SILVA, RDP, Greengenes, NCBI and OTT - how do these taxonomies compare?

Understanding the implicit effects of 16S rRNA gene databases on microbial bioindicator studies

Analysis of the presence and the abundance of microorganisms related to diseases can be used to monitor marine environmental health. Our study evaluated the interference of taxonomic databases (SILVA, Greengenes v13.8, Greengenes2, and RDP) to monitor the distribution of bacterial genera potentially related to diseases in marine organisms (BGPRDs) from low- (Dois Rios Beach-DR), medium- (Abraão Beach-AB) and high (Guanabara Bay-GB) impacted marine environments. The frequency, richness, diversity, and composition of BGPRDs present in DR, AB and GB were significantly influenced by the different databases (p < 0.05). Consequently, the analyses revealed that the use of different databases resulted in controversial results regarding the distribution of BGPRDs in the DR, AB and GB. While Greengenes v13.8 and RDP showed that GB had the highest frequency of BGPRDs (p < 0.05), analysis based on Greengenes2 and SILVA revealed a greater frequency of BGPRDs in AB (p < 0.05). Additionally, there was no congruence of BGPRDs detected by each taxonomic database in DR, AB and GB. In highly-impacted GB, Arcobacter was the main BGPRD obtained with the Greengenes2 and RDP databases, whereas Synechococcus and Alteromonas represented the main BGPRD according to the Greengenes v13.8 and SILVA databases, respectively. Our results showed we cannot determine the exact composition and abundance of BGPRDs in low-, medium- and highly-impacted marine environments. These findings emphasize the critical influence of database choice on microbial community characterization and its implications for effective environmental monitoring and management strategies. Interestingly, alpha diversity indices of BGPRDs obtained from DR, AB and GB were consistent among the different databases and showed greater congruence than did the frequency, richness, distribution and abundance of BGPRDs. The use of diversity indices of BGPRDs can be an alternative to overcome the limitations caused by the bias of taxonomic annotations for biomonitoring marine environments.

Association between the relative abundance of butyrate-producing and mucin-degrading taxa and Parkinson's disease

Parkinson's disease (PD) is a neurodegenerative disorder characterised by motor and non-motor symptoms. Recent evidence suggests a role for gut microbiome composition and diversity in PD aetiology. This study aimed to explore the association between the gut microbiome and PD in a South African population. Gut microbial sequencing data (cases: n = 16; controls: n = 42) was generated using a 16S rRNA gene (V4) primer pair. Alpha- and beta-diversity were calculated using QIIME2, and differential abundance of taxa was evaluated using Analysis of Compositions of Microbiomes with Bias Correction (ANCOM-BC). Beta-diversity was found to differ significantly between cases and controls, with depletion in the relative abundance of Faecalibacterium, Roseburia, Dorea, and Veillonella, and enrichment of the relative abundance of Akkermansia and Victivallis. Our study found a reduction in butyrate-producing bacteria (e.g. Faecalibacterium and Roseburia) and an increase in mucin-degrading bacteria (Akkermansia) in PD cases compared to controls. These alterations might be associated with heightened gut permeability and inflammation. Longitudinal studies should address the question of whether these microbiome differences are a risk factor for, or are consequent to, the development of PD.

Do we need a standardized 16S rRNA gene amplicon sequencing analysis protocol for poultry microbiota research?

Bacteria are the major component of poultry gastrointestinal tract (GIT) microbiota and play an important role in host health, nutrition, physiology regulation, intestinal development, and growth. Bacterial community profiling based on the 16S ribosomal RNA (rRNA) gene amplicon sequencing approach has become the most popular method to determine the taxonomic composition and diversity of the poultry microbiota. The 16S rRNA gene profiling involves numerous steps, including sample collection and storage, DNA isolation, 16S rRNA gene primer selection, Polymerase Chain Reaction (PCR), library preparation, sequencing, raw sequencing reads processing, taxonomic classification, α- and β-diversity calculations, and statistical analysis. However, there is currently no standardized protocol for 16S rRNA gene analysis profiling and data deposition for poultry microbiota studies. Variations in DNA storage and isolation, primer design, and library preparation are known to introduce biases, affecting community structure and microbial population analysis leading to over- or under-representation of individual bacteria within communities. Additionally, different sequencing platforms, bioinformatics pipeline, and taxonomic database selection can affect classification and determination of the microbial taxa. Moreover, detailed experimental design and DNA processing and sequencing methods are often inadequately reported in poultry 16S rRNA gene sequencing studies. Consequently, poultry microbiota results are often difficult to reproduce and compare across studies. This manuscript reviews current practices in profiling poultry microbiota using 16S rRNA gene amplicon sequencing and proposes the development of guidelines for protocol for 16S rRNA gene sequencing that spans from sample collection through data deposition to achieve more reliable data comparisons across studies and allow for comparisons and/or interpretations of poultry studies conducted worldwide.

Complex system modeling reveals oxalate homeostasis is driven by diverse oxalate-degrading bacteria

Decades of research have made clear that host-associated microbiomes touch all facets of health. However, effective therapies that target the microbiome have been elusive given its inherent complexity. Here, we experimentally examined diet-microbe-host interactions through a complex systems framework, centered on dietary oxalate. Using multiple, independent molecular, rodent, and in vitro experimental models, we found that microbiome composition influenced multiple oxalate-microbe-host interfaces. Importantly, the administration of the oxalate-degrading specialist, Oxalobacter formigenes, was only effective against a poor oxalate-degrading microbiota background and gives critical new insights into why clinical intervention trials with this species exhibit variable outcomes. Data suggest that, while heterogeneity in the microbiome impacts multiple diet-host-microbe interfaces, metabolic redundancy among diverse microorganisms in specific diet-microbe axes is a critical variable that may impact the efficacy of bacteriotherapies, which can help guide patient and probiotic selection criteria in probiotic clinical trials.

Trace Element Exposure during Pregnancy Has a Persistent Influence on Perinatal Gut Microbiota in Mother-Infant Dyads

Trace elements have been recognized as the modifiers of the gut microbiota. However, population-based evidence about their effects on maternal gut microbiota dynamics, as well as the intergenerational impacts on neonatal gut microbiota, has been lacking. We examined the longitudinal microbiota data from mother-infant dyads and demonstrated that maternal trace element exposure played a pivotal role in shaping the composition and similarity of the mother-infant gut microbiota. Specifically, serum levels of cobalt (Co), molybdenum (Mo), and rubidium (Rb) were identified to cause further fluctuation in the shift of the maternal gut microbiota. Antibiotic usage shortly before or on the delivery day, as well as maternal zinc (Zn) exposure, affected the gut microbiota similarity within mother-infant dyads. Rb demonstrated an intergenerational effect on meconium Bifidobacterium abundance by altering its abundance in the maternal gut. Notably, this effect was strengthened in the vaginal delivery group without antibiotic usage, while it was attenuated in the c-section delivery group. Our results suggest that maternal trace element exposure has a persistent influence on perinatal gut microbiota, which offers novel insights into promoting mother and infant health.

Temporal changes in cecal luminal and mucosal microbiota of broiler chickens with clinical coccidiosis (Eimeria maxima)

Coccidiosis is a gastrointestinal disease caused by Eimeria parasites which leads to major economic losses in the poultry industry worldwide. Eimeria infection may alter the gut microbiota, which has been associated with chicken health and performance. This study aimed to determine the effects of Eimeria maxima infection on the luminal and mucosal microbiota of the cecum (CeL and CeM) at multiple time points post-infection (days 3, 5, 7, 10, and 14). Infection decreased Shannon diversity at d 3 (P = 0.03), increased observed features (ASVs) at d 5 (P < 0.01), and increased Shannon diversity at d 10 (P = 0.04) in the CeL microbiota compared to the control. In CeM microbiota, infection increased observed features at d 5 (P = 0.03), but later decreased observed features at d 14 (P = 0.01). Relative abundance of potential butyrate-producing bacteria such as [Ruminococcus] torques group in CeL and Butyricicoccus in CeM were decreased in infected birds, and some metabolic pathways related to butyrate production were predicted to be decreased. These findings show E. maxima may affect cecal microbiota alpha diversity in a time-dependent manner and reduce abundance of bacteria potentially important to gut health.

