Systematic Evaluation of Chromatographic Peak Quality for Targeted Mass Spectrometry via Variational Autoencoder.

Targeted mass spectrometry is a powerful technique for quantifying specific proteins or metabolites in complex biological samples. Accurate peak picking is a critical step as it determines the absolute abundance of each analyte by integrating the area under the picked peaks. Although automated software exists for handling such complex tasks, manual intervention is often required to rectify potential errors like misclassification or mis-picking events, which can significantly affect quantification accuracy. Therefore, it is necessary to develop objective scoring functions to evaluate peak-picking results and to identify problematic cases for further inspection. In this study, we present targeted mass spectrometry quality encoder (TMSQE), a data-driven scoring function that summarizes peak quality in three types: transition level, peak group level, and consistency level across samples. Through unsupervised learning from large data sets containing 1,703,827 peak groups, TMSQE establishes a reliable standard for systematic and objective evaluations of chromatographic peak quality in targeted mass spectrometry. TMSQE shows a high degree of consistency with expert experiences and can efficiently capture problematic cases after the automated software. Furthermore, we demonstrate the generalizability of TMSQE by successfully applying it to various data sets, including both peptide and metabolite data sets. Our proposed scoring approach provides a reliable solution for consistent and accurate peak quality evaluation, facilitating peak quality control for targeted mass spectrometry.

HeapMS: An Automatic Peak-Picking Pipeline for Targeted Proteomic Data Powered by 2D Heatmap Transformation and Convolutional Neural Networks.

The process of peak picking and quality assessment for multiple reaction monitoring (MRM) data demands significant human effort, especially for signals with low abundance and high interference. Although multiple peak-picking software packages are available, they often fail to detect peaks with low quality and do not report cases with low confidence. Furthermore, visual examination of all chromatograms is still necessary to identify uncertain or erroneous cases. This study introduces HeapMS, a web service that uses artificial intelligence to assist with peak picking and the quality assessment of MRM chromatograms. HeapMS applies a rule-based filter to remove chromatograms with low interference and high-confidence peak boundaries detected by Skyline. Additionally, it transforms two histograms (representing light and heavy peptides) into a single encoded heatmap and performs a two-step evaluation (quality detection and peak picking) using image convolutional neural networks. HeapMS offers three categories of peak picking: uncertain peak picking that requires manual inspection, deletion peak picking that requires removal or manual re-examination, and automatic peak picking. HeapMS acquires the chromatogram and peak-picking boundaries directly from Skyline output. The output results are imported back into Skyline for further manual inspection, facilitating integration with Skyline. HeapMS offers the benefit of detecting chromatograms that should be deleted or require human inspection. Based on defined categories, it can significantly reduce human workload and provide consistent results. Furthermore, by using heatmaps instead of histograms, HeapMS can adapt to future updates in image recognition models. The HeapMS is available at: https://github.com/ccllabe/HeapMS.

Downregulation of TAZ elicits a mitochondrial redox imbalance and ferroptosis in lung epithelial cells exposed to diesel exhaust particles.

Mitochondrial dysfunction was reported to be involved in the development of lung diseases including chronic obstructive pulmonary disease (COPD). However, molecular regulation underlying metabolic disorders in the airway epithelia exposed to air pollution remains unclear. In the present study, lung bronchial epithelial BEAS-2B and alveolar epithelial A549 cells were treated with diesel exhaust particles (DEPs), the primary representative of ambient particle matter. This treatment elicited cell death accompanied by induction of lipid reactive oxygen species (ROS) production and ferroptosis. Lipidomics analyses revealed that DEPs increased glycerophospholipid contents. Accordingly, DEPs upregulated expression of the electron transport chain (ETC) complex and induced mitochondrial ROS production. Mechanistically, DEP exposure downregulated the Hippo transducer transcriptional co-activator with PDZ-binding motif (TAZ), which was further identified to be crucial for the ferroptosis-associated antioxidant system, including glutathione peroxidase 4 (GPX4), the glutamate-cysteine ligase catalytic subunit (GCLC), and glutathione-disulfide reductase (GSR). Moreover, immunohistochemistry confirmed downregulation of GPX4 and upregulation of lipid peroxidation in the bronchial epithelium of COPD patients and Sprague-Dawley rats exposed to air pollution. Finally, proteomics analyses confirmed alterations of ETC-related proteins in bronchoalveolar lavage from COPD patients compared to healthy subjects. Together, our study discovered that involvement of mitochondrial redox dysregulation plays a vital role in pulmonary epithelial cell destruction after exposure to air pollution.

