Parallel-Meta Suite: Interactive and rapid microbiome data analysis on multiple platforms

Mechanosensory Piezo2 regulated by gut microbiota participates in the development of visceral hypersensitivity and intestinal dysmotility

The gut microbiota plays a crucial role in the manifestation of intestinal dysfunction associated with irritable bowel syndrome (IBS). The mechanosensory Piezo2 has been implicated in the regulation of intestinal function. However, it remains unclear whether Piezo2 is modulated by the gut microbiota, thus contributing to the development of visceral hypersensitivity and gut dysmotility. The study enrolled patients with diarrhea-predominant IBS (IBS-D) alongside healthy controls (HC). Questionnaires, rectal barostat test, and colonoscopy with mucosal biopsy were conducted. Fecal microbiota transplantation (FMT) was performed using samples from HC or IBS-D patients, and interventions with Akkermansia muciniphila or Fusobacterium varium were carried out on colon- or dorsal root ganglion (DRG)- Piezo2 knockdown pseudo-germ-free mice. Visceral sensitivity and intestinal motility were assessed. Piezo2 levels were detected using western blot and immunofluorescence. Fecal 16S rRNA sequencing and cecum untargeted metabolomics analysis, followed by molecular docking predictions of Piezo2, were also performed. The ratio of Piezo2+/5-HT+ cells was lower in IBS-D patients, positively correlated with visceral sensation and intestinal dysbiosis. The mice that received FMT from IBS-D patients exhibited colonic dysmotility and visceral hypersensitivity, along with elevated Piezo2 protein levels in the colon and DRG. Knockdown of Piezo2 in the colon or DRG ameliorated the FMT-induced colonic dysmotility and visceral hypersensitivity. Fecal 16S rRNA sequencing revealed distinct microbiota composition. Notably, Fusobacterium varium, but not Akkermansia muciniphila, induced gut dysmotility and visceral hypersensitivity, effects that could be alleviated by colon or DRG Piezo2 knockdown. Additionally, Fusobacterium varium lead to increased Piezo2 protein levels, as well as elevated levels of indole-3-acetic acid and indole-3-acrylic acid, which were predicted to bind to Piezo2, causing disturbances. Piezo2 can be regulated by gut microbiota and involved in visceral hypersensitivity and colonic dysmotility, with Fusobacterium varium playing a crucial role.

Microbial dysbiosis and its diagnostic potential in androgenetic alopecia: insights from multi-kingdom sequencing and machine learning

Androgenetic alopecia (AGA), the most common form of hair loss, has been linked to dysbiosis of the scalp microbiome. In this study, we collected microbiome samples from the frontal baldness and occipital regions of patients with varying stages of AGA and conducted a comprehensive analysis of bacterial and fungal communities using 16S rRNA and ITS1 sequencing. Our results revealed that although the scalp microbiome dynamics in healthy subjects correlated strongly with chronological age, this trend was disrupted in AGA patients due to severe microbial imbalances, emphasizing the significant impact of AGA on the scalp microecology. Notably, microbial dysbiosis was not confined to the localized areas of hair loss but extended across the entire scalp. Moreover, the degree of dysbiosis was consistent with the severity of AGA. Leveraging multi-kingdom microbial features and machine learning, we developed a microbial index of scalp health (MiSCH), which effectively detects AGA and stratifies its severity. More importantly, MiSCH was able to identify high-risk individuals, those with significantly disrupted microbiome structures but no overt AGA phenotypic characteristics, thereby offering opportunities for early diagnosis, risk assessment, and personalized treatment of AGA.IMPORTANCEBy analyzing the bacteria and fungi on the scalp, this study shows how androgenetic alopecia (AGA) disrupts the balance of microbes not just in the hair loss areas, but across the entire scalp. Thus, we introduce the microbial index of scalp health (MiSCH), which leverages microbiome data for the early detection and severity prediction of AGA. This method is especially valuable for identifying people at risk of developing more severe hair loss, even before visible symptoms appear. By combining microbiome analysis with machine learning, this research offers a potential breakthrough for early diagnosis and personalized treatments, changing how we approach hair loss and offering new hope for managing the condition more effectively.

