Computational Identification of Rho-Independent Terminators in the Genomes of Mycobacterium Tuberculosis

Transcription

1 Thai Journal of Mathematics Volume 6 (2008) Number 1 : Computational Identification of Rho-Independent Terminators in the Genomes of Mycobacterium Tuberculosis C. Thangthong, I Ming Tang and Y. Lenbury Abstract : We have developed an algorithm to identify putative terminators in the genomes of Mycobacterium Tuberculosis H37Rv. We identified all 2,856 and best 1,618 putative terminator sequences beginning with -20 to +270 nucleotides around the stop codon for each gene. Comparison of the putative terminator sequences with the genome sequence databases of the Mycobacterium Tuberculosis CDC1551 and Mycobacterium bovis strains disclosed the same 2,245 sequences. A secondary structure in the nascent RNA followed by a trail of U is believed to be necessary and sufficient to terminate transcription. But many of these structures, such as Mycobacterium Tuberculosis H37Rv, Mycobacterium Tuberculosis CDC1551 and Mycobacterium bovis, are not followed by a U-trail. Keywords : Rho-Independent Terminator, Mycobacterium Tuberculosis, Computational Identification. INTRODUCTION Tuberculosis (often called TB) is a contagious disease that spreads from person to person through the air. The patient who has got the Mycobacterium tuberculosis, known as tubercle bacilli, in his lung, propels the germs into the air by coughing, sneezing, or even laughing or talking. A person can be infected by the bacilli by inhalation. Repeated contacts are usually required for infection. It is important to understand that there is a difference between being infected with TB and having the TB. Someone who is infected with TB will not necessarily become ill with the disease. The human body has an immune system that protects from active TB. When the immune system is weak, one easily becomes sick with the disease. World Health Organization (WHO) reported that only 5-10 percents of the people who are infected with TB germs become sick or infectious at some time during their lifetime. It is also reported that most of TB patients live in Africa and Asia [4]. TB is fast becoming a major world wide problem, especially in Africa where the spread is facilitated by AIDS. HIV weakens an immune system and it drives TB germs in a body to be active. Each of them speeds the other s progress. It is estimated that nearly 1 billion people will become newly infected, over 150 million will become sick, and 36 million will die worldwide between now and 2020 if control is not further strengthened.

2 226 Thai J. Math. 6(2008)/ C. Thangthong, I Ming Tang and Y. Lenbury Mycobacteria are gram-positive [4], rod-shaped bacteria of the Actinomycete family, and therefore are most closely related to the nocardia, corynebacteria, and streptomyces. Their most characteristic feature is their complex cell envelope, containing a high percentage of lipids, which include the large-branched mycolic acids. This envelope makes the bacteria resistant to breakage and relatively impermeable to antibiotics, and is responsible for the acid-fast staining property used to identify the organisms. The genomic DNA contains a high guanine plus cytosine (GC) content, ranging from 58-69%. In Mycobacterium Tuberculosis, it is about 66%. Mycobacteria can infect most species of animals including rodents, birds, and fish. However, their importance lies in the fact that they include major human pathogens. Tuberculosis, caused by Mycobacterium Tuberculosis, remains the most important infectious cause of mortality in the world. In this work, we develop an algorithm adapted from the Gester program [3] to find terminators in the complete genome of Mycobacterium Tuberculosis. Specifically, we study rho-independent terminators in the complete genomes of Mycobacterium Tuberculosis H37Rv, Mycobacterium Tuberculosis CDC1551 and Mycobacterium bovis. For this purpose, we downloaded complete genomes of the above bacteria of current interest from the website of NCBI homepage at ftp://ftp.ncbi.nih.gov/genomes/bacteria/ [5], and used our program to identify their stem-and-loops which are relevant to gene expression, transcription and termination. Gene expression is accomplished by the transfer of genetic information from DNA to RNA molecules and then from RNA to protein molecules. RNS molecules are synthesized by using the base sequence to one strand of DNA as a template in a polymerization reaction that is catalyzed by enzymes called DNA-dependent RNA polymerases or simply RNA polymerases. The process by which RNA molecules are initiated, elongated, and terminated is called transcription. Termination of RNA synthesis occurs at specific bas-sequences in the DNA molecule, called terminators. Some termination sequences allow RNA polymerase to terminate elongation spontaneously. These sequences are called intrinsic terminators. Other terminators require the action of the protein call Rho; they are called rho-dependent terminators. Intrinsic terminators have three characteristic features as follows. First, there is an inverted-repeat base-sequence containing a central nonrepeating segment; that is, the sequence in one DNA strand would read ABCDEF-XYZ- F E E B A in which A and A, B and B, and so on, are complementary bases. This sequence is capable of intra-strand base pairing, forming a stem-and-loop configuration in the RNA transcript and possibly in the DNA strands. The second region is near the loop end of the putative stem (sometimes totally within the stem) and is a sequence having a high G+ C content. A third region is the sequence of A-T pairs (which may begin in the putative stem) that yields in the RNA a sequence of six to eight uracil residues often followed by an adenine.

