Comparative Analysis of 16S rRNA Gene and Metagenome Sequencing in Pediatric Gut Microbiomes

Extracellular vesicles of Limosilactobacillus fermentum SLAM216 ameliorate skin symptoms of atopic dermatitis by regulating gut microbiome on serotonin metabolism

Atopic dermatitis (AD) is a globally prevalent chronic inflammatory skin disorder. Its pathogenesis remains incompletely understood, resulting in considerable therapeutic challenges. Recent studies have highlighted the significance of the interaction between AD and gut microbiome. In this study, we investigated the effects of probiotic-derived extracellular vesicles on AD. Initially, we isolated and characterized extracellular vesicles from Limosilactobacillus fermentum SLAM 216 (LF216EV) and characterized their composition through multi-omics analysis. Gene ontology (GO) and pathway analysis classified LF216EV proteins into biological processes, molecular functions, and cellular components. Importantly, specific abundance in linoleic, oleic, palmitic, sebacic, and stearic acids indicating upregulated fatty acid metabolism were observed by metabolomic analysis. Furthermore, featured lipid profiling including AcylGlcADG and ceramide were observed in LF216EV. Importantly, in an atopic dermatitis-like cell model induced by TNFα/IFNγ, LF216EV significantly modulated the expression of immune regulatory genes (TSLP, TNFα, IL-6, IL-1β, and MDC), indicating its potential functionality in atopic dermatitis. LF216EV alleviated AD-like phenotypes, such as redness, scaling/dryness, and excoriation, induced by DNCB. Histopathological analysis revealed that LF216EV decreased epidermal thickness and mast cell infiltration in the dermis. Furthermore, LF216EV administration reduced mouse scratching and depression-related behaviors, with a faster onset than the classical treatment with dexamethasone. In the quantitative real-time polymerase chain reaction (qRT-PCR) analysis, we observed a significant increase in the expression levels of htrb2c, sert, and tph-1, genes associated with serotonin, in the skin and gut of the LF216EV-treated group, along with a significant increase in the total serum serotonin levels. Gut microbiome analysis of the LF216EV-treated group revealed an altered gut microbiota profile. Correlation analysis revealed that the genera Limosilactobacillus and Desulfovibrio were associated with differences in the intestinal metabolites, including serotonin. Our findings demonstrate that LF216EV mitigates AD-like symptoms by promoting serotonin synthesis through the modulation of gut microbiota and metabolome composition.

Exploiting taxonomic information from metagenomes to infer bacterial bioindicators and environmental quality at salmon aquaculture installations

Environmental DNA (eDNA) metabarcoding has emerged as a powerful method for assessing the environmental impacts of marine Atlantic salmon aquaculture by identifying bacterial bioindicators and inferring biotic indices. However, because this approach relies on the PCR amplification of 16S rRNA gene fragments, it may introduce errors that compromise bioindicator reliability. In contrast, metagenomic analysis which captures the complete set of genetic material directly extracted from environmental samples circumvents biases inherent to PCR amplification. We hypothesized that metagenomic data could offer superior assessments of benthic environmental impacts associated with salmon aquaculture compared to metabarcoding. To test this, we compared bacterial community structures derived from both metabarcoding and metagenomic analyses of 68 sediment samples obtained from aquaculture installation sites characterized by varying degrees of benthic impact as determined by macroinvertebrate inventories. Bacterial bioindicators were identified from each dataset, and Random Forest models were used to predict the degrees of benthic impacts. Metagenomics identified a greater number of bioindicators at both the family and individual sequence variant levels, resulting in higher predictive accuracy for impact assessments. Notably, only a few bioindicators were common to both methods, suggesting that methodological limitations and distorted abundance patterns in metabarcoding data may lead to spurious indicators. These findings highlight both the challenges and potential advantages of employing metagenomics for reliable environmental impact assessments.

Bacteria in the brain: do they have a role in the pathogenesis of Alzheimer's disease?

PURPOSE OF REVIEW

Worldwide efforts continue to unravel the complex pathological pathways that lead to Alzheimer's disease. The gut-brain-microbiome axis, a communication pathway between the gut, brain and microorganisms, is emerging as a potential mechanism involved in Alzheimer's disease pathogenesis. While the gut microbiome's role in Alzheimer's disease has gained significant attention, the brain microbiome remains relatively unexplored. This review summarizes the latest research on the brain microbiome in Alzheimer's disease.

RECENT FINDINGS

In the past 4 years, four out of five studies have found bacteria, such as Streptococcus pneumoniae , in postmortem samples of both control and Alzheimer's disease brains, supporting the idea that the brain is not a sterile environment. Two studies report the overabundance of several bacterial phyla, including Proteobacteria and Actinomycetes, in postmortem Alzheimer's disease brains versus controls. One study reported the presence of Borrelia burgdorferi in a subset of Alzheimer's disease cases compared to controls.

SUMMARY

Limitations and challenges persist in studying the brain microbiome, including the lack of standardized assays and data analysis methods, small sample sizes, and inconsistent use of controls for environmental microbial contamination during sample processing. Well designed studies that employ reproducible and rigorous methods are required to elucidate whether microbes are involved in the pathogenesis of Alzheimer's disease.

Advanced computational tools, artificial intelligence and machine-learning approaches in gut microbiota and biomarker identification

The microbiome of the gut is a complex ecosystem that contains a wide variety of microbial species and functional capabilities. The microbiome has a significant impact on health and disease by affecting endocrinology, physiology, and neurology. It can change the progression of certain diseases and enhance treatment responses and tolerance. The gut microbiota plays a pivotal role in human health, influencing a wide range of physiological processes. Recent advances in computational tools and artificial intelligence (AI) have revolutionized the study of gut microbiota, enabling the identification of biomarkers that are critical for diagnosing and treating various diseases. This review hunts through the cutting-edge computational methodologies that integrate multi-omics data-such as metagenomics, metaproteomics, and metabolomics-providing a comprehensive understanding of the gut microbiome's composition and function. Additionally, machine learning (ML) approaches, including deep learning and network-based methods, are explored for their ability to uncover complex patterns within microbiome data, offering unprecedented insights into microbial interactions and their link to host health. By highlighting the synergy between traditional bioinformatics tools and advanced AI techniques, this review underscores the potential of these approaches in enhancing biomarker discovery and developing personalized therapeutic strategies. The convergence of computational advancements and microbiome research marks a significant step forward in precision medicine, paving the way for novel diagnostics and treatments tailored to individual microbiome profiles. Investigators have the ability to discover connections between the composition of microorganisms, the expression of genes, and the profiles of metabolites. Individual reactions to medicines that target gut microbes can be predicted by models driven by artificial intelligence. It is possible to obtain personalized and precision medicine by first gaining an understanding of the impact that the gut microbiota has on the development of disease. The application of machine learning allows for the customization of treatments to the specific microbial environment of an individual.

Refining microbiome diversity analysis by concatenating and integrating dual 16S rRNA amplicon reads

Understanding the role of human gut microbiota in health and disease requires insights into its taxonomic composition and functional capabilities. This study evaluates whether concatenating paired-end reads enhances data output for gut microbiome analysis compared to the merging approach across various regions of the 16S rRNA gene. We assessed this approach in both mock communities and Korean cohorts with or without ulcerative colitis. Our results indicate that using the direct joining method for the V1-V3 or V6-V8 regions improves taxonomic resolution compared to merging paired-end reads (ME) in post-sequencing data. While predicting microbial function based on 16S rRNA sequencing has inherent limitations, integrating sequencing reads from both the V1-V3 and V6-V8 regions enhanced functional predictions. This was confirmed by whole metagenome sequencing (WMS) of Korean cohorts, where our approach improved taxa detection that was lost using the ME method. Thus, we propose that the integrated dual 16S rRNA sequencing technique serves as a valuable tool for microbiome research by bridging the gap between amplicon sequencing and WMS.

Bacterial DNA Contamination of Commercial PCR Enzymes: Considerations for Microbiome Protocols and Analysis

The microbiome remains a top area of research, and it is now common to examine any organic and inorganic samples for bacterial colonization. However, due to the ubiquity of bacteria in the environment, separating the low-burden colonization of bacteria from the possible contamination of laboratory reagents remains problematic. When examining samples of expected low bacterial burden, it is common to first amplify any bacterial DNA present through PCR before sequencing. In this work, we examined nine different commercial PCR enzymes and their reaction components as possible sources of bacterial DNA contamination. We found contaminating bacterial DNA in seven of the nine reactions, and this DNA was shown to come from a variety of species. Importantly, we were able to perform these studies solely with endpoint PCR and Sanger sequencing, which are more accessible and affordable than high-throughput, short-read sequencing and real-time PCR. This work confirms that there needs to be an increased emphasis on including control reactions in microbiome studies so that contaminating DNA sequences can be identified and addressed, and that this can be achieved with minimal resources.

