A practical guide to amplicon and metagenomic analysis of microbiome data

multiMiAT: an optimal microbiome-based association test for multicategory phenotypes

Microbes can affect the metabolism and immunity of human body incessantly, and the dysbiosis of human microbiome drives not only the occurrence but also the progression of disease (i.e. multiple statuses of disease). Recently, microbiome-based association tests have been widely developed to detect the association between the microbiome and host phenotype. However, the existing methods have not achieved satisfactory performance in testing the association between the microbiome and ordinal/nominal multicategory phenotypes (e.g. disease severity and tumor subtype). In this paper, we propose an optimal microbiome-based association test for multicategory phenotypes, namely, multiMiAT. Specifically, under the multinomial logit model framework, we first introduce a microbiome regression-based kernel association test for multicategory phenotypes (multiMiRKAT). As a data-driven optimal test, multiMiAT then integrates multiMiRKAT, score test and MiRKAT-MC to maintain excellent performance in diverse association patterns. Massive simulation experiments prove the success of our method. Furthermore, multiMiAT is also applied to real microbiome data experiments to detect the association between the gut microbiome and clinical statuses of colorectal cancer as well as for diverse statuses of Clostridium difficile infections.

Multi-omics insights into the interplay between gut microbiota and colorectal cancer in the "microworld" age

Colorectal cancer (CRC) is a multifactorial heterogeneous disease largely due to both genetic predisposition and environmental factors including the gut microbiota, a dynamic microbial ecosystem inhabiting the gastrointestinal tract. Elucidation of the molecular mechanisms by which the gut microbiota interacts with the host may contribute to the pathogenesis, diagnosis, and promotion of CRC. However, deciphering the influence of genetic variants and interactions with the gut microbial ecosystem is rather challenging. Despite recent advancements in single omics analysis, the application of multi-omics approaches to integrate multiple layers of information in the microbiome and host to introduce effective prevention, diagnosis, and treatment strategies is still in its infancy. Here, we integrate host- and microbe-based multi-omics studies, respectively, to provide a strategy to explore potential causal relationships between gut microbiota and colorectal cancer. Specifically, we summarize the recent multi-omics studies such as metagenomics combined with metabolomics and metagenomics combined with genomics. Meanwhile, the sample size and sample types commonly used in multi-omics research, as well as the methods of data analysis, were also generalized. We highlight multiple layers of information from multi-omics that need to be verified by different types of models. Together, this review provides new insights into the clinical diagnosis and treatment of colorectal cancer patients.

Investigation of bacterial and fungal population structure on environmental surfaces of three medical institutions during the COVID-19 pandemic

Objectives

To evaluate the population structure of environmental bacteria and fungi in three different types of medical institutions and the potential risks due to antibiotic resistance during the coronavirus disease 2019 (COVID-19) pandemic.

Methods

One hundred twenty-six environmental surface samples were collected from three medical institutions during the COVID-19 pandemic. A total of 6,093 and 13,514 representative sequences of 16S and ITS ribosomal RNA (rRNA) were obtained by amplicon sequencing analysis. The functional prediction was performed using the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States tool based on the Greengenes database and the FAPROTAX database.

Results

On environmental surfaces in three medical institutions during the COVID-19 pandemic, Firmicutes (51.6%) and Bacteroidetes (25%) were the dominant bacteria, while Ascomycota (39.4%) and Basidiomycota (14.2%) were the dominant fungi. A number of potential bacterial and fungal pathogens were successfully identified by the metagenomic approach. Furthermore, compared with the bacterial results, the fungi showed a generally closer Bray Curtis distance between samples. The overall ratio of Gram-negative bacteria to Gram-positive bacteria was about 3:7. The proportion of stress-tolerant bacteria in medical institutions A, B and C reached 88.9, 93.0 and 93.8%, respectively. Anaerobic bacteria accounted for 39.6% in outdoor environments, 77.7% in public areas, 87.9% in inpatient areas and 79.6% in restricted areas. Finally, the β-Lactam resistance pathway and polymyxin resistance pathway were revealed through functional prediction.

Conclusion

We described the microbial population structure changes in three different types of medical institutions using the metagenomic approach during the COVID-19 pandemic. We found that the disinfection measures performed by three healthcare facilities may be effective on the “ESKAPE” pathogens, but less effective on fungal pathogens. Moreover, emphasis should be given to the prevention and control of β-lactam and polymyxin antibiotics resistance bacteria during the COVID-19 pandemic.

Unveiling the Human Gastrointestinal Tract Microbiome: The Past, Present, and Future of Metagenomics

Over 10¹⁴ symbiotic microorganisms are present in a healthy human body and are responsible for the synthesis of vital vitamins and amino acids, mediating cellular pathways and supporting immunity. However, the deregulation of microbial dynamics can provoke diverse human diseases such as diabetes, human cancers, cardiovascular diseases, and neurological disorders. The human gastrointestinal tract constitutes a hospitable environment in which a plethora of microbes, including diverse species of archaea, bacteria, fungi, and microeukaryotes as well as viruses, inhabit. In particular, the gut microbiome is the largest microbiome community in the human body and has drawn for decades the attention of scientists for its significance in medical microbiology. Revolutions in sequencing techniques, including 16S rRNA and ITS amplicon sequencing and whole genome sequencing, facilitate the detection of microbiomes and have opened new vistas in the study of human microbiota. Especially, the flourishing fields of metagenomics and metatranscriptomics aim to detect all genomes and transcriptomes that are retrieved from environmental and human samples. The present review highlights the complexity of the gastrointestinal tract microbiome and deciphers its implication not only in cellular homeostasis but also in human diseases. Finally, a thorough description of the widely used microbiome detection methods is discussed.

CNN_FunBar: Advanced Learning Technique for Fungi ITS Region Classification

Fungal species identification from metagenomic data is a highly challenging task. Internal Transcribed Spacer (ITS) region is a potential DNA marker for fungi taxonomy prediction. Computational approaches, especially deep learning algorithms, are highly efficient for better pattern recognition and classification of large datasets compared to in silico techniques such as BLAST and machine learning methods. Here in this study, we present CNN_FunBar, a convolutional neural network-based approach for the classification of fungi ITS sequences from UNITE+INSDC reference datasets. Effects of convolution kernel size, filter numbers, k-mer size, degree of diversity and category-wise frequency of ITS sequences on classification performances of CNN models have been assessed at all taxonomic levels (species, genus, family, order, class and phylum). It is observed that CNN models can produce >93% average accuracy for classifying ITS sequences from balanced datasets with 500 sequences per category and 6-mer frequency features at all levels. The comparative study has revealed that CNN_FunBar can outperform machine learning-based algorithms (SVM, KNN, Naïve-Bayes and Random Forest) as well as existing fungal taxonomy prediction software (funbarRF, Mothur, RDP Classifier and SINTAX). The present study will be helpful for fungal taxonomy classification using large metagenomic datasets.

The soil bacterial community regulates germination of Plasmodiophora brassicae resting spores rather than root exudates

Clubroot, caused by Plasmodiophora brassicae, is a severe soil-borne disease that restricts the production of cruciferous crops worldwide. A better understanding of biotic and abiotic factors regulating germination of P. brassicae resting spores in the soil is significant for developing novel control methods. Previous studies reported that root exudates can trigger P. brassicae resting spore germination, thus enabling a targeted attack of P. brassicae on host plant roots. However, we found that native root exudates collected under sterile conditions from host or non-host plants cannot stimulate the germination of sterile spores, indicating that root exudates may not be direct stimulation factors. Instead, our studies demonstrate that soil bacteria are essential for triggering germination. Through 16s rRNA amplicon sequencing analysis, we found that certain carbon sources and nitrate can reshape the initial microbial community to an inducing community leading to the germination of P. brassicae resting spores. The stimulating communities significantly differed in composition and abundance of bacterial taxa compared to the non-stimulating ones. Several enriched bacterial taxa in stimulating community were significantly correlated with spore germination rates and may be involved as stimulation factors. Based on our findings, a multi-factorial 'pathobiome' model comprising abiotic and biotic factors is proposed to represent the putative plant-microbiome-pathogen interactions associated with breaking spore dormancy of P. brassicae in soil. This study presents novel views on P. brassicae pathogenicity and lays the foundation for novel sustainable control strategies of clubroot.

