(Differential) Co-Expression Analysis of Gene Expression: A Survey of Best Practices

Unveiling differential gene co-expression networks and its effects on levodopa-induced dyskinesia

Levodopa-induced dyskinesia (LID) refers to involuntary motor movements of chronic use of levodopa in Parkinson's disease (PD) that negatively impact the overall well-being of people with this disease. The molecular mechanisms involved in LID were investigated through whole-blood transcriptomic analysis for differential gene expression and identification of new co-expression and differential co-expression networks. We found six differentially expressed genes in patients with LID, and 13 in patients without LID. We also identified 12 co-expressed genes exclusive to LID, and six exclusive hub genes involved in 23 gene-gene interactions in patients with LID. Convergently, we identified novel genes associated with PD and LID that play roles in mitochondrial dysfunction, dysregulation of lipid metabolism, and neuroinflammation. We observed significant changes in disease progression, consistent with previous findings of maladaptive plastic changes in the basal ganglia leading to the development of LID, including a chronic pro-inflammatory state in the brain.

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.

ACDC: a general approach for detecting phenotype or exposure associated co-expression

Background

Existing module-based differential co-expression methods identify differences in gene-gene relationships across phenotype or exposure structures by testing for consistent changes in transcription abundance. Current methods only allow for assessment of co-expression variation across a singular, binary or categorical exposure or phenotype, limiting the information that can be obtained from these analyses.

Methods

Here, we propose a novel approach for detection of differential co-expression that simultaneously accommodates multiple phenotypes or exposures with binary, ordinal, or continuous data types.

Results

We report an application to two cohorts of asthmatic patients with varying levels of asthma control to identify associations between gene co-expression and asthma control test scores. Results suggest that both expression levels and covariances of ADORA3, ALOX15, and IDO1 are associated with asthma control.

Conclusion

ACDC is a flexible extension to existing methodology that can detect differential co-expression across varying external variables.

EGFAFS: A Novel Feature Selection Algorithm Based on Explosion Gravitation Field Algorithm

Feature selection (FS) is a vital step in data mining and machine learning, especially for analyzing the data in high-dimensional feature space. Gene expression data usually consist of a few samples characterized by high-dimensional feature space. As a result, they are not suitable to be processed by simple methods, such as the filter-based method. In this study, we propose a novel feature selection algorithm based on the Explosion Gravitation Field Algorithm, called EGFAFS. To reduce the dimensions of the feature space to acceptable dimensions, we constructed a recommended feature pool by a series of Random Forests based on the Gini index. Furthermore, by paying more attention to the features in the recommended feature pool, we can find the best subset more efficiently. To verify the performance of EGFAFS for FS, we tested EGFAFS on eight gene expression datasets compared with four heuristic-based FS methods (GA, PSO, SA, and DE) and four other FS methods (Boruta, HSICLasso, DNN-FS, and EGSG). The results show that EGFAFS has better performance for FS on gene expression data in terms of evaluation metrics, having more than the other eight FS algorithms. The genes selected by EGFAGS play an essential role in the differential co-expression network and some biological functions further demonstrate the success of EGFAFS for solving FS problems on gene expression data.

Transcriptome Profile Reveals Genetic and Metabolic Mechanisms Related to Essential Fatty Acid Content of Intramuscular Longissimus thoracis in Nellore Cattle

Beef is a source of essential fatty acids (EFA), linoleic (LA) and alpha-linolenic (ALA) acids, which protect against inflammatory and cardiovascular diseases in humans. However, the intramuscular EFA profile in cattle is a complex and polygenic trait. Thus, this study aimed to identify potential regulatory genes of the essential fatty acid profile in Longissimus thoracis of Nellore cattle finished in feedlot. Forty-four young bulls clustered in four groups of fifteen animals with extreme values for each FA were evaluated through differentially expressed genes (DEG) analysis and two co-expression methodologies (WGCNA and PCIT). We highlight the ECHS1, IVD, ASB5, and ERLIN1 genes and the TF NFIA, indicated in both FA. Moreover, we associate the NFYA, NFYB, PPARG, FASN, and FADS2 genes with LA, and the RORA and ELOVL5 genes with ALA. Furthermore, the functional enrichment analysis points out several terms related to FA metabolism. These findings contribute to our understanding of the genetic mechanisms underlying the beef EFA profile in Nellore cattle finished in feedlot.

