Comparative genomics and transcriptomics of the Spiroplasma glossinidia strain sGff reveal insights into host interaction and trypanosome resistance in Glossina fuscipes fuscipes

Comparative genomics and transcriptomics of the Spiroplasma glossinidia strain sGff reveal insights into host interaction and trypanosome resistance in Glossina fuscipes fuscipes | 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 Comparative genomics and transcriptomics of the Spiroplasma glossinidia strain s Gff reveal insights into host interaction and trypanosome resistance in Glossina fuscipes fuscipes Daniel J. Bruzzese, Fabian Gstöttenmayer, Brian L. Weiss, Hager Khalil, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7295611/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Nov, 2025 Read the published version in BMC Genomics → Version 1 posted 12 You are reading this latest preprint version Abstract Tsetse ( Glossina spp.) are vectors of African trypanosomes, the causative agents of Human and African Animal trypanosomiases, diseases that remain significant medical and socioeconomic challenges in sub-Saharan Africa. In addition to trypanosomes, tsetse harbor both obligate and facultative symbiotic bacteria that can influence vector competence and reproductive biology. One such facultative symbiont, Spiroplasma glossinidia , infects several tsetse species within the Palpalis subgroup. In Glossina fuscipes fuscipes ( Gff ), the Spiroplasma glossinidia strain s Gff induces a trypanosome-refractory phenotype and negatively impacts reproductive fitness by reducing female fecundity. However, the mechanisms behind these Spiroplasma -derived phenotypes remain poorly understood. Here, we report successful in vitro cultivation of s Gff and present complete genomes from three sources: in vitro cultured s Gff and s Gff isolated from both laboratory-maintained and wild-caught (Uganda) Gff flies. Comparative genomic analyses revealed a high degree of similarity in gene content and synteny among these s Gff samples, confirming that they represent isolates of the same strain. Phylogenomic analyses placed s Gff within the Spiroplasma poulsonii clade. The s Gff genome is highly dynamic, containing numerous mobile genetic elements. Additionally, in silico annotations indicate that s Gff relies on its host for both lipids and carbohydrates and produces several toxins, all of which could be implicated in the observed trypanosome refractory phenotype. Finally, comparative transcriptomic analysis of s Gff from host hemolymph versus in vitro culture provided insights into potential factors relevant to host-symbiont interactions. Our findings provide a foundation for understanding the nutritional dialogue between s Gff and its host and identify symbiotic products that may contribute to trypanosome resistance. Furthermore, the establishment of an in vitro culture system for s Gff represents a significant resource for future functional studies with potential implications for vector control. Spiroplasma Glossina fuscipes fuscipes tsetse symbiosis genome sequencing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Tsetse ( Glossina spp.) transmit African trypanosomes, the causative agents of Human and African Animal Trypanosomiases (HAT and AAT, respectively). Approximately 60 million people in sub-Saharan Africa live in tsetse-infested areas at risk for HAT, while AAT constrains livestock productivity across much of the continent [ 1 , 2 ]. With no vaccines available for either disease, vector-control strategies remain paramount for disease management [ 2 – 4 ]. Complementary approaches that block or reduce trypanosome development in the fly have the potential to enhance disease control efforts [ 5 ]. Successful transmission of trypanosomes is influenced by a combination of intrinsic factors, including the fly’s innate immunity, host and parasite genotypes, as well as host nutritional status at the time of parasite acquisition [ 6 ]. In addition to these intrinsic factors, extrinsic factors, including environmental factors and the composition of fly’s microbiota, also modulate pathogen transmission efficiency [ 5 , 7 ]. In this context, modification of tsetse’s heritable symbiotic microbes, some of which coexist in close proximity to trypanosomes in the midgut, provide a promising avenue for “paratransgenic” interventions [ 8 ]. Tsetse species harbor a complex community of heritable symbionts, each playing distinct roles in host biology. All tsetse species carry the obligate mutualist Wigglesworthia , which supplements the fly’s nutrient-restricted blood diet with essential vitamins necessary for reproductive success [ 7 , 9 ], and proper development of the fly’s immune system [ 10 – 13 ]. Wolbachia infects tsetse species and can induce cytoplasmic incompatibility, potentially influencing population structure and mating compatibility [ 14 ]. Sodalis also colonizes tsetse species, with its presence correlated with trypanosome infection prevalence in some contexts, depending on geographic location and tsetse species [ 15 ]. Finally, some tsetse house Spiroplasma glossinidia , which influences several key tsetse processes, including immune modulation, reproduction, and capacity for trypanosome transmission [ 16 – 18 ]. Despite Spiroplasma's substantial impacts on tsetse biology, the underlying molecular mechanisms governing the bacterium's interactions with tsetse remain largely unknown. Spiroplasma are helical, wall-less Mollicutes, first characterized as plant pathogens [ 19 , 20 ], but are now known to colonize a wide range of arthropod hosts, most commonly infecting insects [ 21 – 27 ]. In insects, Spiroplasma exhibits a broad range of symbiotic phenotypes, including protective effects in different Drosophila species against parasitic wasps and nematodes by either producing ribosome-inactivating proteins (RIPs), which disrupt parasite protein synthesis machinery [ 28 – 30 ], or by competing with the parasite for macronutrients [ 31 ]. Conversely, some Spiroplasma strains act as reproductive parasites in insects, such as in Drosophila [ 32 ], Anisosticta (ladybugs) [ 33 ], and Danaus butterflies [ 34 ], where they induce selective male-killing. Of note, Spiroplasma genomes evolve rapidly, at rates comparable to RNA viruses [ 35 ]. This rapid pace of evolution is attributed to the abundance of mobile genetic elements and absence of key DNA mismatch repair genes, which together promote genomic plasticity, rapid diversification, and horizontal gene transfer of these key symbiosis genes [ 23 , 36 ]. Within Glossina , S. glossinidia infections are limited to species in the Palpalis subgroup, including Glossina fuscipes fuscipes (Gff ), Glossina palpalis palpalis , and G. tachinoides [ 14 , 37 ]. In Uganda, the S. glossinidia strain infecting Gff ( s Gff) is geographically restricted and polymorphic, with prevalence ranging from 5–34% in Northwestern populations, while absent from Central and Southern regions. The infection prevalence in Uganda remains relatively stable across time and space, although seasonality can impact infection dynamics [ 18 ]. Interestingly, in the Gff line reared from the Insect Pest Control laboratory at the International Atomic Energy Agency (IAEA), s Gff infection is also not fixed, but is stably maintained at approximately 50% prevalence [ 38 ]. Laboratory transmission studies in this Gff line indicate that s Gff is maternally inherited with high fidelity, although paternal transmission can also occur [ 17 ]. The bacterium colonizes multiple tissues, such as the gonads, gut, and hemolymph [ 14 , 16 ]. Gff infected with s Gff show altered gene expression in reproductive and gut tissues [ 16 ], reduced hemolymph triacylglyceride (TAG) levels, impaired sperm fitness, and reduced female fecundity [ 17 ]. In addition, s Gff infection is negatively correlated with trypanosome infection prevalence in both field and lab populations, suggesting that presence of the bacterium induces a parasite refractory phenotype in tsetse [ 18 ]. Whether s Gff directly (e.g., via the production of anti-trypanosomal factors) or indirectly (e.g., competition for nutrients) confers the observed parasite resistance phenotype to its tsetse host remains unknown. Transcriptomic analyses of s Gff - infected Gff midguts revealed minimal immune stimulation but indicated elevated oxidative stress and impaired lipid biosynthesis [ 16 ]. These changes may create a hostile gut environment for parasites by increasing production of trypanocidal nitric oxide (NO) and/or by reducing the availability of key metabolites required for trypanosome survival. To investigate the mechanisms by which Spiroplasma alters tsetse’s physiology and impacts vector competence, we leveraged recently developed in vitro methods to culture s Gff. We then sequenced whole genomes of s Gff from in vitro culture, laboratory-reared, and wild-caught Gff individuals using the Oxford Nanopore Technology (ONT) platform. Comparative analysis of the three s Gff genomes revealed subtle isolate-specific genetic variation residing primarily in mobile genetic elements. We also characterized s Gff gene expression from both in vitro s Gff and s Gff-infected hosts, and identified core metabolic pathways and candidate toxins that could mediate the tsetse- s Gff symbiosis. Collectively, this study lays the foundation for dissecting the molecular basis of the s Gff– Gff interactions and for evaluating its potential in trypanosomiasis control strategies. Methods Initiation of sGff in vitro culture Establishment of the s Gff in vitro culture followed the protocol of Masson et al. [39], developed and optimized for the cultivation of s MSRO. This protocol is based on Barbour-Stoenner-Kelly H (BSK-H) media without L-Glutamine (Bio&Sell, Germany), originally designed for Borrelia burgdorferi, supplemented with rabbit serum, Gff fly extract, lipids, antibiotics, and amino acids. Detailed media and cultivation protocols are indicated in Supplementary Document 1. Briefly, hemolymph from female and male Gff from a laboratory line with high s Gff infection prevalence [38] was collected by removing one prothoracic leg and aspirating the exposed hemolymph droplet using a 10 μl pipette tip. Cultures were established in three biological replicates using 10 μl hemolymph and incubated at 25°C for 14 days without agitation under microaerobic atmospheric conditions. Routine microscopy checks were performed by fluorescent microscopy on a Leica DMi8 inverted microscope (Leica Microsystems) following staining with SYTO 9 (0.025 mM; ThermoFisher Scientific). Spiroplasma presence was further confirmed by PCR-amplification using Spiroplasma- specific 16s rRNA primers (Supplementary Table 1). Cultures underwent passaging every 10 - 14 days by diluting cultures 1:1 with fresh media. In vitro culture growth kinetics Samples from s Gff cultures for growth curve analysis were collected in triplicate biological replicates from day 0 to day 15 following the inoculation of BSK-H-spiro media with s Gff precultures. Each day, five μl of culture was diluted in 200 μl nuclease-free double-distilled water (ddH 2 O) in a sterile PCR tube and stored at -20°C. Prior to quantitative PCR (qPCR), samples were lysed via osmotic heat-shock by incubation at 95°C for 10 minutes. qPCR reactions were performed in triplicate technical replicates, in a final volume of 15 μl consisting of 7.5 μl iQ™ SYBR® Green Supermix (Bio-Rad, CA, USA), 5.5 μl PCR-grade water, 0.5 μl each of the forward and reverse Spiroplasma qPCR primers (Supplementary Table 1), and 1 μl heat-shocked culture. To accurately estimate Spiroplasma copy numbers, a standard was prepared by purifying s Gff - specific qPCR amplicons using the Zymo DNA Clean & Concentrator-25 kit (Zymo Research, USA), followed by DNA quantification with a nanodrop spectrophotometer. The purified DNA was then serially diluted 1:10 to create a range of known concentrations. Cycling conditions for the qPCR comprised of an initial denaturation step at 95°C for 2 min, followed by 40 cycles of 95°C for 5 sec, and 56°C for 30 sec on a Bio-Rad CFX96 Touch Real-Time PCR Detection System (Hercules, CA, USA). Raw Ct values were extracted in the CFX maestro software and analyzed using R Studio v4.4.2 [40, 41]. Briefly, the mean value of the three technical replicates of each sample was calculated and the copy number of each sample was inferred based on the standard curve of known copy numbers [42]. Calculation of culture doubling time was performed according to the formula [43]. Injections of in vitro cultured sGff into naïve Gff Teneral females and males from the Gff line were placed in individual cages and their Spiroplasma infection status was determined by PCR-amplification of DNA extracted from one mesothoracic leg using Spiroplasma 16s rRNA and tsetse tubulin gene primers (Supplementary Table 1). Negative- s Gff flies were pooled into two treatment groups (12 females and 6 males per group): (1) high dose and (2) low dose. In addition, 12 females and 6 males screened positive for s Gff were retained as a control group with a natural infection. Gff were injected using 29-gauge insulin needles on a micrometer syringe unit injector (Burkard Manufacturing, UK). Injected copy numbers were estimated via quantitative PCR compared to a standard curve of known copy numbers: High Dose: 1.1 x 10 5 copies in Replicate 1 and 4.3 x 10 5 copies in Replicate 2; Low Dose: 5.7 x 10 2 copies in Replicate 1 and 1.2 x 10 3 copies in Replicate 2. Three hours post-injection, flies were offered their first blood meal and subsequently maintained under standard rearing conditions [44] for 14 days, receiving defibrinated bovine blood three times a week via an artificial membrane feeding system [45]. At designated time points (0, 1, 6, 9 and 14 days), two females and one male were collected, flash-frozen, and stored at -20°C. Genomic DNA from individual whole flies was extracted using the Qiagen DNeasy Blood & Tissue kit (Qiagen, Hilden, GER) according to the manufacturer’s protocols and diluted to 4 ng/μl. The relative abundance of s Gff over time was measured by qPCR, with three technical replicates per sample. Ct values for sGff were normalized to those of the tsetse housekeeping gene β-tubulin (Supplementary Table 1) and relative fold-change was calculated using the 2 -(ΔCt) method [46]. Statistical analysis was performed in RStudio v4.4.2 [40, 41]. Significant differences in s Gff fold-change across timepoints between and within each group were identified using the Kruskal-Wallis test and Dunn’s post-hoc test with Benjamini-Hochberg correction for multiple comparisons. s Gff sequencing and assembly Three Gff Spiroplasma ( s Gff) whole genome assemblies were generated: (1) s Gff-IAEA-CC - derived from the in vitro culture of Spiroplasma established in 2023 from IAEA Gff females and males as described above; (2) s Gff-IAEA-Fly - assembled from a previously sequenced Spiroplasma- infected Gff female obtained in 2023 from the IAEA colony (NCBI BioSample: SAMN47211303 and NCBI SRA: SRR32737746), and (3) s Gff-UG-Tol - assembled from a previously sequenced Spiroplasma -infected Gff female collected in 2019 from Toloyang village in Atiak Sub County, Amuru District, Uganda (3.304883654, 32.37452634) (NCBI BioSample: SAMN47211302 and NCBI SRA: SRR32737747) [47]. For s Gff-IAEA-CC genome, high density in vitro Spiroplasma culture (~10 6 cells) was collected at passage nine, pelleted, and washed with PBS. HMW DNA was extracted using the PureGene kit (Qiagen, Hilden, GER). An ONT LSK14 library was prepared following standard protocols and sequenced on a ONT 10.4.1 minION flow cell for 12 hours. Generated POD5 signal data files were basecalled with Dorado v0.9.1 (https://github.com/nanoporetech/dorado) using the SUP duplex pipeline. Simplex reads were dropped with Samtools v1.18 [48], and remaining sequencing adapters were trimmed using Dorado trim. The produced FASTQ files were quality filtered with nanoq v0.10.0 [49] to have a minimum quality of q10 and minimum 3000 bp read length. To assemble the s Gff-IAEA-CC genome, Autocycler v0.2.1 (https://github.com/rrwick/Autocycler) was used to generate multiple assemblies from four independent subsampled 100X read sets using five assemblers for each read set (Flye v2.9.5 [50], Canu v2.3 [51], miniasm v.3 [52], NECAT [53], and NextDenovo v2.5.2 [54]). A consensus assembly for these 20 assemblies was then determined with Autocycler v0.2.1 and polished with Medaka v2.0.1 using the bacteria model (https://github.com/nanoporetech/medaka). The genome was further polished using short reads made from the same HMW DNA used for ONT sequencing. These reads were sequenced on an Illumina NovaSeq X system (Eurofins Genomics, Germany) and quality trimmed to Q30 with a minimum length of 50 bp with fastp v0.24.0 [55]. Short read polishing was performed with Polypolish v0.6.0 [56] followed by pypolca v0.3.1 [57]. To assemble the s Gff-IAEA-Fly genome, we mapped raw Nanopore simplex reads generated with the SUP Dorado v0.9.0 model from the Gff -IAEA genome assembly (SRR32737746) to the s Gff-IAEA-CC assembly using minimap2 v2.28 [58]. Unmapped reads were dropped with Samtools. The remaining simplex reads were precorrected with Dorado correct to generate near–Hifi quality reads and were assembled using six assemblers with varying parameters (Hifiasm v0.25 [59], Flye, Canu, miniasm, NECAT, and NextDenovo). A consensus assembly was determined with Autocycler v0.2.1. Finally, to assemble the s Gff-UG-Tol genome, we mapped raw Nanopore simplex reads generated with the SUP Dorado v0.9.0 model from the Gff -IAEA genome assembly (SRR32737747) to the s Gff-IAEA-CC assembly using minimap2. Unmapped reads were dropped with Samtools. Due to the shorter read-lengths of this dataset we were unable to utilize Dorado correct to precorrect ONT reads. Instead, we assembled the mapped ONT Simplex reads using five assemblers (Hifiasm (--ont), Flye, miniasm, NECAT, and NextDenovo). A consensus assembly was determined with Autocycler v0.2.1 and was polished with Medaka v2.0.1 using the bacteria model. All completed s Gff circular genomes and plasmids were reoriented to start with the dnaA or repA with Dnaapler v0.7.0 [60]. Assembly stats for the assembled genomes were established using gfastats v1.3.10 [61] and genome completeness was assessed with BUSCO v5.8.3 [62] using the entomoplasmatales_odb10 dataset. Plasmids were clustered with pling v2 [63] to determine similarity and were named in order according to their size. All genomic data were deposited under the NCBI BioProject: PRJNA1235259. Functional annotations Assembled genomes were first annotated using the NCBI Prokaryotic Genome Annotation Pipeline [64]. For the reference s Gff-IAEA-CC genome, additional functional annotations were added with Bakta [65], eggNOG-mapper [66], BlastKOALA [67], and COGclassifier (https://github.com/moshi4/COGclassifier). Mobile genetic elements were annotated using PHASTEST [68] for prophages, ICEberg v3 [69] for integrative conjugative elements, and ISEScan v1.7.3 [70] for insertion sequence elements. Metabolic pathways were characterized with the KEGG terms assigned above with eggNOG-mapper [66] and BlastKOALA [67]. Putative proteins were analyzed using SignalP v6 [71] to determine N-terminal signal peptide sequences, indicative of secretion. Finally, Spiroplasma specific symbiosis genes identified from the literature were manually searched for with BLASTP [72] (Supplementary Table 2). To better characterize the RIP identified in s Gff, we generated a phylogeny using Spiroplasma members of the RIP superfamily from InterPro [73] and our putative RIP sequence (Supplementary Table 3). The tree was constructed using RAxML-NG [74] with the LG+G8+F model, with 25 parsimony and 25 random starting trees with 1000 bootstraps using PRANK [75] aligned amino acid sequences trimmed to only contain RIP domains. Spiroplasma phylogenomics To determine s Gff’s relationship to other Spiroplasma strains, we constructed phylogenetic trees at two scales: (1) using Spiroplasma strains from several clades, including Spiroplasma citri , Spiroplasma mirum, Spiroplasma chrysopicola, and Spiroplasma poulsonii , and (2) using only members from the S. poulsonii clade . Reference genomes and their NCBI RefSeq (or if missing, NCBI GenBank) annotations in GBFF format were downloaded using NCBI datasets [76]. These genomes were clustered using RabbitTClust [77], to iteratively drop Spiroplasma strains from distant clades (e.g. Spiroplasma ixodes or Spiroplasma apis ). For the remaining Spiroplasma strains (Supplementary Table 4), single-copy orthologous core genes (present in > 95%) of taxa were identified with PPanGGOLiN [78]. From this gene set, DNA sequences were extracted, aligned with MAFFT [79], and concatenated for each Spiroplasma taxa. Phylogenetic trees were generated from the multiple sequence alignment using RAxML-NG [74] with the GTR+G substitution model with 50 random and 50 parsimony-based starting trees, and with 1,000 bootstrap replicates. The resulting draft RAxML tree was used to identify duplicate Spiroplasma strains, which were removed before re-running the PPanGGOLiN and RAxML pipeline. Finally, the whole process was repeated at a smaller scale for members of the Spiroplasma poulsonii clade only. Genome comparisons Pairwise average nucleotide identity was calculated between the three assembled s Gff genomes and the closest sister taxa, TU-14 (GCF_001792795.1), using pyani v0.2.13.1 [80]. Whole-genome synteny, SNP, and indel comparisons between s Gff-IAEA-CC, s Gff-IAEA-Fly, and s Gff-UG-Tol were performed by pairwise alignments using NUCmer [81] annotated with SyRI [82], and visualized using plotsr v1.1 [83]. For the s Gff-IAEA-CC, s Gff-IAEA-Fly, s Gff-UG-Tol, and sister taxa TU-14 (GCF_001792795.1), we identified shared and unique gene families by parsing the PPanGGOLiN dataset generated above with for the Spiroplasma poulsonii RAxML tree using R [40] with the tidyverse [84] and VennDiagram [85] packages. Unique gene family amino acid sequences were functionally annotated with Bakta [86]. RNA sequencing and analyses To assess s Gff transcriptomes, five s Gff RNA samples were prepared for RNAseq: three from s Gff cell-culture and two from Gff hemolymph. For the s Gff cell-culture samples, s Gff-IAEA-CC (~10 6 cells) at passage 12 were pelleted at 12,000g for 15 mins at 4°C and resuspended in RNAlater™ (Invitrogen, MA, USA) and stored at -80°C. For the Gff hemolymph samples, female IAEA Gff flies were screened for s Gff infections using the above PCR protocol. Positive flies