Variations in gut microbiome and metabolites of dogs with acute diarrhea in poodles and Labrador retrievers

For different breeds of dogs with acute diarrhea, the gut microbiota and metabolome profiles are unclear. This prospective observational study analyzed the gut microbiomes of poodles with acute diarrhea and Labrador retrievers with acute diarrhea based on 16S amplicon sequencing, with respective healthy dogs as controls. Fecal non-target metabolomics and metagenomics were performed on poodles with acute diarrhea. This study found that the diversity and structure of the microbial community differed significantly between the two breeds in cohorts of healthy dogs. Two breeds of dogs with acute diarrhea demonstrated different changes in microbial communities and metabolic functions. The metabolism of starch and sucrose was significantly decreased in dogs with acute diarrhea, which may be attributed to the reduced activity of dextran dextrinase. Non-targeted metabolomics identified 21 abnormal metabolic pathways exhibited by dogs with acute diarrhea, including starch, amino acid, bile acid metabolism, etc., and were closely related to specific intestinal flora. This study provided new insights into breed specificity and the development of dietary treatment strategy in canine gastrointestinal disease.

Characterization of Gut Microbiota Compositions along the Intestinal Tract in CD163/pAPN Double Knockout Piglets and Their Potential Roles in Iron Absorption

The gut microbiota is known to play a role in regulating host metabolism, yet the mechanisms underlying this regulation are not well elucidated. Our study aimed to characterize the differences in gut microbiota compositions and their roles in iron absorption between wild-type (WT) and CD163/pAPN double-gene-knockout (DKO) weaned piglets. A total of 58 samples along the entire digestive tract were analyzed for microbial community using 16S rRNA gene sequencing. The colonic microbiota and their metabolites were determined by metagenomic sequencing and untargeted liquid chromatography-mass spectrometry (LC-MS), respectively. Our results showed that no alterations in microbial community structure and composition were observed between DKO and WT weaned piglets, with the exception of colonic microbiota. Interestingly, the DKO piglets had selectively increased the relative abundance of the Leeia genus belonging to the Neisseriaceae family and decreased the Ruminococcaceae_UCG_014 genus abundance. Functional capacity analysis showed that organic acid metabolism was enriched in the colon in DKO piglets. In addition, the DKO piglets showed increased iron levels in important tissues compared with WT piglets without any pathological changes. Pearson's correlation coefficient indicated that the specific bacteria such as Leeia and Ruminococcaceae_UCG_014 genus played a key role in host iron absorption. Moreover, the iron levels had significantly (P < 0.05) positive correlation with microbial metabolites, particularly carboxylic acids and their derivatives, which might increase iron absorption by preventing iron precipitation. Overall, this study reveals an interaction between colonic microbiota and host metabolism and has potential significance for alleviating piglet iron deficiency. IMPORTANCE Iron deficiency is a major risk factor for iron deficiency anemia, which is among the most common nutritional disorders in piglets. However, it remains unclear how the gut microbiota interacts with host iron absorption. The current report provides the first insight into iron absorption-microbiome connection in CD163/pAPN double knockout piglets. The present results showed that carboxylic acids and their derivatives contributed to the absorption of nonheme iron by preventing ferric iron precipitation.

Bacteroides vulgatus Ameliorates Lipid Metabolic Disorders and Modulates Gut Microbial Composition in Hyperlipidemic Rats

Hyperlipidemia is a risk factor and key indicator for cardiovascular diseases, and the gut microbiota is highly associated with hyperlipidemia. Bacteroides vulgatus is a prevalent mutualist across human populations and confers multiple health benefits such as immunoregulation, antiobesity, and coronary artery disease intervention. However, its role in antihyperlipidemia has not been systematically characterized. This study sought to identify the effect of B. vulgatus Bv46 on hyperlipidemia. Hyperlipidemic rats were modeled by feeding them a high-fat diet for 6 weeks. The effect of B. vulgatus Bv46 supplementation was evaluated by measuring anthropometric parameters, lipid and inflammation markers, and the liver pathology. Multi-omics was used to explore the underlying mechanisms. The ability of B. vulgatus Bv46 to produce bile salt hydrolase was confirmed by gene annotation and in vitro experiments. Oral administration of B. vulgatus Bv46 in hyperlipidemic rats significantly reduced the body weight gain, food efficiency, and liver index, improved the serum lipid profile, lowered the levels of serum inflammatory cytokines, promoted the loss of fecal bile acids (BAs), and extended the fecal pool of short-chain fatty acids (SCFAs), especially propionate and butyrate. B. vulgatus Bv46 induced compositional shifts of the gut microbial community of hyperlipidemic rats, characterized by a lower ratio of Firmicutes to Bacteroidetes with an increase of genera Bacteroides and Parabacteroides. After intervention, serum metabolite profiling exhibited an adaptation in amino acids and glycerophospholipid metabolism. Transcriptomics further detected altered biological processes, including primary bile acid biosynthesis and fatty acid metabolic process. Taken together, the findings suggest that B. vulgatus Bv46 could be a promising candidate for interventions against hyperlipidemia. IMPORTANCE As a core microbe of the human gut ecosystem, Bacteroides vulgatus has been linked to multiple aspects of metabolic disorders in a collection of associative studies, which, while indicative, warrants more direct experimental evidence to verify. In this study, we experimentally demonstrated that oral administration of B. vulgatus Bv46 ameliorated the serum lipid profile and systemic inflammation of high-fat diet-induced hyperlipidemic rats in a microbiome-regulated manner, which appears to be associated with changes of bile acid metabolism, short-chain fatty acid biosynthesis, and serum metabolomic profile. This finding supports the causal contribution of B. vulgatus in host metabolism and helps to form the basis of novel therapies for the treatment of hyperlipidemia.

Antimicrobial resistomes in food chain microbiomes

The safety and integrity of the global food system is in a constant state of flux with persistent chemical and microbial risks. While chemical risks are being managed systematically, microbial risks pose extra challenges. Antimicrobial resistant microorganism and persistence of related antibiotic resistance genes (ARGs) in the food chain adds an extra dimension to the management of microbial risks. Because the food chain microbiome is a key interface in the global health system, these microbes can affect health in many ways. In this review, we systematically summarize the distribution of ARGs in foods, describe the potential transmission pathway and transfer mechanism of ARGs from farm to fork, and discuss potential food safety problems and challenges. Modulating antimicrobial resistomes in the food chain facilitates a sustainable global food production system.

EasyAmplicon: An easy-to-use, open-source, reproducible, and community-based pipeline for amplicon data analysis in microbiome research

It is difficult for beginners to learn and use amplicon analysis software because there are so many software tools to choose from, and all of them need multiple steps of operation. Herein, we provide a cross-platform, open-source, and community-supported analysis pipeline EasyAmplicon. EasyAmplicon has most of the modules needed for an amplicon analysis, including data quality control, merging of paired-end reads, dereplication, clustering or denoising, chimera detection, generation of feature tables, taxonomic diversity analysis, compositional analysis, biomarker discovery, and publication-quality visualization. EasyAmplicon includes more than 30 cross-platform modules and R packages commonly used in the field. All steps of the pipeline are integrated into RStudio, which reduces learning costs, keeps the flexibility of the analysis process, and facilitates personalized analysis. The pipeline is maintained and updated by the authors and editors of WeChat official account "Meta-genome." Our team will regularly release the latest tutorials both in Chinese and English, read the feedback from users, and provide help to them in the WeChat account and GitHub. The pipeline can be deployed on various platforms, and the installation time is less than half an hour. On an ordinary laptop, the whole analysis process for dozens of samples can be completed within 3 h. The pipeline is available at GitHub (https://github.com/YongxinLiu/EasyAmplicon) and Gitee (https://gitee.com/YongxinLiu/EasyAmplicon).

OmicStudio: A composable bioinformatics cloud platform with real-time feedback that can generate high-quality graphs for publication

OmicStudio focuses on speed, quality together with flexibility. Generally, OmicStudio can not only meet the users' demand of ordinary bioinformatics data analysis, statistics, and visualization, but also provides them freedom of data mining beyond developer's framework. Additionally, unlimited to developer's aesthetics, users can get more elegant graphs through customizing. Available online https://www.omicstudio.cn.

