Farnesol prevents aging-related muscle weakness in mice through enhanced farnesylation of Parkin-interacting substrate.

Peroxisome proliferator-activated receptor-γ coactivator-1α (PGC-1α) is a master regulator of mitochondrial biogenesis. Reduced PGC-1α abundance is linked to skeletal muscle weakness in aging or pathological conditions, such as neurodegenerative diseases and diabetes; thus, elevating PGC-1α abundance might be a promising strategy to treat muscle aging. Here, we performed high-throughput screening and identified a natural compound, farnesol, as a potent inducer of PGC-1α. Farnesol administration enhanced oxidative muscle capacity and muscle strength, leading to metabolic rejuvenation in aged mice. Moreover, farnesol treatment accelerated the recovery of muscle injury associated with enhanced muscle stem cell function. The protein expression of Parkin-interacting substrate (PARIS/Zfp746), a transcriptional repressor of PGC-1α, was elevated in aged muscles, likely contributing to PGC-1α reduction. The beneficial effect of farnesol on aged muscle was mediated through enhanced PARIS farnesylation, thereby relieving PARIS-mediated PGC-1α suppression. Furthermore, short-term exercise increased PARIS farnesylation in the muscles of young and aged mice, whereas long-term exercise decreased PARIS expression in the muscles of aged mice, leading to the elevation of PGC-1α. Collectively, the current study demonstrated that the PARIS-PGC-1α pathway is linked to muscle aging and that farnesol treatment can restore muscle functionality in aged mice through increased farnesylation of PARIS.

Industrializing AI/ML during the end-to-end drug discovery process.

Drug discovery aims to select proper targets and drug candidates to address unmet clinical needs. The end-to-end drug discovery process includes all stages of drug discovery from target identification to drug candidate selection. Recently, several artificial intelligence and machine learning (AI/ML)-based drug discovery companies have attempted to build data-driven platforms spanning the end-to-end drug discovery process. The ability to identify elusive targets essentially leads to the diversification of discovery pipelines, thereby increasing the ability to address unmet needs. Modern ML technologies are complementing traditional computer-aided drug discovery by accelerating candidate optimization in innovative ways. This review summarizes recent developments in AI/ML methods from target identification to molecule optimization, and concludes with an overview of current industrial trends in end-to-end AI/ML platforms.

Diaminodiphenyl sulfone-induced parkin ameliorates age-dependent dopaminergic neuronal loss.

During normal aging, the number of dopaminergic (DA) neurons in the substantia nigra progressively diminishes, although massive DA neuronal loss is a hallmark sign of Parkinson's disease. Unfortunately, there is little known about the molecular events involved in age-related DA neuronal loss. In this study, we found that (1) the level of parkin was decreased in the cerebellum, brain stem, substantia nigra, and striatum of aged mice, (2) diaminodiphenyl sulfone (DDS) restored the level of parkin, (3) DDS prevented age-dependent DA neuronal loss, and (4) DDS protected SH-SY5Y cells from 1-methyl-4-phenylpyridinium and hydrogen peroxide. Furthermore, pretreatment and/or post-treatment of DDS in a 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine-induced Parkinson's disease model attenuated DA neuronal loss and restored motor behavior. DDS transcriptionally activated parkin via protein kinase RNA-like endoplasmic reticulum kinase-activating transcription factor 4 signaling and DDS not only failed to induce parkin expression but also failed to rescue SH-SY5Y cells from 1-methyl-4-phenylpyridinium in the absence of ATF4. Herein, we demonstrated for the first time that DDS increased parkin level and served as a neuroprotective agent for age-dependent DA neuronal loss. Thus, DDS may be a potential therapeutic agent for age-related neurodegeneration.

A Molecular Communication System Model for Particulate Drug Delivery Systems.

The goal of a drug delivery system (DDS) is to convey a drug where the medication is needed, while, at the same time, preventing the drug from affecting other healthy parts of the body. Drugs composed of micro- or nano-sized particles (particulate DDS) that are able to cross barriers which prevent large particles from escaping the bloodstream are used in the most advanced solutions. Molecular communication (MC) is used as an abstraction of the propagation of drug particles in the body. MC is a new paradigm in communication research where the exchange of information is achieved through the propagation of molecules. Here, the transmitter is the drug injection, the receiver is the drug delivery, and the channel is realized by the transport of drug particles, thus enabling the analysis and design of a particulate DDS using communication tools. This is achieved by modeling the MC channel as two separate contributions, namely, the cardiovascular network model and the drug propagation network. The cardiovascular network model allows to analytically compute the blood velocity profile in every location of the cardiovascular system given the flow input by the heart. The drug propagation network model allows the analytical expression of the drug delivery rate at the targeted site given the drug injection rate. Numerical results are also presented to assess the flexibility and accuracy of the developed model. The study of novel optimization techniques for a more effective and less invasive drug delivery will be aided by this model, while paving the way for novel communication techniques for Intrabody communication networks.

Correction: ensemble models of neutrophil trafficking in severe sepsis.

[This corrects the article on p. e1002422 in vol. 8.].

Ensemble models of neutrophil trafficking in severe sepsis.

