Coinfections identified from metagenomic analysis of cervical lymph nodes from tularemia patients

Genomic characterization of Francisella tularensis and other diverse Francisella species from complex samples

Francisella tularensis, the bacterium that causes the zoonosis tularemia, and its genetic near neighbor species, can be difficult or impossible to cultivate from complex samples. Thus, there is a lack of genomic information for these species that has, among other things, limited the development of robust detection assays for F. tularensis that are both specific and sensitive. The objective of this study was to develop and validate approaches to capture, enrich, sequence, and analyze Francisella DNA present in DNA extracts generated from complex samples. RNA capture probes were designed based upon the known pan genome of F. tularensis and other diverse species in the family Francisellaceae. Probes that targeted genomic regions also present in non-Francisellaceae species were excluded, and probes specific to particular Francisella species or phylogenetic clades were identified. The capture-enrichment system was then applied to diverse, complex DNA extracts containing low-level Francisella DNA, including human clinical tularemia samples, environmental samples (i.e., animal tissue and air filters), and whole ticks/tick cell lines, which was followed by sequencing of the enriched samples. Analysis of the resulting data facilitated rigorous and unambiguous confirmation of the detection of F. tularensis or other Francisella species in complex samples, identification of mixtures of different Francisella species in the same sample, analysis of gene content (e.g., known virulence and antimicrobial resistance loci), and high-resolution whole genome-based genotyping. The benefits of this capture-enrichment system include: even very low target DNA can be amplified; it is culture-independent, reducing exposure for research and/or clinical personnel and allowing genomic information to be obtained from samples that do not yield isolates; and the resulting comprehensive data not only provide robust means to confirm the presence of a target species in a sample, but also can provide data useful for source attribution, which is important from a genomic epidemiology perspective.

Detection and Genotyping of Francisella tularensis in Animal Hosts and Vectors from Six Different Natural Landscape Areas, Gansu Province, China

Objective

Tularemia, also known as hare fever, is caused by the bacterium Francisella tularensis (F. tularensis) transmitted through diseased wild animals, blood sucking insects, or contaminated water or food, which is distributed worldwide. The purpose of this study was to investigate F. tularensis infection in animal hosts and vectors from six different natural landscape areas in Gansu Province and to identify the genotypes of the detected F. tularensis.

Methods

Rodents were captured by snap traps, and ticks were collected by dragging a cloth over the vegetation or from domestic animals. After species identification, DNA was isolated from the captured animals and detected by nested PCR assays targeting the F. tularensis fopA gene. The positive samples were further amplified to discriminate the species, and another two short-sequence tandem repeat regions (SSTR) were amplified to identify their genotypes. All positive fragments were sequenced and analyzed by ClustalX (5.0) and DNAClub software.

Results

A total of 407 rodents of 12 species were captured, among which six rodent species were positive for F. tularensis, with an overall prevalence of 3.93%. The geographical difference in infection rate was statistically significant. At the SSTR9 locus, there were 7 genotypes among positive rodent samples. A total of 1864 ticks were tested for evidence of tularemia by nested PCR assays, 69 of which were positive, with an average positive rate of 3.70% for F. tularensis in ticks. The positive rates were significantly different among different regions. Seven genotypes were identified at the SSTR9 locus, one of which seemed dominant in positive tick samples. All positive samples had the same genotype at the SSTR16 locus.

Conclusion

There is natural infection of F. tularensis among animal vectors and hosts in Gansu Province, with diverse genotypes.

Viral coinfections in COVID-19

The most consequential challenge raised by coinfection is perhaps the inappropriate generation of recombinant viruses through the exchange of genetic material among different strains. These genetically similar viruses can interfere with the replication process of each other and even compete for the metabolites required for the maintenance of the replication cycle. Due to the similarity in clinical symptoms of most viral respiratory tract infections, and their coincidence with COVID-19, caused by SARS-CoV-2, it is recommended to develop a comprehensive diagnostic panel for detection of respiratory and nonrespiratory viruses through the evaluation of patient samples. Given the resulting changes in blood markers, such as coagulation factors and white blood cell count following virus infection, these markers can be of diagnostic value in the detection of mixed infection in individuals already diagnosed with a certain viral illness. In this review, we seek to investigate the coinfection of SARS-CoV-2 with other respiratory and nonrespiratory viruses to provide novel insights into the development of highly sensitive diagnostics and effective treatment modalities.

Non-SARS-CoV-2 genome sequences identified in clinical samples from COVID-19 infected patients: Evidence for co-infections

BACKGROUND

In December 2019, an ongoing outbreak of pneumonia caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2/ 2019-nCoV) infection was initially reported in Wuhan, Hubei Province, China. Early in 2020, the World Health Organization (WHO) announced a new name for the 2019-nCoV-caused disease: coronavirus disease 2019 (COVID-19) and declared COVID-19 to be a Public Health Emergency of International Concern (PHEIC). Cellular co-infection is a critical determinant of viral fitness and infection outcomes and plays a crucial role in shaping the host immune response to infections.

METHODS

In this study, 68 public next-generation sequencing data from SARS-CoV-2 infected patients were retrieved from the NCBI Sequence Read Archive database using SRA-Toolkit. Data screening was performed using an alignment-free method based on k-mer mapping and extension, fastv. Taxonomic classification was performed using Kraken 2 on all reads containing one or more virus sequences other than SARS-CoV-2.

RESULTS

SARS-CoV-2 was identified in all except three patients. Influenza type A (H7N9) virus, human immunodeficiency virus, rhabdovirus, human metapneumovirus, Human adenovirus, Human herpesvirus 1, coronavirus NL63, parvovirus, simian virus 40, and hepatitis virus genomes sequences were detected in SARS-CoV-2 infected patients. Besides, a very diverse group of bacterial populations were observed in the samples.

Microbiome applications for pathology: challenges of low microbial biomass samples during diagnostic testing

The human microbiome can play key roles in disease, and diagnostic testing will soon have the ability to examine these roles in the context of clinical applications. Currently, most diagnostic testing in pathology applications focuses on a small number of disease‐causing microbes and dismisses the whole microbial community that causes or is modulated by disease. Microbiome modifications have already provided clinically relevant insights in gut and oral diseases, such as irritable bowel disease, but there are currently limitations when clinically examining microbiomes outside of these body sites. This is critical, as the majority of microbial samples used in pathology originate from body sites that contain low concentrations of microbial DNA, including skin, tissue, blood, and urine. These samples, also known as low microbial biomass samples, are difficult to examine without careful consideration and precautions to mitigate contamination and biases. Here, we present the limitations when analysing low microbial biomass samples using current protocols and techniques and highlight the advantages that microbiome testing can offer diagnostics in the future, if the proper precautions are implemented. Specifically, we discuss the sources of contamination and biases that may result in false assessments for these sample types. Finally, we provide recommendations to mitigate contamination and biases from low microbial biomass samples during diagnostic testing, which will be especially important to effectively diagnose and treat patients using microbiome analyses.