Spatiotemporal Characteristics of Bacterial Communities in Estuarine Mangrove Sediments in Zhejiang Province, China

Mangrove forests are intertidal ecosystems that harbor diverse microbial communities essential for biogeochemical cycles and energy flow. This study investigated the seasonal and spatial patterns of bacterial communities in the artificially introduced mangrove sediments of the Ao River estuary using 16S rRNA gene amplicon high-throughput sequencing. Alpha diversity analyses indicated that the bacterial community diversity in the mangrove sediments of the Ao River estuary was similar to those of natural-formed mangroves, with the Shannon index ranging from 5.16 to 6.54, which was significantly higher in winter compared to other seasons. The dominant bacterial phyla included Proteobacteria (43.65%), Actinobacteria (11.55%), Desulfobacterota (11.16%), and Bacteroidetes (5.52%), while beta diversity analysis revealed substantial differences in bacterial community structure across different seasons and regions. For instance, the relative abundance of Woeseiaceae and Bacteroidota during the summer was significantly higher than that observed in other seasons. And the relative abundance of Bacillaceae in autumn and winter increased by one order of magnitude compared to spring and summer. Woeseiaceae, Desulfobulbaceae, Thermoanaerobaculaceae, and Sva1033 (family of Desulfobacterota) exhibited significantly higher relative abundance in the unvegetated area, whereas Bacillaceae and S085 (family of Chloroflexi) demonstrated greater abundance in the mangrove area. Seasonal variations in bacterial community structure are primarily attributed to changes in environmental factors, including temperature and salinity. Regional differences in bacterial community structure are primarily associated with environmental stressors, such as wave action, fluctuations in salinity, and organic matter content, which are further complicated by seasonal changes. This study is significant for understanding the microbial diversity and seasonal dynamics of estuarine mangrove wetlands, and it contributes to the assessment of mangrove wetland restoration efforts in Zhejiang Province, providing important guidance for the development of strategies to maintain the health of mangrove ecosystems in the future.

Protocol for identifying Mycobacterium tuberculosis infection status through airway microbiome profiling

This protocol describes the steps to determine an airway microbiome signature for identifying Mycobacterium tuberculosis infection status. We outline procedures for processing microbiome data, calculating diversity measures, and fitting Dirichlet multinomial mixture models. Additionally, we provide steps for analyzing taxonomic relative and differential abundances, as well as identifying potential biomarkers associated with infection status. For complete details on the use and execution of this protocol, please refer to Kayongo et al.1.

Brain age mediates gut microbiome dysbiosis-related cognition in older adults

BACKGROUND

Recent studies have focused on improving our understanding of gut microbiome dysbiosis and its impact on cognitive function. However, the relationship between gut microbiome composition, accelerated brain atrophy, and cognitive function has not yet been fully explored.

METHODS

We recruited 292 participants from South Korean memory clinics to undergo brain magnetic resonance imaging, clinical assessments, and collected stool samples. We employed a pretrained brain age model- a measure associated with neurodegeneration. Using cluster analysis, we categorized individuals based on their microbiome profiles and examined the correlations with brain age, Mental State Examination (MMSE) scores, and the Clinical Dementia Rating Sum of Box (CDR-SB).

RESULTS

Two clusters were identified in the microbiota at the phylum level that showed significant differences on a few microbiotas phylum. Greater gut microbiome dysbiosis was associated with worse cognitive function including MMSE and CDR-SB; this effect was partially mediated by greater brain age even when accounting for chronological age, sex, and education.

CONCLUSIONS

Our findings indicate that brain age mediates the link between gut microbiome dysbiosis and cognitive performance. These insights suggest potential interventions targeting the gut microbiome to alleviate age-related cognitive decline.

Complex system modelling reveals oxalate homeostasis is driven by diverse oxalate-degrading bacteria

Decades of research have made clear that host-associated microbiomes touch all facets of health. However, effective therapies that target the microbiome have been elusive given its inherent complexity. Here, we experimentally examined diet-microbe-host interactions through a complex systems framework, centered on dietary oxalate. Using multiple, independent molecular, animal, and in vitro experimental models, we found that microbiome composition influenced multiple oxalate-microbe-host interfaces. Importantly, administration of the oxalate-degrading specialist, Oxalobacter formigenes, was only effective against a poor oxalate-degrading microbiota background and gives critical new insights into why clinical intervention trials with this species exhibit variable outcomes. Data suggest that, while heterogeneity in the microbiome impacts multiple diet-host-microbe interfaces, metabolic redundancy among diverse microorganisms in specific diet-microbe axes is a critical variable that may impact the efficacy of bacteriotherapies, which can help guide patient and probiotic selection criteria in probiotic clinical trials.

Perturbations in gut microbiota composition in patients with autoimmune neurological diseases: a systematic review and meta-analysis

Studies suggest that gut dysbiosis occurs in autoimmune neurological diseases, but a comprehensive synthesis of the evidence is lacking. Our aim was to systematically review and meta-analyze the correlation between the gut microbiota and autoimmune neurological disorders to inform clinical diagnosis and therapeutic intervention. We searched the databases of PubMed, Embase, Web of Science, and the Cochrane Library until 1 March 2024 for research on the correlation between gut microbiota and autoimmune neurological disorders. A total of 62 studies provided data and were included in the analysis (n = 3,126 patients, n = 2,843 healthy individuals). Among the included studies, 42 studies provided data on α-diversity. Regarding α-diversity, except for Chao1, which showed a consistent small decrease (SMD = -0.26, 95% CI = -0.45 to -0.07, p < 0.01), other indices demonstrated no significant changes. While most studies reported significant differences in β-diversity, consistent differences were only observed in neuromyelitis optica spectrum disorders. A decrease in short-chain fatty acid (SCFA)-producing bacteria, including Faecalibacterium and Roseburia, was observed in individuals with autoimmune encephalitis, neuromyelitis optica spectrum disorders, myasthenia gravis, and multiple sclerosis. Conversely, an increase in pathogenic or opportunistic pathogens, including Streptococcus and Escherichia-Shigella, was observed in these patients. Subgroup analyses assessed the confounding effects of geography and immunotherapy use. These findings suggest that disturbances of the gut flora are associated with autoimmune neurological diseases, primarily manifesting as non-specific and shared microbial alterations, including a reduction in SCFA-producing bacteria and an increase in pathogenic or opportunistic pathogens.

Systematic review registration

https://www.crd.york.ac.uk/prospero/, identifier CRD42023410215.

16S rRNA metabarcoding applied to the microbiome of insect products (novel food): a comparative analysis of three reference databases

The 16S rRNA metabarcoding, based on Next-Generation Sequencing (NGS), is used to assess microbial biodiversity in various matrices, including food. The process involves a "dry-lab" phase where NGS data are processed through bioinformatic pipelines, which finally rely on taxonomic unit assignment against reference databases to assign them at order, genus, and species levels. Today, several public genomic reference databases are available for the taxonomic assignment of the 16S rRNA sequences. In this study, 42 insect-based food products were chosen as food models to find out how reference database choice could affect the microbiome results in food matrices. At the same time, this study aims to evaluate the most suitable reference database to assess the microbial composition of these still poorly investigated products. The V3-V4 region was sequenced by Illumina technology, and the R package "DADA2" was used for the bioinformatic analysis. After a bibliographic search, three public databases (SILVA, RDP, NCBI RefSeq) were compared based on amplicon sequence variant (ASV) assignment percentages at different taxonomic levels and diversity indices. SILVA assigned a significantly higher percentage of ASVs to the family and genus levels compared to RefSeq and RDP. However, no significant differences were noted in microbial composition between the databases according to α and β diversity results. A total of 121 genera were identified, with 56.2% detected by all three databases, though some taxa were identified only by one or two. The study highlights the importance of using updated reference databases for accurate microbiome characterization, contributing to the optimization of metabarcoding data analysis in food microbiota studies, including novel foods.

An evaluation of in utero polycyclic aromatic hydrocarbon exposure on the neonatal meconium microbiome

INTRODUCTION

In utero exposure to environmental polycyclic aromatic hydrocarbon (PAH) is associated with neurodevelopmental impairments[1-8], prematurity[9-12] and low birthweight[9,13-15]. The gut microbiome serves as an intermediary between self and external environment; therefore, exploring the impact of PAH on microbiota may elucidate their role in disease. Here, we evaluated the effect of in utero PAH exposure on meconium microbiome.

METHODS

We evaluated 49 mother-child dyads within Fair Start Birth Cohort with full term delivery and adequate meconium sampling. Prenatal PAH was measured using personal active samplers worn for 48 h during third trimester. Post-processing, 35 samples with adequate biomass were evaluated for association between tertile of PAH exposure (high (H) vs low/medium (L/M)) and microbiome diversity.

RESULTS

No significant differences were observed in alpha diversity metrics, Chao1 and Shannon index, between exposure groups for total PAH. However, alpha diversity metrics were negatively associated with log benzo[a]anthracene (BaA) and log chrysene (Chry) with high exposure, but positively associated with log benzo[a]pyrene (BaP) with low/medium exposure. After adjustment for birthweight and sex, alpha diversity metrics were negatively associated with log BaA, BaP, Chry, Indeno (Zhang et al., 2021; Perera et al., 2018)pyrene (IcdP) and total PAH with high exposure. Conversely, with low/medium exposure, alpha diversity metrics positively correlated with log BaP and benzo[b]fluoranthane (BbF). No significant difference in beta diversity was observed across groups using UniFrac, weighted UniFrac, or Bray-Curtis methods. Differential expression analysis showed differentially abundant taxa between exposure groups.