Sedimentary DNA for tracking the long-term changes in biodiversity

Understanding long-term dynamics is vitally important for explaining current biodiversity patterns and setting conservation goals in a changing world. However, the changes in biodiversity in time and space, particularly the dynamics at the centuries or even longer time scales, are poorly documented because of a lack of continuous monitoring data. The sedimentary DNA (sedDNA) has a great potential for paleo-community reconstruction, and it has recently been used as a powerful tool to characterize past dynamics in terms of biodiversity over geological timescales. In particular, it is useful for prokaryotes and eukaryotes that do not fossilize; hence, it is revolutionizing the scope of paleoecological research. Here, a "Research Weaving" method was performed with systematic maps and bibliometric webs based on the Web of Science for Science Citation Index Expanded, presenting a comprehensive landscape of the sedDNA that traces biological dynamics. We identified that most sedDNA-based studies have focused on microbial dynamics and on using samples from multitypes of sediments. This review summarized the advantages and common applications of sedDNA, focused on the biodiversity in microbial communities, and provided an outlook for the future of sedDNA research.

A large scale 16S ribosomal RNA gene amplicon dataset of hand, foot and mouth patients and healthy individuals

There is evidence linking hand, foot and mouth disease (HFMD) to gut microbiota dysbiosis, and this relationship was corroborated in a large HFMD patient population in our previous study. Here, we present a bacterial 16S rRNA gene dataset from faecal samples of 713 individuals (254 HFMD patients, 459 healthy controls) aged 2 to 7 years residing in Heyuan and Jiangmen counties, Guangdong Province, southern China. Microbiome analysis indicated a significant increase in genus Prevotella, Cetobacterium, and Megamonas was observed in patients with HFMD, whereas a large increase in genus Bacteroides, Ruminococcus, and Faecalibacterium were seen in the control group. We also share the bioinformatic analytical pipeline for this analysis, from data preprocessing to data filtering and amplicon sequence variant (ASV) table generation. We expect that the dataset will be reprocessed, evaluated and fully analysed with various analysis methods to further elucidate the role of the gut microbiota in HFMD development.

Supplementation of Weizmannia coagulans BC2000 and Ellagic Acid Inhibits High-Fat-Induced Hypercholesterolemia by Promoting Liver Primary Bile Acid Biosynthesis and Intestinal Cholesterol Excretion in Mice

The probiotic Weizmannia coagulans (W. coagulans) BC2000 can increase the abundance of intestinal transforming ellagic acid (EA) bacteria and inhibit metabolic disorders caused by hyperlipidemia by activating liver autophagy. This study aimed to investigate the inhibitory effects of W. coagulans BC2000 and EA on hyperlipidemia-induced cholesterol metabolism disorders. C57BL/6J mice (n = 10 in each group) were fed a low-fat diet, high-fat diet (HFD), HFD supplemented with EA, HFD supplemented with EA and W. coagulans BC77, HFD supplemented with EA, and W. coagulans BC2000. EA and W. coagulans BC2000 supplementation prevented HFD-induced hypercholesterolemia and promoted fecal cholesterol excretion. Transcriptome analysis showed that primary bile acid biosynthesis in the liver was significantly activated by EA and W. coagulans BC2000 treatments. EA and W. coagulans BC2000 treatment also significantly increased the intestinal Eggerthellaceae abundance and the liver EA metabolites, iso-urolithin A, Urolithin A, and Urolithin B. Therefore, W. coagulans BC2000 supplementation promoted the intestinal transformation of EA, which led to the upregulation of liver bile synthesis, thus preventing hypercholesterolemia.

In vitro human gut microbiota fermentation models: opportunities, challenges, and pitfalls

The human gut microbiota (HGM) plays a pivotal role in health and disease. Consequently, nutritional and medical research focusing on HGM modulation strategies as a means of improving host health is steadily increasing. In vitro HGM fermentation models offer a valid complement to human and animal studies when it comes to the mechanistic exploration of novel modulation approaches and their direct effects on HGM composition and activity, while excluding interfering host effects. However, in vitro cultivation of HGM can be challenging due to its high oxygen sensitivity and the difficulties of accurately modeling the physio-chemical complexity of the gut environment. Despite the increased use of in vitro HGM models, there is no consensus about appropriate model selection and operation, sometimes leading to major deficiencies in study design and result interpretation. In this review paper, we aim to analyze crucial aspects of the application, setup and operation, data validation and result interpretation of in vitro HGM models. When carefully designed and implemented, in vitro HGM modeling is a powerful strategy for isolating and investigating biotic and abiotic factors in the HGM, as well as evaluating their effects in a controlled environment akin to the gut. Furthermore, complementary approaches combining different in vitro and in vivo models can strengthen the design and interpretation of human studies.

Current understanding of the intratumoral microbiome in various tumors

It is estimated that in the future, the number of new cancer cases worldwide will exceed the 19.3 million recorded in 2020, and the number of deaths will exceed 10 million. Cancer remains the leading cause of human mortality and lagging socioeconomic development. Intratumoral microbes have been revealed to exist in many cancer types, including pancreatic, colorectal, liver, esophageal, breast, and lung cancers. Intratumoral microorganisms affect not only the host immune system, but also the effectiveness of tumor chemotherapy. This review concentrates on the characteristics and roles of intratumoral microbes in various tumors. In addition, the potential of therapies targeting intratumoral microbes, as well as the main challenges currently delaying these therapies, are explored. Furthermore, we briefly summarize existing technical methods used to characterize intratumoral microbes. We hope to provide ideas for exploring intratumoral microbes as potential biomarkers and targets for tumor diagnosis, treatment, and prognostication.

Oral microbiome contributes to the failure of orthodontic temporary anchorage devices (TADs)

BACKGROUND

The stability of temporary anchorage devices (TADs) is critical in orthodontic clinics. The failure of TADs is multifactorial, and the role of the oral microbiome has not been clearly defined. Herein, we attempted to analyze the contribution of the oral microbiome to the failure of TADs.

METHODS

Next-generation sequencing was adopted for analyzing the microbiome on the TADs from orthodontic patients. 29 TADs (15 failed TADs and 14 successful TADs) were used for 16S rRNA gene sequencing. A total of 135 TADs (62 failed TADs and 73 successful TADs) were collected to conduct metagenomic sequencing. Additionally, 34 verified samples (18 failed TADs and 16 successful TADs) were collected for quantitative real-time polymerase chain reaction analysis (qRT-PCR).

RESULTS

Successful and failed TADs demonstrated discrepancies in microbiome structure, composition, and function. Clear separations were found in β-diversity in 16S rRNA gene sequencing as well as metagenomic sequencing (p < 0.05). Metagenomic sequencing showed that Prevotella intermedia, Eikenella corrodens, Parvimonas spp., Neisseria elongata, and Catonella morbi were enriched in the failed groups. qRT-PCR also demonstrated that the absolute bacteria load of Prevotella intermedia was higher in failed TADs (p < 0.05). Considering functional aspects, the failed group showed enriched genes involved in flagellar assembly, bacterial chemotaxis, and oxidative phosphorylation.

CONCLUSIONS

This study illustrated the compositional and functional differences of microorganisms found on successful and failed TADs, indicating that controlling bacterial adhesion on the surface of TADs is essential for their success rate.

Polyethylene mulching film degrading bacteria within the plastisphere: Co-culture of plastic degrading strains screened by bacterial community succession

As an ecological niche close to the polymer, microorganisms in the plastisphere possess the advantage of degrading plastics. This study aims to investigate the bacterial community succession and obtain degrading bacteria in the plastisphere, as well as identify the most efficient degradation combination by co-culture of multiple strains. The findings demonstrate the alpha-diversity indices of the plastisphere bacterial community are significantly lower, and the community structure is regularly and significantly altered. With the time of culture, the plastisphere community composition alters regularly, and the hydrocarbon-degrading genera become the core members. Functional prediction of community reveals the potential for Xenobiotics Biodegradation and Metabolism of plastisphere, and the apparent variations detections of polyethylene mulching film (PMF) indicating the PMF degrading ability of plastisphere. Besides, three PMF-degrading bacterial strains, Rhodopseudomonas sp. P1 (P), Rhodanobacter sp. Rs (R) and Microbacterium sp. M1 (M), are screened for co-culture with PMF degrading strain Bacillus aryabhattai 5-3 (B). By considering bacterial growth, biofilm adhesion, and apparent degradation of different samples, RB (R. sp. Rs + B. aryabhattai 5-3) is ultimately selected as the best PMF degradation combination. This study provides a new possibility for plastisphere-related research from the perspective of mitigating plastic pollution on agricultural land.

