Blood Gene Expression Profiles Research Articles

P-1850. Acute Babesia microti Infection in Humans Is Associated With Marked Changes In The Expression Of Peripheral Blood Coding And Noncoding RNA

Abstract Background Babesiosis is a potentially life-threatening disease transmitted by ticks. However, host and parasite determinants of human babesiosis are completely unknown. In the current study we evaluated the impact of B. microti infection, the primary cause of human babesiosis in the US, on the peripheral blood transcriptome. Figure 1. PCA of top 500 differentially expressed genes. PCA distinguishes Babesia patients at presentation from healthy controls. Methods Patients with confirmed B. microti acute infection were enrolled into a 1-year follow up cohort study at Stony Brook University Hospital, NY. Total RNA was isolated from blood using PaxGene RNA tubes and library preparation performed using Illumina's Stranded Total RNAPrep with Ribo-zero plus kit to remove human ribosomal RNA and globin RNA. 100 M RNA paired end reads were sequenced (2 x 151 bps). Low expression RNAs with less than 10 reads in 17 samples were filtered out prior to identification of differentially expressed genes (DEGs) (p< 0.05).Figure 2.Volcano Plot comparing differential gene and noncoding RNA expression between healthy controls and patients with babesiosis at presentation.Red circles represent genes whose expression are increased or decreased in patients with babesiosis at presentation compared to healthy controls. Blue circles represent genes whose expression are not significantly different. Results The peripheral blood transcriptome from 20 healthy controls was compared to 42 patients at presentation for B. microti infection and 1, 3, 6, 9 and 12 months after treatment for patients who completed the longitudinal protocol after the first visit. A total of 1860 genes or noncoding RNAs were differentially expressed in patients with babesiosis at presentation (V0) compared to healthy controls; 370 were decreased in patients with babesiosis and 1490 were increased (adj p < 0.01; expression fold change >+/- 1.) PCA analysis (Fig 1), and the Volcano Plot (Fig. 2) demonstrate that patients with babesiosis have a distinct peripheral blood gene expression profile compared to healthy control individuals. The heat map of DEGs in Fig. 3 shows the peripheral blood transcriptome in patients with babesiosis largely returned to baseline levels by 1-month post-treatment. The Qiagen IPA Graphical Summary based on DEGs is shown in Figure 4 and highlights activation of diverse immune-mediators, including TNF and interferon families, as well as growth factors, and vascularization pathways in response to B. microti infection.Figure 3.Heat map of differentially expressed genes of patients with babesiosis at presentation (0) and 1, 3, 6, 9 and 12 months post-treatment compared to healthy control individuals.Gene expression in patients with babesiosis is markedly different at presentation but is similar to levels for healthy controls by 1 month post-treatment. Conclusion Infection with B. microti results in marked changes in expression of genes and noncoding RNA in peripheral blood that largely returns to baseline by 1-month post-treatment.Figure 4:Qiagen IPA Graphical Summary of pathways likely increased (orange) or decreased (blue) in patients presenting with babesiosis. Disclosures Paul Arnaboldi, PhD, Biopeptides, Corp: Patent Holder|Biopeptides, Corp: Employee|DiaSorin: Advisor/Consultant

Machine Learning-Based Alzheimer's Disease Stage Diagnosis Utilizing Blood Gene Expression and Clinical Data: A Comparative Investigation.

