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1.  Urine-derived stem cells: A novel and versatile progenitor source for cell-based therapy and regenerative medicine 
Genes & diseases  2014;1(1):8-17.
Engineered functional organs or tissues, created with autologous somatic cells and seeded on biodegradable or hydrogel scaffolds, have been developed for use in individuals with tissue damage suffered from congenital disorders, infection, irradiation, or cancer. However, in those patients, abnormal cells obtained by biopsy from the compromised tissue could potentially contaminate the engineered tissues. Thus, an alternative cell source for construction of the neo-organ or functional recovery of the injured or diseased tissues would be useful. Recently, we have found stem cells existing in the urine. These cells are highly expandable, and have self-renewal capacity, paracrine properties, and multi-differentiation potential. As a novel cell source, urine-derived stem cells (USCs) provide advantages for cell therapy and tissue engineering applications in regeneration of various tissues, particularly in the genitourinary tract, because they originate from the urinary tract system. Importantly, USCs can be obtained via a non-invasive, simple, and low-cost approach and induced with high efficiency to differentiate into three dermal cell lineages.
PMCID: PMC4234168  PMID: 25411659
Cell therapy; Genitourinary tract; Stem cells; Tissue regeneration; Urine
2.  MRI-based prediction of pulsed high-intensity focused ultrasound effect on tissue transport in rabbit muscle 
Journal of magnetic resonance imaging : JMRI  2013;38(5):10.1002/jmri.24087.
To design an algorithm for optimizing pulsed high intensity focused ultrasound (p-HIFU) treatment parameters to maximize tissue transport while minimizing thermal necrosis based on MR image guidance.
Materials and Methods
P-HIFU power, duty cycle and treatment duration were varied to generate different levels of thermal and mechanical deposition in rabbit muscle. Changes in T2-weighted and T1 contrast-enhanced (CE) signal were assessed immediately following treatment and at 24 hrs. Transport parameters were extracted via T1-weighted dynamic contrast-enhanced MRI (DCE-MRI) technique at 0 and 24 hr time points.
Successful p-HIFU treatment was indicated by focal hyperintensity on the T2-weighted image immediately post-treatment, suggesting increased fluid (edema), with little intensity change in CE image. After 24h, the affected region expanded along the muscle fiber accompanied by clear hyperintensity in CE image (contrast uptake). Quantitative DCE-MRI analysis revealed statistically significant increases in both leakage rate and extracellular space, accompanied by a decrease in clearance rate.
Successful p-HIFU treatment was mainly correlated to tissue heating. Tissue transport properties following treatment success would result in improved contact between drug and targets in both time and space. MRI is the key to controlling treatment via thermometry and also monitoring efficacy via T2-weighted imaging.
PMCID: PMC3706525  PMID: 23553784
pulsed high-intensity focused ultrasound; MRI-guided focused ultrasound; drug delivery; dynamic contrast enhanced-MRI; tissue permeability; clearance rate
3.  Modeling Cell–Cell Interactions in Regulating Multiple Myeloma Initiating Cell Fate 
Cancer initiating cells have been documented in multiple myeloma and believed to be a key factor that initiates and drives tumor growth, differentiation, metastasis, and recurrence of the diseases. Although myeloma initiating cells (MICs) are likely to share many properties of normal stem cells, the underlying mechanisms regulating the fate of MICs are largely unknown. Studies designed to explore such communication are urgently needed to enhance our ability to predict the fate decisions of MICs (self-renewal, differentiation, and proliferation). In this study, we developed a novel system to understand the intercellular communication between MICs and their niche by seamlessly integrating experimental data and mathematical model. We first designed dynamic cell culture experiments and collected three types of cells (side population cells, progenitor cells, and mature myeloma cells) under various cultural conditions with flow cytometry. Then we developed a lineage model with ordinary differential equations by considering secreted factors, self-renewal, differentiation, and other biological functions of those cells, to model the cell–cell interactions among the three cell types. Particle swarm optimization was employed to estimate the model parameters by fitting the experimental data to the lineage model. The theoretical results show that the correlation coefficient analysis can reflect the feedback loops among the three cell types, the intercellular feedback signaling can regulate cell population dynamics, and the culture strategies can decide cell growth. This study provides a basic framework of studying cell–cell interactions in regulating MICs fate.
PMCID: PMC4104531  PMID: 24058033
Cancer initiating cell; lineage model; mathematical modeling; multiple myeloma (MM); parameter estimation
4.  Integrating Genomics and Proteomics Data to Predict Drug Effects Using Binary Linear Programming 
PLoS ONE  2014;9(7):e102798.
