Seasonal and inter-seasonal RSV activity in the European Region during the COVID-19 pandemic from autumn 2020 to summer 2022.

Background: The emergence of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in early 2020 and subsequent implementation of public health and social measures (PHSM) disrupted the epidemiology of respiratory viruses. This work describes the epidemiology of respiratory syncytial virus (RSV) observed during two winter seasons (weeks 40-20) and inter-seasonal periods (weeks 21-39) during the pandemic between October 2020 and September 2022. Methods: Using data submitted to The European Surveillance System (TESSy) by countries or territories in the World Health Organization (WHO) European Region between weeks 40/2020 and 39/2022, we aggregated country-specific weekly RSV counts of sentinel, non-sentinel and Severe Acute Respiratory Infection (SARI) surveillance specimens and calculated percentage positivity. Results for both 2020/21 and 2021/22 seasons and inter-seasons were compared with pre-pandemic 2016/17 to 2019/20 seasons and inter-seasons. Results: Although more specimens were tested than in pre-COVID-19 pandemic seasons, very few RSV detections were reported during the 2020/21 season in all surveillance systems. During the 2021 inter-season, a gradual increase in detections was observed in all systems. In 2021/22, all systems saw early peaks of RSV infection, and during the 2022 inter-seasonal period, patterns of detections were closer to those seen before the COVID-19 pandemic. Conclusion: RSV surveillance continued throughout the COVID-19 pandemic, with an initial reduction in transmission, followed by very high and out-of-season RSV circulation (summer 2021) and then an early start of the 2021/22 season. As of the 2022/23 season, RSV circulation had not yet normalised.

Bacteroides thetaiotaomicron, a Model Gastrointestinal Tract Species, Prefers Heme as an Iron Source, Yields Protoporphyrin IX as a Product, and Acts as a Heme Reservoir.

Members of the phylum Bacteroidetes are abundant in healthy gastrointestinal (GI) tract flora. Bacteroides thetaiotaomicron is a commensal heme auxotroph and representative of this group. Bacteroidetes are sensitive to host dietary iron restriction but proliferate in heme-rich environments that are also associated with colon cancer. We hypothesized that B. thetaiotaomicron may act as a host reservoir for iron and/or heme. In this study, we defined growth-promoting quantities of iron for B. thetaiotaomicron. B. thetaiotaomicron preferentially consumed and hyperaccumulated iron in the form of heme when presented both heme and nonheme iron sources in excess of its growth needs, leading to an estimated 3.6 to 8.4 mg iron in a model GI tract microbiome consisting solely of B. thetaiotaomicron. Protoporphyrin IX was identified as an organic coproduct of heme metabolism, consistent with anaerobic removal of iron from the heme leaving the intact tetrapyrrole as the observed product. Notably, no predicted or discernible pathway for protoporphyrin IX generation exists in B. thetaiotaomicron. Heme metabolism in congeners of B. thetaiotaomicron has previously been associated with the 6-gene hmu operon, based on genetic studies. A bioinformatics survey demonstrated that the intact operon is widespread in but confined to members of the Bacteroidetes phylum and ubiquitous in healthy human GI tract flora. Anaerobic heme metabolism by commensal Bacteroidetes via hmu is likely a major contributor to human host metabolism of the heme from dietary red meat and a driver for the selective growth of these species in the GI tract consortium. IMPORTANCE Research on bacterial iron metabolism has historically focused on the host-pathogen relationship, where the host suppresses pathogen growth by cutting off access to iron. Less is known about how host iron is shared with bacterial species that live commensally in the anaerobic human GI tract, typified by members of phylum Bacteroidetes. While many facultative pathogens avidly produce and consume heme iron, most GI tract anaerobes are heme auxotrophs whose metabolic preferences we aimed to describe. Understanding iron metabolism by model microbiome species like Bacteroides thetaiotaomicron is essential for modeling the ecology of the GI tract, which serves the long-term biomedical goals of manipulating the microbiome to facilitate host metabolism of iron and remediate dysbiosis and associated pathologies (e.g., inflammation and cancer).

Longitudinal analysis of the Five Sisters hot springs in Yellowstone National Park reveals a dynamic thermoalkaline environment.

Research focused on microbial populations of thermoalkaline springs has been driven in a large part by the lure of discovering functional enzymes with industrial applications in high-pH and high temperature environments. While several studies have focused on understanding the fundamental ecology of these springs, the small molecule profiles of thermoalkaline springs have largely been overlooked. To better understand how geochemistry, small molecule composition, and microbial communities are connected, we conducted a three-year study of the Five Sisters (FS) springs that included high-resolution geochemical measurements, 16S rRNA sequencing of the bacterial and archaeal community, and mass spectrometry-based metabolite and extracellular small molecule characterization. Integration of the four datasets facilitated a comprehensive analysis of the interwoven thermoalkaline spring system. Over the course of the study, the microbial population responded to changing environmental conditions, with archaeal populations decreasing in both relative abundance and diversity compared to bacterial populations. Decreases in the relative abundance of Archaea were associated with environmental changes that included decreased availability of specific nitrogen- and sulfur-containing extracellular small molecules and fluctuations in metabolic pathways associated with nitrogen cycling. This multi-factorial analysis demonstrates that the microbial community composition is more closely correlated with pools of extracellular small molecules than with the geochemistry of the thermal springs. This is a novel finding and suggests that a previously overlooked component of thermal springs may have a significant impact on microbial community composition.