CONCLUSION

Bacterial taxa were detectable in 35/49 (71%) meconium samples. Altered alpha diversity metrics and differentially abundant taxa between groups suggest in utero PAH exposure may impede early colonization. Sample size is limited, but these findings provide supporting evidence for wider scale research. Research on long-term impact of prenatal PAH exposure on childhood health outcomes is ongoing. Differential effects of specific PAHs need further evaluation.

Insight into the potential of algorithms using AI technology as in vitro diagnostics utilizing microbial extracellular vesicles

Recently, the microbiome has been gaining significant attention in the healthcare sector as a next-generation factor. However, there remains a substantial gap in our understanding of the fundamental mechanisms of microbes, particularly regarding the effector microbial products exchanged between the microbiota and the host. Consequently, research on microbial extracellular vesicles (MEVs) has increased. MEVs, which are nano-sized, can circulate throughout the body and penetrate the bloodstream, carrying diverse information. Consequently, they are increasingly being utilized in medical applications. Additionally, AI technologies are being utilized in medicine. The combination of MEVs and AI technology is being explored for the development of algorithm-based in vitro diagnostics (IVD). Therefore, this study aims to review the integration of MEVs and AI technology as diagnostic tools for personalized medicine. This paper reviewed the MEV-based algorithms developed by a variety of human samples and AI technology. Additionally, most of MEV-based diagnostic models showed higher clinical performance. Several important factors are crucial for accurate diagnosis. First, optimizing sample types according to specific diseases is essential. Second, AI technology with higher diagnostic power yields more accurate results. Finally, incorporating additional markers can enhance diagnostic power. However, applying this tool in situ faces several limitations, including method standardization, sample size, and analysis techniques. In the future, we anticipate that research on MEVs will advance our understanding of their role in disease and establish the foundation for precision medicine strategies.

Wise Roles and Future Visionary Endeavors of Current Emperor: Advancing Dynamic Methods for Longitudinal Microbiome Meta-Omics Data in Personalized and Precision Medicine

Understanding the etiological complexity of diseases requires identifying biomarkers longitudinally associated with specific phenotypes. Advanced sequencing tools generate dynamic microbiome data, providing insights into microbial community functions and their impact on health. This review aims to explore the current roles and future visionary endeavors of dynamic methods for integrating longitudinal microbiome multi-omics data in personalized and precision medicine. This work seeks to synthesize existing research, propose best practices, and highlight innovative techniques. The development and application of advanced dynamic methods, including the unified analytical frameworks and deep learning tools in artificial intelligence, are critically examined. Aggregating data on microbes, metabolites, genes, and other entities offers profound insights into the interactions among microorganisms, host physiology, and external stimuli. Despite progress, the absence of gold standards for validating analytical protocols and data resources of various longitudinal multi-omics studies remains a significant challenge. The interdependence of workflow steps critically affects overall outcomes. This work provides a comprehensive roadmap for best practices, addressing current challenges with advanced dynamic methods. The review underscores the biological effects of clinical, experimental, and analytical protocol settings on outcomes. Establishing consensus on dynamic microbiome inter-studies and advancing reliable analytical protocols are pivotal for the future of personalized and precision medicine.

New Insights into Mucosa-Associated Microbiota in Paired Tumor and Non-Tumor Adjacent Mucosal Tissues in Colorectal Cancer Patients

BACKGROUND/OBJECTIVE

Colorectal cancer (CRC) is one of the most common cancers worldwide. Increasing scientific evidence supports the idea that gut microbiota dysbiosis accompanies colorectal tumorigenesis, and these changes could be causative. Implementing gut microbiota analysis in clinical practice is limited by sample type, sequencing platform and taxonomic classification. This article aims to address these limitations, providing new insights into the microbiota associated with CRC pathogenesis and implementing its analyses in personalized medicine.

METHODS

To that aim, we evaluate differences in the bacterial composition of 130 paired tumor and non-tumor adjacent tissues from a cohort of CRC patients from the Biobank of the University of Navarra, Spain. The V3-V4 region of the 16S rRNA gene was amplified, sequenced using the MinION platform, and taxonomically classified using the NCBI database.

RESULTS

To our knowledge, this is the first study to report an increased relative abundance of Streptococcus periodonticum and a decreased relative abundance of Corynebacterium associated with CRC. Genera such as Fusobacterium, Leptotrichia and Streptococcus showed higher relative abundances in tumor than in non-tumor tissues, as previously described in the literature. Specifically, we identified higher levels of Fusobacterium animalis, Fusobacterium nucleatum, Fusobacterium polymorphum and S. periodonticum in tumor tissues. In contrast, genera such as Bacteroides and Corynebacterium showed lower relative abundances in tumor tissues. There were also differences at the taxonomic level between tumor locations.

CONCLUSIONS

These results, consistent with previous studies, further support the hypothesis that Leptotrichia and Fusobacterium contribute to CRC progression, with F. nucleatum and F. animalis proposed as key CRC pathogenic taxa. Overall, these results contribute to a better understanding of the CRC-associated microbiota, addressing critical barriers to its implementation in personalized medicine.

Optimizing microbiome reference databases with PacBio full-length 16S rRNA sequencing for enhanced taxonomic classification and biomarker discovery

Background

The study of the human microbiome is crucial for understanding disease mechanisms, identifying biomarkers, and guiding preventive measures. Advances in sequencing platforms, particularly 16S rRNA sequencing, have revolutionized microbiome research. Despite the benefits, large microbiome reference databases (DBs) pose challenges, including computational demands and potential inaccuracies. This study aimed to determine if full-length 16S rRNA sequencing data produced by PacBio could be used to optimize reference DBs and be applied to Illumina V3-V4 targeted sequencing data for microbial study.

Methods

Oral and gut microbiome data (PRJNA1049979) were retrieved from NCBI. DADA2 was applied to full-length 16S rRNA PacBio data to obtain amplicon sequencing variants (ASVs). The RDP reference DB was used to assign the ASVs, which were then used as a reference DB to train the classifier. QIIME2 was used for V3-V4 targeted Illumina data analysis. BLAST was used to analyze alignment statistics. Linear discriminant analysis Effect Size (LEfSe) was employed for discriminant analysis.

Results

ASVs produced by PacBio showed coverage of the oral microbiome similar to the Human Oral Microbiome Database. A phylogenetic tree was trimmed at various thresholds to obtain an optimized reference DB. This established method was then applied to gut microbiome data, and the optimized gut microbiome reference DB provided improved taxa classification and biomarker discovery efficiency.

Conclusion

Full-length 16S rRNA sequencing data produced by PacBio can be used to construct a microbiome reference DB. Utilizing an optimized reference DB can increase the accuracy of microbiome classification and enhance biomarker discovery.

In Silico Analysis of Probiotic Bacteria Changes Across COVID-19 Severity Stages

The gut microbiota plays a crucial role in modulating the immune response during COVID-19, with several studies reporting significant alterations in specific bacterial genera, including Akkermansia, Bacteroides, Bifidobacterium, Faecalibacterium, Lactobacillus, Oscillospira, and Ruminococcus. These genera are symbionts of the gut microbiota and contribute to host health. However, comparing results across studies is challenging due to differences in analysis methods and reference databases. We screened 16S rRNA raw datasets available in public databases on COVID-19, focusing on the V3-V4 region of the bacterial genome. In total, seven studies were included. All samples underwent the same bioinformatics pipeline, evaluating the differential abundance of these seven bacterial genera at each level of severity. The reanalysis identified significant changes in differential abundance. Bifidobacterium emerged as a potential biomarker of disease severity and a therapeutic target. Bacteroides presented a complex pattern, possibly related to disease-associated inflammation or opportunistic pathogen growth. Lactobacillus showed significant changes in abundance across the COVID-19 stages. On the other hand, Akkermansia and Faecalibacterium did not show significant differences, while Oscillospira and Ruminococcus produced statistically significant results but with limited relevance to COVID-19 severity. Our findings reveal new insights into the differential abundance of key bacterial genera in COVID-19, particularly Bifidobacterium and Bacteroides.

Utilizing Artificial Intelligence for Microbiome Decision-Making: Autism Spectrum Disorder in Children from Bosnia and Herzegovina

BACKGROUND/OBJECTIVES

The study of microbiome composition shows positive indications for application in the diagnosis and treatment of many conditions and diseases. One such condition is autism spectrum disorder (ASD). We aimed to analyze gut microbiome samples from children in Bosnia and Herzegovina to identify microbial differences between neurotypical children and those with ASD. Additionally, we developed machine learning classifiers to differentiate between the two groups using microbial abundance and predicted functional pathways.

METHODS

A total of 60 gut microbiome samples (16S rRNA sequences) were analyzed, with 44 from children with ASD and 16 from neurotypical children. Four machine learning algorithms (Random Forest, Support Vector Classification, Gradient Boosting, and Extremely Randomized Tree Classifier) were applied to create eight classification models based on bacterial abundance at the genus level and KEGG pathways. Model accuracy was evaluated, and an external dataset was introduced to test model generalizability.

