A Goldilocks Principle for the Gut Microbiome: Taxonomic Resolution Matters for Microbiome-Based Classification of Colorectal Cancer

Quantitative microbiome profiling of honey bee (Apis mellifera) guts is predictive of winter colony loss in northern Virginia (USA)

For the past 15 years, the proportion of honey bee hives that fail to survive winter has averaged ~ 30% in the United States. Winter hive loss has significant negative impacts on agriculture, the economy, and ecosystems. Compared to other factors, the role of honey bee gut microbial communities in driving winter hive loss has received little attention. We investigate the relationship between winter survival and honey bee gut microbiome composition of 168 honey bees from 23 hives, nine of which failed to survive through winter 2022. We found that there was a substantial difference in the abundance and community composition of honey bee gut microbiomes based on hive condition, i.e., winter survival or failure. The overall microbial abundance, as assessed using Quantitative Microbiome Profiling (QMP), was significantly greater in hives that survived winter 2022 than in those that failed, and the average overall abundance of each of ten bacterial genera was also greater in surviving hives. There were no significant differences in alpha diversity based on hive condition, but there was a highly significant difference in beta diversity. The bacterial genera Commensalibacter and Snodgrassella were positively associated with winter hive survival. Logistic regression and random forest machine learning models on pooled ASV counts for the genus data were highly predictive of winter outcome, although model performance decreased when samples from the location with no hive failures were excluded from analysis. As a whole, our results show that the abundance and community composition of honey bee gut microbiota is associated with winter hive loss, and can potentially be used as a diagnostic tool in evaluating hive health prior to the onset of winter. Future work on the functional characterization of the honey bee gut microbiome's role in winter survival is warranted.

Proportion-based normalizations outperform compositional data transformations in machine learning applications

BACKGROUND

Normalization, as a pre-processing step, can significantly affect the resolution of machine learning analysis for microbiome studies. There are countless options for normalization scheme selection. In this study, we examined compositionally aware algorithms including the additive log ratio (alr), the centered log ratio (clr), and a recent evolution of the isometric log ratio (ilr) in the form of balance trees made with the PhILR R package. We also looked at compositionally naïve transformations such as raw counts tables and several transformations that are based on relative abundance, such as proportions, the Hellinger transformation, and a transformation based on the logarithm of proportions (which we call "lognorm").

RESULTS

In our evaluation, we used 65 metadata variables culled from four publicly available datasets at the amplicon sequence variant (ASV) level with a random forest machine learning algorithm. We found that different common pre-processing steps in the creation of the balance trees made very little difference in overall performance. Overall, we found that the compositionally aware data transformations such as alr, clr, and ilr (PhILR) performed generally slightly worse or only as well as compositionally naïve transformations. However, relative abundance-based transformations outperformed most other transformations by a small but reliably statistically significant margin.

CONCLUSIONS

Our results suggest that minimizing the complexity of transformations while correcting for read depth may be a generally preferable strategy in preparing data for machine learning compared to more sophisticated, but more complex, transformations that attempt to better correct for compositionality. Video Abstract.

Machine learning classification by fitting amplicon sequences to existing OTUs

The ability to use 16S rRNA gene sequence data to train machine learning classification models offers the opportunity to diagnose patients based on the composition of their microbiome. In some applications, the taxonomic resolution that provides the best models may require the use of de novo operational taxonomic units (OTUs) whose composition changes when new data are added. We previously developed a new reference-based approach, OptiFit, that fits new sequence data to existing de novo OTUs without changing the composition of the original OTUs. While OptiFit produces OTUs that are as high quality as de novo OTUs, it is unclear whether this method for fitting new sequence data into existing OTUs will impact the performance of classification models relative to models trained and tested only using de novo OTUs. We used OptiFit to cluster sequences into existing OTUs and evaluated model performance in classifying a dataset containing samples from patients with and without colonic screen relevant neoplasia (SRN). We compared the performance of this model to standard methods including de novo and database-reference-based clustering. We found that using OptiFit performed as well or better in classifying SRNs. OptiFit can streamline the process of classifying new samples by avoiding the need to retrain models using reclustered sequences. IMPORTANCE There is great potential for using microbiome data to aid in diagnosis. A challenge with de novo operational taxonomic unit (OTU)-based classification models is that 16S rRNA gene sequences are often assigned to OTUs based on similarity to other sequences in the dataset. If data are generated from new patients, the old and new sequences must be reclustered to OTUs and the classification model retrained. Yet there is a desire to have a single, validated model that can be widely deployed. To overcome this obstacle, we applied the OptiFit clustering algorithm to fit new sequence data to existing OTUs allowing for reuse of the model. A random forest model implemented using OptiFit performed as well as the traditional reassign and retrain approach. This result shows that it is possible to train and apply machine learning models based on OTU relative abundance data that do not require retraining or the use of a reference database.

