Learning to explain is a good biomedical few-shot learner.

MOTIVATION: Significant progress has been achieved in biomedical text mining using deep learning methods, which rely heavily on large amounts of high-quality data annotated by human experts. However, the reality is that obtaining high-quality annotated data is extremely challenging due to data scarcity (e.g. rare or new diseases), data privacy and security concerns, and the high cost of data annotation. Additionally, nearly all researches focus on predicting labels without providing corresponding explanations. Therefore, in this paper, we investigate a more realistic scenario, biomedical few-shot learning, and explore the impact of interpretability on biomedical few-shot learning. RESULTS: We present LetEx-Learning to explain-a novel multi-task generative approach that leverages reasoning explanations from large language models (LLMs) to enhance the inductive reasoning ability of few-shot learning. Our approach includes (1) collecting high-quality explanations by devising a suite of complete workflow based on LLMs through CoT prompting and self-training strategies, (2) converting various biomedical NLP tasks into a text-to-text generation task in a unified manner, where collected explanations serve as additional supervision between text-label pairs by multi-task training. Experiments are conducted on three few-shot settings across six biomedical benchmark datasets. The results show that learning to explain improves the performances of diverse biomedical NLP tasks in low-resource scenario, outperforming strong baseline models significantly by up to 6.41%. Notably, the proposed method makes the 220M LetEx perform superior reasoning explanation ability against LLMs. AVAILABILITY AND IMPLEMENTATION: Our source code and data are available at https://github.com/cpmss521/LetEx.

Implicit weight bias: shared neural substrates for overweight and angry facial expressions revealed by cross-adaptation.

The perception of facial expression plays a crucial role in social communication, and it is known to be influenced by various facial cues. Previous studies have reported both positive and negative biases toward overweight individuals. It is unclear whether facial cues, such as facial weight, bias facial expression perception. Combining psychophysics and event-related potential technology, the current study adopted a cross-adaptation paradigm to examine this issue. The psychophysical results of Experiments 1A and 1B revealed a bidirectional cross-adaptation effect between overweight and angry faces. Adapting to overweight faces decreased the likelihood of perceiving ambiguous emotional expressions as angry compared to adapting to normal-weight faces. Likewise, exposure to angry faces subsequently caused normal-weight faces to appear thinner. These findings were corroborated by bidirectional event-related potential results, showing that adaptation to overweight faces relative to normal-weight faces modulated the event-related potential responses of emotionally ambiguous facial expression (Experiment 2A); vice versa, adaptation to angry faces relative to neutral faces modulated the event-related potential responses of ambiguous faces in facial weight (Experiment 2B). Our study provides direct evidence associating overweight faces with facial expression, suggesting at least partly common neural substrates for the perception of overweight and angry faces.

Few-shot biomedical named entity recognition via knowledge-guided instance generation and prompt contrastive learning.

MOTIVATION: Few-shot learning that can effectively perform named entity recognition in low-resource scenarios has raised growing attention, but it has not been widely studied yet in the biomedical field. In contrast to high-resource domains, biomedical named entity recognition (BioNER) often encounters limited human-labeled data in real-world scenarios, leading to poor generalization performance when training only a few labeled instances. Recent approaches either leverage cross-domain high-resource data or fine-tune the pre-trained masked language model using limited labeled samples to generate new synthetic data, which is easily stuck in domain shift problems or yields low-quality synthetic data. Therefore, in this article, we study a more realistic scenario, i.e. few-shot learning for BioNER. RESULTS: Leveraging the domain knowledge graph, we propose knowledge-guided instance generation for few-shot BioNER, which generates diverse and novel entities based on similar semantic relations of neighbor nodes. In addition, by introducing question prompt, we cast BioNER as question-answering task and propose prompt contrastive learning to improve the robustness of the model by measuring the mutual information between query-answer pairs. Extensive experiments conducted on various few-shot settings show that the proposed framework achieves superior performance. Particularly, in a low-resource scenario with only 20 samples, our approach substantially outperforms recent state-of-the-art models on four benchmark datasets, achieving an average improvement of up to 7.1% F1. AVAILABILITY AND IMPLEMENTATION: Our source code and data are available at https://github.com/cpmss521/KGPC.

Chondrocyte autophagy mediated by T-2 toxin via AKT/TSC/Rheb/mTOR signaling pathway and protective effect of CSA-SeNP.

