Detecting complex infections in Trypanosomatids using whole genome sequencing

Detecting complex infections in Trypanosomatids using whole genome sequencing | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Detecting complex infections in Trypanosomatids using whole genome sequencing João Luís Reis-Cunha, Daniel Charlton Jeffares This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4648421/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Oct, 2024 Read the published version in BMC Genomics → Version 1 posted 12 You are reading this latest preprint version Abstract Background Trypanosomatid parasites are a group of protozoans that cause devastating diseases that disproportionately affect developing countries. These protozoans have developed several mechanisms for adaptation to survive in the mammalian host, such as extensive expansion of multigene families enrolled in host-parasite interaction, adaptation to invade and modulate host cells, and the presence of aneuploidy and polyploidy. Two mechanisms might result in “complex” isolates, with more than two haplotypes being present in a single sample: multiplicity of infections (MOI) and polyploidy. We have developed and validated a methodology to identify multiclonal infections and polyploidy using Whole Genome Sequencing reads, based on fluctuations in allelic read depth in heterozygous positions, which can be easily implemented in experiments sequencing genomes from one sample to larger population surveys. Results The methodology estimates the complexity index (CI) of an isolate, and compares real samples with simulated clonal infections at individual and populational level, excluding regions with somy and gene copy number variation. It was primarily validated with simulated MOI and known polyploid isolates respectively from Leishmania and Trypanosoma cruzi . Then, the approach was used to assess the complexity of infection using genome wide SNP data from 530 Trypanosomatid samples from four clades, L. donovani/L. infantum , L. braziliensis , T. cruzi and T. brucei providing an overview of multiclonal infection and polyploidy in these cultured parasites. We show that our method robustly detects complex infections in samples with at least 25x coverage, 100 heterozygous SNPs and where 5–10% of the reads correspond to the secondary clone. We find that relatively small proportions (≤ 7%) of cultured Trypanosomatid isolates are complex. Conclusions The method can accurately identify polyploid isolates, and can identify multiclonal infections in scenarios with sufficient genome read coverage. We pack our method in a single R script that requires only a standard variant call format (VCF) file to run ( https://github.com/jaumlrc/Complex-Infections ). Our analyses indicate that multiclonality and polyploidy do occur in all clades, but not very frequently in cultured Trypanosomatids. We caution that our estimates are lower bounds due to the limitations of current laboratory and bioinformatic methods. Complex infections polyploidy multiplicity of infection Trypanosomatids aneuploidy protozoan parasites Figures Figure 1 Figure 2 Figure 3 Background Trypanosomatid parasites are a group of protozoans that cause devastating diseases, imposing severe health and economic burdens primarily upon developing countries [ 1 – 3 ]; ( https://www.paho.org/en/topics/chagas-disease ). Among them, African trypanosomiasis, American trypanosomiasis and leishmaniasis, caused respectively by Trypanosoma brucei ; Trypanosoma cruzi and species from the Leishmania genus are Neglected Tropical diseases (NTDs), with more than one billion people living at risk of infection. These diseases are a part of the WHO NTDs elimination road map for 2021–2030 (WHO/UCN/NTD/2020.01) [ 3 ]. Various mechanisms for immune evasion and adaptation to survive in the mammalian host have evolved in these parasites; such as antigenic variation in the extracellular parasite T. brucei [ 4 – 7 ]; extensive expansion of multigene families enrolled in host-parasite interaction in T. cruzi [ 8 – 10 ]; adaptation to invade and modulate host cells in T. cruzi and Leishmania [ 11 – 13 ]; and the presence of aneuploidy and polyploidy [ 14 – 16 ]. Genome instability, observable within population by variation in chromosome copy numbers [ 14 ], and frequent formation of triploids and tetraploids [ 17 – 20 ] are also features of these species. There is also evidence of the occurrence of multiplicity of infections (MOI) in these parasites, where more than one diploid genotype is observed in the same host, which might have consequences to the parasite biology [ 21 – 28 ]. MOI is an expected consequence of insect vectors taking more than one blood meal (including from different infected individuals) and important for the resulting meiotic recombination within the vectors [ 29 ]. Both MOI and allopolyploidy will result in complex isolates , with more than two haplotypes being present in a single sample. The complexity of natural infections is relevant to understanding Trypanosomatid biology and disease control, as MOI cases provide direct evidence for genetically diverse infections that could increase the speed in which virulence and drug resistance genes may be shared in the population, may be more challenging to treat, and may result in diverse clinical presentations. In general, parasite diversity allows sub-populations to be selected in different environments, increasing adaptability [ 21 , 28 , 30 ]. MOI has already been described in Leishmania infections [ 22 , 23 , 31 ], where there is usually a dominant genotype combined with rare genotypes of the same species; and in the insect vector [ 32 ], where different species of the parasite may cohabit the same insect [ 33 ], which can result in interspecies hybrids [ 34 ]. Multiclonal infections were also described in T. cruzi using microsatellite and marker genes, where it appears to be more prevalent in mammalian reservoirs, such as rodents and opossums, when compared to human patients [ 24 – 26 ]. There is also evidence of MOI in T. brucei in the mammalian host [ 27 ], and in the inset vector [ 35 ]. In T. brucei , coinfection with two strains in the mammalian host leads to competitive suppression, enhancing host survival [ 36 ], reinforcing that MOI may impact patient clinical outcomes in these parasites. Hybridization leading to temporary trisomy/tetraploidy was already demonstrated in Trypanosomatids. In T. cruzi , experimental hybrids originated from diploid parental strains were mostly tetraploid, and underwent genome erosion throughout culture passages, reverting to trisomy [ 17 ]. In Leishmania , hybridization was shown to generate diploid, triploid or tetraploid strains [ 37 ], both in intra species [ 38 , 39 ], as well as between species hybrids [ 40 ]. This transient presence of four haplotypes (in allotetraploids) in a single cell might increase genetic exchange and recombination, increasing the potential variability, as the parasites revert back to trisomy and disomy by genome erosion. In the context of this analysis, only allotetraploids (containing two different diploid genotypes) will be detected, not autotetraploids (containing two copies of the same diploid genotype). In the present work, we have developed a methodology to identify multiclonal infections and polyploidy in any diploid species using Whole Genome Sequencing (WGS) reads, based on fluctuations in allelic read depth in heterozygous positions, which can be easily implemented in experiments sequencing genomes of one or a few samples, or larger population surveys. This methodology uses the complexity index (CI) proposed in Franssen et al. [ 31 ]. We parameterize this metric by comparing the allelic read depth at heterozygous sites in real samples to simulated clonal infections, which were generated using allelic read depths sampling by binomial trials to generate stochastic allelic depths. This approach was used to assess the complexity of infection in 530 Trypanosomatid isolates from four species/complexes, L. donovani/L. Infantum ( L. donovani complex), L. braziliensis , T. cruzi and T. brucei based on genome-wide markers, providing a large overview of multiclonal infection and polyploidy in these parasites. We show that our method robustly detects complex infections with at least 25x coverage and at least 100 heterozygous SNPs. We find that a relatively small proportion (≤ 7%) of cultured Trypanosomatid isolates are complex. For methodological reasons, these proportions represent a lower bound of complex infections in these species. Methods 2.1 Overview We define the complexity index (CI) as the deviation from the expected 50% of reads in each allele in heterozygous positions, as proposed in Franssen 2021 [ 31 ]. It is estimated by the absolute value of the difference between the alternate allele read depth (ARRD) in heterozygous positions and 0.5, the expected AARD in diploid, clonal heterozygous SNPs. To estimate the CI of an isolate, we have carefully filtered SNP calls, removing SNPs in repetitive regions, aneuploid chromosomes, duplicated genes and samples with low coverage. 2.2 Heterozygous SNP calling and alternate allele read depth (AARD) estimation Representative whole genome sequencing (WGS) read data from Trypanosomatid isolates were downloaded from the National Centre for Biotechnology Information (NCBI) Sequence Read Archive (SRA) using Fastq-dump [ 41 ]. Only Illumina sequencing reads from publicly available datasets were used (Supplementary_Table_1, Supplementary_Table_2 and Supplementary_Table_3). Each read library was filtered using fastp v2.10.7 [ 42 ], with the parameters: average Q20, minimal length 50 and removing the read extremities with base quality lower than Q25. Next, for each species the reads were mapped to an appropriate reference genome, listed in Supplementary_table_4, using BWA-mem v.0.7.17 [ 43 ], retaining only reads with mapping quality 30 or higher and removing PCR duplicates using SAMtools v.1.10 [ 44 ]. The number of mapped reads was estimated using SAMtools v.1.10. The genome coverage was estimated by the mean coverage of all single copy genes in the genome, using SAMtools depth. The single copy genes were selected using OrthoFinder v. 2.5.4 [ 45 ]. For the SNP calls, read groups were assigned for the filtered mapped read libraries, using PicardTools v.2.21.6 ( https://github.com/broadinstitute/picard ). SNPs and indels were called using the Genome Analysis Toolkit (GATK) v.4.1.0.0 HaplotypeCaller and Freebayes v. 1.3.5 ( https://github.com/ekg/freebayes ), with a minimum alternative allele read count of 5. Only SNP/Indel positions that were identified by both callers were kept. For each dataset, the single-sample VCFs were merged with VCFtools v.0.1.16 and regenotyped using Freebayes. Next, the VCF file was filtered using BCFtools v.1.12 [ 46 ], to select only biallelic SNPs, with call quality above 200, coverage greater than half of the mean genome coverage (i.e, at least haploid), and lower than twice the genome coverage (i.e. is not duplicated) with mapping quality 40 or higher and properly paired reads (-m2 -M2 -i ' TYPE="snp" & QUAL > 200 & INFO/DP > Cov/2 & INFO/DP 40 & INFO/MQMR > 40 & INFO/PAIRED > 0.9 & INFO/PAIREDR > 0.9 ). The only exception was the T. cruzi dataset, as several samples were single-end reads, so the “INFO/PAIRED > 0.9 & INFO/PAIREDR > 0.9” were not used. To remove SNP call bias from repetitive regions and paralogous genes, only SNPs in single copy genes were used in subsequent analysis. After filtering, the multisample VCF was split into single sample VCFs, to be used in the complexity pipeline (see below). For the individual sample VCFs, only SNP positions with read depth ≥ 5 in both the reference and alternate alleles were considered as heterozygous. SNPs where the read depth in one allele was > 5, and between 1 and 4 in the other allele were classified as dubious, and not used in the complexity estimation. This was a conservative measure to remove potential noise and sequencing/mapping errors. To control the bias of aneuploidy in the CI estimation, chromosome(s) with coverage higher than 1.15x or lower than 0.85x of the genome coverage in a sample were excluded from downstream analysis. Similarly, to mitigate bias from gene copy number variants (CNVs), SNPs in genes with coverage higher than 1.15x or lower than 0.85x of its chromosome coverage were also removed. The gene coverage was estimated using SAMtools depth and the gene coordinates from the General Feature Format (GFF) obtained in TriTrypDB v.55. The chromosomal somy for each sample was estimated using the median read depth coverage of single copy genes in each chr with non-outlier coverage (Grubb’s tests, with P < 0·05), normalised by genome coverage. Data from Leishmania and Trypanosoma cruzi chromosomes 31 were always excluded, as they are consistently supernumerary in all isolates from these species [ 14 ]. Only read libraries with genome coverage ≥ 25x were used in posterior analysis. 2.3 Complexity evaluation, Cochran-Mantel-Haenszel (CMH) estimation and AARD distribution The classification of an isolate as complex was based on comparisons between the real data with simulated clonal isolates. Samples that were classified as complex had to have: A higher CI than clonal simulated isolates, a significant CMH p-value associating the real sample to deviations from the expected allele read counts, and an alternate allele read depth (ARRD - the read depth proportion (0 to 1) that corresponds to the alternate allele in a SNP position) distribution that deviates from the simulated clonal isolate. Only isolates that were above both Complexity and CMH cutoffs were assumed to be complex. Details are described below. Complexity For each SNP site i , CIi is the absolute value of the difference between the AARD in that position and the expected AARD in diploid, non-mixed SNP positions, within a sample (which is expected to be close to 0.5). To account for the random sampling of reads sequenced from each allele of heterozygous sites, a simulated “clonal-diploid” SNP data sample was generated for each isolate in each population, with the same number of SNPs and read depth as in the real sample, using series of binomial trials. For each SNP position ( i ) in the real sample, we conducted n binomial trials, by randomly sampling from a binary array (0 or 1), where 0 represents the reference allele and 1 the alternate allele, where n is the read depth in the position in the real sample. The AARDi for the i th position in the simulated clone was the sum of the binomial trials ( b ), divided by the total read coverage at site i ( n ); $$ARRDi = \frac{{\sum }_{1}^{n}b }{n}$$ and the complexity index of this position ( CI i ) was calculated as the absolute difference between the expected AARD of 0.5 $$CIi = \left|ARRDi - 0.5\right|$$ The CI of the isolate with i heterozygous SNPs is calculated as the mean of all CIi values (R script available in GitHub: https://github.com/jaumlrc/Complex-Infections.git ). To classify an isolate as “potentially complex” the CI had to be higher than the mean + 3 standard deviations (SD) from all simulated clonal isolates in the population. For an isolate to be classified as “complex” it had to have a CI value > 0.1, which is slightly higher than the cutoff for the simulated data for all trypanosomatid populations (see results section). We recommend the CI threshold of 0.1 be used to classify samples in projects with a small number of samples. CMH test : Another metric used to assess the isolate complexity was the CMH test, which tests the association between binary predictors (expected counts of reference and alternate alleles to generate the expected ARRD of 0.5) and binary outcomes (observed counts of reference and alternate alleles) considering stratification from a third variable (in our case the position in the genome). In this case, it was used to compare the combined effect of all SNP read depth in each allele to classify an isolate as complex, comparing real samples and binomial trial simulated clonal-diploid samples. For each isolate with i heterozygous SNP positions, CMH p-values were generated using i 2x2 contingency tables using row 1; actual read depth of each allele at position i , row 2; the result of n binomial trials (where n is the total read depth at site i ), and columns being the reference and alternate allele counts. We found that a significance level of p ≤ 10 − 10 was a reasonable CMH test threshold Supplementary_Figure1. R scripts for both tests are available on GitHub ( https://github.com/jaumlrc/Complex-Infections.git ). AARD distribution The AARD distribution for all SNP positions in the real sample and its paired simulated clonal sample were generated in R, and deviations from their distributions were accounted as evidence for complexity. 