Systems Biomedicine Research Articles

Single cell atlas reveals multilayered metabolic heterogeneity across tumour types

Metabolic reprogramming plays a pivotal role in cancer progression, contributing to substantial intratumour heterogeneity and influencing tumour behaviour. However, a systematic characterization of metabolic heterogeneity across multiple cancer types at the single-cell level remains limited. We integrated 296 tumour and normal samples spanning six common cancer types to construct a single-cell compendium of metabolic gene expression profiles and identify cell type-specific metabolic properties and reprogramming patterns. A computational approach based on non-negative matrix factorization (NMF) was utilised to identify metabolic meta-programs (MMPs) showing intratumour heterogeneity. In-vitro cell experiments were conducted to confirm the associations between MMPs and chemotherapy resistance, as well as the function of key metabolic regulators. Survival analyses were performed to assess clinical relevance of cellular metabolic properties. Our analysis revealed shared glycolysis upregulation and divergent regulation of citric acid cycle across different cell types. In malignant cells, we identified a colorectal cancer-specific MMP associated with resistance to the cuproptosis inducer elesclomol, validated through in-vitro cell experiments. Furthermore, our findings enabled the stratification of patients into distinct prognostic subtypes based on metabolic properties of specific cell types, such as myeloid cells. This study presents a nuanced understanding of multilayered metabolic heterogeneity, offering valuable insights into potential personalized therapies targeting tumour metabolism. National Key Research and Development Program of China (2021YFA1300601). National Natural Science Foundation of China (key grants 82030081 and 81874235). The Shenzhen High-level Hospital Construction Fund and Shenzhen Basic Research Key Project (JCYJ20220818102811024). The Lam Chung Nin Foundation for Systems Biomedicine.

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Research progress of CTCF in mediating 3D genome formation and regulating gene expression.

In interphase eukaryotic nuclei, chromatin is folded to form a higher-order topological structure. The spatial organization of such chromatin domain has an important impact on the regulation of gene expression. As a key architectural structural protein, CTCF (CCCTC-binding factor) plays an important role in the formation of chromatin three-dimensional chromatin structure. CTCF can also bind to many insulator elements in the genome and insulate enhancers from activating target genes via modulating remote chromatin interactions. A recent study by Dr. Chunliang Li and his team at St. Jude Children's Research Hospital in the United States showed that when CTCF was acutely degraded, significant changes were found in the three-dimensional structure of chromatin. The mechanism by which CTCF binding sites function as insulator elements was investigated by Prof. Qiang Wu's team at Institute of Systems Biomedicine and Shanghai Jiao Tong University in China and Prof. Bing Ren's team at Ludwig Institute for Cancer Research in the United States. Here we mainly review and discuss some of these latest progresses.

Systems Biomedicine of Primary and Metastatic Colorectal Cancer Reveals Potential Therapeutic Targets.

