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In JoVE (1)
Other Publications (19)
- Journal of Chemical Information and Modeling
- Physical Review Letters
- PloS One
- International Journal of Radiation Biology
- Drug Discovery Today
- Environmental Science & Technology
- Journal of Hazardous Materials
- Journal of Hazardous Materials
- Chembiochem : a European Journal of Chemical Biology
- Chembiochem : a European Journal of Chemical Biology
- Methods in Molecular Biology (Clifton, N.J.)
- Molecular Imaging
- Chemical Biology & Drug Design
- Nanotechnology
- PloS One
- Journal of Alzheimer's Disease : JAD
- Current Drug Targets
- Chemical Biology & Drug Design
- Drug Discovery Today
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Articles by Yongliang Yang in JoVE
JoVE Bioengineering
Padronização de plasma de superfície litografia para a Criação de Redes de celular
1Aerospace and Mechanical Engineering, University of Arizona, 2Biomedical Engineering IDP and BIO5 Institute, University of Arizona
A técnica de litografia versátil plasma foi desenvolvido para gerar padrões de superfície estável para orientar adesão celular. Esta técnica pode ser aplicada para criar redes de células, incluindo as que imitam tecidos naturais e tem sido usada para estudar vários tipos de células distintas.
Other articles by Yongliang Yang on PubMed
Virtual Hydrocarbon and Combinatorial Databases for Use with CAVEAT
Three new virtual databases have been developed for use with the bond-orientation-based database searching program CAVEAT. These consist of a database of trisubstituted monocyclic hydrocarbons having ethyl, vinyl, and phenyl substituents; a database of unsubstituted bicyclic hydrocarbons; and a database of core structures from established combinatorial synthetic methods having hydrogen, ethyl, vinyl, and phenyl substituents at the readily varied positions. Each collection of molecules was subjected to a batch conformational search, minimization, and conversion to a vector database for use with CAVEAT.
Quantifying Intrinsic Specificity: a Potential Complement to Affinity in Drug Screening
We report here the investigation of a novel description of specificity in protein-ligand binding based on energy landscape theory. We define a new term, intrinsic specificity ratio (ISR), which describes the level of discrimination in binding free energies of the native basin for a protein-ligand complex from the weaker binding states of the same ligand. We discuss the relationship between the intrinsic specificity we defined here and the conventional definition of specificity. In a docking study of molecules with the enzyme COX-2, we demonstrate a statistical correspondence between ISR value and geometrical shapes of the small molecules binding to COX-2. We further observe that the known selective (nonselective) inhibitors of COX-2 have higher (lower) ISR values. We suggest that intrinsic specificity ratio may be a useful new criterion and a complement to affinity in drug screening and in searching for potential drug lead compounds.
Integrative Genomic Data Mining for Discovery of Potential Blood-borne Biomarkers for Early Diagnosis of Cancer
With the arrival of the postgenomic era, there is increasing interest in the discovery of biomarkers for the accurate diagnosis, prognosis, and early detection of cancer. Blood-borne cancer markers are favored by clinicians, because blood samples can be obtained and analyzed with relative ease. We have used a combined mining strategy based on an integrated cancer microarray platform, Oncomine, and the biomarker module of the Ingenuity Pathways Analysis (IPA) program to identify potential blood-based markers for six common human cancer types.
Synthesis and Application of Molecular Probe for Detection of Hydroxyl Radicals Produced by Na(125)I and Gamma-rays in Aqueous Solution
To synthesize N-(3-(3-aminopropylamino)propyl)-2-oxo-2H-chromene-3-carboxamide (7), a novel DNA-binding, coumarin-based, fluorescent hydroxylradical ((*)OH) indicator and to assess its quantum efficiency compared with that of coumarin-3-carboxylic acid (1) and N1,N12-bis[2-oxo-2H-chromene-3-carbonyl]- 1,12-diamine-4,9-diazadodecane (9).
