Targeting the NTF2-like domain of G3BP1: Novel modulators of intracellular granule dynamics.

Stress granule (SG) is a temporary cellular structure that plays a crucial role in the regulation of mRNA and protein sequestration during various cellular stress conditions. SG enables cells to cope with stress more effectively, conserving vital energy and resources. Focusing on the NTF2-like domain of G3BP1, a key protein in SG dynamics, we explore to identify and characterize novel small molecules involved in SG modulation without external stressors. Through in silico molecular docking approach to simulate the interaction between various compounds and the NTF2-like domain of G3BP1, we identified three compounds as potential candidates that could bind to the NTF2-like domain of G3BP1. Subsequent immunofluorescence experiments demonstrated that these compounds induce the formation of SG-like, G3BP1-positive granules. Importantly, the granule formation by these compounds occurs independent from the phosphorylation of eIF2α, a common mechanism in SG formation, suggesting that it might offer a new strategy for influencing SG dynamics implicated in various diseases.

Machine-Learning Algorithms Using Systemic Inflammatory Markers to Predict the Oncologic Outcomes of Colorectal Cancer After Surgery.

BACKGROUND: This study aimed to investigate the clinical significance of machine-learning (ML) algorithms based on serum inflammatory markers to predict survival outcomes for patients with colorectal cancer (CRC). METHODS: The study included 941 patients with stages I to III CRC. Based on random forest algorithms using 15 compositions of inflammatory markers, four different prediction scores (DFS score-1, DFS score-2, DFS score-3, and DFS score-4) were developed for the Yonsei cohort (training set, n = 803) and tested in the Ulsan cohort (test set, n = 138). The Cox proportional hazards model was used to determine correlation between prediction scores and disease-free survival (DFS). Harrell's concordance index (C-index) was used to compare the predictive ability of prediction scores for each composition. RESULTS: The multivariable analysis showed the DFS score-4 to be an independent prognostic factor after adjustment for clinicopathologic factors in both the training and test sets (hazard ratio [HR], 8.98; 95% confidence interval [CI] 6.7-12.04; P < 0.001 for the training set and HR, 2.55; 95% CI 1.1-5.89; P = 0.028 for the test set]. With regard to DFS, the highest C-index among single compositions was observed in the lymphocyte-to-C-reactive protein ratio (LCR) (0.659; 95% CI 0.656-0.662), and the C-index of DFS score-4 (0.727; 95% CI 0.724-0.729) was significantly higher than that of LCR in the test set. The C-index of DFS score-3 (0.725; 95% CI 0.723-0.728) was similar to that of DFS score-4, but higher than that of DFS score-2 (0.680; 95% CI 0.676-0.683). CONCLUSIONS: The ML-based approaches showed prognostic utility in predicting DFS. They could enhance clinical use of inflammatory markers in patients with CRC.

Washing-Free and Label-Free Onsite Assay for Inorganic Pyrophosphatase Activity Using a Personal Glucose Meter.

In this study, we demonstrated a personal glucose meter-based method for washing-free and label-free inorganic pyrophosphatase (PPase) detection, which relies on the cascade enzymatic reaction (CER) promoted by hexokinase and pyruvate kinase. In principle, the absence of target PPase enables adenosine triphosphate sulfurylase to catalyze the conversion of pyrophosphate (PPi) to ATP, a substrate of CER, which results in the significant reduction of glucose levels by the effective CER process. In contrast, the PPi cleavage activity works in the presence of target PPase by decomposing PPi to orthophosphate (Pi). Therefore, the CER process cannot be effectively executed, leading to the maintenance of the initial high glucose level that may be measured by a portable personal glucose meter. Based on this novel strategy, a quantitative evaluation of the PPase activity may be achieved in a dynamic linear range of 1.5-25 mU/mL with a detection limit of 1.18 mU/mL. Compared with the previous PPase detection methods, this method eliminates the demand for expensive and bulky analysis equipment as well as a complex washing step. More importantly, the diagnostic capability of this method was also successfully verified by reliably detecting PPase present in an undiluted human serum sample with an excellent recovery ratio of 100 ± 2%.

