Comparative transcriptome analysis of geographically distinct virulent and attenuated Babesia bovis strains reveals similar gene expression changes through attenuation
© Pedroni et al.; licensee BioMed Central Ltd. 2013
Received: 13 May 2013
Accepted: 30 October 2013
Published: 6 November 2013
Loss of virulence is a phenotypic adaptation commonly seen in prokaryotic and eukaryotic pathogens. This mechanism is not well studied, especially in organisms with multiple host and life cycle stages such as Babesia, a tick-transmitted hemoparasite of humans and animals. B. bovis, which infects cattle, has naturally occurring virulent strains that can be reliably attenuated in vivo. Previous studies suggest the virulence loss mechanism may involve post-genomic modification. We investigated the transcriptome profiles of two geographically distinct B. bovis virulent and attenuated strain pairs to better understand virulence loss and to gain insight into pathogen adaptation strategies.
Expression microarray and RNA-sequencing approaches were employed to compare transcriptome profiles of two B. bovis strain pairs, with each pair consisting of a virulent parental and its attenuated derivative strain. Differentially regulated transcripts were identified within each strain pair. These included genes encoding for VESA1, SmORFs, undefined membrane and hypothetical proteins. The majority of individual specific gene transcripts differentially regulated within a strain were not shared between the two strains. There was a disproportionately greater number of ves genes upregulated in the virulent parental strains. When compared with their attenuated derivatives, divergently oriented ves genes were included among the upregulated ves genes in the virulent strains, while none of the upregulated ves genes in the attenuated derivatives were oriented head to head. One gene family whose specific members were consistently and significantly upregulated in expression in both attenuated strains was spherical body protein (SBP) 2 encoding gene where SBP2 truncated copies 7, 9 and 11 transcripts were all upregulated.
We conclude that ves heterodimer pair upregulation and overall higher frequency of ves gene expressions in the virulent strains is consistent with the involvement of this gene family in virulence. This is logical given the role of VESA1 proteins in cytoadherence of infected cells to endothelial cells. However, upregulation of some ves genes in the attenuated derivatives suggests that the consequence of upregulation is gene-specific. Furthermore, upregulation of the spherical body protein 2 gene family may play a role in the attenuated phenotype. Exactly how these two gene families may contribute to the loss or gain of virulence is discussed.
KeywordsBabesia bovis Apicomplexans Virulence Attenuation Transcriptome Microarray RNA-sequencing
During pathogenesis, it is advantageous for a pathogen to adjust to the shifting selective pressures exerted by the host. Depending on the environmental milieu there may be strong selection for specific pathogenic phenotypes such as virulence or attenuation acquisition. These phenotypic shifts may be achieved through sexual reproduction where novel recombinations often lead to genome diversity . However for many multi-stage pathogens, this cannot occur during haploid life stages and thus, they depend on genomic mutations or adaptations through non-mutational mechanisms such as phenotypic variation. Mutations associated with virulence (or attenuation) are frequently documented in eubacteria [2–4] and viruses [5, 6]. However, there is a significant gap in knowledge of virulence-associated gene mutations in protozoa, including pathogens that have major impact on global public and animal health. With increased emphasis on the development and delivery of vaccines for hemoparasites such as Babesia, Theileria, and Plasmodium spp. [7–10], the ability to predictably and stably attenuate pathogens would be a significant step toward disease control strategies.
Babesia bovis is a tick-borne apicomplexan protozoan responsible for causing bovine babesiosis . Virulent strains are capable of inducing cerebral babesiosis where >90% of erythrocytes sequestered in cerebral capillaries are infected with B. bovis parasites. The resulting neurovirulent phenotype is clinically similar to cerebral malaria caused by P. falciparum. In nature, there is a wide diversity of B. bovis virulent phenotypes  but experimentally, attenuation can be predictably induced in vivo by serial passages of virulent B. bovis in a splenectomized host. The phenotypic characteristic of neurovirulence is gradually lost, and an attenuated derivative is obtained. Animals infected with the attenuated derivative are protected upon virulent parental challenge, indicating that determinants of virulence may be modulated independently of epitopes responsible for protective immunity. The absence of the spleen during the attenuation process represents a change in the host environment and subsequent change in selection pressure. Although the mechanism of attenuation is undetermined, one plausible explanation is that circulating parasites are not cleared as they would be in spleen-intact animals, resulting in the sequential enrichment of a non-virulent parasite subpopulation.
