The Library of Integrated Network-Based Cellular Signatures (LINCS) is an NIH Common Fund program that catalogs how human cells globally respond to chemical, genetic, and disease perturbations. Resources generated by LINCS include experimental and computational methods, visualization tools, molecular and imaging data, and signatures. By assembling an integrated picture of the range of responses of human cells exposed to many perturbations, the LINCS program aims to better understand human disease and to advance the development of new therapies. Perturbations under study include drugs, genetic perturbations, tissue micro-environments, antibodies, and disease-causing mutations. Responses to perturbations are measured by transcript profiling, mass spectrometry, cell imaging, and biochemical methods, among other assays. The LINCS program focuses on cellular physiology shared among tissues and cell types relevant to an array of diseases, including cancer, heart disease, and neurodegenerative disorders. This Perspective describes LINCS technologies, datasets, tools, and approaches to data accessibility and reusability.

We have previously shown that hepatitis B virus (HBV) replication is inhibited noncytopathically in the livers of transgenic mice following injection of HBV-specific cytotoxic T lymphocytes (CTLs) or infection with unrelated hepatotropic viruses, including lymphocytic choriomeningitis virus (LCMV) and adenovirus. These effects are mediated by gamma interferon (IFN␥), tumor necrosis factor alpha (TNF␣), and IFN␣/␤. In the present study, we crossed HBV transgenic mice with mice genetically deficient for IFN␥ (IFN␥KO), the TNF␣ receptor (TNF␣RKO), or the IFN␣/␤ receptor (IFN␣/␤RKO) in order to determine the relative contribution of each cytokine to the antiviral effects observed in each of these systems. Interestingly, we showed that HBV replicates in unmanipulated IFN␥KO and IFN␣/␤RKO mice at levels higher than those observed in control mice, implying that baseline levels of these cytokines control HBV replication in the absence of inflammation. We also showed that IFN␥ mediates most of the antiviral effect of the CTLs while IFN␣/␤ is primarily responsible for the early inhibitory effect of LCMV and adenovirus on HBV replication. In addition, we showed that the hepatic induction of IFN␣/␤ observed after injection of poly(I ⅐ C) is sufficient to inhibit HBV replication and that a similar antiviral effect is achieved by systemic administration of very high doses of IFN␣. We also compared the relative sensitivity of LCMV and adenovirus to control by IFN␥, TNF␣, or IFN␣/␤ in these animals. Importantly, IFN␣/␤RKO mice, and to a lesser extent IFN␥KO mice, showed higher hepatic levels of LCMV RNA and adenovirus DNA and RNA than control mice, underscoring the importance of both interferons in controlling these other viral infections as well.Hepatitis B virus (HBV) is a noncytopathic, enveloped virus that causes acute and chronic hepatitis and hepatocellular carcinoma (4). We have previously shown that the intrahepatic induction of gamma interferon (IFN␥), tumor necrosis factor alpha (TNF␣), and IFN␣/␤ downregulates HBV replication noncytopathically in the livers of transgenic mice (8, 9). This antiviral effect can be achieved by injecting transgenic mice with HBV-specific cytotoxic T lymphocytes (CTLs) (10) or infecting them with an unrelated hepatotropic virus, such as lymphocytic choriomeningitis virus (LCMV) or adenovirus (3, 7).The CTL-dependent effect occurs within 24 h and appears to be mediated by both IFN␥ and TNF␣, since it is possible to block the regulatory effects of the CTLs by the prior administration of a cocktail of antibodies to these cytokines (10). Whether the antiviral cytokines are produced by the passively transferred CTLs or by host-derived cells is unknown.The LCMV-and adenovirus-dependent effect occurs in two distinct phases. The first phase occurs within 12 to 24 h and is mediated by IFN␣/␤ and/or TNF␣ induced by the infecting virus, since it is blocked by a cocktail of antibodies to these cytokines (3). The second phase occurs 5 to 7 days after infection and is associated with the intrahepatic i...

We have previously shown that interferon and tumor necrosis factor noncytopathically abolish hepatitis B virus (HBV) replication from the hepatocyte and kidney tubular epithelial cells in vivo. Here we show that a persistent lymphocytic choriomeningitis virus (LCMV) infection is cleared from the hepatocyte noncytopathically when the same cytokines are induced in the liver by antigen-nonspecific stimuli. These results indicate that, like HBV, LCMV is also susceptible to intracellular inactivation by cytokine-induced antiviral mechanisms that are operative in the hepatocyte. In contrast, LCMV is not cleared from intrahepatic nonparenchymal cells or splenocytes, indicating that, unlike the hepatocyte, these cells do not produce the factors required to inactivate LCMV. Antiviral mechanisms like these may have evolved to maintain the functional integrity of vital organs in the face of massive infection.

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Most cancer cells harbor multiple drivers whose epistasis and interactions with expression context clouds drug and drug combination sensitivity prediction. We constructed a mechanistic computational model that is context-tailored by omics data to capture regulation of stochastic proliferation and death by pan-cancer driver pathways. Simulations and experiments explore how the coordinated dynamics of RAF/MEK/ERK and PI-3K/AKT kinase activities in response to synergistic mitogen or drug combinations control cell fate in a specific cellular context. In this MCF10A cell context, simulations suggest that synergistic ERK and AKT inhibitor-induced death is likely mediated by BIM rather than BAD, which is supported by prior experimental studies. AKT dynamics explain S-phase entry synergy between EGF and insulin, but simulations suggest that stochastic ERK, and not AKT, dynamics seem to drive cell-to-cell proliferation variability, which in simulations is predictable from pre-stimulus fluctuations in C-Raf/B-Raf levels. Simulations suggest MEK alteration negligibly influences transformation, consistent with clinical data. Tailoring the model to an alternate cell expression and mutation context, a glioma cell line, allows prediction of increased sensitivity of cell death to AKT inhibition. Our model mechanistically interprets context-specific landscapes between driver pathways and cell fates, providing a framework for designing more rational cancer combination therapy.