Rattan Pepper Polysaccharide Regulates DSS-Induced Intestinal Inflammation and Depressive Behavior through Microbiota-Gut-Brain Axis.

Inflammatory bowel disease (IBD) is a chronic and recurrent disease. Increasing evidence suggests a higher incidence of depression in IBD patients compared with the general population, but the underlying mechanism remains uncertain. Rattan pepper polysaccharide (RPP) is an important active ingredient of rattan pepper, yet its effects and mechanisms on intestinal inflammation and depression-like behavior remain largely unknown. This study aims to investigate the ameliorating effect of RPP on dextran sulfate sodium salt (DSS)-induced intestinal inflammation and depression-like behavior as well as to reveal its mechanism. Our results indicate that RPP effectively ameliorated intestinal microbiota imbalance and metabolic disorders of short-chain fatty acids (SCFAs) and bile acids in mice with DSS-induced inflammation, contributing to the recovery of intestinal Th17/Treg homeostasis. Importantly, RPP effectively alleviated brain inflammation caused by intestinal inflammatory factors entering the brain through the blood-brain barrier. This effect may be attributed to the inhibition of the TLR4/NF-κB signaling pathway, which alleviates neuroinflammation, and the activation of the CREB/BDNF signaling pathway, which improves synaptic dysfunction. Therefore, our findings suggest that RPP may play a role in alleviating DSS-induced gut inflammation and depression-like behavior through the microbiota-gut-brain axis.

A unified autonomous Europe? Public opinion of the EU's foreign and security policy.

The Russian invasion of Ukraine has pushed the EU into a new phase where both the political elites and the public start to rethink its foreign and security policies. This paper uses a unique survey in seven European countries in the wake of the war to examine European public opinion on how the EU's foreign and security policies should be made and how autonomous they should be. We find that Europeans not only favour increasing military capacity at the national or NATO level but also prefer increasing military capacity at the EU level, though to a lesser degree. We also show that perceptions of both short-term and long-term threats, European identification and mainstream left-wing ideology lead Europeans to favour a more militarily powerful, unified and autonomous EU.

ACSM3 suppresses the pathogenesis of high-grade serous ovarian carcinoma via promoting AMPK activity.

PURPOSE: Ovarian carcinoma is the fifth commonest malignancy in females and exhibits a high recurrence rate. High-grade serous ovarian carcinoma (HGSOC) is the main histologic subtype. It displays extensive genetic heterogeneity. Here, we aimed to identify potential therapeutic targets for HGSOC. METHODS: Both bioinformatic data from TCGA and 73 pairs of tumor and normal samples from patients were analyzed to reveal the expression level of ACSM3 in HGSOC. Next, cellular and animal experiments, including cell proliferation, colony formation and xenograft assays were performed to explore the suppressive function of ACSM3. Finally, biochemical methods, AMP/ATP ratio measurements and Western blotting were used to elucidate the mechanism underlying the ACSM3-AMPK axis in HGSOC. RESULTS: After analyzing transcriptome data of TCGA HGSOC samples, we found that ACSM3 is down-regulated in patient samples compared with normal controls. This observation was validated using data from primary clinical samples. Proliferation, soft agar colony formation and xenograft assays revealed that ACSM3 is able to suppress HGSOC tumor growth both in vitro and in vivo. Moreover, we found that ACSM3 overexpression increased the AMP/ATP ratio and the phosphorylation level of AMPK at threonine 172. In addition, we found that AMPK silencing in EFO21 and SKOV3 cells completely abolished the anti-oncogenic effect of ACSM3. CONCLUSION: Our data indicate that the ACSM3-AMPK axis is involved in the pathogenesis of HGSOC and, as such, may act as a therapeutic target for this cancer.

Cascading epigenomic analysis for identifying disease genes from the regulatory landscape of GWAS variants.

