The critical roles of propanethiol oxidoreductase and sulfide-quinone oxidoreductase in the propanethiol catabolism pathway in Pseudomonas putida S-1.

Propanethiol (PT) is a hazardous pollutant that poses risks to both the environment and human well-being. Pseudomonas putida S-1 has been identified as a microorganism capable of utilizing PT as its sole carbon source. However, the metabolic pathway responsible for PT degradation in P. putida S-1 has remained poorly understood, impeding its optimization and practical application. In this study, we investigated the catabolic network involved in PT desulfurization with P. putida S-1 and identified key gene modules crucial to this process. Notably, propanethiol oxidoreductase (PTO) catalyzes the initial degradation of PT, a pivotal step for P. putida S-1's survival on PT. PTO facilitates the oxidation of PT, resulting H2S, H2O2, and propionaldehyde (PA). Catalase-peroxidase catalyzes the conversion of H2O2 to oxygen and water, while PA undergoes gradual conversion to Succinyl-CoA, which is subsequently utilized in the tricarboxylic acid cycle. H2S is digested in a comprehensive desulfurization network where sulfide-quinone oxidoreductase (SQOR) predominantly converts it to sulfane sulfur. The transcriptome analysis suggests that sulfur can be finally converted to sulfite or sulfate and exported out of the cell. The PT degradation capacity of P. putida S-1 was enhanced by increasing the transcription level of PTO and SQOR genes in vivo.IMPORTANCEThis work investigated the PT catabolism pathway in Pseudomonas putida S-1, a microorganism capable of utilizing PT as the sole carbon source. Critical genes that control the initiation of PT degradation were identified and characterized, such as pto and sqor. By increasing the transcription level of pto and sqor genes in vivo, we have successfully enhanced the PT degradation efficiency and growth rate of P. putida S-1. This work does not only reveal a unique PT degradation pathway but also highlights the potential of enhancing the microbial desulfurization process in the bioremediation of thiol-contaminated environment.

Effects of dietary supplementation with heat-killed Lactobacillus acidophilus on growth performance, digestive enzyme activity, antioxidant capacity, and inflammatory response of juvenile large yellow croaker (Larimichthys crocea).

A ten-week culture trial in juvenile large yellow croaker (Larimichthys crocea) (10.80 ± 0.10 g) was conducted to assess the impact of supplementing heat-killed Lactobacillus acidophilus (HLA) on growth performance, intestinal digestive enzyme activity, antioxidant capacity and inflammatory response. Five iso-nitrogenous (42 % crude protein) and iso-lipidic (12 % crude lipid) experimental feeds with different levels of HLA (0.0 %, 0.1 %, 0.2 %, 0.4 %, or 0.8 %) were prepared. They were named FO (control group), HLA0.1, HLA0.2, HLA0.4 and HLA0.8, respectively. The results indicated that HLA addition had no impact on survival (P > 0.05). In this experiment, the final body weight, weight gain rate and specific growth rate showed a quadratic regression trend, initially increasing and subsequently decreasing with the increasing in HLA levels, and attained the peak value at 0.2 % HLA supplemental level (P < 0.05). In contrast to the control group, in terms of digestive ability, amylase, lipase and trypsin exhibited a notable linear and quadratic pattern, demonstrating a substantial increase when 0.1% 0.2 % HLA was added in the diets (P < 0.05). Notably, elevated levels of catalase (CAT) activity, superoxide dismutase (SOD) activity, and total antioxidant capacity (T-AOC) were observed in the liver when adding 0.1%-0.2 % HLA, and the level of malondialdehyde (MDA) was significantly decreased and the liver exhibited a notable upregulation in the mRNA expression levels of nrf2, cat, sod2, and sod3 (P < 0.05). Additionally, the mRNA levels of genes associated with tight junctions in the intestines (zo-1, zo-2 and occludin) exhibited a significant upregulation when 0.2 % HLA was added in the feed (P < 0.05). Furthermore, the levels of mRNA expression for proinflammatory genes in the intestines including tnf-α, il-1ß, il-6 and il-8 exhibited a quadratic regression trend, characterized by an initial decline followed by subsequent growth (P < 0.05). Meanwhile, the levels of mRNA expression for genes linked to anti-inflammatory responses in the intestines (including il-10, tgf-ß, and arg1) exhibited a quadratic regression pattern, initially increasing and subsequently decreasing (P < 0.05). Compare with the control group, the levels of tnf-α, il-1ß and il-8 expression were notably downregulated in all HLA addition groups (P < 0.05). When 0.2 % HLA was added, the expression levels of il-10, tgf-ß and arg1 in the intestinal tract were markedly increased (P < 0.05). Overall, the supplementation of 0.2 % HLA in the feed has been shown to enhance the growth performance. The enhancement was attributed to HLA's capacity to improve antioxidant function, intestinal barrier integrity, and mitigate inflammatory responses. This research offers a scientific foundation for the utilization of HLA in aquaculture.

