Human serum proteomics reveals a molecular signature after one night of sleep deprivation.

Study Objectives: Sleep deprivation is highly prevalent and caused by conditions such as night shift work or illnesses like obstructive sleep apnea. Compromised sleep affects cardiovascular-, immune-, and neuronal systems. Recently, we published human serum proteome changes after a simulated night shift. This pilot proteomic study aimed to further explore changes in human blood serum after 6 hours of sleep deprivation at night. Methods: Human blood serum samples from eight self-declared healthy females were analyzed using Orbitrap Eclipse mass spectrometry (MS-MS) and high-pressure liquid chromatography. We used a within-participant design, in which the samples were taken after 6 hours of sleep at night and after 6 hours of sleep deprivation the following night. Systems biological databases and bioinformatic software were used to analyze the data and comparative analysis were done with other published sleep-related proteomic datasets. Results: Out of 494 proteins, 66 were found to be differentially expressed proteins (DEPs) after 6 hours of sleep deprivation. Functional enrichment analysis revealed the associations of these DEPs with several biological functions related to the altered regulation of cellular processes such as platelet degranulation and blood coagulation, as well as associations with different curated gene sets. Conclusions: This study presents serum proteomic changes after 6 hours of sleep deprivation, supports previous findings showing that short sleep deprivation affects several biological processes, and reveals a molecular signature of proteins related to pathological conditions such as altered coagulation and platelet function, impaired lipid and immune function, and cell proliferation. Data are available via ProteomeXchange with identifier PXD045729. This paper is part of the Genetic and other molecular underpinnings of sleep, sleep disorders, and circadian rhythms including translational approaches Collection.

Energy Metabolism Dysregulation in Myocardial Infarction: An Integrative Analysis of Ischemic Cardiomyopathy.

BACKGROUND: Myocardial metabolism is closely related to functional changes after myocardial infarction (MI). OBJECTIVE: This study aimed to present an integrative examination of human ischemic cardiomyopathy. METHODS: We used both GSE121893 single-cell suspension sequencing and GSE19303 transcription microarray data sets from the GEO database, along with a murine MI model for full-spectrum metabolite detection. Through a systematic investigation that involved differential metabolite identification and functional enrichment analysis, we shed light on the pivotal role of energy metabolism dysregulation in the progression of MI. RESULTS: Our findings revealed an association between the core regulatory genes CDKN1A, FOS, ITGB4, and MAP2K1 and the underlying pathophysiology of the disease. These genes are identified as critical elements in the complex landscape of myocardial ischemic disorder, highlighting novel insights into therapeutic targets and the intricate biological mechanisms involved. CONCLUSION: This analysis provides a framework for future research on the metabolic alterations associated with MI.

Direction-aware functional class scoring enrichment analysis of infinium DNA methylation data.

Infinium Methylation BeadChip arrays remain one of the most popular platforms for epigenome-wide association studies, but tools for downstream pathway analysis have their limitations. Functional class scoring (FCS) is a group of pathway enrichment techniques that involve the ranking of genes and evaluation of their collective regulation in biological systems, but the implementations described for Infinium methylation array data do not retain direction information, which is important for mechanistic understanding of genomic regulation. Here, we evaluate several candidate FCS methods that retain directional information. According to simulation results, the best-performing method involves the mean aggregation of probe limma t-statistics by gene followed by a rank-ANOVA enrichment test using the mitch package. This method, which we call 'LAM,' outperformed an existing over-representation analysis method in simulations, and showed higher sensitivity and robustness in an analysis of real lung tumour-normal paired datasets. Using matched RNA-seq data, we examine the relationship of methylation differences at promoters and gene bodies with RNA expression at the level of pathways in lung cancer. To demonstrate the utility of our approach, we apply it to three other contexts where public data were available. First, we examine the differential pathway methylation associated with chronological age. Second, we investigate pathway methylation differences in infants conceived with in vitro fertilization. Lastly, we analyse differential pathway methylation in 19 disease states, identifying hundreds of novel associations. These results show LAM is a powerful method for the detection of differential pathway methylation complementing existing methods. A reproducible vignette is provided to illustrate how to implement this method.

Integration analysis of miRNA-mRNA pairs between two contrasting genotypes reveals the molecular mechanism of jujube (Ziziphus jujuba Mill.) response to high-temperature stress.

