Enrico Giampieri

Methods for the integration of multi-omics data: mathematical aspects.

BackgroundMethods for the integrative analysis of multi-omics data are required to draw a more complete and accurate picture of the dynamics of molecular systems. The complexity of biological systems, the technological limits, the large number of biological variables and the relatively low number of biological samples make the analysis of multi-omics datasets a non-trivial problem.Results and ConclusionsWe review the most advanced strategies for integrating multi-omics datasets, focusing on mathematical and methodological aspects.

The role of low-grade inflammation and metabolic flexibility in aging and nutritional modulation thereof: a systems biology approach

Aging is a biological process characterized by the progressive functional decline of many interrelated physiological systems. In particular, aging is associated with the development of a systemic state of low-grade chronic inflammation (inflammaging), and with progressive deterioration of metabolic function. Systems biology has helped in identifying the mediators and pathways involved in these phenomena, mainly through the application of high-throughput screening methods, valued for their molecular comprehensiveness. Nevertheless, inflammation and metabolic regulation are dynamical processes whose behavior must be understood at multiple levels of biological organization (molecular, cellular, organ, and system levels) and on multiple time scales. Mathematical modeling of such behavior, with incorporation of mechanistic knowledge on interactions between inflammatory and metabolic mediators, may help in devising nutritional interventions capable of preventing, or ameliorating, the age-associated functional decline of the corresponding systems.

mtDNA mutations in human aging and longevity: Controversies and new perspectives opened by high-throughput technologies

The last 30 years of research greatly contributed to shed light on the role of mitochondrial DNA (mtDNA) variability in aging, although contrasting results have been reported, mainly due to bias regarding the population size and stratification, and to the use of analysis methods (haplogroup classification) that resulted to be not sufficiently adequate to grasp the complexity of the phenomenon. A 5-years European study (the GEHA EU project) collected and analyzed data on mtDNA variability on an unprecedented number of long-living subjects (enriched for longevity genes) and a comparable number of controls (matched for gender and ethnicity) in Europe. This very large study allowed a reappraisal of the role of both the inherited and the somatic mtDNA variability in aging, as an association with longevity emerged only when mtDNA variants in OXPHOS complexes co-occurred. Moreover, the availability of data from both nuclear and mitochondrial genomes on a large number of subjects paves the way for an evaluation at a very large scale of the epistatic interactions at a higher level of complexity. This scenario is expected to be even more clarified in the next future with the use of next generation sequencing (NGS) techniques, which are becoming applicable to evaluate mtDNA variability and, then, new mathematical/bioinformatic analysis methods are urgently needed. Recent advances of association studies on age-related diseases and mtDNA variability will also be discussed in this review, taking into account the bias hidden by population stratification. Finally, very recent findings in terms of mtDNA heteroplasmy (i.e. the coexistence of wild type and mutated copies of mtDNA) and aging as well as mitochondrial epigenetic mechanisms will also be discussed.

Immune parameters identify Italian centenarians with a longer five-year survival independent of their health and functional status

Centenarians are rare and exceptional individuals characterized by a peculiar phenotype. They are the best example of healthy aging in humans as most of them have escaped or substantially delayed the onset of major age-related diseases. Within this scenario, the purpose of the present work was to understand if immune status is associated with survival and health status in centenarians. To this aim, 116 centenarians were concomitantly characterized for their immunological, health and functional status, and followed-up for five-year survival. On the basis of previous knowledge we focused on a core of fundamental and basic immune parameters (number of leukocytes, monocytes, total lymphocytes, CD3(+) T lymphocytes, CD4(+) helper T lymphocytes, CD8(+) cytotoxic T lymphocytes, CD19(+) B lymphocytes and plasma levels of IgM), and the most important findings can be summarized as follows: i. a hierarchical cluster analysis was able to define Cluster1 (88 centenarians) and Cluster2 (28 centenarians) characterized by low and high values of all these immune parameters, respectively; ii. centenarians of Cluster2 showed a statistically longer five-year survival and more favorable values of other important immune (naïve, activated/memory and effector/memory T cells) and metabolic (glycemia, insulin and HOMA-IR) parameters, in accord with previous observations that centenarians have a peculiar immune profile, a preserved insulin pathway and a lower incidence of type 2 diabetes; and iii. unexpectedly, parameters related to frailty, as well as functional and cognitive status, did not show any significant correlation with the immune clustering, despite being capable per se of predicting survival. In conclusion, high values of basic immunological parameters and important T cell subsets correlate with five-year survival in centenarians, independent of other phenotypic characteristics. This unexpected biological scenario is compatible with the general hypothesis that in centenarians a progressive disconnection and loss of biological coherence among the different functions of the body occur, where survival/mortality result from the failure of any of these domains which apparently follow an independent age-related trajectory.

