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At least 19 recordsLinked to original sources
PubMed2026

Identifying Single-Cell Expression Quantitative Trait Loci Using a Bootstrap Penalized Hurdle Model.

BACKGROUND: Expression quantitative trait loci (eQTL) analysis links genetic variants to gene expression levels, helping to uncover how genetic variation contributes to gene regulation. While traditional eQTL analyses rely on bulk RNA-seq data, recent advances in single-cell RNA sequencing (scRNA-seq) have made it possible to detect cell-type-specific eQTLs. However, the inherent sparsity and heterogeneity of scRNA-seq data present major challenges for standard modeling approaches. METHODS: In this paper, we propose a novel statistical framework, Bootstrap Penalized Hurdle regression model (BPHurdle), designed specifically for scRNA-seq data. BPHurdle employs a hurdle modeling framework, where a logistic component accounts for the excess zeros in single-cell expression data, and a Poisson component jointly evaluates the effects of multiple SNPs on positive gene expression levels. RESULTS: Through simulation studies, we show that BPHurdle achieves high accuracy and robustness in identifying regulatory variants. We further demonstrate its utility on a real dataset through a case study focusing on a subset of differentially expressed genes, where it successfully identifies reliable cell-type-specific eQTLs. CONCLUSIONS: Overall, BPHurdle offers an advanced and flexible approach for single-cell eQTL mapping, providing deeper insight into the genetic regulation of gene expression at cellular resolution.

Quantitative Trait Loci↗
PubMed2026

A Deep Model Framework for Morphological Trait Imputation Across Taxonomic Groups.

Incomplete morphological trait data pose major hurdles for trait-based analyses, particularly when missing values, multicollinearity, and sparse sampling constrain inference. These issues limit our ability to quantify trait variation and explore broad patterns of functional differentiation across taxa. Here, we introduce FS-DeepRBFNet, which overcomes these pitfalls through integrating correlation-based feature selection with a dual-layer adaptive radial basis function (RBF) network. This end-to-end approach effectively reduces noise and captures both linear allometric trends and nonlinear morphological relationships. We tested the framework on a large species-level morphological trait dataset of Chinese birds and further validated its cross-taxon transferability using the Amphibian Database (Caudata). FS-DeepRBFNet consistently outperformed conventional methods such as KNN, Random Forest, and XGBoost, demonstrating superior predictive accuracy across multiple traits. Beyond improvements, the model revealed biologically interpretable trait associations and stable cross-taxon generalization. These results demonstrate that FS-DeepRBFNet provides a robust and biologically grounded solution for morphological trait prediction, enabling reliable imputation for comparative phylogenetics, functional ecology, and biodiversity forecasting in data-limited situations.

cross‐taxon transferability↗
PubMed2025

From Worms to Tumors: Conserved Strategies of Cellular Arrest and Survival Governing Dormancy.

The recurrence of metastatic lesions months to years after the treatment of primary cancers remains a major contributor to cancer-related mortality, highlighting the need to better understand the mechanisms that govern dormancy and dormancy reawakening. A major hurdle is the lack of adequate in vitro and in vivo models to dissect the complex cascades that trigger tumor cell dissemination, adoption of the dormant state, or tumor cell outgrowth in the new metastatic microenvironmental niche. However, many organisms use dormancy to survive stressful environments or periods of nutrient deprivation. Of these, the dauer state of the free-living nematode Caenorhabditis elegans has unparalleled characterization. In this study, we discuss the remarkable physiologic, signaling, genomic, and metabolic similarities between dormant cancer cells and C. elegans dauers, arguing for the use of dauers as a facile model to help dissect dormancy and reawakening pathways in cancer cells.

Animals↗
PubMed2022

Organoids and microphysiological systems: Promising models for accelerating AAV gene therapy studies.

The FDA has predicted that at least 10-20 gene therapy products will be approved by 2025. The surge in the development of such therapies can be attributed to the advent of safe and effective gene delivery vectors such as adeno-associated virus (AAV). The enormous potential of AAV has been demonstrated by its use in over 100 clinical trials and the FDA's approval of two AAV-based gene therapy products. Despite its demonstrated success in some clinical settings, AAV-based gene therapy is still plagued by issues related to host immunity, and recent studies have suggested that AAV vectors may actually integrate into the host cell genome, raising concerns over the potential for genotoxicity. To better understand these issues and develop means to overcome them, preclinical model systems that accurately recapitulate human physiology are needed. The objective of this review is to provide a brief overview of AAV gene therapy and its current hurdles, to discuss how 3D organoids, microphysiological systems, and body-on-a-chip platforms could serve as powerful models that could be adopted in the preclinical stage, and to provide some examples of the successful application of these models to answer critical questions regarding AAV biology and toxicity that could not have been answered using current animal models. Finally, technical considerations while adopting these models to study AAV gene therapy are also discussed.

