Comparative transcriptomics and proteomics analysis of the symbiotic germination of Paphiopedilum barbigerum with Epulorhiza sp. FQXY019.

Introduction: Paphiopedilum barbigerum is currently the rarest and most endangered species of orchids in China and has significant ornamental value. The mature seeds of P. barbigerum are difficult to germinate owing to the absence of an endosperm and are highly dependent on mycorrhizal fungi for germination and subsequent development. However, little is known about the regulation mechanisms of symbiosis and symbiotic germination of P. barbigerum seeds. Methods: Herein, transcriptomics and proteomics were used to explore the changes in the P. barbigerum seeds after inoculation with (FQXY019 treatment group) or without (control group) Epulorhiza sp. FQXY019 at 90 days after germination. Results: Transcriptome sequencing revealed that a total of 10,961 differentially expressed genes (DEGs; 2,599 upregulated and 8,402 downregulated) were identified in the control and FQXY019 treatment groups. These DEGs were mainly involved in carbohydrate, fatty acid, and amino acid metabolism. Furthermore, the expression levels of candidate DEGs related to nodulin, Ca2+ signaling, and plant lectins were significantly affected in P. barbigerum in the FQXY019 treatment groups. Subsequently, tandem mass tag-based quantitative proteomics was performed to recognize the differentially expressed proteins (DEPs), and a total of 537 DEPs (220 upregulated and 317 downregulated) were identified that were enriched in processes including photosynthesis, photosynthesis-antenna proteins, and fatty acid biosynthesis and metabolism. Discussion: This study provides novel insight on the mechanisms underlying the in vitro seed germination and protocorm development of P. barbigerum by using a compatible fungal symbiont and will benefit the reintroduction and mycorrhizal symbiotic germination of endangered orchids.

Long-term outcomes of 1-2 cm rectal neuroendocrine tumors after local excision or radical resection: A population-based multicenter study.

Objectives: Studies on rectal neuroendocrine tumors (R-NETs) that are 1-2 cm in size are limited, and the optimal treatment for these tumors is not well established. Methods: Data from patients with primary localized R-NETs 1-2 cm in size were retrospectively collected from 17 large-scale referral medical centers in China. Long-term prognosis, quality of life (QOL), and fecal incontinence were evaluated, and the effects of local excision (LE) or radical resection (RR) were elucidated using propensity score matching (PSM). Results: A total of 272 patients were included in this study; 233 underwent LE, and the remaining 39 underwent RR. Patients in the LE group showed lower tumor location, fewer postoperative Clavien-Dindo III-V complications, more G1 tumors, and lower tumor stage. There were no significant differences in the relapse-free survival or overall survival (OS) between the LE and RR groups after PSM. Patients in the LE group reported superior physical, role, emotional, social, and cognitive functions, global QOL, and Wexner fecal incontinence scores compared with those in the RR group (all P < 0.050). Eighteen (6.6%) patients had lymph node metastases. Multivariable analysis revealed that tumor location (odds ratio [OR] = 3.19, 95% confidence interval [CI] 1.04-10.07, P = 0.010), neutrophil-to-lymphocyte ratio (NLR) > 1.80 (OR = 4.50, 1.46-15.89, P = 0.012), and T3-T4 (OR = 36.31, 95% CI 7.85-208.62, P < 0.001) were independent risk factor for lymph node metastasis. Conclusions: R-NETs measuring 1-2 cm generally have a favorable prognosis, and there is no difference in postoperative survival between LE and RR. For patients without lymph node metastasis, LE should be the preferred choice; however, for patients with a higher tumor location, preoperative NLR >1.8 or T3/T4 tumors, RR should be considered.

A modified pancreaticojejunostomy anastomotic technique using double U-sutures for laparoscopic pancreaticoduodenectomy.

