Absolute quantification of microbial taxon abundances

The gut-eye axis: from brain neurodegenerative diseases to age-related macular degeneration

Age-related macular degeneration is a serious neurodegenerative disease of the retina that significantly impacts vision. Unfortunately, the specific pathogenesis remains unclear, and effective early treatment options are consequently lacking. The microbiome is defined as a large ecosystem of microorganisms living within and coexisting with a host. The intestinal microbiome undergoes dynamic changes owing to age, diet, genetics, and other factors. Such dysregulation of the intestinal flora can disrupt the microecological balance, resulting in immunological and metabolic dysfunction in the host, and affecting the development of many diseases. In recent decades, significant evidence has indicated that the intestinal flora also influences systems outside of the digestive tract, including the brain. Indeed, several studies have demonstrated the critical role of the gut-brain axis in the development of brain neurodegenerative diseases, including Alzheimer's disease and Parkinson's disease. Similarly, the role of the "gut-eye axis" has been confirmed to play a role in the pathogenesis of many ocular disorders. Moreover, age-related macular degeneration and many brain neurodegenerative diseases have been shown to share several risk factors and to exhibit comparable etiologies. As such, the intestinal flora may play an important role in age-related macular degeneration. Given the above context, the present review aims to clarify the gut-brain and gut-eye connections, assess the effect of intestinal flora and metabolites on age-related macular degeneration, and identify potential diagnostic markers and therapeutic strategies. Currently, direct research on the role of intestinal flora in age-related macular degeneration is still relatively limited, while studies focusing solely on intestinal flora are insufficient to fully elucidate its functional role in age-related macular degeneration. Organ-on-a-chip technology has shown promise in clarifying the gut-eye interactions, while integrating analysis of the intestinal flora with research on metabolites through metabolomics and other techniques is crucial for understanding their potential mechanisms.

Skyglow increases cyanobacteria abundance and organic matter cycling in lakes

Artificial light propagating towards the night sky can be scattered back to Earth and reach ecosystems tens of kilometres away from the original light source. This phenomenon is known as artificial skyglow. Its consequences on freshwaters are largely unknown. In a large-scale lake enclosure experiment, we found that skyglow at levels of 0.06 and 6 lux increased the abundance of anoxygenic aerobic phototrophs and cyanobacteria by 32 (±22) times. An ecosystem metabolome analysis revealed that skyglow increased the production of algal-derived metabolites, which appeared to stimulate heterotrophic activities as well. Furthermore, we found evidence that skyglow decreased the number of bacteria-bacteria interactions. Effects of skyglow were more pronounced at night, suggesting that responses to skyglow can occur on short time scales. Overall, our results call for considering skyglow as a reality of increasing importance for microbial communities and carbon cycling in lake ecosystems.

Development of reference-based model for improved analysis of bacterial community

Probiotic bacteria play a vital role in maintaining gut microbial homeostasis and are widely used in various commercial products. Although 16S rRNA amplicon-based next-generation sequencing (NGS) is commonly used to analyze probiotic products, biases can arise from various 16S rRNA amplification regions, sequencing platforms, and library kits. In this study, a reference-based bias correction model was developed to correct sequencing biases. The model was validated using eight mock communities and 12 commercial products, which were analyzed across multiple NGS platforms and various 16S rRNA regions. Specific primer-probe assays were developed for accurate bacterial quantification, and their specificity was validated and used in conjunction with droplet digital PCR (ddPCR) to establish initial bacterial ratios within communities. Analysis of the mock communities revealed platform- and region-specific biases, with specific species consistently over- or under-represented. Similarly, commercial product analyses have shown biased outcomes owing to varying sequencing protocols. The correction model, based on PCR efficiencies from the reference communities, successfully corrected biased ratios across different amplification regions and platforms to achieve results that closely matched the proportions predicted by ddPCR. The model effectively corrected the biases arising from the different polymerases. Notably, partial references containing approximately 40 % of the species achieved correction results that were comparable to those of the complete references. This approach demonstrates the potential for improving microbiome analysis accuracy within predictable ranges, and could serve as a model for addressing sequencing bias in metagenomic research.

Incorporating scale uncertainty in microbiome and gene expression analysis as an extension of normalization

Statistical normalizations are used in differential analyses to address sample-to-sample variation in sequencing depth. Yet normalizations make strong, implicit assumptions about the scale of biological systems, such as microbial load, leading to false positives and negatives. We introduce scale models as a generalization of normalizations, which allows researchers to model potential errors in these modeling assumptions, thereby enhancing the transparency and robustness of data analyses. In practice, scale models can drastically reduce false positives and false negatives rates. We introduce updates to the popular ALDEx2 software package, available on Bioconductor, facilitating scale model analysis.

Improvement of the inflammation-damaged intestinal barrier and modulation of the gut microbiota in ulcerative colitis after FMT in the SHIME® model

BACKGROUND

Fecal microbiota transplantation (FMT) seems to be a promising approach in ulcerative colitis (UC) management with the aim of repopulating a patient's dysbiotic microbiota with beneficial bacteria and restore its metabolic activity to its healthy characteristics. Metabolites present after FMT may improve the function and integrity of the intestinal barrier, reduce inflammation, and thus induce remission in an UC patient. In this study we evaluated whether the Simulator of the Human Intestinal Microbial Ecosystem (SHIME®) model may be a suitable non-invasive alternative for studying and modifying the dysbiotic microbiota in UC by FMT application.

METHODS

SHIME® model was used to investigate microbial and metabolic changes in the gut microbiota of UC patient induced by FMT application. FMT-modified metabolites from SHIME® were applied to an in vitro model of the intestinal barrier (differentiated Caco-2 and HT-29-MTX-E12 cell lines) compromised by pro-inflammatory cytokines to study the effect of FMT on the intestinal barrier.

RESULTS

Qualitative and quantitative microbial analyses showed that FMT increased the diversity and variability of the microbiota in UC patient associated with a significant increase in total bacteria, Bacteroidota and Lactobacillus, as well as an increase in butyrate levels. In addition, an increase in the relative abundance of some important species such as Faecalibacterium prausnitzii and Bifidobacterium longum was observed, and there was also an enrichment of the microbiota with new species such as Blautia obeum, Roseburia faecis, Bifidobacterium adolescentis, Fusicatenibacter saccharivorans and Eubacterium rectale. Furthermore, microbial metabolites modulated by FMT from the SHIME® model prevented intestinal barrier damage and inhibited interleukin 8 (IL-8) and monocyte chemoattractant protein 1 (MCP-1) secretion when cell barriers were pretreated with FMT medium for 24 h. In summary, this study confirmed that a single dose of FMT beneficially modulated the composition and metabolic activity of the UC microbiota in the SHIME® model.

CONCLUSIONS

FMT favorably modulates the gut microbiota of UC patient cultured in the SHIME® model. FMT-modulated SHIME-derived microbial metabolites improve intact and inflamed intestinal barrier properties in vitro. Repeated applications are necessary to maintain the beneficial effect of FMT in SHIME® model.

Exploratory study on source identification of saliva stain and its TsD inference based on the microbial relative and absolute abundance

In recent years, it has become a major research trend to obtain the microbial relative abundance in common body fluid stains at the crime scenes through 16S rRNA next generation sequencing to explore the effectiveness in forensic application. However, few scholars have combined the determination of tissue sources of body fluid stains with the inference of time since deposition (TsD) based on the relative and absolute abundance of microorganism in the same sample in a single study. Therefore, we preliminarily used the four abundant saliva-related bacteria to distinguish fresh saliva, saliva stains (exposure ≤60 days) from the four kinds of fresh body fluids and epidermal tissue, simultaneously assessed the temporal variation regularities in both microbial relative and absolute abundance in these saliva stains. Quantitative real-time PCR results demonstrated that fresh saliva samples and saliva stains exposed for up to 60 days still retained two or more abundant saliva-related bacteria, demonstrating sufficient discriminative power to identify saliva stain from other four kinds of body fluids and tissue. Microbial compositions and temporal analyses of 56 saliva samples revealed that many phyla and genera with abundance higher than 1% had different temporal variation regularities in relative and absolute abundance data, except for some genera such as Neisseria, etc. Beta diversity analysis indicated greater differences in absolute quantitative data among fresh saliva samples and saliva stains at different time points compared with relative quantitative data. The support vector machine (svm) model based on microbial relative or absolute abundance both have the prediction accuracy higher than 0.8 in classifying saliva stains deposited at 1 h, 1 day, and 7 to 60 days. This study combined the tissue origin identification and TsD inference of saliva stains, and the absolute quantitative technology was applied for the first time to the TsD inference of saliva stains. And the results indicated that using the absolute quantitative technology might be more suitable for early TsD inference (within 14 days) of saliva stains in this study, which helped to accurately infer the TsD of saliva stains, providing an important clue for forensic investigation.

