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PubMed2026

Preliminary Exploration on Melatonin-Mediated Protective Effects in Intracranial Aneurysms: Transcriptomic, Proteomic, and Metabolomic Profiling of Cerebral Vascular Tissues Combined with in vivo Animal Experiments.

BACKGROUND: Intracranial aneurysm (IA) is a life-threatening cerebrovascular disease with unclear molecular mechanisms and limited drug treatment. Our previous research has shown that melatonin (MLT) has potential protective effects in IA, but its mechanism remains unclear. The purpose of this study is to explore the pathological mechanism of IA and the therapeutic mechanism of MLT by integrating transcriptomic, proteomic and metabolomic analyses. METHODS: In this study, mouse models of IA were successfully established by combining elastase injection with angiotensin II infusion. C57BL/6 mice were divided into control, IA model, IA model+MLT, and IA model+nimodipine groups. The pathological conditions were evaluated by hematoxylin-eosin (HE) staining, TUNEL staining, and scanning electron microscopy. Transcriptomic (n=3 for each group), proteomic (n=3 for each group), and metabolomic (n=6 for each group) analyses were performed based on cerebral vascular tissue samples. The screening thresholds for differentially expressed genes and differentially expressed proteins were P <0.05 and fold change >1.5 and fold change <0.667. The screening criteria for differential metabolites were variable importance for the projection (VIP)> 1.0, fold change >1.2 and fold change <0.833, and P <0.05. RESULTS: MLT alleviated brain tissue damage, vascular endothelial damage, structural disruption, and apoptosis in IA mice. Transcriptomic, proteomic and metabolomic analyses identified numerous differential molecules. Functional annotation revealed that these molecules may be involved in biological pathways and processes such as immune inflammation, vascular remodeling, extracellular matrix remodeling, neuropeptide activity, oxidative stress and metabolic pathways, thereby regulating the occurrence and development of IA or mediating the therapeutic effects of MLT. Furthermore, transcriptomic and proteomic analyses also suggest that there may be extensive post-transcriptional, translational and post-translational regulatory events in the progression of IA and the therapeutic effects of MLT. Integrated transcriptomic and proteomic analyses suggest that Npy may be a key molecule in regulating IA progression and mediating MLT therapeutic effects, and its potential value is further supported by our immunohistochemical validation results. CONCLUSION: Multi-omics integrative analysis preliminarily revealed that the potential mechanisms of MLT may involve the regulation of inflammatory response, vascular remodeling, extracellular matrix remodeling, neuropeptide activity, oxidative stress, metabolic pathways, and post-transcriptional/translational regulation.

Animals↗

PubMed2026

Identification of Drug-resistant Cell Subpopulations in Colorectal Cancer Through Single-cell Analysis and Exploration of Potential Therapeutic Strategies.

INTRODUCTION: The therapeutic efficacy of Colorectal Cancer (CRC) is often compromised by resistance to the standard chemotherapy agent oxaliplatin. METHODS: This study obtained single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) database. Differentially Expressed Genes (DEGs) between resistant and sensitive epithelial subpopulations were identified, followed by enrichment analysis. Pseudotemporal trajectory and cell-cell communication were analyzed using Monocle2 and CellChat, respectively. The candidate drug was predicted by Connectivity Map (cMAP) analysis. External validation included assessment of the EpC2 signature in an oxaliplatin-resistant cell line dataset (GSE76092), survival analysis using The Cancer Genome Atlas (TCGA) cohorts, and re-analysis of the GSE179784 dataset to assess the reproducibility of EpC2-like subpopulations and their DNA Damage Repair (DDR) scores. RESULTS: Cell subpopulations were divided into 10 clusters. Among them, epithelial cells comprised 5 subpopulations, with EPC2 identified as a potential oxaliplatin-resistant subset. DEGs were enriched in the TNF and IL-17 pathways. External validation confirmed the enrichment of EpC2 in resistant cell lines and its association with poor survival. Pseudotemporal trajectory revealed that epithelial cells underwent state transitions, forming two distinct branches. The resistant group exhibited enrichment in RNA splicing and NF-κB pathways. Cell-cell communication analysis revealed interactions involving MDK- NCL and PPIA-BSG. Dasatinib was predicted as a candidate drug. DISCUSSION: We identified an oxaliplatin-resistant subpopulation of Epithelial Cells (EpC2) in CRC, elucidated its multi-layered resistance mechanisms, and integrated multi- omics and cMAP database analyses to predict a potential intervention drug. CONCLUSION: This study provided potential therapeutic possibilities for oxaliplatin resistance, contributing to CRC treatment.

