Phylogenetic trees and data are often stored in incompatible and inconsistent formats. The outputs of software tools that contain trees with analysis findings are often not compatible with each other, making it hard to integrate the results of different analyses in a comparative study. The treeio package is designed to connect phylogenetic tree input and output. It supports extracting phylogenetic trees as well as the outputs of commonly used analytical software. It can link external data to phylogenies and merge tree data obtained from different sources, enabling analyses of phylogeny-associated data from different disciplines in an evolutionary context. Treeio also supports export of a phylogenetic tree with heterogeneous-associated data to a single tree file, including BEAST compatible NEXUS and jtree formats; these facilitate data sharing as well as file format conversion for downstream analysis. The treeio package is designed to work with the tidytree and ggtree packages. Tree data can be processed using the tidy interface with tidytree and visualized by ggtree. The treeio package is released within the Bioconductor and rOpenSci projects. It is available at https://www.bioconductor.org/packages/treeio/.

Reduced bone mineral density (BMD) is associated with an altered microbiota in senile osteoporosis. However, the relationship among gut microbiota, BMD and bone metabolic indexes remains unknown in postmenopausal osteoporosis. In this study, fecal microbiota profiles for 106 postmenopausal individuals with osteopenia (n=33) or osteoporosis (n=42) or with normal BMD (n=31) were determined. An integrated 16S rRNA gene sequencing and LC-MS-based metabolomics approach was applied to explore the association of estrogen-reduced osteoporosis with the gut microbiota and fecal metabolic phenotype. Adjustments were made using several statistical models for potential confounding variables identified from the literature. The results demonstrated decreased bacterial richness and diversity in postmenopausal osteoporosis. Additionally, showed significant differences in abundance levels among phyla and genera in the gut microbial community were found. Moreover, postmenopausal osteopenia-enriched N-acetylmannosamine correlated negatively with BMD, and distinguishing metabolites were closely associated with gut bacterial variation. Both serum procollagen type I N propeptide (P1NP) and C-terminal telopeptide of type I collagen (CTX-1) correlated positively with osteopenia-enriched Allisonella, Klebsiella and Megasphaera. However, we did not find a significant correlation between bacterial diversity and estrogen. These observations will lead to a better understanding of the relationship between bone homeostasis and the microbiota in postmenopausal osteoporosis.

Dysbiosis of gut microbiota during the progression of HBV-related liver disease is not well understood, as there are very few reports that discuss the featured bacterial taxa in different stages. The aim of this study was to reveal the featured bacterial species whose abundances are directly associated with HBV disease progression, that is, progression from healthy subjects to, chronic HBV infection, chronic hepatitis B to liver cirrhosis. Approximately 400 fecal samples were collected, and 97 samples were subjected to 16S rRNA gene sequencing after age and BMI matching. Compared with the healthy individuals, significant gut microbiota alterations were associated with the progression of liver disease. LEfSe results showed that the HBV infected patients had higher Fusobacteria, Veillonella, and Haemophilus abundance while the healthy individuals had higher levels of Prevotella and Phascolarctobacterium. Indicator analysis revealed that 57 OTUs changed as the disease progressed, and their combination produced an AUC value of 90% (95% CI: 86–94%) between the LC and non-LC groups. In addition, the abundances of OTU51 (Dialister succinatiphilus) and OTU50 (Alistipes onderdonkii) decreased as the disease progressed, and these results were further verified by qPCR. The LC patients had the higher bacterial network complexity, which was accompanied with a lower abundance of potential beneficial bacterial taxa, such as Dialister and Alistipes, while they had a higher abundance of pathogenic species within Actinobacteria. The compositional and network changes in the gut microbiota in varied CHB stages, suggest the potential contributions of gut microbiota in CHB disease progression.

The data output from microbiome research is growing at an accelerating rate, yet mining the data quickly and efficiently remains difficult. There is still a lack of effective data structure to represent and manage data, as well as flexible and composable analysis methods. In response to these two issues, we designed and developed the MicrobiotaProcess package. It provides a comprehensive data structure, MPSE, to better integrate the primary and intermediate data, which improves the integration and exploration of the downstream data. Around this data structure, the downstream analysis tasks are decomposed and a set of functions are designed under the tidy framework. These functions independently perform simple tasks and can be combined to perform complex tasks. This gives users the ability to explore data, conduct personalized analysis and develop analysis workflows. Moreover, MicrobiotaProcess can interoperate with other packages in the R community, which further expands its analytical capabilities. This article demonstrates the MicrobiotaProcess for analyzing microbiome data as well as other ecological data through several examples. It connects upstream data, provides flexible downstream analysis components, and provides visualization methods to assist in presenting and interpreting results.