Association of Dental Caries with Muscle Mass, Muscle Strength, and Sarcopenia: A Community-Based Study

OBJECTIVES

Changes in the oral cavity can reflect other changes throughout the body. This study aimed to investigate the association of dental caries with muscle mass, muscle strength, and sarcopenia, and also to describe the microbial diversity, composition, and community structure of severe dental caries and sarcopenia.

DESIGN

Cross-sectional study based on a Chinese population aged from 50 to 85 years.

SETTING

Communities from Lanxi City, Zhejiang Province, China.

PARTICIPANTS

A total of 1,442 participants aged from 50 to 85 years from a general community (62.8% women; median age 61.0 [interquartile range: 55.0, 68.0]).

MEASUREMENTS

Dental caries was assessed by the decayed, missing, and filled teeth (DMFT) index. Sarcopenia was defined as the presence of both low muscle mass (assessed by dual-energy X-ray absorptiometry scanning) and low muscle strength (assessed by handgrip strength). Multivariate logistic regression models were used to analyze the association of dental caries with muscle mass, muscle strength, and sarcopenia. Fecal samples underwent 16S rRNA profiling to evaluate the diversity and composition of the gut microbiota in patients with severe dental caries and/or sarcopenia.

RESULTS

In the fully adjusted logistic models, dental caries was positively associated with low muscle strength (DMFT ≥ 7: OR, 1.61; 95% CI, 1.25-2.06), and sarcopenia (DMFT ≥ 7: OR, 1.51; 95% CI, 1.01-2.26), but not low muscle mass. Severe dental caries was positively associated with higher alpha-diversity indices (richness, chao1, and ACE, all p < 0.05) and associated with beta-diversity based on Bray-Curtis distance (p = 0.006). The severe dental caries group and the sarcopenia group overlapped with 11 depleted and 13 enriched genera.

CONCLUSION

Dental caries was positively associated with low muscle strength and sarcopenia but not muscle mass, and this association was more pronounced in male individuals. Significant differences were observed in gut microbiota composition both in severe dental caries and sarcopenia, and there was an overlap of the genera features. Future longitudinal studies are needed to clarify causal relationships.

Machine Learning and COVID-19: Lessons from SARS-CoV-2

Currently, methods in machine learning have opened a significant number of applications to construct classifiers with capacities to recognize, identify, and interpret patterns hidden in massive amounts of data. This technology has been used to solve a variety of social and health issues against coronavirus disease 2019 (COVID-19). In this chapter, we present some supervised and unsupervised machine learning techniques that have contributed in three aspects to supplying information to health authorities and diminishing the deadly effects of the current worldwide outbreak on the population. First is the identification and construction of powerful classifiers capable of predicting severe, moderate, or asymptomatic responses in COVID-19 patients starting from clinical or high-throughput technologies. Second is the identification of groups of patients with similar physiological responses to improve the triage classification and inform treatments. The final aspect is the combination of machine learning methods and schemes from systems biology to link associative studies with mechanistic frameworks. This chapter aims to discuss some practical applications in the use of machine learning techniques to handle data coming from social behavior and high-throughput technologies, associated with COVID-19 evolution.

Ecological and dynamic analysis of gut microbiota in the early stage of azomethane-dextran sodium sulfate model in mice

The success rate of azomethane-dextran sodium sulfate (AOM-DSS) model in mice has been a long-standing problem. Treatment of AOM and the first round DSS induces acute colitis and is of great significance for the success of AOM-DSS model. In this study, we focused on the role of gut microbiota in the early stage of AOM-DSS model. Few mice with obvious weight loss and high disease-activity score survived from double strike of AOM and the first round DSS. Different ecological dynamics of gut microbiota were observed in AOM-DSS treated mice. Pseudescherichia, Turicibacter, and Clostridium_XVIII were of significance in the model, uncontrolled proliferation of which accompanied with rapid deterioration and death of mice. Akkermansia and Ruthenibacterium were significantly enriched in the alive AOM-DSS treated mice. Decrease of Ligilactobacillus, Lactobacillus, and Limosilactobacillus were observed in AOM-DSS model, but significant drop of these genera could be lethal. Millionella was the only hub genus of gut microbiota network in dead mice, which indicated dysbiosis of the intestinal flora and fragility of microbial network. Our results will provide a better understanding for the role of gut microbiota in the early stage of AOM-DSS model and help improve the success rate of model construction.

Breast Milk-Derived Limosilactobacillus reuteri Prevents Atopic Dermatitis in Mice via Activating Retinol Absorption and Metabolism in Peyer's Patches

SCOPE

Supplementing Limosilactobacillus reuteri Fn041, a breast milk-derived probiotic from agricultural and pastoral areas, to maternal mice during late pregnancy and lactation prevents atopic dermatitis (AD) in offspring. This study aims to elucidate the molecular mechanism of Fn041-mediated immune regulation.

METHODS AND RESULTS

Fn041 is administered prenatal and postnatal to maternal mice, and to offspring after weaning. The ears are administered with calcipotriol to induce AD. Fn041 treatment significantly alleviates ear inflammation, and reduces mast cell infiltration. Fn041 treatment upregulates and downregulates intestinal ZO-1 and Claudin-2 mRNA expression, respectively. Transcriptome analysis of Peyer's patches reveals that pathways related to DNA damage repair are activated in AD mice, which is inhibited by Fn041 treatment. Fn041 activates pathways related to retinol absorption and metabolism. Untargeted metabolomic analysis reveals that Fn041 treatment increases plasma retinol and kynurenine. Fn041 treatment does not significantly alter the overall cecal microbiota profile, only increases the relative abundances of Ligilactobacillus apodemi, Ligilactobacillus murinus, Akkermansia muciniphila, and Bacteroides thetaiotaomicron.

CONCLUSIONS

Fn041 induces anti-AD immune responses directly by promoting the absorption and metabolism of retinol in Peyer's patches, and plays an indirect role by strengthening the mucosal barrier and increasing the abundance of specific anti-AD bacteria in the cecum.

Performance of Gut Microbiome as an Independent Diagnostic Tool for 20 Diseases: Cross-Cohort Validation of Machine-Learning Classifiers

Cross-cohort validation is essential for gut-microbiome-based disease stratification but was only performed for limited diseases. Here, we systematically evaluated the cross-cohort performance of gut microbiome-based machine-learning classifiers for 20 diseases. Using single-cohort classifiers, we obtained high predictive accuracies in intra-cohort validation (~0.77 AUC), but low accuracies in cross-cohort validation, except the intestinal diseases (~0.73 AUC). We then built combined-cohort classifiers trained on samples combined from multiple cohorts to improve the validation of non-intestinal diseases, and estimated the required sample size to achieve validation accuracies of >0.7. In addition, we observed higher validation performance for classifiers using metagenomic data than 16S amplicon data in intestinal diseases. We further quantified the cross-cohort marker consistency using a Marker Similarity Index and observed similar trends. Together, our results supported the gut microbiome as an independent diagnostic tool for intestinal diseases and revealed strategies to improve cross-cohort performance based on identified determinants of consistent cross-cohort gut microbiome alterations.

Exploring microbial functional biodiversity at the protein family level-From metagenomic sequence reads to annotated protein clusters

Metagenomics has enabled accessing the genetic repertoire of natural microbial communities. Metagenome shotgun sequencing has become the method of choice for studying and classifying microorganisms from various environments. To this end, several methods have been developed to process and analyze the sequence data from raw reads to end-products such as predicted protein sequences or families. In this article, we provide a thorough review to simplify such processes and discuss the alternative methodologies that can be followed in order to explore biodiversity at the protein family level. We provide details for analysis tools and we comment on their scalability as well as their advantages and disadvantages. Finally, we report the available data repositories and recommend various approaches for protein family annotation related to phylogenetic distribution, structure prediction and metadata enrichment.

metaGOflow: a workflow for the analysis of marine Genomic Observatories shotgun metagenomics data

BACKGROUND

Genomic Observatories (GOs) are sites of long-term scientific study that undertake regular assessments of the genomic biodiversity. The European Marine Omics Biodiversity Observation Network (EMO BON) is a network of GOs that conduct regular biological community samplings to generate environmental and metagenomic data of microbial communities from designated marine stations around Europe. The development of an effective workflow is essential for the analysis of the EMO BON metagenomic data in a timely and reproducible manner.