Estimating the potential for global dissemination of pandemic pathogens using the global airline network and healthcare development indices.

Pandemics have the potential to incur significant health and economic impacts, and can reach a large number of countries from their origin within weeks. Early identification and containment of a newly emerged pandemic within the source country is key for minimising global impact. To identify a country's potential to control and contain a pathogen with pandemic potential, we compared the quality of a country's healthcare system against its global airline connectivity. Healthcare development was determined using three multi-factorial indices, while detailed airline passenger data was used to identify the global connectivity of all countries. Proximities of countries to a putative 'Worst Case Scenario' (extreme high-connectivity and low-healthcare development) were calculated. We found a positive relationship between a country's connectivity and healthcare metrics. We also identified countries that potentially pose the greatest risk for pandemic dissemination, notably Dominican Republic, India and Pakistan. China and Mexico, both sources of recent influenza and coronavirus pandemics were also identified as among the highest risk countries. Collectively, lower-middle and upper-middle income countries represented the greatest risk, while high income countries represented the lowest risk. Our analysis represents an alternative approach to identify countries where increased within-country disease surveillance and pandemic preparedness may benefit global health.

Isolation and Characterization of Lignocellulose-Degrading Geobacillus thermoleovorans from Yellowstone National Park.

The microbial degradation of lignocellulose in natural ecosystems presents numerous biotechnological opportunities, including biofuel production from agricultural waste and feedstock biomass. To explore the degradation potential of specific thermophiles, we have identified and characterized extremophilic microorganisms isolated from hot springs environments that are capable of biodegrading lignin and cellulose substrates under thermoalkaline conditions, using a combination of culturing, genomics, and metabolomics techniques. Organisms that can use lignin and cellulose as a sole carbon source at 60 to 75°C were isolated from sediment slurry of thermoalkaline hot springs (71 to 81°C and pH 8 to 9) of Yellowstone National Park. Full-length 16S rRNA gene sequencing indicated that these isolates were closely related to Geobacillus thermoleovorans. Interestingly, most of these isolates demonstrated biofilm formation on lignin, a phenotype that is correlated with increased bioconversion. Assessment of metabolite level changes in two Geobacillus isolates from two representative springs were undertaken to characterize the metabolic responses associated with growth on glucose versus lignin carbon source as a function of pH and temperature. Overall, results from this study support that thermoalkaline springs harbor G. thermoleovorans microorganisms with lignocellulosic biomass degradation capabilities and potential downstream biotechnological applications. IMPORTANCE Since lignocellulosic biomass represents a major agro-industrial waste and renewable resource, its potential to replace nonrenewable petroleum-based products for energy production is considerable. Microbial ligninolytic and cellulolytic enzymes are of high interest in biorefineries for the valorization of lignocellulosic biomass, as they can withstand the extreme conditions (e.g., high temperature and high pH) required for processing. Of great interest is the ligninolytic potential of specific Geobacillus thermoleovorans isolates to function at a broad range of pH and temperatures, since lignin is the bottleneck in the bioprocessing of lignocellulose. In this study, results obtained from G. thermoleovorans isolates originating from YNP springs are significant because very few microorganisms from alkaline thermal environments have been discovered to have lignin- and cellulose-biodegrading capabilities, and this work opens new avenues for the biotechnological valorization of lignocellulosic biomass at an industrial scale.

Modelling the global spread of diseases: A review of current practice and capability.

Mathematical models can aid in the understanding of the risks associated with the global spread of infectious diseases. To assess the current state of mathematical models for the global spread of infectious diseases, we reviewed the literature highlighting common approaches and good practice, and identifying research gaps. We followed a scoping study method and extracted information from 78 records on: modelling approaches; input data (epidemiological, population, and travel) for model parameterization; model validation data. We found that most epidemiological data come from published journal articles, population data come from a wide range of sources, and travel data mainly come from statistics or surveys, or commercial datasets. The use of commercial datasets may benefit the modeller, however makes critical appraisal of their model by other researchers more difficult. We found a minority of records (26) validated their model. We posit that this may be a result of pandemics, or far-reaching epidemics, being relatively rare events compared with other modelled physical phenomena (e.g. climate change). The sparsity of such events, and changes in outbreak recording, may make identifying suitable validation data difficult. We appreciate the challenge of modelling emerging infections given the lack of data for both model parameterisation and validation, and inherent complexity of the approaches used. However, we believe that open access datasets should be used wherever possible to aid model reproducibility and transparency. Further, modellers should validate their models where possible, or explicitly state why validation was not possible.