Animals↗
PubMed2015

Perspective on Adeno-Associated Virus Capsid Modification for Duchenne Muscular Dystrophy Gene Therapy.

Duchenne muscular dystrophy (DMD) is a X-linked, progressive childhood myopathy caused by mutations in the dystrophin gene, one of the largest genes in the genome. It is characterized by skeletal and cardiac muscle degeneration and dysfunction leading to cardiac and/or respiratory failure. Adeno-associated virus (AAV) is a highly promising gene therapy vector. AAV gene therapy has resulted in unprecedented clinical success for treating several inherited diseases. However, AAV gene therapy for DMD remains a significant challenge. Hurdles for AAV-mediated DMD gene therapy include the difficulty to package the full-length dystrophin coding sequence in an AAV vector, the necessity for whole-body gene delivery, the immune response to dystrophin and AAV capsid, and the species-specific barriers to translate from animal models to human patients. Capsid engineering aims at improving viral vector properties by rational design and/or forced evolution. In this review, we discuss how to use the state-of-the-art AAV capsid engineering technologies to overcome hurdles in AAV-based DMD gene therapy.

Animals↗
PubMed2025

Development of chloroplast transformation for five species in the genus Nicotiana.

Technologies for the stable genetic transformation of the plastid (chloroplast) genome are currently restricted to a small number of species. The development of highly efficient tissue culture, regeneration, and selection procedures represents the major hurdle that needs to be overcome to extend the species range of the transplastomic technology. Here, we report the development of efficient plastid transformation protocols for five species in the genus Nicotiana: the model species N. benthamiana, the tree tobacco N. glauca, the ornamental plants N. langsdorffii and N. longiflora, and the wild species N. otophora. We have optimized medium composition for efficient regeneration from leaf explants in all five species and determined suitable selection conditions for plastid transformation. We successfully isolated multiple transplastomic lines for each species and also generated lines that express the fluorescent reporter protein DsRed. Molecular and genetic analyses confirmed the homoplasmic state of the transplastomic lines and demonstrated maternal inheritance of the transgenes. Our work makes plastid genome engineering available for a set of new species and enables new applications in horticultural research and ecology. It also informs the future development of plastid transformation technology for other species.

Nicotiana↗
PubMed2026

CRISPR/Cas in gynecologic cancers: A review of experimental and therapeutic applications.

Gynecological malignancies-including cervical, ovarian, and endometrial cancers-remain a major global health challenge, contributing significantly to cancer-related morbidity and mortality among women. Despite advances in conventional treatments such as surgery, chemotherapy, radiotherapy, and immunotherapy, issues such as drug resistance, tumor recurrence, and limited efficacy in advanced-stage disease necessitate novel therapeutic strategies. The emergence of CRISPR/Cas-based genome editing has revolutionized cancer research by enabling precise, efficient, and programmable modifications of specific genomic loci. In gynecologic oncology, CRISPR/Cas systems have been employed to dissect oncogenic mechanisms, identify therapeutic targets, and develop innovative treatment modalities. In cervical cancer, CRISPR-mediated targeting of HPV E6 and E7 oncogenes has shown potential in restoring tumor suppressor pathways and enhancing chemosensitivity. In ovarian cancer, gene editing has been used to modulate chemoresistance, tumor angiogenesis, and metastasis through the knockout of key regulators such as DNMT1, EGFL6, and BRCA1/2. Similarly, in endometrial cancer, CRISPR tools have elucidated mechanisms of hormonal resistance and facilitated the development of in vivo models via somatic gene editing. This review highlights recent advances in the application of CRISPR/Cas technology to gynecologic malignancies, discussing its potential as both a therapeutic and research platform while acknowledging current limitations and translational hurdles.

Humans↗
PubMed1976

[Conditioning of emotional behavior caused by hypothalamic stimulation (3): The learned behavior caused by hypothalamic stimulation in rabbits].