were surface-sterilized in 70% ethanol and hemolymph was collected as described above. Hemolymph was pooled into two samples from 70 and 35 s Gff-positive females and stored in RNAlater™ (Invitrogen, MA, USA) at -80°C. Total RNA was extracted from samples by first diluting the samples stored in RNAlater 1:1 with PBS, pelleting at 5000g for 15 mins at 4°C, and resuspending the pellets in 50 µl PBS. Total RNA was extracted from the PBS washed samples using the Total RNA Miniprep kit (NEB, MA, USA) following standard protocols. Ribodepleted RNA-seq libraries were prepared by the Yale Center for Genomic Analysis and sequenced on the NovaSeq 6000 platform. Raw reads were quality filtered (Phred score >20 and read length >50 bp) and adapter-trimmed using fastp v0.24.0 [55]. Trimming and filtering results were summarized with MultiQC v1.27 [87]. We first used the RNA sequencing data to validate the in silico s Gff-IAEA-CC annotations. We pooled all transcriptomes into a single sample and quantified transcript abundance using Salmon [88] with the s Gff-IAEA-CC genome as a reference. Transcripts were considered to be expressed if their transcripts per million (TPM) count was > 5. Operons were identified with the OpDetect v1.0 [89] pipeline, which utilized STAR v2.7.11b [90] alignments of the transcriptome to the s Gff-IAEA-CC genome. We then quantified differential gene expression for the three culture and two Gff hemolymph samples, again utilizing Salmon [88] to quantify transcript abundance to the s Gff-IAEA-CC reference genome. Differential transcript analysis was performed with edgeR [91] using the quasi-likelihood model. We defined significantly differentially expressed genes as those with a false discovery rate (FDR) 1.5 or < -1.5. Results Establishment of s Gff in vitro cultures Following the published protocol for s MSRO, we successfully established an in vitro culture of the Spiroplasma strain s Gff isolated from the hemolymph of Gff . Microscopic examination of SYTO 9-stained cells revealed a homogenous culture, with cells exhibiting the characteristic helical morphology of Spiroplasma (Fig 1A and 1B). Growth curve analysis of cultures inoculated with frozen preculture aliquots identified two distinct phases of exponential growth, occurring from days 0 to 4 and days 7 to 11, separated by a plateau phase (Fig 1C). The estimated doubling time was 40 hours during the first exponential phase and 85 hours during the second. Fluorescence microscopy revealed that cultures reached high cell densities within 14 days post inoculation. Remarkably, the s Gff cultures have remained viable for over 12 months with routine subculturing every 10-14 days, demonstrating the robustness and long-term stability of the in vitro system. s Gff cultures can establish infections in naïve Gff hosts To assess whether in vitro -cultured s Gff retains its ability to replicate in vivo following prolonged culture, we conducted microinjection experiments using two groups of naïve Gff that were confirmed negative for s Gff by PCR screening of a mesothoracic leg. s Gff-negative teneral flies were injected with either a high- or low-dose of the cultured s Gff and infection dynamics were monitored over a 14-day period. The results demonstrate that in vitro -cultured s Gff retains its replicative capacity within naïve Gff , validating its potential utility for downstream functional studies. Quantification of s Gff in transinfected Gff, alongside naturally infected Gff controls, revealed differences in initial s Gff titers between groups on day 0, with the low-dose group showing the lowest titer (fold-change = 0.014), and high-dose and natural infection groups exhibiting similar titers (fold-change = 2.3 and 2.451, respectively) (Supplementary Table 5). We observed a dose-dependent increase within groups over time (Supplementary Figure 1). Statistical analysis confirmed significant differences in s Gff titers between treatment groups on days 0, 1 and 6 (Kruskal-Wallis: p -values = 0.003, 0.003 and 0.0007, respectively) (Supplementary Table 6) and a significant increase within both high- and low-dose groups over time (Kruskal-Wallis: p- value = 0.000044 for low-dose; p -value = 0.00045 for high-dose) (Supplementary Table 7), with post-hoc tests indicating that titers on days 9 and 14 were significantly higher compared to days 0 and 1 (Supplementary Tables 8 and 9). In contrast, the increase of s Gff titers in the naturally infected control group was not statistically significant, likely due to higher variability among individuals (Supplementary Table 5). s Gff complete genome assembly To generate a high-quality reference genome for s Gff, we sequenced the whole genome and obtained 78,639 Nanopore duplex reads (565.8 Mb), over 200x coverage for the primary assembly. Additionally, we obtained 4,166,901 Illumina short reads (1.2 Gb) for polishing. The nanopore reads were split into four subsets to construct 20 independent s Gff assemblies that were merged into a consensus sequence using Autocycler and subsequently polished with the Illumina data. This approach produced a closed, circular 1.489 Mb reference genome from the in vitro cultured s Gff strain, designated s Gff-IAEA-CC, which was deposited in NCBI (RefSeq: GCF_049669535.1, BioProject: PRJNA1235259). We also assembled two additional s Gff genomes using publicly available NCBI SRA datasets from Gff flies (Bioproject: PRJNA1231403): one from a female IAEA colony fly ( s Gff-IAEA-Fly) and another from a wild-caught female from northwestern Uganda ( s Gff-UG-Tol). For all assemblies, BUSCO scores exceeded 97.3% based on the OrthoDB entomoplasmatales_odb10 dataset, indicating high quality, complete genomes (Table 1). Genome size varied slightly among the assemblies. The s Gff-IAEA-CC reference genome was 1.489 Mb and included four plasmids ranging in size from 6 kb to 16 kb, while the s Gff-IAEA-Fly genome had a slightly smaller chromosome of 1.469 Mb (approximately 20 kb less than the reference s Gff-IAEA-CC genome), but retained all four plasmids. Interestingly, the s Gff - UG-Tol genome was more reduced, with a 1.418 Mb chromosome, and only three plasmids, missing the 13 kb plasmid, p_sGff2 (Table 1). We manually verified the absence of the plasmid p_sGff2 in s Gff-UG-Tol by mapping its reads to the s Gff-IAEA-CC assembly, confirming no coverage over the s Gff-IAEA-CC p_sGff2 region. Gene counts across assemblies showed moderate variation. The s Gff-IAEA-CC genome, including plasmids, contained 1,829 genes, including 1,687 protein-coding genes, 104 pseudogenes, 32 tRNAs, 3 rRNAs, and 3 non-coding RNAs (ncRNAs) (Table 1). In comparison, s Gff-IAEA-Fly encoded 1,795 genes (1,653 protein-coding genes), while s Gff-UG-Tol encoded 1,695 genes (1,553 protein-coding genes) (Table 1). Table 1. Assembly statistics for the three s Gff genomes. BUSCO scores are for the entomoplasmatales_odb10 OrthoDB dataset sGff-IAEA-CC sGff-IAEA-Fly sGff-UG-Tol Chromosome (bp) 1,489,281 1,469,171 1,418,152 p_sGff1 (bp) 16,389 16,389 16,375 p_sGff2 (bp) 13,621 13,621 N/A p_sGff3 (bp) 13,208 13,208 12,717 p_sGff4 (bp) 6,466 6,466 6,460 GC content 27.16% 27.21% 27.29% BioProject PRJNA1235259 PRJNA1235259 PRJNA1235259 BioSample SAMN47572768 SAMN48057343 SAMN48057344 Assembly GCF_049669535.1 GCA_049949145.1 GCA_049949155.1 BUSCO score C:97.6% [S:97.6%,D:0.0%] C:97.6% [S:97.6%,D:0.0%] C:97.3% [S:97.3%,D:0.0%] genes 1,829 1,795 1,695 protein-coding genes 1,687 1,653 1,553 pseudogenes 104 104 104 tRNA 32 32 32 rRNA 3 3 3 ncRNA 3 3 3 s Gff clusters within the Spiroplasma poulsonii clade To place the newly assembled s Gff genomes within a broader phylogenetic framework, we constructed a RAxML maximum likelihood tree based on 93-101 single-copy core orthologous genes conserved in over 95% of representative Spiroplasma taxa from the S. citri , S. mirum, S. chrysopicola, and S. poulsonii clades (Figure 2). The s Gff isolates are grouped most closely with two poorly characterized but genetically identical Spiroplasma strains: s TU-14 and s NBRC_100390. The s TU-14 strain was originally isolated from a contaminated sample of another Mollicute ( Entomoplasma lucivorax PIPN-2) and has an unknown host origin [92] . The s NBRC_100390 strain was initially misclassified as Spiroplasma atrichopogonis GNAT3597, a symbiont of biting midges, but has since been recognized as a distinct Spiroplasma taxon [93]. Together, the s Gff isolates, s TU-14, and s NBRC_100390 form a distinct, well-supported lineage within the S. poulsonii clade (Figure 2). To further resolve their evolutionary relationships, we constructed a higher-resolution RAxML phylogeny using 509-563 single-copy core orthologs shared among >95% of representatives of Spiroplasma taxa within the Poulsonii clade. This second RAxML tree reinforced the same relationships observed in the previous tree. It also resolved the s Gff polytomy, showing that the s Gff-IAEA-CC and s Gff-IAEA-Fly isolates were genetically identical and formed a monophyletic group, with s Gff-UG-Tol positioned as their sister lineage (Supplementary Figure 2). s Gff genomes are highly similar Pairwise genome comparisons among the s Gff assemblies supported the relationships inferred from the RAxML phylogenies. Average Nucleotide Identity (ANI) was 99.9% between the s Gff-IAEA-CC and s Gff-IAEA-Fly genomes, and slightly reduced at 99.8% between the s Gff-IAEA-CC and s Gff-UG-Tol (Figure 3A). These ANI values are a magnitude higher than those observed between any s Gff isolate and its closest Spiroplasma outgroup, strain sTU-14, with pairwise ANI values of 98.5% to s Gff-IAEA-CC, s Gff-IAEA-Fly, and s Gff-UG-Tol (Figure 3A). Gene content analysis revealed subtle differences between the nearly identical s Gff-IAEA isolates and the more divergent s Gff-UG-Tol isolate. Specifically, 38 gene families were unique to the s Gff-IAEA isolates, while 17 were unique to the s Gff-UG-Tol (Figure 3B). Among the 38 s Gff-IAEA-specific gene families, 20 were associated with mobile genetic elements (MGEs) – including twelve from plasmids with six from p_sGff2 that is absent from s Gff-UG-Tol, nine were related to metabolism and symbiosis, and the remaining nine encoded hypothetical proteins (Supplementary Table 10). The 17 s Gff-UG-Tol-specific gene families included four genes located on plasmid p_sGff3, three associated with MGEs, two symbiosis-related genes, and eight hypothetical proteins (Supplementary Table 11). When compared to the nearest sister taxon, s TU-14, we identified 310 unique gene families shared among the s Gff isolates, consisting mostly of MGEs, including Spiroplasma prophages, plasmids, and integrative conjugative elements (ICEs) (Supplementary Table 12). Whole genome alignments further revealed that the two IAEA-derived s Gff genomes, s Gff-IAEA-CC and s Gff-IAEA-Fly, are nearly identical, differing by only three SNPs, 49 insertions/deletions (indels), and one duplication (Figure 3C and Supplementary Table 13). The three SNPs were located in genes encoding a prophage, the DNA-binding protein WhiA , and the fructoselysine transporter frlA (Supplementary Table 14). With no fructose supplemented in the culture medium, the nonsynonymous SNP converting threonine to arginine in frlA could be the result of relaxed selection. Most indels were located within homopolymeric tracts, suggesting potential assembly artifacts. However, we identified a notable 20 kb duplication involving a Spiroplasma prophage region in the s Gff-IAEA-CC genome, but absent compared to s Gff-IAEA-Fly (Figure 3C and Supplementary Table 15). This duplication likely represents a prophage polymorphism in the s Gff-IAEA isolate that became fixed during the in vitro culture process. In contrast, the s Gff-UG-Tol genome exhibited more extensive divergence, with 550 SNPs, 171 indels, five large duplications, and one translocation relative to s Gff-IAEA-CC (Figure 3C and Supplementary Table 16). Of the 550 SNPs, 383 were found within protein coding genes with over 44% associated with MGEs (Supplementary Table 17). While most of the indels were homopolymeric, at least 22 appeared to be genuine sequence differences rather than homopolymeric assembly artifacts. Large-scale structural variation between the s Gff-IAEA-CC and the s Gff-UG-Tol genomes included five duplications and one translocation (Figure 3C). All of these rearrangements were associated with Spiroplasma prophages (Supplementary Table 18). s Gff mobile genetic elements The s Gff-IAEA-CC chromosome encodes a total of 1,778 genes, including 1,637 protein-coding genes, of which 800 are predicted to encode hypothetical proteins with unknown functions. In addition, the genome contains 103 pseudogenes, 32 tRNAs, 3 rRNAs, and 3 non-coding RNAs (ncRNAs) (Table 1, Supplementary table 19). Genes associated with MGEs represented the most prevalent Cluster of Orthologous Groups (COG) functional category, accounting for over 28% of the annotated functions (Figure 4 and Supplementary Figure 3). When the 646 proteins with unknown COG annotations (36.33% of the genome) are excluded, the relative proportion of MGE-associated functions increases to 44.35%, highlighting the central role of MGEs in shaping the s Gff genome. Other prominent COG functional categories include translation, ribosomal structure and biogenesis, replication, recombination and repair, and carbohydrate transport and metabolism (Supplementary Figure 3). The most common MGEs are Spiroplasma prophages, specifically from two families: Plectroviridae ( Plectovirus prophages) and Microviridae ( Spiromicrovirus prophages). Using Phastest, we identified 20 supported prophage regions, along with several other putative prophage-related genes scattered across the genome, including within plasmids (Figure 4). Insertion sequence (IS) elements were also highly prevalent, with 129 identified on the chromosome and many associated with prophage regions. In addition, a single well-supported ICE was identified that encoded a complete Type I restriction-modification system (Figure 4). Like the prophages, ICE-associated genes were dispersed throughout the genome, including some within prophage regions and plasmids. s Gff metabolism We reconstructed the metabolic pathways of s Gff using PGAP-predicted proteins using BLASTKoala and eggNOG-mapper. This analysis revealed that s Gff possesses highly reduced biosynthetic capability and relies primarily on its Gff host for energy and essential metabolites (Supplementary Table 19). The bacterium’s energy metabolism centers on glycolysis, with glucose and fructose serving as its main carbohydrate substrates. We identified four phosphotransferase system (PTS) transporters predicted to facilitate uptake of specific sugars: fructose, glucose, 2-(alpha-D-mannosyl)-D-glycerate, and cellobiose/diacetylchitobiose. Notably, the presence of a diacetylchitobiose transporter, together with putative chitinases, suggests that s Gff may have the capacity to utilize chitin as an alternative energy source. The genome also reveals metabolic versatility through two additional pathways: a functional acetyltransferase-acetate kinase pathway indicating potential acetogenic metabolism, as well as gene-clusters involved with sulfur reduction. Like other Spiroplasma species, s Gff exhibits minimal capacity for de novo lipid synthesis, possessing functionality limited to the citric acid cycle and fatty acid elongation. Instead, it predominantly incorporates host-derived fatty acids and cholesterol directly into its cell membranes [94]. Nevertheless, s Gff retains crucial lipid-modifying capabilities, particularly the complete cardiolipin biosynthesis pathway that converts host-derived diacylglycerols (DAGs) into cardiolipin, a process identical to the experimentally verified pathway in S. poulsonii [95] that is essential for bacterial membrane formation [96]. The genome also encodes the non-mevalonate pathway for terpenoid backbone biosynthesis, providing additional lipid processing capability. Beyond lipid modification, s Gff possesses several other significant biosynthetic capabilities, including a complete folate biosynthesis pathway, supporting one-carbon metabolism essential for nucleotide and amino acid synthesis. Hemolysin-related proteins and ferritin are also produced by s Gff potentially facilitating iron sequestration in the host environment (Supplementary Table 19). We also note that s Gff exhibits limited amino acid biosynthesis as it can only produce three amino acids de novo : aspartic acid, serine, and asparagine, indicating a high degree of dependence on host-derived nutrients. Symbiosis genes In addition to its highly specialized metabolism, s Gff has several mechanisms that may facilitate its persistence within the tsetse host. Among these are lipoproteins, which play essential roles in symbiosis through interactions with the host immune system, host cells, and metabolites present in the hemolymph. Of the 1,637 predicted protein-coding genes in the s Gff genome, 72 encode lipoproteins (Supplementary Table 19). Many of these lipoproteins are associated with Spiroplasma prophage regions, suggesting that horizontal gene transfer and the utilization of prophage-derived lipoproteins may be key to s Gff’s adaptation to the host environment. The most abundant Spiroplasma lipoprotein is Spiralin, which is a well-characterized protein important in host interactions and vertical transmission [97]. We identified one conserved Spiralin gene (WP_424526001.1) as well as two other Spiralin- derived copies containing one and two repeats, respectively (WP_424525940.1 and WP_424525799.1). We note six adhesion-related genes: (WP_424527554.1, WP_424527500.1, WP_424527571.1, WP_424527528.1, WP_424527276.1, and WP_424526807.1) four of which are located on plasmids (one per plasmid) and are likely involved in host cell adhesion and invasion. Similar adhesion genes are essential for midgut cell invasion in bees [98] and adhesion to leafhopper cells [99]. We identified one putative ribosome-inactivating protein (RIP) gene in the s Gff genome (Supplementary Table 19). The gene (WP_424526995.1) encodes a protein with a conserved RIP domain, most similar to s Gff’s sister taxa s NBRC_100390 (Supplementary Figure 4). However, the s Gff RIP lacks a signal peptide, suggesting it is not secreted. Instead, this protein is predicted to be embedded in the outer cell membrane, with the RIP domain exposed to the intracellular environment. Whether this toxin is released via proteolysis by an unidentified peptidase remains unknown. We also identified a more divergent RIP-like protein (WP_424526987.1) that includes a signal peptide, suggesting it may be secreted into the host environment. We identified two overlapping copies of the glycerol-3-phosphatase ( glpO ) gene in the s Gff genome (WP_424526233.1 and WP_424526234.1) (Supplementary Table 19). glpO catalyzes the oxidation of glycerol-3-phosphate, generating reactive oxygen species (ROS) as a byproduct of glycerol metabolism. In Mycoplasma , the gene functions as a virulence factor [100] and in Spiroplasma it may provide protection against parasitoid wasps [36]. Typically, glpO is part of a conserved operon flanked by glpF and glpK , which encode a glycerol transporter and glycerol kinase, respectively. However, in s Gff, glpF appears truncated due to the insertion of a Spiroplasma prophage, with only the first 32 amino acid residues present upstream of the insertion site. Interestingly, in silico analyses suggest that the truncated glpF and the two glpO copies remain potentially functional . Transcriptome validation We performed RNAseq-based transcriptomic analysis utilizing three in vitro samples derived from s Gff culture and two in vivo s Gff samples isolated from Gff hemolymph. The transcriptomic dataset was first used to validate in silico annotations of the s Gff genome. We pooled transcripts from both culture and hemolymph samples, resulting in over 57 million reads mapped to the reference genome. Of the 1,778 annotated genes, 464 showed no detectable expression (TPM < 5) in either condition (Supplementary Table 20). Over 261 of these non-expressed genes were related to MGEs, primarily Spiroplasma prophages and suggests that prophage expression may be actively suppressed in s Gff. Among the 464 non-expressed genes, 52 were annotated as pseudogenes via in silico predictions. Interestingly, 52 of the 104 predicted pseudogenes exhibited transcriptional activity, with 34 annotated as MGEs, including transposases, phage-related proteins, and ICEs, indicating that some may retain regulatory or functional roles, particularly in genome plasticity and host adaptation. Additionally, we confirmed expression for all candidate symbiosis-associated genes identified in our genome analyses (Supplementary Table 20). Transcripts were detected from all four plasmids, indicating that plasmid-encoded genes are expressed under both in vivo and in vitro conditions. We identified the most abundant transcripts from the pooled dataset. Among the top 20 most abundant genes, seven were associated with plasmid functions, three were involved in fructose metabolism and five were involved in core transcription and translation processes. The remaining highly expressed genes included two with unknown functions, a malate permease, a rod shape determining protein, and Spiralin (Supplementary Table 20) . In addition, we used transcriptomic data to predict operon structures in the s Gff genome, as operons may encode co-regulated clusters, including putative secreted effectors relevant to host interactions or trypanocidal effects. Of the 1,675 protein-coding genes analyzed, 1,471 (87.8%) were predicted to be organized into 297 operons. The mean operon length was 3.95 genes, with the largest operon containing 39 genes (Supplementary Table 21). Operon 64 was particularly interesting as it encoded the conserved RIP, MATE family efflux transporters, and Peroxiredoxin, which is a strong antioxidant. Tissue dependent expression of s Gff We next investigated tissue-dependent gene expression differences by comparing transcriptomes of s Gff isolated from hemolymph and from in vitro culture. In total, we identified 45 differentially expressed genes between the two sample types, using log₂ fold change threshold > 1.5 or < -1.5 and a false discovery rate (FDR) < 0.05 (Figure 5A). Of these, 12 genes were upregulated in s Gff from hemolymph relative to the culture, while 33 genes were downregulated (Figure 5B and Supplementary Table 22). Among the 12 upregulated genes in from hemolymph relative to culture, 11 were located on the main chromosome, and one was