Studying the O-GlcNAcome of human placentas using banked tissue samples

O-GlcNAcylation is a dynamic modulator of signaling pathways, equal in magnitude to the widely studied phosphorylation. With the rapid development of tools for its detection at the single protein level, the O-GlcNAc modification rapidly emerged as a novel diagnostic and therapeutic target in human diseases. Yet, mapping the human O-GlcNAcome in various tissues is essential for generating relevant biomarkers. In this study, we used human banked tissue as a sample source to identify O-GlcNAcylated protein targets relevant to human diseases. Using human term placentas, we propose (1) a method to clean frozen banked tissue of blood proteins; (2) an optimized protocol for the enrichment of O-GlcNAcylated proteins using immunoaffinity purification; and (3) a bioinformatic workflow to identify the most promising O-GlcNAc targets. As a proof-of-concept, we used 45 mg of banked placental samples from two pregnancies to generate intracellular protein extracts depleted of blood protein. Then, antibody-based O-GlcNAc enrichment on denatured samples yielded over 2000 unique HexNAc PSMs and 900 unique sites using 300 μg of protein lysate. Due to efficient sample cleanup, we also captured 82 HexNAc proteins with high placental expression. Finally, we provide a bioinformatic tool (CytOVS) to sort the HexNAc proteins based on their cellular localization and extract the most promising O-GlcNAc targets to explore further. To conclude, we provide a simple 3-step workflow to generate a manageable list of O-GlcNAc proteins from human tissue and improve our understanding of O-GlcNAcylation's role in health and diseases.

Gene-gene interaction network analysis indicates CNTN2 is a candidate gene for idiopathic generalized epilepsy

Twin and family studies have established the genetic contribution to idiopathic generalized epilepsy (IGE). The genetic architecture of IGE is generally complex and heterogeneous, and the majority of the genetic burden in IGE remains unsolved. We hypothesize that gene-gene interactions contribute to the complex inheritance of IGE. CNTN2 (OMIM* 615,400) variants have been identified in cases with familial adult myoclonic epilepsy and other epilepsies. To explore the gene-gene interaction network in IGE, we took the CNTN2 gene as an example and investigated its co-occurrent genetic variants in IGE cases. We performed whole-exome sequencing in 114 unrelated IGE cases and 296 healthy controls. Variants were qualified with sequencing quality, minor allele frequency, in silico prediction, genetic phenotype, and recurrent case numbers. The STRING_TOP25 gene interaction network analysis was introduced with the bait gene CNTN2 (denoted as A). The gene-gene interaction pair mode was presumed to be A + c, A + d, A + e, with a leading gene A, or A + B + f, A + B + g, A + B + h, with a double-gene A + B, or other combinations. We compared the number of gene interaction pairs between the case and control groups. We identified three pairs in the case group, CNTN2 + PTPN18, CNTN2 + CNTN1 + ANK2 + ANK3 + SNTG2, and CNTN2 + PTPRZ1, while we did not discover any pairs in the control group. The number of gene interaction pairs in the case group was much more than in the control group (p = 0.021). Taking together the genetic bioinformatics, reported epilepsy cases, and statistical evidence in the study, we supposed CNTN2 as a candidate pathogenic gene for IGE. The gene interaction network analysis might help screen candidate genes for IGE or other complex genetic disorders.

Identification of Protein-Protein Interaction Associated Functions Based on Gene Ontology

Protein-protein interactions (PPIs) involve the physical or functional contact between two or more proteins. Generally, proteins that can interact with each other always have special relationships. Some previous studies have reported that gene ontology (GO) terms are related to the determination of PPIs, suggesting the special patterns on the GO terms of proteins in PPIs. In this study, we explored the special GO term patterns on human PPIs, trying to uncover the underlying functional mechanism of PPIs. The experimental validated human PPIs were retrieved from STRING database, which were termed as positive samples. Additionally, we randomly paired proteins occurring in positive samples, yielding lots of negative samples. A simple calculation was conducted to count the number of positive samples for each GO term pair, where proteins in samples were annotated by GO terms in the pair individually. The similar number for negative samples was also counted and further adjusted due to the great gap between the numbers of positive and negative samples. The difference of the above two numbers and the relative ratio compared with the number on positive samples were calculated. This ratio provided a precise evaluation of the occurrence of GO term pairs for positive samples and negative samples, indicating the latent GO term patterns for PPIs. Our analysis unveiled several nuclear biological processes, including gene transcription, cell proliferation, and nutrient metabolism, as key biological functions. Interactions between major proliferative or metabolic GO terms consistently correspond with significantly reported PPIs in recent literature.

Insights into amyloid precursor protein target through PPI network analysis

Alzheimer's disease (AD) is the leading cause of dementia worldwide with therapeutic lacunae till date. The beta-amyloid (Aβ) accumulation triggers AD pathogenesis, though clinical trials lowering Aβ have not altered disease outcomes suggesting other interacting factors to be identified for drug design of AD. Therefore, it is of interest to identify potential hub proteins interlinked with disease-driving pathways using a network-based approach for AD therapeutic designing. Literature mining was done to identify proteins implicated in AD etiology. Protein-protein interactions (PPIs) were retrieved from the STRING database and merged into a single network using Cytoscape 3.10.1. The hub proteins involved in AD etiology were predicted based on the topological algorithms of CytoHubba. Six major proteins, with STRING database identifiers - APP, BACE1, PSEN1, MAPT, APOE4 and TREM2, were identified to be involved in AD pathogenesis. The merged network of PPIs of these proteins contained 51 nodes and 211 edges, as predicted by Analyzer module of Cytoscape. The Amyloid precursor protein (APP) emerged as the highest-scoring hub protein across multiple centrality measures and topological algorithms. Thus, current data provides evidence to support the ongoing investigation of APP's multifaceted functions and therapeutic potential for AD.

In silico identification of human microRNAs pointing centrin genes in Leishmania donovani: Considering the RNAi-mediated gene control

Leishmaniasis, a parasitic disease caused by different species of the protozoa parasite Leishmania, is a neglected tropical human disease that is endemic in about a hundred countries worldwide. According to the World Health Organization (WHO), the annual incidence of cutaneous leishmaniasis (CL) is estimated to be 0.7-1.2 million cases globally, whereas the annual incidence of visceral leishmaniasis is estimated to be 0.2-0.4 million cases. In many eukaryotic organisms, including human beings and protozoan parasites, centrin genes encode proteins that play essential roles within the centrosome or basal body. Human microRNAs (miRNAs) have been linked to several infectious and non-infectious diseases associated with pathogen-host interactions, and they play the emphatic roles as gene expression regulators. In this study, we used the MirTarget bioinformatics tool, which is a machine learning-based approach implemented in miRDB, to predict the target of human miRNAs in Leishmania donovani centrin genes. For cross-validation, we utilized additional prediction algorithms, namely, RNA22 and RNAhybrid, targeting all five centrin isotypes. The centrin-3 (LDBPK_342160) and putative centrin-5 (NC_018236.1) genes in L. donovani were targeted by eight and twelve human miRNAs, respectively, among 2,635 known miRNAs (miRBase). hsa-miR-5193 consistently targeted both genes. Using TargetScan, TarBase, miRecords, and miRTarBase, we identified miRNA targets and off-targets in human homologs of centrin, inflammation, and immune-responsive genes. Significant targets were screened based on GO terminologies and KEGG pathway-enrichment analysis (Log10 p-value >0.0001). In silico tools that predict the biological roles of human miRNAs as primary gene regulators in pathogen-host interactions help unravel the regulatory patterns of these miRNAs, particularly in the early stages of inflammatory responses. It is also noted that these miRNAs played an important role in the late phase of adaptive immune response, inclusively their impacts on the immune system's response to L. donovani.

An approach for searching genes in signaling pathways or gene-gene interaction networks related to Hypertension in the Mexican population

INTRODUCTION

The selection of single nucleotide polymorphisms (SNPs) to evaluate the genetic susceptibility in complex traits is often conducted in isolation, without considering the entire set of genes. Incorporating signaling pathways or gene-gene interaction search may provide a more comprehensive approach to selecting SNP candidates for further study.

OBJECTIVE

To propose a systematic procedure for identifying SNPs candidates with complex traits such as hypertension and blood pressure.

METHODS

Sequential stages to SNPs selection: 1) literature review to identify SNPs, following the PRISMA methodology, 2) identification and selection of signaling pathways and selection of gene-gene interaction networks using the STRING software, and 3) application of specific criteria for SNPs candidates, including: a) SNPs with minor allele frequency > 5% in the target population, b) SNPs located within genes involved in three or more signaling pathways, and c) SNPs that are not in linkage disequilibrium, with a D'or r2 value < 0.8.

RESULTS

Stage 1) A total of 44 publications were selected, providing information on 230 genes evaluated with blood pressure. Stage 2) Using the STRING software, we selected 7 signaling pathways with a false discovery rate < 0.0001 and strength ≥ 0.8; and we identified 16 genes belonging to gene-gene interaction networks, six of them share ≥ 3 signaling pathways. Stage 3) Finally, 7 SNPs were selected for genotyping in the Health Workers Cohort Study. We observed a positive association between SNPs with hypertension incidence in males (rs1130214, rs3807989) and females (rs5051, rs2493123).

CONCLUSION

Our methodological proposal may be a reliable way for selecting SNPs candidates to study complex traits.