The Library of Integrated Network-Based Cellular Signatures (LINCS) project aims to create a network-based understanding of biology by cataloging changes in gene expression and signal transduction that occur when cells are exposed to a variety of perturbations. It is helpful for understanding cell pathways and facilitating drug discovery. Here, we developed a novel approach to infer cell-specific pathways and identify a compound's effects using gene expression and phosphoproteomics data under treatments with different compounds. Gene expression data were employed to infer potential targets of compounds and create a generic pathway map. Binary linear programming (BLP) was then developed to optimize the generic pathway topology based on the mid-stage signaling response of phosphorylation. To demonstrate effectiveness of this approach, we built a generic pathway map for the MCF7 breast cancer cell line and inferred the cell-specific pathways by BLP. The first group of 11 compounds was utilized to optimize the generic pathways, and then 4 compounds were used to identify effects based on the inferred cell-specific pathways. Cross-validation indicated that the cell-specific pathways reliably predicted a compound's effects. Finally, we applied BLP to re-optimize the cell-specific pathways to predict the effects of 4 compounds (trichostatin A, MS-275, staurosporine, and digoxigenin) according to compound-induced topological alterations. Trichostatin A and MS-275 (both HDAC inhibitors) inhibited the downstream pathway of HDAC1 and caused cell growth arrest via activation of p53 and p21; the effects of digoxigenin were totally opposite. Staurosporine blocked the cell cycle via p53 and p21, but also promoted cell growth via activated HDAC1 and its downstream pathway. Our approach was also applied to the PC3 prostate cancer cell line, and the cross-validation analysis showed very good accuracy in predicting effects of 4 compounds. In summary, our computational model can be used to elucidate potential mechanisms of a compound's efficacy.
PMCID: PMC4103865  PMID: 25036040
5.  Faslodex Inhibits Estradiol-Induced Extracellular Matrix Dynamics and Lung Metastasis in a Model of Lymphangioleiomyomatosis 
Lymphangioleiomyomatosis (LAM) is a destructive lung disease primarily affecting women. Genetic studies indicate that LAM cells carry inactivating tuberous sclerosis complex (TSC)–2 mutations, and metastasize to the lung. We previously discovered that estradiol increases the metastasis of TSC2-deficient cells in mice carrying xenograft tumors. Here, we investigate the molecular basis underlying the estradiol-induced lung metastasis of TSC2-deficient cells, and test the efficacy of Faslodex (an estrogen receptor antagonist) in a preclinical model of LAM. We used a xenograft tumor model in which estradiol induces the lung metastasis of TSC2-deficient cells. We analyzed the impact of Faslodex on tumor size, the extracellular matrix organization, the expression of matrix metalloproteinase (MMP)–2, and lung metastasis. We also examined the effects of estradiol and Faslodex on MMP2 expression and activity in tuberin-deficient cells in vitro. Estradiol resulted in a marked reduction of Type IV collagen deposition in xenograft tumors, associated with 2-fold greater MMP2 concentrations compared with placebo-treated mice. Faslodex normalized the Type IV collagen changes in xenograft tumors, enhanced the survival of the mice, and completely blocked lung metastases. In vitro, estradiol enhanced MMP2 transcripts, protein accumulation, and activity. These estradiol-induced changes in MMP2 were blocked by Faslodex. In TSC2-deficient cells, estradiol increased MMP2 concentrations in vitro and in vivo, and induced extracellular matrix remodeling. Faslodex inhibits the estradiol-induced lung metastasis of TSC2-deficient cells. Targeting estrogen receptors with Faslodex may be of efficacy in the treatment of LAM.
PMCID: PMC3727883  PMID: 23526212
tuberin; estrogen receptor antagonist; matrix metalloproteinase; extracellular matrix
6.  Tracking and Measurement of the Motion of Blood Cells Using Optical Flow Methods 
The investigation of microcirculation is a critical task in biomedical and physiological research. In order to monitor human’s condition and develop effective therapies of some diseases, the microcirculation information, such as flow velocity and vessel density, must be evaluated in a noninvasive manner. As one of the tasks of microcirculation investigation, automatic blood cell tracking presents an effective approach to estimate blood flow velocity. Currently, the most common method for blood cell tracking is based on spatiotemporal image analysis, which has lots of limitations, such as the diameter of microvesssels cannot be too larger than blood cells or tracers, cells or tracers should have fixed velocity, and it requires the image with high qualification. In this paper, we propose an optical flow method for automatic cell tracking. The key algorithm of the method is to align an image to its neighbors in a large image collection consisting of a variety of scenes. Considering the method cannot solve the problems in all cases of cell movement, another optical flow method, SIFT (Scale Invariant Feature Transform) flow, is also presented. The experimental results show that both methods can track the cells accurately. Optical flow is specially robust to the case where the velocity of cell is unstable, while SIFT flow works well when there are large displacement of cell between two adjacent frames. Our proposed methods outperform other methods when doing in vivo cell tracking, which can be used to estimate the blood flow directly and help to evaluate other parameters in microcirculation.
