Computational modeling of light processing in the habenula and dorsal raphe based on laser ablation of functionally-defined cells.

BACKGROUND: The habenula is a major regulator of serotonergic neurons in the dorsal raphe, and thus of brain state. The functional connectivity between these regions is incompletely characterized. Here, we use the ability of changes in irradiance to trigger reproducible changes in activity in the habenula and dorsal raphe of zebrafish larvae, combined with two-photon laser ablation of specific neurons, to establish causal relationships. RESULTS: Neurons in the habenula can show an excitatory response to the onset or offset of light, while neurons in the anterior dorsal raphe display an inhibitory response to light, as assessed by calcium imaging. The raphe response changed in a complex way following ablations in the dorsal habenula (dHb) and ventral habenula (vHb). After ablation of the ON cells in the vHb (V-ON), the raphe displayed no response to light. After ablation of the OFF cells in the vHb (V-OFF), the raphe displayed an excitatory response to darkness. After ablation of the ON cells in the dHb (D-ON), the raphe displayed an excitatory response to light. We sought to develop in silico models that could recapitulate the response of raphe neurons as a function of the ON and OFF cells of the habenula. Early attempts at mechanistic modeling using ordinary differential equation (ODE) failed to capture observed raphe responses accurately. However, a simple two-layer fully connected neural network (NN) model was successful at recapitulating the diversity of observed phenotypes with root-mean-squared error values ranging from 0.012 to 0.043. The NN model also estimated the raphe response to ablation of D-off cells, which can be verified via future experiments. CONCLUSION: Lesioning specific cells in different regions of habenula led to qualitatively different responses to light in the dorsal raphe. A simple neural network is capable of mimicking experimental observations. This work illustrates the ability of computational modeling to integrate complex observations into a simple compact formalism for generating testable hypotheses, and for guiding the design of biological experiments.

Deep learning for de-convolution of Smad2 versus Smad3 binding sites.

BACKGROUND: The transforming growth factor beta-1 (TGF ß-1) cytokine exerts both pro-tumor and anti-tumor effects in carcinogenesis. An increasing body of literature suggests that TGF ß-1 signaling outcome is partially dependent on the regulatory targets of downstream receptor-regulated Smad (R-Smad) proteins Smad2 and Smad3. However, the lack of Smad-specific antibodies for ChIP-seq hinders convenient identification of Smad-specific binding sites. RESULTS: In this study, we use localization and affinity purification (LAP) tags to identify Smad-specific binding sites in a cancer cell line. Using ChIP-seq data obtained from LAP-tagged Smad proteins, we develop a convolutional neural network with long-short term memory (CNN-LSTM) as a deep learning approach to classify a pool of Smad-bound sites as being Smad2- or Smad3-bound. Our data showed that this approach is able to accurately classify Smad2- versus Smad3-bound sites. We use our model to dissect the role of each R-Smad in the progression of breast cancer using a previously published dataset. CONCLUSIONS: Our results suggests that deep learning approaches can be used to dissect binding site specificity of closely related transcription factors.

Machine learning using genetic and clinical data identifies a signature that robustly predicts methotrexate response in rheumatoid arthritis.

OBJECTIVE: To develop a hypothesis-free model that best predicts response to MTX drug in RA patients utilizing biologically meaningful genetic feature selection of potentially functional single nucleotide polymorphisms (pfSNPs) through robust machine learning (ML) feature selection methods. METHODS: MTX-treated RA patients with known response were divided in a 4:1 ratio into training and test sets. From the patients' exomes, potential features for classifier prediction were identified from pfSNPs and non-genetic factors through ML using recursive feature elimination with cross-validation incorporating the random forest classifier. Feature selection was repeated on random subsets of the training cohort, and consensus features were assembled into the final feature set. This feature set was evaluated for predictive potential using six ML classifiers, first by cross-validation within the training set, and finally by analysing its performance with the unseen test set. RESULTS: The final feature set contains 56 pfSNPs and five non-genetic factors. The majority of these pfSNPs are located in pathways related to RA pathogenesis or MTX action and are predicted to modulate gene expression. When used for training in six ML classifiers, performance was good in both the training set (area under the curve: 0.855-0.916; sensitivity: 0.715-0.892; and specificity: 0.733-0.862) and the unseen test set (area under the curve: 0.751-0.826; sensitivity: 0.581-0.839; and specificity: 0.641-0.923). CONCLUSION: Sensitive and specific predictors of MTX response in RA patients were identified in this study through a novel strategy combining biologically meaningful and machine learning feature selection and training. These predictors may facilitate better treatment decision-making in RA management.

