The LARP6 La module from Tetrabaena socialis reveals structural and functional differences from plant and animal LARP6 homologues

This study identified the LARP6 La Module from Tetrabaena socialis (T. socialis), a four-celled green algae, in an effort to better understand the evolution of LARP6 structure and RNA-binding activity in multicellular eukaryotes. Using a combination of sequence alignments, domain boundary screens, and structural modelling, we recombinantly expressed and isolated the TsLARP6 La Module to > 98% purity for in vitro biochemical characterization. The La Module is stably folded and exerts minimal RNA binding activity against single-stranded homopolymeric RNAs. Surprisingly, it exhibits low micromolar binding affinity for the vertebrate LARP6 cognate ligand, a bulged-stem loop found in the 5'UTR of collagen type I mRNA, but does not bind double-stranded RNAs of similar size. These result suggests that the TsLARP6 La Module may prefer structured RNA ligands. In contrast, however, the TsLARP6 La Module does not exhibit the RNA chaperone activity that is observed in vertebrate homologs. Therefore, we conclude that protist LARP6 may have both distinct RNA ligands and binding mechanisms from the previously characterized LARP6 proteins of animals and vascular plants, thus establishing a distinct third class of the LARP6 protein family.

Nuclear envelope and chromatin choreography direct cellular differentiation

The nuclear envelope plays an indispensable role in the spatiotemporal organization of chromatin and transcriptional regulation during the intricate process of cell differentiation. This review outlines the distinct regulatory networks between nuclear envelope proteins, transcription factors and epigenetic modifications in controlling the expression of cell lineage-specific genes during differentiation. Nuclear lamina with its associated nuclear envelope proteins organize heterochromatin via Lamina-Associated Domains (LADs), proximal to the nuclear periphery. Since nuclear lamina is mechanosensitive, we critically examine the impact of extracellular forces on differentiation outcomes. The nuclear envelope is spanned by nuclear pore complexes which, in addition to their central role in transport, are associated with chromatin organization. Furthermore, mutations in the nuclear envelope proteins disrupt differentiation, resulting in developmental disorders. Investigating the underlying nuclear envelope controlled regulatory mechanisms of chromatin remodelling during lineage commitment will accelerate our fundamental understanding of developmental biology and regenerative medicine.

Genomic islands and molecular mechanisms relating to drug-resistance in Clostridioides (Clostridium) difficile PCR ribotype 176

OBJECTIVES

To analyse characteristics of Clostridioides difficile PCR ribotype 176 clinical isolates from Poland, the Czech Republic and Slovakia with regard to the differences in its epidemiology.

METHODS

Antimicrobial susceptibility testing and whole genome sequencing were performed on a selected group of 22 clonally related isolates as determined by multilocus variable-number tandem repeat analysis (n = 509). Heterologous expression and functional analysis of the newly identified methyltransferase were performed.

RESULTS

Core genome multilocus sequence typing found 10-37 allele differences. All isolates were resistant to fluoroquinolones (gyrA_p. T82I), aminoglycosides with aac(6')-Ie-aph(2'')-Ia in six isolates. Erythromycin resistance was detected in 21/22 isolates and 15 were also resistant to clindamycin with ermB gene. Fourteen isolates were resistant to rifampicin with rpoB_p. R505K or p. R505K/H502N, and five to imipenem with pbp1_p. P491L and pbp3_p. N537K. PnimBG together with nimB_p. L155I were detected in all isolates but only five were resistant to metronidazole on chocolate agar. The cfrE, vanZ1 and cat-like genes were not associated with linezolid, teicoplanin and chloramphenicol resistance, respectively. The genome comparison identified six transposons carrying antimicrobial resistance genes. The ermB gene was carried by new Tn7808, Tn6189 and Tn6218-like. The aac(6')-Ie-aph(2'')-Ia were carried by Tn6218-like and new Tn7806 together with cfrE gene. New Tn7807 carried a cat-like gene. Tn6110 and new Tn7806 contained an RlmN-type 23S rRNA methyltransferase, designated MrmA, associated with high-level macrolide resistance in isolates without ermB gene.

CONCLUSIONS

Multidrug-resistant C. difficile PCR ribotype 176 isolates carry already described and unique transposons. A novel mechanism for erythromycin resistance in C. difficile was identified.

Nuclear envelope components in vascular mechanotransduction: emerging roles in vascular health and disease

The vascular network, uniquely sensitive to mechanical changes, translates biophysical forces into biochemical signals for vessel function. This process relies on the cell's architectural integrity, enabling uniform responses to physical stimuli. Recently, the nuclear envelope (NE) has emerged as a key regulator of vascular cell function. Studies implicate nucleoskeletal elements (e.g. nuclear lamina) and the linker of nucleoskeleton and cytoskeleton (LINC) complex in force transmission, emphasizing nucleo-cytoskeletal communication in mechanotransduction. The nuclear pore complex (NPC) and its component proteins (i.e. nucleoporins) also play roles in cardiovascular disease (CVD) progression. We herein summarize evidence on the roles of nuclear lamina proteins, LINC complex members, and nucleoporins in endothelial and vascular cell mechanotransduction. Numerous studies attribute NE components in cytoskeletal-related cellular behaviors to insinuate dysregulation of nucleocytoskeletal feedback and nucleocytoplasmic transport as a mechanism of endothelial and vascular dysfunction, and hence implications for aging and vascular pathophysiology.

Release and degradation of dissolved environmental RNAs from zebrafish cells

The sources and degradation profiles of dissolved environmental RNAs from fish in water remain unknown. In this study, laboratory experiments and mathematical modelling were conducted to investigate the permeability of RNA extracted from zebrafish cells through filters, the release of dissolved environmental RNAs from live and dying zebrafish cells, and the degradation of RNA extracted from zebrafish cells in a non-sterile aqueous environment. This research aimed to provide biological and ecological insights into fish RNAs dissolved in water. The results showed that most of the RNA extracted from zebrafish cells was detected in the filtrates after passage through 0.45 µm filters. Over the course of the 6-day experiment, dynamic levels of the RNAs in the liquid environment containing live or dying zebrafish cells were determined. The release and degradation rates of dissolved environmental RNA from zebrafish cells were calculated using mathematical modelling. RNA extracted from zebrafish cells degraded in non-sterile water in the tubes, and after 2 months, more than 15% of the RNAs in the water remained detectable. The half-life of the RNA in the tubes was approximately 20 ~ 43 days. The modelling results suggest that the levels of the dissolved environmental fish RNAs in natural waters or aquariums could be so low that it would be difficult to detect them using current techniques. The results obtained in this study will help develop new methods for measuring the dynamics of dissolved environmental fish RNAs in water and determining their significance.

Identification of host genetic factors modulating β-lactam resistance in Escherichia coli harbouring plasmid-borne β-lactamase through transposon-sequencing

Since β-lactam antibiotics are widely used, emergence of bacteria with resistance to them poses a significant threat to society. In particular, acquisition of genes encoding β-lactamase, an enzyme that degrades β-lactam antibiotics, has been a major contributing factor in the emergence of bacteria that are resistant to β-lactam antibiotics. However, relatively few genetic targets for killing these resistant bacteria have been identified to date. Here, we used a systematic approach called transposon-sequencing (Tn-Seq), to screen the Escherichia coli genome for host genetic factors that, when mutated, affect resistance to ampicillin, one of the β-lactam antibiotics, in a strain carrying a plasmid that encodes β-lactamase. This approach enabled not just the isolation of genes previously known to affect β-lactam resistance, but the additional loci skp, gshA, phoPQ and ypfN. Individual mutations in these genes modestly but consistently affected antibiotic resistance. We have identified that these genes are not only implicated in β-lactam resistance by itself but also play a crucial role in conditions associated with the expression of β-lactamase. GshA and phoPQ appear to contribute to β-lactam resistance by regulating membrane integrity. Notably, the overexpression of the uncharacterized membrane-associated protein, ypfN, has been shown to significantly enhance β-lactam resistance. We applied the genes identified from the screening into Salmonella Typhimurium and Pseudomonas aeruginosa strains, both critical human pathogens with antibiotic resistance, and observed their significant impact on β-lactam resistance. Therefore, these genes can potentially be utilized as therapeutic targets to control the survival of β-lactamase-producing bacteria.

