The emerging role of extracellular vesicles in fungi: a double-edged sword

Fungi are eukaryotic microorganisms found in nature, which can invade the human body and cause tissue damage, inflammatory reactions, organ dysfunctions, and diseases. These diseases can severely damage the patient’s body systems and functions, leading to a range of clinical symptoms that can be life-threatening. As the incidence of invasive fungal infections has progressively increased in the recent years, a wealth of evidence has confirmed the “double-edged sword” role of fungal extracellular vesicles (EVs) in intercellular communication and pathogen-host interactions. Fungal EVs act as mediators of cellular communication, affecting fungal-host cell interactions, delivering virulence factors, and promoting infection. Fungal EVs can also have an induced protective effect, affecting fungal growth and stimulating adaptive immune responses. By integrating recent studies, we discuss the role of EVs in fungi, providing strong theoretical support for the early prevention and treatment of invasive fungal infections. Finally, we highlight the feasibility of using fungal EVs as drug carriers and in vaccine development

Mendelian randomization analysis identified tumor necrosis factor as being associated with severe COVID-19

Background: Observational studies have shown that anti-tumor necrosis factor (TNF) therapy may be beneficial for patients with coronavirus disease 2019 (COVID-19). Nevertheless, because of the methodological restrictions of traditional observational studies, it is a challenge to make causal inferences. This study involved a two-sample Mendelian randomization analysis to investigate the causal link between nine TNFs and COVID-19 severity using publicly released genome-wide association study summary statistics.Methods: Summary statistics for nine TNFs (21,758 cases) were obtained from a large-scale genome-wide association study. Correlation data between single-nucleotide polymorphisms and severe COVID-19 (18,152 cases vs. 1,145,546 controls) were collected from the COVID-19 host genetics initiative. The causal estimate was calculated by inverse variance-weighted (IVW), MR–Egger, and weighted median methods. Sensitivity tests were conducted to assess the validity of the causal relationship.Results: Genetically predicted TNF receptor superfamily member 6 (FAS) positively correlated with the severity of COVID-19 (IVW, odds ratio = 1.10, 95% confidence interval = 1.01–1.19, p = 0.026), whereas TNF receptor superfamily member 5 (CD40) was protective against severe COVID-19 (IVW, odds ratio = 0.92, 95% confidence interval = 0.87–0.97, p = 0.002).Conclusion: Genetic evidence from this study supports that the increased expression of FAS is associated with the risk of severe COVID-19 and that CD40 may have a potential protective effect against COVID-19

Enhancing Biomedical Text Summarization Using Semantic Relation Extraction

Automatic text summarization for a biomedical concept can help researchers to get the key points of a certain topic from large amount of biomedical literature efficiently. In this paper, we present a method for generating text summary for a given biomedical concept, e.g., H1N1 disease, from multiple documents based on semantic relation extraction. Our approach includes three stages: 1) We extract semantic relations in each sentence using the semantic knowledge representation tool SemRep. 2) We develop a relation-level retrieval method to select the relations most relevant to each query concept and visualize them in a graphic representation. 3) For relations in the relevant set, we extract informative sentences that can interpret them from the document collection to generate text summary using an information retrieval based method. Our major focus in this work is to investigate the contribution of semantic relation extraction to the task of biomedical text summarization. The experimental results on summarization for a set of diseases show that the introduction of semantic knowledge improves the performance and our results are better than the MEAD system, a well-known tool for text summarization

A MEIG1/PACRG complex in the manchette is essential for building the sperm flagella

