Person: Hsiao, Li-Li
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Hsiao
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Li-Li
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Hsiao, Li-Li
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Publication α-Klotho expression determines nitric oxide synthesis in response to FGF-23 in human aortic endothelial cells(Public Library of Science, 2017) Chung, Chih-Ping; Chang, Yu-Chun; Ding, Yan; Lim, Kenneth; Liu, Qinghua; Zhu, Langjing; Zhang, Wei; Lu, Tzong-Shi; Molostvov, Guerman; Zehnder, Daniel; Hsiao, Li-LiEndothelial cells (ECs) express fibroblast growth factor (FGF) receptors and are metabolically active after treatment with FGF-23. It is not known if this effect is α-Klotho independent or mediated by humoral or endogenous endothelial α-Klotho. In the present study, we aimed to characterize EC α-Klotho expression within the human vascular tree and to investigate the potential role of α-Klotho in determining FGF-23 mediated EC regulation. Human tissue and ECs from various organs were used for immunohistochemistry and Western blot. Primary cultures of human aortic endothelial cells (HAECs) and human brain microvascular endothelial cells (HBMECs) were used to generate in vitro cell models. We found endogenous α-Klotho expression in ECs from various organs except in microvascular ECs from human brain. Furthermore, FGF-23 stimulated endothelial nitric oxide synthase (eNOS) expression, nitric oxide (NO) production, and cell proliferation in HAECs. Interestingly, these effects were not observed in our HBMEC model in vitro. High phosphate treatment and endothelial α-Klotho knockdown mitigated FGF-23 mediated eNOS induction, NO production, and cell proliferation in HAECs. Rescue treatment with soluble α-Klotho did not reverse endothelial FGF-23 resistance caused by reduced or absent α-Klotho expression in HAECs. These novel observations provide evidence for differential α-Klotho functional expression in the human endothelium and its presence may play a role in determining the response to FGF-23 in the vascular tree. α-Klotho was not detected in cerebral microvascular ECs and its absence may render these cells nonresponsive to FGF-23.Publication Early Detection of Drug-Induced Renal Hemodynamic Dysfunction Using Sonographic Technology in Rats(MyJove Corporation, 2016) Fisch, Sudeshna; Liao, Ronglih; Hsiao, Li-Li; Lu, TzongshiThe kidney normally functions to maintain hemodynamic homeostasis and is a major site of damage caused by drug toxicity. Drug-induced nephrotoxicity is estimated to contribute to 19- 25% of all clinical cases of acute kidney injury (AKI) in critically ill patients. AKI detection has historically relied on metrics such as serum creatinine (sCr) or blood urea nitrogen (BUN) which are demonstrably inadequate in full assessment of nephrotoxicity in the early phase of renal dysfunction. Currently, there is no robust diagnostic method to accurately detect hemodynamic alteration in the early phase of AKI while such alterations might actually precede the rise in serum biomarker levels. Such early detection can help clinicians make an accurate diagnosis and help in in decision making for therapeutic strategy. Rats were treated with Cisplatin to induce AKI. Nephrotoxicity was assessed for six days using high-frequency sonography, sCr measurement and upon histopathology of kidney. Hemodynamic evaluation using 2D and Color-Doppler images were used to serially study nephrotoxicity in rats, using the sonography. Our data showed successful drug-induced kidney injury in adult rats by histological examination. Color-Doppler based sonographic assessment of AKI indicated that resistive-index (RI) and pulsatile-index (PI) were increased in the treatment group; and peak-systolic velocity (mm/s), end-diastolic velocity (mm/s) and velocity-time integral (VTI, mm) were decreased in renal arteries in the same group. Importantly, these hemodynamic changes evaluated by sonography preceded the rise of sCr levels. Sonography-based indices such as RI or PI can thus be useful predictive markers of declining renal function in rodents. From our sonography-based observations in the kidneys of rats that underwent AKI, we showed that these noninvasive hemodynamic measurements may consider as an accurate, sensitive and robust method in detecting early stage kidney dysfunction. This study also underscores the importance of ethical issues associated with animal