Network biomarkers, interaction networks and dynamical network biomarkers in respiratory diseases

Plasma protein-based identification of neuroimage-driven subtypes in mild cognitive impairment via protein-protein interaction aware explainable graph propagational network

As an early indicator of dementia, mild cognitive impairment (MCI) requires specialized treatment according to its subtypes for the effective prevention and management of dementia progression. Based on the neuropathological characteristics, MCI can be classified into Alzheimer's disease (AD)-related cognitive impairment (ADCI) and subcortical vascular cognitive impairment (SVCI), being more likely to progress to AD and subcortical vascular dementia (SVD), respectively. For identifying MCI subtypes, plasma protein biomarkers are recently seen as promising tools due to their minimal invasiveness and cost-effectiveness in diagnostic procedures. Furthermore, the application of machine learning (ML) has led the preciseness in the biomarker discovery and the resulting diagnostics. Nevertheless, previous ML-based studies often fail to consider interactions between proteins, which are essential in complex neurodegenerative disorders such as MCI and dementia. Although protein-protein interactions (PPIs) have been employed in network models, these models frequently do not fully capture the diverse properties of PPIs due to their local awareness. This limitation increases the likelihood of overlooking critical components and amplifying the impact of noisy interactions. In this study, we introduce a new graph-based ML model for classifying MCI subtypes, called eXplainable Graph Propagational Network (XGPN). The proposed method extracts the globally interactive effects between proteins by propagating the independent effect of plasma proteins on the PPI network, and thereby, MCI subtypes are predicted by estimation of the risk effect of each protein. Moreover, the process of model training and the outcome of subtype classification are fully explainable due to the simplicity and transparency of XGPN's architecture. The experimental results indicated that the interactive effect between proteins significantly contributed to the distinct differences between MCI subtype groups, resulting in an enhanced classification performance with an average improvement of 10.0 % compared to existing methods, also identifying key biomarkers and their impact on ADCI and SVCI.

Multi-omics with dynamic network biomarker algorithm prefigures organ-specific metastasis of lung adenocarcinoma

Efficacious strategies for early detection of lung cancer metastasis are of significance for improving the survival of lung cancer patients. Here we show the marker genes and serum secretome foreshadowing the lung cancer site-specific metastasis through dynamic network biomarker (DNB) algorithm, utilizing two clinical cohorts of four major types of lung cancer distant metastases, with single-cell RNA sequencing (scRNA-seq) of primary lesions and liquid chromatography-mass spectrometry data of sera. Also, we locate the intermediate status of cancer cells, along with its gene signatures, in each metastatic state trajectory that cancer cells at this stage still have no specific organotropism. Furthermore, an integrated neural network model based on the filtered scRNA-seq data is successfully constructed and validated to predict the metastatic state trajectory of cancer cells. Overall, our study provides an insight to locate the pre-metastasis status of lung cancer and primarily examines its clinical application value, contributing to the early detection of lung cancer metastasis in a more feasible and efficacious way.

Identification of molecular subtypes of dementia by using blood-proteins interaction-aware graph propagational network

Plasma protein biomarkers have been considered promising tools for diagnosing dementia subtypes due to their low variability, cost-effectiveness, and minimal invasiveness in diagnostic procedures. Machine learning (ML) methods have been applied to enhance accuracy of the biomarker discovery. However, previous ML-based studies often overlook interactions between proteins, which are crucial in complex disorders like dementia. While protein-protein interactions (PPIs) have been used in network models, these models often fail to fully capture the diverse properties of PPIs due to their local awareness. This drawback increases the chance of neglecting critical components and magnifying the impact of noisy interactions. In this study, we propose a novel graph-based ML model for dementia subtype diagnosis, the graph propagational network (GPN). By propagating the independent effect of plasma proteins on PPI network, the GPN extracts the globally interactive effects between proteins. Experimental results showed that the interactive effect between proteins yielded to further clarify the differences between dementia subtype groups and contributed to the performance improvement where the GPN outperformed existing methods by 10.4% on average.

Personalized decision support system for tailoring IgA nephropathy treatment strategies

BACKGROUND

The ongoing debate surrounding the use of immunosuppressive treatments for IgA nephropathy (IgAN) underscores the demand for personalized and effective strategies.

METHODS

Analyzed data from 807 IgAN patients over 5+ years using three methods: Random Forest with molecular biomarkers, network biomarkers with graph engineering, and an auto-encoder model. All models were trained using identical demographic, clinical, and pathological data, employing an 80-20 split for training and testing purposes.

RESULTS

In the comprehensive assessment of IgAN prognosis, the Random Forest model, employing molecular biomarkers, demonstrated strong performance metrics (AUC = 0.83, sensitivity = 0.51, specificity = 0.96). However, traditional graph feature engineering on patient-specific networks outperformed these results with an AUC of 0.90, sensitivity of 0.64, and specificity of 0.94. The Auto-encoder model showed the best accuracy (AUC = 0.91, sensitivity = 0.46, specificity = 0.96). The findings highlighted the superior predictive capabilities of network biomarkers over molecular biomarkers for adverse renal outcome prediction in IgAN. Consequently, we integrated Auto-encoder-derived Network Biomarkers with Random Forest Models to enhance prognostic precision in diverse IgAN treatment scenarios. The prediction for the prognosis of patients receiving supportive care, glucocorticoid therapy, and immunosuppressant treatment yielded AUC values of 0.95, 0.96, and 1, respectively, indicating high specificity. Drawing from these insights, we pioneered the development of an innovative decision support model for IgAN treatment. This model demonstrated the ability to make medical decisions comparable to those by experienced nephrologists, enabling the customization of personalized disease management strategies.