RESULTS

The highest classification accuracy (80%) was achieved with Random Forest and Extremely Randomized Tree Classifier using genus-level taxa. The Random Forest model also performed well (78%) with KEGG pathways. When tested on an independent dataset, the model maintained high accuracy (79%), confirming its generalizability.

CONCLUSIONS

This study identified significant microbial differences between neurotypical children and children with ASD. Machine learning classifiers, particularly Random Forest and Extremely Randomized Tree Classifier, achieved strong accuracy. Validation with external data demonstrated that the models could generalize across different datasets, highlighting their potential use.

MIMt: a curated 16S rRNA reference database with less redundancy and higher accuracy at species-level identification

MOTIVATION

Accurate determination and quantification of the taxonomic composition of microbial communities, especially at the species level, is one of the major issues in metagenomics. This is primarily due to the limitations of commonly used 16S rRNA reference databases, which either contain a lot of redundancy or a high percentage of sequences with missing taxonomic information. This may lead to erroneous identifications and, thus, to inaccurate conclusions regarding the ecological role and importance of those microorganisms in the ecosystem.

RESULTS

The current study presents MIMt, a new 16S rRNA database for archaea and bacteria's identification, encompassing 47 001 sequences, all precisely identified at species level. In addition, a MIMt2.0 version was created with only curated sequences from RefSeq Targeted loci with 32 086 sequences. MIMt aims to be updated twice a year to include all newly sequenced species. We evaluated MIMt against Greengenes, RDP, GTDB and SILVA in terms of sequence distribution and taxonomic assignments accuracy. Our results showed that MIMt contains less redundancy, and despite being 20 to 500 times smaller than existing databases, outperforms them in completeness and taxonomic accuracy, enabling more precise assignments at lower taxonomic ranks and thus, significantly improving species-level identification.

The effect of peanut skins as a natural antimicrobial feed additive on ileal and cecal microbiota in broiler chickens inoculated with Salmonella enterica Enteritidis

The consumption of poultry products contaminated with Salmonella species is one of the most common causes of Salmonella infections. In vivo studies demonstrated the potential application of peanut skins (PS) as an antimicrobial poultry feed additive to help mitigate the proliferation of Salmonella in poultry environments. Tons of PS, a waste by-product of the peanut industry, are generated and disposed in U.S. landfills annually. Peanut skins and extracts have been shown to possess antimicrobial and antioxidant properties. Hence, we aimed to determine the effect of PS as a feed additive on the gut microbiota of broilers fed a control or PS supplemented (4% inclusion) diet and inoculated with or without Salmonella enterica Enteritidis (SE). At hatch 160 male broilers were randomly assigned to 4 treatments: 1) CON-control diet without SE, 2) PS-PS diet without SE, 3) CONSE-control diet with SE, 4) PSSE-PS diet with SE. On d 3, birds from CONSE and PSSE treatments were inoculated with 4.2 × 109 CFU/mL SE. At termination (4 wk), 10 birds/treatment were euthanized and ileal and cecal contents were collected for 16S rRNA analysis using standard methodologies. Sequencing data were analyzed using QIIME2. No effect of PS or SE was observed on ileal alpha and beta diversity, while evenness, richness, number of amplicon sequence variants (ASV) and Shannon, as well as beta diversity were significantly (P < 0.05) affected in ceca. Similarly, more differentially abundant taxa between treatment groups were identified in ceca than in ileum. However, more microbiota functional changes, based on the PICRUST2 prediction, were observed in ileum. Overall, relatively minor changes in microbiota were observed during SE infection and PS treatment, suggesting that PS addition may not attenuate the SE proliferation, as shown previously, through modulation of microbiota in gastrointestinal tract. However, while further studies are warranted, these results suggest that PS may potentially serve as a functional feed additive for poultry for improvement of animal health.

Temporal Changes in Jejunal and Ileal Microbiota of Broiler Chickens with Clinical Coccidiosis (Eimeria maxima)

Coccidiosis in broiler chickens continues to be a major disease of the gastrointestinal tract, causing economic losses to the poultry industry worldwide. The goal of this study was to generate a symptomatic Eimeria maxima (1000 oocysts) infection to determine its effect on the luminal and mucosal microbiota populations (L and M) in the jejunum and ileum (J and IL). Samples were taken from day 0 to 14 post-infection, and sequencing of 16S rRNA was performed using Illumina technology. Infected birds had significantly (p < 0.0001) lower body weight gain (BWG), higher feed conversion ratio (FCR) (p = 0.0015), increased crypt depth, and decreased villus height (p < 0.05). The significant differences in alpha and beta diversity were observed primarily at height of infection (D7). Analysis of taxonomy indicated that J-L and M were dominated by Lactobacillus, and in IL-M, changeover from Candidatus Arthromitus to Lactobacillus as the major taxon was observed, which occurred quicky in infected animals. LEfSe analysis found that in the J-M of infected chickens, Lactobacillus was significantly more abundant in infected (IF) chickens. These findings show that E. maxima infection affects the microbiota of the small intestine in a time-dependent manner, with different effects on the luminal and mucosal populations.

Turicibacter and Catenibacterium as potential biomarkers in autism spectrum disorders

Autism spectrum disorders (ASD) are neurodevelopmental disorders characterized by social, behavioral, and cognitive impairments. Several comorbidities, including gastrointestinal (GI) dysregulations, are frequently reported in ASD children. Although studies in animals have shown the crucial role of the microbiota in key aspects of neurodevelopment, there is currently no consensus on how the alteration of microbial composition affects the pathogenesis of ASD. Moreover, disruption of the gut-brain axis (GBA) has been reported in ASD although with limited studies conducted on the Mediterranean population. In our study, we aimed to investigate gut microbiota composition in Lebanese ASD subjects, their unaffected siblings, and a control group from various regions in Lebanon using the 16 S-rRNA sequencing (NGS). Our study revealed a lower abundance of Turicibacter and a significant enrichment on Proteobacteria in the ASD and siblings' groups compared to the controls, indicating that gut microbiota is probably affected by dietary habits, living conditions together with host genetic factors. The study also showed evidence of changes in the gut microbiome of ASD children compared to their siblings and the unrelated control. Bacteroidetes revealed a lower abundance in the ASD group compared to controls and siblings, conversely, Catenibacterium and Tenericutes revealed an increased abundance in the ASD group. Notably, our study identifies alterations in the abundance of Turicibacter and Catenibacterium in ASD children suggesting a possible link between these bacterial taxa and ASD and contributing to the growing body of evidence linking the microbiome to ASD.

Host genetics and microbiota data analysis in colorectal cancer research

Colorectal cancer (CRC) is a heterogeneous disease with a complex aetiology influenced by a myriad of genetic and environmental factors. Despite advances in CRC research, it is a major burden of disease, with the second highest incidence and third leading cause of cancer deaths worldwide. To individualise diagnosis, prognosis, and treatment of CRC, developing new strategies combining precision medicine and bioinformatic procedures is promising. Precision medicine is based on omics technologies and aims to individualise the management of CRC based on patient host genetic characteristics and microbiota. Bioinformatics is central to the application of personalised medicine because it enables the analysis of large datasets generated by these technologies. At the level of host genetics, bioinformatics allows the identification of mutations, genes, molecular pathways, biomarkers and drugs relevant to colorectal carcinogenesis. At the microbiota level, bioinformatics is fundamental to analysing microbial communities' composition and functionality and developing biomarkers and personalised microbiota-based therapies. This paper explores the host and microbiota genetic data analysis in CRC research.

Unveiling soil animal community dynamics beneath dominant shrub species in natural desert environment: Implications for ecosystem management and conservation

The Taklimakan Desert, known for extreme aridity and unique ecological challenges, maintains a delicate life balance beneath its harsh surface. This study investigates intricate dynamics of soil animal communities within this desert ecosystem, with a particular focus on vertical profile variations beneath four dominant shrub species (AS-Alhagi sparsifolia, KC-Karelinia caspia, TR- Tamarix ramosissima, CC- Calligonum caput-medusae). Utilizing comprehensive soil sampling and metagenomics techniques, we reveal the diversity and distribution patterns of soil animal communities from the soil surface down to deeper layers (0-100 cm). Our research outcomes have unveiled that Nematoda and Arthropoda emerge as the most predominant classes of soil animals across all studied shrubs. Specifically, Nematoda exhibited notably high abundance in the KC area, while Arthropoda thrived predominantly in the TR region. We also observed a linear decrease in Nematoda populations as soil depth increased, consistent among all shrub species. Moreover, the highest Shannon diversity within soil animal communities was recorded in the KC area, underscoring a trend of declining alpha diversity in the AS region and an increase in other shrub areas as soil depth increased. Notably, the zones dominated by CC and TR displayed the highest levels of beta diversity. Our correlation analysis of soil animals and environmental factors has pinpointed soil water content, available phosphorus, and available potassium as the most influential drivers of variations in the top-classified soil animal communities. This study provides insights into soil animals in deserts, supporting future research to preserve these fragile deserts and enhance our understanding of life below the surface in challenging ecosystems.