Gut dysbiosis was inevitable, but tolerance was not: temporal responses of the murine microbiota that maintain its capacity for butyrate production correlate with sustained antinociception to chronic morphine

The therapeutic benefits of opioids are compromised by the development of analgesic tolerance, which necessitates higher dosing for pain management thereby increasing the liability for drug dependence and addiction. Rodent models indicate opposing roles of the gut microbiota in tolerance: morphine-induced gut dysbiosis exacerbates tolerance, whereas probiotics ameliorate tolerance. Not all individuals develop tolerance, which could be influenced by differences in microbiota, and yet no study design has capitalized upon this natural variation. We leveraged natural behavioral variation in a murine model of voluntary oral morphine self-administration to elucidate the mechanisms by which microbiota influences tolerance. Although all mice shared similar morphine-driven microbiota changes that largely masked informative associations with variability in tolerance, our high-resolution temporal analyses revealed a divergence in the progression of dysbiosis that best explained sustained antinociception. Mice that did not develop tolerance maintained a higher capacity for production of the short-chain fatty acid (SCFA) butyrate known to bolster intestinal barriers and promote neuronal homeostasis. Both fecal microbial transplantation (FMT) from donor mice that did not develop tolerance and dietary butyrate supplementation significantly reduced the development of tolerance independently of suppression of systemic inflammation. These findings could inform immediate therapies to extend the analgesic efficacy of opioids.

Tips and tricks for gut microbiota investigation using scanning electron microscopy (SEM): going from sample preparation to imaging and landscape analysis

The Gut Microbiota (GM) remains a complex microbial ecosystem with many unknown facets despite significant technologic advancement. This study introduces a novel rapid technique using tabletop scanning electron microscopy (SEM) for investigating GM composition, focusing on Clostridioides difficile infection (CDI) as a representative model for dysbiosis-related diseases. Six stool sample preparation protocols were tested on 40 stool samples to develop an optimized SEM protocol. Protocol stability was evaluated after four-month storage. The optimized protocol produced high-resolution micrographs while maintaining sample integrity over time. SEM investigation of GM was done by analyzing ten stool samples (5-control and 5-C. difficile groups), imaged at low and high magnifications. Object detection analysis generated a SEM-based GM components database helping describe and compare microbial diversity variation between the groups. CDI group revealed a reduction in microbial diversity, compared to the controls. Epithelial and red blood cells were more prevalent in CDI group. Statistical analyses of objects proved clear clustering of samples into CDI and control groups. This study pioneers the proof-of-concept for using tabletop SEM to investigate GM components in a dysbiosis-related disease model. This concept emerges as a complementary technique capable of providing deeper insight to describe GM components previously elusive with other methods.

Highly expressed cell wall genes contribute to robustness of sepal size

Reproducibility in organ size and shape is a fascinating trait of living organisms. The mechanisms underlying such robustness remain, however, to be elucidated. Taking the sepal of Arabidopsis as a model, we investigated whether variability of gene expression plays a role in variation of organ size and shape. Previous work from our team identified cell-wall related genes as being enriched among the genes whose expression is highly variable. We then hypothesized that the variation of measured morphological parameters in cell-wall related single knockout mutants could be correlated with the variation in gene expression of the corresponding gene (the knocked-out gene) in wild-type plants. We analyzed sepal size and shape from 16 cell-wall mutants and found that sepal size variability correlates positively, not with gene expression variation, but with mean gene expression of the corresponding gene in wild type. These findings support a contribution of cell-wall related genes to the robustness of sepal size.

PCK2 promotes invasion and epithelial-to-mesenchymal transition in triple-negative breast cancer by promoting TGF-β/SMAD3 signaling through inhibiting TRIM67-mediated SMAD3 ubiquitination

PCK2, which encodes mitochondrial phosphoenolpyruvate carboxykinase (PEPCK-M), is upregulated in various cancers. We demonstrated high expression of PEPCK-M in approximately half of triple-negative breast cancers (TNBCs) previously. TNBC is associated with an aggressive phenotype and a high metastasis rate. In this study, we investigated the role of PCK2 in TNBC. PCK2 knockdown suppressed proliferation and mTOR signaling in TNBC cells. In addition, cell invasion/migration ability and the expression of epithelial-to-mesenchymal transition (EMT) markers were positively correlated with PCK2 expression in TNBC cells via regulation of transforming growth factor-β (TGF-β)/SMAD3 signaling. SMAD3 was positively regulated by PCK2 in TNBC cells. Knockdown of SMAD3 in PCK2-overexpressing TNBC cells reduced the expression levels of EMT markers, Snail and Slug, and suppressed cell invasion/migration. In addition, PCK2 knockdown attenuated the stimulatory effect of TGF-β on SMAD3 phosphorylation in TNBC cells. PEPCK-M promotes the protein and mRNA expression of SMAD3 via competitive binding to tripartite motif-containing 67 (TRIM67), an E3 ubiquitin ligase, to reduce SMAD3 ubiquitination, which leads to promoting nuclear translocation of SMAD3 and autoregulation of SMAD3 transcription. Moreover, high PCK2 mRNA expression was significantly associated with poor survival in TNBC patients. In conclusion, our study revealed for the first time that PCK2 activates TGF-β/SMAD3 signaling by regulating the expression and phosphorylation of SMAD3 by inhibiting TRIM67-mediated SMAD3 ubiquitination and promoting the stimulatory effect of TGF-β to promote TNBC invasion. The regulatory effect of PCK2 on mTOR and TGF-β/SMAD3 signaling suggests that PCK2 is a potential therapeutic target for suppressing TNBC progression.

The role of the early-life gut microbiome in childhood asthma

Asthma is a chronic disease affecting millions of children worldwide, and in severe cases requires hospitalization. The etiology of asthma is multifactorial, caused by both genetic and environmental factors. In recent years, the role of the early-life gut microbiome in relation to asthma has become apparent, supported by an increasing number of population studies, in vivo research, and intervention trials. Numerous early-life factors, which for decades have been associated with the risk of developing childhood asthma, are now being linked to the disease through alterations of the gut microbiome. These factors include cesarean birth, antibiotic use, breastfeeding, and having siblings or pets, among others. Association studies have highlighted several specific microbes that are altered in children developing asthma, but these can vary between studies and disease phenotype. This demonstrates the importance of the gut microbial ecosystem in asthma, and the necessity of well-designed studies to validate the underlying mechanisms and guide future clinical applications. In this review, we examine the current literature on the role of the gut microbiome in childhood asthma and identify research gaps to allow for future microbial-focused therapeutic applications in asthma.

Gut microbiota and microbial metabolites for osteoporosis

Osteoporosis is an age-related bone metabolic disease. As an essential endocrine organ, the skeletal system is intricately connected with extraosseous organs. The crosstalk between bones and other organs supports this view. In recent years, the link between the gut microecology and bone metabolism has become an important research topic, both in preclinical studies and in clinical trials. Many studies have shown that skeletal changes are accompanied by changes in the composition and structure of the gut microbiota (GM). At the same time, natural or artificial interventions targeting the GM can subsequently affect bone metabolism. Moreover, microbiome-related metabolites may have important effects on bone metabolism. We aim to review the relationships among the GM, microbial metabolites, and bone metabolism and to summarize the potential mechanisms involved and the theory of the gut‒bone axis. We also describe existing bottlenecks in laboratory studies, as well as existing challenges in clinical settings, and propose possible future research directions.