2.4 Assessing complexity on mixed samples To estimate the accuracy of the combined CI and CMH tests to identify multiclonal infections with different proportions of the secondary clone, a collection of 24 L. donovani clones from East Africa (EA) described in Franssen 2021 were used (Supplementary_table_1). To create artificial multiclonal samples and assess the impact of the proportion of a secondary clone in complexity estimations, three clones, ERR205809, ERR205816 and ERR205819, were selected. Each of these three read libraries was combined with the full library of another of these three clones, where the secondary strain was downsampled to 2.5, 5, 10, 15 and 50% of the full combined data, using SAMtools v.1.9 [ 44 ], to create ‘MIX’ samples. In each case, the main and secondary clones were permuted, generating a total of 33 combinations. The results from these artificially multiclonal samples were compared with those obtained from clones and simulated clonal isolates, based on their complexity index and CHM test, as previously described. The impact of the number of heterozygous SNPs in the CI evaluation was examined by sub-sampling the number of SNP calls at random to 10, 50, 100, 300 SNPs, in 100 iterations, assessing the number of true positives (number of MIX samples that were classified as complex) and comparing with the estimations with the full set of SNPs. 2.5 Assessing complexity on polyploid samples Besides multiclonality, another source of complexity is polyploidy, having extra full sets of chromosomal copies. To evaluate the impact of polyploidy in the CI estimations, we used samples from the T. cruzi dataset described in Matos 2022 [ 17 ], containing 8 diploid parental clones, and 11 triploid or tetraploid hybrids clones, where the somy of some samples were validated by flow cytometry [ 17 ] (Supplementary_Table_2). As performed for the multiclonal isolates, the SNPs counts for each sample were downsampled to 10, 50, 100, 300 or full set, in 100 iterations, and the accuracy to classify each group as complex was evaluated. 2.6 Assessing complexity on laboratory and field isolates After validating our complexity estimations with simulated/controlled data, we went on to evaluate field isolates (FI) and lab-derived parasites that were available in NCBI SRA. We estimated the complexity of four Trypanosomatid species/complexes: L. donovani/L. Infantum ( L. donovani complex), L. braziliensis , T. cruzi and T. brucei , using a total of 573 WGS data sets from publicly available field isolates and laboratory strains (Table 1 , Supplementary_Table_3). Only read libraries from samples with read coverage ≥ 25x and where at least 100 heterozygous SNPs were called where used in this analysis. For these samples, the whole genome complexity, as well as the complexity estimation for each chromosome was estimated. The proportion of chromosomes that were used in the complexity estimation was calculated for each isolate by dividing the number of chromosomes that were used in complexity estimates (had at least one identifiable SNP and were not aneuploid) by the total number of chromosomes in the species: L. donovani = 36; L. braziliensis = 35; T. brucei = 11; T. cruzi = 41. The proportion of complex chromosomes in an isolate was estimated dividing the number of chromosomes with complexity index ≥ 0.1 by the number of evaluated chromosomes. In practise, most chromosomes were utilised in all CI estimates. Exceptions are isolates with several duplicated chromosomes. The evaluation of statistical differences in the genome coverage and heterozygous SNPs/Kb in complex and non-complex isolates was performed with Mann–Whitney U test, in R. The Pearson correlation between the genome coverage, SNPs/Kb and complexity was estimated in R. For both analyses the heterozygous SNPs/Kb were estimated dividing the heterozygous SNP numbers by the sum of the lengths of the single copy genes, in each set. We also estimated the number of heterozygous SNPs in the maxicircle (kDNA) genome, as well as its coverage (as described for the nuclear genome). To identify kDNA SNPs, the maxicircle sequence ( L. donovani = BK010877.1, L. braziliensis = OY748431, T. cruzi = MW732647, and T. brucei M94286.1; downloaded from NCBI) was combined with the genome reference for the read mapping. Only heterozygous SNPs that were outside the repetitive region and had at least 5% of the kDNA genome coverage were considered. Results 3.1 Genomic phenomena that might alter Trypanosomatid isolate complexity The proportion of reads in each allele in a heterozygous SNP position can be impacted by several phenomena, including multiclonality (multiple clones or genotypes in a sample, often present in different proportions), polyploidy (multiple copies of all chromosomes) and aneuploidy (multiple copies of some chromosomes). (Fig. 1 ). In a non-complex clonal, euploid, diploid isolate, the mean alternate allele read depth (AARD), meaning the proportion of reads that correspond to the alternate allele in heterozygous positions, is expected to be close to 0.5, as there will be a similar number of reads mapping in both alleles (Fig. 1 A). Hence, when all heterozygous SNPs in a genome are evaluated, the distribution of the AARDs will have a peak in 0.5, with a distribution of AARD values expected from ‘random draws’ of reads calling each allele. Some phenomena that have already been observed in Trypanosomatids, such as multiclonality (Fig. 1 -B), polyploidy (Fig. 1 -C) and aneuploidy (Fig. 1 -D) will alter this proportion, changing the distribution peaks or flattening their curve, which may be seen in density plots of ARRD values for all heterozygous SNPs in a sample. To provide a numeric and statistical large-scale evaluation of this deviation from expected AARD, we estimated CI: the absolute value of the deviation from the expected 0.5 proportion in each heterozygous SNP position [ 31 ]; and compared real samples with simulated clonal isolates at individual and populational level. To exclude deviations from AARD due to paralogous genes and aneuploidy, we included only single-copy gene regions and excluded chromosomes with somy variation and gene duplication/losses. We have estimated cutoffs for complex isolates based on the mean complexity of simulated clonal isolates from population genomic data from various Trypanosomatid clades, and used the Cochran-Mantel-Haenszel (CMH) test to support the evaluation in each isolate. 3.2 Assessing the accuracy of the CI to identify multi-clonal and polyploid isolates, using simulated or controlled data To evaluate the accuracy of the CI metric to identify multiclonal isolates, we created sequencing read data sets to represent multiclonal isolates from L. donovani , by combining downsampled read files from laboratory cloned field isolates in various proportions. We evaluated the two features that could impact the complexity estimations in multiclonal infections: the proportion of the secondary clone and the number of heterozygous SNP positions. To simulate multiclonal isolates with different proportion of the secondary clones, WGS data from three L. donovani lab-derived isolates that have been cloned were used: ERR205809, ERR205816 and ERR205819 [ 31 ]. Reads from these clones were combined pairwise in proportions of 2.5, 5, 10, 15, 25 and 50% of the reads from the secondary clone, resulting in 33 datasets. The complexity of these mixed samples (MIX) and the 24 clones from the Frassen 2021 [ 31 ] dataset was assessed based using two parameters: the CI : which had to be higher than the mean + 3 standard deviations (SDEV) from the simulated clonal isolates in the population; and CMH test to evaluate if the real isolate AARD differs from the expected clonal isolate, with a p-value lower than 10 − 10 (Fig. 2 ). Based on these cutoffs, zero clones (0%), and 26 (79%) of the MIX samples were classified as complex isolates. When evaluated separately, the CI parameter was the most specific, as only one clone was classified as complex (false positive), compared to 3 for CMH. However, CI was the least sensitive, as it only classified 26/33 (79%) MIX as complex, when compared to 31/33 (94%) for CMH (Fig. 2 A and B, Supplementary Figure_2). The complexity estimation accuracy was greatly influenced by the proportion of the secondary isolate, where lower proportions resulted in false negative results. None of the six MIX samples where the secondary clone read proportion was 2.5% was classified as complex. Increasing the proportion of the secondary clone resulted in higher accuracy, where five of the six samples where the secondary clone corresponded to 5% of the reads, and all samples where the secondary clone had 10–50% of the reads were classified as complex (Supplementary Figure_2). This was expected, as a low proportion of the secondary clone had a low impact in AARD distributions (Fig. 2 B). Hence, our method can detect complex isolates when the secondary clone represents at least 5–10% of the reads. To evaluate the impact of the number of heterozygous SNPs in the complexity estimation, the heterozygous SNP counts for each MIX sample was downsampled to 10, 50, 100, 300 or full set, and the accuracy to classify each group as complex was assessed (Supplementary Figure_2). To remove potential sampling bias, the analysis was repeated in 100 iterations, re-sampling random SNP positions each time, and the final results are a combination of all iterations. When compared with the full dataset, which had between 978 and 5910 SNPs, the use of 10 SNPs resulted in poor accuracy in all proportions of the secondary clone. By using 100 and 300 SNPs, the results were similar to those observed for the full set, with lower accuracy only for samples with ~ 5% of the reads originating from the secondary clone (Supplementary Figure_2, Supplementary_table_5). Hence, the complexity index estimation requires 100 or more heterozygous SNPs to be accurate. Besides multiclonality, another source of complexity is polyploidy, having extra full sets of chromosomal copies. To evaluate the impact of polyploidy in the complexity estimations, we used the T. cruzi dataset described in Matos 2022, [ 17 ], containing 8 diploid parental clones, and 11 triploid or tetraploid hybrids clones, where the somy of some were validated by flow cytometry Supplementary_Table_2. As performed for the multiclonal isolates, the SNPs counts for each sample were downsampled to 10, 50, 100, 300 or full set, in 100 iterations, and the accuracy to classify each group as complex was evaluated (Fig. 2 C and D; and Supplementary Table 6). Using the combination of CI and CHM cutoffs, on average 4.4, 73.5, 82.5, 90.9 and 100% of the polyploid isolates, respectively for the 10, 50, 100, 300 or the full set of SNPs were correctly classified as complex. No parental diploid clones were classified as complex in any replicate. As expected, the triploid isolates had a distribution of AARD with peak distributions in 0.33 and 0.66, while the tetraploid had peaks in 0.25, 0.5 and 0.75. Both triploid and tetraploid isolates had an CI higher than the observed for the diploid isolates (Fig. 2 C and D, Supplementary_Figure 3). These results suggest that the complexity estimate can also be used to identify polyploid isolates with reasonable sensitivity (~ 80%) when 100 or more heterozygous SNPs are present. Based on these results, we decided to use the combined results of CI and CHM tests to identify complex isolates, and to only evaluate samples with 100 or more SNPs. A conservative approach that minimises false positives, accepting some false negatives, especially in cases where the secondary clone proportion is low. 3.3 Complexity evaluation among Trypanosomatid species: After establishing the accuracy of the CI metric to identify multiclonal and polyploid samples with simulated and controlled data, we estimated the complexity in a total of 530 laboratory and field isolates from L. donovani , L. braziliensis , T. brucei and T. cruzi , identifying a total of 28 complex isolates (Fig. 3 , Table 1 , Supplementary Figs. 4–7). The CI cutoff was similar among the evaluated species, with the lowest value in T. cruzi (0.072) and the highest in L. braziliensis (0.089), which supports the robustness of the method. We propose a global cutoff of 0.1 (slightly higher than the highest cutoff, in L. braziliensis ) as a value that may be used to classify any Trypanosomatid isolate, or possibly other diploid eukaryotic samples, as complex, which will allow any researcher to classify single isolates without the need of population data to estimate a custom cutoff. Samples with CI values lower than the global cutoff but still higher than their species cutoff were classified as “potential complex” and evaluated separately. Only three potentially complex isolates were identified, two in T. cruzi and one in L. donovani . Even though we removed aneuploid chromosomes that had deviations from the mean genome coverage from each isolate, the intra isolate chromosome mosaicism (mosaic aneuploidy, and chromosome imbalance) [ 47 – 51 ] may add noise to complexity measurements in field isolates, by having unbalanced values in a few chromosomes. Hence, we are only considering as “complex”, isolates that had at least 50% of its evaluated chromosomes with a mean complexity value higher than 0.1. The proportion of isolates that were classified as complex varied across clades, where T. cruzi and T. brucei had the lowest (~ 2.5%) and L. braziliensis had the highest (30%) proportion of complex samples in the evaluated dataset. Complexity values also varied in different isolates, where the lowest value was observed in T. brucei SRR17479767 (0.025), and the highest in L. donovani ERR205770 (0.398). Complex isolates have more heterozygous SNPs than non complex samples (Mann-Whitney p-value = 0.003), especially for L. braziliensis (Mann-Whitney p-value 2.87 x 10 − 6 ) and T. brucei (Mann-Whitney, p-value 0.0074). This increase was not observed in the L. donovani evaluated samples (Supplementary_Figure_8). The increase in overall heterozygous SNP counts may reflect the presence of multiclonal infections, where using WGS bulk data, clone specific homozygous SNPs are interpreted as heterozygous SNPs, increasing their counts; or allopolyploids. The genome coverage from each set also varied, and this might have an impact on the CI limit of detection, as we only classified SNPs with coverage ≥ 5 in both alleles as heterozygous to be used in the complexity estimations. The median of the genome coverages for each dataset was 39 for L. braziliensis , 31 for L. donovani , 56 for T. brucei and 45 for T. cruzi (Supplementary_Figure_8 G), which due to our cutoff of at least 5 reads in