Dysregulated inter-mitochondrial crosstalk in glioblastoma cells revealed by in situ cryo-electron tomography.

Glioblastomas (GBMs) are the most lethal primary brain tumors with limited survival, even under aggressive treatments. The current therapeutics for GBMs are flawed due to the failure to accurately discriminate between normal proliferating cells and distinctive tumor cells. Mitochondria are essential to GBMs and serve as potential therapeutical targets. Here, we utilize cryo-electron tomography to quantitatively investigate nanoscale details of randomly sampled mitochondria in their native cellular context of GBM cells. Our results show that compared with cancer-free brain cells, GBM cells own more inter-mitochondrial junctions of several types for communications. Furthermore, our tomograms unveil microtubule-dependent mitochondrial nanotunnel-like bridges in the GBM cells as another inter-mitochondrial structure. These quantified inter-mitochondrial features, together with other mitochondria-organelle and intra-mitochondrial ones, are sufficient to distinguish GBM cells from cancer-free brain cells under scrutiny with predictive modeling. Our findings decipher high-resolution inter-mitochondrial structural signatures and provide clues for diagnosis and therapeutic interventions for GBM and other mitochondria-related diseases.

Kindlin-2 promotes rear focal adhesion disassembly and directional persistence during cell migration.

Cell migration involves front-to-rear asymmetric focal adhesion (FA) dynamics, which facilitates trailing edge detachment and directional persistence. Here, we show that kindlin-2 is crucial for FA sliding and disassembly in migrating cells. Loss of kindlin-2 markedly reduced FA number and selectively impaired rear FA sliding and disassembly, resulting in defective rear retraction and reduced directional persistence during cell migration. Kindlin-2-deficient cells failed to develop serum-induced actomyosin-dependent tension at FAs. At the molecular level, kindlin-2 directly interacted with myosin light chain kinase (MYLK, hereafter referred to as MLCK), which was enhanced in response to serum stimulation. Serum deprivation inhibited rear FA disassembly, which was released in response to serum stimulation. Overexpression of the MLCK-binding kindlin-2 F0F1 fragment (amino acid residues 1-167), which inhibits the interaction of endogenous kindlin-2 with MLCK, phenocopied kindlin-2 deficiency-induced migration defects. Inhibition of MLCK, like loss of kindlin-2, also impaired trailing-edge detachment, rear FA disassembly and directional persistence. These results suggest a role of kindlin-2 in promoting actomyosin contractility at FAs, leading to increased rear FA sliding and disassembly, and directional persistence during cell migration.

Construction of Coarse-Grained Molecular Dynamics with Many-Body Non-Markovian Memory.

We introduce a machine-learning-based coarse-grained molecular dynamics model that faithfully retains the many-body nature of the intermolecular dissipative interactions. Unlike the common empirical coarse-grained models, the present model is constructed based on the Mori-Zwanzig formalism and naturally inherits the heterogeneous state-dependent memory term rather than matching the mean-field metrics such as the velocity autocorrelation function. Numerical results show that preserving the many-body nature of the memory term is crucial for predicting the collective transport and diffusion processes, where empirical forms generally show limitations.

Machine learning assisted coarse-grained molecular dynamics modeling of meso-scale interfacial fluids.

A hallmark of meso-scale interfacial fluids is the multi-faceted, scale-dependent interfacial energy, which often manifests different characteristics across the molecular and continuum scale. The multi-scale nature imposes a challenge to construct reliable coarse-grained (CG) models, where the CG potential function needs to faithfully encode the many-body interactions arising from the unresolved atomistic interactions and account for the heterogeneous density distributions across the interface. We construct the CG models of both single- and two-component polymeric fluid systems based on the recently developed deep coarse-grained potential [Zhang et al., J. Chem. Phys. 149, 034101 (2018)] scheme, where each polymer molecule is modeled as a CG particle. By only using the training samples of the instantaneous force under the thermal equilibrium state, the constructed CG models can accurately reproduce both the probability density function of the void formation in bulk and the spectrum of the capillary wave across the fluid interface. More importantly, the CG models accurately predict the volume-to-area scaling transition for the apolar solvation energy, illustrating the effectiveness to probe the meso-scale collective behaviors encoded with molecular-level fidelity.

Data-driven construction of stochastic reduced dynamics encoded with non-Markovian features.

