Systems biology, proteomics, and the future of health care: toward predictive, preventative, and personalized medicine

Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition)

These guidelines are a consensus work of a considerable number of members of the immunology and flow cytometry community. They provide the theory and key practical aspects of flow cytometry enabling immunologists to avoid the common errors that often undermine immunological data. Notably, there are comprehensive sections of all major immune cell types with helpful Tables detailing phenotypes in murine and human cells. The latest flow cytometry techniques and applications are also described, featuring examples of the data that can be generated and, importantly, how the data can be analysed. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid, all written and peer-reviewed by leading experts in the field, making this an essential research companion.

Morphometric signatures of exposure to endocrine disrupting chemicals in zebrafish eleutheroembryos

Understanding the mode of action of the different pollutants in human and wildlife health is a key step in environmental risk assessment. The aim of this study was to determine signatures that could link morphological phenotypes to the toxicity mechanisms of four Endocrine Disrupting Chemicals (EDCs): bisphenol A (BPA), perfluorooctanesulfonate potassium salt (PFOS), tributyltin chloride (TBT), and 17-ß-estradiol (E2). Zebrafish (Danio rerio) eleutheroembryos were exposed from 2 to 5 dpf to a wide range of BPA, PFOS, TBT and E2 concentrations. At the end of the exposures several morphometric features were assessed. Common and non-specific effects on larvae pigmentation or swim bladder area were observed after exposures to all compounds. BPA specifically induced yolk sac malabsorption syndrome and altered craniofacial parameters, whereas PFOS had specific effects on the notochord formation presenting higher rates of scoliosis and kyphosis. The main effect of E2 was an increase in the body length of the exposed eleutheroembryos. In the case of TBT, main alterations on the morphological traits were related to developmental delays. When integrating all morphometrical parameters, BPA showed the highest rates of malformations in terms of equilethality, followed by PFOS and, distantly, by TBT and E2. In the case of BPA and PFOS, we were able to relate our results with effects on the transcriptome and metabolome, previously reported. We propose that methodized morphometric analyses in zebrafish embryo model can be used as an inexpensive and easy screening tool to predict modes of action of a wide-range number of contaminants.

Dynamical systems approaches to personalized medicine

The complexity of the human body is a major roadblock to diagnosis and treatment of disease. Individuals may be diagnosed with the same disease but exhibit different biomarker profiles or physiological changes and, importantly, they may respond differently to the same risk factors and the same treatment. There is no doubt that computational methods of data analysis and interpretation must be developed for medicine to evolve from the traditional population-based approaches to personalized treatment strategies. We discuss how computational systems biology is contributing to this current evolution.

Molecular Systems Biology of Neurodevelopmental Disorders, Rett Syndrome as an Archetype

Neurodevelopmental disorders represent a challenging biological and medical problem due to their genetic and phenotypic complexity. In many cases, we lack the comprehensive understanding of disease mechanisms necessary for targeted therapeutic development. One key component that could improve both mechanistic understanding and clinical trial design is reliable molecular biomarkers. Presently, no objective biological markers exist to evaluate most neurodevelopmental disorders. Here, we discuss how systems biology and "omic" approaches can address the mechanistic and biomarker limitations in these afflictions. We present heuristic principles for testing the potential of systems biology to identify mechanisms and biomarkers of disease in the example of Rett syndrome, a neurodevelopmental disorder caused by a well-defined monogenic defect in methyl-CpG-binding protein 2 (MECP2). We propose that such an approach can not only aid in monitoring clinical disease severity but also provide a measure of target engagement in clinical trials. By deepening our understanding of the "big picture" of systems biology, this approach could even help generate hypotheses for drug development programs, hopefully resulting in new treatments for these devastating conditions.

Atomically Precise Manufacturing and Responsible Innovation

Although continued investments in nanotechnology are made, atomically precise manufacturing (APM) to date is still regarded as speculative technology. APM, also known as molecular manufacturing, is a token example of a converging technology, has great potential to impact and be affected by other emerging technologies, such as artificial intelligence, biotechnology, and ICT. The development of APM thus can have drastic global impacts depending on how it is designed and used. This article argues that the ethical issues that arise from APM - as both a standalone technology or as a converging one - affects the roles of stakeholders in such a way as to warrant an alternate means furthering responsible innovation in APM research. This article introduces a value-based design methodology called value sensitive design (VSD) that may serve as a suitable framework to adequately cater to the values of stakeholders. Ultimately, it is concluded that VSD is a strong candidate framework for addressing the moral concerns of stakeholders during the preliminary stages of technological development.

Prediction of seed gene function in progressive diabetic neuropathy by a network-based inference method

Guilt by association (GBA) algorithm has been widely used to statistically predict gene functions, and network-based approach increases the confidence and veracity of identifying molecular signatures for diseases. This work proposed a network-based GBA method by integrating the GBA algorithm and network, to identify seed gene functions for progressive diabetic neuropathy (PDN). The inference of predicting seed gene functions comprised of three steps: i) Preparing gene lists and sets; ii) constructing a co-expression matrix (CEM) on gene lists by Spearman correlation coefficient (SCC) method and iii) predicting gene functions by GBA algorithm. Ultimately, seed gene functions were selected according to the area under the receiver operating characteristics curve (AUC) index. A total of 79 differentially expressed genes (DEGs) and 40 background gene ontology (GO) terms were regarded as gene lists and sets for the subsequent analyses, respectively. The predicted results obtained from the network-based GBA approach showed that 27.5% of all gene sets had a good classified performance with AUC >0.5. Most significantly, 3 gene sets with AUC >0.6 were denoted as seed gene functions for PDN, including binding, molecular function and regulation of the metabolic process. In summary, we predicted 3 seed gene functions for PDN compared with non-progressors utilizing network-based GBA algorithm. The findings provide insights to reveal pathological and molecular mechanism underlying PDN.

