Computational Modeling of Biological Processes (Neher)Head of Research Unit Prof. Dr.Richard NeherOverviewMembersPublicationsProjects & CollaborationsProjects & Collaborations OverviewMembersPublicationsProjects & Collaborations Projects & Collaborations 7 foundShow per page10 10 20 50 FAIRification of pathogen bioinformatics resources under the Centre for Pathogen Bioinformatics Research Project | 2 Project MembersImported from Grants Tool 4704247 Surveillance SARS-CoV2 - Analyses Nextstrain/GISAID Research Project | 1 Project MembersImported from Grants Tool 4704592 Nextstrain-SIB Research Project | 2 Project MembersNextstrain is an open-source platform to track the spread and evolution of pathogens and derive actionable inferences for public health interventions from genomic data. Nextstrain is a joint effort of the Neherlab at the University of Basel and the lab of Trevor Bedford at the Fred Hutch Cancer Center in Seattle. The platform consists of a bioinformatics analysis pipeline (augur) and an interactive front-end (auspice) that allows users to explore the data in an intuitive way. We started with an exclusive focus on influenza virus evolution to inform the biannual meetings by the WHO at which the strains for the seasonal influenza vaccine are selected. Nextstrain has since become a trusted resource of the influenza research community and the WHO Collaborating Centers for Influenza. SIB is now funding Nextstrain development and maintenance through inclusion of nextstrain into its resource portfolio. NCCR AntiResist: New approaches to combat antibiotic-resistant bacteria Umbrella Project | 32 Project MembersAntibiotics are powerful and indispensable drugs to treat life threatening bacterial infections such as sepsis or pneumonia. Antibiotics also play a central role in many other areas of modern medicine, in particular to protect patients with compromised immunity during cancer therapies, transplantations or surgical interventions. These achievements are now at risk, with the fraction of bacterial pathogens that are resistant to one or more antibiotics steadily increasing. In addition, development of novel antimicrobials lags behind, suffering from inherently high attrition rates in particular for drug candidates against the most problematic Gram-negative bacteria. Together, these factors increasingly limit the options clinicians have for treating bacterial infections. The overarching goal of NCCR AntiResist is to elucidate the physiological properties of bacterial pathogens in infected human patients in order to find new ways of combatting superbugs. Among the many societal, economic, and scientific factors that impact on the development of alternative strategies for antibiotic discovery, our limited understanding of the physiology and heterogeneity of bacterial pathogens in patients ranks highly. Bacteria growing in tissues of patients experience environments very different from standard laboratory conditions, resulting in radically different microbial physiology and population heterogeneity compared to conditions generally used for antibacterial discovery. There is currently no systematic strategy to overcome this fundamental problem. This has resulted in: (i) suboptimal screens that identify new antibiotics, which do not target the special properties of bacteria growing within the patient; (ii) an inability to properly evaluate the efficacy of non-conventional antibacterial strategies; (iii) missed opportunities for entirely new treatment strategies. This NCCR utilizes patient samples from ongoing clinical studies and establishes a unique multidisciplinary network of clinicians, biologists, engineers, chemists, computational scientists and drug developers that will overcome this problem. We are excited to merge these disciplines in order to determine the properties of pathogens infecting patients, establish conditions in the lab that reproduce these properties and utilize these in-vitro models for antimicrobial discovery and development. In addition, clinical-trial networks and the pharmaceutical industry have major footprints in antimicrobial R&D. Exploiting synergies between these players has great potential for making transformative progress in this critical field of human health. This NCCR maintains active collaborations with Biotech SMEs and large pharmaceutical companies with the goal to: accelerate antibiotic discovery by providing relevant read-outs for early prioritization of compounds; enable innovative screens for non-canonical strategies such as anti-virulence inhibitors and immunomodulators; identify new antibacterial strategies that effectively combat bacteria either by targeting refractory subpopulations or by synergizing with bacterial stresses imposed by the patients' own immune system. This NCCR proposes a paradigm shift in antibiotic discovery by investigating the physiology of bacterial pathogens in human patients. This knowledge will be used to develop assays for molecular analyses and drug screening under relevant conditions and to accelerate antibacterial discovery, improve treatment regimens, and uncover novel targets for eradicating pathogens. Through this concerted effort, this NCCR will make a