Systems Biology and immune aging.

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Immunology Letters xxx (2014) xxx–xxx

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Systems Biology and immune aging José-Enrique O’Connor a,∗ , Guadalupe Herrera b , Alicia Martínez-Romero c , Francisco Sala de Oyanguren a , Laura Díaz a , Angela Gomes a , Susana Balaguer a , Robert C. Callaghan d a

Laboratory of Translational Cytomics, Joint Research Unit, University of Valencia and Principe Felipe Research Center, Valencia, Spain Laboratory of Cytometry, Incliva Foundation, Clinical University Hospital, University of Valencia, Valencia, Spain Cytometry Technological Service, Principe Felipe Research Center, Valencia, Spain d Department of Pathology, Faculty of Medicine, The University of Valencia, Valencia, Spain b c

a r t i c l e

i n f o

Article history: Available online xxx Keywords: Longevity Genomics Proteomics Metabolomics Cytomics Bioinformatics

a b s t r a c t Many alterations of innate and adaptive immunity are common in the aging population, which reflect a deterioration of the immune system, and have lead to the terms “immune aging” or “immunosenescence”. Systems Biology aims to the comprehensive knowledge of the structure, dynamics, control and design that define a given biological system. Systems Biology benefits from the continuous advances in the omics sciences, based on high-throughput and high-content technologies, as well as on bioinformatic tools for data mining and integration. The Systems Biology approach is becoming gradually used to propose and to test comprehensive models of aging, both at the level of the immune system and the whole organism. In this way, immune aging may be described by a dynamic view of the states and interactions of every individual cell and molecule of the immune system and their role in the context of aging and longevity. This mini-review presents a panoramics of the current strategies, tools and challenges for applying Systems Biology to immune aging. © 2014 Elsevier B.V. All rights reserved.

1. Immune aging as a system process Metabolic dysfunction, impaired immune responses to new antigens and inflammation-based disorders are commonly found in the elderly, reflecting the strong link between metabolic regulation and immune responses [1]. Many alterations of innate and adaptive immunity are common in the aging population, which reflect a deterioration of immunity, and have lead to the terms “immune aging” or “immunosenescence” [2]. Many biomarkers of immunosenescence arise from research on T and B cells, which show an altered cytokine pattern, a reduction in clonal expansion and function of antigen-specific T and B cells, and a decline in antigen-presenting cell function. Similarly, the functions of macrophages, neutrophils and natural killer cells, components of the innate immunity, are also decreased [3]. The decline in immune function leads to increased susceptibility of aged individuals to viral, bacterial and fungal infections [4], reactivation of latent viruses and a decreased response to vaccines [5,6].

∗ Corresponding author at: Department of Biochemistry and Molecular Biology, ˜ Faculty of Medicine, University of Valencia. Av. Blasco Ibanez, 17, 46010 Valencia, Spain. Tel.: +34 963 86 4988; fax: +34 963 86 4001. E-mail addresses: [email protected], [email protected] (J.-E. O’Connor).

The relevance of inflammation in the aging process has been consistently confirmed in the recent years, leading to the establishment of the concept of “inflammaging” [2], which identifies the chronic, sub-clinical inflammatory status typical of elderly individuals. Inflammaging coexists with immunosenescence, and indeed several detrimental features collectively denominated as the immune risk phenotype (IRP) [7] have been associated by longitudinal studies on large cohorts of elderly people with increased risk of mortality in Northern Europeans, while centenarians appear to show no IRP [8]. However, recent evidence suggests that inflammaging is in part independent of immunological stimuli and of the total amount of pro-inflammatory mediators, leading to a reappraisal of the involvement of inflammaging in immune senescence of aging, to increase the weight of tissue-environment and celltype related processes [9]. This novel view shows the importance of identifying the molecular mechanisms that regulate the complex interactions between metabolism and immunity in aging, while reinforcing the need for integrative studies that address the multi-factorial and dynamic factors that explain senescence and longevity. Aging in experimental and in the humans can be studied at several levels of complexity, from the molecules to the organism and the metaorganism [10–13], as the classical reductive approach, mostly involving molecular and cellular studies may not be

http://dx.doi.org/10.1016/j.imlet.2014.09.009 0165-2478/© 2014 Elsevier B.V. All rights reserved.

Please cite this article in press as: O’Connor J-E, et al. Systems Biology and immune aging. Immunol Lett (2014), http://dx.doi.org/10.1016/j.imlet.2014.09.009

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informative enough on the highest levels of biological complexity. Recent knowledge shows that different tissues may age at different but coordinated rates, in the so-called “aging mosaic” [14], resulting from particular combinations of genetic, epigenetic, environmental and stochastic factors, leading to a heterogeneous aging phenotype. In parallel, the recent analytical approaches based in the omic sciences, the high-throughput/high-content methodologies and the power of data mining by bioinformatic tools allow for first time to propose and to test comprehensive models of aging, both at the level of the immune system or the whole organism. 2. Strategies for Systems Biology approach to immune aging Systems Biology is an emerging discipline that combines high-content, multiplexed measurements with informatic and computational modeling methods to define biological functions at various scales [15,16]. On this basis, Systems Biology is a relatively novel approach in the study of aging and longevity [10–12,17] and the immune system [18–23], as it extends the classical analysis of isolated entities by integrating individual mechanisms and interactions. The elaboration and testing of comprehensive models in Systems Biology requires complete characterization of an organism in terms of its molecular constituents and their interactions, and how these interactions result in cell function, as well as the spatial and temporal characterization of the molecular responses of the system to external and internal influences. Finally, all such information must be integrated in the form of mathematical models which can be tested by formulating predictions, thus allowing the discovery of new mechanisms and the development of rational strategies for control and manipulation of cells and organisms [5,6,18]. The biological information required by Systems Biology for model building may be gathered either from bottom-up or topdown strategies (Fig. 1). The bottom-up approach involves data collection from different online resources, manual curation of data, simulation of networks through mathematical methods, and validation of generated models by means of literature and database analysis. An example of bottom-up approach is to construct mathematical models from previous kinetics data and predict how a specific protein contributes to aging and antiaging processes These approaches allow researchers to simulate the effect of each gene product in aging by in silico genetic manipulations, such as deletion or over-expression [16,24,25]. The ‘top-down’ strategy is the most frequent in Systems Biology [16]. It starts from a general view of the behavior of the system by providing large and complex omics data, and aims to discover and to characterize biological mechanisms regulating the components and their interactions. Top-down strategies may be potentially complete (i.e., genome-wide) and address the entire levels of biological complexity (metabolome, fluxome, transcriptome, proteome and cytome). A typical example of top-down approach in aging studies would be to predict the role of a specific gene in the aging process by comparing its expression profile and protein–protein interaction pattern with those of known longevity genes. Most currently available studies on immune aging and senescence follow ‘top-down’ strategies [10–12,17]. 3. Tools in Systems Biology 3.1. Genomics and metagenomics 3.1.1. Transcript profiling Transcriptomics implies the study of the complete set of RNAs (transcriptome) produced by the genome of a specific cell or

