Label-free Quantification of Proteins in Single Embryonic Cells with Neural Fate in the Cleavage-Stage Frog (Xenopus laevis) Embryo using CE-ESI-HRMS.

Mol Cell Proteomics. 2016 Jun 17;

Authors: Lombard-Banek C, Reddy S, Moody SA, Nemes P

Abstract
Quantification of protein expression in single cells promises to advance a systems-level understanding of normal development. Using a bottom-up proteomic workflow and multiplexing quantification by tandem mass tags, we recently demonstrated relative quantification between single embryonic cells (blastomeres) in the frog (Xenopus laevis) embryo. In this study, we minimize derivatization steps to enhance analytical sensitivity and use label-free quantification (LFQ) for single Xenopus cells. The technology builds on a custom-designed capillary electrophoresis microflow-electrospray ionization high-resolution mass spectrometry platform and LFQ by MaxLFQ (MaxQuant). By judiciously tailoring performance to peptide separation, ionization, and data-dependent acquisition, we demonstrate an ~75-amol (~11 nM) lower limit of detection and quantification for proteins in complex cell digests. The platform enabled the identification of 438 non-redundant protein groups by measuring 16 ng of protein digest, or <0.2% of the total protein contained in a blastomere in the 16-cell embryo. LFQ intensity was validated as a quantitative proxy for protein abundance. Correlation analysis was performed to compare protein quantities between the embryo and n = 3 different single D11 blastomeres, which are fated to develop into the nervous system. A total of 335 non-redundant protein groups were quantified in union between the single D11 cells spanning a 4 log-order concentration range. LFQ and correlation analysis detected expected proteomic differences between the whole embryo and blastomeres, and also found translational differences between individual D11 cells. LFQ on single cells raises exciting possibilities to study gene expression in other cells and models to help better understand cell processes on a systems biology level.

PMID: 27317400 [PubMed - as supplied by publisher]

Airway remodeling: Systems biology approach, from bench to bedside.

Technol Health Care. 2016 Jun 10;

Authors: Najafi A, Ghanei M, Jamalkandi SA

Abstract
Airway Remodeling, a patho-physiologic process, is considered as a key feature of chronic airway diseases. In recent years, our understanding of the complex diseases has increased significantly by the use of combined approaches, including systems biology, which may contribute to the development of personalized and predictive medicine approaches. Integrative analysis, along with the cooperation of clinicians, computer scientists, research scientists, and bench scientists, has become an important part of the experimental design and therapeutic strategies in the era of omics. The airway remodeling process is the result of the dysregulation of several signaling pathways that modulate the airway regeneration; therefore, high-throughput experiments and systems biology approach can help to understand this process better. The study reviews related literature and is consistent with the existing clinical evidence.

PMID: 27315153 [PubMed - as supplied by publisher]

Differential RNA-seq, Multi-Network Analysis and Metabolic Regulation Analysis of Kluyveromyces marxianus Reveals a Compartmentalised Response to Xylose.

PLoS One. 2016;11(6):e0156242

Authors: Schabort DT, Letebele PK, Steyn L, Kilian SG, du Preez JC

Abstract
We investigated the transcriptomic response of a new strain of the yeast Kluyveromyces marxianus, in glucose and xylose media using RNA-seq. The data were explored in a number of innovative ways using a variety of networks types, pathway maps, enrichment statistics, reporter metabolites and a flux simulation model, revealing different aspects of the genome-scale response in an integrative systems biology manner. The importance of the subcellular localisation in the transcriptomic response is emphasised here, revealing new insights. As was previously reported by others using a rich medium, we show that peroxisomal fatty acid catabolism was dramatically up-regulated in a defined xylose mineral medium without fatty acids, along with mechanisms to activate fatty acids and transfer products of β-oxidation to the mitochondria. Notably, we observed a strong up-regulation of the 2-methylcitrate pathway, supporting capacity for odd-chain fatty acid catabolism. Next we asked which pathways would respond to the additional requirement for NADPH for xylose utilisation, and rationalised the unexpected results using simulations with Flux Balance Analysis. On a fundamental level, we investigated the contribution of the hierarchical and metabolic regulation levels to the regulation of metabolic fluxes. Metabolic regulation analysis suggested that genetic level regulation plays a major role in regulating metabolic fluxes in adaptation to xylose, even for the high capacity reactions, which is unexpected. In addition, isozyme switching may play an important role in re-routing of metabolic fluxes in subcellular compartments in K. marxianus.

