Proteomics: from single molecules to biological pathways

The Importance, Challenges, and Possible Solutions for Sharing Proteomics Data While Safeguarding Individuals' Privacy

Proteomics data sharing has profound benefits at the individual level as well as at the community level. While data sharing has increased over the years, mostly due to journal and funding agency requirements, the reluctance of researchers with regard to data sharing is evident as many shares only the bare minimum dataset required to publish an article. In many cases, proper metadata is missing, essentially making the dataset useless. This behavior can be explained by a lack of incentives, insufficient awareness, or a lack of clarity surrounding ethical issues. Through adequate training at research institutes, researchers can realize the benefits associated with data sharing and can accelerate the norm of data sharing for the field of proteomics, as has been the standard in genomics for decades. In this article, we have put together various repository options available for proteomics data. We have also added pros and cons of those repositories to facilitate researchers in selecting the repository most suitable for their data submission. It is also important to note that a few types of proteomics data have the potential to re-identify an individual in certain scenarios. In such cases, extra caution should be taken to remove any personal identifiers before sharing on public repositories. Data sets that will be useless without personal identifiers need to be shared in a controlled access repository so that only authorized researchers can access the data and personal identifiers are kept safe.

MicroRNA-1 Deficiency Is a Primary Etiological Factor Disrupting Cardiac Contractility and Electrophysiological Homeostasis

BACKGROUND

MicroRNA-1 (miR1), encoded by the genes miR1-1 and miR1-2, is the most abundant microRNA in the heart and plays a critical role in heart development and physiology. Dysregulation of miR1 has been associated with various heart diseases, where a significant reduction (>75%) in miR1 expression has been observed in patient hearts with atrial fibrillation or acute myocardial infarction. However, it remains uncertain whether miR1-deficiency acts as a primary etiological factor of cardiac remodeling.

METHODS

miR1-1 or miR1-2 knockout mice were crossbred to produce 75%-miR1-knockdown (75%KD; miR1-1+/-:miR1-2-/- or miR1-1-/-:miR1-2+/-) mice. Cardiac pathology of 75%KD cardiomyocytes/hearts was investigated by ECG, patch clamping, optical mapping, transcriptomic, and proteomic assays.

RESULTS

In adult 75%KD hearts, the overall miR1 expression was reduced to ≈25% of the normal wild-type level. These adult 75%KD hearts displayed decreased ejection fraction and fractional shortening, prolonged QRS and QT intervals, and high susceptibility to arrhythmias. Adult 75%KD cardiomyocytes exhibited prolonged action potentials with impaired repolarization and excitation-contraction coupling. Comparatively, 75%KD cardiomyocytes showcased reduced Na+ current and transient outward potassium current, coupled with elevated L-type Ca2+ current, as opposed to wild-type cells. RNA sequencing and proteomics assays indicated negative regulation of cardiac muscle contraction and ion channel activities, along with a positive enrichment of smooth muscle contraction genes in 75%KD cardiomyocytes/hearts. miR1 deficiency led to dysregulation of a wide gene network, with miR1's RNA interference-direct targets influencing many indirectly regulated genes. Furthermore, after 6 weeks of bi-weekly intravenous tail-vein injection of miR1 mimics, the ejection fraction and fractional shortening of 75%KD hearts showed significant improvement but remained susceptible to arrhythmias.

CONCLUSIONS

miR1 deficiency acts as a primary etiological factor in inducing cardiac remodeling via disrupting heart regulatory homeostasis. Achieving stable and appropriate microRNA expression levels in the heart is critical for effective microRNA-based therapy in cardiovascular diseases.

Global Proteomics for Identifying the Alteration Pathway of Niemann-Pick Disease Type C Using Hepatic Cell Models

Niemann-Pick disease type C (NPC) is an autosomal recessive disorder with progressive neurodegeneration. Although the causative genes were previously identified, NPC has unclear pathophysiological aspects, and patients with NPC present various symptoms and onset ages. However, various novel biomarkers and metabolic alterations have been investigated; at present, few comprehensive proteomic alterations have been reported in relation to NPC. In this study, we aimed to elucidate proteomic alterations in NPC and perform a global proteomics analysis for NPC model cells. First, we developed two NPC cell models by knocking out NPC1 using CRISPR/Cas9 (KO1 and KO2). Second, we performed a label-free (LF) global proteomics analysis. Using the LF approach, more than 300 proteins, defined as differentially expressed proteins (DEPs), changed in the KO1 and/or KO2 cells, while the two models shared 35 DEPs. As a bioinformatics analysis, the construction of a protein-protein interaction (PPI) network and an enrichment analysis showed that common characteristic pathways such as ferroptosis and mitophagy were identified in the two model cells. There are few reports of the involvement of NPC in ferroptosis, and this study presents ferroptosis as an altered pathway in NPC. On the other hand, many other pathways and DEPs were previously suggested to be associated with NPC, supporting the link between the proteome analyzed here and NPC. Therapeutic research based on these results is expected in the future.

Uncovering Protein Networks in Cardiovascular Proteomics

Biological networks have been widely used in many different diseases to identify potential biomarkers and design drug targets. In the present review, we describe the main computational techniques for reconstructing and analyzing different types of protein networks and summarize the previous applications of such techniques in cardiovascular diseases. Existing tools are critically compared, discussing when each method is preferred such as the use of co-expression networks for functional annotation of protein clusters and the use of directed networks for inferring regulatory associations. Finally, we are presenting examples of reconstructing protein networks of different types (regulatory, co-expression, and protein-protein interaction networks). We demonstrate the necessity to reconstruct networks separately for each cardiovascular tissue type and disease entity and provide illustrative examples of the importance of taking into consideration relevant post-translational modifications. Finally, we demonstrate and discuss how the findings of protein networks could be interpreted using single-cell RNA-sequencing data.

