Systems medicine of inflammaging

Safety and efficacy of autologous adipose tissue-derived stem cell transplantation in aging-related low-grade inflammation patients: a single-group, open-label, phase I clinical trial

BACKGROUND

Inflamm-aging is associated with the rate of aging and is significantly related to diseases such as Alzheimer's disease, Parkinson's disease, atherosclerosis, heart disease, and age-related degenerative diseases such as type II diabetes and osteoporosis. This study aims to evaluate the safety and efficiency of autologous adipose tissue-derived mesenchymal stem cell (AD-MSC) transplantation in aging-related low-grade inflammation patients.

METHODS

This study is a single-group, open-label, phase I clinical trial in which patients treated with 2 infusions (100 million cells i.v) of autologous AD-MSCs were initially evaluated in 12 inflamm-aging patients who concurrently had highly proinflammatory cytokines and 2 of the following 3 diseases: diabetes, dyslipidemia, and obesity. The treatment effects were evaluated based on plasma cytokines.

RESULTS

During the study's follow-up period, no adverse effects were observed in AD-MSC injection patients. Compared to baseline (D-44), the inflammatory cytokines IL-1α, IL-1β, IL-8, IL-6, and TNF-α were significantly reduced after 180 days (D180) of MSC infusion. IL-4/IL-10 at 90 days (D90) and IL-2/IL-10 at D180 increased, reversing the imbalance between proinflammatory and inflammatory ratios in the patients.

CONCLUSION

AD-MSCs represent a potential intervention to prevent age-related inflammation in patients.

TRIAL REGISTRATION

ClinicalTrials.gov number is NCT05827757, first registered on 13th Oct 2020.

Telomere Length and Risk of Incident Fracture and Arthroplasty: Findings From UK Biobank

We investigated independent associations between telomere length and risk of fracture and arthroplasty in UK Biobank participants. Leukocyte telomere length (LTL) was measured in baseline samples using a validated polymerase chain reaction (PCR) method. We used, in men and women separately, Cox proportional hazards models to calculate the hazard ratio (HR) for incident fracture (any, osteoporotic) or arthroplasty (hip or knee) over 1,186,410 person-years of follow-up. Covariates included age, white cell count, ethnicity, smoking, alcohol, physical activity, and menopause (women). In further analyses we adjusted for either estimated bone mineral density (eBMD) from heel quantitative ultrasound, handgrip strength, gait speed, total fat mass (bioimpedance), or blood biomarkers, all measured at baseline (2006-2010). We studied 59,500 women and 51,895 men, mean ± standard deviation (SD) age 56.4 ± 8.0 and 57.0 ± 8.3 years, respectively. During follow-up there were 5619 fractures; 5285 hip and 4261 knee arthroplasties. In confounder-adjusted models, longer LTL was associated with reduced risk of incident knee arthroplasty in both men (HR/SD 0.93; 95% confidence interval [CI], 0.88-0.97) and women (0.92; 95% CI, 0.88-0.96), and hip arthroplasty in men (0.91; 95% CI, 0.87-0.95), but not women (0.98; 95% CI, 0.94-1.01). Longer LTL was weakly associated with reduced risk of any incident fracture in women (HR/SD 0.96; 95% CI, 0.93-1.00) with less evidence in men (0.98; 95% CI, 0.93-1.02). Associations with incident outcomes were not materially altered by adjustment for heel eBMD, grip strength, gait speed, fat mass, or blood biomarker measures. In this, the largest study to date, longer LTL was associated with lower risk of incident knee or hip arthroplasty, but only weakly associated with lower risk of fracture. The relative risks were low at a population level, but our findings suggest that common factors acting on the myeloid and musculoskeletal systems might influence later life musculoskeletal outcomes. © 2022 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Cold-inflammaging: when a state of homeostatic-imbalance associated with ageing, precedes the low-grade pro-inflammatory-state (inflammaging): meaning, evolution, inflammaging phenotypes

