GIP: An open-source computational pipeline for mapping genomic instability from protists to cancer cells

Abstract

One of the hallmarks of eukaryotic pathogens is the ability to genetically adapt to environmental change, causing for instance frequent drug resistance. Even though genome instability has been recognized as a key driver for microbial adaptation, most available computational tools focus just on one mutation type or analytical step. To overcome this limitation and better understand the role of genetic changes in enhancing microbial pathogenicity we established GIP, a novel, powerful bioinformatic pipeline for comparative genome analysis across microbial populations. GIP allows batch processing of whole genome sequencing datasets, including read alignment, normalization, quantification of chromosomes, genes and genomic bins, and detection of single nucleotide and structural variants. GIP produces a comprehensive summary report providing sample statistics together with graphical representations of genomic features and tabulated results. GIP further includes a tool-suite that enables downstream custom comparisons of samples subsets. GIP is broadly applicable to different eukaryotic systems that exploit genome instability for adaptation, including Leishmania, Plasmodium, Candida, and cancer.

Leishmania allelic selection during experimental sand fly infection correlates with mutational signatures of oxidative DNA damage

“…shifts of individual SNV patches towards a 20/80% frequency distribution for chr1,2,3,7,9,10,11,14,16,21,23,25,32,35,36, consistently with the bimodal and tri-modal SNV frequency profiles observed for these chromosomes. Likewise, two SNV patches on chr 31 show a frequency shift from 25% to 40% and 65% (see individual plots in FigureS4for more detail).We observed many sub-chromosomal SNV frequency shifts for all P_P2-SF isolates, with P_P2-SF5 attaining frequencies of close to 100% for SNV patches on chromosomes 1, 2,10,11,13,16,17,20,23,24,25,29,32,35, 36 (Figure …”

“…Here we assess the link of these forms of genome instability to promastigote differentiation, proliferation and genetic adaptation during sand fly infection. We applied read depth analysis on the genome sequences of the various input and corresponding output parasites (see Figure 1) using our Genome Instability Pipeline (GIP) and the giptools analysis package [20]. The chromosomes of the Ama input parasites were largely disomic as judged by their somy score of two, with the exception of the stably tetrasomic chromosome (chr) 31 [6] that showed the expected somy score of four (Figure 2A, upper panel, Table S3).…”

“…The read alignment, the quantification of chromosomes, genomic bins and genes, and the single nucleotide variant analyses were performed with the GIP pipeline and the giptools toolkit (version 1.0.9) [20]. The mapping statistics are provided in Table S2 and the GIP parameters are available in Dataset S1.…”

“…Here we address this important open question conducting experimental sand fly infection with bona fide amastigotes isolated from infected hamster spleen, and derived promastigotes with different karyotypic profiles. Applying our genome instability pipeline (GIP, [20]) on sand fly- recovered parasites revealed signature mutations of oxidative DNA damage that correlated with haplotype shuffling and allelic selection. These seem to drive parasite fitness gain during vector infection as judged by the convergence of allele frequency shifts across independent infection experiments.…”

A comparative genomics approach reveals a local genetic signature ofLeishmania tropicain Morocco

“…Here, we performed a comparative genomics analysis on 14 novel genomes of L. tropica that were isolated from confirmed cases of CL in Morocco. We used the Genome Instability Pipeline (GIP) [20] to assess the diversity between isolates and with respect to the reference genome, and to evaluate the genetic heterogeneity between isolates by analyzing the differences in karyotype, gene copy number, and single nucleotide polymorphisms (SNPs).…”