IMIPMF: Inferring miRNA-disease interactions using probabilistic matrix factorization.

Recently, increasing evidence have reported that microRNAs (miRNAs) play key roles in a variety of biological processes. Therefore, the identification of novel miRNA-disease associations can shed new light on disease etiology and pathogenesis. Till now, various computational methods have been proposed to predict potential miRNA-disease associations by reducing the experimental costs and time consumption. However, most existing methods are highly dependent on known miRNA-disease associations. Therefore, the prediction of new miRNAs (i.e., miRNAs without known associated diseases) and new diseases (i.e., diseases without known associated miRNAs) has become challenging. In this paper, we present IMIPMF, a novel method for predicting miRNA-disease associations using probabilistic matrix factorization (PMF), which is a machine learning technique that is widely used in recommender systems. Predicting the rating scores that a user may assign to each item in a recommender system is analogous to predicting miRNA-disease associations. By applying PMF, our model not only identifies novel miRNA-disease associations, but also overcomes the common problem of incompatibility with miRNAs without any known associated disease, which was a limitation of most previous computational methods. We demonstrated that our proposed model achieved a high performance with a reliable AUC value of 0.891 by performing 5-fold cross-validation. Overall, IMIPMF is a high-performance machine-learning-based model for predicting miRNA-disease associations, although it only considers known miRNA-disease associations and miRNA expression data.

Combinatorial feature embedding based on CNN and LSTM for biomedical named entity recognition.

With the rapid advancement of technology and the necessity of processing large amounts of data, biomedical Named Entity Recognition (NER) has become an essential technique for information extraction in the biomedical field. NER, which is a sequence-labeling task, has been performed using various traditional techniques including dictionary-, rule-, machine learning-, and deep learning-based methods. However, as existing biomedical NER models are insufficient to handle new and unseen entity types from the growing biomedical data, the development of more effective and accurate biomedical NER models is being widely researched. Among biomedical NER models utilizing deep learning approaches, there have been only a few studies involving the design of high-level features in the embedding layer. In this regard, herein, we propose a deep learning NER model that effectively represents biomedical word tokens through the design of a combinatorial feature embedding. The proposed model is based on Bidirectional Long Short-Term Memory (bi-LSTM) with Conditional Random Field (CRF) and enhanced by integrating two different character-level representations extracted from a Convolutional Neural Network (CNN) and bi-LSTM. Additionally, an attention mechanism is applied to the model to focus on the relevant tokens in the sentence, which alleviates the long-term dependency problem of the LSTM model and allows effective recognition of entities. The proposed model was evaluated on two benchmark datasets, the JNLPBA and NCBI-Disease, and a comparative analysis with the existing models is performed. The proposed model achieved a relatively higher performance with an F1-score of 86.93% in case of NCBI-Disease, and a competitive performance for the JNLPBA with an F1-score of 75.31%.

Novel deep learning model for more accurate prediction of drug-drug interaction effects.

BACKGROUND: Predicting the effect of drug-drug interactions (DDIs) precisely is important for safer and more effective drug co-prescription. Many computational approaches to predict the effect of DDIs have been proposed, with the aim of reducing the effort of identifying these interactions in vivo or in vitro, but room remains for improvement in prediction performance. RESULTS: In this study, we propose a novel deep learning model to predict the effect of DDIs more accurately.. The proposed model uses autoencoders and a deep feed-forward network that are trained using the structural similarity profiles (SSP), Gene Ontology (GO) term similarity profiles (GSP), and target gene similarity profiles (TSP) of known drug pairs to predict the pharmacological effects of DDIs. The results show that GSP and TSP increase the prediction accuracy when using SSP alone, and the autoencoder is more effective than PCA for reducing the dimensions of each profile. Our model showed better performance than the existing methods, and identified a number of novel DDIs that are supported by medical databases or existing research. CONCLUSIONS: We present a novel deep learning model for more accurate prediction of DDIs and their effects, which may assist in future research to discover novel DDIs and their pharmacological effects.

