Microbial characteristics of dental caries in HIV positive individuals

Background

Dental caries is a multifactorial disease that affects many people. Even though microorganisms play a crucial role in causing dental caries, diagnosis is routinely macroscopic. In order to improve early detection especially in HIV patients who are disproportionately affected, there is need to reconcile the macroscopic and microscopic characteristics of dental caries. Therefore, the aim of this study was to characterize the oral microbiota profile along the decayed, missing, filled teeth (DMFT) index using amplicon sequencing data.

Methods

Amplicon sequencing of the V6-V8 region of the 16S rRNA gene was done on DNA recovered from whole unstimulated saliva of 59 HIV positive and 29 HIV negative individuals. The microbial structure, composition and co-occurrence networks were characterized using QIIME-2, Phyloseq, Microbiome-1.9.2 and Metacoder in R.

Results

We characterized the oral microbiota into 2,093 operational taxonomic units (OTUs), 21 phyla and 239 genera from 2.6 million high quality sequence reads. While oral microbiota did not cluster participants into distinct groups that track with the DMFT index, we observed the following: (a) The proportion of accessory microbiota was highest in the high DMFT category while the core size (∼50% of richness) remained relatively stable across all categories. (b) The abundance of core genera such as Stomatobaculum, Peptostreptococcus and Campylobacter was high at onset of dental caries, (c) A general difference in oral microbial biomass. (d) The onset of dental caries (low DMFT) was associated with significantly lower oral microbial entropy.

Conclusions

Although oral microbial shifts along the DMFT index were not distinct, we demonstrated the potential utility of microbiota dynamics to characterize oral disease. Therefore, we propose a microbial framework using the DMFT index to better understand dental caries among HIV positive people in resource limited settings.

Fusarium oxysporum Disrupts Microbiome-Metabolome Networks in Arabidopsis thaliana Roots

While the plant host metabolome drives distinct enrichment of detrimental and beneficial members of the microbiome, the mechanistic interomics relationships remain poorly understood. Here, we studied microbiome and metabolome profiles of two Arabidopsis thaliana accessions after Fusarium oxysporum f.sp. mathioli (FOM) inoculation, Landsberg erecta (Ler-0) being susceptible and Col-0 being resistant against FOM. By using bacterial and fungal amplicon sequencing and targeted metabolite analysis, we observed highly dynamic microbiome and metabolome profiles across FOM host progression, while being markedly different between FOM-inoculated and noninoculated Col-0 and Ler-0. Co-occurrence network analysis revealed more robust microbial networks in the resistant Col-0 compared to Ler-0 during FOM infection. Correlation analysis revealed distinct metabolite-OTU correlations in Ler-0 compared with Col-0 which could possibly be explained by missense variants of the Rfo3 and Rlp2 genes in Ler-0. Remarkably, we observed positive correlations in Ler-0 between most of the analyzed metabolites and the bacterial phyla Proteobacteria, Bacteroidetes, Planctomycetes, Acidobacteria, and Verrucomicrobia, and negative correlations with Actinobacteria, Firmicutes, and Chloroflexi. The glucosinolates 4-methyoxyglucobrassicin, glucoerucin and indole-3 carbinol, but also phenolic compounds were strongly correlating with the relative abundances of indicator and hub OTUs and thus could be active in structuring the A. thaliana root-associated microbiome. Our results highlight interactive effects of host plant defense and root-associated microbiota on Fusarium infection and progression. Our findings provide significant insights into plant interomic dynamics during pathogen invasion and could possibly facilitate future exploitation of microbiomes for plant disease control. IMPORTANCE Plant health and fitness are determined by plant-microbe interactions which are guided by host-synthesized metabolites. To understand the orchestration of this interaction, we analyzed the distinct interomic dynamics in resistant and susceptible Arabidopsis ecotypes across different time points after infection with Fusarium oxysporum (FOM). Our results revealed distinct microbial profiles and network resilience during FOM infection in the resistant Col-0 compared with the susceptible Ler-0 and further pinpointed specific microbe-metabolite associations in the Arabidopsis microbiome. These findings provide significant insights into plant interomics dynamics that are likely affecting fungal pathogen invasion and could possibly facilitate future exploitation of microbiomes for plant disease control.

