2023
Wu, Ling-Yi; Pappas, Nikolaos; Wijesekara, Yasas; Piedade, Gonçalo J.; Brussaard, Corina P. D.; Dutilh, Bas E.
Benchmarking Bioinformatic Virus Identification Tools Using Real-World Metagenomic Data across Biomes Journal Article
In: bioRxiv, 2023.
Abstract | Links | BibTeX | Tags: Project 12, Project 13, WP 1.1 Virus identification, WP 1.2 Host prediction
@article{nokey,
title = {Benchmarking Bioinformatic Virus Identification Tools Using Real-World Metagenomic Data across Biomes},
author = {Ling-Yi Wu and Nikolaos Pappas and Yasas Wijesekara and Gonçalo J. Piedade and Corina P.D. Brussaard and Bas E. Dutilh},
doi = {10.1101/2023.04.26.538077},
year = {2023},
date = {2023-04-28},
journal = {bioRxiv},
abstract = {As most viruses remain uncultivated, metagenomics is currently the main method for virus discovery. Detecting viruses in metagenomic data is not trivial. In the past few years, many bioinformatic virus identification tools have been developed for this task, making it challenging to choose the right tools, parameters, and cutoffs. As all these tools measure different biological signals, and use different algorithms and training/reference databases, it is imperative to conduct an independent benchmarking to give users objective guidance. We compared the performance of ten state-of-the-art virus identification tools in thirteen modes on eight paired viral and microbial datasets from three distinct biomes, including a new complex dataset from Antarctic coastal waters. The tools had highly variable true positive rates (0 – 68%) and false positive rates (0 – 15%). PPR-Meta best distinguished viral from microbial contigs, followed by DeepVirFinder, VirSorter2, and VIBRANT. Different tools identified different subsets of the benchmarking data and all tools, except for Sourmash, found unique viral contigs. Tools performance could be improved with adjusted parameter cutoffs, indicating that adjustment of parameter cutoffs before usage should be considered. Together, our independent benchmarking provides guidance on choices of bioinformatic virus identification tools and gives suggestions for parameter adjustments for viromics researchers.},
keywords = {Project 12, Project 13, WP 1.1 Virus identification, WP 1.2 Host prediction},
pubstate = {published},
tppubtype = {article}
}
As most viruses remain uncultivated, metagenomics is currently the main method for virus discovery. Detecting viruses in metagenomic data is not trivial. In the past few years, many bioinformatic virus identification tools have been developed for this task, making it challenging to choose the right tools, parameters, and cutoffs. As all these tools measure different biological signals, and use different algorithms and training/reference databases, it is imperative to conduct an independent benchmarking to give users objective guidance. We compared the performance of ten state-of-the-art virus identification tools in thirteen modes on eight paired viral and microbial datasets from three distinct biomes, including a new complex dataset from Antarctic coastal waters. The tools had highly variable true positive rates (0 – 68%) and false positive rates (0 – 15%). PPR-Meta best distinguished viral from microbial contigs, followed by DeepVirFinder, VirSorter2, and VIBRANT. Different tools identified different subsets of the benchmarking data and all tools, except for Sourmash, found unique viral contigs. Tools performance could be improved with adjusted parameter cutoffs, indicating that adjustment of parameter cutoffs before usage should be considered. Together, our independent benchmarking provides guidance on choices of bioinformatic virus identification tools and gives suggestions for parameter adjustments for viromics researchers.
Liu, Dan; Young, Francesca; Robertson, David L; Yuan, Ke
Prediction of virus-host association using protein language models and multiple instance learning Journal Article
In: bioRxiv, 2023.
