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Study on the effect and mechanisms of piperine against cervical cancer based on network pharmacology and experimental validation

Piperine has immunomodulatory and anti-inflammatory properties, and its potential in treating cervical cancer needs further exploration. Using data from The Cancer Genome Atlas (TCGA), we identified immune-related differentially expressed genes (IRDEGs) in cervical cancer. Predicted targets of piperine were compared with cervical cancer-associated genes from various databases. Protein-protein interaction (PPI) network analysis, enrichment of GO and KEGG pathways, and molecular docking were performed. Kaplan-Meier survival analysis was done to assess prognostic significance. In vitro and in vivo experiments were conducted to confirm findings. We obtained 403 IRDEGs, 125 piperine targets, and 7037 cervical cancer genes. PPI network analysis revealed potential targets and pathways regulated by piperine. Molecular docking showed good binding activity of piperine with specific targets. In vitro, piperine inhibited cervical cancer cell proliferation, migration, and invasion, and promoted apoptosis. In vivo, piperine suppressed tumor growth and downregulated expression of IL-1β and NLRP3 in tumor cells. Piperine also downregulated expression of IL-17A, IL-21, IL-22, and RORγt, and decreased the number of Th17 cells in tumor tissues. Piperine may inhibit cervical cancer progression through modulation of Th17 cell activation mediated by the NLRP3/IL-1β axis. Further studies are warranted to explore the potential of piperine as an immunomodulatory agent in cervical cancer treatment.

Effects of unstable β-PheRS on food avoidance, growth, and development are suppressed by the appetite hormone CCHa2

Amino acyl-tRNA synthetases perform diverse non-canonical functions aside from their essential role in charging tRNAs with their cognate amino acid. The phenylalanyl-tRNA synthetase (PheRS/FARS) is an α2β2 tetramer that is needed for charging the tRNAPhe for its translation activity. Fragments of the α-subunit have been shown to display an additional, translation-independent, function that activates growth and proliferation and counteracts Notch signalling. Here we show in Drosophila that overexpressing the β-subunit in the context of the complete PheRS leads to larval roaming, food avoidance, slow growth, and a developmental delay that can last several days and even prevents pupation. These behavioural and developmental phenotypes are induced by PheRS expression in CCHa2+ and Pros+ cells. Simultaneous expression of β-PheRS, α-PheRS, and the appetite-inducing CCHa2 peptide rescued these phenotypes, linking this β-PheRS activity to the appetite-controlling pathway. The fragmentation dynamic of the excessive β-PheRS points to β-PheRS fragments as possible candidate inducers of these phenotypes. Because fragmentation of human FARS has also been observed in human cells and mutations in human β-PheRS (FARSB) can lead to problems in gaining weight, Drosophila β-PheRS can also serve as a model for the human phenotype and possibly also for obesity.

Enhanced growth of wheat in contaminated fields via synthetic microbiome as revealed by genome-scale metabolic modeling

The relationship between plants and soil microbial communities is complex and subtle, with microbes playing a crucial role in plant growth. Autochthonous bioaugmentation and nutrient biostimulation are promising bioremediation methods for herbicides in contaminated agricultural soils, but how microbes interact to promote biodegradation and plant growth on barren fields, especially in response to the treatment of the herbicide bromoxynil after wheat seedlings, remains poorly understood. In this study, we explored the microbial community reassembly process from the three-leaf stage to the tillering stage of wheat and put forward the idea of using the overlapping results of three methods (network Zi-Pi analysis, LEfSe analysis, and Random Forest analysis) as keystones for the simplification and optimization of key microbial species in the soil. Then we used genome-scale metabolic models (GSMMs) to design a targeted synthetic microbiome for promoting wheat seedling growing. The results showed that carbon source was more helpful in enriching soil microbial diversity and promoting the role of functional microbial communities, which facilitated the degradation of bromoxynil. Designed a multifunctional synthetic consortium consisting of seven non-degraders which unexpectedly assisted in the degradation of indigenous bacteria, which increased the degradation rate of bromoxynil by 2.05 times, and when adding nutritional supplementation, it increased the degradation rate by 3.65 times. In summary, this study provides important insights for rational fertilization and precise microbial consortium management to improve plant seedling growth in contaminated fields.

β-Hydroxybutyrate dehydrogenase functionalized two-dimensional photonic crystals for quantitative and visual sensing of ketone bodies

β-Hydroxybutyrate (BHB) is a substantial physiological ketone body. Its elevated concentration causes ketoacidosis, which is a disorder with a high mortality rate. Therefore, there is an urgent need to develop a simple method for the in-situ monitoring of BHB in urine. In this study, a photonic crystal hydrogel (PCH) sensing material for the detection of urinary ketones was prepared by embedding a two-dimensional polystyrene photonic crystal array (PCA) in a hydrogel functionalized with β-hydroxybutyrate dehydrogenase (BHBDH). BHBDH catalyzes the interconversion between β-hydroxybutyrate and acetoacetic acid and relies on the cofactor nicotinamide adenine dinucleotide (NAD+) to participate in the reaction process. The catalytic cycle of converting β-hydroxybutyrate to acetoacetate generates H+, which reduces the electrostatic repulsion between the carboxyl groups in the hydrogel network, ultimately leading to the shrinkage of the hydrogel volume. The hydrogel volume change was detected by measuring the diameter of the Debye diffraction ring, thus reflecting the concentration of BHB. When the concentration of BHB was increased from 0 to 10 mM, the reflection spectrum of PCH shifted for 117 nm within 60 min, consequently, the structural color of PCH changed from red to green and finally to blue. The material was used for quantitative detection of BHB with a detection limit of 48.94 μM. Then it was used for detection in artificial urine samples. While, this smart and reusable sensing material could provide a more convenient and efficient strategy for the ketone body detection in clinical diagnosis and point-of-care monitoring.

Mechanistic insights into Yifei Sanjie pill's regulation of EMT to enhance gefitinib treatment effect in NSCLC by in silico analysis and experimental validation

ETHNOPHARMACOLOGICAL RELEVANCE

The Yi-Fei San-Jie pill (YFSJ) is a well-known Chinese medicine that has been used to treat non-small cell lung cancer in China for decades.

AIM OF THE STUDY

Previous studies have shown that YFSJ combined with gefitinib can effectively inhibit the proliferation of gefitinib-resistant non-small cell lung cancer (NSCLC) cell lines by promoting apoptosis and autophagy, but the molecular biological mechanisms involved and whether YFSJ combined with gefitinib can have synergistic effects still need to be further explored. Thus, the present study aimed to establish an in silico and experimental framework to decipher the underlying mechanism by which YFSJ augments the efficacy of gefitinib in treating NSCLC.

MATERIALS AND METHODS

Integrated approaches, including microarray analysis, network pharmacology, RNA sequencing, bioinformatics algorithm analysis and in vivo and in vitro experiments, were applied to elucidate the underlying mechanism.

RESULTS

Analysis of microarray datasets indicated that gefitinib may play a role in the regulation of the epithelial-mesenchymal transition (EMT) of PC9 cells. EMT-related Gene Ontology (GO) terms and the MAPK pathway were found to be enriched in the differentially expressed genes (DEGs), and a decreasing trend was observed in the EMT score. Network pharmacology analysis revealed that the potential NSCLC-related targets of YFSJ also showed enrichment in EMT-related GO terms and the MAPK pathway. Experimental findings demonstrated that combined YFSJ-treated serum and gefitinib treatment significantly inhibited PC9 cell migration and invasion. In addition, the combined treatment dramatically reduced the tumour volume in an animal model. The effectiveness of the combination treatment surpassed that of gefitinib alone in both cell and animal experiments. RNA sequencing analysis revealed significant enrichment of DEGs in EMT-related GO terms for the gefitinib treatment group, YFSJ treatment group, and combination treatment group compared to the control group. Notably, the negative regulation of EMT showed significant enrichment in the DEGs of the combination treatment group. The MAPK pathway was significantly enriched among the different groups. Moreover, combined treatment with YFSJ and gefitinib may exert synergistic anti-NSCLC effects by inhibiting the p-p38 MAPK/GSK3β signalling axis, subsequently suppressing downstream EMT processes.

CONCLUSION

Combined treatment with YFSJ and gefitinib could enhance the sensitivity of NSCLC cells to gefitinib by suppressing EMT through the EGFR/p-p38 MAPK/GSK3β signalling axis. YFSJ may serve as an important adjunctive medication for NSCLC patients receiving gefitinib treatment in clinical practice.

