DGCA: A comprehensive R package for Differential Gene Correlation Analysis

Targeting Matrix Metalloproteinase-1, Matrix Metalloproteinase-7, and Serine Protease Inhibitor E1: Implications in preserving lung vascular endothelial integrity and immune modulation in COVID-19

BACKGROUND

SARS-CoV-2 disrupts lung vascular endothelial integrity, contributing to severe COVID-19 complications. However, the molecular mechanisms driving endothelial dysfunction remain underexplored, and targeted therapeutic strategies are lacking.

OBJECTIVE

This study investigates Naringenin-7-O-glucoside (N7G) as a multi-target therapeutic candidate for modulating vascular integrity and immune response by inhibiting MMP1, MMP7, and SERPINE1-key regulators of extracellular matrix (ECM) remodeling and inflammation.

METHODS & RESULTS

RNA-seq analysis of COVID-19 lung tissues identified 17 upregulated N7G targets, including MMP1, MMP7, and SERPINE1, with the latter exhibiting the highest expression. PPI network analysis linked these targets to ECM degradation, IL-17, HIF-1, and AGE-RAGE signaling pathways, and endothelial dysfunction. Disease enrichment associated these genes with idiopathic pulmonary fibrosis and asthma. Molecular docking, 200 ns MD simulations (triplicate), and MMGBSA calculations confirmed N7G's stable binding affinity to MMP1, MMP7, and SERPINE1. Immune profiling revealed increased neutrophils and activated CD4+ T cells, alongside reduced mast cells, NK cells, and naïve B cells, indicating immune dysregulation. Correlation analysis linked MMP1, MMP7, and SERPINE1 to distinct immune cell populations, supporting N7G's immunomodulatory role.

CONCLUSION

These findings suggest that N7G exhibits multi-target therapeutic potential by modulating vascular integrity, ECM remodeling, and immune dysregulation, positioning it as a promising candidate for mitigating COVID-19-associated endothelial dysfunction.

Integrating Pulmonary and Systemic Transcriptomic Profiles to Characterize Lung Injury after Pediatric Hematopoietic Stem Cell Transplant

Hematopoietic stem cell transplantation (HCT) is potentially curative for numerous malignant and non-malignant diseases but can lead to lung injury due to chemoradiation toxicity, infection, and immune dysregulation. Bronchoalveolar lavage (BAL) is the most commonly used procedure for diagnostic sampling of the lung but is invasive, cannot be performed in medically fragile patients, and is challenging to perform serially. We previously showed that BAL transcriptomes representing pulmonary inflammation and cellular injury can phenotype post-HCT lung injury and predict mortality outcomes. However, whether peripheral blood testing is a suitable minimally-invasive surrogate for pulmonary sampling in the HCT population remains unknown. To address this question, we compared 210 paired BAL and peripheral blood transcriptomes obtained from 166 pediatric HCT patients at 27 children's hospitals. BAL and blood mRNA abundance showed minimal overall correlation at the level of individual genes, gene set enrichment scores, imputed cell fractions, and T- and B-cell receptor clonotypes. Instead, we identified significant site-specific transcriptional programs. In BAL, expression of innate and adaptive immune pathways was tightly co-regulated with expression of epithelial mesenchymal transition and hypoxia pathways, and these signatures were associated with mortality. In contrast, in blood, expression of endothelial injury, DNA repair, and cellular metabolism pathways was associated with mortality. Integration of paired BAL and blood transcriptomes dichotomized patients into two groups, of which one group showed twice the rate of hypoxia and significantly worse outcomes within 7 days of enrollment. These findings reveal a compartmentalized injury response, where BAL and peripheral blood transcriptomes provide distinct but complementary insights into local and systemic mechanisms of post-HCT lung injury.

Metabolic Reprogramming of Gastric Cancer Revealed by a Liquid Chromatography-Mass Spectrometry-Based Metabolomics Study

BACKGROUND/OBJECTIVES

Gastric cancer (GC) is a prevalent malignant tumor worldwide, with its pathological mechanisms largely unknown. Understanding the metabolic reprogramming associated with GC is crucial for the prevention and treatment of this disease. This study aims to identify significant alterations in metabolites and pathways related to the development of GC.

METHODS

A liquid chromatography-mass spectrometry-based non-targeted metabolomics data acquisition was performed on paired tissues from 80 GC patients. Differences in metabolic profiles between tumor and adjacent normal tissues were first investigated through univariate and multivariate statistical analyses. Additionally, differential correlation network analysis and a newly proposed network analysis method (NAM) were employed to explore significant metabolite pathways and subnetworks related to tumorigenesis and various TNM stages of GC.

RESULTS

Over half of the annotated metabolites exhibited significant alterations. Phosphatidylcholine (PC)_30_0 and fatty acid C20_3 demonstrated strong diagnostic performance for GC, with AUCs of 0.911 and 0.934 in the discovery and validation sets, respectively. Differential correlation network analysis revealed significant fatty acid-related metabolic reprogramming in GC with elevated levels of medium-chain acylcarnitines and increased activity of medium-chain acyl-CoA dehydrogenase, firstly observed in clinical GC tissues. Of note, using NAM, two correlation subnetworks were identified as having significant alterations across different TNM stages, centered with choline and carnitine C4_0-OH, respectively.

CONCLUSIONS

The identified significant alterations in fatty acid metabolism and TNM-related metabolic subnetworks in GC tissues will facilitate future investigations into the metabolic reprogramming associated with gastric cancer.

Maternal α-cypermethrin and permethrin exert differential effects on fetal growth, placental morphology, and fetal neurodevelopment in mice

Pyrethroid insecticides represent a broad class of chemicals used widely in agriculture and household applications. Human studies show mixed effects of maternal pyrethroid exposure on fetal growth and neurodevelopment. Assessment of shared pyrethroid metabolites as a biomarker for exposure obscures effects of specific chemicals within this broader class. To better characterize pyrethroid effects on fetal development, we investigated maternal exposure to permethrin, a type I pyrethroid, and α-cypermethrin, a type II pyrethroid, on fetal development in mice. Pregnant CD1 mice were exposed to permethrin (1.5, 15, or 50 mg/kg), α-cypermethrin (0.3, 3, or 10 mg/kg), or corn oil vehicle via oral gavage on gestational days (GD) 6-16. Effects on fetal growth, placental toxicity, and neurodevelopment were evaluated at GD 16. Cypermethrin, but not permethrin, significantly reduced fetal growth and altered placental layer morphology. Placental RNAseq analysis revealed downregulation of genes involved in extracellular matrix remodeling in response to α-cypermethrin. Both pyrethroids induced shifts in fetal dorsal forebrain microglia morphology from ramified to ameboid states; however, effects of α-cypermethrin were more pronounced. The α-cypermethrin transcriptome of fetal dorsal forebrain implicated altered glutamate receptor signaling, synaptogenesis, and c-AMP signaling. Coregulated gene modules in individual placenta and fetal dorsal forebrain pairs were correlated and overlapped in biological processes characterizing synapses, mitotic cell cycle, and chromatin organization, suggesting placenta-fetal brain shared mechanisms with α-cypermethrin exposure. In summary, maternal type II pyrethroid α-cypermethrin exposure but not type I pyrethroid permethrin significantly affected placental development, fetal growth, and neurodevelopment, and these effects were linked.

Immunological Network Signature of Naïve Non-Oncogene-Addicted Non-Small Cell Lung Cancer Patients Treated with Anti-PD1 Therapy: A Pilot Study

Background/Objectives: Non-small cell lung cancer (NSCLC) patients without gene driver mutations receive anti-PD1 treatments either as monotherapy or in combination with chemotherapy based on PD-L1 expression in tumor tissue. Anti-PD1 antibodies target various immune system components, perturbing the balance between immune cells and soluble factors. In this study, we identified the immune signatures of NSCLC patients associated with different clinical outcomes through network analysis. Methods: Twenty-seven metastatic NSCLC patients were assessed at baseline for the levels of circulating CD137+ T cells (total, CD4+, and CD8+) via cytofluorimetry, along with 14 soluble checkpoints and 20 cytokines through Luminex analysis. Hierarchical clustering and connectivity heatmaps were executed, analyzing the response to therapy (R vs. NR), performance status (PS = 0 vs. PS > 0), and overall survival (OS < 3 months vs. OS > 3 months). Results: The clustering of immune checkpoints revealed three groups with a significant differential proportion of six checkpoints between patients with PS = 0 and PS > 0 (p < 0.0001). Furthermore, significant pairwise correlations among immune factors evaluated in R were compared to the lack of significant correlations among the same immune factors in NR patients and vice versa. These comparisons were conducted for patients with PS = 0 vs. PS > 0 and OS < 3 months vs. OS > 3 months. The results indicated that NR with PS > 0 and OS ≤ 3 months exhibited an inflammatory-specific signature compared to the contrasting clinical conditions characterized by a checkpoint molecule-based network (p < 0.05). Conclusions: Identifying various connectivity immune profiles linked to response to therapy, PS, and survival in NSCLC patients represents significant findings that can optimize therapeutic choices.

