A multi-model statistical approach for proteomic spectral count quantitation

Mass spectrometry-based retina proteomics

A subfield of neuroproteomics, retina proteomics has experienced a transformative growth since its inception due to methodological advances in enabling chemical, biochemical, and molecular biology techniques. This review focuses on mass spectrometry's contributions to facilitate mammalian and avian retina proteomics to catalog and quantify retinal protein expressions, determine their posttranslational modifications, as well as its applications to study the proteome of the retina in the context of biology, health and diseases, and therapy developments.

Enriched Pathways of Calcium Regulation, Cellular/Oxidative Stress, Inflammation, and Cell Proliferation Characterize Gluteal Muscle of Standardbred Horses between Episodes of Recurrent Exertional Rhabdomyolysis

Certain Standardbred racehorses develop recurrent exertional rhabdomyolysis (RER-STD) for unknown reasons. We compared gluteal muscle histopathology and gene/protein expression between Standardbreds with a history of, but not currently experiencing rhabdomyolysis (N = 9), and race-trained controls (N = 7). Eight RER-STD had a few mature fibers with small internalized myonuclei, one out of nine had histologic evidence of regeneration and zero out of nine degeneration. However, RER-STD versus controls had 791/13,531 differentially expressed genes (DEG). The top three gene ontology (GO) enriched pathways for upregulated DEG (N = 433) were inflammation/immune response (62 GO terms), cell proliferation (31 GO terms), and hypoxia/oxidative stress (31 GO terms). Calcium ion regulation (39 GO terms), purine nucleotide metabolism (32 GO terms), and electron transport (29 GO terms) were the top three enriched GO pathways for down-regulated DEG (N = 305). DEG regulated RYR1 and sarcoplasmic reticulum calcium stores. Differentially expressed proteins (DEP ↑N = 50, ↓N = 12) involved the sarcomere (24% of DEP), electron transport (23%), metabolism (20%), inflammation (6%), cell/oxidative stress (7%), and other (17%). DEP included ↑superoxide dismutase, ↑catalase, and DEP/DEG included several cysteine-based antioxidants. In conclusion, gluteal muscle of RER-susceptible Standardbreds is characterized by perturbation of pathways for calcium regulation, cellular/oxidative stress, inflammation, and cellular regeneration weeks after an episode of rhabdomyolysis that could represent therapeutic targets.

An Optimized Comparative Proteomic Approach as a Tool in Neurodegenerative Disease Research

Recent advances in proteomic technologies now allow unparalleled assessment of the molecular composition of a wide range of sample types. However, the application of such technologies and techniques should not be undertaken lightly. Here, we describe why the design of a proteomics experiment itself is only the first step in yielding high-quality, translatable results. Indeed, the effectiveness and/or impact of the majority of contemporary proteomics screens are hindered not by commonly considered technical limitations such as low proteome coverage but rather by insufficient analyses. Proteomic experimentation requires a careful methodological selection to account for variables from sample collection, through to database searches for peptide identification to standardised post-mass spectrometry options directed analysis workflow, which should be adjusted for each study, from determining when and how to filter proteomic data to choosing holistic versus trend-wise analyses for biologically relevant patterns. Finally, we highlight and discuss the difficulties inherent in the modelling and study of the majority of progressive neurodegenerative conditions. We provide evidence (in the context of neurodegenerative research) for the benefit of undertaking a comparative approach through the application of the above considerations in the alignment of publicly available pre-existing data sets to identify potential novel regulators of neuronal stability.

RNA profiling of laser microdissected human trophoblast subtypes at mid-gestation reveals a role for cannabinoid signaling in invasion

Human placental architecture is complex. Its surface epithelium, specialized for transport, forms by fusion of cytotrophoblast progenitors into multinucleated syncytiotrophoblasts. Near the uterine surface, these progenitors assume a different fate, becoming cancer-like cells that invade its lining and blood vessels. The latter process physically connects the placenta to the mother and shunts uterine blood to the syncytiotrophoblasts. Isolation of trophoblast subtypes is technically challenging. Upon removal, syncytiotrophoblasts disintegrate and invasive cytotrophoblasts are admixed with uterine cells. We used laser capture to circumvent these obstacles. This enabled isolation of syncytiotrophoblasts and two subpopulations of invasive cytotrophoblasts from cell columns and the endovascular compartment of spiral arteries. Transcriptional profiling revealed numerous genes, the placental or trophoblast expression of which was not known, including neurotensin and C4ORF36. Using mass spectrometry, discovery of differentially expressed mRNAs was extended to the protein level. We also found that invasive cytotrophoblasts expressed cannabinoid receptor 1. Unexpectedly, screening agonists and antagonists showed that signals from this receptor promote invasion. Together, these results revealed previously unseen gene expression patterns that translate to the protein level. Our data also suggested that endogenous and exogenous cannabinoids can affect human placental development.