The age-related pro-inflammatory state, discovered and called 'inflammaging' by Franceschi C. et al. (2000) plays an important role in the pathogenesis of age-related chronic diseases. A substantial body of data established that inflammaging is accompanied by a "2-fold-to-4-fold" increase in plasma levels of pro-inflammatory mediators in healthy elderly people, when compared to the healthy adult popultion. This review focuses on the pre-inflammaging phase herein we reported as "Cold-Inflammaging", a state where plasma levels of cytokines are slightly increased, but below the lower limit of "2-fold increase" established for inflammaging. Slightly altered cytokine levels by innate immunity are known to be associated with homeostasis imbalances, this functional pleiotropy of cytokines as signal transducers, have a physiological counterpart, representing an adaptive process aimed at restoring (or achieving a new) homeostatic stability. If a dys-homeostatic state persists, the cytokine response by innate immunity increases and becomes a driver of inflammaging. A scenario where cytokines are characterised as major players in homeostasis imbalances at the beginning (cold-inflammaging) and then in chronic low-grade pro-inflammatory-state (inflammaging). Other important drivers of inflammaging are cellular senescence with its Senescence Associated Secretory Phenotype (SASP), the altered gut microbiota and the age-related dysregulation in the production of endogenous "molecular waste" (Garb-aging). The main purpose of this review being to thoroughly investigate each step of the pathway from cold-inflammaging to overt-inflammaging, because ageing, cold-inflammaging, overt-inflammaging, and the pathogenesis of age-related diseases have been shown to share some establised basic pillars of Geroscience that largely converge on inflammaging.

Resilience integrates concepts in aging research

Aging research is unparalleled in the breadth of disciplines it encompasses, from evolutionary studies examining the forces that shape aging to molecular studies uncovering the underlying mechanisms of age-related functional decline. Despite a common focus to advance our understanding of aging, these disciplines have proceeded along distinct paths with little cross-talk. We propose that the concept of resilience can bridge this gap. Resilience describes the ability of a system to respond to perturbations by returning to its original state. Although resilience has been applied in a few individual disciplines in aging research such as frailty and cognitive decline, it has not been explored as a unifying conceptual framework that is able to connect distinct research fields. We argue that because a resilience-based framework can cross broad physiological levels and time scales it can provide the missing links that connect these diverse disciplines. The resulting framework will facilitate predictive modeling and validation and influence targets and directions in research on the biology of aging.

Inflammaging and Brain: Curcumin and Its Beneficial Potential as Regulator of Microglia Activation

Inflammaging is a term used to describe the tight relationship between low-grade chronic inflammation and aging that occurs during physiological aging in the absence of evident infection. This condition has been linked to a broad spectrum of age-related disorders in various organs including the brain. Inflammaging represents a highly significant risk factor for the development and progression of age-related conditions, including neurodegenerative diseases which are characterized by the progressive dysfunction and degeneration of neurons in the brain and peripheral nervous system. Curcumin is a widely studied polyphenol isolated from Curcuma longa with a variety of pharmacologic properties. It is well-known for its healing properties and has been extensively used in Asian medicine to treat a variety of illness conditions. The number of studies that suggest beneficial effects of curcumin on brain pathologies and age-related diseases is increasing. Curcumin is able to inhibit the formation of reactive-oxygen species and other pro-inflammatory mediators that are believed to play a pivotal role in many age-related diseases. Curcumin has been recently proposed as a potential useful remedy against neurodegenerative disorders and brain ageing. In light of this, our current review aims to discuss the potential positive effects of Curcumin on the possibility to control inflammaging emphasizing the possible modulation of inflammaging processes in neurodegenerative diseases.