Ruminal-buccal microbiota transmission and their diagnostic roles in subacute rumen acidosis in dairy goats

BACKGROUND

Subacute rumen acidosis (SARA) is a common metabolic disorder in ruminants that disrupts the rumen microbiome and animal health, but diagnosis is challenging due to subtle symptoms and invasive testing requirements. This study explores the potential of the buccal (oral) microbiome as a diagnostic indicator for SARA, hypothesizing an interaction with the rumen microbiome.

RESULTS

The study involved 47 dairy goats, including 11 on a control diet and 36 on high-concentrate diets with increasing rumen-degradable starch. Animals were grouped based on dietary exposure and ruminal pH: Control, Low-RDS Tolerance/SARA (LRDST/LRDSS), and High-RDS Tolerance/SARA (HRDST/HRDSS). Transcriptomics of rumen epithelium showed heightened inflammatory pathway gene expression in SARA-susceptible goats compared to controls and tolerant groups. Alpha diversity of ruminal bacteria showed lower Shannon diversity in HRDSS goats compared to HRDST whereas buccal bacteria displayed significantly lower Chao1 diversity in LRDSS goats compared to HRDST. Beta diversity analyses revealed distinct patterns between SARA-affected goats and healthy controls in both ruminal and buccal microbiomes. Prevotellaceae_UCG-003 emerged as a candidate biomarker, with reduced abundance in SARA-susceptible goats in both rumen and buccal samples. Machine learning classifiers achieved high accuracy in distinguishing SARA-susceptible goats using this genus (rumen AUC = 0.807; buccal AUC = 0.779). Source tracking analysis illustrated diminished cross-population of bacteria from the buccal to rumen (2.86% to 0.25%) and vice versa (8.59% to 1.17%), signifying compromised microbial interchange in SARA-affected goats. A microbiota transplant experiment verified SARA microbiota's ability to induce pH decline, escalate inflammation-related gene expression (MAPK10, IL17B, FOSB, SPP1), disrupt microbial transfer, and reduce Prevotellaceae_UCG-003 in recipients.

CONCLUSION

Our findings highlight SARA's dual impact on ruminal and buccal microbiota, exacerbating epithelial inflammation gene expression. Shifts in the buccal microbiome, specifically reductions in Prevotellaceae_UCG-003, mirror ruminal changes and can be influenced by inter-compartmental bacterial transmission, thereby offering a non-invasive diagnostic approach for SARA.

Integrating the milk microbiome signatures in mastitis: milk-omics and functional implications

Mammalian milk contains a variety of complex bioactive and nutritional components and microorganisms. These microorganisms have diverse compositions and functional roles that impact host health and disease pathophysiology, especially mastitis. The advent and use of high throughput omics technologies, including metagenomics, metatranscriptomics, metaproteomics, metametabolomics, as well as culturomics in milk microbiome studies suggest strong relationships between host phenotype and milk microbiome signatures in mastitis. While single omics studies have undoubtedly contributed to our current understanding of milk microbiome and mastitis, they often provide limited information, targeting only a single biological viewpoint which is insufficient to provide system-wide information necessary for elucidating the biological footprints and molecular mechanisms driving mastitis and milk microbiome dysbiosis. Therefore, integrating a multi-omics approach in milk microbiome research could generate new knowledge, improve the current understanding of the functional and structural signatures of the milk ecosystem, and provide insights for sustainable mastitis control and microbiome management.

Early life microbial succession in the gut follows common patterns in humans across the globe

Characterizing the dynamics of microbial community succession in the infant gut microbiome is crucial for understanding child health and development, but no normative model currently exists. Here, we estimate child age using gut microbial taxonomic relative abundances from metagenomes, with high temporal resolution (±3 months) for the first 1.5 years of life. Using 3154 samples from 1827 infants across 12 countries, we trained a random forest model, achieving a root mean square error of 2.56 months. We identified key taxonomic predictors of age, including declines in Bifidobacterium spp. and increases in Faecalibacterium prausnitzii and Lachnospiraceae. Microbial succession patterns are conserved across infants from diverse human populations, suggesting universal developmental trajectories. Functional analysis confirmed trends in key microbial genes involved in feeding transitions and dietary exposures. This model provides a normative benchmark of "microbiome age" for assessing early gut maturation that may be used alongside other measures of child development.

Methodological approaches in 16S sequencing of female reproductive tract in fertility patients: a review

BACKGROUND

The female genital tract microbiome has become a particular area of interest in improving assisted reproductive technology (ART) outcomes with the emergence of next-generation sequencing (NGS) technology. However, NGS assessment of microbiomes currently lacks uniformity and poses significant challenges for accurate and precise bacterial population representation.

OBJECTIVE

As multiple NGS platforms and assays have been developed in recent years for microbiome investigation-including the advent of long-read sequencing technologies-this work aimed to identify current trends and practices undertaken in female genital tract microbiome investigations.

RESULTS

Areas like sample collection and transport, DNA extraction, 16S amplification vs. metagenomics, NGS library preparation, and bioinformatic analysis demonstrated a detrimental lack of uniformity. The lack of uniformity present is a significant limitation characterised by gap discrepancies in generation and interpretation of results. Minimal consistency was observed in primer design, DNA extraction techniques, sample transport, and bioinformatic analyses.

CONCLUSION

With third-generation sequencing technology highlighted as a promising tool in microbiota-based research via full-length 16S rRNA sequencing, there is a desperate need for future studies to investigate and optimise methodological approaches of the genital tract microbiome to ensure better uniformity of methods and results interpretation to improve clinical impact.

Effects of non-pharmacological interventions on gut microbiota and intestinal permeability in older adults: A systematic review: Non-pharmacological interventions on gut microbiota/barrier

This systematic review appraised previous findings of non-pharmacological interventions on gut microbiota and/ or intestinal permeability in older adults. A literature search was performed using PubMed, Scopus, ScienceDirect and the Cochrane Library. Relevant studies were shortlisted based on the inclusion and exclusion criteria, and evaluated for risks of bias using the "Cochrane Collaboration's Risk of Bias 2" and the "NIH Quality Assessment Tool for Before-After (Pre-Post) Studies with No Control Group". The primary outcomes were the effects of non-pharmacological interventions on gut microbiota diversity and composition, and intestinal permeability in older adults. Out of 85,114 studies, 38 were shortlisted. Generally, the non-pharmacological interventions were beneficial against dysbiosis and the leaky gut in older adults. Considering specific interventions with two or more studies that reported consistent outcomes, a pattern was observed amongst the Mediterranean diet (MD), polyphenol-rich (PR) diet and supplements (i.e., probiotics, prebiotics and synbiotics). As for the other interventions, the very few studies that have been conducted did not allow a strong conclusion to be made just yet. The MD (single and multidomain interventions) restored gut microbiota by increasing species richness (alpha diversity) and reduced intestinal permeability (zonulin) and inflammation (CRP). The PR diet only showed slight changes in the gut microbiota but improved the gut barrier by reducing zonulin, CRP and IL-6. Probiotics, prebiotics and synbiotics increased the genus Bifidobacterium spp. which are considered beneficial bacteria. This review has uncovered insights into the relationship between gut microbiota and intestinal epithelial barriers of specific non-pharmacological interventions in older adults.

Gut microbiota in health and disease: advances and future prospects

The gut microbiota plays a critical role in maintaining human health, influencing a wide range of physiological processes, including immune regulation, metabolism, and neurological function. Recent studies have shown that imbalances in gut microbiota composition can contribute to the onset and progression of various diseases, such as metabolic disorders (e.g., obesity and diabetes) and neurodegenerative conditions (e.g., Alzheimer's and Parkinson's). These conditions are often accompanied by chronic inflammation and dysregulated immune responses, which are closely linked to specific forms of cell death, including pyroptosis and ferroptosis. Pathogenic bacteria in the gut can trigger these cell death pathways through toxin release, while probiotics have been found to mitigate these effects by modulating immune responses. Despite these insights, the precise mechanisms through which the gut microbiota influences these diseases remain insufficiently understood. This review consolidates recent findings on the impact of gut microbiota in these immune-mediated and inflammation-associated conditions. It also identifies gaps in current research and explores the potential of advanced technologies, such as organ-on-chip models and the microbiome-gut-organ axis, for deepening our understanding. Emerging tools, including single-bacterium omics and spatial metabolomics, are discussed for their promise in elucidating the microbiota's role in disease development.

