Gene co-expression network analysis reveals common system-level properties of prognostic genes across cancer types

scRDEN: single-cell dynamic gene rank differential expression network and robust trajectory inference

The remarkable advancement of single-cell RNA sequencing (scRNA-seq) technology has empowered researchers to probe gene expression at the single-cell level with unprecedented precision. To gain a profound understanding of the heterogeneity inherent in cell fate determination, a central challenge lies in the comprehensive analysis of the dynamic regulatory alterations that underlie transcriptional differences and the accurate inference of the differentiation trajectory. Here, we propose the method scRDEN, a robust framework that infers important cell sub-populations and differential expression networks of multiple genes along the differentiation directions of each branch by converting the unstable gene expression values in cells into relatively stable gene-gene interactions (global features) and extracting the order of differential expression (network features), and further integrating the expression features of different dimension reduction methods. When applied to five published scRNA-seq datasets from human and mouse cell differentiation, scRDEN not only successfully captures the stable cell subpopulations with potential marker genes, measures the transcriptional differences of gene pairs to identify the rank differential expression network along the differentiation direction of each branch. In addition, in multiple gene rank differential expression networks, the rank expression directly related to transcription factors/marker genes shows a significant strengthening and weakening trend along with their expression changes, and the distribution of diversity and cluster coefficient show a non-monotonic change trend, including the cases of increasing first and then decreasing or decreasing first and then increasing. This may correspond to the mechanism of cells gradually differentiating into stable functions. It is particularly noteworthy that scRDEN method yielded exceptional results when applied to the large-scale, multi-branched, double-batch mouse dentate gyrus data. This outstanding performance provides novel and valuable insights into large-scale, multi-batch trajectory inference and the study of transcriptional mechanism regulation during the processes of differentiation and development.

A Functionally Conserved yet Dynamically Evolving Toolkit Underpinning Molluscan Biomineralization: Insights From Shell and Radula

The molluscan shell and radula constitute pivotal molluscan innovations, each characterized by distinct functions and diverse forms, regulated by the highly specific biomineralization regulatory networks. Despite their paramount importance, the conserved components and adaptive evolutionary processes governing these regulatory networks remain unresolved. To address this knowledge gap, we advocate for the integration of data from less-explored lineages, such as Scaphopoda, as an essential step. This study presents the inaugural comprehensive transcriptome analysis of Pictodentalium vernedei, a representative species of Scaphopoda distinguished by a unique and evolutionarily conserved shell morphology and radula structure. Furthermore, comparative transcriptome/genome analyses are employed to unravel the conservatism and evolutionary innovation of the involved biomineralization regulatory elements. Our findings underscore the central role of secretomes in governing biomineralization processes, and we identified a fundamental set of 26 domains within molluscan secretomes, forming an essential functional protein domain repertoire necessary for the transformation of inorganic ions into biomineralized structures. This core biomineralization toolkit has undergone independent expansion and lineage-specific recruitment, giving rise to novel, modular domain architectures. This may be essential for the functional specialization and morphological diversification of shell and radula structures. These evolutionary processes are driven by the independent co-option of ancient genes and the emergence of novel de novo genes. This comprehensive investigation not only contributes insights into the evolution of molluscan biomineralization structures but also establishes avenues for further scholarly exploration.

Co-expression of miRNA players in advanced laryngeal carcinoma - Insights into the roles of miR-93-5p, miR-145-5p, and miR-210-3p

Advanced laryngeal squamous cell carcinoma (LSCC) is the second most prevalent type of head and neck squamous cell carcinoma (HNSCC). Identifying microRNAs (miRNAs) related to key regulatory molecules or mechanisms could offer an alternative approach to developing new treatment strategies. The aim of our study is to evaluate significant correlations among deregulated miRNAs in advanced laryngeal carcinoma and to analyze, in silico, their strength of association, targets, and the most deregulated pathways. Several miRNAs demonstrated promising co-expression results, specifically miR-93-5p, miR-145-5p, and miR-210-3p. Their expressions were explored and further validated in a large set of in vivo advanced LSCC samples, which were subsequently used for bioinformatics and enrichment analyses. Our results highlight the significant roles of miR-93-5p, miR-145-5p, and miR-210-3p in regulating major pathways linked to the cell cycle via epithelial-to-mesenchymal transition (EMT), PI3K/Akt signaling, hypoxia, metabolism, apoptosis, angiogenesis, and metastasis. The associations between the expressions of these miRNAs and patients' clinical features could be central to the progression of advanced LSCC. Overall, our study provides important insights into the co-expression and regulatory networks of miR-93-5p, miR-145-5p, and miR-210-3p in advanced laryngeal carcinoma, underscoring their potential as therapeutic targets or biomarkers for this aggressive cancer. Further research is needed to elucidate the specific mechanisms through which these miRNAs contribute to the pathogenesis and progression of laryngeal carcinoma.

Deciphering genomic codes using advanced natural language processing techniques: a scoping review

OBJECTIVES

The vast and complex nature of human genomic sequencing data presents challenges for effective analysis. This review aims to investigate the application of natural language processing (NLP) techniques, particularly large language models (LLMs) and transformer architectures, in deciphering genomic codes, focusing on tokenization, transformer models, and regulatory annotation prediction. The goal of this review is to assess data and model accessibility in the most recent literature, gaining a better understanding of the existing capabilities and constraints of these tools in processing genomic sequencing data.

MATERIALS AND METHODS

Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, our scoping review was conducted across PubMed, Medline, Scopus, Web of Science, Embase, and ACM Digital Library. Studies were included if they focused on NLP methodologies applied to genomic sequencing data analysis, without restrictions on publication date or article type.

RESULTS

A total of 26 studies published between 2021 and April 2024 were selected for review. The review highlights that tokenization and transformer models enhance the processing and understanding of genomic data, with applications in predicting regulatory annotations like transcription-factor binding sites and chromatin accessibility.

DISCUSSION

The application of NLP and LLMs to genomic sequencing data interpretation is a promising field that can help streamline the processing of large-scale genomic data while also providing a better understanding of its complex structures. It has the potential to drive advancements in personalized medicine by offering more efficient and scalable solutions for genomic analysis. Further research is also needed to discuss and overcome current limitations, enhancing model transparency and applicability.

CONCLUSION

This review highlights the growing role of NLP, particularly LLMs, in genomic sequencing data analysis. While these models improve data processing and regulatory annotation prediction, challenges remain in accessibility and interpretability. Further research is needed to refine their application in genomics.

Neurotransmitter and metabolic effects of interferon-alpha in association with decreased striatal dopamine in a non-human primate model of cytokine-Induced depression

Inflammatory stimuli administered to humans and laboratory animals affect mesolimbic and nigrostriatal dopaminergic pathways in association with impaired motivation and motor activity. Alterations in dopaminergic corticostriatal reward and motor circuits have also been observed in depressed patients with increased peripheral inflammatory markers. The effects of peripheral inflammation on dopaminergic pathways and associated neurobiologic mechanisms and consequences have been difficult to measure in patients. Postmortem tissue (n = 11) from an established, translationally-relevant non-human primate model of cytokine-induced depressive behavior involving chronic interferon-alpha (IFN-a) administration was utilized herein to explore the molecular mechanisms of peripheral cytokine effects on striatal dopamine. Dopamine (but not serotonin or norepinephrine) was decreased in the nucleus accumbens (NAcc) and putamen of IFN-a-treated animals (p < 0.05). IFN-a had no effect on number of striatal neurons or dopamine terminal density, suggesting no overt neurodegenerative changes. RNA sequencing examined in the caudate, putamen, substantia nigra, and prefrontal cortical subregions revealed that while IFN-a nominally up-regulated limited numbers of genes enriching inflammatory signaling pathways in all regions, robust, whole genome-significant effects of IFN-a were observed specifically in putamen. Genes upregulated in the putamen primarily enriched synaptic signaling, glutamate receptor signaling, and inflammatory/metabolic pathways downstream of IFN-a, including MAPK and PI3K/AKT cascades. Conversely, gene transcripts reduced by IFN-a enriched oxidative phosphorylation (OXPHOS), protein translation, and pathways regulated by dopamine receptors. Unsupervised clustering identified a gene co-expression module in the putamen that was associated with both IFN-a treatment and low dopamine levels, which enriched similar inflammatory, metabolic, and synaptic signaling pathways. IFN-a-induced reductions in dopamine further correlated with genes related to excitotoxic glutamate, kynurenine, and altered dopamine receptor signaling (r = 0.78-97, p < 0.05). These findings provide insight into the immunologic mechanisms and neurobiological consequences of peripheral inflammation effects on dopamine, which may inform novel treatment strategies targeting inflammatory, metabolic or neurotransmitter systems in depressed patients with high inflammation.

