Proteomes最新文献

Proteomes are complex-much more so than genomes or transcriptomes. Thus, simplifying their analysis does not simplify the issue. Proteomes are of proteoforms, not canonical proteins. While having a catalogue of amino acid sequences provides invaluable information, this is the Proteome-lite. To dissect biological mechanisms and identify critical biomarkers/drug targets, we must assess the myriad of proteoforms that arise at any point before, after, and between translation and transcription (e.g., isoforms, splice variants, and post-translational modifications [PTM]), as well as newly defined species. There are numerous analytical methods currently used to address proteome depth and here we critically evaluate these in terms of the current 'state-of-the-field'. We thus discuss both pros and cons of available approaches and where improvements or refinements are needed to quantitatively characterize proteomes. To enable a next-generation approach, we suggest that advances lie in transdisciplinarity via integration of current proteomic methods to yield a unified discipline that capitalizes on the strongest qualities of each. Such a necessary (if not revolutionary) shift cannot be accomplished by a continued primary focus on proteo-genomics/-transcriptomics. We must embrace the complexity. Yes, these are the hard questions, and this will not be easy…but where is the fun in easy?

Skeletal muscle is a major contributor to whole-body glucose homeostasis and is an important endocrine organ. To date, few studies have undertaken the large-scale identification of skeletal muscle-derived secreted proteins (myokines), particularly in response to stimuli that activate pathways governing energy metabolism in health and disease. Whereas the AMP-activated protein kinase (AMPK) and insulin-signaling pathways have received notable attention for their ability to independently regulate skeletal muscle substrate metabolism, little work has examined their ability to re-pattern the secretome. The present study coupled the use of high-resolution MS-based proteomics and bioinformatics analysis of conditioned media derived from 5-aminoimidazole-4-carboxamide ribonucleotide (AICAR-an AMPK activator)- and insulin-treated differentiated C2C12 myotubes. We quantified 858 secreted proteins, including cytokines and growth factors such as fibroblast growth factor-21 (Fgf21). We identified 377 and 118 proteins that were significantly altered by insulin and AICAR treatment, respectively. Notably, the family of insulin growth factor binding-proteins (Igfbp) was differentially regulated by each treatment. Insulin- but not AICAR-induced conditioned media increased the mitochondrial respiratory capacity of myotubes, potentially via secreted factors. These findings may serve as an important resource to elucidate secondary metabolic effects of insulin and AICAR stimulation in skeletal muscle.

We recently discovered three distinct pathophysiological subtypes in Alzheimer's disease (AD) using cerebrospinal fluid (CSF) proteomics: one with neuronal hyperplasticity, a second with innate immune system activation, and a third subtype with blood-brain barrier dysfunction. It remains unclear whether AD proteomic subtype profiles are a consequence of amyloid aggregation, or might exist upstream from aggregated amyloid. We studied this question in 127 older individuals with intact cognition and normal AD biomarkers in two independent cohorts (EMIF-AD MBD and ADNI). We clustered 705 proteins measured in CSF that were previously related to AD. We identified in these cognitively intact individuals without AD pathology three subtypes: two subtypes were seen in both cohorts (n = 49 with neuronal hyperplasticity and n = 44 with blood-brain barrier dysfunction), and one only in ADNI (n = 12 with innate immune activation). The proteins specific for these subtypes strongly overlapped with AD subtype protein profiles (overlap coefficients 92%-71%). Longitudinal p₁₈₁-tau and amyloid β 1-42 (Aβ42) CSF analysis showed that in the hyperplasticity subtype p₁₈₁-tau increased (β = 2.6 pg/mL per year, p = 0.01) and Aβ42 decreased over time (β = -4.4 pg/mL per year, p = 0.03), in the innate immune activation subtype p₁₈₁-tau increased (β = 3.1 pg/mL per year, p = 0.01) while in the blood-brain barrier dysfunction subtype Aβ42 decreased (β = -3.7 pg/mL per year, p = 0.009). These findings suggest that AD proteomic subtypes might already manifest in cognitively normal individuals and may predispose for AD before amyloid has reached abnormal levels.

