Sphingosine kinase 1 regulates HMGB1 translocation by directly interacting with calcium/calmodulin protein kinase II-δ in sepsis-associated liver injury

Previously, we confirmed that sphingosine kinase 1 (SphK1) inhibition improves sepsis-associated liver injury. High-mobility group box 1 (HMGB1) translocation participates in the development of acute liver failure. However, little information is available on the association between SphK1 and HMGB1 translocation during sepsis-associated liver injury. In the present study, we aimed to explore the effect of SphK1 inhibition on HMGB1 translocation and the underlying mechanism during sepsis-associated liver injury. Primary Kupffer cells and hepatocytes were isolated from SD rats. The rat model of sepsis-associated liver damage was induced by intraperitoneal injection with lipopolysaccharide (LPS). We confirmed that Kupffer cells were the cells primarily secreting HMGB1 in the liver after LPS stimulation. LPS-mediated HMGB1 expression, intracellular translocation, and acetylation were dramatically decreased by SphK1 inhibition. Nuclear histone deacetyltransferase 4 (HDAC4) translocation and E1A-associated protein p300 (p300) expression regulating the acetylation of HMGB1 were also suppressed by SphK1 inhibition. HDAC4 intracellular translocation has been reported to be controlled by the phosphorylation of HDAC4. The phosphorylation of HDAC4 is modulated by CaMKII-δ. However, these changes were completely blocked by SphK1 inhibition. Additionally, by performing coimmunoprecipitation and pull-down assays, we revealed that SphK1 can directly interact with CaMKII-δ. The colocalization of SphK1 and CaMKII-δ was verified in human liver tissues with sepsis-associated liver injury. In conclusion, SphK1 inhibition diminishes HMGB1 intracellular translocation in sepsis-associated liver injury. The mechanism is associated with the direct interaction of SphK1 and CaMKII-δ.Subject terms: Hepatotoxicity, Sepsis     

Single-cell landscape of the ecosystem in early-relapse hepatocellular carcinoma

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Diversity of endophytic bacteria and fungi in seeds of Elymus nutans growing in four locations of Qinghai Tibet Plateau,China

Plant and Soil - Seeds are involved in the transmission of microorganisms from one plant generation to the next, acting as initial inoculum for the plant microbiome, therefore provide a key source...     

A study on giant panda recognition based on images of a large proportion of captive pandas

1. As a highly endangered species, the giant panda (panda) has attracted significant attention in the past decades. Considerable efforts have been put on panda conservation and reproduction, offering the promising outcome of maintaining the population size of pandas. To evaluate the effectiveness of conservation and management strategies, recognizing individual pandas is critical. However, it remains a challenging task because the existing methods, such as traditional tracking method, discrimination method based on footprint identification, and molecular biology method, are invasive, inaccurate, expensive, or challenging to perform. The advances of imaging technologies have led to the wide applications of digital images and videos in panda conservation and management, which makes it possible for individual panda recognition in a noninvasive manner by using image‐based panda face recognition method.
2. In recent years, deep learning has achieved great success in the field of computer vision and pattern recognition. For panda face recognition, a fully automatic deep learning algorithm which consists of a sequence of deep neural networks (DNNs) used for panda face detection, segmentation, alignment, and identity prediction is developed in this study. To develop and evaluate the algorithm, the largest panda image dataset containing 6,441 images from 218 different pandas, which is 39.78% of captive pandas in the world, is established.
3. The algorithm achieved 96.27% accuracy in panda recognition and 100% accuracy in detection.
4. This study shows that panda faces can be used for panda recognition. It enables the use of the cameras installed in their habitat for monitoring their population and behavior. This noninvasive approach is much more cost‐effective than the approaches used in the previous panda surveys.

     

Evidence for Inbreeding and Genetic Differentiation among Geographic Populations of the Saprophytic Mushroom Trogia venenata from Southwestern China

During the past 40 years, more than 400 Sudden Unexplained Deaths (SUDs) have occurred in Yunnan, southwestern China. Epidemiological and toxicological analyses suggested that a newly discovered mushroom called Trogia venenata was the leading culprit for SUDs. At present, relatively little is known about the genetics and natural history of this mushroom. In this study, we analyzed the sequence variation at four DNA fragments among 232 fruiting bodies of T. venenata collected from seven locations. Our ITS sequence analyses confirmed that all the isolates belonged to the same species. The widespread presence of sequence heterozygosity within many strains at each of three protein-coding genes suggested that the fruiting bodies were diploid, dikaryotic or heterokaryotic. Within individual geographic populations, we found significant deviations of genotype frequencies from Hardy-Weinberg expectations, with the overall observed heterozygosity lower than that expected under random mating, consistent with prevalent inbreeding within local populations. The geographic populations were overall genetically differentiated. Interestingly, while a positive correlation was found between population genetic distance and geographic distance, there was little correlation between genetic distance and barium concentration difference for the geographic populations. Our results suggest frequent inbreeding, geographic structuring, and limited gene flow among geographic populations of T. venenata from southwestern China.     

