Phylogeographic reconstruction using air transportation data and its application to the 2009 H1N1 influenza A pandemic

Global spread characteristics of CTX-M-type extended-spectrum β-lactamases: A genomic epidemiology analysis

BACKGROUND

Extended-spectrum β-lactamases (ESBLs) producing bacteria have spread worldwide and become a global public health concern. Plasmid-mediated transfer of ESBLs is an important route for resistance acquisition.

METHODS

We collected 1345 complete sequences of plasmids containing CTX-Ms from public database. The global transmission pattern of plasmids and evolutionary dynamics of CTX-Ms have been inferred. We applied the pan-genome clustering based on plasmid genomes and evolution analysis to demonstrate the transmission events.

FINDINGS

Totally, 48 CTX-Ms genotypes and 186 incompatible types of plasmids were identified. The geographical distribution of CTX-Ms showed significant differences across countries and continents. CTX-M-14 and CTX-M-55 were found to be the dominant genotypes in Asia, while CTX-M-1 played a leading role in Europe. The plasmids can be divided into 12 lineages, some of which forming distinct geographical clusters in Asia and Europe, while others forming hybrid populations. The Inc types of plasmids are lineage-specific, with the CTX-M-1_IncI1-I (Alpha) and CTX-M-65_IncFII (pHN7A8)/R being the dominant patterns of cross-host and cross-regional transmission. The IncI-I (Alpha) plasmids with the highest number, were presumed to form communication groups in Europe-Asia and Asia-America-Oceania, showing the transmission model as global dissemination and regional microevolution. Meanwhile, the main kinetic elements of blaCTX-Ms showed genotypic preferences. ISEcpl and IS26 were most frequently involved in the transfer of CTX-M-14 and CTX-M-65, respectively. IS15 has become a crucial participant in mediating the dissemination of blaCTX-Ms. Interestingly, blaTEM and blaCTX-Ms often coexisted in the same transposable unit. Furthermore, antibiotic resistance genes associated with aminoglycosides, sulfonamides and cephalosporins showed a relatively high frequency of synergistic effects with CTX-Ms.

CONCLUSIONS

We recognized the dominant blaCTX-Ms and mainstream plasmids of different continents. The results of this study provide support for a more effective response to the risks associated with the evolution of blaCTX-Ms-bearing plasmids, and lay the foundation for genotype-specific epidemiological surveillance of resistance, which are of important public health implications.

Approaches and challenges to inferring the geographical source of infectious disease outbreaks using genomic data

Genomic data hold increasing potential in the elucidation of transmission dynamics and geographical sources of infectious disease outbreaks. Phylogeographic methods that use epidemiological and genomic data obtained from surveillance enable us to infer the history of spatial transmission that is naturally embedded in the topology of phylogenetic trees as a record of the dispersal of infectious agents between geographical locations. In this Review, we provide an overview of phylogeographic approaches widely used for reconstructing the geographical sources of outbreaks of interest. These approaches can be classified into ancestral trait or state reconstruction and structured population models, with structured population models including popular structured coalescent and birth-death models. We also describe the major challenges associated with sequencing technologies, surveillance strategies, data sharing, and analysis frameworks that became apparent during the generation of large-scale genomic data in recent years, extending beyond inference approaches. Finally, we highlight the role of genomic data in geographical source inference and clarify how this enhances understanding and molecular investigations of outbreak sources.

Will modal shift occur from subway to other modes of transportation in the post-corona world in developing countries?

Developing countries are more fragile in the face of the crippling Covid-19 pandemic. Transportation is one of the major industries that has been hardly hit worldwide, and it is more worrying for these countries that already have challenges such as high modal share of private cars, air pollution, and high fatalities due to car accidents. This paper is one of its first kinds that studies the impact of this pandemic on the transportation of Tehran, the capital of Iran, which is one of the forefronts of the battle. In the first step and to get better insights from the travel behavior of passengers due to the pandemic, an online questionnaire is developed and distributed. Priorities for mode choice before and during the pandemic decrease and increase in the share of different modes and the impact of having a high-risk person in the family is studied. Subway had the most decrease and private cars had the highest increase. Hence, two logit models are developed to explain the variables that affect shifting away from the subway and shifting to private cars. Based on the results, a follow-up survey some months later and ridership trends of public transportation during the pandemic, four scenarios are envisaged for the post-corona world, the most probable one is highlighted and policies are recommended to better manage the situation.

Influence of transportation network on transmission heterogeneity of COVID-19 in China

In this paper, we propose a novel approach to model spatial heterogeneity for epidemic spreading, which combines the relevance of transport proximity in human movement and the excellent estimation accuracy of deep neural network. We apply this model to investigate the effects of various transportation networks on the heterogeneous propagation of COVID-19 in China. We further apply it to predict the development of COVID-19 in China in two scenarios, i.e., i) assuming that different types of traffic restriction policies are conducted and ii) assuming that the epicenter of the COVID-19 outbreak is in Beijing, so as to illustrate the potential usage of the model in generating various policy insights to help the containment of the further spread of COVID-19. We find that the most effective way to prevent the coronavirus from spreading quickly and extensively is to control the routes linked to the epicenter at the beginning of the pandemic. But if the virus has been widely spread, setting restrictions on hub cities would be much more efficient than imposing the same travel ban across the whole country. We also show that a comprehensive consideration of the epicenter location is necessary for disease control.

Haploflow: strain-resolved de novo assembly of viral genomes

With viral infections, multiple related viral strains are often present due to coinfection or within-host evolution. We describe Haploflow, a deBruijn graph-based assembler for de novo genome assembly of viral strains from mixed sequence samples using a novel flow algorithm. We assess Haploflow across multiple benchmark data sets of increasing complexity, showing that Haploflow is faster and more accurate than viral haplotype assemblers and generic metagenome assemblers not aiming to reconstruct strains. We show Haploflow reconstructs viral strain genomes from patient HCMV samples and SARS-CoV-2 wastewater samples identical to clinical isolates.

Computational strategies to combat COVID-19: useful tools to accelerate SARS-CoV-2 and coronavirus research

SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is a novel virus of the family Coronaviridae. The virus causes the infectious disease COVID-19. The biology of coronaviruses has been studied for many years. However, bioinformatics tools designed explicitly for SARS-CoV-2 have only recently been developed as a rapid reaction to the need for fast detection, understanding and treatment of COVID-19. To control the ongoing COVID-19 pandemic, it is of utmost importance to get insight into the evolution and pathogenesis of the virus. In this review, we cover bioinformatics workflows and tools for the routine detection of SARS-CoV-2 infection, the reliable analysis of sequencing data, the tracking of the COVID-19 pandemic and evaluation of containment measures, the study of coronavirus evolution, the discovery of potential drug targets and development of therapeutic strategies. For each tool, we briefly describe its use case and how it advances research specifically for SARS-CoV-2. All tools are free to use and available online, either through web applications or public code repositories. Contact:evbc@unj-jena.de.

Haploflow: Strain-resolved de novo assembly of viral genomes

In viral infections often multiple related viral strains are present, due to coinfection or within-host evolution. We describe Haploflow, a de Bruijn graph-based assembler for de novo genome assembly of viral strains from mixed sequence samples using a novel flow algorithm. We assessed Haploflow across multiple benchmark data sets of increasing complexity, showing that Haploflow is faster and more accurate than viral haplotype assemblers and generic metagenome assemblers not aiming to reconstruct strains. Haplotype reconstructed high-quality strain-resolved assemblies from clinical HCMV samples and SARS-CoV-2 genomes from wastewater metagenomes identical to genomes from clinical isolates.