OBJECTIVE: Kashin-Beck disease (KBD) is an endemic, degenerative, and cartilage-damaging disease for which low selenium and T-2 toxins are considered environmental pathogenic factors. This study aimed to investigate the molecular mechanisms of autophagy in cartilage damage caused by T-2 toxin and the protective effect of chondroitin sulfate A nano-elemental selenium (CSA-SeNP) on the cartilage. METHODS: KBD chondrocytes and C28/I2 human chondrocyte cell lines were used. T-2 toxin, AKT inhibitor, and CSA-SeNP treatment experiments were conducted separately, with a treatment time of 24 h. Autophagy was monitored using MDC staining, and mRFP-GFP-LC3 adenovirus, respectively. RT-qPCR and western blotting were used to detect the expression of the relevant genes and proteins. RESULTS: The suppression of autophagy observed in KBD chondrocytes was replicated by applying 10 ng/mL T-2 toxin to C28/I2 chondrocytes for 24 h. The AKT/TSCR/Rheb/mTOR signaling pathway was activated by T-2 toxin, which inhibits autophagy. The supplementation with CSA-SeNP alleviated the inhibition of autophagy by T-2 toxin through the AKT/TSCR/Rheb/mTOR signaling pathway. CONCLUSIONS: Loss of autophagy regulated by the AKT/TSCR/Rheb/mTOR signaling pathway plays an important role in cartilage damage caused by T-2 toxin. CSA-SeNP supplementation attenuated inhibition of autophagy in chondrocytes by T-2 toxin by modulating this signaling pathway. These findings provide promising new targets for the prevention and treatment of cartilage disease.

Effect of the configuration mixing on the polarization and angular distribution of x-ray line emissions following electron-impact excitation of Ne-like ions.

We present a systematic theoretical study on the angular distribution and linear polarization of x-ray line emissions of neon-like ions following the electron-impact excitation from the ground state to the excited levels [(2p5)1/23d3/2]J=1, [(2p5)3/23d5/2]J=1, [(2p5)3/23d3/2]J=1, and [(2p5)1/23s]J=1. The cross sections are calculated by using the flexible atomic code under configuration-interaction plus many-body perturbation theory method. The angular distribution and linear polarization are obtained based on density matrix theory. Emphasis has been placed on the effect of the configuration mixing on the angular distribution and polarization. It has been proved that the strong mixing of configuration [(2p5)3/23d3/2]J=1 with configuration [(2p5)1/23s]J=1 can result in the abrupt change of Z-dependence of angular distribution and polarization. It indicates that angular distribution and polarization can be expected to serve as a tool for investigation of configuration mixing effect.

A novel disulfide death-related genes prognostic signature identifies the role of IPO4 in glioma progression.

BACKGROUND: "Disulfide death," a form of cellular demise, is triggered by the abnormal accumulation of intracellular disulfides under conditions of glucose deprivation. However, its role in the prognosis of glioma remains undetermined. Therefore, the main objective of this study is to establish prognostic signature based on disulfide death-related genes (DDRGs) and to provide new solutions in choosing the effective treatment of glioma. METHODS: The RNA transcriptome, clinical information, and mutation data of glioma samples were sourced from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA), while normal samples were obtained from the Genotype-Tissue Expression (GTEx). DDRGs were compiled from previous studies and selected through differential analysis and univariate Cox regression analysis. The molecular subtypes were determined through consensus clustering analysis. Further, LASSO analysis was employed to select characteristic genes, and subsequently, a risk model comprising seven DDRGs was constructed based on multivariable Cox analysis. Kaplan-Meier survival curves were employed to assess survival differences between high and low-risk groups. Additionally, functional analyses (GO, KEGG, GSEA) were conducted to explore the potential biological functions and signaling pathways of genes associated with the model. The study also explored immune checkpoint (ICP) genes, immune cell infiltration levels, and immune stromal scores. Finally, the effect of Importin-4(IPO4) on glioma has been further confirmed through RT-qPCR, Western blot, and cell functional experiments. RESULTS: 7 genes associated with disulfide death were obtained and two subgroups of patients with different prognosis and clinical characteristics were identified. Risk signature was subsequently developed and proved to serve as an prognostic predictor. Notably, the high-risk group exhibited an immunosuppressive microenvironment characterized by a high concentration of M2 macrophages and regulatory T cells (Tregs). In contrast, the low-risk group showed lower half-maximal inhibitory concentration (IC50) values. Therefore, patients in the high-risk group may benefit more from immunotherapy, while patients in the low-risk group may benefit more from chemotherapy. In addition, in vitro experiments have shown that inhibition of the expression of IPO4 leads to a significant reduction in the proliferation, migration, and invasion of glioma cells. CONCLUSION: This study identified two glioma subtypes and constructed a prognostic signature based on DDRGs. The signature has the potential to optimize the selection of patients for immune- and chemotherapy and provided a potential therapeutic target for glioma.

