Search Results (94)

Microalgae, with their unparalleled capabilities for sunlight-driven growth, CO₂ fixation, and synthesis of diverse high-value compounds, represent sustainable cell factories for a circular bioeconomy. However, industrial deployment has been hindered by biological constraints and the inadequacy of conventional genetic tools. The advent of CRISPR-Cas systems initially provided precise gene editing via targeted DNA cleavage. This review argues that the true transformative potential lies in moving decisively beyond cutting to harness CRISPR as a versatile synthetic biology “Swiss Army Knife”. We synthesize the rapid evolution of CRISPR-derived tools—including transcriptional modulators (CRISPRa/i), epigenome editors, base/prime editors, multiplexed systems, and biosensor-integrated logic gates—and their revolutionary applications in microalgal engineering. These tools enable tunable gene expression, stable epigenetic reprogramming, DSB-free nucleotide-level precision editing, coordinated rewiring of complex metabolic networks, and dynamic, autonomous control in response to environmental cues. We critically evaluate their deployment to enhance photosynthesis, boost lipid/biofuel production, engineer high-value compound pathways (carotenoids, PUFAs, proteins), improve stress resilience, and optimize carbon utilization. Persistent challenges—species-specific tool optimization, delivery efficiency, genetic stability, scalability, and biosafety—are analyzed, alongside emerging solutions and future directions integrating AI, automation, and multi-omics. The strategic integration of this CRISPR toolkit unlocks the potential to engineer robust, high-productivity microalgal cell factories, finally realizing their promise as sustainable platforms for next-generation biomanufacturing. Full article

Crocin biosynthesis involves a complex network of enzymes with biosynthetic and modifier enzymes, and the manipulation of these pathways holds promise for improving human health through the broad exploitation of these bioactive metabolites. Crocins play a significant role in human nutrition and health, as they exhibit antioxidant and anti-inflammatory activity. Plants that naturally accumulate high levels of crocins are scarce, and the production of crocins is highly limited by the characteristics of the crops and their yield. The CCD2 enzyme, initially identified in saffron, is responsible for converting zeaxanthin into crocetin, which is further modified to crocins by aldehyde dehydrogenases and glucosyltransferase enzymes. Crops like tomato fruits, which naturally contain high levels of carotenoids, offer valuable genetic resources for expanding synthetic biology tools. In an effort to explore CCD2 enzymes with improved activity, two CCD2 alleles from saffron and Crocosmia were introduced into tomato, together with a UGT gene. Furthermore, in order to increase the zeaxanthin pool in the fruit, an RNA interference construct was introduced to limit the conversion of zeaxanthin to violaxanthin. The expression of saffron CCD2, CsCCDD2L, led to the creation of transgenic tomatoes with significantly high crocins levels, reaching concentrations of 4.7 mg/g dry weight. The Crocosmia allele, CroCCD2, also resulted in high crocins levels, reaching a concentration of 2.1 mg/g dry weight. These findings underscore the importance of enzyme variants in synthetic biology, as they enable the development of crops rich in beneficial apocarotenoids. Full article

The advancement of computational genomics has significantly enhanced the use of data-driven solutions in disease prediction and precision medicine. Yet, challenges such as data scarcity, privacy constraints, and biases persist. Synthetic data generation offers a promising solution to these issues. However, existing approaches based on generative artificial intelligence often fail to incorporate biological knowledge, limiting the realism and utility of generated samples. In this work, we present BioGAN, a novel generative framework that, for the first time, incorporates graph neural networks into a generative adversarial network architecture for transcriptomic data generation. By leveraging gene regulatory and co-expression networks, our model preserves biological properties in the generated transcriptomic profiles. We validate its effectiveness on E. coli and human gene expression datasets through extensive experiments using unsupervised and supervised evaluation metrics. The results demonstrate that incorporating a priori biological knowledge is an effective strategy for enhancing both the quality and utility of synthetic transcriptomic data. On human data, BioGAN achieves a 4.3% improvement in precision and an up to 2.6% higher correlation with real profiles compared to state-of-the-art models. In downstream disease and tissue classification tasks, our synthetic data improves prediction performance by an average of 5.7%. Results on E. coli further confirm BioGAN’s robustness, showing consistently strong recall and predictive utility. Full article

