The genomic basis of host and vector specificity in non-pathogenic trypanosomatids

Abstract

Trypanosoma theileri, a non-pathogenic parasite of bovines, has a predicted surface protein architecture that likely aids survival in its mammalian host. Their surface proteins are encoded by genes which account for ∼10% of their genome. A non-pathogenic parasite of sheep, Trypanosoma melophagium, is transmitted by the sheep ked and is closely related to T. theileri. To explore host and vector specificity between these species, we sequenced the T. melophagium genome and transcriptome and an annotated draft genome was assembled. T. melophagium was compared to 43 kinetoplastid genomes, including T. theileri. T. melophagium and T. theileri have an AT biased genome, the greatest bias of publicly available trypanosomatids. This trend may result from selection acting to decrease the genomic nucleotide cost. The T. melophagium genome is 6.3Mb smaller than T. theileri and large families of proteins, characteristic of the predicted surface of T. theileri, were found to be absent or greatly reduced in T. melophagium. Instead, T. melophagium has modestly expanded protein families associated with the avoidance of complement-mediated lysis. We propose that the contrasting genomic features of these species is linked to their mode of transmission from their insect vector to their mammalian host.

Profiling the bloodstream form and procyclic form Trypanosoma brucei cell cycle using single-cell transcriptomics

“…As mechanisms of cell cycle regulation and cyclical transcript changes are largely conserved across the eukaryotes, we hypothesised that genes with CCR transcript levels in T. brucei are more likely to be conserved. To investigate, we extracted the 819 orthogroups which contained the common CCR regulated genes and compared the orthogroup conservation across 44 kinetoplastid proteomes, including trypanosome and leishmania species ( Oldrieve et al, 2022 ). CCR orthogroups were conserved across significantly (p < 0.0001) more proteomes (mean of 41.66 out of 44), compared all orthogroups of the T. brucei Lister427 proteome ( Müller et al, 2018 ) (mean of 18.44), and a random subset of 1000 orthogroups (mean 18.43) ( Figure 4—figure supplement 1 ).…”

“…Orthogroups were identified for each CCR gene common to both BSF and PCF cell cycles, and orthologous protein sequences across 44 kinetoplastida proteomes were extracted from previous analysis ( Oldrieve et al, 2022 ). A distance matrix was created from orthologous protein sequences with ClutalOmega ( Sievers et al, 2011 ).…”

Profiling the bloodstream form and procyclic formTrypanosoma bruceicell cycle using single cell transcriptomics

“…Orthogroups were identified for each CCR gene, common to both BSF and PCF cell cycles, and orthologous protein sequences across 44 kinetoplastida proteomes were extracted from previous analysis 46 . A distance matrix was created from orthologous protein sequences with ClutalOmega 105 .…”

“…As mechanisms of cell cycle regulation and cyclical transcript changes are largely conserved across the eukaryotes, we hypothesised that genes with CCR transcript levels in T. brucei are more likely to be conserved. To investigate, we extracted the 819 orthogroups which contained the common CCR regulated genes and compared their conservation across 44 kinetoplastid proteomes, including trypanosome and leishmania species 46 . CCR orthogroups were conserved across significantly (p < 0.0001) more proteomes (mean of 41.66 out of 44 genomes), compared all orthogroups of the T. brucei Lister427 reference genome 47 (mean of 18.44 genomes), and a random subset of 1,000 orthogroups (mean 18.43) (Fig.…”

TriTrypDB: An integrated functional genomics resource for kinetoplastida

“…Below are a few recent examples of how TriTrypDB data and tools have been utilized by the research community to conduct their own research. TriTrypDB genomes and annotations have been used to characterize individual genes [69][70][71][72][73] or gene families [74,75], identify orthologs across species [55,70,76], conduct genome-wide analyses to study genetic variations such as SNPs, CNVs and hybridization events [77][78][79], perform comparative genomic [80][81][82] and phylogenetic/ phylogenomic [82,83] analyses, and as reference genomes for the assembly and/or annotation of newly generated genomes [16,84]. Similarly, the genome assemblies and annotations have also been utilized for the analysis of differential gene expression [19,72,85], protein expression [85][86][87], the identification of post-translational modifications [72] and potential new genes missing in the official gene sets [88].…”