Microbial Identification Using rRNA Operon Region: Database and Tool for Metataxonomics with Long-Read Sequence

PRONAME: a user-friendly pipeline to process long-read nanopore metabarcoding data by generating high-quality consensus sequences

Background

The study of sample taxonomic composition has evolved from direct observations and labor-intensive morphological studies to different DNA sequencing methodologies. Most of these studies leverage the metabarcoding approach, which involves the amplification of a small taxonomically-informative portion of the genome and its subsequent high-throughput sequencing. Recent advances in sequencing technology brought by Oxford Nanopore Technologies have revolutionized the field, enabling portability, affordable cost and long-read sequencing, therefore leading to a significant increase in taxonomic resolution. However, Nanopore sequencing data exhibit a particular profile, with a higher error rate compared with Illumina sequencing, and existing bioinformatics pipelines for the analysis of such data are scarce and often insufficient, requiring specialized tools to accurately process long-read sequences.

Results

We present PRONAME (PROcessing NAnopore MEtabarcoding data), an open-source, user-friendly pipeline optimized for processing raw Nanopore sequencing data. PRONAME includes precompiled databases for complete 16S sequences (Silva138 and Greengenes2) and a newly developed and curated database dedicated to bacterial 16S-ITS-23S operon sequences. The user can also provide a custom database if desired, therefore enabling the analysis of metabarcoding data for any domain of life. The pipeline significantly improves sequence accuracy, implementing innovative error-correction strategies and taking advantage of the new sequencing chemistry to produce high-quality duplex reads. Evaluations using a mock community have shown that PRONAME delivers consensus sequences demonstrating at least 99.5% accuracy with standard settings (and up to 99.7%), making it a robust tool for genomic analysis of complex multi-species communities.

Conclusion

PRONAME meets the challenges of long-read Nanopore data processing, offering greater accuracy and versatility than existing pipelines. By integrating Nanopore-specific quality filtering, clustering and error correction, PRONAME produces high-precision consensus sequences. This brings the accuracy of Nanopore sequencing close to that of Illumina sequencing, while taking advantage of the benefits of long-read technologies.

rRNA operon improves species-level classification of bacteria and microbial community analysis compared to 16S rRNA

Precise identification of species is fundamental in microbial genomics and is crucial for understanding the microbial communities. While the 16S rRNA gene, particularly its V3-V4 regions, has been extensively employed for microbial identification, however has limitations in achieving species-level resolution. Advancements in long-read sequencing technologies have highlighted the rRNA operon as a more accurate marker for microbial classification and analysis than the 16S rRNA gene. This study aims to compare the accuracy of species classification and microbial community analysis using the rRNA operon versus the 16S rRNA gene. We evaluated the species classification accuracy of the rRNA operon,16S rRNA gene, and 16S rRNA V3-V4 regions using a BLAST-based method and a k-mer matching-based method with public data available from NCBI. We further performed simulations to model microbial community analysis. We accessed the performance using each marker in community composition estimation and differential abundance analysis. Our findings demonstrate that the rRNA operon offers an advantage over the 16S rRNA gene and its V3-V4 regions for species-level classification within the genus. When applied to microbial community analysis, the rRNA operon enables a more accurate determination of composition. Using the rRNA operon yielded more reliable results in differential abundance analysis as well.

IMPORTANCE

We quantitatively demonstrated that the rRNA operon outperformed the 16S rRNA and its V3-V4 regions in accuracy for both individual species identification and species-level microbial community analysis. Our findings can provide guidelines for selecting appropriate markers in the field of microbial research.

Dietary patterns drive loss of fiber-foraging species in the celiac disease patients gut microbiota compared to first-degree relatives

BACKGROUND

Celiac disease is an autoimmune disorder triggered by dietary gluten in genetically predisposed individuals that primarily affects the small intestine. Studies have reported differentially abundant bacterial taxa in the gut microbiota of celiac patients compared with non-celiac controls. However, findings across studies have inconsistencies and no microbial signature of celiac disease has been defined so far.