PMCID: PMC3990667  PMID: 24058034
Optical flow; SIFT Flow; blood cell; in vivo cell tracking; microscopy imaging
7.  Scalable Collaborative Infrastructure for a Learning Healthcare System (SCILHS): Architecture 
We describe the architecture of the Patient Centered Outcomes Research Institute (PCORI) funded Scalable Collaborative Infrastructure for a Learning Healthcare System (SCILHS, clinical data research network, which leverages the $48 billion dollar federal investment in health information technology (IT) to enable a queryable semantic data model across 10 health systems covering more than 8 million patients, plugging universally into the point of care, generating evidence and discovery, and thereby enabling clinician and patient participation in research during the patient encounter. Central to the success of SCILHS is development of innovative ‘apps’ to improve PCOR research methods and capacitate point of care functions such as consent, enrollment, randomization, and outreach for patient-reported outcomes. SCILHS adapts and extends an existing national research network formed on an advanced IT infrastructure built with open source, free, modular components.
PMCID: PMC4078286  PMID: 24821734
Electronic Health Record; Learning Health System; Clinical Trials; Patient Engagement; Distributed Computing
8.  Gene expression analysis uncovers novel Hedgehog interacting protein (HHIP) effects in human bronchial epithelial cells 
Genomics  2013;101(5):263-272.
Hedgehog Interacting Protein (HHIP) was implicated in chronic obstructive pulmonary disease (COPD) by genome-wide association studies (GWAS). However, it remains unclear how HHIP contributes to COPD pathogenesis. To identify genes regulated by HHIP, we performed gene expression microarray analysis in a human bronchial epithelial cell line (Beas-2B) stably infected with HHIP shRNAs. HHIP silencing led to differential expression of 296 genes; enrichment for variants nominally associated with COPD was found. Eighteen of the differentially expressed genes were validated by real-time PCR in Beas-2B cells. Seven of 11 validated genes tested in human COPD and control lung tissues demonstrated significant gene expression differences. Functional annotation indicated enrichment for extracellular matrix and cell growth genes. Network modeling demonstrated that the extracellular matrix and cell proliferation genes influenced by HHIP tended to be interconnected. Thus, we identified potential HHIP targets in human bronchial epithelial cells that may contribute to COPD pathogenesis.
PMCID: PMC3659826  PMID: 23459001
Hedgehog interacting protein (HHIP); Gene expression profiling; COPD (Chronic obstructive pulmonary disease); extracellular matrix (ECM); network modeling
9.  Pathway Bridge Based Multiobjective Optimization Approach for Lurking Pathway Prediction 
BioMed Research International  2014;2014:351095.
Ovarian carcinoma immunoreactive antigen-like protein 2 (OCIAD2) is a protein with unknown function. Frequently methylated or downregulated, OCIAD2 has been observed in kinds of tumors, and TGFβ signaling has been proved to induce the expression of OCIAD2. However, current pathway analysis tools do not cover the genes without reported interactions like OCIAD2 and also miss some significant genes with relatively lower expression. To investigate potential biological milieu of OCIAD2, especially in cancer microenvironment, a nova approach pbMOO was created to find the potential pathways from TGFβ to OCIAD2 by searching on the pathway bridge, which consisted of cancer enriched looping patterns from the complicated entire protein interactions network. The pbMOO approach was further applied to study the modulator of ligand TGFβ1, receptor TGFβR1, intermediate transfer proteins, transcription factor, and signature OCIAD2. Verified by literature and public database, the pathway TGFβ1- TGFβR1- SMAD2/3- SMAD4/AR-OCIAD2 was detected, which concealed the androgen receptor (AR) which was the possible transcription factor of OCIAD2 in TGFβ signal, and it well explained the mechanism of TGFβ induced OCIAD2 expression in cancer microenvironment, therefore providing an important clue for the future functional analysis of OCIAD2 in tumor pathogenesis.
PMCID: PMC4052696  PMID: 24949437
10.  A System Mathematical Model of Cell-cell Communication Network in Amyotrophic Lateral Sclerosis 
Molecular bioSystems  2013;9(3):398-406.
Amyotrophic lateral sclerosis (ALS) is a devastating and chronic neurodegenerative disease without any known cure. In the brain and spinal cord of both patients and animal models with ALS, neuroinflammation is a prominent pathological hallmark which is characterized by infiltrating T cells at sites of motor neurons injury. Their presence in mutant Cu2+/Zn2+ superoxide dismutase (mSOD1) induced ALS plays an important role in shifting the response of microglia from neuroprotective into neurotoxic. In order to better understand how these cells and their communication network collectively modulate the disease progress, we have established a mathematical model integrating diverse cells and cytokines. According to the experimental data sets, we first refined this model by identifying a link between TGFβ and M1 microglia which can produce an optimized model to fit data sets better. Then based on this model, parameters were estimated using genetic algorithm. Sensitivity analysis of these parameters identified several factors such as release rate of IFNγ by T helper 1 (Th1) cells, which may be related to the heterogeneity between the patients with different survival time. Furthermore, the tests on T cell based therapeutic strategies indicated that elimination of Th1 cells is the most effective approach extending survival time. This confirmed the dominant role of Th1 cells in leading the rapid disorder in the later stage of ALS. For the therapies targeting cytokines, injection of IL6 can essentially augment the neuroprotective response and extend the life effectively by elevating the level of IL4, a neuroprotective cytokine, while direct injected IL4 will decay rapidly in ALS microenvironment and cannot provide a persistent protective effect. On the other hand, in spite of the attractive effect of direct elimination of mSOD1 or self-antigen, it is difficult to implement in CNS. As an alternative, elimination of IFNγ can be chosen as another effective therapy. In the future, if we combine the side effect of different therapies, this model can be used to optimize the therapeutic strategies so that they can effectively improve survival rates and quality of life for patients with ALS.
