Publications by authors named "Zhiwei Ji"

27 Publications

  • Page 1 of 1

Single-cell transcriptomics reveals heterogeneous progression and EGFR activation in pancreatic adenosquamous carcinoma.

Int J Biol Sci 2021 22;17(10):2590-2605. Epub 2021 Jun 22.

Department of Hepatobiliary Surgery, Beijing Chaoyang Hospital affiliated to Capital Medical University, Beijing 100020, China.

Pancreatic adenosquamous carcinoma (PASC) - a rare pathological pancreatic cancer (PC) type - has a poor prognosis due to high malignancy. To examine the heterogeneity of PASC, we performed single-cell RNA sequencing (scRNA-seq) profiling with sample tissues from a healthy donor pancreas, an intraductal papillary mucinous neoplasm, and a patient with PASC. Of 9,887 individual cells, ten cell subpopulations were identified, including myeloid, immune, ductal, fibroblast, acinar, stellate, endothelial, and cancer cells. Cancer cells were divided into five clusters. Notably, cluster 1 exhibited stem-like phenotypes expressing UBE2C, ASPM, and TOP2A. We found that S100A2 is a potential biomarker for cancer cells. LGALS1, NPM1, RACK1, and PERP were upregulated from ductal to cancer cells. Furthermore, the copy number variations in ductal and cancer cells were greater than in the reference cells. The expression of EREG, FCGR2A, CCL4L2, and CTSC increased in myeloid cells from the normal pancreas to PASC. The gene sets expressed by cancer-associated fibroblasts were enriched in the immunosuppressive pathways. We demonstrate that EGFR-associated ligand-receptor pairs are activated in ductal-stromal cell communications. Hence, this study revealed the heterogeneous variations of ductal and stromal cells, defined cancer-associated signaling pathways, and deciphered intercellular interactions following PASC progression.
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http://dx.doi.org/10.7150/ijbs.58886DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8315026PMC
June 2021

Evolution of the PB1 gene of human influenza A (H3N2) viruses circulating between 1968 and 2019.

Transbound Emerg Dis 2021 May 25. Epub 2021 May 25.

MOE International Joint Collaborative Research Laboratory for Animal Health and Food Safety & Jiangsu Engineering Laboratory of Animal Immunology, College of Veterinary Medicine, Nanjing Agricultural University, Nanjing, China.

One avian H3N2 influenza virus, providing its PB1 and HA segments, reassorted with one human H2N2 virus and caused a pandemic outbreak in 1968, killing over 1 million people. After its introduction to humanity, the pandemic H3N2 virus continued adapting to humans and has resulted in epidemic outbreaks every influenza season. To understand the functional roles of the originally avian PB1 gene in the circulating strains of human H3N2 influenza viruses, we analyzed the evolution of the PB1 gene in all human H3N2 isolates from 1968 to 2019. We found several specific residues dramatically changed around 2002-2009 and remained stable through to 2019. Then, we verified the functions of these PB1 mutations in the genetic background of the early pandemic virus, A/Hong Kong/1/1968(HK/68), as well as a recent seasonal strain, A/Jiangsu/34/2016 (JS/16). The PB1 V709I or PB1 V113A/K586R/D619N/V709I induced higher polymerase activity of HK/68 in human cells. And the four mutations acted cooperatively that had an increased replication capacity in vitro and in vivo at an early stage of infection. In contrast, the backward mutant, A113V/R586K/N619D/I709V, reduced polymerase activity in human cells. The PB1 I709V decreased viral replication in vitro, but this mutant only showed less effect on mice infection experiment, which suggested influenza A virus evolved in human host was not always consisted with highly replication efficiency and pathogenicity in other mammalian host. Overall, our results demonstrated that the identified PB1 mutations contributed to the viral evolution of human influenza A (H3N2) viruses.
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http://dx.doi.org/10.1111/tbed.14161DOI Listing
May 2021

Multi-scale modeling for systematically understanding the key roles of microglia in AD development.

Comput Biol Med 2021 06 5;133:104374. Epub 2021 Apr 5.

School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin Street, Houston, TX, 77030, USA. Electronic address:

Alzheimer's disease (AD) is the leading cause of age-related dementia, affecting over 5 million people in the United States. Unfortunately, current therapies are largely palliative and several potential drug candidates have failed in late-stage clinical trials. Studies suggest that microglia-mediated neuroinflammation might be responsible for the failures of various therapies. Microglia contribute to Aβ clearance in the early stage of neurodegeneration and may contribute to AD development at the late stage by releasing pro-inflammatory cytokines. However, the activation profile and phenotypic changes of microglia during the development of AD are poorly understood. To systematically understand the key role of microglia in AD progression and predict the optimal therapeutic strategy in silico, we developed a 3D multi-scale model of AD (MSMAD) by integrating multi-level experimental data, to manipulate the neurodegeneration in a simulated system. Based on our analysis, we revealed that how TREM2-related signal transduction leads to an imbalance in the activation of different microglia phenotypes, thereby promoting AD development. Our MSMAD model also provides an optimal therapeutic strategy for improving the outcome of AD treatment.
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http://dx.doi.org/10.1016/j.compbiomed.2021.104374DOI Listing
June 2021

Inhibitory effect of selected hydrocolloids on 2-amino-1-methyl-6-phenylimidazo [4,5-b]pyridine (PhIP) formation in chemical models and beef patties.