The Human Organism as an Integrated Interaction Network: Recent Conceptual and Methodological Challenges

The field of Network Physiology aims to advance our understanding of how physiological systems and sub-systems interact to generate a variety of behaviors and distinct physiological states, to optimize the organism's functioning, and to maintain health. Within this framework, which considers the human organism as an integrated network, vertices are associated with organs while edges represent time-varying interactions between vertices. Likewise, vertices may represent networks on smaller spatial scales leading to a complex mixture of interacting homogeneous and inhomogeneous networks of networks. Lacking adequate analytic tools and a theoretical framework to probe interactions within and among diverse physiological systems, current approaches focus on inferring properties of time-varying interactions-namely strength, direction, and functional form-from time-locked recordings of physiological observables. To this end, a variety of bivariate or, in general, multivariate time-series-analysis techniques, which are derived from diverse mathematical and physical concepts, are employed and the resulting time-dependent networks can then be further characterized with methods from network theory. Despite the many promising new developments, there are still problems that evade from a satisfactory solution. Here we address several important challenges that could aid in finding new perspectives and inspire the development of theoretic and analytical concepts to deal with these challenges and in studying the complex interactions between physiological systems.

Cognitive Decline and Alzheimer's Disease in Old Age: A Sex-Specific Cytokinome Signature

BACKGROUND

Elevated peripheral levels of different cytokines and chemokines in subjects with Alzheimer's disease (AD), as compared with healthy controls (HC), have emphasized the role of inflammation in such a disease. Considering the cross-talking between the central nervous system and the periphery, the inflammatory analytes may provide utility as biomarkers to identify AD at earlier stages.

OBJECTIVE

Using an advanced statistical approach, we can discriminate the interactive network of cytokines/chemokines and propose a useful tool to follow the progression and evolution of AD, also in light of sex differences.

METHODS

A cohort of 289 old-age subjects was screened for cytokine and chemokine profiling, measured in plasma, after a thorough clinical and neuropsychological evaluation. A custom algorithm based on Fisher linear discriminant analysis was applied to ascertain a classification signature able to discriminate HC from mild cognitive impairment (MCI) and AD.

RESULTS

We observed that a joint expression of three proteins (a "signature" composed by IFN-α2, IL-1α, TNFα) can discriminate HC from AD with an accuracy of 65.24%. Using this signature on MCI samples, 84.93% of them were classified as "non-HC". Stratifying MCI samples by sex, we observed that 87.23% of women were classified as "non-HC", and only 57.69% of males. Indeed, in a scatter plot of IFN-α2 and IL-1α, the HC group was better separated from MCI and AD in women as compared with men.

CONCLUSION

These findings suggest that AD is accompanied by a peripheral inflammatory response that can already be present in MCI subjects, thus providing a mean for detecting this at-risk status and allow an anticipated intervention.

Big data analytics in medical engineering and healthcare: methods, advances and challenges

Big data analytics are gaining popularity in medical engineering and healthcare use cases. Stakeholders are finding big data analytics reduce medical costs and personalise medical services for each individual patient. Big data analytics can be used in large-scale genetics studies, public health, personalised and precision medicine, new drug development, etc. The introduction of the types, sources, and features of big data in healthcare as well as the applications and benefits of big data and big data analytics in healthcare is key to understanding healthcare big data and will be discussed in this article. Major methods, platforms and tools of big data analytics in medical engineering and healthcare are also presented. Advances and technology progress of big data analytics in healthcare are introduced, which includes artificial intelligence (AI) with big data, infrastructure and cloud computing, advanced computation and data processing, privacy and cybersecurity, health economic outcomes and technology management, and smart healthcare with sensing, wearable devices and Internet of things (IoT). Current challenges of dealing with big data and big data analytics in medical engineering and healthcare as well as future work are also presented.

Multi-omics integration-a comparison of unsupervised clustering methodologies

With the recent developments in the field of multi-omics integration, the interest in factors such as data preprocessing, choice of the integration method and the number of different omics considered had increased. In this work, the impact of these factors is explored when solving the problem of sample classification, by comparing the performances of five unsupervised algorithms: Multiple Canonical Correlation Analysis, Multiple Co-Inertia Analysis, Multiple Factor Analysis, Joint and Individual Variation Explained and Similarity Network Fusion. These methods were applied to three real data sets taken from literature and several ad hoc simulated scenarios to discuss classification performance in different conditions of noise and signal strength across the data types. The impact of experimental design, feature selection and parameter training has been also evaluated to unravel important conditions that can affect the accuracy of the result.