Computational modeling suggests binding-induced expansion of Epsin disordered regions upon association with AP2.

Intrinsically disordered regions (IDRs) are prevalent in the eukaryotic proteome. Common functional roles of IDRs include forming flexible linkers or undergoing allosteric folding-upon-binding. Recent studies have suggested an additional functional role for IDRs: generating steric pressure on the plasma membrane during endocytosis, via molecular crowding. However, in order to accomplish useful functions, such crowding needs to be regulated in space (e.g., endocytic hotspots) and time (e.g., during vesicle formation). In this work, we explore binding-induced regulation of IDR steric volume. We simulate the IDRs of two proteins from Clathrin-mediated endocytosis (CME) to see if their conformational spaces are regulated via binding-induced expansion. Using Monte-Carlo computational modeling of excluded volumes, we generate large conformational ensembles (3 million) for the IDRs of Epsin and Eps15 and dock the conformers to the alpha subunit of Adaptor Protein 2 (AP2α), their CME binding partner. Our results show that as more molecules of AP2α are bound, the Epsin-derived ensemble shows a significant increase in global dimensions, measured as the radius of Gyration (RG) and the end-to-end distance (EED). Unlike Epsin, Eps15-derived conformers that permit AP2α binding at one motif were found to be more likely to accommodate binding of AP2α at other motifs, suggesting a tendency toward co-accessibility of binding motifs. Co-accessibility was not observed for any pair of binding motifs in Epsin. Thus, we speculate that the disordered regions of Epsin and Eps15 perform different roles during CME, with accessibility in Eps15 allowing it to act as a recruiter of AP2α molecules, while binding-induced expansion of the Epsin disordered region could impose steric pressure and remodel the plasma membrane during vesicle formation.

Transcompp: understanding phenotypic plasticity by estimating Markov transition rates for cell state transitions.

MOTIVATION: Gradual population-level changes in tissues can be driven by stochastic plasticity, meaning rare stochastic transitions of single-cell phenotype. Quantifying the rates of these stochastic transitions requires time-intensive experiments, and analysis is generally confounded by simultaneous bidirectional transitions and asymmetric proliferation kinetics. To quantify cellular plasticity, we developed Transcompp (Transition Rate ANalysis of Single Cells to Observe and Measure Phenotypic Plasticity), a Markov modeling algorithm that uses optimization and resampling to compute best-fit rates and statistical intervals for stochastic cell-state transitions. RESULTS: We applied Transcompp to time-series datasets in which purified subpopulations of stem-like or non-stem cancer cells were exposed to various cell culture environments, and allowed to re-equilibrate spontaneously over time. Results revealed that commonly used cell culture reagents hydrocortisone and cholera toxin shifted the cell population equilibrium toward stem-like or non-stem states, respectively, in the basal-like breast cancer cell line MCF10CA1a. In addition, applying Transcompp to patient-derived cells showed that transition rates computed from short-term experiments could predict long-term trajectories and equilibrium convergence of the cultured cell population. AVAILABILITY AND IMPLEMENTATION: Freely available for download at http://github.com/nsuhasj/Transcompp. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Wnt proteins synergize to activate ß-catenin signaling.

