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Rapid Differentiation of Vector Species: Development of Microsatellite Markers for Population Genetics of Biting Midges and a Potential Tool for Species Identi cation of Culicoides Sonorensis


 Background: Proper vector surveillance relies on the ability to identify species of interest accurately and efficiently, though this can be difficult in groups containing cryptic species. Culicoides is a genus of small biting flies responsible for the transmission of numerous pathogens to a multitude of vertebrates. Regarding pathogen transmission, the C. variipennis species complex is of particular interest in North America. Of the six species within this group, only C. sonorensis is a proven vector of bluetongue virus and epizootic hemorrhagic disease virus. Unfortunately, subtle morphological differences, cryptic species, and mitonuclear discordance make species identification in the C. variipennis complex challenging. Recently, a SNP analysis enabled discrimination between the species of this group; however, this demanding approach is not practical for vector surveillance. Methods: The aim of the current study was to develop a reliable and affordable way of differentiating the species within the C. variipennis complex, especially C. sonorensis. Twenty-five putative microsatellite markers were identified using the C. sonorensis genome and tested for amplification within five species of the C. variipennis complex. Machine learning was then used to determine which markers best explain the genetic differentiation between species. This led to the development of a subset of four and seven markers which were also tested for species differentiation.Results: A total of 21 microsatellite markers were successfully amplified in the species tested. Clustering analyses of all of these markers recover the same species-level identification as the previous SNP data. Additionally, the subset of seven markers was equally capable of accurately differentiating the members of the C. variipennis complex as the 21 microsatellite markers. Finally, one microsatellite marker (C508) was found to be species-specific, only amplifying in the vector species C. sonorensis among the samples tested. Conclusions: These microsatellites provide an affordable way in which to differentiate the species of the C. variipennis complex and could lead to a better understanding of the species dynamics within this group. Additionally, after further testing, marker C508 may allow for the identification of C. sonorensis with a single-tube assay, potentially providing a powerful new tool for vector surveillance in North America.


Introduction
With newly diverged or cryptic species, the boundaries between taxa can be blurred and often di cult to de ne [1,2]. Yet, species delimitation is vitally important as it determines the biological unit on which governmental policies, control programs, evolutionary studies, and conservation efforts rely [3,4]. This is especially true for species that pose a risk to public or animal health, such as pathogen vectors, as misidenti cations will result in unreliable transmission data. Morphological identi cation is commonly used in vector surveillance due to its wide accessibility and cost-effectiveness, though it can require a considerable amount of expertise if the target species closely resembles a sibling species or if it exhibits extensive morphological variation [5]. In many cases, sequencing a common barcoding region (i.e., COI) can be done with far less training while providing a tangible level of taxonomic identi cation [6]. However, barcoding is neither easily implemented nor cost-effective for use in vector surveillance programs that process hundreds, if not thousands, of specimens. For a molecular marker to be feasible in these situations, species-speci c ampli cation is needed, denoting either the presence or absence of the vector species within pools of samples [7].
Culicoides is a genus of small, biting midges that are responsible for the transmission of many pathogens affecting both wildlife and livestock worldwide [8,9]. Viruses, such as bluetongue virus (BTV) and epizootic hemorrhagic disease virus (EHDV), are of particular interest as these can cause a high rate of mortality in infected animals [10,11]. In the past two decades, Culicoides spp. have contributed to notable disease outbreaks in Australia [12], Europe [13,14], and North America [15,16], leading to signi cant morbidity, mortality, and economic loss in these regions [11,17]. These outbreaks highlight the need for Culicoides vector surveillance and population management programs; however, these are complicated by the fact that several of the vector species belong to complexes of closely related species that are not easily distinguishable [18]. The inclusion of a non-vector cryptic species into vector surveillance data can arti cially lower seroprevalence rates, overestimate species distributions, or even interfere with the detection of other vector species. The C. imicola, C. obsoletus, C. pulcairis, and C. variipennis complexes all play a key role in the transmission of BTV and EHDV [9,18]; however, proper species-level identi cation in these groups remains challenging. Molecular tools have been developed to aid in species identi cation in certain groups of Culicoides [19,20,21,22], though cryptic diversity is often noted in Culicoides taxa regarded as a single species [23,24,25]. Additionally, there have been no molecular markers developed for the identi cation of C. sonorensis, the North American vector of BTV and EHDV.