Network pharmacology and molecular dynamic simulation integrated strategy for the screening of active components and mechanisms of phytochemicals from Datura innoxia on Alzheimer and cognitive decline

Alzheimer's disease (AD) ranks as the most prevalent neurodegenerative disorder with dementia and it accounts for more than 70% of all cases. Despite extensive reporting on the experimental investigation of Datura innoxia (DI) and its phytochemical components in the treatment of AD, the urgent need for elucidation of the principle of multi-mechanism and multi-level treatment of AD remains. In this research, molecular docking and network pharmacology were used to evaluate active compounds and molecular targets of DI for the treatment of AD. The phytochemical compounds of DI were obtained from the Indian Medicinal Plants, Phytochemistry, and Therapeutics (IMPPAT) as well as the Traditional Chinese Medicine System Pharmacology (TCMSP) databases. The screening includes the 28 most abundant components of DI and the Swiss Target Prediction database was used to predict targets of these compounds. The GeneCards database was used to collect AD-related genes. Both DI and AD targets were imported into a Venn diagram, and the 28 overlapped genes were identified as potential DI anti-AD targets. The results showed that Dinoxin B, Meteloidine, Scopoline, and Tropic acid had no effect on AD-related genes. Furthermore, the GO enrichment analysis indicates that DI influences molecular functions and biological processes such as learning or memory and modulation of chemical synaptic transmission as well as the membrane raft and membrane microdomain. The KEGG pathway analysis revealed that the key pathways implicated in DI's anti-AD actions include serotonergic synapse, IL-17 signaling pathway, and AGE-RAGE signaling pathway in diabetic complications. Based on the STRING and Cytoscape network-analysis platforms, the top ten anti-AD core targets include APP, CASP3, IL6, BACE1, IL1B, ACE, PSEN1, GAPDH, GSK3B and ACHE. The molecular docking and molecular dynamic simulation of the top two molecules against the top three target proteins confirmed the strong binding affinity and stability at the docked site. Overall, our findings pave the path for further research into the development and optimization of potential anti-AD agents from DI.Communicated by Ramaswamy H. Sarma.

The mechanisms underlying alcohol-induced decreased splenic size: A network meta-analysis study

BACKGROUND

Organ weight change is widely accepted as a measure of toxicologic pathology. We and other groups have shown that excessive alcohol exposure leads to decreased spleen weight in rodents. This study explores the mechanisms underlying alcohol-induced splenic injury through a network meta-analysis.

METHODS

QIAGEN Ingenuity Pathway Analysis (IPA) and Mammalian Phenotype (MP) Ontology were used to identify alcohol-related molecules associated with the small spleen phenotype. Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and IPA bioinformatics tools were then used to analyze the biologic processes and enriched signaling pathways engaging these molecules. In addition, the "downstream effects analysis" algorithm was used to quantify alcohol's effects.

RESULTS

IPA identified 623 molecules affected by alcohol and a Venn diagram revealed that 26 of these molecules overlapped with those associated with the MP Ontology of small spleen. The 26 molecules are TGFB1, CASP8, MTOR, ESR1, CXCR4, CAMK4, NFKBIA, DRD2, BCL2, FAS, PEBP1, TRAF2, ATM, IGHM, EDNRB, MDM2, GLRA1, PRF1, TLR7, IFNG, ALOX5, FOXO1, IL15, APOE, IKBKG, and RORA. Some of the 26 molecules were also associated with the MP Ontology of abnormal white pulp and red pulp morphology of the spleen, abnormal splenic cell ratio, decreased splenocyte number, abnormal spleen physiology, increased splenocyte apoptosis, and reduced splenocyte proliferation. STRING and IPA "Core Analysis" showed that these molecules were mainly involved in pathways related to cell apoptosis, proliferation, migration, and immune responses. IPA's "Molecular Activity Predictor" tool showed that concurrent effects of activation and inhibition of these molecules led to decreased spleen size by modulating apoptosis, proliferation, and migration of splenocytes.

CONCLUSIONS

Our network meta-analysis revealed that excessive alcohol exposure can damage the spleen through a variety of molecular mechanisms, thereby affecting immune function and human health. We found that alcohol-mediated splenic atrophy is largely mediated by increased apoptosis signaling, migration of cells, and inhibition of splenocyte proliferation.

Insights into molecular docking and dynamics to reveal therapeutic potential of natural compounds against P53 protein

P53 is eminent tumour suppressor protein that plays a prominent role in cell cycle arrest, DNA repair, senescence, differentiation and initiation of apoptosis. P53 is an attractive drug target and the high toxicity of some cancer chemotherapy drugs increase the demand for new anti-cancer drugs from natural products. In this current scenario, identification of promising anticancer compounds from natural sources by repurposing approach is still relevant for the early prevention and effective management of cancer. In present study, we docked natural compounds like podophyllotoxin, quercetin and rutin along standard drugs (MG-132 and Bay 61-3606) against p53 protein. ADME/T analysis predicted toxicity of phytochemicals and drugs. In silico docking analysis of podophyllotoxin, quercetin and rutin gave HDOCK docking scores of -187.87, -148. 97 and -143.85, whereas control drugs MG-132 and Bay 61-3606 showed docking scores of -159.59 and -140.71 against p53 respectively. AutoDock analysis of rutin and MG-132 showed highest binding affinity scores of -7.3 and -6.8 kcal/mol against p53. Molecular dynamic simulation for p53 protein displayed stable conformation and convergence. In this study, P53-rutin complex showed free binding energy score of 11.84 kcal/mol and P53-MG-132 complex reported free energy score of 16.3 kcal/mol. Protein contacts atlas gives non-covalent contacts framework by exploring interfaces of individual subunits and protein-ligand interactions. STRING tool predicts physical and functional interactions between proteins. The results of this study revealed that rutin and MG-132 could be promising inhibitors against targeted p53 protein and this could prove detrimental for molecular therapeutics and drug-designing strategies.Communicated by Ramaswamy H. Sarma.

CREB-Regulated Transcriptional Coactivator 2 Proteome Landscape is Modulated by SREBF1

cAMP response element-binding protein (CREB) regulated transcriptional coactivator 2 (CRTC2) is a critical transcription factor that maintains glucose homeostasis by activating CREB. Energy homeostasis is maintained through multiple pathways; therefore, CRTC2 may interact with other transcription factors, particularly under metabolic stress. CRTC2 liver-specific KO mice were created, and the global proteome, phosphoproteome, and acetylome from liver tissue under high-fat diet conditions were analyzed using liquid chromatography-tandem mass spectrometry and bioinformatics analysis. Differentially regulated proteins (DRPs) were enriched in metabolic pathways, which were subsequently corroborated through animal experiments. The consensus DRPs from these datasets were used as seed proteins to generate a protein-protein interaction network using STRING, and GeneMANIA identified fatty acid synthase as a mutually relevant protein. In an additional local-protein-protein interaction analysis of CRTC2 and fatty acid synthase with DRPs, sterol regulatory element binding transcription factor 1 (SREBF1) was the common mediator. CRTC2-CREB and SREBF1 are transcription factors, and DNA-binding motif analysis showed that multiple CRTC2-CREB-regulated genes possess SREBF1-binding motifs. This indicates the possible induction by the CRTC2-SREBF1 complex, which is validated through luciferase assay. Therefore, the CRTC2-SREBF1 complex potentially modulates the transcription of multiple proteins that fine-tune cellular metabolism under metabolic stress.

Evaluation of protein's interaction and the regulatory network of some drought-responsive genes in Canola under drought and re-watering conditions

Drought stress is one of the most important environmental stresses that severely limits the growth and yield of Canola. The re-watering can compensate for the damage caused by drought stress. Investigation of protein's interaction of genes involved in important drought-responsive pathways and their regulatory network by microRNAs (miRNAs) under drought and re-watering conditions are helpful approaches to discovering drought-stress tolerance and recovery mechanisms. In this study, the protein's interaction and functional enrichment analyses of glycolysis, pentose phosphate, glyoxylate cycle, fatty acid biosynthesis, heat shock factor main genes, and the regulatory network of key genes by miRNAs were investigated by in silico analysis. Then, the relative expression of key genes and their related miRNAs were investigated in tolerant and susceptible genotypes of Canola under drought and re-watering conditions by Real-time PCR technique. The bna-miR156b/c/g, bna-miR395d/e/f, bna-miR396a, and all the studied key genes except HSFA1E and PK showed changes in expression levels in one or both genotypes after re-watering. The PPC1 and HSFB2B expression decreased, whereas the MLS and CAC3 expression increased in both genotypes under re-watering treatment after drought stress. It could cause the regulation of oxaloacetate production, the increase of the glyoxylate cycle, lipid biosynthesis, and the reduction of the negative regulation of HSFs under re-watering conditions. It seems that PPC1, G6PD2, MLS, CAC3, and HSFB2B were involved in the recovery mechanisms after drought stress of Canola. They were regulated by drought-responsive miRNAs to respond appropriately to drought stress. Therefore, regulating these genes could be important in plant recovery mechanisms.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12298-023-01345-1.

In silico analysis and molecular docking studies of natural compounds of Withania somnifera against bovine NLRP9

CONTEXT

NLRP9 is a member of nucleotide-binding domain leucine-rich repeat-containing receptors and is found to be associated with many inflammatory diseases. In the current scenario, the identification of promising anti-inflammatory compounds from natural sources by repurposing approach is still relevant for the early prevention and effective management of the disease.