Background/Objectives: This study presents a comparative analysis of the multistage diagnosis of Alzheimer's disease (AD), including mild cognitive impairment (MCI), utilizing two distinct types of biomarkers: blood gene expression and clinical biomarker samples. Both of these samples, obtained from participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI), were independently analyzed utilizing machine learning (ML)-based multiclassifiers. This study applied novel machine learning-based data augmentation techniques to gene expression profile data that are high-dimensional, low-sample-size (HDLSS) and inherently highly imbalanced. The investigation obtained the highest multiclassification performance to date in the multistage diagnosis of Alzheimer's disease utilizing the blood gene expression profiles of Alzheimer's Disease Neuroimaging Initiative (ADNI) participants. Based on the performance results obtained, and other factors such as early prediction capabilities, this study compares the efficacies of the two types of biomarkers for multistage diagnosis. This study presents the sole investigation in which multiclassification-based AD stage diagnosis was conducted utilizing blood gene expression data. We obtained the best multiclassification result in both modalities of the ADNI data in terms of F1-score and were able to identify new genetic biomarkers. Methods: The combination of the XGBoost and SFBS (Sequential Floating Backward Selection) methods was used to select the features. We were able to select the 95 most effective gene probe sets out of 49,386. For the clinical study data, eight of the most effective biomarkers were selected using SFBS. A deep learning (DL) classifier was used to identify the stages-cognitive normal (CN), mild cognitive impairment (MCI), and Alzheimer's disease (AD)/dementia. DL, support vector machine (SVM), gradient boosting (GB), and random forest (RF) classifiers were used for the AD stage detection from gene expression profile data. Because of the high data imbalance in genomic data, borderline oversampling/data augmentation was applied in the model training and original samples for validation. Results: Utilizing clinical data, the highest ROC AUC scores attained were 0.989, 0.927, and 0.907 for the identification of the CN, MCI, and dementia stages, respectively. The highest F1 scores achieved were 0.971, 0.939, and 0.886. Employing gene expression data, we obtained ROC AUC scores of 0.763, 0.761, and 0.706 for the CN, MCI, and dementia stages, respectively, and F1 scores of 0.71, 0.77, and 0.53 for CN, MCI, and dementia, respectively. Conclusions: This represents the best outcome to date for AD stage diagnosis from ADNI blood gene expression profile data utilizing multiclassification techniques. The results indicated that our multiclassification model effectively manages the imbalanced data of a high-dimension, low-sample-size (HDLSS) nature to identify samples of the minority class. MAPK14, PLG, FZD2, FXYD6, and TEP1 are among the novel genes identified as being associated with AD risk.

Multi-omics analysis reveals the genetic aging landscape of Parkinson’s disease

Parkinson’s disease (PD) is the second most common age-related neurodegenerative disease after Alzheimer’s disease. Despite numerous studies, specific age-related factors remain unidentified. This study employed a multi-omics approach to investigate the link between PD and aging. We integrated blood gene expression profiles, expression quantitative trait loci, genome-wide association studies, predictive models, and conducted clinical validation.By analyzing PD datasets, a total of 953 differentially expressed genes (DEGs) and 10 intersecting aging differentially expressed genes (ADEGs) were identified. Enrichment analysis revealed that the regulatory pathways of these ADEGs involve the classical Wnt signaling pathway, endoplasmic reticulum stress, and neuronal apoptosis. Mendelian randomization (MR) analysis showed that the MAP3K5 gene significantly reduces the risk of PD. Multivariate regression analysis identified MXD1, CREB1, and SIRT3 as key diagnostic genes and constructed a predictive model to aid clinical decision-making. Enzyme-linked immunosorbent assay experiments validated the expression levels of these genes in the serum of PD patients.This study utilized a multi-omics approach to identify key ADEGs and their regulatory mechanisms in PD, leading to the establishment of a diagnostic model. The resource is accessible at this link: https://yunhaihupo.shinyapps.io/DynNomapp. This web application can be used as a standalone resource to explore changes in blood transcription profiles in PD and their relationship to clinical and aging aspects, generating new research hypotheses.

Evaluating the feasibility of using whole blood gene expression profiles to identify novel targets in canine septic peritonitis.

To assess gene expression profiles in canine whole blood with and without septic peritonitis to assess workflow feasibility and identify potential blood biomarkers that could be further investigated in future studies. This study enrolled 6 dogs with cytologically confirmed septic peritonitis of any cause and 6 healthy dogs. All dogs had a CBC and biochemistry performed. The dogs with septic peritonitis also had point-of-care lactate and blood oxygen saturation measured for acute patient physiologic and laboratory evaluation score calculation. All dogs then had 2.5 mL of whole blood collected and placed into an RNA stabilization tube, which was processed using a commercial assay based on the hybridization of fluorescent probes for transcript quantification. Quality control, normalization, and data visualization were performed. Raw counts were exported, and differential expression was performed. The evaluation of canine whole blood expression profiles was confirmed to be feasible. Differential expression analysis of septic and nonseptic dogs demonstrated distinct gene expression profile signatures. Five genes of interest were upregulated in septic whole blood including matrix metallopeptidase 9, IL-1 receptor type 2, proliferating cell nuclear antigen, phosphatidylinositol 3-kinase catalytic-γ, and cluster of differentiation 55. The study and associated workflow were feasible and can be scaled in confirmatory studies. Future studies are now proposed to further validate the increased expression of putative biomarkers in a larger cohort of canine septic peritonitis patients with more relevant comparator control cohorts.

