Pre-ribosomal particles from nucleoli to cytoplasm

The analysis of nucleocytoplasmic transport of proteins and messenger RNA has been the focus of advanced microscopic approaches. Recently, it has been possible to identify and visualize individual pre-ribosomal particles on their way through the nuclear pore complex using both electron and light microscopy. In this review, we focused on the transport of pre-ribosomal particles in the nucleus on their way to and through the pores.

Acidic sphingomyelinase interactions with lysosomal membranes and cation amphiphilic drugs: A molecular dynamics investigation

Lysosomes are pivotal in cellular functions and disease, influencing cancer progression and therapy resistance with Acid Sphingomyelinase (ASM) governing their membrane integrity. Moreover, cation amphiphilic drugs (CADs) are known as ASM inhibitors and have anti-cancer activity, but the structural mechanisms of their interactions with the lysosomal membrane and ASM are poorly explored. Our study, leveraging all-atom explicit solvent molecular dynamics simulations, delves into the interaction of glycosylated ASM with the lysosomal membrane and the effects of CAD representatives, i.e., ebastine, hydroxyebastine and loratadine, on the membrane and ASM. Our results confirm the ASM association to the membrane through the saposin domain, previously only shown with coarse-grained models. Furthermore, we elucidated the role of specific residues and ASM-induced membrane curvature in lipid recruitment and orientation. CADs also interfere with the association of ASM with the membrane at the level of a loop in the catalytic domain engaging in membrane interactions. Our computational approach, applicable to various CADs or membrane compositions, provides insights into ASM and CAD interaction with the membrane, offering a valuable tool for future studies.

PANoptosis signaling enables broad immune response in psoriasis: From pathogenesis to new therapeutic strategies

Background

Accumulating evidence suggests that regulated cell death, such as pyroptosis, apoptosis, and necroptosis, is deeply involved in the pathogenesis of psoriasis. As a newly recognized form of systematic cell death, PANoptosis is involved in a variety of inflammatory disorders through amplifying inflammatory and immune cascades, but its role in psoriasis remains elusive.

Objectives

To reveal the role of PANoptosis in psoriasis for a potential therapeutic strategy.

Methods

Multitranscriptomic analysis and experimental validation were used to identify PANoptosis signaling in psoriasis. RNA-seq and scRNA-seq analyses were performed to establish a PANoptosis-mediated immune response in psoriasis, which revealed hub genes through WGCNA and predicted disulfiram as a potential drug. The effect and mechanism of disulfiram were verified in imiquimod (IMQ)-induced psoriasis.

Results

Here, we found a highlighted PANoptosis signature in psoriasis patients through multitranscriptomic analysis and experimental validation. Based on this, two distinct PANoptosis patterns (non/high) were identified, which were the options for clinical classification. The high-PANoptosis-related group had a higher response rate to immune cell infiltration (such as M1 macrophages and keratinocytes). Subsequently, WGCNA showed the hub genes (e.g., S100A12, CYCS, NOD2, STAT1, HSPA4, AIM2, MAPK7), which were significantly associated with clinical phenotype, PANoptosis signature, and identified immune response in psoriasis. Finally, we explored disulfiram (DSF) as a candidate drug for psoriasis through network pharmacology, which ameliorated IMQ-mediated psoriatic symptoms through antipyroptosis-mediated inflammation and enhanced apoptotic progression. By analyzing the specific ligand-receptor interaction pairs within and between cell lineages, we speculated that DSF might exert its effects by targeting keratinocytes directly or targeting M1 macrophages to downregulate the proliferation of keratinocytes.

Conclusions

PANoptosis with its mediated immune cell infiltration provides a roadmap for research on the pathogenesis and therapeutic strategies of psoriasis.

MiRNA-3163 limits ovarian cancer stem-like cells via targeting SOX-2 transcription factor

Cancer stem cells (CSCs) are pivotal in both cancer progression and the acquisition of drug resistance. MicroRNAs (miRNAs) play a crucial role in modulating CSC properties and are being explored as potential targets for therapeutic interventions. MiR-3163 is primarily known for its tumor suppressive properties in various human malignancies, with lower expression reported across different cancer types. However, its role in regulating the ovarian CSC phenotype and the underlying mechanism remain largely unknown. Here, we report a remarkable downregulation of miR-3163 in ovarian cancer stem-like cells (CSLCs). Enforced expression of miR-3163 in ovarian adherent and CSLCs, significantly disrupts the stemness phenotype. Moreover, downregulation of miR-3163 expression in ovarian cancer cells (OV2008 and OVCAR-3) inhibits the stem-like cells characterized by CD44+CD117+ expression. Sphere formation assay results reveal that overexpression of miR-3163 in ovarian cancer cells significantly inhibits spheroid formation ability, confirming the regulatory properties of miR-3163 on ovarian CSLCs. Mechanistic investigation reveals that miR-3163 depletes ovarian CSLCs via targeting SOX-2. Furthermore, we establish SOX-2 as a direct target of miR-3163 through dual-luciferase assay. Taken together, our study demonstrates that overexpression of miR-3163 could be a promising strategy for efficiently eradicating the CSC population to prevent chemoresistance and tumor relapse in ovarian cancer patients.

Evolutionary accumulation of FKS1 mutations from clinical echinocandin-resistant Candida auris

INTRODUCTION

Drug resistance to echinocandins, first-line drugs used to treat Candida auris infection, is rapidly emerging. However, the accumulation of mutations in genes other than FKS1 (before an isolate develops to resistance via FKS1 mutations), remains poorly understood. Methods: Four clinical cases and 29 isolates associated with the incremental process of echinocandin resistance were collected and analyzed using antifungal drug susceptibility testing and genome sequencing to assess the evolution of echinocandin resistance.

FINDINGS

Six echinocandin minimum inhibitory concentration (MIC)-elevated C. auris strains and seven resistant strains were isolated from the urinary system of patients receiving echinocandin treatment. Meanwhile, phylogenetic analyses illustrated that the echinocandin-resistant strains were closely related to other strains in the same patient. Genomic data revealed that the echinocandin-resistant strains had FKS1 mutations. Furthermore, three categories (ECN-S/E/R) of non-synonymous mutant SNP genes (such as RBR3, IFF6, MKC1, MPH1, RAD2, and MYO1) in C. auris appeared to be associated with the three-stage-evolutionary model of echinocandin resistance in C. glabrata: cell wall stress, drug adaptation, and genetic escape (FKS mutation).

INTERPRETATION

Echinocandin-resistant C. auris undergoes spatial and temporal phase changes closely related to echinocandin exposure, particularly in the urinary system. These findings suggest that FKS1 mutations mediate an evolutionary accumulation of echinocandin resistance followed by modulation of chromosome remodelling and DNA repair processes that ultimately lead to FKS1 hot spot mutations and the development of drug resistance. This study provides an in-depth exploration of the molecular pathways involved in the evolution of Candida auris echinocandin resistance.

A survey of k-mer methods and applications in bioinformatics

The rapid progression of genomics and proteomics has been driven by the advent of advanced sequencing technologies, large, diverse, and readily available omics datasets, and the evolution of computational data processing capabilities. The vast amount of data generated by these advancements necessitates efficient algorithms to extract meaningful information. K-mers serve as a valuable tool when working with large sequencing datasets, offering several advantages in computational speed and memory efficiency and carrying the potential for intrinsic biological functionality. This review provides an overview of the methods, applications, and significance of k-mers in genomic and proteomic data analyses, as well as the utility of absent sequences, including nullomers and nullpeptides, in disease detection, vaccine development, therapeutics, and forensic science. Therefore, the review highlights the pivotal role of k-mers in addressing current genomic and proteomic problems and underscores their potential for future breakthroughs in research.

Genetic variation and evolutionary characteristics of Echovirus 11: new variant within genotype D5 associated with neonatal death found in China

Echovirus 11 (E11) has gained attention owing to its association with severe neonatal infections. From 2018 to 2023, a surge in severe neonatal cases and fatalities linked to a novel variant of genotype D5 was documented in China, France, and Italy. However, the prevention and control of E11 variants have been hampered by limited background data on the virus circulation and genetic variance. Therefore, the present study investigated the circulating dynamics of E11 and the genetic variation and molecular evolution of genotype D5 through the collection of strains from the national acute flaccid paralysis (AFP) and hand, foot, and mouth disease (HFMD) surveillance system in China during 2000-2022 and genetic sequences published in the GenBank database. The results of this study revealed a prevalent dynamic of E11 circulation, with D5 being the predominant genotype worldwide. Further phylogenetic analysis of genotype D5 indicated that it could be subdivided into three important geographic clusters (D5-CHN1: 2014-2019, D5-CHN2: 2016-2022, and D5-EUR: 2022-2023). Additionally, variant-specific (144) amino acid mutation sites and positive-selection pressure sites (132, 262) were identified in the VP1 region. Cluster-specific recombination patterns were also identified, with CVB5, E6, and CVB4 as the major recombinant viruses. These findings provide a preliminary landscape of E11 circulation worldwide and basic scientific data for further study of the pathogenicity of E11 variants.

