docs/aims.md

Research Aims

1. Ybx1 in adipogenesis

1.1 Identifying YBX1-CEBPA-cBAF interactions in early, middle, late 3T3-L1 adipogenesis

• Hypothesis: YBX1 cooperates with CEBPA and cBAF complex to regulate temporal gene expression during adipogenesis
• Approach: Time-course analysis of 3T3-L1 differentiation (days 0, 2, 4, 6, 8)
  • ChIP-seq for YBX1, CEBPA, BRG1, SMARCD2, SMARCE1
  • RNA-seq to correlate binding with expression changes
  • Co-IP followed by MS to identify temporal protein-protein interactions
• Expected outcomes: Map dynamic transcriptional networks controlling adipocyte differentiation
• Question: Are we seeing this in all cell types?

1.2 YBX1 in adipogenic 3T3 metabolism

• Hypothesis: YBX1 regulates metabolic reprogramming during adipogenesis
• Approach:
  • Metabolic profiling of control vs YBX1-depleted 3T3-L1 cells during differentiation
  • Seahorse analysis of glycolysis and mitochondrial function
  • Lipidomics to characterize changes in lipid composition
  • Integration with transcriptomic data to identify YBX1-dependent metabolic pathways
• Expected outcomes: Define YBX1's role in adipocyte metabolic adaptation

1.3 Loss of SMARCD2, SMARCE1, and YBX1 in differentiating 3T3 cells

• Hypothesis: cBAF subunits SMARCD2, SMARCE1 cooperate with YBX1 to maintain proper adipogenic program
• Approach:
  • CRISPR/shRNA knockdown of individual and combined factors
  • Oil Red O staining quantification
  • RNA-seq and ATAC-seq to identify chromatin and expression changes
  • Rescue experiments with reconstituted factors
• Expected outcomes: Mechanistic understanding of how chromatin remodeling complex and YBX1 coordinate adipogenesis

2. cBAF-CEBPa regulating mir-101

• Hypothesis: cBAF and CEBPA coordinate to regulate miR-101 expression, impacting lipid metabolism
• Approach:
  • ChIP-seq for cBAF components and CEBPA at miR-101 locus
  • miR-101 overexpression and inhibition studies
  • Target validation using directional RNA-seq and ribosome profiling
  • Lipidomics to assess impact on lipid composition
• Expected outcomes: Novel miRNA-mediated mechanism in metabolic control

3. Ybx1 in metabolic reprogramming of hepatocytes

3.1 Transcriptional network analysis of PPARg, CEBPa, CEBPb, SMARCD2, SMARCE1, BRG1

• Hypothesis: Chronic fat exposure alters binding patterns of master regulators and chromatin remodelers
• Approach:
  • Primary hepatocytes and HepG2 cells exposed to different lipid conditions (acute vs chronic)
  • ChIP-seq for all factors
  • ATAC-seq to assess chromatin accessibility changes
  • Integration with RNA-seq to identify dysregulated pathways
• Expected outcomes: Map how fat exposure reshapes the regulatory landscape in hepatocytes

3.2 Posttranscriptional regulation of lipogenic genes by Ybx1

• Hypothesis: YBX1 regulates mRNA stability and translation of key lipogenic genes
• Approach:
  • RIP-seq for YBX1-bound mRNAs
  • mRNA decay assays in control vs YBX1-depleted cells
  • Polysome profiling coupled with RNA-seq
  • CLIP-seq to map direct YBX1-RNA interactions
• Expected outcomes: Novel posttranscriptional regulatory mechanism in lipid metabolism

3.3 YBX1 INS1 repression

• Hypothesis: YBX1 represses insulin signaling in non-pancreatic tissues
• Approach:
  • Cell-type specific analysis of YBX1 and INS1 expression
  • Reporter assays with INS1 promoter
  • YBX1 ChIP-seq in pancreatic vs non-pancreatic cells
  • CRISPR activation/repression to modulate YBX1 levels
• Key questions:
  • Is YBX1 absent in pancreatic beta cells? If so, how?
  • Can targeting YBX1 be therapeutic for certain types of Type 1 diabetes?

3.4 YBX1-CEBPa-cBAF in lipid-exposed hepatocytes multiomics

• Hypothesis: Chronic lipid exposure alters the cooperative activity of YBX1-CEBPa-cBAF, leading to hepatic steatosis
• Approach: Comprehensive multi-omic analysis integrating:
  • mRNA-seq
  • ATAC-seq
  • ChIP-seq: YBX1, BRG1, CEBPa, CEBPb
  • CelSeq2 for single-cell resolution of heterogeneous responses
  • DUO LINK proximity assays to validate physical interactions
  • Lipidomics to correlate with phenotypic outcomes
• Expected outcomes: Systems-level understanding of transcriptional dysregulation in fatty liver development