Abstract | Links | BibTeX | Tags: Project 10, WP 1.2 Host prediction, WP 1.3 Virus-host interactions
@article{nokey,
title = {Prediction of virus-host association using protein language models and multiple instance learning},
author = {Dan Liu and Francesca Young and David L Robertson and Ke Yuan},
doi = {10.1101/2023.04.07.536023},
year = {2023},
date = {2023-04-08},
journal = {bioRxiv},
abstract = {Predicting virus-host association is essential to understand how viruses interact with host species, and discovering new therapeutics for viral diseases across humans and animals. Currently, the host of the majority of viruses is unknown. Here, we introduce EvoMIL, a deep learning method that predicts virus-host association at the species level from viral sequence only. The method combines a pre-trained large protein language model and attention-based multiple instance learning to allow protein-orientated predictions. Our results show that protein embeddings capture stronger predictive signals than traditional handcrafted features, including amino acids and DNA k-mers, and physio-chemical properties. EvoMIL binary classifiers achieve AUC values of over 0.95 for all prokaryotic and nearly 0.8 for almost all eukaryotic hosts. In multi-host prediction tasks, EvoMIL achieved median performance improvements of 8.6% in prokaryotic hosts and 1.8% in eukaryotic hosts. Furthermore, EvoMIL estimates the importance of single proteins in the prediction and maps them to an embedding landscape of all viral proteins, where proteins with similar functions are distinctly clustered together.},
keywords = {Project 10, WP 1.2 Host prediction, WP 1.3 Virus-host interactions},
pubstate = {published},
tppubtype = {article}
}
Predicting virus-host association is essential to understand how viruses interact with host species, and discovering new therapeutics for viral diseases across humans and animals. Currently, the host of the majority of viruses is unknown. Here, we introduce EvoMIL, a deep learning method that predicts virus-host association at the species level from viral sequence only. The method combines a pre-trained large protein language model and attention-based multiple instance learning to allow protein-orientated predictions. Our results show that protein embeddings capture stronger predictive signals than traditional handcrafted features, including amino acids and DNA k-mers, and physio-chemical properties. EvoMIL binary classifiers achieve AUC values of over 0.95 for all prokaryotic and nearly 0.8 for almost all eukaryotic hosts. In multi-host prediction tasks, EvoMIL achieved median performance improvements of 8.6% in prokaryotic hosts and 1.8% in eukaryotic hosts. Furthermore, EvoMIL estimates the importance of single proteins in the prediction and maps them to an embedding landscape of all viral proteins, where proteins with similar functions are distinctly clustered together.
Graf, Christiana; Fuhrmann, Lara; Lutz, Thomas; Stephan, Christoph; Knecht, Gaby; Gute, Peter; Bickel, Markus; Peiffer, Kai-Henrik; Finkelmeier, Fabian; Dultz, Georg; Mondorf, Antonia; Wetzstein, Nils; Filmann, Natalie; Herrmann, Eva; Zeuzem, Stefan; Beerenwinkel, Niko; Dietz, Julia; Sarrazin, Christoph
Expanding epidemic of recently acquired HCV in HIV-coinfected patients over a period of 10 years Journal Article
In: JHEP Reports, 2023.
Abstract | Links | BibTeX | Tags: Project 03, WP 1.3 Virus-host interactions
@article{nokey,
title = {Expanding epidemic of recently acquired HCV in HIV-coinfected patients over a period of 10 years},
author = {Christiana Graf and Lara Fuhrmann and Thomas Lutz and Christoph Stephan and Gaby Knecht and Peter Gute and Markus Bickel and Kai-Henrik Peiffer and Fabian Finkelmeier and Georg Dultz and Antonia Mondorf and Nils Wetzstein and Natalie Filmann and Eva Herrmann and Stefan Zeuzem and Niko Beerenwinkel and Julia Dietz and Christoph Sarrazin},
doi = {10.1016/j.jhepr.2023.100701},
year = {2023},
date = {2023-02-19},
journal = {JHEP Reports},
abstract = {Background and aims: Ongoing transmission of hepatitis C virus (HCV) infections is associated with risk factors like drug injection, needle stick injuries and men who have sex with men (MSM). Ways of transmission, the course of acute infection, changes of virologic features, and incidence over time are not well known.
Methods: Over a period of 10 years, n=161 patients with recently acquired HCV infection (RAHC) (median follow-up 6.8 years) were prospectively enrolled. NS5B sequencing was performed to re-evaluate the HCV genotype and for phylogenetic analyses.
Results: Patients with RAHC were mainly male (92.5%), MSM (90.1%) and HIV-coinfected (86.3%). Transmission risk factors for MSM and non-MSM were sexual risk behaviour (100 and 6.3%, respectively), injection drug use (9.7 and 37.5%, respectively) and nasal drug use (15.2 and 0%, respectively). Spontaneous and interferon- or direct-acting antiviral (DAA)-based clearance rates were 13.6%, 84.3% and 93.4%, respectively. Mean RAHC declined from 19.8 in the first to 13.2 in the past 5 study years. While the majority of infections was caused by HCV-1a, the frequency of HCV-4d and slightly HCV-3a increased over time. No relevant clustering of HCV isolates was observed in non-MSM. However, 45% of HCV-1a and 100% of HCV-4d MSM-cases clustered with MSM-isolates from other countries. Travel-associated infections were supported by personal data in a MSM subgroup. No international clustering was detected in MSM with HCV-1b or HCV-3a.