Draw+: network-based computational drug repositioning with attention walking and noise filtering

Purpose

Drug repositioning, a strategy that repurposes already-approved drugs for novel therapeutic applications, provides a faster and more cost-effective alternative to traditional drug discovery. Network-based models have been adopted by many computational methodologies, especially those that use graph neural networks to predict drug-disease associations. However, these techniques frequently overlook the quality of the input network, which is a critical factor for achieving accurate predictions.

Methods

We present a novel network-based framework for drug repositioning, named DRAW+, which incorporates noise filtering and feature extraction using graph neural networks and attention mechanisms. The proposed model first constructs a heterogeneous network that integrates the drug-disease association network with the similarity networks of drugs and diseases, which are upgraded through reduced-rank singular value decomposition. Next, a subgraph surrounding the targeted drug-disease node pair is extracted, allowing the model to focus on local structures. Graph neural networks are then applied to extract structural representation, followed by attention walking to capture key features of the subgraph. Finally, a multi-layer perceptron classifies the subgraph as positive or negative, which indicates the presence of the link between the target node pair.

Results

Experimental validation across three benchmark datasets showed that DRAW+ outperformed seven state-of-the-art methods, achieving the highest average AUROC and AUPRC, 0.963 and 0.564, respectively. Moreover, DRAW+ demonstrated its robustness by achieving the best performance across two additional datasets, further confirming its generalizability and effectiveness in diverse settings.

Conclusions

The proposed network-based computational approach, DRAW+, demonstrates exceptional accuracy and robustness, confirming its effectiveness in drug repositioning tasks.

Integrating multi-omics data to reveal the host-microbiota interactome in inflammatory bowel disease

Numerous studies have accelerated the knowledge expansion on the role of gut microbiota in inflammatory bowel disease (IBD). However, the precise mechanisms behind host-microbe cross-talk remain largely undefined, due to the complexity of the human intestinal ecosystem and multiple external factors. In this review, we introduce the interactome concept to systematically summarize how intestinal dysbiosis is involved in IBD pathogenesis in terms of microbial composition, functionality, genomic structure, transcriptional activity, and downstream proteins and metabolites. Meanwhile, this review also aims to present an updated overview of the relevant mechanisms, high-throughput multi-omics methodologies, different types of multi-omics cohort resources, and computational methods used to understand host-microbiota interactions in the context of IBD. Finally, we discuss the challenges pertaining to the integration of multi-omics data in order to reveal host-microbiota cross-talk and offer insights into relevant future research directions.

Translation elongation: measurements and applications

Translation converts genetic information in mRNAs into functional proteins. This process occurs in four major steps: initiation, elongation, termination and ribosome recycling; each of which profoundly impacts mRNA stability and protein yield. Over recent decades, regulatory mechanisms governing these aspects of translation have been identified. In this review, we focus on the elongation phase, reviewing the experimental methods used to measure elongation rates and discussing how the measurements shed light on the factors that regulate elongation and ultimately gene expression.

Genetic variation in gut microbe as a key regulator of host social behavior in C. elegans

Gut microbiota have been shown to influence the social behaviors of their hosts, while variations in host genetics can affect the composition of the microbiome. Nonetheless, the degree to which genetic variations in microbial populations impact host behavior, as well as any potential transgenerational effects, remains inadequately understood. Utilizing C. elegans as a model organism, we identified 77 strains of E. coli from a total of 3,983 mutants that significantly enhanced aggregation behavior through various neurobehavioral pathways. This discovery underscores a collaborative regulatory mechanism between microbial genetics and host behavior. Notably, we observed that some mutant bacteria might affect social behavior via the mitochondrial pathway. Additionally, the modulation of social behavior has been identified as a heritable trait in offspring. Our results provide a novel perspective on the regulatory role of microbial genetic variation in host behavior, which may have significant implications for human studies and the development of genetically engineered probiotics aimed at enhancing well-being across generations.

Prediabetes and type 2 diabetes but not obesity are associated with alterations in bile acid related gut microbe-microbe and gut microbe-host community metabolism

The interplay between bile acids (BAs) and metabolic diseases has gained importance in recent years, with a variety of studies investigating their relationship with diverging results. Therefore, in the present study we performed a detailed analysis of BA metabolism in 492 subjects with different metabolic phenotypes. Besides microbiomics and metabolomics this investigation included in silico analysis of community metabolism to examine metabolic interchange between different microbes as well as microbes and the human host. Our findings revealed distinct changes in the BA profiles of patients with diabetes and prediabetes, whereas obesity alone had no influence on circulating BAs. Impaired glycemic control led to increased circulating BAs, a shift toward more secondary BAs, and an increase in the ratio of glycine to taurine-conjugated BAs. Additional analyses revealed that the ratio of glycine to taurine conjugation demonstrated variations between the single BAs, cholic acid (CA), chenodeoxycholic acid (CDCA) and deoxycholic acid (DCA), regardless of the metabolic status, with CA having a higher fraction of taurine conjugation. Furthermore, we found that microbiome alterations are associated with BAs, independent of diabetes or obesity. Analysis of microbial community metabolism revealed differential relative pathway abundance in relation to diabetes, particularly those related to membrane and polyamine synthesis. Increased bacterial cross-feeding of polyamines, galactose, and D-arabinose also coincided with an increase in BA. Notably, our serum metabolome analysis mirrored several of the previously in silico predicted exchanged metabolites, especially amino acid metabolism. Therefore, targeting BA metabolism may be a future approach for the treatment of metabolic diseases, especially prediabetes and type 2 diabetes.

Coupling mechanisms coordinating mRNA translation with stages of the mRNA lifecycle

Gene expression involves a series of consequential processes, beginning with mRNA synthesis and culminating in translation. Traditionally studied as a linear sequence of events, recent findings challenge this perspective, revealing coupling mechanisms that coordinate key steps of gene expression, even when spatially and temporally distant. In this review, we focus on translation, the final stage of gene expression, and examine its coupling with key stages of mRNA metabolism: synthesis, processing, export, and decay. For each of these processes, we provide an overview of known instances of coupling with translation. Furthermore, we discuss the role of high-throughput technologies in uncovering these intricate interactions on a genome-wide scale. Finally, we highlight key challenges and propose future directions to advance our understanding of how coupling mechanisms orchestrate robust and adaptable gene expression programs.

AUGcontext DB: a comprehensive catalog of the mRNA AUG initiator codon context across eukaryotes

The mRNA translation defines the composition of the cell proteome in all forms of life and diseases. In this process, precise selection of the mRNA translation initiation site (TIS) is crucial, as it establishes the correct open reading frame for triplet decoding. We have gathered and curated all published TIS consensus context sequences. We also included the TIS consensus context from novel 538 fungal genomes available from NCBI's RefSeq database. To do so, we wrote ad hoc programs in PERL to find and extract the TIS for each annotated gene, plus ten bases upstream and three downstream. For each genome, the sequences around the TIS of each gene were obtained, and the consensus was further calculated according to the Cavener rules and by the LOGOS algorithm. We created AUGcontext DB, a portal with a comprehensive collection of TIS context sequences across eukaryotes in a range from -10 to + 6. The compilation covers species of 30 vertebrates, 17 invertebrates, 25 plants, 14 fungi, and 11 protists studied in silico; 23 experimental studies; data on biotechnology; and the discovery of 8 diseases associated with specific mutations. Additionally, TIS context sequences of cellular IRESs were included. AUGcontext DB belongs to the National Institute of Cancer (Instituto Nacional de Cancerología, INCan), Mexico, and is freely available at http://108.161.138.77:8096/. Our catalogue allows us to do comparative studies between species, may help improve the diagnosis of certain diseases, and will be key to maximize the production of recombinant proteins.

