Integrated proteomics reveals autophagy landscape and an autophagy receptor controlling PKA-RI complex homeostasis in neurons.

Autophagy is a conserved, catabolic process essential for maintaining cellular homeostasis. Malfunctional autophagy contributes to neurodevelopmental and neurodegenerative diseases. However, the exact role and targets of autophagy in human neurons remain elusive. Here we report a systematic investigation of neuronal autophagy targets through integrated proteomics. Deep proteomic profiling of multiple autophagy-deficient lines of human induced neurons, mouse brains, and brain LC3-interactome reveals roles of neuronal autophagy in targeting proteins of multiple cellular organelles/pathways, including endoplasmic reticulum (ER), mitochondria, endosome, Golgi apparatus, synaptic vesicle (SV) for degradation. By combining phosphoproteomics and functional analysis in human and mouse neurons, we uncovered a function of neuronal autophagy in controlling cAMP-PKA and c-FOS-mediated neuronal activity through selective degradation of the protein kinase A - cAMP-binding regulatory (R)-subunit I (PKA-RI) complex. Lack of AKAP11 causes accumulation of the PKA-RI complex in the soma and neurites, demonstrating a constant clearance of PKA-RI complex through AKAP11-mediated degradation in neurons. Our study thus reveals the landscape of autophagy degradation in human neurons and identifies a physiological function of autophagy in controlling homeostasis of PKA-RI complex and specific PKA activity in neurons.

International consensus guidelines for the definition, detection, and interpretation of autophagy-dependent ferroptosis.

Macroautophagy/autophagy is a complex degradation process with a dual role in cell death that is influenced by the cell types that are involved and the stressors they are exposed to. Ferroptosis is an iron-dependent oxidative form of cell death characterized by unrestricted lipid peroxidation in the context of heterogeneous and plastic mechanisms. Recent studies have shed light on the involvement of specific types of autophagy (e.g. ferritinophagy, lipophagy, and clockophagy) in initiating or executing ferroptotic cell death through the selective degradation of anti-injury proteins or organelles. Conversely, other forms of selective autophagy (e.g. reticulophagy and lysophagy) enhance the cellular defense against ferroptotic damage. Dysregulated autophagy-dependent ferroptosis has implications for a diverse range of pathological conditions. This review aims to present an updated definition of autophagy-dependent ferroptosis, discuss influential substrates and receptors, outline experimental methods, and propose guidelines for interpreting the results.Abbreviation: 3-MA:3-methyladenine; 4HNE: 4-hydroxynonenal; ACD: accidentalcell death; ADF: autophagy-dependentferroptosis; ARE: antioxidant response element; BH2:dihydrobiopterin; BH4: tetrahydrobiopterin; BMDMs: bonemarrow-derived macrophages; CMA: chaperone-mediated autophagy; CQ:chloroquine; DAMPs: danger/damage-associated molecular patterns; EMT,epithelial-mesenchymal transition; EPR: electronparamagnetic resonance; ER, endoplasmic reticulum; FRET: Försterresonance energy transfer; GFP: green fluorescent protein;GSH: glutathione;IF: immunofluorescence; IHC: immunohistochemistry; IOP, intraocularpressure; IRI: ischemia-reperfusion injury; LAA: linoleamide alkyne;MDA: malondialdehyde; PGSK: Phen Green™ SK;RCD: regulatedcell death; PUFAs: polyunsaturated fatty acids; RFP: red fluorescentprotein;ROS: reactive oxygen species; TBA: thiobarbituricacid; TBARS: thiobarbituric acid reactive substances; TEM:transmission electron microscopy.

Silencing Parkinson's risk allele Rit2 sex-specifically compromises motor function and dopamine neuron viability.

Parkinson's disease (PD) is the second most prevalent neurodegenerative disease and arises from dopamine (DA) neuron death selectively in the substantia nigra pars compacta (SNc). Rit2 is a reported PD risk allele, and recent single cell transcriptomic studies identified a major RIT2 cluster in PD DA neurons, potentially linking Rit2 expression loss to a PD patient cohort. However, it is still unknown whether Rit2 loss itself impacts DA neuron function and/or viability. Here we report that conditional Rit2 silencing in mouse DA neurons drove motor dysfunction that occurred earlier in males than females and was rescued at early stages by either inhibiting the DA transporter (DAT) or with L-DOPA treatment. Motor dysfunction was accompanied by decreased DA release, striatal DA content, phenotypic DAergic markers, DA neurons, and DAergic terminals, with increased pSer129-alpha synuclein and pSer935-LRRK2 expression. These results provide clear evidence that Rit2 loss is causal for SNc cell death and motor dysfunction, and reveal key sex-specific differences in the response to Rit2 loss.

