Genome-wide Analysis for Variants in Philippine Trypanosoma evansi Isolates with ... · 2019. 10. 9. · Genome-wide Analysis for Variants in Philippine Trypanosoma evansi Isolates

Genome-wide Analysis for Variants in Philippine Trypanosoma evansi Isolates with Varying Drug Resistance Profiles

1National Institute of Molecular Biology and Biotechnology, College of Science, University of the Philippines Diliman, Quezon City 1101 Philippines

2Philippine Carabao Center, Department of Agriculture, Science City of Munoz, Nueva Ecija 3120 Philippines

3Philippine Genome Center, University of the Philippines, Diliman, Quezon City 1101 Philippines

§These authors contributed equally to this work

Jose Enrico H. Lazaro1§, Neil Andrew D. Bascos1,3§, Francis A. Tablizo3§, Nancy S. Abes2, Renlyn Ivy DG. Paynaganan1, Michelle A. Miguel2,

Hector M. Espiritu2, Mary Rose D. Uy2, Claro N. Mingala2, and Cynthia P. Saloma1,3*

Philippine Journal of Science148 (S1): 219-233, Special Issue on GenomicsISSN 0031 - 7683Date Received: 21 Mar 2019

*Corresponding Author: [email protected] [email protected]

Surra, a parasitic disease transmitted by hematophagous flies and caused by Trypanosoma evansi, affects many domesticated animals – including water buffaloes, camels, horses, pigs, dogs, and other carnivores – throughout the world. When left untreated, this disease can cause anemia, significant loss of weight, abortion, and death in affected animals. Among Philippine isolates of T. evansi, variability has been reported in terms of virulence as well as response to drug treatment. In this study, trypanosoma-positive blood was obtained from 15 Philippine water buffalo samples from different sites in the country. The collected T. evansi strains were propagated in mice then subjected to in vivo virulence, in vitro drug sensitivity testing, and whole genome sequencing. One strain (O14) was found to be highly virulent in vivo, and was found to be resistant to three commonly used drugs [i.e., isometamidium chloride (IC), diminazene diaceturate (DD), and melarsamine hydrochloride (CY for Cymelarsan®)] in vitro. This highly resistant sample was compared with two less-virulent strains using genome-wide analysis of single nucleotide polymorphisms (SNPs) and short insertions and deletions (indels) relative to the reference strain STIB 805. Variant analysis between O14 and the less virulent strains (M4 and C117) identified a number of distinctive SNPs, many of which corroborate previous data. Genes with relatively high copy numbers were observed in mutation hotspots. These included genes that code for variant surface glycoproteins (VSGs), expression site-associated genes (ESAGs), retrotransposon hot spot (RHS) proteins, and leucine rich repeat proteins. Notable mutations were also predicted from genes coding for membrane transporters and cysteine peptidases, as well as those involved in RNA degradation. The whole genome sequences acquired from the Philippine isolates (O14, M4, and C117) vary greatly from the reference strain (STIB 805). These WGS data serve as a good resource for the discovery of genetic and phenotypic features that may be translated to effective treatment strategies, relevant to the Philippine setting.

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INTRODUCTIONBlood parasitic diseases caused by vector-borne protozoa gravely affect livestock production in many developing countries. Surra, a parasitic disease caused by Trypanosoma evansi, affects many domesticated animal species including buffaloes, camels, and horses in Asia, Africa, and Central and South America. Surra is transmitted by hematophagous flies (e.g., Tabanids and Stomoxys) that act as mechanical vectors of the disease. Infected individuals exhibit highly variable clinical effects, depending on the host and their geographical area (Desquesnes et al. 2013).

Buffaloes are cryptic hosts of T. evansi. Although infected buffaloes do not exhibit common symptoms of the disease (e.g., weakness, edema, pyrexia), infection is known to induce still births and abortions in these animals (Luckins 1988, Reid 2002, Dobson et al. 2009). The latter results in a significant loss in productivity for the Philippine livestock sector. Demographic and epidemic models suggest a net benefit loss of PhP 7 million per year for villages under moderate/high prevalence of surra (Dargantes et al. 2009, Dobson et al. 2009). The Philippines has suffered from several surra outbreaks in the past decades. Manuel (1998) reported that in a span of three years (1994–1996), a total of 1,151 deaths (985 carabaos, 71 cattle, and 95 horses) have been documented all over the country. From 1999 to 2001, at least six municipalities experienced similar serious outbreaks of surra in Central and Western Visayas – with mortality reaching 35% (Dargantes et al. 2009). The high mortality rate associated with local trypanosomal outbreaks suggests increased virulence in the Philippine strains of T. evansi.

Current disease control strategies are principally based on the use of trypanocides and preventive management methods to protect animals from infection (Desquesnes et al. 2013). Three drugs are used by the Philippine Carabao Center (PCC) for treating surra cases in the Philippines – namely IC, DD, and CY. However, treatment failure had been observed with their application on the field (Mungube et al. 2012). These occurrences suggest the development of resistance in some strains of T. evansi for these commonly used drugs.