Cyanophage Lysis of the Cyanobacterium Nodularia spumigena Affects the Variability and Fitness of the Host-Associated Microbiome

Cyanobacteria are intricately linked with its microbiome through multiple metabolic interactions. We assessed how these interactions might be affected by cyanophage infection and lysis in cyanobacterium Nodularia spumigena. The genome-scale metabolic models and analysis of putative metabolic interactions revealed a bidirectional cross-feeding potential within the N. spumigena microbiome, with heterotrophic bacteria exhibiting a greater level of trophic dependency on the cyanobacterium. Our results indicate that microbes associated with N. spumigena rely on the supply of various amino acids, reduced carbon compounds and protein synthesis cofactors released by the cyanobacterial host. We observed that compositional changes in the N. spumigena microbiome were associated with the multiplicity of infection and increased with increasing initial viral load. Higher mortality of N. spumigena led to decreased variability in the relative abundances of bacteria, suggesting an indirect restriction of their niche space. Lysis of N. spumigena resulted in a substantial decline in the estimated absolute abundances of heterotrophic bacteria, indicating reduced fitness of co-occurring bacteria in the absence of N. spumigena. Altogether, we demonstrate how a gradual increase in viral pressure on the photosynthetic host propagates through the co-occurring microbial community, disrupting cooperative nature and microbial connectivity within the N. spumigena microbiome.

Year-Round Quantification, Structure and Dynamics of Epibacterial Communities From Diverse Macroalgae Reveal a Persistent Core Microbiota and Strong Host Specificities

Macroalgae-bacteria interactions play pivotal ecological roles in coastal ecosystems. Previous characterisation of surface microbiota from various macroalgae evidenced fluctuations based on host tissues, physicochemical and environmental parameters. However, the dynamics and degree of similarity of epibacterial communities colonising phylogenetically distant algae from the same habitat are still elusive. We conducted a year-long monthly epimicrobiota sampling on five algal species inhabiting an English Channel rocky shore: Laminaria digitata, Ascophyllum nodosum, Fucus serratus (brown algae), Palmaria palmata (red alga) and Ulva sp. (green alga). To go beyond relative compositional data and estimate absolute variations in taxa abundance, we combined qPCR measurements of 16S rRNA gene copies with amplicon metabarcoding. A core microbiome composed of 10 genera was consistently found year-round on all algae. Notably, the abundant genus Granulosicoccus stood out for being the only one present in all samples and displayed an important microdiversity. Algal host emerged as the primary driver of epibacterial community composition, before seasonality, and bacterial taxa specifically associated with one or several algae were identified. Moreover, the impact of seasons on the epimicrobiota varied depending on algal tissues. Overall, this study provides an extensive characterisation of the microbiota of intertidal macroalgae and enhances our understanding of algal-bacteria holobionts.

Absolute Quantitative Metagenomic Analysis Provides More Accurate Insights for the Anti-Colitis Effect of Berberine via Modulation of Gut Microbiota

Current gut microbiota studies often rely on relative quantitative sequencing. However, under certain circumstances, while the relative quantitative abundance of these bacteria may remain stable, the absolute quantities of specific bacteria can vary considerably. Since the function of bacteria is directly linked to their total numbers, absolute quantification is crucial. This study aims to identify the optimal method for microbiome analysis by comparing relative and absolute quantitative sequencing. Using ulcerative colitis, which is closely associated with gut microbiota, as a disease model and berberine (which affects microbiota) versus sodium butyrate (which does not) as drugs, relative and absolute quantitative methods were used to evaluate the varying effects of the different drugs on the regulation of gut microbiota in UC-affected animals. The regulatory effects of BBR on gut microbiota were further synthesized as identified in earlier studies using an individual-based meta-analysis, and we compared these findings with our absolute sequencing results. The results from absolute sequencing were more consistent with the actual microbial community, suggesting that relative abundance measurements might not accurately reflect the true abundance of microbial species. Moreover, meta-analysis results were only partially consistent with absolute quantitative sequencing and sometimes directly opposed, suggesting that relative quantitative sequencing analyses are prone to misinterpretation and incorrect correlation of results. This study underscores the importance of absolute quantitative analysis in accurately representing the true microbial counts in a sample and evaluating the modulatory effects of drugs on the microbiome, which plays a vital role in the study of the microbiome.

Comparison of Relative and Absolute Abundance and Biomass of Freshwater Phytoplankton Taxa Using Metabarcoding and Microscopy

Phytoplankton is the basis of the food web and an indicator of environmental change in aquatic ecosystems. Phytoplankton assessment uses microscopy, which estimates the composition, absolute abundance and biomass of taxa, and metabarcoding, which estimates the composition, high richness and diversity, and relative abundance of taxa. Problems remain with the consistency of results from these two methods and the quantification of metabarcoding. Using 18S rRNA metabarcoding and microscopy we compared the relative or absolute abundance and biomass of phytoplankton taxa (class or genus/species) in the south basin of Lake Baikal in spring over 3 years. Absolute abundance/biomass of phytoplankton taxa estimated by metabarcoding was obtained by combining relative abundances of amplicon sequence variants (ASV produced by error-correcting method) derived from the V8-V9 region of 18S rRNA gene amplicon sequencing (primers were used that accurately represented the mean relative abundance of different microalgae) with total or class-specific abundance/biomass of phytoplankton estimated by light microscopy. Many Spearman correlations were found between relative (non- or clr-transformed) or absolute abundances/biomasses of the same phytoplankton classes or genus/species. Correlation coefficients were higher between absolute values than between relative values. Correlations were found between relative or absolute abundance/biomass, estimated by both methods, of the classes Bacillariophyceae, Coscinodiscophyceae, Mediophyceae, Chrysophyceae, Cryptophyceae, and Chlorophyceae, but not Dinophyceae and Trebouxiophyceae. Correlations were found between relative or absolute abundance/biomass of dominant species and ASVs of diatoms (Ulnaria, Aulacoseira, Stephanodiscus), Chrysophyceae (Dinobryon), and Cryptophyceae (Cryptomonas). Thus, the consistency of the dynamics of the relative or absolute abundance/biomass of phytoplankton taxa estimated by the two methods was revealed. Absolute abundances/biomasses of taxa estimated by metabarcoding in combination with microscopy improve the accuracy of metabarcoding-based ecological assessment.

Quantity Versus Quality: Links Between Soil Organic Matter and Bacterial Community Composition Along a Geoclimatic Gradient

Soil organic matter (SOM) quantity drives soil bacterial community composition from the regional to global scale. Qualitative characteristics of SOM are known to affect soil bacterial communities in manipulation experiments. However, it remains unresolved how strongly SOM characteristics affect soil bacterial community composition at the macroscale. Here, we investigated how quantity versus qualitative characteristics of SOM shape community composition along a biogeochemical gradient of grassland soils. We assessed relative abundance patterns of soil bacteria and characterised SOM based on scalable methods. Soils with higher SOM content (along a continuum between 0.6% and 18.7% SOC) and acidic pH (along a continuum between pH 4.1-6.7) hosted fewer narrowly distributed taxa (i.e., taxa occurring in few sites) and therefore had lower bacterial alpha diversity. We could explain a larger fraction of bacterial community composition (up to 59.6% of 16S rRNA reads) in these soils. Consequently, we understand community composition in low-SOM soils less than in high-SOM soils, because the drivers of narrowly distributed taxa remain poorly understood. Qualitative SOM characteristics did not strongly affect biogeographical patterns of widely distributed soil bacterial taxa. This suggests that broad aspects of SOM quality do not dominate soil bacterial community composition at the investigated macroscale.

Antibiotic-perturbed microbiota and the role of probiotics

The disruptive effect of antibiotics on the composition and function of the human microbiota is well established. However, the hypothesis that probiotics can help restore the antibiotic-disrupted microbiota has been advanced, with little consideration of the strength of evidence supporting it. Some clinical data suggest that probiotics can reduce antibiotic-related side effects, including Clostridioides difficile-associated diarrhoea, but there are no data that causally link these clinical effects to microbiota protection or recovery. Substantial challenges hinder attempts to address this hypothesis, including the absence of consensus on the composition of a 'normal' microbiota, non-standardized and evolving microbiome measurement methods, and substantial inter-individual microbiota variation. In this Review, we explore these complexities. First, we review the known benefits and risks of antibiotics, the effect of antibiotics on the human microbiota, the resilience and adaptability of the microbiota, and how microbiota restoration might be defined and measured. Subsequently, we explore the evidence for the efficacy of probiotics in preventing disruption or aiding microbiota recovery post-antibiotic treatment. Finally, we offer insights into the current state of research and suggest directions for future research.

