Predicting health and life span with the deep plasma proteome

Polyhexamethylene guanidine aerosol causes irreversible changes in blood proteins that associated with the severity of lung injury

Polyhexamethylene guanidine (PHMG) is a positively charged polymer used as a disinfectant that kills microbes but can cause pulmonary fibrosis if inhaled. After the long-term risks were confirmed in South Korea, it became crucial to measure toxicity through diverse surrogate biomarkers, not only proteins, especially after these hazardous chemicals had cleared from the body. These biomarkers, identified by their biological functions rather than simple numerical calculations, effectively explained the imbalance of pulmonary surfactant caused by fibrosis from PHMG exposure. These long-term studies on children exposed to PHMG has shown that blood protein indicators, primarily related to apolipoproteins and extracellular matrix, can distinguish the degree of exposure to humidifier disinfectants (HDs). We defined the extreme gradient boosting models and computed reflection scores based on just ten selected proteins, which were also verified in adult women exposed to HD. The reflection scores successfully discriminated between the HD-exposed and unexposed groups in both children and adult females (AUROC: 0.957 and 0.974, respectively) and had a strong negative correlation with lung function indicators. Even after an average of more than 10 years, blood is still considered a meaningful specimen for assessing the impact of environmental exposure to toxic substances, with proteins providing in identifying the pathological severity of such conditions.

Proteomic signatures of healthy dietary patterns are associated with lower risks of major chronic diseases and mortality

Healthy dietary patterns have been linked to a decreased risk of chronic diseases. However, it remains uncertain whether proteomic signatures can reflect proteome response to healthy diet patterns, and whether these proteomic signatures are associated with health outcomes. Using data from the UK Biobank including Olink plasma proteins, we identified substantial proteomic variation in relation to adherence to eight healthy dietary patterns. The proteomic signatures, reflecting adherence and proteome response to healthy dietary patterns, were prospectively associated with lower risks of diabetes, cardiovascular diseases, chronic respiratory diseases, chronic kidney diseases and cancers, along with longer life expectancy, even after adjusting for corresponding dietary patterns. These findings suggest proteomic signatures have the potential to complement traditional dietary assessments and deepen our understanding of the relationships between dietary patterns and chronic diseases.

The Proteomic Profile of Interstitial Lung Abnormalities

Rationale: Knowledge on biomarkers of interstitial lung disease is incomplete. Interstitial lung abnormalities (ILAs) are radiologic changes that may present in its early stages. Objectives: To uncover blood proteins associated with ILAs using large-scale proteomics methods. Methods: Data from two prospective cohort studies, the AGES-Reykjavik (Age, Gene/Environment Susceptibility-Reykjavik) study (N = 5,259) for biomarker discovery and the COPDGene (Genetic Epidemiology of COPD) study (N = 4,899) for replication, were used. Blood proteins were measured using DNA aptamers, targeting more than 4,700 protein analytes. The association of proteins with ILAs and ILA progression was assessed with regression modeling, as were associations with genetic risk factors. Adaptive Least Absolute Shrinkage and Selection Operator models were applied to bootstrap data samples to discover sets of proteins predictive of ILAs and their progression. Measurements and Main Results: Of 287 associations, SFTPB (surfactant protein B) (odds ratio [OR], 3.71 [95% confidence interval (CI), 3.20-4.30]; P = 4.28 × 10^-67), SCGB3A1 (Secretoglobin family 3A member 1) (OR, 2.43 [95% CI, 2.13-2.77]; P = 8.01 × 10^-40), and WFDC2 (WAP four-disulfide core domain protein 2) (OR, 2.42 [95% CI, 2.11-2.78]; P = 4.01 × 10^-36) were most significantly associated with ILA in AGES-Reykjavik and were replicated in COPDGene. In AGES-Reykjavik, concentrations of SFTPB were associated with the rs35705950 MUC5B (mucin 5B) promoter polymorphism, and SFTPB and WFDC2 had the strongest associations with ILA progression. Multivariate models of ILAs in AGES-Reykjavik, ILAs in COPDGene, and ILA progression in AGES-Reykjavik had validated areas under the receiver operating characteristic curve of 0.880, 0.826, and 0.824, respectively. Conclusions: Novel, replicated associations of ILA, its progression, and genetic risk factors with numerous blood proteins are demonstrated as well as machine-learning-based models with favorable predictive potential. Several proteins are revealed as potential markers of early fibrotic lung disease.