Namco: a microbiome explorer

16S rRNA gene profiling is currently the most widely used technique in microbiome research and allows the study of microbial diversity, taxonomic profiling, phylogenetics, functional and network analysis. While a plethora of tools have been developed for the analysis of 16S rRNA gene data, only a few platforms offer a user-friendly interface and none comprehensively covers the whole analysis pipeline from raw data processing down to complex analysis. We introduce Namco, an R shiny application that offers a streamlined interface and serves as a one-stop solution for microbiome analysis. We demonstrate Namco's capabilities by studying the association between a rich fibre diet and the gut microbiota composition. Namco helped to prove the hypothesis that butyrate-producing bacteria are prompted by fibre-enriched intervention. Namco provides a broad range of features from raw data processing and basic statistics down to machine learning and network analysis, thus covering complex data analysis tasks that are not comprehensively covered elsewhere. Namco is freely available at https://exbio.wzw.tum.de/namco/.

Hierarchical Microbial Functions Prediction by Graph Aggregated Embedding

Matching 16S rRNA gene sequencing data to a metabolic reference database is a meaningful way to predict the metabolic function of bacteria and archaea, bringing greater insight to the working of the microbial community. However, some operational taxonomy units (OTUs) cannot be functionally profiled, especially for microbial communities from non-human samples cultured in defective media. Therefore, we herein report the development of Hierarchical micrObial functions Prediction by graph aggregated Embedding (HOPE), which utilizes co-occurring patterns and nucleotide sequences to predict microbial functions. HOPE integrates topological structures of microbial co-occurrence networks with k-mer compositions of OTU sequences and embeds them into a lower-dimensional continuous latent space, while maximally preserving topological relationships among OTUs. The high imbalance among KEGG Orthology (KO) functions of microbes is recognized in our framework that usually yields poor performance. A hierarchical multitask learning module is used in HOPE to alleviate the challenge brought by the long-tailed distribution among classes. To test the performance of HOPE, we compare it with HOPE-one, HOPE-seq, and GraphSAGE, respectively, in three microbial metagenomic 16s rRNA sequencing datasets, including abalone gut, human gut, and gut of Penaeus monodon. Experiments demonstrate that HOPE outperforms baselines on almost all indexes in all experiments. Furthermore, HOPE reveals significant generalization ability. HOPE's basic idea is suitable for other related scenarios, such as the prediction of gene function based on gene co-expression networks. The source code of HOPE is freely available at https://github.com/adrift00/HOPE.

Higher Sensitivity of Microbial Network Than Community Structure under Acid Rain

Acid rain (AR), as a global environmental threat, has profoundly adverse effects on natural soil ecosystems. Microorganisms involved in the nitrogen (N) cycle regulate the global N balance and climate stabilization, but little is known whether and how AR influences the structure and complexity of these microbial communities. Herein, we conducted an intact soil core experiment by manipulating the acidity of simulated rain (pH 7.5 (control, CK) vs. pH 4.0 (AR)) in subtropical agricultural soil, to reveal the differences in the structure and complexity of soil nitrifying and denitrifying microbiota using Illumina amplicon sequencing of functional genes (amoA, nirS, and nosZ). Networks of ammonia-oxidizing archaea (AOA) and nirS-carrying denitrifiers in AR treatment were less complex with fewer nodes and lower connectivity, while network of nosZ-carrying denitrifiers in AR treatment had higher complexity and connectivity relative to CK. Supporting this, AR reduced the abundance of keystone taxa in networks of AOA and nirS-carrying denitrifiers, but increased the abundance of keystone taxa in nosZ-carrying denitrifiers network. However, AR did not alter the community structure of AOA, ammonia-oxidizing bacteria (AOB), nirS-, and nosZ-carrying denitrifiers. Moreover, AR did not change soil N₂O emissions during the experimental period. AOB community structure significantly correlated with content of soil available phosphorus (P), while the community structures of nirS- and nosZ-carrying denitrifiers both correlated with soil pH and available P content. Soil N₂O emission was mainly driven by the nirS-carrying denitrifiers. Our results present new perspective on the impacts of AR on soil N-cycle microbial network complexity and keystone taxa in the context of global changes.