J Hazard Mater 2021 01 16;402:123486. Epub 2020 Jul 16.

College of Food Science and Technology, Shanghai Ocean University, Shanghai, 201306, China; Laboratory of Quality and Safety Risk Assessment for Aquatic Products on Storage and Preservation (Shanghai), Ministry of Agriculture, Shanghai, 201306, China; School of Biological Sciences, The University of Hong Kong, Pokfulam Road, Hong Kong, China. Electronic address:

2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP) is a mutagen and a rodent carcinogen mainly formed in thermally processed muscle foods. Hydrocolloids are widely used as thickeners, gelling agents and stabilizers to improve food quality in the food industry. In this study, the inhibitory effects of eight hydrocolloids on the formation of PhIP were investigated in both chemical models and beef patties. 1% (w/w) of carboxymethylcellulose V, κ-carrageenan, alginic acid, and pectin significantly reduced PhIP formation by 53 %, 54 %, 48 %, and 47 %, respectively in chemical models. In fried beef patties, κ-carrageenan appeared to be most capable of inhibiting PhIP formation among the eight tested hydrocolloids. 1% (w/w) of κ-carrageenan caused a decreased formation of PhIP by 90 %. 1% (w/w) of κ-carrageenan also significantly reduced the formation of other heterocyclic aromatic amines including MeIQx and 4,8-DiMeIQx by 64 % and 48 %, respectively in fried beef patties. Further mechanism study showed that κ-carrageenan addition decreased the PhIP precursor creatinine residue and reduced the content of Maillard reaction intermediates including phenylacetaldehyde and aldol condensation product in the chemical model. κ-Carrageenan may inhibit PhIP formation via trapping both creatinine and phenylacetaldehyde. The structures of adducts formed between κ-carrageenan and creatinine and κ-carrageenan and phenylacetaldehyde merits further study.
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http://dx.doi.org/10.1016/j.jhazmat.2020.123486DOI Listing
January 2021

Potential Pathogenic Genes Prioritization Based on Protein Domain Interaction Network Analysis.

IEEE/ACM Trans Comput Biol Bioinform 2021 May-Jun;18(3):1026-1034. Epub 2021 Jun 3.

Pathogenicity-related studies are of great importance in understanding the pathogenesis of complex diseases and improving the level of clinical medicine. This work proposed a bioinformatics scheme to analyze cancer-related gene mutations, and try to figure out potential genes associated with diseases from the protein domain-domain interaction network. Herein, five measures of the principle of centrality lethality had been adopted to implement potential correlation analysis, and prioritize the significance of genes. This method was further applied to KEGG pathway analysis by taking the malignant melanoma as an example. The experimental results show that 25 domains can be found, and 18 of them have high potential to be pathogenically important related to malignant melanoma. Finally, a web-based tool, named Human Cancer Related Domain Interaction Network Analyzer, is developed for potential pathogenic genes prioritization for 26 types of human cancers, and the analysis results can be visualized and downloaded online.
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http://dx.doi.org/10.1109/TCBB.2020.2983894DOI Listing
June 2021

Systematically understanding the immunity leading to CRPC progression.

PLoS Comput Biol 2019 09 10;15(9):e1007344. Epub 2019 Sep 10.

School of Biomedical Informatics, The University of Texas Health science center at Houston, Houston, Texas, United States of America.

Prostate cancer (PCa) is the most commonly diagnosed malignancy and the second leading cause of cancer-related death in American men. Androgen deprivation therapy (ADT) has become a standard treatment strategy for advanced PCa. Although a majority of patients initially respond to ADT well, most of them will eventually develop castration-resistant PCa (CRPC). Previous studies suggest that ADT-induced changes in the immune microenvironment (mE) in PCa might be responsible for the failures of various therapies. However, the role of the immune system in CRPC development remains unclear. To systematically understand the immunity leading to CRPC progression and predict the optimal treatment strategy in silico, we developed a 3D Hybrid Multi-scale Model (HMSM), consisting of an ODE system and an agent-based model (ABM), to manipulate the tumor growth in a defined immune system. Based on our analysis, we revealed that the key factors (e.g. WNT5A, TRAIL, CSF1, etc.) mediated the activation of PC-Treg and PC-TAM interaction pathways, which induced the immunosuppression during CRPC progression. Our HMSM model also provided an optimal therapeutic strategy for improving the outcomes of PCa treatment.
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http://dx.doi.org/10.1371/journal.pcbi.1007344DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6754164PMC
September 2019

The different responses of growth and photosynthesis to NH enrichments between Gracilariopsis lemaneiformis and its epiphytic alga Ulva lactuca grown at elevated atmospheric CO.

Mar Pollut Bull 2019 Jul 16;144:173-180. Epub 2019 May 16.

School of Environment and Energy, South China University of Technology, Guangzhou 510006, China.