Humans↗

PubMed2026

MET Exon 14 Skipping Mutation in NSCLC: From Genomic Discovery to Biomarker-Guided Therapeutic Innovation.

INTRODUCTION: Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, and the MET exon 14 skipping mutation is a key oncogenic driver, which promotes tumor progression and provides a new direction for precision therapy. METHODS: A systematic search of English-language literature and clinical trial data related to the MET exon 14 skipping mutation from 2020-2025 was performed to summarize the role of the mutation and therapeutic advances. RESULTS: DNA-based next-generation sequencing (NGS), RNA-based NGS, and RT-qPCR were employed as the main detection methods. Preclinical models confirmed that mutations promote tumor progression by activating the RAS/MAPK pathway. Clinical trials have reported objective remission rates (ORR) of 46-68% for first-line treatment with MET inhibitors in NSCLC patients harboring MET exon 14 skipping mutations. DISCUSSION: MET exon 14 skipping mutation as a therapeutic target for NSCLC has made significant progress, and MET inhibitors are more advantageous than chemotherapy and immunotherapy, and have been recommended by national and international guidelines as a first-line treatment option. Additionally, NGS technology has the potential to dynamically monitor tumor evolution and drugresistant mutations, thereby helping to realize precision medicine. CONCLUSION: The MET exon 14 skipping mutation is an important target for the precision treatment of NSCLC, and MET-TKIs have remarkable efficacy but a prominent problem with drug resistance. The construction of a precision medicine system encompassing diagnosis, treatment, and drug resistance management through multi-omics research, technological innovation, and international collaboration is a key direction for improving prognosis.

Humans↗

PubMed2026

A Comprehensive Review of Radiomics in Pulmonary Nodule Management: Clinical Applications and Standardization Dilemmas.

Lung cancer is the most common and fatal malignant tumour. Early detection and treatment are likely to reduce mortality, but most pulmonary nodules identified during routine health checks are harmless. Consequently, a clear distinction between benign and malignant nodules is vital to improve early detection and reduce unnecessary interventions. Radiomics, a new omics technology, can be used to extract high-dimensional quantitative features from medical images, providing a profound understanding of tumour pathophysiology. Radiomics has attracted the attention of medical researchers since its formal definition by the Dutch researcher Lambin et al. in 2012. The number of research papers on radiomics has grown tremendously over the past few years. At present, it is used to predict pulmonary nodule malignancy, for noninvasive risk stratification, for integration with genomics to identify genetic mutations associated with lung cancer, and for evaluation of therapeutic responses. With this review, we summarise the literature on radiomics of pulmonary nodules, discuss how it could be used in nodule management, and address the current challenges and future directions for improving precision oncology.

Humans↗

PubMed2005

Thymidylate synthase structure, function and implication in drug discovery.

