limma powers differential expression analyses for RNA-sequencing and microarray studies

Transcriptome analysis unveils PLSCR1 associated with microglial polarization in neuropathic pain

Neuropathic pain (NP) continues to be a significant problem that lacks effective treatment. Our study sought to explore a new promising gene target for the treatment of NP. Differential and enrichment analyses were performed on 24,197 genes and 12,088 genes from the NP microglial microarray and sequencing dataset. Candidate differentially expressed genes (DEGs), functions, and signaling pathways that are closely related to NP were identified by analyzing the bioinformatic results. For in vivo experiments, mice were divided into the sham and NP groups. The expressions of DEGs were validated to screen out the NP hub genes. For in vitro experiments, the hub genes in resting M0-BV2 and polarized M1-BV2 microglia were examined by immunofluorescence, flow cytometry, and qRT-PCR. DEGs in the NP microarray and sequencing data shared five candidate genes, CD244, MEGF9, PCGF2, PLSCR1, and NECAB2. The results of the in vivo experiment showed that the NP model group exhibited higher expression of PLSCR1 and MEGF9 compared to the sham group. The enrichment results of the DEGs revealed the biological processes of "response to lipopolysaccharide". PLSCR1 was highly expressed in the lipopolysaccharide-induced M1-BV2 microglia. PLSCR1 is a potential gene associated with microglial polarization in NP. These findings provide a new view on understanding the pathogenesis mechanism of NP.

Mechanism investigation of Forsythoside A against esophageal squamous cell carcinoma in vitro and in vivo

CONTEXT

Forsythoside A (FSA) was extracted from Forsythia suspensa, a traditional Chinese medicine, which has been demonstrated to exert anti-inflammatory, antibacterial, and other pharmacological effects. However, the anticancer effect of FSA in esophageal squamous cell carcinoma (ESCC) has not been documented.

OBJECTIVE

The present study aimed to elucidate the mechanism of FSA against ESCC.

MATERIALS AND METHODS

Network pharmacology and molecular docking were employed to predict the mechanism. FSA was utilized to treat ESCC cell lines KYSE450 and KYSE30, followed by CCK-8 assay, cell cloning formation assay, flow cytometry, Western blot, RNA-seq analysis, and subsequent in vivo experiments.

RESULTS

Network pharmacology and molecular docking predicted that the therapeutic effect of FSA in ESCC is mediated through proteins such as BCL2 and BAX, influencing KEGG pathways associated with apoptosis. In vitro experiments showed that FSA inhibited cell proliferation and plate clone formation, promoted cell apoptosis and impacted the cell cycle distribution of G2/M phase by regulating BCL2, BAX, and p21. Further RNA-seq in KYSE450 cells showed that FSA regulated the expression of 223 genes, specifically affecting the biological process of epidermal development. In vivo experiments showed that gastric administration of FSA resulted in notable reductions in both tumor volume and weight by regulating BCL2, BAX, and p21. 16S rRNA sequencing showed that FSA led to significant changes of beta diversity. Abundance of 11 specific bacterial taxa were considerably changed following administration of FSA.

CONCLUSIONS

This study presents a novel candidate drug against ESCC and establishes a foundation for future clinical application.

Identification of driving genes of recurrent miscarriage based on transcriptome sequencing and immunoinfiltration analysis

AIMS

Recurrent miscarriage (RM) plagues 1 %-5 % women of childbearing age. Facing the limitations of clinical treatment, its pathological mechanism remains to be clarified.

METHODS

Decidual tissues of three induced abortions and three RM were collected for transcriptome sequencing. The pathological features of RM were identified by differential expression genes (DEGs) analysis, GSEA, GO and KEGG analysis, and a protein-protein interaction network was constructed for DEGs, and six algorithms were used to identify hub genes. In addition, the immune characteristics of RM patients were identified by CIBERSORT, and the correlation between them and hub genes was analyzed. Furthermore, in single-cell level, different cells were grouped according to the expression level of hub genes, and the expression ratio and abundance of hub genes in different cells and their regulation on cell function were explored.

RESULTS

Transcriptome sequencing of patients with RM showed that a large number of genes were down-regulated, which was related to fibroblast proliferation, epithelial cell migration, female pregnancy and cell chemotaxis. Fifteen hub genes were identified by constructing a protein-protein interaction network, among which DUSP1, NR4A1 and THBS1 were involved in cell migration and chemotaxis. Immune cell infiltration analysis showed that the infiltration of T cells, macrophages and NK cells was abnormal, and there was a significant correlation with hub genes. Moreover, we found that compared with the expression of DUSP1, the non-expression of DUSP1 will reduce the extracellular matrix formation of fibroblasts and the chemotaxis of macrophages. At the same time, it is worth noting that the expression ratio and abundance of hub genes are decreased in epithelial cells, fibroblasts, macrophages and NK cells. Furthermore, single-cell analysis and in vitro and in vivo experiments show that DUSP1 and NR4A1 are low-expressed in different cells of RM patients, which is accompanied by the inhibition of fibroblast proliferation and macrophage chemotaxis. Drug prediction and screening based on hub genes show that Cinobufagin and calmidazolium are expected to be candidate drugs for RM.

CONCLUSION

Hub genes such as DUSP1, NR4A1 and THBS1 participate in RM by regulating epithelial cell migration, fibroblast proliferation and macrophage chemotaxis, which will provide new insight for the diagnosis and targeted therapy of RM.

Integrative analysis of anoikis-related genes prognostic signature with immunotherapy and identification of CDKN3 as a key oncogene in lung adenocarcinoma

Anoikis, a form of programmed cell death induced by loss of cell contact, is closely associated with tumor invasion and metastasis, making it highly significant in lung cancer research. We examined the expression patterns and prognostic relevance of Anoikis-related genes (ARGs) in lung adenocarcinoma (LUAD) using the TCGA-LUAD database. This study identified molecular subtypes associated with Anoikis in LUAD and conducted functional enrichment analyses. We constructed an ARG risk score using univariate least absolute shrinkage and selection operator (LASSO) Cox regression, validated externally with GEO datasets and clinical samples. The clinical applicability of the prognostic model was evaluated using nomograms, calibration curves, decision curve analysis (DCA), and time-dependent AUC assessments. We identified four prognostically significant genes (PLK1, SLC2A1, CDKN3, PHLDA2) and two ARG-related molecular subtypes. ARGs were generally upregulated in LUAD and correlated with multiple pathways including the cell cycle and DNA replication. The prognostic model indicated that the low-risk group had better outcomes and significant correlations with clinicopathological features, tumor microenvironment, immune therapy responses, drug sensitivity, and pan-RNA epigenetic modification-related genes. Patients with low-risk LUAD were potential beneficiaries of immune checkpoint inhibitor (ICI) therapy. Prognostic ARGs' distribution and expression across various immune cell types were further analyzed using single-cell RNA sequencing. The pivotal role of CDKN3 in LUAD was confirmed through qRT-PCR and gene knockout experiments, demonstrating that CDKN3 knockdown inhibits tumor cell proliferation, migration, and invasion. Additionally, we constructed a ceRNA network involving CDKN3/hsa-miR-26a-5p/SNHG6, LINC00665, DUXAP8, and SLC2A1/hsa-miR-218-5p/RNASEH1-AS1, providing new insights for personalized and immune therapy decisions in LUAD patients.

Isosilybin A exhibits anti-inflammatory properties in rosacea by inhibiting MAPK pathway and M1 macrophage polarization

Rosacea is a chronic inflammatory skin disease, which is prone to flares and requires continuous management and treatment. However, long-term use of drugs can lead to additional adverse drug reactions. Based on the comorbid relationship between rosacea and Parkinson's disease, bioinformatics and network pharmacology analysis were used to identify a safer drug for rosacea. It has been demonstrated that ISA has an ameliorative impact on the symptoms of Parkinson's disease. The results demonstrated that ISA exhibited anti-inflammatory properties, including reducing erythema areas and inflammatory cell infiltration in rosacea-like mice models, and inhibiting the expression of inflammatory factors in cellular inflammation models. Furthermore, the anti-inflammatory effect of ISA was associated with inhibition of the Erk, p38 and NF-κB signaling pathways and inhibition of macrophage polarization to M1 type. In addition, molecular docking and drug affinity responsive target stability experiment results indicated that VEGFA and RELA were the direct targets of ISA in the treatment for rosacea. In conclusion, these results suggested that ISA may be a potential therapeutic agent for rosacea.

Identification and validation of diagnostic markers related to immunogenic cell death and infiltration of immune cells in diabetic nephropathy

INTRODUCTION

Immunogenic cell death (ICD) is a unique cell death triggered by chemotherapy. However, studies elucidating the potential therapeutic role of ICD and the underlying mechanism in diabetic nephropathy (DN) are limited.

METHODS

WGCNA was conducted on the human kidney biopsy data linked to DN, analyzing gene sets associated with ICD. Gene Set Enrichment Analysis and Gene Set Variation Analysis were utilized to examine the discrepancy in biological function. We used Gene Ontology, the Kyoto Encyclopedia of Genes and Genomes, and the GeneMANIA database to investigate the function of the signature genes. An analysis using the receiver operating characteristic (ROC) was conducted to validate the diagnostic value of hub genes. Additionally, immune infiltration-related analyses were also performed. In conclusion, we examined the association between the glomerular filtration rate, serum creatinine, and hub genes. Hub genes were validated by immunohistochemistry using db/db mice kidneys.

RESULTS

WGCNA revealed that the targets in the turquoise unit (1674 genes) exhibited the highest positive correlation with ICD. Furthermore, 4222 statistically significant DEGs were identified when comparing the DN and healthy control groups. Significantly, the KEGG pathway enrichment analysis indicated a connection between ICD and the nuclear factor-kappa B signaling pathway and the synthesis of cytokines (tumor necrosis factor superfamily). ROC analysis revealed that 16 hub genes exhibited strong discriminatory potential as biomarkers for DN. Therefore, immunohistochemical validation, with the potential involvement of chemokines (CCL11, CCR2, CCR7, CX3CR1, CXCL10, CXCL12, and CXCR5) and immune cells (CD3G, CD5, and CD247) may be crucial for the diagnosis and therapy of DN.

