Gas6/Axl signaling attenuates alveolar inflammation in ischemia-reperfusion-induced acute lung injury by up-regulating SOCS3-mediated pathway.

BACKGROUND: Axl is a cell surface receptor tyrosine kinase, and activation of the Axl attenuates inflammation induced by various stimuli. Growth arrest-specific 6 (Gas6) has high affinity for Axl receptor. The role of Gas6/Axl signaling in ischemia-reperfusion-induced acute lung injury (IR-ALI) has not been explored previously. We hypothesized that Gas6/Axl signaling regulates IR-induced alveolar inflammation via a pathway mediated by suppressor of cytokine signaling 3 (SOCS3). METHODS: IR-ALI was induced by producing 30 min of ischemia followed by 90 min of reperfusion in situ in an isolated and perfused rat lung model. The rats were randomly allotted to a control group and IR groups, which were treated with three different doses of Gas6. Mouse alveolar epithelium MLE-12 cells were cultured in control and hypoxia-reoxygenation (HR) conditions with or without Gas6 and Axl inhibitor R428 pretreatment. RESULTS: We found that Gas6 attenuated IR-induced lung edema, the production of proinflammatory cytokines in perfusates, and the severity of ALI ex vivo. IR down-regulated SOCS3 expression and up-regulated NF-κB, and Gas6 restored this process. In the model of MLE-12 cells with HR, Gas6 suppressed the activation of TRAF6 and NF-κB by up-regulating SOCS3. Axl expression of alveolar epithelium was suppressed in IR-ALI but Gas6 restored phosphorylation of Axl. The anti-inflammatory effect of Gas6 was antagonized by R428, which highlighted that phosphorylation of Axl mediated the protective role of Gas6 in IR-ALI. CONCLUSIONS: Gas6 up-regulates phosphorylation of Axl on alveolar epithelium in IR-ALI. The Gas6/Axl signaling activates the SOCS3-mediated pathway and attenuates IR-related inflammation and injury.

Decomposing the subclonal structure of tumors with two-way mixture models on copy number aberrations.

Multistage tumorigenesis is a dynamic process characterized by the accumulation of mutations. Thus, a tumor mass is composed of genetically divergent cell subclones. With the advancement of next-generation sequencing (NGS), mathematical models have been recently developed to decompose tumor subclonal architecture from a collective genome sequencing data. Most of the methods focused on single-nucleotide variants (SNVs). However, somatic copy number aberrations (CNAs) also play critical roles in carcinogenesis. Therefore, further modeling subclonal CNAs composition would hold the promise to improve the analysis of tumor heterogeneity and cancer evolution. To address this issue, we developed a two-way mixture Poisson model, named CloneDeMix for the deconvolution of read-depth information. It can infer the subclonal copy number, mutational cellular prevalence (MCP), subclone composition, and the order in which mutations occurred in the evolutionary hierarchy. The performance of CloneDeMix was systematically assessed in simulations. As a result, the accuracy of CNA inference was nearly 93% and the MCP was also accurately restored. Furthermore, we also demonstrated its applicability using head and neck cancer samples from TCGA. Our results inform about the extent of subclonal CNA diversity, and a group of candidate genes that probably initiate lymph node metastasis during tumor evolution was also discovered. Most importantly, these driver genes are located at 11q13.3 which is highly susceptible to copy number change in head and neck cancer genomes. This study successfully estimates subclonal CNAs and exhibit the evolutionary relationships of mutation events. By doing so, we can track tumor heterogeneity and identify crucial mutations during evolution process. Hence, it facilitates not only understanding the cancer development but finding potential therapeutic targets. Briefly, this framework has implications for improved modeling of tumor evolution and the importance of inclusion of subclonal CNAs.

Effects of hydrocortisone on activation of inflammation via interleukin-33 in ventilator-induced lung injury.