Aquificae overcomes competition by archaeal thermophiles, and crowding by bacterial mesophiles, to dominate the boiling vent-water of a Trans-Himalayan sulfur-borax spring

Trans-Himalayan hot spring waters rich in boron, chlorine, sodium and sulfur (but poor in calcium and silicon) are known based on PCR-amplified 16S rRNA gene sequence data to harbor high diversities of infiltrating bacterial mesophiles. Yet, little is known about the community structure and functions, primary productivity, mutual interactions, and thermal adaptations of the microorganisms present in the steaming waters discharged by these geochemically peculiar spring systems. We revealed these aspects of a bacteria-dominated microbiome (microbial cell density ~8.5 × 104 mL-1; live:dead cell ratio 1.7) thriving in the boiling (85°C) fluid vented by a sulfur-borax spring called Lotus Pond, situated at 4436 m above the mean sea-level, in the Puga valley of eastern Ladakh, on the Changthang plateau. Assembly, annotation, and population-binning of >15-GB metagenomic sequence illuminated the numeral predominance of Aquificae. While members of this phylum accounted for 80% of all 16S rRNA-encoding reads within the metagenomic dataset, 14% of such reads were attributed to Proteobacteria. Post assembly, only 25% of all protein-coding genes identified were attributable to Aquificae, whereas 41% was ascribed to Proteobacteria. Annotation of metagenomic reads encoding 16S rRNAs, and/or PCR-amplified 16S rRNA genes, identified 163 bacterial genera, out of which 66 had been detected in past investigations of Lotus Pond's vent-water via 16S amplicon sequencing. Among these 66, Fervidobacterium, Halomonas, Hydrogenobacter, Paracoccus, Sulfurihydrogenibium, Tepidimonas, Thermus and Thiofaba (or their close phylogenomic relatives) were presently detected as metagenome-assembled genomes (MAGs). Remarkably, the Hydrogenobacter related MAG alone accounted for ~56% of the entire metagenome, even though only 15 out of the 66 genera consistently present in Lotus Pond's vent-water have strains growing in the laboratory at >45°C, reflecting the continued existence of the mesophiles in the ecosystem. Furthermore, the metagenome was replete with genes crucial for thermal adaptation in the context of Lotus Pond's geochemistry and topography. In terms of sequence similarity, a majority of those genes were attributable to phylogenetic relatives of mesophilic bacteria, while functionally they rendered functions such as encoding heat shock proteins, molecular chaperones, and chaperonin complexes; proteins controlling/modulating/inhibiting DNA gyrase; universal stress proteins; methionine sulfoxide reductases; fatty acid desaturases; different toxin-antitoxin systems; enzymes protecting against oxidative damage; proteins conferring flagellar structure/function, chemotaxis, cell adhesion/aggregation, biofilm formation, and quorum sensing. The Lotus Pond Aquificae not only dominated the microbiome numerically but also acted potentially as the main primary producers of the ecosystem, with chemolithotrophic sulfur oxidation (Sox) being the fundamental bioenergetic mechanism, and reductive tricarboxylic acid (rTCA) cycle the predominant carbon fixation pathway. The Lotus Pond metagenome contained several genes directly or indirectly related to virulence functions, biosynthesis of secondary metabolites including antibiotics, antibiotic resistance, and multi-drug efflux pumping. A large proportion of these genes being attributable to Aquificae, and Proteobacteria (very few were ascribed to Archaea), it could be worth exploring in the future whether antibiosis helped the Aquificae overcome niche overlap with other thermophiles (especially those belonging to Archaea), besides exacerbating the bioenergetic costs of thermal endurance for the mesophilic intruders of the ecosystem.

Metagenomic Study Reveals Phage-Bacterial Interactome Dynamics in Gut and Oral Microbiota in Pancreatic Diseases

Individuals with pancreatic-related health conditions usually show lower diversity and different composition of bacterial and viral species between the gut and oral microbiomes compared to healthy individuals. We performed a thorough microbiome analysis, using deep shotgun sequencing of stool and saliva samples obtained from patients with chronic pancreatitis (CP), pancreatic ductal adenocarcinoma (PDAC), and healthy controls (HCs).We observed similar microbiota composition at the species level in both the gut and oral samples in PDAC patients compared to HCs, among which the most distinctive finding was that the abundance of oral-originated Fusobacterium nucleatum species did not differ between the oral and the gut samples. Moreover, comparing PDAC patients with HCs, Klebsiella oxytoca was significantly more abundant in the stool samples of PDAC patients, while Streptococcus spp. showed higher abundance in both the oral and stool samples of PDAC patients. Finally, the most important finding was the distinctive gut phage-bacterial interactome pattern among PDAC patients. CrAssphages, particularly Blohavirus, showed mutual exclusion with K. oxytoca species, while Burzaovirus showed co-occurrence with Enterobacteriaceae spp., which have been shown to be capable of inducing DNA damage in human pancreatic cells ex vivo. The interactome findings warrant further mechanistic studies, as our findings may provide new insights into developing microbiota-based diagnostic and therapeutic methods for pancreatic diseases.

A streamlined culturomics case study for the human gut microbiota research

Bacterial culturomics is a set of techniques to isolate and identify live bacteria from complex microbial ecosystems. Despite its potential to revolutionize microbiome research, bacterial culturomics has significant challenges when applied to human gut microbiome studies due to its labor-intensive nature. Therefore, we established a streamlined culturomics approach with minimal culture conditions for stool sample preincubation. We evaluated the suitability of non-selective medium candidates for maintaining microbial diversity during a 30-day incubation period based on 16S rRNA gene amplicon analysis. Subsequently, we applied four culture conditions (two preincubation media under an aerobic/anaerobic atmosphere) to isolate gut bacteria on a large scale from eight stool samples of healthy humans. We identified 8141 isolates, classified into 263 bacterial species, including 12 novel species candidates. Our analysis of cultivation efficiency revealed that seven days of aerobic and ten days of anaerobic incubation captured approximately 91% and 95% of the identified species within each condition, respectively, with a synergistic effect confirmed when selected preincubation media were combined. Moreover, our culturomics findings expanded the coverage of gut microbial diversity compared to 16S rRNA gene amplicon sequencing results. In conclusion, this study demonstrated the potential of a streamlined culturomics approach for the efficient isolation of gut bacteria from human stool samples. This approach might pave the way for the broader adoption of culturomics in human gut microbiome studies, ultimately leading to a more comprehensive understanding of this complex microbial ecosystem.

Development of a multiplex PCR assay and quantification of microbial markers by ddPCR for identification of saliva and vaginal fluid

The identification of biological fluids at crime scenes contributes to crime scene reconstruction and provides investigative leads. Traditional methods for body fluid identification are limited in terms of sensitivity and are mostly presumptive. Emerging methods based on mRNA and DNA methylation require high quality template source. An exploitable characteristic of body fluids is their distinct microbial profiles allowing for the discrimination of body fluids based on microbiome content. Microbial DNA is highly abundant within the body, robust and stable and can persist in the environment long after human DNA has degraded. 16S rRNA sequencing is the gold standard for microbial analysis; however, NGS is costly, and requires intricate workflows and interpretation. Also, species level resolution is not always achievable. Based on the current challenges, the first objective of this study was to develop a multiplex conventional PCR assay to identify vaginal fluid and saliva by targeting species-specific 16S rRNA microbial markers. The second objective was to employ droplet digital PCR (ddPCR) as a novel approach to quantify bacterial species alone and in a mixture of body fluids. Lactobacillus crispatus and Streptococcus salivarius were selected because of high abundance within vaginal fluid and saliva respectively. While Fusobacterium nucleatum and Gardnerella vaginalis, though present in healthy humans, are also frequently found in oral and vaginal infections, respectively. The multiplex PCR assay detected L. crispatus and G. vaginalis in vaginal fluid while F. nucleatum and S. salivarius was detected in saliva. Multiplex PCR detected F. nucleatum, S. salivarius and L. crispatus in mixed body fluid samples while, G. vaginalis was undetected in mixtures containing vaginal fluid. For samples exposed at room temperature for 65 days, L. crispatus and G. vaginalis were detected in vaginal swabs while only S. salivarius was detected in saliva swabs. The limit of detection was 0.06 copies/µl for F. nucleatum (2.5 ×10-9 ng/µl) and S. salivarius (2.5 ×10-6 ng/µl). L. crispatus and G. vaginalis had detection limits of 0.16 copies/µl (2.5 ×10-4 ng/µl) and 0.48 copies/µl (2.5 ×10-7 ng/µl). All 4 bacterial species were detected in mixtures and aged samples by ddPCR. No significant differences were observed in quantity of bacterial markers in saliva and vaginal fluid. The present research reports for the first time the combination of the above four bacterial markers for the detection of saliva and vaginal fluid and highlights the sensitivity of ddPCR for bacterial quantification in pure and mixed body fluids.

Size-dependent effects of microplastics on intestinal microbiome for Perna viridis

Microplastics pollution threatens to marine organisms, particularly bivalves that actively ingest and accumulate microplastics of certain sizes, potentially disrupting intestinal homeostasis. This study investigated the microplastic abundance in wild and farmed mussels around Singapore, and examined the size-dependent effects of nano- to micro-scale polystyrene (0.5 µm/5 µm/50 µm) on the mussel intestinal microbiome in the laboratory. The field investigation revealed higher microplastic abundance in farmed mussels compared to wild ones. Experimentally, mussels exposed to 0.6 mg/L of microplastics for 7 days, followed by a 7-day depuration period, showed substantial impacts on Spirochaetes and Proteobacteria, facilitating the proliferation of pathogenic species and differentially affecting their pathogenic contributions. Metagenomics analysis revealed that microplastic exposure reduced Spirochaeta's contribution to virulence and pathogenicity loss, did not affect Vibrio and Oceanispirochaeta's pathogenicity, and increased Treponema and Oceanispirochaeta's contributions to pathogenicity loss. Moreover, microplastics increased transmembrane transporters and impacted oxidative phosphorylation enzymes, impairing energy metabolism. These effects persisted after depuration, indicating lack of resilience in the microbiome. Nano- and micro-scale plastics perturbed the mussel microbiome composition and functions in a size-dependent manner, with nano-plastics being the most disruptive. The increasing use and sale of aquaculture equipment of plastic may exacerbate the intestinal dysbiosis in bivalves, which threatens consumers' health.