CONCLUSIONS

DKK3, NR4A1, NR4A2, VEGFA, and DUSP1 may be associated with the development of DN. The pathogenesis of DN may specifically involve chemokines (CCL11, CCR2, CCR7, CX3CR1, CXCL10, CXCL12, and CXCR5) and immune cells (CD3G, CD5, and CD247), with LCP2 playing a significant role.

Identification of UNC5B as a novel aggressive biomarker for osteosarcoma based on basement membrane genes

BACKGROUND

The prognosis of patients with metastatic osteosarcoma is poor, and the variation of basement membrane genes (BMGs) is associated with cancer metastasis. However, the role of BMGs in osteosarcoma has been poorly studied.

METHODS

BMGs were collected and differentially expressed BMGs (DE-BMGs) were found through difference analysis. DE-BMGs were further screened by univariate Cox regression and Lasso regression analyses, and six key BMGs were identified and defined as basement membrane genes signatures (BMGS). Then, BMGS was used to construct the osteosarcoma BMGS risk score system, and the osteosarcoma patients were divided into high- and low-risk groups based on the median risk score. Single-sample gene set enrichment analysis (ssGSEA) and ESTIMATE scores were used to investigate the differences in immune infiltration between the two scoring groups. Additionally, we investigated whether UNC5B affects various features in tumors by bioinformatic analysis and whether UNC5B was involved in multiple biological functions of osteosarcoma cells by wound healing assay, transwell assay, and western blot.

RESULTS

The osteosarcoma BMGS risk score reliably predicts the risk of metastasis, patient prognosis, and immunity. UNC5B expression was elevated in osteosarcoma, and correlated with various characteristics such as immune infiltration, prognosis, and drug sensitivity. In vitro assays showed that UNC5B knockdown reduced osteosarcoma cells' capacity for migration and invasion, and EMT process.

CONCLUSION

A novel BMGS risk score system that can effectively predict the prognosis of osteosarcoma was developed and validated. The UNC5B gene in this system is one of the key aggressive biomarkers of osteosarcoma.

Identification of key mitochondria-related genes and their potential crosstalk role with immune pattern in Idiopathic pulmonary fibrosis

BACKGROUND

Idiopathic pulmonary fibrosis (IPF) stands out as a life-threatening and one of the most severe interstitial lung diseases. The pathogenesis of IPF is not fully understood, while recent studies have highlighted the association of mitochondrial dysfunction with IPF. This study is dedicated to pinpointing crucial genes related to mitochondria that potentially impact the advancement of IPF, thereby offering new perspectives on the pathogenesis of this condition.

METHODS

The Gene Expression Omnibus (GEO) database was utilized to download three datasets (GSE32537, GSE92592, and GSE150910), following which a comprehensive analysis was conducted to identify differentially expressed mitochondria-related genes (DEMTRGs) in the IPF lung tissues. Subsequently, GO and KEGG enrichment analysis of the DEMTRGs was performed. Next, external datasets and in vivo experiments were performed to validate their expression. Additionally, a Logistic regression model based on key DEMTRGs was constructed, and the model's ability to distinguish between IPF and controls was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC). Finally, gene set enrichment analysis (GSEA) and CIBERSORT algorithm were conducted.

RESULTS

We identified five key DEMTRGs (ALDH18A1, ALDH1B1, MCCC1, ACAT1, and PDHA1), ALDH18A1 and ALDH1B1 exhibited upregulated expression levels, whereas MCCC1, ACAT1, and PDHA1 showed downregulation in the lung tissue of individuals with IPF. The expression levels of these key DEMTRGs were validated by an independent external dataset (GSE53845) and the bleomycin-induced pulmonary fibrosis mice. In addition, the ROCs indicated that the diagnostic model constructed based on key DEMTRGs could effectively distinguish between IPF and controls (AUC>0.8). GSEA analysis and immune-related analysis shed light on the potential mechanisms through which these key DEMTRGs influence IPF.

CONCLUSION

Our research has pinpointed key genes associated with mitochondria that may ultimately contribute to the progression of IPF by exerting regulatory effects on mitochondrial function, thereby influencing multiple cellular processes.

Integrative analysis of single-Cell RNA sequencing and experimental validation in the study of abdominal aortic aneurysm progression

BACKGROUND

Abdominal aortic aneurysm (AAA) is a complex vascular disorder characterized by the progressive dilation of the abdominal aorta, with a high risk of rupture and mortality. Understanding the cellular interactions and molecular mechanisms underlying AAA development is critical for identifying potential therapeutic targets.

METHODS

This study utilized datasets GSE197748, GSE164678 and GSE183464 from the GEO database, encompassing bulk and single-cell RNA sequencing data from AAA and control samples. We performed principal component analysis, differential expression analysis, and functional enrichment analysis to identify key pathways involved in AAA. Cell-cell interactions were investigated using CellPhoneDB, focusing on fibroblasts, vascular smooth muscle cells (VSMCs), and macrophages. We further validated our findings using a mouse model of AAA induced by porcine pancreatic enzyme infusion, followed by gene expression analysis and co-immunoprecipitation experiments.

RESULTS

Our analysis revealed significant alterations in gene expression profiles between AAA and control samples, with a pronounced immune response and cell adhesion pathways being implicated. Single-cell RNA sequencing data highlighted an increased proportion of pro-inflammatory macrophages, along with changes in the composition of fibroblasts and VSMCs in AAA. CellPhoneDB analysis identified critical ligand-receptor interactions, notably collagen type I alpha 1 chain (COL1A1)/COL1A2-CD18 and thrombospondin 1 (THBS1)-CD3, suggesting complex communication networks between fibroblasts and VSMCs. In vivo experiments confirmed the upregulation of these genes in AAA mice and demonstrated the functional interaction between COL1A1/COL1A2 and CD18.

CONCLUSION

The interaction between fibroblasts and VSMCs, mediated by specific ligand-receptor pairs such as COL1A1/COL1A2-CD18 and THBS1-CD3, plays a pivotal role in AAA pathogenesis.

Clinical and proteomic profiles of chronic kidney disease in heart failure with reduced and preserved ejection fraction

BACKGROUND

Chronic kidney disease (CKD) is prevalent and related to poor clinical outcomes in patients with heart failure (HF). The pathophysiology of CKD in HF with a reduced ejection fraction (HFrEF) and HF with a preserved ejection fraction (HFpEF) is not well defined. In this study we compared clinical and proteomic profiles of CKD between patients with HFrEF and HFpEF.

METHODS

We included 478 patients of the Scottish BIOSTAT-CHF cohort, of which 246 had HFrEF and 232 had HFpEF. CKD was defined as an eGFR <60 mL/min/1.73m2. We compared HFrEF-patients with CKD to HFpEF-patients with CKD using logistic- and Cox-regression. We performed a differential expression analysis using 6376 proteins.

RESULTS

The prevalence of CKD was 36 % and 32 % in patients with HFpEF and HFrEF, respectively. CKD patients were on average 7 years older. BMI, higher NT-proBNP, ACE-inhibitors, HDL-cholesterol and Stroke were associated with CKD- patients with HFrEF. In HFpEF, CKD was associated with MRA-use and higher platelet count. CKD was associated with increased risk of death or heart failure hospitalization (HR 1.82, p < 0.001), with similar effect in HFrEF and HFpEF. The pattern of differentially expressed proteins between patients with and without CKD was similar in both HF-groups.

CONCLUSION

Clinical profiles related to CKD- patients with HFrEF were different from CKD-patients with HFrEF. CKD was associated with an increased risk of death or heart failure hospitalization, which was not different between HFpEF and HFrEF. Patterns of circulating proteins were similar between CKD-patients with HFpEF and HFrEF, suggesting no major differences in CKD-pathophysiology.

Overexpression of Basonuclin Zinc Finger Protein 2 in stromal cell is related to mesenchymal phenotype and immunosuppression of mucinous colorectal adenocarcinoma

BACKGROUND

Mucinous carcinoma (MC) is a distinct histologic subtype of colorectal cancer (CRC) that is less studied and associated with poor prognosis. This study aimed to identify MC-specific therapeutic targets and biomarkers to improve the prognosis of this aggressive disease.

METHODS

CRC samples from The Cancer Genome Atlas (TCGA) were categorized into MC and non-MC (NMC) groups based on histologic type. A multi-scale embedded gene co-expression network analysis (MEGENA) was constructed to identify gene modules associated with the MC group. The potential functions of Basonuclin Zinc Finger Protein 2 (BNC2) were further analyzed using the Biomarker Exploration for Solid Tumors (BEST) database. In vivo and in vitro experiments were conducted to validate the predicted results.

RESULTS

We identified the stromal component-related gene, BNC2, in the MC population. This gene is associated with a shorter progression-free interval (PFI) in CRC patients. BNC2 promotes FAP (encoding Fibroblast Activation Protein Alpha) transcription in cancer-associated fibroblasts (CAFs) and is involved in angiogenesis through two pathways. Additionally, BNC2 enhances tumor cell invasiveness in a CAF-dependent manner. Patients with high BNC2 expression benefited less from immunotherapy compared to those with low BNC2 expression.

CONCLUSIONS

Our study highlights the clinical importance of BNC2 in MC, and targeting BNC2 on stromal cells (fibroblasts and endothelial cells) may be an effective strategy for treating MC.

