGT. What should be noted is that the overlap number
between the two methods is small. Such observation is in agreement with
previous research. Ragan observed
that different approaches, when applied to the same genomic sequence,
recognized significantly different subsets of genes as being subject to HT and
the overlap of different methods is less than expected by chance under a
statistical model [12]. This is mainly due to the null hypothesis and
assumptions used by different methods (Table 1). Thus, a multifaceted
approach is needed for HGT identification and HGT might better be represented
as the union, not the intersection, of approaches addressing compatible null
hypotheses. All HGT candidates were stored in database and the flat file can be
downloaded from following website (https://www.abbs.info/sd/hgtdb.xls ).
Other related information, such as the location, function and taxonomy of the
corresponding genomes, and links to databases UNIPRO, TIGR, GO and KEGG, was
integrated into our database.
There are a total of 8893 HT
proteins mapped to the function categories defined by the TIGR (The Institute
of Genomic Research) microbial database [23]. Three uninformative categories
were omitted: disrupted reading frame, unclassified and unknown function. Fig.
1 shows the distribution of all HT genes in each category.
It appears clear that four main categories have significantly high
proportions of horizontally transferred genes: cell envelope (13.2%), energy
metabolism (14.7%), regulatory functions (9.4%), and transport and binding
proteins (14.3%). There are also two categories showing relatively high
distribution: cellular processes (7.2%) and other categories (6.9%). Note that
“other categories” includes genes responsible for prophage functions,
transposon functions and plasmid functions and is usually
pathogenicity-related. It is worth pointing out that energy metabolism, which
was shown to have the lowest proportion of horizontally transferred genes in
the work of Nakamura et al. [4], is among these four categories. To
further investigate this discrepancy, we compared our HGT dataset with the
results of Nakamura et al., and found that genes related to energy
metabolism were mainly identified by methods not used by Nakamura et al..
We postulate that the difference is due to an underestimate of ancient HGT
events in the work of Nakamura et al. Moreover, many previous
researchers also observed that transferred genes involved in energy metabolism
may provide an organism with a new adaptive capability, such as the ability to
utilize a new source of carbon and energy [2]. Thus the transfer of large
numbers of energy metabolism genes is not unexpected.
Similar to the work of Nakamura et al., we also found that
some operational gene categories, including amino acid biosynthesis,
biosynthesis of cofactors or carriers, intermediary metabolism, fatty acid and
phospholipid metabolism, have a low proportion of horizontally transferred
genes. Based on these results, Nakamura et al. suggested that
operational genes, previously considered generally transferable, should be
further classified into two groups, according to their transferability, that
is, the highly transferable genes and the lowly transferable genes. However, it
seems that such conclusions do not clearly explain the abnormal function
categories distribution in some genomes (https://www.abbs.info/sd/funcdistri.xls
). For example, as shown in the supplement, the amino acid biosynthesis
gene category has a comparably high proportion to one “highly” transferable
gene group in four species; in fact, the highly transferable categories also
sometimes show low proportions in certain genomes. We postulated that
horizontal gene transfer is a process dominated by selection pressure from the
demand of function evolution in individual organisms. According to the “genetic
annealing model” [24], HGT will become progressively less likely to happen as
the workings of cellular components improve. In addition, further study also
pointed out that selective barriers in an optimized system make neutral
evolution of alien genes unlikely [9]. Taken together, we believe, after the
formation of a modern organism, the rate of transfer slows down and only when
the organism is in emergency, such as a change of environment, do transfer
events become active. In fact, our research and previous study [25] both
observed that HGT is always related to adaptations to environmental
challenges or responses to new niche expansion. Commonly, such evolutionary
requirements involve the following aspects: novel metabolic capabilities,
antibiotic or resistance characteristics, virulence or pathogenity-related
function, new cell surface structure and the change of regulation mechanisms.
All of these are related to the observed function categories with high
proportions of horizontally transferred genes. Therefore, we suggest that the
biased function distribution should be ascribed to the guide of evolutionary
demand of recipent genomes, reflecting the adaptive response to enviroment
challenges. Furthermore, function demands are different in different
organisms. Not all above-mentioned functions are in need and, in addition, some
species may have particular requirements, such as the alternative amino acid
and biosynthesis route. This may explain why some organisms did not show
classical function category distribution. In general, it is more reliable to
analyze the biased function distribution in the evolutionary context of
specific species.