FINDINGS

Based on the established MGnify resource, we developed metaGOflow. metaGOflow supports the fast inference of taxonomic profiles from GO-derived data based on ribosomal RNA genes and their functional annotation using the raw reads. Thanks to the Research Object Crate packaging, relevant metadata about the sample under study, and the details of the bioinformatics analysis it has been subjected to, are inherited to the data product while its modular implementation allows running the workflow partially. The analysis of 2 EMO BON samples and 1 Tara Oceans sample was performed as a use case.

CONCLUSIONS

metaGOflow is an efficient and robust workflow that scales to the needs of projects producing big metagenomic data such as EMO BON. It highlights how containerization technologies along with modern workflow languages and metadata package approaches can support the needs of researchers when dealing with ever-increasing volumes of biological data. Despite being initially oriented to address the needs of EMO BON, metaGOflow is a flexible and easy-to-use workflow that can be broadly used for one-sample-at-a-time analysis of shotgun metagenomics data.

Altered gut microbiomes are associated with the symptomatic status of unruptured intracranial aneurysms

Background

Gut microbiome has recently been recognized as an important environmental factor affecting the occurrence and development of unruptured intracranial aneurysms (UIA). This study aimed to investigate the relationship between gut microbiome and symptomatic UIA, which is a predictor of instability and a high propensity to rupture.

Methods

A total of 132 patients including 86 asymptomatic UIA and 46 symptomatic UIA were recruited in the study. The composition of gut bacterial communities was determined by 16S ribosomal RNA gene sequencing. In addition, Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) was used to predict the functional composition of the gut microbiome.

Results

There is no difference in the fecal microbial alpha diversity between symptomatic and asymptomatic UIA, but gut microbiome composition changed significantly. At the order level, the relative abundance of Clostridiales was significantly enriched in the symptomatic compared with asymptomatic UIA (p = 0.043). In addition, similar alterations were observed at the family levels of Ruminococcaceae. The Linear discriminant analysis (LEfSe) revealed Fournierella, Ruthenibacterium, and Anaerotruncus as discriminative features in the symptomatic group. Notably, functional differences in gut microbiome of patients with symptomatic UIA included decreased propionate metabolism pathway and enrichment of peptidoglycan biosynthesis pathways.

Conclusion

The present study comprehensively characterizes gut microbiome in a large cohort of different risk statuses of UIA patients and demonstrates the potential biological function of gut microbiome involved in the development of UIA. It may provide additional benefits in guiding UIA management and improving patient outcomes.

Bio-Organic Fertilizer Promotes Pear Yield by Shaping the Rhizosphere Microbiome Composition and Functions

Bio-organic fertilizers (BOF) containing both organic amendments and beneficial microorganisms have been consistently shown to improve soils fertility and yield. However, the exact mechanisms which link amendments and yields remain disputed, and the complexity of bio-organic fertilizers may work in parallel in several ways. BOF may directly improve yield by replenishing soil nutrients or introducing beneficial microbial genes or indirectly by altering the soil microbiome to enrich native beneficial microorganisms. In this work, we aim to disentangle the relative contributions of direct and indirect effects on pear yield. We treated pear trees with either chemical fertilizer or organic fertilizer with/without the plant-beneficial bacterium Bacillus velezensis SQR9. We then assessed, in detail, soil physicochemical and biological properties (metagenome sequencing) as well as pear yield. We then evaluated the relative importance of direct and indirect effects of soil amendments on pear yield. Both organic treatments increased plant yield by up to 20%, with the addition of bacteria tripling the increase driven by organic fertilizer alone. This increase could be linked to alterations in soil physicochemical properties, bacterial community function, and metabolism. Supplementation of organic fertilizer SQR9 increased rhizosphere microbiome richness and functional diversity. Fertilizer-sensitive microbes and functions responded as whole guilds. Pear yield was most positively associated with the Mitsuaria- and Actinoplanes-dominated ecological clusters and with gene clusters involved in ion transport and secondary metabolite biosynthesis. Together, these results suggested that bio-organic fertilizers mainly act indirectly on plant yield by creating soil chemical properties which promote a plant-beneficial microbiome. IMPORTANCE Bio-organic fertilization is a widely used, eco-friendly, sustainable approach to increasing plant productivity in the agriculture and fruit industries. However, it remains unclear whether the promotion of fruit productivity is related to specific changes in microbial inoculants, the resident microbiome, and/or the physicochemical properties of rhizosphere soils. We found that bio-organic fertilizers alter soil chemical properties, thus manipulating specific microbial taxa and functions within the rhizosphere microbiome of pear plants to promote yield. Our work unveils the ecological mechanisms which underlie the beneficial impacts of bio-organic fertilizers on yield promotion in fruit orchards, which may help in the design of more efficient biofertilizers to promote sustainable fruit production.

Multiomics Characterization of the Canada Goose Fecal Microbiome Reveals Selective Efficacy of Simulated Metagenomes

16S rRNA amplicon sequences are predominantly used to identify the taxonomic composition of a microbiome, but they can also be used to generate simulated metagenomes to circumvent costly empirical shotgun sequencing. The effectiveness of using "simulated metagenomes" (shotgun metagenomes simulated from 16S rRNA amplicons using a database of full genomes closely related to the amplicons) in nonmodel systems is poorly known. We sought to determine the accuracy of simulated metagenomes in a nonmodel organism, the Canada goose (Branta canadensis), by comparing metagenomes and metatranscriptomes to simulated metagenomes derived from 16S amplicon sequencing. We found significant differences between the metagenomes, metatranscriptomes, and simulated metagenomes when comparing enzymes, KEGG orthologies (KO), and metabolic pathways. The simulated metagenomes accurately identified the majority (>70%) of the total enzymes, KOs, and pathways. The simulated metagenomes accurately identified the majority of the short-chain fatty acid metabolic pathways crucial to folivores. When narrowed in scope to specific genes of interest, the simulated metagenomes overestimated the number of antimicrobial resistance genes and underestimated the number of genes related to the breakdown of plant matter. Our results suggest that simulated metagenomes should not be used in lieu of empirical sequencing when studying the functional potential of a nonmodel organism's microbiome. Regarding the function of the Canada goose microbiome, we found unexpected amounts of fermentation pathways, and we found that a few taxa are responsible for large portions of the functional potential of the microbiome. IMPORTANCE The taxonomic composition of a microbiome is predominately identified using amplicon sequencing of 16S rRNA genes, but as a single marker, it cannot identify functions (genes). Metagenome and metatranscriptome sequencing can determine microbiome function but can be cost prohibitive. Therefore, computational methods have been developed to generate simulated metagenomes derived from 16S rRNA sequences and databases of full-length genomes. Simulated metagenomes can be an effective alternative to empirical sequencing, but accuracy depends on the genomic database used and whether the database contains organisms closely related to the 16S sequences. These tools are effective in well-studied systems, but the accuracy of these predictions in a nonmodel system is less known. Using a nonmodel bird species, we characterized the function of the microbiome and compared the accuracy of 16S-derived simulated metagenomes to sequenced metagenomes. We found that the simulated metagenomes reflect most but not all functions of empirical metagenome sequencing.

Land use intensification in a dry-hot valley reduced the constraints of water content on soil microbial diversity and multifunctionality but increased CO2 production

Conversion of abandoned land (mainly savanna) into cropland generally occurs in fragile ecosystems such as dry-hot valleys (DHVs) in southwest China, with the intent of increasing land productivity and conducting ecological restoration. However, the effects of conversion on soil microbial communities and carbon turnover of savanna ecosystems remain unclear, since savannas could be a vital but overlooked carbon sink. To illustrate the ecological consequences of land-use change (LUC) for savanna ecosystems, a 1-year field experiment was conducted in DHVs of southwest China. The soil properties, microbial respiration, and metagenomics from two different land-use types (grassland and mango plantation) were examined to reveal the effects of regional LUC on soil C turnover and microbial traits. Conversion from degraded grassland into cropland increased the contribution of soil microclimate to the microbial community composition, reduced the constraints of soil water content (SWC), and further decreased nutrient availability. LUC reshaped the composition and structure of soil bacterial communities. Specifically, soil dominant microbes that belonged to Actinobacteria and Proteobacteria were significantly enriched by conversion, while rare microbes that belonged to a wider range of phyla were generally depleted, leading to an overall decrease in community diversity. In addition, LUC-induced changes in soil characteristics and microbial communities further decreased soil multifunctionality as well as the carbon use efficiency of microbes. Intensified microbial respiration and a significant increase in the soil CO₂ efflux were observed following LUC, which could drive changes in soil microbial community composition and functions (such as growth and regeneration). In summary, through simultaneously reducing constraints on SWC and decreasing nutrient availability, conversion from degraded grassland to cropland in a DHV decreased soil microbial diversity and multifunctionality, and increased microbial respiration and soil CO₂ efflux. Our study provides new insights for understanding the role and mechanisms of LUC in soil carbon turnover in ecologically fragile areas such as DHVs.