the rarer allele, would only allow the identification of multiclonal infections where the secondary clone proportion of reads was respectively of at least 17%, 16%, 8% and 11%. When each dataset was evaluated separately, from the 85 evaluated L. donovani samples, 6 were classified as complex (7%), and one as potentially complex. Among the 6 complex isolates, three ERR205724 (MHOM/SD/82/GILANI), ERR205770 (MHOM/IT/02/ISS2429) and ERR205774 (MHOM/BR/2003/MAM), were already classified as multiclonal by Fransen 2020 [ 52 ], and one, ERR3956121 (1052_ToD_1_primary_neg), was classified as complex by Frassen 2021. In fact, ERR205774 also presented a higher count of heterozygous SNPs in the maxicircle sequence, which further corroborates that it is a multiclonal infection (Supplementary Figure_9). Two isolates, ERR205748 (MHOM/CY/2006/CH32) and ERR205789 (MHOM/SD/62/LRC-L61), were classified by Frassen 2020 as hybrids, and had a ARRD distribution compatible with triploidy in our analysis. Hybridizations in trypanosomatids may result in polyploid lineages, which might revert back to diploidy by genome erosion [ 17 ]. The sample that was classified as potential complex ERR3956143 (1073_ToD_1_primary_neg), corresponds to a field isolate obtained from a patient from Ethiopia, which might be multiclonal. For the L. braziliensis dataset, from the 42 evaluated samples, 13 were classified as complex and all had previous evidence of being polyploid (30%). From these 13, 10 corresponded to experimental tetraploid hybrids, described in [ 53 ], while SRR21604774 corresponded to a triploid L. braziliensis and Leishmania guyanensis hybrid. Finally the last two samples, ERR2508271 and ERR2508272, correspond to read libraries used in the assembly of the triploid L. braziliensis M2904 genome [ 16 , 54 ]. We found no strong evidence of multiclonal infections in any of the evaluated L. braziliensis samples. From the 211 T. cruzi evaluated samples, five were classified as complex, and two were classified as potential complex. From the complex set, three were isolated from the insect vector: SRR8503553 ( Panstrongylus lignarius in Peru); SRR3676272 and SRR3676273 ( Triatoma dimidiata in Texas) [ 55 – 57 ]. The AARD density peaks in these three samples are similar to those expected for triploid isolates (0.33 and 0.66), suggesting that they are polyploid. The other two complex samples were isolated from chronic chagasic human patients in Panama (SRR3676281, SRR3676310) [ 55 ], and had AARD peaks that are not similar to what is expected for tri or tetraploid isolates. This suggests that they might be multiclonal infections. In fact, SRR3676310 had also a higher count of heterozygous SNPs in the maxicircle sequence when compared to other T. cruzi isolates (Supplementary_Figure_9), which further support that it is potentially a multiclonal infection. Finally, for T. brucei we identified 4 complex isolates in a dataset of 159 samples. From those, SRR17479764 corresponds to a triploid hybrid from the J10 and KETRI 1738 strains [ 58 ], while SRR17479766 was previously suggested to be a multiclonal infection [ 58 ]. The final two complex T. brucei strains, ERR270813 and SRR6052140 have AARD profiles that are similar to what is respectively expected for tetraploid and triploid isolates. Table 1 Complexity evaluation of each Trypanosomatid group of samples. In the Complex samples column, the number in parentheses indicates the number of potential complex samples. Species Sample number CI threshold Max. CI Min. CI Complex samples Complex % Assessment L. donovani 85 0.083 0.398 0.041 6 (+ 1) 7 4 multiclonal 2 polyploid L. braziiensis 42 0.089 0.194 0.035 13 30 13 polyploid T. brucei 159 0.077 0.167 0.025 4 2.5 1 multiclonal 3 polyploid T. cruzi 211 0.072 0.227 0.030 5 (+ 2) 2.3 2 multiclonal (chronic cases) 3 triploid (insect source) Simulated 33 0.075 0.3 0.05 25 75 - Polyploid 11 0.084 0.22 0.134 11 100 - Taken together these results suggest that complex isolates represent a small percentage of the cultured field isolates for all the TriTryp species evaluated. This corresponds to the lower bound of potential complex infections compared to what is observed in natural conditions due to limitations in parasite isolation, culture and a low proportion of the secondary clone, which hampers complexity detection by our method. We suggest that the CI should be estimated in all new WGS from Trypanosomatids isolates obtained in the future, to identify polyploid/multiclonal isolates before proceeding with downstream analysis. To facilitate this, we provide the R code for this method on github ( https://github.com/jaumlrc/Complex-Infections.git ), which requires only a variant call format (VCF) file to produce complexity estimates. Discussion In the present study, we identified and characterised two genome modifications that result in more than two haplotypes being present in a single parasite isolate in Trypanosomatids: multiclonal infections and polyploidy. We developed and validated a method to assess the complexity of Trypanosomatid samples based on WGS reads, and implemented the method to evaluate complexity in a representative collection of Leishmania , T. cruzi and T. brucei field-isolates and lab strains. Our method only uses chromosomes with similar somy as the genome ploidy, and removes genes with evidence of duplication/loss, as these could be confounding factors in the estimations. We have identified complex (polyploidy or multiclonal) infections in all evaluated species, and proposed a global complexity index cutoff that can be used in any Trypanosomatid single sample, and likely other diploid eukaryote samples. We provide an R script that can estimate complexity directly from VCF files ( https://github.com/jaumlrc/Complex-Infections.git ). In the last decade, the reduction in sequencing costs and the relevance of questions that may be answered with genomic data have resulted in a large increase in the number of studies that generate population WGS data for trypanosomatid parasites [ 31 , 50 , 52 , 55 , 59 – 64 ]. However, the occurrence of multiclonal infection and polyploidy is not always assessed in these studies. Complex infections also occur in bacterial infections, viruses and some protozoan parasites as Plasmodium , where the main stage that infects humans and other mammalian hosts is haploid [ 65 , 66 ]. Trypanosomatid parasites are usually diploid, and often aneuploid and/or polyploid [ 16 , 20 , 50 , 67 ], where the somy of different chromosomes can vary even within clones [ 47 ]. This increases the challenge of estimating complex infections in these parasites. Hence, a method is needed to identify complex infections using WGS in these species at scale, that takes into account gene copy number variants and aneuploidy, which might be used to evaluate publicly available datasets, as well as future in projects. By using WGS reads, the method that we propose has the advantage of assessing genome-wide SNP variation as evidence for complexity simply and robustly [ 22 , 52 ]. When compared to methods based on microsatellite loci and marker genes [ 23 – 25 , 27 ], the use of WGS data allows the removal of aneuploid chromosomes and duplicated genes by read depth values, which may add noise if not removed. A clonal aneuploid isolate with a trisomic chromosome containing three different alleles (one in each chromosome) in a microsatellite locus might be classified as “multiclonal” in microsatellite analysis, for having more than two alleles. This is especially relevant if the marker is in the trypanosomatid ancestral supernumerary chromosome (TASC), which has been shown to have four stable copies and a higher sequence variability when compared to other chromosomes [ 14 ]. By evaluating the complexity in each euploid chromosome from an isolate we could separate complex infections (multiclonal/polyploid) from “chromosome instability” (CIN) and “mosaic aneuploidy” events [ 16 , 47 , 49 , 50 ]. This was achieved by only classifying as complex field isolates that have complexity evidence supported by at least half of the evaluated chromosomes. In the current study, we identified a low proportion of complex infections in all Trypanosomatid field isolates and lab derived strains, with clear perturbations of the AARD distributions, and high CI values. We identified around 7% of complex infections for the L. donovani group, where 4 isolates had evidence of multiclonal infections and 2 isolates had evidence of polyploidy. This is in accordance with what was identified in [ 23 ], where even though different Leishmania genotypes were identified in different tissues, the number of isolates with MOI in the same tissue was low. For T. cruzi , we identified a very low proportion of complex infections (~ 2%), which is lower than the ~ 15–17% that was reported in the literature for inter Discrete Typing Units (DTU) [ 68 ] mixed infections in human patients from Latin America [ 24 , 69 , 70 ]; and to the ~ 13% of MOI in the vector Triatoma infestans [ 71 ]. This might be caused in part as a significant proportion of the evaluated T. cruzi isolates were cloned prior to sequencing [ 61 ], which would remove multiclonal infections but not polyploidy. As cloned samples could be polyploid, they were still evaluated in this work. Although most of the T. cruzi isolates had an AARD distribution that matched the expectation from a “non-complex clonal, euploid, diploid isolate”, there were some non-complex isolates with perturbations in the AARD distribution and high CI; such as SRR3676315, SRR3676316, SRR3676317, SRR3676318, SRR3676319; that had a distribution pattern similar to the potential complex isolate SRR3676320. These isolates had a high CI in less than half of the evaluated chromosomes, which suggests that they have a high level of mosaic aneuploidy and CIN [ 20 , 49 – 51 ], rather than being polyploid or multiclonal infections. T. brucei isolates also had a low proportion of complex infections identified in the WGS data (~ 2%) with only one isolate with strong evidence of multiclonal infection, which is lower than the 8–20% of multiclonal infections reported in humans and vector infections in East Africa [ 27 ]. Potential limitations of complexity estimation based on WGS are data collection, processing and genome coverage. Most Trypanosomatid WGS data is obtained from parasites that are isolated from the host and cultured in axenic media or used to infect mice, which might reduce complexity when compared to the variation present in the patient [ 22 , 23 ]. Different strains might have different growth rates in media, and secondary clones in low proportions might be outcompeted in culture. The methodologies to sequence parasite genomes directly from patient tissue, such as Selective Whole Genome Amplification (SWGA) [ 72 – 74 ], SureSelect [ 22 , 75 ] and Nanopore adaptive sampling [ 76 , 77 ] are improving in the last years, and might allow WGS in Trypanosomatids to be done in large scale without a culturing step in a affordable way in the future. This might allow complex infection assessments using WGS data to be performed directly from host tissues, as long as allelic proportions are not altered by these methods. Another potential limitation of complexity estimations in multiclonal infections in WGS data is the proportion of the secondary clone. The proposed method was able to identify complex infections when the secondary clone corresponded to at least 5–10% of the sequencing reads. This is in the range of what was observed WGS in clinical samples with Sure-select sequencing, where in the three identified complex infections, the proportion of the secondary clone was ~ 6–10% both in SureSelect and in cultured samples [ 22 ]. However, the mean genome coverage of the laboratory and field isolates evaluated here varied from 29 to 56 among the datasets, limiting our potential to identify multiclonal infections to cases where the secondary clone corresponded to at least 8–17% of the total reads in the sample. This might lead to an underestimation of the total frequency of complex infections identified. The multiclonal isolate with the lowest genome coverage was ERR3956121, from the L. donovani group, with a genome coverage of 27. Differently to what is observed for multiclonal infections, the low coverage should not have a great impact on polyploidy estimations, as the proportion of the rarer allele should be higher in this case. Due to ethical and health limitations, Trypanosomatid parasite isolates are usually obtained from only one tissue during diagnostic procedures in human patients, such as bone marrow, lymph nodes, spleen, heart, gut or blood. This might mitigate the complex infection frequency estimation, as different organs might harbour different parasite strains/variants [ 23 , 78 ]. A potential option to assess the complexity of Trypanosomatids infection in different organs is using samples from reservoirs, as dogs with visceral leishmaniasis in Brazil, where samples can be collected from any tissue post-mortem , with the approval of owners [ 23 ]. Similar analysis might be performed on other reservoirs for T. cruzi and T. brucei . While polyploidy appears to be a relatively common occurrence within Trypanosomatids [ 14 , 17 – 20 ], including an example of a two-species allotriploid [ 79 ], we detect few multiclonal infections in the Trypanosomatid read libraries examined here (Table 1 ; six L. donovani multiclonal, one T. brucei and two T. cruzi ). However, all these Trypanosomatid species do undergo some degree of sexual recombination and outcrossing [ 29 ], indicating that different genotypes must be present in the same insect at some point to undergo meiosis and outcrossing. We imagine two, non mutually exclusive, explanations for these results. First, the occurrence of multiclonal infections may be underestimated. Laboratory culture of strains is likely to result in loss of genotypes that are less fit in culture, reducing the apparent complexity of the infections below our threshold of detection. Consistent with this view, a study of infections in HIV + and HIV- visceral leishmaniasis cases Ethiopia found that 6 of the 68 infections (9%) were multiclonal [ 31 ], and a study of canine leishmaniasis in the state of Mato Grosso, Brazil identified 9 multilocus (polyclonal) genotypes in different organs, out of 23 genotyped (39%) [ 23 ]. These studies indicate that substantial proportions of multiclonal infections do occur in some populations. In other populations, multiclonal infections and outcrossing may be rare. Bottlenecks in the number of parasites transferred to or from vectors may reduce genetic diversity of infections at the outset, and long incubation of parasites within mammalian hosts with selection for the fittest parasite genotype may reduce genetically complex populations to a single clone. A reduction in within-host diversity would be expected to reduce outcrossing, unless vectors frequently feed on more than one host. We can expect that there will be alternative explanations for different species, populations, depending on the frequencies of transmission, endemicity and within host/within vector population dynamics. It is our perspective that more study of these factors will enhance our understanding of transmission dynamics in Trypanosomatids. Conclusions The method we describe can accurately identify polyploid isolates; and can identify multiclonal infections in samples sequenced with modest read depth (> 25x), as little as 100 heterozygous SNPs, and as little as 5–10% of the secondary genotype. We find that multiclonality and polyploidy are not frequent in cultured Trypanosomatid field isolates, although there are good reasons to expect that our estimates are lower bounds. Future projects could explore new sequencing methods to identify multiclonal infections, such as single-cell sequencing [ 80 ] that could directly identify different clones; and long-read sequencing followed by haplotype phasing, to identify different haplotypes in a sample [ 81 – 83 ]. These methods could quantify the proportion and number of the different clones in a mixed infection. Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Competing interests The authors declare that they have no competing interests Funding: J.L.R.-C. and D.C.J. are supported by a MRC New Investigator Research grant (MR/T016019/1) and by MRC Newton as a component of the UK:Brazil Joint Centre Partnership in Leishmaniasis (MR/S019472/1). Author Contribution J.L.R.-C and D.C.J. conceived, designed the study, drafted and revised the manuscript. Acknowledgement We thank the funding agencies that provided funds for this study, the Medical Research Council (MRC), and the Newton UK:Brazil Joint CentrePartnership in Leishmaniasis. This project was undertaken on the Viking Cluster, which is a high-performance compute facility provided by the University of York. We thank the University of York High Performance Computing service, Viking, and the Research Computing team for computational support. Data Availability All the read libraries used in this are available in NCBI (see Supplementary Tables 1, 2 and 3). The script and test set can be obtained from GitHub: https://github.com/jaumlrc/Complex-Infections.git References Burza S, Croft SL, Boelaert M, Leishmaniasis. Lancet. 2018;392:951–70. Kennedy PGE. Update on human African trypanosomiasis (sleeping sickness). J Neurol. 2019;266:2334–7. Horn D. A profile of research on the parasitic trypanosomatids and the diseases they cause. PLoS Negl Trop Dis. 2022;16:e0010040. Vickerman K. 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Santi-Rocca J, Fernandez-Cortes F, Chillón-Marinas C, González-Rubio M-L, Martin D, Gironès N, et al. A multi-parametric analysis of Trypanosoma cruzi infection: common pathophysiologic patterns beyond extreme heterogeneity of host responses. Sci Rep. 2017;7:8893. Van den Broeck F, Heeren S, Maes I, Sanders M, Cotton JA, Cupolillo E, et al. Genome Analysis of Triploid Hybrid Leishmania Parasite from the Neotropics. Emerg Infect Dis. 2023;29:1076–8. Nawy T. Single-cell sequencing. Nat Methods. 2014;11:18. Maestri S, Maturo MG, Cosentino E, Marcolungo L, Iadarola B, Fortunati E et al. A Long-Read Sequencing Approach for Direct Haplotype Phasing in Clinical Settings. Int J Mol Sci. 2020;21. Kronenberg ZN, Rhie A, Koren S, Concepcion GT, Peluso P, Munson KM, et al. Extended haplotype-phasing of long-read de novo genome assemblies using Hi-C. Nat Commun. 2021;12:1935. Hosch S, Wagner P, Giger JN, Dubach N, Saavedra E, Perno CF, et al. PHARE: a bioinformatics pipeline for compositional profiling of multiclonal Plasmodium falciparum infections from long-read Nanopore sequencing data. J Antimicrob Chemother. 2024;79:987–96. Additional Declarations No competing interests reported. Supplementary Files ReisCunha2024ComplexinfectionsSupplementalfigures27062024.docx Supplementarytable1.xlsx Supplementarytable2.xlsx Supplementarytable3.xlsx Supplementarytable4.xlsx Supplementarytable5.xlsx Supplementarytable6.xlsx Cite Share Download PDF Status: Published Journal Publication published 29 Oct, 2024 Read the published version in BMC Genomics → Version 1 posted Editorial decision: Revision requested 23 Jul, 2024 Reviews received at journal 20 Jul, 2024 Reviews received at journal 19 Jul, 2024 Reviews received at journal 16 Jul, 2024 Reviewers agreed at journal 08 Jul, 2024 Reviewers agreed at journal 05 Jul, 2024 Reviewers agreed at journal 01 Jul, 2024 Reviewers agreed at journal 01 Jul, 2024 Reviewers invited by journal 28 Jun, 2024 Editor assigned by journal 28 Jun, 2024 Submission checks completed at journal 27 Jun, 2024 First submitted to journal 27 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4648421","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":328789591,"identity":"3979e306-14a0-4436-8c30-d070e04708fc","order_by":0,"name":"João Luís Reis-Cunha","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYBACxgYow4CB+QCQSkAVJaCFLYE4LXBgwMBjQJwW5vbmhw8Yag7nmbOf+SbxcU8aA3/7ATbJGfgc1nPM2IDh2OFiy57cbZIznuUwSJxJYJPcgE/LjAQzCcaGw4kbDuRuk+Y5UMHAcIOBTfIBXi3p3yBazr95BtYiT1hLDtSWGzlsQC05DAYgLXgd1nOm2CDhWDpQyzNjyxkH0ngMzyQ2W+LzvmF7+8YHH2qsgQ5Lfnjjw4FkObnjhw/e7MGnpYEBFhkMLBJAgodgRMojsZk/4FU6CkbBKBgFIxYAALBIUaYnWutKAAAAAElFTkSuQmCC","orcid":"","institution":"University of York","correspondingAuthor":true,"prefix":"","firstName":"João","middleName":"Luís","lastName":"Reis-Cunha","suffix":""},{"id":328789595,"identity":"b312b3ea-4ec0-4be8-871e-67808d761663","order_by":1,"name":"Daniel Charlton Jeffares","email":"","orcid":"","institution":"University of York","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"Charlton","lastName":"Jeffares","suffix":""}],"badges":[],"createdAt":"2024-06-27 12:07:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4648421/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4648421/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12864-024-10862-6","type":"published","date":"2024-10-29T16:04:58+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":60710314,"identity":"a62f1b44-30d8-4ef1-b6d7-523242b7fe7d","added_by":"auto","created_at":"2024-07-19 20:01:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":309521,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhenomena that may alter the complexity of Trypanosomatid isolates.\u003c/strong\u003e \u003cstrong\u003eA)\u003c/strong\u003e Non-complex clonal, euploid, diploid isolate. In this example, the proportion of AARD in a heterozygous position is expected to be close to 0.5, and the genomic distribution of AARD in the real sample (purple) is similar to the simulated-clonal isolate generated from random-draw binomial trials (cyan). \u003cstrong\u003eB)\u003c/strong\u003eMulticlonality: having more than one parasite clone in an isolate, will result in deviations from the expected distribution and mean of 0.5 AARD, as the different clones may have different SNP sites or a SNP position may be homozygous in one clone and heterozygous in the other. This will result in different distributions of genome-wide AARD and a higher CI, which also vary depending on the proportion of the secondary clone (see Figure 2). \u003cstrong\u003eC)\u003c/strong\u003e Polyploidy, having extra copies of the whole chromosomal set also alters the AARD distribution. While triploid isolates have AARD peaks of ~0.33 and ~0.66, tetraploid isolates will have a combination of AARD peaks in ~0.25, ~0.5 and ~0.75. Higher ploidy would result in even more complex patterns\u003cstrong\u003e D)\u003c/strong\u003e Aneuploidy, having an unbalanced number of chromosomal copies will result in different AARD distributions in different chromosomes, which could impact whole genome CI estimations.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4648421/v1/d4f54f95a083d8bb92f6b644.png"},{"id":60710318,"identity":"6e714c86-e9e9-4c3c-a079-c6cc1d4750b7","added_by":"auto","created_at":"2024-07-19 20:01:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":505019,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssessing the impact of the proportion of the secondary clone and polyploidy in complexity estimates. A)\u003c/strong\u003e Complexity estimations in MIX samples. The X and Y axis represents, respectively, the mean CI and CI standard deviation. The sample origin is represented by shape, where each individual triangle represents a simulated clone, and circles represent the MIX samples. The proportion of the secondary clone is represented by colours. The red dotted line represents the complexity CI cutoff. The red arrow points to a clonal isolate that had a higher CI than the cutoff. However, its classification as complex was not supported by the CMH test. \u003cstrong\u003eB) \u003c/strong\u003eDensity distributions of the AARD proportion in heterozygous positions for increasing proportions of the secondary clone, varying from 2.5-50%. Each panel corresponds to a different isolate, including the clones and the MIX samples, from ERR205809 (main) and ERR205816 (secondary). The purple distribution corresponds to the isolate data, while the cyan distribution represents the simulated clone, with the same number of SNPs and read depth as the real sample. \u003cstrong\u003eC) \u003c/strong\u003eComplexity estimations in each sample from the \u003cem\u003eT. cruzi \u003c/em\u003eparental isolates and hybrid polyploid progeny. The X and Y axis represents, respectively, the CI and CI standard deviation. Each dot corresponds to an isolate (circles) or simulated clone (triangles), where parental diploid, triploid or tetraploid isolates are respectively represented in light-blue, dark-green and light green. Simulated clonal isolates are represented in dark-blue. The red dotted lines represent the complexity CI cutoff. \u003cstrong\u003eD) \u003c/strong\u003eExamples of diploid (SRR15686198), triploid (SRR15686206) and tetraploid (SRR15686203) AARD distributions plots. The top panels are density plots, where the purple distribution corresponds to the real data, while the cyan distribution represents the simulated clone, with the same number of SNPs and read depth as the real sample. Bottom panels represent the AARD distribution in each heterozygous SNP in the chromosome 36 from a diploid (SRR15686198), triploid (SRR15686206) and tetraploid (SRR15686203) sample. The Y axis represents the AARD and the X axis the chromosomal position.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4648421/v1/bd8f0ad160a9d986636c49d6.png"},{"id":60710321,"identity":"f930c831-b818-432a-9219-09e15aee5a6f","added_by":"auto","created_at":"2024-07-19 20:01:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1061012,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverall complexity estimation in Trypanosomatids field isolates. A)\u003c/strong\u003eComplexity estimations in each sample from each species. Each box corresponds to a clade. From top to bottom: \u003cem\u003eL. braziliensis\u003c/em\u003e, \u003cem\u003eL. donovani\u003c/em\u003e, \u003cem\u003eT. brucei\u003c/em\u003e, \u003cem\u003eT. cruzi\u003c/em\u003e. Each dot corresponds to a complex (circles), potential complex (diamond) or non-complex (triangles) isolates. The X and Y axis represents, respectively, the CI and proportion of the evaluated chromosomes that had a CI ≥ 0.1. The colour corresponds to the proportion of chromosomes that were evaluated in the isolate. The vertical dotted lines represent complexity cutoffs, and are coloured in accordance to the species from whom they were estimated. The red vertical line is the global complexity cutoff of 0.1, which separates the potential complex from the complex isolates. \u003cstrong\u003eB-E)\u003c/strong\u003e AARD distribution from the complex (red) and potential complex (orange) isolates. From top to bottom: \u003cem\u003eL. braziliensis\u003c/em\u003e;\u003cstrong\u003e \u003c/strong\u003e\u003cem\u003eL. donovani\u003c/em\u003e; \u003cem\u003eT. brucei\u003c/em\u003e;\u003cem\u003e T. cruzi\u003c/em\u003e. The distribution of the simulated clone is represented in white. The AARD density plot for all isolates from each population can be seen in the Supplementary Figures 4 to 7.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4648421/v1/92fead66932e2439124e1392.png"},{"id":68206625,"identity":"fd44c6bb-9608-44bc-9b27-82fd6a0227a5","added_by":"auto","created_at":"2024-11-04 16:33:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2576439,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4648421/v1/fb05029c-e552-4b56-ae17-3740c151f208.pdf"},{"id":60710315,"identity":"3e8fc91b-a0c9-4b6e-b6a2-d80275f92d3b","added_by":"auto","created_at":"2024-07-19 20:01:37","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":6108560,"visible":true,"origin":"","legend":"","description":"","filename":"ReisCunha2024ComplexinfectionsSupplementalfigures27062024.docx","url":"https://assets-eu.researchsquare.com/files/rs-4648421/v1/205f7cb14deba371f73c4024.docx"},{"id":60710316,"identity":"d7e0cb38-2a01-4432-acb3-ca5988d50efd","added_by":"auto","created_at":"2024-07-19 20:01:37","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":13294,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4648421/v1/dabe91361e4db7bfe2e9e565.xlsx"},{"id":60710670,"identity":"09c86b03-3952-4260-a85c-34a0cb7d254b","added_by":"auto","created_at":"2024-07-19 20:09:37","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":13067,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4648421/v1/6f2fe463f3c969846d1a37af.xlsx"},{"id":60710320,"identity":"976891e6-4d0d-45c1-b823-d01dfa6bc09d","added_by":"auto","created_at":"2024-07-19 20:01:37","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":42466,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4648421/v1/fd314c3d7708572b72d371f6.xlsx"},{"id":60710324,"identity":"176fe07f-4ca7-41fb-abf3-f72be2e40133","added_by":"auto","created_at":"2024-07-19 20:01:38","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":8807,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4648421/v1/cc095f6f236ae4e7102e0b10.xlsx"},{"id":60710671,"identity":"a0ddc073-1ee0-4d95-b585-3cf32118fcf4","added_by":"auto","created_at":"2024-07-19 20:09:37","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":11488,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4648421/v1/3ddbaeb4220f6db5aa819742.xlsx"},{"id":60710322,"identity":"3ddec95d-5508-47db-8f7f-326a2a789ea5","added_by":"auto","created_at":"2024-07-19 20:01:37","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":9032,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable6.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4648421/v1/b412952321f19349aee55466.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Detecting complex infections in Trypanosomatids using whole genome sequencing","fulltext":[{"header":"Background","content":"\u003cp\u003eTrypanosomatid parasites are a group of protozoans that cause devastating diseases, imposing severe health and economic burdens primarily upon developing countries [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]; (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.paho.org/en/topics/chagas-disease\u003c/span\u003e\u003cspan address=\"https://www.paho.org/en/topics/chagas-disease\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e Among them, African trypanosomiasis, American trypanosomiasis and leishmaniasis, caused respectively by \u003cem\u003eTrypanosoma brucei\u003c/em\u003e; \u003cem\u003eTrypanosoma cruzi\u003c/em\u003e and species from the \u003cem\u003eLeishmania\u003c/em\u003e genus are Neglected Tropical diseases (NTDs), with more than one billion people living at risk of infection. These diseases are a part of the WHO NTDs elimination road map for 2021\u0026ndash;2030 (WHO/UCN/NTD/2020.01) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eVarious mechanisms for immune evasion and adaptation to survive in the mammalian host have evolved in these parasites; such as antigenic variation in the extracellular parasite \u003cem\u003eT. brucei\u003c/em\u003e [\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]; extensive expansion of multigene families enrolled in host-parasite interaction in \u003cem\u003eT. cruzi\u003c/em\u003e [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]; adaptation to invade and modulate host cells in \u003cem\u003eT. cruzi\u003c/em\u003e and \u003cem\u003eLeishmania\u003c/em\u003e [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]; and the presence of aneuploidy and polyploidy [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Genome instability, observable within population by variation in chromosome copy numbers [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and frequent formation of triploids and tetraploids [\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] are also features of these species. There is also evidence of the occurrence of multiplicity of infections (MOI) in these parasites, where more than one diploid genotype is observed in the same host, which might have consequences to the parasite biology [\u003cspan additionalcitationids=\"CR22 CR23 CR24 CR25 CR26 CR27\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. MOI is an expected consequence of insect vectors taking more than one blood meal (including from different infected individuals) and important for the resulting meiotic recombination within the vectors [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Both MOI and allopolyploidy will result in \u003cem\u003ecomplex isolates\u003c/em\u003e, with more than two haplotypes being present in a single sample.