Transcriptomic profile of longissimus thoracis associated with fatty acid content in Nellore beef cattle

The beef fatty acid (FA) profile has the potential to impact human health, and displays polygenic and complex features. This study aimed to identify the transcriptomic FA profile in the longissimus thoracis muscle in Nellore beef cattle finished in feedlot. Forty-four young bulls were sampled to assess the beef FA profile by considering 14 phenotypes and including differentially expressed genes (DEG), co-expressed (COE), and differentially co-expressed genes (DCO) analyses. All samples (n = 44) were used for COE analysis, whereas 30 samples with extreme phenotypes for the beef FA profile were used for DEG and DCO. A total of 912 DEG were identified, and the polyunsaturated (n = 563) and unsaturated ω-3 (n = 346) FA sums groups were the most frequently observed. The COE analyses identified three modules, of which the blue module (n = 1776) was correlated with eight of 14 FA phenotypes. Also, 759 DCO genes were listed, and the oleic acid (n = 358) and monounsaturated fatty acids sum (n = 120) were the most frequent. Furthermore, 243 and 13, 319 and seven, and 173 and 12 gene ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways were enriched respectively for the DEG, COE, and DCO analyses. Combining the results, we highlight the unexplored GIPC2, ASB5, and PPP5C genes in cattle. Besides LIPE and INSIG2 genes in COE modules, the ACSL3, ECI1, DECR2, FITM1, and SDHB genes were signaled in at least two analyses. These findings contribute to understand the genetic mechanisms underlying the beef FA profile in Nellore beef cattle finished in feedlot.

Analysis of Gene Expression Microarray Data Reveals Androgen-Responsive Genes of Muscles in Polycystic Ovarian Syndrome Patients

Polycystic ovarian syndrome (PCOS) is an endocrine disorder that is characterized by hyperandrogenism. Therefore, information about androgen-induced molecular changes can be obtained using the tissues of patients with PCOS. We analyzed two microarray datasets of normal and PCOS muscle samples (GSE8157 and GSE6798) to identify androgen-responsive genes (ARGs). Differentially expressed genes were determined using the t-test and a meta-analysis of the datasets. The overlap between significant results of the meta-analysis and ARGs predicted from an external database was determined, and differential coexpression analysis was then applied between these genes and the other genes. We found 313 significant genes in the meta-analysis using the Benjamini–Hochberg multiple testing correction. Of these genes, 61 were in the list of predicted ARGs. When the differential coexpression between these 61 genes and 13,545 genes filtered by variance was analyzed, 540 significant gene pairs were obtained using the Benjamini–Hochberg correction. While no significant results were obtained regarding the functional enrichment of the differentially expressed genes, top-level gene ontology terms were significantly enriched in the list of differentially coexpressed genes, which indicates that a broad range of cellular processes is affected by androgen administration. Our findings provide valuable information for the identification of ARGs.

GeneCloudOmics: A Data Analytic Cloud Platform for High-Throughput Gene Expression Analysis

