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

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.

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.

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.

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.

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.

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.

Compositional Changes in the Vaginal Bacterial Microbiome of Healthy Pregnant Women across the Three Gestational Trimesters in Ismailia, Egypt

The composition of the vaginal microbiome may lead to adverse pregnancy outcomes. Normal pregnancy is associated with changes in the vaginal bacterial community composition, which tend to be more enriched with one or two Lactobacillus species promoting a healthy vagina and favorable birth outcomes. The aim of the current study was to determine compositional changes in the healthy vaginal microbiome composition during the three trimesters of pregnancy in Ismailia, Egypt using Illumina MiSeq sequencing of the V3-V4 region of the 16S rRNA. The phylum Firmicutes and the genus Lactobacillus dominated across the three trimesters of pregnancy. L. iners was the most abundant species. However, L. coleohominis and L. reuteri represented the least dominant vaginal lactobacilli. Core microbiome analyses showed the Lactobacillus genus and L. iners species to have the highest prevalence in all the samples of our study groups. The phylum Firmicutes was found to be negatively correlated with almost all other vaginal phyla during pregnancy. Likewise, a negative correlation between Lactobacillus and almost all other genera was detected, including significant negative correlations with Dialister and Prevotella. Furthermore, negative correlations of L. iners were detected with almost all other species, including a significant negative correlation with L. helveticus, G. vaginalis, S. anginosus, and S. agalactiae.

Bioinformatic and Statistical Analysis of Microbiome Data

Since advances in next-generation sequencing (NGS) technique enabled to investigate uncultured microbiota and their genomes in unbiased manner, many microbiome researches have been reporting strong evidences for close links of microbiome to human health and disease. Bioinformatic and statistical analysis of NGS-based microbiome data are essential components in those microbiome researches to explore the complex composition of microbial community and understand the functions of community members in relation to host and environment. This chapter introduces bioinformatic analysis methods that generate taxonomy and functional feature count table along with phylogenetic tree from raw NGS microbiome data and then introduce statistical methods and machine learning approaches for analyzing the outputs of the bioinformatic analysis to infer the biodiversity of a microbial community and unravel host-microbiome association. Understanding the advantages and limitations of the analysis methods will help readers use the methods correctly in microbiome data analysis and may give a new opportunity to develop new analytic techniques for microbiome research.

DNA extraction protocol impacts ocular surface microbiome profile

Purpose

The aim of this study is to provide a reference frame to allow the comparison and interpretation of currently published studies on 16S ribosomal ribonucleic acid amplicon sequencing of ocular microbiome samples using different DNA extraction protocols. Alongside, the quantitative and qualitative yield and the reproducibility of different protocols has been assessed.

Methods

Both eyes of 7 eligible volunteers were sampled. Five commercially available DNA extraction protocols were selected based on previous publications in the field of the ocular surface microbiome and 2 host DNA depletion protocols were added based on their reported effective host DNA depletion without significant reduction in bacterial DNA concentration. The V3-V4 region of the 16S rRNA gene was targeted using Illumina MiSeq sequencing. The DADA2 pipeline in R was used to perform the bio-informatic processing and taxonomical assignment was done using the SILVA v132 database. The Vegdist function was used to calculate Bray-Curtis distances and the Galaxy web application was used to identify potential metagenomic biomarkers via linear discriminant analysis Effect Size (LEfSe). The R package Decontam was applied to control for potential contaminants.

Results

Samples analysed with PowerSoil, RNeasy and NucleoSpin had the highest DNA yield. The host DNA depletion kits showed a very low microbial DNA yield; and these samples were pooled per kit before sequencing. Despite pooling, 1 of both failed to construct a library.Looking at the beta-diversity, clear microbial compositional differences - dependent on the extraction protocol used - were observed and remained present after decontamination. Eighteen genera were consistently retrieved from the ocular surface of every volunteer by all non-pooled extraction kits and a comprehensive list of differentially abundant bacteria per extraction method was generated using LefSe analysis.

Conclusion

High-quality papers have been published in the field of the ocular surface microbiome but consensus on the importance of the extraction protocol used are lacking. Potential contaminants and discriminative genera per extraction protocol used, were introduced and a reference frame was built to facilitate both the interpretation of currently published papers and to ease future choice - making based on the research question at hand.