TaxaCal: enhancing species-level profiling accuracy of 16S amplicon data

BACKGROUND

16S rRNA amplicon sequencing is a widely used method for microbiome composition analysis due to its cost-effectiveness and lower data requirements compared to metagenomic whole-genome sequencing (WGS). However, inherent limitations in 16S-based approach often lead to profiling discrepancies, particularly at the species level, compromising the accuracy and reliability of findings.

RESULTS

To address this issue, we present TaxaCal (Taxonomic Calibrator), a machine learning algorithm designed to calibrate species-level taxonomy profiles in 16S amplicon data using a two-tier correction strategy. Validation on in-house produced and public datasets shows that TaxaCal effectively reduces biases in amplicon sequencing, mitigating discrepancies between microbial profiles derived from 16S and WGS. Moreover, TaxaCal enables seamless cross-platform comparisons between these two sequencing approaches, significantly improving disease detection in 16S-based microbiome data.

CONCLUSIONS

Therefore, TaxaCal offers a cost-effective solution for generating high-resolution microbiome species profiles that closely align with WGS results, enhancing the utility of 16S-based profiling in microbiome research. As microbiome-based diagnostics continue to evolve, TaxaCal has the potential to be a crucial tool in advancing the utility of 16S sequencing in clinical and research settings.

Preconception maternal gut dysbiosis affects enteric nervous system development and disease susceptibility in offspring via the GPR41-GDNF/RET/SOX10 signaling pathway

Maternal health, specifically changes in the gut microbiota, can profoundly impact offspring health; however, our understanding of how gut microbiota alterations during the preconception period influence the offspring remains limited. In this study, we investigated the impact and mechanisms of preconception maternal gut dysbiosis on the development of the enteric nervous system (ENS) in mice. We found that preconception maternal exposure to antibiotics led to the abnormal development of the ENS in offspring, increasing their susceptibility to water avoidance stress at the adult stage. Metagenomic, targeted metabolomic, and transcriptomic analyses revealed that preconception antibiotic exposure disrupted the expression of genes crucial for embryonic ENS development by altering maternal gut microbiota composition. Multi-omics analysis combined with Limosilactobacillus reuteri and propionate gestational supplementation demonstrated that the maternal gut microbiota and metabolites may influence embryonic ENS development via the GPR41-GDNF/RET/SOX10 signaling pathway. Our findings highlight the critical importance of maintaining a healthy maternal gut microbiota before conception to support normal ENS development in offspring.

Differences in clinical manifestations and the fecal microbiome between irritable bowel syndrome and small intestinal bacterial overgrowth

BACKGROUND

Irritable bowel syndrome (IBS) and small intestinal bacterial overgrowth (SIBO) share similar abdominal symptoms; however, their differentiation remains controversial.

AIMS

To illustrate the differences between the two conditions.

METHODS

Patients and healthy controls completed questionnaires and provided stool samples for analysis.

RESULTS

IBS presented with the most severe symptoms and was specifically characterized by intense abdominal pain and frequent episodes of diarrhea. Patients with IBS displayed more dysregulated taxonomy within the fecal microbiota than SIBO. Opportunistic pathogens, including Lachnoclostridium, Escherichia-Shigella, and Enterobacter were enriched in the IBS group which contributed to increased bacterial pathogenicity and positively correlated with abdominal pain and bloating, meanwhile, Lachnoclostridium and Escherichia-Shigella were found to be associated with metabolites affiliated to bile acids, alcohols and derivatives. Bacteria enriched in SIBO group correlated with constipation. The bacterial co-occurrence network within the SIBO group was the most intricate. Ruminococcaceae Group were defined as core bacteria in SIBO. Differential metabolites affiliated to androstane steroids and phenylacetic acids were associated with core bacteria.

