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PubMed2025

Multi-Omics and Integrative Analytics in Natural Products Discovery.

Natural products (NPs) have long been an essential source of new bioactive compounds for drug discovery; however, traditional methods for screening and isolating these compounds can be slow and often yield diminishing returns. Fortunately, advanced multi-omics and computational approaches present powerful solutions to these challenges. This review highlights innovative methodologies that integrate metabolomics, genomics, transcriptomics, and proteomics with bioinformatics and analytical chemistry to accelerate NP discovery. For instance, untargeted metabolomics platforms like high-resolution liquid chromatography-tandem mass spectrometry (LC-MS/MS) and Global Natural Products Social (GNPS) molecular networking allow for comprehensive profiling of new compounds, while targeted isotope-labeling strategies enhance this process. Additionally, genome and metagenome mining tools such as antibiotics and secondary metabolite analysis shell (antiSMASH), Deep Biosynthetic Gene Cluster (DeepBGC), and Pipeline for Reconstructing Integrated Syntheses of Metabolites (PRISM) quickly identify biosynthetic gene clusters (BGCs) in both cultured and uncultured organisms, often using heterologous expression to validate products. Transcriptomic analyses, including RNA sequencing (RNA-seq), co-expression networks, and fluxomics, help clarify how pathways are regulated, while quantitative proteomics techniques like tandem mass tags/isobaric tags for relative and absolute quantitation (TMT/iTRAQ) and label-free methods, along with chemoproteomics approaches such as cellular thermal shift assay and thermal proteome profiling (TPP), uncover molecular targets and their mechanisms of action. This review also places significant emphasis on the role of artificial intelligence (AI) and machine learning (ML) in integrating multi-omics data, spanning activities from constructing gene-metabolite correlation networks to leveraging knowledge graphs and graph neural networks for data fusion and functional prediction. Finally, this review concludes by discussing the synergistic benefits of multi-omics for natural-product discovery, addressing current technical challenges, and exploring future directions toward high-throughput, intelligent data integration for next-generation NP research.

Biological Products↗

PubMedDate not supplied

Integrated approach for manual evaluation of peptides identified by searching protein sequence databases with tandem mass spectra.

Quantitative proteomics relies on accurate protein identification, which often is carried out by automated searching of a sequence database with tandem mass spectra of peptides. When these spectra contain limited information, automated searches may lead to incorrect peptide identifications. It is therefore necessary to validate the identifications by careful manual inspection of the mass spectra. Not only is this task time-consuming, but the reliability of the validation varies with the experience of the analyst. Here, we report a systematic approach to evaluating peptide identifications made by automated search algorithms. The method is based on the principle that the candidate peptide sequence should adequately explain the observed fragment ions. Also, the mass errors of neighboring fragments should be similar. To evaluate our method, we studied tandem mass spectra obtained from tryptic digests of E. coli and HeLa cells. Candidate peptides were identified with the automated search engine Mascot and subjected to the manual validation method. The method found correct peptide identifications that were given low Mascot scores (e.g., 20-25) and incorrect peptide identifications that were given high Mascot scores (e.g., 40-50). The method comprehensively detected false results from searches designed to produce incorrect identifications. Comparison of the tandem mass spectra of synthetic candidate peptides to the spectra obtained from the complex peptide mixtures confirmed the accuracy of the evaluation method. Thus, the evaluation approach described here could help boost the accuracy of protein identification, increase number of peptides identified, and provide a step toward developing a more accurate next-generation algorithm for protein identification.

Algorithms↗

PubMed2005

The impact of computing technology on pharmaceutical and biotech research.

Pharmaceutical and biotechnology companies continue to make investments in research techniques including those using high-performance computing technology. Much of this research is fueled by the need to analyze the ever-growing store of genomic and proteomic data. However, many research and development investments, including techniques to mine newly produced genomic data, have not provided the anticipated improvements in productivity. This raises the questions of whether advancements expected from next-generation computing technology can translate into tangible benefits for pharmaceutical and biotechnology researchers, and whether researchers can capitalize on an increased understanding of biological systems using tools made more readily available in high-performance computing. The author provides some background on the source of the advancements in the computing industry and offers examples from other scientific applications that point to potential benefits in life science research.

Biotechnology↗

PubMed2025

A Comprehensive Bioinformatics Approach to Analysis of Variants: Variant Calling, Annotation, and Prioritization.

