Bio-Active Lipid Research Team

BALR team

What’s BALR team?

BALR stands for “Bio-Active Lipid Research”. We are a biomedical research team who interested to study the role of lipids play in a variety of biomedical problems/issues, e.g., metabolic syndrome, cardiovascular disease, cancer, autoimmune disease, aging related osteoporosis…etc. The BALR team is composed by six different function cores, and 8 members/laboratories across from five institutions in Taiwan. The six cores including clinical, bioinformatics, lipid drug synthesis, nano-liposome delivery, 3D printing Organ-On-A-Chip, and preclinical trial…etc. The members are from five institutions, including NCHU清華大學, NCKU成功大學, CMU中國醫藥大學, KMU高雄醫學大學, and CCH彰化基督教醫院.

The team have built up platforms and implementing cutting edge technology for lipid biology/biomedical research. We have built up cancer cell lines lipidome database, lipid profiling and data processing, lipoprotein trans-omics technology, and transcriptome-lipidome trans-omics, which achieved by co-PI, Wei-Chung Cheng. We have synthetic chemistry expert and nature product compound bank builder for the team, which was developed in co-PI Juan-Cheng Yang’s lab. The nanoparticle liposome drug delivery system built up by one of the co-PI, Professor Yunching Chen, which allowed us to practice the concept of lipoprotein-mimicking liposome to practice. In addition, we have a strong 3D-printing team, co-PI Evin Chen, who lead the team for developing Organ-On-A-Chip for testing drug delivery. Last, but not least, the PI Wen-Lung Ma developed the concept of lipidome biomed research for the team, and provide experimental animal model for testing concepts.

In brief, we strive for solving biomedical issue from lipid perspectives. With the multi-discipline calibers in technology, knowledge, and novel approaches, BALR team will elucidate the pathway of human diseases and develop potential therapies in the future.


Who we are?

The team is composed by 8 members/PIs, which conceptually divided into six cores in terms of functionality.

  1. Clinical core: The are three PI/clinician from three different locations/hospitals.
    1. Professor Yan-Shang Shen: Dean of School of Medicine of National Cheng-Kung University. Professor Shen is one leading expert in the field of Surgical Gastroenterology, particularly in the surgery for gastric cancer, pancreatic cancer. He has been studied the roles of cancer stem cells and cancer associated fibroblast microenvironmental regulation for years.
    2. Professor Hsiang-Chun Lee: Chair of Lipid Science and Aging Research Center (LSARC) of Kaohsiung Medical University Hospital (KMUH). Professor Lee is an cardiologist. She is one of the pioneers to study the roles of metabolic syndrome related Very Low Density Lipoprotein and its receptor VLDLR play in pathogenesis of atrial cardiomyopathy.
    3. Professor Yao-Li Chen: Deputy Dean of hang-Hwa Christian Hospital (CCH). Professor Chen is an expert is the field of Hepatic surgery, especially hepatectomy/liver transplantation surgery for liver cancers or cirrhotic patients.
  2. Trans-Omics Bioinformatics Core: The PI, Professor Wei-Chung Cheng, who is one of the leading bioinformatics researcher in Taiwan. He has developed database based cancer gene analysis platforms which have been published in prestigious journals. His major effort in the team will be the establishment of Trans-Omics (proteome, lipidome, and transcriptome).
  3. Lipid Drug synthesis Core: The PI, Professor Juan-Cheng Yang, is pharmaceutics professional expert in molecular pharmacology and nature product. This core is responsible for lipid-drug design, synthesis, and purification, validation.
  4. Nano-liposome delivery: Professor Yunching Chen from nano-particle liposome Laboratory of NCHU is expert in nanomedicine. She developed novel liposome delivery vehicle for liver cancer therapy with reputation publication records. In the BALR team, she is contributed in the design and liposome prototype development.
  5. 3D printing Organ-On-A-Chip: Professor Evin Chen is the deputy director of 3D-printing medical center of China Medical University (CMU) who lead the development of Organ-On-A-Chip for verifying lipoprotein-mimicking drug delivery efficacy. She has been received many prestigious award, including National Innovation Award four time in the past 5 years.
  6. Preclinical trial: Professor Wen-Lung Ma is the molecular endocrinologist of CMU, who is the developer of endocrine organ-like tumor (EOLT) hypothesis. The newly established concept of lipoprotein/receptor route of lipid delivery for cancer development/progression was discover and characterized in Ma’s lab.





































What do we want to do?

We strive for solving biomedical issue from lipid perspectives. With the multi-discipline calibers in technology, knowledge, and novel approaches, BALR team will elucidate the pathway of human diseases and develop potential therapies in the future.


