Proteomics for understanding pathogenesis, immune modulation and host pathogen interactions in aquaculture

Ahmed, Fatma; Kumar, Gokhlesh; Soliman, Faiza M.; Adly, Mohamed A.; Soliman, Hatem; El‐Matbouli, Mansour; Saleh, Mona

doi:10.1016/j.cbd.2019.100625

Cited by 18 publications

(15 citation statements)

References 148 publications

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“…For any treatment to be effective, in-depth understanding of the pathogenic mechanisms of disease and immune system of fish is required (Sudhagar et al 2018), and this often involves studying the transcriptomic and proteomic changes and dynamics (for review on the role of transcriptomics in aquaculture, see Chandhini & Kumar 2019; for review on the role of proteomics in disease pathogenesis in aquaculture, see Ahmed et al 2019). However, as described in the works of Chandhini and Kumar (2019) and Ahmed et al (2019), the use of genomics and proteomics in disease management of aquaculture species allows direct prediction or identification of the involved proteins, but not the interactions with other proteins, and this greatly hinders the in-depth understanding of disease pathogenesis and subsequent management. Therefore, the application of the PPI network in aquaculture species may offer new insights into their defence mechanisms against incoming stressors.…”

Section: Introductionmentioning

confidence: 99%

“…2018), and this often involves studying the transcriptomic and proteomic changes and dynamics (for review on the role of transcriptomics in aquaculture, see Chandhini & Kumar 2019; for review on the role of proteomics in disease pathogenesis in aquaculture, see Ahmed et al . 2019). However, as described in the works of Chandhini and Kumar (2019) and Ahmed et al .…”

Section: Introductionmentioning

confidence: 99%

“…However, as described in the works of Chandhini and Kumar (2019) and Ahmed et al . (2019), the use of genomics and proteomics in disease management of aquaculture species allows direct prediction or identification of the involved proteins, but not the interactions with other proteins, and this greatly hinders the in‐depth understanding of disease pathogenesis and subsequent management. Therefore, the application of the PPI network in aquaculture species may offer new insights into their defence mechanisms against incoming stressors.…”

Section: Introductionmentioning

confidence: 99%

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Protein–protein interaction network: an emerging tool for understanding fish disease in aquaculture

Waiho

Afiqah‐Aleng

Iryani

et al. 2020

Reviews in Aquaculture

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Protein-protein interactions (PPIs) play integral roles in a wide range of biological processes that regulate the overall growth, development, physiology and disease in living organisms. With the advancement of high-throughput sequencing technologies, increasing numbers of PPI networks are being predicted and annotated, and these contribute greatly towards the understanding of pathogenesis and the discovery of novel drug targets for the treatment of diseases. The use of this tool is gaining popularity in the identification, understanding and treatment of diseases in humans and plants. Due to the importance of aquaculture in tackling the global food crisis by producing cheap and high-quality protein source, the maintenance of the overall health status of aquaculture species is essential. With the increasing omics data on aquaculture species, the PPI network is an emerging tool for fish health maintenance. In this review, we first introduce the concept of PPI network, how they are discovered and their general application. Then, the current status of aquaculture and disease in aquaculture are discussed. The different applications of PPI network in aquaculture fish disease management such as biomarker identification, mechanism prediction, understanding of host-pathogen interaction, understanding of pathogen co-infection interaction, and potential development of vaccines and treatments are subsequently highlighted. It is hoped that this emerging tool-PPI networkwould deepen our understanding of the pathogenesis of various diseases and hasten the prevention and treatment processes in aquaculture species.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Protein–protein interaction network: an emerging tool for understanding fish disease in aquaculture

Waiho

Afiqah‐Aleng

Iryani

et al. 2020

Reviews in Aquaculture

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“…In post-genomic area, transcriptomics is the global analysis of mRNA, which is the product of transcription process [3]. Proteomics is another phoneme, which is the analysis of all gene-encoded proteins in cells and also post-translational modifications on protein structure [4]. Metabolomics is analysis of metabolites, which are side or end products of biological reactions (Figure 1) [5].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of MS-Dial and Mzmine2 Softwares for Clinical Lipidomics Analysis

Koçak

2020

Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng.