Next-Generation Sequencing (NGS), also known as high-throughput sequencing technologies, has enabled rapid and efficient sequencing of large amounts of DNA and RNA. These technologies have revolutionized the field of genomics, transcriptomics, and proteomics and have been widely used in cancer research, leading to advances in clinical diagnosis and treatment. Improvements in the NGS technologies enabled millions of fragments to be sequenced simultaneously in a time- and cost-effective manner and resulted in large amount of genomic data which require efficient analysis methods. Analysis of the genomic data requires both efficient computer resources and bioinformatics approaches. This chapter details a comprehensive computational approach and analysis steps for genomic data analysis.

Computational Biology↗

PubMed2006

Chemical methods for glycoprotein discovery.

An important frontier in glycoproteomics is the discovery of proteins with post-translational glycan modifications. The first step in glycoprotein identification is the isolation of glycosylated proteins from the remainder of the proteome. New enzymatic and metabolic methods are being used to chemically tag proteins to enable their isolation. Once isolated, glycoproteins can be identified by mass spectrometry. Additional information can be obtained by using either enzymatic or chemoselective reactions to incorporate isotope labels at specific sites of glycosylation. Isotopic labeling facilitates mass spectrometry-based confirmation of glycoprotein identity, identification of glycosylation sites, and quantification of the extent of modification. By combining chemical tagging for isolation and isotope labeling for mass spectrometry analysis, researchers are developing highly effective strategies for glycoproteomics. These techniques are enabling cancer biologists to identify biomarkers whose glycosylation state correlates with disease states, and developmental biologists to characterize stage-specific changes in glycoprotein expression. Next-generation methods will make functional analyses of the glycoproteome possible, including the discovery of glycoprotein interaction partners and the identification of enzymes responsible for synthesis of particular glycan structures.

Biomarkers↗

PubMed2025

Marine-Inspired Antimicrobial Peptides Disrupt Gene Expression at the DNA Level.

Genome mining of Streptomyces sp. H-KF8 combined with sequence engineering yielded two serum-stable, noncytotoxic, nonlytic antimicrobial peptides, L3 and L3-K. Initial studies in uropathogenic Escherichia coli suggested membrane effects and nucleoid relaxation, prompting a comprehensive investigation of their mode of action. In this study tandem mass tag (TMT)-based quantitative proteomics revealed extensive proteome remodeling, with 175 and 120 differentially expressed proteins (DEPs) after treatment with L3 and L3-K, respectively. L3 induced predominantly upregulated responses linked to metabolism, RNA processing, transport, and homeostasis, whereas L3-K mainly caused the downregulation of proteins involved in metabolism, transport, and cell structure. Both peptides disrupted ABC transporter-mediated nutrient uptake and elicited stress responses, while L3 specifically perturbed the mal regulon, indicative of broader transcriptional dysregulation. Complementary fluorescent dye displacement and in vitro transcription/translation assays demonstrated nonspecific DNA binding, stronger for L3 than L3-K, and potent inhibition of transcriptional and translational processes. Strikingly, inhibitory concentrations paralleled their minimum inhibitory concentrations, directly linking DNA binding and interference with central information processing to antimicrobial activity. These findings reveal that L3 and L3-K primarily act by targeting DNA and interfering with the transcription-translation machinery. Beyond offering mechanistic insights, this study underscores peptides' potential to act as scaffolds for next-generation antimicrobial peptides with DNA-binding and nonmembrane-lytic activity.

Antimicrobial Peptides↗

PubMed2003

Next-generation protein-handling method: puromycin analogue technology.

Puromycin is a well-known antibiotic that inhibits protein synthesis by competitive incorporation against an aminoacyl tRNA on the ribosome A site. Novel technology using this property of puromycin has been developed for convenient handling methods in protein research. Puromycin modified with another molecule is incorporated into a protein at the C-terminus, thus linking the desired molecule to the protein. Combination of in vitro translation with puromycin analogues has resulted in novel technologies such as display technology for screening, fluorescence labeling, affinity purification, and protein chip for proteomics.

Biotechnology↗

PubMed2006

Mass spectrometry-based immunoassays for the next phase of clinical applications.