Gene expression profiling techniques, such as DNA microarray and RNA-Sequencing, have provided significant impact on our understanding of biological systems. They contribute to almost all aspects of biomedical research, including studying developmental biology, host-parasite relationships, disease progression and drug effects. However, the high-throughput data generations present challenges for many wet experimentalists to analyze and take full advantage of such rich and complex data. Here we present GeneCloudOmics, an easy-to-use web server for high-throughput gene expression analysis that extends the functionality of our previous ABioTrans with several new tools, including protein datasets analysis, and a web interface. GeneCloudOmics allows both microarray and RNA-Seq data analysis with a comprehensive range of data analytics tools in one package that no other current standalone software or web-based tool can do. In total, GeneCloudOmics provides the user access to 23 different data analytical and bioinformatics tasks including reads normalization, scatter plots, linear/non-linear correlations, PCA, clustering (hierarchical, k-means, t-SNE, SOM), differential expression analyses, pathway enrichments, evolutionary analyses, pathological analyses, and protein-protein interaction (PPI) identifications. Furthermore, GeneCloudOmics allows the direct import of gene expression data from the NCBI Gene Expression Omnibus database. The user can perform all tasks rapidly through an intuitive graphical user interface that overcomes the hassle of coding, installing tools/packages/libraries and dealing with operating systems compatibility and version issues, complications that make data analysis tasks challenging for biologists. Thus, GeneCloudOmics is a one-stop open-source tool for gene expression data analysis and visualization. It is freely available at http://combio-sifbi.org/GeneCloudOmics.

An Integrative Network Science and Artificial Intelligence Drug Repurposing Approach for Muscle Atrophy in Spaceflight Microgravity

Muscle atrophy is a side effect of several terrestrial diseases which also affects astronauts severely in space missions due to the reduced gravity in spaceflight. An integrative graph-theoretic network-based drug repurposing methodology quantifying the interplay of key gene regulations and protein-protein interactions in muscle atrophy conditions is presented. Transcriptomic datasets from mice in spaceflight from GeneLab have been extensively mined to extract the key genes that cause muscle atrophy in organ muscle tissues such as the thymus, liver, and spleen. Top muscle atrophy gene regulators are selected by Bayesian Markov blanket method and gene-disease knowledge graph is constructed using the scalable precision medicine knowledge engine. A deep graph neural network is trained for predicting links in the network. The top ranked diseases are identified and drugs are selected for repurposing using drug bank resource. A disease drug knowledge graph is constructed and the graph neural network is trained for predicting new drugs. The results are compared with machine learning methods such as random forest, and gradient boosting classifiers. Network measure based methods shows that preferential attachment has good performance for link prediction in both the gene-disease and disease-drug graphs. The receiver operating characteristic curves, and prediction accuracies for each method show that the random walk similarity measure and deep graph neural network outperforms the other methods. Several key target genes identified by the graph neural network are associated with diseases such as cancer, diabetes, and neural disorders. The novel link prediction approach applied to the disease drug knowledge graph identifies the Monoclonal Antibodies drug therapy as suitable candidate for drug repurposing for spaceflight induced microgravity. There are a total of 21 drugs identified as possible candidates for treating muscle atrophy. Graph neural network is a promising deep learning architecture for link prediction from gene-disease, and disease-drug networks.

SeqNet: An R Package for Generating Gene-Gene Networks and Simulating RNA-Seq Data

Gene expression data provide an abundant resource for inferring connections in gene regulatory networks. While methodologies developed for this task have shown success, a challenge remains in comparing the performance among methods. Gold-standard datasets are scarce and limited in use. And while tools for simulating expression data are available, they are not designed to resemble the data obtained from RNA-seq experiments. SeqNet is an R package that provides tools for generating a rich variety of gene network structures and simulating RNA-seq data from them. This produces in silico RNA-seq data for benchmarking and assessing gene network inference methods. The package is available on CRAN and on GitHub at https://github.com/tgrimes/SeqNet.

