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Assessing the Adjuvant Potential of Chinese Hamster Ovary Host Cell Proteins Using an In Vitro Dendritic Cell Assay.
No full textHost cell proteins (HCPs) are process-related impurities of therapeutic protein production and may affect product quality or patient safety. In clinical trials, certain HCPs (e.g., PLBL2 or CCL2) that co-purify with the therapeutic protein have been associated with immune reactions in patients. In this study, we examined the adjuvant potential of six commonly detected HCPs from CHO cells (PRDX1, S100A4, PLBL2, CCL2, CLU, and YWHAE) using an in vitro dendritic cell (DC) maturation assay. Recombinant HCPs were expressed in CHO cells to mimic manufacturing conditions. PRDX1, S100A4, and PLBL2 caused a slight increase in the expression of maturation markers on DCs, while YWHAE, CLU, and CCL2 did not. Interestingly, CLU and CCL2 reduced the DC maturation induced by rituximab. In addition, we observed that process parameters such as elution conditions during chromatographic purification can influence HCP aggregation, which in turn can mask or enhance the intrinsic adjuvant potential of an HCP. These findings not only provide initial insights into the adjuvant potential of individual HCPs but also indicate that the quantity as well as the degree of aggregation of HCPs might influence adjuvanticity
Dissecting regulators of hepatocyte identity using a CRISPR activation screening in iPSC-derived hepatocytes
No full textInduced pluripotent stem cell-derived hepatocytes (iHEPs) hold significant promise for
disease modeling, drug testing, and regenerative medicine. However, their limited
maturity compared to primary human hepatocytes remains a major challenge. This
study employed a CRISPR activation (CRISPRa) system in induced pluripotent stem
cells to investigate the role of selected transcription factors (TFs) in enhancing iHEP
maturation during differentiation. Following sgRNA-mediated activation of 14 target
TFs, bulk RNA sequencing and subsequent transcriptomic analyses—including
UMAP visualization, MuSIC deconvolution, gene set enrichment analysis (GSEA),
and hepatocyte zonation marker assessment—were performed to evaluate changes
in cell identity and function.
Our results demonstrate that activation of AR and CEBPA significantly promotes
hepatocyte differentiation and metabolic function, as evidenced by increased
proportions of hepatocyte-like cells, enrichment of hepatocyte-specific gene sets, and
activation of key liver metabolic pathways. Conversely, TFs such as IRF1 and IRF2
biased differentiation toward cholangiocyte lineage, while NCOA2 and MYC activation
favored mesenchymal cell fate indicating epithelial-to-mesenchymal transition.
Notably, HNF4A activation unexpectedly failed to enhance hepatocyte maturation,
suggesting potential technical or biological complexities. Comparative analyses
revealed that hepatocyte enrichment strongly correlates with the activation of
metabolic pathways, underscoring the functional relevance of AR and CEBPA-driven
maturation.
