Download RYGB에 의한 당뇨 치료기전 발굴

SY – Master Presentation
Bioinformatics and Synthetic Biology Lab., KAIST
Multi-Model, Multi-Omics Convergence in Drug Discovery2
Link Clinical Phenotype,
Animal Model Phenotype
and Cell System Phenotypes
with Advanced Cell Function
Assay System and Omics Marker
oriented Bioinformatics
Bioinformatics and Synthetic Biology Lab., KAIST
Cell Function Assay based Disease Profiling
Efficacious Disease
Phenotypes
Bioinformatics and Synthetic Biology Lab., KAIST
3
Cell Oriented Multi Dimensional Omics Marker Mapping 4
Animal Disease Model
Marker Expression Profile
Link Clinical Omics Marker,
Animal Model Omics Marker
and Cell System Omics Marker
with Multi-Omics Integration Strategy
Multi-Omics Integration
&
Multi Dimension Correlation
Patient Clinical Marker
& Omics Marker
Expression Profile
Bioinformatics and Synthetic Biology Lab., KAIST
Disease Cell Model System
Marker Expression Profile
Technological Mission Overview
Computational Research
Feature Extraction
& Feature Mapping
Feature Extraction
Algorithms
Drug Synergism
Prediction
HT Cell Image Analysis
Omics Integration
and Model Generation
Pharm.-pore
Screen & 3D Dock
Pursuit of
Expertise
HT-Experiments
HCS - Phenome Analysis
MCMT Drug Discovery
Drug Research
Bioinformatics and Synthetic Biology Lab., KAIST
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Multi Dimensional Fat Cell Biology for T2DM Drug Discovery
Literature
Survey
Browning
Literature Survey
Animal and Clinical
Studies
Literature and Trend Survey
(Basic Knowledge & Cell Assays)
Fat Cell Controlling Agents Discovery Knowledge Build up
T2DM Bioinformatics
(Patient Sample & Animal Model
Fat Cell Bioinformatics
(Expression Marker & Controlling Agents)
Fat Cell HCS
(Assay Systems and Bio-image Informatics)
HCS based Phenotype Discovery
Bioinformatics and Synthetic Biology Lab., KAIST
Fat-Cell Bioinformatics
6
Type 2 Diabetes Mellitus Drug Discovery: Case Studies 7
Drug Discovery Case Studies
• JNK-JIP Inhibitor: BI-78D3
• PPARg ligand blocker: SR1664
• PPARg agonist: Harmine
• GLP-1R G-protein-biased agonist: P5
• Browning Agent: R406
Multi-target/Multi-Component Drug discovery
• Horizon Discovery
• OncoReference Data Set
• Chalice Analyzer
• Dream Challenge
• Dream NCI
• Dream AstraZeneca
Fat Cell Disease Model - Regulatory Network Learning
• Dream Challenge 8
Bioinformatics and Synthetic Biology Lab., KAIST
Type 2 Diabetes Mellitus Drug Discovery: Case Studies 8
High-throughput Phenotype Screening for T2DM Drug Discovery
Bioinformatics and Synthetic Biology Lab., KAIST
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Bioinformatics and Synthetic Biology Lab., KAIST
Type 2 Diabetes Mellitus Drug Discovery: Current Practice10
1. Metabolic Disease Drug Discovery—‘‘Hitting the Target’’ Is Easier Said Than
Done, Cell Metabolism, 2012
2. Suneng Fu et al., Phenotypic assays identify azoramide as a small-molecule
modulator of the unfolded protein response with antidiabetic activity, Sci. Trs.