The genome of Lupinus mutabilis: Evolution and genetics of an emerging bio-based crop

Lupinus mutabilis is an under-domesticated legume species from the Andean region of South America. It belongs to the New World lupins clade, which groups several lupin species displaying large genetic variation and adaptability to highly different environments. L. mutabilis is attracting interest as a potential multipurpose crop to diversify the European supply of plant proteins, increase agricultural biodiversity, and fulfill bio-based applications. This study reports the first high-quality L. mutabilis genome assembly, which is also the first sequenced assembly of a New World lupin species. Through comparative genomics and phylogenetics, the evolution of L. mutabilis within legumes and lupins is described, highlighting both genomic similarities and patterns specific to L. mutabilis, potentially linked to environmental adaptations. Furthermore, the assembly was used to study the genetics underlying important traits for the establishment of L. mutabilis as a novel crop, including protein and quinolizidine alkaloids contents in seeds, genomic patterns of classic resistance genes, and genomic properties of L. mutabilis mycorrhiza-related genes. These analyses pointed out copy number variation, differential genomic gene contexts, and gene family expansion through tandem duplications as likely important drivers of the genomic diversity observed for these traits between L. mutabilis and other lupins and legumes. Overall, the L. mutabilis genome assembly will be a valuable resource to conduct genetic research and enable genomic-based breeding approaches to turn L. mutabilis into a multipurpose legume crop.

Elucidating the material basis and potential mechanisms of Daqinglong Decoction acting on influenza by UPLC-Q-TOF/MS and network pharmacology

Daqinglong Decoction (DQLD), a traditional Chinese medicine (TCM) prescription firstly recorded in Shang han lun (the treatise on febrile diseases), has been used hundreds of years for the clinical treatment of influenza. However, the chemical composition and therapeutic mechanism of this prescription are unclear. UPLC-Q-TOF/MS was employed to analyze the chemical compounds in both methanol and boiling water extracts of DQLD. The compounds were then screened, characterized, and filtered using the TCMSP, TCMIP, TCM-ID and SymMap database, with a focus on their oral bioavailability and drug-likeness values. The resulting data were analyzed and optimized using the R language platform, Autodock and Gromacs software to identify biological processes and pathways. A total of 121 compounds were identified, of which 5 showed good binding ability to influenza virus targets (1L1B, IL10, CASP3, STAT3, TNF, and others). The active ingredient-target-influenza virus pathway was constructed using a network drug target analysis model prediction of DQLD, which was mainly enriched in Human cytomegalovirus infection, PI3K-Akt, HIF-1, and other signaling pathways through 1L1B, IL10 and other targets. Those pathways highly correlated to the body's inflammatory response, improve immunity, and exert anti-influenza virus effects. In summary, this study demonstrated that DQLD's active ingredients can effectively bind to influenza virus targets and exert anti-influenza virus effects by reducing inflammation and improving immunity through Human cytomegalovirus infection, PI3K-Akt and HIF-1 signaling pathways. These findings offer important insights into the potential mechanisms of action of DQLD and its potential use as a TCM against influenza and other viral infections.Communicated by Ramaswamy H. Sarma.

An in-silico investigation and network pharmacology based approach to explore the anti-breast-cancer potential of Tecteria coadunata (Wall.) C. Chr

Uncontrolled cell proliferation is a common definition of cancer. After lung carcinoma, breast neoplasm is the second-most prevalent kind of cancer. The majority of breast cancer cells and healthy breast cells both have receptors for circulating oestrogen and progesterone. In order to promote the development and division of cancer cells, oestrogen and progesterone bind to the receptors and may collaborate with growth factors (such as oncogenes and mutant tumour suppressor genes). As per the literature, Tecteria coadunata (Wall.) C. Chr. has anticancer, antioxidant and anti-inflammatory potential. After the hydroalcoholic extraction of this rhizome, total of 200 phytochemicals were retrieved from HR-LCMS analysis. In this current study, Network pharmacology was carried out to explore the rationale of Tecteria coadunata (Wall.) C. Chr. by using different database using Cytoscape software. The network depicted the interaction of Bioactives with their targets and their association with several disease, especially breast cancer. Tecteria coadunata (Wall.) C. Chr. has offered new relationship with variety of genes and its applications in different types of breast cancers. Further Gene Ontology was carried out and it showed key targets were TP53, BRCA2, PGR and CHEK 2. Further Signalling pathways were also enriched. Flex-X software was used for molecular docking studies, and it verified that Dopaxanthin, Dantrolene and Orotidin shows the highest binding affinities with key targets. Additionally, Pharmacokinetic analysis revealed that all top three lead compounds which follows the Lipinski Rule (Rule of three) without interrupting the conditions of bioavailability with minimal toxicity.Communicated by Ramaswamy H. Sarma.

A stepwise guide for pangenome development in crop plants: an alfalfa (Medicago sativa) case study

BACKGROUND

The concept of pangenomics and the importance of structural variants is gaining recognition within the plant genomics community. Due to advancements in sequencing and computational technology, it has become feasible to sequence the entire genome of numerous individuals of a single species at a reasonable cost. Pangenomes have been constructed for many major diploid crops, including rice, maize, soybean, sorghum, pearl millet, peas, sunflower, grapes, and mustards. However, pangenomes for polyploid species are relatively scarce and are available in only few crops including wheat, cotton, rapeseed, and potatoes.

MAIN BODY

In this review, we explore the various methods used in crop pangenome development, discussing the challenges and implications of these techniques based on insights from published pangenome studies. We offer a systematic guide and discuss the tools available for constructing a pangenome and conducting downstream analyses. Alfalfa, a highly heterozygous, cross pollinated and autotetraploid forage crop species, is used as an example to discuss the concerns and challenges offered by polyploid crop species. We conducted a comparative analysis using linear and graph-based methods by constructing an alfalfa graph pangenome using three publicly available genome assemblies. To illustrate the intricacies captured by pangenome graphs for a complex crop genome, we used five different gene sequences and aligned them against the three graph-based pangenomes. The comparison of the three graph pangenome methods reveals notable variations in the genomic variation captured by each pipeline.

CONCLUSION

Pangenome resources are proving invaluable by offering insights into core and dispensable genes, novel gene discovery, and genome-wide patterns of variation. Developing user-friendly online portals for linear pangenome visualization has made these resources accessible to the broader scientific and breeding community. However, challenges remain with graph-based pangenomes including compatibility with other tools, extraction of sequence for regions of interest, and visualization of genetic variation captured in pangenome graphs. These issues necessitate further refinement of tools and pipelines to effectively address the complexities of polyploid, highly heterozygous, and cross-pollinated species.

PWAS Hub for exploring gene-based associations of common complex diseases

PWAS (proteome-wide association study) is an innovative genetic association approach that complements widely used methods like GWAS (genome-wide association study). The PWAS approach involves consecutive phases. Initially, machine learning modeling and probabilistic considerations quantify the impact of genetic variants on protein-coding genes' biochemical functions. Secondly, for each individual, aggregating the variants per gene determines a gene-damaging score. Finally, standard statistical tests are activated in the case-control setting to yield statistically significant genes per phenotype. The PWAS Hub offers a user-friendly interface for an in-depth exploration of gene-disease associations from the UK Biobank (UKB). Results from PWAS cover 99 common diseases and conditions, each with over 10,000 diagnosed individuals per phenotype. Users can explore genes associated with these diseases, with separate analyses conducted for males and females. For each phenotype, the analyses account for sex-based genetic effects, inheritance modes (dominant and recessive), and the pleiotropic nature of associated genes. The PWAS Hub showcases its usefulness for asthma by navigating through proteomic-genetic analyses. Inspecting PWAS asthma-listed genes (a total of 27) provide insights into the underlying cellular and molecular mechanisms. Comparison of PWAS-statistically significant genes for common diseases to the Open Targets benchmark shows partial but significant overlap in gene associations for most phenotypes. Graphical tools facilitate comparing genetic effects between PWAS and coding GWAS results, aiding in understanding the sex-specific genetic impact on common diseases. This adaptable platform is attractive to clinicians, researchers, and individuals interested in delving into gene-disease associations and sex-specific genetic effects.

