Search Results (30)

Background/Objectives: Autoimmunity towards podocyte antigens causes membranous nephropathy (MN). Numerous MN target antigens (MNTAgs) have been reported, including PLA2R1, THSD7A, NTNG1, TGFBR3, HTRA1, NDNF, SEMA3B, FAT1, EXT1, CNTN1, NELL1, PCDH7, EXT2, PCSK6, and NCAM1, but their podocyte expression has not been thoroughly studied. Methods: We screened CZ CELLxGene single-cell RNA (scRNA) sequence datasets for those of adult, fetal, and mouse kidneys and analyzed the above MNTAgs’ expression. Results: In adult kidneys, most MNTAgs are present in podocytes, except PCSK6 and NCAM1. PLA2R1 is expressed significantly more than other MNTAgs in podocytes and is a major podocyte marker, consistent with PLA2R1 as the dominant MNTAg. Additionally, PLA2R1 is a top-upregulated gene in the podocytes of chronic kidney disease, acute kidney injury, and diabetic nephropathy, indicating its general role in causing podocyte injury. PLA2R1, NTNG1, HTRA1, and NDNF display podocyte-enriched expression along with elevated chromatin accessibility in podocytes, suggesting transcription initiation contributing to their preference expression in podocytes. In the fetal kidney, most MNTAgs are expressed in podocytes. While PLA2R1 is weakly present in podocytes, SEMA3B is abundantly expressed in immature and mature podocytes, supporting SEMA3B as a childhood MNTAg. In mouse kidneys, Thsd7a is the only MNTAg with a prominent level and podocyte-specific expression. Conclusions: Most MNTAgs are present in podocytes in adults and during renal development. In adults, PLA2R1 expression is highly enriched in podocytes and significantly upregulated in multiple kidney diseases accompanied by proteinuria. In mouse kidneys, Thsd7a is specifically expressed in podocytes at an elevated level. Full article

Background: In eukaryotes with a double-stranded linear DNA genome, the loss of terminal DNA during replication is inevitable due to an end-replication problem; here, telomeres serve as a buffer against DNA loss. Thus, the activation of the telomere maintenance mechanism (TMM) is a prerequisite for malignant transformation. Methods: We compared neurofibroma (NF, benign) and malignant peripheral nerve sheath tumors (MPNSTs) occurring in the same patient with type 1 neurofibromatosis, where each NF–MPNST pair shared the same genetic background and differentiation lineage; this minimizes the genetic bias and contrasts only those changes that are related to malignant transformation. A total of 20 NF–MPNST pairs from 20 NF1 patients were analyzed. Whole-transcriptome sequencing (WTS) was conducted to profile the transcriptional relationship, and whole-genome sequencing (WGS) was performed to measure the telomere length. Results: We identified 22 differentially expressed genes (DEGs) during the malignant transformation of MPNSTs. Among them, NELL2 activated PAX7, which sequentially activated RAD52, the recombinase of RAD52-dependent alternative lengthening of telomeres (ALT). RAD52 elongated MPNSTs–telomeres (p = 0.017). Otherwise, neither NELL2 nor PAX7 affected telomere length (p = 0.647 and p = 0.354, respectively). RAD52 increased MPNSTs–telomeres length, independently of NELL2 and PAX7 in multiple analyses (p = 0.021). The group with increased telomere length during the malignant transformation showed inferior overall survival (OS) (HR = 3.809, p = 0.038) to the group without increased telomere length. Accordingly, the group with increased PAX7 showed inferior OS (HR = 4.896, p = 0.046) and metastasis-free survival (MFS) (HR = 9.129, p = 0.007) in comparison to the group without increased PAX7; the group with increased RAD52 showed inferior MFS (HR = 8.669, p = 0.011) in comparison to the group without increased RAD52. Conclusions: We suggest that the NELL2-PAX7 transcriptional cascade activates RAD52-dependent ALT to increase telomere length during the malignant transformation of MPNSTs, resulting in a poor prognosis. Full article