Target Discovery from Data Mining Approaches
Data mining of available biomedical data and information has greatly boosted target discovery in the 'omics' era. Target discovery is the key step in the biomarker and drug discovery pipeline to diagnose and fight human diseases. In biomedical science, the 'target' is a broad concept ranging from molecular entities (such as genes, proteins and miRNAs) to biological phenomena (such as molecular functions, pathways and phenotypes). Within the context of biomedical science, data mining refers to a bioinformatics approach that combines biological concepts with computer tools or statistical methods that are mainly used to discover, select and prioritize targets. In response to the huge demand of data mining for target discovery in the 'omics' era, this review explicates various data mining approaches and their applications to target discovery with emphasis on text and microarray data analysis. Two emerging data mining approaches, chemogenomic data mining and proteomic data mining, are briefly introduced. Also discussed are the limitations of various data mining approaches found in the level of database integration, the quality of data annotation, sample heterogeneity and the performance of analytical and mining tools. Tentative strategies of integrating different data sources for target discovery, such as integrated text mining with high-throughput data analysis and integrated mining with pathway databases, are introduced.
Perfluorinated Compounds in Tap Water from China and Several Other Countries
The recent development of a sensitive and accurate analytical method for the analysis of 20 perfluorinated compounds (PFCs), including several short-chain PFCs, has enabled their quantification in tap water collected in China, Japan, India, the United States, and Canada between 2006 and 2008. Of the PFCs measured, PFOS, PFHxS, PFBS, PFPrS, PFEtS, PFOSA, N-EtFOSAA, PFDoDA, PFUnDA, PFDA, PFNA, PFHpA, PFHxA, PFPeA, PFBA, and PFPrA were found at detectable concentrations in the tap water samples. The water samples from Shanghai (China) contained the greatest concentrations of total PFCs (arithmetic mean = 130 ng/L), whereas those from Toyama (Japan) contained only 0.62 ng/L. In addition to PFOS and PFOA, short-chain PFCs such as PFHxS, PFBS, PFHxA, and PFBA were found to be prevalent in drinking water. According to the health-based values (HBVs) and advisory guidelines derived for PFOS, PFOA, PFBA, PFHxS, PFBS, PFHxA, and PFPeA by the U.S.EPA and the Minnesota Department of Health, tap water may not pose an immediate health risk to consumers.
Immobilization of Zn, Cu, and Pb in Contaminated Soils Using Phosphate Rock and Phosphoric Acid
Considerable research has been done on P-induced Pb immobilization in Pb-contaminated soils. However, application of P to soils contaminated with multiple heavy metals is limited. The present study examined effectiveness of phosphoric acid (PA) and/or phosphate rock (PR) in immobilizing Pb, Cu, and Zn in two contaminated soils. The effectiveness was evaluated using water extraction, plant uptake, and a simple bioaccessibility extraction test (SBET) mimicking metal uptake in the acidic environment of human stomach. The possible mechanisms for metal immobilization were elucidated using X-ray diffraction, scanning electron microscopy, and chemical speciation program Visual MINTEQ. Compared to the control, all P amendments significantly reduced Pb water solubility, phytoavailability, and bioaccessibility by 72-100%, 15-86%, and 28-92%, respectively. The Pb immobilization was probably attributed to the formation of insoluble Pb phosphate minerals. Phosphorus significantly reduced Cu and Zn water solubility by 31-80% and 40-69%, respectively, presumably due to their sorption on minerals (e.g., calcite and phosphate phases) following CaO addition. However, P had little effect on the Cu and Zn phytoavailability; while the acid extractability of Cu and Zn induced by SBET (pH 2) were even elevated by up to 48% and 40%, respectively, in the H(3)PO(4) treatments (PA and PR+PA). Our results indicate that phosphate was effective in reducing Pb availability in terms of water solubility, bioaccessibility, and phytoavailability. Caution should be exercised when H(3)PO(4) was amended to the soil co-contaminated with Cu and Zn since the acidic condition of SBET increased Cu and Zn bioaccessibility though their water solubility was reduced.
