16S-ITGDB: An Integrated Database for Improving Species Classification of Prokaryotic 16S Ribosomal RNA Sequences

Fire-associated microbial shifts in soils of western conifer forests with Armillaria root disease

Fires in coniferous forests throughout the northern United States alter ecosystem processes and ecological communities, including the diversity and composition of microbial communities living in the soil. In addition to its influence on ecosystem processes and functions, the soil microbiome can interact with soilborne pathogens to facilitate or suppress plant disease development. Altering the microbiome composition to promote taxa that inhibit pathogenic activity has been suggested as a management strategy for forest diseases, including Armillaria root disease caused by Armillaria solidipes, which causes growth loss and mortality of conifers. These forest ecosystems are experiencing increased wildfire burn severity that could influence A. solidipes activity and interactions of the soil microbiome with Armillaria root disease. In this research, we examine changes to the soil microbiome following three levels of burn severity in a coniferous forest in northern Idaho, United States, where Armillaria root disease is prevalent. We further determine how these changes correspond to the soil microbiomes associated with the pathogen A. solidipes, and a putatively beneficial species, A. altimontana. At 15-months post-fire, we found significant differences in richness and diversity between bacterial communities associated with unburned and burned areas, yet no significant changes to these metrics were found in fungal communities following fire. However, both bacterial and fungal communities showed compositional changes associated with burn severity, including microbial taxa with altered relative abundance. Further, significant differences in the relative abundance of certain microbial taxa in communities associated with the three burn severity levels overlapped with taxa associated with various Armillaria spp. Following severe burn, we observed a decreased relative abundance of beneficial ectomycorrhizal fungi associated with the microbial communities of A. altimontana, which may contribute to the antagonistic activity of this soil microbial community. Additionally, A. solidipes and associated microbial taxa were found to dominate following high-severity burns, suggesting that severe fires provide suitable environmental conditions for these species. Overall, our results suggest that shifts in the soil microbiome and an associated increase in the activity of A. solidipes following high-severity burns in similar conifer forests may result in priority areas for monitoring and proactive management of Armillaria root disease.

IMPORTANCE

With its influence on ecosystem processes and functions, the soil microbiome can interact with soilborne pathogens to facilitate or suppress plant disease development. These forest ecosystems are experiencing increased wildfire frequency and burn severity that could influence the fungal root pathogen, Armillaria solidipes, and interactions with the soil microbiome. We examined changes to the soil microbiome following three levels of burn severity, and examined how these changes correspond with A. solidipes, and a putatively beneficial species, A. altimontana. Following severe burn, there was a decreased relative abundance of ectomycorrhizal fungi associated A. altimontana. A. solidipes and associated microbial taxa dominated following high-severity burns, suggesting that severe fires provide suitable environmental conditions for these species. Our results suggest that shifts in the soil microbiome and an associated increase in the activity of A. solidipes following high-severity burns in conifer forests may result in priority areas for monitoring and proactive management of Armillaria root disease.

SpecGMM: Integrating Spectral analysis and Gaussian Mixture Models for taxonomic classification and identification of discriminative DNA regions

Motivation

Genomic signal processing (GSP), which transforms biomolecular sequences into discrete signals for spectral analysis, has provided valuable insights into DNA sequence, structure, and evolution. However, challenges persist with spectral representations of variable-length sequences for tasks like species classification and in interpreting these spectra to identify discriminative DNA regions.