Phage-Host Prediction Using a Computational Tool Coupled with 16S rRNA Gene Amplicon Sequencing

Metagenomics studies have revealed tremendous viral diversity in aquatic environments. Yet, while the genomic data they have provided is extensive, it is unannotated. For example, most phage sequences lack accurate information about their bacterial host, which prevents reliable phage identification and the investigation of phage-host interactions. This study aimed to take this knowledge further, using a viral metagenomic framework to decipher the composition and diversity of phage communities and to predict their bacterial hosts. To this end, we used water and sediment samples collected from seven sites with varying contamination levels in the Ebrié Lagoon in Abidjan, Ivory Coast. The bacterial communities were characterized using the 16S rRNA metabarcoding approach, and a framework was developed to investigate the virome datasets that: (1) identified phage contigs with VirSorter and VIBRANT; (2) classified these contigs with MetaPhinder using the phage database (taxonomic annotation); and (3) predicted the phages' bacterial hosts with a machine learning-based tool: the Prokaryotic Virus-Host Predictor. The findings showed that the taxonomic profiles of phages and bacteria were specific to sediment or water samples. Phage sequences assigned to the Microviridae family were widespread in sediment samples, whereas phage sequences assigned to the Siphoviridae, Myoviridae and Podoviridae families were predominant in water samples. In terms of bacterial communities, the phyla Latescibacteria, Zixibacteria, Bacteroidetes, Acidobacteria, Calditrichaeota, Gemmatimonadetes, Cyanobacteria and Patescibacteria were most widespread in sediment samples, while the phyla Epsilonbacteraeota, Tenericutes, Margulisbacteria, Proteobacteria, Actinobacteria, Planctomycetes and Marinimicrobia were most prevalent in water samples. Significantly, the relative abundance of bacterial communities (at major phylum level) estimated by 16S rRNA metabarcoding and phage-host prediction were significantly similar. These results demonstrate the reliability of this novel approach for predicting the bacterial hosts of phages from shotgun metagenomic sequencing data.

Alterations of the vaginal microbiome in healthy pregnant women positive for group B Streptococcus colonization during the third trimester

BACKGROUND

Streptococcus agalactiae or group B Streptococcus (GBS) asymptomatically colonizes the genitourinary tracts of up to 30% of pregnant women. Globally, GBS is an important cause of neonatal morbidity and mortality. GBS has recently been linked to adverse pregnancy outcomes. The potential interactions between GBS and the vaginal microbiome composition remain poorly understood. In addition, little is known about the vaginal microbiota of pregnant Egyptian women.

RESULTS

Using V3-V4 16S rRNA next-generation sequencing, we examined the vaginal microbiome in GBS culture-positive pregnant women (22) and GBS culture-negative pregnant women (22) during the third trimester in Ismailia, Egypt. According to the alpha-diversity indices, the vaginal microbiome of pregnant GBS culture-positive women was significantly more diverse and less homogenous. The composition of the vaginal microbiome differed significantly based on beta-diversity between GBS culture-positive and culture-negative women. The phylum Firmicutes and the family Lactobacillaceae were significantly more abundant in GBS-negative colonizers. In contrast, the phyla Actinobacteria, Tenericutes, and Proteobacteria and the families Bifidobacteriaceae, Mycoplasmataceae, Streptococcaceae, Corynebacteriaceae, Staphylococcaceae, and Peptostreptococcaceae were significantly more abundant in GBS culture-positive colonizers. On the genus and species levels, Lactobacillus was the only genus detected with significantly higher relative abundance in GBS culture-negative status (88%), and L. iners was the significantly most abundant species. Conversely, GBS-positive carriers exhibited a significant decrease in Lactobacillus abundance (56%). In GBS-positive colonizers, the relative abundance of the genera Ureaplasma, Gardnerella, Streptococcus, Corynebacterium, Staphylococcus, and Peptostreptococcus and the species Peptostreptococcus anaerobius was significantly higher. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to the metabolism of cofactors and vitamins, phosphatidylinositol signaling system, peroxisome, host immune system pathways, and host endocrine system were exclusively enriched among GBS culture-positive microbial communities. However, lipid metabolism KEGG pathways, nucleotide metabolism, xenobiotics biodegradation and metabolism, genetic information processing pathways associated with translation, replication, and repair, and human diseases (Staphylococcus aureus infection) were exclusively enriched in GBS culture-negative communities.

