An independent evaluation in a CRC patient cohort of microbiome 16S rRNA sequence analysis methods: OTU clustering, DADA2, and Deblur

Standardizing a microbiome pipeline for body fluid identification from complex crime scene stains

Recent advances in next-generation sequencing have opened up new possibilities for applying the human microbiome in various fields, including forensics. Researchers have capitalized on the site-specific microbial communities found in different parts of the body to identify body fluids from biological evidence. Despite promising results, microbiome-based methods have not been integrated into forensic practice due to the lack of standardized protocols and systematic testing of methods on forensically relevant samples. Our study addresses critical decisions in establishing these protocols, focusing on bioinformatics choices and the use of machine learning to present microbiome results in case reports for forensically relevant and challenging samples. In our study, we propose using operational taxonomic units (OTUs) for read data processing and generating heterogeneous training data sets for training a random forest classifier. We incorporated six forensically relevant classes: saliva, semen, skin from hand, penile skin, urine, and vaginal/menstrual fluid, and our classifier achieved a high weighted average F1 score of 0.89. Systematic testing on mock forensic samples, including mixed-source samples and underwear, revealed reliable detection of at least one component of the mixture and the identification of vaginal fluid from underwear substrates. Additionally, when investigating the sexually shared microbiome (sexome) of heterosexual couples, our classifier could potentially infer the nature of sexual activity. We therefore highlight the value of the sexome for assessing the nature of sexual activities in forensic investigations while delineating areas that warrant further research.IMPORTANCEMicrobiome-based analyses combined with machine learning offer potential avenues for use in forensic science and other applied fields, yet standardized protocols remain lacking. Moreover, machine learning classifiers have shown promise for predicting body sites in forensics, but they have not been systematically evaluated on complex mixed-source samples. Our study addresses key decisions for establishing standardized protocols and, to our knowledge, is the first to report classification results from uncontrolled mixed-source samples, including sexome (sexually shared microbiome) samples. In our study, we explore both the strengths and limitations of classifying the mixed-source samples while also providing options for tackling the limitations.

Microbial methods matter: Identifying discrepancies between microbiome denoising pipelines using a leaf biofilm taphonomic dataset

Premise

The occurrence of different microorganisms on aquatic macrophyte fossils suggests that biofilm microbes may facilitate leaf preservation. Understanding the impact of microorganisms on leaf preservation requires studies on living plants coupled with microbial amplicon sequencing. Choosing the most suitable bioinformatic pipeline is pivotal to accurate data interpretation, as it can lead to considerably different estimations of microbial community composition.

Methods

We analyze biofilms from floating and submerged leaves of Nymphaea alba and Nuphar lutea and mock communities using primers for the 16S ribosomal RNA (rRNA), 18S rRNA, and ITS amplicon regions and compare the microbial community compositions derived from three bioinformatic pipelines: DADA2, Deblur, and UNOISE.

Results

The choice of denoiser alters the total number of sequences identified and differs in the identified taxa. Results from all three denoising pipelines show that the leaf microbial communities differed between depths and that the effect of the environment varied depending on the amplicon region.

Discussion

Considering the performance of denoising algorithms and the identification of amplicon sequence variants (ASVs), we recommend DADA2 for analyzing 16S rRNA and 18S rRNA. For the ITS region, the choice is more nuanced, as Deblur identified the most ASVs and was compositionally similar to DADA2.

Long-read 16S rRNA amplicon sequencing reveals microbial characteristics in patients with colorectal adenomas and carcinoma lesions in Egypt

BACKGROUND

Colorectal cancer (CRC) is among the five leading causes of cancer incidence and mortality. During the past decade, the role of the gut microbiota and its dysbiosis in colorectal tumorigenesis has been emphasized. Metagenomics and amplicon-based microbiome profiling provided insights into the potential role of microbial dysbiosis in the development of CRC.

AIM

To address the scarcity of information on differential microbiome composition of tumor tissue in comparison to adenomas and the lack of such data from Egyptian patients with CRC.

MATERIALS AND METHODS

Long-read nanopore sequencing of 16S rRNA amplicons was used to profile the colonic microbiota from fresh colonoscopic biopsy samples of Egyptian patients with CRC and patients with colonic polyps.