\u003c/p\u003e \u003cp\u003eThe complexity of natural infections is relevant to understanding Trypanosomatid biology and disease control, as MOI cases provide direct evidence for genetically diverse infections that could increase the speed in which virulence and drug resistance genes may be shared in the population, may be more challenging to treat, and may result in diverse clinical presentations. In general, parasite diversity allows sub-populations to be selected in different environments, increasing adaptability [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMOI has already been described in \u003cem\u003eLeishmania\u003c/em\u003e infections [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], where there is usually a dominant genotype combined with rare genotypes of the \u003cem\u003esame\u003c/em\u003e species; and in the insect vector [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], where \u003cem\u003edifferent\u003c/em\u003e species of the parasite may cohabit the same insect [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], which can result in interspecies hybrids [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Multiclonal infections were also described in \u003cem\u003eT. cruzi\u003c/em\u003e using microsatellite and marker genes, where it appears to be more prevalent in mammalian reservoirs, such as rodents and opossums, when compared to human patients [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. There is also evidence of MOI in \u003cem\u003eT. brucei\u003c/em\u003e in the mammalian host [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], and in the inset vector [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In \u003cem\u003eT. brucei\u003c/em\u003e, coinfection with two strains in the mammalian host leads to competitive suppression, enhancing host survival [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], reinforcing that MOI may impact patient clinical outcomes in these parasites.\u003c/p\u003e \u003cp\u003eHybridization leading to temporary trisomy/tetraploidy was already demonstrated in Trypanosomatids. In \u003cem\u003eT. cruzi\u003c/em\u003e, experimental hybrids originated from diploid parental strains were mostly tetraploid, and underwent genome erosion throughout culture passages, reverting to trisomy [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In \u003cem\u003eLeishmania\u003c/em\u003e, hybridization was shown to generate diploid, triploid or tetraploid strains [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], both in intra species [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], as well as between species hybrids [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. This transient presence of four haplotypes (in allotetraploids) in a single cell might increase genetic exchange and recombination, increasing the potential variability, as the parasites revert back to trisomy and disomy by genome erosion. In the context of this analysis, only allotetraploids (containing two different diploid genotypes) will be detected, not autotetraploids (containing two copies of the same diploid genotype).\u003c/p\u003e \u003cp\u003eIn the present work, we have developed a methodology to identify multiclonal infections and polyploidy in any diploid species using Whole Genome Sequencing (WGS) reads, based on fluctuations in allelic read depth in heterozygous positions, which can be easily implemented in experiments sequencing genomes of one or a few samples, or larger population surveys. This methodology uses the complexity index (CI) proposed in Franssen \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. We parameterize this metric by comparing the allelic read depth at heterozygous sites in real samples to simulated clonal infections, which were generated using allelic read depths sampling by binomial trials to generate stochastic allelic depths. This approach was used to assess the complexity of infection in 530 Trypanosomatid isolates from four species/complexes, \u003cem\u003eL. donovani/L. Infantum\u003c/em\u003e (\u003cem\u003eL. donovani\u003c/em\u003e complex), \u003cem\u003eL. braziliensis\u003c/em\u003e, \u003cem\u003eT. cruzi\u003c/em\u003e and \u003cem\u003eT. brucei\u003c/em\u003e based on genome-wide markers, providing a large overview of multiclonal infection and polyploidy in these parasites. We show that our method robustly detects complex infections with at least \u003cb\u003e25x\u003c/b\u003e coverage and at least 100 heterozygous SNPs. We find that a relatively small proportion (\u0026le;\u0026thinsp;7%) of cultured Trypanosomatid isolates are complex. For methodological reasons, these proportions represent a lower bound of complex infections in these species.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Overview\u003c/h2\u003e \u003cp\u003eWe define the complexity index (CI) as the deviation from the expected 50% of reads in each allele in heterozygous positions, as proposed in Franssen 2021 [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. It is estimated by the absolute value of the difference between the alternate allele read depth (ARRD) in heterozygous positions and 0.5, the expected AARD in diploid, clonal heterozygous SNPs. To estimate the CI of an isolate, we have carefully filtered SNP calls, removing SNPs in repetitive regions, aneuploid chromosomes, duplicated genes and samples with low coverage.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Heterozygous SNP calling and alternate allele read depth (AARD) estimation\u003c/h2\u003e \u003cp\u003eRepresentative whole genome sequencing (WGS) read data from Trypanosomatid isolates were downloaded from the National Centre for Biotechnology Information (NCBI) Sequence Read Archive (SRA) using Fastq-dump [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Only Illumina sequencing reads from publicly available datasets were used (Supplementary_Table_1, Supplementary_Table_2 and Supplementary_Table_3). Each read library was filtered using fastp v2.10.7 [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], with the parameters: average Q20, minimal length 50 and removing the read extremities with base quality lower than Q25. Next, for each species the reads were mapped to an appropriate reference genome, listed in Supplementary_table_4, using BWA-mem v.0.7.17 [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], retaining only reads with mapping quality 30 or higher and removing PCR duplicates using SAMtools v.1.10 [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The number of mapped reads was estimated using SAMtools v.1.10. The genome coverage was estimated by the mean coverage of all single copy genes in the genome, using SAMtools depth. The single copy genes were selected using OrthoFinder v. 2.5.4 [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor the SNP calls, read groups were assigned for the filtered mapped read libraries, using PicardTools v.2.21.6 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/broadinstitute/picard\u003c/span\u003e\u003cspan address=\"https://github.com/broadinstitute/picard\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). SNPs and indels were called using the Genome Analysis Toolkit (GATK) v.4.1.0.0 HaplotypeCaller and Freebayes v. 1.3.5 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/ekg/freebayes\u003c/span\u003e\u003cspan address=\"https://github.com/ekg/freebayes\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), \u003cb\u003ewith a minimum alternative allele read count of 5.\u003c/b\u003e Only SNP/Indel positions that were identified by both callers were kept. For each dataset, the single-sample VCFs were merged with VCFtools v.0.1.16 and regenotyped using Freebayes. Next, the VCF file was filtered using BCFtools v.1.12 [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], to select only biallelic SNPs, with call quality above 200, coverage greater than half of the mean genome coverage (i.e, at least haploid), and lower than twice the genome coverage (i.e. is not duplicated) with mapping quality 40 or higher and properly paired reads (-m2 -M2 -i ' TYPE=\"snp\" \u0026amp; QUAL \u0026gt; 200 \u0026amp; INFO/DP \u0026gt; Cov/2 \u0026amp; INFO/DP \u0026lt; Cov*2 \u0026amp; INFO/MQM \u0026gt; 40 \u0026amp; INFO/MQMR \u0026gt; 40 \u0026amp; INFO/PAIRED \u0026gt; 0.9 \u0026amp; INFO/PAIREDR \u0026gt; 0.9 ). The only exception was the \u003cem\u003eT. cruzi\u003c/em\u003e dataset, as several samples were single-end reads, so the “INFO/PAIRED \u0026gt; 0.9 \u0026amp; INFO/PAIREDR \u0026gt; 0.9” were not used. To remove SNP call bias from repetitive regions and paralogous genes, only SNPs in single copy genes were used in subsequent analysis. After filtering, the multisample VCF was split into single sample VCFs, to be used in the complexity pipeline (see below). For the individual sample VCFs, only SNP positions with read depth ≥ 5 in both the reference and alternate alleles were considered as heterozygous. SNPs where the read depth in one allele was \u0026gt; 5, and between 1 and 4 in the other allele were classified as dubious, and not used in the complexity estimation. This was a conservative measure to remove potential noise and sequencing/mapping errors.\u003c/p\u003e \u003cp\u003eTo control the bias of aneuploidy in the CI estimation, chromosome(s) with coverage higher than 1.15x or lower than 0.85x of the genome coverage in a sample were excluded from downstream analysis. Similarly, to mitigate bias from gene copy number variants (CNVs), SNPs in genes with coverage higher than 1.15x or lower than 0.85x of its chromosome coverage were also removed. The gene coverage was estimated using SAMtools depth and the gene coordinates from the General Feature Format (GFF) obtained in TriTrypDB v.55. The chromosomal somy for each sample was estimated using the median read depth coverage of single copy genes in each chr with non-outlier coverage (Grubb’s tests, with P \u0026lt; 0·05), normalised by genome coverage. Data from \u003cem\u003eLeishmania\u003c/em\u003e and \u003cem\u003eTrypanosoma cruzi\u003c/em\u003e chromosomes 31 were always excluded, as they are consistently supernumerary in all isolates from these species [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Only read libraries with genome coverage ≥ 25x were used in posterior analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Complexity evaluation, Cochran-Mantel-Haenszel (CMH) estimation and AARD distribution\u003c/h2\u003e \u003cp\u003eThe classification of an isolate as complex was based on comparisons between the real data with simulated clonal isolates. Samples that were classified as complex had to have: A higher CI than clonal simulated isolates, a significant CMH p-value associating the real sample to deviations from the expected allele read counts, and an alternate allele read depth (ARRD - the read depth proportion (0 to 1) that corresponds to the alternate allele in a SNP position) distribution that deviates from the simulated clonal isolate. Only isolates that were above both Complexity and CMH cutoffs were assumed to be complex. Details are described below.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eComplexity\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eFor each SNP site \u003cem\u003ei\u003c/em\u003e, \u003cem\u003eCIi\u003c/em\u003e is the absolute value of the difference between the AARD in that position and the expected AARD in diploid, non-mixed SNP positions, within a sample (which is expected to be close to 0.5). To account for the random sampling of reads sequenced from each allele of heterozygous sites, a simulated “clonal-diploid” SNP data sample was generated for each isolate in each population, with the same number of SNPs and read depth as in the real sample, using series of binomial trials. For each SNP position (\u003cem\u003ei\u003c/em\u003e) in the real sample, we conducted \u003cem\u003en\u003c/em\u003e binomial trials, by randomly sampling from a binary array (0 or 1), where 0 represents the reference allele and 1 the alternate allele, where \u003cem\u003en\u003c/em\u003e is the read depth in the position in the real sample. The \u003cem\u003eAARDi\u003c/em\u003e for the \u003cem\u003ei\u003c/em\u003eth position in the simulated clone was the sum of the binomial trials (\u003cem\u003eb\u003c/em\u003e), divided by the total read coverage at site \u003cem\u003ei\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e);\u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$ARRDi = \\frac{{\\sum }_{1}^{n}b }{n}$$\u003c/div\u003e \u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eand the complexity index of this position (\u003cem\u003eCI\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e) was calculated as the absolute difference between the expected AARD of 0.5\u003c/p\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$CIi = \\left|ARRDi - 0.5\\right|$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e \u003cp\u003eThe CI of the isolate with \u003cem\u003ei\u003c/em\u003e heterozygous SNPs is calculated as the mean of all \u003cem\u003eCIi\u003c/em\u003e values (R script available in GitHub: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/jaumlrc/Complex-Infections.git\u003c/span\u003e\u003cspan address=\"https://github.com/jaumlrc/Complex-Infections.git\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). To classify an isolate as “potentially complex” the CI had to be higher than the mean + 3 standard deviations (SD) from all simulated clonal isolates in the population. For an isolate to be classified as “complex” it had to have a CI value \u0026gt; 0.1, which is slightly higher than the cutoff for the simulated data for all trypanosomatid populations (see results section). We recommend the CI threshold of 0.1 be used to classify samples in projects with a small number of samples.