Recent methodologies applied to the drug discovery process, such as genomics and proteomics, have greatly implemented our basic understanding of drug action and are giving more input to medicinal chemists, in finding genuinely new targets and opportunities for the development of drugs with original mechanisms of action. In this paper, an example of the successful application of some new techniques to the target enzymes with the Thymidylate Synthase (TS) function is given. The improved knowledge of the complex mechanism of the biological pathways in which thymidylate synthase is involved represents a unique chance to find new mechanism-based inhibitors, aimed to treat not only cancerous diseases, but also infectious pathologies. Thymidylate synthase (TS or ThyA) has long been considered as one of the best-known drug targets in the anti-cancer area, after which old and new drugs, such as 5-fluoro uracil and the anti-folate ZD1694, have been introduced into chemotherapy to treat solid tumours. Only a few attempts have been made to find non-classical anti-folate inhibitors that are dissimilar to the folate co-factor, with the aim of finding unshared protein target domains on the enzyme structure, in order to specifically inhibit TS enzymes from pathogens. Only recently from omic studies, a new Thymidylate Synthase Complementing Protein (TSCP or ThyX) has been identified in a number of pathogens, showing a different structure with respect to human TS, thus opening new avenues to specific inhibitions. A depiction of the most recent progress in the study of Thymidylate Synthase enzymes is presented in the following sections.

Antineoplastic Agents↗

PubMed2002

In vivo toxicity screening programs using metabonomics.

Metabonomics is an emerging technology that enables rapid in vivo screening for toxicity, disease state, or drug efficacy. The technology combines the power of high-resolution nuclear magnetic resonance (NMR) techniques with statistical data analysis methods to rapidly evaluate the metabolic "status" of an animal. Complimentary to other profiling technologies like proteomics and genomics, metabonomics provides a fingerprint of the small-molecules contained in a given biofluid through the time course of a study. This article reviews the steps in implementing a metabonomics-based screening program from study design through data analysis. While metabonomics is still a relatively new technology in comparison to the other "omics", published results from metabonomics studies demonstrate its potential impact in the drug discovery process by enabling the incorporation of safety endpoints much earlier in the drug discovery process, reducing the likelihood (and cost) of later stage attrition.

Algorithms↗

PubMed2006

High-throughput functional genomic methods to analyze the effects of dietary lipids.

The applications of 'omics' (genomics, transcriptomics, proteomics and metabolomics) technologies in nutritional studies have opened new possibilities to understand the effects and the action of different diets both in healthy and diseased states and help to define personalized diets and to develop new drugs that revert or prevent the negative dietary effects. Several single nucleotide polymorphisms have already been investigated for potential gene-diet interactions in the response to different lipid diets. It is also well-known that besides the known cellular effects of lipid nutrition, dietary lipids influence gene expression in a tissue, concentration and age-dependent manner. Protein expression and post-translational changes due to different diets have been reported as well. To understand the molecular basis of the effects and roles of dietary lipids high-throughput functional genomic methods such as DNA- or protein microarrays, high-throughput NMR and mass spectrometry are needed to assess the changes in a global way at the genome, at the transcriptome, at the proteome and at the metabolome level. The present review will focus on different high-throughput technologies from the aspects of assessing the effects of dietary fatty acids including cholesterol and polyunsaturated fatty acids. Several genes were identified that exhibited altered expression in response to fish-oil treatment of human lung cancer cells, including protein kinase C, natriuretic peptide receptor-A, PKNbeta, interleukin-1 receptor associated kinase-1 (IRAK-1) and diacylglycerol kinase genes by using high-throughput quantitative real-time PCR. Other results will also be mentioned obtained from cholesterol and polyunsaturated fatty acid fed animals by using DNA- and protein microarrays.

Animals↗

PubMed2005

Toxicogenomics of cationic lipid-based vectors for gene therapy: impact of microarray technology.

