Compared Gene Expression Profiles Research Articles

Weighted Gene Co-Expression Network Based on Transcriptomics: Unravelling the Differentiation Dynamics of 3T3-L1 Preadipocytes and the Regulatory Mechanism of Protopanaxatriol

The intricate regulatory mechanisms governing adipocyte differentiation are pivotal in elucidating the complex pathophysiology underlying obesity. This study aims to explore the dynamic changes in gene expression during the differentiation of 3T3-L1 adipocytes using transcriptomics methods. Protopanaxatriol (PPT) significantly inhibited adipocyte differentiation. To uncover the molecular mechanisms, we conducted an extensive transcriptomic analysis of adipocytes throughout various differentiation stages, comparing gene expression profiles before and after PPT treatment. The construction of 16 co-expression modules was achieved using weighted gene co-expression network analysis (WGCNA). The 838 differentially expressed genes in the blue module were highly correlated with PPT treatment. Further analysis revealed that PIKfyve, STAT3, JAK1, CTTN, TYK2, JAK3, STAT2, STAT5b, SOCS3, and IRF9 were core genes closely associated with adipocyte differentiation. This discovery underscores the potential pivotal function of these ten genes in regulating adipocyte differentiation. This study elucidated that PPT, an active ingredient in ginseng, could reduce lipid accumulation by inhibiting the differentiation of adipocyte precursors through the negative regulation of genes such as PIKfyve, STAT3, and JAK1. Finally, molecular docking identified potential binding sites for PPT on PIKfyve and JAK1. This study provides potential drug targets for preventing obesity and related metabolic diseases.

Comprehensive gene expression analysis in gallbladder mucosal epithelial cells of dogs with gallbladder mucocele.

Gallbladder mucocele (GBM) is a common disease in the canine gallbladder. Although the pathogenesis of GBM remains unclear, we recently reported that the excessive accumulation of mucin in the gallbladder is not a result of overproduction by gallbladder epithelial cells (GBECs). Changes in the function of GBECs other than the production of mucin are associated with the pathogenesis of GBM. We performed an RNA sequencing (RNA-seq) analysis to comprehensively search for abnormalities in gene expression profiles of GBECs in dogs with GBM. Fifteen dogs with GBM and 8 dogs euthanized for reasons other than gallbladder disease were included. The GBECs were isolated from gallbladder tissues to extract RNA. The RNA-seq analysis was performed using the samples from 3 GBM cases and 3 dogs with normal gallbladders, and the gene expression profiles were compared between the 2 groups. Differences in mRNA expression levels of the extracted differentially expressed genes (DEGs) were validated by quantitative reverse transcription polymerase chain reaction (RT-qPCR) using samples of 15 GBM cases and 8 dogs with normal gallbladders. Comparison of gene expression profiles by RNA-seq extracted 367 DEGs, including ANO1, a chloride channel associated with changes in mucin morphology, and HTR4, which regulates the function of chloride channels. The ANO1 and HTR4 genes were confirmed to be downregulated in the GBM group by RT-qPCR. Our results suggest that GBM may be associated with decreased function of chloride channels expressed in GBECs.

Discovery of RNA Biomarkers for Prostate Cancer Using Cross-Platform Transcriptomics

Microarray and Single-Molecule Molecular Inversion Probe (smMIP)-based targeted RNA sequencing are two RNA profiling platforms for identifying disease-associated biomarkers. The microarray uses a GeneChip array with oligonucleotide probes to measure expression levels across thousands of genes, while smMIPs capture and quantify RNA transcripts and transcript variants via next-generation sequencing. To evaluate the strengths and weaknesses of both platforms, a comparative gene expression profiling study was conducted using RNA samples from 52 prostate tissues (normal, benign prostatic hyperplasia (BPH) and various prostate cancer (PCa) grades). Of all genes covered by both platforms, only 35% of the expression levels aligned, with 45% showing discrepancies. Both platforms identified the same 17 genes as potential PCa biomarkers. Microarray analysis identified an additional 253 genes that were not covered or not identified by smMIP technology, while smMIP technology identified eight markers not covered or not identified in the microarray core gene analysis, including fusion genes and splice variants. For high-grade prostate cancer (HG-PCa), the smMIP-method identified 8 markers, and the microarray identified 17 markers, with FOLH1, FAP and CLDN3 being common across both platforms. The choice of RNA expression analysis technology depends on research objectives; microarray technology is useful for the evaluation of a wide range of genes but has low throughput. In contrast, smMIP-based RNA sequencing enables sensitive analysis with minimal RNA in a medium- to high-throughput setting.