With global warming, high temperature (HT) has become one of the most common abiotic stresses resulting in significant crop yield losses, especially for jujube (Ziziphus jujuba Mill.), an important temperate economic crop cultivated worldwide. This study aims to explore the coping mechanism of jujube to HT stress at the transcriptional and post-transcriptional levels, including identifying differentially expressed miRNAs and mRNAs as well as elucidating the critical pathways involved. High-throughput sequencing analyses of miRNA and mRNA were performed on jujube leaves, which were collected from "Fucumi" (heat-tolerant) and "Junzao" (heat-sensitive) cultivars subjected to HT stress (42 °C) for 0, 1, 3, 5, and 7 days, respectively. The results showed that 45 known miRNAs, 482 novel miRNAs, and 13,884 differentially expressed mRNAs (DEMs) were identified. Among them, integrated analysis of miRNA target genes prediction and mRNA-seq obtained 1306 differentially expressed miRNAs-mRNAs pairs, including 484, 769, and 865 DEMIs-DEMs pairs discovered in "Fucuimi", "Junzao" and two genotypes comparative groups, respectively. Furthermore, functional enrichment analysis of 1306 DEMs revealed that plant-pathogen interaction, starch and sucrose metabolism, spliceosome, and plant hormone signal transduction were crucial pathways in jujube leaves response to HT stress. The constructed miRNA-mRNA network, composed of 20 DEMIs and 33 DEMs, displayed significant differently expressions between these two genotypes. This study further proved the regulatory role of miRNAs in the response to HT stress in plants and will provide a theoretical foundation for the innovation and cultivation of heat-tolerant varieties.

TES and SLC40A1 as Potential Biomarkers for Predicting Survival in T-Cell Acute Lymphoblastic Leukemia.

INTRODUCTION: Identifying patients with high-risk T-cell acute lymphoblastic leukemia (T-ALL) is crucial for personalized therapy; however, the lack of robust biomarkers hinders prognosis assessment. To address this issue, our study aimed to screen and validate genes whose expression may serve as predictive indicators of outcomes in T-ALL patients while also investigating the underlying molecular mechanisms. METHODS: Differentially expressed genes (DEGs) between T-ALL patients and healthy controls were identified by integrating data from three independent public datasets. Functional annotation of these DEGs and protein-protein interactions were also conducted. Further, we enrolled a prospective cohort of T-ALL patients (n = 20) at our center, conducting RNA-seq analysis on their bone marrow samples. Survival-based univariate Cox analysis was employed to identify gene expressions related to survival, and an intersection algorithm was sequentially applied. Furthermore, we validated the identified genes using cases from the Therapeutically Applicable Research to Generate Effective Treatments database, plotting Kaplan-Meier curves for secondary validation. RESULTS: Through the integration of survival-related genes with DEGs identified in T-ALL, our analysis revealed six T-ALL-specific genes, the expression levels of which were linked to prognostic value. Notably, the independent prognostic value of SLC40A1 and TES expression levels was confirmed in both an external cohort and a prospective cohort at our center. CONCLUSION: In summary, our preliminary study indicates that the expression levels of TES and SLC40A1 genes show promise as potential indicators for predicting survival outcomes in T-ALL patients.

A whole-exome sequencing study of patent foramen ovale: investigating genetic variants and their association with cardiovascular disorders.

Background: Patent foramen ovale (PFO) has a genetic predisposition and is closely associated with cryptogenic stroke (CS), migraine, decompression sickness, and hypoxemia. Identifying PFO-related mutant genes through whole-exome sequencing (WES) can help in the early recognition of cardiovascular genetic risk factors, guide timely clinical intervention, and reduce the occurrence of cardiovascular events. Methods: We analyzed mutant genes from ClinVar and OMIM databases. WES was performed on 25 PFO patients from Zhejiang Provincial Hospital of Chinese Medicine. Pathogenicity of variants was evaluated using American College of Medical Genetics and Genomics (ACMG) and Association for Molecular Pathology. (AMP) guidelines. Results: In ClinVar (4 Feb 2023), 113 coding gene mutations were found, including 83 associated with PFO. From OMIM (18 Apr 2023), 184 gene mutations were analyzed, with 110 mutant coding genes. WES identified pathogenic mutations in two of 25 PFO patients (8%). LDLR, SDHC, and NKX2-5 genes were linked to PFO and primarily involved in myocardial tissue function. NKX2-5 may play a crucial role in PFO development, interacting with NOTCH1, GATA4, MYH6, SCN5A signaling pathways regulating cardiomyocyte characteristics. Conclusion: We identified pathogenic mutations in LDLR, SDHC, and NKX2-5 genes, implying their role in PFO development. Functional enrichment analysis revealed NKX2-5's interaction with signaling pathways regulating cardiomyocyte function. These findings enhance our understanding of PFO's genetic basis, suggesting potential therapeutic targets for future research.

