From systems biology to P4 medicine: applications in respiratory medicine

Network Pharmacology-driven therapeutic interventions for Interstitial Lung Diseases using Traditional medicines: A Narrative Review

This review explores the progressive domain of network pharmacology and its potential to revolutionize therapeutic approaches for Interstitial Lung Diseases (ILDs), a collective term encompassing Interstitial Pneumonia, Pneumoconiosis, Connective Tissue Disease-related ILDs, and Sarcoidosis. The exploration focuses on the profound legacy of traditional medicines, particularly Ayurveda and Traditional Chinese Medicines (TCM), and their largely unexplored capacity in ILD treatment. These ancient healing systems, characterized by their holistic methodologies and multifaceted treatment modalities, offer a promising foundation for discovering innovative therapeutic strategies. Moreover, the review underscores the amalgamation of artificial intelligence (AI) and machine learning (ML) methodologies with bioinformatics, creating a computational synergy capable of deciphering the intricate biological networks associated with ILDs. Network pharmacology has tailored the hypothesis from the conventional "one target, one drug" towards a "network target, multi-component therapeutics" approach. The fusion of traditional literature and computational technology can unveil novel drugs, targets, and pathways, augmenting effective therapies and diminishing adverse effects related to current medications. In conclusion, this review provides a comprehensive exposition of how Network Pharmacology tools can leverage the insights of Ayurveda and TCM to craft efficacious therapeutic solutions for ILDs. It sets the stage for future investigations in this captivating interdisciplinary domain, validating the use of traditional medicines worldwide.

Pathophysiology and genomics of bronchiectasis

Bronchiectasis is a complex and heterogeneous inflammatory chronic respiratory disease with an unknown cause in around 30-40% of patients. The presence of airway infection together with chronic inflammation, airway mucociliary dysfunction and lung damage are key components of the vicious vortex model that better describes its pathophysiology. Although bronchiectasis research has significantly increased over the past years and different endotypes have been identified, there are still major gaps in the understanding of the pathophysiology. Genomic approaches may help to identify new endotypes, as has been shown in other chronic airway diseases, such as COPD.Different studies have started to work in this direction, and significant contributions to the understanding of the microbiome and proteome diversity have been made in bronchiectasis in recent years. However, the systematic application of omics approaches to identify new molecular insights into the pathophysiology of bronchiectasis (endotypes) is still limited compared with other respiratory diseases.Given the complexity and diversity of these technologies, this review describes the key components of the pathophysiology of bronchiectasis and how genomics can be applied to increase our knowledge, including the study of new techniques such as proteomics, metabolomics and epigenomics. Furthermore, we propose that the novel concept of trained innate immunity, which is driven by microbiome exposures leading to epigenetic modifications, can complement our current understanding of the vicious vortex. Finally, we discuss the challenges, opportunities and implications of genomics application in clinical practice for better patient stratification into new therapies.

Galectin-10 in serum extracellular vesicles reflects asthma pathophysiology

BACKGROUND

Novel biomarkers (BMs) are urgently needed for bronchial asthma (BA) with various phenotypes and endotypes.

OBJECTIVE

We sought to identify novel BMs reflecting tissue pathology from serum extracellular vesicles (EVs).

METHODS

We performed data-independent acquisition of serum EVs from 4 healthy controls, 4 noneosinophilic asthma (NEA) patients, and 4 eosinophilic asthma (EA) patients to identify novel BMs for BA. We confirmed EA-specific BMs via data-independent acquisition validation in 61 BA patients and 23 controls. To further validate these findings, we performed data-independent acquisition for 6 patients with chronic rhinosinusitis without nasal polyps and 7 patients with chronic rhinosinusitis with nasal polyps.

