Differential Analysis Research Articles

Differential Analysis of Fecal SCFAs and Their Contribution to Adipogenesis in UCP1 Knock-In Pigs

This study aimed to investigate the changes in fecal short-chain fatty acids (SCFAs) content in UCP1 knock-in pigs (KI pigs) and their effect on adipogenesis. Fecal samples from five 6-month-old wild-type (WT) and KI pigs were collected for targeted metabolomics and 16s rRNA sequencing analyses to identify differences in SCFAs and gut microbiota that may contribute to regulating fat deposition in pigs. The metabolome of pig fecal samples targeted for an analysis of SCFAs identified seven SCFAs, with caproic acid (except isovaleric acid) being the significantly different one. The results of the fecal 16s rRNA analysis demonstrated a notable reduction in the abundance of Streptococcus spp. in the KI pigs in comparison to the WT pigs, with a statistically significant difference. Correlation analyses demonstrated a statistically significant positive correlation between the abundance of Streptococcus spp. and SCFAs, as well as pig body weight and fatness. It was postulated that the reduction in SCFAs in the intestinal tracts of KI pigs may be associated with a reduction in Streptococcus spp. abundance. Compared to WT pigs, the concentration of fecal SCFAs in KI pigs was significantly reduced, which may be related to the decreased abundance of Streptococcus. The in vitro experiments showed that caproic acid could significantly enhance the differentiation efficiency of porcine SVF cells into mature adipocytes by activating the FFAR4 gene.

Ecological speciation with gene flow followed initial large-scale geographic speciation in the enigmatic afroalpine giant senecios (Dendrosenecio).

Mountains have highly heterogeneous environments that generate ample opportunities for lineage differentiation through ecological adaptation, geographic isolation and secondary contact. The geographic and ecological isolation of the afroalpine vegetation fragments on the East African mountain tops makes them an excellent system to study speciation. The initial diversification within the afroalpine endemic genus Dendrosenecio was shown to occur via allopatric divergence among four isolated mountain groups, but the potential role of ecological speciation within these groups and the role of gene flow in speciation remained uncertain. Here we extend the sampling of Dendrosenecio and use phylogenomics to assess the importance of gene flow in the diversification of the genus. Then, population genomics, demographic modelling and habitat differentiation analyses are used to study ecological speciation in two sister species occurring on Mount Kenya. We found that two sympatric sister species on Mt Kenya occupy distinct microhabitats, and our analyses support that they originated in situ via ecological speciation with gene flow. In addition, we obtained signals of admixture history between mountain groups. Taken together, these results suggest that geographic isolation shaped main lineages, while ecologically mediated speciation occurred within a single mountain.

Differential Gene Expression Analysis and Machine Learning identified Structural, TFs, Cytokine and Glycoproteins, including SOX2, TOP2A, SPP1, COL1A1, and TIMP1 as potential drivers of Lung Cancer

