Bioinformatics Tools for Proteomics Data Interpretation

Uncovering domain motif interactions using high-throughput protein-protein interaction detection methods

Protein-protein interactions (PPIs) are often mediated by short linear motifs (SLiMs) in one protein and domain in another, known as domain-motif interactions (DMIs). During the past decade, SLiMs have been studied to find their role in cellular functions such as post-translational modifications, regulatory processes, protein scaffolding, cell cycle progression, cell adhesion, cell signalling and substrate selection for proteasomal degradation. This review provides a comprehensive overview of the current PPI detection techniques and resources, focusing on their relevance to capturing interactions mediated by SLiMs. We also address the challenges associated with capturing DMIs. Moreover, a case study analysing the BioGrid database as a source of DMI prediction revealed significant known DMI enrichment in different PPI detection methods. Overall, it can be said that current high-throughput PPI detection methods can be a reliable source for predicting DMIs.

Fucoxanthin, a marine derived carotenoid, attenuates surgery-induced cognitive impairments via activating Akt and ERK pathways in aged mice

BACKGROUND

Fucoxanthin is the most abundant marine carotenoid derived from brown seaweeds, possesses antioxidant, anti-inflammatory, and neuroprotective properties, and might be benefit for the treatment of neurological disorders. Post-operative cognitive dysfunction (POCD) is a neurological symptom with learning and memory impairments, mainly affecting the elderly after surgery. However, there is no effective treatments for this symptom.

PURPOSES

In this study, we evaluated the neuroprotective effects of fucoxanthin against POCD in aged mice after surgery.

STUDY DESIGN AND METHODS

The animal model of POCD was established in 12 - 14 month aged mice with a laparotomy. Curcumin was used as a positive control. The beneficial effects of fucoxanthin on POCD was analyzed by behavioral tests. Pro-inflammatory cytokines were measured by Enzyme-linked Immunosorbent Assay (ELISA). And the expressions of key proteins in the Akt and ERK signaling pathways were analyzed by Western blotting analysis. The morphology of microglial cells and astrocytes was explored by immunohistochemical staining. The activity of antioxidant superoxide dismutase (SOD) and catalase (CAT) were measured by anti-oxidative enzyme activity assays.

RESULTS

Fucoxanthin at 100 - 200 mg/kg significantly attenuated cognitive dysfunction, with a similar potency as curcumin, in aged mice after surgery. In addition, fucoxanthin and curcumin significantly increased the expression of pAkt, prevented the activation of microglial cells and astrocytes, and inhibited the secretion of pro-inflammatory interleukin-1β (IL - 1β) and tumor necrosis factor-α (TNF-α). Furthermore, fucoxanthin and curcumin elevated the ERK pathway and potently increased the activity of antioxidant enzymes. Most importantly, U0126, an inhibitor of the ERK pathway, and wortmannin, an inhibitor of the Akt pathway, significantly abolished the cognitive-enhancing effects, as well as the inhibition of neuroinflammation and the reduction of oxidative stress, induced by fucoxanthin in aged mice after surgery.

CONCLUSION

Fucoxanthin might be developed as a functional food or drug for the treatment of POCD by inhibiting neuroinflammation and enhancing antioxidant capacity via the activation of the Akt and ERK signaling pathways.

asmbPLS: Adaptive Sparse Multi-block Partial Least Square for Survival Prediction using Multi-Omics Data

Background

As high-throughput studies advance, more and more high-dimensional multi-omics data are available and collected from the same patient cohort. Using multi-omics data as predictors to predict survival outcomes is challenging due to the complex structure of such data.

Results

In this article, we introduce an adaptive sparse multi-block partial least square (asmbPLS) regression method by assigning different penalty factors to different blocks in different PLS components for feature selection and prediction. We compared the proposed method with several competitive algorithms in many aspects including prediction performance, feature selection and computation efficiency. The performance and the efficiency of our method were demonstrated using both the simulated and the real data.

Conclusions

In summary, asmbPLS achieved a competitive performance in prediction, feature selection, and computation efficiency. We anticipate asmbPLS to be a valuable tool for multi-omics research. An R package called asmbPLS implementing this method is made publicly available on GitHub.

Metaproteomics insights into fermented fish and vegetable products and associated microbes

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Increasing global population means higher demand for healthy food.

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Fish and vegetables are healthy foods, but overproduction leads to spoilage.

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Fermentation of fish/vegetables elongate their shelf lives, improved flavour and functions.

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Microbes associated with Fish/vegetable fermentation produce health conferring peptides.

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There is little review on peptides elicited during fish/vegetable fermentations.