RESULTS

Here, we showed, by comparing celiac patients with their non-celiac 1st-degree relatives, that bacterial communities of related individuals have similar species occurrence and abundance compared with non-relatives, regardless the disease status. We also found in celiac patients a loss of bacterial species associated with fiber degradation, and host metabolic and immune modulation, as ruminiclostridia, ruminococci, Prevotella, and Akkermansia muciniphila species. We demonstrated that the differential abundance of bacterial species correlates to different dietary patterns observed between the two groups. For instance, Ruminiclostridium siraeum, Ruminococcus bicirculans, and Bacteroides plebeious, recognized as fiber-degraders, appear more abundant in non-celiac 1st-degree relatives, which have a vegetable consumption pattern higher than celiac patients. Pattern of servings per day also suggests a possible link between these species' abundance and daily calorie intake.

CONCLUSIONS

Overall, we evidenced that a kinship approach could be valuable in unveiling potential celiac disease microbial traits, as well as the significance of dietary factors in shaping microbial profiles and their influence on disease development and progression. Our results pave the way for designing and adopting novel dietary strategies based on gluten-free fiber-enriched ingredients to improve disease management and patients' quality of life.

Potentially pathogenic culturable bacteria in hemodialysis waters

BACKGROUND

Hemodialysis patients are at risk of acquiring healthcare-related infections due to using non-sterile water to prepare hemodialysis fluid. Therefore, microbiological control and monitoring of used water are of crucial importance.

MATERIALS AND METHODS

In this work, we identified bacterial populations occupying a hemodialysis water distribution system for almost a 6-month period in Ahvaz city, southwest of Iran. A total of 18 samples from three points were collected. We found high colony counts of bacteria on R2A agar. 31 bacteria with different morphological and biochemical characteristics were identified by molecular-genetic methods based on 16 S rRNA gene sequencing. Endotoxin concentrations were measured, using Endosafe® Rapid LAL Single-Test Vials.

RESULTS

A diverse bacterial community was identified, containing predominantly Gram-negative bacilli. The most frequently isolated genus was Sphingomonas. Five species including M. fortuitum, M. lentiflavum, M.szulgai, M. barrassiae, and M. gordonae was identified .Despite the presence of Gram-negative bacteria the endotoxin analysis of all samples revealed that their endotoxin values were below the detection limit.

CONCLUSION

The members of Sphingomonas genus along with Bosea and mycobacteria could be regarded as pioneers in surface colonization and biofilm creation. These bacteria with others like Pelomonas, Bradyrhizobium, staphylococcus, and Microbacterium may represent a potential health risk to patients under hemodialysis treatment.

GROND: a quality-checked and publicly available database of full-length 16S-ITS-23S rRNA operon sequences

Sequence comparison of 16S rRNA PCR amplicons is an established approach to taxonomically identify bacterial isolates and profile complex microbial communities. One potential application of recent advances in long-read sequencing technologies is to sequence entire rRNA operons and capture significantly more phylogenetic information compared to sequencing of the 16S rRNA (or regions thereof) alone, with the potential to increase the proportion of amplicons that can be reliably classified to lower taxonomic ranks. Here we describe GROND (Genome-derived Ribosomal Operon Database), a publicly available database of quality-checked 16S-ITS-23S rRNA operons, accompanied by multiple taxonomic classifications. GROND will aid researchers in analysis of their data and act as a standardised database to allow comparison of results between studies.

Customized 16S-23S rDNA ITS Amplicon Metagenomics for Acetic Acid Bacteria Species Identification in Vinegars and Kombuchas

Acetic acid bacteria (AAB) are involved in food and beverage production bioprocesses, like those in vinegar and kombucha. They oxidize sugars and alcohols into various metabolites, resulting in the final products' pleasant taste and aroma. The 16S rDNA amplicon metagenomics using Illumina technology is usually used to follow the microbiological development of these processes. However, the 16S rRNA gene sequences among different species of AAB are very similar, thus not enabling a reliable identification down to the species level but only to the genus. In this study, we have constructed primers for amplifying half of the 16S-23S rRNA gene internal transcribed spacer (ITS) for library construction and further sequencing using Illumina technology. This approach was successfully used to estimate the relative abundance of AAB species in defined consortia. Further application of this method for the analysis of different vinegar and kombucha samples proves it suitable for assessing the relative abundance of AAB species when these bacteria represent a predominant part of a microbial community.