We investigated how elevated CO affects the responses of Gracilariopsis lemaneiformis and Ulva lactuca to NH enrichments. All algae were incubated under four nutritional conditions (zero addition, 100, 500, and 2500 μM NH), and two CO levels (390 ppm and 1000 ppm). The growth, photosynthesis, and soluble protein contents of both species increased under the eutrophication condition (100 μM NH). However, the growth and carotenoid contents of the two species declined when NH concentration increased. Under the super eutrophication condition (2500 μM NH), all indexes measured in G. lemaneiformis were suppressed, while the growth and photosynthesis in U. lactuca changed indistinctively, both compared with the control. Moreover, under the super eutrophication condition, elevated CO reduced the suppression in the growth of G. lemaneiformis, but decreased the growth of U. lactuca. Nonetheless, G. lemaneiformis displayed much lower growth rates than U. lactuca under the super eutrophication and elevated CO condition.
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http://dx.doi.org/10.1016/j.marpolbul.2019.04.049DOI Listing
July 2019

Cross-situation consistency of mobile App users' psychological needs.

PLoS One 2019 24;14(4):e0215819. Epub 2019 Apr 24.

Department of psychology and Behavioral Sciences, Zhejiang University, Hangzhou, Zhejiang Province, China.

Previous studies showed that individuals' traits could be used to explain the similarity of behavioral patterns across different occasions. Such studies have typically focused on personality traits, and have not been extended to psychological needs. Our study used a large dataset of 1,715,078 anonymous users' App usage records to examine whether the individual's needs-based profiles of App usage were consistent across different situations (as indexed by categories of App functions). Results showed a high level of consistency across situations in a user's choice of Apps based on the needs the Apps could satisfy. These results provide clear evidence in support of cross-category App recommendation systems.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0215819PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6481827PMC
January 2020

Genome-Wide Association and Mechanistic Studies Indicate That Immune Response Contributes to Alzheimer's Disease Development.

Front Genet 2018 24;9:410. Epub 2018 Sep 24.

School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States.

Alzheimer's disease (AD) is the most common cause of dementia. Although genome-wide association study (GWAS) have reported hundreds of single-nucleotide polymorphisms (SNPs) and genes linked to AD, the mechanisms about how these SNPs modulate the development of AD remain largely unknown. In this study, we performed GWAS for three traits in cerebrospinal fluid (CSF) and one clinical trait in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Our analysis identified five most significant AD related SNPs (FDR < 0.05) within or proximal to APOE, APOC1, and TOMM40. One of the SNPs was co-inherited with APOE allele 4, which is the most important genetic risk factor for AD. Three of the five SNPs were located in promoter or enhancer regions, and transcription factor (TF) binding affinity calculations showed dramatic changes (| Log2FC| > 2) of three TFs (PLAG1, RREB1, and ZBTB33) for two motifs containing SNPs rs2075650 and rs157580. In addition, our GWAS showed that both rs2075650 and rs157580 were significantly associated with the poliovirus receptor-related 2 (PVRL2) gene (FDR < 0.25), which is involved in spreading of herpes simplex virus (HSV). The altered regulation of PVRL2 may increase the susceptibility AD patients to HSV and other virus infections of the brain. Our work suggests that AD is a type of immune disorder driven by viral or microbial infections of the brain during aging.
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http://dx.doi.org/10.3389/fgene.2018.00410DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6166008PMC
September 2018

The impact of elevated atmospheric CO on cadmium toxicity in Pyropia haitanensis (Rhodophyta).

Environ Sci Pollut Res Int 2018 Nov 26;25(33):33361-33369. Epub 2018 Sep 26.

College of life Sciences, Huaibei Normal University, Huaibei, 235000, China.

Cadmium is one of the major heavy metal pollutions in coastal waters, and it is well known that cadmium at trace concentration is toxic to macroalgae. Change in marine carbonate system and ocean acidification caused by elevated atmospheric CO also alter physiological characteristics of macroalgae. However, less research is focused on the combined impacts of elevated CO and cadmium pollution on the growth and physiology in macroalgae. In this study, the maricultivated macroalga Pyropia haitanensis (Rhodophyta) was cultured at three levels of Cd (control, 4 and 12 mg L) and two concentrations of CO, the ambient CO (AC, 410 ppm) and elevated CO (HC, 1100 ppm). The results showed that 12 mg L Cd significantly suppressed the relative growth rate and superoxide dismutase activity in AC-grown P. haitanensis, while such inhibition extents by Cd were alleviated in HC-grown algae. Cd had no effects on efficiency of electron transport (α) and maximum electron transport rate (ETR), but α was increased by elevated CO. Cd dramatically suppressed the maximum net photosynthesis oxygen evolution rate (NPR) and the minimum saturation irradiance (I) when the algal thalli were grown at AC, while such suppression of NPR by Cd was much decreased when the thalli were grown at HC. Collectively, our results suggested that elevated CO would alleviate Cd toxicity on P. haitanensis.
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http://dx.doi.org/10.1007/s11356-018-3289-zDOI Listing
November 2018

Integrating MicroRNA Expression Profiling Studies to Systematically Evaluate the Diagnostic Value of MicroRNAs in Pancreatic Cancer and Validate Their Prognostic Significance with the Cancer Genome Atlas Data.