crucial and unique contribution to winning the race against superbugs. Recombination and reassortement in viral evolution Research Project | 1 Project MembersVery few organisms evolve exclusively by asexual vertical transmission of their genomes. Instead, most populations exchange genetic information horizontally via a diverse set of mechanisms with profound effects on genetic diversity, the evolutionary dynamics, and adaptive potential. Theoretical models predict that recombination accelerates adaptation and reduces mutation load. Recombination or horizontal transfer are hence expected to be beneficial on the long run despite its short-term costs. Empirical evidence for this effect, however, is sparse and largely limited to short evolution experiments. RNA viruses like HIV and influenza are ideal systems to study the interplay of recombination and adaptive evolution since they evolve rapidly, are densely sampled, and their biology is well understood. Recombination rates of HIV and influenza viruses are high enough to matter, but low enough that substantial linkage and clonal interference remains. Hence the competing effects of recombination and selection are directly observable. In microbial evolution, recombination and reassortment are often the most impactful evolutionary events. Yet most phylogenetic analyses of viruses ignore recombination or seek to remove sequences with evidence of recombination. Here, we propose to develop computationally efficient and scalable methods to analyze recombination and reassortment in viruses and investigate how recombination facilitates adaptation and reduces recombination load. Insights from this undertaking will not only help to address pressing public health problems such as predicting future circulating influenza viruses, but will also shed light on fundamental problems in evolutionary biology. High-throughput experiments to guide influenza vaccine strain selection Research Project | 2 Project MembersEvery year, seasonal influenza infects 5-15% of the human population, resulting in over 250,000 deaths worldwide. The annual influenza vaccine is the primary public-health intervention against these epidemics. The strains in the vaccine must be selected before the influenza season. Unfortunately, the selected strains sometimes fail to closely match those that end up actually circulating in the human population; such strain mismatches reduce vaccine efficacy. Methods for better selecting vaccine strains are therefore of paramount importance to public health. We will use innovative new experimental and computational techniques to guide better vaccine-strain selection. Two key properties determine which influenza strains dominate a season: successful strains have high inherent fitness (manifested by a low load of deleterious mutations) and an abundance of antigenic mutations in the epitopes recognized by human immunity. We will measure how each of these properties is affected by every possible amino-acid mutation to the viral surface protein hemagglutinin. To make these high-throughput measurements, we will generate pools of viruses carrying all possible codon mutations to hemagglutinin, and then passage these mutant viruses in the presence and absence of human serum. We will then use ultra-accurate deep sequencing to count the frequency of every mutation pre- and post-selection, enabling us to quantify how each mutation affects both deleterious load and antigenic recognition by serum from a cross-section of the human population. To improve vaccine-strain selection, we will use a real-time web platform to overlay our measurements of deleterious mutational load and the antigenic change onto an influenza phylogeny. This platform will enable decision makers to intuitively visualize the "Big Data" generated by our experiments as they weigh all sources of evidence during the strain-selection process. In addition, we will make our data and computer code readily available, so that others can leverage our work for their own efforts to better predict influenza strain dynamics. This work has direct relevance to public health in that it will help guide better vaccine-strain selection at a fraction of the cost of current approaches, and thereby improve seasonal influenza vaccine effectiveness. Real-time Evolutionary Tracking for Pathogen Surveillance and Epidemiological Investigation Research Project | 2 Project MembersGenome sequences of viral pathogens have the capacity to provide valuable insight into epidemic transmission patterns and viral evolution. But to inform public health interventions in acute public health crises, genomic data has to be analyzed and results diseminated in near real-time. The goal of this project is to promote open sharing of viral genomic data and harness this data to make epidemiologically actionable inferences. For this project, we are developing an integrated framework for real-time molecular epidemiology and evolutionary analysis of emerging epidemics, such as Ebola virus, MERS-CoV and Zika virus. This framework includes an online visualization platform deployed to the website nextstrain.org that is continually updated as new data becomes available. This platform pools data from across research groups thereby synthesizing disparate datasets and serves to promote open science in the face of public health crises. All source code is publicly available at github.com/nextstrain. 1 1 OverviewMembersPublicationsProjects & Collaborations
