Although many different genes can be mutated in cancer, the overall malignant behavior of tumors is remarkably consistent: uncontrolled growth, invasion, and sometimes metastasis. This raises the question of whether diverse cancer-causing mutations ultimately converge on a shared set of molecular targets that drive these core features.
Using a multi-omic approach, we uncovered that translation and ribosome biogenesis—processes central to protein production—are common downstream targets of major oncogenic pathways across many cancer types. Proteomic analysis and cell-based experiments highlighted nucleolar and coiled-body phosphoprotein 1 (NOLC1) as a critical regulator of tumor cell growth, controlled at both the transcriptional and post-translational level. These findings suggest that while different oncogenic pathways may vary by cancer type, they often funnel into the same growth-regulating mechanisms. Identifying such shared nodes not only deepens our understanding of cancer biology but also points to new potential targets for therapy and prevention.
The genetic code is defined by how transfer RNAs bind to mRNA, while the gene regulatory code is defined by how transcription factors (TFs) bind to DNA and work together to control gene expression. Unlike the genetic code, the regulatory code is extremely complex, as humans have over 1,600 TFs that frequently interact with one another.
To better understand this complexity, we applied a method called CAP-SELEX, which can identify how individual TFs bind DNA, how they interact in pairs, and the DNA sequences they recognize together. Screening more than 58,000 TF combinations revealed over 2,000 interacting pairs, many with unique binding patterns or entirely new composite motifs not seen in the individual factors. These novel motifs were often linked to specific cell types and developmental processes, showing how TFs with similar DNA-binding abilities can still drive distinct outcomes in different contexts. This work provides an important step toward decoding the human gene regulatory code.
We describe a competitive genome editing method that measures the effect of mutations on molecular functions, based on precision CRISPR editing using template libraries with either the original or altered sequence, and a sequence tag, enabling direct comparison between original and mutated cells. Using the example of the MYC oncogene, we identify important transcriptional targets and show that E-box mutations at MYC target gene promoters reduce cellular fitness.
While DNA encodes the instructions for gene expression, the complete set of sequence rules that control when and where genes are expressed has remained unclear. To address this, we used massively parallel reporter assays (MPRAs) to test DNA sequences spanning a space nearly 100 times larger than the human genome. Machine learning analysis revealed that transcription factors (TFs) generally act in an additive manner with limited “grammar,” and that most enhancers boost promoter activity without requiring specific TF-TF interactions. Enhancers could be grouped into three categories—classical, closed chromatin, and chromatin-dependent—each influencing gene regulation in distinct ways.
The study also found that only a small number of TFs show strong activity in a given cell, with most TF activities shared across cell types. Importantly, individual TFs can perform multiple regulatory roles, from opening chromatin to shaping transcription start site activity, underscoring the central role of TF binding motifs as the fundamental building blocks of gene expression.
The packaging of DNA on nucleosomes makes it more difficult for transcription factors to access DNA. This new study shows that these proteins have evolved several different mechanisms to get around the problem, allowing them to read the important messages in our genome that tell cells how to construct and maintain our tissues and organs.
The reported findings uncover a rich, interactive landscape between transcription factors and the nucleosome, thus paving a way to a thorough understanding of the complicated DNA decoding mechanisms in higher organisms. The findings also provide a basis for future studies aimed at understanding transcriptional regulation based on biochemical principles. As aberrant transcription factor activity is linked to many human diseases, including cancer, the findings are also relevant to understanding mechanisms of human disease.
We developed a massively parallel protein activity assay (Active Transcription Factor Identification, ATF) that can measure the DNA binding activity of all TFs in a particular cell type. By applying the technique to mouse tissues and embryonic stem cells, we found that only a small number of TFs demonstrated strong DNA binding in each of the tissues studied. These results suggest that, despite there being a huge number of TFs present in most tissues, just a handful of TFs may determine the gene expression landscape of a cell, and that the pattern of gene regulatory interactions may be far less complex and more hierarchical than previously thought.
Speaking about the research, Professor Jussi Taipale explained: "The finding that some transcription factors are much more active than others indicates that the regulatory system is far simpler than what we had imagined. We previously thought that all transcription factors can work together in millions of different ways to regulate genes. Instead, it now looks like weaker transcription factors need to work with the strong ones to get anything done. This makes the regulatory system very hierarchical, and simplifies the task of evolution. In a hierarchical system it is easier to evolve sets of co-expressed genes that work together to accomplish a particular task."