PMCID: PMC3752652  PMID: 23287963
11.  Targeting the Biophysical Properties of the Myeloma Initiating Cell Niches: A Pharmaceutical Synergism Analysis Using Multi-Scale Agent-Based Modeling 
PLoS ONE  2014;9(1):e85059.
Multiple myeloma, the second most common hematological cancer, is currently incurable due to refractory disease relapse and development of multiple drug resistance. We and others recently established the biophysical model that myeloma initiating (stem) cells (MICs) trigger the stiffening of their niches via SDF-1/CXCR4 paracrine; The stiffened niches then promote the colonogenesis of MICs and protect them from drug treatment. In this work we examined in silico the pharmaceutical potential of targeting MIC niche stiffness to facilitate cytotoxic chemotherapies. We first established a multi-scale agent-based model using the Markov Chain Monte Carlo approach to recapitulate the niche stiffness centric, pro-oncogenetic positive feedback loop between MICs and myeloma-associated bone marrow stromal cells (MBMSCs), and investigated the effects of such intercellular chemo-physical communications on myeloma development. Then we used AMD3100 (to interrupt the interactions between MICs and their stroma) and Bortezomib (a recently developed novel therapeutic agent) as representative drugs to examine if the biophysical properties of myeloma niches are drugable. Results showed that our model recaptured the key experimental observation that the MBMSCs were more sensitive to SDF-1 secreted by MICs, and provided stiffer niches for these initiating cells and promoted their proliferation and drug resistance. Drug synergism analysis suggested that AMD3100 treatment undermined the capability of MICs to modulate the bone marrow microenvironment, and thus re-sensitized myeloma to Bortezomib treatments. This work is also the first attempt to virtually visualize in 3D the dynamics of the bone marrow stiffness during myeloma development. In summary, we established a multi-scale model to facilitate the translation of the niche-stiffness centric myeloma model as well as experimental observations to possible clinical applications. We concluded that targeting the biophysical properties of stem cell niches is of high clinical potential since it may re-sensitize tumor initiating cells to chemotherapies and reduce risks of cancer relapse.
PMCID: PMC3903473  PMID: 24475036
12.  Computational Modeling of 3D Tumor Growth and Angiogenesis for Chemotherapy Evaluation 
PLoS ONE  2014;9(1):e83962.
Solid tumors develop abnormally at spatial and temporal scales, giving rise to biophysical barriers that impact anti-tumor chemotherapy. This may increase the expenditure and time for conventional drug pharmacokinetic and pharmacodynamic studies. In order to facilitate drug discovery, we propose a mathematical model that couples three-dimensional tumor growth and angiogenesis to simulate tumor progression for chemotherapy evaluation. This application-oriented model incorporates complex dynamical processes including cell- and vascular-mediated interstitial pressure, mass transport, angiogenesis, cell proliferation, and vessel maturation to model tumor progression through multiple stages including tumor initiation, avascular growth, and transition from avascular to vascular growth. Compared to pure mechanistic models, the proposed empirical methods are not only easy to conduct but can provide realistic predictions and calculations. A series of computational simulations were conducted to demonstrate the advantages of the proposed comprehensive model. The computational simulation results suggest that solid tumor geometry is related to the interstitial pressure, such that tumors with high interstitial pressure are more likely to develop dendritic structures than those with low interstitial pressure.
PMCID: PMC3880288  PMID: 24404145
13.  Accuracy of a Computer-Aided Surgical Simulation (CASS) Protocol for Orthognathic Surgery: A Prospective Multicenter Study 
The purpose of this prospective multicenter study was to assess the accuracy of a computer-aided surgical simulation (CASS) protocol for orthognathic surgery.
Materials and Methods
The accuracy of the CASS protocol was assessed by comparing planned and postoperative outcomes of 65 consecutive patients enrolled from 3 centers. Computer-generated surgical splints were used for all patients. For the genioplasty, one center utilized computer-generated chin templates to reposition the chin segment only for patients with asymmetry. Standard intraoperative measurements were utilized without the chin templates for the remaining patients. The primary outcome measurements were linear and angular differences for the maxilla, mandible and chin when the planned and postoperative models were registered at the cranium. The secondary outcome measurements were: maxillary dental midline difference between the planned and postoperative positions; and linear and angular differences of the chin segment between the groups with and without the use of the template. The latter was measured when the planned and postoperative models were registered at mandibular body. Statistical analyses were performed, and the accuracy was reported using root mean square deviation (RMSD) and Bland and Altman's method for assessing measurement agreement.