of Potential Noncoding RNAs Related to Spinal Cord Injury Based on Competing Endogenous RNAs.

This study aims to elucidate the key regulatory molecules, specifically messenger RNAs (mRNAs), long noncoding RNAs (lncRNAs), and microRNAs (miRNAs) and their roles in the development and progression of spinal cord injury (SCI). Expression profiles (GSE45006, GSE19890, and GSE125630) for SCI were sourced from the Gene Expression Omnibus (GEO) database. By comparing rats with SCI at various time points against those without SCI, we identified differentially expressed mRNAs (DEmRNAs), lncRNAs (DElncRNAs), and miRNAs (DEmiRNAs). The GSE45006 dataset facilitated the production of DEmRNAs, which were then clustered using Mfuzz. Subsequently, we constructed a protein-protein interaction (PPI) network and anticipated interaction pairs between miRNA-mRNA and lncRNA-mRNA. These pairs were instrumental in forming a regulatory network involving lncRNA-miRNA-mRNA interactions. Additionally, we conducted functional enrichment studies on the DEmRNAs within these gene networks. A total of 2313 DEmRNAs were identified using the GSE45006 dataset, alongside 111 DEmiRNAs from GSE19890. From GSE125630, we extracted 154 DElncRNAs and 2322 DEmRNAs. Our analysis revealed 294 up-regulated DEmRNAs, grouped into the up-cluster, and 407 down-regulated DEmRNAs, forming the down-cluster. Key hub genes in the PPI network, such as Rhof, Vav1, Lyz2, Rab3a, Lyn, Cyfip1, Gns, and Nckap1l, were identified. Additionally, the study successfully constructed a competing endogenous RNA (ceRNA) network, revealing 55 unique lncRNA-miRNA-mRNA link pairs. Our research established a ceRNA network associated with SCI, identifying several critical lncRNA-miRNA-mRNA connection pairs integral to the disease's onset and progression. Notably, significant associations, including the AABR07041411.1-miR-125a-5p-Slc4a7 and the Smg1-rno-miR-331-3p-Tlr4 pairs, were observed to exert a significant influence within this biological context.

Effect of Calebin-A on Critical Genes Related to NAFLD: A Protein-Protein Interaction Network and Molecular Docking Study.

Background: Calebin-A is a minor phytoconstituent of turmeric known for its activity against inflammation, oxidative stress, cancerous, and metabolic disorders like Non-alcoholic fatty liver disease(NAFLD). Based on bioinformatic tools. Subsequently, the details of the interaction of critical proteins with Calebin-A were investigated using the molecular docking technique. Methods: We first probed the intersection of genes/ proteins between NAFLD and Calebin-A through online databases. Besides, we performed an enrichment analysis using the ClueGO plugin to investigate signaling pathways and gene ontology. Next, we evaluate the possible interaction of Calebin-A with significant hub proteins involved in NAFLD through a molecular docking study. Results: We identified 87 intersection genes Calebin-A targets associated with NAFLD. PPI network analysis introduced 10 hub genes (TP53, TNF, STAT3, HSP90AA1, PTGS2, HDAC6, ABCB1, CCT2, NR1I2, and GUSB). In KEGG enrichment, most were associated with Sphingolipid, vascular endothelial growth factor A (VEGFA), C-type lectin receptor, and mitogen-activated protein kinase (MAPK) signaling pathways. The biological processes described in 87 intersection genes are mostly concerned with regulating the apoptotic process, cytokine production, and intracellular signal transduction. Molecular docking results also directed that Calebin-A had a high affinity to bind hub proteins linked to NAFLD. Conclusion: Here, we showed that Calebin-A, through its effect on several critical genes/ proteins and pathways, might repress the progression of NAFLD.

