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Interactions of liposomes with mammalian cells.

In this review we have attempted to highlight each of the major areas of interest in liposome-cell interactions: the purely physical chemical, the cell biological, and the medical. Liposomes can be generated in a number of ways and are classified as small unilamellar, large unilamellar, and multilamellar vesicles. Although liposomes are easy to prepare, it is important to consider the effects of impurities, and also the possible changes in liposome properties with time (particularly at or below the phase transition temperature). Intelligent application of liposomes to cell biological and clinical problems requires an understanding of their mechanisms of interaction with cells. The mechanisms thus far delineated, largely by studies in vitro, are fusion, endocytosis, lipid transfer, and stable adsorption. In practice, demonstrating the occurrence of a given mechanism in an actual system is difficult because these are not mutually exclusive. Cell type, conditions of incubation, and liposome properties (charge, fluidity, size) are important in determining mechanism and appear to organize the literature effectively. However, this may be an oversimplification resulting from the sketchiness of current information. Liposomes have been used in cell biology to alter the phospholipid and cholesterol composition of cells, to bypass the membrane permeability barrier to normally impermeant solutes, and to promote cell-cell fusion. Perhaps the most fruitful of these applications has been the alteration of cholesterol, which can result in changes in cell permeability and morphology. On the other hand, delivery into cells of liposome-entrapped, water-soluble materials has not yet proved an effective tool in cell biology; delivery, and consequent physiological changes, have been demonstrated, but generally to answer questions about liposome-cell interactions, not to answer questions about the cells. Much of the current interest in liposomes derives from their potential applications in vivo. Liposomes are envisioned as pharmacological capsules for delivery of therapeutic agents in treatment of such conditions as diabetes, enzyme deficiencies, heavy metal poisoning, and neoplasms. Although much of the literature to date has been concerned with the end applications, it seems clear that a more systematic approach to the pharmacokinetics of liposomes will be necessary. In particular, such aspects as their leakage rates and their ability to cross cell and anatomical barriers require further study. Targeting of liposomes to particular cells or tissues will be essential for many applications. Finally, it must be remembered that all of these in vivo applications of liposomes are future tense; as with other technologies, passage from demonstration of the phenomenon to practical application is likely to be arduous.

Adsorption

ssHiCstuff: a package for the design and analysis of ssDNA-specific Hi-C experiments.

MOTIVATION: Single-strand DNA-specific Hi-C (ssHi-C) is a recently developed technique enabling the capture of chromatin interactions involving single-stranded DNA (ssDNA), an intermediate of various DNA metabolic processes. ssHi-C entails the restoration of restriction sites in ssDNA regions of interest upon introduction of designer, internally barcoded "annealing oligonucleotides" prior to the restriction digestion step of Hi-C. The design of these "annealing oligonucleotides," as well as the analysis of the resulting ssHi-C data presents specific challenges, such as (i) differentiating ssDNA from dsDNA-derived contacts, (ii) tracking probe-specific interactions, and (iii) calibrating the amount of ssDNA contacts across biological samples. Dedicated computational tools are therefore needed to facilitate the design of, and extract biological information from, ssHi-C experiments. RESULTS: We present ssHiCstuff, a Rust- and Python-based package for the design of key reagents for ssHi-C experiments and for the analysis of ssHi-C data. ssHiCstuff provides (i) an automated annealing oligonucleotides design module, (ii) an end-to-end analyses pipeline, and (iii) a graphical user interface. ssHiCstuff simplifies the high-resolution analysis of ssDNA interactions at genome-wide scale. A graphical user interface (GUI) implemented in Python is also available for biologists without coding skills. AVAILABILITY: ssHiCstuff is freely available at https://github.com/Piazzalab/ssHiCstuff and https://zenodo.org/records/19677479 (https://doi.org/10.5281/zenodo.19677479) under the GPL 3.0 license. The annealing oligonucleotides design and the visualization modules are additionally freely available on a web browser at https://bioshiny.ens-lyon.fr/public/app/sshicstuff. A test dataset is available at https://zenodo.org/records/20035366 (https://doi.org/10.5281/zenodo.20035366).

DNA, Single-Stranded

Unlocking the Full Potential of Spatial Omics in Plants: Practical Challenges, Solutions, and a Path Forward.