One important problem in constructing the reduced dynamics of molecular systems is the accurate modeling of the non-Markovian behavior arising from the dynamics of unresolved variables. The main complication emerges from the lack of scale separations, where the reduced dynamics generally exhibits pronounced memory and non-white noise terms. We propose a data-driven approach to learn the reduced model of multi-dimensional resolved variables that faithfully retains the non-Markovian dynamics. Different from the common approaches based on the direct construction of the memory function, the present approach seeks a set of non-Markovian features that encode the history of the resolved variables and establishes a joint learning of the extended Markovian dynamics in terms of both the resolved variables and these features. The training is based on matching the evolution of the correlation functions of the extended variables that can be directly obtained from the ones of the resolved variables. The constructed model essentially approximates the multi-dimensional generalized Langevin equation and ensures numerical stability without empirical treatment. We demonstrate the effectiveness of the method by constructing the reduced models of molecular systems in terms of both one-dimensional and four-dimensional resolved variables.

Network pharmacology and molecular docking analysis on molecular targets: Mechanisms of baicalin and baicalein against hyperuricemic nephropathy.

Oxidative stress and inflammation in kidney are the main causes for hyperuricemic nephropathy (HN). Baicalin and baicalein, two flavonoids, have anti-inflammatory and anti-oxidative effects and they are interconvertible in the body. In this study, both baicalin and baicalein were administered by intragastric administration (i.g.) or intraperitoneal injection (i.p.) at the dose of 50 mg kg-1, once a day for 15 consecutive days to HN mice, a model established by i.g. of yeast extract combined with i.p. of potassium oxonate. In HN mice, baicalin and baicalein reduced serum uric acid (SUA) levels and protected kidneys by anti-inflammatory and anti-oxidative effects. Mechanistically, the effect of baicalin and baicalein on reducing SUA levels might due to their inhibitory effect on xanthine oxidase (XO) activity in vivo and in vitro. Furthermore, the mechanisms of baicalin and baicalein against HN were analyzed with network pharmacology and molecular docking technology. The network pharmacology indicated that the protective effects of baicalin and baicalein against HN were mainly related to their down-regulating effects on TLRs, NF-κB, MAPK, PI3K/AKT and NOD-like receptor signaling pathways. Molecular docking indicated high binding affinity of baicalin/baicalein to targets such as AKT1 and MAPK1. In summary, baicalin and baicalein are promising drug candidates for the treatment of HN by inhibiting XO activity, reducing inflammation and cell apoptosis through down-regulating TLRs/NLRP3/NF-κB, MAPK, PI3K/AKT/NF-κB pathways.

Petrov-Galerkin methods for the construction of non-Markovian dynamics preserving nonlocal statistics.

A common observation in coarse-graining a molecular system is the non-Markovian behavior, primarily due to the lack of scale separations. This is reflected in the strong memory effect and the non-white noise spectrum, which must be incorporated into a coarse-grained description to correctly predict dynamic properties. To construct a stochastic model that gives rise to the correct non-Markovian dynamics, we propose a Galerkin projection approach, which transforms the exhausting effort of finding an appropriate model to choosing appropriate subspaces in terms of the derivatives of the coarse-grained variables and, at the same time, provides an accurate approximation to the generalized Langevin equation. We introduce the notion of fractional statistics that embodies nonlocal properties. More importantly, we show how to pick subspaces in the Galerkin projection so that those statistics are automatically matched.

Accurate Determination of Barium Isotopic Compositions in Sequentially Leached Phases from Carbonates by Double Spike-Thermal Ionization Mass Spectrometry (DS-TIMS).

