Protein-protein networks analysis of differentially expressed genes unveils the key phenomenon of biological process with respect to reproduction in endangered catfish, C. magur (2023)


Reproduction, being an important biological process, provide mechanisms for the continued existence of a species. In vertebrates, the nervous and the endocrine systems jointly work to regulate reproduction (Zohar et al., 2010). Like other vertebrates, brain is actively engaged in fishes in controlling reproduction either by maintaining suitable endocrine status or by reacting to the external environment through a neuroendocrine mechanism via hypothalamus-pituitary-gonadal (HPG) axis, which is evolutionarily conserved. Hypothalamus is the main site for the production of neuropeptide, gonadotropin-releasing hormone (GnRH) etc. GnRH regulates the secretion and release of gonadotropins (Pozor et al., 1991, McCann and Ojeda, 1996, Bancroft, 2005), which control the development and maturation of gonads and prompt steroidogenesis and spermatogenesis in testes (Pierce and Parsons, 1981) and folliculogenesis and oogenesis in ovaries (Pogrmic-Majkic et al., 2014). Many fish species are reported to have three groups of GnRHs (GnRH1, GnRH2 and GnRH3) in the brain. Several studies in teleosts have also reported the presence of kisspeptin coding genes (kiss1 and kiss2) and kisspeptin receptor genes (kissr1andkissr2), a potent regulator (Bousfield et al., 1994, Tena-Sempere, 2006, Roseweir and Millar, 2009) of GnRH release. Various evidences (Pogrmic-Majkic et al., 2014, Tohyama et al., 2015, Gabilondo et al., 2021) support the uniqueness and importance of neuroendocrine signalling pathways in the reproduction management of teleosts. Likewise, numerous studies have depicted the participation and significance of the nervous system in the regulation of reproduction in fishes (Tohyama et al., 2015, Joy and Chaube, 2015, Gabilondo et al., 2021, Senthilkumaran and Kar, 2021).

Hormone released by hypothalamus, pituitary gland, testis and ovary from the brain-pituitary-gonad axis are involved in regulation of the maturation and spawning in the reproduction process in fishes, including magur (Weltzien et al., 2004, Raghuveer et al., 2011, Anbazhagan and Senthilkumaran, 2014). Besides this, various other studies have revealed the involvement of novel genes in understanding the mechanism of brain-reproduction correlation (Raghuveer and Senthilkumaran, 2009, Agarwal et al., 2020).

Artificial breeding technique is used in normal culture systems, wherein GnRH analogues are injected to the brooders and stripping of milt and eggs is achieved after 12-18 h of procedure (Manickam and Joy, 1989, Basu et al., 2000; Murthy et al., 2020). In magur, milt from the male is difficult to obtain by simple stripping (Viveiros et al., 2003) due to the presence of very less quantity of seminal fluid in the testis (Majhi et al., 2020). Hence, milt for fertilization is collected either through partial harvesting of the testis by surgery or complete harvesting of testis by sacrificing male (Das, 2002, Viveiros et al., 2003, Majhi et al., 2020). The core cause of low fertility in magur seems to be the ineptitude of testis to release milt and the complex mating behaviour of the species. Several studies at genome and transcriptome levels have been conducted to understand the physiology of male testis in magur (Raghuveer and Senthilkumaran, 2009, Agarwal et al., 2020) and other fishes, including vertebrates describing the gene expression, gene pathways etc. (Soghomonian and Martin, 1998; Luu-The et al., 2001; Ortega et al., 2002, Wang and Ge, 2004, Elliott et al., 2016).

Protein-protein interaction (PPI) network analysis is key mathematical modelling to understand the biological processes from a system biology perspective. It links diverse data to a knowledge base to understand the insight mechanism and molecular as well as cellular interactions between the genes and the proteins. PPI network analysis has been undertaken in other fish species to identify key genes involved in various process like immunology, reproduction etc. Kumari et al. (2020) utilized PPI network analysis to understand the role of kissspeptins in gonad maturation in Heteropneustes fossilis. Li et al. (2022) identified a set of core genes involved in immune response of egg-protecting behavior in Webfoot octopus (Octopus ocellatus). Wang et al. (2022) identified 16 hub genes involved in disease resistance in Olive flounder (Paralichthys olivaceus). Understanding the gene-gene interactions in magur would provide an insight in biological mechanisms and pathways between the tissues involved in reproduction process at the system network biology level. It will be helpful in discovering the key genes involved in magur reproduction and may pave way to understand the probable causes behind the low seminal content or failure in milt release.