Environmental, socioeconomic, and health factors associated with gut microbiome species and strains in isolated Honduras villages

Despite a growing interest in the gut microbiome of non-industrialized countries, data linking deeply sequenced microbiomes from such settings to diverse host phenotypes and situational factors remain uncommon. Using metagenomic data from a community-based cohort of 1,871 people from 19 isolated villages in the Mesoamerican highlands of western Honduras, we report associations between bacterial species and human phenotypes and factors. Among them, socioeconomic factors account for 51.44% of the total associations. Meta-analysis of species-level profiles across several datasets identified several species associated with body mass index, consistent with previous findings. Furthermore, the inclusion of strain-phylogenetic information modifies the overall relationship between the gut microbiome and the phenotypes, especially for some factors like household wealth (e.g., wealthier individuals harbor different strains of Eubacterium rectale). Our analysis suggests a role that gut microbiome surveillance can play in understanding broad features of individual and public health.

Impact of Prophylactic Antibiotic Use in Ornamental Fish Tanks on Microbial Communities and Pathogen Selection in Carriage Water in Hong Kong Retail Shops

Antibiotics are routinely added to ornamental fish tanks for treating bacterial infection or as a prophylactic measure. However, the overuse or subtherapeutical application of antibiotics could potentially facilitate the selection of antibiotic resistance in bacteria, yet no studies have investigated antibiotic use in the retail ornamental fish sector and its impact on microbial communities. The present study analyzed the concentrations of twenty antibiotics in the carriage water (which also originates from fish tanks in retail shops) collected monthly from ten local ornamental fish shops over a duration of three months. The antibiotic concentrations were correlated with the sequenced microbial community composition, and the risk of resistance selection in bacteria was assessed. Results revealed that the detected concentrations of tetracyclines were the highest among samples, followed by fluoroquinolones and macrolides. The concentrations of oxytetracycline (44.3 to 2,262,064.2 ng L-1) detected across three months demonstrated a high risk for resistance selection at most of the sampled shops. Zoonotic pathogens (species of Rhodococcus, Legionella, and Citrobacter) were positively correlated with the concentrations of oxytetracycline, tetracycline, chlortetracycline, and enrofloxacin. This suggests that antibiotic use in retail shops may increase the likelihood of selecting for zoonotic pathogens. These findings shed light on the potential for ornamental fish retail shops to create a favorable environment for the selection of pathogens with antibiotics, thereby highlighting the urgent need for enhanced antibiotic stewardship within the industry.

Inactivation of Cronobacter sakazakii in powdered infant formula with probiotics and metagenomic analysis

In the present study, proper manual for powdered infant formula with probiotics (PIF-P) to prevent the contamination of Cronobacter sakazakii was investigated. First, the population of C. sakazakii and LAB in three different PIF-P samples were quantitatively analyzed after reconstituted with hydrothermal treatments. When C. sakazakii was inoculated into reconstituted infant formula with probiotics (RIF-P), it was immediately reduced below the detection limit by 60-65 °C hydrothermal treatment whereas reduction levels of LAB was 1-2 log CFU/g. When heat resistance of C. sakazakii inoculated to PIF-P with 4 h drying was compared with that inoculated to RIF-P samples, the heat resistance of C. sakazakii increased significantly after the inoculation in PIF-P with drying. Metagenomic analysis revealed that Lactobacillus and Bifidobacterium were dominant genus in all three groups and there was no significant difference in the microbial community of untreated PIF sample and hydrothermal treated samples.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10068-023-01503-x.

MicroPredict: predicting species-level taxonomic abundance of whole-shotgun metagenomic data using only 16S amplicon sequencing data

BACKGROUND

The importance of the human microbiome in the analysis of various diseases is emerging. The two main methods used to profile the human microbiome are 16S rRNA gene sequencing (16S sequencing) and whole-genome shotgun sequencing (WGS). Owing to the full coverage of the genome in sequencing, WGS has multiple advantages over 16S sequencing, including higher taxonomic profiling resolution at the species-level and functional profiling analysis. However, 16S sequencing remains widely used because of its relatively low cost. Although WGS is the standard method for obtaining accurate species-level data, we found that 16S sequencing data contained rich information to predict high-resolution species-level abundances with reasonable accuracy.

OBJECTIVE

In this study, we proposed MicroPredict, a method for accurately predicting WGS-comparable species-level abundance data using 16S taxonomic profile data.

METHODS

We employed a mixed model using two key strategies: (1) modeling both sample- and species-specific information for predicting WGS abundances, and (2) accounting for the possible correlations among different species.

RESULTS

We found that MicroPredict outperformed the other machine learning methods.

CONCLUSION

We expect that our approach will help researchers accurately approximate the species-level abundances of microbiome profiles in datasets for which only cost-effective 16S sequencing has been applied.

Short-duration selective decontamination of the digestive tract infection control does not contribute to increased antimicrobial resistance burden in a pilot cluster randomised trial (the ARCTIC Study)

OBJECTIVE

Selective decontamination of the digestive tract (SDD) is a well-studied but hotly contested medical intervention of enhanced infection control. Here, we aim to characterise the changes to the microbiome and antimicrobial resistance (AMR) gene profiles in critically ill children treated with SDD-enhanced infection control compared with conventional infection control.

DESIGN

We conducted shotgun metagenomic microbiome and resistome analysis on serial oropharyngeal and faecal samples collected from critically ill, mechanically ventilated patients in a pilot multicentre cluster randomised trial of SDD. The microbiome and AMR profiles were compared for longitudinal and intergroup changes. Of consented patients, faecal microbiome baseline samples were obtained in 89 critically ill children. Additionally, samples collected during and after critical illness were collected in 17 children treated with SDD-enhanced infection control and 19 children who received standard care.

RESULTS

SDD affected the alpha and beta diversity of critically ill children to a greater degree than standard care. At cessation of treatment, the microbiome of SDD patients was dominated by Actinomycetota, specifically Bifidobacterium, at the end of mechanical ventilation. Altered gut microbiota was evident in a subset of SDD-treated children who returned late longitudinal samples compared with children receiving standard care. Clinically relevant AMR gene burden was unaffected by the administration of SDD-enhanced infection control compared with standard care. SDD did not affect the composition of the oral microbiome compared with standard treatment.

CONCLUSION

Short interventions of SDD caused a shift in the microbiome but not of the AMR gene pool in critically ill children at the end mechanical ventilation, compared with standard antimicrobial therapy.

The longitudinal microbial and metabolic landscape of infant cystic fibrosis: the gut-lung axis

BACKGROUND AND AIM

In cystic fibrosis, gastrointestinal dysfunction and lower airway infection occur early and are independently associated with poorer outcomes in childhood. This study aimed to define the relationship between the microbiota at each niche during the first 2 years of life, its association with growth and airway inflammation, and explanatory features in the metabolome.

MATERIALS AND METHODS

67 bronchoalveolar lavage fluid (BALF), 62 plasma and 105 stool samples were collected from 39 infants with cystic fibrosis between 0 and 24 months who were treated with prophylactic antibiotics. 16S rRNA amplicon and shotgun metagenomic sequencing were performed on BALF and stool samples, respectively; metabolomic analyses were performed on all sample types. Sequencing data from healthy age-matched infants were used as controls.

RESULTS

Bacterial diversity increased over the first 2 years in both BALF and stool, and microbial maturation was delayed in comparison to healthy controls from the RESONANCE cohort. Correlations between their respective abundance in both sites suggest stool may serve as a noninvasive alternative for detecting BALF Pseudomonas and Veillonella. Multisite metabolomic analyses revealed age- and growth-related changes, associations with neutrophilic airway inflammation, and a set of core systemic metabolites. BALF Pseudomonas abundance was correlated with altered stool microbiome composition and systemic metabolite alterations, highlighting a complex gut-plasma-lung interplay and new targets with therapeutic potential.

CONCLUSION

Exploration of the gut-lung microbiome and metabolome reveals diverse multisite interactions in cystic fibrosis that emerge in early life. Gut-lung metabolomic links with airway inflammation and Pseudomonas abundance warrant further investigation for clinical utility, particularly in non-expectorating patients.