Analysis of Bacterial Microbiota of Aerated Compost Teas and Effect on Tomato Growth

Mature composts and their water-based extracts, known as aerated compost teas (ACTs), are biofertilizers that share bioactive effects like soil restoration and plant health promotion, widely used for sustainable agriculture. Bioactive effects of compost and ACTs could be associated with their physicochemical and biological characteristics, like carbon/nitrogen (C/N) ratio and microbiota structure respectively. In our study, we elaborated ACTs using mature homemade compost, wheat bran, and grass clippings, following the C/N ratio criteria. Irrigation of tomato plantlets with ACT whose C/N ratio was close to the expected C/N ratio for mature compost evidenced plant growth promotion. Exploring the bacterial microbiota of elaborated ACTs and origin compost revealed significant structural differences, including phyla involved in N mineralization and free-living N-fixing bacteria. Therefore, ACTs harbor diverse bacterial microbiota involved in the N cycle, which would enrich plant and soil bacterial communities at the taxonomic and functional levels. Furthermore, ACTs are considered a part of agroecological and circular economy approaches.

Reshuffling of the Coral Microbiome during Dormancy

Quiescence, or dormancy, is a response to stressful conditions in which an organism slows or halts physiological functioning. Although most species that undergo dormancy maintain complex microbiomes, there is little known about how dormancy influences and is influenced by the host's microbiome, including in the temperate coral Astrangia poculata. Northern populations of A. poculata undergo winter quiescence. Here, we characterized wild A. poculata microbiomes in a high-resolution sampling time series before, during, and after quiescence using 16S rRNA gene sequencing on active (RNA) and present (DNA) microbiomes. We observed a restructuring of the coral microbiome during quiescence that persisted after reemergence. Upon entering quiescence, corals shed copiotrophic microbes, including putative pathogens, suggesting a removal of these taxa as corals cease normal functioning. During and after quiescence, bacteria and archaea associated with nitrification were enriched, suggesting that the quiescent microbiome may replace essential functions through supplying nitrate to corals and/or microbes. Overall, this study demonstrates that key microbial groups related to quiescence in A. poculata may play a role in the onset or emergence from dormancy and long-term regulation of the microbiome composition. The predictability of dormancy in A. poculata provides an ideal natural manipulation system to further identify factors that regulate host-microbial associations. IMPORTANCE Using a high-resolution sampling time series, this study is the first to demonstrate a persistent microbial community shift with quiescence (dormancy) in a marine organism, the temperate coral Astrangia poculata. Furthermore, during this period of community turnover, there is a shedding of putative pathogens and copiotrophs and an enhancement of the ammonia-oxidizing bacteria (Nitrosococcales) and archaea ("Candidatus Nitrosopumilus"). Our results suggest that quiescence represents an important period during which the coral microbiome can reset, shedding opportunistic microbes and enriching for the reestablishment of beneficial associates, including those that may contribute nitrate while the coral animal is not actively feeding. We suggest that this work provides foundational understanding of the interplay of microbes and the host's dormancy response in marine organisms.

Nucleic acid testing of SARS-CoV-2: A review of current methods, challenges, and prospects

Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and has brought a huge threat to public health and the global economy. Rapid identification and isolation of SARS-CoV-2-infected individuals are regarded as one of the most effective measures to control the pandemic. Because of its high sensitivity and specificity, nucleic acid testing has become the major method of SARS-CoV-2 detection. A deep understanding of different diagnosis methods for COVID-19 could help researchers make an optimal choice in detecting COVID-19 at different symptom stages. In this review, we summarize and evaluate the latest developments in current nucleic acid detection methods for SARS-CoV-2. In particular, we discuss biosensors and CRISPR-based diagnostic systems and their characteristics and challenges. Furthermore, the emerging COVID-19 variants and their impact on SARS-CoV-2 diagnosis are systematically introduced and discussed. Considering the disease dynamics, we also recommend optional diagnostic tests for different symptom stages. From sample preparation to results readout, we conclude by pointing out the pain points and future directions of COVID-19 detection.

Comparison of Metagenomics and Metatranscriptomics Tools: A Guide to Making the Right Choice

The study of microorganisms is a field of great interest due to their environmental (e.g., soil contamination) and biomedical (e.g., parasitic diseases, autism) importance. The advent of revolutionary next-generation sequencing techniques, and their application to the hypervariable regions of the 16S, 18S or 23S ribosomal subunits, have allowed the research of a large variety of organisms more in-depth, including bacteria, archaea, eukaryotes and fungi. Additionally, together with the development of analysis software, the creation of specific databases (e.g., SILVA or RDP) has boosted the enormous growth of these studies. As the cost of sequencing per sample has continuously decreased, new protocols have also emerged, such as shotgun sequencing, which allows the profiling of all taxonomic domains in a sample. The sequencing of hypervariable regions and shotgun sequencing are technologies that enable the taxonomic classification of microorganisms from the DNA present in microbial communities. However, they are not capable of measuring what is actively expressed. Conversely, we advocate that metatranscriptomics is a "new" technology that makes the identification of the mRNAs of a microbial community possible, quantifying gene expression levels and active biological pathways. Furthermore, it can be also used to characterise symbiotic interactions between the host and its microbiome. In this manuscript, we examine the three technologies above, and discuss the implementation of different software and databases, which greatly impact the obtaining of reliable results. Finally, we have developed two easy-to-use pipelines leveraging Nextflow technology. These aim to provide everything required for an average user to perform a metagenomic analysis of marker genes with QIMME2 and a metatranscriptomic study using Kraken2/Bracken.

Pathways to engineering the phyllosphere microbiome for sustainable crop production

Current disease resistance breeding, which is largely dependent on the exploitation of resistance genes in host plants, faces the serious challenges of rapidly evolving phytopathogens. The phyllosphere is the largest biological surface on Earth and an untapped reservoir of functional microbiomes. The phyllosphere microbiome has the potential to defend against plant diseases. However, the mechanisms of how the microbiota assemble and function in the phyllosphere remain largely elusive, and this restricts the exploitation of the targeted beneficial microbes in the field. Here we review the endogenous and exogenous cues impacting microbiota assembly in the phyllosphere and how the phyllosphere microbiota in turn facilitate the disease resistance of host plants. We further construct a holistic framework by integrating of holo-omics, genetic manipulation, culture-dependent characterization and emerging artificial intelligence techniques, such as deep learning, to engineer the phyllosphere microbiome for sustainable crop production.

TCM-Suite: A comprehensive and holistic platform for Traditional Chinese Medicine component identification and network pharmacology analysis

DNA-based biological ingredient identification and downstream pharmacology network analysis are commonly used in research for Traditional Chinese Medicine preparations (TCM formulas). Advancements in bioinformatics tools and the accumulation of related data have become driving forces for progress in this field. However, a lack of a platform integrating biological ingredient identification and downstream pharmacology network analysis hinders the deep understanding of TCM. In this study, we developed the TCM-Suite platform composed of two sub-databases, Holmes-Suite and Watson-Suite, for TCM biological ingredient identification and network pharmacology investigation, respectively, both are among the most complete: In the Holmes-Suite, we collected and processed six types of marker gene sequences, accounting for 1,251,548 marker gene sequences. In the Watson-Suite, we curated and integrated a massive number of entries from more than 10 public databases. Importantly, we developed a comprehensive pipeline to integrate TCM biological ingredient identification and downstream network pharmacology research, allowing users to simultaneously identify components of a TCM formula and analyze its potential pharmacology mechanism. Furthermore, we designed search engines and a user-friendly platform to better search and visualize these rich resources. TCM-Suite is a comprehensive and holistic platform for TCM-based drug discovery and repurposing. TCM-Suite website: http://TCM-Suite.AImicrobiome.cn.