PMID: 27315089 [PubMed - as supplied by publisher]

Heterologous vaccine effects.

Vaccine. 2016 Jun 13;

Authors: Saadatian-Elahi M, Aaby P, Shann F, Netea MG, Levy O, Louis J, Picot V, Greenberg M, Warren W

Abstract
The heterologous or non-specific effects (NSEs) of vaccines, at times defined as "off-target effects" suggest that they can affect the immune response to organisms other than their pathogen-specific intended purpose. These NSEs have been the subject of clinical, immunological and epidemiological studies and are increasingly recognized as an important biological process by a growing group of immunologists and epidemiologists. Much remain to be learned about the extent and underlying mechanisms for these effects. The conference "Off-target effects of vaccination" held in Annecy-France (June 8-10 2015) intended to take a holistic approach drawing from the fields of immunology, systems biology, epidemiology, bioinformatics, public health and regulatory science to address fundamental questions of immunological mechanisms, as well as translational questions about vaccines NSEs. NSE observations were examined using case-studies on live attenuated vaccines and non-live vaccines followed by discussion of studies of possible biological mechanisms. Some possible pathways forward in the study of vaccines NSE were identified and discussed by the expert group.

PMID: 27312214 [PubMed - as supplied by publisher]

A Systems Biology Approach for Identifying Hepatotoxicant Groups Based on Similarity in Mechanisms of Action and Chemical Structure.

Methods Mol Biol. 2016;1425:339-59

Authors: Hebels DG, Rasche A, Herwig R, van Westen GJ, Jennen DG, Kleinjans JC

Abstract
When evaluating compound similarity, addressing multiple sources of information to reach conclusions about common pharmaceutical and/or toxicological mechanisms of action is a crucial strategy. In this chapter, we describe a systems biology approach that incorporates analyses of hepatotoxicant data for 33 compounds from three different sources: a chemical structure similarity analysis based on the 3D Tanimoto coefficient, a chemical structure-based protein target prediction analysis, and a cross-study/cross-platform meta-analysis of in vitro and in vivo human and rat transcriptomics data derived from public resources (i.e., the diXa data warehouse). Hierarchical clustering of the outcome scores of the separate analyses did not result in a satisfactory grouping of compounds considering their known toxic mechanism as described in literature. However, a combined analysis of multiple data types may hypothetically compensate for missing or unreliable information in any of the single data types. We therefore performed an integrated clustering analysis of all three data sets using the R-based tool iClusterPlus. This indeed improved the grouping results. The compound clusters that were formed by means of iClusterPlus represent groups that show similar gene expression while simultaneously integrating a similarity in structure and protein targets, which corresponds much better with the known mechanism of action of these toxicants. Using an integrative systems biology approach may thus overcome the limitations of the separate analyses when grouping liver toxicants sharing a similar mechanism of toxicity.

PMID: 27311473 [PubMed - in process]

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

Chin J Cancer. 2016;35(1):53

Authors: Van Laere S, Dirix L, Vermeulen P

Abstract
Interpreting molecular profiles in a biological context requires specialized analysis strategies. Initially, lists of relevant genes were screened to identify enriched concepts associated with pathways or specific molecular processes. However, the shortcoming of interpreting gene lists by using predefined sets of genes has resulted in the development of novel methods that heavily rely on network-based concepts. These algorithms have the advantage that they allow a more holistic view of the signaling properties of the condition under study as well as that they are suitable for integrating different data types like gene expression, gene mutation, and even histological parameters.