In the present study, the learned behavior caused by hypothalamic electrical stimulation was examined in order to determine the effects of psychotropic drugs. Subjects were albino male rabbits with electrodes chronically implanted in the hypothalamic area. A shuttle box, which was adjusted for behavioral pharmacological estimation of drugs in rabbits, was used. A buzzer sound (85dB) and electrical stimulation of hypothalamus (100 HZ, 1 msec, 1.2-2.0V) were used as the conditional stimulation (CS) and unconditional stimulation (UCS), respectively. The same animal was trained in habituation to a buzzer sound as the CS. For avoidance conditioning in a two-compartment situation, the animal was placed in a shuttle box divided by a hurdle situated at the middle of two-compartments. After the CS was presented for 10 sec, the UCS was given. The animals were subjected to 15 conditioning trials per day. The avoidance and escape behavior model became as distinct by hypothalamic stimulation as by UCS. After termination of the experiments, extinction trials were carried out after which the animals were sacrificed, and localization of the stimulating electrodes was determined histologically.

Animals↗
PubMed2025

Whole-genome phenotype prediction with machine learning: open problems in bacterial genomics.

MOTIVATION: How can we identify causal genetic mechanisms governing bacterial traits? Initial efforts entrusting machine learning models to handle the task of predicting phenotype from genotype yield high accuracy scores. However, attempts to extract meaningful interpretations from the predictive models are found to be corrupted by falsely identified 'causal' features. Relying solely on pattern recognition and correlations is unreliable, significantly so in bacterial genomics settings where high-dimensionality and spurious associations are the norm. Though it is not yet clear whether we can overcome this hurdle, significant efforts are being made towards discovering potential high-risk bacterial genetic variants. In view of this, we set up open problems surrounding phenotype prediction from bacterial whole-genome datasets and extending those approaches to learning causal effects, and discuss challenges that impact the reliability of a machine's decision-making when faced with datasets of this nature. RESULTS: We identify major sources of non-injectivity in the formulation of the genotype-to-phenotype mapping function-linkage-disequilibrium, limited sampling, information loss in representations, unmeasured confounders and observational noise-and analyse their implications for machine learning applications. Using a collection of 4,140 Staphylococcus aureus isolates, we illustrate challenges surrounding the defined open problems. AVAILABILITY AND IMPLEMENTATION: Raw sequencing data are available from the European Nucleotide Archive (ENA) under project accessions ERP001012, PRJEB3174, PRJEB2655, PRJEB2756, and PRJEB2944. Assemblies and annotations were generated with the Sanger bacterial pipeline (https://github.com/sanger-pathogens/vr-codebase) and unitigs extracted using DBGWAS (https://gitlab.com/leoisl/dbgwas).

Machine Learning↗
PubMed1992

EAE: a model for immune intervention with synthetic peptides.

The cellular and molecular requirements for the autoimmune disease EAE are being defined in increasing detail through intense scrutiny of critical autoantigenic peptides, class II MHC molecules, and alpha beta TCRs involved in the disease process. This study has led to novel immunotherapeutic approaches, many of which are based on the administration of synthetic peptides. Since short peptides are understood to be the minimal antigenic units bound by MHC molecules for recognition by T cells, they are attractive experimental tools for finely modulating specific immune responses. It is clear that a large number of defined peptides can dramatically influence the course of EAE. Table IV lists a number of potential mechanisms which may mediate disease prevention. Increasing evidence supports the idea that prevention of autoimmune disease can result from MHC-blockade by peptides which competitively bind to class II molecules. However, for some peptides such as the perplexing partial agonist Ac1-11[4A], the mechanism by which these precisely defined units act is not yet fully understood. Numerous hurdles hinder immediate clinical application of peptide-based immunotherapy. Nevertheless, the knowledge gained by probing experimental autoimmunity with defined peptides promises to inspire original and practical approaches to treating human autoimmune disease.

Amino Acid Sequence↗
PubMed2026

Development and validation of a novel LC-MS/MS method for simultaneous quantification of fidaxomicin and metabolite (OP-1118) from feces for gut pharmacobiome studies.