Community effort endorsing multiscale modelling, multiscale data science and multiscale computing for systems medicine

Systems medicine holds many promises, but has so far provided only a limited number of proofs of principle. To address this road block, possible barriers and challenges of translating systems medicine into clinical practice need to be identified and addressed. The members of the European Cooperation in Science and Technology (COST) Action CA15120 Open Multiscale Systems Medicine (OpenMultiMed) wish to engage the scientific community of systems medicine and multiscale modelling, data science and computing, to provide their feedback in a structured manner. This will result in follow-up white papers and open access resources to accelerate the clinical translation of systems medicine.

Frailness and resilience of gene networks predicted by detection of co-occurring mutations via a stochastic perturbative approach

In recent years complex networks have been identified as powerful mathematical frameworks for the adequate modeling of many applied problems in disparate research fields. Assuming a Master Equation (ME) modeling the exchange of information within the network, we set up a perturbative approach in order to investigate how node alterations impact on the network information flow. The main assumption of the perturbed ME (pME) model is that the simultaneous presence of multiple node alterations causes more or less intense network frailties depending on the specific features of the perturbation. In this perspective the collective behavior of a set of molecular alterations on a gene network is a particularly adapt scenario for a first application of the proposed method, since most diseases are neither related to a single mutation nor to an established set of molecular alterations. Therefore, after characterizing the method numerically, we applied as a proof of principle the pME approach to breast cancer (BC) somatic mutation data downloaded from Cancer Genome Atlas (TCGA) database. For each patient we measured the network frailness of over 90 significant subnetworks of the protein-protein interaction network, where each perturbation was defined by patient-specific somatic mutations. Interestingly the frailness measures depend on the position of the alterations on the gene network more than on their amount, unlike most traditional enrichment scores. In particular low-degree mutations play an important role in causing high frailness measures. The potential applicability of the proposed method is wide and suggests future development in the control theory context.

Anti-aging Effects of Calorie Restriction (CR) and CR Mimetics based on the Senoinflammation Concept

Chronic inflammation, a pervasive feature of the aging process, is defined by a continuous, multifarious, low-grade inflammatory response. It is a sustained and systemic phenomenon that aggravates aging and can lead to age-related chronic diseases. In recent years, our understanding of age-related chronic inflammation has advanced through a large number of investigations on aging and calorie restriction (CR). A broader view of age-related inflammation is the concept of senoinflammation, which has an outlook beyond the traditional view, as proposed in our previous work. In this review, we discuss the effects of CR on multiple phases of proinflammatory networks and inflammatory signaling pathways to elucidate the basic mechanism underlying aging. Based on studies on senoinflammation and CR, we recognized that senescence-associated secretory phenotype (SASP), which mainly comprises cytokines and chemokines, was significantly increased during aging, whereas it was suppressed during CR. Further, we recognized that cellular metabolic pathways were also dysregulated in aging; however, CR mimetics reversed these effects. These results further support and enhance our understanding of the novel concept of senoinflammation, which is related to the metabolic changes that occur in the aging process. Furthermore, a thorough elucidation of the effect of CR on senoinflammation will reveal key insights and allow possible interventions in aging mechanisms, thus contributing to the development of new therapies focused on improving health and longevity.

The peculiar aging of human liver: A geroscience perspective within transplant context