Blood Gene Expression Profiling and Donor-derived Cell-free DNA to Noninvasively Diagnose Clinical and Subclinical Kidney Transplant Rejection: A Real-life Appraisal Study

Background. Peripheral blood biomarkers aim to noninvasively diagnose kidney allograft rejection, but most lack robust independent validation. TruGraf is intended to exclude subclinical cellular rejection (TCMR), whereas donor-derived cell-free DNA Viracor-TRAC has proven value in excluding antibody-mediated rejection (AMR). We aim to validate both biomarkers for accurate rejection diagnosis in a real-world clinical setting. Methods. We prospectively included 230 unselected, consecutive kidney transplants from 6 centers undergoing for-cause and protocol biopsies with paired blood samples from December 2021 to 2022. TruGraf and Viracor-TRAC were blindly run by a central laboratory. Results. The incidence of rejection was 22.6% (17.3% surveillance; 27% for-cause biopsies). Inflammation was associated with higher TRAC levels, with AMR/mixed and microvascular inflammation (MVI) showing the highest levels (P < 0.05). TruGraf did not associate with any specific allograft injury. No biomarkers, individually or combined, accurately diagnosed any rejection (area under the receiver operating characteristic curve [AUROC] < 0.65). However, high TRAC levels, when combined with DSA in for-cause biopsies, predicted AMR/mixed rejection or MVI (AUROC = 0.817; P < 0.001), outperforming serum creatinine and DSA (AUROC < 0.65). Conclusions. In this large, prospective, observational real-life study, we were unable to validate TruGraf and TRAC to diagnose rejection but found a useful context of use for TRAC to noninvasively diagnose AMR/mixed or MVI in conjunction with DSA in dysfunctioning graft.

Can Blood Gene Expression Profile and Donor-Derived Cellfree DNA Guide Postrejection Management among Kidney Transplant Recipients?

Can Blood Gene Expression Profile and Donor-Derived Cellfree DNA Guide Postrejection Management among Kidney Transplant Recipients?

Dog Domestication Strongly Relied on Translation Regulation According to Differential Gene Expression Analysis.

Domestication of dogs from their shared ancestors with wolves occurred more than 15,000 years ago and affected many characteristics of the species. We analyzed the blood RNA sequence data of 12 dogs and 11 wolves from Europe and Asia to shed more light on the domestication history of dogs. We implemented a differential gene expression analysis, a weighted gene correlation network analysis, gene ontology and genetic pathway analyses. We found that both the sample origin (Europe or Asia) and the species had a significant effect on the blood gene expression profiles of the animals. We identified 1567 differentially expressed genes between wolves and dogs and found several significantly overrepresented gene ontology terms, such as RNA polymerase II transcription regulatory region sequence-specific DNA binding or translation. We identified 11 significant gene co-expression networks, hosting a total of 4402 genes, related to DNA replication, metabolism of RNA or metabolism of proteins, for example. Our findings suggest that gene expression regulation played a cardinal role in dog domestication. We recommend further diversifying the analyzed dog and wolf populations in the future by including individuals from different dog breeds and geographical origins, in order to enhance the specificity of detecting significant, true positive genes related to domestication as well as to reduce the false positive rate.

362.4: Pilot study of clinical use of peripheral blood gene expression profile in the care of kidney transplant recipients between 2- and 5-years post-transplant.

362.4: Pilot study of clinical use of peripheral blood gene expression profile in the care of kidney transplant recipients between 2- and 5-years post-transplant.

Detection of Subclinical Rejection in Pediatric Kidney Transplantation: Current and Future Practices.