MetaTrass: A high-quality metagenome assembler of the human gut microbiome by cobarcoding sequencing reads

Metagenomic evidence of great genetic diversity within the nonconserved regions of the human gut microbial genomes appeals for new methods to elucidate the species-level variability at high resolution. However, current approaches cannot satisfy this methodologically challenge. In this study, we proposed an efficient binning-first-and-assembly-later strategy, named MetaTrass, to recover high-quality species-resolved genomes based on public reference genomes and the single-tube long fragment read (stLFR) technology, which enables cobarcoding. MetaTrass can generate genomes with longer contiguity, higher completeness, and lower contamination than those produced by conventional assembly-first-and-binning-later strategies. From a simulation study on a mock microbial community, MetaTrass showed the potential to improve the contiguity of assembly from kb to Mb without accuracy loss, as compared to other methods based on the next-generation sequencing technology. From four human fecal samples, MetaTrass successfully retrieved 178 high-quality genomes, whereas only 58 ones were provided by the optimal performance of other conventional strategies. Most importantly, these high-quality genomes confirmed the high level of genetic diversity among different samples and unveiled much more. MetaTrass was designed to work with metagenomic reads sequenced by stLFR technology, but is also applicable to other types of cobarcoding libraries. With the high capability of assembling high-quality genomes of metagenomic data sets, MetaTrass seeks to facilitate the study of spatial characters and dynamics of complex microbial communities at enhanced resolution. The open-source code of MetaTrass is available at https://github.com/BGI-Qingdao/MetaTrass.

Microbiological Advances in Orthodontics: An Overview and Detailed Analysis of Temporary Anchorage Devices

Dental biofilm is the initiating factor of oral diseases, such as periodontitis and caries. Orthodontic treatment could alter the microbiome structure balance, and increase the risk of such diseases. Furthermore, fixed appliances can induce temporary changes in the microbiome community, and the changes that clear aligners bring are smaller by comparison. Temporary anchorage devices (TADs) are skeletal anchorages that are widely used in orthodontic treatment. Microorganisms affect the occurrence and development of inflammation surrounding TADs. At present, existing researches have verified the existence of plaque biofilm on the surface of TADs, but the formation of plaque biofilm and plaque composition under different stable conditions have not been fully understood. The development of high-throughput sequencing, molecular biology experiments, and metabonomics have provided new research ideas to solve this problem. They can become an effective means to explore the microbiome surrounding TADs.

Do we need to change our perspective about gut biomarkers? A public data mining approach to identify differentially abundant bacteria in intestinal inflammatory diseases

Introduction

The gut microbiome is involved in multiple processes that influence host physiology, and therefore, disruptions in microbiome homeostasis have been linked to diseases or secondary infections. Given the importance of the microbiome and the communities of microorganisms that compose it (microbiota), the term biomarkers were coined, which are bacteria correlated with disease states, diets, and the lifestyle of the host. However, a large field in the study of intestinal biomarkers remains unexplored because the bacterial communities associated with a given disease state have not been exactly defined yet.

Methods

Here, we analyzed public data of studies focused on describing the intestinal microbiota of patients with some intestinal inflammatory diseases together with their respective controls. With these analyses, we aimed to identify differentially abundant bacteria between the subjects with the disease and their controls.

Results

We found that frequently reported bacteria such as Fusobacterium, Streptococcus, and Escherichia/Shigella were differentially abundant between the groups, with a higher abundance mostly in patients with the disease in contrast with their controls. On the other hand, we also identified potentially beneficial bacteria such as Faecalibacterium and Phascolarctobacterium, with a higher abundance in control patients.

Discussion

Our results of the differentially abundant bacteria contrast with what was already reported in previous studies on certain inflammatory diseases, but we highlight the importance of considering more comprehensive approaches to redefine or expand the definition of biomarkers. For instance, the intra-taxa diversity within a bacterial community must be considered, as well as environmental and genetic factors of the host, and even consider a functional validation of these biomarkers through in vivo and in vitro approaches. With the above, these key bacterial communities in the intestinal microbiota may have potential as next-generation probiotics or may be functional for the design of specific therapies in certain intestinal diseases.

Analysis and Interpretation of metagenomics data: an approach

Advances in next-generation sequencing technologies have accelerated the momentum of metagenomic studies, which is increasing yearly. The metagenomics field is one of the versatile applications in microbiology, where any interaction in the environment involving microorganisms can be the topic of study. Due to this versatility, the number of applications of this omics technology reached its horizons. Agriculture is a crucial sector involving crop plants and microorganisms interacting together. Hence, studying these interactions through the lenses of metagenomics would completely disclose a new meaning to crop health and development. The rhizosphere is an essential reservoir of the microbial community for agricultural soil. Hence, we focus on the R&D of metagenomic studies on the rhizosphere of crops such as rice, wheat, legumes, chickpea, and sorghum. These recent developments are impossible without the continuous advancement seen in the next-generation sequencing platforms; thus, a brief introduction and analysis of the available sequencing platforms are presented here to have a clear picture of the workflow. Concluding the topic is the discussion about different pipelines applied to analyze data produced by sequencing techniques and have a significant role in interpreting the outcome of a particular experiment. A plethora of different software and tools are incorporated in the automated pipelines or individually available to perform manual metagenomic analysis. Here we describe 8-10 advanced, efficient pipelines used for analysis that explain their respective workflows to simplify the whole analysis process.

Geographical patterns of Fejervarya limnocharis gut microbiota by latitude along mainland China’s coastline

The gut microbiota affects many aspects of host biology and plays key roles in the coevolutionary association with its host. Geographical gradients may play a certain role on gut microbiota variation in the natural environment. However, the distribution pattern of amphibian gut microbiota in the latitudinal gradient remains largely unexplored. Here, we sampled six natural populations of Fejervarya limnocharis along the eastern coastline of mainland China (spanning 20°–30° N = 1,300 km) using 16S rRNA amplicon sequencing to characterize the gut microbiota. First of all, a significant correlation between gut microbial diversity and latitude was observed in our research system. Second, we discovered that latitude influenced the composition of the gut microbiota of F. limnocharis. Finally, we detected that geographical distance could not determine gut microbiota composition in F. limnocharis. These results indicate that latitude can play an important role in shaping the gut microbial diversity of amphibian. Our study offers the first evidence that gut microbial diversity of amphibian presents a latitudinal pattern and highlights the need for increased numbers of individuals to be sampled during microbiome studies in wild populations along environmental gradients.

The microbial community associated with Parascaris spp. infecting juvenile horses

Background

Parasitic nematodes, including large roundworms colloquially known as ascarids, affect the health and well-being of livestock animals worldwide. The equine ascarids, Parascaris spp., are important parasites of juvenile horses and the first ascarids to develop widespread anthelmintic resistance. The microbiota has been shown to be an important factor in the fitness of many organisms, including parasitic nematodes, where endosymbiotic Wolbachia have been exploited for treatment of filariasis in humans.

Methods

This study used short-read 16S rRNA sequences and Illumina sequencing to characterize and compare microbiota of whole worm small intestinal stages and microbiota of male and female intestines and gonads. Diversity metrics including alpha and beta diversity, and the differential abundance analyses DESeq2, ANCOM-BC, corncob, and metagenomeSeq were used for comparisons.

Results

Alpha and beta diversity of whole worm microbiota did not differ significantly between groups, but Simpson alpha diversity was significantly different between female intestine (FI) and male gonad (MG) (P= 0.0018), and Shannon alpha diversity was significantly different between female and male gonads (P = 0.0130), FI and horse jejunum (HJ) (P = 0.0383), and FI and MG (P= 0.0001). Beta diversity (Fig. 2B) was significantly different between female and male gonads (P = 0.0006), male intestine (MI) and FG (P = 0.0093), and MG and FI (P = 0.0041). When comparing organs, Veillonella was differentially abundant for DESeq2 and ANCOM-BC (p < 0.0001), corncob (P = 0.0008), and metagenomeSeq (P = 0.0118), and Sarcina was differentially abundant across four methods (P < 0.0001). Finally, the microbiota of all individual Parascaris spp. specimens were compared to establish shared microbiota between groups.

Conclusions

Overall, this study provided important information regarding the Parascaris spp. microbiota and provides a first step towards determining whether the microbiota may be a viable target for future parasite control options.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s13071-022-05533-y.