Conclusions: RAHC were mainly diagnosed in MSM-HIV-coinfected patients and were associated with sexual risk behaviour. Spontaneous clearance rates were low and phylogenetic clusters were observed in the majority of patients.},
keywords = {Project 03, WP 1.3 Virus-host interactions},
pubstate = {published},
tppubtype = {article}
}
Background and aims: Ongoing transmission of hepatitis C virus (HCV) infections is associated with risk factors like drug injection, needle stick injuries and men who have sex with men (MSM). Ways of transmission, the course of acute infection, changes of virologic features, and incidence over time are not well known.
Methods: Over a period of 10 years, n=161 patients with recently acquired HCV infection (RAHC) (median follow-up 6.8 years) were prospectively enrolled. NS5B sequencing was performed to re-evaluate the HCV genotype and for phylogenetic analyses.
Results: Patients with RAHC were mainly male (92.5%), MSM (90.1%) and HIV-coinfected (86.3%). Transmission risk factors for MSM and non-MSM were sexual risk behaviour (100 and 6.3%, respectively), injection drug use (9.7 and 37.5%, respectively) and nasal drug use (15.2 and 0%, respectively). Spontaneous and interferon- or direct-acting antiviral (DAA)-based clearance rates were 13.6%, 84.3% and 93.4%, respectively. Mean RAHC declined from 19.8 in the first to 13.2 in the past 5 study years. While the majority of infections was caused by HCV-1a, the frequency of HCV-4d and slightly HCV-3a increased over time. No relevant clustering of HCV isolates was observed in non-MSM. However, 45% of HCV-1a and 100% of HCV-4d MSM-cases clustered with MSM-isolates from other countries. Travel-associated infections were supported by personal data in a MSM subgroup. No international clustering was detected in MSM with HCV-1b or HCV-3a.
Conclusions: RAHC were mainly diagnosed in MSM-HIV-coinfected patients and were associated with sexual risk behaviour. Spontaneous clearance rates were low and phylogenetic clusters were observed in the majority of patients.
Methods: Over a period of 10 years, n=161 patients with recently acquired HCV infection (RAHC) (median follow-up 6.8 years) were prospectively enrolled. NS5B sequencing was performed to re-evaluate the HCV genotype and for phylogenetic analyses.
Results: Patients with RAHC were mainly male (92.5%), MSM (90.1%) and HIV-coinfected (86.3%). Transmission risk factors for MSM and non-MSM were sexual risk behaviour (100 and 6.3%, respectively), injection drug use (9.7 and 37.5%, respectively) and nasal drug use (15.2 and 0%, respectively). Spontaneous and interferon- or direct-acting antiviral (DAA)-based clearance rates were 13.6%, 84.3% and 93.4%, respectively. Mean RAHC declined from 19.8 in the first to 13.2 in the past 5 study years. While the majority of infections was caused by HCV-1a, the frequency of HCV-4d and slightly HCV-3a increased over time. No relevant clustering of HCV isolates was observed in non-MSM. However, 45% of HCV-1a and 100% of HCV-4d MSM-cases clustered with MSM-isolates from other countries. Travel-associated infections were supported by personal data in a MSM subgroup. No international clustering was detected in MSM with HCV-1b or HCV-3a.
Conclusions: RAHC were mainly diagnosed in MSM-HIV-coinfected patients and were associated with sexual risk behaviour. Spontaneous clearance rates were low and phylogenetic clusters were observed in the majority of patients.
2022
Rummel, Teresa; Sakellaridi, Lygeri; Erhard, Florian
grandR: a comprehensive package for nucleotide conversion sequencing data analysis Journal Article
In: bioRxiv, 2022.