3 Computational Identification of Rho-Independent Rho-dependent terminators lack sequences with a stretch of adenine residues in the template strand. Rho acts by binding to a special sequence on the nascent RNA and forcibly pulls the RNA away from its contact with the DNA in the transcription-elongation complex with RNA polymerase. Rho derives its force from the hydrolysis of ATP molecules. The RNA polymerase remains bound to the DNA and continues transcription until it reaches a terminator sequence (stop signal) at the end of the transcription unit. The most common stop signal is an RNA hairpin in which the RNA transcript is self-complementary. As a result, the RNA can form a stable hairpin structure with a stem and a loop. Commonly, the stem structure is very GC-rich, favoring its base pairing stability due to the additional stability of G-C base pairs over A-U base pairs. The RNA hairpin is often followed by a sequence of four or more U residues. It seems that the polymerase pauses immediately after it has synthesized the hairpin RNA. The subsequent stretch of U residues in the RNA base pairs only weakly with the corresponding A residues in the antisense DNA strand. This favors dissociation of the RNA from the complex with the template strand of the DNA. The RNA is therefore released from the transcription complex. The non-base-paired antisense strand of the DNA then re-anneals with the sense DNA strand and core enzyme disassociates from the DNA. While the RNA polymerase can self-terminate at a hairpin structure followed by a stretch of U residues, other known terminator sites may not form strong hairpins. They use an accessory factor, the rho protein (ρ) to mediate transcription termination. Rho is a hexameric protein that hydrolyzes ATP in the presence of single-stranded RNA. The protein appears to bind to a stretch of 72 nucleotides in RNA, probably through recognition of a specific structural feature rather than a consensus sequence. Rho moves along the nascent RNA towards the transcription complex. There, it enables the RNA polymerase to terminate at rho-dependent transcriptional terminators. Like rho-independent terminators, these signals are recognized in the newly synthesized RNA rather than in the template DNA. Sometimes, the rho-dependent terminators are hairpin structures, which lack the subsequent stretch of U residues which are required for rho-independent termination. The main objective in this research is to identify Rho-independent terminators in genomes of Mycobacterium Tubercolusis by using mathematical techniques. The details are given in the next section. We wrote the program by using Fortran. The program was then applied to search for stem-and-loops in genomes of Mycobacterium Tubercolusis, M. TB. H37Rv, M. TB. CDC1551 and M. Bovis. We already know that all 3 genomes have almost the same structure so that we hypothesize that they have some of the same stem-and-loops as well. In 2002, Shyam Unniraman, Ranjana Prakash and Valakunja Nagaraja presented a program, Gester [3], to identify putative terminators. Upon our studying this program, since we are in possession of their source code programs, some mistakes were discovered. Some values of free energy in their program were wrong and the total free energy on the complementary strand was computed with a wrong direction. 5 3 direction is for the regular strand and 3 5 direction is for the complementary strand. However, Gester used direction 5 3 on the