Best practices for developing microbiome-based disease diagnostic classifiers through machine learning

The human gut microbiome, crucial in various diseases, can be utilized to develop diagnostic models through machine learning (ML). The specific tools and parameters used in model construction such as data preprocessing, batch effect removal and modeling algorithms can impact model performance and generalizability. To establish an generally applicable workflow, we divided the ML process into three above-mentioned steps and optimized each sequentially using 83 gut microbiome cohorts across 20 diseases. We tested a total of 156 tool-parameter-algorithm combinations and benchmarked them according to internal- and external- AUCs. At the data preprocessing step, we identified four data preprocessing methods that performed well for regression-type algorithms and one method that excelled for non-regression-type algorithms. At the batch effect removal step, we identified the "ComBat" function from the sva R package as an effective batch effect removal method and compared the performance of various algorithms. Finally, at the ML algorithm selection step, we found that Ridge and Random Forest ranked the best. Our optimized work flow performed similarly comparing with previous exhaustive methods for disease-specific optimizations, thus is generally applicable and can provide a comprehensive guideline for constructing diagnostic models for a range of diseases, potentially serving as a powerful tool for future medical diagnostics.

Pharmacomicrobiomics: a new field contributing to optimizing drug therapy in Parkinson's disease

Gut microbiota, which act as a determinant of pharmacokinetics, have long been overlooked. In recent years, a growing body of evidence indicates that the gut microbiota influence drug metabolism and efficacy. Conversely, drugs also exert a substantial influence on the function and composition of the gut microbiota. Pharmacomicrobiomics, an emerging field focusing on the interplay of drugs and gut microbiota, provides a potential foundation for making certain advances in personalized medicine. Understanding the communication between gut microbiota and antiparkinsonian drugs is critical for precise treatment of Parkinson's disease. Here, we provide a historical overview of the interplay between gut microbiota and antiparkinsonian drugs. Moreover, we discuss potential mechanistic insights into the complex associations between gut microbiota and drug metabolism. In addition, we also draw attention to microbiota-based biomarkers for predicting antiparkinsonian drug efficacy and examine current state-of-the-art knowledge of microbiota-based strategies to optimize drug therapy in Parkinson's disease.

Mapping the rapid growth of multi-omics in tumor immunotherapy: Bibliometric evidence of technology convergence and paradigm shifts

This study aims to fill the knowledge gap in systematically mapping the evolution of omics-driven tumor immunotherapy research through a bibliometric lens. While omics technologies (genomics, transcriptomics, proteomics, metabolomics)provide multidimensional molecular profiling, their synergistic potential with immunotherapy remains underexplored in large-scale trend analyses. A comprehensive search was conducted using the Web of Science Core Collection for literature related to omics in tumor immunotherapy, up to August 2024. Bibliometric analyses, conducted using R version 4.3.3, VOSviewer 1.6.20, and Citespace 6.2, examined publication trends, country and institutional contributions, journal distributions, keyword co-occurrence, and citation bursts. This analysis of 9,494 publications demonstrates rapid growth in omics-driven tumor immunotherapy research since 2019, with China leading in output (63% of articles) yet exhibiting limited multinational collaboration (7.9% vs. the UK's 61.8%). Keyword co-occurrence and citation burst analyses reveal evolving frontiers: early emphasis on "PD-1/CTLA-4 blockade" has transitioned toward "machine learning," "multi-omics," and "lncRNA," reflecting a shift to predictive modeling and biomarker discovery. Multi-omics integration has facilitated the development of immune infiltration-based prognostic models, such as TIME subtypes, which have been validated across multiple tumor types, which inform clinical trial design (e.g. NCT06833723). Additionally, proteomic analysis of melanoma patients suggests that metabolic biomarkers, particularly oxidative phosphorylation and lipid metabolism, may stratify responders to PD-1 blockade therapy. Moreover, spatial omics has confirmed ENPP1 as a potential novel therapeutic target in Ewing sarcoma. Citation trends underscore clinical translation, particularly mutation-guided therapies. Omics technologies are transforming tumor immunotherapy by enhancing biomarker discovery and improving therapeutic predictions. Future advancements will necessitate longitudinal omics monitoring, AI-driven multi-omics integration, and international collaboration to accelerate clinical translation. This study presents a systematic framework for exploring emerging research frontiers and offers insights for optimizing precision-driven immunotherapy.

Translational regulation of PKD1 by evolutionarily conserved upstream open reading frames

Mutations in PKD1 coding sequence and abnormal PKD1 expression levels contribute to the development of autosomal-dominant polycystic kidney disease, the most common genetic disorder. Regulation of PKD1 expression by factors located in the promoter and 3´ UTR have been extensively studied. Less is known about its regulation by 5´ UTR elements. In this study, we investigated the effects of uORFs and uORF-affecting variants by combining bioinformatic analyses, luciferase reporter assays, RT-qPCR and immunoblotting experiments. Our analyses demonstrate that PKD1 mRNA contains two evolutionarily conserved translation-inhibitory uORFs. uORF1 is translatable, and uORF2 is likely not translatable. The 5´ UTR and uORFs do not modulate downstream protein output under endoplasmic reticulum stress and oxidative stress conditions. Some of uORF-perturbing variants in the SNP database are predicted to affect gene translation. Luciferase reporter assays and RT-qPCR results reveal that rs2092942382 and rs1596636969 increase, while rs2092942900 decreases main gene translation without affecting transcription. Antisense oligos targeting the uORFs reduce luciferase protein levels without altering luciferase mRNA levels. Our results establish PKD1 as a novel target of uORF-mediated translational regulation and mutations that perturb uORFs may dysregulate PKD1 protein level.

SARS-CoV-2 enhances complement-mediated endothelial injury via the suppression of membrane complement regulatory proteins

Complement hyperactivation and thrombotic microangiopathy are closely associated with severe COVID-19. Endothelial dysfunction is a key mechanism underlying thrombotic microangiopathy. To address the relationship between endothelial injury, complement activation and thrombotic microangiopathy of severe COVID-19, we wonder whether, and if so, what and how SARS-CoV-2 factors make endothelial cells (ECs) sensitive to complement-mediated cytotoxicity. We revealed that multiple SARS-CoV-2 proteins enhanced complement-mediated cytotoxicity to ECs by inhibiting membrane complement regulatory proteins (CRPs) and enhancing the deposition of complement-recognizing component FCN1. By screening with CRISPR/Cas9-gRNA libraries, we identified that ADAMTS9, SYAP1, and HIGD1A as intrinsic regulators of CD59 on ECs, which were inhibited by the SARS-CoV-2 M, NSP16, and ORF9b proteins. IFN-γ, GM-CSF, and IFN-α upregulated CD55 and CD59, while IFN-γ antagonized the inhibition of CD59 by the three SARS-CoV-2 proteins. So, the deficiency of IFN-γ weakened the protection of ECs by CRPs against complement-mediated injury which may be enhanced during infection. Our findings illustrated the regulation of protection against complement-mediated attack on self-cells by SARS-CoV-2 infection and immune responses, providing insights into endothelial injury, thrombotic microangiopathy, and potential targets for treating severe COVID-19.