Wnt ligands are involved in diverse signaling pathways that are active during development, maintenance of tissue homeostasis and in various disease states. While signaling regulated by individual Wnts has been extensively studied, Wnts are rarely expressed alone, and the consequences of Wnt gene co-expression are not well understood. Here, we studied the effect of co-expression of Wnts on the ß-catenin signaling pathway. While some Wnts are deemed 'non-canonical' due to their limited ability to activate ß-catenin when expressed alone, unexpectedly, we find that multiple Wnt combinations can synergistically activate ß-catenin signaling in multiple cell types. WNT1- and WNT7B-mediated synergistic Wnt signaling requires FZD5, FZD8 and LRP6, as well as the WNT7B co-receptors GPR124 (also known as ADGRA2) and RECK. Unexpectedly, this synergistic signaling occurs downstream of ß-catenin stabilization, and is correlated with increased lysine acetylation of ß-catenin. Wnt synergy provides a general mechanism to confer increased combinatorial control over this important regulatory pathway.

Millennial-scale sustainability of the Chesapeake Bay Native American oyster fishery.

Estuaries around the world are in a state of decline following decades or more of overfishing, pollution, and climate change. Oysters (Ostreidae), ecosystem engineers in many estuaries, influence water quality, construct habitat, and provide food for humans and wildlife. In North America's Chesapeake Bay, once-thriving eastern oyster (Crassostrea virginica) populations have declined dramatically, making their restoration and conservation extremely challenging. Here we present data on oyster size and human harvest from Chesapeake Bay archaeological sites spanning ∼3,500 y of Native American, colonial, and historical occupation. We compare oysters from archaeological sites with Pleistocene oyster reefs that existed before human harvest, modern oyster reefs, and other records of human oyster harvest from around the world. Native American fisheries were focused on nearshore oysters and were likely harvested at a rate that was sustainable over centuries to millennia, despite changing Holocene climatic conditions and sea-level rise. These data document resilience in oyster populations under long-term Native American harvest, sea-level rise, and climate change; provide context for managing modern oyster fisheries in the Chesapeake Bay and elsewhere around the world; and demonstrate an interdisciplinary approach that can be applied broadly to other fisheries.

A prediction tool for hospital-acquired pressure ulcers among surgical patients: Surgical pressure ulcer risk score.

Surgical patients are prone to developing hospital-acquired pressure ulcers (HAPU). Therefore, a better prediction tool is needed to predict risk using preoperative data. This study aimed to determine, from previously published HAPU risk factors, which factors are significant among our surgical population and to develop a prediction tool that identifies pressure ulcer risk before the operation. A literature review was first performed to elicit all the published HAPU risk factors before conducting a retrospective case-control study using medical records. The known HAPU risks were compared between patients with HAPU and without HAPU who underwent operations during the same period (July 2015-December 2016). A total of 80 HAPU cases and 189 controls were analysed. Multivariate logistic regression analyses identified eight significant risk factors: age ≥ 75 years, female gender, American Society of Anaesthesiologists ≥ 3, body mass index < 23, preoperative Braden score ≤ 14, anaemia, respiratory disease, and hypertension. The model had bootstrap-corrected c-statistic 0.78 indicating good discrimination. A cut-off score of ≥6 is strongly predictive, with a positive predictive value of 73.2% (confidence interval [CI]: 59.7%-84.2%) and a negative predictive value of 80.7% (CI: 74.3%-86.1%). SPURS contributes to the preoperative identification of pressure ulcer risk that could help nurses implement preventive measures earlier.

Synergistic target combination prediction from curated signaling networks: Machine learning meets systems biology and pharmacology.

Given a signaling network, the target combination prediction problem aims to predict efficacious and safe target combinations for combination therapy. State-of-the-art in silico methods use Monte Carlo simulated annealing (mcsa) to modify a candidate solution stochastically, and use the Metropolis criterion to accept or reject the proposed modifications. However, such stochastic modifications ignore the impact of the choice of targets and their activities on the combination's therapeutic effect and off-target effects, which directly affect the solution quality. In this paper, we present mascot, a method that addresses this limitation by leveraging two additional heuristic criteria to minimize off-target effects and achieve synergy for candidate modification. Specifically, off-target effects measure the unintended response of a signaling network to the target combination and is often associated with toxicity. Synergy occurs when a pair of targets exerts effects that are greater than the sum of their individual effects, and is generally a beneficial strategy for maximizing effect while minimizing toxicity. mascot leverages on a machine learning-based target prioritization method which prioritizes potential targets in a given disease-associated network to select more effective targets (better therapeutic effect and/or lower off-target effects); and on Loewe additivity theory from pharmacology which assesses the non-additive effects in a combination drug treatment to select synergistic target activities. Our experimental study on two disease-related signaling networks demonstrates the superiority of mascot in comparison to existing approaches.