Die-Attach Structure of Silicon-on-Glass MEMS Devices Considering Asymmetric Packaging Stress and Thermal Stress.

Die attach is a typical process that induces thermal stress in the fabrication of microelectromechanical system (MEMS) devices. One solution to this problem is attaching a portion of the die to the package. In such partial die bonding, the lack of control over the spreading of the adhesive can cause non-uniform attachment. In this case, asymmetric packaging stress could be generated and transferred to the die. The performance of MEMS devices, which employ the differential outputs of the sensing elements, is directly affected by the asymmetric packaging stress. In this paper, we proposed a die-attach structure with a pillar to reduce the asymmetric packaging stress and the changes in packaging stress due to changes in the device temperature. To verify the proposed structure, we fabricated four types of differential resonant accelerometers (DRA) with the silicon-on-glass process. We confirmed experimentally that the pillar can control the spreading of the adhesive and that the asymmetric packaging stress is considerably reduced. The simulation and experimental results indicated that the DRAs manufactured using glass-on-silicon wafers as handle substrates instead of conventional glass wafers have a structure that compensates for the thermal stress.

PISTON: Predicting drug indications and side effects using topic modeling and natural language processing.

The process of discovering novel drugs to treat diseases requires a long time and high cost. It is important to understand side effects of drugs as well as their therapeutic effects, because these can seriously damage the patients due to unexpected actions of the derived candidate drugs. In order to overcome these limitations, computational methods for predicting the therapeutic effects and side effects have been proposed. In particular, text mining is a widely used technique in the field of systems biology, because it can discover hidden relationships between drugs, genes and diseases from a large amount of literature data. Compared with in vivo/in vitro experiments, text mining derives meaningful results with less time and cost. In this study, we propose an algorithm for predicting novel drug-phenotype associations and drug-side effect associations using topic modeling and natural language processing (NLP). We extract sentences in which drugs and genes co-occur from the abstracts of the literature and identify words that describe the relationship between them using NLP. Considering the characteristics of the identified words, we determine if the drug has an up-regulation effect or a down-regulation effect on the gene. Based on genes that affect drugs and their regulatory relationships, we group the frequently occurring genes and regulatory relationships into topics, and build a drug-topic probability matrix by calculating the score that the drug will have a topic using topic modeling. Using the matrix, a classifier is constructed for predicting the novel indications and side effects of drugs considering the characteristics of known drug-phenotype associations or drug-side effect associations. The proposed method predicts both indications and side effects with a single algorithm, and it can exclude drugs with serious side effects or side effects that patients do not want to experience from among the candidate drugs provided for the treatment of the phenotype. Furthermore, lists of novel candidate drugs for phenotypes and side effects can be continuously updated with our algorithm every time a document is added. More than a thousand documents are produced per day, and it is possible for our algorithm to efficiently derive candidate drugs because it requires less cost than the existing drug repositioning methods. The resource of PISTON is available at databio.gachon.ac.kr/tools/PISTON.

Drug voyager: a computational platform for exploring unintended drug action.

BACKGROUND: The dominant paradigm in understanding drug action focuses on the intended therapeutic effects and frequent adverse reactions. However, this approach may limit opportunities to grasp unintended drug actions, which can open up channels to repurpose existing drugs and identify rare adverse drug reactions. Advances in systems biology can be exploited to comprehensively understand pharmacodynamic actions, although proper frameworks to represent drug actions are still lacking. RESULTS: We suggest a novel platform to construct a drug-specific pathway in which a molecular-level mechanism of action is formulated based on pharmacologic, pharmacogenomic, transcriptomic, and phenotypic data related to drug response ( http://databio.gachon.ac.kr/tools/ ). In this platform, an adoption of three conceptual levels imitating drug perturbation allows these pathways to be realistically rendered in comparison to those of other models. Furthermore, we propose a new method that exploits functional features of the drug-specific pathways to predict new indications as well as adverse reactions. For therapeutic uses, our predictions significantly overlapped with clinical trials and an up-to-date drug-disease association database. Also, our method outperforms existing methods with regard to classification of active compounds for cancers. For adverse reactions, our predictions were significantly enriched in an independent database derived from the Food and Drug Administration (FDA) Adverse Event Reporting System and meaningfully cover an Adverse Reaction Database provided by Health Canada. Lastly, we discuss several predictions for both therapeutic indications and side-effects through the published literature. CONCLUSIONS: Our study addresses how we can computationally represent drug-signaling pathways to understand unintended drug actions and to facilitate drug discovery and screening.

