Hepatitis C virus whole genome position weight matrix and robust primer design

Abstract

Background

The high degree of sequence heterogeneity found in Hepatitis C virus (HCV) isolates, makes robust nucleic acid-based assays difficult to generate. Polymerase chain reaction based techniques, require efficient and specific sequence recognition. Generation of robust primers capable of recognizing a wide range of isolates is a difficult task.

Results

A position weight matrix (PWM) and a consensus sequence were built for each region of HCV and subsequently assembled into a whole genome consensus sequence and PWM. For each of the 10 regions, the number of occurrences of each base at a given position was compiled. These counts were converted to frequencies that were used to calculate log odds scores. Using over 100 complete and 14,000 partial HCV genomes from GenBank, a consensus HCV genome sequence was generated along with a PWM reflecting heterogeneity at each position. The PWM was used to identify the most conserved regions for primer design.

Conclusions

This approach allows rapid identification of conserved regions for robust primer design and is broadly applicable to sets of genomes with all levels of genetic heterogeneity.

Background

Genetic heterogeneity is a hallmark of RNA viruses in general, and the hepatitis C virus (HCV) in particular, due to the lack of fidelity of viral RNA-dependent RNA polymerases [1,2]. In HCV, this genetic diversity has been organized into six major genotypes and numerous subtypes (over 80). Isolates of the same genotype have an average DNA sequence identity of 95%, but different genotypes have DNA sequence identity close to 65% on average [2-5].

Nucleic acid-based assays, such as the polymerase chain reaction (PCR), the ligase chain reaction (LCR), nucleic acid sequence-based amplification (NASBA), branched chain DNA (bDNA) and sequence analysis itself, rely on the efficient hybridization of oligonucleotides to the targeted sequence. Mismatches between the oligonucleotides and the targeted nucleic acid can affect duplex stability and may compromise the ability of a system to amplify and detect the targeted sequences. Numerous factors determine the effect of mismatches, including: the length of the oligonucleotide, the nature and position of the mismatches, the temperature of hybridization, the presence of co-solvents and the concentrations of oligonucleotides, as well as monovalent and divalent cations [6].

The sequence heterogeneity of HCV challenges efficient detection with nucleic acid-based assays. PCR is widely used for the detection of HCV specific nucleic acids due to its sensitivity. Generally speaking however, effective primers require the genotype of the sample to be known in advance and even then will often be less than 100% effective due to minor variations in the isolates.

Design of robust primers to maximize success with unsequenced isolates (i.e. clinical samples), is a common challenge facing the molecular virologists. A number of software products exist to facilitate primer selection with defined genomes. Many factors are considered in these programs, for example, melting temperature of primers, avoiding primer dimers, avoiding self-complementary primers etc (e.g., Primer Premier [7], Primer3 [8], PRIDE [9]). These algorithms deal mostly with a single template or a small number of sequences. Little effort has been made to handle large number of heterogeneous variants of a given genome.

A large number of HCV related sequences have been deposited in GenBank, making genome wide comparison of all different HCV genotypes and subtypes possible. In this report, more than 100 complete and 14,000 partial sequences deposited in GenBank (Release 129, April 15, 2002) have been used to generate a genome wide consensus sequence and Position Weight Matrix (PWM) for the HCV genome. A PWM based approach for identifying highly conserved regions is proposed which should aid in robust primer design for nucleic acid-based assays. This approach is general enough to be used to optimize any set of genomes with a high degree of heterogeneity.

Results and Discussion

Aligning genomes and generating a position weight matrix(PWM)

One HCV genome (D90208) was used as a template and was separated into pieces based on known gene boundaries. All complete and partial sequences that contained a given region were collected by TBLASTN [10] against HCV sequences from the GenBank non-redundant nucleotide sequence database (nt). An alignment was then made for each part of the genome using ClustalW [11]. A weight score for each position in each fragment was calculated and a PWM was created for that fragment. A whole genome PWM was created by joining the individual PWMs. Finally a 25-bp window, (representative of a typical primer), was walked through the genome/PWM to identify the most conserved locations for primer design.

Due to the extreme genetic heterogeneity of the HCV genome and the nature and large number of complete and partial sequences in the public database, a direct genome wide sequence alignment was not feasible. The approach taken, to break the HCV genome into 10 pieces according to the gene boundaries, proved to be successful. HCV sequence D90208 was chosen arbitrarily as the template sequence and the number of sequences included for alignment of each region is indicated in Table 1.

Table 1.