organism at a specific time and/or under a specific set of conditions. Transcriptomic approaches using microarrays and, more recently, RNA sequencing, have been applied repeatedly to the study of aging in humans and in animal models [14]. Most transcriptomic studies are aimed to identify genes that are differentially expressed with chronological age, and in many cases, have been focused on blood samples [26] but many of the expression changes found in human blood have also been found in genes related to lymphocytes and the immune system [27]. Signatures of aging have been also found to be consistent across tissue and species [28] and the gene pathways showing most altered gene expression with age were related to the immune response, including complement activation, antigen processing, apoptosis and anti-apoptosis. Some studies have compared normally aging individuals with younger patients with progeria syndromes and have found similarity in the majority of age-related expression changes [29]. Transcriptomics study of total RNA in blood mononuclear cells (PBMC) of healthy young and middle-age versus healthy old individuals showed that quantitative changes of expression were accessible biomarkers of aging. Some differential expression, like CD28, CD69, LCK (decreased abundance in old subjects), CD86, Cathepsins D, H and S (increased abundance in old subjects) reflected the low-grade pro-inflammatory status in old persons and suggested a reduced response of T-cells together with an increase in antigen presentation potential. In addition, genes involved in the oxidative stress response were found more active in PBMC from elderly subjects [30]. 3.1.2. Next generation sequencing The array-based transcriptomic studies are limited in part by the lack of sensitivity to low abundance transcripts and by the issue of inter-laboratory reproducibility due to the use of different microarray platforms, as shown by multicentric studies using different platforms but identical RNA samples [31]. Current gene expression array data are also limited in that they do not provide information on microRNAs (miRNAs). Part of these limitations may be overcome by the use of RNA sequencing approaches, as shown recently by comprehensive comparison of transcriptome analysis obtained by RNA-sequencing and microarrays [32]. Next generation sequencing (NGS) uses massive parallel analyses of individually amplified DNA fragments [33]. NGS systems process beyond 1 gigabase of sequence per run. In this way, genome-wide analyses allow to extend sequencing the entire genome by specialized determinations, such as the quantitative analysis of expressed mRNA, epigenetically-modified DNA, nucleic acid bound to a specific protein, or DNA sensitive to enzymatic degradation. NGS has been recently applied to explore the basic principles of immune-receptor repertoire selection, and its relation to disease and vaccination. The size, diversity, and affinity of this repertoire are closely linked to the immune response and, likely, may be affected by aging. Hence, exploring this diversity and its clinical implications in an individual or a population is of high importance. Through the design of primers flanking regions of interest deep sequencing of antibody and TCR sequences [34], as well as of HLA regions [35], have been obtained. A recent study [36] has applied high-throughput long read sequencing to perform immunogenomic characterization of expressed human antibody repertoires in the context of influenza vaccination. Informatic analysis of 5 million antibody heavy chain sequences from healthy individuals showed that elderly subjects have a decreased number of lineages but an increased prevaccination mutation load in their repertoire and that some of these subjects have an oligoclonal character to their repertoire in which




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Fig. 1. Comparison of bottom-up and top-down strategies in Systems Biology. (A) In the bottom-up approach, information gathered from existing databases of from omics experiments is provided as an input to start building the computational models. Model draft reconstruction requires bioinformatics for automated metabolic network prediction from DNA sequence. This stage usually leads to an incomplete draft, which needs manual curation by consulting through organism- and pathway-specific databases, allowing the conversion of the refined draft into a mathematical model by stoichiometric calculations and visualization with Matlab-embedded tools. At this step, mathematical models can be simulated and evaluated under optimal conditions. If inconsistencies are found in the model, then it must be re-evaluated by manual curation. If model works correctly in the final stage, then it may be used for further computational applications. (B) In the top-down approach, after designing an experiment, the pre-analytical phase involves collection and preparation of biological samples. In the analytical phase, different types of omics experiments are performed, leading typically to the generation of large matrices of data. At the following stage, such data must be normalized to subtract noise and to assess the influence of relevant aspects of the experimental design. Thus, high-quality data may be subjected to statistical analysis and significance validation. In the last stage, significant data are mined and interpreted by using suitable bioinformatics approaches that allow knowledge discovery and development of a new scientific hypothesis.

