The cluster D-trace loss for differential network analysis

Differential network analysis investigates how the network of connected genes changes from one condition to another and has become a prevalent tool to provide a deeper and more comprehensive understanding of the molecular etiology of complex diseases. Based on the asymptotically normal estimation of large Gaussian graphical model (GGM) in the high-dimensional setting, we developed a computationally efficient test for differential network analysis through testing the equality of two precision matrices, which summarize the conditional dependence network structures of the genes. Additionally, we applied a multiple testing procedure to infer the differential network structure with false discovery rate (FDR) control. Through extensive simulation studies with different combinations of parameters including sample size, number of vertices, level of heterogeneity and graph structure, we demonstrated that our method performed much better than the current available methods in terms of accuracy and computational time. In real data analysis on lung adenocarcinoma, we revealed a differential network with 3503 nodes and 2550 edges, which consisted of 50 clusters with an FDR threshold at 0.05. Many of the top gene pairs in the differential network have been reported relevant to human cancers. Our method represents a powerful tool of network analysis for high-dimensional biological data.

Differential network analysis has become a widely used technique to investigate changes of interactions among different conditions. Although the relationship between observed interactions and biochemical mechanisms is hard to establish, differential network analysis can provide useful insights about dysregulated pathways and candidate biomarkers. The available methods to detect differential interactions are heterogeneous and often rely on assumptions that are unrealistic in many applications. To address these issues, we develop a novel method for differential network analysis, using the so-called disparity filter as network reduction technique. In addition, we propose a classification model based on the inferred network interactions. The main novelty of this work lies in its ability to preserve connections that are statistically significant with respect to a null model without favouring any resolution scale, as a hard threshold would do, and without Gaussian assumptions. The method was tested using a published metabolomic dataset on colorectal cancer (CRC). Detected hub metabolites were consistent with recent literature and the classifier was able to distinguish CRC from polyp and healthy subjects with great accuracy. In conclusion, the proposed method provides a new simple and effective framework for the identification of differential interaction patterns and improves the biological interpretation of metabolomics data.

Host resistance is the primary means to control Verticillium dahliae, a soil-borne pathogen causing major losses on a broad range of plants, including tomato. The tissues and mechanisms responsible for resistance remain obscure. In the field, resistant tomato used as rootstocks does not confer resistance. Here, we created bi-grafted plants with near-isogenic lines (NILs) exhibiting (Ve1) or lacking (ve1) resistance to V. dahliae race 1. Ten days after inoculation, scion and rootstock tissues were subjected to differential gene expression and co-expression network analyses. Symptoms only developed in susceptible scions regardless of the rootstock. Infection caused more dramatic alteration of tomato gene expression in susceptible compared to resistant tissues, including pathogen receptor, signaling pathway, PR protein, and cell wall modification genes. Differences were observed between scions and rootstocks, primarily related to physiological processes in these tissues. Gene expression in scions was influenced by the rootstock genotype. A few genes were associated with the Ve1 genotype, which was independent of infection or tissue type. Several were physically clustered, some near the Ve1 locus on chromosome 9. Transcripts mapped to V. dahliae were dominated by secreted candidate effector proteins. These findings advance knowledge of molecular mechanisms underlying the tomato-V. dahliae interaction.

Multidrug-resistant Escherichia coli poses a significant threat to the healthcare system by causing treatment failure in infected patients. The use of a beta-lactam in combination with a beta-lactamase inhibitor has been shown to be an effective strategy to solve this problem. In vitro antimicrobial susceptibility experiments have demonstrated the antimicrobial activity of aztreonam and clavulanate. In this investigation, we conducted a transcriptomic analysis to reveal the downstream differential gene expression in E. coli ymmD45 (a strain newly isolated and found to carry the New Delhi metallo-β-lactamase gene) following exposure to aztreonam and clavulanate separately, as well as their combination. Differential gene expression, pathway enrichment, and gene network analyses demonstrated the polygenic nature of the response to the combination treatment, which suppressed the expression of pivotal virulence genes, disrupted two-component regulatory systems for bacteria to resist external stress, and interfered with the formation of the cellular membrane. Results from single-step mutant selection combined with deep whole-genome sequencing also revealed the spontaneous origin of the resistance mutations and confirmed action mechanisms during the combination treatment. Our study contributes valuable insights into the impact of antibiotic exposure on gene expression, laying the groundwork for understanding antibiotic resistance development in the treatment of multi-drug resistant infections through in vitro studies.IMPORTANCEMultidrug-resistant Escherichia coli is a major challenge in treating infections effectively. Aztreonam and clavulanate combination is promising in combating these resistant bacteria. By investigating the antimicrobial activity of aztreonam and clavulanate using transcriptomic analysis and mutant selection, this research sheds light on the mechanisms underlying antibiotic resistance and the effectiveness of combination therapies. The findings highlight how this particular antibiotic combination suppresses virulence genes, disrupts bacterial regulatory systems, and interferes with cellular functions critical for resistance. Moreover, the study lays the groundwork for understanding antibiotic resistance development in the treatment of multi-drug resistant infections through in vitro studies, offering insights that could inform future strategies in clinical settings. Ultimately, our findings could guide the development of better treatment strategies for multidrug-resistant infections, improving patient outcomes and helping to manage antibiotic resistance in healthcare.