Colorectal cancer (CRC) is one of the major causes of cancer deaths across the world. Patients’ survival at time of diagnosis depends mainly on stage of the tumor. Therefore, understanding the molecular mechanisms from low-grade to high-grade stages of cancer that lead to cellular migration from one tissue/organ to another tissue/organ is essential for implementing therapeutic approaches. To this end, we performed a unique meta-analysis flowchart by identifying differentially expressed genes (DEGs) between normal, primary (primary sites), and metastatic samples (Colorectal metastatic lesions in liver and lung) in some Test datasets. DEGs were employed to construct a protein-protein interaction (PPI) network. A smaller network containing 39 DEGs was then extracted from the PPI network whose nodes expression induction or suppression alone or in combination with each other would inhibit tumor progression or metastasis. These DEGs were then verified by gene expression profiling, survival analysis, and multiple Validation datasets. We suggested for the first time that downregulation of mitochondrial genes, including ETHE1, SQOR, TST, and GPX3, would help colorectal cancer cells to produce more energy under hypoxic conditions through mechanisms that are different from “Warburg Effect”. Augmentation of given antioxidants and repression of P4HA1 and COL1A2 genes could be a choice of CRC treatment. Moreover, promoting active GSK-3β together with expression control of EIF2B would prevent EMT. We also proposed that OAS1 expression enhancement can induce the anti-cancer effects of interferon-gamma, while suppression of CTSH hinders formation of focal adhesions. ATF5 expression suppression sensitizes cancer cells to anchorage-dependent death signals, while LGALS4 induction recovers cell-cell junctions. These inhibitions and inductions would be another combinatory mechanism that inhibits EMT and cell migration. Furthermore, expression inhibition of TMPO, TOP2A, RFC3, GINS1, and CKS2 genes could prevent tumor growth. Besides, TRIB3 suppression would be a promising target for anti−angiogenic therapy. SORD is a poorly studied enzyme in cancer, found to be upregulated in CRC. Finally, TMEM131 and DARS genes were identified in this study whose roles have never been interrogated in any kind of cancer, neither as a biomarker nor curative target. All the mentioned mechanisms must be further validated by experimental wet-lab techniques.

The dynamics of TCAM integration in the Indian public health system: Medical dominance, countervailing power and co-optation

Hierarchies of power among healthcare professionals are well documented, nonetheless, power remains neglected, understudied and under-theorised in health systems analysis and policy discussions, especially in the domain of Traditional, Complementary and Alternative Medicine (TCAM). Sociological and public health scholarship has documented the persistence of medical dominance in the health system, theorised as the limitation, subordination, exclusion and incorporation of other professions. This paper explores how interprofessional power dynamics shape the integration of TCAM into Indian primary healthcare centres, as part of a nationwide policy of TCAM integration and medical pluralism implemented since 2005. We conducted interviews (n = 37) with health system administrators, nurses, pharmacists, TCAM and biomedicine doctors, and observed day-to-day activities of primary healthcare centres for six months in Odisha state, India. Thematic analysis enabled the identification of themes and exploration of sub-themes. The analysis revealed multilayered forms of medical dominance within the primary healthcare system and identified multiple sites where everyday power is mobilised. Biomedicine practitioners exercised authoritative power and restricted TCAM doctors' access to facility-level resources, i.e. financial and workforce support, which inhibited the integration policy implementation. Significantly, TCAM doctors were ‘ordered’ to practice biomedicine at primary healthcare centres, which was beyond the scope of the integration policy. However, TCAM doctors were also able to exercise countervailing power in their day-to-day activities in the primary healthcare centres and sought to assist patients' health behaviour change through their authoritative knowledge about ‘how to live a healthy life’. The health system actors involved in policy implementation hold a range of forms of power specific to the circumstances, influencing the integration processes. We explain these dynamics in relation to existing theories of medical dominance and countervailing power, while introducing a previously unreported dimension of dominance: ‘co-optation’, which enrols TCAM practitioners in the practice of biomedicine.

Translational Medicine and Exercise Prescription (TMEP): Advancing the Era of Exercise Medicine

Translational Medicine and Exercise Prescription (TMEP): Advancing the Era of Exercise Medicine

PredictProtein - Predicting Protein Structure and Function for 29 Years.

Since 1992 PredictProtein (https://predictprotein.org) is a one-stop online resource for protein sequence analysis with its main site hosted at the Luxembourg Centre for Systems Biomedicine (LCSB) and queried monthly by over 3,000 users in 2020. PredictProtein was the first Internet server for protein predictions. It pioneered combining evolutionary information and machine learning. Given a protein sequence as input, the server outputs multiple sequence alignments, predictions of protein structure in 1D and 2D (secondary structure, solvent accessibility, transmembrane segments, disordered regions, protein flexibility, and disulfide bridges) and predictions of protein function (functional effects of sequence variation or point mutations, Gene Ontology (GO) terms, subcellular localization, and protein-, RNA-, and DNA binding). PredictProtein's infrastructure has moved to the LCSB increasing throughput; the use of MMseqs2 sequence search reduced runtime five-fold (apparently without lowering performance of prediction methods); user interface elements improved usability, and new prediction methods were added. PredictProtein recently included predictions from deep learning embeddings (GO and secondary structure) and a method for the prediction of proteins and residues binding DNA, RNA, or other proteins. PredictProtein.org aspires to provide reliable predictions to computational and experimental biologists alike. All scripts and methods are freely available for offline execution in high-throughput settings.