Background Lung cancer is a primary global health concern, responsible for a considerable portion of cancer-related fatalities worldwide. Understanding its molecular complexities is crucial for identifying potential targets for treatment. The goal is to slow disease progression and intervene early to prevent the development of advanced lung cancer cases. Hence, there's an urgent need for new biomarkers that can detect lung cancer in its early stages. Methods: The study conducted RNA-Seq analysis of lung cancer samples from the publicly available SRA database (NCBI SRP009408), including both control and tumour samples. The genes with differential expression between tumour and healthy tissues were identified using R and Bioconductor. Machine learning (ML) techniques, Random Forest, Lasso, XGBoost, Gradient Boosting, and Elastic Net were employed to pinpoint significant genes followed by classifiers, Multilayer Perceptron (MLP), Support Vector Machines (SVM), and k-Nearest Neighbors (k-NN). Gene ontology and pathway analyses were performed on the significant differentially expressed genes (DEGs). The top genes from DEG and machine learning analyses were combined for protein-protein interaction (PPI) analysis, identifying 10 hub genes essential for lung cancer progression. Results: The integrated analysis of ML and DEGs revealed the significance of specific genes in lung cancer samples, identified the top five upregulated genes (COL11A1, TOP2A, SULF1, DIO2, MIR196A2) and the top five downregulated genes (PDK4, FOSB, FLYWCH1, CYB5D2, MIR328), along with their associated genes implicated in pathways or co-expression networks were identified. Among the various algorithms employed, Random Forest and XGBoost proved effective in identifying common genes, underscoring their potential significance in lung cancer pathogenesis. The MLP exhibited the highest accuracy in classifying samples using all genes. Additionally, the protein-protein interaction (PPI) analysis identified 10 hub genes that are pivotal in lung cancer pathogenesis: COL1A1, SOX2, SPP1, THBS2, POSTN, COL5A1, COL11A1, TIMP1, TOP2A, and PKP1. Conclusion The study contributes to the early prediction of lung cancer by identifying potential biomarkers that could enhance early diagnosis and pave the way for practical clinical applications in the future. Integrating DEGs and machine learning-derived significant genes for PPI analysis offers a robust approach to uncovering critical molecular targets for lung cancer treatment.

Bioinformatics analysis combined with experimental validation reveals the biological role of the ILK gene in prostate cancer

BackgroundProstate cancer (PCa) is a prevalent urological malignancy. The integrin-linked kinase (ILK) gene has been identified as an oncogenic driver in hormonal cancers, including PCa.MethodsTo identify key genes in PCa, we utilized differential gene expression analysis and Weighted Gene Co-expression Network Analysis (WGCNA). The ILK gene was silenced using short interfering RNA (siRNA), and subsequent experiments focusing on cellular functionality were conducted to evaluate its impact on cell proliferation, apoptosis, and cell cycle. We examined the expression of autophagy-related and cell cycle-related proteins, including MAP1LC3A, BECN1, C-MYC, TP53, and MDM2. Moreover, we conducted Mfuzz expression pattern clustering analysis, gene set enrichment analysis (GSEA), immune function analysis, transcription factor (TF) analysis, and drug prediction.Results544 significant genes were identified by WGCNA. The protein–protein interaction (PPI) network analysis revealed that MYC was the central regulatory gene, with the intersected genes mainly involved in regulating cell adhesion and drug metabolism in prostate cancer (PCa). Experimental results showed LNCaP cell proliferation was significantly inhibited in the knockdown groups (P < 0.001). Moreover, ILK silencing increased apoptosis in LNCaP cells compared to normal cells and empty vectors, and transfected LNCaP cells were arrested in the S phase of the cell cycle. Notably, C-MYC expression decreased following ILK silencing. Subsequently, we further identified ILK-related regulatory biomarkers.ConclusionsThe ILK is an oncogene mainly through influencing the C-MYC in PCa. Inhibition of ILK expression would be a promising method for treating the development and progression of PCa.

MetaQ: fast, scalable and accurate metacell inference via single-cell quantization

To overcome the computational barriers of analyzing large-scale single-cell sequencing data, we introduce MetaQ, a metacell algorithm that scales to arbitrarily large datasets with linear runtime and constant memory usage. Inspired by cellular development, MetaQ conceptualizes each metacell as a collective ancestor of biologically similar cells. By quantizing cells into a discrete codebook, where each entry represents a metacell capable of reconstructing the original cells it quantizes, MetaQ identifies homogeneous cell subsets for efficient and accurate metacell inference. This approach reduces computational complexity from exponential to linear while maintaining or surpassing the performance of existing metacell algorithms. Extensive experiments demonstrate that MetaQ excels in downstream tasks such as cell type annotation, developmental trajectory inference, batch integration, and differential expression analysis. Thanks to its superior efficiency and effectiveness, MetaQ makes analyzing datasets with millions of cells practical, offering a powerful solution for single-cell studies in the era of high-throughput profiling.