Spatial omics technologies are providing new opportunities for plant biology by enabling molecular profiling within structurally intact tissues, revealing spatially organised cell states, developmental gradients, and regulatory interactions. While spatial transcriptomics has driven early advances, the field is rapidly expanding toward integrated spatial multi-omics by combining single-cell and spatial transcriptomic, epigenomic, proteomic, and metabolomic data. These approaches offer new opportunities to study development, physiology, and plant biotic and abiotic interactions in spatially preserved cellular contexts. However, despite rapid adoption, the field remains constrained by plant-specific challenges when applying technologies largely developed for animal systems. Compared with animal systems, plant tissues pose additional challenges due to rigid cell walls, and diverse chemistries, complicating sample preparation, cell and subcellular segmentation, signal detection, and data integration. As a result, many studies rely on bespoke protocols and analysis pipelines that are often difficult to reproduce or generalise. Here, we provide a practical, solution-oriented synthesis of current bottlenecks across experimental and computational pipelines, highlight emerging strategies to overcome these limitations, and propose a roadmap for community-driven protocol sharing, benchmarking, and integration across spatial and multi-omics modalities. Addressing these challenges will be essential to establish spatial omics as a routine and scalable tool for plant biology.

Journal Article

Serum protein levels as anthropological markers: a statistical analysis in Binga Pygmies and Italians.

Data on serum protein levels in Binga Pygmies are presented, and these are compared with the levels in healthy Italians and in Italians suffering from liver diseases. Principal Component Analysis carried out on the three groups points out similarities in protein levels between the Pygmies and the Italian Hepatopaths on one hand and between healthy and liver diseased Italians on the other. Discriminant Analyses reveal the important differences between the populations. It is suggested that such analyses of protein levels could serve as tools in population biology.

Adolescent

Immune-Like Malignant Epithelial Programs Shape Tumor-Immune Interactions and Inform Prognostic Stratification in Lung Adenocarcinoma.

Lung adenocarcinoma (LUAD) is characterized by marked cellular heterogeneity, yet how malignant epithelial states contribute to immune regulation and clinical outcomes remains incompletely defined. We integrated single-cell RNA-sequencing data to map the cellular landscape of LUAD and identify malignant epithelial cells based on inferred copy-number alterations. Epithelial states were further examined through trajectory inference, transcription factor analysis, and cell-cell communication profiling. Single-cell-derived genes were subsequently integrated with TCGA and independent GEO cohorts to construct and validate a machine learning-based prognostic signature. Malignant epithelial cells displayed distinct functional programs, including an immune-like state associated with genomic instability, immune-related transcriptional activity, tumor-immune communication, and patient outcomes. The resulting immune-like malignant epithelial cell signature (IMEC-Sig) consistently stratified survival across multiple cohorts. Low IMEC-Sig scores were accompanied by greater immune infiltration, higher immune checkpoint expression, and increased immunophenoscore, whereas high scores were linked to a comparatively immunosuppressive phenotype. Pan-cancer analyses further identified KRT8 as a gene associated with unfavorable prognosis, and functional experiments showed that KRT8 silencing suppressed proliferation, migration, invasion, and colony formation in LUAD cells. Together, these findings connect malignant epithelial heterogeneity with the immune context and clinical outcomes, support IMEC-Sig as a biologically informed prognostic tool, and nominate KRT8 as a potential therapeutic target in LUAD.

Humans

Automated Machine Learning Tools to Build Regression Models for Schizosaccharomyces pombe Omics Data.

Machine learning is a powerful tool for analyzing biological data and making useful predictions. The surge of biological data from high-throughput omics technologies has raised the need for modeling approaches capable of tackling such amounts of data, which is pivotal to understanding the nature of complex molecular systems. Here, we show how to construct a simple model using automated machine learning (AutoML) to predict protein abundance in Schizosaccharomyces pombe, using data obtained from codon usage bias and quantitative proteomics.

Machine Learning

Membrane and proteome allocation constraints in Escherichia coli models during overflow metabolism.