Background The rewiring of molecular interactions in various conditions leads to distinct phenotypic outcomes. Differential network analysis (DINA) is dedicated to exploring these rewirings within gene and protein networks. Leveraging statistical learning and graph theory, DINA algorithms scrutinize alterations in interaction patterns derived from experimental data.Results Introducing a novel approach to differential network analysis, we incorporate differential gene expression based on sex and gender attributes. We hypothesize that gene expression can be accurately represented through non-Gaussian processes. Our methodology involves quantifying changes in non-parametric correlations among gene pairs and expression levels of individual genes.ConclusionsApplying our method to public expression datasets concerning diabetes mellitus and atherosclerosis in liver tissue, we identify gender-specific differential networks. Results underscore the biological relevance of our approach in uncovering meaningful molecular distinctions.

Atypical frontotemporal lobar degeneration with ubiquitin-positive inclusions (aFTLD-U) is a rare cause of frontotemporal lobar degeneration (FTLD), characterized postmortem by neuronal inclusions of the FET family of proteins (FTLD-FET). The recent discovery of TAF15 amyloid filaments in aFTLD-U brains represents a significant step toward improved diagnostic and therapeutic strategies. However, our understanding of the etiology of this FTLD subtype remains limited, which severely hampers translational research efforts. To explore the transcriptomic changes in aFTLD-U, we performed bulk RNA sequencing on the frontal cortex tissue of 21 aFTLD-U patients and 20 control individuals. Cell-type deconvolution revealed loss of excitatory neurons and a higher proportion of astrocytes in aFTLD-U relative to controls. Differential gene expression and co-expression network analysis, adjusted for the shift in cell-type proportions, showed dysregulation of mitochondrial pathways, transcriptional regulators, and upregulation of the Sonic hedgehog (Shh) pathway, including the GLI1 transcription factor, in aFTLD-U. Overall, oligodendrocyte and astrocyte-enriched genes were significantly over-represented among the differentially expressed genes. Differential splicing analysis confirmed the dysregulation of non-neuronal cell types with significant splicing alterations, particularly in oligodendrocyte-enriched genes, including myelin basic protein (MBP), a crucial component of myelin. Immunohistochemistry in frontal cortex brain tissue also showed reduced myelin levels in aFTLD-U patients compared to controls. Together, these findings highlight a central role for glial cells, particularly astrocytes and oligodendrocytes, in the pathogenesis of aFTLD-U, with disruptions in mitochondrial activity, RNA metabolism, Shh signaling, and myelination as possible disease mechanisms. This study offers the first transcriptomic insight into aFTLD-U and presents new avenues for research into FTLD-FET.Supplementary InformationThe online version contains supplementary material available at 10.1007/s00401-025-02919-x.

Colon cancer has been well studied using a variety of molecular techniques, including whole genome sequencing. However, genetic markers that could be used to predict lymph node (LN) involvement, which is the most important prognostic factor for colon cancer, have not been identified. In the present study, we compared LN(+) and LN(−) colon cancer patients using differential gene expression and network analysis. Colon cancer gene expression data were obtained from the Cancer Genome Atlas and divided into two groups, LN(+) and LN(−). Gene expression networks were constructed using LASSO (Least Absolute Shrinkage and Selection Operator) regression. We identified hub genes, such as APBB1, AHSA2, ZNF767, and JAK2, that were highly differentially expressed. Survival analysis using selected hub genes, such as AHSA2, CDK10, and CWC22, showed that their expression levels were significantly associated with the survival rate of colon cancer patients, which indicates their possible use as prognostic markers. In addition, protein-protein interaction network, GO enrichment, and KEGG pathway analysis were performed with selected hub genes from each group to investigate the regulatory relationships between hub genes and LN involvement in colon cancer; these analyses revealed differences between the LN(−) and LN(+) groups. Our network analysis may help narrow down the search for novel candidate genes for the treatment of colon cancer, in addition to improving our understanding of the biological processes underlying LN involvement. All R implementation codes are available at journal website as Supplementary Materials.

BackgroundCoronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, has been associated with neurological and neuropsychiatric illness in many individuals. We sought to further our understanding of the relationship between brain tropism, neuro-inflammation, and host immune response in acute COVID-19 cases.MethodsThree brain regions (dorsolateral prefrontal cortex, medulla oblongata, and choroid plexus) from 5 patients with severe COVID-19 and 4 controls were examined. The presence of the virus was assessed by western blot against viral spike protein, as well as viral transcriptome analysis covering > 99% of SARS-CoV-2 genome and all potential serotypes. Droplet-based single-nucleus RNA sequencing (snRNA-seq) was performed in the same samples to examine the impact of COVID-19 on transcription in individual cells of the brain.ResultsQuantification of viral spike S1 protein and viral transcripts did not detect SARS-CoV-2 in the postmortem brain tissue. However, analysis of 68,557 single-nucleus transcriptomes from three distinct regions of the brain identified an increased proportion of stromal cells, monocytes, and macrophages in the choroid plexus of COVID-19 patients. Furthermore, differential gene expression, pseudo-temporal trajectory, and gene regulatory network analyses revealed transcriptional changes in the cortical microglia associated with a range of biological processes, including cellular activation, mobility, and phagocytosis.ConclusionsDespite the absence of detectable SARS-CoV-2 in the brain at the time of death, the findings suggest significant and persistent neuroinflammation in patients with acute COVID-19.

BackgroundMany cancer cells show distorted epigenetic landscapes. The Cancer Genome Atlas (TCGA) project profiles thousands of tumors, allowing the discovery of somatic alterations in the epigenetic machinery and the identification of potential cancer drivers among members of epigenetic protein families.MethodsWe integrated mutation, expression, and copy number data from 5943 tumors from 13 cancer types to train a classification model that predicts the likelihood of being an oncogene (OG), tumor suppressor (TSG) or neutral gene (NG). We applied this predictor to epigenetic regulator genes (ERGs), and used differential expression and correlation network analysis to identify dysregulated ERGs along with co-expressed cancer genes. Furthermore, we quantified global proteomic changes by mass spectrometry after EZH2 inhibition.ResultsMutation-based classifiers uncovered the OG-like profile of DNMT3A and TSG-like profiles for several ERGs. Differential gene expression and correlation network analyses revealed that EZH2 is the most significantly over-expressed ERG in cancer and is co-regulated with a cell cycle network. Proteomic analysis showed that EZH2 inhibition induced down-regulation of cell cycle regulators in lymphoma cells.ConclusionsUsing classical driver genes to train an OG/TSG predictor, we determined the most predictive features at the gene level. Our predictor uncovered one OG and several TSGs among ERGs. Expression analyses elucidated multiple dysregulated ERGs including EZH2 as member of a co-expressed cell cycle network.

Bisphenol A (BPA), an endocrine-disrupting substance commonly found in plastics and receipts, is associated with adverse effects, including endocrine disorders, reduced fertility, and metabolic issues. To gain insights into its effects on biological systems, we observed the adverse effects of BPA in male Institute of Cancer Research (ICR) mice exposed to BPA at the lowest observed adverse effect level for 6 weeks, in comparison with the control groups. We constructed a comprehensive transcriptome profile using 20 different tissues to analyze the changes in the whole-body systems. This involved employing differential gene expression, tissue-specific gene, and gene co-expression network analyses. The study revealed that BPA exposure led to significant differences in the transcriptome in the thymus, suggesting activation of T-cell differentiation and maturation in response to BPA treatment. Furthermore, various tissues exhibited immune response activation, potentially due to the migration of immune cells from the thymus. BPA exposure also caused immune-related functional changes in the colon, liver, and kidney, as well as abnormal signaling responses in the sperm. The transcriptome analysis serves as a valuable resource for understanding the functional impact of BPA, providing profound insights into the effects of BPA exposure and emphasizing the need for further research on potential associated health risks.

Differential network analysis is an important tool to investigate the rewiring of gene interactions under different conditions. Several computational methods have been developed to estimate differential networks from gene expression data, but most of them do not consider that gene network rewiring may be driven by the differential expression of individual genes. New differential network analysis methods that simultaneously take account of the changes in gene interactions and changes in expression levels are needed. : In this article, we propose a differential network analysis method that considers the differential expression of individual genes when identifying differential edges. First, two hypothesis test statistics are used to quantify changes in partial correlations between gene pairs and changes in expression levels for individual genes. Then, an optimization framework is proposed to combine the two test statistics so that the resulting differential network has a hierarchical property, where a differential edge can be considered only if at least one of the two involved genes is differentially expressed. Simulation results indicate that our method outperforms current state-of-the-art methods. We apply our method to identify the differential networks between the luminal A and basal-like subtypes of breast cancer and those between acute myeloid leukemia and normal samples. Hub nodes in the differential networks estimated by our method, including both differentially and nondifferentially expressed genes, have important biological functions. All the datasets underlying this article are publicly available. Processed data and source code can be accessed through the Github repository at https://github.com/Zhangxf-ccnu/chNet. Supplementary data are available at Bioinformatics online.

