The Impact of Bioinformatics Pipelines on Microbiota Studies: Does the Analytical "Microscope" Affect the Biological Interpretation?

Comparison of different microbiome analysis pipelines to validate their reproducibility of gastric mucosal microbiome composition

Microbiome analysis has become a crucial tool for basic and translational research due to its potential for translation into clinical practice. However, there is ongoing controversy regarding the comparability of different bioinformatic analysis platforms and a lack of recognized standards, which might have an impact on the translational potential of results. This study investigates how the performance of different microbiome analysis platforms impacts the final results of mucosal microbiome signatures. Across five independent research groups, we compared three distinct and frequently used microbiome analysis bioinformatic packages (DADA2, MOTHUR, and QIIME2) on the same subset of fastQ files. The source data set encompassed 16S rRNA gene raw sequencing data (V1-V2) from gastric biopsy samples of clinically well-defined gastric cancer (GC) patients (n = 40; with and without Helicobacter pylori [H. pylori] infection) and controls (n = 39, with and without H. pylori infection). Independent of the applied protocol, H. pylori status, microbial diversity and relative bacterial abundance were reproducible across all platforms, although differences in performance were detected. Furthermore, alignment of the filtered sequences to the old and new taxonomic databases (i.e., Ribosomal Database Project, Greengenes, and SILVA) had only a limited impact on the taxonomic assignment and thus on global analytical outcomes. Taken together, our results clearly demonstrate that different microbiome analysis approaches from independent expert groups generate comparable results when applied to the same data set. This is crucial for interpreting respective studies and underscores the broader applicability of microbiome analysis in clinical research, provided that robust pipelines are utilized and thoroughly documented to ensure reproducibility.IMPORTANCEMicrobiome analysis is one of the most important tools for basic and translational research due to its potential for translation into clinical practice. However, there is an ongoing controversy about the comparability of different bioinformatic analysis platforms and a lack of recognized standards. In this study, we investigate how the performance of different microbiome analysis platforms affects the final results of mucosal microbiome signatures. Five independent research groups used three different and commonly used bioinformatics packages for microbiome analysis on the same data set and compared the results. This data set included microbiome sequencing data from gastric biopsy samples of GC patients. Regardless of the protocol used, Helicobacter pylori status, microbial diversity, and relative bacterial abundance were reproducible across all platforms. The results show that different microbiome analysis approaches provide comparable results. This is crucial for the interpretation of corresponding studies and underlines the broader applicability of microbiome analysis.

Integrating Metagenomic and Culture-Based Techniques to Detect Foodborne Pathogens and Antimicrobial Resistance Genes in Malaysian Produce

Foodborne illnesses pose a significant global health threat, often caused by pathogens like Escherichia coli, Listeria monocytogenes, and Salmonella spp. The emergence of antibiotic-resistant strains further exacerbates food safety challenges. This study combines shotgun metagenomics and culture-based approaches to detect foodborne pathogens and antimicrobial resistance genes (ARGs) in Malaysian produce and meats from the Kinta Valley region. A total of 27 samples comprising vegetables, meats, and fruits were analyzed. Metagenomics provided comprehensive microbial profiles, revealing diverse bacterial communities with species-level taxonomic resolution. Culture-based methods complemented these findings by identifying viable pathogens. Key foodborne pathogens were detected, with Listeria monocytogenes identified in meats and vegetables and Shigella flexneri detected inconsistently between the methods. ARGs analysis highlighted significant resistance to cephalosporins and penams, particularly in raw chicken and vegetable samples, underscoring the potential public health risks. While deli meats and fruits exhibited a lower antimicrobial resistance prevalence, resistant genes linked to E. coli and Salmonella strains were identified. Discrepancies between the methods suggest the need for integrated approaches to improve the pathogen detection accuracy. This study demonstrates the potential of metagenomics in advancing food safety research and supports its adoption as a complementary tool alongside culture-based methods for comprehensive foodborne pathogen surveillance and ARG profiling in Malaysian food systems.

