Jana S. Huisman

Bio: Jana S. Huisman is an academic researcher from Swiss Institute of Bioinformatics. The author has contributed to research in topics: Plasmid & Medicine. The author has an hindex of 11, co-authored 27 publications receiving 564 citations. Previous affiliations of Jana S. Huisman include École Polytechnique Fédérale de Lausanne & ETH Zurich.

Practical considerations for measuring the effective reproductive number, Rt.

Abstract: Estimation of the effective reproductive number Rt is important for detecting changes in disease transmission over time. During the Coronavirus Disease 2019 (COVID-19) pandemic, policy makers and public health officials are using Rt to assess the effectiveness of interventions and to inform policy. However, estimation of Rt from available data presents several challenges, with critical implications for the interpretation of the course of the pandemic. The purpose of this document is to summarize these challenges, illustrate them with examples from synthetic data, and, where possible, make recommendations. For near real-time estimation of Rt, we recommend the approach of Cori and colleagues, which uses data from before time t and empirical estimates of the distribution of time between infections. Methods that require data from after time t, such as Wallinga and Teunis, are conceptually and methodologically less suited for near real-time estimation, but may be appropriate for retrospective analyses of how individuals infected at different time points contributed to the spread. We advise caution when using methods derived from the approach of Bettencourt and Ribeiro, as the resulting Rt estimates may be biased if the underlying structural assumptions are not met. Two key challenges common to all approaches are accurate specification of the generation interval and reconstruction of the time series of new infections from observations occurring long after the moment of transmission. Naive approaches for dealing with observation delays, such as subtracting delays sampled from a distribution, can introduce bias. We provide suggestions for how to mitigate this and other technical challenges and highlight open problems in Rt estimation.

Salmonella persisters promote the spread of antibiotic resistance plasmids in the gut

Abstract: The emergence of antibiotic-resistant bacteria through mutations or the acquisition of genetic material such as resistance plasmids represents a major public health issue1,2. Persisters are subpopulations of bacteria that survive antibiotics by reversibly adapting their physiology3–10, and can promote the emergence of antibiotic-resistant mutants11. We investigated whether persisters can also promote the spread of resistance plasmids. In contrast to mutations, the transfer of resistance plasmids requires the co-occurrence of both a donor and a recipient bacterial strain. For our experiments, we chose the facultative intracellular entero-pathogen Salmonella enterica serovar Typhimurium (S. Typhimurium) and Escherichia coli, a common member of the microbiota12. S. Typhimurium forms persisters that survive antibiotic therapy in several host tissues. Here we show that tissue-associated S. Typhimurium persisters represent long-lived reservoirs of plasmid donors or recipients. The formation of reservoirs of S. Typhimurium persisters requires Salmonella pathogenicity island (SPI)-1 and/or SPI-2 in gut-associated tissues, or SPI-2 at systemic sites. The re-seeding of these persister bacteria into the gut lumen enables the co-occurrence of donors with gut-resident recipients, and thereby favours plasmid transfer between various strains of Enterobacteriaceae. We observe up to 99% transconjugants within two to three days of re-seeding. Mathematical modelling shows that rare re-seeding events may suffice for a high frequency of conjugation. Vaccination reduces the formation of reservoirs of persisters after oral infection with S. Typhimurium, as well as subsequent plasmid transfer. We conclude that—even without selection for plasmid-encoded resistance genes—small reservoirs of pathogen persisters can foster the spread of promiscuous resistance plasmids in the gut. The re-seeding of antibiotic-resistant persister subpopulations of Salmonella enterica into the gut lumen favours the transfer of resistance plasmids to gut-resident enterobacteria, showing that even small reservoirs of persister bacteria facilitate the spread of antibiotic resistance.

Practical considerations for measuring the effective reproductive number, Rt.

Abstract: Estimation of the effective reproductive number, Rt, is important for detecting changes in disease transmission over time. During the COVID-19 pandemic, policymakers and public health officials are using Rt to assess the effectiveness of interventions and to inform policy. However, estimation of Rt from available data presents several challenges, with critical implications for the interpretation of the course of the pandemic. The purpose of this document is to summarize these challenges, illustrate them with examples from synthetic data, and, where possible, make methodological recommendations. For near real-time estimation of Rt, we recommend the approach of Cori et al. (2013), which uses data from before time t and empirical estimates of the distribution of time between infections. Methods that require data from after time t, such as Wallinga and Teunis (2004), are conceptually and methodologically less suited for near real-time estimation, but may be appropriate for some retrospective analyses. We advise against using methods derived from Bettencourt and Ribeiro (2008), as the resulting Rt estimates may be biased if the underlying structural assumptions are not met. A challenge common to all approaches is reconstruction of the time series of new infections from observations occurring long after the moment of transmission. Naive approaches for dealing with observation delays, such as subtracting delays sampled from a distribution, can introduce bias. We provide suggestions for how to mitigate this and other technical challenges and highlight open problems in Rt estimation.

The origin and early spread of SARS-CoV-2 in Europe.