use in research.Publication Drop-on-Demand Single Cell Isolation and Total RNA Analysis(Public Library of Science, 2011) Moon, Sangjun; Kim, Yun-Gon; Dong, Lingsheng; Lombardi, Michael; Haeggstrom, Edward Olof; Jensen, Roderick V.; Hsiao, Li-Li; Demirci, UtkanTechnologies that rapidly isolate viable single cells from heterogeneous solutions have significantly contributed to the field of medical genomics. Challenges remain both to enable efficient extraction, isolation and patterning of single cells from heterogeneous solutions as well as to keep them alive during the process due to a limited degree of control over single cell manipulation. Here, we present a microdroplet based method to isolate and pattern single cells from heterogeneous cell suspensions (10% target cell mixture), preserve viability of the extracted cells (97.0\(\pm\)10.8%), and obtain genomic information from isolated cells compared to the non-patterned controls. The cell encapsulation process is both experimentally and theoretically analyzed. Using the isolated cells, we identified 11 stem cell markers among 1000 genes and compare to the controls. This automated platform enabling high-throughput cell manipulation for subsequent genomic analysis employs fewer handling steps compared to existing methods.Publication Differential expression patterns of housekeeping genes increase diagnostic and prognostic value in lung cancer(PeerJ Inc., 2018) Chang, Yu-Chun; Ding, Yan; Dong, Lingsheng; Zhu, Lang-Jing; Jensen, Roderick V.; Hsiao, Li-LiBackground: Using DNA microarrays, we previously identified 451 genes expressed in 19 different human tissues. Although ubiquitously expressed, the variable expression patterns of these “housekeeping genes” (HKGs) could separate one normal human tissue type from another. Current focus on identifying “specific disease markers” is problematic as single gene expression in a given sample represents the specific cellular states of the sample at the time of collection. In this study, we examine the diagnostic and prognostic potential of the variable expressions of HKGs in lung cancers. Methods: Microarray and RNA-seq data for normal lungs, lung adenocarcinomas (AD), squamous cell carcinomas of the lung (SQCLC), and small cell carcinomas of the lung (SCLC) were collected from online databases. Using 374 of 451 HKGs, differentially expressed genes between pairs of sample types were determined via two-sided, homoscedastic t-test. Principal component analysis and hierarchical clustering classified normal lung and lung cancers subtypes according to relative gene expression variations. We used uni- and multi-variate cox-regressions to identify significant predictors of overall survival in AD patients. Classifying genes were selected using a set of training samples and then validated using an independent test set. Gene Ontology was examined by PANTHER. Results: This study showed that the differential expression patterns of 242, 245, and 99 HKGs were able to distinguish normal lung from AD, SCLC, and SQCLC, respectively. From these, 70 HKGs were common across the three lung cancer subtypes. These HKGs have low expression variation compared to current lung cancer markers (e.g., EGFR, KRAS) and were involved in the most common biological processes (e.g., metabolism, stress response). In addition, the expression pattern of 106 HKGs alone was a significant classifier of AD versus SQCLC. We further highlighted that a panel of 13 HKGs was an independent predictor of overall survival and cumulative risk in AD patients. Discussion Here we report HKG expression patterns may be an effective tool for evaluation of lung cancer states. For example, the differential expression pattern of 70 HKGs alone can separate normal lung tissue from various lung cancers while a panel of 106 HKGs was a capable class predictor of subtypes of non-small cell carcinomas. We also reported that HKGs have significantly lower variance compared to traditional cancer markers across samples, highlighting the robustness of a panel of genes over any one specific biomarker. Using RNA-seq data, we showed that the expression pattern of 13 HKGs is a significant, independent predictor of overall survival for AD patients. This reinforces the predictive power of a HKG panel across different gene expression measurement platforms. Thus, we propose the expression patterns of HKGs alone may be sufficient for the diagnosis and prognosis of individuals with lung cancer.