Recent genomic comparison between three virulent and attenuated B. bovis strain pairs indicate that there are no consistent changes among the protein-coding genes shared between strain pairs that could explain the divergent phenotypes. However, an overall genome reduction in all three attenuated strains was observed . These data suggest that (i) differences between parental and derivative strain pairs may lie in the non-coding regions which influence transcriptomic variability between virulent and attenuated strains, (ii) the loss of genome content during the attenuation process is the key to attenuation, or (iii) both.
In this study, we investigated the transcriptome profiles of two geographically unrelated B. bovis strain pairs to determine if attenuation is a result of common or distinct strain-specific transcriptomic regulation. Although both B. bovis strains were subjected to identical treatment/selection pressure for attenuation acquisition, transcriptome profiles of these two strains through attenuation were different. Detailed discussion of differential gene expression in their contribution to attenuation is discussed.
Results and discussion
T2Bo and L17 transcriptome approaches
In order to determine if virulence loss is controlled at the transcriptional level, we compared the global transcriptomes of two strain pairs of B. bovis. These strain pairs, T2Bo_V irulent parent and A ttenuated derivative and L17_V irulent parent and A ttenuated derivative, are geographically distinct field isolates from Mexico and Argentina, respectively . Biological replicate (BR) sample pairs were prepared for transcriptomic analyses. For the generation of BR samples, animals were inoculated with 1 × 107 parasitized erythrocytes. When clinical symptoms associated with acute babesiosis were observed and blood smears detected parasitemia, infected blood was collected to establish short-term (<30 days) culture to obtain sufficient starting material for RNA extraction (TriZol and RNeasy Mini-elute kit, Invitrogen. This short-term in vitro culture of virulent and attenuated B. bovis followed by in vivo evaluation of phenotype in infected animals were performed to ensure the original phenotypes were maintained post cultivation (unpublished data). Using the published T2Bo genome (http://www.piroplasmaDB.org), we generated an expression microarray that represented >99.4% of the protein-coding genes to investigate transcriptome differences between T2Bo_V and _A. Due to the incomplete L17 genome, L17_V and _A transcriptome comparison was performed by RNA-sequencing. The utilization of the T2Bo genome-based microarray for L17 transcriptome investigation would reflect gene expressions shared between T2Bo and L17 and exclude L17-specific transcripts as gene family member variations are known to exist between different B. bovis strains .
Expression microarray analysis of B. bovis T2Bo transcriptomes
Summary of the T2Bo genome-based microarray statistics
# genes detected
% genes detected
T2Bo_A1 vs. V1
T2Bo_A2 vs. V2
# differentially expressed genes*
# differentially expressed genes with 2x difference
In addition to ves genes that were upregulated in both parental virulent and attenuated T2Bo, two (9%) smORF (sm all o pen r eading f rame) genes were upregulated in T2Bo_V while 13 smORF (36%) were upregulated significantly in T2Bo_A (Figure 2B-C). SmORF is the second largest gene family with 43 members in T2Bo . Although the function of the SmORF proteins is unknown, the proximity of these genes with ves led to speculation that their expression may be coordinated with ves’. However, since only two smORF s but 16 ves genes in T2Bo_V, and 13 smORF s but five ves genes in T2Bo_A, were upregulated, and none of the smORF gene locations were associated with those of ves, smORF gene transcriptional regulation may be independent from ves after all. The higher frequency of upregulated smORF gene transcripts in T2Bo_A is noteworthy, but its transcription in B. bovis is likely to be independent of the virulence/attenuation phenotype (Figure 2B-C).
Spherical body protein 2 gene family members (SBP2) that were significantly upregulated in attenuated (A) T2Bo and L17 strains
T2Bo_A (fold changes)*
L17_A (fold changes)*
SBP2 truncated copy 1
SBP2 truncated copy 4
SBP2 truncated copy 5
SBP2 truncated copy 7
SBP2 truncated copy 9
SBP2 truncated copy 11
Among the randomly selected transcripts chosen for microarray data validation in this study (Figure 3), 70% confirmed significant differential regulation while 20% showed differential regulation but were not statistically significant and one transcript showed the opposite trend from the array data (II007790) (p < .05). Including additional validated genes (manuscript in prep) , greater than 95% of randomly chosen transcripts were validated suggesting that the microarray assessment of the T2Bo virulent and attenuated transcriptome profiles is representative. One possible explanation to why we were unable to validate a small percentage differentially expressed transcripts may be due to sample pairs used in BR preparation are not clonal, thus may exhibit population variability. Transcripts whose differential expression profiles are not shared between biological replicates may not contribute in the attenuation process.