Recent applications of affinity mass spectrometry into clinical laboratories brought a renewed interest in immunoaffinity mass spectrometry as a more specific affinity method capable of selectively targeting and studying protein biomarkers. In mass spectrometry-based immunoassays, proteins are affinity retrieved from biological samples via surface-immobilized antibodies, and are then detected via mass spectrometric analysis. The assays benefit from dual specificity, which is brought about by the affinity of the antibody and the protein mass readout. The mass spectrometry aspect of the assays enables single-step detection of protein isoforms and their individual quantification. This review offers a comprehensive review of mass spectrometry-based immunoassays, from historical perspectives in the development of the immunoaffinity mass spectrometry, to current applications of the assays in clinical and population proteomic endeavors. Described in more detail are two types of mass spectrometry-based immunoassays, one of which incorporates surface plasmon resonance detection for protein quantification. All mass spectrometry-based immunoassays offer high-throughput targeted protein investigation, with clear implications in clinical research, encompassing biomarker discovery and validation, and in diagnostic settings as the next-generation immunoassays.

Animals↗

PubMed2006

Proteomic profiling and identification of immunodominant spore antigens of Bacillus anthracis, Bacillus cereus, and Bacillus thuringiensis.

Differentially expressed and immunogenic spore proteins of the Bacillus cereus group of bacteria, which includes Bacillus anthracis, Bacillus cereus, and Bacillus thuringiensis, were identified. Comparative proteomic profiling of their spore proteins distinguished the three species from each other as well as the virulent from the avirulent strains. A total of 458 proteins encoded by 232 open reading frames were identified by matrix-assisted laser desorption ionization-time-of-flight mass spectrometry analysis for all the species. A number of highly expressed proteins, including elongation factor Tu (EF-Tu), elongation factor G, 60-kDa chaperonin, enolase, pyruvate dehydrogenase complex, and others exist as charge variants on two-dimensional gels. These charge variants have similar masses but different isoelectric points. The majority of identified proteins have cellular roles associated with energy production, carbohydrate transport and metabolism, amino acid transport and metabolism, posttranslational modifications, and translation. Novel vaccine candidate proteins were identified using B. anthracis polyclonal antisera from humans postinfected with cutaneous anthrax. Fifteen immunoreactive proteins were identified in B. anthracis spores, whereas 7, 14, and 7 immunoreactive proteins were identified for B. cereus and in the virulent and avirulent strains of B. thuringiensis spores, respectively. Some of the immunodominant antigens include charge variants of EF-Tu, glyceraldehyde-3-phosphate dehydrogenase, dihydrolipoamide acetyltransferase, Delta-1-pyrroline-5-carboxylate dehydrogenase, and a dihydrolipoamide dehydrogenase. Alanine racemase and neutral protease were uniquely immunogenic to B. anthracis. Comparative analysis of the spore immunome will be of significance for further nucleic acid- and immuno-based detection systems as well as next-generation vaccine development.

Antigens, Bacterial↗

PubMed2026

Engineering cold stress resilience in capsicum annuum through functional genomics and precision breeding.

This review synthesizes the molecular mechanisms of cold tolerance in pepper, integrating multi-omics data,genome editing, and precision breeding strategies to accelerate the development of cold-resilient cultivars. Cold stress is a significant environmental factor that affects the growth, productivity, and fruit quality of Capsicum annuum by impairing membrane integrity photosynthesis and cellular redox homeostasis. Although pepper has several endogenous cold-responsive regulators such as CaNAC035 and CabHLH035, along with antioxidant defense systems, its cold tolerance remains limited due to low transcriptional activation of key regulators, functional redundancy among cold-responsive genes, and the polygenicity of cold tolerance. These complexities, combined with low genetic diversity and linkage drag, have hindered the improvement of cold-resistant cultivars through conventional breeding. This review brings together the recent progress in understanding the molecular mechanisms of cold stress perception, signal transduction, transcriptional regulation, metabolic reprogramming, and phytohormone interactions in pepper. Precision Breeding 2.0 is a new innovation that combines the integration of multi-omics-based target identification with next-generation genome-editing techniques, allowing precise and multiplex engineering of complex and interconnected regulatory networks instead of single genes. We cover new approaches such as engineering the DREB/CBF pathway, allele-specific editing and targeted disruption of negative regulators to enhance the pathway(s) involved in cold response. Moreover, we propose a roadmap for integration of transcriptomics, proteomics, metabolomics, high-throughput phenomics, and speed breeding to accelerate the identification, validation, and deployment of superior alleles to boost cold tolerance. This review provides a foundation for developing climate-resilient pepper cultivars by connecting functional genomics with precision genome engineering approaches to maintain productivity under variable environmental conditions.

Capsicum↗

PubMed2026

Ligand-Mediated Reprogramming Redirects Liver-Tropic Ionizable Lipid Nanoparticles for Lung-Selective mRNA Delivery.