Polychlorinated Biphenyls, Polychlorinated Dibenzo-p-dioxins and Dibenzofurans in Marine and Lacustrine Sediments from the Shandong Peninsula, China
Concentrations of polychlorinated biphenyls (PCBs), polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) in sediments from Bohai Sea and Yellow Sea coastal waters and lakes in Shandong Peninsula were determined. The total PCB concentrations of the measured 50 congeners (Sigma50PCBs) in the sediments ranged from 273.7 to 644.5 pg g(-1) dw (dry weight). The PCB congener profiles in the lacustrine sediments were different from those in the marine sediments. TriCBs and TetraCBs were the dominant homologues in marine sediments, whereas in the sediments from the Nansi Lakes, contributions of PCB homologues were similar. The total concentrations of 2,3,7,8-PCDD/Fs ranged from 6.2 to 27.4 pg g(-1) dw. The congener profiles of 2,3,7,8-substituted PCDD/Fs for the sediments were generally similar for both the lakes and the coastal sea areas in Shandong Peninsula. They were characterized by high OCDD, followed by 1,2,3,4,6,7,8-HpCDD and OCDF. The congener profiles of PCDD/Fs in the sediments were consistent with the profiles of main dominant PCDD/Fs in pentachlorophenol and sodium pentachlorophenate products in China. PCDD/F-TEQ ranged from 0.11 to 0.80 pg TEQg(-1) dw. The dioxin-like PCB-TEQ had concentrations ranging from 0.03 to 0.08 pg TEQ g(-1) dw, mainly from PCB126. PCBs and PCDD/Fs concentrations found in the sediments were from background to low polluted levels.
Synthesis, Evaluation, and Mechanism of N,N,N-Trimethyl-D-glucosamine-(1→4)-chitooligosaccharides As Selective Inhibitors of Glycosyl Hydrolase Family 20 β-N-Acetyl-D-hexosaminidases
GH20 β-N-acetyl-D-hexosaminidases are enzymes involved in many vital processes. Inhibitors that specifically target GH20 enzymes in pests are of agricultural and economic importance. Structural comparison has revealed that the bacterial chitindegrading β-N-acetyl-D-hexosaminidases each have an extra +1 subsite in the active site; this structural difference could be exploited for the development of selective inhibitors. N,N,Ntrimethyl-D-glucosamine (TMG)-chitotriomycin, which contains three GlcNAc residues, is a natural selective inhibitor against bacterial and insect β-N-acetyl-D-hexosaminidases. However, our structural alignment analysis indicated that the two GlcNAc residues at the reducing end might be unnecessary. To prove this hypothesis, we designed and synthesized a series of TMG-chitotriomycin analogues containing one to four GlcNAc units. Inhibitory kinetics and molecular docking showed that TMG-(GlcNAc)(2), is as active as TMG-chitotriomycin [TMG-(GlcNAc)(3)]. The selective inhibition mechanism of TMG-chitotriomycin was also explained.
Synthesis, Evaluation, and Mechanism of N,N,N-trimethyl-D-glucosamine-(1→4)-chitooligosaccharides As Selective Inhibitors of Glycosyl Hydrolase Family 20 β-N-acetyl-D-hexosaminidases
GH20 β-N-acetyl-D-hexosaminidases are enzymes involved in many vital processes. Inhibitors that specifically target GH20 enzymes in pests are of agricultural and economic importance. Structural comparison has revealed that the bacterial chitindegrading β-N-acetyl-D-hexosaminidases each have an extra +1 subsite in the active site; this structural difference could be exploited for the development of selective inhibitors. N,N,Ntrimethyl-D-glucosamine (TMG)-chitotriomycin, which contains three GlcNAc residues, is a natural selective inhibitor against bacterial and insect β-N-acetyl-D-hexosaminidases. However, our structural alignment analysis indicated that the two GlcNAc residues at the reducing end might be unnecessary. To prove this hypothesis, we designed and synthesized a series of TMG-chitotriomycin analogues containing one to four GlcNAc units. Inhibitory kinetics and molecular docking showed that TMG-(GlcNAc)(2), is as active as TMG-chitotriomycin [TMG-(GlcNAc)(3)]. The selective inhibition mechanism of TMG-chitotriomycin was also explained.