The C. variipennis species complex is found throughout much of North America and is comprised of at least six species (C. albertensis, C. australis, C. occidentalis, C. sonorensis, C. variipennis, and C. mullensi) [26], only one of which (C. sonorensis) is a proven vector [27,28,29,30]. Species delimitation within the C. variipennis complex is particularly challenging due to subtle morphological differences between these species [31]. Species identi cation is further hampered by a lack of segregation between mitochondrial haplotypes of three of these species, including the vector species C. sonorensis (plus C. albertensis and C. variipennis) [32]. The absence of mitochondrial discrimination prevents genetic identi cation using the traditional COI barcode [33,34,35], and to further complicate the situation, C. sonorensis occurs in sympatry with each of the other members of this species complex [36]. Overall, the lack of clear morphological differences, the unavailability of readily applied genetic identi cation, and the occurrence of several species within a single location have introduced ambiguity to vector surveillance in this group. Recently, genomic analyses using a SNP dataset shed light on species delimitation in the C. variipennis complex and served as a useful tool for population genomic analyses [32]. However, this method is expensive and requires bioinformatic analyses, rendering it unsuitable for the rapid and affordable species identi cation necessary for effective vector surveillance.
Here, we rst aimed to provide an easy and cost-effective way to identify species within the C. variipennis complex, especially the vector species C. sonorensis. We developed microsatellite markers from the available genome of C. sonorensis and tested these markers for species differentiation within the C. variipennis complex. These results were compared to the species delimitation obtained through SNP analyses from Shults et al. (2021) [32]. We then used machine-learning analyses to estimate the in uence of each microsatellite marker in discriminating between the different species in the C. variipennis complex. This analysis was used to determine the minimum number of markers required for identi cation while still maintaining a high level of con dence in species differentiation. Finally, a single marker was found to uniquely amplify in the C. sonorensis samples tested and may prove to be a fast and inexpensive tool for discriminating between this species and the non-vector species of the C. variipennis complex.

Microsatellite Marker Selection
The reference genome of C. sonorensis (RefSeq GCA_900258525.2) [37] was assessed with the software QDD v. 3.1 [38] to determine suitable microsatellite repeat motifs. Microsatellite repeats containing less than ve repetitions, as well as mononucleotide repeats, were discarded. For each microsatellite repeat, 200bp anking regions on either side of the repeat were extracted. Overall, microsatellite repeat motifs were identi ed in 60,026 reads. To maximize polymorphism, loci with the highest number of repeats were selected, all of which had dinucleotide repeats. Twenty-ve loci were selected, and their corresponding primers were generated using the online Primer-BLAST software through NCBI (https://www.ncbi.nlm.nih.gov/tools/primer-blast). A broad range of PCR products (110-490) were targeted to ease the development of multiplex arrangements. Primer sequences, microsatellite repeat information, and product size are displayed for each of the microsatellite markers in Table 1.