The successes in the field of pediatric kidney transplantation over the past 60 years have been extraordinary. Year over year, there have been significant improvements in short-term graft survival. However, improvements in longer-term outcomes have been much less apparent. One important contributor has been the phenomenon of low-level rejection in the absence of clinical manifestations-so-called subclinical rejection (SCR). Traditionally, rejection has been diagnosed by changes in clinical parameters, including but not limited to serum creatinine and proteinuria. This review examines the shortcomings of this approach, the effects of SCR on kidney allograft outcome, the benefits and drawbacks of surveillance biopsies to identify SCR, and new urine and blood biomarkers that define the presence or absence of SCR. Serum creatinine is an unreliable index of SCR. Surveillance biopsies are the method most utilized to detect SCR. However, these have significant drawbacks. New biomarkers show promise. These biomarkers include blood gene expression profiles and donor derived-cell free DNA; urine gene expression profiles; urinary cytokines, chemokines, and metabolomics; and other promising blood and urine tests. Specific emphasis is placed on studies carried out in pediatric kidney transplant recipients. ClinicalTrials.gov: NCT03719339.

Clinical Value of Peripheral Blood Gene Expression Profile and Donor-Derived Cellfree DNA for Identifying Persistent Rejection.

Persistent rejection is an increasingly recognized barrier to long-term kidney allograft survival. A noninvasive method to help identify patients with persistent rejection in need of biopsy would be valuable. This was a post-hoc analysis of a multicenter observational study. Subjects that had a biopsy-proven acute rejection and had another biopsy within 9 months (270 days) and had a biopsy-paired biomarker sample were included. A total of 64 "index" rejections in 58 subjects with repeat biopsies were identified with a median time to repeat biopsy of 100 days. Persistent rejection was present in 61%; 69% of follow-up biopsies were performed in clinically stable patients. Peripheral blood gene expression profile (GEP) demonstrated 59% sensitivity, 76% specificity, PPV of 79%, and NPV of 54%. Donor-derived cell-free DNA (dd-cfDNA) demonstrated sensitivity of 62%, specificity of 86%, PPV of 88%, and NPV of 56%. For repeat biopsies within 90 days of rejection in clinically stable patients (63% of repeat biopsies), both GEP and dd-cfDNA had specificities and PPVs of 100%. GEP was more likely to be positive in TCMR, while dd-cfDNA was more likely to be positive in AMR. Both GEP and dd-cfDNA may have utility at identifying clinically stable patients with persistent rejection in need of biopsy, however they identify different types of rejection.

Biomarkers in Kidney Transplantation: A Rapidly Evolving Landscape.

The last decade has seen an explosion in clinical research focusing on the use of noninvasive biomarkers in kidney transplantation. Much of the published literature focuses on donor-derived cell-free DNA (dd-cfDNA). Although initially studied as a noninvasive means of identifying acute rejection, it is now clear that dd-cfDNA is more appropriately described as a marker of severe injury and irrespective of the etiology, elevated dd-cfDNA ≥0.5% portends worse graft outcomes. Blood gene expression profiling is also commercially available and has mostly been studied in the context of early identification of subclinical rejection, although additional data is needed to validate these findings. Torque teno virus, a ubiquitous DNA virus, has emerged as a biomarker of immunosuppression exposure as peripheral blood Torque teno virus copy numbers might mirror the intensity of host immunosuppression. Urinary chemokine tests including C-X-C motif chemokine ligand 9 and C-X-C motif chemokine ligand 10 have recently been assessed in large clinical trials and hold promising potential for early diagnosis of both subclinical and acute rejection, as well as, for long-term prognosis. Urinary cellular messenger RNA and exosome vesicular RNA based studies require additional validation. Although current data does not lend itself to conclusion, future studies on multimodality testing may reveal the utility of serial surveillance for individualization of immunosuppression and identify windows of opportunity to intervene early and before the irreversible allograft injury sets in.

Clinical Utility of the Molecular Microscope Diagnostic System in a Real-World Transplant Cohort: Moving Towards a New Paradigm.