Integrated Bioinformatics Analysis for Cancer Target Identification
The exponential growth of high-throughput Omics data has provided an unprecedented opportunity for new target identification to fuel the dried-up drug discovery pipeline. However, the bioinformatics analysis of large amount and heterogeneous Omics data has posed a great deal of technical challenges for experimentalists who lack statistical skills. Moreover, due to the complexity of human diseases, it is essential to analyze the Omics data in the context of molecular networks to detect meaningful biological targets and understand disease processes. Here, we describe an integrated bioinformatics analysis strategy and provide a running example to identify suitable targets for our in-house Enzyme-Mediated Cancer Imaging and Therapy (EMCIT) technology. In addition, we go through a few key concepts in the process, including corrected false discovery rate (FDR), Gene Ontology (GO), pathway analysis, and tissue specificity. We also describe popular programs and databases which allow the convenient annotation and network analysis of Omics data. We provide a practical guideline for researchers to quickly follow the protocol described and identify those targets that are pertinent to their work.
General Approach to Identifying Potential Targets for Cancer Imaging by Integrated Bioinformatics Analysis of Publicly Available Genomic Profiles
Molecular imaging has moved to the forefront of drug development and biomedical research. The identification of appropriate imaging targets has become the touchstone for the accurate diagnosis and prognosis of human cancer. Particularly, cell surface- or membrane-bound proteins are attractive imaging targets for their aberrant expression, easily accessible location, and unique biochemical functions in tumor cells. Previously, we published a literature mining of potential targets for our in-house enzyme-mediated cancer imaging and therapy technology. Here we present a simple and integrated bioinformatics analysis approach that assembles a public cancer microarray database with a pathway knowledge base for ascertaining and prioritizing upregulated genes encoding cell surface- or membrane-bound proteins, which could serve imaging targets. As examples, we obtained lists of potential hits for six common and lethal human tumors in the prostate, breast, lung, colon, ovary, and pancreas. As control tests, a number of well-known cancer imaging targets were detected and confirmed by our study. Further, by consulting gene-disease and protein-disease databases, we suggest a number of significantly upregulated genes as promising imaging targets, including cell surface-associated mucin-1, prostate-specific membrane antigen, hepsin, urokinase plasminogen activator receptor, and folate receptors. By integrating pathway analysis, we are able to organize and map "focused" interaction networks derived from significantly dysregulated entity pairs to reflect important cellular functions in disease processes. We provide herein an example of identifying a tumor cell growth and proliferation subnetwork for prostate cancer. This systematic mining approach can be broadly applied to identify imaging or therapeutic targets for other human diseases.
Human Placental Alkaline Phosphatase-mediated Hydrolysis Correlates Tightly with the Electrostatic Contribution from Tail Group
Human placental alkaline phosphatase has been identified as a hydrolase that is significantly overexpressed on the surface of various solid tumor cells, and is therefore a suitable prodrug design target for non-invasive cancer imaging and therapy. Structure-based prediction of enzymatic activities is essential for rational prodrug design. We have been probing the catalytic proficiency--(k(cat) /K(M) )/k(w)--of placental alkaline phosphatase toward several widely diverse substrate structures experimentally and correlating these results to in silico predictions that are based on the free energy estimates obtained from docking of each substrate structure with placental alkaline phosphatase. We have found that electrostatic contribution from the tail group is the most crucial factor to determine the catalytic efficiencies of the substrates. The electrostatic contribution and the total binding energy of the tail group are well correlated with catalytic efficiencies (R² = 0.79 and 0.89, respectively). However, hydrophobic contribution from the tail group does not correlate with the catalytic efficiencies (negative correlation, R² = 0.27). This supports the prior hypothesis stating that alkaline phosphatase-mediated differential hydrolysis of its substrates is attributable to the differential interactions with the tail group, determined by the electrostatic contributions from the non-bridging oxygen atoms. Calculation of the electrostatic potentials within the active site of human placental alkaline phosphatase also suggests that the local positive electrostatic environment may account for its capability to distinguish various substrates. Our study is likely to have immediate implications in the design of prodrugs against human placental alkaline phosphatase and other esterases overexpressed by human tumor cells.
