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Abstract: Formin-like 1 (FMNL1) is a formin-related protein highly expressed in hematopoietic cells and overexpressed in leukemias as well as diverse transformed cell lines. It has been described to play a role in diverse functions of hematopoietic cells such as phagocytosis of macrophages as well as polarization and cytotoxicity of T cells. However, the specific role of FMNL1 in these processes has not been clarified yet and regulation by interaction partners in primary hematopoietic cells has never been investigated. We performed a proteomic screen for investigation of the interactome of FMNL1 in primary hematopoietic cells resulting in the identification of a number of interaction partners. Bioinformatic analysis considering semantic similarity suggested the giant protein AHNAK1 to be an essential interaction partner of FMNL1. We confirmed AHNAK1 as a general binding partner for FMNL1 in diverse hematopoietic cells and demonstrate that the N-terminal part of FMNL1 binds to the C-terminus of AHNAK1. Moreover, we show that the constitutively activated form of FMNL1 (FMNL1γ) induces localization of AHNAK1 to the cell membrane. Finally, we provide evidence that overexpression or knock down of FMNL1 has an impact on the capacitative calcium influx after ionomycin-mediated activation of diverse cell lines and primary cells. [Copyright &y& Elsevier]

Immunotherapy shows great promise for treating advanced cancers, but its effectiveness varies widely among different patients and cancer types. Identifying biomarkers and developing robust predictive models to discern which patients are most likely to benefit from immunotherapy is of great importance. In this context, we have developed the tumor immunotherapy gene expression R package (tigeR 1.0) to address the increasing need for effective tools to explore biomarkers and construct predictive models. tigeR encompasses four distinct yet closely interconnected modules. The Biomarker Evaluation module enables researchers to evaluate whether the biomarkers of interest are associated with immunotherapy response via built‐in or custom immunotherapy gene expression data. The Tumor Microenvironment Deconvolution module integrates 10 open‐source algorithms to obtain the proportions of different cell types within the tumor microenvironment, facilitating the investigation of the association between immune cell populations and immunotherapy response. The Prediction Model Construction module equips users with the ability to construct sophisticated prediction models using a range of built‐in machine‐learning algorithms. The Response Prediction module predicts the immunotherapy response for the patients from gene expression data using our pretrained machine learning models or public gene expression signatures. By providing these diverse functionalities, tigeR aims to simplify the process of analyzing immunotherapy gene expression data, thus making it accessible to researchers without advanced programming skills. The source code and example for the tigeR project can be accessed at http://github.com/YuLab-SMU/tigeR. Highlights: Tumor Immunotherapy Gene Expression R package (tigeR) is an effective R package to explore biomarkers and construct predictive models to predict immunotherapeutic outcomes.tigeR enables the flexibility to load built‐in or custom gene expression data with immunotherapy outcome information.tigeR encompasses four distinct yet closely interconnected modules, including the Biomarker Evaluation module, Tumor Microenvironment Deconvolution module, Prediction Model Construction module, and Response Prediction module. [ABSTRACT FROM AUTHOR]

Background: Recent advances in high-throughput technologies dramatically increase biological data generation. However, many research groups lack computing facilities and specialists. This is an obstacle that remains to be addressed. Here, we present a Linux distribution, LXtoo, to provide a flexible computing platform for bioinformaticsanalysis.Findings: Unlike most of the existing live Linux distributions for bioinformatics limiting their usage to sequence analysis and protein structure prediction, LXtoo incorporates a comprehensive collection of bioinformatics software, including data mining tools for microarray and proteomics, protein-protein interaction analysis, and computationally complex tasks like molecular dynamics. Moreover, most of the programs have been configured and optimized for high performance computing.Conclusions: LXtoo aims to provide well-supported computing environment tailored for bioinformatics research, reducing duplication of efforts in building computing infrastructure. LXtoo is distributed as a Live DVD and freely available at http://bioinformatics.jnu.edu.cn/LXtoo. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 [ABSTRACT FROM AUTHOR]

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. Author summary: Since the beginning of the COVID-19 pandemic, over 15 million SARS-CoV-2 genome sequences have been made publicly available. As sequencing cost decreases, the rate of genome sequencing is expected to greatly increase in the future and will generate numerous sequences where conventional de novo phylogenetic inference may no longer be suitable. TIPars allows rapid and memory-efficient expansion of phylogeny at high accuracy. This enables real-time monitoring of pathogen transmission during a pandemic using large-scale global phylogenetic analysis such as the ever-increasing SARS-CoV-2 genome sequences. We believe that the development of next-generation phylogenetic methods is imperative for analysing enormous, fast-growing genome sequence datasets to gain critical evolutionary insights that, as evident in this pandemic, have real-world applications. [ABSTRACT FROM AUTHOR]

The article discusses the features and updates of the ggVennDiagram software, which is used for creating Venn diagrams to illustrate relationships among multiple datasets. The software, built upon existing tools like VennDiagram and RVenn, offers precise region/subset filling and easy-to-use features. The latest version, 1.5, includes improvements such as easier installation, native support for Upset plots, a full functional Venn calculator, and the availability of an official Shiny app and TBtools plugin for interactive use. The authors acknowledge the contributions of various individuals and institutions, and the work was supported by funding from the National Natural Science Foundation of China. The software and related materials are openly available for use. [Extracted from the article]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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). [ABSTRACT FROM AUTHOR]