Caring for trafficked and unidentified patients in the EHR shadows: Shining a light by sharing the data

OBJECTIVE

Healthcare providers have key roles in the prevention of, detection of, and interventions for human trafficking. Yet caring for trafficked persons is particularly challenging: patients whose identities are unknown, unreliable, or false could receive subpar care from providers delivering care in a vacuum of relevant information. The application of precision medicine principles and integration of biometric data (including genetic information) could facilitate patient identification, enable longitudinal medical records, and improve continuity and quality of care for this vulnerable patient population. Scant empirical data exist regarding healthcare system preparedness and care for the needs of this vulnerable population nor data on perspectives on the use and risks of biometrics or genetic information for trafficked patients.

METHODS

To address this gap, we conducted mixed-methods research involving semi-structured interviews with key informants, which informed a subsequent broad survey of physicians and registered nurses.

RESULTS

Our findings support the perception that trafficked persons obtain care yet remain unnoticed or undocumented in the electronic health record. Our survey findings further reveal that healthcare providers remain largely unaware of human trafficking issues and are inadequately prepared to provide patient-centered care for trafficked and unidentified patients.

CONCLUSION

Meaningful efforts to design and implement precision medicine initiatives in an inclusive way that optimizes impacts are unlikely to succeed without concurrent efforts to increase general awareness of and preparedness to care for trafficked persons. Additional research is needed to examine properly the potential utility for biometrics to improve the delivery of care for trafficked patients.

The Recent Applications and Developments of Bioinformatics and Omics Technologies in Traditional Chinese Medicine

Background:

Traditional Chinese Medicine (TCM) is widely utilized as complementary health care in China whose acceptance is still hindered by conventional scientific research methodology, although it has been exercised and implemented for nearly 2000 years. Identifying the molecular mechanisms, targets and bioactive components in TCM is a critical step in the modernization of TCM because of the complexity and uniqueness of the TCM system. With recent advances in computational approaches and high throughput technologies, it has become possible to understand the potential TCM mechanisms at the molecular and systematic level, to evaluate the effectiveness and toxicity of TCM treatments. Bioinformatics is gaining considerable attention to unearth the in-depth molecular mechanisms of TCM, which emerges as an interdisciplinary approach owing to the explosive omics data and development of computer science. Systems biology, based on the omics techniques, opens up a new perspective which enables us to investigate the holistic modulation effect on the body.

Objective:

This review aims to sum up the recent efforts of bioinformatics and omics techniques in the research of TCM including Systems biology, Metabolomics, Proteomics, Genomics and Transcriptomics.

Conclusion:

Overall, bioinformatics tools combined with omics techniques have been extensively used to scientifically support the ancient practice of TCM to be scientific and international through the acquisition, storage and analysis of biomedical data.

Development of Strategic Plans for Advancing Nursing in Korea

PURPOSE

The aim of the study is to evaluate the current and prospective status of nursing in Korea and develop a strategic framework and plan to accommodate the increased demands on nurses in the changing health-care system.

METHODS

This study used a mixed-methods approach including a literature review, an online survey with health-care consumers, expert panel interviews, and an analysis of strengths, weaknesses, opportunities, and threats to develop the strategic plans and framework.

RESULTS

The vision of the strategic framework involved improving health and quality of life, and its mission was to elevate the status of Korea's nursing sector as a key health-care profession through high-quality and innovative nursing education, research, and practice. The five values in accordance with the mission and vision were innovation, creation, collaboration, excellence, and authenticity. Three strategic goals, namely, education, research, and practice, were identified, and 31 related strategic tasks were developed.

CONCLUSION

In response to the rising social demand for a paradigm shift in nursing care services, there is a need for advancements in nursing education, research, and practice in Korea. This study provide some recommendations to achieve these aims.

Application of Machine Learning Models for Survival Prognosis in Breast Cancer Studies

The application of machine learning models for prediction and prognosis of disease development has become an irrevocable part of cancer studies aimed at improving the subsequent therapy and management of patients. The application of machine learning models for accurate prediction of survival time in breast cancer on the basis of clinical data is the main objective of the presented study. The paper discusses an approach to the problem in which the main factor used to predict survival time is the originally developed tumor-integrated clinical feature, which combines tumor stage, tumor size, and age at diagnosis. Two datasets from corresponding breast cancer studies are united by applying a data integration approach based on horizontal and vertical integration by using proper document-oriented and graph databases which show good performance and no data losses. Aside from data normalization and classification, the applied machine learning methods provide promising results in terms of accuracy of survival time prediction. The analysis of our experiments shows an advantage of the linear Support Vector Regression, Lasso regression, Kernel Ridge regression, K-neighborhood regression, and Decision Tree regression—these models achieve most accurate survival prognosis results. The cross-validation for accuracy demonstrates best performance of the same models on the studied breast cancer data. As a support for the proposed approach, a Python-based workflow has been developed and the plans for its further improvement are finally discussed in the paper.

Effect of benzo[a]pyrene on the expression of miR-126, miR-190a and their target genes EGFL7, TP53INP1 and PHLPP1 in primary endometrial cells

The main topic of this study was to investigate the effect of benzo[a]pyrene (BP) on microRNAs and their target genes expression levels in primary cell cultures from normal and malignant endometrial tissue. MicroRNA-126 (miR-126) and miR-190a were most sensitive to BP treatment. The treatment of both cultures with BP was accompanied by a decrease of miR-126 level and an increase of EGFL7 gene expression level. BP-induced upregulation of miR-190a was detected only in normal cells and it was accompanied with decrease of mRNA levels of TP53INP1 and PHLPP1 genes. Taking into account that BP promoted the proliferation of normal cells and amplified apoptosis of cancer cells, it is possible that miR-190a is involved in general cellular response to BP. The findings of this study indicate that miR-190a and its target genes may be involved in the regulation of cell fate under BP treatment.