Crop-GPA: an integrated platform of crop gene-phenotype associations.

With the increasing availability of large-scale biology data in crop plants, there is an urgent demand for a versatile platform that fully mines and utilizes the data for modern molecular breeding. We present Crop-GPA ( https://crop-gpa.aielab.net ), a comprehensive and functional open-source platform for crop gene-phenotype association data. The current Crop-GPA provides well-curated information on genes, phenotypes, and their associations (GPAs) to researchers through an intuitive interface, dynamic graphical visualizations, and efficient online tools. Two computational tools, GPA-BERT and GPA-GCN, are specifically developed and integrated into Crop-GPA, facilitating the automatic extraction of gene-phenotype associations from bio-crop literature and predicting unknown relations based on known associations. Through usage examples, we demonstrate how our platform enables the exploration of complex correlations between genes and phenotypes in crop plants. In summary, Crop-GPA serves as a valuable multi-functional resource, empowering the crop research community to gain deeper insights into the biological mechanisms of interest.

Molecular profiling of human substantia nigra identifies diverse neuron types associated with vulnerability in Parkinson's disease.

Parkinson's disease (PD) is characterized pathologically by the loss of dopaminergic (DA) neurons in the substantia nigra (SN). Whether cell types beyond DA neurons in the SN show vulnerability in PD remains unclear. Through transcriptomic profiling of 315,867 high-quality single nuclei in the SN from individuals with and without PD, we identified cell clusters representing various neuron types, glia, endothelial cells, pericytes, fibroblasts, and T cells and investigated cell type-dependent alterations in gene expression in PD. Notably, a unique neuron cluster marked by the expression of RIT2, a PD risk gene, also displayed vulnerability in PD. We validated RIT2-enriched neurons in midbrain organoids and the mouse SN. Our results demonstrated distinct transcriptomic signatures of the RIT2-enriched neurons in the human SN and implicated reduced RIT2 expression in the pathogenesis of PD. Our study sheds light on the diversity of cell types, including DA neurons, in the SN and the complexity of molecular and cellular changes associated with PD pathogenesis.

PDDGCN: A Parasitic Disease-Drug Association Predictor Based on Multi-view Fusion Graph Convolutional Network.

The precise identification of associations between diseases and drugs is paramount for comprehending the etiology and mechanisms underlying parasitic diseases. Computational approaches are highly effective in discovering and predicting disease-drug associations. However, the majority of these approaches primarily rely on link-based methodologies within distinct biomedical bipartite networks. In this study, we reorganized a fundamental dataset of parasitic disease-drug associations using the latest databases, and proposed a prediction model called PDDGCN, based on a multi-view graph convolutional network. To begin with, we fused similarity networks with binary networks to establish multi-view heterogeneous networks. We utilized neighborhood information aggregation layers to refine node embeddings within each view of the multi-view heterogeneous networks, leveraging inter- and intra-domain message passing to aggregate information from neighboring nodes. Subsequently, we integrated multiple embeddings from each view and fed them into the ultimate discriminator. The experimental results demonstrate that PDDGCN outperforms five state-of-the-art methods and four compared machine learning algorithms. Additionally, case studies have substantiated the effectiveness of PDDGCN in identifying associations between parasitic diseases and drugs. In summary, the PDDGCN model has the potential to facilitate the discovery of potential treatments for parasitic diseases and advance our comprehension of the etiology in this field. The source code is available at https://github.com/AhauBioinformatics/PDDGCN .

Named entity recognition of rice genes and phenotypes based on BiGRU neural networks.