PMID: 27311441 [PubMed - in process]

Overexpression of Catalase Diminishes Oxidative Cysteine Modifications of Cardiac Proteins.

PLoS One. 2015;10(12):e0144025

Authors: Yao C, Behring JB, Shao D, Sverdlov AL, Whelan SA, Elezaby A, Yin X, Siwik DA, Seta F, Costello CE, Cohen RA, Matsui R, Colucci WS, McComb ME, Bachschmid MM

Abstract
Reactive protein cysteine thiolates are instrumental in redox regulation. Oxidants, such as hydrogen peroxide (H2O2), react with thiolates to form oxidative post-translational modifications, enabling physiological redox signaling. Cardiac disease and aging are associated with oxidative stress which can impair redox signaling by altering essential cysteine thiolates. We previously found that cardiac-specific overexpression of catalase (Cat), an enzyme that detoxifies excess H2O2, protected from oxidative stress and delayed cardiac aging in mice. Using redox proteomics and systems biology, we sought to identify the cysteines that could play a key role in cardiac disease and aging. With a 'Tandem Mass Tag' (TMT) labeling strategy and mass spectrometry, we investigated differential reversible cysteine oxidation in the cardiac proteome of wild type and Cat transgenic (Tg) mice. Reversible cysteine oxidation was measured as thiol occupancy, the ratio of total available versus reversibly oxidized cysteine thiols. Catalase overexpression globally decreased thiol occupancy by ≥1.3 fold in 82 proteins, including numerous mitochondrial and contractile proteins. Systems biology analysis assigned the majority of proteins with differentially modified thiols in Cat Tg mice to pathways of aging and cardiac disease, including cellular stress response, proteostasis, and apoptosis. In addition, Cat Tg mice exhibited diminished protein glutathione adducts and decreased H2O2 production from mitochondrial complex I and II, suggesting improved function of cardiac mitochondria. In conclusion, our data suggest that catalase may alleviate cardiac disease and aging by moderating global protein cysteine thiol oxidation.

PMID: 26642319 [PubMed - indexed for MEDLINE]

Systems biology of IL-6, IL-12 family cytokines.

Cytokine Growth Factor Rev. 2015 Oct;26(5):595-602

Authors: Dittrich A, Hessenkemper W, Schaper F

Abstract
Interleukin-6-type cytokines play important roles in the communication between cells of multicellular organisms. They are involved in the regulation of complex cellular processes such as proliferation and differentiation and act as key player during inflammation and immune response. A major challenge is to understand how these complex non-linear processes are connected and regulated. Systems biology approaches are used to tackle this challenge in an iterative process of quantitative experimental and mathematical analyses. Here we review quantitative experimental studies and systems biology approaches dealing with the function of Interleukin-6-type cytokines in physiological and pathophysiological conditions. These approaches cover the analyses of signal transduction on a cellular level up to pharmacokinetic and pharmacodynamic studies on a whole organism level.

PMID: 26187858 [PubMed - indexed for MEDLINE]

Community-level cohesion without cooperation.

Elife. 2016 Jun 16;5

Authors: Tikhonov M

Abstract
Recent work draws attention to community-community encounters ('coalescence') as likely an important factor shaping natural ecosystems. This work builds on MacArthur's classic model of competitive coexistence to investigate such community-level competition in a minimal theoretical setting. It is shown that the ability of a species to survive a coalescence event is best predicted by a community-level 'fitness' of its native community rather than the intrinsic performance of the species itself. The model presented here allows formalizing a macroscopic perspective whereby a community harboring organisms at varying abundances becomes equivalent to a single organism expressing genes at different levels. While most natural communities do not satisfy the strict criteria of multicellularity developed by multi-level selection theory, the effective cohesion described here is a generic consequence of resource partitioning, requires no cooperative interactions, and can be expected to be widespread in microbial ecosystems.