Long-read MinION™ sequencing of 16S and 16S-ITS-23S rRNA genes provides species-level resolution of Lactobacillaceae in mixed communities

The Lactobacillaceae are lactic acid bacteria harnessed to deliver important outcomes across numerous industries, and their unambiguous, species-level identification from mixed community environments is an important endeavor. Amplicon-based metataxonomics using short-read sequencing of partial 16S rRNA gene regions is widely used to support this, however, the high genetic similarity among Lactobacillaceae species restricts our ability to confidently describe these communities even at genus level. Long-read sequencing (LRS) of the whole 16S rRNA gene or the near complete rRNA operon (16S-ITS-23S) has the potential to improve this. We explored species ambiguity amongst Lactobacillaceae using in-silico tool RibDif2, which identified allele overlap when various partial and complete 16S rRNA gene and 16S-ITS-23S rRNA regions were amplified. We subsequently implemented LRS by MinION™ to compare the capacity of V3-V4, 16S and 16S-ITS-23S rRNA amplicons to accurately describe the diversity of a 20-species Lactobacillaceae mock community in practice. In-silico analysis identified more instances of allele/species overlap with V3-V4 amplicons (n = 43) compared to the 16S rRNA gene (n = 11) and partial (n = up to 15) or complete (n = 0) 16S-ITS-23S rRNA amplicons. With subsequent LRS of a DNA mock community, 80% of target species were identified using V3-V4 amplicons whilst the 16S rRNA gene and 16S-ITS-23S rRNA region amplicons resulted in 95 and 100% of target species being identified. A considerable reduction in false-positive identifications was also seen with 16S rRNA gene (n = 3) and 16S-ITS-23S rRNA region (n = 9) amplicons compared with V3-V4 amplicons (n = 43). Whilst the target species affected by allele overlap in V3-V4 and 16S rRNA gene sequenced mock communities were predicted by RibDif2, unpredicted species ambiguity was observed in 16S-ITS-23S rRNA sequenced communities. Considering the average nucleotide identity (ANI) between ambiguous species (~97%) and the basecall accuracy of our MinION™ sequencing protocol (96.4%), the misassignment of reads between closely related taxa is to be expected. With basecall accuracy exceeding 99% for recent MinION™ releases, the increased species-level differentiating power promised by longer amplicons like the 16S-ITS-23S rRNA region, may soon be fully realized.

The newest Oxford Nanopore R10.4.1 full-length 16S rRNA sequencing enables the accurate resolution of species-level microbial community profiling

The long-read amplicon provides a species-level solution for the community. With the improvement of nanopore flowcells, the accuracy of Oxford Nanopore Technologies (ONT) R10.4.1 has been substantially enhanced, with an average of approximately 99%. To evaluate its effectiveness on amplicons, three types of microbiomes were analyzed by 16S ribosomal RNA (hereinafter referred to as "16S") amplicon sequencing using Novaseq, Pacbio sequel II, and Nanopore PromethION platforms (R9.4.1 and R10.4.1) in the current study. We showed the error rate, recall, precision, and bias index in the mock sample. The error rate of ONT R10.4.1 was greatly reduced, with a better recall in the case of the synthetic community. Meanwhile, in different types of environmental samples, ONT R10.4.1 analysis resulted in a composition similar to Pacbio data. We found that classification tools and databases influence ONT data. Based on these results, we conclude that the ONT R10.4.1 16S amplicon can also be used for application in environmental samples. IMPORTANCE The long-read amplicon supplies the community with a species-level solution. Due to the high error rate of nanopore sequencing early on, it has not been frequently used in 16S studies. Oxford Nanopore Technologies (ONT) introduced the R10.4.1 flowcell with Q20+ reagent to achieve more than 99% accuracy as sequencing technology advanced. However, there has been no published study on the performance of commercial PromethION sequencers with R10.4.1 flowcells on 16S sequencing or on the impact of accuracy improvement on taxonomy (R9.4.1 to R10.4.1) using 16S ONT data. In this study, three types of microbiomes were investigated by 16S ribosomal RNA (rRNA) amplicon sequencing using Novaseq, Pacbio sequel II, and Nanopore PromethION platforms (R9.4.1 and R10.4.1). In the mock sample, we displayed the error rate, recall, precision, and bias index. We observed that the error rate in ONT R10.4.1 is significantly lower, especially when deletions are involved. First and foremost, R10.4.1 and Pacific Bioscience platforms reveal a similar microbiome in environmental samples. This study shows that the R10.4.1 full-length 16S rRNA sequences allow for species identification of environmental microbiota.