MHIF-MSEA: a novel model of miRNA set enrichment analysis based on multi-source heterogeneous information fusion.

Introduction: MicroRNAs (miRNAs) are a class of non-coding RNA molecules that play a crucial role in the regulation of diverse biological processes across various organisms. Despite not encoding proteins, miRNAs have been found to have significant implications in the onset and progression of complex human diseases. Methods: Conventional methods for miRNA functional enrichment analysis have certain limitations, and we proposed a novel method called MiRNA Set Enrichment Analysis based on Multi-source Heterogeneous Information Fusion (MHIF-MSEA). Three miRNA similarity networks (miRSN-DA, miRSN-GOA, and miRSN-PPI) were constructed in MHIF-MSEA. These networks were built based on miRNA-disease association, gene ontology (GO) annotation of target genes, and protein-protein interaction of target genes, respectively. These miRNA similarity networks were fused into a single similarity network with the averaging method. This fused network served as the input for the random walk with restart algorithm, which expanded the original miRNA list. Finally, MHIF-MSEA performed enrichment analysis on the expanded list. Results and Discussion: To determine the optimal network fusion approach, three case studies were introduced: colon cancer, breast cancer, and hepatocellular carcinoma. The experimental results revealed that the miRNA-miRNA association network constructed using miRSN-DA and miRSN-GOA exhibited superior performance as the input network. Furthermore, the MHIF-MSEA model performed enrichment analysis on differentially expressed miRNAs in breast cancer and hepatocellular carcinoma. The achieved p-values were 2.17e(-75) and 1.50e(-77), and the hit rates improved by 39.01% and 44.68% compared to traditional enrichment analysis methods, respectively. These results confirm that the MHIF-MSEA method enhances the identification of enriched miRNA sets by leveraging multiple sources of heterogeneous information, leading to improved insights into the functional implications of miRNAs in complex diseases.

Identification and validation of regulatory T cell-associated gene signatures to predict colon adenocarcinoma prognosis.

Colon adenocarcinoma (COAD) is a common cause of cancer-related death. Due to the difficulty in early diagnosis and drug resistance, conventional treatments are difficult to be effective. Some studies have found that the functional recovery of T cells in the tumor microenvironment, especially regulatory T cells (Tregs), plays an important role in the progression of cancer. This study used the TCGA data set, clinical information and RNA-seq data of COAD patients to construct a Tregs-related risk score (TRS) through methods such as WGCNA, single-factor Cox, multi-factor Cox and random survival forest (RSF). Moreover, we also used the TCGA test set and internal validation set to verify the predictive ability of TRS, and used functional enrichment analysis and somatic mutation analysis to mine genes related to TRS, such as like thrombin/trypsin receptor 2 (F2RL2), inhibin subunit beta B (INHBB) and melanoma antigen family A12 (MAGEA12). Moreover, this study confirmed the expression of these prognostic genes using scRNA-seq data. We also performed qPCR analysis of various genes in normal and cancerous colon cancer cell lines to verify that these genes indeed play a role in CODA patients. We also constructed a mouse CODA model to study and evaluate the impact of key genes such as MAGEA12 on tumor growth in mice. This study explores the important role of Treg cells in the prognosis of COAD and discovers some potential biomarkers for the occurrence and development of COAD, which provides some new ideas for the treatment of COAD.

A System Biology Approach Reveals New Targets for Human Thyroid Gland Toxicity in Embryos and Adult Individuals.

Compounds of natural or synthetic origin present in personal care products, food additives, and packaging may interfere with hormonal regulation and are called endocrine-disrupting chemicals (EDCs). The thyroid gland is an important target of these compounds. The objective of this study was to analyze public data on the human thyroid transcriptome and investigate potential new targets of EDCs in the embryonic and adult thyroid glands. We compared the public transcriptome data of adult and embryonic human thyroid glands and selected 100 up- or downregulated genes that were subsequently subjected to functional enrichment analysis. In the embryonic thyroid, the most highly expressed gene was PRMT6, which methylates arginine-4 of histone H2A (86.21%), and the downregulated clusters included plasma lipoprotein particles (39.24%) and endopeptidase inhibitory activity (24.05%). For the adult thyroid gland, the most highly expressed genes were related to the following categories: metallothionein-binding metals (56.67%), steroid hormone biosynthetic process (16.67%), and cellular response to vascular endothelial growth factor stimulus (6.67%). Several compounds ranging from antihypertensive drugs to enzyme inhibitors were identified as potentially harmful to thyroid gland development and adult function.