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCMH test\u003c/b\u003e: Another metric used to assess the isolate complexity was the CMH test, which tests the association between binary predictors (expected counts of reference and alternate alleles to generate the expected ARRD of 0.5) and binary outcomes (observed counts of reference and alternate alleles) considering stratification from a third variable (in our case the position in the genome). In this case, it was used to compare the combined effect of all SNP read depth in each allele to classify an isolate as complex, comparing real samples and binomial trial simulated clonal-diploid samples. For each isolate with \u003cem\u003ei\u003c/em\u003e heterozygous SNP positions, CMH p-values were generated using \u003cem\u003ei\u003c/em\u003e 2x2 contingency tables using row 1; actual read depth of each allele at position \u003cem\u003ei\u003c/em\u003e, row 2; the result of \u003cem\u003en\u003c/em\u003e binomial trials (where \u003cem\u003en\u003c/em\u003e is the total read depth at site \u003cem\u003ei\u003c/em\u003e), and columns being the reference and alternate allele counts. We found that a significance level of p ≤ 10\u003csup\u003e− 10\u003c/sup\u003e was a reasonable CMH test threshold Supplementary_Figure1. R scripts for both tests are available on GitHub (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/jaumlrc/Complex-Infections.git\u003c/span\u003e\u003cspan address=\"https://github.com/jaumlrc/Complex-Infections.git\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eAARD distribution\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eThe AARD distribution for all SNP positions in the real sample and its paired simulated clonal sample were generated in R, and deviations from their distributions were accounted as evidence for complexity.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Assessing complexity on mixed samples\u003c/h2\u003e \u003cp\u003eTo estimate the accuracy of the combined CI and CMH tests to identify multiclonal infections with different proportions of the secondary clone, a collection of 24 \u003cem\u003eL. donovani\u003c/em\u003e clones from East Africa (EA) described in Franssen 2021 were used (Supplementary_table_1). To create artificial multiclonal samples and assess the impact of the proportion of a secondary clone in complexity estimations, three clones, ERR205809, ERR205816 and ERR205819, were selected. Each of these three read libraries was combined with the full library of another of these three clones, where the secondary strain was downsampled to 2.5, 5, 10, 15 and 50% of the full combined data, using SAMtools v.1.9 [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], to create ‘MIX’ samples. In each case, the main and secondary clones were permuted, generating a total of 33 combinations. The results from these artificially multiclonal samples were compared with those obtained from clones and simulated clonal isolates, based on their complexity index and CHM test, as previously described.\u003c/p\u003e \u003cp\u003eThe impact of the number of heterozygous SNPs in the CI evaluation was examined by sub-sampling the number of SNP calls at random to 10, 50, 100, 300 SNPs, in 100 iterations, assessing the number of true positives (number of MIX samples that were classified as complex) and comparing with the estimations with the full set of SNPs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Assessing complexity on polyploid samples\u003c/h2\u003e \u003cp\u003eBesides multiclonality, another source of complexity is polyploidy, having extra full sets of chromosomal copies. To evaluate the impact of polyploidy in the CI estimations, we used samples from the \u003cem\u003eT. cruzi\u003c/em\u003e dataset described in Matos 2022 [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], containing 8 diploid parental clones, and 11 triploid or tetraploid hybrids clones, where the somy of some samples were validated by flow cytometry [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] (Supplementary_Table_2). As performed for the multiclonal isolates, the SNPs counts for each sample were downsampled to 10, 50, 100, 300 or full set, in 100 iterations, and the accuracy to classify each group as complex was evaluated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Assessing complexity on laboratory and field isolates\u003c/h2\u003e \u003cp\u003eAfter validating our complexity estimations with simulated/controlled data, we went on to evaluate field isolates (FI) and lab-derived parasites that were available in NCBI SRA. We estimated the complexity of four Trypanosomatid species/complexes: \u003cem\u003eL. donovani/L. Infantum\u003c/em\u003e (\u003cem\u003eL. donovani\u003c/em\u003e complex), \u003cem\u003eL. braziliensis\u003c/em\u003e, \u003cem\u003eT. cruzi\u003c/em\u003e and \u003cem\u003eT. brucei\u003c/em\u003e, using a total of 573 WGS data sets from publicly available field isolates and laboratory strains (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Supplementary_Table_3). Only read libraries from samples with read coverage ≥ 25x and where at least 100 heterozygous SNPs were called where used in this analysis. For these samples, the whole genome complexity, as well as the complexity estimation for each chromosome was estimated. The proportion of chromosomes that were used in the complexity estimation was calculated for each isolate by dividing the number of chromosomes that were used in complexity estimates (had at least one identifiable SNP and were not aneuploid) by the total number of chromosomes in the species: \u003cem\u003eL. donovani\u003c/em\u003e = 36; \u003cem\u003eL. braziliensis\u003c/em\u003e = 35; \u003cem\u003eT. brucei\u003c/em\u003e = 11; \u003cem\u003eT. cruzi\u003c/em\u003e = 41. The proportion of complex chromosomes in an isolate was estimated dividing the number of chromosomes with complexity index ≥ 0.1 by the number of evaluated chromosomes. In practise, most chromosomes were utilised in all CI estimates. Exceptions are isolates with several duplicated chromosomes.\u003c/p\u003e \u003cp\u003eThe evaluation of statistical differences in the genome coverage and heterozygous SNPs/Kb in complex and non-complex isolates was performed with Mann–Whitney U test, in R. The Pearson correlation between the genome coverage, SNPs/Kb and complexity was estimated in R. For both analyses the heterozygous SNPs/Kb were estimated dividing the heterozygous SNP numbers by the sum of the lengths of the single copy genes, in each set.\u003c/p\u003e \u003cp\u003eWe also estimated the number of heterozygous SNPs in the maxicircle (kDNA) genome, as well as its coverage (as described for the nuclear genome). To identify kDNA SNPs, the maxicircle sequence (\u003cem\u003eL. donovani\u003c/em\u003e = BK010877.1, \u003cem\u003eL. braziliensis\u003c/em\u003e = OY748431, \u003cem\u003eT. cruzi\u003c/em\u003e = MW732647, and \u003cem\u003eT. brucei\u003c/em\u003e M94286.1; downloaded from NCBI) was combined with the genome reference for the read mapping. Only heterozygous SNPs that were outside the repetitive region and had at least 5% of the kDNA genome coverage were considered.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e "},{"header":"Results","content":"\u003ch2\u003e3.1 Genomic phenomena that might alter Trypanosomatid isolate complexity\u003c/h2\u003e\u003cp\u003eThe proportion of reads in each allele in a heterozygous SNP position can be impacted by several phenomena, including multiclonality (multiple clones or genotypes in a sample, often present in different proportions), polyploidy (multiple copies of all chromosomes) and aneuploidy (multiple copies of some chromosomes). (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In a non-complex clonal, euploid, diploid isolate, the mean alternate allele read depth (AARD), meaning the proportion of reads that correspond to the alternate allele in heterozygous positions, is expected to be close to 0.5, as there will be a similar number of reads mapping in both alleles (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Hence, when all heterozygous SNPs in a genome are evaluated, the distribution of the AARDs will have a peak in 0.5, with a distribution of AARD values expected from ‘random draws’ of reads calling each allele. Some phenomena that have already been observed in Trypanosomatids, such as multiclonality (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e-B), polyploidy (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e-C) and aneuploidy (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e-D) will alter this proportion, changing the distribution peaks or flattening their curve, which may be seen in density plots of ARRD values for all heterozygous SNPs in a sample.\u003c/p\u003e\u003cp\u003eTo provide a numeric and statistical large-scale evaluation of this deviation from expected AARD, we estimated CI: the absolute value of the deviation from the expected 0.5 proportion in each heterozygous SNP position [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]; and compared real samples with simulated clonal isolates at individual and populational level. To exclude deviations from AARD due to paralogous genes and aneuploidy, we included only single-copy gene regions and excluded chromosomes with somy variation and gene duplication/losses. We have estimated cutoffs for complex isolates based on the mean complexity of simulated clonal isolates from population genomic data from various Trypanosomatid clades, and used the Cochran-Mantel-Haenszel (CMH) test to support the evaluation in each isolate.\u003c/p\u003e\u003cp\u003e \u003cb\u003e3.2 Assessing the accuracy of the CI to identify multi-clonal and polyploid isolates, using simulated or controlled data\u003c/b\u003e \u003c/p\u003e\u003cp\u003eTo evaluate the accuracy of the CI metric to identify multiclonal isolates, we created sequencing read data sets to represent multiclonal isolates from \u003cem\u003eL. donovani\u003c/em\u003e, by combining downsampled read files from laboratory cloned field isolates in various proportions. We evaluated the two features that could impact the complexity estimations in multiclonal infections: the proportion of the secondary clone and the number of heterozygous SNP positions.\u003c/p\u003e\u003cp\u003eTo simulate multiclonal isolates with different proportion of the secondary clones, WGS data from three \u003cem\u003eL. donovani\u003c/em\u003e lab-derived isolates that have been cloned were used: ERR205809, ERR205816 and ERR205819 [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Reads from these clones were combined pairwise in proportions of 2.5, 5, 10, 15, 25 and 50% of the reads from the secondary clone, resulting in 33 datasets. The complexity of these mixed samples (MIX) and the 24 clones from the Frassen 2021 [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] dataset was assessed based using two parameters: the \u003cb\u003eCI\u003c/b\u003e: which had to be higher than the mean + 3 standard deviations (SDEV) from the simulated clonal isolates in the population; and \u003cb\u003eCMH\u003c/b\u003e test to evaluate if the real isolate AARD differs from the expected clonal isolate, with a p-value lower than 10\u003csup\u003e− 10\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Based on these cutoffs, zero clones (0%), and 26 (79%) of the MIX samples were classified as complex isolates. When evaluated separately, the CI parameter was the most specific, as only one clone was classified as complex (false positive), compared to 3 for CMH. However, CI was the least sensitive, as it only classified 26/33 (79%) MIX as complex, when compared to 31/33 (94%) for CMH (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and B, Supplementary Figure_2).\u003c/p\u003e\u003cp\u003eThe complexity estimation accuracy was greatly influenced by the proportion of the secondary isolate, where lower proportions resulted in false negative results. None of the six MIX samples where the secondary clone read proportion was 2.5% was classified as complex. Increasing the proportion of the secondary clone resulted in higher accuracy, where five of the six samples where the secondary clone corresponded to 5% of the reads, and all samples where the secondary clone had 10–50% of the reads were classified as complex (Supplementary Figure_2). This was expected, as a low proportion of the secondary clone had a low impact in AARD distributions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Hence, our method can detect complex isolates when the secondary clone represents at least 5–10% of the reads.\u003c/p\u003e\u003cp\u003eTo evaluate the impact of the number of heterozygous SNPs in the complexity estimation, the heterozygous SNP counts for each MIX sample was downsampled to 10, 50, 100, 300 or full set, and the accuracy to classify each group as complex was assessed (Supplementary Figure_2). To remove potential sampling bias, the analysis was repeated in 100 iterations, re-sampling random SNP positions each time, and the final results are a combination of all iterations. When compared with the full dataset, which had between 978 and 5910 SNPs, the use of 10 SNPs resulted in poor accuracy in all proportions of the secondary clone. By using 100 and 300 SNPs, the results were similar to those observed for the full set, with lower accuracy only for samples with ~ 5% of the reads originating from the secondary clone (Supplementary Figure_2, Supplementary_table_5). Hence, the complexity index estimation requires 100 or more heterozygous SNPs to be accurate.\u003c/p\u003e\u003cp\u003eBesides multiclonality, another source of complexity is polyploidy, having extra full sets of chromosomal copies. To evaluate the impact of polyploidy in the complexity estimations, we used the \u003cem\u003eT. cruzi\u003c/em\u003e dataset described in Matos 2022, [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], containing 8 diploid parental clones, and 11 triploid or tetraploid hybrids clones, where the somy of some were validated by flow cytometry Supplementary_Table_2. As performed for the multiclonal isolates, the SNPs counts for each sample were downsampled to 10, 50, 100, 300 or full set, in 100 iterations, and the accuracy to classify each group as complex was evaluated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC and D; and Supplementary Table\u0026nbsp;6).\u003c/p\u003e\u003cp\u003eUsing the combination of CI and CHM cutoffs, on average 4.4, 73.5, 82.5, 90.9 and 100% of the polyploid isolates, respectively for the 10, 50, 100, 300 or the full set of SNPs were correctly classified as complex. No parental diploid clones were classified as complex in any replicate. As expected, the triploid isolates had a distribution of AARD with peak distributions in 0.33 and 0.66, while the tetraploid had peaks in 0.25, 0.5 and 0.75. Both triploid and tetraploid isolates had an CI higher than the observed for the diploid isolates (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC and D, Supplementary_Figure 3). These results suggest that the complexity estimate can also be used to identify polyploid isolates with reasonable sensitivity (~ 80%) when 100 or more heterozygous SNPs are present.\u003c/p\u003e\u003cp\u003eBased on these results, we decided to use the combined results of CI and CHM tests to identify complex isolates, and to only evaluate samples with 100 or more SNPs. A conservative approach that minimises false positives, accepting some false negatives, especially in cases where the secondary clone proportion is low.\u003c/p\u003e\u003ch2\u003e3.3 Complexity evaluation among Trypanosomatid species:\u003c/h2\u003e\u003cp\u003eAfter establishing the accuracy of the CI metric to identify multiclonal and polyploid samples with simulated and controlled data, we estimated the complexity in a total of 530 laboratory and field isolates from \u003cem\u003eL. donovani\u003c/em\u003e, \u003cem\u003eL. braziliensis\u003c/em\u003e, \u003cem\u003eT. brucei\u003c/em\u003e and \u003cem\u003eT. cruzi\u003c/em\u003e, identifying a total of 28 complex isolates (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Supplementary Figs.