Category Archives: 研究發表

麥如村助理教授研究團隊發表研究成果於Am J Cancer Res

連結網址：https://pubmed.ncbi.nlm.nih.gov/37293175/

Abstract

This study aimed to investigate the use of organic fertilizers instead of modified f/2 medium for Chlorella sp. cultivation, and the extracted lutein of the microalga to protect mammal cells against blue-light irradiation. The biomass productivity and lutein content of Chlorella sp. cultured in 20 g/L fertilizer medium for 6 days were 1.04 g/L/d and 4.41 mg/g, respectively. These values are approximately 1.3- and 1.4-fold higher than those achieved with the modified f/2 medium, respectively. The cost of medium per gram of microalgal biomass reduced by about 97%. The microalgal lutein content was further increased to 6.03 mg/g in 20 g/L fertilizer medium when supplemented with 20 mM urea, and the cost of medium per gram lutein reduced by about 96%. When doses of ≥1 μM microalgal lutein were used to protect mammal NIH/3T3 cells, there was a significant reduction in the levels of reactive oxygen species (ROS) produced by the cells in the following blue-light irradiation treatments. The results show that microalgal lutein produced by fertilizers with urea supplements has the potential to develop anti-blue-light oxidation products and reduce the economic challenges of microalgal biomass applied to carbon biofixation and biofuel production.

楊進木教授研究團隊發表研究成果於Environment International

連結網址：https://www.sciencedirect.com/science/article/pii/S0160412023003008?via%3Dihub

Abstract

Over 400,000 people are estimated to have been exposed to World Trade Center particulate matter (WTC_PM) since the attack on the Twin Towers in Lower Manhattan on September 11, 2001. Epidemiological studies have found that exposure to dust may cause respiratory ailments and cardiovascular diseases. However, limited studies have performed a systematic analysis of transcriptomic data to elucidate the biological responses to WTC_PM exposure and the therapeutic options. Here, we developed an in vivo mouse exposure model of WTC_PM and administered two drugs (i.e., rosoxacin and dexamethasone) to generate transcriptomic data from lung samples. WTC_PM exposure increased the inflammation index, and this index was significantly reduced by both drugs. We analyzed the transcriptomics derived omics data using a hierarchical systems biology model (HiSBiM) with four levels, including system, subsystem, pathway, and gene analyses. Based on the selected differentially expressed genes (DEGs) from each group, WTC_PM and the two drugs commonly affected the inflammatory responses, consistent with the inflammation index. Among these DEGs, the expression of 31 genes was affected by WTC_PM exposure and consistently reversed by the two drugs, and these genes included Psme2, Cldn18, and Prkcd, which are involved in immune- and endocrine-related subsystems and pathways such as thyroid hormone synthesis, antigen processing and presentation, and leukocyte transendothelial migration. Furthermore, the two drugs reduced the inflammatory effects of WTC_PM through distinct pathways, e.g., vascular-associated signaling by rosoxacin, whereas mTOR-dependent inflammatory signaling was found to be regulated by dexamethasone. To the best of our knowledge, this study constitutes the first investigation of transcriptomics data of WTC_PM and an exploration of potential therapies. We believe that these findings provide strategies for the development of promising optional interventions and therapies for airborne particle exposure.

陸志豪副教授研究團隊發表研究成果於 Antiviral Research

連結網址：https://www.sciencedirect.com/science/article/pii/S0166354223001316?dgcid=coauthor

Abstract

The main protease (Mpro) of SARS-CoV-2 is essential for viral replication, which suggests that the Mpro is a critical target in the development of small molecules to treat COVID-19. This study used an in-silico prediction approach to investigate the complex structure of SARS-CoV-2 Mpro in compounds from the United States National Cancer Institute (NCI) database, then validate potential inhibitory compounds against the SARS-CoV-2 Mpro in cis- and trans-cleavage proteolytic assays. Virtual screening of ∼280,000 compounds from the NCI database identified 10 compounds with highest site-moiety map scores. Compound NSC89640 (coded C1) showed marked inhibitory activity against the SARS-CoV-2 Mpro in cis-/trans-cleavage assays. C1 strongly inhibited SARS-CoV-2 Mpro enzymatic activity, with a half maximal inhibitory concentration (IC50) of 2.69 μM and a selectivity index (SI) of >74.35. The C1 structure served as a template to identify structural analogs based on AtomPair fingerprints to refine and verify structure-function associations. Mpro-mediated cis-/trans-cleavage assays conducted with the structural analogs revealed that compound NSC89641 (coded D2) exhibited the highest inhibitory potency against SARS-CoV-2 Mpro enzymatic activity, with an IC50 of 3.05 μM and a SI of >65.57. Compounds C1 and D2 also displayed inhibitory activity against MERS-CoV-2 with an IC50 of <3.5 μM. Thus, C1 shows potential as an effective Mpro inhibitor of SARS-CoV-2 and MERS-CoV. Our rigorous study framework efficiently identified lead compounds targeting the SARS-CoV-2 Mpro and MERS-CoV Mpro.

