A Novel Mechanism of Mesenchymal Stromal Cell-Mediated Protection against Sepsis: Restricting Inflammasome Activation in Macrophages by Increasing Mitophagy and Decreasing Mitochondrial ROS

Sepsis, a systemic inflammatory response to infection, is the leading cause of death in the intensive care unit (ICU). Previous studies indicated that mesenchymal stromal cells (MSCs) might have therapeutic potential against sepsis. The current study was designed to investigate the effects of MSCs on sepsis and the underlying mechanisms focusing on inflammasome activation in macrophages. The results demonstrated that the bone marrow-derived mesenchymal stem cells (BMSCs) significantly increased the survival rate and organ function in cecal ligation and puncture (CLP) mice compared with the control-grouped mice. BMSCs significantly restricted NLRP3 inflammasome activation, suppressed the generation of mitochondrial ROS, and decreased caspase-1 and IL-1β activation when cocultured with bone marrow-derived macrophages (BMDMs), the effects of which could be abolished by Mito-TEMPO. Furthermore, the expression levels of caspase-1, IL-1β, and IL-18 in BMDMs were elevated after treatment with mitophagy inhibitor 3-MA. Thus, BMSCs exert beneficial effects on inhibiting NLRP3 inflammasome activation in macrophages primarily via both enhancing mitophagy and decreasing mitochondrial ROS. These findings suggest that restricting inflammasome activation in macrophages by increasing mitophagy and decreasing mitochondrial ROS might be a crucial mechanism for MSCs to combat sepsis

H+-pyrophosphatases enhance low nitrogen stress tolerance in transgenic Arabidopsis and wheat by interacting with a receptor-like protein kinase

IntroductionNitrogen is a major abiotic stress that affects plant productivity. Previous studies have shown that plant H+-pyrophosphatases (H+-PPases) enhance plant resistance to low nitrogen stress. However, the molecular mechanism underlying H+-PPase-mediated regulation of plant responses to low nitrogen stress is still unknown. In this study, we aimed to investigate the regulatory mechanism of AtAVP1 in response to low nitrogen stress.Methods and ResultsAtAVP1 in Arabidopsis thaliana and EdVP1 in Elymus dahuricus belong to the H+-PPase gene family. In this study, we found that AtAVP1 overexpression was more tolerant to low nitrogen stress than was wild type (WT), whereas the avp1-1 mutant was less tolerant to low nitrogen stress than WT. Plant height, root length, aboveground fresh and dry weights, and underground fresh and dry weights of EdVP1 overexpression wheat were considerably higher than those of SHI366 under low nitrogen treatment during the seedling stage. Two consecutive years of low nitrogen tolerance experiments in the field showed that grain yield and number of grains per spike of EdVP1 overexpression wheat were increased compared to those in SHI366, which indicated that EdVP1 conferred low nitrogen stress tolerance in the field. Furthermore, we screened interaction proteins in Arabidopsis; subcellular localization analysis demonstrated that AtAVP1 and Arabidopsis thaliana receptor-like protein kinase (AtRLK) were located on the plasma membrane. Yeast two-hybrid and luciferase complementary imaging assays showed that the AtRLK interacted with AtAVP1. Under low nitrogen stress, the Arabidopsis mutants rlk and avp1-1 had the same phenotypes.DiscussionThese results indicate that AtAVP1 regulates low nitrogen stress responses by interacting with AtRLK, which provides a novel insight into the regulatory pathway related to H+-pyrophosphatase function in plants