This study investigated Philippine T. evansi isolates with varied drug resistance and virulence profiles in the mouse model. Whole genome sequences of these Philippine isolates were acquired and compared in search of genetic markers that could be correlated with virulence and drug response. Large scale structural variations were found in genes of resistant strains, some of which have been previously implicated in virulence. These include VSGs and flagellar proteins. Conserved regions in the encoded cellular structures serve as potential targets for vaccine

design. A similar strategy has been employed to generate protective antibodies against the parasites that cause malaria (Martin 2004). The observed features in these genomes provide leads on possible avenues for more efficient drug therapies and potential vaccine targets that would be relevant for the prevention of surra in Philippine domesticated animals.

MATERIALS AND METHODS

IACUC Permit for Animal StudiesAll procedures on the use of mice for trypanosome propagation and antibiotic drug testing were approved by the UP Diliman Institutional Animal Care and Use Committee (UPD IACUC) under Protocol Permit No. PAF-NIMBB-2015-02.

Sample Collection and PropagationBlood samples infected with parasites were obtained from the PCC in two forms: cryopreserved in liquid nitrogen and extracted from mice infected with parasites. Cryopreserved blood samples were thawed and then inoculated into mice via intraperitoneal injections. Blood parasite examination was done everyday to monitor parasite numbers. Parasites were collected once peak parasitemia was observed (usually on Day 3) by cardiac puncture, washed, resuspended in buffer, and either used immediately for in vivo and in vitro testing or for subsequent cryopreservation. Methods were done as described previously (Tavares et al. 2011).

In Vivo Virulence and In Vitro Drug Sensitivity AssaysTo propagate parasites, ICR mice were inoculated with cryopreserved parasites via intraperitoneal injection. ICR mice are outbred mice named after the Institute of Cancer Research, USA, where the mouse strain was developed. Parasitemia was monitored daily by tail blood wet mount. Once peak parasitemia was observed (> 60 parasites per view at 40X magnification), mice were anaesthetized and blood was collected via cardiac puncture. Collected blood was mixed with 4:6 phosphate buffered saline with 1% glucose (PBSG, pH 7.2) in a 1:1 ratio. The mixture was centrifuged for 30 min at 8 x g in 4 °C. The buffy coat was carefully collected and applied into equilibrated DEAE resin-packed mini columns in the same buffer. Once all parasites were eluted, their concentrations within the samples were estimated using a hemocytometer. Parasites were maintained in culture media (25 mM HEPES, 1 g/L glucose, 2.2 g/L NaHCO3, 10 mL/L 100x MEM non-essential amino acids, 0.2 mM 2-mercaptoethanol, 2 mM Na-pyruvate, 0.1 mM hypoxanthine, 0.016 mM

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thymidine, 15% heat inactivated rabbit serum, 1x pen-strep, pH 7.2) until further treatment (Baltz et al. 1985).

To measure in vivo virulence, eight-week-old female ICR mice were acclimatized for one week and maintained under standard laboratory conditions. The mice were administered cyclophosphamide to induce immunosuppression. Cyclophosphamide in phosphate buffered saline was administered 24 h before inoculation. About 1 x 105 parasites suspended in culture medium were inoculated by intraperitoneal injection into each mouse at a maximum volume of 0.2 mL. Virulence was measured based on the number of days to reach peak parasitemia post-inoculation (greater than 60 parasites per field of view at 40X magnification).

To measure in vitro drug sensitivity, IC, DD, and CY were initially dissolved in dimethyl sulfoxide (DMSO) to produce stock solutions. Aliquots were added to the complete culture media resulting in a final DMSO concentration of less than 10% in the working solutions. This concentration of DMSO was shown to have no effect on parasite survival. Twenty-five microliters (25 µL) of the working solutions were loaded into the microtiter plates and were serially diluted two-fold. A volume of 100 µL of parasites at a density of 2,000 parasites/µL was then loaded into flat-bottomed microtiter wells. The highest final concentrations of the drugs were 2 mg/mL IC, 10 mg/mL DD, and 40 µg/mL CY. Cultures were exposed to the drugs and 10 µL of Alamar Blue for 6 h at 37 °C and 5% CO2 prior to analysis. The assays were performed in triplicate for each drug.

Control setups containing media with parasites and no drug treatments were prepared in parallel with the drug assays. Standard curves for the control samples were prepared by serially diluting two-fold from an initial density of 5,000 parasites/µL in 125 uL in a 96-well microplate. Alamar Blue dye was added to each well prior to incubation at 37 °C and 5% CO2 for 6 h.

The analysis was done using a Varioskan Flash Multimode Reader. One hundred microliters (100 µL) of well contents from treatment and control samples were transferred into black flat-bottom microplates for fluorescence readings. Fluorescence excitation and emission were done at 530 nm and 590 nm, respectively. Median inhibitory concentration (IC50) was calculated using Compusyn (www.combosyn.com)

Genomic DNA Extraction from T. evansi IsolatesParasites isolated and purified using the DEAE columns were pelleted and subjected to DNA extraction. The QIAGEN DNA Mini kit was used following the manufacturer’s instructions. The extracted genomic DNA was then subjected to gel electrophoresis (1% agarose, 100V for 30

min) and Qubit™ fluorometric quantitation (Thermo Fisher Scientific) to verify DNA quality and quantity. The identity of the isolates as T. evansi was verified using targeted PCR amplification of the extracted DNA.