CONCLUSIONS

Our study elucidates the differences between IBS and SIBO in terms of symptoms, microbiota and functions, which provides insights into a better understanding of both diseases and evidence for different treatment strategies.

USEARCH 12: Open-source software for sequencing analysis in bioinformatics and microbiome

The well-known bioinformatic software USEARCH v12 was open sourced. Its meaning encourages the microbiome research community to constantly develop excellent bioinformatic software based on the codes. The open source and popularization of artificial intelligence (AI) will make a better infrastructure for microbiome research.

Comparative analysis of the gut microbiome of ungulate species from Qinghai-Xizang plateau

Several studies have investigated the gut bacterial composition of wild ungulates in the Qinghai-Xizang Plateau. However, the relationship between their gut microbiome dendrograms and their phylogenetic tree remains unclear. In this study, we analyzed 45 amplicons (V3-V4 region of the 16S rRNA gene) from five wild ungulates-Pseudois nayaur, Pantholops hodgsonii, Gazella subgutturosa, Bos grunniens, and Equus kiang-from the Qinghai-Xizang Plateau to clarify the relationship between their phylogenies and gut microbiome dendrograms. The unweighted pair group method with arithmetic mean analysis and hierarchical clustering analysis indicated that G. subgutturosa is closely related to P. nayaur; however, these results were inconsistent with their phylogenetic trees. Additionally, the indicator genera in the microbiome of each wild ungulate showed strong associations with the diets and habitats of their host. Thus, diet and space niche differentiation may primarily account for the differences between the gut microbiome characteristics of these wild ungulates and their phylogeny. In summary, our research provides insights into the evolutionary factors influencing the gut microbiome of wild ungulates in the Qinghai-Xizang Plateau.

Molecular and epigenetic ex vivo profiling of testis cancer-associated fibroblasts and their interaction with germ cell tumor cells and macrophages

Germ cell tumors (GCT) are the most common solid tumors in young men of age 15 - 40. In previous studies, we profiled the interaction of GCT cells with cells of the tumor microenvironment (TM), which showed that especially the 3D interaction of fibroblasts (FB) or macrophages with GCT cells influenced the growth behavior and cisplatin response as well as the transcriptome and secretome of the tumor cells, suggesting that the crosstalk of these cells with GCT cells is crucial for tumor progression and therapy outcome. In this study, we shed light on the mechanisms of activation of cancer-associated fibroblasts (CAF) in the GCT setting and their effects on GCT cells lines and the monocyte cell line THP-1. Ex vivo cultures of GCT-derived CAF were established and characterized molecularly and epigenetically by performing DNA methylation arrays, RNA sequencing, and mass spectrometry-based secretome analysis. We demonstrated that the activation state of CAF is influenced by their former prevailing tumor environment in which they have resided. Hereby, we postulate that seminoma (SE) and embryonal carcinoma (EC) activate CAF, while teratoma (TER) play only a minor role in CAF formation. In turn, CAF influence proliferation and the expression of cisplatin sensitivity-related factors in GCT cells lines as well as polarization of in vitro-induced macrophages by the identified effector molecules IGFBP1, LGALS3BP, LYVE1, and PTX3. Our data suggests that the vital interaction of CAF with GCT cells and with macrophages has a huge influence on shaping the extracellular matrix as well as on recruitment of immune cells to the TM. In conclusion, therapeutically interfering with CAF and / or macrophages in addition to the standard therapy might slow-down progression of GCT and re-shaping of the TM to a tumor-promoting environment. Significance: The interaction of CAF with GCT and macrophages considerably influences the microenvironment. Thus, therapeutically interfering with CAF might slow-down progression of GCT and re-shaping of the microenvironment to a tumor-promoting environment.