Investigating the molecular mechanisms between type 1 diabetes and mild cognitive impairment using bioinformatics analysis, with a focus on immune response

The immune system is involved in the development and progression of several diseases. Type 1 diabetes mellitus (T1DM), an autoimmune disorder, influences the progression of several other conditions; however, the link between T1DM and mild cognitive impairment (MCI) remains unclear. This study investigated the underlying immune response mechanisms that contribute to the development and progression of T1DM and MCI. Microarray datasets for MCI (GSE63060) and T1DM (GSE30208) were retrieved from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified using the limma package. To explore the functional implications of these DEGs, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were conducted using ClusterProfiler. Protein-protein interaction networks for the DEGs were constructed using the STRING database and visualized using Cytoscape. The Molecular Complex Detection algorithm was used to analyze DEGs. Immune cell infiltration in patients with T1DM and MCI was analyzed using the xCell method. Gene set enrichment analysis was used to gain in-depth insights into the functional characteristics of T1DM and MCI. Immune-related genes were obtained from the GeneCards and ImmPort databases. Machine learning algorithms were used to identify potential hub genes associated with immunity for T1DM and MCI diagnosis, and the diagnostic value was assessed using the receiver operating characteristic curve. The identified genes were validated using quantitative polymerase chain reaction. In the T1DM and MCI datasets, 610 DEGs showed consistent trends, of which 232 and 378 were upregulated and downregulated, respectively. Immune response analysis revealed significant changes in the immune cells associated with MCI and T1DM. Using immune-related genes, DEGs, and machine learning techniques, we identified CD3D in the MCI and T1DM groups. We observed a gradual decline in the cognitive function of mice with T1DM as the disease progressed. CD3D expression increased with increasing disease severity; CD3D primarily affected CD4+ T cells. This study revealed a complex interaction between T1DM and MCI, providing novel insights into the intricate relationship between immune dysregulation and cognitive impairment and expanding our understanding of these two interconnected disorders. These findings will facilitate the development of therapeutic interventions and identification of potential therapeutic targets.

Identification and experimental validation of genes associated with programmed cell death in dendritic cells of the thyroid tissue in Hashimoto's thyroiditis

INTRODUCTION

Hashimoto's thyroiditis (HT) is a chronic autoimmune disorder. As antigen-presenting cells, dendritic cells(DCs) play a pivotal role in inducing programmed cell death (PCD) types, contributing to immune disorders. This study aimed to identify genes associated with multiple PCD pathways in dendritic cells within the thyroid tissue of patients with HT.

METHODS

The single-cell RNA-sequencing dataset HRA001684 was obtained from the National Genomics Data Center (NGDC) to calculate the area under the curve (AUC) scores for PCD-related genes. Additionally, mRNA sequencing datasets GSE138198 and HRA001684 were sourced from the Gene Expression Omnibus(GEO) and NGDC, respectively. Differentially expressed genes (DEGs) were identified by comparing normal and HT groups in GSE138198 and HRA001684. The intersection of these DEGs with PCD-related genes led to the identification of 17 PCD-related DEGs(PCDDEGs).

RESULTS

AUC scores revealed that DCs in HT exhibited significantly elevated levels of necroptosis, ferroptosis, pyroptosis, autophagy, and PANoptosis, expressing six key PCDDEGs: TNFAIP3, CYBB, PTPN6, STAT1, TGFB1, and NLRP3. These genes displayed an AUC>0.8 for HT in the GSE29315, GSE138198, and HRA001684 datasets, confirming their diagnostic accuracy. Moreover, their expression was positively correlated with the serum levels of thyroid peroxidase and thyroglobulin antibodies, while the expression of all PCDDEGs was negatively correlated with the abundance of thyroid follicular epithelial cells. qRT-PCR, WB, IHC, and IF experiments further confirmed the differences in PCDDEGs gene and protein levels in HT patients.

DISCUSSION

These findings highlight the crucial role of DCs in mediating PCD within the thyroid tissues of HT patients. The identified PCDDEGs-TNFAIP3, CYBB, PTPN6, STAT1, TGFB1, and NLRP3-may significantly contribute to HT pathogenesis through PCD pathways.

SORBS2-Mediated inhibition of malignant behaviors in esophageal squamous cell carcinoma through TIMP3

BACKGROUND

Esophageal squamous cell carcinoma (ESCC) is characterized by high invasiveness and poor prognosis. The role of Sorbin and SH3 domain-containing protein 2 (SORBS2) in ESCC remains largely unexplored.

METHODS

The expression levels of SORBS2 in ESCC were detected using RNA-seq and proteomics data. The biological functions of SORBS2 in ESCC were investigated through in vivo and in vitro experiments. The mechanism of SORBS2 was explored using RIP-seq technology, which identified the key downstream molecule metalloproteinase-3 (TIMP3). The interaction between SORBS2 and TIMP3, including specific binding sites, was validated through RIP-qPCR and RNA pull-down assays. The impact of altered SORBS2 expression in ESCC on HUVECs was assessed using endothelial tube formation assays.

RESULTS

SORBS2 expression was significantly downregulated in ESCC tissues, and its decreased expression was associated with poor prognosis. Overexpression of SORBS2 in ESCC cell lines inhibited cell proliferation, migration, and invasion both in vitro and in vivo. Mechanistically, SORBS2 bound to the 3' UTR of TIMP3 mRNA, enhancing its stability and thereby regulating TIMP3 expression. Rescue experiments demonstrated that increased TIMP3 expression could reverse the promotive effects of SORBS2 knockdown on ESCC, confirming TIMP3 as a critical downstream molecule of SORBS2. Furthermore, downregulation of SORBS2 in ESCC cells was associated with activation of HUVEC functions, whereas upregulation of TIMP3 could reverse this effect. The SORBS2/TIMP3 axis may exert tumor suppressive effects by influencing extracellular matrix degradation.

CONCLUSION

This study confirms that SORBS2 inhibits ESCC tumor progression by regulating extracellular matrix degradation through TIMP3, providing a potential therapeutic target for future treatment interventions.

IFIT2 mediates iron retention and cholesterol efflux in atherosclerosis

BACKGROUND

Abnormalities in iron and lipid metabolism are recognized as key contributors to atherosclerosis (AS). Therefore, this study proposes to characterize the biomarker related to iron and lipid metabolism in AS using bioinformatics, animal, and cell experiments.

METHODS

The limma package was utilized to identify differentially expressed genes (DEGs) in GSE70126 and GSE70619 datasets, and biomarkers were screened using enrichment analysis and PPI networks. IFIT2 was knocked down using shRNA lentivirus in a high fat diet (HFD)-induced APOE-/- AS model to investigate its effects of IFIT2 on the pathology, iron retention, and lipid accumulation. Iron storage-related and cholesterol efflux-related proteins were evaluated following exogenous modulation of IFIT2 expression in ox-LDL-induced foamy macrophages.

RESULTS

Compared to non-foamy macrophages from the aorta, 189 and 4152 DEGs were identified in foamy macrophages within the GSE70126 and GSE70619 datasets, respectively. Moreover, intersecting DEGs may modulate immune responses, cell adhesion, vascular permeability, and oxidative stress through NF-kappa B, Wnt, TNF and HIF-1 signaling pathways. Notably, IFIT2 was significantly upregulated in foamy macrophages and AS models. In vivo, IFIT2 co-localized with foamy macrophages, and its knockdown led to reductions in plasma lipid levels, plaque area, immune infiltration, iron retention, and lipid accumulation. In vitro, IFIT2 knockdown alleviated the ox-LDL-induced increase in iron storage-related proteins (Ferritin-L and Ferritin-H) and iron (Fe2+ and Fe3+) in foamy macrophages. Furthermore, IFIT2 knockdown reduced lipid accumulation and upregulated cholesterol efflux-related proteins (PPARγ, LXRα, ABCA1, and ABCG1) in foamy macrophages.

CONCLUSION

IFIT2 knockdown attenuates iron retention and lipid accumulation in AS plaques, and facilitated cholesterol efflux from foamy macrophages via the PPARγ/LXRα/ABCA1-ABCG1 pathway.

Chicoric acid acts as an ALOX15 inhibitor to prevent ferroptosis in asthma

BACKGROUND

Chicoric acid (CA) is a crucial immunologically active compound found in chicory and echinacea, possessing a range of biological activities. Ferroptosis, a type of iron-dependent cell death induced by lipid peroxidation, plays a key role in the development and advancement of asthma. Targeting ferroptosis could be a potential therapeutic strategy for treating asthma.

PURPOSE

The purpose of this study was to explore the screening of ALOX15, a pivotal target of ferroptosis in asthma, and potential therapeutic agents, as well as to investigate the promising potential of CA as an ALOX15 inhibitor for modulating ferroptosis in asthma.

METHODS

Through high-throughput data processing of bronchial epithelial RNA from asthma patients using bioinformatics and machine learning, the key target of ferroptosis in asthma, ALOX15, was identified. An inhibitor of ALOX15 was then obtained through high-throughput molecular docking and molecular dynamics simulation tests. In vitro experiments were conducted using a 16HBE cell model induced by house dust mite (HDM) and lipopolysaccharide (LPS), which were treated with the ALOX15 inhibitor (PD146176), CA treatment, or ALOX15 knockdown. In vivo experiments were also carried out using a mouse model induced by HDM and LPS.

RESULTS

The composite model of ALOX15 and CA in molecular dynamics simulations shows good stability and flexibility. Network pharmacological analysis reveals that CA regulates ferroptosis through ALOX15 in treating asthma. In vitro studies show that ALOX15 is highly expressed in HDM and LPS treatments, while CA inhibits HDM and LPS-induced ferroptosis in 16HBE cells by reducing ALOX15 expression. Knockdown of ALOX15 has the opposite effect. Metabolomics analysis identifies key compounds associated with ferroptosis, including L-Targinine, eicosapentaenoic acid, 16-hydroxy hexadecanoic acid, and succinic acid. In vivo experiments demonstrate that CA suppresses ALOX15 expression, inhibits ferroptosis, and improves asthma symptoms in mice.