Multi-tissue network analysis reveals the effect of JNK inhibition on dietary sucrose-induced metabolic dysfunction in rats

Excessive consumption of sucrose, in the form of sugar-sweetened beverages, has been implicated in the pathogenesis of metabolic dysfunction-associated fatty liver disease (MAFLD) and other related metabolic syndromes. The c-Jun N-terminal kinase (JNK) pathway plays a crucial role in response to dietary stressors, and it was demonstrated that the inhibition of the JNK pathway could potentially be used in the treatment of MAFLD. However, the intricate mechanisms underlying these interventions remain incompletely understood given their multifaceted effects across multiple tissues. In this study, we challenged rats with sucrose-sweetened water and investigated the potential effects of JNK inhibition by employing network analysis based on the transcriptome profiling obtained from hepatic and extrahepatic tissues, including visceral white adipose tissue, skeletal muscle, and brain. Our data demonstrate that JNK inhibition by JNK-IN-5A effectively reduces the circulating triglyceride accumulation and inflammation in rats subjected to sucrose consumption. Coexpression analysis and genome-scale metabolic modeling reveal that sucrose overconsumption primarily induces transcriptional dysfunction related to fatty acid and oxidative metabolism in the liver and adipose tissues, which are largely rectified after JNK inhibition at a clinically relevant dose. Skeletal muscle exhibited minimal transcriptional changes to sucrose overconsumption but underwent substantial metabolic adaptation following the JNK inhibition. Overall, our data provides novel insights into the molecular basis by which JNK inhibition exerts its metabolic effect in the metabolically active tissues. Furthermore, our findings underpin the critical role of extrahepatic metabolism in the development of diet-induced steatosis, offering valuable guidance for future studies focused on JNK-targeting for effective treatment of MAFLD.

A graph neural network approach for hierarchical mapping of breast cancer protein communities

BACKGROUND

Comprehensively mapping the hierarchical structure of breast cancer protein communities and identifying potential biomarkers from them is a promising way for breast cancer research. Existing approaches are subjective and fail to take information from protein sequences into consideration. Deep learning can automatically learn features from protein sequences and protein-protein interactions for hierarchical clustering.

RESULTS

Using a large amount of publicly available proteomics data, we created a hierarchical tree for breast cancer protein communities using a novel hierarchical graph neural network, with the supervision of gene ontology terms and assistance of a pre-trained deep contextual language model. Then, a group-lasso algorithm was applied to identify protein communities that are under both mutation burden and survival burden, undergo significant alterations when targeted by specific drug molecules, and show cancer-dependent perturbations. The resulting hierarchical map of protein communities shows how gene-level mutations and survival information converge on protein communities at different scales. Internal validity of the model was established through the convergence on BRCA2 as a breast cancer hotspot. Further overlaps with breast cancer cell dependencies revealed SUPT6H and RAD21, along with their respective protein systems, HOST:37 and HOST:861, as potential biomarkers. Using gene-level perturbation data of the HOST:37 and HOST:861 gene sets, three FDA-approved drugs with high therapeutic value were selected as potential treatments to be further evaluated. These drugs include mercaptopurine, pioglitazone, and colchicine.

CONCLUSION

The proposed graph neural network approach to analyzing breast cancer protein communities in a hierarchical structure provides a novel perspective on breast cancer prognosis and treatment. By targeting entire gene sets, we were able to evaluate the prognostic and therapeutic value of genes (or gene sets) at different levels, from gene-level to system-level biology. Cancer-specific gene dependencies provide additional context for pinpointing cancer-related systems and drug-induced alterations can highlight potential therapeutic targets. These identified protein communities, in conjunction with other protein communities under strong mutation and survival burdens, can potentially be used as clinical biomarkers for breast cancer.

Exploring glioma heterogeneity through omics networks: from gene network discovery to causal insights and patient stratification

Gliomas are primary malignant brain tumors with a typically poor prognosis, exhibiting significant heterogeneity across different cancer types. Each glioma type possesses distinct molecular characteristics determining patient prognosis and therapeutic options. This study aims to explore the molecular complexity of gliomas at the transcriptome level, employing a comprehensive approach grounded in network discovery. The graphical lasso method was used to estimate a gene co-expression network for each glioma type from a transcriptomics dataset. Causality was subsequently inferred from correlation networks by estimating the Jacobian matrix. The networks were then analyzed for gene importance using centrality measures and modularity detection, leading to the selection of genes that might play an important role in the disease. To explore the pathways and biological functions these genes are involved in, KEGG and Gene Ontology (GO) enrichment analyses on the disclosed gene sets were performed, highlighting the significance of the genes selected across several relevent pathways and GO terms. Spectral clustering based on patient similarity networks was applied to stratify patients into groups with similar molecular characteristics and to assess whether the resulting clusters align with the diagnosed glioma type. The results presented highlight the ability of the proposed methodology to uncover relevant genes associated with glioma intertumoral heterogeneity. Further investigation might encompass biological validation of the putative biomarkers disclosed.

AH-6809 mediated regulation of lung adenocarcinoma metastasis through NLRP7 and prognostic analysis of key metastasis-related genes

Introduction

Lung adenocarcinoma (LUAD) has become one of the leading causes of cancer-related deaths globally, with metastasis representing the most lethal stage of the disease. Despite significant advances in diagnostic and therapeutic strategies for LUAD, the mechanisms enabling cancer cells to breach the blood-brain barrier remain poorly understood. While genomic profiling has shed light on the nature of primary tumors, the genetic drivers and clinical relevance of LUAD metastasis are still largely unexplored.

Objectives

This study aims to investigate the genomic differences between brain-metastatic and non-brain-metastatic LUAD, identify potential prognostic biomarkers, and evaluate the efficacy of AH-6809 in modulating key molecular pathways involved in LUAD metastasis, with a focus on post-translational modifications (PTMs).

Methods

Genomic analyses were performed using data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) between brain-metastatic and non-metastatic LUAD samples were identified. Key gene modules were determined using Weighted Gene Co-expression Network Analysis (WGCNA), and their prognostic significance was assessed through Kaplan-Meier analysis. Cellular experiments, including CCK8 and qRT-PCR assays, were conducted to evaluate the anti-cancer effects of AH-6809 in LUAD cells. Apoptosis and inflammatory marker expression were assessed using immunofluorescence.

Results

Genomic analysis differentiated brain-metastatic from non-brain-metastatic LUAD and identified NLRP7, FIBCD1, and ELF5 as prognostic markers. AH-6809 significantly suppressed LUAD cell proliferation, promoted apoptosis, and modulated epithelial-mesenchymal transition (EMT) markers. These effects were reversed upon NLRP7 knockdown, highlighting its role in metastasis. Literature analysis further supported AH-6809's tumor-suppressive activity, particularly in NLRP7 knockdown cells, where it inhibited cell growth and facilitated apoptosis. AH-6809 was also found to affect SUMO1-mediated PTMs and downregulate EMT markers, including VIM and CDH2. NLRP7 knockdown partially reversed these effects. Immunofluorescence revealed enhanced apoptosis and inflammation in lung cancer cells, especially in NLRP7 knockdown cells treated with AH-6809. The regulatory mechanisms involve SUMO1-mediated post-translational modifications and NQO1. Further studies are required to elucidate the molecular mechanisms and assess the clinical potential of these findings.

Conclusion

These findings demonstrate the critical role of NLRP7 and associated genes in LUAD metastasis and suggest that AH-6809 holds promise as a potential therapeutic agent for brain-metastatic LUAD.

PyNetCor: a high-performance Python package for large-scale correlation analysis

The development of multi-omics technologies has generated an abundance of biological datasets, providing valuable resources for investigating potential relationships within complex biological systems. However, most correlation analysis tools face computational challenges when dealing with these high-dimensional datasets containing millions of features. Here, we introduce pyNetCor, a fast and scalable tool for constructing correlation networks on large-scale and high-dimensional data. PyNetCor features optimized algorithms for both full correlation coefficient matrix computation and top-k correlation search, outperforming other tools in the field in terms of runtime and memory consumption. It utilizes a linear interpolation strategy to rapidly estimate P-values and achieve false discovery rate control, demonstrating a speedup of over 110 times compared to existing methods. Overall, pyNetCor supports large-scale correlation analysis, a crucial foundational step for various bioinformatics workflows, and can be easily integrated into downstream applications to accelerate the process of extracting biological insights from data.

Deciphering genomic codes using advanced NLP techniques: a scoping review

Objectives

The vast and complex nature of human genomic sequencing data presents challenges for effective analysis. This review aims to investigate the application of Natural Language Processing (NLP) techniques, particularly Large Language Models (LLMs) and transformer architectures, in deciphering genomic codes, focusing on tokenization, transformer models, and regulatory annotation prediction. This review aims to assess data and model accessibility in the most recent literature, gaining a better understanding of the existing capabilities and constraints of these tools in processing genomic sequencing data.