Whole Genome SequencingGenomes of the three T. evansi isolates were sequenced using the Illumina MiSeq platform (paired-end, 300 bp read length). Libraries were prepared using the Nextera DNA Library Preparation kit for simultaneous fragmentation (600 bp) and tagging with barcoded sequencing adapters. The samples were then pooled and were run through the Illumina MiSeq sequencer at > 100X coverage at the Philippine Genome Center DNA Sequencing Core Facility.

Variant Calling, Gene Ontology, and Functional AnalysisThe quality of the raw sequence data was initially assessed using the tool FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Quality trimming of low-quality sequence ends (min quality = 20; min length = 50) was then done using TrimGalore (http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/). The remaining sequences were then subjected to quality filtering (min quality = 20; % passing bases = 80%) using the tool fastq_quality_filter included in the FASTX-Toolkit (http://hannonlab.cshl.edu/fastx_ toolkit/). A final assessment of the quality control reads was done using FastQC and the pairing of the remaining reads was restored using an in-house script. The quality assessment metrics before and after quality control are provided in Table I.

The T. evansi sequencing reads were mapped against the sequences of the annotated reference T. evansi STIB 805 strain obtained from TriTrypDB (tritrypdb.org) using the Burrows-Wheeler Aligner software (Li and Durbin 2010). The mapping metrics – including the number of mapped reads, the total number of mapped bases, and average mapping coverage – can be seen in Table II.

To determine single nucleotide variants and short indels, the best practices workflow suggested by GATK version 3.7 (McKenna et al. 2010) was implemented. Briefly, the mapping files were sorted according to reference coordinates using the tool SortSam from PICARD Tools (http://broadinstitute.github.io/picard). Duplicate reads were marked using the MarkDuplicates tool of PICARD (https://broadinstitute.github.io/picard/). Read groups were added to each of the mapping files using the PICARD tool AddOrReplaceReadGroups. Realignment of mapped reads around indel regions was done using the RealignerTargetCreator and IndelRealigner tools in GATK. Finally, variants were called using the

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HaplotypeCaller tool of GATK. The single nucleotide variants and short indels were annotated using SNPEff (Cingolani et al. 2012).

Genes implicated in the variations were then used as input for sequence homology search against the non-redundant protein database of the National Center for Biotechnology Information (NCBI) using the tool DIAMOND (Buchfink et al. 2015) and subjected to gene ontology analysis using Blast2GO (Conesa and Götz 2008). Visualization and plotting of the associated ontology terms were done using in-house R scripts.

Further investigations on the functional significance of the variants observed only in resistant samples were done through the sequence and structural alignments. Sequence alignment was primarily done using the Basic Local Alignment Search Tool (BLAST) (Altschul et al. 1990) of NCBI. Structural analysis was done by searching the protein structures available in the Protein Data Bank (PDB; www.pdb.org) for matches to the observed mutations in the resistant strains. In cases where no PDB matches were found, structures from UniProt (www.uniprot.org) were used instead. Predictions made for the structural significance of the SNPs were based on the location of these mutations in functional domains of the related proteins.

RESULTS AND DISCUSSION

In Vivo Virulence and in Vitro Drug Sensitivity Assay in MiceA total of 15 strains were tested for virulence in vivo and drug sensitivity in vitro. Assessment of in vivo virulence was based on daily parasitemia and symptomatology observed in mice. Of these strains, one was highly virulent (+++), four were intermediate (++), and 10 were weakly virulent (+). Strain O14 was the only highly virulent strain, consistently killing mice in a 24-h period (Table 1).

IC50 served as an additional indicator of virulence. The IC50 values could be compared for experiments carried out under the same conditions of dose and exposure. Prior experiments to establish optimal exposure conditions showed that all isolates began to decrease in number after 10–12 h in culture in the absence of drug, although there are still a few viable parasites at 24 h. Doses of all three drugs were optimized to effect a sigmoidal dose-response over a 6-h period to ensure that parasites have not adapted to exposure in possibly varying ways.

Kumar et al. (2015) found that parasites could remain viable between 48 h and 72 h depending on media. The media used in the present study is comparable to medium C referred to by Kumar et al. (2015) with parasites able

Table 1. Drug resistance profiles of Philippine T. evansi samples. (+) reached peak parasitemia in 5 days or more; (++) peak parasitemia in 3–4 days; (+++) peak parasitemia in 1–2 days.

Strain LocationIC50 (mg/mL) Phenotypic virulence

Isometamidium chloride

Diminazene diaceturate

Cymelasan

M4 Mindanao 144 7,194 7 +

C117 Cagayan 165 24,889 8 +

O14 Nueva Ecija 439 22,478 25 +++

M6 Mindanao 332 15,943 6 ++

C120 Cagayan 134 8,126 18 +

C110 Cagayan 137 15,509 6 ++

5UP 03021 UP Los Baños 220 18,024 4 +

05141 Nueva Ecija 84 20,245 13 ++

M2 Mindanao 95 18,231 5 +

C1 Cagayan 252 4,640 4 +

M7 Mindanao 217 11,694 11 ++

P75 Nueva Ecija 119 12,341 7 +

2NIZ 11038 Nueva Ecija 356 12,374 4 +

M5 Mindanao 131 5,428 2 +

TRYPS 3+ Luzon 248 15,907 7 +Highly sensitive + Reaches peak parasitemia at 5 days onwardMildly sensitive ++ Reaches peak parasitemia at 3-4 daysMildly resistant +++ Reaches peak parasitemia at 1-2 daysHighly resistant Peak parasitemia: > 60 parasites per field of view at 40x manification


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to be sustained for 72 h. In medium C, parasite numbers could go down within 24 h then increase to remain viable for more than 30 d. Using long exposure, Birhanu et al. (2016) found an IC50 of 7 ng/mL for isometamidium, 18 ng/mL for diminazene diaceturate, and 3 ng/mL for melarsamine hydrochloride for an exposure of 30 d. Although these IC50 values are much smaller than those presented here, the relative magnitudes among the three drugs are somewhat preserved. However, as a screen, Bulus et al. (2016) working on T. brucei, exposed parasites for 24 h but found it difficult to estimate an IC50 for diminazene diaceturate, although a visual examination suggested a value less than 40 μg/mL.