Inhibition of RAN attenuates influenza a virus replication and nucleoprotein nuclear export

Nuclear export of the viral ribonucleoprotein (vRNP) is a critical step in the influenza A virus (IAV) life cycle and may be an effective target for the development of anti-IAV drugs. The host factor ras-related nuclear protein (RAN) is known to participate in the life cycle of several viruses, but its role in influenza virus replication remains unknown. In the present study, we aimed to determine the function of RAN in influenza virus replication using different cell lines and subtype strains. We found that RAN is essential for the nuclear export of vRNP, as it enhances the binding affinity of XPO1 toward the viral nuclear export protein NS2. Depletion of RAN constrained the vRNP complex in the nucleus and attenuated the replication of various subtypes of influenza virus. Using in silico compound screening, we identified that bepotastine could dissociate the RAN-XPO1-vRNP trimeric complex and exhibit potent antiviral activity against influenza virus both in vitro and in vivo. This study demonstrates the important role of RAN in IAV replication and suggests its potential use as an antiviral target.

Integration of miRNA expression analysis of purified leukocytes and whole blood reveals blood-borne candidate biomarkers for lung cancer

Changes in leukocyte populations may confound the disease-associated miRNA signals in the blood of cancer patients. We aimed to develop a method to detect differentially expressed miRNAs from lung cancer whole blood samples that are not influenced by variations in leukocyte proportions. The Ref-miREO method identifies differential miRNAs unaffected by changes in leukocyte populations by comparing the within-sample relative expression orderings (REOs) of miRNAs from healthy leukocyte subtypes and those from lung cancer blood samples. Over 77% of the differential miRNAs observed between lung cancer and healthy blood samples overlapped with those between myeloid-derived and lymphoid-derived leukocytes, suggesting the potential impact of changes in leukocyte populations on miRNA profile. Ref-miREO identified 16 differential miRNAs that target 19 lung adenocarcinoma-related genes previously linked to leukocytes. These miRNAs showed enrichment in cancer-related pathways and demonstrated high potential as diagnostic biomarkers, with the LASSO regression models effectively distinguishing between healthy and lung cancer blood or serum samples (all AUC > 0.85). Additionally, 12 of these miRNAs exhibited significant prognostic correlations. The Ref-miREO method offers valuable candidates for circulating biomarker detection in cancer that are not affected by changes in leukocyte populations.

Cardiac tissue-resident vesicles differentially modulate anti-fibrotic phenotype by age and sex through synergistic miRNA effects

Aging is a risk factor for cardiovascular disease, the leading cause of death worldwide. Cardiac fibrosis is a harmful result of repeated myocardial infarction that increases risk of morbidity and future injury. Interestingly, both rates and outcomes of cardiac fibrosis differ between young and aged individuals, as well as men and women. Here, for the first time, we identify and isolate matrix-bound extracellular vesicles from the left ventricles (LVs) of young or aged males and females in both human and murine models. These LV vesicles (LVVs) show differences in morphology and content between these four cohorts in both humans and mice. LVV effects on fibrosis were also investigated in vitro, and aged male LVVs were pro-fibrotic while other LVVs were anti-fibrotic. From these LVVs, we could identify therapeutic miRNAs to promote anti-fibrotic effects. Four miRNAs were identified and together, but not individually, demonstrated significant cardioprotective effects when transfected. This suggests that miRNA synergy can regulate cell response, not just individual miRNAs, and also indicates that biological agent-associated therapeutic effects may be recapitulated using non-immunologically active agents. Furthermore, that chronic changes in LVV miRNA content may be a major factor in sex- and age-dependent differences in clinical outcomes of cardiac fibrosis.

Investigation of in silico studies for cytochrome P450 isoforms specificity

Cytochrome P450 (CYP450) enzymes comprise a highly diverse superfamily of heme-thiolate proteins that responsible for catalyzing over 90 % of enzymatic reactions associated with xenobiotic metabolism in humans. Accurately predicting whether chemicals are substrates or inhibitors of different CYP450 isoforms can aid in pre-selecting hit compounds for the drug discovery process, chemical toxicology studies, and patients treatment planning. In this work, we investigated in silico studies on CYP450s specificity over past twenty years, categorizing these studies into structure-based and ligand-based approaches. Subsequently, we utilized 100 of the most frequently prescribed drugs to test eleven machine learning-based prediction models which were published between 2015 and 2024. We analyzed various aspects of the evaluated models, such as their datasets, algorithms, and performance. This will give readers with a comprehensive overview of these prediction models and help them choose the most suitable one to do prediction. We also provide our insights for future research trend in both structure-based and ligand-based approaches in this field.

Smad4 regulates TGF-β1-mediated hedgehog activation to promote epithelial-to-mesenchymal transition in pancreatic cancer cells by suppressing Gli1 activity

Pancreatic cancer (PC) is an aggressive and metastatic gastrointestinal tumor with a poor prognosis. Persistent activation of the TGF-β/Smad signaling induces PC cell (PCC) invasion and infiltration via epithelial-to-mesenchymal transition (EMT). Hedgehog signaling is a crucial pathway for the development of PC via the transcription factors Gli1/2/3. This study aimed to investigate the underlying molecular mechanisms of action of hedgehog activation in TGF-β1-triggered EMT in PCCs (PANC-1 and BxPc-3). In addition, overexpression and shRNA techniques were used to evaluate the role of Smad4 in TGF-β1-treated PCCs. Our data showed that TGF-β1 promoted PCC invasion and infiltration via Smad2/3-dependent EMT. Hedgehog-Gli signaling axis in PCCs was activated upon TGF-β1 stimulation. Inhibition of hedgehog with cyclopamine effectively antagonized TGF-β1-induced EMT, thereby suggesting that the hedgehog signaling may act as a downstream cascade signaling of TGF-β1. As a key protein that assists the nuclear translocation of Smad2/3, Smad4 was highly expressed in PANC-1 cells, but not in BxPc-3 cells. Conversely, Gli1 expression was low in PANC-1 cells, but high in BxPc-3 cells. Furthermore, knockdown of Smad4 in PANC-1 cells by shRNA inhibited TGF-β1-mediated EMT and collagen deposition. Overexpression of Smad4 did not affect TGF-β1-mediated EMT due to the lack of significant increase in nuclear expression of Smad4. Importantly, Gli1 activity was upregulated by Smad4 knockdown in PANC-1 cells and downregulated by Smad4 overexpression in BxPc-3 cells, indicating that Gli1 may be a negative target protein downstream of Smad4. Thus, Smad4 regulates TGF-β1-mediated hedgehog activation to promote EMT in PCCs by suppressing Gli1 activity.

Whole genome sequencing unravels cryptic circulation of divergent dengue virus lineages in the rainforest region of Nigeria

Dengue is often misclassified and underreported in Africa due to inaccurate differential diagnoses of nonspecific febrile illnesses such as malaria, sparsity of diagnostic testing and poor clinical and genomic surveillance. There are limited reports on the seroprevalence and genetic diversity of dengue virus (DENV) in humans and vectors in Nigeria. In this study, we investigated the epidemiology and genetic diversity of dengue in the rainforest region of Nigeria. We screened 515 febrile patients who tested negative for malaria and typhoid fever in three hospitals in Oyo and Ekiti States in southern Nigeria with a combination of anti-dengue IgG/IgM/NS1 rapid test kits and metagenomic sequencing. We found that approximately 28% of screened patients had previous DENV exposure, with the highest prevalence in persons over sixty. Approximately 8% of the patients showed evidence of recent or current infection, and 2.7% had acute infection. Following sequencing of sixty samples, we assembled twenty DENV-1 genomes (3 complete and 17 partial). We found that all assembled genomes belonged to DENV-1 genotype III. Our phylogenetic analyses showed evidence of prolonged cryptic circulation of divergent DENV lineages in Oyo state. We were unable to resolve the source of DENV in Nigeria owing to limited sequencing data from the region. However, our sequences clustered closely with sequences in Tanzania and sequences reported in Chinese with travel history to Tanzania in 2019. This may reflect the wider unsampled bidirectional transmission of DENV-1 in Africa, which strongly emphasizes the importance of genomic surveillance in monitoring ongoing DENV transmission in Africa.