Preclinical Unit

Research expertise:

Members:

Bioinfromatic Unit

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Lipid-drug synthesis Unit

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Liposome-drug prototype Unit

Research expertise:

Organ-on-a-chip Unit

Research expertise:

Members:

Clinical unit

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Clinical unit

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Clinical unit

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Activity

Year End Party

3rd CMU-NUS Joint Symposium and Translational Lipidome Research









BALR team assembling








17th CGCM Meeting of the consortium of Chinese Medicine

BALR team visit to NUS

第一屆 江浙台 精準醫學研討會

Technique

Lipid profiling technology:

Lipid profiling technology offers unique lipid analysis services from organelles, microorganisms, cultured cells to tissues and organs. The advantages are such as broad coverage of lipids (>2300 absolutely quantified lipids), small sample amounts (e.g. 100.000 cells), two weeks delivery time of results, comprehensive statistical analysis and graphical presentation of data.

BROAD COVERAGE OF LIPIDS

Lipid profiling Shotgun Lipidomics Technology provides a broad coverage of membrane lipids, but also of storage lipids. Our analysis covers 30 different lipid classes(e.g. TAG or PC) on the level of lipid species (e.g. TAG 54:0) or subspecies (e.g. PC 18:0_16:0, including the fatty acid information) – in total more than 2300 individual lipids. Moreover our platform allows for the analysis of various sample types – from organelles, bacteria, yeast and other microorganisms, cultured cells and blood plasma to tissues and organs: muscles, liver, brain, and many others. The crucial point: while our technology supports a wide range of sample types it requires only minimal sample amounts per analysis, e.g. 1 µl or less of blood plasma.

FULL HIGH-THROUGHPUT

Lipid profiling uses Shotgun Lipidomics Technology without time-consuming chromatographic separation of lipids before analysis. The advantages of cutting-edge mass spectrometry, combined with automated sample extraction, processing and data analysis. In this way, standardized platform allows for the analysis of hundreds of samples per day, offering unprecedented delivery time of weeks instead months for complete results and associated reports.

ABSOLUTE QUANTIFICATION

The quantification is achieved using lipid class-specific internal standards that allow for unbiased and direct quantitation of individual lipids directly from their mass spectra intensities. Therefore, the results expressed in absolute and not in relative values, which provides the basis for a direct comparison of different samples and experiments.























Transcriptome vs. lipidome trans-omics approaches for potential molecular regulations. A. Heat-map lipid profiling of six types of EOC cells, including serous (SKOV3, OVCAR3), endometrioid (MDAH-2774, TOV-112D), and clear-cell (ES2, TOV-21G) lines. The spectrum from blue to red indicates the variation of lipid species among the cells. B. Lipidome analyses of lipid species comparing shLuc vs. shLDLR in MDAH-2774 and TOV-21G cells. The Y-axis shows the lipid species change p-value, where the threshold > -log10(1.4) indicates significant alteration (red-colored). LPC was increased and PE O- was decreased when the LDLR was knocked down. C. Replicated transcriptome analysis by RNAseq was performed to compare shLuc vs. shLDLR in MDAH-2774 and TOV-21G cells. The overlapped transcriptome showed that 1404 genes were consistently altered. D. Pathway enrichment analysis by GO-term followed with GSEA analysis. The top-10 enriched pathways were ranked from highest to lowest p-value(-log10). E. Trans-omics analyses of transcripto2me and lipidome profiles. The four selection criteria were implemented in the analyses. Strategy 1: transmembrane receptor activity genes were revealed by transcriptome analyses; Strategy 2: lyso-phospholipid-related phospholipase genes were revealed in transcriptome analyses; Strategy 3: phospholipase expression was negatively correlated with LDLR expression (for knockdown experimental design); Strategy 4: phospholipase genes significantly correlated to cancer survival were revealed in TCGA database. The results came out SNCB (1 ∩ 2 ∩ 3), FAM83B (1 ∩ 3), FGFR1/3 (2 ∩ 4), and FGFR1 (1 ∩ 4). The lipid profiling data were from two replicated experiments, and the gene expression and cytotoxic assay results were from at least three independent experiments.


Serological metabolome technology:

We have developed the NMR-based blood metabolome screen technology with the co-development of Nightingale Co (based in Finland) and T-BAL Ltd. Co. (based in Taiwan), which comprises of biomarkers that allow comprehensive research into the interplay of health, lifestyle factors, genetics as well as future disease risk. The platform enables simultaneous quantification of numerous groups of biomarkers, for instance, routine lipids, lipoprotein subclass profiling (with lipid concentrations within 14 subclasses), fatty acid composition and various low-molecular metabolites.



