METHODS

In the present study, we docked bioactives of Ashwagandha (Withanoside IV, Withanoside V, Withanolide A, Withanolide B, and Sitoindoside IX) and two control drugs against bovine NLRP9 protein. ADME/T analysis was used to determine the physiochemical properties of compounds and standard drugs. Molecular modeling was used to evaluate the correctness and quality of protein structures. In silico docking analysis revealed Withanolide B had the highest binding affinity score of -10.5 kcal/mol, whereas, among control drugs, doxycycline hydrochloride was most effective (-10.3 kcal/mol). The results of this study revealed that bioactives of Withania somnifera could be promising inhibitors against bovine NLRP9. In the present study, molecular simulation was used to measure protein conformational changes over time. The Rg value was found to be 34.77A°. RMSD and B-factor were also estimated to provide insights into the flexibility and mobile regions of protein structure. A functional protein network interaction was constructed from information collected from non-curative sources as protein-protein interactions (PPI) that play an important role in determining the function of the target protein and the ability of the drug molecule. Thus, in the present situation, it is important to identify bioactives with the potential to combat inflammatory diseases and provide strength and immunity to the host. However, there is still a need to study in vitro and in vivo to further support these findings.

Data-independent acquisition mass spectrometry reveals comprehensive plasma protein profiles in the natural history of patients with hereditary transthyretin amyloidosis (ATTRv)

OBJECTIVES

Hereditary transthyretin amyloidosis (ATTRv) is a rare, fatal, autosomal dominant disease with more than 140 mutations discovered. Three phenotypes of amyloid infiltration are neuropathy (ATTRv-PN), cardiopathy (ATTRv-CM), and neuropathy + cardiopathy (ATTRv-MIX). The lack of ATTR-specific biomarkers, difficulties in biopsy evidence, and limited known pathogenic mechanisms have made diagnosis difficult. Newly emerging noninvasive measures for monitoring progression and disease-modifying therapies have improved early diagnosis and patient management.

METHODS

Our research applies the latest technology, Data-Independent Acquisition-Based Quantitative Proteomics (DIA), to reveal comprehensive plasma protein profiles in the natural history of Chinese patients with hereditary transthyretin amyloidosis (ATTRv). We analyzed differentially expressed proteins (DEPs) in three phenotypes (ATTRv-PN, ATTRv-CM, and ATTRv-MIX).

RESULTS

Serum samples were collected from a total of 18 patients (6 ATTRv-PN, 5 ATTRv-CM, and 7 ATTRv-MIX patients) and 20 healthy participants as a control group. Combined with the results of the proteomic and bioinformatic analyses, we found 30 DEPs and protein interaction networks clustered in KRT family proteins and DSC3 between ATTRv-PN and the control, which were rich in the estrogen signaling pathway and the cell adhesion molecule (CAM) pathway.

CONCLUSION

This study demonstrates a global and significant proteomic profile in different stages of ATTRv.

The Interactome of Protein, DNA, and RNA

Proteins participate in many processes of the organism and are very important for maintaining the health of the organism. However, proteins cannot function independently in the body. They must interact with proteins, DNA, RNA, and other substances to perform biological functions and maintain the body's health. At present, there are many experimental methods and software tools that can detect and predict the interaction between proteins and other substances. There are also many databases that record the interaction between proteins and other substances. This article mainly describes protein-protein, protein-DNA, and protein-RNA interactions in detail by introducing some commonly used experimental methods, the software tools produced with the accumulation of experimental data and the rapid development of machine learning, and the related databases that record the relationship between proteins and some substances. By this review, we hope that through the analysis and summary of various aspects, it will be convenient for researchers to conduct further research on protein interactions.

Cytoscape stringApp 2.0: Analysis and Visualization of Heterogeneous Biological Networks

Biological networks are often used to represent complex biological systems, which can contain several types of entities. Analysis and visualization of such networks is supported by the Cytoscape software tool and its many apps. While earlier versions of stringApp focused on providing intraspecies protein-protein interactions from the STRING database, the new stringApp 2.0 greatly improves the support for heterogeneous networks. Here, we highlight new functionality that makes it possible to create networks that contain proteins and interactions from STRING as well as other biological entities and associations from other sources. We exemplify this by complementing a published SARS-CoV-2 interactome with interactions from STRING. We have also extended stringApp with new data and query functionality for protein-protein interactions between eukaryotic parasites and their hosts. We show how this can be used to retrieve and visualize a cross-species network for a malaria parasite, its host, and its vector. Finally, the latest stringApp version has an improved user interface, allows retrieval of both functional associations and physical interactions, and supports group-wise enrichment analysis of different parts of a network to aid biological interpretation. stringApp is freely available at https://apps.cytoscape.org/apps/stringapp.

Influence of Padina gymnospora on Apoptotic Proteins of Oral Cancer Cells-a Proteome-Wide Analysis

Oral carcinoma is one of the most vicious forms of cancer with a very low survival rate, as its patients often respond poorly to conventional chemotherapy. Presently several researchers are attempting to pursue an alternative to this therapy using natural products. Considering the promising strategy and induction of apoptosis to target the cancer cells, we evaluated the influence of a seaweed Padina gymnospora (15 µg/ml and 20 µg/ml) in enhancing apoptosis of oral cancer cells (KB-CHR-8-5) after 24-h incubation. The morphological changes indicating apoptosis were primarily assessed using a light microscope after which the apoptosis was confirmed by performing AO/EB staining method. Subsequently, MMP and ROS levels in the cells were assessed using Rh 123 and DCFH-DA staining procedures, respectively. All the above tests confirmed the ability of P. gymnospora to accelerate apoptosis in the oral cancer cells. As a next step, wide proteome analysis was performed where the proteins from P. gymnospora-treated cells were separated using the 2D electrophoresis technique and compared with that of control cells to isolate the differentially expressed proteins. This procedure resulted in the isolation of 10 proteins which were identified using MALDI-TOF/TOF MS, which established that most of the isolated proteins were part of the apoptotic process of the cell. The proteins identified are part of huge and complex pathways where it gets linked with many more genes which are also associated with apoptosis. Bioinformatics of these identified proteins was analyzed using STRING and PANTHER databases. These proteins contribute to cell apoptosis by affecting various functions, biological processes, and the synthesis of cellular components. PANTHER also demonstrated that these proteins belong to the classes of proteins that take part in several vital pathways of the cell among which the apoptotic pathway is the predominant one.

PPARγ Gene as a Possible Link between Acquired and Congenital Lipodystrophy and its Modulation by Dietary Fatty Acids

Lipodystrophy syndromes are rare diseases that could be of genetic or acquired origin. The main complication of lipodystrophy is the dysfunction of adipose tissue, which leads to an ectopic accumulation of triglycerides in tissues such as the liver, pancreas and skeletal muscle. This abnormal fat distribution is associated with hypertriglyceridemia, insulin resistance, liver steatosis, cardiomyopathies and chronic inflammation. Although the origin of acquired lipodystrophies remains unclear, patients show alterations in genes related to genetic lipodystrophy, suggesting that this disease could be improved or aggravated by orchestrating gene activity, for example by diet. Nowadays, the main reason for adipose tissue dysfunction is an imbalance in metabolism, caused in other pathologies associated with adipose tissue dysfunction by high-fat diets. However, not all dietary fats have the same health implications. Therefore, this article aims to summarize the main genes involved in the pathophysiology of lipodystrophy, identify connections between them and provide a systematic review of studies published between January 2017 and January 2022 of the dietary fats that can modulate the development of lipodystrophy through transcriptional regulation or the regulation of protein expression in adipocytes.

Investigation of proteins' interaction network and the expression pattern of genes involved in the ABA biogenesis and antioxidant system under methanol spray in drought-stressed rapeseed

Drought is one of the most critical abiotic stresses, which significantly impair rapeseed (Brassica napus L.) productivity. Several factors can regulate the stress response, including changes in gene expression in biological pathways, extensive protein interaction networks, and post-translational regulatory factors like microRNAs. External factors can also affect the intensity of the stress response. Therefore, this study investigated protein-protein interactions of some essential genes involved in abscisic acid (ABA) production, antioxidant system, and Krebs cycle. The expression of phyton synthase (PSY), 9-cis-epoxycarotenoid dioxygenase (NCED3), aldehyde oxidase (AAO3), thioredoxin reductase (NTRC), and glutathione reductase (GR) genes in two rapeseed genotypes, i.e., Hyola308 (drought-sensitive) and SLM046 (drought-tolerant) were evaluated using qRT-PCR technique under 72 h of drought stress and methanol foliar application. In the SLM046 (tolerant) genotype, the expression levels of PYS, NCED, AAO3, and GR genes were increased after 8 h of foliar application. The expression level of the NTR gene was increased 8 and 24 h after stress and methanol treatment. In the Hyola308 genotype, PYS, AAO3, NTR, and GR genes' expression level was increased 8 h after methanol foliar application, and the NCED gene was increased 24 h after stress with methanol treatment. In general, methanol foliar application increased the expression levels of several genes. Particularly, the gene expression was considerably higher in the SLM046 genotype than in Hyola308. Bioinformatics prediction of microRNAs targeting PSY, NCED, GR, NTRC, and AAO3 genes was performed, and 38, 38, 13, 11, and 11 microRNAs were predicted for these genes, respectively. The study of effective microRNAs showed that sometimes more than one type of microRNA could affect the desired gene, and in some cases, a conserved family of microRNAs caused the main effect on gene expression. Overall, our results lay the foundation for functional characterization of these genes or gene-miRNA modules in regulating drought stress tolerance in rapeseed.