A key event in the process of spermiogenesis is the formation of the flagella, which enables sperm to reach eggs for fertilization. Yeast two-hybrid studies revealed that meiosis-expressed gene 1 (MEIG1) and Parkin co-regulated gene (PACRG) interact, and that sperm-associated antigen 16, which encodes an axoneme central apparatus protein, is also a binding partner of MEIG1. In spermatocytes of wild-type mice, MEIG1 is expressed in the whole germ cell bodies, but the protein migrates to the manchette, a unique structure at the base of elongating spermatid that directs formation of the flagella. In the elongating spermatids of wild-type mice, PACRG colocalizes with α-tubulin, a marker for the manchette, whereas this localization was not changed in the few remaining elongating spermatids of Meig1-deficient mice. In addition, MEIG1 no longer localizes to the manchette in the remaining elongating spermatids of Pacrg-deficient mice, indicating that PACRG recruits MEIG1 to the manchette. PACRG is not stable in mammalian cells, but can be stabilized by MEIG1 or by inhibition of proteasome function. SPAG16L is present in the spermatocyte cytoplasm of wild-type mice, and in the manchette of elongating spermatids, but in the Meig1 or Pacrg-deficient mice, SPAG16L no longer localizes to the manchette. By contrast, MEIG1 and PACRG are still present in the manchette of Spag16L-deficient mice, indicating that SPAG16L is a downstream partner of these two proteins. Together, our studies demonstrate that MEIG1/PACRG forms a complex in the manchette and that this complex is necessary to transport cargos, such as SPAG16L, to build the sperm flagella

Sperm Associated Antigen 6 (SPAG6) Regulates Fibroblast Cell Growth, Morphology, Migration and Ciliogenesis

Mammalian Spag6 is the orthologue of Chlamydomonas PF16, which encodes a protein localized in the axoneme central apparatus, and regulates flagella/cilia motility. Most Spag6-deficient mice are smaller in size than their littermates. Because SPAG6 decorates microtubules, we hypothesized that SPAG6 has other roles related to microtubule function besides regulating flagellar/cilia motility. Mouse embryonic fibroblasts (MEFs) were isolated from Spag6-deficient and wild-type embryos for these studies. Both primary and immortalizedSpag6-deficient MEFs proliferated at a much slower rate than the wild-type MEFs, and they had a larger surface area. Re-expression of SPAG6 in the Spag6-deficient MEFs rescued the abnormal cell morphology. Spag6-deficient MEFs were less motile than wild-type MEFs, as shown by both chemotactic analysis and wound-healing assays. Spag6-deficient MEFs also showed reduced adhesion associated with a non-polarized F-actin distribution. Multiple centrosomes were observed in theSpag6-deficient MEF cultures. The percentage of cells with primary cilia was significantly reduced compared to the wild-type MEFs, and some Spag6-deficient MEFs developed multiple cilia. Furthermore, SPAG6 selectively increased expression of acetylated tubulin, a microtubule stability marker. The Spag6-deficient MEFs were more sensitive to paclitaxel, a microtubule stabilizer. Our studies reveal new roles for SPAG6 in modulation of cell morphology, proliferation, migration, and ciliogenesis

DUTIR-BioNLP@BC8 Track 3: Genetic Phenotype Extraction and Normalization with Biomedical Pre-trained Language Models

<h3><strong>Abstract</strong></h3><p>It is important to automatically extract and normalize key medical findings from the observation results written during the physical examination of teratology. The BioCreative VIII Track 3 endeavors to facilitate the advancement and assessment of systems designed to automatically extract and normalize the phenotype entities from electronic health records (EHRs). This paper describes our method used to create our submissions to the track. Our pipelined method for the phenotype concept extraction partitions the process into two subtasks: Named Entity Recognition and Named Entity Normalization. The cutting-edge biomedical pre-trained language models are used for both subtasks. Then the ensemble method is further used to improve the final performance. The official results on the test set show that our best submission achieves the F1-scores of 0.7632 on Subtask 3a and 0.7112 on Subtask 3b.</p><p> </p><p>This article is part of the <a href="https://zenodo.org/doi/10.5281/zenodo.10103190">Proceedings of the BioCreative VIII Challenge and Workshop: Curation and Evaluation in the era of Generative Models</a>.</p&gt