Med., 2015
3. Disease Modeling and Phenotypic Drug Screening for Diabetic Cardiomyopathy
using Human Induced Pluripotent Stem Cells, Cell Reports, 2014
4. Autocrine selection of a GLP-1R G-protein biased agonist with potent
antidiabetic effects, Nat. Comm., 2015
Bioinformatics and Synthetic Biology Lab., KAIST
Diet and Fat Metabolism
Free Fatty Acids, FFA
Monoglycerides
Triglyceride, TG
Lipase
11
+
Dietary Fat
Chylomicrons
Bioinformatics and Synthetic Biology Lab., KAIST
Fat Storage and Release
Bioinformatics and Synthetic Biology Lab., KAIST
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Energy Metabolism
Bioinformatics and Synthetic Biology Lab., KAIST
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Lipolisys
Bioinformatics and Synthetic Biology Lab., KAIST
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Lipolisys
15
The different shades of fat
Vivian Peirce,
Stefania Carobbio & Antonio Vidal-Puig
Nature 510, 76–83 (05 June 2014) doi:10.1038/nature13477
Bioinformatics and Synthetic Biology Lab., KAIST
Fatty Acids, Glycerol and Trigrycerides
Stearic Acid – saturated fatty acid
Esterification
RCO2H + R’OH  RCO2R’ + H2O
3SA + 1G  1TG + 3H2O
Triglyceride
Bioinformatics and Synthetic Biology Lab., KAIST
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Mono, di, and triglycerides
Monoglyceride
Diglyceride
Triglyceride
Bioinformatics and Synthetic Biology Lab., KAIST
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Historical Perspectives in Fat Cell Biology
Bioinformatics and Synthetic Biology Lab., KAIST
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History of Fat Cell Biology
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Around beginning of the 20th century, adipose tissue was considered to be a
connective tissue filled with droplets of fat. Adipose tissue was believed to be
limited in the body to an insulating role against heat loss and to provide
mechanical support for certain tissues.
In the 1940s, deuterium oxide was used to study the conversion of carbohydrates
to fatty acids.
Shapiro and Wertheimer (1948) showed conversion of carbohydrates to fat in
adipose tissue and the direct effect of insulin on the transformation of glucose into
fatty acids. They proposed that adipose tissue was metabolically active and that
deposition and mobilization of fat are regulated by nervous and endocrine factors.
In 1965, over 4,000 references on adipose tissue was published in handbook of
physiology
Bioinformatics and Synthetic Biology Lab., KAIST
History of Fat Cell Biology
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In the 19th Century, discovery of adipocyte
In 1957, Two major fat cell types – white and brown cells were discovered
In 1957, Study of histology of adipose tissue
• Histology of adipose tissue is similar in several species
• Morphologies of developing white fat and brown fat are similar
• During the differentiation of white fat, the cells go through a stage (e.g., multilocular stage) that, at
the level of light microscopy, has the morphological appearance of brown fat cells.
Bioinformatics and Synthetic Biology Lab., KAIST
연구요약
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신규 당뇨 약물 발굴 기법 개발
1.
Literature Survey
I.
II.
당뇨 연구 동향
신규 당뇨 치료 기전 발굴
<RYGB에 의한 당뇨 치료기전 연구>
1.
Literature Survey
당뇨 약물 개발 동향
I.
RYGB에 의한 당뇨 치료
III. 당뇨 약물 발굴 기법 동향
II.
RYGB에 의한 당뇨 치료 기전 연구
2.
신규 당뇨 약물 표적발굴 Scheme
3.
Current Status
2.
Network 기반 RYGB Induced 당뇨 치료 기전
발굴 연구 Scheme
3.
Current Status
<BAT 분화 촉진 약물 발굴>
<BAT Thermogenesis에 의한 당뇨치료>
1.
1.
Literature Survey
Literature Survey
I.
Biology of FAT 연구 동향
I.
Biology of FAT 연구 동향
II.
Beige Adipocyte 분화 관련 연구 동향
II.
Beige Adipocyte 분화 관련 연구 동향
2.
WA to BA 분화 약물 발굴 연구 Scheme
3.
Current Status
2.
Network 기반 WA to BA 분화 마커 발굴 연구
Scheme
3.
Current Status
신규 당뇨약물 표적발굴
Bioinformatics and Synthetic Biology Lab., KAIST
당뇨 연구동향
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Monogenic  Multi-genic, Heterogenic, Systemic
One drug therapy  Managing overall patient condition (body weight,
personalized and combinatorial drugs usage, complications)
Target specific organ  Target chronic inflammation and fat accumulation
Causal gene study  Causal multi-factor study (genes, SNPs, microbiome, etc.)