Signaling Pathways Associated with Chronic Wound Progression: A Systems Biology Approach

Previously we have shown that several oxidative stress-driven pathways in cutaneous chronic wounds are dysregulated in the first 48 h post-wounding. Here, we performed an RNASeq analysis of tissues collected up to day 20 after wounding, when we have determined full chronicity is established. Weighted Gene Correlation Network Analysis was performed in R segregating the genes into 14 modules. Genes in the modules significantly correlated (p < 0.05) to early and full chronicity were used for pathway analysis using pathfindR. In early chronicity, we observed enrichment of several pathways. Dysregulation of Ephrin/Eph signaling leads to growth cone collapse and impairs neuronal regeneration. Adra2b and Adra2a overexpression in early and full chronicity, respectively, decreased cAMP production and impaired re-epithelialization and granulation tissue formation. Several pathways involving a Smooth-muscle-actin (Acta1) were also enriched with Acta1 overexpression contributing to impaired angiogenesis. During full chronicity, the ‘JAK-STAT’ pathway was suppressed undermining host defenses against infection. Wnt signaling was also suppressed, impairing re-epithelialization and granulation tissue formation. Biomarkers of cancer such as overexpression of SDC1 and constitutive activation of ErbB2/HER2 were also identified. In conclusion, we show that during progression to full chronicity, numerous signaling pathways are dysregulated, including some related to carcinogenesis, suggesting that chronic wounds behave much like cancer. Experimental verification in vivo could identify candidates for treatment of chronic wounds.

Bioinformatics of Differentially Expressed Genes in Phorbol 12-Myristate 13-Acetate-Induced Megakaryocytic Differentiation of K562 Cells by Microarray Analysis

Megakaryocytes are large hematopoietic cells present in the bone marrow cavity, comprising less than 0.1% of all bone marrow cells. Despite their small number, megakaryocytes play important roles in blood coagulation, inflammatory responses, and platelet production. However, little is known about changes in gene expression during megakaryocyte maturation. Here we identified the genes whose expression was changed during K562 leukemia cell differentiation into megakaryocytes using an Affymetrix GeneChip microarray to determine the multifunctionality of megakaryocytes. K562 cells were differentiated into mature megakaryocytes by treatment for 7 days with phorbol 12-myristate 13-acetate, and a microarray was performed using RNA obtained from both types of cells. The expression of 44,629 genes was compared between K562 cells and mature megakaryocytes, and 954 differentially expressed genes (DEGs) were selected based on a p-value < 0.05 and a fold change >2. The DEGs was further functionally classified using five major megakaryocyte function-associated clusters—inflammatory response, angiogenesis, cell migration, extracellular matrix, and secretion. Furthermore, interaction analysis based on the STRING database was used to generate interactions between the proteins translated from the DEGs. This study provides information on the bioinformatics of the DEGs in mature megakaryocytes after K562 cell differentiation.

Network analysis of atherosclerotic genes elucidates druggable targets

Background

Atherosclerosis is one of the major causes of cardiovascular disease. It is characterized by the accumulation of atherosclerotic plaque in arteries under the influence of inflammatory responses, proliferation of smooth muscle cell, accumulation of modified low density lipoprotein. The pathophysiology of atherosclerosis involves the interplay of a number of genes and metabolic pathways. In traditional translation method, only a limited number of genes and pathways can be studied at once. However, the new paradigm of network medicine can be explored to study the interaction of a large array of genes and their functional partners and their connections with the concerned disease pathogenesis. Thus, in our study we employed a branch of network medicine, gene network analysis as a tool to identify the most crucial genes and the miRNAs that regulate these genes at the post transcriptional level responsible for pathogenesis of atherosclerosis.

Result

From NCBI database 988 atherosclerotic genes were retrieved. The protein–protein interaction using STRING database resulted in 22,693 PPI interactions among 872 nodes (genes) at different confidence score. The cluster analysis of the 872 genes using MCODE, a plug-in of Cytoscape software revealed a total of 18 clusters, the topological parameter and gene ontology analysis facilitated in the selection of four influential genes viz., AGT, LPL, ITGB2, IRS1 from cluster 3. Further, the miRNAs (miR-26, miR-27, and miR-29 families) targeting these genes were obtained by employing MIENTURNET webtool.

Conclusion

Gene network analysis assisted in filtering out the 4 probable influential genes and 3 miRNA families in the pathogenesis of atherosclerosis. These genes, miRNAs can be targeted to restrict the occurrence of atherosclerosis. Given the importance of atherosclerosis, any approach in the understanding the genes involved in its pathogenesis can substantially enhance the health care system.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-022-01195-y.

Analyzing large scale gene expression data in colorectal cancer reveals important clues; CLCA1 and SELENBP1 downregulated in CRC not in normal and not in adenoma

Early detection of colorectal cancer (CRC) increases the chances of survival and reduces the therapeutic problems and costs of treatment. Since molecular biomarkers can help us diagnose colorectal cancer early, we need to identify novel gene for predicting the early stages of tumorigenesis. Here, we integrated five independent CRC gene expression datasets derived from expression profiling by array comparing CRC with normal samples in: GSE21510, GSE4107, GSE25071, GSE15781 dataset, and GSE8671 dataset, including 64 samples from 32 patients comparing 32 colonic normal mucosa with 32 colorectal adenoma. To detect genes that expressed differentially in experimental circumstances of these datasets, we used web tool of GEO2R to compare groups of samples in the GEO data series. Furthermore, we constructed the protein-protein interactions network by STRING database for mostly downregulated genes and the expression of their members in PPI network were studied into five datasets separately. Also, the level of expression of selected biomarker genes in different stages of CRC compared to normal was studied. Our data revealed 17 common downregulated genes (average fold change (FC) in five tests ≥6) in CRC in comparison with normal (Test 1 to Test 4) and in adenoma compared with normal (Test 5). Studying of gene expression of PPI network members of these downregulated genes led to identifying of CLCA1, SELENBP1, CWC25, ACOT11, GUCY2C and ALDH1A1 as suppressor genes and PTGS2, PROCR, MOCS3 and NFS1 as oncogenes which respectively downregulated and upregulated in CRC. Since decreasing of gene expression was seen in CRC comparing with normal and due to no different expression seen for these 10 genes in adenoma, they, especially CLCA1 and SELENBP1, could be considered as biomarkers for early detection of CRC. Before using these signature genes in the clinic; however, further validations are required.

Analysis of protein association networks regulating the neuroactive metabolites production in Lactobacillus species

Human population is intensively suffering from mental disorders and stress. Microbial metabolites may alter the brain activity, which seems to be an effective approach in the treatment of psychological distress. Earlier, microbial neuroactive metabolites such as trimethylamine, imidazolone propionate and taurine have been shown to alter the brain activity. In the present study proteins regulating their production and activity were explored in Lactobacillus species with the help of STRING (11.5) as a bioinformatic tool. Dataset network of urocanate hydratase, glycine radical enzyme and taurine ABC transporter protein (ATP-dependent transporter) have been identified in Lactobacillus nodensis, Lactobacillus vini and Lactobacillus paraplantarum strains. Further, cluster analysis of network resulted with groups of homologous proteins that most likely related to reductive monocarboxylic acid cycle, pyruvate fermentation to acetate IV and L-histidine degradation I pathway. The findings emphasize on the use and evaluation of selected Lactobacillus strains as psychoactive bacteria for the prevention and treatment of certain neurological and neurophysiological conditions.

Proteomic analysis of the colistin-resistant E. coli clinical isolate: Explorations of the resistome

BACKGROUND

Antimicrobial resistance is a worldwide problem after the emergence of colistin resistance since it was the last option left to treat carbapenemase-resistant bacterial infections. The mcr gene and its variants are one of the causes for colistin resistance. Besides mcr genes, some other intrinsic genes are also involved in colistin resistance but still need to be explored.

OBJECTIVE

The aim of this study was to investigate differential proteins expression of colistin-resistant E. coli clinical isolate and to understand their interactive partners as future drug targets.

METHODS

In this study, we have employed the whole proteome analysis through LC-MS/MS. The advance proteomics tools were used to find differentially expressed proteins in the colistin-resistant Escherichia coli clinical isolate compared to susceptible isolate. Gene ontology and STRING were used for functional annotation and protein-protein interaction networks, respectively.

RESULTS

LC-MS/MS analysis showed overexpression of 47 proteins and underexpression of 74 proteins in colistin-resistant E. coli. These proteins belong to DNA replication, transcription and translational process; defense and stress related proteins; proteins of phosphoenol pyruvate phosphotransferase system (PTS) and sugar metabolism. Functional annotation and protein-protein interaction showed translational and cellular metabolic process, sugar metabolism and metabolite interconversion.

CONCLUSION

We conclude that these protein targets and their pathways might be used to develop novel therapeutics against colistin-resistant infections. These proteins could unveil the mechanism of colistin resistance.