Gene-based microbiome representation enhances host phenotype classification

With the concomitant advances in both the microbiome and machine learning fields, the gut microbiome has become of great interest for the potential discovery of biomarkers to be used in the classification of the host health status. Shotgun metagenomics data derived from the human microbiome is composed of a high-dimensional set of microbial features. The use of such complex data for the modeling of host-microbiome interactions remains a challenge as retaining de novo content yields a highly granular set of microbial features. In this study, we compared the prediction performances of machine learning approaches according to different types of data representations derived from shotgun metagenomics. These representations include commonly used taxonomic and functional profiles and the more granular gene cluster approach. For the five case-control datasets used in this study (Type 2 diabetes, obesity, liver cirrhosis, colorectal cancer, and inflammatory bowel disease), gene-based approaches, whether used alone or in combination with reference-based data types, allowed improved or similar classification performances as the taxonomic and functional profiles. In addition, we show that using subsets of gene families from specific functional categories of genes highlight the importance of these functions on the host phenotype. This study demonstrates that both reference-free microbiome representations and curated metagenomic annotations can provide relevant representations for machine learning based on metagenomic data. IMPORTANCE Data representation is an essential part of machine learning performance when using metagenomic data. In this work, we show that different microbiome representations provide varied host phenotype classification performance depending on the dataset. In classification tasks, untargeted microbiome gene content can provide similar or improved classification compared to taxonomical profiling. Feature selection based on biological function also improves classification performance for some pathologies. Function-based feature selection combined with interpretable machine learning algorithms can generate new hypotheses that can potentially be assayed mechanistically. This work thus proposes new approaches to represent microbiome data for machine learning that can potentiate the findings associated with metagenomic data.

Potential role of intratumor bacteria outside the gastrointestinal tract: More than passengers

INTRODUCTION

Tumor-associated bacteria and gut microbiota have gained significant attention in recent years due to their potential role in cancer development and therapeutic response. This review aims to discuss the contributions of intratumor bacteria outside the gastrointestinal tract, in addition to exploring the mechanisms, functions, and implications of these bacteria in cancer therapy.

METHODS

We reviewed current literature on intratumor bacteria and their impact on tumorigenesis, progression, metastasis, drug resistance, and anti-tumor immune modulation. Additionally, we examined techniques used to detect intratumor bacteria, precautions necessary when handling low microbial biomass tumor samples, and the recent progress in bacterial manipulation for tumor treatment.

RESULTS

Research indicates that each type of cancer uniquely interacts with its microbiome, and bacteria can be detected even in non-gastrointestinal tumors with low bacterial abundance. Intracellular bacteria have the potential to regulate tumor cells' biological behavior and contribute to critical aspects of tumor development. Furthermore, bacterial-based anti-tumor therapies have shown promising results in cancer treatment.

CONCLUSIONS

Understanding the complex interactions between intratumor bacteria and tumor cells could lead to the development of more precise cancer treatment strategies. Further research into non-gastrointestinal tumor-associated bacteria is needed to identify new therapeutic approaches and expand our knowledge of the microbiota's role in cancer biology.

Accessory microbiomes

In microbiome research, considerable effort has been invested in finding core microbiomes, which have been hypothesized to contain the species most important for host function. Much less attention has been paid to microbiome members that are present in only a subset of hosts. Such accessory microbiomes must in large part consist of species that have no effect on fitness, but some will have deleterious effects on fitness (pathogens), and it is also possible that some accessory microbiome members benefit an ecologically distinct subset of hosts. This short paper discusses what we know about accessory microbiomes, specifically by comparing it with the concept of accessory genomes.

Population structure discovery in meta-analyzed microbial communities and inflammatory bowel disease using MMUPHin

Microbiome studies of inflammatory bowel diseases (IBD) have achieved a scale for meta-analysis of dysbioses among populations. To enable microbial community meta-analyses generally, we develop MMUPHin for normalization, statistical meta-analysis, and population structure discovery using microbial taxonomic and functional profiles. Applying it to ten IBD cohorts, we identify consistent associations, including novel taxa such as Acinetobacter and Turicibacter, and additional exposure and interaction effects. A single gradient of dysbiosis severity is favored over discrete types to summarize IBD microbiome population structure. These results provide a benchmark for characterization of IBD and a framework for meta-analysis of any microbial communities.

Berberine suppresses colorectal cancer by regulation of Hedgehog signaling pathway activity and gut microbiota

BACKGROUND

A growing body of evidence reveals that dysregulation of Hedgehog signaling pathway and dysbiosis of gut microbiota are associated with the pathogenesis of colorectal cancer (CRC). Berberine, a botanical benzylisoquinoline alkaloid, possesses powerful activities against various malignancies including CRC, with the underlying mechanisms to be illuminated.

PURPOSE

The present study investigated the potencies of berberine on CRC and deciphered the action mechanisms in the context of Hedgehog signaling cascade and gut microbiota.

METHODS

The effects of berberine on the malignant phenotype, apoptosis, cell cycle and Hedgehog signaling of CRC cells were examined in vitro. In azoxymethane/dextran sulfate sodium-caused mouse CRC, the efficacies of berberine on the carcinogenesis, pathological profile, apoptosis, cell cycle and Hedgehog signaling were determined in vivo. Also, the influences of berberine on gut microbiota in CRC mice were assessed by high-throughput DNA sequencing analysis of 16S ribosomal RNA of fecal microbiome in CRC mice.

RESULTS

In the present study, berberine was found to dampen the proliferation, migration, invasion and colony formation of CRC cells, without toxicity to normal colonic cells. Additionally, berberine induced apoptosis and arrested cell cycle at G₀/G₁ phase in CRC cells, accompanied by reduced Hedgehog signaling pathway activity in vitro. In mouse CRC, berberine suppressed tumor growth, ameliorated pathological manifestations, and potentially induced the apoptosis and cell cycle arrest of CRC, with lowered Hedgehog signaling cascade in vivo. Additionally, berberine decreased β-diversity of gut microbiota in CRC mice, without influence on α-diversity. Berberine also enriched probiotic microbes and depleted pathogenic microbes, and modulated the functionality of gut microbiota in CRC mice.

CONCLUSIONS

Overall, berberine may suppress colorectal cancer, orchestrated by down-regulation of Hedgehog signaling pathway activity and modulation of gut microbiota.