An integrative metabolomics and transcriptomics study to identify metabolic alterations in aged skin of humans in vivo

Kuehne, Andreas; Hildebrand, Janosch; Soehle, Joern; Wenck, Horst; Terstegen, Lara; Gallinat, Stefan; Knott, Anja; Winnefeld, Marc; Zamboni, Nicola

doi:10.1186/s12864-017-3547-3

Cited by 59 publications

(72 citation statements)

References 90 publications

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“…Among the most reduced proteins with age in the buttock were phosphoglycerate mutase 1, proteasome alpha subunits and inositol monophosphatase 2. A recent transcriptome study found inositol monophosphatase 2 to be higher in aged skin [53], however in this study we found the opposite to be true at the protein level. It is possible that the increase in the transcription of this enzyme is in response to lower cellular levels of the protein, if with age the rate of turnover increases or the rate of translation reduces.…”

Section: Protein Abundance In Young and Aged Photoexposed Forearm Skincontrasting

confidence: 97%

Mass spectrometry‐based proteomics reveals the distinct nature of the skin proteomes of photoaged compared to intrinsically aged skin

Newton

Riba-Garcia

Griffiths

et al. 2019

Intern J of Cosmetic Sci

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Objective With increasing age, skin is subject to alterations in its organization, which impact on its function as well as having clinical consequences. Proteomics is a useful tool for non‐targeted, semi‐quantitative simultaneous investigation of high numbers of proteins. In the current study, we utilize proteomics to characterize and contrast age‐associated differences in photoexposed and photoprotected skin, with a focus on the epidermis, dermal–epidermal junction and papillary dermis. Methods Skin biopsies from buttock (photoprotected) and forearm (photoexposed) of healthy volunteers (aged 18–30 or ≥65 years) were transversely sectioned from the stratum corneum to a depth of 250 μm. Following SDS‐PAGE, each sample lane was segmented prior to analysis by liquid chromatography‐mass spectrometry/mass spectrometry. Pathway analysis was carried out using Ingenuity IPA. Results Comparison of skin proteomes at buttock and forearm sites revealed differences in relative protein abundance. Ageing in skin on the photoexposed forearm resulted in 80% of the altered proteins being increased with age, in contrast to the photoprotected buttock where 74% of altered proteins with age were reduced. Functionally, age‐altered proteins in the photoexposed forearm were associated with conferring structure, energy and metabolism. In the photoprotected buttock, proteins associated with gene expression, free‐radical scavenging, protein synthesis and protein degradation were most frequently altered. Conclusion This study highlights the necessity of not considering photoageing as an accelerated intrinsic ageing, but as a distinct physiological process.

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Section: Protein Abundance In Young and Aged Photoexposed Forearm Skincontrasting

confidence: 97%

Mass spectrometry‐based proteomics reveals the distinct nature of the skin proteomes of photoaged compared to intrinsically aged skin

Newton

Riba-Garcia

Griffiths

et al. 2019

Intern J of Cosmetic Sci

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“…Since the microbiome and metabolite profiles are influenced by the environment, nutrition, age, and lifestyle of the host, in addition to genetics, these concatenated profiles provide a unique snapshot of individual health. 19,22,31,[23][24][25][26][27][28][29][30] Indeed, while these complex profiles can be examined independently, changes in the collective abundance patterns of metabolites and microbes may indicate deeper homeostatic disturbances, which may be reflected through changes in interleukin signaling. The membership of the bodies microbial communities have dynamic interconnected relationships with one another and the host that change under states of disease and stress.…”

Section: Discussionmentioning

confidence: 99%

“…The metabolome and microbiome 21 are also unique portraits of the individual patient as they are not only influenced by genetics and disease state but also by the environment, nutrition, age, and lifestyle 19,22,31,[23][24][25][26][27][28][29][30] . Thus the microbiome and metabolome are very different from transcriptomic or proteomic biomarkers.…”

Section: Introductionmentioning

confidence: 99%

Discovery and Predictive Modeling of Urine Microbiome, Metabolite and Cytokine Biomarkers in Hospitalized Patients with Community Acquired Pneumonia