Identification of molecular biomarkers and pathways of NSCLC: insights from a systems biomedicine perspective

BackgroundWorldwide, more than 80% of identified lung cancer cases are associated to the non-small cell lung cancer (NSCLC). We used microarray gene expression dataset GSE10245 to identify key biomarkers and associated pathways in NSCLC. ResultsTo collect Differentially Expressed Genes (DEGs) from the dataset GSE10245, we applied the R statistical language. Functional analysis was completed using the Database for Annotation Visualization and Integrated Discovery (DAVID) online repository. The DifferentialNet database was used to construct Protein–protein interaction (PPI) network and visualized it with the Cytoscape software. Using the Molecular Complex Detection (MCODE) method, we identify clusters from the constructed PPI network. Finally, survival analysis was performed to acquire the overall survival (OS) values of the key genes. One thousand eighty two DEGs were unveiled after applying statistical criterion. Functional analysis showed that overexpressed DEGs were greatly involved with epidermis development and keratinocyte differentiation; the under-expressed DEGs were principally associated with the positive regulation of nitric oxide biosynthetic process and signal transduction. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway investigation explored that the overexpressed DEGs were highly involved with the cell cycle; the under-expressed DEGs were involved with cell adhesion molecules. The PPI network was constructed with 474 nodes and 2233 connections. ConclusionsUsing the connectivity method, 12 genes were considered as hub genes. Survival analysis showed worse OS value for SFN, DSP, and PHGDH. Outcomes indicate that Stratifin may play a crucial role in the development of NSCLC.

Non-Coding RNAs in the Brain-Heart Axis: The Case of Parkinson's Disease.

Parkinson’s disease (PD) is a complex and heterogeneous disorder involving multiple genetic and environmental influences. Although a wide range of PD risk factors and clinical markers for the symptomatic motor stage of the disease have been identified, there are still no reliable biomarkers available for the early pre-motor phase of PD and for predicting disease progression. High-throughput RNA-based biomarker profiling and modeling may provide a means to exploit the joint information content from a multitude of markers to derive diagnostic and prognostic signatures. In the field of PD biomarker research, currently, no clinically validated RNA-based biomarker models are available, but previous studies reported several significantly disease-associated changes in RNA abundances and activities in multiple human tissues and body fluids. Here, we review the current knowledge of the regulation and function of non-coding RNAs in PD, focusing on microRNAs, long non-coding RNAs, and circular RNAs. Since there is growing evidence for functional interactions between the heart and the brain, we discuss the benefits of studying the role of non-coding RNAs in organ interactions when deciphering the complex regulatory networks involved in PD progression. We finally review important concepts of harmonization and curation of high throughput datasets, and we discuss the potential of systems biomedicine to derive and evaluate RNA biomarker signatures from high-throughput expression data.

