Network analysis reveals different hub genes and molecular pathways for pig in vitro fertilized early embryos and parthenogenotes

The role of serum-glucocorticoid regulated kinase 1 in reproductive viability: implications from prenatal programming and senescence

OBJECTIVE

Organisms and cellular viability are of paramount importance to living creatures. Disruption of the balance between cell survival and apoptosis results in compromised viability and even carcinogenesis. One molecule involved in keeping this homeostasis is serum-glucocorticoid regulated kinase (SGK) 1. Emerging evidence points to a significant role of SGK1 in cell growth and survival, cell metabolism, reproduction, and life span, particularly in prenatal programming and reproductive senescence by the same token. Whether the hormone inducible SGK1 kinase is a major driver in the pathophysiological processes of prenatal programming and reproductive senescence?

METHOD

The PubMed/Medline, Web of Science, Embase/Ovid, and Elsevier Science Direct literature databases were searched for articles in English focusing on SGK1 published up to July 2023 RESULT: Emerging evidence is accumulating pointing to a pathophysiological role of the ubiquitously expressed SGK1 in the cellular and organismal viability. Under the regulation of specific hormones, extracellular stimuli, and various signals, SGK1 is involved in several biological processes relevant to viability, including cell proliferation and survival, cell migration and differentiation. In line, SGK1 contributes to the development of germ cells, embryos, and fetuses, whereas SGK1 inhibition leads to abnormal gametogenesis, embryo loss, and truncated reproductive lifespan.

CONCLUTION

SGK1 integrates a broad spectrum of effects to maintain the homeostasis of cell survival and apoptosis, conferring viability to multiple cell types as well as both simple and complex organisms, and thus ensuring appropriate prenatal development and reproductive lifespan.

Construction of a co-expression network affecting intramuscular fat content and meat color redness based on transcriptome analysis

Introduction: Intramuscular fat content (IFC) and meat color are vital indicators of pork quality. Methods: A significant positive correlation between IFC and redness of meat color (CIE a* value) indicates that these two traits are likely to be regulated by shared molecular pathways.To identify candidate genes, hub genes, and signaling pathways that regulate these two traits, we measured the IFC and CIE a* value in 147 hybrid pigs, and selected individuls with extreme phenotypes for transcriptome analysis. Results: The results revealed 485 and 394 overlapping differentially expressed genes (DEGs), using the DESeq2, limma, and edgeR packages, affecting the IFC and CIE a* value, respectively. Weighted gene co-expression network analysis (WGCNA) identified four modules significantly correlated with the IFC and CIE a* value. Moreover, we integrated functional enrichment analysis results based on DEGs, GSEA, and WGCNA conditions to identify candidate genes, and identified 47 and 53 candidate genes affecting the IFC and CIE a* value, respectively. The protein protein interaction (PPI) network analysis of candidate genes showed that 5 and 13 hub genes affect the IFC and CIE a* value, respectively. These genes mainly participate in various pathways related to lipid metabolism and redox reactions. Notably, four crucial hub genes (MYC, SOX9, CEBPB, and PPAGRC1A) were shared for these two traits. Discussion and conclusion: After functional annotation of these four hub genes, we hypothesized that the SOX9/CEBPB/PPARGC1A axis could co-regulate lipid metabolism and the myoglobin redox response. Further research on these hub genes, especially the SOX9/CEBPB/PPARGC1A axis, will help to understand the molecular mechanism of the co-regulation of the IFC and CIE a* value, which will provide a theoretical basis for improving pork quality.

Literature-Based Discovery Predicts Antihistamines Are a Promising Repurposed Adjuvant Therapy for Parkinson's Disease

Parkinson's disease (PD) is a movement disorder caused by a dopamine deficit in the brain. Current therapies primarily focus on dopamine modulators or replacements, such as levodopa. Although dopamine replacement can help alleviate PD symptoms, therapies targeting the underlying neurodegenerative process are limited. The study objective was to use artificial intelligence to rank the most promising repurposed drug candidates for PD. Natural language processing (NLP) techniques were used to extract text relationships from 33+ million biomedical journal articles from PubMed and map relationships between genes, proteins, drugs, diseases, etc., into a knowledge graph. Cross-domain text mining, hub network analysis, and unsupervised learning rank aggregation were performed in SemNet 2.0 to predict the most relevant drug candidates to levodopa and PD using relevance-based HeteSim scores. The top predicted adjuvant PD therapies included ebastine, an antihistamine for perennial allergic rhinitis; levocetirizine, another antihistamine; vancomycin, a powerful antibiotic; captopril, an angiotensin-converting enzyme (ACE) inhibitor; and neramexane, an N-methyl-D-aspartate (NMDA) receptor agonist. Cross-domain text mining predicted that antihistamines exhibit the capacity to synergistically alleviate Parkinsonian symptoms when used with dopamine modulators like levodopa or levodopa-carbidopa. The relationship patterns among the identified adjuvant candidates suggest that the likely therapeutic mechanism(s) of action of antihistamines for combatting the multi-factorial PD pathology include counteracting oxidative stress, amending the balance of neurotransmitters, and decreasing the proliferation of inflammatory mediators. Finally, cross-domain text mining interestingly predicted a strong relationship between PD and liver disease.

Cross-Domain Text Mining to Predict Adverse Events from Tyrosine Kinase Inhibitors for Chronic Myeloid Leukemia

Tyrosine kinase inhibitors (TKIs) are prescribed for chronic myeloid leukemia (CML) and some other cancers. The objective was to predict and rank TKI-related adverse events (AEs), including under-reported or preclinical AEs, using novel text mining. First, k-means clustering of 2575 clinical CML TKI abstracts separated TKIs by significant (p < 0.05) AE type: gastrointestinal (bosutinib); edema (imatinib); pulmonary (dasatinib); diabetes (nilotinib); cardiovascular (ponatinib). Next, we propose a novel cross-domain text mining method utilizing a knowledge graph, link prediction, and hub node network analysis to predict new relationships. Cross-domain text mining of 30+ million articles via SemNet predicted and ranked known and novel TKI AEs. Three physiology-based tiers were formed using unsupervised rank aggregation feature importance. Tier 1 ranked in the top 1%: hematology (anemia, neutropenia, thrombocytopenia, hypocellular marrow); glucose (diabetes, insulin resistance, metabolic syndrome); iron (deficiency, overload, metabolism), cardiovascular (hypertension, heart failure, vascular dilation); thyroid (hypothyroidism, hyperthyroidism, parathyroid). Tier 2 ranked in the top 5%: inflammation (chronic inflammatory disorder, autoimmune, periodontitis); kidney (glomerulonephritis, glomerulopathy, toxic nephropathy). Tier 3 ranked in the top 10%: gastrointestinal (bowel regulation, hepatitis, pancreatitis); neuromuscular (autonomia, neuropathy, muscle pain); others (secondary cancers, vitamin deficiency, edema). Results suggest proactive TKI patient AE surveillance levels: regular surveillance for tier 1, infrequent surveillance for tier 2, and symptom-based surveillance for tier 3.