Re-purposing software for functional characterization of the microbiome

Application and Comparison of Machine Learning and Database-Based Methods in Taxonomic Classification of High-Throughput Sequencing Data

The advent of high-throughput sequencing technologies has not only revolutionized the field of bioinformatics but has also heightened the demand for efficient taxonomic classification. Despite technological advancements, efficiently processing and analyzing the deluge of sequencing data for precise taxonomic classification remains a formidable challenge. Existing classification approaches primarily fall into two categories, database-based methods and machine learning methods, each presenting its own set of challenges and advantages. On this basis, the aim of our study was to conduct a comparative analysis between these two methods while also investigating the merits of integrating multiple database-based methods. Through an in-depth comparative study, we evaluated the performance of both methodological categories in taxonomic classification by utilizing simulated data sets. Our analysis revealed that database-based methods excel in classification accuracy when backed by a rich and comprehensive reference database. Conversely, while machine learning methods show superior performance in scenarios where reference sequences are sparse or lacking, they generally show inferior performance compared with database methods under most conditions. Moreover, our study confirms that integrating multiple database-based methods does, in fact, enhance classification accuracy. These findings shed new light on the taxonomic classification of high-throughput sequencing data and bear substantial implications for the future development of computational biology. For those interested in further exploring our methods, the source code of this study is publicly available on https://github.com/LoadStar822/Genome-Classifier-Performance-Evaluator. Additionally, a dedicated webpage showcasing our collected database, data sets, and various classification software can be found at http://lab.malab.cn/~tqz/project/taxonomic/.

Altered salivary microbiota profile in patients with abdominal aortic aneurysm

Evidence suggests that the DNA of oral pathogens is detectable in the dilated aortic tissue of abdominal aortic aneurysm (AAA), one of the most fatal cardiovascular diseases. However, the association between oral microbial homeostasis and aneurysm formation remains largely unknown. In this study, a cohort of individuals, including 53 AAA patients and 30 control participants (CTL), was recruited for salivary microbiota investigation by 16S rRNA gene sequencing and bioinformatics analysis. Salivary microbial diversity was decreased in AAA compared with CTL, and the microbial structures were significantly separated between the two groups. Additionally, significant taxonomic and functional changes in the salivary microbiota of AAA participants were observed. The genera Streptococcus and Gemella were remarkably enriched, while Selenomonas, Leptotrichia, Lautropia and Corynebacterium were significantly depleted in AAA. Co-occurrence network analysis showed decreased potential interactions among the differentially abundant microbial genera in AAA. A machine-learning model predicted AAA using the combination of 5 genera and 14 differentially enriched functional pathways, which could distinguish AAA from CTL with an area under the receiver-operating curve of 90.3 %. Finally, 16 genera were found to be significantly positively correlated with the morphological parameters of AAA. Our study is the first to show that AAA patients exhibit oral microbial dysbiosis, which has high predictive power for AAA, and the over-representation of specific salivary bacteria may be associated with AAA disease progression. Further studies are needed to better understand the function of putative oral bacteria in the etiopathogenesis of AAA.

Importance

Host microbial dysbiosis has recently been linked to AAA as a possible etiology. To our knowledge, studies of the oral microbiota and aneurysms remain scarce, although previous studies have indicated that the DNA of some oral pathogens is detectable in aneurysms by PCR method. We take this field one step further by investigating the oral microbiota composition of AAA patients against control participants via high-throughput sequencing technologies and unveiling the potential microbial biomarker associated with AAA formation. Our study will provide new insights into AAA etiology, treatment and prevention from a microecological perspective and highlight the effects of oral microbiota on vascular health.

Exploring microbial worlds: a review of whole genome sequencing and its application in characterizing the microbial communities

The classical microbiology techniques have inherent limitations in unraveling the complexity of microbial communities, necessitating the pivotal role of sequencing in studying the diversity of microbial communities. Whole genome sequencing (WGS) enables researchers to uncover the metabolic capabilities of the microbial community, providing valuable insights into the microbiome. Herein, we present an overview of the rapid advancements achieved thus far in the use of WGS in microbiome research. There was an upsurge in publications, particularly in 2021 and 2022 with the United States, China, and India leading the metagenomics research landscape. The Illumina platform has emerged as the widely adopted sequencing technology, whereas a significant focus of metagenomics has been on understanding the relationship between the gut microbiome and human health where distinct bacterial species have been linked to various diseases. Additionally, studies have explored the impact of human activities on microbial communities, including the potential spread of pathogenic bacteria and antimicrobial resistance genes in different ecosystems. Furthermore, WGS is used in investigating the microbiome of various animal species and plant tissues such as the rhizosphere microbiome. Overall, this review reflects the importance of WGS in metagenomics studies and underscores its remarkable power in illuminating the variety and intricacy of the microbiome in different environments.