4 228 Thai J. Math. 6(2008)/ C. Thangthong, I Ming Tang and Y. Lenbury complementary strand to calculate free energy. They did not employ appropriate mathematical method so that they could not find all possible stem-and-loops. We have adapted the program to discover them all. We then compared our results with those reported by Carafa et al. [1]. In order to locate the Rho-independent terminator, we used a statistical method to test our hypothesis that more stemand-loops usually reside near the stop condon. MATERIALS AND METHOD Materials In this section, the materials and methodology for finding all possible stems and loops will be explained. We obtained the genome data from the GenBank database, the public genome library of the National Center for Biotechnology Information [5]. For this study, we used complete genomes of Mycobacterium tuberculosis H37Rv (locus NC gbk, size 4,411,529 bp, 12.9 MB), Mycobacterium tuberculosis CDC1551 (locus NC gbk, size 4,403,836 bp, 9.70 MB) and Mycobacterium bovis (locus NC gbk, size 4,345,492 bp, 12.5 MB), downloaded from ftp://ftp.ncbi.nih.gov/genomes/bacteria/ with a high performance computer Intel Pentium M, processor 1.70 GHz and 504 MB of ram. We used the Compaq visual Fortran professional edition for our source code. Definition of Stem-and-Loop The stem-and-loop structures are as follows: 1. Stem structure : the bases can make bonds with the opposite ones. 2. Hairpin Structure : the bases at the top of all structures that can not make a bond in this structure. If there is no base here, it is called a perfect palindrome. 3. Mismatch structure : the bases lie between stem structure that can not make bonds with the opposite bases. The opposite side must have some bases. 4. Gap structure : like the mismatch structure but there is no pair of base in the opposite side. 5. Trail : the sequence after the last base in the stem structure. Please see Figure 1. Figure 1: The structure of stem-and-loop.

5 Computational Identification of Rho-Independent Concerning the direction of stem-and-loops, we considered them to be the same as the direction of RNA. That is, 5 3 direction is for the regular strand, and 3 5 direction is for the complementary strand. The direction plays a significant role in searching for stem-and-loops. Computer Algorithm Our algorithm consists of three steps. The first step separates the data in the whole genome sequences in Genbank format into two parts, (i) details of genes and (ii) DNA sequence on regular strain. At the end of this first step, we have the position, start and stop, and the name of each gene, and the RNA sequence for both regular and complementary strains. The second step searches for the region surrounded by -20 to +270 nucleotides around the stop codon for each gene, without entering adjacent coding regions, the terminator sequences will be identified. Next, we have used the mathematical technique to find all of the possible hairpin loop structures with the range 4 to 30 nucleotides for stem length, 3 to 9 nucleotides for loop length, 0 to 3 unpaired nucleotides for mismatch and gap. Also, our possible palindromes should have at least 50% of the GC content on the stem. Our algorithm has searched for the putative terminators by calculating for all RNA subsequences (using slide window) with -20 to +270 nucleotides around the stop codon for each gene satisfying the following criteria; the energy score ( G) is below the cut-off value, stem length is constrained to be in the range 4 to 30 nucleotide pairs with at least 50% of the GC content in that stem, loop length must range from 3 to 9 nucleotides. In addition, there are unpaired regions that interrupt the stem called mismatch and gap. We allowed none up to 3 unpaired nucleotides [3]. We may summarize the conditions adopted in our algorithm as follows stem length %GC content of stems 50% 3. 3 loop length mismatch gap 3 6. G cut-off G Mathematical Method Suppose that we have some sequence around the stop codon ready for us to find all possible stem-and-loops. First, we let the length of these sequences (the amount of bases in sequence) is n. We have b 1, b{ 2,..., b n where b i is the i th position 1, b i make a bond with b j of base in the sequence. We define d(b i, b j ) = 0, else The values of d(b i, b j ) are showed in Table 1.