Implementation of the high-throughput microarray gene expression profiling technology towards "toxicogenomics" has advanced identification process for safer drugs in the century of 'omics' technology. Applying such technology, in fact, to identify mechanisms for cellular toxicity can provide a means to clarify safety liabilities early in the drug discovery and developments process. The underlying principle in gene therapy is primarily targeting a specific gene (e.g., for silencing). Hence, massive efforts have been devoted to validate the gene-based therapeutics, regardless of toxicogenomics potential of delivery systems. Of the gene delivery systems, viral and non-viral vectors, as two main paradigms, have so far been widely used for delivering of the genome-based therapeutics such as oligonucleotide, small interfering RNA and DNA. However, the use of viral vectors was narrowed due to the safety concerns. Non-viral vectors were utilized as safer alternatives for gene delivery in vitro and ex-vivo; though their success for in vivo gene therapy has been limited due to low efficiency and safety issues. Fundamental principle for gene therapy is to deliver gene-based therapeutics into target cells for specific gene targeting ideally with minimal cellular toxicity. Until now, few works have been conducted about geno-compatibility of delivery systems itself, including cationic lipid-based nanosystems. Inadvertent toxicogenomic impact of gene delivery systems (e.g., cationic lipids) may intrinsically affect the outcome of gene therapy, where often only a single desired genetic change is sought. Further, there exists a possibility that gene changes induced by the lipid delivery system itself could exacerbate, attenuate or even mask the desired effects of the gene-based therapeutics. This review will focus on toxicogenomics impact of the cationic lipid-based formulations for gene therapy.

Animals↗

PubMed2026

Mutual Information-based Prognostic Biomarker Discovery in Cancer Genomics: Conceptual Framework and Representative Applications of MI-POG.

Mutual information (MI)-based approaches have increasingly been applied to cancer genomics; however, their use for genome-wide prognostic biomarker discovery remains relatively underexplored. The present article summarizes the conceptual workflow of Mutual Information-based Prognostic Omics Gene (MI-POG) based on previously published applications in breast cancer, lower-grade glioma, and other cancer datasets. The framework consists of clinical endpoint discretization, genome-wide MI-based screening, candidate ranking, and downstream validation using conventional survival-analysis approaches. Previous MI-POG applications identified solute carrier family 20 member 1 (SLC20A1) as a prognostic biomarker in hormone receptor-positive breast cancer. Elevated SLC20A1 expression was associated with unfavorable survival outcomes and was independently validated in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) cohort. Methodological analyses demonstrated how survival endpoints can be integrated into an information-theoretic framework through fixed-time outcome discretization, enabling model-independent assessment of molecular-clinical dependencies. Applications across multiple cancer datasets suggested the potential applicability of the framework across biologically distinct tumor types, although further validation will be required to establish its robustness and generalizability. In conclusion, MI-POG can be formalized as an information-theoretic framework for genome-wide identification of prognostic biomarkers by quantifying molecular-clinical dependencies using mutual information. Representative applications from previously published studies suggest that MI-POG may complement conventional survival-analysis approaches and provide a useful strategy for biomarker discovery, although additional benchmarking and prospective validation will be required.

Humans↗

PubMed2026

Integrative Cross-platform Analysis of Kinase Inhibitor Effects on Statin-relevant Cardioprotective Pathways in Human Cardiomyocytes.

BACKGROUND/AIM: Kinase inhibitors (KIs) can cause cardiotoxicity through mechanisms overlapping with statin cardioprotective pathways, yet their effects on these pathways in cardiomyocytes remain uncertain. We evaluated six literature-defined statin-relevant gene sets using transcriptomic and proteomic data. MATERIALS AND METHODS: Pre-ranked gene set enrichment analysis was performed for 23 KIs in primary cardiac cells (GSE146096; n=319) and iPSC-derived cardiomyocytes (GSE217421; n=541), with cross-platform analysis of 21 KIs by shotgun proteomics (PXD014791; n=300). Pathway-specific concordance was assessed by Spearman correlation with Benjamini-Hochberg correction; protein scores were estimated after adjustment for cell line. RESULTS: KI effects were heterogeneous. The anti-fibrotic pathway showed nominal concordance across the two transcriptomic datasets (ρ=0.495, p=0.016, q=0.098; 91% direction concordance) and significant cell-line-adjusted transcriptomic-proteomic concordance (ρ=0.644, p=0.0016, q=0.0081). Nilotinib reproducibly upregulated NF-κB pathway genes [normalized enrichment score (NES)=+2.29 and +2.18 in discovery and validation], with targeted inter-gene-correlation-adjusted testing supporting higher NF-κB expression than under rosuvastatin (CAMERA p=3.54×10-8). No global cross-omics summary remained significant after harmonizing pathway universes and accounting for repeated pathways. CONCLUSION: KI effects on statin-relevant pathways were pathway-specific. Anti-fibrotic concordance and nilotinib-associated NF-κB upregulation are hypothesis-generating candidates for experimental validation.