Identification of target gene and prognostic evaluation for lung adenocarcinoma using gene expression meta-analysis, network analysis and neural network algorithms.

Genomic coevolution of papillomavirus and immune system in placental mammals indicates the role of IFN-γ in the emergence of new variants.

BackgroundThe recent emergence of COVID-19, rapid worldwide spread, and incomplete knowledge of molecular mechanisms underlying SARS-CoV-2 infection have limited development of therapeutic strategies. Our objective was to systematically investigate molecular regulatory mechanisms of COVID-19, using a combination of high throughput RNA-sequencing-based transcriptomics and systems biology approaches.MethodsRNA-Seq data from peripheral blood mononuclear cells (PBMCs) of healthy persons, mild and severe 17 COVID-19 patients were analyzed to generate a gene expression matrix. Weighted gene co-expression network analysis (WGCNA) was used to identify co-expression modules in healthy samples as a reference set. For differential co-expression network analysis, module preservation and module-trait relationships approaches were used to identify key modules. Then, protein-protein interaction (PPI) networks, based on co-expressed hub genes, were constructed to identify hub genes/TFs with the highest information transfer (hub-high traffic genes) within candidate modules.ResultsBased on differential co-expression network analysis, connectivity patterns and network density, 72% (15 of 21) of modules identified in healthy samples were altered by SARS-CoV-2 infection. Therefore, SARS-CoV-2 caused systemic perturbations in host biological gene networks. In functional enrichment analysis, among 15 non-preserved modules and two significant highly-correlated modules (identified by MTRs), 9 modules were directly related to the host immune response and COVID-19 immunopathogenesis. Intriguingly, systemic investigation of SARS-CoV-2 infection identified signaling pathways and key genes/proteins associated with COVID-19’s main hallmarks, e.g., cytokine storm, respiratory distress syndrome (ARDS), acute lung injury (ALI), lymphopenia, coagulation disorders, thrombosis, and pregnancy complications, as well as comorbidities associated with COVID-19, e.g., asthma, diabetic complications, cardiovascular diseases (CVDs), liver disorders and acute kidney injury (AKI). Topological analysis with betweenness centrality (BC) identified 290 hub-high traffic genes, central in both co-expression and PPI networks. We also identified several transcriptional regulatory factors, including NFKB1, HIF1A, AHR, and TP53, with important immunoregulatory roles in SARS-CoV-2 infection. Moreover, several hub-high traffic genes, including IL6, IL1B, IL10, TNF, SOCS1, SOCS3, ICAM1, PTEN, RHOA, GDI2, SUMO1, CASP1, IRAK3, HSPA5, ADRB2, PRF1, GZMB, OASL, CCL5, HSP90AA1, HSPD1, IFNG, MAPK1, RAB5A, and TNFRSF1A had the highest rates of information transfer in 9 candidate modules and central roles in COVID-19 immunopathogenesis.ConclusionThis study provides comprehensive information on molecular mechanisms of SARS-CoV-2-host interactions and identifies several hub-high traffic genes as promising therapeutic targets for the COVID-19 pandemic.

Previous studies have shown that asthma is a risk factor for lung cancer, while the mechanisms involved remain unclear. We attempted to further explore the association between asthma and non-small cell lung cancer (NSCLC) via bioinformatics analysis. We obtained GSE143303 and GSE18842 from the GEO database. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) groups were downloaded from the TCGA database. Based on the results of differentially expressed genes (DEGs) between asthma and NSCLC, we determined common DEGs by constructing a Venn diagram. Enrichment analysis was used to explore the common pathways of asthma and NSCLC. A protein-protein interaction (PPI) network was constructed to screen hub genes. KM survival analysis was performed to screen prognostic genes in the LUAD and LUSC groups. A Cox model was constructed based on hub genes and validated internally and externally. Tumor Immune Estimation Resource (TIMER) was used to evaluate the association of prognostic gene models with the tumor microenvironment (TME) and immune cell infiltration. Nomogram model was constructed by combining prognostic genes and clinical features. 114 common DEGs were obtained based on asthma and NSCLC data, and enrichment analysis showed that significant enrichment pathways mainly focused on inflammatory pathways. Screening of 5 hub genes as a key prognostic gene model for asthma progression to LUAD, and internal and external validation led to consistent conclusions. In addition, the risk score of the 5 hub genes could be used as a tool to assess the TME and immune cell infiltration. The nomogram model constructed by combining the 5 hub genes with clinical features was accurate for LUAD. Five-hub genes enrich our understanding of the potential mechanisms by which asthma contributes to the increased risk of lung cancer.