Cell Physiol Biochem 2018 30;49(2):678-695. Epub 2018 Aug 30.

Department of Hepatobiliary Surgery, Beijing Chaoyang Hospital Affiliated to Capital Medical University, Beijing, China.

Background/aims: MicroRNAs (miRNAs) are promising biomarkers for pancreatic cancer (PaCa). However, systemic and unified evaluations of the diagnostic value of miRNAs are lacking. Therefore, we performed a systematic evaluation based on miRNA expression profiling studies.

Methods: We obtained miRNA expression profiling studies from Gene Expression Omnibus (GEO) and ArrayExpress (AE) databases and calculated the pooled sensitivity, specificity, and summary area under a receiver operating characteristic (ROC) curve for every miRNA. According to the area under the curve (AUC), we identified the miRNAs with diagnostic potentiality and validated their prognostic role in The Cancer Genome Atlas (TCGA) data. Gene Ontology (GO) annotations and pathway enrichments of the target genes of the miRNAs were evaluated using bioinformatics tools.

Results: Ten miRNA expression profiling studies including 958 patients were used in this diagnostic meta-analysis. A total of 693 miRNAs were measured in more than 9 studies. The top 50 miRNAs with high predictive values for PaCa were identified. Among them, miR-130b had the best predictive value for PaCa (pooled sensitivity: 0.73 [95% confidence intervals (CI) 0.44-0.91], specificity: 0.81 [95% CI 0.59-0.93], and AUC: 0.84 [95% CI 0.73-0.95]). We identified nine miRNAs (miR-23a, miR-30a, miR-125a, miR-129-1, miR-181b-1, miR-203, miR-221, miR-222, and miR-1301) associated with overall survival in PaCa patients by combining our results with TCGA data. The results of a Cox model revealed that two miRNAs (miR-30a [hazard ratio (HR)=2.43, 95% CI 1.05-5.59; p=0.037] and miR-203 [HR=3.14, 95% CI 1.28-7.71; p=0.012]) were independent risk factors for prognosis in PaCa patients. In total, 405 target genes of the nine miRNAs were enriched with Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and cancer-associated pathways such as Ras signaling pathways, phospholipase D signaling pathway, and AMP-activated protein kinase (AMPK) signaling pathway were revealed among the top 20 enriched pathways. There were significant negative correlations between miR-181b-1 and miR-125a expression levels and the methylation status of their promoter region.

Conclusion: Our study performed a systematic evaluation of the diagnostic value of miRNAs based on miRNA expression profiling studies. We identified that miR-23a, miR-30a, miR-125a, miR-129-1, miR-181b-1, miR-203, miR-221, miR-222, and miR-1301 had moderate diagnostic value for PaCa and predicted overall survival in PaCa patients.
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http://dx.doi.org/10.1159/000493033DOI Listing
September 2018

Detection and Recognition for Life State of Cell Cancer Using Two-Stage Cascade CNNs.

IEEE/ACM Trans Comput Biol Bioinform 2020 May-Jun;17(3):887-898. Epub 2017 Dec 11.

Cancer cell detection and its stages recognition of life cycle are an important step to analyze cellular dynamics in the automation of cell based-experiments. In this work, a two-stage hierarchical method is proposed to detect and recognize different life stages of bladder cells by using two cascade Convolutional Neural Networks (CNNs). Initially, a hybrid object proposal algorithm (called EdgeSelective) by combining EdgeBoxes and Selective Search is proposed to generate candidate object proposals instead of a single Selective Search method in Region-CNN (R-CNN), and it can exploit the advantages of different mechanisms for generating proposals so that each cell in the image can be fully contained by at least one proposed region during the detection process. Then, the obtained cells from the previous step are used to train and extract features by employing CNNs for the purpose of cell life stage recognition. Finally, a series of comparison experiments are implemented. The results show that the proposed method can obtain better performance than traditional methods either in the stage of cell detection or cell life stage recognition, and it encourages and suggests the application in the development of new anticancer drug and cytopathology analysis of cancer patients in the near future.
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http://dx.doi.org/10.1109/TCBB.2017.2780842DOI Listing
April 2021

A linear programming computational framework integrates phosphor-proteomics and prior knowledge to predict drug efficacy.

BMC Syst Biol 2017 12 21;11(Suppl 7):127. Epub 2017 Dec 21.

School of Information & Electronic Engineering, Zhejiang Gongshang University, 18 Xuezheng Road, Hangzhou, 310018, China.

Background: In recent years, the integration of 'omics' technologies, high performance computation, and mathematical modeling of biological processes marks that the systems biology has started to fundamentally impact the way of approaching drug discovery. The LINCS public data warehouse provides detailed information about cell responses with various genetic and environmental stressors. It can be greatly helpful in developing new drugs and therapeutics, as well as improving the situations of lacking effective drugs, drug resistance and relapse in cancer therapies, etc.