Polyethylene terephthalate (PET) is one of the most frequently used synthetic polymers and it plays a major role in plastic pollution in aquatic environments. As PET undergoes environmental degradation, it sheds microplastics and chemical leachates, which have an effect on microbial communities, including benthic cyanobacteria. This review focuses on the molecular processes by which PET microplastics and their associated leachate affect the growth, physiological performance, and ecological performance of benthic cyanobacteria. We explore how PET-derived compounds serve as carbon and energy sources or signaling molecules, possibly affecting photosynthesis, nitrogen fixation, or stress response pathways through changes in gene expression. Moreover, the function of PET leachates as environmental modulators of microbial community structure, generators of reactive oxygen species (ROS), and disruptors of hormonal and quorum sensing networks are also outlined. Knowledge of these interactions is essential for the evaluation of the wider ecological risks resulting from plastic pollution and the likelihood of cyanobacterial blooms in PET-polluted environments. This review synthesizes evidence on how PET microplastics and leachates act as carbon sources and stressors, modulating gene expression to promote benthic cyanobacterial growth and toxin production, potentially exacerbating ecological risks in polluted aquatic systems. Full article

It is a challenge to infer a gene regulatory network from time-series gene expression data in the systems biology field. A lack of gene expression data samples is a factor limiting the performance of the inference methods. To resolve this problem, we propose a novel autoencoder-based approach that synthesizes virtual gene expression data to be used as input to the inference method. Through intensive experiments, we showed that using synthetic gene expression as input improves the performance of the network inference method compared to that without it. In particular, the performance improvement was stable against the discretization level of gene expression, the number of time steps in the observed gene expression, and the number of genes. Full article

Soil nitrogen (N) is critical for crop yield. Although previous studies have shown that straw return enhances soil mineral N availability, the response of soil aggregate microbes to straw return and its impact on soil mineral N availability remains unclear. We conducted a 13-year experiment to explore how soil N mineralization potential, fungi, and bacteria within soil aggregates responded to straw return. Our findings indicated that straw return significantly increased mineral N concentrations in soil macroaggregates, with no statistically significant effect observed on microaggregate composition. We observed increased microbial community α-diversity, enhanced co-occurrence network stability, and an increase in functional groups associated with N (nitrate respiration, denitrification, nitrite denitrification) and carbon (saprotrophs, saprotroph–symbiotrophs, patho-saprotrophs) cycling within the aggregates. Additionally, microorganisms in macroaggregates were influenced by total N, while those in microaggregates were affected by soil total organic carbon and C–N ratio. A sensitivity network analysis identified specific microorganisms responding to straw return. Within macroaggregates, microbial community shifts explained 42.88% of mineral N variation, with bacterial and fungal β-diversity contributing 27.82% and 12.58%, respectively. Moreover, straw return upregulated N-cycling genes (N ammonification: sub, ureC, and chiA; nitrification: amoA-AOB; denitrification: nirK, nirS, nosZ, norB, and narG; and N fixation: nifH) in macroaggregates. Partial least squares path modeling revealed that N availability in macroaggregates was mainly driven by ammonification, with bacterial β-diversity explaining 23.22% and fungal β-diversity 15.16% of the variation. Our study reveals that macroaggregates, which play a crucial role in soil N supply, are highly sensitive to tillage practices. This finding provides a practical approach to reducing reliance on synthetic N fertilizers by promoting microbial-mediated N cycling, while sustaining high crop yields in intensive agricultural systems. Full article