Translational behavioral medicine for population and individual health: gaps, opportunities, and vision for practice-based translational behavior change research

In this commentary, we propose a vision for "practice-based translational behavior change research," which we define as clinical and public health practice-embedded research on the implementation, optimization, and fundamental mechanisms of behavioral interventions. This vision intends to be inclusive of important research elements for behavioral intervention development, testing, and implementation. We discuss important research gaps and conceptual and methodological advances in three key areas along the discovery (development) to delivery (implementation) continuum of evidence-based interventions to improve behavior and health that could help achieve our vision of practice-based translational behavior change research. We expect our proposed vision to be refined and evolve over time. Through highlighting critical gaps that can be addressed by integrating modern theoretical and methodological approaches across disciplines in behavioral medicine, we hope to inspire the development and funding of innovative research on more potent and implementable behavior change interventions for optimal population and individual health.

Regression-Based Network Estimation for High-Dimensional Genetic Data

Given the continuous advancement in genome sequencing technology, large volumes of gene expression data can be easily obtained. However, the corresponding increase in genetic information necessitates adoption of a new approach for network estimation. Data dimensions increase with the progress in genome sequencing technology, thereby making it difficult to estimate gene networks by causing multicollinearity. Furthermore, such a problem also occurs when hub nodes exist, where gene networks are known to have regulator genes that can be interpreted as hub nodes. This study aims at developing methods that demonstrate good performance when handling high-dimensional data with hub nodes. We propose regression-based approaches as feasible solutions in this article. Elastic-net and adaptive elastic-net penalty regressions were applied to compensate for the disadvantages of existing regression-based approaches employing LASSO or adaptive LASSO. Experiments were performed to compare the proposed regression-based approaches with other conventional methods. We confirmed the superior performance of the regression-based approaches and applied it to actual genetic data to verify the suitability to estimate gene networks. As results, robustness of the proposed methods was demonstrated with respect to high-dimensional gene expression data.

Making the Case for Functional Proteomics

"Making the Case for Functional Proteomics" first differentiates the Functional Proteome from the products of genetic protein expression. Qualitatively, the prevalence of posttranslational modifications (PTMs) virtually insures that individual, functional proteins do not equate to their genetic expression counterparts. Quantitatively, considering the frequency of PTMs and a conservative estimate of the number of functional entities arising from protein interactions, the size of the Functional Proteome exceeds that of the human genome by at least two orders of magnitude. The human genome does not, cannot, map the Functional Proteome. Further, the collective genome of the human microbiome dwarfs the human genome. With these facts established, "Making the Case…" proceeds to examine Functional Proteomics (of which both "gene expression" and "epigenetics" are but parts of a larger whole) within the context of Systems Biology, concluding that functionally related networks comprise the dominant motif for biological activity. Creating just such a network focus is essential in not only expanding basic knowledge but also in applying that knowledge in the pragmatic efforts of drug and biomarker development. Outlines for development of drugs and biomarkers, as well as the realization of precision medicine, within a functional proteomics-based, network motif are provided. The chapter proceeds to asses both the knowledge base and the tools to fully embrace Functional Proteomics. Given the decades-long infatuation with the reductionism of genomics, it is not surprising that both the proteomics knowledge base and tools are assessed as poor to fair. However, even a minor shift in research funding and a renewed challenge to methods developers will rapidly improve the current situation. Adoption of the included "Roadmap" will realistically make the twenty-first century the century of a long-awaited revolution in biology.

Therapeutic targeting of angiogenesis molecular pathways in angiogenesis-dependent diseases

Angiogenesis is a critical step in the progression of almost all human malignancies and some other life-threatening diseases. Anti-angiogenic therapy is a novel and effective approach for treatment of angiogenesis-dependent diseases such as cancer, diabetic retinopathy, and age-related macular degeneration. In this article, we will review the main strategies developed for anti-angiogenic therapies beside their clinical applications, the major challenges, and the latest advances in the development of anti-angiogenesis-based targeted therapies.

User Perspectives of a Web-Based Data-Sharing Platform (Open Humans) on Ethical Oversight in Participant-Led Research: Protocol for a Quantitative Study

BACKGROUND

Advances in medicine rely to a great extent on people's willingness to share their data with researchers. With increasingly widespread use of digital technologies, several Web-based communities have emerged aiming to enable their users to share large amounts of data, some of which can possibly be employed for research purposes by scientists, or to conduct participant-led research (PLR). Scholarship has recently addressed the necessity of interrogating how existing ethical standards can and should be applied and adapted in view of the specificities of such Web-based activities. So far, no study has explored participants' beliefs about and attitudes toward ethical oversight when it comes to platforms that involve medical data sharing.

OBJECTIVE

This paper presents the protocol for a survey study aimed at understanding users' beliefs about Web-based data-sharing platforms regarding how research ethics principles should be applied in such a setting. Furthermore, the study aims at quantitatively assessing the relationship between participants' perspectives on ethical oversight and other variables such as previous participation in research, beliefs about data sharing, and attitudes toward self-experimentation.

METHODS

We are conducting a Web-based survey with users of a popular Web-based data-sharing platform, Open Humans. The survey has been sent to approximately 4640 users registered for the Open Humans newsletter. To fill out the survey, participants need to have an account on Open Humans. We expect a 5%-10% response rate (between 200 and 400 completed surveys out of approximately 4000 survey invitations sent). Independent variables include past data-sharing behavior and intention, beliefs about data sharing, past participation in research, attitudes toward self-experimentation, perceived knowledge of the platform's guidelines and terms, perceived importance of having transparent guidelines, and governance-related beliefs. The main dependent variable is participants' expectations regarding who should ensure that ethical requirements are met within research projects conducted on open data-sharing platforms, based on Emanuel et al's ethical framework. We will use chi-square tests to assess the relationship between participants' expectations regarding ethical oversight and their past behavior, future intentions, beliefs, attitudes, and knowledge.