Prediction of deleterious non-synonymous SNPs of human MDC1 gene: an in silico approach

MDC1 (Mediator of DNA damage Checkpoint protein 1) functions to facilitate the localization of numerous DNA damage response (DDR) components to DNA double-strand break sites. MDC1 is an integral component in preserving genomic stability and appropriate DDR regulation. There haven't been systematic investigations of MDC1 mutations that induce cancer and genomic instability. Variations in nsSNPs have the potential to modify the protein chemistry and their function. Describing functional SNPs in disease-associated genes presents a significant conundrum for investigators, it is possible to assess potential functional SNPs before conducting larger population examinations. Multiple sequences and structure-based bioinformatics strategies were implemented in the current in-silico investigation to discern potential nsSNPs of the MDC1 genes. The nsSNPs were identified with SIFT, SNAP2, Align GVGD, PolyPhen-2, and PANTHER, and their stability was determined with MUpro. The conservation, solvent accessibility, and structural effects of the mutations were identified with ConSurf, NetSurfP-2.0, and SAAFEC-SEQ respectively. Cancer-related analysis of the nsSNPs was conducted using cBioPortal and TCGA web servers. The present study appraised five nsSNPs (P1426T, P69S, P194R, P203L, and H131Y) as probably mutilating due to their existence in highly conserved regions and propensity to deplete protein stability. The nsSNPs P194R, P203L, and H131Y were concluded as deleterious and possibly damaging from the 5 prediction tools. The functional nsSNP P194R mutation is associated with skin cutaneous melanoma while no significant records were found for other nsSNPs. The present study concludes that the highly deleterious P194R mutations can potentially induce genomic instability and contribute to various cancers' pathogenesis. Developing drugs targeting these mutations can undoubtedly be advantageous in large population-based studies, particularly in the development of precision medicine.

RBProkCNN: Deep learning on appropriate contextual evolutionary information for RNA binding protein discovery in prokaryotes

RNA-binding proteins (RBPs) are central to key functions such as post-transcriptional regulation, mRNA stability, and adaptation to varied environmental conditions in prokaryotes. While the majority of research has concentrated on eukaryotic RBPs, recent developments underscore the crucial involvement of prokaryotic RBPs. Although computational methods have emerged in recent years to identify RBPs, they have fallen short in accurately identifying prokaryotic RBPs due to their generic nature. To bridge this gap, we introduce RBProkCNN, a novel machine learning-driven computational model meticulously designed for the accurate prediction of prokaryotic RBPs. The prediction process involves the utilization of eight shallow learning algorithms and four deep learning models, incorporating PSSM-based evolutionary features. By leveraging a convolutional neural network (CNN) and evolutionarily significant features selected through extreme gradient boosting variable importance measure, RBProkCNN achieved the highest accuracy in five-fold cross-validation, yielding 98.04% auROC and 98.19% auPRC. Furthermore, RBProkCNN demonstrated robust performance with an independent dataset, showcasing a commendable 95.77% auROC and 95.78% auPRC. Noteworthy is its superior predictive accuracy when compared to several state-of-the-art existing models. RBProkCNN is available as an online prediction tool (https://iasri-sg.icar.gov.in/rbprokcnn/), offering free access to interested users. This tool represents a substantial contribution, enriching the array of resources available for the accurate and efficient prediction of prokaryotic RBPs.

Emergent microenvironments of nucleoli

In higher eukaryotes, the nucleolus harbors at least three sub-phases that facilitate multiple functionalities including ribosome biogenesis. The three prominent coexisting sub-phases are the fibrillar center (FC), the dense fibrillar component (DFC), and the granular component (GC). Here, we review recent efforts in profiling sub-phase compositions that shed light on the types of physicochemical properties that emerge from compositional biases and territorial organization of specific types of macromolecules. We highlight roles played by molecular grammars which refers to protein sequence features including the substrate binding domains, the sequence features of intrinsically disordered regions, and the multivalence of these distinct types of domains / regions. We introduce the concept of a barcode of emergent physicochemical properties of nucleoli. Although our knowledge of the full barcode remains incomplete, we hope that the concept prompts investigations into undiscovered emergent properties and engenders an appreciation for how and why unique microenvironments control biochemical reactions.

RES-Xre toxin-antitoxin locus knaAT maintains the stability of the virulence plasmid in Klebsiella pneumoniae

Hypervirulent Klebsiella pneumoniae isolates have been increasingly reported worldwide, especially hypervirulent drug-resistant variants owing to the acquisition of a mobilizable virulence plasmid by a carbapenem-resistant strain. This pLVPK-like mobilizable plasmid encodes various virulence factors; however, information about its genetic stability is lacking. This study aimed to investigate the type II toxin-antitoxin (TA) modules that facilitate the virulence plasmid to remain stable in K. pneumoniae. More than 3,000 TA loci in 2,000 K. pneumoniae plasmids were examined for their relationship with plasmid cargo genes. TA loci from the RES-Xre family were highly correlated with virulence plasmids of hypervirulent K. pneumoniae. Overexpression of the RES toxin KnaT, encoded by the virulence plasmid-carrying RES-Xre locus knaAT, halts the cell growth of K. pneumoniae and E. coli, whereas co-expression of the cognate Xre antitoxin KnaA neutralizes the toxicity of KnaT. knaA and knaT were co-transcribed, representing the characteristics of a type II TA module. The knaAT deletion mutation gradually lost its virulence plasmid in K. pneumoniae, whereas the stability of the plasmid in E. coli was enhanced by adding knaAT, which revealed that the knaAT operon maintained the genetic stability of the large virulence plasmid in K. pneumoniae. String tests and mouse lethality assays subsequently confirmed that a loss of the virulence plasmid resulted in reduced pathogenicity of K. pneumoniae. These findings provide important insights into the role of the RES-Xre TA pair in stabilizing virulence plasmids and disseminating virulence genes in K. pneumoniae.

Investigating the overlap of machine learning algorithms in the final results of RNA-seq analysis on gene expression estimation

Advances in computer science in combination with the next-generation sequencing have introduced a new era in biology, enabling advanced state-of-the-art analysis of complex biological data. Bioinformatics is evolving as a union field between computer Science and biology, enabling the representation, storage, management, analysis and exploration of many types of data with a plethora of machine learning algorithms and computing tools. In this study, we used machine learning algorithms to detect differentially expressed genes between different types of cancer and showing the existence overlap to final results from RNA-sequencing analysis. The datasets were obtained from the National Center for Biotechnology Information resource. Specifically, dataset GSE68086 which corresponds to PMID:200,068,086. This dataset consists of 171 blood platelet samples collected from patients with six different tumors and healthy individuals. All steps for RNA-sequencing analysis (preprocessing, read alignment, transcriptome reconstruction, expression quantification and differential expression analysis) were followed. Machine Learning- based Random Forest and Gradient Boosting algorithms were applied to predict significant genes. The Rstudio statistical tool was used for the analysis.

The limits of prediction: Why intrinsically disordered regions challenge our understanding of antimicrobial peptides

Antimicrobial peptides (AMPs) are molecules found in most organisms, playing a vital role in innate immune defense against pathogens. Their mechanism of action involves the disruption of bacterial cell membranes, causing leakage of cellular contents and ultimately leading to cell death. While AMPs typically lack a defined structure in solution, they often assume a defined conformation when interacting with bacterial membranes. Given this structural flexibility, we investigated whether intrinsically disordered regions (IDRs) with AMP-like properties could exhibit antimicrobial activity. We tested 14 peptides from different IDRs predicted to have antimicrobial activity and found that nearly all of them did not display the anticipated effects. These peptides failed to adopt a defined secondary structure and had compromised membrane interactions, resulting in a lack of antimicrobial activity. We hypothesize that evolutionary constraints may prevent IDRs from folding, even in membrane-like environments, limiting their antimicrobial potential. Moreover, our research reveals that current antimicrobial predictors fail to accurately capture the structural features of peptides when dealing with intrinsically unstructured sequences. Hence, the results presented here may have far-reaching implications for designing and improving antimicrobial strategies and therapies against infectious diseases.