A neural network study on the dynamic identification of a fermentation system

The objective of this paper is to propose neural networks for the study of dynamic identification and prediction of a fermentation system which produces mainly 2,3-butanediol (2,3-BDL). The metabolic products of the fermentation, acetic acid, acetoin, ethanol, and 2,3-BDL were measured on-line via a mass spectrometer modified by the insertion of a dimethylvinylsilicone membrane probe. The measured data at different sampling times were included as the input and output nodes, at different learning batches, of the network. A fermentation system is usually nonlinear and dynamic in nature. Measured fermentation data obtained from the complex metabolic pathways are often difficult to be entirely included in a static process model, therefore, a dynamic model was suggested instead. In this work, neural networks were provided by a dynamic learning and prediction process that moved along the time sequence batchwise. In other words, a scheme of two-dimensional moving window (number of input nodes by the number of training data) was proposed for reading in new data while forgetting part of the old data. Proper size of the network including proper number of input/output nodes were determined by trained with the real-time fermentation data. Different number of hidden nodes under the consideration of both learning performance and computation efficiency were tested. The data size for each learning batch was determined. The performance of the learning factors such as the learning coefficient η and the momentum term coefficient α were also discussed. The effect of different dynamic learning intervals, with different starting points and the same ending point, both on the learning and prediction performance were studied. On the other hand, the effect of different dynamic learning intervals, with the same starting point and different ending points, was also investigated. The size of data sampling interval was also discussed. The performance from four different types of transfer functions, x/(1+|x|), sgn(x)·x ²/(1+x ²), 2/(1+e ^{? x} )?1, and 1/(1+e ^{? x} ) was compared. A scaling factor b was added to the transfer function and the effect of this factor on the learning was also evaluated. The prediction results from the time-delayed neural networks were also studied.     

Functional analysis of a miRNA‐like small RNA derived from Bombyx mori cytoplasmic polyhedrosis virus

Bombyx mori cytoplasmic polyhedrosis virus (BmCPV) is a major pathogen of the economic insect silkworm, Bombyx mori. Virus‐encoded microRNAs (miRNAs) have been proven to play important roles in host–pathogen interactions. In this study we identified a BmCPV‐derived miRNA‐like 21 nt small RNA, BmCPV‐miR‐1, from the small RNA deep sequencing of BmCPV‐infected silkworm larvae by stem‐loop quantitative real‐time PCR (qPCR) and investigated its functions with qPCR and lentiviral expression systems. Bombyx mori inhibitor of apoptosis protein (BmIAP) gene was predicted by both target prediction software miRanda and Targetscan to be one of its target genes with a binding site for BmCPV‐miR‐1 at the 5′ untranslated region. It was found that the expression of BmCPV‐miR‐1 and its target gene BmIAP were both up‐regulated in BmCPV‐infected larvae. At the same time, it was confirmed that BmCPV‐miR‐1 could up‐regulate the expression of BmIAP gene in HEK293T cells with lentiviral expression systems and in BmN cells by transfecting mimics. Furthermore, BmCPV‐miR‐1 mimics could up‐regulate the expression level of BmIAP gene in midgut and fat body in the silkworm. In the midgut of BmCPV‐infected larvae, BmCPV‐miR‐1 mimics could be further up‐regulated and inhibitors could lower the virus‐mediated expression of BmIAP gene. With the viral genomic RNA segments S1 and S10 as indicators, BmCPV‐miR‐1 mimics could up‐regulate and inhibitors down‐regulate their replication in the infected silkworm. These results implied that BmCPV‐miR‐1 could inhibit cell apoptosis in the infected silkworm through up‐regulating BmIAP expression, providing the virus with a better cell circumstance for its replication.     

Effects of SOX2 on Proliferation,Migration and Adhesion of Human Dental Pulp Stem Cells

As a key factor for cell pluripotent and self-renewing phenotypes, SOX2 has attracted scientists’ attention gradually in recent years. However, its exact effects in dental pulp stem cells (DPSCs) are still unclear. In this study, we mainly investigated whether SOX2 could affect some biological functions of DPSCs. DPSCs were isolated from the dental pulp of human impacted third molar. SOX2 overexpressing DPSCs (DPSCs-SOX2) were established through retroviral infection. The effect of SOX2 on cell proliferation, migration and adhesion ability was evaluated with CCK-8, trans-well system and fibronectin-induced cell attachment experiment respectively. Whole genome expression of DPSCs-SOX2 was analyzed with RNA microarray. Furthermore, a rescue experiment was performed with SOX2-siRNA in DPSC-SOX2 to confirm the effect of SOX2 overexpression in DPSCs. We found that SOX2 overexpression could result in the enhancement of cell proliferation, migration, and adhesion in DPSCs obviously. RNA microarray analysis indicated that some key genes in the signal pathways associated with cell cycle, migration and adhesion were upregulated in different degree, and the results were further confirmed with qPCR and western-blot. Finally, DPSC-SOX2 transfected with SOX2-siRNA showed a decrease of cell proliferation, migration and adhesion ability, which further confirmed the biological effect of SOX2 in human DPSCs. This study indicated that SOX2 could improve the cell proliferation, migration and adhesion ability of DPSCs through regulating gene expression about cell cycle, migration and adhesion, and provided a novel strategy to develop seed cells with strong proliferation, migration and adhesion ability for tissue engineering.