Methods

Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, our scoping review was conducted across PubMed, Medline, Scopus, Web of Science, Embase, and ACM Digital Library. Studies were included if they focused on NLP methodologies applied to genomic sequencing data analysis, without restrictions on publication date or article type.

Results

A total of 26 studies published between 2021 and April 2024 were selected for review. The review highlights that tokenization and transformer models enhance the processing and understanding of genomic data, with applications in predicting regulatory annotations like transcription-factor binding sites and chromatin accessibility.

Discussion

The application of NLP and LLMs to genomic sequencing data interpretation is a promising field that can help streamline the processing of large-scale genomic data while providing a better understanding of its complex structures. It can potentially drive advancements in personalized medicine by offering more efficient and scalable solutions for genomic analysis. Further research is needed to discuss and overcome limitations, enhancing model transparency and applicability.

Understanding complex systems through differential causal networks

In the evolving landscape of data science and computational biology, Causal Networks (CNs) have emerged as a robust framework for modelling causal relationships among elements of complex systems derived from experimental data. CNs can efficiently model causal relationships emerging in a single system while comparing multiple systems, allowing to understand rewiring in different cells, tissues, and physiological states with a deeper perspective. Despite the existence of network models, namely differential networks, that have been used to compare coexpression and correlation structures, causality needs to be introduced in differential analysis to robustly provide direction to the edges of such networks, in order to better understand the flows of information, and also to better intervene in their functioning, for example for agricultural or pharmacological purposes. Resolved to reach this ambitious goal, we introduce Differential Causal Networks (DCNs), a novel framework that represents differences between two existing CNs. A DCN is obtained from experimental data by comparing two CNs, and it is a power tool for highlighting differences in causal relations. After a careful definition and design of DCNs, we test our algorithm to model possible differential causal relationships between genes responsible for the onset of type 2 diabetes mellitus-related pathologies considering patients' sex at the tissue level. DCNs allowed us to shed light on causal differences between sexes across nine tissues. We also compare differences among three possible definitions of DCNs to highlight similarities and differences of biological importance. Code, Data and Supplementary Information are available at https://github.com/hguzzi/DifferentialCausalNetworks .

Structured feature ranking for genomic marker identification accommodating multiple types of networks

Numerous statistical methods have been developed to search for genomic markers associated with the development, progression, and response to treatment of complex diseases. Among them, feature ranking plays a vital role due to its intuitive formulation and computational efficiency. However, most of the existing methods are based on the marginal importance of molecular predictors and share the limitation that the dependence (network) structures among predictors are not well accommodated, where a disease phenotype usually reflects various biological processes that interact in a complex network. In this paper, we propose a structured feature ranking method for identifying genomic markers, where such network structures are effectively accommodated using Laplacian regularization. The proposed method innovatively investigates multiple network scenarios, where the networks can be known a priori and data-dependently estimated. In addition, we rigorously explore the noise and uncertainty in the networks and control their impacts with proper selection of tuning parameters. These characteristics make the proposed method enjoy especially broad applicability. Theoretical result of our proposal is rigorously established. Compared to the original marginal measure, the proposed network structured measure can achieve sure screening properties with a faster convergence rate under mild conditions. Extensive simulations and analysis of The Cancer Genome Atlas melanoma data demonstrate the improvement of finite sample performance and practical usefulness of the proposed method.

Multi-organ transcriptomics analysis of a slowly growing fish rock carp (Procypris rabaudi) reveals insights into mechanism of growth rate regulation

To explore the patterns of differentially expressed genes (DEGs) associated with different growth rates in rock carp (Procypris rabaudi), transcriptome sequencing was performed on the muscle, liver, and brain tissues of rock carp. Subsequently, bioinformatics analysis was conducted, and 2129, 1380, and 415 DEGs were identified in the muscle, liver, and brain tissues, respectively. GO enrichment and KEGG pathway analysis revealed that genes related to appetite regulation, protein degradation and digestion, lipid transport and metabolisms, and glycolysis/gluconeogenesis were upregulated in individuals with slower growth rates. Differential expression analysis identified 21 genes associated with feeding and metabolism across three tissues, including mc4r, npy, and npry in brain tissue; fatp, fabp, pparα, and apo in liver tissue; and prss, ctrl, and cela in muscle tissue. All these genes were upregulated in the slow-growing fish. Furthermore, weighted gene co-expression network analyses, including three modules (yellow, turquoise, and brown), significantly associated with growth. A network map that included these three modules enabled the identification of a series of hub genes, including rp13a, ube2o, h6pd, etc. These genes may be key candidate genes regulating the growth of rock carp. This study contributes to a deeper understanding of the growth control mechanism in rock carp and offers a scientific basis for efficient breeding and species improvement.

CSER: a gene regulatory network construction method based on causal strength and ensemble regression

Introduction

Gene regulatory networks (GRNs) reveal the intricate interactions between and among genes, and understanding these interactions is essential for revealing the molecular mechanisms of cancer. However, existing algorithms for constructing GRNs may confuse regulatory relationships and complicate the determination of network directionality.

Methods

We propose a new method to construct GRNs based on causal strength and ensemble regression (CSER) to overcome these issues. CSER uses conditional mutual inclusive information to quantify the causal associations between genes, eliminating indirect regulation and marginal genes. It considers linear and nonlinear features and uses ensemble regression to infer the direction and interaction (activation or regression) from regulatory to target genes.

Results

Compared to traditional algorithms, CSER can construct directed networks and infer the type of regulation, thus demonstrating higher accuracy on simulated datasets. Here, using real gene expression data, we applied CSER to construct a colorectal cancer GRN and successfully identified several key regulatory genes closely related to colorectal cancer (CRC), including ADAMDEC1, CLDN8, and GNA11.

Discussion

Importantly, by integrating immune cell and microbial data, we revealed the complex interactions between the CRC gene regulatory network and the tumor microenvironment, providing additional new biomarkers and therapeutic targets for the early diagnosis and prognosis of CRC.

Risk assessment with gene expression markers in sepsis development

Infection is a commonplace, usually self-limiting, condition but can lead to sepsis, a severe life-threatening dysregulated host response. We investigate the individual phenotypic predisposition to developing uncomplicated infection or sepsis in a large cohort of non-infected patients undergoing major elective surgery. Whole-blood RNA sequencing analysis was performed on preoperative samples from 267 patients. These patients developed postoperative infection with (n = 77) or without (n = 49) sepsis, developed non-infectious systemic inflammatory response (n = 31), or had an uncomplicated postoperative course (n = 110). Machine learning classification models built on preoperative transcriptomic signatures predict postoperative outcomes including sepsis with an area under the curve of up to 0.910 (mean 0.855) and sensitivity/specificity up to 0.767/0.804 (mean 0.746/0.769). Our models, confirmed by quantitative reverse-transcription PCR (RT-qPCR), potentially offer a risk prediction tool for the development of postoperative sepsis with implications for patient management. They identify an individual predisposition to developing sepsis that warrants further exploration to better understand the underlying pathophysiology.

Discovery and validation of a 10-gene predictive signature for response to adjuvant chemotherapy in stage II and III colon cancer

Identifying patients with stage II and III colon cancer who will benefit from 5-fluorouracil (5-FU)-based adjuvant chemotherapy is crucial for the advancement of personalized cancer therapy. We employ a semi-supervised machine learning approach to analyze a large dataset with 933 stage II and III colon cancer samples. Our analysis leverages gene regulatory networks to discover an 18-gene prognostic signature and to explore a 10-gene signature that potentially predicts chemotherapy benefits. The 10-gene signature demonstrates strong prognostic power and shows promising potential to predict chemotherapy benefits. We establish a robust clinical assay on the NanoString nCounter platform, validated in a retrospective formalin-fixed paraffin-embedded (FFPE) cohort, which represents an important step toward clinical application. Our study lays the groundwork for improving adjuvant chemotherapy and potentially expanding into immunotherapy decision-making in colon cancer. Future prospective studies are needed to validate and establish the clinical utility of the 10-gene signature in clinical settings.

Boosting Volatile fatty acids (VFAs) production in fermentation microorganisms through genes expression control: Unraveling the role of iron homeostasis transcription factors

Iron (Fe0, Fe (II), and Fe (III)) has been previously documented to upregulate the expression of key genes, enhancing the production of volatile fatty acids (VFAs) to achieve waste/wastewater resource recovery. However, the precise mechanism by why iron influences gene expression remains unclear. This study applied iron-assisted fermentation systems to explore the behind enhancing mechanism by constructing regulon networks among genes, microbes, and transcription factors. In iron-conditioned systems, a significant enhancement in VFAs production and upregulation of genes expression (1.19-3.92 folds) related to organic conversion and the electron transfer chain was observed. Besides, gene co-expression network and Procrustes analysis identified ten hub transcription factors (e.g., arsR, crp, iscR, perR) and their major contributors (genus) (e.g., Paludibacter, Acinetobacter, Tolumonas). Further analysis suggested that most of hub transcription factors were implicated in iron homeostasis regulation, which speculated that the induced iron homeostasis transcription factors probably effectively regulated the expression of genes encoding enzymes involving in VFAs production and electron transfer of functional microbes, in the case of Paludibacter, Acinetobacter, and Tolumonas while regulating the iron homeostasis, resulting in the efficient production of VFAs in iron-conditioned systems. This study might contribute to an enhanced understanding of the underlying genetic mechanisms by why iron influences gene expression regulation of microbes, which also provides a genetic theoretical basis for improving system VFAs production and resource recovery.