The role of microbial ecology in improving the performance of anaerobic digestion of sewage sludge

The use of next-generation diagnostic tools to optimise the anaerobic digestion of municipal sewage sludge has the potential to increase renewable natural gas recovery, improve the reuse of biosolid fertilisers and help operators expand circular economies globally. This review aims to provide perspectives on the role of microbial ecology in improving digester performance in wastewater treatment plants, highlighting that a systems biology approach is fundamental for monitoring mesophilic anaerobic sewage sludge in continuously stirred reactor tanks. We further highlight the potential applications arising from investigations into sludge ecology. The principal limitation for improvements in methane recoveries or in process stability of anaerobic digestion, especially after pre-treatment or during co-digestion, are ecological knowledge gaps related to the front-end metabolism (hydrolysis and fermentation). Operational problems such as stable biological foaming are a key problem, for which ecological markers are a suitable approach. However, no biomarkers exist yet to assist in monitoring and management of clade-specific foaming potentials along with other risks, such as pollutants and pathogens. Fundamental ecological principles apply to anaerobic digestion, which presents opportunities to predict and manipulate reactor functions. The path ahead for mapping ecological markers on process endpoints and risk factors of anaerobic digestion will involve numerical ecology, an expanding field that employs metrics derived from alpha, beta, phylogenetic, taxonomic, and functional diversity, as well as from phenotypes or life strategies derived from genetic potentials. In contrast to addressing operational issues (as noted above), which are effectively addressed by whole population or individual biomarkers, broad improvement and optimisation of function will require enhancement of hydrolysis and acidogenic processes. This will require a discovery-based approach, which will involve integrative research involving the proteome and metabolome. This will utilise, but overcome current limitations of DNA-centric approaches, and likely have broad application outside the specific field of anaerobic digestion.

Molecular Microbial Community Analysis as an Analysis Tool for Optimal Biogas Production

The microbial diversity in anaerobic digestion (AD) is important because it affects process robustness. High-throughput sequencing offers high-resolution data regarding the microbial diversity and robustness of biological systems including AD; however, to understand the dynamics of microbial processes, knowing the microbial diversity is not adequate alone. Advanced meta-omic techniques have been established to determine the activity and interactions among organisms in biological processes like AD. Results of these methods can be used to identify biomarkers for AD states. This can aid a better understanding of system dynamics and be applied to producing comprehensive models for AD. The paper provides valuable knowledge regarding the possibility of integration of molecular methods in AD. Although meta-genomic methods are not suitable for on-line use due to long operating time and high costs, they provide extensive insight into the microbial phylogeny in AD. Meta-proteomics can also be explored in the demonstration projects for failure prediction. However, for these methods to be fully realised in AD, a biomarker database needs to be developed.

Integrative transcriptome, proteome, and microRNA analysis reveals the effects of nitrogen sufficiency and deficiency conditions on theanine metabolism in the tea plant (Camellia sinensis)

Nitrogen (N) is associated with amino acid metabolism in higher plants. Theanine is an important amino acid in tea plants. To explore the relationship between theanine metabolism and N conditions, we examined the differentially expressed genes (DEGs), proteins (DEPs), and microRNAs (DEMs) involved in theanine metabolism in tea plant shoots and roots under N sufficiency and deficiency conditions. Transcriptome, proteome, and microRNA analyses were performed on tea plant shoots and roots under N sufficiency and deficiency conditions. The contents of theanine, expression levels of genes involved in theanine metabolism, contents of proteinogenic amino acids, and activity of enzymes were analyzed. The DEP-DEG correlation pairs and negative DEM-DEG interactions related to theanine metabolism were identified based on correlation analyses. The expression profiles of DEGs and negative DEM-DEG pairs related to theanine biosynthesis were consistent with the sequencing results. Our results suggest that the molecular and physiological mechanism of theanine accumulation is significantly affected by N sufficiency and deficiency conditions. The DEGs, DEPs, and DEMs and the activity of the enzymes involved in theanine biosynthesis might play vital roles in theanine accumulation under N sufficiency and deficiency conditions in the shoots and roots of tea plants.

Is proteomics of value in cardiovascular risk assessment?

PURPOSE OF REVIEW

To briefly summarize recently published evidence in the field of cardiovascular proteomics, focusing on its ability to improve cardiovascular risk stratification and critically discussing still open and burning issues and future perspectives of proteomics research.

RECENT FINDINGS

Several epidemiological studies have demonstrated an improvement in cardiovascular risk prediction beyond traditional risk factors by adding novel biomarkers, identified by both discovery and targeted proteomics. However, only a moderate improvement in risk discrimination over clinical variables was observed. Moreover, despite different outcomes there was also a strong overlap of identified candidates, with several of them being already well established cardiovascular risk markers such as growth differentiation factor 15, natriuretic peptides, C-reactive protein, interleukins, and metalloproteases.

SUMMARY

Although proteomics plays a crucial role in biomarker discovery, the modest discriminative ability of this technique raises the possibility that there are still hidden mechanisms in protein regulatory networks, which urgently need to be evaluated to improve a cardiovascular risk assessment to a clinically significant extent.

Differentially Expressed Protein Are Involved in Dynamic Changes of Catechins Contents in Postharvest Tea Leaves under Different Temperatures

In this study, isobaric tags for relative and absolute quantitation (iTRAQ) technology were used to investigate three samples from postharvest tea leaves that were treated at room temperature (25 °C, control group), high temperature (38 °C), and low temperature (4 °C) for 4 h. In heat and cold treatments, a total of 635 and 566 differentially expressed proteins (DEPs) were determined, respectively. DEPs were annotated to GO and KEGG databases, which revealed that DEPs involved in various aspects of biological process. Three catechins-related DEPs, CsCHI, CsF3H, and CsANR, were identified. Both catechins contents and the expression profiles of catechins biosynthesis-related genes changed significantly under different temperature treatments. The correlations between catechins contents, gene expression profiles, and DEPs were analyzed. This study provides potential new insights into the molecular basis for tea production of postharvest leaves and catechins content changes at diverse temperature conditions and will guide the improvement of tea-processing technology.

Integration of Transcriptome, Proteome, and Metabolome Provides Insights into How Calcium Enhances the Mechanical Strength of Herbaceous Peony Inflorescence Stems

Weak stem mechanical strength severely restrains cut flowers quality and stem weakness can be alleviated by calcium (Ca) treatment, but the mechanisms underlying Ca-mediated enhancement of stem mechanical strength remain largely unknown. In this study, we performed a comparative transcriptomic, proteomic, and metabolomic analysis of herbaceous peony (Paeonia lactiflora Pall.) inflorescence stems treated with nanometer Ca carbonate (Nano-CaCO₃). In total, 2643 differentially expressed genes (DEGs) and 892 differentially expressed proteins (DEPs) were detected between the Control and nano-CaCO₃ treatment. Among the 892 DEPs, 152 were coregulated at both the proteomic and transcriptomic levels, and 24 DEPs related to the secondary cell wall were involved in signal transduction, energy metabolism, carbohydrate metabolism and lignin biosynthesis, most of which were upregulated after nano-CaCO₃ treatment during the development of inflorescence stems. Among these four pathways, numerous differentially expressed metabolites (DEMs) related to lignin biosynthesis were identified. Furthermore, structural observations revealed the thickening of the sclerenchyma cell walls, and the main wall constitutive component lignin accumulated significantly in response to nano-CaCO₃ treatment, thereby indicating that Ca can enhance the mechanical strength of the inflorescence stems by increasing the lignin accumulation. These results provided insights into how Ca treatment enhances the mechanical strength of inflorescence stems in P. lactiflora.

