Integrating One Health into Systems Science

One Health Systems Science. The three subsystems of One Health (ecosystem, human and animal health) are integrated in the Systems Science concept, where objects or adaptive agents (circles) interact with a dynamic environment, and Systems Thinking can lead it intervations (Systems Design) generating a change in One Health outcomes. Real-time genomic data retrieved from the three subsystems porvide information fo Systems Thinking and Systems Design.Unlabelled Image.

Big Data for a Small World: A Review on Databases and Resources for Studying Microbiomes

Microorganisms are ubiquitous in nature and form complex community networks to survive in various environments. This community structure depends on numerous factors like nutrient availability, abiotic factors like temperature and pH as well as microbial composition. Categorising accessible biomes according to their habitats would help in understanding the complexity of the environment-specific communities. Owing to the recent improvements in sequencing facilities, researchers have started to explore diverse microbiomes rapidly and attempts have been made to study microbial crosstalk. However, different metagenomics sampling, preprocessing, and annotation methods make it difficult to compare multiple studies and hinder the recycling of data. Huge datasets originating from these experiments demand systematic computational methods to extract biological information beyond microbial compositions. Further exploration of microbial co-occurring patterns across the biomes could help us in designing cross-biome experiments. In this review, we catalogue databases with system-specific microbiomes, discussing publicly available common databases as well as specialised databases for a range of microbiomes. If the new datasets generated in the future could maintain at least biome-specific annotation, then researchers could use those contemporary tools for relevant and bias-free analysis of complex metagenomics data.

A comprehensive evaluation of single-end sequencing data analyses for environmental microbiome research

Illumina sequencing platforms have been widely used for amplicon-based environmental microbiome research. Analyses of amplicon data of environmental samples, generated from Illumina MiSeq platform illustrate the reverse (R2) reads in the PE datasets to have low quality towards the 3' end of the reads which affect the sequencing depth of samples and ultimately impact the sample size which may possibly lead to an altered outcome. This study evaluates the usefulness of single-end (SE) sequencing data in microbiome research when the Illumina MiSeq PE dataset shows significantly high number of low-quality reverse reads. In this study, the amplicon data (V1V3, V3V4, V4V5 and V6V8) from 128 environmental (soil) samples, downloaded from SRA, demonstrate the efficiency of single-end (SE) sequencing data analyses in microbiome research. The SE datasets were found to infer the core microbiome structure as comparable to the PE dataset. Conspicuously, the forward (R1) datasets inferred a higher number of taxa as compared to PE datasets for most of the amplicon regions, except V3V4. Thus, analyses of SE sequencing data, especially R1 reads, in environmental microbiome studies could ameliorate the problems arising on sample size of the study due to low quality reverse reads in the dataset. However, care must be taken while interpreting the microbiome structure as few taxa observed in the PE datasets were absent in the SE datasets. In conclusion, this study demonstrates the availability of choices in analyzing the amplicon data without having the need to remove samples with low quality reverse reads.

Environmental dynamics of hospital microbiome upon transfer from a major hospital to a new facility

BACKGROUND

Infection control is critical to safe hospital care. However, how bacteria within nosocomial environments relate to space utilisation and occupancy remains poorly understood. Our aim was to characterise the hospital microbiome in the context of the closure of a tertiary hospital and the opening of a new facility.

METHODS

Environmental swabs were collected from common and inpatient areas in the old and new hospitals during a 12-month transition period. Microbiota characteristics were determined by 16S rRNA gene sequencing and quantitative (q)PCR. Targeted assays were used to detect Methicillin-resistant Staphylococcus aureus (MRSA) and vanB-positive Vancomycin-Resistant Enterococci (VRE).

RESULTS

The transition to full occupancy in the new facility was associated with an increase in bacterial load (inpatient areas, 3 months p = 0.001; common areas, 6 months p = 0.039) and a change in microbiota composition (baseline-12 months, PERMANOVA p = 0.002). These changes were characterised by an increase in human microbiota-associated taxa, including Acinetobacter and Veillonella. Closure of the existing facility was associated with a decrease in bacterial load (p = 0.040). Detection of MRSA did not differ significantly between sites.

CONCLUSIONS

Occupancy is a major determinant of bacterial dispersion within hospital environments. Steady-state bacterial levels and microbiota composition provide a basis for assessment of infection control measures.

Meeting report of the fourth annual Tri-Service Microbiome Consortium symposium

The Tri-Service Microbiome Consortium (TSMC) was founded to enhance collaboration, coordination, and communication of microbiome research among U.S. Department of Defense (DoD) organizations. The annual TSMC symposium is designed to enable information sharing between DoD scientists and leaders in the field of microbiome science, thereby keeping DoD consortium members informed of the latest advances within the microbiome community and facilitating the development of new collaborative research opportunities. The 2020 annual symposium was held virtually on 24-25 September 2020. Presentations and discussions centered on microbiome-related topics within four broad thematic areas: (1) Enabling Technologies; (2) Microbiome for Health and Performance; (3) Environmental Microbiome; and (4) Microbiome Analysis and Discovery. This report summarizes the presentations and outcomes of the 4th annual TSMC symposium.