Fidaxomicin is a first-line antibiotic for treating Clostridioides difficile infection. While it has low systemic absorption and reaches high colonic concentrations, it is hydrolyzed to a less active metabolite, OP-1118. Few studies have completely described critical experimental details of liquid chromatography-tandem mass spectrometry (LC-MS/MS) for quantifying fecal fidaxomicin and OP-1118. This study developed and validated a simple, fast, and sensitive LC-MS/MS method to quantify fidaxomicin and OP-1118 in human and mouse feces. This method simplified fecal sample preparation without the use of solid phase extraction and optimized LC-MS/MS parameters. A broad working range (0.3-1000 ng/ml) in both diluted human and murine fecal matrices was achieved with good intra- and inter-day accuracy (93-107%), precision (1-7%), and recovery (70-105%) as well as little IS-normalized matrix effects. This method was utilized to quantify fidaxomicin and OP-1118 in human and murine fecal samples. This novel method was simple, fast, sensitive, and accurate in analyzing fecal fidaxomicin and OP-1118 and could be deployed to facilitate gut pharmacobiome research.

Feces↗
PubMed2026

Ultrasound-Actuated Gene Editing in Human Kidney Organoids.

Efficient delivery of gene editing ribonucleoproteins (RNPs) into the interior of solid tissues remains a key hurdle to the clinical translation of non-viral CRISPR-Cas9 technologies. Here, we report acoustically-actuated peptide nanoemulsions (NPeps) that can be spatiotemporally guided and activated by ultrasound to ballistically deliver RNPs into cells within the bulk of dense 3D cellular structures. Using human kidney organoids as a model, we demonstrate NPep vectors improve the spatial profile of gene editing in the organoid mass relative to commercial lipofection reagents, without disruption of tissue structure or qualitative viability features. This technologic paradigm is poised to advance imaging-guided, deep tissue RNP delivery modalities to expand the clinical diagnostic and therapeutic potential of CRISPR-Cas9 editing strategies.

Humans↗
PubMed2026

Post-Translational Modifications in Traumatic Brain Injury: Decoding the Proteomic Landscape and Molecular Mechanisms of Secondary Injury.

Traumatic brain injury (TBI) initiates a complex secondary injury cascade that significantly contributes to long-term neurological deficits, with post-translational modifications (PTMs) emerging as pivotal molecular regulators of this process. Unlike primary mechanical damage, secondary injury evolves over hours to years and involves intricate proteomic alterations that changes in gene expression alone cannot fully explain. PTMs-including phosphorylation, ubiquitination, acetylation, SUMOylation, glycosylation, and emerging modifications such as succinylation, lactylation, and nitrosylation-serve as dynamic molecular switches that fine-tune protein function, stability, localization, and interactions in response to TBI-induced stressors. These modifications play dual roles: they can either promote neuroprotection and recovery or drive pathological processes such as neuronal cell death (via apoptosis, necroptosis, and ferroptosis), neuroinflammation through glial activation and inflammasome signaling, blood-brain barrier disruption, mitochondrial dysfunction, and impaired synaptic plasticity. Critically, extensive crosstalk exists among different PTM pathways-such as the interplay between phosphorylation and ubiquitination in protein degradation or the competitive balance between acetylation and SUMOylation-that collectively shape cellular fate after injury. This nuanced regulatory network presents both challenges and opportunities for therapeutic intervention. Targeting PTM-related enzymes, including kinases, phosphatases, E3 ligases, and histone deacetylases, has shown promise in preclinical models, while novel strategies like Proteolysis-Targeting Chimeras (PROTACs) and repurposed drugs (e.g., metformin, resveratrol) offer innovative avenues for modulating the PTM landscape. Advances in high-throughput proteomics and mass spectrometry are enabling the mapping of TBI-specific PTM signatures across spatiotemporal phases, facilitating the identification of pro-survival versus pro-death modification thresholds. Despite hurdles in clinical translation-such as blood-brain barrier penetration and off-target effects-the growing understanding of PTM dynamics underscores their potential as both biomarkers and therapeutic targets. Future TBI management may thus rely on precision medicine approaches that integrate multi-PTM profiling to guide combination therapies aimed at tipping the balance toward neural repair and functional recovery.

Brain Injuries, Traumatic↗
PubMed2026

Toward simple, rapid, and deep plant proteome analysis with an in-cell proteomics strategy.