Muscle tissue serves as a key nutrient reservoir that dairy cows utilize to meet energy and amino acid requirements for fetal growth and milk production. Circadian clocks act as homeostatic regulators so that organisms can anticipate regular environmental changes. The objective of this study was to use liquid chromatography tandem mass spectrometry (LC-MS/MS) to determine how chronic circadian disruption in late gestation affected the muscle tissue proteome. At five weeks before expected calving (BEC), multiparous Holstein cows were assigned to either a control (CON, n = 8) or a 6 h forward phase shift (PS, n = 8) of the light-dark cycle every 3 days. At calving, all animals were exposed to CON light-dark cycles. Muscle biopsies were collected from longissimus dorsi muscles at 21 days BEC and at 21 days postpartum (PP). At p < 0.1, 116 and 121 proteins were differentially abundant between PS and CON at 21 days BEC and 21 days PP, respectively. These proteins regulate beta oxidation and glycolysis. Between pregnancy and lactation, 134 and 145 proteins were differentially abundant in CON and PS cows, respectively (p < 0.1). At both timepoints, PS cows exhibited an oxidative stress signature. Thus, dairy cattle management strategies that minimize circadian disruptions may ensure optimal health and production performance.

Proteomic technology has improved at a staggering pace in recent years, with even practitioners challenged to keep up with new methods and hardware. The most common metric used for method performance is the number of peptides and proteins identified. While this metric may be helpful for proteomics researchers shopping for new hardware, this is often not the most biologically relevant metric. Biologists often utilize proteomics in the search for protein regulators that are of a lower relative copy number in the cell. In this review, I re-evaluate untargeted proteomics data using a simple graphical representation of the absolute copy number of proteins present in a single cancer cell as a metric. By comparing single-shot proteomics data to the coverage of the most in-depth proteomic analysis of that cell line acquired to date, we can obtain a rapid metric of method performance. Using a simple copy number metric allows visualization of how proteomics has developed in both sensitivity and overall dynamic range when using both relatively long and short acquisition times. To enable reanalysis beyond what is presented here, two available web applications have been developed for single- and multi-experiment comparisons with reference protein copy number data for multiple cell lines and organisms.

Saliva, an essential oral secretion involved in protecting the oral cavity's hard and soft tissues, is readily available and straightforward to collect. Recent studies have analyzed the salivary proteome in children and adolescents with extensive carious lesions to identify diagnostic and prognostic biomarkers. The current study aimed to investigate saliva's diagnostic ability through proteomics to detect the potential differential expression of proteins specific for the occurrence of carious lesions. For this study, we performed bioinformatics and functional analysis of proteomic datasets, previously examined by our group, from samples of adolescents with regulated and unregulated type 1 diabetes, as they compare with healthy controls. Among the differentially expressed proteins relevant to caries pathology, alpha-amylase 2B, beta-defensin 4A, BPI fold containing family B member 2, protein S100-A7, mucin 5B, statherin, salivary proline-rich protein 2, and interleukin 36 gamma were significantly downregulated in poorly-controlled patients compared to healthy subjects. In addition, significant biological pathways (defense response to the bacterium, beta-defensin activity, proline-rich protein activity, oxygen binding, calcium binding, and glycosylation) were deregulated in this comparison, highlighting specific molecular characteristics in the cariogenic process. This analysis contributes to a better understanding of the mechanisms involved in caries vulnerability in adolescents with unregulated diabetes.

Non-invasive urinary peptide biomarkers are able to detect and predict chronic kidney disease (CKD). Moreover, specific urinary peptides enable discrimination of different CKD etiologies and offer an interesting alternative to invasive kidney biopsy, which cannot always be performed. The aim of this study was to define a urinary peptide classifier using mass spectrometry technology to predict the degree of renal interstitial fibrosis and tubular atrophy (IFTA) in CKD patients. The urinary peptide profiles of 435 patients enrolled in this study were analyzed using capillary electrophoresis coupled with mass spectrometry (CE-MS). Urine samples were collected on the day of the diagnostic kidney biopsy. The proteomics data were divided into a training (n = 200) and a test (n = 235) cohort. The fibrosis group was defined as IFTA ≥ 15% and no fibrosis as IFTA < 10%. Statistical comparison of the mass spectrometry data enabled identification of 29 urinary peptides with differential occurrence in samples with and without fibrosis. Several collagen fragments and peptide fragments of fetuin-A and others were combined into a peptidomic classifier. The classifier separated fibrosis from non-fibrosis patients in an independent test set (n = 186) with area under the curve (AUC) of 0.84 (95% CI: 0.779 to 0.889). A significant correlation of IFTA and FPP_BH29 scores could be observed Rho = 0.5, p < 0.0001. We identified a peptidomic classifier for renal fibrosis containing 29 peptide fragments corresponding to 13 different proteins. Urinary proteomics analysis can serve as a non-invasive tool to evaluate the degree of renal fibrosis, in contrast to kidney biopsy, which allows repeated measurements during the disease course.