RESULTS

We identified 3032 proteins, 23 of which exhibited differential expression in EA. Ingenuity pathway analysis revealed that protein signatures from each phenotype reflected disease characteristics. Validation revealed 5 EA-specific BMs, including galectin-10 (Gal10), eosinophil peroxidase, major basic protein, eosinophil-derived neurotoxin, and arachidonate 15-lipoxygenase. The potential of Gal10 in EVs was superior to that of eosinophils in terms of diagnostic capability and detection of airway obstruction. In rhinosinusitis patients, 1752 and 8413 proteins were identified from EVs and tissues, respectively. Among 11 BMs identified in EVs and tissues from patients with chronic rhinosinusitis with nasal polyps, 5 (including Gal10 and eosinophil peroxidase) showed significant correlations between EVs and tissues. Gal10 release from EVs was implicated in eosinophil extracellular trapped cell death in vitro and in vivo.

CONCLUSION

Novel BMs such as Gal10 from serum EVs reflect disease pathophysiology in BA and may represent a new target for liquid biopsy approaches.

New management strategies for primary headache disorders: Insights from P4 medicine

Primary headache disorder is the main cause of headache attacks, leading to significant disability and impaired quality of life. This disorder is increasingly recognized as a heterogeneous condition with a complex network of genetic, environmental, and lifestyle factors. However, the timely diagnosis and effective treatment of these headaches remain challenging. Precision medicine is a potential strategy based on P4 (predictive, preventive, personalized, and participatory) medicine that may bring new insights for headache care. Recent machine learning advances and widely available molecular biology and imaging data have increased the usefulness of this medical strategy. Precision medicine emphasizes classifying headaches according to their risk factors, clinical presentation, and therapy responsiveness to provide individualized headache management. Furthermore, early preventive strategies, mainly utilizing predictive tools, are critical in reducing headache attacks and improving the quality of life of individuals with headaches. The current review comprehensively discusses the potential application value of P4 medicine in headache management.

Ayurgenomics-based frameworks in precision and integrative medicine: Translational opportunities

In today's globalized and flat world, a patient can access and seek multiple health and disease management options. A digitally enabled participatory framework that allows an evidence-based informed choice is likely to assume an immense importance in the future. In India, traditional knowledge systems, like Ayurveda, coexist with modern medicine. However, due to limited crosstalk between the clinicians of both disciplines, a patient attempts integrative medicine by seeking both options independently with limited understanding and evidence. There is a need for an integrative medicine platform with a formalized approach, which allows practitioners from the two diverse systems to crosstalk, coexist, and coevolve for an informed cross-referral that benefits the patients. To be successful, this needs frameworks that enable the bridging of disciplines through a common interface with shared ontologies. Ayurgenomics is an emerging discipline that explores the principles and practices of Ayurveda combined with genomics approaches for mainstream integration. The present review highlights how in conjunction with different disciplines and technologies this has provided frameworks for (1) the discovery of molecular correlates to build ontological links between the two systems, (2) the discovery of biomarkers and targets for early actionable interventions, (3) understanding molecular mechanisms of drug action from its usage perspective in Ayurveda with applications in repurposing, (4) understanding the network and P4 medicine perspective of Ayurveda through a common organizing principle, (5) non-invasive stratification of healthy and diseased individuals using a compendium of system-level phenotypes, and (6) developing evidence-based solutions for practice in integrative medicine settings. The concordance between the two contrasting streams has been built through extensive explorations and iterations of the concepts of Ayurveda and genomic observations using state-of-the-art technologies, computational approaches, and model system studies. These highlight the enormous potential of a trans-disciplinary approach in evolving solutions for personalized interventions in integrative medicine settings.

Functional characterization of nutraceuticals using spectral clustering: Centrality of caveolae-mediated endocytosis for management of nitric oxide and vitamin D deficiencies and atherosclerosis

It is well recognized that redox imbalance, nitric oxide (NO), and vitamin D deficiencies increase risk of cardiovascular, metabolic, and infectious diseases. However, clinical studies assessing efficacy of NO and vitamin D supplementation have failed to produce unambiguous efficacy outcomes suggesting that the understanding of the pharmacologies involved is incomplete. This raises the need for using systems pharmacology tools to better understand cause-effect relationships at biological systems levels. We describe the use of spectral clustering methodology to analyze protein network interactions affected by a complex nutraceutical, Cardio Miracle (CM), that contains arginine, citrulline, vitamin D, and antioxidants. This examination revealed that interactions between protein networks affected by these substances modulate functions of a network of protein complexes regulating caveolae-mediated endocytosis (CME), TGF beta activity, vitamin D efficacy and host defense systems. Identification of this regulatory scheme and the working of embedded reciprocal feedback loops has significant implications for treatment of vitamin D deficiencies, atherosclerosis, metabolic and infectious diseases such as COVID-19.