Results

We introduce SpecGMM, a novel framework that integrates sliding window-based Spectral analysis with a Gaussian Mixture Model to transform variable-length DNA sequences into fixed-dimensional spectral representations for taxonomic classification. SpecGMM's hyperparameters were selected using a dataset of plant sequences, and applied unchanged across diverse datasets, including mitochondrial DNA, viral and bacterial genome, and 16S rRNA sequences. Across these datasets, SpecGMM outperformed a baseline method, with 9.45% average and 35.55% maximum improvement in test accuracies for a Linear Discriminant classifier. Regarding interpretability, SpecGMM revealed discriminative hypervariable regions in 16S rRNA sequences-particularly V3/V4 for discriminating higher taxa and V2/V3 for lower taxa-corroborating their known classification relevance. SpecGMM's spectrogram video analysis helped visualize species-specific DNA signatures. SpecGMM thus provides a robust and interpretable method for spectral DNA analysis, opening new avenues in GSP research.

Availability and implementation

SpecGMM's source code is available at https://github.com/BIRDSgroup/SpecGMM.

Revisiting Socransky's Complexes: A Review Suggesting Updated New Bacterial Clusters (GF-MoR Complexes) for Periodontal and Peri-Implant Diseases and Conditions

This review aimed to identify newly discovered bacteria from individuals with periodontal/peri-implant diseases and organize them into new clusters (GF-MoR complexes) to update Socransky's complexes (1998). For methodological development, the PCC (Population, Concept, Context) strategy was used for the focus question construction: "In patients with periodontal and/or peri-implant disease, what bacteria (microorganisms) were detected through laboratory assays?" The search strategy was applied to PubMed/MEDLINE, PubMed Central, and Embase. The search key terms, combined with Boolean markers, were (1) bacteria, (2) microbiome, (3) microorganisms, (4) biofilm, (5) niche, (6) native bacteria, (7) gingivitis), (8) periodontitis, (9) peri-implant mucositis, and (10) peri-implantitis. The search was restricted to the period 1998-2024 and the English language. The bacteria groups in the oral cavity obtained/found were retrieved and included in the GF-MoR complexes, which were based on the disease/condition, presenting six groups: (1) health, (2) gingivitis, (3) peri-implant mucositis, (4) periodontitis, (5) peri-implantitis, and (6) necrotizing and molar-incisor (M-O) pattern periodontitis. The percentual found per group refers to the number of times a specific bacterium was found to be associated with a particular disease. A total of 381 articles were found: 162 articles were eligible for full-text reading (k = 0.92). Of these articles, nine were excluded with justification, and 153 were included in this review (k = 0.98). Most of the studies reported results for the health condition, periodontitis, and peri-implantitis (3 out of 6 GF-MoR clusters), limiting the number of bacteria found in the other groups. Therefore, it became essential to understand that bacterial colonization is a dynamic process, and the bacteria present in one group could also be present in others, such as those observed with the bacteria found in all groups (Porphyromonas gingivalis, Tannarela forsythia, Treponema denticola, and Aggregatibacter actinomycetemcomitans) (GF-MoR's red triangle). The second most observed bacteria were grouped in GF-MoR's blue triangle: Porphyromonas spp., Prevotela spp., and Treponema spp., which were present in five of the six groups. The third most detected bacteria were clustered in the grey polygon (GF-MoR's grey polygon): Fusobacterium nucleatum, Prevotella intermedia, Campylobacter rectus, and Eikenella corrodens. These three geometric shapes had the most relevant bacteria to periodontal and peri-implant diseases. Specifically, per group, GF-MoR's health group had 58 species; GF-MoR's gingivitis group presented 16 bacteria; GF-MoR's peri-implant mucositis included 17 bacteria; GF-MoR's periodontitis group had 101 different bacteria; GF-MoR's peri-implantitis presented 61 bacteria; and the last group was a combination of necrotizing diseases and molar-incisor (M-I) pattern periodontitis, with seven bacteria. After observing the top seven bacteria of all groups, all of them were found to be gram-negative. Groups 4 and 5 (periodontitis and peri-implantitis) presented the same top seven bacteria. For the first time in the literature, GF-MoR's complexes were presented, gathering bacteria data according to the condition found and including more bacteria than in Socransky's complexes. Based on this understanding, this study could drive future research into treatment options for periodontal and peri-implant diseases, guiding future studies and collaborations to prevent and worsen systemic conditions. Moreover, it permits the debate about the evolution of bacterial clusters.