RESULTS

Species richness of CRC lesions was significantly higher than that in colonic polyps (p-value = 0.0078), while evenness of the CRC group was significantly lower than the colonic polyps group (p-value = 0.0055). Both species richness and Shannon diversity index of the late onset CRC samples were significantly higher than those of the early onset ones. The Firmicutes-to-Bacteroidetes (F/B) ratio was significantly higher in the CRC group than in the colonic polyps group (p-value = 0.0054), and significantly higher in samples from early-onset CRC. The Enterococcus spp. were significantly overabundant in patients with rectal cancer and early-onset CRC, while Staphylococcus spp. were significantly higher in patients with sigmoid cancer and late-onset CRC. In addition, the relative abundance of Fusobacterium nucleatum was significantly higher in CRC patients.

CONCLUSION

Differentiating trends were identified at phylum, genus, and species levels, despite the inter-individual differences. In summary, this study addressed the microbial dysbiosis associated with CRC and colonic polyps groups, paving the way for a better understanding of the pathogenesis of early and late-onset CRC in Egyptian patients.

Comparing subsampling strategies for metagenomic analysis in microbial studies using amplicon sequence variants versus operational taxonomic units

The microbiome is increasingly regarded as a key component of human health, and analysis of microbiome data can aid in the development of precision medicine. Due to the high cost of shotgun metagenomic sequencing (SM-seq), microbiome analyses can be done cost-effectively in two phases: Phase 1-sequencing of 16S ribosomal RNA, and Phase 2-SM-seq of an informative subsample. Existing research suggests strategies to select the subsample based on biological diversity and dissimilarity metrics calculated using operational taxonomic units (OTUs). However, the microbiome field has progressed towards amplicon sequencing variants (ASVs), as they provide more precise microbe identification and sample diversity information. The aim of this work is to compare the subsampling strategies for two-phase metagenomic studies when using ASVs instead of OTUs, and to propose data driven strategies for subsample selection through dimension reduction techniques. We used 199 samples of infant-gut microbiome data from the DIABIMMUNE project to generate ASVs and OTUs, then generated subsamples based on five existing biologically driven subsampling methods and two data driven methods. Linear discriminant analysis Effect Size (LEfSe) was used to assess differential representation of taxa between the subsamples and the overall sample. The use of ASVs showed a 50-93% agreement in the subsample selection with the use of OTUs for the subsampling methods evaluated, and showed a similar bacterial representation across all methods. Although sampling using ASVs and OTUs typically lead to similar results for each subsample, ASVs had more clades that differed in expression levels between allergic and non-allergic individuals across all sample sizes compared to OTUs, and led to more biomarkers discovered at Phase 2-SM-seq level.

Distinct microbes, metabolites, and the host genome define the multi-omics profiles in right-sided and left-sided colon cancer

BACKGROUND

Studies have reported clinical heterogeneity between right-sided colon cancer (RCC) and left-sided colon cancer (LCC). However, none of these studies used multi-omics analysis combining genetic regulation, microbiota, and metabolites to explain the site-specific difference.

METHODS

Here, 494 participants from a 16S rRNA gene sequencing cohort (50 RCC, 114 LCC, and 100 healthy controls) and a multi-omics cohort (63 RCC, 79 LCC, and 88 healthy controls) were analyzed. 16S rRNA gene, metagenomic sequencing, and metabolomics analyses of fecal samples were evaluated to identify tumor location-related bacteria and metabolites. Whole-exome sequencing (WES) and transcriptome sequencing (RNA-seq) were conducted to obtain the mutation burden and genomic expression pattern.

RESULTS

We found unique profiles of the intestinal microbiome, metabolome, and host genome between RCC and LCC. The bacteria Flavonifractor plautii (Fp) and Fusobacterium nucleatum, the metabolites L-phenylalanine, and the host genes PHLDA1 and WBP1 were the key omics features of RCC; whereas the bacteria Bacteroides sp. A1C1 (B.A1C1) and Parvimonas micra, the metabolites L-citrulline and D-ornithine, and the host genes TCF25 and HLA-DRB5 were considered the dominant omics features in LCC. Multi-omics correlation analysis indicated that RCC-enriched Fp was related to the accumulation of the metabolite L-phenylalanine and the suppressed WBP1 signal in RCC patients. In addition, LCC-enriched B.A1C1 was associated with the accumulation of the metabolites D-ornithine and L-citrulline as well as activation of the genes TCF25, HLA-DRB5, and AC079354.1.

CONCLUSION

Our findings identify previously unknown links between intestinal microbiota alterations, metabolites, and host genomics in RCC vs. LCC, suggesting that it may be possible to treat colorectal cancer (CRC) by targeting the gut microbiota-host interaction. Video Abstract.