Smooth Muscle Overexpression of PGC1α Attenuates Atherosclerosis in Rabbits.

[Figure: see text].

Glycerol monolaurate improved intestinal barrier, antioxidant capacity, inflammatory response and microbiota dysbiosis in large yellow croaker (Larimichthys crocea) fed with high soybean oil diets.

Glycerol monolaurate (GML) is a potential candidate for regulating metabolic syndrome and inflammatory response. However, the role of GML in modulating intestinal health in fish has not been well determined. In this study, a 70-d feeding trial was conducted to evaluate the effect of GML on intestinal barrier, antioxidant capacity, inflammatory response and microbiota community of large yellow croaker (13.05 ± 0.09 g) fed with high level soybean oil (SO) diets. Two basic diets with fish oil (FO) or SO were formulated. Based on the SO group diet, three different levels of GML 0.02% (SO0.02), 0.04% (SO0.04) and 0.08% (SO0.08) were supplemented respectively. Results showed that intestinal villus height and perimeter ratio were increased in SO0.04 treatment compared with the SO group. The mRNA expressions of intestinal physical barrier-related gene odc and claudin-11 were significantly up-regulated in different addition of GML treatments compared with the SO group. Fish fed SO diet with 0.04% GML addition showed higher activities of acid phosphatase and lysozyme compared with the SO group. The content of malonaldehyde was significantly decreased and activities of catalase and superoxide dismutase were significantly increased in 0.02% and 0.04% GML groups compared with those in the SO group. The mRNA transcriptional levels of inflammatory response-related genes (il-1ß, il-6, tnf-α and cox-2) in 0.04% GML treatment were notably lower than those in the SO group. Meanwhile, sequencing analysis of bacterial 16S rRNA V4-V5 region showed that GML addition changed gut microbiota structure and increased alpha diversity of large yellow croaker fed diets with a high level of SO. The correlation analysis results indicated that the change of intestinal microbiota relative abundance strongly correlated with intestinal health indexes. In conclusion, these results demonstrated that 0.02%-0.04% GML addition could improve intestinal morphology, physical barrier, antioxidant capacity, inflammatory response and microbiota dysbiosis of large yellow croaker fed diets with a high percentage of SO.

Interpretable Molecular Property Predictions Using Marginalized Graph Kernels.

Marginalized graph kernels have shown competitive performance in molecular machine learning tasks but currently lack measures of interpretability, which are important to improve trust in the models, detect biases, and inform molecular optimization campaigns. We here conceive and implement two interpretability measures for Gaussian process regression using a marginalized graph kernel (GPR-MGK) to quantify (1) the contribution of specific training data to the prediction and (2) the contribution of specific nodes of the graph to the prediction. We demonstrate the applicability of these interpretability measures for molecular property prediction. We compare GPR-MGK to graph neural networks on four logic and two real-world toxicology data sets and find that the atomic attribution of GPR-MGK generally outperforms the atomic attribution of graph neural networks. We also perform a detailed molecular attribution analysis using the FreeSolv data set, showing how molecules in the training set influence machine learning predictions and why Morgan fingerprints perform poorly on this data set. This is the first systematic examination of the interpretability of GPR-MGK and thereby is an important step in the further maturation of marginalized graph kernel methods for interpretable molecular predictions.

Efficient Exploration of Chemical Compound Space Using Active Learning for Prediction of Thermodynamic Properties of Alkane Molecules.