Easy and Rapid Approach to Obtaining the Binding Affinity of Biomolecular Interactions Based on the Deep Learning Boost.

Recently, the deep learning (DL) dimension of artificial intelligence has received much attention from biochemical researchers and thus has gradually become the key approach adopted in the area of biosensing applications. Studies have shown that the use of DL techniques for sensing can not only shorten the time of data analysis but also significantly increase the accuracy of data analysis and prediction, resulting in the performance improvement of biosensing systems in comparison to conventional methods. However, obtaining reliable equilibrium and rate constants of biomolecular interactions during the detection process remains difficult and time-consuming to date. In this study, we propose a transformed model based on the deep transfer learning and sequence-to-sequence autoencoder that can successfully transfer the SPR sensorgram to the protein-binding constants, that is, the association rate constant (ka) and dissociation rate constant (kd), which provide crucial information to understand the mechanisms of drug action and the functional structures of biomolecules. Experimentally, we first trained and tested the pre-trained model using the Langmuir model which generated ideal SPR sensorgrams and then we fine-tuned the pre-trained model through the augmented SPR sensorgrams which were synthesized by using the synthesized minority oversampling technique (SMOTE) through the moderate-scale experiment. Next, the fine-tuned model was inputted with a short experimental SPR sensorgram that only needs 110 s, and the sensorgram was directly transformed into a reconstructed ideal sensorgram. Finally, the binding kinetic constants, that is, ka and kd, as outputs, were obtained through fitting the reconstructed ideal sensorgram. The results showed that the prediction errors of ka and kd obtained by our model were less than 12 and 24%, respectively. Based on the convenience, accuracy, and reliability of the proposed DL approach, we believe our strategy significantly boosts the feasibility to monitor the binding affinity of antibodies online during production.

Association of exacerbation phenotype with the sputum microbiome in chronic obstructive pulmonary disease patients during the clinically stable state.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a progressive, life-threatening lung disease with increasing prevalence and incidence worldwide. Increasing evidence suggests that lung microbiomes might play a physiological role in acute exacerbations of COPD. The objective of this study was to characterize the association of the microbiota and exacerbation risk or airflow limitation in stable COPD patients. METHODS: The sputum microbiota from 78 COPD outpatients during periods of clinical stability was investigated using 16S rRNA V3-V4 amplicon sequencing. The microbiome profiles were compared between patients with different risks of exacerbation, i.e., the low risk exacerbator (LRE) or high risk exacerbator (HRE) groups, and with different airflow limitation severity, i.e., mild to moderate (FEV1 ≥ 50; PFT I) or severe to very severe (FEV1 < 50; PFT II). RESULTS: The bacterial diversity (Chao1 and observed OTUs) was significantly decreased in the HRE group compared to that in the LRE group. The top 3 dominant phyla in sputum were Firmicutes, Actinobacteria, and Proteobacteria, which were similar in the HRE and LRE groups. At the genus level, compared to that in the LRE group (41.24%), the proportion of Streptococcus was slightly decreased in the HRE group (28.68%) (p = 0.007). However, the bacterial diversity and the proportion of dominant bacteria at the phylum and genus levels were similar between the PFT I and PFT II groups. Furthermore, the relative abundances of Gemella morbillorum, Prevotella histicola, and Streptococcus gordonii were decreased in the HRE group compared to those in the LRE group according to linear discriminant analysis effect size (LEfSe). Microbiome network analysis suggested altered bacterial cooperative regulation in different exacerbation phenotypes. The proportions of Proteobacteria and Neisseria were negatively correlated with the FEV1/FVC value. According to functional prediction of sputum bacterial communities through Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) analysis, genes involved in lipopolysaccharide biosynthesis and energy metabolism were enriched in the HRE group. CONCLUSION: The present study revealed that the sputum microbiome changed in COPD patients with different risks of exacerbation. Additionally, the bacterial cooperative networks were altered in the HRE patients and may contribute to disease exacerbation. Our results provide evidence that sputum microbiome community dysbiosis is associated with different COPD phenotypes, and we hope that by understanding the lung microbiome, a potentially modifiable clinical factor, further targets for improved COPD therapies during the clinically stable state may be elucidated.

mSignatureDB: a database for deciphering mutational signatures in human cancers.

