Integrative Biology最新文献

This paper describes the manufacture of geometrically inverted mammary organoids encapsulating primary mammary preadipocytes and adipocytes. Material manipulation in an array of 192 hanging drops induces cells to self-assemble into inside-out organoids where an adipose tissue core is enveloped by a cell-produced basement membrane, indicated by laminin V staining and then a continuous layer of mammary epithelial cells. This inverted tissue structure enables investigation of multiple mammary cancer subtypes, with a significantly higher extent of invasion by triple-negative MDA-MB-231 breast cancer cells compared to MCF7 cells. By seeding cancer cells into co-culture around pre-formed organoids with encapsulated preadipocytes/adipocytes, invasion through the epithelium, then into the adipose core is observable through acquisition of confocal image stacks of whole mount specimens. Furthermore, in regions of the connective tissue core where invasion occurs, there is an accumulation of collagen in the microenvironment. Suggesting that this collagen may be conducive to increased invasiveness, the anti-fibrotic drug pirfenidone shows efficacy in this model by slowing invasion. Comparison of adipose tissue derived from three different donors shows method consistency as well as the potential to evaluate donor cell-based biological variability. Insight box Geometrically inverted mammary organoids encapsulating primary preadipocytes/adipocytes (P/As) are bioengineered using a minimal amount of Matrigel scaffolding. Use of this eversion-free method is key to production of adipose mammary organoids (AMOs) where not only the epithelial polarity but also the entire self-organizing arrangement, including adipose position, is inside-out. While an epithelial-only structure can analyze cancer cell invasion, P/As are required for invasion-associated collagen deposition and efficacy of pirfenidone to counteract collagen deposition and associated invasion. The methods described strike a balance between repeatability and preservation of biological variability: AMOs form consistently across multiple adipose cell donors while revealing cancer cell invasion differences.

Breast cancer ranks as one of the most prevalent forms of cancer and stands as the primary global cause of mortality among women. Overexpression of EGFR and ER receptors or their genomic alterations leads to malignant transformation, disease aggression and is linked to poor patient survival outcomes. The clinical breast cancer patient's genomic expression, survival analysis, and computational drug-targeting approaches were used to identify best-hit phytochemicals for therapeutic purposes. Breast cancer patients have genomic alterations in EGFR (4%, n = 5699) and ER (9%, n = 8461), with the highest proportion being missense mutations. No statistically significant difference was observed in the patient survival rates between the altered and unaltered ER groups, unlike EGFR, with the lowest survival rates in the altered group. Computational screening of natural compound libraries (7711) against each EGFR (3POZ) and ER (3ERT) receptor shortlists the best-hit 3 compounds with minimum docking score (ΔG = -7.9 to -10.8), MMGBSA (-40.16 to -51.91 kcal/mol), strong intermolecular H-bonding, drug-like properties with least kd, and ki. MD simulation studies display stable RMSD, RMSF, and good residual correlation of best-hit common compounds (PubChem ID: 5281672 and 5280863) targeting both EGFR and ER receptors. In vitro, studies revealed that these common drugs exhibited a high anti-proliferative effect on MCF-7 and MDA-MB-231 breast cancer cells, with effective IC50 values (15-40 μM) and lower free energy, kd, and ki (5281672 > 5280863 > 5330286) much affecting HEK-293 non-cancerous cells, indicating the safety profile. The experimental and computational correlation studies suggest that the highly expressed EGFR and ER receptors in breast cancer patients having poor survival rates can be effectively targeted with best-hit common potent drugs with a multi-target therapeutic approach. Insight Box: The findings of this study provide valuable insights into the genomic/proteomic data, breast cancer patient's survival analysis, and EGFR and ER receptor variants structural analysis. The genetic alterations analysis of EGFR and ER/ESR1 in breast cancer patients reveals the high frequency of mutation types, which affect patient's survival rate and targeted therapies. The common best-hit compounds affect the cell survival patterns with effective IC50, drug-like properties having lower equilibrium and dissociation constants demonstrating the anti-proliferative effects. This work integrates altered receptor structural analysis, molecular interaction-based simulations, and ADMET properties to illuminate the identified best hits phytochemicals potential efficacy targeting both EGFR and ER receptors, demonstrating a multi-target therapeutic approach.

