Integrative Biology最新文献

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.

The kidney tubule consists of a single layer of epithelial cells supported by the tubular basement membrane (TBM), a thin layer of specialized extracellular matrix (ECM). The mechanical properties of the ECM are important for regulating a wide range of cell functions including proliferation, differentiation and cell survival. Increased ECM stiffness plays a role in promoting multiple pathological conditions including cancer, fibrosis and heart disease. How changes in TBM mechanics regulate tubular epithelial cell behavior is not fully understood. Here we introduce a cell culture system that utilizes in vivo-derived TBM to investigate cell-matrix interactions in kidney proximal tubule cells. Basement membrane mechanics was controlled using genipin, a biocompatibility crosslinker. Genipin modification resulted in a dose-dependent increase in matrix stiffness. Crosslinking had a marginal but statistically significant impact on the diffusive molecular transport properties of the TBM, likely due to a reduction in pore size. Both native and genipin-modified TBM substrates supported tubular epithelial cell growth. Cells were able to attach and proliferate to form confluent monolayers. Tubular epithelial cells polarized and assembled organized cell-cell junctions. Genipin modification had minimal impact on cell viability and proliferation. Genipin stiffened TBM increased gene expression of pro-fibrotic cytokines and altered gene expression for N-cadherin, a proximal tubular epithelial specific cell-cell junction marker. This work introduces a new cell culture model for cell-basement membrane mechanobiology studies that utilizes in vivo-derived basement membrane. We also demonstrate that TBM stiffening affects tubular epithelial cell function through altered gene expression of cell-specific differentiation markers and induced increased expression of pro-fibrotic growth factors.

Saponins are amphipathic glycosides with detergent properties present in vegetables. These compounds, when ingested, can cause difficulties in absorbing nutrients from food and even induce inflammatory processes in the intestine. There is already some evidence that saponins can be degraded by β-glucosidases of the GH3 family. In the present study, we evaluated, through computational tools, the possibility of a β-glucosidase (AMBGL17) obtained from a metagenomic analysis of the Amazonian soil, to catalytically interact with a saponin present in soybean. For this, the amino acid sequence of AMBGL17 was used in a phylogenetic analysis to estimate its origin and to determine its three-dimensional structure. The 3D structure of the enzyme was used in a molecular docking analysis to evaluate its interaction with soy saponin as a ligand. The results of the phylogenetic analysis showed that AMBGL17 comes from a microorganism of the phylum Chloroflexi, probably related to species of the order Aggregatinales. Molecular docking showed that soybean saponin can interact with the catalytic site of AMBGL17, with the amino acid GLY345 being important in this catalytic interaction, especially with a β-1,2 glycosidic bond present in the carbohydrate portion of saponin. In conclusion, AMBGL17 is an enzyme with interesting biotechnological potential in terms of mitigating the anti-nutritional and pro-inflammatory effects of saponins present in vegetables used for human and animal food.

Multi-phenotypic screening of multiple zebrafish larvae plays an important role in enhancing the quality and speed of biological assays. Many microfluidic platforms have been presented for zebrafish phenotypic assays, but multi-organ screening of multiple larvae, from different needed orientations, in a single device that can enable rapid and large-sample testing is yet to be achieved. Here, we propose a multi-phenotypic quadruple-fish microfluidic chip for simultaneous monitoring of heart activity and fin movement of 5-7-day postfertilization zebrafish larvae trapped in the chip. In each experiment, fin movements of four larvae were quantified in the dorsal view in terms of fin beat frequency (FBF). Positioning of four optical prisms next to the traps provided the lateral views of the four larvae and enabled heart rate (HR) monitoring. The device's functionality in chemical testing was validated by assessing the impacts of ethanol on heart and fin activities. Larvae treated with 3% ethanol displayed a significant drop of 13.2 and 35.8% in HR and FBF, respectively. Subsequent tests with cadmium chloride highlighted the novel application of our device for screening the effect of heavy metals on cardiac and respiratory function at the same time. Exposure to 5 $mu$g/l cadmium chloride revealed a significant increase of 8.2% and 39.2% in HR and FBF, respectively. The device can be employed to monitor multi-phenotypic behavioral responses of zebrafish larvae induced by chemical stimuli in various chemical screening assays, in applications such as ecotoxicology and drug discovery.

Wound healing is an intrinsic process directed towards the restoration of damaged or lost tissue. The development of a dressing material having the ability to control the multiple aspects of the wound environment would be an ideal strategy to improve wound healing. Though natural silk proteins, fibroin, and sericin have demonstrated tissue regenerative properties, the efficacy of bioengineered silk proteins on wound healing is seldom assessed. Furthermore, silk proteins sans contaminants, having low molecular masses, and combining with other bioactive factors can hasten the wound healing process. Herein, recombinant silk proteins, fibroin and sericin, and their fusions with cecropin B were evaluated for their wound-healing effects using in vivo rat model. The recombinant silk proteins demonstrated accelerated wound closure in comparison to untreated wounds and treatment with Povidone. Among all groups, the treatment with recombinant sericin-cecropin B (RSC) showed significantly faster healing, greater than 90% wound closure by Day 12 followed by recombinant fibroin-cecropin B (RFC) (88.86%). Furthermore, histological analysis and estimation of hydroxyproline showed complete epithelialization, neovascularization, and collagenisation in groups treated with recombinant silk proteins. The wound healing activity was further verified by in vitro scratch assay using HADF cells, where the recombinant silk proteins induced cell proliferation and cell migration to the wound area. Additionally, wound healing-related gene expression showed recombinant silk proteins stimulated the upregulation of EGF and VEGF and regulated the expression of TGF-β1 and TGF-β3. Our results demonstrated the enhanced healing effects of the recombinant silk fusion proteins in facilitating complete tissue regeneration with scar-free healing. Therefore, the recombinant silks and their fusion proteins have great potential to be developed as smart bandages for wound healing.

During chemotherapy, structural and mechanical changes in malignant cells have been observed in several cancers, including leukaemia and pancreatic and prostate cancer. Such cellular changes may act as physical biomarkers for chemoresistance and cancer recurrence. This study aimed to determine how exposure to paclitaxel affects the intracellular stiffness of human oesophageal cancer of South African origin in vitro. A human oesophageal squamous cell carcinoma cell line WHCO1 was cultured on glass substrates (2D) and in collagen gels (3D) and exposed to paclitaxel for up to 48 h. Cellular morphology and stiffness were assessed with confocal microscopy, visually aided morpho-phenotyping image recognition and mitochondrial particle tracking microrheology at 24 and 48 h. In the 2D environment, the intracellular stiffness was higher for the paclitaxel-treated than for untreated cells at 24 and 48 h. In the 3D environment, the paclitaxel-treated cells were stiffer than the untreated cells at 24 h, but no statistically significant differences in stiffness were observed at 48 h. In 2D, paclitaxel-treated cells were significantly larger at 24 and 48 h and more circular at 24 but not at 48 h than the untreated controls. In 3D, there were no significant morphological differences between treated and untreated cells. The distribution of cell shapes was not significantly different across the different treatment conditions in 2D and 3D environments. Future studies with patient-derived primary cancer cells and prolonged drug exposure will help identify physical cellular biomarkers to detect chemoresistance onset and assess therapy effectiveness in oesophageal cancer patients.