Conservation and evolution of gene coexpression networks in human and chimpanzee brains

A spectral framework to map QTLs affecting joint differential networks of gene co-expression

Studying the mechanisms underlying the genotype-phenotype association is crucial in genetics. Gene expression studies have deepened our understanding of the genotype  →  expression  →  phenotype mechanisms. However, traditional expression quantitative trait loci (eQTL) methods often overlook the critical role of gene co-expression networks in translating genotype into phenotype. This gap highlights the need for more powerful statistical methods to analyze genotype  →  network  →  phenotype mechanism. Here, we develop a network-based method, called spectral network quantitative trait loci analysis (snQTL), to map quantitative trait loci affecting gene co-expression networks. Our approach tests the association between genotypes and joint differential networks of gene co-expression via a tensor-based spectral statistics, thereby overcoming the ubiquitous multiple testing challenges in existing methods. We demonstrate the effectiveness of snQTL in the analysis of three-spined stickleback (Gasterosteus aculeatus) data. Compared to conventional methods, our method snQTL uncovers chromosomal regions affecting gene co-expression networks, including one strong candidate gene that would have been missed by traditional eQTL analyses. Our framework suggests the limitation of current approaches and offers a powerful network-based tool for functional loci discoveries.

How a territorial challenge changes sex steroid-related gene networks in the female brain: A field experiment with the tree swallow

Territorial competition can stimulate secretion of testosterone (T), which is thought to act on neural circuits of aggression to promote further aggression. Here, we test the hypothesis that competition modulates sex steroid sensitivity and conversion in the brain, focused on the female tree swallow (Tachycineta bicolor). In this bird species, exogenous T enhances female aggression, but social competition for limited nesting territories does not stimulate systemic T elevation. We exposed free-living females to simulated territorial intrusions and sampled five regions of the vertebrate social behavior network (SBN). Using quantitative PCR, we measured mRNA abundance of: androgen receptor, 5-alpha reductase, estrogen receptor alpha, and aromatase. Using standard analyses, we found essentially no treatment effect on mRNA abundance in any one brain area; however, network analyses revealed marked socially-induced changes in gene co-expression across the SBN. After a territorial challenge, gene expression was more positively correlated with T, and genes specific to the androgen-signaling pathway were also more positively correlated with one another. The challenged brain also exhibited stronger negative correlations among genes in the nucleus taeniae, but stronger positive correlations between the lateral septum and bed nucleus of the stria terminalis. Together, these findings suggest that, in response to female-female territorial challenges, T acts on androgen-mediated circuits of aggression, with some divergence in gene regulation in the nucleus taeniae. The post-transcriptional consequences of these shifts require more research, but their existence underscores insights to be gained from analyzing the neuroendocrine properties of the SBN using network-level perspectives.

Ecological Trait Differences Are Associated with Gene Expression in the Primary Visual Cortex of Primates

Primate species differ drastically from most other mammals in how they visually perceive their environments, which is particularly important for foraging, predator avoidance, and detection of social cues.

BACKGROUND/OBJECTIVES

Although it is well established that primates display diversity in color vision and various ecological specializations, it is not understood how visual system characteristics and ecological adaptations may be associated with gene expression levels within the primary visual cortex (V1).

METHODS

We performed RNA-Seq on V1 tissue samples from 28 individuals, representing 13 species of primates, including hominoids, cercopithecoids, and platyrrhines. We explored trait-dependent differential expression (DE) by contrasting species with differing visual system phenotypes and ecological traits.

RESULTS

Between 4-25% of genes were determined to be differentially expressed in primates that varied in type of color vision (trichromatic or polymorphic di/trichromatic), habitat use (arboreal or terrestrial), group size (large or small), and primary diet (frugivorous, folivorous, or omnivorous).

CONCLUSIONS

Interestingly, our DE analyses revealed that humans and chimpanzees showed the most marked differences between any two species, even though they are only separated by 6-8 million years of independent evolution. These results show a combination of species-specific and trait-dependent differences in the evolution of gene expression in the primate visual cortex.

FLEXIBILITY IN GENE COEXPRESSION AT DEVELOPMENTAL AND EVOLUTIONARY TIMESCALES

The explosion of next-generation sequencing technologies has allowed researchers to move from studying single genes, to thousands of genes, and thereby to also consider the relationships within gene networks. Like others, we are interested in understanding how developmental and evolutionary forces shape the expression of individual genes, as well as the interactions among genes. To this end, we characterized the effects of genetic background and developmental environment on brain gene coexpression in two parallel, independent evolutionary lineages of Trinidadian guppies (Poecilia reticulata). We asked whether connectivity patterns among genes differed based on genetic background and rearing environment, and whether a gene's connectivity predicted its propensity for expression divergence. In pursuing these questions, we confronted the central challenge that standard approaches fail to control the Type I error and/or have low power in the presence of high dimensionality (i.e., large number of genes) and small sample size, as in many gene expression studies. Using our data as a case study, we detail central challenges, discuss sample size guidelines, and provide rigorous statistical approaches for exploring coexpression differences with small sample sizes. Using these approaches, we find evidence that coexpression relationships differ based on both genetic background and rearing environment. We report greater expression divergence in less connected genes and suggest this pattern may arise and be reinforced by selection.

Deciphering molecular landscape of breast cancer progression and insights from functional genomics and therapeutic explorations followed by in vitro validation

Breast cancer is caused by aberrant breast cells that proliferate and develop into tumors. Tumors have the potential to spread throughout the body and become lethal if ignored. Metastasis is the process by which invasive tumors move to neighboring lymph nodes or other organs. Metastasis can be lethal and perhaps fatal. The objective of our study was to elucidate the molecular mechanisms underlying the transition of Ductal Carcinoma In Situ (DCIS) to Invasive Ductal Carcinoma (IDC), with a particular focus on hub genes and potential therapeutic agents. Using Weighted Gene Co-expression Network Analysis (WGCNA), we built a comprehensive network combining clinical and phenotypic data from both DCIS and IDC. Modules within this network, correlated with specific phenotypic traits, were identified, and hub genes were identified as critical markers. Receiver Operating Characteristic (ROC) analysis assessed their potential as biomarkers, while survival curve analysis gauged their prognostic value. Furthermore, molecular docking predicted interactions with potential therapeutic agents. Ten hub genes-CDK1, KIF11, NUF2, ASPM, CDCA8, CENPF, DTL, EXO1, KIF2C, and ZWINT-emerged as pivotal fibroblast-specific genes potentially involved in the DCIS to IDC transition. These genes exhibited pronounced positive correlations with key pathways like the cell cycle and DNA repair, Molecular docking revealed Fisetin, an anti-inflammatory compound, effectively binding to both CDK1 and DTL underscoring their role in orchestrating cellular transformation. CDK1 and DTL were selected for molecular docking with CDK1 inhibitors, revealing effective binding of Fisetin, an anti-inflammatory compound, to both. Of the identified hub genes, DTL-an E3 ubiquitin ligase linked to the CRL4 complex-plays a central role in cancer progression, impacting tumor growth, invasion, and metastasis, as well as cell cycle regulation and epithelial-mesenchymal transition (EMT). CDK1, another hub gene, is pivotal in cell cycle progression and associated with various biological processes. In conclusion, our study offers insights into the complex mechanisms driving the transition from DCIS to IDC. It underscores the importance of hub genes and their potential interactions with therapeutic agents, particularly Fisetin. By shedding light on the interplay between CDK1 and DTL expression, our findings contribute to understanding the regulatory landscape of invasive ductal carcinoma and pave the way for future investigations and novel therapeutic avenues.

Gene Expression Shifts in Emperor Penguin Adaptation to the Extreme Antarctic Environment

Gene expression can accelerate ecological divergence by rapidly tweaking the response of an organism to novel environments, with more divergent environments exerting stronger selection and supposedly, requiring faster adaptive responses. Organisms adapted to extreme environments provide ideal systems to test this hypothesis, particularly when compared to related species with milder ecological niches. The Emperor penguin (Aptenodytes forsteri) is the only endothermic vertebrate breeding in the harsh Antarctic winter, in stark contrast with the less cold-adapted sister species, the King penguin (A. patagonicus). Assembling the first de novo transcriptomes and analysing multi-tissue (brain, kidney, liver, muscle, skin) RNA-Seq data from natural populations of both species, we quantified the shifts in tissue-enhanced genes, co-expression gene networks, and differentially expressed genes characterising Emperor penguin adaptation to the extreme Antarctic. Our analyses revealed the crucial role played by muscle and liver in temperature homeostasis, fasting, and whole-body energy metabolism (glucose/insulin regulation, lipid metabolism, fatty acid beta-oxidation, and blood coagulation). Repatterning at the regulatory level appears as more important in the brain of the Emperor penguin, showing the lowest signature of differential gene expression, but the largest co-expression gene network shift. Nevertheless, over-expressed genes related to mTOR signalling in the brain and the liver support their central role in cold and fasting responses. Besides contributing to understanding the genetics underlying complex traits, like body energy reservoir management, our results provide a first insight into the role of gene expression in adaptation to one of the most extreme environmental conditions endured by an endotherm.