RibDif2: expanding amplicon analysis to full genomes

Motivation

As previously described, amplicon analysis of the bacterial 16S gene has several limitations owing to fundamental characteristics of both the 16S gene and technological restrictions. Previously, RibDif was introduced to help quantify these limitations by detailed analysis of a given genera and the 16S gene profile of its members, notably multiplicity and divergence of 16S alleles within genomes as well as shared alleles between species. Apart from using amplicon analysis for only the 16S gene, amplicons derived from genus-specific genes or even functional genes are increasingly being utilized. Moreover, long-read technologies are progressively being used to sequence longer amplicons, and since these inherently contain more information, they may likely alleviate the issues proposed in RibDif.

Results

Taking these phenomena into account, we here propose RibDif2. RibDif2 retains the 16S-optimized functionality of the original RibDif but can now run any set of primers on any part of the genome in any set of organisms, be it prokaryote, eukaryote, or archaea. We demonstrate this new functionality by showing full species resolution of Pseudoalteromonas using complete rRNA-operon amplicons, as well as selection of optimally discriminatory primers for Staphylococcus and Pseudomonas. Moreover, we show a potential bias toward terrestrial bacteria relative to marine ones for primers amplifying biosynthetic gene clusters and lastly suggest optimal primers to differentiate the members of the insect genus Drosophila. We believe that RibDif2 will facilitate the work of all scientists using amplicon sequencing, especially in the era of long-read sequencing.

Availability and implementation

Ribdif2 is freely available at https://github.com/Rob-murphys/ribdif.

RESCUE: a validated Nanopore pipeline to classify bacteria through long-read, 16S-ITS-23S rRNA sequencing

Despite the advent of third-generation sequencing technologies, modern bacterial ecology studies still use Illumina to sequence small (~400 bp) hypervariable regions of the 16S rRNA SSU for phylogenetic classification. By sequencing a larger region of the rRNA gene operons, the limitations and biases of sequencing small portions can be removed, allowing for more accurate classification with deeper taxonomic resolution. With Nanopore sequencing now providing raw simplex reads with quality scores above Q20 using the kit 12 chemistry, the ease, cost, and portability of Nanopore play a leading role in performing differential bacterial abundance analysis. Sequencing the near-entire rrn operon of bacteria and archaea enables the use of the universally conserved operon holding evolutionary polymorphisms for taxonomic resolution. Here, a reproducible and validated pipeline was developed, RRN-operon Enabled Species-level Classification Using EMU (RESCUE), to facilitate the sequencing of bacterial rrn operons and to support import into phyloseq. Benchmarking RESCUE showed that fully processed reads are now parallel or exceed the quality of Sanger, with median quality scores of approximately Q20+, using the R10.4 and Guppy SUP basecalling. The pipeline was validated through two complex mock samples, the use of multiple sample types, with actual Illumina data, and across four databases. RESCUE sequencing is shown to drastically improve classification to the species level for most taxa and resolves erroneous taxa caused by using short reads such as Illumina.

Morphological and molecular characterization of bacterial endophytes from Centella asiatica leaves

BACKGROUND

Endophytes are a rich source of novel, distinct, and applicable compounds of interest in agricultural, medical, cosmetic, and pharmaceutical industries. In this respect, they have been attracting growing interest in the past few years. Endophytes are defined as microorganisms such as bacteria and fungi which have a mutualistic relationship with their host plants without causing any harm to their host. In this study, we isolated and identified bacterial endophytes from Centella asiatica collected in Western Cape, South Africa.