A hallmark of severe sepsis is systemic inflammation which activates leukocytes and can result in their misdirection. This leads to both impaired migration to the locus of infection and increased infiltration into healthy tissues. In order to better understand the pathophysiologic mechanisms involved, we developed a coarse-grained phenomenological model of the acute inflammatory response in CLP (cecal ligation and puncture)-induced sepsis in rats. This model incorporates distinct neutrophil kinetic responses to the inflammatory stimulus and the dynamic interactions between components of a compartmentalized inflammatory response. Ensembles of model parameter sets consistent with experimental observations were statistically generated using a Markov-Chain Monte Carlo sampling. Prediction uncertainty in the model states was quantified over the resulting ensemble parameter sets. Forward simulation of the parameter ensembles successfully captured experimental features and predicted that systemically activated circulating neutrophils display impaired migration to the tissue and neutrophil sequestration in the lung, consequently contributing to tissue damage and mortality. Principal component and multiple regression analyses of the parameter ensembles estimated from survivor and non-survivor cohorts provide insight into pathologic mechanisms dictating outcome in sepsis. Furthermore, the model was extended to incorporate hypothetical mechanisms by which immune modulation using extracorporeal blood purification results in improved outcome in septic rats. Simulations identified a sub-population (about 18% of the treated population) that benefited from blood purification. Survivors displayed enhanced neutrophil migration to tissue and reduced sequestration of lung neutrophils, contributing to improved outcome. The model ensemble presented herein provides a platform for generating and testing hypotheses in silico, as well as motivating further experimental studies to advance understanding of the complex biological response to severe infection, a problem of growing magnitude in humans.

Modeling and analysis of retinoic acid induced differentiation of uncommitted precursor cells.

Manipulation of differentiation programs has therapeutic potential in a spectrum of human cancers and neurodegenerative disorders. In this study, we integrated computational and experimental methods to unravel the response of a lineage uncommitted precursor cell-line, HL-60, to Retinoic Acid (RA). HL-60 is a human myeloblastic leukemia cell-line used extensively to study human differentiation programs. Initially, we focused on the role of the BLR1 receptor in RA-induced differentiation and G1/0-arrest in HL-60. BLR1, a putative G protein-coupled receptor expressed following RA exposure, is required for RA-induced cell-cycle arrest and differentiation and causes persistent MAPK signaling. A mathematical model of RA-induced cell-cycle arrest and differentiation was formulated and tested against BLR1 wild-type (wt) knock-out and knock-in HL-60 cell-lines with and without RA. The current model described the dynamics of 729 proteins and protein complexes interconnected by 1356 interactions. An ensemble strategy was used to compensate for uncertain model parameters. The ensemble of HL-60 models recapitulated the positive feedback between BLR1 and MAPK signaling. The ensemble of models also correctly predicted Rb and p47phox regulation and the correlation between p21-CDK4-cyclin D formation and G1/0-arrest following exposure to RA. Finally, we investigated the robustness of the HL-60 network architecture to structural perturbations and generated experimentally testable hypotheses for future study. Taken together, the model presented here was a first step toward a systematic framework for analysis of programmed differentiation. These studies also demonstrated that mechanistic network modeling can help prioritize experimental directions by generating falsifiable hypotheses despite uncertainty.

Ensembles of signal transduction models using Pareto Optimal Ensemble Techniques (POETs).

Mathematical modeling of complex gene expression programs is an emerging tool for understanding disease mechanisms. However, identification of large models sometimes requires training using qualitative, conflicting or even contradictory data sets. One strategy to address this challenge is to estimate experimentally constrained model ensembles using multiobjective optimization. In this study, we used Pareto Optimal Ensemble Techniques (POETs) to identify a family of proof-of-concept signal transduction models. POETs integrate Simulated Annealing (SA) with Pareto optimality to identify models near the optimal tradeoff surface between competing training objectives. We modeled a prototypical-signaling network using mass-action kinetics within an ordinary differential equation (ODE) framework (64 ODEs in total). The true model was used to generate synthetic immunoblots from which the POET algorithm identified the 117 unknown model parameters. POET generated an ensemble of signaling models, which collectively exhibited population-like behavior. For example, scaled gene expression levels were approximately normally distributed over the ensemble following the addition of extracellular ligand. Also, the ensemble recovered robust and fragile features of the true model, despite significant parameter uncertainty. Taken together, these results suggest that experimentally constrained model ensembles could capture qualitatively important network features without exact parameter information.

Modeling and analysis of the molecular basis of pain in sensory neurons.

Intracellular calcium dynamics are critical to cellular functions like pain transmission. Extracellular ATP plays an important role in modulating intracellular calcium levels by interacting with the P2 family of surface receptors. In this study, we developed a mechanistic mathematical model of ATP-induced P2 mediated calcium signaling in archetype sensory neurons. The model architecture, which described 90 species connected by 162 interactions, was formulated by aggregating disparate molecular modules from literature. Unlike previous models, only mass action kinetics were used to describe the rate of molecular interactions. Thus, the majority of the 252 unknown model parameters were either association, dissociation or catalytic rate constants. Model parameters were estimated from nine independent data sets taken from multiple laboratories. The training data consisted of both dynamic and steady-state measurements. However, because of the complexity of the calcium network, we were unable to estimate unique model parameters. Instead, we estimated a family or ensemble of probable parameter sets using a multi-objective thermal ensemble method. Each member of the ensemble met an error criterion and was located along or near the optimal trade-off surface between the individual training data sets. The model quantitatively reproduced experimental measurements from dorsal root ganglion neurons as a function of extracellular ATP forcing. Hypothesized architecture linking phosphoinositide regulation with P2X receptor activity explained the inhibition of P2X-mediated current flow by activated metabotropic P2Y receptors. Sensitivity analysis using individual and the whole system outputs suggested which molecular subsystems were most important following P2 activation. Taken together, modeling and analysis of ATP-induced P2 mediated calcium signaling generated qualitative insight into the critical interactions controlling ATP induced calcium dynamics. Understanding these critical interactions may prove useful for the design of the next generation of molecular pain management strategies.