Subtyping subtypes  T2DM class separation (Obesity vs non-obesity, genetic vs
environmental origin of T2DM)
• Li Li et al., Science translational medicine 2015
Bioinformatics and Synthetic Biology Lab., KAIST
당뇨약물 개발동향
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Current Options
• Insulin 계열 – Enhance PK properties of Insulin
• Metformin 계열 – Suppressing Hepatic glucose production (target mitochondria GPD)
• Sulphonylureas 계열 - Stimulate insulin release (KATP target) in Pancreas
• TZDs 계열 (Glitazones) - PPARg agonist
• GLP-1 and GIP – Increase insulin secretion and decrease glucagon secretion
• DPP-4 Inhibitors – Inhibit degradation of GLP-1
• SGLT2 Inhibitors – inhibit glucose re-absorbption in kidney
• Amylinomimetic 계열 – reducing glucagon secretion & reduces appetite (amylin analog)
• Alpha-glucosidase inhibitors – inhibit starch to glucose reaction
• Dopamine Agonist – Unknown, possibly regulating hepatic glucose production
Combinatorial Drugs – combine drugs which have different target and mechanism
• Metformin, Sulphonylureas, Tzds, and Alpha-glucosidase inhibitors
• GLP and DPP-4 inhibitors
• A + SGLT2 inhibitors
당뇨약물 개발동향
Clinical Trial 동향
• 세포 Signaling Target (MAPK, GPR, etc.)
• TF Target (PPAR Family, RXR)
• Immuno-Modulators (IL, TNF, CD protein Target)
• Incretin Modulators (GLP, GIP, L-cell Target)
• Diabetes-CVD Co-targeted Drugs (ACE Inhibitors, ACC Inhibitors, etc.)
• Hormonal Targets (Glucagon R, Dopamine R, Serotonin R, etc.)
• Enzyme Targets (GSK, GK, Hyaluronic acid, etc.)
• Unusual Targets (VEGF, FGFR, Microbiome, etc.)
25
List of Candidate Targets
26
IGF-1, PTP-1B, CD3, RIP140, GCGR, HSP60, CTLA-4, FGFR-21, IL-1B, PTHR,
GCCR, JNK, Antidiuretic hormone, GR, PPAR-a, PPAR-g, ACE, C-II, PR,
11beta-HSD, AMPK, MTP, ZR, CB1R, VCAM-1, SGLT-1/2, FBPase, GKA, NA,
SR, DP, Beta3AR, GCP, GDIR, SIRT1, DGAT1, 5-HT2C, CB1, CCK A, PPAR-d,
SGK-1, CB1R, IL-6R, CB1R/2R, Ghrelin-R, CCR2, ABC-T, IL-1R, GPR119,
Sykk, Let-7, GAD, CPT-I, Hyaluronic acid, MC1R/MC4R, GP, Y2R, GPR40,
NPY1, HM74a, Mnk, BAR, BMP4R-1a, Sirtuin 1, DG070, IL-1B, GSK-3, HSP65,
s-EH, SCD-1, Sigma-1R, IL-18, Amylin and leptin R, CD80, CD86, CD52, MNC4 R, MetAP2, NNR-alpha7, ACC, RXR, GPR120, C-peptide R, Calcitonin R,
EGFR, Gastrin R, VEGF-B, ApoA-1, ACL, NK cell R, SERCA2b, IBAT, TGR5 in
L cells, CD28, MCH R, IL-7R, IAPP, PDHK
Z:\2012-21-MCMT\0-사업단\2단계공통소재\소재후보_이관수\References\기
타 Reference\Diabetes-Clinical-Trials(All).xlsx
Diabetes-CVD Co-target 발굴전략
1. Constructing Diabetes Related Genetic Network
2. Constructing CVD related Genetic Network
3. Combine 2 Networks
4. Annotate the combined network with current drug options
5. Set the rule for the co-target discovery (Guilt-by-Association, Network
Propagation, Context Associated Hub)
6. Searching Candidate Drug for the target
7. Experimental validation
Bioinformatics and Synthetic Biology Lab., KAIST
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Current Status
Diabetes Associated Gene List – 확보 & update 중
• Diabetes associated genes (mechanistically)
• Disease associated SNPs and related genes
• Drug target & Drug target related genes
• Z:\3-Disease\1-Diabetes-기민난영\ Disease-gene-in-T2DM.xls
Network Analysis Methods
• BIML 2016 Network Modeling and Analysis for Data-driven Biology 강의 수강
• Resource & Tools Study 중
• 해당 Protocol Update 예정
Bioinformatics and Synthetic Biology Lab., KAIST
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RYGB에 의한 당뇨 치료기전 발굴
Bioinformatics and Synthetic Biology Lab., KAIST
RYGB and remission of T2DM
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After RYGB, 80% of T2DM patients show remission of T2DM (Filip K. Knop,
diabetes cares, 2013)
Patients who treated with insulin, After 12 month later, 62% of patients stop
insulin treatment (Ali Ardestani et al, diabetes cares, 2015)
Currently no clear mechanism has been proposed. There are two main arguments
– food restriction and GI factors (Incretin and other GI derived hormones)
There are some study also proposed microbiome effects
RYGB에 의한 당뇨 치료기전 연구
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Clinical Study
• Sarah Steven et al., Diabetes Care, 2015
• Comparing between RYGB patients who have T2DM and have normal glucose tolerance.