Identification of therapeutic targets for Alzheimer’s Disease Treatment using bioinformatics and machine learning

Alzheimer’s disease (AD) is a complex neurodegenerative disorder that currently lacks effective treatment options. This study aimed to identify potential therapeutic targets for the treatment of AD using comprehensive bioinformatics methods and machine learning algorithms. By integrating differential gene expression analysis, weighted gene co-expression network analysis, Mfuzz clustering, single-cell RNA sequencing, and machine learning algorithms including LASSO regression, SVM-RFE, and random forest, five hub genes related to AD, including PLCB1, NDUFAB1, KRAS, ATP2A2, and CALM3 were identified. PLCB1, in particular, exhibited the highest diagnostic value in AD and showed significant correlation with Braak stages and neuronal expression. Furthermore, Noscapine, PX-316, and TAK-901 were selected as potential therapeutic drugs for AD based on PLCB1. This research provides a comprehensive and reliable method for the discovery of AD therapeutic targets and the construction of diagnostic models, offering important insights and directions for future AD treatment strategies and drug development.

Multi-omics dissection of metabolic dysregulation associated with immune recovery in people living with HIV-1

BackgroundDespite the success of antiretroviral therapy (ART) in suppressing HIV-1 replication, some people living with HIV-1 (PLWH) fail to achieve an optimal recovery of CD4 T cells, and precise metabolic regulation underlying immune recovery remained poorly understood.MethodsIn this cross-sectional study, mass spectrometry was used for quantitative analysis of plasma metabolome and lipidome in 24 treatment-naïve PLWH (TNs), 33 immunological responders (IRs), 35 immunological non-responders (INRs), and 16 healthy controls (HCs). The data were analyzed using the Mann–Whitney U-test, Kruskal–Wallis test, Spearman correlation, and LASSO regression analysis.ResultsSignificant metabolic dysregulation was observed in TNs, IRs and INRs compared to HCs. In TNs, metabolomic analysis revealed increased levels of 3-hydroxyoctanoic acid, 3-oxododecanoic acid, 5-hydroxy-L-tryptophan, 5-hydroxyindoleacetic acid, L-kynurenine, oleoylcarnitine, and pseudouridine that were positively correlated with CD8 T cell activation and inflammation-related markers, and decreased levels of phosphorylcholine, ribothymidine, and thymine that were negatively correlated. Notably, 3-hydroxyoctanoic acid and thymine were consistently associated with CD4 T cell counts and inflammation-related markers in PLWH, regardless of ART. Pathway analysis uncovered the biosynthesis of unsaturated fatty acids as the major dysregulated pathway in TNs, IRs, and INRs, while primary bile acid biosynthesis was the dysregulated pathway specifically in INRs. Lipidomic analysis indicated higher plasma triacylglycerols, free fatty acids, ceramide, and monosialodihexosyl gangliosides (GM3) in TNs, IRs, and INRs compared to HCs. Pathway enrichment and differential correlation analyses underscore perturbed systemic lipid metabolism in treatment response to ART, possibly mediated by host-commensal metabolic interactions. Ultimately, we identified two panels, one consisting of 9 metabolites and another of 8 lipids, that can effectively distinguish INRs from IRs.ConclusionsMetabolic aberrations induced by chronic HIV-1 infection persist and do not recover with ART. Abnormal primary bile acid biosynthesis pathway and levels of DHA-containing lipids are closely associated with CD4 T cell recovery. These finding provide new intervention targets to achieve better immune recovery in PLWH.

Identification of potential biomarkers associated with cuproptosis and immune microenvironment analysis in acute myocardial infarction: A diagnostic accuracy study.