PMID: 27310530 [PubMed - as supplied by publisher]

Mechanistic Mathematical Modeling Tests Hypotheses of the Neurovascular Coupling in fMRI.

PLoS Comput Biol. 2016 Jun;12(6):e1004971

Authors: Lundengård K, Cedersund G, Sten S, Leong F, Smedberg A, Elinder F, Engström M

Abstract
Functional magnetic resonance imaging (fMRI) measures brain activity by detecting the blood-oxygen-level dependent (BOLD) response to neural activity. The BOLD response depends on the neurovascular coupling, which connects cerebral blood flow, cerebral blood volume, and deoxyhemoglobin level to neuronal activity. The exact mechanisms behind this neurovascular coupling are not yet fully investigated. There are at least three different ways in which these mechanisms are being discussed. Firstly, mathematical models involving the so-called Balloon model describes the relation between oxygen metabolism, cerebral blood volume, and cerebral blood flow. However, the Balloon model does not describe cellular and biochemical mechanisms. Secondly, the metabolic feedback hypothesis, which is based on experimental findings on metabolism associated with brain activation, and thirdly, the neurotransmitter feed-forward hypothesis which describes intracellular pathways leading to vasoactive substance release. Both the metabolic feedback and the neurotransmitter feed-forward hypotheses have been extensively studied, but only experimentally. These two hypotheses have never been implemented as mathematical models. Here we investigate these two hypotheses by mechanistic mathematical modeling using a systems biology approach; these methods have been used in biological research for many years but never been applied to the BOLD response in fMRI. In the current work, model structures describing the metabolic feedback and the neurotransmitter feed-forward hypotheses were applied to measured BOLD responses in the visual cortex of 12 healthy volunteers. Evaluating each hypothesis separately shows that neither hypothesis alone can describe the data in a biologically plausible way. However, by adding metabolism to the neurotransmitter feed-forward model structure, we obtained a new model structure which is able to fit the estimation data and successfully predict new, independent validation data. These results open the door to a new type of fMRI analysis that more accurately reflects the true neuronal activity.

PMID: 27310017 [PubMed - as supplied by publisher]

Mitochondrial dysfunction remodels one-carbon metabolism in human cells.

Elife. 2016;5

Authors: Bao XR, Ong SE, Goldberger O, Peng J, Sharma R, Thompson DA, Vafai SB, Cox AG, Marutani E, Ichinose F, Goessling W, Regev A, Carr SA, Clish CB, Mootha VK

Abstract
Mitochondrial dysfunction is associated with a spectrum of human disorders, ranging from rare, inborn errors of metabolism to common, age-associated diseases such as neurodegeneration. How these lesions give rise to diverse pathology is not well understood, partly because their proximal consequences have not been well-studied in mammalian cells. Here we provide two lines of evidence that mitochondrial respiratory chain dysfunction leads to alterations in one-carbon metabolism pathways. First, using hypothesis-generating metabolic, proteomic, and transcriptional profiling, followed by confirmatory experiments, we report that mitochondrial DNA depletion leads to an ATF4-mediated increase in serine biosynthesis and transsulfuration. Second, we show that lesioning the respiratory chain impairs mitochondrial production of formate from serine, and that in some cells, respiratory chain inhibition leads to growth defects upon serine withdrawal that are rescuable with purine or formate supplementation. Our work underscores the connection between the respiratory chain and one-carbon metabolism with implications for understanding mitochondrial pathogenesis.

PMID: 27307216 [PubMed - in process]

Advanced Multidimensional Separations in Mass Spectrometry: Navigating the Big Data Deluge.