Genomic signatures suggesting adaptation to ocean acidification in a coral holobiont from volcanic CO2 seeps

Ocean acidification, caused by anthropogenic CO₂ emissions, is predicted to have major consequences for reef-building corals, jeopardizing the scaffolding of the most biodiverse marine habitats. However, whether corals can adapt to ocean acidification and how remains unclear. We addressed these questions by re-examining transcriptome and genome data of Acropora millepora coral holobionts from volcanic CO₂ seeps with end-of-century pH levels. We show that adaptation to ocean acidification is a wholistic process involving the three main compartments of the coral holobiont. We identified 441 coral host candidate adaptive genes involved in calcification, response to acidification, and symbiosis; population genetic differentiation in dinoflagellate photosymbionts; and consistent transcriptional microbiome activity despite microbial community shifts. Coral holobionts from natural analogues to future ocean conditions harbor beneficial genetic variants with far-reaching rapid adaptation potential. In the face of climate change, these populations require immediate conservation strategies as they could become key to coral reef survival.

An exploratory study of the gut microbiota in major depression with anxious distress

OBJECTIVES

To explore differences in the diversity and composition of the gut microbiome between major depressive disorder (MDD) with and without anxious distress.

METHODS

The study comprised 117 participants (79 female, 36 male, 2 other, mean age 38.2 ± 13.4 years) with a current major depressive episode (MDE) with (n = 63) and without (n = 54) the anxious distress specifier. A clinical psychologist administered the structured clinical interview for the DSM-5-RV to confirm a diagnosis of depression. Participants provided stool samples which were immediately frozen and stored at -80 °C. These samples were analysed using the Illumina 16S Metagenomics sequencing protocol in which the sequencing primers target the V3 and V4 regions of the 16S rRNA gene. Participants also completed mental health questionnaires to assess severity of depression (BDI-II), generalized anxiety (GAD-7), and stress (PSS).

RESULTS

There were no significant group differences in α-diversity (Shannon's diversity Index; Simpson Index), richness (ACE; Chao1), (Pielou's) evenness, or beta diversity (Bray-Curtis dissimilarity index and weighted UniFrac distance) of gut bacteria. Significant group differences in the relative abundance of gut microbiota however were observed at each taxonomical level, including across 15 genera and 18 species.

LIMITATIONS

This was an exploratory study that needs to be replicated across larger samples and compared with a healthy control group.

CONCLUSIONS

The research contributes to knowledge of the depressive gut microbial profile unique to the anxious distress subtype of MDD.

Functional profiling of COVID-19 respiratory tract microbiomes

In response to the ongoing global pandemic, characterizing the molecular-level host interactions of the new coronavirus SARS-CoV-2 responsible for COVID-19 has been at the center of unprecedented scientific focus. However, when the virus enters the body it also interacts with the micro-organisms already inhabiting the host. Understanding the virus-host-microbiome interactions can yield additional insights into the biological processes perturbed by viral invasion. Alterations in the gut microbiome species and metabolites have been noted during respiratory viral infections, possibly impacting the lungs via gut-lung microbiome crosstalk. To better characterize microbial functions in the lower respiratory tract during COVID-19 infection, we carry out a functional analysis of previously published metatranscriptome sequencing data of bronchoalveolar lavage fluid from eight COVID-19 cases, twenty-five community-acquired pneumonia patients, and twenty healthy controls. The functional profiles resulting from comparing the sequences against annotated microbial protein domains clearly separate the cohorts. By examining the associated metabolic pathways, distinguishing functional signatures in COVID-19 respiratory tract microbiomes are identified, including decreased potential for lipid metabolism and glycan biosynthesis and metabolism pathways, and increased potential for carbohydrate metabolism pathways. The results include overlap between previous studies on COVID-19 microbiomes, including decrease in the glycosaminoglycan degradation pathway and increase in carbohydrate metabolism. The results also suggest novel connections to consider, possibly specific to the lower respiratory tract microbiome, calling for further research on microbial functions and host-microbiome interactions during SARS-CoV-2 infection.