CONCLUSION

Our system accurately predicted IgAN prognosis and evaluated various treatment efficacies, aiding physicians in devising optimal therapeutic strategies for patients.

Identification of marker genes for spinal cord injury

Introduction

Spinal cord injury (SCI) is a profoundly disabling and devastating neurological condition, significantly impacting patients' quality of life. It imposes unbearable psychological and economic pressure on both patients and their families, as well as placing a heavy burden on society.

Methods

In this study, we integrated datasets GSE5296 and GSE47681 as training groups, analyzed gene variances between sham group and SCI group mice, and conducted Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis based on the differentially expressed genes. Subsequently, we performed Weighted Gene Correlation Network Analysis (WGCNA) and Lasso regression analyses.

Results

We identified four characteristic disease genes: Icam1, Ch25h, Plaur and Tm4sf1. We examined the relationship between SCI and immune cells, and validated the expression of the identified disease-related genes in SCI rats using PCR and immunohistochemistry experiments.

Discussion

In conclusion, we have identified and verified four genes related to SCI: Icam1, Ch25h, Plaur and Tm4sf1, which could offer insights for SCI treatment.

An Interaction Network Driven Approach for Identifying Cervical, Endometrial, Vulvar Carcinomic Biomarkers and Their Multi-targeted Inhibitory Agents from Few Widely Available Medicinal Plants

Differently expressed genes (DEGs) across cervical (CC), endometrial (EC), and vulvar carcinoma (VC) may serve as potential biomarkers for these progressive tumor conditions. In this study, DEGs of cervical (CC), endometrial (EC), and vulvar carcinoma (VC) were identified by microarray analysis. The interaction network between the identified 124 DEGs was constructed and analyzed to identify the hub genes and genes with high stress centrality. DEGs, namely, CDK1 and MMP9, were found to show highest degree and highest stress centrality respectively from the gene interaction network of 124 nodes and 1171 edges. DEG CDK1 is found to be overlapping in both cervical and endometrial carcinomic conditions while DEG MMP9 is found in vulvar carcinomic condition. Further, as it is studied that many phytochemicals play an important role as medicinal drugs, we have identified phytochemicals from few widely available medicinal plants and performed comprehensive computational study to identify a multi-targeted phytochemical against the identified DEGs, which are crucially responsible for the progression of these carcinomic conditions. Virtual screening of the phytochemicals against the target DEG protein structures with PDB IDs 4Y72 and 1GKC resulted in identifying the multi-targeted phytochemical against both the proteins. The molecular docking and dynamics simulation studies reveal that luteolin can act as a multi-targeted agent. Thus, the interactional and structural insights of luteolin toward the DEG proteins signify that it can be further explored as a multi-targeted agent against the cervical, endometrial, and vulvar carcinoma.

Networks as Biomarkers: Uses and Purposes

Networks-based approaches are often used to analyze gene expression data or protein-protein interactions but are not usually applied to study the relationships between different biomarkers. Given the clinical need for more comprehensive and integrative biomarkers that can help to identify personalized therapies, the integration of biomarkers of different natures is an emerging trend in the literature. Network analysis can be used to analyze the relationships between different features of a disease; nodes can be disease-related phenotypes, gene expression, mutational events, protein quantification, imaging-derived features and more. Since different biomarkers can exert causal effects between them, describing such interrelationships can be used to better understand the underlying mechanisms of complex diseases. Networks as biomarkers are not yet commonly used, despite being proven to lead to interesting results. Here, we discuss in which ways they have been used to provide novel insights into disease susceptibility, disease development and severity.

Molecular biomarkers, network biomarkers, and dynamic network biomarkers for diagnosis and prediction of rare diseases

The difficulty of converting scientific research findings into novel pharmacological treatments for rare and life-threatening diseases is enormous. Biomarkers related to multiple biological processes involved in cell growth, proliferation, and disease occurrence have been identified in recent years with the development of immunology, molecular biology, and genomics technologies. Biomarkers are capable of reflecting normal physiological processes, pathological processes, and the response to therapeutic intervention; as such, they play vital roles in disease diagnosis, prevention, drug response, and other aspects of biomedicine. The discovery of valuable biomarkers has become a focal point of current research. Numerous studies have identified molecular biomarkers based on the differential expression/concentration of molecules (e.g., genes/proteins) for disease state diagnosis, characterization, and treatment. Although technological breakthroughs in molecular analysis platforms have enabled the identification of a large number of candidate biomarkers for rare diseases, only a small number of these candidates have been properly validated for use in patient treatment. The traditional molecular biomarkers may lose vital information by ignoring molecular associations/interactions, and thus the concept of network biomarkers based on differential associations/correlations of molecule pairs has been established. This approach promises to be more stable and reliable in diagnosing disease states. Furthermore, the newly-emerged dynamic network biomarkers (DNBs) based on differential fluctuations/correlations of molecular groups are able to recognize pre-disease states or critical states instead of disease states, thereby achieving rare disease prediction or predictive/preventative medicine and providing deep insight into the dynamic characteristics of disease initiation and progression.