Recent studies have proposed barium isotopes as a novel proxy for studying primary productivity in paleo-oceangraphical studies and elements cycling through the critical zone. Pristine marine carbonates are generally assumed to preserve Ba isotope compositions of ancient seawater. However, Ba incorporated in or adsorbed on detrital minerals such as clays in impure carbonates may limit the accurate application of the Ba isotope proxy for paleo-ocean environmental reconstruction purposes. We present here a sequential extraction procedure and show that a considerable range of Ba concentrations can be associated with the four operationally defined sequential leaching fractions (water-soluble, exchangeable, carbonate, and oxidizable fractions). Chemical separation of Ba from these leachates is achieved with a recovery of >98.6% by our modified ion exchange procedure. Potential instrumental mass bias effects and barium isotope fractionation during the chemical separation are corrected using a carefully optimized 130Ba-135Ba double-spike method. A long-term reproducibility better than ±0.03‰ (2SD) for δ137/134Ba has been achieved by using the double spike-thermal ionization mass spectrometry (DS-TIMS) in this study. We demonstrate that significant variations of δ137/134Ba in the analyzed leachates suggest a considerable Ba isotope fractionation between carbonate mineral phase and noncarbonate phases of marine carbonate rocks. The barium isotope distribution in a set of standard reference materials and natural geological samples under various geological settings has been presented. When utilizing Ba isotopes as a proxy for primary productivity and the biogeochemical cycling of Ba, our new findings from sequential Ba extraction as well as our modified precise DS-TIMS analytical setup should be taken into account.

A simple two-stage column chromatographic separation scheme for strontium, lead, neodymium and hafnium isotope analyses in geological samples by thermal ionization mass spectrometry or multi-collector inductively coupled plasma mass spectrometry.

Here, a two-stage column separation scheme is developed for the concomitant isolation of Sr, Pb, Nd, and Hf from geological samples. The first column, which consists of three resin layers (AG50W-X8 ion exchange resin + Ln specific resin + Sr specific resin), separates the high field strength element + rare earth element, Sr and Pb from the matrices. Subsequently, Nd and Hf are further separated from the high field strength element + rare earth element fraction on the second column using 1 mL of Ln specific resin. The two-stage column process can be completed within about seven and a half hours for a batch of samples (20-30). The separated Sr fraction was ready for isotope ratio measurements by thermal ionization mass spectrometry. The Pb, Nd, and Hf fractions were converted to nitrate prior to isotopic analyses by multi-collector inductively coupled plasma mass spectrometry. The feasibility of this new procedure is confirmed by the analyses of four international rock standards (BCR-2, AGV-2, BHVO-2, and JB-3), which yielded isotope ratios that were in good agreement with other published data.

Data-driven parameterization of the generalized Langevin equation.

We present a data-driven approach to determine the memory kernel and random noise in generalized Langevin equations. To facilitate practical implementations, we parameterize the kernel function in the Laplace domain by a rational function, with coefficients directly linked to the equilibrium statistics of the coarse-grain variables. We show that such an approximation can be constructed to arbitrarily high order and the resulting generalized Langevin dynamics can be embedded in an extended stochastic model without explicit memory. We demonstrate how to introduce the stochastic noise so that the second fluctuation-dissipation theorem is exactly satisfied. Results from several numerical tests are presented to demonstrate the effectiveness of the proposed method.

Citrus Tree Segmentation from UAV Images Based on Monocular Machine Vision in a Natural Orchard Environment.

The segmentation of citrus trees in a natural orchard environment is a key technology for achieving the fully autonomous operation of agricultural unmanned aerial vehicles (UAVs). Therefore, a tree segmentation method based on monocular machine vision technology and a support vector machine (SVM) algorithm are proposed in this paper to segment citrus trees precisely under different brightness and weed coverage conditions. To reduce the sensitivity to environmental brightness, a selective illumination histogram equalization method was developed to compensate for the illumination, thereby improving the brightness contrast for the foreground without changing its hue and saturation. To accurately differentiate fruit trees from different weed coverage backgrounds, a chromatic aberration segmentation algorithm and the Otsu threshold method were combined to extract potential fruit tree regions. Then, 14 color features, five statistical texture features, and local binary pattern features of those regions were calculated to establish an SVM segmentation model. The proposed method was verified on a dataset with different brightness and weed coverage conditions, and the results show that the citrus tree segmentation accuracy reached 85.27% ± 9.43%; thus, the proposed method achieved better performance than two similar methods.

A data-driven framework for sparsity-enhanced surrogates with arbitrary mutually dependent randomness.