The walking catfish,Clarias magur (Family: Clariidae), is a high-potential freshwater catfish of Indian subcontinent and is widely distributed in many countries including India, Nepal and Bangladesh (Ng and Kottelat, 2008). The species is assessed as ‘ENDANGERED’ in IUCN Red List in the year 2010. The population is severely fragmented and declining throughout its distribution range due to drying up of the wetlands and pesticide pollution of agricultural lands in their breeding tracts. Other major reason behind the endangered status is its low fertility, overfishing and several other external factors. Keeping in view of breeding-led reproductive issues in magur, the present study was undertaken with objectives to investigate the gene-gene interaction and expression pathways among the brain and gonads of male and female magur using the protein-protein interaction network biology-based computational model. This may be helpful in better understanding of the key genes involved in its reproduction pathways and to increase abundance of the magur.

Section snippets

Data collection

Information on DEGs in male brain (MB), female brain (FB), male testis (MT) and female ovary (FO) were retrieved from the transcriptome data (unpublished) generated in our laboratory. In brief, three adults male and three adults female magur, weighing approximately 175-200 g, were obtained from ICAR-NBFGR fish farm and acclimatized for a period of 15 days before sample collection. The magur individuals were fed twice daily at the rate of 5% body weight with the feed containing 40% protein. All

PPI and Cluster Networks

The PPI network, constructed by inputting DEGs of 4 tissues in STRING database, resulted in 98 edges between 302 nodes (p-value <0.00639) for female ovary (Fig. 1), 27 edges between 219 nodes (p-value <0.00687) for male testis (Fig. 2), 142 edges between 40 nodes (p-value <1.0e-16) for female brain (Fig. 3) and 194 edges between 45 nodes (p-value <1.0e-16) for male brain (Fig. 4). Further, the cluster subnetwork, built for all 4 tissues using MCODE, resulted into 109 DEGs in the male testis and


C. magur is known to be the highly favoured, economically potential catfish species in its geographical distribution. Unfortunately, due to low fecundity in female brooders and problematic assisted breeding in males, the availability and production of magur has declined considerably. Induced breeding is one of the methods to increase production, but it needs sacrifice of the males in most cases. Therefore, to combat the problem, it is needed to understand the mechanism of reproduction in magur


The protein-protein networks analysis of C. magur in this study unveils the key phenomenon of a biological process with respect to the reproductive system from a systems biology perspective. It also has implications in prioritizing the key genes and their interaction levels among the vast array of genes in the network. This can lead to the exploration of new strategies for breeding as well as culture practices. The network and their functional annotation analysis in this important catfish will

Uncited references

Bai et al., 2021, Kumari et al., 2022, Li et al., 2021a, Li et al., 2021b, Luu-The, 2001, Murthy et al., 2013.

CRediT authorship contribution statement

Basdeo Kushwaha: Conceptualization. Neha Srivastava: . Murali S Kumar: . Ravindra Kumar: .

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


The authors are thankful to the Director, ICAR-National Bureau of Fish Genetic Resources, Lucknow, for providing the necessary support, guidance and facilities to carry out this work.

© 2023 Elsevier B.V. All rights reserved.


How do you analyze DEGs? ›

RNA-Seq - DEG Analysis Methods
  1. Read quality assessment, filtering and trimming.
  2. Map reads against reference genome.
  3. Perform read counting for required ranges (e.g. exonic gene ranges)
  4. Normalization of read counts.
  5. Identification of differentially expressed genes (DEGs)
  6. Clustering of gene expression profiles.
Mar 22, 2022

What does string analysis tell you? ›

In molecular biology, STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) is a biological database and web resource of known and predicted protein–protein interactions.

What is DEGs in bioinformatics? ›

Identification of Differentially Expressed Genes (DEGs) Relevant to Prognosis of Ovarian Cancer by Use of Integrated Bioinformatics Analysis and Validation by Immunohistochemistry Assay. Med Sci Monit.

What is protein-protein interaction network? ›

Protein-protein interaction networks (PPIN) are mathematical representations of the physical contacts between proteins in the cell. These contacts: are specific. occur between defined binding regions in the proteins. have a particular biological meaning (i.e., they serve a specific function)

What does differential gene expression tell us? ›

Differential gene expression (DGE) analysis is an RNA-seq technology where the gene expression of cells is evaluated, quantified, and compared. DGE analysis determines which genes are actively being expressed, how often those genes are being expressed, and which genes are silenced.

What is the purpose of differential gene expression analysis? ›

Differential gene expression (DGE) analysis is one of the most common applications of RNA-sequencing (RNA-seq) data. This process allows for the elucidation of differentially expressed genes across two or more conditions and is widely used in many applications of RNA-seq data analysis.

What does string DB show? ›

STRING is a database of known and predicted protein-protein interactions. The interactions include direct (physical) and indirect (functional) associations; they stem from computational prediction, from knowledge transfer between organisms, and from interactions aggregated from other (primary) databases.