Metabolic modelling as a powerful tool to identify critical components of Pneumocystis growth medium

Establishing suitable in vitro culture conditions for microorganisms is crucial for dissecting their biology and empowering potential applications. However, a significant number of bacterial and fungal species, including Pneumocystis jirovecii, remain unculturable, hampering research efforts. P. jirovecii is a deadly pathogen of humans that causes life-threatening pneumonia in immunocompromised individuals and transplant patients. Despite the major impact of Pneumocystis on human health, limited progress has been made in dissecting the pathobiology of this fungus. This is largely due to the fact that its experimental dissection has been constrained by the inability to culture the organism in vitro. We present a comprehensive in silico genome-scale metabolic model of Pneumocystis growth and metabolism, to identify metabolic requirements and imbalances that hinder growth in vitro. We utilise recently published genome data and available information in the literature as well as bioinformatics and software tools to develop and validate the model. In addition, we employ relaxed Flux Balance Analysis and Reinforcement Learning approaches to make predictions regarding metabolic fluxes and to identify critical components of the Pneumocystis growth medium. Our findings offer insights into the biology of Pneumocystis and provide a novel strategy to overcome the longstanding challenge of culturing this pathogen in vitro.

Construction of Protein Sequence Databases for Metaproteomics: A Review of the Current Tools and Databases

In metaproteomics studies, constructing a reference protein sequence database that is both comprehensive and not overly large is critical for the peptide identification step. Therefore, the availability of well-curated reference databases and tools for custom database construction is essential to enhance the performance of metaproteomics analyses. In this review, we first provide an overview of metaproteomics by presenting a concise historical background, outlining a typical experimental and bioinformatics workflow, emphasizing the crucial step of constructing a protein sequence database for metaproteomics. We then delve into the current tools available for building such databases, highlighting their individual approaches, utility, and advantages and limitations. Next, we examine existing protein sequence databases, detailing their scope and relevance in metaproteomics research. Then, we provide practical recommendations for constructing protein sequence databases for metaproteomics, along with an overview of the current challenges in this area. We conclude with a discussion of anticipated advancements, emerging trends, and future directions in the construction of protein sequence databases for metaproteomics.

Pseudogymnoascus destructans invasion stage impacts the skin microbial functions of highly vulnerable Myotis lucifugus

The role of the skin microbiome in resistance and susceptibility of wildlife to fungal pathogens has been examined from a taxonomic perspective but skin microbial function, in the context of fungal infection, has yet to be studied. Our objective was to understand effects of a bat fungal pathogen site infection status and course of invasion on skin microbial function. We sampled seven hibernating colonies of Myotis lucifugus covering three-time points over the course of Pseudogymnoascus destructans (Pd) invasion and white nose syndrome (pre-invasion, epidemic, and established). Our results support three new hypotheses about Pd and skin functional microbiome: (1) there is an important effect of Pd invasion stage, especially at the epidemic stage; (2) disruption by the fungus at the epidemic stage could decrease anti-fungal functions with potential negative effects on the microbiome and bat health; (3) the collection site might have a larger influence on microbiomes at the pre-invasion stage rather than at epidemic and established stages. Future studies with larger sample sizes and using meta-omics approaches will help confirm these hypotheses, and determine the influence of the microbiome on wildlife survival to fungal disease.

Rational and Semirational Approaches for Engineering Salicylate Production in Escherichia coli

Salicylate plays a pivotal role as a pharmaceutical intermediate in drugs, such as aspirin and lamivudine. The low catalytic efficiency of key enzymes and the inherent toxicity of salicylates to cells pose significant challenges to large-scale microbial production. In this study, we introduced the salicylate synthase Irp9 into an l-phenylalanine-producing Escherichia coli, constructing the shortest salicylate biosynthetic pathway. Subsequent protein engineering increased the catalytic efficiency of Irp9 by 33.5%. Furthermore, by integrating adaptive evolution with transcriptome analysis, we elucidated the crucial mechanism of efflux proteins in salicylate tolerance. The elucidation of this mechanism guided us in the targeted modification of these transport proteins, achieving a reported maximum level of 3.72 g/L of salicylate in a shake flask. This study highlights the importance of efflux proteins for enhancing the productivity of microbial cell factories in salicylate production, which also holds potential for application in the green synthesis of other phenolic acids.

Combining Quantitative Proteomics and Interactomics for a Deeper Insight into Molecular Differences between Human Cell Lines

In modern biomedical research, cultivable cell lines are an indispensable tool, and the selection of cell lines that exhibit specific functional profiles is often critical to success. Cellular functional pathways have evolved through the selection of protein intra- and intermolecular interactions collectively referred to as the interactome. In the present work, quantitative in vivo protein cross-linking and mass spectrometry were used to probe large-scale protein interactome differences among three commonly employed human cell lines, namely, HEK293, MCF-7, and HeLa cells. These data illustrated highly reproducible quantitative interactome levels with R2 values larger than 0.8 for all biological replicates. Proteome abundance levels were also measured using data-independent acquisition quantitative proteomics methods. Combining quantitative interactome and proteome information allowed the visualization of cell type-specific interactome changes mediated by proteome level adaptations and independently regulated interactome changes to gain deeper insight into possible drivers of these changes. Among the largest detected alterations in protein interactions and conformations are changes in cytoskeletal proteins, RNA-binding proteins, chromatin remodeling complexes, mitochondrial proteins, and others. Overall, these data demonstrate the utility and reproducibility of quantitative cross-linking to study system-level interactome variations. Moreover, these results illustrate how combined quantitative interactomics and proteomics can provide unique insight into cellular functional landscapes.

Optical scattering methods for the label-free analysis of single biomolecules

Single-molecule techniques to analyze proteins and other biomolecules involving labels and tethers have allowed for new understanding of the underlying biophysics; however, the impact of perturbation from the labels and tethers has recently been shown to be significant in several cases. New approaches are emerging to measure single proteins through light scattering without the need for labels and ideally without tethers. Here, the approaches of interference scattering, plasmonic scattering, microcavity sensing, nanoaperture optical tweezing, and variants are described and compared. The application of these approaches to sizing, oligomerization, interactions, conformational dynamics, diffusion, and vibrational mode analysis is described. With early commercial successes, these approaches are poised to have an impact in the field of single-molecule biophysics.

A structurally informed human protein-protein interactome reveals proteome-wide perturbations caused by disease mutations

To assist the translation of genetic findings to disease pathobiology and therapeutics discovery, we present an ensemble deep learning framework, termed PIONEER (Protein-protein InteractiOn iNtErfacE pRediction), that predicts protein-binding partner-specific interfaces for all known protein interactions in humans and seven other common model organisms to generate comprehensive structurally informed protein interactomes. We demonstrate that PIONEER outperforms existing state-of-the-art methods and experimentally validate its predictions. We show that disease-associated mutations are enriched in PIONEER-predicted protein-protein interfaces and explore their impact on disease prognosis and drug responses. We identify 586 significant protein-protein interactions (PPIs) enriched with PIONEER-predicted interface somatic mutations (termed oncoPPIs) from analysis of approximately 11,000 whole exomes across 33 cancer types and show significant associations of oncoPPIs with patient survival and drug responses. PIONEER, implemented as both a web server platform and a software package, identifies functional consequences of disease-associated alleles and offers a deep learning tool for precision medicine at multiscale interactome network levels.

Mechanism of Yi-Qi-Bu-Shen Recipe for the Treatment of Diabetic Nephropathy Complicated with Cognitive Dysfunction Based on Network Pharmacology and Experimental Validation

Context

Diabetic nephropathy (DN) and cognitive dysfunction (CD) are common complications of diabetes. Yi-Qi-Bu-Shen Recipe (YQBS) can effectively reduce blood glucose, improve insulin resistance, and delay the progression of diabetic complications. The underlying mechanisms of its effects need to be further studied.

Objective

This study elucidates the mechanism of YQBS in DN with CD through network pharmacology and experimental validation.

Materials and Methods

Protein-protein interaction, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed. Male Sprague-Dawley (SD) rats were divided into 6 groups: model, YQBS (2, 4, 8 g/kg), positive control (metformin, 200 mg/kg), and control; the DN model was established by high sugar and high fat diet combined with intraperitoneal streptozotocin injection. After the DN model was established, the rats were gavaged for 10 weeks. Serum, kidneys, and hippocampus tissues were collected to measure the expression levels of TLR4, NF-κB, TNF-α, and IL-6.