In the primary outcome measurements, there was no statistically significant difference among the 3 centers for the maxilla and mandible. The largest RMSD was 1.0mm and 1.5° for the maxilla, and 1.1mm and 1.8° for the mandible. For the chin, there was a statistically significant difference between the groups with and without the use of the chin template. The chin template group showed excellent accuracy with largest positional RMSD of 1.0mm and the largest orientational RSMD of 2.2°. However, larger variances were observed in the group not using the chin template. This was significant in anteroposterior and superoinferior directions, as in pitch and yaw orientations. In the secondary outcome measurements, the RMSD of maxillary dental midline positions was 0.9mm. When registered at the body of the mandible, the linear and angular differences of the chin segment between the groups with and without the use of the chin template were consistent with the results found in the primary outcome measurements.
Using the CASS protocol, the computerized plan can be accurately and consistently transferred to the patient to position the maxilla and mandible at the time of surgery. The computer-generated chin template provides more accuracy in repositioning the chin segment than the intraoperative measurements.
PMCID: PMC3443525  PMID: 22695016
computer-aided surgical simulation; orthognathic surgery; computer-generated surgical splint; computer-generated chin template; intraoperative measurements; dentofacial deformity
14.  Skeletal Myogenic Differentiation of Urine-Derived Stem Cells and Angiogenesis Using Microbeads Loaded with Growth Factors 
Biomaterials  2012;34(4):1311-1326.
To provide site-specific delivery and targeted release of growth factors to implanted urine-derived stem cells (USCs), we prepared microbeads of alginate containing growth factors. The growth factors included VEGF, IGF-1, FGF-1, PDGF, HGF and NGF. Radiolabeled growth factors were loaded separately and used to access the in vitro release from the microbeads with a gamma counter over 4 weeks. In vitro endothelial differentiation of USCs by the released VEGF from the microbeads in a separate experiment confirmed that the released growth factors from the microbeads were bioactive. USCs and microbeads were mixed with the collagen gel type 1 (2 mg/ml) and used for in vivo studies through subcutaneous injection into nude mice. Four weeks after subcutaneous injection, we found that grafted cell survival was improved and more cells expressed myogenic and endothelial cell transcripts and markers compared to controls. More vessel formation and innervations were observed in USCs combined with six growth factors cocktail incorporated in microbeads compared to controls. In conclusion, a combination of growth factors released locally from the alginate microbeads induced USCs to differentiate into a myogenic lineage, enhanced revascularization and innervation, and stimulated resident cell growth in vivo. This approach could potentially be used for cell therapy in the treatment of stress urinary incontinence.
PMCID: PMC3513922  PMID: 23137393
Drug delivery; Stem cells; Alginate microbeads; Controlled release; Growth factors
15.  Systems Modeling of Anti-apoptotic Pathways in Prostate Cancer: Psychological Stress Triggers a Synergism Pattern Switch in Drug Combination Therapy 
PLoS Computational Biology  2013;9(12):e1003358.
Prostate cancer patients often have increased levels of psychological stress or anxiety, but the molecular mechanisms underlying the interaction between psychological stress and prostate cancer as well as therapy resistance have been rarely studied and remain poorly understood. Recent reports show that stress inhibits apoptosis in prostate cancer cells via epinephrine/beta2 adrenergic receptor/PKA/BAD pathway. In this study, we used experimental data on the signaling pathways that control BAD phosphorylation to build a dynamic network model of apoptosis regulation in prostate cancer cells. We then compared the predictive power of two different models with or without the role of Mcl-1, which justified the role of Mcl-1 stabilization in anti-apoptotic effects of emotional stress. Based on the selected model, we examined and quantitatively evaluated the induction of apoptosis by drug combination therapies. We predicted that the combination of PI3K inhibitor LY294002 and inhibition of BAD phosphorylation at S112 would produce the best synergistic effect among 8 interventions examined. Experimental validation confirmed the effectiveness of our predictive model. Moreover, we found that epinephrine signaling changes the synergism pattern and decreases efficacy of combination therapy. The molecular mechanisms responsible for therapeutic resistance and the switch in synergism were explored by analyzing a network model of signaling pathways affected by psychological stress. These results provide insights into the mechanisms of psychological stress signaling in therapy-resistant cancer, and indicate the potential benefit of reducing psychological stress in designing more effective therapies for prostate cancer patients.