Abstract | Links | BibTeX | Tags: Project 06, WP 1.3 Virus-host interactions
@article{Rummel2022,
title = {grandR: a comprehensive package for nucleotide conversion sequencing data analysis},
author = {Teresa Rummel and Lygeri Sakellaridi and Florian Erhard },
doi = {10.1101/2022.09.12.507665},
year = {2022},
date = {2022-09-15},
urldate = {2022-09-15},
journal = {bioRxiv},
abstract = {Metabolic labeling of RNA is a powerful technique for studying the temporal dynamics of gene expression. Nucleotide conversion approaches greatly facilitate the generation of data but introduce challenges for their analysis. We here present grandR, a comprehensive package for quality control, differential gene expression analysis, kinetic modeling, and visualization of such data. We compare several existing methods for inference of RNA synthesis rates and half-lives using progressive labeling time courses. We demonstrate the need for recalibration of effective labeling times and introduce a Bayesian approach to study the temporal dynamics of RNA using snapshot experiments.},
keywords = {Project 06, WP 1.3 Virus-host interactions},
pubstate = {published},
tppubtype = {article}
}
Metabolic labeling of RNA is a powerful technique for studying the temporal dynamics of gene expression. Nucleotide conversion approaches greatly facilitate the generation of data but introduce challenges for their analysis. We here present grandR, a comprehensive package for quality control, differential gene expression analysis, kinetic modeling, and visualization of such data. We compare several existing methods for inference of RNA synthesis rates and half-lives using progressive labeling time courses. We demonstrate the need for recalibration of effective labeling times and introduce a Bayesian approach to study the temporal dynamics of RNA using snapshot experiments.
Jakob, Celia; Paul-Stansilaus, Rithu; Schwemmle, Martin; Marquet, Roland; Bolte, Hardin
The influenza A virus genome packaging network - complex, flexible and yet unsolved Journal Article
In: Nucleic Acids Res., 2022.
Abstract | Links | BibTeX | Tags: Project 02, WP 1.4 Virus regulation
@article{nokey,
title = {The influenza A virus genome packaging network - complex, flexible and yet unsolved},
author = {Celia Jakob and Rithu Paul-Stansilaus and Martin Schwemmle and Roland Marquet and Hardin Bolte},
doi = {10.1093/nar/gkac688},
year = {2022},
date = {2022-08-22},
journal = {Nucleic Acids Res.},
abstract = {The genome of influenza A virus (IAV) consists of eight unique viral RNA segments. This genome organization allows genetic reassortment between co-infecting IAV strains, whereby new IAVs with altered genome segment compositions emerge. While it is known that reassortment events can create pandemic IAVs, it remains impossible to anticipate reassortment outcomes with pandemic prospects. Recent research indicates that reassortment is promoted by a viral genome packaging mechanism that delivers the eight genome segments as a supramolecular complex into the virus particle. This finding holds promise of predicting pandemic IAVs by understanding the intermolecular interactions governing this genome packaging mechanism. Here, we critically review the prevailing mechanistic model postulating that IAV genome packaging is orchestrated by a network of intersegmental RNA-RNA interactions. Although we find supporting evidence, including segment-specific packaging signals and experimentally proposed RNA-RNA interaction networks, this mechanistic model remains debatable due to a current shortage of functionally validated intersegmental RNA-RNA interactions. We speculate that identifying such functional intersegmental RNA-RNA contacts might be hampered by limitations of the utilized probing techniques and the inherent complexity of the genome packaging mechanism. Nevertheless, we anticipate that improved probing strategies combined with a mutagenesis-based validation could facilitate their discovery.},
keywords = {Project 02, WP 1.4 Virus regulation},
pubstate = {published},
tppubtype = {article}
}
The genome of influenza A virus (IAV) consists of eight unique viral RNA segments. This genome organization allows genetic reassortment between co-infecting IAV strains, whereby new IAVs with altered genome segment compositions emerge. While it is known that reassortment events can create pandemic IAVs, it remains impossible to anticipate reassortment outcomes with pandemic prospects. Recent research indicates that reassortment is promoted by a viral genome packaging mechanism that delivers the eight genome segments as a supramolecular complex into the virus particle. This finding holds promise of predicting pandemic IAVs by understanding the intermolecular interactions governing this genome packaging mechanism. Here, we critically review the prevailing mechanistic model postulating that IAV genome packaging is orchestrated by a network of intersegmental RNA-RNA interactions. Although we find supporting evidence, including segment-specific packaging signals and experimentally proposed RNA-RNA interaction networks, this mechanistic model remains debatable due to a current shortage of functionally validated intersegmental RNA-RNA interactions. We speculate that identifying such functional intersegmental RNA-RNA contacts might be hampered by limitations of the utilized probing techniques and the inherent complexity of the genome packaging mechanism. Nevertheless, we anticipate that improved probing strategies combined with a mutagenesis-based validation could facilitate their discovery.