We introduce an exploratory active learning (AL) algorithm using Gaussian process regression and marginalized graph kernel (GPR-MGK) to sample chemical compound space (CCS) at minimal cost. Targeting 251,728 enumerated alkane molecules with 4-19 carbon atoms, we applied the AL algorithm to select a diverse and representative set of molecules and then conducted high-throughput molecular simulations on these selected molecules. To demonstrate the power of the AL algorithm, we built directed message-passing neural networks (D-MPNN) using simulation data as the training set to predict liquid densities, heat capacities, and vaporization enthalpies of the CCS. Validations show that D-MPNN models built on the smallest training set considered in this work, which consists of 313 molecules or 0.124% of the original CCS, predict the properties with R2 > 0.99 against the computational data and R2 > 0.94 against the experimental data. The advantage of the presented AL algorithm is that the predicted uncertainty of GPR depends on only the molecular structures, which renders it compatible with high-throughput data generation.

Impact of dietary sucralose and sucrose-sweetened water intake on lipid and glucose metabolism in male mice.

AIMS: Overconsumption of sugar-sweetened beverages (SSBs) is associated with an increased risk of metabolic disorders, including obesity and diabetes. However, accumulating evidence also suggests the potential negative impact of consuming nonnutritive sweeteners (NNSs) on weight and glycaemic control. The metabolic effects of sucralose, the most widely used NNS, remain controversial. This study aimed to compare the impact of intake of dietary sucralose (acceptable daily intake dose, ADI dose) and sucrose-sweetened water (at the same sweetness level) on lipid and glucose metabolism in male mice. MATERIALS AND METHODS: Sucralose (0.1 mg/mL) or sucrose (60 mg/mL) was added to the drinking water of 8-week-old male C57BL/6 mice for 16 weeks, followed by oral glucose and intraperitoneal insulin tolerance tests, and measurements of bone mineral density, plasma lipids, and hormones. After the mice were sacrificed, the duodenum and ileum were used for examination of sweet taste receptors (STRs) and glucose transporters. RESULTS: A significant increase in fat mass was observed in the sucrose group of mice after 16 weeks of sweetened water drinking. Sucrose consumption also led to increased levels of plasma LDL, insulin, lipid deposition in the liver, and increased glucose intolerance in mice. Compared with the sucrose group, mice consuming sucralose showed much lower fat accumulation, hyperlipidaemia, liver steatosis, and glucose intolerance. In addition, the daily dose of sucralose only had a moderate effect on T1R2/3 in the intestine, without affecting glucose transporters and plasma insulin levels. CONCLUSION: Compared with mice consuming sucrose-sweetened water, daily drinking of sucralose within the ADI dose had a much lower impact on glucose and lipid homeostasis.

Effects of dietary lysolecithin on growth performance, serum biochemical indexes, antioxidant capacity, lipid metabolism and inflammation-related genes expression of juvenile large yellow croaker (Larimichthys crocea).

A 70-day feeding trial was conducted to investigate effects of dietary lysolecithin on growth performance, serum biochemical indexes, antioxidant capacity, lipid metabolism and inflammation-related genes expression of juvenile large yellow croaker (Larimichthys crocea) with initial weight of 6.04 ± 0.08 g. A formulated diet containing approximately 42% crude protein and 12.5% crude lipid was used as the control diet (CON). The other three experimental diets were formulated with supplementation of 0.2%, 0.4% and 0.6% lysolecithin based on the control diet, respectively. Results showed that weight gain rate (WGR) and specific growth rate (SGR) significantly increased in fish fed diets with lysolecithin compared with those in the control diet (P < 0.05). Fish fed diets with 0.4% and 0.6% lysolecithin had notably higher lipid content in muscle than that in the control diet (P < 0.05). When fish were fed diets with lysolecithin, serum high-density lipoprotein cholesterol (HDL-c) content was notably higher than that in the control diet (P < 0.05), while fish fed the diet with 0.6% lysolecithin had a significant lower serum low-density lipoprotein cholesterol (LDL-c) content than that in the control diet (P < 0.05). Meanwhile, serum aspartate transaminase (AST) and alanine transaminase (ALT) activities in fish fed diets with lysolecithin were remarkably lower than those in the control diet (P < 0.05). With the increase of dietary lysolecithin from 0.2% to 0.6%, mRNA expression of stearoyl-coenzyme A desaturase 1 (scd1), diacylglycerol acyltransferase 2 (dgat2) and sterol-regulatory element binding protein 1 (srebp1) showed decreasing trends. Furthermore, mRNA expression of carnitine palmitoyl transferase 1 (cpt1) and lipoprotein lipase (lpl) among each dietary lysolecithin treatment were significantly higher than those in the control diet (P < 0.05). In terms of inflammation, mRNA expression of tumor necrosis factor α (tnf-α) and interleukin-1 ß (il-1ß) were significantly down-regulated in fish fed diets with lysolecithin compared with those in the control diet (P < 0.05), while the mRNA expression of interleukin-10 (il-10) was significantly higher than that in the control diet (P < 0.05). In conclusion, dietary lysolecithin could promote the growth performance, improve hepatic lipid metabolism and regulate inflammation response in juvenile large yellow croaker, and the optimal supplement level of lysolecithin was approximately 0.4% in this study.