Hub genes and key pathways of non-small lung cancer identified using bioinformatics

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, accounting for ~80% of all lung cancer cases. The aim of the present study was to identify key genes and pathways in NSCLC, in order to improve understanding of the mechanism of lung cancer. The GSE33532 gene expression dataset, containing 20 normal and 80 NSCLC samples, was used. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to obtain the enrichment data of differently expressed genes (DEGs). Disease modules within NSCLC were constructed by Cytoscape, using protein-protein interaction (PPI) from the Search Tool for the Retrieval of Interacting Genes database. In addition, the Kaplan Meier plotter KMplot was used to assess the top hub genes in the PPI network. As a result, 1,795 genes were identified in NSCLC; 729 were upregulated and 1,066 were downregulated. The results of the GO analysis indicated that the upregulated DEGs were significantly enriched in 'biological processes' (BP), including 'cell cycle and nuclear division'; the downregulated DEGs were also significantly enriched in BP, including 'response to wounding', 'anatomical structure morphogenesis' and 'response to stimulus'. Upregulated DEGs were also enriched in 'cell cycle', 'DNA replication' and the 'tumor protein 53 signaling pathway', while the downregulated DEGs were also enriched in 'complement and coagulation cascades', 'malaria' and 'cell adhesion molecules'. The top 9 hub genes were cyclin-dependent kinase 9 (CDK1), polo-like kinase 1, aurora kinase B, cell division cycle 20, baculoviral initiator of apoptosis repeat containing 5, mitotic checkpoint serine/threonine kinase B, proliferating cell nuclear antigen (PCNA), centromere protein A and MAD2 mitotic arrest deficient-like 1, and the KMplot results revealed that the high expression levels of these genes resulted in significantly low survival rates, compared with low expression samples (P<0.05), with the exception of PCNA and CDK1. In the pathway crosstalk analysis, 26 nodes and 41 interactions were divided into two groups: One module of the two groups primarily included 'metabolism of amino acid' and the other primarily contained 'tumor necrosis signaling' pathways. In conclusion, the present study assisted in improving the understanding of the molecular mechanisms underlying NSCLC development, and the results may help the understanding of the biological mechanism of NSCLC.

Integrating Genes Affecting Coronary Artery Disease in Functional Networks by Multi-OMICs Approach

Coronary artery disease (CAD) and myocardial infarction (MI) remain among the leading causes of mortality worldwide, urgently demanding a better understanding of disease etiology, and more efficient therapeutic strategies. Genetic predisposition as well as the environment and lifestyle are thought to contribute to disease risk. It is likely that non-linear and complex interactions occur between these multiple factors, involving simultaneous pathological changes in diverse cell types, tissues, and organs, at multiple molecular levels. Recent technological advances have exponentially expanded the breadth of available -omics data, from genome, epigenome, transcriptome, proteome, metabolome to even the microbiome. Integration of multiple layers of information across several -omics domains, i.e., the so-called multi-omics approach, currently holds the promise as a path toward precision medicine. Indeed, a more meaningful interpretation of genotype-phenotype relationships and the development of successful therapeutics tailored to individual patients are urgently needed. In this review, we will summarize recent findings and applications of integrative multi-omics in elucidating the etiology of CAD/MI; with a special focus on established disease susceptibility loci sequentially identified in genome-wide association studies (GWAS) over the last 10 years. Moreover, in addition to the autosomal genome, we will also consider the genetic variation in our “second genome”—the mitochondrial genome. Finally, we will summarize the current challenges in the field and point to future research directions required in order to successfully and effectively apply these approaches for precision medicine.

Proteomes and Phosphoproteomes of Anther and Pollen: Availability and Progress

In flowering plants, anther development plays crucial role in sexual reproduction. Within the anther, microspore mother cells meiosis produces microspores, which further develop into pollen grains that play decisive role in plant reproduction. Previous studies on anther biology mainly focused on single gene functions relying on genetic and molecular methods. Recently, anther development has been expanded from multiple OMICS approaches like transcriptomics, proteomics/phosphoproteomics, and metabolomics. The development of proteomics techniques allowing increased proteome coverage and quantitative measurements of proteins which can characterize proteomes and their modulation during normal development, biotic and abiotic stresses in anther development. In this review, we summarize the achievements of proteomics and phosphoproteomics with anther and pollen organs from model plant and crop species (i.e. Arabidopsis, rice, tobacco). The increased proteomic information facilitated translation of information from the models to crops and thus aid in agricultural improvement.

CsGOGAT Is Important in Dynamic Changes of Theanine Content in Postharvest Tea Plant Leaves under Different Temperature and Shading Spreadings

We analyzed the changes of theanine content in postharvest tea leaves under high temperature (38 °C), low temperature (4 °C), and shading spreadings by using ultrahigh-performance liquid chromatography. The differentially expressed proteins (DEPs), CsFd-GOGAT and CsNADH-GOGAT, which are involved in theanine biosynthesis pathway, were identified from the corresponding proteome data. The protein-protein interactions of CsFd-GOGAT and CsNADH-GOGAT, CsTS1, or CsNiR were verified by yeast two-hybrid technology. The expression profiles of 17 genes in theanine metabolism, including CsFd-GOGAT and CsNADH-GOGAT, were analyzed by quantitative real-time polymerase chain reaction. The correlations between the dynamic changes of theanine content and expression profiles of related genes and DEPs were analyzed. This study preliminarily proved the importance of CsGOGAT in dynamic changes of theanine content in postharvest tea leaves during spreading.

Opportunities and challenges for the discovery and validation of proteomic biomarkers for common arthritic diseases

Osteoarthritis (OA) and rheumatoid arthritis (RA) are most prevalent among all the rheumatic diseases, and currently, there are no reliable biochemical measures for early diagnosis or for predicting who is likely to progress. Early diagnosis is important for making decisions on treatment options and for better management of patients. This narrative review highlights the first-generation biomarkers identified over the last two decades and focuses on the discovery and validation of candidate OA biomarkers from recent mass-spectrometry-based proteomic studies for diagnosis and monitoring disease outcomes in human. It discusses the challenges and opportunities for discovery of novel biomarkers and progress in the development of techniques for measuring biomarkers, and provides directions for future discovery and validation of biomarkers for OA and rheumatoid arthritis.