Construction of rBCG carrying the IL-2-BZLF1 fusion gene and its immunological function

In this research, a recombinant Bacillus Calmette Guerin (rBCG) vector vaccine carrying a human IL-2 and EBV BZLF1 fusion gene (IL-2-BZLF1-rBCG) was constructed. The IL-2-BZLF1-rBCG construct was successfully generated and stably expressed the IL-2 and BZLF1 proteins. IL-2-BZLF1-rBCG activated the immune system and promoted the secretion of IFN-γ and TNF-α by CD4+ and CD8+ T cells. IL-2-BZLF1-rBCG activated lymphocytes to effectively kill EBV-positive NPC cells in vitro. Additionally, IL-2-BZLF1-rBCG stimulated the proliferation of NK cells and lymphocytes in vivo, activated related immune responses, and effectively treated EBV-positive NPC. The immune response to and pharmacological effect of IL-2-BZLF1-rBCG were explored in vitro and in vivo to provide a theoretical and experimental basis for the prevention and treatment of EBV-positive tumors with an rBCG vector vaccine. KEY POINTS: • rBCG with human IL-2 and BZLF1 of EB virus was constructed • The IL-2-BZLF1 fusion gene was stably expressed with rBCG • rBCG with IL-2-BZLF1 has an obvious immune response in vitro and in vivo.

Recombinant T7 RNA polymerase production using ClearColi BL21(DE3) and animal-free media for in vitro transcription

In vitro transcription (IVT) using T7 RNA polymerase (RNAP) is integral to RNA research, yet producing this enzyme in E. coli presents challenges regarding endotoxins and animal-sourced toxins. This study demonstrates the viable production and characterization of T7 RNAP using ClearColi BL21(DE3) (an endotoxin-free E. coli strain) and animal-free media. Compared to BL21(DE3) with animal-free medium, soluble T7 RNAP expression is ~50% lower in ClearColi BL21(DE3). Optimal soluble T7 RNAP expression in flask fermentation is achieved through the design of experiments (DoE). Specification and functional testing showed that the endotoxin-free T7 RNAP has comparable activity to conventional T7 RNAP. After Ni-NTA purification, endotoxin levels were approximately 109-fold lower than T7 RNAP from BL21(DE3) with animal-free medium. Furthermore, a full factorial DoE created an optimal IVT system that maximized mRNA yield from the endotoxin-free and animal-free T7 RNAP. This work addresses critical challenges in recombinant T7 RNAP production through innovative host and medium combinations, avoided endotoxin risks and animal-derived toxins. Together with an optimized IVT reaction system, this study represents a significant advance for safe and reliable reagent manufacturing and RNA therapeutics. KEY POINTS: • Optimized IVT system maximizes mRNA yields, enabling the synthesis of long RNAs. • Novel production method yields endotoxin-free and animal-free T7 RNAP. • The T7 RNAP has equivalent specifications and function to conventional T7 RNAP.

Optimization of welan gum extraction and purification using lysozyme and alkaline protease

Welan gum, a natural polysaccharide produced by Sphingomonas sp. ATCC 31555, has attracted considerable attention in the scientific community due to its desirable properties. However, challenges, such as high viscosity, residual bacterial cells, carotenoids, and protein complexation, hinder the widespread application of welan gum. In this study, we established a method for the extraction and purification of welan gum using a synergistic approach with lysozyme and alkaline protease. Lysozyme hydrolysis conditions were optimized by applying response surface methodology, and the best results for bacterial cell removal were achieved at 11 000 U/g, 44 °C, and pH 9 after 3 h of treatment. Subsequently, we evaluated protein hydrolysis through computer simulation and identified alkaline protease as the most suitable enzyme. Through experimental investigations, we found that the optimal conditions for alkaline protease hydrolysis were 7500 U/g, 50 °C, pH 10, and 600 rpm. These conditions resulted in a sugar recovery rate of 76.1%, carotenoid removal rate of 89.5%, bacterial removal rate of 95.2%, and protein removal rate of 87.3% after 3 h of hydrolysis. The purified welan gum exhibited high transparency and purity. Structural characterization and antioxidant activity evaluation revealed that enzymatically purified welan gum has potential application prospects. Our study provides valuable insights into the optimal method for the enzymatic extraction and purification of welan gum. Such a method is conducive to the development of the multiple potential applications of welan gum. KEY POINTS: • A novel process for the synergistic purification of welan gum using lysozyme and alkaline protease was established. • In silico virtual digestion was employed to select the purification enzyme. • Welan gum with high transparency and purity was obtained.

Methanogenic partner influences cell aggregation and signalling of Syntrophobacterium fumaroxidans

For several decades, the formation of microbial self-aggregates, known as granules, has been extensively documented in the context of anaerobic digestion. However, current understanding of the underlying microbial-associated mechanisms responsible for this phenomenon remains limited. This study examined morphological and biochemical changes associated with cell aggregation in model co-cultures of the syntrophic propionate oxidizing bacterium Syntrophobacterium fumaroxidans and hydrogenotrophic methanogens, Methanospirillum hungatei or Methanobacterium formicicum. Formerly, we observed that when syntrophs grow for long periods with methanogens, cultures tend to form aggregates visible to the eye. In this study, we maintained syntrophic co-cultures of S. fumaroxidans with either M. hungatei or M. formicicum for a year in a fed-batch growth mode to stimulate aggregation. Millimeter-scale aggregates were observed in both co-cultures within the first 5 months of cultivation. In addition, we detected quorum sensing molecules, specifically N-acyl homoserine lactones, in co-culture supernatants preceding the formation of macro-aggregates (with diameter of more than 20 μm). Comparative transcriptomics revealed higher expression of genes related to signal transduction, polysaccharide secretion and metal transporters in the late-aggregation state co-cultures, compared to the initial ones. This is the first study to report in detail both biochemical and physiological changes associated with the aggregate formation in syntrophic methanogenic co-cultures. KEYPOINTS: • Syntrophic co-cultures formed mm-scale aggregates within 5 months of fed-batch cultivation. • N-acyl homoserine lactones were detected during the formation of aggregates. • Aggregated co-cultures exhibited upregulated expression of adhesins- and polysaccharide-associated genes.

AnoChem: Prediction of chemical structural abnormalities based on machine learning models

De novo drug design aims to rationally discover novel and potent compounds while reducing experimental costs during the drug development stage. Despite the numerous generative models that have been developed, few successful cases of drug design utilizing generative models have been reported. One of the most common challenges is designing compounds that are not synthesizable or realistic. Therefore, methods capable of accurately assessing the chemical structures proposed by generative models for drug design are needed. In this study, we present AnoChem, a computational framework based on deep learning designed to assess the likelihood of a generated molecule being real. AnoChem achieves an area under the receiver operating characteristic curve score of 0.900 for distinguishing between real and generated molecules. We utilized AnoChem to evaluate and compare the performances of several generative models, using other metrics, namely SAscore and Fréschet ChemNet distance (FCD). AnoChem demonstrates a strong correlation with these metrics, validating its effectiveness as a reliable tool for assessing generative models. The source code for AnoChem is available at https://github.com/CSB-L/AnoChem.

Heterologous biosynthesis of myxobacterial lanthipeptides melittapeptins

The myxobacteria are an attractive bioresource for bioactive compounds since the large size genome contains many biosynthetic gene clusters of secondary metabolites. The genome of the myxobacterium Melittangium boletus contains three biosynthetic gene clusters for lanthipeptide production. One of the gene clusters includes genes coding lanthipeptide precursor (melA), class II lanthipeptide synthetase (melM), and transporter (melT). The amino acid sequence of melA indicated similarity with that of known lanthipeptides mersacidin and lichenicidin A1 by the alignment. To perform heterologous production of new lanthipeptides, the expression vector containing the essential genes (melA and melM) was constructed by utilizing codon-optimized synthetic genes. The co-expression of two genes in the host bacterial cells of Escherichia coli BL21 (DE3) afforded new lanthipeptides named melittapeptins A-C. The structures of melittapeptins A-C including lanthionine/methyllanthionine bridge pattern were proposed based on protease digestion and MS/MS experiments. The native strain of M. boletus did not produce melittapeptins A-C, so heterologous production using the biosynthetic gene cluster was effective in obtaining the lanthipeptides. Melittapeptins A-C showed specific and potent antibacterial activity to the Gram-positive bacterium Micrococcus luteus. To the best of our knowledge, this is the first report of antibacterial lanthipeptides derived from myxobacterial origin. KEY POINTS: • New lanthipeptides melittapeptins were heterologously produced in Escherichia coli. • Melittapeptins showed specific antibacterial activity against Micrococcus luteus. • Melittapeptins were the first antibacterial lanthipeptides of myxobacterial origin.

