A bottleneck in drug discovery is the identification of the molecular targets of a compound (mode of action, MoA) and of its off-target effects. Previous approaches to elucidate drug MoA include analysis of chemical structures, transcriptional responses following treatment, and text mining. Methods based on transcriptional responses require the least amount of information and can be quickly applied to new compounds. Available methods are inefficient and are not able to support network pharmacology. We developed an automatic and robust approach that exploits similarity in gene expression profiles following drug treatment, across multiple cell lines and dosages, to predict similarities in drug effect and MoA. We constructed a "drug network" of 1,302 nodes (drugs) and 41,047 edges (indicating similarities between pair of drugs). We applied network theory, partitioning drugs into groups of densely interconnected nodes (i.e., communities). These communities are significantly enriched for compounds with similar MoA, or acting on the same pathway, and can be used to identify the compoundtargeted biological pathways. New compounds can be integrated into the network to predict their therapeutic and off-target effects. Using this network, we correctly predicted the MoA for nine anticancer compounds, and we were able to discover an unreported effect for a well-known drug. We verified an unexpected similarity between cyclin-dependent kinase 2 inhibitors and Topoisomerase inhibitors. We discovered that Fasudil (a Rho-kinase inhibitor) might be "repositioned" as an enhancer of cellular autophagy, potentially applicable to several neurodegenerative disorders. Our approach was implemented in a tool (Mode of Action by NeTwoRk Analysis, MANTRA, http://mantra.tigem.it).computational drug discovery | drug repurposing | systems biology | chemotherapy

The thyroid-stimulating hormone͞thyrotropin (TSH) is the most relevant hormone in the control of thyroid gland physiology in adulthood. TSH effects on the thyroid gland are mediated by the interaction with a specific TSH receptor (TSHR). We studied the role of TSH͞TSHR signaling on gland morphogenesis and differentiation in the mouse embryo using mouse lines deprived either of TSH (pit dw ͞pit dw ) or of a functional TSHR (tshr hyt ͞tshr hyt and TSHRknockout lines). The results reported here show that in the absence of either TSH or a functional TSHR, the thyroid gland develops to a normal size, whereas the expression of thyroperoxidase and the sodium͞iodide symporter are reduced greatly. Conversely, no relevant changes are detected in the amounts of thyroglobulin and the thyroid-enriched transcription factors TTF-1, TTF-2, and Pax8. These data suggest that the major role of the TSH͞TSHR pathway is in controlling genes involved in iodide metabolism such as sodium͞iodide symporter and thyroperoxidase. Furthermore, our data indicate that in embryonic life TSH does not play an equivalent role in controlling gland growth as in the adult thyroid. T he mouse thyroid gland begins to develop at embryonic day (E)8.5 as an endodermal thickening in the floor of the primitive pharynx. After loosing all connections with the pharynx, the thyroid bud migrates caudally, reaching its final position in front of the trachea ϷE13 (1). Only after completion of migration do thyroid follicular cells begin their differentiative program and express thyroid-specific genes such as thyroglobulin (Tg), thyroid-stimulating hormone͞thyrotropin (TSH) receptor (TSHR), thyroperoxidase (TPO), and the sodium͞iodide symporter (NIS) (2). Finally, primitive follicles appear, and the gland displays its final morphological organization. Since E8.5, thyroid precursor cells express a combination of transcription factors such as TTF-1 (encoded by the titf1͞nkx2.1 gene) (3), TTF-2 (encoded by the titf2͞foxe1 gene) (4), and Pax8 (5). Gene-targeting experiments demonstrated that all these factors are required for the early stages of thyroid development (6-8). However, it still is unclear what the mechanisms are that lead to the initiation of functional differentiation that only occurs at E14.TSH is known as the main regulator of the adult thyroid gland. Indeed, after binding to its receptor, TSH stimulates the thyroid cells in almost every aspect of their metabolism including synthesis and secretion of thyroid hormones (9). Several groups have demonstrated clearly that TSH regulates mRNA levels of several thyroid-specific genes such as Tg (10-13), TPO (13-15), and NIS (16,17).TSH also stimulates the aggregation of porcine thyroid cells in follicles (18), and its presence is necessary to maintain the follicular architecture (19). In the rat, there is a temporal correlation between the increased expression of TSHR and the formation of follicles. Indeed, TSHR mRNA is expressed by E15 (3, 20), and its expression increases on E17-E18. At this stage, thyroid-speci...

Alpha-1-anti-trypsin deficiency is the most common genetic cause of liver disease in children and liver transplantation is currently the only available treatment. Enhancement of liver autophagy increases degradation of mutant, hepatotoxic alpha-1-anti-trypsin (ATZ). We investigated the therapeutic potential of liver-directed gene transfer of transcription factor EB (TFEB), a master gene that regulates lysosomal function and autophagy, in PiZ transgenic mice, recapitulating the human hepatic disease. Hepatocyte TFEB gene transfer resulted in dramatic reduction of hepatic ATZ, liver apoptosis and fibrosis, which are key features of alpha-1-anti-trypsin deficiency. Correction of the liver phenotype resulted from increased ATZ polymer degradation mediated by enhancement of autophagy flux and reduced ATZ monomer by decreased hepatic NFκB activation and IL-6 that drives ATZ gene expression. In conclusion, TFEB gene transfer is a novel strategy for treatment of liver disease of alpha-1-anti-trypsin deficiency. This study may pave the way towards applications of TFEB gene transfer for treatment of a wide spectrum of human disorders due to intracellular accumulation of toxic proteins.

We collected a massive and heterogeneous dataset of 20 255 gene expression profiles (GEPs) from a variety of human samples and experimental conditions, as well as 8895 GEPs from mouse samples. We developed a mutual information (MI) reverse-engineering approach to quantify the extent to which the mRNA levels of two genes are related to each other across the dataset. The resulting networks consist of 4 817 629 connections among 20 255 transcripts in human and 14 461 095 connections among 45 101 transcripts in mouse, with a inter-species conservation of 12%. The inferred connections were compared against known interactions to assess their biological significance. We experimentally validated a subset of not previously described protein–protein interactions. We discovered co-expressed modules within the networks, consisting of genes strongly connected to each other, which carry out specific biological functions, and tend to be in physical proximity at the chromatin level in the nucleus. We show that the network can be used to predict the biological function and subcellular localization of a protein, and to elucidate the function of a disease gene. We experimentally verified that granulin precursor (GRN) gene, whose mutations cause frontotemporal lobar degeneration, is involved in lysosome function. We have developed an online tool to explore the human and mouse gene networks.