Flow Cytometry in Microbiology: A Review of the Current State in Microbiome Research, Probiotics, and Industrial Manufacturing

Flow cytometry (FC) is a versatile and powerful tool in microbiology, enabling precise analysis of single cells for a variety of applications, including the detection and quantification of bacteria, viruses, fungi, as well as algae, phytoplankton, and parasites. Its utility in assessing cell viability, metabolic activity, immune responses, and pathogen-host interactions makes it indispensable in both research and diagnostics. The analysis of microbiota (community of microorganisms) and microbiome (collective genomes of the microorganisms) has become essential for understanding the intricate role of microbial communities in health, disease, and physiological functions. FC offers a promising complement, providing rapid, cost-effective, and dynamic profiling of microbial communities, with the added ability to isolate and sort bacterial populations for further analysis. In the probiotic industry, FC facilitates fast, affordable, and versatile analyses, helping assess both probiotics and postbiotics. It also supports the study of bacterial viability under stress conditions, including gastric acid and bile, improving insight into probiotic survival and adhesion to the intestinal mucosa. Additionally, the integration of Machine Learning in microbiology research has transformative potential, improving data analysis and supporting advances in personalized medicine and probiotic formulations. Despite the need for further standardization, FC continues to evolve as a key tool in modern microbiology and clinical diagnostics.

Impact of Saharan Dust and SERPINA1 Gene Variants on Bacterial/Fungal Balance in Asthma Patients

The Canary Islands, a region with high asthma prevalence, are frequently exposed to Saharan Dust Intrusions (SDIs), as are a wide range of countries in Europe. Alpha-1 antitrypsin (SERPINA1 gene) regulates the airway's inflammatory response. This study analyzed the combined effect of SDI exposure and SERPINA1 variants on bacterial/fungal DNA concentrations in saliva and pharyngeal samples from asthmatic patients. Bacterial and fungal DNAs were quantified by qPCR in 211 asthmatic patients (GEMAS study), grouped based on their exposure to daily PM10 concentrations. Associations between SDI exposure, microbial DNA concentrations, and nine variants in SERPINA1 were tested using linear regression models adjusted for confounders. The ratio between bacterial and fungal DNA was similar in saliva and pharyngeal samples. SDI exposure for 1-3 days was enough to observe significant microbial DNA change. Increased bacterial DNA concentration was detected when SDI exposure occurred 4-10 days prior to sampling, while exposure between days 1 and 3 led to a reduction in the fungal DNA concentration. The T-allele of SERPINA1 SNV rs2854254 prevented the increase in the bacterial/fungal DNA ratio in pharyngeal samples after SDI exposure. The bacterial/fungal DNA ratio represents a potential tool to monitor changes in the microbiome of asthmatic patients.

Diverse vaginal microbiota in healthy Japanese women: a combined relative and quantitative analyses

Introduction

This cross-sectional study aimed to characterize the viable vaginal microbiota and identify host factors influencing this microbiota by employing a combination of relative and quantitative analyses.

Methods

Twenty-four vaginal fluid samples were collected from healthy adult Japanese women for analysis. Vaginal fluid pH was measured using a portable pH meter. DNA was extracted from the vaginal fluid, and the 16S ribosomal RNA gene sequences in the V3-V4 regions were analyzed to identify bacterial species. Additionally, the vaginal fluid was cultured on four types of selective agar plates. The predominant species in the growing colonies were identified using colony polymerase chain reaction, and the colonies were counted.

Results

The vaginal microbiota was classified into four categories based on the characterization of the dominant bacterial population: Lactobacillus crispatus, Lactobacillus iners, Lactobacillus gasseri, and a diversity group. The predominant bacterial species were consistent across methods; however, the levels of the viable population varied significantly. Body mass index had a significant influence on the total number of viable bacteria and vaginal pH, while age only affected vaginal pH.

Conclusions

Our findings indicate that the vaginal microbiome of healthy Japanese women is not only highly diverse but also affected by host factors such as BMI and age.

Climate Change Drives Changes in the Size and Composition of Fungal Communities Along the Soil-Seedling Continuum of Schima superba

Plant microbiomes have a major influence on forest structure and functions, as well as tree fitness and evolution. However, a comprehensive understanding of variations in fungi along the soil-plant continuum, particularly within tree seedlings, under global warming is lacking. Here, we investigated the dynamics of fungal communities across different compartments (including bulk soil and rhizosphere soil) and plant organs (including the endosphere of roots, stems and leaves) of Schima superba seedlings exposed to experimental warming and drought using AccuITS absolute quantitative sequencing. Our results revealed that warming and drought significantly reduced the number of specific fungal amplicon sequence variants (ASVs) in the bulk soil and rhizosphere soil, respectively. Variations in fungal communities were mainly explained by compartments and plant organs, with the composition of endophytic fungal communities within leaves (primarily attributed to species gain or loss) being most influenced by climate change. Moreover, warming significantly reduced the migration of Ascomycota, soil saprotrophs, wood saprotrophs and yeasts from the bulk soil to the rhizosphere soil but increased that of plant pathogens from the roots to the stems. Drought significantly decreased the absolute abundances of Chytridiomycota, Glomeromycota and Rozellomycota, as well as the migration of ectomycorrhizal fungi from the bulk soil to the rhizosphere soil but increased that of plant pathogens. Warming could indirectly reduce leaf area by increasing the diversity of leaf pathogens. These findings have potential implications for enhancing the resilience and functioning of natural forest ecosystems under climate change through the manipulation of plant microbiomes, as demonstrated in agroecosystems.

The quest for environmental analytical microbiology: absolute quantitative microbiome using cellular internal standards

BACKGROUND

High-throughput sequencing has revolutionized environmental microbiome research, providing both quantitative and qualitative insights into nucleic acid targets in the environment. The resulting microbial composition (community structure) data are essential for environmental analytical microbiology, enabling characterization of community dynamics and assessing microbial pollutants for the development of intervention strategies. However, the relative abundances derived from sequencing impede comparisons across samples and studies.

RESULTS

This review systematically summarizes various absolute quantification (AQ) methods and their applications to obtain the absolute abundance of microbial cells and genetic elements. By critically comparing the strengths and limitations of AQ methods, we advocate the use of cellular internal standard-based high-throughput sequencing as an appropriate AQ approach for studying environmental microbiome originated from samples of complex matrices and high heterogeneity. To minimize ambiguity and facilitate cross-study comparisons, we outline essential reporting elements for technical considerations, and provide a checklist as a reference for environmental microbiome research.

CONCLUSIONS

In summary, we propose absolute microbiome quantification using cellular internal standards for environmental analytical microbiology, and we anticipate that this approach will greatly benefit future studies. Video Abstract.

Quantifying synthetic bacterial community composition with flow cytometry: efficacy in mock communities and challenges in co-cultures

Determination of bacterial community composition in synthetic communities is critical for understanding microbial systems. The community composition is typically determined through bacterial plating or through PCR-based methods, which can be labor-intensive, expensive, or prone to bias. Simultaneously, flow cytometry has been suggested as a cheap and fast alternative. However, since the technique captures the phenotypic state of bacterial cells, accurate determination of community composition could be affected when bacteria are co-cultured. We investigated the performance of flow cytometry for quantifying oral synthetic communities and compared it to the performance of strain specific qPCR and 16S rRNA gene amplicon sequencing. Therefore, axenic cultures, mock communities and co-cultures of oral bacteria were prepared. Random forest classifiers trained on flow cytometry data of axenic cultures were used to determine the composition of the synthetic communities, as well as strain specific qPCR and 16S rRNA gene amplicon sequencing. Flow cytometry was shown to have a lower average root mean squared error and outperformed the PCR-based methods in even mock communities (flow cytometry: 0.11 ± 0.04; qPCR: 0.26 ± 0.09; amplicon sequencing: 0.15 ± 0.01). When bacteria were co-cultured, neither flow cytometry, strain-specific qPCR, nor 16S rRNA gene amplicon sequencing resulted in similar community composition. Performance of flow cytometry was decreased compared with mock communities due to changing phenotypes. Finally, discrepancies between flow cytometry and strain-specific qPCR were found. These findings highlight the challenges ahead for quantifying community composition in co-cultures by flow cytometry.IMPORTANCEQuantification of bacterial composition in synthetic communities is crucial for understanding and steering microbial interactions. Traditional approaches like plating, strain-specific qPCR, and amplicon sequencing are often labor-intensive and expensive and limit high-throughput experiments. Recently, flow cytometry has been suggested as a swift and cheap alternative for quantifying communities and has been successfully demonstrated on simple bacterial mock communities. However, since flow cytometry measures the phenotypic state of cells, measurements can be affected by differing phenotypes. Especially, changing phenotypes resulting from co-culturing bacteria can have a profound effect on the applicability of the technique in this context. This research illustrates the feasibility and challenges of flow cytometry for the determination of community structure in synthetic mock communities and co-cultures.