In this article, we mapped our horizontally transferred gene
candidates onto the connectivity structure map of organism-specific pathways
listed in KEGG. We found that, among the total of 5150 protein pathways in
KEGG, 1421 pathways (approximately 28%) involved horizontally transferred
genes. The full list of those pathways is available (https://www.abbs.info/sd/allhgtpath.doc
). We found that, with the increase of involved transferred members, the
number of related pathways sharply decreases. Of particular note is that only
1095 pathways (77%) have two or less horizontally transferred members. To some
extent, the recruitment of one or two transferred genes into one pathway may
only affect a few specific steps, if not a single step, through the replacement
of original function members or the introduction of novel preferred proteins.
They may affect only a very small number of members of the whole pathway
structure. To evaluate the extent to which alien genes are integrated in each
pathway, we used hypergeometric probability distribution to model the chance
probability of finding at least H horizontally transferred genes in one
specific pathway by chance.
Through this approach, we found that only 114 pathways (2%) may be
seriously affected by HGT. A list of these 114 pathways is also available (https://www.abbs.info/sd/sighgtpath.doc
). It seems that HGT influences only specific pathways and, in most cases,
pathways organization conserve well. Such results further support previous
research conclusions [10]. This phenomenon can be well explained from an
evolutionary point of view. Considering a pathway is a complex system, such an
observation is not unexpected. After long-standing co-evolution, closely
functional or structural interactions exist between original members, making a
pathway a fine-tuned machine. Thus, in the evolutionary process after the
formation of modern species, most pathways will hold specific organizational
properties and the massive incorporation of alien genes are constrained to keep
the original optimal pathway organization. Above all, we suggest that HGT, an
important evolutionary source, may provide many opportunities for pathway
improvement and innovation. However, in most cases, the process of integrating
transferred genes into existing pathways is under strong selection pressure because
of the co-evolution of tightly function-related host genes. The overall pathway
organization structure will preserve well throughout evolution history.
Another interesting observation is that, in the 114 pathways
significantly affected by HGT, operon structure may have an important influence
on the integration of alien genes into existing pathways. The operons were
identified by approaches similar to previous work [26,27]. When one operon has
a significantly high proportion (more than 60%) of HT genes, it may possibly
play a significant role in bringing the related HT genes into the
corresponding pathway. Among the total 221 operons in 114 pathways, 77 (35%)
have such characteristic. For comparison, random tests (100,000 replicates)
were used by randomly picking genes with the same number of HT genes in each
pathway. The expected value of random occurrence is 20 and the standard
deviation is 2.72, which indicates that HT genes are more likely to be arranged
in operon structure (significant level 0.001). Such results indicate that
operon structure may play an essential and directional role in bringing HT
genes into pathways. In fact, the integration of alien genes in operon form
has its selective advantage, which can bring the recipient pathway new functional
units and lighten the damage to original pathway organization at the same
time.
To further understand the impact of HGT on pathway evolution, we
also classified the 114 pathways according to the KEGG pathway categories to
detect what type of pathways are more likely to integrate horizontally
transferred genes. As expected, the most mobile pathways are related to the
metabolism category (97 pathways), which is in good agreement with the
observation that genes in the metabolic function group are more apt to be
transferred. Such transfer may be preferred by the adaptive value of improving
the metabolic capability in some pathways. There are also some pathways
involved in genetic information processing including the protein secretion
system (mainly the type III secretion system), aminoacyl tRNA biosynthesis and
ribosome complex (14 pathways). The type III secretion system has been proved
to be pathogenetic-related which is often the important part of the
pathogenetic island [28]. But it is surprising that some subunits of ribosome
complex are also horizontally transferred. We speculated that this transfer
occurred before the formation of translation machinery, that is, at the
“progenotes time” as the “genetic annealing model” stated [24]. At that time,
the horizontal gene transfer is pervasive, even in the primitive translation
machine. The other pathways are involved in environmental information
processing (4 pathways). All of those are related to the ABC transporters
system: a hot spot of gene transfer as observed in many studies.
In this paper, we try to
answer two basic questions about HGT. The clarification of these two issues may
shed more light on the understanding of the behavior and mode of HGT in
prokaryotic genome evolution. In the future, more accurate data such as the
validated list of transferred genes and a more accurate pathway graph will
certainly refine these results.
Acknowledgement
We thank Dr. Haixu TANG (Indiana University School of Informatics, Bloomington,
USA) for assisting in the preparation of this manuscript.
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