Dissecting genetic regulation of metabolic coordination

Understanding genetic regulation of metabolism is critical for gaining insights into the causes of metabolic diseases. Traditional metabolome-based genome-wide association studies (mGWAS) focus on static associations between single nucleotide polymorphisms (SNPs) and metabolite levels, overlooking the changing relationships caused by genotypes within the metabolic network. Notably, some metabolites exhibit changes in correlation patterns with other metabolites under certain physiological conditions while maintaining their overall abundance level. In this manuscript, we develop Metabolic Differential-coordination GWAS (mdGWAS), an innovative framework that detects SNPs associated with the changing correlation patterns between metabolites and metabolic pathways. This approach transcends and complements conventional mean-based analyses by identifying latent regulatory factors that govern the system-level metabolic coordination. Through comprehensive simulation studies, mdGWAS demonstrated robust performance in detecting SNP-metabolite-metabolite associations. Applying mdGWAS to genotyping and mass spectrometry (MS)-based metabolomics data of the METabolic Syndrome In Men (METSIM) Study revealed novel SNPs and genes potentially involved in the regulation of the coordination between metabolic pathways.

Multi-Cohort Analysis Reveals Altered Archaea in Colorectal Cancer Fecal Samples Across Populations

BACKGROUND & AIMS

Archaea are important components of the host microbiome, but their roles in colorectal cancer (CRC) remain largely unclear. We aimed to elucidate the contribution of gut archaea to CRC across multiple populations.

METHODS

This study incorporated fecal metagenomic data from 10 independent cohorts across 7 countries and an additional in-house cohort, totaling 2101 metagenomes (748 CRC, 471 adenoma, and 882 healthy controls [HCs]). Taxonomic profiling was performed using Kraken2 against the Genome Taxonomy Database. Alterations of archaeal communities and their interactions with bacteria, as well as methanogenic functions were analyzed. A Random Forest model was used to identify multicohort diagnostic microbial biomarkers in CRC.

RESULTS

The overall archaeal alpha diversity shifted from HCs, patients with adenoma, to patients with CRC with the Methanobacteriota phylum enriched while the order Methanomassiliicoccales depleted. At the species level, Methanobrevibacter_A smithii and Methanobrevibacter_A sp002496065 were enriched, whereas 8 species, including Methanosphaera stadtmanae and Methanomassiliicoccus_A intestinalis, were depleted in patients with CRC across multiple cohorts. Among them, M stadtmanae, Methanobrevibacter_A sp900314695, and Methanocorpusculum sp001940805 exhibited a progressive decrease in the HC-adenoma-CRC sequence. CRC-depleted methanogenic archaea exhibited enhanced co-occurring interactions with butyrate-producing bacteria. Consistently, methanogenesis-related genes and pathways were enriched in patients with CRC. A model incorporating archaeal and bacterial biomarkers outperformed single-kingdom models in discriminating patients with CRC from healthy individuals with the area under the curve ranging from 0.744 to 0.931 in leave-one-cohort-out analysis.

CONCLUSIONS

This multicohort analysis uncovered significant alterations in gut archaea and their interactions with bacteria in healthy individuals, patients with adenoma, and patients with CRC. Archaeal biomarkers, combined with bacterial features, have potential as noninvasive diagnostic biomarkers for CRC.

Discovery of robust and highly specific microbiome signatures of non-alcoholic fatty liver disease

BACKGROUND

The pathogenesis of non-alcoholic fatty liver disease (NAFLD) with a global prevalence of 30% is multifactorial and the involvement of gut bacteria has been recently proposed. However, finding robust bacterial signatures of NAFLD has been a great challenge, mainly due to its co-occurrence with other metabolic diseases.

RESULTS

Here, we collected public metagenomic data and integrated the taxonomy profiles with in silico generated community metabolic outputs, and detailed clinical data, of 1206 Chinese subjects w/wo metabolic diseases, including NAFLD (obese and lean), obesity, T2D, hypertension, and atherosclerosis. We identified highly specific microbiome signatures through building accurate machine learning models (accuracy = 0.845-0.917) for NAFLD with high portability (generalizable) and low prediction rate (specific) when applied to other metabolic diseases, as well as through a community approach involving differential co-abundance ecological networks. Moreover, using these signatures coupled with further mediation analysis and metabolic dependency modeling, we propose synergistic defined microbial consortia associated with NAFLD phenotype in overweight and lean individuals, respectively.

CONCLUSION

Our study reveals robust and highly specific NAFLD signatures and offers a more realistic microbiome-therapeutics approach over individual species for this complex disease. Video Abstract.

Uncovering NK cell sabotage in gut diseases via single cell transcriptomics

The identification of immune environments and cellular interactions in the colon microenvironment is essential for understanding the mechanisms of chronic inflammatory disease. Despite occurring in the same organ, there is a significant gap in understanding the pathophysiology of ulcerative colitis (UC) and colorectal cancer (CRC). Our study aims to address the distinct immunopathological response of UC and CRC. Using single-cell RNA sequencing datasets, we analyzed the profiles of immune cells in colorectal tissues obtained from healthy donors, UC patients, and CRC patients. The colon tissues from patients and healthy participants were visualized by immunostaining followed by laser confocal microscopy for select targets. Natural killer (NK) cells from UC patients on medication showed reduced cytotoxicity compared to those from healthy individuals. Nonetheless, a UC-specific pathway called the BAG6-NCR3 axis led to higher levels of inflammatory cytokines and increased the cytotoxicity of NCR3+ NK cells, thereby contributing to the persistence of colitis. In the context of colorectal cancer (CRC), both NK cells and CD8+ T cells exhibited significant changes in cytotoxicity and exhaustion. The GALECTIN-9 (LGALS9)-HAVCR2 axis was identified as one of the CRC-specific pathways. Within this pathway, NK cells solely communicated with myeloid cells under CRC conditions. HAVCR2+ NK cells from CRC patients suppressed NK cell-mediated cytotoxicity, indicating a reduction in immune surveillance. Overall, we elucidated the comprehensive UC and CRC immune microenvironments and NK cell-mediated immune responses. Our findings can aid in selecting therapeutic targets that increase the efficacy of immunotherapy.

A powerful framework for differential co-expression analysis of general risk factors

Differential co-expression analysis (DCA) aims to identify genes in a pathway whose shared expression depends on a risk factor. While DCA provides insights into the biological activity of diseases, existing methods are limited to categorical risk factors and/or suffer from bias due to batch and variance-specific effects. We propose a new framework, Kernel-based Differential Co-expression Analysis (KDCA), that harnesses correlation patterns between genes in a pathway to detect differential co-expression arising from general (i.e., continuous, discrete, or categorical) risk factors. Using various simulated pathway architectures, we find that KDCA accounts for common sources of bias to control the type I error rate while substantially increasing the power compared to the standard eigengene approach. We then applied KDCA to The Cancer Genome Atlas thyroid data set and found several differentially co-expressed pathways by age of diagnosis and BRAF mutation status that were undetected by the eigengene method. Collectively, our results demonstrate that KDCA is a powerful testing framework that expands DCA applications in expression studies.

Fine construction of gene coexpression network analysis using GTOM and RECODE detected a critical module of neuroblastoma stages 4 and 4S

BACKGROUND

Stage 4 neuroblastoma (NBL), a solid tumor of childhood, has a poor prognosis. Despite intensive molecular genetic studies, no targetable gene abnormalities have been identified. Stage 4S NBL has a characteristic of spontaneous regression, and elucidation of the mechanistic differences between stages 4 and 4S may improve treatment. Conventional NBL studies have mainly focused on the detection of abnormalities in individual genes and have rarely examined abnormalities in gene networks. While the gene coexpression network is expected to contribute to the detection of network abnormalities, the fragility of the network due to data noise and the extraction of arbitrary topological structures for the high-dimensional network are issues.

RESULTS

The present paper concerns the classification method of stages 4 and 4S NBL patients using highly accurate gene coexpression network analysis based on RNA-sequencing data of transcription factors (TFs). In particular, after applying a noise reduction method RECODE, generalized topological overlapping measure (GTOM), which weighs the connections of nodes in the network structure, succeeded in extracting a cluster of TFs that showed high classification performance for stages 4 and 4S. In addition, we investigated how these clusters correspond to clinical information and to TFs which control the normal adrenal tissue and NBL characters.