The challenge of quantifying uncertainty propagation in real-world systems is rooted in the high-dimensionality of the stochastic input and the frequent lack of explicit knowledge of its probability distribution. Traditional approaches show limitations for such problems, especially when the size of the training data is limited. To address these difficulties, we have developed a general framework of constructing surrogate models on spaces of stochastic input with arbitrary probability measure irrespective of the mutual dependencies between individual components of the random inputs and the analytical form. The present Data-driven Sparsity-enhancing Rotation for Arbitrary Randomness (DSRAR) framework includes a data-driven construction of multivariate polynomial basis for arbitrary mutually dependent probability measures and a sparsity enhancement rotation procedure. This sparsity-enhancing rotation method was initially proposed in our previous work [1] for Gaussian density distributions, which may not be feasible for non-Gaussian distributions due to the loss of orthogonality after the rotation. To remedy such difficulties, we developed a new data-driven approach to construct orthonormal polynomials for arbitrary mutually dependent randomness, ensuring the constructed basis maintains the orthogonality/near-orthogonality with respect to the density of the rotated random vector, where directly applying the regular polynomial chaos including arbitrary polynomial chaos (aPC) [2] shows limitations due to the assumption of the mutual independence between the components of the random inputs. The developed DSRAR framework leads to accurate recovery, with only limited training data, of a sparse representation of the target functions. The effectiveness of our method is demonstrated in challenging problems such as partial differential equations and realistic molecular systems within high-dimensional (O(10)) conformational spaces where the underlying density is implicitly represented by a large collection of sample data, as well as systems with explicitly given non-Gaussian probabilistic measures.

Secretomic Analysis of Host-Pathogen Interactions Reveals That Elongation Factor-Tu Is a Potential Adherence Factor of Helicobacter pylori during Pathogenesis.

The secreted proteins of bacteria are usually accompanied by virulence factors, which can cause inflammation and damage host cells. Identifying the secretomes arising from the interactions of bacteria and host cells could therefore increase understanding of the mechanisms during initial pathogenesis. The present study used a host-pathogen coculture system of Helicobacter pylori and monocytes (THP-1 cells) to investigate the secreted proteins associated with initial H. pylori pathogenesis. The secreted proteins from the conditioned media from H. pylori, THP-1 cells, and the coculture were collected and analyzed using SDS-PAGE and LC-MS/MS. Results indicated the presence of 15 overexpressed bands in the coculture. Thirty-one proteins were identified-11 were derived from THP-1 cells and 20 were derived from H. pylori. A potential adherence factor from H. pylori, elongation factor-Tu (EF-Tu), was selected for investigation of its biological function. Results from confocal microscopic and flow cytometric analyses indicated the contribution of EF-Tu to the binding ability of H. pylori in THP-1. The data demonstrated that fluorescence of EF-Tu on THP-1 cells increased after the addition of the H. pylori-conditioned medium. This study reports a novel secretory adherence factor in H. pylori, EF-Tu, and further elucidates mechanisms of H. pylori adaptation for host-pathogen interaction during pathogenesis.

Antibacterial activity of food-grade chitosan against Vibrio parahaemolyticus biofilms.

Biofilm is a community composed of microbes and the extracellular polymeric substances. This special architecture poses a significant public health risk as it increases the fitness of bacteria in harsh conditions and renders bacterial resistance to antimicrobial agents and cleaning. In this study, we investigated the inhibition and eradication effects of chitosan on the biofilm of Vibrio parahaemolyticus, an important food-borne pathogen. The crystal violet staining, [2, 3-bis (2-methoxy-4-nitro-5- sulfophenyl)-2H-tetrazolium-5-carboxanilide] (XTT) reduction method, phenol-sulfuric acid method, fluorescence microscope and confocal laser scanning microscope (CLSM) observation were conducted. The results indicated that the minimum inhibitory concentration (MIC) of chitosan was 1.25 mg/mL. Sub-MIC of chitosan could significantly inhibit biofilm formation, reduce the metabolic activities and the secretion of extracellular polysaccharide (EPS). Moreover, chitosan at 4MIC could eradicate 85.06% mature biofilm of V. parahaemolyticus, and decrease 81.43% EPS in mature biofilm. These results were also confirmed by the visual images obtained from fluorescence microscopy and CLSM. This study elucidated that chitosan was not only effective to prevent biofilm formation, but also eradicate mature biofilms of V. parahaemolyticus.

Inflow/Outflow Boundary Conditions for Particle-Based Blood Flow Simulations: Application to Arterial Bifurcations and Trees.

