Achieving sustainable biomanufacturing requires understanding how process format and facility design influence environmental performance. Using process-modeling software, this study compared three monoclonal antibody (mAb) drug-substance manufacturing scenarios: single-use fed-batch (SU FB), stainless-steel fed-batch (SS FB), and single-use continuous (SU Cont). Each scenario utilized a single bioreactor facility at an equivalent annual output to assess water and materials intensity as well as facility energy demand and greenhouse gas (CO₂e) emissions. Differences in overall water intensity were primarily driven by facility type, with stainless-steel systems requiring additional cleaning water, while non-cleaning process water usage remained similar across scenarios. Variations in raw materials and consumables intensity were linked to differences in single-use versus stainless-steel manufacturing as well as process format. Energy modeling showed that facility operations (e.g., HVAC, lighting) accounted for roughly 85% of total energy use, at the scale evaluated, i.e. 500 L to 2000 L, varying with facility footprint; thus, maximizing product output relative to facility size reduced both energy and carbon intensity. Sensitivity analyses on facility optimization—such as lower cleanroom classifications, increased equipment density, more efficient utilities, and even site location due to climate—indicated potential reductions in total energy use and emissions of up to approximately 35%. Overall, the results underscore the critical role of integrated process and facility design in advancing more carbon-efficient, sustainable biomanufacturing.
Transcriptomic adaptation plays a central role in the phenotypic plasticity of Chinese hamster ovary (CHO) cells. While various gene expression studies have provided insights into condition-specific responses, a comprehensive, systems-level understanding of how CHO cell transcriptomes dynamically adapt across diverse experimental contexts remains limited. Here, we present a large-scale investigation of more than 600 RNA-seq samples across diverse cell lines, culture conditions and experimental designs to systematically dissect stable and adaptive components of the transcriptome. We consolidate robust expression variance measures across datasets to identify a global axis of gene expression variability that summarizes transcriptional adaptation across common experimental settings. Functional enrichment analyses reveal that highly variable genes are associated with stress response mechanisms, extracellular interactions, signaling and apoptosis, whereas low-variability genes are enriched for RNA metabolism, intracellular transport and basal transcription. Analyses of regulatory features of genes with different variabilities show distinct promoter architectures and epigenetic signatures consistent with dynamic transcriptional control. Beyond this global axis of transcriptional adaptation, we capture context-specific transcriptional programs linked to experimental designs, including temperature downshift and growth phase transitions. Together, these findings delineate a common, low-variability transcriptional core alongside a variable, flexible layer that enables adaptive responses. Our work advances the understanding of transcriptional robustness and plasticity in CHO cells, provides practical guidance for reference gene selection and may offer potential for rational engineering for more robust cell lines.
Ergothioneine (EGT) is a bioactive, rare variant of histidine with many applications in the medical, pharmaceutical, and food fields. Therefore, we aimed to investigate in this study the impact of genomic and physicochemical factors on EGT production by the industrial filamentous fungus Aspergillus oryzae. Firstly, to facilitate efficient EGT production, we analyzed the subcellular localization of the three EGT biosynthetic enzymes present in A. oryzae. During screening for the most potent producer of EGT among bioengineered transformants, the strain EgtACO overexpressing both AoegtA and AoegtC showed promising EGT production in DPY medium. Five days of incubation was the optimum period, and CZYP medium was the optimum medium for EGT production. Co-cultivation with the nisin Z-producing Lactococcus lactis JCM 7638 yielded EGT production equivalent to that of the EgtACO strain alone. Having broad-spectrum antimicrobial activity without suppressing growth of the EgtACO strain suggested that bacteriocin may help reduce the risk of contamination during long-term cultivation. Moreover, supplementing the production medium with L-methionine or zinc sulfate improved EGT production (1468.5 or 1565 mg/L, respectively). Furthermore, repeated inoculation of the producer strain EgtACO and incubation in blue light were the optimum conditions for EGT production (1895 mg/L). Finally, we achieved cost-effective EGT production using A. oryzae strain EgtACO under the optimal culture conditions using agricultural wastes: potato peel and sweet potato peel (293 and 308 mg/L, respectively).
Riboswitches are RNA regulatory elements that sense small molecules and regulate gene expression through ligand-induced conformational changes in an aptamer domain. They enable synthetic biology applications such as biosensing and gene circuit design. Accurate prediction of riboswitch-ligand binding affinity, quantified by the dissociation constant (Kd), is essential for rational riboswitch engineering. We present a neuro-symbolic framework in which LLM-derived embeddings encode riboswitch sequences, secondary structures, and ligand representations into a unified representation that captures riboswitch-ligand interaction context, while domain-informed rules encode explicit biochemical priors. Despite limited labeled data, this framework achieves strong predictive performance and improved data efficiency. It predicts min-max log-scaled pKd values and supports binary classification of binding strength, particularly by mitigating systematic overestimation of affinity. More broadly, the results suggest that LLM-based neuro-symbolic representations provide an effective route for modeling riboswitch-ligand interactions under data-limited conditions.
Hairy root cultures offer a sustainable alternative to field-grown plants for the production of bioactive compounds, but their translation to industrial-scale processes is limited by the challenges of high-volume cultivation and downstream processing. In the current study, a large-scale production pipeline for basil (Ocimum basilicum) hairy roots, a rich source of specialized bioactive metabolites, is described. The production of hairy root biomass was optimized by developing a robust and scalable cultivation protocol, enabling consistent and reproducible growth in bioreactor systems with working volumes of up to 1000 L. The entire production pipeline from initial Petri dish cultures to industrial-scale bioreactors was established by adopting a systematic, stepwise methodology, ensuring process reliability and scalability. A customized bioreactor with controlled aeration maintained biomass integrity and metabolic stability during long-term axenic cultivation, yielding 96.4 kg of biomass fresh weight within 84 days. Complementary solvent-based extraction and pulsed electric field processing allowed the recovery of metabolites with a broad range of polarities. Untargeted metabolomics revealed a chemically diverse profile comprising primary metabolites, phenolic acids, flavonoids, terpenoids and triterpenoids with relevance for cosmetic applications. These results demonstrate the feasibility of industrial-scale basil hairy root cultures and support their use as a source of plant-derived cosmetic ingredients.