王雲銘教授研究團隊發表研究成果於 Sensors and Actuators B: Chemical

連結網址：

https://www.sciencedirect.com/science/article/pii/S0925400523006755

Abstract

The COVID-19 pandemic has become a global catastrophe, affecting the health and economy of the human community. It is required to mitigate the impact of pandemics by developing rapid molecular diagnostics for SARS-CoV-2 virus detection. In this context, developing a rapid point-of-care (POC) diagnostic test is a holistic approach to the prevention of COVID-19. In this context, this study aims at presenting a real-time, biosensor chip for improved molecular diagnostics including recombinant SARS-CoV-2 spike glycoprotein and SARS-CoV-2 pseudovirus detection based on one-step-one-pot hydrothermally derived CoFeBDCNH2-CoFe2O4 MOF-nanohybrids. This study was tested on a PalmSens-EmStat Go POC device, showing a limit of detection (LOD) for recombinant SARS-CoV-2 spike glycoprotein of 6.68 fg/mL and 6.20 fg/mL in buffer and 10% serum-containing media, respectively. To validate virus detection in the POC platform, an electrochemical instrument (CHI6116E) was used to perform dose dependent studies under similar experimental conditions to the handheld device. The results obtained from these studies were comparable indicating the capability and high detection electrochemical performance of MOF nanocomposite derived from one-step-one-pot hydrothermal synthesis for SARS-CoV-2 detection for the first time. Further, the performance of the sensor was tested in the presence of Omicron BA.2 and wild-type D614G pseudoviruses.

李宗夷教授研究團隊發表研究成果於Briefings in Bioinformatics

連結網址：https://pubmed.ncbi.nlm.nih.gov/36810579/

Abstract

Phosphorylation is an essential mechanism for regulating protein activities. Determining kinase-specific phosphorylation sites by experiments involves time- consuming and expensive analyzes. Although several studies proposed computational methods to model kinase-specific phosphorylation sites, they typically required abundant experimentally verified phosphorylation sites to yield reliable predictions. Nevertheless, the number of experimentally verified phosphorylation sites for most kinases is relatively small, and the targeting phosphorylation sites are still unidentified for some kinases. In fact, there is little research related to these understudied kinases in the literature. Thus, this study aims to create predictive models for these understudied kinases. A kinase-kinase similarity network was generated by merging
the sequence-, functional-, protein-domain- and ‘STRING’-related similarities. Thus, besides sequence data, protein-protein interactions and functional pathways were also considered to aid predictive modelling. This similarity network was then integrated with a classification of kinase groups to yield highly similar kinases to a specific understudied type of kinase. Their experimentally verified phosphorylation sites were leveraged as positive sites to train predictive models. The experimentally verified phosphorylation sites of the understudied kinase were used for validation. Results demonstrate that 82 out of 116 understudied kinases were predicted with adequate performance via the proposed modelling strategy, achieving a balanced accuracy of 0.81, 0.78, 0.84, 0.84, 0.85, 0.82, 0.90, 0.82 and 0.85, for the ‘TK’, ‘Other’, ‘STE’, ‘CAMK’, ‘TKL’, ‘CMGC’, ‘AGC’, ‘CK1’ and ‘Atypical’ groups, respectively. Therefore, this study demonstrates that web-like predictive networks can reliably capture the underlying patterns in such understudied kinases by harnessing relevant sources of similarities to predict their specific phosphorylation sites.