6 230 Thai J. Math. 6(2008)/ C. Thangthong, I Ming Tang and Y. Lenbury b j b i A U G C A d(a,a) = 0 d(a,u) = 1 d(a,g) = 0 d(a,c) = 0 U d(u,a) = 1 d(u,u) = 0 d(u,g) = 1 d(u,c) = 0 G d(g,a) = 0 d(g,u) = 1 d(g,g) = 0 d(g,c) = 1 C d(c,a) = 0 d(c,u) = 0 d(c,g) = 1 d(c,c) = 0 Table 1 The values of d(b i, b j ) Next, we compute d(b i, b j ), i = 1, 2,..., n 1, and for each i we let j = i + 1, i + 2,..., n. Then, we have (n 1)n 2 values of d(b i, b j ). For the sequence to form a stem-and- loop structure, the value of d(b 1, b n ) must be 1. If d(b 1, b n ) = 1, then we will consider the values of d(b 1+k, b n k ), k = 1, 2,..., [ n 2 ] 1. If d(b i, b j ) = 1, then b i and b j are in stem structure, but if d(b i, b j ) = 0, b i and b j are in mismatch or gap structure. For example, let s consider the sequence CCGCAUCGCAUCACGGCGUGCGCGG. Here, n = 25. First, we compute (24)(25) 2 = 300 values of d(b i, b j ), i = 1, 2,..., 24, and for each i we let j = i+1, i+2,..., 25. The resulting d(b i, b j ) are shown in Table 2. We can see that the value of d(b 1, b 25 ) = 1, which means that this sequence can form stem-and-loop structure. C C G C A U C G C A U C A C G G C G U G C G C G G C C G C A U C G C A U C A C G G C G U G C G 1 1 C 0 G G Table 2 The values of d(b i, b j ) for sequence CCGCAUCGCAUCACGGCGUGCGCGG Next we consider the values of d(b 1+k, b 25 k ), k = 1, 2,..., 11 (since [ 25 2 ] = [12.5] = 12 ). For each k, if d(b 1+k, b 25 k ) = 1, we increase the value of k to k + 1. But if d(b 1+k, b25 k) = 0, then we stop. In our example, we stop the

7 Computational Identification of Rho-Independent process when k = 4, because d(b 5, b 21 ) = 0. Then we have four bonds in stem structure, b 1 b 2 b 3 b 4 making bond with b 25 b 24 b 23 b 22. In this research, bases in a stem structure are represented outside the ( ). Next, we have 9 different possibilities, since the maximum number of bases in mismatch or gap is 3 and we have 2 sides of stem-and-loop structure, called right and left sides. For each side, we have 3 posibilities of the values of i and j along the value of d(b i, b j ). Continuing with our example, now k = 4 and we have i = 5(1 + k), j = 21(25 k). Since d(b i, b j ) = 0, we will consider the value of d(b i+m, b j r ), m = 0, 1, 2, and r = 0, 1, 2. If d(b i+m, b j r ) = 0, then b i+m and b j r are in mismatch or gap structure. b i+m are on the right side and b j r are on the left side of a mismatch or a gap. Bases in a mismatch or a gap structure are represented inside the ( ). One of possible event is shown in Table 3. C C G C A U C G C A U C A C G G C G U G C G C G G C C G C A U C G C A U C A C G G C G U G C G 1 1 C 0 G G Table 3. The values of d(b i, b j ) for sequence CCGC(AU)CGCAUCACGGCG(UGC)GCGG Next, we will use the same method as in the second step in Table 2. That is, starting with d(b i, b j ) = 1, consider d(b i+k, b j k ), k = 1, 2,... until the value of d(b i+k, b j k ) = 0. If d(b i+k, b j k ) = 1, then b i+k andb j k are in the stem structure. Continue in the same manner as in the third step in Table 3 and repeat both of steps until k = [ n 2 ] 1. If the [ n 2 ]th position of the bases is in the mismatch, then all bases in that mismatch are in hairpin loop structure. However, if it is in a stem structure, then we call it a perfect palindrome (not having hairpin loop). Bases in the hairpin loop structure are represented inside the {{ }}. In the third step, the total free energy for each putative terminator is calculated. The overall free energy of a terminator is given by the general equation: G total = G doublet + G hairpinloop + G stackingmismatch + G unpair +

