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Background: The long-term mortality and morbidity of patients with severe fever with thrombocytopenia syndrome (SFTS) remain unclear. Methods: This retrospective cohort study was conducted using the National Health Insurance Service dataset on hospitalized patients with SFTS aged ≥20 years between 2016 and 2021 (n = 1,217). Each SFTS case was matched with three controls hospitalized for non-SFTS-related diseases using propensity score matching. The all-cause mortality of patients with SFTS was evaluated during the one-year follow-up and compared with that of controls. Post-discharge events were investigated to determine the effects of SFTS on post-acute sequelae. Results: Finally, 1,105 patients with SFTS and 3,315 controls were included. Patients with SFTS had a higher risk of death during the one-year follow-up than that of controls (hazard ratio [HR], 2·26; 95% confidence interval [CI], 1·82–2·81). Thirty-day mortality was significantly higher in the SFTS group (HR, 3·99; 95% CI, 3·07–5·19) than in the control group. An increased risk of death after 31–365 days was observed among controls, though this difference was significant only among patients in their 80s (HR, 0·18; 95% CI, 0·06–0·57). For post-discharge events, patients in the SFTS group exhibited a higher risk of readmission (HR, 1·17; 95% CI, 1·04–1·32) and emergency room visit (HR, 2·32; 95% CI, 1·96–2·76) than those in the control group. Conclusion: SFTS induces a higher risk of short-term mortality and post-acute sequelae in hospitalized patients during a one-year follow-up than non-SFTS-related diseases. Our results provide guidance for the management of SFTS. Author summary: Severe fever with thrombocytopenia syndrome (SFTS) is a life-threatening viral disease, which has expanded beyond Asian countries. However, most studies have focused on the short-term mortality of patients with SFTS, and knowledge of their long-term outcomes is limited. This nationwide cohort study evaluated long-term outcomes of hospitalized patients with SFTS between 2016 and 2021 using the national dataset. We found that hospitalized patients with SFTS had higher one-year mortality (HR, 2.26) than those without SFTS. An increased mortality risk was attributed to death in the first 30 days of hospitalisation (HR, 3.99). Although the long-term mortality (31–365 days) did not differ between patients with and without SFTS, except for those in their 80s, patients with SFTS suffered from post-acute events, including readmission (HR, 1.17) or emergency room visit (HR, 2.32) during the one-year follow-up. SFTS is an emerging infectious disease that demands attention. The disease burden extends beyond the acute phase, with patients often experiencing ongoing health problems even after hospital discharge. Our findings underscore the post-acute burden of SFTS and offer valuable insights for the development of effective management plans for acute and post-acute illnesses in patients with SFTS. [ABSTRACT FROM AUTHOR]

Targeted therapies for inhibiting the growth of cancer cells or inducing apoptosis are urgently needed for effective rhabdomyosarcoma (RMS) treatment. However, identifying cancer-targeting compounds with few side effects, among the many potential compounds, is expensive and time-consuming. A computational approach to reduce the number of potential candidate drugs can facilitate the discovery of attractive lead compounds. To address this and obtain reliable predictions of novel cell-line-specific drugs, we apply prediction models that have the potential to improve drug discovery approaches for RMS treatment. The results of two prediction models were ensemble and validated via in vitro experiments. The computational models were trained using data extracted from the Genomics of Drug Sensitivity in Cancer database and tested on two RMS cell lines to select potential RMS drug candidates. Among 235 candidate drugs, 22 were selected following the result of the computational approach, and three candidate drugs were identified (NSC207895, vorinostat, and belinostat) that showed selective effectiveness in RMS cell lines in vitro via the induction of apoptosis. Our in vitro experiments have demonstrated that our proposed methods can effectively identify and repurpose drugs for treating RMS. [ABSTRACT FROM AUTHOR]