This comprehensive transcriptomic study highlights the critical influence of TF
selection on iHEP fate decisions and functional maturation. Identifying TFs that
robustly drive hepatocyte differentiation—particularly AR and CEBPA—offers valuable
insights for optimizing differentiation protocols aimed at producing functionally mature
hepatocytes for biomedical applications
Empowering Micellar Catalysis and Representation Learning with Limited Data Availability: Surfactant Design Principle Can Boost Yield Predictions
No full textAccurate prediction of chemical reaction yields is crucial for advancing synthetic chemistry, particularly in process de-velopment. Traditional trial-and-error methods for reaction optimization are increasingly inadequate due to high time and resource consumption. This study presents the development of an AI-driven model for predicting reaction yields in mi-cellar catalysis, leveraging representation learning and predictive analytics to reduce waste, and promote sustainable micellar methodologies. Despite the challenge of data scarcity, we trained the model based on limited available data for micellar catalysis and selected the closely related data from traditional organic solvents based on the design principle of surfactant PS-750-M and its intrinsic polarity match with organic solvents. The data set was manually curated from pa-tents and journals, ensuring robust model performance. The model employed a hybrid representation learning frame-work, integrating autoencoders for dimensionality reduction with a gradient-boosting regressor for prediction tasks. This approach demonstrated high predictive accuracy, with experimental validation showing yields closely resembling pre-dicted values. The findings highlight the potential of AI to transform synthetic micellar chemistry by enabling resource-efficient and environmentally sound amide couplings as a chemical transformation. This work lays a strong foundation for integrating advanced AI strategies into micellar catalysis, addressing current data limitations, and paving the way for future advancements in sustainable chemical design
Double-stranded RNA induces retinal pigment epithelium cell degeneration and inflammation
No full textRIG-I signaling has been previously implicated as a driver of inflammation to the retinal pigment epithelium (RPE) during age-related macular degeneration (AMD). Double-stranded RNA (dsRNA) is known to initiate RIG-I signaling and lead to a type I interferon response. We show through shRNA knockdown that RIG-I is essential for initiating an interferon response in iPS-RPE in response to both synthetic dsRNA-mimetic 3p-hpRNA and the double-stranded retrotransposable element Alu. Analysis of human tissue from patients suffering from AMD show accumulation of dsRNA, peaking at the geographic atrophy (GA) stage. Using a new murine model of 3p-hpRNA subretinal challenge to RPE cells, we confirmed that accumulation of dsRNA initiates a type I interferon response, as well as RPE and photoreceptor degeneration. Although RPE response to synthetic dsRNA was acute, extensive leukocyte migration was observed. The results from this study verify the importance of RIG-I signaling in regulating inflammation in the subretinal space and implicates dsRNA accumulation as a driver of AMD pathogenesis
Direct N–SF5 and N–SF4CF3 Bond Formation through Strain-Release Functionalization of 3-Substituted [1.1.0]Azabicyclobutanes
No full textIn comparison to modern methods for carbon–SF5 bond formation, methods for direct heteroatom–SF5 bond formation are exceptionally scarce, rendering motifs such as “N–SF5” virtually unexplored in the context of organic and medici-nal chemistry. Herein, we demonstrate that direct N–SF5 bond formation can be accomplished through strain-release pentafluorosulfanylation of 3-aryl [1.1.0]azabicyclobutanes (ABBs), using an easy-to-access solution of SF5Cl. To our surprise, the resultant N–SF5 azetidines proved to be remarkably chemically stable and amenable to peripheral syn-thetic modifications (e.g., amination, cross-coupling, oxidation, dehalogenation, SN1, and SNAr reactions). The meth-odology also extends to direct N–SF4CF3 bond formation using trans-CF3SF4Cl, enabling comparative studies through-out this work. From a mechanistic standpoint, DFT calculations, Hammett analyses, and radical trapping experiments support our proposed radical chain propagation mechanism. From a fundamental standpoint, considering N–SF5 and N–SF4CF3 azetidines are heretofore unknown molecular motifs, this work analyzes their dynamic, spectroscopic, and crystallographic features, as well as computed properties (e.g., BDE and pKb values), to provide foundational knowledge and inform downstream applications. While the carbon-bound SF5 group has been employed as a bi-oisostere for a CF3 group, we posited the N–SF5 motif could be a potential replacement for a small sulfonamide. Ac-cordingly, we synthesized an N–SF5 derivative of a spleen tyrosine kinase inhibitor reported in the patent literature for comparative ADME studies; results from in vitro profiling indicate that an N–SF5 azetidine could indeed be an al-ternative for an N–SO2Me azetidine, in instances where enhanced lipophilicity is desirable