Peng, Xue; Ru, Jinlong; Mirzaei, Mohammadali Khan; Deng, Li
Replidec - Use naive Bayes classifier to identify virus lifecycle from metagenomics data Journal Article
In: bioRxiv, 2022.
Abstract | Links | BibTeX | Tags: Project 05, WP 1.2 Host prediction
@article{nokey,
title = {Replidec - Use naive Bayes classifier to identify virus lifecycle from metagenomics data},
author = {Xue Peng and Jinlong Ru and Mohammadali Khan Mirzaei and Li Deng
},
doi = {10.1101/2022.07.18.500415},
year = {2022},
date = {2022-07-19},
journal = {bioRxiv},
abstract = {Motivation: Viruses are the most abundant biological entities on earth. The majority of these entities are bacterial viruses or phages which specifically infect bacteria. Phages can use different replication strategies to invade their hosts including lytic, lysogenic, chronic cycle and pseudolysogeny. While the determination of the replication strategy used by phages is important to explore the phage-bacteria relationships in different ecosystems there are not many tools that can predict this in metagenomic data. In addition, most of the tools available can only predict lytic and lysogenic cycles. To address this issue, we have developed a new software called Replidec to identify three most common phage replication cycles (virulent, temperate, chronic) in viral sequences.
Results: Replidec uses Naive Bayes classifier combined with alignment-based methods to improve the prediction accuracy in metagenomic data. We test Replidec on viral genomes with known replication cycle and simulated metagenomic sequences. Replidec perform relatively good both in isolated genomes (F1 score: 92.29% ± 0.81; mcc: 89.14% ± 1.22) and simulated metagenomic sequences(F1 score: 87.55% ± 2.12; mcc: 88.23% ± 2.55). Moreover, Replidec can also accurately predict the replication cycle in small viral fragments(∼3000bp). In conclusion, Replidec can achieve the best performance in simulated metagenomic data compared to most prediction softwares including BACPHLIP.},
keywords = {Project 05, WP 1.2 Host prediction},
pubstate = {published},
tppubtype = {article}
}
Motivation: Viruses are the most abundant biological entities on earth. The majority of these entities are bacterial viruses or phages which specifically infect bacteria. Phages can use different replication strategies to invade their hosts including lytic, lysogenic, chronic cycle and pseudolysogeny. While the determination of the replication strategy used by phages is important to explore the phage-bacteria relationships in different ecosystems there are not many tools that can predict this in metagenomic data. In addition, most of the tools available can only predict lytic and lysogenic cycles. To address this issue, we have developed a new software called Replidec to identify three most common phage replication cycles (virulent, temperate, chronic) in viral sequences.
Results: Replidec uses Naive Bayes classifier combined with alignment-based methods to improve the prediction accuracy in metagenomic data. We test Replidec on viral genomes with known replication cycle and simulated metagenomic sequences. Replidec perform relatively good both in isolated genomes (F1 score: 92.29% ± 0.81; mcc: 89.14% ± 1.22) and simulated metagenomic sequences(F1 score: 87.55% ± 2.12; mcc: 88.23% ± 2.55). Moreover, Replidec can also accurately predict the replication cycle in small viral fragments(∼3000bp). In conclusion, Replidec can achieve the best performance in simulated metagenomic data compared to most prediction softwares including BACPHLIP.
Results: Replidec uses Naive Bayes classifier combined with alignment-based methods to improve the prediction accuracy in metagenomic data. We test Replidec on viral genomes with known replication cycle and simulated metagenomic sequences. Replidec perform relatively good both in isolated genomes (F1 score: 92.29% ± 0.81; mcc: 89.14% ± 1.22) and simulated metagenomic sequences(F1 score: 87.55% ± 2.12; mcc: 88.23% ± 2.55). Moreover, Replidec can also accurately predict the replication cycle in small viral fragments(∼3000bp). In conclusion, Replidec can achieve the best performance in simulated metagenomic data compared to most prediction softwares including BACPHLIP.