Acute myocardial infarction (AMI), a critical cardiovascular condition, is often associated with serious health risks. Recent studies suggest a link between copper-induced apoptosis and immune cell infiltration. Specifically, abnormal accumulation of copper ions can lead to intracellular oxidative stress and apoptosis, while also affecting immune cell function and infiltration. Nevertheless, studies exploring this relationship in the context of AMI are notably scarce, underscoring the necessity of identifying biomarkers associated with cuproptosis in AMI. Consensus clustering analysis was employed to classify distinct subtypes of AMI in the GSE66360 dataset. Concurrently, differential expression analysis was performed to identify differentially expressed genes (DEGs) across subtypes and between AMI and control samples. We employed Venn diagrams to validate the selection of cuproptosis-related DEGs in patients with AMI. A protein-protein interaction network was constructed to pinpoint potential candidate genes. Receiver operating characteristic curves were generated to identify promising biomarkers. The immune infiltration milieu was analyzed using CIBERSORT algorithms. Finally, the expression levels of identified cuproptosis-related biomarkers were validated at the transcriptional level. We classified AMI into 2 distinct cuproptosis-related subtypes, leading to the identification of 157 cuproptosis-related DEGs. Further analysis refined this list to 10 potential candidate genes. Among these, 5 emerged as significant biomarkers for AMI: granzyme A (GZMA), GTPase immunity-associated proteins (GIMPAs) GIMAP7, GIMAP5, GIMAP6, and TRAF3 interacting protein 3 (TRAF3IP3). A comprehensive examination of immune infiltration in AMI samples revealed significant differences in the levels of 11 types of immune cells, with GZMA displaying the highest correlation with activated mast cells and CD8 + T cells. We observed markedly lower expression levels of GZMA, GIMAP6, and TRAF3IP3 in the AMI group compared to controls. This study identified 5 cuproptosis-related biomarkers (GZMA, GIMAP7, GIMAP5, GIMAP6, and TRAF3IP3) associated with AMI, laying a theoretical foundation for the treatment of AMI.

Identification of Common Angiogenesis Marker Genes in Chronic Lung Diseases and Their Relationship with Immune Infiltration Based on Bioinformatics Approaches

Objective: This study aims to explore the role of angiogenesis-related genes in chronic lung diseases (ILD and COPD) using bioinformatics methods, with the goal of identifying novel therapeutic targets to slow disease progression and prevent its deterioration into fibrosis or pulmonary artery hypertension. Methods: The research methods encompassed differential analysis, WGCNA (Weighted Gene Co-expression Network Analysis), and multiple machine learning approaches to screen for key genes. Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO), and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were utilized to assess related biological functions and pathways. Additionally, immune cell infiltration was analyzed to evaluate the immune status of the disease and the correlation between genes and immunity. Results: COPD and ILD are closely associated with pathways related to angiogenesis, immune responses, and others, with differential genes in both groups linked to inflammation-related signaling pathways. The study established a chronic lung disease-related gene set comprising 171 genes and further screened out 21 genes related to angiogenesis. Ultimately, four key genes—COL10A1, EDN1, MMP1, and RRAS—were identified through machine learning methods. These four genes are closely related to angiogenesis and immune processes, and clustering analysis based on them can reflect different disease states and variations in immune cell infiltration. Conclusions: COL10A1, EDN1, MMP1, and RRAS represent potential therapeutic targets for slowing the progression of chronic lung diseases and preventing their deterioration. Furthermore, monocytes exhibited consistent infiltration patterns across disease and control groups, as well as among different subgroups, suggesting their potential significant role in the development of chronic lung diseases.

Interneuron loss and microglia activation by transcriptome analyses in the basal ganglia of Tourette disorder.