Although recent advances in laparoscopic technology have popularized laparoscopic pancreatoduodenectomy (LPD), laparoscopic pancreaticojejunostomy anastomosis (PJA) still presents a major technical challenge. From February 2021 to January 2023, 42 patients underwent LPD with modified double U-suture PJA. Data on the demographic characteristics and clinical results of these patients were investigated. The median operation time was 316 min (249-596 min). The median PJA time was 32 min (25-40 min). The median intraoperative blood loss was 150 mL (50-500 mL). The median postoperative stay was 12 days (7-30 days). Complications occurred in 10 (23.8%) patients, including two cases (4.8%) of delayed gastric emptying and nine cases (21.4%) of postoperative pancreatic fistula (POPF). One patient presented delayed gastric emptying and POPF. Eight patients (19.0%) experienced biochemical leakage, and one patient (2.4%) had grade B POPF. Laparoscopic double U-suture PJA is a feasible and safe technique for performing LPD.

A combined pre- and intra-operative nomogram in evaluation of degrees of liver cirrhosis predicts post-hepatectomy liver failure: a multicenter prospective study.

Background: Adequate evaluation of degrees of liver cirrhosis is essential in surgical treatment of hepatocellular carcinoma (HCC) patients. The impact of the degrees of cirrhosis on prediction of post-hepatectomy liver failure (PHLF) remains poorly defined. This study aimed to construct and validate a combined pre- and intra-operative nomogram based on the degrees of cirrhosis in predicting PHLF in HCC patients using prospective multi-center's data. Methods: Consecutive HCC patients who underwent hepatectomy between May 18, 2019 and Dec 19, 2020 were enrolled at five tertiary hospitals. Preoperative cirrhotic severity scoring (CSS) and intra-operative direct liver stiffness measurement (DSM) were performed to correlate with the Laennec histopathological grading system. The performances of the pre-operative nomogram and combined pre- and intra-operative nomogram in predicting PHLF were compared with conventional predictive models of PHLF. Results: For 327 patients in this study, histopathological studies showed the rates of HCC patients with no, mild, moderate, and severe cirrhosis were 41.9%, 29.1%, 22.9%, and 6.1%, respectively. Either CSS or DSM was closely correlated with histopathological stages of cirrhosis. Thirty-three (10.1%) patients developed PHLF. The 30- and 90-day mortality rates were 0.9%. Multivariate regression analysis showed four pre-operative variables [HBV-DNA level, ICG-R15, prothrombin time (PT), and CSS], and one intra-operative variable (DSM) to be independent risk factors of PHLF. The pre-operative nomogram was constructed based on these four pre-operative variables together with total bilirubin. The combined pre- and intra-operative nomogram was constructed by adding the intra-operative DSM. The pre-operative nomogram was better than the conventional models in predicting PHLF. The prediction was further improved with the combined pre- and intra-operative nomogram. Conclusions: The combined pre- and intra-operative nomogram further improved prediction of PHLF when compared with the pre-operative nomogram. Trial Registration: Clinicaltrials.gov Identifier: NCT04076631.

CRISPR-Cas9-Induced Gene Editing in Primary Human Monocytes.

Monocytes play important and diverse roles in both homeostatic and inflammatory immune responses. The CRISPR-Cas9 system in lentiviral vectors has been widely used to manipulate specific genes of immortal monocyte cell lines to study monocyte functions. However, human primary monocytes are refractory to this method with low gene knockout (KO) efficiency. In this chapter, we developed an in vitro gene-editing procedure for primary human monocytes with a consistent and high-gene KO efficiency via a ribonucleoprotein (RNP) complex consisting of Cas9 protein and single-guide RNA (sgRNA). This method can be adapted to study the functions of targeted signaling molecules involved in modulating monocyte polarization in primary human monocytes.

Identification of keystone taxa in rhizosphere microbial communities using different methods and their effects on compounds of the host Cinnamomum migao.

