VIPERA: Viral Intra-Patient Evolution Reporting and Analysis

Abstract

[Background] Viral mutations within patients nurture the adaptive potential of SARS-CoV-2 during chronic infections, which are a potential source of variants of concern. However, there is no integrated framework for the evolutionary analysis of intrapatient SARS-CoV-2 serial samples. Herein we describe VIPERA, a new software that integrates the evaluation of the intrapatient ancestry of SARS-CoV-2 sequences with the analysis of evolutionary trajectories of serial sequences from the same viral infection.

[Methods] VIPERA is a pipeline implemented in Snakemake that analyzes a set of SARS-CoV-2 samples serially-collected from the same patient and produces a HTML report with three sections. Firstly, a summary of the samples is reported including lineage designation. Secondly, to ascertain if all samples were collected from the same infection, lineage admixture analyses, phylogenetic analyses and diversity analyses are produced and reported. For the phylogenetic analyses and the diversity analyses, a set of context samples is automatically fetched from GISAID. Thirdly, evolutionary analyses such as dN/dS analysis or the characterization of the nucleotide variants that appear in the studied samples are also carried out and reported. To validate the power to detect serially-sampled infections using VIPERA, we have used it with two validation datasets. The positive control dataset includes 30 sequences collected in Yale from the same patient (Chaguza C et al.,Cell Rep Med, 2023). The negative control dataset includes a mixture of 12 samples collected from one patient in Barcelona in 2020 with 3 other samples from another patient but collected in the same location and dates. Finally, we use VIPERA to carry out the whole intrapatient-evolution analysis with the 12 samples from the same patient that take part in the negative control.

[Results] The positive and negative control datasets showed very different lineage composition, with more homogeneous lineage composition in the positive control. Phylogenetic three showed samples from positive control were monophyletic, while sequences from negative control were paraphyletic. Finally, the nucleotide diversity was significantly reduced in the studied sequences compared to the context ones in the positive control, as we expect for sequences derived from the same infection. On the contrary, the nucleotide diversity was not significantly reduced in the studied sequences compared to the context ones in the negative control. Our validation demonstrated that VIPERA has the power to detect serially-sampled infections. Additionally, we analyzed a case study using VIPERA. First we confirmed all sequences derived from a serially-sampled infection because lineage admixture was homogeneous, sequences were monophyletic and the nucleotide diversity was significantly lower than in the context sequences. Intrapatient-evolution analyses reported by VIPERA identified the appearance of mutations that are concerning due to their associated immune scape phenotype. We also detect positive selection along the infection using the dN/dS analysis.

[Conclusions] VIPERA provides an aggregate of analysis for detecting whether there is a serially-sampled infection or not, including novel approaches such as genetic diversity and genetic distance at the population level approaches. It also provides a description of the within-host evolution observed in the studied samples.