Tourette disorder is characterized by motor hyperactivity and tics that are believed to originate in basal ganglia. Postmortem immunocytochemical analyses previously revealed decreases in cholinergic, parvalbumin, and somatostatin interneurons (IN) within the caudate/putamen of individuals with TS. We obtained transcriptome and open chromatin datasets by snRNAseq and snATAC-seq, respectively, from caudate/putamen postmortem specimens of 6 adult TS and 6 matched normal control (NC). Differential gene expression and differential chromatin accessibility analyses were performed in identified cell types. The data reproduced the known cellular composition of the human striatum, including a majority of medium spiny neurons (MSN) and small populations of GABAergic and cholinergic IN. IN were decreased by ∼50% in TS brains, with no difference in other cell types. Differential gene expression analysis suggested that mitochondrial oxidative metabolism in MSN and synaptic adhesion and function in IN were both decreased in TS subjects, while there was activation of immune response in microglia. Gene expression changes correlated with changes in activity of cis-regulatory elements, suggesting a relationship of transcriptomic and regulatory abnormalities in MSN, OL and AST of TS brains. This initial analysis of the TS basal ganglia transcriptome at the single cell level confirms the loss and synaptic dysfunction of basal ganglia IN, consistent with in vivo basal ganglia hyperactivity. In parallel, oxidative metabolism was decreased in MSN and correlated with activation of microglia cells, attributable at least in part to dysregulated activity of putative enhancers, implicating altered epigenomic regulation in TS.

Understanding Intrinsic Electrochemical Properties of NiCo–Metal–Organic Framework-Derived NiCo2O4 as a Li-Ion Battery Anode

This study explores the electrochemical properties of additive-free NiCo₂O₄ derived from NiCo–metal–organic frameworks (MOFs) as a high-performance anode material for lithium-ion batteries (LIBs), excluding the effect of additives. NiCo-MOF was synthesized via an ultrasonic-assisted method and deposited on stainless steel foils using alternating current electrophoretic deposition (AC-EPD). The resulting thin films exhibited outstanding cycling stability and rate performance, maintaining a specific capacity of ~1200 mAh/g over 250 cycles at a high current density of 2.35 A/g, with nearly 100% Coulombic efficiency. Differential capacity analysis revealed enhanced redox activity at 0.8 V and 1.7 V during lithiation and delithiation, attributed to the decomposition of NiCo₂O₄ into metallic Ni and Co, followed by their oxidation to Ni2⁺ and Co3⁺, respectively. The gradual activation of electroactive sites, coupled with improved electrode kinetics and structural adjustments, contributed to the observed capacity increase over cycles. These findings underscore the potential of NiCo₂O₄ as a robust and efficient anode material for next-generation LIBs.

Characterization of Luffa-reinforced Polyaniline Films

Herein, Polyaniline (PANI)/polyethylene oxide (PEO) - luffa cylindrica bio-composite films of various mass fractions (%) have been prepared via casting solution of emeraldine base polyaniline and cellulose extracted from luffa. The biopolymer films were structurally and thermally characterized using X-ray diffraction (XRD), Fournier Transform-InfraRed (FT-IR) spectroscopy, and differential scanning calorimetry analysis (DSC). Moreover, the electrical properties of conductive biopolymer solutions were measured by using the conductivity meter. According to the obtained results, treated luffa increases the conductivity of biopolymers. XRD results reveal that luffa increases the crystallinity of bio-composite films in comparison to PANI/PEO films. FTIR analysis proved the presence of functional groups of PANI, PEO, and luffa in the film structure. Also, an increase in the weight of luffa in the bio-composite film brings about an increase in the peak intensities of the O-H group. It is determined that luffa enhances the thermal stability of composites via the results of DSC analysis.

Genome-wide transcriptome differences associated with perceived discrimination in an urban, community-dwelling middle-aged cohort.