Detection of Genes in Arabidopsis thaliana L. Responding to DNA Damage from Radiation and Other Stressors in Spaceflight

Ionizing radiation present in extraterrestrial environment is an important factor that affects plants grown in spaceflight. Pearson correlation-based gene regulatory network inferencing from transcriptional responses of the plant Arabidopsis thaliana L. grown in real and simulated spaceflight conditions acquired by GeneLab, followed by topological and spectral analysis of the networks is performed. Gene regulatory subnetworks are extracted for DNA damage response processes. Analysis of radiation-induced ATR/ATM protein-protein interactions in Arabidopsis reveals interaction profile similarities under low radiation doses suggesting novel mechanisms of DNA damage response involving non-radiation-induced genes regulating other stress responses in spaceflight. The Jaccard similarity index shows that the genes AT2G31320, AT4G21070, AT2G46610, and AT3G27060 perform similar functions under low doses of radiation. The incremental association Markov blanket method reveals non-radiation-induced genes linking DNA damage response to root growth and plant development. Eighteen radiation-induced genes and sixteen non-radiation-induced gene players have been identified from the ATR/ATM protein interaction complexes involved in heat, salt, water, osmotic stress responses, and plant organogenesis. Network analysis and logistic regression ranking detected AT3G27060, AT1G07500, AT5G66140, and AT3G21280 as key gene players involved in DNA repair processes. High atomic weight, high energy, and gamma photon radiation result in higher intensity of DNA damage response in the plant resulting in elevated values for several network measures such as spectral gap and girth. Nineteen flavonoid and carotenoid pigment activations involved in pigment biosynthesis processes are identified in low radiation dose total light spaceflight environment but are not found to have significant regulations under very high radiation dose environment.

Transcriptomic meta-analysis of disuse muscle atrophy vs. resistance exercise-induced hypertrophy in young and older humans

BACKGROUND

Skeletal muscle atrophy manifests across numerous diseases; however, the extent of similarities/differences in causal mechanisms between atrophying conditions in unclear. Ageing and disuse represent two of the most prevalent and costly atrophic conditions, with resistance exercise training (RET) being the most effective lifestyle countermeasure. We employed gene-level and network-level meta-analyses to contrast transcriptomic signatures of disuse and RET, plus young and older RET to establish a consensus on the molecular features of, and therapeutic targets against, muscle atrophy in conditions of high socio-economic relevance.

METHODS

Integrated gene-level and network-level meta-analysis was performed on publicly available microarray data sets generated from young (18-35 years) m. vastus lateralis muscle subjected to disuse (unilateral limb immobilization or bed rest) lasting ≥7 days or RET lasting ≥3 weeks, and resistance-trained older (≥60 years) muscle.

RESULTS

Disuse and RET displayed predominantly separate transcriptional responses, and transcripts altered across conditions were mostly unidirectional. However, disuse and RET induced directly inverted expression profiles for mitochondrial function and translation regulation genes, with COX4I1, ENDOG, GOT2, MRPL12, and NDUFV2, the central hub components of altered mitochondrial networks, and ZMYND11, a hub gene of altered translation regulation. A substantial number of genes (n = 140) up-regulated post-RET in younger muscle were not similarly up-regulated in older muscle, with young muscle displaying a more pronounced extracellular matrix (ECM) and immune/inflammatory gene expression response. Both young and older muscle exhibited similar RET-induced ubiquitination/RNA processing gene signatures with associated PWP1, PSMB1, and RAF1 hub genes.

CONCLUSIONS

Despite limited opposing gene profiles, transcriptional signatures of disuse are not simply the converse of RET. Thus, the mechanisms of unloading cannot be derived from studying muscle loading alone and provides a molecular basis for understanding why RET fails to target all transcriptional features of disuse. Loss of RET-induced ECM mechanotransduction and inflammatory profiles might also contribute to suboptimal ageing muscle adaptations to RET. Disuse and age-dependent molecular candidates further establish a framework for understanding and treating disuse/ageing atrophy.

GWENA: gene co-expression networks analysis and extended modules characterization in a single Bioconductor package

BACKGROUND

Network-based analysis of gene expression through co-expression networks can be used to investigate modular relationships occurring between genes performing different biological functions. An extended description of each of the network modules is therefore a critical step to understand the underlying processes contributing to a disease or a phenotype. Biological integration, topology study and conditions comparison (e.g. wild vs mutant) are the main methods to do so, but to date no tool combines them all into a single pipeline.