Hence, relative to other strains in our experiments, O14 had the maximum value of IC50 for two of the three drugs, and an IC50 for diminazene diaceturate comparable to the highest (Figure 1). Two other strains were selected for comparison – C117 and M4. They were both weakly virulent in vivo. However, C117 had the highest IC50 for diminazene diaceturate but an IC50 for the other two drugs that were near the median, giving it an intermediate profile between the other two strains. The IC50 values obtained for strain M4 were all at or below the median.

The three strains were from provinces with a high prevalence of surra. They were selected for whole genome sequencing at the PGC DSCF and analyzed at the Core Facility for Bioinformatics.

SNPs and Short Indels in Local T. evansi Isolates with Varying Drug Resistance ProfilesThe three strains with varying drug resistance profiles were coded as O14 (resistant to isometamidium, diminazine, and cymelarsan); C117 (resistant to diminazene only); and M4 (sensitive to all three drugs). Search for SNPs and short indels identified a total of 252,376 variants in 246,813 genomic loci across the three T. evansi samples (Table 2). Considering the sheer number of sequence variations detected, isolates showing drug resistance were found to have a substantially higher number of variations (191,528 for C117 and 183,958 for O14) than the drug-sensitive sample M4 (144,021). Although the majority of the observed variant loci are shared by the three isolates (96, 212), M4 harbors the least number of unique variations as well as the least number of variants shared with another isolate (Figure 2). For variations that affect gene-coding regions, the ratio of missense (non-

Figure 1. Distribution of IC50 of 15 strains tested against isometamidium chloride (IC), diminazene diaceturate (DD), and melarsamine hydrochloride (CY). IC50 values are presented in µg/mL. Strain O14 had the highest IC50 values for IC and CY drugs and the second-highest for DD. Strain M4 had IC50 values that were at or below the median for all drugs. Strain C117 had the highest IC50 for DD and had IC50 values close to the median for IC and CY.



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synonymous) to silent (synonymous) mutation counts in each of the three isolates all fall around the value 1.7 (Table 2). The occurrence of these non-neutral mutations may be due to their exposure to various environmental pressures, including host responses and drug treatments.

Based on the mutation effects, 13,272 variants involve significant changes in amino acid properties, stop-loss or -gain, and frameshifts that are predicted to have a high impact on the resulting protein product. The majority (4,226) of these high impact mutations are shared by the three isolates, whereas 1,125 are unique to M4, 1,995 are unique to O14, and 2,337 are unique to C117. The resistant samples (O14 and C117) share mutations in 1,958 genes. The notable sequence variants are summarized in Tables 3–5 and are subsequently discussed.

Gene ontology analysis of the unique variants per isolate, as well as those shared by the resistant samples, revealed their association with several ontology terms. The

Table 2. Summary of statistics for SNPs and short indels.

Variant type Count Percent

Single nucleotide 204,843 81.17

Insertions 24,956 9.89

Deletions 22,577 8.95

Number of effects by impact

High 13,982 1.19

Moderate 55,280 4.70

Low 30,742 2.61

Modifier 1,075,642 91.49

Number of effects by functional class

Missense 53,002 62.06

Nonsense 1,685 1.97

Silent 30,714 35.96

Missense to silent ratio 1.72

Number of effects by region

Downstream 464,667 39.52

Exon 99,619 8.47

Intergenic 152,543 12.97

Intron 33 0.003

Splice site acceptor 132 0.01

Splice site donor 118 0.01

Splice site region 134 0.01

Upstream 458,399 38.99

Total number of SNPs and short indels 252,376

Note: A given variant may be counted multiple times for a particular category (e.g., a variant may have a Modifier and Low impact effect and may occur upstream of a gene and downstream of another). Data is taken from the output of the tool SNPEff.

Figure 2. Venn diagram for (A) all observed variants and (B) high impact variants in the T. evansi isolates. Majority of the observed variant loci (96,212) are shared by the three isolates. However, the drug-susceptible isolate M4 harbors the least number of unique variations (16,370) among all samples. Furthermore, the drug-resistant samples O14 and C117 were found to share considerably more variants (39,820) than either of them does with M4 (C117xM4 – 15,033; O14xM4 – 13,114). A similar trend was observed for the predicted high impact variants.


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following are the highest GO terms observed in each of the three main domains: “integral component of membrane” under the cellular component, “macromolecule metabolic process” under biological process, and “nucleic acid binding” under molecular function (Figures I–IV). The other observed enriched ontologies are also related to the three aforementioned GO domains. However, it is worth noting that while many of the GO terms are shared with both drug-resistant and susceptible T. evansi strains, the scale of gene counts for the ontology terms in the drug-susceptible M4 strain is substantially lower than those in the other two drug-resistant isolates O14 and C117, raising the possibility that a higher number of the relevant genes suggested by the GO data are affected by high impact mutations in resistant isolates.