Cancer is a genetic disease caused by somatic mutations; however, the understanding of the causative biological processes generating these mutations is limited. A cancer genome bears the cumulative effects of mutational processes during tumor development. Deciphering mutational signatures in cancer is a new topic in cancer research. The Wellcome Trust Sanger Institute (WTSI) has categorized 30 reference signatures in the COSMIC database based on the analyses of ∼10 000 sequencing datasets from TCGA and ICGC. Large cohorts and bioinformatics skills are required to perform the same analysis as WTSI. The quantification of known signatures in custom cohorts is not possible under the current framework of the COSMIC database, which motivates us to construct a database for mutational signatures in cancers and make such analyses more accessible to general researchers. mSignatureDB (http://tardis.cgu.edu.tw/msignaturedb) integrates R packages and in-house scripts to determine the contributions of the published signatures in 15 780 individual tumors from 73 TCGA/ICGC cancer projects, making comparison of signature patterns within and between projects become possible. mSignatureDB also allows users to perform signature analysis on their own datasets, quantifying contributions of signatures at sample resolution, which is a unique feature of mSignatureDB not available in other related databases.

Pathogenic Protist Transmembranome database (PPTdb): a web-based platform for searching and analysis of protist transmembrane proteins.

BACKGROUND: Pathogenic protist membrane transporter proteins play important roles not only in exchanging molecules into and out of cells but also in acquiring nutrients and biosynthetic compounds from their hosts. Currently, there is no centralized protist membrane transporter database published, which makes system-wide comparisons and studies of host-pathogen membranomes difficult to achieve. RESULTS: We analyzed over one million protein sequences from 139 protists with full or partial genome sequences. Putative transmembrane proteins were annotated by primary sequence alignments, conserved secondary structural elements, and functional domains. We have constructed the PPTdb (Pathogenic Protist Transmembranome database), a comprehensive membrane transporter protein portal for pathogenic protists and their human hosts. The PPTdb is a web-based database with a user-friendly searching and data querying interface, including hierarchical transporter classification (TC) numbers, protein sequences, functional annotations, conserved functional domains, batch sequence retrieving and downloads. The PPTdb also serves as an analytical platform to provide useful comparison/mining tools, including transmembrane ability evaluation, annotation of unknown proteins, informative visualization charts, and iterative functional mining of host-pathogen transporter proteins. CONCLUSIONS: The PPTdb collected putative protist transporter proteins and offers a user-friendly data retrieving interface. Moreover, a pairwise functional comparison ability can provide useful information for identifying functional uniqueness of each protist. Finally, the host and non-host protein similarity search can fulfill the needs of comprehensive studies of protists and their hosts. The PPTdb is freely accessible at http://pptdb.cgu.edu.tw .

VAReporter: variant reporter for cancer research of massive parallel sequencing.

BACKGROUND: High throughput sequencing technologies have been an increasingly critical aspect of precision medicine owing to a better identification of disease targets, which contributes to improved health care cost and clinical outcomes. In particular, disease-oriented targeted enrichment sequencing is becoming a widely-accepted application for diagnostic purposes, which can interrogate known diagnostic variants as well as identify novel biomarkers from panels of entire human coding exome or disease-associated genes. RESULTS: We introduce a workflow named VAReporter to facilitate the management of variant assessment in disease-targeted sequencing, the identification of pathogenic variants, the interpretation of biological effects and the prioritization of clinically actionable targets. State-of-art algorithms that account for mutation phenotypes are used to rank the importance of mutated genes through visual analytic strategies. We established an extensive annotation source by integrating a wide variety of biomedical databases and followed the American College of Medical Genetics and Genomics (ACMG) guidelines for interpretation and reporting of sequence variations. CONCLUSIONS: In summary, VAReporter is the first web server designed to provide a "one-stop" resource for individual's diagnosis and large-scale cohort studies, and is freely available at http://rnd.cgu.edu.tw/vareporter .

CMPD: cancer mutant proteome database.