CONCLUSIONS

A clustering method is presented for finding intermediate-scale clusters of TFs that give considerable separation performance for distinguishing between stages 4 and 4S. It is suggested that this method is useful as a way to extract factors that contribute to the separation of groups from multiple pieces of information such as gene expression levels.

Decoding the transcriptomic signatures of psychological trauma in human cortex and amygdala

Psychological trauma has profound effects on brain function and precipitates psychiatric disorders in vulnerable individuals, however, the molecular mechanisms linking trauma with psychiatric risk remain incompletely understood. Using RNA-seq data postmortem brain tissue of a cohort of 304 donors (N=136 with trauma exposure), we investigated transcriptional signatures of trauma exposures in two cortical regions (dorsolateral prefrontal cortex, and dorsal anterior cingulate cortex) and two amygdala regions (medial amygdala and basolateral amygdala) associated with stress processing and regulation. We focused on dissecting heterogeneity of traumatic experiences in these transcriptional signatures by investigating exposure to several trauma types (childhood, adulthood, complex, single acute, combat, and interpersonal traumas) and interactions with sex. Overall, amygdala regions were more vulnerable to childhood traumas, whereas cortical regions were more vulnerable to adulthood trauma (regardless of childhood experience). Using cell-type-specific expression imputation, we identified a strong transcriptional response of medial amygdala excitatory neurons to childhood trauma, which coincided with dysregulation observed in a human induced pluripotent stem cell (hiPSC)-derived glutamatergic neurons exposed to hydrocortisone. We resolved multiscale coexpression networks for each brain region and identified modules enriched in trauma signatures and whose connectivity was altered with trauma. Trauma-associated coexpression modules provide insight into coordinated functional dysregulation with different traumas and point to potential gene targets for further dissection. Together, these data provide a characterization of the long-lasting human encoding of traumatic experiences in corticolimbic regions of human brain.

Tumor draining lymph nodes connected to cold triple-negative breast cancers are characterized by Th2-associated microenvironment

Tumor draining lymph nodes (TDLN) represent a key component of the tumor-immunity cycle. There are few studies describing how TDLNs impact lymphocyte infiltration into tumors. Here we directly compare tumor-free TDLNs draining "cold" and "hot" human triple negative breast cancers (TDLNCold and TDLNHot). Using machine-learning-based self-correlation analysis of immune gene expression, we find unbalanced intranodal regulations within TDLNCold. Two gene pairs (TBX21/GATA3-CXCR1) with opposite correlations suggest preferential priming of T helper 2 (Th2) cells by mature dendritic cells (DC) within TDLNCold. This is validated by multiplex immunofluorescent staining, identifying more mature-DC-Th2 spatial clusters within TDLNCold versus TDLNHot. Associated with this Th2 priming preference, more IL4 producing mast cells (MC) are found within sinus regions of TDLNCold. Downstream, Th2-associated fibrotic TME is found in paired cold tumors with increased Th2/T-helper-1-cell (Th1) ratio, upregulated fibrosis growth factors, and stromal enrichment of cancer associated fibroblasts. These findings are further confirmed in a validation cohort and public genomic data. Our results reveal a potential role of IL4+ MCs within TDLNs, associated with Th2 polarization and reduced immune infiltration into tumors.

Lipidomics and metabolomics investigation into the effect of DAG dietary intervention on hyperuricemia in athletes

The occurrence of hyperuricemia (HUA; elevated serum uric acid) in athletes is relatively high despite that exercise can potentially reduce the risk of developing this condition. Although recent studies have shown the beneficial properties of DAG in improving overall metabolic profiles, a comprehensive understanding of the effect of DAG in modulating HUA in athletes is still lacking. In this study, we leveraged combinatorial lipidomics and metabolomics to investigate the effect of replacing TAG with DAG in the diet of athletes with HUA. A total of 1,074 lipids and metabolites from 94 classes were quantitated in serum from 33 athletes, who were categorized into responders and non-responders based on whether serum uric acid levels returned to healthy levels after the DAG diet intervention. Lipidomics and metabolomics analyses revealed lower levels of xanthine and uric acid in responders, accompanied by elevated plasmalogen phosphatidylcholines and diminished acylcarnitine levels. Our results highlighted the mechanisms behind how the DAG diet circumvented the risk and effects associated with high uric acid via lowered triglycerides at baseline influencing the absorption of DAG resulting in a decline in ROS and uric acid production, increased phospholipid levels associated with reduced p-Cresol metabolism potentially impacting on intestinal excretion of uric acid as well as improved ammonia recycling contributing to decreased serum uric acid levels in responders. These observed alterations might be suggestive that successful implementation of the DAG diet can potentially minimize the likelihood of a potentially vicious cycle occurring in high uric acid, elevated ROS, and impaired mitochondrial metabolism environment.

Uncovering waterlogging-responsive genes in cucumber through machine learning and differential gene correlation analysis

As climate change intensifies, the frequency and severity of waterlogging are expected to increase, necessitating a deeper understanding of the cucumber response to this stress. In this study, three public RNA-seq datasets (PRJNA799460, PRJNA844418, and PRJNA678740) comprising 36 samples were analyzed. Various feature selection algorithms including Uncertainty, Relief, SVM (Support Vector Machine), Correlation, and logistic least absolute shrinkage, and selection operator (LASSO) were performed to identify the most significant genes related to the waterlogging stress response. These feature selection techniques, which have different characteristics, were used to reduce the complexity of the data and thereby identify the most significant genes related to the waterlogging stress response. Uncertainty, Relief, SVM, Correlation, and LASSO identified 4, 4, 10, 21, and 13 genes, respectively. Differential gene correlation analysis (DGCA) focusing on the 36 selected genes identified changes in correlation patterns between the selected genes under waterlogged versus control conditions, providing deeper insights into the regulatory networks and interactions among the selected genes. DGCA revealed significant changes in the correlation of 13 genes between control and waterlogging conditions. Finally, we validated 13 genes using the Random Forest (RF) classifier, which achieved 100% accuracy and a 1.0 Area Under the Curve (AUC) score. The SHapley Additive exPlanations (SHAP) values clearly showed the significant impact of LOC101209599, LOC101217277, and LOC101216320 on the model's predictive power. In addition, we employed the Boruta as a wrapper feature selection method to further validate our gene selection strategy. Eight of the 13 genes were common across the four feature weighting algorithms, LASSO, DGCA, and Boruta, underscoring the robustness and reliability of our gene selection strategy. Notably, the genes LOC101209599, LOC101217277, and LOC101216320 were among genes identified by multiple feature selection methods from different categories (filtering, wrapper, and embedded). Pathways associated with these specific genes play a pivotal role in regulating stress tolerance, root development, nutrient absorption, sugar metabolism, gene expression, protein degradation, and calcium signaling. These intricate regulatory mechanisms are crucial for cucumbers to adapt effectively to waterlogging conditions. These findings provide valuable insights for uncovering targets in breeding new cucumber varieties with enhanced stress tolerance.

Multi-omic analysis of Huntington's disease reveals a compensatory astrocyte state

The mechanisms underlying the selective regional vulnerability to neurodegeneration in Huntington's disease (HD) have not been fully defined. To explore the role of astrocytes in this phenomenon, we used single-nucleus and bulk RNAseq, lipidomics, HTT gene CAG repeat-length measurements, and multiplexed immunofluorescence on HD and control post-mortem brains. We identified genes that correlated with CAG repeat length, which were enriched in astrocyte genes, and lipidomic signatures that implicated poly-unsaturated fatty acids in sensitizing neurons to cell death. Because astrocytes play essential roles in lipid metabolism, we explored the heterogeneity of astrocytic states in both protoplasmic and fibrous-like (CD44+) astrocytes. Significantly, one protoplasmic astrocyte state showed high levels of metallothioneins and was correlated with the selective vulnerability of distinct striatal neuronal populations. When modeled in vitro, this state improved the viability of HD-patient-derived spiny projection neurons. Our findings uncover key roles of astrocytic states in protecting against neurodegeneration in HD.

Deciphering KDM8 dysregulation and CpG methylation in hepatocellular carcinoma using multi-omics and machine learning

Aim: This study investigates the altered expression and CpG methylation patterns of histone demethylase KDM8 in hepatocellular carcinoma (HCC), aiming to uncover insights and promising diagnostics biomarkers.Materials & methods: Leveraging TCGA-LIHC multi-omics data, we employed R/Bioconductor libraries and Cytoscape to analyze and construct a gene correlation network, and LASSO regression to develop an HCC-predictive model.Results: In HCC, KDM8 downregulation is correlated with CpGs hypermethylation. Differential gene correlation analysis unveiled a liver carcinoma-associated network marked by increased cell division and compromised liver-specific functions. The LASSO regression identified a highly accurate HCC prediction signature, prominently featuring CpG methylation at cg02871891.Conclusion: Our study uncovers CpG hypermethylation at cg02871891, possibly influencing KDM8 downregulation in HCC, suggesting these as promising biomarkers and targets.