An appraisal of recent data highlighting aspects inspired by the new Geroscience perspective are here discussed. The main findings are summarized as follows: i) liver has to be considered an immunological organ, and new studies suggest a role for the recently described cells named telocytes; ii) the liver-gut axis represents a crucial connection with environment and life style habits and may influence liver diseases onset; iii) the physiological aging of liver shows relatively modest alterations. Nevertheless, several molecular changes appear to be relevant: a) an increase of microRNA-31-5p; -141-3p; -200c-3p expressions after 60 years of age; b) a remodeling of genome-wide DNA methylation profile evident until 60 years of age and then plateauing; c) changes in transcriptome including the metabolic zones of hepatocyte lobules; d) liver undergoes an accelerated aging in presence of chronic inflammation/liver diseases in a sort of continuum, largely as a consequence of unhealthy life styles and exposure to environmental noxious agents. We argue that chronic liver inflammation has all the major characteristics of "inflammaging" and likely sustains the onset and progression of liver diseases. Finally, we propose to use a combination of parameters, mostly obtained by omics such as transcriptomics and epigenomics, to evaluate in deep both the biological age of liver (in comparison with the chronological age) and the effects of donor-recipient age-mismatches in the context of liver transplant.

Multi-omic analysis of signalling factors in inflammatory comorbidities

BACKGROUND

Inflammation is a core element of many different, systemic and chronic diseases that usually involve an important autoimmune component. The clinical phase of inflammatory diseases is often the culmination of a long series of pathologic events that started years before. The systemic characteristics and related mechanisms could be investigated through the multi-omic comparative analysis of many inflammatory diseases. Therefore, it is important to use molecular data to study the genesis of the diseases. Here we propose a new methodology to study the relationships between inflammatory diseases and signalling molecules whose dysregulation at molecular levels could lead to systemic pathological events observed in inflammatory diseases.

RESULTS

We first perform an exploratory analysis of gene expression data of a number of diseases that involve a strong inflammatory component. The comparison of gene expression between disease and healthy samples reveals the importance of members of gene families coding for signalling factors. Next, we focus on interested signalling gene families and a subset of inflammation related diseases with multi-omic features including both gene expression and DNA methylation. We introduce a phylogenetic-based multi-omic method to study the relationships between multi-omic features of inflammation related diseases by integrating gene expression, DNA methylation through sequence based phylogeny of the signalling gene families. The models of adaptations between gene expression and DNA methylation can be inferred from pre-estimated evolutionary relationship of a gene family. Members of the gene family whose expression or methylation levels significantly deviate from the model are considered as the potential disease associated genes.

CONCLUSIONS

Applying the methodology to four gene families (the chemokine receptor family, the TNF receptor family, the TGF- β gene family, the IL-17 gene family) in nine inflammation related diseases, we identify disease associated genes which exhibit significant dysregulation in gene expression or DNA methylation in the inflammation related diseases, which provides clues for functional associations between the diseases.

Network integration of multi-tumour omics data suggests novel targeting strategies

We characterize different tumour types in search for multi-tumour drug targets, in particular aiming for drug repurposing and novel drug combinations. Starting from 11 tumour types from The Cancer Genome Atlas, we obtain three clusters based on transcriptomic correlation profiles. A network-based analysis, integrating gene expression profiles and protein interactions of cancer-related genes, allows us to define three cluster-specific signatures, with genes belonging to NF-κB signaling, chromosomal instability, ubiquitin-proteasome system, DNA metabolism, and apoptosis biological processes. These signatures have been characterized by different approaches based on mutational, pharmacological and clinical evidences, demonstrating the validity of our selection. Moreover, we define new pharmacological strategies validated by in vitro experiments that show inhibition of cell growth in two tumour cell lines, with significant synergistic effect. Our study thus provides a list of genes and pathways that could possibly be used, singularly or in combination, for the design of novel treatment strategies.

Tumours of different tissues can show similarities in genomic alterations. Here, the authors combine tumour transcriptome and protein interaction data in a network-based analysis of 11 tumours types, and identify clusters of tumours with specific signatures for multi-tumour drug targeting and survival prognosis.