A Standard Pipeline for Analyzing the Endometrial Microbiome

The endometrial microbiome is a rapidly advancing field of research, particularly in obstetrics and gynecology, as it has been found to be linked with obstetric complications and potential impacts on fertility. The diversity of microorganisms presents in the endometrium, along with their metabolites, can influence reproductive outcomes by modulating the local immune environment of the uterus. However, a major challenge in advancing our understanding of the endometrial microbiota lies in the heterogeneity of available studies, which vary in terms of patient selection, control groups, collection methods and analysis methodologies. In this study, we propose a detailed pipeline for endometrial microbiome analysis, based on the most comprehensive prospective of 64 studies that have investigated the endometrial microbiome up to the present. Additionally, our review suggests that a dominance of Lactobacilli in the endometrium may be associated with improved reproductive prognosis, including higher implantation rates and lower miscarriage rates. By establishing a standardized pipeline, we aim to facilitate future research, enabling better comparison and correlation of bacterial communities with the health status of patients, including fertility-related issues.

Comparative analysis of bacterial community structure and physicochemical quality in high-temperature Daqu of different colors in Qingzhou production area

To compare the effects of differences in Daqu making technology and production regions on the bacterial composition and physicochemical properties of high-temperature Daqu (HTD), this study analyzed the bacterial community structure of three colors of HTD in the Qingzhou production area and measured their physicochemical quality. At the same time, a comparative analysis was conducted on the bacterial composition of Qingzhou and Xiangyang regions. The results revealed that the HTD in the Qingzhou area exhibited a diverse bacterial community dominated by Lentibacillus, Scopulibacillus, and Staphylococcus. The black HTD displayed the lowest bacterial richness (P < 0.05) and a relatively unique microbial structure. Significant variations were observed in the physicochemical qualities of the three colors of HTD. Notably, white HTD demonstrated higher moisture and ash content, saccharification and liquor-producing power. Yellow HTD exhibited higher amino nitrogen and protein content, and black HTD displayed higher water activity, acidity, and starch content. The variation in Bacillus, Limosilactobacillus, and Weissella distributions across different colors of HTD primarily contributed to these findings. From the HTD samples in the Qingzhou area, Bacillus (61.90 %) and lactic acid bacteria (17.46 %) being the predominant cultivable communities. Cluster analysis identified significant differences in bacterial communities among HTD samples from various production areas. It can enhance the understanding of HTD quality in the Qingzhou area and offer insights for optimizing HTD and Maotai-flavor Baijiu quality.

From flesh to bones: Multi-omics approaches in forensic science

Recent advancements in omics techniques have revolutionised the study of biological systems, enabling the generation of high-throughput biomolecular data. These innovations have found diverse applications, ranging from personalised medicine to forensic sciences. While the investigation of multiple aspects of cells, tissues or entire organisms through the integration of various omics approaches (such as genomics, epigenomics, metagenomics, transcriptomics, proteomics and metabolomics) has already been established in fields like biomedicine and cancer biology, its full potential in forensic sciences remains only partially explored. In this review, we have presented a comprehensive overview of state-of-the-art analytical platforms employed in omics research, with specific emphasis on their application in the forensic field for the identification of the cadaver and the cause of death. Moreover, we have conducted a critical analysis of the computational integration of omics approaches, and highlighted the latest advancements in employing multi-omics techniques for forensic investigations.

Effects of Eimeria acervulina infection on the luminal and mucosal microbiota of the cecum and ileum in broiler chickens

Coccidiosis, an intestinal disease caused by Eimeria parasites, is responsible for major losses in the poultry industry by impacting chicken health. The gut microbiota is associated with health factors, such as nutrient exchange and immune system modulation, requiring understanding on the effects of Eimeria infection on the gut microbiota. This study aimed to determine the effects of Eimeria acervulina infection on the luminal and mucosal microbiota of the cecum (CeL and CeM) and ileum (IlL and IlM) at multiple time points (days 3, 5, 7, 10, and 14) post-infection. E. acervulina infection decreased evenness in CeL microbiota at day 10, increased richness in CeM microbiota at day 3 before decreasing richness at day 14, and decreased richness in IlL microbiota from day 3 to 10. CeL, CeM, and IlL microbiota differed between infected and control birds based on beta diversity at varying time points. Infection reduced relative abundance of bacterial taxa and some predicted metabolic pathways known for short-chain fatty acid production in CeL, CeM, and IlL microbiota, but further understanding of metabolic function is required. Despite E. acervulina primarily targeting the duodenum, our findings demonstrate the infection can impact bacterial diversity and abundance in the cecal and ileal microbiota.

Analysis of fungal diversity in the gut feces of wild takin (Budorcas taxicolor)

Introduction

The composition of the intestinal microbiome correlates significantly with an animal's health status. Hence, this indicator is highly important and sensitive for protecting endangered animals. However, data regarding the fungal diversity of the wild Budorcas taxicolor (takin) gut remain scarce. Therefore, this study analyzes the fungal diversity, community structure, and pathogen composition in the feces of wild B. taxicolor.

Methods

To ensure comprehensive data analyses, we collected 82 fecal samples from five geographical sites. Amplicon sequencing of the internal transcribed spacer (ITS) rRNA was used to assess fecal core microbiota and potential pathogens to determine whether the microflora composition is related to geographical location or diet. We further validated the ITS rRNA sequencing results via amplicon metagenomic sequencing and culturing of fecal fungi.

Results and discussion

The fungal diversity in the feces of wild Budorcas taxicolor primarily comprised three phyla (99.69%): Ascomycota (82.19%), Fungi_unclassified (10.37%), and Basidiomycota (7.13%). At the genus level, the predominant fungi included Thelebolus (30.93%), Functional_unclassified (15.35%), and Ascomycota_unclassified (10.37%). Within these genera, certain strains exhibit pathogenic properties, such as Thelebolus, Cryptococcus, Trichosporon, Candida, Zopfiella, and Podospora. Collectively, this study offers valuable information for evaluating the health status of B. taxicolor and formulating protective strategies.

Effect of wheat crops on the persistence and attenuation of antibiotic resistance genes in soil after swine wastewater application

Swine wastewater (SW) application introduces antibiotic resistance genes (ARGs) into farmland soils. However, ARG attenuation in SW-fertigated soils, especially those influenced by staple crops and soil type, remains unclear. This study investigated twelve soil ARGs and one mobile genetic element (MGE) in sandy loam, loam, and silt loam soils before and after SW application in wheat-planted and unplanted soils. The results revealed an immediate increase in the abundance of ARGs in soil by two orders of magnitude above background levels following SW application. After SW application, the soil total ARG abundance was attenuated, reaching background levels at 54 days; However, more individual ARGs were detected above the detection limit than pre-application. Among the 13 genes, acc(6')-lb, tetM, and tetO tended to persist in the soil during wheat harvest. ARG half-lives were up to four times longer in wheat-planted soils than in bare soils. Wheat planting decreased the persistence of acc(6')-lb, ermB, ermF, and intI2 but increased the persistence of others such as sul1 and sul2. Soil type had no significant impact on ARG and MGE fates. Our findings emphasize the need for strategic SW application and the consideration of crop cultivation effects to mitigate ARG accumulation in farmland soils.

Microbiologic surveys for Baijiu fermentation are affected by experimental design

The traditional Chinese alcoholic beverage Baijiu is produced by spontaneous fermentation of grains under anaerobic conditions. While numerous studies have used metagenomic technology to investigate the microbiome of Baijiu brewing, the microbial succession mechanism of Baijiu brewing has not been fully clarified, and metagenomic strategies for microecology surveys have not been comprehensively evaluated. Using the fermentation process of strong-flavor Baijiu as a model, we compared the data for bacterial communities based on short read 16S rRNA variable regions, V3-V4, and full-length 16S regions, V1-V9, to whole metagenomic shotgun sequencing (WMS) to measure the effect of technology selection on phylogenetic and functional profiles. The results showed differences in bacterial compositions and their relation to volatiles and physicochemical variables between sequencing methods. Furthermore, the percentage of V3-V4 sequences assigned to species level was higher than the percentage of V1-V9 sequences according to SILVA taxonomy, but lower according to NCBI taxonomy (P < 0.05). In both SILVA and NCBI taxonomies, the relative abundances of bacterial communities at both the genus and family levels were more positively correlated with WMS data in the V3-V4 dataset than in the V1-V9 dataset. The WMS identified changes in abundances of multiple metabolic pathways during fermentation (P < 0.05), including "starch and sucrose metabolism," "galactose metabolism," and "fatty acid biosynthesis." Although functional predictions derived from 16S data show similar patterns to WMS, most metabolic pathway changes are uncorrelated (P > 0.05). This study provided new technical and biological insights into Baijiu production that may assist in selection of methodologies for studies of fermentation systems.