Association between atrial fibrillation and periodontal disease: A bioinformatics analysis

IntroductionAtrial fibrillation (AF), which is an aging disease, and periodontal disease (PD) are prevalent, and AF and PD may be associated with each other. Inflammation plays a role in the two pathologies. However, there is a need for a deeper understanding of the mechanisms associated with these conditions. MethodsA bioinformatics analysis was performed to compare gene expression profiles in the blood and left atrial tissue samples of patients with AF and in the blood samples of patients with PD. Our analysis involved comparing gene expression profiles between patients with AF, those with sinus rhythm, and individuals with and without PD. This comparative approach enabled us to identify upregulated genes in both the AF and PD cohorts. ResultsThe expression of 40 genes was upregulated in patients with AF and PD, with a significant enrichment for genes associated with inflammation. Notably, a network analysis revealed distinct functional associations among these genes. Interestingly, the expressions of three lysosome-related genes in all analyzed tissues (AF blood, PD blood, and AF left atrium) were upregulated. ConclusionInflammation played a role in AF and PD. Further, based on the upregulated expression of lysosome-related genes across all samples that were evaluated, in addition to inflammation, lysosomal function had a possible role in the co-existence of the two diseases.

Comparative Analysis of Digital Transcriptomics Between Pre- and Post-Treatment Samples of Patients with Locally Advanced Cervical Cancer: A Preliminary Study

Cervical cancer remains a leading cause of cancer-related deaths in women worldwide, with limited treatment options for advanced stages and therapy-resistant cases. Despite advances in treatment, the variability in the patient response to standard therapies underscores the need for molecular biomarkers to guide personalized treatment strategies. This study aimed to explore the transcriptomic changes associated with the therapeutic response in locally advanced cervical cancer, focusing on 770 immune-related genes. We employed a digital multiplexed gene expression analysis, comparing gene expression profiles between matching pre- and post-treatment samples. The results revealed the significant upregulation of C7 and EGR2 in the post-treatment samples, suggesting that enhanced immune activity is a key factor in therapeutic success. Conversely, IL17RB, S100A7, and SAA1 were upregulated in the pre-treatment samples, potentially indicating resistance mechanisms. Pathway enrichment analysis highlighted that the immune response and apoptosis pathways are crucial to post-treatment changes. These findings suggest that C7, EGR2, and IL17RB may serve as biomarkers for predicting therapeutic outcomes and could inform the development of more effective, individualized treatment strategies for cervical cancer. This study provides new insights into the molecular mechanisms underlying treatment response and resistance.

Deciphering the role of lipid metabolism-related genes in Alzheimer's disease: a machine learning approach integrating Traditional Chinese Medicine.

Alzheimer's disease (AD) represents a progressive neurodegenerative disorder characterized by the accumulation of misfolded amyloid beta protein, leading to the formation of amyloid plaques and the aggregation of tau protein into neurofibrillary tangles within the cerebral cortex. The role of carbohydrates, particularly apolipoprotein E (ApoE), is pivotal in AD pathogenesis due to its involvement in lipid and cholesterol metabolism, and its status as a genetic predisposition factor for the disease. Despite its significance, the mechanistic contributions of Lipid Metabolism-related Genes (LMGs) to AD remain inadequately elucidated. This research endeavor seeks to bridge this gap by pinpointing biomarkers indicative of early-stage AD, with an emphasis on those linked to immune cell infiltration. To this end, advanced machine-learning algorithms and data derived from the Gene Expression Omnibus (GEO) database have been employed to facilitate the identification of these biomarkers. Differentially expressed genes (DEGs) were identified by comparing gene expression profiles between healthy individuals and Alzheimer's disease (AD) patients, using data from two Gene Expression Omnibus (GEO) datasets: GSE5281 and GSE138260. Functional enrichment analysis was conducted to elucidate the biological relevance of the DEGs. To ensure the reliability of the results, samples were randomly divided into training and validation sets. The analysis focused on lipid metabolism-related DEGs (LMDEGs) to explore potential biomarkers for AD. Machine learning algorithms, including Support Vector Machine-Recursive Feature Elimination (SVM-RFE) and the Least Absolute Shrinkage and Selection Operator (LASSO) regression model, were applied to identify a key gene biomarker. Additionally, immune cell infiltration and its relationship with the gene biomarker were assessed using the CIBERSORT algorithm. The Integrated Traditional Chinese Medicine (ITCM) database was also referenced to identify Chinese medicines related to lipid metabolism and their possible connection to AD. This comprehensive strategy aims to integrate modern computational methods with traditional medicine to deepen our understanding of AD and its underlying mechanisms. The study identified 137 genes from a pool of 751 lipid metabolism-related genes (LMGs) significantly associated with autophagy and immune response mechanisms. Through the application of LASSO and SVM-RFE machine-learning techniques, four genes-choline acetyl transferase (CHAT), member RAS oncogene family (RAB4A), acyl-CoA binding domain-containing protein 6 (ACBD6), and alpha-galactosidase A (GLA)-emerged as potential biomarkers for Alzheimer's disease (AD). These genes demonstrated strong therapeutic potential due to their involvement in critical biological pathways. Notably, nine Chinese medicine compounds were identified to target these marker genes, offering a novel treatment approach for AD. Further, ceRNA network analysis revealed complex regulatory interactions involving these genes, underscoring their importance in AD pathology. CIBERSORT analysis highlighted a potential link between changes in the immune microenvironment and CHAT expression levels in AD patients, providing new insights into the immunological dimensions of the disease. The discovery of these gene markers offers substantial promise for the diagnosis and understanding of Alzheimer's disease (AD). However, further investigation is necessary to validate their clinical utility. This study illuminates the role of Lipid Metabolism-related Genes (LMGs) in AD pathogenesis, offering potential targets for therapeutic intervention. It enhances our grasp of AD's complex mechanisms and paves the way for future research aimed at refining diagnostic and treatment strategies.