Results

The network pharmacology analysis showed that quercetin and kaempferol were the main active components of YQBS. TNF and IL-6 were the key targets, and TLR4/NF-κB pathway was crucial to YQBS in treating DN complicated with CD. Experimental validation showed that the intervention of YQBS can reduce TNF-α and IL-6 in serum, and also significantly decreases the protein expression of TLR4 and NF-κB.

Conclusion

YQBS exerts anti-inflammatory effects on DN with CD through TLR4/NF-κB pathway. This study provides a biological basis for the scientific usage of YQBS in inflammation diseases and supplies experimental evidence for future traditional Chinese medicine development.

LncPepAtlas: a comprehensive resource for exploring the translational landscape of long non-coding RNAs

Long non-coding RNAs were commonly viewed as non-coding elements. However, they are increasingly recognized for their ability to be translated into proteins, thereby playing a significant role in various cellular processes and diseases. With developments in biotechnology and computational algorithms, a range of novel approaches are being applied to investigate the translation of long non-coding RNA (lncRNAs). Herein, we developed the LncPepAtlas database (http://www.cnitbiotool.net/LncPepAtlas/), which aims to compile multiple evidences for the translation of lncRNAs and annotations for the upstream regulation of lncRNAs across various species. LncPepAtlas integrated compelling evidence from nine distinct sources for the translation of lncRNAs. These include a dataset comprising 2631 publicly available Ribo-seq samples from nine species, which has been collected and analysed. LncPepAtlas offers extensive annotation for lncRNA upstream regulation and expression profiles across various cancers, tissues or cell lines at transcriptional and translational levels. Importantly, it enables novel antigen predictions for lncRNA-encoded peptides. By identifying numerous peptide candidates that could potentially bind to major histocompatibility complex class I and II molecules, this work may provide new insights into cancer immunotherapy. The function of peptides were inferred by aligning them with experimentally detected proteins. LncPepAtlas aims to become a convenient resource for exploring translatable lncRNAs.

Protein A-like Peptide Design Based on Diffusion and ESM2 Models

Proteins are the foundation of life, and designing functional proteins remains a key challenge in biotechnology. Before the development of AlphaFold2, the focus of design was primarily on structure-centric approaches such as using the well-known open-source software Rosetta3. Following the development of AlphaFold2, deep-learning techniques for protein design gained prominence. This study proposes a new method to generate functional proteins using the diffusion model and ESM2 protein language model. Diffusion models, which are widely used in image and natural language generation, are used here for protein design, facilitating the controlled generation of new sequences. The ESM2 model, trained on the basis of large-scale protein sequence data, provides a deep understanding of the context of the sequence, thus improving the model's ability to generate biologically relevant proteins. In this study, we used the Protein A-like peptide as a model study object, combined the diffusion model and the ESM2 model to generate new peptide sequences from minimal input data, and verified their biological activities through experiments such as the BLI affinity test. In conclusion, we developed a new method for protein design that provides a novel strategy to meet the challenges of generic protein generation.

Network Pharmacology Combined with Experimental Validation to Investigate the Mechanism of the Anti-Hyperuricemia Action of Portulaca oleracea Extract

BACKGROUND/OBJECTIVES

Hyperuricemia (HUA) is a common metabolic disease caused by purine metabolic disorders in the body. Portulaca oleracea L. (PO) is an edible wild vegetable.

METHODS

In this study, the regulatory effect of PO on HUA and its potential mechanism were initially elucidated through network pharmacology and experimental validation.

RESULTS

The results showed that PO from Sichuan province was superior to the plant collected from other habitats in inhibiting xanthine oxidase (XOD) activity. Berberine and stachydrine were isolated and identified from PO for the first time by UPLC-Q-Exactive Orbitrap MS. The potential molecular targets and related signaling pathways were predicted by network pharmacology and molecular docking techniques. Molecular docking showed that berberine had strong docking activity with XOD, and the results of in vitro experiments verified this prediction. Through experimental analysis of HUA mice, we found that PO can reduce the production of uric acid (UA) in the organism by inhibiting XOD activity. On the other hand, PO can reduce the body 's reabsorption of urate and aid in its excretion out of the body by inhibiting the urate transporter proteins (GLUT9, URAT1) and promoting the high expression of urate excretory protein (ABCG2). The results of H/E staining showed that, compared with the positive drug (allopurinol and benzbromarone) group, there was no obvious renal injury in the middle- and high-dose groups of PO extract.

CONCLUSIONS

In summary, our findings reveal the potential of wild plant PO as a functional food for the treatment of hyperuricemia.

Breaking resistance: in silico subtractive and comparative genomics approaches for drug targeting in Bacteroides fragilis

The purpose of this study was to identify potential novel drug targets for Bacteroides fragilis infections using bioinformatics techniques, such as subtractive and comparative genomics. Bacteroides fragilis is a frequently isolated anaerobic pathogen, particularly in the human digestive tract, where its pathogenesis and persistence are influenced by various virulence factors. By understanding these factors, the study aims to explore alternative therapeutic strategies and provide insights for the development of treatments against B. fragilis infections, particularly as alternatives to antibiotic therapy. A comparative subtractive genomic analysis was performed against the B. fragilis (strain CL07T12C05) to identify unique drug targets. The analysis includes the identification of non-paralogous, non-homologous, essential, and drug target like proteins. Moreover, a comprehensive structural analysis of the protein was conducted utilizing structure modeling and validation techniques, along with network topology analysis. Furthermore, a library comprising approximately 9000 FDA-approved compounds accessible in the DrugBank database was employed to conduct virtual screenings for compounds effective against the designated drug target. The top shortlisted compounds were further studied by employing MD simulations using GROMACS. This approach was chosen due to the established safety, efficacy, pharmacokinetics, and toxicity profiles of these compounds. As a result, B. fragilis (strain CL07T12C05) was found to possess 4595 proteins. Among these, 3518 were identified as non-homologous, 1508 deemed essential for bacterial viability, 348 exhibited drug-like properties, 203 were implicated in virulence, and 135 displayed antibiotic resistance. Following an extensive literature review, the protein Sialic acid O-acetyltransferase was chosen through a hierarchical shortlisting process as a potential therapeutic target. The ongoing research facilitated the repurposing of drug compounds: DB12411, DB02112, DB03591, and DB00192, as cost-effective medications against B. fragilis related infections. MD simulations analysis showed that DB12411 may be a potential drug candidate against Sialic acid O-acetyltransferase from B. fragilis. Through subtractive and comparative genomic analysis, Sialic acid O-acetyltransferase was identified as a promising drug target against Bacteroides fragilis. The findings indicate that compounds targeting this protein could potentially be effective in treating B. fragilis infections. However, further experimental validation is required to conclusively confirm their efficacy.

The genome sequence of the common awl robberfly, Neoitamus cyanurus (Loew, 1849)

We present a genome assembly from an individual female Neoitamus cyanurus (the common awl robberfly; Arthropoda; Insecta; Diptera; Asilidae). The genome sequence has a total length of 365.5 megabases. Most of the assembly is scaffolded into 10 chromosomal pseudomolecules. The mitochondrial genome has also been assembled and is 16.63 kilobases in length. Gene annotation of this assembly on Ensembl identified 12,046 protein-coding genes.

The genome sequence of the willow leaf beetle, Lochmaea capreae Linnaeus, 1758

We present a genome assembly from an individual female Lochmaea capreae (the willow leaf beetle; Arthropoda; Insecta; Coleoptera; Chrysomelidae). The genome sequence is 534.7 megabases in span. Most of the assembly is scaffolded into 17 chromosomal pseudomolecules. The mitochondrial genome has also been assembled and is 18.85 kilobases in length. Gene annotation of this assembly on Ensembl identified 12,254 protein coding genes.

Reconstruction of the human amylase locus reveals ancient duplications seeding modern-day variation

Previous studies suggested that the copy number of the human salivary amylase gene, AMY1, correlates with starch-rich diets. However, evolutionary analyses are hampered by the absence of accurate, sequence-resolved haplotype variation maps. We identified 30 structurally distinct haplotypes at nucleotide resolution among 98 present-day humans, revealing that the coding sequences of AMY1 copies are evolving under negative selection. Genomic analyses of these haplotypes in archaic hominins and ancient human genomes suggest that a common three-copy haplotype, dating as far back as 800 KYA, has seeded rapidly evolving rearrangements through recurrent non-allelic homologous recombination. Additionally, haplotypes with more than three AMY1 copies have significantly increased in frequency among European farmers over the past 4,000 years, potentially as an adaptive response to increased starch digestion.