A Full Bayesian Approach to Sparse Network Inference Using Heterogeneous Datasets

Network inference has been attracting increasing attention in several fields, notably systems biology and biomedicine. Indeed, one of our biggest challenges is to uncover and understand complex molecular networks behind cells and organisms. A network is mainly characterized by its topology and internal dynamics. In particular, sparse topologies with stable dynamics are properties present in most real-world networks. Moreover, experiments typically measure a partial set of nodes. Linear systems have been used as approximations of complex nonlinear systems in a wide range of applications. Although they exhibit simpler dynamics, they can easily model unmeasured nodes, e.g., via transfer functions, which is not possible for nonlinear systems. This article explores these properties and considers general linear network models. It develops a method, based on reversible jump Markov chain Monte Carlo, to estimate the confidence of all links and produce the most likely topology. Monte Carlo simulations indicate that our approach consistently produces more accurate networks compared with other state-of-the-art methods, including kernel-based methods, nonlinear ordinary-differential-equation-based methods (iCheMA), and machine-learning-based methods (dynGINIE3). We show that this is also true on a well-known biological nonlinear model. The proposed method can be used in a wide range of applications, such as systems biology and biomedicine, and fault detection and diagnosis.

A Novel Circulating 4-miRNA Prognostic Model in Diffuse Large B-Cell Lymphoma: Role of MDSCs and Th17 Cells on Lymphoma Progression

Background: MicroRNAs (miRNAs) have been emerging as prognostic biomarkers in diffuse large B-cell lymphoma (DLBCL). The aim of this study is to translate these findings into clinical prediction on lymphoma progression. Methods: Using genome-wide miRNA expression in serum samples of 20 patients with DLBCL at diagnosis, remission and at relapse, candidate miRNAs were identified to establish circulating prognostic model. DNA- and RNA-sequencing were performed on tumor samples to detect genetic mutations and signaling pathway dysregulation. Mechanism of action of miRNAs on oncogenic signaling and tumor microenvironment was analyzed in vitro and in vivo. Findings: A novel circulating 4-miRNA prognostic model (miR21, miR130b, miR155, and miR28) significantly predicted clinical outcome of DLBCL, independent on International Prognostic Index in the training cohort [n=279, HR=2.83 (95%CI 2.14-3.51), P<0.001] and in the validation cohort [n=225, NCT01852435, HR=2.71 (95%CI 1.91-3.50), P<0.001]. DNA-sequencing data showed no significant difference of tumor mutation burden between low-risk and high-risk groups of the 4-miRNA model. RNA-sequencing data showed that 4-miRNA model correlated with aberrant Ras signaling transduction. In B-lymphoma cells, modulation of the 4 miRNAs regulated IGF1 and JUN expression, resulting in alterations in MDSCs and Th17 cells. Clinically, comparing with low-risk group of the 4-miRNA model, high-risk group presented Ras signaling activation, increased MDSCs and Th17 cells, and immunosuppressive status in DLBCL. Interpretations: The easy-to-use circulating 4-miRNA prognostic model was effective to predict relapse and survival in DLBCL. Tumor microenvironment contributes to the underlying mechanism of the 4-miRNA model on DLBCL progression. Funding: This study was supported, in part, by research funding from the National Natural Science Foundation of China (81520108003, 81830007, 81670176, 81700191 and 81900193), Chang Jiang Scholars Program,Shanghai Municipal Education Commission Gaofeng Clinical Medicine Grant Support (20152206 and 20152208), Shanghai Sailing Program (19YF1430900), Clinical Research Plan of Shanghai hospital development center (SHDC, 16CR2017A), Multicenter Clinical Research Project by Shanghai Jiao Tong University School of Medicine (DLY201601), Multicenter Hematology-Oncology Programs Evaluation System (M-HOPES), Collaborative Innovation Center of Systems Biomedicine, and the Samuel Waxman Cancer Research Foundation. Declaration of Interests: All authors declare no potential conflict of interest. Ethics Approval Statement: The study was approved by the Institutional Review Boards of all M-HOPES centers with informed consent obtained from all patients in accordance with the Declaration of Helsinki.