Integrative methylome and transcriptome analysis reveals epigenetic regulation of Fusobacterium nucleatum in laryngeal cancer.

The aetiological mechanisms of Fusobacterium nucleatum in laryngeal cancer remain unclear. This study aimed to reveal the epigenetic signature induced by F. nucleatum in laryngeal squamous cell carcinoma (LSCC). Combined analysis of methylome and transcriptome data was performed to address the functional role of F. nucleatum in laryngeal cancer. Twenty-nine differentially expressed methylation-driven genes were identified by mapping the methylation levels of significant differential methylation sites to the expression levels of related genes. The combined analysis revealed that F. nucleatum promoted Janus kinase 3 (JAK3) gene expression in LSCC. Further validation found decreased methylation and elevated expression of JAK3 in the F. nucleatum-treated LSCC cell group; F. nucleatum abundance and JAK3 gene expression had a positive correlation in tumour tissues. This analysis provides a novel understanding of the impact of F. nucleatum in the methylome and transcriptome of laryngeal cancer. Identification of these epigenetic regulatory mechanisms opens up new avenues for mechanistic studies to explore novel therapeutic strategies.

Gene expression profiling of peripheral blood in patients with steroid-induced osteonecrosis of the femoral head.

Aim: Steroid-induced osteonecrosis of the femoral head (SONFH) is a severe complication following glucocorticoid therapy. This study aimed to identify the differential mRNA expression and investigate the molecular mechanisms of SONFH. Materials & methods: RNA sequencing was performed in eight SONFH patients, five non-SONFH patients and five healthy individuals. Results: A total of 1555, 3997 and 5276 differentially expressed mRNAs existed between the following combinations: SONFH versus non-SONFH, SONFH versus healthy subjects and non-SONFH versus healthy subjects. Increased ISM1 expression might contribute to a high risk of SONFH through antiangiogenesis. Decreased FOLR3 expression might affect the metabolism of homocysteine, leading to avascular necrosis of the femoral head. KCNJ2, which plays a pivotal role in regulating bone development, was also deregulated. Conclusion: ISM1, FOLR3 and KCNJ2 might be related to the occurrence of SONFH.

[Box: see text].

In Silico Models to Validate Novel Blood-Based Biomarkers.

Biological validation of preliminary findings is a key prerequisite in biomarker discovery. In recent years, the development of advanced large-scale sequencing technologies combined with high-throughput computational analysis methods led to the extraction of considerable amount of data from healthy and diseased tissues. Stored in large open-access repositories, these data can be accessed and interrogated by researchers aiming at understanding the biological rationale behind their results. These so called in silico analyses, in opposite to in vitro analyses, have gained increasing importance in recent years, becoming a major component of research projects and publications. However, making sense of the large amount of data available can be challenging and may lead to a misinterpretation of the data. To reduce the dimensionality of this data, recent years have seen the development of statistical m\ethods and advanced graph analytics which help researchers summarize the available data and draw appropriate conclusions. In this chapter we will describe three in silico methods to investigate the biological underpinnings of a panel of seven blood-based biomarkers of Alzheimer's disease.

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.

Proteomic Profiling of Dilated Cardiomyopathy Plasma Samples ─ Searching for Biomarkers with Potential to Predict the Outcome of Therapy.

Determination of the prognosis and treatment outcomes of dilated cardiomyopathy is a serious problem due to the lack of valid specific protein markers. Using in-depth proteome discovery analysis, we compared 49 plasma samples from patients suffering from dilated cardiomyopathy with plasma samples from their healthy counterparts. In total, we identified 97 proteins exhibiting statistically significant dysregulation in diseased plasma samples. The functional enrichment analysis of differentially expressed proteins uncovered dysregulation in biological processes like inflammatory response, wound healing, complement cascade, blood coagulation, and lipid metabolism in dilated cardiomyopathy patients. The same proteome approach was employed in order to find protein markers whose expression differs between the patients well-responding to therapy and nonresponders. In this case, 45 plasma proteins revealed statistically significant different expression between these two groups. Of them, fructose-1,6-bisphosphate aldolase seems to be a promising biomarker candidate because it accumulates in plasma samples obtained from patients with insufficient treatment response and with worse or fatal outcome. Data are available via ProteomeXchange with the identifier PXD046288.