Therapeutic Effects of Zanthoxyli Pericarpium on Intestinal Inflammation and Network Pharmacological Mechanism Analysis in a Dextran Sodium Sulfate-Induced Colitis Mouse Model

(1) Background: IBD (inflammatory bowel disease) is characterized by chronic intestinal inflammation leading to persistent symptoms and a lack of effective treatments. ZP (Zanthoxyli Pericarpium) has been used in traditional Chinese medicine for its anti-inflammatory and antioxidant properties for the management of intestinal disorders. (2) Methods: This study aimed to investigate the components of ZP, their specific targets, and associated diseases using the TCMSP (Traditional Chinese Medicine Systems Pharmacology) analysis platform, TCMBank database, and ETCM2.0 (Encyclopedia of Traditional Chinese Medicine 2.0) database. Additionally, we explored the protective effects of ZP on the colon and the underlying molecular mechanisms in the treatment of IBD. (3) Results: We identified 59 compounds in ZP that target 38 genes related to IBD, including PTGS2, PPARG, and GPBAR1. In a mice model of DSS (dextran sodium sulfate)-induced colitis, ZP significantly reduced colonic epithelial damage and oxidative stress markers, such as iNOS and nitrotyrosine, demonstrating its antioxidant properties. (4) Conclusions: These findings suggest that ZP has protective effects against DSS-induced colonic damage owing to its anti-inflammatory and antioxidant properties, making it a potential candidate for IBD treatment. However, further research and clinical trials are required to confirm its therapeutic potential and safety in humans.

Quantifying constraint in the human mitochondrial genome

Mitochondrial DNA (mtDNA) has an important yet often overlooked role in health and disease. Constraint models quantify the removal of deleterious variation from the population by selection and represent powerful tools for identifying genetic variation that underlies human phenotypes1-4. However, nuclear constraint models are not applicable to mtDNA, owing to its distinct features. Here we describe the development of a mitochondrial genome constraint model and its application to the Genome Aggregation Database (gnomAD), a large-scale population dataset that reports mtDNA variation across 56,434 human participants5. Specifically, we analyse constraint by comparing the observed variation in gnomAD to that expected under neutrality, which was calculated using a mtDNA mutational model and observed maximum heteroplasmy-level data. Our results highlight strong depletion of expected variation, which suggests that many deleterious mtDNA variants remain undetected. To aid their discovery, we compute constraint metrics for every mitochondrial protein, tRNA and rRNA gene, which revealed a range of intolerance to variation. We further characterize the most constrained regions within genes through regional constraint and identify the most constrained sites within the entire mitochondrial genome through local constraint, which showed enrichment of pathogenic variation. Constraint also clustered in three-dimensional structures, which provided insight into functionally important domains and their disease relevance. Notably, we identify constraint at often overlooked sites, including in rRNA and noncoding regions. Last, we demonstrate that these metrics can improve the discovery of deleterious variation that underlies rare and common phenotypes.

Immunopeptidomics of Salmonella enterica Serovar Typhimurium-Infected Pig Macrophages Genotyped for Class II Molecules

Salmonellosis is a zoonotic infection that has a major impact on human health; consuming contaminated pork products is the main source of such infection. Vaccination responses to classic vaccines have been unsatisfactory; that is why peptide subunit-based vaccines represent an excellent alternative. Immunopeptidomics was used in this study as a novel approach for identifying antigens coupled to major histocompatibility complex class II molecules. Three homozygous individuals having three different haplotypes (Lr-0.23, Lr-0.12, and Lr-0.21) were thus selected as donors; peripheral blood macrophages were then obtained and stimulated with Salmonella typhimurium (MOI 1:40). Although similarities were observed regarding peptide length distribution, elution patterns varied between individuals; in total, 1990 unique peptides were identified as follows: 372 for Pig 1 (Lr-0.23), 438 for Pig 2 (Lr.0.12) and 1180 for Pig 3 (Lr.0.21). Thirty-one S. typhimurium unique peptides were identified; most of the identified peptides belonged to outer membrane protein A and chaperonin GroEL. Notably, 87% of the identified bacterial peptides were predicted in silico to be elution ligands. These results encourage further in vivo studies to assess the immunogenicity of the identified peptides, as well as their usefulness as possible protective vaccine candidates.

The genome sequence of fat-hen, Chenopodium album L

We present a genome assembly from an individual Chenopodium album (fat-hen; Streptophyta; Magnoliopsida; Caryophyllales; Chenopodiaceae). The genome sequence has a total length of 1,593.80 megabases. Most of the assembly (99.61%) is scaffolded into 27 chromosomal pseudomolecules suggesting the individual is an allohexaploid (2 n = 6 x = 54). The mitochondrial and plastid genome assemblies have lengths of 312.95 kilobases and 152.06 kilobases, respectively. Gene annotation of this assembly on Ensembl identified 50,077 protein-coding genes.

The genome sequence of the Vapourer moth, Orgyia antiqua (Linnaeus, 1758)

We present a genome assembly from an individual male Orgyia antiqua specimen (the Vapourer moth; Arthropoda; Insecta; Lepidoptera; Erebidae). The genome sequence is 480.1 megabases in span. Most of the assembly is scaffolded into 14 chromosomal pseudomolecules, including the Z sex chromosome. The mitochondrial genome has also been assembled and is 15.4 kilobases in length. Gene annotation of this assembly on Ensembl identified 12,475 protein coding genes.

Comprehensive Assessment of BERT-Based Methods for Predicting Antimicrobial Peptides

In recent years, the prediction of antimicrobial peptides (AMPs) has gained prominence due to their high antibacterial activity and reduced susceptibility to drug resistance, making them potential antibiotic substitutes. To advance the field of AMP recognition, an increasing number of natural language processing methods are being applied. These methods exhibit diversity in terms of pretraining models, pretraining data sets, word vector embeddings, feature encoding methods, and downstream classification models. Here, we provide a comprehensive survey of current BERT-based methods for AMP prediction. An independent benchmark test data set is constructed to evaluate the predictive capabilities of the surveyed tools. Furthermore, we compared the predictive performance of these computational methods based on six different AMP public databases. LM_pred (BFD) outperformed all other surveyed tools due to abundant pretraining data set and the unique vector embedding approach. To avoid the impact of varying training data sets used by different methods on prediction performance, we performed the 5-fold cross-validation experiments using the same data set, involving retraining. Additionally, to explore the applicability and generalization ability of the models, we constructed a short peptide data set and an external data set to test the retrained models. Although these prediction methods based on BERT can achieve good prediction performance, there is still room for improvement in recognition accuracy. With the continuous enhancement of protein language model, we proposed an AMP prediction method based on the ESM-2 pretrained model called iAMP-bert. Experimental results demonstrate that iAMP-bert outperforms other approaches. iAMP-bert is freely accessible to the public at http://iamp.aielab.cc/.

The genome sequence of the Currant Clearwing moth, Synanthedon tipuliformis (Clerck, 1759)

We present a genome assembly from an individual male Synanthedon tipuliformis (the Currant Clearwing; Arthropoda; Insecta; Lepidoptera; Sesiidae). The genome sequence is 295.8 megabases in span. Most of the assembly (99.98%) is scaffolded into 31 chromosomal pseudomolecules, including the Z sex chromosome. The mitochondrial genome has also been assembled and is 27.05 kilobases in length. Gene annotation of this assembly on Ensembl identified 11,878 protein-coding genes.

gGN: Representing the Gene Ontology as low-rank Gaussian distributions

Computational representations of knowledge graphs are critical for several tasks in bioinformatics, including large-scale graph analysis and gene function characterization. In this study, we introduce gGN, an unsupervised neural network for learning node representations as Gaussian distributions. Unlike prior efforts, where the covariance matrices of these distributions are simplified to diagonal, we propose representing them with a low-rank approximation. This representation not only maintains manageable learning complexity, allowing for scaling to large graphs, but is also more effective for modeling the structural features of knowledge graphs, such as their hierarchical and directional relationships between nodes. To learn the low-rank Gaussian distributions, we introduce a semantic-based loss function that effectively preserves these structural features. Systematic experiments reveal that gGN preserves structural features more effectively than existing approaches and scales efficiently on large knowledge graphs. Furthermore, applying gGN to represent the Gene Ontology, a widely used knowledge graph in bioinformatics, outperformed multiple baseline methods in ubiquitous gene characterization tasks. Altogether, the proposed low-rank Gaussian distributions not only effectively represent knowledge graphs but also open new avenues for enhancing bioinformatics tasks. gGN is publicly available as an easily installable package at https://github.com/aedera/ggn.