When blood flows through a bifurcation, red blood cells (RBCs) travel into side branches at different hematocrit levels, and it is even possible that all RBCs enter into one branch only, leading to a complete separation of plasma and RBCs. To quantify this phenomenon via particle-based mesoscopic simulations, we developed a general framework for open boundary conditions in multiphase flows that is effective even for high hematocrit levels. The inflow at the inlet is duplicated from a fully developed flow generated in a pilot simulation with periodic boundary conditions. The outflow is controlled by adaptive forces to maintain the flow rate and velocity gradient at fixed values, while the particles leaving the arteriole at the outlet are removed from the system. Upon validation of this approach, we performed systematic 3D simulations to study plasma skimming in arterioles of diameters 20 to 32 microns. For a flow rate ratio 6:1 at the branches, we observed the "all-or-nothing" phenomenon with plasma only entering the low flow rate branch. We then simulated blood-plasma separation in arteriolar bifurcations with different bifurcation angles and same diameter of the daughter branches. Our simulations predict a significant increase in RBC flux through the main daughter branch as the bifurcation angle is increased. Finally, we demonstrated the effectiveness of the new methodology in simulations of blood flow in vessels with multiple inlets and outlets, constructed using an angiogenesis model.

Probing vasoocclusion phenomena in sickle cell anemia via mesoscopic simulations.

Vasoocclusion crisis is a key hallmark of sickle cell anemia. Although early studies suggest that this crisis is caused by blockage of a single elongated cell, recent experiments have revealed that vasoocclusion is a complex process triggered by adhesive interactions among different cell groups in multiple stages. However, the quantification of the biophysical characteristics of sickle cell anemia remains an open issue. Based on dissipative particle dynamics, we develop a multiscale model for the sickle red blood cells (SS-RBCs), accounting for diversity in both shapes and cell rigidities, to investigate the precise mechanism of vasoocclusion. First, we investigate the adhesive dynamics of a single SS-RBC in shear flow and static conditions, and find that the different cell groups (SS2: young-deformable SS-RBCs, ISCs: rigid-irreversible SS-RBCs) exhibit heterogeneous adhesive behavior due to the diverse cell morphologies and membrane rigidities. We quantify the observed adhesion behavior (in static conditions) in terms of a balance of free energies due to cell adhesion and deformation, and propose a power law that relates the free-energy increase as a function of the contact area. We further simulate postcapillary flow of SS-RBC suspensions with different cell fractions. The more adhesive SS2 cells interact with the vascular endothelium and trap ISC cells, resulting in vasoocclusion in vessels less than 12-14 µm depending on the hematocrit. Under inflammation, adherent leukocytes may also trap ISC cells, resulting in vasoocclusion in even larger vessels.

Modeling nanoscale hydrodynamics by smoothed dissipative particle dynamics.

Thermal fluctuation and hydrophobicity are two hallmarks of fluid hydrodynamics on the nano-scale. It is a challenge to consistently couple the small length and time scale phenomena associated with molecular interaction with larger scale phenomena. The development of this consistency is the essence of mesoscale science. In this study, we use a nanoscale fluid model based on smoothed dissipative particle dynamics that accounts for the phenomena associated with density fluctuations and hydrophobicity. We show consistency in the fluctuation spectrum across scales. In doing so, it is necessary to account for finite fluid particle size. Furthermore, we demonstrate that the present model can capture the void probability and solvation free energy of nonpolar hard particles of different sizes. The present fluid model is well suited for an understanding of emergent phenomena in nano-scale fluid systems.

Size-dependent attenuation of TLR9 signaling by gold nanoparticles in macrophages.

Gold nanoparticles (GNPs), which are generally thought to be bio-inert and non-cytotoxic, have become one of the most ideal nanomaterials for medical applications. Once engulfed by phagocytes, the immunological effects of GNPs are still of concern and require detailed investigation. Therefore, this study explored the immunological significance of GNPs on TLR-mediated innate immunity in murine macrophages. GNP causes specific inhibition of TLR9 (CpG oligodeoxynucleotides; CpG-ODNs) signal in macrophages. The impaired CpG-ODN-induced TNF-α production is GNP concentration- and size-dependent in murine Raw264.7 cells: a GNP of 4 nm in size is more potent than a GNP of 11, 19, 35, or 45 nm in size. Consistent with cytokine inhibition, the CpG-ODN-induced phosphorylation of NF-κB and JNK as well as NF-κB activation are suppressed by GNPs. GNPs accumulate in lysosomes after phagocytosis and also increase TLR9-associated lysosomal cathepsin expression and activities, but this is irrelevant to TLR9 inhibition by GNPs in our studies. In addition, GNPs affected TLR9 translocation in response to CpG-ODNs and to phagosomes. Further exploring how GNPs inhibited TLR9 function, we found that GNPs could bind to high-mobility group box-1 (which is involved in the regulation of TLR9 signaling) inside the lysosomes. The current studies demonstrate that size-dependent inhibition of TLR9 function by GNP may be attributed to its binding to high-mobility group box-1.