Whole-exome sequencing, which centres on the protein coding regions of disease/cancer associated genes, represents the most cost-effective method to-date for deciphering the association between genetic alterations and diseases. Large-scale whole exome/genome sequencing projects have been launched by various institutions, such as NCI, Broad Institute and TCGA, to provide a comprehensive catalogue of coding variants in diverse tissue samples and cell lines. Further functional and clinical interrogation of these sequence variations must rely on extensive cross-platforms integration of sequencing information and a proteome database that explicitly and comprehensively archives the corresponding mutated peptide sequences. While such data resource is a critical for the mass spectrometry-based proteomic analysis of exomic variants, no database is currently available for the collection of mutant protein sequences that correspond to recent large-scale genomic data. To address this issue and serve as bridge to integrate genomic and proteomics datasets, CMPD (http://cgbc.cgu.edu.tw/cmpd) collected over 2 millions genetic alterations, which not only facilitates the confirmation and examination of potential cancer biomarkers but also provides an invaluable resource for translational medicine research and opportunities to identify mutated proteins encoded by mutated genes.

PARRoT- a homology-based strategy to quantify and compare RNA-sequencing from non-model organisms.

BACKGROUND: Next-generation sequencing promises the de novo genomic and transcriptomic analysis of samples of interests. However, there are only a few organisms having reference genomic sequences and even fewer having well-defined or curated annotations. For transcriptome studies focusing on organisms lacking proper reference genomes, the common strategy is de novo assembly followed by functional annotation. However, things become even more complicated when multiple transcriptomes are compared. RESULTS: Here, we propose a new analysis strategy and quantification methods for quantifying expression level which not only generate a virtual reference from sequencing data, but also provide comparisons between transcriptomes. First, all reads from the transcriptome datasets are pooled together for de novo assembly. The assembled contigs are searched against NCBI NR databases to find potential homolog sequences. Based on the searched result, a set of virtual transcripts are generated and served as a reference transcriptome. By using the same reference, normalized quantification values including RC (read counts), eRPKM (estimated RPKM) and eTPM (estimated TPM) can be obtained that are comparable across transcriptome datasets. In order to demonstrate the feasibility of our strategy, we implement it in the web service PARRoT. PARRoT stands for Pipeline for Analyzing RNA Reads of Transcriptomes. It analyzes gene expression profiles for two transcriptome sequencing datasets. For better understanding of the biological meaning from the comparison among transcriptomes, PARRoT further provides linkage between these virtual transcripts and their potential function through showing best hits in SwissProt, NR database, assigning GO terms. Our demo datasets showed that PARRoT can analyze two paired-end transcriptomic datasets of approximately 100 million reads within just three hours. CONCLUSIONS: In this study, we proposed and implemented a strategy to analyze transcriptomes from non-reference organisms which offers the opportunity to quantify and compare transcriptome profiles through a homolog based virtual transcriptome reference. By using the homolog based reference, our strategy effectively avoids the problems that may cause from inconsistencies among transcriptomes. This strategy will shed lights on the field of comparative genomics for non-model organism. We have implemented PARRoT as a web service which is freely available at http://parrot.cgu.edu.tw .

Vanno: a visualization-aided variant annotation tool.

Next-generation sequencing (NGS) technologies have revolutionized the field of genetics and are trending toward clinical diagnostics. Exome and targeted sequencing in a disease context represent a major NGS clinical application, considering its utility and cost-effectiveness. With the ongoing discovery of disease-associated genes, various gene panels have been launched for both basic research and diagnostic tests. However, the fundamental inconsistencies among the diverse annotation sources, software packages, and data formats have complicated the subsequent analysis. To manage disease-associated NGS data, we developed Vanno, a Web-based application for in-depth analysis and rapid evaluation of disease-causative genome sequence alterations. Vanno integrates information from biomedical databases, functional predictions from available evaluation models, and mutation landscapes from TCGA cancer types. A highly integrated framework that incorporates filtering, sorting, clustering, and visual analytic modules is provided to facilitate exploration of oncogenomics datasets at different levels, such as gene, variant, protein domain, or three-dimensional structure. Such design is crucial for the extraction of knowledge from sequence alterations and translating biological insights into clinical applications. Taken together, Vanno supports almost all disease-associated gene tests and exome sequencing panels designed for NGS, providing a complete solution for targeted and exome sequencing analysis. Vanno is freely available at http://cgts.cgu.edu.tw/vanno.