Effects of different colored polyethylene mulching films on bacterial communities from soil during enrichment incubation

Studies have shown that mulching agricultural fields with plastic residues can influence microbial communities in the environment, but few studies have investigated the differences in the soil microbial communities in distinct areas under mulching with different colored plastic products. Thus, in this study, we explored how different colored polyethylene mulching films (PMFs) might affect soil bacterial communities during enrichment incubation. We found significant differences in the bacterial communities under different colored PMFs after incubation. Treatment with the same colored PMF obtained more similar bacterial community compositions. For instance, at the class level, Gammaproteobacteria and Bacteroidia were most abundant with black PMF, whereas Actinobacteria and Bacteroidia were most abundant with white PMF. The most abundant genera were Acinetobacter and Chryseobacterium with black PMF but Rhodanobacter and Paenarthrobacter with white PMF. Polyethylene- and hydrocarbon-degrading bacteria were the core members detected under both treatments, and the bacterial communities were predicted to have the potential for the biodegradation and metabolism of xenobiotics after enrichment culture according to the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) tool. In addition, the bacterial communities in soil from Xinjiang treated with white PMF and in soil from Yangling treated with black PMF were strongly correlated and stable. Our results suggest that the color of the PMF applied affected the soil bacterial communities, where plastics with the same color may have recruited similar species of microorganisms, although the origins of these microorganisms were not the same.

Visualization and quantification of carbon "rusty sink" by rice root iron plaque: Mechanisms, functions, and global implications

Paddies contain 78% higher organic carbon (C) stocks than adjacent upland soils, and iron (Fe) plaque formation on rice roots is one of the mechanisms that traps C. The process sequence, extent and global relevance of this C stabilization mechanism under oxic/anoxic conditions remains unclear. We quantified and localized the contribution of Fe plaque to organic matter stabilization in a microoxic area (rice rhizosphere) and evaluated roles of this C trap for global C sequestration in paddy soils. Visualization and localization of pH by imaging with planar optodes, enzyme activities by zymography, and root exudation by ¹⁴ C imaging, as well as upscale modeling enabled linkage of three groups of rhizosphere processes that are responsible for C stabilization from the micro- (root) to the macro- (ecosystem) levels. The ¹⁴ C activity in soil (reflecting stabilization of rhizodeposits) with Fe²⁺ addition was 1.4-1.5 times higher than that in the control and phosphate addition soils. Perfect co-localization of the hotspots of β-glucosidase activity (by zymography) with root exudation (¹⁴ C) showed that labile C and high enzyme activities were localized within Fe plaques. Fe²⁺ addition to soil and its microbial oxidation to Fe³⁺ by radial oxygen release from rice roots increased Fe plaque (Fe³⁺ ) formation by 1.7-2.5 times. The C amounts trapped by Fe plaque increased by 1.1 times after Fe²⁺ addition. Therefore, Fe plaque formed from amorphous and complex Fe (oxyhydr)oxides on the root surface act as a "rusty sink" for organic matter. Considering the area of coverage of paddy soils globally, upscaling by model revealed the radial oxygen loss from roots and bacterial Fe oxidation may trap up to 130 Mg C in Fe plaques per rice season. This represents an important annual surplus of new and stable C to the existing C pool under long-term rice cropping.

The response of bacterial community to UVB was significantly different between immature periphyton and mature periphyton, but not for physiological indicators

Periphyton has important ecological functions. It can even exist in environments with strong ultraviolet radiation. However, knowledge of periphyton under ultraviolet is limited, which limits the understanding and application of periphyton in environments with high ultraviolet radiation. In this study, immature periphyton (IMP) and mature periphyton (MP) under ultraviolet B (UVB) irradiation were investigated and compared in terms of physiological characteristics and bacterial community. Analysis of the physiological characteristics showed that the response patterns of IMP and MP to UVB were similar. IMP and MP could adapt to UVB of 1 W/m² well. However, high-intensity UVB (2 and 3 W/m²) reduced the periphyton biomass, inhibited photosynthesis and antioxidant enzyme activity and caused severe lipid peroxidation in both IMP and MP. Integrated Biological Response (IBR) analysis and toxicological model fitting showed that the ED₅₀ values of UVB for IMP and MP were 1.25 and 1.50 W/m², respectively. 16 S rRNA gene analysis showed that in both IMP and MP, bacterial community composition, assembly and function were affected by UVB. In addition, the response of the bacterial community in IMP to UVB was stronger than that in MP. The diversity of the IMP community was inhibited by UVB, but that of the MP community was not. Proteobacteria and Deinococcus-Thermus are key microorganisms responsible for tolerance to UVB stress. Neutral community model fitting showed that both UVB and the development process caused the determinism of bacterial succession. However, UVB may weaken the deterministic process caused by development. Functional prediction showed that many metabolic functions of periphyton were inhibited by UVB in IMP and MP. However, UVB caused different changes (enhancement or inhibition) of some ecological functions in them. This study provides valuable information for understanding periphyton in environments with UVB radiation, which may be used to improve the application of periphyton in these environments.

Understanding of mercury and methylmercury transformation in sludge composting by metagenomic analysis

Municipal sewage especially the produced sewage sludge is a significant source releasing mercury (Hg) to the environment. However, the Hg speciation especially methylmercury (MeHg) transformation in sewage sludge treatment process remains poorly understood. This study investigated the transformation of Hg speciation especially MeHg in sludge composting. The distribution of Hg transformation related gene pairs hgcAB and merAB, and their putative microbial hosts were comprehensively analyzed. Both Hg (from 3.16±0.22 mg/kg to 3.20±0.19 mg/kg) and MeHg content (from 4.77±0.64 ng/g to 4.36±0.37 ng/g) were not obviously changed before and after composting, but about 19.69% of Hg and 27.36% of MeHg were lost according to mass balance calculation. The metagenomic analysis further revealed that anaerobes (Desulfobacterota and Euryarchaeota) were the mainly putative Hg methylators especially carrying high abundance of hgcA gene in the initial periods of composting. Among the 151 reconstructed metagenome-assembled genomes (MAGs), only 4 hgcA gene carriers (Myxococcota, Firmicutes, Cyclobacteriaceae, and Methanothermobacter) and 16 merB gene carriers were identified. But almost all of the MAGs carried hgcB gene and merA gene. The merA gene was widely distributed in genomes, which indicated the widespread functionality of microbes for reducing Hg(II) to Hg(0). The hgcA carrying microbes tends to present the similar metabolic pathways including methanogenesis and sulfur metabolism. Besides, both the irregular distribution of hgcA in various species (including Actinobacteria, Archaea, Bacteroidetes, Desulfobacterota, Euryarchaeota, and Nitrospirae, etc.) and opposite evolution trends between hgcA gene abundance and its host genome abundance can be an indication of horizontal gene transfer or gene deletions of hgcA during composting. Our findings thus revealed that sludge composting is not only a hotspot for Hg speciation transformation, but also a potential hotspot for MeHg transformation.

CRAMdb: a comprehensive database for composition and roles of microbiome in animals

CRAMdb (a database for composition and roles of animal microbiome) is a comprehensive resource of curated and consistently annotated metagenomes for non-human animals. It focuses on the composition and roles of the microbiome in various animal species. The main goal of the CRAMdb is to facilitate the reuse of animal metagenomic data, and enable cross-host and cross-phenotype comparisons. To this end, we consistently annotated microbiomes (including 16S, 18S, ITS and metagenomics sequencing data) of 516 animals from 475 projects spanning 43 phenotype pairs to construct the database that is equipped with 9430 bacteria, 278 archaea, 2216 fungi and 458 viruses. CRAMdb provides two main contents: microbiome composition data, illustrating the landscape of the microbiota (bacteria, archaea, fungi, and viruses) in various animal species, and microbiome association data, revealing the relationships between the microbiota and various phenotypes across different animal species. More importantly, users can quickly compare the composition of the microbiota of interest cross-host or body site and the associated taxa that differ between phenotype pairs cross-host or cross-phenotype. CRAMdb is freely available at (http://www.ehbio.com/CRAMdb).

The Role of the Cutaneous Mycobiome in Atopic Dermatitis

Atopic dermatitis is a chronic inflammatory skin disorder characterized by eczematous lesions, itch, and a significant deterioration in the quality of life. Recently, microbiome dysbiosis has been implicated in the pathogenesis of atopic dermatitis. Changes in the fungal microbiome (also termed mycobiome) appear to be an important factor influencing the clinical picture of this entity. This review summarizes the available insights into the role of the cutaneous mycobiome in atopic dermatitis and the new research possibilities in this field. The prevalence and characteristics of key fungal species, the most important pathogenesis pathways, as well as classic and emerging therapies of fungal dysbiosis and infections complicating atopic dermatitis, are presented.