plasmid-encoded. The upregulated main chromosomal genes contained five metabolism-related genes, including a fructose-specific phosphotransferase system (PTS) transporter, two translation-related genes, a detoxifying-related gene, a repair-related gene, a stress-related gene, and a pseudogene. Of the 33 downregulated in s Gff hemolymph compared to culture, 21 were located on the main chromosome and 12 were plasmid-encoded. The genes on the chromosome included seven hypothetical proteins, six metabolism-related genes, including a glucose-specific PTS transporter, six prophage-related genes, two lipoproteins, a DNA polymerase, and a serine protease. These differential expression patterns likely reflect environmental differences between the in vivo (hemolymph) and in vitro (culture) conditions. The upregulation of a fructose-specific PTS transporter and downregulation of a glucose-specific PTS transporter may indicate a shift in available carbohydrate sources between the hemolymph and the culture medium, as fructose is absent from the culture media (Supplementary Document 1). Reduced expression of prophage genes could also suggest s Gff can suppress prophage expression in certain environments. Discussion This study advances our understanding of the interactions between Glossina fuscipes fuscipes ( Gff ) and its symbiont Spiroplasma glossinidia ( s Gff) through the establishment of an in vitro culture system, whole genome sequencing of multiple s Gff isolates, and transcriptomic comparisons between s Gff derived from culture and from Gff hemolymph. While Spiroplasma citri was first cultured in 1971 [ 101 ] and some Spiroplasma strains were propagated using both cell-based and cell-free culture systems [ 102 , 103 ], optimizing growth conditions for many strains remained challenging, likely due to the bacterium’s specialized adaptations to its specific insect host environment [ 104 ]. More recently, the use of Barbour-Stoenner-Kelly (BSK-H) media, supplemented with specific nutrients, has facilitated the sustained in vitro growth of the previously unculturable s MSRO strain [ 39 ], and now s Gff. The initial growth pattern of cultured s Gff was characterized by two exponential phases separated by a plateau phase. Culture plateaus at specific density thresholds were also observed in s MSRO [ 105 ], however, the sGff culture resumed growth after the plateau phase, possibly reflecting adaptation to the culture environment or modulation of endogenous prophages, which can impact growth dynamics in related strains [ 106 , 107 ]. The growth rate of sGff was slower than s MSRO, with a doubling time of 40 hours compared to 30 hours, which may reflect strain-specific metabolic traits [ 39 ]. Further optimization of the s Gff culture media, informed by insights from its genome-derived metabolic profile [ 104 ], may improve these growth rates. Importantly, s Gff retained capacity for infection after prolonged in vitro cultivation, as demonstrated by successful colonization of naïve Gff following experimental infection, with higher inoculum doses resulting in higher s Gff titers. It will be important to establish whether s Gff infections can recapitulate the same tissue tropism and transmission dynamics of natural infections. Overall, the availability of a reliable culture system provides a powerful tool for dissecting the molecular dialogue between s Gff and its tsetse host, including the potential for genetic manipulation of the symbiont. Using a hybrid sequencing approach that combined Nanopore and Illumina reads, we assembled a high-quality, closed genome from the s Gff culture consisting of a 1.489 Mb chromosome along with four plasmids ranging in size from 6 to 16 kb. This reference genome ( s Gff-IAEA-CC) was compared to two additional s Gff assemblies – one from a colony fly ( s Gff-IAEA-Fly) and another from a fly collected in Northwest Uganda ( s Gff-UG-Tol). Comparative analysis confirmed that all three assemblies represent closely related isolates of the same strain, differing by several SNPs, indels, and unique gene families, with very little genomic variation between the sGff-IAEA-CC and sGff-IAEA-Fly genomes. The few genomic differences observed between the two IAEA-fly derived s Gff genomes could be attributed to either natural polymorphisms that became fixed during cultivation or perhaps from an elevated mutation rate due to the absence of genes in the mismatch repair pathway, namely MutS , MutL , and MutH , which is a trait shared across the Spiroplasma genus [ 35 ]. In contrast, more pronounced genomic variation was observed between the IAEA-derived genomes and field-derived s Gff-UG-Tol isolate. This divergence is consistent with the moderate genetic differentiation between their respective Gff hosts, as s Gff-UG-Tol isolate was obtained from a Northwestern Ugandan population, while the IAEA Gff colony originated from flies collected in the Central African Republic in 1986 [ 47 , 108 ]. Nearly all the genomic variation between these isolates was localized to MGEs, including genes associated with Spiroplasma prophages. Notably, one plasmid present in the IAEA isolates was absent in the Uganda field isolate. Studies characterizing the range of genetic variation found in s Gff across the landscape will be important for understanding the full extent of genetic diversity in this symbiont. Phylogenetic analysis placed the s Gff isolates within the S. poulsonii clade, a group that contains several other well-characterized Spiroplasma strains known for protecting their hosts against nematodes, parasitoids, and viruses [ 109 – 111 ]. Among these sister strains is s MSRO, whose culture protocol we successfully adopted for s Gff and likely explains the ease with which s Gff was cultivated. The closest known relatives of s Gff are two genetically identical but poorly characterized strains: s TU-14 and s NBRC_100390. These strains share 98.5% genome-wide identity with s Gff and have 778 gene families in common. However, s Gff possesses over 310 unique gene families, consisting primarily of hypothetical proteins and MGEs. These shared and unique gene families highlight both its close relationship to its sister taxa as well as its substantial repertoire of distinctive genetic features. The s Gff genome consists of a remarkably high proportion of MGEs, including prophages, integrative conjugative elements (ICEs), and insertion sequence elements (ISEs), which together comprise over 28% of the genome. When hypothetical proteins are excluded, MGE-associated content increases to more than 44% of the annotated genome. This high MGE load is consistent with observations in several other S. poulsonii and S. citri strains [ 35 , 112 ]. In total, we identified 20 distinct prophage regions, consisting largely of Spiromicrovirus and Plectrovirus prophages, along with one ICE, and 129 ISEs. We also identified additional prophage- and ICE-related genes dispersed throughout the genome and plasmids – all pointing to a dynamic genome characterized by extensive structural and functional plasticity. Prophages and MGEs can play major roles in the evolution and virulence of their hosts [ 113 – 116 ], acting as a reservoir of adaptive genes that can either be co-opted or horizontally transferred to enhance symbiotic capabilities [ 116 , 117 ]. For example, in Wolbachia , key symbiosis-associated genes, such as the cif genes responsible for cytoplasmic incompatibility [ 118 ], and wmk , a male-killing factor [ 119 ], are both derived from and horizontally transferred via prophages [ 120 , 121 ]. In s Gff, as well as other Spiroplasma strains, this pattern holds true [ 35 , 122 ]. In s Gff, key symbiosis genes, such as RIP and glpO , are flanked by prophage sequences. Several prophages in s Gff encode lipoproteins, which could mediate interactions with host cells, metabolites, and lipids, contributing to symbiotic function [ 123 – 125 ]. The abundance of prophages present in the s Gff genome may also play a mechanistic role in regulating Spiroplasma symbiosis. In Wolbachia , the genomic island octomom modulates bacterial density to prevent overproliferation [ 126 ]. Although s Gff lacks a clearly defined octomom ortholog, prophage induction may serve a similar density-dependent regulatory function, by triggering bacterial lysis in response to stress, such as overcrowding or resource limitation, [ 127 , 128 ] or via a quorum sensing mechanism as observed in Vibrio cholerae [ 129 ] or E. coli [ 130 ]. Our transcriptomic comparison between cultured s Gff and s Gff isolated from tsetse hemolymph supports this hypothesis, as prophage-associated genes were highly expressed in the dense in vitro cultures as compared to hemolymph-derived s Gff. Additionally, prophage induction can also modulate biofilm formation [ 131 ], interbacterial competition [ 132 ], or be induced via reactive oxygen species (ROS) [ 133 ]. Future studies comparing prophage activity in trypanosome-infected and uninfected tsetse will be key in determining whether s Gff prophages contribute to trypanosome resistance or influence interactions with other prominent tsetse symbionts, including Sodalis and Wigglesworthia . Our metabolic profiling confirms that, like other Spiroplasma strains [ 134 , 135 ], s Gff has limited biosynthetic capacity and relies on its host for essential metabolites. Unlike its sister taxon s MSRO that utilizes only glucose [ 36 ], s Gff appears to use both fructose and glucose as primary carbon sources. Notably, a functional trehalose transporter was absent, which may reduce s Gff pathogenicity, as trehalose is abundant in tsetse [ 136 ]. The inability of sGff to utilize trehalose could limit its replication within the host and reduce host resource exploitation, thereby supporting a more commensal relationship. Transcriptomic analysis revealed condition-specific regulation of carbohydrate transporters, where fructose-specific transporters were upregulated in hemolymph-derived s Gff, while glucose-specific transporters were downregulated. This finding likely reflects differences in nutrient availability, as fructose is absent from the culture medium, and suggests that fructose supplementation may accelerate in vitro growth. Nutrient-driven tissue tropism may also influence s Gff distribution in its Gff host. High fructose levels in reproductive tissues [ 137 ] could explain the elevated s Gff titers observed in these organs, while post-blood meal glucose spikes [ 138 ] may promote gut colonization and proliferation. We also identified a transporter for diacetylchitobiose, a sugar derived from chitin. While s Gff encodes several putative chitinases, it may access chitin breakdown products indirectly, potentially through Sodalis -derived secreted chitinase enzymes [ 139 ]. Beyond carbohydrates, s Gff scavenges host-derived fatty acids and cholesterol, and like other Spiroplasma strains, can incorporate them directly into its membrane [ 94 ]. Pathway reconstructions also indicate that s Gff can metabolize host diacylglycerols (DAGs) to synthesize cardiolipins, which are key membrane lipids in Spiroplasma [ 94 ] that could be cytotoxic [ 36 ] and in Drosophila , serve as important virulence factors by depleting host DAGs [ 95 ]. In Gff flies, the lipid-depleting phenotype associated with s Gff is supported by previous work that shows decreased expression of Gff genes involved in fatty acid synthesis in s Gff infected flies, suggesting reduced lipid availability [ 16 ]. In addition, triacylglycerol (TAG) levels are decreased in the fat bodies of s Gff-infected tsetse [ 17 ] and since TAGs are synthesized from DAGs, sGff’s consumption of host DAGs may be contributing to this decrease. Functional annotations also indicated that s Gff can produce ferritin and a hemolysin-related protein, both of which are used for iron sequestration. Iron sequestration is an important aspect of nutritional immunity, limiting microbe overproliferation and providing pathogen resistance [ 111 , 140 , 141 ] and In Drosophila , the iron-binding protein transferrin is upregulated in the presence of s MSRO, which relies on host transferrin-bound iron for survival [ 142 ]. Finally, consistent with other Spiroplasma species, s Gff is unable to synthesize most amino acids, and instead must import them from the host, further emphasizing its nutritional dependence on Gff [ 97 , 143 ]. Our metabolic analysis reveals an intriguing potential competitive dynamic between s Gff and trypanosomes within the tsetse gut, as both microorganisms require the same host-derived resources. In the Gff gut, both organisms prefer glucose as their primary carbohydrate source, with trypanosomes later switching to proline [ 138 ] – an amino acid that s Gff also catabolizes. Trypanosomes, like s Gff, scavenge host lipids and cholesterol [ 144 ] and both reduce the expression of Gff genes associated with lipid biosynthesis [ 16 ] and deplete host TAG reserves [ 17 , 145 ]. While trypanosomes acquire transferrin-bound iron through a high affinity receptor [ 146 ], s Gff likely acquires transferrin-bound iron in a manner similar to its sister s MSRO strain [ 142 ]. This potential multifaceted resource competition may contribute to the trypanosome-refractory phenotype observed in s Gff-infected flies [ 18 ]. By limiting parasite access to essential nutrients, s Gff may delay parasite proliferation, potentially providing the host sufficient time to mount an effective immune response. These competitive dynamics require new studies to characterize sGff’s metabolic demands in vivo and to test how resource competition influences host-pathogen dynamics, perhaps contributing to trypanosome resistance in tsetse. In addition to a nutritional impact, Spiroplasma produces a diverse repertoire of toxins that can impact host fitness via reproductive manipulation or protection against parasites and pathogens [ 109 , 110 ]. In s Gff, we identified a limited set of potential encoded toxins: two putative Glycerol-3-phosphatase ( glpO ) genes and one ribosome-inactivating-protein (RIP) gene – all of which are expressed according to our transcriptome data. The glpO enzyme generates reactive oxygen species (ROS) and is a major virulence factor in Mycoplasma [ 147 ] and may protect s MSRO-infected Drosophila against parasitoids [ 36 ]. In response to trypanosome infection in the gut, tsetse express nitric-oxide synthase (NOS), which catalyzes the production of trypanocidal nitric oxide (NO) and ROS [ 16 ]. These ROS are an essential part of the tsetse innate immune response, such that supplementing infected blood meals with antioxidants significantly enhances trypanosome infection success [ 148 ]. Thus, the ROS produced by glpO encoded by s Gff could contribute to Gff’s immune response and enhance its trypanocidal effect. We identified one well-conserved RIP gene in the s Gff genome, which is transcriptionally active. RIPs act by depurinating the sarcin-ricin loop of 28s rRNA, thereby damaging host ribosomes and causing cell-death [ 149 ]. In sister S. poulsonii strains, s MSRO, s HYD, and s NEO, RIPs can protect their hosts from parasitic nematodes and parasitoid wasps [ 28 , 29 ]. Unlike most RIP genes present in the S. poulsonii clade, which are typically secreted, the s Gff RIP gene lacks a signal peptide, [ 109 ] and is predicted to be anchored in the cell membrane. In dense microenvironments such as plaques, high concentration of the membrane-bound RIPs could exert toxic effects in close proximity, while having limited effects at a distance. Alternatively, the RIP may be proteolytically cleaved from the membrane and released into the extracellular space, enabling it to act on specific targets, or perhaps secreted by MATE family efflux transporters encoded within the same operon. Given that secreted RIPs exhibit high substrate specificity, future work is needed to determine the nature of its molecular targets and whether this RIP remains membrane-associated or is secreted. Furthermore, other potential RIP functions need to be evaluated, including enzymatic activity such as chitinase or phosphatase activity on lipids [ 150 ]. RIPs can also impact host tissues and incur fitness costs [ 28 ], but such effects may be tolerated or advantageous in contexts where protection against parasites or pathogens improves host survival. This trade-off could explain the polymorphic s Gff distribution in Gff populations in northern Uganda, where it persists at about ~ 30% prevalence [ 18 ]. The potential for RIPs to mediate protective benefits in a population-specific manner warrants future research, particularly to determine whether s Gff-derived RIPs are active against trypanosomes. Conclusions Here, we report the first successful in vitro cultivation and complete genome assembly of the Spiroplasma glossinidia strain s Gff — the Spiroplasma symbiont of Gff associated with deleterious effects on host metabolism and a trypanosome-refractory phenotype. We assembled a closed, circular ~1.5 Mb genome that clusters within the S. poulsonii clade. Comparative genomic analyses of cultured, laboratory- and field-derived s Gff isolates revealed only minor isolate-specific genetic variations, residing primarily in mobile genetic elements in field-derived s Gff. Metabolic profiling confirmed that s Gff has limited biosynthetic capacity and relies on its Gff host for essential carbohydrates, lipids, and amino acids. We also identified two putative toxin genes: RIP and glpO that may contribute to its trypanocidal potential. Our genomic discoveries and the availability of a stable culture system will enable future functional studies to elucidate this symbiosis and identify potential implications for trypanosome transmission control. Symbionts can protect their hosts through several mechanisms, including immune priming, nutrient supplementation, competitive exclusion of pathogens, or secretion of effector proteins that target pathogens. In the case of s Gff, previous work found no evidence for activation of the tsetse immune system [16], and our work here identified no key nutrient supplementation apart from folate, which is already produced in excess by Gff’s obligate symbiont Wigglesworthia . However, we identified several points of potential metabolic competition between s Gff and trypanosomes, including glucose, cholesterol, fatty acids and iron, which could collectively have an additive effect to limit parasite fitness. In addition, the two toxin genes identified, glpO and RIP, represent potential candidates for direct trypanocidal activity. These genomic discoveries, along with the establishment of a stable culture system, lay the foundation for functional studies to dissect the mechanisms of trypanosome resistance. Such studies may involve metabolomics, RNAseq, culture media modifications, tsetse transfections, or perhaps s Gff gene knockouts. Future work also needs to address fundamental aspects of s Gff biology, including its tissue specific distribution, specific roles of s Gff within different host compartments, and strategies it uses for vertical transmission. Expanding transcriptomic analyses to additional tissues, such as the reproductive organs, salivary glands, and midgut, will help reveal whether s Gff gene expression is spatially regulated and potentially linked to the diverse physiological phenotypes observed in the tsetse host. Abbreviations AAT: African Animal Trypanosomiasis ANI: Average Nucleotide Identity BLASTP: Basic Local Alignment Search Tool for proteins BSK-H medium: Barbour-Stoenner-Kelly H medium BUSCO: Benchmarking Universal Single-Copy Orthologs CDS: Coding Sequences COG : Cluster of Orthologous Groups DAGs: Diacylglycerols DNA: Deoxyribonucleic acid E. coli : Escherichia coli FDR: False-Discovery Rate GC content: Guanine-Cytosine content Gff : Glossina fuscipes fuscipes HAT: Human African Trypanosomiasis HMW DNA: High Molecular Weight Deoxyribonucleic Acid IAEA: International Atomic Energy Agency ICEs: Integrative Conjugative Elements ISEs: Insertion Sequence Elements KEGG: Kyoto Encyclopedia of Genes and Genomes MGEs: Mobile Genetic Elements ncRNAs: non-coding Ribonucleic Acids NCBI: National Center for Biotechnology Information NCBI SRA: National Center for Biotechnology Information Sequence Read Archive NO: Nitric Oxide ONT: Oxford Nanopore Technologies PCR: Polymerase Chain Reaction p_sGff: Plasmid of Spiroplasma endosymbiont of Glossina fuscipes fuscipes PTS transporters: Phosphotransferase System Transporter qPCR: quantitative Polymerase Chain Reaction RIPs: Ribosome-inactivating Proteins RNA / rRNA: Ribonucleic Acid / ribosomal Ribonucleic Acid RNAseq: Ribonucleic Acid Sequencing ROS: Reactive Oxygen Species s Gff: Spiroplasma endosymbiont of Glossina fuscipes fuscipes / strain s Gff s MSRO: Spiroplasma poulsonii Melanogaster Sex-Ratio Organism SNPs: Single Nucleotide Polymorphisms Sp AID: Spiroplasma poulsonii Androcidin TAG: Triacylglyceride TPM: Transcripts per Million tRNA: transfer Ribonucleic Acids Declarations Ethics approval and consent to participate Not applicable. Availability of data and materials All genomic and transcriptomic data generated in this study is available at the NCBI BioProject: PRJNA1235259. Raw data from culture growth kinetics and injection experiments is deposited under https://doi.org/10.60600/YU/2979VU. Competing interests The authors declare no competing interests. Funding This work was generously supported with funding from Ambrose Monell Foundation (to SA), National Institutes of Health (R01AI068932 and R01AI139525 to SA) and National Institutes of Health (R21AI163969 to SA and B.L.W). This work also was funded by the International Atomic Energy Agency under the coordinated research project D42017 and the regular budget of the Joint FAO/IAEA Centre of Nuclear Techniques in Food and Agriculture for the project 2.1.4.2: Management of transboundary livestock insect pests for sustainable agriculture and rural development. The funders played no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Authors' contributions D.J.B., F.G., B.L.W., R.M., A.M.A. and S.A. conceived and designed the study. F.G. initiated in vitro cultures. D.J.B., F.G. and A.M.A. collected samples and generated sequencing data. F.G. and H.K. performed injection experiments. D.J.B. and F.G. analyzed and interpreted data. D.J.B., F.G., B.L.W. and S.A. wrote the manuscript. All authors contributed to the manuscript with comments and edits. B.L.W., S.A. and A.M.A. provided project supervision. D.J.B. and F.G. are equally contributing first authors. Acknowledgements The authors would like to acknowledge all involved personnel at the Joint FAO/IAEA Centre of Nuclear Techniques in Food and Agriculture, Insect Pest Control Subprogram for tsetse rearing and colony management. We are grateful to Florent Masson who provided detailed insights into in vitro cultivation methods. 