What is string DB used for? ›

The STRING database aims to integrate all known and predicted associations between proteins, including both physical interactions as well as functional associations.

How do you analyze protein-protein interactions? ›

Characterizing protein–protein interactions through methods such as co-immunoprecipitation (co-IP), pull-down assays, crosslinking, label transfer, and far–western blot analysis is critical to understand protein function and the biology of the cell.

What is differential protein expression analysis? ›

Differential protein expression analysis determines the relative abundances (molar ratios) of identical proteins in two or more samples (multi-protein mixtures) representing different conditions (groups) – e.g. control vs patient, treated vs untreated, etc.

What is identification of DEGs? ›

Identification of DEGs

Hierarchical clustering analysis was applied to categorize the data into two groups of different expression patterns. Significance analysis by Student's t-test and fold-change (FC) in the expression of genes between each pair of cancerous and normal tissues were jointly used to identify DEGs.

How is gene enrichment analysis performed? ›

Methods. Gene set enrichment analysis uses a priori gene sets that have been grouped together by their involvement in the same biological pathway, or by proximal location on a chromosome. A database of these predefined sets can be found at the Molecular signatures database (MSigDB).

What diseases are caused by protein-protein interaction? ›

New undesired protein interactions are the main causes of several diseases, including Huntington's disease (see introduction), cystic fibrosis, and Alzheimer's disease.

What are the three types of protein-protein interactions? ›

Proteins bind to each other through a combination of hydrophobic bonding, van der Waals forces, and salt bridges at specific binding domains on each protein. These domains can be small binding clefts or large surfaces and can be just a few peptides long or span hundreds of amino acids.

What are the benefits of protein-protein interaction? ›

Abstract. Protein–protein interactions (PPIs) participate in all important biological processes in living organisms, such as catalyzing metabolic reactions, DNA replication, DNA transcription, responding to stimuli and transporting molecules from one location to another.

What affects differential gene expression? ›

Differential gene expression is often associated with methylation of DNA, and until recently the prevailing idea was that increased cytosine methylation nearby and within genes correlated with reduced gene expression.

Why is gene expression so significant? ›

Gene expression is important because a specific protein can be produced only when its gene is turned on. But it takes more than one step to get from gene to protein, and the process of building proteins is a key step in the gene expression pathway that can be altered in cancer.

What controls differential gene expression? ›

Gene expression is primarily controlled at the level of transcription, largely as a result of binding of proteins to specific sites on DNA.

What is differential gene expression quizlet? ›

differential gene expression. the process by which cells become different from one another based upon the unique combo of genes that are active or expressed. Expressing genes -> express different proteins -> different cell types. 1.

What is the difference between string and GeneMANIA? ›

STRING relies on the phylogeny to infer the functional interaction (protein networks) with direct interaction to score nodes, while GeneMANIA uses functional genomic data with label propagation to score nodes and generate gene networks (Figure 3).

What does string score mean? ›

In STRING, each protein-protein interaction is annotated with one or more 'scores'. Importantly, these scores do not indicate the strength or the specificity of the interaction. Instead, they are indicators of confidence, i.e. how likely STRING judges an interaction to be true, given the available evidence.

What is a string code? ›

In computer programming, a string is traditionally a sequence of characters, either as a literal constant or as some kind of variable. The latter may allow its elements to be mutated and the length changed, or it may be fixed (after creation).

Is string manipulation important? ›

String manipulation is an important skill for novice programmers to master as most applications deal with text and/or interact with the user. The analysis shows most novice program- mers (88%) were able to sketch their own programming plan to print a word in pyramid style.

What is the threshold for string DB score? ›

STRING, for example, suggests thresholds of 0.15 (low confidence), 0.40 (medium confidence), 0.70 (high confidence) or 0.90 (highest confidence), whereas HitPredict identifies all interactions scoring below 0.28 as medium-high confidence, and interactions scoring above as high confidence.

What are the 3 analytical methods for determining protein content? ›

The most frequently used methods for measuring protein content in foods include the Kjeldahl method, Dumas method, direct measurement methods using UV-spectroscopy and refractive index measurement.

Which method is best for protein analysis? ›

The Lowry method has been widely used for protein determination for many decades, due to its simplicity and availability. However, besides aromatic amino acids, a wide range of other compounds react with the Folin–Ciocalteu reagent [30].

What are 3 ways of assessing protein quality? ›

The quality of a protein is vital when considering the nutritional benefits that it can provide. Determining the quality of a protein is determined by assessing its essential amino acid composition, digestibility and bioavailability of amino acids (FAO/WHO, 1990).

How do you identify a genome mutation? ›

Identification of causal mutations typically begins with genetic mapping, followed by candidate gene sequencing and complementation studies using transformation.