Alterations of Gut Microbiome in Patients with Colorectal Advanced Adenoma by Metagenomic Analyses

BACKGROUND/AIMS

Colorectal cancer (CRC) is one of the deadliest cancers worldwide, mostly arising from adenomatous polyps. Mounting evidence has demonstrated that changes in the gut microbiome play key roles in CRC progression, while quite few studies focused on the altered microbiota architecture of advanced adenoma (AA), a crucial precancerous stage of CRC. Thus, we aimed to investigate the microbial profiles of AA patients.

MATERIALS AND METHODS

Fecal samples were collected from 26 AA patients and 26 age- and sex-matched normal controls (NC), and analyzed by shotgun metagenomic sequencing.

RESULTS

Gut microbial dysbiosis was observed in AA patients with lower alpha diversity. Advanced adenoma was characterized by an increased Bacillota/Bacteroidota ratio and higher Pseudomonadota levels compared to normal individuals. Linear discriminant analysis effect size (LEfSe) analysis was performed and identified 14 microbiota with significantly different abundance levels between AA and NC groups. Functional analysis revealed that tryptophan metabolism was upregulated in AA. Correspondingly, the expressions of gut microbes implicated in tryptophan metabolism also changed, including Akkermansia muciniphila, Bacteroides ovatus, Clostridium sporogenes, and Limosilactobacillus reuteri. The microbial network suggested that AA exhibited decreased correlation complexity, with Escherichia coli and Enterobacteriaceae unclassified harboring the strongest connectivity. A diagnostic model consisting of 3 microbial species was established based on random forest, yielding an area under the curve (AUC) of 0.799.

CONCLUSION

Our study profiled the alterations of the gut microbiome in AA patients, which may enrich the knowledge of microbial signatures along with colorectal tumorigenesis and provide promising biomarkers for AA diagnosis.

Improved diagnostic efficiency of CRC subgroups revealed using machine learning based on intestinal microbes

BACKGROUND

Colorectal cancer (CRC) is a common cancer that causes millions of deaths worldwide each year. At present, numerous studies have confirmed that intestinal microbes play a crucial role in the process of CRC. Additionally, studies have shown that CRC can be divided into several consensus molecular subtypes (CMS) based on tumor gene expression, and CRC microbiomes have been reported related to CMS. However, most previous studies on intestinal microbiome of CRC have only compared patients with healthy controls, without classifying of CRC patients based on intestinal microbial composition.

RESULTS

In this study, a CRC cohort including 339 CRC samples and 333 healthy controls was selected as the discovery set, and the CRC samples were divided into two subgroups (234 Subgroup1 and 105 Subgroup2) using PAM clustering algorithm based on the intestinal microbial composition. We found that not only the microbial diversity was significantly different (Shannon index, p-value < 0.05), but also 129 shared genera altered (p-value < 0.05) between the two CRC subgroups, including several marker genera in CRC, such as Fusobacterium and Bacteroides. A random forest algorithm was used to construct diagnostic models, which showed significantly higher efficiency when the CRC samples were divided into subgroups. Then an independent cohort including 187 CRC samples (divided into 153 Subgroup1 and 34 Subgroup2) and 123 healthy controls was chosen to validate the models, and confirmed the results.

CONCLUSIONS

These results indicate that the divided CRC subgroups can improve the efficiency of disease diagnosis, with various microbial composition in the subgroups.

Taxonomic composition and functional potentials of gastrointestinal microbiota in 12 wild-stranded cetaceans

Cetaceans play a crucial role in marine ecosystems; however, research on their gastrointestinal microbiota remains limited due to sampling constraints. In this study, we collected hindgut samples from 12 stranded cetaceans and performed 16S rRNA gene amplicon sequencing to investigate microbial composition and functional potentials. Analysis of ZOTUs profiles revealed that the phyla Firmicutes, Proteobacteria, and Bacteroidetes dominated all hindgut samples. However, unique microbial profiles were observed among different cetacean species, with significant separation of gut microbiota communities according to biological evolutionary lineages. Different genera that contain pathogens were observed distinguishing delphinids from physeteroids/ziphiids. Delphinid samples exhibited higher abundances of Vibrio, Escherichia, and Paeniclostridium, whereas physeteroid and ziphiid samples showed higher abundances of Pseudomonas, Enterococcus, and Intestinimonas. Functional analysis indicated convergence in the gut microbiota among all cetaceans, with shared bacterial infection pathways across hindgut samples. In addition, a comparison of the gastrointestinal microbial composition between a stranded short-finned pilot whale (Globicephala macrorhynchus) and a stranded rough-toothed dolphin (Steno bredanensis) using 16S rRNA gene sequencing revealed distinct microbial community structures and functional capacities. To the best of our knowledge, this study represents the first report on the gastrointestinal microbiota of the pantropical spotted dolphin (Stenella attenuata), Blainville's beaked whale (Mesoplodon densirostris), and rough-toothed dolphin, with various comparisons conducted among different cetacean species. Our findings enhance the understanding of microbial composition and diversity in cetacean gastrointestinal microbiota, providing new insights into co-evolution and complex interactions between cetacean microbes and hosts.