A proteogenomic signature of age-related macular degeneration in blood

Age-related macular degeneration (AMD) is one of the most common causes of visual impairment in the elderly, with a complex and still poorly understood etiology. Whole-genome association studies have discovered 34 genomic regions associated with AMD. However, the genes and cognate proteins that mediate the risk, are largely unknown. In the current study, we integrate levels of 4782 human serum proteins with all genetic risk loci for AMD in a large population-based study of the elderly, revealing many proteins and pathways linked to the disease. Serum proteins are also found to reflect AMD severity independent of genetics and predict progression from early to advanced AMD after five years in this population. A two-sample Mendelian randomization study identifies several proteins that are causally related to the disease and are directionally consistent with the observational estimates. In this work, we present a robust and unique framework for elucidating the pathobiology of AMD.

Age related macular degeneration is a common cause of visual impairment in the elderly, but the etiology is not fully understood. Here, the authors use genetic data, serum proteomics, and AMD phenotypic data from a large Icelandic cohort to discover proteins altered in, causally related to AMD or signifying progression of advanced AMD.

Coding and regulatory variants are associated with serum protein levels and disease

Circulating proteins can be used to diagnose and predict disease-related outcomes. A deep serum proteome survey recently revealed close associations between serum protein networks and common disease. In the current study, 54,469 low-frequency and common exome-array variants were compared to 4782 protein measurements in the serum of 5343 individuals from the AGES Reykjavik cohort. This analysis identifies a large number of serum proteins with genetic signatures overlapping those of many diseases. More specifically, using a study-wide significance threshold, we find that 2021 independent exome array variants are associated with serum levels of 1942 proteins. These variants reside in genetic loci shared by hundreds of complex disease traits, highlighting serum proteins' emerging role as biomarkers and potential causative agents of a wide range of diseases.

iTRAQ-based proteomics of testicular interstitial fluid during aging in mice

Advancing age is associated with a decline in fertility and testicular function in males. The testicular interstitial fluid (TIF) bathing the seminiferous tubules and testicular interstitium is considered an important part of the testicular microenvironment. However, the TIF proteome in mice and whether it changes with age remain unclear. This study aimed to map the TIF proteome and identify differentially abundant proteins (DEPs) among young, middle-aged, and old mice using isobaric tags for relative and absolute quantification (iTRAQ) coupled with liquid chromatography-tandem mass spectrometry. A total of 1477 proteins were identified in murine TIF. The abundance of 706 proteins showed a linear change trend with age, of which 360 and 346 proteins increased and decreased, respectively. In addition, 45 age-related DEPs were identified (P < 0.05, with at least 1.2-fold up- or downregulation). Bioinformatic analyses revealed that these proteins were involved in "actin cytoskeleton organization," "intrinsic apoptotic signaling pathway," and "regulation of protein transport." Comparative analysis with relevant proteomes previously reported further revealed the characteristics of mouse TIF proteome. Moreover, two of the age-related DEPs (Fga and Qsox1) were also found to be age-related differentially expressed proteins in human blood plasma and senescence-related secretome of human peritubular myoid cells. Taken together, these findings may represent the foundation for a better understanding of the molecular function of TIF and testicular aging.

Aged-related Function Disorder of Liver is Reversed after Exposing to Young Milieu via Conversion of Hepatocyte Ploidy

Previous study showed that senescent hepatocytes from aged liver could be rejuvenated after repopulated in the young recipient liver. The proliferative capacity of hepatocytes was restored with the senescence reversal. However, it is unknown whether metabolic and homeostatic function of aged liver, as well as age-dependent liver steatosis could be rejuvenated or alleviated. Here, we found that senescent hepatocytes from aged liver were rejuvenated after exposing to young blood. An autonomous proliferation of senescent hepatocytes which resulting in ploidy reversal might be the underlying mechanism of senescent reversal. After performing 2/3 partial hepatectomy (2/3PHx) in young blood exposed old liver, delayed DNA synthesis of senescent hepatocytes was rescued and the number of BrdU positive hepatocytes was restored from 4.39±2.30% to 17.85±3.21%, similarly to that in the young mice at 36 hours post 2/3PHx. Moreover, Cyclin A2 and Cyclin E1 overexpression of hepatocytes in aged liver facilitating the G1/S phase transition was contributed to enhance liver regeneration. Furthermore, lipid droplet spread widely in the elderly human liver and old mouse liver, but this aged-associated liver steatosis was alleviated as senescence reversal. Collectively, our study provides new thoughts for effectively preventing age-related liver diseases.