Pierre

Akbilgiç

Smallwood

et al. 2020

Preprint

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Pneumonia is the leading cause of infectious related death costing 12 billion dollars annually in the United States alone. Despite improvements in clinical care, total mortality remains around 4%, with inpatient mortality reaching 5-10%. For unknown reasons, mortality risk remains high even after hospital discharge and there is a need to identify those patients most at risk. Also of importance, clinical symptoms alone do not distinguish viral from bacterial infection which may delay appropriate treatment and may contribute to short-term and long-term mortality.Biomarkers have the potential to provide point of care diagnosis, identify high-risk patients, and increase our understanding of the biology of disease. However, there have been mixed results on the diagnostic performance of many of the analytes tested to date. Urine represents a largely untapped source for biomarker discovery and is highly accessible. To test this hypothesis, we collected urine from hospitalized patients with community-acquired pneumonia (CAP) and performed a comprehensive screen for urinary tract microbiota signatures, metabolite, and cytokine profiles. CAP patients were diagnosed with influenza or bacterial (S. aureus and S. pneumoniae) etiologies and compared with healthy volunteers. Microbiome signatures showed marked shifts in taxonomic levels in patients with bacterial etiology versus influenza and CAP versus normal. Predictive modeling of 291 microbial and metabolite values achieved a +90% accuracy with LASSO in predicting specific pneumonia etiology. This study demonstrates that urine from patients hospitalized with pneumonia may serve as a reliable and accessible sample to evaluate biomarkers that may diagnose etiology and predict clinical outcomes. Author SummaryUrine has been classically considered sterile since most microorganisms are not readily culturable under healthy circumstances. Further, many pneumonia patients are immediately placed on antibiotics rendering culture-based techniques useless. However, the advent of next generation sequencing has enabled unprecedented analysis of the microbial communitiesliving or detected as free DNA -found in many niches of the human body. Here, we describe a urine microbiome as well as metabolites and cytokines measured in patients newly admitted to the hospital diagnosed with influenza or bacterial (S. aureus and S. pneumoniae) infection pneumonia, compared with healthy controls. Using these parameters alone, we were able to achieve high success in predicting patient pneumonia. This study provides a proof of concept that urine samples, which are easily accessible in outpatient and inpatient settings, could provide additional diagnostic insights to patient infectious status and future risk factor for complication. Physical Examination and Laboratory FindingsHeart rate (Beats

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“…Integrative metabolomics and transcriptomics were later applied to show global metabolic adaptations occurring in epidermal skin during aging. [ 115 ] Microarray analysis of the transcriptome was performed on epidermal tissue samples from 24 young and 24 old donors, of which 23 donors of each group were also included in the metabolomics analysis. The results of the study revealed age‐dependent reduction of coenzyme Q10 levels, as well as altered activity in upper glycolysis and glycerolipid biosynthesis or decreased protein and polyamine biosynthesis.…”

Section: Omics‐based Molecule‐pattern Biomarkersmentioning

confidence: 99%

“…The results of the study revealed age‐dependent reduction of coenzyme Q10 levels, as well as altered activity in upper glycolysis and glycerolipid biosynthesis or decreased protein and polyamine biosynthesis. [ 115 ] Importantly, the identified compendium of age‐dependent genes (1053 transcripts with decreased and 932 transcripts with increased levels) had no significant overlap with reported genes predicted to be involved in aging in humans in other tissues. This indicates that each tissue of our body has its own age‐dependent gene expression pattern.…”

Section: Omics‐based Molecule‐pattern Biomarkersmentioning

confidence: 99%

Aging Biomarkers: From Functional Tests to Multi‐Omics Approaches

Kudryashova¹,

Burka²,

Kulaga

et al. 2020

Proteomics

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Aging results in various deleterious changes in the human body that may lead to loss of function and the manifestation of chronic diseases. While diseases can generally be reliably diagnosed, the aging process itself requires more sophisticated approaches to evaluate its progression. Numerous attempts have been made to establish biomarkers to quantify human aging at the cellular, tissue, and organismal level. Here, an up‐to‐date overview of biomarkers related to human aging with an emphasis on biomarkers that take into account different mechanisms of aging between individuals is provided. Classical discrete molecular and non‐molecular biomarkers handpicked by researches on the base of their strong correlation with age, as well as emerging omics‐based biomarkers, are discussed and potential future directions and developments in the field of aging assessment are outlined.

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An integrative metabolomics and transcriptomics study to identify metabolic alterations in aged skin of humans in vivo

Cited by 59 publications

References 90 publications

Mass spectrometry‐based proteomics reveals the distinct nature of the skin proteomes of photoaged compared to intrinsically aged skin

Mass spectrometry‐based proteomics reveals the distinct nature of the skin proteomes of photoaged compared to intrinsically aged skin

Discovery and Predictive Modeling of Urine Microbiome, Metabolite and Cytokine Biomarkers in Hospitalized Patients with Community Acquired Pneumonia

Aging Biomarkers: From Functional Tests to Multi‐Omics Approaches

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