Exome Sequencing Reveals Germline SMAD9 Mutation That Reduces Phosphatase and Tensin Homolog Expression and Is Associated With Hamartomatous Polyposis and Gastrointestinal Ganglioneuromas.

Hamartomatous polyposis syndromes (HPS) account for a small but appreciable proportion of inherited gastrointestinal cancer predisposition syndromes; patients with HPS have an increased risk for colon and extracolonic malignancies. We present a unique case of familial juvenile polyposis syndrome associated with gastrointestinal ganglioneuromas of unknown etiology. The patient was tested for HPS-associated genes, but no mutation was detected. Exome sequencing identified a germline heterozygous mutation in SMAD9 (SMAD9(V90M)). This mutation was predicted to be an activating mutation. HEK cells transfected to express SMAD9(V90M) had reduced expression of phosphatase and tensin homolog; this reduction was also observed in a polyp from the patient. We have therefore identified a new susceptibility locus for HPS. Patients with hamartomatous polyposis in the colon associated with ganglioneuromatosis should be referred for genetic assessments.

TENET: topological feature-based target characterization in signalling networks.

MOTIVATION: Target characterization for a biochemical network is a heuristic evaluation process that produces a characterization model that may aid in predicting the suitability of each molecule for drug targeting. These approaches are typically used in drug research to identify novel potential targets using insights from known targets. Traditional approaches that characterize targets based on their molecular characteristics and biological function require extensive experimental study of each protein and are infeasible for evaluating larger networks with poorly understood proteins. Moreover, they fail to exploit network connectivity information which is now available from systems biology methods. Adopting a network-based approach by characterizing targets using network features provides greater insights that complement these traditional techniques. To this end, we present Tenet (Target charactErization using NEtwork Topology), a network-based approach that characterizes known targets in signalling networks using topological features. RESULTS: Tenet first computes a set of topological features and then leverages a support vector machine-based approach to identify predictive topological features that characterizes known targets. A characterization model is generated and it specifies which topological features are important for discriminating the targets and how these features should be combined to quantify the likelihood of a node being a target. We empirically study the performance of Tenet from a wide variety of aspects, using several signalling networks from BioModels with real-world curated outcomes. Results demonstrate its effectiveness and superiority in comparison to state-of-the-art approaches. AVAILABILITY AND IMPLEMENTATION: Our software is available freely for non-commercial purposes from: https://sites.google.com/site/cosbyntu/softwares/tenet CONTACT: hechua@ntu.edu.sg or assourav@ntu.edu.sg SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Non-canonical Activation of Akt in Serum-Stimulated Fibroblasts, Revealed by Comparative Modeling of Pathway Dynamics.

The dynamic behaviors of signaling pathways can provide clues to pathway mechanisms. In cancer cells, excessive phosphorylation and activation of the Akt pathway is responsible for cell survival advantages. In normal cells, serum stimulation causes brief peaks of extremely high Akt phosphorylation before reaching a moderate steady-state. Previous modeling assumed this peak and decline behavior (i.e., "overshoot") was due to receptor internalization. In this work, we modeled the dynamics of the overshoot as a tool for gaining insight into Akt pathway function. We built an ordinary differential equation (ODE) model describing pathway activation immediately upstream of Akt phosphorylation at Thr308 (Aktp308). The model was fit to experimental measurements of Aktp308, total Akt, and phosphatidylinositol (3,4,5)-trisphosphate (PIP3), from mouse embryonic fibroblasts with serum stimulation. The canonical Akt activation model (the null hypothesis) was unable to recapitulate the observed delay between the peak of PIP3 (at 2 minutes), and the peak of Aktp308 (at 30-60 minutes). From this we conclude that the peak and decline behavior of Aktp308 is not caused by PIP3 dynamics. Models for alternative hypotheses were constructed by allowing an arbitrary dynamic curve to perturb each of 5 steps of the pathway. All 5 of the alternative models could reproduce the observed delay. To distinguish among the alternatives, simulations suggested which species and timepoints would show strong differences. Time-series experiments with membrane fractionation and PI3K inhibition were performed, and incompatible hypotheses were excluded. We conclude that the peak and decline behavior of Aktp308 is caused by a non-canonical effect that retains Akt at the membrane, and not by receptor internalization. Furthermore, we provide a novel spline-based method for simulating the network implications of an unknown effect, and we demonstrate a process of hypothesis management for guiding efficient experiments.