Molecular Techniques
A total of 79 individuals from ve selected species of the C. variipennis complex (14 for C. albertensis, 15 for C. mullensi, 16 for C. occidentalis, 19 for C. sonorensis, and 15 for C. variipennis; Supplementary Information Table S1) were used for testing primer ampli cation. These individuals were previously assigned to species based on genomic analyses of 3609 SNP loci from Shults et al. (2021) and were selected to cover most of the geographic distributions of the different species (Figure 1a & 1b). A modi ed Gentra Puregene extraction method (Gentra Systems, Inc. Minneapolis, MN, USA) was used to extract the genomic DNA of each individual. Each of the 25 primers was ampli ed in standard simplex PCR conditions using a Bio-Rad thermocycler T100 (Bio-Rad, Pleasanton, CA, USA). Each PCR reaction contained 2.0 µl of DNA, 0.75 µM of a primer pair, 5.0 µl of 5x reaction buffer, 0.15 µl of Taq, and 16.35 µl of deionized water. The cycling conditions used for the ampli cation of microsatellite markers consisted of 95°C for 3 min, followed by 35 cycles of 95°C for 1 min, 57°C for 1.5 min, and 72°C for 2 min, with a nal extension step at 72°C for 5 min. All microsatellite markers were tested at 57°C, regardless of their species of origin. The M13-tailed primer method was used to label amplicons to facilitate multiplexing after PCR ampli cation. Each forward primer had an M13 tail attached, which was 5'-uorescently labeled with 6-NED, VIC, PET, or FAM. An ABI 3500 capillary sequencer with a LIZ500 internal standard (Applied Biosystems, Foster City, CA, USA) was used to visualize PCR products. Alleles were scored using Geneious v.9.1 (Biomatters, Auckland, New Zealand) [39]. Four primers were discarded due to inconsistent or nonexistent ampli cation. The nal primer set includes 21 microsatellite markers grouped in ve different multiplexes (  [42] was used to estimate the number of genetic clusters (K) and determine whether individuals from distinct species cluster together. Simulations were run with values of K from 1 to 20 and repeated 10 times for each K-value. Each run consisted of a 5 x 10 4 burn-in period followed by 1 x 10 5 iterations of the MCMC. The most likely number of genetic clusters (K) was inferred using the method in Puechmaille (2016) [43] implemented in the web-based software StructureSelector [44]. The outputs were visualized using CLUMPAK [45].

Selecting a Subset of Markers for Optimized Species Differentiation
A random forest (RF) classi cation analysis was carried out on an 18 microsatellite dataset using the R package randomForest [46]. Three markers (C424, C995, and C508) were discarded due to the presence of missing data (e.g., non-amplifying marker in some species), which cannot be handled by an RF analysis. This analysis aims at estimating the con dence rate in determining an individual's species of origin for each of the microsatellite markers developed. The RF analysis was performed using 1,000 trees.
The default values were used for the number of input variables randomly selected to build each node of the tree, and for the number of observations not used for building the tree (i.e., the out of bag sample -OOB). The OOB samples were used to build the confusion matrix and to estimate the OOB error rate. Low OOB error rates indicate a high ability of the variables in predicting the species of origin of the individual.
In addition, RF analysis on the 18-marker dataset was used to determine the importance of each microsatellite marker in classifying the individuals in the ve species. This analysis enables the selection of a subset of microsatellite markers most capable of differentiating species.
The markers determined to have the highest in uence in differentiating species were grouped into two subsets (a four-and seven-marker dataset), from which a PCA and STRUCTURE analysis were subsequently applied. STRUCTURE assignments using the four-and seven-marker datasets were compared to the assignments from the entire 21 microsatellite marker dataset as well as the SNP dataset. In addition, RF analyses were re-applied on these datasets to estimate the con dence (i.e., OOB error rate) in estimating an individual's species of origin using only four or seven microsatellite markers.

Results
The 21 selected microsatellite markers ampli ed in most of the species of the C. variipennis complex, with a few exceptions (Table 3.) All 21 markers were found to be polymorphic, with the number of alleles ranging from 11 to 37 (Mean +-SD = 26.4 ± 7.4; Table 2). More speci cally, allelic diversity ranged from 3 to 15 (Mean ± SD = 8.6 ± 3.4) alleles per marker for C. albertensis, 4 to 12 (8.4 ± 3.2) for C. mullensi, 4 to 16 (8.0 ± 3.9) for C. occidentalis, 4 to 20 (13.0 ± 3.8) for C. sonorensis, and 4 to 14 (8.6 ± 3.7) for C. variipennis. Deviation from HWE was observed for most markers per species. This result originated from signi cantly positive F IS inbreeding coe cients observed for the majority of the markers and most species, with levels of observed heterozygosity lowered than expected (Table 3). It is important to note that the positive F IS values can be overestimated due to the sampling of a few individuals per species over an expansive range (i.e., the Wahlund effect). Results from the linkage disequilibrium analysis suggest that most genotypes at one locus were independent from genotypes at another locus. An exception was that markers C45, C728, and C995 appeared to be linked (P = 0.004, 0.03 and 0.058), as were markers C94 and C2085 (P = 0.05) (Supplementary Table S2). Note that only a single marker from each of these two groups was later used in the four-and seven-marker datasets.