RESULTS

Data collection started on June 13, 2018. A reminder to fill out the survey was sent to participants in mid-July. We expect to gain insights on users' perspectives on the ethical oversight of Web-based data-sharing platforms and on the associated experiences, beliefs, and sociodemographic characteristics.

CONCLUSIONS

When digital tools allow people to engage in PLR including medical data, understanding how people interpret and envision the ethical oversight of their data-sharing practices is crucial. This will be the first study to explore users' perspectives on ethical oversight of Web-based data-sharing platforms. The results will help inform the development of a framework that can be employed for platforms hosting various kinds of research projects to accommodate participants' ethical oversight needs.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR1-10.2196/10939.

Exhaled breath profiling by electronic nose enabled discrimination of allergic rhinitis and extrinsic asthma

AIM

To assess whether an e-nose could discriminate between subjects affected by allergic rhinitis with and without concomitant extrinsic asthma, as well as from healthy controls, in terms of exhaled VOC-profile.

METHODS

Fourteen patients with Extrinsic Asthma and Allergic Rhinitis (AAR), 14 patients with Allergic Rhinitis without asthma (AR) and 14 healthy controls (HC) participated in a cross-sectional study. Exhaled breath was collected by a standardized method and sampled by an e-nose (Cyranose 320). Raw data were reduced by Principal component analysis and analyzed by canonical discriminant analysis. Cross-validation accuracy (CVA) and Receiver Operating Characteristic(ROC)-curves were calculated. External validation in newly recruited patients (7 AAR, 7 AR and 7 HC) was tested using the previous training model.

RESULTS

Breathprints of patients with AR clustered from those with AAR (CVA = 85.7%), as well as HC (CVA = 82.1%). Breathprints from AAR were also separated from those of HC (CVA = 75.0%). External validation confirmed the above findings.

CONCLUSIONS

An e-nose can discriminate exhaled breath from subjects with allergic rhinitis with and without extrinsic asthma, which represent two different diseases with partly overlapping features. This supports the view of using breath profiling to diagnose asthma also in patients with allergic rhinitis.

'Omic' technologies as a helpful tool in radioecological research

This article presents a brief review of the modern 'omic' technologies, namely genomics, epigenomics, transcriptomics, proteomics, and metabolomics, as well as the examples of their possible use in radioecology. For each technology, a short description of advances, limitations, and instrumental applications is given. In addition, the review contains examples of successful use of 'omic' technologies in the assessment of biological effects of pollutants in the field conditions.

Application of Electronic-Nose Technologies and VOC-Biomarkers for the Noninvasive Early Diagnosis of Gastrointestinal Diseases †

Conventional methods utilized for clinical diagnosis of gastrointestinal (GI) diseases have employed invasive medical procedures that cause stress, anxiety and pain to patients. These methods are often expensive, time-consuming, and require sophisticated chemical-analysis instruments and advanced modeling procedures to achieve diagnostic interpretations. This paper reviews recent applications of simpler, electronic-nose (e-nose) devices for the noninvasive early diagnosis of a wide range of GI diseases by collective analysis of headspace volatile organic compound (VOC)-metabolites from clinical samples to produce disease-specific aroma signatures (VOC profiles). A different "metabolomics" approach to GI disease diagnostics, involving identifications and quantifications of disease VOC-metabolites, are compared to the electronic-nose approach based on diagnostic costs, accuracy, advantages and disadvantages. The importance of changes in gut microbiome composition that result from disease are discussed relative to effects on disease detection. A new diagnostic approach, which combines the use of e-nose instruments for early rapid prophylactic disease-screenings with targeted identification of known disease biomarkers, is proposed to yield cheaper, quicker and more dependable diagnostic results. Some priority future research needs and coordination for bringing e-nose instruments into routine clinical practice are summarized.

Precision Medicine and Radiogenomics in Breast Cancer: New Approaches toward Diagnosis and Treatment

Precision medicine is medicine optimized to the genotypic and phenotypic characteristics of an individual and, when present, his or her disease. It has a host of targets, including genes and their transcripts, proteins, and metabolites. Studying precision medicine involves a systems biology approach that integrates mathematical modeling and biology genomics, transcriptomics, proteomics, and metabolomics. Moreover, precision medicine must consider not only the relatively static genetic codes of individuals, but also the dynamic and heterogeneous genetic codes of cancers. Thus, precision medicine relies not only on discovering identifiable targets for treatment and surveillance modification, but also on reliable, noninvasive methods of identifying changes in these targets over time. Imaging via radiomics and radiogenomics is poised for a central role. Radiomics, which extracts large volumes of quantitative data from digital images and amalgamates these together with clinical and patient data into searchable shared databases, potentiates radiogenomics, which is the combination of genetic and radiomic data. Radiogenomics may provide voxel-by-voxel genetic information for a complete, heterogeneous tumor or, in the setting of metastatic disease, set of tumors and thereby guide tailored therapy. Radiogenomics may also quantify lesion characteristics, to better differentiate between benign and malignant entities, and patient characteristics, to better stratify patients according to risk for disease, thereby allowing for more precise imaging and screening. This report provides an overview of precision medicine and discusses radiogenomics specifically in breast cancer. ^© RSNA, 2018.

Network‑based gene function inference method to predict optimal gene functions associated with fetal growth restriction

The guilt by association (GBA) principle has been widely used to predict gene functions, and a network‑based approach may enhance the confidence and stability of the analysis compared with focusing on individual genes. Fetal growth restriction (FGR), is the second primary cause of perinatal mortality. Therefore, the present study aimed to predict the optimal gene functions for FGR using a network‑based GBA method. The method was comprised of four parts: Identification of differentially‑expressed genes (DEGs) between patients with FGR and normal controls based on gene expression data; construction of a co‑expression network (CEN) dependent on DEGs, using the Spearman correlation coefficient algorithm; collection of gene ontology (GO) data on the basis of a known confirmed database and DEGs; and prediction of optimal gene functions using the GBA algorithm, for which the area under the receiver operating characteristic curve (AUC) was obtained for each GO term. A total of 115 DEGs and 109 GO terms were obtained for subsequent analysis. All DEGs were mapped to the CEN and formed 6,555 edges. The results of GBA algorithm demonstrated that 78 GO terms had a good classification performance with AUC >0.5. In particular, the AUC for 5 of the GO terms was >0.7, and these were defined as optimal gene functions, including defense response, immune system process, response to stress, cellular response to chemical stimulus and positive regulation of biological process. In conclusion, the results of the present study provided insights into the pathological mechanism underlying FGR, and provided potential biomarkers for early detection and targeted treatment of this disease. However, the interactions between the 5 GO terms remain unclear, and further studies are required.