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Additional Declarations No competing interests reported. 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As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7295611","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":504855922,"identity":"5c894517-83be-4877-b78a-e1615c26ba3c","order_by":0,"name":"Daniel J. 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Scale bar: 5 μm.\u003c/p\u003e\n\u003cp\u003eB) High density \u003cem\u003es\u003c/em\u003eGff culture at passage nine. Scale bar: 100 μm.\u003c/p\u003e\n\u003cp\u003eC) Growth kinetics of\u003cem\u003es\u003c/em\u003eGff in supplemented BSK-H media at 25°C under microaerobic conditions, assessed by qPCR with threefold technical and biological replication.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7295611/v1/d7fb50d4ca272a87997aa63c.png"},{"id":89801640,"identity":"cc0b7791-267b-4527-81a0-390245f9dcab","added_by":"auto","created_at":"2025-08-25 08:21:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":121254,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhylogenetic placement of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003es\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eGff among representative \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eSpiroplasma\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e genomes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMaximum likelihood (RAxML) tree showing the relationship between \u003cem\u003es\u003c/em\u003eGff and representative \u003cem\u003eSpiroplasma\u003c/em\u003e species from the \u003cem\u003eSpiroplasma citri\u003c/em\u003e, \u003cem\u003eSpiroplasma mirum, Spiroplasma chrysopicola, \u003c/em\u003eand \u003cem\u003eSpiroplasma poulsonii \u003c/em\u003eclades (see Supplementary Table 4 for accession details). Bootstrap support values are indicated on branches and the tree is midpoint rooted.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7295611/v1/a1f7079b6b7ba33ca7953880.png"},{"id":89802777,"identity":"81ca00d3-5e0b-4f76-a89e-a887cb5cf745","added_by":"auto","created_at":"2025-08-25 08:37:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":42185,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGenome comparisons between \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003es\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eGff isolates and their closest relative, \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003es\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eTU-14.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA) Average Nucleotide Identity (ANI) values between \u003cem\u003es\u003c/em\u003eGff isolates and \u003cem\u003es\u003c/em\u003eTU-14, indicating high sequence similarity.\u003c/p\u003e\n\u003cp\u003eB) Venn diagram showing shared and unique gene families for \u003cem\u003es\u003c/em\u003eGff isolates and \u003cem\u003es\u003c/em\u003eTU-14.\u003c/p\u003e\n\u003cp\u003eC) Genome synteny plot illustrating conserved gene order among \u003cem\u003es\u003c/em\u003eGff isolates, highlighting structural similarities and isolate-specific variations.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7295611/v1/1282763639be143d05481314.png"},{"id":89801649,"identity":"15b09c21-8c60-4222-adb8-68b24225bdf3","added_by":"auto","created_at":"2025-08-25 08:21:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":85650,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCircular genome plot of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003es\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eGff-IAEA-CC showing the distribution of Mobile Genetic Elements (MGE). \u003c/strong\u003eThe genome is represented in nine concentric rings (1-9), each corresponding to a feature labeled in the center of the plot. The figure highlights the high proportion and broad distribution of MGE-associated genes across the chromosome\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7295611/v1/9cb7351e32d6d6c4ab429a3e.png"},{"id":89801647,"identity":"187c7a01-11ca-4ce0-8533-bbd6cf934b69","added_by":"auto","created_at":"2025-08-25 08:21:21","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":155664,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential gene expression between culture- and hemolymph-derived \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003es\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eGff.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA) MA plot depicting differentially expressed genes (DEGs) and their expression levels, with positive log-fold change values representing genes up-regulated in hemolymph-derived \u003cem\u003es\u003c/em\u003eGff relative to culture-derived \u003cem\u003es\u003c/em\u003eGff, and negative log-fold change values representing down-regulation. Genes were considered differentially expressed if they had a log₂-fold change \u0026gt; 1.5 with FDR \u0026lt; 0.05 (Supplementary Table 22). Labeled genes correspond to those highlighted in panel B.\u003c/p\u003e\n\u003cp\u003eB) Functional annotations and genomic locations (chromosome or plasmid) of the DEGs shown in panel A.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7295611/v1/b6875eaf2c7eeb78d7c95b40.png"},{"id":96650380,"identity":"44506eaf-76f1-46de-ad96-1509eed5b45b","added_by":"auto","created_at":"2025-11-24 16:11:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7502053,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7295611/v1/b1299813-27c1-432f-8fd6-f0badf5a3c84.pdf"},{"id":89801641,"identity":"2adbea64-cbcd-4562-917c-35fedba686d9","added_by":"auto","created_at":"2025-08-25 08:21:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":184945,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryDocument1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7295611/v1/0d2375c797dfed79fb004c28.pdf"},{"id":89801646,"identity":"23d19b55-9bc9-406d-b709-0bf471b387f4","added_by":"auto","created_at":"2025-08-25 08:21:21","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1001703,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables122.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7295611/v1/f2e13c5555b6debf559385aa.xlsx"},{"id":89802550,"identity":"680b46ad-28aa-4339-88d7-d6b753e4831a","added_by":"auto","created_at":"2025-08-25 08:29:21","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":387280,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-7295611/v1/8bce08378578b699a64a05ee.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eComparative genomics and transcriptomics of the \u003cem\u003eSpiroplasma glossinidia\u003c/em\u003e strain \u003cem\u003es\u003c/em\u003eGff reveal insights into host interaction and trypanosome resistance in \u003cem\u003eGlossina fuscipes fuscipes\u003c/em\u003e\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eTsetse (\u003cem\u003eGlossina\u003c/em\u003e spp.) transmit African trypanosomes, the causative agents of Human and African Animal Trypanosomiases (HAT and AAT, respectively). Approximately 60\u0026nbsp;million people in sub-Saharan Africa live in tsetse-infested areas at risk for HAT, while AAT constrains livestock productivity across much of the continent [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. With no vaccines available for either disease, vector-control strategies remain paramount for disease management [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Complementary approaches that block or reduce trypanosome development in the fly have the potential to enhance disease control efforts [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Successful transmission of trypanosomes is influenced by a combination of intrinsic factors, including the fly\u0026rsquo;s innate immunity, host and parasite genotypes, as well as host nutritional status at the time of parasite acquisition [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In addition to these intrinsic factors, extrinsic factors, including environmental factors and the composition of fly\u0026rsquo;s microbiota, also modulate pathogen transmission efficiency [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In this context, modification of tsetse\u0026rsquo;s heritable symbiotic microbes, some of which coexist in close proximity to trypanosomes in the midgut, provide a promising avenue for \u0026ldquo;paratransgenic\u0026rdquo; interventions [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTsetse species harbor a complex community of heritable symbionts, each playing distinct roles in host biology. All tsetse species carry the obligate mutualist \u003cem\u003eWigglesworthia\u003c/em\u003e, which supplements the fly\u0026rsquo;s nutrient-restricted blood diet with essential vitamins necessary for reproductive success [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], and proper development of the fly\u0026rsquo;s immune system [\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. \u003cem\u003eWolbachia\u003c/em\u003e infects tsetse species and can induce cytoplasmic incompatibility, potentially influencing population structure and mating compatibility [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. \u003cem\u003eSodalis\u003c/em\u003e also colonizes tsetse species, with its presence correlated with trypanosome infection prevalence in some contexts, depending on geographic location and tsetse species [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Finally, some tsetse house \u003cem\u003eSpiroplasma glossinidia\u003c/em\u003e, which influences several key tsetse processes, including immune modulation, reproduction, and capacity for trypanosome transmission [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Despite \u003cem\u003eSpiroplasma's\u003c/em\u003e substantial impacts on tsetse biology, the underlying molecular mechanisms governing the bacterium's interactions with tsetse remain largely unknown.\u003c/p\u003e\u003cp\u003e\u003cem\u003eSpiroplasma\u003c/em\u003e are helical, wall-less Mollicutes, first characterized as plant pathogens [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], but are now known to colonize a wide range of arthropod hosts, most commonly infecting insects [\u003cspan additionalcitationids=\"CR22 CR23 CR24 CR25 CR26\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In insects, \u003cem\u003eSpiroplasma\u003c/em\u003e exhibits a broad range of symbiotic phenotypes, including protective effects in different \u003cem\u003eDrosophila\u003c/em\u003e species against parasitic wasps and nematodes by either producing ribosome-inactivating proteins (RIPs), which disrupt parasite protein synthesis machinery [\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], or by competing with the parasite for macronutrients [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Conversely, some \u003cem\u003eSpiroplasma\u003c/em\u003e strains act as reproductive parasites in insects, such as in \u003cem\u003eDrosophila\u003c/em\u003e [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], \u003cem\u003eAnisosticta\u003c/em\u003e (ladybugs) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], and \u003cem\u003eDanaus\u003c/em\u003e butterflies [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], where they induce selective male-killing. Of note, \u003cem\u003eSpiroplasma\u003c/em\u003e genomes evolve rapidly, at rates comparable to RNA viruses [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. This rapid pace of evolution is attributed to the abundance of mobile genetic elements and absence of key DNA mismatch repair genes, which together promote genomic plasticity, rapid diversification, and horizontal gene transfer of these key symbiosis genes [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWithin \u003cem\u003eGlossina\u003c/em\u003e, \u003cem\u003eS. glossinidia\u003c/em\u003e infections are limited to species in the \u003cem\u003ePalpalis\u003c/em\u003e subgroup, including \u003cem\u003eGlossina fuscipes fuscipes (Gff\u003c/em\u003e), \u003cem\u003eGlossina palpalis palpalis\u003c/em\u003e, and \u003cem\u003eG. tachinoides\u003c/em\u003e [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In Uganda, the \u003cem\u003eS. glossinidia\u003c/em\u003e strain infecting \u003cem\u003eGff\u003c/em\u003e (\u003cem\u003es\u003c/em\u003eGff) is geographically restricted and polymorphic, with prevalence ranging from 5\u0026ndash;34% in Northwestern populations, while absent from Central and Southern regions. The infection prevalence in Uganda remains relatively stable across time and space, although seasonality can impact infection dynamics [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Interestingly, in the \u003cem\u003eGff\u003c/em\u003e line reared from the Insect Pest Control laboratory at the International Atomic Energy Agency (IAEA), \u003cem\u003es\u003c/em\u003eGff infection is also not fixed, but is stably maintained at approximately 50% prevalence [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Laboratory transmission studies in this \u003cem\u003eGff\u003c/em\u003e line indicate that \u003cem\u003es\u003c/em\u003eGff is maternally inherited with high fidelity, although paternal transmission can also occur [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The bacterium colonizes multiple tissues, such as the gonads, gut, and hemolymph [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. \u003cem\u003eGff\u003c/em\u003e infected with \u003cem\u003es\u003c/em\u003eGff show altered gene expression in reproductive and gut tissues [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], reduced hemolymph triacylglyceride (TAG) levels, impaired sperm fitness, and reduced female fecundity [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In addition, \u003cem\u003es\u003c/em\u003eGff infection is negatively correlated with trypanosome infection prevalence in both field and lab populations, suggesting that presence of the bacterium induces a parasite refractory phenotype in tsetse [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWhether \u003cem\u003es\u003c/em\u003eGff directly (e.g., via the production of anti-trypanosomal factors) or indirectly (e.g., competition for nutrients) confers the observed parasite resistance phenotype to its tsetse host remains unknown. Transcriptomic analyses of \u003cem\u003es\u003c/em\u003eGff\u003cem\u003e-\u003c/em\u003einfected \u003cem\u003eGff\u003c/em\u003e midguts revealed minimal immune stimulation but indicated elevated oxidative stress and impaired lipid biosynthesis [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These changes may create a hostile gut environment for parasites by increasing production of trypanocidal nitric oxide (NO) and/or by reducing the availability of key metabolites required for trypanosome survival.\u003c/p\u003e\u003cp\u003eTo investigate the mechanisms by which \u003cem\u003eSpiroplasma\u003c/em\u003e alters tsetse\u0026rsquo;s physiology and impacts vector competence, we leveraged recently developed \u003cem\u003ein vitro\u003c/em\u003e methods to culture \u003cem\u003es\u003c/em\u003eGff. We then sequenced whole genomes of \u003cem\u003es\u003c/em\u003eGff from \u003cem\u003ein vitro\u003c/em\u003e culture, laboratory-reared, and wild-caught \u003cem\u003eGff\u003c/em\u003e individuals using the Oxford Nanopore Technology (ONT) platform. Comparative analysis of the three \u003cem\u003es\u003c/em\u003eGff genomes revealed subtle isolate-specific genetic variation residing primarily in mobile genetic elements. We also characterized \u003cem\u003es\u003c/em\u003eGff gene expression from both \u003cem\u003ein vitro s\u003c/em\u003eGff and \u003cem\u003es\u003c/em\u003eGff-infected hosts, and identified core metabolic pathways and candidate toxins that could mediate the tsetse-\u003cem\u003es\u003c/em\u003eGff symbiosis. Collectively, this study lays the foundation for dissecting the molecular basis of the \u003cem\u003es\u003c/em\u003eGff\u0026ndash;\u003cem\u003eGff\u003c/em\u003e interactions and for evaluating its potential in trypanosomiasis control strategies.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch3\u003e\u003cstrong\u003eInitiation of \u003cem\u003esGff\u003c/em\u003e \u003cem\u003ein vitro\u003c/em\u003e culture\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eEstablishment of the \u003cem\u003es\u003c/em\u003eGff\u003cem\u003e\u0026nbsp;in vitro\u0026nbsp;\u003c/em\u003eculture followed the protocol of Masson et al. [39], developed and optimized for the cultivation of \u003cem\u003es\u003c/em\u003eMSRO. This protocol is based on Barbour-Stoenner-Kelly H (BSK-H) media without L-Glutamine (Bio\u0026amp;Sell, Germany), originally designed for \u003cem\u003eBorrelia\u003c/em\u003e \u003cem\u003eburgdorferi,\u0026nbsp;\u003c/em\u003esupplemented with rabbit serum, \u003cem\u003eGff\u0026nbsp;\u003c/em\u003efly extract, lipids, antibiotics, and amino acids.\u003cem\u003e\u0026nbsp;\u003c/em\u003eDetailed media and cultivation protocols are indicated in Supplementary Document 1. Briefly, hemolymph from female and male \u003cem\u003eGff\u0026nbsp;\u003c/em\u003efrom a laboratory line with high \u003cem\u003es\u003c/em\u003eGff infection prevalence [38] was collected by removing one prothoracic leg and aspirating the exposed hemolymph droplet using a 10 \u0026mu;l pipette tip. Cultures were established in three biological replicates using 10 \u0026mu;l hemolymph and incubated at 25\u0026deg;C for 14 days without agitation under microaerobic atmospheric conditions. Routine microscopy checks were performed by fluorescent microscopy on a Leica DMi8 inverted microscope (Leica Microsystems) following staining with SYTO 9 (0.025 mM; ThermoFisher Scientific). \u003cem\u003eSpiroplasma\u0026nbsp;\u003c/em\u003epresence was further confirmed by PCR-amplification using \u003cem\u003eSpiroplasma-\u003c/em\u003especific\u003cem\u003e\u0026nbsp;\u003c/em\u003e16s rRNA primers (Supplementary Table 1). Cultures underwent passaging every 10 - 14 days by diluting cultures 1:1 with fresh media.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e\u003cem\u003eIn vitro\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;culture growth kinetics\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eSamples from \u003cem\u003es\u003c/em\u003eGff cultures for growth curve analysis were collected in triplicate biological replicates from day 0 to day 15 following the inoculation of BSK-H-spiro media with \u003cem\u003es\u003c/em\u003eGff precultures. Each day, five \u0026mu;l of culture was diluted in 200 \u0026mu;l nuclease-free double-distilled water (ddH\u003csub\u003e2\u003c/sub\u003eO) in a sterile PCR tube and stored at -20\u0026deg;C. Prior to quantitative PCR (qPCR), samples were lysed via osmotic heat-shock by incubation at 95\u0026deg;C for 10 minutes. qPCR reactions were performed in triplicate technical replicates, in a final volume of 15 \u0026mu;l consisting of 7.5 \u0026mu;l iQ\u0026trade; SYBR\u0026reg; Green Supermix (Bio-Rad, CA, USA), 5.5 \u0026mu;l PCR-grade water, 0.5 \u0026mu;l each of the forward and reverse \u003cem\u003eSpiroplasma\u003c/em\u003e qPCR primers (Supplementary Table 1), and 1 \u0026mu;l heat-shocked culture. To accurately estimate \u003cem\u003eSpiroplasma\u0026nbsp;\u003c/em\u003ecopy numbers, a standard was prepared by purifying \u003cem\u003es\u003c/em\u003eGff\u003cem\u003e-\u003c/em\u003especific qPCR amplicons using the Zymo DNA Clean \u0026amp; Concentrator-25 kit (Zymo Research, USA), followed by DNA quantification with a nanodrop spectrophotometer. The purified DNA was then serially diluted 1:10 to create a range of known concentrations. Cycling conditions for the qPCR comprised of an initial denaturation step at 95\u0026deg;C for 2 min, followed by 40 cycles of 95\u0026deg;C for 5 sec, and 56\u0026deg;C for 30 sec on a\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eBio-Rad CFX96 Touch Real-Time PCR Detection System (Hercules, CA, USA). Raw Ct values were extracted in the CFX maestro software and analyzed using R Studio v4.4.2 [40, 41]. Briefly, the mean value of the three technical replicates of each sample was calculated and the copy number of each sample was inferred based on the standard curve of known copy numbers [42]. Calculation of culture doubling time was performed according to the formula \u003cimg width=\"92\" height=\"22\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u0026nbsp;[43]. \u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eInjections of \u003cem\u003ein vitro\u003c/em\u003e cultured \u003cem\u003esGff\u003c/em\u003e into na\u0026iuml;ve \u003cem\u003eGff\u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eTeneral females and males from the \u003cem\u003eGff\u003c/em\u003e line were placed in individual cages and their \u003cem\u003eSpiroplasma\u0026nbsp;\u003c/em\u003einfection status was determined by PCR-amplification of DNA extracted from one mesothoracic leg using \u003cem\u003eSpiroplasma\u0026nbsp;\u003c/em\u003e16s rRNA and tsetse tubulin gene primers (Supplementary Table 1). Negative-\u003cem\u003es\u003c/em\u003eGff flies were pooled into two treatment groups (12 females and 6 males per group): (1) high dose and (2) low dose. In addition, 12 females and 6 males screened positive for \u003cem\u003es\u003c/em\u003eGff\u003cem\u003e\u0026nbsp;\u003c/em\u003ewere retained as a control group with a natural infection. \u003cem\u003eGff\u003c/em\u003e were injected using 29-gauge insulin needles on a micrometer syringe unit injector (Burkard Manufacturing, UK). Injected copy numbers were estimated via quantitative PCR compared to a standard curve of known copy numbers: High Dose: 1.1 x 10\u003csup\u003e5\u0026nbsp;\u003c/sup\u003ecopies in Replicate 1 and 4.3 x 10\u003csup\u003e5\u003c/sup\u003e copies in Replicate 2; Low Dose: 5.7 x 10\u003csup\u003e2\u003c/sup\u003e copies in Replicate 1 and 1.2 x 10\u003csup\u003e3\u003c/sup\u003e copies in Replicate 2. Three hours post-injection, flies were offered their first blood meal and subsequently maintained under standard rearing conditions [44] for 14 days, receiving defibrinated bovine blood three times a week via an artificial membrane feeding system [45]. At designated time points (0, 1, 6, 9 and 14 days), two females and one male were collected, flash-frozen, and stored at -20\u0026deg;C. Genomic DNA from individual whole flies was extracted using the Qiagen DNeasy Blood \u0026amp; Tissue kit (Qiagen, Hilden, GER) according to the manufacturer\u0026rsquo;s protocols and diluted to 4 ng/\u0026mu;l. The relative abundance of \u003cem\u003es\u003c/em\u003eGff over time was measured by qPCR, with three technical replicates per sample. Ct values for \u003cem\u003esGff\u003c/em\u003e were normalized to those of the tsetse housekeeping gene \u0026beta;-tubulin (Supplementary Table 1) and relative fold-change was calculated using the 2\u003csup\u003e-(\u0026Delta;Ct) \u0026nbsp;\u003c/sup\u003emethod [46]. Statistical analysis was performed in RStudio v4.4.2 [40, 41]. Significant differences in \u003cem\u003es\u003c/em\u003eGff fold-change across timepoints between and within each group were identified using the Kruskal-Wallis test and Dunn\u0026rsquo;s post-hoc test with Benjamini-Hochberg correction for multiple comparisons.