The genome sequence of the common green Tenthredo, Tenthredo mesomela Linnaeus, 1758

We present a genome assembly from an individual female Tenthredo mesomela (the common green Tenthredo; Arthropoda; Insecta; Hymenoptera; Tenthredinidae). The genome sequence is 392.8 megabases in span. Most of the assembly is scaffolded into 10 chromosomal pseudomolecules. The mitochondrial genome has also been assembled and is 15.6 kilobases in length. Gene annotation of this assembly on Ensembl has identified 11,086 protein coding genes.

Protein-Sequence-Based Search of Nonreceptor ITAM-Like Regions to Identify Cytosolic Syk-Recruiting Proteins

The recruitment of the protein spleen tyrosine kinase (Syk) to membrane-bound immune receptors is an essential step in initiating an immune response mediated through the activated receptors. Syk recognizes intracellular phosphorylated regions of membrane receptors known as immunoreceptor tyrosine-based activation motifs (ITAMs) defined by a sequence with two tyrosine (Y) amino acids separated by a certain spacing of six to eight residues: YXX(I/L)X6-8YXX(I/L). Syk with doubly phosphorylated ITAM is high-affinity and negatively regulated when Syk itself becomes phosphorylated. While the role of Syk in immune signaling is well characterized, recent information affords new functionality to Syk related to cytoplasmic processes, including the clearance of stress granules and P-bodies, both formed by liquid-liquid phase separation. Little to nothing is known about the molecular interactions involving Syk in these cytoplasmic processes. Given the essential role of receptor ITAMs in recruiting and localizing Syk for immune signaling, we explore here the possibility of a similar localization mechanism occurring for cytoplasmic processes by searching sequences of proteins related to Syk cytoplasmic function for regions that resemble receptor ITAMs. Protein sequence databases were generated from a Syk-dependent phosphoproteome and from genes related to P-bodies. A search of these databases for ITAM-like sequences yielded 102 unique hits, and 33 of these were synthesized and tested experimentally for binding to Syk tandem SH2 domains. The equilibrium dissociation constants were 0.1-50 μM for 28 peptides, and binding was negatively regulated by phosphorylation for many peptides. These results identify cytoplasmic proteins with potential for regulating the localization of Syk in a phosphorylation-dependent manner to nonmembrane cellular regions.

Identifying compound-protein interactions with knowledge graph embedding of perturbation transcriptomics

The emergence of perturbation transcriptomics provides a new perspective for drug discovery, but existing analysis methods suffer from inadequate performance and limited applicability. In this work, we present PertKGE, a method designed to deconvolute compound-protein interactions from perturbation transcriptomics with knowledge graph embedding. By considering multi-level regulatory events within biological systems that share the same semantic context, PertKGE significantly improves deconvoluting accuracy in two critical "cold-start" settings: inferring targets for new compounds and conducting virtual screening for new targets. We further demonstrate the pivotal role of incorporating multi-level regulatory events in alleviating representational biases. Notably, it enables the identification of ectonucleotide pyrophosphatase/phosphodiesterase-1 as the target responsible for the unique anti-tumor immunotherapy effect of tankyrase inhibitor K-756 and the discovery of five novel hits targeting the emerging cancer therapeutic target aldehyde dehydrogenase 1B1 with a remarkable hit rate of 10.2%. These findings highlight the potential of PertKGE to accelerate drug discovery.

The genome sequence of the Black Medic, Medicago lupulina L

We present a genome assembly from a specimen of Black Medic, Medicago lupulina (Streptophyta; Magnoliopsida; Fabales; Fabaceae). The genome sequence has a total length of 575.40 megabases. Most of the assembly is scaffolded into 8 chromosomal pseudomolecules. The mitochondrial and plastid genome assemblies have lengths of 294.12 kilobases and 123.99 kilobases, respectively. Gene annotation of this assembly on Ensembl identified 27,424 protein-coding genes.

Novel Sex-Specific Genes and Diverse Interspecific Expression in the Antennal Transcriptomes of Ithomiine Butterflies

The olfactory sense is crucial for organisms, facilitating environmental recognition and interindividual communication. Ithomiini butterflies exemplify this importance not only because they rely strongly on olfactory cues for both inter- and intra-sexual behaviors, but also because they show convergent evolution of specialized structures within the antennal lobe, called macroglomerular complexes (MGCs). These structures, widely absent in butterflies, are present in moths where they enable heightened sensitivity to, and integration of, information from various types of pheromones. In this study, we investigate chemosensory evolution across six Ithomiini species and identify possible links between expression profiles and neuroanatomical. To enable this, we sequenced four new high-quality genome assemblies and six sex-specific antennal transcriptomes for three of these species with different MGC morphologies. With extensive genomic analyses, we found that the expression of antennal transcriptomes across species exhibit profound divergence, and identified highly expressed ORs, which we hypothesize may be associated to MGCs, as highly expressed ORs are absent in Methona, an Ithomiini lineage which also lacks MGCs. More broadly, we show how antennal sexual dimorphism is prevalent in both chemosensory genes and non-chemosensory genes, with possible relevance for behavior. As an example, we show how lipid-related genes exhibit consistent sexual dimorphism, potentially linked to lipid transport or host selection. In this study, we investigate the antennal chemosensory adaptations, suggesting a link between genetic diversity, ecological specialization, and sensory perception with the convergent evolution of MCGs. Insights into chemosensory gene evolution, expression patterns, and potential functional implications enhance our knowledge of sensory adaptations and sexual dimorphisms in butterflies, laying the foundation for future investigations into the genetic drivers of insect behavior, adaptation, and speciation.

Improved deep learning prediction of antigen-antibody interactions

Identifying antibodies that neutralize specific antigens is crucial for developing effective immunotherapies, but this task remains challenging for many target antigens. The rise of deep learning-based computational approaches presents a promising avenue to address this challenge. Here, we assess the performance of a deep learning approach through two benchmark tests aimed at predicting antibodies for the receptor-binding domain of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein. Three different strategies for constructing input sequence alignments are employed for predicting structural models of antigen-antibody complexes. In our initial testing set, which comprises known experimental structures, these strategies collectively yield a significant top-ranked prediction for 61% of cases and a success rate of 47%. Notably, one strategy that utilizes the sequences of known antigen binders outperforms the other two, achieving a precision of 90% in a subsequent test set of ~1,000 antibodies, balanced between true and control antibodies for the antigen, albeit with a lower recall of 25%. Our results underscore the potential of integrating deep learning methods with single B cell sequencing techniques to enhance the prediction accuracy of antigen-antibody interactions.

Scar/WAVE drives actin protrusions independently of its VCA domain using proline-rich domains

Cell migration requires the constant modification of cellular shape by reorganization of the actin cytoskeleton. Fine-tuning of this process is critical to ensure new actin filaments are formed only at specific times and in defined regions of the cell. The Scar/WAVE complex is the main catalyst of pseudopod and lamellipodium formation during cell migration. It is a pentameric complex highly conserved through eukaryotic evolution and composed of Scar/WAVE, Abi, Nap1/NCKAP1, Pir121/CYFIP, and HSPC300/Brk1. Its function is usually attributed to activation of the Arp2/3 complex through Scar/WAVE's VCA domain, while other parts of the complex are expected to mediate spatial-temporal regulation and have no direct role in actin polymerization. Here, we show in both B16-F1 mouse melanoma and Dictyostelium discoideum cells that Scar/WAVE without its VCA domain still induces the formation of morphologically normal, actin-rich protrusions, extending at comparable speeds despite a drastic reduction of Arp2/3 recruitment. However, the proline-rich regions in Scar/WAVE and Abi subunits are essential, though either is sufficient for the generation of actin protrusions in B16-F1 cells. We further demonstrate that N-WASP can compensate for the absence of Scar/WAVE's VCA domain and induce lamellipodia formation, but it still requires an intact WAVE complex, even if without its VCA domain. We conclude that the Scar/WAVE complex does more than directly activating Arp2/3, with proline-rich domains playing a central role in promoting actin protrusions. This implies a broader function for the Scar/WAVE complex, concentrating and simultaneously activating many actin-regulating proteins as a lamellipodium-producing core.