Multi-layer Bundling as a New Approach for Determining Multi-scale Correlations Within a High-Dimensional Dataset

The growing complexity of biological data has spurred the development of innovative computational techniques to extract meaningful information and uncover hidden patterns within vast datasets. Biological networks, such as gene regulatory networks and protein-protein interaction networks, hold critical insights into biological features' connections and functions. Integrating and analyzing high-dimensional data, particularly in gene expression studies, stands prominent among the challenges in deciphering these networks. Clustering methods play a crucial role in addressing these challenges, with spectral clustering emerging as a potent unsupervised technique considering intrinsic geometric structures. However, spectral clustering's user-defined cluster number can lead to inconsistent and sometimes orthogonal clustering regimes. We propose the Multi-layer Bundling (MLB) method to address this limitation, combining multiple prominent clustering regimes to offer a comprehensive data view. We call the outcome clusters "bundles". This approach refines clustering outcomes, unravels hierarchical organization, and identifies bridge elements mediating communication between network components. By layering clustering results, MLB provides a global-to-local view of biological feature clusters enabling insights into intricate biological systems. Furthermore, the method enhances bundle network predictions by integrating the bundle co-cluster matrix with the affinity matrix. The versatility of MLB extends beyond biological networks, making it applicable to various domains where understanding complex relationships and patterns is needed.

Massively integrated coexpression analysis reveals transcriptional regulation, evolution and cellular implications of the yeast noncanonical translatome

BACKGROUND

Recent studies uncovered pervasive transcription and translation of thousands of noncanonical open reading frames (nORFs) outside of annotated genes. The contribution of nORFs to cellular phenotypes is difficult to infer using conventional approaches because nORFs tend to be short, of recent de novo origins, and lowly expressed. Here we develop a dedicated coexpression analysis framework that accounts for low expression to investigate the transcriptional regulation, evolution, and potential cellular roles of nORFs in Saccharomyces cerevisiae.

RESULTS

Our results reveal that nORFs tend to be preferentially coexpressed with genes involved in cellular transport or homeostasis but rarely with genes involved in RNA processing. Mechanistically, we discover that young de novo nORFs located downstream of conserved genes tend to leverage their neighbors' promoters through transcription readthrough, resulting in high coexpression and high expression levels. Transcriptional piggybacking also influences the coexpression profiles of young de novo nORFs located upstream of genes, but to a lesser extent and without detectable impact on expression levels. Transcriptional piggybacking influences, but does not determine, the transcription profiles of de novo nORFs emerging nearby genes. About 40% of nORFs are not strongly coexpressed with any gene but are transcriptionally regulated nonetheless and tend to form entirely new transcription modules. We offer a web browser interface ( https://carvunislab.csb.pitt.edu/shiny/coexpression/ ) to efficiently query, visualize, and download our coexpression inferences.

CONCLUSIONS

Our results suggest that nORF transcription is highly regulated. Our coexpression dataset serves as an unprecedented resource for unraveling how nORFs integrate into cellular networks, contribute to cellular phenotypes, and evolve.

ScHGSC-IGDC: Identifying genes with differential correlations of high-grade serous ovarian cancer based on single-cell RNA sequencing analysis

Due to the high heterogeneity of ovarian cancer (OC), it occupies the main cause of cancer-related death among women. As the most aggressive and frequent subtype of OC, high-grade serous cancer (HGSC) represents around 70 % of all patients. With the booming progress of single-cell RNA sequencing (scRNA-seq), unique and subtle changes among different cell states have been identified including novel risk genes and pathways. Here, our present study aims to identify differentially correlated core genes between normal and tumor status through HGSC scRNA-seq data analysis. R package high-dimension Weighted Gene Co-expression Network Analysis (hdWGCNA) was implemented for building gene interaction networks based on HGSC scRNA-seq data. DiffCorr was integrated for identifying differentially correlated genes between tumor and their adjacent normal counterparts. Software Cytoscape was implemented for constructing and visualizing biological networks. Real-time qPCR (RT-qPCR) was utilized to confirm expression pattern of new genes. We introduced ScHGSC-IGDC (Identifying Genes with Differential Correlations of HGSC based on scRNA-seq analysis), an in silico framework for identifying core genes in the development of HGSC. We detected thirty-four modules in the network. Scores of new genes with opposite correlations with others such as NDUFS5, TMSB4X, SERPINE2 and ITPR2 were identified. Further survival and literature validation emphasized their great values in the HGSC management. Meanwhile, RT-qPCR verified expression pattern of NDUFS5, TMSB4X, SERPINE2 and ITPR2 in human OC cell lines and tissues. Our research offered novel perspectives on the gene modulatory mechanisms from single cell resolution, guiding network based algorithms in cancer etiology field.

Coordinated inflammation and immune response transcriptional regulation in breast cancer molecular subtypes

Breast cancer, characterized by its complexity and diversity, presents significant challenges in understanding its underlying biology. In this study, we employed gene co-expression network analysis to investigate the gene composition and functional patterns in breast cancer subtypes and normal breast tissue. Our objective was to elucidate the detailed immunological features distinguishing these tumors at the transcriptional level and to explore their implications for diagnosis and treatment. The analysis identified nine distinct gene module clusters, each representing unique transcriptional signatures within breast cancer subtypes and normal tissue. Interestingly, while some clusters exhibited high similarity in gene composition between normal tissue and certain subtypes, others showed lower similarity and shared traits. These clusters provided insights into the immune responses within breast cancer subtypes, revealing diverse immunological functions, including innate and adaptive immune responses. Our findings contribute to a deeper understanding of the molecular mechanisms underlying breast cancer subtypes and highlight their unique characteristics. The immunological signatures identified in this study hold potential implications for diagnostic and therapeutic strategies. Additionally, the network-based approach introduced herein presents a valuable framework for understanding the complexities of other diseases and elucidating their underlying biology.

Comparative analyses of gene networks mediating cancer metastatic potentials across lineage types

Studies have identified genes and molecular pathways regulating cancer metastasis. However, it remains largely unknown whether metastatic potentials of cancer cells from different lineage types are driven by the same or different gene networks. Here, we aim to address this question through integrative analyses of 493 human cancer cells' transcriptomic profiles and their metastatic potentials in vivo. Using an unsupervised approach and considering both gene coexpression and protein-protein interaction networks, we identify different gene networks associated with various biological pathways (i.e. inflammation, cell cycle, and RNA translation), the expression of which are correlated with metastatic potentials across subsets of lineage types. By developing a regularized random forest regression model, we show that the combination of the gene module features expressed in the native cancer cells can predict their metastatic potentials with an overall Pearson correlation coefficient of 0.90. By analyzing transcriptomic profile data from cancer patients, we show that these networks are conserved in vivo and contribute to cancer aggressiveness. The intrinsic expression levels of these networks are correlated with drug sensitivity. Altogether, our study provides novel comparative insights into cancer cells' intrinsic gene networks mediating metastatic potentials across different lineage types, and our results can potentially be useful for designing personalized treatments for metastatic cancers.

Revolutionizing Cancer Treatment: The Promising Horizon of Zein Nanosystems

Various nanomaterials have recently become fascinating tools in cancer diagnostic applications because of their multifunctional and inherent molecular characteristics that support efficient diagnosis and image-guided therapy. Zein nanoparticles are a protein derived from maize. It belongs to the class of prolamins possessing a spherical structure with conformational properties similar to those of conventional globular proteins like ribonuclease and insulin. Zein nanoparticles have gained massive interest over the past couple of years owing to their natural hydrophilicity, ease of functionalization, biodegradability, and biocompatibility, thereby improving oral bioavailability, nanoparticle targeting, and prolonged drug administration. Thus, zein nanoparticles are becoming a promising candidate for precision cancer drug delivery. This review highlights the clinical significance of applying zein nanosystems for cancer theragnostic─moreover, the role of zein nanosystems for cancer drug delivery, anticancer agents, and gene therapy. Finally, the difficulties and potential uses of these NPs in cancer treatment and detection are discussed. This review will pave the way for researchers to develop theranostic strategies for precision medicine utilizing zein nanosystems.