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;s\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003eGff sequencing and assembly\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThree \u003cem\u003eGff Spiroplasma\u0026nbsp;\u003c/em\u003e(\u003cem\u003es\u003c/em\u003eGff) whole genome assemblies were generated: (1) \u003cem\u003es\u003c/em\u003eGff-IAEA-CC - derived from the\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003ein vitro\u003c/em\u003e culture of \u003cem\u003eSpiroplasma\u003c/em\u003e established in 2023 from IAEA \u003cem\u003eGff\u003c/em\u003e females and males as described above; (2) \u003cem\u003es\u003c/em\u003eGff-IAEA-Fly \u003cem\u003e-\u0026nbsp;\u003c/em\u003eassembled from a previously sequenced \u003cem\u003eSpiroplasma-\u003c/em\u003einfected \u003cem\u003eGff\u0026nbsp;\u003c/em\u003efemale obtained in 2023 from the IAEA colony (NCBI BioSample: SAMN47211303 and NCBI SRA: SRR32737746), and (3) \u003cem\u003es\u003c/em\u003eGff-UG-Tol - assembled from a previously sequenced \u003cem\u003eSpiroplasma\u003c/em\u003e-infected \u003cem\u003eGff\u0026nbsp;\u003c/em\u003efemale collected in 2019 from Toloyang village in Atiak Sub County, Amuru District, Uganda (3.304883654, 32.37452634) (NCBI BioSample: SAMN47211302 and NCBI SRA: SRR32737747) [47].\u003c/p\u003e\n\u003cp\u003eFor \u003cem\u003es\u003c/em\u003eGff-IAEA-CC genome, high density\u003cem\u003e\u0026nbsp;in vitro\u003c/em\u003e \u003cem\u003eSpiroplasma\u0026nbsp;\u003c/em\u003eculture (~10\u003csup\u003e6\u003c/sup\u003e cells) was collected at passage nine, pelleted, and washed with PBS. HMW DNA was extracted using the PureGene kit (Qiagen, Hilden, GER). An ONT LSK14 library was prepared following standard protocols and sequenced on a ONT 10.4.1 minION flow cell for 12 hours. Generated POD5 signal data files were basecalled with Dorado v0.9.1 (https://github.com/nanoporetech/dorado) using the SUP duplex pipeline. Simplex reads were dropped with Samtools v1.18 [48], and remaining sequencing adapters were trimmed using Dorado trim. The produced FASTQ files were quality filtered with nanoq v0.10.0 [49] to have a minimum quality of q10 and minimum 3000 bp read length. To assemble the \u003cem\u003es\u003c/em\u003eGff-IAEA-CC genome, Autocycler v0.2.1 (https://github.com/rrwick/Autocycler) was used to generate multiple assemblies from four independent subsampled 100X read sets using five assemblers for each read set (Flye v2.9.5 [50], Canu v2.3 [51], miniasm v.3 [52], NECAT [53], and NextDenovo v2.5.2 [54]). A consensus assembly for these 20 assemblies was then determined with Autocycler v0.2.1 and polished with Medaka v2.0.1 using the bacteria model (https://github.com/nanoporetech/medaka). The genome was further polished using short reads made from the same HMW DNA used for ONT sequencing. These reads were sequenced on an Illumina NovaSeq X system (Eurofins Genomics, Germany) and quality trimmed to Q30 with a minimum length of 50 bp with fastp v0.24.0 [55]. Short read polishing was performed with Polypolish v0.6.0 [56] followed by pypolca v0.3.1 [57].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo assemble the \u003cem\u003es\u003c/em\u003eGff-IAEA-Fly genome, we mapped raw Nanopore simplex reads generated with the SUP Dorado v0.9.0 model from the \u003cem\u003eGff\u003c/em\u003e-IAEA genome assembly (SRR32737746) to the \u003cem\u003es\u003c/em\u003eGff-IAEA-CC assembly using minimap2 v2.28 [58]. Unmapped reads were dropped with Samtools. The remaining simplex reads were precorrected with Dorado correct to generate near\u0026ndash;Hifi quality reads and were assembled using six assemblers with varying parameters (Hifiasm v0.25 [59], Flye, Canu, miniasm, NECAT, and NextDenovo). A consensus assembly was determined with Autocycler v0.2.1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinally, to assemble the \u003cem\u003es\u003c/em\u003eGff-UG-Tol genome, we mapped raw Nanopore simplex reads generated with the SUP Dorado v0.9.0 model from the \u003cem\u003eGff\u003c/em\u003e-IAEA genome assembly (SRR32737747) to the \u003cem\u003es\u003c/em\u003eGff-IAEA-CC assembly using minimap2. Unmapped reads were dropped with Samtools. Due to the shorter read-lengths of this dataset we were unable to utilize Dorado correct to precorrect ONT reads. Instead, we assembled the mapped ONT Simplex reads using five assemblers (Hifiasm (--ont), Flye, miniasm, NECAT, and NextDenovo). A consensus assembly was determined with Autocycler v0.2.1 and was polished with Medaka v2.0.1 using the bacteria model.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;All completed \u003cem\u003es\u003c/em\u003eGff circular genomes and plasmids were reoriented to start with the \u003cem\u003ednaA\u003c/em\u003e or \u003cem\u003erepA\u003c/em\u003e with Dnaapler v0.7.0 [60]. Assembly stats for the assembled genomes were established using gfastats v1.3.10 [61] and genome completeness was assessed with BUSCO v5.8.3 [62] using the entomoplasmatales_odb10 dataset. Plasmids were clustered with pling v2 [63] to determine similarity and were named in order according to their size. All genomic data were deposited under the NCBI BioProject: PRJNA1235259.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eFunctional annotations\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eAssembled genomes were first annotated using the NCBI Prokaryotic Genome Annotation Pipeline [64]. For the reference \u003cem\u003es\u003c/em\u003eGff-IAEA-CC genome, additional functional annotations were added with Bakta [65], eggNOG-mapper [66], BlastKOALA [67], and COGclassifier (https://github.com/moshi4/COGclassifier). Mobile genetic elements were annotated using PHASTEST [68] for prophages, ICEberg v3 [69] for integrative conjugative elements, and ISEScan v1.7.3 [70] for insertion sequence elements. Metabolic pathways were characterized with the KEGG terms assigned above with eggNOG-mapper [66] and BlastKOALA [67]. Putative proteins were analyzed using SignalP v6 [71] to determine N-terminal signal peptide sequences, indicative of secretion. Finally, \u003cem\u003eSpiroplasma\u0026nbsp;\u003c/em\u003especific symbiosis genes identified from the literature were manually searched for with BLASTP [72] (Supplementary Table 2). To better characterize the RIP identified in \u003cem\u003es\u003c/em\u003eGff, we generated a phylogeny using \u003cem\u003eSpiroplasma\u0026nbsp;\u003c/em\u003emembers of the RIP superfamily from InterPro [73] and our putative RIP sequence (Supplementary Table 3). The tree was constructed using RAxML-NG [74] with the LG+G8+F model, with 25 parsimony and 25 random starting trees with 1000 bootstraps using PRANK [75] aligned amino acid sequences trimmed to only contain RIP domains.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e\u003cem\u003eSpiroplasma\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cstrong\u003ephylogenomics\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eTo determine \u003cem\u003es\u003c/em\u003eGff\u0026rsquo;s relationship to other \u003cem\u003eSpiroplasma\u0026nbsp;\u003c/em\u003estrains, we constructed phylogenetic trees at two scales: (1) using \u003cem\u003eSpiroplasma\u0026nbsp;\u003c/em\u003estrains from several clades, including \u003cem\u003eSpiroplasma citri\u003c/em\u003e, \u003cem\u003eSpiroplasma mirum, Spiroplasma chrysopicola,\u0026nbsp;\u003c/em\u003eand \u003cem\u003eSpiroplasma poulsonii\u003c/em\u003e, and (2) using only members from the \u003cem\u003eS. poulsonii\u0026nbsp;\u003c/em\u003eclade\u003cem\u003e.\u003c/em\u003e Reference genomes and their NCBI RefSeq (or if missing, NCBI GenBank) annotations in GBFF format were downloaded using NCBI datasets [76]. These genomes were clustered using RabbitTClust [77], to iteratively drop \u003cem\u003eSpiroplasma\u0026nbsp;\u003c/em\u003estrains from distant clades (e.g. \u003cem\u003eSpiroplasma ixodes\u003c/em\u003e or \u003cem\u003eSpiroplasma apis\u003c/em\u003e). For the remaining \u003cem\u003eSpiroplasma\u0026nbsp;\u003c/em\u003estrains (Supplementary Table 4), single-copy orthologous core genes (present in \u0026gt; 95%) of taxa were identified with PPanGGOLiN [78]. From this gene set, DNA sequences were extracted, aligned with MAFFT [79], and concatenated for each \u003cem\u003eSpiroplasma\u0026nbsp;\u003c/em\u003etaxa. Phylogenetic trees were generated from the multiple sequence alignment using RAxML-NG [74] with the GTR+G substitution model with 50 random and 50 parsimony-based starting trees, and with 1,000 bootstrap replicates. The resulting draft RAxML tree was used to identify duplicate \u003cem\u003eSpiroplasma\u0026nbsp;\u003c/em\u003estrains, which were removed before re-running the PPanGGOLiN and RAxML pipeline. Finally, the whole process was repeated at a smaller scale for members of the \u003cem\u003eSpiroplasma poulsonii\u0026nbsp;\u003c/em\u003eclade only. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eGenome comparisons\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003ePairwise average nucleotide identity was calculated between the three assembled \u003cem\u003es\u003c/em\u003eGff genomes and the closest sister taxa, TU-14 (GCF_001792795.1), using pyani v0.2.13.1 [80]. Whole-genome synteny, SNP, and indel comparisons between \u003cem\u003es\u003c/em\u003eGff-IAEA-CC, \u003cem\u003es\u003c/em\u003eGff-IAEA-Fly, and \u003cem\u003es\u003c/em\u003eGff-UG-Tol were performed by pairwise alignments using NUCmer [81] annotated with SyRI [82], and visualized using plotsr v1.1 [83]. For the \u003cem\u003es\u003c/em\u003eGff-IAEA-CC, \u003cem\u003es\u003c/em\u003eGff-IAEA-Fly, \u003cem\u003es\u003c/em\u003eGff-UG-Tol, and sister taxa TU-14 (GCF_001792795.1), we identified shared and unique gene families by parsing the PPanGGOLiN \u0026nbsp;dataset generated above with for the \u003cem\u003eSpiroplasma poulsonii\u0026nbsp;\u003c/em\u003eRAxML tree using R [40] with the tidyverse [84] and VennDiagram [85] packages. Unique gene family amino acid sequences were functionally annotated with Bakta [86].\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eRNA sequencing and analyses\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eTo assess \u003cem\u003es\u003c/em\u003eGff transcriptomes, five \u003cem\u003es\u003c/em\u003eGff RNA samples were prepared for RNAseq: three from \u003cem\u003es\u003c/em\u003eGff cell-culture and two from \u003cem\u003eGff\u0026nbsp;\u003c/em\u003ehemolymph. For the \u003cem\u003es\u003c/em\u003eGff cell-culture samples, \u003cem\u003es\u003c/em\u003eGff-IAEA-CC (~10\u003csup\u003e6\u003c/sup\u003e cells) at passage 12 were pelleted at 12,000g for 15 mins at 4\u0026deg;C and resuspended in RNAlater\u0026trade; (Invitrogen, MA, USA) and stored at -80\u0026deg;C. For the \u003cem\u003eGff\u0026nbsp;\u003c/em\u003ehemolymph samples, female IAEA \u003cem\u003eGff\u003c/em\u003e flies were screened for \u003cem\u003es\u003c/em\u003eGff infections\u003cem\u003e\u0026nbsp;\u003c/em\u003eusing the above PCR protocol. Positive flies were surface-sterilized in 70% ethanol and hemolymph was collected as described above. Hemolymph was pooled into two samples from 70 and 35 \u003cem\u003es\u003c/em\u003eGff-positive females and stored in RNAlater\u0026trade; (Invitrogen, MA, USA) at -80\u0026deg;C. Total RNA was extracted from samples by first diluting the samples stored in RNAlater 1:1 with PBS, pelleting at 5000g for 15 mins at 4\u0026deg;C, and resuspending the pellets in 50 \u0026micro;l PBS. Total RNA was extracted from the PBS washed samples using the Total RNA Miniprep kit (NEB, MA, USA) following standard protocols. Ribodepleted RNA-seq libraries were prepared by the Yale Center for Genomic Analysis and sequenced on the NovaSeq 6000 platform. Raw reads were quality filtered (Phred score \u0026gt;20 and read length \u0026gt;50 bp) and adapter-trimmed using fastp v0.24.0 [55]. Trimming and filtering results were summarized with MultiQC v1.27 [87].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe first used the RNA sequencing data to validate the \u003cem\u003ein silico s\u003c/em\u003eGff-IAEA-CC annotations. We pooled all transcriptomes into a single sample and quantified transcript abundance using Salmon [88] with the \u003cem\u003es\u003c/em\u003eGff-IAEA-CC genome as a reference. Transcripts were considered to be expressed if their transcripts per million (TPM) count was \u0026gt; 5. Operons were identified with the OpDetect v1.0 [89] pipeline, which utilized STAR v2.7.11b [90] alignments of the transcriptome to the \u003cem\u003es\u003c/em\u003eGff-IAEA-CC genome. We then quantified differential gene expression for the three culture and two \u003cem\u003eGff\u0026nbsp;\u003c/em\u003ehemolymph samples, again utilizing Salmon [88] to quantify transcript abundance to the \u003cem\u003es\u003c/em\u003eGff-IAEA-CC reference genome. Differential transcript analysis was performed with edgeR [91] using the quasi-likelihood model. We defined significantly differentially expressed genes as those with a false discovery rate (FDR) \u0026lt; 0.05 and log-fold-change (LFC) \u0026gt; 1.5 or \u0026lt; -1.5.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003ch3\u003e\u003cstrong\u003eEstablishment of \u003cem\u003es\u003c/em\u003eGff \u003cem\u003ein vitro\u0026nbsp;\u003c/em\u003ecultures\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eFollowing the published protocol for \u003cem\u003es\u003c/em\u003eMSRO,\u003cem\u003e\u0026nbsp;\u003c/em\u003ewe successfully established an\u003cem\u003e\u0026nbsp;in vitro\u0026nbsp;\u003c/em\u003eculture of\u003cem\u003e\u0026nbsp;\u003c/em\u003ethe \u003cem\u003eSpiroplasma\u0026nbsp;\u003c/em\u003estrain \u003cem\u003es\u003c/em\u003eGff isolated from the hemolymph of \u003cem\u003eGff\u003c/em\u003e. Microscopic examination of SYTO 9-stained cells revealed a homogenous culture, with cells exhibiting the characteristic helical morphology of \u003cem\u003eSpiroplasma\u003c/em\u003e (Fig 1A and 1B). Growth curve analysis of cultures inoculated with frozen preculture aliquots identified two distinct phases of exponential growth, occurring from days 0 to 4 and days 7 to 11, separated by a plateau phase (Fig 1C). The estimated doubling time was 40 hours during the first exponential phase and 85 hours during the second. Fluorescence microscopy revealed that cultures reached high cell densities within 14 days post inoculation. Remarkably, the \u003cem\u003es\u003c/em\u003eGff cultures have remained viable for over 12 months with routine subculturing every 10-14 days, demonstrating the robustness and long-term stability of the \u003cem\u003ein vitro\u003c/em\u003e system.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e\u003cem\u003es\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003eGff cultures can establish infections in na\u0026iuml;ve \u003cem\u003eGff\u0026nbsp;\u003c/em\u003ehosts\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eTo assess whether \u003cem\u003ein vitro\u003c/em\u003e-cultured \u003cem\u003es\u003c/em\u003eGff retains its ability to replicate\u003cem\u003e\u0026nbsp;in vivo\u003c/em\u003e following \u0026nbsp;prolonged culture, we conducted microinjection experiments using two groups of na\u0026iuml;ve \u003cem\u003eGff\u0026nbsp;\u003c/em\u003ethat were confirmed negative for \u003cem\u003es\u003c/em\u003eGff by PCR screening of a mesothoracic leg. \u003cem\u003es\u003c/em\u003eGff-negative teneral flies were injected with either a high- or low-dose of the cultured \u003cem\u003es\u003c/em\u003eGff and infection dynamics were monitored over a 14-day period. The results demonstrate that \u003cem\u003ein vitro\u003c/em\u003e-cultured \u003cem\u003es\u003c/em\u003eGff retains its replicative capacity within na\u0026iuml;ve \u003cem\u003eGff\u003c/em\u003e, validating its potential utility for downstream functional studies. Quantification of \u003cem\u003es\u003c/em\u003eGff in transinfected \u003cem\u003eGff,\u0026nbsp;\u003c/em\u003ealongside naturally infected \u003cem\u003eGff\u003c/em\u003e controls, revealed differences in initial \u003cem\u003es\u003c/em\u003eGff titers between groups on day 0, with the low-dose group showing the lowest titer (fold-change = 0.014), and high-dose and natural infection groups exhibiting similar titers (fold-change = 2.3 and 2.451, respectively) (Supplementary Table 5). We observed a dose-dependent increase within groups over time (Supplementary Figure 1). Statistical analysis confirmed significant differences in \u003cem\u003es\u003c/em\u003eGff titers between treatment groups on days 0, 1 and 6 (Kruskal-Wallis: \u003cem\u003ep\u003c/em\u003e-values = 0.003, 0.003 and 0.0007, respectively) (Supplementary Table 6) and a significant increase within both high- and low-dose groups over time (Kruskal-Wallis: \u003cem\u003ep-\u003c/em\u003evalue \u003cem\u003e=\u003c/em\u003e 0.000044 for low-dose; \u003cem\u003ep\u003c/em\u003e-value = 0.00045 for high-dose) (Supplementary Table 7), with post-hoc tests indicating that titers on days 9 and 14 were significantly higher compared to days 0 and 1 (Supplementary Tables 8 and 9). In contrast, the increase of \u003cem\u003es\u003c/em\u003eGff titers in the naturally infected control group was not statistically significant, likely due to higher variability among individuals (Supplementary Table 5).\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e\u003cem\u003es\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003eGff\u003cem\u003e\u0026nbsp;\u003c/em\u003ecomplete genome assembly\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eTo generate a high-quality reference genome for \u003cem\u003es\u003c/em\u003eGff, we sequenced the whole genome and obtained 78,639 Nanopore duplex reads (565.8 Mb), over 200x coverage for the primary assembly. Additionally, we obtained 4,166,901 Illumina short reads (1.2 Gb) for polishing. The nanopore reads were split into four subsets to construct 20 independent \u003cem\u003es\u003c/em\u003eGff assemblies that were merged into a consensus sequence using Autocycler and subsequently polished with the Illumina data. This approach produced a closed, circular 1.489 Mb reference genome from the \u003cem\u003ein vitro\u0026nbsp;\u003c/em\u003ecultured \u003cem\u003es\u003c/em\u003eGff strain, designated \u003cem\u003es\u003c/em\u003eGff-IAEA-CC, which was deposited in NCBI (RefSeq: GCF_049669535.1, BioProject: PRJNA1235259). We also assembled two additional \u003cem\u003es\u003c/em\u003eGff genomes using publicly available NCBI SRA datasets from \u003cem\u003eGff\u0026nbsp;\u003c/em\u003eflies (Bioproject: PRJNA1231403): one from a female IAEA colony fly (\u003cem\u003es\u003c/em\u003eGff-IAEA-Fly) and another from a wild-caught female from northwestern Uganda (\u003cem\u003es\u003c/em\u003eGff-UG-Tol).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor all assemblies, BUSCO scores exceeded 97.3% based on the OrthoDB entomoplasmatales_odb10 dataset, indicating high quality, complete genomes (Table 1). Genome size varied slightly among the assemblies. The \u003cem\u003es\u003c/em\u003eGff-IAEA-CC reference genome was 1.489 Mb and included four plasmids ranging in size from 6 kb to 16 kb, while the \u003cem\u003es\u003c/em\u003eGff-IAEA-Fly genome had a slightly smaller chromosome of 1.469 Mb (approximately 20 kb less than the reference \u003cem\u003es\u003c/em\u003eGff-IAEA-CC genome), but retained all four plasmids. Interestingly, the \u003cem\u003es\u003c/em\u003eGff\u003cem\u003e-\u003c/em\u003eUG-Tol genome was more reduced, with a 1.418 Mb chromosome, and only three plasmids, missing the 13 kb plasmid, p_sGff2 (Table 1). We manually verified the absence of the plasmid p_sGff2 in \u003cem\u003es\u003c/em\u003eGff-UG-Tol by mapping its\u003cem\u003e\u0026nbsp;\u003c/em\u003ereads to the \u003cem\u003es\u003c/em\u003eGff-IAEA-CC assembly, confirming no coverage over the \u003cem\u003es\u003c/em\u003eGff-IAEA-CC p_sGff2 region. Gene counts across assemblies showed moderate variation. The \u003cem\u003es\u003c/em\u003eGff-IAEA-CC genome, including plasmids, contained 1,829 genes, including 1,687 protein-coding genes, 104 pseudogenes, 32 tRNAs, 3 rRNAs, and 3 non-coding RNAs (ncRNAs) (Table 1). In comparison, \u003cem\u003es\u003c/em\u003eGff-IAEA-Fly encoded 1,795 genes (1,653 protein-coding genes), while \u003cem\u003es\u003c/em\u003eGff-UG-Tol encoded 1,695 genes (1,553 protein-coding genes) (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Assembly statistics for the three \u003cem\u003es\u003c/em\u003eGff genomes.