Clinically Relevant Genes and Proteins Modulated by Tocotrienols in Human Colon Cancer Cell Lines: Systematic Scoping Review

The last decade has witnessed tremendous growth in tocotrienols (T3s) research, especially in the field of oncology, owing to potent anticancer property. Among the many types of cancers, colorectal cancer (CRC) is growing to become a serious global health threat to humans. Chemoprevention strategies in recent days are open to exploring alternative interventions to inhibit or delay carcinogenesis, especially with the use of bioactive natural compounds, such as tocotrienols. This scoping review aims to distil the large bodies of literature from various databases to identify the genes and their encoded modulations by tocotrienols and to explicate important mechanisms via which T3s combat CRC. For this scoping review, research papers published from 2010 to early 2021 related to T3s and human CRC cells were reviewed in compliance with the PRISMA guidelines. The study included research articles published in English, searchable on four literature databases (Ovid MEDLINE, PubMed, Scopus, and Embase) that reported differential expression of genes and proteins in human CRC cell lines following exposure to T3s. A total of 12 articles that fulfilled the inclusion and exclusion criteria of the study were short-listed for data extraction and analysis. The results from the analysis of these 12 articles showed that T3s, especially its γ and δ analogues, modulated the expression of 16 genes and their encoded proteins that are associated with several important CRC pathways (apoptosis, transcriptional dysregulation in cancer, and cancer progression). Further studies and validation work are required to scrutinize the specific role of T3s on these genes and proteins and to propose the use of T3s to develop adjuvant or multi-targeted therapy for CRC.

GWAS Meta-Analysis Reveals Shared Genes and Biological Pathways between Major Depressive Disorder and Insomnia

Major depressive disorder (MDD) is one of the most prevalent and disabling mental disorders worldwide. Among the symptoms of MDD, sleep disturbance such as insomnia is prominent, and the first reason patients may seek professional help. However, the underlying pathophysiology of this comorbidity is still elusive. Recently, genome-wide association studies (GWAS) have begun to unveil the genetic background of several psychiatric disorders, including MDD and insomnia. Identifying the shared genomic risk loci between comorbid psychiatric disorders could be a valuable strategy to understanding their comorbidity. This study seeks to identify the shared genes and biological pathways between MDD and insomnia based on their shared genetic variants. First, we performed a meta-analysis based on the GWAS summary statistics of MDD and insomnia obtained from Psychiatric Genomics Consortium and UK Biobank, respectively. Next, we associated shared genetic variants to genes using two gene mapping strategies: (a) positional mapping based on genomic proximity and (b) expression quantitative trait loci (eQTL) mapping based on gene expression linkage across multiple tissues. As a result, a total of 719 shared genes were identified. Over half (51%) of them are protein-coding genes. Functional enrichment analysis shows that the most enriched biological pathways are related to epigenetic modification, sensory perception, and immunologic signatures. We also identified druggable targets using a network approach. Together, these results may provide insights into understanding the genetic predisposition and underlying biological pathways of comorbid MDD and insomnia symptoms.

TCF19 Impacts a Network of Inflammatory and DNA Damage Response Genes in the Pancreatic β-Cell

Transcription factor 19 (TCF19) is a gene associated with type 1 diabetes (T1DM) and type 2 diabetes (T2DM) in genome-wide association studies. Prior studies have demonstrated that Tcf19 knockdown impairs β-cell proliferation and increases apoptosis. However, little is known about its role in diabetes pathogenesis or the effects of TCF19 gain-of-function. The aim of this study was to examine the impact of TCF19 overexpression in INS-1 β-cells and human islets on proliferation and gene expression. With TCF19 overexpression, there was an increase in nucleotide incorporation without any change in cell cycle gene expression, alluding to an alternate process of nucleotide incorporation. Analysis of RNA-seq of TCF19 overexpressing cells revealed increased expression of several DNA damage response (DDR) genes, as well as a tightly linked set of genes involved in viral responses, immune system processes, and inflammation. This connectivity between DNA damage and inflammatory gene expression has not been well studied in the β-cell and suggests a novel role for TCF19 in regulating these pathways. Future studies determining how TCF19 may modulate these pathways can provide potential targets for improving β-cell survival.

Molecular Human Targets of Bioactive Alkaloid-Type Compounds from Tabernaemontana cymose Jacq

Alkaloids are a group of secondary metabolites that have been widely studied for the discovery of new drugs due to their properties on the central nervous system and their anti-inflammatory, antioxidant and anti-cancer activities. Molecular docking was performed for 10 indole alkaloids identified in the ethanol extract of Tabernaemontana cymosa Jacq. with 951 human targets involved in different diseases. The results were analyzed through the KEGG and STRING databases, finding the most relevant physiological associations for alkaloids. The molecule 5-oxocoronaridine proved to be the most active molecule against human proteins (binding energy affinity average = −9.2 kcal/mol) and the analysis of the interactions between the affected proteins pointed to the PI3K/ Akt/mTOR signaling pathway as the main target. The above indicates that indole alkaloids from T. cymosa constitute a promising source for the search and development of new treatments against different types of cancer.

The Participation of the Intrinsically Disordered Regions of the bHLH-PAS Transcription Factors in Disease Development

The basic helix–loop–helix/Per-ARNT-SIM (bHLH-PAS) proteins are a family of transcription factors regulating expression of a wide range of genes involved in different functions, ranging from differentiation and development control by oxygen and toxins sensing to circadian clock setting. In addition to the well-preserved DNA-binding bHLH and PAS domains, bHLH-PAS proteins contain long intrinsically disordered C-terminal regions, responsible for regulation of their activity. Our aim was to analyze the potential connection between disordered regions of the bHLH-PAS transcription factors, post-transcriptional modifications and liquid-liquid phase separation, in the context of disease-associated missense mutations. Highly flexible disordered regions, enriched in short motives which are more ordered, are responsible for a wide spectrum of interactions with transcriptional co-regulators. Based on our in silico analysis and taking into account the fact that the functions of transcription factors can be modulated by posttranslational modifications and spontaneous phase separation, we assume that the locations of missense mutations inducing disease states are clearly related to sequences directly undergoing these processes or to sequences responsible for their regulation.

Identification of key pathways and genes responsible for aggressive behavior

Aggression is a complex behavior, underpinned by cross talk between several biomolecules. To date a composite molecular network of the behavioral disorder has not been constructed. The present study aims to develop the same from the system network analyses recruiting genes with empirical evidence demonstrating their role in the incidence and progression of aggression. In short, 327 genes were recruited in the study after extensive literature survey and subsequent shortlisting by sieving out the comorbidities like cancer and other pathological and physiological ailments, other languages and repeated citations. Subsequent String network analysis coalesces 275 genes in a network with 2223 edges. The developed network was then subjected to delineate modules using MCODE which via gene clustering on the basis of gene ontology segregate all genes into 14 modules. Of these, as expected top 5 modules involved entailing of neuronal signaling pathways with redundant repetitions. Finally, 10 genes (known) were picked randomly, accounting average module size, and subjected to the network analysis with 100,000 bootstrap replicates. This results in the detection of certain novel genes that lacks empirical evidence for their association with the aggression. Amongst those, most notable are genes involved in protein turnover regulation like UBC, UBA, mitogenic proteins such as Rho and Myc, transcription factors like Tp53. The findings in turn fill caveats in the molecular resolution of cross talk that underscore the development of aggressive behavior and may then be exploited as screening biomarker and/or therapeutic intervention for aggression.

STRING data mining of GWAS data in canine hereditary pigment-associated deafness

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Genome-wide association studies may fail to identify significant associations between a disorder and causative genes in complex hereditary disorders.

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STRING software is a bioinformatics data mining tool that identifies known and predicted physical and functional relationship networks among the proteins of candidate genes.

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STRING analysis provides a mechanism to identify gene-gene interactions that might not otherwise have been recognized.

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Relationships identified from STRING analysis can uncover function-based gene-gene relationships that may not be easily extracted from literature, thereby providing genes for pursuit as a cause of a complex hereditary disorder.

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In this study STRING analysis was applied to identification of candidate genes to pursue as the cause of pigment-associated hereditary deafness in dogs.

Most canine deafness is linked to white pigmentation caused by the piebald locus, shown to be the gene MITF (melanocyte inducing transcription factor), but studies have failed to identify a deafness cause. The coding regions of MITF have not been shown to be mutated in deaf dogs, leading us to pursue genes acting on or controlled by MITF. We have genotyped DNA from 502 deaf and hearing Australian cattle dogs, Dalmatians, and English setters, breeds with a high deafness prevalence. Genome-wide significance was not attained in any of our analyses, but we did identify several suggestive associations. Genome-wide association studies (GWAS) in complex hereditary disorders frequently fail to identify causative gene variants, so advanced bioinformatics data mining techniques are needed to extract information to guide future studies.

STRING diagrams are graphical representations of known and predicted networks of protein-protein interactions, identifying documented relationships between gene proteins based on the scientific literature, to identify functional gene groupings to pursue for further scrutiny. The STRING program predicts associations at a preset confidence level and suggests biological functions based on the identified genes.

Starting with (1) genes within 500 kb of GWAS-suggested SNPs, (2) known pigmentation genes, (3) known human deafness genes, and (4) genes identified from proteomic analysis of the cochlea, we generated STRING diagrams that included these genes. We then reduced the number of genes by excluding genes with no relationship to auditory function, pigmentation, or relevant structures, and identified clusters of genes that warrant further investigation.