Despite mechanical ventilation being a very important life-saving intervention, ventilator-induced lung injury (VILI) is related with inflammatory effects and causes high mortality. Our previous study demonstrated that the interleukin-33 (IL-33) cytokine pathway is a biomarker of VILI. The purpose of this study was to further explore the effects of hydrocortisone sodium succinate (HC) on pro-inflammatory IL-33 activation by VILI. The rats were intubated and received ventilation at 20 cmH2O of inspiratory pressure (PC20) by a G5 ventilator for 4 h as a control group, and an intervention group received the same inspiratory pressure as well as treated with HC at 1 mg/kg at the third hour of ventilation (PC20+HC). The hemodynamic and respiratory data showed similar changes in the two groups that were exposed to VILI. The pathophysiological results showed that the HC treatment attenuated the VILI severity. Treatment of HC increased IL-33 expression in the bronchoalveolar lavage fluid (BALF). These results demonstrated that IL-33 is involved in VILI processing and HC treatment attenuated IL-33 involvement in inflammatory activation in VILI. In conclusion, IL-33 may play an important role in VILI.

Somatic copy number alterations detected by ultra-deep targeted sequencing predict prognosis in oral cavity squamous cell carcinoma.

BACKGROUND: Ultra-deep targeted sequencing (UDT-Seq) has advanced our knowledge on the incidence and functional significance of somatic mutations. However, the utility of UDT-Seq in detecting copy number alterations (CNAs) remains unclear. With the goal of improving molecular prognostication and identifying new therapeutic targets, we designed this study to assess whether UDT-Seq may be useful for detecting CNA in oral cavity squamous cell carcinoma (OSCC). METHODS: We sequenced a panel of clinically actionable cancer mutations in 310 formalin-fixed paraffin-embedded OSCC specimens. A linear model was developed to overcome uneven coverage across target regions and multiple samples. The 5-year rates of secondary primary tumors, local recurrence, neck recurrence, distant metastases, and survival served as the outcome measures. We confirmed the prognostic significance of the CNA signatures in an independent sample of 105 primary OSCC specimens. RESULTS: The CNA burden across 10 targeted genes was found to predict prognosis in two independent cohorts. FGFR1 and PIK3CAamplifications were associated with prognosis independent of clinical risk factors. Genes exhibiting CNA were clustered in the proteoglycan metabolism, the FOXO signaling, and the PI3K-AKT signaling pathways, for which targeted drugs are already available or currently under development. CONCLUSIONS: UDT-Seq is clinically useful to identify CNA, which significantly improve the prognostic information provided by traditional clinicopathological risk factors in OSCC patients.

MicroRNAs MiR-218, MiR-125b, and Let-7g predict prognosis in patients with oral cavity squamous cell carcinoma.

MicroRNAs (miRNAs) have a major impact on regulatory networks in human carcinogenesis. In this study, we sought to investigate the prognostic significance of miRNAs in patients with oral cavity squamous cell carcinoma (OSCC). In a discovery phase, RNA was extracted from 58 OSCC tumor samples and paired normal tissues. MiRNAs expression was evaluated with TaqMan Array Card and TaqMan MicroRNA assays. The prognostic significance of the miRNA signature identified in the discovery phase was validated by qRT-PCR in a replication set consisting of 141 formalin-fixed, paraffin-embedded (FFPE) samples. We identified a miRNA regulatory network centered on the three hub genes (SP1, MYC, and TP53) that predicted distinct clinical endpoints. Three miRNAs (miR-218, miR-125b, and let-7g) and their downstream response genes had a concordant prognostic significance on disease-free survival and disease-specific survival rates. In addition, patients with a reduced expression of miR-218, miR-125b, and let-7g have a higher risk of poor outcomes in presence of specific risk factors (p-stage III-IV, pT3-4, or pN+). Our findings indicate that specific miRNAs have prognostic significance in OSCC patients and may improve prognostic stratification over traditional risk factors.

Causal inference of gene regulation with subnetwork assembly from genetical genomics data.