PM-profiler: a high-resolution and fast tool for taxonomy annotation of amplicon-based microbiome

Amplicon sequencing stands as a cornerstone in microbiome profiling, yet concerns persist regarding its resolution and accuracy. The enhancement of reference databases and annotations marks a new era for 16S rRNA-based profiling. Capitalizing on this potential, we introduce PM-profiler, a novel tool for profiling amplicon short reads. PM-profiler is implemented by C++-based advanced algorithms, such as pre-allocated hash for reference construction, hybrid and dynamic short-read matching, big-data-guided dual-mode hierarchical taxonomy annotation strategy, and full-procedure parallel computing. This tool delivers species-level resolution and ultrafast speed for large-scale microbiomes, surpassing alignment-based approaches and the Naïve-Bayesian model. Furthermore, recognizing the global uneven distribution of microbes, we delineate optimal annotation strategies for each sampling habitat based on microbial patterns over 270,000 microbiomes. Integrated with the established workflow of Parallel-Meta Suite and the latest curated reference databases, this endeavor offers a swift and dependable solution for high-precision microbiome surveys.IMPORTANCEOur study introduces PM-profiler, a new tool that deciphers the complexity of microbial communities. With advanced algorithms, flexible annotation strategies, and well-organized big-data, PM-profiler provides a faster and more accurate way to study on microbiomes, paving the way for discoveries that could improve our understanding of microbiomes and their impact on the world.

Fecal Coprococcus, hidden behind abdominal symptoms in patients with small intestinal bacterial overgrowth

BACKGROUND

Small intestinal bacterial overgrowth (SIBO) is the presence of an abnormally excessive amount of bacterial colonization in the small bowel. Hydrogen and methane breath test has been widely applied as a non-invasive method for SIBO. However, the positive breath test representative of bacterial overgrowth could also be detected in asymptomatic individuals.

METHODS

To explore the relationship between clinical symptoms and gut dysbiosis, and find potential fecal biomarkers for SIBO, we compared the microbial profiles between SIBO subjects with positive breath test but without abdominal symptoms (PBT) and healthy controls (HC) using 16S rRNA amplicon sequencing.

RESULTS

Fecal samples were collected from 63 SIBO who complained of diarrhea, distension, constipation, or abdominal pain, 36 PBT, and 55 HC. For alpha diversity, the Shannon index of community diversity on the genus level showed a tendency for a slight increase in SIBO, while the Shannon index on the predicted function was significantly decreased in SIBO. On the genus level, significantly decreased Bacteroides, increased Coprococcus_2, and unique Butyrivibrio were observed in SIBO. There was a significant positive correlation between saccharolytic Coprococcus_2 and the severity of abdominal symptoms. Differently, the unique Veillonella in the PBT group was related to amino acid fermentation. Interestingly, the co-occurrence network density of PBT was larger than SIBO, which indicates a complicated interaction of genera. Coprococcus_2 showed one of the largest betweenness centrality in both SIBO and PBT microbiota networks. Pathway analysis based on the Kyoto Encyclopedia of Genes and Genome (KEGG) database reflected that one carbon pool by folate and multiple amino acid metabolism were significantly down in SIBO.

CONCLUSIONS

This study provides valuable insights into the fecal microbiota composition and predicted metabolic functional changes in patients with SIBO. Butyrivibrio and Coprococcus_2, both renowned for their role in carbohydrate fermenters and gas production, contributed significantly to the symptoms of the patients. Coprococcus's abundance hints at its use as a SIBO marker. Asymptomatic PBT individuals show a different microbiome, rich in Veillonella. PBT's complex microbial interactions might stabilize the intestinal ecosystem, but further study is needed due to the core microbiota similarities with SIBO. Predicted folate and amino acid metabolism reductions in SIBO merit additional validation.