Microbial and Metabolic Gut Profiling across Seven Malignancies Identifies Fecal Faecalibacillus intestinalis and Formic Acid as Commonly Altered in Cancer Patients

The key association between gut dysbiosis and cancer is already known. Here, we used whole-genome shotgun sequencing (WGS) and gas chromatography/mass spectrometry (GC/MS) to conduct metagenomic and metabolomic analyses to identify common and distinct taxonomic configurations among 40, 45, 71, 34, 50, 60, and 40 patients with colorectal cancer, stomach cancer, breast cancer, lung cancer, melanoma, lymphoid neoplasms and acute myeloid leukemia (AML), respectively, and compared the data with those from sex- and age-matched healthy controls (HC). α-diversity differed only between the lymphoid neoplasm and AML groups and their respective HC, while β-diversity differed between all groups and their HC. Of 203 unique species, 179 and 24 were under- and over-represented, respectively, in the case groups compared with HC. Of these, Faecalibacillus intestinalis was under-represented in each of the seven groups studied, Anaerostipes hadrus was under-represented in all but the stomach cancer group, and 22 species were under-represented in the remaining five case groups. There was a marked reduction in the gut microbiome cancer index in all case groups except the AML group. Of the short-chain fatty acids and amino acids tested, the relative concentration of formic acid was significantly higher in each of the case groups than in HC, and the abundance of seven species of Faecalibacterium correlated negatively with most amino acids and formic acid, and positively with the levels of acetic, propanoic, and butanoic acid. We found more differences than similarities between the studied malignancy groups, with large variations in diversity, taxonomic/metabolomic profiles, and functional assignments. While the results obtained may demonstrate trends rather than objective differences that correlate with different types of malignancy, the newly developed gut microbiota cancer index did distinguish most of the cancer cases from HC. We believe that these data are a promising step forward in the search for new diagnostic and predictive tests to assess intestinal dysbiosis among cancer patients.

Alterations in gut microbiota caused by major depressive disorder or a low FODMAP diet and where they overlap

Beneficial changes in microbiota observed in individuals with a major depressive disorder (MDD) may be initiated with a low fermentable oligosaccharide, disaccharide, monosaccharide, and polyol (FODMAP) elimination diet. Academic Search Ultimate, APA PsychINFO, Cochrane Library, MEDLINE, Scopus and Web of Science were searched for original research documenting differences in microbiota in MDD or changes with a low FODMAP diet in adults (age 18 years +). Studies with fecal microbiota, 16 s RNA sequencing and QIIME pipelines were included. Studies using antibiotics, probiotics, and medications such as antidepressants were excluded. Additionally, studies based on a single gender were excluded as gender impacts microbiota changes in MDD. Four studies addressed differences in microbiota with MDD and another four assessed shifts occurring with a low FODMAP diet. The abundance of Bacteroidetes, Bacteroidaceae and Bacteroides were lower in individuals with MDD but increased with a low FODMAP diet. Abundance of Ruminoccaceae was lower and Bilophila was higher with both a low FODMAP diet and MDD. These results provide preliminary evidence that a low FODMAP diet might drive changes in microbiota that also benefit people with MDD. Further research to assess whether a low FODMAP diet can treat MDD through modification of targeted microbiota is warranted.

Mock microbial community meta-analysis using different trimming of amplicon read lengths

Trimming of sequencing reads is a pre-processing step that aims to discard sequence segments such as primers, adapters and low quality nucleotides that will interfere with clustering and classification steps. We evaluated the impact of trimming length of paired-end 16S and 18S rRNA amplicon reads on the ability to reconstruct the taxonomic composition and relative abundances of communities with a known composition in both even and uneven proportions. We found that maximizing read retention maximizes recall but reduces precision by increasing false positives. The presence of expected taxa was accurately predicted across broad trim length ranges but recovering original relative proportions remains a difficult challenge. We show that parameters that maximize taxonomic recovery do not simultaneously maximize relative abundance accuracy. Trim length represents one of several experimental parameters that have non-uniform impact across microbial clades, making it a difficult parameter to optimize. This study offers insights, guidelines, and helps researchers assess the significance of their decisions when trimming raw reads in a microbiome analysis based on overlapping or non-overlapping paired-end amplicons.