the diversity of the lineages is greatly reduced relative to younger subjects. 3.1.3. miRNA profiling The recently discovered miRNAs have a key regulatory role, as shown in numerous cell types, including immune cells [37]. The technology to profile miRNAs and characterize their function has been developed rapidly based on well-established nucleic acid-based methods, leading to the generation of miRNA expression profiles in normal and pathological conditions [38]. Similarly,

analysis of the function of specific miRNAs has been facilitated by the development of specific antisense inhibitors and synthetic miRNAs [34]. Such small RNA reagents can be delivered into cells as for siRNA, and used for routinely screening groups to assess the influence of miRNAs on specific cellular processes. Experimental investigations of miRNA expression and function can be complemented by computational approaches for miRNA target prediction [18].miRNA studies in immune cells have shown conserved target sites for single miRNAs or miRNA families within a common regulatory process, suggesting that these miRNAs, which change




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expression in response to cellular stimulation, may act as negative or positive feedback regulators. For example, several phosphatases downregulating signaling through the T cell receptor are targets for miR181 [39]. In the other hand, several key components involved in pathways of monocyte response to pathogens, such as IRAK1 and TRAF6, are targets for miR146 [40], and recent data suggest that the anti-inflammatory effects of IL-10 may be promoted through direct inhibition of LPS-induced miR155 expression [41], supporting that miRNAs may also be involved in the interaction between inflammatory mediators. 3.1.4. RNAi screening The discovery of RNA interference (RNAi) has provided an excellent tool for wide-scale and rapid genetic screening [42]. RNAi takes advantage of endogenous RNA processing machinery, which permits the silencing of mRNA transcripts with small complementary dsRNA sequences. The first genome-wide functional genomic screen for longevity genes [43] was performed in Caenorhabdytis elegans genes using large-scale RNAi, and found that RNAi inactivation of 89 genes extend Caenorhabdytis elegans lifespan, including genes encoding proteins that contain domains that might act as immune cell receptors, such as EGF-like domains and Ig domains. In addition, siRNA screens have been significantly employed to identify key components involved in immune responses. RNAi screening led to the discovery of several molecules involved in the process of store-operated Ca2+ influx through plasma membrane channels [44], and the subsequent activation of transcription factor NFAT [45], which are fundamental to lymphocyte activation and function. Further recent discoveries using RNAi include demonstration of key roles for the kinase RIP3 in programmed necrosis following viral inflammation [46] and AIM2 in sensing cytoplasmic DNA for the inflammasome [47]. 3.2. Proteomics Proteomic studies are highly relevant to current Systems Biology as they determine the actual molecular constituents of the cell and not the coding potential provided by transcriptomic methods. Proteomic and protein-chemistry studies may also be used to determine rates of association/dissociation for molecular pairs and higher order complexes, and to calculate enzyme kinetic rates. However, the system-oriented analysis of the proteome progresses at a slower pace than the Systems Biology approaches based on nucleic acids and more tools are available to study the genome than the proteome. 3.2.1. Mass spectrometry Mass spectrometry (MS) allows identifying and characterizing single proteins from protein preparations or from SDS-PAGE bands or spots. Any soluble protein can in principle be analyzed and identified based on the measurement of the mass-to-charge ratio (m/z) of ions in gas phase. Ion sources can be coupled with different mass analyzers. Mass analyzers sort the ions based on the m/z by applying electromagnetic fields [18]. MS is usually applied in proteomic studies for determination of protein expression levels, identification and quantification of post-translational modifications, and characterization of protein–protein interactions. MS is still a low-throughput approach, and the results are measurements of population averages, single-cell based measurements being yet impossible, although recent multiplexing approaches allow parallel quantification of up to eight samples [48]. Accurate absolute quantification methodologies in MS proteomics are still under development. This fact limits the role of MS to support development of quantitative models for simulation, as quantitative parameters must be used for dynamic modeling.

Urine proteomics analysis, a non-invasive and reproducible diagnostic method, has been applied to determine which metabolic processes were weakened or strengthened in aging humans [49]. In this study, as much as 19 proteins were differentially expressed in different age groups (young, intermediate, and old age). In particular, the oldest group showed protein changes reflective of declining immune function and altered extracellular matrix turnover, related to reported changes in immune disorders and cardiovascular tissue remodeling in the elderly. In a very recent and large multicentric study [50] proteomics has revealed age-related differences in the host immune response to sepsis caused by community-acquired pneumonia (CAP). In this nested case–control study of 2320 patients with CAP, a total, 772 proteins were identified, of which 58 proteins exhibited statistically significant differences in expression levels among patients with severe sepsis as a function of age. Differentially expressed proteins were involved in pathways such as acute phase response, coagulation signaling, atherosclerosis signaling, lipid metabolism, and production of nitric oxide and reactive oxygen species. This study provides insight into factors that may explain age-related differences in incidence of severe sepsis in the elderly. Related to humoral immune response in the eledery, proteomic approaches have allowed the profiling of N-glycans on human serum glycoproteins (including immunoglobulins) in healthy individuals from different age groups, which revealed substantial changes with increasing age [51]. Importantly, levels of N-glycans were also found to correlate with human longevity and one of the longevity-related glycans is also associated with cardiovascular disease [52]. 3.2.2. Antibody-based array methods Immunoblot (Western blot) and ELISA (enzyme-linked immunosorbent assay) are classical low-throughput assays. In the recent years, multiplex immunoassays have been developed which provide quantitative data via parallel analyses and require substantially less sample and reagents than the traditional ELISA. Two basic assay formats allow simultaneous quantification of multiple antigens: solid-matrix or planar array assays and microbead assays. In the first format, different capture antibodies are spotted at defined coordinates on a bidimensional array. In the second type, capture antibodies are conjugated to different populations of microbeads, which are identified specifically by their fluorescence intensity in dedicated or conventional flow cytometers. Among affinity-based solid-matrix assays, the most widely used technology is protein microarray technology. Protein microarrays can be used to quantify protein abundance in the samples as well as to explore protein function, for example kinase activity measured by substrate phosphorylation levels [18]. A recently developed protein microarray method, the nucleic acid programmable protein array (NAPPA), is based on in situ translation of functional proteins from DNA printed on the spots with the use of in vitro transcription–translation system [53]. In the DAPA method (DNA array to protein array), proteins translated in vitro on a cDNA array diffuse through a membrane infused with cell-free extract to a surface with capture molecules and high density peptide – and protein chips can be produced by immediate immobilization of proteins synthesized [54]. Reverse-phase protein microarray (RPMA) is a highly miniaturized dot-blot platform that enables the simultaneous quantification of protein expression in a large number of biological samples. [55]. The second family of affinity arrays is based on bead technology. Several platforms allowing simultaneous measurement of protein analytes are available. Most popular among them are the bead array multiplex assays based on the Luminex technology [56]. The multiplexing can be achieved because up to 100 distinct Luminex color-coded microsphere bead sets can be coated with a reagent




