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Abstract A group of vascular plants called homoiochlorophyllous resurrection plants evolved unique capabilities to protect their photosynthetic machinery against desiccation-induced damage. This study examined whether the ontogenetic status of the resurrection plant Craterostigma pumilum has an impact on how the plant responds to dehydration at the thylakoid membrane level to prepare cells for the desiccated state. Thus, younger plants (<4 months) were compared with their older (>6 months) counterparts. Ultrastructural analysis provided evidence that younger plants suppressed senescence-like programs that are realized in older plants. During dehydration, older plants degrade specific subunits of the photosynthetic apparatus such as the D1 subunit of PSII and subunits of the cytochrome b6f complex. The latter leads to a controlled down-regulation of linear electron transport. In contrast, younger plants increased photoprotective high-energy quenching mechanisms and maintained a high capability to replace damaged D1 subunits. It follows that depending on the ontogenetic state, either more degradation-based or more photoprotective mechanisms are employed during dehydration of Craterostigma pumilum.
Segregated pathways enable AtRGS1 endocytosis to transduce signal-specific responses in Arabidopsis .
Corrections for five figures and six tables are noted for the paper, which do not affect the conclusions reported.
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Rubisco activase (Rca) facilitates the release of sugar-phosphate inhibitors at Rubisco catalytic sites during CO2 fixation. Most plant species express two Rca isoforms, the larger Rca-α and the shorter Rca-β, either by alternative splicing from a single gene or expression from separate genes. The mechanism of Rubisco activation by Rca isoforms has been intensively studied in C3 plants. However, the functional role of Rca in C4 plants where Rubisco and Rca are located in a much higher [CO2] compartment is less clear. In this study, we selected four C4 bioenergy grasses and the model C4 grass setaria ( Setaria viridis ) to investigate the role of Rca in C4 photosynthesis. All five C4 grass species contained two Rca genes, one encoding Rca-α and the other Rca-β, which were positioned closely together in the genomes. A variety of abiotic stress-related motifs were identified in the Rca-α promoter of each grass, and while the Rca-β gene was constantly highly expressed at ambient temperature, Rca-α isoforms were expressed only at high temperature but never surpassed 30% of Rca-β content. The pattern of Rca-α induction on transition to high temperature and reduction on return to ambient temperature was the same in all five C4 grasses. In sorghum ( Sorghum bicolor ), sugarcane ( Saccharum officinarum ), and setaria, the induction rate of Rca-α was similar to the recovery rate of photosynthesis and Rubisco activation at high temperature. This association between Rca-α isoform expression and maintenance of Rubisco activation at high temperature suggests that Rca-α has a functional thermo-protective role in carbon fixation in C4 grasses by sustaining Rubisco activation at high temperature.
As the design complexity of modern accelerators grows, there is more interest in using advanced simulations that have fast execution time or yield additional insights like gradients. The FAST/IOTA facility has been working on implementing and experimentally validating an end-to-end digital twin that is both fast and gradient-aware, allowing for rapid prototyping of new software and experiments with minimal beam time costs. Our framework integrates physics and ML codes for linac and ring simulation through a set of generic interfaces between surrogate and physics-based sections. To reproduce device inputs and outputs, system state is exposed as a deterministic event loop in a specialized discrete event simulator architecture. Because Fermilab is undergoing control system transition, several APIs were implemented as final user interfaces - a fully asynchronous EPICS soft IOC, a gRPC-based Data Pool Manager (DPM), and legacy ACNET protocols. We discuss implementation details as well as challenges handling live data assimilation and future plans to extend modelling to main complex proton accelerators like PIPII and Booster.
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Highlights: • PLD and PIP5K1 are involved in the regulation of oligodendroglial cell morphological differentiation. • Inhibition of PLD or PIP5K1 decreases differentiation or myelin marker expression. • PLD and PIP5K1 regulate differentiation of oligodendrocyte precursor cells. • Inhibition of PLD or PIP5K1 decreases differentiation marker expression of oligodendrocyte precursor cells. Oligodendroglial cells (oligodendrocytes) differentiate to form the myelin that wraps neuronal axons in the central nervous system (CNS). This myelin sheath supports the propagation of saltatory conduction and protects axons from physical stresses. When oligodendrocytes do not normally differentiate to myelinate axons, their key functions as oligodendrocytes in the CNS are severely impaired. The molecular mechanics that control differentiation still remain to be clarified. Arf6 belongs to the small GTPase family and is known to be a positive regulator of oligodendrocyte differentiation. Here, we show that the phospholipase D (PLD) and phosphatidylinositol-4-phosphate 5-kinase 1 (PIP5K1) molecules, the major effectors of Arf6, are involved in the regulation of oligodendrocyte differentiation. Knockdown of PLD1 or PIP5K type 1γ (PIP5K1C) by their respective specific siRNAs in mouse oligodendroglial FBD-102b cells inhibited morphological differentiation into structures bearing myelin-like processes; this finding is consistent with the concurrent changes in expression of differentiation and myelin marker proteins. Treatment with VU0155069 or UNC3230, specific inhibitors of PLD and PIP5K1, respectively, blunted morphological differentiation and decreased expression of myelin and differentiation marker proteins. Similar results have been obtained in studies using primary oligodendrocytes. These results suggest that the major Arf6 effector molecules PLD and PIP5K1 are among the molecules involved in the regulation of morphological differentiation in oligodendrocytes prior to myelination.
