Rodent reservoirs: unraveling spectrum of zoonotic and pathogenic bacteria

Journal of Ideas in Health, Journal Year: 2024, Volume and Issue: 7(3), P. 1061 - 1067

Published: June 30, 2024

Background: Zoonotic diseases are the major public health threat, with over 70% originating from wildlife. Rodents, while beneficial to environment, transmit many zoonotic such as hemorrhagic fevers, plague, tularemia, and leptospirosis, mainly due increased agriculture land use changes. Understanding rodent-borne pathogens is essential for effective intervention. Therefore, this study aimed identify pathogenic bacteria in rodents rodent species area. Methods: A total of 116 achieved samples (101 oral-pharyngeal 15 rectal swabs) collected Kibondo, Uvinza Kyerwa were used study. Total RNA (Ribonucleic Acid) was extracted each swab sample then pooled based on species, location types make twelve pools. portion swabs polyadenylated metagenomics sequence libraries preparation. 16S rRNA (ribosomal Ribonucleic sequencing performed 12 pools by using MinIon platform order microbial diversity. Results: 13 different communities includinng identified; where, families potentially pathogenic, unknown potential also identified. These included Mycobacteriacea, Helicobacteriacea, Enterobacteriacea, Vibrionacea, Staphylococcaceae, Nocardiaceae, Bacillaceae, Pasteurellaceae, Streptococcaceae, Campylobacteraceae, Leptospiraceae, Brachyspiraceae, Moraxellaceae, Enterococcaea, Flavobacteriacea. Potentially including Mycobacterium tuberculosis, Vibrio cholerae, Helicobacter pylori parahaemolyticus reported Conclusion: This identifies several veterinary importance, highlighting possibility risk human infection cross-transmission between rodents, humans, animals given proximity humans animals. While no concrete evidence rodent-to-human transmission found, we hypothesize that a source, especially resource-poor areas close rodent-human contact.

Language: Английский

---

Benchmarking bacterial taxonomic classification using nanopore metagenomics data of several mock communities

Scientific Data, Journal Year: 2024, Volume and Issue: 11(1)

Published: Aug. 10, 2024

Taxonomic classification is crucial in identifying organisms within diverse microbial communities when using metagenomics shotgun sequencing. While second-generation Illumina sequencing still dominates, third-generation nanopore promises improved through longer reads. However, extensive benchmarking studies on data are lacking. We systematically evaluated performance of bacterial taxonomic for several commonly used classifiers, standardized reference sequence databases, the largest collection publicly available defined mock thus far (nine samples), representing different research domains and application scopes. Our results categorize classifiers into three categories: low precision/high recall; medium precision/medium recall, high recall. Most fall first group, although precision can be without excessively penalizing recall with suitable abundance filtering. No definitive 'best' classifier emerges, selection depends scope practical requirements. Although few designed long reads exist, they generally exhibit better performance. comprehensive provides concrete recommendations, supported by code reassessment fine-tuning other scientists.

Language: Английский

---

Filtering out the noise: metagenomic classifiers optimize ancient DNA mapping

Briefings in Bioinformatics, Journal Year: 2024, Volume and Issue: 26(1)

Published: Nov. 22, 2024

Abstract Contamination with exogenous DNA presents a significant challenge in ancient (aDNA) studies of single organisms. Failure to address contamination from microbes, reagents, and present-day sources can impact the interpretation results. Although field laboratory protocols exist limit contamination, there is still need accurately distinguish between endogenous data computationally. Here, we propose workflow reduce based on metagenomic classifier. Unlike previous methods that relied exclusively sequencing reads mapping specificity reference genome remove contaminating reads, our approach uses Kraken2-based filtering before genome. Using both simulated empirical shotgun aDNA data, show this simple efficient method be used wide range computational environments—including personal machines. We strategies build specific databases profile take into consideration available resources prior knowledge about target taxa likely contaminants. Our significantly reduces overall required during process total runtime by up ~94%. The most impacts are observed low samples. Importantly, contaminants would map filtered out using strategy, reducing false positive alignments. also results negligible loss no measurable downstream population genetics analyses.

Language: Английский

---