Molecular Modelling Hurdle in the Next-Generation Sequencing Era

There are challenges in the genetic diagnosis of rare diseases, and pursuing an optimal strategy to identify the cause of the disease is one of the main objectives of any clinical genomics unit. A range of techniques are currently used to characterize the genomic variability within the human genome to detect causative variants of specific disorders. With the introduction of next-generation sequencing (NGS) in the clinical setting, geneticists can study single-nucleotide variants (SNVs) throughout the entire exome/genome. In turn, the number of variants to be evaluated per patient has increased significantly, and more information has to be processed and analyzed to determine a proper diagnosis. Roughly 50% of patients with a Mendelian genetic disorder are diagnosed using NGS, but a fair number of patients still suffer a diagnostic odyssey. Due to the inherent diversity of the human population, as more exomes or genomes are sequenced, variants of uncertain significance (VUSs) will increase exponentially. Thus, assigning relevance to a VUS (non-synonymous as well as synonymous) in an undiagnosed patient becomes crucial to assess the proper diagnosis. Multiple algorithms have been used to predict how a specific mutation might affect the protein's function, but they are far from accurate enough to be conclusive. In this work, we highlight the difficulties of genomic variability determined by NGS that have arisen in diagnosing rare genetic diseases, and how molecular modelling has to be a key component to elucidate the relevance of a specific mutation in the protein's loss of function or malfunction. We suggest that the creation of a multi-omics data model should improve the classification of pathogenicity for a significant amount of the detected genomic variability. Moreover, we argue how it should be incorporated systematically in the process of variant evaluation to be useful in the clinical setting and the diagnostic pipeline.

Prioritizing Molecular Biomarkers in Asthma and Respiratory Allergy Using Systems Biology

Highly prevalent respiratory diseases such as asthma and allergy remain a pressing health challenge. Currently, there is an unmet need for precise diagnostic tools capable of predicting the great heterogeneity of these illnesses. In a previous study of 94 asthma/respiratory allergy biomarker candidates, we defined a group of potential biomarkers to distinguish clinical phenotypes (i.e. nonallergic asthma, allergic asthma, respiratory allergy without asthma) and disease severity. Here, we analyze our experimental results using complex algorithmic approaches that establish holistic disease models (systems biology), combining these insights with information available in specialized databases developed worldwide. With this approach, we aim to prioritize the most relevant biomarkers according to their specificity and mechanistic implication with molecular motifs of the diseases. The Therapeutic Performance Mapping System (Anaxomics’ TPMS technology) was used to generate one mathematical model per disease: allergic asthma (AA), non-allergic asthma (NA), and respiratory allergy (RA), defining specific molecular motifs for each. The relationship of our molecular biomarker candidates and each disease was analyzed by artificial neural networks (ANNs) scores. These analyses prioritized molecular biomarkers specific to the diseases and to particular molecular motifs. As a first step, molecular characterization of the pathophysiological processes of AA defined 16 molecular motifs: 2 specific for AA, 2 shared with RA, and 12 shared with NA. Mechanistic analysis showed 17 proteins that were strongly related to AA. Eleven proteins were associated with RA and 16 proteins with NA. Specificity analysis showed that 12 proteins were specific to AA, 7 were specific to RA, and 2 to NA. Finally, a triggering analysis revealed a relevant role for AKT1, STAT1, and MAPK13 in all three conditions and for TLR4 in asthmatic diseases (AA and NA). In conclusion, this study has enabled us to prioritize biomarkers depending on the functionality associated with each disease and with specific molecular motifs, which could improve the definition and usefulness of new molecular biomarkers.