To evaluate the clinical implications of adjunctive molecular gene expression analysis (MMDx ) of biopsy specimens in heart transplant (HT ) recipients with suspected rejection. Histopathological evaluation remains the standard method for rejection diagnosis in HT. However, the wide interobserver variability combined with a relatively common incidence of "biopsy-negative" rejection has raised concerns about the likelihood of false-negative results. MMDx, which uses gene expression to detect early signs of rejection, is a promising test to further refine the assessment of HT rejection. Single-center prospective study of 418 consecutive for-cause endomyocardial biopsies performed between November 2022 and May 2024. Each biopsy was graded based on histology and assessed for rejection patterns using MMDx. MMDx results were deemed positive if borderline or definitive rejection was present. The impact of MMDx results on clinical management was evaluated. Primary outcomes were 1-year survival and graft dysfunction following MMDx-guided clinical management. Secondary outcomes included changes in donor-specific antibodies, MMDx gene transcripts, and donor-derived cell-free DNA (dd-cfDNA) levels. We analyzed 418 molecular samples from 237 unique patients. Histology identified rejection in 32 cases (7.7%), while MMDx identified rejection in 95 cases (22.7%). Notably, in 79 of the 95 cases where MMDx identified rejection, histology results were negative, with the majority of these cases being antibody-mediated rejection (62.1%). Samples with rejection on MMDx were more likely to show a combined elevation of dd-cfDNA and peripheral blood gene expression profiling than those with borderline or negative MMDx results (36.7% vs 28.0% vs 10.3%; p<0.001). MMDx results led to the implementation of specific antirejection protocols or changes in immunosuppression in 20.4% of cases, and in 73.4% of cases where histology was negative and MMDx showed rejection. 1-year survival was better in the positive MMDx group where clinical management was guided by MMDx results (87.0% vs 78.6%; log rank p=0.0017). In our cohort, MMDx results more frequently indicated rejection than histology, often leading to the initiation of antirejection treatment. Intervention guided by positive MMDx results was associated with improved outcomes.

Study of hub nodes of transcription factor-target gene regulatory network and immune mechanism for type 2 diabetes based on chip analysis of GEO database

Identification of novel therapeutic targets for type 2 diabetes is a key area of contemporary research. In this study, we screened differentially expressed genes in type 2 diabetes through the GEO database and sought to identify the key virulence factors for type 2 diabetes through a transcription factor regulatory network. Our findings may help identify new therapeutic targets for type 2 diabetes. Data pertaining to the humoral (whole blood) gene expression profile of diabetic patients were obtained from the NCBI’s GEO Datasets database and gene sets with differential expression were identified. Subsequently, the TRED transcriptional regulatory element database was integrated to build a gene regulatory network for type 2 diabetes. Functional analysis (GO-Analysis) and Pathway-analysis of differentially expressed genes were performed using the DAVID database and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Finally, gene-disease correlation analysis was performed using the DAVID online annotation tool. A total of 236 pathogenic genes, four transcription factors related to the pathogenic genes, and 261 corresponding target genes were identified. A transcription factor-target gene regulatory network for type 2 diabetes was constructed. Most of the key factors of the transcription factor-target gene regulatory network for type 2 diabetes were found closely related to the immune metabolic system and the functions of cell proliferation and transformation.

Effect of Obstructive Sleep Apnea during Pregnancy on Fetal Development: Gene Expression Profile of Cord Blood.

Obstructive sleep apnea (OSA) is quite prevalent during pregnancy and is associated with adverse perinatal outcomes, but its potential influence on fetal development remains unclear. This study investigated maternal OSA impact on the fetus by analyzing gene expression profiles in whole cord blood (WCB). Ten women in the third trimester of pregnancy were included, five OSA and five non-OSA cases. WCB RNA expression was analyzed by microarray technology to identify differentially expressed genes (DEGs) under OSA conditions. After data normalization, 3238 genes showed significant differential expression under OSA conditions, with 2690 upregulated genes and 548 downregulated genes. Functional enrichment was conducted using gene set enrichment analysis (GSEA) applied to Gene Ontology annotations. Key biological processes involved in OSA were identified, including response to oxidative stress and hypoxia, apoptosis, insulin response and secretion, and placental development. Moreover, DEGs were confirmed through qPCR analyses in additional WCB samples (7 with OSA and 13 without OSA). This highlighted differential expression of several genes in OSA (EGR1, PFN1 and PRKAR1A), with distinct gene expression profiles observed during rapid eye movement (REM)-OSA in pregnancy (PFN1, UBA52, EGR1, STX4, MYC, JUNB, and MAPKAP). These findings suggest that OSA, particularly during REM sleep, may negatively impact various biological processes during fetal development.

Peripheral Blood Gene Expression Profiling Reveals Molecular Pathways Associated with Cervical Artery Dissection.