Gut microbiome composition and metabolic activity in metabolic-associated fatty liver disease

Metabolic Associated Fatty Liver Disease (MAFLD) impacts approximately 25% of the global population. Between April 2023 and July 2023, 60 patients with MAFLD, along with 60 age, ethnicity, and sex-matched healthy controls (HCs), were enrolled from the Inner Mongolia Autonomous Region, China. Analysis of gut microbiota composition and plasma metabolic profiles was conducted using metagenome sequencing and LC-MS. LEfSe analysis identified five pivotal species: Eubacterium rectale, Dialister invisus, Pseudoruminococcus massiliensis, GGB3278 SGB4328, and Ruminococcaceae bacteria. In subgroup analysis, Eubacterium rectale tended to increase by more than 2 times and more than double in the non-obese MAFLD group, and MAFLD with moderate hepatic steatosis (HS), respectively. Plasma samples identified 172 metabolites mainly composed of fatty acid metabolites such as propionic acid and butyric acid analogues. Ruminococcaceae bacteria have a strong positive correlation with β-alanine, uric acid, and L-valine. Pseudoruminococcus massiliensis has a strong positive correlation with β-alanine. Combinations of phenomics and metabolomics yielded the highest accuracy (AUC = 0.97) in the MAFLD diagnosis. Combinations of phenomics and metagenomics yielded the highest accuracy (AUC = 0.94) in the prediction of the MAFLD HS progress. Increases in Eubacterium rectale and decreases in Dialister invisus seem to be indicative of MAFLD patients. Eubacterium rectale may predict HS degree of MAFLD and play an important role in the development of non-obese MAFLD. Eubacterium rectale can generate more propionic acid and butyric acid analogues to absorb energy and increase lipid synthesis and ultimately cause MAFLD.

The ladder of regulatory stringency and balance: an application to the US FDA's regulation of bacterial live therapeutics

The three main types of live bacterial therapies - probiotics, fecal/microbiome transplants, and engineered bacterial therapies - hold immense potential to revolutionize medicine. While offering targeted and personalized treatments for various diseases, these therapies also carry risks such as adverse immune reactions, antibiotic resistance, and the potential for unintended consequences. Therefore, developing and deploying these therapies necessitates a robust regulatory framework to protect public health while fostering innovation. In this paper, we propose a novel conceptual tool - the Ladder of Regulatory Stringency and Balance-which can assist in the design of robust regulatory regimes which encompass medicine practices based not only on definitive Randomized Controlled Trials (RCTs), but also on meta-analyses, observational studies, and clinicians experience. Regulatory stringency refers to the strictness of regulations, while regulatory balance concerns the degree of alignment between the regulatory framework governing a technology and the actual risks posed by specific products within that technology. Focusing on the US regulatory environment, we subsequently position the three types of live bacterial therapies on the Ladder. The insight gained from this exercise demonstrates that probiotics are generally positioned at the bottom of the Ladder, corresponding to low-stringency regulation, with a proportionate regulatory balance. However, probiotics intended for high-risk populations are currently subject to low-stringency regulations, resulting in under-regulation. Our analysis also supports the conclusion that fecal microbiota transplants (FMT) for recurrent Clostridium difficile infection should be positioned close to but below the threshold for under regulation by the U.S. Food and Drug Administration (FDA), and we recommend improved donor screening procedures, preservation and processing, storage, and distribution. Our framework can serve as a scale to assess regulatory gaps for live bacterial therapies and to identify potential solutions where such gaps exist.

Induced pluripotent stem cell-related approaches to generate dopaminergic neurons for Parkinson's disease

The progressive loss of dopaminergic neurons in affected patient brains is one of the pathological features of Parkinson's disease, the second most common human neurodegenerative disease. Although the detailed pathogenesis accounting for dopaminergic neuron degeneration in Parkinson's disease is still unclear, the advancement of stem cell approaches has shown promise for Parkinson's disease research and therapy. The induced pluripotent stem cells have been commonly used to generate dopaminergic neurons, which has provided valuable insights to improve our understanding of Parkinson's disease pathogenesis and contributed to anti-Parkinson's disease therapies. The current review discusses the practical approaches and potential applications of induced pluripotent stem cell techniques for generating and differentiating dopaminergic neurons from induced pluripotent stem cells. The benefits of induced pluripotent stem cell-based research are highlighted. Various dopaminergic neuron differentiation protocols from induced pluripotent stem cells are compared. The emerging three-dimension-based brain organoid models compared with conventional two-dimensional cell culture are evaluated. Finally, limitations, challenges, and future directions of induced pluripotent stem cell-based approaches are analyzed and proposed, which will be significant to the future application of induced pluripotent stem cell-related techniques for Parkinson's disease.

Exploring the role of N-acetyltransferases in diseases: a focus on N-acetyltransferase 9 in neurodegeneration

Acetyltransferases, required to transfer an acetyl group on protein are highly conserved proteins that play a crucial role in development and disease. Protein acetylation is a common post-translational modification pivotal to basic cellular processes. Close to 80%-90% of proteins are acetylated during translation, which is an irreversible process that affects protein structure, function, life, and localization. In this review, we have discussed the various N-acetyltransferases present in humans, their function, and how they might play a role in diseases. Furthermore, we have focused on N-acetyltransferase 9 and its role in microtubule stability. We have shed light on how N-acetyltransferase 9 and acetylation of proteins can potentially play a role in neurodegenerative diseases. We have specifically discussed the N-acetyltransferase 9-acetylation independent function and regulation of c-Jun N-terminal kinase signaling and microtubule stability during development and neurodegeneration.

Discovery of specific protein markers in multiple body fluids and their application in forensic science

Identification of multiple body fluids is crucial for the reconstruction and corroboration of crime event. However, for the body fluids with high component similarities, such as peripheral blood and menstrual blood, reliable distinguishing markers are still lacking. Furthermore, a comprehensive protein marker assay for multiple body fluids is urgently necessary for complex crime events. Herein, we established a highly specific and detectable method for discovering protein markers in peripheral blood, menstrual blood, saliva, semen and vaginal fluid through integrating in-depth discovery proteomics and a two-step targeted screening approach. Four menstrual blood markers with high endometrial specificities were identified for differentiation from peripheral blood and exhibited moderate protein concentrations for reproducible analysis with a protein quantitation CV value of 8.66%. Finally, a targeted discrimination method with 16 protein markers was established. We successfully identified 47 blind samples with 100% specificity and detection rate, sourced from five types of body fluids and presented on matrices such as cotton, tissues, slides or fluid. Overall, this work developed an effective method for discovering body fluid biomarkers, obtained specific protein markers to identify five kinds of body fluids and their targeted monitoring will show great significance for forensic science.

Identification of plasma biomarkers for non-invasive diagnosis of hepatitis B cirrhosis

Hepatitis B virus (HBV) infection represents a major public health challenge due to its potential progression to liver cirrhosis and hepatocellular carcinoma, underscoring the importance of early diagnosis for effective management. This study aimed to identify plasma biomarkers for the non-invasive diagnosis of hepatitis B cirrhosis (HBC). We employed quantitative proteomic analysis via liquid chromatography-tandem mass spectrometry on plasma samples from 27 individuals, including 13 patients with HBC and 14 with chronic hepatitis B (CHB). Bioinformatics analysis of 963 identified proteins revealed 234 differential expressed proteins, comprising 115 upregulated and 119 downregulated proteins. Four candidate biomarkers-CHI3L1, IGFBP1, SHBG, and TIMP2-were subsequently selected and validated using ELISA in a cohort of 158 patients, all demonstrating elevated levels in HBC patients. The four-biomarker panel (4MP) demonstrated superior diagnostic performance, achieving an area under the curve (AUC) of 0.902 for distinguishing HBC from CHB. For differentiating decompensated HBC from CHB, the 4MP achieved an AUC of 0.993, with a sensitivity of 93.75 % and specificity of 98.73 %. Mostly, the 4MP also performed well in identifying severe HBC from non-severe HBC, achieving an AUC of 0.911, with a sensitivity of 81.25% and specificity of 93.65%. In conclusion, this study identifies four novel plasma biomarkers for HBC, highlighting their potential to enhance non-invasive diagnostic strategies for monitoring HBC progression.