Comparison of MALDI-TOF mass spectrometry and 16S rDNA sequencing for identification of environmental bacteria: a case study of cave mussel-associated culturable microorganisms

Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) is routinely used as a rapid and cost-effective method for pathogen identification in clinical settings. In comparison, its performance in other microbiological fields, such as environmental microbiology, is still being tested, although isolates of environmental microbes are essential for in-depth in vivo studies of their biology, including biotechnological applications. We investigated the applicability of MALDI-TOF MS for the identification of bacterial isolates from a highly oligotrophic environment - Dinaric Karst caves, which likely harbor specific microorganisms. We cultured bacteria from the shell surface of the endemic mussel Congeria jalzici, one of the three known cave mussels in the world that lives in the Dinaric karst underground. The bacterial isolates were obtained by swabbing the shell surface of mussels living in microhabitats with different amounts of water: 10 air-exposed mussels, 10 submerged mussels, and 10 mussels in the hygropetric zone. A collection of 87 pure culture isolates was obtained, mostly belonging to the phylum Bacillota (72%), followed by Pseudomonadota (16%), Actinomycetota (11%), and Bacteroidota (1%). We compared the results of MALDI-TOF MS identification (Bruker databases DB-5989 and version 11, v11) with the results of 16S rDNA-based phylogenetic analysis, a standard procedure for bacterial identification. Identification to the genus level based on 16S rDNA was possible for all isolates and clearly outperformed the results from MALDI-TOF MS, although the updated MALDI-TOF MS database v11 gave better results than the DB-5989 version (85% versus 62%). However, identification to the species-level by 16S rDNA sequencing was achieved for only 17% of isolates, compared with 14% and 40% for the MALDI-TOF MS databases DB-5989 and v11 database, respectively. In conclusion, our results suggest that continued enrichment of MALDI-TOF MS libraries will result with this method soon becoming a rapid, accurate, and efficient tool for assessing the diversity of culturable bacteria from different environmental niches.

Dynamics of soil biota and nutrients at varied depths in a Tamarix ramosissima-dominated natural desert ecosystem: Implications for nutrient cycling and desertification management

The underground community of soil organisms, known as soil biota, plays a critical role in terrestrial ecosystems. Different ecosystems exhibit varied responses of soil organisms to soil physical and chemical properties (SPCPs). However, our understanding of how soil biota react to different soil depths in naturally established population of salinity tolerant Tamarix ramosissima in desert ecosystems, remains limited. To address this, we employed High-Throughput Illumina HiSeq Sequencing to examine the population dynamics of soil bacteria, fungi, archaea, protists, and metazoa at six different soil depths (0-100 cm) in the naturally occurring T. ramosissima dominant zone within the Taklimakan desert of China. Our observations reveal that the alpha diversity of bacteria, fungi, metazoa, and protists displayed a linear decrease with the increase of soil depth, whereas archaea exhibited an inverse pattern. The beta diversity of soil biota, particularly metazoa, bacteria, and protists, demonstrated noteworthy associations with soil depths through Non-Metric Dimensional Scaling analysis. Among the most abundant classes of soil organisms, we observed Actinobacteria, Sordariomycetes, Halobacteria, Spirotrichea, and Nematoda for bacteria, fungi, archaea, protists, and metazoa, respectively. Additionally, we identified associations between the vertical distribution of dominant biotic communities and SPCPs. Bacterial changes were mainly influenced by total potassium, available phosphorus (AP), and soil water content (SWC), while fungi were impacted by nitrate (NO3-) and available potassium (AK). Archaea showed correlations with total carbon (TC) and AK thus suggesting their role in methanogenesis and methane oxidation, protists with AP and SWC, and metazoa with AP and pH. These correlations underscore potential connections to nutrient cycling and the production and consumption of greenhouse gases (GhGs). This insight establishes a solid foundation for devising strategies to mitigate nutrient cycling and GHG emissions in desert soils, thereby playing a pivotal role in the advancement of comprehensive approaches to sustainable desert ecosystem management.

Considerations for the inclusion of metabarcoding data in the plant protection product risk assessment of the soil microbiome

There is increasing interest in further developing the plant protection product (PPP) environmental risk assessment, particularly within the European Union, to include the assessment of soil microbial community composition, as measured by metabarcoding approaches. However, to date, there has been little discussion as to how this could be implemented in a standardized, reliable, and robust manner suitable for regulatory decision-making. Introduction of metabarcoding-based assessments of the soil microbiome into the PPP risk assessment would represent a significant increase in the degree of complexity of the data that needs to be processed and analyzed in comparison to the existing risk assessment on in-soil organisms. The bioinformatics procedures to process DNA sequences into community compositional data sets currently lack standardization, while little information exists on how these data should be used to generate regulatory endpoints and the ways in which these endpoints should be interpreted. Through a thorough and critical review, we explore these challenges. We conclude that currently, we do not have a sufficient degree of standardization or understanding of the required bioinformatics and data analysis procedures to consider their use in an environmental risk assessment context. However, we highlight critical knowledge gaps and the further research required to understand whether metabarcoding-based assessments of the soil microbiome can be utilized in a statistically and ecologically relevant manner within a PPP risk assessment. Only once these challenges are addressed can we consider if and how we should use metabarcoding as a tool for regulatory decision-making to assess and monitor ecotoxicological effects on soil microorganisms within an environmental risk assessment of PPPs. Integr Environ Assess Manag 2024;20:337-358. © 2023 SETAC.

Species-level resolution for the vaginal microbiota with short amplicons

Specific bacterial species have been found to play important roles in human vagina. Achieving high species-level resolution is vital for analyzing vaginal microbiota data. However, contradictory conclusions were yielded from different methodological studies. More comprehensive evaluation is needed for determining an optimal pipeline for vaginal microbiota. Based on the sequences of vaginal bacterial species downloaded from NCBI, we conducted simulated amplification with various primer sets targeting different 16S regions as well as taxonomic classification on the amplicons applying different combinations of algorithms (BLAST+, VSEARCH, and Sklearn) and reference databases (Greengenes2, SILVA, and RDP). Vaginal swabs were collected from participants with different vaginal microecology to construct 16S full-length sequenced mock communities. Both computational and experimental amplifications were performed on the mock samples. Classification accuracy of each pipeline was determined. Microbial profiles were compared between the full-length and partial 16S sequencing samples. The optimal pipeline was further validated in a multicenter cohort against the PCR results of common STI pathogens. Pipeline V1-V3_Sklearn_Combined had the highest accuracy for classifying the amplicons generated from both the NCBI downloaded data (84.20% ± 2.39%) and the full-length sequencing data (95.65% ± 3.04%). Vaginal samples amplified and sequenced targeting the V1-V3 region but merely employing the forward reads (223 bp) and classified using the optimal pipeline, resembled the mock communities the most. The pipeline demonstrated high F1-scores for detecting STI pathogens within the validation cohort. We have determined an optimal pipeline to achieve high species-level resolution for vaginal microbiota with short amplicons, which will facilitate future studies.IMPORTANCEFor vaginal microbiota studies, diverse 16S rRNA gene regions were applied for amplification and sequencing, which affect the comparability between different studies as well as the species-level resolution of taxonomic classification. We conducted comprehensive evaluation on the methods which influence the accuracy for the taxonomic classification and established an optimal pipeline to achieve high species-level resolution for vaginal microbiota with short amplicons, which will facilitate future studies.

Time-Course Responses of Apple Leaf Endophytes to the Infection of Gymnosporangium yamadae

Apple rust, caused by Gymnosporangium yamadae, poses a significant challenge to apple production. Prior studies have underscored the pivotal role played by endophytic microbial communities, intimately linked with the host, in influencing plant diseases and their pathogenic outcomes. The objective of this study is to scrutinize alternations in endophytic microbial communities within apple leaves at different stages of apple rust using high-throughput sequencing technology. The findings revealed a discernible pattern characterized by an initial increase and subsequent decrease in the alpha diversity of microbial communities in diseased leaves. A microbial co-occurrence network analysis revealed that the complexity of the bacterial community in diseased leaves diminished initially and then rebounded during the progression of the disease. Additionally, employing the PICRUSt2 platform, this study provided preliminary insights into the functions of microbial communities at specific disease timepoints. During the spermogonial stage, endophytic bacteria particularly exhibited heightened activity in genetic information processing, metabolism, and environmental information processing pathways. Endophytic fungi also significantly enriched a large number of metabolic pathways during the spermogonial stage and aecial stage, exhibiting abnormally active life activities. These findings establish a foundation for comprehending the role of host endophytes in the interaction between pathogens and hosts. Furthermore, they offer valuable insights for the development and exploitation of plant endophytic resources, thereby contributing to enhanced strategies for managing apple rust.

