Identification of bacterial pathogens in sudden unexpected death in infancy and childhood using 16S rRNA gene sequencing

Background Sudden unexpected death in infancy (SUDI) is the most common cause of post-neonatal death in the developed world. Following an extensive investigation, the cause of ~40% of deaths remains unknown. It is hypothesized that a proportion of deaths are due to an infection that remains undetected due to limitations in routine techniques. This study aimed to apply 16S rRNA gene sequencing to post-mortem (PM) tissues collected from cases of SUDI, as well as those from the childhood equivalent (collectively known as sudden unexpected death in infancy and childhood or SUDIC), to investigate whether this molecular approach could help identify potential infection-causing bacteria to enhance the diagnosis of infection. Methods In this study, 16S rRNA gene sequencing was applied to de-identified frozen post-mortem (PM) tissues from the diagnostic archive of Great Ormond Street Hospital. The cases were grouped depending on the cause of death: (i) explained non-infectious, (ii) infectious, and (iii) unknown. Results and conclusions In the cases of known bacterial infection, the likely causative pathogen was identified in 3/5 cases using bacterial culture at PM compared to 5/5 cases using 16S rRNA gene sequencing. Where a bacterial infection was identified at routine investigation, the same organism was identified by 16S rRNA gene sequencing. Using these findings, we defined criteria based on sequencing reads and alpha diversity to identify PM tissues with likely infection. Using these criteria, 4/20 (20%) cases of unexplained SUDIC were identified which may be due to bacterial infection that was previously undetected. This study demonstrates the potential feasibility and effectiveness of 16S rRNA gene sequencing in PM tissue investigation to improve the diagnosis of infection, potentially reducing the number of unexplained deaths and improving the understanding of the mechanisms involved

Quantifying dominant bacterial genera detected in metagenomic data from fish eggs and larvae using genus‐specific primers

The goal of this study was to design genus-specific primers for rapid evaluation of the most abundant bacterial genera identified using amplicon-based sequencing of the 16S rRNA gene in fish-related samples and surrounding water. Efficient genus-specific primers were designed for 11 bacterial genera including Alkalimarinus, Colwellia, Enterovibrio, Marinomonas, Massilia, Oleispira, Phaeobacter, Photobacterium, Polarbacerium, Pseudomonas, and Psychrobium. The specificity of the primers was confirmed by the phylogeny of the sequenced polymerase chain reaction (PCR) amplicons that indicated primers were genus-specific except in the case of Colwellia and Phaeobacter. Copy number of the 16S rRNA gene obtained by quantitative PCR using genus-specific primers and the relative abundance obtained by 16S rRNA gene sequencing using universal primers were well correlated for the five analyzed abundant bacterial genera. Low correlations between quantitative PCR and 16S rRNA gene sequencing for Pseudomonas were explained by the higher coverage of known Pseudomonas species by the designed genus-specific primers than the universal primers used in 16S rRNA gene sequencing. The designed genus-specific primers are proposed as rapid and cost-effective tools to evaluate the most abundant bacterial genera in fish-related or potentially other metagenomics samples.info:eu-repo/semantics/publishedVersio

Randomized lasso links microbial taxa with aquatic functional groups inferred from flow cytometry

High-nucleic-acid (HNA) and low-nucleic-acid (LNA) bacteria are two operational groups identified by flow cytometry (FCM) in aquatic systems. A number of reports have shown that HNA cell density correlates strongly with heterotrophic production, while LNA cell density does not. However, which taxa are specifically associated with these groups, and by extension, productivity has remained elusive. Here, we addressed this knowledge gap by using a machine learning-based variable selection approach that integrated FCM and 16S rRNA gene sequencing data collected from 14 freshwater lakes spanning a broad range in physicochemical conditions. There was a strong association between bacterial heterotrophic production and HNA absolute cell abundances (R-2 = 0.65), but not with the more abundant LNA cells. This solidifies findings, mainly from marine systems, that HNA and LNA bacteria could be considered separate functional groups, the former contributing a disproportionately large share of carbon cycling. Taxa selected by the models could predict HNA and LNA absolute cell abundances at all taxonomic levels. Selected operational taxonomic units (OTUs) ranged from low to high relative abundance and were mostly lake system specific (89.5% to 99.2%). A subset of selected OTUs was associated with both LNA and HNA groups (12.5% to 33.3%), suggesting either phenotypic plasticity or within-OTU genetic and physiological heterogeneity. These findings may lead to the identification of system-specific putative ecological indicators for heterotrophic productivity. Generally, our approach allows for the association of OTUs with specific functional groups in diverse ecosystems in order to improve our understanding of (microbial) biodiversity-ecosystem functioning relationships. IMPORTANCE A major goal in microbial ecology is to understand how microbial community structure influences ecosystem functioning. Various methods to directly associate bacterial taxa to functional groups in the environment are being developed. In this study, we applied machine learning methods to relate taxonomic data obtained from marker gene surveys to functional groups identified by flow cytometry. This allowed us to identify the taxa that are associated with heterotrophic productivity in freshwater lakes and indicated that the key contributors were highly system specific, regularly rare members of the community, and that some could possibly switch between being low and high contributors. Our approach provides a promising framework to identify taxa that contribute to ecosystem functioning and can be further developed to explore microbial contributions beyond heterotrophic production

Characterization of the urogenital microbiome in patients with urinary tract infections