CONCLUSION

Our research initially identified CA as a promising asthma treatment that effectively blocks ferroptosis by specifically targeting ALOX15. This study not only highlights CA as a potential therapeutic agent for asthma but also introduces novel targets and treatment options for this condition, along with innovative approaches for utilizing natural compounds to target diseases associated with ferroptosis.

Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review

In recent years, the role of bioinformatics and computational biology together with omics techniques and transcriptomics has gained tremendous importance in biomedicine and healthcare, particularly for the identification of biomarkers for precision medicine and drug discovery. Differential gene expression (DGE) analysis is one of the most used techniques for RNA-sequencing (RNA-seq) data analysis. This tool, which is typically used in various RNA-seq data processing applications, allows the identification of differentially expressed genes across two or more sample sets. Functional enrichment analyses can then be performed to annotate and contextualize the resulting gene lists. These studies provide valuable information about disease-causing biological processes and can help in identifying molecular targets for novel therapies. This review focuses on differential gene expression (DGE) analysis pipelines and bioinformatic techniques commonly used to identify specific biomarkers and discuss the advantages and disadvantages of these techniques.

Alpha1-antitrypsin impacts innate host-pathogen interactions with Candida albicans by stimulating fungal filamentation

Our immune system possesses sophisticated mechanisms to cope with invading microorganisms, while pathogens evolve strategies to deal with threats imposed by host immunity. Human plasma protein α1-antitrypsin (AAT) exhibits pleiotropic immune-modulating properties by both preventing immunopathology and improving antimicrobial host defence. Genetic associations suggested a role for AAT in candidemia, the most frequent fungal blood stream infection in intensive care units, yet little is known about how AAT influences interactions between Candida albicans and the immune system. Here, we show that AAT differentially impacts fungal killing by innate phagocytes. We observed that AAT induces fungal transcriptional reprogramming, associated with cell wall remodelling and downregulation of filamentation repressors. At low concentrations, the cell-wall remodelling induced by AAT increased immunogenic β-glucan exposure and consequently improved fungal clearance by monocytes. Contrastingly, higher AAT concentrations led to excessive C. albicans filamentation and thus promoted fungal immune escape from monocytes and macrophages. This underscores that fungal adaptations to the host protein AAT can differentially define the outcome of encounters with innate immune cells, either contributing to improved immune recognition or fungal immune escape.

Integration of miRNA expression analysis of purified leukocytes and whole blood reveals blood-borne candidate biomarkers for lung cancer

Changes in leukocyte populations may confound the disease-associated miRNA signals in the blood of cancer patients. We aimed to develop a method to detect differentially expressed miRNAs from lung cancer whole blood samples that are not influenced by variations in leukocyte proportions. The Ref-miREO method identifies differential miRNAs unaffected by changes in leukocyte populations by comparing the within-sample relative expression orderings (REOs) of miRNAs from healthy leukocyte subtypes and those from lung cancer blood samples. Over 77% of the differential miRNAs observed between lung cancer and healthy blood samples overlapped with those between myeloid-derived and lymphoid-derived leukocytes, suggesting the potential impact of changes in leukocyte populations on miRNA profile. Ref-miREO identified 16 differential miRNAs that target 19 lung adenocarcinoma-related genes previously linked to leukocytes. These miRNAs showed enrichment in cancer-related pathways and demonstrated high potential as diagnostic biomarkers, with the LASSO regression models effectively distinguishing between healthy and lung cancer blood or serum samples (all AUC > 0.85). Additionally, 12 of these miRNAs exhibited significant prognostic correlations. The Ref-miREO method offers valuable candidates for circulating biomarker detection in cancer that are not affected by changes in leukocyte populations.

ExomiRHub: A comprehensive database for hosting and analyzing human disease-related extracellular microRNA transcriptomics data

Extracellular microRNA (miRNA) expression data generated by different laboratories exhibit heterogeneity, which poses challenges for biologists without bioinformatics expertise. To address this, we introduce ExomiRHub (http://www.biomedical-web.com/exomirhub/), a user-friendly database designed for biologists. This database incorporates 191 human extracellular miRNA expression datasets associated with 112 disease phenotypes, 62 treatments, and 24 genotypes, encompassing 29,198 and 23 sample types. ExomiRHub also integrates 16,012 miRNA transcriptomes of 156 cancer subtypes from The Cancer Genome Atlas. All the data in ExomiRHub were further standardized and curated with annotations. The platform offers 25 analytical functions, including differential expression, co-expression, Weighted Gene Co-Expression Network Analysis (WGCNA), feature selection, and functional enrichment, enabling users to select samples, define groups, and customize parameters for analyses. Moreover, ExomiRHub provides a web service that allows biologists to analyze their uploaded miRNA expression data. Four additional tools were developed to evaluate the functions and targets of miRNAs and miRNA variations. Through ExomiRHub, we identified extracellular miRNA biomarkers associated with angiogenesis for monitoring glioma progression, demonstrating its potential to significantly accelerate the discovery of extracellular miRNA biomarkers.

Identification and functional characterisation of DNA methylation differences between East- and West-originating Finns

Eastern and Western Finns show a striking difference in coronary heart disease-related mortality; genetics is a known contributor for this discrepancy. Here, we discuss the potential role of DNA methylation in mediating the discrepancy in cardiometabolic disease-risk phenotypes between the sub-populations. We used data from the Young Finns Study (n = 969) to compare the genome-wide DNA methylation levels of East- and West-originating Finns. We identified 21 differentially methylated loci (FDR < 0.05; Δβ >2.5%) and 7 regions (smoothed FDR < 0.05; CpGs ≥ 5). Methylation at all loci and regions associates with genetic variants (p < 5 × 10-8). Independently of genetics, methylation at 11 loci and 4 regions associates with transcript expression, including genes encoding zinc finger proteins. Similarly, methylation at 5 loci and 4 regions associates with cardiometabolic disease-risk phenotypes including triglycerides, glucose, cholesterol, as well as insulin treatment. This analysis was also performed in LURIC (n = 2371), a German cardiovascular patient cohort, and results replicated for the association of methylation at cg26740318 and DMR_11p15 with diabetes-related phenotypes and methylation at DMR_22q13 with triglyceride levels. Our results indicate that DNA methylation differences between East and West Finns may have a functional role in mediating the cardiometabolic disease discrepancy between the sub-populations.

Identification and validation of autophagy‑related genes in hypertrophic cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is an autosomal dominant cardiac disorder characterized by ventricular hypertrophy resulting from the disordered arrangement of myocardial cells, which leads to impaired cardiac function or death. Autophagy (AT) is a biochemical process through which lysosomes degrade and recycle damaged or discarded intracellular components to protect cells against external environmental conditions, such as hypoxia and oxidative stress. AT is closely related to HCM, and thus, serves an important role in myocardial hypertrophy. However, the precise mechanism underlying the regulation of AT in cardiac hypertrophy remains elusive. The present study aimed to examine the role and mechanisms of AT-related genes (ARGs) in HCM through bioinformatics analysis and experimental validation and to identify potential targeted drugs for HCM. In this study, cardiac samples were obtained from healthy individuals and patients with HCM from the GEO database, and screened for differentially expressed ARGs to further investigate their potential interactions and functional pathways. These genes were subjected to functional enrichment analysis to identify potential crosstalk and involved pathways. Based on a protein-protein interaction network, EIF4EBP1, MCL1, PIK3R1, CCND1 and PPARG were identified as potential biomarkers for the diagnosis and treatment of HCM. Furthermore, 10 components with therapeutic potential for HCM were predicted based on the aforementioned hub genes. The results of bioinformatics analysis were validated using H9c2 cells stimulated with angiotensin II, which represented an in vitro model of cardiac hypertrophy. Overall, the present study demonstrated that the expression levels of ARGs were substantially altered in HCM. Therefore, these genes may be used as diagnostic biomarkers and therapeutic targets for HCM.

Transcriptomic analysis and validation study of key genes and the HIF‑1α/HO‑1 pathway associated with ferroptosis in neutrophilic asthma

Ferroptosis, as a unique form of cell death caused by iron overload and lipid peroxidation, is involved in the pathogenesis of various inflammatory diseases of the airways. Inhibition of ferroptosis has become a novel strategy for reducing airway epithelial cell death and improving airway inflammation. The aim of the present study was to analyze and validate the key genes and signaling pathways associated with ferroptosis by bioinformatic methods combined with experimental analyzes in vitro and in vivo to aid the diagnosis and treatment of neutrophilic asthma. A total of 1,639 differentially expressed genes (DEGs) were identified in the transcriptome dataset. After overlapping with ferroptosis-related genes, 11 differentially expressed ferroptosis-related genes (DE-FRGs) were obtained. A new diagnostic model was constructed by these DE-FRGs from the transcriptome dataset with those from the GSE108417 dataset. The receiver operating characteristic curve analysis indicated that the area under the curve had good diagnostic performance (>0.8). As a result, four key DE-FRGs (CXCL2, HMOX1, IL-6 and SLC7A5) and biological pathway [hypoxia-inducible factor 1 (HIF-1) signaling pathway] associated with ferroptosis in neutrophilic asthma were identified by the bioinformatics analysis combined with experimental validation. The upstream regulatory network of key DE-FRGs and target drugs were predicted and the molecular docking results from screened 37 potential therapeutic drugs revealed that the 13 small-molecule drugs exhibited a higher stable binding to the primary proteins of key DE-FRGs. The results suggested that four key DE-FRGs and the HIF-1α/heme oxygenase 1 pathway associated with ferroptosis have potential as novel markers or targets for the diagnosis or treatment of neutrophilic asthma.