Medical care of rare and undiagnosed diseases: Prospects and challenges

Rare and undiagnosed diseases tend to be diverse, misdiagnosed, and difficult to diagnose. In some cases, the disease is progressive and life-threatening. Yet, to date, an estimated 95% of rare diseases have no approved therapy. Therefore, rare and undiagnosed diseases are considered the ultimate challenges for understanding human diseases. Here, we review the research progress, research frontiers, and important scientific issues related to rare and undiagnosed diseases. We mainly focus on five topics: (1) the identification and functional analysis of disease-causing genes; (2) the construction of cells, organoids, and animal models for mechanism validation; (3) subtyping and diagnosis; (4) treatment and drug screening based on causative genes and mutations; and (5) new technologies and methods for studying rare and undiagnosed diseases. In this review, we briefly update and discuss the pathogenic mechanisms and precision medicine for rare and undiagnosed diseases.

TPD: a web tool for tipping-point detection based on dynamic network biomarker

Tipping points or critical transitions widely exist during the progression of many biological processes. It is of great importance to detect the tipping point with the measured omics data, which may be a key to achieving predictive or preventive medicine. We present the tipping point detector (TPD), a web tool for the detection of the tipping point during the dynamic process of biological systems, and further its leading molecules or network, based on the input high-dimensional time series or stage course data. With the solid theoretical background of dynamic network biomarker (DNB) and a series of computational methods for DNB detection, TPD detects the potential tipping point/critical state from the input omics data and outputs multifarious visualized results, including a suggested tipping point with a statistically significant P value, the identified key genes and their functional biological information, the dynamic change in the DNB/leading network that may drive the critical transition and the survival analysis based on DNB scores that may help to identify ‘dark’ genes (nondifferential in terms of expression but differential in terms of DNB scores). TPD fits all current browsers, such as Chrome, Firefox, Edge, Opera, Safari and Internet Explorer. TPD is freely accessible at http://www.rpcomputationalbiology.cn/TPD.

Connections for Matters of the Heart: Network Medicine in Cardiovascular Diseases

Cardiovascular diseases (CVD) are diverse disorders affecting the heart and vasculature in millions of people worldwide. Like other fields, CVD research has benefitted from the deluge of multiomics biomedical data. Current CVD research focuses on disease etiologies and mechanisms, identifying disease biomarkers, developing appropriate therapies and drugs, and stratifying patients into correct disease endotypes. Systems biology offers an alternative to traditional reductionist approaches and provides impetus for a comprehensive outlook toward diseases. As a focus area, network medicine specifically aids the translational aspect of in silico research. This review discusses the approach of network medicine and its application to CVD research.

Determination of tipping point in course of PM2.5 organic extracts-induced malignant transformation by dynamic network biomarkers

The dynamic network biomarkers (DNBs) are designed to identify the tipping point and specific molecules in initiation of PM_2.5-induced lung cancers. To discover early-warning signals, we analyzed time-series gene expression datasets over a course of PM_2.5 organic extraction-induced human bronchial epithelial (HBE) cell transformation (0^th~16^th week). A composition index of DNB (CI_DNB) was calculated to determine correlations and fluctuations in molecule clusters at each timepoint. We identified a group of genes with the highest CI_DNB at the 10^th week, implicating a tipping point and corresponding DNBs. Functional experiments revealed that manipulating respective DNB genes at the tipping point led to remarkable changes in malignant phenotypes, including four promoters (GAB2, NCF1, MMP25, LAPTM5) and three suppressors (BATF2, DOK3, DAP3). Notably, co-altered expression of seven core DNB genes resulted in an enhanced activity of malignant transformation compared to effects of single-gene manipulation. Perturbation of pathways (EMT, HMGB1, STAT3, NF-κB, PTEN) appeared in HBE cells at the tipping point. The core DNB genes were involved in regulating lung cancer cell growth and associated with poor survival, indicating their synergistic effects in initiation and development of lung cancers. These findings provided novel insights into the mechanism of dynamic networks attributable to PM_2.5-induced cell transformation.

Impaired Airway Epithelial Barrier Integrity in Response to Stenotrophomonas maltophilia Proteases, Novel Insights Using Cystic Fibrosis Bronchial Epithelial Cell Secretomics

Stenotrophomonas maltophilia is a Gram-negative opportunistic pathogen that can chronically colonize the lungs of people with cystic fibrosis (CF) and is associated with lethal pulmonary hemorrhage in immunocompromised patients. Its secreted virulence factors include the extracellular serine proteases StmPR1, StmPR2, and StmPR3. To explore the impact of secreted virulence determinants on pulmonary mucosal defenses in CF, we examined the secretome of human CFBE41o- bronchial epithelial cells in response to treatment with S. maltophilia K279a cell culture supernatant (CS) using a liquid-chromatography-tandem mass spectrometry (LC-MS/MS) based label-free quantitative (LFQ) shotgun proteomics approach for global profiling of the cell secretome. Secretome analysis identified upregulated pathways mainly relating to biological adhesion and epithelial cell signaling in infection, whereas no specific pathways relating to the immune response were enriched. Further exploration of the potentially harmful effects of K279a CS on CF bronchial epithelial cells, demonstrated that K279a CS caused CFBE41o- cell condensation and detachment, reversible by the serine protease inhibitor PMSF. K279a CS also decreased trans-epithelial electrical resistance in CFBE41o- cell monolayers suggestive of disruption of tight junction complexes (TJC). This finding was corroborated by an observed increase in fluorescein isothiocyanate (FITC) dextran permeability and by demonstrating PMSF-sensitive degradation of the tight junction proteins ZO-1 and occludin, but not JAM-A or claudin-1. These observations demonstrating destruction of the CFBE41o- TJC provide a novel insight regarding the virulence of S. maltophilia and may explain the possible injurious effects of this bacterium on the CF bronchial epithelium and the pathogenic mechanism leading to lethal pulmonary hemorrhage.