• After Surgery, weight loss was similar in two groups but physiological factors related to T2DM
remarkably improved in T2DM patient group compared to NGT group. This phenotype related to
decreased in pancreatic fat.
• They concluded that the fall in intra-pancreatic triacylglycerol in T2DM, which occurs during
weight loss, is associated with the condition itself rather than decreased total body fat.
• Carsten Dirksen et al., Diabetes Cares, 2010
• Comparing peroral feeding (위를 통과하도록 관을 연결하여 영양분 주입) and gastroduodenal
feeding (장까지 관을 연결시켜 영양분 주입) in T2DM patients who had RYGB
• In two cases, there are no significant difference in GIP level but both beta cell function (2 fold)
and GIP level (5 fold) were significantly improved than pre-operational levels.
• They concluded that Improvement in postprandial glucose metabolism after gastric bypass is an
immediate and direct consequence of the gastrointestinal rearrangement, associated with
exaggerated GLP-1 release and independent of changes in insulin sensitivity, weight loss, and
caloric restriction.
Bioinformatics and Synthetic Biology Lab., KAIST
RYGB에 의한 당뇨 치료기전 연구
32
Animal Study
• Jibin Li et al., Molecular Medicine, 2012
• For 60 male GK rats (non obese diabetic animal model, shows spontaneous T2DM phenotype),
comparing RYGB, Sham operation, and control.
• After 20 to 30 days, PPARg and GLUT4 protein level increased in fat cell (site is not specified) of
RYGB group compared to control group. Phosphorylation of PI3K was also increased. Instead,
TNF-alpha level was decreased.
• They concluded that, RYGB may improve insulin resistance and treat T2DM through upregulation
of the PPARγ2 protein, downregulation of TNFα mRNA transcription, through the autocrine
pathway, upregulation of PI3Kp85α expression, upregulation of GLUT4 mRNA transcripts and by
inducing translocation of GLUT4 in adipose tissue
• Hongwei Yu et al., J Gastrointest Surg, 2013
• For GK rats, gene expression profile of islets and GI hormone levels were assessed.
• After surgery, GI Hormone level was improved and Ca2+ concentration changed in pancreas. Ca2+
channel activity also improved and RYGB induced pancreatic islet beta-cell proliferation and
improve function of beta-cell.
• They concluded that, RYGB promoted a new metabolic environment while triggering changes to
adapt to the new environment. These changes promoted the cellular proliferation of islets and
improved the function of beta cells. The quantity of beta cells increased, and their quality
improved, ultimately leading to insulin secretion enhancement.
RYGB에 의한 당뇨 치료기전 발굴전략
1. RYGB Gene expression Data 수집 (rat, human)
2. Co-expression network 구축
3. Gene Set Enrichment Analysis 수행
4. Negative case 분석 수행 (human trial 중 T2DM remission 실패한 case)
5. 공통 기전 분석 및 결론 도출
Bioinformatics and Synthetic Biology Lab., KAIST
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Current Status
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RYGB Gene expression data 수집
• GSE8314 – rat RYGB Dataset (Hypothalamus, subcutaneous abdominal fat)
• GSE68812 – rat RYGB Dataset (small intestine)
• GSE5109 – human RYGB Dataset (obesity, muscle)
• GSE24297 – human RYGB Dataset (subcutaneous adipose)
• GSE32575 – human RYGB Dataset (circulating monocyte)
• GSE69248 – human RYGB Dataset (32 gene signature in liver)
Co-expression network 구축 및 분석 수행 중
• MAQC I~III (2006 ~ 2014) – Micro array Quality Control Study paper 분석
• MAQC I – microarray quality control
• MAQC II – microarray prediction quality control
• MAQC III – SEQC, RNA-seq quality contorl
• Probe selection & gene-probe mapping, platform-to-platform variation control guideline
study 중 (NIH Standard)
Bioinformatics and Synthetic Biology Lab., KAIST
Beige Adipose Target 약물 및 치료기전 발굴
Bioinformatics and Synthetic Biology Lab., KAIST
White, Beige, and Brown Adipose Tissues
There is persuasive evidence from animal models that enhancement of the
function of brown adipocytes, beige adipocytes or both in humans could be very
effective for treating type 2 diabetes and obesity. Moreover, there are now an
extensive variety of factors and pathways that could potentially be targeted for
therapeutic effects, including PPAR-gamma – the target of TZD.