Analysis on the change of gut microbiota and metabolome in lung transplant patients

Previous studies have shown that the gut microbiota and its metabolites are associated with the success of organ transplantation. However, the specific changes in the gut microbiota of lung transplant patients remain unclear. Hence, this study aimed to elucidate the interplay between the gut microbiota, metabolome, and lung transplantation outcomes. Using 16S metagenomics sequencing and untargeted metabolic profiling, we conducted a comprehensive analysis of gut microbial and metabolic alterations in lung transplant recipients relative to non-transplant group. Our findings revealed the predominance of Enterococcus and Streptococcus genera within the lung transplant cohort, accompanied by the significant reduction in Bacteroides, Epulopiscium, Faecalibacterium, and Prevotella abundance. In addition, a significant reduction in ATRA (all-trans retinoic acid) levels and suppression of IgA production were observed in lung transplant recipients, which were found to be closely associated with the Enterococcus genus. It was speculated that the association might have implications for the prognosis of lung transplant patients. Notably, the differences in gut microbial composition and metabolomic profiles between successful transplant recipients and those experiencing chronic rejection were not statistically significant. These novel insights shed light on the putative implications of the gut microbiota and metabolome in shaping lung transplantation outcomes, and provide a foundation for future investigations and targeted therapeutic interventions.

IMPORTANCE

This study has profound implications for lung transplantation as it uncovers the important role of gut microbiota and metabolome in shaping transplantation outcomes. The identification of dominant bacterial genera, such as Enterococcus and Streptococcus, within the lung transplant cohort, along with the significant decrease in Bacteroides, Epulopiscium, Faecalibacterium, and Prevotella abundance, reveals potential microbial imbalances associated with lung transplantation. In addition, a significant reduction in ATRA (all-trans retinoic acid) levels and suppression of IgA production were observed in lung transplant recipients, which were found to be closely associated with the Enterococcus genus. It was speculated that the association might have implications for the prognosis of lung transplant patients. These findings hold immense clinical significance as they lay the groundwork for future research and targeted therapeutic interventions. Understanding the impact of the gut microbiota and metabolome on lung transplantation outcomes offers promising avenues for improving transplantation patient prognosis.

Amplidiff: an optimized amplicon sequencing approach to estimating lineage abundances in viral metagenomes

BACKGROUND

Metagenomic profiling algorithms commonly rely on genomic differences between lineages, strains, or species to infer the relative abundances of sequences present in a sample. This observation plays an important role in the analysis of diverse microbial communities, where targeted sequencing of 16S and 18S rRNA, both well-known hypervariable genomic regions, have led to insights into microbial diversity and the discovery of novel organisms. However, the variable nature of discriminatory regions can also act as a double-edged sword, as the sought-after variability can make it difficult to design primers for their amplification through PCR. Moreover, the most variable regions are not necessarily the most informative regions for the purpose of differentiation; one should focus on regions that maximize the number of lineages that can be distinguished.

RESULTS

Here we present AmpliDiff, a computational tool that simultaneously finds highly discriminatory genomic regions in viral genomes of a single species, as well as primers allowing for the amplification of these regions. We show that regions and primers found by AmpliDiff can be used to accurately estimate relative abundances of SARS-CoV-2 lineages, for example in wastewater sequencing data. We obtain errors that are comparable with using whole genome information to estimate relative abundances. Furthermore, our results show that AmpliDiff is robust against incomplete input data and that primers designed by AmpliDiff also bind to genomes sampled months after the primers were selected.

CONCLUSIONS

With AmpliDiff we provide an effective, cost-efficient alternative to whole genome sequencing for estimating lineage abundances in viral metagenomes.

Exploring Cereal Metagenomics: Unravelling Microbial Communities for Improved Food Security

Food security is an urgent global challenge, with cereals playing a crucial role in meeting the nutritional requirements of populations worldwide. In recent years, the field of metagenomics has emerged as a powerful tool for studying the microbial communities associated with cereal crops and their impact on plant health and growth. This chapter aims to provide a comprehensive overview of cereal metagenomics and its role in enhancing food security through the exploration of beneficial and pathogenic microbial interactions. Furthermore, we will examine how the integration of metagenomics with other tools can effectively address the adverse effects on food security. For this purpose, we discuss the integration of metagenomic data and machine learning in providing novel insights into the dynamic interactions shaping plant-microbe relationships. We also shed light on the potential applications of leveraging microbial diversity and epigenetic modifications in improving crop resilience and yield sustainability. Ultimately, cereal metagenomics has revolutionized the field of food security by harnessing the potential of beneficial interactions between cereals and their microbiota, paving the way for sustainable agricultural practices.

A comprehensive overview of microbiome data in the light of machine learning applications: categorization, accessibility, and future directions

Metagenomics, Metabolomics, and Metaproteomics have significantly advanced our knowledge of microbial communities by providing culture-independent insights into their composition and functional potential. However, a critical challenge in this field is the lack of standard and comprehensive metadata associated with raw data, hindering the ability to perform robust data stratifications and consider confounding factors. In this comprehensive review, we categorize publicly available microbiome data into five types: shotgun sequencing, amplicon sequencing, metatranscriptomic, metabolomic, and metaproteomic data. We explore the importance of metadata for data reuse and address the challenges in collecting standardized metadata. We also, assess the limitations in metadata collection of existing public repositories collecting metagenomic data. This review emphasizes the vital role of metadata in interpreting and comparing datasets and highlights the need for standardized metadata protocols to fully leverage metagenomic data's potential. Furthermore, we explore future directions of implementation of Machine Learning (ML) in metadata retrieval, offering promising avenues for a deeper understanding of microbial communities and their ecological roles. Leveraging these tools will enhance our insights into microbial functional capabilities and ecological dynamics in diverse ecosystems. Finally, we emphasize the crucial metadata role in ML models development.

Bacterial pathogens in Xpert MTB/RIF Ultra-negative sputum samples of patients with presumptive tuberculosis in a high TB burden setting: a 16S rRNA analysis

In patients with presumptive tuberculosis (TB) in whom the diagnosis of TB was excluded, understanding the bacterial etiology of lower respiratory tract infections (LRTIs) is important for optimal patient management. A secondary analysis was performed on a cohort of 250 hospitalized patients with symptoms of TB. Bacterial DNA was extracted from sputum samples for Illumina 16S rRNA sequencing to identify bacterial species based on amplicon sequence variant level. The bacterial pathogen most likely to be responsible for the patients' LRTI could only be identified in a minority (6.0%, 13/215) of cases based on 16S rRNA amplicon sequencing: Mycoplasma pneumoniae (n = 7), Bordetella pertussis (n = 2), Acinetobacter baumanii (n = 2), and Pseudomonas aeruginosa (n = 2). Other putative pathogens were present in similar proportions of Xpert Ultra-positive and Xpert Ultra-negative sputum samples. The presence of Streptococcus (pseudo)pneumoniae appeared to increase the odds of radiological abnormalities (aOR 2.5, 95% CI 1.12-6.16) and the presence of S. (pseudo)pneumoniae (aOR 5.31, 95% CI 1.29-26.6) and Moraxella catarrhalis/nonliquefaciens (aOR 12.1, 95% CI 2.67-72.8) increased the odds of 6-month mortality, suggesting that these pathogens might have clinical relevance. M. pneumoniae, B. pertussis, and A. baumanii appeared to be the possible causes of TB-like symptoms. S. (pseudo)pneumoniae and M. catarrhalis/nonliquefaciens also appeared of clinical relevance based on 16S rRNA amplicon sequencing. Further research using tools with higher discriminatory power than 16S rRNA sequencing is required to develop optimal diagnostic and treatment strategies for this population.IMPORTANCEThe objective of this study was to identify possible bacterial lower respiratory tract infection (LRTI) pathogens in hospitalized patients who were initially suspected to have TB but later tested negative using the Xpert Ultra test. Although 16S rRNA was able to identify some less common or difficult-to-culture pathogens such as Mycoplasma pneumoniae and Bordetella pertussis, one of the main findings of the study is that, in contrast to what we had hypothesized, 16S rRNA is not a method that can be used to assist in the management of patients with presumptive TB having a negative Xpert Ultra test. Even though this could be considered a negative finding, we believe it is an important finding to report as it highlights the need for further research using different approaches.

On the limits of 16S rRNA gene-based metagenome prediction and functional profiling

Molecular profiling techniques such as metagenomics, metatranscriptomics or metabolomics offer important insights into the functional diversity of the microbiome. In contrast, 16S rRNA gene sequencing, a widespread and cost-effective technique to measure microbial diversity, only allows for indirect estimation of microbial function. To mitigate this, tools such as PICRUSt2, Tax4Fun2, PanFP and MetGEM infer functional profiles from 16S rRNA gene sequencing data using different algorithms. Prior studies have cast doubts on the quality of these predictions, motivating us to systematically evaluate these tools using matched 16S rRNA gene sequencing, metagenomic datasets, and simulated data. Our contribution is threefold: (i) using simulated data, we investigate if technical biases could explain the discordance between inferred and expected results; (ii) considering human cohorts for type two diabetes, colorectal cancer and obesity, we test if health-related differential abundance measures of functional categories are concordant between 16S rRNA gene-inferred and metagenome-derived profiles and; (iii) since 16S rRNA gene copy number is an important confounder in functional profiles inference, we investigate if a customised copy number normalisation with the rrnDB database could improve the results. Our results show that 16S rRNA gene-based functional inference tools generally do not have the necessary sensitivity to delineate health-related functional changes in the microbiome and should thus be used with care. Furthermore, we outline important differences in the individual tools tested and offer recommendations for tool selection.