The majority of genetic variants detected in genome wide association studies (GWAS) exert their effects on phenotypes through gene regulation. Motivated by this observation, we propose a multi-omic integration method that models the cascading effects of genetic variants from epigenome to transcriptome and eventually to the phenome in identifying target genes influenced by risk alleles. This cascading epigenomic analysis for GWAS, which we refer to as CEWAS, comprises two types of models: one for linking cis genetic effects to epigenomic variation and another for linking cis epigenomic variation to gene expression. Applying these models in cascade to GWAS summary statistics generates gene level statistics that reflect genetically-driven epigenomic effects. We show on sixteen brain-related GWAS that CEWAS provides higher gene detection rate than related methods, and finds disease relevant genes and gene sets that point toward less explored biological processes. CEWAS thus presents a novel means for exploring the regulatory landscape of GWAS variants in uncovering disease mechanisms.

Polyamide/Chitosan/Tetraethyl Orthosilicate Electrospun Nanofibers for a Novel and Promising Drug Carrier.

Chitosan (CS), the only alkaline polysaccharose available in nature, has always been a promising candidate for drug delivery owing to its excellent biodegradability and biocompatibility. However, inherent solubility and polycationic properties of CS largely hinder electrospinning, which is an efficient method of fabricating nanofibers for drug carriers. To solve this problem and extend the applications of CS, polyamide/chitosan/tetraethyl orthosilicate (PA/CS/TEOS) composite nanofibers were successfully prepared as drug carriers in this study via electrospinning. The PA/CS/TEOS ratios significantly influenced the nanofiber morphology. As the content of each was increased, the beads among the membranes increased initially and then decreased, determined by scanning electron microscopy (SEM). The morphology of the optimum membranes with the ratio of 1:0.13:0.67 was smoother with less beads and uniform fiber diameter. Finally, the membranes with optimum ratios were used as carriers of ofloxacin in the study of drug release performance to identify their future feasibility, which revealed an initial fast release followed by a relatively stable release.

Deep learning of immune cell differentiation.

Although we know many sequence-specific transcription factors (TFs), how the DNA sequence of cis-regulatory elements is decoded and orchestrated on the genome scale to determine immune cell differentiation is beyond our grasp. Leveraging a granular atlas of chromatin accessibility across 81 immune cell types, we asked if a convolutional neural network (CNN) could learn to infer cell type-specific chromatin accessibility solely from regulatory DNA sequences. With a tailored architecture and an ensemble approach to CNN parameter interpretation, we show that our trained network ("AI-TAC") does so by rediscovering ab initio the binding motifs for known regulators and some unknown ones. Motifs whose importance is learned virtually as functionally important overlap strikingly well with positions determined by chromatin immunoprecipitation for several TFs. AI-TAC establishes a hierarchy of TFs and their interactions that drives lineage specification and also identifies stage-specific interactions, like Pax5/Ebf1 vs. Pax5/Prdm1, or the role of different NF-κB dimers in different cell types. AI-TAC assigns Spi1/Cebp and Pax5/Ebf1 as the drivers necessary for myeloid and B lineage fates, respectively, but no factors seemed as dominantly required for T cell differentiation, which may represent a fall-back pathway. Mouse-trained AI-TAC can parse human DNA, revealing a strikingly similar ranking of influential TFs and providing additional support that AI-TAC is a generalizable regulatory sequence decoder. Thus, deep learning can reveal the regulatory syntax predictive of the full differentiative complexity of the immune system.

Multimodal Brain Parcellation Based on Functional and Anatomical Connectivity.

Brain parcellation is often a prerequisite for network analysis due to the statistical challenges, computational burdens, and interpretation difficulties arising from the high dimensionality of neuroimaging data. Predominant approaches are largely unimodal with functional magnetic resonance imaging (fMRI) being the primary modality used. These approaches thus neglect other brain attributes that relate to brain organization. In this paper, we propose an approach for integrating fMRI and diffusion MRI (dMRI) data. Our approach introduces a nonlinear mapping between the connectivity values of two modalities, and adaptively balances their weighting based on their voxel-wise test-retest reliability. An efficient region level extension that additionally incorporates structural information on gyri and sulci is further presented. To validate, we compare multimodal parcellations with unimodal parcellations and existing atlases on the Human Connectome Project data. We show that multimodal parcellations achieve higher reproducibility, comparable/higher functional homogeneity, and comparable/higher leftout data likelihood. The boundaries of multimodal parcels are observed to align to those based on cyto-architecture, and subnetworks extracted from multimodal parcels matched well with established brain systems. Our results thus show that multimodal information improves brain parcellation.