Visualizing nuclear pore complex plasticity with pan-expansion microscopy

The exploration of cell-type and environmentally responsive nuclear pore complex (NPC) plasticity requires new, accessible tools. Using pan-expansion microscopy (pan-ExM), NPCs were identified by machine learning-facilitated segmentation. They exhibited a large range of diameters with a bias for dilated NPCs at the basal nuclear surface in clusters suggestive of local islands of nuclear envelope tension. Whereas hyperosmotic shock constricted NPCs analogously to those found in annulate lamellae, depletion of LINC complexes specifically eliminated the modest nuclear surface diameter biases. Therefore, LINC complexes may contribute locally to nuclear envelope tension to toggle NPC diameter between dilated, but not constricted, states. Lastly, POM121 shifts from the nuclear ring to the inner ring of the NPC specifically in induced pluripotent stem cell-derived neurons from a patient with C9orf72 amyotrophic lateral sclerosis. Thus, pan-ExM is a powerful tool to visualize NPC plasticity in physiological and pathological contexts at single NPC resolution.

Programming anti-ribozymes to sense trigger RNAs for modulating gene expression in mammalian cells

Synthetic RNA-based switches provide distinctive merits in modulating gene expression. Simple and flexible RNA-based switches are crucial for advancing the field of gene regulation, paving the way for innovative tools that can sense and manipulate cellular processes. In this research, we have developed programmable ribozymes that are capable of suppressing gene expression in response to specific, endogenously expressed trigger RNAs. We engineer ribozymes by introducing upstream antisense sequences (anti-ribozymes) to inhibit the self-cleaving activity of the hammerhead ribozyme and open the expression of the target gene. The trigger RNA is designed to recognize and bind to complementary sequences within the anti-ribozymes, thereby inhibiting their ability to direct protein synthesis. The anti-ribozyme performance is optimized by regulating the essential sequence modules that play a crucial role in determining the specificity and efficiency of the anti-ribozyme's interaction with its trigger RNA. By applying this switch mechanism to various ribozyme designs, we have shown that it is possible to achieve control over gene expression across a wide range of trigger RNAs. By exploiting these programmable anti-ribozymes, we aim to create a powerful tool for controlling gene expression in mammalian cells, which could have important implications for basic research, disease diagnosis, and therapeutic interventions.

Unravelling microbial interactions in a synthetic broad bean paste microbial community

The biotic factors governing the assembly and functionality of broad bean paste microbiota remain largely unexplored due to its highly complex fermentation ecosystem. This study constructed a synthetic community comprising Zygosaccharomyces rouxii, Staphylococcus carnosus, Bacillus subtilis, Bacillus amyloliquefaciens, Tetragenococcus halophilus and Weissella confusa, representing key microorganisms involved in broad bean paste fermentation. The generalized Lotka-Volterra (gLV) model revealed that the microbial interaction network among the six species was dominated by pairwise interactions. The abundances of most species in the multi-species communities at 2 and 4 days were accurately predicted using the gLV model, based on pairwise species combinations outcomes. Among pairwise interactions, negative interactions (57 %) were significantly more prevalent than positive interactions (37 %), with the former generally being stronger. Subsequent investigations demonstrated that the tested Z. rouxii inhibited acid accumulation by acid-producing bacteria, while the two strains belonging to the genus Bacillus stimulated lactic acid bacteria growth and lactic acid accumulation. The sequential inoculation strategy, informed by the interaction network, enhanced the synthetic community's bioaugmentation in broad bean paste, significantly improving ester and mellow flavors, reducing unpleasant odors, and increasing volatile flavor substances to 9.43 times that of natural fermentation. Overall, this study revealed the interaction network of six key microorganisms in broad bean paste using the gLV model and guided the application of the synthetic community in its fermentation, significantly enhancing flavor quality.

VBNC induction and persistence of Listeria monocytogenes Scott A as a defence mechanism against free chlorine stress

Sodium hypochlorite (SH) belongs to the chlorine-releasing agents (CRAs) and is widely used as a disinfectant or a bleaching agent for sanitizing in the food processing environment and fresh-cut industry. In the present study, the potential induction of dormancy states, i.e. the VBNC state and persistence, in Listeria monocytogenes, Scott A strain, was evaluated after exposure to SH for 3 h at 20 °C. Our results showed that the concentration of free chlorine after cells (109.5 CFU/mL) resuspension into the working solution decreased down to 3.7 ppm (SD ± 0.4 ppm; pH 6.64 ± 0.1). To detect VBNC fractions we evaluated comparatively the results of plate counting with fluorescence microscopy, using 5(6)-carboxy-fluorescein diacetate (CFDA; metabolic activity) and propidium iodide (PI; death) staining. The resuscitation capacity of L. monocytogenes stressed single cells was monitored real-time on TSAYE at 37°C, using time-lapse microscopy. Thus, colony outgrowth kinetics were estimated and non-diving fractions were detected. Furthermore, variability in the division time per generation was examined. Our analyses showed that SH induces the VBNC state and persistence in L. monocytogenes. Phenotypic variants of "high" fitness, i.e. size colony variations (SCVs) were also detected in response to SH stress. L. monocytogenes cells presented a prolonged lag time after exposure to SH. This phenomenon is a defence mechanism that allows cells to tolerate stress and maximize population fitness. The investigation of the VBNC state is of high importance for the food industry, as the impacts of VBNC induction and single cell outgrowth heterogeneity can contribute to false-negative detection outcomes.

Efficient production of ectoine from Jerusalem artichoke using engineered Escherichia coli

In this study, a recombinant Escherichia coli strain was constructed to produce ectoine from Jerusalem artichoke through modular pathway engineering. First, a promoter-optimized ectoine synthesis module was integrated into the chromosome using multiple copies. Then, the introduction and expression of inulin hydrolase was optimized because inulin cannot be directly utilized. Subsequently, Fructose transport and phosphorylation, glycolysis, and oxaloacetate supply module were enhanced separately and in combination to improve ectoine production and substrate utilization. The strain ETC16 (co-expression of gapA, ppc, and fruK, ΔiclR) produced 6.51 g/L ectoine with 0.13 g/g inulin. Furthermore, the raw inulin extract and monosodium glutamate (MSG) residue were optimized for ectoine production. Finally, 35.60 g/L of ectoine with a yield of 0.36 g/g inulin was achieved in a 7.5 L fermenter. This study revealed a potential method of non-food fermentation to produce high-value products.

Invariant set theory for predicting potential failure of antibiotic cycling

Collateral sensitivity, where resistance to one drug confers heightened sensitivity to another, offers a promising strategy for combating antimicrobial resistance, yet predicting resultant evolutionary dynamics remains a significant challenge. We propose here a mathematical model that integrates fitness trade-offs and adaptive landscapes to predict the evolution of collateral sensitivity pathways, providing insights into optimizing sequential drug therapies. Our approach embeds collateral information into a network of switched systems, allowing us to abstract the effects of sequential antibiotic exposure on antimicrobial resistance. We analyze the system stability at disease-free equilibrium and employ set-control theory to tailor therapeutic windows. Consequently, we propose a computational algorithm to identify effective sequential therapies to counter antibiotic resistance. By leveraging our theory with data on collateral sensivity interactions, we predict scenarios that may prevent bacterial escape for chronic Pseudomonas aeruginosa infections.

Taco-DDI: accurate prediction of drug-drug interaction events using graph transformer-based architecture and dynamic co-attention matrices

Drug-drug interactions (DDIs) are critical in pharmaceutical research, as adverse interactions can pose significant risks for patient treatment plans. Accurate prediction of DDI events risk levels can provide valuable guidance for designing safer and more effective medical regimens. However, existing approaches often focus on interaction networks while overlooking the inherent molecular properties of drugs. In this study, we present Taco-DDI, a novel drug representation learning framework that utilizes a graph transformer-based model combined with a dynamic co-attention mechanism. Taco-DDI leverages the transformer architecture to derive atom-level feature encodings, capturing comprehensive molecular representations. Furthermore, it employs an adaptive co-attention matrix to identify essential substructures in drug molecules solely from structural information. Our results demonstrate that Taco-DDI achieves a 6.59 % relative accuracy improvement in DDI events risk levels prediction. Additionally, interpretability analysis confirms that Taco-DDI provides meaningful insights into DDI mechanisms, highlighting its practical utility as a robust tool for identifying DDI events risk levels.