Multi-hop reasoning provides a means for inferring indirect relationships and missing information from knowledge graphs (KGs). Reinforcement learning (RL) was recently employed for multi-hop reasoning. Although RL-based methods provide explainability, they face challenges such as sparse rewards, spurious paths, large action spaces, and long training and running times. In this study, we present a novel approach that combines KG embeddings and RL strategies for multi-hop reasoning called path-based multi-hop reasoning (PMHR). We address the issues of sparse rewards and spurious paths by incorporating a well-designed reward function that combines soft rewards with rule-based rewards. The rewards are adjusted based on the target entity and the path to it. Furthermore, we perform action filtering and utilize the vectors of entities and relations acquired through KG embeddings to initialize the environment, thereby significantly reducing the runtime. Experiments involving a comprehensive performance evaluation, efficiency analysis, ablation studies, and a case study were performed. The experimental results on benchmark datasets demonstrate the effectiveness of PMHR in improving KG reasoning accuracy while preserving interpretability. Compared to existing state-of-the-art models, PMHR achieved Hit@1 improvements of 0.63%, 2.02%, and 3.17% on the UMLS, Kinship, and NELL-995 datasets, respectively. PMHR provides not only improved reasoning accuracy and explainability but also optimized computational efficiency, thereby offering a robust solution for multi-hop reasoning. Full article

White striping (WS) is a myopathy characterized by the appearance of white stripes parallel to the muscle fibers in the breast of broiler chickens, composed of adipose and connective tissues. This condition causes economic losses and, although common, its etiology remains poorly understood. Hence, the objective was to identify genes and biological mechanisms involved in the early stages of WS using a paternal broiler line that grows slightly slower than commercial ones, at 35 days of age, through the RNA sequencing of the pectoralis major muscle. Thirty genes were differentially expressed between normal and WS-affected chickens, with 23 upregulated and 7 downregulated in the affected broilers. Of these, 14 genes are novel candidates for WS and are implicated in biological processes related to muscle development (CEPBD, DUSP8, METTL21EP, NELL2, and UBE3D), lipid metabolism (PDK4, DDIT4, FKBP5, DGAT2, LIPG, TDH, and RGCC), and collagen (COL4A5 and COL4A6). Genes related to changes in muscle fiber type and the processes of apoptosis, autophagy, proliferation, and differentiation are possibly involved with the initial stage of WS development. In contrast, the genes linked to lipid metabolism and collagen may have their expression altered due to the progression of the myopathy. Full article

Benzo[a]pyrene (B[a]P) is known to inhibit neurodifferentiation and induce neurodegeneration. Agarwood or Aquilaria crassna (AC), a plant with health-promoting properties, may counteract the neurotoxic effects of B[a]P by promoting neuronal growth and survival. This study investigated the protective effect of AC leaf ethanolic extract (ACEE) on the B[a]P-induced impairment of neuronal differentiation. A transcriptomic analysis identified the canonical pathway, the biological network, and the differentially expressed genes (DEGs) that are changed in response to neuronal differentiation and neurogenesis. Several genes, including CXCR4, ENPP2, GAP43, GFRA2, NELL2, NFASC, NSG2, NGB, BASP1, and NEUROD1, in B[a]P-treated SH-SY5Y cells were up-regulated after treatment with ACEE. Notably, a Western blot analysis further confirmed that ACEE increased the protein levels of GAP43 and neuroglobin. B[a]P treatment led to decreased phosphorylation of Akt and increased phosphorylation of ERK in SH-SY5Y cells; however, ACEE was able to reverse these effects. Clionasterol and lupenone were identified in ACEE. Molecular docking showed that these two phytochemicals had significant interactions with CXCR4, GDNF family receptor alpha (GFRA), and retinoid X receptors (RXRs). In conclusion, ACEE may be a potential alternative medicine for the prevention of impaired neuronal differentiation and neurodegenerative diseases. Full article