The role and application of small extracellular vesicles in glioma.

Small extracellular vesicles (sEVs) are cell-derived, nanometer-sized particles enclosed by a lipid bilayer. All kinds of biological molecules, including proteins, DNA fragments, RNA, lipids, and metabolites, can be selectively loaded into sEVs and transmitted to recipient cells that are near and distant. Growing shreds of evidence show the significant biological function and the clinical significance of sEVs in cancers. Numerous recent studies have validated that sEVs play an important role in tumor progression and can be utilized to diagnose, stage, grading, and monitor early tumors. In addition, sEVs have also served as drug delivery nanocarriers and cancer vaccines. Although it is still infancy, the field of basic and translational research based on sEVs has grown rapidly. In this review, we summarize the latest research on sEVs in gliomas, including their role in the malignant biological function of gliomas, and the potential of sEVs in non-invasive diagnostic and therapeutic approaches, i.e., as nanocarriers for drug or gene delivery and cancer vaccines.

Integrative analysis of a novel super-enhancer-associated lncRNA prognostic signature and identifying LINC00945 in aggravating glioma progression.

BACKGROUND: Super-enhancers (SEs), driving high-level expression of genes with tumor-promoting functions, have been investigated recently. However, the roles of super-enhancer-associated lncRNAs (SE-lncRNAs) in tumors remain undetermined, especially in gliomas. We here established a SE-lncRNAs expression-based prognostic signature to choose the effective treatment of glioma and identify a novel therapeutic target. METHODS: Combined analysis of RNA sequencing (RNA-seq) data and ChIP sequencing (ChIP-seq) data of glioma patient-derived glioma stem cells (GSCs) screened SE-lncRNAs. Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) datasets served to construct and validate SE-lncRNA prognostic signature. The immune profiles and potential immuno- and chemotherapies response prediction value of the signature were also explored. Moreover, we verified the epigenetic activation mechanism of LINC00945 via the ChIP assay, and its effect on glioma was determined by performing the functional assay and a mouse xenograft model. RESULTS: 6 SE-lncRNAs were obtained and identified three subgroups of glioma patients with different prognostic and clinical features. A risk signature was further constructed and demonstrated to be an independent prognostic factor. The high-risk group exhibited an immunosuppressive microenvironment and was higher enrichment of M2 macrophage, regulatory T cells (Tregs), and Cancer-associated fibroblasts (CAFs). Patients in the high-risk group were better candidates for immunotherapy and chemotherapeutics. The SE of LINC00945 was further verified via ChIP assay. Mechanistically, BRD4 may mediate epigenetic activation of LINC00945. Additionally, overexpression of LINC00945 promoted glioma cell proliferation, EMT, migration, and invasion in vitro and xenograft tumor formation in vivo. CONCLUSION: Our study constructed the first prognostic SE-lncRNA signature with the ability to optimize the choice of patients receiving immuno- and chemotherapies and provided a potential therapeutic target for glioma.

A Head-to-Head Comparison of EQ-HWB and EQ-5D-5L in Patients, Carers, and General Public in China.