A 3D approach to understanding heterogeneity in early developing autisms

BACKGROUND

Phenotypic heterogeneity in early language, intellectual, motor, and adaptive functioning (LIMA) features are amongst the most striking features that distinguish different types of autistic individuals. Yet the current diagnostic criteria uses a single label of autism and implicitly emphasizes what individuals have in common as core social-communicative and restricted repetitive behavior difficulties. Subtype labels based on the non-core LIMA features may help to more meaningfully distinguish types of autisms with differing developmental paths and differential underlying biology.

METHODS

Unsupervised data-driven subtypes were identified using stability-based relative clustering validation on publicly available Mullen Scales of Early Learning (MSEL) and Vineland Adaptive Behavior Scales (VABS) data (n = 615; age = 24-68 months) from the National Institute of Mental Health Data Archive (NDA). Differential developmental trajectories between subtypes were tested on longitudinal data from NDA and from an independent in-house dataset from UCSD. A subset of the UCSD dataset was also tested for subtype differences in functional and structural neuroimaging phenotypes and relationships with blood gene expression. The current subtyping model was also compared to early language outcome subtypes derived from past work.

RESULTS

Two autism subtypes can be identified based on early phenotypic LIMA features. These data-driven subtypes are robust in the population and can be identified in independent data with 98% accuracy. The subtypes can be described as Type I versus Type II autisms differentiated by relatively high versus low scores on LIMA features. These two types of autisms are also distinguished by different developmental trajectories over the first decade of life. Finally, these two types of autisms reveal striking differences in functional and structural neuroimaging phenotypes and their relationships with gene expression and may highlight unique biological mechanisms.

LIMITATIONS

Sample sizes for the neuroimaging and gene expression dataset are relatively small and require further independent replication. The current work is also limited to subtyping based on MSEL and VABS phenotypic measures.

CONCLUSIONS

This work emphasizes the potential importance of stratifying autism by a Type I versus Type II distinction focused on LIMA features and which may be of high prognostic and biological significance.

De novo FRMD5 Missense Variants in Patients with Childhood-Onset Ataxia, Prominent Nystagmus, and Seizures

BACKGROUND

FRMD5 variants were recently identified in patients with developmental delay, ataxia, and eye movement abnormalities.

OBJECTIVES

We describe 2 patients presenting with childhood-onset ataxia, nystagmus, and seizures carrying pathogenic de novo FRMD5 variants. Weighted gene co-expression network analysis (WGCNA) was performed to gain insights into the function of FRMD5 in the brain.

METHODS

Trio-based whole-exome sequencing was performed in both patients, and CoExp web tool was used to conduct WGCNA.

RESULTS

Both patients presented with developmental delay, childhood-onset ataxia, nystagmus, and seizures. Previously unreported findings were diffuse choreoathetosis and dystonia of the hands (patient 1) and areas of abnormal magnetic resonance imaging signal in the white matter (patient 2). WGCNA showed that FRMD5 belongs to gene networks involved in neurodevelopment and oligodendrocyte function.

CONCLUSIONS

We expanded the phenotype of FRMD5-related disease and shed light on its role in brain function and development. We recommend including FRMD5 in the genetic workup of childhood-onset ataxia and nystagmus. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Single-cell spatial transcriptomics reveals a dystrophic trajectory following a developmental bifurcation of myoblast cell fates in facioscapulohumeral muscular dystrophy

Facioscapulohumeral muscular dystrophy (FSHD) is linked to abnormal derepression of the transcription activator DUX4. This effect is localized to a low percentage of cells, requiring single-cell analysis. However, single-cell/nucleus RNA-seq cannot fully capture the transcriptome of multinucleated large myotubes. To circumvent these issues, we use multiplexed error-robust fluorescent in situ hybridization (MERFISH) spatial transcriptomics that allows profiling of RNA transcripts at a subcellular resolution. We simultaneously examined spatial distributions of 140 genes, including 24 direct DUX4 targets, in in vitro differentiated myotubes and unfused mononuclear cells (MNCs) of control, isogenic D4Z4 contraction mutant and FSHD patient samples, as well as the individual nuclei within them. We find myocyte nuclei segregate into two clusters defined by the expression of DUX4 target genes, which is exclusively found in patient/mutant nuclei, whereas MNCs cluster based on developmental states. Patient/mutant myotubes are found in "FSHD-hi" and "FSHD-lo" states with the former signified by high DUX4 target expression and decreased muscle gene expression. Pseudotime analyses reveal a clear bifurcation of myoblast differentiation into control and FSHD-hi myotube branches, with variable numbers of DUX4 target-expressing nuclei found in multinucleated FSHD-hi myotubes. Gene coexpression modules related to extracellular matrix and stress gene ontologies are significantly altered in patient/mutant myotubes compared with the control. We also identify distinct subpathways within the DUX4 gene network that may differentially contribute to the disease transcriptomic phenotype. Taken together, our MERFISH-based study provides effective gene network profiling of multinucleated cells and identifies FSHD-induced transcriptomic alterations during myoblast differentiation.

A molecular and cellular perspective on human brain evolution and tempo

The evolution of the modern human brain was accompanied by distinct molecular and cellular specializations, which underpin our diverse cognitive abilities but also increase our susceptibility to neurological diseases. These features, some specific to humans and others shared with related species, manifest during different stages of brain development. In this multi-stage process, neural stem cells proliferate to produce a large and diverse progenitor pool, giving rise to excitatory or inhibitory neurons that integrate into circuits during further maturation. This process unfolds over varying time scales across species and has progressively become slower in the human lineage, with differences in tempo correlating with differences in brain size, cell number and diversity, and connectivity. Here we introduce the terms 'bradychrony' and 'tachycrony' to describe slowed and accelerated developmental tempos, respectively. We review how recent technical advances across disciplines, including advanced engineering of in vitro models, functional comparative genetics and high-throughput single-cell profiling, are leading to a deeper understanding of how specializations of the human brain arise during bradychronic neurodevelopment. Emerging insights point to a central role for genetics, gene-regulatory networks, cellular innovations and developmental tempo, which together contribute to the establishment of human specializations during various stages of neurodevelopment and at different points in evolution.

A 3D approach to understanding heterogeneity in early developing autisms

Phenotypic heterogeneity in early language, intellectual, motor, and adaptive functioning (LIMA) features are amongst the most striking features that distinguish different types of autistic individuals. Yet the current diagnostic criteria uses a single label of autism and implicitly emphasizes what individuals have in common as core social-communicative and restricted repetitive behavior difficulties. Subtype labels based on the non-core LIMA features may help to more meaningfully distinguish types of autisms with differing developmental paths and differential underlying biology. Using relatively large (n=615) publicly available data from early developing (24-68 months) standardized clinical tests tapping LIMA features, we show that stability-based relative cluster validation analysis can identify two robust and replicable clusters in the autism population with high levels of generalization accuracy (98%). These clusters can be described as Type I versus Type II autisms differentiated by relatively high versus low scores on LIMA features. These two types of autisms are also distinguished by different developmental trajectories over the first decade of life. Finally, these two types of autisms reveal striking differences in functional and structural neuroimaging phenotypes and their relationships with gene expression. This work emphasizes the potential importance of stratifying autism by a Type I versus Type II distinction focused on LIMA features and which may be of high prognostic and biological significance.

Dissecting gene expression networks in the developing hippocampus through the lens of NEIL3 depletion

Gene regulation in the hippocampus is fundamental for its development, synaptic plasticity, memory formation, and adaptability. Comparisons of gene expression among different developmental stages, distinct cell types, and specific experimental conditions have identified differentially expressed genes contributing to the organization and functionality of hippocampal circuits. The NEIL3 DNA glycosylase, one of the DNA repair enzymes, plays an important role in hippocampal maturation and neuron functionality by shaping transcription. While differential gene expression (DGE) analysis has identified key genes involved, broader gene expression patterns crucial for high-order hippocampal functions remain uncharted. By utilizing the weighted gene co-expression network analysis (WGCNA), we mapped gene expression networks in immature (p8-neonatal) and mature (3 m-adult) hippocampal circuits in wild-type and NEIL3-deficient mice. Our study unveiled intricate gene network structures underlying hippocampal maturation, delineated modules of co-expressed genes, and pinpointed highly interconnected hub genes specific to the maturity of hippocampal subregions. We investigated variations within distinct gene network modules following NEIL3 depletion, uncovering NEIL3-targeted hub genes that influence module connectivity and specificity. By integrating WGCNA with DGE, we delve deeper into the NEIL3-dependent molecular intricacies of hippocampal maturation. This study provides a comprehensive systems-level analysis for assessing the potential correlation between gene connectivity and functional connectivity within the hippocampal network, thus shaping hippocampal function throughout development.