Role of the intestinal microbiome and its therapeutic intervention in cardiovascular disorder

The gut microbiome is a heterogeneous population of microbes comprising viruses, bacteria, fungi, and protozoa. Such a microbiome is essential for sustaining host equilibrium, and its impact on human health can be altered by a variety of factors such as external variables, social behavior, age, nutrition, and genetics. Gut microbes' imbalances are related to a variety of chronic diseases including cancer, obesity, and digestive disorders. Globally, recent findings show that intestinal microbes have a significant role in the formation of cardiovascular disease (CVD), which is still the primary cause of fatalities. Atherosclerosis, hypertension, diabetes, inflammation, and some inherited variables are all cardiovascular risk variables. However, studies found correlations between metabolism, intestinal flora, and dietary intake. Variations in the diversity of gut microbes and changes in their activity are thought to influence CVD etiology. Furthermore, the gut microbiota acts as an endocrine organ, producing bioactive metabolites such as TMA (trimethylamine)/TMAO (trimethylamine N-oxide), SCFA (short-chain fatty acids), and bile acids, which have a substantial impact on host wellness and disease by multiple mechanisms. The purpose of this overview is to compile current evidence highlighting the intricate links between gut microbiota, metabolites, and the development of CVD. It focuses on how intestinal dysbiosis promotes CVD risk factors such as heart failure, hypertension, and atherosclerosis. This review explores the normal physiology of intestinal microbes and potential techniques for targeting gut bacteria for CVD treatment using various microbial metabolites. It also examines the significance of gut bacteria in disease treatment, including supplements, prebiotics, probiotics, antibiotic therapies, and fecal transplantation, which is an innovative approach to the management of CVD. As a result, gut bacteria and metabolic pathways become increasingly attractive as potential targets for CVD intervention.

Multi-omics analysis reveals associations between host gene expression, gut microbiota, and metabolites in chickens

Egg-laying is an important trait in chickens, and it is affected by many factors, such as hormones regulated by the hypothalamic-pituitary axis and precursors synthesized by the liver. Recent studies showed that gut microbiota was associated with egg-laying, however, its underlying mechanism remains unclear. We comprehensively analyzed the host transcriptome, gut microbiota, and metabolome in broiler breeder hens during the pre-laying, peak-laying, and late-laying periods. The transcriptome analysis of the tissues related to the hypothalamic-pituitary-liver (HPL) axis revealed dynamic gene expression during egg-laying periods. Differentially expressed genes (DEGs) (i.e., PENK, NPY, AVP, PRL, RLN3, and FST) from the hypothalamus and pituitary gland were involved in female gonadal development, hormone secretion, response to endogenous stimulus, liver development, and amide metabolism. In liver, DEGs (i.e., FABP3, VTG1, LPL, APOA5, APOV1, and RBP5) were enriched in efferocytosis, sphingolipid metabolism, amide, and peptide biosynthesis. Alpha and beta diversity changed significantly in cecum microbiota during different laying periods. The abundance of Firmicutes was decreased and the abundance of Bacteroidota was increased during the peak-laying period. Functional analysis showed that the biosynthesis of secondary metabolites, amino acids, purine, and steroid hormones was altered during laying. The metabolome analysis from cecal contents showed that amino acid metabolism and steroid hormone biosynthesis changed during laying. Integrated analysis of the cecal microbiota and metabolites showed the genus Megasphaera was involved in amino acid metabolism, which included 3-phenyllatic acid, quinic acid, caffeic acid, and folic acid, and the genus Hungatella participated in steroid hormone biosynthesis through its strong correlation with estradiol. These results explored the dynamic changes in tissues related to the HPL axis and cecal microbiota and provided new insights into the interaction between the host and microbiota during egg-laying in chickens.

Metagenomics and metabolomics approaches in the study of Candida albicans colonization of host niches: a framework for finding microbiome-based antifungal strategies

In silico and experimental approaches have allowed an ever-growing understanding of the interactions within the microbiota. For instance, recently acquired data have increased knowledge of the mechanisms that support, in the gut and vaginal microbiota, the resistance to colonization by Candida albicans, an opportunistic fungal pathogen whose overgrowth can initiate severe infections in immunocompromised patients. Here, we review how bacteria from the microbiota interact with C. albicans. We show how recent OMICs-based pipelines, using metagenomics and/or metabolomics, have identified bacterial species and metabolites modulating C. albicans growth. We finally discuss how the combined use of cutting-edge OMICs-based and experimental approaches could provide new means to control C. albicans overgrowth within the microbiota and prevent its consequences.

Simultaneous analysis of seven 16S rRNA hypervariable gene regions increases efficiency in marine bacterial diversity detection

Environmental DNA sequencing is the gold standard to reveal microbial community structures. In most applications, a one-fragment PCR approach is applied to amplify a taxonomic marker gene, usually a hypervariable region of the 16S rRNA gene. We used a new reverse complement (RC)-PCR-based assay that amplifies seven out of the nine hypervariable regions of the 16S rRNA gene, to interrogate bacterial communities in sediment samples collected from different coastal marine sites with an impact gradient. In parallel, we employed a traditional one-fragment analysis of the hypervariable V3-V4 region to investigate whether the RC-PCR reveals more of the 'unseen' diversity obtained by the one-fragment approach. As a benchmark for the full deck of diversity, we subjected the samples to PCR-free metagenomic sequencing. None of the two PCR-based approaches recorded the full taxonomic repertoire obtained from the metagenomics datasets. However, the RC-PCR approach detected 2.8 times more bacterial genera compared to the near-saturation sequenced V3-V4 samples. RC-PCR is an ideal compromise between the standard one-fragment approach and metagenomics sequencing and may guide future environmental sequencing studies, in which bacterial diversity is a central subject.

Gut microbiomes of agropastoral children from the Adadle region of Ethiopia reflect their unique dietary habits

The composition and function of the intestinal microbiota are major determinants of human health and are strongly influenced by diet, antibiotic treatment, lifestyle and geography. Nevertheless, we currently have only little data on microbiomes of non-westernized communities. We assess the stool microbiota composition in 59 children aged 2-5 years from the Adadle district of Ethiopia, Somali Regional State. Here, milk and starch-rich food are predominant components of the local diet, where the inhabitants live a remote, traditional agropastoral lifestyle. Microbiota composition, function and the resistome were characterized by both 16S rRNA gene amplicon and shotgun metagenomic sequencing and compared to 1471 publicly available datasets from children living in traditional, transitional, and industrial communities with different subsistence strategies. Samples from the Adadle district are low in Bacteroidaceae, and Prevotellaceae, the main bacterial representatives in the feces of children living in industrialized and non-industrialized communities, respectively. In contrast, they had a higher relative abundance in Streptococcaceae, Bifidobacteriaceae and Erysipelatoclostridiaceae. Further, genes involved in degradation pathways of lactose, D-galactose and simple carbohydrates were enriched. Overall, our study revealed a unique composition of the fecal microbiota of these agropastoral children, highlighting the need to further characterize the fecal bacterial composition of human populations living different lifestyles.

Gut Microbiota, Inflammation, and Probiotic Supplementation in Fetal Growth Restriction-A Comprehensive Review of Human and Animal Studies

Fetal growth restriction (FGR) is a pathological state that represents a fetus's inability to achieve adequate growth during pregnancy. Several maternal, placental, and fetal factors are likely associated with FGR etiology. FGR is linked to severe fetal and neonatal complications, as well as adverse health consequences in adulthood. Numerous randomized controlled trials (RCTs) have demonstrated improved growth in FGR fetuses with promising treatment strategies such as maternal micronutrient, amino acid, and nitric oxide supplementation. Elevated inflammation in pregnant women diagnosed with FGR has been associated with an imbalance between pro- and anti-inflammatory cytokines. Gut microbiota dysbiosis may result in increased FGR-related inflammation. Probiotic treatment may relieve FGR-induced inflammation and improve fetal growth. The aim of this review is to provide an overview of the gut microbiota and inflammatory profiles associated with FGR and explore the potential of probiotics in treating FGR.

Establishing the link between microbial communities in bovine liver abscesses and the gastrointestinal tract

BACKGROUND

Liver abscesses (LAs) are one of the most common and important problems faced by the beef industry. The most efficacious method for the prevention of LAs in North America is through dietary inclusion of low doses of antimicrobial drugs such as tylosin, but the mechanisms by which this treatment prevents LAs are not fully understood. LAs are believed to result from mucosal barrier dysfunction in the gastrointestinal tract (GIT) allowing bacterial translocation to the liver via the portal vein, yet differences in the GIT microbiome of cattle with and without LAs have not been explored. Here, we characterized microbial communities from LAs, rumen, ileum, and colon from the same cattle for the first time.