OBJECTIVES: This study aimed to understand the psychometric properties of EQ Health and Wellbeing (EQ-HWB) and to examine its relationship with EQ-5D-5L in a sample covering patients, carers, and general public. METHODS: A cross-sectional study was conducted in Guizhou Province, China. The acceptability, convergent validity (using Spearman correlation coefficients), internal structure (using exploratory factor analysis), and known-group validity of EQ-HWB, EQ-HWB-Short (EQ-HWB-S), and EQ-5D-5L were reported and compared. RESULTS: A total of 323 participants completed the survey, including 106 patients, 101 carers, and 116 individuals from the general public. Approximately 7.4% of participants had at least 1 missing response. In the EQ-HWB and EQ-5D-5L items related to activities, there were more level 1 responses. The correlations between EQ-HWB and EQ-5D-5L items ranged from low to high, confirming the convergent validity of similar aspects between the 2 instruments. Notably, EQ-HWB measures 2 additional factors compared with EQ-5D-5L or EQ-HWB-S, both of which share 3 common factors. When the patient group was included, EQ-5D-5L had the largest effect size, but it failed to differentiate between the groups of general public and carers. Both EQ-HWB and EQ-HWB-S demonstrated better known-group validity results when carers were included. CONCLUSIONS: EQ-HWB measures a broader quality of life construct that goes beyond health measured by EQ-5D-5L. By encompassing a broader scope, the impact of healthcare interventions may become diluted, given that other factors can influence well-being outcomes as significantly as health conditions do.

Valuation of Multi-Dimensional Health States With a Bolt-On: Is There a Shortcut?

OBJECTIVES: To use the EQ-5D questionnaire with bolt-on dimensions in economic evaluation studies, new value sets are needed. In this study, we explored the feasibility of a new approach called the scaling factor model, which estimates bolt-on value sets using estimated EQ-5D dimensional weights. METHODS: We designed a 2-arm study, inviting university students to value health states with and without bolt-on items using the composite time trade-off method. We selected 25 health states from an orthogonal array and added the 5 mildest EQ-5D states in the design. In arm 1, EQ-5D without self-care and standard EQ-5D states were valued, and in arm 2, standard EQ-5D states and EQ-5D with vision were valued. By arm, we compared the mean observed values of health states with and without bolt-on item. Next, by arm, we estimated value sets for the EQ-5D with bolt-on states using both standard model and scaling factor model. Model performances were compared in terms of prediction accuracy and correlation with likelihood-based mean values. RESULTS: Adding a five-level bolt-on to EQ-5D resulted in statistically lower values. This effect was consistent across 2 arms and bolt-on items. The scaling factor models outperformed the standard models in all statistics. CONCLUSIONS: The scaling factor model offers a methodologically viable and low-cost option for producing value sets for EQ-5D supplemented with bolt-on items. Future studies should further test this method using other bolt-on items and more relevant study populations.

Testing 2 Alternative Time Trade-Off Methods for Valuation of Children's Health States.

OBJECTIVES: Composite time trade-off (cTTO) values for EQ-5D-Y-3L health states tend to be high, raising concerns about sensitivity particularly for mild or moderate health states. We conceptualized and pilot tested 2 alternative time trade-off (TTO) variants: the caregiver TTO (CGTTO) and lag-time TTO (LTTO). METHODS: We collected CGTTO and LTTO data in China for 10 EQ-5D-Y-3L health states and compared the resulting values, respondent feedback, and task completion times with those from an EQ-5D-Y-3L valuation study using cTTO. We also examined how age and parental status of respondents could affect TTO values. RESULTS: A total of 304 participants were included in this study. Overall, cTTO showed statistically better results in all feedback questions. On a 5-point Likert scale where lower score means greater agreement, the mean (SD) feedback scores for cTTO, LTTO, and CGTTO were 1.18 (0.58), 1.45 (0.91), and 1.65 (1.02) for "easy to understand"; 1.45 (0.91), 1.94 (1.08), and 1.86 (1.24) for "easy to differentiate"; and 3.61 (1.29), 2.97 (1.33), and 3.02 (1.50) for "difficult to decide," respectively. The mean (SD) TTO values of all 10 states were 0.463 (0.494), 0.387 (0.555), and 0.123 (0.710) for cTTO, LTTO, and CGTTO, respectively. The effects of age and parental status on TTO values differed by the 3 methods. CONCLUSIONS: LTTO and CGTTO produce values with good characteristics and merit further investigation. Researchers need to be aware of the differences in design and values when using the TTO method to value children's health states.

Testing "Pits" Time Trade-Off: Can Data Quality be Improved by Removing Death From Valuation of Health States?