\u0026nbsp;4–7). The CI cutoff was similar among the evaluated species, with the lowest value in \u003cem\u003eT. cruzi\u003c/em\u003e (0.072) and the highest in \u003cem\u003eL. braziliensis\u003c/em\u003e (0.089), which supports the robustness of the method. We propose a global cutoff of 0.1 (slightly higher than the highest cutoff, in \u003cem\u003eL. braziliensis\u003c/em\u003e) as a value that may be used to classify any Trypanosomatid isolate, or possibly other diploid eukaryotic samples, as complex, which will allow any researcher to classify single isolates without the need of population data to estimate a custom cutoff. Samples with CI values lower than the global cutoff but still higher than their species cutoff were classified as “potential complex” and evaluated separately. Only three potentially complex isolates were identified, two in \u003cem\u003eT. cruzi\u003c/em\u003e and one in \u003cem\u003eL. donovani\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eEven though we removed aneuploid chromosomes that had deviations from the mean genome coverage from each isolate, the intra isolate chromosome mosaicism (mosaic aneuploidy, and chromosome imbalance) [\u003cspan additionalcitationids=\"CR48 CR49 CR50\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e–\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] may add noise to complexity measurements in field isolates, by having unbalanced values in a few chromosomes. Hence, we are only considering as “complex”, isolates that had at least 50% of its evaluated chromosomes with a mean complexity value higher than 0.1.\u003c/p\u003e\u003cp\u003eThe proportion of isolates that were classified as complex varied across clades, where \u003cem\u003eT. cruzi\u003c/em\u003e and \u003cem\u003eT. brucei\u003c/em\u003e had the lowest (~ 2.5%) and \u003cem\u003eL. braziliensis\u003c/em\u003e had the highest (30%) proportion of complex samples in the evaluated dataset. Complexity values also varied in different isolates, where the lowest value was observed in \u003cem\u003eT. brucei\u003c/em\u003e SRR17479767 (0.025), and the highest in \u003cem\u003eL. donovani\u003c/em\u003e ERR205770 (0.398). Complex isolates have more heterozygous SNPs than non complex samples (Mann-Whitney p-value = 0.003), especially for \u003cem\u003eL. braziliensis\u003c/em\u003e (Mann-Whitney p-value 2.87 x 10\u003csup\u003e− 6\u003c/sup\u003e) and \u003cem\u003eT. brucei\u003c/em\u003e (Mann-Whitney, p-value 0.0074). This increase was not observed in the \u003cem\u003eL. donovani\u003c/em\u003e evaluated samples (Supplementary_Figure_8). The increase in overall heterozygous SNP counts may reflect the presence of multiclonal infections, where using WGS bulk data, clone specific homozygous SNPs are interpreted as heterozygous SNPs, increasing their counts; or allopolyploids.\u003c/p\u003e\u003cp\u003eThe genome coverage from each set also varied, and this might have an impact on the CI limit of detection, as we only classified SNPs with coverage ≥ 5 in both alleles as heterozygous to be used in the complexity estimations. The median of the genome coverages for each dataset was 39 for \u003cem\u003eL. braziliensis\u003c/em\u003e, 31 for \u003cem\u003eL. donovani\u003c/em\u003e, 56 for \u003cem\u003eT. brucei\u003c/em\u003e and 45 for \u003cem\u003eT. cruzi\u003c/em\u003e (Supplementary_Figure_8 G), which due to our cutoff of at least 5 reads in the rarer allele, would only allow the identification of multiclonal infections where the secondary clone proportion of reads was respectively of at least 17%, 16%, 8% and 11%.\u003c/p\u003e\u003cp\u003eWhen each dataset was evaluated separately, from the 85 evaluated \u003cem\u003eL. donovani\u003c/em\u003e samples, 6 were classified as complex (7%), and one as potentially complex. Among the 6 complex isolates, three ERR205724 (MHOM/SD/82/GILANI), ERR205770 (MHOM/IT/02/ISS2429) and ERR205774 (MHOM/BR/2003/MAM), were already classified as multiclonal by Fransen 2020 [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], and one, ERR3956121 (1052_ToD_1_primary_neg), was classified as complex by Frassen 2021. In fact, ERR205774 also presented a higher count of heterozygous SNPs in the maxicircle sequence, which further corroborates that it is a multiclonal infection (Supplementary Figure_9). Two isolates, ERR205748 (MHOM/CY/2006/CH32) and ERR205789 (MHOM/SD/62/LRC-L61), were classified by Frassen 2020 as hybrids, and had a ARRD distribution compatible with triploidy in our analysis. Hybridizations in trypanosomatids may result in polyploid lineages, which might revert back to diploidy by genome erosion [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The sample that was classified as potential complex ERR3956143 (1073_ToD_1_primary_neg), corresponds to a field isolate obtained from a patient from Ethiopia, which might be multiclonal.\u003c/p\u003e\u003cp\u003eFor the \u003cem\u003eL. braziliensis\u003c/em\u003e dataset, from the 42 evaluated samples, 13 were classified as complex and all had previous evidence of being polyploid (30%). From these 13, 10 corresponded to experimental tetraploid hybrids, described in [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], while SRR21604774 corresponded to a triploid \u003cem\u003eL. braziliensis\u003c/em\u003e and \u003cem\u003eLeishmania guyanensis\u003c/em\u003e hybrid. Finally the last two samples, ERR2508271 and ERR2508272, correspond to read libraries used in the assembly of the triploid \u003cem\u003eL. braziliensis\u003c/em\u003e M2904 genome [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. We found no strong evidence of multiclonal infections in any of the evaluated \u003cem\u003eL. braziliensis\u003c/em\u003e samples.\u003c/p\u003e\u003cp\u003eFrom the 211 \u003cem\u003eT. cruzi\u003c/em\u003e evaluated samples, five were classified as complex, and two were classified as potential complex. From the complex set, three were isolated from the insect vector: SRR8503553 (\u003cem\u003ePanstrongylus lignarius\u003c/em\u003e in Peru); SRR3676272 and SRR3676273 (\u003cem\u003eTriatoma dimidiata\u003c/em\u003e in Texas) [\u003cspan additionalcitationids=\"CR56\" citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e–\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. The AARD density peaks in these three samples are similar to those expected for triploid isolates (0.33 and 0.66), suggesting that they are polyploid. The other two complex samples were isolated from chronic chagasic human patients in Panama (SRR3676281, SRR3676310) [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], and had AARD peaks that are not similar to what is expected for tri or tetraploid isolates. This suggests that they might be multiclonal infections. In fact, SRR3676310 had also a higher count of heterozygous SNPs in the maxicircle sequence when compared to other \u003cem\u003eT. cruzi\u003c/em\u003e isolates (Supplementary_Figure_9), which further support that it is potentially a multiclonal infection.\u003c/p\u003e\u003cp\u003eFinally, for \u003cem\u003eT. brucei\u003c/em\u003e we identified 4 complex isolates in a dataset of 159 samples. From those, SRR17479764 corresponds to a triploid hybrid from the J10 and KETRI 1738 strains [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], while SRR17479766 was previously suggested to be a multiclonal infection [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. The final two complex \u003cem\u003eT. brucei\u003c/em\u003e strains, ERR270813 and SRR6052140 have AARD profiles that are similar to what is respectively expected for tetraploid and triploid isolates.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eComplexity evaluation of each Trypanosomatid group of samples.\u003c/b\u003e In the Complex samples column, the number in parentheses indicates the number of potential complex samples.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003cp\u003enumber\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCI threshold\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMax. CI\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin. CI\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eComplex samples\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eComplex\u003c/p\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAssessment\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eL. donovani\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.398\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6 (+ 1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4 multiclonal\u003c/p\u003e \u003cp\u003e2 polyploid\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eL. braziiensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13 polyploid\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eT. brucei\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e159\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 multiclonal\u003c/p\u003e \u003cp\u003e3 polyploid\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eT. cruzi\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e211\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5 (+ 2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2 multiclonal\u003c/p\u003e \u003cp\u003e(chronic cases)\u003c/p\u003e \u003cp\u003e3 triploid (insect source)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSimulated\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolyploid\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eTaken together these results suggest that complex isolates represent a small percentage of the cultured field isolates for all the TriTryp species evaluated. This corresponds to the lower bound of potential complex infections compared to what is observed in natural conditions due to limitations in parasite isolation, culture and a low proportion of the secondary clone, which hampers complexity detection by our method. We suggest that the CI should be estimated in all new WGS from Trypanosomatids isolates obtained in the future, to identify polyploid/multiclonal isolates before proceeding with downstream analysis. To facilitate this, we provide the R code for this method on github (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/jaumlrc/Complex-Infections.git\u003c/span\u003e\u003cspan address=\"https://github.com/jaumlrc/Complex-Infections.git\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which requires only a variant call format (VCF) file to produce complexity estimates.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the present study, we identified and characterised two genome modifications that result in more than two haplotypes being present in a single parasite isolate in Trypanosomatids: multiclonal infections and polyploidy. We developed and validated a method to assess the complexity of Trypanosomatid samples based on WGS reads, and implemented the method to evaluate complexity in a representative collection of \u003cem\u003eLeishmania\u003c/em\u003e, \u003cem\u003eT. cruzi\u003c/em\u003e and \u003cem\u003eT. brucei\u003c/em\u003e field-isolates and lab strains. Our method only uses chromosomes with similar somy as the genome ploidy, and removes genes with evidence of duplication/loss, as these could be confounding factors in the estimations. We have identified complex (polyploidy or multiclonal) infections in all evaluated species, and proposed a global complexity index cutoff that can be used in any Trypanosomatid single sample, and likely other diploid eukaryote samples. We provide an R script that can estimate complexity directly from VCF files (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/jaumlrc/Complex-Infections.git\u003c/span\u003e\u003cspan address=\"https://github.com/jaumlrc/Complex-Infections.git\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn the last decade, the reduction in sequencing costs and the relevance of questions that may be answered with genomic data have resulted in a large increase in the number of studies that generate population WGS data for trypanosomatid parasites [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan additionalcitationids=\"CR60 CR61 CR62 CR63\" citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e–\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. However, the occurrence of multiclonal infection and polyploidy is not always assessed in these studies. Complex infections also occur in bacterial infections, viruses and some protozoan parasites as \u003cem\u003ePlasmodium\u003c/em\u003e, where the main stage that infects humans and other mammalian hosts is haploid [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Trypanosomatid parasites are usually diploid, and often aneuploid and/or polyploid [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e], where the somy of different chromosomes can vary even within clones [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. This increases the challenge of estimating complex infections in these parasites. Hence, a method is needed to identify complex infections using WGS in these species at scale, that takes into account gene copy number variants and aneuploidy, which might be used to evaluate publicly available datasets, as well as future in projects.\u003c/p\u003e\u003cp\u003eBy using WGS reads, the method that we propose has the advantage of assessing genome-wide SNP variation as evidence for complexity simply and robustly [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. When compared to methods based on microsatellite loci and marker genes [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e–\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], the use of WGS data allows the removal of aneuploid chromosomes and duplicated genes by read depth values, which may add noise if not removed. A clonal aneuploid isolate with a trisomic chromosome containing three different alleles (one in each chromosome) in a microsatellite locus might be classified as “multiclonal” in microsatellite analysis, for having more than two alleles. This is especially relevant if the marker is in the \u003cem\u003etrypanosomatid ancestral supernumerary chromosome\u003c/em\u003e (TASC), which has been shown to have four stable copies and a higher sequence variability when compared to other chromosomes [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. By evaluating the complexity in each euploid chromosome from an isolate we could separate complex infections (multiclonal/polyploid) from “chromosome instability” (CIN) and “mosaic aneuploidy” events [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. This was achieved by only classifying as complex field isolates that have complexity evidence supported by at least half of the evaluated chromosomes.