Abstract: The investigation of migratory patterns during the SARS-CoV-2 pandemic before spring 2020 border closures in Europe is a crucial first step toward an in-depth evaluation of border closure policies. Here we analyze viral genome sequences using a phylodynamic model with geographic structure to estimate the origin and spread of SARS-CoV-2 in Europe prior to border closures. Based on SARS-CoV-2 genomes, we reconstruct a partial transmission tree of the early pandemic and coinfer the geographic location of ancestral lineages as well as the number of migration events into and between European regions. We find that the predominant lineage spreading in Europe during this time has a most recent common ancestor in Italy and was probably seeded by a transmission event in either Hubei, China or Germany. We do not find evidence for preferential migration paths from Hubei into different European regions or from each European region to the others. Sustained local transmission is first evident in Italy and then shortly thereafter in the other European regions considered. Before the first border closures in Europe, we estimate that the rate of occurrence of new cases from within-country transmission was within the bounds of the estimated rate of new cases from migration. In summary, our analysis offers a view on the early state of the epidemic in Europe and on migration patterns of the virus before border closures. This information will enable further study of the necessity and timeliness of border closures.

COVID-19 infectivity profile correction.

Abstract: The infectivity profile of an individual with COVID-19 is attributed to the paper Temporal dynamics in viral shedding and transmissibility of COVID-19 by He et al., published in Nature Medicine in April 2020. However, the analysis within this paper contains a mistake such that the published infectivity profile is incorrect and the conclusion that infectiousness begins 2.3 days before symptom onset is no longer supported. In this document we discuss the error and compute the correct infectivity profile. We also establish confidence intervals on this profile, quantify the difference between the published and the corrected profiles, and discuss an issue of normalisation when fitting serial interval data. This infectivity profile plays a central role in policy and decision making, thus it is crucial that this issue is corrected with the utmost urgency to prevent the propagation of this error into further studies and policies. We hope that this preprint will reach all researchers and policy makers who are using the incorrect infectivity profile to inform their work.

SPAdes, a new genome assembly algorithm and its applications to single-cell sequencing ( 7th Annual SFAF Meeting, 2012)

Abstract: The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.

Detection of a SARS-CoV-2 variant of concern in South Africa.

Abstract: Continued uncontrolled transmission of SARS-CoV-2 in many parts of the world is creating conditions for substantial evolutionary changes to the virus1,2. Here we describe a newly arisen lineage of SARS-CoV-2 (designated 501Y.V2; also known as B.1.351 or 20H) that is defined by eight mutations in the spike protein, including three substitutions (K417N, E484K and N501Y) at residues in its receptor-binding domain that may have functional importance3-5. This lineage was identified in South Africa after the first wave of the epidemic in a severely affected metropolitan area (Nelson Mandela Bay) that is located on the coast of the Eastern Cape province. This lineage spread rapidly, and became dominant in Eastern Cape, Western Cape and KwaZulu-Natal provinces within weeks. Although the full import of the mutations is yet to be determined, the genomic data-which show rapid expansion and displacement of other lineages in several regions-suggest that this lineage is associated with a selection advantage that most plausibly results from increased transmissibility or immune escape6-8.

Emergence and rapid spread of a new severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) lineage with multiple spike mutations in South Africa

Abstract: Summary Continued uncontrolled transmission of the severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) in many parts of the world is creating the conditions for significant virus evolution. Here, we describe a new SARS-CoV-2 lineage (501Y.V2) characterised by eight lineage-defining mutations in the spike protein, including three at important residues in the receptor-binding domain (K417N, E484K and N501Y) that may have functional significance. This lineage emerged in South Africa after the first epidemic wave in a severely affected metropolitan area, Nelson Mandela Bay, located on the coast of the Eastern Cape Province. This lineage spread rapidly, becoming within weeks the dominant lineage in the Eastern Cape and Western Cape Provinces. Whilst the full significance of the mutations is yet to be determined, the genomic data, showing the rapid displacement of other lineages, suggest that this lineage may be associated with increased transmissibility.

Rapid epidemic expansion of the SARS-CoV-2 Omicron variant in southern Africa

Abstract: The SARS-CoV-2 epidemic in southern Africa has been characterized by three distinct waves. The first was associated with a mix of SARS-CoV-2 lineages, while the second and third waves were driven by the Beta (B.1.351) and Delta (B.1.617.2) variants, respectively1-3. In November 2021, genomic surveillance teams in South Africa and Botswana detected a new SARS-CoV-2 variant associated with a rapid resurgence of infections in Gauteng province, South Africa. Within three days of the first genome being uploaded, it was designated a variant of concern (Omicron, B.1.1.529) by the World Health Organization and, within three weeks, had been identified in 87 countries. The Omicron variant is exceptional for carrying over 30 mutations in the spike glycoprotein, which are predicted to influence antibody neutralization and spike function4. Here we describe the genomic profile and early transmission dynamics of Omicron, highlighting the rapid spread in regions with high levels of population immunity.

Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions.

Abstract: As COVID-19 is rapidly spreading across the globe, short-term modeling forecasts provide time-critical information for decisions on containment and mitigation strategies. A major challenge for short-term forecasts is the assessment of key epidemiological parameters and how they change when first interventions show an effect. By combining an established epidemiological model with Bayesian inference, we analyze the time dependence of the effective growth rate of new infections. Focusing on COVID-19 spread in Germany, we detect change points in the effective growth rate that correlate well with the times of publicly announced interventions. Thereby, we can quantify the effect of interventions, and we can incorporate the corresponding change points into forecasts of future scenarios and case numbers. Our code is freely available and can be readily adapted to any country or region.