Deciphering the causal networks of gene interactions is critical for identifying disease pathways and disease-causing genes. We introduce a method to reconstruct causal networks based on exploring phenotype-specific modules in the human interactome and including the expression quantitative trait loci (eQTLs) that underlie the joint expression variation of each module. Closely associated eQTLs help anchor the orientation of the network. To overcome the inherent computational complexity of causal network reconstruction, we first deduce the local causality of individual subnetworks using the selected eQTLs and module transcripts. These subnetworks are then integrated to infer a global causal network using a random-field ranking method, which was motivated by animal sociology. We demonstrate how effectively the inferred causality restores the regulatory structure of the networks that mediate lymph node metastasis in oral cancer. Network rewiring clearly characterizes the dynamic regulatory systems of distinct disease states. This study is the first to associate an RXRB-causal network with increased risks of nodal metastasis, tumor relapse, distant metastases and poor survival for oral cancer. Thus, identifying crucial upstream drivers of a signal cascade can facilitate the discovery of potential biomarkers and effective therapeutic targets.

Dynamic transcript profiling of Candida albicans infection in zebrafish: a pathogen-host interaction study.

Candida albicans is responsible for a number of life-threatening infections and causes considerable morbidity and mortality in immunocompromised patients. Previous studies of C. albicans pathogenesis have suggested several steps must occur before virulent infection, including early adhesion, invasion, and late tissue damage. However, the mechanism that triggers C. albicans transformation from yeast to hyphae form during infection has yet to be fully elucidated. This study used a systems biology approach to investigate C. albicans infection in zebrafish. The surviving fish were sampled at different post-infection time points to obtain time-lapsed, genome-wide transcriptomic data from both organisms, which were accompanied with in sync histological analyses. Principal component analysis (PCA) was used to analyze the dynamic gene expression profiles of significant variations in both C. albicans and zebrafish. The results categorized C. albicans infection into three progressing phases: adhesion, invasion, and damage. Such findings were highly supported by the corresponding histological analysis. Furthermore, the dynamic interspecies transcript profiling revealed that C. albicans activated its filamentous formation during invasion and the iron scavenging functions during the damage phases, whereas zebrafish ceased its iron homeostasis function following massive hemorrhage during the later stages of infection. Most of the immune related genes were expressed as the infection progressed from invasion to the damage phase. Such global, inter-species evidence of virulence-immune and iron competition dynamics during C. albicans infection could be crucial in understanding control fungal pathogenesis.

Feature Identification of Compensatory Gene Pairs without Sequence Homology in Yeast.

Genetic robustness refers to a compensatory mechanism for buffering deleterious mutations or environmental variations. Gene duplication has been shown to provide such functional backups. However, the overall contribution of duplication-based buffering for genetic robustness is rather small. In this study, we investigated whether transcriptional compensation also exists among genes that share similar functions without sequence homology. A set of nonhomologous synthetic-lethal gene pairs was assessed by using a coexpression network, protein-protein interactions, and other types of genetic interactions in yeast. Our results are notably different from those of previous studies on buffering paralogs. The low expression similarity and the conditional coexpression alone do not play roles in identifying the functionally compensatory genes. Additional properties such as synthetic-lethal interaction, the ratio of shared common interacting partners, and the degree of coregulation were, at least in part, necessary to extract functional compensatory genes. Our network-based approach is applicable to select several well-documented cases of compensatory gene pairs and a set of new pairs. The results suggest that transcriptional reprogramming plays a limited role in functional compensation among nonhomologous genes. Our study aids in understanding the mechanism and features of functional compensation more in detail.

A novel molecular signature identified by systems genetics approach predicts prognosis in oral squamous cell carcinoma.