Sustained bacterial N2O reduction at acidic pH

Nitrous oxide (N2O) is a climate-active gas with emissions predicted to increase due to agricultural intensification. Microbial reduction of N2O to dinitrogen (N2) is the major consumption process but microbial N2O reduction under acidic conditions is considered negligible, albeit strongly acidic soils harbor nosZ genes encoding N2O reductase. Here, we study a co-culture derived from acidic tropical forest soil that reduces N2O at pH 4.5. The co-culture exhibits bimodal growth with a Serratia sp. fermenting pyruvate followed by hydrogenotrophic N2O reduction by a Desulfosporosinus sp. Integrated omics and physiological characterization revealed interspecies nutritional interactions, with the pyruvate fermenting Serratia sp. supplying amino acids as essential growth factors to the N2O-reducing Desulfosporosinus sp. Thus, we demonstrate growth-linked N2O reduction between pH 4.5 and 6, highlighting microbial N2O reduction potential in acidic soils.

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

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

PM-CNN: microbiome status recognition and disease detection model based on phylogeny and multi-path neural network

Motivation

The human microbiome, found throughout various body parts, plays a crucial role in health dynamics and disease development. Recent research has highlighted microbiome disparities between patients with different diseases and healthy individuals, suggesting the microbiome's potential in recognizing health states. Traditionally, microbiome-based status classification relies on pre-trained machine learning (ML) models. However, most ML methods overlook microbial relationships, limiting model performance.

Results

To address this gap, we propose PM-CNN (Phylogenetic Multi-path Convolutional Neural Network), a novel phylogeny-based neural network model for multi-status classification and disease detection using microbiome data. PM-CNN organizes microbes based on their phylogenetic relationships and extracts features using a multi-path convolutional neural network. An ensemble learning method then fuses these features to make accurate classification decisions. We applied PM-CNN to human microbiome data for status and disease detection, demonstrating its significant superiority over existing ML models. These results provide a robust foundation for microbiome-based state recognition and disease prediction in future research and applications.

Availability and implementation

PM-CNN software is available at https://github.com/qdu-bioinfo/PM_CNN.

Metagenomics and metatranscriptomics as potential driving forces for the exploration of diversity and functions of micro-eukaryotes in soil

Micro-eukaryotes are ubiquitous and play vital roles in diverse ecological systems, yet their diversity and functions are scarcely known. This may be due to the limitations of formerly used conventional culture-based methods. Metagenomics and metatranscriptomics are enabling to unravel the genomic, metabolic, and phylogenetic diversity of micro-eukaryotes inhabiting in different ecosystems in a more comprehensive manner. The in-depth study of structural and functional characteristics of micro-eukaryote community residing in soil is crucial for the complete understanding of this major ecosystem. This review provides a deep insight into the methodologies employed under these approaches to study soil micro-eukaryotic organisms. Furthermore, the review describes available computational tools, pipelines, and database sources and their manipulation for the analysis of sequence data of micro-eukaryotic origin. The challenges and limitations of these approaches are also discussed in detail. In addition, this review summarizes the key findings of metagenomic and metatranscriptomic studies on soil micro-eukaryotes. It also highlights the exploitation of these methods to study the structural as well as functional profiles of soil micro-eukaryotic community and to screen functional eukaryotic protein coding genes for biotechnological applications along with the future perspectives in the field.