specific to a particular bioassay, allowing the capture and detection of specific analytes from a sample. A similar bead-based array strategy can be performed by flow cytometry, taking advantage of the wide distribution of flow cytometers in the clinical and basic immunology laboratories. Currently, several commercial variants of the flow cytometric bead array are available and applied to a wide variety of immunological studies [57–60]. 3.3. Interactomics Protein–protein interactions (PPI) are key elements for the normal functioning of a living cell. Most of the present knowledge on cellular processes are based on identifying and characterizing the interactions between cellular proteins and other biomolecules. In Systems Biology, modeling of complex biological phenomena requires a detailed understanding of both the potential binding partners of each molecule, and the context and kinetics of the interaction(s). The study of the complex web of interactions that link biological molecules in a cell is the subject of interactomics, one of the fastest moving fields in molecular biology. Although interactions among molecules of diverse chemical composition are important, most current efforts are devoted to the protein interactome. There are three principal methods in use for studying PPI, namely (i) yeast 2-hybrid (Y2H), (ii) affinity purification of protein complexes followed by mass spectrometry (AP/MS) and (iii) protein complementation assays [18,61–63]. Bioinformatics tools can also predict with a good accuracy PPIs in silico. PPIs databases are now numerous and topological analysis has led to interesting insights into the nature of network connection [18]. In addition to the characterization of cellular networks among host proteins, PPI techniques have key applications in the field of infectious disease in determining the mechanisms of immune response evasion through the interaction of pathogen-derived proteins with the host proteome. A bioinformatic analysis of all published host-pathogen interactions showed that of more than 10,000 reported interactions, more than 98% involve viruses and over 77% result from HIV studies [64], showing the lack of data addressing bacterial–host interactomes, where interaction networks based on Y2H data were recently published for anthrax, Francisella and Yersinia [65]. 3.4. Cytomics Cytomics aims to determine the molecular phenotype of single cells, by means of the investigation of multiple biochemical features of the heterogeneous cellular systems [66,67]. Cytomics can be considered, thus, as the science of single cell-based analyses that links genomics and proteomics with the dynamics of cell and tissue function, as modulated by external influences. Inherent to cytomics are the use of sensitive, scarcely invasive, multiparametric methods and the event-integrating concept of individual cells to understand the complexity and behavior of tissues and organisms. Among cytomic technologies, conventional, fluorescence-based flow cytometry (FCM) [68] and other recent technologies based on non-fluorescent markers [69] and on single-cell bioimaging and bioinformatic tools have become an important tool in Systems Biology, because of both high content and high-throughput [70]. 3.4.1. Polychromatic and spectral flow cytometry FCM is a most successful technology in the field of immunology. Based on the increased capability of the current instruments and the availability of new fluorescent labels, the state-of-the-art in conventional FCM is polychromatic FCM (PFCM), which allows analyzing simultaneously up to 20 parameters in a single cell,

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while processing millions of cells per sample in a few minutes [71]. PFCM has been extensively applied for the investigation of immune cell phenotypes in a large range of experimental and clinical settings [72–74], and novel developments of PFCM enhancing higher content, throughput and sensitivity include combinatorial strategies [75], fluorescent ‘barcoding’ of samples [76] and analysis of highly selected and rare subpopulations [73,77]. The most important challenges to PFCM derive from the optical characteristics of the available fluorescent dyes. Spectral flow cytometry allows resolving the issue of fluorescence overlap, by means of more sophisticated analysis of fluorescence-based signals [78]. Spectral flow cytometers collect the complete fluorescence emission spectrum simultaneously from all fluorochromes. The spectrum is then deconvoluted to quantify individual fluorochromes. In this way, fluorochromes with highly overlapping emission spectra can be distinguished more accurately than by conventional flow cytometry. Recent developments of intracellular immunophenotyping allow the quantification of intracellular signaling proteins, and their phosphorylated epitopes, the so-called ‘phospho flow’ [79,80]. This is a flow cytometric approach to determine phosphoepitope levels involving cell fixation and permeabilization prior to staining with combinations of phospho-specific antibodies labeled with different fluorophores. Phosphoflow has become an essential tool in single-cell proteomics, as it provides a rapid and efficient means to measure the levels of a variety of intracellular phosphoepitopes for mapping cell signaling networks [80]. Conventional FCM and PFCM have been applied for many years in studies of immunosenescence and immune aging. Early applications of FCM in the field were reviewed already in 1997 [81], including work with healthy centenarians. Conventional FCM showed that a continuous remodeling of the immune system occurs with age, being characterized by complex changes in humoraland cellular immunity and signaling molecules, together with a decreased sensitivity to apoptosis in peripheral blood lymphocyte, associated with a well preserved mitochondria function. PFCM by eight-color FCM analysis has been applied to investigate the T cell compartment in young, middle-aged, and centenarians [82]. T cell subsets were identified by their immunophenotype and the high number of T-cell subsets generated was organized into a matrix and subjected to cluster- and principal component analyses. Using this novel bioinformatic strategy, cluster analysis allowed to group people of different ages according to their T-cell immunophenotypic profile. Thus, while the majority of subsets identifified by PFCM were not fundamental for the comprehension of T cell dynamics during the aging process, some crucial populations arose from the analyses, and principal component analysis of the cellular subsets identifified centenarians within a different cluster for both CD4 and CD8 T-cells from young donors, while middle-aged donors were located between these groups. A clear example of how integration of cytomics and other omics may benefit immune senescence-related studies is an ongoing clinical trial managed by the Stanford University and the National Institute of Allergy and Infectious Diseases (http://clinicaltrials. gov/ct2/show/NCT01827462?term=NCT01827462&rank=1). The aim of this study on immune senescence in the elderly is the comparison of immune responses to influenza vaccine in groups of adults of different ages (18–30, 60–79, or 80–100 years). The Systems Biology approach includes integration of demographic and clinical parameters with a cellular array of primary outcome measures, namely (i) abundance levels of cell subsets, serum cytokines, and mRNA transcripts in blood; (ii) single cell phosphoprotein abundance changes in response to immune perturbations; and (iii) serum antibody titers. Whole-genome gene expression analysis is performed by Agilent microarrays. Multiplex analysis of 26