Uncovering the exercise-related proteome signature in skeletal muscle

Exercise training has been recommended as a nonpharmacological strategy for the prevention and attenuation of skeletal muscle atrophy in distinct pathophysiological conditions. Despite the well-established phenotypic alterations, the molecular mechanisms underlying exercise-induced skeletal muscle remodeling are poorly characterized. Proteomics based on mass spectrometry have been successfully applied for the characterization of skeletal muscle proteome, representing a pivotal approach for the wide characterization of the molecular networks that lead to skeletal muscle remodeling. Nevertheless, few studies were performed to characterize the exercise-induced proteome remodeling of skeletal muscle, with only six research papers focused on the cross-talk between exercise and pathophysiological conditions. In order to add new insights on the impact of distinct exercise programs on skeletal muscle proteome, molecular network analysis was performed with bioinformatics tools. This analysis highlighted an exercise-related proteome signature characterized by the up-regulation of the capacity for ATP generation, oxygen delivery, antioxidant capacity and regulation of mitochondrial protein synthesis. Chronic endurance training up-regulates the tricarboxylic acid cycle and oxidative phosphorylation system, whereas the release of calcium ion into cytosol and amino acid metabolism are the biological processes up-regulated by a single bout of exercise. Other issues as exercise intensity, load, mode and regimen as well as muscle type also influence the exercise-induced proteome signature. The comprehensive analysis of the molecular networks modulated by exercise training in health and disease, taking in consideration all these variables, might not only support the therapeutic effect of exercise but also highlight novel targets for the development of enhanced pharmacological strategies.

Systems biology-opportunities and challenges: the application of proteomics to study the cardiovascular extracellular matrix

Systems biology approaches including proteomics are becoming more widely used in cardiovascular research. In this review article, we focus on the application of proteomics to the cardiac extracellular matrix (ECM). ECM remodelling is a hallmark of many cardiovascular diseases. Proteomic techniques using mass spectrometry (MS) provide a platform for the comprehensive analysis of ECM proteins without a priori assumptions. Proteomics overcomes various constraints inherent to conventional antibody detection. On the other hand, studies that use whole tissue lysates for proteomic analysis mask the identification of the less abundant ECM constituents. In this review, we first discuss decellularization-based methods that enrich for ECM proteins in cardiac tissue, and how targeted MS allows for accurate protein quantification. The second part of the review will focus on post-translational modifications including hydroxylation and glycosylation and on the release of matrix fragments with biological activity (matrikines), all of which can be interrogated by proteomic techniques.

Systems Medicine as an Emerging Tool for Cardiovascular Genetics

Cardiovascular disease (CVD) is a major contributor to morbidity and mortality worldwide. However, the pathogenesis of CVD is complex and remains elusive. Within the last years, systems medicine has emerged as a novel tool to study the complex genetic, molecular, and physiological interactions leading to diseases. In this review, we provide an overview about the current approaches for systems medicine in CVD. They include bioinformatical and experimental tools such as cell and animal models, omics technologies, network, and pathway analyses. Additionally, we discuss challenges and current literature examples where systems medicine has been successfully applied for the study of CVD.

An 'Omics' Perspective on Cardiomyopathies and Heart Failure

Pathological enlargement of the heart, represented by hypertrophic cardiomyopathy (HCM) and dilated cardiomyopathy (DCM), occurs in response to many genetic and non-genetic factors. The clinical course of cardiac hypertrophy is remarkably variable, ranging from lifelong absence of symptoms to rapidly declining heart function and sudden cardiac death (SCD). Unbiased omics studies have begun to provide a glimpse into the molecular framework underpinning altered mechanotransduction, mitochondrial energetics, oxidative stress, and extracellular matrix in the heart undergoing physiological and pathological hypertrophy. Omics analyses indicate that post-transcriptional regulation of gene expression plays an overriding role in the normal and diseased heart. Studies to date highlight a need for more effective bioinformatics to better integrate patient omics data with their comprehensive clinical histories.

The Role of Proteomics in Biomarker Development for Improved Patient Diagnosis and Clinical Decision Making in Prostate Cancer

Prostate Cancer (PCa) is the second most commonly diagnosed cancer in men worldwide. Although increased expression of prostate-specific antigen (PSA) is an effective indicator for the recurrence of PCa, its intended use as a screening marker for PCa is of considerable controversy. Recent research efforts in the field of PCa biomarkers have focused on the identification of tissue and fluid-based biomarkers that would be better able to stratify those individuals diagnosed with PCa who (i) might best receive no treatment (active surveillance of the disease); (ii) would benefit from existing treatments; or (iii) those who are likely to succumb to disease recurrence and/or have aggressive disease. The growing demand for better prostate cancer biomarkers has coincided with the development of improved discovery and evaluation technologies for multiplexed measurement of proteins in bio-fluids and tissues. This review aims to (i) provide an overview of these technologies as well as describe some of the candidate PCa protein biomarkers that have been discovered using them; (ii) address some of the general limitations in the clinical evaluation and validation of protein biomarkers; and (iii) make recommendations for strategies that could be adopted to improve the successful development of protein biomarkers to deliver improvements in personalized PCa patient decision making.

Nutrigenomics, the Microbiome, and Gene-Environment Interactions: New Directions in Cardiovascular Disease Research, Prevention, and Treatment: A Scientific Statement From the American Heart Association

Cardiometabolic diseases are the leading cause of death worldwide and are strongly linked to both genetic and nutritional factors. The field of nutrigenomics encompasses multiple approaches aimed at understanding the effects of diet on health or disease development, including nutrigenetic studies investigating the relationship between genetic variants and diet in modulating cardiometabolic risk, as well as the effects of dietary components on multiple "omic" measures, including transcriptomics, metabolomics, proteomics, lipidomics, epigenetic modifications, and the microbiome. Here, we describe the current state of the field of nutrigenomics with respect to cardiometabolic disease research and outline a direction for the integration of multiple omics techniques in future nutrigenomic studies aimed at understanding mechanisms and developing new therapeutic options for cardiometabolic disease treatment and prevention.

What gastric cancer proteomic studies show about gastric carcinogenesis?