A spectral framework to map QTLs affecting joint differential networks of gene co-expression

Studying the mechanisms underlying the genotype-phenotype association is crucial in genetics. Gene expression studies have deepened our understanding of the genotype → expression → phenotype mechanisms. However, traditional expression quantitative trait loci (eQTL) methods often overlook the critical role of gene co-expression networks in translating genotype into phenotype. This gap highlights the need for more powerful statistical methods to analyze genotype → network → phenotype mechanism. Here, we develop a network-based method, called snQTL, to map quantitative trait loci affecting gene co-expression networks. Our approach tests the association between genotypes and joint differential networks of gene co-expression via a tensor-based spectral statistics, thereby overcoming the ubiquitous multiple testing challenges in existing methods. We demonstrate the effectiveness of snQTL in the analysis of three-spined stickleback (Gasterosteus aculeatus) data. Compared to conventional methods, our method snQTL uncovers chromosomal regions affecting gene co-expression networks, including one strong candidate gene that would have been missed by traditional eQTL analyses. Our framework suggests the limitation of current approaches and offers a powerful network-based tool for functional loci discoveries.

Glioblastoma evolution and heterogeneity from a 3D whole-tumor perspective

Treatment failure for the lethal brain tumor glioblastoma (GBM) is attributed to intratumoral heterogeneity and tumor evolution. We utilized 3D neuronavigation during surgical resection to acquire samples representing the whole tumor mapped by 3D spatial coordinates. Integrative tissue and single-cell analysis revealed sources of genomic, epigenomic, and microenvironmental intratumoral heterogeneity and their spatial patterning. By distinguishing tumor-wide molecular features from those with regional specificity, we inferred GBM evolutionary trajectories from neurodevelopmental lineage origins and initiating events such as chromothripsis to emergence of genetic subclones and spatially restricted activation of differential tumor and microenvironmental programs in the core, periphery, and contrast-enhancing regions. Our work depicts GBM evolution and heterogeneity from a 3D whole-tumor perspective, highlights potential therapeutic targets that might circumvent heterogeneity-related failures, and establishes an interactive platform enabling 360° visualization and analysis of 3D spatial patterns for user-selected genes, programs, and other features across whole GBM tumors.

Astrocytes Drive Divergent Metabolic Gene Expression in Humans and Chimpanzees

The human brain utilizes ∼20% of all of the body's metabolic resources, while chimpanzee brains use <10%. Although previous work shows significant differences in metabolic gene expression between the brains of primates, we have yet to fully resolve the contribution of distinct brain cell types. To investigate cell type-specific interspecies differences in brain gene expression, we conducted RNA-seq on neural progenitor cells, neurons, and astrocytes generated from induced pluripotent stem cells from humans and chimpanzees. Interspecies differential expression analyses revealed that twice as many genes exhibit differential expression in astrocytes (12.2% of all genes expressed) than neurons (5.8%). Pathway enrichment analyses determined that astrocytes, rather than neurons, diverged in expression of glucose and lactate transmembrane transport, as well as pyruvate processing and oxidative phosphorylation. These findings suggest that astrocytes may have contributed significantly to the evolution of greater brain glucose metabolism with proximity to humans.

Interactome Analysis of Visceral Adipose Tissue Elucidates Gene Regulatory Networks and Novel Gene Candidates in Obesity

Objective

Visceral adiposity is associated with increased proinflammatory activity, insulin resistance, diabetes risk and mortality rate. Numerous individual genes have been associated with obesity, but studies investigating gene-regulatory networks in human visceral obesity are lacking.

Methods

We analyzed gene-regulatory networks in human visceral adipose tissue (VAT) from 48 obese and 11 non-obese Chinese subjects using gene co-expression and network construction with RNA-sequencing data. We also conducted RNA interference-based tests on selected genes for adipocyte differentiation effects.

Results

A scale-free gene co-expression network was constructed from 360 differentially expressed genes between obese and non-obese VAT (absolute log fold-change >1, FDR<0.05) with edge probability >0.8. Gene regulatory network analysis identified candidate transcription factors associated with differentially expressed genes. Fifteen subnetworks (communities) displayed altered connectivity patterns between obese and non-obese networks. Genes in pro-inflammatory pathways showed increased network connectivities in obese VAT whereas the oxidative phosphorylation pathway displayed reduced connections (enrichment FDR<0.05). Functional screening via RNA interference identified SOX30 and OSBPL3 as potential network-derived gene candidates influencing adipocyte differentiation.

Conclusions

This interactome-based approach highlights the network architecture, identifies novel candidate genes, and leads to new hypotheses regarding network-assisted gene regulation in obese vs. non-obese VAT.What is already known about this subject?: Visceral adipose tissue (VAT) is associated with increased levels of proinflammatory activity, insulin resistance, diabetes risk and mortality rate.Gene expression studies have identified candidate genes associated with proinflammatory function in VAT.What are the new findings in your manuscript?: Using integrative network-science, we identified co-expression and gene regulatory networks that are differentially regulated in VAT samples from subjects with and without obesityWe used functional testing (adipocyte differentiation) to validate a subset of novel candidate genes with minimal prior reported associations to obesityHow might your results change the direction of research or the focus of clinical practice: Network biology-based investigation provides a new avenue to our understanding of gene function in visceral adiposityFunctional validation screen allows for the identification of novel gene candidates that may be targeted for the treatment of adipose tissue dysfunction in obesity.

Therapeutic effect of microRNA-21 on differentially expressed hub genes in gastric cancer based on systems biology

Gastric cancer (GC) is a leading cause of mortality for many people. Cancer's initiating factors are poorly understood. miR-21 has a crucial function in several malignancies, particularly GC. Furthermore, it has been shown that miR-21 is critical for the emergence and advancement of GC. This work intends to identify new genes which expression is associated with the activity of mir-21 in GC and to investigate the effect of downregulation of mir-21 on these genes and gastric tumorigenesis. We utilized the gene expression profiles of GCs from an Array database (GSE13911) from the Gene Expression Omnibus (GEO) dataset to find differentially expressed genes (DEGs) between control and gastric cancer groups. Using weighted gene correlation network analysis (WGCNA) in R, the Gene co-expression network was reconstructed. The microRNA-mRNA network was then reconstructed using the miRWalk database, and by investigating the microRNA-mRNA network, the genes that have an association with mir-21 were found. To implement the functional investigation, MKN and AGS cell lines were transfected with anti-miR-21 next. Subsequently, MTT proliferation was utilized to assess the cell's vitality. qRT-PCR was then used to evaluate the anticipated levels of gene expression in both GC cell lines. This study discovered and predicted CCL28, NR3C2, and SNYPO2 as the targets of miR-21 (GC), which are downregulated through gastric tumorigenesis, showing great potential as therapeutic and diagnostic targets. The suppression of miR-21 in gastric GC cells led to the inhibition of cell proliferation and decreased expression of CCL28, NR3C2, and SNYPO2 genes. This study established that miR-21, via downregulating these genes, contributes significantly to the development of GC. In addition, systems biology techniques identified CCL28, NR3C2, and SNYPO2 genes as possible GC surveillance and therapy components.

Novel insights into molecular signatures and pathogenic cell populations shared by systemic lupus erythematosus and vascular dementia

Although vascular dementia (VD) and systemic lupus erythematosus (SLE) may share immune-mediated pathophysiologic processes, the underlying mechanisms are unclear. This study investigated shared gene signatures in SLE versus VD, as well as their potential molecular mechanisms. Bulk RNA sequencing (RNAseq) and single-cell or single-nucleus RNAseq (sc/snRNAseq) datasets from SLE blood samples and VD brain samples were obtained from Gene Expression Omnibus. The identification of genes associated with both SLE and VD was performed using the weighted gene co-expression network analysis (WGCNA) and machine learning algorithms. For the sc/snRNAseq data, an unbiased clustering pipeline based on Seurat and CellChat was used to determine the cellular landscape profile and examine intracellular communication, respectively. The results were subsequently validated using a mice model of SLE with cognitive dysfunction (female MRL/lpr mice). WGCNA and machine learning identified C1QA, LY96, CD163, and MS4A4A as key genes for SLE and VD. sc/snRNAseq analyses revealed that CD163 and MS4A4A were upregulated in mononuclear phagocytes (MPs) from SLE and VD samples and were associated with monocyte-macrophage differentiation. Intriguingly, LGALS9-associated molecular pathway, as the only signaling pathway common between SLE and VD via CellChat analysis, exhibited significant upregulation in cortical microglia of MRL/lpr mice. Our analyses identified C1QA, LY96, CD163, and MS4A4A as potential biomarkers for SLE and VD. Moreover, the upregulation of CD163/MS4A4A and activation of LGALS9 signaling in MPs may contribute to the pathogenesis of VD with SLE. These findings offer novel insight into the mechanisms underlying VD in SLE patients.