Summary: Transcriptomic and proteomic profiling of laser captured human trophoblasts showed that placental cells lining uterine arteries express cannabinoid receptor 1. Functional analyses suggest that endogenous/exogenous cannabinoids could affect placentation.

The DREAM complex represses growth in response to DNA damage in Arabidopsis

The DNA of all organisms is constantly damaged by physiological processes and environmental conditions. Upon persistent damage, plant growth and cell proliferation are reduced. Based on previous findings that RBR1, the only Arabidopsis homolog of the mammalian tumor suppressor gene retinoblastoma, plays a key role in the DNA damage response in plants, we unravel here the network of RBR1 interactors under DNA stress conditions. This led to the identification of homologs of every DREAM component in Arabidopsis, including previously not recognized homologs of LIN52. Interestingly, we also discovered NAC044, a mediator of DNA damage response in plants and close homolog of the major DNA damage regulator SOG1, to directly interact with RBR1 and the DREAM component LIN37B. Consistently, not only mutants in NAC044 but also the double mutant of the two LIN37 homologs and mutants for the DREAM component E2FB showed reduced sensitivities to DNA-damaging conditions. Our work indicates the existence of multiple DREAM complexes that work in conjunction with NAC044 to mediate growth arrest after DNA damage.

Multiple Imputation Approaches Applied to the Missing Value Problem in Bottom-Up Proteomics

Analysis of differential abundance in proteomics data sets requires careful application of missing value imputation. Missing abundance values widely vary when performing comparisons across different sample treatments. For example, one would expect a consistent rate of “missing at random” (MAR) across batches of samples and varying rates of “missing not at random” (MNAR) depending on the inherent difference in sample treatments within the study. The missing value imputation strategy must thus be selected that best accounts for both MAR and MNAR simultaneously. Several important issues must be considered when deciding the appropriate missing value imputation strategy: (1) when it is appropriate to impute data; (2) how to choose a method that reflects the combinatorial manner of MAR and MNAR that occurs in an experiment. This paper provides an evaluation of missing value imputation strategies used in proteomics and presents a case for the use of hybrid left-censored missing value imputation approaches that can handle the MNAR problem common to proteomics data.

Sex Differences in the Ventral Tegmental Area and Nucleus Accumbens Proteome at Baseline and Following Nicotine Exposure

Sex differences in behaviors relevant to nicotine addiction have been observed in rodent models and human subjects. Behavioral, imaging, and epidemiological studies also suggest underlying sex differences in mesolimbic dopamine signaling pathways. In this study we evaluated the proteome in the ventral tegmental area (VTA) and nucleus accumbens (NAc) shell in male and female mice. Experimental groups included two mouse strains (C3H/HeJ and C57BL/6J) at baseline, a sub-chronic, rewarding regimen of nicotine in C3H/HeJ mice, and chronic nicotine administration and withdrawal in C57BL/6J mice. Isobaric labeling with a TMT 10-plex system, sample fractionation, and tandem mass spectrometry were used to quantify changes in protein abundance. In C3H/HeJ mice, similar numbers of proteins were differentially regulated between sexes at baseline compared with within each sex after sub-chronic nicotine administration. In C57BL/6J mice, there were significantly greater numbers of proteins differentially regulated between sexes at baseline compared with within each sex after chronic nicotine administration and withdrawal. Despite differences by sex, strain, and nicotine exposure parameters, glial fibrillary acidic protein (GFAP) and dopamine and cAMP-regulated phosphoprotein of 32 kDa (DARPP-32, Ppp1r1b) were repeatedly identified as significantly altered proteins, especially in the VTA. Further, network analyses showed sex- and nicotine-dependent regulation of a number of signaling pathways, including dopaminergic signaling. Sub-chronic nicotine exposure in female mice increased proteins related to dopaminergic signaling in the NAc shell but decreased them in the VTA, whereas the opposite pattern was observed in male mice. In contrast, dopaminergic signaling pathways were similarly upregulated in both male and female VTA after chronic nicotine and withdrawal. Overall, this study identifies significant sex differences in the proteome of the mesolimbic system, at baseline and after nicotine reward or withdrawal, which may help explain differential trajectories and susceptibility to nicotine addiction in males and females.