Gastric cancer is a complex, heterogeneous, and multistep disease. Over the past decades, several studies have aimed to determine the molecular factors that lead to gastric cancer development and progression. After completing the human genome sequencing, proteomic technologies have presented rapid progress. Differently from the relative static state of genome, the cell proteome is dynamic and changes in pathologic conditions. Proteomic approaches have been used to determine proteome profiles and identify differentially expressed proteins between groups of samples, such as neoplastic and nonneoplastic samples or between samples of different cancer subtypes or stages. Therefore, proteomic technologies are a useful tool toward improving the knowledge of gastric cancer molecular pathogenesis and the understanding of tumor heterogeneity. This review aimed to summarize the proteins or protein families that are frequently identified by using high-throughput screening methods and which thus may have a key role in gastric carcinogenesis. The increased knowledge of gastric carcinogenesis will clearly help in the development of new anticancer treatments. Although the studies are still in their infancy, the reviewed proteins may be useful for gastric cancer diagnosis, prognosis, and patient management.

Proteomic analysis in cardiovascular research

Advances in mass spectrometry technology and bioinformatics using clinical human samples have expanded quantitative proteomics in cardiovascular research. There are two major proteomic strategies: namely, "gel-based" or "gel-free" proteomics coupled with either "top-down" or "bottom-up" mass spectrometry. Both are introduced into the proteomic analysis using plasma or serum sample targeting 'biomarker" searches of aortic aneurysm and tissue samples, such as from the aneurysmal wall, calcific aortic valve, or myocardial tissue, investigating pathophysiological protein interactions and post-translational modifications. We summarize the proteomic studies that analyzed human samples taken during cardiovascular surgery to investigate disease processes, in order to better understand the system-wide changes behind known molecular factors and specific signaling pathways.

Integrative analyses of proteomics and RNA transcriptomics implicate mitochondrial processes, protein folding pathways and GWAS loci in Parkinson disease

Background

Parkinson disease (PD) is a neurodegenerative disease characterized by the accumulation of alpha-synuclein (SNCA) and other proteins in aggregates termed “Lewy Bodies” within neurons. PD has both genetic and environmental risk factors, and while processes leading to aberrant protein aggregation are unknown, past work points to abnormal levels of SNCA and other proteins. Although several genome-wide studies have been performed for PD, these have focused on DNA sequence variants by genome-wide association studies (GWAS) and on RNA levels (microarray transcriptomics), while genome-wide proteomics analysis has been lacking.

Methods

This study employed two state-of-the-art technologies, three-stage Mass Spectrometry Tandem Mass Tag Proteomics (12 PD, 12 controls) and RNA-sequencing transcriptomics (29 PD, 44 controls), evaluated in the context of PD GWAS implicated loci and microarray transcriptomics (19 PD, 24 controls). The technologies applied for this study were performed in a set of overlapping prefrontal cortex (Brodmann area 9) samples obtained from PD patients and sex and age similar neurologically healthy controls.

Results

After appropriate filters, proteomics robustly identified 3558 unique proteins, with 283 of these (7.9 %) significantly different between PD and controls (q-value < 0.05). RNA-sequencing identified 17,580 protein-coding genes, with 1095 of these (6.2 %) significantly different (FDR p-value < 0.05); only 166 of the FDR significant protein-coding genes (0.94 %) were present among the 3558 proteins characterized. Of these 166, eight genes (4.8 %) were significant in both studies, with the same direction of effect. Functional enrichment analysis of the proteomics results strongly supports mitochondrial-related pathways, while comparable analysis of the RNA-sequencing results implicates protein folding pathways and metallothioneins. Ten of the implicated genes or proteins co-localized to GWAS loci. Evidence implicating SNCA was stronger in proteomics than in RNA-sequencing analyses.

Conclusions

We report the largest analysis of proteomics in PD to date, and the first to combine this technology with RNA-sequencing to investigate GWAS implicated loci. Notably, differentially expressed protein-coding genes were more likely to not be characterized in the proteomics analysis, which lessens the ability to compare across platforms. Combining multiple genome-wide platforms offers novel insights into the pathological processes responsible for this disease by identifying pathways implicated across methodologies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12920-016-0164-y) contains supplementary material, which is available to authorized users.

A trial proteomics fingerprint analysis of HepaRG cells by FD-LC-MS/MS

A proteomics profile analysis was performed on a human hepatocyte carcinoma cell line (HepaRG) by using the FD-LC-MS/MS method. One hundred and fifty-eight proteins were newly identified for the first time of which 10 were found to be specific to human hepatocytes. These proteins are a "proteomics fingerprint" that can be used to characterize HepaRG cells.

Microvascular and mitochondrial dysfunction in the female F1 generation after gestational TiO2 nanoparticle exposure

Due to the ongoing evolution of nanotechnology, there is a growing need to assess the toxicological outcomes in under-studied populations in order to properly consider the potential of engineered nanomaterials (ENM) and fully enhance their safety. Recently, we and others have explored the vascular consequences associated with gestational nanomaterial exposure, reporting microvascular dysfunction within the uterine circulation of pregnant dams and the tail artery of fetal pups. It has been proposed (via work derived by the Barker Hypothesis) that mitochondrial dysfunction and subsequent oxidative stress mechanisms as a possible link between a hostile gestational environment and adult disease. Therefore, in this study, we exposed pregnant Sprague-Dawley rats to nanosized titanium dioxide aerosols after implantation (gestational day 6). Pups were delivered, and the progeny grew into adulthood. Microvascular reactivity, mitochondrial respiration and hydrogen peroxide production of the coronary and uterine circulations of the female offspring were evaluated. While there were no significant differences within the maternal or litter characteristics, endothelium-dependent dilation and active mechanotransduction in both coronary and uterine arterioles were significantly impaired. In addition, there was a significant reduction in maximal mitochondrial respiration (state 3) in the left ventricle and uterus. These studies demonstrate microvascular dysfunction and coincide with mitochondrial inefficiencies in both the cardiac and uterine tissues, which may represent initial evidence that prenatal ENM exposure produces microvascular impairments that persist throughout multiple developmental stages.

[Differential proteomics on synthetic antimicrobial decapeptide against Streptococcus mutans]

OBJECTIVE

To compare the protein profiles between decapeptide-treated and untreated planktonic cells of Streptococcus mutans (S. mutans) by differential proteomic analysis to determine and identify the key proteins.

METHODS

In our previous study, we investigated decapeptide (KKVVFKVKFK-NH2), which was a novel adenosine monophosphate. Compared with other oral pathogens tested, decapeptide had a preferential antibacterial activity against S. mutans. It also inhibited S. mutans biofilm formation and reduced the one-day developed biofilm. In the present study, we first synthesized decapeptide, and then compared the protein profiles between decapeptide-treated and untreated planktonic cells of S. mutans by two-dimensional gel electrophoresis and matrix-assisted laser desorption ionization time-of-flight mass spectrometry. We also verified different expressions of key protein enolase in the protein level.