Integrated analysis of long non-coding RNAs and mRNAs associated with condyloma acuminatum.

Condyloma acuminatum (CA) is a prevalent sexually transmitted disease caused by low-risk human papillomavirus infection, characterized by high transmission and recurrence rates. Long non-coding RNAs (lncRNAs) play a crucial role in regulating gene transcription and are involved in various biological processes. Although recent studies have demonstrated the involvement of lncRNAs in cervical cancer, their expression profile and function in CA remain poorly understood. In this study, we aimed to identify messenger RNA (mRNA) and lncRNA expression patterns in CA using high-throughput lncRNA sequencing. We found that 3033 differentially expressed genes (DEGs) and 1090 differentially expressed lncRNAs (DELs) were significantly altered in CA compared to healthy controls. The results from quantitative reverse transcription polymerase chain reaction and immunohistochemical staining are in accordance with the observed trends in the sequencing data. Functional enrichment analysis revealed that upregulated DEGs in CA were involved in biological processes such as virus response, immune response, cell cycle regulation, the tumor necrosis factor signaling pathway, and the P53 signaling pathway. Co-expression network analysis identified potential target genes of DELs, with enrichment in biological processes such as cell differentiation, the intrinsic apoptotic signaling pathway, and pathways such as virus infection, pathways in cancer, T helper 17 cell differentiation, the mitogen-activated protein kinase signaling pathway, and the Wnt signaling pathway. Collectively, our findings indicate significant changes in the transcriptome profile, including mRNAs and lncRNAs, in CA compared to healthy controls. Our study provides new insights into the potential functions of lncRNAs in the pathogenesis of CA and identifies potential therapeutic targets for this disease.

Gene signatures to therapeutics: Assessing the potential of ivermectin against t(4;14) multiple myeloma.

BACKGROUND: Multiple myeloma (MM) is a terminal differentiated B-cell tumor disease characterized by clonal proliferation of malignant plasma cells and excessive levels of monoclonal immunoglobulins in the bone marrow. The translocation, (t)(4;14), results in high-risk MM with limited treatment alternatives. Thus, there is an urgent need for identification and validation of potential treatments for this MM subtype. Microarray data and sequencing information from public databases could offer opportunities for the discovery of new diagnostic or therapeutic targets. AIM: To elucidate the molecular basis and search for potential effective drugs of t(4;14) MM subtype by employing a comprehensive approach. METHODS: The transcriptional signature of t(4;14) MM was sourced from the Gene Expression Omnibus. Two datasets, GSE16558 and GSE116294, which included 17 and 15 t(4;14) MM bone marrow samples, and five and four normal bone marrow samples, respectively. After the differentially expressed genes were identified, the Cytohubba tool was used to screen for hub genes. Then, the hub genes were analyzed using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis. Using the STRING database and Cytoscape, protein-protein interaction networks and core targets were identified. Potential small-molecule drugs were identified and validated using the Connectivity Map database and molecular docking analysis, respectively. RESULTS: In this study, a total of 258 differentially expressed genes with enriched functions in cancer pathways, namely cytokine receptor interactions, nuclear factor (NF)-κB signaling pathway, lipid metabolism, atherosclerosis, and Hippo signaling pathway, were identified. Ten hub genes (cd45, vcam1, ccl3, cd56, app, cd48, btk, ccr2, cybb, and cxcl12) were identified. Nine drugs, including ivermectin, deforolimus, and isoliquiritigenin, were predicted by the Connectivity Map database to have potential therapeutic effects on t (4;14) MM. In molecular docking, ivermectin showed strong binding affinity to all 10 identified targets, especially cd45 and cybb. Ivermectin inhibited t(4;14) MM cell growth via the NF-κB pathway and induced MM cell apoptosis in vitro. Furthermore, ivermectin increased reactive oxygen species accumulation and altered the mitochondrial membrane potential in t(4;14) MM cells. CONCLUSION: Collectively, the findings offer valuable molecular insights for biomarker validation and potential drug development in t(4;14) MM diagnosis and treatment, with ivermectin emerging as a potential therapeutic alternative.