Genes related to microglia polarization and immune infiltration in Alzheimer's Disease

Alzheimer's Disease (AD) remains a significant challenge due to its complex etiology and socio-economic burden. In this study, we investigated the roles of macrophage polarization-related hub genes in AD pathology, focusing on their impact on immune infiltration and gene regulation in distinct brain regions. Using Gene Expression Omnibus (GEO) datasets GSE110226 (choroid plexus) and GSE1297 (hippocampal CA1), we identified key genes-EDN1, HHLA2, KL, TREM2, and WWTR1-associated with AD mechanisms and immune responses. Based on these findings, we developed a diagnostic model demonstrating favorable calibration and clinical applicability. Furthermore, we explored molecular interactions within mRNA-transcription factor and mRNA-miRNA regulatory networks, providing deeper insights into AD progression and identifying potential therapeutic targets. The novel identification of WWTR1 and HHLA2 as biomarkers expands the diagnostic toolkit for AD, offering new perspectives on the disease's underlying immune dynamics. However, external dataset validation and further in vitro and in vivo studies are required to confirm these results and their clinical relevance.

Methods in DNA methylation array dataset analysis: A review

Understanding the intricate relationships between gene expression levels and epigenetic modifications in a genome is crucial to comprehending the pathogenic mechanisms of many diseases. With the advancement of DNA Methylome Profiling techniques, the emphasis on identifying Differentially Methylated Regions (DMRs/DMGs) has become crucial for biomarker discovery, offering new insights into the etiology of illnesses. This review surveys the current state of computational tools/algorithms for the analysis of microarray-based DNA methylation profiling datasets, focusing on key concepts underlying the diagnostic/prognostic CpG site extraction. It addresses methodological frameworks, algorithms, and pipelines employed by various authors, serving as a roadmap to address challenges and understand changing trends in the methodologies for analyzing array-based DNA methylation profiling datasets derived from diseased genomes. Additionally, it highlights the importance of integrating gene expression and methylation datasets for accurate biomarker identification, explores prognostic prediction models, and discusses molecular subtyping for disease classification. The review also emphasizes the contributions of machine learning, neural networks, and data mining to enhance diagnostic workflow development, thereby improving accuracy, precision, and robustness.

Identification and Validation of Aging Related Genes Signature in Chronic Obstructive Pulmonary Disease

PURPOSE

Chronic Obstructive Pulmonary Disease (COPD) is regarded as an accelerated aging disease. Aging-related genes in COPD are still poorly understood.

METHOD

Data set GSE76925 was obtained from the Gene Expression Omnibus (GEO) database. The "limma" package identified the differentially expressed genes. The weighted gene co-expression network analysis (WGCNA) constructes co-expression modules and detect COPD-related modules. The least absolute shrinkage and selection operator (LASSO) and the support vector machine recursive feature elimination (SVM-RFE) algorithms were chosen to identify the hub genes and the diagnostic ability. Three external datasets were used to identify differences in the expression of hub genes. Real-time reverse transcription polymerase chain reaction (RT-qPCR) was used to verify the expression of hub genes.

RESULT

We identified 15 differentially expressed genes associated with aging (ARDEGs). The SVM-RFE and LASSO algorithms pinpointed four potential diagnostic biomarkers. Analysis of external datasets confirmed significant differences in PIK3R1 expression. RT-qPCR results indicated decreased expression of hub genes. The ROC curve demonstrated that PIK3R1 exhibited strong diagnostic capability for COPD.

CONCLUSION

We identified 15 differentially expressed genes associated with aging. Among them, PIK3R1 showed differences in external data sets and RT-qPCR results. Therefore, PIK3R1 may play an essential role in regulating aging involved in COPD.

Epithelial-specific loss of Smad4 alleviates the fibrotic response in an acute colitis mouse model

Mucosal healing is associated with better clinical outcomes in patients with inflammatory bowel disease. But the epithelial-specific contribution to mucosal healing in vivo is poorly understood. We evaluated mucosal healing in an acute dextran sulfate sodium mouse model that shows an alleviated colitis response after epithelial-specific loss of Smad4. We find that enhanced epithelial wound healing alleviates the fibrotic response. Dextran sulfate sodium caused increased mesenchymal collagen deposition-indicative of fibrosis-within a week in the WT but not in the Smad4 KO colon. The fibrotic response correlated with decreased epithelial proliferation in the WT, whereas uninterrupted proliferation and an expanded zone of proliferation were observed in the Smad4 KO colon epithelium. Furthermore, the Smad4 KO colon showed epithelial extracellular matrix alterations that promote epithelial regeneration. Our data suggest that epithelium is a key determinant of the mucosal healing response in vivo, implicating mucosal healing as a strategy against fibrosis in inflammatory bowel disease patients.

Identifying critical modules and biomarkers of intervertebral disc degeneration by using weighted gene co-expression network

Background

Intervertebral disc degeneration (IVDD) is an age-related orthopedic degenerative disease characterized by recurrent episodes of lower back pain, the pathogenesis of which is not fully understood. This study aimed to identify key biomarkers of IVDD and its causes.

Methods

We acquired three gene expression profiles from the Gene Expression Omnibus (GEO) database, GSE56081, GSE124272, and GSE153761, and used limma fast differential analysis to identify differentially expressed genes (DEGs) between normal and IVDD samples after removing batch effects. We applied weighted gene co-expression network (WGCNA) to identify the key modular genes in GSE124272 and intersected these with DEGs. Next, A protein-protein interaction network (PPI) was constructed, and Cytoscape was used to identify the Top 10 hub genes. Functional enrichment analyses were performed using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Three key genes were validated using Western Blot (WB) and qRT-PCR. Additionally, we predicted miRNAs involved in hub gene co-regulation and analyzed miRNA microarray data from GSE116726 to identify four differentially expressed miRNAs.

Results

We identified 10 hub genes using bioinformatics analysis, gene function enrichment analysis revealed that they were primarily enriched in pathways, such as the TNF signaling pathway. We chose JUNB, SOCS3, and CEBPB as hub genes and used WB and qRT-PCR to confirm their expression. All three genes were overexpressed in the IVDD model group compared to the control group. Furthermore, we identified four miRNAs involved in the co-regulation of the hub genes using miRNet prediction: mir-191-5p, mir-20a-5p, mir-155-5p, and mir-124-3p. Using limma difference analysis, we discovered that mir-191-5p, mir-20a-5p, and mir-155-5p were all down-regulated and expressed in IVDD samples, but mir-124-3p showed no significant change.

Conclusion

JUNB, SOCS3, and CEBPB were identified as key genes in IVDD, regulated by specific miRNAs, providing potential biomarkers for early diagnosis and therapeutic targets.

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.

Identifying new molecular signatures and potential therapeutics for idiopathic pulmonary fibrosis: a network medicine approach

Idiopathic pulmonary fibrosis (IPF) is a progressive and fatal lung disease characterized by excessive collagen deposition and fibrosis of the lung parenchyma, leading to respiratory failure. The molecular mechanisms underlying IPF pathogenesis remain incompletely understood, hindering the development of effective therapeutic strategies. We have used a network medicine approach to comprehensively analyze molecular interactions and identify novel molecular signatures and potential therapeutics associated with IPF progression. Our integrative analysis revealed dysregulated molecular networks that are central to IPF pathophysiology. We have highlighted key molecular players and signaling pathways that are implicated in aberrant fibrotic processes. This systems-level understanding enables the identification of new biomarkers and therapeutic targets for IPF, providing potential avenues for precision medicine. Drug repurposing analysis revealed several drug candidates with anti-fibrotic, anti-inflammatory, and anti-cancer activities that could potentially slow fibrotic progression and improve patient outcomes. This study offers new insights into the molecular underpinnings of IPF and highlights network medicine approaches in uncovering complex disease mechanisms. The molecular signatures and therapeutic targets identified hold promise for developing precision therapies tailored to individual patients, ultimately advancing the management of this debilitating lung disease.

High-Dimensional Bayesian Semiparametric Models for Small Samples: A Principled Approach to the Analysis of Cytokine Expression Data

In laboratory medicine, due to the lack of sample availability and resources, measurements of many quantities of interest are commonly collected over a few samples, making statistical inference particularly challenging. In this context, several hypotheses can be tested, and studies are not often powered accordingly. We present a semiparametric Bayesian approach to effectively test multiple hypotheses applied to an experiment that aims to identify cytokines involved in Crohn's disease (CD) infection that may be ongoing in multiple tissues. We assume that the positive correlation commonly observed between cytokines is caused by latent groups of effects, which in turn result from a common cause. These clusters are effectively modeled through a Dirichlet Process (DP) that is one of the most popular choices as nonparametric prior in Bayesian statistics and has been proven to be a powerful tool for model-based clustering. We use a spike-slab distribution as the base measure of the DP. The nonparametric part has been included in an additive model whose parametric component is a Bayesian hierarchical model. We include simulations that empirically demonstrate the effectiveness of the proposed testing procedure in settings that mimic our application's sample size and data structure. Our CD data analysis shows strong evidence of a cytokine gradient in the external intestinal tissue.

Transcriptome-wide analysis uncovers regulatory elements of the antennal transcriptome repertoire of bumblebee at different life stages

Bumblebees are crucial pollinators, providing essential ecosystem services and global food production. The success of pollination services relies on the interaction between sensory organs and the environment. The antenna functions as a versatile multi-sensory organ, pivotal in mediating chemosensory/olfactory information, and governs adaptive responses to environmental changes. Despite an increasing number of RNA-sequencing studies on insect antenna, comprehensive antennal transcriptome studies at the different life stages were not elucidated systematically. Here, we quantified the expression profile and dynamics of coding/microRNA genes of larval head and antennal tissues from early- and late-stage pupa to the adult of Bombus terrestris as suitable model organism among pollinators. We further performed Pearson correlation analyses on the gene expression profiles of the antennal transcriptome from larval head tissue to adult stages, exploring both positive and negative expression trends. The positively correlated coding genes were primarily enriched in sensory perception of chemical stimuli, ion transport, transmembrane transport processes and olfactory receptor activity. Negatively correlated genes were mainly enriched in organic substance biosynthesis and regulatory mechanisms underlying larval body patterning and the formation of juvenile antennal structures. As post-transcriptional regulators, miR-1000-5p, miR-13b-3p, miR-263-5p and miR-252-5p showed positive correlations, whereas miR-315-5p, miR-92b-3p, miR-137-3p, miR-11-3p and miR-10-3p exhibited negative correlations in antennal tissue. Notably, based on the inverse expression relationship, positively and negatively correlated microRNA (miRNA)-mRNA target pairs revealed that differentially expressed miRNAs predictively targeted genes involved in antennal development, shaping antennal structures and regulating antenna-specific functions. Our data serve as a foundation for understanding stage-specific antennal transcriptomes and large-scale comparative analysis of transcriptomes in different insects.