Semantic technology plays a key role in various domains, from conversation understanding to algorithm analysis. As the most efficient semantic tool, ontology can represent, process and manage the widespread knowledge. Nowadays, many researchers use ontology to collect and organize data's semantic information in order to maximize research productivity. In this paper, we firstly describe our work on the development of a remote sensing data ontology, with a primary focus on semantic fusion-driven research for big data. Our ontology is made up of 1,264 concepts and 2,030 semantic relationships. However, the growth of big data is straining the capacities of current semantic fusion and reasoning practices. Considering the massive parallelism of DNA strands, we propose a novel DNA-based semantic fusion model. In this model, a parallel strategy is developed to encode the semantic information in DNA for a large volume of remote sensing data. The semantic information is read in a parallel and bit-wise manner and an individual bit is converted to a base. By doing so, a considerable amount of conversion time can be saved, i.e., the cluster-based multi-processes program can reduce the conversion time from 81,536 seconds to 4,937 seconds for 4.34 GB source data files. Moreover, the size of result file recording DNA sequences is 54.51 GB for parallel C program compared with 57.89 GB for sequential Perl. This shows that our parallel method can also reduce the DNA synthesis cost. In addition, data types are encoded in our model, which is a basis for building type system in our future DNA computer. Finally, we describe theoretically an algorithm for DNA-based semantic fusion. This algorithm enables the process of integration of the knowledge from disparate remote sensing data sources into a consistent, accurate, and complete representation. This process depends solely on ligation reaction and screening operations instead of the ontology. [ABSTRACT FROM AUTHOR]

Phylogenetic analysis of the highly pathogenic avian influenza (HPAI) H5 viruses causing recent outbreaks in Taiwan showed that they belonged to the Asian HPAI H5 lineage, clade 2.3.4.4 viruses, and were apparently introduced by migratory birds. These viruses reassorted with Eurasian influenza gene pool viruses and formed five genotypic variants. As Taiwan has a similar influenza ecosystem to southern China, the HPAI H5 lineage could become established and enzootic in the island. [ABSTRACT FROM AUTHOR]

Streptococcus pneumoniae is a Gram-positive pathogen that causes a variety of infection diseases in human. In this project, we determined the antibacterial activity of a Ru(II) complex X-03 against S. pneumoniae in vitro, by comparing its toxicity to host cells A549 and HBE. We performed two-dimensional gel electrophoresis (2-DE)-based proteomic analysis to characterize the protein alterations in S. pneumoniae after treatment with X-03. In total, 50 proteins exhibiting significant differential expressions were identified. RT-PCR was used to confirm the expression differences for selected proteins. Bioinformatics analysis on the proteomic alterations suggested that Ru(II) complex X-03 may obstruct bacterial fatty acid synthesis and oxidation–reduction process to suppress the growth of S. pneumoniae . Metal-uptake experiments revealed that iron-acquisition pathway in the bacterium may be interfered by X-03. These results provide useful clues for further investigations on the mechanism of the antibacterial action of metal compounds. Biological significance The appearance of bacterial strains with broad antibiotic resistance is becoming an alarming global health concern. The development of novel efficient antibacterial compound is urgently needed. In the present study, we found that Ru(II) complex X-03 has a significant antibacterial activity and applied proteomic technology combined with bioinformatics analysis to investigate its antimicrobial mechanism in S. pneumoniae . Many proteins were found to be dysregulated, implicating that X-03 may affect various molecular pathways leading to the inhibition of bacterial growth. Metal-uptake experiments demonstrated that X-03 treatment reduced the iron content in the bacterium, suggesting the interference with iron acquisition systems by the complex. This disturbance in iron acquisition may directly or indirectly induce the proteomic response that involved many pathways. In addition, X-03 could selectively suppress Gram-positive bacteria but execute less cytotoxicity to Gram-negative bacteria, with almost no effect on human cells, implicating its potential to be developed as a specific antimicrobial agent. These results provide useful information for further investigations on the mechanism of the antibacterial action of metal drugs and development of efficient antibacterial drugs. [ABSTRACT FROM AUTHOR]

AbstractStreptococcus pneumoniaeis a Gram-positive human pathogen that causes a variety of serious mucosal and invasive diseases in human. Bacterial membrane proteins play crucial roles in host–pathogen interactions and bacterial pathogenesis, and thus are potential drug targets or vaccine candidates. In this study, membranes from Streptococcus pneumoniaeD39 were enriched by mechanical grinding and ultracentrifugation, and then the membrane proteins were extracted with trifluroethanol and chloroform. Around 60% of the extracted proteins were identified to be membrane proteins with 2-DE coupled with MALDI-MS/MS and 2D-LC-ESI-MS/MS. These identified membrane proteins can be functionally categorized into various groups involved in nutriment transport, signal transduction, protein folding or secretion, oxidation, carbohydrate metabolism, and other physiological processes. A protein interaction network was constructed for understanding the regulation relationship of the membrane proteins. This study represents the first global characterization of membrane proteome from Gram-positive streptococcus species of bacteria, providing valuable clues for further investigation aiming at identifying drug/vaccine targets for the bacterial infection. [ABSTRACT FROM AUTHOR]