Named Entity Recognition (NER) is a fundamental but crucial task in natural language processing (NLP) and big data analysis, with wide application range. NER for rice genes and phenotypes is a technique to identify genes and phenotypes from a large amount of text. NER for rice genes and phenotypes can facilitate the acquisition of information in the field of crops and provide references for our research on higher quality crops. At the same time, named entity recognition still faces many challenges. In this paper, we propose an improved bidirectional gated recurrent unit neural network (BI-GRU) method, which is used to automatically identify the required entities (i.e. gene names, rice phenotypes) from relevant rice literature and patents. The neural network model is combined with the Softmax function to directly output the probabilities of labels, forming the BI-GRU-SF model. With the ability of deep learning methods, the semantic information in the context can be learned without the need for feature engineering. Finally, we conducted experiments, and the results showed that our proposed model provided better performance compared to other models. All datasets and resource codes of BI-GRU-SF are available at https://github.com/qqeeqq/NER for academic use.

APDDD: Animal parasitic diseases and drugs database.

Animal parasitic diseases not only have an economic impact, but also have serious social and public health impacts. Although antiparasitic drugs can treat these diseases, it seems difficult for users to comprehensively utilize the information, due to incomplete and difficult data collection. Thus, there is an urgent need to establish a comprehensive database, that includes parasitic diseases and related drugs. In this paper, we develop a knowledge database dedicated to collecting and analyzing animal parasitic diseases and related drugs, named Animal Parasitic Diseases and Drugs Database (APDDD). The current version of APDDD includes animal parasitic disease data of 8 major parasite classifications that cause common parasitic diseases and 96 subclass samples mined from many literature and authoritative books, as well as 182 antiparasitic drugs. Furthermore, we utilized APDDD data to add a knowledge graph representing the relationships between parasitic diseases, drugs, and the targeted gene of drugs acting on parasites. We hope that APDDD will become a good database for animal parasitic diseases and antiparasitic drugs research and that users can gain a more intuitive understanding of the relationships between parasitic diseases, drugs, and targeted genes through the knowledge graph.

The experiences of reproductive concerns in cancer survivors: A systematic review and meta-synthesis of qualitative studies.

AIM: The aim of this study was to synthesize qualitative research evidence on cancer survivors' experiences with reproductive concerns (RC). METHODS: We conducted a systematic search of qualitative studies and utilized the meta-aggregation approach. The database searches were extended up to May 14, 2023, encompassing 12 databases, specifically MEDLINE, CINAHL, PubMed, EMBASE, Scopus, Web of Science (Core Collection), AMED, PsycINFO, The Cochrane Library, CNKI, Wan Fang Data, and VIP. RESULTS: Three overarching themes were synthesized from the analysis of 21 studies that explored cancer patients' awareness of reproductive concerns, their perceptions, needs, and coping styles. These themes encapsulate the multifaceted aspects of cancer patients' reproductive concerns: "Gender differences in fertility concerns among cancer patients: Perspectives from men and women"; "The influence of age: Experiences with fertility issues among cancer patients at different life stages"; "The impact of treatment stages on fertility concerns: The evolution of perception and coping strategies in the course of cancer treatment". CONCLUSION: Our study presents an in-depth exploration of the reproductive concerns experienced by cancer patients from various perspectives. We found that the internal experiences of reproductive concerns, their perceptions, needs, and coping mechanisms differ based on their roles. This comprehensive understanding of the complex emotions and needs of cancer patients when confronted with fertility issues can guide clinicians in providing more effective medical assistance, psychological counseling, and fertility-related information services.

Circulating senescent myeloid cells infiltrate the brain and cause neurodegeneration in histiocytic disorders.

Neurodegenerative diseases (ND) are characterized by progressive loss of neuronal function. Mechanisms of ND pathogenesis are incompletely understood, hampering the development of effective therapies. Langerhans cell histiocytosis (LCH) is an inflammatory neoplastic disorder caused by hematopoietic progenitors expressing mitogen-activated protein kinase (MAPK)-activating mutations that differentiate into senescent myeloid cells that drive lesion formation. Some individuals with LCH subsequently develop progressive and incurable neurodegeneration (LCH-ND). Here, we showed that LCH-ND was caused by myeloid cells that were clonal with peripheral LCH cells. Circulating BRAFV600E+ myeloid cells caused the breakdown of the blood-brain barrier (BBB), enhancing migration into the brain parenchyma where they differentiated into senescent, inflammatory CD11a+ macrophages that accumulated in the brainstem and cerebellum. Blocking MAPK activity and senescence programs reduced peripheral inflammation, brain parenchymal infiltration, neuroinflammation, neuronal damage and improved neurological outcome in preclinical LCH-ND. MAPK activation and senescence programs in circulating myeloid cells represent targetable mechanisms of LCH-ND.