Graphene oxide stimulated low-temperature denitrification activity of microbial communities in lake sediments by enhancing anabolism and inhibiting cellular respiration

Nitrate pollution in fresh water is becoming increasingly serious. In this study, the effects of temperature and graphene oxide materials on the potential functions of denitrification communities in lake sediments were investigated by metagenome. The addition of graphene oxide significantly affected the abundance of denitrification genes such as Nap, Nos, and enhanced the contribution of Pseudomonas, making low temperature and material addition conducive to the denitrification process. Module network implied that low temperature increased the centrality of denitrification in community functions. At low temperatures, graphene oxide enhanced community anabolism by stimulation organic carbon consumption and regulating the gene abundance in the citric acid cycle and the semi-phosphorylation Entner-Doudoroff, thus possibly stimulating extracellular polymeric substances (EPS) synthesis and secretion. In addition, graphene oxide may also regulate the transfer of reducing electrons from NADH to denitrifying enzymes by affecting the gene abundances of complex I and complex IV.

Unlocking Prognostic Genes and Multi-Targeted Therapeutic Bioactives from Herbal Medicines to Combat Cancer-Associated Cachexia: A Transcriptomics and Network Pharmacology Approach

Cachexia is a devastating fat tissue and muscle wasting syndrome associated with every major chronic illness, including cancer, chronic obstructive pulmonary disease, kidney disease, AIDS, and heart failure. Despite two decades of intense research, cachexia remains under-recognized by oncologists. While numerous drug candidates have been proposed for cachexia treatment, none have achieved clinical success. Only a few drugs are approved by the FDA for cachexia therapy, but a very low success rate is observed among patients. Currently, the identification of drugs from herbal medicines is a frontier research area for many diseases. In this milieu, network pharmacology, transcriptomics, cheminformatics, and molecular docking approaches were used to identify potential bioactive compounds from herbal medicines for the treatment of cancer-related cachexia. The network pharmacology approach is used to select the 32 unique genes from 238 genes involved in cachexia-related pathways, which are targeted by 34 phytocompounds identified from 12 different herbal medicines used for the treatment of muscle wasting in many countries. Gene expression profiling and functional enrichment analysis are applied to decipher the role of unique genes in cancer-associated cachexia pathways. In addition, the pharmacological properties and molecular interactions of the phytocompounds were analyzed to find the target compounds for cachexia therapy. Altogether, combined omics and network pharmacology approaches were used in the current study to untangle the complex prognostic genes involved in cachexia and phytocompounds with anti-cachectic efficacy. However, further functional and experimental validations are required to confirm the efficacy of these phytocompounds as commercial drug candidates for cancer-associated cachexia.

The Role of Transcription Factors in the Loss of Inter-Chromosomal Co-Expression for Breast Cancer Subtypes

Breast cancer encompasses a diverse array of subtypes, each exhibiting distinct clinical characteristics and treatment responses. Unraveling the underlying regulatory mechanisms that govern gene expression patterns in these subtypes is essential for advancing our understanding of breast cancer biology. Gene co-expression networks (GCNs) help us identify groups of genes that work in coordination. Previous research has revealed a marked reduction in the interaction of genes located on different chromosomes within GCNs for breast cancer, as well as for lung, kidney, and hematopoietic cancers. However, the reasons behind why genes on the same chromosome often co-express remain unclear. In this study, we investigate the role of transcription factors in shaping gene co-expression networks within the four main breast cancer subtypes: Luminal A, Luminal B, HER2+, and Basal, along with normal breast tissue. We identify communities within each GCN and calculate the transcription factors that may regulate these communities, comparing the results across different phenotypes. Our findings indicate that, in general, regulatory behavior is to a large extent similar among breast cancer molecular subtypes and even in healthy networks. This suggests that transcription factor motif usage does not fully determine long-range co-expression patterns. Specific transcription factor motifs, such as CCGGAAG, appear frequently across all phenotypes, even involving multiple highly connected transcription factors. Additionally, certain transcription factors exhibit unique actions in specific subtypes but with limited influence. Our research demonstrates that the loss of inter-chromosomal co-expression is not solely attributable to transcription factor regulation. Although the exact mechanism responsible for this phenomenon remains elusive, this work contributes to a better understanding of gene expression regulatory programs in breast cancer.

Genome-wide family prediction unveils molecular mechanisms underlying the regulation of agronomic traits in Urochloa ruziziensis

Tropical forage grasses, particularly those belonging to the Urochloa genus, play a crucial role in cattle production and serve as the main food source for animals in tropical and subtropical regions. The majority of these species are apomictic and tetraploid, highlighting the significance of U. ruziziensis, a sexual diploid species that can be tetraploidized for use in interspecific crosses with apomictic species. As a means to support breeding programs, our study investigates the feasibility of genome-wide family prediction in U. ruziziensis families to predict agronomic traits. Fifty half-sibling families were assessed for green matter yield, dry matter yield, regrowth capacity, leaf dry matter, and stem dry matter across different clippings established in contrasting seasons with varying available water capacity. Genotyping was performed using a genotyping-by-sequencing approach based on DNA samples from family pools. In addition to conventional genomic prediction methods, machine learning and feature selection algorithms were employed to reduce the necessary number of markers for prediction and enhance predictive accuracy across phenotypes. To explore the regulation of agronomic traits, our study evaluated the significance of selected markers for prediction using a tree-based approach, potentially linking these regions to quantitative trait loci (QTLs). In a multiomic approach, genes from the species transcriptome were mapped and correlated to those markers. A gene coexpression network was modeled with gene expression estimates from a diverse set of U. ruziziensis genotypes, enabling a comprehensive investigation of molecular mechanisms associated with these regions. The heritabilities of the evaluated traits ranged from 0.44 to 0.92. A total of 28,106 filtered SNPs were used to predict phenotypic measurements, achieving a mean predictive ability of 0.762. By employing feature selection techniques, we could reduce the dimensionality of SNP datasets, revealing potential genotype-phenotype associations. The functional annotation of genes near these markers revealed associations with auxin transport and biosynthesis of lignin, flavonol, and folic acid. Further exploration with the gene coexpression network uncovered associations with DNA metabolism, stress response, and circadian rhythm. These genes and regions represent important targets for expanding our understanding of the metabolic regulation of agronomic traits and offer valuable insights applicable to species breeding. Our work represents an innovative contribution to molecular breeding techniques for tropical forages, presenting a viable marker-assisted breeding approach and identifying target regions for future molecular studies on these agronomic traits.

Comparative Transcriptome and WGCNA Analysis Reveal Molecular Responses to Salinity Change in Larvae of the Iwagaki Oyster Crassostrea Nippona

The Iwagaki oyster Crassostrea nippona is an important aquaculture species with significant potential for large-scale oyster farming. It is susceptible to the fluctuated salinity in the coastal area. In this study, we compared the transcriptome of Crassostrea nippona larvae under variant conditions with low-salinity stress (28, 20, 15, 10, and 5 practical salinity units (psu)) for 24 h. KEGG enrichment analysis of differentially expressed genes (DEGs) from pairwise comparisons identified several free amino acid metabolism pathway (taurine and hypotaurine, arginine and proline, glycine, and beta-alanine) contributing to the salinity change adaptation and activated "lysosome" and "apoptosis" pathway in response to the low-salinity stress (10 and 5 psu). Trend analysis revealed sustained upregulation of transmembrane transport-related genes (such as SLC family) and downregulation of ribosomal protein synthesis genes faced with decreasing salinities. In addition, 9 biomarkers in response to low-salinity stress were identified through weighted gene co-expression network analysis (WGCNA) and validated by qRT-PCR. Our transcriptome analysis provides a comprehensive view of the molecular mechanisms and regulatory networks underlying the adaptive responses of oyster larvae to hypo-salinity conditions. These findings contribute to our understanding of the complex biological processes involved in oyster resilience and adaptation to changing environmental conditions.

Repeated social defeat stress leads to immunometabolic shifts in innate immune cells of the spleen

Psychosocial stress has been shown to prime peripheral innate immune cells, which take on hyper-inflammatory phenotypes and are implicated in depressive-like behavior in mouse models. However, the impact of stress on cellular metabolic states that are thought to fuel inflammatory phenotypes in immune cells are unknown. Using single cell RNA-sequencing, we investigated mRNA enrichment of immunometabolic pathways in innate immune cells of the spleen in mice subjected to repeated social defeat stress (RSDS) or no stress (NS). RSDS mice displayed a significant increase in the number of splenic macrophages and granulocytes (p < 0.05) compared to NS littermates. RSDS-upregulated genes in macrophages, monocytes, and granulocytes significantly enriched immunometabolic pathways thought to play a role in myeloid-driven inflammation (glycolysis, HIF-1 signaling, MTORC1 signaling) as well as pathways related to oxidative phosphorylation (OXPHOS) and oxidative stress (p < 0.05 and FDR<0.1). These results suggest that the metabolic enhancement reflected by upregulation of glycolytic and OXPHOS pathways may be important for cellular proliferation of splenic macrophages and granulocytes following repeated stress exposure. A better understanding of these intracellular metabolic mechanisms may ultimately help develop novel strategies to reverse the impact of stress and associated peripheral immune changes on the brain and behavior.