Proteomics of serum extracellular vesicles identifies a novel COPD biomarker, fibulin-3 from elastic fibres

There is an unmet need for novel biomarkers in the diagnosis of multifactorial COPD. We applied next-generation proteomics to serum extracellular vesicles (EVs) to discover novel COPD biomarkers.

EVs from 10 patients with COPD and six healthy controls were analysed by tandem mass tag-based non-targeted proteomics, and those from elastase-treated mouse models of emphysema were also analysed by non-targeted proteomics. For validation, EVs from 23 patients with COPD and 20 healthy controls were validated by targeted proteomics.

Using non-targeted proteomics, we identified 406 proteins, 34 of which were significantly upregulated in patients with COPD. Of note, the EV protein signature from patients with COPD reflected inflammation and remodelling. We also identified 63 upregulated candidates from 1956 proteins by analysing EVs isolated from mouse models. Combining human and mouse biomarker candidates, we validated 45 proteins by targeted proteomics, selected reaction monitoring. Notably, levels of fibulin-3, tripeptidyl-peptidase 2, fibulin-1, and soluble scavenger receptor cysteine-rich domain-containing protein were significantly higher in patients with COPD. Moreover, six proteins; fibulin-3, tripeptidyl-peptidase 2, UTP-glucose-1-phosphate uridylyl transferase, CD81, CD177, and oncoprotein-induced transcript 3, were correlated with emphysema. Upregulation of fibulin-3 was confirmed by immunoblotting of EVs and immunohistochemistry in lungs. Strikingly, fibulin-3 knockout mice spontaneously developed emphysema with age, as evidenced by alveolar enlargement and elastin destruction.

We discovered potential pathogenic biomarkers for COPD using next-generation proteomics of EVs. This is a novel strategy for biomarker discovery and precision medicine.

This study identified novel biomarkers for COPD using next-generation proteomics of serum extracellular vesicles. Notably, the expression of fibulin-3 is correlated with lung function and emphysema. This could be useful for personalised medicine.https://bit.ly/2JfRCgk

Network and Systems Medicine: Position Paper of the European Collaboration on Science and Technology Action on Open Multiscale Systems Medicine

Introduction: Network and systems medicine has rapidly evolved over the past decade, thanks to computational and integrative tools, which stem in part from systems biology. However, major challenges and hurdles are still present regarding validation and translation into clinical application and decision making for precision medicine. Methods: In this context, the Collaboration on Science and Technology Action on Open Multiscale Systems Medicine (OpenMultiMed) reviewed the available advanced technologies for multidimensional data generation and integration in an open-science approach as well as key clinical applications of network and systems medicine and the main issues and opportunities for the future. Results: The development of multi-omic approaches as well as new digital tools provides a unique opportunity to explore complex biological systems and networks at different scales. Moreover, the application of findable, applicable, interoperable, and reusable principles and the adoption of standards increases data availability and sharing for multiscale integration and interpretation. These innovations have led to the first clinical applications of network and systems medicine, particularly in the field of personalized therapy and drug dosing. Enlarging network and systems medicine application would now imply to increase patient engagement and health care providers as well as to educate the novel generations of medical doctors and biomedical researchers to shift the current organ- and symptom-based medical concepts toward network- and systems-based ones for more precise diagnoses, interventions, and ideally prevention. Conclusion: In this dynamic setting, the health care system will also have to evolve, if not revolutionize, in terms of organization and management.

Sputum and serum autoantibody profiles and their clinical correlation patterns in COPD patients with and without eosinophilic airway inflammation

Background

Autoimmunity plays a role in the pathogenesis of chronic obstructive pulmonary disease (COPD). However, the autoantibody responses and their clinical correlation patterns in COPD patients with and without airway eosinophilic inflammation are unknown. The aim of this study was to compare the autoantibody profiles and their clinical associations in stable COPD patients, stratified by airway inflammatory phenotypes.