RESULTS

Here we present GWENA, a new R package that integrates gene co-expression network construction and whole characterization of the detected modules through gene set enrichment, phenotypic association, hub genes detection, topological metric computation, and differential co-expression. To demonstrate its performance, we applied GWENA on two skeletal muscle datasets from young and old patients of GTEx study. Remarkably, we prioritized a gene whose involvement was unknown in the muscle development and growth. Moreover, new insights on the variations in patterns of co-expression were identified. The known phenomena of connectivity loss associated with aging was found coupled to a global reorganization of the relationships leading to expression of known aging related functions.

CONCLUSION

GWENA is an R package available through Bioconductor ( https://bioconductor.org/packages/release/bioc/html/GWENA.html ) that has been developed to perform extended analysis of gene co-expression networks. Thanks to biological and topological information as well as differential co-expression, the package helps to dissect the role of genes relationships in diseases conditions or targeted phenotypes. GWENA goes beyond existing packages that perform co-expression analysis by including new tools to fully characterize modules, such as differential co-expression, additional enrichment databases, and network visualization.

Navigating differential structures in complex networks

Structural changes in a network representation of a system, due to different experimental conditions, different connectivity across layers, or to its time evolution, can provide insight on its organization, function, and on how it responds to external perturbations. The deeper understanding of how gene networks cope with diseases and treatments is maybe the most incisive demonstration of the gains obtained through this differential network analysis point of view, which led to an explosion of new numeric techniques in the last decade. However, where to focus one's attention, or how to navigate through the differential structures in the context of large networks, can be overwhelming even for a few experimental conditions. In this paper, we propose a theory and a methodological implementation for the characterization of shared "structural roles" of nodes simultaneously within and between networks. Inspired by recent methodological advances in chaotic phase synchronization analysis, we show how the information about the shared structures of a set of networks can be split and organized in an automatic fashion, in scenarios with very different (i) community sizes, (ii) total number of communities, and (iii) even for a large number of 100 networks compared using numerical benchmarks generated by a stochastic block model. Then, we investigate how the network size, number of networks, and mean size of communities influence the method performance in a series of Monte Carlo experiments. To illustrate its potential use in a more challenging scenario with real-world data, we show evidence that the method can still split and organize the structural information of a set of four gene coexpression networks obtained from two cell types × two treatments (interferon-β stimulated or control). Aside from its potential use as for automatic feature extraction and preprocessing tool, we discuss that another strength of the method is its "story-telling"-like characterization of the information encoded in a set of networks, which can be used to pinpoint unexpected shared structure, leading to further investigations and providing new insights. Finally, the method is flexible to address different research-field-specific questions, by not restricting what scientific-meaningful characteristic (or relevant feature) of a node shall be used.

Differential Co-Expression Analyses Allow the Identification of Critical Signalling Pathways Altered during Tumour Transformation and Progression

Biological systems respond to perturbations through the rewiring of molecular interactions, organised in gene regulatory networks (GRNs). Among these, the increasingly high availability of transcriptomic data makes gene co-expression networks the most exploited ones. Differential co-expression networks are useful tools to identify changes in response to an external perturbation, such as mutations predisposing to cancer development, and leading to changes in the activity of gene expression regulators or signalling. They can help explain the robustness of cancer cells to perturbations and identify promising candidates for targeted therapy, moreover providing higher specificity with respect to standard co-expression methods. Here, we comprehensively review the literature about the methods developed to assess differential co-expression and their applications to cancer biology. Via the comparison of normal and diseased conditions and of different tumour stages, studies based on these methods led to the definition of pathways involved in gene network reorganisation upon oncogenes' mutations and tumour progression, often converging on immune system signalling. A relevant implementation still lagging behind is the integration of different data types, which would greatly improve network interpretability. Most importantly, performance and predictivity evaluation of the large variety of mathematical models proposed would urgently require experimental validations and systematic comparisons. We believe that future work on differential gene co-expression networks, complemented with additional omics data and experimentally tested, will considerably improve our insights into the biology of tumours.