The best practice for microbiome analysis using R

With the gradual maturity of sequencing technology, many microbiome studies have published, driving the emergence and advance of related analysis tools. R language is the widely used platform for microbiome data analysis for powerful functions. However, tens of thousands of R packages and numerous similar analysis tools have brought major challenges for many researchers to explore microbiome data. How to choose suitable, efficient, convenient, and easy-to-learn tools from the numerous R packages has become a problem for many microbiome researchers. We have organized 324 common R packages for microbiome analysis and classified them according to application categories (diversity, difference, biomarker, correlation and network, functional prediction, and others), which could help researchers quickly find relevant R packages for microbiome analysis. Furthermore, we systematically sorted the integrated R packages (phyloseq, microbiome, MicrobiomeAnalystR, Animalcules, microeco, and amplicon) for microbiome analysis, and summarized the advantages and limitations, which will help researchers choose the appropriate tools. Finally, we thoroughly reviewed the R packages for microbiome analysis, summarized most of the common analysis content in the microbiome, and formed the most suitable pipeline for microbiome analysis. This paper is accompanied by hundreds of examples with 10,000 lines codes in GitHub, which can help beginners to learn, also help analysts compare and test different tools. This paper systematically sorts the application of R in microbiome, providing an important theoretical basis and practical reference for the development of better microbiome tools in the future. All the code is available at GitHub github.com/taowenmicro/EasyMicrobiomeR.

The effects of arsenic on dechlorination of trichloroethene by consortium DH: Microbial response and resistance

Inorganic arsenic and organochlorines are frequently co-occurring contaminants in anoxic groundwater environments, and the bioremediation of their composite pollution has long been a rigorous predicament. Currently, the dechlorination behaviors and stress responses of microbial dechlorination consortia to arsenic are not yet fully understood. This study assessed the reductive dechlorination performance of a Dehalococcoides-bearing microcosm DH under gradient concentrations of arsenate [As(V)] or arsenite [As(III)] and investigated the response patterns of different functional microorganisms. Our results demonstrated that although the dechlorination rates declined with increasing arsenic concentrations in both As(III/V) scenarios, the inhibitory impact was more pronounced in As(III)-amended groups compared to As(V)-amended groups. Moreover, the vinyl chloride (VC)-to-ethene step was more susceptible to arsenic exposure compared to the trichloroethene (TCE)-to-dichloroethane (DCE) step, while high levels of arsenic exposure [e.g. As(III) > 75 μM] can induce significant accumulation of VC. Functional gene variations and microbial community analyses revealed that As(III/V) affected reductive dechlorination by directly inhibiting organohalide-respiring bacteria (OHRB) and indirectly inhibiting synergistic populations such as acetogens. Metagenomic results indicated that arsenic metabolic and efflux mechanisms were identical among different Dhc strains, and variations in arsenic uptake pathways were possibly responsible for their differential responses to arsenic exposures. By comparison, fermentative bacteria showed high potential for arsenic resistance due to their inherent advantages in arsenic detoxification and efflux mechanisms. Collectively, our findings expanded the understanding of the response patterns of different functional populations to arsenic stress in the dechlorinating consortium and provided insights into modifying bioremediation strategies at co-contaminated sites for furtherance.

Gut microbiome predicts selenium supplementation efficiency across different Chinese adult cohorts using hybrid modeling and feature refining

Selenium (Se) is an essential trace element that plays a vital role in various physiological functions of the human body, despite its small proportion. Due to the inability of the human body to synthesize selenium, there has been increasing concern regarding its nutritional value and adequate intake as a micronutrient. The efficiency of selenium absorption varies depending on individual biochemical characteristics and living environments, underscoring the importance of accurately estimating absorption efficiency to prevent excessive or inadequate intake. As a crucial digestive organ in the human body, gut harbors a complex and diverse microbiome, which has been found to have a significant correlation with the host's overall health status. To investigate the relationship between the gut microbiome and selenium absorption, a two-month intervention experiment was conducted among Chinese adult cohorts. Results indicated that selenium supplementation had minimal impact on the overall diversity of the gut microbiome but was associated with specific subsets of microorganisms. More importantly, these dynamics exhibited variations across regions and sequencing batches, which complicated the interpretation and utilization of gut microbiome data. To address these challenges, we proposed a hybrid predictive modeling method, utilizing refined gut microbiome features and host variable encoding. This approach accurately predicts individual selenium absorption efficiency by revealing hidden microbial patterns while minimizing differences in sequencing data across batches and regions. These efforts provide new insights into the interaction between micronutrients and the gut microbiome, as well as a promising direction for precise nutrition in the future.