8 232 Thai J. Math. 6(2008)/ C. Thangthong, I Ming Tang and Y. Lenbury Gbonus + G penalty The terms on the right hand side are calculated by using the parameters of Mathews et al.[2] and the Gester program [3]. After we have calculated the total free energy for each putative terminator, we compare G total with the cut off G. We used the cut off G for any genome that is computed as follows [3]: G cut off = (12/10.5)[ 0.294(%GC) ]. Our putative terminators have G total less than or equal G cut off. For example, if we have a subsequence CCCGGCCGCGGCUAACCGUGGCGGG then the total free energy is kcal/mol. It consists of 10 bonds in stem, 4 bases on loop and 1 mismatch. The details of the total free energy computation is shown in Figure 2. Figure 2 An example of the total free energy computation. RESULTS AND DISCUSSION Our program searches for putative terminators in the genome of Mycobacterium Tuberculosis H37Rv, Mycobacterium Tuberculosis CDC1551 and Mycobacterium bovis. The stability of each structure is calculated by using the parameters of Mathews et al.[2]. For each gene, the strongest structure is called the best candidate for the primary terminator. The amount of stem-and-loops for each genome is shown in Table 4. In this table, we show two kinds of stem-and-loops.

9 Computational Identification of Rho-Independent The first group is all of the stem-and-loops that we found in each genome, denoted by All. This means that for each gene in each genome we can find more than one stem-and-loop. The other group corresponds to the stem-and-loops that are selected from the lowest free energy stem-and-loops for each gene, denoted by Best. Genome All Best M. TB. H37Rv 2,856 1,618 M. TB. CDC1551 3,028 1,721 M. bovis 2,863 1,613 Table 4: The amount of stem-and-loops for genomes of M.TB. H37Rv, M.TB.CDC1551 and M. bovis. From the results in Table 4, we can see that all of the 3 genomes have nearly equal amount of stem-and-loops. It agrees with our proposition. Next, we found that there are the same number 2,245 stem-and-loops, in all three genomes. And the same number 1,237 best stem-and-loops for each gene in all three genomes. Although all three genomes have similar sequences of DNA, there are some stemand-loops that are not like those on other genomes. Our results are shown as a Venn diagram in Figure 3 for all stem-and-loops, and in Figure 3.2 for best stem-and-loops for each gene. Figure 3: Venn diagram for the amount of all stem-and-loops in genomes of M.TB. H37Rv, M.TB.CDC1551 and M. bovis.

10 234 Thai J. Math. 6(2008)/ C. Thangthong, I Ming Tang and Y. Lenbury Figure 4: Venn diagram for the amount of best stem-and-loops in genomes of M.TB. H37Rv, M.TB.CDC1551 and M. bovis. Next, the frequency of the variation in the distances between stem-and-loops and stop condons is computed. The maximal frequency occured at 1 base from the stop codon. We show the top ten frequencies in Table 5. Most hair pin loops reside close to the stop codons. The graph of the frequncy versus the distance between all stems and loops and stop condons of M.TB.H37Rv is plotted in Figure 5. distance (base) frequency Table 5: Top ten distances between all stem-and-loops and stop condons with high frequency for M.TB.H37Rv.

11 Computational Identification of Rho-Independent Figure 5: The graph of the frequency versus the distance between all stem-and-loops and stop condons of M.TB.H37Rv. The conclusion in the case of the genome of M.TB. CDC1551, is the same as that for M.TB. H37Rv. We show the top ten frequencies in Table 6. Most hair pin loops reside close to the stop codons. The graph of the frequncy versus the distance between all stem-and-loops and stop condons of M.TB.CDC1551 is plotted in Figure 6. distance (base) frequency Table 6: Top ten distances between all stem-and-loops and stop condons with high frequency for M.TB.CDC1551. Figure 6: The graph of the frequency versus the distance between all stem-and-loops and stop condons of M.TB.CDC1551.