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Fig. 2. An example of the application of integrative Systems Biology in a clinical trial related to immune aging. The figure summarizes the main features, the involvement of omics techniques and the scope of the clinical trial “Immune Senescence in the Elderly: Comparison of Immune Responses to Influenza Vaccine in Adults of Different Ages” (http://clinicaltrials.gov/ct2/show/NCT01827462?term=NCT01827462&rank=1). This project applies state-of-the-art technology developed by the Stanford Human Immune Monitoring Center to survey older individuals for signs of immune system aging and to gather information about the factors associated with the decline of immune function. Individuals aged 18–30, 60–79, or 80–100 years at time of initial enrollment, are treated with trivalent inactivated influenza vaccine. In whole blood samples obtained preand post-treatment, genomics, proteomics and cytomics are applied. Gene expression is determined using Agilent mRNA microarrays that survey global gene expression. Proteomic analysis of 26 different cytokines in serum is performed with the Panomics/Luminex system. Cytomics (flow cytometry) is used to quantify the relative abundance and the activation state of the leukocyte populations and lymphocyte subsets, as well as to normalize the gene expression data. Functional flow cytometry is also applied to assess the monocyte activation potential, by determining the monocyte response in vitro to cytokines and LPS, as measured by expression of cell surface activation markers and intracellular phosphoproteins. Omics data are integrated with biochemical and clinical parameters of vaccination outcome, namely serum antibody responses, viral hemagglutinin inhibition and clinical status of patients, in order to provide new knowledge on relevant signs of immune system aging and to discover factors associated with the decline of immune function.

different serum cytokines involves using the Panomics/Luminex system. PFCM is used to determine the precise number of each white blood cell type and their activation state (B cells, T cell subtypes, NK cells, monocytes, dendritic cells, with a particular interest in CD8+ CD28− T cells or ␥␦ T cells). Phosphoflow techniques are used to assess monocyte activation by the phosphorylation status of intracellular signaling proteins, like p38 or STATs (Fig. 2). 3.4.2. Mass-spectrometry flow cytometry Mass-spectrometry flow cytometry (MSFC) quantifies the abundance of heavy metal isotope labels on antibodies and other probes on single cells using mass spectroscopy [69]. Regarding PFCM, MSFC overcomes the challenge of fluorescence spectral overlap by using rare-earth-metal isotopes detected distinctly [83]. Reduced biological background is another benefit of MSFC. Staining panels are designed similar to PFCM and many cell-associated parameters, such as phosphorylated molecules, intracellular cytokines and surface proteins, can be determined simultaneously [84]. Currently, isotopic-label antibody panels spanning over 35 proteins are already run regularly via mass cytometry (110 different proteins are the upper limit), and much more information may be obtained from each cell. A limitation to current MFSFC is that it does not permit cell sorting, as particles are completely disintegrated during the analysis. Other limitations include the analysis of only a subset of the applied cell sample, reduced sensitivity for the low

antigen expression and slower cell throughput than conventional flow cytometers. To date, data from mass cytometry have been reported mostly for phenotypic and functional analyses of subsets of lymphocytes. Studies of antigen-specific CD8+ T cells, for example, have confirmed the interrelatedness of subsets previously described by conventional FCM and the continuous nature of CD8+ T-cell differentiation. These data also showed much greater complexity in this T cell compartment, with a nearly combinatorial pattern of cytokine expression by virus-specific cells [84]. A recent 35parameter analysis of human natural killer (NK) cells by MSFC has revealed up to 30,000 different NK cell phenotypes in one person [85], suggesting that the established assumptions about immunecell lineages described precisely by a few discrete subsets may not apply. A particular advance in the MSFC has been the analysis of the generated cell subpopulations by clustering in multiple dimensions. Computational tools, such as ‘spanning-tree progression analysis of density-normalized events’ (SPADE), provide unique visualization options for major cell populations [86] but may be problematic for rare subsets of cells, such as antigen-specific cells or those associated with minimal residual disease (MRD). Notably, for MRD, a new computational tool called visual interactive stochastic neighbor embedding (viSNE) can offer more robust phenotyping of rare subsets than SPADE [87].