Bioinformatic Analysis of Temporal and Spatial Proteome Alternations During Infections

Microbial pathogens have evolved numerous mechanisms to hijack host's systems, thus causing disease. This is mediated by alterations in the combined host-pathogen proteome in time and space. Mass spectrometry-based proteomics approaches have been developed and tailored to map disease progression. The result is complex multidimensional data that pose numerous analytic challenges for downstream interpretation. However, a systematic review of approaches for the downstream analysis of such data has been lacking in the field. In this review, we detail the steps of a typical temporal and spatial analysis, including data pre-processing steps (i.e., quality control, data normalization, the imputation of missing values, and dimensionality reduction), different statistical and machine learning approaches, validation, interpretation, and the extraction of biological information from mass spectrometry data. We also discuss current best practices for these steps based on a collection of independent studies to guide users in selecting the most suitable strategies for their dataset and analysis objectives. Moreover, we also compiled the list of commonly used R software packages for each step of the analysis. These could be easily integrated into one's analysis pipeline. Furthermore, we guide readers through various analysis steps by applying these workflows to mock and host-pathogen interaction data from public datasets. The workflows presented in this review will serve as an introduction for data analysis novices, while also helping established users update their data analysis pipelines. We conclude the review by discussing future directions and developments in temporal and spatial proteomics and data analysis approaches. Data analysis codes, prepared for this review are available from https://github.com/BabuLab-UofR/TempSpac, where guidelines and sample datasets are also offered for testing purposes.

Jejunal mucosa proteomics unravel metabolic adaptive processes to mild chronic heat stress in dairy cows

Climate change affects the duration and intensity of heat waves during summer months and jeopardizes animal health and welfare. High ambient temperatures cause heat stress in dairy cows resulting in a reduction of milk yield, feed intake, and alterations in gut barrier function. The objectives of this study were to investigate the mucosal amino acid, glucose and lactate metabolism, as well as the proteomic response of the small intestine in heat stressed (HS) Holstein dairy cows. Cows of the HS group (n = 5) were exposed for 4 days to 28 °C (THI = 76) in a climate chamber. Percentage decrease in daily ad libitum intake of HS cows was calculated to provide isocaloric energy intake to pair-fed control cows kept at 15 °C (THI = 60) for 4 days. The metabolite, mRNA and proteomic analyses revealed that HS induced incorrect protein folding, cellular destabilization, increased proteolytic degradation and protein kinase inhibitor activity, reduced glycolysis, and activation of NF-κB signaling, uronate cycling, pentose phosphate pathway, fatty acid and amino acid catabolism, mitochondrial respiration, ATPase activity and the antioxidative defence system. Our results highlight adaptive metabolic and immune mechanisms attempting to maintain the biological function in the small intestine of heat-stressed dairy cows.

Integrated proteomic and transcriptomic profiling identifies aberrant gene and protein expression in the sarcomere, mitochondrial complex I, and the extracellular matrix in Warmblood horses with myofibrillar myopathy

Background

Myofibrillar myopathy in humans causes protein aggregation, degeneration, and weakness of skeletal muscle. In horses, myofibrillar myopathy is a late-onset disease of unknown origin characterized by poor performance, atrophy, myofibrillar disarray, and desmin aggregation in skeletal muscle. This study evaluated molecular and ultrastructural signatures of myofibrillar myopathy in Warmblood horses through gluteal muscle tandem-mass-tag quantitative proteomics (5 affected, 4 control), mRNA-sequencing (8 affected, 8 control), amalgamated gene ontology analyses, and immunofluorescent and electron microscopy.