Results: In this study, we developed a Ternary status based Integer Linear Programming (TILP) method to infer cell-specific signaling pathway network and predict compounds' treatment efficacy. The novelty of our study is that phosphor-proteomic data and prior knowledge are combined for modeling and optimizing the signaling network. To test the power of our approach, a generic pathway network was constructed for a human breast cancer cell line MCF7; and the TILP model was used to infer MCF7-specific pathways with a set of phosphor-proteomic data collected from ten representative small molecule chemical compounds (most of them were studied in breast cancer treatment). Cross-validation indicated that the MCF7-specific pathway network inferred by TILP were reliable predicting a compound's efficacy. Finally, we applied TILP to re-optimize the inferred cell-specific pathways and predict the outcomes of five small compounds (carmustine, doxorubicin, GW-8510, daunorubicin, and verapamil), which were rarely used in clinic for breast cancer. In the simulation, the proposed approach facilitates us to identify a compound's treatment efficacy qualitatively and quantitatively, and the cross validation analysis indicated good accuracy in predicting effects of five compounds.

Conclusions: In summary, the TILP model is useful for discovering new drugs for clinic use, and also elucidating the potential mechanisms of a compound to targets.
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http://dx.doi.org/10.1186/s12918-017-0501-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5763468PMC
December 2017

Molecular Skin Surface-Based Transformation Visualization between Biological Macromolecules.

J Healthc Eng 2017 20;2017:4818604. Epub 2017 Apr 20.

College of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China.

Molecular skin surface (MSS), proposed by Edelsbrunner, is a continuous smooth surface modeling approach of biological macromolecules. Compared to the traditional methods of molecular surface representations (e.g., the solvent exclusive surface), MSS has distinctive advantages including having no self-intersection and being decomposable and transformable. For further promoting MSS to the field of bioinformatics, transformation between different MSS representations mimicking the macromolecular dynamics is demanded. The transformation process helps biologists understand the macromolecular dynamics processes visually in the atomic level, which is important in studying the protein structures and binding sites for optimizing drug design. However, modeling the transformation between different MSSs suffers from high computational cost while the traditional approaches reconstruct every intermediate MSS from respective intermediate union of balls. In this study, we propose a novel computational framework named general MSS transformation framework (GMSSTF) between two MSSs without the assistance of union of balls. To evaluate the effectiveness of GMSSTF, we applied it on a popular public database PDB (Protein Data Bank) and compared the existing MSS algorithms with and without GMSSTF. The simulation results show that the proposed GMSSTF effectively improves the computational efficiency and is potentially useful for macromolecular dynamic simulations.
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http://dx.doi.org/10.1155/2017/4818604DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5415869PMC
July 2019

Accelerating smooth molecular surface calculation.

J Math Biol 2018 02 8;76(3):779-793. Epub 2017 Jul 8.

College of Information Engineering, China Jiliang University, 258 Xueyuan Street, Hangzhou, 310018, China.

This study proposes a novel approach, namely, skin flow complex algorithm (SFCA), to decompose the molecular skin surface into topological disks. The main contributions of SFCA include providing a simple decomposition and fast calculation of the molecular skin surface. Unlike most existing works which partition the molecular skin surface into sphere and hyperboloid patches, SFCA partitions the molecular skin surface into triangular quadratic patches and rectangular quadratic patches. Each quadratic patch is proven to be a topological disk and rendered by a rational Bézier patch. The skin surface is constructed by assembling all rational Bézier patches. Experimental results show that the SFCA is more efficient than most existing algorithms, and produces a triangulation of molecular skin surface which is decomposable, deformable, smooth, watertight and feature-preserved.
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http://dx.doi.org/10.1007/s00285-017-1156-zDOI Listing
February 2018

Mathematical and Computational Modeling in Complex Biological Systems.

Biomed Res Int 2017 13;2017:5958321. Epub 2017 Mar 13.

School of Information & Electronic Engineering, Zhejiang Gongshang University, 18 Xuezheng Road, Hangzhou 310018, China.

The biological process and molecular functions involved in the cancer progression remain difficult to understand for biologists and clinical doctors. Recent developments in high-throughput technologies urge the systems biology to achieve more precise models for complex diseases. Computational and mathematical models are gradually being used to help us understand the omics data produced by high-throughput experimental techniques. The use of computational models in systems biology allows us to explore the pathogenesis of complex diseases, improve our understanding of the latent molecular mechanisms, and promote treatment strategy optimization and new drug discovery. Currently, it is urgent to bridge the gap between the developments of high-throughput technologies and systemic modeling of the biological process in cancer research. In this review, we firstly studied several typical mathematical modeling approaches of biological systems in different scales and deeply analyzed their characteristics, advantages, applications, and limitations. Next, three potential research directions in systems modeling were summarized. To conclude, this review provides an update of important solutions using computational modeling approaches in systems biology.
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http://dx.doi.org/10.1155/2017/5958321DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5366773PMC
April 2017

Predicting the impact of combined therapies on myeloma cell growth using a hybrid multi-scale agent-based model.