Enhancing the synthesis efficiency of galacto-oligosaccharides of a β-galactosidase from Paenibacillus barengoltzii by engineering the active and distal sites

Previously, a glycoside hydrolase (GH) family 2 β-galactosidase (PbBGal2A) from Paenibacillus barengoltzii is characterized for its high transglycosylation capability. Here, the cryo-electron microscopy (cryo-EM) structure of PbBGal2A was determined, revealing an enlarged acidic catalytic pocket that facilitate the binding of carbohydrate substrates. Three structure-based strategies as well as machine learning MECE platform (method for enhancing the catalytic efficiency) were employed to identify active and distal mutations with enhanced galacto-oligosaccharides (GOS) synthesis and their synergistic effects were evaluated. The best H331V mutation yielded a maximum GOS production of 76.57 % at 4 h when 35 % (w/v) of lactose was used as a substrate. Molecular dynamics (MD) simulation analysis further indicated that distal mutations increase the rigidity of the loops surrounding the catalytic pocket. This research sheds light on the structural and catalytic mechanisms of PbBGal2A, highlighting the importance of both active and distal mutations in the efficient design of customized β-galactosidases.

Portable electrophoretic lateral flow biosensing for ultra-sensitive human lactate dehydrogenase detection in serum samples

Lateral flow immunoassays are globally recognized for their simplicity, cost-effectiveness, and rapid qualitative and semiquantitative analyses, making them indispensable as point-of-care screening tools. However, their limited sensitivity restricts their application in clinical settings, requiring the detection of ultralow analyte concentrations in complex sample matrices. To address these challenges, we present a portable biosensing platform integrating battery-powered electrokinetic-driven microfluidics to enhance sensitivity while preserving point-of-care functionality. Our lightweight (151 g), 3D-printed electrophoretic device (€82) supports the simultaneous analysis of three samples and operates with an ultra-low power consumption of 225 mAh-1, enabling 44 h of operation on a single charge. By optimizing key parameters such as Joule heating, buffer evaporation, and electroosmotic flow, the device enables iterative incubation and washing steps directly on the nitrocellulose strip, capabilities unattainable with conventional capillarity-driven LFIAs. This advanced biosensing platform achieves a detection limit of 70 pg mL-1 for human lactate dehydrogenase (h-LDH), a key cancer biomarker, using gold nanoparticles as signal transducers. This result means a 367-fold improvement in sensitivity. Offering rapid, cost-effective, and ultra-sensitive biomarker quantification, this approach holds significant promise for transforming precision medicine, particularly in monitoring LDH-related cancer therapies.

Sustainable remediation of butyl xanthate-contaminated mine wastewater by combining emergent macrophyte Cyperus alternifolius with a versatile bacterial isolate

Butyl xanthate (BuX) is an emerging pollutant due to wide use as flotation collector, posing a serious threat to ecosystem health in mining areas. Here we develop a combinational plant-microbe remediation strategy for restoration of BuX-contaminated mining areas. A novel bacterial strain that completely degraded up to 1000 mg/L of BuX within 12 h was isolated and identified as Pseudomonas monteilii W50. It was found to harbor good tolerance to extreme environmental conditions and multiple plant growth-promoting traits such as phosphate and potassium solubilization, indole-3-acetic acid and gibberellin production, and cellulose degradation. This strain can colonize in the rhizosphere of an emergent macrophyte Cyperus alternifolius, improving removal of BuX and chemical oxygen demand (COD) from simulated wastewater. Compared to the phytoremediation alone, the removal of BuX and COD increased from 70 % to 98 % and from 21 % to 46 % respectively in the combined remediation The strain W50 protected the macrophyte from the phytotoxicity of BuX and the macrophyte provided it with a suitable habitat for return, benefiting each other. Compared to the individual treatment using C. alternifolius or strain W50, the combinational treatment significantly improved the plant growth and the residence of inoculated bacteria. Overall, C. alternifolius and strain W50 are the perfect combination for efficient and sustainable remediation of BuX-contaminated mine wastewater, overcoming the constraints of individual phytoremediation or bioaugmentation methods.

Incorporating time as a third dimension in transcriptomic analysis using machine learning and explainable AI

Transcriptomic data analysis entails the measurement of RNA transcript (gene expression products) abundance in a cell or a cell population at a single point in time. In other words, transcriptomics as it is currently practiced is two-dimensional (2DTA). Gene expression profiling by 2DTA has proven invaluable in furthering our understanding of numerous biological processes in health and disease. That said, shortcomings including technical variability, small sample size, differential rates of transcript decay, and the lack of linearity between transcript abundance and functionality or the formation of functional proteins limit the interpretive utility and generalizability of transcriptomic data. 2DTA utility may also be constrained by its reliance on RNA extracts obtained at a single time point. In other words, much like judging a movie by a single frame, 2DTA can only provide a snapshot of the transcriptome at time of RNA extraction. Whether this perceived "temporality" problem is real and whether it has any bearing on transcriptomic data interpretation have yet to be addressed. To investigate this problem, 25 publicly available datasets relating to MCF-7 cells, where RNA extracts obtained at 12- or 48-hours post-culture were subjected to transcriptomic analysis. The individual datasets were downloaded and compiled into two separate datasets (MCF-7 U12hr and MCF-7 U48hr). To comparatively analyze the two compiled datasets, three machine learning approaches (decision trees (DT), random forests (RF), and XGBoost (Extreme Gradient Boosting)) were used as classifiers to search for genes with distinct expression patterns between the two groups. Shapley additive explanation (SHAP), an explainable AI method, was used to assess the fundamental principles of the DT, RF, and XGBoost models. Coefficient of Determination (DC), Mean Absolute Error (MAE), and Mean Squared Error (MSE) were used to evaluate the models. The results show that the two datasets exhibited very significant gene expression patterns. The XGBoost model performed better than the DT or RF models with MSE, MAE, and DC values of 0.00028, 0.00028, and 0.95778 respectively. These observations suggest that time, as a third dimension, can impact transcriptomic data interpretation and that machine learning and explainable AI are useful tools in resolving the temporality problem in transcriptomics.

OPT: Codon optimize gene sequences for E. coli protein overexpression

The ability to overexpress proteins is valuable for biotechnology, but not all sequences are compatible with high yield. We previously analyzed the sequence features and mRNA folding stability of a large data set of 6,384 distinct gene constructs, and developed a model for protein yield. Our OPT.williams.edu server (1) predicts the probability an input sequence will produce protein at a high level when overexpressed in E. coli, and (2) returns optimized synonymous sequences designed to boost protein expression. Here we also present experimental evidence of the high yields of our OPT constructs for eight commercially produced proteins.

Transcriptional regulator-based biosensors for biomanufacturing in Corynebacterium glutamicum

Intracellular biosensors based on transcriptional regulators have become essential instruments in biomanufacturing, extensively employed for the semi-quantitative assessment of intracellular metabolites, high-throughput screening of production strains, and the directed evolution of enzymes. Corynebacterium glutamicum serves as an industrial chassis for the production of amino acids and a variety of high-value-added chemicals. This paper discusses the varieties and modes of action of transcriptional regulators employed in the construction of intracellular biosensors in C. glutamicum. It also reviews the design principles and progress in the application of transcriptional regulator-based biosensors. Furthermore, measures designed to improve the efficacy of these biosensors are delineated. The challenges and future prospects of biosensors based on transcriptional regulators in practical applications are analyzed. This review seeks to offer theoretical direction for the systematic design and development of transcriptional regulator-based biosensors and to aid researchers in enhancing the growth and productivity of microbial production strains.