RESULTS

Results demonstrate that tylosin supplementation was associated with differences in microbial community structure in the rumen and small intestine, largely because of differences in the predominance of Clostridia. Importantly, we show for the first time that microbial communities from multiple LAs in one animal's liver are highly similar, suggesting that abscesses found at different locations in the liver may originate from a localized source in the GIT (rather than disparate locations). A large portion of abscesses were dominated by microbial taxa that were most abundant in the hindgut. Further, we identified taxa throughout the GIT that were differentially abundant between animals with and without liver abscesses. Bifidobacterium spp.-a bacteria commonly associated with a healthy GIT in several species-were more abundant in the rumen and ileum of animals without LAs compared to those with LAs.

CONCLUSIONS

Together these results provide the first direct comparison of GIT and LA microbial communities within the same animal, add considerable evidence to the hypothesis that some LA microbial communities arise from the hindgut, and suggest that barrier dysfunction throughout the GIT may be the underlying cause of LA formation in cattle.

Comparative analysis of shotgun metagenomics and 16S rDNA sequencing of gut microbiota in migratory seagulls

Background

Shotgun metagenomic and 16S rDNA sequencing are commonly used methods to identify the taxonomic composition of microbial communities. Previously, we analysed the gut microbiota and intestinal pathogenic bacteria configuration of migratory seagulls by using 16S rDNA sequencing and culture methods.

Methods

To continue in-depth research on the gut microbiome and reveal the applicability of the two methods, we compared the metagenome and 16S rDNA amplicon results to further demonstrate the features of this animal.

Results

The number of bacterial species detected by metagenomics gradually increased from the phylum to species level, consistent with 16S rDNA sequencing. Several taxa were commonly shared by both sequencing methods. However, Escherichia, Shigella, Erwinia, Klebsiella, Salmonella, Escherichia albertii, Shigella sonnei, Salmonella enterica, and Shigella flexneri were unique taxa for the metagenome compared with Escherichia-Shigella, Hafnia-Obesumbacterium, Catellicoccus marimammalium, Lactococcus garvieae, and Streptococcus gallolyticus for 16S rDNA sequencing. The largest differences in relative abundance between the two methods were identified at the species level, which identified many pathogenic bacteria to humans using metagenomic sequencing. Pearson correlation analysis indicated that the correlation coefficient for the two methods gradually decreased with the refinement of the taxonomic levels. The high consistency of the correlation coefficient was identified at the genus level for the beta diversity of the two methods.

Conclusions

In general, relatively consistent patterns and reliability could be identified by both sequencing methods, but the results varied following the refinement of taxonomic levels. Metagenomic sequencing was more suitable for the discovery and detection of pathogenic bacteria of gut microbiota in seagulls. Although there were large differences in the numbers and abundance of bacterial species of the two methods in terms of taxonomic levels, the patterns and reliability results of the samples were consistent.

Integrative analysis of microbial 16S gene and shotgun metagenomic sequencing data improves statistical efficiency

Background

The most widely used technologies for profiling microbial communities are 16S marker-gene sequencing and shotgun metagenomic sequencing. Interestingly, many microbiome studies have performed both sequencing experiments on the same cohort of samples. The two sequencing datasets often reveal consistent patterns of microbial signatures, highlighting the potential for an integrative analysis to improve power of testing these signatures. However, differential experimental biases, partially overlapping samples, and differential library sizes pose tremendous challenges when combining the two datasets. Currently, researchers either discard one dataset entirely or use different datasets for different objectives.

Methods

In this article, we introduce the first method of this kind, named Com-2seq, that combines the two sequencing datasets for testing differential abundance at the genus and community levels while overcoming these difficulties. The new method is based on our LOCOM model (Hu et al., 2022), which employs logistic regression for testing taxon differential abundance while remaining robust to experimental bias. To benchmark the performance of Com-2seq, we introduce two ad hoc approaches: applying LOCOM to pooled taxa count data and combining LOCOM p-values from analyzing each dataset separately.

Results

Our simulation studies indicate that Com-2seq substantially improves statistical efficiency over analysis of either dataset alone and works better than the two ad hoc approaches. An application of Com-2seq to two real microbiome studies uncovered scientifically plausible findings that would have been missed by analyzing individual datasets.

Conclusions

Com-2seq performs integrative analysis of 16S and metagenomic sequencing data, which improves statistical efficiency and has the potential to accelerate the search of microbial communities and taxa that are involved in human health and diseases.

Exploring the microbiome: Uncovering the link with lung cancer and implications for diagnosis and treatment

Lung cancer is the leading cause of cancer-related deaths worldwide. Tobacco smoking and air pollution are believed to be responsible for more than 90% of lung cancers. Respiratory pathogens are also known to be associated with the initiation and development of lung cancer. Despite the fact that the bacterial biomass in the lungs is lower than that in the intestinal tract, emerging evidence indicates that the lung is colonized by a diverse array of microbes. However, there is limited knowledge regarding the role of dysbiosis of the lung microbiota in the progression of lung cancer. In this review, we summarize the current information about the relationship between the microbiome and lung cancer. The objective is to provide an overview of the core composition of the microbiota in lung cancer as well as the role of specific dysbiosis of the lung microbiota in the progression of lung cancer and treatment of the disease.

A Two-Step Single Plex PCR Method for Evaluating Key Colonic Microbiota Markers in Young Mexicans with Autism Spectrum Disorders: Protocol and Pilot Epidemiological Application

Many neurological disorders have a distinctive colonic microbiome (CM) signature. Particularly, children with autism spectrum disorders (ASD) exhibit a very dissimilar CM when compared to neurotypical (NT) ones, mostly at the species level. Thus far, knowledge on this matter comes from high-throughput (yet very expensive and time-consuming) analytical platforms, such as massive high-throughput sequencing of bacterial 16S rRNA. Here, pure (260/280 nm, ~1.85) stool DNA samples (200 ng.µL^-1) from 48 participants [39 ASD, 9 NT; 3-13 y] were used to amplify four candidate differential CM markers [Bacteroides fragilis (BF), Faecalibacterium prausnitzii (FP), Desulfovibrio vulgaris (DV), Akkermansia muciniphila (AM)], using micro-organism-specific oligonucleotide primers [265 bp (BF), 198 bp (FP), 196 bp (DV), 327 bp (AM)] and a standardized two-step [low (step 1: °Tm-5 °C) to high (stage 2: °Tm-0 °C) astringent annealing] PCR protocol (2S-PCR). The method was sensitive enough to differentiate all CM biomarkers in the studied stool donors [↑ abundance: NT (BF, FP, AM), ASD (DV)], and phylogenetic analysis confirmed the primers' specificity.

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.

Integrative analysis of microbial 16S gene and shotgun metagenomic sequencing data improves statistical efficiency

The most widely used technologies for profiling microbial communities are 16S marker-gene sequencing and shotgun metagenomic sequencing. Interestingly, many microbiome studies have performed both sequencing experiments on the same cohort of samples. The two sequencing datasets often reveal consistent patterns of microbial signatures, highlighting the potential for an integrative analysis to improve power of testing these signatures. However, differential experimental biases, partially overlapping samples, and differential library sizes pose tremendous challenges when combining the two datasets. Currently, researchers either discard one dataset entirely or use different datasets for different objectives. In this article, we introduce the first method of this kind, named Com-2seq, that combines the two sequencing datasets for the objective of testing differential abundance at the genus and community levels while overcoming these difficulties. We demonstrate that Com-2seq substantially improves statistical efficiency over analysis of either dataset alone and works better than two ad hoc approaches.

Cefquinome shows a higher impact on the pig gut microbiome and resistome compared to ceftiofur

Cephalosporins are licensed for treatment of severe bacterial infections in different species. However, the effect of these antimicrobials on the fecal microbiome and potential spread of resistance-associated genes causes great concern. This highlights the need to understand the impact of cephalosporins on the porcine fecal microbiome and resistome. A combination of long-read 16S rRNA gene and shotgun metagenomic sequencing was applied to investigate the effect of conventional treatment with either ceftiofur (3 mg.kg^-1 intramuscular, 3 consecutive days) or cefquinome (2 mg.kg^-1 intramuscular, 5 consecutive days) on the porcine microbiome and resistome. Fecal samples were collected from 17 pigs (6 ceftiofur treated, 6 cefquinome treated, 5 control pigs) at four different timepoints. Treatment with ceftiofur resulted in an increase in Proteobacteria members on microbiome level, while on resistome level selection in TetQ containing Bacteroides, CfxA6 containing Prevotella and bla_TEM-1 containing Escherichia coli was observed. Cefquinome treatment resulted in a decline in overall species richness (α-diversity) and increase in Proteobacteria members. On genus level, administration of cefquinome significantly affected more genera than ceftiofur (18 vs 8). On resistome level, cefquinome resulted in a significant increase of six antimicrobial resistance genes, with no clear correlation with certain genera. For both antimicrobials, the resistome levels returned back to the control levels 21 days post-treatment. Overall, our study provides novel insights on the effect of specific cephalosporins on the porcine gut microbiome and resistome after conventional intramuscular treatment. These results might contribute to better tailoring of the most ideal treatment strategy for some bacterial infections.