Exploring keystone taxa affecting microbial community stability and host function is crucial for understanding ecosystem functions. However, identifying keystone taxa from humongous microbial communities remains challenging. We collected 344 rhizosphere and bulk soil samples from the endangered plant C. migao for 2 years consecutively. Used high-throughput sequencing 16S rDNA and ITS to obtain the composition of bacterial and fungal communities. We explored keystone taxa and the applicability and limitations of five methods (SPEC-OCCU, Zi-Pi, Subnetwork, Betweenness, and Module), as well as the impact of microbial community domain, time series, and rhizosphere boundary on the identification of keystone taxa in the communities. Our results showed that the five methods, identified abundant keystone taxa in rhizosphere and bulk soil microbial communities. However, the keystone taxa shared by the rhizosphere and bulk soil microbial communities over time decreased rapidly decrease in the five methods. Among five methods on the identification of keystone taxa in the rhizosphere community, Module identified 113 taxa, SPEC-OCCU identified 17 taxa, Betweenness identified 3 taxa, Subnetwork identified 3 taxa, and Zi-Pi identified 4 taxa. The keystone taxa are mainly conditionally rare taxa, and their ecological functions include chemoheterotrophy, aerobic chemoheterotrophy, nitrate reduction, and anaerobic photoautotrophy. The results of the random forest model and structural equation model predict that keystone taxa Mortierella and Ellin6513 may have an effects on the accumulation of 1, 4, 7, - Cycloundecatriene, 1, 5, 9, 9-tetramethyl-, Z, Z, Z-, beta-copaene, bicyclogermacrene, 1,8-Cineole in C. migao fruits, but their effects still need further evidence. Our study evidence an unstable microbial community in the bulk soil, and the definition of microbial boundary and ecologically functional affected the identification of keystone taxa in the community. Subnetwork and Module are more in line with the definition of keystone taxa in microbial ecosystems in terms of maintaining community stability and hosting function.

Local Excision Versus Radical Resection for Grade 2 Rectal Neuroendocrine Tumors: A Multicenter Propensity Score-Matched Analysis.

BACKGROUND: Studies on the grade 2 rectal neuroendocrine tumors are limited and the optimal treatment for these tumors is not well established. OBJECTIVE: To compare the oncologic results of local excision versus radical resection for the treatment of grade 2 rectal neuroendocrine tumors. DESIGN: Retrospective multicenter propensity score-matched study to minimize heterogeneity between groups and focus on the difference between surgery strategies. SETTINGS: Seventeen Chinese large-scale medical centers participated in this study. PATIENTS: A total of 144 patients with pathologically confirmed grade 2 rectal neuroendocrine tumors were retrospectively analyzed. MAIN OUTCOME MEASURES: Cancer-specific survival and relapse-free survival were assessed to compare surgery strategies. RESULTS: A total of 144 patients with grade 2 rectal neuroendocrine tumors were enrolled in this study. Twenty-seven patients underwent endoscopic resection, 55 underwent transanal excision, 50 underwent radical resection, and 12 underwent palliative surgery or biopsy for distant metastasis. Of the 50 patients who underwent radical resection, 30 (60.0%) had clinically positive lymph nodes based on the histopathology results. The optimal cutoff value for tumor size to predict cancer-specific survival was 1.5 cm. In patients with grade 2 rectal neuroendocrine tumors ≤ 1.5 cm, there were no significant differences in cancer-specific survival and relapse-free survival between local excision and radical resection groups (P >0.05). In patients with grade 2 rectal neuroendocrine tumors > 1.5 cm, relapse-free survival was significantly lower in the local excision group than in the radical resection group (P = 0.04). LIMITATIONS: The nature of retrospective review and relatively short follow-up period are limitations of this study. CONCLUSIONS: Grade 2 rectal neuroendocrine tumors have a nonnegligible rate of lymph node metastasis. Local excision is a feasible choice for tumors ≤ 1.5 cm without metastasis, while radical resection is more beneficial in those > 1.5 cm. See Video Abstract.

Deep Multiview Module Adaption Transfer Network for Subject-Specific EEG Recognition.

Transfer learning is one of the popular methods to solve the problem of insufficient data in subject-specific electroencephalogram (EEG) recognition tasks. However, most existing approaches ignore the difference between subjects and transfer the same feature representations from source domain to different target domains, resulting in poor transfer performance. To address this issue, we propose a novel subject-specific EEG recognition method named deep multiview module adaption transfer (DMV-MAT) network. First, we design a universal deep multiview (DMV) network to generate different types of discriminative features from multiple perspectives, which improves the generalization performance by extensive feature sets. Second, module adaption transfer (MAT) is designed to evaluate each module by the feature distributions of source and target samples, which can generate an optimal weight sharing strategy for each target subject and promote the model to learn domain-invariant and domain-specific features simultaneously. We conduct extensive experiments in two EEG recognition tasks, i.e., motor imagery (MI) and seizure prediction, on four datasets. Experimental results demonstrate that the proposed method achieves promising performance compared with the state-of-the-art methods, indicating a feasible solution for subject-specific EEG recognition tasks. Implementation codes are available at https://github.com/YangLibuaa/DMV-MAT.