Single-cell and spatial atlases of spinal cord injury in the Tabulae Paralytica

Here, we introduce the Tabulae Paralytica-a compilation of four atlases of spinal cord injury (SCI) comprising a single-nucleus transcriptome atlas of half a million cells, a multiome atlas pairing transcriptomic and epigenomic measurements within the same nuclei, and two spatial transcriptomic atlases of the injured spinal cord spanning four spatial and temporal dimensions. We integrated these atlases into a common framework to dissect the molecular logic that governs the responses to injury within the spinal cord1. The Tabulae Paralytica uncovered new biological principles that dictate the consequences of SCI, including conserved and divergent neuronal responses to injury; the priming of specific neuronal subpopulations to upregulate circuit-reorganizing programs after injury; an inverse relationship between neuronal stress responses and the activation of circuit reorganization programs; the necessity of re-establishing a tripartite neuroprotective barrier between immune-privileged and extra-neural environments after SCI and a failure to form this barrier in old mice. We leveraged the Tabulae Paralytica to develop a rejuvenative gene therapy that re-established this tripartite barrier, and restored the natural recovery of walking after paralysis in old mice. The Tabulae Paralytica provides a window into the pathobiology of SCI, while establishing a framework for integrating multimodal, genome-scale measurements in four dimensions to study biology and medicine.

Demultiplexing of single-cell RNA-sequencing data using interindividual variation in gene expression

Motivation

Pooled designs for single-cell RNA sequencing, where many cells from distinct samples are processed jointly, offer increased throughput and reduced batch variation. This study describes expression-aware demultiplexing (EAD), a computational method that employs differential co-expression patterns between individuals to demultiplex pooled samples without any extra experimental steps.

Results

We use synthetic sample pools and show that the top interindividual differentially co-expressed genes provide a distinct cluster of cells per individual, significantly enriching the regulation of metabolism. Our application of EAD to samples of six isogenic inbred mice demonstrated that controlling genetic and environmental effects can solve interindividual variations related to metabolic pathways. We utilized 30 samples from both sepsis and healthy individuals in six batches to assess the performance of classification approaches. The results indicate that combining genetic and EAD results can enhance the accuracy of assignments (Min. 0.94, Mean 0.98, Max. 1). The results were enhanced by an average of 1.4% when EAD and barcoding techniques were combined (Min. 1.25%, Median 1.33%, Max. 1.74%). Furthermore, we demonstrate that interindividual differential co-expression analysis within the same cell type can be used to identify cells from the same donor in different activation states. By analysing single-nuclei transcriptome profiles from the brain, we demonstrate that our method can be applied to nonimmune cells.

Availability and implementation

EAD workflow is available at https://isarnassiri.github.io/scDIV/ as an R package called scDIV (acronym for single-cell RNA-sequencing data demultiplexing using interindividual variations).

Distinctive Lipid Characteristics of Colorectal Cancer Revealed through Non-targeted Lipidomics Analysis of Tongue Coating

The metabolites and microbiota in tongue coating display distinct characteristics in certain digestive disorders, yet their relationship with colorectal cancer (CRC) remains unexplored. Here, we employed liquid chromatography coupled with tandem mass spectrometry to analyze the lipid composition of tongue coating using a nontargeted approach in 30 individuals with colorectal adenomas (CRA), 32 with CRC, and 30 healthy controls (HC). We identified 21 tongue coating lipids that effectively distinguished CRC from HC (AUC = 0.89), and 9 lipids that differentiated CRC from CRA (AUC = 0.9). Furthermore, we observed significant alterations in the tongue coating lipid composition in the CRC group compared to HC/CRA groups. As the adenoma-cancer sequence progressed, there was an increase in long-chain unsaturated triglycerides (TG) levels and a decrease in phosphatidylethanolamine plasmalogen (PE-P) levels. Furthermore, we noted a positive correlation between N-acyl ornithine (NAOrn), sphingomyelin (SM), and ceramide phosphoethanolamine (PE-Cer), potentially produced by members of the Bacteroidetes phylum. The levels of inflammatory lipid metabolite 12-HETE showed a decreasing trend with colorectal tumor progression, indicating the potential involvement of tongue coating microbiota and tumor immune regulation in early CRC development. Our findings highlight the potential utility of tongue coating lipid analysis as a noninvasive tool for CRC diagnosis.

Multivariate differential association analysis

Identifying how dependence relationships vary across different conditions plays a significant role in many scientific investigations. For example, it is important for the comparison of biological systems to see if relationships between genomic features differ between cases and controls. In this paper, we seek to evaluate whether relationships between two sets of variables are different or not across two conditions. Specifically, we assess: do two sets of high-dimensional variables have similar dependence relationships across two conditions? We propose a new kernel-based test to capture the differential dependence. Specifically, the new test determines whether two measures that detect dependence relationships are similar or not under two conditions. We introduce the asymptotic permutation null distribution of the test statistic and it is shown to work well under finite samples such that the test is computationally efficient, significantly enhancing its usability in analyzing large datasets. We demonstrate through numerical studies that our proposed test has high power for detecting differential linear and non-linear relationships. The proposed method is implemented in an R package kerDAA.

CancerGATE: Prediction of cancer-driver genes using graph attention autoencoders

Discovery of the cancer type specific-driver genes is important for understanding the molecular mechanisms of each cancer type and for providing proper treatment. Recently, graph deep learning methods became widely used in finding cancer-driver genes. However, previous methods had limited performance in individual cancer types due to a small number of cancer-driver genes used in training and biases toward the cancer-driver genes used in training the models. Here, we introduce a novel pipeline, CancerGATE that predicts the cancer-driver genes using graph attention autoencoder (GATE) to learn in a self-supervised manner and can be applied to each of the cancer types. CancerGATE utilizes biological network topology and multi-omics data from 15 types of cancer of 20,079 samples from the cancer genome atlas (TCGA). Attention coefficients calculated in the model are used to prioritize cancer-driver genes by comparing coefficients of cancer and normal contexts. CancerGATE shows a higher AUPRC with a difference ranging from 1.5 % to 36.5 % compared to the previous graph deep learning models in each cancer type. We also show that CancerGATE is free from the bias toward cancer-driver genes used in training, revealing mechanisms of the cancer-driver genes in specific cancer types. Finally, we propose novel cancer-driver gene candidates that could be therapeutic targets for specific cancer types.

Alzheimer's disease rewires gene coexpression networks coupling different brain regions

Connectome studies have shown how Alzheimer's disease (AD) disrupts functional and structural connectivity among brain regions. But the molecular basis of such disruptions is less studied, with most genomic/transcriptomic studies performing within-brain-region analyses. To inspect how AD rewires the correlation structure among genes in different brain regions, we performed an Inter-brain-region Differential Correlation (Inter-DC) analysis of RNA-seq data from Mount Sinai Brain Bank on four brain regions (frontal pole, superior temporal gyrus, parahippocampal gyrus and inferior frontal gyrus, comprising 264 AD and 372 control human post-mortem samples). An Inter-DC network was assembled from all pairs of genes across two brain regions that gained (or lost) correlation strength in the AD group relative to controls at FDR 1%. The differentially correlated (DC) genes in this network complemented known differentially expressed genes in AD, and likely reflects cell-intrinsic changes since we adjusted for cell compositional effects. Each brain region used a distinctive set of DC genes when coupling with other regions, with parahippocampal gyrus showing the most rewiring, consistent with its known vulnerability to AD. The Inter-DC network revealed master dysregulation hubs in AD (at genes ZKSCAN1, SLC5A3, RCC1, IL17RB, PLK4, etc.), inter-region gene modules enriched for known AD pathways (synaptic signaling, endocytosis, etc.), and candidate signaling molecules that could mediate region-region communication. The Inter-DC network generated in this study is a valuable resource of gene pairs, pathways and signaling molecules whose inter-brain-region functional coupling is disrupted in AD, thereby offering a new perspective of AD etiology.