Low-Concentration Hypochlorous Acid Drinking Water Alleviates Broiler Gut Microbial Load While Preserving Overall Growth Performance

Hypochlorous acid has been attempted as an additive to animal drinking water in practical animal farming processes for water microbial quality control. Despite its potential, there is still a knowledge gap concerning the effects of hypochlorous acid on both poultry growth performance and gut microbial load. To address this gap, an animal study was conducted using flow cytometry to quantify the age-related microbial load in broiler manure and gut contents. We observed that the effect on growth performance was sustained only during the starter phase, with no significant impact throughout the entire production cycle. The treatment could reduce the microbial load of both fresh broiler manure and cecal contents. Despite this convergence in the duodenum, significant differences in microbial loads between the control and treatment groups persisted in the manure and cecal contents throughout the later stages. Our findings demonstrate that consuming low-concentration hypochlorous acid water over the long term can lower the microbial load in the broiler gut throughout the entire growth cycle without impacting overall growth performance. Future research on drinking or feed additives should incorporate microbial absolute quantification methods to achieve a more precise assessment of microbiota.

Fecal microbial load is a major determinant of gut microbiome variation and a confounder for disease associations

The microbiota in individual habitats differ in both relative composition and absolute abundance. While sequencing approaches determine the relative abundances of taxa and genes, they do not provide information on their absolute abundances. Here, we developed a machine-learning approach to predict fecal microbial loads (microbial cells per gram) solely from relative abundance data. Applying our prediction model to a large-scale metagenomic dataset (n = 34,539), we demonstrated that microbial load is the major determinant of gut microbiome variation and is associated with numerous host factors, including age, diet, and medication. We further found that for several diseases, changes in microbial load, rather than the disease condition itself, more strongly explained alterations in patients' gut microbiome. Adjusting for this effect substantially reduced the statistical significance of the majority of disease-associated species. Our analysis reveals that the fecal microbial load is a major confounder in microbiome studies, highlighting its importance for understanding microbiome variation in health and disease.

Synthetic DNA spike-in standards for cross-domain absolute quantification of microbiomes by rRNA gene amplicon sequencing

Microbiome studies using high-throughput sequencing are increasingly incorporating absolute quantitative approaches to overcome the inherent limitations of relative abundances. In this study, we have designed and experimentally validated a set of 12 unique synthetic rRNA operons, which we refer to as rDNA-mimics, to serve as spike-in standards for quantitative profiling of fungal/eukaryotic and bacterial microbiomes. The rDNA-mimics consist of conserved sequence regions from natural rRNA genes to act as binding sites for common universal PCR primers, and bioinformatically designed variable regions that allow their robust identification in any microbiome sample. All constructs cover multiple rRNA operon regions commonly targeted in fungal/eukaryotic microbiome studies (SSU-V9, ITS1, ITS2, and LSU-D1D2) and two of them also include an artificial segment of the bacterial 16S rRNA gene (SSU-V4) for cross-domain application. We validated the quantitative performance of the rDNA-mimics using defined mock communities and representative environmental samples. In particular, we show that rDNA-mimics added to extracted DNA or directly to the samples prior to DNA extraction precisely reflects the total amount of fungal and/or bacterial rRNA genes in the samples. We demonstrate that this allows accurate estimation of differences in microbial loads between samples, thereby confirming that the rDNA-mimics are suitable for absolute quantitative analyses of differential microbial abundances.

Moderate permeability enhanced microbial community turnover and copper extraction during bioleaching of low-grade copper ores

Heap bioleaching is one of the most promising technologies for extracting valuable metals from low-grade ores. However, the effects of permeability of the heap on microbial community and bioleaching efficiency remain unclear. In this study, heap bioleaching systems with different permeability were constructed. Despite the high content of larger particles had better permeability (0.25 cm/s) and oxygen transfer efficiency (0.14), there was a 25 % decrease in copper extraction compared with the moderate permeability group (81.2 %), while low permeability (0.025 cm/s) could cut the extraction in half (48.5 %). The fine profiles of microbial communities based on relatively and absolutely quantitative technologies suggested that permeability significantly affected microbial diversity, biomass, and composition. Microbial community evenness was crucial to improving extraction than biomass. Additionally, Thermoplasmatales except for Acidiplasma and Ferroplasma played vital roles in bioleaching. This study highlighted the delicate trade-off of particle size-mediated permeability for intensifying bioleaching efficiency of low-grade copper ores.

Microbiota in tumors: new factor influencing cancer development

Tumor microbiota research is a new field in oncology. With the advancement of high-throughput sequencing, there is growing evidence that a microbial community exists within tumor tissue. How these bacteria access tumor cells varies, including through the invasion of mucous membranes, the bloodstream, or the gut-organ axis. Previous literature has shown that microbes promote the development and progression of cancer through various mechanisms, such as affecting the host's immune system, promoting inflammation, regulating metabolism, and activating invasion and transfer. The study of the tumor microbiota offers a new perspective for the diagnosis and treatment of cancer, and it holds the potential for the development of new diagnostic tools and therapies. The role of the tumor microbiota in the pathogenesis of cancer is becoming increasingly evident, and future research will continue to uncover the specific mechanisms of action of these microbes, potentially shedding light on new strategies and methods for cancer prevention and therapy. This article reviews the latest advancements in this field, including how intratumor microbes migrate, their carcinogenic mechanisms, and the characteristics of different types of tumor microbes as well as the application of relevant methods in tumor microbiota research and the clinical values of targeting tumor microbes in cancer therapy.

Real-time PCR-based quantitative microbiome profiling elucidates the microbial dynamic succession in backslopping fermentation of Taiwanese pickled cabbage

BACKGROUND

Microbiota succession determines the flavor and quality of fermented foods. Quantitative PCR-based quantitative microbiome profiling (QMP) has been applied broadly for microbial analysis from absolute abundance perspectives, transforming microbiota ratios into counts by normalizing 16S ribosomal RNA (16S rRNA) gene sequencing data with gene copies quantified by quantitative PCR. However, the application of QMP in fermented foods is still limited.

RESULTS

QMP elucidated microbial succession of Taiwanese pickled cabbage. In the spontaneous first-round fermentation (FR), the 16S rRNA gene copies of total bacteria increased from 6.1 to 10 log copies mL-1. The dominant lactic acid bacteria genera were successively Lactococcus, Leuconostoc and Lactiplantibacillus. Despite the decrease in the proportion of Lactococcus during the succession, the absolute abundance of Lactococcus still increased. In the backslopping second-round fermentation (SR), the total bacteria 16S rRNA gene copies increased from 7.6 to 9.9 log copies mL-1. The addition of backslopping starter and vinegar rapidly led to a homogenous microbial community dominated by Lactiplantibacillus. The proportion of Lactiplantibacillus remained consistently around 90% during SR, whereas its absolute abundance exhibited a continuous increase. In SR without vinegar, Leuconostoc consistently dominated the fermentation.

CONCLUSION

The present study highlights that compositional analysis would misinterpret microbial dynamics, whereas QMP reflected the real succession profiles and unveiled the essential role of vinegar in promoting Lactiplantibacillus dominance in backslopping fermentation of Taiwanese pickled cabbage. Quantitative microbiome profiling (QMP) was found to be a more promising approach for the detailed observation of microbiome succession in food fermentation compared to compositional analysis. © 2024 Society of Chemical Industry.

Emergence of community behaviors in the gut microbiota upon drug treatment

Pharmaceuticals can directly inhibit the growth of gut bacteria, but the degree to which such interactions manifest in complex community settings is an open question. Here, we compared the effects of 30 drugs on a 32-species synthetic community with their effects on each community member in isolation. While most individual drug-species interactions remained the same in the community context, communal behaviors emerged in 26% of all tested cases. Cross-protection during which drug-sensitive species were protected in community was 6 times more frequent than cross-sensitization, the converse phenomenon. Cross-protection decreased and cross-sensitization increased at higher drug concentrations, suggesting that the resilience of microbial communities can collapse when perturbations get stronger. By metabolically profiling drug-treated communities, we showed that both drug biotransformation and bioaccumulation contribute mechanistically to communal protection. As a proof of principle, we molecularly dissected a prominent case: species expressing specific nitroreductases degraded niclosamide, thereby protecting both themselves and sensitive community members.