Results

We identified 93/1533 proteins and 47/27,690 genes that were significantly differentially expressed. The top significantly differentially expressed protein CSRP3 and three other differentially expressed proteins, including, PDLIM3, SYNPO2, and SYNPOL2, are integrally involved in Z-disc signaling, gene transcription and subsequently sarcomere integrity. Through immunofluorescent staining, both desmin aggregates and CSRP3 were localized to type 2A fibers. The highest differentially expressed gene CHAC1, whose protein product degrades glutathione, is associated with oxidative stress and apoptosis. Amalgamated transcriptomic and proteomic gene ontology analyses identified 3 enriched cellular locations; the sarcomere (Z-disc & I-band), mitochondrial complex I and the extracellular matrix which corresponded to ultrastructural Z-disc disruption and mitochondrial cristae alterations found with electron microscopy.

Conclusions

A combined proteomic and transcriptomic analysis highlighted three enriched cellular locations that correspond with MFM ultrastructural pathology in Warmblood horses. Aberrant Z-disc mechano-signaling, impaired Z-disc stability, decreased mitochondrial complex I expression, and a pro-oxidative cellular environment are hypothesized to contribute to the development of myofibrillar myopathy in Warmblood horses. These molecular signatures may provide further insight into diagnostic biomarkers, treatments, and the underlying pathophysiology of MFM.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07758-0.

Protein Interaction Networks: Functional and Statistical Approaches

The evolution of next-generation sequencing and high-throughput technologies has created new opportunities and challenges in data science. Currently, a classic proteomics analysis can be complemented by going a step beyond the individual analysis of the proteome by using integrative approaches. These integrations can be focused either on inferring relationships among proteins themselves, with other molecular levels, phenotype, or even environmental data, giving the researcher new tools to extract and determine the most relevant information in biological terms. Furthermore, it is also important the employ of visualization methods that allow a correct and deep interpretation of data.To carry out these analyses, several bioinformatics and biostatistical tools are required. In this chapter, different workflows that enable the creation of interaction networks are proposed. Resulting networks reduce the complexity of original datasets, depicting complex statistical relationships (through PLS analysis and variants), functional networks (STRING, shinyGO), and a combination of both approaches. Recently developed methods for integrating different omics levels, such as coinertial analyses or DIABLO, are also described. Finally, the use of Cytoscape or Gephi was described for the representation and mining of the different networks.This approach constitutes a new way of acquiring a deeper knowledge of the function of proteins, such as the search for specific connections of each group to identify differentially connected modules, which may reflect involved protein complexes and key pathways.

Identification of candidate genetic variants and altered protein expression in neural stem and mature neural cells support altered microtubule function to be an essential component in bipolar disorder

Identification of causative genetic variants leading to the development of bipolar disorder (BD) could result in genetic tests that would facilitate diagnosis. A better understanding of affected genes and pathways is also necessary for targeting of genes that may improve treatment strategies. To date several susceptibility genes have been reported from genome-wide association studies (GWAS), but little is known about specific variants that affect disease development. Here, we performed quantitative proteomics and whole-genome sequencing (WGS). Quantitative proteomics revealed NLRP2 as the most significantly up-regulated protein in neural stem cells and mature neural cells obtained from BD-patient cell samples. These results are in concordance with our previously published transcriptome analysis. Furthermore, the levels of FEZ2 and CADM2 proteins were also significantly differentially expressed in BD compared to control derived cells. The levels of FEZ2 were significantly downregulated in neural stem cells (NSC) while CADM2 was significantly up-regulated in mature neuronal cell culture. Promising novel candidate mutations were identified in the ANK3, NEK3, NEK7, TUBB, ANKRD1, and BRD2 genes. A literature search of candidate variants and deregulated proteins revealed that there are several connections to microtubule function for the molecules putatively involved. Microtubule function in neurons is critical for axon structure and axonal transport. A functional dynamic microtubule is also needed for an advocate response to cellular and environmental stress. If microtubule dynamics is compromised by mutations, it could be followed by deregulated expression forming a possible explanation for the inherited vulnerability to stressful life events that have been proposed to trigger mood episodes in BD patients.