Cyclosporine A-induced systemic metabolic perturbations in rats: A comprehensive metabolome analysis

A better understanding of cyclosporine A (CsA)-induced nephro- and hepatotoxicity at the molecular level is necessary for safe and effective use. Utilizing a sophisticated study design, this study explored metabolic alterations after long-term CsA treatment in vivo. Rats were exposed to CsA with 4, 10, and 25 mg/kg for 4 weeks and then sacrificed to obtain liver, kidney, urine, and serum for untargeted metabolomics analysis. Differential network analysis was conducted to explore the biological relevance of metabolites significantly altered by toxicity-induced disturbance. Dose-dependent toxicity was observed in all biospecimens. The toxic effects were characterized by alterations of metabolites related to energy metabolism and cellular membrane composition, which could lead to the cholestasis-induced accumulation of bile acids in the tissues. The unfavorable impacts were also demonstrated in the serum and urine. Intriguingly, phenylacetylglycine was increased in the kidney, urine, and serum treated with high doses versus controls. Differential correlation network analysis revealed the strong correlations of deoxycytidine and guanosine with other metabolites in the network, which highlighted the influence of repeated CsA exposure on DNA synthesis. Overall, prolonged CsA administration had system-level dose-dependent effects on the metabolome in treated rats, suggesting the need for careful usage and dose adjustment.

The effect of probiotic Bifidobacterium lactis Bl-04 on innate antiviral responses invitro

Consumption of certain probiotic strains may be beneficial for reducing the risk of acute upper respiratory tract infections (URTIs), however, underlying immunological mechanisms are elusive. Bifidobacterium lactis Bl-04™ has been reported in humans to significantly reduce the risk of URTIs, affect the innate immunity in the nasal mucosa, and reduce nasal lavage virus titer after a rhinovirus (RV) challenge. To study the immunological mechanisms, we investigated the effect of Bl-04 on cytokine production and transcriptomes of human monocyte-derived macrophages (Mfs) and dendritic cells (DCs), and further on RV replication and cytokine production in MRC-5 fibroblasts. The results showed that Bl-04 modulates antiviral immune responses and potentiates cytokine production during viral challenge mimic in immune cells. However, effect of Bl-04 on RV replication and cytokine production in fibroblasts was negligible. Overall, the findings suggest that Bl-04 mildly stimulates antiviral immunity in Mfs and DCs, and potentially influences viral replication in fibroblasts that however warrants further investigations.

CoVar: A generalizable machine learning approach to identify the coordinated regulators driving variational gene expression

Network inference is used to model transcriptional, signaling, and metabolic interactions among genes, proteins, and metabolites that identify biological pathways influencing disease pathogenesis. Advances in machine learning (ML)-based inference models exhibit the predictive capabilities of capturing latent patterns in genomic data. Such models are emerging as an alternative to the statistical models identifying causative factors driving complex diseases. We present CoVar, an ML-based framework that builds upon the properties of existing inference models, to find the central genes driving perturbed gene expression across biological states. Unlike differentially expressed genes (DEGs) that capture changes in individual gene expression across conditions, CoVar focuses on identifying variational genes that undergo changes in their expression network interaction profiles, providing insights into changes in the regulatory dynamics, such as in disease pathogenesis. Subsequently, it finds core genes from among the nearest neighbors of these variational genes, which are central to the variational activity and influence the coordinated regulatory processes underlying the observed changes in gene expression. Through the analysis of simulated as well as yeast expression data perturbed by the deletion of the mitochondrial genome, we show that CoVar captures the intrinsic variationality and modularity in the expression data, identifying key driver genes not found through existing differential analysis methodologies.

Deciphering the impact of microbial interactions on COPD exacerbation: An in-depth analysis of the lung microbiome

In microbiome studies, the diversity and types of microbes have been extensively explored; however, the significance of microbial ecology is equally paramount. The comprehension of metabolic interactions among the wide array of microorganisms in the lung microbiota is indispensable for understanding chronic pulmonary disease and for the development of potent treatments. In this investigation, metabolic networks were simulated, and ecological theory was employed to assess the diagnosis of COPD, subsequently suggesting innovative treatment strategies for COPD exacerbation. Lung sputum 16S rRNA paired-end data from 112 COPD patients were utilized, and a supervised machine-learning algorithm was applied to identify taxa associated with sex and mortality. Subsequently, an OTU table with Greengenes 99 % dataset was generated. Finally, the interactions between bacterial species were analyzed using a simulated metabolic network. A total of 1781 OTUs and 1740 bacteria at the genus level were identified. We employed an additional dataset to validate our analyses. Notably, among the more abundant genera, Pseudomonas was detected in females, while Lactobacillus was detected in males. Additionally, a decrease in bacterial diversity was observed during COPD exacerbation, and mortality was associated with the high abundance of the Staphylococcus and Pseudomonas genera. Moreover, an increase in Proteobacteria abundance was observed during COPD exacerbations. In contrast, COPD patients exhibited decreased levels of Firmicutes and Bacteroidetes. Significant connections between microbial ecology and bacterial diversity in COPD patients were discovered, highlighting the critical role of microbial ecology in the understanding of COPD. Through the simulation of metabolic interactions among bacteria, the observed dysbiosis in COPD was elucidated. Furthermore, the prominence of anaerobic bacteria in COPD patients was revealed to be influenced by parasitic relationships. These findings have the potential to contribute to improved clinical management strategies for COPD patients.

Patient-specific analysis of co-expression to measure biological network rewiring in individuals

To effectively understand the underlying mechanisms of disease and inform the development of personalized therapies, it is critical to harness the power of differential co-expression (DCE) network analysis. Despite the promise of DCE network analysis in precision medicine, current approaches have a major limitation: they measure an average differential network across multiple samples, which means the specific etiology of individual patients is often overlooked. To address this, we present Cosinet, a DCE-based single-sample network rewiring degree quantification tool. By analyzing two breast cancer datasets, we demonstrate that Cosinet can identify important differences in gene co-expression patterns between individual patients and generate scores for each individual that are significantly associated with overall survival, recurrence-free interval, and other clinical outcomes, even after adjusting for risk factors such as age, tumor size, HER2 status, and PAM50 subtypes. Cosinet represents a remarkable development toward unlocking the potential of DCE analysis in the context of precision medicine.

Cecal microbiota and mammary gland microRNA signatures are related and modifiable by dietary flaxseed with implications for breast cancer risk

IMPORTANCE

Breast cancer is a leading cause of cancer mortality worldwide. There is a growing interest in using dietary approaches, including flaxseed (FS) and its oil and lignan components, to mitigate breast cancer risk. Importantly, there is recognition that pubertal processes and lifestyle, including diet, are important for breast health throughout life. Mechanisms remain incompletely understood. Our research uncovers a link between mammary gland miRNA expression and the gut microbiota in young female mice. We found that this relationship is modifiable via a dietary intervention. Using data from The Cancer Genome Atlas, we also show that the expression of miRNAs involved in these relationships is altered in breast cancer in humans. These findings highlight a role for the gut microbiome as a modulator, and thus a target, of interventions aiming at reducing breast cancer risk. They also provide foundational knowledge to explore the effects of early life interventions and mechanisms programming breast health.

Integrative transcriptome- and DNA methylation analysis of brain tissue from the temporal pole in suicide decedents and their controls

Suicide rates have increased steadily world-wide over the past two decades, constituting a serious public health crisis that creates a significant burden to affected families and the society as a whole. Suicidal behavior involves a multi-factorial etiology, including psychological, social and biological factors. Since the molecular neural mechanisms of suicide remain vastly uncharacterized, we examined transcriptional- and methylation profiles of postmortem brain tissue from subjects who died from suicide as well as their neurotypical healthy controls. We analyzed temporal pole tissue from 61 subjects, largely free from antidepressant and antipsychotic medication, using RNA-sequencing and DNA-methylation profiling using an array that targets over 850,000 CpG sites. Expression of NPAS4, a key regulator of inflammation and neuroprotection, was significantly downregulated in the suicide decedent group. Moreover, we identified a total of 40 differentially methylated regions in the suicide decedent group, mapping to seven genes with inflammatory function. There was a significant association between NPAS4 DNA methylation and NPAS4 expression in the control group that was absent in the suicide decedent group, confirming its dysregulation. NPAS4 expression was significantly associated with the expression of multiple inflammatory factors in the brain tissue. Overall, gene sets and pathways closely linked to inflammation were significantly upregulated, while specific pathways linked to neuronal development were suppressed in the suicide decedent group. Excitotoxicity as well as suppressed oligodendrocyte function were also implicated in the suicide decedents. In summary, we have identified central nervous system inflammatory mechanisms that may be active during suicidal behavior, along with oligodendrocyte dysfunction and altered glutamate neurotransmission. In these processes, NPAS4 might be a master regulator, warranting further studies to validate its role as a potential biomarker or therapeutic target in suicidality.