Annu Rev Anal Chem (Palo Alto Calif). 2016 Jun 12;9(1):387-409

Authors: May JC, McLean JA

Abstract
Hybrid analytical instrumentation constructed around mass spectrometry (MS) is becoming the preferred technique for addressing many grand challenges in science and medicine. From the omics sciences to drug discovery and synthetic biology, multidimensional separations based on MS provide the high peak capacity and high measurement throughput necessary to obtain large-scale measurements used to infer systems-level information. In this article, we describe multidimensional MS configurations as technologies that are big data drivers and review some new and emerging strategies for mining information from large-scale datasets. We discuss the information content that can be obtained from individual dimensions, as well as the unique information that can be derived by comparing different levels of data. Finally, we summarize some emerging data visualization strategies that seek to make highly dimensional datasets both accessible and comprehensible.

PMID: 27306312 [PubMed - in process]

Qualitative dynamics semantics for SBGN process description.

BMC Syst Biol. 2016;10(1):42

Authors: Rougny A, Froidevaux C, Calzone L, Paulevé L

Abstract
BACKGROUND: Qualitative dynamics semantics provide a coarse-grain modeling of networks dynamics by abstracting away kinetic parameters. They allow to capture general features of systems dynamics, such as attractors or reachability properties, for which scalable analyses exist. The Systems Biology Graphical Notation Process Description language (SBGN-PD) has become a standard to represent reaction networks. However, no qualitative dynamics semantics taking into account all the main features available in SBGN-PD had been proposed so far.
RESULTS: We propose two qualitative dynamics semantics for SBGN-PD reaction networks, namely the general semantics and the stories semantics, that we formalize using asynchronous automata networks. While the general semantics extends standard Boolean semantics of reaction networks by taking into account all the main features of SBGN-PD, the stories semantics allows to model several molecules of a network by a unique variable. The obtained qualitative models can be checked against dynamical properties and therefore validated with respect to biological knowledge. We apply our framework to reason on the qualitative dynamics of a large network (more than 200 nodes) modeling the regulation of the cell cycle by RB/E2F.
CONCLUSION: The proposed semantics provide a direct formalization of SBGN-PD networks in dynamical qualitative models that can be further analyzed using standard tools for discrete models. The dynamics in stories semantics have a lower dimension than the general one and prune multiple behaviors (which can be considered as spurious) by enforcing the mutual exclusiveness between the activity of different nodes of a same story. Overall, the qualitative semantics for SBGN-PD allow to capture efficiently important dynamical features of reaction network models and can be exploited to further refine them.

PMID: 27306057 [PubMed - in process]

The analysis of the antibiotic resistome offers new opportunities for therapeutic intervention.

Future Med Chem. 2016 Jun 15;

Authors: Corona F, Blanco P, Alcalde-Rico M, Hernando-Amado S, Lira F, Bernardini A, Sánchez MB, Martínez JL

Abstract
Most efforts in the development of antimicrobials have focused on the screening of lethal targets. Nevertheless, the constant expansion of antimicrobial resistance makes the antibiotic resistance determinants themselves suitable targets for finding inhibitors to be used in combination with antibiotics. Among them, inhibitors of antibiotic inactivating enzymes and of multidrug efflux pumps are suitable candidates for improving the efficacy of antibiotics. In addition, the application of systems biology tools is helping to understand the changes in bacterial physiology associated to the acquisition of resistance, including the increased susceptibility to other antibiotics displayed by some antibiotic-resistant mutants. This information is useful for implementing novel strategies based in metabolic interventions or combination of antibiotics for improving the efficacy of antibacterial therapy.

PMID: 27304087 [PubMed - as supplied by publisher]

Molecular and genetic inflammation networks in major human diseases.