While liquid chromatography-mass spectrometry (LCMS) has revolutionized plant proteomics over the past decade, plant sample preparation remains a major challenge due to rigid cell walls, abundant secondary metabolites, and wide dynamic range of protein abundance. These hurdles demand laborious tissue disruption, complex precipitation, and extensive cleanup prior to LCMS analysis, limiting the widespread adoption of proteomic technologies within the plant biology community. To overcome these barriers, we introduced an "in-cell proteomics" strategy that bypasses cell lysis and protein extraction by performing digestion directly inside methanol-fixed cells. We systematically benchmarked this strategy against conventional lysate-based workflows across 4 model plants (Arabidopsis thaliana, Nicotiana benthamiana, Zea mays, and Sorghum bicolor) and 3 tissue types (leaves, pollen, and seeds). Combined with minimal input material and single-shot LCMS, the in-cell approach consistently identified 9,000 to 12,000 proteins from leaves, 7,000 to 9,000 from pollen grains, and approximately 8,000 from seeds. Our comprehensive dataset demonstrates that this in-cell digestion approach substantially simplifies plant sample preparation while delivering proteomic performance equivalent to established workflows. Finally, to demonstrate the biological utility of this approach, we characterized the proteomes of N. benthamiana leaves infected with 2 fungal strains that exhibit different host specificities. Our in-depth proteomic data revealed distinct host response signatures differentiating the host-adapted Colletotrichum destructivum from the nonhost-adapted Colletotrichum sublineola strain. Overall, this study provides a simple, unbiased alternative for plant proteomic analysis that can be readily applied to tackle complex agricultural and physiological challenges in plant biology.

Proteomics↗
PubMed2026

Bridging Organ-on-a-Chip and Omics: A Multi-Dimensional Frontier in Biomedical Research.

Organ-on-a-Chip (OOC) technology offers a powerful platform for replicating human tissue-specific microenvironments, thereby narrowing the translational gap between conventional biomedical models and actual human physiology. Concurrently, omics technologies deliver comprehensive molecular-level insights into biological systems. This review highlights the transformative potential of integrating OOC platforms with high-throughput omics methodologies. We systematically examine the classification, structural configurations, and engineering principles underlying OOC systems, alongside the defining attributes of key omics domains-genomics, transcriptomics, proteomics, and metabolomics. The convergence of dynamic OOC models with advanced omics technologies enables high-resolution, multi-dimensional analyses across numerous biomedical applications, including drug metabolism, disease mechanisms, environmental toxicity assessments, and host-microbiome interactions. This interdisciplinary integration is driving a paradigm shift in precision and translational medicine. However, several challenges remain to be addressed, such as the development of whole-organ mimetics, adaptation of sample collection techniques, and real-time artificial intelligence-based integration of biosensor data with multi-omics datasets. Addressing these hurdles will be vital for unlocking the full potential of this technological synergy in biomedical science.

Multiomics↗
PubMed2025

Redundant and Singular Regulatory Elements Underlie the Rapidly Evolving Pigmentation of Drosophila.

A major hurdle in understanding the molecular changes responsible for metazoan diversity is the characterization of cis-regulatory elements (CREs) for gene regulatory networks (GRNs). CRE changes are suspected to be commonplace in trait evolution, since such changes circumvent the deleterious effects of pleiotropy. A growing list of genes, though, is known to be regulated by redundant CREs. Such redundant CRE architectures complicate the characterization of GRN evolution, as they compound the effort to characterize each locus, and raise the questions of how and whether genes with redundant architectures evolve expression. Here, we used the evolution of sexually dimorphic abdomen pigmentation of Drosophila (D.) melanogaster as a model to study the function and evolution of CREs. Numerous sequences were evaluated that were previously predicted as potential abdomen CREs. Most of these predictions were validated, including two, four, and ten that, respectively, reside in the homothorax, grainy head, and Eip74EF transcription factor loci. The homothorax CREs were found to be partially redundant for this gene's pigmentation function, and pupal-stage Homothorax expression and the CRE activities were conserved among Drosophila species with the derived dimorphic and ancestral monomorphic phenotypes. Similarly, the Eip74EF CREs were conserved in the monomorphic D. willistoni. Thus, this gene's extensive CRE spatiotemporal redundancy has been conserved for over 30 million years, predating the dimorphic trait. Pigmentation evolution has been connected elsewhere to changes in nonredundant CREs. When these traits evolve, GRN changes may be biased towards the genes with singular nonredundant CREs, while the expression of redundantly regulated genes remains conserved.