Deciphering and designing microbial communities by genome-scale metabolic modelling

Microbial communities are shaped by the complex interactions among organisms and the environment. Genome-scale metabolic models (GEMs) can provide deeper insights into the complexity and ecological properties of various microbial communities, revealing their intricate interactions. Many researchers have modified GEMs for the microbial communities based on specific needs. Thus, GEMs need to be comprehensively summarized to better understand the trends in their development. In this review, we summarized the key developments in deciphering and designing microbial communities using different GEMs. A timeline of selected highlights in GEMs indicated that this area is evolving from the single-strain level to the microbial community level. Then, we outlined a framework for constructing GEMs of microbial communities. We also summarized the models and resources of static and dynamic community-level GEMs. We focused on the role of external environmental and intracellular resources in shaping the assembly of microbial communities. Finally, we discussed the key challenges and future directions of GEMs, focusing on the integration of GEMs with quorum sensing mechanisms, microbial ecology interactions, machine learning algorithms, and automatic modeling, all of which contribute to consortia-based applications in different fields.

Genetic, virulence, and antimicrobial resistance characteristics associated with distinct morphotypes in ST11 carbapenem-resistant Klebsiella pneumoniae

ST11 is the most common lineage among carbapenem-resistant Klebsiella pneumoniae (CRKP) infections in Asia. Diverse morphotypes resulting from genetic mutations are associated with significant differences in microbial characteristics among K. pneumoniae isolates. Here, we investigated the genetic determinants and critical characteristics associated with distinct morphotypes of ST11 CRKP. An ST11-KL47 CRKP isolate carrying a pLVPK-like virulence plasmid was isolated from a patient with a bloodstream infection; the isolate had the "mcsw" morphotype. Two distinct morphotypes ("ntrd" and "msdw") were derived from this strain during in vitro passage. Whole genome sequencing was used to identify mutations that cause the distinct morphotypes of ST11 CRKP. Transmission electron microscopy, antimicrobial susceptibility tests, growth assays, biofilm formation, virulence assays, membrane permeability assays, and RNA-seq analysis were used to investigate the specific characteristics associated with different morphotypes of ST11 CRKP. Compared with the parental mcsw morphotype, the ntrd morphotype resulted from mutation of genes involved in capsular polysaccharide biosynthesis (wza, wzc, and wbaP), a result validated by gene knockout experiments. This morphotype showed capsule deficiency and lower virulence potential, but higher biofilm production. By contrast, the msdw morphotype displayed competition deficiency and increased susceptibility to chlorhexidine and polymyxin B. Further analyses indicated that these characteristics were caused by interruption of the sigma factor gene rpoN by insertion mutations and deletion of the rpoN gene, which attenuated membrane integrity presumably by downregulating the phage shock protein operon. These data expand current understanding of genetic, virulence, and antimicrobial resistance characteristics associated with distinct morphotypes in ST11 CRKP.

The pathogenicity and virulence of the opportunistic pathogen Staphylococcus epidermidis

The pervasive presence of Staphylococcus epidermidis and other coagulase-negative staphylococci on the skin and mucous membranes has long underpinned a casual disregard for the infection risk that these organisms pose to vulnerable patients in healthcare settings. Prior to the recognition of biofilm as an important virulence determinant in S. epidermidis, isolation of this microorganism in diagnostic specimens was often overlooked as clinically insignificant with potential delays in diagnosis and onset of appropriate treatment, contributing to the establishment of chronic infection and increased morbidity or mortality. While impressive progress has been made in our understanding of biofilm mechanisms in this important opportunistic pathogen, research into other virulence determinants has lagged S. aureus. In this review, the broader virulence potential of S. epidermidis including biofilm, toxins, proteases, immune evasion strategies and antibiotic resistance mechanisms is surveyed, together with current and future approaches for improved therapeutic interventions.

Methods in DNA methylation array dataset analysis: A review

Understanding the intricate relationships between gene expression levels and epigenetic modifications in a genome is crucial to comprehending the pathogenic mechanisms of many diseases. With the advancement of DNA Methylome Profiling techniques, the emphasis on identifying Differentially Methylated Regions (DMRs/DMGs) has become crucial for biomarker discovery, offering new insights into the etiology of illnesses. This review surveys the current state of computational tools/algorithms for the analysis of microarray-based DNA methylation profiling datasets, focusing on key concepts underlying the diagnostic/prognostic CpG site extraction. It addresses methodological frameworks, algorithms, and pipelines employed by various authors, serving as a roadmap to address challenges and understand changing trends in the methodologies for analyzing array-based DNA methylation profiling datasets derived from diseased genomes. Additionally, it highlights the importance of integrating gene expression and methylation datasets for accurate biomarker identification, explores prognostic prediction models, and discusses molecular subtyping for disease classification. The review also emphasizes the contributions of machine learning, neural networks, and data mining to enhance diagnostic workflow development, thereby improving accuracy, precision, and robustness.

MKG-GC: A multi-task learning-based knowledge graph construction framework with personalized application to gastric cancer

Over the past decade, information for precision disease medicine has accumulated in the form of textual data. To effectively utilize this expanding medical text, we proposed a multi-task learning-based framework based on hard parameter sharing for knowledge graph construction (MKG), and then used it to automatically extract gastric cancer (GC)-related biomedical knowledge from the literature and identify GC drug candidates. In MKG, we designed three separate modules, MT-BGIPN, MT-SGTF and MT-ScBERT, for entity recognition, entity normalization, and relation classification, respectively. To address the challenges posed by the long and irregular naming of medical entities, the MT-BGIPN utilized bidirectional gated recurrent unit and interactive pointer network techniques, significantly improving entity recognition accuracy to an average F1 value of 84.5% across datasets. In MT-SGTF, we employed the term frequency-inverse document frequency and the gated attention unit. These combine both semantic and characteristic features of entities, resulting in an average Hits@ 1 score of 94.5% across five datasets. The MT-ScBERT integrated cross-text, entity, and context features, yielding an average F1 value of 86.9% across 11 relation classification datasets. Based on the MKG, we then developed a specific knowledge graph for GC (MKG-GC), which encompasses a total of 9129 entities and 88,482 triplets. Lastly, the MKG-GC was used to predict potential GC drugs using a pre-trained language model called BioKGE-BERT and a drug-disease discriminant model based on CNN-BiLSTM. Remarkably, nine out of the top ten predicted drugs have been previously reported as effective for gastric cancer treatment. Finally, an online platform was created for exploration and visualization of MKG-GC at https://www.yanglab-mi.org.cn/MKG-GC/.

Combining Off-flow, a Nextflow-coded program, and whole genome sequencing reveals unintended genetic variation in CRISPR/Cas-edited iPSCs

Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-Cas nucleases and human induced pluripotent stem cell (iPSC) technology can reveal deep insight into the genetic and molecular bases of human biology and disease. Undesired editing outcomes, both on-target (at the edited locus) and off-target (at other genomic loci) hinder the application of CRISPR-Cas nucleases. We developed Off-flow, a Nextflow-coded bioinformatic workflow that takes a specific guide sequence and Cas protein input to call four separate off-target prediction programs (CHOPCHOP, Cas-Offinder, CRISPRitz, CRISPR-Offinder) to output a comprehensive list of predicted off-target sites. We applied it to whole genome sequencing (WGS) data to investigate the occurrence of unintended effects in human iPSCs that underwent repair or insertion of disease-related variants by homology-directed repair. Off-flow identified a 3-base-pair-substitution and a mono-allelic genomic deletion at the target loci, KCNQ2, in 2 clones. Unbiased WGS analysis further identified off-target missense variants and a mono-allelic genomic deletion at the targeted locus, GNAQ, in 10 clones. On-target substitution and deletions had escaped standard PCR and Sanger sequencing analysis, while missense variants at other genomic loci were not detected by Off-flow. We used these results to filter out iPSC clones for subsequent functional experiments. Off-flow, which we make publicly available, works for human and mouse genomes currently and can be adapted for other genomes. Off-flow and WGS analysis can improve the integrity of studies using CRISPR/Cas-edited cells and animal models.

Heterochronous multiplex real-time PCR with intercalating dye using uracil-DNA N-glycosylase (UNG) and multiple primer pairs to revaluate post PCR product

Real-time PCR with intercalating dyes can only be performed once. The expensive fluorescent hydrolysis probes are target specific and are suitable to detect multiplex targets. Uracil-DNA N-glycosylase (UNG), which specifically hydrolyzes and degrades any uracil-containing PCR products, is often applied before PCR to reduce carryover contamination. We developed an optimized protocol for recovering DNA from PCR products and revaluating by real-time PCR with intercalating dye using UNG processing, which is particularly useful when the sample volume is very small and insufficient for multiple assays of real-time PCR.•A real-time PCR master mix with dUTP instead of dTTP was used.•UNG at 1 % and 10 % concentrations of PCR product volumes were used for the first and second processing.•The second real-time PCR was performed with different primer pairs than the first real-time PCR.