Microbiome depiction through user-adapted bioinformatic pipelines and parameters

Introduction. The role of the microbiome in health and disease continues to be increasingly recognized. However, there is significant variability in the bioinformatic protocols for analysing genomic data. This, in part, has impeded the potential incorporation of microbiomics into the clinical setting and has challenged interstudy reproducibility. In microbial compositional analysis, there is a growing recognition for the need to move away from a one-size-fits-all approach to data processing.Gap Statement. Few evidence-based recommendations exist for setting parameters of programs that infer microbiota community profiles despite these parameters significantly impacting the accuracy of taxonomic inference.Aim. To compare three commonly used programs (DADA2, QIIME2, and mothur) and optimize them into four user-adapted pipelines for processing paired-end amplicon reads. We aim to increase the accuracy of compositional inference and help standardize microbiomic protocol.Methods. Two key parameters were isolated across four pipelines: filtering sequence reads based on a whole-number error threshold (maxEE) and truncating read ends based on a quality score threshold (QTrim). Closeness of sample inference was then evaluated using a mock community of known composition.Results. We observed that raw genomic data lost were proportionate to how stringently parameters were set. Exactly how much data were lost varied by pipeline. Accuracy of sample inference correlated with increased sequence read retention. Falsely detected taxa and unaccounted for microbial constituents were unique to pipeline and parameter. Implementation of optimized parameter values led to better approximation of the known mock community.Conclusions. Microbial compositions generated based on the 16S rRNA marker gene should be interpreted with caution. To improve microbial community profiling, bioinformatic protocols must be user-adapted. Analysis should be performed with consideration for the select target amplicon, pipelines and parameters used, and taxa of interest.

A next-generation sequencing approach for the detection of mixed species in canned tuna

Tuna cans are relevant seafood products for which mixtures of different tuna species are not allowed according to European regulations. In order to support the prevention of food fraud and mislabelling, a next-generation sequencing methodology based on mitochondrial cytochrome b and control region markers has been tested. Analyses of defined mixtures of DNA, fresh tissue and canned tissue revealed a qualitative and, to some extent, semiquantitative identification of tuna species. While the choice of the bioinformatic pipeline had no influence in the results (p = 0.71), quantitative differences occurred depending on the treatment of the sample, marker, species, and mixture (p < 0.01). The results revealed that matrix-specific calibrators or normalization models should also be used in NGS. The method represents an important step towards a semiquantitative method for routine control of this analytically challenging food matrix. Tests of commercial samples uncovered mixed species in some cans, being not in compliance with EU regulations.

A Systematic Review and Meta-analysis of Dietary Interventions Modulating Gut Microbiota and Cardiometabolic Diseases-Striving for New Standards in Microbiome Studies

BACKGROUND & AIMS

Cardiometabolic diseases (CMDs) have shared properties and causes. Insulin resistance is a risk factor and characteristic of CMDs and has been suggested to be modulated by plasma metabolites derived from gut microbiota (GM). Because diet is among the most important modulators of GM, we performed a systematic review of the literature to assess whether CMDs can be modulated via dietary interventions targeting the GM.

METHODS

A systematic review of the literature for clinical studies was performed on Ovid MEDLINE and Ovid Embase. Studies were assessed for risk of bias and patterns of intervention effects. A meta-analysis with random effects models was used to evaluate the effect of dietary interventions on clinical outcomes.

RESULTS

Our search yielded 4444 unique articles, from which 15 randomized controlled trials and 6 nonrandomized clinical trials were included. The overall risk of bias was high in all studies. In general, most dietary interventions changed the GM composition, but no consistent effect could be found. Results of the meta-analyses showed that only diastolic blood pressure is decreased across interventions compared with controls (mean difference: -3.63 mm Hg; 95% confidence interval, -7.09 to -0.17; I² = 0%, P = .04) and that a high-fiber diet was associated with reduced triglyceride levels (mean difference: -0.69 mmol/L; 95% confidence interval, -1.36 to -0.02; I² = 59%, P = .04). Other CMD parameters were not affected.

CONCLUSIONS

Dietary interventions modulate GM composition, blood pressure, and circulating triglycerides. However, current studies have a high methodological heterogeneity and risk of bias. Well-designed and controlled studies are thus necessary to better understand the complex interaction between diet, microbiome, and CMDs.