Transcriptional survey of abiotic stress response in maize (Zea mays) in the level of gene co-expression network and differential gene correlation analysis

Abstract. Maize may be exposed to several abiotic stresses in the field. Therefore, identifying the tolerance mechanisms of natural field stress is mandatory. Gene expression data of maize upon abiotic stress were collected, and 560 differentially expressed genes (DEGs) were identified through meta-analysis. The most significant gene ontology terms in up-regulated genes were 'response to abiotic stress' and 'chitinase activity'. 'Phosphorelay signal transduction system' was the most significant enriched biological process in down-regulated DEGs. The co-expression analysis unveiled seven modules of DEGs, with a notable positive correlation between the modules and abiotic stress. Furthermore, the statistical significance was strikingly high for the turquoise, green and yellow modules. The turquoise group played a central role in orchestrating crucial adaptations in metabolic and stress response pathways in maize when exposed to abiotic stress. Within three up-regulated modules, Zm.7361.1.A1_at, Zm.10386.1.A1_a_at and Zm.10151.1.A1_at emerged as hub genes. These genes might introduce novel candidates implicated in stress tolerance mechanisms, warranting further comprehensive investigation and research. In parallel, the R package glmnet was applied to fit a logistic LASSO regression model on the DEGs profile to select candidate genes associated with abiotic responses in maize. The identified hub genes and LASSO regression genes were validated on an independent microarray dataset. Additionally, Differential Gene Correlation Analysis (DGCA) was performed on LASSO and hub genes to investigate the gene-gene regulatory relationship. The P value of DGCA of 16 pairwise gene comparisons was lower than 0.01, indicating a gene-gene significant change in correlation between control and abiotic stress. Integrated weighted gene correlation network analysis and logistic LASSO analysis revealed Zm.11185.1.S1_at, Zm.2331.1.S1_x_at and Zm.17003.1.S1_at. Notably, these 3 genes were identified in the 16 gene-pair comparisons. This finding highlights the notable significance of these genes in the abiotic stress response. Additional research into maize stress tolerance may focus on these three genes.

Limitation of permutation-based differential correlation analysis

The comparison of biological systems, through the analysis of molecular changes under different conditions, has played a crucial role in the progress of modern biological science. Specifically, differential correlation analysis (DCA) has been employed to determine whether relationships between genomic features differ across conditions or outcomes. Because ascertaining the null distribution of test statistics to capture variations in correlation is challenging, several DCA methods utilize permutation which can loosen parametric (e.g., normality) assumptions. However, permutation is often problematic for DCA due to violating the assumption that samples are exchangeable under the null. Here, we examine the limitations of permutation-based DCA and investigate instances where the permutation-based DCA exhibits poor performance. Experimental results show that the permutation-based DCA often fails to control the type I error under the null hypothesis of equal correlation structures.

PRANA: an R package for differential co-expression network analysis with the presence of additional covariates

BACKGROUND

Advances in sequencing technology and cost reduction have enabled an emergence of various statistical methods used in RNA-sequencing data, including the differential co-expression network analysis (or differential network analysis). A key benefit of this method is that it takes into consideration the interactions between or among genes and do not require an established knowledge in biological pathways. As of now, none of existing softwares can incorporate covariates that should be adjusted if they are confounding factors while performing the differential network analysis.

RESULTS

We develop an R package PRANA which a user can easily include multiple covariates. The main R function in this package leverages a novel pseudo-value regression approach for a differential network analysis in RNA-sequencing data. This software is also enclosed with complementary R functions for extracting adjusted p-values and coefficient estimates of all or specific variable for each gene, as well as for identifying the names of genes that are differentially connected (DC, hereafter) between subjects under biologically different conditions from the output.

CONCLUSION

Herewith, we demonstrate the application of this package in a real data on chronic obstructive pulmonary disease. PRANA is available through the CRAN repositories under the GPL-3 license: https://cran.r-project.org/web/packages/PRANA/index.html .

Differential Correlation of Transcriptome Data Reveals Gene Pairs and Pathways Involved in Treatment of Citrobacter rodentium Infection with Bioactive Punicalagin

This study is part of the work investigating bioactive fruit enzymes as sustainable alternatives to parasite anthelmintics that can help reverse the trend of lost efficacy. The study looked to define biological and molecular interactions that demonstrate the ability of the pomegranate extract punicalagin against intracellular parasites. The study compared transcriptomic reads of two distinct conditions. Condition A was treated with punicalagin (PA) and challenged with Citrobacter rodentium, while condition B (CM) consisted of a group that was challenged and given mock treatment of PBS. To understand the effect of punicalagin on transcriptomic changes between conditions, a differential correlation analysis was conducted. The analysis examined the regulatory connections of genes expressed between different treatment conditions by statistically querying the relationship between correlated gene pairs and modules in differing conditions. The results indicated that punicalagin treatment had strong positive correlations with the over-enriched gene ontology (GO) terms related to oxidoreductase activity and lipid metabolism. However, the GO terms for immune and cytokine responses were strongly correlated with no punicalagin treatment. The results matched previous studies that showed punicalagin to have potent antioxidant and antiparasitic effects when used to treat parasitic infections in mice and livestock. Overall, the results indicated that punicalagin enhanced the effect of tissue-resident genes.

Gut ecological networks reveal associations between bacteria, exercise, and clinical profile in non-alcoholic fatty liver disease patients

IMPORTANCE

Our study is applying a community-based approach to examine the influence of exercise on gut microbiota (GM) and discover GM structures linked with NAFLD improvements during exercise. The majority of microbiome research has focused on finding specific species that may contribute to the development of human diseases. However, we believe that complex diseases, such as NAFLD, would be more efficiently treated using consortia of species, given that bacterial functionality is based not only on its own genetic information but also on the interaction with other microorganisms. Our results revealed that exercise significantly changes the GM interaction and that structural alterations can be linked with improvements in intrahepatic lipid content and metabolic functions. We believe that the identification of these characteristics in the GM enhances the development of exercise treatment for NAFLD and will attract general interest in this field.

Global co-expression network for key factor selection on environmental stress RNA-seq dataset in Capsicum annuum

Environmental stresses significantly affect plant growth, development, and productivity. Therefore, a deeper understanding of the underlying stress responses at the molecular level is needed. In this study, to identify critical genetic factors associated with environmental stress responses, the entire 737.3 Gb clean RNA-seq dataset across abiotic, biotic stress, and phytohormone conditions in Capsicum annuum was used to perform individual differentially expressed gene analysis and to construct gene co-expression networks for each stress condition. Subsequently, gene networks were reconstructed around transcription factors to identify critical factors involved in the stress responses, including the NLR gene family, previously implicated in resistance. The abiotic and biotic stress networks comprise 233 and 597 hubs respectively, with 10 and 89 NLRs. Each gene within the NLR groups in the network exhibited substantial expression to particular stresses. The integrated analysis strategy of the transcriptome network revealed potential key genes for complex environmental conditions. Together, this could provide important clues to uncover novel key factors using high-throughput transcriptome data in other species as well as plants.

Distinct patterns of gene expression in the medial preoptic area are related to gregarious singing behavior in European starlings (Sturnus vulgaris)

BACKGROUND

Song performed in flocks by European starlings (Sturnus vulgaris), referred to here as gregarious song, is a non-sexual, social behavior performed by adult birds. Gregarious song is thought to be an intrinsically reinforced behavior facilitated by a low-stress, positive affective state that increases social cohesion within a flock. The medial preoptic area (mPOA) is a region known to have a role in the production of gregarious song. However, the neurochemical systems that potentially act within this region to regulate song remain largely unexplored. In this study, we used RNA sequencing to characterize patterns of gene expression in the mPOA of male and female starlings singing gregarious song to identify possibly novel neurotransmitter, neuromodulator, and hormonal pathways that may be involved in the production of gregarious song.

RESULTS

Differential gene expression analysis and rank rank hypergeometric analysis indicated that dopaminergic, cholinergic, and GABAergic systems were associated with the production of gregarious song, with multiple receptor genes (e.g., DRD2, DRD5, CHRM4, GABRD) upregulated in the mPOA of starlings who sang at high rates. Additionally, co-expression network analyses identified co-expressing gene clusters of glutamate signaling-related genes associated with song. One of these clusters contained five glutamate receptor genes and two glutamate scaffolding genes and was significantly enriched for genetic pathways involved in neurodevelopmental disorders associated with social deficits in humans. Two of these genes, GRIN1 and SHANK2, were positively correlated with performance of gregarious song.

CONCLUSIONS

This work provides new insights into the role of the mPOA in non-sexual, gregarious song in starlings and highlights candidate genes that may play a role in gregarious social interactions across vertebrates. The provided data will also allow other researchers to compare across species to identify conserved systems that regulate social behavior.

Metabolomics acts as a powerful tool for comprehensively evaluating vaccines approved under emergency: a CoronaVac retrospective study

INTRODUCTION

To control the COVID-19 pandemic, great efforts have been made to realize herd immunity by vaccination since 2020. Unfortunately, most of the vaccines against COVID-19 were approved in emergency without a full-cycle and comprehensive evaluation process as recommended to the previous vaccines. Metabolome has a close tie with the phenotype and can sensitively reflect the responses to stimuli, rendering metabolomic analysis have the potential to appraise and monitor vaccine effects authentically.