RNA-sequencing analysis on B. bovis L17 transcriptomes
The pattern of differential expression of smORF genes in the two strains is unique to each strain. There were fewer upregulated smORF genes in T2Bo_V than in L17_V with little overlap between the strains, suggesting that subsequent SmORF proteins may not play a significant role in the virulence phenotype (Figure 8).
Among the genes encoding proteins that were significantly upregulated in L17_A (n = 66), hypothetical (30%, n = 20), putative membrane (18%, n = 12), SmORF (9%, n = 9) and VESA1 (5%, n = 3) remain the major groups (Figure 6C) with 10.6% of the 66 of the total upregulated genes in L17_A also upregulated in T2Bo_A. As similarly found in the T2Bo_A transcriptome profile, the upregulated hypothetical and putative membrane protein encoding genes likely participate in strain-specific activities. Their involvement in virulence loss is still possible but this idea is further dampened by the fact that some of these L17_A-associated upregulated genes were found to be upregulated in T2Bo_V (e.g. I001360, II002820, II007850 and III002360) (Additional file 1: Table S4 and S7).
Genes encoding for SBP2 truncated copies (1, 4, 5, 7, 9 and 11) were upregulated in both attenuated strains (Figure 8). Specifically, sbp 2 truncated copies 4, 7,9 and 11 were upregulated in T2Bo_A while truncated copies 1, 5, 7 9 and 11 were upregulated in L17_A. This makes sbp 2 truncated copies 7, 9 and 11 upregulated in both attenuated strains (Table 2, Additional file 1: Table S5 and S7). Multiple sequence alignments of sbp 2_1, 4, 5, 7, 9 and 11 indicated that they share extremely high sequence homology (e-200–887, data not shown) and thus sbp 2_4 that was upregulated in T2Bo_A could potientially be substituted by sbp 2_1 and 5 in L17_A. Again, exactly how the truncated copies participate in SBP2’s function and why their upregulation may contribute to virulence loss are unknown. SBP2 has been reported to reside in the cytoplasmic side of the infected erythrocytes. It is possible that this protein family interacts with either parasite- or host-derived proteins, a scenario not foreign to the apicomplexan field (e.g. knob-associated histidine rich protein, KAHRP ). Perhaps upregulation of SBP2 family genes and subsequent overexpression of the proteins disrupt the normal matrix of protein-protein interaction, resulting in virulence loss. Experiments investigating into such a phenomenon are currently underway.
In addition to transcripts described above, some miscellaneous transcripts encoding proteins were exclusively upregulated in L17_A by at least 2 fold. These include transcripts encoding for NifU-domain contain protein, HesB-domain containing protein, HAD hydrolase, u6-snRNA associated sm-like Lsm2 protein and oxidoreductase NAD-binding domain containing protein (Additional file 1: Table S7). NifU-domain containing proteins perform basic cellular functions and are among the most highly conserved proteins . HesB-domain containing proteins participate in the formation of metallo-sulfur cluster assembly . HAD hydrolases function in housekeeping detoxification, modulation of sugar-phosphate balance in plants  while u6-snRNA associated sm-like Lsm2 proteins have been reported to be involved in RNA processing and may function in a chaperone-like manner in eukaryotes . Lastly, oxidoreductase NAD-binding proteins are known to be enrolled in both aerobic and anaerobic metabolism which include glycolysis, tricarboxylic acid (TCA) cycle, oxidative phosphorylation, and amino acid metabolism . As all these transcripts were solely upregulated in L17_A and not in T2Bo_A, we hypothesize that these transcripts and subsequent proteins, if translated, likely function in processes unrelated to virulence loss.