TFEB regulates lysosomal proteostasis

Loss-of-function diseases are often caused by destabilizing mutations that lead to protein misfolding and degradation. Modulating the innate protein homeostasis (proteostasis) capacity may lead to rescue of native folding of the mutated variants, thereby ameliorating the disease phenotype. In lysosomal storage disorders (LSDs), a number of highly prevalent alleles have missense mutations that do not impair the enzyme's catalytic activity but destabilize its native structure, resulting in the degradation of the misfolded protein. Enhancing the cellular folding capacity enables rescuing the native, biologically functional structure of these unstable mutated enzymes. However, proteostasis modulators specific for the lysosomal system are currently unknown. Here, we investigate the role of the transcription factor EB (TFEB), a master regulator of lysosomal biogenesis and function, in modulating lysosomal proteostasis in LSDs. We show that TFEB activation results in enhanced folding, trafficking and lysosomal activity of a severely destabilized glucocerebrosidase (GC) variant associated with the development of Gaucher disease (GD), the most common LSD. TFEB specifically induces the expression of GC and of key genes involved in folding and lysosomal trafficking, thereby enhancing both the pool of mutated enzyme and its processing through the secretory pathway. TFEB activation also rescues the activity of a β-hexosaminidase mutant associated with the development of another LSD, Tay–Sachs disease, thus suggesting general applicability of TFEB-mediated proteostasis modulation to rescue destabilizing mutations in LSDs. In summary, our findings identify TFEB as a specific regulator of lysosomal proteostasis and suggest that TFEB may be used as a therapeutic target to rescue enzyme homeostasis in LSDs

Cell transcriptomic atlas of the non-human primate Macaca fascicularis.

Studying tissue composition and function in non-human primates (NHPs) is crucial to understand the nature of our own species. Here we present a large-scale cell transcriptomic atlas that encompasses over 1 million cells from 45 tissues of the adult NHP Macaca fascicularis. This dataset provides a vast annotated resource to study a species phylogenetically close to humans. To demonstrate the utility of the atlas, we have reconstructed the cell-cell interaction networks that drive Wnt signalling across the body, mapped the distribution of receptors and co-receptors for viruses causing human infectious diseases, and intersected our data with human genetic disease orthologues to establish potential clinical associations. Our M. fascicularis cell atlas constitutes an essential reference for future studies in humans and NHPs.We thank W. Liu and L. Xu from the Huazhen Laboratory Animal Breeding Centre for helping in the collection of monkey tissues, D. Zhu and H. Li from the Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory) for technical help, G. Guo and H. Sun from Zhejiang University for providing HCL and MCA gene expression data matrices, G. Dong and C. Liu from BGI Research, and X. Zhang, P. Li and C. Qi from the Guangzhou Institutes of Biomedicine and Health for experimental advice or providing reagents. This work was supported by the Shenzhen Basic Research Project for Excellent Young Scholars (RCYX20200714114644191), Shenzhen Key Laboratory of Single-Cell Omics (ZDSYS20190902093613831), Shenzhen Bay Laboratory (SZBL2019062801012) and Guangdong Provincial Key Laboratory of Genome Read and Write (2017B030301011). In addition, L.L. was supported by the National Natural Science Foundation of China (31900466), Y. Hou was supported by the Natural Science Foundation of Guangdong Province (2018A030313379) and M.A.E. was supported by a Changbai Mountain Scholar award (419020201252), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA16030502), a Chinese Academy of Sciences–Japan Society for the Promotion of Science joint research project (GJHZ2093), the National Natural Science Foundation of China (92068106, U20A2015) and the Guangdong Basic and Applied Basic Research Foundation (2021B1515120075). M.L. was supported by the National Key Research and Development Program of China (2021YFC2600200).S

Simfusion: measuring similarity using unified relationship matrix

In this paper we use a Unified Relationship Matrix (URM) to represent a set of heterogeneous data objects (e.g., web pages, queries) and their interrelationships (e.g., hyperlinks, user clickthrough sequences). We claim that iterative computations over the URM can help overcome the data sparseness problem and detect latent relationships among heterogeneous data objects, thus, can improve the quality of information applications that require com-bination of information from heterogeneous sources. To support our claim, we present a unified similarity-calculating algorithm, SimFusion. By iteratively computing over the URM, SimFusion can effectively integrate relationships from heterogeneous sources when measuring the similarity of two data objects. Experiments based on a web search engine query log and a web page collection demonstrate that SimFusion can improve similarity measurement of web objects over both traditional content based algorithms and the cutting edge SimRank algorithm

Can We Get A Better Retrieval Function From Machine?