Highlights: • Exosomes from TSLP-treated DCs promote Th17 differentiation. • Exosomes from TSLP-treated DCs inhibit Tregs differentiation. • MiR-21 is highly expressed in exosomes from TSLP-treated DCs. • Exosomal miR-21 regulates Th17/Treg differentiation by targeting Smad7. Thymic stromal lymphopoietin (TSLP) is associated with fungal keratitis. This work aims to investigate whether TSLP can regulate T helper (Th) 17 and regulatory T cell (Treg) differentiation. We separated dendritic cells (DCs) from peripheral blood of healthy volunteers. DCs were treated with TSLP to activate DCs, and exosomes were obtained. CD{sup +} T cells were incubated with exosomes from TSLP-treated DCs. We found that exosomes from TSLP-treated DCs notably promoted the proportions of Th17 cells and inhibited the proportions of Tregs in the CD4{sup +} T cells. Moreover, exosomes from TSLP-treated DCs enhanced the expression of retinoid-related orphan receptor γt (RORγt) and interleukin 17 (IL-17), and repressed the expression of forkhead box protein P3 (Foxp3) and interleukin 10 (IL-10) in the CD4{sup +} T cells. Furthermore, miR-21 was highly expressed in exosomes from TSLP-treated DCs. Exosomes from TSLP-treated miR-21-silenced DCs promoted Treg differentiation and suppressed Th17 differentiation. Smad7 up-regulation repressed Th17 differentiation and enhanced Treg differentiation, which was abolished by miR-21 overexpression. Smad7 overexpression rescued the effect of exosomes from TSLP-treated DCs on Th17/Treg differentiation. In conclusion, our article confirms that TSLP induces DCs to deliver miR-21 by secreting exosomes, and thus miR-21 regulates Th17/Treg differentiation by inhibiting Smad7. Thus, this work further reveals the biological role of miR-21 in fungal keratitis.
Highlights: • Dex decreased Sema3A expression by suppressing the PI3K/Akt pathway. • Sema3A reversed Dex-induced suppression of osteoblast differentiation. • Sema3A reversed the Dex-mediated reduced transcriptional activity of β-catenin. Glucocorticoids(GCs) are extensively used to treat inflammatory and autoimmune diseases. Excessive prolonged exposure to glucocorticoids is associated with an increased risk of osteoporosis. The inhibition of osteoblast differentiation by GCs is suggested as a major cause for GCs-induced osteoporosis (GIO). However, the precise mechanism underlying the role of GCs in osteoblasts differentiation is not fully elucidated. Semaphorin 3A (Sema3A), a secreted member of the Semaphorin family, enhances bone formation and promotes fracture healing, which is known to increase osteoblastic differentiation and stimulate osteogenesis in bone metabolism. Here, the present study explored the effect of Sema3A in osteoblast differentiation using dexamethasone (Dex) treatment of bone marrow stromal cells (BMSCs). Dex treatment decreased Sema3A expression in BMSCs in a dose-dependent manner. Moreover, Dex stimulation suppressed the differentiation of osteoblasts by reducing alkaline phosphatase (ALP) activity, osteoblastic marker genes expression and mineralization, but all of these effects were ameliorated by exogenous recombinant Sema3A administration. Furthermore, exogenous Sema3A administration reversed the Dex-mediated decrease in nuclear accumulation of β-catenin and β-catenin activity in BMSCs. Meanwhile, Dex was capable of simultaneously suppressing the phosphorylation of protein kinase B(Akt) and the expression level of Sema3A in BMSCs. These changes were significantly abolished by the PI3K/Akt agonist. These results suggest that Dex inhibits osteoblast differentiation by suppressing Sema3A expression via the PI3K/Akt pathway. These data provide new insights into the molecular mechanisms of Dex-induced osteoblast differentiation inhibition.