The interest in proteomic studies of fermented food is increasing; the role of proteins derived from fermentation extends beyond preservation, they also improve the organoleptic, anti-pathogenic, anti-cancer, anti-obesogenic properties, and other health conferring properties of fermented food. Traditional fermentation processes are still in use in certain cultures, but recently, the controlled process is gaining wider acceptance due to consistency and predictability. Scientists use modern biotechnological approaches to evaluate reactions and component yields from fermentation processes. Pieces of literature on fermented fish and vegetable end-products are scanty (compared to milk and meat), even though fish and vegetables are considered health conferring diets with high nutritional contents. Evaluations of peptides from fermented fish and vegetables show they have anti-obesity, anti-oxidative, anti-inflammatory, anti-pathogenic, anti-anti-nutrient, improves digestibility, taste, nutrient content, texture, aroma properties, etc. Despite challenges impeding the wider applications of the metaproteomic analysis of fermented fish and vegetables, their potential benefits cannot be underestimated.

Metabolic networks of the Nicotiana genus in the spotlight: content, progress and outlook

Manually curated metabolic databases residing at the Sol Genomics Network comprise two taxon-specific databases for the Solanaceae family, i.e. SolanaCyc and the genus Nicotiana, i.e. NicotianaCyc as well as six species-specific databases for Nicotiana tabacum TN90, N. tabacum K326, Nicotiana benthamiana, N. sylvestris, N. tomentosiformis and N. attenuata. New pathways were created through the extraction, examination and verification of related data from the literature and the aid of external database guided by an expert-led curation process. Here we describe the curation progress that has been achieved in these databases since the first release version 1.0 in 2016, the curation flow and the curation process using the example metabolic pathway for cholesterol in plants. The current content of our databases comprises 266 pathways and 36 superpathways in SolanaCyc and 143 pathways plus 21 superpathways in NicotianaCyc, manually curated and validated specifically for the Solanaceae family and Nicotiana genus, respectively. The curated data have been propagated to the respective Nicotiana-specific databases, which resulted in the enrichment and more accurate presentation of their metabolic networks. The quality and coverage in those databases have been compared with related external databases and discussed in terms of literature support and metabolic content.

Presumed cancer-associated retinopathy (CAR) mimicking Sudden Acquired Retinal Degeneration Syndrome (SARDS) in canines

OBJECTIVE

To describe functional and structural features of presumed cancer-associated retinopathy (CAR) mimicking sudden acquired retinal degeneration syndrome (SARDS) in dogs and describe treatment outcomes.

ANIMALS

Subjects were 17 dogs from 8 eight US states and Canada diagnosed with SARDS or immune-mediated retinitis (IMR) by 12 ophthalmologists. Nine eyes from seven deceased patients were used for microarray (MA), histology, or immunohistochemical (IHC) analysis.

PROCEDURES

Dogs underwent complete ophthalmic examination, including retinal photography, optical coherence tomography (OCT), chromatic pupil light reflex testing (cPLR), and electroretinography (ERG), in addition to complete systemic examination. Histology, microarray, and IHC analysis were performed in CAR retinas to evaluate histological and molecular changes in retinal tissue.

RESULTS

None of the patients evaluated satisfied previously established criteria for diagnosis of SARDS (flat ERG+ no red - good blue PLR), and all were diagnosed with IMR. All patients were diagnosed with a cancer: meningioma (24%), sarcoma (18%), pituitary tumor (12%), and squamous cell carcinoma (12%), other (34%). Median survival time was 6 months from diagnosis (range 1-36 months). Most frequent systemic abnormalities were as follows: proteinuria (78%); elevated liver enzymes (47%); and metabolic changes (PU/PD, polyphagia - 24%). Immunosuppressive therapy resulted in the reversal of blindness in 44% of treated patients, with 61% of all treated patients recovering and/or maintaining vision. Median time for preservation of vision was 5 months (range 1-35 months).

CONCLUSIONS

Observed changes are highly suggestive of immune-mediated damage in IMR-CAR eyes. A relatively high percentage of patients with CAR responded positively to immunosuppressive therapy.

An Integrated Genomic Approach Identifies HOXC8 as an Upstream Regulator in Ovarian Endometrioma

PURPOSE

To identify the upstream regulators (URs) involved in the onset and pathogenesis of ovarian endometrioma.

METHODS

Recently, a method called Significance-based Modules Integrating the Transcriptome and Epigenome (SMITE) that uses transcriptome data in combination with publicly available data for identifying URs of cellular processes has been developed. Here, we used SMITE with transcriptome data from ovarian endometrioma stromal cells (ovESCs) and eutopic endometrium stromal cells (euESCs) in combination with publicly available gene regulatory network data. To confirm the URs identified by SMITE, we developed a Boolean network simulation to see if correcting aberrant expressions of the identified genes could restore the entire gene expression profile of ovESCs to a profile similar to that of euESCs. We then established euESCs overexpressing the identified gene and characterized them by cell function assays and transcriptome analysis.