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Adipocyte Browning Agent
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Adipocyte Browning Agent
Bioinformatics and Synthetic Biology Lab., KAIST
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Dietary Adipocyte Browning Agent
Bioinformatics and Synthetic Biology Lab., KAIST
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Why Browning Matter?
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In humans, low BAT activity is correlated with ageing, obesity and measures of
metabolic disease is pivotal. This relationship suggests a causal link between
decreased BAT activity and weight gain.
Most of the standard metabolic parameters routinely assessed by physicians, such
as blood glucose and lipid levels, are influenced by the activity of brown (and
possibly also beige) adipocytes.
The induction of browning represents one important approach that could
potentially convert deleteriously lipid-overloaded WAT into metabolically active
and healthy BAT
Bartelt, A. & Heeren, J. Nat. Rev. Endocrinol. 10, 24–36 (2014)
Beige adipocyte 분화 연관 약물/기전 발굴 전략 41
1. Beige adipocyte 분화 연관 연구 Literature Survey
2. white/beige adipocyte gene expression Data 수집
3. 3T3 L1 기반 white/beige adipocyte gene expression Data 분석 및 genetic
marker selection
4. 3T3 L1 derived beige adipocyte 분화 실험 Setting
5. 3T3 L1 derived white adipocyte browning agent 실험 및 phenotype 분석
6. Genotype, Phenotype 연계 분석
7. 약물 유사성 분석 기반 신규 Browning Agent 발굴
8. 신규 Browning Agent characterization – gene, protein expression, phenotype
Bioinformatics and Synthetic Biology Lab., KAIST
Current Status
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1. Beige adipocyte 분화관련 Literature Survey – 관련 연구 정리 및 update 중
2. BAT 관련 Expression data 수집
• GSE8044 Mouse BAT, WAT tissue
• GSE60441/2 Mouse White and EBF2 Over expressed adipose tissue
• GSE55080 PRDM16 KO Brown adipose Tissue
• GSE39562 Isolated Beige Adipocyte in mouse
3. 3T3 L1 관련 Expression data 수집
• GSE69313 3T3-L1, FTO KO
• GSE53249 3T3-L1, FTO, METTL3 deficient RNA-Seq
• GSE12929 3T3-L1, PPARg KO
• GSE14810 3T3-L1, TZD (rosiglitazone) treatment
• GSE1458 3T3-L1, TZD (pioglitazone, rosiglitazone, troglitazone) treatment
• GSE64075 3T3-L1, TZD (rosiglitazone), NSAIDs (Diclofenac, Indomethacin, Ibuprofen)
• GSE34150 3T3-L1, Insulin, Dexamethasone treatment
• GSE56440 3T3-L1, Berberine Treatment
• GSE49176 3T3-L1, control
4. 3T3 L1 expression data 분석 진행중
MCMT 당뇨연관 약물 및 치료기전 발굴
MCMT 당뇨 연관 약물/치료기전 발굴 주제
• 당뇨 및 심혈관 질환 공통 표적 약물 및 치료기전 발굴
• RYGB 치료기전 발굴 및 기전 유사 약물 발굴
• White Adipocyte Browning 약물 발굴 및 약물 작용 기전 분석
Current Status
• 기본 Literature Survey
• 필요 데이터 확보
• 분석 방법 Study
추후계획
• Literature Survey 결과 누적 Update
• 데이터 분석 및 실험 검증 계획 구체화
Bioinformatics and Synthetic Biology Lab., KAIST
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Method of organizing your research
Writing paper is the best way of organizing your research
Ask,
• What is the problem
• Why you trying to solve the problem
• How are you solving it
• What have you done
• Who cares
English
• Outline
• Words right
• Strunk & White
• Simple version of English, as clear as you can make it, as short as you can make it,
Organize transparently, written flawlessly grammatically, using simple English words.
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Background – Type 2 Diabetes and Drug Discovery
Problem Setting
Methods and Process
Preliminary Data
Future Plan
Bioinformatics and Synthetic Biology Lab., KAIST