Multi-omics approaches to studying gastrointestinal microbiome in the context of precision medicine and machine learning

The human gastrointestinal (gut) microbiome plays a critical role in maintaining host health and has been increasingly recognized as an important factor in precision medicine. High-throughput sequencing technologies have revolutionized -omics data generation, facilitating the characterization of the human gut microbiome with exceptional resolution. The analysis of various -omics data, including metatranscriptomics, metagenomics, glycomics, and metabolomics, holds potential for personalized therapies by revealing information about functional genes, microbial composition, glycans, and metabolites. This multi-omics approach has not only provided insights into the role of the gut microbiome in various diseases but has also facilitated the identification of microbial biomarkers for diagnosis, prognosis, and treatment. Machine learning algorithms have emerged as powerful tools for extracting meaningful insights from complex datasets, and more recently have been applied to metagenomics data via efficiently identifying microbial signatures, predicting disease states, and determining potential therapeutic targets. Despite these rapid advancements, several challenges remain, such as key knowledge gaps, algorithm selection, and bioinformatics software parametrization. In this mini-review, our primary focus is metagenomics, while recognizing that other -omics can enhance our understanding of the functional diversity of organisms and how they interact with the host. We aim to explore the current intersection of multi-omics, precision medicine, and machine learning in advancing our understanding of the gut microbiome. A multidisciplinary approach holds promise for improving patient outcomes in the era of precision medicine, as we unravel the intricate interactions between the microbiome and human health.

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.

Streptomyces Diversity Maps Reveal Distinct High-Specificity Biogeographical and Environmental Patterns Compared to the Overall Bacterial Diversity

Despite their enormous impact on the environment and humans, the distribution and variety of the biggest natural secondary metabolite producers, the genus Streptomyces, have not been adequately investigated. We developed representative maps from public EMP 16S rRNA amplicon sequences microbiomics data. Streptomyces ASVs were extracted from the EMP overall bacterial community, demonstrating Streptomyces diversity and identifying crucial diversity patterns. Our findings revealed that while the EMP primarily distinguished bacterial communities as host-associated or free-living (EMPO level 1), the Streptomyces community showed no significant difference but exhibited distinctions between categories in EMPO level 2 (animal, plant, non-saline, and saline). Multiple linear regression analysis demonstrated that pH, temperature, and salinity significantly predicted Streptomyces richness, with richness decreasing as these factors increased. However, latitude and longitude do not predict Streptomyces richness. Our Streptomyces maps revealed that additional samplings in Africa and Southeast Asia are needed. Additionally, our findings indicated that a greater number of samples did not always result in greater Streptomyces richness; future surveys may not necessitate extensive sampling from a single location. Broader sampling, rather than local/regional sampling, may be more critical in answering microbial biogeograph questions. Lastly, using 16S rRNA gene sequencing data has some limitations, which should be interpreted cautiously.

Multicenter evaluation of gut microbiome profiling by next-generation sequencing reveals major biases in partial-length metabarcoding approach

Next-generation sequencing workflows, using either metabarcoding or metagenomic approaches, have massively contributed to expanding knowledge of the human gut microbiota, but methodological bias compromises reproducibility across studies. Where these biases have been quantified within several comparative analyses on their own, none have measured inter-laboratory reproducibility using similar DNA material. Here, we designed a multicenter study involving seven participating laboratories dedicated to partial- (P1 to P5), full-length (P6) metabarcoding, or metagenomic profiling (MGP) using DNA from a mock microbial community or extracted from 10 fecal samples collected at two time points from five donors. Fecal material was collected, and the DNA was extracted according to the IHMS protocols. The mock and isolated DNA were then provided to the participating laboratories for sequencing. Following sequencing analysis according to the laboratories' routine pipelines, relative taxonomic-count tables defined at the genus level were provided and analyzed. Large variations in alpha-diversity between laboratories, uncorrelated with sequencing depth, were detected among the profiles. Half of the genera identified by P1 were unique to this partner and two-thirds of the genera identified by MGP were not detected by P3. Analysis of beta-diversity revealed lower inter-individual variance than inter-laboratory variances. The taxonomic profiles of P5 and P6 were more similar to those of MGP than those obtained by P1, P2, P3, and P4. Reanalysis of the raw sequences obtained by partial-length metabarcoding profiling, using a single bioinformatic pipeline, harmonized the description of the bacterial profiles, which were more similar to each other, except for P3, and closer to the profiles obtained by MGP. This study highlights the major impact of the bioinformatics pipeline, and primarily the database used for taxonomic annotation. Laboratories need to benchmark and optimize their bioinformatic pipelines using standards to monitor their effectiveness in accurately detecting taxa present in gut microbiota.

Microbial metabolites regulate social novelty via CaMKII neurons in the BNST

Social novelty is a cognitive process that is essential for animals to interact strategically with conspecifics based on their prior experiences. The commensal microbiome in the gut modulates social behavior through various routes, including microbe-derived metabolite signaling. Short-chain fatty acids (SCFAs), metabolites derived from bacterial fermentation in the gastrointestinal tract, have been previously shown to impact host behavior. Herein, we demonstrate that the delivery of SCFAs directly into the brain disrupts social novelty through distinct neuronal populations. We are the first to observe that infusion of SCFAs into the lateral ventricle disrupted social novelty in microbiome-depleted mice without affecting brain inflammatory responses. The deficit in social novelty can be recapitulated by activating calcium/calmodulin-dependent protein kinase II (CaMKII)-labeled neurons in the bed nucleus of the stria terminalis (BNST). Conversely, chemogenetic silencing of the CaMKII-labeled neurons and pharmacological inhibition of fatty acid oxidation in the BNST reversed the SCFAs-induced deficit in social novelty. Our findings suggest that microbial metabolites impact social novelty through a distinct neuron population in the BNST.

Social and psychological adversity are associated with distinct mother and infant gut microbiome variations

Health disparities are driven by underlying social disadvantage and psychosocial stressors. However, how social disadvantage and psychosocial stressors lead to adverse health outcomes is unclear, particularly when exposure begins prenatally. Variations in the gut microbiome and circulating proinflammatory cytokines offer potential mechanistic pathways. Here, we interrogate the gut microbiome of mother-child dyads to compare high-versus-low prenatal social disadvantage, psychosocial stressors and maternal circulating cytokine cohorts (prospective case-control study design using gut microbiomes from 121 dyads profiled with 16 S rRNA sequencing and 89 dyads with shotgun metagenomic sequencing). Gut microbiome characteristics significantly predictive of social disadvantage and psychosocial stressors in the mothers and children indicate that different discriminatory taxa and related pathways are involved, including many species of Bifidobacterium and related pathways across several comparisons. The lowest inter-individual gut microbiome similarity was observed among high-social disadvantage/high-psychosocial stressors mothers, suggesting distinct environmental exposures driving a diverging gut microbiome assembly compared to low-social disadvantage/low-psychosocial stressors controls (P = 3.5 × 10-5 for social disadvantage, P = 2.7 × 10-15 for psychosocial stressors). Children's gut metagenome profiles at 4 months also significantly predicted high/low maternal prenatal IL-6 (P = 0.029), with many bacterial species overlapping those identified by social disadvantage and psychosocial stressors. These differences, based on maternal social and psychological status during a critical developmental window early in life, offer potentially modifiable targets to mitigate health inequities.

Comparisons of the Oral Microbiota from Seven Species of Wild Venomous Snakes in Taiwan Using the High-Throughput Amplicon Sequencing of the Full-Length 16S rRNA Gene

A venomous snake's oral cavity may harbor pathogenic microorganisms that cause secondary infection at the wound site after being bitten. We collected oral samples from 37 individuals belonging to seven species of wild venomous snakes in Taiwan, including Naja atra (Na), Bungarus multicinctus (Bm), Protobothrops mucrosquamatus (Pm), Trimeresurus stejnegeri (Ts), Daboia siamensis (Ds), Deinagkistrodon acutus (Da), and alpine Trimeresurus gracilis (Tg). Bacterial species were identified using full-length 16S rRNA amplicon sequencing analysis, and this is the first study using this technique to investigate the oral microbiota of multiple Taiwanese snake species. Up to 1064 bacterial species were identified from the snake's oral cavities, with 24 pathogenic and 24 non-pathogenic species among the most abundant ones. The most abundant oral bacterial species detected in our study were different from those found in previous studies, which varied by snake species, collection sites, sampling tissues, culture dependence, and analysis methods. Multivariate analysis revealed that the oral bacterial species compositions in Na, Bm, and Pm each were significantly different from the other species, whereas those among Ts, Ds, Da, and Tg showed fewer differences. Herein, we reveal the microbial diversity in multiple species of wild snakes and provide potential therapeutic implications regarding empiric antibiotic selection for wildlife medicine and snakebite management.