RESULTS

SMITE identified 12 potential URs in ovarian endometrioma that were confirmed by the Boolean simulation. One of the URs, HOXC8, was confirmed to be overexpressed in ovESCs. HOXC8 overexpression significantly enhanced cell proliferation, migration, adhesion, and fibrotic activities, and altered expression statuses of the genes involved in transforming growth factor (TGF)-β signaling. HOXC8 overexpression also increased the expression levels of phosphorylated SMAD2/SMAD3. The increased adhesion and fibrosis activities by HOXC8 were significantly inhibited by E-616452, a selective inhibitor of TGF-β receptor type I kinases.

MAIN CONCLUSIONS

Integrated genomic approaches identified HOXC8 as an UR in ovarian endometrioma. The pathological features of ovarian endometrioma including cell proliferation, adhesion, and fibrosis were induced by HOXC8 and its subsequent activation of TGF-β signaling.

Searching for Small Molecules with an Atomic Sort

The discovery of biologically active small molecules requires sifting through large amounts of data to identify unique or unusual arrangements of atoms. Here, we develop, test and evaluate an atom-based sort to identify novel features of secondary metabolites and demonstrate its use to evaluate novelty in marine microbial and sponge extracts. This study outlines an important ongoing advance towards the translation of autonomous systems to identify, and ultimately elucidate, atomic novelty within a complex mixture of small molecules.

Prognostic value and prospective molecular mechanism of miR-100-5p in hepatocellular carcinoma: A comprehensive study based on 1,258 samples

The prognostic value and molecular mechanism of microRNA-100-5p (miR-100-5p) in hepatocellular carcinoma (HCC) are still unclear. To explore the prognostic value and the mechanism of miR-100-5p in HCC, the present study analyzed the results of 18 previous studies and bioinformatic datasets. The clinical significance of miR-100-5p and its targets in HCC were investigated using The Cancer Genome Atlas and the Gene Expression Omnibus, as well as relevant literature. In total, 12 online tools were used to predict the target genes of miR-100-5p. Bioinformatics analysis and Spearman correlation analysis were performed, and genomic alterations of the hub genes were evaluated. A meta-analysis with 1,258 samples revealed that miR-100-5p was significantly downregulated in HCC [standard mean difference (SMD), −0.94; 95% confidence interval (CI), −1.14 to −0.74; I², 35.2%]. Lower miR-100-5p expression was associated with poorer clinical characteristics and a poorer prognosis for patients with HCC. Additionally, bioinformatics analysis revealed that the ‘regulation of transcription’, ‘chromatin remodeling complex’, ‘transcription regulator activity’, ‘pathways in cancer’ and ‘heparan sulfate biosynthesis’ were the most enriched terms. Furthermore, expression of histone deacetylase (HDAC)2, HDAC3, SHC-transforming protein 1 (SHC1), Ras-related protein Rac1 (RAC1) and E3 ubiquitin-protein ligase CBL (CBL) was negatively correlated with miR-100-5p expression. Among these, upregulated HDAC2 [hazard ratio (HR), 1.910; 95% CI, 1.309–2.787; P=0.0007], HDAC3 (HR, 1.474; 95% CI, 1.012–2.146; P=0.0435), SHC1 (HR, 1.52; 95% CI, 1.043–2.215; P=0.0281) and RAC1 (HR, 1.817; 95% CI, 1.248–2.645; P=0.0022) were associated with shorter survival. Alterations in HDAC2, SHC1, RAC1 and IGF1R were linked with a poorer outcome for HCC, and alternative splicing of SHC and RAC1 were significantly decreased and increased in HCC, respectively. In summary, the downregulation of miR-100-5p may be involved in the progression and prognosis of HCC. The upregulation of HDAC2, HDAC3, SHC1 and RAC1 may indicate a poorer survival rate for patients with HCC. Thus, miR-100-5p and these 4 potential target genes may provide novel therapeutic targets and prognostic predictors for patients with HCC.

Bioinformatics to Tackle the Biological Meaning of Human Cerebrospinal Fluid Proteome

Cerebrospinal fluid (CSF) is a source of valuable information concerning brain disorders. The technical advances of high-throughput omics platforms to analyze body fluids can generate a huge amount of data, whose translation to biological meaning is a challenge. Several bioinformatic tools have emerged to help handling this data into systems biology comprehensively. Herein, we describe a step-by-step tutorial for CSF proteome data analysis in the set of neurodegenerative diseases using (1) ClueGO+CluePedia tool to perform cluster-based analysis envisioning the characterization of the biological processes dysregulated in neurodegenerative diseases including Alzheimer's and Parkinson's diseases; (2) Cytoscape to map disease-specific proteins; (3) SecretomeP to inquire the secretion pathway of CSF proteins; and (4) STRING to identify biological processes modulated by secreted CSF proteins based on protein-protein interaction analysis. This step-by-step guide might help researchers to better characterize disease pathogenesis and to identify putative disease biomarkers.