Longitudinal serum autoantibody repertoire profiling identifies surgery-associated biomarkers in lung adenocarcinoma

Autoantibodies against tumor associated antigens are highly related to cancer progression. Autoantibodies could serve as indicators of tumor burden, and have the potential to monitor the response of treatment and tumor recurrence. However, how the autoantibody repertoire changes in response to cancer treatment are largely unknown. Sera of five lung adenocarcinoma patients before and after surgery, were collected longitudinally. These sera were analyzed on a human proteome microarray of 20,240 recombinant proteins to acquire dynamic autoantibody repertoire in response to surgery, as well as to identify the antigens with decreased antibody response after tumor excision or surgery, named as surgery-associated antigens. The identified candidate antigens were then used to construct focused microarray and validated by longitudinal sera collected from a variety of time points of the same patient and a larger cohort of 45 sera from lung adenocarcinoma patients. The autoantibody profiles are highly variable among patients. Meanwhile, the autoantibody profiles of the sera from the same patient were surprisingly stable for at least 3 months after surgery. Six surgery-associated antigens were identified and validated. All the five patients have at least one surgery-associated antigen, demonstrating this type of biomarkers is prevalent, while specific antigens are poorly shared among individuals. The prevalence of each antigen is 2%-14% according to the test with a larger cohort. To our knowledge, this is the first study of dynamically profiling of autoantibody repertoires before/after surgery of cancer patients. The high prevalence of surgery-associated antigens implies the possible broad application for monitoring of tumor recurrence in population, while the low prevalence of specific antigens allows personalized medicine. After the accumulation and analysis of more longitudinal samples, the surgery-associated serum biomarkers, combined as a panel, may be applied to alarm the recurrence of tumor in a personalized manner. Research supported by grants from National Key Research and Development Program of China Grant (No. 2016YFA0500600), National Natural Science Foundation of China (No. 31970130, 31600672, 31670831, and 31370813), Open Foundation of Key Laboratory of Systems Biomedicine (No. KLSB2017QN-01), Science and Technology Commission of Shanghai Municipality Medical Guidance Science &Technology Support Project (16411966100), Shanghai Municipal Education Commission-Gaofeng Clinical Medicine Grant Support (20172005), Shanghai Municipal Commission of Health and Family Planning Outstanding Academic Leaders Training Program (2017BR055) and National Natural Science Foundation of China (81871882).

Clinical Efficacy and Epigenetic Biomarkers in a Phase II Study of Chidamide Plus R-CHOP in Elderly Patients with Newly Diagnosed Diffuse Large B-Cell Lymphoma

Background: Elderly patients with diffuse large B-cell lymphoma (DLBCL) present with poor clinical outcome and intolerance to intensive chemotherapy. Histone deacetylase inhibitors (HDACIs) show anti-lymphoma activities and can be combined with R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) in treating DLBCL. Methods: Elderly patients with newly diagnosed DLBCL (IPI≥2) received oral HDACI chidamide (20mg twice a week for two weeks) and R-CHOP every 21 days (CR-CHOP) for six cycles. The primary endpoint was complete response rate at the end of the treatment. Whole exome sequencing, targeted sequencing and RNA-sequencing were performed in patients with available tumor samples. Findings: Among 49 patients, the complete response rate was 86%, with overall response rate achieving 94%. 2-year PFS and OS rate were 68% (95%CI 52-80) and 80% (95%CI 64-90), respectively. There was no difference in 2-year PFS (83% vs 65%, P=0.210) or OS (91% vs 81%, P=0.313) rate between double-expressor lymphoma (DEL) and non-DEL phenotype. However, 2-year PFS and OS rate of DEL patients was significantly improved by CR-CHOP, comparing with historical control (NCT01852435, P=0.030 and 0.036). Instead of histone acetyltransferases CREBBP / EP300 , histone methyltransferases KMT2D mutations were associated with inferior PFS and OS (P=0.043 and <0.001). Grade 3-4 neutropenia was reported in 171, grade 3-4 thrombocytopenia in 27, and grade 3 anemia in 11 of 283 cycles. No grade 4 non-hematological adverse event was reported. Interpretation: CR-CHOP is effective and safe in elderly patients with newly diagnosed DLBCL. Relevance of DEL phenotype and epigenetic factors on CR-CHOP response warrants further investigation in DLBCL. Trial Registration: This trial is registered with ClinicalTrials.gov, number NCT02753647 Funding Statement: This study was supported, in part, by research funding from the National Natural Science Foundation of China (81520108003, 81830007, and 81670176), Chang Jiang Scholars Program, Shanghai Municipal Education Commission Gaofeng Clinical Medicine Grant Support (20152206 and 20152208), Clinical Research Plan of Shanghai hospital development center (SHDC, 16CR2017A), Multicenter Clinical Research Project by Shanghai Jiao Tong University School of Medicine (DLY201601), Collaborative Innovation Center of Systems Biomedicine, and the Samuel Waxman Cancer Research Foundation. Declaration of Interests: All authors declare no conflict of interest. Ethics Approval Statement: The study was approved by ethics committee and institutional review board of Shanghai Rui Jin Hospital. Written informed consent was obtained from all participants in accordance with the Declaration of Helsinki.