The overall dataset of 21 markers was successful in the species-level differentiation of all specimens, though the clustering of individuals using a PCA revealed that two species, C. albertensis and C. variipennis, slightly overlap (Figure 2a). The clustering of individuals using a STRUCTURE analysis suggested the presence of ve distinct clusters in the dataset (best K = 5; Figure 1d; individual assignments for other values of K are provided in Supplementary gure S1). This clustering using microsatellite markers corresponds to ve different species, as it closely mirrors the results of the SNP dataset with the same samples from Shults et al. (2021) (Figure 1c). Importantly, individuals mostly belonged (>85% [mean = 98%] assignment probability) to a single genetic cluster when using the overall dataset of 21 microsatellite markers (i.e., unambiguous assignment to the correct species). RF analysis on the overall dataset also suggests that markers C226, C728, C838, and C1450 had the highest in uence in differentiating species, followed by markers C589, C2085, and C1241 ( Figure S2). When using most of the microsatellite markers (18-marker dataset), the OOB error rate was 1.3%. The confusion matrix found that a potential low rate of misidenti cations might occur with C. sonorensis and C. variipennis samples, while no misidenti cation occurs among samples from the three other species ( Figure S3).
When the seven-marker dataset was analyzed (i.e., C226, C728, C838, C1450, C589, C2085, and C1241), almost no overlap was found between individuals from distinct species on the PCA (Figure 2b). Similarly, STRUCTURE analysis revealed con dent segregation of the individuals into the different species (Figure  1e), as most individuals (N = 75) were unambiguously assigned to the correct species (> 85% [mean = 95%] assignment probability). Only four samples had assignment probabilities lower than 85% to the correct species cluster, with one sample of C. occidentalis (63%), one sample of C. albertensis (71%), and two samples of C. variipennis (80 and 83%). Additionally, the clustering closely mirrored the results from both the entire 21 microsatellite marker dataset and the SNP dataset. This nding suggests robust segregation of samples into the different species using seven microsatellite markers. The RF analysis provides further support for species delineation using these markers, revealing an OOB error rate of 1.9% ( Figure S3). The confusion matrix found a misclassi ed sample of C. mullensi, C. sonorensis, and C. variipennis.
When plotting individuals on a PCA using the four-marker dataset (i.e., C226, C728, C838, and C1450), individuals within the same species mostly clustered together, despite small overlap (Figure 2c). Similarly, the STRUCTURE analysis revealed that individuals mostly cluster into their respective species (Figure 1f), with most individuals (N = 65) being correctly assigned (> 85% [mean = 87%] assignment probability). However, 14 individuals had a mixed assignment (< 85% assignment probability), with four of them having less than 50% assignment to their correct species, hampering full con dence in identifying species using only four markers. This nding was con rmed by an RF analysis that revealed a small, but non-negligible OOB error rate of 6.3% ( Figure S3). The confusion matrix revealed multiple misclassi ed samples belonging to C. mullensi, C. sonorensis, and C. variipennis.
Lastly, the microsatellite locus C508 was found to amplify only in C. sonorensis (Figure 3), the only proven vector species within the C. variipennis complex. In total, 79 individuals spanning 14 geographic locations were tested at this marker: C. albertensis from four populations, C. mullensi from one population, C. occidentalis from three populations, C. sonorensis from nine populations, and C. variipennis from four populations (Figure 1a and Table S1). Many more samples and populations need to be tested to con rm this species-speci c ampli cation; however, in the samples tested here, there does not appear to be any geographical bias in ampli cation. Individuals of C. albertensis, C. occidentalis, and C. variipennis collected from the same location as individuals of C. sonorensis showed no ampli cation at this marker. It is also important to note that this marker was not included in the RF analyses above due to the substantial amount of missing data (i.e., non-ampli cation in four of the sibling species) ( Table 3).