Circulating senescent myeloid cells drive blood brain barrier breakdown and neurodegeneration.

Neurodegenerative diseases (ND) are characterized by progressive loss of neuronal function. Mechanisms of ND pathogenesis are incompletely understood, hampering the development of effective therapies. Langerhans cell histiocytosis (LCH) is an inflammatory neoplastic disorder caused by hematopoietic progenitors expressing MAPK activating mutations that differentiate into senescent myeloid cells that drive lesion formation. Some patients with LCH subsequently develop progressive and incurable neurodegeneration (LCH-ND). Here, we show that LCH-ND is caused by myeloid cells that are clonal with peripheral LCH cells. We discovered that circulating BRAF V600E + myeloid cells cause the breakdown of the blood-brain barrier (BBB), enhancing migration into the brain parenchyma where they differentiate into senescent, inflammatory CD11a + macrophages that accumulate in the brainstem and cerebellum. Blocking MAPK activity and senescence programs reduced parenchymal infiltration, neuroinflammation, neuronal damage and improved neurological outcome in preclinical LCH-ND. MAPK activation and senescence programs in circulating myeloid cells represent novel and targetable mechanisms of ND.

Cell type-specific NRBF2 orchestrates autophagic flux and adult hippocampal neurogenesis in chronic stress-induced depression.

Dysfunctional autophagy and impairment of adult hippocampal neurogenesis (AHN) each contribute to the pathogenesis of major depressive disorder (MDD). However, whether dysfunctional autophagy is linked to aberrant AHN underlying MDD remains unclear. Here we demonstrate that the expression of nuclear receptor binding factor 2 (NRBF2), a component of autophagy-associated PIK3C3/VPS34-containing phosphatidylinositol 3-kinase complex, is attenuated in the dentate gyrus (DG) under chronic stress. NRBF2 deficiency inhibits the activity of the VPS34 complex and impairs autophagic flux in adult neural stem cells (aNSCs). Moreover, loss of NRBF2 disrupts the neurogenesis-related protein network and causes exhaustion of aNSC pool, leading to the depression-like phenotype. Strikingly, overexpressing NRBF2 in aNSCs of the DG is sufficient to rescue impaired AHN and depression-like phenotype of mice. Our findings reveal a significant role of NRBF2-dependent autophagy in preventing chronic stress-induced AHN impairment and suggest the therapeutic potential of targeting NRBF2 in MDD treatment.

Rit2 silencing in dopamine neurons drives a progressive Parkinsonian phenotype.

Parkinson's disease (PD) is the second most prevalent neurodegenerative disease and arises from dopamine (DA) neuron death selectively in the substantia nigra pars compacta (SNc). Rit2 is a reported PD risk allele, and recent single cell transcriptomic studies identified a major RIT2 cluster in PD DA neurons, potentially linking Rit2 expression anomalies to a PD patient cohort. However, it is still unknown whether Rit2 loss itself is causative for PD or PD-like symptoms. Here we report that conditional Rit2 silencing in mouse DA neurons drove a progressive motor dysfunction that was more rapid in males than females and was rescued at early stages by either inhibiting the DA transporter (DAT) or with L-DOPA treatment. Motor dysfunction was accompanied by decreases in DA release, striatal DA content, phenotypic DAergic markers, and a loss of DA neurons, with increased pSer129-alpha synuclein expression. These results provide the first evidence that Rit2 loss is causal for SNc cell death and a PD-like phenotype, and reveal key sex-specific differences in the response to Rit2 loss.

A convolutional network and attention mechanism-based approach to predict protein-RNA binding residues.