Dynamics of drinking water biofilm formation associated with Legionella spp. colonization

Understanding how Legionella spp. proliferate in multispecies biofilms is essential to develop strategies to control their presence in building plumbing. Here, we analyzed biofilm formation and Legionella spp. colonization on new plumbing material during 8 weeks. Biofilm formation was characterized by an initial increase in intact cell concentrations up to 9.5 × 105 cells/cm2, followed by a steady decrease. We identified Comamonas, Caulobacter, Schlegella, Blastomonas and Methyloversatilis as pioneer genera in the biofilm formation process. Importantly, L. pneumophila was the dominant Legionella spp. and rapidly colonized the biofilms, with culturable cell concentrations peaking at 3.1 × 104 MPN/cm2 after 4 weeks already. Moreover, several Legionella species co-occurred and had distinct dynamics of biofilm colonization. Vermamoeba vermiformis (V. vermiformis) was the dominant protist identified with 18S rRNA gene amplicon sequencing. Together our results highlight that biofilm formation upon introduction of new building plumbing material is a dynamic process where pathogenic Legionella species can be part of the earliest colonizers.

Implementing high-throughput insect barcoding in microbiome studies: impact of non-destructive DNA extraction on microbiome reconstruction

Background

Symbiotic relationships with diverse microorganisms are crucial for many aspects of insect biology. However, while our understanding of insect taxonomic diversity and the distribution of insect species in natural communities is limited, we know much less about their microbiota. In the era of rapid biodiversity declines, as researchers increasingly turn towards DNA-based monitoring, developing and broadly implementing approaches for high-throughput and cost-effective characterization of both insect and insect-associated microbial diversity is essential. We need to verify whether approaches such as high-throughput barcoding, a powerful tool for identifying wild insects, would permit subsequent microbiota reconstruction in these specimens.

Methods

High-throughput barcoding ("megabarcoding") methods often rely on non-destructive approaches for obtaining template DNA for PCR amplification by leaching DNA out of insect specimens using alkaline buffers such as HotSHOT. This study investigated the impact of HotSHOT on microbial abundance estimates and the reconstructed bacterial community profiles. We addressed this question by comparing quantitative 16S rRNA amplicon sequencing data for HotSHOT-treated or untreated specimens of 16 insect species representing six orders and selected based on the expectation of limited variation among individuals.

Results

We find that in 13 species, the treatment significantly reduced microbial abundance estimates, corresponding to an estimated 15-fold decrease in amplifiable 16S rRNA template on average. On the other hand, HotSHOT pre-treatment had a limited effect on microbial community composition. The reconstructed presence of abundant bacteria with known significant effects was not affected. On the other hand, we observed changes in the presence of low-abundance microbes, those close to the reliable detection threshold. Alpha and beta diversity analyses showed compositional differences in only a few species.

Conclusion

Our results indicate that HotSHOT pre-treated specimens remain suitable for microbial community composition reconstruction, even if abundance may be hard to estimate. These results indicate that we can cost-effectively combine barcoding with the study of microbiota across wild insect communities. Thus, the voucher specimens obtained using megabarcoding studies targeted at characterizing insect communities can be used for microbiome characterizations. This can substantially aid in speeding up the accumulation of knowledge on the microbiomes of abundant and hyperdiverse insect species.

The impact of DNA extraction on the quantification of Legionella, with implications for ecological studies

Monitoring the levels of opportunistic pathogens in drinking water is important to plan interventions and understand the ecological niches that allow them to proliferate. Quantitative PCR is an established alternative to culture methods that can provide a faster, higher-throughput, and more precise enumeration of the bacteria in water samples. However, PCR-based methods are still not routinely applied for Legionella monitoring, and techniques, such as DNA extraction, differ notably between laboratories. Here, we quantify the impact that DNA extraction methods had on downstream PCR quantification and community sequencing. Through a community science campaign, we collected 50 water samples and corresponding shower hoses, and compared two commonly used DNA extraction methodologies to the same biofilm and water phase samples. The two methods showed clearly different extraction efficacies, which were reflected in both the quantity of DNA extracted and the concentrations of Legionella enumerated in both the matrices. Notably, one method resulted in higher enumeration in nearly all samples by about one order of magnitude and detected Legionella in 21 samples that remained undetected by the other method. 16S rRNA amplicon sequencing revealed that the relative abundance of individual taxa, including sequence variants of Legionella, significantly varied depending on the extraction method employed. Given the implications of these findings, we advocate for improvement in documentation of the performance of DNA extraction methods used in drinking water to detect and quantify Legionella, and characterize the associated microbial community.IMPORTANCEMonitoring for the presence of the waterborne opportunistic pathogen Legionella is important to assess the risk of infection and plan remediation actions. While monitoring is traditionally carried on through cultivation, there is an ever-increasing demand for rapid and high-throughput molecular-based approaches for Legionella detection. This paper provides valuable insights on how DNA extraction affects downstream molecular analysis such as the quantification of Legionella through droplet digital PCR and the characterization of natural microbial communities through sequencing analysis. We analyze the results from a risk-assessment, legislative, and ecological perspective, showing how initial DNA processing is an important step to take into account when shifting to molecular-based routine monitoring and discuss the central role of consistent and detailed reporting of the methods used.

Advancing environmental biotechnology with microbial community modeling rooted in functional 'omics

Emerging biotechnologies that solve pressing environmental and climate emergencies will require harnessing the vast functional diversity of the underlying microbiomes driving such engineered processes. Modeling is a critical aspect of process engineering that informs system design as well as aids diagnostic optimization of performance. 'Conventional' bioprocess models assume homogenous biomass within functional guilds and thus fail to predict emergent properties of diverse microbial physiologies, such as product specificity and community interactions. Yet, recent advances in functional 'omics-based approaches can provide a 'lens' through which we can probe and measure in situ ecophysiologies of environmental microbiomes. Here, we overview microbial community modeling approaches that incorporate functional 'omics data, which we posit can advance our ability to design and control new environmental biotechnologies going forward.

Microbiome modeling: a beginner's guide

Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with each other, their environment, and hosts via ecological interactions. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metaomics methods, common modeling formalisms, and how models facilitate microbiome control. It concludes with guidelines and repositories for modeling. Each section provides entry-level information, example applications, and important references, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.

Gradient boosted regression as a tool to reveal key drivers of temporal dynamics in a synthetic yeast community

Microbial communities are vital to our lives, yet their ecological functioning and dynamics remain poorly understood. This understanding is crucial for assessing threats to these systems and leveraging their biotechnological applications. Given that temporal dynamics are linked to community functioning, this study investigated the drivers of community succession in the wine yeast community. We experimentally generated population dynamics data and used it to create an interpretable model with a gradient boosted regression tree approach. The model was trained on temporal data of viable species populations in various combinations, including pairs, triplets, and quadruplets, and was evaluated for predictive accuracy and input feature importance. Key findings revealed that the inoculation dosage of non-Saccharomyces species significantly influences their performance in mixed cultures, while Saccharomyces cerevisiae consistently dominates regardless of initial abundance. Additionally, we observed multispecies interactions where the dynamics of Wickerhamomyces anomalus were influenced by Torulaspora delbrueckii in pairwise cultures, but this interaction was altered by the inclusion of S. cerevisiae. This study provides insights into yeast community succession and offers valuable machine learning-based analysis techniques applicable to other microbial communities, opening new avenues for harnessing microbial communities.

A new spike-in-based method for quantitative metabarcoding of soil fungi and bacteria

Metabarcoding is a powerful tool to characterize biodiversity in biological samples. The interpretation of taxonomic profiles from metabarcoding data has been hindered by their compositional nature. Several strategies have been proposed to transform compositional data into quantitative data, but they have intrinsic limitations. Here, I propose a workflow based on bacterial and fungal cellular internal standards (spike-ins) for absolute quantification of the microbiota in soil samples. These standards were added to the samples before DNA extraction in amounts estimated after qPCRs, to target around 1-2% coverage in the sequencing run. In bacteria, proportions of spike-in reads in the sequencing run were very similar (< 2-fold change) to those predicted by the qPCR assessment, and for fungi they differed up to 40-fold. The low variation among replicates highlights the reproducibility of the method. Estimates based on multiple bacterial spike-ins were highly correlated (r = 0.99). Procrustes analysis evidenced significant biological effects on the community composition when normalizing compositional data. A protocol based on qPCR estimation of input amounts of cellular spikes is proposed as a cheap and reliable strategy for quantitative metabarcoding of biological samples.