Humans↗

PubMed2025

Rehabilomics Strategies Enabled by Cloud-Based Rehabilitation: Scoping Review.

BACKGROUND: Rehabilomics, or the integration of rehabilitation with genomics, proteomics, metabolomics, and other "-omics" fields, aims to promote personalized approaches to rehabilitation care. Cloud-based rehabilitation offers streamlined patient data management and sharing and could potentially play a significant role in advancing rehabilomics research. This study explored the current status and potential benefits of implementing rehabilomics strategies through cloud-based rehabilitation. OBJECTIVE: This scoping review aimed to investigate the implementation of rehabilomics strategies through cloud-based rehabilitation and summarize the current state of knowledge within the research domain. This analysis aims to understand the impact of cloud platforms on the field of rehabilomics and provide insights into future research directions. METHODS: In this scoping review, we systematically searched major academic databases, including CINAHL, Embase, Google Scholar, PubMed, MEDLINE, ScienceDirect, Scopus, and Web of Science to identify relevant studies and apply predefined inclusion criteria to select appropriate studies. Subsequently, we analyzed 28 selected papers to identify trends and insights regarding cloud-based rehabilitation and rehabilomics within this study's landscape. RESULTS: This study reports the various applications and outcomes of implementing rehabilomics strategies through cloud-based rehabilitation. In particular, a comprehensive analysis was conducted on 28 studies, including 16 (57%) focused on personalized rehabilitation and 12 (43%) on data security and privacy. The distribution of articles among the 28 studies based on specific keywords included 3 (11%) on the cloud, 4 (14%) on platforms, 4 (14%) on hospitals and rehabilitation centers, 5 (18%) on telehealth, 5 (18%) on home and community, and 7 (25%) on disease and disability. Cloud platforms offer new possibilities for data sharing and collaboration in rehabilomics research, underpinning a patient-centered approach and enhancing the development of personalized therapeutic strategies. CONCLUSIONS: This scoping review highlights the potential significance of cloud-based rehabilomics strategies in the field of rehabilitation. The use of cloud platforms is expected to strengthen patient-centered data management and collaboration, contributing to the advancement of innovative strategies and therapeutic developments in rehabilomics.

Cloud Computing↗

PubMed2025

Generative AI Models in Time-Varying Biomedical Data: Scoping Review.

BACKGROUND: Trajectory modeling is a long-standing challenge in the application of computational methods to health care. In the age of big data, traditional statistical and machine learning methods do not achieve satisfactory results as they often fail to capture the complex underlying distributions of multimodal health data and long-term dependencies throughout medical histories. Recent advances in generative artificial intelligence (AI) have provided powerful tools to represent complex distributions and patterns with minimal underlying assumptions, with major impact in fields such as finance and environmental sciences, prompting researchers to apply these methods for disease modeling in health care. OBJECTIVE: While AI methods have proven powerful, their application in clinical practice remains limited due to their highly complex nature. The proliferation of AI algorithms also poses a significant challenge for nondevelopers to track and incorporate these advances into clinical research and application. In this paper, we introduce basic concepts in generative AI and discuss current algorithms and how they can be applied to health care for practitioners with little background in computer science. METHODS: We surveyed peer-reviewed papers on generative AI models with specific applications to time-series health data. Our search included single- and multimodal generative AI models that operated over structured and unstructured data, physiological waveforms, medical imaging, and multi-omics data. We introduce current generative AI methods, review their applications, and discuss their limitations and future directions in each data modality. RESULTS: We followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines and reviewed 155 articles on generative AI applications to time-series health care data across modalities. Furthermore, we offer a systematic framework for clinicians to easily identify suitable AI methods for their data and task at hand. CONCLUSIONS: We reviewed and critiqued existing applications of generative AI to time-series health data with the aim of bridging the gap between computational methods and clinical application. We also identified the shortcomings of existing approaches and highlighted recent advances in generative AI that represent promising directions for health care modeling.