Jahn, Katharina; Dreifuss, David; Topolsky, Ivan; Kull, Anina; Ganesanandamoorthy, Pravin; Fernandez-Cassi, Xavier; Bänziger, Carola; Devaux, Alexander J.; Stachler, Elyse; Caduff, Lea; Cariti, Federica; Corzón, Alex Tuñas; Fuhrmann, Lara; Chen, Chaoran; Jablonski, Kim Philipp; Nadeau, Sarah; Feldkamp, Mirjam; Beisel, Christian; Aquino, Catharine; Stadler, Tanja; Ort, Christoph; Kohn, Tamar; & Niko Beerenwinkel, Timothy R. Julian
Early detection and surveillance of SARS-CoV-2 genomic variants in wastewater using COJAC Journal Article
In: Nat Microbiol, 2022.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {Early detection and surveillance of SARS-CoV-2 genomic variants in wastewater using COJAC},
author = {Katharina Jahn and David Dreifuss and Ivan Topolsky and Anina Kull and Pravin Ganesanandamoorthy and Xavier Fernandez-Cassi and Carola Bänziger and Alexander J. Devaux and Elyse Stachler and Lea Caduff and Federica Cariti and Alex Tuñas Corzón and Lara Fuhrmann and Chaoran Chen and Kim Philipp Jablonski and Sarah Nadeau and Mirjam Feldkamp and Christian Beisel and Catharine Aquino and Tanja Stadler and Christoph Ort and Tamar Kohn and Timothy R. Julian & Niko Beerenwinkel},
doi = {10.1038/s41564-022-01185-x},
year = {2022},
date = {2022-07-18},
journal = {Nat Microbiol},
abstract = {The continuing emergence of SARS-CoV-2 variants of concern and variants of interest emphasizes the need for early detection and epidemiological surveillance of novel variants. We used genomic sequencing of 122 wastewater samples from three locations in Switzerland to monitor the local spread of B.1.1.7 (Alpha), B.1.351 (Beta) and P.1 (Gamma) variants of SARS-CoV-2 at a population level. We devised a bioinformatics method named COJAC (Co-Occurrence adJusted Analysis and Calling) that uses read pairs carrying multiple variant-specific signature mutations as a robust indicator of low-frequency variants. Application of COJAC revealed that a local outbreak of the Alpha variant in two Swiss cities was observable in wastewater up to 13 d before being first reported in clinical samples. We further confirmed the ability of COJAC to detect emerging variants early for the Delta variant by analysing an additional 1,339 wastewater samples. While sequencing data of single wastewater samples provide limited precision for the quantification of relative prevalence of a variant, we show that replicate and close-meshed longitudinal sequencing allow for robust estimation not only of the local prevalence but also of the transmission fitness advantage of any variant. We conclude that genomic sequencing and our computational analysis can provide population-level estimates of prevalence and fitness of emerging variants from wastewater samples earlier and on the basis of substantially fewer samples than from clinical samples. Our framework is being routinely used in large national projects in Switzerland and the UK.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The continuing emergence of SARS-CoV-2 variants of concern and variants of interest emphasizes the need for early detection and epidemiological surveillance of novel variants. We used genomic sequencing of 122 wastewater samples from three locations in Switzerland to monitor the local spread of B.1.1.7 (Alpha), B.1.351 (Beta) and P.1 (Gamma) variants of SARS-CoV-2 at a population level. We devised a bioinformatics method named COJAC (Co-Occurrence adJusted Analysis and Calling) that uses read pairs carrying multiple variant-specific signature mutations as a robust indicator of low-frequency variants. Application of COJAC revealed that a local outbreak of the Alpha variant in two Swiss cities was observable in wastewater up to 13 d before being first reported in clinical samples. We further confirmed the ability of COJAC to detect emerging variants early for the Delta variant by analysing an additional 1,339 wastewater samples. While sequencing data of single wastewater samples provide limited precision for the quantification of relative prevalence of a variant, we show that replicate and close-meshed longitudinal sequencing allow for robust estimation not only of the local prevalence but also of the transmission fitness advantage of any variant. We conclude that genomic sequencing and our computational analysis can provide population-level estimates of prevalence and fitness of emerging variants from wastewater samples earlier and on the basis of substantially fewer samples than from clinical samples. Our framework is being routinely used in large national projects in Switzerland and the UK.