Highly accurate disease diagnosis and highly reproducible biomarker identification with PathFormer

Biomarker identification is critical for precise disease diagnosis and understanding disease pathogenesis in omics data analysis, like using fold change and regression analysis. Graph neural networks (GNNs) have been the dominant deep learning model for analyzing graph-structured data. However, we found two major limitations of existing GNNs in omics data analysis, i.e., limited-prediction/diagnosis accuracy and limited-reproducible biomarker identification capacity across multiple datasets. The root of the challenges is the unique graph structure of biological signaling pathways, which consists of a large number of targets and intensive and complex signaling interactions among these targets. To resolve these two challenges, in this study, we presented a novel GNN model architecture, named PathFormer , which systematically integrate signaling network, priori knowledge and omics data to rank biomarkers and predict disease diagnosis. In the comparison results, PathFormer outperformed existing GNN models significantly in terms of highly accurate prediction capability (~ 30% accuracy improvement in disease diagnosis compared with existing GNN models) and high reproducibility of biomarker ranking across different datasets. The improvement was confirmed using two independent Alzheimer's Disease (AD) and cancer transcriptomic datasets. The PathFormer model can be directly applied to other omics data analysis studies.

Multiscale network modeling reveals the gene regulatory landscape driving cancer prognosis in 32 cancer types

Cancer is a complex disease with diverse molecular mechanisms that affect patient prognosis. Network-based approaches are effective in revealing a holistic picture of cancer prognosis and gene interactions. However, a comprehensive landscape of coexpression networks and prognostic gene modules across multiple cancer types remains elusive. In this study, we performed a systematic analysis of coexpression networks in 32 cancer types. Our analysis identified 4749 prognostic modules that play a vital role in regulating cancer progression. Integrative epigenomic analyses revealed that these modules were regulated by interactions between gene expression and methylation. Coregulated genes of network modules were enriched in chromosome cytobands and preferentially localized in open chromatin regions. The preserved network modules formed 330 module clusters that resided in chromosome hot spots. The cancer-type-specific prognostic modules participated in unique essential biological processes in different cancer types. Overall, our study provides rich resources of prevalent gene networks and underlying multiscale regulatory mechanisms driving cancer prognosis, which lay a foundation for biomarker discovery and therapeutic target development.

Involvement of CCL2 and CH25H Genes and TNF signaling pathways in mast cell activation and pathogenesis of chronic spontaneous urticaria

Chronic spontaneous urticaria (CSU), a mast cell-driven disease, substantially affects the quality of life. While genetics affect CSU susceptibility and severity, the specific genetic factors associated with mast cell activation in CSU remain elusive. We aimed to identify key genetic factors and investigate their roles in CSU pathogenesis. Two gene expression datasets from the Gene Expression Omnibus were merged and validated using principal component analysis and boxplots. The merged dataset was subjected to limma and weighted gene co-expression network analyses. Genes whose expression correlated highly with CSU were identified and analyzed using Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. As GSEA, GO, and KEGG analyses highlighted the importance of chemokine (C-C motif) ligand 2 (CCL2) and cholesterol 25-hydroxylase (CH25H) gene and tumor necrosis factor (TNF) signaling pathways in CSU; the three corresponding genes were knocked down in human mast cell line-1 (HMC-1), followed by incubation with thrombin to mimic CSU pathogenesis. CCL2, CH25H, and TNF knockdown reduced excitability and cytokine production in HMC-1. Our findings suggest that genes involved in the CCL2, CH25H, and TNF pathways play crucial roles in CSU pathogenesis, providing insights into potential therapeutic targets for CSU treatment.

Pseudogenes in Cancer: State of the Art

Pseudogenes are duplicates of protein-coding genes that have accumulated multiple detrimental alterations, rendering them unable to produce the protein they encode. Initially disregarded as "junk DNA" due to their perceived lack of functionality, research on their biological roles has been hindered by this assumption. Nevertheless, recent focus has shifted towards these molecules due to their abnormal expression in cancer phenotypes. In this review, our objective is to provide a thorough overview of the current understanding of pseudogene formation, the mechanisms governing their expression, and the roles they may play in promoting tumorigenesis.

A systematic review of computational approaches to understand cancer biology for informed drug repurposing

Cancer is the second leading cause of death globally, trailing only heart disease. In the United States alone, 1.9 million new cancer cases and 609,360 deaths were recorded for 2022. Unfortunately, the success rate for new cancer drug development remains less than 10%, making the disease particularly challenging. This low success rate is largely attributed to the complex and poorly understood nature of cancer etiology. Therefore, it is critical to find alternative approaches to understanding cancer biology and developing effective treatments. One such approach is drug repurposing, which offers a shorter drug development timeline and lower costs while increasing the likelihood of success. In this review, we provide a comprehensive analysis of computational approaches for understanding cancer biology, including systems biology, multi-omics, and pathway analysis. Additionally, we examine the use of these methods for drug repurposing in cancer, including the databases and tools that are used for cancer research. Finally, we present case studies of drug repurposing, discussing their limitations and offering recommendations for future research in this area.

Integrative network-based analysis on multiple Gene Expression Omnibus datasets identifies novel immune molecular markers implicated in non-alcoholic steatohepatitis

Introduction

Non-alcoholic steatohepatitis (NASH), an advanced subtype of non-alcoholic fatty liver disease (NAFLD), has becoming the most important aetiology for end-stage liver disease, such as cirrhosis and hepatocellular carcinoma. This study were designed to explore novel genes associated with NASH.

Methods

Here, five independent Gene Expression Omnibus (GEO) datasets were combined into a single cohort and analyzed using network biology approaches.

Results

11 modules identified by weighted gene co-expression network analysis (WGCNA) showed significant association with the status of NASH. Further characterization of four gene modules of interest demonstrated that molecular pathology of NASH involves the upregulation of hub genes related to immune response, cholesterol and lipid metabolic process, extracellular matrix organization, and the downregulation of hub genes related to cellular amino acid catabolic, respectively. After DEGs enrichment analysis and module preservation analysis, the Turquoise module associated with immune response displayed a remarkably correlation with NASH status. Hub genes with high degree of connectivity in the module, including CD53, LCP1, LAPTM5, NCKAP1L, C3AR1, PLEK, FCER1G, HLA-DRA and SRGN were further verified in clinical samples and mouse model of NASH. Moreover, single-cell RNA-seq analysis showed that those key genes were expressed by distinct immune cells such as microphages, natural killer, dendritic, T and B cells. Finally, the potential transcription factors of Turquoise module were characterized, including NFKB1, STAT3, RFX5, ILF3, ELF1, SPI1, ETS1 and CEBPA, the expression of which increased with NASH progression.

Discussion

In conclusion, our integrative analysis will contribute to the understanding of NASH and may enable the development of potential biomarkers for NASH therapy.

Monocyte, neutrophil, and whole blood transcriptome dynamics following ischemic stroke

BACKGROUND

After ischemic stroke (IS), peripheral leukocytes infiltrate the damaged region and modulate the response to injury. Peripheral blood cells display distinctive gene expression signatures post-IS and these transcriptional programs reflect changes in immune responses to IS. Dissecting the temporal dynamics of gene expression after IS improves our understanding of immune and clotting responses at the molecular and cellular level that are involved in acute brain injury and may assist with time-targeted, cell-specific therapy.

METHODS

The transcriptomic profiles from peripheral monocytes, neutrophils, and whole blood from 38 ischemic stroke patients and 18 controls were analyzed with RNA-seq as a function of time and etiology after stroke. Differential expression analyses were performed at 0-24 h, 24-48 h, and >48 h following stroke.

RESULTS

Unique patterns of temporal gene expression and pathways were distinguished for monocytes, neutrophils, and whole blood with enrichment of interleukin signaling pathways for different time points and stroke etiologies. Compared to control subjects, gene expression was generally upregulated in neutrophils and generally downregulated in monocytes over all times for cardioembolic, large vessel, and small vessel strokes. Self-organizing maps identified gene clusters with similar trajectories of gene expression over time for different stroke causes and sample types. Weighted Gene Co-expression Network Analyses identified modules of co-expressed genes that significantly varied with time after stroke and included hub genes of immunoglobulin genes in whole blood.

CONCLUSIONS

Altogether, the identified genes and pathways are critical for understanding how the immune and clotting systems change over time after stroke. This study identifies potential time- and cell-specific biomarkers and treatment targets.

Contrast subgraphs allow comparing homogeneous and heterogeneous networks derived from omics data

BACKGROUND

Biological networks are often used to describe the relationships between relevant entities, particularly genes and proteins, and are a powerful tool for functional genomics. Many important biological problems can be investigated by comparing biological networks between different conditions or networks obtained with different techniques.