Artificial Intelligence↗

PubMed2006

Monitoring the health to disease continuum with global metabolic profiling and systems biology.

Global metabolic profiling, which includes both metabolomics and metabonomics studies, is the latest 'omics' research platform that is being applied to understand the health and disease continuum. Metabolic profiling analyses have been demonstrated for the investigation of inborn errors of metabolism, organ transplant rejection, drug toxicity, disease diagnosis and prognosis, drug efficacy and nutritional status. Combining information generated from a metabolic profiling platform with that obtained based on genetics, transcriptomics and proteomics research paradigms will pave the way for a better understanding of the mechanisms of disease and toxicity. Metabolomics and nutrition will lay the groundwork for the application of personalized medicine in the 21st century.

Animals↗

PubMed2006

Metabolomics as a functional genomic tool for understanding lipid dysfunction in diabetes, obesity and related disorders.

With the rise of systems biology, a number of approaches have been developed to globally profile a tier of organization in a cell, tissue or organism. Metabolomics is an approach that attempts to profile all the metabolites in a biological matrix. One of the major challenges of this approach, as with other 'omic' technologies, is that the metabolome is context-dependent and will vary with pathology, developmental stage and environmental factors. Thus, the possibility of globally profiling the metabolome of an organism is a genuine analytical challenge, as by definition this must also take into consideration all relevant factors that influence metabolism. Despite these challenges, the approach has already been applied to understand the metabolism in a range of animal models, and has more recently started to be projected into the clinical situation. In this review, the technologies currently being used in metabolomics will be assessed prior to examining their use to study diseases related to the metabolic syndrome, including Type II diabetes, obesity, cardiovascular disease and fatty liver disease.

Animals↗

PubMed2006

Major advances in fundamental dairy cattle nutrition.

Fundamental nutrition seeks to describe the complex biochemical reactions involved in assimilation and processing of nutrients by various tissues and organs, and to quantify nutrient movement (flux) through those processes. Over the last 25 yr, considerable progress has been made in increasing our understanding of metabolism in dairy cattle. Major advances have been made at all levels of biological organization, including the whole animal, organ systems, tissues, cells, and molecules. At the whole-animal level, progress has been made in delineating metabolism during late pregnancy and the transition to lactation, as well as in whole-body use of energy-yielding substrates and amino acids for growth in young calves. An explosion of research using multicatheterization techniques has led to better quantitative descriptions of nutrient use by tissues of the portal-drained viscera (digestive tract, pancreas, and associated adipose tissues) and liver. Isolated tissue preparations have provided important information on the interrelationships among glucose, fatty acid, and amino acid metabolism in liver, adipose tissue, and mammary gland, as well as the regulation of these pathways during different physiological states. Finally, the last 25 yr has witnessed the birth of "molecular biology" approaches to understanding fundamental nutrition. Although measurements of mRNA abundance for proteins of interest already have provided new insights into regulation of metabolism, the next 25 yr will likely see remarkable advances as these techniques continue to be applied to problems of dairy cattle biology. Integration of the "omics" technologies (functional genomics, proteomics, and metabolomics) with measurements of tissue metabolism obtained by other methods is a particularly exciting prospect for the future. The result should be improved animal health and well being, more efficient dairy production, and better models to predict nutritional requirements and provide rations to meet those requirements.