Gene expression profile comparison of primary and pulmonary metastatic lesions in a dog with appendicular osteosarcoma and hypertrophic osteopathy

Hypertrophic osteopathy (HO) is a paraneoplastic syndrome, and the most notable cause in dogs is pulmonary metastatic osteosarcoma (OSA). Although many molecular factors in canine OSA have been shown in metastasis, little is known about the gene expression profile of HO secondary to metastatic OSA. Therefore, the purpose of this study was to compare the gene expression profiles between primary and metastatic OSA lesions from the same dog and to look for gene expression changes that can elucidate the molecular mechanism of metastases and HO. Tumoral samples were obtained from a 2-year-old, intact male, Labrador retriever. At the first visit, the patient presented with an appendicular OSA as the primary lesion. About 10 months later, the dog developed HO due to a single pulmonary metastasis. Using these primary and metastatic samples from the same dog, as well as normal canine osteoblasts, we investigated the gene expression profiling using the NanoString nCounter® Canine IO panel. A total of 180 differentially expressed genes were identified between malignant OSA cells and non-malignant canine osteoblasts. Furthermore, 5 genes (CCL17, VEGFC, C3, C4BPA, and FOS) were differentially expressed in comparison between primary and metastatic OSA samples. CCL17 and VEGFC were upregulated in the primary lesion compared to the metastatic lesion, while C3, C4BPA, and FOS were downregulated in the primary lesion relative to the metastatic lesion. Given that the metastatic lesion was relevant to the development of HO, the different gene expression profiles may be relevant to understanding the pathophysiology of HO.

Identifying High Recurrence Risk in Breast Carcinoma Patients Through Spatial Transcriptomic Analysis.

Comparing gene expression profiles according to recurrence risk using spatially resolved transcriptomic analysis has not been reported. This study aimed to identify distinct genetic features of breast carcinoma associated with a high Oncotype DX Recurrence Score (ORS). Patients were categorized into two groups, ORS-high (ORS-H; two patients) and ORS-non-high (ORS-NH; five patients). We performed digital spatial profiling and bioinformatic analyses to investigate the spatial transcriptomic profiles. Lysozyme (LYZ), complement C1q C chain (C1QC), and complement C1q B chain (C1QB) exhibited the highest fold changes in the stromal compartment of the ORS-H group. Gene ontology enrichment analysis of the ORS-H group revealed significant up-regulation of genes associated with immune response in the stromal compartment, including lymphocyte-mediated immunity, adaptive immune response related to the immunoglobulin superfamily, and leukocyte-mediated immunity. Gene set enrichment analysis showed significant positive enrichment of gene sets associated with interferon (IFN) response and complement pathways in the stromal compartment. This study highlights significant differences in gene expression profiles and spatially resolved transcriptional activities between ORS-H and ORS-NH breast carcinomas. The significant up-regulation of genes and pathways associated with cell-mediated immunity, IFN response, and complement C1q in the stromal compartment of the ORS-H group warrants further evaluation with larger population cohorts.

Tumor and α-SMA-expressing stromal cells in pancreatic neuroendocrine tumors have a distinct RNA profile depending on tumor grade.