Replacement of Dietary Fishmeal Protein with Degossypolized Cottonseed Protein on Growth Performance, Nonspecific Immune Response, Antioxidant Capacity, and Target of Rapamycin Pathway of Juvenile Large Yellow Croaker (Larimichthys crocea).

A 70-day feeding experiment was carried out to assess the replacement of dietary fishmeal (FM) protein with degossypolized cottonseed protein (DCP) on large yellow croaker (Larimichthys crocea) with initial body weight (13.09 ± 0.50 g). Five isonitrogenous and isolipidic diets replaced fishmeal protein with 0%, 20%, 40%, 60%, and 80% DCP were formulated and named as FM (the control group), DCP20, DCP40, DCP60, and DCP80, respectively. Results displayed that weight gain rate (WGR) and specific growth rate (SGR) in the DCP20 group (263.91% and 1.85% d-1) were significantly increased compared with the control group (194.79% and 1.54% d-1) (P < 0.05). Furthermore, fish fed the diet with 20% DCP significantly increased the activity of hepatic superoxide dismutase (SOD) compared with the control group (P < 0.05). Meanwhile, the content of hepatic malondialdehyde (MDA) in the DCP20, DCP40, and DCP80 groups was significantly lower than that in the control group (P < 0.05). The activity of intestinal trypsin in the DCP20 group was significantly degraded compared with that in the control group (P < 0.05). The transcription of hepatic proinflammatory cytokine genes (interleukin-6 (il-6); tumor necrosis factor-α (tnf-α); and interferon-γ (ifn-γ)) in the DCP20 and DCP40 groups was significantly upregulated compared with that in the control group (P < 0.05). As to the target of rapamycin (TOR) pathway, the transcription of hepatic target of rapamycin (tor) and ribosomal protein (s6) was significantly up-regulated, while the transcription of hepatic eukaryotic translation initiation factor 4E binding protein 1 (4e-bp1) gene was significantly downregulated in the DCP group compared with the control group (P < 0.05). In summary, based on the broken line regression model analysis of WGR and SGR against dietary DCP replacement levels, the optimal replacement level was recommended to be 8.12% and 9.37% for large yellow croaker, respectively. These results revealed that FM protein replaced with 20% DCP could promote digestive enzyme activities and antioxidant capacity and further activate immune response and the TOR pathway so that growth performance of juvenile large yellow croaker was improved.

A Type VI Secretion System Facilitates Fitness, Homeostasis, and Competitive Advantages for Environmental Adaptability and Efficient Nicotine Biodegradation.

Gram-negative bacteria employ secretion systems to translocate proteinaceous effectors from the cytoplasm to the extracellular milieu, thus interacting with the surrounding environment or microniche. It is known that bacteria can benefit from the type VI secretion system (T6SS) by transporting ions to combat reactive oxygen species (ROS). Here, we report that T6SS activities conferred tolerance to nicotine-induced oxidative stress in Pseudomonas sp. strain JY-Q, a highly active nicotine degradation strain isolated from tobacco waste extract. AA098_13375 was identified to encode a dual-functional effector with antimicrobial and anti-ROS activities. Wild-type strain JY-Q grew better than the AA098_13375 deletion mutant in nicotine-containing medium by antagonizing increased intracellular ROS levels. It was, therefore, tentatively designated TseN (type VI secretion system effector for nicotine tolerance), homologs of which were observed to be broadly ubiquitous in Pseudomonas species. TseN was identified as a Tse6-like bacteriostatic toxin via monitoring intracellular NAD+ TseN presented potential antagonism against ROS to fine tune the heavy traffic of nicotine metabolism in strain JY-Q. It is feasible that the dynamic tuning of NAD+ driven by TseN could satisfy demands from nicotine degradation with less cytotoxicity. In this scenario, T6SS involves a fascinating accommodation cascade that prompts constitutive biotransformation of N-heterocyclic aromatics by improving bacterial robustness/growth. In summary, the T6SS in JY-Q mediated resistance to oxidative stress and promoted bacterial fitness via a contact-independent growth competitive advantage, in addition to the well-studied T6SS-dependent antimicrobial activities.IMPORTANCE Mixtures of various pollutants and the coexistence of numerous species of organisms are usually found in adverse environments. Concerning biodegradation of nitrogen-heterocyclic contaminants, the scientific community has commonly focused on screening functional enzymes that transform pollutants into intermediates of attenuated toxicity or for primary metabolism. Here, we identified dual roles of the T6SS effector TseN in Pseudomonas sp. strain JY-Q, which is capable of degrading nicotine. The T6SS in strain JY-Q is able to deliver TseN to kill competitors and provide a growth advantage by a contact-independent pattern. TseN could monitor the intracellular NAD+ level by its hydrolase activity, causing cytotoxicity in competitive rivals but metabolic homeostasis on JY-Q. Moreover, JY-Q could be protected from TseN toxicity by the immunity protein TsiN. In conclusion, we found that TseN with cytotoxicity to bacterial competitors facilitated the nicotine tolerance of JY-Q. We therefore reveal a working model between T6SS and nicotine metabolism. This finding indicates that multiple diversified weapons have been evolved by bacteria for their growth and robustness.