Absorption↗

PubMed2004

Integromic analysis of the NCI-60 cancer cell lines.

Microarray-based transcript profiling has become exceedingly popular, particularly for breast cancer. However, other 'omic' profiling technologies at the DNA, RNA, protein, functional, and pharmacological levels are also becoming increasingly practical. We define 'integromics' as the melding of such diverse types of data from different experimental platforms. The whole can sometimes be more than the sum of its parts. We describe here a set of integromic studies in which we have profiled the 60 human cancer cell lines (the NCI-60) used by the National Cancer Institute to screen >100,000 chemical compounds over the last 13 years. Patterns of potency in the screen can be mapped into molecular structures of the compounds or into molecular characteristics of the cells. Here we discuss conceptual and experimental aspects of the profiling, as well as a number of bioinformatic computer programs (CIMminer, MedMiner, MatchMiner, and GoMiner) that we have developed for biological interpretation of the profiles. As briefly reviewed here, we have used the combination of NCI-60 data types to identify markers for distinguishing tumor types and to obtain pharmacogenomic clues for possible individualization of a cancer therapy.

Journal Article↗

PubMed2005

The use of histologically defined specific biomarkers in drug development with special reference to the glutathione S-transferases.

Failure of drug candidates late in development is very expensive. This can be reduced by using more specific biomarkers of effect and toxicity during the preclinical and development testing. However, traditional toxicity tests have not been developed to study toxicology and so may lack sufficient sensitivity and specificity hence the search for new biomarkers using the many "-omics" technologies. Important aspects of useful biomarkers are that their origin is known and localised so that one knows what is being observed. Furthermore, biomarkers with a localised origin are less likely to be subject to background variation and have a wider dynamic range. This paper will discuss how using biomarkers with a known cellular origin, toxic effects can be found earlier and at lower doses of compound.

Adaptation, Biological↗

PubMed2026

Molecular biology and integrated strategies for activating cryptic biosynthetic gene clusters toward next-generation antibiotic discovery.

Antimicrobial resistance (AMR) has been identified as one of the 21st century's severest global public health crises. AMR led to an estimated 4.95 million deaths in 2019 and will claim 10 million lives a year by 2050 in the absence of targeted interventions. During the same period, the number of novel antibiotics discovered has decreased drastically as many researchers are rediscovering known antibiotics, non-model microorganisms are poorly understood or difficult to culture and antibiotic research and development investment has declined drastically. However, high-throughput whole genome sequencing and the subsequent application of bioinformatics in bacterial and fungal genomes have shown that a numerous of cryptic or silent biosynthetic gene clusters (BGCs) remain latent at ambient laboratory conditions since their genes are transcriptionally inactive. Cryptic BGCs represent a vast source of unique secondary metabolites, many of which may yield novel antibacterial, antifungal, anti-cancer and other potentially valuable natural products. This review discusses the biological relevance of cryptic BGCs, the major limiting factors that restricts their activation and novel strategies that have been employed to activate them and exploit their potential to produce novel natural products. The review focuses on biological approaches including CRISPR-Cas mediation for the activation of cryptic BGCs, promoter engineering, pathway refactoring, and heterologous expression; biochemical strategies such as Osman, OsMAC, Precursor Feeding, Chemical Elicitation, Epigenetic Regulation and Co-cultivation and technology-based strategies such as Genome mining, Microfluidic Cultivation systems, High-Throughput Screening, Metabolomics, Molecular Networking and Artificial Intelligence and Machine Learning based prediction of BGCs and their metabolites. The use of multi-omics technologies combined with synthetic biology to achieve better discovery, characterization and large-scale production of novel natural products is also discussed herein. Finally, we will talk about the ecological significance and evolutionary advantage of cryptic BGCs' role in interactions between microorganisms, such as competition, communication, symbiosis and environmental adaptability, so as to provide a useful background for accelerating next-generation antibiotics.

CRISPR-Cas activation↗