Projects & Collaborations 7 foundShow per page10 10 20 50 FAIRification of pathogen bioinformatics resources under the Centre for Pathogen Bioinformatics Research Project | 2 Project MembersImported from Grants Tool 4704247 Surveillance SARS-CoV2 - Analyses Nextstrain/GISAID Research Project | 1 Project MembersImported from Grants Tool 4704592 Nextstrain-SIB Research Project | 2 Project MembersNextstrain is an open-source platform to track the spread and evolution of pathogens and derive actionable inferences for public health interventions from genomic data. Nextstrain is a joint effort of the Neherlab at the University of Basel and the lab of Trevor Bedford at the Fred Hutch Cancer Center in Seattle. The platform consists of a bioinformatics analysis pipeline (augur) and an interactive front-end (auspice) that allows users to explore the data in an intuitive way. We started with an exclusive focus on influenza virus evolution to inform the biannual meetings by the WHO at which the strains for the seasonal influenza vaccine are selected. Nextstrain has since become a trusted resource of the influenza research community and the WHO Collaborating Centers for Influenza. SIB is now funding Nextstrain development and maintenance through inclusion of nextstrain into its resource portfolio. NCCR AntiResist: New approaches to combat antibiotic-resistant bacteria Umbrella Project | 32 Project MembersAntibiotics are powerful and indispensable drugs to treat life threatening bacterial infections such as sepsis or pneumonia. Antibiotics also play a central role in many other areas of modern medicine, in particular to protect patients with compromised immunity during cancer therapies, transplantations or surgical interventions. These achievements are now at risk, with the fraction of bacterial pathogens that are resistant to one or more antibiotics steadily increasing. In addition, development of novel antimicrobials lags behind, suffering from inherently high attrition rates in particular for drug candidates against the most problematic Gram-negative bacteria. Together, these factors increasingly limit the options clinicians have for treating bacterial infections. The overarching goal of NCCR AntiResist is to elucidate the physiological properties of bacterial pathogens in infected human patients in order to find new ways of combatting superbugs. Among the many societal, economic, and scientific factors that impact on the development of alternative strategies for antibiotic discovery, our limited understanding of the physiology and heterogeneity of bacterial pathogens in patients ranks highly. Bacteria growing in tissues of patients experience environments very different from standard laboratory conditions, resulting in radically different microbial physiology and population heterogeneity compared to conditions generally used for antibacterial discovery. There is currently no systematic strategy to overcome this fundamental problem. This has resulted in: (i) suboptimal screens that identify new antibiotics, which do not target the special properties of bacteria growing within the patient; (ii) an inability to properly evaluate the efficacy of non-conventional antibacterial strategies; (iii) missed opportunities for entirely new treatment strategies. This NCCR utilizes patient samples from ongoing clinical studies and establishes a unique multidisciplinary network of clinicians, biologists, engineers, chemists, computational scientists and drug developers that will overcome this problem. We are excited to merge these disciplines in order to determine the properties of pathogens infecting patients, establish conditions in the lab that reproduce these properties and utilize these in-vitro models for antimicrobial discovery and development. In addition, clinical-trial networks and the pharmaceutical industry have major footprints in antimicrobial R&D. Exploiting synergies between these players has great potential for making transformative progress in this critical field of human health. This NCCR maintains active collaborations with Biotech SMEs and large pharmaceutical companies with the goal to: accelerate antibiotic discovery by providing relevant read-outs for early prioritization of compounds; enable innovative screens for non-canonical strategies such as anti-virulence inhibitors and immunomodulators; identify new antibacterial strategies that effectively combat bacteria either by targeting refractory subpopulations or by synergizing with bacterial stresses imposed by the patients' own immune system. This NCCR proposes a paradigm shift in antibiotic discovery by investigating the physiology of bacterial pathogens in human patients. This knowledge will be used to develop assays for molecular analyses and drug screening under relevant conditions and to accelerate antibacterial discovery, improve treatment regimens, and uncover novel targets for eradicating pathogens. Through this concerted effort, this