It was unexpected and disappointing that none of the differentially regulated transcripts with some annotation data involved in immune evasion, infection or reproduction efficiency were detected. This, by no means, suggests that such transcripts were not involved as they may be one of the putative membrane or the hypothetical proteins that were differentially regulated in both strains.
Two B. bovis strain pairs (T2Bo and L17) were used to investigate if virulence loss in protozoans is controlled at the transcriptional level through common strategies. For this study, we utilized a virulent parental phenotype and its attenuated derivative of each strain. We developed and independently validated data generated from an expression microarray for T2Bo and RNA-sequencing for L17, the latter of which yielded three times more differentially regulated genes in the L17 strain pair. We attribute this difference to the heightened sensitivity of RNA-sequencing. Although some differentially expressed transcript families between the two strains were shared, the majority of the specific differentially expressed members were different, suggesting that strain-specific members within common gene families may participate in virulence loss.
We identified upregulation of ves αβ pairs in both virulent strains and hypothesize that these “actively and transcriptionally coordinated” ves αβ pairs expressed in the virulent strains may contribute to the virulence phenotype. This is consistent with our previous study where we showed that additional in vivo serial passage is required to attenuate T2Bo than L17 and more severe clinical pathologies are associated with animals infected with T2Bo_V than those infected with L17_V, all suggest that T2Bo may be more virulent than L17 . Specifically, we identified more divergently oriented and actively transcribed αβ pairs in T2Bo_V than L17_V and speculated that T2Bo may be a more heterologous B. bovis population with higher virulence than L17. The observation that none of the putative αβ pairs were shared between the two virulent strains could simply mean that different strains utilize different ves αβ pairs for the same function.
We also demonstrated that genes encoding for SBP2 truncated copies were significantly upregulated in both attenuated strains, and among these, the specific genes sbp 2_7, 9 and 11 were upregulated in both. We speculated that sbp 2 truncated copies 1, 4 and 5, which were not shared, may be used substitutively by the parasite strains in attenuation. Although function of SBP2 is not known, we hypothesized that the upregulation of sbp2 truncated copies may result in the over expression of the proteins and affect binding partners in the cytoplasm of the infected erythrocytes.
There were additional upregulated transcripts with limited annotational information but they were all strain specific in their expression and may not contribute to the phenotype change due to attenuation.
In summary, our study demonstrates that two B. bovis strains underwent identical manipulation/exposure to selection eventually resulting in virulence loss. Despite some similar gene expression profiles shared between the two attenuated derivatives, much of the differential gene regulation is strain specific. Outcome of this study demonstrates the independent adaptation of individual B. bovis strains to similar environmental milieu. These data raise further questions inquiring into the composition of the virulent parental strains. Ongoing experiments exploring population dynamics of these virulent B. bovis are currently underway.
Parasite strains, in vitro cultivation of B. bovis and RNA isolation
Two geographical distinct, virulent (V) B. bovis (T2Bo and L17 strains) and the generation of their attenuated (A) derivatives were propagated as previously described . Infected blood was collected and frozen stabilates were prepared and stored until use. All animals used for the generation of parasitized erythrocytes were used according to all guideline approved by the Animal Use Committee of the Instituto Nacional de Tecnología Agropecuaria, Argentina. Two and three BR sample pairs for T2Bo and L17 B. bovis strains were used in the generation of transcriptome profiles, respectively. These were prepared at different times during a 1-year duration using different animals. For microarray and RNA-sequencing validation, additional BR sample pairs were generated (see below). Quality of the total RNA was evaluated using an Agilent Bioanalyzer.
Microarray sample preparation
Ten micrograms of total RNA from T2Bo V1-2 and A1-2 were converted to enriched mRNA using Applied Biosystem’s (Ambion) MicrobExpress Tm Kit (AM1905). The manufacturer’s protocol was followed. Enriched mRNA was analyzed by the Agilent 2100 Bioanalyzer. One - 2.5 μg of enriched mRNA from 10 μg of total RNA was obtained. The enriched mRNA (200 ng) was converted to cRNA using Applied Biosystem’s (Ambion) MessageAmp™ II-Bacteria RNA Amplification Kit (AM1790). Amino-allyl-UTP was incorporated into the cRNA during the IVT reaction as described in the manufacturer’s protocol. One hundred μg from 200 ng was obtained. The amine reactive dye Alexa Fluor 555 (Invitrogen A32756) was coupled to cRNA (amino allyl moieties) following the manufacturer’s instructions. Unincorporated dye was removed using RNeasy Mini Columns (Qiagen 74104). The Qiagen quick clean-up protocol was followed. cRNA coupled to fluorescent dye was fragmented using proprietary technology. The expected mean fragment length was 100–125 nucleotides. Fragmented cRNA was considered sufficiently fragmented for use on MYcroarrays if the mean fragment length less than 200 nucleotides.