CONCLUSIONS

Understanding how perturbations of the vaginal microbiome contribute to pregnancy complications may result in the development of alternative, targeted prevention strategies to prevent maternal GBS colonization. We hypothesized associations between inferred microbial function and GBS status that would need to be confirmed in larger cohorts.

Exploring the Connection Between the Gut Microbiome and Parkinson's Disease Symptom Progression and Pathology: Implications for Supplementary Treatment Options

The contribution of the microbiota to induce gastrointestinal inflammation is hypothesized to be a key component of alpha-synuclein (aSyn) aggregation within the gastrointestinal (GI) tract in the pathological progression of Parkinson's disease (PD). The function of the GI tract is governed by a system of neurons that form part of the enteric nervous system (ENS). The ENS hosts  100-500 million nerve cells within two thin layers lining the GI tract. The gut-brain axis (GBA) is the major communication pathway between the ENS and the central nervous system. It has become increasingly clear that the microbiota in the gut are key regulators of GBA function and help to maintain homeostasis in the immune and endocrine systems. The GBA may act as a possible etiological launching pad for the pathogenesis of age-related neurodegenerative diseases, such as PD, because of an imbalance in the gut microbiota. PD is a multi-faceted illness with multiple biological, immunological, and environmental factors contributing to its pathological progression. Interestingly, individuals with PD have an altered gut microbiota compared to healthy individuals. However, there is a lack of literature describing the relationship between microbiota composition in the gut and symptom progression in PD patients. This review article examines how the pathology and symptomology of PD may originate from dysregulated signaling in the ENS. We then discuss by targeting the imbalance within the gut microbiota such as prebiotics and probiotics, some of the prodromal symptoms might be alleviated, possibly curtailing the pathological spread of aSyn and ensuing debilitating motor symptoms.

Oral Microbiome in Nonsmoker Patients with Oral Cavity Squamous Cell Carcinoma, Defined by Metagenomic Shotgun Sequencing

Objectives: Smoking is the commonest cause of oral cavity squamous cell carcinoma (OC-SCC), but the etiology of OC-SCC in nonsmokers is unknown. Our primary goal was to use metagenomic shotgun sequencing (MSS) to define the taxonomic composition and functional potential of oral metagenome in nonsmokers with OC-SCC. Methods: We conducted a case-control study with 42 OC-SCC case and 45 control nonsmokers. MSS was performed on DNA extracted from mouthwash samples. Taxonomic analysis and pathway analysis were done using MetaPhlAn2 and HUMAnN2, respectively. Statistical difference was determined using the Mann-Whitney test controlling false discovery rate. Results: There was no significant difference in age, sex, race, or alcohol consumption between OC-SCC and control patients. There was a significant difference in beta diversity between OC-SCC and controls. At the phylum level, Bacteroidetes and Synergistetes were overly represented in OC-SCC while Actinobacteria and Firmicutes were overly represented in controls. At the genus level, Fusobacterium was overly represented in OC-SCC compared with controls, while Corynebacterium, Streptococcus, Actinomyces, Cryptobacterium, and Selenomonas were overly represented in controls. Bacterial pathway analysis identified overrepresentation in OC-SCC of pathways related to metabolism of flavin, biotin, thiamin, heme, sugars, fatty acids, peptidoglycans, and tRNA and overrepresentation of nucleotides and essential amino acids in controls. Conclusions: The oral microbiome in nonsmoker patients with OC-SCC is significantly different from that of nonsmoker control patients in taxonomic compositions and functional potentials. Our study's MSS findings matched with previous 16S-based methods in taxonomic differentiation but varied greatly in functional differentiation of microbiomes in OC-SCC and controls.

Multi-Omics Nutritional Approaches Targeting Metabolic-Associated Fatty Liver Disease

Currently, metabolic-associated fatty liver disease (MAFLD) is a leading global cause of chronic liver disease, and is expected to become one of the most common indications of liver transplantation. MAFLD is associated with obesity, involving multiple mechanisms such as alterations in lipid metabolism, insulin resistance, hyperinflammation, mitochondrial dysfunction, cell apoptosis, oxidative stress, and extracellular matrix formation. However, the onset and progression of MAFLD is variable among individuals, being influenced by intrinsic (personal) and external environmental factors. In this context, sequence structural variants across the human genome, epigenetic phenomena (i.e., DNA methylation, histone modifications, and long non-coding RNAs) affecting gene expression, gut microbiota dysbiosis, and metabolomics/lipidomic fingerprints may account for differences in MAFLD outcomes through interactions with nutritional features. This knowledge may contribute to gaining a deeper understanding of the molecular and physiological processes underlying MAFLD pathogenesis and phenotype heterogeneity, as well as facilitating the identification of biomarkers of disease progression and therapeutic targets for the implementation of tailored nutritional strategies. This comprehensive literature review highlights the potential of nutrigenetic, nutriepigenetic, nutrimetagenomic, nutritranscriptomics, and nutrimetabolomic approaches for the prevention and management of MAFLD in humans through the lens of precision nutrition.