Mycolyltransferase is important for biofilm formation and pathogenesis of Tsukamurella keratitis

Tsukamurella, a group of multi-drug resistant, Gram-positive, aerobic, and partially acid-fast bacteria, are emerging causes of bacterial conjunctivitis and keratitis. However, the pathogenesis of Tsukamurella keratitis is largely unknown. To address this, we used New Zealand White rabbits to develop the first eye infection model and conducted in vitro tests to study the pathogenesis mechanisms of Tsukamurella. There is increasing evidence that biofilms play a significant role in ocular infections, leading us to hypothesize that biofilm formation is crucial for effective Tsukamurella infection. In order to look for potential candidate genes which are important in biofilm formation and Tsukamurella keratitis. We performed genome sequencing of two ocular isolates, T. pulmonis-PW1004 and T. tyrosinosolvens-PW899, to identify potential virulence factors. Through in vitro and in vivo studies, we characterized their biological roles in mediating Tsukamurella keratitis. Our findings confirmed that Tsukamurella is an ocular pathogen by fulfilling Koch's postulates, and using genome sequence data, we identified tmytC, encoding a mycolyltransferase, as a crucial gene in biofilm formation and causing Tsukamurella keratitis in the rabbit model. This is the first report demonstrating the novel role of mycolyltransferase in causing ocular infections. Overall, our findings contribute to a better understanding of Tsukamurella pathogenesis and provide a potential target for treatment. Specific inhibitors targeting TmytC could serve as an effective treatment option for Tsukamurella infections.

Structure-based protein and small molecule generation using EGNN and diffusion models: A comprehensive review

Recent breakthroughs in deep learning have revolutionized protein sequence and structure prediction. These advancements are built on decades of protein design efforts, and are overcoming traditional time and cost limitations. Diffusion models, at the forefront of these innovations, significantly enhance design efficiency by automating knowledge acquisition. In the field of de novo protein design, the goal is to create entirely novel proteins with predetermined structures. Given the arbitrary positions of proteins in 3-D space, graph representations and their properties are widely used in protein generation studies. A critical requirement in protein modelling is maintaining spatial relationships under transformations (rotations, translations, and reflections). This property, known as equivariance, ensures that predicted protein characteristics adapt seamlessly to changes in orientation or position. Equivariant graph neural networks offer a solution to this challenge. By incorporating equivariant graph neural networks to learn the score of the probability density function in diffusion models, one can generate proteins with robust 3-D structural representations. This review examines the latest deep learning advancements, specifically focusing on frameworks that combine diffusion models with equivariant graph neural networks for protein generation.

Study of the multitarget mechanism of Astragalus (HUANGQI) in the treatment of Alzheimer's disease based on network pharmacology and molecular docking technology

CONTEXT

In China, HUANGQI is widely used for the treatment of Alzheimer's disease (AD). However, a comprehensive understanding of its mechanism of anti-AD effects is lacking.

OBJECTIVE

To explore the active ingredients of HUANGQI and its potential targets and mechanisms of action in AD.

MATERIALS AND METHODS

The active ingredients and targets of HUANGQI were screened from databases (TCSMP, ETCM, and BATMan), and AD-related genes were obtained from DrugBank and GeneCards. The same target genes were screened, and a drug-target disease network was constructed. The PPI network was constructed and GO and KEGG pathway enrichment analyses of the targets. The Cell Counting Kit-8 (CCK-8) assay was used to determine suitable HUANGQI treatment concentrations for HT-22 cells between 0-480 μg/mL. CCK-8, FITC-phalloidin and propidium iodide (PI) assays were used to examine the protective effect of (0, 60, 120, 240 μg/mL) of HUANGQI on 20 μM Aβ1-42-induced HT-22 cell cytotoxicity.

RESULTS

Twelve active ingredients of HUANGQI were selected, with 679 common targets associated with AD. GO and KEGG analysis revealed that the therapeutic mechanisms of HUANGQI involve TNF, AGE, the NF-κB pathway, and nuclear receptor activity-related processes. The CCK-8 assay indicated that HUANGQI was not cytotoxic to HT-22 cells at concentrations less than 240 μg/mL and was able to attenuate Aβ1-42-induced cellular damage (EC50 = 83.46 μg/mL). FITC-phalloidin and PI assays suggested that HUANGQI could alleviate 20 μM Aβ1-42-induced neuronal cell cytotoxicity in a dose-dependent manner.

CONCLUSION

HUANGQI has a protective effect on Aβ1-42-induced nerve cell injury; further mechanism research was needed.

Systematic data analysis pipeline for quantitative morphological cell phenotyping

Quantitative morphological phenotyping (QMP) is an image-based method used to capture morphological features at both the cellular and population level. Its interdisciplinary nature, spanning from data collection to result analysis and interpretation, can lead to uncertainties, particularly among those new to this actively growing field. High analytical specificity for a typical QMP is achieved through sophisticated approaches that can leverage subtle cellular morphological changes. Here, we outline a systematic workflow to refine the QMP methodology. For a practical review, we describe the main steps of a typical QMP; in each step, we discuss the available methods, their applications, advantages, and disadvantages, along with the R functions and packages for easy implementation. This review does not cover theoretical backgrounds, but provides several references for interested researchers. It aims to broaden the horizons for future phenome studies and demonstrate how to exploit years of endeavors to achieve more with less.

A dual-gene panel of two fragments of methylated IRF4 and one of ZEB2 in plasma cell-free DNA for gastric cancer detection

Early detection is crucial for increasing the survival rate of gastric cancer (GC). We aimed to identify a methylated cell-free DNA (cfDNA) marker panel for detecting GC. The differentially methylated CpGs (DMCs) were selected from datasets of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The selected DMCs were validated and further selected in tissue samples (40 gastric cancer and 36 healthy white blood cell samples) and in a quarter sample volume of plasma samples (37 gastric cancer, 12 benign gastric disease, and 43 healthy individuals). The marker combination selected was then evaluated in a normal sample volume of plasma samples (35 gastric cancer, 39 control diseases, and 40 healthy individuals) using real-time methylation-specific PCR (MSP). The analysis of the results compared methods based on 2-ΔΔCt values and Ct values. In the results, 30 DMCs were selected through bioinformatics methods, and then 5 were selected for biological validation. The marker combination of two fragments of IRF4 (IRF4-1 and IRF4-2) and one of ZEB2 was selected due to its good performance. The Ct-based method was selected for its good results and practical advantages. The assay, IRF4-1 and IRF4-2 in one fluorescence channel and ZEB2 in another, obtained 74.3% sensitivity for the GC group at any stage, at 92.4% specificity. In conclusion, the panel of IRF4 and ZEB2 in plasma cfDNA demonstrates good diagnostic performance and application potential in clinical settings.

Identification and Validation of Aging Related Genes Signature in Chronic Obstructive Pulmonary Disease

PURPOSE

Chronic Obstructive Pulmonary Disease (COPD) is regarded as an accelerated aging disease. Aging-related genes in COPD are still poorly understood.

METHOD

Data set GSE76925 was obtained from the Gene Expression Omnibus (GEO) database. The "limma" package identified the differentially expressed genes. The weighted gene co-expression network analysis (WGCNA) constructes co-expression modules and detect COPD-related modules. The least absolute shrinkage and selection operator (LASSO) and the support vector machine recursive feature elimination (SVM-RFE) algorithms were chosen to identify the hub genes and the diagnostic ability. Three external datasets were used to identify differences in the expression of hub genes. Real-time reverse transcription polymerase chain reaction (RT-qPCR) was used to verify the expression of hub genes.

RESULT

We identified 15 differentially expressed genes associated with aging (ARDEGs). The SVM-RFE and LASSO algorithms pinpointed four potential diagnostic biomarkers. Analysis of external datasets confirmed significant differences in PIK3R1 expression. RT-qPCR results indicated decreased expression of hub genes. The ROC curve demonstrated that PIK3R1 exhibited strong diagnostic capability for COPD.

CONCLUSION

We identified 15 differentially expressed genes associated with aging. Among them, PIK3R1 showed differences in external data sets and RT-qPCR results. Therefore, PIK3R1 may play an essential role in regulating aging involved in COPD.