Nicotinamide adenine dinucleotide (NAD+) is an essential co-factor for cellular metabolism and serves as a substrate in enzymatic processes. NAD+ is produced by de novo synthesis or salvage pathways in nearly all bacterial species. Haemophilus influenzae lacks the capacity for de novo synthesis, so it is dependent on import of NAD+ from the external environment or salvage biosynthetic pathways for recycling of NAD+ precursors and breakdown products. However, the actual sources of NAD+ utilized by H. influenzae in the respiratory tract are not well defined. In this study, we found that a variety of bacteria, including species found in the upper airway of humans, released NAD+ that was readily detectable in extracellular culture fluid, and which supported growth of H. influenzae in vitro. By contrast, certain strains of Streptococcus pyogenes (group A streptococcus or GAS) inhibited growth of H. influenzae in vitro by secreting NAD+-glycohydrolase (NADase), which degraded extracellular NAD+. Conversely, GAS strains that lacked enzymatically active NADase released extracellular NAD+, which could support H. influenzae growth. Our results suggest that many bacterial species, including normal flora of the upper airway, release NAD+ into the environment. GAS is distinctive in its ability to both release and degrade NAD+. Thus, colonization of the airway with H. influenzae may be promoted or restricted by co-colonization with GAS in a strain-specific manner that depends, respectively, on release of NAD+ or secretion of active NADase. We suggest that, in addition to its role as a cytotoxin for host cells, NADase may serve a separate function by restricting growth of H. influenzae in the human respiratory tract. [ABSTRACT FROM AUTHOR]

Nicotinamide adenine dinucleotide (NAD+) is an essential co-factor for cellular metabolism and serves as a substrate in enzymatic processes. NAD+ is produced by de novo synthesis or salvage pathways in nearly all bacterial species. Haemophilus influenzae lacks the capacity for de novo synthesis, so it is dependent on import of NAD+ from the external environment or salvage biosynthetic pathways for recycling of NAD+ precursors and breakdown products. However, the actual sources of NAD+ utilized by H. influenzae in the respiratory tract are not well defined. In this study, we found that a variety of bacteria, including species found in the upper airway of humans, released NAD+ that was readily detectable in extracellular culture fluid, and which supported growth of H. influenzae in vitro. By contrast, certain strains of Streptococcus pyogenes (group A streptococcus or GAS) inhibited growth of H. influenzae in vitro by secreting NAD+-glycohydrolase (NADase), which degraded extracellular NAD+. Conversely, GAS strains that lacked enzymatically active NADase released extracellular NAD+, which could support H. influenzae growth. Our results suggest that many bacterial species, including normal flora of the upper airway, release NAD+ into the environment. GAS is distinctive in its ability to both release and degrade NAD+. Thus, colonization of the airway with H. influenzae may be promoted or restricted by co-colonization with GAS in a strain-specific manner that depends, respectively, on release of NAD+ or secretion of active NADase. We suggest that, in addition to its role as a cytotoxin for host cells, NADase may serve a separate function by restricting growth of H. influenzae in the human respiratory tract. [ABSTRACT FROM AUTHOR]

Understanding the role of genes in human disease is of high importance. However, identifying genes associated with human diseases requires laborious experiments that involve considerable effort and time. Therefore, a computational approach to predict candidate genes related to complex diseases including cancer has been extensively studied. In this study, we propose a convolutional neural network-based knowledge graph-embedding model (KGED), which is based on a biological knowledge graph with entity descriptions to infer relationships between biological entities. As an application demonstration, we generated gene-interaction networks for each cancer type using gene-gene relationships inferred by KGED. We then analyzed the constructed gene networks using network centrality measures, including betweenness, closeness, degree, and eigenvector centrality metrics, to rank the central genes of the network and identify highly correlated cancer genes. Furthermore, we evaluated our proposed approach for prostate, breast, and lung cancers by comparing the performance with that of existing approaches. The KGED model showed improved performance in predicting cancer-related genes using the inferred gene-gene interactions. Thus, we conclude that gene-gene interactions inferred by KGED can be helpful for future research, such as that aimed at future research on pathogenic mechanisms of human diseases, and contribute to the field of disease treatment discovery. [ABSTRACT FROM AUTHOR]

Background: Many new medicines have been derived from natural sources such as plants, which have a long history of being used for disease treatment. Thus, their benefits and side effects have been studied, and plant-related information including plant and disease relations have been accumulated in Medline articles. Because numerous articles are available in Medline and are written in natural language, text-mining is important. However, a corpus of plant and disease relations is not available yet. Thus, we aimed to construct such a corpus. Methods and results: In this study, we designed and annotated a plant–disease relations corpus, and proposed a computational model to predict plant–disease relations using the corpus. We categorized plant and disease relations into four types: treatments of diseases, causes of diseases, associations, and negative relations. To construct a corpus of plant–disease relations, we first created its annotation guidelines and randomly selected 200 Medline abstracts. From these abstracts, we identified 1,405 and 1,755 plant and disease mentions, annotated to 105 and 237 unique plant and disease identifiers, respectively. When we selected sentences containing at least one plant and one disease mention, we extracted 878 plant and 1,077 disease entities, which finally generated a corpus of plant-disease relations including 1,309 relations from 199 abstracts. To verify the effectiveness of the corpus, we proposed a convolutional neural network model with the shortest dependency path (SDP-CNN) and applied it to the constructed corpus. The micro F-score with ten-fold cross-validation was found to be 0.764. We also applied the proposed SDP-CNN model to all Medline abstracts. When we measured its performance for 483 randomly selected plant-disease co-occurring sentences, the model showed a precision of 0.707. Conclusion: The plant–disease relations corpus is unique and represents an important resource for biomedical text-mining. The corpus of plant and disease relations is available at . [ABSTRACT FROM AUTHOR]