Microbial dynamics and vertical transmission of Escherichia coli across consecutive life stages of the black soldier fly (Hermetia illucens)

BACKGROUND

The black soldier fly (BSF, Hermetia illucens L.) is one of the most promising insects for bioconversion of organic waste, which often carry a high microbial load with potential foodborne pathogens. Although horizontal transmission (from rearing substrate to larvae) has been extensively studied, less is known about vertical transmission of microorganisms, and particularly of foodborne pathogens, across different BSF life stages.

RESULTS

This study investigated the microbial dynamics and vertical transmission of Escherichia coli across different life stages (larvae, prepupae, pupae and adults) of one BSF life cycle and its associated substrate (chicken feed) and frass, based on a combination of general microbial counts (based on culture-dependent techniques) and the bacterial community composition (based on 16S rRNA gene sequencing). Multiple interactions between the microbiota of the substrate, frass and BSF larvae were affirmed. The larvae showed relative consistency among both the microbial counts and bacterial community composition. Diversification of the bacterial communities started during the pupal stage, while most notable changes of the microbial counts and bacterial community compositions occurred during metamorphosis to adults. Furthermore, vertical transmission of E. coli was investigated after substrate inoculation with approximately 7.0 log cfu/g of kanamycin-resistant E. coli, and monitoring E. coli counts from larval to adult stage. Although the frass still contained substantial levels of E. coli (> 4.5 log cfu/g) and E. coli was taken up by the larvae, limited vertical transmission of E. coli was observed with a decreasing trend until the prepupal stage. E. coli counts were below the detection limit (1.0 log cfu/g) for all BSF samples from the end of the pupal stage and the adult stage. Additionally, substrate inoculation of E. coli did not have a substantial impact on the bacterial community composition of the substrate, frass or different BSF life stages.

CONCLUSIONS

The fluctuating microbial counts and bacterial community composition underscored the dynamic character of the microbiota of BSF life stages. Additionally, vertical transmission throughout one BSF life cycle was not observed for E. coli. Hence, these findings paved the way for future case studies on vertical transmission of foodborne pathogens across consecutive BSF life stages or other insect species.

GSR-DB: a manually curated and optimized taxonomical database for 16S rRNA amplicon analysis

Amplicon-based 16S ribosomal RNA sequencing remains a widely used method to profile microbial communities, especially in low biomass samples, due to its cost-effectiveness and low-complexity approach. Reference databases are a mainstay for taxonomic assignments, which typically rely on popular databases such as SILVA, Greengenes, Genome Taxonomy Database (GTDB), or Ribosomal Database Project (RDP). However, the inconsistency of the nomenclature across databases and the presence of shortcomings in the annotation of these databases are limiting the resolution of the analysis. To overcome these limitations, we created the GSR database (Greengenes, SILVA, and RDP database), an integrated and manually curated database for bacterial and archaeal 16S amplicon taxonomy analysis. Unlike previous integration approaches, this database creation pipeline includes a taxonomy unification step to ensure consistency in taxonomical annotations. The database was validated with three mock communities, two real data sets, and a 10-fold cross-validation method and compared with existing 16S databases such as Greengenes, Greengenes 2, GTDB, ITGDB, SILVA, RDP, and MetaSquare. Results showed that the GSR database enhances taxonomical annotations of 16S sequences, outperforming current 16S databases at the species level, based on the evaluation of the mock communities. This was confirmed by the 10-fold cross-validation, except for Greengenes 2. The GSR database is available for full-length 16S sequences and the most commonly used hypervariable regions: V4, V1-V3, V3-V4, and V3-V5.IMPORTANCETaxonomic assignments of microorganisms have long been hindered by inconsistent nomenclature and annotation issues in existing databases like SILVA, Greengenes, Greengenes2, Genome Taxonomy Database, or Ribosomal Database Project. To overcome these issues, we created Greengenes-SILVA-RDP database (GSR-DB), accurate and comprehensive taxonomic annotations of 16S amplicon data. Unlike previous approaches, our innovative pipeline includes a unique taxonomy unification step, ensuring consistent and reliable annotations. Our evaluation analyses showed that GSR-DB outperforms existing databases in providing species-level resolution, especially based on mock-community analysis, making it a game-changer for microbiome studies. Moreover, GSR-DB is designed to be accessible to researchers with limited computational resources, making it a powerful tool for scientists across the board. Available for full-length 16S sequences and commonly used hypervariable regions, including V4, V1-V3, V3-V4, and V3-V5, GSR-DB is a go-to database for robust and accurate microbial taxonomy analysis.