DeCoST: A New Approach in Drug Repurposing From Control System Theory

In this paper, we propose DeCoST (Drug Repurposing from Control System Theory) framework to apply control system paradigm for drug repurposing purpose. Drug repurposing has become one of the most active areas in pharmacology since the last decade. Compared to traditional drug development, drug repurposing may provide more systematic and significantly less expensive approaches in discovering new treatments for complex diseases. Although drug repurposing techniques rapidly evolve from "one: disease-gene-drug" to "multi: gene, dru" and from "lazy guilt-by-association" to "systematic model-based pattern matching," mathematical system and control paradigm has not been widely applied to model the system biology connectivity among drugs, genes, and diseases. In this paradigm, our DeCoST framework, which is among the earliest approaches in drug repurposing with control theory paradigm, applies biological and pharmaceutical knowledge to quantify rich connective data sources among drugs, genes, and diseases to construct disease-specific mathematical model. We use linear-quadratic regulator control technique to assess the therapeutic effect of a drug in disease-specific treatment. DeCoST framework could classify between FDA-approved drugs and rejected/withdrawn drug, which is the foundation to apply DeCoST in recommending potentially new treatment. Applying DeCoST in Breast Cancer and Bladder Cancer, we reprofiled 8 promising candidate drugs for Breast Cancer ER+ (Erbitux, Flutamide, etc.), 2 drugs for Breast Cancer ER- (Daunorubicin and Donepezil) and 10 drugs for Bladder Cancer repurposing (Zafirlukast, Tenofovir, etc.).

Advances in metabolome information retrieval: turning chemistry into biology. Part I: analytical chemistry of the metabolome

Metabolites are small molecules produced by enzymatic reactions in a given organism. Metabolomics or metabolic phenotyping is a well-established omics aimed at comprehensively assessing metabolites in biological systems. These comprehensive analyses use analytical platforms, mainly nuclear magnetic resonance spectroscopy and mass spectrometry, along with associated separation methods to gather qualitative and quantitative data. Metabolomics holistically evaluates biological systems in an unbiased, data-driven approach that may ultimately support generation of hypotheses. The approach inherently allows the molecular characterization of a biological sample with regard to both internal (genetics) and environmental (exosome, microbiome) influences. Metabolomics workflows are based on whether the investigator knows a priori what kind of metabolites to assess. Thus, a targeted metabolomics approach is defined as a quantitative analysis (absolute concentrations are determined) or a semiquantitative analysis (relative intensities are determined) of a set of metabolites that are possibly linked to common chemical classes or a selected metabolic pathway. An untargeted metabolomics approach is a semiquantitative analysis of the largest possible number of metabolites contained in a biological sample. This is part I of a review intending to give an overview of the state of the art of major metabolic phenotyping technologies. Furthermore, their inherent analytical advantages and limits regarding experimental design, sample handling, standardization and workflow challenges are discussed.

Comparison of Protein Gut Samples from Rhipicephalus spp. Using a Crude and an Innovative Preparation Method for Proteome Analysis

Tick populations are controlled through the application of chemical pesticides. However, the rise in chemical resistance has prompted the investigation of other control methods such as the use of tick vaccines. Proteomic analysis provides valuable information about the possible function and localization of proteins, as candidate vaccine proteins are often either secreted or localized on the cell-surface membrane. Progress in the utilization of proteomics for the identification of novel treatment targets has been significant. However, their use in tick-specific investigations is still quite novel, with the continual development of tick-specific methodologies essential. In this study, an innovative sample preparation method was utilized to isolate epithelial cells from tick midguts to identify the membrane-bound proteins. Proteomic analysis was conducted comparing crude and innovative sample preparation methods with 692 and 1242 tick-specific proteins, 108 and 314 surface proteins respectively, isolated from the midguts of semi-engorged Rhipicephalus microplus adult female ticks. This research reports a novel preparation protocol for the analysis of tick midgut proteins which reduces host protein contamination.

E-health beyond technology: analyzing the paradigm shift that lies beneath

Information and computer technology has come to play an increasingly important role in medicine, to the extent that e-health has been described as a disruptive innovation or revolution in healthcare. The attention is very much focused on the technology itself, and advances that have been made in genetics and biology. This leads to the question: What is changing in medicine today concerning e-health? To what degree could these changes be characterized as a 'revolution'? We will apply the work of Thomas Kuhn, Larry Laudan, Michel Foucault and other philosophers-which offers an alternative understanding of progress and revolution in medicine to the classic discovery-oriented approach-to our analysis. Nowadays, the long-standing curative or reactive paradigm in medicine is facing a crisis due to an aging population, a significant increase in chronic diseases and the development of more expensive diagnostic tools and therapies. This promotes the evolution towards a new paradigm with an emphasis on preventive medicine. E-health constitutes an essential part of this new paradigm that seeks to solve the challenges presented by an aging population, skyrocketing costs and so forth. Our approach changes the focus from the technology itself toward the underlying paradigm shift in medicine. We will discuss the relevance of this approach by applying it to the surge in digital self-tracking through health apps and wearables: the recognition of the underlying paradigm shift leads to a more comprehensive understanding of self-tracking than a solely discovery-oriented or technology-focused view can provide.