Chronic hepatitis B: dynamic change in Traditional Chinese Medicine syndrome by dynamic network biomarkers

Background

In traditional Chinese medicine (TCM) clinical practice, TCM syndromes help to understand human homeostasis and guide individualized treatment. However, the TCM syndrome changes with disease progression, of which the scientific basis and mechanism remain unclear.

Methods

To demonstrate the underlying mechanism of dynamic changes in the TCM syndrome, we applied a dynamic network biomarker (DNB) algorithm to obtain the DNBs of changes in the TCM syndrome, based on the transcriptomic data of patients with chronic hepatitis B and typical TCM syndromes, including healthy controls and patients with liver-gallbladder dampness-heat syndrome (LGDHS), liver-depression spleen-deficiency syndrome (LDSDS), and liver-kidney yin-deficiency syndrome (LKYDS). The DNB model exploits collective fluctuations and correlations of the observed genes, then diagnoses the critical state.

Results

Our results showed that the DNBs of TCM syndromes were comprised of 52 genes and the tipping point occurred at the LDSDS stage. Meanwhile, there were numerous differentially expressed genes between LGDHS and LKYDS, which highlighted the drastic changes before and after the tipping point, implying the 52 DNBs could serve as early-warning signals of the upcoming change in the TCM syndrome. Next, we validated DNBs by cytokine profiling and isobaric tags for relative and absolute quantitation (iTRAQ). The results showed that PLG (plasminogen) and coagulation factor XII (F12) were significantly expressed during the progression of TCM syndrome from LGDHS to LKYDS.

Conclusions

This study provides a scientific understanding of changes in the TCM syndrome. During this process, the cytokine system was involved all the time. The DNBs PLG and F12 were confirmed to significantly change during TCM-syndrome progression and indicated a potential value of DNBs in auxiliary diagnosis of TCM syndrome in CHB.

Trial registration Identifier: NCT03189992. Registered on June 4, 2017. Retrospectively registered (http://www.clinicaltrials.gov)

Identifying Interaction Clusters for MiRNA and MRNA Pairs in TCGA Network

Existing methods often fail to recognize the conversions for the biological roles of the pairs of genes and microRNAs (miRNAs) between the tumor and normal samples. We have developed a novel cluster scoring method to identify messenger RNA (mRNA) and miRNA interaction pairs and clusters while considering tumor and normal samples jointly. Our method has identified 54 significant clusters for 15 cancer types selected from The Cancer Genome Atlas project. We also determined the shared clusters across tumor types and/or subtypes. In addition, we compared gene and miRNA overlap between lists identified in our liver hepatocellular carcinoma (LIHC) study and regulatory relationships reported from human and rat nonalcoholic fatty liver disease studies (NAFLD). Finally, we analyzed biological functions for the single significant cluster in LIHC and uncovered a significantly enriched pathway (phospholipase D signaling pathway) with six genes represented in the cluster, symbols: DGKQ, LPAR2, PDGFRB, PIK3R3, PTGFR and RAPGEF3.

Functional criticality in the human brain: Physiological, behavioral and neurodevelopmental correlates

Understanding the critical features of the human brain at multiple time scales is vital for both normal development and disease research. A recently proposed method, the vertex-wise Index of Functional Criticality (vIFC) based on fMRI, has been testified as a sensitive neuroimaging marker to characterize critical transitions of human brain dynamics during Alzheimer's disease progression. However, it remains unclear whether vIFC in healthy brains is associated with neuropsychological and neurophysiological measurements. Using the Nathan Kline Institute/Rockland lifespan cross-sectional datasets and openfMRI single participant longitudinal datasets, we found consistent spatial patterns of vIFC across the entire cortical mantle: the inferior parietal and the precuneus exhibited high vIFC. On a time scale of years, we observed that vIFC increased with age in the left ventral posterior cingulate gyrus. On a time scale of days and weeks, vIFC demonstrated the capacity to identify a link between anxiety and pulse. These results showed that vIFC can serve as a useful neuroimaging marker for detecting physiological, behavioral, and neurodevelopmental transitions. Based on the criticality theory in nonlinear dynamics, the current vIFC study sheds new light on human brain studies from a nonlinear perspective and opens potential new avenues for normal and abnormal human brain studies.

Simultaneous quantification of Cyt c interactions with HSP27 and Bcl-xL using molecularly imprinted polymers (MIPs) coupled with liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based targeted proteomics

Cytochrome c (Cyt c) plays an important role in cell apoptosis. However, it could be functionally compromised by interaction with anti-apoptosis proteins, known as protein-protein interactions (PPIs). Among the proteins potentially interacting with Cyt c, both HSP27 and Bcl-xL serve as pivotal anti-apoptosis proteins. Because multiple PPIs, especially those involve the same protein, could affect each other, their simultaneous and quantitative detection is highly needed. In this study, a combined approach of molecularly imprinted polymers (MIPs) and LC-MS/MS-based targeted proteomics was developed for simultaneous quantification of Cyt c-HSP27 and Cyt c-Bcl-xL interactions. Surrogate peptides of Cyt c, HSP27 and Bcl-xL were first selected and used for the corresponding proteins quantification in targeted proteomics analysis. For MIPs, epitope approach was employed and a short peptide of Cyt c was selected as template for protein complexes recognition and enrichment. The characteristics of the synthesized MIPs including adsorption capacity, kinetics and efficiency were then evaluated. After validation, this combined assay was applied to quantitative analysis of total Cyt c including Cyt c in mitochondria and cytosol, total HSP27, total Bcl-xL and Cyt c-HSP27 and Cyt c-Bcl-xL protein complexes in breast cells. The result was also compared with that using Co-IP/Western Blotting. SIGNIFICANCE: Protein-protein interactions (PPIs) are essential for many cellular processes and the changes of PPIs are often associated with cellular dysfunction. More importantly, each protein typically has more than one interaction partner and multiple PPIs, especially those involve the same protein, could affect each other. The selectivity of these interactions determines the activities of proteins and further the developmental potential of the cell. Thus, simultaneous and quantitative detection of multiple PPIs is highly needed in biological research and related disciplines. However, it is still challenging to even qualitatively or semi-quantitatively analyze multiple PPIs because of the limitations of current experimental techniques for interaction detection. In this study, molecularly imprinted polymers (MIPs) epitope approach was combined with liquid chromatography-tandem mass spectrometry (LC-MS/MS) targeted proteomics for the simultaneous and quantitative detection of Cyt c-HSP27 and Cyt c-Bcl-xL interactions in breast cancer. Given high sensitivity, high selectivity and wide dynamic range of LC-MS/MS, MIPs approach was employed here to separate and enrich protein complexes prior to targeted proteomics analysis.