Sequence numbers for each region that was used to construct the whole genome PWM

Region	Start	Stop	# of Sequences
5' UTR	1	329	1333
CORE	330	899	1818
E1	900	1475	3830
E2(p7)	1476	2564	3792
NS2	2565	3407	1496
NS3	3408	5300	520
NS4A	5301	5462	277
NS4B	5463	6245	345
NS5A	6246	7586	1571
NS5B	7587	9413	1914

Some regions of the HCV genome share only 50 percent identity across strains. Figure 1 shows a plot of conservation score using a 10-bp window for the whole HCV genome. Region 1–350, which corresponds to the 5' UTR is very conserved across all strains while region 1860–2230 corresponding to the E2 protein, is very heterogeneous. In addition, third position wobble causes mismatching at virtually every third base (in the coding region), leading as expected, to less identity at the DNA level [12]. In the process of collecting sequences for each HCV region, using a nucleotide level comparison algorithm like BLASTN, a lot of sequence entries will be missed. To solve this problem, a protein level comparison algorithm TBLASTN was used via a six-frame translation. Different stringency scores were used to ensure that as many sequences as possible were retrieved. A sequence was chosen for alignment (for a given region) if it shared at least 50% identity over a 30 amino acid stretch or 65 % identity over a 20 amino acid stretch, or over 90% identity over a 10 amino acid stretch with the template sequence. These cutoffs were chosen following inspection of the blast hits for the different regions. Only 4.9% of the available sequences were discarded due to failure to meet the aforementioned criteria.

Figure 1.

HCV genome conservation score distribution.

For each regional alignment, flanking sequences were trimmed prior to generating the PWM. The genome wide PWM was created by combining all individual PWMs (see additional file 1). Insertions (represented by '-' in additional file 1) were added to the template sequence only if greater than 1% of the sequences contained this insertion. This was done to reduce the inclusion of spurious insertions that are caused by sequencing errors or that exist in only a single isolate. A consensus sequence was derived by picking the most frequently occurring base at each position.

Choosing a conserved region for optimized primer design

Using the PWM, the most conserved stretches were rapidly identified making possible the design of robust primers based on the criteria described in Methods. The 25-bp segments in Table 2 and Table 3 are listed by positions in the genome. The higher the odds score, the more conserved the region. Figure 2 shows samples from the final PWM; one 10-bp region in NS5B has a very low conservation score (A), a second 10-bp region shows a very high conservation score (B). This approach allows rapid identification of the most conserved regions of the genome with no regard for self-complementarity of primers, optimizing melting temperature, avoiding primer dimers, etc. Once potential regions of interest are identified, other primer design algorithms can then be used to ensure that self-complementarity etc. will not be a problem. This two step strategy for designing robust primers can be applied to any set of genomes with a high degree of heterogeneity such as viruses, bacterial genes etc. Once a specific sequence has been identified, partially degenerate or multiple oligonucleotides can easily be generated as deemed appropriate for the particular application. Empirical validation of all primers is still prudent.

Table 2.

