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.