3.4.3. Multispectral imaging flow cytometry The previously described variants of FCM are based on the quantification of whole-cell associated intensity values, and lack the capability of providing direct images of the analyzed cells. A recent development combining image technology and FCM is multispectral imaging flow cytometry (MSIFC) [88]. Similar to FCM, MSIFC instruments analyze cells in suspension. Samples are introduced into a fluidic system where cells are hydrodynamically focused into a core stream and illuminated by a bright-field light source and one or several lasers. Transmitted light, scattered light and emitted fluorescence are collected by a microscope objective and further fluorescence light is decomposed into individual images in defined ranges of wavelengths by a dichroic filter stack. Signals are further directed onto the surface of a CCD camera with several detectors. Contrary to conventional FCM, current MSIFC instruments collect images continuously for about 10 ms duration per object. This allows the detection of low fluorescence intensity signals even when the cell image is acquired at high speed [89]. The process of spectral decomposition into individual images and multispectral data collection allow multimode imaging and separate quantification of signals from overlapping regions of the cell, thereby allowing calculation and display of more than 200 various morphometric, photometric and dynamic features for each cell, including quantitative measurements of size, shape, texture, and location of probes within, on or between cells. Because measurements are performed on individual cells, it is also possible to execute complex assays in a growing list of applications which include studies of immune cell phenotype and function [88–91]. 3.4.4. High-content bioimaging by automated microscopy Fluorescence microscopy is by far the most commonly employed technique in cell-based studies, but its applications to Systems Biology are limited by their low throughput rate and the difficulty of obtaining single-cell based measurements for large populations of cells. A relatively recent approach to overcome such limitation involves automated fluorescence-microscopybased systems, which allow a high-content analysis (HCA) of cells in solid support. HCA allows to capture and to analyze tens of thousands of images in a day, where each image may contain hundreds to thousands of cells, monitoring hundreds of parameters per cell. Individual cells or intracellular regions of interests are segmented by means of powerful software algorithms [92]. In this way, large-scale screens and functional studies rich in multiparameter subcellular information can be performed. Application of HCA for screening large libraries of compounds or genetic material is referred to as high-content screening (HCS) [93]. HCA and HCS have been applied to live cell imaging of cell structure, function and signaling [92–95], including aging studies of senescence phenotypes in normal human fibroblasts [96]. 3.4.5. Microtools for single-cell level genomic and proteomic analysis In the recent years several technologies have been evolved in the microliter- to picoliter-scale (‘microtools’) that permit precise handling and measurements of single cells or single-cell lysates [97]. Microtools alone, or complementing already established cytomic technologies, provide new classes of dynamic functional and transcriptional measurements, as they offer unique possibilities to approach dynamics of immune cells that are not possible by FCM or by bulk measurements. With these tools, the exact address of each cell is determined by imaging throughout an experiment, enabling multiple measurements for the same class of data over time, or combinations of measurements of multiple classes in the same experiment [70]. To date, two main classes of microtools are in use for monitoring the status of the immune system: microfluidic systems and simple spatial arrays of nanoliter-scale wells.

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Microfluidic chips enable thousands of parallel qPCR reactions for up to 96 samples at a time [98] or multiplex the preparation of up to 96 cells for single-cell sequencing (whole-genome and RNA sequencing) [99] and facilitate multiplexed measurements of single-cell secretomes [100]. In contrast, simple dense arrays that contain tens of thousands of addressable, subnanoliter compartments make it straightforward to isolate and monitor large numbers of single cells in parallel. These devices enable many new methods of studying unique aspects of single-cell phenotypes at scales relevant for detecting rare cells such as antigen-specific T cells and B cells [101,102], for revealing the dynamics and functional outcomes of interactions between human NK cells and target cells [103], and for describing variance among highly heterogeneous populations [104]. 3.4.6. Single-cell polymerase chain reaction PCR is used for amplification, detection, and quantification of nucleic acids. Quantitative reverse transcription PCR (qRT-PCR) incorporates reverse transcription and a fluorescent probe or dsDNA binding dye that allow sensitive measurement and detection of starting material down to single molecule range. The use of PCR for single cell analysis is well established but multiplex qPCR is limited in the number of reactions one can analyze [105]. Microfluidic chips overcome these limitations by combinatorially mixing the samples and gene detectors and by performing thousands of reactions in parallel on a single chip [106]. Microfluidic chips are also useful for automated single cell isolation and allow for more efficient RNA purification and amplification [107]. Microfluidic devices have been used to compare the expression levels of genes responsible for scavenging reactive oxygen species in single cells or to study the immune response to different prime-boost vector combinations [108]. 3.4.7. Single-cell genomic sequencing Microarrays enable measurement of thousands of genes at once by hybridization of a fluorescently labeled biological sample to an array consisting with thousands of synthetic oligonuclotide probes. There exist two principal technical requirements for singlecell sequencing, (a) the physical isolation of a single cell, and (b) the preparation of that material for DNA sequencing [109]. With respect to the first challenge, several approaches have been successfully implemented, including microfluidic flow [108] and FCM-cell sorting [110]. Modern tiling microarrays can be used for high resolution genomic measurements and whole transcriptome measurements, and can also detect non-coding transcripts and miRNAs [109]. Single-cell RNA sequencing (scRNA-seq) is a novel extension of transcriptomic technologies, allowing genome-wide profiling of cellular mRNA expression. This method enables analysis of other transcriptional features in single cells, and has already revealed subsets of cells not previously observed using other single-cell measurements [111]. scRNA-seq is still limited to the characterization of small numbers of cells at a time. Despite these current limitations, scRNA-seq paired with cell enrichment using FCM promises to expand largely the knowledge of static single-cell phenotypes [111,112]. 3.4.8. Bioinformatic tools for single-cell data mining The growing use of high-content and high-throughput cytomics requires managing large amounts of data in an automatic fashion, as well as integrating such data with those generated by other omic techniques [72]. FCM bioinformatics can be defined as the application of bioinformatics to FCM data, which involves storing, retrieving, organizing, and analyzing FCM data using extensive computational resources and tools [113]. Pioneer work on application of novel bioinformatics to PFCM was performed by