NCCR will make a crucial and unique contribution to winning the race against superbugs. Recombination and reassortement in viral evolution Research Project | 1 Project MembersVery few organisms evolve exclusively by asexual vertical transmission of their genomes. Instead, most populations exchange genetic information horizontally via a diverse set of mechanisms with profound effects on genetic diversity, the evolutionary dynamics, and adaptive potential. Theoretical models predict that recombination accelerates adaptation and reduces mutation load. Recombination or horizontal transfer are hence expected to be beneficial on the long run despite its short-term costs. Empirical evidence for this effect, however, is sparse and largely limited to short evolution experiments. RNA viruses like HIV and influenza are ideal systems to study the interplay of recombination and adaptive evolution since they evolve rapidly, are densely sampled, and their biology is well understood. Recombination rates of HIV and influenza viruses are high enough to matter, but low enough that substantial linkage and clonal interference remains. Hence the competing effects of recombination and selection are directly observable. In microbial evolution, recombination and reassortment are often the most impactful evolutionary events. Yet most phylogenetic analyses of viruses ignore recombination or seek to remove sequences with evidence of recombination. Here, we propose to develop computationally efficient and scalable methods to analyze recombination and reassortment in viruses and investigate how recombination facilitates adaptation and reduces recombination load. Insights from this undertaking will not only help to address pressing public health problems such as predicting future circulating influenza viruses, but will also shed light on fundamental problems in evolutionary biology. High-throughput experiments to guide influenza vaccine strain selection Research Project | 2 Project MembersEvery year, seasonal influenza infects 5-15% of the human population, resulting in over 250,000 deaths worldwide. The annual influenza vaccine is the primary public-health intervention against these epidemics. The strains in the vaccine must be selected before the influenza season. Unfortunately, the selected strains sometimes fail to closely match those that end up actually circulating in the human population; such strain mismatches reduce vaccine efficacy. Methods for better selecting vaccine strains are therefore of paramount importance to public health. We will use innovative new experimental and computational techniques to guide better vaccine-strain selection. Two key properties determine which influenza strains dominate a season: successful strains have high inherent fitness (manifested by a low load of deleterious mutations) and an abundance of antigenic mutations in the epitopes recognized by human immunity. We will measure how each of these properties is affected by every possible amino-acid mutation to the viral surface protein hemagglutinin. To make these high-throughput measurements, we will generate pools of viruses carrying all possible codon mutations to hemagglutinin, and then passage these mutant viruses in the presence and absence of human serum. We will then use ultra-accurate deep sequencing to count the frequency of every mutation pre- and post-selection, enabling us to quantify how each mutation affects both deleterious load and antigenic recognition by serum from a cross-section of the human population. To improve vaccine-strain selection, we will use a real-time web platform to overlay our measurements of deleterious mutational load and the antigenic change onto an influenza phylogeny. This platform will enable decision makers to intuitively visualize the "Big Data" generated by our experiments as they weigh all sources of evidence during the strain-selection process. In addition, we will make our data and computer code readily available, so that others can leverage our work for their own efforts to better predict influenza strain dynamics. This work has direct relevance to public health in that it will help guide better vaccine-strain selection at a fraction of the cost of current approaches, and thereby improve seasonal influenza vaccine effectiveness. Real-time Evolutionary Tracking for Pathogen Surveillance and Epidemiological Investigation Research Project | 2 Project MembersGenome sequences of viral pathogens have the capacity to provide valuable insight into epidemic transmission patterns and viral evolution. But to inform public health interventions in acute public health crises, genomic data has to be analyzed and results diseminated in near real-time. The goal of this project is to promote open sharing of viral genomic data and harness this data to make epidemiologically actionable inferences. For this project, we are developing an integrated framework for real-time molecular epidemiology and evolutionary analysis of emerging epidemics, such as Ebola virus, MERS-CoV and Zika virus. This framework includes an online visualization platform deployed to the website nextstrain.org that is continually updated as new data becomes available. This platform pools data from across research groups thereby synthesizing disparate datasets and serves to promote open science in the face of public health crises. All source code is publicly available at github.com/nextstrain. 1 1