The allocation of finite cellular resources is a fundamental principle that dictates microbial metabolic strategies and gives rise to complex phenomena, such as overflow metabolism, characterized by the production of respiro-fermentative by-products, including acetate, during rapid growth. Although proteome-constrained models have successfully predicted overflow metabolism in Escherichia coli, they often overlook the distinct biophysical and energetic costs associated with protein localization. The cellular membrane, in particular, represents a critical and constrained compartment where competition for space and synthesis machinery can create significant metabolic bottlenecks. To investigate this, we developed the membrane-associated constrained flux balance analysis (MAFBA), a scalable, genome-scale metabolic model that introduces a tunable constraint on the total protein mass allocated to the cellular membrane. Our model demonstrates that the overall and membrane-associated proteome allocation constraints interact to improve the accuracy of predicting the onset of overflow metabolism. It mechanistically reveals that at high growth rates, competition for limited membrane allocation forces a trade-off between growth-essential functions and respiratory capacity, leading to acetate production. Furthermore, MAFBA quantitatively explains the widely observed experimental phenomenon that expressing heterologous membrane proteins imposes a significantly higher metabolic burden than expressing cytosolic proteins. This study establishes membrane resource allocation as a key constraint governing bacterial physiology, acting in concert with overall proteome limitations. The resulting MAFBA framework provides a powerful and accessible tool for synthetic biology and metabolic engineering, enabling the prediction of metabolic costs associated with expressing membrane-bound proteins and guiding strain design strategies, holding promise for applications in bioproduction and metabolic engineering.

Escherichia coli

Machine learning-assisted plasma PEA proteomics enables differential diagnosis of melancholic depression and bipolar disorder.

Differentiating bipolar disorder (BD) from major depressive disorder (MDD) remains a critical unmet need in psychiatry due to overlapping clinical presentations and the absence of reliable biological markers. In this study, we assessed the capacity of multivariate machine learning models to accurately differentiate BD from MDD with melancholic features using plasma proteomic profiles obtained via Proximity Extension Assay (PEA) technology. A total of 67 participants were included (23 BD, 20 MDD, and 24 HC), and plasma protein expression was assessed using the Olink Target 96 Neurology panel. Differential proteomic analysis revealed distinct disorder-specific expression patterns, identifying 21 differentially expressed proteins in BD versus MDD, 18 in BD versus healthy controls, and 7 in MDD versus healthy controls. Using a stepwise feature reduction strategy, machine learning models were trained on three feature sets comprising all proteins, the top 20 most informative proteins, and the top 5 most beneficial proteins, and evaluated across BD-MDD, BD-HC, and MDD-HC classification tasks using five algorithms. For BD-MDD discrimination, the Random Forest model achieved the highest performance when trained on the top 5 protein set (LXN, HAGH, MATN3, PLXNB1, and CTSC), yielding an AUC of 0.905, with similarly strong performance observed using the top 20 protein set. Feature importance analysis highlighted proteins involved in neurodevelopmental processes, immune regulation, and extracellular matrix organization. Overall, these findings demonstrate that integrating plasma proteomics with machine learning enables robust differentiation between BD and MDD with melancholic features, supporting the development of scalable and biologically informed diagnostic tools for precision psychiatry.

Bipolar disorder

Efficient scarless gene editing in Pichia pastoris via survival stress-based intramolecular homologous recombination.

To overcome low efficiency and/or genomic instability induced by DNA cleavage in current genome-editing approaches, a novel pop-in/pop-out-based editing system was developed for Pichia pastoris. An ingenious arrangement of components leads to a more efficient screening by permitting the only type of DNA recombination under defined pressure conditions, in terms of the overall efficiency of gene editing, the system virtually depends on the integration efficiency mediated by single-crossover recombination. It does not rely on exogenous recombinases or programmable nucleases such as Cas9, thereby avoiding nuclease induced double strand breaks and associated off target mutations or chromatin fatigue. This strategy preserves high editing efficiency with no modification to the host's inherent genetic properties. Relative to site-specific recombination methods, its dual MazF counterselection enables seamless editing, avoiding scar sequence-induced genomic instability. In this study, nearly 100% knockout efficiency and over 86.67% integration efficiency were achieved in the described experimental cases with this system, which provides a new gene-editing tool for synthetic biology in Pichia pastoris.

Efficient scarless editing

Analog epigenetic memory revealed by targeted chromatin editing.

Cells store information by means of chromatin modifications that persist through cell divisions and can hold gene expression silenced over generations. However, how these modifications may maintain other gene expression states has remained unclear. This study shows that chromatin modifications can maintain a wide range of gene expression levels over time, thus uncovering analog epigenetic memory. By engineering a genomic reporter and epigenetic effectors, we tracked the gene expression dynamics following targeted perturbations to the chromatin state. We found that distinct grades of DNA methylation led to corresponding, persistent gene expression levels. Altering the DNA methylation grade, in turn, resulted in permanent loss of gene expression memory. Consistent with experiments, our chromatin modification model indicates that analog memory arises when the positive feedback between DNA methylation and repressive histone modifications is lacking. This discovery will lead to a deeper understanding of epigenetic memory and to new tools for synthetic biology.