FINDINGS

We show that contrast subgraphs, a recently introduced technique to identify the most important structural differences between 2 networks, provide a versatile tool for comparing gene and protein networks of diverse origin. We demonstrate the use of contrast subgraphs in the comparison of coexpression networks derived from different subtypes of breast cancer, coexpression networks derived from transcriptomic and proteomic data, and protein-protein interaction networks assayed in different cell lines.

CONCLUSIONS

These examples demonstrate how contrast subgraphs can provide new insight in functional genomics by extracting the gene/protein modules whose connectivity is most altered between 2 conditions or experimental techniques.

Pan-cancer surveys indicate cell cycle-related roles of primate-specific genes in tumors and embryonic cerebrum

BACKGROUND

Despite having been extensively studied, it remains largely unclear why humans bear a particularly high risk of cancer. The antagonistic pleiotropy hypothesis predicts that primate-specific genes (PSGs) tend to promote tumorigenesis, while the molecular atavism hypothesis predicts that PSGs involved in tumors may represent recently derived duplicates of unicellular genes. However, these predictions have not been tested.

RESULTS

By taking advantage of pan-cancer genomic data, we find the upregulation of PSGs across 13 cancer types, which is facilitated by copy-number gain and promoter hypomethylation. Meta-analyses indicate that upregulated PSGs (uPSGs) tend to promote tumorigenesis and to play cell cycle-related roles. The cell cycle-related uPSGs predominantly represent derived duplicates of unicellular genes. We prioritize 15 uPSGs and perform an in-depth analysis of one unicellular gene-derived duplicate involved in the cell cycle, DDX11. Genome-wide screening data and knockdown experiments demonstrate that DDX11 is broadly essential across cancer cell lines. Importantly, non-neutral amino acid substitution patterns and increased expression indicate that DDX11 has been under positive selection. Finally, we find that cell cycle-related uPSGs are also preferentially upregulated in the highly proliferative embryonic cerebrum.

CONCLUSIONS

Consistent with the predictions of the atavism and antagonistic pleiotropy hypotheses, primate-specific genes, especially those PSGs derived from cell cycle-related genes that emerged in unicellular ancestors, contribute to the early proliferation of the human cerebrum at the cost of hitchhiking by similarly highly proliferative cancer cells.

Gene expression changes implicate specific peripheral immune responses to Deep and Lobar Intracerebral Hemorrhages in humans

The peripheral immune system response to Intracerebral Hemorrhage (ICH) may differ with ICH in different brain locations. Thus, we investigated peripheral blood mRNA expression of Deep ICH, Lobar ICH, and vascular risk factor-matched control subjects (n = 59). Deep ICH subjects usually had hypertension. Some Lobar ICH subjects had cerebral amyloid angiopathy (CAA). Genes and gene networks in Deep ICH and Lobar ICH were compared to controls. We found 774 differentially expressed genes (DEGs) and 2 co-expressed gene modules associated with Deep ICH, and 441 DEGs and 5 modules associated with Lobar ICH. Pathway enrichment showed some common immune/inflammatory responses between locations including Autophagy, T Cell Receptor, Inflammasome, and Neuroinflammation Signaling. Th2, Interferon, GP6, and BEX2 Signaling were unique to Deep ICH. Necroptosis Signaling, Protein Ubiquitination, Amyloid Processing, and various RNA Processing terms were unique to Lobar ICH. Finding amyloid processing pathways in blood of Lobar ICH patients suggests peripheral immune cells may participate in processes leading to perivascular/vascular amyloid in CAA vessels and/or are involved in its removal. This study identifies distinct peripheral blood transcriptome architectures in Deep and Lobar ICH, emphasizes the need for considering location in ICH studies/clinical trials, and presents potential location-specific treatment targets.

Identifying Hub Genes Associated with Neoadjuvant Chemotherapy Resistance in Breast Cancer and Potential Drug Repurposing for the Development of Precision Medicine

Breast cancer is the second leading cause of morbidity and mortality in women worldwide. Despite advancements in the clinical application of neoadjuvant chemotherapy (NAC), drug resistance remains a major concern hindering treatment efficacy. Thus, identifying the key genes involved in driving NAC resistance and targeting them with known potential FDA-approved drugs could be applied to advance the precision medicine strategy. With this aim, we performed an integrative bioinformatics study to identify the key genes associated with NAC resistance in breast cancer and then performed the drug repurposing to identify the potential drugs which could use in combination with NAC to overcome drug resistance. In this study, we used publicly available RNA-seq datasets from the samples of breast cancer patients sensitive and resistant to chemotherapy and identified a total of 1446 differentially expressed genes in NAC-resistant breast cancer patients. Next, we performed gene co-expression network analysis to identify significantly co-expressed gene modules, followed by MCC (Multiple Correlation Clustering) clustering algorithms and identified 33 key hub genes associated with NAC resistance. mRNA–miRNA network analysis highlighted the potential impact of these hub genes in altering the regulatory network in NAC-resistance breast cancer cells. Further, several hub genes were found to be significantly involved in the poor overall survival of breast cancer patients. Finally, we identified FDA-approved drugs which could be useful for potential drug repurposing against those hub genes. Altogether, our findings provide new insight into the molecular mechanisms of NAC resistance and pave the way for drug repurposing techniques and personalized treatment to overcome NAC resistance in breast cancer.

Transcriptome and Metabolome Analysis of Color Changes during Fruit Development of Pepper (Capsicum baccatum)

Fruit color is one of the most critical characteristics of pepper. In this study, pepper (Capsicum baccatum L.) fruits with four trans-coloring periods were used as experimental materials to explore the color conversion mechanism of pepper fruit. By transcriptome and metabolome analysis, we identified a total of 307 flavonoid metabolites, 68 carotenoid metabolites, 29 DEGs associated with flavonoid biosynthesis, and 30 DEGs related to carotenoid biosynthesis. Through WGCNA (weighted gene co-expression network analysis) analysis, positively correlated modules with flavonoids and carotenoids were identified, and hub genes associated with flavonoid and carotenoid synthesis and transport were anticipated. We identified Pinobanksin, Naringenin Chalcone, and Naringenin as key metabolites in the flavonoid biosynthetic pathway catalyzed by the key genes chalcone synthase (CHS CQW23_29123, CQW23_29380, CQW23_12748), cinnamic acid 4-hydroxylase (C4H CQW23_16085, CQW23_16084), cytochrome P450 (CYP450 CQW23_19845, CQW23_24900). In addition, phytoene synthase (PSY CQW23_09483), phytoene dehydrogenase (PDS CQW23_11317), zeta-carotene desaturase (ZDS CQW23_19986), lycopene beta cyclase (LYC CQW23_09027), zeaxanthin epoxidase (ZEP CQW23_05387), 9-cis-epoxycarotenoid dioxygenase (NCED CQW23_17736), capsanthin/capsorubin synthase (CCS CQW23_30321) are key genes in the carotenoid biosynthetic pathway, catalyzing the synthesis of key metabolites such as Phytoene, Lycopene, β-carotene and ε-carotene. We also found that transcription factor families such as p450 and NBARC could play important roles in the biosynthesis of flavonoids and carotenoids in pepper fruits. These results provide new insights into the interaction mechanisms of genes and metabolites involved in the biosynthesis of flavonoids and carotenoids in pepper fruit leading to color changes in pepper fruit.

Gene coexpression network analysis identifies hubs in hepatitis B virus-associated hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is among the leading causes of cancer-related death worldwide. The molecular pathogenesis of HCC involves multiple signaling pathways. This study utilizes systems and bioinformatic approaches to investigate the pathogenesis of HCC.

METHODS

Gene expression microarray data were obtained from 50 patients with chronic hepatitis B and HCC. There were 1649 differentially expressed genes inferred from tumorous and nontumorous datasets. Weighted gene coexpression network analysis (WGCNA) was performed to construct clustered coexpressed gene modules. Statistical analysis was used to study the correlation between gene coexpression networks and demographic features of patients. Functional annotation and pathway inference were explored for each coexpression network. Network analysis identified hub genes of the prognostic gene coexpression network. The hub genes were further validated with a public database.

RESULT

Five distinct gene coexpression networks were identified by WGCNA. A distinct coexpressed gene network was significantly correlated with HCC prognosis. Pathway analysis of this network revealed extensive integration with cell cycle regulation. Ten hub genes of this gene network were inferred from protein-protein interaction network analysis and further validated in an external validation dataset. Survival analysis showed that lower expression of the 10-gene signature had better overall survival and recurrence-free survival.

CONCLUSION

This study identified a crucial gene coexpression network associated with the prognosis of hepatitis B virus-related HCC. The identified hub genes may provide insights for HCC pathogenesis and may be potential prognostic markers or therapeutic targets.