Discussion
As only about 2% of the known species of Culicoides are vectors [8], differentiating these from non-vector species remains vital to surveillance efforts. Since this can be complicated by morphologically similar cryptic taxa, molecular species delimitation tools are needed. This study identi ed a relatively simple, reproducible, and economical tool for the molecular differentiation of the species within the C. variipennis species complex. We generated a set of 21 microsatellite markers that can assign species-level identities to the members of this complex. These markers also exhibit consistent polymorphism for each species and should lead to a better understanding of the population structure and species dynamics within this group. Machine learning was utilized to detect a set of seven microsatellite markers optimal for species differentiation, further reducing costs. Finally, the locus C508 was found to only amplify in C. sonorensis and appears to be a promising marker to improve vector surveillance for this species, though additional testing is needed.
In populations with closely related or cryptic species, approaching species delimitation at a population level can help identify independent, or mostly independent, gene pools. Shults et al. (2021) [32] provided insight into the number of biological species within the C. variipennis complex; however, SNP data is expensive to produce and cannot be easily combined with new datasets. Conversely, the microsatellite data produced here was far less expensive while achieving the same level of species delimitation as the SNPs. This will allow these new markers to be integrated into most studies of this species complex to improve accurate species identi cation. It is highly likely that the species distribution records and serological data within this group need to be revisited. Additionally, as morphological identi cation of the larvae within the C. variipennis complex is not possible, these markers will help to decipher the immature habitat of each species. Hybridization is possible between C. sonorensis and several other members of the C. variipennis complex [32,47], but it remains to be seen if microsatellites can be used to identify hybrid individuals. Unfortunately, this study was unable to obtain specimens of the newly elevated C. australis [26]; however, if this is truly a valid species, these markers should differentiate it as well.
While these microsatellite markers will be helpful to future studies of the C. variipennis complex, their practicality in vector surveillance may be limited. However, locus C508 could be incredibly useful for the rapid identi cation of the vector species, C. sonorensis. If ampli cation of this marker is speci c to this species, screening individuals or pools of individuals can be completed with a single PCR and agarose gel. Of the samples tested here, ampli cation was 100% for C. sonorensis, which included individuals from nine populations across its known range (Table S1). Conversely, no ampli cation was seen in the other members of the C. variipennis complex (Figure 3). More samples need to be tested to be con dent in locus C508's ability to identify C. sonorensis in all populations. Additionally, this marker has yet to be tested on Culicoides species outside of this complex. Should this marker be cross-reactive with another species, it would produce a false positive if used as the sole method for identifying C. sonorensis. Fortunately, the C. variipennis complex is morphologically distinguishable from other species of Culicoides [26,48], thus if this were the case, a combination of the two methods would still allow for rapid identi cation of the vector species. Single-tube molecular identi cation assays already exist for vector species in other Culicoides species complexes; however, most of these are based on mitochondrial data. If similar patterns of mitonuclear discordance (as seen in the C. variipennis complex) exist in these groups, these assays have the potential to miss cryptic species. As microsatellite markers have already been developed for several of these groups [49,50], it would be interesting to compare the number of species recovered between these mitochondrial and nuclear markers.

Tables
Due to technical limitations, tables 1 to 3 are only available as a download in the Supplemental Files section. Figure 1 (a.) The sites used to collect specimens of the C. variipennis complex. Each location is colored (corresponding to the phylogenetic tree) to represent which species were tested from these collection sites. corresponding to the phylogenetic tree.   The PCR product of marker C508 imaged after gel electrophoresis for individuals of the C. variipennis complex. This marker is roughly 350 bp in length and, of the samples tested, shows ampli cation in only C. sonorensis.