Methods

Matched sputum and serum, obtained from 62 stable COPD patients and 14 age-matched controls, were assayed for the presence of IgG and IgM antibodies against 13 autoantigens using protein array. A sputum eosinophil count ≥3% was used as cut-off value to stratify COPD patients into eosinophilic and non-eosinophilic groups. Correlation network analysis was used to evaluate the correlation patterns among autoantibody and clinical variables in each group.

Results

There were no significant differences of clinical parameters and autoantibody levels between the two COPD groups. In non-eosinophilic COPD, sputum anti-CytochromeC_IgG and anti-Aggrecan_IgM were significantly higher than those in healthy controls, and prior exacerbation was positively associated with lung function and sputum anti-Collagen-IV_IgG. While in eosinophilic COPD, sputum/serum anti-heat shock protein (HSP)47_IgG, serum anti-HSP70_IgG and serum anti-Amyloid-beta_IgG were significantly lower than those in healthy controls, and no significant correlation between prior exacerbations and lung function was found. Differences were also observed in network hubs, with the network for non-eosinophilic COPD possessing 9 hubs comprising two lung function parameters and seven autoantibodies, compared with eosinophilic COPD possessing 12 hubs all comprising autoantibodies.

Conclusions

Autoantibody responses were heterogeneous and differentially correlated with the exacerbation risk and other clinical parameters in COPD patients of different inflammatory phenotypes. These findings provide useful insight into the need for personalized management for preventing COPD exacerbations.

Molecular networks in Network Medicine: Development and applications

Network Medicine applies network science approaches to investigate disease pathogenesis. Many different analytical methods have been used to infer relevant molecular networks, including protein-protein interaction networks, correlation-based networks, gene regulatory networks, and Bayesian networks. Network Medicine applies these integrated approaches to Omics Big Data (including genetics, epigenetics, transcriptomics, metabolomics, and proteomics) using computational biology tools and, thereby, has the potential to provide improvements in the diagnosis, prognosis, and treatment of complex diseases. We discuss briefly the types of molecular data that are used in molecular network analyses, survey the analytical methods for inferring molecular networks, and review efforts to validate and visualize molecular networks. Successful applications of molecular network analysis have been reported in pulmonary arterial hypertension, coronary heart disease, diabetes mellitus, chronic lung diseases, and drug development. Important knowledge gaps in Network Medicine include incompleteness of the molecular interactome, challenges in identifying key genes within genetic association regions, and limited applications to human diseases. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Translational, Genomic, and Systems Medicine > Translational Medicine Analytical and Computational Methods > Analytical Methods Analytical and Computational Methods > Computational Methods.

The current state of omics technologies in the clinical management of asthma and allergic diseases

OBJECTIVE

To review the state of omics science specific to asthma and allergic diseases and discuss the current and potential applicability of omics in clinical disease prediction, treatment, and management.

DATA SOURCES

Studies and reviews focused on the use of omics technologies in asthma and allergic disease research and clinical management were identified using PubMed.

STUDY SELECTIONS

Publications were included based on relevance, with emphasis placed on the most recent findings.

RESULTS

Omics-based research is increasingly being used to differentiate asthma and allergic disease subtypes, identify biomarkers and pathological mediators, predict patient responsiveness to specific therapies, and monitor disease control. Although most studies have focused on genomics and transcriptomics approaches, increasing attention is being placed on omics technologies that assess the effect of environmental exposures on disease initiation and progression. Studies using omics data to identify biological targets and pathways involved in asthma and allergic disease pathogenesis have primarily focused on a specific omics subtype, providing only a partial view of the disease process.

CONCLUSION

Although omics technologies have advanced our understanding of the molecular mechanisms underlying asthma and allergic disease pathology, omics testing for these diseases are not standard of care at this point. Several important factors need to be addressed before these technologies can be used effectively in clinical practice. Use of clinical decision support systems and integration of these systems within electronic medical records will become increasingly important as omics technologies become more widely used in the clinical setting.