The role of extracellular matrix in mouse and human corneal neovascularization

Corneal neo-vascularization (CNV) is a highly prevalent medical condition which impairs visual acuity. The role of specific proteins in modulating CNV has been extensively reported, although no studies have described the entire human proteome in CNV corneas. In this paper, we performed a proteomic analysis of vascularized vs healthy corneal stroma, in a CNV mouse model and in CNV-affected patients, with a specific focus on extracellular matrix (ECM) proteins. We identified and quantified 2315 murine proteins, 691 human proteins and validated 5 proteins which are differentially expressed in vascularized samples and conserved in mice and humans: tenascin-C and fibronectin-1 were upregulated, while decorin, lumican and collagen-VI were downregulated in CNV samples. Interestingly, among CNV patients, those affected with Acanthamoeba keratitis showed the highest levels of fibronectin-1 and tenascin-C, suggesting a specific role of these two proteins in Acanthamoeba driven corneal CNV. On a broader picture, our findings support the hypothesis that the corneal stroma in CNV samples is disorganized and less compact. We are confident that the dissection of the human corneal proteome may shed new light on the complex pathophysiology of human CNV, and finally lead to improved treatments.

Simultaneous Improvement in the Precision, Accuracy, and Robustness of Label-free Proteome Quantification by Optimizing Data Manipulation Chains

The label-free proteome quantification (LFQ) is multistep workflow collectively defined by quantification tools and subsequent data manipulation methods that has been extensively applied in current biomedical, agricultural, and environmental studies. Despite recent advances, in-depth and high-quality quantification remains extremely challenging and requires the optimization of LFQs by comparatively evaluating their performance. However, the evaluation results using different criteria (precision, accuracy, and robustness) vary greatly, and the huge number of potential LFQs becomes one of the bottlenecks in comprehensively optimizing proteome quantification. In this study, a novel strategy, enabling the discovery of the LFQs of simultaneously enhanced performance from thousands of workflows (integrating 18 quantification tools with 3,128 manipulation chains), was therefore proposed. First, the feasibility of achieving simultaneous improvement in the precision, accuracy, and robustness of LFQ was systematically assessed by collectively optimizing its multistep manipulation chains. Second, based on a variety of benchmark datasets acquired by various quantification measurements of different modes of acquisition, this novel strategy successfully identified a number of manipulation chains that simultaneously improved the performance across multiple criteria. Finally, to further enhance proteome quantification and discover the LFQs of optimal performance, an online tool (https://idrblab.org/anpela/) enabling collective performance assessment (from multiple perspectives) of the entire LFQ workflow was developed. This study confirmed the feasibility of achieving simultaneous improvement in precision, accuracy, and robustness. The novel strategy proposed and validated in this study together with the online tool might provide useful guidance for the research field requiring the mass-spectrometry-based LFQ technique.

The human tubal lavage proteome reveals biological processes that may govern the pathology of hydrosalpinx

Hydrosalpinx, the blockage of fallopian tubes, can result from pelvic inflammatory disease. Hydrosalpinx is a cause of infertility and negatively impacts in vitro fertilization. To better understand the pathobiology of hydrosalpinx, we compared the proteome of lavages from disease vs. healthy fallopian tubes. Results indicate a disruption of redox homeostasis and activation of the complement system, immune cell infiltration, and phagocytosis; pathways that may drive tubal injury. To our surprise among the most prominent proteins with hydrosalpinx was mesothelin (MSLN), which until now has only been associated with epithelial malignancies. Analogous to mesothelioma and ovarian carcinoma, a significant increase of MSLN was detected in plasma from patients with hydrosalpinx. This finding suggests MSLN may provide clinical diagnosis in lieu of the current approaches that require invasive imaging. Importantly, these findings implicate MSLN in a benign disease, indicating that the activation and role of MSLN is not restricted to cancer.

Discovery and Validation of a Novel Neutrophil Activation Marker Associated with Obesity