Custom 3 × 20 K B. bovis arrays
Custom B. bovis (Bb) MYcroarrays were manufactured (Mycroarray.com). Briefly, each MYcroarray slide contains three arrays. Each 20 K Bb array had 21,280 potential addresses for features. On each 20 K Bb array, there were 18,490 spots that contain 45mer probes for Bb genes, 2,110 features were “empty” (i.e., they contain no probe), 608 features contain MYcroarray in-house QC probes and 72 features contain positive control probes for assessing hybridization and washing stringency. There were 5 identical replicates, i.e. technical replicates, of each unique Bb probe sequence. A total of 3,698 Bb genes were interrogated on each array, one probe sequence per gene. Designated probes that could cross hybridize due to conserved sequences were noted (Additional file 1: Table S1). A total of 24 genes (23 apicoplast-encoded and 1 nuclear-encoded) were not represented in the array due to the failure of single gene-specific probe design (Additional file 1: Table S2).
Single color hybridizations
cRNA derived from all samples was coupled to Alexa Fluor 555. Each target (5 μg) was hybridized separately to one array. Hybridization was performed for >18 hrs at 50°C in a proprietary hybridization buffer and subsequent wash steps in MYcorarray’s 1X wash solutionwas carried out twice at 22°C for 3 min, once at 50°C for 3 min with gentle agitation. The arrays were allowed to cool to room temperature in fresh 1X wash solution, transferred to 0.25× wash solution for 30 sec, spun dry in a microarray centrifuge and were scanned in an Axon 4000B Scanner (Molecular Devices) set at 5 micron pixel resolution. The photo multiplier tube (PMT) setting was adjusted to detect the maximum dynamic range of signal (0 to 65,000). This was accomplished by increasing the PMT setting until just a few features (spots) were saturated. The background turned out to be unusually high on two slides. Consequently, the washing process was repeated in exactly the same way with one exception. The 50°C wash was increased to 58°C. Following the second wash procedure, the arrays were scanned at a PMT setting of 380 in the 532 nm (“green”) channel.
Data were extracted from the scanned images using GenePix Pro Software (version 188.8.131.52). The GAL file (“Load Array List” in GenePix) was positioned over the image. For signal extraction, circular feature indicators (30 μm diameter) were centered over each spot. Median feature pixel intensity was extracted. Flagged features were handled as follows: if a spot was manually flagged as “bad” (a value of −100 in the “flags” field of the .gpr file) no present call was made for this probe and the signal value was replaced with a blank field. Only one array had manually flagged “bad” features (sample A2) and only 303 features were flagged “bad” out of a total of 18,490 (1.6%) Bb features. Since a trimmed mean (see below) was used to estimate the signal for each probe set, the absent value for flagged spots was dropped. Saturated features were handled as follows: To ascertain the full dynamic range of signal intensity, the PMT setting was adjusted until saturation was achieved. In this study, a PMT setting of 380 resulted in some saturated features. There were a total of 211 features with saturation equal to or greater than 10%. This is about 0.2% of the Bb features in the study. The features with ≥10% saturation were handled identically to features that were flagged “bad”. To correct for background, an equation was used: background corrected signal = [Max (signal–background, 0.1) × SF] + C, where C =25 and SF = Scale Factor. In other words, if background subtraction produced a value less than zero, 0.1 replaced the background corrected signal. The background corrected signal was then multiplied by the scaling factor (see normalization below) to adjust for inter-array brightness differences. Finally, the entire signal distribution was shifted to the right by adding constant value (25) to all background corrected and scaled signal values. The shift of the entire signal distribution to the right helped to avoid confoundment of differentiate low signal values from residual noise in the system background noise.
A scaled F532 median signal for each probe was calculated by multiplying the background adjusted median F532 signal for each feature by the scale factor for each array. Scaling is a minimalistic normalization approach. There are numerous normalization techniques for single color microarray analysis.