Co-Supplementation of Baobab Fiber and Arabic Gum Synergistically Modulates the In Vitro Human Gut Microbiome Revealing Complementary and Promising Prebiotic Properties

Arabic gum, a high molecular weight heteropolysaccharide, is a promising prebiotic candidate as its fermentation occurs more distally in the colon, which is the region where most chronic colonic diseases originate. Baobab fiber could be complementary due to its relatively simple structure, facilitating breakdown in the proximal colon. Therefore, the current study aimed to gain insight into how the human gut microbiota was affected in response to long-term baobab fiber and Arabic gum supplementation when tested individually or as a combination of both, allowing the identification of potential complementary and/or synergetic effects. The validated Simulator of the Human Intestinal Microbial Ecosystem (SHIME®), an in vitro gut model simulating the entire human gastrointestinal tract, was used. The microbial metabolic activity was examined, and quantitative 16S-targeted Illumina sequencing was used to monitor the gut microbial composition. Moreover, the effect on the gut microbial metabolome was quantitatively analyzed. Repeated administration of baobab fiber, Arabic gum, and their combination had a significant effect on the metabolic activity, diversity index, and community composition of the microbiome present in the simulated proximal and distal colon with specific impacts on Bifidobacteriaceae and Faecalibacterium prausnitzii. Despite the lower dosage strategy (2.5 g/day), co-supplementation of both compounds resulted in some specific synergistic prebiotic effects, including a biological activity throughout the entire colon, SCFA synthesis including a synergy on propionate, specifically increasing abundance of Akkermansiaceae and Christensenellaceae in the distal colon region, and enhancing levels of spermidine and other metabolites of interest (such as serotonin and ProBetaine).

Implementing high-throughput insect barcoding in microbiome studies: impact of non-destructive DNA extraction on microbiome reconstruction

Background

Symbiotic relationships with diverse microorganisms are crucial for many aspects of insect biology. However, while our understanding of insect taxonomic diversity and the distribution of insect species in natural communities is limited, we know much less about their microbiota. In the era of rapid biodiversity declines, as researchers increasingly turn towards DNA-based monitoring, developing and broadly implementing approaches for high-throughput and cost-effective characterization of both insect and insect-associated microbial diversity is essential. We need to verify whether approaches such as high-throughput barcoding, a powerful tool for identifying wild insects, would permit subsequent microbiota reconstruction in these specimens.

Methods

High-throughput barcoding ("megabarcoding") methods often rely on non-destructive approaches for obtaining template DNA for PCR amplification by leaching DNA out of insect specimens using alkaline buffers such as HotSHOT. This study investigated the impact of HotSHOT on microbial abundance estimates and the reconstructed bacterial community profiles. We addressed this question by comparing quantitative 16S rRNA amplicon sequencing data for HotSHOT-treated or untreated specimens of 16 insect species representing six orders and selected based on the expectation of limited variation among individuals.

Results

We find that in 13 species, the treatment significantly reduced microbial abundance estimates, corresponding to an estimated 15-fold decrease in amplifiable 16S rRNA template on average. On the other hand, HotSHOT pre-treatment had a limited effect on microbial community composition. The reconstructed presence of abundant bacteria with known significant effects was not affected. On the other hand, we observed changes in the presence of low-abundance microbes, those close to the reliable detection threshold. Alpha and beta diversity analyses showed compositional differences in only a few species.

Conclusion

Our results indicate that HotSHOT pre-treated specimens remain suitable for microbial community composition reconstruction, even if abundance may be hard to estimate. These results indicate that we can cost-effectively combine barcoding with the study of microbiota across wild insect communities. Thus, the voucher specimens obtained using megabarcoding studies targeted at characterizing insect communities can be used for microbiome characterizations. This can substantially aid in speeding up the accumulation of knowledge on the microbiomes of abundant and hyperdiverse insect species.

Beyond Normalization: Incorporating Scale Uncertainty in Microbiome and Gene Expression Analysis

Though statistical normalizations are often used in differential abundance or differential expression analysis to address sample-to-sample variation in sequencing depth, we offer a better alternative. These normalizations often make strong, implicit assumptions about the scale of biological systems (e.g., microbial load). Thus, analyses are susceptible to even slight errors in these assumptions, leading to elevated rates of false positives and false negatives. We introduce scale models as a generalization of normalizations so researchers can model potential errors in assumptions about scale. By incorporating scale models into the popular ALDEx2 software, we enhance the reproducibility of analyses while often drastically decreasing false positive and false negative rates. We design scale models that are guaranteed to reduce false positives compared to equivalent normalizations. At least in the context of ALDEx2, we recommend using scale models over normalizations in all practical situations.

Effects of operational parameters on bacterial communities in Hong Kong and global wastewater treatment plants

Wastewater treatment plants (WWTPs) are indispensable biotechnology facilities for modern cities and play an essential role in modern urban infrastructure by employing microorganisms to remove pollutants in wastewater, thus protecting public health and the environment. This study conducted a 13-month bacterial community survey of six full-scale WWTPs in Hong Kong with samples of influent, activated sludge (AS), and effluent to explore their synchronism and asynchronism of bacterial community. Besides, we compared AS results of six Hong Kong WWTPs with data from 1,186 AS amplicon data in 269 global WWTPs and a 9-year metagenomic sequencing survey of a Hong Kong WWTP. Our results showed the compositions of bacterial communities varied and the bacterial community structure of AS had obvious differences across Hong Kong WWTPs. The co-occurrence analysis identified 40 pairs of relationships that existed among Hong Kong WWTPs to show solid associations between two species and stochastic processes took large proportions for the bacterial community assembly of six WWTPs. The abundance and distribution of the functional bacteria in worldwide and Hong Kong WWTPs were examined and compared, and we found that ammonia-oxidizing bacteria had more diversity than nitrite-oxidizing bacteria. Besides, Hong Kong WWTPs could make great contributions to the genome mining of microbial dark matter in the global "wanted list." Operational parameters had important effects on OTUs' abundance, such as the temperature to the genera of Tetrasphaera, Gordonia and Nitrospira. All these results obtained from this study can deepen our understanding of the microbial ecology in WWTPs and provide foundations for further studies.

IMPORTANCE

Wastewater treatment plants (WWTPs) are an indispensable component of modern cities, as they can remove pollutants in wastewater to prevent anthropogenic activities. Activated sludge (AS) is a fundamental wastewater treatment process and it harbors a highly complex microbial community that forms the main components and contains functional groups. Unveiling "who is there" is a long-term goal of the research on AS microbiology. High-throughput sequencing provides insights into the inventory diversity of microbial communities to an unprecedented level of detail. At present, the analysis of communities in WWTPs usually comes from a specific WWTP and lacks comparisons and verification among different WWTPs. The wide-scale and long-term sampling project and research in this study could help us evaluate the AS community more accurately to find the similarities and different results for different WWTPs in Hong Kong and other regions of the world.

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

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

Intestinal toxicity alleviation and efficacy potentiation through therapeutic administration of Lactobacillus paracasei GY-1 in the treatment of gout flares with colchicine

Gout flares have emerged as a significant public health concern. Colchicine (COL) is a first-line and standard drug for treating gout flares. However, its clinical use is limited due to various adverse effects. Besides, COL fails to adequately meet the needs of patients, particularly young patients. In this study, we investigate the therapeutic administration of Lactobacillus paracasei GY-1 (GY-1) to overcome the limitations of COL. Our results demonstrate that GY-1 attenuates COL toxicity in terms of body weight loss, decreased feed intake, mortality, reduced locomotor activity, colon shortening, increased oxidative stress, histological damage, and impaired gut permeability. Meanwhile, we demonstrate that GY-1 enhances the therapeutic effect for gout flares when combined with COL, as evidenced by the reduction in paw swelling, decreased levels of proinflammatory cytokines including IL-1β and TNF-α, and an increase in the anti-inflammatory cytokine IL-10. Additionally, the absolute quantification of the gut microbiota shows that GY-1 restores the gut microbiota imbalance caused by COL. Furthermore, GY-1 reduces the abundance of 4 Alistipes species and 6 Porphyromonadaceae species, which may be responsible for toxicity alleviation. At the same time, GY-1 increases the abundance of Bacteroides sartorii and Enterococcus sp., which may contribute to its therapeutic efficacy. This study demonstrates the feasibility of developing probiotic-based adjuvant therapy or bacteriotherapy for treating gout flares. To our knowledge, GY-1 is the first probiotic that could be used as an alternative synergetic agent with COL for the therapeutic treatment of gout flares.