Cat-E: A comprehensive web tool for exploring cancer targeting strategies

Identifying potential cancer-associated genes and drug targets from omics data is challenging due to its diverse sources and analyses, requiring advanced skills and large amounts of time. To facilitate such analysis, we developed Cat-E (Cancer Target Explorer), a novel R/Shiny web tool designed for comprehensive analysis with evaluation according to cancer-related omics data. Cat-E is accessible at https://cat-e.bioinfo-wuerz.eu/. Cat-E compiles information on oncolytic viruses, cell lines, gene markers, and clinical studies by integrating molecular datasets from key databases such as OvirusTB, TCGA, DrugBANK, and PubChem. Users can use all datasets and upload their data to perform multiple analyses, such as differential gene expression analysis, metabolic pathway exploration, metabolic flux analysis, GO and KEGG enrichment analysis, survival analysis, immune signature analysis, single nucleotide variation analysis, dynamic analysis of gene expression changes and gene regulatory network changes, and protein structure prediction. Cancer target evaluation by Cat-E is demonstrated here on lung adenocarcinoma (LUAD) datasets. By offering a user-friendly interface and detailed user manual, Cat-E eliminates the need for advanced computational expertise, making it accessible to experimental biologists, undergraduate and graduate students, and oncology clinicians. It serves as a valuable tool for investigating genetic variations across diverse cancer types, facilitating the identification of novel diagnostic markers and potential therapeutic targets.

The molecular subtypes of autoimmune diseases

Autoimmune diseases (ADs) are characterized by their complexity and a wide range of clinical differences. Despite patients presenting with similar symptoms and disease patterns, their reactions to treatments may vary. The current approach of personalized medicine, which relies on molecular data, is seen as an effective method to address the variability in these diseases. This review examined the pathologic classification of ADs, such as multiple sclerosis and lupus nephritis, over time. Acknowledging the limitations inherent in pathologic classification, the focus shifted to molecular classification to achieve a deeper insight into disease heterogeneity. The study outlined the established methods and findings from the molecular classification of ADs, categorizing systemic lupus erythematosus (SLE) into four subtypes, inflammatory bowel disease (IBD) into two, rheumatoid arthritis (RA) into three, and multiple sclerosis (MS) into a single subtype. It was observed that the high inflammation subtype of IBD, the RA inflammation subtype, and the MS "inflammation & EGF" subtype share similarities. These subtypes all display a consistent pattern of inflammation that is primarily driven by the activation of the JAK-STAT pathway, with the effective drugs being those that target this signaling pathway. Additionally, by identifying markers that are uniquely associated with the various subtypes within the same disease, the study was able to describe the differences between subtypes in detail. The findings are expected to contribute to the development of personalized treatment plans for patients and establish a strong basis for tailored approaches to treating autoimmune diseases.

Widespread genomic de novo DNA methylation occurs following CD8+ T cell activation and proliferation

This research investigates the intricate dynamics of DNA methylation in the hours following CD8+ T cell activation, during a critical yet understudied temporal window. DNA methylation is an epigenetic modification central to regulation of gene expression and directing immune responses. Our investigation spanned 96-h post-activation and unveils a nuanced tapestry of global and site-specific methylation changes. We identified 15,626 significant differentially methylated CpGs spread across the genome, with the most significant changes occurring within the genes ADAM10, ICA1, and LAPTM5. While many changes had modest effect sizes, approximately 120 CpGs exhibited a log2FC above 1.5, with cell activation and proliferation pathways the most affected. Relatively few of the differentially methylated CpGs occurred along adjacent gene regions. The exceptions were seven differentially methylated gene regions, with the Human T cell Receptor Alpha Joining Genes demonstrating consistent methylation change over a 3kb window. We also investigated whether an inflammatory environment could alter DNA methylation during activation, with proliferating cells exposed to the oxidant glycine chloramine. No substantial differential methylation was observed in this context. The temporal perspective of early activation adds depth to the evolving field of epigenetic immunology, offering insights with implications for therapeutic innovation and expanding our understanding of epigenetic modulation in immune function.

Construction of ferroptosis-related gene signatures for identifying potential biomarkers and immune cell infiltration in osteoarthritis

Osteoarthritis (OA) is a comprehensive joint disorder. The specific genes that trigger OA and the strategies for its effective management are not fully understood. This study focuses on identifying key genes linked to iron metabolism that could influence both the diagnosis and therapeutic approaches for OA. Analysis of GEO microarray data and iron metabolism genes identified 15 ferroptosis-related DEGs, enriched in hypoxia and HIF-1 pathways. Ten key hub genes (ATM, GCLC, PSEN1, CYBB, ATG7, MAP1LC3B, PLIN2, GRN, APOC1, SIAH2) were identified. Through stepwise regression, we screened 4 out of the above 10 genes, namely, GCLC, GRN, APOC1, and SIAH2, to obtain the optimal model. AUROCs for diagnosis of OA for the four hub genes were 0.81 and 0.80 of training and validation sets, separately. According to immune infiltration results, OA was related to significantly increased memory B cells, M0 macrophages, regulatory T cells, and resting mast cells but decreased activated dendritic cells. The four hub genes showed a close relation to them. It is anticipated that this model will aid in diagnosing osteoarthritis by assessing the expression of specific genes in blood samples. Moreover, studying these hub genes may further elucidate the pathogenesis of osteoarthritis.

Screening biomarkers for spinal cord injury using weighted gene co-expression network analysis and machine learning

JOURNAL/nrgr/04.03/01300535-202412000-00028/figure1/v/2024-04-08T165401Z/r/image-tiff Immune changes and inflammatory responses have been identified as central events in the pathological process of spinal cord injury. They can greatly affect nerve regeneration and functional recovery. However, there is still limited understanding of the peripheral immune inflammatory response in spinal cord injury. In this study, we obtained microRNA expression profiles from the peripheral blood of patients with spinal cord injury using high-throughput sequencing. We also obtained the mRNA expression profile of spinal cord injury patients from the Gene Expression Omnibus (GEO) database (GSE151371). We identified 54 differentially expressed microRNAs and 1656 differentially expressed genes using bioinformatics approaches. Functional enrichment analysis revealed that various common immune and inflammation-related signaling pathways, such as neutrophil extracellular trap formation pathway, T cell receptor signaling pathway, and nuclear factor-κB signal pathway, were abnormally activated or inhibited in spinal cord injury patient samples. We applied an integrated strategy that combines weighted gene co-expression network analysis, LASSO logistic regression, and SVM-RFE algorithm and identified three biomarkers associated with spinal cord injury: ANO10, BST1, and ZFP36L2. We verified the expression levels and diagnostic performance of these three genes in the original training dataset and clinical samples through the receiver operating characteristic curve. Quantitative polymerase chain reaction results showed that ANO10 and BST1 mRNA levels were increased and ZFP36L2 mRNA was decreased in the peripheral blood of spinal cord injury patients. We also constructed a small RNA-mRNA interaction network using Cytoscape. Additionally, we evaluated the proportion of 22 types of immune cells in the peripheral blood of spinal cord injury patients using the CIBERSORT tool. The proportions of naïve B cells, plasma cells, monocytes, and neutrophils were increased while the proportions of memory B cells, CD8+ T cells, resting natural killer cells, resting dendritic cells, and eosinophils were markedly decreased in spinal cord injury patients increased compared with healthy subjects, and ANO10, BST1 and ZFP26L2 were closely related to the proportion of certain immune cell types. The findings from this study provide new directions for the development of treatment strategies related to immune inflammation in spinal cord injury and suggest that ANO10, BST1, and ZFP36L2 are potential biomarkers for spinal cord injury. The study was registered in the Chinese Clinical Trial Registry (registration No. ChiCTR2200066985, December 12, 2022).

Integrative Bioinformatics Analysis: Unraveling Variant Signatures and Single-Nucleotide Polymorphism Markers Associated with 5-FU-Based Chemotherapy Resistance in Colorectal Cancer Patients

BACKGROUND

Drug resistance in colorectal cancer (CRC) is modulated by multiple molecular factors, which can be ascertained through genetic investigation. Single nucleotide polymorphisms (SNPs) within key genes have the potential to impair the efficacy of chemotherapeutic agents such as 5-fluorouracil (5-FU). Therefore, the identification of SNPs linked to drug resistance can significantly contribute to the advancement of tailored therapeutic approaches and the enhancement of treatment outcomes in patients with CRC.

MATERIAL AND METHOD

To identify dysregulated genes in 5-FU-based chemotherapy responder or non-responder CRC patients, a meta-analysis was performed. Next, the protein-protein interaction (PPI) network of the identified genes was analyzed using the STRING database. The most significant module was chosen for further analysis. In addition, a literature review was conducted to identify drug resistance-related genes. Enrichment analysis was conducted to validate the main module genes and the genes identified from the literature review. The associations between SNPs and drug resistance were investigated, and the consequences of missense variants were assessed using in silico tools.