Comparative single-cell transcriptomic analysis of primate brains highlights human-specific regulatory evolution

Enhanced cognitive function in humans is hypothesized to result from cortical expansion and increased cellular diversity. However, the mechanisms that drive these phenotypic innovations remain poorly understood, in part because of the lack of high-quality cellular resolution data in human and non-human primates. Here, we take advantage of single-cell expression data from the middle temporal gyrus of five primates (human, chimp, gorilla, macaque and marmoset) to identify 57 homologous cell types and generate cell type-specific gene co-expression networks for comparative analysis. Although orthologue expression patterns are generally well conserved, we find 24% of genes with extensive differences between human and non-human primates (3,383 out of 14,131), which are also associated with multiple brain disorders. To assess the functional significance of gene expression differences in an evolutionary context, we evaluate changes in network connectivity across meta-analytic co-expression networks from 19 animals. We find that a subset of these genes has deeply conserved co-expression across all non-human animals, and strongly divergent co-expression relationships in humans (139 out of 3,383, <1% of primate orthologues). Genes with human-specific cellular expression and co-expression profiles (such as NHEJ1, GTF2H2, C2 and BBS5) typically evolve under relaxed selective constraints and may drive rapid evolutionary change in brain function.

GeCoNet-Tool: a software package for gene co-expression network construction and analysis

BACKGROUND

Network analysis is a powerful tool for studying gene regulation and identifying biological processes associated with gene function. However, constructing gene co-expression networks can be a challenging task, particularly when dealing with a large number of missing values.

RESULTS

We introduce GeCoNet-Tool, an integrated gene co-expression network construction and analysis tool. The tool comprises two main parts: network construction and network analysis. In the network construction part, GeCoNet-Tool offers users various options for processing gene co-expression data derived from diverse technologies. The output of the tool is an edge list with the option of weights associated with each link. In network analysis part, the user can produce a table that includes several network properties such as communities, cores, and centrality measures. With GeCoNet-Tool, users can explore and gain insights into the complex interactions between genes.

Insight on the hub gene associated signatures and potential therapeutic agents in epilepsy and glioma

OBJECTIVE

The relationship between epilepsy and glioma has long been widely recognized, but the mechanisms of interaction remain unclear. This study aimed to investigate the shared genetic signature and treatment strategies between epilepsy and glioma.

METHODS

We subjected hippocampal tissue samples from patients with epilepsy and glioma to transcriptomic analysis to identify differential genes and associated pathways, respectively. Weight gene co-expression network (WGCNA) analysis was performed to identify conserved modules in epilepsy and glioma and to obtain differentially expressed conserved genes. Prognostic and diagnostic models were built using lasso regression. We also focused on building transcription factor-gene interaction networks and assessing the proportion of immune invading cells in epilepsy patients. Finally, drug compounds were inferred using a drug signature database (DSigDB) based on core targets.

RESULTS

We discovered 88 differently conserved genes, most of which are involved in synaptic signaling and calcium ion pathways. We used lasso regression model to reduce 88 characteristic genes, and finally screened out 14 genes (EIF4A2, CEP170B, SNPH, EPHA4, KLK7, GNG3, MYOP, ANKRD29, RASD2, PRRT3, EFR3A, SGIP1, RAB6B, CNNM1) as the features of glioma prognosis model whose ROC curve is 0.9. Then, we developed a diagnosis model for epilepsy patients using 8 genes (PRRT3, RASD2, MYPOP, CNNM1, ANKRD29, GNG3, SGIP1, KLK7) with area under ROC curve (AUC) values near 1. According to the ssGSEA method, we observed an increase in activated B cells, eosinophils, follicular helper T cells and type 2T helper cells, and a decrease in monocytes in patients with epilepsy. Notably, the great majority of these immune cells showed a negative correlation with hub genes. To reveal the transcriptional-level regulation mechanism, we also built a TF-gene network. In addition, we discovered that patients with glioma-related epilepsy may benefit more from gabapentin and pregabalin.

CONCLUSION

This study reveals the modular conserved phenotypes of epilepsy and glioma and constructs effective diagnostic and prognostic markers. It provides new biological targets and ideas for the early diagnosis and effective treatment of epilepsy.

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.

Morphometric dis-similarity between cortical and subcortical areas underlies cognitive function and psychiatric symptomatology: a preadolescence study from ABCD

Preadolescence is a critical period characterized by dramatic morphological changes and accelerated cortico-subcortical development. Moreover, the coordinated development of cortical and subcortical regions underlies the emerging cognitive functions during this period. Deviations in this maturational coordination may underlie various psychiatric disorders that begin during preadolescence, but to date these deviations remain largely uncharted. We constructed a comprehensive whole-brain morphometric similarity network (MSN) from 17 neuroimaging modalities in a large preadolescence sample (N = 8908) from Adolescent Brain Cognitive Development (ABCD) study and investigated its association with 10 cognitive subscales and 27 psychiatric subscales or diagnoses. Based on the MSNs, each brain was clustered into five modules with distinct cytoarchitecture and evolutionary relevance. While morphometric correlation was positive within modules, it was negative between modules, especially between isocortical and paralimbic/subcortical modules; this developmental dissimilarity was genetically linked to synapse and neurogenesis. The cortico-subcortical dissimilarity becomes more pronounced longitudinally in healthy children, reflecting developmental differentiation of segregated cytoarchitectonic areas. Higher cortico-subcortical dissimilarity (between the isocortical and paralimbic/subcortical modules) were related to better cognitive performance. In comparison, children with poor modular differentiation between cortex and subcortex displayed higher burden of externalizing and internalizing symptoms. These results highlighted cortical-subcortical morphometric dissimilarity as a dynamic maturational marker of cognitive and psychiatric status during the preadolescent stage and provided insights into brain development.

Cross-species cell-type assignment from single-cell RNA-seq data by a heterogeneous graph neural network

Cross-species comparative analyses of single-cell RNA sequencing (scRNA-seq) data allow us to explore, at single-cell resolution, the origins of the cellular diversity and evolutionary mechanisms that shape cellular form and function. Cell-type assignment is a crucial step to achieve that. However, the poorly annotated genome and limited known biomarkers hinder us from assigning cell identities for nonmodel species. Here, we design a heterogeneous graph neural network model, CAME, to learn aligned and interpretable cell and gene embeddings for cross-species cell-type assignment and gene module extraction from scRNA-seq data. CAME achieves significant improvements in cell-type characterization across distant species owing to the utilization of non-one-to-one homologous gene mapping ignored by early methods. Our large-scale benchmarking study shows that CAME significantly outperforms five classical methods in terms of cell-type assignment and model robustness to insufficiency and inconsistency of sequencing depths. CAME can transfer the major cell types and interneuron subtypes of human brains to mouse and discover shared cell-type-specific functions in homologous gene modules. CAME can align the trajectories of human and macaque spermatogenesis and reveal their conservative expression dynamics. In short, CAME can make accurate cross-species cell-type assignments even for nonmodel species and uncover shared and divergent characteristics between two species from scRNA-seq data.

Feature learning and network structure from noisy node activity data

In the studies of network structures, much attention has been devoted to developing approaches to reconstruct networks and predict missing links when edge-related information is given. However, such approaches are not applicable when we are only given noisy node activity data with missing values. This work presents an unsupervised learning framework to learn node vectors and construct networks from such node activity data. First, we design a scheme to generate random node sequences from node context sets, which are generated from node activity data. Then, a three-layer neural network is adopted training the node sequences to obtain node vectors, which allow us to construct networks and capture nodes with synergistic roles. Furthermore, we present an entropy-based approach to select the most meaningful neighbors for each node in the resulting network. Finally, the effectiveness of the method is validated through both synthetic and real data.

Selection-driven adaptation to the extreme Antarctic environment in the Emperor penguin

The eco-evolutionary history of penguins is characterised by shifting from temperate to cold environments. Breeding in Antarctica, the Emperor penguin appears as an extreme outcome of this process, with unique features related to insulation, heat production and energy management. However, whether this species actually diverged from a less cold-adapted ancestor, more ecologically similar to its sister species, the King penguin, is still an open question. As the Antarctic colonisation likely resulted in vast changes in selective pressure experienced by the Emperor penguin, the relative quantification of the genomic signatures of selection, unique to each sister species, could answer this question. Applying phylogeny-based selection tests on 7651 orthologous genes, we identified a more pervasive selection shift in the Emperor penguin than in the King penguin, supporting the hypothesis that its extreme cold adaptation is a derived state. Furthermore, among candidate genes under selection, four (TRPM8, LEPR, CRB1, and SFI1) were identified before in other cold-adapted homeotherms, like the woolly Mammoth, while other 161 genes can be assigned to biological functions relevant to cold adaptation identified in previous studies. Location and structural effects of TRPM8 substitutions in Emperor and King penguin lineages support their functional role with putative divergent effects on thermal adaptation. We conclude that extreme cold adaptation in the Emperor penguin largely involved unique genetic options which, however, affect metabolic and physiological traits common to other cold-adapted homeotherms.