Comprehensive Assessment of 16S rRNA Gene Amplicon Sequencing for Microbiome Profiling across Multiple Habitats

The 16S rRNA gene works as a rapid and effective marker for the identification of microorganisms in complex communities; hence, a huge number of microbiomes have been surveyed by 16S amplicon-based sequencing. The resolution of the 16S rRNA gene is always considered only at the genus level; however, it has not been verified on a wide range of microbes yet. To fully explore the ability and potential of the 16S rRNA gene in microbial profiling, here, we propose Qscore, a comprehensive method to evaluate the performance of amplicons by integrating the amplification rate, multitier taxonomic annotation, sequence type, and length. Our in silico assessment by a "global view" of 35,889 microbe species across multiple reference databases summarizes the optimal sequencing strategy for 16S short reads. On the other hand, since microbes are unevenly distributed according to their habitats, we also provide the recommended configuration for 16 typical ecosystems based on the Qscores of 157,390 microbiomes in the Microbiome Search Engine (MSE). Detailed data simulation further proves that the 16S amplicons produced with Qscore-suggested parameters exhibit high precision in microbiome profiling, which is close to that of shotgun metagenomes under CAMI metrics. Therefore, by reconsidering the precision of 16S-based microbiome profiling, our work not only enables the high-quality reusability of massive sequence legacy that has already been produced but is also significant for guiding microbiome studies in the future. We have implemented the Qscore as an online service at http://qscore.single-cell.cn to parse the recommended sequencing strategy for specific habitats or expected microbial structures. IMPORTANCE 16S rRNA has long been used as a biomarker to identify distinct microbes from complex communities. However, due to the influence of the amplification region, sequencing type, sequence processing, and reference database, the accuracy of 16S rRNA has not been fully verified on a global range. More importantly, the microbial composition of different habitats varies greatly, and it is necessary to adopt different strategies according to the corresponding target microbes to achieve optimal analytical performance. Here, we developed Qscore, which evaluates the comprehensive performance of 16S amplicons from multiple perspectives, thus providing the best sequencing strategies for common ecological environments by using big data.

Flex Meta-Storms elucidates the microbiome local beta-diversity under specific phenotypes

MOTIVATION

Beta-diversity quantitatively measures the difference among microbial communities thus enlightening the association between microbiome composition and environment properties or host phenotypes. The beta-diversity analysis mainly relies on distances among microbiomes that are calculated by all microbial features. However, in some cases, only a small fraction of members in a community plays crucial roles. Such a tiny proportion is insufficient to alter the overall distance, which is always missed by end-to-end comparison. On the other hand, beta-diversity pattern can also be interfered due to the data sparsity when only focusing on nonabundant microbes.

RESULTS

Here, we develop Flex Meta-Storms (FMS) distance algorithm that implements the "local alignment" of microbiomes for the first time. Using a flexible extraction that considers the weighted phylogenetic and functional relations of microbes, FMS produces a normalized phylogenetic distance among members of interest for microbiome pairs. We demonstrated the advantage of FMS in detecting the subtle variations of microbiomes among different states using artificial and real datasets, which were neglected by regular distance metrics. Therefore, FMS effectively discriminates microbiomes with higher sensitivity and flexibility, thus contributing to in-depth comprehension of microbe-host interactions, as well as promoting the utilization of microbiome data such as disease screening and prediction.

AVAILABILITY AND IMPLEMENTATION

FMS is implemented in C++, and the source code is released at https://github.com/qdu-bioinfo/flex-meta-storms.

