Protein-protein interaction network analysis in chronic obstructive pulmonary disease

Knockdown of lncRNA PCAT6 suppresses the growth of non-small cell lung cancer cells by inhibiting macrophages M2 polarization via miR-326/KLF1 axis

Non-small cell lung cancer (NSCLC) is the most common malignant tumor of lung, which seriously threatens the life of people. It has been reported that lncRNA prostate cancer-associated transcript 6 (PCAT6) could facilitate the metastasis of NSCLC cells. However, whether lncRNA PCAT6 in NSCLC cells could affect the tumor microenvironment (TME) remains unclear. In the present study, the level of PCAT6 in NSCLC cells was detected using RT-qPCR. The effects of PCAT6 knockdown on the viability and apoptosis in NSCLC cells were detected with CCK-8 and flow cytometry assay. NSCLC cell-derived exosomes were isolated with ultracentrifugation. Next, transwell assay was conducted to assess the migration and invasion of NSCLC cells. Dual-luciferase reporter assay was performed to verify the relationship among PCAT6, miR-326, and KLF1 in A549 cells. In addition, nanoparticle tracking analysis (NTA) was applied to detect the particle size of isolated exosomes. Moreover, ELISA assay was performed to detect the levels of IL-1β and IL-10 in the supernatant of macrophage. We found knockdown of PCAT6 significantly inhibited the viability, migration, and invasion of NSCLC cells. In addition, dual-luciferase reporter assay illustrated that miR-326 was the target of PCAT6 and KLF1 was the target of miR-326 in NSCLC cells. Moreover, NSCLC cells-derived exosomes could promote macrophages M2 polarization by transporting PCAT6. Meanwhile, macrophages M2 polarization was able to promote the metastasis and epithelial-mesenchymal transition (EMT) process of NSCLC cells via regulating PCAT6/miR-326/KLF1 axis. Taken together, knockdown of lncRNA PCAT6 suppressed the growth of NSCLC cells by inhibiting macrophages M2 polarization via miR-326/KLF1 axis.

A review of connectivity map and computational approaches in pharmacogenomics

Large-scale perturbation databases, such as Connectivity Map (CMap) or Library of Integrated Network-based Cellular Signatures (LINCS), provide enormous opportunities for computational pharmacogenomics and drug design. A reason for this is that in contrast to classical pharmacology focusing at one target at a time, the transcriptomics profiles provided by CMap and LINCS open the door for systems biology approaches on the pathway and network level. In this article, we provide a review of recent developments in computational pharmacogenomics with respect to CMap and LINCS and related applications.

Airway remodeling: Systems biology approach, from bench to bedside

Airway Remodeling, a patho-physiologic process, is considered as a key feature of chronic airway diseases. In recent years, our understanding of the complex diseases has increased significantly by the use of combined approaches, including systems biology, which may contribute to the development of personalized and predictive medicine approaches. Integrative analysis, along with the cooperation of clinicians, computer scientists, research scientists, and bench scientists, has become an important part of the experimental design and therapeutic strategies in the era of omics. The airway remodeling process is the result of the dysregulation of several signaling pathways that modulate the airway regeneration; therefore, high-throughput experiments and systems biology approach can help to understand this process better. The study reviews related literature and is consistent with the existing clinical evidence.

Systems Medicine: from molecular features and models to the clinic in COPD

Background and hypothesis

Chronic Obstructive Pulmonary Disease (COPD) patients are characterized by heterogeneous clinical manifestations and patterns of disease progression. Two major factors that can be used to identify COPD subtypes are muscle dysfunction/wasting and co-morbidity patterns. We hypothesized that COPD heterogeneity is in part the result of complex interactions between several genes and pathways. We explored the possibility of using a Systems Medicine approach to identify such pathways, as well as to generate predictive computational models that may be used in clinic practice.

Objective and method

Our overarching goal is to generate clinically applicable predictive models that characterize COPD heterogeneity through a Systems Medicine approach. To this end we have developed a general framework, consisting of three steps/objectives: (1) feature identification, (2) model generation and statistical validation, and (3) application and validation of the predictive models in the clinical scenario. We used muscle dysfunction and co-morbidity as test cases for this framework.

Results

In the study of muscle wasting we identified relevant features (genes) by a network analysis and generated predictive models that integrate mechanistic and probabilistic models. This allowed us to characterize muscle wasting as a general de-regulation of pathway interactions. In the co-morbidity analysis we identified relevant features (genes/pathways) by the integration of gene-disease and disease-disease associations. We further present a detailed characterization of co-morbidities in COPD patients that was implemented into a predictive model. In both use cases we were able to achieve predictive modeling but we also identified several key challenges, the most pressing being the validation and implementation into actual clinical practice.

Conclusions

The results confirm the potential of the Systems Medicine approach to study complex diseases and generate clinically relevant predictive models. Our study also highlights important obstacles and bottlenecks for such approaches (e.g. data availability and normalization of frameworks among others) and suggests specific proposals to overcome them.

Synergy-COPD: a systems approach for understanding and managing chronic diseases

Chronic diseases (CD) are generating a dramatic societal burden worldwide that is expected to persist over the next decades. The challenges posed by the epidemics of CD have triggered a novel health paradigm with major consequences on the traditional concept of disease and with a profound impact on key aspects of healthcare systems. We hypothesized that the development of a systems approach to understand CD together with the generation of an ecosystem to transfer the acquired knowledge into the novel healthcare scenario may contribute to a cost-effective enhancement of health outcomes. To this end, we designed the Synergy-COPD project wherein the heterogeneity of chronic obstructive pulmonary disease (COPD) was addressed as a use case representative of CD. The current manuscript describes main features of the project design and the strategies put in place for its development, as well the expected outcomes during the project life-span. Moreover, the manuscript serves as introductory and unifying chapter of the different papers associated to the Supplement describing the characteristics, tools and the objectives of Synergy-COPD.