In recent years, three-dimensional (3D) cell culture has been attracting attention as a cell culture model that mimics an environment closer to that of a living organism. It is known that there is a close relationship between cell nuclear shape and cellular function, which highlights the importance of cell nucleus shape analysis in the 3D culture. On the other hand, it is difficult to observe the cell nuclei inside the 3D culture models because the penetration depth of the laser light under a microscope is limited. In this study, we adopted an aqueous iodixanol solution to the 3D osteocytic spheroids derived from mouse osteoblast precursor cells to make the spheroids transparent for 3D quantitative analysis. With a custom-made image analysis pipeline in Python, we found that the aspect ratio of the cell nuclei near the surface of the spheroid was significantly greater than that at the center, suggesting that the nuclei on the surface were deformed more than those at the center. The results also quantitatively showed that the orientation of nuclei in the center of the spheroid was randomly distributed, whereas those on the surface of the spheroid were oriented parallel to the surface of the spheroid. Our 3D quantitative method with an optical clearing technique will contribute to the 3D culture models including various organoid models to elucidate the nuclear deformation during the development of the organs. Insight box Although 3D cell culture has been a powerful tool in the fields of fundamental biology and tissue engineering, it raises the demand for quantification techniques for cell nuclear morphology in the 3D culture model. In this study, we attempted to optically clear a 3D osteocytic spheroid model using iodixanol solution for the nuclear observation inside the spheroid. Moreover, using a custom-made image analysis pipeline in Python, we successfully quantified the nuclear morphology regarding aspect ratio and orientation. Our quantitative method with the optical clearing technique will contribute to the 3D culture models such as various organoid models to elucidate the nuclear deformation during the development of the organs.

Radiation therapy, one of the most effective therapies to treat cancer, is highly toxic to healthy tissue. The delivery of radiation at ultra-high dose rates, FLASH radiation therapy (FLASH), has been shown to maintain therapeutic anti-tumor efficacy while sparing normal tissues compared to conventional dose rate irradiation (CONV). Though promising, these studies have been limited mainly to murine models. Here, we leveraged enteroids, three-dimensional cell clusters that mimic the intestine, to study human-specific tissue response to radiation. We observed enteroids have a greater colony growth potential following FLASH compared with CONV. In addition, the enteroids that reformed following FLASH more frequently exhibited proper intestinal polarity. While we did not observe differences in enteroid damage across groups, we did see distinct transcriptomic changes. Specifically, the FLASH enteroids upregulated the expression of genes associated with the WNT-family, cell-cell adhesion, and hypoxia response. These studies validate human enteroids as a model to investigate FLASH and provide further evidence supporting clinical study of this therapy. Insight Box Promising work has been done to demonstrate the potential of ultra-high dose rate radiation (FLASH) to ablate cancerous tissue, while preserving healthy tissue. While encouraging, these findings have been primarily observed using pre-clinical murine and traditional two-dimensional cell culture. This study validates the use of human enteroids as a tool to investigate human-specific tissue response to FLASH. Specifically, the work described demonstrates the ability of enteroids to recapitulate previous in vivo findings, while also providing a lens through which to probe cellular and molecular-level responses to FLASH. The human enteroids described herein offer a powerful model that can be used to probe the underlying mechanisms of FLASH in future studies.

Neurodegenerative disorders (NDDs) are known to exhibit genetic overlap and shared pathophysiology. This study aims to find the shared genetic architecture of Alzheimer's disease (AD) and Parkinson's disease (PD), two major age-related progressive neurodegenerative disorders. The gene expression profiles of GSE67333 (containing samples from AD patients) and GSE114517 (containing samples from PD patients) were retrieved from the Gene Expression Omnibus (GEO) functional genomics database managed by the National Center for Biotechnology Information. The web application GREIN (GEO RNA-seq Experiments Interactive Navigator) was used to identify differentially expressed genes (DEGs). A total of 617 DEGs (239 upregulated and 379 downregulated) were identified from the GSE67333 dataset. Likewise, 723 DEGs (378 upregulated and 344 downregulated) were identified from the GSE114517 dataset. The protein-protein interaction networks of the DEGs were constructed, and the top 50 hub genes were identified from the network of the respective dataset. Of the four common hub genes between two datasets, C-X-C chemokine receptor type 4 (CXCR4) was selected due to its gene expression signature profile and the same direction of differential expression between the two datasets. Mavorixafor was chosen as the reference drug due to its known inhibitory activity against CXCR4 and its ability to cross the blood-brain barrier. Molecular docking and molecular dynamics simulation of 51 molecules having structural similarity with Mavorixafor was performed to find two novel molecules, ZINC49067615 and ZINC103242147. This preliminary study might help predict molecular targets and diagnostic markers for treating Alzheimer's and Parkinson's diseases. Insight Box Our research substantiates the therapeutic relevance of CXCR4 inhibitors for the treatment of Alzheimer's and Parkinson's diseases. We would like to disclose the following insights about this study. We found common signatures between Alzheimer's and Parkinson's diseases at transcriptional levels by analyzing mRNA sequencing data. These signatures were used to identify putative therapeutic agents for these diseases through computational analysis. Thus, we proposed two novel compounds, ZINC49067615 and ZINC103242147, that were stable, showed a strong affinity with CXCR4, and exhibited good pharmacokinetic properties. The interaction of these compounds with major residues of CXCR4 has also been described.