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applying cluster- and principal component analysis [114], which was used to objectively classify T cell populations from centenarians versus young and middle-aged donors [82]. A similar strategy for analyzing mass- and flow-cytometry data is spanning-tree progression analysis of density-normalized events (SPADE) [86]. Currently, computational methods exist to assist in the preprocessing of FCM data, identifying the cell populations generated, matching those cell populations across samples, and performing diagnosis and discovery from the results of previous steps. Open standards, data, and software are also key issues in FCM bioinformatics. Data standards include the widely adopted flow cytometry standard (FCS) defining how data from cytometers should be stored. Availability of open data is growing thanks to recently created public databases and repositories, such as CytoBank database (https://www.cytobank.org) and FlowRepository (http://flowrepository.org), both of which allow users to freely distribute their data. Open software is most widely available in the form of a suite of Bioconductor packages, but is also available for web execution on the GenePattern platform. Such software and its performance in automatic FCM have been recently compared and assessed, suggesting that automated methods have reached a sufficient level of maturity and accuracy for reliable use in FCM data analysis. As a part of the collective effort to create databases of immunological data and to standardize the analytical procedures, the National Institute of Allergy and Infectious Diseases of the US National Institutes of Health have created the Human Immunology Project Consortium (http://www.immuneprofiling.org). Several collaborative subprojects focus on cytometry data, seeking to ensure proper assay standardization from the data generation to the data analysis steps, both in PFCM, MSFC and Luminex assays. The key role of bioinformatics in FCM and cytomics has been reviewed recently [113]. 3.5. Metabolomics Metabolomics is a well-established system approach to characterize the metabolic phenotype resulting from a coordinated response to multiple factors, including environment, drugs, dietary habits, lifestyle, genetics, and microbiome [14]. Mass spectrometry is the choice tool for metabolomic analysis of small-molecule metabolites from biological samples, especially for biomarker discovery. The most often-used instrument combination is gas chromatography coupled with mass spectrometry, although in some cases the molecules can be directly infused into the mass spectrometer. Developments in mass spectrometry-based metabolomics have been reviewed recently [115]. Metabolomics has been applied repeatedly to analyze metabolite changes associated with age and longevity, and large populational metabolomic studies have shown significant correlations with age for many metabolites. Thus, metabolic signatures of extreme longevity in centenarians revealed a complex remodeling of lipids, amino acids, and gut microbiota metabolism [116]. Consistent findings in age-related studies support an incomplete mitochondrial fatty acid oxidation associated to aging [117], while improved antioxidant capacity and more efficient ␤-oxidation might be involved in increased lifespan in humans [118]. A detailed metabolomic profiling of longevity by combining holistic NMR profiling and targeted MS approaches, provided for the first time a metabolic phenotype of longevity in a well characterized human aging cohort comprising mostly female centenarians, elderly, and young individuals. With increasing age, targeted MS profiling of blood serum displayed a marked decrease in tryptophan concentration and specific alterations of glycerophospholipids and sphingolipids, likely reflecting the centenarians’ unique capacity to adapt to and respond to accumulating oxidative and chronic inflammatory conditions [119].

Metabolomics has also shown that the longevity process affects the structure and composition of the human gut microbiota, which is extensively affected by diet. A recent study demonstrated dietdriven alterations in varying rates of health decline upon aging [120]. In that study, a reduced coding capacity for producing shortchain fatty acids in frail subjects correlated with lower levels of butyrate, acetate and propionate in the fecal metabolome. The metabonomic profile of centenarians allowed the identification of some urine metabotypes which were strongly connected with extreme aging and some intestinal microbiota elements [116], suggesting that potential increase of consumption of tryptophan by the gut microbiota reduces its bioavailability within the host, in agreement with the reduction of tryptophan found in serum of centenarians [119]. 3.6. Epigenetics Gene expression is modulated along the life course of an individual by epigenetic factors such as histone modifications and DNA methylation [121]. Among epigenetic modifications, DNA methylation is the best characterized. Many studies demonstrate the occurrence of age-associated modifications in the DNA-methylation pattern [121–124]. Epigenetic variations have been suggested to have an important role in cellular senescence, tumorigenesis and in several diseases including type-2 diabetes, cardiovascular and autoimmune diseases, obesity and Alzheimer disease [121]. Most studies support that aging is associated with a relaxation of epigenetic control, with a decrease in global cytosine methylation [123] and an age-related hypermethylation in promoter regions of specific genes, involved in cell cycle regulation, tumor-cell invasion, apoptosis, metabolism, cell signaling and DNA repair [124,125]. Correlations of epigenetic DNA modifications (level and distribution of DNA methylation) to human lifespan [126] and quality of aging [127] have also been demonstrated. Current DNA methylation analysis may be performed at high throughput by technologies such as microarray platforms, which facilitate simultaneous analysis of near 30,000 CpG sites, which are associated with promoter regions of more than 14,000 annotated genes [14]. Studies of age-associated changes in primary tissues have even identified an Epigenetic-Aging-Signature which was tested and validated, and found applicable in many tissues to predict donor age [122]. Similar studies have shown changes in DNA methylation during both cellular senescence and in vivo aging [128]. However, these arrays are biased toward gene promoters, and the findings cannot be extrapolated to the whole genome, but a study of whole-genome, comparing cord blood from newborn infants with peripheral blood from centenarians found an overall marked decrease in global methylation and CpG island promoter hypermethylation with extreme age [129]. 4. Data integration in Systems Biology of immune aging Biological systems cannot be understood by the analysis of single-type data sets. Thus, one of the main challenges of Systems Biology is the cross-examination and integration and of a large amount of different types of data, reflecting different levels of biological complexity. Omics data integration requires network analysis and annotation of the involved pathways and their interactions with environmental factors. In addition, there is also a need for the integration of omics and clinical data. Currently, these relevant questions are approached by research and development activities at four sequential steps: (i) designing and conducting research projects to investigate biological systems at different biological levels; (ii) generating large sets of heterogeneous data; (iii) designing novel bioinformatics methodologies to analyze the heterogeneous




