Robust expansion of phylogeny for fast-growing genome sequence data.

Massive sequencing of SARS-CoV-2 genomes has urged novel methods that employ existing phylogenies to add new samples efficiently instead of de novo inference. 'TIPars' was developed for such challenge integrating parsimony analysis with pre-computed ancestral sequences. It took about 21 seconds to insert 100 SARS-CoV-2 genomes into a 100k-taxa reference tree using 1.4 gigabytes. Benchmarking on four datasets, TIPars achieved the highest accuracy for phylogenies of moderately similar sequences. For highly similar and divergent scenarios, fully parsimony-based and likelihood-based phylogenetic placement methods performed the best respectively while TIPars was the second best. TIPars accomplished efficient and accurate expansion of phylogenies of both similar and divergent sequences, which would have broad biological applications beyond SARS-CoV-2. TIPars is accessible from https://tipars.hku.hk/ and source codes are available at https://github.com/id-bioinfo/TIPars.

ggmsa: a visual exploration tool for multiple sequence alignment and associated data.

The identification of the conserved and variable regions in the multiple sequence alignment (MSA) is critical to accelerating the process of understanding the function of genes. MSA visualizations allow us to transform sequence features into understandable visual representations. As the sequence-structure-function relationship gains increasing attention in molecular biology studies, the simple display of nucleotide or protein sequence alignment is not satisfied. A more scalable visualization is required to broaden the scope of sequence investigation. Here we present ggmsa, an R package for mining comprehensive sequence features and integrating the associated data of MSA by a variety of display methods. To uncover sequence conservation patterns, variations and recombination at the site level, sequence bundles, sequence logos, stacked sequence alignment and comparative plots are implemented. ggmsa supports integrating the correlation of MSA sequences and their phenotypes, as well as other traits such as ancestral sequences, molecular structures, molecular functions and expression levels. We also design a new visualization method for genome alignments in multiple alignment format to explore the pattern of within and between species variation. Combining these visual representations with prime knowledge, ggmsa assists researchers in discovering MSA and making decisions. The ggmsa package is open-source software released under the Artistic-2.0 license, and it is freely available on Bioconductor (https://bioconductor.org/packages/ggmsa) and Github (https://github.com/YuLab-SMU/ggmsa).

CBNplot: Bayesian network plots for enrichment analysis.

SUMMARY: When investigating gene expression profiles, determining important directed edges between genes can provide valuable insights in addition to identifying differentially expressed genes. In the subsequent functional enrichment analysis (EA), understanding how enriched pathways or genes in the pathway interact with one another can help infer the gene regulatory network (GRN), important for studying the underlying molecular mechanisms. However, packages for easy inference of the GRN based on EA are scarce. Here, we developed an R package, CBNplot, which infers the Bayesian network (BN) from gene expression data, explicitly utilizing EA results obtained from curated biological pathway databases. The core features include convenient wrapping for structure learning, visualization of the BN from EA results, comparison with reference networks, and reflection of gene-related information on the plot. As an example, we demonstrate the analysis of bladder cancer-related datasets using CBNplot, including probabilistic reasoning, which is a unique aspect of BN analysis. We display the transformability of results obtained from one dataset to another, the validity of the analysis as assessed using established knowledge and literature, and the possibility of facilitating knowledge discovery from gene expression datasets. AVAILABILITY AND IMPLEMENTATION: The library, documentation and web server are available at https://github.com/noriakis/CBNplot. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

ggtreeExtra: Compact Visualization of Richly Annotated Phylogenetic Data.

We present the ggtreeExtra package for visualizing heterogeneous data with a phylogenetic tree in a circular or rectangular layout (https://www.bioconductor.org/packages/ggtreeExtra). The package supports more data types and visualization methods than other tools. It supports using the grammar of graphics syntax to present data on a tree with richly annotated layers and allows evolutionary statistics inferred by commonly used software to be integrated and visualized with external data. GgtreeExtra is a universal tool for tree data visualization. It extends the applications of the phylogenetic tree in different disciplines by making more domain-specific data to be available to visualize and interpret in the evolutionary context.

Treeio: An R Package for Phylogenetic Tree Input and Output with Richly Annotated and Associated Data.