Wochenende — modular and flexible alignment-based shotgun metagenome analysis

Background

Shotgun metagenome analysis provides a robust and verifiable method for comprehensive microbiome analysis of fungal, viral, archaeal and bacterial taxonomy, particularly with regard to visualization of read mapping location, normalization options, growth dynamics and functional gene repertoires. Current read classification tools use non-standard output formats, or do not fully show information on mapping location. As reference datasets are not perfect, portrayal of mapping information is critical for judging results effectively.

Results

Our alignment-based pipeline, Wochenende, incorporates flexible quality control, trimming, mapping, various filters and normalization. Results are completely transparent and filters can be adjusted by the user. We observe stringent filtering of mismatches and use of mapping quality sharply reduces the number of false positives. Further modules allow genomic visualization and the calculation of growth rates, as well as integration and subsequent plotting of pipeline results as heatmaps or heat trees. Our novel normalization approach additionally allows calculation of absolute abundance profiles by comparison with reads assigned to the human host genome.

Conclusion

Wochenende has the ability to find and filter alignments to all kingdoms of life using both short and long reads, and requires only good quality reference genomes. Wochenende automatically combines multiple available modules ranging from quality control and normalization to taxonomic visualization. Wochenende is available at https://github.com/MHH-RCUG/nf_wochenende.

Proof of principle study replicating microbial clusters in connection to birth mode and diet in the early life intestine

The human gut ecosystem starts developing at birth and is influenced by many factors during early life. In this study we make use of a Belgian cohort of 64 children, followed until the age of 6 years, to analyze different phases of microbiota development. We analyzed fecal samples taken before weaning (age 1 month), shortly after weaning (age 6 months), when milk feeding has been discontinued completely (age 1 year), and at the age of 6 years. We performed 16S rRNA gene amplicon sequencing on the collected fecal samples and analyzed the compositional data in relation to dietary metadata and birth mode. Human and formula milk feeding promotes a microbiota dominated by either Bacteroides or Bifidobacterium, respectively. Into later life stages, the microbiota composition follows distinct microbiota clusters, related to abundance dynamics of certain bacterial groups. Furthermore, it becomes apparent that a formula diet leads to early maturation of the infant gut microbiota. Despite other clinical variables within the infant cohort, they did not significantly contribute to the microbiota patterns we observed. Our data provide a proof of principle study of the importance of diet to the development of the microbiota in early life that replicates earlier findings in other cohorts.

Long-Read 16S rRNA Amplicon and Metagenomic Data of Swine Feed-Additive Probiotics Product

Swine feed-additive probiotics products play a major role in swine performance and welfare by promoting gut health. Here, we present two types of data, including a full-length 16S rRNA amplicon sequence data and a long-read metagenomic sequence data obtained from the same commercial probiotic product.

Functionally diverse microbial communities show resilience in response to a record-breaking rain event

Estuaries provide many ecosystem services and host a majority of the world's population. Here, the response of microbial communities after a record-breaking flood event in a highly urbanized estuary was followed. Hurricane Harvey (hereafter Harvey) was a category 4 hurricane that made landfall on the Texas coast in 2017 and lashed the Houston area with 1.4-1.7 × 1010 m3 of rainfall, disrupting the natural gradients of nutrients and salinity. Here, we utilized metagenomics to analyze how Harvey altered the microbial community of Galveston Bay over five weeks following the storm. We hypothesized that the community would shift from a marine dominated community to that of a terrestrial and freshwater origin. We found that following the storm there were changes in the distribution of species with specific metabolic capacities, such as Cyanobacteria, enriched in oxygenic photosynthesis and nitrogen fixation genes, as well as Verrucomicrobia and Betaproteobacteria, with high prevalence of the SOX complex and anoxygenic photosynthesis genes. On the other hand, dominant members of the community with more diverse metabolic capabilities showed less fluctuations in their distribution. Our results highlight how massive precipitation disturbances can alter microbial communities and how the coalescence of diverse microorganisms creates a resilient community able to maintain ecosystem services even when the system is in an altered state.