RESULT

The meta-analysis identified 796 dysregulated genes. Then, to conduct PPI analysis and enrichment analysis, we were able to discover 23 genes that are intricately involved in the cell cycle pathway. Consequently, these 23 genes were chosen for SNP analysis. By using the dbSNP database and ANNOVAR, we successfully detected and labeled SNPs in these specific genes. Additionally, after careful exclusion of SNPs with allele frequencies below 0.01, we evaluated 6 SNPs from the HDAC1, MCM2, CDK1, BUB1B, CDC14B, and CCNE1 genes using 8 bioinformatics tools. Therefore, these SNPs were identified as potentially harmful by multiple computational tools. Specifically, rs199958833 in CDK1 (Val124Gly) was predicted to be damaging by all tools used. Our analysis strongly indicates that this specific SNP could negatively affect the stability and functionality of the CDK1 protein.

CONCLUSION

Based on our current understanding, the evaluation of CDK1 polymorphisms in the context of drug resistance in CRC has yet to be undertaken. In this investigation, we showed that rs199958833 variant in the CDK1 gene may favor resistance to 5-FU-based chemotherapy. However, these findings need validation in an independent cohort of patients.

Effects of water stress on apoplastic barrier formation in soil grown roots differ from hydroponically grown roots: Histochemical, biochemical and molecular evidence

In root research, hydroponic plant cultivation is commonly used and soil experiments are rare. We investigated the response of 12-day-old barley roots, cultivated in soil-filled rhizotrons, to different soil water potentials (SWP) comparing a modern cultivar (cv. Scarlett) with a wild accession ICB181243 from Pakistan. Water potentials were quantified in soils with different relative water contents. Root anatomy was studied using histochemistry and microscopy. Suberin and lignin amounts were quantified by analytical chemistry. Transcriptomic changes were observed by RNA-sequencing. Compared with control with decreasing SWP, total root length decreased, the onset of endodermal suberization occurred much closer towards the root tips, amounts of suberin and lignin increased, and corresponding biosynthesis genes were upregulated in response to decreasing SWP. We conclude that decreasing water potentials enhanced root suberization and lignification, like osmotic stress experiments in hydroponic cultivation. However, in soil endodermal cell suberization was initiated very close towards the root tip, and root length as well as suberin amounts were about twofold higher compared with hydroponic cultivation.

Challenges and opportunities in processing NanoString nCounter data

NanoString nCounter is a medium-throughput technology used in mRNA and miRNA differential expression studies. It offers several advantages, including the absence of an amplification step and the ability to analyze low-grade samples. Despite its considerable strengths, the popularity of the nCounter platform in experimental research stabilized in 2022 and 2023, and this trend may continue in the upcoming years. Such stagnation could potentially be attributed to the absence of a standardized analytical pipeline or the indication of optimal processing methods for nCounter data analysis. To standardize the description of the nCounter data analysis workflow, we divided it into five distinct steps: data pre-processing, quality control, background correction, normalization and differential expression analysis. Next, we evaluated eleven R packages dedicated to nCounter data processing to point out functionalities belonging to these steps and provide comments on their applications in studies of mRNA and miRNA samples.

Metabolic response to a heterologous poly-3-hydroxybutyrate (PHB) pathway in Phaeodactylum tricornutum

The marine diatom Phaeodactylum tricornutum is an emerging host for metabolic engineering, but little is known about how introduced pathways are integrated into the existing metabolic framework of the host or influence transgene expression. In this study, we expressed the heterologous poly-3-hydroxybutyrate (PHB) pathway using episomal expression, which draws on the precursor acetyl coenzyme-A (AcCoA). By experimentally perturbing cultivation conditions, we gained insight into the regulation of the endogenous metabolism in transgenic lines under various environmental scenarios, as well as on alterations in AcCoA flux within the host cell. Biosynthesis of PHB led to distinct shifts in the metabolome of the host, and further analysis revealed a condition-dependent relationship between endogenous and transgenic metabolic pathways. Under N limitation, which induced a significant increase in neutral lipid content, both metabolic and transcriptomic data suggest that AcCoA was preferably shunted into the endogenous pathway for lipid biosynthesis over the transgenic PHB pathway. In contrast, supply of organic carbon in the form of glycerol supported both fatty acid and PHB biosynthesis, suggesting cross-talk between cytosolic and plastidial AcCoA precursors. This is the first study to investigate the transcriptomic and metabolomic response of diatom cell lines expressing a heterologous multi-gene pathway under different environmental conditions, providing useful insights for future engineering attempts for pathways based on the precursor AcCoA. KEY POINTS: • PHB expression had minimal effects on transcription of adjacent pathways. • N limitation favoured native lipid rather than transgenic PHB synthesis. • Glycerol addition allowed simultaneous lipid and PHB accumulation.

Bipolar disorder: Construction and analysis of a joint diagnostic model using random forest and feedforward neural networks

Background

To construct a diagnostic model for Bipolar Disorder (BD) depressive phase using peripheral tissue RNA data from patients and combining Random Forest with Feedforward Neural Network methods.

Methods

Datasets GSE23848, GSE39653, and GSE69486 were selected, and differential gene expression analysis was conducted using the limma package in R. Key genes from the differentially expressed genes were identified using the Random Forest method. These key genes' expression levels in each sample were used to train a Feedforward Neural Network model. Techniques like L1 regularization, early stopping, and dropout layers were employed to prevent model overfitting. Model performance was then validated, followed by GO, KEGG, and protein-protein interaction network analyses.

Results

The final model was a Feedforward Neural Network with two hidden layers and two dropout layers, comprising 2345 trainable parameters. Model performance on the validation set, assessed through 1000 bootstrap resampling iterations, demonstrated a specificity of 0.769 (95 % CI 0.571-1.000), sensitivity of 0.818 (95 % CI 0.533-1.000), AUC value of 0.832 (95 % CI 0.642-0.979), and accuracy of 0.792 (95 % CI 0.625-0.958). Enrichment analysis of key genes indicated no significant enrichment in any known pathways.

Conclusion

Key genes with biological significance were identified based on the decrease in Gini coefficient within the Random Forest model. The combined use of Random Forest and Feedforward Neural Network to establish a diagnostic model showed good classification performance in Bipolar Disorder.

The astrocyte-enriched gene deathstar plays a crucial role in the development, locomotion, and lifespan of D. melanogaster

The Drosophila melanogaster brain is a complex organ with various cell types, orchestrating the development, physiology, and behaviors of the fly. While each cell type in Drosophila brain is known to express a unique gene set, their complete genetic profile is still unknown. Advances in the RNA sequencing techniques at single-cell resolution facilitate identifying novel cell type markers and/or re-examining the specificity of the available ones. In this study, exploiting a single-cell RNA sequencing data of Drosophila optic lobe, we categorized the cells based on their expression pattern for known markers, then the genes with enriched expression in astrocytes were identified. CG11000 was identified as a gene with a comparable expression profile to the Eaat1 gene, an astrocyte marker, in every individual cell inside the Drosophila optic lobe and midbrain, as well as in the entire Drosophila brain throughout its development. Consistent with our bioinformatics data, immunostaining of the brains dissected from transgenic adult flies showed co-expression of CG11000 with Eaat1 in a set of single cells corresponding to the astrocytes in the Drosophila brain. Physiologically, inhibiting CG11000 through RNA interference disrupted the normal development of male D. melanogaster, while having no impact on females. Expression suppression of CG11000 in adult flies led to decreased locomotion activity and also shortened lifespan specifically in astrocytes, indicating the gene's significance in astrocytes. We designated this gene as 'deathstar' due to its crucial role in maintaining the star-like shape of glial cells, astrocytes, throughout their development into adult stage.

Construction of a five-gene-based prognostic model for relapsed/refractory acute lymphoblastic leukemia

BACKGROUND

Relapsed/refractory acute lymphoblastic leukemia (R/R ALL) continues to be a major cause of mortality in children worldwide, with around 15% of ALL patients experiencing relapse and approximately 10% eventually dying from the disease. Early identification of R/R ALL in children has posed a longstanding clinical challenge.

METHOD

Genetic analysis of survival outcomes in pediatric patients with ALL from the TARGET-ALL dataset revealed five risk score factors identified through the intersection of differential genes (relapse/non-relapse) from the GSE17703 and GSE6092 databases. A risk score equation was formulated using these factors and validated against prognostic data from 46 ALL cases at our institution. Patients from multiple datasets were stratified into high and low-score groups based on this equation. Protein-protein interaction networks (PPI) were then constructed using the intersecting differential genes from all three datasets to identify hub nodes and predict interacting transcription factors. Additionally, genes related to cell pyroptosis with varying expression across these datasets were screened, and a multifactorial ROC curve (incorporating risk score and differential expression of pyroptosis-related genes) was generated. Furthermore, relationships among variables in the predictive model were depicted using a nomogram, and model efficacy was assessed through decision curve analysis (DCA).

RESULTS

By analyzing the TARGET-ALL, GSE17703, and GSE6092 databases, we developed a prognostic risk assessment model for pediatric ALL incorporating BAG2, EPHA4, FBXO9, SNX10, and WNK1. Validation of this model was conducted using data from 46 pediatric ALL cases obtained from our institution. Following the identification of 27 differentially expressed genes, we constructed a PPI and identified the top 10 hub genes (PTPRC, BTK, LCK, PRKCQ, CD3D, CD27, CD3G, BLNK, RASGRP1, VPREB1). Using this network, we predicted the top 5 transcription factors (HOXB4, MYC, SOX2, E2F1, NANOG). ROC and DCA were conducted on pyroptosis-related genes exhibiting differential expression and risk scores. Subsequently, a nomogram was generated, demonstrating the effectiveness of the risk score in predicting prognosis for pediatric ALL patients.

CONCLUSIONS

We have developed a risk prediction model for pediatric R/R ALL utilizing the genes BAG2, EPHA4, FBXO9, SNX10, and WNK1. This model provides a scientific foundation for early identification of R/R ALL in children.