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/.

Two Methods for Mapping and Visualizing Associated Data on Phylogeny Using Ggtree.

Ggtree is a comprehensive R package for visualizing and annotating phylogenetic trees with associated data. It can also map and visualize associated external data on phylogenies with two general methods. Method 1 allows external data to be mapped on the tree structure and used as visual characteristic in tree and data visualization. Method 2 plots the data with the tree side by side using different geometric functions after reordering the data based on the tree structure. These two methods integrate data with phylogeny for further exploration and comparison in the evolutionary biology context. Ggtree is available from http://www.bioconductor.org/packages/ggtree.

Using meshes for MeSH term enrichment and semantic analyses.

Summary: Medical Subject Headings (MeSH) is the NLM controlled vocabulary used to manually index articles for MEDLINE/PubMed. MeSH provides unique and comprehensive annotations for life science. The meshes package implements measurement of the semantic similarity of MeSH terms and gene products to help using MeSH vocabulary in knowledge mining. Enrichment analysis to extract the biological meanings from gene list, expression profile and genomic regions is also provided using MeSH annotation. Meshes supports more than 70 species and provides high quality visualization methods to help interpreting analysis results. Availability and implementation: meshes is released under Artistic-2.0 License. The source code and documents are freely available through Bioconductor (https://www.bioconductor.org/packages/meshes). Supplementary information: Supplementary data are available at Bioinformatics online.

ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization.

UNLABELLED: ChIPseeker is an R package for annotating ChIP-seq data analysis. It supports annotating ChIP peaks and provides functions to visualize ChIP peaks coverage over chromosomes and profiles of peaks binding to TSS regions. Comparison of ChIP peak profiles and annotation are also supported. Moreover, it supports evaluating significant overlap among ChIP-seq datasets. Currently, ChIPseeker contains 15 000 bed file information from GEO database. These datasets can be downloaded and compare with user's own data to explore significant overlap datasets for inferring co-regulation or transcription factor complex for further investigation. AVAILABILITY AND IMPLEMENTATION: ChIPseeker is released under Artistic-2.0 License. The source code and documents are freely available through Bioconductor (http://www.bioconductor.org/packages/release/bioc/html/ChIPseeker.html).

DOSE: an R/Bioconductor package for disease ontology semantic and enrichment analysis.

SUMMARY: Disease ontology (DO) annotates human genes in the context of disease. DO is important annotation in translating molecular findings from high-throughput data to clinical relevance. DOSE is an R package providing semantic similarity computations among DO terms and genes which allows biologists to explore the similarities of diseases and of gene functions in disease perspective. Enrichment analyses including hypergeometric model and gene set enrichment analysis are also implemented to support discovering disease associations of high-throughput biological data. This allows biologists to verify disease relevance in a biological experiment and identify unexpected disease associations. Comparison among gene clusters is also supported. AVAILABILITY AND IMPLEMENTATION: DOSE is released under Artistic-2.0 License. The source code and documents are freely available through Bioconductor (http://www.bioconductor.org/packages/release/bioc/html/DOSE.html). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. CONTACT: gcyu@connect.hku.hk or tqyhe@jnu.edu.cn.

ACK1 promotes gastric cancer epithelial-mesenchymal transition and metastasis through AKT-POU2F1-ECD signalling.

Amplification of the activated Cdc42-associated kinase 1 (ACK1) gene is frequent in gastric cancer (GC). However, little is known about the clinical roles and molecular mechanisms of ACK1 abnormalities in GC. Here, we found that the ACK1 protein level and ACK1 phosphorylation at Tyr 284 were frequently elevated in GC and associated with poor patient survival. Ectopic ACK1 expression in GC cells induced epithelial-mesenchymal transition (EMT) and promoted migration and invasion in vitro, and metastasis in vivo; the depletion of ACK1 induced the opposite effects. We utilized SILAC quantitative proteomics to discover that the level of the cell cycle-related protein ecdysoneless homologue (ECD) was markedly altered by ACK1. Overexpression of ECD promoted EMT, migration, and invasion in GC, similar to the effects of ACK1 overexpression. Silencing of ECD completely blocked the augmentation of ACK1 overexpression-induced EMT, migration, and invasion. Mechanistically, ACK1 phosphorylated AKT at Thr 308 and Ser 473 and activated the AKT pathway to up-regulate the transcription factor POU2F1, which directly bound to the promoter region of its novel target gene ECD and thus regulated ECD expression in GC cells. Furthermore, the phosphorylation levels of AKT at Thr 308 and Ser 473 and POU2F1 and ECD levels were positively associated with ACK1 levels in clinical GC specimens. Collectively, we have demonstrated that ACK1 promotes EMT, migration, and invasion by activating AKT-POU2F1-ECD signalling in GC cells. ACK1 may be employed as a new prognostic factor and therapeutic target for GC.