'One Health' summarises the idea that human health and animal health are interdependent and bound to the health of ecosystems. The purpose of proteomics methodologies and studies is to determine proteins present in samples of interest and to quantify changes in protein expression during pathological conditions. The objectives of this paper are to review the application of proteomics technologies within the One Health concept and to appraise their role in the elucidation of diseases and situations relevant to One Health. The paper develops in three sections. Proteomics Applications in Zoonotic Infections part discusses proteomics applications in zoonotic infections and explores the use of proteomics for studying pathogenetic pathways, transmission dynamics, diagnostic biomarkers and novel vaccines in prion, viral, bacterial, protozoan and metazoan zoonotic infections. Proteomics Applications in Antibiotic Resistance part discusses proteomics applications in mechanisms of resistance development and discovery of novel treatments for antibiotic resistance. Proteomics Applications in Food Safety part discusses the detection of allergens, exposure of adulteration, identification of pathogens and toxins, study of product traits and characterisation of proteins in food safety. Sensitive analysis of proteins, including low-abundant ones in complex biological samples, will be achieved in the future, thus enabling implementation of targeted proteomics in clinical settings, shedding light on biomarker research and promoting the One Health concept.

Cellular senescence is a state of terminal proliferative arrest that plays key roles in aging by preventing stem cell renewal and by inducing the expression of a series of inflammatory factors including many secreted proteins with paracrine effects. The in vivo identification of senescent cells is difficult due to the absence of universal biomarkers. Chromatin modifications are key aspects of the senescence transition and may provide novel biomarkers. We used a combined protein profiling and bottom-up mass spectrometry approach to characterize the isoforms and post-translational modifications of chromatin proteins over time in post-mitotic human fibroblasts in vitro. We show that the H2B type 1-K variant is specifically enriched in deep senescent cells with persistent DNA damage. This accumulation was not observed in quiescent cells or in cells induced into senescence without DNA damage by expression of the RAF kinase. Similarly, HMGA1a di-methylated and HMGA1b tri-phosphorylated forms accumulated exclusively in the chromatin of cells in deep senescent conditions with persistent DNA damage. H2B type 1-K and modified HMGA1 may thus represent novel biomarkers of senescent cells containing persistent DNA damage.

The complexity of data has burgeoned to such an extent that scientists of every realm are encountering the incessant challenge of data management. Modern-day analytical approaches with the help of free source tools and programming languages have facilitated access to the context of the various domains as well as specific works reported. Here, with this article, an attempt has been made to provide a systematic analysis of all the available reports at PubMed on Proteome using text mining. The work is comprised of scientometrics as well as information extraction to provide the publication trends as well as frequent keywords, bioconcepts and most importantly gene-gene co-occurrence network. Out of 33,028 PMIDs collected initially, the segregation of 24,350 articles under 28 Medical Subject Headings (MeSH) was analyzed and plotted. Keyword link network and density visualizations were provided for the top 1000 frequent Mesh keywords. PubTator was used, and 322,026 bioconcepts were able to extracted under 10 classes (such as Gene, Disease, CellLine, etc.). Co-occurrence networks were constructed for PMID-bioconcept as well as bioconcept-bioconcept associations. Further, for creation of subnetwork with respect to gene-gene co-occurrence, a total of 11,100 unique genes participated with mTOR and AKT showing the highest (64) number of connections. The gene p53 was the most popular one in the network in accordance with both the degree and weighted degree centrality, which were 425 and 1414, respectively. The present piece of study is an amalgam of bibliometrics and scientific data mining methods looking deeper into the whole scale analysis of available literature on proteome.