Ubiquitylation activates a peptidase that promotes cleavage and destabilization of its activating E3 ligases and diverse growth regulatory proteins to limit cell proliferation in Arabidopsis

The characteristic shapes and sizes of organs are established by cell proliferation patterns and final cell sizes, but the underlying molecular mechanisms coordinating these are poorly understood. Here we characterize a ubiquitin-activated peptidase called DA1 that limits the duration of cell proliferation during organ growth in Arabidopsis thaliana. The peptidase is activated by two RING E3 ligases, Big Brother (BB) and DA2, which are subsequently cleaved by the activated peptidase and destabilized. In the case of BB, cleavage leads to destabilization by the RING E3 ligase PROTEOLYSIS 1 (PRT1) of the N-end rule pathway. DA1 peptidase activity also cleaves the deubiquitylase UBP15, which promotes cell proliferation, and the transcription factors TEOSINTE BRANCED 1/ CYCLOIDEA/PCF 15 (TCP15) and TCP22, which promote cell proliferation and repress endoreduplication. We propose that DA1 peptidase activity regulates the duration of cell proliferation and the transition to endoreduplication and differentiation during organ formation in plants by coordinating the destabilization of regulatory proteins

Food safety news events classification via a hierarchical transformer model

In light of the significance of regulatory authorities and the rising demand for information disclosure, a vast amount of information on food safety news reports is readily accessible on the Internet. The extraction of such information for precise classification and provision of appropriate safety alerts based on their respective categories has emerged as a challenging problem for academic research. Given that most food safety-related events in news reports comprise lengthy text, the pre-trained language models currently employed for text analysis are generally limited in their capability to handle long documents. This paper proposes a long-text classification model utilising hierarchical Transformers. We categorise information in long documents into two distinct types: (1) multiple text chunks meeting the length constraint and (2) essential sentences within long documents, such as headings, paragraph start and end sentences, etc. Initially, our proposed model utilises the text chunks as input to the BERT model. Then, it concatenates the output of the BERT model with the important sentences from the document and use them as input to the Transformer model for feature transformation. Finally, we utilise a classifier for food safety news classification. We conducted several comparative experiments with various commonly used text classification models on a dataset constructed from publicly available information on food regulatory websites. Our proposed method outperforms existing methods, establishing itself as the leading approach in terms of performance. [Abstract copyright: © 2023 The Author(s).

The unique immune ecosystems in pediatric brain tumors: integrating single-cell and bulk RNA-sequencing

BackgroundThe significant progress of immune therapy in non-central nervous system tumors has sparked interest in employing the same strategy for adult brain tumors. However, the advancement of immunotherapy in pediatric central nervous system (CNS) tumors is not yet on par. Currently, there is a lack of comprehensive comparative studies investigating the immune ecosystem in pediatric and adult CNS tumors at a high-resolution single-cell level.MethodsIn this study, we comprehensively analyzed over 0.3 million cells from 171 samples, encompassing adult gliomas (IDH wild type and IDH mutation) as well as four major types of pediatric brain tumors (medulloblastoma (MB), ependymoma (EPN), H3K27M-mutation (DIPG), and pediatric IDH-mutation glioma (P-IDH-M)). Our approach involved integrating publicly available and newly generated single-cell datasets. We compared the immune landscapes in different brain tumors, as well as the detailed functional phenotypes of T-cell and myeloid subpopulations. Through single-cell analysis, we identified gene sets associated with major cell types in the tumor microenvironment (gene features from single-cell data, scFes) and compared them with existing gene sets such as GSEA and xCell. The CBTTC and external GEO cohort was used to analyze and validate the immune-stromal-tumor patterns in pediatric brain tumors which might potentially respond to the immunotherapy.ResultsFrom the perspective of single-cell analysis, it was observed that major pediatric brain tumors (MB, EPN, P-IDH-M, DIPG) exhibited lower immune contents compared with adult gliomas. Additionally, these pediatric brain tumors displayed diverse immunophenotypes, particularly in regard to myeloid cells. Notably, the presence of HLA-enriched myeloid cells in MB was found to be independently associated with prognosis. Moreover, the scFes, when compared with commonly used gene features, demonstrated superior performance in independent single-cell datasets across various tumor types. Furthermore, our study revealed the existence of heterogeneous immune ecosystems at the bulk-RNA sequencing level among different brain tumor types. In addition, we identified several immune-stromal-tumor patterns that could potentially exhibit significant responses to conventional immune checkpoint inhibitors.ConclusionThe single-cell technique provides a rational path to deeply understand the unique immune ecosystem of pediatric brain tumors. In spite of the traditional attitudes of “cold” tumor towards pediatric brain tumor, the immune-stroma-tumor patterns identified in this study suggest the feasibility of immune checkpoint inhibitors and pave the way for the upcoming tide of immunotherapy in pediatric brain tumors