Chemicals interact with genes in the process of disease development and treatment. Although much biomedical research has been performed to understand relationships among genes, chemicals, and diseases, which have been reported in biomedical articles in Medline, there are few studies that extract disease–gene–chemical relationships from biomedical literature at a PubMed scale. In this study, we propose a deep learning model based on bidirectional long short-term memory to identify the evidence sentences of relationships among genes, chemicals, and diseases from Medline abstracts. Then, we develop the search engine DigChem to enable disease–gene–chemical relationship searches for 35,124 genes, 56,382 chemicals, and 5,675 diseases. We show that the identified relationships are reliable by comparing them with manual curation and existing databases. DigChem is available at . [ABSTRACT FROM AUTHOR]

With the increasing availability of multi-dimensional biological datasets for the same samples (i.e., gene expression, microRNAs, copy numbers, mutations, methylations), it has now become possible to systematically understand the regulatory mechanisms operating in a cancer cell. For this task, it is important to discover a set of co-expressed genes with functions, representing a so-called functional gene module, because co-expressed genes tend to be co-regulated by the same regulators, including transcription factors, microRNAs, and copy number aberrations. Several algorithms have been used to identify such gene modules, including hierarchical clustering and non-negative matrix factorization. Although these algorithms have been applied to many microarray datasets, only a few systematic analyses of these algorithms have been performed for RNA-sequencing (RNA-Seq) data to date. Although gene expression levels determined based on microarray and RNA-Seq datasets tend to be highly correlated, the expression levels of some genes differ depending on the platforms used for analysis, which may result in the construction of different gene modules for the same samples. Here, we compare several module detection algorithms applied to both microarray and RNA-seq datasets. We further propose a new functional gene module detection algorithm (FGMD), which is based on a hierarchical clustering algorithm that was modified to reflect actual biological observations, including the fact that a single gene may be involved in multiple biological pathways. Application of existing algorithms and the new FGMD algorithm to breast cancer and ovarian cancer datasets from The Cancer Genome Atlas showed that the FGMD algorithm had the best performance for most of the functional pathway enrichment tests and in the transcription factor enrichment test. We expect that the FGMD algorithm will contribute to improving the identification of functional gene modules related to cancer. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) are responsible for the regulation of target genes involved in various biological processes, and may play oncogenic or tumor suppressive roles. Many studies have investigated the relationships between miRNAs and their target genes, using mRNA and miRNA expression data. However, mRNA expression levels do not necessarily represent the exact gene expression profiles, since protein translation may be regulated in several different ways. Despite this, large-scale protein expression data have been integrated rarely when predicting gene-miRNA relationships. This study explores two approaches for the investigation of gene-miRNA relationships by integrating mRNA expression and protein expression data. First, miRNAs were ranked according to their effects on cancer development. We calculated influence scores for each miRNA, based on the number of significant mRNA-miRNA and protein-miRNA correlations. Furthermore, we constructed modules containing mRNAs, proteins, and miRNAs, in which these three molecular types are highly correlated. The regulatory interactions between miRNA and genes in these modules have been validated based on the direct regulations, indirect regulations, and co-regulations through transcription factors. We applied our approaches to glioblastomas (GBMs), ranked miRNAs depending on their effects on GBM, and obtained 52 GBM-related modules. Compared with the miRNA rankings and modules constructed using only mRNA expression data, the rankings and modules constructed using mRNA and protein expression data were shown to have better performance. Additionally, we experimentally verified that miR-504, highly ranked and included in the identified modules, plays a suppressive role in GBM development. We demonstrated that the integration of both expression profiles allows a more precise analysis of gene-miRNA interactions and the identification of a higher number of cancer-related miRNAs and regulatory mechanisms. [ABSTRACT FROM AUTHOR]