Is the whole greater than the sum of its parts? De novo assembly strategies for bacterial genomes based on paired-end sequencing.

BACKGROUND: Whole genome sequence construction is becoming increasingly feasible because of advances in next generation sequencing (NGS), including increasing throughput and read length. By simply overlapping paired-end reads, we can obtain longer reads with higher accuracy, which can facilitate the assembly process. However, the influences of different library sizes and assembly methods on paired-end sequencing-based de novo assembly remain poorly understood. RESULTS: We used 250 bp Illumina Miseq paired-end reads of different library sizes generated from genomic DNA from Escherichia coli DH1 and Streptococcus parasanguinis FW213 to compare the assembly results of different library sizes and assembly approaches. Our data indicate that overlapping paired-end reads can increase read accuracy but sometimes cause insertion or deletions. Regarding genome assembly, merged reads only outcompete original paired-end reads when coverage depth is low, and larger libraries tend to yield better assembly results. These results imply that distance information is the most critical factor during assembly. Our results also indicate that when depth is sufficiently high, assembly from subsets can sometimes produce better results. CONCLUSIONS: In summary, this study provides systematic evaluations of de novo assembly from paired end sequencing data. Among the assembly strategies, we find that overlapping paired-end reads is not always beneficial for bacteria genome assembly and should be avoided or used with caution especially for genomes containing high fraction of repetitive sequences. Because increasing numbers of projects aim at bacteria genome sequencing, our study provides valuable suggestions for the field of genomic sequence construction.

Novel insights into the molecular events linking to cell death induced by tetracycline in the amitochondriate protozoan Trichomonas vaginalis.

Trichomonas vaginalis colonizes the human urogenital tract and causes trichomoniasis, the most common nonviral sexually transmitted disease. Currently, 5-nitroimidazoles are the only recommended drugs for treating trichomoniasis. However, increased resistance of the parasite to 5-nitroimidazoles has emerged as a highly problematic public health issue. Hence, it is essential to identify alternative chemotherapeutic agents against refractory trichomoniasis. Tetracycline (TET) is a broad-spectrum antibiotic with activity against several protozoan parasites, but the mode of action of TET in parasites remains poorly understood. The in vitro effect of TET on the growth of T. vaginalis was examined, and the mode of cell death was verified by various apoptosis-related assays. Next-generation sequencing-based RNA sequencing (RNA-seq) was employed to elucidate the transcriptome of T. vaginalis in response to TET. We show that TET has a cytotoxic effect on both metronidazole (MTZ)-sensitive and -resistant T. vaginalis isolates, inducing some features resembling apoptosis. RNA-seq data reveal that TET significantly alters the transcriptome via activation of specific pathways, such as aminoacyl-tRNA synthetases and carbohydrate metabolism. Functional analyses demonstrate that TET disrupts the hydrogenosomal membrane potential and antioxidant system, which concomitantly elicits a metabolic shift toward glycolysis, suggesting that the hydrogenosomal function is impaired and triggers cell death. Collectively, we provide in vitro evidence that TET is a potential alternative therapeutic choice for treating MTZ-resistant T. vaginalis. The in-depth transcriptomic signatures in T. vaginalis upon TET treatment presented here will shed light on the signaling pathways linking to cell death in amitochondriate organisms.

ChIPseek, a web-based analysis tool for ChIP data.