\u003c/p\u003e\u003cp\u003eIn the current study, we identified a low proportion of complex infections in all Trypanosomatid field isolates and lab derived strains, with clear perturbations of the AARD distributions, and high CI values. We identified around 7% of complex infections for the \u003cem\u003eL. donovani\u003c/em\u003e group, where 4 isolates had evidence of multiclonal infections and 2 isolates had evidence of polyploidy. This is in accordance with what was identified in [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], where even though different \u003cem\u003eLeishmania\u003c/em\u003e genotypes were identified in different tissues, the number of isolates with MOI in the same tissue was low. For \u003cem\u003eT. cruzi\u003c/em\u003e, we identified a very low proportion of complex infections (~ 2%), which is lower than the ~ 15–17% that was reported in the literature for inter Discrete Typing Units (DTU) [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e] mixed infections in human patients from Latin America [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]; and to the ~ 13% of MOI in the vector \u003cem\u003eTriatoma infestans\u003c/em\u003e [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. This might be caused in part as a significant proportion of the evaluated \u003cem\u003eT. cruzi\u003c/em\u003e isolates were cloned prior to sequencing [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e], which would remove multiclonal infections but not polyploidy. As cloned samples could be polyploid, they were still evaluated in this work. Although most of the \u003cem\u003eT. cruzi\u003c/em\u003e isolates had an AARD distribution that matched the expectation from a “non-complex clonal, euploid, diploid isolate”, there were some non-complex isolates with perturbations in the AARD distribution and high CI; such as SRR3676315, SRR3676316, SRR3676317, SRR3676318, SRR3676319; that had a distribution pattern similar to the potential complex isolate SRR3676320. These isolates had a high CI in less than half of the evaluated chromosomes, which suggests that they have a high level of mosaic aneuploidy and CIN [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan additionalcitationids=\"CR50\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e–\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], rather than being polyploid or multiclonal infections. \u003cem\u003eT. brucei\u003c/em\u003e isolates also had a low proportion of complex infections identified in the WGS data (~ 2%) with only one isolate with strong evidence of multiclonal infection, which is lower than the 8–20% of multiclonal infections reported in humans and vector infections in East Africa [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePotential limitations of complexity estimation based on WGS are data collection, processing and genome coverage. Most Trypanosomatid WGS data is obtained from parasites that are isolated from the host and cultured in axenic media or used to infect mice, which might reduce complexity when compared to the variation present in the patient [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Different strains might have different growth rates in media, and secondary clones in low proportions might be outcompeted in culture. The methodologies to sequence parasite genomes directly from patient tissue, such as Selective Whole Genome Amplification (SWGA) [\u003cspan additionalcitationids=\"CR73\" citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e–\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e], SureSelect [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e] and Nanopore adaptive sampling [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e] are improving in the last years, and might allow WGS in Trypanosomatids to be done in large scale without a culturing step in a affordable way in the future. This might allow complex infection assessments using WGS data to be performed directly from host tissues, as long as allelic proportions are not altered by these methods.\u003c/p\u003e\u003cp\u003eAnother potential limitation of complexity estimations in multiclonal infections in WGS data is the proportion of the secondary clone. The proposed method was able to identify complex infections when the secondary clone corresponded to at least 5–10% of the sequencing reads. This is in the range of what was observed WGS in clinical samples with Sure-select sequencing, where in the three identified complex infections, the proportion of the secondary clone was ~ 6–10% both in SureSelect and in cultured samples [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, the mean genome coverage of the laboratory and field isolates evaluated here varied from 29 to 56 among the datasets, limiting our potential to identify multiclonal infections to cases where the secondary clone corresponded to at least 8–17% of the total reads in the sample. This might lead to an underestimation of the total frequency of complex infections identified. The multiclonal isolate with the lowest genome coverage was ERR3956121, from the \u003cem\u003eL. donovani\u003c/em\u003e group, with a genome coverage of 27. Differently to what is observed for multiclonal infections, the low coverage should not have a great impact on polyploidy estimations, as the proportion of the rarer allele should be higher in this case.\u003c/p\u003e\u003cp\u003eDue to ethical and health limitations, Trypanosomatid parasite isolates are usually obtained from only one tissue during diagnostic procedures in human patients, such as bone marrow, lymph nodes, spleen, heart, gut or blood. This might mitigate the complex infection frequency estimation, as different organs might harbour different parasite strains/variants [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. A potential option to assess the complexity of Trypanosomatids infection in different organs is using samples from reservoirs, as dogs with visceral leishmaniasis in Brazil, where samples can be collected from any tissue \u003cem\u003epost-mortem\u003c/em\u003e, with the approval of owners [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Similar analysis might be performed on other reservoirs for \u003cem\u003eT. cruzi\u003c/em\u003e and \u003cem\u003eT. brucei\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eWhile polyploidy appears to be a relatively common occurrence within Trypanosomatids [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e–\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], including an example of a two-species allotriploid [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e], we detect few multiclonal infections in the Trypanosomatid read libraries examined here (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; six \u003cem\u003eL. donovani\u003c/em\u003e multiclonal, one \u003cem\u003eT. brucei\u003c/em\u003e and two \u003cem\u003eT. cruzi\u003c/em\u003e). However, all these Trypanosomatid species do undergo some degree of sexual recombination and outcrossing [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], indicating that different genotypes must be present in the same insect at some point to undergo meiosis and outcrossing. We imagine two, non mutually exclusive, explanations for these results. First, the occurrence of multiclonal infections may be underestimated. Laboratory culture of strains is likely to result in loss of genotypes that are less fit in culture, reducing the apparent complexity of the infections below our threshold of detection. Consistent with this view, a study of infections in HIV + and HIV- visceral leishmaniasis cases Ethiopia found that 6 of the 68 infections (9%) were multiclonal [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], and a study of canine leishmaniasis in the state of Mato Grosso, Brazil identified 9 multilocus (polyclonal) genotypes in different organs, out of 23 genotyped (39%) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. These studies indicate that substantial proportions of multiclonal infections do occur in some populations. In other populations, multiclonal infections and outcrossing may be rare. Bottlenecks in the number of parasites transferred to or from vectors may reduce genetic diversity of infections at the outset, and long incubation of parasites within mammalian hosts with selection for the fittest parasite genotype may reduce genetically complex populations to a single clone. A reduction in within-host diversity would be expected to reduce outcrossing, unless vectors frequently feed on more than one host. We can expect that there will be alternative explanations for different species, populations, depending on the frequencies of transmission, endemicity and within host/within vector population dynamics. It is our perspective that more study of these factors will enhance our understanding of transmission dynamics in Trypanosomatids.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe method we describe can accurately identify polyploid isolates; and can identify multiclonal infections in samples sequenced with modest read depth (\u0026gt;\u0026thinsp;25x), as little as 100 heterozygous SNPs, and as little as 5\u0026ndash;10% of the secondary genotype. We find that multiclonality and polyploidy are not frequent in cultured Trypanosomatid field isolates, although there are good reasons to expect that our estimates are lower bounds. Future projects could explore new sequencing methods to identify multiclonal infections, such as single-cell sequencing [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e] that could directly identify different clones; and long-read sequencing followed by haplotype phasing, to identify different haplotypes in a sample [\u003cspan additionalcitationids=\"CR82\" citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]. These methods could quantify the proportion and number of the different clones in a mixed infection.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003ch2\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e \u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eJ.L.R.-C. and D.C.J. are supported by a MRC New Investigator Research grant (MR/T016019/1) and by MRC Newton as a component of the UK:Brazil Joint Centre Partnership in Leishmaniasis (MR/S019472/1).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJ.L.R.-C and D.C.J. conceived, designed the study, drafted and revised the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank the funding agencies that provided funds for this study, the Medical Research Council (MRC), and the Newton UK:Brazil Joint CentrePartnership in Leishmaniasis. This project was undertaken on the Viking Cluster, which is a high-performance compute facility provided by the University of York. We thank the University of York High Performance Computing service, Viking, and the Research Computing team for computational support.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll the read libraries used in this are available in NCBI (see Supplementary Tables 1, 2 and 3). The script and test set can be obtained from GitHub: https://github.com/jaumlrc/Complex-Infections.git\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBurza S, Croft SL, Boelaert M, Leishmaniasis. Lancet. 2018;392:951\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKennedy PGE. Update on human African trypanosomiasis (sleeping sickness). J Neurol. 2019;266:2334\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHorn D. 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Int J Mol Sci. 2020;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKronenberg ZN, Rhie A, Koren S, Concepcion GT, Peluso P, Munson KM, et al. Extended haplotype-phasing of long-read de novo genome assemblies using Hi-C. Nat Commun. 2021;12:1935.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHosch S, Wagner P, Giger JN, Dubach N, Saavedra E, Perno CF, et al. PHARE: a bioinformatics pipeline for compositional profiling of multiclonal Plasmodium falciparum infections from long-read Nanopore sequencing data. J Antimicrob Chemother. 2024;79:987\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Complex infections, polyploidy, multiplicity of infection, Trypanosomatids, aneuploidy, protozoan parasites","lastPublishedDoi":"10.21203/rs.3.rs-4648421/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4648421/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eTrypanosomatid parasites are a group of protozoans that cause devastating diseases that disproportionately affect developing countries. These protozoans have developed several mechanisms for adaptation to survive in the mammalian host, such as extensive expansion of multigene families enrolled in host-parasite interaction, adaptation to invade and modulate host cells, and the presence of aneuploidy and polyploidy. Two mechanisms might result in \u0026ldquo;complex\u0026rdquo; isolates, with more than two haplotypes being present in a single sample: multiplicity of infections (MOI) and polyploidy. We have developed and validated a methodology to identify multiclonal infections and polyploidy using Whole Genome Sequencing reads, based on fluctuations in allelic read depth in heterozygous positions, which can be easily implemented in experiments sequencing genomes from one sample to larger population surveys.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe methodology estimates the complexity index (CI) of an isolate, and compares real samples with simulated clonal infections at individual and populational level, excluding regions with somy and gene copy number variation. It was primarily validated with simulated MOI and known polyploid isolates respectively from \u003cem\u003eLeishmania\u003c/em\u003e and \u003cem\u003eTrypanosoma cruzi\u003c/em\u003e. Then, the approach was used to assess the complexity of infection using genome wide SNP data from 530 Trypanosomatid samples from four clades, \u003cem\u003eL. donovani/L. infantum\u003c/em\u003e, \u003cem\u003eL. braziliensis\u003c/em\u003e, \u003cem\u003eT. cruzi\u003c/em\u003e and \u003cem\u003eT. brucei\u003c/em\u003e providing an overview of multiclonal infection and polyploidy in these cultured parasites. We show that our method robustly detects complex infections in samples with at least 25x coverage, 100 heterozygous SNPs and where 5\u0026ndash;10% of the reads correspond to the secondary clone. We find that relatively small proportions (\u0026le;\u0026thinsp;7%) of cultured Trypanosomatid isolates are complex.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe method can accurately identify polyploid isolates, and can identify multiclonal infections in scenarios with sufficient genome read coverage. We pack our method in a single R script that requires only a standard variant call format (VCF) file to run (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/jaumlrc/Complex-Infections\u003c/span\u003e\u003cspan address=\"https://github.com/jaumlrc/Complex-Infections\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Our analyses indicate that multiclonality and polyploidy do occur in all clades, but not very frequently in cultured Trypanosomatids. We caution that our estimates are lower bounds due to the limitations of current laboratory and bioinformatic methods.\u003c/p\u003e","manuscriptTitle":"Detecting complex infections in Trypanosomatids using whole genome sequencing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-19 20:01:32","doi":"10.21203/rs.3.rs-4648421/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-23T09:04:06+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-20T22:33:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-19T18:20:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-16T21:20:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"181780317111744939106335473925944171359","date":"2024-07-08T08:13:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"70140253174825097760560567373318604391","date":"2024-07-05T15:29:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"129908585565634110726403975392093539421","date":"2024-07-01T13:54:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"96929478202793977621546683910235433484","date":"2024-07-01T09:25:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-28T10:56:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-28T10:09:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-28T01:13:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Genomics","date":"2024-06-27T12:06:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ba3ce6bb-68ae-4ce0-96d3-50386b48f589","owner":[],"postedDate":"July 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-11-04T16:22:24+00:00","versionOfRecord":{"articleIdentity":"rs-4648421","link":"https://doi.org/10.1186/s12864-024-10862-6","journal":{"identity":"bmc-genomics","isVorOnly":false,"title":"BMC Genomics"},"publishedOn":"2024-10-29 16:04:58","publishedOnDateReadable":"October 29th, 2024"},"versionCreatedAt":"2024-07-19 20:01:32","video":"","vorDoi":"10.1186/s12864-024-10862-6","vorDoiUrl":"https://doi.org/10.1186/s12864-024-10862-6","workflowStages":[]},"version":"v1","identity":"rs-4648421","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4648421","identity":"rs-4648421","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}