Next Issue
Volume 7, December
Previous Issue
Volume 7, June

Table of Contents

Proteomes, Volume 7, Issue 3 (September 2019)

  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Readerexternal link to open them.
Order results
Result details
Select all
Export citation of selected articles as:
Open AccessArticle
Towards Understanding Non-Infectious Growth-Rate Retardation in Growing Pigs
Proteomes 2019, 7(3), 31; https://doi.org/10.3390/proteomes7030031 - 11 Sep 2019
Viewed by 322
Abstract
For growth-rate retardation in commercial growing pigs suffering from non-infectious diseases, no biomarker is available for early detection and prevention of the condition or for the diagnosis of affected animals. The point in question is that the underlying pathological pathway of the condition [...] Read more.
For growth-rate retardation in commercial growing pigs suffering from non-infectious diseases, no biomarker is available for early detection and prevention of the condition or for the diagnosis of affected animals. The point in question is that the underlying pathological pathway of the condition is still unknown and multiple nutritional or management issues could be the cause of the disease. Common health status markers such as acute phase proteins, adenosine deaminase activity or total antioxidant capacity did not show any alteration in the saliva of animals with growth-rate retardation, so other pathways should be affected. The present study investigates saliva samples from animals with the same commercial crossbreed, sex and age, comparing control pigs and pigs with growth-rate retardation. A proteomics approach based on two-dimensional gel electrophoresis including mass spectrometry together with validation experiments was applied for the search of proteins that could help understand disease mechanisms and be used for early disease detection. Two proteins were detected as possible markers of growth-rate retardation, specifically S100A12 and carbonic anhydrase VI. A decrease in innate immune response was confirmed in pigs with growth-rate retardation, however further studies should be necessary to understand the role of the different CA VI proteoforms observed. Full article
(This article belongs to the Special Issue Top-down Proteomics: In Memory of Dr. Alfred Yergey)
Show Figures

Figure 1

Open AccessArticle
Network Analysis of a Membrane-Enriched Brain Proteome across Stages of Alzheimer’s Disease
Proteomes 2019, 7(3), 30; https://doi.org/10.3390/proteomes7030030 - 27 Aug 2019
Viewed by 654
Abstract
Previous systems-based proteomic approaches have characterized alterations in protein co-expression networks of unfractionated asymptomatic (AsymAD) and symptomatic Alzheimer’s disease (AD) brains. However, it remains unclear how sample fractionation and sub-proteomic analysis influences the organization of these protein networks and their relationship to clinicopathological [...] Read more.
Previous systems-based proteomic approaches have characterized alterations in protein co-expression networks of unfractionated asymptomatic (AsymAD) and symptomatic Alzheimer’s disease (AD) brains. However, it remains unclear how sample fractionation and sub-proteomic analysis influences the organization of these protein networks and their relationship to clinicopathological traits of disease. In this proof-of-concept study, we performed a systems-based sub-proteomic analysis of membrane-enriched post-mortem brain samples from pathology-free control, AsymAD, and AD brains (n = 6 per group). Label-free mass spectrometry based on peptide ion intensity was used to quantify the 18 membrane-enriched fractions. Differential expression and weighted protein co-expression network analysis (WPCNA) were then used to identify and characterize modules of co-expressed proteins most significantly altered between the groups. We identified a total of 27 modules of co-expressed membrane-associated proteins. In contrast to the unfractionated proteome, these networks did not map strongly to cell-type specific markers. Instead, these modules were principally organized by their associations with a wide variety of membrane-bound compartments and organelles. Of these, the mitochondrion was associated with the greatest number of modules, followed by modules linked to the cell surface compartment. In addition, we resolved networks with strong associations to the endoplasmic reticulum, Golgi apparatus, and other membrane-bound organelles. A total of 14 of the 27 modules demonstrated significant correlations with clinical and pathological AD phenotypes. These results revealed that the proteins within individual compartments feature a heterogeneous array of AD-associated expression patterns, particularly during the preclinical stages of disease. In conclusion, this systems-based analysis of the membrane-associated AsymAD brain proteome yielded a unique network organization highly linked to cellular compartmentalization. Further study of this membrane-associated proteome may reveal novel insight into the complex pathways governing the earliest stages of disease. Full article
Show Figures

Figure 1

Open AccessFeature PaperReview
What is Normalization? The Strategies Employed in Top-Down and Bottom-Up Proteome Analysis Workflows
Proteomes 2019, 7(3), 29; https://doi.org/10.3390/proteomes7030029 - 22 Aug 2019
Viewed by 604
Abstract
The accurate quantification of changes in the abundance of proteins is one of the main applications of proteomics. The maintenance of accuracy can be affected by bias and error that can occur at many points in the experimental process, and normalization strategies are [...] Read more.
The accurate quantification of changes in the abundance of proteins is one of the main applications of proteomics. The maintenance of accuracy can be affected by bias and error that can occur at many points in the experimental process, and normalization strategies are crucial to attempt to overcome this bias and return the sample to its regular biological condition, or normal state. Much work has been published on performing normalization on data post-acquisition with many algorithms and statistical processes available. However, there are many other sources of bias that can occur during experimental design and sample handling that are currently unaddressed. This article aims to cast light on the potential sources of bias and where normalization could be applied to return the sample to its normal state. Throughout we suggest solutions where possible but, in some cases, solutions are not available. Thus, we see this article as a starting point for discussion of the definition of and the issues surrounding the concept of normalization as it applies to the proteomic analysis of biological samples. Specifically, we discuss a wide range of different normalization techniques that can occur at each stage of the sample preparation and analysis process. Full article
(This article belongs to the Special Issue Top-down Proteomics: In Memory of Dr. Alfred Yergey)
Show Figures

Figure 1

Open AccessErratum
Dowling, P.; et al. Characterization of Contractile Proteins from Skeletal Muscle Using Gel-Based Top-Down Proteomics. Proteomes 2019, 7, 25
Proteomes 2019, 7(3), 28; https://doi.org/10.3390/proteomes7030028 - 15 Jul 2019
Viewed by 670
Abstract
The authors wish to make the following correction to their paper [...] Full article
(This article belongs to the Special Issue Top-down Proteomics: In Memory of Dr. Alfred Yergey)
Show Figures

Figure 2

Open AccessEditorial
Proteomic Analysis of Plasma-Derived Exosomes in Defining Their Role as Biomarkers of Disease Progression, Response to Therapy and Outcome
Proteomes 2019, 7(3), 27; https://doi.org/10.3390/proteomes7030027 - 30 Jun 2019
Cited by 1 | Viewed by 1008
Abstract
Extracellular vesicles (EVs) have recently emerged as an intercellular communication system that plays an important role in health and becomes dysfunctional in disease [...] Full article
(This article belongs to the Special Issue Proteomes of Extracellular Vesicles)
Previous Issue
Next Issue
Back to TopTop