BACKGROUND: Chromatin is a dynamic but highly regulated structure. DNA-binding proteins such as transcription factors, epigenetic and chromatin modifiers are responsible for regulating specific gene expression pattern and may result in different phenotypes. To reveal the identity of the proteins associated with the specific region on DNA, chromatin immunoprecipitation (ChIP) is the most widely used technique. ChIP assay followed by next generation sequencing (ChIP-seq) or microarray (ChIP-chip) is often used to study patterns of protein-binding profiles in different cell types and in cancer samples on a genome-wide scale. However, only a limited number of bioinformatics tools are available for ChIP datasets analysis. RESULTS: We present ChIPseek, a web-based tool for ChIP data analysis providing summary statistics in graphs and offering several commonly demanded analyses. ChIPseek can provide statistical summary of the dataset including histogram of peak length distribution, histogram of distances to the nearest transcription start site (TSS), and pie chart (or bar chart) of genomic locations for users to have a comprehensive view on the dataset for further analysis. For examining the potential functions of peaks, ChIPseek provides peak annotation, visualization of peak genomic location, motif identification, sequence extraction, and comparison between datasets. Beyond that, ChIPseek also offers users the flexibility to filter peaks and re-analyze the filtered subset of peaks. ChIPseek supports 20 different genome assemblies for 12 model organisms including human, mouse, rat, worm, fly, frog, zebrafish, chicken, yeast, fission yeast, Arabidopsis, and rice. We use demo datasets to demonstrate the usage and intuitive user interface of ChIPseek. CONCLUSIONS: ChIPseek provides a user-friendly interface for biologists to analyze large-scale ChIP data without requiring any programing skills. All the results and figures produced by ChIPseek can be downloaded for further analysis. The analysis tools built into ChIPseek, especially the ones for selecting and examine a subset of peaks from ChIP data, provides invaluable helps for exploring the high through-put data from either ChIP-seq or ChIP-chip. ChIPseek is freely available at http://chipseek.cgu.edu.tw.

Techno-Functions and Safety Concerns of Plant-Based Peptides in Food Matrices.

Plant-based peptides (PBPs) benefit functional food development and environmental sustainability. Proteolysis remains the primary method of peptide production because it is a mild and nontoxic technique. However, potential safety concerns still emanate from toxic or allergenic sequences, amino acid racemization, iso-peptide bond formation, Maillard reaction, dose usage, and frequency. The main aim of this review is to investigate the techno-functions of PBPs in food matrices, as well as their safety concerns. The distinctive characteristics of PBPs exhibit their techno-functions for improving food quality and functionality by contributing to several crucial food formulations and processing. The techno-functions of PBPs include solubility, hydrophobicity, bitterness, foaming, oil-binding, and water-holding capacities, which subsequently affect food matrices. The safety and quality of foodstuff containing PBPs depend on the proper source of plant proteins, the selection of processing approaches, and compliance with legal regulations for allergen labeling and safety evaluations. The safety concerns in allergenicity and toxicity were discussed. The conclusion is that food technologists must apply safe limits and consider potential allergenic components generated during the development of food products with PBPs. Therefore, functional food products containing PBPs can be a promising strategy to provide consumers with wholesome health benefits.

Anti-obesity and anti-diabetic bioactive peptides: A comprehensive review of their sources, properties, and techno-functional challenges.

The scourge of obesity arising from obesogens and poor dieting still ravages our planet as half of the global population may be overweight and obese by 2035. This metabolic disorder is intertwined with type 2 diabetes (T2D), both of which warrant alternative therapeutic options other than clinically approved drugs like orlistat with their tendency of abuse and side effects. In this review, we comprehensively describe the global obesity problem and its connection to T2D. Obesity, overconsumption of fats, the mechanism of fat digestion, obesogenic gut microbiota, inhibition of fat digestion, and natural anti-obesity compounds are discussed. Similar discussions are made for diabetes with regard to glucose regulation, the diabetic gut microbiota, and insulinotropic compounds. The sources and production of anti-obesity bioactive peptides (AOBPs) and anti-diabetic bioactive peptides (ADBPs) are also described while explaining their structure-function relationships, gastrointestinal behaviors, and action mechanisms. Finally, the techno-functional applications of AOBPs and ADBPs are highlighted.

Plant-based Meat Analogs: Perspectives on Their Meatiness, Nutritional Profile, Environmental Sustainability, Acceptance and Challenges.

PURPOSE OF REVIEW: Plant-based meat analogs (PBMAs) have been the subject of interest over the past few years due to consumers' demand for environmentally friendly, healthful, and non-animal-based foods. A better comprehension of the composition, structure, texture, nutrition, and sustainability of these PBMAs is necessary. RECENT FINDINGS: This review articulates the protein sources and composition of PBMAs and their "meatiness" with respect to texture, structure, and flavor enhancement. The components used in the analogs, such as unsaturated fats, fibers, vitamins, minerals, carbohydrates, and plant-based oils enriching their nutritional profile, are described. The study identifies the environmental and sustainability impact of PBMAs, as crucial to the survival and maintenance of biodiversity. More studies are warranted to scope and underscore the significance of the analogs and comprehend the texture or structure-function relationships. Further product development and testing thereof may ultimately result in quality meat analogs that respect meat taste, health and acceptance of consumers, environmental sustainability, animal welfare, and current challenges.