Knowledge graph reasoning can deduce new facts and relationships, which is an important research direction of knowledge graphs. Most of the existing methods are based on end-to-end reasoning which cannot effectively use the knowledge graph, so consequently the performance of the method still needs to be improved. Therefore, we combine causal inference with reinforcement learning and propose a new framework for knowledge graph reasoning. By combining the counterfactual method in causal inference, our method can obtain more information as prior knowledge and integrate it into the control strategy in the reinforcement model. The proposed method mainly includes the steps of relationship importance identification, reinforcement learning framework design, policy network design, and the training and testing of the causal reinforcement learning model. Specifically, a prior knowledge table is first constructed to indicate which relationship is more important for the problem to be queried; secondly, designing state space, optimization, action space, state transition and reward, respectively, is undertaken; then, the standard value is set and compared with the weight value of each candidate edge, and an action strategy is selected according to the comparison result through prior knowledge or neural network; finally, the parameters of the reinforcement learning model are determined through training and testing. We used four datasets to compare our method to the baseline method and conducted ablation experiments. On dataset NELL-995 and FB15k-237, the experimental results show that the MAP scores of our method are 87.8 and 45.2, and the optimal performance is achieved. Full article

The inductive relation prediction of knowledge graphs, as an important research topic, aims at predicting the missing relation between unknown entities with many real-world applications. Existing approaches toward this problem mostly use enclosing subgraphs to extract the features of target nodes to make predictions; however, there is a tendency to ignore the neighboring relations outside the enclosing subgraph, thus leading to inaccurate predictions. In addition, they also neglect the rich commonsense information that can help filter out less convincing results. In order to address the above issues, this paper proposes a commonsense-guided inductive relation prediction method with a dual attention mechanism called CNIA. Specifically, in addition to the enclosing subgraph, we added the multi-hop neighboring relations of target nodes, thereby forming a neighbor-enriched subgraph where the initial embeddings are generated. Next, we obtained the subgraph representations with a dual attention (i.e., edge-aware and relation-aware) mechanism, as well as the neighboring relational path embeddings. Then, we concatenated the two embeddings before feeding them into the supervised learning model. A commonsense re-ranking mechanism was introduced to filter the results that conformed to commonsense. Extensive experiments on WN18RR, FB15k-237, and NELL995 showed that CNIA achieves better prediction results when compared to the state-of-the-art models. The results suggested that our proposed model can be considered as an effective and state-of-the-art solution for inductive relation prediction. Full article

The construction of medical knowledge graphs (MKGs) is steadily progressing from manual to automatic methods, which inevitably introduce noise, which could impair the performance of downstream healthcare applications. Existing error detection approaches depend on the topological structure and external labels of entities in MKGs to improve their quality. Nevertheless, due to the cost of manual annotation and imperfect automatic algorithms, precise entity labels in MKGs cannot be readily obtained. To address these issues, we propose an approach named Enhancing error detection on Medical knowledge graphs via intrinsic labEL (EMKGEL). Considering the absence of hyper-view KG, we establish a hyper-view KG and a triplet-level KG for implicit label information and neighborhood information, respectively. Inspired by the success of graph attention networks (GATs), we introduce the hyper-view GAT to incorporate label messages and neighborhood information into representation learning. We leverage a confidence score that combines local and global trustworthiness to estimate the triplets. To validate the effectiveness of our approach, we conducted experiments on three publicly available MKGs, namely PharmKG-8k, DiseaseKG, and DiaKG. Compared with the baseline models, the Precision@K value improved by 0.7%, 6.1%, and 3.6%, respectively, on these datasets. Furthermore, our method empirically showed that it significantly outperformed the baseline on a general knowledge graph, Nell-995. Full article

