OrthoID: profiling dynamic proteomes through time and space using mutually orthogonal chemical tools.

Identifying proteins at organelle contact sites, such as mitochondria-associated endoplasmic reticulum membranes (MAM), is essential for understanding vital cellular processes, yet challenging due to their dynamic nature. Here we report "OrthoID", a proteomic method utilizing engineered enzymes, TurboID and APEX2, for the biotinylation (Bt) and adamantylation (Ad) of proteins close to the mitochondria and endoplasmic reticulum (ER), respectively, in conjunction with high-affinity binding pairs, streptavidin-biotin (SA-Bt) and cucurbit[7]uril-adamantane (CB[7]-Ad), for selective orthogonal enrichment of Bt- and Ad-labeled proteins. This approach effectively identifies protein candidates associated with the ER-mitochondria contact, including LRC59, whose roles at the contact site were-to the best of our knowledge-previously unknown, and tracks multiple protein sets undergoing structural and locational changes at MAM during mitophagy. These findings demonstrate that OrthoID could be a powerful proteomics tool for the identification and analysis of spatiotemporal proteins at organelle contact sites and revealing their dynamic behaviors in vital cellular processes.

Ratiometric measurement of MAM Ca2+ dynamics using a modified CalfluxVTN.

Mitochondria-associated ER membrane (MAM) is a structure where these calcium-regulating organelles form close physical contact sites for efficient Ca2+ crosstalk. Despite the central importance of MAM Ca2+ dynamics in diverse biological processes, directly and specifically measuring Ca2+ concentrations inside MAM is technically challenging. Here, we develop MAM-Calflux, a MAM-specific BRET-based Ca2+ indicator. The successful application of the bimolecular fluorescence complementation (BiFC) concept highlights Ca2+-responsive BRET signals in MAM. The BiFC strategy imparts dual functionality as a Ca2+ indicator and quantitative structural marker specific for MAM. As a ratiometric Ca2+ indicator, MAM-Calflux estimates steady-state MAM Ca2+ levels. Finally, it enables the visualization of uneven intracellular distribution of MAM Ca2+ and the elucidation of abnormally accumulated MAM Ca2+ from the neurons of Parkinson's disease mouse model in both steady-state and stimulated conditions. Therefore, we propose that MAM-Calflux can be a versatile tool for ratiometrically measuring dynamic inter-organellar Ca2+ communication.

Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine.

Despite the tremendous success, pitfalls have been observed in every step of a clinical metabolomics workflow, which impedes the internal validity of the study. Furthermore, the demand for logistics, instrumentations, and computational resources for metabolic phenotyping studies has far exceeded our expectations. In this conceptual review, we will cover inclusive barriers of a metabolomics-based clinical study and suggest potential solutions in the hope of enhancing study robustness, usability, and transferability. The importance of quality assurance and quality control procedures is discussed, followed by a practical rule containing five phases, including two additional "pre-pre-" and "post-post-" analytical steps. Besides, we will elucidate the potential involvement of machine learning and demonstrate that the need for automated data mining algorithms to improve the quality of future research is undeniable. Consequently, we propose a comprehensive metabolomics framework, along with an appropriate checklist refined from current guidelines and our previously published assessment, in the attempt to accurately translate achievements in metabolomics into clinical and epidemiological research. Furthermore, the integration of multifaceted multi-omics approaches with metabolomics as the pillar member is in urgent need. When combining with other social or nutritional factors, we can gather complete omics profiles for a particular disease. Our discussion reflects the current obstacles and potential solutions toward the progressing trend of utilizing metabolomics in clinical research to create the next-generation healthcare system.

An Integrative Data Mining and Omics-Based Translational Model for the Identification and Validation of Oncogenic Biomarkers of Pancreatic Cancer.