Discrimination is a social adversity that is linked to several age-related outcomes. However, the molecular drivers of these observations are poorly understood. Social adverse factors are associated with proinflammatory and interferon gene expression, but little is known about whether additional genes are associated with discrimination among both African American and White adults. In this study, we examined how perceived discrimination in African American and White adults was associated with genome-wide transcriptome differences using RNA sequencing. Perceived discrimination was measured based on responses to self-reported lifetime discrimination and racial discrimination. Differential gene expression and pathway analysis were conducted in a cohort (N = 59) stratified by race, sex, and overall discrimination level. We found 28 significantly differentially expressed genes associated with race among those reporting high discrimination. Several of the upregulated genes for African American versus White adults reporting discrimination were related to immune function IGLV2-11, S100B, IGKV3-20, and IGKV4-1; the most significantly downregulated genes were associated with immune modulation and cancer, LUCAT1, THBS1, and ARPIN. The most enriched gene ontology biological process between African American and White men reporting high discrimination was the regulation of cytokine biosynthetic processes. The immune response biological process was significantly lower for African American women compared to White women reporting high discrimination. Discrimination was associated with the expression of small nucleolar RNAs, long noncoding RNAs, and microRNAs associated with energy homeostasis, cancer, and actin. Understanding the pathways through which adverse social factors like discrimination are associated with gene expression is crucial in advancing knowledge of age-related health disparities.

NTHi killing activity is reduced in COPD patients and is associated with a differential microbiome

Chronic obstructive pulmonary disease (COPD) is a chronic lung disease characterized by airway obstruction and inflammation. Non-typeable Haemophilus influenzae (NTHi) lung infections are common in COPD, promoting frequent exacerbations and accelerated lung function decline. The relationship with immune responses and NTHi are poorly understood. Herein, we comprehensively characterized the respiratory microbiome and mycobiome of patients while investigating microbial dynamics and host immune changes attributable to NTHi killing activity. Mild-to-moderate COPD patients encompassing frequent and infrequent exacerbators and healthy volunteers (HV) were enrolled. Microbial composition, proteomics and NTHi killing activity was analyzed using bronchoalveolar lavage fluid (BALF). In addition, antigen–antibody titers in sera to COPD pathogens were determined using a multiplex assay. Differential abundance analysis revealed an enrichment of Actinobacteria and Bacteroidetes in the BALF of COPD and HV subjects respectively. Significant differences in the IgA titer response were observed against NTHi antigens in COPD vs. HV. Notably, there was also significantly greater killing activity against NTHi in BALF from COPD vs. HV subjects (OR = 5.64; 95% CI = 1.75–20.20; p = 0.001). Stratification of COPD patients by NTHi killing activity identified unique microbial and protein signatures wherein Firmicutes, Actinobacteria and haptoglobin were enriched in patients with killing activity. We report that differences in host immune responses and NTHi-killing activity are associated with microbiome changes in mild-to-moderate COPD. This is suggestive of a potential link between the respiratory microbiome and immune activity against NTHi in the context of COPD pathogenesis even at this disease stage.

Single-Cell Hi-C Technologies and Computational Data Analysis.

Single-cell chromatin conformation capture (scHi-C) techniques have evolved to provide significant insights into the structural organization and regulatory mechanisms in individual cells. Although many scHi-C protocols have been developed, they often involve intricate procedures and the resulting data are sparse, leading to computational challenges for systematic data analysis and limited applicability. This review provides a comprehensive overview, quantitative evaluation of thirteen protocols and practical guidance on computational topics. It is first assessed the efficiency of these protocols based on the total number of contacts recovered per cell and the cis/trans ratio. It is then provided systematic considerations for scHi-C quality control and data imputation. Additionally, the capabilities and implementations of various analysis methods, covering cell clustering, A/B compartment calling, topologically associating domain (TAD) calling, loop calling, 3D reconstruction, scHi-C data simulation and differential interaction analysis is summarized. It is further highlighted key computational challenges associated with the specific complexities of scHi-C data and propose potential solutions.