The gene ontology terms that were found to be highest invariant regions have been previously associated with the mechanisms for infectivity, disease progression, and drug resistance in trypanosomes. These may involve variations in surface and transporter proteins, accompanied by changes in gene expression and high metabolic demands (Habila et al. 2012). Trypanosomes have a repertoire of VSGs whose sequential expression results in changes in the parasite’s antigenic pattern, allowing them to evade the host’s immune response (Habila et al. 2012). In a study on T. brucei rhodesiense, mutations in genes coding for adenosine transporter AT1, aquaporin AQP2, and RNA-binding protein UBP1 were associated with resistance against the drugs melarsoprol and pentamidine (Graf et al. 2016). In T. congolense, copy number variations in different transporter and transmembrane products were also observed to accompany resistance against the drug IC (Tihon et al. 2017).

Mutation Hotspots in Subtelomeric Regions Rich with VSGsThe mutation rates for the three variants were analyzed by mapping the occurrence of variance across the different chromosome positions (Figure 3). For all of the three isolates, the majority of the mutations occurred near the chromosome ends and across the entire length of chromosome 11_02 and 11_03. Second, these locations coincide with the distribution of VSG gene loci. Interestingly, chromosomes 11_02 and 11_03 are comprised of genes annotated as VSGs and ESAGs. These genes are known to be mostly concentrated in the subtelomeric regions of chromosomes (Carnes et al. 2015) – coinciding with the observed mutation hotspots. These results corroborate previous observations on the high rate of expansion and mutation of VSGs in the T. evansi genome (Carnes et al. 2015). This supports experimental findings on the hypervariability of the VSG, which has been related to the parasite’s defense against immunogenic attack. The observation of VSG hypervariability in all

three strains, which may not be directly related to drug resistance, points to a conserved mechanism of survival that persists independent of the drug resistance properties of a given strain.

VSGs and ESAGs are part of polycistronic units known as bloodstream expression sites whose differential expression throughout the parasite’s life cycle results in stochastic switching of antigenic VSGs and, consequently, evasion of the host immune system (Daniels et al. 2010). Having a higher mutation rate for these genes suggests another layer of variability that will further expand the antigenic landscape of the parasite.

In the genome of T. brucei, only a small proportion of the annotated VSGs (~7%) appear to encode the necessary features to be fully functional. Other potential VSGs are marked as either pseudogenes or gene fragments (Berriman et al. 2005). This observation suggests that most of the VSG gene scan harbor more neutral mutations than genes involved in essential functions. A previous analysis of the T. evansi genome revealed that, on the average, purifying selection is significantly much weaker in VSG than in non-VSG genes with mutations in VSGs to be mostly neutral and do not have substantial functional effects. The observed diversity of VSGs may, therefore, be a consequence of increased recombination and not due to selection pressure brought about by the host immune response (Carnes et al. 2015).

Additional features found in mutation hotspots are other high copy number genes – including those that code for RHS proteins, leucine-rich repeat proteins, UDP-N-acetylglucosamine (UDP-GlcNAc)-dependent glycosyl transferase, and receptor-type adenylate cyclase GRESAG 4. Previous studies have looked at some of these proteins as possible drug targets to control trypanosomes. For instance, synthesis of UDP-GlcNAc was found to be essential for the growth of the bloodstream form of T. brucei (Stokes et al. 2008) and the therapeutic potential of an allosteric inhibitor to a step in the UDP-GlcNAc biosynthesis has also been described (Urbaniak et al. 2013). More recently, RHS proteins were found to be involved in RNA polymerase II transcription in T. brucei and the depletion of these factors impair RNA synthesis (Florini et al. 2018). Similar to VSGs, these genes have relatively high copy numbers and may allow for more neutral mutations without sacrificing function.

Notable Sequence VariantsConsidering that isolates O14 and C117 are both highly resistant to diminazene, mutations shared only by these two isolates may provide insights on the development of resistance towards the said drug. While diminazene exhibits its direct trypanocidal activity by preferentially interacting



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Figure 3. Mutation hotspots per chromosome. The x-axis represents base position within the chromosome, binned every 10 kilobases. The y-axis shows variant counts which were plotted per bin. Counts are highlighted with line markers colored yellow for the drug-susceptible sample M4, blue for the diminazene-resistant sample C117, and red for the highly-resistant sample 014. Majority of the observed variation hotspots were found to coincide with the presence of VSGs and ESAGs, which are known to be mostly concentrated in the subtelomeric regions of the chromosomes and have relatively high expansion and mutation rates.


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with A-T regions in the kinetoplast DNA and affecting host immune responses (Kuriakose et al. 2012), mutations for the resistant isolates show the potential involvement of genes involved in transport and RNA degradation.