Changes of putative pathogenic species within the water bacterial community in large-scale drinking water treatment and distribution systems

Although the management of microbes in drinking water is of paramount importance for public health, there remain challenges in comprehensively examining pathogenic bacteria in the water supply system at the species level. In this study, high-throughput sequencing of nearly full-length 16S rRNA genes was performed to investigate the changes of the water bacterial community in three large-scale drinking water treatment plants (DWTPs) and their corresponding distribution systems during winter and summer. Our findings revealed significant differences in the bacterial community structure between winter and summer water samples for each DWTP and its distribution management area (DMA). In the groundwater-fed DWTP, selective enrichment of mycobacterial species was observed in both seasons, and the subsequent DMA also exhibited strong selection for specific mycobacterial species. In one of the surface water-fed DWTPs, certain Legionella species present in the source water in winter were selectively enriched in the bacterial community after pre-oxidation, although they were susceptible to the subsequent purification steps. A variety of putative pathogenic species (n = 83) were identified based on our pathogen identification pipeline, with the dominant species representing opportunistic pathogens commonly found in water supply systems. While pathogen removal primarily occurred during the purification processes of DWTPs, especially for surface water-fed plants, the relative abundance of pathogenic bacteria in the DMA water flora was lower than that in the DWTP effluent flora, indicating a diminished competitiveness of pathogens within the DMA ecosystem.

Gut microbiota and metabolic profiles in chronic intermittent hypoxia-induced rats: disease-associated dysbiosis and metabolic disturbances

Aim

Chronic intermittent hypoxia (CIH) is a key characteristic of obstructive sleep apnea (OSA) syndrome, a chronic respiratory disorder. The mechanisms of CIH-induced metabolic disturbance and histopathological damage remain unclear.

Methods

CIH-induced rats underwent daily 8-h CIH, characterized by oxygen levels decreasing from 21% to 8.5% over 4 min, remaining for 2 min, and quickly returning to 21% for 1 min. The control rats received a continuous 21% oxygen supply. The levels of hypersensitive C reactive protein (h-CRP), tumor necrosis factor-α (TNF-α), interleukin 6 (IL-6), interleukin 8 (IL-8), and nuclear factor kappa-B (NF-κB) were measured by ELISA. Histological analysis of the soft palates was conducted using HE staining. The microbial profiling of fecal samples was carried out by Accu16STM assay. Untargeted metabolomics of serum and soft palate tissue samples were analyzed by UPLC-MS. The protein expression of cAMP-related pathways in the soft palate was determined by Western blot.

Results

After 28 h of CIH induction, a significant increase in pro-inflammatory cytokines was observed in the serum, along with mucosal layer thickening and soft palate tissue hypertrophy. CIH induction altered the diversity and composition of fecal microbiota, specifically reducing beneficial bacteria while increasing harmful bacteria/opportunistic pathogens. Notably, CIH induction led to a significant enrichment of genera such as Dorea, Oscillibacter, Enteractinococcus, Paenibacillus, Globicatella, and Flaviflexus genera. Meanwhile, Additionally, CIH induction had a notable impact on 108 serum marker metabolites. These marker metabolites, primarily involving amino acids, organic acids, and a limited number of flavonoids or sterols, were associated with protein transport, digestion and absorption, amino acid synthesis and metabolism, as well as cancer development. Furthermore, these differential serum metabolites significantly affected 175 differential metabolites in soft palate tissue, mainly related to cancer development, signaling pathways, amino acid metabolism, nucleotide precursor or intermediate metabolism, respiratory processes, and disease. Importantly, CIH induction could significantly affect the expression of the cAMP pathway in soft palate tissue.

Conclusions

Our findings suggest that targeting differential metabolites in serum and soft palate tissue may represent a new approach to clinical intervention and treatment of OSA simulated by the CIH.

Combining compositional data sets introduces error in covariance network reconstruction

Microbial communities are diverse biological systems that include taxa from across multiple kingdoms of life. Notably, interactions between bacteria and fungi play a significant role in determining community structure. However, these statistical associations across kingdoms are more difficult to infer than intra-kingdom associations due to the nature of the data involved using standard network inference techniques. We quantify the challenges of cross-kingdom network inference from both theoretical and practical points of view using synthetic and real-world microbiome data. We detail the theoretical issue presented by combining compositional data sets drawn from the same environment, e.g. 16S and ITS sequencing of a single set of samples, and we survey common network inference techniques for their ability to handle this error. We then test these techniques for the accuracy and usefulness of their intra- and inter-kingdom associations by inferring networks from a set of simulated samples for which a ground-truth set of associations is known. We show that while the two methods mitigate the error of cross-kingdom inference, there is little difference between techniques for key practical applications including identification of strong correlations and identification of possible keystone taxa (i.e. hub nodes in the network). Furthermore, we identify a signature of the error caused by transkingdom network inference and demonstrate that it appears in networks constructed using real-world environmental microbiome data.

High-throughput identification and quantification of bacterial cells in the microbiota based on 16S rRNA sequencing with single-base accuracy using BarBIQ

Bacteria often function as a community, called the microbiota, consisting of many different bacterial species. The accurate identification of bacterial types and the simultaneous quantification of the cells of each bacterial type will advance our understanding of microbiota; however, this cannot be performed by conventional 16S rRNA sequencing methods as they only identify and quantify genes, which do not always represent cells. Here, we present a protocol for our developed method, barcoding bacteria for identification and quantification (BarBIQ). In BarBIQ, the 16S rRNA genes of single bacterial cells are amplified and attached to a unique cellular barcode in a droplet. Sequencing the tandemly linked cellular barcodes and 16S rRNA genes from many droplets (representing many cells with unique cellular barcodes) and clustering the sequences using the barcodes determines both the bacterial type for each cell based on 16S rRNA gene and the number of cells for each bacterial type based on the quantity of barcode types sequenced. Single-base accuracy for 16S rRNA sequencing is achieved via the barcodes and by avoiding chimera formation from 16S rRNA genes of different bacteria using droplets. For data processing, an easy-to-use bioinformatic pipeline is available ( https://github.com/Shiroguchi-Lab/BarBIQ_Pipeline_V1_2_0 ). This protocol allows researchers with experience in molecular biology but without bioinformatics experience to perform the process in ~2 weeks. We show the application of BarBIQ in mouse gut microbiota analysis as an example; however, this method is also applicable to other microbiota samples, including those from the mouth and skin, marine environments, soil and plants, as well as those from other terrestrial environments.

A quantitative sequencing method using synthetic internal standards including functional and phylogenetic marker genes

The method of spiking synthetic internal standard genes (ISGs) to samples for amplicon sequencing, generating sequences and converting absolute gene numbers from read counts has been used only for phylogenetic markers and has not been applied to functional markers. In this study, we developed ISGs, including gene sequences of the 16S rRNA, pmoA, encoding a subunit of particulate methane monooxygenase and amoA, encoding a subunit of ammonia monooxygenase. We added ISGs to the samples, amplified the target genes and performed amplicon sequencing. For the mock community, the copy numbers converted from read counts using ISGs were equivalent to those obtained by the quantitative real-time polymerase chain reaction (4.0 × 104 versus 4.1 × 104 and 3.0 × 103 versus 4.0 × 103 copies μL-DNA-1 for 16S rRNA and pmoA genes, respectively), but we also identified underestimation, possibly due to primer coverage (7.8 × 102 versus 3.7 × 103  μL-DNA-1 for amoA gene). We then applied this method to environmental samples and analysed phylogeny, functional diversity and absolute quantities. One Methylocystis population was most abundant in the sludge samples [16S rRNA gene (3.8 × 109 copies g-1 ) and the pmoA gene (2.3 × 109 copies g-1 )] and were potentially interrelated. This study demonstrates that ISG spiking is useful for evaluating sequencing data processing and quantifying functional markers.