Giant Piezoresistance of P-type Nano-thick Silicon Induced by Interface Electron Trapping Instead of 2D Quantum Confinement
The p-type silicon giant piezoresistive coefficient is measured in top-down fabricated nano-thickness single-crystalline-silicon strain-gauge resistors with a macro-cantilever bending experiment. For relatively thicker samples, the variation of piezoresistive coefficient in terms of silicon thickness obeys the reported 2D quantum confinement effect. For ultra-thin samples, however, the variation deviates from the quantum-effect prediction but increases the value by at least one order of magnitude (compared to the conventional piezoresistance of bulk silicon) and the value can change its sign (e.g. from positive to negative). A stress-enhanced Si/SiO(2) interface electron-trapping effect model is proposed to explain the 'abnormal' giant piezoresistance that should be originated from the carrier-concentration change effect instead of the conventional equivalent mobility change effect for bulk silicon piezoresistors. An interface state modification experiment gives preliminary proof of our analysis.
The Discovery of Putative Urine Markers for the Specific Detection of Prostate Tumor by Integrative Mining of Public Genomic Profiles
Urine has emerged as an attractive biofluid for the noninvasive detection of prostate cancer (PCa). There is a strong imperative to discover candidate urinary markers for the clinical diagnosis and prognosis of PCa. The rising flood of various omics profiles presents immense opportunities for the identification of prospective biomarkers. Here we present a simple and efficient strategy to derive candidate urine markers for prostate tumor by mining cancer genomic profiles from public databases. Prostate, bladder and kidney are three major tissues from which cellular matters could be released into urine. To identify urinary markers specific for PCa, upregulated entities that might be shed in exosomes of bladder cancer and kidney cancer are first excluded. Through the ontology-based filtering and further assessment, a reduced list of 19 entities encoding urinary proteins was derived as putative PCa markers. Among them, we have found 10 entities closely associated with the process of tumor cell growth and development by pathway enrichment analysis. Further, using the 10 entities as seeds, we have constructed a protein-protein interaction (PPI) subnetwork and suggested a few urine markers as preferred prognostic markers to monitor the invasion and progression of PCa. Our approach is amenable to discover and prioritize potential markers present in a variety of body fluids for a spectrum of human diseases.
Identification of Putative Molecular Imaging Probes for BACE-1 by Accounting for Protein Flexibility in Virtual Screening
β-secretase (BACE-1), an enzyme critical in the process of amyloid-β (Aβ) peptides deposition in human brain, is closely associated with the onset and progression of Alzheimer's disease (AD). A strong need exists, therefore, to identify molecular imaging probes homing at BACE-1 for use with positron emission tomography (PET) that is recognized as an effective tool for detecting AD. Through this imaging, an early diagnosis of AD could be made. Herein, to identify suitable molecular probes for use with PET, we searched the Molecular Imaging and Contrast Agent Database (MICAD), an online database warehousing scientific information regarding molecular imaging and contrast agents, and applied a virtual screening approach against the different confirmations of BACE-1 obtained from the World Wide Protein Database. The lack of considering receptor flexibility is a key drawback in virtual screening for drug discovery. Therefore, we incorporated protein flexibility into the virtual screening by using an ensemble of 143 experimental BACE-1 structures derived from the Protein Data Bank. Finally, the best performing affinity was recorded and used in the ranking of each ligand. To the best of our knowledge, this is the first virtual screening approach used to identify four new molecular probes that could target BACE-1 with favorable affinity, a discovery that can lead to the development of new PET probes for the early detection and therapy of AD. However, the actual utility of these probes can only be ascertained after in vitro and in vivo investigations.