METHODS

In this study, a retrospective study was carried out for 330 Chinese volunteers receiving recommended two-dose CoronaVac, a vaccine approved in emergency in 2020. Venous blood was sampled before and after vaccination at 5 separate time points for all the recipients. Routine clinical laboratory analysis, metabolomic and lipidomic analysis data were collected.

RESULTS AND DISCUSSION

It was found that the serum antibody-positive rate of this population was around 81.82%. Most of the laboratory parameters were slightly perturbated within the relevant reference intervals after vaccination. The metabolomic and lipidomic analyses showed that the metabolic shift after inoculation was mainly in the glycolysis, tricarboxylic acid cycle, amino acid metabolism, urea cycle, as well as microbe-related metabolism (bile acid metabolism, tryptophan metabolism and phenylalanine metabolism). Time-course metabolome changes were found in parallel with the progress of immunity establishment and peripheral immune cell counting fluctuation, proving metabolomics analysis was an applicable solution to evaluate immune effects complementary to traditional antibody detection. Taurocholic acid, lysophosphatidylcholine 16:0 sn-1, glutamic acid, and phenylalanine were defined as valuable metabolite markers to indicate the establishment of immunity after vaccination. Integrated with the traditional laboratory analysis, this study provided a feasible metabolomics-based solution to relatively comprehensively evaluate vaccines approved under emergency.

Genomic virulence features of Beauveria bassiana as a biocontrol agent for the mountain pine beetle population

BACKGROUND

The mountain pine beetle, Dendroctonus ponderosae, is an irruptive bark beetle that causes extensive mortality to many pine species within the forests of western North America. Driven by climate change and wildfire suppression, a recent mountain pine beetle (MPB) outbreak has spread across more than 18 million hectares, including areas to the east of the Rocky Mountains that comprise populations and species of pines not previously affected. Despite its impacts, there are few tactics available to control MPB populations. Beauveria bassiana is an entomopathogenic fungus used as a biological agent in agriculture and forestry and has potential as a management tactic for the mountain pine beetle population. This work investigates the phenotypic and genomic variation between B. bassiana strains to identify optimal strains against a specific insect.

RESULTS

Using comparative genome and transcriptome analyses of eight B. bassiana isolates, we have identified the genetic basis of virulence, which includes oosporein production. Genes unique to the more virulent strains included functions in biosynthesis of mycotoxins, membrane transporters, and transcription factors. Significant differential expression of genes related to virulence, transmembrane transport, and stress response was identified between the different strains, as well as up to nine-fold upregulation of genes involved in the biosynthesis of oosporein. Differential correlation analysis revealed transcription factors that may be involved in regulating oosporein production.

CONCLUSION

This study provides a foundation for the selection and/or engineering of the most effective strain of B. bassiana for the biological control of mountain pine beetle and other insect pests populations.

UHRF1 is a mediator of KRAS driven oncogenesis in lung adenocarcinoma

KRAS is a frequent driver in lung cancer. To identify KRAS-specific vulnerabilities in lung cancer, we performed RNAi screens in primary spheroids derived from a Kras mutant mouse lung cancer model and discovered an epigenetic regulator Ubiquitin-like containing PHD and RING finger domains 1 (UHRF1). In human lung cancer models UHRF1 knock-out selectively impaired growth and induced apoptosis only in KRAS mutant cells. Genome-wide methylation and gene expression analysis of UHRF1-depleted KRAS mutant cells revealed global DNA hypomethylation leading to upregulation of tumor suppressor genes (TSGs). A focused CRISPR/Cas9 screen validated several of these TSGs as mediators of UHRF1-driven tumorigenesis. In vivo, UHRF1 knock-out inhibited tumor growth of KRAS-driven mouse lung cancer models. Finally, in lung cancer patients high UHRF1 expression is anti-correlated with TSG expression and predicts worse outcomes for patients with KRAS mutant tumors. These results nominate UHRF1 as a KRAS-specific vulnerability and potential target for therapeutic intervention.

Molecular differences in brain regional vulnerability to aging between males and females

Background

Aging-related cognitive decline is associated with brain structural changes and synaptic loss. However, the molecular mechanisms of cognitive decline during normal aging remain elusive.

Results

Using the GTEx transcriptomic data from 13 brain regions, we identified aging-associated molecular alterations and cell-type compositions in males and females. We further constructed gene co-expression networks and identified aging-associated modules and key regulators shared by both sexes or specific to males or females. A few brain regions such as the hippocampus and the hypothalamus show specific vulnerability in males, while the cerebellar hemisphere and the anterior cingulate cortex regions manifest greater vulnerability in females than in males. Immune response genes are positively correlated with age, whereas those involved in neurogenesis are negatively correlated with age. Aging-associated genes identified in the hippocampus and the frontal cortex are significantly enriched for gene signatures implicated in Alzheimer's disease (AD) pathogenesis. In the hippocampus, a male-specific co-expression module is driven by key synaptic signaling regulators including VSNL1, INA, CHN1 and KCNH1; while in the cortex, a female-specific module is associated with neuron projection morphogenesis, which is driven by key regulators including SRPK2, REPS2 and FXYD1. In the cerebellar hemisphere, a myelination-associated module shared by males and females is driven by key regulators such as MOG, ENPP2, MYRF, ANLN, MAG and PLP1, which have been implicated in the development of AD and other neurodegenerative diseases.

Conclusions

This integrative network biology study systematically identifies molecular signatures and networks underlying brain regional vulnerability to aging in males and females. The findings pave the way for understanding the molecular mechanisms of gender differences in developing neurodegenerative diseases such as AD.

Single-cell RNA sequencing data reveals rewiring of transcriptional relationships in Alzheimer's Disease associated with risk variants

Understanding how genetic risk variants contribute to Alzheimer's Disease etiology remains a challenge. Single-cell RNA sequencing (scRNAseq) allows for the investigation of cell type specific effects of genomic risk loci on gene expression. Using seven scRNAseq datasets totalling >1.3 million cells, we investigated differential correlation of genes between healthy individuals and individuals diagnosed with Alzheimer's Disease. Using the number of differential correlations of a gene to estimate its involvement and potential impact, we present a prioritization scheme for identifying probable causal genes near genomic risk loci. Besides prioritizing genes, our approach pin-points specific cell types and provides insight into the rewiring of gene-gene relationships associated with Alzheimer's.

Integrated analysis of ovarian cancer patients from prospective transcription factor activity reveals subtypes of prognostic significance

Transcription factors are protein molecules that act as regulators of gene expression. Aberrant protein activity of transcription factors can have a significant impact on tumor progression and metastasis in tumor patients. In this study, 868 immune-related transcription factors were identified from the transcription factor activity profile of 1823 ovarian cancer patients. The prognosis-related transcription factors were identified through univariate Cox analysis and random survival tree analysis, and two distinct clustering subtypes were subsequently derived based on these transcription factors. We assessed the clinical significance and genomics landscape of the two clustering subtypes and found statistically significant differences in prognosis, response to immunotherapy, and chemotherapy among ovarian cancer patients with different subtypes. Multi-scale Embedded Gene Co-expression Network Analysis was used to identify differential gene modules between the two clustering subtypes, which allowed us to conduct further analysis of biological pathways that exhibited significant differences between them. Finally, a ceRNA network was constructed to analyze lncRNA-miRNA-mRNA regulatory pairs with differential expression levels between two clustering subtypes. We expected that our study may provide some useful references for stratifying and treating patients with ovarian cancer.

Multimodal plasma metabolomics and lipidomics in elucidating metabolic perturbations in tuberculosis patients with concurrent type 2 diabetes

Type 2 diabetes mellitus (DM) poses a major burden for the treatment and control of tuberculosis (TB). Characterization of the underlying metabolic perturbations in DM patients with TB infection would yield insights into the pathophysiology of TB-DM, thus potentially leading to improvements in TB treatment. In this study, a multimodal metabolomics and lipidomics workflow was applied to investigate plasma metabolic profiles of patients with TB and TB-DM. Significantly different biological processes and biomarkers in TB-DM vs. TB were identified using a data-driven, knowledge-based framework. Changes in metabolic and signaling pathways related to carbohydrate and amino acid metabolism were mainly captured by amide HILIC column metabolomics analysis, while perturbations in lipid metabolism were identified by the C18 metabolomics and lipidomics analysis. Compared to TB, TB-DM exhibited elevated levels of bile acids and molecules related to carbohydrate metabolism, as well as the depletion of glutamine, retinol, lysophosphatidylcholine, and phosphatidylcholine. Moreover, arachidonic acid metabolism was determined as a potential important factor in the interaction between TB and DM pathophysiology. In a correlation network of the significantly altered molecules, among the central nodes, chenodeoxycholic acid was robustly associated with TB and DM. Fatty acid (22:4) was a component of all significant modules. In conclusion, the integration of multimodal metabolomics and lipidomics provides a thorough picture of the metabolic changes associated with TB-DM. The results obtained from this comprehensive profiling of TB patients with DM advance the current understanding of DM comorbidity in TB infection and contribute to the development of more effective treatment.