Encoding biological metaverse: Advancements and challenges in neural fields from macroscopic to microscopic.

Neural fields can efficiently encode three-dimensional (3D) scenes, providing a bridge between two-dimensional (2D) images and virtual reality. This method becomes a trendsetter in bringing the metaverse into vivo life. It has initially captured the attention of macroscopic biology, as demonstrated by computed tomography and magnetic resonance imaging, which provide a 3D field of view for diagnostic biological images. Meanwhile, it has also opened up new research opportunities in microscopic imaging, such as achieving clearer de novo protein structure reconstructions. Introducing this method to the field of biology is particularly significant, as it is refining the approach to studying biological images. However, many biologists have yet to fully appreciate the distinctive meaning of neural fields in transforming 2D images into 3D perspectives. This article discusses the application of neural fields in both microscopic and macroscopic biological images and their practical uses in biomedicine, highlighting the broad prospects of neural fields in the future biological metaverse. We stand at the threshold of an exciting new era, where the advancements in neural field technology herald the dawn of exploring the mysteries of life in innovative ways.

ggVennDiagram: Intuitive Venn diagram software extended.

Highlights of ggVennDiagram include: (1) Subset/Region filling Venn diagram up to seven sets; (2) Upset plot with unlimited sets; (3) Venn Calculator for two or more sets; (4) Provide as R package, Shiny App, and TBtools plugin.

Gut microbiome and metabolome to discover pathogenic bacteria and probiotics in ankylosing spondylitis.

Objective: Previous research has partially revealed distinct gut microbiota in ankylosing spondylitis (AS). In this study, we performed non-targeted fecal metabolomics in AS in order to discover the microbiome-metabolome interface in AS. Based on prospective cohort studies, we further explored the impact of the tumor necrosis factor inhibitor (TNFi) on the gut microbiota and metabolites in AS. Methods: To further understand the gut microbiota and metabolites in AS, along with the influence of TNFi, we initiated a prospective cohort study. Fecal samples were collected from 29 patients with AS before and after TNFi therapy and 31 healthy controls. Metagenomic and metabolomic experiments were performed on the fecal samples; moreover, validation experiments were conducted based on the association between the microbiota and metabolites. Results: A total of 7,703 species were annotated using the metagenomic sequencing system and by profiling the microbial community taxonomic composition, while 50,046 metabolites were identified using metabolite profiling. Differential microbials and metabolites were discovered between patients with AS and healthy controls. Moreover, TNFi was confirmed to partially restore the gut microbiota and the metabolites. Multi-omics analysis of the microbiota and metabolites was performed to determine the associations between the differential microbes and metabolites, identifying compounds such as oxypurinol and biotin, which were correlated with the inhibition of the pathogenic bacteria Ruminococcus gnavus and the promotion of the probiotic bacteria Bacteroides uniformis. Through experimental studies, the relationship between microbes and metabolites was further confirmed, and the impact of these two types of microbes on the enterocytes and the inflammatory cytokine interleukin-18 (IL-18) was explored. Conclusion: In summary, multi-omics exploration elucidated the impact of TNFi on the gut microbiota and metabolites and proposed a novel therapeutic perspective: supplementation of compounds to inhibit potential pathogenic bacteria and to promote potential probiotics, therefore controlling inflammation in AS.

shinyTempSignal: an R shiny application for exploring temporal and other phylogenetic signals.