Substantial alterations at the multi-omics level of pancreatic cancer (PC) impede the possibility to diagnose and treat patients in early stages. Herein, we conducted an integrative omics-based translational analysis, utilizing next-generation sequencing, transcriptome meta-analysis, and immunohistochemistry, combined with statistical learning, to validate multiplex biomarker candidates for the diagnosis, prognosis, and management of PC. Experiment-based validation was conducted and supportive evidence for the essentiality of the candidates in PC were found at gene expression or protein level by practical biochemical methods. Remarkably, the random forests (RF) model exhibited an excellent diagnostic performance and LAMC2, ANXA2, ADAM9, and APLP2 greatly influenced its decisions. An explanation approach for the RF model was successfully constructed. Moreover, protein expression of LAMC2, ANXA2, ADAM9, and APLP2 was found correlated and significantly higher in PC patients in independent cohorts. Survival analysis revealed that patients with high expression of ADAM9 (Hazard ratio (HR)OS = 2.2, p-value < 0.001), ANXA2 (HROS = 2.1, p-value < 0.001), and LAMC2 (HRDFS = 1.8, p-value = 0.012) exhibited poorer survival rates. In conclusion, we successfully explore hidden biological insights from large-scale omics data and suggest that LAMC2, ANXA2, ADAM9, and APLP2 are robust biomarkers for early diagnosis, prognosis, and management for PC.

High-Throughput Omics and Statistical Learning Integration for the Discovery and Validation of Novel Diagnostic Signatures in Colorectal Cancer.

The advancement of bioinformatics and machine learning has facilitated the discovery and validation of omics-based biomarkers. This study employed a novel approach combining multi-platform transcriptomics and cutting-edge algorithms to introduce novel signatures for accurate diagnosis of colorectal cancer (CRC). Different random forests (RF)-based feature selection methods including the area under the curve (AUC)-RF, Boruta, and Vita were used and the diagnostic performance of the proposed biosignatures was benchmarked using RF, logistic regression, naïve Bayes, and k-nearest neighbors models. All models showed satisfactory performance in which RF appeared to be the best. For instance, regarding the RF model, the following were observed: mean accuracy 0.998 (standard deviation (SD) < 0.003), mean specificity 0.999 (SD < 0.003), and mean sensitivity 0.998 (SD < 0.004). Moreover, proposed biomarker signatures were highly associated with multifaceted hallmarks in cancer. Some biomarkers were found to be enriched in epithelial cell signaling in Helicobacter pylori infection and inflammatory processes. The overexpression of TGFBI and S100A2 was associated with poor disease-free survival while the down-regulation of NR5A2, SLC4A4, and CD177 was linked to worse overall survival of the patients. In conclusion, novel transcriptome signatures to improve the diagnostic accuracy in CRC are introduced for further validations in various clinical settings.

Efficacy of Integrating a Novel 16-Gene Biomarker Panel and Intelligence Classifiers for Differential Diagnosis of Rheumatoid Arthritis and Osteoarthritis.

Introducing novel biomarkers for accurately detecting and differentiating rheumatoid arthritis (RA) and osteoarthritis (OA) using clinical samples is essential. In the current study, we searched for a novel data-driven gene signature of synovial tissues to differentiate RA from OA patients. Fifty-three RA, 41 OA, and 25 normal microarray-based transcriptome samples were utilized. The area under the curve random forests (RF) variable importance measurement was applied to seek the most influential differential genes between RA and OA. Five algorithms including RF, k-nearest neighbors (kNN), support vector machines (SVM), naïve-Bayes, and a tree-based method were employed for the classification. We found a 16-gene signature that could effectively differentiate RA from OA, including TMOD1, POP7, SGCA, KLRD1, ALOX5, RAB22A, ANK3, PTPN3, GZMK, CLU, GZMB, FBXL7, TNFRSF4, IL32, MXRA7, and CD8A. The externally validated accuracy of the RF model was 0.96 (sensitivity = 1.00, specificity = 0.90). Likewise, the accuracy of kNN, SVM, naïve-Bayes, and decision tree was 0.96, 0.96, 0.96, and 0.91, respectively. Functional meta-analysis exhibited the differential pathological processes of RA and OA; suggested promising targets for further mechanistic and therapeutic studies. In conclusion, the proposed genetic signature combined with sophisticated classification methods may improve the diagnosis and management of RA patients.