CLEC5A is critical for dengue-virus-induced lethal disease.

Dengue haemorrhagic fever and dengue shock syndrome, the most severe responses to dengue virus (DV) infection, are characterized by plasma leakage (due to increased vascular permeability) and low platelet counts. CLEC5A (C-type lectin domain family 5, member A; also known as myeloid DAP12-associating lectin (MDL-1)) contains a C-type lectin-like fold similar to the natural-killer T-cell C-type lectin domains and associates with a 12-kDa DNAX-activating protein (DAP12) on myeloid cells. Here we show that CLEC5A interacts with the dengue virion directly and thereby brings about DAP12 phosphorylation. The CLEC5A-DV interaction does not result in viral entry but stimulates the release of proinflammatory cytokines. Blockade of CLEC5A-DV interaction suppresses the secretion of proinflammatory cytokines without affecting the release of interferon-alpha, supporting the notion that CLEC5A acts as a signalling receptor for proinflammatory cytokine release. Moreover, anti-CLEC5A monoclonal antibodies inhibit DV-induced plasma leakage, as well as subcutaneous and vital-organ haemorrhaging, and reduce the mortality of DV infection by about 50% in STAT1-deficient mice. Our observation that blockade of CLEC5A-mediated signalling attenuates the production of proinflammatory cytokines by macrophages infected with DV (either alone or complexed with an enhancing antibody) offers a promising strategy for alleviating tissue damage and increasing the survival of patients suffering from dengue haemorrhagic fever and dengue shock syndrome, and possibly even other virus-induced inflammatory diseases.

Protective effects of Lactococcus lactis subsp. lactis HFY14 supplementation on the brain, intestines, and motor function of antibiotic-treated mice.

Introduction: This study aimed to explore the anti-oxidative and anti-inflammatory properties of Lactococcus lactis subsp. lactis HFY14 (LLSLHFY14) and investigate its effects on the intestinal barrier, cranial nerve, and motor function in mice treated with antibiotics. Methods: Mice were administered an antibiotic mixture (neomycin 5 mg/mL, vancomycin 25 mg/mL, amphotericin B 0.1 mg/mL, ampicillin 10 mg/mL, metronidazole file 5 mg/mL, and lipopolysaccharide 1.5 µg/mL) intraperitoneally, and oxidative stress and inflammatory markers in the serum and brain tissues, and liver index were measured. H&E staining was performed to detect pathological alterations in brain tissues. The expression of intestinal-barrier-related genes and that of genes involved in inflammatory pathways in the brain were detected using polymerase chain reaction (PCR). Results: LLSLHFY14 administration extended the weight-loaded swimming and running times of mice and decreased the liver index. Moreover, the levels of malondialdehyde (MDA), interleukin-6 (IL-6), and tumor necrosis factor alpha (TNF-α) in the serum and brain tissue were reduced, whereas those of superoxide dismutase (SOD), glutathione (GSH), and interleukin-10 (IL-10) were elevated. Elevated brain expression of the protein kinase B (AKT)/cAMP-response element binding protein (CREB)/brain-derived neurotrophic factor (BDNF)/extracellular signal-regulated kinase 1 (ERK1) pathway, decreased brain expression of the IL-6 gene, and elevated cecum expression of zonula occludens-1 (ZO-1), occludin-1, and claudin-1 genes were noted. LLSLHFY14 supplementation significantly increased Bacteroidetes expression but decreased Firmicutes expression, thus increasing the Bacteroidetes/Firmicutes ratio. Discussion: Overall, LLSLHFY14 supplementation ameliorated antibiotic-induced oxidative stress and inflammation in the mouse central nervous system, intestinal barrier dysfunction, and increased motor function, thus confirming its potential application as probiotics.