The Rivera-Mulia Lab - RT Networks

Cell fate commitment is accompanied by specific changes in RT. In our lab, we are exploiting RT networks to dissect the mechanisms that regulate lineage specification and cellular identity maintenance.

We use the dynamic changes in the temporal order of DNA replication during human development to identify gene regulatory interactions.

Using genome-wide RT programs of distinct cell types and intermediate differentiation stages we identify gene regulatory interactions. We identify thousands of genes highly correlated in their RT patterns and used them to construct distinc models of RT Networks.

1. Correlated RT networks

Correlated RT networks are based on the coordinated RT changes. Pairwise correlations between all human genes are calculated and gene interactions are established between the highest correlated gene pairs. RT networks are then constructed where distances between genes are established according to their correlation strenght. Ontology analysis of RT networks allow to identify sub-network communities of highly interconnected nodes of genes involved in specific cellular functions.

These correlated RT networks identify gene regulatory interactions exploiting the cell-type–specific RT programs.

Functional annotation of RT networks. RT-correlated gene pairs from human development were identified and exploited to construct a RT network. Detailed subnetwork connectivity is shown as well as the corresponding ontology terms. Rivera-Mulia, et al., 2019.

2. Directional RT networks

Directional RT networks based on the temporal order of RT changes during cell differentiation. These RT networks take advantage of RT programs collected at multiple intermediate differentiation stages to determine the directionality of gene interactions.

Directed RT networks identify the earliest genes to change RT during cell fate commitment and the connected genes that change in subsequent stages of differentiation.

Construction of directed RT networks allows characterization of the hierarchical relationships in gene regulatory interactions and predict potential targets for key regulators.

Directed RT network of FOXA1 during pancreatic differentiation. FOXA1 gene encodes for a key transcription factor and changes from late to early replication during the earliest stages of differentiation; potential downstream genes were identified as those genes that change in subsequent differentiation stages. Analysis of protein–protein interactions validated gene interactions predicted by the directed RT network. Rivera-Mulia, et al., 2019.

3. Bipartite networks

To analyze the relationships between the temporal order of DNA replication and gene expression we developed a model of Bipartite networks.

Bipartite networks exploit RT and transcriptome programs collected at multiple stages of differentiation. These networks consist of two independent but interconnected networks: transcriptional regulatory networks (TRNs) contain coexpressed genes and the RT network contains genes whose RT patterns correlate with the expression changes from the TRN.

Bipartite networks allowed us to identify hundreds of genes whose RT correlated with expression levels of coexpressed transcription factors (TFs). Chromatin immunoprecipitation (ChIP-seq) signals confirms co-occupancy of multple TFs at the promoter of the predicted RT genes.

These findings suggest that establishment of complex regulatory TFs networks, might be required to remodel the RT program and 3D genome organization during development.

Bipartite networks. Expression patterns of exemplary key transcription factors (TFs) correlate with the RT of downstream genes. Bipartite networks are constructed based on the correlation between transcriptional levels of the TF genes in the TRN side and RT changes of genes in the RT side. ChIP-seq signals validate the co-occupancy of mulitple TFs at the promoter of downstream targets predicted by the bipartite network. Rivera-Mulia, et al., 2019.