ProFun: A web server for functional enrichment analysis of parasitic protozoan genes.

BACKGROUND: The initial step to interpreting putative biological functions from comparative multi-omics studies usually starts from a differential expressed gene list followed by functional enrichment analysis (FEA). However, most FEA packages are designed exclusively for humans and model organisms. Although parasitic protozoan is the most important pathogen in the tropics, no FEA package is available for protozoan functional (ProFun) enrichment analysis. To speed up comparative multi-omics research on parasitic protozoans, we constructed ProFun, a web-based, user-friendly platform for the research community. METHODS: ProFun utilizes the Docker container, ShinyProxy, and R Shiny to construct a scalable web service with load-balancing infrastructure. We have integrated a series of visual analytic functions, in-house scripts, and custom-made annotation packages to create three analytical modules for 40 protozoan species: (1) Gene Overlaps; (2) Over-representation Analysis (ORA); (3) Gene Set Enrichment Analysis (GSEA). RESULTS: We have established ProFun, a web server for functional enrichment analysis of differentially expressed genes. FEA becomes as simple as pasting a list of gene IDs into the textbox of our website. Users can customize enrichment parameters and results with just one click. The intuitive web interface and publication-ready charts enable users to reveal meaningful biological events and pinpoint potential targets for further studies. CONCLUSION: ProFun is the first web application that enables gene functional enrichment analysis of parasitic protozoans. In addition to supporting FEA analysis, ProFun also allows the comparison of FEA results across complicated experimental designs. ProFun is freely available at http://dalek.cgu.edu.tw:8080/app/profun.

vHDvDB 2.0: Database and Group Comparison Server for Hepatitis Delta Virus.

The hepatitis delta virus (HDV) is a unique pathogen with significant global health implications, affecting individuals who are coinfected with the hepatitis B virus (HBV). HDV infection has profound clinical consequences, manifesting either as coinfection with HBV, resulting in acute hepatitis and potential liver failure, or as superinfection in chronic HBV cases, substantially increasing the risk of cirrhosis and hepatocellular carcinoma. Given the complex dynamics of HDV infection and the urgent need for advanced research tools, this article introduces vHDvDB 2.0, a comprehensive HDV full-length sequence database. This innovative platform integrates data preprocessing, secondary structure prediction, and epidemiological research tools. The primary goal of vHDvDB 2.0 is to consolidate HDV sequence data into a user-friendly repository, thereby facilitating access for researchers and enhancing the broader scientific understanding of HDV. The significance of this database lies in its potential to streamline HDV research by providing a centralized resource for analyzing viral sequences and exploring genotype-specific characteristics. It will also enable more in-depth research within the HDV sequence domains.

Arginine Methylation of DDX3 by PRMT1 Mediates Mitochondrial Homeostasis to Promote Breast Cancer Metastasis.

Dysregulated mitochondrial dynamics and metabolism play important roles in tumorigenesis. Metastasizing tumor cells predominantly utilize mitochondrial metabolism, and regulators of metabolic reprogramming may provide reliable biomarkers for diagnosing cancer metastasis. Here, we identified a type I arginine methyltransferase-DEAD-box polypeptide 3, X-linked (PRMT1-DDX3) axis that promotes breast cancer metastasis by coordinating mitochondrial biogenesis and mitophagy to ensure mitochondrial quality control. Mechanistically, PRMT1 induces arginine methylation of DDX3, which enhances its protein stability and prevents proteasomal degradation. DDX3 mediates mitochondrial homeostasis by translocating to mitochondria where it facilitates phosphatase and tensin homology-induced kinase 1 translation in response to mitochondrial stress. Inhibition of DDX3 suppresses mitochondrial biogenesis and mitophagy, resulting in diminished cancer stemness and metastatic properties. Overall, this study uncovers a mechanism by which the PRMT1-DDX3 axis regulates mitochondrial homeostasis to support breast cancer metastasis, suggesting strategies for targeting metabolic vulnerabilities to treat metastatic breast cancer. Significance: DDX3 is stabilized by PRMT1-mediated arginine methylation and coordinates mitophagy and mitochondrial biogenesis by upregulating PINK1 to facilitate breast cancer progression.