Metaproteogenomics resolution of a high-CO2 aquifer community reveals a complex cellular adaptation of groundwater Gracilibacteria to a host-dependent lifestyle

BACKGROUND

Bacteria of the candidate phyla radiation (CPR), constituting about 25% of the bacterial biodiversity, are characterized by small cell size and patchy genomes without complete key metabolic pathways, suggesting a symbiotic lifestyle. Gracilibacteria (BD1-5), which are part of the CPR branch, possess alternate coded genomes and have not yet been cultivated. The lifestyle of Gracilibacteria, their temporal dynamics, and activity in natural ecosystems, particularly in groundwater, has remained largely unexplored. Here, we aimed to investigate Gracilibacteria activity in situ and to discern their lifestyle based on expressed genes, using the metaproteogenome of Gracilibacteria as a function of time in the cold-water geyser Wallender Born in the Volcanic Eifel region in Germany.

RESULTS

We coupled genome-resolved metagenomics and metaproteomics to investigate a cold-water geyser microbial community enriched in Gracilibacteria across a 12-day time-series. Groundwater was collected and sequentially filtered to fraction CPR and other bacteria. Based on 725 Gbps of metagenomic data, 1129 different ribosomal protein S3 marker genes, and 751 high-quality genomes (123 population genomes after dereplication), we identified dominant bacteria belonging to Gallionellales and Gracilibacteria along with keystone microbes, which were low in genomic abundance but substantially contributing to proteomic abundance. Seven high-quality Gracilibacteria genomes showed typical limitations, such as limited amino acid or nucleotide synthesis, in their central metabolism but no co-occurrence with potential hosts. The genomes of these Gracilibacteria were encoded for a high number of proteins involved in cell to cell interaction, supporting the previously surmised host-dependent lifestyle, e.g., type IV and type II secretion system subunits, transporters, and features related to cell motility, which were also detected on protein level.

CONCLUSIONS

We here identified microbial keystone taxa in a high-CO2 aquifer, and revealed microbial dynamics of Gracilibacteria. Although Gracilibacteria in this ecosystem did not appear to target specific organisms in this ecosystem due to lack of co-occurrence despite enrichment on 0.2-µm filter fraction, we provide proteomic evidence for the complex machinery behind the host-dependent lifestyle of groundwater Gracilibacteria. Video Abstract.

Transcriptomic analysis of Chaetoceros muelleri in response to different nitrogen concentrations reveals the activation of pathways to enable efficient nitrogen uptake

Nitrogen is the principal nutrient deficiency that increases lipids and carbohydrate content in diatoms but negatively affects biomass production. Marine diatom Chaetoceros muelleri is characterized by lipid and carbohydrate accumulation under low nitrogen concentration without affecting biomass. To elucidate the molecular effects of nitrogen concentrations, we performed an RNA-seq analysis of C. muelleri grown under four nitrogen concentrations (3.53 mM, 1.76 mM, 0.44 mM, and 0.18 mM of NaNO3). This research revealed that changes in global transcription in C. muelleri are differentially expressed by nitrogen concentration. "Energetic metabolism", "Carbohydrate metabolism" and "Lipid metabolism" pathways were identified as the most upregulated by N deficiency. Due to N limitation, alternative pathways to self-supply nitrogen employed by microalgal cells were identified. Additionally, nitrogen limitation decreased chlorophyll content and caused a greater response at the transcriptional level with a higher number of unigenes differentially expressed. By contrast, the highest N concentration (3.53 mM) recorded the lowest number of differentially expressed genes. Amt1, Nrt2, Fad2, Skn7, Wrky19, and Dgat2 genes were evaluated by RT-qPCR. In conclusion, C. muelleri modify their metabolic pathways to optimize nitrogen utilization and minimize nitrogen losses. On the other hand, the assembled transcriptome serves as the basis for metabolic engineering focused on improving the quantity and quality of the diatom for biotechnological applications. However, proteomic and metabolomic analysis is also required to compare gene expression, protein, and metabolite accumulation.

Adaptation to space conditions of novel bacterial species isolated from the International Space Station revealed by functional gene annotations and comparative genome analysis

BACKGROUND

The extreme environment of the International Space Station (ISS) puts selective pressure on microorganisms unintentionally introduced during its 20+ years of service as a low-orbit science platform and human habitat. Such pressure leads to the development of new features not found in the Earth-bound relatives, which enable them to adapt to unfavorable conditions.

RESULTS

In this study, we generated the functional annotation of the genomes of five newly identified species of Gram-positive bacteria, four of which are non-spore-forming and one spore-forming, all isolated from the ISS. Using a deep-learning based tool-deepFRI-we were able to functionally annotate close to 100% of protein-coding genes in all studied species, overcoming other annotation tools. Our comparative genomic analysis highlights common characteristics across all five species and specific genetic traits that appear unique to these ISS microorganisms. Proteome analysis mirrored these genomic patterns, revealing similar traits. The collective annotations suggest adaptations to life in space, including the management of hypoosmotic stress related to microgravity via mechanosensitive channel proteins, increased DNA repair activity to counteract heightened radiation exposure, and the presence of mobile genetic elements enhancing metabolism. In addition, our findings suggest the evolution of certain genetic traits indicative of potential pathogenic capabilities, such as small molecule and peptide synthesis and ATP-dependent transporters. These traits, exclusive to the ISS microorganisms, further substantiate previous reports explaining why microbes exposed to space conditions demonstrate enhanced antibiotic resistance and pathogenicity.

CONCLUSION

Our findings indicate that the microorganisms isolated from ISS we studied have adapted to life in space. Evidence such as mechanosensitive channel proteins, increased DNA repair activity, as well as metallopeptidases and novel S-layer oxidoreductases suggest a convergent adaptation among these diverse microorganisms, potentially complementing one another within the context of the microbiome. The common genes that facilitate adaptation to the ISS environment may enable bioproduction of essential biomolecules need during future space missions, or serve as potential drug targets, if these microorganisms pose health risks. Video Abstract.

Analysis and Visualization of Quantitative Proteomics Data Using FragPipe-Analyst

The FragPipe computational proteomics platform is gaining widespread popularity among the proteomics research community because of its fast processing speed and user-friendly graphical interface. Although FragPipe produces well-formatted output tables that are ready for analysis, there is still a need for an easy-to-use and user-friendly downstream statistical analysis and visualization tool. FragPipe-Analyst addresses this need by providing an R shiny web server to assist FragPipe users in conducting downstream analyses of the resulting quantitative proteomics data. It supports major quantification workflows, including label-free quantification, tandem mass tags, and data-independent acquisition. FragPipe-Analyst offers a range of useful functionalities, such as various missing value imputation options, data quality control, unsupervised clustering, differential expression (DE) analysis using Limma, and gene ontology and pathway enrichment analysis using Enrichr. To support advanced analysis and customized visualizations, we also developed FragPipeAnalystR, an R package encompassing all FragPipe-Analyst functionalities that is extended to support site-specific analysis of post-translational modifications (PTMs). FragPipe-Analyst and FragPipeAnalystR are both open-source and freely available.

AmpClass: an Antimicrobial Peptide Predictor Based on Supervised Machine Learning

In the last decades, antibiotic resistance has been considered a severe problem worldwide. Antimicrobial peptides (AMPs) are molecules that have shown potential for the development of new drugs against antibiotic-resistant bacteria. Nowadays, medicinal drug researchers use supervised learning methods to screen new peptides with antimicrobial potency to save time and resources. In this work, we consolidate a database with 15945 AMPs and 12535 non-AMPs taken as the base to train a pool of supervised learning models to recognize peptides with antimicrobial activity. Results show that the proposed tool (AmpClass) outperforms classical state-of-the-art prediction models and achieves similar results compared with deep learning models.