Systems medicine of inflammaging

Systems Medicine (SM) can be defined as an extension of Systems Biology (SB) to Clinical-Epidemiological disciplines through a shifting paradigm, starting from a cellular, toward a patient centered framework. According to this vision, the three pillars of SM are Biomedical hypotheses, experimental data, mainly achieved by Omics technologies and tailored computational, statistical and modeling tools. The three SM pillars are highly interconnected, and their balancing is crucial. Despite the great technological progresses producing huge amount of data (Big Data) and impressive computational facilities, the Bio-Medical hypotheses are still of primary importance. A paradigmatic example of unifying Bio-Medical theory is the concept of Inflammaging. This complex phenotype is involved in a large number of pathologies and patho-physiological processes such as aging, age-related diseases and cancer, all sharing a common inflammatory pathogenesis. This Biomedical hypothesis can be mapped into an ecological perspective capable to describe by quantitative and predictive models some experimentally observed features, such as microenvironment, niche partitioning and phenotype propagation. In this article we show how this idea can be supported by computational methods useful to successfully integrate, analyze and model large data sets, combining cross-sectional and longitudinal information on clinical, environmental and omics data of healthy subjects and patients to provide new multidimensional biomarkers capable of distinguishing between different pathological conditions, e.g. healthy versus unhealthy state, physiological versus pathological aging.

Systemic Age-Associated DNA Hypermethylation of ELOVL2 Gene: In Vivo and In Vitro Evidences of a Cell Replication Process.

Epigenetic remodeling is one of the major features of the aging process. We recently demonstrated that DNA methylation of ELOVL2 and FHL2 CpG islands is highly correlated with age in whole blood. Here we investigated several aspects of age-associated hypermethylation of ELOVL2 and FHL2. We showed that ELOVL2 methylation is significantly different in primary dermal fibroblast cultures from donors of different ages. Using epigenomic data from public resources, we demonstrated that most of the tissues show ELOVL2 and FHL2 hypermethylation with age. Interestingly, ELOVL2 hypermethylation was not found in tissues with very low replication rate. We demonstrated that ELOVL2 hypermethylation is associated with in vitro cell replication rather than with senescence. We confirmed intra-individual hypermethylation of ELOVL2 and FHL2 in longitudinally assessed participants from the Doetinchem Cohort Study. Finally we showed that, although the methylation of the two loci is not associated with longevity/mortality in the Leiden Longevity Study, ELOVL2 methylation is associated with cytomegalovirus status in nonagenarians, which could be informative of a higher number of replication events in a fraction of whole-blood cells. Collectively, these results indicate that ELOVL2 methylation is a marker of cell divisions occurring during human aging.