Clinical Efficacy and Molecular Biomarkers of Chidamide Plus R-CHOP In&amp;nbsp;Elderly Patients with Newly Diagnosed Diffuse Large B-Cell Lymphoma: An Open-Label Phase II Clinical Trial

Background: Elderly patients with diffuse large B-cell lymphoma (DLBCL) present with poor clinical outcome and intolerance to intensive chemotherapy. Histone deacetylase inhibitors (HDACIs) show anti-lymphoma activities and can be combined with R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) in treating DLBCL. Methods: Elderly patients with newly diagnosed DLBCL (IPI≥2) received oral HDACI chidamide (20mg twice a week for two weeks) and R-CHOP every 21 days (CR-CHOP) for six cycles. The primary endpoint was complete response rate at the end of the treatment. Whole exome sequencing, targeted sequencing and RNA-sequencing were performed in patients with available tumor samples. Findings: Among 49 patients, the complete response rate was 86%, with overall response rate achieving 94%. 2-year PFS and OS rate were 68% (95%CI 52-80) and 80% (95%CI 64-90), respectively. There was no difference in 2-year PFS (83% vs 65%, P=0.210) or OS (91% vs 81%, P=0.313) rate between double-expressor lymphoma (DEL) and non-DEL phenotype. However, 2-year PFS and OS rate of DEL patients was significantly improved by CR-CHOP, comparing with historical control (NCT01852435, P=0.030 and 0.036). Instead of histone acetyltransferases CREBBP/EP300, histone methyltransferases KMT2D mutations were associated with inferior PFS and OS (P=0.043 and <0.001). Grade 3-4 neutropenia was reported in 171, grade 3-4 thrombocytopenia in 27, and grade 3 anemia in 11 of 283 cycles. No grade 4 non-hematological adverse event was reported. Interpretation: CR-CHOP is effective and safe in elderly patients with newly diagnosed DLBCL. Relevance of DEL phenotype and molecular biomarkers on CR-CHOP response warrants further investigation in DLBCL. Trial Registration: This trial is registered with ClinicalTrials.gov, number NCT02753647. Funding Statement: This study was supported, in part, by research funding from the National Natural Science Foundation of China (81520108003, 81830007, and 81670176), Chang Jiang Scholars Program, Shanghai Municipal Education Commission Gaofeng Clinical Medicine Grant Support (20152206 and 20152208), Clinical Research Plan of Shanghai hospital development center (SHDC, 16CR2017A), Multicenter Clinical Research Project by Shanghai Jiao Tong University School of Medicine (DLY201601), Collaborative Innovation Center of Systems Biomedicine, and the Samuel Waxman Cancer Research Foundation Declaration of Interests: All authors declare no conflict of interest. Ethics Approval Statement: The study was approved by ethics committee and institutional review board of Shanghai Rui Jin Hospital. Written informed consent was obtained from all participants in accordance with the Declaration of Helsinki

DAISY: A Data Information System for accountability under the General Data Protection Regulation.