Identification of Potential Growth-Related Proteins in Chick Vitreous during Emmetropization Using SWATH-MS and Targeted-Based Proteomics (MRMHR)

The vitreous humor (VH) is a transparent gelatin-like substance that occupies two-thirds of the eyeball and undergoes the most significant changes during eye elongation. Quantitative proteomics on the normal growth period in the VH could provide new insights into understanding its progression mechanism in the early stages of myopia. In this study, a data-independent acquisition (SWATH-MS) was combined with targeted LC-ESI-MS/MS to identify and quantify the relative protein changes in the vitreous during the normal growth period (4, 7, 14, 21 and 28 days old) in the chick model. Chicks were raised under normal growing conditions (12/12 h Dark/light cycle) for 28 days, where ocular measurements, including refractive and biometric measurements, were performed on days 4 (baseline), 7, 14, 21 and 28 (n = 6 chicks at each time point). Extracted vitreous proteins from individual animals were digested and pooled into a left eye pool and a right pool at each time point for protein analysis. The vitreous proteome for chicks was generated using an information-dependent acquisition (IDA) method by combining injections from individual time points. Using individual pool samples, SWATH-MS was employed to quantify proteins between each time point. DEPs were subsequently confirmed in separate batches of animals individually on random eyes (n = 4) using MRMHR between day 7 and day 14. Refraction and vitreous chamber depth (VCD) were found to be significantly changed (p < 0.05, n = 6 at each time point) during the period. A comprehensive vitreous protein ion library was built with 1576 non-redundant proteins (22987 distinct peptides) identified at a 1% false discovery rate (FDR). A total of 12 up-regulated and 26 down-regulated proteins were found across all time points compared to day 7 using SWATH-MS. Several DEPs, such as alpha-fetoprotein, the cadherin family group, neurocan, and reelin, involved in structural and growth-related pathways, were validated for the first time using MRMHR under this experimental condition. This study provided the first comprehensive spectral library of the vitreous for chicks during normal growth as well as a list of potential growth-related protein biomarker candidates using SWATH-MS and MRMHR during the emmetropization period.

Exploring the Mechanism of Sempervirine Inhibiting Glioblastoma Invasion Based on Network Pharmacology and Bioinformatics

Background: Invasion is an important characteristic of the malignancy of glioblastoma (GBM) and a significant prognostic factor. Sempervirine (SPV), a yohimbine-type alkaloid, has been proven to inhibit GBM cells proliferation in previous research and found to have a potential effect in anti-invasion, but its mechanism of anti-invasion is still unknown. Methods: To explore its pharmacodynamics in inhibiting GBM cell invasion in this study, we combined network pharmacology and bioinformatics to comprehensive exploratory analysis of SPV and verified the mechanism in vitro. Results: Firstly, targets of SPV and invasion-related genes were collected from public databases. Moreover, GBM samples were obtained to analyze differentially expressed genes (DEGs) from The Cancer Genome Atlas (TCGA). Then, the relevant targets of SPV inhibiting GBM invasion (SIGI) were obtained through the intersection of the three gene sets. Further, GO and KEGG analysis showed that the targets of SIGI were heavily enriched in the AKT signaling pathway. Subsequently, based on the method of machine learning, a clinical prognostic model of the relevant targets of SIGI was constructed using GBM samples from TCGA and the Gene Expression Omnibus (GEO). A four-genes model (DUSP6, BMP2, MMP2, and MMP13) was successfully constructed, and Vina Scores of MMP2 and MMP13 in molecular docking were higher, which may be the main targets of SIGI. Then, the effect of SIGI was confirmed via functional experiments on invasion, migration, and adhesion assay, and the effect involved changes in the expressions of p-AKT, MMP2 and MMP13. Finally, combined with AKT activator (SC79) and inhibitor (MK2206), we further confirmed that SPV inhibits GBM invasion through AKT phosphorylation. Conclusions: This study provides valuable and an expected point of view into the regulation of AKT phosphorylation and inhibition of GBM invasion by SPV.

Proteogenomics-guided functional venomics resolves the toxin arsenal and activity of Deinagkistrodon acutus venom

Snakebite primarily impacts rural communities of Africa, Asia, and Latin America. The sharp-nosed viper (Deinagkistrodon acutus) is among the snakes of highest medical importance in Asia. Despite various studies on its venom using modern venomics techniques, a comprehensive understanding of composition and function of this species' venom remains lacking. We combined proteogenomics with extensive bioactivity profiling to present the first genome-level catalogue of D. acutus venom proteins and their exochemistry. Our analysis identified an unusually simple venom containing 45 components from 20 distinct protein families. Relative toxin abundances indicate that C-type lectin and C-type lectin-related protein (CTL), snake venom metalloproteinase (svMP), snake venom serine protease (svSP), and phospholipase A2 (PLA2) constitute 90 % of the venom. Bioassays targeting key aspects of viperid envenomation showed considerable concentration-dependent cytotoxicity, particularly in kidney and lung cells, and potent protease and PLA2 activity. Factor Xa and thrombin activities were minor, and no plasmin activity was observed. Effects on haemolysis, intracellular calcium (Ca2+) release, and nitric oxide (NO) synthesis were negligible. Our analysis provides the first holistic genome-based overview of the toxin arsenal of D. acutus, predicting the molecular and functional basis of its life-threatening effects, and opens novel avenues for treating envenomation by this highly dangerous snake.

Characteristics of blood-brain barrier heterogeneity between brain regions revealed by profiling vascular and perivascular cells

The blood-brain barrier (BBB) protects the brain and maintains neuronal homeostasis. BBB properties can vary between brain regions to support regional functions, yet how BBB heterogeneity occurs is poorly understood. Here, we used single-cell and spatial transcriptomics to compare the mouse median eminence, one of the circumventricular organs that has naturally leaky blood vessels, with the cortex. We identified hundreds of molecular differences in endothelial cells (ECs) and perivascular cells, including astrocytes, pericytes and fibroblasts. Using electron microscopy and an aqueous-based tissue-clearing method, we revealed distinct anatomical specializations and interaction patterns of ECs and perivascular cells in these regions. Finally, we identified candidate regionally enriched EC-perivascular cell ligand-receptor pairs. Our results indicate that both molecular specializations in ECs and unique EC-perivascular cell interactions contribute to BBB functional heterogeneity. This platform can be used to investigate BBB heterogeneity in other regions and may facilitate the development of central nervous system region-specific therapeutics.

Integrating genetic and proteomic data to elucidate the association between immune system and blood-brain barrier dysfunction with multiple sclerosis risk and severity

BACKGROUND

Immune system dysfunction and blood-brain barrier (BBB) impairment are implicated in multiple sclerosis (MS) risk and severity. However, the causal relationships and potential therapeutic targets remain unclear.

METHODS

Leveraging the MRC IEU OpenGWAS data infrastructure, we extracted 1254 peripheral immune systems and 792 BBB biomarkers as genetic instruments for exposure. MS risk data from the International Multiple Sclerosis Genetics Consortium (IMSGC) (47,429 MS cases, 68,374 controls) served as one outcome, replicated in FinnGen (1048 cases, 217,141 controls) and the UK Biobank (1679 cases, 461,254 controls). Genetic associations with MS severity derived from IMSGC and MultipleMS Consortium GWAS data (12,584 cases). Two-sample, bidirectional, and protein drug-target MR analyses were conducted, along with interaction analysis of identified proteins and druggability assessment.

RESULTS

Causal relationships between 45 immunological markers, 15 BBB markers, and MS risk were strongly supported. In peripheral immunity, the causal associations with MS are predominantly concentrated in CD4+ T cells and CD8+ T cells. Notably, anti-Epstein-Barr virus nuclear antigen (EBNA) IgG levels exhibited the most significant causal effect on MS risk (OR = 225.62, P = 5.63E-208), replicated in the MS severity (OR = 1.11, P = 0.04). Weak causal evidence was found between 62 immunological markers, 35 BBB markers, and MS severity. Reverse MR analysis suggested potential causal effects of MS risk on 8 markers. Drug-targeted MR analysis indicated potential therapeutic benefits in reducing MS risk for CD40 (OR = 0.71, P = 7.24E-13, PPH4 = 97.6 %), AHSG (OR = 0.88, P = 2.91E-05, PPH4 = 94.4 %), and FCRL3 (Sun BB et al.: OR = 0.83, P = 8.93E-09, PPH4 = 94.2 %, Suhre K et al.: OR = 0.88, P = 5.20E-08, PPH4 = 99.2 %).

CONCLUSIONS

This study provides evidence supporting the causal effects of immune system and BBB dysfunction on MS risk and severity. It emphasizes the significant role of anti-EBNA IgG levels, CD4+ T cells, and CD8+ T cells in MS, and delineates the potential therapeutic benefits of targeting three proteins associated with MS risk: CD40, AHSG, and FCRL3.