Protein-RNA interactions play a key role in various biological cellular processes, and many experimental and computational studies have been initiated to analyze their interactions. However, experimental determination is quite complex and expensive. Therefore, researchers have worked to develop efficient computational tools to detect protein-RNA binding residues. The accuracy of existing methods is limited by the features of the target and the performance of the computational models; there remains room for improvement. To solve the problem of the accurate detection of protein-RNA binding residues, we propose a convolutional network model named PBRPre based on improved MobileNet. First, by extracting the position information of the target complex and the 3-mer amino acid feature data, the position-specific scoring matrix (PSSM) is improved by using spatial neighbor smoothing processing and discrete wavelet transform to fully exploit the spatial structure information of the target and enrich the feature dataset. Second, the deep learning model MobileNet is used to integrate and optimize the potential features in the target complexes; then, by introducing the Vision Transformer (ViT) network classification layer, the deep-level information of the target is mined to enhance the processing ability of the model for global information and to improve the detection accuracy of the classifiers. The results show that the AUC value of the model can reach 0.866 in the independent testing dataset, which shows that PBRPre can effectively realize the detection of protein-RNA binding residues. All datasets and resource codes of PBRPre are available at https://github.com/linglewu/PBRPre for academic use.

Rit2 silencing in dopamine neurons drives a Parkinsonian phenotype.

Parkinson's disease (PD) is the second most prevalent neurodegenerative disease and arises from dopamine (DA) neuron death selectively in the substantia nigra pars compacta (SNc). Rit2 is a reported PD risk allele, and recent single cell transcriptomic studies identified a major RIT2 cluster in PD DA neurons, potentially linking Rit2 expression loss to a PD patient cohort. However, it is still unknown whether Rit2 loss itself is causative for PD or PD-like symptoms. Here we report that conditional Rit2 silencing in mouse DA neurons drove motor dysfunction that occurred earlier in males than females and was rescued at early stages by either inhibiting the DA transporter (DAT) or with L-DOPA treatment. Motor dysfunction was accompanied by decreased DA release, striatal DA content, phenotypic DAergic markers, DA neurons, and DAergic terminals, with increased pSer129-alpha synuclein and pSer935-LRRK2 expression. These results provide the first evidence that Rit2 loss is causal for SNc cell death and a PD-like phenotype, and reveal key sex-specific differences in the response to Rit2 loss.

Autophagy enables microglia to engage amyloid plaques and prevents microglial senescence.

Dysfunctional autophagy has been implicated in the pathogenesis of Alzheimer's disease (AD). Previous evidence suggested disruptions of multiple stages of the autophagy-lysosomal pathway in affected neurons. However, whether and how deregulated autophagy in microglia, a cell type with an important link to AD, contributes to AD progression remains elusive. Here we report that autophagy is activated in microglia, particularly of disease-associated microglia surrounding amyloid plaques in AD mouse models. Inhibition of microglial autophagy causes disengagement of microglia from amyloid plaques, suppression of disease-associated microglia, and aggravation of neuropathology in AD mice. Mechanistically, autophagy deficiency promotes senescence-associated microglia as evidenced by reduced proliferation, increased Cdkn1a/p21Cip1, dystrophic morphologies and senescence-associated secretory phenotype. Pharmacological treatment removes autophagy-deficient senescent microglia and alleviates neuropathology in AD mice. Our study demonstrates the protective role of microglial autophagy in regulating the homeostasis of amyloid plaques and preventing senescence; removal of senescent microglia is a promising therapeutic strategy.

PredinID: Predicting Pathogenic Inframe Indels in Human Through Graph Convolution Neural Network With Graph Sampling Technique.

Inframe insertion/deletion (indel) variants may alter protein sequence and function, which are closely related to an extensive variety of diseases. Although recent researches have paid attention to the associations between inframe indels and diseases, modeling indels in silico and interpreting their pathogenicity remain challenging, mainly due to the lack of experimental information and computational methodologies. In this article, we propose a novel computational method named PredinID (Predictor for inframe InDels) via graph convolutional network (GCN). PredinID leverages k-nearest neighbor algorithm to construct the feature graph for aggregating more informative representation, regarding the pathogenic inframe indel prediction as a node classification task. An edge-based sampling strategy is designed for extracting information from both the potential connections of feature space and the topological structure of subgraphs. Evaluated by 5-fold cross-validations, the PredinID method achieves satisfactory performance and is superior to four classic machine learning algorithms and two GCN methods. Comprehensive experiments show that PredinID has superior performances when compared with the state-of-the-art methods on the independent test set. Moreover, we also implement a web server at http://predinid.bio.aielab.cc/, to facilitate the use of the model.