何信瑩教授研究團隊發表研究成果於Human Genetics and Genomics Advances

連結網址：https://pubmed.ncbi.nlm.nih.gov/37124139/

Abstract

The ability to detect cancer at an early stage in patients who would benefit from effective therapy is a key factor in increasing survivability. This work proposes an evolutionary supervised learning method called CancerSig to identify cancer stage-specific microRNA (miRNA) signatures for early cancer predictions. CancerSig established a compact panel of miRNA signatures as potential markers from 4,667 patients with 15 different types of cancers for the cancer stage prediction, and achieved a mean performance: 10-fold cross-validation accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of 84.27% ± 6.31%, 0.81 ± 0.12, 0.80 ± 0.10, and 0.80 ± 0.06, respectively. The pan-cancer analysis of miRNA signatures suggested that three miRNAs, hsa-let-7i-3p, hsa-miR-362-3p, and hsa-miR-3651, contributed significantly toward stage prediction across 8 cancers, and each of the 67 miRNAs of the panel was a biomarker of stage prediction in more than one cancer. CancerSig may serve as the basis for cancer screening and therapeutic selection.

李宗夷教授研究團隊發表研究成果於Microbiology Spectrum

連結網址：https://pubmed.ncbi.nlm.nih.gov/37042778/

Abstract

In clinical microbiology, matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOF MS) is frequently employed for rapid microbial identification. However, rapid identification of antimicrobial resistance (AMR) in Escherichia coli based on a large amount of MALDI-TOF MS data has not yet been reported. This may be because building a prediction model to cover all E. coli isolates would be challenging given the high diversity of the E. coli population. This study aimed to develop a MALDI-TOF MS-based, data-driven, two-stage framework for characterizing different AMRs in E. coli. Specifically, amoxicillin (AMC), ceftazidime (CAZ), ciprofloxacin (CIP), ceftriaxone (CRO), and cefuroxime (CXM) were used. In the first stage, we split the data into two groups based on informative peaks according to the importance of the random forest. In the second stage, prediction models were constructed using four different machine learning algorithms-logistic regression, support vector machine, random forest, and extreme gradient boosting (XGBoost). The findings demonstrate that XGBoost outperformed the other four machine learning models. The values of the area under the receiver operating characteristic curve were 0.62, 0.72, 0.87, 0.72, and 0.72 for AMC, CAZ, CIP, CRO, and CXM, respectively. This implies that a data-driven, two-stage framework could improve accuracy by approximately 2.8%. As a result, we developed AMR prediction models for E. coli using a data-driven two-stage framework, which is promising for assisting physicians in making decisions. Further, the analysis of informative peaks in future studies could potentially reveal new insights. IMPORTANCE Based on a large amount of matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOF MS) clinical data, comprising 37,918 Escherichia coli isolates, a data-driven two-stage framework was established to evaluate the antimicrobial resistance of E. coli. Five antibiotics, including amoxicillin (AMC), ceftazidime (CAZ), ciprofloxacin (CIP), ceftriaxone (CRO), and cefuroxime (CXM), were considered for the two-stage model training, and the values of the area under the receiver operating characteristic curve (AUC) were 0.62 for AMC, 0.72 for CAZ, 0.87 for CIP, 0.72 for CRO, and 0.72 for CXM. Further investigations revealed that the informative peak m/z 9714 appeared with some important peaks at m/z 6809, m/z 7650, m/z 10534, and m/z 11783 for CIP and at m/z 6809, m/z 10475, and m/z 8447 for CAZ, CRO, and CXM. This framework has the potential to improve the accuracy by approximately 2.8%, indicating a promising potential for further research.

楊慕華教授與林峻宇助理教授團隊共同發表研究成果於Advanced Science

連結網址：https://pubmed.ncbi.nlm.nih.gov/37026630/

Abstract

As a type of immunogenic cell death, ferroptosis participates in the creation of immunoactive tumor microenvironments. However, knowledge of spatial location of tumor cells with ferroptosis signature in tumor environments and the role of ferroptotic stress in inducing the expression of immune-related molecules in cancer cells is limited. Here the spatial association of the transcriptomic signatures is demonstrated for ferroptosis and inflammation/immune activation located in the invasive front of head and neck squamous cell carcinoma (HNSCC). The association between ferroptosis signature and inflammation/immune activation is more prominent in HPV-negative HNSCC compared to HPV-positive ones. Ferroptotic stress induces PD-L1 expression through reactive oxygen species (ROS)-elicited NF-κB signaling pathway and calcium influx. Priming murine HNSCC with the ferroptosis inducer sensitizes tumors to anti-PD-L1 antibody treatment. A positive correlation between the ferroptosis signature and the active immune cell profile is shown in the HNSCC samples. This study reveals a subgroup of ferroptotic HNSCC with immune-active signatures and indicates the potential of priming HNSCC with ferroptosis inducers to increase the antitumor efficacy of immune checkpoint inhibitors.