Phthalates, a group of synthetic non-persistent organic chemicals commonly used as solvents and plasticisers, have been associated with a range of detrimental health effects. These endocrine disrupting chemicals (ECDs) may exert their effects through epigenetic changes such as altered microRNA (miRNA) expression. miRNAs are short non-coding endogenous RNA transcripts that are preferentially expressed in various tissues and cell types and can circulate in body fluids, thereby regulating gene expression and acting as mediators for intercellular communication. As miRNAs mostly target protein-coding transcripts, they are involved in nearly all networks that regulate developmental and pathological processes. In this review, we provide an overview of human, in vivo and in vitro studies assessing altered miRNA expression due to phthalate exposure and their biological effects. Importantly, this study suggests that the mechanism of phthalate action may in part be mediated by epigenetic changes, affecting a large number of different proteins. This is indicative that alterations in miRNA expression induced by phthalate exposure are then implicated in a wide range of health conditions, including reproductive dysfunction, oncogenesis, metabolic disorders, and neurodevelopmental outcomes. Exposure to phthalates and their metabolites predominantly results in the upregulation of miRNAs. Dysregulation of miR-34a, miR-15b, miR-141, miR-184, miR-19a, miR-125, and miR-let-7 were observed across several studies. More research involving human participants combined with mechanistic studies integrating mRNA target analysis would be beneficial in understanding the downstream effects of phthalate exposure on gene expression and grasping the broader biological implications. Full article

Background/Objectives: Ginseng has been a traditional remedy for centuries, known for its diverse benefits such as anti-inflammation, antioxidant, bactericidal, fungicidal antidiabetic, and anticancer effects. This study employs a network pharmacology approach with molecular dynamics simulation to investigate the potential mechanisms through which ginseng-derived compounds control hypertension. Methods: The total of 70 bioactive compounds were identified from the literature and classified as ginsenosides, which fall under Protopanaxadiol-type ginsenosides, Protopanaxatriol-type ginsenosides, and Ocotillol-type saponins. The target proteins related to hypertension were collected from the drug bank, and interactions between proteins network were examined using STRING 12.0 and Cytoscape 3.10.1. Bioinformatics tools were used to analyze the biological enrichment of genes. The core targets extracted through network pharmacology were subjected to molecular docking studies. Similarly, the docking score below −6.0 kcal/mol was further visualized by performing molecular dynamics simulation to see the binding affinity between the complexes. Finally, pharmacokinetics and toxicity of the compounds were evaluated using computational tools. Results: Molecular docking and simulation results revealed that Floralquinquenoside C, Ginsenoside Rg6, Notoginsenoside T1, and Floralquinquenoside B exhibited strong binding and stability with Angiotensin-converting enzyme (ACE) and Carbonic Anhydrase-I (CA-I), which alters the renin–angiotensin system, calcium signaling pathway, adrenergic signaling in cardiomyocytes, c-GMP-PKG signaling pathway, etc., to regulate high blood pressure. Conclusions: The results show that the phytochemicals from ginseng could act as potential candidates for the management of hypertension, which may help minimize the side effects caused by synthetic anti-hypertensive drugs available on the market. Full article

Transcription factors play a key role in plant growth and development. As the largest family of plant-specific transcription factors, the NAC family plays a central role in coordinating plant growth and development and environmental adaptation through its unique molecular design paradigm of “fixed N-terminal structural domain + variable C-terminal regulatory domain”. This review systematically analyses the multidimensional regulatory mechanisms of NAC transcription factors in developmental processes such as cell wall remodelling, root system architecture, leaf senescence and fruit ripening, and reveals their molecular basis for responding to biotic/abiotic stresses through strategies such as hormone signalling integration (ABA, SA, JA, etc.), antioxidant defence activation and metabolic reprogramming. The study found that NAC proteins precisely control plant growth through multiple regulatory mechanisms and have evolved to form both conservative and diverse functional modules, which are of great value for crop improvement. However, research still faces three major challenges: the NAC regulatory network in different crops is still unclear, the coordinated response to multiple stresses has not been solved, and the ecological risks of gene editing have not been assessed. To this end, this paper proposes to build an ‘NAC regulatory map database’ and use synthetic biology and artificial intelligence technology to design smarter, stress-tolerant and high-yielding crops, overcoming the limitations of traditional research. Full article