Upper panels: NMR-based metabolome analysis on CMUH HCC cohort patient related VLDL lipidome in association with VLDLR expressions. Left-handed side graph represented phospholipids amount in VLDL particles from normal control, VLDLR–, and VLDLR+ HCC patients. Right-handed side graph represented cholesterol-ester amount in VLDL particles from normal control, VLDLR–, and VLDLR+ HCC patients.
Lower panels: Detailed listing of the lipid content in VLDL particle from CMUH HCC cohort.


Lipid profiling of LIPOPROTEINS:

We have established procedure for analyze lipid profiling technology with the collaboration of Lipotype GmbH (based in Germany) and T-BAL Ltd. Co.(based in Taiwan). The following example is the successful case on the analysis of post-menopausal osteoporosis to compared with post-menopausal non-osteoporosis women.



























Example of lipoprotein lipidome analysis on human post-menopausal osteoporosis patients. A. Hierarchical lipidome profiles of LDL samples from post-menopausal OP and non-OP individuals. Heatmap representing levels of various lipid species in LDL samples from OP patients (red color bar on top of the heatmap) and non-OP individuals (green color bar on top of the heatmap) is shown. The blue (lower level) and red color (lower level) intensity spectrum represents fold change in the amount of each lipid species compared to the average normal level. B. Enriched lipid classes included Cer: ceramide; DAG: diacylglycerol; HexCer: hexosyceramide; LPC: lyso-phosphatidylcholine; LPE: lyso-phosphatidylethanolamine; PC: phosphatidylcholine; PC O-: ether-link phosphatidylcholine; PE: phatidylethanolamine; PE O-: ether-link phatidylethanolamine; PI: phosphatidylinositol; SM: sphingomyelin; TAG: triacylglycerol. The upper panel shows the LDL lipid classes that were downregulated, and the lower panel shows showing the LDL lipid classes those that were upregulated in OP patients compared to non-OP individuals. Y-axis indicates -log10(p-value). Red colored bar indicates significant alteration. C. Double bond number in LDL lipids of OP patients divided by that of non-OP individual. Y-axis indicates fold change in the number of double bonds as log2 values. D. Number of hydroxyl group number in LDL lipids of OP patients divided by that of non-OP individuals. The red line represents the significant level of change as -log10(p-value). Red colored bars indicate significant alteration.












Hypothetical model of systemic lipid metabolism. BMI and serum cholesterol levels were decreased in postmenopausal OP. Total LDL levels were decreased, and characteristics of LDL lipids were also changed, including increased number of double bonds and hydroxyl groups, and decreased ceramide levels. Changes in the expression of LDLR or LRP5 genetic variations might contribute to bone homeostasis or micro-architecture remodeling. The lipid oxidative stress clearance lipoprotein, HDL, was also altered with decreased number of double bond and increased LPE levels. These changes may be related to the pathogenesis of postmenopausal OP.


Lipidomic analysis platform:

Lipidomic analysis including: (A)Lipid amount of individual samples. (B)Lipid expression distribution of grouped samples. (C)Lipid composition. (D)Expression of individual lipid classes. (E)Hierarchical clustering. (F)Accumulation in lipid expression. (G)Differentially expressed lipid classes. (H)Lipid class enrichment.

















Trans-Omics analysis platform:

The Omics study has prompted biomedical research to speed up, and advance fast. In the BALR team, we have successful experiences working on trans-omics studies, including Transcriptome vs protein-interactome levels (Theranostics, 2018), Lipidome vs. Transcriptome (Endocrine-Related Cancer, 2020), and Lipidome vs. Proteome of lipoproteins (unpublished result).























Transcriptome vs. lipidome trans-omics approaches for potential molecular regulations. A. Heat-map lipid profiling of six types of EOC cells, including serous (SKOV3, OVCAR3), endometrioid (MDAH-2774, TOV-112D), and clear-cell (ES2, TOV-21G) lines. The spectrum from blue to red indicates the variation of lipid species among the cells. B. Lipidome analyses of lipid species comparing shLuc vs. shLDLR in MDAH-2774 and TOV-21G cells. The Y-axis shows the lipid species change p-value, where the threshold > -log10(1.4) indicates significant alteration (red-colored). LPC was increased and PE O- was decreased when the LDLR was knocked down. C. Replicated transcriptome analysis by RNAseq was performed to compare shLuc vs. shLDLR in MDAH-2774 and TOV-21G cells. The overlapped transcriptome showed that 1404 genes were consistently altered. D. Pathway enrichment analysis by GO-term followed with GSEA analysis. The top-10 enriched pathways were ranked from highest to lowest p-value(-log10). E. Trans-omics analyses of transcripto2me and lipidome profiles. The four selection criteria were implemented in the analyses. Strategy 1: transmembrane receptor activity genes were revealed by transcriptome analyses; Strategy 2: lyso-phospholipid-related phospholipase genes were revealed in transcriptome analyses; Strategy 3: phospholipase expression was negatively correlated with LDLR expression (for knockdown experimental design); Strategy 4: phospholipase genes significantly correlated to cancer survival were revealed in TCGA database. The results came out SNCB (1 ∩ 2 ∩ 3), FAM83B (1 ∩ 3), FGFR1/3 (2 ∩ 4), and FGFR1 (1 ∩ 4). The lipid profiling data were from two replicated experiments, and the gene expression and cytotoxic assay results were from at least three independent experiments. (Endocrine-Related Cancer, 2020)