Finding disease modules for cancer and COVID-19 in gene co-expression networks with the Core&Peel method

Genes are organized in functional modules (or pathways), thus their action and their dysregulation in diseases may be better understood by the identification of the modules most affected by the disease (aka disease modules, or active subnetworks). We describe how an algorithm based on the Core&Peel method is used to detect disease modules in co-expression networks of genes. We first validate Core&Peel for the general task of functional module detection by comparison with 42 methods participating in the Disease Module Identification DREAM challenge. Next, we use four specific disease test cases (colorectal cancer, prostate cancer, asthma, and rheumatoid arthritis), four state-of-the-art algorithms (ModuleDiscoverer, Degas, KeyPathwayMiner, and ClustEx), and several pathway databases to validate the proposed algorithm. Core&Peel is the only method able to find significant associations of the predicted disease module with known validated relevant pathways for all four diseases. Moreover, for the two cancer datasets, Core&Peel detects further eight relevant pathways not discovered by the other methods used in the comparative analysis. Finally, we apply Core&Peel and other methods to explore the transcriptional response of human cells to SARS-CoV-2 infection, finding supporting evidence for drug repositioning efforts at a pre-clinical level.

Whole transcriptomic network analysis using Co-expression Differential Network Analysis (CoDiNA)

Biological and medical sciences are increasingly acknowledging the significance of gene co-expression-networks for investigating complex-systems, phenotypes or diseases. Typically, complex phenotypes are investigated under varying conditions. While approaches for comparing nodes and links in two networks exist, almost no methods for the comparison of multiple networks are available and—to best of our knowledge—no comparative method allows for whole transcriptomic network analysis. However, it is the aim of many studies to compare networks of different conditions, for example, tissues, diseases, treatments, time points, or species. Here we present a method for the systematic comparison of an unlimited number of networks, with unlimited number of transcripts: Co-expression Differential Network Analysis (CoDiNA). In particular, CoDiNA detects links and nodes that are common, specific or different among the networks. We developed a statistical framework to normalize between these different categories of common or changed network links and nodes, resulting in a comprehensive network analysis method, more sophisticated than simply comparing the presence or absence of network nodes. Applying CoDiNA to a neurogenesis study we identified candidate genes involved in neuronal differentiation. We experimentally validated one candidate, demonstrating that its overexpression resulted in a significant disturbance in the underlying gene regulatory network of neurogenesis. Using clinical studies, we compared whole transcriptome co-expression networks from individuals with or without HIV and active tuberculosis (TB) and detected signature genes specific to HIV. Furthermore, analyzing multiple cancer transcription factor (TF) networks, we identified common and distinct features for particular cancer types. These CoDiNA applications demonstrate the successful detection of genes associated with specific phenotypes. Moreover, CoDiNA can also be used for comparing other types of undirected networks, for example, metabolic, protein-protein interaction, ecological and psychometric networks. CoDiNA is publicly available as an R package in CRAN (https://CRAN.R-project.org/package=CoDiNA).

Integrative network analysis identifies differential regulation of neuroimmune system in Schizophrenia and Bipolar disorder

Background

Neuropsychiatric disorders such as Schizophrenia (SCZ) and Bipolar disorder (BPD) pose a broad range of problems with different symptoms mainly characterized by some combination of abnormal thoughts, emotions, behaviour, etc. However, in depth molecular and pathophysiological mechanisms among different neuropsychiatric disorders have not been clearly understood yet. We have used RNA-seq data to investigate unique and overlapping molecular signatures between SCZ and BPD using an integrative network biology approach.