Pharmacotherapy of cardiovascular diseases from herbs and pills to nucleic acids A report from the European Society of Cardiology Cardiovascular Roundtable
No full textCardiovascular (CV) diseases continue to cause substantial morbidity and mortality. Risk factors are inadequately controlled, compliance with medi�cation remains suboptimal, and treatments are not sufficient to fully prevent the progression of atherosclerotic CV disease, heart failure, arrhyth�mias, and valvular heart diseases. An increased understanding of the genetic basis of CV diseases and advances in the technology of therapeutics have led to the development of nucleic acid–based therapies (NATs) for prevention and treatment of CV risk factors and diseases. Nucleic acid–based therapies can target disease pathways at the translational level preventing the formation of disease-causing proteins that could not be effectively targeted by other pharmacological therapeutics and will likely improve treatment adherence by providing long-acting effects over many months rather than daily treatment. These therapies include RNA-targeted therapeutics, gene editing therapeutics, and gene therapies. Challenges around
the use of NATs may be unique with each new drug and new target and may include long-term unanticipated side effects, and issues around spe�cificity, targeting, and stability. Assessing NATs for marketing approval continues to pose challenges for regulatory agencies. These include their di�verse nature, limited data on pharmacology, clinical safety and efficacy, and the lack of long-term results. Barriers in clinical practice may include the lack of specific education, fear of off target effects, costs, and ethical challenges. Implementation of these novel therapies will require careful patient selection and education. Despite potentially high treatment costs, possible long-term cost savings could result from fewer healthcare visits due to infrequent NAT administrations, and lower rates of disease progression, hospitalization, and CV events due to sustained improvement in control of disease pathways and risk factors
Triggering AHR resolves TGF-β1 induced fibroblast activation and promotes AT1 cell regeneration in alveolar organoids.
No full textRegeneration of the alveolar epithelium is necessary to restore tissue architecture and gas exchange capabilities in chronic pulmonary diseases such as fibrosing interstitial lung disease. While it is known alveolar type 2 (AT2) cells give rise to alveolar type 1 (AT1) cells to repair the alveolar epithelium after injury, methods to promote this process under pathological settings are poorly understood. Here, using a complex 3D organoid culture with TGF-β1 dependent impaired AT1 spheroid formation, we performed a high-throughput screen (HTS) with ~16,800 compounds to identify small molecules that increase number of AT1 spheroids. Longitudinal single cell RNA sequencing (scRNA-seq) revealed that DB-11-BE87 increased AT1 regeneration by reducing TGF-β1 induced fibroblast activation, concurrently with AHR activation in those cells. These studies highlight a HTS system to identify factors that can promote AT1 differentiation and suggest AHR activation as a method to counteract pathological TGF-β1 signaling in pulmonary disease
Deep Learning-Driven MRI for Accurate Brain Volumetry in Murine Models of Neurodegenerative Diseases
No full textBrain atrophy as assessed by magnetic resonance imaging (MRI) is a key measure of neurodegeneration and a predictor of disability progression in Alzheimer’s disease and multiple sclerosis (MS) patients. While MRI-based brain volumetry is valuable for analyzing neurodegeneration in murine models as well, achieving high spatial resolution at sufficient signal-to-noise ratio is challenging due to the small size of the mouse brain. Ex vivo analysis offers greater resolution but is limited by potential tissue distortions and shrinkage due to preparation processes. In vivo imaging allows for longitudinal studies and repeated assessments, enhancing statistical power and enabling pharmacological evaluations. However, the need for anesthesia necessitates compromises in acquisition times and voxel sizes. We demonstrate the application of deep learning for reliable quantification of brain region volumes, such as the hippocampus, caudate putamen, and cerebellum, from T2-weighted images with a pixel volume of 78x78x250 µm³ acquired in 4.3 minutes at 7 Tesla. The reproducibility of the fully automatic segmentation pipeline was validated in healthy C57BL/6J mice and subsequently applied to models of amyotrophic lateral sclerosis, cuprizone-induced demyelination, and MS. Our approach offers a robust and efficient method for in vivo brain volumetry in preclinical mouse studies, facilitating the evaluation of neurodegenerative processes and therapeutic interventions. The dramatic reduction in acquisition time achieved with our AI-based approach significantly enhances animal welfare (3R). This advancement allows brain volumetry to be seamlessly integrated into additional analyses, providing comprehensive insights without substantially increasing study duration