Hepatocellular carcinoma (HCC) is the fifth most common cancer worldwide. Despite the prevalence of HCC, there is no effective, systemic treatment. The transcription factor LSF is a promising protein target for chemotherapy; it is highly expressed in HCC patient samples and cell lines, and promotes oncogenesis in rodent xenograft models of HCC. Here, we identify small molecules that effectively inhibit LSF cellular activity. The lead compound, factor quinolinone inhibitor 1 (FQI1), inhibits LSF DNA-binding activity both in vitro, as determined by electrophoretic mobility shift assays, and in cells, as determined by ChIP. Consistent with such inhibition, FQI1 eliminates transcriptional stimulation of LSF-dependent reporter constructs. FQI1 also exhibits antiproliferative activity in multiple cell lines. In LSF-overexpressing cells, including HCC cells, cell death is rapidly induced; however, primary or immortalized hepatocytes are unaffected by treatment with FQI1. The highly concordant structure–activity relationship of a panel of 23 quinolinones strongly suggests that the growth inhibitory activity is due to a single biological target or family. Coupled with the striking agreement between the concentrations required for antiproliferative activity (GI 50 s) and for inhibition of LSF transactivation (IC 50 s), we conclude that LSF is the specific biological target of FQIs. Based on these in vitro results, we tested the efficacy of FQI1 in inhibiting HCC tumor growth in a mouse xenograft model. As a single agent, tumor growth was dramatically inhibited with no observable general tissue cytotoxicity. These findings support the further development of LSF inhibitors for cancer chemotherapy.

ABSTRACT

Understanding the systemic biological pathways and the key cellular mechanisms that dictate disease states, drug response, and altered cellular function poses a significant challenge. Although high-throughput measurement techniques, such as transcriptional profiling, give some insight into the altered state of a cell, they fall far short of providing by themselves a complete picture. Some improvement can be made by using enrichment-based methods to, for example, organize biological data of this sort into collections of dysregulated pathways. However, such methods arguably are still limited to primarily a transcriptional view of the cell. Augmenting these methods still further with networks and additional -omics data has been found to yield pathways that play more fundamental roles. We propose a previously undescribed method for identification of such pathways that takes a more direct approach to the problem than any published to date. Our method, called latent pathway identification analysis (LPIA), looks for statistically significant evidence of dysregulation in a network of pathways constructed in a manner that implicitly links pathways through their common function in the cell. We describe the LPIA methodology and illustrate its effectiveness through analysis of data on ( i ) metastatic cancer progression, ( ii ) drug treatment in human lung carcinoma cells, and ( iii ) diagnosis of type 2 diabetes. With these analyses, we show that LPIA can successfully identify pathways whose perturbations have latent influences on the transcriptionally altered genes.

Hydrogels are of keen interest for a wide range of medical and biotechnological applications including as 3D substrate structures for the detection of proteins, nucleic acids, and cells. Hydrogel parameters such as polymer wt % and crosslink density are typically altered for a specific application; now, fluorescence can be incorporated into such criteria by specific macromonomer selection. Intrinsic fluorescence was observed at λmax 445 nm from hydrogels polymerized from lysine and aldehyde- terminated poly(ethylene glycol) macromonomers upon excitation with visible light. The hydrogel’s photochemical properties are consistent with formation of a nitrone functionality. Printed hydrogels of 150 μm were used to detect individual cell adherence via a decreased in fluorescence. The use of such intrinsically fluorescent hydrogels as a platform for cell sorting and detection expands the current repertoire of tools available.

BackgroundGenome-wide microarrays have been useful for predicting chemical-genetic interactions at the gene level. However, interpreting genome-wide microarray results can be overwhelming due to the vast output of gene expression data combined with off-target transcriptional responses many times induced by a drug treatment. This study demonstrates how experimental and computational methods can interact with each other, to arrive at more accurate predictions of drug-induced perturbations. We present a two-stage strategy that links microarray experimental testing and network training conditions to predict gene perturbations for a drug with a known mechanism of action in a well-studied organism.ResultsS. cerevisiae cells were treated with the antifungal, fluconazole, and expression profiling was conducted under different biological conditions using Affymetrix genome-wide microarrays. Transcripts were filtered with a formal network-based method, sparse simultaneous equation models and Lasso regression (SSEM-Lasso), under different network training conditions. Gene expression results were evaluated using both gene set and single gene target analyses, and the drug’s transcriptional effects were narrowed first by pathway and then by individual genes. Variables included: (i) Testing conditions – exposure time and concentration and (ii) Network training conditions – training compendium modifications. Two analyses of SSEM-Lasso output – gene set and single gene – were conducted to gain a better understanding of how SSEM-Lasso predicts perturbation targets.ConclusionsThis study demonstrates that genome-wide microarrays can be optimized using a two-stage strategy for a more in-depth understanding of how a cell manifests biological reactions to a drug treatment at the transcription level. Additionally, a more detailed understanding of how the statistical model, SSEM-Lasso, propagates perturbations through a network of gene regulatory interactions is achieved.