Accurate prediction of gene regulatory rules is important towards understanding of cellular processes. Existing computational algorithms devised for bulk transcriptomics typically require a large ...number of time points to infer gene regulatory networks (GRNs), are applicable for a small number of genes and fail to detect potential causal relationships effectively. Here, we propose a novel approach 'TENET' to reconstruct GRNs from single cell RNA sequencing (scRNAseq) datasets. Employing transfer entropy (TE) to measure the amount of causal relationships between genes, TENET predicts large-scale gene regulatory cascades/relationships from scRNAseq data. TENET showed better performance than other GRN reconstructors, in identifying key regulators from public datasets. Specifically from scRNAseq, TENET identified key transcriptional factors in embryonic stem cells (ESCs) and during direct cardiomyocytes reprogramming, where other predictors failed. We further demonstrate that known target genes have significantly higher TE values, and TENET predicted higher TE genes were more influenced by the perturbation of their regulator. Using TENET, we identified and validated that Nme2 is a culture condition specific stem cell factor. These results indicate that TENET is uniquely capable of identifying key regulators from scRNAseq data.
Alzheimer's disease (AD) is associated with progressive neuronal degeneration as amyloid-beta (Aβ) and tau proteins accumulate in the brain. Glial cells were recently reported to play an important ...role in the development of AD. However, little is known about the role of oligodendrocytes in AD pathogenesis. Here, we describe a disease-associated subpopulation of oligodendrocytes that is present during progression of AD-like pathology in the male App
and male 5xFAD AD mouse brains and in postmortem AD human brains using single-cell RNA sequencing analysis. Aberrant Erk1/2 signaling was found to be associated with the activation of disease-associated oligodendrocytes (DAOs) in male App
mouse brains. Notably, inhibition of Erk1/2 signaling in DAOs rescued impaired axonal myelination and ameliorated Aβ-associated pathologies and cognitive decline in the male App
AD mouse model.
Examining enzyme kinetics is critical for understanding cellular systems and for using enzymes in industry. The Michaelis-Menten equation has been widely used for over a century to estimate the ...enzyme kinetic parameters from reaction progress curves of substrates, which is known as the progress curve assay. However, this canonical approach works in limited conditions, such as when there is a large excess of substrate over enzyme. Even when this condition is satisfied, the identifiability of parameters is not always guaranteed, and often not verifiable in practice. To overcome such limitations of the canonical approach for the progress curve assay, here we propose a Bayesian approach based on an equation derived with the total quasi-steady-state approximation. In contrast to the canonical approach, estimates obtained with this proposed approach exhibit little bias for any combination of enzyme and substrate concentrations. Importantly, unlike the canonical approach, an optimal experiment to identify parameters with certainty can be easily designed without any prior information. Indeed, with this proposed design, the kinetic parameters of diverse enzymes with disparate catalytic efficiencies, such as chymotrypsin, fumarase, and urease, can be accurately and precisely estimated from a minimal amount of timecourse data. A publicly accessible computational package performing such accurate and efficient Bayesian inference for enzyme kinetics is provided.
Period (PER) protein phosphorylation is a critical regulator of circadian period, yet an integrated understanding of the role and interaction between phosphorylation sites that can both increase and ...decrease PER2 stability remains elusive. Here, we propose a phosphoswitch model, where two competing phosphorylation sites determine whether PER2 has a fast or slow degradation rate. This mathematical model accurately reproduces the three-stage degradation kinetics of endogenous PER2. We predict and demonstrate that the phosphoswitch is intrinsically temperature sensitive, slowing down PER2 degradation as a result of faster reactions at higher temperatures. The phosphoswitch provides a biochemical mechanism for circadian temperature compensation of circadian period. This phosphoswitch additionally explains the phenotype of Familial Advanced Sleep Phase (FASP) and CK1εtau genetic circadian rhythm disorders, metabolic control of PER2 stability, and how drugs that inhibit CK1 alter period. The phosphoswitch provides a general mechanism to integrate diverse stimuli to regulate circadian period.
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•PER2 degradation follows three-stage kinetics, consistent with a phosphoswitch model•Inclusion of a phosphoswitch suggests a temperature compensation mechanism•This phosphoswitch integrates diverse environmental stimuli to tune PER2 stability•The phosphoswitch is a design feature of the clock
It’s been long known that circadian clocks compensate for changes in temperature, but the molecular mechanism has been elusive. Zhou, Kim, and coworkers describe a dual-kinase, multisite phosphoswitch that regulates stability of the PERIOD protein and provides for environmental and metabolic control of the clock, including temperature compensation.
A challenge of synthetic biology is the creation of cooperative microbial systems that exhibit population–level behaviors. Such systems use cellular signaling mechanisms to regulate gene expression ...across multiple cell types. We describe the construction of a synthetic microbial consortium consisting of two distinct cell types–an "activator" strain and a "repressor" strain. These strains produced two orthogonal cell-signaling molecules that regulate gene expression within a synthetic circuit spanning both strains. The two strains generated emergent, population-level oscillations only when cultured together. Certain network topologies of the two-strain circuit were better at maintaining robust oscillations than others. The ability to program population-level dynamics through the genetic engineering of multiple cooperative strains points the way toward engineering complex synthetic tissues and organs with multiple cell types.