Animals↗
PubMed2025

Foundations of Artificial Intelligence in Hepatology: What a Clinician Needs to Know.

This review focuses on foundational knowledge about artificial intelligence (AI) in hepatology, exploring how AI, including machine learning and deep learning, leverages large-scale clinical data to transform the diagnosis, risk assessment, prognostication, and management of liver diseases. Online resources are described to offer fundamental AI knowledge and essential technical skills and to facilitate clinician participation across the entire AI lifecycle, ensuring they contribute not only as end users but also in development and deployment. Unlike traditional statistical approaches that prioritize interpretable parameters and clinical insight, AI focuses on maximizing predictive accuracy by identifying complex, often non-linear patterns using high-dimensional data, albeit often at the cost of model interpretability. AI is demonstrating clinical utility in liver histopathology and radiological imaging, significantly improving detection accuracy for cirrhosis, clinically significant portal hypertension, and hepatocellular carcinoma. Beyond diagnostics, AI-driven prediction models are emerging to provide personalized risk stratification for the development of liver-related complications and treatment guidance, based on complex data including longitudinal laboratory results, comorbidities, and co-medication use to monitor disease progression and therapy response. The field is rapidly expanding into novel areas such as analyzing patient-reported outcomes, genomic data, and real-time liver function monitoring, offering deeper mechanistic insights alongside clinical tools. Despite the potential to revolutionize hepatology practice and research, successful integration into routine care faces challenges. These include seamless workflow integration with existing electronic health records, establishing clear liability frameworks, and guaranteeing protection of patient privacy. Addressing these hurdles requires collaborative efforts from clinicians, researchers, and regulators to develop best practices and governance. Understanding the transformative capabilities, current applications, emerging frontiers, and essential implementation considerations is crucial for clinicians navigating the evolving AI landscape and responsibly utilizing its power for improved patient outcomes.

PROBAST+AI↗
PubMed2026

Harnessing Probiotic LAB and Bacteriocins for Clean-Label Food Processing and Biopreservation: Omics, Molecular Innovations and Industrial Applications.

The persistence of microbial agents in foods, especially spore forming bacteria is one of the most significant challenges to food preservation and safety, undermining product quality, shelf life, and consumer health. The use of traditional control methods, including thermal processing and chemical preservatives, are increasingly limited by consumer demands for minimally processed foods, and the emergence of resistant microbial strains. Advances have been made in the use of probiotics like lactic acid bacteria (LAB) and their biometabolites like bacteriocins in food processing and preservation, particularly to control biofilm and endospore forming pathogens including Bacillus sp., Listeria sp., Staphylococcus sp., Clostridium sp., E. coli etc. in foods and food processing plants/surfaces. Given the ability of these organisms to cause foodborne illness and form resilient biofilms in the food processing ecosystem and their resistance to the conventional method of their elimination, the antimicrobial peptides (bacteriocins) are gaining increasing prominence as useful alternatives to synthetic antimicrobials in enhancing food safety and combating the threats of these pathogens. This review addresses current information on the inhibition of persistent microbial spoilage contaminants, biofilm-forming pathogens, and spore formers of interest to the food industry using LAB and their bacteriocins. Current developments in isolation, characterization, and mode of action of bacteriocins are explored, including synergistic activity with other preservative hurdle techniques such as encapsulation, and nanobiotechnology. Importantly, there is a focus on the utilization of molecular and omics-based approaches to enable a better understanding of bacteriocin biosynthesis, gene regulation, host-microbe interactions and gut microbiome regulation potential of probiotic LABs, permitting the rational development of targeted and strain-specific interventions. Developments in the incorporation of bacteriocin-producing LAB into functional starter cultures and bio-protective products, and challenges in stability, regulatory approval, and scalability for industrial use, are also discussed in the paper. Despite their considerable potential, broader translation remains constrained by regulatory requirements, production and formulation costs, variable efficacy in complex food matrices, and the limited validation of many candidate bacteriocins beyond laboratory and model-food systems. Collectively, these advances position LAB and their bacteriocins at the leading edge of developing sustainable, clean-label, and efficacious functional foods and food preservation systems. Their functionality can be expanded by integrating genomics, synthetic biology, and predictive modeling for the maximization of their biopreservative potential in diverse food matrices and in gut microbiota modulation.

Bioactive Peptides↗