Defense Response in Chickpea Pod Wall due to Simulated Herbivory Unfolds Differential Proteome Profile

The pod wall of legumes is known to protect the developing seeds from pests and pathogens. However, the mechanism of conferring defense against insects has not yet been deciphered. Here, we have utilized 2-dimensional gel electrophoresis (2D-GE) coupled with mass spectrometry (MS/MS) to identify over expressed proteins in the pod wall of two different cultivars (commercial cultivar: JG 11 and tolerant cultivar: ICC 506-EB) of chickpea after 12 h of application of Helicoverpa armigera oral secretions (simulated herbivory). The assays were performed with a view that larvae are a voracious feeder and cause substantial damage to the pod within 12 h. A total of 600 reproducible protein spots were detected on gels, and the comparative analysis helped identify 35 (12 up-regulated, 23 down-regulated) and 20 (10 up-regulated, 10 down-regulated) differentially expressed proteins in JG 11 and ICC 506-EB, respectively. Functional classification of protein spots of each cultivar after MS/MS indicated that the differentially expressed proteins were associated with various metabolic activities. Also, stress-related proteins such as mannitol dehydrogenase (MADH), disease resistance-like protein-CSA1, serine/threonine kinase (D6PKL2), endoglucanase-19 etc. were up-regulated due to simulated herbivory. The proteins identified with a possible role in defense were further analyzed using the STRING database to advance our knowledge on their interacting partners. It decoded the involvement of several reactive oxygen species (ROS) scavengers and other proteins involved in cell wall reinforcement. The biochemical analysis also confirmed the active role of ROS scavengers during simulated herbivory. Thus, our study provides valuable new insights on chickpea-H.armigera interactions at the protein level.

Hepatic proteome analysis reveals altered mitochondrial metabolism and suppressed acyl-CoA synthetase-1 in colon-26 tumor-induced cachexia

Cachexia is a life-threatening complication of cancer traditionally characterized by weight loss and muscle dysfunction. Cachexia, however, is a systemic disease that also involves remodeling of nonmuscle organs. The liver exerts major control over systemic metabolism, yet its role in cancer cachexia is not well understood. To advance the understanding of how the liver contributes to cancer cachexia, we used quantitative proteomics and bioinformatics to identify hepatic pathways and cellular processes dysregulated in mice with moderate and severe colon-26 tumor-induced cachexia; ~300 differentially expressed proteins identified during the induction of moderate cachexia were also differentially regulated in the transition to severe cachexia. KEGG pathway enrichment revealed representation by oxidative phosphorylation, indicating altered hepatic mitochondrial function as a common feature across cachexia severity. Glycogen catabolism was also observed in cachexic livers along with decreased pyruvate dehydrogenase protein X component (Pdhx), increased lactate dehydrogenase A chain (Ldha), and increased lactate transporter Mct1. Together this suggests altered lactate metabolism and transport in cachexic livers, which may contribute to energetically inefficient interorgan lactate cycling. Acyl-CoA synthetase-1 (ACSL1), known for activating long-chain fatty acids, was decreased in moderate and severe cachexia based on LC-MS/MS analysis and immunoblotting. ACSL1 showed strong linear relationships with percent body weight change and muscle fiber size (R² = 0.73-0.76, P < 0.01). Mitochondrial coupling efficiency, which is compromised in cachexic livers to potentially increase energy expenditure and weight loss, also showed a linear relationship with ACSL1. Findings suggest altered mitochondrial and substrate metabolism of the liver in cancer cachexia, and possible hepatic targets for intervention.

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.

Protein-Protein interaction network analyses of human WNT proteins involved in neural development

WNT proteins are involved from initial of neural tissue specification to the end of cell fate determination and organ development. The present work was carried out to understand the involvement of different WNT isoforms (WNT3a, WNT5a and WNT7b) in neural development. A total of 718, 546 and 1004 PPIs for WNT3a, WNT5a and WNT7b respectively, were predicted by STRING database with confidence score more than 0.400. A network carrying all the selected PPIs of targeted proteins was constructed by using Cytoscape by assigning source node, target node and combined score as edge attribute. A total 2268 interactions of WNT3a, WNT5a and WNT7b were predicted to be involved in multiple signaling pathways and developmental processes. 43 of 2268 PPIs were refined after analyzing role of targeted proteins specifically in brain and neural development. WNT3a, WNT5a and WNT7a were predicted to be interacting with 18, 17 and 11 proteins, respectively, with average node degree score of 1.89, 2.12 and 1.82 respectively. The CytoHubba algorithm identified WNT3a, WNT5a, and WNT7b as hub proteins in neural development ranked on the basis of EPC (Edge Percolated Component) score of 9.352, 9.258 and 8.387.

Analysis of Caenorhabditis elegans phosphoproteome reveals the involvement of a molecular chaperone, HSP-90 protein during Salmonella enterica Serovar Typhi infection

Being a primary and prerequisite Post Translational Modification (PTM), protein phosphorylation mediates the defense mechanisms that presides host defense against a pathogen attack. Hence, the current study was intended to uncover the role of regulatory proteins and their PTMs with special attention to phosphorylation during pathogen attack, using C. elegans as a host and S. Typhi as an interacting pathogen. The study was initiated with the identification of differential regulation of the crucial immune regulatory kinases such as PMK-1, JNK-1 and SGK-1 through immunoblotting analysis, which revealed up-regulation of kinases during 48 h of S. Typhi infection. Subsequent the phosphoproteome profiling of S. Typhi infected C. elegans, using TiO₂ Column Chromatography followed by MALDI-ToF-ToF-MS, uncovered the regulated phosphoprotein players resulting in the identification of 166 and 54 proteins from gel-free and gel-based analysis, respectively. HSP-90 was found to be a central player from the interactome analyses and its role during pathogenic defense was validated using immunoblotting. Furthermore, the protein disorders of the identified phosphoproteins have been extensively analysed in silico. This study suggests that S. Typhi interferes with the homeostasis of chaperone molecules by kinetically interfering with the phosphorylation of the downstream pathway players of MAPK and JNK.

The construction and analysis of the aberrant lncRNA-miRNA-mRNA network in non-small cell lung cancer

Background

Non-small cell lung cancer (NSCLC) is the most common cancer and the pathogenesis remain unclear. According to the competing endogenous RNA (ceRNA) theory, long noncoding RNA (lncRNA) have a competition with mRNAs for the connecting with miRNAs that affecting the level of mRNA. In this work, the ceRNA network and the important genes to predict the survival prognosis were explored.

Methods

In the study, we recognized differently expressed genes (mRNAs, lncRNAs and miRNAs) between NSCLC and normal tissues from The Cancer Genome Atlas database (fold change >2, P<0.01) using edgeR. Then, the interaction between lncRNA and miRNA or mRNA and miRNA was explored by miRcode, miRDB, TargetScan, and miRanda. Furthermore, the functions and KEGG pathway were analyzed with DAVID and KOBAS. The connections of these mRNAs were explored by STRING online database. The relation between genes in the network and survival time were further explored by survival package in R.

Results

By bioinformatics tools, we explored 155 lncRNAs, 30 miRNAs and 68 mRNAs and constructed ceRNA network. The functions and KEGG pathway of 68 mRNAs were further analyzed. AQP2, EGF, SLC12A1, TRPV5 and AVPR2 was in the center of network and may play key roles in the development of NSCLC. And mRNA (CCNB1, COL1A1, E2F7, EGLN3, FOXG1 and PFKP), miRNA (miR-31, miR-144 and miR-192) and lncRNA (AC080129.1, AC100791.1, AL163952.1, AP000525.1, AP003064.2, C2orf48, C10orf91, FGF12-AS2, HOTAIR, LINC00518, LNX1-AS1, MED4-AS1, MIG31HG, MUC2, TTTY16 and UCA1) were closely related with overall survival (OS).

Conclusions

In summary, the present study provides a deeper understanding of the lncRNA-related ceRNA network in NSCLC and some genes may be new target to treat for NSCLC patients.

Proteome profiling of carbapenem-resistant K. pneumoniae clinical isolate (NDM-4): Exploring the mechanism of resistance and potential drug targets

The emergence of carbapenem resistance has become a major problem worldwide. This has made treatment of K. pneumoniae infections a difficult task. In this study, we have explored the whole proteome of the carbapenem-resistant Klebsiella pneumonia clinical isolate (NDM-4) under the meropenem stress. Proteomics (LC-MS/MS) and bioinformatics approaches were employed to uncover the novel mystery of the resistance over the existing mechanisms. Gene ontology, KEGG and STRING were used for functional annotation, pathway enrichment and protein-protein interaction (PPI) network respectively. LC-MS/MS analysis revealed that 52 proteins were overexpressed (≥10 log folds) under meropenem stress. These proteins belong to four major groups namely protein translational machinery complex, DNA/RNA modifying enzymes or proteins, proteins involved in carbapenems cleavage, modifications & transport and energy metabolism & intermediary metabolism-related proteins. Among the total 52 proteins 38 {matched to Klebsiella pneumonia subsp. pneumoniae (strain ATCC 700721/MGH 78578)} were used for functional annotation, pathways enrichment and protein-protein interaction. These were significantly enriched in the "intracellular" (14 of 38), "cytoplasm" (12 of 38) and "ribosome" (10 of 38). We suggest that these 52 over expressed proteins and their interactive proteins cumulatively contributed in survival of bacteria and meropenem resistance through various mechanisms or enriched pathways. These proteins targets and their pathways might be used for development of novel therapeutics against the resistance; therefore, the situation of the emergence of "bad-bugs" could be controlled.