Pneumolysin (Ply) and pneumococcal histidine triad protein D (PhtD) are candidate proteins for a next-generation pneumococcal vaccine. We aimed to analyze the genetic diversity and antigenic heterogeneity of Ply and PhtD for 173 pneumococci isolated from invasive diseases in Korean children. Allele was designated based on the variation of amino acid sequence. Antigenicity was predicted by the amino acid hydrophobicity of the region. There were seven and 39 allele types for the ply and phtD genes, respectively. The nucleotide sequence identity was 97.2%-99.9% for ply and 91.4%-98.0% for phtD gene. Only minor variations in hydrophobicity were noted among the antigenicity plots of Ply and PhtD. Overall, the allele types of the ply and phtD genes were remarkably homogeneous, and the antigenic diversity of the corresponding proteins was very limited. The Ply and PhtD could be useful antigens for universal pneumococcal vaccines. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) play key roles in the initiation and progression of various cancers by regulating genes. Regulatory interactions between genes and miRNAs are complex, as multiple miRNAs can regulate multiple genes. In addtion, these interactions vary from patient to patient and even among patients with the same cancer type, as cancer development is a heterogeneous process. These relationships are more complicated because transcription factors and other regulatory molecules can also regulate miRNAs and genes. Hence, it is important to identify the complex relationships between genes and miRNAs in cancer. In this study, we propose a computational approach to constructing modules that represent these relationships by integrating the expression data of genes and miRNAs with gene-gene interaction data. First, we used a biclustering algorithm to construct modules consisting of a subset of genes and a subset of samples to incorporate the heterogeneity of cancer cells. Second, we combined gene-gene interactions to include genes that play important roles in cancer-related pathways. Then, we selected miRNAs that are closely associated with genes in the modules based on a Gaussian Bayesian network and Bayesian Information Criteria. When we applied our approach to ovarian cancer and glioblastoma (GBM) data sets, 33 and 54 modules were constructed, respectively. In these modules, 91% and 94% of ovarian cancer and GBM modules, respectively, were explained either by direct regulation between genes and miRNAs or by indirect relationships via transcription factors. In addition, 48.4% and 74.0% of modules from ovarian cancer and GBM, respectively, were enriched with cancer-related pathways, and 51.7% and 71.7% of miRNAs in modules were ovarian cancer-related miRNAs and GBM-related miRNAs, respectively. Finally, we extensively analyzed significant modules and showed that most genes in these modules were related to ovarian cancer and GBM. [ABSTRACT FROM AUTHOR]

Sub-networks can expose complex patterns in an entire bio-molecular network by extracting interactions that depend on temporal or condition-specific contexts. When genes interact with each other during cellular processes, they may form differential co-expression patterns with other genes across different cell states. The identification of condition-specific sub-networks is of great importance in investigating how a living cell adapts to environmental changes. In this work, we propose the weighted MAXimum clique (WMAXC) method to identify a condition-specific sub-network. WMAXC first proposes scoring functions that jointly measure condition-specific changes to both individual genes and gene-gene co-expressions. It then employs a weaker formula of a general maximum clique problem and relates the maximum scored clique of a weighted graph to the optimization of a quadratic objective function under sparsity constraints. We combine a continuous genetic algorithm and a projection procedure to obtain a single optimal sub-network that maximizes the objective function (scoring function) over the standard simplex (sparsity constraints). We applied the WMAXC method to both simulated data and real data sets of ovarian and prostate cancer. Compared with previous methods, WMAXC selected a large fraction of cancer-related genes, which were enriched in cancer-related pathways. The results demonstrated that our method efficiently captured a subset of genes relevant under the investigated condition. [ABSTRACT FROM AUTHOR]

Computational methods for predicting drug-target interactions have become important in drug research because they can help to reduce the time, cost, and failure rates for developing new drugs. Recently, with the accumulation of drug-related data sets related to drug side effects and pharmacological data, it has became possible to predict potential drug-target interactions. In this study, we focus on drug-drug interactions (DDI), their adverse effects () and pharmacological information (), and investigate the relationship among chemical structures, side effects, and DDIs from several data sources. In this study, data from the STITCH database, from drugs.com, and drug-target pairs from ChEMBL and SIDER were first collected. Then, by applying two machine learning approaches, a support vector machine (SVM) and a kernel-based L1-norm regularized logistic regression (KL1LR), we showed that DDI is a promising feature in predicting drug-target interactions. Next, the accuracies of predicting drug-target interactions using DDI were compared to those obtained using the chemical structure and side effects based on the SVM and KL1LR approaches, showing that DDI was the data source contributing the most for predicting drug-target interactions. [ABSTRACT FROM AUTHOR]