Transcriptional response of Saccharomyces cerevisiae to lactic acid enantiomers

The model yeast, Saccharomyces cerevisiae, is a popular object for both fundamental and applied research, including the development of biosensors and industrial production of pharmaceutical compounds. However, despite multiple studies exploring S. cerevisiae transcriptional response to various substances, this response is unknown for some substances produced in yeast, such as D-lactic acid (DLA). Here, we explore the transcriptional response of the BY4742 strain to a wide range of DLA concentrations (from 0.05 to 45 mM), and compare it to the response to 45 mM L-lactic acid (LLA). We recorded a response to 5 and 45 mM DLA (125 and 113 differentially expressed genes (DEGs), respectively; > 50% shared) and a less pronounced response to 45 mM LLA (63 DEGs; > 30% shared with at least one DLA treatment). Our data did not reveal natural yeast promoters quantitatively sensing DLA but provide the first description of the transcriptome-wide response to DLA and enrich our understanding of the LLA response. Some DLA-activated genes were indeed related to lactate metabolism, as well as iron uptake and cell wall structure. Additional analyses showed that at least some of these genes were activated only by acidic form of DLA but not its salt, revealing the role of pH. The list of LLA-responsive genes was similar to those published previously and also included iron uptake and cell wall genes, as well as genes responding to other weak acids. These data might be instrumental for optimization of lactate production in yeast and yeast co-cultivation with lactic acid bacteria. KEY POINTS: • We present the first dataset on yeast transcriptional response to DLA. • Differential gene expression was correlated with yeast growth inhibition. • The transcriptome response to DLA was richer in comparison to LLA.

Resource: A curated database of brain-related functional gene sets (Brain.GMT)

Transcriptional profiling has become a common tool for investigating the nervous system. During analysis, differential expression results are often compared to functional ontology databases, which contain curated gene sets representing well-studied pathways. This dependence can cause neuroscience studies to be interpreted in terms of functional pathways documented in better studied tissues (e.g., liver) and topics (e.g., cancer), and systematically emphasizes well-studied genes, leaving other findings in the obscurity of the brain "ignorome". To address this issue, we compiled a curated database of 918 gene sets related to nervous system function, tissue, and cell types ("Brain.GMT") that can be used within common analysis pipelines (GSEA, limma, edgeR) to interpret results from three species (rat, mouse, human). Brain.GMT includes brain-related gene sets curated from the Molecular Signatures Database (MSigDB) and extracted from public databases (GeneWeaver, Gemma, DropViz, BrainInABlender, HippoSeq) and published studies containing differential expression results. Although Brain.GMT is still undergoing development and currently only represents a fraction of available brain gene sets, "brain ignorome" genes are already better represented than in traditional Gene Ontology databases. Moreover, Brain.GMT substantially improves the quantity and quality of gene sets identified as enriched with differential expression in neuroscience studies, enhancing interpretation. •We compiled a curated database of 918 gene sets related to nervous system function, tissue, and cell types ("Brain.GMT").•Brain.GMT can be used within common analysis pipelines (GSEA, limma, edgeR) to interpret neuroscience transcriptional profiling results from three species (rat, mouse, human).•Although Brain.GMT is still undergoing development, it substantially improved the interpretation of differential expression results within our initial use cases.

Identification the role of necroptosis in rheumatoid arthritis by WGCNA network

Rheumatoid arthritis (RA) is the predominant manifestation of inflammatory arthritis, distinguished by an increasing burden of morbidity and mortality. The intricate interplay of genes and signalling pathways involved in synovial inflammation in patients with RA remains inadequately comprehended. This study aimed to ascertain the role of necroptosis in RA, as along with their associations with immune cell infiltration. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were employed to identify central genes for RA. In this study, identified total of 28 differentially expressed genes (DEGs) were identified in RA. Utilising WGCNA, two co-expression modules were generated, with one module demonstrating the strongest correlation with RA. Through the integration of differential gene expression analysis, a total of 5 intersecting genes were discovered. These 5 hub genes, namely fused in sarcoma (FUS), transformer 2 beta homolog (TRA2B), eukaryotic translation elongation factor 2 (EEF2), cleavage and polyadenylation specific factor 6 (CPSF6) and signal transducer and activator of transcription 3 (STAT3) were found to possess significant diagnostic value as determined by receiver operating characteristic (ROC) curve analysis. The close association between the concentrations of various immune cells is anticipated to contribute to the diagnosis and treatment of RA. Furthermore, the infiltration of immune cells mentioned earlier is likely to exert a substantial influence on the initiation of this disease.

Identification and prognostic analysis of metabolic genes associated with TP53 mutation in multiple myeloma

BACKGROUND

TP53 gene mutation is crucial in determining the prognosis of Multiple Myeloma (MM) patients. Understanding metabolic genes linked to TP53 mutation is vital for developing targeted therapies for these patients.

METHOD

We analyzed The Cancer Genome Atlas (TCGA) dataset to identify genes related to TP53 mutation and metabolism. Using univariate Cox regression and protein-protein interaction (PPI) analysis, we identified key genes. We categorized patients into high and low metabolism groups via non-negative matrix factorization (NMF) clustering, which led to the discovery of relevant differential genes. Integrating these with genes from the Gene Expression Omnibus (GEO) datasets and PPI interactions, we pinpointed crucial metabolic genes associated with TP53 mutation in MM. Additionally, we conducted prognostic analyses involving survival curves and receiver operating characteristic (ROC) charts.

RESULTS

Our study reveals that the metabolic gene ribonucleotide reductase M2 (RRM2), linked to TP53 mutation, correlates positively with the International Staging System (ISS) stage in MM patients and is an independent prognostic risk factor. In the TCGA dataset, among the 767 patients, the 35 MM patients with TP53 mutation generally had poor survival outcomes. Specifically, the patients with both TP53 mutation and high RRM2 expression had a 2-year survival rate of only 38.87%, whereas those with normal TP53 function and low RRM2 expression had a 2-year survival rate of 86.31% (p < 0.001).

CONCLUSION

RRM2 significantly impacts MM prognosis and is associated with TP53 mutation, presenting itself as a potential therapeutic target and prognostic marker for MM.

Serum and urinary levels of MIF, CD74, DDT and CXCR4 among patients with type 1 diabetes mellitus, type 2 diabetes and healthy individuals: Implications for further research

Background

Macrophage migration inhibitory factor (MIF) is a highly conserved cytokine with pleiotropic properties, mainly pro-inflammatory. MIF seems to exert its pro-inflammatory features by binding to its transmembrane cellular receptor CD74. MIF also has CXCR4, which acts as a co-receptor in this inflammatory process. Apart from MIF, D-dopachrome tautomerase (DDT) or MIF2, which belongs to the MIF superfamily, also binds to receptor CD74. Therefore, these molecules, MIF, CD74, DDT and CXCR4 are suggested to work together orchestrating an inflammatory process. Diabetes mellitus is characterised by chronic low-grade inflammation. Therefore, the aim of the present study was to evaluate serum and urinary levels of the aforementioned molecules among patients with type 1 diabetes mellitus (T1DM), type 2 diabetes mellitus (T2DM) and among healthy controls.

Methods

We enrolled 13 patients with T1DM, 74 patients with T2DM and 25 healthy individuals as controls. Levels of CD74, CXCR4, DDT, and MIF were measured using ELISA Kits according to the manufacturer's instructions.

Results

We documented increased serum MIF levels together with higher urinary CD74 levels among patients with T1DM, when compared to patients with T2DM and healthy adults. In particular, patients with T1DM showed significantly increased levels of MIF compared to T2DM (p = 0.011) and healthy controls (p = 0.0093). CD74 in urine were significantly higher in patients with T1DM compared to those affected with T2DM (p = 0.0302) and healthy group (p = 0.0099). On the contrary, serum CD74 were similar among the three groups. No statistical differences were identified in CXCR4 levels both in serum and in urine of all groups. Patients with T2DM and overweight/obesity had increased urinary levels of CD74, when compared to lean patients with T2DM.

Conclusion

The increased serum MIF levels and urinary CD74 levels among patients with T1DM may be attributed to the autoimmune milieu, which characterises patients with T1DM, when compared to patients with T2DM. These two findings merit further attention as they could pave the way for further research regarding the potential beneficial effects of inhibitors of MIF among patients with T1DM, especially in the early stages of T1DM. Finally, the role of inhibitors of MIF could be further explored in the context of obesity among patients with T2DM.

Extracellular vesicles from virulent P. brasiliensis induce TLR4 and dectin-1 expression in innate cells and promote enhanced Th1/Th17 response

Extracellular vesicles (EVs) are membrane-enclosed nanoparticles that transport several biomolecules and are involved in important mechanisms and functions related to the pathophysiology of fungal diseases. EVs from Paracoccidioides brasiliensis, the main causative agent of Paracoccidioidomycosis (PCM), modulate the immune response of macrophages. In this study, we assessed the EVs proteome from a virulent P. brasiliensis isolated from granulomatous lesions and compared their immunomodulatory ability with EVs isolated from the fungus before the animal passage (control EVs) when challenging macrophages and dendritic cells (DCs). Proteome showed that virulent EVs have a higher abundance of virulence factors such as GP43, protein 14-3-3, GAPDH, as well as virulence factors never described in PCM, such as aspartyl aminopeptidase and a SidJ analogue compared with control EVs. Virulent extracellular vesicles induced higher expression of TLR4 and Dectin-1 than control EVs in macrophages and dendritic cells (DCs). In opposition, a lower TLR2 expression was induced by virulent EVs. Additionally, virulent EVs induced lower expression of CD80, CD86 and TNF-α, but promoted a higher expression of IL-6 and IL-10, suggesting that EVs isolated from virulent P. brasiliensis-yeast promote a milder DCs and macrophage maturation. Herein, we showed that EVs from virulent fungi stimulated a higher frequency of Th1/Tc1, Th17, and Treg cells, which gives new insights into fungal extracellular vesicles. Taken together, our results suggest that P. brasiliensis utilizes its EVs as virulence bags that manipulate the immune system in its favour, creating a milder immune response and helping with fungal evasion from the immune system.