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Epidermal growth factor receptor EGFR major driver mutations may affect downstream molecular networks and pathways, which would influence treatment outcomes of non-small cell lung cancer (NSCLC). This study aimed to unveil profiles of mutant proteins expressed in lung adenocarcinomas of 36 patients harboring representative driver EGFR mutations (Ex19del, nine; L858R, nine; no Ex19del/L858R, 18). Surprisingly, the orthogonal partial least squares discriminant analysis performed for identified mutant proteins demonstrated the profound differences in distance among the different EGFR mutation groups, suggesting that cancer cells harboring L858R or Ex19del emerge from cellular origins different from L858R/Ex19del-negative cells. Weighted gene coexpression network analysis, together with over-representative analysis, identified 18 coexpressed modules and their eigen proteins. Pathways enriched differentially for both the L858R and Ex19del mutations included carboxylic acid metabolic process, cell cycle, developmental biology, cellular responses to stress, mitotic prophase, cell proliferation, growth, epithelial to mesenchymal transition (EMT), and immune system. The IPA causal network analysis identified the highly activated networks of PARPBP, HOXA1, and APH1 under the L858R mutation, whereas those of ASGR1, APEX1, BUB1, and MAPK10 were highly activated under the Ex19del mutation. Interestingly, the downregulated causal network of osimertinib intervention showed the highest significance in overlap p-value among most causal networks predicted under the L858R mutation. We also identified the causal network of MAPK interacting serine/threonine kinase 1/2 (MNK1/2) highly activated differentially under the L858R mutation. Tumor-suppressor AMOT, a component of the Hippo pathways, was highly inhibited commonly under both L858R and Ex19del mutations. Our results could identify disease-related protein molecular networks from the landscape of single amino acid variants. Our findings may help identify potential therapeutic targets and develop therapeutic strategies to improve patient outcomes.

Neural decoding and neuromodulation technologies hold great promise for treating mood and other brain disorders in next-generation therapies that manipulate functional brain networks. Here we perform a novel causal network analysis to decode multiregional communication in the primate mood processing network and determine how neuromodulation, short-burst tetanic microstimulation (sbTetMS), alters multiregional network communication. The causal network analysis revealed a mechanism of network excitability that regulates when a sender stimulation site communicates with receiver sites. Decoding network excitability from neural activity at modulator sites predicted sender-receiver communication, whereas sbTetMS neuromodulation temporarily disrupted sender-receiver communication. These results reveal specific network mechanisms of multiregional communication and suggest a new generation of brain therapies that combine neural decoding to predict multiregional communication with neuromodulation to disrupt multiregional communication.

Fatty acids are important sources of energy and possible predictors and etiologic factors in many common complex pathologies such as cardiovascular disease, diabetes, and certain forms of cancers. While fatty acids are thought to covary with each other, their underlying causal networks have not been fully elucidated. This study reports the identification and analysis of a statistical causal network among 15 mostly long-chain fatty acids. In an African-American population sample and using the Genome granularity-Directed Acyclic Graph (GDAG) algorithm, we determined directions or causal relationships in the fatty acid metabolome. A directed causal network was constructed that revealed 29 significant edges among the 15 nodes (p

Dynamics as well as localization of Ca2+ transients plays a vital role in liver function under homeostatic conditions, repair, and disease. In response to circulating hormonal stimuli, hepatocytes exhibit intracellular Ca2+ responses that propagate through liver lobules in a wave-like fashion. Although intracellular processes that control cell autonomous Ca2+ spiking behavior have been studied extensively, the intra- and inter-cellular signaling factors that regulate lobular scale spatial patterns and wave-like propagation of Ca2+ remain to be determined. To address this need, we acquired images of cytosolic Ca2+ transients in 1300 hepatocytes situated across several mouse liver lobules over a period of 1600 seconds. We analyzed this time series data using correlation network analysis, causal network analysis, and computational modeling, to characterize the spatial distribution of heterogeneity in intracellular Ca2+ signaling components as well as intercellular interactions that control lobular scale Ca2+ waves. Our causal network analysis revealed that hepatocytes are causally linked to multiple other co-localized hepatocytes, but these influences are not necessarily aligned uni-directionally along the sinusoids. Our computational model-based analysis showed that spatial gradients of intracellular Ca2+ signaling components as well as intercellular molecular exchange are required for lobular scale propagation of Ca2+ waves. Additionally, our analysis suggested that causal influences of hepatocytes on Ca2+ responses of multiple neighbors lead to robustness of Ca2+ wave propagation through liver lobules.

The understanding of the mechanisms associated with the action of chemotherapeutic agents is fundamental to assess and account for possible side-effects of such treatments. Casiope'inas have demonstrated a cytotoxic effect by activation of pro-apoptotic processes in malignant cells. Such processes have been proved to activate the apoptotic intrinsic route, as well as cell cycle arrest. Despite this knowledge, the whole mechanism of action of Casiope'inas is yet to be completely understood. In this work, we implement a systems biology approach based on two pathway analysis tools (Over-Representation Analysis and Causal Network Analysis), to observe changes in some hallmarks of cancer, induced by this copper-based chemotherapeutic agent in HeLa cell lines. We find that the metabolism of metal ions is exacerbated, as well as cell division processes being globally diminished. We also show that cellular migration and proliferation events are decreased. Moreover, the molecular mechanisms of liver protection are increased in the cell cultures under the actions of Casiope'inas, unlike the case in many other cytotoxic drugs. We argue that this chemotherapeutic agent may be promising, given its protective hepatic function, concomitant with its cytotoxic participation in the onset of apoptotic processes in malignant cells.

Introduction Plasma triglyceride levels are a risk factor for coronary heart disease. Triglyceride metabolism is well characterized, but challenges remain to identify novel paths to lower levels. A metabolomics analysis may help identify such novel pathways and, therefore, provide hints about new drug targets. Objectives In an observational study, causal relationships in the metabolomics level of granularity are taken into account to distinguish metabolites and pathways having a direct effect on plasma triglyceride levels from those which are only associated with or have indirect effect on triglyceride. Method The analysis began by leveraging near-complete information from the genome level of granularity using the GDAG algorithm to identify a robust causal network over 122 metabolites in an upper level of granularity. Knowing the metabolomics causal relationships, we enter the triglyceride variable in the model to identify metabolites with direct effect on plasma triglyceride levels. We carried out the same analysis on triglycerides measured over five different visits spanning 24 years. Result Nine metabolites out of 122 metabolites under consideration influenced directly plasma triglyceride levels. Given these nine metabolites, the rest of metabolites in the study do not have a significant effect on triglyceride levels at significance level alpha = 0.001. Therefore, for the further analysis and interpretations about triglyceride levels, the focus should be on these nine metabolites out of 122 metabolites in the study. The metabolites with the strongest effects at the baseline visit were arachidonate and carnitine, followed by 9-hydroxy-octadecadenoic acid and palmitoylglycerophosphoinositol. The influence of arachidonate on triglyceride levels remained significant even at the fourth visit, which was 10 years after the baseline visit. Conclusion These results demonstrate the utility of integrating multi-omics data in a granularity framework to identify novel candidate pathways to lower risk factor levels. Electronic supplementary material The online version of this article (doi:10.1007/s11306-016-1045-2) contains supplementary material, which is available to authorized users.