Functional orderly topography of brain networks associated with gene expression heterogeneity

The human cerebral cortex is vastly expanded relative to nonhuman primates and rodents, leading to a functional orderly topography of brain networks. Here, we show that functional topography may be associated with gene expression heterogeneity. The neocortex exhibits greater heterogeneity in gene expression, with a lower expression of housekeeping genes, a longer mean path length, fewer clusters, and a lower degree of ordering in networks than archicortical and subcortical areas in human, rhesus macaque, and mouse brains. In particular, the cerebellar cortex displays greater heterogeneity in gene expression than cerebellar deep nuclei in the human brain, but not in the mouse brain, corresponding to the emergence of novel functions in the human cerebellar cortex. Moreover, the cortical areas with greater heterogeneity, primarily located in the multimodal association cortex, tend to express genes with higher evolutionary rates and exhibit a higher degree of functional connectivity measured by resting-state fMRI, implying that such a spatial distribution of gene expression may be shaped by evolution and is favourable for the specialization of higher cognitive functions. Together, the cross-species imaging and genetic findings may provide convergent evidence to support the association between the orderly topography of brain function networks and gene expression.

Comparative analysis of human, macaque and mouse function and genetic heterogeneity in the brain reveals links between gene expression and orderly topography of functional networks.

Longitudinal multi-omics analysis identifies early blood-based predictors of anti-TNF therapy response in inflammatory bowel disease

Background and aims

Treatment with tumor necrosis factor α (TNFα) antagonists in IBD patients suffers from primary non-response rates of up to 40%. Biomarkers for early prediction of therapy success are missing. We investigated the dynamics of gene expression and DNA methylation in blood samples of IBD patients treated with the TNF antagonist infliximab and analyzed the predictive potential regarding therapy outcome.

Methods

We performed a longitudinal, blood-based multi-omics study in two prospective IBD patient cohorts receiving first-time infliximab therapy (discovery: 14 patients, replication: 23 patients). Samples were collected at up to 7 time points (from baseline to 14 weeks after therapy induction). RNA-sequencing and genome-wide DNA methylation data were analyzed and correlated with clinical remission at week 14 as a primary endpoint.

Results

We found no consistent ex ante predictive signature across the two cohorts. Longitudinally upregulated transcripts in the non-remitter group comprised TH2- and eosinophil-related genes including ALOX15, FCER1A, and OLIG2. Network construction identified transcript modules that were coherently expressed at baseline and in non-remitting patients but were disrupted at early time points in remitting patients. These modules reflected processes such as interferon signaling, erythropoiesis, and platelet aggregation. DNA methylation analysis identified remission-specific temporal changes, which partially overlapped with transcriptomic signals. Machine learning approaches identified features from differentially expressed genes cis-linked to DNA methylation changes at week 2 as a robust predictor of therapy outcome at week 14, which was validated in a publicly available dataset of 20 infliximab-treated CD patients.

Conclusions

Integrative multi-omics analysis reveals early shifts of gene expression and DNA methylation as predictors for efficient response to anti-TNF treatment. Lack of such signatures might be used to identify patients with IBD unlikely to benefit from TNF antagonists at an early time point.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-022-01112-z.

Comparative transcriptome in large-scale human and cattle populations

BACKGROUND

Cross-species comparison of transcriptomes is important for elucidating evolutionary molecular mechanisms underpinning phenotypic variation between and within species, yet to date it has been essentially limited to model organisms with relatively small sample sizes.

RESULTS

Here, we systematically analyze and compare 10,830 and 4866 publicly available RNA-seq samples in humans and cattle, respectively, representing 20 common tissues. Focusing on 17,315 orthologous genes, we demonstrate that mean/median gene expression, inter-individual variation of expression, expression quantitative trait loci, and gene co-expression networks are generally conserved between humans and cattle. By examining large-scale genome-wide association studies for 46 human traits (average n = 327,973) and 45 cattle traits (average n = 24,635), we reveal that the heritability of complex traits in both species is significantly more enriched in transcriptionally conserved than diverged genes across tissues.

CONCLUSIONS

In summary, our study provides a comprehensive comparison of transcriptomes between humans and cattle, which might help decipher the genetic and evolutionary basis of complex traits in both species.

Wnt Signaling Interactor WTIP (Wilms Tumor Interacting Protein) Underlies Novel Mechanism for Cardiac Hypertrophy

BACKGROUND

The study of hypertrophic cardiomyopathy (HCM) can yield insight into the mechanisms underlying the complex trait of cardiac hypertrophy. To date, most genetic variants associated with HCM have been found in sarcomeric genes. Here, we describe a novel HCM-associated variant in the noncanonical Wnt signaling interactor WTIP (Wilms tumor interacting protein) and provide evidence of a role for WTIP in complex disease.

METHODS

In a family affected by HCM, we used exome sequencing and identity-by-descent analysis to identify a novel variant in WTIP (p.Y233F). We knocked down WTIP in isolated neonatal rat ventricular myocytes with lentivirally delivered short hairpin ribonucleic acids and in Danio rerio via morpholino injection. We performed weighted gene coexpression network analysis for WTIP in human cardiac tissue, as well as association analysis for WTIP variation and left ventricular hypertrophy. Finally, we generated induced pluripotent stem cell-derived cardiomyocytes from patient tissue, characterized size and calcium cycling, and determined the effect of verapamil treatment on calcium dynamics.

RESULTS

WTIP knockdown caused hypertrophy in neonatal rat ventricular myocytes and increased cardiac hypertrophy, peak calcium, and resting calcium in D rerio. Network analysis of human cardiac tissue indicated WTIP as a central coordinator of prohypertrophic networks, while common variation at the WTIP locus was associated with human left ventricular hypertrophy. Patient-derived WTIP p.Y233F-induced pluripotent stem cell-derived cardiomyocytes recapitulated cellular hypertrophy and increased resting calcium, which was ameliorated by verapamil.

CONCLUSIONS

We demonstrate that a novel genetic variant found in a family with HCM disrupts binding to a known Wnt signaling protein, misregulating cardiomyocyte calcium dynamics. Further, in orthogonal model systems, we show that expression of the gene WTIP is important in complex cardiac hypertrophy phenotypes. These findings, derived from the observation of a rare Mendelian disease variant, uncover a novel disease mechanism with implications across diverse forms of cardiac hypertrophy.

Deeper insights into long-term survival heterogeneity of pancreatic ductal adenocarcinoma (PDAC) patients using integrative individual- and group-level transcriptome network analyses

Pancreatic ductal adenocarcinoma (PDAC) is categorized as the leading cause of cancer mortality worldwide. However, its predictive markers for long-term survival are not well known. It is interesting to delineate individual-specific perturbed genes when comparing long-term (LT) and short-term (ST) PDAC survivors and integrate individual- and group-based transcriptome profiling. Using a discovery cohort of 19 PDAC patients from CHU-Liège (Belgium), we first performed differential gene expression analysis comparing LT to ST survivor. Second, we adopted systems biology approaches to obtain clinically relevant gene modules. Third, we created individual-specific perturbation profiles. Furthermore, we used Degree-Aware disease gene prioritizing (DADA) method to develop PDAC disease modules; Network-based Integration of Multi-omics Data (NetICS) to integrate group-based and individual-specific perturbed genes in relation to PDAC LT survival. We identified 173 differentially expressed genes (DEGs) in ST and LT survivors and five modules (including 38 DEGs) showing associations to clinical traits. Validation of DEGs in the molecular lab suggested a role of REG4 and TSPAN8 in PDAC survival. Via NetICS and DADA, we identified various known oncogenes such as CUL1 and TGFB1. Our proposed analytic workflow shows the advantages of combining clinical and omics data as well as individual- and group-level transcriptome profiling.

Integrated Drug Mining Reveals Actionable Strategies Inhibiting Plexiform Neurofibromas

Neurofibromatosis Type 1 (NF1) is one of the most common genetic tumor predisposition syndromes, affecting up to 1 in 2500 individuals. Up to half of patients with NF1 develop benign nerve sheath tumors called plexiform neurofibromas (PNs), characterized by biallelic NF1 loss. PNs can grow to immense sizes, cause extensive morbidity, and harbor a 15% lifetime risk of malignant transformation. Increasingly, molecular sequencing and drug screening data from various preclinical murine and human PN cell lines, murine models, and human PN tissues are available to help identify salient treatments for PNs. Despite this, Selumetinib, a MEK inhibitor, is the only currently FDA-approved pharmacotherapy for symptomatic and inoperable PNs in pediatric NF1 patients. The discovery of alternative and additional treatments has been hampered by the rarity of the disease, which makes prioritizing drugs to be tested in future clinical trials immensely important. Here, we propose a gene regulatory network-based integrated analysis to mine high-throughput cell line-based drug data combined with transcriptomes from resected human PN tumors. Conserved network modules were characterized and served as drug fingerprints reflecting the biological connections among drug effects and the inherent properties of PN cell lines and tissue. Drug candidates were ranked, and the therapeutic potential of drug combinations was evaluated via computational predication. Auspicious therapeutic agents and drug combinations were proposed for further investigation in preclinical and clinical trials.