The self-limiting dynamics of TGF-ß signaling in silico and in vitro, with negative feedback through PPM1A upregulation.

The TGF-ß/Smad signaling system decreases its activity through strong negative regulation. Several molecular mechanisms of negative regulation have been published, but the relative impact of each mechanism on the overall system is unknown. In this work, we used computational and experimental methods to assess multiple negative regulatory effects on Smad signaling in HaCaT cells. Previously reported negative regulatory effects were classified by time-scale: degradation of phosphorylated R-Smad and I-Smad-induced receptor degradation were slow-mode effects, and dephosphorylation of R-Smad was a fast-mode effect. We modeled combinations of these effects, but found no combination capable of explaining the observed dynamics of TGF-ß/Smad signaling. We then proposed a negative feedback loop with upregulation of the phosphatase PPM1A. The resulting model was able to explain the dynamics of Smad signaling, under both short and long exposures to TGF-ß. Consistent with this model, immuno-blots showed PPM1A levels to be significantly increased within 30 min after TGF-ß stimulation. Lastly, our model was able to resolve an apparent contradiction in the published literature, concerning the dynamics of phosphorylated R-Smad degradation. We conclude that the dynamics of Smad negative regulation cannot be explained by the negative regulatory effects that had previously been modeled, and we provide evidence for a new negative feedback loop through PPM1A upregulation. This work shows that tight coupling of computational and experiments approaches can yield improved understanding of complex pathways.

SPEDRE: a web server for estimating rate parameters for cell signaling dynamics in data-rich environments.

Cell signaling pathways and metabolic networks are often modeled using ordinary differential equations (ODEs) to represent the production/consumption of molecular species over time. Regardless whether a model is built de novo or adapted from previous models, there is a need to estimate kinetic rate constants based on time-series experimental measurements of molecular abundance. For data-rich cases such as proteomic measurements of all species, spline-based parameter estimation algorithms have been developed to avoid solving all the ODEs explicitly. We report the development of a web server for a spline-based method. Systematic Parameter Estimation for Data-Rich Environments (SPEDRE) estimates reaction rates for biochemical networks. As input, it takes the connectivity of the network and the concentrations of the molecular species at discrete time points. SPEDRE is intended for large sparse networks, such as signaling cascades with many proteins but few reactions per protein. If data are available for all species in the network, it provides global coverage of the parameter space, at low resolution and with approximate accuracy. The output is an optimized value for each reaction rate parameter, accompanied by a range and bin plot. SPEDRE uses tools from COPASI for pre-processing and post-processing. SPEDRE is a free service at http://LTKLab.org/SPEDRE.

Computational modelling of LY303511 and TRAIL-induced apoptosis suggests dynamic regulation of cFLIP.