RESULTS

The results showed that decapeptide altered the protein expression of planktonic S. mutans. These proteins were functionally involved in carbohydrate degradation by glycolysis, protein folding, conjunction, transport, translation, adenosine triphosphate binding, protein binding, sequence-specific DNA binding, transcription factor activity, and two-component response regulator activity. Western blot results showed that enolase protein expression decreased obviously in decapeptide-treated cells of S. mutans.

CONCLUSION

The protein expression of S. mutans significantly changed after synthetic antimicrobial decapeptide treatment, suggesting that decapeptide may present a preferential effect on oral caries by changing the expression of certain key proteins, such as enolase protein.

[Differential proteomics on synthetic antimicrobial decapeptide against Streptococcus mutans]

OBJECTIVE

To compare the protein profiles between decapeptide-treated and untreated planktonic cells of Streptococcus mutans (S. mutans) by differential proteomic analysis to determine and identify the key proteins.

METHODS

In our previous study, we investigated decapeptide (KKVVFKVKFK-NH2), which was a novel adenosine monophosphate. Compared with other oral pathogens tested, decapeptide had a preferential antibacterial activity against S. mutans. It also inhibited S. mutans biofilm formation and reduced the one-day developed biofilm. In the present study, we first synthesized decapeptide, and then compared the protein profiles between decapeptide-treated and untreated planktonic cells of S. mutans by two-dimensional gel electrophoresis and matrix-assisted laser desorption ionization time-of-flight mass spectrometry. We also verified different expressions of key protein enolase in the protein level.

RESULTS

The results showed that decapeptide altered the protein expression of planktonic S. mutans. These proteins were functionally involved in carbohydrate degradation by glycolysis, protein folding, conjunction, transport, translation, adenosine triphosphate binding, protein binding, sequence-specific DNA binding, transcription factor activity, and two-component response regulator activity. Western blot results showed that enolase protein expression decreased obviously in decapeptide-treated cells of S. mutans.

CONCLUSION

The protein expression of S. mutans significantly changed after synthetic antimicrobial decapeptide treatment, suggesting that decapeptide may present a preferential effect on oral caries by changing the expression of certain key proteins, such as enolase protein.

High throughput proteomic analysis and a comparative review identify the nuclear chaperone, Nucleophosmin among the common set of proteins modulated in Chikungunya virus infection

Global re-emergence of Chikungunya virus (CHIKV) has renewed the interest in its cellular pathogenesis. We subjected CHIKV-infected Human Embryo Kidney cells (HEK293), a widely used cell-based system for CHIKV infection studies, to a high throughput expression proteomics analysis by Liquid Chromatography–tandem mass spectrometry. A total of 1047 differentially expressed proteins were identified in infected cells, consistently in three biological replicates. Proteins involved in transcription, translation, apoptosis and stress response were the major ones among the 209 proteins that had significant up-regulation. In the set of 45 down-regulated proteins, those involved in carbohydrate and lipid metabolism predominated. A STRING network analysis revealed tight interaction of proteins within the apoptosis, stress response and protein synthesis pathways. We short-listed a common set of 30 proteins that can be implicated in cellular pathology of CHIKV infection by comparing our results and results of earlier CHIKV proteomics studies. Modulation of eight proteins selected from this set was re-confirmed at transcript level. One among them, Nucleophosmin, a nuclear chaperone, showed temporal modulation and cytoplasmic aggregation upon CHIKV infection in double immunofluorescence staining and confocal microscopy. The short-listed cellular proteins will be potential candidates for targeted study of the molecular interactions of CHIKV with host cells.

Biological significance

Chikungunya remained as a neglected tropical disease till its re-emergence in 2005 in the La RéUnion islands and subsequently, in India and many parts of South East Asia. These and the epidemics that followed in subsequent years ran an explosive course leading to extreme morbidity and attributed mortality to this originally benign virus infection. Apart from classical symptoms of acute fever and debilitating polyarthralgia lasting for several weeks, a number of complications were documented. These included aphthous-like ulcers and vesiculo-bullous eruptions on the skin, hepatic involvement, central nervous system complications such as encephalopathy and encephalitis, and transplacental transmission. The disease has recently spread to the Americas with its initial documentation in the Caribbean islands. The Asian genotype of this positive-stranded RNA virus of the Alphavirus genus has been attributed in these outbreaks. However, the disease ran a similar course as the one caused by the East, Central and South African (ECSA) genotype in the other parts of the world. Studies have documented a number of mutations in the re-emerging strains of the virus that enhances mosquito adaptability and modulates virus infectivity. This might support the occurrence of fiery outbreaks in the absence of herd immunity in affected population. Several research groups work to understand the pathogenesis of chikungunya and the mechanisms of complications using cellular and animal models. A few proteomics approaches have been employed earlier to understand the protein level changes in the infected cells. Our present study, which couples a high throughput proteomic analysis and a comparative review of these earlier studies, identifies a few critical molecules as hypothetical candidates that might be important in this infection and for future study.

•

High throughput expression proteomics analysis in HEK293 cells

•

Identified four major cellular pathways affected in Chikungunya virus infection

•

Short-listed 30 key proteins modulated by a comparative review

•

Confirmed modulation of Nucleophosmin and other selected proteins upon infection

[Development and application of FD-LC-MS/MS proteomics analysis revealing protein expression and biochemical events in tissues and cells]

It is routine to search for and recognized genetic defects in human disorders to provide knowledge for diagnosis, treatment, and protection against diseases. It is also important to investigate and demonstrate the cause of a disease from the proteomic perspective, because intracellular signaling systems depend on protein dynamics. Demonstrating changes in protein levels enables us to understand biochemical events during the initiation and progression of a disease. To understand changes in protein levels in tissues and cells, we have developed a novel proteomics approach, FD-LC-MS/ MS. This consists of fluorogenic derivatization (FD), HPLC separation and detection/quantification of proteins in a biological sample, followed by the isolation and tryptic digestion of target proteins, and then their identification using HPLC and tandem mass spectrometry (MS/MS) with a database-searching algorithm. The method is highly sensitive (femtomole-level detection) through the use of less noisy fluorogenic rather than fluorescence derivatization, and enables precise and comprehensive relative quantitation of protein levels (between-day relative standard deviation of peak heights of ca. 20%) by combining FD with HPLC separation. In this paper, after a simple review of differential profiling using FD-LC-MS/MS, for example the analysis of stimulated vs. unstimulated samples, we introduce the development and application of the FD-LC-MS/MS method for comprehensive differential proteomics of several tissues, including mouse liver, mouse brain, and breast cancer cell lines, to reveal protein levels and biochemical events in tissues and cells.