OBJECTIVES: Composite time trade-off (cTTO) often exhibits issues such as a value clustering at -1, potentially due to suboptimal valuation task design. We developed a novel time trade-off which uses the "pits" state as an anchor state and enables a unified time trade-off procedure for all health states (referred to as "pTTO"). This study aimed to test the pTTO for valuation of EQ-5D-5L health states. METHODS: A total of 120 members of the general public were invited to value 3 pairs of dominant/dominated EQ-5D-5L states using both cTTO and pTTO and the "pits" state using cTTO. We compared the pTTO with cTTO in terms of feasibility (interviewer-rated task-completing difficulty), acceptability (participant-reported task experience), value distribution, logical consistency, and discriminatory ability (using the standardized response mean, an effect size measure, derived from 3 pairwise comparisons of states with dominant/dominated relationship). RESULTS: The pTTO demonstrated similar feasibility to cTTO but slightly lower acceptability. The pTTO values exhibited a smoother and more continuous distribution compared with cTTO values. Relative to the cTTO, the pTTO showed higher individual-level logical consistency rate for the pairs comprising severe states (stringent criterion: 53.04% vs 17.39%; less stringent criterion: 82.61% vs 78.26%). The standardized response mean value based on pTTO values were higher than those based on cTTO values for the pair comprising severe states (0.388 vs -0.140). CONCLUSIONS: The pTTO appeared to be feasible and acceptable to value EQ-5D-5L states. The pTTO values were less clustered, more logically consistent, and discriminative compared with the cTTO values for severe health states.

The methodological quality of systematic reviews regarding the Core Outcome Set (COS) development.

BACKGROUND: The Core Outcome Measures in Effectiveness Trials (COMET) working group proposed core outcome sets (COS) to address the heterogeneity in outcome measures in clinical studies. According to the recommendations of COMET, performing systematic reviews (SRs) usually was the first step for COS development. However, the SRs that serve as a basis for COS are not specifically appraised by organizations such as COMET regarding their quality. Here, we investigated the status of SRs related to development of COS and evaluated their methodological quality. METHODS: We conducted a search on PubMed to identify SRs related to COS development published from inception to May 2022. We qualitatively summarized the disease included in SR topics, and the studies included in the SRs. We evaluated the methodological quality of the SRs using AMSTAR 2.0 and compared the overall quality of SRs with and without protocols using the Mann-Whitney U test. RESULTS: We included 175 SRs from 23 different countries or regions, and they mainly focused on five diseases: musculoskeletal system or connective tissue disease (n = 19, 10.86%), injury, poisoning, or certain other consequences of external causes (n = 18, 10.29%), digestive system disease (n = 16, 9.14%), nervous system disease (n = 15, 8.57%), and genitourinary system disease (n = 15, 8.57%). Although 88.00% of SRs included randomized controlled trials (RCTs), only a few SRs (23.38%) employed appropriate tools to assess the risk of bias in RCTs. The assessment results on the basis of AMSTAR 2.0 indicated that most SRs (93.71%) were rated as ''critically low'' to ''low'' in terms of overall confidence. The overall confidence of SRs with protocols was significantly higher than that without protocols (P <.001). Compared to the SRs with protocols on Core Outcome Measures in Effectiveness Trials (COMET), SRs with protocols on PROSPERO were of better overall confidence (P = .017). CONCLUSION: The overall quality of published SRs regarding COS development was poor. Our findings emphasize the need for researchers to carefully select the disease topic and strictly adhere to the requirements of optimal methodology when conducting a SR for the establishment of a COS.

A patient information mining network for drug recommendation.

As an important task of natural language processing, medication recommendation aims to recommend medication combinations according to the electronic health record, which can also be regarded as a multi-label classification task. But patients often have multiple diseases simultaneously, and the model must consider drug-drug interactions (DDI) of medication combinations when recommending medications, making medication recommendation more difficult. There is little existing work to explore the changes in patient conditions. However, these changes may point to future trends in patient conditions that are critical for reducing DDI rates in recommended drug combinations. In this paper, we proposed the Patient Information Mining Network (PIMNet), which models the current core medications of patient by mining the temporal and spatial changes of patient medication order and patient condition vector, and allocates some auxiliary medications as the currently recommended medication combination. The experimental results show that the proposed model greatly reduces the recommended DDI of medications while achieving results no lower than the state-of-the-art results.

SSGU-CD: A combined semantic and structural information graph U-shaped network for document-level Chemical-Disease interaction extraction.