Papillary thyroid carcinoma with tall cell features is as aggressive as tall cell variant: a meta-analysis.

There are still ongoing debates as to which cut-off percentage of tall cell (TC) should be used to define tall cell variant (TCV) papillary thyroid carcinoma (PTC). In this meta-analysis, we aimed to investigate the clinicopathological significance of PTC with tall cell features (PTC-TCF, PTC with 10-50% of TCs) in comparison with classical PTC and TCVPTC (PTC with more than 50% of TCs) to clarify the controversial issue. Four electronic databases including PubMed, Web of Science, Scopus and Virtual Health Library were accessed to search for relevant articles. We extracted data from published studies and pooled into odds ratio (OR) and its corresponding 95% confidence intervals (CIs) using random-effect modeling. Nine studies comprising 403 TCVPTCs, 325 PTC-TCFs and 3552 classical PTCs were included for meta-analyses. Overall, the clinicopathological profiles of PTC-TCF including multifocality, extrathyroidal extension, lymph node metastasis, distant metastasis and patient mortality were not statistically different from those of TCVPTC. Additionally, PTC-TCF and TCVPTC were both associated with an increased risk for aggressive clinical courses as compared to classical PTC. The prevalence of BRAF mutation in PTC-TCF and TCVPTC was comparable and both were significantly higher than that in classical PTC. The present meta-analysis demonstrated that even a PTC comprising only 10% of TCs might be associated with a poor clinical outcome. Therefore, the proportions of PTC in PTC should be carefully estimated and reported even when the TC component is as little as 10%.

A systematic review on metabolomics-based diagnostic biomarker discovery and validation in pancreatic cancer.

INTRODUCTION: Metabolomics is an emerging approach for early detection of cancer. Along with the development of metabolomics, high-throughput technologies and statistical learning, the integration of multiple biomarkers has significantly improved clinical diagnosis and management for patients. OBJECTIVES: In this study, we conducted a systematic review to examine recent advancements in the oncometabolomics-based diagnostic biomarker discovery and validation in pancreatic cancer. METHODS: PubMed, Scopus, and Web of Science were searched for relevant studies published before September 2017. We examined the study designs, the metabolomics approaches, and the reporting methodological quality following PRISMA statement. RESULTS AND CONCLUSION: The included 25 studies primarily focused on the identification rather than the validation of predictive capacity of potential biomarkers. The sample size ranged from 10 to 8760. External validation of the biomarker panels was observed in nine studies. The diagnostic area under the curve ranged from 0.68 to 1.00 (sensitivity: 0.43-1.00, specificity: 0.73-1.00). The effects of patients' bio-parameters on metabolome alterations in a context-dependent manner have not been thoroughly elucidated. The most reported candidates were glutamic acid and histidine in seven studies, and glutamine and isoleucine in five studies, leading to the predominant enrichment of amino acid-related pathways. Notably, 46 metabolites were estimated in at least two studies. Specific challenges and potential pitfalls to provide better insights into future research directions were thoroughly discussed. Our investigation suggests that metabolomics is a robust approach that will improve the diagnostic assessment of pancreatic cancer. Further studies are warranted to validate their validity in multi-clinical settings.

Systematic assessment of cervical cancer initiation and progression uncovers genetic panels for deep learning-based early diagnosis and proposes novel diagnostic and prognostic biomarkers.