Mol Biosyst. 2016 Jun 15;

Authors: Zhao Y, Forst CV, Sayegh CE, Wang IM, Yang X, Zhang B

Abstract
It has been well-recognized that inflammation alongside tissue repair and damage maintaining tissue homeostasis determines the initiation and progression of complex diseases. Albeit with the accomplishment of having captured the most critical inflammation-involved molecules, genetic susceptibilities, epigenetic factors, and environmental factors, our schemata on the role of inflammation in complex diseases remain largely patchy, in part due to the success of reductionism in terms of research methodology per se. Omics data alongside the advances in data integration technologies have enabled reconstruction of molecular and genetic inflammation networks which shed light on the underlying pathophysiology of complex diseases or clinical conditions. Given the proven beneficial role of anti-inflammation in coronary heart disease as well as other complex diseases and immunotherapy as a revolutionary transition in oncology, it becomes timely to review our current understanding of the molecular and genetic inflammation networks underlying major human diseases. In this review, we first briefly discuss the complexity of infectious diseases and then highlight recently uncovered molecular and genetic inflammation networks in other major human diseases including obesity, type II diabetes, coronary heart disease, late onset Alzheimer's disease, Parkinson's disease, and sporadic cancer. The commonality and specificity of these molecular networks are addressed in the context of genetics based on genome-wide association study (GWAS). The double-sword role of inflammation, such as how the aberrant type 1 and/or type 2 immunity leads to chronic and severe clinical conditions, remains open in terms of the inflammasome and the core inflammatome network features. Increasingly available large Omics and clinical data in tandem with systems biology approaches have offered an exciting yet challenging opportunity toward reconstruction of more comprehensive and dynamic molecular and genetic inflammation networks, which hold great promise in transiting network snapshots to video-style multi-scale interplays of disease mechanisms, in turn leading to effective clinical intervention.

PMID: 27303926 [PubMed - as supplied by publisher]

The natural defense system and the normative self model.

F1000Res. 2016;5:797

Authors: Kourilsky P

Abstract
Infectious agents are not the only agressors, and the immune system is not the sole defender of the organism. In an enlarged perspective, the 'normative self model' postulates that a 'natural defense system' protects man and other complex organisms against the environmental and internal hazards of life, including infections and cancers. It involves multiple error detection and correction mechanisms that confer robustness to the body at all levels of its organization. According to the model, the self relies on a set of physiological norms, and NONself (meaning : Non Obedient to the Norms of the self) is anything 'off-norms'. The natural defense system comprises a set of 'civil defenses' (to which all cells in organs and tissues contribute), and a 'professional army ', made of a smaller set of mobile cells. Mobile and non mobile cells differ in their tuning abilities. Tuning extends the recognition capabilities of NONself by the mobile cells, which increase their defensive function. To prevent them to drift, which would compromise self/NONself discrimination, the more plastic mobile cells need to periodically refer to the more stable non mobile cells to keep within physiological standards.

PMID: 27303629 [PubMed]

A signaling visualization toolkit to support rational design of combination therapies and biomarker discovery: SiViT.

Oncotarget. 2016 May 18;

Authors: Bown JL, Shovman M, Robertson P, Boiko A, Goltsov A, Mullen P, Harrison DJ

Abstract
Targeted cancer therapy aims to disrupt aberrant cellular signalling pathways. Biomarkers are surrogates of pathway state, but there is limited success in translating candidate biomarkers to clinical practice due to the intrinsic complexity of pathway networks. Systems biology approaches afford better understanding of complex, dynamical interactions in signalling pathways targeted by anticancer drugs. However, adoption of dynamical modelling by clinicians and biologists is impeded by model inaccessibility. Drawing on computer games technology, we present a novel visualization toolkit, SiViT, that converts systems biology models of cancer cell signalling into interactive simulations that can be used without specialist computational expertise. SiViT allows clinicians and biologists to directly introduce for example loss of function mutations and specific inhibitors. SiViT animates the effects of these introductions on pathway dynamics, suggesting further experiments and assessing candidate biomarker effectiveness. In a systems biology model of Her2 signalling we experimentally validated predictions using SiViT, revealing the dynamics of biomarkers of drug resistance and highlighting the role of pathway crosstalk. No model is ever complete: the iteration of real data and simulation facilitates continued evolution of more accurate, useful models. SiViT will make accessible libraries of models to support preclinical research, combinatorial strategy design and biomarker discovery.