Highlights: • FDXR expression is increased in sebaceous cells of acne lesions. • FDXR may enhance sebocyte differentiation and lipogenesis via ROS production. • FDXR might inhibit proliferation through inducing G2/S blockade. • IGF-1 could facilitate sebaceous differentiation and lipogenesis via PI3K/Akt/FDXR pathway. Ferredoxin reductase (FDXR), a mitochondrial membrane-associated flavoprotein, is essential for electron transfer and modulates p53-dependent apoptosis in cancer cells.FDXR may be implicated in epidermal and sebocytic differentiation, but its explicit function in sebocytes remains to be elucidated. In the present study, immunohistochemistry revealed that FDXR expression was increased in sebaceous cells of acne lesions. FDXR, PPARγ, LXRα/β, SREBP1 and Sox9 expression was incremental during sebocyte differentiation. FDXR overexpression induced by Ad-GFP-FDXR infection enhanced differentiation, reactive oxygen species (ROS), lipogenesis and PPARγ expression, and consequnently inhibited proliferation in SZ95 sebocytes. Flow cytometry showed that FDXR overexpression induced significant blockade of G2/M phase but had no effect on sub-G1 (apoptotic) sebocytes. Insulin-like growth factor-1 (IGF-1)-induced FDXR and PPARγ expression and lipogenesis were abolished by pretreatment with PI3K inhibitor LY294002. These results suggest that FDXR overexpression might promote differentiation and lipogenesis via ROS production and suppress proliferation via G2/S blockade in SZ95 sebocytes. IGF-1 could facilitate differentiation and lipogenesis through PI3K/Akt/FDXR pathway. FDXR could serve as a potential marker of advanced sebaceous differentiation, and its overexpression may be involved in the development of acne lesions.
Differential sensitivity analysis is indispensable in fitting parameters, understanding uncertainty, and forecasting the results of both thought and lab experiments. Although there are many methods currently available for performing differential sensitivity analysis of biological models, it can be difficult to determine which method is best suited for a particular model. In this paper, we explain a variety of differential sensitivity methods and assess their value in some typical biological models. First, we explain the mathematical basis for three numerical methods: adjoint sensitivity analysis, complex perturbation sensitivity analysis, and forward mode sensitivity analysis. We then carry out four instructive case studies. (a) The CARRGO model for tumor-immune interaction highlights the additional information that differential sensitivity analysis provides beyond traditional naive sensitivity methods, (b) the deterministic SIR model demonstrates the value of using second-order sensitivity in refining model predictions, (c) the stochastic SIR model shows how differential sensitivity can be attacked in stochastic modeling, and (d) a discrete birth-death-migration model illustrates how the complex perturbation method of differential sensitivity can be generalized to a broader range of biological models. Finally, we compare the speed, accuracy, and ease of use of these methods. We find that forward mode automatic differentiation has the quickest computational time, while the complex perturbation method is the simplest to implement and the most generalizable.
We investigate identifying differential equations in the frequency domain. Fourier analysis is an important tool in theoretical analysis and numerical solvers of differential equations, yet there is limited work in exploring this connection in the identification of differential equations. This paper aims to identify the underlying differential equation in the frequency domain, from a given single realization of the differential equation perturbed by noise. Such setting imposes difficulties which are different from other identification methods where computation is carried out in the physical domain. We propose several ways to mitigate the challenges arising from noise in data and large differences in the magnitudes of frequency responses. The main takeaways are that identifying differential equations solely in the frequency domain is challenging, the method we propose is based on a form of domain partitions in the frequency domain, and this method shows benefits for complex data even with high level of noise. We introduce a Fourier feature denoising, and define the meaningful data region and the core regions of features to reduce the effect of noise in the frequency domain and to enhance the accuracy in coefficient identification. The proposed method is tested on various differential equations with linear, nonlinear, and high-order derivative feature terms, and shows advantages on complex data with many frequency modes, even under high level of noise.
Highlights: • Cyclic mechanical stretch increases the nuclear accumulation of Nrf2 in PDLSCs. • T-BHQ promotes osteogenic differentiation under cyclic mechanical stretch. • Nrf2 activation may improve alveolar bone remodeling during orthodontic treatment. During orthodontic treatment, mechanical stretch serves a crucial function in osteogenic differentiation of periodontal ligament stem cells (PDLSCs). Up-regulated reactive oxygen species (ROS) level is a result of cyclic mechanical stretch in many cell types. Nuclear factor erythroid-2-related factor-2 (Nrf2) is a master regulator in various antioxidants expression. However, it is not known whether cyclic mechanical stretch could induce the ROS generation in PDLSCs and whether Nrf2 participated in this process. The present study was aimed to investigate the role of Nrf2 in PDLSCs under cyclic mechanical stretch. Our results showed that cyclic mechanical stretch increased ROS level and the nuclear accumulation of Nrf2 during osteoblast differentiation. Knocking down Nrf2 by siRNA transfection increased ROS formation and suppressed osteogenic differentiation in PDLSCs. T-BHQ, a Nrf2 activator, promoted the osteogenic differentiation in PDLSCs under cyclic mechanical stretch, and improved the microstructure of alveolar bone during orthodontic tooth movement in rats by employing micro-CT system. Taken together, Nrf2 activation was involved in osteogenic differentiation under cyclic mechanical stretch in PDLSCs. T-BHQ could promote the osteogenic differentiation in vitro and in vivo, suggesting a promising option for the remodeling of the alveolar bone during orthodontic tooth movement.