Obesity is accompanied by low-grade systemic inflammation that etiologically contributes to obesity-induced cardiovascular disease (CVD). Growing evidence supports that neutrophil, the most abundant type of leukocytes in human, is most likely to be the target peripheral leukocyte subtype initiating the inflammatory cascade in obesity. However, few studies have systematically assessed the genome wide changes in neutrophils associated with obesity. In this study, a hypothesis-free OMIC approach (i.e. the discovery phase) and a target approach (i.e. the validation phase) were used to identify obesity related neutrophil activation markers and their roles on CVD risks. In the discovery phase, genome wide DNA methylation, RNA-sequencing and quantitative proteomics were obtained from purified neutrophils (12 obese vs. 12 lean). In the validation phase, gene expression levels of the promising genes from the OMIC platforms were measured in 81 obese cases vs. 83 lean controls, and the association between the expression levels and CVD risks were evaluated. Significant difference was found for one gene, alkaline phosphatase, liver/bone/kidney (ALPL), across 3 OMIC platforms. In the validation phase, the gene expression levels of ALPL in leukocytes were significantly higher in obese compared with lean subjects (p < 0.05). Within the obese population, we observed that ALPL expression level showed significantly positive association with CVD risk factors (p < 0.05) including systolic blood pressure, diastolic blood pressure, mean arterial pressure, carotid intima–media thickness and borderline significance with fasting insulin (p = 0.08). This study identified one novel marker ALPL of neutrophil activation in response to obesity and provided evidence that obesity induced change in ALPL expression was associated with CVD risk factors.

Extended Multiplexing of Tandem Mass Tags (TMT) Labeling Reveals Age and High Fat Diet Specific Proteome Changes in Mouse Epididymal Adipose Tissue

The lack of high-throughput methods to analyze the adipose tissue protein composition limits our understanding of the protein networks responsible for age and diet related metabolic response. We have developed an approach using multiple-dimension liquid chromatography tandem mass spectrometry and extended multiplexing (24 biological samples) with tandem mass tags (TMT) labeling to analyze proteomes of epididymal adipose tissues isolated from mice fed either low or high fat diet for a short or a long-term, and from mice that aged on low versus high fat diets. The peripheral metabolic health (as measured by body weight, adiposity, plasma fasting glucose, insulin, triglycerides, total cholesterol levels, and glucose and insulin tolerance tests) deteriorated with diet and advancing age, with long-term high fat diet exposure being the worst. In response to short-term high fat diet, 43 proteins representing lipid metabolism (e.g. AACS, ACOX1, ACLY) and red-ox pathways (e.g. CPD2, CYP2E, SOD3) were significantly altered (FDR < 10%). Long-term high fat diet significantly altered 55 proteins associated with immune response (e.g. IGTB2, IFIT3, LGALS1) and rennin angiotensin system (e.g. ENPEP, CMA1, CPA3, ANPEP). Age-related changes on low fat diet significantly altered only 18 proteins representing mainly urea cycle (e.g. OTC, ARG1, CPS1), and amino acid biosynthesis (e.g. GMT, AKR1C6). Surprisingly, high fat diet driven age-related changes culminated with alterations in 155 proteins involving primarily the urea cycle (e.g. ARG1, CPS1), immune response/complement activation (e.g. C3, C4b, C8, C9, CFB, CFH, FGA), extracellular remodeling (e.g. EFEMP1, FBN1, FBN2, LTBP4, FERMT2, ECM1, EMILIN2, ITIH3) and apoptosis (e.g. YAP1, HIP1, NDRG1, PRKCD, MUL1) pathways. Using our adipose tissue tailored approach we have identified both age-related and high fat diet specific proteomic signatures highlighting a pronounced involvement of arginine metabolism in response to advancing age, and branched chain amino acid metabolism in early response to high fat feeding. Data are available via ProteomeXchange with identifier PXD005953.

Tag-Count Analysis of Large-Scale Proteomic Data

Label-free quantitative methods are advantageous in bottom-up (shotgun) proteomics because they are robust and can easily be applied to different workflows without additional cost. Both label-based and label-free approaches are routinely applied to discovery-based proteomics experiments and are widely accepted as semiquantitative. Label-free quantitation approaches are segregated into two distinct approaches: peak-abundance-based approaches and spectral counting (SpC). Peak abundance approaches like MaxLFQ, which is integrated into the MaxQuant environment, require precursor peak alignment that is computationally intensive and cannot be routinely applied to low-resolution data. Not limited by these constraints, SpC approaches simply use the number of peptide identifications corresponding to a given protein as a measurement of protein abundance. We show here that spectral counts from multidimensional proteomic data sets have a mean-dispersion relationship that can be modeled in edgeR. Furthermore, by simulating spectral counts, we show that this approach can routinely be applied to large-scale discovery proteomics data sets to determine differential protein expression.