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths, with immunotherapy being a first-line treatment at the advanced stage and beyond. Hypoxia plays a critical role in tumor progression and resistance to therapy. This study develops and validates an artificial intelligence (AI) model based on publicly available genomic datasets to predict hypoxia-related immunotherapy responses. Based on the HCC-Hypoxia Overlap (HHO) and immunotherapy response to hypoxia (IRH) genes selected by differential expression and enrichment analyses, a hypoxia model was built and validated on the TCGA-LIHC and GSE233802 datasets, respectively. The training and test sets were assembled from the EGAD00001008128 dataset of 290 HCC patients, and the response and non-response classes were balanced using the Synthetic Minority Over-sampling Technique. With the genes selected via the minimum Redundancy Maximum Relevance and stepwise forward methods, a Kolmogorov–Arnold Network (KAN) model was trained. Support Vector Machine (SVM) combined the Hypoxia and KAN models to predict immunotherapy response. The hypoxia model was constructed using 10 genes (IRH and HHO). The KAN model with 11 genes achieved a test accuracy of 0.7. The SVM integrating the hypoxia and KAN models achieved a test accuracy of 0.725. The established AI model can predict immunotherapy response based on hypoxia risk and genomic factors potentially intervenable in HCC patients. Full article

The global shortage of protein resources has highlighted microbial processes as a promising solution for protein production. Photosynthetic bacteria (PSB) offer advantages in protein synthesis, yet the mechanisms underlying nitrogen conversion to protein remain insufficiently understood. To clarify these mechanisms, nitrogen metabolism-related genes and networks were analyzed using high-throughput sequencing. Synthetic sugar wastewater served as the initial substrate. The results showed that at a nitrogen concentration of 200 mg/L with a combined NH₄-N + NO₃-N supply, the nitrogen conversion rate reached 3.3, and protein production peaked at 130.35 mg/(L·d). Under these conditions, 68.4% of the utilized nitrogen originated from NH₄-N, and 31.6% from NO₃-N, leading to an increase in pro-N to 12.46 mg. Transcriptome analysis revealed high expression of nirK, norB, and nosZ, confirming significant denitrification, while the absence of nitrate reductase, GLDH, GDH, and GltS in Rp. palustris corresponded to a lower protein yield of 53.28 mg/(L·d). Additionally, genes related to nitrogen transport (amtB, nrtABC), ammonium assimilation (glnA, gltB, gltD), and nitrate reduction (nasA, narB) were upregulated, facilitating nitrogen utilization. These findings provide insights into optimizing nitrogen utilization for improved protein synthesis in PSB-based wastewater treatment. Full article

Background: The cell cycle, a critical and intricate biological process, comprises various phases, and its dysregulation plays a pivotal role in tumorigenesis and metastasis. The exploration of cell cycle-based molecular subtypes across pan-cancers, along with the application of synthetic lethality concepts, holds promise for advancing cancer therapies. Methods: A pan-cancer analysis was conducted to assess the cell cycle serves as a reliable signature for classifying molecular subtypes and to understand the potential clinical application of genes as potential drug targets based on synthetic lethality. Results: Molecular subtypes derived from cell cycle features in certain cancers, particularly kidney-related malignancies, exhibited distinct immune characteristics. Synthetic lethal interactions within the cell cycle pathway were common, with significant genetic interactions further identifying potential drug targets through the exploitation of genetic relationships with key driver genes. Additionally, miRNAs and lncRNAs may influence the cell cycle through miRNA:mRNA interactions and ceRNA networks, thereby enriching the genetic interaction landscape. Conclusions: These findings suggest that the cell cycle pathway could serve as a promising molecular subtype signature to enhance cancer prognostication and offer potential targets for anticancer drug development through synthetic lethality. Full article