Document-level interaction extraction for Chemical-Disease is aimed at inferring the interaction relations between chemical entities and disease entities across multiple sentences. Compared with sentence-level relation extraction, document-level relation extraction can capture the associations between different entities throughout the entire document, which is found to be more practical for biomedical text information. However, current biomedical extraction methods mainly concentrate on sentence-level relation extraction, making it difficult to access the rich structural information contained in documents in practical application scenarios. We put forward SSGU-CD, a combined Semantic and Structural information Graph U-shaped network for document-level Chemical-Disease interaction extraction. This framework effectively stores document semantic and structure information as graphs and can fuse the original context information of documents. Using the framework, we propose a balanced combination of cross-entropy loss function to facilitate collaborative optimization among models with the aim of enhancing the ability to extract Chemical-Disease interaction relations. We evaluated SSGU-CD on the document-level relation extraction dataset CDR and BioRED, and the results demonstrate that the framework can significantly improve the extraction performance.

Location-enhanced syntactic knowledge for biomedical relation extraction.

Biomedical relation extraction has long been considered a challenging task due to the specialization and complexity of biomedical texts. Syntactic knowledge has been widely employed in existing research to enhance relation extraction, providing guidance for the semantic understanding and text representation of models. However, the utilization of syntactic knowledge in most studies is not exhaustive, and there is often a lack of fine-grained noise reduction, leading to confusion in relation classification. In this paper, we propose an attention generator that comprehensively considers both syntactic dependency type information and syntactic position information to distinguish the importance of different dependency connections. Additionally, we integrate positional information, dependency type information, and word representations together to introduce location-enhanced syntactic knowledge for guiding our biomedical relation extraction. Experimental results on three widely used English benchmark datasets in the biomedical domain consistently outperform a range of baseline models, demonstrating that our approach not only makes full use of syntactic knowledge but also effectively reduces the impact of noisy words.

Differences and common ground in the frameworks of health-related quality of life in traditional Chinese medicine and modern medicine: a systematic review.

PURPOSE: This systematic review aims to explore the conceptualization of health-related quality of life (HRQoL) in China. With HRQoL influenced by both modern medicine (MM) and traditional Chinese medicine (TCM), the study seeks to identify differences and common ground between the frameworks of MM and TCM as defined in the literature. METHOD: A systematic literature search was conducted across three Chinese databases and four English databases. The data was extracted including title, author(s), publication year, region, aim, method, category, and result. When sorting data, we broke down the HRQoL frameworks into concepts, domains and facets, with a focus on overlapped facets between the frameworks of MM and TCM. RESULTS: A total of 31 studies were included. In the perspective of TCM, HRQoL is centered around three key 'concepts': (1) 'xingshentongyi' (unity of body and spirit), (2) 'tianrenheyi' (harmony between man and nature), and (3) 'qiqing' (seven emotional forms). In contrast, the MM framework comprises 'physical,' 'mental,' 'social,' and 'environment' domains. Out of the 59 unique facets identified, 28 are common to both TCM and MM, 9 specific to TCM, and 22 specific to MM. 'Appetite,' 'sleep,' and 'energy' are the most frequently mentioned facets in both frameworks. CONCLUSION: The concept of HRQoL in China encompasses frameworks rooted in both TCM and MM. While TCM and MM have distinct healthcare approaches, they share overlapping domains when measuring HRQoL through questionnaires. Furthermore, TCM and MM demonstrate considerable convergence in terms of HRQoL facets, showing the potential for utilizing HRQoL instruments across different cultural settings.

A novel fungal sensor (Ngs1) of N-acetylglucosamine (GlcNAc) mediates the fungal response to GlcNAc in the interaction between entomopathogenic Beauveria bassiana and insect host.

As N-acetylglucosamine (GlcNAc) ubiquitously exists in both insect cuticle and fungal cell walls, the GlcNAc sensor (Ngs1) potentially plays important roles in the interactions between entomopathogenic fungi and their insect hosts. However, the roles of the Ngs1 derived from the entomopathogens in response to the host's cuticle remain completely unexplored. In this study, a putative Ngs1 homolog was identified in the entomopathogenic fungus Beauveria bassiana. Deletion of Ngs1 significantly reduced virulence towards Galleria mellonella larvae either through cuticle infection (by 23%) or by bypassing the cuticle (by 44%). To investigate the role of Ngs1 in fungal virulence, an analysis of the transcriptome induced by Locusta migratoria exoskeleton was conducted, highlighting the regulatory mechanism of Ngs1 in carbohydrate metabolic process, particularly chitin metabolism and GlcNAc metabolism. Consistent with the transcriptomic data, Ngs1-deletion mutants showed reduced activities of both secreted chitinase (17% reduction) and Pr1 protease (35% reduction). Loss of Ngs1 down-regulated the transcript levels of GlcNAc-catabolism genes, resulting in a 17% decrease in fungal growth on GlcNAc-supported media. Furthermore, Ngs1 deficiency attenuated the fungal response to GlcNAc, leading to the alteration of fungal resistance to diverse stress cues. All of these changes contribute to the reduction in virulence in Ngs1-deficient B. bassiana. These findings support that Ngs1 plays a critical role in responding to insect-derived GlcNAc, affecting the production of cuticle-degrading enzymes to penetrate insect epidermis, GlcNAc-induced changes of stress resistance, and contribute to the fungal virulence against insects.