Alpha-smooth muscle actin (α-SMA) expression in the stroma is linked to the presence of cancer-associated fibroblasts and is known to correlate with worse outcomes in various tumors. In this study, using a GeoMx digital spatial profiling approach, we characterized the gene expression of the tumor and α-SMA-expressing stromal cell compartments in pancreatic neuroendocrine tumors (PanNETs). The profiling was performed on tissues from eight retrospective cases (three grade 1, four grade 2, and one grade3). Selected regions of interest were segmented geometrically based on tissue morphology and fluorescent signals from synaptophysin and α-SMA markers. The α-SMA-expressing stromal-cell-associated genes were involved in pathways of extracellular matrix modification, whereas, in tumor cells, the gene expression profiles were associated with pathways involved in cell proliferation. The comparison of gene expression profiles across all three PanNET grades revealed that the differences between grades are not only present at the level of the tumor but also in the α-SMA-expressing stromal cells. Furthermore, the tumor cells from regions with a rich presence of adjacent α-SMA-expressing stromal cells revealed an upregulation of matrix metalloproteinase-9 (MMP9) expression in grade 3 tumors. This study provides an in-depth characterization of gene expression profiles in α-SMA-expressing stromal and tumor cells, and outlines potential crosstalk mechanisms.

Cross-species imputation and comparison of single-cell transcriptomic profiles.

Cross-species comparison and prediction of gene expression profiles are important to understand regulatory changes during evolution and to transfer knowledge learned from model organisms to humans. Single-cell RNA-seq (scRNA-seq) profiles enable us to capture gene expression profiles with respect to variations among individual cells; however, cross-species comparison of scRNA-seq profiles is challenging because of data sparsity, batch effects, and the lack of one-to-one cell matching across species. Moreover, single-cell profiles are challenging to obtain in certain biological contexts, limiting the scope of hypothesis generation. Here we developed Icebear, a neural network framework that decomposes single-cell measurements into factors representing cell identity, species, and batch factors. Icebear enables accurate prediction of single-cell gene expression profiles across species, thereby providing high-resolution cell type and disease profiles in under-characterized contexts. Icebear also facilitates direct cross-species comparison of single-cell expression profiles for conserved genes that are located on the X chromosome in eutherian mammals but on autosomes in chicken. This comparison, for the first time, revealed evolutionary and diverse adaptations of X-chromosome upregulation in mammals.

Comprehensive analysis of hub genes associated with cisplatin-resistance in ovarian cancer and screening of therapeutic drugs through bioinformatics and experimental validation

BackgroundTo identify key genes associated with cisplatin resistance in ovarian cancer, a comprehensive analysis was conducted on three datasets from the GEO database and through experimental validation.MethodsGene expression profiles were retrieved from the GEO database. DEGs were identified by comparing gene expression profiles between cisplatin-sensitive and resistant ovarian cancer cell lines. The identified genes were further subjected to GO, KEGG, and PPI network analysis. Potential inhibitors of key genes were identified through methods such as LibDock nuclear molecular docking. In vitro assays and RT-qPCR were performed to assess the expression levels of key genes in ovarian cancer cell lines. The sensitivity of cells to chemotherapy and proliferation of key gene knockout cells were evaluated through CCK8 and Clonogenic assays.ResultsResults showed that 12 genes influenced the chemosensitivity of the ovarian cancer cell line SKOV3, and 9 genes were associated with the prognosis and survival outcomes of ovarian cancer patients. RT-qPCR results revealed NDRG1, CYBRD1, MT2A, CNIH3, DPYSL3, and CARMIL1 were upregulated, whereas ERBB4, ANK3, B2M, LRRTM4, EYA4, and SLIT2 were downregulated in cisplatin-resistant cell lines. NDRG1, CYBRD1, and DPYSL3 knock-down significantly inhibited the proliferation of cisplatin-resistant cell line SKOV3. Finally, photofrin, a small-molecule compound targeting CYBRD1, was identified.ConclusionThis study reveals changes in the expression level of some genes associated with cisplatin-resistant ovarian cancer. In addition, a new small molecule compound was identified for the treatment of cisplatin-resistant ovarian cancer.

P-518 Differentially gene expression identified by RNA seq in aneuploid human blastocysts is more influenced by the phenomenon of aneuploidy than the involved chromosome