Mining cancer biology through bioinformatic analysis of proteomic data

Introduction: Discovery proteomics for cancer research generates complex datasets of diagnostic, prognostic, and therapeutic significance in human cancer. With the advent of high-resolution mass spectrometers, able to identify thousands of proteins in complex biological samples, only the application of bioinformatics can lead to the interpretation of data which can be relevant for cancer research. Areas covered: Here, we give an overview of the current bioinformatic tools used in cancer proteomics. Moreover, we describe their applications in cancer proteomics studies of cell lines, serum, and tissues, highlighting recent results and critically evaluating their outcomes. Expert opinion: The use of bioinformatic tools is a fundamental step in order to manage the large amount of proteins (from hundreds to thousands) that can be identified and quantified in a cancer biological samples by proteomics. To handle this challenge and obtain useful data for translational medicine, it is important the combined use of different bioinformatic tools. Moreover, a particular attention to the global experimental design, and the integration of multidisciplinary skills are essential for best setting of tool parameters and best interpretation of bioinformatics output.

A decade of Nucleic Acid Programmable Protein Arrays (NAPPA) availability: News, actors, progress, prospects and access

Understanding the dynamic of the proteome is a critical challenge because it requires high sensitive methodologies in high-throughput formats in order to decipher its modifications and complexity. While molecular biology provides relevant information about cell physiology that may be reflected in post-translational changes, High-Throughput (HT) experimental proteomic techniques are essential to provide valuable functional information of the proteins, peptides and the interconnections between them. Hence, many methodological developments and innovations have been reported during the last decade. To study more dynamic protein networks and fine interactions, Nucleic Acid Programmable Protein Arrays (NAPPA) was introduced a decade ago. The tool is rapidly maturing and serving as a gateway to characterize biological systems and diseases thanks primarily to its accuracy, reproducibility, throughput and flexibility. Currently, NAPPA technology has proved successful in several research areas adding valuable information towards innovative diagnostic and therapeutic applications. Here, the basic and latest advances within this modern technology in basic, translational research are reviewed, in addition to presenting its exciting new directions. Our final goal is to encourage more scientists/researchers to incorporate this method, which can help to remove bottlenecks in their particular research or biomedical projects. SIGNIFICANCE: Nucleic Acid Programmable Protein Arrays (NAPPA) is becoming an essential tool for functional proteomics and protein-protein interaction studies. The technology impacts decisively on projects aiming massive screenings and the latest innovations like the multiplexing capability or printing consistency make this a promising method to be integrated in novel and combinatorial proteomic approaches.

Integration of Proteomics and Metabolomics in Exploring Genetic and Rare Metabolic Diseases

BACKGROUND

Inherited metabolic disorders or inborn errors of metabolism are caused by deficiency of enzymatic activities in the catabolism of amino acids, carbohydrates, or lipids. These disorders include aminoacidopathies, urea cycle defects, organic acidemias, defects of oxidation of fatty acids, and lysosomal storage diseases. Inborn errors of metabolism constitute a significant proportion of genetic diseases, particularly in children; however, they are individually rare. Clinical phenotypes are very variable, some of them remain asymptomatic, others manifest metabolic decompensation in neonatal age, and others encompass mental retardation at later age. The clinical manifestation of these disorders can involve different organs and/or systems. Some disorders are easily managed if promptly diagnosed and treated, whereas in other cases neither diet, vitamin therapy, nor transplantation appears to prevent multi-organ impairment.

SUMMARY

Here, we discuss the principal challenges of metabolomics and proteomics in inherited metabolic disorders. We review the recent developments in mass spectrometry-based proteomic and metabolomic strategies. Mass spectrometry has become the most widely used platform in proteomics and metabolomics because of its ability to analyze a wide range of molecules, its optimal dynamic range, and great sensitivity. The fast measurement of a broad spectrum of metabolites in various body fluids, also collected in small samples like dried blood spots, have been facilitated by the use of mass spectrometry-based techniques. These approaches have enabled the timely diagnosis of inherited metabolic disorders, thereby facilitating early therapeutic intervention. Due to its analytical features, proteomics is suited for the basic investigation of inborn errors of metabolism. Modern approaches enable detailed functional characterization of the pathogenic biochemical processes, as achieved by quantification of proteins and identification of their regulatory chemical modifications.

KEY MESSAGE

Mass spectrometry-based "omics" approaches most frequently used to study the molecular mechanisms underlying inherited metabolic disorders pathophysiology are described.