Crosstalk between mucosal microbiota, host gene expression, and sociomedical factors in the progression of colorectal cancer

Various omics-based biomarkers related to the occurrence, progression, and prognosis of colorectal cancer (CRC) have been identified. In this study, we attempted to identify gut microbiome-based biomarkers and detect their association with host gene expression in the initiation and progression of CRC by integrating analysis of the gut mucosal metagenome, RNA sequencing, and sociomedical factors. We performed metagenome and RNA sequencing on colonic mucosa samples from 13 patients with advanced CRC (ACRC), 10 patients with high-risk adenoma (HRA), and 7 normal control (NC) individuals. All participants completed a questionnaire on sociomedical factors. The interaction and correlation between changes in the microbiome and gene expression were assessed using bioinformatic analysis. When comparing HRA and NC samples, which can be considered to represent the process of tumor initiation, 28 genes and five microbiome species were analyzed with correlation plots. When comparing ACRC and HRA samples, which can be considered to represent the progression of CRC, seven bacterial species and 21 genes were analyzed. When comparing ACRC and NC samples, 16 genes and five bacterial species were analyzed, and four correlation plots were generated. A network visualizing the relationship between bacterial and host gene expression in the initiation and progression of CRC indicated that Clostridium spiroforme and Tyzzerella nexilis were hub bacteria in the development and progression of CRC. Our study revealed the interactions of and correlation between the colonic mucosal microbiome and host gene expression to identify potential roles of the microbiome in the initiation and progression of CRC. Our results provide gut microbiome-based biomarkers that may be potential diagnostic markers and therapeutic targets in patients with CRC.

Effect of Intensity and Duration of Exercise on Gut Microbiota in Humans: A Systematic Review

(1) Background: The gut microbiota might play a part in affecting athletic performance and is of considerable importance to athletes. The aim of this study was to search the recent knowledge of the protagonist played by high-intensity and high-duration aerobic exercise on gut microbiota composition in athletes and how these effects could provide disadvantages in sports performance. (2) Methods: This systematic review follows the PRISMA guidelines. An exhaustive bibliographic search in Web of Science, PubMed, and Scopus was conducted considering the articles published in the last 5 years. The selected articles were categorized according to the type of study. The risk of bias was assessed using the Joanna Briggs Institute's Critical Appraisal Tool for Systematic Reviews. (3) Results: Thirteen studies had negative effects of aerobic exercise on intestinal microbiota such as an upsurge in I-FABP, intestinal distress, and changes in the gut microbiota, such as an increase in Prevotella, intestinal permeability and zonulin. In contrast, seven studies observed positive effects of endurance exercise, including an increase in the level of bacteria such as increased microbial diversity and increased intestinal metabolites. (4) Conclusions: A large part of the studies found reported adverse effects on the intestinal microbiota when performing endurance exercises. In studies carried out on athletes, more negative effects on the microbiota were found than in those carried out on non-athletic subjects.

Effect of Tanshinone IIA on Gut Microbiome in Diabetes-Induced Cognitive Impairment

Diabetes-induced cognitive impairment (DCI) presents a major public health risk among the aging population. Previous clinical attempts on known therapeutic targets for DCI, such as depleted insulin secretion, insulin resistance, and hyperglycaemia have delivered poor patient outcomes. However, recent evidence has demonstrated that the gut microbiome plays an important role in DCI by modulating cognitive function through the gut–brain crosstalk. The bioactive compound tanshinone IIA (TAN) has shown to improve cognitive and memory function in diabetes mellitus models, though the pharmacological actions are not fully understood. This study aims to investigate the effect and underlying mechanism of TAN in attenuating DCI in relation to regulating the gut microbiome. Metagenomic sequencing analyses were performed on a group of control rats, rats with diabetes induced by a high-fat/high-glucose diet (HFD) and streptozotocin (STZ) (model group) and TAN-treated diabetic rats (TAN group). Cognitive and memory function were assessed by the Morris water maze test, histopathological assessment of brain tissues, and immunoblotting of neurological biomarkers. The fasting blood glucose (FBG) level was monitored throughout the experiments. The levels of serum lipopolysaccharide (LPS) and tumor necrosis factor-α (TNF-α) were measured by enzyme-linked immunoassays to reflect the circulatory inflammation level. The morphology of the colon barrier was observed by histopathological staining. Our study confirmed that TAN reduced the FBG level and improved the cognitive and memory function against HFD- and STZ-induced diabetes. TAN protected the endothelial tight junction in the hippocampus and colon, regulated neuronal biomarkers, and lowered the serum levels of LPS and TNF-α. TAN corrected the reduced abundance of Bacteroidetes in diabetic rats. At the species level, TAN regulated the abundance of B. dorei, Lachnoclostridium sp. YL32 and Clostridiodes difficile. TAN modulated the lipid metabolism and biosynthesis of fatty acids in related pathways as the main functional components. TAN significantly restored the reduced levels of isobutyric acid and butyric acid. Our results supported the use of TAN as a promising therapeutic agent for DCI, in which the underlying mechanism may be associated with gut microbiome regulation.