McCallin, Shawna; Menzi, Carmen; Lassen, Swenja; Daraspe, Jean; Oechslin, Frank; Moreillon, Philippe
Antibiotic Exposure Leads to Reduced Phage Susceptibility in Vancomycin Intermediate Staphylococcus aureus (VISA) Journal Article
In: Antimicrob Agents Chemother, 2022.
Abstract | Links | BibTeX | Tags: Project 07, WP 1.5 Virus products
@article{McCallin2022,
title = {Antibiotic Exposure Leads to Reduced Phage Susceptibility in Vancomycin Intermediate Staphylococcus aureus (VISA)},
author = {Shawna McCallin and Carmen Menzi and Swenja Lassen and Jean Daraspe and Frank Oechslin and Philippe Moreillon
},
doi = {10.1128/aac.02247-21},
year = {2022},
date = {2022-06-16},
urldate = {2022-06-16},
journal = {Antimicrob Agents Chemother},
abstract = {In the time of antimicrobial resistance, phage therapy is frequently suggested as a possible solution for such difficult-to-treat infections. Vancomycin-intermediate Staphylococcus aureus (VISA) remains a relatively rare yet increasing occurrence in the clinic for which phage therapy may be an option. However, the data presented herein suggest a potential cross-resistance mechanism to phage following vancomycin exposure in VISA strains. When comparing genetically similar strains differing in their susceptibility to vancomycin, those with intermediate levels of vancomycin resistance displayed decreased sensitivity to phage in solid and liquid assays. Serial passaging with vancomycin induced both reduced vancomycin susceptibility and phage sensitivity. As a consequence, the process of phage infection was shown to be interrupted after DNA ejection from adsorbed phage but prior to phage DNA replication, as demonstrated through adsorption assays, lysostaphin sensitivity assays, electron microscopy, and quantitative PCR (qPCR). At a time when phage products are being used for experimental treatments and tested in clinical trials, it is important to understand possible interference between mechanisms underlying antibiotic and phage resistance in order to design effective therapeutic regimens.},
keywords = {Project 07, WP 1.5 Virus products},
pubstate = {published},
tppubtype = {article}
}
In the time of antimicrobial resistance, phage therapy is frequently suggested as a possible solution for such difficult-to-treat infections. Vancomycin-intermediate Staphylococcus aureus (VISA) remains a relatively rare yet increasing occurrence in the clinic for which phage therapy may be an option. However, the data presented herein suggest a potential cross-resistance mechanism to phage following vancomycin exposure in VISA strains. When comparing genetically similar strains differing in their susceptibility to vancomycin, those with intermediate levels of vancomycin resistance displayed decreased sensitivity to phage in solid and liquid assays. Serial passaging with vancomycin induced both reduced vancomycin susceptibility and phage sensitivity. As a consequence, the process of phage infection was shown to be interrupted after DNA ejection from adsorbed phage but prior to phage DNA replication, as demonstrated through adsorption assays, lysostaphin sensitivity assays, electron microscopy, and quantitative PCR (qPCR). At a time when phage products are being used for experimental treatments and tested in clinical trials, it is important to understand possible interference between mechanisms underlying antibiotic and phage resistance in order to design effective therapeutic regimens.
2021
Fuhrmann, Lara; Jablonski, Kim Philipp; Beerenwinkel, Niko
Quantitative measures of within-host viral genetic diversity Journal Article
In: Curr Opin Virol, vol. 49, pp. 157-163, 2021.
Abstract | Links | BibTeX | Tags: Project 03, WP 1.3 Virus-host interactions
@article{nokey,
title = {Quantitative measures of within-host viral genetic diversity},
author = {Lara Fuhrmann and Kim Philipp Jablonski and Niko Beerenwinkel
},
doi = {10.1016/j.coviro.2021.06.002},
year = {2021},
date = {2021-06-18},
urldate = {2021-06-18},
journal = {Curr Opin Virol},
volume = {49},
pages = {157-163},
abstract = {The genetic diversity of virus populations within their hosts is known to influence disease progression, treatment outcome, drug resistance, cell tropism, and transmission risk, and the study of dynamic changes of genetic heterogeneity can provide insights into the evolution of viruses. Several measures to quantify within-host genetic diversity capturing different aspects of diversity patterns in a sample or population are used, based on incidence, relative frequencies, pairwise distances, or phylogenetic trees. Here, we review and compare several of these measures.},
keywords = {Project 03, WP 1.3 Virus-host interactions},
pubstate = {published},
tppubtype = {article}
}
The genetic diversity of virus populations within their hosts is known to influence disease progression, treatment outcome, drug resistance, cell tropism, and transmission risk, and the study of dynamic changes of genetic heterogeneity can provide insights into the evolution of viruses. Several measures to quantify within-host genetic diversity capturing different aspects of diversity patterns in a sample or population are used, based on incidence, relative frequencies, pairwise distances, or phylogenetic trees. Here, we review and compare several of these measures.