Cervical artery dissection (CeAD) is the primary cause of ischemic stroke in young adults. Monogenic heritable connective tissue diseases account for fewer than 5% of cases of CeAD. The remaining sporadic cases have known risk factors. The clinical, radiological, and histological characteristics of systemic vasculopathy and undifferentiated connective tissue dysplasia are present in up to 70% of individuals with sporadic CeAD. Genome-wide association studies identified CeAD-associated genetic variants in the non-coding genomic regions that may impact the gene transcription and RNA processing. However, global gene expression profile analysis has not yet been carried out for CeAD patients. We conducted bulk RNA sequencing and differential gene expression analysis to investigate the expression profile of protein-coding genes in the peripheral blood of 19 CeAD patients and 18 healthy volunteers. This was followed by functional annotation, heatmap clustering, reports on gene-disease associations and protein-protein interactions, as well as gene set enrichment analysis. We found potential correlations between CeAD and the dysregulation of genes linked to nucleolar stress, senescence-associated secretory phenotype, mitochondrial malfunction, and epithelial-mesenchymal plasticity.

Abstract 2114: Differences In The Circulating Transcriptome Between Peripheral Artery Disease And Coronary Artery Disease

Introduction: Despite common risk factors, peripheral artery disease (PAD) and coronary artery disease (CAD) exhibit distinct pathological presentations. Previous gene expression profiling of blood from CAD and PAD patients shows alterations in inflammatory and immune related transcripts relative to controls. Studies have not directly compared gene expression in PAD versus CAD. Here, we aimed to use whole-blood RNA sequencing to investigate transcriptomic differences between PAD and CAD. Methods: Whole blood from subjects age 40-65 years with symptomatic PAD (n=20, mean age 59, 75% male, 35% white) and CAD (n=51, mean age 52, 82% male, 78% white) was collected into PAXgene tubes. Patients with concomitant CAD and PAD were excluded. RNA was isolated, sequenced and analyzed to investigate differential circulating gene expression between PAD and CAD subjects adjusting for age, sex, and race/ethnicity. Cell type deconvolution method dtangle was applied to characterize cell type proportions in the whole blood transcriptome. Results: Between PAD and CAD subjects, 106 transcripts were differentially expressed (adjP &lt; 0.1), of which 38 were enriched in PAD and 68 were enriched in CAD. Gene set enrichment analysis (GSEA) revealed 29 pathways differentially expressed (FDR 0.05) between PAD and CAD, of which 8 were enriched in PAD and 21 enriched in CAD. The top enriched pathways in PAD were “specific granule” (Normalized enrichment score [NES] = 2.1, q value = 2.5 x 10 -5 ) and “secretory granule membrane” (NES = 1.9, q = 1.0 x 10 -4 ). Top enriched pathways in CAD were “T cell receptor complex” (NES = 3.1, q = 2.2 x 10 -7 ) and “response to interferon-alpha” (NES = 3.1, q = 2.2 x 10 -9 ). Cellular deconvolution using dtangle showed increased CD8 T-cells (p=0.01) and CD4 Naïve T-cells (p=0.007) in subjects with CAD versus PAD. Conclusion: Whole blood transcriptomics revealed significant differences in the circulating transcriptome between patients presenting with PAD and CAD. Our data suggest upregulation of pathways related to secretory granules in PAD, and enrichment of CD8 and CD4 Naïve T-cells and pathways related to T-cell signaling in CAD. Differences in the circulating transcriptome may help explain pathological and clinical differences between PAD and CAD.

Ensemble Learning for Higher Diagnostic Precision in Schizophrenia Using Peripheral Blood Gene Expression Profile.