Algunas consideraciones sobre las opciones de Centroamérica en sus relaciones comerciales con los Estados Unidos

Incluye Bibliografí

Seasonal variation and nutrient jointly drive the community structure of macrophytes in lakes with different trophic states

IntroductionMacrophytes are essential for maintaining the health of shallow lake ecosystems, however, the driving and responsive relationship between ecological factors (such as seasonal changes and nutrition, etc.) and plant communities is not yet clear.MethodsIn this study, we conducted seasonal surveys of macrophyte community composition in lakes with different nutrient states, aiming to understand the incidence relation between macrophyte community diversity, seasonal changes and environmental factors.ResultsAccording to the classification criteria of comprehensive nutritional index, there were significant differences in the trophic status of the three lakes. Among them, the Xihu Lake has reached mild eutrophication with a TLI value of 56.33, both Cibi Lake and Haixihai Lake are mesotrophic with TLI value of 36.03 and 33.48, respectively. The results of diversity analysis showed a significant negative correlation between α-diversity (include Species richness, Shannon-Wiener index, Simpson index and Pielou index) and lake nutrient status. Among them, Xihu Lake showed the lowest α-diversity in all seasons, Haixihai Lake exhibited the middle α-diversity, Cibi Lake indicated the highest α-diversity. Non-metric multidimensional ordination showed that there were obvious spatial structures differences among the macrophyte communities in the three lakes. Macrophyte community composition in the three lakes was more similar in summer and autumn, but there was a wider gap in spring and winter. The redundancy analysis indicated distinct differences between diversity index and ecological factors, the eigenvalues of Axis 1 and Axis 2 being, respectively, 36.13% and 8.15%. Environmental factors could explain 44.8% of the total variation in macrophyte communities structure. Among these, nitrogen, phosphorus, water transparency and water temperature contributed 50.2%, 3.5%, 3.8% and 27.5%, respectively.ConclusionsIn summary, the community structure of macrophytes in plateau shallow lakes is co-regulated by seasons and nutrients

Predicting potential microbe-disease associations with graph attention autoencoder, positive-unlabeled learning, and deep neural network

BackgroundMicrobes have dense linkages with human diseases. Balanced microorganisms protect human body against physiological disorders while unbalanced ones may cause diseases. Thus, identification of potential associations between microbes and diseases can contribute to the diagnosis and therapy of various complex diseases. Biological experiments for microbe–disease association (MDA) prediction are expensive, time-consuming, and labor-intensive.MethodsWe developed a computational MDA prediction method called GPUDMDA by combining graph attention autoencoder, positive-unlabeled learning, and deep neural network. First, GPUDMDA computes disease similarity and microbe similarity matrices by integrating their functional similarity and Gaussian association profile kernel similarity, respectively. Next, it learns the feature representation of each microbe–disease pair using graph attention autoencoder based on the obtained disease similarity and microbe similarity matrices. Third, it selects a few reliable negative MDAs based on positive-unlabeled learning. Finally, it takes the learned MDA features and the selected negative MDAs as inputs and designed a deep neural network to predict potential MDAs.ResultsGPUDMDA was compared with four state-of-the-art MDA identification models (i.e., MNNMDA, GATMDA, LRLSHMDA, and NTSHMDA) on the HMDAD and Disbiome databases under five-fold cross validations on microbes, diseases, and microbe-disease pairs. Under the three five-fold cross validations, GPUDMDA computed the best AUCs of 0.7121, 0.9454, and 0.9501 on the HMDAD database and 0.8372, 0.8908, and 0.8948 on the Disbiome database, respectively, outperforming the other four MDA prediction methods. Asthma is the most common chronic respiratory condition and affects ~339 million people worldwide. Inflammatory bowel disease is a class of globally chronic intestinal disease widely existed in the gut and gastrointestinal tract and extraintestinal organs of patients. Particularly, inflammatory bowel disease severely affects the growth and development of children. We used the proposed GPUDMDA method and found that Enterobacter hormaechei had potential associations with both asthma and inflammatory bowel disease and need further biological experimental validation.ConclusionThe proposed GPUDMDA demonstrated the powerful MDA prediction ability. We anticipate that GPUDMDA helps screen the therapeutic clues for microbe-related diseases