Diversity and composition of gut microbiome of cervical cancer patients: Do results of 16S rRNA sequencing and whole genome sequencing approaches align?

Integrative analysis of microbial 16S gene and shotgun metagenomic sequencing data improves statistical efficiency

Background

The most widely used technologies for profiling microbial communities are 16S marker-gene sequencing and shotgun metagenomic sequencing. Interestingly, many microbiome studies have performed both sequencing experiments on the same cohort of samples. The two sequencing datasets often reveal consistent patterns of microbial signatures, highlighting the potential for an integrative analysis to improve power of testing these signatures. However, differential experimental biases, partially overlapping samples, and differential library sizes pose tremendous challenges when combining the two datasets. Currently, researchers either discard one dataset entirely or use different datasets for different objectives.

Methods

In this article, we introduce the first method of this kind, named Com-2seq, that combines the two sequencing datasets for testing differential abundance at the genus and community levels while overcoming these difficulties. The new method is based on our LOCOM model (Hu et al., 2022), which employs logistic regression for testing taxon differential abundance while remaining robust to experimental bias. To benchmark the performance of Com-2seq, we introduce two ad hoc approaches: applying LOCOM to pooled taxa count data and combining LOCOM p-values from analyzing each dataset separately.

Results

Our simulation studies indicate that Com-2seq substantially improves statistical efficiency over analysis of either dataset alone and works better than the two ad hoc approaches. An application of Com-2seq to two real microbiome studies uncovered scientifically plausible findings that would have been missed by analyzing individual datasets.

Conclusions

Com-2seq performs integrative analysis of 16S and metagenomic sequencing data, which improves statistical efficiency and has the potential to accelerate the search of microbial communities and taxa that are involved in human health and diseases.

Integrative analysis of microbial 16S gene and shotgun metagenomic sequencing data improves statistical efficiency

The most widely used technologies for profiling microbial communities are 16S marker-gene sequencing and shotgun metagenomic sequencing. Interestingly, many microbiome studies have performed both sequencing experiments on the same cohort of samples. The two sequencing datasets often reveal consistent patterns of microbial signatures, highlighting the potential for an integrative analysis to improve power of testing these signatures. However, differential experimental biases, partially overlapping samples, and differential library sizes pose tremendous challenges when combining the two datasets. Currently, researchers either discard one dataset entirely or use different datasets for different objectives. In this article, we introduce the first method of this kind, named Com-2seq, that combines the two sequencing datasets for the objective of testing differential abundance at the genus and community levels while overcoming these difficulties. We demonstrate that Com-2seq substantially improves statistical efficiency over analysis of either dataset alone and works better than two ad hoc approaches.

HPV-related anal cancer is associated with changes in the anorectal microbiome during cancer development

Background

Squamous cell carcinoma of the anus (SCCA) is a rare gastrointestinal cancer. Factors associated with progression of HPV infection to anal dysplasia and cancer are unclear and screening guidelines and approaches for anal dysplasia are less clear than for cervical dysplasia. One potential contributing factor is the anorectal microbiome. In this study, we aimed to identify differences in anal microbiome composition in the settings of HPV infection, anal dysplasia, and anal cancer in this rare disease.

Methods

Patients were enrolled in two prospective studies. Patients with anal dysplasia were part of a cross-sectional cohort that enrolled women with high-grade lower genital tract dysplasia. Anorectal tumor swabs were prospectively collected from patients with biopsy-confirmed locally advanced SCCA prior to receiving standard-of-care chemoradiotherapy (CRT). Patients with high-grade lower genital tract dysplasia without anal dysplasia were considered high-risk (HR Normal). 16S V4 rRNA Microbiome sequencing was performed for anal swabs. Alpha and Beta Diversity and composition were compared for HR Normal, anal dysplasia, and anal cancer.

Results

60 patients with high-grade lower genital tract dysplasia were initially enrolled. Seven patients had concurrent anal dysplasia and 44 patients were considered HR Normal. Anorectal swabs from 21 patients with localized SCCA were included, sequenced, and analyzed in the study. Analysis of weighted and unweighted UniFrac distances demonstrated significant differences in microbial community composition between anal cancer and HR normal (p=0.018). LEfSe identified that all three groups exhibited differential enrichment of specific taxa. Peptoniphilus (p=0.028), Fusobacteria (p=0.0295), Porphyromonas (p=0.034), and Prevotella (p=0.029) were enriched in anal cancer specimens when compared to HR normal.

Conclusion

Although alpha diversity was similar between HR Normal, dysplasia and cancer patients, composition differed significantly between the three groups. Increased anorectal Peptoniphilus, Fusobacteria, Porphyromonas, and Prevotella abundance were associated with anal cancer. These organisms have been reported in various gastrointestinal cancers with roles in facilitating the proinflammatory microenvironment and neoplasia progression. Future work should investigate a potential role of microbiome analysis in screening for anal dysplasia and investigation into potential mechanisms of how these microbial imbalances influence the immune system and anal carcinogenesis.