FNDC3B is associated with ER stress and poor prognosis in cervical cancer

Currently, the occurrence and mortality rate of cervical cancer is high, particularly in low-to-middle-income countries. Therefore, the development of novel diagnostic and treatment strategies for cervical cancer is urgently required. The aim of the present study was to assess the prognostic significance of fibronectin type III domain containing 3B (FNDC3B) expression in patients with cervical cancer and to determine the underlying mechanism of FNDC3 in tumor development. Analysis of the ONCOMINE database revealed that FNDC3B was significantly upregulated in cervical cancer tissue compared with normal tissue. Additionally, FNDC3B expression data and the clinical characteristics of patients with cervical cancer were obtained from the cBioPortal database. Correlations between FNDC3B expression and overall survival were subsequently investigated. The results revealed that increased FNDC3B expression was significantly correlated with a lower overall survival in patients with cervical cancer. A co-expression network was subsequently constructed to elucidate the function of FNDC3B in cervical cancer. Co-expression genes for FNDC3B were obtained from the cBioPortal database and were subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses. The results demonstrated that the genes were enriched in pathways associated with migration, invasion, endoplasmic reticulum (ER) stress and the unfolded protein response (UPR). Furthermore, immunofluorescence results obtained from the Human Protein Atlas revealed that the FNDC3B protein was localized to the ER. The results revealed that upregulated FNDC3B expression may be a biomarker for poor prognosis for patients with cervical cancer. Additionally, the results revealed that FNDC3B may serve an oncogenic role in cancer development via ER stress, UPR, cell migration and invasion. However, further studies are required to determine the exact molecular mechanism of FNDC3B in the development of cervical cancer and to assess its potential as a novel therapeutic target for the treatment of this disease.

System-based approaches as prognostic tools for glioblastoma

BACKGROUND

The evasion of apoptosis is a hallmark of cancer. Understanding this process holistically and overcoming apoptosis resistance is a goal of many research teams in order to develop better treatment options for cancer patients. Efforts are also ongoing to personalize the treatment of patients. Strategies to confirm the therapeutic efficacy of current treatments or indeed to identify potential novel additional options would be extremely beneficial to both clinicians and patients. In the past few years, system medicine approaches have been developed that model the biochemical pathways of apoptosis. These systems tools incorporate and analyse the complex biological networks involved. For their successful integration into clinical practice, it is mandatory to integrate systems approaches with routine clinical and histopathological practice to deliver personalized care for patients.

RESULTS

We review here the development of system medicine approaches that model apoptosis for the treatment of cancer with a specific emphasis on the aggressive brain cancer, glioblastoma.

CONCLUSIONS

We discuss the current understanding in the field and present new approaches that highlight the potential of system medicine approaches to influence how glioblastoma is diagnosed and treated in the future.

A Systems Biology Approach for Personalized Medicine in Refractory Epilepsy

Epilepsy refers to a common chronic neurological disorder that affects all age groups. Unfortunately, antiepileptic drugs are ineffective in about one-third of patients. The complex interindividual variability influences the response to drug treatment rendering the therapeutic failure one of the most relevant problems in clinical practice also for increased hospitalizations and healthcare costs. Recent advances in the genetics and neurobiology of epilepsies are laying the groundwork for a new personalized medicine, focused on the reversal or avoidance of the pathophysiological effects of specific gene mutations. This could lead to a significant improvement in the efficacy and safety of treatments for epilepsy, targeting the biological mechanisms responsible for epilepsy in each individual. In this review article, we focus on the mechanism of the epilepsy pharmacoresistance and highlight the use of a systems biology approach for personalized medicine in refractory epilepsy.