Epigenesis, Genetic

Advancing proteomic discovery through optimized multi-stage scoring and deep learning-enhanced open search.

MOTIVATION: Protein search engines are essential for interpreting mass spectrometry data into biological insight. Current tools often face limitations in sensitivity when analyzing complex modern datasets, and lack a unified framework that effectively integrates deep learning features for both restricted and open searches, especially for scenarios aimed at discovering unknown modifications. RESULTS: We present pFind+, a high-performance search engine for data-dependent acquisition (DDA) proteomics, extending pFind. It introduces an enhanced raw scoring that delivers substantially improved pre-filtering ability, while recovering most of the computational overhead through a tailored acceleration strategy. Coupled with an enhanced rescoring framework that effectively integrates deep learning features, pFind+ uniquely supports high-sensitivity, DL-enhanced open search, enabling comprehensive PTM discovery while incorporating hardware-aware inference optimizations for practical deployment. Evaluations across diverse datasets demonstrate its superior sensitivity, with gains of 12.7%-29.3% (average 17.9%) in restricted search and 8.0%-38.4% (average 25.8%) in open search over the best existing tools.

Deep Learning

seq2ribo: structure-aware integration of machine learning and simulation to predict ribosome location profiles from RNA sequences.

MOTIVATION: Ribosome dynamics are vital in the process of protein expression. Current methods rely on ribosome profiling (Ribo-seq), RNA-seq profiles, and full genomic context. This restricts their use in de novo sequence design, like messenger RNA (mRNA) vaccines. Simulation-only approaches like the Totally Asymmetric Simple Exclusion Process (TASEP) oversimplify translation by focusing solely on codon elongation times. RESULTS: We present seq2ribo, a hybrid simulation and machine learning framework that predicts ribosome A-site locations using only an mRNA sequence as input. Our method first employs a novel structure-aware TASEP (sTASEP), which models translation using a comprehensive set of fitted parameters that include codon wait times and structural features, such as local angles, base-pairing, and discrete positional buckets. The ribosome locations generated by sTASEP are then processed by a polisher model, which learns to refine the simulated ribosome distributions. seq2ribo provides high-fidelity predictions of ribosome locations across diverse cell types (iPSC, HEK293, LCL, and RPE-1), significantly outperforming baselines. seq2ribo is the first method to achieve meaningful positional correlation with observed ribosome profiles from sequence alone, reaching transcript-level Pearson correlations up to 0.920 and within-transcript shape correlations up to 0.186, where all baselines yield near-zero values on these metrics. seq2ribo also reduces elementwise error by up to 37.7% relative to the sequence-only Translatomer baseline. By adding a task-specific head, seq2ribo achieves Pearson correlations up to 0.732 with experimental translation efficiency (TE) across several cell lines, and up to 0.903 with measured protein expression. By operating from sequence alone, seq2ribo provides a new tool for synthetic biology, enabling the rational design and optimization of mRNA sequences without the need for expression-level data or genomic context. AVAILABILITY: seq2ribo is available at https://github.com/Kingsford-Group/seq2ribo.

Machine Learning

Genomic exploration of Bacillus paralicheniformis TB197: an agrobiotechnological tool from the Sonoran Desert.

Climate change and the harmful effects of extensive agrochemical use for plant nutrition and pest control on soils, the environment, and human health are driving the search for sustainable alternatives that reduce their use while increasing plant resilience. In regenerative agriculture, microorganisms have become valuable tools, acting as biological control agents or biostimulants, such as plant growth-promoting rhizobacteria, and/or to enhance plant performance under abiotic stress. The genus Bacillus is well known for its versatile interactions with plants. Specifically, Bacillus paralicheniformis TB197 has demonstrated high efficacy in controlling phytopathogenic nematodes and adapting to diverse soil and crop conditions. Based on these traits, we explored the agricultural potential of this strain through genomic analysis and in vitro and in vivo assays. Gene analysis identified functions related to three main areas: (i) stress resistance and plant colonization, (ii) plant growth promotion, and (iii) phytopathogen control. The strain showed high tolerance to salinity and temperature, promoted plant growth, and exhibited strong antifungal activity. These findings highlight the potential of the TB197 strain as a promising candidate for developing next-generation bioinoculants.IMPORTANCEThe use of beneficial microorganisms is a pivotal strategy for mitigating the environmental impacts of intensive agriculture while preserving crop productivity. Bacillus paralicheniformis TB197 is a native desert soil bacterium with genetic traits associated with stress tolerance, plant growth promotion, and suppression of plant pathogens. In this study, we employed a multifaceted approach integrating genomic analysis and functional assays to demonstrate the strain's multifunctional potential as an agricultural bioinoculant. The results of the study demonstrate that a singular bacterial strain can integrate multiple beneficial functions relevant to sustainable agriculture. This work contributes to the field of applied microbiology by expanding the understanding of how environmentally adapted bacteria can serve as biological alternatives to chemical inputs in agroecosystems.