Keratinocytes undergo a complex process of differentiation to form the stratified stratum corneum layer of the skin. In most biomimetic skin models, a 3D hydrogel fabricated out of collagen type I is used to mimic human skin. However, native skin also contains keratin, which makes up 90% of the epidermis and is produced by the keratinocytes present. We hypothesized that the addition of keratin (KTN) in our collagen hydrogel may aid in the process of keratinocyte differentiation compared to a pure collagen hydrogel. Keratinocytes were seeded on top of a 100% collagen or 50/50 C/KTN hydrogel cultured in either calcium-free (Ca-free) or calcium+ (Ca+) media. Our study demonstrates that the addition of keratin and calcium in the media increased lysosomal activity by measuring the glucocerebrosidase (GBA) activity and lysosomal distribution length, an indication of greater keratinocyte differentiation. We also found that the presence of KTN in the hydrogel also increased the expression of involucrin, a differentiation marker, compared to a pure collagen hydrogel. We demonstrate that a combination (i.e., containing both collagen and kerateine or “C/KTN”) hydrogel was able to increase keratinocyte differentiation compared to a pure collagen hydrogel, and the addition of calcium further increased the differentiation of keratinocytes. This multi-protein hydrogel shows promise in future models or treatments to increase keratinocyte differentiation into the stratum corneum.
Cosmological fluctuations retain a memory of the physics that generated them in their spatial correlations. The strength of correlations varies smoothly as a function of external kinematics, which is encoded in differential equations satisfied by cosmological correlation functions. In this work, we provide a broader perspective on the origin and structure of these differential equations. As a concrete example, we study conformally coupled scalar fields in a power-law cosmology. The wavefunction coefficients in this model have integral representations, with the integrands being the product of the corresponding flat-space results and “twist factors” that depend on the cosmological evolution. Similar twisted integrals arise for loop amplitudes in dimensional regularization, and their recent study has led to the discovery of rich mathematical structures and powerful new tools for computing multi-loop Feynman integrals in quantum field theory. The integrals of interest in cosmology are also part of a finite-dimensional basis of master integrals, which satisfy a system of first-order differential equations. We develop a formalism to derive these differential equations for arbitrary tree graphs. The results can be represented in graphical form by associating the singularities of the differential equations with a set of graph tubings. Upon differentiation, these tubings grow in a local and predictive fashion. In fact, a few remarkably simple rules allow us to predict — by hand — the equations for all tree graphs. While the rules of this “kinematic flow” are defined purely in terms of data on the boundary of the spacetime, they reflect the physics of bulk time evolution. We also study the analogous structures in tr ϕ 3 theory, and see some glimpses of hidden structure in the sum over planar graphs. This suggests that there is an autonomous combinatorial or geometric construction from which cosmological correlations, and the associated spacetime, emerge.
Data-driven identification of differential equations is an interesting but challenging problem, especially when the given data are corrupted by noise. When the governing differential equation is a linear combination of various differential terms, the identification problem can be formulated as solving a linear system, with the feature matrix consisting of linear and nonlinear terms multiplied by a coefficient vector. This product is equal to the time derivative term, and thus generates dynamical behaviors. The goal is to identify the correct terms that form the equation to capture the dynamics of the given data. We propose a general and robust framework to recover differential equations using a weak formulation with two new mechanisms, narrow-fit and trimming, for both ordinary and partial differential equations (ODEs and PDEs). The weak formulation facilitates an efficient and robust way to handle noise, and two new mechanisms, narrow-fit and trimming, improve the coefficient support and value recoveries respectively. For each sparsity level, Subspace Pursuit is utilized to find an initial set of support from the large dictionary. Then, we focus on highly dynamic regions (rows of the feature matrix), and error normalize the feature matrix in the narrow-fit step. The support is further updated via trimming the terms that contribute the least. Finally, the support set of features with the smallest Cross-Validation error is chosen as the result. A comprehensive set of numerical experiments are presented for both systems of ODEs and PDEs with various noise levels. The proposed method gives a robust recovery of the coefficients, and a significant denoising effect which can handle up to 100% noise-to-signal ratio for some equations. We compare the proposed method with several state-of-the-art algorithms for the recovery of differential equations.