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Lipidomics covers analysis of all lipid species in an organism. Lipid metabolism is one of the key factors to understand cellular processes at molecular level. Lipidomics has been used to find diagnostic and prognostic biomarkers in clinical sample (plasma, serum, urine, tissue). Today mass spectroscopy based approach dominates lipidomics and several computational platforms have been developed to process raw mass spectra data. However, there is no routine procedure for data processing in lipidomics. In present work, two different bioinformatics platforms, which are MS-DIAL and MZmine2, was compared for lipidomics analysis of plasma sample. Peak detection, identification and quantification parameters were investigated to understand advantages and disadvantages. In peak detection process, it was observed that MZmine2 detected more peak than MS-DIAL at same threshold level. In identification process, Lipidmaps database was used for identification. MZmine2 identifies more lipid than MS-DIAL. Semi-quantification is very important to find differentially expressed lipid species and biomarkers in clinical studies. MS-DIAL and MZmine2 calculated normalized peak intensities and results were compared to understand reproducibility. Average relative standard deviation of all peaks was calculated and results showed that MS-DIAL gives more reproducible results than MZmine2. In conclusion, MZmine2 and MS-DIAL could be used in clinical lipidomics studies.

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“…Proteomics uses two-dimensional electrophoresis, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry, surface-enhanced laser desorption/ionization time-of-flight mass spectrometry, isobaric tags for relative and absolute quantification (iTRAQ), and other proteomic techniques to study the protein composition and their change laws in the cell, tissue, or organism, including the level of protein synthesis, posttranslation modification, protein and protein interaction, etc. 8,9 In this way, we can get a comprehensive understanding of the physiological and pathological processes of organisms at the protein level. Old anterior myocardial infarction (OAMI) is an integral part of ICM.…”

mentioning

confidence: 99%

Serum biomarker analysis at the protein level on pulmonary hypertension secondary to old anterior myocardial infarction

You

et al. 2020

Pulm. circ.

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Pulmonary hypertension (PH) related to old anterior myocardial infarction (OAMI) always accompanies a bad prognosis, and thus we aimed to screen serum biomarkers related to PH in OAMI patients. According to right ventricular systolic pressure (RVSP), we divided mice into sham, OAMI, and PH-OAMI groups and evaluated body, heart and lung weight, heart function, pulmonary blood flow velocity, cardiac fibrotic area, and pulmonary arteriole condition. Lung and serum were under the proteomic analysis. Levels of three identified proteins were measured. Compared with sham and OAMI mice, PH-OAMI mice showed heart dysfunction, low pulmonary blood flow, high RVSP, heavy heart and lung weight, large cardiac fibrotic area, and pathological pulmonary arteriole remodeling (P<0.05 or P<0.01). Haptoglobin, annexin A5 and Ig mu chain C region of lung and serum were changed significantly in PH-OAMI mice (P<0.01). Then, we collected serum and clinical data, measured three serum protein levels, and performed multivariate regression and receiver operating characteristic curve (ROC) in patients (normal, OAMI, and PH-OAMI groups). Compared with normal and OAMI patients, serum levels of three proteins in PH-OAMI patients were also altered notably (P<0.01). These three proteins can predict PH in OAMI patients (P<0.01). ROC analysis revealed haptoglobin (cut-off value: 78.295, sensitivity: 62.8%, Specificity: 94.4%), annexin A5 (cut-off value: 151.925, sensitivity: 41.9%, Specificity: 82.4%), and Ig mu chain C region (cut-off value: 168.885, sensitivity: 86.0%, Specificity: 79.6%) (P<0.01). Three circulating serum proteins can be useful for the categorization of OAMI patients with and without PH.

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Proteomics for understanding pathogenesis, immune modulation and host pathogen interactions in aquaculture

Cited by 18 publications

References 148 publications

Protein–protein interaction network: an emerging tool for understanding fish disease in aquaculture

Protein–protein interaction network: an emerging tool for understanding fish disease in aquaculture

Evaluation of MS-Dial and Mzmine2 Softwares for Clinical Lipidomics Analysis

Serum biomarker analysis at the protein level on pulmonary hypertension secondary to old anterior myocardial infarction

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