RESULTS

Twenty bacterial endophytes were isolated from Centella asiatica and characterized by using morphological and molecular techniques. Based on molecular traits, the isolates were identified as Pseudomonas sp. strain SGM1, Pseudomonas sp. strain SGM2, Pseudomonas sp. strain SGM3, Pseudomonas sp. strain SGM4, Pseudomonas sp. strain SGM5, Pseudomonas sp. strain SGM6, Pseudomonas sp. strain SGM7, Novosphingobium sp. strain SGM8, Pseudomonas sp. strain SGM9, Pseudomonas sp. strain SGM10, Chryseobacterium sp. strain SGM11, Enterobacter sp. strain SGM12, Enterobacter sp. strain SGM13, Pseudomonas sp. strain SGM14, Enterobacter sp. strain SGM15, Enterobacter sp. strain SGM16, Agrobacterium sp. strain SGM17, Pantoea sp. strain SGM18, Paraburkholderia sp. strain SGM19, and Pseudomonas sp. strain SGM20. Pseudomonas genus was dominant with eleven isolates. Morphological trait results showed that all isolates were gram-negative rod-shaped bacteria.

CONCLUSION

According to our understanding, this study revealed the first twenty endophytic bacteria isolated from Centella asiatica growing in the Western Cape Province, South Africa. Data obtained in the current study will increase the knowledge of the already existing microbial diversity associated with Centella asiatica. Further work is needed to evaluate the antioxidant and antibacterial activities in vitro and assess the growth and medicinal compounds of the identified endophytic bacteria in a laboratory scale bioreactors.

The Application of Metagenomics to Study Microbial Communities and Develop Desirable Traits in Fermented Foods

The microbial communities present within fermented foods are diverse and dynamic, producing a variety of metabolites responsible for the fermentation processes, imparting characteristic organoleptic qualities and health-promoting traits, and maintaining microbiological safety of fermented foods. In this context, it is crucial to study these microbial communities to characterise fermented foods and the production processes involved. High Throughput Sequencing (HTS)-based methods such as metagenomics enable microbial community studies through amplicon and shotgun sequencing approaches. As the field constantly develops, sequencing technologies are becoming more accessible, affordable and accurate with a further shift from short read to long read sequencing being observed. Metagenomics is enjoying wide-spread application in fermented food studies and in recent years is also being employed in concert with synthetic biology techniques to help tackle problems with the large amounts of waste generated in the food sector. This review presents an introduction to current sequencing technologies and the benefits of their application in fermented foods.

The microbiota composition drives personalized nutrition: Gut microbes as predictive biomarkers for the success of weight loss diets

The investigation of the human gut microbiome during recent years has permitted us to understand its relevance for human health at a systemic level, making it possible to establish different functional axes (e.g., the gut-brain, gut-liver, and gut-lung axes), which support the organ-like status conferred to this microecological component of our body. The human gut microbiota is extremely variable but modifiable via diet, a fact that allows targeting of microbes through defined dietary strategies to uncover cost-effective therapies to minimize the burden of non-communicable diseases such as pandemic obesity and overweight and its metabolic comorbidities. Nevertheless, randomly controlled dietary interventions regularly exhibit low to moderate degrees of success in weight control, making their implementation difficult in clinical practice. Here, we review the predictive value of the baseline gut microbiota configurations to anticipate the success of dietary interventions aimed at weight loss, mostly based on caloric restriction regimes and oral fiber supplementation. This emergent research concept fits into precision medicine by considering different diet patterns and adopting the best one, based on the individual microbiota composition, to reach significant adiposity reduction and improve metabolic status. We review the results from this fresh perspective of investigation, taking into account studies released very recently. We also discuss some future outlooks in the field and potential pitfalls to overcome with the aim of gaining knowledge in the field and achieving breakthroughs in personalized nutrition.

Combination of Whole Genome Sequencing and Metagenomics for Microbiological Diagnostics

Whole genome sequencing (WGS) provides the highest resolution for genome-based species identification and can provide insight into the antimicrobial resistance and virulence potential of a single microbiological isolate during the diagnostic process. In contrast, metagenomic sequencing allows the analysis of DNA segments from multiple microorganisms within a community, either using an amplicon- or shotgun-based approach. However, WGS and shotgun metagenomic data are rarely combined, although such an approach may generate additive or synergistic information, critical for, e.g., patient management, infection control, and pathogen surveillance. To produce a combined workflow with actionable outputs, we need to understand the pre-to-post analytical process of both technologies. This will require specific databases storing interlinked sequencing and metadata, and also involves customized bioinformatic analytical pipelines. This review article will provide an overview of the critical steps and potential clinical application of combining WGS and metagenomics together for microbiological diagnosis.