Inhibitory effects of extracts from Prunella vulgaris on biofilm formation of Staphylococcus aureus

Staphylococcus aureus (S. aureus) is a highly prevalent pathogen capable of strongly adhering to food processing equipment and the contact surfaces, where it forms resilient biofilms that are difficult to eliminate. Prunella vulgaris (P. vulgaris), a traditional Chinese herbal medicine, has demonstrated strong potential in inhibiting S. aureus biofilm formation. This study investigated the inhibitory mechanisms of P. vulgaris extracts against S. aureus growth and biofilm formation, evaluating the biofilm inhibitory concentration, bactericidal concentration and their effects on ica operon gene expression. The P. vulgaris extracts exhibited a minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of 1.25 mg/mL. At the MIC level, the extracts not only suppressed S. aureus growth and metabolic viability but also inhibited polysaccharide intercellular adhesion (PIA), prevented biofilm formation and disrupted mature biofilms. Furthermore, P. vulgaris extracts demonstrated concentration-dependent effects on extracellular polymeric substances (EPS) production: while 1/2 MIC concentrations stimulated EPS synthesis, double-MIC concentrations markedly suppressed it. Notably, the extracts consistently downregulated icaA and icaD expression at both MIC and 2 × MIC concentrations. Therefore, P. vulgaris exhibits significant potential against S. aureus-induced foodborne diseases, demonstrating promise as a novel antibacterial agent for future applications in both pharmaceutical development and food safety enhancement.

Expanding the genetic spectrum of short rib polydactyly syndrome: Novel DYNC2H1 variants and functional insights

INTRODUCTION

Short rib polydactyly syndrome (SRPS), with or without polydactyly, also known as Verma-Naumoff/Saldino-Noonan syndrome, is a type of skeletal ciliopathy. Initially, variants in the IFT80 gene were implicated; however, approximately half of the SRPS cases are associated with variants in the DYNC2H1 gene. Additionally, digenic variants involving DYNC2H1 and NEK1 can contribute to the syndrome.

MATERIALS AND METHODS

This case report describes a male patient presenting with characteristic SRPS features, including a constricted thorax and shortened limbs. Exome sequencing was performed to identify causative variants, followed by functional analyses to assess the pathogenicity of the identified variants, including a synonymous variant.

RESULTS

Exome sequencing identified compound heterozygous variants in the DYNC2H1 gene: a novel missense variant c.6439G>T p.(Asp2147Tyr) and a synonymous variant c.6477G>A p.(Gln2159=). Functional analyses confirmed that the synonymous variant triggers nonsense-mediated decay of the affected allele.

CONCLUSION

This study expands the spectrum of DYNC2H1 variants associated with SRPS and emphasizes the importance of functional analyses in genetic diagnostics. Demonstrating pathogenicity for a synonymous variant highlights the necessity for comprehensive variant assessments to improve diagnostic accuracy and enable early intervention. These findings have significant implications for molecular diagnostics and personalized therapy strategies in skeletal ciliopathies.

CircITSN2-miR-17-5p/20a-5p/20b-5p-PD-L1 regulatory network is a potential molecular mechanism of PD-L1 gene involving in immune response to IBDV

RESEARCH HIGHLIGHTS

PD-L1 gene is correlated with IBDV immune response in chickens.PD-L1 is a key gene regulating the immune functions of the heart, lung, and proventriculus.CircITSN2-miR-17-5p/20a-5p/20b-5p-PD-L1 is a potential mechanism in IBDV immunity.

PILOT: Deep Siamese network with hybrid attention improves prediction of mutation impact on protein stability

Evaluating the mutation impact on protein stability (ΔΔG) is essential in the study of protein engineering and understanding molecular mechanisms of disease-associated mutations. Here, we propose a novel deep learning framework, PILOT, for improved prediction of ΔΔG using a Siamese network with hybrid attention mechanism. The PILOT framework leverages multiple attention modules to effectively extract representations for amino acids, atoms, and protein sequences, respectively. This approach significantly ensures the deep fusion of structural information at both residue and atom levels, the seamless integration of structural and sequence representations, and the effective capture of both long-range and short-range dependencies among amino acids. Our extensive evaluations demonstrate that PILOT greatly outperforms other state-of-the-art methods. We also showcase that PILOT identifies exceptional patterns for different mutation types. Moreover, we illustrate the clinical applicability of PILOT in highlighting pathogenic variants from benign variants and VUS (variants of uncertain significance), and distinguishing de novo mutations in disease cases and controls. In summary, PILOT presents a robust deep learning tool that could offer significant insights into drug design, medical applications, and protein engineering studies.

Debugging the dynamics of protein corona: Formation, composition, challenges, and applications at the nano-bio interface

The intricate interplay between nanomaterials and the biological molecules has garnered considerable interest in understanding the dynamics of protein corona formation at the nano-bio interface. This review provides an in-depth exploration of protein-nanoparticle interactions, elucidating their structural dynamics and thermodynamics at the nano-Bio interface and further on emphasizing its formation, composition, challenges, and applications in the biomedical and nanotechnological domains, such as drug delivery, theranostics, and the translational medicine. We delve the nuanced mechanisms governing protein corona formation on nanoparticle surfaces, highlighting the influence of nanoparticle and biological factors, and review the impact of corona formation on the biological identity and functionality of nanoparticles. Notably, emerging applications of artificial intelligence and machine learning have begun to revolutionize this field, enabling accurate prediction of corona composition and related biological outcomes. These tools not only enhance efficiency over traditional experimental methods but also help decode complex protein-nanoparticle interaction patterns, offering new insights into corona-driven cellular responses and disease diagnostics. Additionally, it discusses recent advancements in the field of protein corona, particularly in translational nanomedicine and associated applications entailing current and future strategies which are aimed at mitigating the adverse effects of protein-nanoparticle interactions at the biological interface, for tailoring the protein coronas by engineering of the nanomaterials. This comprehensive assessment from chemical, technological, and biological aspects serves as a guiding beacon for the development of future nanomedicine, enabling the more effective emulation of the biological milieu and the design of protein-NP systems for enhanced biomedical applications.

Elucidation of the possible synergistic effect of Torulaspora delbrueckii and ciprofloxacin in a rat model of induced pulmonary fibrosis and infected with Klebsiella pneumonia: An in vivo study

The lungs are constantly subjected to enormous amounts of air and potentially transmitted agents, leading to a high incidence of severe and complex ailments urging the demand for defensive actions to maintain their regular function. Numerous studies have demonstrated how certain probiotics have many advantages including hindering pulmonary exacerbations in individuals with cystic fibrosis, which encourages the idea of combining them with approved antibiotics as a therapeutic choice for treatment patients with lung fibrosis who also have bacterial infections. This investigation aimed to test the possibility of a combination of Torulaspora delbrueckii as a probiotic with ciprofloxacin in an animal model having pulmonary fibrosis with a moderate load of Klebsiella pneumonia. Ninety adult male rats were split into six groups (15 rats/each): GI (control), GII (lung fibrosis), GIII (lung fibrosis infected by K. pneumonia), GIV (lung fibrosis infected by K. pneumonia then treated with ciprofloxacin), GV (lung fibrosis infected by K. pneumonia and fed with T. delbrueckii) and GVI (lung fibrosis infected by K. pneumonia then treated with combined therapy of ciprofloxacin and T. delbrueckii) for 28 days. Survival rate and bacterial load were determined in various experimental animal groups. Histological variations were examined as well as scanning electron microscopy. Gene expression, various levels of cytokines, redox enzymes, and oxidative stress markers were assessed and compared in different tested groups. The treatment using a combination of T. delbrueckii and ciprofloxacin is suggested as a new method to treat induced lung fibrosis in animals and infected with K. pneumonia as a possible option in complicated infection.