Galanin contributes to ultraviolet irradiation-induced inflammation in human skin.

Exposure of the skin to ultraviolet (UV) irradiation causes various consequences such as inflammation and photoageing. Galanin is an active neuropeptide expressed widely in the central nervous system and peripheral tissues including the skin. Galanin promotes or inhibits inflammation in a context-dependent manner, but its role in UV irradiation-induced responses in human skin was still unknown. UV irradiation induced a substantial expression of galanin in primary epidermal keratinocytes in vitro and in human epidermis in vivo. Galanin knock-down by siRNA transfection markedly inhibited UV irradiation-induced expression of matrix metalloproteinase (MMP)-1, interleukin (IL)-1ß, IL-6 and cyclooxygenase (COX)-2. Moreover, siRNA-mediated knock-down of GAL2 , a principal galanin receptor in the skin, led to a considerable decrease in these mediators in keratinocytes. Collectively, our findings suggest that galanin is an important messenger between the neuroendocrine system and UV irradiation-damaged skin.

IMA: Identifying disease-related genes using MeSH terms and association rules.

Genes play an important role in several diseases. Hence, in biology, identifying relationships between diseases and genes is important for the analysis of diseases, because mutated or dysregulated genes play an important role in pathogenesis. Here, we propose a method to identify disease-related genes using MeSH terms and association rules. We identified genes by analyzing the MeSH terms and extracted information on gene-gene interactions based on association rules. By integrating the extracted interactions, we constructed gene-gene networks and identified disease-related genes. We applied the proposed method to study five cancers, including prostate, lung, breast, stomach, and colorectal cancer, and demonstrated that the proposed method is more useful for identifying disease-related and candidate disease-related genes than previously published methods. In this study, we identified 20 genes for each disease. Among them, we presented 34 important candidate genes with evidence that supports the relationship of the candidate genes with diseases.

Vasoactive intestinal peptide stimulates melanogenesis in B16F10 mouse melanoma cells via CREB/MITF/tyrosinase signaling.

Vasoactive intestinal peptide (VIP), one of the major skin neuropeptides, has been suggested to have active roles in the pathogenesis of inflammatory skin disorders such as atopic dermatitis and psoriasis, which can commonly cause post-inflammatory hyperpigmentation. However, the effect of VIP on melanogenesis remains unknown. In this study, we showed that the melanin contents, tyrosinase activity, and gene expression of tyrosinase and microphthalmia-associated transcription factor (MITF) were significantly increased by treatment with VIP in B16F10 mouse melanoma cells and the stimulatory melanogenic effect was further examined in human epidermal melanocytes (HEMns). In addition, phosphorylated levels of CRE-binding protein (CREB) and protein kinase A (PKA) were markedly increased after VIP treatment, but not p38 mitogen-activated protein kinase (p38 MAPK), extracellular signal-regulated kinase (ERK), or Akt, indicating the possible PKA-CREB signaling pathway involved in VIP-induced melanogenesis. This result was further verified by the fact that VIP induced increased melanin synthesis, and protein levels of phosphorylated CREB, MITF, tyrosinase were significantly attenuated by H89 (a specific PKA inhibitor). These data suggest that VIP-induced upregulation of tyrosinase through the CREB-MITF signaling pathway plays an important role in finding new treatment strategy for skin inflammatory diseases related pigmentation disorders.