From systems biology to P4 medicine: applications in respiratory medicine

Human health and disease are emergent properties of a complex, nonlinear, dynamic multilevel biological system: the human body. Systems biology is a comprehensive research strategy that has the potential to understand these emergent properties holistically. It stems from advancements in medical diagnostics, “omics” data and bioinformatic computing power. It paves the way forward towards “P4 medicine” (predictive, preventive, personalised and participatory), which seeks to better intervene preventively to preserve health or therapeutically to cure diseases. In this review, we: 1) discuss the principles of systems biology; 2) elaborate on how P4 medicine has the potential to shift healthcare from reactive medicine (treatment of illness) to predict and prevent illness, in a revolution that will be personalised in nature, probabilistic in essence and participatory driven; 3) review the current state of the art of network (systems) medicine in three prevalent respiratory diseases (chronic obstructive pulmonary disease, asthma and lung cancer); and 4) outline current challenges and future goals in the field.

Systems biology and network medicine have the potential to transform medical research and practicehttp://ow.ly/r3jR30hf35x

Revolution of Alzheimer Precision Neurology. Passageway of Systems Biology and Neurophysiology

The Precision Neurology development process implements systems theory with system biology and neurophysiology in a parallel, bidirectional research path: a combined hypothesis-driven investigation of systems dysfunction within distinct molecular, cellular, and large-scale neural network systems in both animal models as well as through tests for the usefulness of these candidate dynamic systems biomarkers in different diseases and subgroups at different stages of pathophysiological progression. This translational research path is paralleled by an "omics"-based, hypothesis-free, exploratory research pathway, which will collect multimodal data from progressing asymptomatic, preclinical, and clinical neurodegenerative disease (ND) populations, within the wide continuous biological and clinical spectrum of ND, applying high-throughput and high-content technologies combined with powerful computational and statistical modeling tools, aimed at identifying novel dysfunctional systems and predictive marker signatures associated with ND. The goals are to identify common biological denominators or differentiating classifiers across the continuum of ND during detectable stages of pathophysiological progression, characterize systems-based intermediate endophenotypes, validate multi-modal novel diagnostic systems biomarkers, and advance clinical intervention trial designs by utilizing systems-based intermediate endophenotypes and candidate surrogate markers. Achieving these goals is key to the ultimate development of early and effective individualized treatment of ND, such as Alzheimer's disease. The Alzheimer Precision Medicine Initiative (APMI) and cohort program (APMI-CP), as well as the Paris based core of the Sorbonne University Clinical Research Group "Alzheimer Precision Medicine" (GRC-APM) were recently launched to facilitate the passageway from conventional clinical diagnostic and drug development toward breakthrough innovation based on the investigation of the comprehensive biological nature of aging individuals. The APMI movement is gaining momentum to systematically apply both systems neurophysiology and systems biology in exploratory translational neuroscience research on ND.

Applications of Machine Learning in Cancer Prediction and Prognosis

Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to “learn” from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets. This capability is particularly well-suited to medical applications, especially those that depend on complex proteomic and genomic measurements. As a result, machine learning is frequently used in cancer diagnosis and detection. More recently machine learning has been applied to cancer prognosis and prediction. This latter approach is particularly interesting as it is part of a growing trend towards personalized, predictive medicine. In assembling this review we conducted a broad survey of the different types of machine learning methods being used, the types of data being integrated and the performance of these methods in cancer prediction and prognosis. A number of trends are noted, including a growing dependence on protein biomarkers and microarray data, a strong bias towards applications in prostate and breast cancer, and a heavy reliance on “older” technologies such artificial neural networks (ANNs) instead of more recently developed or more easily interpretable machine learning methods. A number of published studies also appear to lack an appropriate level of validation or testing. Among the better designed and validated studies it is clear that machine learning methods can be used to substantially (15–25%) improve the accuracy of predicting cancer susceptibility, recurrence and mortality. At a more fundamental level, it is also evident that machine learning is also helping to improve our basic understanding of cancer development and progression.

Research and Discovery Science and the Future of Dental Education and Practice

Dental graduates of 2040 will face new and complex challenges. If they are to meet these challenges, dental schools must develop a research and discovery mission that will equip graduates with the new knowledge required to function in a modern health care environment. The dental practitioner of 2040 will place greater emphasis on risk assessment, disease prevention, and health maintenance; and the emerging discipline of precision medicine and systems biology will revolutionize disease diagnosis and reveal new targeted therapies. The dental graduate of 2040 will be expected to function effectively in a collaborative, learning health care system and to understand the impact of health care policy on local, national, and global communities. Emerging scientific fields such as big data analytics, stem cell biology, tissue engineering, and advanced biomimetics will impact dental practice. Despite all the warning signs indicating how the changing scientific and heath care landscape will dramatically alter dental education and dental practice, dental schools have yet to reconsider their research and educational priorities and clinical practice objectives. Until dental schools and the practicing community come to grips with these challenges, this persistent attitude of complacency will likely be at the dental profession's peril. This article was written as part of the project "Advancing Dental Education in the 21^st Century."

The Role of Omics in the Application of Adverse Outcome Pathways for Chemical Risk Assessment

In conjunction with the second International Environmental Omics Symposium (iEOS) conference, held at the University of Liverpool (United Kingdom) in September 2014, a workshop was held to bring together experts in toxicology and regulatory science from academia, government and industry. The purpose of the workshop was to review the specific roles that high-content omics datasets (eg, transcriptomics, metabolomics, lipidomics, and proteomics) can hold within the adverse outcome pathway (AOP) framework for supporting ecological and human health risk assessments. In light of the growing number of examples of the application of omics data in the context of ecological risk assessment, we considered how omics datasets might continue to support the AOP framework. In particular, the role of omics in identifying potential AOP molecular initiating events and providing supportive evidence of key events at different levels of biological organization and across taxonomic groups was discussed. Areas with potential for short and medium-term breakthroughs were also discussed, such as providing mechanistic evidence to support chemical read-across, providing weight of evidence information for mode of action assignment, understanding biological networks, and developing robust extrapolations of species-sensitivity. Key challenges that need to be addressed were considered, including the need for a cohesive approach towards experimental design, the lack of a mutually agreed framework to quantitatively link genes and pathways to key events, and the need for better interpretation of chemically induced changes at the molecular level. This article was developed to provide an overview of ecological risk assessment process and a perspective on how high content molecular-level datasets can support the future of assessment procedures through the AOP framework.