FAIRification of pathogen bioinformatics resources under the Centre for Pathogen Bioinformatics Research Project | 2 Project MembersImported from Grants Tool 4704247
Surveillance SARS-CoV2 - Analyses Nextstrain/GISAID Research Project | 1 Project MembersImported from Grants Tool 4704592
Nextstrain-SIB Research Project | 2 Project MembersNextstrain is an open-source platform to track the spread and evolution of pathogens and derive actionable inferences for public health interventions from genomic data. Nextstrain is a joint effort of the Neherlab at the University of Basel and the lab of Trevor Bedford at the Fred Hutch Cancer Center in Seattle. The platform consists of a bioinformatics analysis pipeline (augur) and an interactive front-end (auspice) that allows users to explore the data in an intuitive way. We started with an exclusive focus on influenza virus evolution to inform the biannual meetings by the WHO at which the strains for the seasonal influenza vaccine are selected. Nextstrain has since become a trusted resource of the influenza research community and the WHO Collaborating Centers for Influenza. SIB is now funding Nextstrain development and maintenance through inclusion of nextstrain into its resource portfolio.
NCCR AntiResist: New approaches to combat antibiotic-resistant bacteria Umbrella Project | 32 Project MembersAntibiotics are powerful and indispensable drugs to treat life threatening bacterial infections such as sepsis or pneumonia. Antibiotics also play a central role in many other areas of modern medicine, in particular to protect patients with compromised immunity during cancer therapies, transplantations or surgical interventions. These achievements are now at risk, with the fraction of bacterial pathogens that are resistant to one or more antibiotics steadily increasing. In addition, development of novel antimicrobials lags behind, suffering from inherently high attrition rates in particular for drug candidates against the most problematic Gram-negative bacteria. Together, these factors increasingly limit the options clinicians have for treating bacterial infections. The overarching goal of NCCR AntiResist is to elucidate the physiological properties of bacterial pathogens in infected human patients in order to find new ways of combatting superbugs. Among the many societal, economic, and scientific factors that impact on the development of alternative strategies for antibiotic discovery, our limited understanding of the physiology and heterogeneity of bacterial pathogens in patients ranks highly. Bacteria growing in tissues of patients experience environments very different from standard laboratory conditions, resulting in radically different microbial physiology and population heterogeneity compared to conditions generally used for antibacterial discovery. There is currently no systematic strategy to overcome this fundamental problem. This has resulted in: (i) suboptimal screens that identify new antibiotics, which do not target the special properties of bacteria growing within the patient; (ii) an inability to properly evaluate the efficacy of non-conventional antibacterial strategies; (iii) missed opportunities for entirely new treatment strategies. This NCCR utilizes patient samples from ongoing clinical studies and establishes a unique multidisciplinary network of clinicians, biologists, engineers, chemists, computational scientists and drug developers that will overcome this problem. We are excited to merge these disciplines in order to determine the properties of pathogens infecting patients, establish conditions in the lab that reproduce these properties and utilize these in-vitro models for antimicrobial discovery and development. In addition, clinical-trial networks and the pharmaceutical industry have major footprints in antimicrobial R&D. Exploiting synergies between these players has great potential for making transformative progress in this critical field of human health. This NCCR maintains active collaborations with Biotech SMEs and large pharmaceutical companies with the goal to: accelerate antibiotic discovery by providing relevant read-outs for early prioritization of compounds; enable innovative screens for non-canonical strategies such as anti-virulence inhibitors and immunomodulators; identify new antibacterial strategies that effectively combat bacteria either by targeting refractory subpopulations or by synergizing with bacterial stresses imposed by the patients' own immune system. This NCCR proposes a paradigm shift in antibiotic discovery by investigating the physiology of bacterial pathogens in human patients. This knowledge will be used to develop assays for molecular analyses and drug screening under relevant conditions and to accelerate antibacterial discovery, improve treatment regimens, and uncover novel targets for eradicating pathogens. Through this concerted effort, this NCCR will make a crucial and unique contribution to winning the race against superbugs.