Suggested forward primer regions based on HCV whole genome PWM

Primer Start	Primer End	Conservation Score	Primer Start	Primer End	Conservation Score	Primer Start	Primer End	Conservation Score
7	31	1.804	465	489	1.936	5672	5696	1.821
8	32	1.805	470	494	1.938	6263	6287	1.74
32	56	1.94	471	495	1.939	6302	6326	1.775
33	57	1.94	559	583	1.716	6317	6341	1.732
40	64	1.954	560	584	1.736	6318	6342	1.734
54	78	1.957	580	604	1.873	6323	6347	1.78
93	117	1.911	581	605	1.873	6356	6380	1.786
94	118	1.912	582	606	1.873	6376	6400	1.728
106	130	1.962	600	624	1.89	6424	6448	1.744
107	131	1.962	610	634	1.757	6431	6455	1.77
141	165	1.956	611	635	1.779	6542	6566	1.736
142	166	1.956	612	636	1.779	6556	6580	1.707
143	167	1.957	619	643	1.798	6976	7000	1.708
165	189	1.865	707	731	1.614	7018	7042	1.748
168	192	1.866	1297	1321	1.782	7081	7105	1.862
178	202	1.897	1298	1322	1.782	7097	7121	1.922
204	228	1.841	1302	1326	1.766	7098	7122	1.925
212	236	1.871	1303	1327	1.804	7249	7273	1.66
213	237	1.881	1402	1426	1.719	7250	7274	1.686
214	238	1.881	1403	1427	1.733	7251	7275	1.686
215	239	1.882	1404	1428	1.733	7288	7312	1.68
218	242	1.893	1447	1471	1.765	7568	7592	1.782
219	243	1.893	1448	1472	1.795	7569	7593	1.782
220	244	1.899	1449	1473	1.794	7581	7605	1.829
221	245	1.9	1754	1778	1.587	7587	7611	1.902
222	246	1.901	1755	1779	1.626	7689	7713	1.734
225	249	1.922	2511	2535	1.676	7879	7903	1.792
237	261	1.922	2572	2596	1.796	7895	7919	1.812
238	262	1.922	2692	2716	1.856	7985	8009	1.849
263	287	1.952	2715	2739	1.928	8015	8039	1.804
264	288	1.954	2746	2770	1.789	8016	8040	1.803
337	361	1.833	2747	2771	1.791	8034	8058	1.835
345	369	1.849	2748	2772	1.792	8035	8059	1.852
351	375	1.883	2820	2844	1.928	8040	8064	1.878
364	388	1.934	3247	3271	1.728	8089	8113	1.88
381	405	1.918	3308	3332	1.806	8090	8114	1.88
387	411	1.899	3328	3352	1.851	8159	8183	1.872
395	419	1.929	3329	3353	1.871	8192	8216	1.794
405	429	1.914	3330	3354	1.87	8233	8257	1.791
406	430	1.914	3439	3463	1.591	8234	8258	1.811
407	431	1.937	3607	3631	1.729	8295	8319	1.807
408	432	1.936	3703	3727	1.852	8312	8336	1.728
422	446	1.923	3886	3910	1.779	8726	8750	1.739
443	467	1.908	3892	3916	1.762	8893	8917	1.686
444	468	1.908	4955	4979	1.788	8926	8950	1.816
447	471	1.907	5049	5073	1.898	8962	8986	1.74
448	472	1.907	5186	5210	1.751	8963	8987	1.749
451	475	1.909	5187	5211	1.774	9048	9072	1.642
452	476	1.922	5198	5222	1.801	9114	9138	1.717
453	477	1.922	5354	5378	1.84	9180	9204	1.794
454	478	1.922	5355	5379	1.843	9202	9226	1.745
455	479	1.922	5499	5523	1.811	9315	9339	1.826
456	480	1.921	5636	5660	1.724	9475	9499	1.704
464	488	1.929	5657	5681	1.678

To ensure optimal polymerization, the 3' end and the penultimate position were required to be G or C with frequencies of ≥0.98 and the upstream position, (3' -2), a G or C with a frequency of ≥0.90 or alternatively an A or T with a frequency of ≥0.95.

Table 3.

Suggested reverse primer regions based on HCV whole genome PWM.

Primer Start	Primer End	Conservation Score	Primer Start	Primer End	Conservation Score	Primer Start	Primer End	Conservation Score
30	54	1.94	467	491	1.938	3352	3376	1.822
31	55	1.94	470	494	1.938	3630	3654	1.808
55	79	1.956	471	495	1.939	3726	3750	1.684
63	87	1.942	474	498	1.938	5043	5067	1.876
77	101	1.929	475	499	1.931	5072	5096	1.835
116	140	1.972	476	500	1.926	5209	5233	1.818
117	141	1.972	477	501	1.923	5210	5234	1.792
129	153	1.954	478	502	1.891	5221	5245	1.763
164	188	1.865	487	511	1.918	5377	5401	1.754
165	189	1.865	488	512	1.918	5378	5402	1.732
166	190	1.865	493	517	1.89	5522	5546	1.745
187	211	1.9	494	518	1.89	5659	5683	1.695
188	212	1.891	572	596	1.805	5680	5704	1.882
191	215	1.89	582	606	1.873	5695	5719	1.866
197	221	1.878	603	627	1.811	6286	6310	1.782
201	225	1.846	604	628	1.77	6325	6349	1.815
209	233	1.868	605	629	1.77	6340	6364	1.79
235	259	1.923	633	657	1.897	6341	6365	1.746
236	260	1.922	634	658	1.864	6379	6403	1.648
237	261	1.922	642	666	1.743	6454	6478	1.783
238	262	1.922	691	715	1.542	6579	6603	1.838
241	265	1.915	730	754	1.484	7120	7144	1.868
242	266	1.914	856	880	1.58	7121	7145	1.868
243	267	1.913	1297	1321	1.782	7272	7296	1.794
244	268	1.912	1320	1344	1.893	7273	7297	1.754
245	269	1.912	1325	1349	1.862	7311	7335	1.674
260	284	1.953	1326	1350	1.834	7591	7615	1.856
261	285	1.953	1425	1449	1.762	7712	7736	1.821
286	310	1.924	1426	1450	1.729	7902	7926	1.813
287	311	1.922	1439	1463	1.716	8038	8062	1.878
360	384	1.936	1470	1494	1.67	8057	8081	1.912
387	411	1.899	1471	1495	1.62	8058	8082	1.912
403	427	1.914	1777	1801	1.532	8092	8116	1.864
407	431	1.937	2534	2558	1.737	8112	8136	1.833
418	442	1.894	2715	2739	1.928	8256	8280	1.833
428	452	1.914	2769	2793	1.918	8293	8317	1.806
429	453	1.914	2770	2794	1.907	8916	8940	1.839
430	454	1.914	2771	2795	1.894	8985	9009	1.684
445	469	1.908	3270	3294	1.759	9203	9227	1.742
452	476	1.922	3331	3355	1.854	9498	9522	1.574
466	490	1.936	3351	3375	1.84