Obesity is linked to adipose tissue dysfunction, a dynamic endocrine organ. Two-dimensional cultures present technical hurdles hampering their ability to follow individual or cell groups for metabolic disease research. Three-dimensional type I collagen microgels with embedded adipocytes have not been thoroughly investigated to evaluate adipogenic maintenance as instrument for studying metabolic disorders. We aimed to develop a novel tunable Col-I microgel simulating the adipocyte microenvironment to maintain differentiated cells with only insulin as in vitro model for obesity research. Adipocytes were cultured and encapsulated in collagen microgels at different concentrations (2, 3 and 4 mg/mL). Collagen microgels at 3 and 4 mg/mL were more stable after 8 days of culture. However, cell viability and metabolic activity were maintained at 2 and 3 mg/mL, respectively. Cell morphology, lipid mobilization and adipogenic gene expression demonstrated the maintenance of adipocyte phenotype in an in vitro microenvironment. We demonstrated the adequate stability and biocompatibility of the collagen microgel at 3 mg/mL. Cell and molecular analysis confirmed that adipocyte phenotype is maintained over time in the absence of adipogenic factors. These findings will help better understand and open new avenues for research on adipocyte metabolism and obesity. Insight box In the context of adipose tissue dysfunction research, new struggles have arisen owing to the difficulty of cellular maintenance in 2D cultures. Herein, we sought a novel approach using a 3D type I collagen-based biomaterial to adipocyte culture with only insulin. This component was tailored as a microgel in different concentrations to support the growth and survival of adipocytes. We demonstrate that adipocyte phenotype is maintained and key adipogenesis regulators and markers are over time. The cumulative results unveil the practical advantage of this microgel platform as an in vitro model to study adipocyte dysfunction and obesity.

Ovarian cancer (OC) is the second most common cancer of the female reproductive system. Due to the asymptomatic nature of early stages of OC and an increasingly poor prognosis in later stages, methods of screening for OC are much desired. Furthermore, screening and diagnosis processes, in order to justify use on asymptomatic patients, must be convenient and non-invasive. Recent developments in machine-learning technologies have made this possible via techniques in the field of metabolomics. The objective of this research was to use existing metabolomics data on OC and various analytic methods to develop a machine-learning model for the classification of potentially OC-related metabolite biomarkers. Pathway analysis and metabolite-set enrichment analysis were performed on gathered metabolite sets. Quantitative molecular descriptors were then used with various machine-learning classifiers for the diagnostics of OC using related metabolites. We elucidated that the metabolites associated with OC used for machine-learning models are involved in five metabolic pathways linked to OC: Nicotinate and Nicotinamide Metabolism, Glycolysis/Gluconeogenesis, Aminoacyl-tRNA Biosynthesis, Valine, Leucine and Isoleucine Biosynthesis, and Alanine, Aspartate and Glutamate Metabolism. Several classification models for the identification of OC using related metabolites were created and their accuracies were confirmed through testing with 10-fold cross-validation. The most accurate model was able to achieve 85.29% accuracy. The elucidation of biological pathways specific to OC using metabolic data and the observation of changes in these pathways in patients have the potential to contribute to the development of screening techniques for OC. Our results demonstrate the possibility of development of the machine-learning models for OC diagnostics using metabolomics data.