Investigation of Family 18 Chitinases and Inhibitors by Computer-aided Approaches
Chitinases belong to family 18 glycosyl hydrolases that can hydrolyze chitin by cleaving β-1,4-glycosidic bond, and are at key points in the life cycles of organism. The inhibitors of chitinases not only have chemotherapeutic potential against fungi, insects, but also hold anti-inflammatory efficacy against asthma and allergic disease in human. This review summarizes the structural characters of chitinases, the proposed catalytic mechanism, furthermore, also gives descriptions of currently existing inhibitors. In addition, computational studies of the interaction modes of chitinases with different inhibitors and substrates, as well as the inhibitor design of chitinases, are summarized so as to obtain an overall understanding for chitinases.
Computational and Biological Evaluation of Quinazolinone Prodrug for Targeting Pancreatic Cancer
Our concept of Enzyme-Mediated Cancer Imaging and Therapy aims to use radiolabeled compounds to target hydrolases over-expressed on the extracellular surface of solid tumors. A data-mining approach identified extracellular sulfatase 1 (SULF1) as an enzyme expressed on the surface of pancreatic cancer cells. We designed, synthesized, and characterized 2-(2'-sulfooxyphenyl)-6-iodo-4-(3H)-quinazolinone (IQ(2-S) ) as well as its radioiodinated form ((125) IQ(2-S) ) as a prodrug with potential for hydrolysis by SULF1. IQ(2-S) was successfully docked in silico into three enzymes - homolog of SULF1, alkaline phosphatase, and prostatic acid phosphatase. The incubation of (125) IQ(2-S) and (125) IQ(2-P) with the three enzymes in solution confirms the docking results and enzyme selectivity for the analogs. The hydrolysis of both radioactive compounds produces the water-insoluble, fluorescent product 2-(2'-hydroxyphenyl)-6-[(125) I]iodo-4-(3H)-quinazolinone ((125) IQ(2-OH) ). The in vitro incubation of (127) IQ(2-S) and (127) IQ(2-P) with pancreatic, ovarian and prostate cancer cells expressing studied hydrolases also results in their hydrolysis and the precipitation of (127) IQ(2-OH) fluorescent crystals on the cell surface. To our knowledge, these findings are the first to report the targeting of a radioactive substrate to SULF1 and that this prodrug may be potentially useful in the imaging ((123) I/(124) I/(131) I) and radiotherapy ((131) I) of pancreatic cancer. © 2012 John Wiley & Sons A/S.
Target Discovery from Data Mining Approaches
Data mining of available biomedical data and information has greatly boosted target discovery in the 'omics' era. Target discovery is the key step in the biomarker and drug discovery pipeline to diagnose and fight human diseases. In biomedical science, the 'target' is a broad concept ranging from molecular entities (such as genes, proteins and miRNAs) to biological phenomena (such as molecular functions, pathways and phenotypes). Within the context of biomedical science, data mining refers to a bioinformatics approach that combines biological concepts with computer tools or statistical methods that are mainly used to discover, select and prioritize targets. In response to the huge demand of data mining for target discovery in the 'omics' era, this review explicates various data mining approaches and their applications to target discovery with emphasis on text and microarray data analysis. Two emerging data mining approaches, chemogenomic data mining and proteomic data mining, are briefly introduced. Also discussed are the limitations of various data mining approaches found in the level of database integration, the quality of data annotation, sample heterogeneity and the performance of analytical and mining tools. Tentative strategies of integrating different data sources for target discovery, such as integrated text mining with high-throughput data analysis and integrated mining with pathway databases, are introduced.