Translational precision medicine: an industry perspective

In the era of precision medicine, digital technologies and artificial intelligence, drug discovery and development face unprecedented opportunities for product and business model innovation, fundamentally changing the traditional approach of how drugs are discovered, developed and marketed. Critical to this transformation is the adoption of new technologies in the drug development process, catalyzing the transition from serendipity-driven to data-driven medicine. This paradigm shift comes with a need for both translation and precision, leading to a modern Translational Precision Medicine approach to drug discovery and development. Key components of Translational Precision Medicine are multi-omics profiling, digital biomarkers, model-based data integration, artificial intelligence, biomarker-guided trial designs and patient-centric companion diagnostics. In this review, we summarize and critically discuss the potential and challenges of Translational Precision Medicine from a cross-industry perspective.

Proteomics in aging research: A roadmap to clinical, translational research

The identification of plasma proteins that systematically change with age and, independent of chronological age, predict accelerated decline of health is an expanding area of research. Circulating proteins are ideal translational "omics" since they are final effectors of physiological pathways and because physicians are accustomed to use information of plasma proteins as biomarkers for diagnosis, prognosis, and tracking the effectiveness of treatments. Recent technological advancements, including mass spectrometry (MS)-based proteomics, multiplexed proteomic assay using modified aptamers (SOMAscan), and Proximity Extension Assay (PEA, O-Link), have allowed for the assessment of thousands of proteins in plasma or other biological matrices, which are potentially translatable into new clinical biomarkers and provide new clues about the mechanisms by which aging is associated with health deterioration and functional decline. We carried out a detailed literature search for proteomic studies performed in different matrices (plasma, serum, urine, saliva, tissues) and species using multiple platforms. Herein, we identified 232 proteins that were age-associated across studies. Enrichment analysis of the 232 age-associated proteins revealed metabolic pathways previously connected with biological aging both in animal models and in humans, most remarkably insulin-like growth factor (IGF) signaling, mitogen-activated protein kinases (MAPK), hypoxia-inducible factor 1 (HIF1), cytokine signaling, Forkhead Box O (FOXO) metabolic pathways, folate metabolism, advance glycation end products (AGE), and receptor AGE (RAGE) metabolic pathway. Information on these age-relevant proteins, likely expanded and validated in longitudinal studies and examined in mechanistic studies, will be essential for patient stratification and the development of new treatments aimed at improving health expectancy.

It's in Our Blood: A Glimpse of Personalized Medicine

Recent advances in protein profiling technology has facilitated simultaneous measurement of thousands of proteins in large population studies, exposing the depth and complexity of the plasma and serum proteomes. This revealed that proteins in circulation were organized into regulatory modules under genetic control and closely associated with current and future common diseases. Unlike networks in solid tissues, serum protein networks comprise members synthesized across different tissues of the body. Genetic analysis reveals that this cross-tissue regulation of the serum proteome participates in systemic homeostasis and mirrors the global disease state of individuals. Here, we discuss how application of this information in routine clinical evaluations may transform the future practice of medicine.

Definition and Independent Validation of a Proteomic-Classifier in Ovarian Cancer

Simple Summary

The heterogeneity of epithelial ovarian cancer and its associated molecular biological characteristics are continuously integrated in the development of therapy guidelines. In a next step, future therapy recommendations might also be able to focus on the patient’s systemic status, not only the tumor’s molecular pattern. Therefore, new methods to identify and validate host-related biomarkers need to be established. Using mass spectrometry, we developed and independently validated a blood-based proteomic classifier, stratifying epithelial ovarian cancer patients into good and poor survival groups. We also determined an age dependence of the prognostic performance of this classifier and its association with important biological processes. This work highlights that, just like molecular markers of the tumor itself, the systemic condition of a patient (partly reflected in proteomic patterns) also influences survival and therapy response and could therefore be integrated into future processes of therapy planning.

Abstract

Mass-spectrometry-based analyses have identified a variety of candidate protein biomarkers that might be crucial for epithelial ovarian cancer (EOC) development and therapy response. Comprehensive validation studies of the biological and clinical implications of proteomics are needed to advance them toward clinical use. Using the Deep MALDI method of mass spectrometry, we developed and independently validated (development cohort: n = 199, validation cohort: n = 135) a blood-based proteomic classifier, stratifying EOC patients into good and poor survival groups. We also determined an age dependency of the prognostic performance of this classifier, and our protein set enrichment analysis showed that the good and poor proteomic phenotypes were associated with, respectively, lower and higher levels of complement activation, inflammatory response, and acute phase reactants. This work highlights that, just like molecular markers of the tumor itself, the systemic condition of a patient (partly reflected in proteomic patterns) also influences survival and therapy response in a subset of ovarian cancer patients and could therefore be integrated into future processes of therapy planning.