Hi-C deconvolution of a textile dye-related microbiome reveals novel taxonomic landscapes and links phenotypic potential to individual genomes

Microbial biodiversity is represented by a variety of genomic landscapes adapted to dissimilar environments on Earth. These genomic landscapes contain functional signatures connected with the community phenotypes. Here, we assess the genomic microbial diversity landscape at a high-resolution level of a polluted river-associated microbiome (Morelos, México), cultured in a medium enriched with anthraquinone Deep Blue 35 dye. We explore the resultant textile dye microbiome to infer links between predicted biodegradative functions, and metagenomic and metabolic potential, especially using the information obtained from individual reconstructed genomes. By using Hi-C proximity-ligation deconvolution method, we deconvoluted 97 genome composites (80% potentially novel species). The main taxonomic determinants were Methanobacterium, Clostridium, and Cupriavidus genera constituting 50, 22, and 11% of the total community profile. Also, we observed a rare biosphere of novel taxa without clear taxonomic standing. Removal of 50% chemical oxygen demand with 23% decolorization was observed after 30 days of dye enrichment. Genes related to catalase-peroxidase, polyphenol oxidase, and laccase enzymes were predicted as associated with textile dye biodegradation phenotype under our study conditions, highlighting the potential of metagenome-wide analysis to predict biodegradative determinants. This study prompts high-resolution screening of individual genomes within textile dye river sediment microbiomes or complex communities under environmental pressures.

Meeting report of the third annual Tri-Service Microbiome Consortium symposium

The Tri-Service Microbiome Consortium (TSMC) was founded to enhance collaboration, coordination, and communication of microbiome research among U.S. Department of Defense (DoD) organizations and to facilitate resource, material and information sharing among consortium members. The 2019 annual symposium was held 22-24 October 2019 at Wright-Patterson Air Force Base in Dayton, OH. Presentations and discussions centered on microbiome-related topics within five broad thematic areas: 1) human microbiomes; 2) transitioning products into Warfighter solutions; 3) environmental microbiomes; 4) engineering microbiomes; and 5) microbiome simulation and characterization. Collectively, the symposium provided an update on the scope of current DoD microbiome research efforts, highlighted innovative research being done in academia and industry that can be leveraged by the DoD, and fostered collaborative opportunities. This report summarizes the presentations and outcomes of the 3rd annual TSMC symposium.

Urban-associated diseases: Candidate diseases, environmental risk factors, and a path forward

BACKGROUND

Cities are home to over half the global population; that proportion is expected to rise to 70% by mid-century. The urban environment differs greatly from that in which humans evolved, with potentially important consequences for health. Rates for allergic, inflammatory and auto-immune diseases appear to rise with urbanization and be higher in the more urbanized nations of the world which has led some to suggest that cities promote the occurrence of these diseases. However, there are no syntheses outlining what urban-associated diseases are and what characteristics of cities promote their occurrence.

OBJECTIVES

To synthesize the current understanding of "urban-associated diseases", and discover the common, potentially modifiable features of cities that may be driving these associations.

METHODS

We focus on any diseases that have been associated with cities or are particularly prominent in today's urban societies. We draw on expertise across diverse health fields to examine the evidence for urban connections and drivers.

DISCUSSION

We found evidence for urban associations across allergic, auto-immune, inflammatory, lifestyle and infectious disease categories. Some conditions (e.g. obesity and diabetes) have complex relationships with cities that have been insufficiently explored. Other conditions (e.g. allergies and asthma) have more evidence demonstrating their relationship with cities and the mechanisms driving that association. Unsurprisingly, air pollution was the characteristic of cities most frequently associated with disease. Other identified urban risk factors are not as widely known: altered microbial exposure and a disconnect from environmental microbiomes, vitamin D deficiency, noise and light pollution, and a transient, over-crowded, impoverished population. However, many complexities and caveats to these relationships beg clarification; we highlight the current knowledge gaps and outline ways to fill those gaps. Identifying urban-associated diseases and their drivers will allow us to prepare for the urban-disease burden of the future and create healthy cities that mitigate that disease burden.

The structure and diversity of human, animal and environmental resistomes

BACKGROUND

Antibiotic resistance genes (ARGs) are widespread but cause problems only when present in pathogens. Environments where selection and transmission of antibiotic resistance frequently take place are likely to be characterized by high abundance and diversity of horizontally transferable ARGs. Large-scale quantitative data on ARGs is, however, lacking for most types of environments, including humans and animals, as is data on resistance genes to potential co-selective agents, such as biocides and metals. This paucity prevents efficient identification of risk environments.

RESULTS

We provide a comprehensive characterization of resistance genes, mobile genetic elements (MGEs) and bacterial taxonomic compositions for 864 metagenomes from humans (n = 350), animals (n = 145) and external environments (n = 369), all deeply sequenced using Illumina technology. Environment types showed clear differences in both resistance profiles and bacterial community compositions. Human and animal microbial communities were characterized by limited taxonomic diversity and low abundance and diversity of biocide/metal resistance genes and MGEs but a relatively high abundance of ARGs. In contrast, external environments showed consistently high taxonomic diversity which in turn was linked to high diversity of both biocide/metal resistance genes and MGEs. Water, sediment and soil generally carried low relative abundance and few varieties of known ARGs, whereas wastewater/sludge was on par with the human gut. The environments with the largest relative abundance and/or diversity of ARGs, including genes encoding resistance to last resort antibiotics, were those subjected to industrial antibiotic pollution and a limited set of deeply sequenced air samples from a Beijing smog event.

CONCLUSIONS

Our study identifies air and antibiotic-polluted environments as under-investigated transmission routes and reservoirs for antibiotic resistance. The high taxonomic and genetic diversity of external environments supports the hypothesis that these also form vast sources of unknown resistance genes, with potential to be transferred to pathogens in the future.