Standard microbiological culture techniques can only identify a fraction of the urogenital microbiome. Meanwhile, identifying and characterizing infectious microorganisms are very important for the success of diagnosis and treatments, especially for Urinary Tract Infection (UTI) patients. This study aimed to characterize the urogenital microbiome of UTI patients using 16S rRNA gene sequencing. We sequenced two pooled DNA samples from voided urine of UTI patients (21 females and 13 males). To determine the structure and composition of taxa in the samples, 16S rRNA gene sequencing was performed using the Illumina Mi‐Seq paired‐end platform. The most abundant genera were Burkholderia‐Caballeronia‐Paraburkholderia (71%) followed by Prevotella (33%), Escherichia‐Shigella (24%), Klebsiella (23%) and Sneathia (10%). The female microbiome was dominated by Prevotella bivia (28%), Escherichia coli (24%), Sneathia sanguinegens (7%) and Klebsiella pneumoniae (4%). On the other hand, the male microbiome was dominated by K. pneumoniae (23%) and E. coli (2%). K. pneumoniae and E. coli were the most abundant species found in both microbiomes. The 16S rRNA gene sequencing used in this study successfully uncovered the composition of the urogenital microbiome, which might not have been possible with conventional culture methods

Diversity of culturable moderately halophilic and halotolerant bacteria in a marsh and two salterns a protected ecosystem of Lower Loukkos (Morocco)

To study the biodiversity of halophilic bacteria in a protected wetland located in Loukkos (Northwest, Morocco), a total of 124 strains were recovered from sediment samples from a marsh and salterns. 120 isolates (98%) were found to be moderately halophilic bacteria; growing in salt ranges of 0.5 to 20%. Of 124 isolates, 102 were Gram-positive while 22 were Gram negative. All isolates were identified based on 16S rRNA gene phylogenetic analysis and characterized phenotypically and by screening for extracellular hydrolytic enzymes. The Gram-positive isolates were dominated by the genus Bacillus (89%) and the others were assigned to Jeotgalibacillus, Planococcus, Staphylococcus and Thalassobacillus. The Gram negative isolates were dominated by the genus Vibrio (41%) and the others were assigned to Halomonas, Psychrobacter, Marinobacterium, Pseudoalteromonas, Salinivibrio and Photobacterium. The growth of strains obtained under different physico-chemical conditions and the screening for hydrolytic enzymes showed a high diversity even within the same species

Considerations and best practices in animal science 16S ribosomal RNA gene sequencing microbiome studies

Microbiome studies in animal science using 16S rRNA gene sequencing have become increasingly common in recent years as sequencing costs continue to fall and bioinformatic tools become more powerful and user-friendly. The combination of molecular biology, microbiology, microbial ecology, computer science, and bioinformatics—in addition to the traditional considerations when conducting an animal science study—makes microbiome studies sometimes intimidating due to the intersection of different fields. The objective of this review is to serve as a jumping-off point for those animal scientists less familiar with 16S rRNA gene sequencing and analyses and to bring up common issues and concerns that arise when planning an animal microbiome study from design through analysis. This review includes an overview of 16S rRNA gene sequencing, its advantages, and its limitations; experimental design considerations such as study design, sample size, sample pooling, and sample locations; wet lab considerations such as field handing, microbial cell lysis, low biomass samples, library preparation, and sequencing controls; and computational considerations such as identification of contamination, accounting for uneven sequencing depth, constructing diversity metrics, assigning taxonomy, differential abundance testing, and, finally, data availability. In addition to general considerations, we highlight some special considerations by species and sample type

Analysis of 16S rRNA for identification of new bacteria

The major problem associated with bacteria classification, identification and identification is due to alack of data and methods.  Correct data is crucial for the nomenclature of bacteria.  Bacteria in thisregard, molecular approach is advantageous because it reveals the identitiy of bacteria.  The 16SrRNA gene as molecular characteristic have been developed to be a useful marker molecular ofprocaryote for systematics. Identification of bacteria by 16S rRNA gene sequencing  is considered tobe more accurate than phenotypic characteristics. In conclusion, the sequence analysis of 16S rRNAproved to be a useful  molecular data for identification new strain of pathogenic bacteria. The isolate was identifiedas Bacillus cereus strain C4 after 16S rRNA gene sequencing and alignment by BLAST. Keywords : NA, 16S rRNA, Bacillus cereus, phylogenetic stud

Taxonomy of bacterial fish pathogens

Bacterial taxonomy has progressed from reliance on highly artificial culture-dependent techniques involving the study of phenotype (including morphological, biochemical and physiological data) to the modern applications of molecular biology, most recently 16S rRNA gene sequencing, which gives an insight into evolutionary pathways (= phylogenetics). The latter is applicable to culture-independent approaches, and has led directly to the recognition of new uncultured bacterial groups, i.e. "Candidatus", which have been associated as the cause of some fish diseases, including rainbow trout summer enteritic syndrome. One immediate benefit is that 16S rRNA gene sequencing has led to increased confidence in the accuracy of names allocated to bacterial pathogens. This is in marked contrast to the previous dominance of phenotyping, and identifications, which have been subsequently challenged in the light of 16S rRNA gene sequencing. To date, there has been some fluidity over the names of bacterial fish pathogens, with some, for example Vibrio anguillarum, being divided into two separate entities (V. anguillarum and V. ordalii). Others have been combined, for example V. carchariae, V. harveyi and V. trachuri as V. harveyi. Confusion may result with some organisms recognized by more than one name; V. anguillarum was reclassified as Beneckea and Listonella, with Vibrio and Listonella persisting in the scientific literature. Notwithstanding, modern methods have permitted real progress in the understanding of the taxonomic relationships of many bacterial fish pathogens

The presence of bacteria varies between colorectal adenocarcinomas, precursor lesions and non-malignant tissue

Tissue samples used for 16S rRNA gene sequencing. Quantification cycles obtained using qPCR and clinical information for each clinical sample investigated using Illumina sequencing of the V4 region of the 16S rRNA gene. (XLSX 31 kb