謝仁俊教授研究團隊發表研究成果於Rhinology

連結網址：https://pubmed.ncbi.nlm.nih.gov/37000430/

Abstract

Background: Olfactory capacity increases during the period of ovulation, perhaps as an adjunct to mate selection; however, researchers have yet to elucidate the neural underpinning of menstrual cycle-dependent variations in olfactory performance.

Methodology: A cohort of healthy volunteers (n = 88, grand cohort) underwent testing for gonadal hormone levels and resting-state functional magnetic resonance imaging with a focus on intrinsic functional connectivity (FC) in the olfactory network based on a priori seeds (piriform cortex and orbitofrontal cortex) during the periovulatory (POV) and menstrual (MEN) phases. A subcohort (n = 20, olfaction cohort) returned to the lab to undergo testing of olfactory performance during the POV and MEN phases of a subsequent menstrual cycle.

Results: Olfactory performance and FC were both stronger in the periovulatory phase than in the menstrual phase. Enhanced FC was observed in the network targeting the cerebellum in both the grand and olfaction cohorts, while enhanced FC was observed in the middle temporal gyrus, lingual gyrus, dorsal medial prefrontal cortex, and postcentral gyrus in the grand cohort. Periovulatory progesterone levels in the grand cohort were positively correlated with FC in the network targeting the insula and paracentral lobule.

Conclusion: Our analysis revealed that superior olfactory function in the periovulatory period is associated with enhanced intrinsic connectivity in the olfactory network. These findings can be appreciated in the context of evolutionary biology.

趙瑞益教授研究團隊發表研究成果於Anti-Cancer Agents in Medicinal Chemistry

連結網址：https://www.eurekaselect.com/article/129735

Abstract

Background: Lung cancer has become one of the leading causes of cancer incidence and mortality worldwide. Non-small cell lung carcinoma (NSCLC) is the most common type among all lung cancer cases. NSCLC patients contained high levels of activating epidermal growth factor receptor (EGFR) mutations, such as exon 19 deletion, L858R and T790M. Osimertinib, a third-generation of EGFR tyrosine kinase inhibitor (EGFR-TKI), has therapeutic efficacy on the EGFR-T790M mutation of NSCLC patients; however, treatment of osimertinib still can induce drug resistance in lung cancer patients. Therefore, investigation of the drug resistance mechanisms of osimertinib will provide novel strategies for lung cancer therapy.

Methods: The H1975OR osimertinib-resistant cell line was established by prolonged exposure with osimertinib derived from the H1975 cells. The cell proliferation ability was evaluated by the cell viability and cell growth assays. The cell migration ability was determined by the Boyden chamber assays. The differential gene expression profile was analyzed by genome-wide RNA sequencing. The protein expression and location were analyzed by western blot and confocal microscopy.

Results: In this study, we established the osimertinib-resistant H1975 (T790M/L858R) cancer cells, named the H1975OR cell line. The cell growth ability was decreased in the H1975OR cells by comparison with the H1975 parental cells. Conversely, the cell migration ability was elevated in the H1975OR cells. We found the differential gene expression profile of cell proliferation and migration pathways between the H1975OR and H1975 parental cells. Interestingly, the protein levels of phospho-EGFR, PD-L1, E-cadherin and β-catenin were decreased, but the survivin and N-cadherin proteins were increased in the H1975OR drug-resistant cells.

Conclusion: Osimertinib induces the opposite effect of proliferation and migration in the drug resistance of EGFRT790M lung cancer cells. We suggest that differential gene and protein expressions in the cell proliferation and migration pathways may mediate the drug resistance of osimertinib in lung cancer cells. Understanding the molecular drugresistant mechanisms of proliferation and migration pathways of osimertinib may provide novel targets and strategies for the clinical treatment of EGFR-TKIs in lung cancer patients.