Oncotarget 2017 Jan;8(5):7647-7665

Division of Radiologic Sciences and Center for Bioinformatics and Systems Biology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC, USA 27157.

Multiple myeloma is a malignant still incurable plasma cell disorder. This is due to refractory disease relapse, immune impairment, and development of multi-drug resistance. The growth of malignant plasma cells is dependent on the bone marrow (BM) microenvironment and evasion of the host's anti-tumor immune response. Hence, we hypothesized that targeting tumor-stromal cell interaction and endogenous immune system in BM will potentially improve the response of multiple myeloma (MM). Therefore, we proposed a computational simulation of the myeloma development in the complicated microenvironment which includes immune cell components and bone marrow stromal cells and predicted the effects of combined treatment with multi-drugs on myeloma cell growth. We constructed a hybrid multi-scale agent-based model (HABM) that combines an ODE system and Agent-based model (ABM). The ODEs was used for modeling the dynamic changes of intracellular signal transductions and ABM for modeling the cell-cell interactions between stromal cells, tumor, and immune components in the BM. This model simulated myeloma growth in the bone marrow microenvironment and revealed the important role of immune system in this process. The predicted outcomes were consistent with the experimental observations from previous studies. Moreover, we applied this model to predict the treatment effects of three key therapeutic drugs used for MM, and found that the combination of these three drugs potentially suppress the growth of myeloma cells and reactivate the immune response. In summary, the proposed model may serve as a novel computational platform for simulating the formation of MM and evaluating the treatment response of MM to multiple drugs.
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http://dx.doi.org/10.18632/oncotarget.13831DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5352350PMC
January 2017

Prediction of treatment efficacy for prostate cancer using a mathematical model.

Sci Rep 2016 Feb 12;6:21599. Epub 2016 Feb 12.

Division of Radiologic Sciences - Center for Bioinformatics and Systems Biology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157, USA.

Prostate immune system plays a critical role in the regulation of prostate cancer development regarding androgen-deprivation therapy (ADT) and/or immunotherapy (vaccination). In this study, we developed a mathematical model to explore the interactions between prostate tumor and immune microenvironment. This model was used to predict treatment outcomes for prostate cancer with ADT, vaccination, Treg depletion and/or IL-2 neutralization. Animal data were used to guide construction, parameter selection, and validation of our model. Our analysis shows that Treg depletion and/or IL-2 neutralization can effectively improve the treatment efficacy of combined therapy with ADT and vaccination. Treg depletion has a higher synergetic effect than that from IL-2 neutralization. This study highlights a potential therapeutic strategy in effectively managing prostate tumor growth and provides a framework of systems biology approach in studying tumor-related immune mechanism and consequent selection of therapeutic regimens.
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http://dx.doi.org/10.1038/srep21599DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4751505PMC
February 2016

NMFBFS: A NMF-Based Feature Selection Method in Identifying Pivotal Clinical Symptoms of Hepatocellular Carcinoma.

Comput Math Methods Med 2015 12;2015:846942. Epub 2015 Oct 12.

Machine Learning & Systems Biology Lab, School of Electronics and Information Engineering, Tongji University, 4800 Caoan Road, Shanghai 201804, China ; The Advanced Research Institute of Intelligent Sensing Network, Tongji University, 4800 Caoan Road, Shanghai 201804, China ; The Key Laboratory of Embedded System and Service Computing, Tongji University, 4800 Caoan Road, Shanghai 201804, China.

Background: Hepatocellular carcinoma (HCC) is a highly aggressive malignancy. Traditional Chinese Medicine (TCM), with the characteristics of syndrome differentiation, plays an important role in the comprehensive treatment of HCC. This study aims to develop a nonnegative matrix factorization- (NMF-) based feature selection approach (NMFBFS) to identify potential clinical symptoms for HCC patient stratification.

Methods: The NMFBFS approach consisted of three major steps. Firstly, statistics-based preliminary feature screening was designed to detect and remove irrelevant symptoms. Secondly, NMF was employed to infer redundant symptoms. Based on NMF-derived basis matrix, we defined a novel similarity measurement of intersymptoms. Finally, we converted each group of redundant symptoms to a new single feature so that the dimension was further reduced.

Results: Based on a clinical dataset consisting of 407 patient samples of HCC with 57 symptoms, NMFBFS approach detected 8 irrelevant symptoms and then identified 16 redundant symptoms within 6 groups. Finally, an optimal feature subset with 39 clinical features was generated after compressing the redundant symptoms by groups. The validation of classification performance shows that these 39 features obviously improve the prediction accuracy of HCC patients.

Conclusions: Compared with other methods, NMFBFS has obvious advantages in identifying important clinical features of HCC.
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http://dx.doi.org/10.1155/2015/846942DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4633688PMC
August 2016

miR-193b Modulates Resistance to Doxorubicin in Human Breast Cancer Cells by Downregulating MCL-1.

Biomed Res Int 2015 7;2015:373574. Epub 2015 Oct 7.

Department of Clinical Laboratory, Tongde Hospital of Zhejiang Province, 234 Gucui Road, Hangzhou 310012, China.