Capturing the dynamics of microbial interactions through individual-specific networks

Longitudinal analysis of multivariate individual-specific microbiome profiles over time or across conditions remains dauntin. Most statistical tools and methods that are available to study microbiomes are based on cross-sectional data. Over the past few years, several attempts have been made to model the dynamics of bacterial species over time or across conditions. However, the field needs novel views on handling microbial interactions in temporal analyses. This study proposes a novel data analysis framework, MNDA, that combines representation learning and individual-specific microbial co-occurrence networks to uncover taxon neighborhood dynamics. As a use case, we consider a cohort of newborns with microbiomes available at 6 and 9 months after birth, and extraneous data available on the mode of delivery and diet changes between the considered time points. Our results show that prediction models for these extraneous outcomes based on an MNDA measure of local neighborhood dynamics for each taxon outperform traditional prediction models solely based on individual-specific microbial abundances. Furthermore, our results show that unsupervised similarity analysis of newborns in the study, again using the notion of a taxon's dynamic neighborhood derived from time-matched individual-specific microbial networks, can reveal different subpopulations of individuals, compared to standard microbiome-based clustering, with potential relevance to clinical practice. This study highlights the complementarity of microbial interactions and abundances in downstream analyses and opens new avenues to personalized prediction or stratified medicine with temporal microbiome data.

Evolving approaches to profiling the microbiome in skin disease

Despite its harsh and dry environment, human skin is home to diverse microbes, including bacteria, fungi, viruses, and microscopic mites. These microbes form communities that may exist at the skin surface, deeper skin layers, and within microhabitats such as the hair follicle and sweat glands, allowing complex interactions with the host immune system. Imbalances in the skin microbiome, known as dysbiosis, have been linked to various inflammatory skin disorders, including atopic dermatitis, acne, and psoriasis. The roles of abundant commensal bacteria belonging to Staphylococcus and Cutibacterium taxa and the fungi Malassezia, where particular species or strains can benefit the host or cause disease, are increasingly appreciated in skin disorders. Furthermore, recent research suggests that the interactions between microorganisms and the host's immune system on the skin can have distant and systemic effects on the body, such as on the gut and brain, known as the "skin-gut" or "skin-brain" axes. Studies on the microbiome in skin disease have typically relied on 16S rRNA gene sequencing methods, which cannot provide accurate information about species or strains of microorganisms on the skin. However, advancing technologies, including metagenomics and other functional 'omic' approaches, have great potential to provide more comprehensive and detailed information about the skin microbiome in health and disease. Additionally, inter-species and multi-kingdom interactions can cause cascading shifts towards dysbiosis and are crucial but yet-to-be-explored aspects of many skin disorders. Better understanding these complex dynamics will require meta-omic studies complemented with experiments and clinical trials to confirm function. Evolving how we profile the skin microbiome alongside technological advances is essential to exploring such relationships. This review presents the current and emerging methods and their findings for profiling skin microbes to advance our understanding of the microbiome in skin disease.

Nanopore Sequencing Discloses Compositional Quality of Commercial Probiotic Feed Supplements

The market for the application of probiotics as a livestock health improvement supplement has increased in recent years. However, most of the available products are quality-controlled using low-resolution techniques and un-curated databases, resulting in misidentification and incorrect product labels. In this work, we deployed two workflows and compared results obtained by full-length 16S rRNA genes (16S) and metagenomic (Meta) data to investigate their reliability for the microbial composition of both liquid and solid forms of animal probiotic products using Oxford Nanopore long-read-only (without short-read). Our result revealed that 16S amplicon data permits to detect the bacterial microbiota even with the low abundance in the samples. Moreover, the 16S approach has the potential to provide species-level resolution for prokaryotes but not for assessing yeast communities. Whereas, Meta data has more power to recover of high-quality metagenome-assembled genomes that enables detailed exploration of both bacterial and yeast populations, as well as antimicrobial resistance genes, and functional genes in the population. Our findings clearly demonstrate that implementing these workflows with long-read-only monitoring could be applied to assessing the quality and safety of probiotic products for animals and evaluating the quality of probiotic products on the market. This would benefit the sustained growth of the livestock probiotic industry.

The Gut Microbiome, Microsatellite Status and the Response to Immunotherapy in Colorectal Cancer

There is increasing evidence in a range of cancer types that the microbiome plays a direct role in modulating the anti-cancer immune response both at the gut level and systemically. Differences in the gut microbiota have been shown to correlate with differences in immunotherapy responses in a range of non-gastrointestinal tract cancers. DNA mismatch repair-deficient (dMMR) colorectal cancer (CRC) is radically different to DNA mismatch repair-proficient (pMMR) CRC in clinical phenotype and in its very good responses to immunotherapy. While this has usually been thought to be due to the high mutational burden in dMMR CRC, the gut microbiome is radically different in dMMR and pMMR CRC in terms of both composition and diversity. It is probable that differences in the gut microbiota contribute to the varied responses to immunotherapy in dMMR versus pMMR CRC. Targeting the microbiome offers a way to boost the response and increase the selection of patients who might benefit from this therapy. This paper reviews the available literature on the role of the microbiome in the response to immunotherapy in dMMR and pMMR CRC, explores the potential causal relationship and discusses future directions for study in this exciting and rapidly changing field.

Common Variable Immunodeficiency Patient Fecal Microbiota Transplant Recapitulates Gut Dysbiosis

Purpose

Patients with non-infectious complications have worse clinical outcomes in common variable immunodeficiency (CVID) than those with infections-only. Non-infectious complications are associated with gut microbiome aberrations, but there are no reductionist animal models that emulate CVID. Our aim in this study was to uncover potential microbiome roles in the development of non-infectious complications in CVID.

Methods

We examined fecal whole genome shotgun sequencing from patients CVID, and non-infectious complications, infections-only, and their household controls. We also performed Fecal Microbiota transplant from CVID patients to Germ-Free Mice.

Results

We found potentially pathogenic microbes Streptococcus parasanguinis and Erysipelatoclostridium ramosum were enriched in gut microbiomes of CVID patients with non-infectious complications. In contrast, Fusicatenibacter saccharivorans and Anaerostipes hadrus, known to suppress inflammation and promote healthy metabolism, were enriched in gut microbiomes of infections-only CVID patients. Fecal microbiota transplant from non-infectious complications, infections-only, and their household controls into germ-free mice revealed gut dysbiosis patterns in recipients from CVID patients with non-infectious complications, but not infections-only CVID, or household controls recipients.

Conclusion

Our findings provide a proof of concept that fecal microbiota transplant from CVID patients with non-infectious complications to Germ-Free mice recapitulates microbiome alterations observed in the donors.

Role of microbiota and microbiota-derived short-chain fatty acids in PDAC

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive lethal diseases among other cancer types. Gut microbiome and its metabolic regulation play a crucial role in PDAC. Metabolic regulation in the gut is a complex process that involves microbiome and microbiome-derived short-chain fatty acids (SCFAs). SCFAs regulate inflammation, as well as lipid and glucose metabolism, through different pathways. This review aims to summarize recent developments in PDAC in the context of gut and oral microbiota and their associations with short-chain fatty acid (SCFA). In addition to this, we discuss possible therapeutic applications using microbiota in PDAC.

Exploring taxonomic and functional microbiome of Hawaiian stream and spring irrigation water systems using Illumina and Oxford Nanopore sequencing platforms

Irrigation water is a common source of contamination that carries plant and foodborne human pathogens and provides a niche for proliferation and survival of microbes in agricultural settings. Bacterial communities and their functions in irrigation water were investigated by analyzing samples from wetland taro farms on Oahu, Hawaii using different DNA sequencing platforms. Irrigation water samples (stream, spring, and storage tank water) were collected from North, East, and West sides of Oahu and subjected to high quality DNA isolation, library preparation and sequencing of the V3-V4 region, full length 16S rRNA, and shotgun metagenome sequencing using Illumina iSeq100, Oxford Nanopore MinION and Illumina NovaSeq, respectively. Illumina reads provided the most comprehensive taxonomic classification at the phylum level where Proteobacteria was identified as the most abundant phylum in the stream source and associated water samples from wetland taro fields. Cyanobacteria was also a dominant phylum in samples from tank and spring water, whereas Bacteroidetes were most abundant in wetland taro fields irrigated with spring water. However, over 50% of the valid short amplicon reads remained unclassified and inconclusive at the species level. In contrast, Oxford Nanopore MinION was a better choice for microbe classification at the genus and species levels as indicated by samples sequenced for full length 16S rRNA. No reliable taxonomic classification results were obtained while using shotgun metagenome data. In functional analyzes, only 12% of the genes were shared by two consortia and 95 antibiotic resistant genes (ARGs) were detected with variable relative abundance. Full descriptions of microbial communities and their functions are essential for the development of better water management strategies aimed to produce safer fresh produce and to protect plant, animal, human and environmental health. Quantitative comparisons illustrated the importance of selecting the appropriate analytical method depending on the level of taxonomic delineation sought in each microbiome.