A trimmed mean statistic was used to estimate the signal value for each probe set. The median pixel signal for each feature was used after background subtraction and scaling. For each probe set (n = 5) the maximum and minimum value were dropped and the remaining three values were averaged. If there were less than five probe signal values due to either a bad or saturated feature, the trimmed mean was simply calculated on the remaining probes. Using the trimmed mean to estimate signal on an array, is a very simple yet robust approach that helps to mitigate the influence of outlier values and helps normalize intra-array measurements.
An estimation of whether or not a transcript was detected by a probe was calculated. The estimation is a “present” call. The present call is based on the signal value for each gene (probe) relative to the local median background level surrounding each spot. For each probe, if the signal was greater than five times the background signal, then the transcript was considered “present”.
A list of differentially regulated genes was generated. Briefly, each sample group (either A1 vs V1 or A2 vs V2) was compared separately. The filtering criteria were as follows:
1. For each gene, at least 4 out of 5 probes had to have a present call in one condition (either “A” or “V”).
2. For the genes that satisfied the present call filter (#1 above), a Student’s T-test was performed on the Log2 transformed signal data and only genes with at least p < 0.05 were considered.
3. Finally, a gene was added to the list if both a fold change ratio (A/V was used arbitrarily) of at least 1.5 combined with a T-test p < 0.01.
Total RNA from L17_V 1–3 and _A1-3 samples were used for the constructions of the libraries as described by the manufacturer (Illumina TruSeq v.2) and carried out at the Fred Hutchison Genomic Center, Seattle. All six samples were multiplexed in a single lane with 50 cycles pair-end runs. The estimated reads per library were 25 million. Following the sequencing process, image analysis and base calling were performed with Illumina RTA v1.13. Reads were aligned to B. bovis T2Bo genome (http://www.piroplasmaDB.org) using Tophat v.2.0.4 and Bowtie v.0.12.8. Counts were generated using htseq-count v.0.5.3p9 with the default “union” overlapping mode. Certain genes were removed from consideration if count < 1 per 2 samples. Therefore, the starting # of genes was 3,754 and the post-filtered # of genes was 3,630. Differentially expressed genes pairing the samples by their biological source were identified using edgeR v.2.6.12. Genes were considered significantly and differentially regulated if |log fold change (FC)| ≥ 1 & false detection rate (FDR) < 5%. Additional file 1: Table S3 summaries the htseq counts of the 6 L17 samples.
Array and RNA- sequencing validation
In order to validate the data obtained by the microarray and RNA-sequencing, qPCR was performed using an independently generated T2Bo or L17 BR sample pair and BBOV_III004820 to normalize the qPCR assay. BBVO_III004820 encodes for a putative topoisomerase II and although its orthologue in P. falciparum has been shown to have a four-fold fluctuation between different asexual stages within the erythrocytes , its transcript level is indifferent between B. bovis V and A via RT-PCR . Primers used for the validation are listed in Additional file 1: Table S8. All primers were confirmed for specificity via cloning and sequencing prior to use (data not shown). Four technical replicates were performed per gene. Cycle thresholds (CT) for a selection of differentially regulated genes were measured. The selection criteria of genes used for the validation process are based on the list of genes that was identified to be significantly differentially expressed in both strains. However, differentially expressed genes selected for validation for the RNA-sequencing data were those that were highly differentially expressed. Significant differentially expressed transcripts with low copy numbers detected by RNA-sequencing likely will not be detectable via qPCR due to its lower sensitivity threshold compared to RNA-sequencing. Upon normalization of the qPCR, relative values of expression were represented as cycle threshold ratios (CT ratio) as previously described . A 1-way ANOVA with Bonferroni post-test analysis was conducted (Graphpad Prism v.5.0a). The list of differentially expressed genes chosen for validation for both assays is in Additional file 1: Table S8.
Availability of supporting data
All the microarray data have been incorporated into the corresponding genomic data of the B. bovis strain (http://www.piroplasmadb.org) while the RNA-sequencing data have been deposited in NCBI’s Gene Expression Omnibus and are accessible through GEO Series accession number GSE51560 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE51560).
This study was partially funded by Washington State University College of Veterinary Medicine Intramural grant (AOTL).
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