The impact of gut microbiome enterotypes on ulcerative colitis: identifying key bacterial species and revealing species co-occurrence networks using machine learning

Ulcerative colitis (UC) is a chronic inflammatory intestinal disease affecting the colon and rectum, with its pathogenesis attributed to genetic background, environmental factors, and gut microbes. This study aimed to investigate the role of enterotypes in UC by conducting a hierarchical analysis, determining differential bacteria using machine learning, and performing Species Co-occurrence Network (SCN) analysis. Fecal bacterial data were collected from UC patients, and a 16S rRNA metagenomic analysis was performed using the QIIME2 bioinformatics pipeline. Enterotype clustering was conducted at the family level, and deep neural network (DNN) classification models were trained for UC and healthy controls (HC) in each enterotype. Results from eleven 16S rRNA gut microbiome datasets revealed three enterotypes: Bacteroidaceae (ET-B), Lachnospiraceae (ET-L), and Clostridiaceae (ET-C). Ruminococcus (R. gnavus) abundance was significantly higher in UC subjects with ET-B and ET-C than in those with ET-L. R. gnavus also showed a positive correlation with Clostridia in UC SCN for ET-B and ET-C subjects, with a higher correlation in ET-C subjects. Conversely, Odoribacter (O.) splanchnicus and Bacteroides (B.) uniformis exhibited a positive correlation with tryptophan metabolism and AMP-activated protein kinase (AMPK) signaling pathways, while R. gnavus showed a negative correlation. In vitro co-culture experiments with Clostridium (C.) difficile demonstrated that fecal microbiota from ET-B subjects had a higher abundance of C. difficile than ET-L subjects. In conclusion, the ET-B enterotype predisposes individuals to UC, with R. gnavus as a potential risk factor and O. splanchnicus and B. uniformis as protective bacteria, and those with UC may have ultimately become ET-C.

Amplicon-Based Microbiome Profiling: From Second- to Third-Generation Sequencing for Higher Taxonomic Resolution

The 16S rRNA amplicon-based sequencing approach represents the most common and cost-effective strategy with great potential for microbiome profiling. The use of second-generation sequencing (NGS) technologies has led to protocols based on the amplification of one or a few hypervariable regions, impacting the outcome of the analysis. Nowadays, comparative studies are necessary to assess different amplicon-based approaches, including the full-locus sequencing currently feasible thanks to third-generation sequencing (TGS) technologies. This study compared three different methods to achieve the deepest microbiome taxonomic characterization: (a) the single-region approach, (b) the multiplex approach, covering several regions of the target gene/region, both based on NGS short reads, and (c) the full-length approach, which analyzes the whole length of the target gene thanks to TGS long reads. Analyses carried out on benchmark microbiome samples, with a known taxonomic composition, highlighted a different classification performance, strongly associated with the type of hypervariable regions and the coverage of the target gene. Indeed, the full-length approach showed the greatest discriminating power, up to species level, also on complex real samples. This study supports the transition from NGS to TGS for the study of the microbiome, even if experimental and bioinformatic improvements are still necessary.