PMID: 27302920 [PubMed - as supplied by publisher]

Advances in targeted proteomics and applications to biomedical research.

Proteomics. 2016 Jun 15;

Authors: Shi T, Song E, Nie S, Rodland KD, Liu T, Qian WJ, Smith RD

Abstract
Targeted proteomics technique has emerged as a powerful protein quantification tool in systems biology, biomedical research, and increasing for clinical applications. The most widely used targeted proteomics approach, selected reaction monitoring (SRM), also known as multiple reaction monitoring (MRM), can be used for quantification of cellular signaling networks and preclinical verification of candidate protein biomarkers. As an extension to our previous review on advances in SRM sensitivity (Shi et al., Proteomics, 12, 1074-1092, 2012) herein we review recent advances in the method and technology for further enhancing SRM sensitivity (from 2012 to present), and highlighting its broad biomedical applications in human bodily fluids, tissue and cell lines. Furthermore, we also review two recently introduced targeted proteomics approaches, parallel reaction monitoring (PRM) and data-independent acquisition (DIA) with targeted data extraction on fast scanning high-resolution accurate-mass (HR/AM) instruments. Such HR/AM targeted quantification with monitoring all target product ions addresses SRM limitations effectively in specificity and multiplexing; whereas when compared to SRM, PRM and DIA are still in the infancy with a limited number of applications. Thus, for HR/AM targeted quantification we focus our discussion on method development, data processing and analysis, and its advantages and limitations in targeted proteomics. Finally, general perspectives on the potential of achieving both high sensitivity and high sample throughput for large-scale quantification of hundreds of target proteins are discussed. This article is protected by copyright. All rights reserved.

PMID: 27302376 [PubMed - as supplied by publisher]

Personalized medicine. Closing the gap between knowledge and clinical practice.

Autoimmun Rev. 2016 Jun 11;

Authors: Anaya JM, Duarte-Rey C, Sarmiento-Monroy JC, Bardey D, Castiblanco J, Rojas-Villarraga A

Abstract
Personalized medicine encompasses a broad and evolving field informed by a patient distinctive information and biomarker profile. Although terminology is evolving and some semantic interpretations exist (e.g., personalized, individualized, precision), in a broad sense personalized medicine can be coined as: "To practice medicine as it once used to be in the past using the current biotechnological tools." A humanized approach to personalized medicine would offer the possibility of exploiting systems biology and its concept of P5 medicine, where predictive factors for developing a disease should be examined within populations in order to establish preventive measures on at-risk individuals, for whom healthcare should be personalized and participatory. Herein, the process of personalized medicine is presented together with the options that can be offered in health care systems with limited resources for diseases like rheumatoid arthritis and type 1 diabetes.

PMID: 27302209 [PubMed - as supplied by publisher]

Dynamic zonation of liver polyploidy.

Cell Tissue Res. 2016 Jun 15;

Authors: Tanami S, Ben-Moshe S, Elkayam A, Mayo A, Bahar Halpern K, Itzkovitz S

Abstract
The liver is a polyploid organ, consisting of hepatocytes with one or two nuclei each containing 2, 4, 8 or more haploid chromosome sets. The dynamic changes in the spatial distributions of polyploid classes across the liver lobule, its repeating anatomical unit, have not been characterized. Identifying these spatial patterns is important for understanding liver homeostatic and regenerative turnover, as well as potential division of labor among ploidy classes. Here, we use single molecule-based tissue imaging to reconstruct the spatial zonation profiles of liver polyploid classes in mice of different ages. We find that liver polyploidy proceeds in spatial waves, advancing more rapidly in the mid-lobule zone compared to the periportal and perivenous zones. We also measure the spatial zonation profiles of S-phase entry at different ages and identify more rapid S-phase entry in the mid-lobule zone at older ages. Our findings reveal fundamental features of liver spatial heterogeneity and highlight their dynamic changes during development and aging.

PMID: 27301446 [PubMed - as supplied by publisher]