The molecular clock model is fundamental for inferring species divergence times from molecular sequences. However, its direct application may introduce significant biases due to sequencing errors, recombination events, and inaccurately labeled sampling times. Improving accuracy necessitates rigorous quality control measures to identify and remove potentially erroneous sequences. Furthermore, while not all branches of a phylogenetic tree may exhibit a clear temporal signal, specific branches may still adhere to the assumptions, with varying evolutionary rates. Supporting a relaxed molecular clock model better aligns with the complexities of evolution. The root-to-tip regression method has been widely used to analyze the temporal signal in phylogenetic studies and can be generalized for detecting other phylogenetic signals. Despite its utility, there remains a lack of corresponding software implementations for broader applications. To address this gap, we present shinyTempSignal, an interactive web application implemented with the shiny framework, available as an R package and publicly accessible at https://github.com/YuLab-SMU/shinyTempSignal. This tool facilitates the analysis of temporal and other phylogenetic signals under both strict and relaxed models. By extending the root-to-tip regression method to diverse signals, shinyTempSignal helps in the detection of evolving features or traits, thereby laying the foundation for deeper insights and subsequent analyses.

Quantitative proteomics characterization on the antitumor effects of isodeoxyelephantopin against nasopharyngeal carcinoma.

Isolated from Elephantopus scaber L., a Chinese medicinal herb that is widely used to prevent and treat cancers in China, isodeoxyelephantopin (ESI) exerted antitumor effects on several cancer cells. However, its antitumor mechanism is still not clear. In this study, we found that ESI could induce G2/M arrest and subsequently stimulate cell apoptosis in dose- and time-dependent manners. We used SILAC quantitative proteomics to identify ESI-regulated proteins in cancer cells, and found that 124 proteins were significantly altered in expression. Gene ontology and Ingenuity Pathway Analysis revealed that these proteins were mainly involved in the regulation of oxidative stress and inflammation response. Functional studies demonstrated that ESI induced G2/M arrest and apoptosis by inducing ROS generation, and that antioxidant N-acetyl-l-cysteine could block the ESI-induced antitumor effects. Accumulated ROS resulted in DNA breakage, subsequent G2/M arrest and mitochondrial-mediated apoptosis. ESI upregulated the expression of anticancer inflammation factors IL-12a, IFN-α, and IFN-ß through ROS-dependent and independent pathways. The current work reveals that ESI exerts its antitumor effects through ROS-dependent DNA damage, mitochondrial-mediated apoptosis mechanism and antitumor inflammation factor pathway.

A novel andrographolide derivative AL-1 exerts its cytotoxicity on K562 cells through a ROS-dependent mechanism.

Andrographolide-lipoic acid conjugate (AL-1) is a new in-house synthesized chemical entity, which was derived by covalently linking andrographolide with lipoic acid. However, its anti-cancer effect and cytotoxic mechanism remains unknown. In this study, we found that AL-1 could significantly inhibit cell viability of human leukemia K562 cells by inducing G2/M arrest and apoptosis in a dose-dependent manner. Thirty-one AL-1-regulated protein alterations were identified by proteomics analysis. Gene ontology and ingenuity pathway analysis revealed that a cluster of proteins of oxidative redox state and apoptotic cell death-related proteins, such as PRDX2, PRDX3, PRDX6, TXNRD1, and GLRX3, were regulated by AL-1. Functional studies confirmed that AL-1 induced apoptosis of K562 cells through a ROS-dependent mechanism, and anti-oxidant, N-acetyl-L-cysteine, could completely block AL-1-induced cytotoxicity, implicating that ROS generation played a vital role in AL-1 cytotoxicity. Accumulated ROS resulted in oxidative DNA damage and subsequent G2/M arrest and mitochondrial-mediated apoptosis. The current work reveals that a novel andrographolide derivative AL-1 exerts its anticancer cytotoxicity through a ROS-dependent DNA damage and mitochondrial-mediated apoptosis mechanism.

MMINP: A computational framework of microbe-metabolite interactions-based metabolic profiles predictor based on the O2-PLS algorithm.