The respiratory microbiota and its impact on health and disease in dogs and cats: A One Health perspective

Healthy lungs were long thought of as sterile, with presence of bacteria identified by culture representing contamination. Recent advances in metagenomics have refuted this belief by detecting rich, diverse, and complex microbial communities in the healthy lower airways of many species, albeit at low concentrations. Although research has only begun to investigate causality and potential mechanisms, alterations in these microbial communities (known as dysbiosis) have been described in association with inflammatory, infectious, and neoplastic respiratory diseases in humans. Similar studies in dogs and cats are scarce. The microbial communities in the respiratory tract are linked to distant microbial communities such as in the gut (ie, the gut-lung axis), allowing interplay of microbes and microbial products in health and disease. This review summarizes considerations for studying local microbial communities, key features of the respiratory microbiota and its role in the gut-lung axis, current understanding of the healthy respiratory microbiota, and examples of dysbiosis in selected respiratory diseases of dogs and cats.

Inferring microbial co-occurrence networks from amplicon data: a systematic evaluation

Microbes commonly organize into communities consisting of hundreds of species involved in complex interactions with each other. 16S ribosomal RNA (16S rRNA) amplicon profiling provides snapshots that reveal the phylogenies and abundance profiles of these microbial communities. These snapshots, when collected from multiple samples, can reveal the co-occurrence of microbes, providing a glimpse into the network of associations in these communities. However, the inference of networks from 16S data involves numerous steps, each requiring specific tools and parameter choices. Moreover, the extent to which these steps affect the final network is still unclear. In this study, we perform a meticulous analysis of each step of a pipeline that can convert 16S sequencing data into a network of microbial associations. Through this process, we map how different choices of algorithms and parameters affect the co-occurrence network and identify the steps that contribute substantially to the variance. We further determine the tools and parameters that generate robust co-occurrence networks and develop consensus network algorithms based on benchmarks with mock and synthetic data sets. The Microbial Co-occurrence Network Explorer, or MiCoNE (available at https://github.com/segrelab/MiCoNE) follows these default tools and parameters and can help explore the outcome of these combinations of choices on the inferred networks. We envisage that this pipeline could be used for integrating multiple data sets and generating comparative analyses and consensus networks that can guide our understanding of microbial community assembly in different biomes. IMPORTANCE Mapping the interrelationships between different species in a microbial community is important for understanding and controlling their structure and function. The surge in the high-throughput sequencing of microbial communities has led to the creation of thousands of data sets containing information about microbial abundances. These abundances can be transformed into co-occurrence networks, providing a glimpse into the associations within microbiomes. However, processing these data sets to obtain co-occurrence information relies on several complex steps, each of which involves numerous choices of tools and corresponding parameters. These multiple options pose questions about the robustness and uniqueness of the inferred networks. In this study, we address this workflow and provide a systematic analysis of how these choices of tools affect the final network and guidelines on appropriate tool selection for a particular data set. We also develop a consensus network algorithm that helps generate more robust co-occurrence networks based on benchmark synthetic data sets.

Biofilm colonization of stone materials from an Australian outdoor sculpture: Importance of geometry and exposure

The safeguarding of Australian outdoor stone heritage is currently limited by a lack of information concerning mechanisms responsible for the degradation of the built heritage. In this study, the bacterial community colonizing the stone surface of an outdoor sculpture located at the Church of St. John the Evangelist in Melbourne was analysed, providing an overview of the patterns of microbial composition associated with stone in an anthropogenic context. Illumina MiSeq 16S rRNA gene sequencing together with confocal laser microscope investigations highlighted the bacterial community was composed of both phototrophic and chemotrophic microorganisms characteristic of stone and soil, and typical of arid, salty and urban environments. Cardinal exposure, position and surface geometry were the most important factors in determining the structure of the microbial community. The North-West exposed areas on the top of the sculpture with high light exposure gave back the highest number of sequences and were dominated by Cyanobacteria. The South and West facing in middle and lower parts of the sculpture received significantly lower levels of radiation and were dominated by Actinobacteria. Proteobacteria were observed as widespread on the sculpture. This pioneer research provided an in-depth investigation of the microbial community structure on a deteriorated artistic stone in the Australian continent and provides information for the identification of deterioration-associated microorganisms and/or bacteria beneficial for stone preservation.

Gut dysbiosis in Thai intrahepatic cholangiocarcinoma and hepatocellular carcinoma

Primary liver cancer (PLC), which includes intrahepatic cholangiocarcinoma (iCCA) and hepatocellular carcinoma (HCC), has the highest incidence of all cancer types in Thailand. Known etiological factors, such as viral hepatitis and chronic liver disease do not fully account for the country's unusually high incidence. However, the gut-liver axis, which contributes to carcinogenesis and disease progression, is influenced by the gut microbiome. To investigate this relationship, fecal matter from 44 Thai PLC patients and 76 healthy controls were subjected to whole-genome metagenomic shotgun sequencing and then analyzed by marker gene-based and assembly based methods. Results revealed greater gut microbiome heterogeneity in iCCA compared to HCC and healthy controls. Two Veillonella species were found to be more abundant in iCCA samples and could distinguish iCCA from HCC and healthy controls. Conversely, Ruminococcus gnavus was depleted in iCCA patients and could distinguish HCC from iCCA samples. High Veillonella genus counts in the iCCA group were associated with enriched amino acid biosynthesis and glycolysis pathways, while enriched phospholipid and thiamine metabolism pathways characterized the HCC group with high Blautia genus counts. These findings reveal distinct landscapes of gut dysbiosis among Thai iCCA and HCC patients and warrant further investigation as potential biomarkers.

Intelligent Biological Networks: Improving Anti-Microbial Resistance Resilience through Nutritional Interventions to Understand Protozoal Gut Infections

Enteric protozoan pathogenic infections significantly contribute to the global burden of gastrointestinal illnesses. Their occurrence is considerable within remote and indigenous communities and regions due to reduced access to clean water and adequate sanitation. The robustness of these pathogens leads to a requirement of harsh treatment methods, such as medicinal drugs or antibiotics. However, in addition to protozoal infection itself, these treatments impact the gut microbiome and create dysbiosis. This often leads to opportunistic pathogen invasion, anti-microbial resistance, or functional gastrointestinal disorders, such as irritable bowel syndrome. Moreover, these impacts do not remain confined to the gut and are reflected across the gut-brain, gut-liver, and gut-lung axes, among others. Therefore, apart from medicinal treatment, nutritional supplementation is also a key aspect of providing recovery from this dysbiosis. Future proteins, prebiotics, probiotics, synbiotics, and food formulations offer a good solution to remedy this dysbiosis. Furthermore, nutritional supplementation also helps to build resilience against opportunistic pathogens and potential future infections and disorders that may arise due to the dysbiosis. Systems biology techniques have shown to be highly effective tools to understand the biochemistry of these processes. Systems biology techniques characterize the fundamental host-pathogen interaction biochemical pathways at various infection and recovery stages. This same mechanism also allows the impact of the abovementioned treatment methods of gut microbiome remediation to be tracked. This manuscript discusses system biology approaches, analytical techniques, and interaction and association networks, to understand (1) infection mechanisms and current global status; (2) cross-organ impacts of dysbiosis, particularly within the gut-liver and gut-lung axes; and (3) nutritional interventions. This study highlights the impact of anti-microbial resistance and multi-drug resistance from the perspective of protozoal infections. It also highlights the role of nutritional interventions to add resilience against the chronic problems caused by these phenomena.

The Microbiome of Osteoarthritic Hip and Knee Joints: A Prospective Multicenter Investigation

BACKGROUND

Recent advances in high-throughput DNA sequencing technologies have made it possible to characterize the microbial profile in anatomical sites previously assumed to be sterile. We used this approach to explore the microbial composition within joints of osteoarthritic patients.

METHODS

This prospective multicenter study recruited 113 patients undergoing hip or knee arthroplasty between 2017 and 2019. Demographics and prior intra-articular injections were noted. Matched synovial fluid, tissue, and swab specimens were obtained and shipped to a centralized laboratory for testing. Following DNA extraction, microbial 16S-rRNA sequencing was performed.

RESULTS

Comparisons of paired specimens indicated that each was a comparable measure for microbiological sampling of the joint. Swab specimens were modestly different in bacterial composition from synovial fluid and tissue. The 5 most abundant genera were Escherichia, Cutibacterium, Staphylococcus, Acinetobacter, and Pseudomonas. Although sample size varied, the hospital of origin explained a significant portion (18.5%) of the variance in the microbial composition of the joint, and corticosteroid injection within 6 months before arthroplasty was associated with elevated abundance of several lineages.

CONCLUSIONS

The findings revealed that prior intra-articular injection and the operative hospital environment may influence the microbial composition of the joint. Furthermore, the most common species observed in this study were not among the most common in previous skin microbiome studies, suggesting that the microbial profiles detected are not likely explained solely by skin contamination. Further research is needed to determine the relationship between the hospital and a "closed" microbiome environment. These findings contribute to establishing the baseline microbial signal and identifying contributing variables in the osteoarthritic joint, which will be valuable as a comparator in the contexts of infection and long-term arthroplasty success.

LEVEL OF EVIDENCE

Diagnostic Level II . See Instructions for Authors for a complete description of levels of evidence.