BackgroundThe new European legislation on data protection, namely, the General Data Protection Regulation (GDPR), has introduced comprehensive requirements for the documentation about the processing of personal data as well as informing the data subjects of its use. GDPR’s accountability principle requires institutions, projects, and data hubs to document their data processings and demonstrate compliance with the GDPR. In response to this requirement, we see the emergence of commercial data-mapping tools, and institutions creating GDPR data register with such tools. One shortcoming of this approach is the genericity of tools, and their process-based model not capturing the project-based, collaborative nature of data processing in biomedical research.FindingsWe have developed a software tool to allow research institutions to comply with the GDPR accountability requirement and map the sometimes very complex data flows in biomedical research. By analysing the transparency and record-keeping obligations of each GDPR principle, we observe that our tool effectively meets the accountability requirement.ConclusionsThe GDPR is bringing data protection to center stage in research data management, necessitating dedicated tools, personnel, and processes. Our tool, DAISY, is tailored specifically for biomedical research and can help institutions in tackling the documentation challenge brought about by the GDPR. DAISY is made available as a free and open source tool on Github. DAISY is actively being used at the Luxembourg Centre for Systems Biomedicine and the ELIXIR-Luxembourg data hub.

Identification of molecular signatures and pathways to identify novel therapeutic targets in Alzheimer's disease: Insights from a systems biomedicine perspective

Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by the accumulation of amyloid plaques and neurofibrillary tangles in the brain. However, there are no peripheral biomarkers available that can detect AD onset. This study aimed to identify the molecular signatures in AD through an integrative analysis of blood gene expression data. We used two microarray datasets (GSE4226 and GSE4229) comparing peripheral blood transcriptomes of AD patients and controls to identify differentially expressed genes (DEGs). Gene set and protein overrepresentation analysis, protein-protein interaction (PPI), DEGs-Transcription Factors (TFs) interactions, DEGs-microRNAs (miRNAs) interactions, protein-drug interactions, and protein subcellular localizations analyses were performed on DEGs common to the datasets. We identified 25 common DEGs between the two datasets. Integration of genome scale transcriptome datasets with biomolecular networks revealed hub genes (NOL6, ATF3, TUBB, UQCRC1, CASP2, SND1, VCAM1, BTF3, VPS37B), common transcription factors (FOXC1, GATA2, NFIC, PPARG, USF2, YY1) and miRNAs (mir-20a-5p, mir-93-5p, mir-16-5p, let-7b-5p, mir-708-5p, mir-24-3p, mir-26b-5p, mir-17-5p, mir-193-3p, mir-186-5p). Evaluation of histone modifications revealed that hub genes possess several histone modification sites associated with AD. Protein-drug interactions revealed 10 compounds that affect the identified AD candidate biomolecules, including anti-neoplastic agents (Vinorelbine, Vincristine, Vinblastine, Epothilone D, Epothilone B, CYT997, and ZEN-012), a dermatological (Podofilox) and an immunosuppressive agent (Colchicine). The subcellular localization of molecular signatures varied, including nuclear, plasma membrane and cytosolic proteins.In the present study, it was identified blood-cell derived molecular signatures that might be useful as candidate peripheral biomarkers in AD. It was also identified potential drugs and epigenetic data associated with these molecules that may be useful in designing therapeutic approaches to ameliorate AD.

