# Presentation: 071076

##      MINDY: An Algorithm for the Genome-wide Discovery of Modulators of Transcriptional Interactions

**Introduction**
- Cellular interactions are regulated by a variety of genetic and epigenetic mechanisms, resulting in cellular networks that are both dynamic and context-dependent.
- This dynamical nature is mostly due to post-transcriptional modulation events (e.g. co-factor binding, protein activation, degradation etc), which can not be captured by pairwise analysis of mRNA co-expressions. Interaction of higher-orders must be considered.
- In this paper we explored a particular type of third order interactions that are biologically important and computationally tractable, by explicitly modeling the dependency of a transcriptional interaction (e.g. between a transcription factor and its targets) on the availability of another post-transcriptional effector (a **modulator**). We demonstrated that such modulators can be efficiently identified, given a  large expression profile sets where their expression variability is sufficient.

**Algorithm**
- Our algorithm for systematic discovery of modulator genes is TF-specific and relies on a three-step process:
**STEP 1**: Given a TF of interest, gTF, select a set of candidate modulator genes that:
-  has sufficient expression range
-  is statistically independent with gTF
**STEP 2**: For all triplets (gTF, gt, gm), where gTFgt and gmgt, compute conditional MI difference ΔI(gTF,gt | gm). Statistical significance of ‘ΔI’s is assessed using a null hypothesis with random conditionings.
**STEP 3**: Significant triplets are further pruned by Data Processing Inequality (DPI), if the interaction between gTF and gt is inferred as indirect in both conditions of gm

**Application to human B Cells**
**Dataset**: 254 gene expression profiles (HG-U95Av2, ~12,000 probe sets) from 27 distinct natural occurring and tumor derived B cell phenotypes
**TF of interest**: MYC proto-oncogene.
**Results**:
-  Identified 296 putative modulators of MYC, (p<0.05, Bonferroni corrected), which are responsible for modulating 612 regulatory relationships between MYC and its 303b inferred targets in an interaction-specific fashion.
-  Top enriched GO Molecular Function categories (e.g., DNA binding, acyltransferase, transcription regulator, protein kinase) represent functions consistent with their activities as modulators of cellular interactions.
-  Literature validation
-  Known MYC co-factors: MAX, MIZ-1
-  MYC protein modification: CSNK1D
-  Protein degradation: UBE2G1, USP6
-  mRNA processing: APOBEC3B, HNRPDL
-  Chromatin accessibility: DNMT1
-  Known pathways affecting MYC activities
-  BCR signaling: HCK, MAP4K4, NFKB
-  NFKB & NFKBIA
-  BCL2 & BIK
-  Promoter analysis

**Kai Wang****1,2****, Ilya Nemenman****3****, Nilanjana Banerjee****1,2****, ****Adam A. Margolin****1,2****, ****Andrea Califano****1,2**
- 1 Department of Biomedical Informatics, 2 Joint Centers for Systems Biology, Columbia University, New York, NY 10032
- 3 Los Alamos National Laboratory, MS B256 Los Alamos, NM 87545

**Model**
- A transistor-like 3-way interaction model
- A modulator gene: a gene whose expression levels are monotonically associated with the regulatory relationship between other genes (e.g. a TF and its target)
- gm is a modulator: Information between gTF and gt, conditional on gm, is a monotonic function of gm
- Novelty: the first method that attempts to model post-transcriptional regulation of genes at expression level

**Synthetic Network & Simulation Study**
- Two modulation events, i.e. co-factor binding and post-translational activation, were modeled with realistic biochemical kinetics.
- Use mutual information (MI) to measure the statistical dependency between gene expressions.

**Conclusion**
- Cellular interactions are dynamic and are reconfigured based on many genetic and epigenetic factors.
- Signaling pathway (fast) availability is transcriptionally modulated (slow) based on cellular phenotype.
- We have shown a promising new computational approach to systematically identify modulators of transcriptional interactions, capturing a wide spectrum of post-transcriptional mechanism of gene expression regulation.

**Acknowledgements**
- We thank R. Dalla-Favera, K. Basso and U. Klein for sharing the B Cell gene expression profile dataset and helpful discussions. Funding was provided by
- NCI, NIAID and the NIH Roadmap.

- Due to the monotonic relationship between the conditional MI and the modulator expression, we define the metric, conditional MI difference,
- using the highest (M+) and lowest (M-) expression range of the modulator, in order to achieve the greatest statistical power in detecting a difference.

***Schematic representation of the synthetic network****.* *TF: transcription factor; CF: transcription co-factor; Ki: protein kinase. Molecules start with ‘m’ are mRNA molecules, while ‘p’ indicates the protein molecules. Red arrows in the figure are transcriptional activation/inhibitions; Blue arrows represent translation, while black arrows represent post-translational modification. **The mRNA levels of the TF, CF and Ki were independently sampled from their respective normal distributions, and the system was let evolve till steady state. *
*Steady state mRNA concentrations were measured with a multiplicative measurement noise.*

*Unconditional MI, conditional MI and conditional MI difference for the TF interactions conditioning on the expression level of the PK and the coTF; entries colored in red are determined to be statistically significant, while those in pink are only marginally significant.*

*Transistor-like behaviour for both modulation events. (a) conditional mutual information on CF as a function of the expression levels of CF. (b) the same for Ki.*

- (a)

- (b)

| TF Interaction | I 
(P-value) | IKI+ | IKI- | ΔIKI 
(P-value) | ICF+ | ICF- | ΔICF 
(P-value) |
| --- | --- | --- | --- | --- | --- | --- | --- |
| KI | 0.0075 (0.999) | --- | --- | --- | 0.0180 | 0.0176 | 0.0004 (0.999) |
| CF | 0.0112 (0.999) | 0.0139 | 0.0347 | 0.0208
 (0.869) | --- | --- | --- |
| Gene1 | 0.3109 (2.08×10-23) | 0.3343 | 0.3218 | 0.0125 (0.999) | 0.2450 | 0.2735 | 0.0285 (0.999) |
| Gene2 | 0.0529 (0.0063) | 0.0456 | 0.0572 | 0.0116
 (0.999) | 0.0204 | 0.1475 | 0.1271 (6.62×10-4) |
| Gene3 | 0.0435 (0.0342) | 0.0180 | 0.1619 | 0.1439
 (3.45×10-5) | 0.0507 | 0.0437 | 0.0070 (0.999) |