Biology subjects

de Bernardi Schneider, A.

Publications and source records attributed to de Bernardi Schneider, A..

8 recordsLinked to original sources

bioRxiv2023

SARS-CoV-2 lineage assignments using phylogenetic placement/UShER are superior to pangoLEARN machine learning method

With the rapid spread and evolution of SARS-CoV-2, the ability to monitor its transmission and distinguish among viral lineages is critical for pandemic response efforts. The most commonly used software for the lineage assignment of newly isolated SARS-CoV-2 genomes is pangolin, which offers two methods of assignment, pangoLEARN and pUShER. PangoLEARN rapidly assigns lineages using a machine learning algorithm, while pUShER performs a phylogenetic placement to identify the lineage corresponding to a newly sequenced genome. In a preliminary study, we observed that pangoLEARN (decision tree model), while substantially faster than pUShER, offered less consistency across different versions of pangolin v3. Here, we expand upon this analysis to include v3 and v4 of pangolin, which moved the default algorithm for lineage assignment from pangoLEARN in v3 to pUShER in v4, and perform a thorough analysis confirming that pUShER is not only more stable across versions but also more accurate. Our findings suggest that future lineage assignment algorithms for various pathogens should consider the value of phylogenetic placement.

bioinformatics↗

bioRxiv2023

Automated Agnostic Designation of Pathogen Lineages

Pathogen nomenclature systems are a key component of effective communication and collaboration for researchers and public health workers. Since February 2021, the Pango nomenclature for SARS-CoV-2 has been sustained by crowdsourced lineage proposals as new isolates were added to a growing global dataset. This approach to dynamic lineage designation is dependent on a large and active epidemiological community identifying and curating each new lineage. This is vulnerable to time-critical delays as well as regional and personal bias. To address these issues, we developed a simple heuristic approach that divides a phylogenetic tree into lineages based on shared ancestral genotypes. We additionally provide a framework that automatically prioritizes the lineages by growth rate and association with key mutations or locations, extensible to any pathogen. Our implementation is efficient on extremely large phylogenetic trees and produces similar results to existing Pango lineage designations when applied to SARS-CoV-2. This method offers a simple, automated and consistent approach to pathogen nomenclature that can assist researchers in developing and maintaining phylogeny-based classifications in the face of ever increasing genomic datasets.

evolutionary biology↗

bioRxiv2022

Multiple-trait subsampling for optimized ancestral trait reconstruction

Large datasets along with sampling bias represent a challenge for phylodynamic reconstructions, particularly when the study data are obtained from various heterogeneous sources and/or through convenience sampling. In this study, we evaluate the presence of unbalanced sampled distribution by collection date, location, and risk group of HIV-1 subtype C using a compre-hensive subsampling strategy, and assess their impact on the reconstruction of the viral spatial and risk group dynamics using phylogenetic comparative methods. Our study shows that the most suitable dataset for ancestral trait reconstruction can be obtained through subsampling by collection date, location, and risk group, particularly using multigene datasets. We also demonstrate that sampling bias is inflated when considerable information for a given trait is unavailable or of poor quality, as we observed for the risk group in the analysis of HIV-1 subtype C. In conclusion, we suggest that, even if traits are not well recorded, including them deliberately optimizes the representativeness of the original dataset rather than completely excluding them. Therefore, we advise the inclusion of as many traits as possible with the aid of subsampling approaches in order to optimize the dataset for phylodynamic analysis while reducing the computational burden. This will benefit research communities investigating the evolutionary and spatiotemporal patterns of infectious diseases.

bioinformatics↗

bioRxiv2021

Dynamic molecular epidemiology reveals lineage-associated single-nucleotide variants that alter RNA structure in Chikungunya virus

Chikungunya virus (CHIKV) is an emerging Alphavirus which causes millions of human infections every year. Outbreaks have been reported in Africa and Asia since the early 1950s, from three CHIKV lineages: West African, East Central South African, and Asian Urban. As new outbreaks occurred in the Americas, individual strains from the known lineages have evolved, creating new monophyletic groups that generated novel geographic-based lineages. Building on a recently updated phylogeny of CHIKV, we report here the availability of an interactive CHIKV phylodynamics dataset, which is based on more than 900 publicly available CHIKV genomes. We provide an interactive view of CHIKV molecular epidemiology built on Nextstrain, a web-based visualization framework for real-time tracking of pathogen evolution. CHIKV molecular epidemiology reveals single nucleotide variants that change the stability and fold of locally stable RNA structures. We propose alternative RNA structure formation in different CHIKV lineages by predicting more than a dozen RNA elements that are subject to perturbation of the structure ensemble upon variation of a single nucleotide.