An Integrated Multi-Omic Network Analysis Identifies Seizure-Associated Dysregulated Pathways in the GAERS Model of Absence Epilepsy

Absence epilepsy syndromes are part of the genetic generalized epilepsies, the pathogenesis of which remains poorly understood, although a polygenic architecture is presumed. Current focus on single molecule or gene identification to elucidate epileptogenic drivers is unable to fully capture the complex dysfunctional interactions occurring at a genetic/proteomic/metabolomic level. Here, we employ a multi-omic, network-based approach to characterize the molecular signature associated with absence epilepsy-like phenotype seen in a well validated rat model of genetic generalized epilepsy with absence seizures. Electroencephalographic and behavioral data was collected from Genetic Absence Epilepsy Rats from Strasbourg (GAERS, n = 6) and non-epileptic controls (NEC, n = 6), followed by proteomic and metabolomic profiling of the cortical and thalamic tissue of rats from both groups. The general framework of weighted correlation network analysis (WGCNA) was used to identify groups of highly correlated proteins and metabolites, which were then functionally annotated through joint pathway enrichment analysis. In both brain regions a large protein-metabolite module was found to be highly associated with the GAERS strain, absence seizures and associated anxiety and depressive-like phenotype. Quantitative pathway analysis indicated enrichment in oxidative pathways and a downregulation of the lysine degradation pathway in both brain regions. GSTM1 and ALDH2 were identified as central regulatory hubs of the seizure-associated module in the somatosensory cortex and thalamus, respectively. These enzymes are involved in lysine degradation and play important roles in maintaining oxidative balance. We conclude that the dysregulated pathways identified in the seizure-associated module may be involved in the aetiology and maintenance of absence seizure activity. This dysregulated activity could potentially be modulated by targeting one or both central regulatory hubs.

CBNplot: Bayesian network plots for enrichment analysis

SUMMARY

When investigating gene expression profiles, determining important directed edges between genes can provide valuable insights in addition to identifying differentially expressed genes. In the subsequent functional enrichment analysis (EA), understanding how enriched pathways or genes in the pathway interact with one another can help infer the gene regulatory network (GRN), important for studying the underlying molecular mechanisms. However, packages for easy inference of the GRN based on EA are scarce. Here, we developed an R package, CBNplot, which infers the Bayesian network (BN) from gene expression data, explicitly utilizing EA results obtained from curated biological pathway databases. The core features include convenient wrapping for structure learning, visualization of the BN from EA results, comparison with reference networks, and reflection of gene-related information on the plot. As an example, we demonstrate the analysis of bladder cancer-related datasets using CBNplot, including probabilistic reasoning, which is a unique aspect of BN analysis. We display the transformability of results obtained from one dataset to another, the validity of the analysis as assessed using established knowledge and literature, and the possibility of facilitating knowledge discovery from gene expression datasets.

AVAILABILITY AND IMPLEMENTATION

The library, documentation and web server are available at https://github.com/noriakis/CBNplot.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Identification of Key Modules and Genes Associated with Major Depressive Disorder in Adolescents

Major depressive disorder (MDD) is a leading cause of disability worldwide. Adolescence is a crucial period for the occurrence and development of depression. There are essential distinctions between adolescent and adult depression patients, and the etiology of depressive disorder is unclear. The interactions of multiple genes in a co-expression network are likely to be involved in the physiopathology of MDD. In the present study, RNA-Seq data of mRNA were acquired from the peripheral blood of MDD in adolescents and healthy control (HC) subjects. Co-expression modules were constructed via weighted gene co-expression network analysis (WGCNA) to investigate the relationships between the underlying modules and MDD in adolescents. In the combined MDD and HC groups, the dynamic tree cutting method was utilized to assign genes to modules through hierarchical clustering. Moreover, functional enrichment analysis was conducted on those co-expression genes from interested modules. The results showed that eight modules were constructed by WGCNA. The blue module was significantly associated with MDD after multiple comparison adjustment. Several Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways associated with stress and inflammation were identified in this module, including histone methylation, apoptosis, NF-kappa β signaling pathway, and TNF signaling pathway. Five genes related to inflammation, immunity, and the nervous system were identified as hub genes: CNTNAP3, IL1RAP, MEGF9, UBE2W, and UBE2D1. All of these findings supported that MDD was associated with stress, inflammation, and immune responses, helping us to obtain a better understanding of the internal molecular mechanism and to explore biomarkers for the diagnosis or treatment of depression in adolescents.

Network Preservation Analysis Reveals Dysregulated Synaptic Modules and Regulatory Hubs Shared Between Alzheimer’s Disease and Temporal Lobe Epilepsy

Background: There is increased prevalence of epilepsy in patients with Alzheimer’s disease (AD). Although shared pathological and clinical features have been identified, the underlying pathophysiology and cause-effect relationships are poorly understood. We aimed to identify commonly dysregulated groups of genes between these two disorders.

Methods: Using publicly available transcriptomic data from hippocampal tissue of patients with temporal lobe epilepsy (TLE), late onset AD and non-AD controls, we constructed gene coexpression networks representing all three states. We then employed network preservation statistics to compare the density and connectivity-based preservation of functional gene modules between TLE, AD and controls and used the difference in significance scores as a surrogate quantifier of module preservation.

Results: The majority (>90%) of functional gene modules were highly preserved between all coexpression networks, however several modules identified in the TLE network showed various degrees of preservation in the AD network compared to that of control. Of note, two synaptic signalling-associated modules and two metabolic modules showed substantial gain of preservation, while myelination and immune system-associated modules showed significant loss of preservation. The genes SCN3B and EPHA4 were identified as central regulatory hubs of the highly preserved synaptic signalling-associated module. GABRB3 and SCN2A were identified as central regulatory hubs of a smaller neurogenesis-associated module, which was enriched for multiple epileptic activity and seizure-related human phenotype ontologies.

Conclusion: We conclude that these hubs and their downstream signalling pathways are common modulators of synaptic activity in the setting of AD and TLE, and may play a critical role in epileptogenesis in AD.

Chronic aryl hydrocarbon receptor activity phenocopies smoking-induced skeletal muscle impairment

BACKGROUND

Chronic obstructive pulmonary disease (COPD) patients exhibit skeletal muscle atrophy, denervation, and reduced mitochondrial oxidative capacity. Whilst chronic tobacco smoke exposure is implicated in COPD muscle impairment, the mechanisms involved are ambiguous. The aryl hydrocarbon receptor (AHR) is a ligand-activated transcription factor that activates detoxifying pathways with numerous exogenous ligands, including tobacco smoke. Whereas transient AHR activation is adaptive, chronic activation can be toxic. On this basis, we tested the hypothesis that chronic smoke-induced AHR activation causes adverse muscle impact.

METHODS

We used clinical patient muscle samples, and in vitro (C2C12 myotubes) and in vivo models (mouse), to perform gene expression, mitochondrial function, muscle and neuromuscular junction morphology, and genetic manipulations (adeno-associated virus-mediated gene transfer).

RESULTS

Sixteen weeks of tobacco smoke exposure in mice caused muscle atrophy, neuromuscular junction degeneration, and reduced oxidative capacity. Similarly, smoke exposure reprogrammed the muscle transcriptome, with down-regulation of mitochondrial and neuromuscular junction genes. In mouse and human patient specimens, smoke exposure increased muscle AHR signalling. Mechanistically, experiments in cultured myotubes demonstrated that smoke condensate activated the AHR, caused mitochondrial impairments, and induced an AHR-dependent myotube atrophy. Finally, to isolate the role of AHR activity, expression of a constitutively active AHR mutant without smoke exposure caused atrophy and mitochondrial impairments in cultured myotubes, and muscle atrophy and neuromuscular junction degeneration in mice.

CONCLUSIONS

These results establish that chronic AHR activity, as occurs in smokers, phenocopies the atrophy, mitochondrial impairment, and neuromuscular junction degeneration caused by chronic tobacco smoke exposure.

Re-wiring and gene expression changes of AC025034.1 and ATP2B1 play complex roles in early-to-late breast cancer progression

Background

Elucidating the dynamic topological changes across different stages of breast cancer, called stage re-wiring, could lead to identifying key latent regulatory signatures involved in cancer progression. Such dynamic regulators and their functions are mostly unknown. Here, we reconstructed differential co-expression networks for four stages of breast cancer to assess the dynamic patterns of cancer progression. A new computational approach was applied to identify stage-specific subnetworks for each stage. Next, prognostic traits of genes and the efficiency of stage-related groups were evaluated and validated, using the Log-Rank test, SVM classifier, and sample clustering. Furthermore, by conducting the stepwise VIF-feature selection method, a Cox-PH model was developed to predict patients’ risk. Finally, the re-wiring network for prognostic signatures was reconstructed and assessed across stages to detect gain/loss, positive/negative interactions as well as rewired-hub nodes contributing to dynamic cancer progression.