Cellular and immune landscape of chronic liver diseases: insights from immunophenotyping

BackgroundChronic liver disease due to alcohol-related liver disease and chronic viral hepatitis pose a substantial burden on healthcare systems. Chronic liver disease may predispose to hepatocellular carcinoma, for which therapeutic options are limited. This study aimed to explore the immune cell characteristics of the clinical conditions.MethodsExplant liver samples were collected from 25 patients for bulk RNA sequencing and flow cytometry analysis. Immune cell populations were characterized by flow cytometry from isolated hepatic and peripheral mononuclear cells.ResultsSignificant differences in immune cell characteristics were observed among patients with three clinical conditions. Viral hepatitis and peri-tumor samples exhibited higher hepatic B cell counts compared to alcohol-related liver disease. Additionally, chronic liver disease patients showed higher levels of CD57+ T cells, suggestive of T cell differentiation. Differential expression analysis identified several genes associated with immune regulation, including downregulation of CD27 and upregulation of granzyme B in ARLD, consistent with a highly differentiated phenotype. LAG3 and PDCD1 were upregulated in peri-tumor samples. The NK cell count was lower in peri-tumor liver specimens compared to ARLD, and an upregulation of TIGIT, an inhibitory marker, was observed in those peri-tumor specimens.ConclusionThis study contributes to the understanding of immune dynamics in chronic liver disease among different etiologies. B lymphocytes are relatively reduced in alcohol-related liver disease compared to other groups, and T cells exhibit a more differentiated subtype. The peritumor microenvironment in HCC suggests a relatively diminished presence of NK cells and a potential tendency toward increased inhibitory characteristics.

Transcriptomic signatures of severe acute mountain sickness during rapid ascent to 4,300 m

IntroductionAcute mountain sickness (AMS) is a common altitude illness that occurs when individuals rapidly ascend to altitudes ≥2,500 m without proper acclimatization. Genetic and genomic factors can contribute to the development of AMS or predispose individuals to susceptibility. This study aimed to investigate differential gene regulation and biological pathways to diagnose AMS from high-altitude (HA; 4,300 m) blood samples and predict AMS-susceptible (AMS+) and AMS-resistant (AMS─) individuals from sea-level (SL; 50 m) blood samples.MethodsTwo independent cohorts were used to ensure the robustness of the findings. Blood samples were collected from participants at SL and HA. RNA sequencing was employed to profile gene expression. Differential expression analysis and pathway enrichment were performed to uncover transcriptomic signatures associated with AMS. Biomarker panels were developed for diagnostic and predictive purposes.ResultsAt HA, hemoglobin-related genes (HBA1, HBA2, and HBB) and phosphodiesterase 5A (PDE5A) emerged as key differentiators between AMS+ and AMS− individuals. The cAMP response element-binding protein (CREB) pathway exhibited contrasting regulatory patterns at SL and HA, reflecting potential adaptation mechanisms to hypoxic conditions. Diagnostic and predictive biomarker panels were proposed based on the identified transcriptomic signatures, demonstrating strong potential for distinguishing AMS+ from AMS− individuals.DiscussionThe findings highlight the importance of hemoglobin-related genes and the CREB pathway in AMS susceptibility and adaptation to hypoxia. The differential regulation of these pathways provides novel insights into the biological mechanisms underlying AMS. The proposed biomarker panels offer promising avenues for the early diagnosis and prediction of AMS risk, which could enhance preventive and therapeutic strategies.

Differential Metabolite Analysis of Anthocyanins in Variously Colored Petal Types During Different Developmental Stages of Sophora japonica L

Differential Metabolite Analysis of Anthocyanins in Variously Colored Petal Types During Different Developmental Stages of Sophora japonica L

ZINQ-L: a zero-inflated quantile approach for differential abundance analysis of longitudinal microbiome data

