Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2024.02.17.580826

Exploring the Genomic Landscape of the GP63 family in Trypanosoma cruzi: Evolutionary Dynamics and Functional Peculiarities

Berna, L.·Chiribao, M. L.·Pita, S.·Alvarez-Valin, F.·Parodi-Talice, A.
View original source ↗Export metadata ↓

Abstract

We analyzed the complete set of GP63 sequences from the parasitic protozoa Trypanosoma cruzi. Our analysis allowed us to refine annotation of sequences previously identified as functional and pseudogenes. Concerning the latter, we unified pseudogenic fragments derived from the same functional gene and excluded sequences incorrectly annotated as GP63 pseudogenes. We were able to identify eleven GP63 gene groups, which are sharply defined and have a high intra-group sequence identity. The sequences of each group showed a strong preference for genomic compartments. Some groups are located in the core and others in disruptive compartments of the T. cruzi genome. Groups located in the core compartment often contain tandem arrays of GP63 genes. On the contrary, genes from groups located in the disruptive compartment tend to be surrounded by genes encoding surface proteins such as MASP, mucins and trans-sialidases. Analysis of the immediate GP63 environments showed differences that may be the result of different genomic dynamics in these two compartments. Interestingly, each GP63 group showed a particular mRNA expression profile and some groups contain members that are differentially expressed between life cycle stages, being expressed at higher levels in trypomastigotes than in the replicative forms. This suggests that these groups of GP63 proteins may play a relevant role in the infective stage. The analysis of the M8 domain, that defines the GP63 protein family, allowed us to recognize that each group presented peculiarities in the conserved sites as well as in the presence of the predicted signal peptide and GPI anchor site. Phylogenetic analysis of the GP63 sequences, including other species of the genus Trypanosoma as well as other kinetoplastids, showed that ten of the 11 groups of T. cruzi not only are also present in the other Trypanosoma species but also are exclusive of genus, suggesting that the diversification of these subfamilies took place before speciation. However, each species then followed a different evolutionary path, amplifying specific groups in unique ways. Data summaryThe authors confirm all supporting data, code and protocols have been provided within the article or through supplementary data files. Impact statementOur study contributes to the understanding of the GP63 gene family in Trypanosoma cruzi, a crucial protein for the parasites infectivity and evolution. We refined the annotation of GP63 sequences, identifying eleven distinct gene groups with distinctive preferences for genomic compartments -some in the core, others in the disruptive compartment. This distribution hints at varied genomic dynamics and potential roles in the parasites life cycle, especially since some groups show enhanced expression in infective stages, suggesting their importance in disease transmission. Our exploration into the GP63 sequences M8 domain revealed group-specific peculiarities in conserved sites and structural motifs, emphasizing functional diversity. Phylogenetic analysis across Trypanosoma species highlighted the evolutionary uniqueness of these gene subfamilies within the genus, underscoring their role in the species distinct evolutionary paths and amplification patterns.

Explore related subjects

genomicsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Berna, L., Chiribao, M. L., Pita, S., Alvarez-Valin, F., Parodi-Talice, A.. 2024-02-20. Exploring the Genomic Landscape of the GP63 family in Trypanosoma cruzi: Evolutionary Dynamics and Functional Peculiarities. https://doi.org/10.1101/2024.02.17.580826

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Structural variation in repeat elements is widespread in normal human tissues and in tumorigenesis

Somatic mosaicism contributes to genomic variation, yet postzygotic structural variants remain under-characterized. We performed long- and short-read WGS from multiple individuals (n=47 normal tissues; n=168 samples) and identified mosaic structural variants in all individuals and germ layers, impacting a median 285.2 kb/genome. Nearly half of breakpoints were independently validated, with tissue distributions reflecting both early and late developmental origins. Most mosaic variants were repeat-mediated and 8.3% overlapped functional elements, an enrichment compared to germline variants. To extend these analyses in samples where long-read sequencing is infeasible, we measured repeat alterations from short-read sequencing, recapitulating mosaic tissue-specific differences. We characterized tumor- and tissue- specific variation in repeats across 15 cancer types and found tumor-related repeat variation to be similar in scale to that of normal mosaic variation. Tracking repeat changes in cell-free DNA provided a noninvasive approach for tumor monitoring. Our analyses revealed widespread repeat-driven structural variation in health and disease.

genomics↗
bioRxiv2026

RNA isoform-resolved multiplexed sequencing with bioorthogonal barcoding

RNA isoform dysregulation drives disease pathogenesis and is the target of FDA-approved splice-switching therapeutics. However, multiplexed sequencing methods discard splice junction information because only 3' termini are barcoded and counted. Here, we repurpose acylation and click chemistries to conjugate bioorthogonal barcodes (bobcodes) directly onto multiple internal positions along cellular RNAs. Bobcoded RNAs from multiple samples are pooled for multiplexed cDNA synthesis, during which reverse transcriptase switches from each RNA template onto its tethered bobcode with greater than 99% accuracy in species mixing experiments. Bobcode attachment intervals set cDNA insert sizes without a library fragmentation step, and priming with poly(dT) or random hexamers selects between 3'-end counting and full-length isoform capture. A bioorthogonal barcode-sequencing (BOB-seq v0.1) drug screen identifies transcriptome-wide on- and off-target RNA splicing effects and outperforms existing multiplexing RNA sequencing methods in workflow simplicity, sample-to-sample variability, and barcoding accuracy. Bobcodes add isoform resolution to scalable multiplexed RNA sequencing.

genomics↗
bioRxiv2026

Structural polymorphism and population-variable coding capacity of HERV-K(HML-2) in human pangenomes

Approximately 8% of the human genome is derived from ancient retroviral infections. The most recently integrated of these endogenous retroviruses is the HERV-K(HML-2) clade, whose expression has been associated with cancer, amyotrophic lateral sclerosis, and embryogenesis. Studies of HERV expression, particularly HML-2, have relied predominantly on short-read sequencing. However, the high similarity among HML-2 proviruses prevents many short reads from being assigned uniquely to individual loci. We therefore compared haplotype-resolved long-read genome assemblies from 292 donors to resolve variation in proviral structure and coding capacity. Several loci previously thought to be fixed were structurally polymorphic. Tandem arrays occurred at 13 loci and contained up to six proviral copies in a single array. At 8q11.23, we identified a previously undescribed full-length provirus in one haplotype. All 583 other haplotypes carried a solo-LTR. We found that standard reference genomes failed to represent the coding capacity retained in many individuals, whose proviruses contained intact open reading frames despite disruptive mutations in the reference sequences. Short-read genotypes left 32.5% of the tested donor-variant pairs unresolved at sites associated with viral reading frames. These findings show why HML-2 expression must be interpreted in the context of the structural and coding alleles each individual carries.

genomics↗