Results

After having implemented our new approach, we could identify four stage-specific core biological pathways. We could also detect an essential non-coding RNA, AC025034.1, which is not the only antisense to ATP2B1 (cell proliferation regulator), but also revealed a statistically significant stage-descending pattern; Moreover, AC025034.1 revealed both a dynamic topological pattern across stages and prognostic trait. We also identified a high-performance Overall-Survival-Risk model, including 12 re-wired genes to predict patients’ risk (c-index = 0.89). Finally, breast cancer-specific prognostic biomarkers of LINC01612, AC092142.1, and AC008969.1 were identified.

Conclusions

In summary new scoring method highlighted stage-specific core pathways for early-to-late progressions. Moreover, detecting the significant re-wired hub nodes indicated stage-associated traits, which reflects the importance of such regulators from different perspectives.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12863-021-01015-9.

Modular Evolution of the Drosophila Metabolome

Comparative phylogenetic studies offer a powerful approach to study the evolution of complex traits. Although much effort has been devoted to the evolution of the genome and to organismal phenotypes, until now relatively little work has been done on the evolution of the metabolome, despite the fact that it is composed of the basic structural and functional building blocks of all organisms. Here we explore variation in metabolite levels across 50 My of evolution in the genus Drosophila, employing a common garden design to measure the metabolome within and among 11 species of Drosophila. We find that both sex and age have dramatic and evolutionarily conserved effects on the metabolome. We also find substantial evidence that many metabolite pairs covary after phylogenetic correction, and that such metabolome coevolution is modular. Some of these modules are enriched for specific biochemical pathways and show different evolutionary trajectories, with some showing signs of stabilizing selection. Both observations suggest that functional relationships may ultimately cause such modularity. These coevolutionary patterns also differ between sexes and are affected by age. We explore the relevance of modular evolution to fitness by associating modules with lifespan variation measured in the same common garden. We find several modules associated with lifespan, particularly in the metabolome of older flies. Oxaloacetate levels in older females appear to coevolve with lifespan, and a lifespan-associated module in older females suggests that metabolic associations could underlie 50 My of lifespan evolution.

Identified Three Interferon Induced Proteins as Novel Biomarkers of Human Ischemic Cardiomyopathy

Ischemic cardiomyopathy is the most frequent type of heart disease, and it is a major cause of myocardial infarction (MI) and heart failure (HF), both of which require expensive medical treatment. Precise biomarkers and therapy targets must be developed to enhance improve diagnosis and treatment. In this study, the transcriptional profiles of 313 patients' left ventricle biopsies were obtained from the PubMed database, and functional genes that were significantly related to ischemic cardiomyopathy were screened using the Weighted Gene Co-Expression Network Analysis and protein-protein interaction (PPI) networks enrichment analysis. The rat myocardial infarction model was developed to validate these findings. Finally, the putative signature genes were blasted through the common Cardiovascular Disease Knowledge Portal to explore if they were associated with cardiovascular disorder. Three interferon stimulated genes (IFIT2, IFIT3 and IFI44L), as well as key pathways, have been identified as potential biomarkers and therapeutic targets for ischemic cardiomyopathy, and their alternations or mutations have been proven to be strongly linked to cardiac disorders. These novel signature genes could be utilized as bio-markers or potential therapeutic objectives in precise clinical diagnosis and treatment of ischemic cardiomyopathy.

Profiling senescent cells in human brains reveals neurons with CDKN2D/p19 and tau neuropathology

Senescent cells contribute to pathology and dysfunction in animal models1. Their sparse distribution and heterogenous phenotype have presented challenges for detecting them in human tissues. We developed a senescence eigengene approach to identify these rare cells within large, diverse populations of postmortem human brain cells. Eigengenes are useful when no single gene reliably captures a phenotype, like senescence; they also help to reduce noise, which is important in large transcriptomic datasets where subtle signals from low-expressing genes can be lost. Each of our eigengenes detected ~2% senescent cells from a population of ~140,000 single nuclei derived from 76 postmortem human brains with various levels of Alzheimer's disease (AD) pathology. More than 97% of the senescent cells were excitatory neurons and overlapped with tau-containing neurofibrillary tangles (NFTs). Cyclin dependent kinase inhibitor 2D (CDKN2D/p19) was predicted as the most significant contributor to the primary senescence eigengene. RNAscope and immunofluorescence confirmed its elevated expression in AD brain tissue whereby p19-expressing neurons had 1.8-fold larger nuclei and significantly more cells with lipofuscin than p19-negative neurons. These hallmark senescence phenotypes were further elevated in the presence of NFTs. Collectively, CDKN2D/p19-expressing neurons with NFTs represent a unique cellular population in human AD with a senescence phenotype. The eigengenes developed may be useful in future senescence profiling studies as they accurately identified senescent cells in snRNASeq datasets and predicted biomarkers for histological investigation.

A novel approach to co-expression network analysis identifies modules and genes relevant for moulting and development in the Atlantic salmon louse (Lepeophtheirus salmonis)

BACKGROUND

The salmon louse (Lepeophtheirus salmonis) is an obligate ectoparasitic copepod living on Atlantic salmon and other salmonids in the marine environment. Salmon lice cause a number of environmental problems and lead to large economical losses in aquaculture every year. In order to develop novel parasite control strategies, a better understanding of the mechanisms of moulting and development of the salmon louse at the transcriptional level is required.

METHODS

Three weighted gene co-expression networks were constructed based on the pairwise correlations of salmon louse gene expression profiles at different life stages. Network-based approaches and gene annotation information were applied to identify genes that might be important for the moulting and development of the salmon louse. RNA interference was performed for validation. Regulatory impact factors were calculated for all the transcription factor genes by examining the changes in co-expression patterns between transcription factor genes and deferentially expressed genes in middle stages and moulting stages.

RESULTS

Eight gene modules were predicted as important, and 10 genes from six of the eight modules have been found to show observable phenotypes in RNA interference experiments. We knocked down five hub genes from three modules and observed phenotypic consequences in all experiments. In the infection trial, no copepodids with a RAB1A-like gene knocked down were found on fish, while control samples developed to chalimus-1 larvae. Also, a FOXO-like transcription factor obtained highest scores in the regulatory impact factor calculation.

CONCLUSIONS

We propose a gene co-expression network-based approach to identify genes playing an important role in the moulting and development of salmon louse. The RNA interference experiments confirm the effectiveness of our approach and demonstrated the indispensable role of a RAB1A-like gene in the development of the salmon louse. We propose that our approach could be generalized to identify important genes associated with a phenotype of interest in other organisms.

Gene correlation network analysis to identify regulatory factors in sciatic nerve injury

BACKGROUND

Sciatic nerve injury (SNI), which frequently occurs under the traumatic hip and hip fracture dislocation, induces serious complications such as motor and sensory loss, muscle atrophy, or even disabling. The present work aimed to determine the regulating factors and gene network related to the SNI pathology.

METHODS

Sciatic nerve injury dataset GSE18803 with 24 samples was divided into adult group and neonate group. Weighted gene co-expression network analysis (WGCNA) was carried out to identify modules associated with SNI in the two groups. Moreover, differentially expressed genes (DEGs) were determined from every group, separately. Subsequently, co-expression network and protein-protein interaction (PPI) network were overlapped to identify hub genes, while functional enrichment and Reactome analysis were used for a comprehensive analysis of potential pathways. GSE30165 was used as the test set for investigating the hub gene involvement within SNI. Gene set enrichment analysis (GSEA) was performed separately using difference between samples and gene expression level as phenotype label to further prove SNI-related signaling pathways. In addition, immune infiltration analysis was accomplished by CIBERSORT. Finally, Drug-Gene Interaction database (DGIdb) was employed for predicting the possible therapeutic agents.

RESULTS

14 SNI status modules and 97 DEGs were identified in adult group, while 15 modules and 21 DEGs in neonate group. A total of 12 hub genes was overlapping from co-expression and PPI network. After the results from both test and training sets were overlapped, we verified that the ten real hub genes showed remarkably up-regulation within SNI. According to functional enrichment of hub genes, the above genes participated in the immune effector process, inflammatory responses, the antigen processing and presentation, and the phagocytosis. GSEA also supported that gene sets with the highest significance were mostly related to the cytokine-cytokine receptor interaction. Analysis of hub genes possible related signaling pathways using gene expression level as phenotype label revealed an enrichment involved in Lysosome, Chemokine signaling pathway, and Neurotrophin signaling pathway. Immune infiltration analysis showed that Macrophages M2 and Regulatory T cells may participate in the development of SNI. At last, 25 drugs were screened from DGIdb to improve SNI treatment.

CONCLUSIONS

The gene expression network is determined in the present work based on the related regulating factors within SNI, which sheds more light on SNI pathology and offers the possible biomarkers and therapeutic targets in subsequent research.