MicroRNAs (miRNAs) family, which is involved in cancer development, proliferation, apoptosis, and drug resistance, is a group of noncoding RNAs that modulate the expression of oncogenes and antioncogenes. Doxorubicin is an active cytotoxic agent for breast cancer treatment, but the acquisition of doxorubicin resistance is a common and critical limitation to cancer therapy. The aim of this study was to investigate whether miR-193b mediated the resistance of breast cancer cells to doxorubicin by targeting myeloid cell leukemia-1 (MCL-1). In this study, we found that miR-193b levels were significantly lower in doxorubicin-resistant MCF-7 (MCF-7/DOXR) cells than in the parental MCF-7 cells. We observed that exogenous miR-193b significantly suppressed the ability of MCF-7/DOXR cells to resist doxorubicin. It demonstrated that miR-193b directly targeted MCL-1 3'-UTR (3'-Untranslated Regions). Further studies indicated that miR-193b sensitized MCF-7/DOXR cells to doxorubicin through a mechanism involving the downregulation of MCL-1. Together, our findings provide evidence that the modulation of miR-193b may represent a novel therapeutic target for the treatment of breast cancer.
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http://dx.doi.org/10.1155/2015/373574DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4615858PMC
September 2016

Systemic modeling myeloma-osteoclast interactions under normoxic/hypoxic condition using a novel computational approach.

Sci Rep 2015 Aug 18;5:13291. Epub 2015 Aug 18.

Division of Radiologic Sciences - Center for Bioinformatics and Systems Biology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC, USA 27157.

Interaction of myeloma cells with osteoclasts (OC) can enhance tumor cell expansion through activation of complex signaling transduction networks. Both cells reside in the bone marrow, a hypoxic niche. How OC-myeloma interaction in a hypoxic environment affects myeloma cell growth and their response to drug treatment is poorly understood. In this study, we i) cultured myeloma cells in the presence/absence of OCs under normoxia and hypoxia conditions and did protein profiling analysis using reverse phase protein array; ii) computationally developed an Integer Linear Programming approach to infer OC-mediated myeloma cell-specific signaling pathways under normoxic and hypoxic conditions. Our modeling analysis indicated that in the presence OCs, (1) cell growth-associated signaling pathways, PI3K/AKT and MEK/ERK, were activated and apoptotic regulatory proteins, BAX and BIM, down-regulated under normoxic condition; (2) β1 Integrin/FAK signaling pathway was activated in myeloma cells under hypoxic condition. Simulation of drug treatment effects by perturbing the inferred cell-specific pathways showed that targeting myeloma cells with the combination of PI3K and integrin inhibitors potentially (1) inhibited cell proliferation by reducing the expression/activation of NF-κB, S6, c-Myc, and c-Jun under normoxic condition; (2) blocked myeloma cell migration and invasion by reducing the expression of FAK and PKC under hypoxic condition.
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http://dx.doi.org/10.1038/srep13291DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4539608PMC
August 2015

Detecting protein-protein interactions with a novel matrix-based protein sequence representation and support vector machines.

Biomed Res Int 2015 27;2015:867516. Epub 2015 Apr 27.

School of Electronics and Information Engineering, Tongji University, Shanghai 200092, China.

Proteins and their interactions lie at the heart of most underlying biological processes. Consequently, correct detection of protein-protein interactions (PPIs) is of fundamental importance to understand the molecular mechanisms in biological systems. Although the convenience brought by high-throughput experiment in technological advances makes it possible to detect a large amount of PPIs, the data generated through these methods is unreliable and may not be completely inclusive of all possible PPIs. Targeting at this problem, this study develops a novel computational approach to effectively detect the protein interactions. This approach is proposed based on a novel matrix-based representation of protein sequence combined with the algorithm of support vector machine (SVM), which fully considers the sequence order and dipeptide information of the protein primary sequence. When performed on yeast PPIs datasets, the proposed method can reach 90.06% prediction accuracy with 94.37% specificity at the sensitivity of 85.74%, indicating that this predictor is a useful tool to predict PPIs. Achieved results also demonstrate that our approach can be a helpful supplement for the interactions that have been detected experimentally.
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http://dx.doi.org/10.1155/2015/867516DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4426769PMC
February 2016

Silibinin, a natural flavonoid, induces autophagy via ROS-dependent mitochondrial dysfunction and loss of ATP involving BNIP3 in human MCF7 breast cancer cells.

Oncol Rep 2015 Jun 17;33(6):2711-8. Epub 2015 Apr 17.

Department of Clinical Laboratory, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang 310012, P.R. China.