Precise Metabolomics Defines Systemic Metabolic Dysregulation Distinct to Acute Myocardial Infarction Associated With Diabetes

BACKGROUND

Acute myocardial infarction (AMI) is a leading cause of death and disability. Diabetes is an important risk factor and a common comorbidity in AMI patients. The higher mortality risk of diabetes-AMI relative to nondiabetes-AMI indicates a need for specific treatment to improve clinical outcome. However, the global metabolic dysregulation of AMI complicated with diabetes is still unclear. We aim to systematically interrogate changes in the metabolic microenvironment immediate to AMI episodes in the absence or presence of diabetes.

METHODS

In this work, quantitative metabolomics was used to investigate plasma metabolic differences between diabetes-AMI (n=59) and nondiabetes-AMI (n=59) patients. A diverse array of perturbed metabolic pathways involving carbohydrate metabolism, lipid metabolism, glycolysis, tricarboxylic acid cycle, and amino acid metabolism emerged.

RESULTS

In all, our omics-oriented approach defined a metabolic signature of afflicted mitochondrial function aggravated by concurrent diabetes in AMI patients. In particular, our analyses uncovered N-lactoyl-phenylalanine and lysophosphatidylcholines as key functional metabolites that skewed the metabolic picture of diabetes-AMI relative to nondiabetes-AMI. N-lactoyl-phenylalanine was strongly associated with metabolic indicators reflective of mitochondrial overload and negatively correlated with HbA1c (glycosylated hemoglobin, type A1C) specifically in hyperglycemic AMI, suggestive of its central role in glucose utilization and mitochondrial energy production instrumental to the clinical outcome of diabetes-AMI. Reductions in lysophosphatidylcholines, which were negatively correlated with blood glucose and inflammatory markers, might further compromise glucose expenditure and aggravate inflammation leading to poorer prognosis in diabetes-AMI.

CONCLUSIONS

As circulating metabolite levels are amenable to therapeutic intervention, such shifts in metabolic signatures provide new clues and potential therapeutic targets specific to the treatment of diabetes-AMI.

Large-scale correlation network construction for unraveling the coordination of complex biological systems

Advanced measurement and data storage technologies have enabled high-dimensional profiling of complex biological systems. For this, modern multiomics studies regularly produce datasets with hundreds of thousands of measurements per sample, enabling a new era of precision medicine. Correlation analysis is an important first step to gain deeper insights into the coordination and underlying processes of such complex systems. However, the construction of large correlation networks in modern high-dimensional datasets remains a major computational challenge owing to rapidly growing runtime and memory requirements. Here we address this challenge by introducing CorALS (Correlation Analysis of Large-scale (biological) Systems), an open-source framework for the construction and analysis of large-scale parametric as well as non-parametric correlation networks for high-dimensional biological data. It features off-the-shelf algorithms suitable for both personal and high-performance computers, enabling workflows and downstream analysis approaches. We illustrate the broad scope and potential of CorALS by exploring perspectives on complex biological processes in large-scale multiomics and single-cell studies.

Developmental programming: Adipose depot-specific regulation of non-coding RNAs and their relation to coding RNA expression in prenatal testosterone and prenatal bisphenol-A -treated female sheep

Inappropriate developmental exposure to steroids is linked to metabolic disorders. Prenatal testosterone excess or bisphenol A (BPA, an environmental estrogen mimic) leads to insulin resistance and adipocyte disruptions in female lambs. Adipocytes are key regulators of insulin sensitivity. Metabolic tissue-specific differences in insulin sensitivity coupled with adipose depot-specific changes in key mRNAs, were previously observed with prenatal steroid exposure. We hypothesized that depot-specific changes in the non-coding RNA (ncRNA) - regulators of gene expression would account for the direction of changes seen in mRNAs. Non-coding RNA (lncRNA, miRNA, snoRNA, snRNA) from various adipose depots of prenatal testosterone and BPA-treated animals were sequenced. Adipose depot-specific changes in the ncRNA that are consistent with the depot-specific mRNA expression in terms of directionality of changes and functional implications in insulin resistance, adipocyte differentiation and cardiac hypertrophy were found. Importantly, the adipose depot-specific ncRNA changes were model-specific and mutually exclusive, suggestive of different regulatory entry points in this regulation.

Diversity and Differential Expression of MicroRNAs in the Human Skeletal Muscle with Distinct Fiber Type Composition

The ratio of fast- and slow-twitch fibers in human skeletal muscle is variable and largely determined by genetic factors. In this study, we investigated the contribution of microRNA (miRNA) in skeletal muscle fiber type composition. The study involved biopsy samples of the vastus lateralis muscle from 24 male participants with distinct fiber type ratios. The miRNA study included samples from five endurance athletes and five power athletes with the predominance of slow-twitch (61.6-72.8%) and fast-twitch (69.3-80.7%) fibers, respectively. Total and small RNA were extracted from tissue samples. Total RNA sequencing (N = 24) revealed 352 differentially expressed genes between the groups with the predominance of fast- and slow-twitch muscle fibers. Small RNA sequencing showed upregulation of miR-206, miR-501-3p and miR-185-5p, and downregulation of miR-499a-5p and miR-208-5p in the group of power athletes with fast-twitch fiber predominance. Two miRtronic miRNAs, miR-208b-3p and miR-499a-5p, had strong correlations in expression with their host genes (MYH7 and MYH7B, respectively). Correlations between the expression of miRNAs and their experimentally validated messenger RNA (mRNA) targets were calculated, and 11 miRNA-mRNA interactions with strong negative correlations were identified. Two of them belonged to miR-208b-3p and miR-499a-5p, indicating their regulatory links with the expression of CDKN1A and FOXO4, respectively.

Differential co-expression network analysis with DCoNA reveals isomiR targeting aberrations in prostate cancer

MOTIVATION

One of the standard methods of high-throughput RNA sequencing analysis is differential expression. However, it does not detect changes in molecular regulation. In contrast to the standard differential expression analysis, differential co-expression one aims to detect pairs or clusters whose mutual expression changes between two conditions.

RESULTS

We developed Differential Co-expression Network Analysis (DCoNA)-an open-source statistical tool that allows one to identify pair interactions, which correlation significantly changes between two conditions. Comparing DCoNA with the state-of-the-art analog, we showed that DCoNA is a faster, more accurate and less memory-consuming tool. We applied DCoNA to prostate mRNA/miRNA-seq data collected from The Cancer Genome Atlas (TCGA) and compared predicted regulatory interactions of miRNA isoforms (isomiRs) and their target mRNAs between normal and cancer samples. As a result, almost all highly expressed isomiRs lost negative correlation with their targets in prostate cancer samples compared to ones without the pathology. One exception to this trend was the canonical isomiR of hsa-miR-93-5p acquiring cancer-specific targets. Further analysis showed that cancer aggressiveness simultaneously increased with the expression level of this isomiR in both TCGA primary tumor samples and 153 blood plasma samples of P. Hertsen Moscow Oncology Research Institute patients' cohort analyzed by miRNA microarrays.

AVAILABILITY AND IMPLEMENTATION

Source code and documentation of DCoNA are available at https://github.com/zhiyanov/DCoNA.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Differential expression and cross-correlation between global regulator and pho regulon genes involved in decision-making under phosphate stress

The differential gene expression under phosphate stress conditions leads to cross-talk between the global regulator, pho regulon, and metabolic genes. Promoter activity analysis of the selected 23 genes reveals the dynamic nature of real-time gene expression under different phosphate conditions. The expression profiles of the global regulator (rpoD, soxR, soxS, arcB, and fur), pho regulon (phoH, phoR, phoB, and ugpB), and metabolic genes (sdh, pfkA, ldh) varied significantly on phosphate level variation. Under stress conditions, soxR switches expression partners and co-expresses with rpoS instead of soxS. The partner-switching behavior of the genes under a challenging environment represents the intelligence of functional execution and ensures cell survival. The dynamic expression profile of the selected genes applies a time-lagged correlation to provide insight into the differential gene interaction between time-shifted expression profiles. Under different phosphate conditions, the minimum spanning tree graph revealed a different clustering pattern of selected genes depending on the computed distance and its proximity to other promoters.