Silica‐filled NR compounds are prepared by mixing rubber with organic‐networked silica and additives such as curatives, processing and coupling agents, and stabilizer in a dried state (dry‐mixed NR, ...DNR) and mixing a silica‐NR wet masterbatch prepared in liquid phase with the additives (wet‐mixed NR, WNR). The effects of mixing method and time of DNR and WNR compounds on their cure, process, tensile, and dynamic properties are investigated. The mixing of DNR crushes silica aggregates to smaller ones, but also forms occasionally large ones, while the mixing of WNR produces small aggregates without forming larger ones because silica aggregates are already covered with rubber. The high silica dispersion, suppressed silica aggregation, low Payne effect, and low‐loss modulus of the WNR compounds prepared by mixing for a sufficient time result in their low viscosity, high scorch safety, and low rolling resistance without sacrificing other rubber properties.
To identify causation, model-free inference methods, such as Granger Causality, have been widely used due to their flexibility. However, they have difficulty distinguishing synchrony and indirect ...effects from direct causation, leading to false predictions. To overcome this, model-based inference methods that test the reproducibility of data with a specific mechanistic model to infer causality were developed. However, they can only be applied to systems described by a specific model, greatly limiting their applicability. Here, we address this limitation by deriving an easily testable condition for a general monotonic ODE model to reproduce time-series data. We built a user-friendly computational package, General ODE-Based Inference (GOBI), which is applicable to nearly any monotonic system with positive and negative regulations described by ODE. GOBI successfully inferred positive and negative regulations in various networks at both the molecular and population levels, unlike existing model-free methods. Thus, this accurate and broadly applicable inference method is a powerful tool for understanding complex dynamical systems.
Strong circadian (~24h) rhythms in heart rate (HR) are critical for flexible regulation of cardiac pacemaking function throughout the day. While this circadian flexibility in HR is sustained in ...diverse conditions, it declines with age, accompanied by reduced maximal HR performance. The intricate regulation of circadian HR involves the orchestration of the autonomic nervous system (ANS), circadian rhythms of body temperature (CRBT), and local circadian rhythmicity (LCR), which has not been fully understood. Here, we developed a mathematical model describing ANS, CRBT, and LCR in sinoatrial nodal cells (SANC) that accurately captures distinct circadian patterns in adult and aged mice. Our model underscores how the alliance among ANS, CRBT, and LCR achieves circadian flexibility to cover a wide range of firing rates in SANC, performance to achieve maximal firing rates, while preserving robustness to generate rhythmic firing patterns irrespective of external conditions. Specifically, while ANS dominates in promoting SANC flexibility and performance, CRBT and LCR act as primary and secondary boosters, respectively, to further enhance SANC flexibility and performance. Disruption of this alliance with age results in impaired SANC flexibility and performance, but not robustness. This unexpected outcome is primarily attributed to the age-related reduction in parasympathetic activities, which maintains SANC robustness while compromising flexibility. Our work sheds light on the critical alliance of ANS, CRBT, and LCR in regulating time-of-day cardiac pacemaking function and dysfunction, offering insights into novel therapeutic targets for the prevention and treatment of cardiac arrhythmias.
Mammalian physiology exhibits 24-hour cyclicity due to circadian rhythms of gene expression controlled by transcription factors that constitute molecular clocks. Core clock transcription factors bind ...to the genome at enhancer sequences to regulate circadian gene expression, but not all binding sites are equally functional. We found that in mice, circadian gene expression in the liver is controlled by rhythmic chromatin interactions between enhancers and promoters. Rev-erbα, a core repressive transcription factor of the clock, opposes functional loop formation between Rev-erbα-regulated enhancers and circadian target gene promoters by recruitment of the NCoR-HDAC3 co-repressor complex, histone deacetylation, and eviction of the elongation factor BRD4 and the looping factor MED1. Thus, a repressive arm of the molecular clock operates by rhythmically modulating chromatin loops to control circadian gene transcription.
Delamination is one of the detrimental defects in laminated composite materials that often arose due to manufacturing defects or in-service loadings (e.g., low/high velocity impacts). Most of the ...contemporary research efforts are dedicated to high-frequency guided wave and mode shape-based methods for the assessment (i.e., detection, quantification, localization) of delamination. This paper presents a deep learning framework for structural vibration-based assessment of delamination in smart composite laminates. A number of small-sized (4.5% of total area) inner and edge delaminations are simulated using an electromechanically coupled model of the piezo-bonded laminated composite. Healthy and delaminated structures are stimulated with random loads and the corresponding transient responses are transformed into spectrograms using optimal values of window size, overlapping rate, window type, and fast Fourier transform (FFT) resolution. A convolutional neural network (CNN) is designed to automatically extract discriminative features from the vibration-based spectrograms and use those to distinguish the intact and delaminated cases of the smart composite laminate. The proposed architecture of the convolutional neural network showed a training accuracy of 99.9%, validation accuracy of 97.1%, and test accuracy of 94.5% on an unseen data set. The testing confusion chart of the pre-trained convolutional neural network revealed interesting results regarding the severity and detectability for the in-plane and through the thickness scenarios of delamination.
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