Serum amyloid A1 secreted from UV-irradiated keratinocytes induces matrix metalloproteinase-1 in fibroblasts through toll-like receptor 4.

Ultraviolet (UV) irradiation on skin triggers photoageing-related phenotypes such as formation of wrinkles. UV ray upregulates matrix metalloproteinase-1 (MMP-1), which in turn degrades extracellular matrix proteins, mostly collagens. Serum amyloid A1 (SAA1) is an acute-phase protein of which plasma concentration increases in response to inflammation. Although the expression of SAA1 in the skin was reported, its function in the skin is yet to be studied. In this research, we found that the expression of SAA1 was increased in acute UV-irradiated buttock skin and photoaged forearm skin in vivo. UV irradiation also increased SAA1 in normal human epidermal keratinocytes (NHEK), and treatment of recombinant human SAA1 (rhSAA1) induced MMP-1 in normal human dermal fibroblasts (NHDF) but not in NHEK. Next, we demonstrated that NHDF treated with UV-irradiated keratinocyte-conditioned media showed the increased MMP-1 expression; however, this increase of MMP-1 in NHDF was inhibited by knockdown of SAA1 in NHEK. In addition, knockdown of Toll-like receptor 4 (TLR4) inhibited rhSAA1-induced MMP-1 expression in NHDF. Taken together, our data showed that UV-induced SAA1 production in NHEK, and this secreted SAA1 induced MMP-1 expression in NHDF in a paracrine manner through TLR4 signalling pathway. Therefore, our results suggest that SAA1 can be a potential mediator for UV-induced MMP-1 expression in human skin.

Photothermally induced local dissociation of collagens for harvesting of cell sheets.

The local heating of poly(3,4-ethylenedioxythiophene) (PEDOT) by a photothermal effect directed by near-infrared (NIR) light induces unfolding of absorbed collagen triple helices, yielding soluble collagen single-helical structures. This dissociation of collagens allowed the harvesting of a living idiomorphic cell sheet, achieved upon irradiation with NIR light (λ=808 nm). The PEDOT layer was patterned and cells were successfully cultured on the patterned substrate. Cell sheets of various shapes mirroring the PEDOT pattern could be detached after a few minutes of irradiation with NIR light. The PEDOT patterns guided not only the entire shape of the cell sheets but also the spreading direction of the cells in the sheets. This photothermally induced dissociation of collagen provided a fast non-invasive harvesting method and tailor-made cell-sheet patterns.

PIDiff: Physics informed diffusion model for protein pocket-specific 3D molecular generation.

Designing drugs capable of binding to the structure of target proteins for treating diseases is essential in drug development. Recent remarkable advancements in geometric deep learning have led to unprecedented progress in three-dimensional (3D) generation of ligands that can bind to the protein pocket. However, most existing methods primarily focus on modeling the geometric information of ligands in 3D space. Consequently, these methods fail to consider that the binding of proteins and ligands is a phenomenon driven by intrinsic physicochemical principles. Motivated by this understanding, we propose PIDiff, a model for generating molecules by accounting in the physicochemical principles of protein-ligand binding. Our model learns not only the structural information of proteins and ligands but also to minimize the binding free energy between them. To evaluate the proposed model, we introduce an experimental framework that surpasses traditional assessment methods by encompassing various essential aspects for the practical application of generative models to actual drug development. The results confirm that our model outperforms baseline models on the CrossDocked2020 benchmark dataset, demonstrating its superiority. Through diverse experiments, we have illustrated the promising potential of the proposed model in practical drug development.

Identification of functional CNV region networks using a CNV-gene mapping algorithm in a genome-wide scale.