Application of machine learning in rheumatic disease research

Over the past decade, there has been a paradigm shift in how clinical data are collected, processed and utilized. Machine learning and artificial intelligence, fueled by breakthroughs in high-performance computing, data availability and algorithmic innovations, are paving the way to effective analyses of large, multi-dimensional collections of patient histories, laboratory results, treatments, and outcomes. In the new era of machine learning and predictive analytics, the impact on clinical decision-making in all clinical areas, including rheumatology, will be unprecedented. Here we provide a critical review of the machine-learning methods currently used in the analysis of clinical data, the advantages and limitations of these methods, and how they can be leveraged within the field of rheumatology.

Predictive, Personalized, Preventive and Participatory (4P) Medicine Applied to Telemedicine and eHealth in the Literature

The main objective of this work is to provide a review of existing research work into predictive, personalized, preventive and participatory medicine in telemedicine and ehealth. The academic databases used for searches are IEEE Xplore, PubMed, Science Direct, Web of Science and ResearchGate, taking into account publication dates from 2010 up to the present day. These databases cover the greatest amount of information on scientific texts in multidisciplinary fields, from engineering to medicine. Various search criteria were established, such as ("Predictive" OR "Personalized" OR "Preventive" OR "Participatory") AND "Medicine" AND ("eHealth" OR "Telemedicine") selecting the articles of most interest. A total of 184 publications about predictive, personalized, preventive and participatory (4P) medicine in telemedicine and ehealth were found, of which 48 were identified as relevant. Many of the publications found show how the P4 medicine is being developed in the world and the benefits it provides for patients with different illnesses. After the revision that was undertaken, it can be said that P4 medicine is a vital factor for the improvement of medical services. It is hoped that one of the main contributions of this study is to provide an insight into how P4 medicine in telemedicine and ehealth is being applied, as well as proposing outlines for the future that contribute to the improvement of prevention and prediction of illnesses.

Recent Advances on the Molecular Mechanism of Cervical Carcinogenesis Based on Systems Biology Technologies

Cervical cancer is one of the common malignancies in women worldwide. Exploration of pathogenesis and molecular mechanism of cervical cancer is pivotal for development of effective treatment for this disease. Recently, systems biology approaches based on high-throughput technologies have been carried out to investigate the expression of some genes and proteins in genomics, transcriptomics, proteomics, and metabonomics of cervical cancer. Compared with traditional methods，systems biology technology has been shown to provide large of information regarding prognostic biomarkers and therapeutic targets for cervical cancer. These molecular signatures from system biology technology could be useful to understand the molecular mechanisms of cervical cancer development and progression, and help physicians to design targeted therapeutic strategies for patients with cervical cancer.

Synthetic consortia of nanobody-coupled and formatted bacteria for prophylaxis and therapy interventions targeting microbiome dysbiosis-associated diseases and co-morbidities

Designed nanobody-linked synthetic consortia for microbiota dysbiosis therapies. A. Nanobodies (Nb) are selected for specific antigens on target bacteria destined for a synthetic therapy consortium that may consist of two (B) or multiple (C) members. For the treatment of dysbiosis co-morbidities requiring two functionally distinct consortia, these may be linked through a common member to generate a single bi-functional microbiota therapy (D).

Insights into respiratory disease through bioinformatics

Respiratory diseases such as asthma, chronic obstructive pulmonary disease and lung cancer represent a critical area for medical research as millions of people are affected globally. The development of new strategies for treatment and/or prevention, and the identification of biomarkers for patient stratification and early detection of disease inception are essential to reducing the impact of lung diseases. The successful translation of research into clinical practice requires a detailed understanding of the underlying biology. In this regard, the advent of next-generation sequencing and mass spectrometry has led to the generation of an unprecedented amount of data spanning multiple layers of biological regulation (genome, epigenome, transcriptome, proteome, metabolome and microbiome). Dealing with this wealth of data requires sophisticated bioinformatics and statistical tools. Here, we review the basic concepts in bioinformatics and genomic data analysis and illustrate the application of these tools to further our understanding of lung diseases. We also highlight the potential for data integration of multi-omic profiles and computational drug repurposing to define disease subphenotypes and match them to targeted therapies, paving the way for personalized medicine.