Unraveling the exercise-related proteome signature in heart

Exercise training is a well-known non-pharmacological strategy for the prevention and treatment of cardiovascular diseases. Despite the established phenotypic knowledge, the molecular signature of exercise-induced cardiac remodeling remains poorly characterized. The great majority of studies dedicated to this topic use conventional reductionist methods, which only allow analyzing individual protein candidates. Nowadays, several methodologies based on mass spectrometry are available and have been successfully applied for the characterization of heart proteome, representing an attractive approach for the wide characterization of the complex molecular networks that underlie exercise-induced cardiac remodeling. Still, few studies have used these methodologies to understand the impact of exercise training on the remodeling of cardiac proteome. The present study analyzes the few available data obtained from mass spectrometry (MS)-based proteomic studies assessing the impact of distinct types of exercise training on the protein profile of heart (left ventricle and isolated mitochondria) and the potential cross-tolerance between exercise training and diseases as myocardial infarction and obesity. Network analysis was performed with bioinformatics to integrate data from distinct research papers, based on distinct exercise training protocols, animal models and methodological approaches applied in the characterization of heart proteome. The analysis revealed that exercise training confers a unique proteome signature characterized by the up-regulation of lipid and organic metabolic processes, vasculogenesis and tissue regeneration. Data retrieved from this analysis also suggested that cardiac mitochondrial proteome is highly dynamic in response to exercise training due, in part, to the action of specific kinases as PKA and PKG. Regarding to the type of exercise, treadmill training seems to have a greater effect on the modulation of cardiac proteome than swimming. Data from the present review will certainly open new perspectives on cardiac proteomics and will help to envisage future studies targeting the identification of the regulatory mechanisms underlying cardiac adaptive and maladaptive remodeling.

Implementation strategies of Systems Medicine in clinical research and home care for cardiovascular disease patients

Insights from the "-omics" science have recently emphasized the need to implement an overall strategy in medical research. Here, the development of Systems Medicine has been indicated as a potential tool for clinical translation of basic research discoveries. Systems Medicine also gives the opportunity of improving different steps in medical practice, from diagnosis to healthcare management, including clinical research. The development of Systems Medicine is still hampered however by several challenges, the main one being the development of computational tools adequate to record, analyze and share a large amount of disparate data. In addition, available informatics tools appear not yet fully suitable for the challenge because they are not standardized, not universally available, or with ethical/legal concerns. Cardiovascular diseases (CVD) are a very promising area for translating Systems Medicine into clinical practice. By developing clinically applied technologies, the collection and analysis of data may improve CV risk stratification and prediction. Standardized models for data recording and analysis can also greatly broaden data exchange, thus promoting a uniform management of CVD patients also useful for clinical research. This advance however requires a great organizational effort by both physicians and health institutions, as well as the overcoming of ethical problems. This narrative review aims at providing an update on the state-of-art knowledge in the area of Systems Medicine as applied to CVD, focusing on current critical issues, providing a road map for its practical implementation.

Protein deep sequencing applied to biobank samples from patients with pancreatic cancer

PURPOSE

Pancreatic cancer is commonly detected at advanced stages when the tumor is no longer amenable to surgical resection. Therefore, finding biomarkers for early stage disease is urgent. Here, we show that high-definition mass spectrometry (HDMS(E)) can be used to identify serum protein alterations associated with early stage pancreatic cancer.

METHODS

We analyzed serum samples from patients with resectable pancreatic cancer, benign pancreatic disease, and healthy controls. The SYNAPT G2-Si platform was used in a data-independent manner coupled with ion mobility. The dilution of the samples with yeast alcohol dehydrogenase tryptic digest of known concentration allowed the estimated amounts of each identified protein to be calculated (Silva et al. in Anal Chem 77:2187-2200, 2005; Silva et al. in Mol Cell Proteomics 5:144-156, 2006). A global protein expression comparison of the three study groups was made using label-free quantification and bioinformatic analyses.

RESULTS

Two-way unsupervised hierarchical clustering revealed 134 proteins that successfully classified pancreatic cancer patients from the controls, and identified 40 proteins that showed a significant up-regulation in the pancreatic cancer group. This discrimination reliability was further confirmed by principal component analysis. The differentially expressed candidates were aligned with protein network analyses and linked to biological pathways related to pancreatic tumorigenesis. Pancreatic disease link associations could be made for BAZ2A, CDK13, DAPK1, DST, EXOSC3, INHBE, KAT2B, KIF20B, SMC1B, and SPAG5, by pathway network linkages to p53, the most frequently altered tumor suppressor in pancreatic cancer.

CONCLUSION

These pancreatic cancer study candidates may provide new avenues of research for a noninvasive blood-based diagnosis for pancreatic tumor stratification.

Quantitative proteomics in cardiovascular research: global and targeted strategies

Extensive technical advances in the past decade have substantially expanded quantitative proteomics in cardiovascular research. This has great promise for elucidating the mechanisms of cardiovascular diseases and the discovery of cardiac biomarkers used for diagnosis and treatment evaluation. Global and targeted proteomics are the two major avenues of quantitative proteomics. While global approaches enable unbiased discovery of altered proteins via relative quantification at the proteome level, targeted techniques provide higher sensitivity and accuracy, and are capable of multiplexed absolute quantification in numerous clinical/biological samples. While promising, technical challenges need to be overcome to enable full utilization of these techniques in cardiovascular medicine. Here, we discuss recent advances in quantitative proteomics and summarize applications in cardiovascular research with an emphasis on biomarker discovery and elucidating molecular mechanisms of disease. We propose the integration of global and targeted strategies as a high-throughput pipeline for cardiovascular proteomics. Targeted approaches enable rapid, extensive validation of biomarker candidates discovered by global proteomics. These approaches provide a promising alternative to immunoassays and other low-throughput means currently used for limited validation.