MOTIVATION: Identifying functional relation of copy number variation regions (CNVRs) and gene is an essential process in understanding the impact of genotypic variations on phenotype. There have been many related works, but only a few attempts were made to normal populations. RESULTS: To analyze the functions of genome-wide CNVRs, we applied a novel correlation measure called Correlation based on Sample Set (CSS) to paired Whole Genome TilePath array and messenger RNA (mRNA) microarray data from 210 HapMap individuals with normal phenotypes and calculated the confident CNVR-gene relationships. Two CNVR nodes form an edge if they regulate a common set of genes, allowing the construction of a global CNVR network. We performed functional enrichment on the common genes that were trans-regulated from CNVRs clustered together in our CNVR network. As a result, we observed that most of CNVR clusters in our CNVR network were reported to be involved in some biological processes or cellular functions, while most CNVR clusters from randomly constructed CNVR networks showed no evidence of functional enrichment. Those results imply that CSS is capable of finding related CNVR-gene pairs and CNVR networks that have functional significance. AVAILABILITY: http://embio.yonsei.ac.kr/~ Park/cnv_net.php. CONTACT: sanghyun@cs.yonsei.ac.kr SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

[6]-Shogaol inhibits melanogenesis in B16 mouse melanoma cells through activation of the ERK pathway.

AIM: To investigate the effect of [6]-shogaol, an active ingredient in ginger, on melanogenesis and the underlying mechanisms. METHODS: B16F10 mouse melanoma cells were tested. Cell viability was determined with the MTT assay. Melanin content and tyrosinase activity were analyzed with a spectrophotometer. The protein expression of tyrosinase and microphthalmia associated transcription factor (MITF), as well as phosphorylated or total ERK1/2 and Akt were measured using Western blot. RESULTS: Treatment of the cells with [6]-shogaol (1, 5, 10 µmol/L) reduced the melanin content in a concentration-dependent manner. [6]-Shogaol (5 and 10 µmol/L) significantly decreased the intracellular tyrosinase activity, and markedly suppressed the expression levels of tyrosinase and MITF proteins in the cells. Furthermore, [6]-shogaol (10 µmol/L) activated ERK, which was known to negatively regulate melanin synthesis in these cells. Pretreatment with the specific ERK pathway inhibitor PD98059 (20 µmol/L) greatly attenuated the inhibition of melanin synthesis by [6]-shogaol (10 µmol/L). CONCLUSION: The results demonstrate that [6]-shogaol inhibits melanogenesis in B16F10 mouse melanoma cells via activating the ERK pathway.

Background-filtered telomerase activity assay with cyclic DNA cleavage amplification.

Overexpression of telomerase incites the abnormal proliferation of cancer cells. Thus, it has been regarded as a cancer biomarker and a potential therapeutic target. Existing assays suggest a promising sensing scheme to detect telomerase activity. However, they are complicated in terms of assay preparation and implementation. We herein report a Quenching-Exempt invader Signal Amplification Test, termed 'QUEST'. The assay leverages on a high turnover, specific cleaving enzyme, flap endonuclease I (FEN1), and graphene oxide (GO) for background (BG) filtering. In response to the target, FEN1 significantly boosts the signal with invader signal amplification. To distinguish the target signal, GO filters out the BG. It captures residual reporter invader probes (RP) to quench undesired signals. QUEST is straightforward without any pre-preparatory steps and washing/separation. Its probe design is simple and cost-effective. With QUEST, we investigated telomerase activities in various cell lines. Notably, we discriminated cancer cell lines from normal cell lines. In addition, a candidate inhibitor for telomerase was screened, which showed the promising potential of QUEST in real applications.

Fire accelerant classification from GC-MS data of suspected arson cases using machine-learning models.

Using a practical GC-MS dataset containing approximately 4000 suspected arson cases, three machine-learning based classification models were developed and their performances were evaluated. All models trained for classifying the data from fire residue into six categories; no fire accelerants detected or else one of fire accelerants was used within gasoline, kerosene, diesel, solvents, or candle. The classification accuracies of the random forest, supporting vector machine, and convolutional neural network model were 0.88, 0.88, and 0.92, respectively. By calculating feature importance of the random forest model, several potential chemical fingerprints of fire accelerants were discovered.