Expression patterns and clinical value of key m6A RNA modification regulators in smoking patients with coronary artery disease

Even though N6-methyladenosine (m6A) RNA modifications are increasingly being implicated in human disease, their mechanisms are not fully understood in smokers with coronary artery disease (CAD). Thirty m6A-related regulators' expression (MRRE) in CAD individuals (smokers and non-smokers) were analyzed from GEO. Support Vector Machine, random forest, and nomogram models were constructed to assess its clinical value. Consensus clustering, principal component analysis, and ssGSEA were used to construct a full picture of m6A-related regulators in smokers with CAD. Oxygen-glucose deprivation (OGD) and qRT-PCR were used to validate hypoxia's effect on MRRE. A comparison between smokers with CAD and controls revealed lower expression levels of RBM15B, YTHDC2, and ZC3H13. Based on three key MRREs, all models showed good clinical value, and smokers with CAD were divided into two distinct molecular subgroups. The correlations were found between key MRRE and the degree of immune infiltration. Three key MRREs in HUVECs and FMC84 mouse cardiomyocytes were reduced in the OGD group. Through hypoxia, smoking might reduce the expression levels of RBM15B, YTHDC2, and ZC3H13 in smokers with CAD. Our findings provide an important theoretical basis for the treatment of smokers with CAD.

In-cell bioluminescence resonance energy transfer (BRET)-based assay uncovers ceritinib and CA-074 as SARS-CoV-2 papain-like protease inhibitors

Papain-like protease (PLpro) is an attractive anti-coronavirus target. The development of PLpro inhibitors, however, is hampered by the limitations of the existing PLpro assay and the scarcity of validated active compounds. We developed a novel in-cell PLpro assay based on BRET and used it to evaluate and discover SARS-CoV-2 PLpro inhibitors. The developed assay demonstrated remarkable sensitivity for detecting the reduction of intracellular PLpro activity while presenting high reliability and performance for inhibitor evaluation and high-throughput screening. Using this assay, three protease inhibitors were identified as novel PLpro inhibitors that are structurally disparate from those previously known. Subsequent enzymatic assays and ligand-protein interaction analysis based on molecular docking revealed that ceritinib directly inhibited PLpro, showing high geometric complementarity with the substrate-binding pocket in PLpro, whereas CA-074 methyl ester underwent intracellular hydrolysis, exposing a free carboxyhydroxyl group essential for hydrogen bonding with G266 in the BL2 groove, resulting in PLpro inhibition.

The perinucleolar compartment: structure, function, and utility in anti-cancer drug development

The perinucleolar compartment (PNC) was initially identified as a nuclear structure enriched for the polypyrimidine tract-binding protein. Since then, the PNC has been implicated in carcinogenesis. The prevalence of this compartment is positively correlated with disease progression in various types of cancer, and its expression in primary tumors is linked to worse patient outcomes. Using the PNC as a surrogate marker for anti-cancer drug efficacy has led to the development of a clinical candidate for anti-metastasis therapies. The PNC is a multicomponent nuclear body situated at the periphery of the nucleolus. Thus far, several non-coding RNAs and RNA-binding proteins have been identified as the PNC components. Here, we summarize the current understanding of the structure and function of the PNC, as well as its recurrent links to cancer progression and metastasis.

Comparison of two peroxidases with high potential for biotechnology applications - HRP vs. APEX2

Peroxidases are essential elements in many biotechnological applications. An especially interesting concept involves split enzymes, where the enzyme is separated into two smaller and inactive proteins that can dimerize into a fully active enzyme. Such split forms were developed for the horseradish peroxidase (HRP) and ascorbate peroxidase (APX) already. Both peroxidases have a high potential for biotechnology applications. In the present study, we performed biophysical comparisons of these two peroxidases and their split analogues. The active site availability is similar for all four structures. The split enzymes are comparable in stability with their native analogues, meaning that they can be used for further biotechnology applications. Also, the tertiary structures of the two peroxidases are similar. However, differences that might help in choosing one system over another for biotechnology applications were noticed. The main difference between the two systems is glycosylation which is not present in the case of APX/sAPEX2, while it has a high impact on the HRP/sHRP stability. Further differences are calcium ions and cysteine bridges that are present only in the case of HRP/sHRP. Finally, computational results identified sAPEX2 as the systems with the smallest structural variations during molecular dynamics simulations showing its dominant stability comparing to other simulated proteins. Taken all together, the sAPEX2 system has a high potential for biotechnological applications due to the lack of glycans and cysteines, as well as due to high stability.

Enhancing Mass spectrometry-based tumor immunopeptide identification: machine learning filter leveraging HLA binding affinity, aliphatic index and retention time deviation

Accurately identifying neoantigens is crucial for developing effective cancer vaccines and improving tumor immunotherapy. Mass spectrometry-based immunopeptidomics has emerged as a promising approach to identifying human leukocyte antigen (HLA) peptides presented on the surface of cancer cells, but false-positive identifications remain a significant challenge. In this study, liquid chromatography-tandem mass spectrometry-based proteomics and next-generation sequencing were utilized to identify HLA-presenting neoantigenic peptides resulting from non-synonymous single nucleotide variations in tumor tissues from 18 patients with renal cell carcinoma or pancreatic cancer. Machine learning was utilized to evaluate Mascot identifications through the prediction of MS/MS spectral consistency, and four descriptors for each candidate sequence: the max Mascot ion score, predicted HLA binding affinity, aliphatic index and retention time deviation, were selected as important features in filtering out identifications with inadequate fragmentation consistency. This suggests that incorporating rescoring filters based on peptide physicochemical characteristics could enhance the identification rate of MS-based immunopeptidomics compared to the traditional Mascot approach predominantly used for proteomics, indicating the potential for optimizing neoantigen identification pipelines as well as clinical applications.

Moura A, Filho ODMM, Pessoa C, Miranda Furtado CL. Hypomethylation at H19DMR in penile squamous cell carcinoma is not related to HPV infection

Penile squamous cell carcinoma (SCC) is a rare and aggressive tumour mainly related to lifestyle behaviour and human papillomavirus (HPV) infection. Environmentally induced loss of imprinting (LOI) at the H19 differentially methylated region (H19DMR) is associated with many cancers in the early events of tumorigenesis and may be involved in the pathogenesis of penile SCC. We sought to evaluate the DNA methylation pattern at H19DMR and its association with HPV infection in men with penile SCC by bisulfite sequencing (bis-seq). We observed an average methylation of 32.2% ± 11.6% at the H19DMR of penile SCC and did not observe an association between the p16INK4a+ (p = 0.59) and high-risk HPV+ (p = 0.338) markers with methylation level. The average methylation did not change according to HPV positive for p16INK4a+ or hrHPV+ (35.4% ± 10%) and negative for both markers (32.4% ± 10.1%) groups. As the region analysed has a binding site for the CTCF protein, the hypomethylation at the surrounding CpG sites might alter its insulator function. In addition, there was a positive correlation between intense polymorphonuclear cell infiltration and hypomethylation at H19DMR (p = 0.035). Here, we report that hypomethylation at H19DMR in penile SCC might contribute to tumour progression and aggressiveness regardless of HPV infection.

Epigenetic changes in pyloric caeca of Atlantic salmon fed diets containing increasing levels of lipids and choline

An earlier study of ours investigating the effect of dietary lipid levels on the choline requirement of Atlantic salmon showed increasing severity of intestinal steatosis with increasing lipid levels. As choline is involved in epigenetic regulation by being the key methyl donor, pyloric caeca samples from the study were analysed for epigenetic effects of dietary lipid and choline levels. The diets varied in lipid levels between 16% and 28%, and choline levels between 1.9 and 2.3 g/kg. The diets were fed for 8 weeks to Atlantic salmon of 25 g of initial weight. Using reduced representation bisulfite sequencing (RRBS), this study revealed that increasing dietary lipid levels induced methylation differences in genes involved in membrane transport and signalling pathways, and in microRNAs important for the regulation of lipid homoeostasis. Increasing choline levels also affected genes involved in fatty acid biosynthesis and transport, lipolysis, and lipogenesis, as well as important immune genes. Our observations confirmed that choline is involved in epigenetic regulation in Atlantic salmon, as has been reported for higher vertebrates. This study showed the need for the inclusion of biomarkers of epigenetic processes in studies that must be conducted to define optimal choline levels in diets for Atlantic salmon.