Author Summary
Psychological stress and anxiety are often experienced by prostate cancer patients, but the underlying mechanisms of interactions between psychological stress and cancer development, as well as drug resistance, are unclear. Here, we employed a systems biology approach to study interactions between stress-activated epinephrine/beta2 adrenergic receptor/protein kinase A signaling and a regulatory network that controls apoptosis in prostate cancer cells. We developed a dynamic network model of signaling pathways that control apoptosis in prostate cancer cells and quantitatively evaluated the effects of stress-activated signaling on apoptosis induced by drug combinations. Experimental data were used to guide modeling, to fit the unknown parameters and validate the model. Based on our model we found that epinephrine/beta2 adrenergic receptor/protein kinase A signaling can decrease drug efficiency, and can shift the effect of drug combination from synergy to antagonism. We also predicted that in addition to BAD phosphorylation Mcl-1 expression could be upregulated by stress/epinephrine signaling to inhibit apoptosis. This study provides insights into the mechanisms of psychological stress signaling in therapy-resistant cancer, and suggests that reducing psychological stress could help to make prostate cancer treatment more effective.
PMCID: PMC3854132  PMID: 24339759
16.  Systematically Studying Kinase Inhibitor Induced Signaling Network Signatures by Integrating Both Therapeutic and Side Effects 
PLoS ONE  2013;8(12):e80832.
Substantial effort in recent years has been devoted to analyzing data based large-scale biological networks, which provide valuable insight into the topologies of complex biological networks but are rarely context specific and cannot be used to predict the responses of cell signaling proteins to specific ligands or compounds. In this work, we proposed a novel strategy to investigate kinase inhibitor induced pathway signatures by integrating multiplex data in Library of Integrated Network-based Cellular Signatures (LINCS), e.g. KINOMEscan data and cell proliferation/mitosis imaging data. Using this strategy, we first established a PC9 cell line specific pathway model to investigate the pathway signatures in PC9 cell line when perturbed by a small molecule kinase inhibitor GW843682. This specific pathway revealed the role of PI3K/AKT in modulating the cell proliferation process and the absence of two anti-proliferation links, which indicated a potential mechanism of abnormal expansion in PC9 cell number. Incorporating the pathway model for side effects on primary human hepatocytes, it was used to screen 27 kinase inhibitors in LINCS database and PF02341066, known as Crizotinib, was finally suggested with an optimal concentration 4.6 uM to suppress PC9 cancer cell expansion while avoiding severe damage to primary human hepatocytes. Drug combination analysis revealed that the synergistic effect region can be predicted straightforwardly based on a threshold which is an inherent property of each kinase inhibitor. Furthermore, this integration strategy can be easily extended to other specific cell lines to be a powerful tool for drug screen before clinical trials.
PMCID: PMC3855094  PMID: 24339888
17.  A novel missense-mutation-related feature extraction scheme for ‘driver’ mutation identification 
Bioinformatics  2012;28(22):2948-2955.
Motivation: It becomes widely accepted that human cancer is a disease involving dynamic changes in the genome and that the missense mutations constitute the bulk of human genetic variations. A multitude of computational algorithms, especially the machine learning-based ones, has consequently been proposed to distinguish missense changes that contribute to the cancer progression (‘driver’ mutation) from those that do not (‘passenger’ mutation). However, the existing methods have multifaceted shortcomings, in the sense that they either adopt incomplete feature space or depend on protein structural databases which are usually far from integrated.
Results: In this article, we investigated multiple aspects of a missense mutation and identified a novel feature space that well distinguishes cancer-associated driver mutations from passenger ones. An index (DX score) was proposed to evaluate the discriminating capability of each feature, and a subset of these features which ranks top was selected to build the SVM classifier. Cross-validation showed that the classifier trained on our selected features significantly outperforms the existing ones both in precision and robustness. We applied our method to several datasets of missense mutations culled from published database and literature and obtained more reasonable results than previous studies.
Availability: The software is available online at and
Supplementary information: Supplementary data are available at Bioinformatics online.
PMCID: PMC3496432  PMID: 23044540
18.  Discovering Transcription and Splicing Networks in Myelodysplastic Syndromes 
PLoS ONE  2013;8(11):e79118.
More and more transcription factors and their motifs have been reported and linked to specific gene expression levels. However, focusing only on transcription is not sufficient for mechanism research. Most genes, especially in eukaryotes, are alternatively spliced to different isoforms. Some of these isoforms increase the biodiversity of proteins. From this viewpoint, transcription and splicing are two of important mechanisms to modulate expression levels of isoforms. To integrate these two kinds of regulation, we built a linear regression model to select a subset of transcription factors and splicing factors for each co-expressed isoforms using least-angle regression approach. Then, we applied this method to investigate the mechanism of myelodysplastic syndromes (MDS), a precursor lesion of acute myeloid leukemia. Results suggested that expression levels of most isoforms were regulated by a set of selected regulatory factors. Some of the detected factors, such as EGR1 and STAT family, are highly correlated with progression of MDS. We discovered that the splicing factor SRSF11 experienced alternative splicing switch, and in turn induced different amino acid sequences between MDS and controls. This splicing switch causes two different splicing mechanisms. Polymerase Chain Reaction experiments also confirmed that one of its isoforms was over-expressed in MDS. We analyzed the regulatory networks constructed from the co-expressed isoforms and their regulatory factors in MDS. Many of these networks were enriched in the herpes simplex infection pathway which involves many splicing factors, and pathways in cancers and acute or chronic myeloid leukemia.