Autophagic Regulation of Adipogenesis Through TP53INP2: Insights from In Silico and In Vitro Analysis.

Obesity is an epidemic disease associated with multimorbidity resulting in higher mortality risk. The imbalance between energy storage and expenditure is the prime factor in the prognosis of the disease. Specifically, excessive lipid storage through adipogenesis leads to obesity. Adipogenesis is the process that converts preadipocytes into mature adipocytes by regulating major transcription factors like PPARγ and C/EBPα, contributes to lipid storage in adipose tissue. On the contrary, autophagy is a self-degradative process that maintains homeostasis in adipose tissue by regulating adipogenesis and lipolysis. TP53INP2 is a key player that regulates the autophagy process, and it negatively regulates adipogenesis and lipid storage. The gene expression profile GSE93637 was retrieved from the GEO database and analyzed using an integrated bioinformatics approach. The differentially expressed genes (DEGs) were analyzed using R-Bioconductor for TP53INP2 knockdown microarray dataset of 3T3L1 cells, and the DEGs were analyzed for the functional enrichment analysis. Further, the genes involved in the potential biological and molecular functions were evaluated for pathway enrichment analysis by KEGG (Kyoto Encyclopedia of Genes and Genomes). A total of 726 DEGs were found including 391 upregulated and 335 downregulated genes. Further, the functional and pathway enrichment analysis was employed to identify the highly interacting genes, and we identified a total of 56 genes that are highly interacting through a protein-protein interaction network. The DEGs mainly regulate the Peroxisome proliferator-activated receptor (PPAR) signaling pathway, lipolysis, and autophagy. Further, we investigated the associated Hub genes for enriched pathway genes and found the involvement of two autophagic genes ATG7 and sequestosome 1 (p62). In addition, in vitro studies of qRT-PCR (Quantitative real-time polymerase chain reaction) and Western blot analysis revealed that increased autophagy resulted in reduced lipid storage through down-regulation of the adipogenic gene. Moreover, increased expression of autophagic gene TP53INP2 and ATG7 facilitates the down-regulation of p62 and PPARγ gene resulting in lipolysis in mature adipocytes through autophagy. There is no specific treatment to reduce obesity other than a caloric diet and exercise. Hence, this study provides sufficient evidence to conclude that TP53INP2 negatively regulates adipogenesis and increases the degradation of lipids in mature adipocytes which is crucial for reducing obesity. Therefore, it is plausible to consider TP53INP2 as a promising therapeutic target for managing adipogenesis and obesity. However, further studies are necessary to validate their functional and molecular pathway analysis in the regulation of adipogenesis and obesity.

NUSAP1 promotes pancreatic ductal adenocarcinoma progression by drives the epithelial-mesenchymal transition and reduces AMPK phosphorylation.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) has a poor prognosis, and its molecular mechanisms are unclear. Nucleolar and spindle-associated protein 1 (NUSAP1), an indispensable mitotic regulator, has been reported to be involved in the development of several types of tumors. The biological function and molecular mechanism of NUSAP1 in PDAC remain controversial. This study explored the effects and mechanism of NUSAP1 in PDAC. METHODS: Differentially expressed genes (DEGs) were screened. A protein‒protein interaction (PPI) network was constructed to identify hub genes. Experimental studies and tissue microarray (TMA) analysis were performed to investigate the effects of NUSAP1 in PDAC and explore its mechanism. RESULTS: Network analysis revealed that NUSAP1 is an essential hub gene in the PDAC transcriptome. Genome heterogeneity analysis revealed that NUSAP1 is related to tumor mutation burden (TMB), loss of heterozygosity (LOH) and homologous recombination deficiency (HRD) in PDAC. NUSAP1 is correlated with the levels of infiltrating immune cells, such as B cells and CD8 T cells. High NUSAP1 expression was found in PDAC tissues and was associated with a poor patient prognosis. NUSAP1 promoted cancer cell proliferation, migration and invasion, drives the epithelial-mesenchymal transition and reduces AMPK phosphorylation. CONCLUSIONS: NUSAP1 is an essential hub gene that promotes PDAC progression and leads to a dismal prognosis by drives the epithelial-mesenchymal transition and reduces AMPK phosphorylation.