Generation and optimization of off-the-shelf immunotherapeutics targeting TCR-Vß2+ T cell malignancy.

Current treatments for T cell malignancies encounter issues of disease relapse and off-target toxicity. Using T cell receptor (TCR)Vß2 as a model, here we demonstrate the rapid generation of an off-the-shelf allogeneic chimeric antigen receptor (CAR)-T platform targeting the clone-specific TCR Vß chain for malignant T cell killing while limiting normal cell destruction. Healthy donor T cells undergo CRISPR-induced TRAC, B2M and CIITA knockout to eliminate T cell-dependent graft-versus-host and host-versus-graft reactivity. Second generation 4-1BB/CD3zeta CAR containing high affinity humanized anti-Vß scFv is expressed efficiently on donor T cells via both lentivirus and adeno-associated virus transduction with limited detectable pre-existing immunoreactivity. Our optimized CAR-T cells demonstrate specific and persistent killing of Vß2+ Jurkat cells and Vß2+ patient derived malignant T cells, in vitro and in vivo, without affecting normal T cells. In parallel, we generate humanized anti-Vß2 antibody with enhanced antibody-dependent cellular cytotoxicity (ADCC) by Fc-engineering for NK cell ADCC therapy.

The CUL5 E3 ligase complex negatively regulates central signaling pathways in CD8+ T cells.

CD8+ T cells play an important role in anti-tumor immunity. Better understanding of their regulation could advance cancer immunotherapies. Here we identify, via stepwise CRISPR-based screening, that CUL5 is a negative regulator of the core signaling pathways of CD8+ T cells. Knocking out CUL5 in mouse CD8+ T cells significantly improves their tumor growth inhibiting ability, with significant proteomic alterations that broadly enhance TCR and cytokine signaling and their effector functions. Chemical inhibition of neddylation required by CUL5 activation, also enhances CD8 effector activities with CUL5 validated as a major target. Mechanistically, CUL5, which is upregulated by TCR stimulation, interacts with the SOCS-box-containing protein PCMTD2 and inhibits TCR and IL2 signaling. Additionally, CTLA4 is markedly upregulated by CUL5 knockout, and its inactivation further enhances the anti-tumor effect of CUL5 KO. These results together reveal a negative regulatory mechanism for CD8+ T cells and have strong translational implications in cancer immunotherapy.

Bioorthogonal Aptamer-ATTEC Conjugates for Degradation of Alpha-Synuclein via Autophagy-Lysosomal Pathway.

Autophagosome-tethering compound (ATTEC) technology has recently been emerging as a novel approach for degrading proteins of interest (POIs). However, it still faces great challenges in how to design target-specific ATTEC molecules. Aptamers are single-stranded DNA or RNA oligonucleotides that can recognize their target proteins with high specificity and affinity. Here, ATTEC is combined with aptamers for POIs degradation. As a proof of concept, pathological protein α-synuclein (α-syn) is chosen as the target and an efficient α-syn degrader is generated. Aptamer as a targeting warhead of α-syn is conjugated with LC3B-binding compound 5,7-dihydroxy-4-phenylcoumarin (DP) via bioorthogonal click reaction. It is demonstrated that the aptamer conjugated with DP is capable of clearing α-syn through LC3 and autophagic degradation. These results indicate that aptamer-based ATTECs are a versatile approach to degrade POIs by taking advantage of the well-defined different aptamers for targeting diverse proteins, which provides a new way for the design of ATTECs to degradation of targeted proteins.

Symbiotic seed germination and seedling growth of mycorrhizal fungi in Paphiopedilum hirsutissimun (Lindl.Ex Hook.) Stein from China.