A Comparative Study of Marginalized Graph Kernel and Message-Passing Neural Network.

This work proposes a state-of-the-art hybrid kernel to calculate molecular similarity. Combined with Gaussian process models, the performance of the hybrid kernel in predicting molecular properties is comparable to that of the directed message-passing neural network (D-MPNN). The hybrid kernel consists of a marginalized graph kernel (MGK) and a radial basis function (RBF) kernel that operate on molecular graphs and global molecular features, respectively. Bayesian optimization was used to obtain the optimal hyperparameters for both models. The comparisons are performed on 11 publicly available data sets. Our results show that their performances are similar, their prediction errors are correlated, and the ensemble predictions of the two models perform better than either of them. Through principal component analysis, we found that the molecular embeddings of the hybrid kernel and the D-MPNN are also similar. The advantage of D-MPNN lies in the computational efficiency and scalability of large-scale data, while the advantage of the graph kernel models lies in the accurate uncertainty quantification.

Graphical Gaussian process regression model for aqueous solvation free energy prediction of organic molecules in redox flow batteries.

The solvation free energy of organic molecules is a critical parameter in determining emergent properties such as solubility, liquid-phase equilibrium constants, pKa and redox potentials in an organic redox flow battery. In this work, we present a machine learning (ML) model that can learn and predict the aqueous solvation free energy of an organic molecule using the Gaussian process regression method based on a new molecular graph kernel. To investigate the performance of the ML model for electrostatic interaction, the nonpolar interaction contribution of the solvent and the conformational entropy of the solute in the solvation free energy, three data sets with implicit or explicit water solvent models, and contribution of the conformational entropy of the solute are tested. We demonstrate that our ML model can predict the solvation free energy of molecules at chemical accuracy with a mean absolute error of less than 1 kcal mol-1 for subsets of the QM9 dataset and the Freesolv database. To solve the general data scarcity problem for a graph-based ML model, we propose a dimension reduction algorithm based on the distance between molecular graphs, which can be used to examine the diversity of the molecular data set. It provides a promising way to build a minimum training set to improve prediction for certain test sets where the space of molecular structures is predetermined.

Predicting Single-Substance Phase Diagrams: A Kernel Approach on Graph Representations of Molecules.

This work presents a Gaussian process regression (GPR) model on top of a novel graph representation of chemical molecules that predicts thermodynamic properties of pure substances in single, double, and triple phases. A transferable molecular graph representation is proposed as the input for a marginalized graph kernel, which is the major component of the covariance function in our GPR models. Radial basis function kernels of temperature and pressure are also incorporated into the covariance function when necessary. We predicted three types of representative properties of pure substances in single, double, and triple phases, i.e., critical temperature, vapor-liquid equilibrium (VLE) density, and pressure-temperature density. The accuracy of the models is nearly identical to the precision of the experimental measurements. Moreover, the reliability of our predictions can be quantified on a per-sample basis using the posterior uncertainty of the GPR model. We compare our model against Morgan fingerprints and a graph neural network to further demonstrate the advantage of the proposed method.

Prediction of atomization energy using graph kernel and active learning.