Straightforward proteomic analysis reveals real dynamics of proteins in cells

To reveal real dynamics of proteins in cells, we have developed a novel type of straightforward proteomic analysis named FD-LC-MS/MS. This technique consists of fluorogenic derivatization (FD) of intact proteins, followed by high performance liquid chromatographic (LC) separation, detection and quantification of the derivatized proteins, isolation of the subject proteins, enzymatic digestion of the isolated proteins, and identification of the proteins using HPLC and MS/MS with a database-searching algorithm. The method is uncomplicated, sensitive, reproducible, and easily quantifies and identifies intact proteins in tissues and cells. Additionally, in contrast to other proteomic approaches, our method does not require any pretreatment steps, such as precipitation and clean-up, except for the derivatization, resulting in high reproducibility and the same or higher detectability than that of other methods. In this article, after a brief review of other types of proteomic analyses, we introduce the development and application of the FD-LC-MS/MS method. We also discuss the features and perspectives of this method.

Proteomic profiling of the dystrophin-deficient mdx phenocopy of dystrophinopathy-associated cardiomyopathy

Cardiorespiratory complications are frequent symptoms of Duchenne muscular dystrophy, a neuromuscular disorder caused by primary abnormalities in the dystrophin gene. Loss of cardiac dystrophin initially leads to changes in dystrophin-associated glycoproteins and subsequently triggers secondarily sarcolemmal disintegration, fibre necrosis, fibrosis, fatty tissue replacement, and interstitial inflammation. This results in progressive cardiac disease, which is the cause of death in a considerable number of patients afflicted with X-linked muscular dystrophy. In order to better define the molecular pathogenesis of this type of cardiomyopathy, several studies have applied mass spectrometry-based proteomics to determine proteome-wide alterations in dystrophinopathy-associated cardiomyopathy. Proteomic studies included both gel-based and label-free mass spectrometric surveys of dystrophin-deficient heart muscle from the established mdx animal model of dystrophinopathy. Comparative cardiac proteomics revealed novel changes in proteins associated with mitochondrial energy metabolism, glycolysis, signaling, iron binding, antibody response, fibre contraction, basal lamina stabilisation, and cytoskeletal organisation. This review summarizes the importance of studying cardiomyopathy within the field of muscular dystrophy research, outlines key features of the mdx heart and its suitability as a model system for studying cardiac pathogenesis, and discusses the impact of recent proteomic findings for exploring molecular and cellular aspects of cardiac abnormalities in inherited muscular dystrophies.

Proteomics in heart failure: top-down or bottom-up?

The pathophysiology of heart failure (HF) is diverse, owing to multiple etiologies and aberrations in a number of cellular processes. Therefore, it is essential to understand how defects in the molecular pathways that mediate cellular responses to internal and external stressors function as a system to drive the HF phenotype. Mass spectrometry (MS)-based proteomics strategies have great potential for advancing our understanding of disease mechanisms at the systems level because proteins are the effector molecules for all cell functions and, thus, are directly responsible for determining cell phenotype. Two MS-based proteomics strategies exist: peptide-based bottom-up and protein-based top-down proteomics--each with its own unique strengths and weaknesses for interrogating the proteome. In this review, we will discuss the advantages and disadvantages of bottom-up and top-down MS for protein identification, quantification, and analysis of post-translational modifications, as well as highlight how both of these strategies have contributed to our understanding of the molecular and cellular mechanisms underlying HF. Additionally, the challenges associated with both proteomics approaches will be discussed and insights will be offered regarding the future of MS-based proteomics in HF research.

Protein Quantification by MRM for Biomarker Validation

In this chapter we describe how mass spectrometry-based quantitative protein measurements by multiple reaction monitoring (MRM) have opened up the opportunity for the assembly of large panels of candidate protein biomarkers that can be simultaneously validated in large clinical cohorts to identify diagnostic protein biomarker signatures. We outline a workflow in which candidate protein biomarker panels are initially assembled from multiple diverse sources of discovery data, including proteomics and transcriptomics experiments, as well as from candidates found in the literature. Subsequently, the individual candidates in these large panels may be prioritised by application of a range of bioinformatics tools to generate a refined panel for which MRM assays may be developed. We describe a process for MRM assay design and implementation, and illustrate how the data generated from these multiplexed MRM measurements of prioritised candidates may be subjected to a range of statistical tools to create robust biomarker signatures for further clinical validation in large patient sample cohorts. Through this overall approach MRM has the potential to not only support individual biomarker validation but also facilitate the development of clinically useful protein biomarker signatures.

Linking microbial community structure, interactions and function in anaerobic digesters using new molecular techniques

Over the last decade there has been a rapid development in culture-independent techniques for exploring microbial communities, which have led to new insights into their structure and function in both natural environments and engineered systems. This review focuses on some of the most important recent advances and their applications to the diverse microbial communities associated with anaerobic digestion. The use of these approaches in combination with complementary imaging techniques, chemical isotope analyses and detailed reactor performance measurements provides a new opportunity to develop a fundamental understanding of how microbial community dynamics, interactions and functionality influence digester efficiency and stability.

Systems biology in drug discovery and development

The complexity of human biology makes it challenging to develop safe and effective new medicines. Systems biology omics-based efforts have led to an explosion of high-throughput data and focus is now shifting to the integration of diverse data types to connect molecular and pathway information to predict disease outcomes. Better models of human disease biology, including more integrated network-based models that can accommodate multiple omics data types, as well as more relevant experimental systems, will help predict drug effects in patients, enabling personalized medicine, improvement of the success rate of new drugs in the clinic, and the finding of new uses for existing drugs.

A conceptual review on systems biology in health and diseases: from biological networks to modern therapeutics

Human physiology is an ensemble of various biological processes spanning from intracellular molecular interactions to the whole body phenotypic response. Systems biology endures to decipher these multi-scale biological networks and bridge the link between genotype to phenotype. The structure and dynamic properties of these networks are responsible for controlling and deciding the phenotypic state of a cell. Several cells and various tissues coordinate together to generate an organ level response which further regulates the ultimate physiological state. The overall network embeds a hierarchical regulatory structure, which when unusually perturbed can lead to undesirable physiological state termed as disease. Here, we treat a disease diagnosis problem analogous to a fault diagnosis problem in engineering systems. Accordingly we review the application of engineering methodologies to address human diseases from systems biological perspective. The review highlights potential networks and modeling approaches used for analyzing human diseases. The application of such analysis is illustrated in the case of cancer and diabetes. We put forth a concept of cell-to-human framework comprising of five modules (data mining, networking, modeling, experimental and validation) for addressing human physiology and diseases based on a paradigm of system level analysis. The review overtly emphasizes on the importance of multi-scale biological networks and subsequent modeling and analysis for drug target identification and designing efficient therapies.