Prediction of hot spots in protein-DNA binding interfaces based on discrete wavelet transform and wavelet packet transform.

BACKGROUND: Identification of hot spots in protein-DNA binding interfaces is extremely important for understanding the underlying mechanisms of protein-DNA interactions and drug design. Since experimental methods for identifying hot spots are time-consuming and expensive, and most of the existing computational methods are based on traditional protein-DNA features to predict hot spots, unable to make full use of the effective information in the features. RESULTS: In this work, a method named WTL-PDH is proposed for hot spots prediction. To deal with the unbalanced dataset, we used the Synthetic Minority Over-sampling Technique to generate minority class samples to achieve the balance of dataset. First, we extracted the solvent accessible surface area features and structural features, and then processed the traditional features using discrete wavelet transform and wavelet packet transform to extract the wavelet energy information and wavelet entropy information, and obtained a total of 175 dimensional features. In order to obtain the best feature subset, we systematically evaluate these features in various feature selection strategies. Finally, light gradient boosting machine (LightGBM) was used to establish the model. CONCLUSIONS: Our method achieved good results on independent test set with AUC, MCC and F1 scores of 0.838, 0.533 and 0.750, respectively. WTL-PDH can achieve generally better performance in predicting hot spots when compared with state-of-the-art methods. The dataset and source code are available at https://github.com/chase2555/WTL-PDH .

Development and validation of a machine learning algorithm prediction for dense granule proteins in Apicomplexa.

BACKGROUND: Apicomplexa consist of numerous pathogenic parasitic protistan genera that invade host cells and reside and replicate within the parasitophorous vacuole (PV). Through this interface, the parasite exchanges nutrients and affects transport and immune modulation. During the intracellular life-cycle, the specialized secretory organelles of the parasite secrete an array of proteins, among which dense granule proteins (GRAs) play a major role in the modification of the PV. Despite this important role of GRAs, a large number of potential GRAs remain unidentified in Apicomplexa. METHODS: A multi-view attention graph convolutional network (MVA-GCN) prediction model with multiple features was constructed using a combination of machine learning and genomic datasets, and the prediction was performed on selected Neospora caninum protein data. The candidate GRAs were verified by a CRISPR/Cas9 gene editing system, and the complete NcGRA64(a,b) gene knockout strain was constructed and the phenotypes of the mutant were analyzed. RESULTS: The MVA-GCN prediction model was used to screen N. caninum candidate GRAs, and two novel GRAs (NcGRA64a and NcGRA64b) were verified by gene endogenous tagging. Knockout of complete genes of NcGRA64(a,b) in N. caninum did not affect the parasite's growth and replication in vitro and virulence in vivo. CONCLUSIONS: Our study showcases the utility of the MVA-GCN deep learning model for mining Apicomplexa GRAs in genomic datasets, and the prediction model also has certain potential in mining other functional proteins of apicomplexan parasites.

GRA-GCN: Dense Granule Protein Prediction in Apicomplexa Protozoa Through Graph Convolutional Network.

Dense granule proteins (GRAs) are secreted by Apicomplexa protozoa, which are closely related to an extensive variety of farm animal diseases. Predicting GRAs is an integral part in prevention and treatment of parasitic diseases. Considering that biological experiment approach is time-consuming and labor-intensive, computational method is a superior choice. Hence, developing an effective computational method for GRAs prediction is of urgency. In this paper, we present a novel computational method named GRA-GCN through graph convolutional network. In terms of the graph theory, the GRAs prediction can be regarded as a node classification task. GRA-GCN leverages k-nearest neighbor algorithm to construct the feature graph for aggregating more informative representation. To our knowledge, this is the first attempt to utilize computational approach for GRAs prediction. Evaluated by 5-fold cross-validations, the GRA-GCN method achieves satisfactory performance, and is superior to four classic machine learning-based methods and three state-of-the-art models. The analysis of the comprehensive experiment results and a case study could offer valuable information for understanding complex mechanisms, and would contribute to accurate prediction of GRAs. Moreover, we also implement a web server at http://dgpd.tlds.cc/GRAGCN/index/, for facilitating the process of using our model.