Metaomics in Clinical Laboratory: Potential Driving Force for Innovative Disease Diagnosis

Currently, more and more studies suggested that reductionism was lack of holistic and integrative view of biological processes, leading to limited understanding of complex systems like microbiota and the associated diseases. In fact, microbes are rarely present in individuals but normally live in complex multispecies communities. With the recent development of a variety of metaomics techniques, microbes could be dissected dynamically in both temporal and spatial scales. Therefore, in-depth understanding of human microbiome from different aspects such as genomes, transcriptomes, proteomes, and metabolomes could provide novel insights into their functional roles, which also holds the potential in making them diagnostic biomarkers in many human diseases, though there is still a huge gap to fill for the purpose. In this mini-review, we went through the frontlines of the metaomics techniques and explored their potential applications in clinical diagnoses of human diseases, e.g., infectious diseases, through which we concluded that novel diagnostic methods based on human microbiomes shall be achieved in the near future, while the limitations of these techniques such as standard procedures and computational challenges for rapid and accurate analysis of metaomics data in clinical settings were also examined.

Flow cytometry can reliably capture gut microbial composition in healthy adults as well as dysbiosis dynamics in patients with aggressive B-cell non-Hodgkin lymphoma

Modulation of commensal gut microbiota is increasingly recognized as a promising strategy to reduce mortality in patients with malignant diseases, but monitoring for dysbiosis is generally not routine clinical practice due to equipment, expertise and funding required for sequencing analysis. A low-threshold alternative is microbial diversity profiling by single-cell flow cytometry (FCM), which we compared to 16S rRNA sequencing in human fecal samples and employed to characterize longitudinal changes in the microbiome composition of patients with aggressive B-cell non-Hodgkin lymphoma undergoing chemoimmunotherapy. Diversity measures obtained from both methods were correlated and captured identical trends in microbial community structures, finding no difference in patients' pretreatment alpha or beta diversity compared to healthy controls and a significant and progressive loss of alpha diversity during chemoimmunotherapy. Our results highlight the potential of FCM-based microbiome profiling as a reliable and accessible diagnostic tool that can provide novel insights into cancer therapy-associated dysbiosis dynamics.

Longitudinal and Comparative Analysis of Gut Microbiota of Tunisian Newborns According to Delivery Mode

Microbiota colonization is a dynamic process that impacts the health status during an individual's lifetime. The composition of the gut microbiota of newborns is conditioned by multiple factors, including the delivery mode (DM). Nonetheless, the DM's influence remains uncertain and is still the subject of debate. In this context, the medical indication and the emergency of a cesarean delivery might have led to confounding conclusions regarding the composition and diversity of the neonatal microbiome. Herein, we used high-resolution shotgun sequencing to decipher the composition and dynamics of the gut microbiota composition of Tunisian newborns. Stool samples were collected from 5 elective cesarean section (ECS) and 5 vaginally delivered (VD) newborns at the following time points: Day 0, Day 15, and Day 30. The ECS and VD newborns showed the same level of bacterial richness and diversity. In addition, our data pointed to a shift in microbiota community composition during the first 2 weeks, regardless of the DM. Both ECS and VD showed a profile dominated by Proteobacteria, Actinobacteria, and Firmicutes. However, ECS showed an underrepresentation of Bacteroides and an enrichment of opportunistic pathogenic species of the ESKAPE group, starting from the second week. Besides revealing the intestinal microbiota of Tunisian newborns, this study provides novel insights into the microbiota perturbations caused by ECS.