12 236 Thai J. Math. 6(2008)/ C. Thangthong, I Ming Tang and Y. Lenbury Also, the conclusion in the case of the genome of M.bovis, is the same as that for M.TB. H37Rv and M.TB. CDC1551. We show the top ten frequencies in Table 7. Most hair pin loops reside close to the stop codons. The graph of the frequncy versus the distance between all stem-and-loops and stop condons of M.bovis is plotted in Figure 7. distance (base) frequency Table 7: Top ten distances between all stem-and-loops and stop condons with high frequency for M. bovis. Figure 7: The graph of the frequency versus the distance between all stem-and-loops and stop condons of M.bovis. Moreover, the region of our search for stem-and-loops is surrounded by -20 to +270 nucleotides around the stop codon for each gene. We found that the

13 Computational Identification of Rho-Independent maximum distance between all stem-and-loops and stop condons is close to +241 nucleotides around the stop codons for each genes of all three genomes. The distance between all stem-and-loops and stop condons with low frequency for all three genomes are shown in Table 8. We can conclude that the regions for stem-andloops of genomes of M.TB. H37Rv, M.TB.CDC1551 and M. bovis are surrounded by -20 to +242 nucleotides around the stop codons for each gene. distance (base) frequency H37Rv CDC1551 M.bovis Table 8: The high distances between all stem-and-loops and stop condons for M.TB. H37Rv, M.TB. CDC1551 and M.bovis. CONCLUSION Based on the Gester program [3], we have developed a program to study complete genomes of M.TB. H37Rv, M.TB.CDC1551 and M. bovis, downloaded from the GenBank database, the public genome library of the National Center for Biotechnology Information [5]. Here, the same region as that in the Gester program was used, namely the subsequence of RNA beginning with -20 to +270 nucleotides around the stop codon for each gene. The difference between our program and the Gester program is in the data input process. The Gester

14 238 Thai J. Math. 6(2008)/ C. Thangthong, I Ming Tang and Y. Lenbury program moved a stem inward the region until a mismatch is found. On the other hand, our program uses a slide window for a subsequence of RNA starting with -20 to 0 nucleotides around the stop codon for each gene and calculates all possibility events that can form stems and loops in that subsequence. After that, we add one base into the subsequence or starting with -20 to 1 nucleotides around the stop codon for each gene and compute them again until the last base is in the last position of possible region (+80 nucleotides around the stop codon for each gene). By using our method, we obtain all possible stem-and-loops for each gene. For each stem-and-loop, we calculated the free energy ( G) by using the parameters of Mathews et al. [2]. We reported only stem-and-loops that have the G lower than cut-off G. The computer program in FORTRAN utilized in this study and our output may be found on In 1990, Yves d Aubenton Carafa, Edward Brody and Claude Thermes reported their findings on 148 Escherichia coli rho-indendent transcription terminators [1]. We compared our results with theirs. By using our program, we found all stem-and-loops while the Gester program found only 80% of them [3]. In this manner, we believe that our algorithm is more powerful than the Gester program. The output of our work provides valuable information relevant to the study of gene expression, transcription, and termination, especially in connection to TB which has become a disease of major concern world wide. REFERENCES 1. Carafa YA, Brody E and Thermes C (1990) Prediction of rho-independent Escherichia coli transcription terminators. J Mol Biol 216, Mathews DH, Sabina J, Zuker M, Turner DH (1999) Expanded sequence dependence of thermodynamic parameters improves prediction of RNA secondary structure. J Mol Biol 288, Unniraman S, Prakash R and Nagaraja V (2002) Conserved economics of transcription termination in eubacteria. Nucleic Acids Res 30, (Received 30 May 2007) Chaiporn Thangthong a, I Ming Tang b, Yongwimon Lenbury a a Department of Mathematics, Mahidol University, Bangkok 10400, Thailand. b Department of Physics, Mahidol University, Bangkok 10400, Thailand. Corresponding author, scylb@mahidol.ac.th