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBUSCO scores are for the entomoplasmatales_odb10 OrthoDB dataset\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"525\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.4762%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24.7619%;\"\u003e\n \u003cp\u003e\u003cstrong\u003esGff-IAEA-CC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.2857%;\"\u003e\n \u003cp\u003e\u003cstrong\u003esGff-IAEA-Fly\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.4762%;\"\u003e\n \u003cp\u003e\u003cstrong\u003esGff-UG-Tol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.4762%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChromosome (bp)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24.7619%;\"\u003e\n \u003cp\u003e1,489,281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.2857%;\"\u003e\n \u003cp\u003e1,469,171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.4762%;\"\u003e\n \u003cp\u003e1,418,152\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.4762%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep_sGff1 (bp)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24.7619%;\"\u003e\n \u003cp\u003e16,389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.2857%;\"\u003e\n \u003cp\u003e16,389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.4762%;\"\u003e\n \u003cp\u003e16,375\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.4762%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep_sGff2 (bp)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24.7619%;\"\u003e\n \u003cp\u003e13,621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.2857%;\"\u003e\n \u003cp\u003e13,621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.4762%;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.4762%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep_sGff3 (bp)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24.7619%;\"\u003e\n \u003cp\u003e13,208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.2857%;\"\u003e\n \u003cp\u003e13,208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.4762%;\"\u003e\n \u003cp\u003e12,717\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.4762%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep_sGff4 (bp)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24.7619%;\"\u003e\n \u003cp\u003e6,466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.2857%;\"\u003e\n \u003cp\u003e6,466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.4762%;\"\u003e\n \u003cp\u003e6,460\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.4762%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGC content\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24.7619%;\"\u003e\n \u003cp\u003e27.16%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.2857%;\"\u003e\n \u003cp\u003e27.21%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.4762%;\"\u003e\n \u003cp\u003e27.29%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.4762%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBioProject\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24.7619%;\"\u003e\n \u003cp\u003ePRJNA1235259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.2857%;\"\u003e\n \u003cp\u003ePRJNA1235259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.4762%;\"\u003e\n \u003cp\u003ePRJNA1235259\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.4762%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBioSample\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24.7619%;\"\u003e\n \u003cp\u003eSAMN47572768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.2857%;\"\u003e\n \u003cp\u003eSAMN48057343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.4762%;\"\u003e\n \u003cp\u003eSAMN48057344\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.4762%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAssembly\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24.7619%;\"\u003e\n \u003cp\u003eGCF_049669535.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.2857%;\"\u003e\n \u003cp\u003eGCA_049949145.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.4762%;\"\u003e\n \u003cp\u003eGCA_049949155.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.4762%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBUSCO score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24.7619%;\"\u003e\n \u003cp\u003eC:97.6% [S:97.6%,D:0.0%]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.2857%;\"\u003e\n \u003cp\u003eC:97.6% [S:97.6%,D:0.0%]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.4762%;\"\u003e\n \u003cp\u003eC:97.3% [S:97.3%,D:0.0%]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.4762%;\"\u003e\n \u003cp\u003e\u003cstrong\u003egenes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24.7619%;\"\u003e\n \u003cp\u003e1,829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.2857%;\"\u003e\n \u003cp\u003e1,795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.4762%;\"\u003e\n \u003cp\u003e1,695\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.4762%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eprotein-coding genes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24.7619%;\"\u003e\n \u003cp\u003e1,687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.2857%;\"\u003e\n \u003cp\u003e1,653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.4762%;\"\u003e\n \u003cp\u003e1,553\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.4762%;\"\u003e\n \u003cp\u003e\u003cstrong\u003epseudogenes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24.7619%;\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.2857%;\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.4762%;\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.4762%;\"\u003e\n \u003cp\u003e\u003cstrong\u003etRNA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24.7619%;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.2857%;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.4762%;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.4762%;\"\u003e\n \u003cp\u003e\u003cstrong\u003erRNA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24.7619%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.2857%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.4762%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.4762%;\"\u003e\n \u003cp\u003e\u003cstrong\u003encRNA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24.7619%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.2857%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.4762%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003e\u003cstrong\u003e\u003cem\u003es\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003eGff clusters within the \u003cem\u003eSpiroplasma poulsonii\u0026nbsp;\u003c/em\u003eclade\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eTo place the newly assembled \u003cem\u003es\u003c/em\u003eGff genomes within a broader phylogenetic framework, we constructed a RAxML maximum likelihood tree based on 93-101 single-copy core orthologous genes conserved in over 95% of representative \u003cem\u003eSpiroplasma\u0026nbsp;\u003c/em\u003etaxa from the \u003cem\u003eS. citri\u003c/em\u003e, \u003cem\u003eS. mirum, S. chrysopicola,\u0026nbsp;\u003c/em\u003eand \u003cem\u003eS. poulsonii\u0026nbsp;\u003c/em\u003eclades (Figure 2). The \u003cem\u003es\u003c/em\u003eGff\u003cem\u003e\u0026nbsp;\u003c/em\u003eisolates are grouped most closely with two poorly characterized but genetically identical \u003cem\u003eSpiroplasma\u0026nbsp;\u003c/em\u003estrains: \u003cem\u003es\u003c/em\u003eTU-14 and \u003cem\u003es\u003c/em\u003eNBRC_100390. The \u003cem\u003es\u003c/em\u003eTU-14 strain was originally isolated from a contaminated sample of another Mollicute (\u003cem\u003eEntomoplasma lucivorax\u003c/em\u003e PIPN-2) and has an unknown host origin [92]\u003cem\u003e.\u0026nbsp;\u003c/em\u003eThe\u003cem\u003e\u0026nbsp;s\u003c/em\u003eNBRC_100390 strain was initially misclassified as \u003cem\u003eSpiroplasma atrichopogonis\u003c/em\u003e GNAT3597, a symbiont of biting midges, but has since been recognized as a distinct \u003cem\u003eSpiroplasma\u0026nbsp;\u003c/em\u003etaxon [93]. Together, the \u003cem\u003es\u003c/em\u003eGff isolates, \u003cem\u003es\u003c/em\u003eTU-14, and \u003cem\u003es\u003c/em\u003eNBRC_100390 form a distinct, well-supported lineage within the \u003cem\u003eS. poulsonii\u0026nbsp;\u003c/em\u003eclade (Figure 2). To further resolve their evolutionary relationships, we constructed a higher-resolution RAxML phylogeny using 509-563 single-copy core orthologs shared among \u0026gt;95% of representatives of \u003cem\u003eSpiroplasma\u0026nbsp;\u003c/em\u003etaxa within the \u003cem\u003ePoulsonii\u0026nbsp;\u003c/em\u003eclade. This second RAxML tree reinforced the same relationships observed in the previous tree. It also resolved the \u003cem\u003es\u003c/em\u003eGff\u003cem\u003e\u0026nbsp;\u003c/em\u003epolytomy, showing that the \u003cem\u003es\u003c/em\u003eGff-IAEA-CC and \u003cem\u003es\u003c/em\u003eGff-IAEA-Fly isolates were genetically identical and formed a monophyletic group, with \u003cem\u003es\u003c/em\u003eGff-UG-Tol positioned as their sister lineage (Supplementary Figure 2).\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e\u003cem\u003es\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003eGff genomes are highly similar\u003c/strong\u003e\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003ePairwise genome comparisons among the \u003cem\u003es\u003c/em\u003eGff assemblies supported the relationships inferred from the RAxML phylogenies. Average Nucleotide Identity (ANI) was 99.9% between the \u003cem\u003es\u003c/em\u003eGff-IAEA-CC and \u003cem\u003es\u003c/em\u003eGff-IAEA-Fly genomes, and slightly reduced at 99.8% between the \u003cem\u003es\u003c/em\u003eGff-IAEA-CC and \u003cem\u003es\u003c/em\u003eGff-UG-Tol (Figure 3A). These ANI values are a magnitude higher than those observed between any \u003cem\u003es\u003c/em\u003eGff isolate and its closest \u003cem\u003eSpiroplasma\u0026nbsp;\u003c/em\u003eoutgroup, strain sTU-14, with pairwise ANI values of 98.5% to \u003cem\u003es\u003c/em\u003eGff-IAEA-CC, \u003cem\u003es\u003c/em\u003eGff-IAEA-Fly, and \u003cem\u003es\u003c/em\u003eGff-UG-Tol (Figure 3A).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Gene content analysis revealed subtle differences between the nearly identical \u003cem\u003es\u003c/em\u003eGff-IAEA isolates and the more divergent \u003cem\u003es\u003c/em\u003eGff-UG-Tol isolate. Specifically, 38 gene families were unique to the \u003cem\u003es\u003c/em\u003eGff-IAEA isolates, while 17 were unique to the \u003cem\u003es\u003c/em\u003eGff-UG-Tol (Figure 3B). Among the 38 \u003cem\u003es\u003c/em\u003eGff-IAEA-specific gene families, 20 were associated with mobile genetic elements (MGEs) \u0026ndash; including twelve from plasmids with six from p_sGff2 that is absent from \u003cem\u003es\u003c/em\u003eGff-UG-Tol, nine were related to metabolism and symbiosis, and the remaining nine encoded hypothetical proteins (Supplementary Table 10). The 17 \u003cem\u003es\u003c/em\u003eGff-UG-Tol-specific gene families included four genes located on plasmid p_sGff3, three associated with MGEs, two symbiosis-related genes, and eight hypothetical proteins (Supplementary Table 11). When compared to the nearest sister taxon, \u003cem\u003es\u003c/em\u003eTU-14, we identified 310 unique gene families shared among the \u003cem\u003es\u003c/em\u003eGff isolates, consisting mostly of MGEs, including \u003cem\u003eSpiroplasma\u0026nbsp;\u003c/em\u003eprophages, plasmids, and integrative conjugative elements (ICEs) (Supplementary Table 12).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhole genome alignments further revealed that the two IAEA-derived \u003cem\u003es\u003c/em\u003eGff genomes, \u003cem\u003es\u003c/em\u003eGff-IAEA-CC and \u003cem\u003es\u003c/em\u003eGff-IAEA-Fly, are nearly identical, differing by only three SNPs, 49 insertions/deletions (indels), and one duplication (Figure 3C and Supplementary Table 13). The three SNPs were located in genes encoding a prophage, the DNA-binding protein \u003cem\u003eWhiA\u003c/em\u003e, and the fructoselysine transporter \u003cem\u003efrlA\u0026nbsp;\u003c/em\u003e(Supplementary Table 14). With no fructose supplemented in the culture medium, the nonsynonymous SNP converting threonine to arginine in \u003cem\u003efrlA\u0026nbsp;\u003c/em\u003ecould be the result of relaxed selection. Most indels were located within homopolymeric tracts, suggesting potential assembly artifacts. However, we identified a notable 20 kb duplication involving a \u003cem\u003eSpiroplasma\u0026nbsp;\u003c/em\u003eprophage region in the \u003cem\u003es\u003c/em\u003eGff-IAEA-CC genome, but absent compared to \u003cem\u003es\u003c/em\u003eGff-IAEA-Fly (Figure 3C and Supplementary Table 15). This duplication likely represents a prophage polymorphism in the \u003cem\u003es\u003c/em\u003eGff-IAEA isolate that became fixed during the \u003cem\u003ein vitro\u0026nbsp;\u003c/em\u003eculture process. In contrast, the \u003cem\u003es\u003c/em\u003eGff-UG-Tol genome exhibited more extensive divergence, with 550 SNPs, 171 indels, five large duplications, and one translocation relative to \u003cem\u003es\u003c/em\u003eGff-IAEA-CC (Figure 3C and Supplementary Table 16). Of the 550 SNPs, 383 were found within protein coding genes with over 44% associated with MGEs (Supplementary Table 17). While most of the indels were homopolymeric, at least 22 appeared to be genuine sequence differences rather than homopolymeric assembly artifacts. Large-scale structural variation between the \u003cem\u003es\u003c/em\u003eGff-IAEA-CC and the \u003cem\u003es\u003c/em\u003eGff-UG-Tol genomes included five duplications and one translocation (Figure 3C). All of these rearrangements were associated with \u003cem\u003eSpiroplasma\u0026nbsp;\u003c/em\u003eprophages (Supplementary Table 18).\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e\u003cem\u003es\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003eGff\u003cem\u003e\u0026nbsp;\u003c/em\u003emobile genetic elements\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe \u003cem\u003es\u003c/em\u003eGff-IAEA-CC chromosome encodes a total of 1,778 genes, including 1,637 protein-coding genes, of which 800 are predicted to encode hypothetical proteins with unknown functions. In addition, the genome contains 103 pseudogenes, 32 tRNAs, 3 rRNAs, and 3 non-coding RNAs (ncRNAs) (Table 1, Supplementary table 19). Genes associated with MGEs represented the most prevalent Cluster of Orthologous Groups (COG) functional category, accounting for over 28% of the annotated functions (Figure 4 and Supplementary Figure 3). When the 646 proteins with unknown COG annotations (36.33% of the genome) are excluded, the relative proportion of MGE-associated functions increases to 44.35%, highlighting the central role of MGEs in shaping the \u003cem\u003es\u003c/em\u003eGff genome. Other prominent COG functional categories include translation, ribosomal structure and biogenesis, replication, recombination and repair, and carbohydrate transport and metabolism (Supplementary Figure 3).\u003c/p\u003e\n\u003cp\u003eThe most common MGEs are \u003cem\u003eSpiroplasma\u003c/em\u003e prophages, specifically from two families: Plectroviridae (\u003cem\u003ePlectovirus\u003c/em\u003e prophages) and Microviridae (\u003cem\u003eSpiromicrovirus\u0026nbsp;\u003c/em\u003eprophages). Using Phastest, we identified 20 supported prophage regions, along with several other putative prophage-related genes scattered across the genome, including within plasmids (Figure 4). Insertion sequence (IS) elements were also highly prevalent, with 129 identified on the chromosome and many associated with prophage regions. In addition, a single well-supported ICE was identified that encoded a complete Type I restriction-modification system (Figure 4). Like the prophages, ICE-associated genes were dispersed throughout the genome, including some within prophage regions and plasmids.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e\u003cem\u003es\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003eGff metabolism\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eWe reconstructed the metabolic pathways of \u003cem\u003es\u003c/em\u003eGff using PGAP-predicted proteins using BLASTKoala and eggNOG-mapper. This analysis revealed that \u003cem\u003es\u003c/em\u003eGff possesses highly reduced biosynthetic capability and relies primarily on its \u003cem\u003eGff\u0026nbsp;\u003c/em\u003ehost for energy and essential metabolites (Supplementary Table 19). The bacterium\u0026rsquo;s energy metabolism centers on glycolysis, with glucose and fructose serving as its main carbohydrate substrates. We identified four phosphotransferase system (PTS) transporters predicted to facilitate uptake of specific sugars: fructose, glucose, 2-(alpha-D-mannosyl)-D-glycerate, and cellobiose/diacetylchitobiose. Notably, the presence of a diacetylchitobiose transporter, together with putative chitinases, suggests that \u003cem\u003es\u003c/em\u003eGff may have the capacity to utilize chitin as an alternative energy source. The genome also reveals metabolic versatility through two additional pathways: a functional acetyltransferase-acetate kinase pathway indicating potential acetogenic metabolism, as well as gene-clusters involved with sulfur reduction.\u003c/p\u003e\n\u003cp\u003eLike other \u003cem\u003eSpiroplasma\u003c/em\u003e species, \u003cem\u003es\u003c/em\u003eGff exhibits minimal capacity for \u003cem\u003ede novo\u003c/em\u003e lipid synthesis, possessing functionality limited to the citric acid cycle and fatty acid elongation. Instead, it predominantly incorporates host-derived fatty acids and cholesterol directly into its cell membranes [94]. Nevertheless, \u003cem\u003es\u003c/em\u003eGff retains crucial lipid-modifying capabilities, particularly the complete cardiolipin biosynthesis pathway that converts host-derived diacylglycerols (DAGs) into cardiolipin, a process identical to the experimentally verified pathway in \u003cem\u003eS. poulsonii\u003c/em\u003e [95] that is essential for bacterial membrane formation [96]. The genome also encodes the non-mevalonate pathway for terpenoid backbone biosynthesis, providing additional lipid processing capability.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBeyond lipid modification, \u003cem\u003es\u003c/em\u003eGff possesses several other significant biosynthetic capabilities, including a complete folate biosynthesis pathway, supporting one-carbon metabolism essential for nucleotide and amino acid synthesis. Hemolysin-related proteins and ferritin are also produced by \u003cem\u003es\u003c/em\u003eGff potentially facilitating iron sequestration in the host environment (Supplementary Table 19). We also note that \u003cem\u003es\u003c/em\u003eGff exhibits limited amino acid biosynthesis as it can only produce three amino acids \u003cem\u003ede novo\u003c/em\u003e: aspartic acid, serine, and asparagine, indicating a high degree of dependence on host-derived nutrients.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eSymbiosis genes\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eIn addition to its highly specialized metabolism, \u003cem\u003es\u003c/em\u003eGff has several mechanisms that may facilitate its persistence within the tsetse host. Among these are lipoproteins, which play essential roles in symbiosis through interactions with the host immune system, host cells, and metabolites present in the hemolymph. Of the 1,637 predicted protein-coding genes in the \u003cem\u003es\u003c/em\u003eGff genome, 72 encode lipoproteins (Supplementary Table 19). Many of these lipoproteins are associated with \u003cem\u003eSpiroplasma\u0026nbsp;\u003c/em\u003eprophage regions, suggesting that horizontal gene transfer and the utilization of prophage-derived lipoproteins may be key to \u003cem\u003es\u003c/em\u003eGff\u0026rsquo;s adaptation to the host environment. The most abundant \u003cem\u003eSpiroplasma\u0026nbsp;\u003c/em\u003elipoprotein is \u003cem\u003eSpiralin,\u0026nbsp;\u003c/em\u003ewhich is a well-characterized protein important in host interactions and vertical transmission [97].\u003cem\u003e\u0026nbsp;\u003c/em\u003eWe identified one conserved \u003cem\u003eSpiralin\u0026nbsp;\u003c/em\u003egene (WP_424526001.1) as well as two other \u003cem\u003eSpiralin-\u003c/em\u003ederived copies containing one and two repeats, respectively (WP_424525940.1 and WP_424525799.1). We note six adhesion-related genes: (WP_424527554.1, WP_424527500.1, WP_424527571.1, WP_424527528.1, WP_424527276.1, and WP_424526807.1) four of which are located on plasmids (one per plasmid) and are likely involved in host cell adhesion and invasion. Similar adhesion genes are essential for midgut cell invasion in bees [98] and adhesion to leafhopper cells [99].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe identified one putative ribosome-inactivating protein (RIP) gene in the \u003cem\u003es\u003c/em\u003eGff genome (Supplementary Table 19). The gene (WP_424526995.1) encodes a protein with a conserved RIP domain, most similar to \u003cem\u003es\u003c/em\u003eGff\u0026rsquo;s sister taxa \u003cem\u003es\u003c/em\u003eNBRC_100390 (Supplementary Figure 4). However, the \u003cem\u003es\u003c/em\u003eGff RIP lacks a signal peptide, suggesting it is not secreted. Instead, this protein is predicted to be embedded in the outer cell membrane, with the RIP domain exposed to the intracellular environment. Whether this toxin is released via proteolysis by an unidentified peptidase remains unknown. We also identified a more divergent RIP-like protein (WP_424526987.1) that includes a signal peptide, suggesting it may be secreted into the host environment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe identified two overlapping copies of the \u003cem\u003eglycerol-3-phosphatase\u0026nbsp;\u003c/em\u003e(\u003cem\u003eglpO\u003c/em\u003e) gene in the\u003cem\u003e\u0026nbsp;s\u003c/em\u003eGff genome (WP_424526233.1 and WP_424526234.1) (Supplementary Table 19). \u003cem\u003eglpO\u0026nbsp;\u003c/em\u003ecatalyzes the oxidation of glycerol-3-phosphate, generating reactive oxygen species (ROS) as a byproduct of glycerol metabolism. In \u003cem\u003eMycoplasma\u003c/em\u003e, the gene functions as a virulence factor [100] and in \u003cem\u003eSpiroplasma\u003c/em\u003e it may provide protection against parasitoid wasps [36]. Typically, \u003cem\u003eglpO\u0026nbsp;\u003c/em\u003eis part of a conserved operon flanked by \u003cem\u003eglpF\u0026nbsp;\u003c/em\u003eand \u003cem\u003eglpK\u003c/em\u003e, which encode a glycerol transporter and glycerol kinase, respectively. However, in\u003cem\u003e\u0026nbsp;s\u003c/em\u003eGff, \u003cem\u003eglpF\u0026nbsp;\u003c/em\u003eappears truncated due to the insertion of a \u003cem\u003eSpiroplasma\u0026nbsp;\u003c/em\u003eprophage, with only the first 32 amino acid residues present upstream of the insertion site. Interestingly, \u003cem\u003ein silico\u0026nbsp;\u003c/em\u003eanalyses suggest that the truncated \u003cem\u003eglpF\u0026nbsp;\u003c/em\u003eand the two \u003cem\u003eglpO\u0026nbsp;\u003c/em\u003ecopies\u003cem\u003e\u0026nbsp;\u003c/em\u003eremain potentially functional\u003cem\u003e.