Data-driven prediction of molecular properties presents unique challenges to the design of machine learning methods concerning data structure/dimensionality, symmetry adaption, and confidence management. In this paper, we present a kernel-based pipeline that can learn and predict the atomization energy of molecules with high accuracy. The framework employs Gaussian process regression to perform predictions based on the similarity between molecules, which is computed using the marginalized graph kernel. To apply the marginalized graph kernel, a spatial adjacency rule is first employed to convert molecules into graphs whose vertices and edges are labeled by elements and interatomic distances, respectively. We then derive formulas for the efficient evaluation of the kernel. Specific functional components for the marginalized graph kernel are proposed, while the effects of the associated hyperparameters on accuracy and predictive confidence are examined. We show that the graph kernel is particularly suitable for predicting extensive properties because its convolutional structure coincides with that of the covariance formula between sums of random variables. Using an active learning procedure, we demonstrate that the proposed method can achieve a mean absolute error of 0.62 ± 0.01 kcal/mol using as few as 2000 training samples on the QM7 dataset.

An atomistic fingerprint algorithm for learning ab initio molecular force fields.

Molecular fingerprints, i.e., feature vectors describing atomistic neighborhood configurations, is an important abstraction and a key ingredient for data-driven modeling of potential energy surface and interatomic force. In this paper, we present the density-encoded canonically aligned fingerprint algorithm, which is robust and efficient, for fitting per-atom scalar and vector quantities. The fingerprint is essentially a continuous density field formed through the superimposition of smoothing kernels centered on the atoms. Rotational invariance of the fingerprint is achieved by aligning, for each fingerprint instance, the neighboring atoms onto a local canonical coordinate frame computed from a kernel minisum optimization procedure. We show that this approach is superior over principal components analysis-based methods especially when the atomistic neighborhood is sparse and/or contains symmetry. We propose that the "distance" between the density fields be measured using a volume integral of their pointwise difference. This can be efficiently computed using optimal quadrature rules, which only require discrete sampling at a small number of grid points. We also experiment on the choice of weight functions for constructing the density fields and characterize their performance for fitting interatomic potentials. The applicability of the fingerprint is demonstrated through a set of benchmark problems.

OpenRBC: A Fast Simulator of Red Blood Cells at Protein Resolution.

We present OpenRBC, a coarse-grained molecular dynamics code, which is capable of performing an unprecedented in silico experiment-simulating an entire mammal red blood cell lipid bilayer and cytoskeleton as modeled by multiple millions of mesoscopic particles-using a single shared memory commodity workstation. To achieve this, we invented an adaptive spatial-searching algorithm to accelerate the computation of short-range pairwise interactions in an extremely sparse three-dimensional space. The algorithm is based on a Voronoi partitioning of the point cloud of coarse-grained particles, and is continuously updated over the course of the simulation. The algorithm enables the construction of the key spatial searching data structure in our code, i.e., a lattice-free cell list, with a time and space cost linearly proportional to the number of particles in the system. The position and the shape of the cells also adapt automatically to the local density and curvature. The code implements OpenMP parallelization and scales to hundreds of hardware threads. It outperforms a legacy simulator by almost an order of magnitude in time-to-solution and >40 times in problem size, thus providing, to our knowledge, a new platform for probing the biomechanics of red blood cells.

GPU-accelerated Red Blood Cells Simulations with Transport Dissipative Particle Dynamics.

Mesoscopic numerical simulations provide a unique approach for the quantification of the chemical influences on red blood cell functionalities. The transport Dissipative Particles Dynamics (tDPD) method can lead to such effective multiscale simulations due to its ability to simultaneously capture mesoscopic advection, diffusion, and reaction. In this paper, we present a GPU-accelerated red blood cell simulation package based on a tDPD adaptation of our red blood cell model, which can correctly recover the cell membrane viscosity, elasticity, bending stiffness, and cross-membrane chemical transport. The package essentially processes all computational workloads in parallel by GPU, and it incorporates multi-stream scheduling and non-blocking MPI communications to improve inter-node scalability. Our code is validated for accuracy and compared against the CPU counterpart for speed. Strong scaling and weak scaling are also presented to characterizes scalability. We observe a speedup of 10.1 on one GPU over all 16 cores within a single node, and a weak scaling efficiency of 91% across 256 nodes. The program enables quick-turnaround and high-throughput numerical simulations for investigating chemical-driven red blood cell phenomena and disorders.