In both the diminazene-resistant isolates O14 and C117, a number of mutations were found in an adenosine transporter 2 gene (TevSTIB805.2.3490) at chromosome 2 and an ATP-binding cassette transporter gene (multidrug resistance protein E; p-glycoprotein; TevSTBI805.4.4670) at chromosome 4 (Table 3). In particular, an A to G transition at position 220 of the adenosine transporter gene results in a Met74Val mutation, whereas an A to G transition at position 464 of the p-glycoprotein results in an Asp155Gly change. A nucleoporin gene (TevSTIB805.4.3000) at chromosome 4 was also found to harbor a T to C transition at position 3736, which translates to a Tyr1246His amino acid substitution. None of these three point mutations were observed in the diminazene-susceptible isolate (M4). The location of these mutations coincides with recent findings on the central role of membrane transporters for trypanosome drug sensitivity (Zoltner et al. 2016). A previous study in T. cruzi demonstrated the importance of the p-glycoprotein efflux pump in drug resistance development (Campos et al. 2013). Although the actual effects of the Met74Val, Asp155Gly, and Tyr1246His mutations are yet to be determined, their presence in only the resistant samples O14 and C117 may facilitate the development of markers for diminazene resistance.

Another set of mutations shared only by diminazene-resistant isolates are found in genes that are involved in

RNA degradation. These include a C to G transversion resulting in a Pro116Ala change in the NOT1 gene (TevSTIB805.10.1620) at chromosome 10 and a T to C transition leading to a Val76Ala mutation in the Ribonuclease H gene (TevSTIB805.7.5350) at chromosome 7 (Table 3). To the best of our knowledge, there are no previous reports implicating the NOT1 and Ribonuclease H genes in drug resistance. We hypothesize that their roles in gene expression and transcriptional regulation allow the parasite to escape the effect of diminazene binding to kinetoplast DNA. A possible mechanism may involve the high binding affinity of diminazene to RNA-based G-quadruplexes. This involves higher binding affinity (103 stronger) compared to its AT-rich duplex DNA target (Zhou et al. 2014). Decreased RNA degradation would, therefore, promote the sequestration of cytoplasmic diminazene and hinder the accumulation of irreversible damages towards the kinetoplast DNA leading to parasite death.

The highly resistant sample O14 is the only isolate resistant to the drugs cymelarsan and isometamidium. The unique mutations observed in this isolate suggest the involvement of several genes in resistance generation. We found an A to G transition at position 1469 (Asn490Ser) of the pteridine transporter gene (TevSTIB805.1.2770) in chromosome 1 (Table 3). Interestingly, a pteridine transporter gene was shown to contribute to methotrexate resistance in the trypanosomatid Leishmania tarentolae (Kündig et al. 1999).

We also found a putative cation transporter gene (TevSTIB805.11_01.9250) at chromosome 11_01 that

Table 3. Notable mutations found only in drug-resistant isolates.

Chr ChrPos Gene Name Gene ID G e n e Pos

N u c l e o t i d e Change

Amino Acid Change

Shared by diminazene-resistant samples (C117 and O14)

2 1105712 Adenosine Transporter 2 TevSTIB805.2.3490 220 A -> G Met74Val

4 1202184 P-glycoprotein TevSTBI805.4.4670 464 A ->G Asp155Gly

4 758913 Nucleoporin TevSTIB805.4.3000 3736 T -> C Tyr1246His

10 396672 NOT1 TevSTIB805.10.1620 346 C ->G Pro116Ala

7 1295816 Ribonuclease H TevSTIB805.7.5350 227 T ->C Val76Ala

Only in the highly resistant sample (O14)

1 639160 Pteridine transporter TevSTIB805.1.2770 1469 A ->G Asn490Ser

11_01 2414063 Putative cation transporter TevSTIB805.11_01.9250 689 G -> A Arg230Lys

11_01 2414074 Putative cation transporter TevSTIB805.11_01.9250 700 T -> G Ser234Ala

5 438649 64-kDa invariant surface glycoprotein

TevSTIB805.5.1560 663 delA Frameshift

11_01 305307 Calpain-like cysteine peptidase

TevSTIB805.11_01.1120 5264 T -> C Val1755Ala

6 393111 Metacaspase 3 TevSTIB805.6.1000 161 A ->G Lys54Arg

6 393114 Metacaspase 3 TevSTIB805.6.1000 158 C ->G Pro53Arg



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was observed to harbor two mutations, a G to A transition at position 689 (Arg230Lys), and a T to G transversion at position 700 (Ser234Ala) (Table 3). While direct links between cation transporters and drug resistance have been suggested, we hypothesize that these transporters influence drug uptake through the modulation of metal co-factor concentration. Enzymes, such as those involved in drug interactions, often require metal co-factors (Wittung-Stafshede 2002), making the activity of cation transporters crucial in the development of resistance.

We further found a single nucleotide deletion at position 663 of a 64-kDa invariant surface glycoprotein (ISG) gene (TevSTIB805.5.1580) in chromosome 5 (Table 3). ISGs, particularly ISG75, were implicated in the cellular uptake of the trypanocidal drug suramin (Zoltner et al. 2016). Apart from membrane transporters, unique mutations were also found in two cysteine peptidase genes of isolate O14. These were for metacaspase MCA3 (TevSTIB805.6.1000) in chromosome 6 and a calpain-like cysteine peptidase (TevSTIB805.11_01.1120) in chromosome 11_01 (Table 3). Point mutations in two consecutive codons were found in the metacaspase gene (including an A to G transition) resulting in a Lys54Arg change, and a C to G transversion leading to a Pro53Arg amino acid substitution. For the calpain-like cysteine peptidase, a T to C transition was found at position 5264, resulting in a Val1755Ala mutation. A number of studies have demonstrated the importance of cysteine peptidases in bloodstream forms of T. brucei (Helms 2006, Troeberg et al. 1999). These have also been found to be crucial for the replication of the kinetoplast (Grewal et al. 2016), differentiation and virulence of the bloodstream form (Santos et al. 2007), and even crossing the blood-brain barrier (Nikolskaia et al. 2008). Mutations in cysteine peptidases that are possibly associated with resistance recommend caution about this class of peptidase as possible therapeutic targets unless combined with other drugs with different modes of action (Branquinha et al. 2013, Roy et al. 2010).