Bacillus

seq2ribo: Structure-aware integration of machine learning and simulation to predict ribosome location profiles from RNA sequences.

MOTIVATION: Ribosome dynamics are vital in the process of protein expression. Current methods rely on ribosome profiling (Ribo-seq), RNA-seq profiles, and full genomic context. This restricts their use in de novo sequence design, like messenger RNA (mRNA) vaccines. Simulation-only approaches like the Totally Asymmetric Simple Exclusion Process (TASEP) oversimplify translation by focusing solely on codon elongation times. RESULTS: We present seq2ribo, a hybrid simulation and machine learning framework that predicts ribosome A-site locations using only an mRNA sequence as input. Our method first employs a novel structure-aware TASEP (sTASEP), which models translation using a comprehensive set of fitted parameters that include codon wait times and structural features, such as local angles, base-pairing, and discrete positional buckets. The ribosome locations generated by sTASEP are then processed by a polisher model, which learns to refine the simulated ribosome distributions. seq2ribo provides high-fidelity predictions of ribosome locations across diverse cell types (iPSC, HEK293, LCL, and RPE-1), significantly outperforming baselines. seq2ribo is the first method to achieve meaningful positional correlation with observed ribosome profiles from sequence alone, reaching transcript-level Pearson correlations up to 0.920 and within-transcript shape correlations up to 0.186, where all baselines yield near-zero values on these metrics. seq2ribo also reduces elementwise error by up to 37.7% relative to the sequence-only Translatomer baseline. By adding a task-specific head, seq2ribo achieves Pearson correlations up to 0.732 with experimental translation efficiency (TE) across several cell lines, and up to 0.903 with measured protein expression. By operating from sequence alone, seq2ribo provides a new tool for synthetic biology, enabling the rational design and optimization of mRNA sequences without the need for expression-level data or genomic context.

Journal Article

Microassay of cyclic nucleotides in vessel wall. IV. Cyclic GMP phosphodiesterase activity.

Following our microassay for cyclic AMP phosphodiesterase (1978, Microvascular Res. 15:229), a new microassay for cyclic GMP phosphodiesterase (c-GMPPDE) activity was devised, combining the quantitative histochemical method of O.H. Lowry and J.V. Passonneau (1971, A Flexible System of Enzymatic Analysis, Academic Press, New York) with the thin-layer chromatography method of W.A. Scott and B. Solomon (1973, Biochem. Biophys. Res. Comm., 53, 1024). Using this method, c-GMPPDE activity can be accurately measured in a 250 microgram dry weight sample of tissue from the aortic wall. The optimal amount of sample and incubation time were studied, and two Km values were obtained. Low Km is 4.00 x 10(-6) and high Km is 1.25 x 10(-5). The activity of this enzyme was measured in the intima and media of the aorta of three rabbits, three cows and three pigs. The cyclic GMPPDE activities in tissue from cows, pigs and rabbits were 26.61 +/- 2.19, 20.40 +/- 1.35, 43.08 +/- 4.11 pmole/mg dry weight/min in the intima; 52.56 +/- 2.73, 16.07 +/- 3.30 and 66.51 +/- 4.60 pmole/mg dry weight/min in the media. With regard to the relationship between levels of cAMP and cGMP, the activities of cGMPPDE were 10-20 times higher than those of cAMPPDE. These assay systems should provide accurate tools for researching biological, physiological and pathological states of arterial tissues, particularly in the case of atherosclerosis.

3',5'-Cyclic-GMP Phosphodiesterases

Development of ptxD/Phi as a new dominant selection system for genetic manipulation in Cryptococcus neoformans.