BackgroundIdentifying bacterial taxa associated with disease phenotypes or clinical treatments over time is critical for understanding the underlying biological mechanism. Association testing for microbiome data is already challenging due to its complex distribution that involves sparsity, over-dispersion, heavy tails, etc. The longitudinal nature of the data adds another layer of complexity - one needs to account for the within-subject correlations to avoid biased results. Existing longitudinal differential abundance approaches usually depend on strong parametric assumptions, such as zero-inflated normal or negative binomial. However, the complex microbiome data frequently violate these distributional assumptions, leading to inflated false discovery rates. In addition, the existing methods are mostly mean-based, unable to identify heterogeneous associations such as tail events or subgroup effects, which could be important biomedical signals.MethodsWe propose a zero-inflated quantile approach for longitudinal (ZINQ-L) microbiome differential abundance test. A mixed-effects quantile rank-score-based test was proposed for hypothesis testing, which consists of a test in mixed-effects logistic model for the presence-absence status of the investigated taxon, and a series of mixed-effects quantile rank-score tests adjusted for zero inflation given its presence. As a regression method with minimal distributional assumptions, it is robust to the complex microbiome data, controlling false discovery rate, and is flexible to adjust for important covariates. Its comprehensive examination of the abundance distribution enables the identification of heterogeneous associations, improving the testing power.ResultsExtensive simulation studies and an application to a real kidney transplant microbiome study demonstrate the improved power of ZINQ-L in detecting true signals while controlling false discovery rates.ConclusionZINQ-L is a zero-inflated quantile-based approach for detecting individual taxa associated with outcomes or exposures in longitudinal microbiome studies, providing a robust and powerful option to improve and complement the existing methods in the field.

P-1831. Clostridioides difficile infection alters the prefrontal cortex transcriptomics in a preclinical model

Abstract Background Dementia is associated with poor outcomes of Clostridioides difficile infection (CDI) in older adults. Changes in brain metabolites profile was detected in preclinical model of CDI. Recently, we discovered that CDI-promoted brain neuroinflammation occurs after peak of the infection. Here, we investigated how CDI changes the transcriptomics of prefrontal cortex of C. difficile-infected mice. Methods 6-month-old mice were infected with C. difficile and euthanized on day 7 post-infection (pi) and bulk RNA-sequencing was performed on prefrontal cortex. Changes in the main upregulated or downregulated genes were confirmed by protein assays. Spearman correlation analyses were also performed. Results Infected mice developed diarrhea and weight loss, resulting in maximum weight loss and diarrhea on day 3 pi, which were resolved by day 7 pi. Differential gene expression analysis pointed 136 differentially expressed genes: 75 upregulated (Lcn2, Plin4, Fkbp5, Slc38a5, Lrg1, Ucp2, Slc2a1, Spint1, and Trpv4) and 61 downregulated (Pprc1, Zfp169, Bnip5, Chrm4, Grm3, Hmgcr, Zfp804b, Gpm6b, and Ncam) on day 7. Gene set enrichment (KEGG) analysis showed upregulation of genes associated with oxidative phosphorylation, Parkinson’s disease and Alzheimer’s disease, as well as downregulation of genes associated with axon guidance and neuroactive ligand-receptor interaction and T cell receptor signaling pathway in infected mice. Using protein assays, prefrontal cortex levels of GFAP, a marker of astrocytes, were increased in the infected mice, pointing to reactive astrocytes response. Increased levels of Lcn2, which can be secreted by astrocytes, followed by increased MPO and calprotectin in prefrontal cortex, cecum and serum of infected mice were detected. Levels of systemic MPO, a marker of inflammation, and fecal Lcn2, a marker of epithelial barrier disruption, correlates positively with the levels of Lnc2 in prefrontal cortex. Conclusion Our findings demonstrate that CDI induces transcriptomic changes in the prefrontal cortex, including genes associated with oxidative phosphorylation and dementia secondary to Parkinson’s and Alzheimer’s disease, among others. Astrocytes-derived Lnc2 in the prefrontal cortex may contribute to the neuroinflammatory effect associated with CDI. Disclosures Jae Hyun Shin, MD, Ferring: Grant/Research Support