To further investigate the functional significance of the discussed variants, two search protocols were employed. The first used the BLAST algorithm of NCBI to find the best matches to the mutated sequences. The BLAST algorithm predicts similarities between query sequences and entries within the database on the basis of sequence identity. This provides an unbiased method of locating potential reference proteins without restricting the search to proteins which are “known” to be related to the query. The average sequence identity for the matches with this protocol was 45.9% – with average query coverage of 32.4%. The query coverage spanned values from 2% to 97% and, unfortunately, mutations from the sequenced samples were mostly found in areas that were outside the aligned sequences, making predictions on their functional

effects difficult. The results of these are shown in Table III. It must be noted, however, that this search protocol did yield a match of high query coverage (97%) and similarity (87.3%) for the mutated metacaspase protein. Mutations P53R and K54R were observed to only occur in the highly resistant sample O14. Both of these mutations are predicted to occur near the catalytic triad of the enzyme, possibly increasing efficiency for processing metacaspase 4 – an enzyme related to parasite virulence – and survival in the bloodstream (Proto et al. 2011).

A second protocol for assigning reference proteins for structural comparisons was also utilized. This protocol used a direct search of the PDB for structures that were similar or related to the expected products of the mutated genes. The names of the mutated genes were used as keywords in the search for related structures, and the top hits in PDB were evaluated for their similarity. For example, a keyword search for Metacaspase 2 reveals the presence of an archived structure for Metacaspase 3 that may be evaluated. The locations of the observed mutations were mapped to the domains of the matched proteins to predict their possible effects. In the absence of available reference structures in the PDB, predicted protein structures in UniProt were used as references. The results of these are shown in Table 4.

The predicted effects of the SNPs related altered transport of molecules through the plasma membrane and the nuclear membrane to the observed drug resistance in the C117 and O14 samples. Interestingly, the highly resistant sample (O14) was observed to have additional modifications in the processing of enzymes (i.e., Metacaspase 4) essential for parasite survival in the bloodstream. It must be noted that while some matched proteins (e.g., from Trypanosoma brucei) had a sequence identity of 86%, the average sequence identities observed for the matches made by this search was 28.1%. This highlights the need for more structural studies in the identified variant proteins for more accurate analyses of their functional significance.

CONCLUSIONSThis study utilized whole-genome sequencing to compare three T. evansi isolates from the Philippines with varying levels of drug resistance. Through WGS, regions that can differentiate strains as related to their drug resistance have been identified and highlight the value of a genomic approach to design regions that could target specific strains. From the analysis of genomic variants, several were found to be unique to either single-drug-resistant or multidrug-resistant strains. Affected genes in the drug-resistant strains linked genes for transport (e.g., for adenosine, pteridine, glycoproteins, etc.); proteases; and


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Table 4. Protein domain locations of notable mutations in drug-resistant isolates. Reference proteins are based on Top PDB result. In the absence of available reference structures in the PDB, predicted structures in UniProt were used to infer the structural significance of the observed SNPs. UniProt entries were chosen based on the first matching structures that contained annotations of the documented SNP positions. *Predictions were based on unreviewed predicted structures.

Chr. Chr. Pos. Gene name Amino acid change

Location Predicted effect Reference structure (PDBID)

Shared by diminazene-resistant samples (C117 and O14)

2 1105712 A d e n o s i n e Transporter 2

Met74Val Scaffold domain; IH1 helix

Maps to 5L26 (Gly80)

Stabilization of scaffold domain a s s o c i a t i o n with the plasma membrane

C o n c e n t r a t i v e Nucleotide Transporter (5L26)19.2% identity

4 1202184 P-glycoprotein ( m e m b r a n e -e m b e d d e d transporter)

Asp155Gly Maps to a region before the N-terminus of the structure in PDB (4Q9H).

Uniprot Predicted Structure: C 9 Z M P 2 - T R Y B 9 (Unreviewed)Predicts the mutation to occur in a transmembrane helix (AA 138–160)

*May modulate the function of this membrane-e m b e d d e d transporter

P-glycoprotein(4Q9H)24.0% identity

4 758913 Nucleoporin Tyr1246His Nucleoporin tail region Maps to 2RFO (Ser 780)

Stabilization of the Nuclear Pore Complex (NPC)

Nucleoporin Nic96(2RFO)21.2% identity

10 396672 NOT1 Pro116Ala Maps to a region before the N-terminus of the structure in PDB (4B8B).Uniprot Predicted Structure: A 8 D Y 8 2 - D R O M E (Unreviewed)Predicts the mutation to occur in a disordered region of the protein (AA 70–123). AA 116 occurs in a polyampholyte region (AA 108–123) that succeeds a polar region of this structure (AA 70–107).