Stigma contributes to a significant part of the burden of schizophrenia (SCZ), therefore reducing false positives from the diagnosis would be liberating for the individuals with SCZ and desirable for the clinicians. The stigmatization associated with schizophrenia advocates the need for high-precision diagnosis. In this study, we present an ensemble learning-based approach for high-precision diagnosis of SCZ using peripheral blood gene expression profiles. The machine learning (ML) models, support vector machines (SVM), and prediction analysis for microarrays (PAM) were developed using differentially expressed genes (DEGs) as features. The SCZ samples were classified based on a voting ensemble classifier of SVM and PAM. Further, microarray-based learning was used to classify RNA sequencing (RNA-Seq) samples from our case-control study (Pune-SCZ) to assess cross-platform compatibility. Ensemble learning using ML models resulted in a significantly higher precision of 80.41% (SD: 0.04) when compared to the individual models (SVM-radial: 71.69%, SD: 0.04 and PAM 77.20%, SD: 0.02). The RNA sequencing samples from our case-control study (Pune-SCZ) resulted in a moderate precision (59.92%, SD: 0.05). The feature genes used for model building were enriched for biological processes such as response to stress, regulation of the immune system, and metabolism of organic nitrogen compounds. The network analysis identified RBX1, CUL4B, DDB1, PRPF19, and COPS4 as hub genes. In summary, this study developed robust models for higher diagnostic precision in psychiatric disorders. Future efforts will be directed towards multi-omic integration and developing "explainable" diagnostic models.

CircAMN1-Mediated Ferroptosis Regulates the Expulsion of Placenta in Trophoblast Cells.

After delivery, the death of trophoblast cells can promote the expulsion of the placenta. Ferroptosis, an iron-dependent programmed cell death, is involved in mammalian development. Circular RNAs are associated with placental development; however, it is unclear whether circular RNAs regulate the expulsion of fetal membranes through ferroptosis. The gene expression profiles in the tail vein blood of Holstein cows with normal and retained placentas were investigated using RNA sequencing and a GSE214588 dataset. circAMN1 and SLC39A8 expression was significantly downregulated in the blood of cows with a retained placenta, whereas miR-205_R-1 expression was significantly upregulated. We validated erastin-induced ferroptosis in trophoblast cells. Transfection with si-circAMN1 and miR-205_R-1 mimic reduced intracellular total iron, Fe2+, and glutathione disulfide levels; increased intracellular glutathione levels and glutathione/glutathione disulfide; and enhanced cell viability in these cells. In contrast, transfection with pcDNA3.1 circAMN1 and an miR-205_R-1 inhibitor promoted ferroptosis. As an miR-205_R-1 sponge, circAMN1 regulated the expression of SLC39A8 to control erastin-induced ferroptosis and regulated the proliferation, invasion, and migration of trophoblast cells. Our findings provide a theoretical basis for studying the mechanism by which programmed cell death regulates fetal membrane expulsion and indicate its potential as a therapeutic target for placenta retention.

Integrated analysis of transcriptional changes in major depressive disorder: Insights from blood and anterior cingulate cortex

BackgroundMajor depressive disorder (MDD) was involved in widely transcriptional changes in central and peripheral tissues. While, previous studies focused on single tissues, making it difficult to represent systemic molecular changes throughout the body. Thus, there is an urgent need to explore the central and peripheral biomarkers with intrinsic correlation. MethodsWe systematically retrieved gene expression profiles of blood and anterior cingulate cortex (ACC). 3 blood datatsets (84 MDD and 88 controls) and 6 ACC datasets (100 MDD and 100 controls) were obtained. Differential expression analysis, RobustRankAggreg (RRA) analysis, functional enrichment analysis, immune associated analysis and protein-protein interaction networks (PPI) were integrated. Furthermore, the key genes were validated in an independent ACC dataset (12 MDD and 15 controls) and a cohort with 120 MDD and 117 controls. ResultsDifferential expression analysis identified 2211 and 2021 differential expressed genes (DEGs) in blood and ACC, respectively. RRA identified 45 and 25 robust DEGs in blood and ACC based on DEGs, and all of them were closely associated with immune cells. Functional enrichment results showed both the robust DEGs in blood and ACC were enriched in humoral immune response. Furthermore, PPI identified 8 hub DEGs (CD79A, CD79B, CD19, MS4A1, PLP1, CLDN11, MOG, MAG) in blood and ACC. Independent ACC dataset showed the area under the curve (AUC) based on these hub DEGs was 0.77. Meanwhile, these hub DEGs were validated in the serum of MDD patients, and also showed a promising diagnostic power. ConclusionsThe biomarker panel based on hub DEGs yield a promising diagnostic efficacy, and all of these hub DEGs were strongly correlated with immunity. Humoral immune response may be the key link between the brain and blood in MDD, and our results may provide further understanding for MDD.

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

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