MBPD: A multiple bacterial pathogen detection pipeline for One Health practices

Bacterial pathogens are one of the major threats to biosafety and environmental health, and advanced assessment is a prerequisite to combating bacterial pathogens. Currently, 16S rRNA gene sequencing is efficient in the open-view detection of bacterial pathogens. However, the taxonomic resolution and applicability of this method are limited by the domain-specific pathogen database, taxonomic profiling method, and sequencing target of 16S variable regions. Here, we present a pipeline of multiple bacterial pathogen detection (MBPD) to identify the animal, plant, and zoonotic pathogens. MBPD is based on a large, curated database of the full-length 16S genes of 1986 reported bacterial pathogen species covering 72,685 sequences. In silico comparison allowed MBPD to provide the appropriate similarity threshold for both full-length and variable-region sequencing platforms, while the subregion of V3-V4 (mean: 88.37%, accuracy rate compared to V1-V9) outperformed other variable regions in pathogen identification compared to full-length sequencing. Benchmarking on real data sets suggested the superiority of MBPD in a broader range of pathogen detections compared with other methods, including 16SPIP and MIP. Beyond detecting the known causal agent of animal, human, and plant diseases, MBPD is capable of identifying cocontaminating pathogens from biological and environmental samples. Overall, we provide a MBPD pipeline for agricultural, veterinary, medical, and environmental monitoring to achieve One Health.

Droplet microfluidics-based high-throughput bacterial cultivation for validation of taxon pairs in microbial co-occurrence networks

Co-occurrence networks inferred from the abundance data of microbial communities are widely applied to predict microbial interactions. However, the high workloads of bacterial isolation and the complexity of the networks themselves constrained experimental demonstrations of the predicted microbial associations and interactions. Here, we integrate droplet microfluidics and bar-coding logistics for high-throughput bacterial isolation and cultivation from environmental samples, and experimentally investigate the relationships between taxon pairs inferred from microbial co-occurrence networks. We collected Potamogeton perfoliatus plants (including roots) and associated sediments from Beijing Olympic Park wetland. Droplets of series diluted homogenates of wetland samples were inoculated into 126 96-well plates containing R2A and TSB media. After 10 days of cultivation, 65 plates with > 30% wells showed microbial growth were selected for the inference of microbial co-occurrence networks. We cultivated 129 bacterial isolates belonging to 15 species that could represent the zero-level OTUs (Zotus) in the inferred co-occurrence networks. The co-cultivations of bacterial isolates corresponding to the prevalent Zotus pairs in networks were performed on agar plates and in broth. Results suggested that positively associated Zotu pairs in the co-occurrence network implied complicated relations including neutralism, competition, and mutualism, depending on bacterial isolate combination and cultivation time.

ImageGP: An easy-to-use data visualization web server for scientific researchers

Data visualization plays a crucial role in illustrating results and sharing knowledge among researchers. Though many types of visualization tools are widely used, most of them require enough coding experience or are designed for specialized usages, or are not free. Here, we present ImageGP, a specialized visualization platform designed for biology and chemistry data illustration. ImageGP could generate generalized plots like lines, bars, scatters, boxes, sets, heatmaps, and histograms with the most common input content in a user-friendly interface. Normally plotting using ImageGP only needs a few mouse clicks. For some plots, one only needs to just paste data and click submit to get the visualization results. Additionally, ImageGP supplies up to 26 parameters to meet customizable requirements. ImageGP also contains specialized plots like volcano plot, functional enrichment plot for most omics-data analysis, and other four specialized functions for microbiome analysis. Since 2017, ImageGP has been running for nearly 5 years and serving 336,951 visits from all over the world. Together, ImageGP (http://www.ehbio.com/ImageGP/) is an effective and efficient tool for experimental researchers to comprehensively visualize and interpret data generated from wet-lab and dry-lab.

iMeta: Integrated meta-omics for biology and environments

The cover of iMeta's inaugural issue. The galaxy represents the complexity and value of bioinformatics and metagenomics. DNA, which represents genetic components that guide biological diversity, is at the center of the galaxy. The spiral arms are the microbiome welcoming scientists from all over the world to make novel discoveries. Let us usher in the metaverse era of the microbiome.