Systemic delivery of messenger RNA (mRNA) to target tissues and cells using lipid nanoparticles (LNPs) holds transformative potential for gene therapy. However, most clinically validated LNP exhibit strong liver tropism, and redirecting their organ specificity without redesigning entirely new chemistries remains challenging. Here we present a ligand-mediated lipid reprogramming approach that repurposes chemically defined, liver-tropic, ionizable lipids (lipidoids) for mRNA delivery beyond the liver. From a library of 90 degradable lipidoids, we identified 2-t6b as a potent liver-targeting platform. By site-specific displaying of small molecule ligands onto 2-t6b headgroup, we engineered a series of reconfigured lipidoids that achieve lung-specific targeting while retaining the parent delivery scaffold. Ligand7-2-t6b-lipid-functionalized LNP achieved over 200-fold higher mRNA translation in the lungs compared to the parent liver-tropic LNP. Proteomics and molecular docking analysis revealed enhanced binding of the modified lipid to vitronectin, a serum glycoprotein that improves integrin binding and thus promotes cellular uptake and translation efficiency. Ligand-mediated 2-t6b/ligand7 LNPs achieved outperformed efficacy and therapeutic potential in lung-specific genome editing relative to SORT-constructed 2-t6b LNP system. Our modular reprogramming strategy provides a generalizable framework to upgrade existing liver-biased LNPs into lung-selective mRNA carriers, advancing next-generation tissue-specific mRNA therapies for gene editing, protein replacement therapy, and regenerative medicine.

RNA, Messenger↗

PubMed2026

Immunopeptidomics-guided cancer vaccine design: Advances, challenges, and emerging opportunities.

Selecting clinically relevant tumor antigens remains a major challenge in the development of therapeutic cancer vaccines. Although computational approaches have considerably improved neoantigen prediction, many candidate epitopes identified in silico are not ultimately presented on the tumor cell surface. The emergence of immunopeptidomics has provided direct access to naturally processed HLA-associated peptides and has offered new opportunities for antigen discovery. Increasing evidence has shown that information derived from the immunopeptidome becomes considerably more informative when interpreted alongside genomic, transcriptomic, and proteomic data. This integrative view has broadened the spectrum of targetable antigens and has also revealed important limitations related to peptide abundance, HLA diversity, tumor heterogeneity, and the imperfect relationship between antigen presentation and immunogenicity. These issues have renewed interest in multi-antigen vaccine strategies designed to better reflect the complexity of tumor antigen landscapes. Advances in bioinformatics and artificial intelligence are facilitating the interpretation of increasingly complex datasets and are beginning to support more systematic approaches to antigen prioritization. In this review, we discuss how immunopeptidomics is contributing to next-generation cancer vaccine development, summarize the major translational challenges, and highlight emerging concepts that may improve the clinical applicability of immunopeptidomics-guided immunotherapy.

Cancer immunotherapy↗

PubMed2026

Recent Advances in the Comprehension of Molecular and Genetic Mechanisms Underlying Yeast Biocontrol Efficacy Against Fungal Pathogens in Agriculture.

Recent advances in biotechnologies have enabled scientists to uncover biological processes across multiple research fields. Still, the molecular and genetic mechanisms underlying the biological control efficacy of yeast biocontrol agents (YBCAs) against fungal plant pathogens remain incompletely elucidated. This review focuses on recent insights into the regulatory bases and molecular interplay underlying successful disease control by YBCAs. It provides a detailed description of core antagonistic molecular mechanisms-nutrient and iron competition, mycoparasitism via cell wall degradation, antifungal compounds production, oxidative stress resistance, biofilm formation and colonization, and induction of the host defense responses-and integrates genomic, transcriptomic, proteomic, and metabolomic evidence to elucidate each mechanism. Further, how genetic engineering-based approaches that leverage omics data and functional genetics can help overcoming obstacles to translate YBCAs efficacy from laboratory conditions to the field are also discussed. Finally, the use of the CRISPR-Cas technology is recommended to better exploit how master transcription factors coordinate multiple mechanisms simultaneously; these factors are crucial for the synergistic antifungal effect, which is critical for developing highly effective YBCAs. Ultimately, the mechanism-based perspective provides a unified conceptual framework for understanding YBCAs efficacy and can guide the rational design of next-generation biocontrol agents for sustainable agriculture.