*May modulate the poly-A s p e c i f i c r i b o n u c l e a s e activity of this enzyme

N-terminal domain of yeast Not1(4B8B)19.2% identity

7 1295816 Ribonuclease H Val76Ala Maps to a region just before the N-terminus of the structure in PDB (2RN2)

Uniprot Structure: Related protein, Ribonuclease H2O75792-RNH2A-HUMAN (Reviewed)Predicts the mutation to occur in the 4th helix (AA 73–84).

*May modulate the ribonuclease activity of this enzyme. A K69A mutation near the affected region has been documented to strongly reduce enzyme activity (Figiel et al. 2011)

PDB Match:E. coli Ribonuclease H(2RN2)31.0% identity

Uniprot Match:Ribonuclease H2PDBID:3P5619.2% identity

Only in the highly resistant sample (O14)



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1 639160 P t e r i d i n e transporter

Asn490Ser No Structure Available in the PDB.

Uniprot Predicted Structure: Q38A66-TRYB2 (Unreviewed)Predicts the mutation to occur in a transmembrane helix (AA 473-494)

*May modulate p t e r i d i n e t r a n s p o r t e r function

No structure available

11_01 2414063 Putative cation transporter

Arg230Lys Central PoreMaps to 2HN2 (R1232)

Modulation of Cation transport

CorA Mg+2 Transporter(2HN2)18.2% identity

11_01 2414074 Putative cation transporter

Ser234Ala Central PoreMaps to 2HN2 (S1236)

Modulation of Cation transport

CorA Mg+2 Transporter(2HN2)18.2% identity

5 438649 6 4 - k D a invariant surface glycoprotein

Frameshift:T r u n c a t i o n (434aa 224aa)

C-terminal helixMaps to 5VTL (A205)

Truncation of protein product.

Trypanosoma brucei metacyclic invariant surface protein(5VTL)24.4% identity

11_01 305307 C a l p a i n -like cysteine peptidase

Val1755Ala Outside available structures in PDB

Uniprot Structure: Related protein, Calpain-type Cysteine Protease, DEK1Q8RVL2-DEK1-ARATH (Reviewed)Predicts the mutation to occur in the cytoplasmic domain (AA 1072-2151). The Val1755Ala mutation is located near a predicted active site (AA 1761)

May modulate the protease activity of this enzyme

PDB matchHomo sapiensm-Calpain form II(1KFU)21.4% Identity to L subunit;21.7% identity to S subunit

6 393114 Metacaspase 3 Pro53Arg N-terminal region; external; near catalytic dyad

Maps to 4AFV (R30)

Processing of M e t a c a s p a s e 4; required for parasite survival in the bloodstream

Trypanosoma bruceiMetacaspase 2(4AFV)85.9% identity

6 393111 Metacaspase 3 Lys54Arg N-terminal region; external; near catalytic dyad

Maps to 4AFV (R31)T. brucei metacaspase 2 structure

Processing of M e t a c a s p a s e 4; required for parasite survival in the bloodstream

Trypanosoma bruceiMetacaspase 2(4AFV)85.9% identity

Table 4 continuation . . . .


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surface glycoproteins with trypanocide resistance. In addition, novel mutations observed in the study suggest the possible involvement of new genes (e.g., for RNA degradation, cation transport) with drug resistance. A comparison of the single-drug and multi-drug resistant strains suggests an additive effect of modulations in cation transport and metacaspase processing for increased drug resistance. Further investigation of the mechanisms through which these genes confer drug resistance will reveal potential targets for disruption, for which novel therapeutics may be designed.

ACKNOWLEDGMENTSThis project was funded by a research grant from the PCC. We thank the Philippine Genome Center DNA Sequencing and Bioinformatics Core Facilities for analyzing our samples. We also acknowledge the help of Ms. Shella Badong in facilitating this research project.

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APPENDIX

Table I. Quality assessment metrics.

Sample Pair #Initial assessment Final assessment

Final pairedTotal # of seqs Length %GC Total # of seqs Length %GC

O14 1 4,783,175 35–301 44 4,392,053 50–301 44 3,669,087

O14 2 4,783,175 35–301 45 3,812,875 50–301 44 3,669,087

C117 1 6,685,345 35–301 45 6,148,269 50–301 44 5,501,386

C117 2 6,685,345 35–301 45 5,660,139 50–301 44 5,501,386

M4 1 4,356,245 35–301 45 3,986,201 50–301 45 3,130,302

M4 2 4,356,245 35–301 45 3,261,811 50–301 44 3,130,302

Table II. Mapping metrics.

Sample # Mapped reads # Mapped bases Ave. mapping depth % Coverage breadth (> 5x)

O14 3,103,615 1,089,734,939 42.85 96.71

C117 4,362,439 1,283,185,682 50.46 96.56

M4 2,685,760 982,807,110 38.64 94.24

Figure I. Gene ontology results for genes affected by high impact mutations in the highly resistant isolate O14.

Figure II. Gene ontology results for genes affected by high impact mutations in the diminazene-resistant isolate C117.


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Figure III. Gene ontology results for genes affected by high impact mutations in the drug-susceptible isolate M4.

Figure IV. Gene ontology results for genes affected by high impact mutations and shared only by isolates O14 and C117.



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