Goettsch, Winfried; Beerenwinkel, Niko; Deng, Li; Dölken, Lars; Dutilh, Bas E.; Erhard, Florian; Kaderali, Lars; von Kleist, Max; Marquet, Roland; Matthijnssens, Jelle; McCallin, Shawna; McMahon, Dino; Rattei, Thomas; van Rij, Ronald P.; Robertson, David L.; Schwemmle, Martin; Stern-Ginossar, Noam; Marz, Manja
ITN -- VIROINF: Understanding (Harmful) Virus-Host Interactions by Linking Virology and Bioinformatics Journal Article
In: Viruses, vol. 13, no. 5, pp. 766, 2021.
Abstract | Links | BibTeX | Tags: Project 01, Project 02, Project 03, Project 04, Project 05, Project 06, Project 07, Project 08, Project 09, Project 10, Project 11, Project 12, Project 13, Project 14, Project 15, WP 1.1 Virus identification, WP 1.2 Host prediction, WP 1.3 Virus-host interactions, WP 1.4 Virus regulation, WP 1.5 Virus products, WP 2.1 Microevolution: Virus quasispecies, WP 2.2 Macroevolution: Natural selection of viruses
@article{nokey,
title = {ITN -- VIROINF: Understanding (Harmful) Virus-Host Interactions by Linking Virology and Bioinformatics},
author = {Winfried Goettsch and Niko Beerenwinkel and Li Deng and Lars Dölken and Bas E. Dutilh and Florian Erhard and Lars Kaderali and Max von Kleist and Roland Marquet and Jelle Matthijnssens and Shawna McCallin and Dino McMahon and Thomas Rattei and Ronald P. {van Rij} and David L. Robertson and Martin Schwemmle and Noam Stern-Ginossar and Manja Marz},
doi = {10.3390/v13050766},
year = {2021},
date = {2021-04-27},
urldate = {2021-04-27},
journal = {Viruses},
volume = {13},
number = {5},
pages = {766},
abstract = {Many recent studies highlight the fundamental importance of viruses. Besides their important role as human and animal pathogens, their beneficial, commensal or harmful functions are poorly understood. By developing and applying tailored bioinformatical tools in important virological models, the Marie Skłodowska-Curie Initiative International Training Network VIROINF will provide a better understanding of viruses and the interaction with their hosts. This will open the door to validate methods of improving viral growth, morphogenesis and development, as well as to control strategies against unwanted microorganisms. The key feature of VIROINF is its interdisciplinary nature, which brings together virologists and bioinformaticians to achieve common goals.},
keywords = {Project 01, Project 02, Project 03, Project 04, Project 05, Project 06, Project 07, Project 08, Project 09, Project 10, Project 11, Project 12, Project 13, Project 14, Project 15, WP 1.1 Virus identification, WP 1.2 Host prediction, WP 1.3 Virus-host interactions, WP 1.4 Virus regulation, WP 1.5 Virus products, WP 2.1 Microevolution: Virus quasispecies, WP 2.2 Macroevolution: Natural selection of viruses},
pubstate = {published},
tppubtype = {article}
}
Many recent studies highlight the fundamental importance of viruses. Besides their important role as human and animal pathogens, their beneficial, commensal or harmful functions are poorly understood. By developing and applying tailored bioinformatical tools in important virological models, the Marie Skłodowska-Curie Initiative International Training Network VIROINF will provide a better understanding of viruses and the interaction with their hosts. This will open the door to validate methods of improving viral growth, morphogenesis and development, as well as to control strategies against unwanted microorganisms. The key feature of VIROINF is its interdisciplinary nature, which brings together virologists and bioinformaticians to achieve common goals.