An interaction network driven approach for identifying biomarkers for progressing cervical intraepithelial neoplasia

Overlapping genes across high-grade squamous intraepithelial lesions (CIN2 and 3) and cancer may serve as potential biomarkers for this progressive disease. Differentially expressed genes (DEGs) of dysplastic (CIN2 and CIN3) and cancer cells were identified by microarray data analysis. Gene interaction network was constructed using the 98 common DEGs among the dysplastic and cancer cells and analysed for the identification of common modules, hubs and significant motifs. Two significant modules and 10 hubs of the common gene interaction network, with 125 nodes and 201 edges were found. DEGs namely NDC80, ZWINT, CDC7, MCM4, MCM2 and MCM6 were found to be common in both the significant modules as well as the hubs. Of these, ZWINT, CDC7, MCM4, MCM2 and MCM6 were further identified to be part of most significant motifs. This overlapping relationship provides a list of common disease related genes among pre-cancerous and cancer stages which could help in targeting the proliferating cancerous cells during onset. Capitalizing upon and targeting Minichromosome maintenance protein complex - specifically the MCM2, MCM4 and MCM6 subunits, ZWINT and CDC7 for experimental validation, may provide valuable insights in understanding and detection of progressing cervical neoplasia to cervical cancer at an early stage.

Sparse Pathway-Induced Dynamic Network Biomarker Discovery for Early Warning Signal Detection in Complex Diseases

In many complex diseases, the transition process from the healthy stage to the catastrophic stage does not occur gradually. Recent studies indicate that the initiation and progression of such diseases are comprised of three steps including healthy stage, pre-disease stage, and disease stage. It has been demonstrated that a certain set of trajectories can be observed in the genetic signatures at the molecular level, which might be used to detect the pre-disease stage and to take necessary medical interventions. In this paper, we propose two optimization-based algorithms for extracting the dynamic network biomarkers responsible for catastrophic transition into the disease stage, and to open new horizons to reverse the disease progression at an early stage through pinpointing molecular signatures provided by high-throughput microarray data. The first algorithm relies on meta-heuristic intelligent search to characterize dynamic network biomarkers represented as a complete graph. The second algorithm induces sparsity on the adjacency matrix of the genes by taking into account the biological signaling and metabolic pathways, since not all the genes in the ineractome are biologically linked. Comprehensive numerical and meta-analytical experiments verify the effectiveness of the results of the proposed approaches in terms of network size, biological meaningfulness, and verifiability.

Impaired Functional Criticality of Human Brain during Alzheimer's Disease Progression

The progression of Alzheimer’s Disease (AD) has been proposed to comprise three stages, subjective cognitive decline (SCD), mild cognitive impairment (MCI), and AD. Was brain dynamics across the three stages smooth? Was there a critical transition? How could we characterize and study functional criticality of human brain? Based on dynamical characteristics of critical transition from nonlinear dynamics, we proposed a vertex-wise Index of Functional Criticality (vIFC) of fMRI time series in this study. Using 42 SCD, 67 amnestic MCI (aMCI), 34 AD patients as well as their age-, sex-, years of education-matched 54 NC, our new method vIFC successfully detected significant patient-normal differences for SCD and aMCI, as well as significant negative correlates of vIFC in the right middle temporal gyrus with total scores of Montreal Cognitive Assessment (MoCA) in SCD. In comparison, standard deviation of fMRI time series only detected significant differences between AD patients and normal controls. As an index of functional criticality of human brain derived from nonlinear dynamics, vIFC could serve as a sensitive neuroimaging marker for future studies; considering much more vIFC impairments in aMCI compared to SCD and AD, our study indicated aMCI as a critical stage across AD progression.

Can glypican-3 be a disease-specific biomarker?

Background

Glypican-3 (GPC3) is a cell surface-bound proteoglycan which has been identified as a potential biomarker candidate in hepatocellular carcinoma, lung carcinoma, severe pneumonia, and acute respiratory distress syndrome (ARDS). The aim of our review is to evaluate whether GPC3 has utility as a disease-specific biomarker, to discuss the potential involvement of GPC3 in cell biology, and to consider the changes of GPC3 gene and protein expression and regulation in hepatocellular carcinoma, lung cancer, severe pneumonia, and ARDS.

Results

Immunohistochemical studies have suggested that over-expression of GPC3 is associated with a poorer prognosis for hepatocellular carcinoma patients. Expression of GPC3 leads to an increased apoptosis response in human lung carcinoma tumor cells, and is considered to be a candidate lung tumor suppressor gene. Increased serum levels of GPC3 have been demonstrated in ARDS patients with severe pneumonia.