Abstract Study question What insights do RNA-Seq profiles provide about gene expression in aneuploid blastocysts with small and large chromosomes compared to euploid blastocysts? Summary answer The difference in gene expression is affected by aneuploidy and is more related to the presence of aneuploidy than the size of the aneuploid chromosomes. What is known already Embryonic aneuploidy is one of the main common causes of implantation failure and miscarriage. RNA-Seq investigations of the embryo in the early stages of preimplantation indicate that with the gradual replacement of the embryonic profile and the reduction of the oocyte residues, the gene expression profile in the blastocyst is more dependent on the embryo’s genome. A recent study by comparing the gene expression profile of euploid blastocysts and specific aneuploidies such as trisomy 21, trisomy 18, XXY and XO syndromes using RNA-Seq, showed how the type of aneuploidy might affect gene expression. Study design, size, duration This study was conducted on surplus embryos of the patients with history of recurrent implantation failure or recurrent miscarriage following PGT by Day-5 trophectoderm biopsy. Inclusion criteria involved maternal age ≤37, normal parental karyotypes, and ovulation by long stimulation protocol. As advanced maternal age and ovarian stimulation method can affect the aneuploidy rate and expression profile, embryos belonging to mothers > 37 years and included only patients with the long stimulation protocol were excluded. Participants/materials, setting, methods The normal embryos and those with single aneuploidy were selected for comparison of gene expression profiles using RNA sequencing. The single aneuploid embryos were classified in two groups: with defined aneuploidy(DA) of small chromosomes (including chromosomes 16, 20, 21, 22), and with undefined aneuploidy(UA) as single aneuploids for other chromosomes. To neutralize the effect of genomic content on gene expression profile in DA group, embryos were pooled reciprocally (trisomy and monosomy for the same chromosome). Main results and the role of chance Principal Component Analysis (PCA) validated embryos classification considering their differentially expressed genes (DEG) clustering into three distinct groups. To minimize the role of chance, only genes with significant adjusted P-values in both STAR and Hisat data analyses were included in consequent interpretations. In addition to comparison between and among the DA, UA and normal groups, the entire set of aneuploid groups was compared with the normal group. The DEG in both aneuploid groups compared to the normal group represented eminent upregulation rather than downregulation. Gene Ontology analyses indicated an upregulation of transcription, ubiquitination, autophagy, mitochondrial, chromosome segregation and DNA repair pathways in both DA and UA compared to normal group. Overexpression of the DNA repair pathway and other validated genes of interest in aneuploid embryos was in agreement with qPCR findings. Translation and protein synthesis pathways also showed overexpression as part of the functional response to aneuploidy. Upregulation of apoptotic process pathway was evident in the UA group. While comparison of gene expression between DA and UA showed overexpression of 143 genes, but cellular lipid synthesis was the only pathway with a significant difference. The distribution of DEG on chromosomes in the DA group was not associated with selected aneuploidies. Limitations, reasons for caution The number of biological replicates in each category was limited in order to provide more confident data by minimizing the time interval between providing the samples and RNA sequencing. Larger studies might change some affected pathways due to differences in identified DEG. Wider implications of the findings This study shed light on the black box of human preimplantation aneuploidy. Considering that the transcriptome is affected by the genomic status and some debates on genomic based current PGT approaches, this study is a step for selection of healthy and viable embryos by revealing the molecular mechanisms. Trial registration number not applicable

Microarray Analysis of Tumor Suppressor Proteins p53 and p63: Their Role in Tumor Cell Dynamics

Background: Microarray technology has become essential in cancer research, providing insights into gene expression patterns and regulatory networks governed by key transcription factors. This review focuses on using microarray technology to elucidate genes regulated by tumor suppressor proteins p53 and p63. These proteins maintain genomic stability, regulate the cell cycle, and induce programmed cell death. Dysregulation of p53 and p63 is common in various cancers, highlighting their significance in cancer biology. Methods: Microarray technology enables high-throughput analysis of thousands of genes simultaneously. Researchers compare gene expression profiles in cancer cells and tissues with functional p53 and p63 against those with impaired function. This comparative analysis identifies numerous downstream target genes and pathways influenced by these transcription factors. Results: Microarray studies have revealed a wide array of genes regulated by p53 and p63, involved in DNA repair, apoptosis, cell cycle control, and epithelial differentiation. These findings enhance our understanding of molecular mechanisms driving cancer initiation, progression, and therapeutic resistance. Additionally, microarray technology enables the stratification of cancer subtypes based on distinct gene expression signatures associated with p53 and p63 status, offering insights into patient prognosis, treatment responses, and personalized therapeutic strategies. Conclusion: Microarray technology has significantly advanced our knowledge of gene regulatory networks orchestrated by p53 and p63 in cancer. Despite limitations like data interpretation and cross-hybridization, advancements in bioinformatics and complementary technologies are improving study accuracy and reliability, promising better cancer diagnostics, prognostics, and targeted therapies.

