Research » 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 = {},

pubstate = {published},

tppubtype = {article}

}

@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 = {},

pubstate = {published},

tppubtype = {article}

}

@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 = {},

pubstate = {published},

tppubtype = {article}

}

@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 = {},

pubstate = {published},

tppubtype = {article}

}

@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 = {},

pubstate = {published},

tppubtype = {article}

}