Similar to other orchid species, Paphiopedilum hirsutissimum (Lindl.ex Hook.) Stein, relies on nutrients provided by mycorrhizal fungus for seed germination and seedling development in the wild owing to a lack of endosperm in its seeds. Therefore, obtaining suitable and specialized fungi to enhance seed germination, seedling formation, and further development is considered a powerful tool for orchid seedling propagation, reintroduction, and species conservation. In this study, we investigated the diversity, abundance, and frequency of endophytic fungal strains in the root organs of P. hirsutissimum. One family and five genera of the fungi were isolated and identified through rDNA-ITS sequencing. The ability of isolated fungi to germinate in vitro from the seeds of this species was evaluated, and the development of P. hirsutissimum protocorm has been described. The findings showed that the treatments inoculated with endophytic fungal DYXY033 may successfully support the advanced developmental stage of seedlings up to stage 5. In addition, scanning electron microscopy (SEM) revealed that the mycelium of this strain began to invade from either end of the seeds up to the embryo, extending rapidly from the inside to the outside. Its lengthening resulted in the bursting of the seed coat to form protocorms, which developed into seedlings. The results showed that DYXY033 has a high degree of mycobiont specificity under in vitro symbiotic seed germination conditions and is a representative mycorrhizal fungus with ecological value for the species. In summary, this strain may particularly be significant for the protection of P. hirsutissimum species that are endangered in China. In the long run, it may also contribute to global efforts in reintroducing orchid species and in realizing in situ restorations of threatened orchid populations.

Dynamic Multi-Graph Convolution-Based Channel-Weighted Transformer Feature Fusion Network for Epileptic Seizure Prediction.

Electroencephalogram (EEG) based seizure prediction plays an important role in the closed-loop neuromodulation system. However, most existing seizure prediction methods based on graph convolution network only focused on constructing the static graph, ignoring multi-domain dynamic changes in deep graph structure. Moreover, the existing feature fusion strategies generally concatenated coarse-grained epileptic EEG features directly, leading to the suboptimal seizure prediction performance. To address these issues, we propose a novel multi-branch dynamic multi-graph convolution based channel-weighted transformer feature fusion network (MB-dMGC-CWTFFNet) for the patient-specific seizure prediction with the superior performance. Specifically, a multi-branch (MB) feature extractor is first applied to capture the temporal, spatial and spectral representations fromthe epileptic EEG jointly. Then, we design a point-wise dynamic multi-graph convolution network (dMGCN) to dynamically learn deep graph structures, which can effectively extract high-level features from the multi-domain graph. Finally, by integrating the local and global channel-weighted strategies with the multi-head self-attention mechanism, a channel-weighted transformer feature fusion network (CWTFFNet) is adopted to efficiently fuse the multi-domain graph features. The proposed MB-dMGC-CWTFFNet is evaluated on the public CHB-MIT EEG and a private intracranial sEEG datasets, and the experimental results demonstrate that our proposed method achieves outstanding prediction performance compared with the state-of-the-art methods, indicating an effective tool for patient-specific seizure warning. Our code will be available at: https://github.com/Rockingsnow/MB-dMGC-CWTFFNet.

CLEP: Contrastive Learning for Epileptic Seizure Prediction Using a Spatio-Temporal-Spectral Network.

Seizure prediction of epileptic preictal period through electroencephalogram (EEG) signals is important for clinical epilepsy diagnosis. However, recent deep learning-based methods commonly employ intra-subject training strategy and need sufficient data, which are laborious and time-consuming for a practical system and pose a great challenge for seizure predicting. Besides, multi-domain characterizations, including spatio-temporal-spectral dependencies in an epileptic brain are generally neglected or not considered simultaneously in current approaches, and this insufficiency commonly leads to suboptimal seizure prediction performance. To tackle the above issues, in this paper, we propose Contrastive Learning for Epileptic seizure Prediction (CLEP) using a Spatio-Temporal-Spectral Network (STS-Net). Specifically, the CLEP learns intrinsic epileptic EEG patterns across subjects by contrastive learning. The STS-Net extracts multi-scale temporal and spectral representations under different rhythms from raw EEG signals. Then, a novel triple attention layer (TAL) is employed to construct inter-dimensional interaction among multi-domain features. Moreover, a spatio dynamic graph convolution network (sdGCN) is proposed to dynamically model the spatial relationships between electrodes and aggregate spatial information. The proposed CLEP-STS-Net achieves a sensitivity of 96.7% and a false prediction rate of 0.072/h on the CHB-MIT scalp EEG database. We also validate the proposed method on clinical intracranial EEG (iEEG) database from our Xuanwu Hospital of Capital Medical University, and the predicting system yielded a sensitivity of 95%, a false prediction rate of 0.087/h. The experimental results outperform the state-of-the-art studies which validate the efficacy of our method. Our code is available at https://github.com/LianghuiGuo/CLEP-STS-Net.