The Molecular Network behind Volatile Aroma Formation in Pear (Pyrus spp. Panguxiang) Revealed by Transcriptome Profiling via Fatty Acid Metabolic Pathways

Pears are popular table fruits, grown and consumed worldwide for their excellent color, aroma, and taste. Volatile aroma is an important factor affecting fruit quality, and the fatty acid metabolism pathway is important in synthesizing volatile aromas. Most of the white pear varieties cultivated in China are not strongly scented, which significantly affects their overall quality. Panguxiang is a white pear cultivar, but its aroma has unique components and is strong. The study of the mechanisms by which aroma is formed in Panguxiang is, therefore, essential to improving the quality of the fruit. The study analyzed physiological and transcriptome factors to reveal the molecular network behind volatile aroma formation in Panguxiang. The samples of Panguxiang fruit were collected in two (fruit development at 60, 90, 120, and 147 days, and fruit storage at 0, 7, 14, 21, and 28 days) periods. A total of nine sample stages were used for RNA extraction and paired-end sequencing. In addition, RNA quantification and qualification, library preparation and sequencing, data analysis and gene annotation, gene co-expression network analysis, and validation of DEGs through quantitative real-time PCR (qRT-;PCR) were performed in this study. The WGCNA identified yellow functional modules and several biological and metabolic pathways related to fatty acid formation. Finally, we identified seven and eight hub genes in the fatty acid synthesis and fatty acid metabolism pathways, respectively. Further analysis of the co-expression network allowed us to identify several key transcription factors related to the volatile aroma, including AP2/ERF-ERF, C3H, MYB, NAC, C2H2, GRAS, and Trihelix, which may also be involved in the fatty acid synthesis. This study lays a theoretical foundation for studying volatile compounds in pear fruits and provides a theoretical basis for related research in other fruits.

Game-theoretic link relevance indexing on genome-wide expression dataset identifies putative salient genes with potential etiological and diapeutics role in colorectal cancer

Diapeutics gene markers in colorectal cancer (CRC) can help manage mortality caused by the disease. We applied a game-theoretic link relevance Index (LRI) scoring on the high-throughput whole-genome transcriptome dataset to identify salient genes in CRC and obtained 126 salient genes with LRI score greater than zero. The biomarkers database lacks preliminary information on the salient genes as biomarkers for all the available cancer cell types. The salient genes revealed eleven, one and six overrepresentations for major Biological Processes, Molecular Function, and Cellular components. However, no enrichment with respect to chromosome location was found for the salient genes. Significantly high enrichments were observed for several KEGG, Reactome and PPI terms. The survival analysis of top protein-coding salient genes exhibited superior prognostic characteristics for CRC. MIR143HG, AMOTL1, ACTG2 and other salient genes lack sufficient information regarding their etiological role in CRC. Further investigation in LRI methodology and salient genes to augment the existing knowledge base may create new milestones in CRC diapeutics.

Gene networks under circadian control exhibit diurnal organization in primate organs

Mammalian organs are individually controlled by autonomous circadian clocks. At the molecular level, this process is defined by the cyclical co-expression of both core transcription factors and their downstream targets across time. While interactions between these molecular clocks are necessary for proper homeostasis, these features remain undefined. Here, we utilize integrative analysis of a baboon diurnal transcriptome atlas to characterize the properties of gene networks under circadian control. We found that 53.4% (8120) of baboon genes are oscillating body-wide. Additionally, two basic network modes were observed at the systems level: daytime and nighttime mode. Daytime networks were enriched for genes involved in metabolism, while nighttime networks were enriched for genes associated with growth and cellular signaling. A substantial number of diseases only form significant disease modules at either daytime or nighttime. In addition, a majority of SARS-CoV-2-related genes and modules are rhythmically expressed, which have significant network proximities with circadian regulators. Our data suggest that synchronization amongst circadian gene networks is necessary for proper homeostatic functions and circadian regulators have close interactions with SARS-CoV-2 infection.

Integrative analysis of the high-resolution baboon diurnal transcriptome, provides insights into the effect of circadian rhythm on the whole-body primate gene network.

Comparative Analysis of Gene Correlation Networks of Breast Cancer Patients Based on Mutations in TP53

Breast cancer is one of the most prevalent cancers in females, with more than 450,000 deaths each year worldwide. Among the subtypes of breast cancer, basal-like breast cancer, also known as triple-negative breast cancer, shows the lowest survival rate and does not have effective treatments yet. Somatic mutations in the TP53 gene frequently occur across all breast cancer subtypes, but comparative analysis of gene correlations with respect to mutations in TP53 has not been done so far. The primary goal of this study is to identify gene correlations in two groups of breast cancer patients and to derive potential prognostic gene pairs for breast cancer. We partitioned breast cancer patients into two groups: one group with a mutated TP53 gene (mTP53) and the other with a wild-type TP53 gene (wtTP53). For every gene pair, we computed the hazard ratio using the Cox proportional hazard model and constructed gene correlation networks (GCNs) enriched with prognostic information. Our GCN is more informative than typical GCNs in the sense that it indicates the type of correlation between genes, the concordance index, and the prognostic type of a gene. Comparative analysis of correlation patterns and survival time of the two groups revealed several interesting findings. First, we found several new gene pairs with opposite correlations in the two GCNs and the difference in their correlation patterns was the most prominent in the basal-like subtype of breast cancer. Second, we obtained potential prognostic genes for breast cancer patients with a wild-type TP53 gene. From a comparative analysis of GCNs of mTP53 and wtTP53, we found several gene pairs that show significantly different correlation patterns in the basal-like breast cancer subtype and obtained prognostic genes for patients with a wild-type TP53 gene. The GCNs and prognostic genes identified in this study will be informative for the prognosis of survival and for selecting a drug target for breast cancer, in particular for basal-like breast cancer. To the best of our knowledge, this is the first attempt to construct GCNs for breast cancer patients with or without mutations in the TP53 gene and to find prognostic genes accordingly.

Cellular and immunometabolic mechanisms of inflammation in depression: Preliminary findings from single cell RNA sequencing and a tribute to Bruce McEwen

Inflammation is associated with symptoms of anhedonia, a core feature of major depression (MD). We have shown that MD patients with high inflammation as measured by plasma C-reactive protein (CRP) and anhedonia display gene signatures of metabolic reprograming (e.g., shift to glycolysis) necessary to sustain cellular immune activation. To gain preliminary insight into the immune cell subsets and transcriptomic signatures that underlie increased inflammation and its relationship with behavior in MD at the single-cell (sc) level, herein we conducted scRNA-Seq on peripheral blood mononuclear cells from a subset of medically-stable, unmedicated MD outpatients. Three MD patients with high CRP (>3 mg/L) before and two weeks after anti-inflammatory challenge with the tumor necrosis factor antagonist infliximab and three patients with low CRP (≤3 mg/L) were studied. Cell clusters were identified using a Single Cell Wizard pipeline, followed by pathway analysis. CD14⁺ and CD16⁺ monocytes were more abundant in MD patients with high CRP and were reduced by 29% and 55% respectively after infliximab treatment. Within CD14⁺ and CD16⁺ monocytes, genes upregulated in high CRP patients were enriched for inflammatory (phagocytosis, complement, leukocyte migration) and immunometabolic (hypoxia-inducible factor [HIF]-1, aerobic glycolysis) pathways. Shifts in CD4⁺ T cell subsets included ∼30% and ∼10% lower abundance of CD4⁺ central memory (T_CM) and naïve cells and ∼50% increase in effector memory-like (T_EM-like) cells in high versus low CRP patients. T_CM cells of high CRP patients displayed downregulation of the oxidative phosphorylation (OXPHOS) pathway, a main energy source in this cell type. Following infliximab, changes in the number of CD14⁺ monocytes and CD4⁺ T_EM-like cells predicted improvements in anhedonia scores (r = 1.0, p < 0.001). In sum, monocytes and CD4⁺ T cells from MD patients with increased inflammation exhibited immunometabolic reprograming in association with symptoms of anhedonia. These findings are the first step toward determining the cellular and molecular immune pathways associated with inflammatory phenotypes in MD, which may lead to novel immunomodulatory treatments of psychiatric illnesses with increased inflammation.

Gene Co-Expression Network Analysis Reveals the Correlation Patterns Among Genes in Different Temperature Stress Adaptation of Manila Clam

The Manila clam (Ruditapes philippinarum) is one of the most important aquaculture species and widely distributed along the coasts of China, Japan, and Korea. Due to its wide distribution, it can tolerate a wide range of temperature. Studying the gene expression profiles of clam gills had found differentially expressed genes (DEGs) and pathway involved in temperature stress tolerance. A systematic study of cellular response to temperature stress may provide insights into the mechanism of acquired tolerance. Here, weighted gene co-expression network analysis (WGCNA) was carried out using RNA-seq data from gill transcriptome in response to high and low temperature stress. There are a total 32 gene modules, of which 18 gene modules were identified as temperature-related modules. Blue module was one significantly correlated with temperature which was associated with cellular metabolism, apoptosis pathway, ER stress, and others.