Conclusions

Glypican-3 could be considered as a clinically useful biomarker in hepatocellular carcinoma, lung carcinoma, and ARDS, but further research is needed to confirm and expand on these findings.

A global view of regulatory networks in lung cancer: An approach to understand homogeneity and heterogeneity

A number of new biotechnologies are used to identify potential biomarkers for the early detection of lung cancer, enabling a personalized therapy to be developed in response. The combinatorial cross-regulation of hundreds of biological function-specific transcription factors (TFs) is defined as the understanding of regulatory networks of molecules within the cell. Here we integrated global databases with 537 patients with lung adenocarcinoma (ADC), 140 with lung squamous carcinoma (SCC), 9 with lung large-cell carcinoma (LCC), 56 with small-cell lung cancer (SCLC), and 590 without cancer with the understanding of TF functions. The present review aims at the homogeneity or heterogeneity of gene expression profiles among subtypes of lung cancer. About 5, 136, 52, or 16 up-regulated or 19, 24, 122, or 97down-regulated type-special TF genes were identified in ADC, SCC, LCC or SCLC, respectively. DNA-binding and transcription regulator activity associated genes play a dominant role in the differentiation of subtypes in lung cancer. Subtype-specific TF gene regulatory networks with elements should be an alternative for diagnostic and therapeutic targets for early identification of lung cancer and can provide insightful clues to etiology and pathogenesis.

Tomorrow's genome medicine in lung cancer

Tomorrow's genome medicine in lung cancer should focus more on the homogeneity and heterogeneity of lung cancer which play an important role in the development of drug resistance, genetic complexity, as well as confusion and difficulty of early diagnosis and therapy. Chromosome positioning and repositioning may contribute to the sensitivity of lung cancer cells to therapy, the heterogeneity associated with drug resistance, and the mechanism of lung carcinogenesis. The CCCTC-binding factor plays critical roles in genome topology and function, increased risk of carcinogenicity, and potential of lung cancer-specific mediations. Chromosome reposition in lung cancer can be regulated by CCCTC binding factor. Single-cell gene sequencing, as part of genome medicine, was paid special attention in lung cancer to understand mechanical phenotypes, single-cell biology, heterogeneity, and chromosome positioning and function of single lung cancer cells. We at first propose to develop an intelligent single-cell robot of human cells to integrate together systems information of molecules, genes, proteins, organelles, membranes, architectures, signals, and functions. It can be a powerful automatic system to assist clinicians in the decision-making, molecular understanding, risk analyzing, and prognosis predicting.

New strategies for targeting drug combinations to overcome mutation-driven drug resistance

Targeted therapies are suggested as an effective alternative for patients with cancer that harbor mutations, but treatment outcomes are frequently limited by primary or acquired drug resistance. The present review describes potential mechanisms of primary or acquired drug resistances to provide a resource for considering how to be overcome. We focus on strategies of targeted drug combinations to minimize the development of drug resistance within the context how resistance develops. Strategies benefit from the combined use of "omics" technologies, i.e., high-throughput functional genomics data, pharmacogenomics, or genome-wide CRISPR-Cas9 screening, to analyze and design targeted drug combinations for mutation-driven drug resistance. We also introduce new insights towards pathway-centric combined therapies as an alternative to overcome the heterogeneity and benefit patient prognoses.

Application of clinical bioinformatics in lung cancer-specific biomarkers

The fact that lung cancer is a heterogeneous disease suggests that there is a high likelihood that effective lung cancer biomarkers will need to address patient-specific molecular defects, clinical characters, and aspects of the tumor microenvironment. In this transition, clinical bioinformatics tools and resources are the most appropriate means to improve the analysis, as major biological databases are now containing clinical data alongside genomics, proteomics, and other biological data. Clinical bioinformatics comprises a series of concepts and approaches that have been used successfully both to delineate novel biological mechanisms and to drive translational advances in individualized healthcare. In this article, we outline several of emerging clinical bioinformatics-based strategies as they apply specifically to lung cancer.

Gene heterogeneity in metastasis of colorectal cancer to the lung

Colorectal cancer (CRC) as a heterogeneous disease, is one of the most common and serious cancers with high metastases and mortality. Lung is one of the most common sites of CRC metastases with high heterogeneity between cells, pathways, or molecules. The present review will focus on potential roles of gene heterogeneity in KRAS pathway in the development of CRC metastasis to lung and clinical therapies, which would lead to better understanding of the metastatic control and benefit to the treatment of metastases. KRAS is the central relay for pathways originating at the epidermal growth factor receptor (EGFR) family. KRAS mutation exists in about 40% CRC, associated with higher cumulative incidence of CRC lung metastasis, and acts as an independent predictor of metastasis to lung. Mutations in KRAS can lead to poor response of patients to panitumumab, and inferior progression-free survival. However, most patients with KRAS wild-type tumors still do not respond, which indicates other mutations. Phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA) mutation was associated with lung metastases in metastatic colorectal cancer. PIK3CA mutation in exon 20 was found to be correlated with patient survival in the metastatic setting after the treatment with cetuximab and chemotherapy. The heterogeneity of KRAS pathway was found in the phosphatase and tensin homologue deleted on chromosome ten loss, disheveled binding antagonist of beta catenin 2 overexpression and increased dual-specificity protein phosphatase 4 expression of CRC lung metastasis.