Limit Cycle of a Single-Neuron System With Smooth Continuous and Binary-Value Activation Functions and Its Circuitry Design.

In this brief, we investigate the limit cycle of a single-neuron system with smooth continuous and binary-value activation functions and its circuit design. By transforming the system into Liénard-type and using Poincaré-Bendixson theorem as well as the symmetry of these systems, we obtain the existence conditions of limit cycle of the system. Then, by comparing the integral value of the differential of positive definite function along two assumed limit cycles, we prove that the system cannot produce two coexisting limit cycles, which means that the system has at most one limit cycle. In addition, according to the two specific functions, i.e., smooth continuous and binary-value activation functions of the system, we give the numerical simulation and realize the circuit design of the single-neuron system by using Multisim modeling, respectively. The waveform diagram and phase diagram of the numerical simulation and circuit simulation are obtained. By comparing the results of numerical and circuit simulation, the effectiveness of our mathematical analysis and the feasibility of circuit design are better illustrated.

A Hybrid Recommender System Based on Autoencoder and Latent Feature Analysis.

A recommender system (RS) is highly efficient in extracting valuable information from a deluge of big data. The key issue of implementing an RS lies in uncovering users' latent preferences on different items. Latent Feature Analysis (LFA) and deep neural networks (DNNs) are two of the most popular and successful approaches to addressing this issue. However, both the LFA-based and the DNNs-based models have their own distinct advantages and disadvantages. Consequently, relying solely on either the LFA or DNN-based models cannot ensure optimal recommendation performance across diverse real-world application scenarios. To address this issue, this paper proposes a novel hybrid recommendation model that combines Autoencoder and LFA techniques, termed AutoLFA. The main idea of AutoLFA is two-fold: (1) It leverages an Autoencoder and an LFA model separately to construct two distinct recommendation models, each residing in a unique metric representation space with its own set of strengths; and (2) it integrates the Autoencoder and LFA model using a customized self-adaptive weighting strategy, thereby capitalizing on the merits of both approaches. To evaluate the proposed AutoLFA model, extensive experiments on five real recommendation datasets are conducted. The results demonstrate that AutoLFA achieves significantly better recommendation performance than the seven related state-of-the-art models.

[Retracted] MicroRNA­212 inhibits colorectal cancer cell viability and invasion by directly targeting PIK3R3.

Following the publication of this paper, it was drawn to the Editor's attention by a concerned reader that the western blotting data shown in Fig. 4A were strikingly similar to data appearing in different form in other articles by different authors at different research institutes, several of which have been retracted. Owing to the fact that the contentious data in the above article were already under consideration for publication prior to its submission to Molecular Medicine Reports, the Editor has decided that this paper should be retracted from the Journal. The authors were asked for an explanation to account for these concerns, but the Editorial Office did not receive a reply. The Editor apologizes to the readership for any inconvenience caused. [Molecular Medicine Reports 16: 7864­7872, 2017; DOI: 10.3892/mmr.2017.7552].

A Recurrent Neural Network Approach for Constrained Distributed Fuzzy Convex Optimization.