A diagram showing the roles of ERα and ERβ in the process of LR. The ERα promotes LR via regulating Chd1 expression to increase hepatic cell number, whereas ERβ ensures the quality of LR via regulating Ube3a expression to facilitate hepatic progenitor cell differentiation. The cooperation between ERα and ERβ maximizes LR efficiency.







































The caliber to conduct VLDL cargo (proteome and lipidome) profiling and Trans-Omics analysis. A. Protein profiling of norm- vs. MetS-VLDL patients. The red-colored dot represent the protein significantly different while comparing MetS/norm-VLDL, and circle dot represented Apolipoprotein, and triangle dot represent other proteins. B. Differential association of Lipidome with proteome profile in MetS/norm-VLDL particles. C. Correlation of total protein amount of VLDL particle to sum of Phosphatidylcholine (PC) and cholesterol (coat lipid; panel on the left), or to TAG (core lipid; panel in the middle), or to the sum of TAG and cholesterol ester (core lipids). D. Correlation of apoliprotein C-III (ApoC-III) amount of VLDL particle to total protein. E. Correlation of ApoC-III amount of VLDL particle to the sum of PC and Cholesterol. F. Correlation of ApoC-III amount of VLDL particle to the sum of Triacylglycerol (TAG) and Cholesterol-ester (CE). G. Illustration of significant difference of MetS-VLDL compared to norm-VLDL. The potential biological significant different including ApoC-III and ApoA-II, SERPINa1a (anti-trypsin A1), increased core lipids, and decreased coat lipids.


Organ On A Chip:

Organ-on-a-chip engineering aims to create artificial living organs that mimic the complex and physiological responses of real organs, in order to test drugs by precisely manipulating the cells and their microenvironments. To achieve this, the artificial organs should to be microfabricated with an extracellular matrix (ECM) and various types of cells, and should recapitulate morphogenesis, cell differentiation, and functions according to the native organ. A promising strategy is 3D printing, which precisely controls the spatial distribution and layer-by-layer assembly of cells, ECMs, and other biomaterials. Owing to this unique advantage, integration of 3D printing into organ-on-a-chip engineering can facilitate the creation of micro-organs with heterogeneity, a desired 3D cellular arrangement, tissue-specific functions, or even cyclic movement within a microfluidic device. Moreover, fully 3D-printed organs-on-chips more easily incorporate other mechanical and electrical components with the chips, and can be commercialized via automated massive production. Herein, we discuss the recent advances and the potential of 3D cell-printing technology in engineering organs-on-chips, and provides the future perspectives of this technology to establish the highly reliable and useful drug-screening platforms.







Diagram and illustration of 3D-printing Organ-On-A-Chip.


Nano technology and novel Liposome for drug delivery:

The anticancer efficacy of TNF-related apoptosis-inducing ligand (TRAIL)-based therapy is limited because of systemic toxicity, poor bioavailability, and development of TRAIL resistance. We developed a tumor-targeted LCPP (lipid/calcium/phosphate/protamine) nanoparticle (NP) to deliver TRAIL plasmid DNA (pDNA) into hepatocellular carcinoma (HCC) cells in a mouse model of HCC. TRAIL pDNA was encapsulated in a pH stimuli-responsive calcium phosphate (CaP) core, and protamine was added to facilitate nuclear delivery of pDNA. In addition, intracellular release of Ca2+ from the CaP core overcame TRAIL resistance by calcium influx-dependent DR5 up-regulation. TRAIL expression also attenuated fibrosis in liver tissues surrounding HCCs by reverting activated hepatic stellate cells (HSCs) to a quiescent state or by directly inducing apoptosis in activated HSCs.








TRAIL pDNA delivered by HCC-targeted LCPP NPs in combination with conventional sorafenib treatment attenuated HCC progression as well as liver fibrosis. Overall, our study presents an effective TRAIL-based cancer therapy that could be developed for clinical applications.


Contact us

Ma Lab

Name: Wen-Lung Ma 馬文隆

Tel: +886-4-22052121 #2053

E-mail: maverick@mail.cmu.edu.tw