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eTranscriptome validation\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eWe performed RNAseq-based transcriptomic analysis utilizing three \u003cem\u003ein vitro\u0026nbsp;\u003c/em\u003esamples derived from \u003cem\u003es\u003c/em\u003eGff culture and two \u003cem\u003ein vivo\u003c/em\u003e \u003cem\u003es\u003c/em\u003eGff samples isolated from \u003cem\u003eGff\u0026nbsp;\u003c/em\u003ehemolymph. The transcriptomic dataset was first used to validate \u003cem\u003ein silico\u0026nbsp;\u003c/em\u003eannotations of the \u003cem\u003es\u003c/em\u003eGff genome. We pooled transcripts from both culture and hemolymph samples, resulting in over 57 million reads mapped to the reference genome. Of the 1,778 annotated genes, 464 showed no detectable expression (TPM \u0026lt; 5) in either condition (Supplementary Table 20). Over 261 of these non-expressed genes were related to MGEs, primarily \u003cem\u003eSpiroplasma\u0026nbsp;\u003c/em\u003eprophages and suggests that prophage expression may be actively suppressed in \u003cem\u003es\u003c/em\u003eGff. Among the 464 non-expressed genes, 52 were annotated as pseudogenes via \u003cem\u003ein silico\u0026nbsp;\u003c/em\u003epredictions. Interestingly, 52 of the 104 predicted pseudogenes exhibited transcriptional activity, with 34 annotated as MGEs, including transposases, phage-related proteins, and ICEs, indicating that some may retain regulatory or functional roles, particularly in genome plasticity and host adaptation. Additionally, we confirmed expression for all candidate symbiosis-associated genes identified in our genome analyses (Supplementary Table 20). Transcripts were detected from all four plasmids, indicating that plasmid-encoded genes are expressed under both \u003cem\u003ein vivo\u003c/em\u003e and \u003cem\u003ein vitro\u003c/em\u003e conditions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe identified the most abundant transcripts from the pooled dataset. Among the top 20 most abundant genes, seven were associated with plasmid functions, three were involved in fructose metabolism and five were involved in core transcription and translation processes. The remaining highly expressed genes included two with unknown functions, a malate permease, a rod shape determining protein, and \u003cem\u003eSpiralin\u0026nbsp;\u003c/em\u003e(Supplementary Table 20)\u003cem\u003e.\u0026nbsp;\u003c/em\u003eIn addition, we used transcriptomic data to predict operon structures in the \u003cem\u003es\u003c/em\u003eGff genome, as operons may encode co-regulated clusters, including putative secreted effectors relevant to host interactions or trypanocidal effects. Of the 1,675 protein-coding genes analyzed, 1,471 (87.8%) were predicted to be organized into 297 operons. The mean operon length was 3.95 genes, with the largest operon containing 39 genes (Supplementary Table 21). Operon 64 was particularly interesting as it encoded the conserved RIP, MATE family efflux transporters, and Peroxiredoxin, which is a strong antioxidant.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eTissue dependent expression of\u003cem\u003e\u0026nbsp;s\u003c/em\u003eGff\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eWe next investigated tissue-dependent gene expression differences by comparing transcriptomes of \u003cem\u003es\u003c/em\u003eGff isolated from hemolymph and from \u003cem\u003ein vitro\u003c/em\u003e culture. In total, we identified 45 differentially expressed genes between the two sample types, using log₂ fold change threshold \u0026gt; 1.5 or \u0026lt; -1.5 and a false discovery rate (FDR) \u0026lt; 0.05 (Figure 5A).\u003cem\u003e\u0026nbsp;\u003c/em\u003eOf these, 12 genes were upregulated in \u003cem\u003es\u003c/em\u003eGff from hemolymph relative to the culture, while 33 genes were downregulated (Figure 5B and Supplementary Table 22). Among the 12 upregulated genes in from hemolymph relative to culture, 11 were located on the main chromosome, and one was plasmid-encoded. The upregulated main chromosomal genes contained five metabolism-related genes, including a fructose-specific phosphotransferase system (PTS) transporter, two translation-related genes, a detoxifying-related gene, a repair-related gene, a stress-related gene, and a pseudogene. Of the 33 downregulated in \u003cem\u003es\u003c/em\u003eGff hemolymph compared to culture, 21 were located on the main chromosome and 12 were plasmid-encoded. The genes on the chromosome included seven hypothetical proteins, six metabolism-related genes, including a glucose-specific PTS transporter, six prophage-related genes, two lipoproteins, a DNA polymerase, and a serine protease.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThese differential expression patterns likely reflect environmental differences between the \u003cem\u003ein vivo\u003c/em\u003e (hemolymph) and \u003cem\u003ein vitro\u003c/em\u003e (culture) conditions. The upregulation of a fructose-specific PTS transporter and downregulation of a glucose-specific PTS transporter may indicate a shift in available carbohydrate sources between the hemolymph and the culture medium, as fructose is absent from the culture media (Supplementary Document 1). Reduced expression of prophage genes could also suggest \u003cem\u003es\u003c/em\u003eGff can suppress prophage expression in certain environments.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study advances our understanding of the interactions between \u003cem\u003eGlossina fuscipes fuscipes\u003c/em\u003e (\u003cem\u003eGff\u003c/em\u003e) and its symbiont \u003cem\u003eSpiroplasma glossinidia\u003c/em\u003e (\u003cem\u003es\u003c/em\u003eGff) through the establishment of an \u003cem\u003ein vitro\u003c/em\u003e culture system, whole genome sequencing of multiple \u003cem\u003es\u003c/em\u003eGff isolates, and transcriptomic comparisons between \u003cem\u003es\u003c/em\u003eGff derived from culture and from \u003cem\u003eGff\u003c/em\u003e hemolymph.\u003c/p\u003e\u003cp\u003eWhile \u003cem\u003eSpiroplasma citri\u003c/em\u003e was first cultured in 1971 [\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e] and some \u003cem\u003eSpiroplasma\u003c/em\u003e strains were propagated using both cell-based and cell-free culture systems [\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e, \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e], optimizing growth conditions for many strains remained challenging, likely due to the bacterium\u0026rsquo;s specialized adaptations to its specific insect host environment [\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e]. More recently, the use of Barbour-Stoenner-Kelly (BSK-H) media, supplemented with specific nutrients, has facilitated the sustained \u003cem\u003ein vitro\u003c/em\u003e growth of the previously unculturable \u003cem\u003es\u003c/em\u003eMSRO strain [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], and now \u003cem\u003es\u003c/em\u003eGff. The initial growth pattern of cultured \u003cem\u003es\u003c/em\u003eGff was characterized by two exponential phases separated by a plateau phase. Culture plateaus at specific density thresholds were also observed in \u003cem\u003es\u003c/em\u003eMSRO [\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e], however, the \u003cem\u003esGff\u003c/em\u003e culture resumed growth after the plateau phase, possibly reflecting adaptation to the culture environment or modulation of endogenous prophages, which can impact growth dynamics in related strains [\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e, \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e]. The growth rate of \u003cem\u003esGff\u003c/em\u003e was slower than \u003cem\u003es\u003c/em\u003eMSRO, with a doubling time of 40 hours compared to 30 hours, which may reflect strain-specific metabolic traits [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Further optimization of the \u003cem\u003es\u003c/em\u003eGff culture media, informed by insights from its genome-derived metabolic profile [\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e], may improve these growth rates. Importantly, \u003cem\u003es\u003c/em\u003eGff retained capacity for infection after prolonged \u003cem\u003ein vitro\u003c/em\u003e cultivation, as demonstrated by successful colonization of na\u0026iuml;ve \u003cem\u003eGff\u003c/em\u003e following experimental infection, with higher inoculum doses resulting in higher \u003cem\u003es\u003c/em\u003eGff titers. It will be important to establish whether \u003cem\u003es\u003c/em\u003eGff infections can recapitulate the same tissue tropism and transmission dynamics of natural infections. Overall, the availability of a reliable culture system provides a powerful tool for dissecting the molecular dialogue between \u003cem\u003es\u003c/em\u003eGff and its tsetse host, including the potential for genetic manipulation of the symbiont.\u003c/p\u003e\u003cp\u003eUsing a hybrid sequencing approach that combined Nanopore and Illumina reads, we assembled a high-quality, closed genome from the \u003cem\u003es\u003c/em\u003eGff culture consisting of a 1.489 Mb chromosome along with four plasmids ranging in size from 6 to 16 kb. This reference genome (\u003cem\u003es\u003c/em\u003eGff-IAEA-CC) was compared to two additional \u003cem\u003es\u003c/em\u003eGff assemblies \u0026ndash; one from a colony fly (\u003cem\u003es\u003c/em\u003eGff-IAEA-Fly) and another from a fly collected in Northwest Uganda (\u003cem\u003es\u003c/em\u003eGff-UG-Tol). Comparative analysis confirmed that all three assemblies represent closely related isolates of the same strain, differing by several SNPs, indels, and unique gene families, with very little genomic variation between the sGff-IAEA-CC and sGff-IAEA-Fly genomes. The few genomic differences observed between the two IAEA-fly derived \u003cem\u003es\u003c/em\u003eGff genomes could be attributed to either natural polymorphisms that became fixed during cultivation or perhaps from an elevated mutation rate due to the absence of genes in the mismatch repair pathway, namely \u003cem\u003eMutS\u003c/em\u003e, \u003cem\u003eMutL\u003c/em\u003e, and \u003cem\u003eMutH\u003c/em\u003e, which is a trait shared across the \u003cem\u003eSpiroplasma\u003c/em\u003e genus [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In contrast, more pronounced genomic variation was observed between the IAEA-derived genomes and field-derived \u003cem\u003es\u003c/em\u003eGff-UG-Tol isolate. This divergence is consistent with the moderate genetic differentiation between their respective \u003cem\u003eGff\u003c/em\u003e hosts, as \u003cem\u003es\u003c/em\u003eGff-UG-Tol isolate was obtained from a Northwestern Ugandan population, while the IAEA \u003cem\u003eGff\u003c/em\u003e colony originated from flies collected in the Central African Republic in 1986 [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e]. Nearly all the genomic variation between these isolates was localized to MGEs, including genes associated with \u003cem\u003eSpiroplasma\u003c/em\u003e prophages. Notably, one plasmid present in the IAEA isolates was absent in the Uganda field isolate. Studies characterizing the range of genetic variation found in \u003cem\u003es\u003c/em\u003eGff across the landscape will be important for understanding the full extent of genetic diversity in this symbiont.\u003c/p\u003e\u003cp\u003ePhylogenetic analysis placed the \u003cem\u003es\u003c/em\u003eGff isolates within the \u003cem\u003eS. poulsonii\u003c/em\u003e clade, a group that contains several other well-characterized \u003cem\u003eSpiroplasma\u003c/em\u003e strains known for protecting their hosts against nematodes, parasitoids, and viruses [\u003cspan additionalcitationids=\"CR110\" citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e]. Among these sister strains is \u003cem\u003es\u003c/em\u003eMSRO, whose culture protocol we successfully adopted for \u003cem\u003es\u003c/em\u003eGff and likely explains the ease with which \u003cem\u003es\u003c/em\u003eGff was cultivated. The closest known relatives of \u003cem\u003es\u003c/em\u003eGff are two genetically identical but poorly characterized strains: \u003cem\u003es\u003c/em\u003eTU-14 and \u003cem\u003es\u003c/em\u003eNBRC_100390. These strains share 98.5% genome-wide identity with \u003cem\u003es\u003c/em\u003eGff and have 778 gene families in common. However, \u003cem\u003es\u003c/em\u003eGff possesses over 310 unique gene families, consisting primarily of hypothetical proteins and MGEs. These shared and unique gene families highlight both its close relationship to its sister taxa as well as its substantial repertoire of distinctive genetic features.\u003c/p\u003e\u003cp\u003eThe \u003cem\u003es\u003c/em\u003eGff genome consists of a remarkably high proportion of MGEs, including prophages, integrative conjugative elements (ICEs), and insertion sequence elements (ISEs), which together comprise over 28% of the genome. When hypothetical proteins are excluded, MGE-associated content increases to more than 44% of the annotated genome. This high MGE load is consistent with observations in several other \u003cem\u003eS. poulsonii\u003c/em\u003e and \u003cem\u003eS. citri\u003c/em\u003e strains [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e]. In total, we identified 20 distinct prophage regions, consisting largely of \u003cem\u003eSpiromicrovirus\u003c/em\u003e and \u003cem\u003ePlectrovirus\u003c/em\u003e prophages, along with one ICE, and 129 ISEs. We also identified additional prophage- and ICE-related genes dispersed throughout the genome and plasmids \u0026ndash; all pointing to a dynamic genome characterized by extensive structural and functional plasticity. Prophages and MGEs can play major roles in the evolution and virulence of their hosts [\u003cspan additionalcitationids=\"CR114 CR115\" citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e], acting as a reservoir of adaptive genes that can either be co-opted or horizontally transferred to enhance symbiotic capabilities [\u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e, \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e117\u003c/span\u003e]. For example, in \u003cem\u003eWolbachia\u003c/em\u003e, key symbiosis-associated genes, such as the \u003cem\u003ecif\u003c/em\u003e genes responsible for cytoplasmic incompatibility [\u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e118\u003c/span\u003e], and \u003cem\u003ewmk\u003c/em\u003e, a male-killing factor [\u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e119\u003c/span\u003e], are both derived from and horizontally transferred via prophages [\u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e120\u003c/span\u003e, \u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e121\u003c/span\u003e]. In \u003cem\u003es\u003c/em\u003eGff, as well as other \u003cem\u003eSpiroplasma\u003c/em\u003e strains, this pattern holds true [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e122\u003c/span\u003e]. In \u003cem\u003es\u003c/em\u003eGff, key symbiosis genes, such as \u003cem\u003eRIP\u003c/em\u003e and \u003cem\u003eglpO\u003c/em\u003e, are flanked by prophage sequences. Several prophages in \u003cem\u003es\u003c/em\u003eGff encode lipoproteins, which could mediate interactions with host cells, metabolites, and lipids, contributing to symbiotic function [\u003cspan additionalcitationids=\"CR124\" citationid=\"CR123\" class=\"CitationRef\"\u003e123\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e125\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe abundance of prophages present in the \u003cem\u003es\u003c/em\u003eGff genome may also play a mechanistic role in regulating \u003cem\u003eSpiroplasma\u003c/em\u003e symbiosis. In \u003cem\u003eWolbachia\u003c/em\u003e, the genomic island \u003cem\u003eoctomom\u003c/em\u003e modulates bacterial density to prevent overproliferation [\u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e126\u003c/span\u003e]. Although \u003cem\u003es\u003c/em\u003eGff lacks a clearly defined \u003cem\u003eoctomom\u003c/em\u003e ortholog, prophage induction may serve a similar density-dependent regulatory function, by triggering bacterial lysis in response to stress, such as overcrowding or resource limitation, [\u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e127\u003c/span\u003e, \u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e128\u003c/span\u003e] or via a quorum sensing mechanism as observed in \u003cem\u003eVibrio cholerae\u003c/em\u003e [\u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e129\u003c/span\u003e] or \u003cem\u003eE. coli\u003c/em\u003e [\u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e130\u003c/span\u003e]. Our transcriptomic comparison between cultured \u003cem\u003es\u003c/em\u003eGff and \u003cem\u003es\u003c/em\u003eGff isolated from tsetse hemolymph supports this hypothesis, as prophage-associated genes were highly expressed in the dense \u003cem\u003ein vitro\u003c/em\u003e cultures as compared to hemolymph-derived \u003cem\u003es\u003c/em\u003eGff. Additionally, prophage induction can also modulate biofilm formation [\u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e131\u003c/span\u003e], interbacterial competition [\u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e132\u003c/span\u003e], or be induced via reactive oxygen species (ROS) [\u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e133\u003c/span\u003e]. Future studies comparing prophage activity in trypanosome-infected and uninfected tsetse will be key in determining whether \u003cem\u003es\u003c/em\u003eGff prophages contribute to trypanosome resistance or influence interactions with other prominent tsetse symbionts, including \u003cem\u003eSodalis\u003c/em\u003e and \u003cem\u003eWigglesworthia\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eOur metabolic profiling confirms that, like other \u003cem\u003eSpiroplasma\u003c/em\u003e strains [\u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e134\u003c/span\u003e, \u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e135\u003c/span\u003e], \u003cem\u003es\u003c/em\u003eGff has limited biosynthetic capacity and relies on its host for essential metabolites. Unlike its sister taxon \u003cem\u003es\u003c/em\u003eMSRO that utilizes only glucose [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], \u003cem\u003es\u003c/em\u003eGff appears to use both fructose and glucose as primary carbon sources. Notably, a functional trehalose transporter was absent, which may reduce \u003cem\u003es\u003c/em\u003eGff pathogenicity, as trehalose is abundant in tsetse [\u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e136\u003c/span\u003e]. The inability of sGff to utilize trehalose could limit its replication within the host and reduce host resource exploitation, thereby supporting a more commensal relationship. Transcriptomic analysis revealed condition-specific regulation of carbohydrate transporters, where fructose-specific transporters were upregulated in hemolymph-derived \u003cem\u003es\u003c/em\u003eGff, while glucose-specific transporters were downregulated. This finding likely reflects differences in nutrient availability, as fructose is absent from the culture medium, and suggests that fructose supplementation may accelerate \u003cem\u003ein vitro\u003c/em\u003e growth. Nutrient-driven tissue tropism may also influence \u003cem\u003es\u003c/em\u003eGff distribution in its \u003cem\u003eGff\u003c/em\u003e host. High fructose levels in reproductive tissues [\u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e137\u003c/span\u003e] could explain the elevated \u003cem\u003es\u003c/em\u003eGff titers observed in these organs, while post-blood meal glucose spikes [\u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e138\u003c/span\u003e] may promote gut colonization and proliferation. We also identified a transporter for diacetylchitobiose, a sugar derived from chitin. While \u003cem\u003es\u003c/em\u003eGff encodes several putative chitinases, it may access chitin breakdown products indirectly, potentially through \u003cem\u003eSodalis\u003c/em\u003e-derived secreted chitinase enzymes [\u003cspan citationid=\"CR139\" class=\"CitationRef\"\u003e139\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBeyond carbohydrates, \u003cem\u003es\u003c/em\u003eGff scavenges host-derived fatty acids and cholesterol, and like other \u003cem\u003eSpiroplasma\u003c/em\u003e strains, can incorporate them directly into its membrane [\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e]. Pathway reconstructions also indicate that \u003cem\u003es\u003c/em\u003eGff can metabolize host diacylglycerols (DAGs) to synthesize cardiolipins, which are key membrane lipids in \u003cem\u003eSpiroplasma\u003c/em\u003e [\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e] that could be cytotoxic [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] and in \u003cem\u003eDrosophila\u003c/em\u003e, serve as important virulence factors by depleting host DAGs [\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e]. In \u003cem\u003eGff\u003c/em\u003e flies, the lipid-depleting phenotype associated with \u003cem\u003es\u003c/em\u003eGff is supported by previous work that shows decreased expression of \u003cem\u003eGff\u003c/em\u003e genes involved in fatty acid synthesis in \u003cem\u003es\u003c/em\u003eGff infected flies, suggesting reduced lipid availability [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In addition, triacylglycerol (TAG) levels are decreased in the fat bodies of \u003cem\u003es\u003c/em\u003eGff-infected tsetse [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and since TAGs are synthesized from DAGs, sGff\u0026rsquo;s consumption of host DAGs may be contributing to this decrease. Functional annotations also indicated that \u003cem\u003es\u003c/em\u003eGff can produce ferritin and a hemolysin-related protein, both of which are used for iron sequestration. Iron sequestration is an important aspect of nutritional immunity, limiting microbe overproliferation and providing pathogen resistance [\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e, \u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e140\u003c/span\u003e, \u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e141\u003c/span\u003e] and In \u003cem\u003eDrosophila\u003c/em\u003e, the iron-binding protein transferrin is upregulated in the presence of \u003cem\u003es\u003c/em\u003eMSRO, which relies on host transferrin-bound iron for survival [\u003cspan citationid=\"CR142\" class=\"CitationRef\"\u003e142\u003c/span\u003e]. Finally, consistent with other \u003cem\u003eSpiroplasma\u003c/em\u003e species, \u003cem\u003es\u003c/em\u003eGff is unable to synthesize most amino acids, and instead must import them from the host, further emphasizing its nutritional dependence on \u003cem\u003eGff\u003c/em\u003e [\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e, \u003cspan citationid=\"CR143\" class=\"CitationRef\"\u003e143\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOur metabolic analysis reveals an intriguing potential competitive dynamic between \u003cem\u003es\u003c/em\u003eGff and trypanosomes within the tsetse gut, as both microorganisms require the same host-derived resources. In the \u003cem\u003eGff\u003c/em\u003e gut, both organisms prefer glucose as their primary carbohydrate source, with trypanosomes later switching to proline [\u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e138\u003c/span\u003e] \u0026ndash; an amino acid that \u003cem\u003es\u003c/em\u003eGff also catabolizes. Trypanosomes, like \u003cem\u003es\u003c/em\u003eGff, scavenge host lipids and cholesterol [\u003cspan citationid=\"CR144\" class=\"CitationRef\"\u003e144\u003c/span\u003e] and both reduce the expression of \u003cem\u003eGff\u003c/em\u003e genes associated with lipid biosynthesis [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and deplete host TAG reserves [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR145\" class=\"CitationRef\"\u003e145\u003c/span\u003e]. While trypanosomes acquire transferrin-bound iron through a high affinity receptor [\u003cspan citationid=\"CR146\" class=\"CitationRef\"\u003e146\u003c/span\u003e], \u003cem\u003es\u003c/em\u003eGff likely acquires transferrin-bound iron in a manner similar to its sister \u003cem\u003es\u003c/em\u003eMSRO strain [\u003cspan citationid=\"CR142\" class=\"CitationRef\"\u003e142\u003c/span\u003e]. This potential multifaceted resource competition may contribute to the trypanosome-refractory phenotype observed in \u003cem\u003es\u003c/em\u003eGff-infected flies [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. By limiting parasite access to essential nutrients, \u003cem\u003es\u003c/em\u003eGff may delay parasite proliferation, potentially providing the host sufficient time to mount an effective immune response. These competitive dynamics require new studies to characterize sGff\u0026rsquo;s metabolic demands \u003cem\u003ein vivo\u003c/em\u003e and to test how resource competition influences host-pathogen dynamics, perhaps contributing to trypanosome resistance in tsetse.