MOTIVATION: TRAIL has been widely studied for the ability to kill cancer cells selectively, but its clinical usefulness has been hindered by the development of resistance. Multiple compounds have been identified that sensitize cancer cells to TRAIL-induced apoptosis. The drug LY303511 (LY30), combined with TRAIL, caused synergistic (greater than additive) killing of multiple cancer cell lines. We used mathematical modelling and ordinary differential equations to represent how LY30 and TRAIL individually affect HeLa cells, and to predict how the combined treatment achieves synergy. RESULTS: Model-based predictions were compared with in vitro experiments. The combination treatment model was successful at mimicking the synergistic levels of cell death caused by LY30 and TRAIL combined. However, there were significant failures of the model to mimic upstream activation at early time points, particularly the slope of caspase-8 activation. This flaw in the model led us to perform additional measurements of early caspase-8 activation. Surprisingly, caspase-8 exhibited a transient decrease in activity after LY30 treatment, prior to strong activation. cFLIP, an inhibitor of caspase-8 activation, was up-regulated briefly after 30 min of LY30 treatment, followed by a significant down-regulation over prolonged exposure. A further model suggested that LY30-induced fluctuation of cFLIP might result from tilting the ratio of two key species of reactive oxygen species (ROS), superoxide and hydrogen peroxide. Computational modelling extracted novel biological implications from measured dynamics, identified time intervals with unexplained effects, and clarified the non-monotonic effects of the drug LY30 on cFLIP during cancer cell apoptosis.

Systematic parameter estimation in data-rich environments for cell signalling dynamics.

MOTIVATION: Computational models of biological signalling networks, based on ordinary differential equations (ODEs), have generated many insights into cellular dynamics, but the model-building process typically requires estimating rate parameters based on experimentally observed concentrations. New proteomic methods can measure concentrations for all molecular species in a pathway; this creates a new opportunity to decompose the optimization of rate parameters. RESULTS: In contrast with conventional parameter estimation methods that minimize the disagreement between simulated and observed concentrations, the SPEDRE method fits spline curves through observed concentration points, estimates derivatives and then matches the derivatives to the production and consumption of each species. This reformulation of the problem permits an extreme decomposition of the high-dimensional optimization into a product of low-dimensional factors, each factor enforcing the equality of one ODE at one time slice. Coarsely discretized solutions to the factors can be computed systematically. Then the discrete solutions are combined using loopy belief propagation, and refined using local optimization. SPEDRE has unique asymptotic behaviour with runtime polynomial in the number of molecules and timepoints, but exponential in the degree of the biochemical network. SPEDRE performance is comparatively evaluated on a novel model of Akt activation dynamics including redox-mediated inactivation of PTEN (phosphatase and tensin homologue). AVAILABILITY AND IMPLEMENTATION: Web service, software and supplementary information are available at www.LtkLab.org/SPEDRE SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Topological defects in self-assembled patterns of mesenchymal stromal cells in vitro are predictive attributes of condensation and chondrogenesis.

Mesenchymal stromal cells (MSCs) are promising therapeutic agents for cartilage regeneration, including the potential of cells to promote chondrogenesis in vivo. However, process development and regulatory approval of MSCs as cell therapy products benefit from facile in vitro approaches that can predict potency for a given production run. Current standard in vitro approaches include a 21 day 3D differentiation assay followed by quantification of cartilage matrix proteins. We propose a novel biophysical marker that is cell population-based and can be measured from in vitro monolayer culture of MSCs. We hypothesized that the self-assembly pattern that emerges from collective-cell behavior would predict chondrogenesis motivated by our observation that certain features in this pattern, namely, topological defects, corresponded to mesenchymal condensations. Indeed, we observed a strong predictive correlation between the degree-of-order of the pattern at day 9 of the monolayer culture and chondrogenic potential later estimated from in vitro 3D chondrogenic differentiation at day 21. These findings provide the rationale and the proof-of-concept for using self-assembly patterns to monitor chondrogenic commitment of cell populations. Such correlations across multiple MSC donors and production batches suggest that self-assembly patterns can be used as a candidate biophysical attribute to predict quality and efficacy for MSCs employed therapeutically for cartilage regeneration.

Metabolic modulation to improve MSC expansion and therapeutic potential for articular cartilage repair.