Silibinin, derived from the milk thistle plant (Silybum marianum), has anticancer and chemopreventive properties. Silibinin has been reported to inhibit the growth of various types of cancer cells. However, the mechanisms by which silibinin exerts an anticancer effect are poorly defined. The present study aimed to investigate whether silibinin-induced cell death might be attributed to autophagy and the underlying mechanisms in human MCF7 breast cancer cells. Our results showed that silibinin-induced cell death was greatly abrogated by two specific autophagy inhibitors, 3-methyladenine (3-MA) and bafilomycin-A1 (Baf-A1). In addition, silibinin triggered the conversion of light chain 3 (LC3)-I to LC3-II, promoted the upregulation of Atg12-Atg5 formation, increased Beclin-1 expression, and decreased the Bcl-2 level. Moreover, we noted elevated reactive oxygen species (ROS) generation, concomitant with the dissipation of mitochondrial transmembrane potential (ΔΨm) and a drastic decline in ATP levels following silibinin treatment, which were effectively prevented by the antioxidants, N-acetylcysteine and ascorbic acid. Silibinin stimulated the expression of Bcl-2 adenovirus E1B 19-kDa-interacting protein 3 (BNIP3), a pro-death Bcl-2 family member, and silencing of BNIP3 greatly inhibited silibinin-induced cell death, decreased ROS production, and sustained ΔΨm and ATP levels. Taken together, these findings revealed that silibinin induced autophagic cell death through ROS-dependent mitochondrial dysfunction and ATP depletion involving BNIP3 in MCF7 cells.
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http://dx.doi.org/10.3892/or.2015.3915DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4431438PMC
June 2015

Integrating genomics and proteomics data to predict drug effects using binary linear programming.

PLoS One 2014 18;9(7):e102798. Epub 2014 Jul 18.

Division of Radiologic Sciences - Center for Bioinformatics and Systems Biology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, North Carolina, United States of America.

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.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0102798PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4103865PMC
November 2015

Identifying potential clinical syndromes of hepatocellular carcinoma using PSO-based hierarchical feature selection algorithm.

Authors:
Zhiwei Ji Bing Wang

Biomed Res Int 2014 17;2014:127572. Epub 2014 Mar 17.

School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China ; The Advanced Research Institute of Intelligent Sensing Network, Tongji University, Shanghai 201804, China ; The Key Laboratory of Embedded System and Service Computing, Tongji University, Ministry of Education, Shanghai 201804, China.

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors. Clinical symptoms attributable to HCC are usually absent, thus often miss the best therapeutic opportunities. Traditional Chinese Medicine (TCM) plays an active role in diagnosis and treatment of HCC. In this paper, we proposed a particle swarm optimization-based hierarchical feature selection (PSOHFS) model to infer potential syndromes for diagnosis of HCC. Firstly, the hierarchical feature representation is developed by a three-layer tree. The clinical symptoms and positive score of patient are leaf nodes and root in the tree, respectively, while each syndrome feature on the middle layer is extracted from a group of symptoms. Secondly, an improved PSO-based algorithm is applied in a new reduced feature space to search an optimal syndrome subset. Based on the result of feature selection, the causal relationships of symptoms and syndromes are inferred via Bayesian networks. In our experiment, 147 symptoms were aggregated into 27 groups and 27 syndrome features were extracted. The proposed approach discovered 24 syndromes which obviously improved the diagnosis accuracy. Finally, the Bayesian approach was applied to represent the causal relationships both at symptom and syndrome levels. The results show that our computational model can facilitate the clinical diagnosis of HCC.
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http://dx.doi.org/10.1155/2014/127572DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3976846PMC
January 2015

Systematically studying kinase inhibitor induced signaling network signatures by integrating both therapeutic and side effects.

PLoS One 2013 5;8(12):e80832. Epub 2013 Dec 5.

Department of Radiology, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America ; Department of Modern Mechanics, University of Science and Technology of China, Hefei, Anhui, P.R. China.

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.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0080832PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3855094PMC
March 2015

Prediction of peptide drift time in ion mobility mass spectrometry from sequence-based features.

BMC Bioinformatics 2013 9;14 Suppl 8:S9. Epub 2013 May 9.

The Advanced Research Institute of Intelligent Sensing Network, Tongji University, shanghai, 201804, China.

Background: Ion mobility-mass spectrometry (IMMS), an analytical technique which combines the features of ion mobility spectrometry (IMS) and mass spectrometry (MS), can rapidly separates ions on a millisecond time-scale. IMMS becomes a powerful tool to analyzing complex mixtures, especially for the analysis of peptides in proteomics. The high-throughput nature of this technique provides a challenge for the identification of peptides in complex biological samples. As an important parameter, peptide drift time can be used for enhancing downstream data analysis in IMMS-based proteomics.

Results: In this paper, a model is presented based on least square support vectors regression (LS-SVR) method to predict peptide ion drift time in IMMS from the sequence-based features of peptide. Four descriptors were extracted from peptide sequence to represent peptide ions by a 34-component vector. The parameters of LS-SVR were selected by a grid searching strategy, and a 10-fold cross-validation approach was employed for the model training and testing. Our proposed method was tested on three datasets with different charge states. The high prediction performance achieve demonstrate the effectiveness and efficiency of the prediction model.

Conclusions: Our proposed LS-SVR model can predict peptide drift time from sequence information in relative high prediction accuracy by a test on a dataset of 595 peptides. This work can enhance the confidence of protein identification by combining with current protein searching techniques.
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http://dx.doi.org/10.1186/1471-2105-14-S8-S9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3654891PMC
November 2013
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