This article investigates a class of constrained distributed fuzzy convex optimization problems, where the objective function is the sum of a set of local fuzzy convex objective functions, and the constraints include partial order relation and closed convex set constraints. In undirected connected node communication network, each node only knows its own objective function and constraints, and the local objective function and partial order relation functions may be nonsmooth. To solve this problem, a recurrent neural network approach based on differential inclusion framework is proposed. The network model is constructed with the help of the idea of penalty function, and the estimation of penalty parameters in advance is eliminated. Through theoretical analysis, it is proven that the state solution of the network enters the feasible region in finite time and does not escape again, and finally reaches consensus at an optimal solution of the distributed fuzzy optimization problem. Furthermore, the stability and global convergence of the network do not depend on the selection of the initial state. A numerical example and an intelligent ship output power optimization problem are given to illustrate the feasibility and effectiveness of the proposed approach.

CX3CR1 modulates SLE-associated glomerulonephritis and cardiovascular disease in MRL/lpr mice.

OBJECTIVE: Patients with systemic lupus erythematosus (SLE) often develop multi-organ damages including heart and kidney complications. We sought to better define the underlying mechanisms with a focus on the chemokine receptor CX3CR1. METHODS: We generated Cx3cr1-deficient MRL/lpr lupus-prone mice through backcrossing. We then employed heterozygous intercross to generate MRL/lpr littermates that were either sufficient or deficient of CX3CR1. The mice were also treated with either Lactobacillus spp. or a high-fat diet (HFD) followed by assessments of the kidney and heart, respectively. RESULTS: Cx3cr1-/- MRL/lpr mice exhibited a distinct phenotype of exacerbated glomerulonephritis compared to Cx3cr1+/+ littermates, which was associated with a decrease of spleen tolerogenic marginal zone macrophages and an increase of double-negative T cells. Interestingly, upon correction of the gut microbiota with Lactobacillus administration, the phenotype of exacerbated glomerulonephritis was reversed, suggesting that CX3CR1 controls glomerulonephritis in MRL/lpr mice through a gut microbiota-dependent mechanism. Upon treatment with HFD, Cx3cr1-/- MRL/lpr mice developed significantly more atherosclerotic plaques that were promoted by Ly6C+ monocytes. Activated monocytes expressed ICOS-L that interacted with ICOS-expressing follicular T-helper cells, which in turn facilitated a germinal center reaction to produce more autoantibodies. Through a positive feedback mechanism, the increased circulatory autoantibodies further promoted the activation of Ly6C+ monocytes and their display of ICOS-L. CONCLUSIONS: We uncovered novel, Cx3cr1 deficiency-mediated pathogenic mechanisms contributing to SLE-associated glomerulonephritis and cardiovascular disease.

Arbuscular Mycorrhizal Fungi Improve the Growth, Water Status, and Nutrient Uptake of Cinnamomum migao and the Soil Nutrient Stoichiometry under Drought Stress and Recovery.

Drought greatly influences the growth and ecological stoichiometry of plants in arid and semi-arid regions such as karst areas, where Cinnamomum migao (C. migao) is an endemic tree species that is used as a bioenergy resource. Arbuscular mycorrhizal fungi (AMF) play a key role in nutrient uptake in the soil-plant continuum, increasing plant tolerance to drought. However, few studies have examined the contribution of AMF in improving the growth of C. migao seedlings and the soil nutrient stoichiometry under drought-stress conditions. A pot experiment was conducted under natural light in a plastic greenhouse to investigate the effects of individual inoculation and Co-inoculation of AMF [Funneliformis mosseae (F. mosseae) and Claroideoglomus etunicatum (C. etunicatum)] on the growth, water status, and nutrient uptake of C. migao as well as the soil nutrient stoichiometry under well-watered (WW) and drought-stress (DS) conditions. The results showed that compared with non-AMF control (CK), AM symbiosis significantly stimulated plant growth and had higher dry mass. Mycorrhizal plants had better water status than corresponding CK plants. AMF colonization notably increased the total nitrogen and phosphorus content of C. migao seedlings compared with CK. Mycorrhizal plants had higher leaf and stem total carbon concentrations than CK. The results indicated that AM symbiosis protects C. migao seedlings against drought stress by improving growth, water status, and nutrient uptake. In general, the C. migao seedlings that formed with C. etunicatum showed the most beneficial effect on plant growth, water status, and nutrient uptake among all treatments. In the future, we should study more about the biological characteristics of each AMF in the field study to understand more ecological responses of AMF under drought stress, which can better provide meaningful guidance for afforestation projects in karst regions.