The quality of an information retrieval system heavily depends on its retrieval function, which returns a similarity measurement between the query and each document in the collection. Documents are sorted according to their similarity values with the query and those with high rank are assumed to be relevant. Okapi BM25 and their variations are very popular retrieval functions and they seem to be the default retrieval function for the IR research community; and there are many other widely used and well studied functions, for example, Pivoted TFIDF and INQUERY. Most of these retrieval functions being used today are made based on probabilistic theories and they are adjusted in real world according to different contexts and information needs. In this paper, we propose the idea that a good retrieval function can be discovered by a pure machine learning approach, without using probabilistic theories and knowledge-based techniques. Two machine learning algorithms, Support Vector Machine (SVM) and Genetic Programming (GP) are used for retrieval function discovery, and GP is found to be a more effective approach. The retrieval functions discovered by GP might be hard for human interpretation, but their performance is superior to Okapi BM25, one of the most popular functions. The new retrieval function is combined with query expansion techniques and the retrieval performance is improved significantly. Based on our observations in the empirical study, the GP function is more reliable and effective than Okapi BM25 when query expansion techniques are used

Ranking function optimization for effective web search by genetic programming: An empirical study

Abstract — Web search engines have become indispensable in our daily life to help us find the information we need. Although search engines are very fast in search response time, their effectiveness in finding useful and relevant documents at the top of the search hit list needs to be improved. In this paper, we report our experience applying Genetic Programming (GP) to the ranking function discovery problem leveraging the structural information of HTML documents. Our empirical experiments using the web track data from recent TREC conferences show that we can discover better ranking functions than existing well-known ranking strategies from IR, such as Okapi, Ptfidf. The performance is even comparable to those obtained by Support Vector Machine. I

Improving web search results using affinity graph

In this paper, we propose a novel ranking scheme named Affinity Ranking (AR) to re-rank search results by optimizing two metrics: (1) diversity-- which indicates the variance of topics in a group of documents; (2) information richness-- which measures the coverage of a single document to its topic. Both of the two metrics are calculated from a directed link graph named Affinity Graph (AG). AG models the structure of a group of documents based on the asymmetric content similarities between each pair of documents. Experimental results in Yahoo! Directory, ODP Data, and Newsgroup data demonstrate that our proposed ranking algorithm significantly improves the search performance. Specifically, the algorithm achieves 31 % improvement in diversity and 12 % improvement in information richness relatively within the top 10 search results

Ranking Function Discovery by Genetic Programming for Robust Retrieval

Ranking functions are instrumental for the success of an information retrieval (search engine) system. However nearly all existing ranking functions are manually designed based on experience, observations and probabilistic theories. This paper tested a novel ranking function discovery technique proposed in [Fan 2003a, Fan2003b] – ARRANGER (Automatic geneRation of RANking functions by GEnetic pRogramming), which uses Genetic Programming (GP) to automatically learn the “best ” ranking function, for the robust retrieval task. Ranking function discovery is essentially an optimization problem. As the search space here is not a coordinate system, most of the traditional optimization algorithms could not work. However, this ranking discovery problem could be easily tackled by ARRANGER. In our evaluations on 150 queries from the ad-hoc track of TREC 6, 7, and 8, the performance of our system (in average precision) was improved by nearly 16%, after replacing Okapi BM25 function with a function automatically discovered by ARRANGER. By applying pseudo-relevance feedback and ranking fusion on newly discovered functions, we improved the retrieval performance by up to 30%. The results of our experiments showed that our ranking function discovery technique – ARRANGER – is very effective in discovering high-performing ranking functions. 1

VT at TREC-2003: The Web Track Report

This year, we participated in the Web Track in addition to the Robust Track. We submitted results on both topic distillation and home page/named page finding tasks. As our time and human resources were limited for taking two tasks simultaneously, in this task we only concentrate on testing our ranking function discovery technique, ARRANGER (Automatic Rendering of RANking functions by GEnetic pRogramming) [Fan 2003a, Fan 2003b]