Health relevance of the modification of low grade inflammation in ageing (inflammageing) and the role of nutrition

Ageing of the global population has become a public health concern with an important socio-economic dimension. Ageing is characterized by an increase in the concentration of inflammatory markers in the bloodstream, a phenomenon that has been termed "inflammageing". The inflammatory response is beneficial as an acute, transient reaction to harmful conditions, facilitating the defense, repair, turnover and adaptation of many tissues. However, chronic and low grade inflammation is likely to be detrimental for many tissues and for normal functions. We provide an overview of low grade inflammation (LGI) and determine the potential drivers and the effects of the "inflamed" phenotype observed in the elderly. We discuss the role of gut microbiota and immune system crosstalk and the gut-brain axis. Then, we focus on major health complications associated with LGI in the elderly, including mental health and wellbeing, metabolic abnormalities and infections. Finally, we discuss the possibility of manipulating LGI in the elderly by nutritional interventions. We provide an overview of the evidence that exists in the elderly for omega-3 fatty acid, probiotic, prebiotic, antioxidant and polyphenol interventions as a means to influence LGI. We conclude that slowing, controlling or reversing LGI is likely to be an important way to prevent, or reduce the severity of, age-related functional decline and the onset of conditions affecting health and well-being; that there is evidence to support specific dietary interventions as a strategy to control LGI; and that a continued research focus on this field is warranted.

Inflammation-a Critical Appreciation of the Role of Myeloid Cells

What is inflammation's big idea? In this brief overview of the role of myeloid cells in inflammation, we will critically discuss what drives the initiation, amplification, and resolution of inflammation in different anatomical sites in response to different pathological stimuli. It can be argued that we have a good understanding of the basic principles that underlie myeloid cell activation and the mobilization of innate immune cells to sites of injury and infection in acute inflammation. The challenge now for inflammation biologists is to understand how resolution of this normal physiological response goes wrong in hyperacute and chronic inflammation. A better understanding of how inflammation is regulated will allow us to develop new anti-inflammatory drugs that will reduce the burden of inflammatory disease without compromising the patient's immune defenses against infectious disease. Ideally such drugs should encourage a return to homeostasis and enhance tissue repair processes.

Interdisciplinary approach towards a systems medicine toolbox using the example of inflammatory diseases

Electronic access to multiple data types, from generic information on biological systems at different functional and cellular levels to high-throughput molecular data from human patients, is a prerequisite of successful systems medicine research. However, scientists often encounter technical and conceptual difficulties that forestall the efficient and effective use of these resources. We summarize and discuss some of these obstacles, and suggest ways to avoid or evade them.The methodological gap between data capturing and data analysis is huge in human medical research. Primary data producers often do not fully apprehend the scientific value of their data, whereas data analysts maybe ignorant of the circumstances under which the data were collected. Therefore, the provision of easy-to-use data access tools not only helps to improve data quality on the part of the data producers but also is likely to foster an informed dialogue with the data analysts.We propose a means to integrate phenotypic data, questionnaire data and microbiome data with a user-friendly Systems Medicine toolbox embedded into i2b2/tranSMART. Our approach is exemplified by the integration of a basic outlier detection tool and a more advanced microbiome analysis (alpha diversity) script. Continuous discussion with clinicians, data managers, biostatisticians and systems medicine experts should serve to enrich even further the functionality of toolboxes like ours, being geared to be used by 'informed non-experts' but at the same time attuned to existing, more sophisticated analysis tools.

Inflammaging and human longevity in the omics era

Inflammaging is a recent theory of aging originally proposed in 2000 where data and conceptualizations regarding the aging of the immune system (immunosenescence) and the evolution of immune responses from invertebrates to mammals converged. This theory has received an increasing number of citations and experimental confirmations. Here we present an updated version of inflammaging focused on omics data - particularly on glycomics - collected on centenarians, semi-supercentenarians and their offspring. Accordingly, we arrived to the following conclusions: i) inflammaging has a structure where specific combinations of pro- and anti-inflammatory mediators are involved; ii) inflammaging is systemic and more complex than we previously thought, as many organs, tissues and cell types participate in producing pro- and anti-inflammatory stimuli defined "molecular garbage"; iii) inflammaging is dynamic, can be propagated locally to neighboring cells and systemically from organ to organ by circulating products and microvesicles, and amplified by chronic age-related diseases constituting a "local fire", which in turn produces additional inflammatory stimuli and molecular garbage; iv) an integrated Systems Medicine approach is urgently needed to let emerge a robust and highly informative set/combination of omics markers able to better grasp the complex molecular core of inflammaging in elderly and centenarians.