Building a glaucoma interaction network using a text mining approach

Background

The volume of biomedical literature and its underlying knowledge base is rapidly expanding, making it beyond the ability of a single human being to read through all the literature. Several automated methods have been developed to help make sense of this dilemma. The present study reports on the results of a text mining approach to extract gene interactions from the data warehouse of published experimental results which are then used to benchmark an interaction network associated with glaucoma. To the best of our knowledge, there is, as yet, no glaucoma interaction network derived solely from text mining approaches. The presence of such a network could provide a useful summative knowledge base to complement other forms of clinical information related to this disease.

Results

A glaucoma corpus was constructed from PubMed Central and a text mining approach was applied to extract genes and their relations from this corpus. The extracted relations between genes were checked using reference interaction databases and classified generally as known or new relations. The extracted genes and relations were then used to construct a glaucoma interaction network. Analysis of the resulting network indicated that it bears the characteristics of a small world interaction network. Our analysis showed the presence of seven glaucoma linked genes that defined the network modularity. A web-based system for browsing and visualizing the extracted glaucoma related interaction networks is made available at http://neurogene.spd.louisville.edu/GlaucomaINViewer/Form1.aspx.

Conclusions

This study has reported the first version of a glaucoma interaction network using a text mining approach. The power of such an approach is in its ability to cover a wide range of glaucoma related studies published over many years. Hence, a bigger picture of the disease can be established. To the best of our knowledge, this is the first glaucoma interaction network to summarize the known literature. The major findings were a set of relations that could not be found in existing interaction databases and that were found to be new, in addition to a smaller subnetwork consisting of interconnected clusters of seven glaucoma genes. Future improvements can be applied towards obtaining a better version of this network.

Electronic supplementary material

The online version of this article (doi:10.1186/s13040-016-0096-2) contains supplementary material, which is available to authorized users.

Potentials of single-cell biology in identification and validation of disease biomarkers

Single-cell biology is considered a new approach to identify and validate disease-specific biomarkers. However, the concern raised by clinicians is how to apply single-cell measurements for clinical practice, translate the message of single-cell systems biology into clinical phenotype or explain alterations of single-cell gene sequencing and function in patient response to therapies. This study is to address the importance and necessity of single-cell gene sequencing in the identification and development of disease-specific biomarkers, the definition and significance of single-cell biology and single-cell systems biology in the understanding of single-cell full picture, the development and establishment of whole-cell models in the validation of targeted biological function and the figure and meaning of single-molecule imaging in single cell to trace intra-single-cell molecule expression, signal, interaction and location. We headline the important role of single-cell biology in the discovery and development of disease-specific biomarkers with a special emphasis on understanding single-cell biological functions, e.g. mechanical phenotypes, single-cell biology, heterogeneity and organization of genome function. We have reason to believe that such multi-dimensional, multi-layer, multi-crossing and stereoscopic single-cell biology definitely benefits the discovery and development of disease-specific biomarkers.

Effects of anti-human T lymphocyte immune globulins in patients: new or old

Multiple studies demonstrated that anti‐human T lymphocyte immune globulins (ATG) can decrease the incidence of acute and chronic graft rejection in cell or organ transplants. However, further in‐depth study indicates that different subgroups may benefit from either different regimes or alteration of them. Studies among renal transplant patients indicate that low immunological risk patients may not gain the same amount of benefit and thus tilt the risk versus benefit consideration. This may hold true for low immunological risk patients receiving other organ transplants and would be worth further investigation. The recovery time of T cells and natural killer (NK) cells also bears consideration and the impact that it has on the severity and incidence of opportunistic infections closely correlated with the dosage of ATG. The use of lower doses of ATG in combination with other induction medications may offer a solution. The finding that ATG may lose efficacy in cases of multiple transplants or re‐transplants in the case of heart transplants may hold true for other transplantations. This may lead to reconsideration of which induction therapies would be most beneficial in the clinical setting. These studies on ATG done on different patient groups will naturally not be applicable to all, but the evidence accrued from them as a whole may offer us new and different perspectives on how to approach and potentially solve the clinical question of how to best reduce the mortality associated with chronic host‐versus‐graft disease.

Discovering a critical transition state from nonalcoholic hepatosteatosis to nonalcoholic steatohepatitis by lipidomics and dynamical network biomarkers

Nonalcoholic fatty liver disease (NAFLD) is a major risk factor for type 2 diabetes and metabolic syndrome. However, accurately differentiating nonalcoholic steatohepatitis (NASH) from hepatosteatosis remains a clinical challenge. We identified a critical transition stage (termed pre-NASH) during the progression from hepatosteatosis to NASH in a mouse model of high fat-induced NAFLD, using lipidomics and a mathematical model termed dynamic network biomarkers (DNB). Different from the conventional biomarker approach based on the abundance of molecular expressions, the DNB model exploits collective fluctuations and correlations of different metabolites at a network level. We found that the correlations between the blood and liver lipid species drastically decreased after the transition from steatosis to NASH, which may account for the current difficulty in differentiating NASH from steatosis based on blood lipids. Furthermore, most DNB members in the blood circulation, especially for triacylglycerol (TAG), are also identified in the liver during the disease progression, suggesting a potential clinical application of DNB to diagnose NASH based on blood lipids. We further identified metabolic pathways responsible for this transition. Our study suggests that the transition from steatosis to NASH is not smooth and the existence of pre-NASH may be partially responsible for the current clinical limitations to diagnose NASH. If validated in humans, our study will open a new avenue to reliably diagnose pre-NASH and achieve early intervention of NAFLD.