Promoters, as core elements in the regulation of gene expression, play a pivotal role in genetic engineering and synthetic biology. The accurate prediction and optimization of promoter strength are essential for advancing these fields. Here, we present the first promoter strength database tailored to Halomonas, an extremophilic microorganism, and propose a novel promoter design and prediction method based on generative adversarial networks (GANs) and multi-model fusion. The GAN model effectively learns the key features of Halomonas promoter sequences, such as the GC content and Moran’s coefficients, to generate biologically plausible promoter sequences. To enhance prediction accuracy, we developed a multi-model fusion framework integrating deep learning and machine learning approaches. Deep learning models, incorporating BiLSTM and CNN architectures, capture k-mer and PSSM features, whereas machine learning models utilize engineered string and non-string features to construct comprehensive feature matrices for the multidimensional analysis and prediction of promoter strength. Using the proposed framework, newly generated promoters via mutation were predicted, and their functional validity was experimentally confirmed. The integration of multiple models significantly reduced the experimental validation space through an intersection-based strategy, achieving a notable improvement in top quantile prediction accuracy, particularly within the top five quantiles. The robustness and applicability of this model were further validated on diverse datasets, including test sets and out-of-sample promoters. This study not only introduces an innovative approach for promoter design and prediction in Halomonas but also lays a foundation for advancing industrial biotechnology. Additionally, the proposed strategy of GAN-based generation coupled with multi-model prediction demonstrates versatility, offering a valuable reference for promoter design and strength prediction in other extremophiles. Our findings highlight the promising synergy between artificial intelligence and synthetic biology, underscoring their profound academic and practical implications. Full article

Changes in root traits and rhizosphere microbiome are important ways to optimize plant phosphorus (P) efficiency and promote multifunctionality in intercropping. However, whether and how synthetic microbial communities isolated from polyculture systems can facilitate plant growth and P uptake are still largely unknown. A field experiment was first carried out to assess the rice yield and P uptake in the rice/soybean intercropping systems, and a synthetic microbial community (SynCom) isolated from intercropped rice was then constructed to elucidate the potential mechanisms of growth-promoting effects on rice growth and P uptake in a series of pot experiments. Our results showed that the yield and P uptake of intercropped rice were lower than those of rice grown in monoculture. However, bacterial networks in the rice rhizosphere were more stable in polyculture, exhibiting more hub nodes and greater modularity compared to the rice monoculture. A bacterial synthetic community (SynCom) composed of four bacterial strains (Variovorax paradoxus, Novosphingobium subterraneum, Hydrogenophaga pseudoflava, Acidovorax sp.) significantly enhanced the biomass and P uptake of potted rice plants. These growth-promoting effects are underpinned by multiple pathways, including the direct activation of soil available P, increased root surface area and root tip number, reduced root diameter, and promotion of root-to-shoot P translocation through up-regulation of Pi transporter genes (OsPht1;1, OsPht1;2, OsPht1;4, OsPht1;6). This study highlights the potential of harnessing synthetic microbial communities to enhance nutrient acquisition and improve crop production. Full article

Abiotic stresses, such as drought, salinity, and extreme temperatures, limit plant growth and development, reducing crop yields. Therefore, a more comprehensive understanding of the signaling mechanisms and responses of plants to changing environmental conditions is crucial for improving sustainable agricultural productivity. Chemical screening was conducted to find novel small compounds that act as regulators of the abiotic stress signaling pathway using the ABA-inducible transgenic reporter line. Small molecules called stress response regulators (SRRs) were isolated by screening a synthetic library composed of 14,400 small compounds, affecting phenotypes such as seed germination, root growth, and gene expression in response to multiple abiotic stresses. Seeds pretreated with SRR compounds positively affected the germination rate and radicle emergence of Arabidopsis and tomato plants under abiotic stress conditions. The SRR-priming treatment enhanced the transcriptional responses of abiotic stress-responsive genes in response to subsequent salt stress. The isolation of the novel molecules SRR1 and SRR2 will provide a tool to elucidate the complex molecular networks underlying the plant stress-tolerant responses. Full article