PROSPERO

CRD42020188405.

Multi-Omics Strategies for Investigating the Microbiome in Toxicology Research

Microbial communities on and within the host contact environmental pollutants, toxic compounds, and other xenobiotic compounds. These communities of bacteria, fungi, viruses, and archaea possess diverse metabolic potential to catabolize compounds and produce new metabolites. Microbes alter chemical disposition thus making the microbiome a natural subject of interest for toxicology. Sequencing and metabolomics technologies permit the study of microbiomes altered by acute or long-term exposure to xenobiotics. These investigations have already contributed to and are helping to re-interpret traditional understandings of toxicology. The purpose of this review is to provide a survey of the current methods used to characterize microbes within the context of toxicology. This will include discussion of commonly used techniques for conducting omic-based experiments, their respective strengths and deficiencies, and how forward-looking techniques may address present shortcomings. Finally, a perspective will be provided regarding common assumptions that currently impede microbiome studies from producing causal explanations of toxicologic mechanisms.

A systematic review of gut microbiota composition in observational studies of major depressive disorder, bipolar disorder and schizophrenia

The emerging understanding of gut microbiota as 'metabolic machinery' influencing many aspects of physiology has gained substantial attention in the field of psychiatry. This is largely due to the many overlapping pathophysiological mechanisms associated with both the potential functionality of the gut microbiota and the biological mechanisms thought to be underpinning mental disorders. In this systematic review, we synthesised the current literature investigating differences in gut microbiota composition in people with the major psychiatric disorders, major depressive disorder (MDD), bipolar disorder (BD) and schizophrenia (SZ), compared to 'healthy' controls. We also explored gut microbiota composition across disorders in an attempt to elucidate potential commonalities in the microbial signatures associated with these mental disorders. Following the PRISMA guidelines, databases were searched from inception through to December 2021. We identified 44 studies (including a total of 2510 psychiatric cases and 2407 controls) that met inclusion criteria, of which 24 investigated gut microbiota composition in MDD, seven investigated gut microbiota composition in BD, and 15 investigated gut microbiota composition in SZ. Our syntheses provide no strong evidence for a difference in the number or distribution (α-diversity) of bacteria in those with a mental disorder compared to controls. However, studies were relatively consistent in reporting differences in overall community composition (β-diversity) in people with and without mental disorders. Our syntheses also identified specific bacterial taxa commonly associated with mental disorders, including lower levels of bacterial genera that produce short-chain fatty acids (e.g. butyrate), higher levels of lactic acid-producing bacteria, and higher levels of bacteria associated with glutamate and GABA metabolism. We also observed substantial heterogeneity across studies with regards to methodologies and reporting. Further prospective and experimental research using new tools and robust guidelines hold promise for improving our understanding of the role of the gut microbiota in mental and brain health and the development of interventions based on modification of gut microbiota.

Robustness analysis of metabolic predictions in algal microbial communities based on different annotation pipelines

Animals, plants, and algae rely on symbiotic microorganisms for their development and functioning. Genome sequencing and genomic analyses of these microorganisms provide opportunities to construct metabolic networks and to analyze the metabolism of the symbiotic communities they constitute. Genome-scale metabolic network reconstructions rest on information gained from genome annotation. As there are multiple annotation pipelines available, the question arises to what extent differences in annotation pipelines impact outcomes of these analyses. Here, we compare five commonly used pipelines (Prokka, MaGe, IMG, DFAST, RAST) from predicted annotation features (coding sequences, Enzyme Commission numbers, hypothetical proteins) to the metabolic network-based analysis of symbiotic communities (biochemical reactions, producible compounds, and selection of minimal complementary bacterial communities). While Prokka and IMG produced the most extensive networks, RAST and DFAST networks produced the fewest false positives and the most connected networks with the fewest dead-end metabolites. Our results underline differences between the outputs of the tested pipelines at all examined levels, with small differences in the draft metabolic networks resulting in the selection of different microbial consortia to expand the metabolic capabilities of the algal host. However, the consortia generated yielded similar predicted producible compounds and could therefore be considered functionally interchangeable. This contrast between selected communities and community functions depending on the annotation pipeline needs to be taken into consideration when interpreting the results of metabolic complementarity analyses. In the future, experimental validation of bioinformatic predictions will likely be crucial to both evaluate and refine the pipelines and needs to be coupled with increased efforts to expand and improve annotations in reference databases.