Complex network-based approaches to biomarker discovery

Many studies on biomarker discovery have been done by analyzing mutations in DNA sequences and differences in gene expression patterns. As a new branch of the latter approach, the concept of network biomarkers has been proposed, in which expression data of small subnetworks are used as markers. Furthermore, network biomarkers have been extended to dynamical network biomarkers, in which time series expression data of subnetworks are used as markers. On the other hand, the methodologies in complex networks have also been applied to biomarker discovery. For example, various centrality measures and the concept of observability have been applied. In this article, we review these new approaches for biomarker discovery with focusing on the computational/methodological aspects.

Using Multi-objective Optimization to Identify Dynamical Network Biomarkers as Early-warning Signals of Complex Diseases

Biomarkers have gained immense scientific interest and clinical value in the practice of medicine. With unprecedented advances in high-throughput technologies, research interest in identifying novel and customized disease biomarkers for early detection, diagnosis, or drug responses is rapidly growing. Biomarkers can be identified in different levels of molecular biomarkers, networks biomarkers and dynamical network biomarkers (DNBs). The latter is a recently developed concept which relies on the idea that a cell is a complex system whose behavior is emerged from interplay of various molecules, and this network of molecules dynamically changes over time. A DNB can serve as an early-warning signal of disease progression, or as a leading network that drives the system into the disease state, and thus unravels mechanisms of disease initiation and progression. It is therefore of great importance to identify DNBs efficiently and reliably. In this work, the problem of DNB identification is defined as a multi-objective optimization problem, and a framework to identify DNBs out of time-course high-throughput data is proposed. Temporal gene expression data of a lung injury with carbonyl chloride inhalation exposure has been used as a case study, and the functional role of the discovered biomarker in the pathogenesis of lung injury has been thoroughly analyzed.

mRNAs and miRNAs in whole blood associated with lung hyperplasia, fibrosis, and bronchiolo-alveolar adenoma and adenocarcinoma after multi-walled carbon nanotube inhalation exposure in mice

Inhalation exposure to multi-walled carbon nanotubes (MWCNT) in mice results in inflammation, fibrosis and the promotion of lung adenocarcinoma; however, the molecular basis behind these pathologies is unknown. This study determined global mRNA and miRNA profiles in whole blood from mice exposed by inhalation to MWCNT that correlated with the presence of lung hyperplasia, fibrosis, and bronchiolo-alveolar adenoma and adenocarcinoma. Six-week-old, male, B6C3F1 mice received a single intraperitoneal injection of either the DNA-damaging agent methylcholanthrene (MCA, 10 µg g(-1) body weight) or vehicle (corn oil). One week after injections, mice were exposed by inhalation to MWCNT (5 mg m(-3), 5 hours per day, 5 days per week) or filtered air (control) for a total of 15 days. At 17 months post-exposure, mice were euthanized and examined for the development of pathological changes in the lung, and whole blood was collected and analyzed using microarray analysis for global mRNA and miRNA expression. Numerous mRNAs and miRNAs in the blood were significantly up- or down-regulated in animals developing pathological changes in the lung after MCA/corn oil administration followed by MWCNT/air inhalation, including fcrl5 and miR-122-5p in the presence of hyperplasia, mthfd2 and miR-206-3p in the presence of fibrosis, fam178a and miR-130a-3p in the presence of bronchiolo-alveolar adenoma, and il7r and miR-210-3p in the presence of bronchiolo-alveolar adenocarcinoma, among others. The changes in miRNA and mRNA expression, and their respective regulatory networks, identified in this study may potentially serve as blood biomarkers for MWCNT-induced lung pathological changes.

Critical transitions in chronic disease: transferring concepts from ecology to systems medicine

Ecosystems and biological systems are known to be inherently complex and to exhibit nonlinear dynamics. Diseases such as microbiome dysregulation or depression can be seen as complex systems as well and were shown to exhibit patterns of nonlinearity in their response to perturbations. These nonlinearities can be revealed by a sudden shift in system states, for instance from health to disease. The identification and characterization of early warning signals which could predict upcoming critical transitions is of primordial interest as prevention of disease onset is a major aim in health care. In this review, we focus on recent evidence for critical transitions in diseases and discuss the potential of such studies for therapeutic applications.

Enhanced frequency and potential mechanism of B regulatory cells in patients with lung cancer

BACKGROUND

Regulatory T cells (Tregs) and B cells (Bregs) play an important role in the development of lung cancer. The present study aimed to investigate the phenotype of circulating Tregs and Bregs in patients with lung cancer and explore potential mechanism by which lung cancer cells act on the development of both.

METHODS

Patients with lung cancer (n = 268) and healthy donors (n = 65) were enrolled in the study. Frequencies of Tregs and Bregs were measured by flow cytometry with antibodies against CD4, CD25, CD127, CD45RA, CD19, CD24, CD27 and IL-10 before and after co-cultures. qRT-PCR was performed to evaluate the mRNA levels of RANTES, MIP-1α, TGF-β, IFN-γ and IL-4.

RESULTS

We found a lower frequency of Tregs and a higher frequency of Bregs in patients with lung cancer compared to healthy donors. Co-culture of lung cancer cells with peripheral blood mononuclear cells could polarize the lymphocyte phenotype in the similar pattern. Lipopolysaccharide (LPS)-stimulated lung cancer cells significantly modulated regulatory cell number and function in an in vitro model.

CONCLUSION

We provide initial evidence that frequencies of peripheral Tregs decreased or Bregs increased in patients with lung cancer, which may be modulated directly by lung cancer cells. It seems cancer cells per se plays a crucial role in the development of tumor immunity.