PMCID: PMC3828332  PMID: 24244432
19.  Cytokine Combination Therapy Prediction for Bone Remodeling in Tissue Engineering Based on the Intracellular Signaling Pathway 
Biomaterials  2012;33(33):8265-8276.
The long-term performance of tissue-engineered bone grafts is determined by a dynamic balance between bone regeneration and resorption. We proposed using embedded cytokine slow-releasing hydrogels to tune this balance toward a desirable final bone density. In this study we established a systems biology model, and quantitatively explored the combinatorial effects of delivered cytokines from hydrogels on final bone density. We hypothesized that: 1) bone regeneration was driven by transcription factors Runx2 and Osterix, which responded to released cytokines, such as Wnt, BMP2, and TGFβ, drove the development of osteoblast lineage, and contributed to bone mass generation; and 2) the osteoclast lineage, on the other hand, governed the bone resorption, and communications between these two lineages determined the dynamics of bone remodeling. In our model, Intracellular signaling pathways were represented by ordinary differential equations, while the intercellular communications and cellular population dynamics were modeled by stochastic differential equations. Effects of synergistic cytokine combinations were evaluated by Loewe index and Bliss index. Simulation results revealed that the Wnt/BMP2 combinations released from hydrogels showed best control of bone regeneration and synergistic effects, and suggested optimal dose ratios of given cytokine combinations released from hydrogels to most efficiently control the long-term bone remodeling. We revealed the characteristics of cytokine combinations of Wnt/BMP2 which could be used to guide the design of in vivo bone scaffolds and the clinical treatment of some diseases such as osteoporosis.
PMCID: PMC3444627  PMID: 22910219
Bone tissue engineering; Bone remodeling; Cytokine combination therapy; Systems biology; Osteogenic differentiation; Signaling pathway
20.  Incremental Kernel Ridge Regression for the Prediction of Soft Tissue Deformations 
This paper proposes a nonlinear regression model to predict soft tissue deformation after maxillofacial surgery. The feature which served as input in the model is extracted with Finite Element Model (FEM). The output in the model is the facial deformation calculated from the preoperative and postoperative 3D data. After finding the relevance between feature and facial deformation by using the regression model, we establish a general relationship which can be applied to all the patients. As a new patient comes, we predict his/her facial deformation by combining the general relationship and the new patient’s biomechanical properties. Thus, our model is biomechanical relevant and statistical relevant. Validation on eleven patients demonstrates the effectiveness and efficiency of our method.
PMCID: PMC3754788  PMID: 23285540
kernel ridge regression; finite element model; maxillofacial surgery; soft tissue deformation
21.  3D Segmentation of Maxilla in Cone-beam Computed Tomography Imaging Using Base Invariant Wavelet Active Shape Model on Customized Two-manifold Topology 
Recent advances in cone-beam computed tomography (CBCT) have rapidly enabled widepsread applications of dentomaxillofacial imaging and orthodontic practices in the past decades due to its low radiation dose, high spatial resolution, and accessibility. However, low contrast resolution in CBCT image has become its major limitation in building skull models. Intensive hand-segmentation is usually required to reconstruct the skull models. One of the regions affected by this limitation the most is the thin bone images. This paper presents a novel segmentation approach based on wavelet density model (WDM) for a particular interest in the outer surface of anterior wall of maxilla. Nineteen CBCT datasets are used to conduct two experiments. This mode-based segmentation approach is validated and compared with three different segmentation approaches. The results show that the performance of this model-based segmentation approach is better than those of the other approaches. It can achieve 0.25 ± 0.2mm of surface error from ground truth of bone surface.
PMCID: PMC3735231  PMID: 23694914
3D segmentation; Active shape model (ASM); Statistical shape model (SSM); Craniomaxillofacial (CMF) surgeries; Cone-beam computed tomography (CBCT)
22.  SNVHMM: predicting single nucleotide variants from next generation sequencing 
BMC Bioinformatics  2013;14:225.
The rapid development of next generation sequencing (NGS) technology provides a novel avenue for genomic exploration and research. Single nucleotide variants (SNVs) inferred from next generation sequencing are expected to reveal gene mutations in cancer. However, NGS has lower sequence coverage and poor SNVs detection capability in the regulatory regions of the genome. Post probabilistic based methods are efficient for detection of SNVs in high coverage regions or sequencing data with high depth. However, for data with low sequencing depth, the efficiency of such algorithms remains poor and needs to be improved.