Bistability in the chemical master equation for dual phosphorylation cycles

Dual phospho/dephosphorylation cycles, as well as covalent enzymatic-catalyzed modifications of substrates are widely diffused within cellular systems and are crucial for the control of complex responses such as learning, memory, and cellular fate determination. Despite the large body of deterministic studies and the increasing work aimed at elucidating the effect of noise in such systems, some aspects remain unclear. Here we study the stationary distribution provided by the two-dimensional chemical master equation for a well-known model of a two step phospho/dephosphorylation cycle using the quasi-steady state approximation of enzymatic kinetics. Our aim is to analyze the role of fluctuations and the molecules distribution properties in the transition to a bistable regime. When detailed balance conditions are satisfied it is possible to compute equilibrium distributions in a closed and explicit form. When detailed balance is not satisfied, the stationary non-equilibrium state is strongly influenced by the chemical fluxes. In the last case, we show how the external field derived from the generation and recombination transition rates, can be decomposed by the Helmholtz theorem, into a conservative and a rotational (irreversible) part. Moreover, this decomposition allows to compute the stationary distribution via a perturbative approach. For a finite number of molecules there exists diffusion dynamics in a macroscopic region of the state space where a relevant transition rate between the two critical points is observed. Further, the stationary distribution function can be approximated by the solution of a Fokker-Planck equation. We illustrate the theoretical results using several numerical simulations.

Stochastic neutral modelling of the Gut Microbiota's relative species abundance from next generation sequencing data.

BackgroundInterest in understanding the mechanisms that lead to a particular composition of the Gut Microbiota is highly increasing, due to the relationship between this ecosystem and the host health state. Particularly relevant is the study of the Relative Species Abundance (RSA) distribution, that is a component of biodiversity and measures the number of species having a given number of individuals. It is the universal behaviour of RSA that induced many ecologists to look for theoretical explanations. In particular, a simple stochastic neutral model was proposed by Volkov et al. relying on population dynamics and was proved to fit the coral-reefs and rain forests RSA. Our aim is to ascertain if this model also describes the Microbiota RSA and if it can help in explaining the Microbiota plasticity.ResultsWe analyzed 16S rRNA sequencing data sampled from the Microbiota of three different animal species by Jeraldo et al. Through a clustering procedure (UCLUST), we built the Operational Taxonomic Units. These correspond to bacterial species considered at a given phylogenetic level defined by the similarity threshold used in the clustering procedure. The RSAs, plotted in the form of Preston plot, were fitted with Volkov’s model. The model fits well the Microbiota RSA, except in the tail region, that shows a deviation from the neutrality assumption. Looking at the model parameters we were able to discriminate between different animal species, giving also a biological explanation. Moreover, the biodiversity estimator obtained by Volkov’s model also differentiates the animal species and is in good agreement with the first and second order Hill’s numbers, that are common evenness indexes simply based on the fraction of individuals per species.ConclusionsWe conclude that the neutrality assumption is a good approximation for the Microbiota dynamics and the observation that Volkov’s model works for this ecosystem is a further proof of the RSA universality. Moreover, the ability to separate different animals with the model parameters and biodiversity number are promising results if we think about future applications on human data, in which the Microbiota composition and biodiversity are in close relationships with a variety of diseases and life-styles.

Statistical strategies and stochastic predictive models for the MARK-AGE data

MARK-AGE aims at the identification of biomarkers of human aging capable of discriminating between the chronological age and the effective functional status of the organism. To achieve this, given the structure of the collected data, a proper statistical analysis has to be performed, as the structure of the data are non trivial and the number of features under study is near to the number of subjects used, requiring special care to avoid overfitting. Here we described some of the possible strategies suitable for this analysis. We also include a description of the main techniques used, to explain and justify the selected strategies. Among other possibilities, we suggest to model and analyze the data with a three step strategy.

Analysis of noise-induced bimodality in a Michaelis–Menten single-step enzymatic cycle

In this paper we study noise-induced bistability in a specific circuit with many biological implications, namely a single-step enzymatic cycle described by Michaelis Menten equations with quasi-steady state assumption. We study the system both with a Master Equation formalism, and with the Fokker-Planck continuous approximation, characterizing the conditions in which the continuous approach is a good approximation of the exact discrete model. An analysis of the stationary distribution in both cases shows that bimodality can not occur in such a system. We discuss which additional requirements can generate stochastic bimodality, by coupling the system with a chemical reaction involving enzyme production and turnover. This extended system shows a bistable behaviour only in specific parameter windows depending on the number of molecules involved, providing hints about which should be a feasible system size in order that such a phenomenon could be exploited in real biological systems.