Considering the complexity of entity pair relations and the information contained in the target neighborhood in few-shot knowledge graphs (KG), existing few-shot KG completion methods generally suffer from insufficient relation representation learning capabilities and neglecting the contextual semantics of entities. To tackle the above problems, we propose a Few-shot Relation Learning-based Knowledge Graph Completion model (FRL-KGC). First, a gating mechanism is introduced during the aggregation of higher-order neighborhoods of entities in formation, enriching the central entity representation while reducing the adverse effects of noisy neighbors. Second, during the relation representation learning stage, a more accurate relation representation is learned by using the correlation between entity pairs in the reference set. Finally, an LSTM structure is incorporated into the Transformer learner to enhance its ability to learn the contextual semantics of entities and relations and predict new factual knowledge. We conducted comparative experiments on the publicly available NELL-One and Wiki-One datasets, comparing FRL-KGC with six few-shot knowledge graph completion models and five traditional knowledge graph completion models for five-shot link prediction. The results showed that FRL-KGC outperformed all comparison models in terms of MRR, Hits@10, Hits@5, and Hits@1 metrics. Full article

Telomeres are terminal DNA regions of chromosomes that prevent chromosomal fusion and degradation during cell division. In cattle, leukocyte telomere length (LTL) is associated with longevity, productive lifespan, and disease susceptibility. However, the genetic basis of LTL in this species is less studied than in humans. In this study, we utilized the whole-genome resequencing data of 239 animals from 17 cattle breeds for computational leukocyte telomere length estimation and subsequent genome-wide association study of LTL. As a result, we identified 42 significant SNPs, of which eight were found in seven genes (EXOC6B, PTPRD, RPS6KC1, NSL1, AGBL1, ENSBTAG00000052188, and GPC1) when using covariates for two major breed groups (Turano–Mongolian and European). Association analysis with covariates for breed effect detected 63 SNPs, including 13 in five genes (EXOC6B, PTPRD, RPS6KC1, ENSBTAG00000040318, and NELL1). The PTPRD gene, demonstrating the top signal in analysis with breed effect, was previously associated with leukocyte telomere length in cattle and likely is involved in the mechanism of alternative lengthening of telomeres. The single nucleotide variants found could be tested for marker-assisted selection to improve telomere-length-associated traits. Full article

Bovine mastitis is the most frequent disease on dairy farms, which leads to a decrease in the health welfare of the animals and great economic losses. This study was aimed at determining the quantitative variations in the milk proteome caused by natural infection by Staphylococcus and Streptococcus species in order to gain further understanding of any discrepancies in pathophysiology and host immune responses, independent of the mastitis level. After identification of Staphylococcus (N = 51) and Streptococcus (N = 67) spp., tandem mass tag (TMT)-labeled quantitative proteomic and liquid chromatography-mass spectrometry (LC-MS/MS) techniques on a modular Ultimate 3000 RSLCnano system coupled to a Q Exactive Plus was applied on aseptically sampled milk from Holstein cows. Proteome Discoverer was used for protein identification and quantitation through the SEQUEST algorithm. Statistical analysis employing R was used to identify differentially abundant proteins between the groups. Protein classes, functions and functional-association networks were determined using the PANTHER and STRING tools and pathway over-representation using the REACTOME. In total, 156 master bovine proteins were identified (two unique peptides, p < 0.05 and FDR < 0.001), and 20 proteins showed significantly discrepant abundance between the genera (p < 0.05 and FDR < 0.5). The most discriminatory proteins per group were odorant-binding protein (higher in staphylococci) and fibrinogen beta chain protein (higher in streptococci). The receiver operating characteristic (ROC) curve showed that protein kinase C-binding protein NELL2, thrombospondin-1, and complement factor I have diagnostic potential for differentiating staphylococci and streptococci intramammary infection and inflammation. Improved understanding of the host response mechanisms and recognition of potential biomarkers of specific-pathogen mastitis, which may aid prompt diagnosis for control implementation, are potential benefits of this study. Full article