Therapeutic targets for gastrointestinal diseases: proteome-wide Mendelian randomization and colocalization analyses.

PURPOSE: This study was aimed to identify serum proteins linked with gastrointestinal diseases by proteome-wide Mendelian randomization analysis. METHODS: We determined the casual relationship between 732 kinds of circulating proteins and the 24 kinds of gastrointestinal diseases via Mendelian randomization analysis. RESULTS: Four circulating proteins (FCGR3B, IL-12B, MAPKAPK2, and IL-23R) were associated with the occurrence of ulcerative colitis (UC), and IL23R was also correlated with risk of Crohn's disease (CD). Genetically predicted levels of IL23R were strongly correlated with the risk of UC and CD based on the high supporting evidence of colocalization analysis. Five circulating proteins (NOV, EFEMP1, ADGRE2, LCT, and SEMA3G) were associated with the risk of diverticulosis disease. With high supporting evidence of colocalization, genetically predicted levels of NOV and SEMA3G were inversely correlated with the risk of diverticulosis disease. Five circulating proteins (FUT3, FUT5, CRHBP, SULT2A1, and QPCTL) were associated with the occurrence of cholelithiasis. With high supporting evidence of colocalization, genetically predicted levels of FUT3 and CRHBP were inversely correlated with the risk of cholelithiasis. CONCLUSIONS: The proteome-wide Mendelian randomization investigation identified several circulating proteins associated with the risk of UC, CD, diverticular disease and cholelithiasis, which reinforced the understanding of molecular pathogenesis and design of therapeutic targets.

N-acetylglucosamine kinase (BbHxk1) has pleiotropic effects on vegetative growth, cell wall integrity, morphological transition, cuticle infection, and metabolic modulation in the biological pesticide Beauveria bassiana.

Beauveria bassiana is a popular and eco-friendly biopesticide. During its pathogen-pest interaction, both N-acetylglucosamine (GlcNAc) catabolism and anabolism are crucial for nutrient supply and cell-wall construction. The initiation of GlcNAc metabolism relies on the catalysis of GlcNAc kinase, which has been extensively studied in the human pathogen Candida albicans. However, the physiological function of GlcNAc kinase remains poorly understood in entomopathogenic fungi. In the present study, a GlcNAc kinase homolog was identified and designated as BbHxk1 in B. bassiana. Deletion of BbHxk1 resulted in viable but reduced vegetative growth on various carbon sources. ΔBbHxk1 mutants displayed severe defects in cell wall integrity, making them more susceptible to cell wall stress cues. Furthermore, the absence of BbHxk1 resulted in an increase in conidial yield and blastospore production, and a faster rate of germination and filamentation, potentially attributed to higher intracellular ATP levels. BbHxk1 deficiency led to a reduction in the activities of cuticle-degrading enzymes, which might contribute to the attenuated pathogenicity specifically through cuticle penetration rather than hemocoel infection towards Galleria mellonella larvae. Being different from C. albicans Hxk1, which facultatively acts as a catalyzing enzyme and transcriptional regulator, BbHxk1 primarily acts as a catalyzing enzyme and metabolic regulator. The altered metabolomic profiling correlated with the phenotypic defects in ΔBbHxk1 mutants, further implicating a potential metabolism-dependent mechanism of BbHxk1 in mediating physiologies of B. bassiana. These findings not only unveil a novel role for GlcNAc kinase in B. bassiana, but also provide a solid theoretical basis to guide metabolic reprogramming in order to maintain or even enhance the efficiency of fungi for practical applications.