Methods

RNA-seq count data were collected from NCBI-GEO database generated on post-mortem brain tissues of controls (n = 24) and patients of BPD (n = 24) and SCZ (n = 24). Differentially expressed genes (DEGs) were identified using the consensus of DESeq2 and edgeR tools and used for downstream analysis. Weighted gene correlation networks were constructed to find non-preserved (NP) modules for SCZ, BPD and control conditions. Topological analysis and functional enrichment analysis were performed on NP modules to identify unique and overlapping expression signatures during SCZ and BPD conditions.

Results

We have identified four NP modules from the DEGs of BPD and SCZ. Eleven overlapping genes have been identified between SCZ and BPD networks, and they were found to be highly enriched in inflammatory responses. Among these eleven genes, TNIP2, TNFRSF1A and AC005840.1 had higher sum of connectivity exclusively in BPD network. In addition, we observed that top five genes of NP module from SCZ network were downregulated which may be a key factor for SCZ disorder.

Conclusions

Differential activation of the immune system components and pathways may drive the common and unique pathogenesis of the BPD and SCZ.

Use of gene expression studies to investigate the human immunological response to malaria infection

Background

Transcriptional profiling of the human immune response to malaria has been used to identify diagnostic markers, understand the pathogenicity of severe disease and dissect the mechanisms of naturally acquired immunity (NAI). However, interpreting this body of work is difficult given considerable variation in study design, definition of disease, patient selection and methodology employed. This work details a comprehensive review of gene expression profiling (GEP) of the human immune response to malaria to determine how this technology has been applied to date, instances where this has advanced understanding of NAI and the extent of variability in methodology between studies to allow informed comparison of data and interpretation of results.

Methods

Datasets from the gene expression omnibus (GEO) including the search terms; ‘plasmodium’ or ‘malaria’ or ‘sporozoite’ or ‘merozoite’ or ‘gametocyte’ and ‘Homo sapiens’ were identified and publications analysed. Datasets of gene expression changes in relation to malaria vaccines were excluded.

Results

Twenty-three GEO datasets and 25 related publications were included in the final review. All datasets related to Plasmodium falciparum infection, except two that related to Plasmodium vivax infection. The majority of datasets included samples from individuals infected with malaria ‘naturally’ in the field (n = 13, 57%), however some related to controlled human malaria infection (CHMI) studies (n = 6, 26%), or cells stimulated with Plasmodium in vitro (n = 6, 26%). The majority of studies examined gene expression changes relating to the blood stage of the parasite. Significant heterogeneity between datasets was identified in terms of study design, sample type, platform used and method of analysis. Seven datasets specifically investigated transcriptional changes associated with NAI to malaria, with evidence supporting suppression of the innate pro-inflammatory response as an important mechanism for this in the majority of these studies. However, further interpretation of this body of work was limited by heterogeneity between studies and small sample sizes.

Conclusions

GEP in malaria is a potentially powerful tool, but to date studies have been hypothesis generating with small sample sizes and widely varying methodology. As CHMI studies are increasingly performed in endemic settings, there will be growing opportunity to use GEP to understand detailed time-course changes in host response and understand in greater detail the mechanisms of NAI.

Comparison of Methods for Differential Co-expression Analysis for Disease Biomarker Prediction

In the recent past, a number of methods have been developed for analysis of biological data. Among these methods, gene co-expression networks have the ability to mine functionally related genes with similar co-expression patterns, because of which such networks have been most widely used. However, gene co-expression networks cannot identify genes, which undergo condition specific changes in their relationships with other genes. In contrast, differential co-expression analysis enables finding co-expressed genes exhibiting significant changes across disease conditions. In this paper, we present some significant outcomes of a comparative study of four co-expression network module detection techniques, namely, THD-Module Extractor, DiffCoEx, MODA, and WGCNA, which can perform differential co-expression analysis on both gene and miRNA expression data (microarray and RNA-seq) and discuss the applications to Alzheimer's disease and Parkinson's disease research. Our observations reveal that compared to other methods, THD-Module Extractor is the most effective in finding modules with higher functional relevance and biological significance.