Screening of potential hub genes involved in Kidney Wilms tumor via bioinformatics analysis and experimental validation

BackgroundWilms tumor (WT) is the most common pediatric embryonal tumor. Improving patient outcomes requires advances in understanding and targeting the multiple genes and cellular control pathways, but its pathogenesis is currently not well-researched. We aimed to identify the potential molecular biological mechanism of WT and develop new prognostic markers and molecular targets by comparing gene expression profiles of Wilms tumors and fetal normal kidneys.MethodsDifferential gene expression analysis was performed on Wilms tumor transcriptomic data from the GEO and TARGET databases. For biological functional analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were utilized. Out of 24 hub genes identified, nine were found to be prognostic-related through univariate Cox regression analysis. These nine genes underwent LASSO regression analysis to enhance the predictive capability of the model. The key hub genes were validated in the GSE73209 datasets, and cell function experiments were conducted to identify the genes’ functions in WiT-49 cells.ResultsThe enrichment analysis revealed that DEGs were significantly involved in the regulation of angiogenesis and regulation of cell differentiation. 24 DEGs were identified through PPI networks and the MCODE algorithm, and 9 of 24 genes were related to WT patients’ prognosis. EMCN and CCNA1 were identified as key hub genes, and related to the progression of WT. Functionally, over-expression of EMCN and CCNA1 knockdown inhibited cell viability, proliferation, migration, and invasion of Wilms tumor cells.ConclusionsEMCN and CCNA1 were identified as key prognostic markers in Wilms tumor, suggesting their potential as therapeutic targets. Differential gene expression and enrichment analyses indicate significant roles in angiogenesis and cell differentiation.

Comparing gene expression profiles of adults with isolated spinal tuberculosis to disseminated spinal tuberculosis identified by 18FDG-PET/CT at time of diagnosis, 6- and 12-months follow-up: classifying clinical stages of spinal tuberculosis and monitoring treatment response (Spinal TB X cohort study)

BackgroundTuberculosis (TB) is one of the top ten causes of death worldwide, with approximately 10 million cases annually. Focus has been on pulmonary TB, while extrapulmonary TB (EPTB) has received little attention. Diagnosis of EPTB remains challenging due to the invasive procedures required for sample collection. Spinal TB (STB) accounts for 10% of EPTB and often leads to lifelong debilitating disease due to devastating spinal deformation and compression of neural structures. Little is known about the extent of disease, although both isolated STB and a disseminated form of STB have been described. In our Spinal TB X cohort study, we aim to describe the clinical phenotype of STB using whole-body 18FDG-PET/CT, identify a specific gene expression profile for different stages of dissemination and compare findings to previously described gene expression signatures for latent and active pulmonary TB.MethodsA single-centre, prospective cohort study will be established to describe the distributional pattern of STB detected by whole-body 18FDG-PET/CT and gene expression profile of patients with suspected STB on magnetic resonance imaging (MRI) at point of diagnosis, six months, and 12 months. Blood biobanking will be performed at these time points. Specimens for microbiology will be obtained from sputum/urine, from easily accessible sites of disease (e.g., lymph nodes, abscess) identified in the first 18FDG-PET/CT, from CT-guided biopsy and/or surgery. Clinical parameters and functional scores will be collected at every physical visit. Data will be entered into RedCap® database; data cleaning, validation and analysis will be performed by the study team. The University of Cape Town Ethics Committee approved the protocol (243/2022).DiscussionThe Spinal TB X cohort study is the first prospective cohort study using whole-body 18FDG-PET/CT scans in patients with microbiologically confirmed spinal tuberculosis. Dual imaging techniques of the spine using 18FDG-PET/CT and magnetic resonance imaging as well as tissue diagnosis (microbiology and histopathology) will allow us to develop a virtual biopsy model. If successful, a distinct gene-expression profile will aid in blood-based diagnosis (point of care testing) as well as treatment monitoring and would lead to earlier diagnosis of this devastating disease.Trial registration: The study has been registered on ClinicalTrials.gov (NCT05610098).

Molecular profiling of frontal and occipital subcortical white matter hyperintensities in Alzheimer's disease.

White matter hyperintensities (WMHs) are commonly detected on T2-weighted magnetic resonance imaging (MRI) scans, occurring in both typical aging and Alzheimer's disease. Despite their frequent appearance and their association with cognitive decline, the molecular factors contributing to WMHs remain unclear. In this study, we investigated the transcriptomic profiles of two commonly affected brain regions with coincident AD pathology-frontal subcortical white matter (frontal-WM) and occipital subcortical white matter (occipital-WM)-and compared with age-matched healthy controls. Through RNA-sequencing in frontal- and occipital-WM bulk tissues, we identified an upregulation of genes associated with brain vasculature function in AD white matter. To further elucidate vasculature-specific transcriptomic features, we performed RNA-seq analysis on blood vessels isolated from these white matter regions, which revealed an upregulation of genes related to protein folding pathways. Finally, comparing gene expression profiles between AD individuals with high- versus low-WMH burden showed an increased expression of pathways associated with immune function. Taken together, our study characterizes the diverse molecular profiles of white matter changes in AD compared to normal aging and provides new mechanistic insights processes underlying AD-related WMHs.