4. Using artificial intelligence to enhance biological insights

4.1 Geo stacking app

• Project goal: Develop tool to integrate and visualize multiple GEO datasets
• Potential applications: Meta-analysis of metabolic disease datasets

4.2 Transregulator-ATAC pattern finder

• Project goal: Machine learning tool to predict transcription factor binding from ATAC-seq data
• Approach: Train models using paired ATAC-seq and ChIP-seq datasets

4.3 Increase read mapping speed

• Project goal: Optimize computational pipeline for multi-omic data analysis

4.4 Lab agent

• Project goal: Develop AI-assisted laboratory workflow management system
• Applications: Experiment planning, protocol optimization, data analysis

4.5 ML model for oil-red O image quantification

• Project goal: Automated quantification of lipid accumulation in cell culture
• Approach: Computer vision models trained on labeled microscopy images

5. Regulation of Ybx1 cyto-nuclear translocation

• Hypothesis: Nutrient status regulates YBX1 localization and function
• Approach:
  • Subcellular fractionation followed by western blot
  • Live-cell imaging with fluorescently tagged YBX1
  • Mass spectrometry to identify post-translational modifications
  • Mutagenesis of key regulatory sites
• Expected outcomes: Understanding of how metabolic signals control YBX1 localization and function

6. Metabolic regulation of gene expression

• Hypothesis: Different nutrient environments reshape the epigenetic landscape
• Approach:
  • Treat hepatocytes with various conditions:
    • Insulin stimulation
    • Beta-oxidation inhibitors
    • Glycolysis inhibitors
  • ATAC-seq to map chromatin accessibility changes
  • RNA-seq to identify expression changes
  • Metabolomics to correlate with cellular metabolic state
• Expected outcomes: Map how specific metabolic pathways influence gene regulation

7. Environmental effects on hepatocyte cell fate

• Hypothesis: Environmental factors reprogram hepatocytes through KLF-mediated mechanisms
• Approach:
  • HepaRG differentiation under various conditions
  • ChIP-seq for KLF family members
  • CelSeq2 for single-cell trajectory analysis
  • Functional validation of key target genes

8. Mice studies

• Hypothesis: Hepatocyte-specific YBX1 deletion protects against diet-induced fatty liver
• Approach:
  • Generate hepatocyte-specific YBX1 knockout mice
  • High-fat diet challenge
  • Histological and biochemical analysis
  • Multi-omic profiling of liver tissue

9. Miscellaneous projects

10. Spheroids/Organoids

• Hypothesis: 3D culture systems better recapitulate YBX1 function in vivo
• Approach:
  • Establish liver spheroid/organoid cultures
  • Manipulate YBX1 expression
  • Single-cell RNA-seq for heterogeneity analysis
  • Lipid loading experiments and imaging

11. Robot

• Goal: Automated high-throughput screening platform
• Applications:
  • Drug screening for metabolic disease
  • Systematic CRISPR screening
  • Automated lipid accumulation assays

12. Directional RNAseq

• Hypothesis: Antisense transcription contributes to metabolic gene regulation
• Approach:
  • Directional RNA-seq in normal vs lipid-exposed cells
  • Integration with ChIP-seq data
  • Functional validation of key antisense transcripts

13. DuoLink

• Goal: Map protein-protein interactions in situ
• Applications:
  • YBX1-CEBP interactions in different cellular compartments
  • Dynamic changes in protein complexes during lipid stress

14. FA Uptake

• Hypothesis: YBX1 regulates expression of fatty acid transporters
• Approach:
  • Fluorescent fatty acid uptake assays in control vs YBX1-depleted cells
  • ChIP-seq for YBX1 at fatty acid transporter gene loci
  • Rescue experiments with transporter overexpression

15. Proliferation

• Hypothesis: YBX1 balances proliferation and differentiation in hepatocytes
• Approach:
  • EdU incorporation assays
  • Cell cycle analysis in YBX1-manipulated cells
  • Integration with RNA-seq data

16. Lipidomics

• Goal: Comprehensive profiling of lipid species changes
• Applications:
  • YBX1 knockout effects on hepatocyte lipid composition
  • Temporal changes during fat-induced cellular reprogramming

17. Oxylipins

• Hypothesis: YBX1 regulates inflammatory signaling via oxylipin metabolism
• Approach:
  • Targeted oxylipin profiling
  • Expression analysis of oxylipin biosynthetic enzymes
  • Functional validation with specific inhibitors

18. CelSeq time series

• Hypothesis: Fat exposure creates heterogeneous cell populations with distinct trajectories
• Approach:
  • CelSeq2 time course during fat exposure
  • Trajectory analysis and pseudotime ordering
  • Identification of cell state markers

19. In vitro cytonuclear proteomics

• Goal: Map protein localization changes during metabolic stress
• Approach:
  • Subcellular fractionation followed by mass spectrometry
  • YBX1 interactome in different cellular compartments
  • Specific focus: Epigenetic memory mechanisms and chromatin mismatch repair