CRISPR-Cas technology↗

PubMed2006

Molecular imaging promotes progress in orthopedic research.

Modern orthopedic research is directed towards the understanding of molecular mechanisms that determine development, maintenance and health of musculoskeletal tissues. In recent years, many genetic and proteomic discoveries have been made which necessitate investigation under physiological conditions in intact, living tissues. Molecular imaging can meet this demand and is, in fact, the only strategy currently available for noninvasive, quantitative, real-time biology studies in living subjects. In this review, techniques of molecular imaging are summarized, and applications to bone and joint biology are presented. The imaging modality most frequently used in the past was optical imaging, particularly bioluminescence and near-infrared fluorescence imaging. Alternate technologies including nuclear and magnetic resonance imaging were also employed. Orthopedic researchers have applied molecular imaging to murine models including transgenic mice to monitor gene expression, protein degradation, cell migration and cell death. Within the bone compartment, osteoblasts and their stem cells have been investigated, and the organic and mineral bone phases have been assessed. These studies addressed malignancy and injury as well as repair, including fracture healing and cell/gene therapy for skeletal defects. In the joints, molecular imaging has focused on the inflammatory and tissue destructive processes that cause arthritis. As described in this review, the feasibility of applying molecular imaging to numerous areas of orthopedic research has been demonstrated and will likely result in an increase in research dedicated to this powerful strategy. Molecular imaging holds great promise in the future for preclinical orthopedic research as well as next-generation clinical musculoskeletal diagnostics.

Animals↗

PubMed2026

Next-Generation Disease Profiling by Integrating Histopathology with Spatial Multi-Omics Data.

The field of pathology has experienced several transformative changes in recent years with the advent of digital pathology and spatial multi-omics. These technologies have enhanced every aspect of pathology practice, from streamlining daily workflows to generating high-fidelity multi-omics data that provide pathologists with novel tools to refine disease profiling and clinical diagnosis. Each layer of multimodal data (genomic, metabolomic, proteomic, or transcriptomic) has uncovered a distinct facet of disease pathologies, and combined with machine learning/artificial intelligence-based data analysis and pattern recognition models, has provided holistic understanding of regulatory mechanisms underpinning them. However, high-dimensional data have far exceeded the volume, scale, and complexity of immunostaining methods implemented by pathologists and, thus, have generated significant challenges related to deconvolution, interpretation, and clinical translation. Furthermore, these multimodal studies have predominantly relied on computational methods to process data and extract disease-relevant insights, thus raising questions around relevance or role of a pathologist in this new era of multi-omics. This review will provide a perspective on the evolving fields of molecular histopathology and spatial -omics, leveraging them to approach disease profiling, and redefining the role of a pathologist during this process.

Humans↗

PubMed2025

Worldwide Innovative Network (WIN) Consortium in Personalized Cancer Medicine: Bringing next-generation precision oncology to patients.

The human genome project ushered in a genomic medicine era that was largely unimaginable three decades ago. Discoveries of druggable cancer drivers enabled biomarker-driven gene- and immune-targeted therapy and transformed cancer treatment. Minimizing treatment not expected to benefit, and toxicity-including financial and time-are important goals of modern oncology. The Worldwide Innovative Network (WIN) Consortium in Personalized Cancer Medicine founded by Drs. John Mendelsohn and Thomas Tursz provided a vision for innovation, collaboration and global impact in precision oncology. Through pursuit of transcriptomic signatures, artificial intelligence (AI) algorithms, global precision cancer medicine clinical trials and input from an international Molecular Tumor Board (MTB), WIN has led the way in demonstrating patient benefit from precision-therapeutics through N-of-1 molecularly-driven studies. WIN Next-Generation Precision Oncology (WINGPO) trials are being developed in the neoadjuvant, adjuvant or metastatic settings, incorporate real-world data, digital pathology, and advanced algorithms to guide MTB prioritization of therapy combinations for a diverse global population. WIN has pursued combinations that target multiple drivers/hallmarks of cancer in individual patients. WIN continues to be impactful through collaboration with industry, government, sponsors, funders, academic and community centers, patient advocates, and other stakeholders to tackle challenges including drug access, costs, regulatory barriers, and patient support. WIN's collaborative next generation of precision oncology trials will guide treatment selection for patients with advanced cancers through MTB and AI algorithms based on serial liquid and tissue biopsies and exploratory omics including transcriptomics, proteomics, metabolomics and functional precision medicine. Our vision is to accelerate the future of precision oncology care.

Humans↗