Insights into the regulatory role of bacterial sncRNA and its extracellular delivery via OMVs

Small noncoding RNAs (sncRNAs) play important regulatory roles in bacterial physiological processes and host-pathogen interactions. Meanwhile, bacterial outer membrane vesicles (OMVs), as naturally secreted outer membrane structures, play a vital role in the interaction between bacteria and their living environment, including the host environment. However, most current studies focus on the biological functions of sncRNAs in bacteria or hosts, while neglecting the roles and regulatory mechanisms of the OMVs that encapsulate these sncRNAs. Therefore, this review aims to summarize the intracellular regulatory roles of bacterial sncRNAs in promoting pathogen survival by regulating virulence, modulating bacterial drug resistance, and regulating iron metabolism, and their extracellular regulatory function for influencing host immunity through host-pathogen interactions. Additionally, we introduce the key role played by OMVs, which serve as important cargoes in bacterial sncRNA-host interactions. We propose emerging pathways of sncRNA action to further discuss the mode of host-pathogen interactions, highlighting that the inhibition of sncRNA delivery by OMVs may prevent the occurrence of infection to some extent. Hence, this review lays the foundation for future prophylactic treatments against bacterial infections and strategies for addressing bacterial drug resistance. KEY POINTS: •sncRNAs have intracellular and extracellular regulatory functions in bacterial physiological processes and host-pathogen interactions. •OMVs are potential mediators between bacterial sncRNAs and host cells. •OMVs encapsulating sncRNAs have more potential biological functions.

Bacteriophage cocktail for biocontrol of soft rot disease caused by Pectobacterium species in Chinese cabbage

Pectobacterium spp. are necrotrophic plant pathogens that cause the soft rot disease in Chinese cabbage, resulting in severe yield loss. The use of conventional antimicrobial agents, copper-based bactericides, and antibiotics has encountered several limitations, such as bioaccumulation on plants and microbial resistance. Bacteriophages (phages) are considered promising alternative antimicrobial agents against diverse phytopathogens. In this study, we isolated and characterized two virulent phages (phiPccP-2 and phiPccP-3) to develop a phage cocktail. Morphological and genomic analyses revealed that two phages belonged to the Tevenvirinae and Mccorquodalevirinae subfamilies, respectively. The phiPccP-2 and phiPccP-3 phages, which have a broad host range, were stable at various environmental conditions, such as various pHs and temperatures and exposure to ultraviolet light. The phage cocktail developed using these two lytic phages inhibited the emergence of phage-resistant bacteria compared to single-phage treatments in in vitro challenge assays. The phage cocktail treatment effectively prevented the development of soft rot symptom in matured Chinese cabbage leaves. Additionally, the phage cocktail comprising three phages (phiPccP-1, phiPccP-2, and phiPccP-3) showed superior biocontrol efficacy against the mixture of Pectobacterium strains in Chinese cabbage seedlings. These results suggest that developing phage cocktails is an effective approach for biocontrol of soft rot disease caused by Pectobacterium strains in crops compared to single-phage treatments. KEY POINTS: •Two newly isolated Pectobacterium phages, phiPccP-2 and phiPccP-3, infected diverse Pectobacterium species and effectively inhibited the emergence of phage-resistant bacteria. •Genomic and physiological analyses suggested that both phiPccP-2 and phiPccP-3 are lytic phages and that their lytic activities are stable in the environmental conditions under which Chinese cabbage grows. •Treatment using a phage cocktail comprising phiPccP-2 and phiPccP-3 efficiently suppressed soft rot disease in detached mature leaves and seedlings of Chinese cabbage, indicating the applicability of the phage cocktail as an alternative antimicrobial agent.

Integrated omics analysis reveals the alteration of gut microbiota and fecal metabolites in Cervus elaphus kansuensis

The gut microbiota is the largest and most complex microecosystem in animals. It is influenced by the host's dietary habits and living environment, and its composition and diversity play irreplaceable roles in animal nutrient metabolism, immunity, and adaptation to the environment. Although the gut microbiota of red deer has been studied, the composition and function of the gut microbiota in Gansu red deer (Cervus elaphus kansuensis), an endemic subspecies of red deer in China, has not been reported. In this study, the composition and diversity of the gut microbiome and fecal metabolomics of C. elaphus kansuensis were identified and compared for the first time by using 16S rDNA sequencing, metagenomic sequencing, and LC-MS/MS. There were significant differences in gut microbiota structure and diversity between wild and farmed C. elaphus kansuensis. The 16S rDNA sequencing results showed that the genus UCRD-005 was dominant in both captive red deer (CRD) and wild red deer (WRD). Metagenomic sequencing showed similar results to those of 16S rDNA sequencing for gut microbiota in CRD and WRD at the phylum and genus levels. 16S rDNA and metagenomics sequencing data suggested that Bacteroides and Bacillus might serve as marker genera for CRD and WRD, respectively. Fecal metabolomics results showed that 520 metabolites with significant differences were detected between CRD and WRD and most differential metabolites were involved in lipid metabolism. The results suggested that large differences in gut microbiota composition and fecal metabolites between CRD and WRD, indicating that different dietary habits and living environments over time have led to the development of stable gut microbiome characteristics for CRD and WRD to meet their respective survival and reproduction needs. KEY POINTS: • Environment and food affected the gut microbiota and fecal metabolites in red deer • Genera Bacteroides and Bacillus may play important roles in CRD and WRD, respectively • Flavonoids and ascorbic acid in fecal metabolites may influence health of red deer.

Combined transcriptomic and pangenomic analyses guide metabolic amelioration to enhance tiancimycins production

Exploration of high-yield mechanism is important for further titer improvement of valuable antibiotics, but how to achieve this goal is challenging. Tiancimycins (TNMs) are anthraquinone-fused enediynes with promising drug development potentials, but their prospective applications are limited by low titers. This work aimed to explore the intrinsic high-yield mechanism in previously obtained TNMs high-producing strain Streptomyces sp. CB03234-S for the further titer amelioration of TNMs. First, the typical ribosomal RpsL(K43N) mutation in CB03234-S was validated to be merely responsible for the streptomycin resistance but not the titer improvement of TNMs. Subsequently, the combined transcriptomic, pan-genomic and KEGG analyses revealed that the significant changes in the carbon and amino acid metabolisms could reinforce the metabolic fluxes of key CoA precursors, and thus prompted the overproduction of TNMs in CB03234-S. Moreover, fatty acid metabolism was considered to exert adverse effects on the biosynthesis of TNMs by shunting and reducing the accumulation of CoA precursors. Therefore, different combinations of relevant genes were respectively overexpressed in CB03234-S to strengthen fatty acid degradation. The resulting mutants all showed the enhanced production of TNMs. Among them, the overexpression of fadD, a key gene responsible for the first step of fatty acid degradation, achieved the highest 21.7 ± 1.1 mg/L TNMs with a 63.2% titer improvement. Our studies suggested that comprehensive bioinformatic analyses are effective to explore metabolic changes and guide rational metabolic reconstitution for further titer improvement of target products. KEY POINTS: • Comprehensive bioinformatic analyses effectively reveal primary metabolic changes. • Primary metabolic changes cause precursor enrichment to enhance TNMs production. • Strengthening of fatty acid degradation further improves the titer of TNMs.

Current models in bacterial hemicellulase-encoding gene regulation

The discovery and characterization of bacterial carbohydrate-active enzymes is a fundamental component of biotechnology innovation, particularly for renewable fuels and chemicals; however, these studies have increasingly transitioned to exploring the complex regulation required for recalcitrant polysaccharide utilization. This pivot is largely due to the current need to engineer and optimize enzymes for maximal degradation in industrial or biomedical applications. Given the structural simplicity of a single cellulose polymer, and the relatively few enzyme classes required for complete bioconversion, the regulation of cellulases in bacteria has been thoroughly discussed in the literature. However, the diversity of hemicelluloses found in plant biomass and the multitude of carbohydrate-active enzymes required for their deconstruction has resulted in a less comprehensive understanding of bacterial hemicellulase-encoding gene regulation. Here we review the mechanisms of this process and common themes found in the transcriptomic response during plant biomass utilization. By comparing regulatory systems from both Gram-negative and Gram-positive bacteria, as well as drawing parallels to cellulase regulation, our goals are to highlight the shared and distinct features of bacterial hemicellulase-encoding gene regulation and provide a set of guiding questions to improve our understanding of bacterial lignocellulose utilization. KEY POINTS: • Canonical regulatory mechanisms for bacterial hemicellulase-encoding gene expression include hybrid two-component systems (HTCS), extracytoplasmic function (ECF)-σ/anti-σ systems, and carbon catabolite repression (CCR). • Current transcriptomic approaches are increasingly being used to identify hemicellulase-encoding gene regulatory patterns coupled with computational predictions for transcriptional regulators. • Future work should emphasize genetic approaches to improve systems biology tools available for model bacterial systems and emerging microbes with biotechnology potential. Specifically, optimization of Gram-positive systems will require integration of degradative and fermentative capabilities, while optimization of Gram-negative systems will require bolstering the potency of lignocellulolytic capabilities.