In an attempt to understand the role of dysregulated circadian rhythm in glioma, our recent findings highlighted the existence of a feed-forward loop between tumour metabolite lactate, pro-inflammatory cytokine IL-1β and circadian CLOCK. To further elucidate the implication of this complex interplay, we developed a mathematical model that quantitatively describes this lactate dehydrogenase A (LDHA)-IL-1β-CLOCK/BMAL1 circuit and predicts potential therapeutic targets. The model was calibrated on quantitative western blotting data in two glioma cell lines in response to either lactate inhibition or IL-1β stimulation. The calibrated model described the experimental data well and most of the parameters were identifiable, thus the model was predictive. Sensitivity analysis identified IL-1β and LDHA as potential intervention points. Mathematical models described here can be useful to understand the complex interrelationship between metabolism, inflammation and circadian rhythm, and in designing effective therapeutic strategies. Our findings underscore the importance of including the circadian clock when developing pharmacological approaches that target aberrant tumour metabolism and inflammation. Insight box  The complex interplay of metabolism-inflammation-circadian rhythm in tumours is not well understood. Our recent findings provided evidence of a feed-forward loop between tumour metabolite lactate, pro-inflammatory cytokine IL-1β and circadian CLOCK/BMAL1 in glioma. To elucidate the implication of this complex interplay, we developed a mathematical model that quantitatively describes this LDHA-IL-1β-CLOCK/BMAL1 circuit and integrates experimental data to predict potential therapeutic targets. The study employed a multi-start optimization strategy and profile likelihood estimations for parameter estimation and assessing identifiability. The simulations are in reasonable agreement with the experimental data. Sensitivity analysis found LDHA and IL-1β as potential therapeutic points. Mathematical models described here can provide insights to understand the complex interrelationship between metabolism, inflammation and circadian rhythm, and in identifying effective therapeutic targets.

Live cell calcium (Ca2+) imaging is one of the important tools to record cellular activity during in vitro and in vivo preclinical studies. Specially, high-resolution microscopy can provide valuable dynamic information at the single cell level. One of the major challenges in the implementation of such imaging schemes is to extract quantitative information in the presence of significant heterogeneity in Ca2+ responses attained due to variation in structural arrangement and drug distribution. To fill this gap, we propose time-lapse imaging using spinning disk confocal microscopy and machine learning-enabled framework for automated grouping of Ca2+ spiking patterns. Time series analysis is performed to correlate the drug induced cellular responses to self-assembly pattern present in multicellular systems. The framework is designed to reduce the large-scale dynamic responses using uniform manifold approximation and projection (UMAP). In particular, we propose the suitability of hierarchical DBSCAN (HDBSCAN) in view of reduced number of hyperparameters. We find UMAP-assisted HDBSCAN outperforms existing approaches in terms of clustering accuracy in segregation of Ca2+ spiking patterns. One of the novelties includes the application of non-linear dimension reduction in segregation of the Ca2+ transients with statistical similarity. The proposed pipeline for automation was also proved to be a reproducible and fast method with minimal user input. The algorithm was used to quantify the effect of cellular arrangement and stimulus level on collective Ca2+ responses induced by GPCR targeting drug. The analysis revealed a significant increase in subpopulation containing sustained oscillation corresponding to higher packing density. In contrast to traditional measurement of rise time and decay ratio from Ca2+ transients, the proposed pipeline was used to classify the complex patterns with longer duration and cluster-wise model fitting. The two-step process has a potential implication in deciphering biophysical mechanisms underlying the Ca2+ oscillations in context of structural arrangement between cells.

Fibroblasts play an essential role in tissue repair and regeneration as they migrate to wounded areas to secrete and remodel the extracellular matrix. Fibroblasts recognize chemical substances such as growth factors, which enhance their motility towards the wounded tissues through chemotaxis. Although several studies have characterized single-cell fibroblast motility before, the migration patterns of fibroblasts in response to external factors have not been fully explored in 3D environments. We present a study that combines experimental and computational efforts to characterize the effect of chemical stimuli on the invasion of 3D collagen matrices by fibroblasts. Experimentally, we used microfluidic devices to create chemical gradients using collagen matrices of distinct densities. We evaluated how cell migration patterns were affected by the presence of growth factors and the mechanical properties of the matrix. Based on these results, we present a discrete-based computational model to simulate cell motility, which we calibrated through the quantitative comparison of experimental and computational data via Bayesian optimization. By combining these approaches, we predict that fibroblasts respond to both the presence of chemical factors and their spatial location. Furthermore, our results show that the presence of these chemical gradients could be reproduced by our computational model through increases in the magnitude of cell-generated forces and enhanced cell directionality. Although these model predictions require further experimental validation, we propose that our framework can be applied as a tool that takes advantage of experimental data to guide the calibration of models and predict which mechanisms at the cellular level may justify the experimental findings. Consequently, these new insights may also guide the design of new experiments, tailored to validate the variables of interest identified by the model.