Molecular methods for predicting prognosis in patients with oral cavity squamous cell carcinoma (OSCC) are urgently needed, considering its high recurrence rate and tendency for metastasis. The present study investigated the genetic basis of variations in gene expression associated with poor prognosis in OSCC using Affymetrix SNP 6.0 and Affymetrix GeneChip Human Gene 1.0 ST arrays. We identified recurrent DNA amplifications scattered from 8q22.2 to 8q24.3 in 112 OSCC specimens. These amplicons demonstrated significant associations with increased incidence of extracapsular spread, development of second primary malignancies, and poor survival. Fluorescence in situ hybridization, in a validation panel consisting of 295 cases, confirmed these associations. Assessment of the effects of copy number variations (CNVs) on genome-wide variations in gene expression identified a total of 85 CNV-associated transcripts enriched in the MYC-centered regulatory network. Twenty-four transcripts associated with increased risk of second primary malignancies, tumor relapse, and poor survival. Besides MYC itself, a novel dysregulated MYC module plays a key role in OSCC carcinogenesis. This study identified a candidate molecular signature associated with poor prognosis in OSCC patients, which may ultimately facilitate patient-tailored selection of therapeutic strategies.

A genetic algorithm-based Boolean delay model of intracellular signal transduction in inflammation.

BACKGROUND: Signal transduction is the major mechanism through which cells transmit external stimuli to evoke intracellular biochemical responses. Understanding relationship between external stimuli and corresponding cellular responses, as well as the subsequent effects on downstream genes, is a major challenge in systems biology. Thus, a systematic approach to integrate experimental data and qualitative knowledge to identify the physiological consequences of environmental stimuli is needed. RESULTS: In present study, we employed a genetic algorithm-based Boolean model to represent NF-κB signaling pathway. We were able to capture feedback and crosstalk characteristics to enhance our understanding on the acute and chronic inflammatory response. Key network components affecting the response dynamics were identified. CONCLUSIONS: We designed an effective algorithm to elucidate the process of immune response using comprehensive knowledge about network structure and limited experimental data on dynamic responses. This approach can potentially be implemented for large-scale analysis on cellular processes and organism behaviors.

Computational modeling with forward and reverse engineering links signaling network and genomic regulatory responses: NF-kappaB signaling-induced gene expression responses in inflammation.

BACKGROUND: Signal transduction is the major mechanism through which cells transmit external stimuli to evoke intracellular biochemical responses. Diverse cellular stimuli create a wide variety of transcription factor activities through signal transduction pathways, resulting in different gene expression patterns. Understanding the relationship between external stimuli and the corresponding cellular responses, as well as the subsequent effects on downstream genes, is a major challenge in systems biology. Thus, a systematic approach is needed to integrate experimental data and theoretical hypotheses to identify the physiological consequences of environmental stimuli. RESULTS: We proposed a systematic approach that combines forward and reverse engineering to link the signal transduction cascade with the gene responses. To demonstrate the feasibility of our strategy, we focused on linking the NF-kappaB signaling pathway with the inflammatory gene regulatory responses because NF-kappaB has long been recognized to play a crucial role in inflammation. We first utilized forward engineering (Hybrid Functional Petri Nets) to construct the NF-kappaB signaling pathway and reverse engineering (Network Components Analysis) to build a gene regulatory network (GRN). Then, we demonstrated that the corresponding IKK profiles can be identified in the GRN and are consistent with the experimental validation of the IKK kinase assay. We found that the time-lapse gene expression of several cytokines and chemokines (TNF-alpha, IL-1, IL-6, CXCL1, CXCL2 and CCL3) is concordant with the NF-kappaB activity profile, and these genes have stronger influence strength within the GRN. Such regulatory effects have highlighted the crucial roles of NF-kappaB signaling in the acute inflammatory response and enhance our understanding of the systemic inflammatory response syndrome. CONCLUSION: We successfully identified and distinguished the corresponding signaling profiles among three microarray datasets with different stimuli strengths. In our model, the crucial genes of the NF-kappaB regulatory network were also identified to reflect the biological consequences of inflammation. With the experimental validation, our strategy is thus an effective solution to decipher cross-talk effects when attempting to integrate new kinetic parameters from other signal transduction pathways. The strategy also provides new insight for systems biology modeling to link any signal transduction pathways with the responses of downstream genes of interest.