Identifying micro-inversions using high-throughput sequencing reads.

BACKGROUND: The identification of inversions of DNA segments shorter than read length (e.g., 100 bp), defined as micro-inversions (MIs), remains challenging for next-generation sequencing reads. It is acknowledged that MIs are important genomic variation and may play roles in causing genetic disease. However, current alignment methods are generally insensitive to detect MIs. Here we develop a novel tool, MID (Micro-Inversion Detector), to identify MIs in human genomes using next-generation sequencing reads. RESULTS: The algorithm of MID is designed based on a dynamic programming path-finding approach. What makes MID different from other variant detection tools is that MID can handle small MIs and multiple breakpoints within an unmapped read. Moreover, MID improves reliability in low coverage data by integrating multiple samples. Our evaluation demonstrated that MID outperforms Gustaf, which can currently detect inversions from 30 bp to 500 bp. CONCLUSIONS: To our knowledge, MID is the first method that can efficiently and reliably identify MIs from unmapped short next-generation sequencing reads. MID is reliable on low coverage data, which is suitable for large-scale projects such as the 1000 Genomes Project (1KGP). MID identified previously unknown MIs from the 1KGP that overlap with genes and regulatory elements in the human genome. We also identified MIs in cancer cell lines from Cancer Cell Line Encyclopedia (CCLE). Therefore our tool is expected to be useful to improve the study of MIs as a type of genetic variant in the human genome. The source code can be downloaded from: http://cqb.pku.edu.cn/ZhuLab/MID .

Comprehensive analysis of CYBB as a prognostic marker and therapeutic target in glioma: A bioinformatics approach.

Background: In the central nervous system, glioma is the most common malignant tumor, and patients have a poor prognosis. Identification of novel marker genes and establishment of prognostic models are important for early diagnosis and prognosis determination. Methods: Download glioma data from the CGGA and TCG databases. Application of bioinformatics to analyze the impact of CYBB on the clinicopathological characteristics, immunological features and prognosis of gliomas. Using single-cell sequencing data from 7 glioblastoma patients in the CGGA database, the role of CYBB in the tumor microenvironment was analyzed. In addition, a prognostic model was constructed based on CYBB high and low differentially expressed genes and mitochondrial genes. Results: The expression of CYBB is closely related to various clinical features, immune cell infiltration level, immune checkpoint and survival time of patients. A 10-gene prediction model was constructed based on the differentially expressed genes of low and high CYBB and mitochondria-related genes. Glioma patients with higher risk scores had significantly lower survival probabilities. Receiver operating characteristic curves and nomograms were plotted over time to show the predictive accuracy and predictive value of the 10-gene prognostic model. Conclusions: Our study shows that CYBB is strongly correlated with clinical characteristics features and prognosis of glioma patients, and can be used as a potential therapeutic target. Prognostic models based on CYBB and mitochondrial genes have good performance in predicting prognosis of glioma patients.

Glucose regulates the HMGB1 signaling pathway through SIRT1 in glioma.

BACKGROUND: Glucose is a fundamental substance for numerous cancers, including glioma. However, its influence on tumor cells regulatory mechanisms remains uncertain. SIRT1 is a regulator of deacetylation and a key player in the progression of malignant tumors. The objective of this study was to examine the role of glucose and SIRT1 in glioma. METHODS: This study investigated the association of SIRT1 expression with clinicopathological features and prognosis in glioma patients using the TCGA database. The Western blotting technique was used to identify the expression of SIRT1 protein in glioma cells. The study also examined the impact of differing glucose concentrations on the biological functions of glioma cells. The study investigated the expression of SIRT1 and HMGB1 signaling pathways in glioma. Additionally, resilience experiments were conducted utilizing SRT1720. RESULTS: SIRT1 is a gene that suppresses tumors and is low expressed in gliomas. Low expression of this gene is strongly linked to a poor prognosis in patients with glioma. High concentrations of glucose can promote the proliferation, migration, and invasion of glioma cells, while also inhibiting apoptosis. The findings of this mechanistic study provide evidence that glucose can down-regulate SIRT1 expression, leading to increased levels of acetylated HMGB1. This in turn promotes the ex-nuclear activation of HMGB1 and associated signaling pathways, ultimately driving glioma malignancy. CONCLUSION: Glucose has the ability to regulate the HMGB1 associated signaling pathway through SIRT1, thus promoting glioma progression. This holds significant research value.