Personalized Medicine in a New Genomic Era: Ethical and Legal Aspects

The genome of two completely unrelated individuals is quite similar apart from minor variations called single nucleotide polymorphisms which contribute to the uniqueness of each and every person. These single nucleotide polymorphisms are of great interest clinically as they are useful in figuring out the susceptibility of certain individuals to particular diseases and for recognizing varied responses to pharmacological interventions. This gives rise to the idea of 'personalized medicine' as an exciting new therapeutic science in this genomic era. Personalized medicine suggests a unique treatment strategy based on an individual's genetic make-up. Its key principles revolve around applied pharmaco-genomics, pharmaco-kinetics and pharmaco-proteomics. Herein, the ethical and legal aspects of personalized medicine in a new genomic era are briefly addressed. The ultimate goal is to comprehensively recognize all relevant forms of genetic variation in each individual and be able to interpret this information in a clinically meaningful manner within the ambit of ethical and legal considerations. The authors of this article firmly believe that personalized medicine has the potential to revolutionize the current landscape of medicine as it makes its way into clinical practice.

17beta-hydroxysteroid dehydrogenase type 5 is negatively correlated to apoptosis inhibitor GRP78 and tumor-secreted protein PGK1, and modulates breast cancer cell viability and proliferation

17beta-hydroxysteroid dehydrogenase type 5 (17β-HSD5) is an important enzyme associated with sex steroid metabolism in hormone-dependent cancer. However, reports on its expression and its prognostic value in breast cancer are inconsistent. Here, we demonstrate the impact of 17β-HSD5 expression modulation on the proteome of estrogen receptor-positive (ER+) breast cancer cells. RNA interference technique (siRNA) was used to knock down 17β-HSD5 gene expression in the ER+ breast cancer cell line MCF-7 and the proteome of the 17β-HSD5-knockdown cells was compared to that of MCF-7 cells using two-dimensional (2-D) gel electrophoresis followed by mass spectrometry analysis. Ingenuity pathway analysis (IPA) was additionally used to assess functional enrichment analyses of the proteomic dataset, including protein network and canonical pathways. Our proteomic analysis revealed only four differentially expressed protein spots (fold change > 2, p<0.05) between the two cell lines. The four spots were up-regulated in 17β-HSD5-knockdown MCF-7 cells, and comprised 21 proteins involved in two networks and in functions that include apoptosis inhibition, regulation of cell growth and differentiation, signal transduction and tumor metastasis. Among the proteins are nucleoside diphosphate kinase A (NME1), 78kDa glucose-regulated protein (GRP78) and phosphoglycerate kinase 1 (PGK1). We also showed that expression of 17β-HSD5 and that of the apoptosis inhibitor GRP78 are strongly but negatively correlated. Consistent with their opposite regulation, GRP78 knockdown decreased MCF-7 cell viability whereas 17β-HSD5 knockdown or inhibition increased cell viability and proliferation. Besides, IPA analysis revealed that ubiquitination pathway is significantly affected by 17β-HSD5 knockdown. Furthermore, IPA predicted the proto-oncogene c-Myc as an upstream regulator linked to the tumor-secreted protein PGK1. The latter is over-expressed in invasive ductal breast carcinoma as compared with normal breast tissue and its expression increased following 17β-HSD5 knockdown. Our present results indicate a 17β-HSD5 role in down-regulating breast cancer development. We thus propose that 17β-HSD5 may not be a potent target for breast cancer treatment but its low expression could represent a poor prognosis factor.

A genetic screen in combination with biochemical analysis in Saccharomyces cerevisiae indicates that phenazine-1-carboxylic acid is harmful to vesicular trafficking and autophagy

The environmentally friendly antibiotic phenazine-1-carboxylic acid (PCA) protects plants, mammals and humans effectively against various fungal pathogens. However, the mechanism by which PCA inhibits or kills fungal pathogens is not fully understood. We analyzed the effects of PCA on the growth of two fungal model organisms, Saccharomyces cerevisiae and Candida albicans, and found that PCA inhibited yeast growth in a dose-dependent manner which was inversely dependent on pH. In contrast, the commonly used antibiotic hygromycin B acted in a dose-dependent manner as pH increased. We then screened a yeast mutant library to identify genes whose mutation or deletion conferred resistance or sensitivity to PCA. We isolated 193 PCA-resistant or PCA-sensitive mutants in clusters, including vesicle-trafficking- and autophagy-defective mutants. Further analysis showed that unlike hygromycin B, PCA significantly altered intracellular vesicular trafficking under growth conditions and blocked autophagy under starvation conditions. These results suggest that PCA inhibits or kills pathogenic fungi in a complex way, in part by disrupting vesicular trafficking and autophagy.

Systems Medicine-Complexity Within, Simplicity Without

This paper presents a brief history of Systems Theory, progresses to Systems Biology, and its relation to the more traditional investigative method of reductionism. The emergence of Systems Medicine represents the application of Systems Biology to disease and clinical issues. The challenges faced by this transition from Systems Biology to Systems Medicine are explained; the requirements of physicians at the bedside, caring for patients, as well as the place of human-human interaction and the needs of the patients are addressed. An organ-focused transition to Systems Medicine, rather than a genomic-, molecular-, or cell-based effort is emphasized. Organ focus represents a middle-out approach to ease this transition and to maximize the benefits of scientific discovery and clinical application. This method manages the perceptions of time and space, the massive amounts of human- and patient-related data, and the ensuing complexity of information.