Single-cell profiling indicates a high similarity between immune cells in the cerebrospinal fluid and in meningeal ectopic lymphoid tissue in experimental autoimmune encephalomyelitis.

B cell depleting anti-CD20 monoclonal antibodies (aCD20 mAbs) are highly effective in treatment of multiple sclerosis (MS) but fail to halt the formation of meningeal ectopic lymphoid tissue (mELT) in the murine model experimental autoimmune encephalomyelitis (EAE). While mELT can be examined in EAE, it is not accessible in vivo in MS patients. Our key objectives were to compare the immune cells in cerebrospinal fluid (CSF), which is accessible in patients, with those in mELT, and to study the effects of aCD20 mAbs on CSF and mELT in EAE. Applying single cell RNA sequencing, we compared gene expression profiles in immune cells from (1) CSF with mELT and (2) aCD20 mAbs treated with control treated mice in a spontaneous 2D2xTh EAE model. The immune cell composition in CSF and mELT was very similar. Gene expression profiles and pathway enrichment analysis revealed no striking differences between the two compartments. aCD20 mAbs led not only to a virtually complete depletion of B cells in the CSF but also to a reduction of naïve CD4+ T cells and marked increase of macrophages. No remarkable differences in regulated genes or pathways were observed. Our results suggest that immune cells in the CSF may serve as a surrogate for mELT in EAE. Future studies are required to confirm this in MS patients. The observed increase of macrophages in B cell depleted CSF is a novel finding and requires verification in CSF of aCD20 mAbs treated MS patients. Due to unresolved technical challenges, we were unable to study the effects of aCD20 mAbs on mELT. This should be addressed in future studies.

Whole transcriptome analysis to identify non-coding RNA regulators and hub genes in sperm of non-obstructive azoospermia by microarray, single-cell RNA sequencing, weighted gene co-expression network analysis, and mRNA-miRNA-lncRNA interaction analysis

BackgroundThe issue of male fertility is becoming increasingly common due to genetic differences inherited over generations. Gene expression and evaluation of non-coding RNA (ncRNA), crucial for sperm development, are significant factors. This gene expression can affect sperm motility and, consequently, fertility. Understanding the intricate protein interactions that play essential roles in sperm differentiation and development is vital. This knowledge could lead to more effective treatments and interventions for male infertility.Materials and methodsOur research aim to identify new and key genes and ncRNA involved in non-obstructive azoospermia (NOA), improving genetic diagnosis and offering more accurate estimates for successful sperm extraction based on an individual’s genotype.ResultsWe analyzed the transcript of three NOA patients who tested negative for genetic sperm issues, employing comprehensive genome-wide analysis of approximately 50,000 transcript sequences using microarray technology. This compared gene expression profiles between NOA sperm and normal sperm. We found significant gene expression differences: 150 genes were up-regulated, and 78 genes were down-regulated, along with 24 ncRNAs up-regulated and 13 ncRNAs down-regulated compared to normal conditions. By cross-referencing our results with a single-cell genomics database, we identified overexpressed biological process terms in differentially expressed genes, such as “protein localization to endosomes” and “xenobiotic transport.” Overrepresented molecular function terms in up-regulated genes included “voltage-gated calcium channel activity,” “growth hormone-releasing hormone receptor activity,” and “sialic acid transmembrane transporter activity.” Analysis revealed nine hub genes associated with NOA sperm: RPL34, CYB5B, GOL6A6, LSM1, ARL4A, DHX57, STARD9, HSP90B1, and VPS36.ConclusionsThese genes and their interacting proteins may play a role in the pathophysiology of germ cell abnormalities and infertility.

Comparison of Brain Gene Expression Profiles Associated with Auto-Grooming Behavior between Apis cerana and Apis mellifera Infested by Varroa destructor.

The grooming behavior of honeybees serves as a crucial auto-protective mechanism against Varroa mite infestations. Compared to Apis mellifera, Apis cerana demonstrates more effective grooming behavior in removing Varroa mites from the bodies of infested bees. However, the underlying mechanisms regulating grooming behavior remain elusive. In this study, we evaluated the efficacy of the auto-grooming behavior between A. cerana and A. mellifera and employed RNA-sequencing technology to identify differentially expressed genes (DEGs) in bee brains with varying degrees of grooming behavior intensity. We observed that A. cerana exhibited a higher frequency of mite removal between day 5 and day 15 compared to A. mellifera, with day-9 bees showing the highest frequency of mite removal in A. cerana. RNA-sequencing results revealed the differential expression of the HTR2A and SLC17A8 genes in A. cerana and the CCKAR and TpnC47D genes in A. mellifera. Subsequent homology analysis identified the HTR2A gene and SLC17A8 gene of A. cerana as homologous to the HTR2A gene and SLC17A7 gene of A. mellifera. These DEGs are annotated in the neuroactive ligand-receptor interaction pathway, the glutamatergic synaptic pathway, and the calcium signaling pathway. Moreover, CCKAR, TpnC47D, HTR2A, and SLC17A7 may be closely related to the auto-grooming behavior of A. mellifera, conferring resistance against Varroa infestation. Our results further explain the relationship between honeybee grooming behavior and brain function at the molecular level and provide a reference basis for further studies of the mechanism of honeybee grooming behavior.