Network diffusion-based analysis of high-throughput data for the detection of differentially enriched modules

A relation exists between network proximity of molecular entities in interaction networks, functional similarity and association with diseases. The identification of network regions associated with biological functions and pathologies is a major goal in systems biology. We describe a network diffusion-based pipeline for the interpretation of different types of omics in the context of molecular interaction networks. We introduce the network smoothing index, a network-based quantity that allows to jointly quantify the amount of omics information in genes and in their network neighbourhood, using network diffusion to define network proximity. The approach is applicable to both descriptive and inferential statistics calculated on omics data. We also show that network resampling, applied to gene lists ranked by quantities derived from the network smoothing index, indicates the presence of significantly connected genes. As a proof of principle, we identified gene modules enriched in somatic mutations and transcriptional variations observed in samples of prostate adenocarcinoma (PRAD). In line with the local hypothesis, network smoothing index and network resampling underlined the existence of a connected component of genes harbouring molecular alterations in PRAD.

Inflammation Thread Runs across Medical Laboratory Specialities

We work on the assumption that four major specialities or sectors of medical laboratory assays, comprising clinical chemistry, haematology, immunology, and microbiology, embraced by genome sequencing techniques, are routinely in use. Medical laboratory markers for inflammation serve as model: they are allotted to most fields of medical lab assays including genomics. Incessant coding of assays aligns each of them in the long lists of big data. As exemplified with the complement gene family, containing C2, C3, C8A, C8B, CFH, CFI, and ITGB2, heritability patterns/risk factors associated with diseases with genetic glitch of complement components are unfolding. The C4 component serum levels depend on sufficient vitamin D whilst low vitamin D is inversely related to IgG1, IgA, and C3 linking vitamin sufficiency to innate immunity. Whole genome sequencing of microbial organisms may distinguish virulent from nonvirulent and antibiotic resistant from nonresistant varieties of the same species and thus can be listed in personal big data banks including microbiological pathology; the big data warehouse continues to grow.

Potential therapeutic effects of the MTOR inhibitors for preventing ageing and progeria-related disorders

The mammalian target of rapamycin (mTOR) pathway is an highly conserved signal transduction axis involved in many cellular processes, such as cell growth, survival, transcription, translation, apoptosis, metabolism, motility and autophagy. Recently, this signalling pathway has come to the attention of the scientific community owing to the unexpected finding that inhibition of mTOR by rapamycin, an antibiotic with immunosuppressant and chemotherapeutic properties, extends lifespan in diverse animal models. Moreover, rapamycin has been reported to rescue the cellular phenotype in a progeroid syndrome [Hutchinson–Gilford Progeria syndrome (HGPS)] that recapitulates most of the traits of physiological ageing. The promising perspectives raised by these results warrant a better understanding of mTOR signalling and the potential applications of mTOR inhibitors to counteract ageing‐associated diseases and increase longevity. This review is focused on these issues.

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

Worldwide population is aging, and a large part of the growing burden associated with age-related conditions can be prevented or delayed by promoting healthy lifestyle and normalizing metabolic risk factors. However, a better understanding of the pleiotropic effects of available nutritional interventions and their influence on the multiple processes affected by aging is needed to select and implement the most promising actions. New methods of analysis are required to tackle the complexity of the interplay between nutritional interventions and aging, and to make sense of a growing amount of -omics data being produced for this purpose. In this paper, we review how various systems biology-inspired methods of analysis can be applied to the study of the molecular basis of nutritional interventions promoting healthy aging, notably caloric restriction and polyphenol supplementation. We specifically focus on the role that different versions of network analysis, molecular signature identification and multi-omics data integration are playing in elucidating the complex mechanisms underlying nutrition, and provide some examples on how to extend the application of these methods using available microarray data.