Rethinking the pragmatic systems biology and systems-theoretical biology divide: Toward a complexity-inspired epistemology of systems biomedicine

This paper examines some methodological and epistemological issues underlying the ongoing “artificial” divide between pragmatic-systems biology and systems-theoretical biology. The pragmatic systems view of biology has encountered problems and constraints on its explanatory power because pragmatic systems biologists still tend to view systems as mere collections of parts, not as “emergent realities” produced by adaptive interactions between the constituting components. As such, they are incapable of characterizing the higher-level biological phenomena adequately. The attempts of systems-theoretical biologists to explain these “emergent realities” using mathematics also fail to produce satisfactory results.Given the increasing strategic importance of systems biology, both from theoretical and research perspectives, we suggest that additional epistemological and methodological insights into the possibility of further integration between traditional experimental studies and complex modeling are required. This integration will help to improve the currently underdeveloped pragmatic-systems biology and system-theoretical biology.The “epistemology of complexity,” I contend, acts as a glue that connects and integrates different and sometimes opposing viewpoints, perspectives, streams, and practices, thus maintaining intellectual and research coherence of systems research of life. It allows scientists to shift the focus from traditional experimental research to integrated, modeling-based holistic practices capable of providing a comprehensive knowledge of organizing principles of living systems. It also opens the possibility of the development of new practical and theoretical foundations of systems biology to build a better understanding of complex organismic functions.

Integration of imaging biomarkers into systems biomedicine: a renaissance for medical imaging

Systems biomedicine consists in the integration of biosciences, medicine and computer sciences. Systems biomedicine is supposed to allow a holistic approach to the human subject and its disease states. This paper outlines the basic concepts and open issues in this field and provides an outlook for the integration of medical imaging procedures in the growing area of systems biomedicine. The terms “Systems biomedicine”, “Systems medicine” were used for bibliographic search in Pubmed and Web of sciences. Most relevant papers were selected for inclusion in this paper; a synthesis of the papers is presented. An integration of methods is required to best exploit the potential of the multi-‘omics biobanks, in which imaging biomarker data represent an added value. To obtain such integration, imaging biomarker data from different “systems” should be in a manageable format. The recent evolution of AI and the hardware improvements by parallel and fast computing are bringing us towards a new age of molecular and morphologic imaging. Although there will always be a qualitative aspect to imaging, AI and quantitative metrics will supplement and complement the current “human” methods of interpretation of imaging data in a holistic approach to individual patient management.

Community-driven roadmap for integrated disease maps.

The Disease Maps Project builds on a network of scientific and clinical groups that exchange best practices, share information and develop systems biomedicine tools. The project aims for an integrated, highly curated and user-friendly platform for disease-related knowledge. The primary focus of disease maps is on interconnected signaling, metabolic and gene regulatory network pathways represented in standard formats. The involvement of domain experts ensures that the key disease hallmarks are covered and relevant, up-to-date knowledge is adequately represented. Expert-curated and computer readable, disease maps may serve as a compendium of knowledge, allow for data-supported hypothesis generation or serve as a scaffold for the generation of predictive mathematical models. This article summarizes the 2nd Disease Maps Community meeting, highlighting its important topics and outcomes. We outline milestones on the roadmap for the future development of disease maps, including creating and maintaining standardized disease maps; sharing parts of maps that encode common human disease mechanisms; providing technical solutions for complexity management of maps; and Web tools for in-depth exploration of such maps. A dedicated discussion was focused on mathematical modeling approaches, as one of the main goals of disease map development is the generation of mathematically interpretable representations to predict disease comorbidity or drug response and to suggest drug repositioning, altogether supporting clinical decisions.

Roles of Single Cell Systems Biomedicine in Lung Diseases.

Single cell sequencing is important to detect the gene heterogeneity between cells, as the part of single-cell systems biology which combines computational science, mathematical modelling and high-throughput technologies with biological function and organization in the cell. We initially arise the question how to integrate the outcomes of single-cell systems biology with clinical phenotype, interpret alterations of single-cell gene sequencing and function in patient response to therapies, and understand the significance of single-cell systems biology in the discovery and development of new molecular diagnostics and therapeutics. The present review furthermore focuses the significance of singe cell systems biology in respiratory diseases and calls the special attention from scientists who are working on single cell systems biology to improve the diagnosis and therapy for patients with lung diseases.