BIOCOM-PIPE: a new user-friendly metabarcoding pipeline for the characterization of microbial diversity from 16S, 18S and 23S rRNA gene amplicons

BACKGROUND

The ability to compare samples or studies easily using metabarcoding so as to better interpret microbial ecology results is an upcoming challenge. A growing number of metabarcoding pipelines are available, each with its own benefits and limitations. However, very few have been developed to offer the opportunity to characterize various microbial communities (e.g., archaea, bacteria, fungi, photosynthetic microeukaryotes) with the same tool.

RESULTS

BIOCOM-PIPE is a flexible and independent suite of tools for processing data from high-throughput sequencing technologies, Roche 454 and Illumina platforms, and focused on the diversity of archaeal, bacterial, fungal, and photosynthetic microeukaryote amplicons. Various original methods were implemented in BIOCOM-PIPE to (1) remove chimeras based on read abundance, (2) align sequences with structure-based alignments of RNA homologs using covariance models, and (3) a post-clustering tool (ReClustOR) to improve OTUs consistency based on a reference OTU database. The comparison with two other pipelines (FROGS and mothur) and Amplicon Sequence Variant definition highlighted that BIOCOM-PIPE was better at discriminating land use groups.

CONCLUSIONS

The BIOCOM-PIPE pipeline makes it possible to analyze 16S, 18S and 23S rRNA genes in the same packaged tool. The new post-clustering approach defines a biological database from previously analyzed samples and performs post-clustering of reads with this reference database by using open-reference clustering. This makes it easier to compare projects from various sequencing runs, and increased the congruence among results. For all users, the pipeline was developed to allow for adding or modifying the components, the databases and the bioinformatics tools easily, giving high modularity for each analysis.

Biological observations in microbiota analysis are robust to the choice of 16S rRNA gene sequencing processing algorithm: case study on human milk microbiota

BACKGROUND

In recent years, the microbiome field has undergone a shift from clustering-based methods of operational taxonomic unit (OTU) designation based on sequence similarity to denoising algorithms that identify exact amplicon sequence variants (ASVs), and methods to identify contaminating bacterial DNA sequences from low biomass samples have been developed. Although these methods improve accuracy when analyzing mock communities, their impact on real samples and downstream analysis of biological associations is less clear.

RESULTS

Here, we re-processed our recently published milk microbiota data using Qiime1 to identify OTUs, and Qiime2 to identify ASVs, with or without contaminant removal using decontam. Qiime2 resolved the mock community more accurately, primarily because Qiime1 failed to detect Lactobacillus. Qiime2 also considerably reduced the average number of ASVs detected in human milk samples (364 ± 145 OTUs vs. 170 ± 73 ASVs, p < 0.001). Compared to the richness, the estimated diversity measures had a similar range using both methods albeit statistically different (inverse Simpson index: 14.3 ± 8.5 vs. 15.6 ± 8.7, p = 0.031) and there was strong consistency and agreement for the relative abundances of the most abundant bacterial taxa, including Staphylococcaceae and Streptococcaceae. One notable exception was Oxalobacteriaceae, which was overrepresented using Qiime1 regardless of contaminant removal. Downstream statistical analyses were not impacted by the choice of algorithm in terms of the direction, strength, and significance of associations of host factors with bacterial diversity and overall community composition.

CONCLUSION

Overall, the biological observations and conclusions were robust to the choice of the sequencing processing methods and contaminant removal.

Evolving Technologies in Gastrointestinal Microbiome Era and Their Potential Clinical Applications

The human gastrointestinal microbiota (GIM) is a complex and diverse ecosystem that consists of community of fungi, viruses, protists and majorly bacteria. The association of several human illnesses, such as inflammatory bowel disease, allergy, metabolic syndrome and cancers, have been linked directly or indirectly to compromise in the integrity of the GIM, for which some medical interventions have been proposed or attempted. This review highlights and gives update on various technologies, including microfluidics, high-through-put sequencing, metabolomics, metatranscriptomics and culture in GIM research and their applications in gastrointestinal microbiota therapy, with a view to raise interest in the evaluation, validation and eventual use of these technologies in diagnosis and the incorporation of therapies in routine clinical practice.