Recombination and reassortement in viral evolution Research Project | 1 Project MembersVery few organisms evolve exclusively by asexual vertical transmission of their genomes. Instead, most populations exchange genetic information horizontally via a diverse set of mechanisms with profound effects on genetic diversity, the evolutionary dynamics, and adaptive potential. Theoretical models predict that recombination accelerates adaptation and reduces mutation load. Recombination or horizontal transfer are hence expected to be beneficial on the long run despite its short-term costs. Empirical evidence for this effect, however, is sparse and largely limited to short evolution experiments. RNA viruses like HIV and influenza are ideal systems to study the interplay of recombination and adaptive evolution since they evolve rapidly, are densely sampled, and their biology is well understood. Recombination rates of HIV and influenza viruses are high enough to matter, but low enough that substantial linkage and clonal interference remains. Hence the competing effects of recombination and selection are directly observable. In microbial evolution, recombination and reassortment are often the most impactful evolutionary events. Yet most phylogenetic analyses of viruses ignore recombination or seek to remove sequences with evidence of recombination. Here, we propose to develop computationally efficient and scalable methods to analyze recombination and reassortment in viruses and investigate how recombination facilitates adaptation and reduces recombination load. Insights from this undertaking will not only help to address pressing public health problems such as predicting future circulating influenza viruses, but will also shed light on fundamental problems in evolutionary biology.
High-throughput experiments to guide influenza vaccine strain selection Research Project | 2 Project MembersEvery year, seasonal influenza infects 5-15% of the human population, resulting in over 250,000 deaths worldwide. The annual influenza vaccine is the primary public-health intervention against these epidemics. The strains in the vaccine must be selected before the influenza season. Unfortunately, the selected strains sometimes fail to closely match those that end up actually circulating in the human population; such strain mismatches reduce vaccine efficacy. Methods for better selecting vaccine strains are therefore of paramount importance to public health. We will use innovative new experimental and computational techniques to guide better vaccine-strain selection. Two key properties determine which influenza strains dominate a season: successful strains have high inherent fitness (manifested by a low load of deleterious mutations) and an abundance of antigenic mutations in the epitopes recognized by human immunity. We will measure how each of these properties is affected by every possible amino-acid mutation to the viral surface protein hemagglutinin. To make these high-throughput measurements, we will generate pools of viruses carrying all possible codon mutations to hemagglutinin, and then passage these mutant viruses in the presence and absence of human serum. We will then use ultra-accurate deep sequencing to count the frequency of every mutation pre- and post-selection, enabling us to quantify how each mutation affects both deleterious load and antigenic recognition by serum from a cross-section of the human population. To improve vaccine-strain selection, we will use a real-time web platform to overlay our measurements of deleterious mutational load and the antigenic change onto an influenza phylogeny. This platform will enable decision makers to intuitively visualize the "Big Data" generated by our experiments as they weigh all sources of evidence during the strain-selection process. In addition, we will make our data and computer code readily available, so that others can leverage our work for their own efforts to better predict influenza strain dynamics. This work has direct relevance to public health in that it will help guide better vaccine-strain selection at a fraction of the cost of current approaches, and thereby improve seasonal influenza vaccine effectiveness.
Real-time Evolutionary Tracking for Pathogen Surveillance and Epidemiological Investigation Research Project | 2 Project MembersGenome sequences of viral pathogens have the capacity to provide valuable insight into epidemic transmission patterns and viral evolution. But to inform public health interventions in acute public health crises, genomic data has to be analyzed and results diseminated in near real-time. The goal of this project is to promote open sharing of viral genomic data and harness this data to make epidemiologically actionable inferences. For this project, we are developing an integrated framework for real-time molecular epidemiology and evolutionary analysis of emerging epidemics, such as Ebola virus, MERS-CoV and Zika virus. This framework includes an online visualization platform deployed to the website nextstrain.org that is continually updated as new data becomes available. This platform pools data from across research groups thereby synthesizing disparate datasets and serves to promote open science in the face of public health crises. All source code is publicly available at github.com/nextstrain.