Although many outstanding achievements in the management of cervical cancer (CxCa) have obtained, it still imposes a major burden which has prompted scientists to discover and validate new CxCa biomarkers to improve the diagnostic and prognostic assessment of CxCa. In this study, eight different gene expression data sets containing 202 cancer, 115 cervical intraepithelial neoplasia (CIN), and 105 normal samples were utilized for an integrative systems biology assessment in a multi-stage carcinogenesis manner. Deep learning-based diagnostic models were established based on the genetic panels of intrinsic genes of cervical carcinogenesis as well as on the unbiased variable selection approach. Survival analysis was also conducted to explore the potential biomarker candidates for prognostic assessment. Our results showed that cell cycle, RNA transport, mRNA surveillance, and one carbon pool by folate were the key regulatory mechanisms involved in the initiation, progression, and metastasis of CxCa. Various genetic panels combined with machine learning algorithms successfully differentiated CxCa from CIN and normalcy in cross-study normalized data sets. In particular, the 168-gene deep learning model for the differentiation of cancer from normalcy achieved an externally validated accuracy of 97.96% (99.01% sensitivity and 95.65% specificity). Survival analysis revealed that ZNF281 and EPHB6 were the two most promising prognostic genetic markers for CxCa among others. Our findings open new opportunities to enhance current understanding of the characteristics of CxCa pathobiology. In addition, the combination of transcriptomics-based signatures and deep learning classification may become an important approach to improve CxCa diagnosis and management in clinical practice.

Research trends in evidence-based medicine: a joinpoint regression analysis of more than 50 years of publication data.

BACKGROUND: Evidence-based medicine (EBM) has developed as the dominant paradigm of assessment of evidence that is used in clinical practice. Since its development, EBM has been applied to integrate the best available research into diagnosis and treatment with the purpose of improving patient care. In the EBM era, a hierarchy of evidence has been proposed, including various types of research methods, such as meta-analysis (MA), systematic review (SRV), randomized controlled trial (RCT), case report (CR), practice guideline (PGL), and so on. Although there are numerous studies examining the impact and importance of specific cases of EBM in clinical practice, there is a lack of research quantitatively measuring publication trends in the growth and development of EBM. Therefore, a bibliometric analysis was constructed to determine the scientific productivity of EBM research over decades. METHODS: NCBI PubMed database was used to search, retrieve and classify publications according to research method and year of publication. Joinpoint regression analysis was undertaken to analyze trends in research productivity and the prevalence of individual research methods. FINDINGS: Analysis indicates that MA and SRV, which are classified as the highest ranking of evidence in the EBM, accounted for a relatively small but auspicious number of publications. For most research methods, the annual percent change (APC) indicates a consistent increase in publication frequency. MA, SRV and RCT show the highest rate of publication growth in the past twenty years. Only controlled clinical trials (CCT) shows a non-significant reduction in publications over the past ten years. CONCLUSIONS: Higher quality research methods, such as MA, SRV and RCT, are showing continuous publication growth, which suggests an acknowledgement of the value of these methods. This study provides the first quantitative assessment of research method publication trends in EBM.

Scientific Productivity on Research in Ethical Issues over the Past Half Century: A JoinPoint Regression Analysis.

BACKGROUND: Ethics is one of the main pillars in the development of science. We performed a JoinPoint regression analysis to analyze the trends of ethical issue research over the past half century. The question is whether ethical issues are neglected despite their importance in modern research. METHOD: PubMed electronic library was used to retrieve publications of all fields and ethical issues. JoinPoint regression analysis was used to identify the significant time trends of publications of all fields and ethical issues, as well as the proportion of publications on ethical issues to all fields over the past half century. Annual percent changes (APC) were computed with their 95% confidence intervals, and a p-value < 0.05 was considered statistically significant. RESULTS: We found that publications of ethical issues increased during the period of 1965-1996 but slightly fell in recent years (from 1996 to 2013). When comparing the absolute number of ethics related articles (APEI) to all publications of all fields (APAF) on PubMed, the results showed that the proportion of APEI to APAF statistically increased during the periods of 1965-1974, 1974-1986, and 1986-1993, with APCs of 11.0, 2.1, and 8.8, respectively. However, the trend has gradually dropped since 1993 and shown a marked decrease from 2002 to 2013 with an annual percent change of -7.4%. CONCLUSIONS: Scientific productivity in ethical issues research on over the past half century rapidly increased during the first 30-year period but has recently been in decline. Since ethics is an important aspect of scientific research, we suggest that greater attention is needed in order to emphasize the role of ethics in modern research.