Cervicovaginal Microbiota Profiles in Precancerous Lesions and Cervical Cancer among Ethiopian Women

Although high-risk human papillomavirus infection is a well-established risk factor for cervical cancer, other co-factors within the local microenvironment may play an important role in the development of cervical cancer. The current study aimed to characterize the cervicovaginal microbiota in women with premalignant dysplasia or invasive cervical cancer compared with that of healthy women. The study comprised 120 Ethiopian women (60 cervical cancer patients who had not received any treatment, 25 patients with premalignant dysplasia, and 35 healthy women). Cervicovaginal specimens were collected using either an Isohelix DNA buccal swab or an Evalyn brush, and ribosomal RNA sequencing was used to characterize the cervicovaginal microbiota. Shannon and Simpson diversity indices were used to evaluate alpha diversity. Beta diversity was examined using principal coordinate analysis of weighted UniFrac distances. Alpha diversity was significantly higher in patients with cervical cancer than in patients with dysplasia and in healthy women (p < 0.01). Beta diversity was also significantly different in cervical cancer patients compared with the other groups (weighted UniFrac Bray-Curtis, p < 0.01). Microbiota composition differed between the dysplasia and cervical cancer groups. Lactobacillus iners was particularly enriched in patients with cancer, and a high relative abundance of Lactobacillus species was identified in the dysplasia and healthy groups, whereas Porphyromonas, Prevotella, Bacteroides, and Anaerococcus species predominated in the cervical cancer group. In summary, we identified differences in cervicovaginal microbiota diversity, composition, and relative abundance between women with cervical cancer, women with dysplasia, and healthy women. Additional studies need to be carried out in Ethiopia and other regions to control for variation in sample collection.

Role of microbiota and microbiota-derived short-chain fatty acids in PDAC

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive lethal diseases among other cancer types. Gut microbiome and its metabolic regulation play a crucial role in PDAC. Metabolic regulation in the gut is a complex process that involves microbiome and microbiome-derived short-chain fatty acids (SCFAs). SCFAs regulate inflammation, as well as lipid and glucose metabolism, through different pathways. This review aims to summarize recent developments in PDAC in the context of gut and oral microbiota and their associations with short-chain fatty acid (SCFA). In addition to this, we discuss possible therapeutic applications using microbiota in PDAC.

Resources to Facilitate Use of the Altered Schaedler Flora (ASF) Mouse Model to Study Microbiome Function

Animals colonized with a defined microbiota represent useful experimental systems to investigate microbiome function. The altered Schaedler flora (ASF) represents a consortium of eight murine bacterial species that have been used for more than 4 decades where the study of mice with a reduced microbiota is desired. In contrast to germ-free mice, or mice colonized with only one or two species, ASF mice show the normal gut structure and immune system development. To further expand the utility of the ASF, we have developed technical and bioinformatic resources to enable a systems-based analysis of microbiome function using this model. Here, we highlighted four distinct applications of these resources that enable and improve (i) measurements of the abundance of each ASF member by quantitative PCR; (ii) exploration and comparative analysis of ASF genomes and the metabolic pathways they encode that comprise the entire gut microbiome; (iii) global transcriptional profiling to identify genes whose expression responds to environmental changes within the gut; and (iv) discovery of genetic changes resulting from the evolutionary adaptation of the microbiota. These resources were designed to be accessible to a broad community of researchers that, in combination with conventionally-reared mice (i.e., with complex microbiome), should contribute to our understanding of microbiome structure and function. IMPORTANCE Improved experimental systems are needed to advance our understanding of how the gut microbiome influences processes of the mammalian host as well as microbial community structure and function. An approach that is receiving considerable attention is the use of animal models that harbor a stable microbiota of known composition, i.e., defined microbiota, which enables control over an otherwise highly complex and variable feature of mammalian biology. The altered Schaedler flora (ASF) consortium is a well-established defined microbiota model, where mice are stably colonized with 8 distinct murine bacterial species. To take better advantage of the ASF, we established new experimental and bioinformatics resources for researchers to make better use of this model as an experimental system to study microbiome function.

Comparison Between Full-Length 16S rRNA Metabarcoding and Whole Metagenome Sequencing Suggests the Use of Either Is Suitable for Large-Scale Microbiome Studies

Since the number of studies of the microbial communities related to food and food-associated matrices almost completely reliant on next-generation sequencing techniques is rising, evaluations of these high-throughput methods are critical. Currently, the two most used sequencing methods to profile the microbiota of complex samples, including food and food-related matrices, are the 16S ribosomal RNA (rRNA) metabarcoding and the whole metagenome sequencing (WMS), both of which are powerful tools for the monitoring of foodborne pathogens and the investigation of the microbiome. Herein, the microbial profiles of 20 bulk tank milk filters from different dairy farms were investigated using both the full-length 16S (FL-16S) rRNA metabarcoding, a third-generation sequencing method whose application in food and food-related matrices is yet in its infancy, and the WMS, to evaluate the correlation and the reliability of these two methods to explore the microbiome of food-related matrices. Metabarcoding and metagenomic data were generated on a MinION platform (Oxford Nanopore Technologies) and on a Illumina NovaSeq 6000 platform, respectively. Our findings support the greater resolution of WMS in terms of both increased detection of bacterial taxa and enhanced detection of diversity; in contrast, FL-16S rRNA metabarcoding has proven to be a promising, less expensive, and more practical tool to profile most abundant taxa. The significant correlation of the two technologies both in terms of taxa diversity and richness, together with the similar profiles defined for both highly abundant taxa and core microbiomes, including Acinetobacter, Bacillus, and Escherichia genera, highlights the possible application of both methods for different purposes. This study allowed the first comparison of FL-16S rRNA sequencing and WMS to investigate the microbial composition of a food-related matrix, pointing out the advantageous use of FL-16S rRNA to identify dominant microorganisms and the superior power of WMS for the taxonomic detection of low abundant microorganisms and to perform functional analysis of the microbial communities.