Cellular behavior is determined not by a single molecule but by many molecules that interact strongly with one another and form a complex network. It is unclear whether cellular behavior can be controlled by regulating certain molecular components in the network. By analyzing a variety of biomolecular regulatory networks, we discovered that only a small fraction of the network components need to be regulated to govern the network dynamics and control cellular behavior. We defined a minimal set of network components that must be regulated to make the cell reach a desired stable state as the control kernel and developed a general algorithm for identifying it. We found that the size of the control kernel was related to both the topological and logical characteristics of a network. Intriguingly, the control kernel of the human signaling network included many drug targets and chemical-binding interactions, suggesting therapeutic application of the control kernel.

Accurate prediction of gene regulatory rules is important towards understanding of cellular processes. Existing computational algorithms devised for bulk transcriptomics typically require a large number of time points to infer gene regulatory networks (GRNs), are applicable for a small number of genes and fail to detect potential causal relationships effectively. Here, we propose a novel approach ‘TENET’ to reconstruct GRNs from single cell RNA sequencing (scRNAseq) datasets. Employing transfer entropy (TE) to measure the amount of causal relationships between genes, TENET predicts large-scale gene regulatory cascades/relationships from scRNAseq data. TENET showed better performance than other GRN reconstructors, in identifying key regulators from public datasets. Specifically from scRNAseq, TENET identified key transcriptional factors in embryonic stem cells (ESCs) and during direct cardiomyocytes reprogramming, where other predictors failed. We further demonstrate that known target genes have significantly higher TE values, and TENET predicted higher TE genes were more influenced by the perturbation of their regulator. Using TENET, we identified and validated that Nme2 is a culture condition specific stem cell factor. These results indicate that TENET is uniquely capable of identifying key regulators from scRNAseq data.

Recent advances in genome and transcriptome analysis have contributed to the identification of many potential cancerrelated genes. Furthermore, biological and clinical investigations of the candidate genes provide us with a better understanding of carcinogenesis and development of cancer treatment. Here, we report a novel role of KIAA1324 as a tumor suppressor in gastric cancer. We observed that KIAA1324 was downregulated in most gastric cancers from transcriptome sequencing data and found that histone deacetylase was involved in the suppression of KIAA1324. Low KIAA1324 levels were associated with poor prognosis in gastric cancer patients. In the xenograft model, KIAA1324 significantly reduced tumor formation of gastric cancer cells and decreased development of preformed tumors. KIAA1324 also suppressed proliferation, invasion, and drug resistance and induced apoptosis in gastric cancer cells. Through protein interaction analysis, we identified GRP78 (glucose-regulated protein 78 kDa) as a KIAA1324-binding partner. KIAA1324 blocked oncogenic activities of GRP78 by inhibiting GRP78-caspase-7 interaction and suppressing GRP78-mediated AKT activation, thereby inducing apoptosis. In conclusion, our study reveals a tumor suppressive role of KIAA1324 via inhibition of GRP78 oncoprotein activities and provides new insight into the diagnosis and treatment of gastric cancer. Cancer Res; 75(15); 3087-97. Ó2015 AACR.

Understanding the specific survival of the rare chronic myelogenous leukaemia (CML) stem cell population could provide a target for therapeutics aimed at eradicating these cells. However, little is known about how survival signalling is regulated in CML stem cells. In this study, we survey global metabolic differences between murine normal haematopoietic stem cells (HSCs) and CML stem cells using metabolomics techniques. Strikingly, we show that CML stem cells accumulate significantly higher levels of certain dipeptide species than normal HSCs. Once internalized, these dipeptide species activate amino-acid signalling via a pathway involving p38MAPK and the stemness transcription factor Smad3, which promotes CML stem cell maintenance. Importantly, pharmacological inhibition of dipeptide uptake inhibits CML stem cell activity in vivo. Our results demonstrate that dipeptide species support CML stem cell maintenance by activating p38MAPK–Smad3 signalling in vivo, and thus point towards a potential therapeutic target for CML treatment.