Intriguing Interaction of Bacteriophage-Host Association: An Understanding in the Era of Omics

Innovations in next-generation sequencing technology have introduced new avenues in microbial studies through “omics” approaches. This technology has considerably augmented the knowledge of the microbial world without isolation prior to their identification. With an enormous volume of bacterial “omics” data, considerable attempts have been recently invested to improve an insight into virosphere. The interplay between bacteriophages and their host has created a significant influence on the biogeochemical cycles, microbial diversity, and bacterial population regulation. This review highlights various concepts such as genomics, transcriptomics, proteomics, and metabolomics to infer the phylogenetic affiliation and function of bacteriophages and their impact on diverse microbial communities. Omics technologies illuminate the role of bacteriophage in an environment, the influences of phage proteins on the bacterial host and provide information about the genes important for interaction with bacteria. These investigations will reveal some of bio-molecules and biomarkers of the novel phage which demand to be unveiled.

Molecular Elucidation of Disease Biomarkers at the Interface of Chemistry and Biology

Disease-related biomarkers are objectively measurable molecular signatures of physiological status that can serve as disease indicators or drug targets in clinical diagnosis and therapy, thus acting as a tool in support of personalized medicine. For example, the prostate-specific antigen (PSA) biomarker is now widely used to screen patients for prostate cancer. However, few such biomarkers are currently available, and the process of biomarker identification and validation is prolonged and complicated by inefficient methods of discovery and few reliable analytical platforms. Therefore, in this Perspective, we look at the advanced chemistry of aptamer molecules and their significant role as molecular probes in biomarker studies. As a special class of functional nucleic acids evolved from an iterative technology termed Systematic Evolution of Ligands by Exponential Enrichment (SELEX), these single-stranded oligonucleotides can recognize their respective targets with selectivity and affinity comparable to those of protein antibodies. Because of their fast turnaround time and exceptional chemical properties, aptamer probes can serve as novel molecular tools for biomarker investigations, particularly in assisting identification of new disease-related biomarkers. More importantly, aptamers are able to recognize biomarkers from complex biological environments such as blood serum and cell surfaces, which can provide direct evidence for further clinical applications. This Perspective highlights several major advancements of aptamer-based biomarker discovery strategies and their potential contribution to the practice of precision medicine.

Metabolomic Biomarker Identification in Presence of Outliers and Missing Values

Metabolomics is the sophisticated and high-throughput technology based on the entire set of metabolites which is known as the connector between genotypes and phenotypes. For any phenotypic changes, potential metabolite (biomarker) identification is very important because it provides diagnostic as well as prognostic markers and can help to develop new biomolecular therapy. Biomarker identification from metabolomics data analysis is hampered by the use of high-throughput technology that provides high dimensional data matrix which contains missing values as well as outliers. However, missing value imputation and outliers handling techniques play important role in identifying biomarker correctly. Although several missing value imputation techniques are available, outliers deteriorate the accuracy of imputation as well as the accuracy of biomarker identification. Therefore, in this paper we have proposed a new biomarker identification technique combining the groupwise robust singular value decomposition, t-test, and fold-change approach that can identify biomarkers more correctly from metabolomics dataset. We have also compared the performance of the proposed technique with those of other traditional techniques for biomarker identification using both simulated and real data analysis in absence and presence of outliers. Using our proposed method in hepatocellular carcinoma (HCC) dataset, we have also identified the four upregulated and two downregulated metabolites as potential metabolomic biomarkers for HCC disease.

Decision support systems for personalized and participative radiation oncology

A paradigm shift from current population based medicine to personalized and participative medicine is underway. This transition is being supported by the development of clinical decision support systems based on prediction models of treatment outcome. In radiation oncology, these models 'learn' using advanced and innovative information technologies (ideally in a distributed fashion - please watch the animation: http://youtu.be/ZDJFOxpwqEA) from all available/appropriate medical data (clinical, treatment, imaging, biological/genetic, etc.) to achieve the highest possible accuracy with respect to prediction of tumor response and normal tissue toxicity. In this position paper, we deliver an overview of the factors that are associated with outcome in radiation oncology and discuss the methodology behind the development of accurate prediction models, which is a multi-faceted process. Subsequent to initial development/validation and clinical introduction, decision support systems should be constantly re-evaluated (through quality assurance procedures) in different patient datasets in order to refine and re-optimize the models, ensuring the continuous utility of the models. In the reasonably near future, decision support systems will be fully integrated within the clinic, with data and knowledge being shared in a standardized, dynamic, and potentially global manner enabling truly personalized and participative medicine.

Prediction models for the risk of gestational diabetes: a systematic review

BACKGROUND

Numerous prediction models for gestational diabetes mellitus (GDM) have been developed, but their methodological quality is unknown. The objective is to systematically review all studies describing first-trimester prediction models for GDM and to assess their methodological quality.

METHODS

MEDLINE and EMBASE were searched until December 2014. Key words for GDM, first trimester of pregnancy, and prediction modeling studies were combined. Prediction models for GDM performed up to 14 weeks of gestation that only include routinely measured predictors were eligible.Data was extracted by the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS). Data on risk predictors and performance measures were also extracted. Each study was scored for risk of bias.

RESULTS

Our search yielded 7761 articles, of which 17 were eligible for review (14 development studies and 3 external validation studies). The definition and prevalence of GDM varied widely across studies. Maternal age and body mass index were the most common predictors. Discrimination was acceptable for all studies. Calibration was reported for four studies. Risk of bias for participant selection, predictor assessment, and outcome assessment was low in general. Moderate to high risk of bias was seen for the number of events, attrition, and analysis.

CONCLUSIONS

Most studies showed moderate to low methodological quality, and few prediction models for GDM have been externally validated. External validation is recommended to enhance generalizability and assess their true value in clinical practice.