Recently, computational approaches integrating copy number aberrations (CNAs) and gene expression (GE) have been extensively studied to identify cancer-related genes and pathways. In this work, we integrate these two data sets with protein-protein interaction (PPI) information to find cancer-related functional modules. To integrate CNA and GE data, we first built a gene-gene relationship network from a set of seed genes by enumerating all types of pairwise correlations, e.g. GE-GE, CNA-GE, and CNA-CNA, over multiple patients. Next, we propose a voting-based cancer module identification algorithm by combining topological and data-driven properties (VToD algorithm) by using the gene-gene relationship network as a source of data-driven information, and the PPI data as topological information. We applied the VToD algorithm to 266 glioblastoma multiforme (GBM) and 96 ovarian carcinoma (OVC) samples that have both expression and copy number measurements, and identified 22 GBM modules and 23 OVC modules. Among 22 GBM modules, 15, 12, and 20 modules were significantly enriched with cancer-related KEGG, BioCarta pathways, and GO terms, respectively. Among 23 OVC modules, 19, 18, and 23 modules were significantly enriched with cancer-related KEGG, BioCarta pathways, and GO terms, respectively. Similarly, we also observed that 9 and 2 GBM modules and 15 and 18 OVC modules were enriched with cancer gene census (CGC) and specific cancer driver genes, respectively. Our proposed module-detection algorithm significantly outperformed other existing methods in terms of both functional and cancer gene set enrichments. Most of the cancer-related pathways from both cancer data sets found in our algorithm contained more than two types of gene-gene relationships, showing strong positive correlations between the number of different types of relationship and CGC enrichment -values (0.64 for GBM and 0.49 for OVC). This study suggests that identified modules containing both expression changes and CNAs can explain cancer-related activities with greater insights. [ABSTRACT FROM AUTHOR]

Background: Platelet-derived growth factor receptor β (PDGFRβ) is a tyrosine kinase receptor known to affect vascular development. The zebrafish is an excellent model to study specific regulators of vascular development, yet the role of PDGF signaling has not been determined in early zebrafish embryos. Furthermore, vascular mural cells, in which PDGFRβ functions cell autonomously in other systems, have not been identified in zebrafish embryos younger than 72 hours post fertilization. Methodology/Principal Findings: In order to investigate the role of PDGFRβ in zebrafish vascular development, we cloned the highly conserved zebrafish homolog of PDGFRβ. We found that pdgfrβ is expressed in the hypochord, a developmental structure that is immediately dorsal to the dorsal aorta and potentially regulates blood vessel development in the zebrafish. Using a PDGFR tyrosine kinase inhibitor, a morpholino oligonucleotide specific to PDGFRβ, and a dominant negative PDGFRβ transgenic line, we found that PDGFRβ is necessary for angiogenesis of the intersegmental vessels. Significance/Conclusion: Our data provide the first evidence that PDGFRβ signaling is required for zebrafish angiogenesis. We propose a novel mechanism for zebrafish PDGFRβ signaling that regulates vascular angiogenesis in the absence of mural cells.

Nicotinamide adenine dinucleotide (NAD+) is an essential co-factor for cellular metabolism and serves as a substrate in enzymatic processes. NAD+ is produced by de novo synthesis or salvage pathways in nearly all bacterial species. Haemophilus influenzae lacks the capacity for de novo synthesis, so it is dependent on import of NAD+ from the external environment or salvage biosynthetic pathways for recycling of NAD+ precursors and breakdown products. However, the actual sources of NAD+ utilized by H. influenzae in the respiratory tract are not well defined. In this study, we found that a variety of bacteria, including species found in the upper airway of humans, released NAD+ that was readily detectable in extracellular culture fluid, and which supported growth of H. influenzae in vitro. By contrast, certain strains of Streptococcus pyogenes (group A streptococcus or GAS) inhibited growth of H. influenzae in vitro by secreting NAD+-glycohydrolase (NADase), which degraded extracellular NAD+. Conversely, GAS strains that lacked enzymatically active NADase released extracellular NAD+, which could support H. influenzae growth. Our results suggest that many bacterial species, including normal flora of the upper airway, release NAD+ into the environment. GAS is distinctive in its ability to both release and degrade NAD+. Thus, colonization of the airway with H. influenzae may be promoted or restricted by co-colonization with GAS in a strain-specific manner that depends, respectively, on release of NAD+ or secretion of active NADase. We suggest that, in addition to its role as a cytotoxin for host cells, NADase may serve a separate function by restricting growth of H. influenzae in the human respiratory tract., Competing Interests: The authors have declared that no competing interests exist.