Improved normalization of species count data in ecology by scaling with ranked subsampling (SRS): application to microbial communities

Background

Analysis of species count data in ecology often requires normalization to an identical sample size. Rarefying (random subsampling without replacement), which is the current standard method for normalization, has been widely criticized for its poor reproducibility and potential distortion of the community structure. In the context of microbiome count data, researchers explicitly advised against the use of rarefying. Here we introduce a normalization method for species count data called scaling with ranked subsampling (SRS) and demonstrate its suitability for the analysis of microbial communities.

Methods

SRS consists of two steps. In the scaling step, the counts for all species or operational taxonomic units (OTUs) are divided by a scaling factor chosen in such a way that the sum of scaled counts equals the selected total number of counts C_min. The relative frequencies of all OTUs remain unchanged. In the subsequent ranked subsampling step, non-integer count values are converted into integers by an algorithm that minimizes subsampling error with regard to the population structure (relative frequencies of species or OTUs) while keeping the total number of counts equal C_min. SRS and rarefying were compared by normalizing a test library representing a soil bacterial community. Common parameters of biodiversity and population structure (Shannon index H’, species richness, species composition, and relative abundances of OTUs) were determined for libraries normalized to different size by rarefying as well as SRS with 10,000 replications each. An implementation of SRS in R is available for download (https://doi.org/10.20387/BONARES-2657-1NP3).

Results

SRS showed greater reproducibility and preserved OTU frequencies and alpha diversity better than rarefying. The variance in Shannon diversity increased with the reduction of the library size after rarefying but remained zero for SRS. Relative abundances of OTUs strongly varied among libraries generated by rarefying, whereas libraries normalized by SRS showed only negligible variation. Bray–Curtis index of dissimilarity among replicates of the same library normalized by rarefying revealed a large variation in species composition, which reached complete dissimilarity (not a single OTU shared) among some libraries rarefied to a small size. The dissimilarity among replicated libraries normalized by SRS remained negligibly low at each library size. The variance in dissimilarity increased with the decreasing library size after rarefying, whereas it remained either zero or negligibly low after SRS.

Conclusions

Normalization of OTU or species counts by scaling with ranked subsampling preserves the original community structure by minimizing subsampling errors. We therefore propose SRS for the normalization of biological count data.

Deep Insights into Gut Microbiota in Four Carnivorous Coral Reef Fishes from the South China Sea

Investigations of gut microbial diversity among fish to provide baseline data for wild marine fish, especially the carnivorous coral reef fishes of the South China Sea, are lacking. The present study investigated the gut microbiota of four carnivorous coral reef fishes, including Oxycheilinus unifasciatus, Cephalopholis urodeta, Lutjanus kasmira, and Gnathodentex aurolineatus, from the South China Sea for the first time using high-throughput Illumina sequencing. Proteobacteria, Firmicutes, and Bacteroidetes constituted 98% of the gut microbiota of the four fishes, and 20 of the gut microbial genera recovered in this study represent new reports from marine fishes. Comparative analysis indicated that the four fishes shared a similar microbial community, suggesting that diet type (carnivorous) might play a more important role in shaping the gut microbiota of coral reef fishes than the species of fish. Furthermore, the genera Psychrobacter, Escherichia-Shigella, and Vibrio constituted the core microbial community of the four fishes, accounting for 61–91% of the total sequences in each fish. The lack of the genus Epulopiscium in the four fishes was in sharp contrast to what has been found in coral reef fishes from the Red Sea, in which Epulopiscium was shown to be the most dominant gut microbial genus in seven herbivorous coral reef fishes. In addition, while unique gut microbial genera accounted for a small proportion (8–13%) of the total sequences, many such genera were distributed in each coral reef fish species, including several genera (Endozoicomonas, Clostridium, and Staphylococcus) that are frequently found in marine fishes and 11 new reports of gut microbes in marine fishes. The present study expands our knowledge of the diversity and specificity of gut microbes associated with coral reef fishes.