\u003c/p\u003e\u003cp\u003eIn addition to a nutritional impact, \u003cem\u003eSpiroplasma\u003c/em\u003e produces a diverse repertoire of toxins that can impact host fitness via reproductive manipulation or protection against parasites and pathogens [\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e, \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e]. In \u003cem\u003es\u003c/em\u003eGff, we identified a limited set of potential encoded toxins: two putative Glycerol-3-phosphatase (\u003cem\u003eglpO\u003c/em\u003e) genes and one ribosome-inactivating-protein (RIP) gene \u0026ndash; all of which are expressed according to our transcriptome data. The \u003cem\u003eglpO\u003c/em\u003e enzyme generates reactive oxygen species (ROS) and is a major virulence factor in \u003cem\u003eMycoplasma\u003c/em\u003e [\u003cspan citationid=\"CR147\" class=\"CitationRef\"\u003e147\u003c/span\u003e] and may protect \u003cem\u003es\u003c/em\u003eMSRO-infected \u003cem\u003eDrosophila\u003c/em\u003e against parasitoids [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In response to trypanosome infection in the gut, tsetse express nitric-oxide synthase (NOS), which catalyzes the production of trypanocidal nitric oxide (NO) and ROS [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These ROS are an essential part of the tsetse innate immune response, such that supplementing infected blood meals with antioxidants significantly enhances trypanosome infection success [\u003cspan citationid=\"CR148\" class=\"CitationRef\"\u003e148\u003c/span\u003e]. Thus, the ROS produced by \u003cem\u003eglpO\u003c/em\u003e encoded by \u003cem\u003es\u003c/em\u003eGff could contribute to \u003cem\u003eGff\u0026rsquo;s\u003c/em\u003e immune response and enhance its trypanocidal effect.\u003c/p\u003e\u003cp\u003eWe identified one well-conserved RIP gene in the \u003cem\u003es\u003c/em\u003eGff genome, which is transcriptionally active. RIPs act by depurinating the sarcin-ricin loop of 28s rRNA, thereby damaging host ribosomes and causing cell-death [\u003cspan citationid=\"CR149\" class=\"CitationRef\"\u003e149\u003c/span\u003e]. In sister \u003cem\u003eS. poulsonii\u003c/em\u003e strains, \u003cem\u003es\u003c/em\u003eMSRO, \u003cem\u003es\u003c/em\u003eHYD, and \u003cem\u003es\u003c/em\u003eNEO, RIPs can protect their hosts from parasitic nematodes and parasitoid wasps [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Unlike most RIP genes present in the \u003cem\u003eS. poulsonii\u003c/em\u003e clade, which are typically secreted, the \u003cem\u003es\u003c/em\u003eGff RIP gene lacks a signal peptide, [\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e] and is predicted to be anchored in the cell membrane. In dense microenvironments such as plaques, high concentration of the membrane-bound RIPs could exert toxic effects in close proximity, while having limited effects at a distance. Alternatively, the RIP may be proteolytically cleaved from the membrane and released into the extracellular space, enabling it to act on specific targets, or perhaps secreted by MATE family efflux transporters encoded within the same operon. Given that secreted RIPs exhibit high substrate specificity, future work is needed to determine the nature of its molecular targets and whether this RIP remains membrane-associated or is secreted. Furthermore, other potential RIP functions need to be evaluated, including enzymatic activity such as chitinase or phosphatase activity on lipids [\u003cspan citationid=\"CR150\" class=\"CitationRef\"\u003e150\u003c/span\u003e]. RIPs can also impact host tissues and incur fitness costs [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], but such effects may be tolerated or advantageous in contexts where protection against parasites or pathogens improves host survival. This trade-off could explain the polymorphic \u003cem\u003es\u003c/em\u003eGff distribution in \u003cem\u003eGff\u003c/em\u003e populations in northern Uganda, where it persists at about\u0026thinsp;~\u0026thinsp;30% prevalence [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The potential for RIPs to mediate protective benefits in a population-specific manner warrants future research, particularly to determine whether \u003cem\u003es\u003c/em\u003eGff-derived RIPs are active against trypanosomes.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eHere, we report the first successful \u003cem\u003ein vitro\u003c/em\u003e cultivation and complete genome assembly of the \u003cem\u003eSpiroplasma glossinidia\u0026nbsp;\u003c/em\u003estrain \u003cem\u003es\u003c/em\u003eGff \u0026mdash; the \u003cem\u003eSpiroplasma\u0026nbsp;\u003c/em\u003esymbiont of \u003cem\u003eGff\u003c/em\u003e associated with deleterious effects on host metabolism and a trypanosome-refractory phenotype. We assembled a closed, circular ~1.5 Mb genome that clusters within the \u003cem\u003eS. poulsonii\u0026nbsp;\u003c/em\u003eclade. Comparative genomic analyses of cultured, laboratory- and field-derived \u003cem\u003es\u003c/em\u003eGff isolates revealed only minor isolate-specific genetic variations, residing primarily in mobile genetic elements in field-derived \u003cem\u003es\u003c/em\u003eGff. Metabolic profiling confirmed that \u003cem\u003es\u003c/em\u003eGff has limited biosynthetic capacity and relies on its \u003cem\u003eGff\u0026nbsp;\u003c/em\u003ehost for essential carbohydrates, lipids, and amino acids. We also identified two putative toxin genes: \u003cem\u003eRIP\u0026nbsp;\u003c/em\u003eand \u003cem\u003eglpO\u0026nbsp;\u003c/em\u003ethat may contribute to its trypanocidal potential.\u003cem\u003e\u0026nbsp;\u003c/em\u003eOur genomic discoveries and the availability of a stable culture system will enable future functional studies to elucidate this symbiosis and identify potential implications for trypanosome transmission control.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSymbionts can protect their hosts through several mechanisms, including immune priming, nutrient supplementation, competitive exclusion of pathogens, or secretion of effector proteins that target pathogens. In the case of \u003cem\u003es\u003c/em\u003eGff, previous work found no evidence for activation of the tsetse immune system [16], and our work here identified no key nutrient supplementation apart from folate, which is already produced in excess by \u003cem\u003eGff\u0026rsquo;s\u0026nbsp;\u003c/em\u003eobligate symbiont \u003cem\u003eWigglesworthia\u003c/em\u003e. However, we identified several points of potential metabolic competition between \u003cem\u003es\u003c/em\u003eGff and trypanosomes, including glucose, cholesterol, fatty acids and iron, which could collectively have an additive effect to limit parasite fitness. In addition, the two toxin genes identified,\u003cem\u003e\u0026nbsp;glpO\u003c/em\u003e and \u003cem\u003eRIP,\u003c/em\u003e represent potential candidates for direct trypanocidal activity. These genomic discoveries, along with the establishment of a stable culture system, lay the foundation for functional studies to dissect the mechanisms of trypanosome resistance. \u0026nbsp;Such studies may involve metabolomics, RNAseq, culture media modifications, tsetse transfections, or perhaps \u003cem\u003es\u003c/em\u003eGff gene knockouts. Future work also needs to address fundamental aspects of \u003cem\u003es\u003c/em\u003eGff \u0026nbsp;biology, including its tissue specific distribution, specific roles of \u003cem\u003es\u003c/em\u003eGff within different host compartments, and strategies it uses for vertical transmission. Expanding transcriptomic analyses to additional tissues, such as the reproductive organs, salivary glands, and midgut, will help reveal whether \u003cem\u003es\u003c/em\u003eGff gene expression is spatially regulated and potentially linked to the diverse physiological phenotypes observed in the tsetse host.\u0026nbsp;\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eAAT:\u003c/strong\u003e African Animal Trypanosomiasis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eANI:\u003c/strong\u003e Average Nucleotide Identity\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBLASTP:\u003c/strong\u003e Basic Local Alignment Search Tool for proteins\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBSK-H medium:\u0026nbsp;\u003c/strong\u003eBarbour-Stoenner-Kelly H medium\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBUSCO:\u003c/strong\u003e Benchmarking Universal Single-Copy Orthologs\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCDS:\u003c/strong\u003e Coding Sequences\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCOG\u003c/strong\u003e: Cluster of Orthologous Groups\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDAGs:\u003c/strong\u003e Diacylglycerols\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDNA:\u003c/strong\u003e Deoxyribonucleic acid\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eE. coli\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e \u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFDR:\u003c/strong\u003e False-Discovery Rate\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGC content:\u003c/strong\u003e Guanine-Cytosine content\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGff\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e \u003cem\u003eGlossina fuscipes fuscipes\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHAT:\u003c/strong\u003e Human African Trypanosomiasis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHMW DNA:\u003c/strong\u003e High Molecular Weight Deoxyribonucleic Acid\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIAEA:\u003c/strong\u003e International Atomic Energy Agency\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eICEs:\u003c/strong\u003e Integrative Conjugative Elements\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eISEs:\u003c/strong\u003e Insertion Sequence Elements\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKEGG:\u003c/strong\u003e Kyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMGEs:\u003c/strong\u003e Mobile Genetic Elements\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003encRNAs:\u003c/strong\u003e non-coding Ribonucleic Acids\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNCBI:\u003c/strong\u003e National Center for Biotechnology Information\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNCBI SRA:\u003c/strong\u003e National Center for Biotechnology Information Sequence Read Archive\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNO:\u003c/strong\u003e Nitric Oxide\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eONT:\u003c/strong\u003e Oxford Nanopore Technologies\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePCR:\u003c/strong\u003e Polymerase Chain Reaction\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ep_sGff:\u003c/strong\u003e Plasmid of \u003cem\u003eSpiroplasma\u003c/em\u003e endosymbiont of \u003cem\u003eGlossina fuscipes fuscipes\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePTS transporters:\u003c/strong\u003e Phosphotransferase System Transporter\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eqPCR:\u003c/strong\u003e quantitative Polymerase Chain Reaction\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRIPs:\u003c/strong\u003e Ribosome-inactivating Proteins\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRNA / rRNA:\u003c/strong\u003e Ribonucleic Acid / ribosomal Ribonucleic Acid\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRNAseq:\u003c/strong\u003e Ribonucleic Acid Sequencing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eROS:\u003c/strong\u003e Reactive Oxygen Species\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003es\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003eGff:\u0026nbsp;\u003c/strong\u003e\u003cem\u003eSpiroplasma\u003c/em\u003e endosymbiont of \u003cem\u003eGlossina fuscipes fuscipes\u003c/em\u003e / strain \u003cem\u003es\u003c/em\u003eGff\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003es\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003eMSRO:\u003c/strong\u003e \u003cem\u003eSpiroplasma poulsonii\u003c/em\u003e Melanogaster Sex-Ratio Organism\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSNPs:\u003c/strong\u003e Single Nucleotide Polymorphisms\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSp\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003eAID:\u003c/strong\u003e \u003cem\u003eSpiroplasma poulsonii\u003c/em\u003e Androcidin\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTAG:\u003c/strong\u003e Triacylglyceride\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTPM:\u003c/strong\u003e Transcripts per Million\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003etRNA:\u003c/strong\u003e transfer Ribonucleic Acids\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eNot applicable. \u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eAll genomic and transcriptomic data generated in this study is available at the NCBI BioProject: PRJNA1235259. Raw data from culture growth kinetics and injection experiments is deposited under https://doi.org/10.60600/YU/2979VU. \u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe authors declare no competing interests. \u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThis work was generously supported with funding from Ambrose Monell Foundation (to SA), National Institutes of Health (R01AI068932 and R01AI139525 to SA) and National Institutes of Health (R21AI163969 to SA and B.L.W). This work also was funded by the International Atomic Energy Agency under the coordinated research project D42017 and the regular budget of the Joint FAO/IAEA Centre of Nuclear Techniques in Food and Agriculture for the project 2.1.4.2: Management of transboundary livestock insect pests for sustainable agriculture and rural development. The funders played no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eD.J.B., F.G., B.L.W., R.M., A.M.A. and S.A. conceived and designed the study. F.G. initiated \u003cem\u003ein vitro\u0026nbsp;\u003c/em\u003ecultures. D.J.B., F.G. and A.M.A. collected samples and generated sequencing data. F.G. and H.K. performed injection experiments. D.J.B. and F.G. analyzed and interpreted data. D.J.B., F.G., B.L.W. and S.A. wrote the manuscript. All authors contributed to the manuscript with comments and edits. B.L.W., S.A. and A.M.A. provided project supervision. D.J.B. and F.G. are equally contributing first authors.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe authors would like to acknowledge all involved personnel at the Joint FAO/IAEA Centre of Nuclear Techniques in Food and Agriculture, Insect Pest Control Subprogram for tsetse rearing and colony management. We are grateful to Florent Masson who provided detailed insights into \u003cem\u003ein vitro\u0026nbsp;\u003c/em\u003ecultivation methods. We thank the Yale Center for Research Computing for maintenance and use of the research computing infrastructure.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSimarro PP, Diarra A, Ruiz Postigo JA, Franco JR, Jannin JG. The human African trypanosomiasis control and surveillance programme of the World Health Organization 2000\u0026ndash;2009: the way forward. 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Toxins. 2017;9:123. https://doi.org/10.3390/toxins9040123.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":true,"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":"Spiroplasma, Glossina fuscipes fuscipes, tsetse, symbiosis, genome sequencing","lastPublishedDoi":"10.21203/rs.3.rs-7295611/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7295611/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTsetse (\u003cem\u003eGlossina\u003c/em\u003e spp.) are vectors of African trypanosomes, the causative agents of Human and African Animal trypanosomiases, diseases that remain significant medical and socioeconomic challenges in sub-Saharan Africa. In addition to trypanosomes, tsetse harbor both obligate and facultative symbiotic bacteria that can influence vector competence and reproductive biology. One such facultative symbiont, \u003cem\u003eSpiroplasma glossinidia\u003c/em\u003e, infects several tsetse species within the \u003cem\u003ePalpalis\u003c/em\u003e subgroup. In \u003cem\u003eGlossina fuscipes fuscipes\u003c/em\u003e (\u003cem\u003eGff\u003c/em\u003e), the \u003cem\u003eSpiroplasma glossinidia\u003c/em\u003e strain \u003cem\u003es\u003c/em\u003eGff induces a trypanosome-refractory phenotype and negatively impacts reproductive fitness by reducing female fecundity. However, the mechanisms behind these \u003cem\u003eSpiroplasma\u003c/em\u003e-derived phenotypes remain poorly understood. Here, we report successful \u003cem\u003ein vitro\u003c/em\u003e cultivation of \u003cem\u003es\u003c/em\u003eGff and present complete genomes from three sources: \u003cem\u003ein vitro\u003c/em\u003e cultured \u003cem\u003es\u003c/em\u003eGff and \u003cem\u003es\u003c/em\u003eGff isolated from both laboratory-maintained and wild-caught (Uganda) \u003cem\u003eGff\u003c/em\u003e flies. Comparative genomic analyses revealed a high degree of similarity in gene content and synteny among these \u003cem\u003es\u003c/em\u003eGff samples, confirming that they represent isolates of the same strain. Phylogenomic analyses placed \u003cem\u003es\u003c/em\u003eGff within the \u003cem\u003eSpiroplasma poulsonii\u003c/em\u003e clade. The \u003cem\u003es\u003c/em\u003eGff genome is highly dynamic, containing numerous mobile genetic elements. Additionally, \u003cem\u003ein silico\u003c/em\u003e annotations indicate that \u003cem\u003es\u003c/em\u003eGff relies on its host for both lipids and carbohydrates and produces several toxins, all of which could be implicated in the observed trypanosome refractory phenotype. Finally, comparative transcriptomic analysis of \u003cem\u003es\u003c/em\u003eGff from host hemolymph versus \u003cem\u003ein vitro\u003c/em\u003e culture provided insights into potential factors relevant to host-symbiont interactions. Our findings provide a foundation for understanding the nutritional dialogue between \u003cem\u003es\u003c/em\u003eGff and its host and identify symbiotic products that may contribute to trypanosome resistance. Furthermore, the establishment of an \u003cem\u003ein vitro\u003c/em\u003e culture system for \u003cem\u003es\u003c/em\u003eGff represents a significant resource for future functional studies with potential implications for vector control.\u003c/p\u003e","manuscriptTitle":"Comparative genomics and transcriptomics of the Spiroplasma glossinidia strain sGff reveal insights into host interaction and trypanosome resistance in Glossina fuscipes fuscipes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-25 08:21:16","doi":"10.21203/rs.3.rs-7295611/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-19T18:02:29+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-18T20:19:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-04T15:21:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"297155093954503616908310268756320093427","date":"2025-08-24T16:12:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"74930136856397360617785615388033969335","date":"2025-08-24T10:42:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-22T09:01:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"184795335932456803246685753927098898307","date":"2025-08-12T07:22:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-09T07:16:45+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-08T16:39:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-07T22:14:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-07T22:14:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Genomics","date":"2025-08-05T02:43:27+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":"0eb2ec6d-074f-49bf-8292-7d62c66d72cd","owner":[],"postedDate":"August 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-24T16:07:28+00:00","versionOfRecord":{"articleIdentity":"rs-7295611","link":"https://doi.org/10.1186/s12864-025-12351-w","journal":{"identity":"bmc-genomics","isVorOnly":false,"title":"BMC Genomics"},"publishedOn":"2025-11-22 15:57:38","publishedOnDateReadable":"November 22nd, 2025"},"versionCreatedAt":"2025-08-25 08:21:16","video":"","vorDoi":"10.1186/s12864-025-12351-w","vorDoiUrl":"https://doi.org/10.1186/s12864-025-12351-w","workflowStages":[]},"version":"v1","identity":"rs-7295611","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7295611","identity":"rs-7295611","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}