Abstract PO1-03-09: Transcriptomic profiling of ER-negative and ER-low, HER2-negative breast cancer and its implications on immune regulation

Abstract Background: The recommended cut-off of < 1% positive cells to define estrogen receptor (ER) negative status in breast cancer (BC) is highly debated. Few data exist on transcriptomic features of ER-low BC, suggesting no major differences vs ER-neg. Among HER2-negative BCs, we compared gene-expression profiling (including PAM50 intrinsic subtyping) according to ER status (ER-neg: 0% vs. ER-low: 1-9%) and assessed its impact on Tumor-infiltrating lymphocytes (TILs) levels. Methods: Across two cancer centers, stage I-III HER2-negative BCs FFPE samples were reviewed for ER status. All ER-low (ER 1-9%) samples were identified and a cohort of ER-neg BC samples, matched for age and stage, was also retrieved. A small cohort of pts with intermediate ER expression (ER-int: 10-50%) was included as a control cohort. Expression of 776 BC–related genes was evaluated by nCounter® (Breast Cancer 360TM Panel) and intrinsic molecular subtyping was determined using the PAM50 subtype predictor. A False Discovery Rate (FDR) corrected unpaired two-class SAM was used to identify genes differentially expressed in different subgroups. TILs were evaluated on archival H&E slides following guidelines. Results: Of 116 stage I-III HER2-neg BC pts included, 39 had ER-neg BC, 65 had ER-low BC, and 12 had ER-int disease. PAM50 intrinsic subtype distribution was similar in ER-neg and ER-low BCs, (with an enrichment in basal-like tumors), while both subgroups differed significantly from ER-int samples (Table). As compared to ER-neg tumors, ER-low BCs showed significantly higher expression of GATA3 gene and lower expression of EDN1 and PROM1 genes by SAM analysis (FDR< 5%); however, when the analysis was limited to PAM50 basal-like tumors (N=77) we only identified two genes that were downregulated in ER-low BCs (EDN1, PROM1) as compared to ER-neg BCs. On the contrary, significant differences in transcriptomic regulation were observed between ER-low and ER-int BCs, with ER-low BCs showing significantly higher expression of 53 genes and lower expression of 398 genes, as compared to ER-int (SAM analysis FDR< 5%). We previously reported similar TIL levels in ER-low and ER-neg tumors. In both ER-low and ER-int BCs, PAM50 basal-like subtype was associated with significantly higher TILs: median TIL levels 20 (range 0-80) and 6 (range 1-40) for PAM50 Basal-like BCs versus other PAM50 subtypes respectively in ER-low samples (p< 0.001); median TIL levels 53 (range 25-80) and 5 (range 0-10) for PAM50 Basal-like BCs versus other PAM50 subtypes respectively in ER-int samples (p=0.036). No significant difference in TIL levels according to PAM50 subtyping were observed in ER-neg tumors. No significant difference in mRNA levels for PD-1 (PDCD1) and PD-L1 (CD274) genes was observed between ER-low and ER-neg tumors. Conclusions: Among patients with HER2-neg BC, gene-expression profiling is similar in ER-neg (i.e., < 1%) vs. ER-low tumors with a high prevalence of PAM50 Basal-like tumors and few differentially expressed genes. In both ER-low and ER-int HER2-neg BCs, PAM50 Basal-like subtype is also associated with higher TIL levels. Our results strongly indicate that HER2-neg/ER-low BCs and HER2-neg/ER-neg BCs are similar biological entities, thus further supporting the use of similar treatments in patients with early-stage ER-low and triple-negative BC, including immune checkpoint inhibitors. PAM50 intrinsic subtype distribution according to ER levels Citation Format: Gaia Griguolo, Claudio Vernieri, Davide Massa, Lorenzo Nicolè, Claudia Pinato, Federica Miglietta, Andrea Vingiani, Silvia Brich, Riccardo Lobefaro, Francesca Schiavi, Matteo Fassan, Giancarlo Pruneri, Valentina Guarneri, Maria Vittoria Dieci. Transcriptomic profiling of ER-negative and ER-low, HER2-negative breast cancer and its implications on immune regulation [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-03-09.