Addressing erroneous scale assumptions in microbe and gene set enrichment analysis

By applying Differential Set Analysis (DSA) to sequence count data, researchers can determine whether groups of microbes or genes are differentially enriched. Yet sequence count data suffer from a scale limitation: these data lack information about the scale (i.e., size) of the biological system under study, leading some authors to call these data compositional (i.e., proportional). In this article, we show that commonly used DSA methods that rely on normalization make strong, implicit assumptions about the unmeasured system scale. We show that even small errors in these scale assumptions can lead to positive predictive values as low as 9%. To address this problem, we take three novel approaches. First, we introduce a sensitivity analysis framework to identify when modeling results are robust to such errors and when they are suspect. Unlike standard benchmarking studies, this framework does not require ground-truth knowledge and can therefore be applied to both simulated and real data. Second, we introduce a statistical test that provably controls Type-I error at a nominal rate despite errors in scale assumptions. Finally, we discuss how the impact of scale limitations depends on a researcher's scientific goals and provide tools that researchers can use to evaluate whether their goals are at risk from erroneous scale assumptions. Overall, the goal of this article is to catalyze future research into the impact of scale limitations in analyses of sequence count data; to illustrate that scale limitations can lead to inferential errors in practice; yet to also show that rigorous and reproducible scale reliant inference is possible if done carefully.

Ketogenic Diet Has Moderate Effects on the Fecal Microbiota of Wild-Type Mice

The ketogenic diet (KD) is a high-fat, low-carbohydrate diet that has been reported to have neuroprotective effects. The health effects of KD might be linked to an altered gut microbiome, which plays a major role in host health, leading to neuroprotective effects via the gut-brain axis. However, results from different studies, most often based on the 16S rRNA gene and metagenome sequencing, have been inconsistent. In this study, we assessed the effect of a 4-week KD compared to a western diet (WD) on the colonic microbiome of female C57Bl/6J mice by analyzing fecal samples using fluorescence in situ hybridization. Our results showed distinct changes in the total number of gut bacteria following the 4-week KD, in addition to changes in the composition of the microbiome. KD-fed mice showed higher absolute numbers of Actinobacteria (especially Bifidobacteria spp.) and lower absolute levels of Proteobacteria, often linked to gut inflammation, in comparison with WD-fed mice. Furthermore, an increased abundance of the typically rare genus Atopobium was observed. These changes may indicate the possible anti-inflammatory effects of the KD. However, since the overall changes in the microbiota seem low, the KD effects might be linked to the differential abundance of only a few key genera in mice.

Overview of data preprocessing for machine learning applications in human microbiome research

Although metagenomic sequencing is now the preferred technique to study microbiome-host interactions, analyzing and interpreting microbiome sequencing data presents challenges primarily attributed to the statistical specificities of the data (e.g., sparse, over-dispersed, compositional, inter-variable dependency). This mini review explores preprocessing and transformation methods applied in recent human microbiome studies to address microbiome data analysis challenges. Our results indicate a limited adoption of transformation methods targeting the statistical characteristics of microbiome sequencing data. Instead, there is a prevalent usage of relative and normalization-based transformations that do not specifically account for the specific attributes of microbiome data. The information on preprocessing and transformations applied to the data before analysis was incomplete or missing in many publications, leading to reproducibility concerns, comparability issues, and questionable results. We hope this mini review will provide researchers and newcomers to the field of human microbiome research with an up-to-date point of reference for various data transformation tools and assist them in choosing the most suitable transformation method based on their research questions, objectives, and data characteristics.

Full-electric microfluidic platform to capture, analyze and selectively release single cells

Current single-cell technologies require large and expensive equipment, limiting their use to specialized labs. In this paper, we present for the first time a microfluidic device which demonstrates a combined method for full-electric cell capturing, analyzing, and selectively releasing with single-cell resolution. All functionalities are experimentally demonstrated on Saccharomyces cerevisiae. Our microfluidic platform consists of traps centered around a pair of individually accessible coplanar electrodes, positioned under a microfluidic channel. Using this device, we validate our novel Two-Voltage method for trapping single cells by positive dielectrophoresis (pDEP). Cells are attracted to the trap when a high voltage (VH) is applied. A low voltage (VL) holds the already trapped cell in place without attracting additional cells, allowing full control over the number of trapped cells. After trapping, the cells are analyzed by broadband electrochemical impedance spectroscopy. These measurements allow the detection of single cells and the extraction of cell parameters. Additionally, these measurements show a strong correlation between average phase change and cell size, enabling the use of our system for size measurements in biological applications. Finally, our device allows selectively releasing trapped cells by turning off the pDEP signal in their trap. The experimental results show the techniques potential as a full-electric single-cell analysis tool with potential for miniaturization and automation which opens new avenues towards small-scale, high throughput single-cell analysis and sorting lab-on-CMOS devices.

Validation of 16S rRNA gene sequencing and metagenomics for evaluating microbial immigration in a methanogenic bioreactor

To quantitatively evaluate the impact of microbial immigration from an upstream community on the microbial assembly of a downstream community, an ecological genomics (ecogenomics)-based mass balance (EGMB) model coupled with 16S rRNA gene sequencing was previously developed. In this study, a mock community was used to further validate the EGMB models and demonstrate the feasibility of using metagenome-based EGMB model to reveal both microbial activity and function. The mock community consisting of Aeromonas, Escherichia, and Pseudomonas was fed into a lab-scale methanogenic bioreactor together with dissolved organic substrate. Using qPCR, 16S rRNA gene, 16S rRNA gene copy number normalization (GCN), and metagenome, results showed highly comparable community profiles in the feed. In the bioreactor, Aeromonas and Pseudomonas exhibited negative growth rates throughout the experiment by all approaches. Escherichia's growth rate was negative by most biomarkers but was slightly positive by 16S rRNA gene. Still, all approaches showed a decreasing trend toward negative in the growth rate of Escherichia as reactor operation time increased. Uncultivated populations of phyla Desulfobacterota, Chloroflexi, Actinobacteriota, and Spirochaetota were observed to increase in abundance, suggesting their contribution in degrading the feed biomass. Based on metabolic reconstruction of metagenomes, these populations possessed functions of hydrolysis, fermentation, fatty acid degradation, or acetate oxidation. Overall results supported the application of both 16S rRNA gene- and metagenome-based EGMB models to measure the growth rate of microbes in the bioreactor, and the latter had advantage in providing insights into the microbial functions of uncultivated populations.

Deciphering the brewing process of Cantonese-style rice vinegar: Main flavors, key physicochemical factors, and important microorganisms

Cantonese-style rice vinegar is one of the most important Chinese rice vinegars and is quite popular all over the southeast coast of China, especially in Guangdong. This study identified 31 volatile compounds, including 11 esters, 6 alcohols, 3 aldehydes, 3 acids, 2 ketones, 1 phenol, and 5 alkanes, using headspace solid-phase microextraction-gas chromatography-mass spectrometry. Six organic acids were detected by high performance liquid chromatography. The ethanol content was detected by gas chromatography. During acetic acid fermentation, physicochemical analysis showed that the initial concentrations of reducing sugar and ethanol were 0.0079 g/L and 23.81 g/L, respectively, and the final value of total acid was 46.5 g/L, and the pH value was stable at 3.89. High-throughput sequencing was used to identify the microorganisms, and Acetobacter, Komagataeibacter, and Ralstonia were the top three bacterial genera. Quantitative real-time polymerase chain reaction revealed patterns that were different from those of high-throughput sequencing. The co-occurrence network of microorganisms and the correlation analysis between microorganisms and flavor substances indicate that Acetobacter and Ameyamaea played crucial roles as the main functional AAB, and the failure of Cantonese-style rice vinegar fermentation can be attributed to the abnormal increase in Komagataeibacter. Microbial co-occurrence network analysis indicated that Oscillibacter, Parasutterella, and Alistipes were the top three microorganisms. Redundancy analysis disclosed that total acid and ethanol were the key environmental factors influencing the microbial community. Fifteen microorganisms closely related to the metabolites were identified using the bidirectional orthogonal partial least squares model. Correlation analysis showed that these microorganisms were strongly associated with flavor metabolites and environmental factors. The findings of this study deepen our understanding of the fermentation of traditional Cantonese-style rice vinegar.

Opportunities in optical and electrical single-cell technologies to study microbial ecosystems

New techniques are revolutionizing single-cell research, allowing us to study microbes at unprecedented scales and in unparalleled depth. This review highlights the state-of-the-art technologies in single-cell analysis in microbial ecology applications, with particular attention to both optical tools, i.e., specialized use of flow cytometry and Raman spectroscopy and emerging electrical techniques. The objectives of this review include showcasing the diversity of single-cell optical approaches for studying microbiological phenomena, highlighting successful applications in understanding microbial systems, discussing emerging techniques, and encouraging the combination of established and novel approaches to address research questions. The review aims to answer key questions such as how single-cell approaches have advanced our understanding of individual and interacting cells, how they have been used to study uncultured microbes, which new analysis tools will become widespread, and how they contribute to our knowledge of ecological interactions.