Datasets of fungal diversity and pseudo-chromosomal genomes of mangrove rhizosphere soil in China

With climate change and anthropic influence on the coastal ecosystems, mangrove ecosystems are disappearing at an alarming rate. Accordingly, it becomes important to track, study, record and store the mangrove microbial community considering their ecological importance and potential for biotechnological applications. Here, we provide information on mangrove fungal community composition and diversity in mangrove ecosystems with different plant species and from various locations differing in relation to anthropic influences. We describe twelve newly assembled genomes, including four chromosomal-level genomes of fungal isolates from the mangrove ecosystems coupled with functional annotations. We envisage that these data will be of value for future studies including comparative genome analysis and large-scale temporal and/or spatial research to elucidate the potential mechanisms by which mangrove fungal communities assemble and evolve. We further anticipate that the genomes represent valuable resources for bioprospecting related to industrial or clinical uses.

Investigating the causal role of the gut microbiota in esophageal cancer and its subtypes: a two-sample Mendelian randomization study

BACKGROUND

Through research on the gut microbiota (GM), increasing evidence has indicated that the GM is associated with esophageal cancer (ESCA). However, the specific cause-and-effect relationship remains unclear. In this study, Mendelian randomization (MR) analysis was applied to investigate the causal relationship between the GM and ESCA, including its subtypes.

METHODS

We collected information on 211 GMs and acquired data on ESCA and its subtypes through genome-wide association studies (GWASs). The causal relationship was primarily assessed using the inverse variance weighted (IVW) method. Additionally, we applied the weighted median estimator (WME) method, MR-Egger method, weighted mode, and simple mode to provide further assistance. Subsequent to these analyses, sensitivity analysis was conducted using the MR-Egger intercept test, MR-PRESSO global test, and leave-one-out method.

RESULT

Following our assessment using five methods and sensitivity analysis, we identified seven GMs with potential causal relationships with ESCA and its subtypes. At the genus level, Veillonella and Coprobacter were positively correlated with ESCA, whereas Prevotella9, Eubacterium oxidoreducens group, and Turicibacter were negatively correlated with ESCA. In the case of esophageal adenocarcinoma (EAC), Flavonifractor exhibited a positive correlation, while Actinomyces exhibited a negative correlation.

CONCLUSION

Our study revealed the potential causal relationship between GM and ESCA and its subtypes, offering novel insights for the advancement of ESCA diagnosis and treatment.

Prokaryotic taxonomy and functional diversity assessment of different sequencing platform in a hyper-arid Gobi soil in Xinjiang Turpan Basin, China

Turpan Basin located in the eastern Xinjiang is a typical arid inland basin with extremely scarce water resources and a fragile ecosystem. Prokaryotic communities with unique genetic and physiological modifications can survive and function in such harsh environments, offering diverse microbial resources. However, numerous microbes can enter the viable but non-culturable state because of drought stress in the desert soil. In this work, next generation sequencing (NGS) technology based on DNA nanoball sequencing platform (DNBSEQ-G400) and sequencing-by-synthesis platform (NovaSeq 6000) were applied to analyze the prokaryotic diversity in three hyper-arid Gobi soils from Flaming Mountain, Toksun, and Kumtag. The comparison between two platforms indicated that DNBSEQ-G400 had better repeatability and could better reflect the prokaryotic community of this hyper-arid region. The diversity analysis based on DNBSEQ-G400 identified a total of 36 bacterial phyla, including Pseudomonadota, Bacteroidota, Bacillota, Actinomycetota, Methanobacteriota, Acidobacteriota, Nitrososphaerota, and Planctomycetota. The environmental factors, including soluble salt, available potassium, total nitrogen, and organic matter, were positively correlated with the abundance of most prokaryote. In addition, the prokaryotic community assembly in hyper-arid soil was well described by neutral-based models, indicating that the community assembly was mainly controlled by stochastic processes. Finally, the phylogenetic analysis of Actinomycetota proved that such extremophiles played an important role in the ecosystems they colonize. Overall, our result provides a reference for choosing the appropriate sequencing platform and a perspective for the utilization of soil microbial resources from hyper-arid regions.