BACKGROUND: Articular cartilage degeneration can result from injury, age, or arthritis, causing significant joint pain and disability without surgical intervention. Currently, the only FDA cell-based therapy for articular cartilage injury is Autologous Chondrocyte Implantation (ACI); however, this procedure is costly, time-intensive, and requires multiple treatments. Mesenchymal stromal cells (MSCs) are an attractive alternative autologous therapy due to their availability and ability to robustly differentiate into chondrocytes for transplantation with good safety profiles. However, treatment outcomes are variable due to donor-to-donor variability as well as intrapopulation heterogeneity and unstandardized MSC manufacturing protocols. Process improvements that reduce cell heterogeneity while increasing donor cell numbers with improved chondrogenic potential during expansion culture are needed to realize the full potential of MSC therapy. METHODS: In this study, we investigated the potential of MSC metabolic modulation during expansion to enhance their chondrogenic commitment by varying the nutrient composition, including glucose, pyruvate, glutamine, and ascorbic acid in culture media. We tested the effect of metabolic modulation in short-term (one passage) and long-term (up to seven passages). We measured metabolic state, cell size, population doubling time, and senescence and employed novel tools including micro-magnetic resonance relaxometry (µMRR) relaxation time (T2) to characterize the effects of AA on improved MSC expansion and chondrogenic potential. RESULTS: Our data show that the addition of 1 mM L-ascorbic acid-2-phosphate (AA) to cultures for one passage during MSC expansion prior to initiation of differentiation improves chondrogenic differentiation. We further demonstrate that AA treatment reduced the proportion of senescent cells and cell heterogeneity also allowing for long-term expansion that led to a > 300-fold increase in yield of MSCs with enhanced chondrogenic potential compared to untreated cells. AA-treated MSCs with improved chondrogenic potential showed a robust shift in metabolic profile to OXPHOS and higher µMRR T2 values, identifying critical quality attributes that could be implemented in MSC manufacturing for articular cartilage repair. CONCLUSIONS: Our results suggest an improved MSC manufacturing process that can enhance chondrogenic potential by targeting MSC metabolism and integrating process analytic tools during expansion.

Multi-omic analysis of bat versus human fibroblasts reveals altered central metabolism.

Bats have unique characteristics compared to other mammals, including increased longevity and higher resistance to cancer and infectious disease. While previous studies have analyzed the metabolic requirements for flight, it is still unclear how bat metabolism supports these unique features, and no study has integrated metabolomics, transcriptomics, and proteomics to characterize bat metabolism. In this work, we performed a multi-omics data analysis using a computational model of metabolic fluxes to identify fundamental differences in central metabolism between primary lung fibroblast cell lines from the black flying fox fruit bat (Pteropus alecto) and human. Bat cells showed higher expression levels of Complex I components of electron transport chain (ETC), but, remarkably, a lower rate of oxygen consumption. Computational modeling interpreted these results as indicating that Complex II activity may be low or reversed, similar to an ischemic state. An ischemic-like state of bats was also supported by decreased levels of central metabolites and increased ratios of succinate to fumarate in bat cells. Ischemic states tend to produce reactive oxygen species (ROS), which would be incompatible with the longevity of bats. However, bat cells had higher antioxidant reservoirs (higher total glutathione and higher ratio of NADPH to NADP) despite higher mitochondrial ROS levels. In addition, bat cells were more resistant to glucose deprivation and had increased resistance to ferroptosis, one of the characteristics of which is oxidative stress. Thus, our studies revealed distinct differences in the ETC regulation and metabolic stress responses between human and bat cells.

A high-density microfluidic bioreactor for the automated manufacturing of CAR T cells.

The manufacturing of autologous chimaeric antigen receptor (CAR) T cells largely relies either on fed-batch and manual processes that often lack environmental monitoring and control or on bioreactors that cannot be easily scaled out to meet patient demands. Here we show that human primary T cells can be activated, transduced and expanded to high densities in a 2 ml automated closed-system microfluidic bioreactor to produce viable anti-CD19 CAR T cells (specifically, more than 60 million CAR T cells from donor cells derived from patients with lymphoma and more than 200 million CAR T cells from healthy donors). The in vitro secretion of cytokines, the short-term cytotoxic activity and the long-term persistence and proliferation of the cell products, as well as their in vivo anti-leukaemic activity, were comparable to those of T cells produced in a gas-permeable well. The manufacturing-process intensification enabled by the miniaturized perfusable bioreactor may facilitate the analysis of the growth and metabolic states of CAR T cells during ex vivo culture, the high-throughput optimization of cell-manufacturing processes and the scale out of cell-therapy manufacturing.