data; and (iv) organizing scientific meetings to disseminate and to foster further integration efforts. These issues have been recently reviewed [130], but have only been partially implemented in omics studies related to immunity and aging. 4.1. Bioinformatics and resources for Systems Biology in human immunology The National Institute of Allergy and Infectious Diseases of the US National Institutes of Health created the Human Immunology Project Consortium (http://www.immuneprofiling.org). This competitive grants program currently consists of seven research centers, which are building large data sets on human subjects undergoing influenza vaccination or who are infected with pathogens. A subcommittee collaboratively works to create an infrastructure to assist the entire international immunology community, through development of experimental protocols, standards for data collection, and state-of-the-art tools for modeling and integration of heterogeneous immunological data. A central database and analysis engine, ImmuneSpace (http://www.immunespace.org/), based on the LabKey system, provides public access to all data generated by the consortium. To support the wide range of immunological experiments, the consortium is taking advantage of the infrastructure already developed as part of the NIAID Immunology Database and Analysis Portal (ImmPort) system (https://immport.niaid.nih.gov/). The Immunological Genome Project (ImmGen) [131] is a public resource aimed to characterize the mouse immunological system, on the basis of microarray profiling of mRNA of most immune cell types under carefully standardized conditions. ImmGen project is a combined effort between immunologists and computational biologists, and has become a key resource for research on murine and human immune systems. In addition, Immgen has developed and applied the most advanced network inference methodology, such as Ontogenet [132] to identify transcription factors acting as stage-specific regulators of mouse hematopoiesis. Bioinformatics resources available for immunology studies have been recently reviewed [22]. 4.2. Bioinformatics and resources for Systems Biology in human aging

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contains miRNA-regulated PPI networks for longevity, age-related diseases and aging-associated processes. In addition to the specific aging databases, numerous secondary databases and tools may facilitate the analysis of aging-related data. These include the Gene Ontology which provides exhaustive functional annotation for many genes. The ArrayExpress Archive and associated Gene Expression Atlas as well as the Gene Expression Omnibus allow searching and downloading of data related to functional genomics experiments, many of which have some association with aging. Bioinformatics resources available for aging-related studies have been recently reviewed [133]. 5. Conclusion The individual omic technologies available for analyzing immune responses with genome-wide or single-cell or singlemolecule resolution are advancing rapidly, but the extraordinary promise that represents Systems Biology will only be fulfilled by the integration of the measurements resulting from the wise combination of these experimental tools, the application of powerful bioinformatic tools for mining the complex data generated and the easy accessibility to public data organized in dynamic, interactive repositories. In this way, the static phenotypic picture that still dominates the studies of immune aging will ideally be turned into a dynamic landscape modelized to define the states and interactions of every individual cell and molecule of the Immune system and their role in the context of aging and longevity. For several omics, such as glycomics, metabolomics and metagenomics, clear age correlations have been observed, but much data remain to be collected and analyzed. For transcriptomic studies, substantial changes with age have so far been mainly identified as tissue- and species-specific. In addition, changes seen in the transcriptome do not always reflect what is happening at the protein level. Some of the most substantial advances have come from epigenetics, in which a large number of studies showed consistent patterns of DNA methylation across tissues and strong age dependence, although comprehensive longitudinal data are needed. The studies reported so far demonstrate that omics technologies are increasingly important and are promising tools to understand the complexities of aging. Acknowledgements

The recent application of omics technologies and the derived Systems Biology approach to the field of human aging and longevity has led to a very large amount of public data, most often based on high-throughput genomic and proteomic technologies. Open access to existing data and to their computational analysis, is a very important issue to gain knowledge on the causes and effects of aging. Text-mining, for instance, should become a useful tool for aging research with near 240,000 articles found in PubMed when searching for ‘aging’ [133]. Even though the data originating from diverse aging studies require different methods for their interpretation, they rely heavily on bioinformatics methods. Bioinformatics is essential from data handling to interpretation and integration to complex comparisons, hypotheses and model building. Primary databases of aging studies include GenAge (http://genomics.senescene.info/genes), a database of genes related to longevity or aging, and AnAge (http://genomics.senescene.info/species), a database of longevity and aging in animal species. GenAge and AnAge are subsections of the Human Aging Genomic Resources (HAGR). Gene Aging Nexus (http://gan.usc.edu) is a data mining platform for the biogerontological-geriatric research community. AgeID (http://uwaging.org/genesdb) is a database for aging genes and interventions. The NetAge database (http://netage-project.org)

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WITHDRAWN: Systems Biology and Immune Aging.

Systems biology of aging.

Computational systems biology for aging research.

Editorial: Biology of Cognitive Aging: Model Systems, Technologies, and Beyond.

Metabolism and Aging: From Molecular Physiology to Systems Biology.

Systems biology and reproduction.

A systems biology approach to the effect of aging, immunosenescence and vaccine response.

Systems biology approaches to study the molecular effects of caloric restriction and polyphenols on aging processes.

Systems biology as an integrated platform for bioinformatics, systems synthetic biology, and systems metabolic engineering.

Chemokines and Heart Disease: A Network Connecting Cardiovascular Biology to Immune and Autonomic Nervous Systems.

Systems biology, emergence and antireductionism.

Introduction: systems biology and reproduction.

The cell biology of aging.

Integration of 'omics' data in aging research: from biomarkers to systems biology.

Integrative radiation systems biology.

Image-based systems biology.

Root systems biology.

Perspectives in systems biology.

The biology and physiology of aging.

Complex systems biology.

Systems biology for hepatologists.

Systems biology approach to developing "systems therapeutics".

Fostering synergy between cell biology and systems biology.

Molecular profiles to biology and pathways: a systems biology approach.