To ensure optimal polymerization, the 3' end and the penultimate position were required to be G or C with frequencies of ≥0.98 and the upstream position, (3' -2), a G or C with a frequency of ≥ 0.90 or alternatively an A or T with a frequency of ≥ 0.95.

Figure 2.

Comparison of two 10-bp regions in NS5B: the first with a very low conservation score (A), the second with a very high conservation score (B). Conservation scores were calculated by taking the average of the highest log odds score for each position (see Methods). The sequence shown on top of the matrices is the consensus sequence.

Methods

Databases and Resources

Genbank Release 129 was downloaded from ftp://ncbi.nlm.nih.gov. Pairwise alignment TBLASTN [13] was used to determine whether or not two sequences share similarity. ClustalW [11] was used for multiple sequence alignment. All non-commercial softwares used in this study were written in PERL 5.0.

Construction of alignment

All HCV related sequences were extracted from GenBank (Release 129) by using keyword HCV or Hepatitis C. D90208 was chosen as the organizing template for its fully annotated genome in the GenBank. (Other organizing HCV genomes yielded virtually identical consensus sequences and PWM profiles.) The genomes were separated into 10 regions according to D90208's annotation: 5' UTR, core, E1, E2(P7), NS2, NS3, NS4A, NS4B, NS5A, NS5B. The DNA sequences for each of these regions were retrieved and used for TBLASTN analysis against all HCV sequences. If a sequence shared 50% identity over 90-bp (30 amino acids), 65% identity over 60-bp (20 amino acids) or 90% over 30-bp (10 amino acids) with the query template region, it was considered to contain part of the corresponding gene from a HCV genome in that region, and therefore was used for multiple sequences alignments of this region. For each region, a multiple alignment was done using ClustalW. Alignment was manually curated to eliminate obvious false alignments due to bad sequence quality or inappropriate BLAST hits.

Construction of PWM

A PWM and a consensus sequence were built for each region of HCV and subsequently assembled into a whole genome consensus sequence and PWM. For each of the 10 regions, the number of occurrences of each base at a given position was compiled. These counts were converted to frequencies that were used to calculate log odds scores. The odds score is the frequency observed divided by the theoretical frequency expected (i.e., the background frequency of the base, usually averaged over the genome ~0.25/base). For example, if the base frequency is 0.79 and the estimated background frequency is 0.25, the odds score would be 0.79/0.25 = 3.16. Finally, odds scores were converted to log odds scores by taking the logarithm base 2.

W_i,j = log₂(F_i,j/P_i,)

Where

W_i,j is the scoring matrix value of base i in position j

F_i,j is the frequency of base i in position j, P_i is the background frequency of base i

As the logarithm of zero is infinity, a zero occurrence of a particular base in the matrix creates a problem. In this case, a large negative log odds score may be used at such a position in a scoring matrix. A formula proposed by Hertz and Stormo [14] was used instead in our calculations.

W_i,j = log₂ [(C_i,j + P_i)/{(N + 1)P_i}] ≈ log₂(F_i,j/P_i,)

Where C_i,j is the count of base i in position j, N is the total number of sequences.

Choosing a conserved region for primer design

By sliding 25 bp window (representing average primer length) incrementally along the genome in 1-bp intervals, an average of the highest log odds scores for each position (either A, C, G or T) was calculated to generate the overall degree of conservation (conservation score) for this 25-bp region.

where L is the length of the region (25-bp in this case).

For PCR applications (or those involving polymerization, where homology at the 3' end of the primer is most critical), it is recommended that the 3' end and the penultimate position be G or C with frequencies of ≥0.98. It is also beneficial if the previous position (3' -2) is a G or C with a frequency of ≥0.90 or alternatively, an A or T with a frequency of ≥0.95. Regions that contain insertions, should in general, be avoided.

Authors' contributions

PQ carried out the data analysis. PQ, XC, LW, JG and BM participated in the design of the study. PQ and BM drafted the manuscript.

All authors read and approved the final manuscript.

Supplementary Material

Additional file 1

HCV whole genome PWM. The first line is the consensus sequence and the second line is the template sequence, D90208.