Ribosomal maturation factor (RimP) is essential for survival of nontuberculous mycobacteria Mycobacterium fortuitum under in vitro acidic stress conditions

Mycobacterium fortuitum is an important human pathogenic NTM, which resists stress conditions inside macrophages by exploitation of specific genes. TnphoA-based transposon mutagenesis was employed to identify membrane genes responsible for survival of M. fortuitum under such stress conditions. A library of about 450 mutants was constructed after electroporation of vector pRT291 into wild-type M. fortuitum. On the basis of blue color development and alkaline phosphatase assay, 20 mutants were shortlisted to screen for growth and survival under acidic stress at pH 6.5, 5.5, 4.5, and 3.5. Mutant MT727 showed reduced growth and survival under acidic stress. The acid susceptible mutant MT727 was subjected to other in vitro stress conditions prevalent inside macrophages including oxidative, nutrient starvation and nitrosative stress. However, the mutant showed no appreciable difference in growth behavior under oxidative, nutrient starvation and nitrosative stress conditions in comparison to the wild type. Genomic and bioinformatics analysis of MT727 led to identification of putative ribosomal maturation factor RimP of M. fortuitum to be affected by mutagenesis, showing closest homology to M. abscessus RimP. In silico functional interaction of RimP protein using STRING database showed its interaction with proteins of ribosomal assembly and maturation. Results indicate role of rimP gene in survival of M. fortuitum under acidic stress conditions which may be further explored for use as a potential drug target against M. fortuitum and other mycobacterial infections.

The Importance of Se-Related Genes in the Chondrocyte of Kashin-Beck Disease Revealed by Whole Genomic Microarray and Network Analysis

Kashin-Beck disease (KBD) is an endemic, chronic, and degenerative osteoarthropathy. Selenium (Se) deficiency plays important role in the pathogenesis of KBD. We aimed to screen Se-related gene from chondrocytes of patients with KBD. Whole-genome oligonucleotide microarrays were used to detect differentially expressed genes. qRT-PCR was used to confirm the microarray results. Comparative Toxicogenomics Database (CTD) was used to screen Se-related genes from differentially expressed genes. Gene Ontology (GO) classifications and network analysis of Se-related genes were constituted by STRING online system. Three hundred ninety-nine differentially expressed genes were obtained from microarray. Among them, 54 Se-related genes were identified by CTD. The qRT-PCR validation showed that four genes expressed similarly with the ones in the microarray transcriptional profiles. The Se-related genes were categorized into 6 cellular components, 8 molecular functions, 44 biological processes, 10 pathways, and 1 network by STRING. The Se-related gene insulin-like growth factor binding protein 2 (IGFBP2), insulin-like growth factor binding protein 3 (IGFBP3), interleukin 6 (IL6), BCL2, apoptosis regulator (BCL2), and BCL2-associated X, apoptosis regulator (BAX), which involved in many molecular functions, biological processes, and apoptosis pathway may play important roles in the pathogenesis of KBD.

Identification of target gene and prognostic evaluation for lung adenocarcinoma using gene expression meta-analysis, network analysis and neural network algorithms

BACKGROUND

Lung adenocarcinoma (LUAD) is a heterogeneous disease with poor survival in the advanced stage and a high incidence rate in the world. Novel drug targets are urgently required to improve patient treatment. Therefore, we aimed to identify therapeutic targets for LUAD based on protein-protein and protein-drug interaction network analysis with neural network algorithms using mRNA expression profiles.

RESULTS

A comprehensive meta-analysis of selective non-small cell lung cancer (NSCLC) mRNA expression profile datasets from Gene Expression Omnibus were used to identify potential biomarkers and the molecular mechanisms related to the prognosis of NSCLC patients. Using the Network Analyst tool, based on combined effect size (ES) methods, we recognized 6566 differentially expressed genes (DEGs), which included 3036 downregulated and 3530 upregulated genes linked to NSCLC patient survival. ClueGO, a Cytoscape plugin, was exploited to complete the function and pathway enrichment analysis, which disclosed "regulated exocytosis", "purine nucleotide binding", "pathways in cancer", and "cell cycle" between exceptionally supplemented terms. Enrichr, a web tool examination, demonstrated "early growth response protein 1 (EGR-1)", "hepatocyte nuclear factor 4α (HNF4A)", "mitogen-activated protein kinase 14 (MAP3K14)", and "cyclin-dependent kinase 1 (CDK1)" to be among the most prevalent TFs and kinases associated with NSCLC. Our meta-analysis identified that MAPK1 and aurora kinase (AURKA) are the most obvious class of hub nodes. Furthermore, protein-drug interaction network and neural network algorithms identified candidate drugs such as phosphothreonine and 4-(4-methylpiperazin-1-yl)-n-[5-(2-thienylacetyl)-1,5-dihydropyrrolo[3,4-c]pyrazol-3-yl] benzamide and for the targets MAPK1 and AURKA, respectively.

CONCLUSION

Our study has identified novel candidate biomarkers, pathways, transcription factors (TFs), and kinases associated with NSCLC prognosis, as well as drug candidates, which may assist treatment strategy for NSCLC patients.

Application of Ferulic Acid for Alzheimer's Disease: Combination of Text Mining and Experimental Validation

Alzheimer's disease (AD) is an increasing concern in human health. Despite significant research, highly effective drugs to treat AD are lacking. The present study describes the text mining process to identify drug candidates from a traditional Chinese medicine (TCM) database, along with associated protein target mechanisms. We carried out text mining to identify literatures that referenced both AD and TCM and focused on identifying compounds and protein targets of interest. After targeting one potential TCM candidate, corresponding protein-protein interaction (PPI) networks were assembled in STRING to decipher the most possible mechanism of action. This was followed by validation using Western blot and co-immunoprecipitation in an AD cell model. The text mining strategy using a vast amount of AD-related literature and the TCM database identified curcumin, whose major component was ferulic acid (FA). This was used as a key candidate compound for further study. Using the top calculated interaction score in STRING, BACE1 and MMP2 were implicated in the activity of FA in AD. Exposure of SHSY5Y-APP cells to FA resulted in the decrease in expression levels of BACE-1 and APP, while the expression of MMP-2 and MMP-9 increased in a dose-dependent manner. This suggests that FA induced BACE1 and MMP2 pathways maybe novel potential mechanisms involved in AD. The text mining of literature and TCM database related to AD suggested FA as a promising TCM ingredient for the treatment of AD. Potential mechanisms interconnected and integrated with Aβ aggregation inhibition and extracellular matrix remodeling underlying the activity of FA were identified using in vitro studies.

Insights from the protein-protein interaction network analysis of Mycobacterium tuberculosis toxin-antitoxin systems

Protein-protein interaction (PPI) network analysis is a powerful strategy to understand M. tuberculosis (Mtb) system level physiology in the identification of hub proteins. In the present study, the PPI network of 79 Mtb toxin-antitoxin (TA) systems comprising of 167 nodes and 234 edges was investigated. The topological properties of PPI network were examined by 'Network analyzer' a cytoscape plugin app and STRING database. The key enriched biological processes and the molecular functions of Mtb TA systems were analyzed by STRING. Manual curation of the PPI data identified four proteins (i.e. Rv2762c, VapB14, VapB42 and VapC42) to possess the highest number of interacting partners. The top 15% hub proteins were identified in the PPI network by employing two statistical measures, i.e. betweenness and radiality by employing cytohubba. Insights gained from the molecular protein models of VapC9 and VapC10 are also documented.

Serum Proteomic Variability Associated with Clinical Phenotype in Familial Transthyretin Amyloidosis (ATTRm)

Transthyretin (TTR), normally a plasma circulating protein, can become misfolded and aggregated, ultimately leading to extracellular deposition of amyloid fibrils usually targeted to heart or nerve tissues. Referred to as TTR-associated amyloidoses (ATTR), this group of diseases is frequently life threatening and fatal if untreated. ATTR, caused by amyloid-forming variant TTR proteins (ATTRm) that arise from point mutations in the TTR gene, were classically referred to as familial amyloid cardiomyopathy (FAC) or familial amyloid polyneuropathy (FAP), reflecting the clinical phenotype. FAC and FAP are pathologies that can be challenging to diagnose as there are no definitive biomarkers of disease; moreover, disease-specific measures of progression are lacking, and treatment options are limited. Thus, the discovery of sensitive and specific indicators of disease has the potential to improve recognition, enable accurate measurement of amyloid progression and response to treatment, and reveal key information regarding FAC and FAP pathobiological mechanisms. In this study, the goal was to investigate serum proteomic features unique to FAC and FAP types of ATTRm. Multiple-reaction monitoring mass spectrometry (MRM-MS), a powerful technique in profiling proteomes, was used to measure the serum concentrations of 160 proteins in samples from FAC and FAP patients. Results were compared to data from healthy control sera obtained from individuals matched to age (≥60 years), gender (male), and race (Caucasian). Proteomic analyses of ATTRm (FAC and FAP) and control samples showed significant concentration differences in 107 of 192 (56%) of the serum proteins that were studied. In comparing FAC to FAP, differences in concentrations as well as interactions and functions of several proteins were identified as unique to each disease; significantly lower levels of TTR were specific to FAC, but not to FAP. Annotated functional clustering identified extracellular region, signal, and signal peptide as terms common to FAC and FAP. Conversely, disulfide bond was unique to FAC; secreted, glycosylation site: N-linked, glycosylation, glycoprotein, polymorphism, and sequence variant were associated solely with FAP. Predicted protein-protein associations in FAC were seen for reaction, binding, and activation processes; no associations were found in FAP. This study demonstrates significant proteomic differences between ATTRm patient and control sera, as well as ATTRm phenotype-associated variations in the circulating levels of several proteins including TTR. The identification of serum proteins unique to FAC and FAP may have diagnostic and prognostic utility and could possibly provide important clues about disease mechanisms.