The gut metabolome acts as an intermediary between the gut microbiota and host, and has tremendous diagnostic and therapeutic potential. Several studies have utilized bioinformatic tools to predict metabolites based on the different aspects of the gut microbiome. Although these tools have contributed to a better understanding of the relationship between the gut microbiota and various diseases, most of them have focused on the impact of microbial genes on the metabolites and the relationship between microbial genes. In contrast, relatively little is known regarding the effect of metabolites on the microbial genes or the relationship between these metabolites. In this study, we constructed a computational framework of Microbe-Metabolite INteractions-based metabolic profiles Predictor (MMINP), based on the Two-Way Orthogonal Partial Least Squares (O2-PLS) algorithm to predict the metabolic profiles associated with gut microbiota. We demonstrated the predictive value of MMINP relative to that of similar methods. Additionally, we identified the features that would profoundly impact the prediction performance of data-driven methods (O2-PLS, MMINP, MelonnPan, and ENVIM), including the training sample size, host disease state, and the upstream data processing methods of the different technical platforms. We suggest that when using data-driven methods, similar host disease states and preprocessing methods, and a sufficient number of training samples are necessary to achieve accurate prediction.

MMINP fully considers internal and mutual correlations in metabolites and microbial genes and infers metabolite information through their real joint parts.The feasibility of predicting metabolic profiles using gut microbiome data should be based on the premise of similar host disease states, similar preprocessing methods, and a sufficient number of training samples.Although the accuracy of predicted specific metabolites is affected by multiple factors, the systematic conclusions presented for predicted metabolites at higher levels (e.g., class level) are accurate, allowing metabolite prediction to be applied to the discovery of potential metabolite markers.

MicrobiotaProcess: A comprehensive R package for deep mining microbiome.

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 an 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 a 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 analyses, 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.

The impact of pre-existing influenza antibodies and inflammatory status on the influenza vaccine responses in older adults.

Age-associated immune changes and pre-existing influenza immunity are hypothesized to reduce influenza vaccine effectiveness in older adults, although the contribution of each factor is unknown. Here, we constructed influenza-specific IgG landscapes and determined baseline concentrations of cytokines typically associated with chronic inflammation in older adults (TNF-α, IL-10, IL-6, and IFN-γ) in 30 high and 29 low influenza vaccine responders (HR and LR, respectively). In a background of high H3 antibody titers, vaccine-specific H3, but not H1, antibody titers were boosted in LRs to titers comparable to HRs. Pre-vaccination concentrations of IL-10 were higher in LRs compared with HRs and inversely correlated with titers of pre-existing influenza antibodies. Baseline TNF-α concentrations were positively correlated with fold-increases in antibody titers in HRs. Our findings indicate that baseline inflammatory status is an important determinant for generating post-vaccination hemagglutinin-inhibition antibodies in older adults, and IgG responses can be boosted in the context of high pre-existing immunity.

Behind the Indolent Facade: Uncovering the Molecular Features and Malignancy Potential in Lung Minimally Invasive Adenocarcinoma by Single-Cell Transcriptomics.

The increased use of low-dose computed tomography screening has led to more frequent detection of early stage lung tumors, including minimally invasive adenocarcinoma (MIA). To unravel the intricacies of tumor cells and the immune microenvironment in MIA, this study performs a comprehensive single-cell transcriptomic analysis and profiles the transcriptomes of 156,447 cells from fresh paired MIA and invasive adenocarcinoma (IA) tumor samples, peripheral blood mononuclear cells, and adjacent normal tissue samples from three patients with synchronous multiple primary lung adenocarcinoma. This study highlights a connection and heterogeneity between the tumor ecosystem of MIA and IA. MIA tumor cells exhibited high expression of aquaporin-1 and angiotensin II receptor type 2 and a basal-like molecular character. Furthermore, it identifies that cathepsin B+ tumor-associated macrophages may over-activate CD8+ T cells in MIA, leading to an enrichment of granzyme K+ senescent CD8+ T cells, indicating the possibility of malignant progression behind the indolent appearance of MIA. These findings are further validated in 34 MIA and 35 IA samples by multiplexed immunofluorescence. These findings provide valuable insights into the mechanisms that maintain the indolent nature and prompt tumor progression of MIA and can be used to develop more effective therapeutic targets and strategies for MIA patients.