Crohn’s disease (CD) is a chronic relapsing inflammatory bowel disease of unknown etiology. Genetic predisposition and dysbiotic gut microbiota are important factors in the pathogenesis of CD. In this study, we analyzed the taxonomic composition of the gut microbiota and genotypes of 24 single nucleotide polymorphisms (SNP) associated with the risk of CD. The studied cohorts included 96 CD patients and 24 healthy volunteers from Russia. Statistically significant differences were found in the allele frequencies for 8 SNPs and taxonomic composition of the gut microbiota in CD patients compared with controls. In addition, two types of gut microbiota communities were identified in CD patients. The main distinguishing driver of bacterial families for the first community type are Bacteroidaceae and unclassified members of the Clostridiales order, and the second type is characterized by increased abundance of Streptococcaceae and Enterobacteriaceae. Differences in the allele frequencies of the rs9858542 (BSN), rs3816769 (STAT3), and rs1793004 (NELL1) were also found between groups of CD patients with different types of microbiota communities. These findings confirm the complex multifactorial nature of CD. Full article

The architecture of the Soviet Avant-garde represents an important part in the history of the world’s architecture. It has become and continues to be a subject of interest for numerous researchers all over the world since the second half of the 20th century. However, was it well-known before, and who was the first to spread that knowledge? This article aims to study the critical legacy of Italian artist and architect Vinicio Paladini and his role as the first disseminator of the ideas of Soviet Avant-garde architecture in Italy in the 1920s with his article “Lo spirito moderno e la nuova architettura nell’U.R.S.S.” This article provides an in-depth analysis of chosen projects and architects as well as attribution of illustrative material alongside the archival research. It establishes the origins of Paladini’s interest in the art and architecture of the USSR, surfaces his perception of the characteristics of Soviet architecture, and highlights the importance of his role in promoting Russian modernism in Italy. Full article

At a time when wars and pandemics have disrupted the world order and deeply damaged international agreements, more people are talking about the energy and food independence of the nation-states. It is clear that the achievement of these objectives will not be rapid, but it is also clear that land use must take account of these important aspects. The over-dimensioning of the settled areas presents illogically in almost all municipal urban planning plans regardless of their geographical location, demographic dynamics, and values of the administered territory; it certainly constitutes an obstacle to effective land use. The work presented here aims to analyze the effects on agricultural production of the transformations envisaged by the plans of the provinces of Modena and Reggio nell’Emilia, while also providing solutions that could promote the sustainability of the interventions planned in these areas of high agri-food value. Full article

Long-term selection or evolution is an important factor governing the development of disease resistance in pigs. To better clarify the molecular mechanisms underlying different levels of disease resistance, we used transcriptomics and proteomics analysis to characterize differences in the immunities between six resistant (Min pig) and six susceptible (Large White, LW) pigs which were raised in the same environment. A total of 135 proteins and 791 genes were identified as being differentially expressed between the Large White and Min pig groups. Protein expression clustering and functional analysis revealed that proteins related to immune system process, humoral immune response, the B cell receptor signaling pathway, lymphocyte-mediated immunity, and innate immune responses were more highly expressed in Min pigs. Transcriptome gene set enrichment analysis was used to reveal that pathways of cell adhesion molecules and antigen processing and presentation are significantly enriched in Min pigs. Integrated proteomics and transcriptomics data analysis identified 16 genes that are differentially expressed at both the mRNA and protein levels. In addition, 13 out of these 16 genes were related to the quantitative trait loci of immune diseases, including neural EGFL-like 2 (NELL2) and lactate dehydrogenase B (LDHB), which are involved in innate immunity. Correlation analysis between the genes/proteins and cytokines shows upregulated proteins in LW pigs in association with immunosuppressive/pro-inflammatory cytokines, such as interleukin (IL) 10, IL6, and tumor necrosis factor alpha. This was further validated using parallel reaction monitoring analysis. In summary, we discovered several potential candidate pathways and key genes/proteins involved in determining differences in disease resistance between the two studied pig breeds, which could provide new insights into the breeding of pigs for disease resistance. Full article