evolutionary biology↗

bioRxiv2019

Updated phylogeny of Chikungunya virus reveals lineage-specific RNA architecture

Chikungunya virus (CHIKV), a mosquito-borne alphavirus of the family Togaviridae, has recently emerged in the Americas from lineages from two continents, Asia and Africa. Historically, CHIKV circulated as at least four lineages worldwide with both enzootic and epidemic transmission cycles. To understand the recent patterns of emergence and the current status of the CHIKV spread, updated analyses of the viral genetic data and metadata are needed. Here, we performed phylogenetic and comparative genomics screens of CHIKV genomes, taking advantage of the public availability of many recently sequenced isolates. Based on these new data and analyses, we derive a revised phylogeny from nucleotide sequences in coding regions. Using this phylogeny, we uncover the presence of several distinct lineages in Africa that were previously considered a single one. In parallel, we performed thermodynamic modeling of CHIKV untranslated regions (UTRs), which revealed evolutionarily conserved structured and unstructured RNA elements in the 3UTR. We provide evidence for duplication events in recently emerged American isolates of the Asian CHIKV lineage and propose the existence of a flexible 3UTR architecture among different CHIKV lineages.

bioinformatics↗

bioRxiv2019

Enhanced genome annotation strategy provides novel insights on the phylogeny of Flaviviridae

The ongoing and severe public health threat of viruses of the family Flaviviridae, including dengue, hepatitis C, West Nile, yellow fever, and zika, demand a greater understanding of how these viruses evolve, emerge and spread in order to respond. Central to this understanding is an updated phylogeny of the entire family. Unfortunately, most cladograms of Flaviviridae focus on specific lineages, ignore outgroups, and rely on midpoint rooting, hampering their ability to test ingroup monophyly and estimate ingroup relationships. This problem is partly due to the lack of fully annotated genomes of Flaviviridae, which has genera with slightly different gene content, hindering genome analysis without partitioning. To tackle these problems, we developed an annotation pipeline for Flaviviridae that uses a combination of ab initio and homology-based strategies. The pipeline recovered 100% of the genes in reference genomes and annotated over 97% of the expected genes in the remaining non curated sequences. We further demonstrate that the combined analysis of genomes of all genera of Flaviviridae (Flavivirus, Hepacivirus, Pegivirus, and Pestivirus), as made possible by our annotation strategy, enhances the phylogenetic analyses of these viruses for all optimality criteria that we tested (parsimony, maximum likelihood, and posterior probability). The final tree sheds light on the phylogenetic relationship of viruses that are divergent from most Flaviviridae and should be reclassified, especially the soybean cyst nematode virus 5 (SbCNV-5) and the Tamana bat virus. We also corroborate the close phylogenetic relationship of dengue and zika viruses with an unprecedented degree of support.

evolutionary biology↗

bioRxiv2019

StrainHub: A phylogenetic tool to construct pathogen transmission networks

In exploring the epidemiology of infectious diseases, networks have been used to reconstruct contacts among individuals and/or populations. Summarizing networks using pathogen metadata (e.g., host species and place of isolation) and a phylogenetic tree is a nascent, alternative approach. In this paper, we introduce a tool for reconstructing transmission networks in arbitrary space from phylogenetic information and metadata. Our goals are to provide a means of deriving new insights and infection control strategies based on the dynamics of the pathogen lineages derived from networks and centrality metrics. We created a web-based application, called StrainHub, in which a user can input a phylogenetic tree based on genetic or other data along with characters derived from metadata using their preferred tree search method. StrainHub generates a transmission network based on character state changes in meta-data, such as place or source of isolation, mapped on the phylogenetic tree. The user has the option to calculate centrality metrics on the nodes including betweenness, closeness, degree, and a new metric, the source/hub ratio. The outputs include the network with values for metrics on its nodes and the tree with characters reconstructed. All of these results can be exported for further analysis.\n\nAvailabilityhttps://github.com/abschneider/StrainHub and strainhub.io

bioinformatics↗

bioRxiv2018

Musashi binding elements in Zika and related Flavivirus 3’UTRs: A comparative study in silico

Zika virus (ZIKV) belongs to a class of neurotropic viruses that have the ability to cause congenital infection, which can result in microcephaly or fetal demise. Recently, the RNA-binding protein Musashi-1 (Msi1), which mediates the maintenance and self-renewal of stem cells and acts as a translational regulator, has been associated with promoting ZIKV replication, neurotropism, and pathology. Msi1 predominantly binds to single-stranded UAG motifs in the 3UTR of RNA. We systematically analyzed the properties of Musashi binding elements (MBEs) in the 3UTR of flaviviruses based on a thermodynamic model for RNA folding. Our results indicate that MBEs in ZIKV 3UTRs occur predominantly in unpaired, single-stranded structural context, thus corroborating experimental observations by a biophysical model of RNA structure formation. Statistical analysis and comparison with related viruses shows that ZIKV MBEs are maximally accessible among all mosquito-borne flaviviruses. Our study addresses the broader question whether other emerging arboviruses can cause similar neurotropic effects through the same mechanism in the developing fetus. In this line, we establish a link between the biophysical properties of viral RNA and teratogenicity. Moreover, our thermodynamic model can explain recent experimental findings and predict the Msi1-related neurotropic potential of other viruses.

bioinformatics↗