How do you verify gene expression? ›

Gene expression measurement is usually achieved by quantifying levels of the gene product, which is often a protein. Two common techniques used for protein quantification include Western blotting and enzyme-linked immunosorbent assay or ELISA.

How do you identify a mutation in a gene? ›

There are many genetic tests that require a sample of your blood, skin, hair, amniotic fluid or tissues to identify changes to your genes, chromosomes or proteins. Genetic testing can locate mutated genes or chromosomes that cause genetic conditions.

Why is gene enrichment analysis important? ›

Gene set enrichment analysis (GSEA) is a powerful tool for the interpretation of high-throughput expression studies such as mass spectrometry-based proteomics or Next-Generation Sequencing, in order to identify insights into biological processes or pathways underlying a given phenotype.

Which technique is used for gene expression analysis? ›

Currently the most widely used techniques are qPCR, expression microarrays, and RNAseq for the transcriptome analysis. In this chapter, these techniques will be reviewed. Keywords: Gene expression; Microarrays; RNA sequencing; Transcriptome; qPCR.

How do you investigate protein expressions? ›

A technique called mass spectrometry permits scientists to sequence the amino acids in a protein. After a sequence is known, comparing its amino acid sequence with databases allows scientists to discover if there are related proteins whose function is already known.

How do you analyze a Crispr? ›

The gold standard of analyzing CRISPR editing involves targeted next-generation sequencing to perform deep sequencing on the region of interest. Targeted NGS is an extremely sensitive method of detecting editing outcomes, and the high-throughput sequence-based data provides a comprehensive view of the indels generated.

How do you analyze RNA-seq results? ›

RNA-seq data analysis typically involves several steps: trimming, alignment, counting and normalization of the sequenced reads, and, very often, differential expression (DE) analysis across conditions.

How do you analyze gene expression from cultured cells? ›

The standard workflow for gene expression analysis in cell cultures is a multistep process that requires harvesting of cells, isolation of RNA, removal of contaminating DNA, cDNA synthesis, and finally qPCR, where cell harvesting and RNA isolation are the rate-limiting steps.

How does CRISPR Covid test work? ›

To perform this test, viral RNA is first isolated and extracted from patient samples. Then, the purified RNA is subjected to reverse transcription quantitative polymerase chain reaction (RT-qPCR), which converts the virus RNA to DNA and amplifies the DNA to produce millions of copies.

What diseases can CRISPR detect? ›

Scientists are studying CRISPR for many conditions, including high cholesterol, HIV, and Huntington's disease. Researchers have also used CRISPR to cure muscular dystrophy in mice. Most likely, the first disease CRISPR helps cure will be caused by just one flaw in a single gene, like sickle cell disease.

What is the most controversial use of CRISPR? ›

DNA replacement in human embryos (germline genome therapy) The most controversial usage of CRISPR-Cas9 is the modification of human embryo DNA, or, in other words, its use for germline genome therapy.

Why do we need RNA sequencing? ›

RNA-Seq allows researchers to detect both known and novel features in a single assay, enabling the identification of transcript isoforms, gene fusions, single nucleotide variants, and other features without the limitation of prior knowledge.

Can RNA-seq identify mutations? ›

Detection of mutations by RNA-Seq was more successful for mutations present at a high allelic frequency. Mutations were more often missed by RNA-Seq when present at low frequency or when tested on FNA samples. All TERT mutations were missed by RNA-Seq.

How do you visualize RNA-seq data? ›

Heatmaps are commonly used to visualize RNA-Seq results. They are useful for visualizing the expression of genes across the samples.

Which technique can be used to analyze gene expression? ›

In addition to Northern blot tests and SAGE analyses, there are several other techniques for analyzing gene expression. Most of these techniques, including microarray analysis and reverse transcription polymerase chain reaction (RT-PCR), work by measuring mRNA levels.

What method is used is used to identify gene expression of multiple genes to compare between samples such as tumor vs healthy patient samples using DNA probes? ›

RNA sequencing (RNA-Seq), which directly sequences and counts the mRNA molecules in the whole transcriptome, can be selected to measure gene expression to detect genomic changes in disease states.

What is the purpose of microarray analysis? ›

Microarray helps in analyzing large amount of samples which have either been recorded previously or new samples; it even helps to test the incidence of a particular marker in tumors.

What do the results from a microarray tell you? ›

A microarray is a special genetic test that looks in detail at a person's chromosomes to see if there are any extra or missing sections which might account for problems they have been experiencing.

What is the advantage of measuring gene expression using microarray method? ›

Microarray experiments allow description of genome-wide expression changes in health and disease. The results of such experiments are expected to change the methods employed in the diagnosis and prognosis of disease in obstetrics and gynecology.

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