Cryptococcus neoformans is a globally distributed pathogenic fungus posing a significant threat to immunocompromised individuals, particularly those with HIV/AIDS. Effective genetic manipulation tools are essential for understanding its biology and developing new therapies. However, current genetic tools, including the variation of versatile selectable markers, are limited. This study develops and validates the phosphite dehydrogenase gene (ptxD)/phosphite (Phi) selection system as a non-antibiotic selectable marker for genetic manipulation in C. neoformans. A codon-optimized ptxD gene from Pseudomonas stutzeri was cloned under the TEF promoter. Using the transient CRISPR-Cas9 coupled with electroporation system, we integrated the ptxD gene into the C. neoformans genome and assessed the impact of ptxD integration on cell growth and virulence factors. The ptxD/Phi system effectively selected transformed cells on Phi-containing media. Growth assays showed that ptxD integration did not adversely affect cell growth or key virulence factors, including pleomorphism, capsule size, and melanin production. Additionally, we successfully disrupted the ADE2 gene using this system, confirming its applicability for gene deletion. Taken together, the ptxD/Phi system provides a robust and versatile tool for genetic manipulation in C. neoformans, facilitating further research into its biology and pathogenicity.IMPORTANCECryptococcus neoformans is a type of fungus that can cause serious illnesses in people who have weakened immune systems, like those with HIV/AIDS. To better study this fungus and find new treatments, scientists need tools to change its genes in precise ways. However, the current tools available for this are somewhat limited. This research introduces a new tool called the phosphite dehydrogenase gene/phosphite system, which does not rely on antibiotics to work. It uses a gene from a different bacterium that helps select and grow only the fungus cells that have successfully incorporated new genetic information. This is particularly useful because it does not interfere with the normal growth of the fungus or the features that make it harmful (like its ability to change shape or produce protective coatings). By making it easier and more effective to manipulate the genetics of C. neoformans, this tool opens up new possibilities for understanding how this fungus operates and for developing therapies to combat its infections. This is crucial for improving the treatment of infections in vulnerable populations.

Cryptococcus neoformans

Malaria-GENOMAP: a web-based tool for exploring genomic variation of malaria parasites.

MOTIVATION: Malaria, caused by Plasmodium parasites, imposes a significant public health burden. While Plasmodium falciparum remains the primary target of elimination strategies due to its high mortality rate, lesser-known species such as P. malariae, P. vivax, and P. knowlesi continue to contribute to substantial human morbidity. Genomic approaches, including whole-genome sequencing, offer powerful tools for understanding the biology, transmission, and emerging drug resistance of these neglected Plasmodium species. However, there is an urgent need for informatic tools to summarize and visualize the high-dimensional and complex genomic data generated. RESULTS: We developed Malaria-GENOMAP, a user-friendly web-based tool, which integrates genomic variant data, such as allele frequencies, with geographical maps and chromosome-wide to gene views for in-depth exploration. The tool includes variation from P. knowlesi (n = 139), P. malariae (n = 158), P. ovale curtisi (n = 36), P. ovale wallikeri (n = 47), P. simium (n = 38), and P. vivax (n = 1359). It enables the investigation of population structure, geographic associations of mutations, and putative drug resistance markers, offering valuable insights for malaria control efforts. AVAILABILITY AND IMPLEMENTATION: Malaria-GENOMAP is available online at https://genomics.lshtm.ac.uk/malaria-genomaps.

Internet

Activation of secondary metabolism in Aspergillus and related filamentous fungi through regulatory engineering.

Filamentous fungi are major contributors to diverse secondary metabolites with broad applications to medicine, agriculture, and biotechnology. Advances in genome sequencing and bioinformatic tools have revealed that fungal genomes encode far more biosynthetic gene clusters (BGCs) than are expressed under normal laboratory conditions, leaving much biosynthetic potential transcriptionally silent. Overcoming this gap between predicted and observed secondary metabolism has become a major challenge in fungal natural product discovery. In this review, we summarize current strategies for activating silent or weakly expressed fungal BGCs through regulatory engineering, with an emphasis on approaches validated in Aspergillus, Penicillium, Monascus, and related filamentous fungi. We focus on genetic and chemical manipulations that enable coordinated activation of multiple biosynthetic pathways through chromatin-level modifiers, global transcriptional regulators, and developmental regulators. By framing these regulators as practical tools rather than solely biological components, we demonstrate their strengths, limitations, and applications in Aspergillus and related filamentous fungi. We further discuss emerging combinatorial and integrative approaches that use regulatory engineering alongside omics technologies and predictive tools, outlining alternatives and future directions for improving the interpretability of silent pathway activation.

Journal Article