Aging at evolutionary crossroads: longitudinal gene co-expression network analyses of proximal and ultimate causes of aging in bats

How, when, and why do organisms, their tissues, and their cells age remain challenging issues, although researchers have identified multiple mechanistic causes of aging, and three major evolutionary theories have been developed to unravel the ultimate causes of organismal aging. A central hypothesis of these theories is that the strength of natural selection decreases with age. However, empirical evidence on when, why, and how organisms age is phylogenetically limited, especially in natural populations. Here, we developed generic comparisons of gene co-expression networks that quantify and dissect the heterogeneity of gene co-expression in conspecific individuals from different age-classes to provide topological evidence about some mechanical and fundamental causes of organismal aging. We applied this approach to investigate the complexity of some proximal and ultimate causes of aging phenotypes in a natural population of the greater mouse-eared bat Myotis myotis, a remarkably long-lived species given its body size and metabolic rate, with available longitudinal blood transcriptomes. M. myotis gene co-expression networks become increasingly fragmented with age, suggesting an erosion of the strength of natural selection and a general dysregulation of gene co-expression in aging bats. However, selective pressures remain sufficiently strong to allow successive emergence of homogeneous age-specific gene co-expression patterns, for at least 7 years. Thus, older individuals from long-lived species appear to sit at an evolutionary crossroad: as they age, they experience both a decrease in the strength of natural selection and a targeted selection for very specific biological processes, further inviting to refine a central hypothesis in evolutionary aging theories.

Beyond "consistent with" adaptation: Is there a robust test for music adaptation?

In their article, Mehr et al. conclude that the design features of music are consistent with adaptations for credible signaling. Although appealing to design may seem like a plausible basis for identifying adaptations, probing adaptive theories of music must be done at the genomic level and will require a functional understanding of the genomic, phenotypic, and fitness properties of music.

Biomarkers for Traumatic Brain Injury: Data Standards and Statistical Considerations

Recent biomarker innovations hold potential for transforming diagnosis, prognostic modeling, and precision therapeutic targeting of traumatic brain injury (TBI). However, many biomarkers, including brain imaging, genomics, and proteomics, involve vast quantities of high-throughput and high-content data. Management, curation, analysis, and evidence synthesis of these data are not trivial tasks. In this review, we discuss data management concepts and statistical and data sharing strategies when dealing with biomarker data in the context of TBI research. We propose that application of biomarkers involves three distinct steps-discovery, evaluation, and evidence synthesis. First, complex/big data has to be reduced to useful data elements at the stage of biomarker discovery. Second, inferential statistical approaches must be applied to these biomarker data elements for assessment of biomarker clinical utility and validity. Last, synthesis of relevant research is required to support practice guidelines and enable health decisions informed by the highest quality, up-to-date evidence available. We focus our discussion around recent experiences from the International Traumatic Brain Injury Research (InTBIR) initiative, with a specific focus on four major clinical projects (Transforming Research and Clinical Knowledge in TBI, Collaborative European NeuroTrauma Effectiveness Research in TBI, Collaborative Research on Acute Traumatic Brain Injury in Intensive Care Medicine in Europe, and Approaches and Decisions in Acute Pediatric TBI Trial), which are currently enrolling subjects in North America and Europe. We discuss common data elements, data collection efforts, data-sharing opportunities, and challenges, as well as examine the statistical techniques required to realize successful adoption and use of biomarkers in the clinic as a foundation for precision medicine in TBI.

Comparative Analyses of Gene Co-expression Networks: Implementations and Applications in the Study of Evolution

Similarities and differences in the associations of biological entities among species can provide us with a better understanding of evolutionary relationships. Often the evolution of new phenotypes results from changes to interactions in pre-existing biological networks and comparing networks across species can identify evidence of conservation or adaptation. Gene co-expression networks (GCNs), constructed from high-throughput gene expression data, can be used to understand evolution and the rise of new phenotypes. The increasing abundance of gene expression data makes GCNs a valuable tool for the study of evolution in non-model organisms. In this paper, we cover motivations for why comparing these networks across species can be valuable for the study of evolution. We also review techniques for comparing GCNs in the context of evolution, including local and global methods of graph alignment. While some protein-protein interaction (PPI) bioinformatic methods can be used to compare co-expression networks, they often disregard highly relevant properties, including the existence of continuous and negative values for edge weights. Also, the lack of comparative datasets in non-model organisms has hindered the study of evolution using PPI networks. We also discuss limitations and challenges associated with cross-species comparison using GCNs, and provide suggestions for utilizing co-expression network alignments as an indispensable tool for evolutionary studies going forward.

COVID-19 data analysis using HJ-Biplot method: A study case

COVID-19 is a new viral disease declared as a pandemic by the World Health Organization in 2020. This highly infectious disease, caused by the virus Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has shown a different impact on young individuals, old individuals, and people with other health conditions. In this study, we aim to identify the different relationships between COVID-19 with other health complications. Additionally, our purpose is to ordinate the ages of individuals according to COVID-19 affection. For this, we apply the HJ-Biplot along with Cluster multivariate analysis techniques to a data set of patients diagnosed with COVID-19, Pneumonia, smoking Habits, Diabetes, Obesity, Chronic Obstructive Pulmonary Disease, Asthma, Immunosuppression, Hypertension, Cardiovascular Problems, Chronic Renal Insufficiency, and other health problems. The data set that we use in this work was obtained from the Mexico Government website. This exploratory research illustrates that COVID-19 disease presents different relationships with other health problems and individuals. For example, this viral disease is highly correlated with Hypertension, Diabetes, smoking habits, and Obesity health conditions. Other high correlations are found among Chronic Obstructive Pulmonary Disease, Cardiovascular problems, Pneumonia, Chronic Renal Insufficiency, Hypertension, and Diabetes. Also, the ordination of individuals according to COVID-19 affection shows us that 60 to 89 years old people are highly affected by this disease. Furthermore, we identify the formation of two clusters of individuals. On the one hand, the first Cluster is formed by old individuals highly affected by many diseases. On the other hand, the second Cluster is formed mainly by young individuals lowly affected by this study's health problems. Identifying COVID-19 correlated health conditions and the age of the most affected individuals is crucial to the correct handle of the pandemic.

As above, so below: Whole transcriptome profiling demonstrates strong molecular similarities between avian dorsal and ventral pallial subdivisions

Over the last two decades, beginning with the Avian Brain Nomenclature Forum in 2000, major revisions have been made to our understanding of the organization and nomenclature of the avian brain. However, there are still unresolved questions on avian pallial organization, particularly whether the cells above the vestigial ventricle represent distinct populations to those below it or similar populations. To test these two hypotheses, we profiled the transcriptomes of the major avian pallial subdivisions dorsal and ventral to the vestigial ventricle boundary using RNA sequencing and a new zebra finch genome assembly containing about 22,000 annotated, complete genes. We found that the transcriptomes of neural populations above and below the ventricle were remarkably similar. Each subdivision in dorsal pallium (Wulst) had a corresponding molecular counterpart in the ventral pallium (dorsal ventricular ridge). In turn, each corresponding subdivision exhibited shared gene co‐expression modules that contained gene sets enriched in functional specializations, such as anatomical structure development, synaptic transmission, signaling, and neurogenesis. These findings are more in line with the continuum hypothesis of avian brain subdivision organization above and below the vestigial ventricle space, with the pallium as a whole consisting of four major cell populations (intercalated pallium, mesopallium, hyper‐nidopallium, and arcopallium) instead of seven (hyperpallium apicale, interstitial hyperpallium apicale, intercalated hyperpallium, hyperpallium densocellare, mesopallium, nidopallium, and arcopallium). We suggest adopting a more streamlined hierarchical naming system that reflects the robust similarities in gene expression, neural connectivity motifs, and function. These findings have important implications for our understanding of overall vertebrate brain evolution.

DNA methylation analysis improves the prognostication of acute myeloid leukemia

Integration of orthogonal data could provide new opportunities to pinpoint the underlying molecular mechanisms of hematologic disorders. Using a novel gene network approach, we integrated DNA methylation data from The Cancer Genome Atlas (n = 194 cases) with the corresponding gene expression profile. Our integrated gene network analysis classified AML patients into low-, intermediate-, and high-risk groups. The identified high-risk group had significantly shorter overall survival compared to the low-risk group (p-value ≤10-11). Specifically, our approach identified a particular subgroup of nine high-risk AML cases that died within 2 years after diagnosis. These high-risk cases otherwise would be incorrectly classified as intermediate-risk solely based on cytogenetics, mutation profiles, and common molecular characteristics of AML. We confirmed the prognostic value of our integrative gene network approach using two independent datasets, as well as through comparison with European LeukemiaNet and LSC17 criteria. Our approach could be useful in the prognostication of a subset of borderline AML cases. These cases would not be classified into appropriate risk groups by other approaches that use gene expression, but not DNA methylation data. Our findings highlight the significance of epigenomic data, and they indicate integrating DNA methylation data with gene coexpression networks can have a synergistic effect.