Bioinformatics Tools for Proteomics Data Interpretation

Biological systems function via intricate cellular processes and networks in which RNAs, metabolites, proteins and other cellular compounds have a precise role and are exquisitely regulated (Kumar and Mann, FEBS Lett 583(11):1703-1712, 2009). The development of high-throughput technologies, such as the Next Generation DNA Sequencing (NGS) and DNA microarrays for sequencing genomes or metagenomes, have triggered a dramatic increase in the last few years in the amount of information stored in the GenBank and UniProt Knowledgebase (UniProtKB). GenBank release 210, reported in October 2015, contains 202,237,081,559 nucleotides corresponding to 188,372,017 sequences, whilst there are only 1,222,635,267,498 nucleotides corresponding to 309,198,943 sequences from Whole Genome Shotgun (WGS) projects. In the case of UniProKB/Swiss-Prot, release 2015_12 (December 9, 2015) contains 196,219,159 amino acids that correspond to 550,116 entries. Meanwhile, UniProtKB/TrEMBL (release 2015_12 of December 9 2015) contains 1,838,851,8871 amino acids corresponding to 555,270,679 entries. Proteomics has also improved our knowledge of proteins that are being expressed in cells at a certain time of the cell cycle. It has also allowed the identification of molecules forming part of multiprotein complexes and an increasing number of posttranslational modifications (PTMs) that are present in proteins, as well as the variants of proteins expressed.

Effects of bone marrow on the microenvironment of the human pancreatic islet: A Protein Profile Approach

Stem cells are a new therapeutic modality that may support the viability and function of human organs and tissue. Our previous studies have revealed that human allogeneic bone marrow (BM) sustains pancreatic β cell function and survival. This paper examines whether BM creates a microenvironment that supports human pancreatic islets in vitro by evaluating 107 proteins in culture media from BM, islet, and islet/bone marrow (IB) with mass spectrometry. Proteins were considered up- or down-regulated if p-values < 0.05 and fold change was greater than 2 fold I VS. IB. In addition, proteins identified that were uniquely found in islets co-cultured with bone marrow, but not in islets or bone marrow. A 95% protein probability was used as a threshold. Twenty three proteins were upregulated, and sixteen proteins were downregulated. The function of each protein is listed based on the protein database, which include structural proteins (9 upregulated, 4 downregulated); anti-protease and anti-endopeptidase enzymes (8 upregulated); cation binding proteins (6 up-regulated). Six proteins were uniquely identified in islet co-cultured with bone marrow. Three are anti-proteases or anti-endopeptidases, and 1 is a structural protein. These findings suggest that BM, by changing culture media proteins, may be one of mechanisms to maintain human islet function and survival.

Differential proteome profiling in the hippocampus of amnesic mice

Amnesia or memory loss is associated with brain aging and several neurodegenerative pathologies including Alzheimer's disease (AD). This can be induced by a cholinergic antagonist scopolamine but the underlying molecular mechanism is poorly understood. This study of proteome profiling in the hippocampus could provide conceptual insights into the molecular mechanisms involved in amnesia. To reveal this, mice were administered scopolamine to induce amnesia and memory impairment was validated by novel object recognition test. Using two-dimensional gel electrophoresis coupled with MALDI-MS/MS, we have analyzed the hippocampal proteome and identified 18 proteins which were differentially expressed. Out of these proteins, 11 were downregulated and 7 were upregulated in scopolamine-treated mice as compared to control. In silico analysis showed that the majority of identified proteins are involved in metabolism, catalytic activity, and cytoskeleton architectural functions. STRING interaction network analysis revealed that majority of identified proteins exhibit common association with Actg1 cytoskeleton and Vdac1 energy transporter protein. Furthermore, interaction map analysis showed that Fascin1 and Coronin 1b individually interact with Actg1 and regulate the actin filament dynamics. Vdac1 was significantly downregulated in amnesic mice and showed interaction with other proteins in interaction network. Therefore, we silenced Vdac1 in the hippocampus of normal young mice and found similar impairment in recognition memory of Vdac1 silenced and scopolamine-treated mice. Thus, these findings suggest that Vdac1-mediated disruption of energy metabolism and cytoskeleton architecture might be involved in scopolamine-induced amnesia.

How to use and integrate bioinformatics tools to compare proteomic data from distinct conditions? A tutorial using the pathological similarities between Aortic Valve Stenosis and Coronary Artery Disease as a case-study

Nowadays we are surrounded by a plethora of bioinformatics tools, powerful enough to deal with the large amounts of data arising from proteomic studies, but whose application is sometimes hard to find. Therefore, we used a specific clinical problem - to discriminate pathophysiology and potential biomarkers between two similar cardiovascular diseases, aortic valve stenosis (AVS) and coronary artery disease (CAD) - to make a step-by-step guide through four bioinformatics tools: STRING, DisGeNET, Cytoscape and ClueGO. Proteome data was collected from articles available on PubMed centered on proteomic studies enrolling subjects with AVS or CAD. Through the analysis of gene ontology provided by STRING and ClueGO we could find specific biological phenomena associated with AVS, such as down-regulation of elastic fiber assembly, and with CAD, such as up-regulation of plasminogen activation. Moreover, through Cytoscape and DisGeNET we could pinpoint surrogate markers either for AVS (e.g. popeye domain containing protein 2 and 28S ribosomal protein S36, mitochondrial) or for CAD (e.g. ankyrin repeat and SOCS box protein 7) which deserve future validation. Data recycling and integration as well as research orientation are among the main advantages of resorting to bioinformatics analysis, hence these tutorials can be of great convenience for proteomics investigators.

BIOLOGICAL SIGNIFICANCE

As we saw for aortic valve stenosis and coronary artery disease, it can be of great relevance to perform preliminary bioinformatics analysis with already published proteomics data. It not only saves us time in the lab (avoiding work duplication) as it points out new hypothesis to explain the phenotypical presentation of the diseases as well as new surrogate markers with clinical relevance, deserving future scrutiny. These essential steps can be easily overcome if one follows the steps proposed in our tutorial for STRING, DisGeNET, Cytoscape and ClueGO utilization.

Blood Proteomic Profiling in Inherited (ATTRm) and Acquired (ATTRwt) Forms of Transthyretin-Associated Cardiac Amyloidosis

Transthyretin-associated forms of cardiac amyloidosis are fatal protein misfolding diseases that can be inherited (ATTRm) or acquired (ATTRwt). An accurate diagnosis of ATTR amyloidosis can be challenging as biopsy evidence, usually from the affected organ, is required. Precise biomarkers for ATTR disease identification and monitoring are undiscovered, disease-specific therapeutic options are needed, and the current understanding of ATTR molecular pathogenesis is limited. The aim of this study was to investigate and compare the serum proteomes in ATTRm and ATTRwt cardiac amyloidosis to identify differentially expressed blood proteins that were disease-specific. Using multiple-reaction monitoring mass spectrometry (MRM-MS), the concentrations of 160 proteins were analyzed in serum samples from ATTRm and ATTRwt patients, and a healthy control group. Patient and control sera were matched to age (≥60 years), gender (male), and race (Caucasian). The circulating concentrations of 123/160 proteins were significantly different in patient vs control sera; TTR and retinol-binding protein (RBP4) levels were significantly decreased (p < 0.03) in ATTRm compared to controls. In ATTRm, 14/123 proteins were identified as unique to that group and found generally to be lower than controls; moreover, the concentrations of RBP4 and 6 other proteins in this group were significantly different (p < 0.04) compared to ATTRwt. Predicted interactions among the 14 proteins unique to ATTRm were categorized as reaction and binding associations. Alternatively, 27 proteins were found to be unique to ATTRwt with associated interactions defined as activation, catalysis, and inhibition, in addition to reaction and binding. This study demonstrates significant proteomic differences between ATTR patient and control sera, and disease-associated variations in circulating levels of several proteins including TTR and RBP4. The identification of serum proteins unique to ATTRm and ATTRwt cardiac amyloidosis may have diagnostic and prognostic utility, and may provide important clues about disease mechanisms.

Protein kinase A signalling in Schistosoma mansoni cercariae and schistosomules

Cyclic AMP (cAMP)-dependent protein kinase/protein kinase A regulates multiple processes in eukaryotes by phosphorylating diverse cellular substrates, including metabolic and signalling enzymes, ion channels and transcription factors. Here we provide insight into protein kinase A signalling in cercariae and 24h in vitro cultured somules of the blood parasite, Schistosoma mansoni, which causes human intestinal schistosomiasis. Functional mapping of activated protein kinase A using anti-phospho protein kinase A antibodies and confocal laser scanning microscopy revealed activated protein kinase A in the central and peripheral nervous system, oral-tip sensory papillae, oesophagus and excretory system of intact cercariae. Cultured 24h somules, which biologically represent the skin-resident stage of the parasite, exhibited similar activation patterns in oesophageal and nerve tissues but also displayed striking activation at the tegument and activation in a region resembling the germinal 'stem' cell cluster. The adenylyl cyclase activator, forskolin, stimulated somule protein kinase A activation and produced a hyperkinesia phenotype. The biogenic amines, serotonin and dopamine known to be present in skin also induced protein kinase A activation in somules, whereas neuropeptide Y or [Leu(31),Pro(34)]-neuropeptide Y attenuated protein kinase A activation. However, neuropeptide Y did not block the forskolin-induced somule hyperkinesia. Bioinformatic investigation of potential protein associations revealed 193 medium confidence and 59 high confidence protein kinase A interacting partners in S. mansoni, many of which possess putative protein kinase A phosphorylation sites. These data provide valuable insight into the intricacies of protein kinase A signalling in S. mansoni and a framework for further physiological investigations into the roles of protein kinase A in schistosomes, particularly in the context of interactions between the parasite and the host.