A new tool SNVHMM basing on a discrete hidden Markov model (HMM) was developed to infer the genotype for each position on the genome. We incorporated the mapping quality of each read and the corresponding base quality on the reads into the emission probability of HMM. The context information of the whole observation as well as its confidence were completely utilized to infer the genotype for each position on the genome in study. Therefore, more probability power can be gained over the Bayes based methods, which is very useful for SNVs detection for data with low sequencing depth. Moreover, our model was verified by testing against two sets of lobular breast tumor and Myelodysplastic Syndromes (MDS) data each. Comparing against a recently published SNVs calling algorithm SNVMix2, our model improved the performance of SNVMix2 largely when the sequencing depth is low and also outperformed SNVMix2 when SNVMix2 is well trained by large datasets.
SNVHMM can detect SNVs from NGS cancer data efficiently even if the sequence depth is very low. The training data size can be very small for SNVHMM to work. SNVHMM incorporated the base quality and mapping quality of all observed bases and reads, and also provides the option for users to choose the confidence of the observation for SNVs prediction.
PMCID: PMC3718670  PMID: 23855743
23.  Multi-scale agent-based modeling on melanoma and its related angiogenesis analysis 
Recently, melanoma has become the most malignant and commonly occurring skin cancer. Melanoma is not only the major source (75%) of deaths related to skin cancer, but also it is hard to be treated by the conventional drugs. Recent research indicated that angiogenesis is an important factor for tumor initiation, expansion, and response to therapy. Thus, we proposed a novel multi-scale agent-based computational model that integrates the angiogenesis into tumor growth to study the response of melanoma cancer under combined drug treatment.
Our multi-scale agent-based model can simulate the melanoma tumor growth with angiogenesis under combined drug treatment. The significant synergistic effects between drug Dox and drug Sunitinib demonstrated the clinical potential to interrupt the communication between melanoma cells and its related vasculatures. Also, the sensitivity analysis of the model revealed that diffusivity related to the micro-vasculatures around tumor tissues closely correlated with the spread, oscillation and destruction of the tumor.
Simulation results showed that the 3D model can represent key features of melanoma growth, angiogenesis, and its related micro-environment. The model can help cancer researchers understand the melanoma developmental mechanism. Drug synergism analysis suggested that interrupting the communications between melanoma cells and the related vasculatures can significantly increase the drug efficacy against tumor cells.
PMCID: PMC3694033  PMID: 23800293
Microenvironment; Drug synergism; Agent-based model; Multi-scale; Melanoma; Anti-angiogenesis
24.  FusionQ: a novel approach for gene fusion detection and quantification from paired-end RNA-Seq 
BMC Bioinformatics  2013;14:193.
Gene fusions, which result from abnormal chromosome rearrangements, are a pathogenic factor in cancer development. The emerging RNA-Seq technology enables us to detect gene fusions and profile their features.
In this paper, we proposed a novel fusion detection tool, FusionQ, based on paired-end RNA-Seq data. This tool can detect gene fusions, construct the structures of chimerical transcripts, and estimate their abundances. To confirm the read alignment on both sides of a fusion point, we employed a new approach, “residual sequence extension”, which extended the short segments of the reads by aggregating their overlapping reads. We also proposed a list of filters to control the false-positive rate. In addition, we estimated fusion abundance using the Expectation-Maximization algorithm with sparse optimization, and further adopted it to improve the detection accuracy of the fusion transcripts. Simulation was performed by FusionQ and another two stated-of-art fusion detection tools. FusionQ exceeded the other two in both sensitivity and specificity, especially in low coverage fusion detection. Using paired-end RNA-Seq data from breast cancer cell lines, FusionQ detected both the previously reported and new fusions. FusionQ reported the structures of these fusions and provided their expressions. Some highly expressed fusion genes detected by FusionQ are important biomarkers in breast cancer. The performances of FusionQ on cancel line data still showed better specificity and sensitivity in the comparison with another two tools.
FusionQ is a novel tool for fusion detection and quantification based on RNA-Seq data. It has both good specificity and sensitivity performance. FusionQ is free and available at
PMCID: PMC3691734  PMID: 23768108
Fusion detection; chimerical transcripts quantification; EM algorithm
25.  Cancer bioinformatics: detection of chromatin states, SNP-containing motifs, and functional enrichment modules 
Chinese Journal of Cancer  2013;32(4):153-154.
In this editorial preface, I briefly review cancer bioinformatics and introduce the four articles in this special issue highlighting important applications of the field: detection of chromatin states; detection of SNP-containing motifs and association with transcription factor-binding sites; improvements in functional enrichment modules; and gene association studies on aging and cancer. We expect this issue to provide bioinformatics scientists, cancer biologists, and clinical doctors with a better understanding of how cancer bioinformatics can be used to identify candidate biomarkers and targets and to conduct functional analysis.
PMCID: PMC3845569  PMID: 23544450
Chromatin sates; SNP-containing motifs; functional enrichment analysis; gene association

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