Comprehensive Transcriptome Meta-analysis to Characterize Host Immune Responses in Helminth Infections

Transcriptome meta-analysis and validation to discovery of hub genes and pathways in focal and segmental glomerulosclerosis

BACKGROUND

Focal segmental glomerulosclerosis (FSGS), a histologic pattern of injury in the glomerulus, is one of the leading glomerular causes of end-stage renal disease (ESRD) worldwide. Despite extensive research, the underlying biological alterations causing FSGS remain poorly understood. Studying variations in gene expression profiles offers a promising approach to gaining a comprehensive understanding of FSGS molecular pathogenicity and identifying key elements as potential therapeutic targets. This work is a meta-analysis of gene expression profiles from glomerular samples of FSGS patients. The main aims of this study are to establish a consensus list of differentially expressed genes in FSGS, validate these findings, understand the disease's pathogenicity, and identify novel therapeutic targets.

METHODS

After a thorough search in the GEO database and subsequent quality control assessments, seven gene expression datasets were selected for the meta-analysis: GSE47183 (GPL14663), GSE47183 (GPL11670), GSE99340, GSE108109, GSE121233, GSE129973, and GSE104948. The random effect size method was applied to identify differentially expressed genes (meta-DEGs), which were then used to construct a regulatory network (STRING, MiRTarBase, and TRRUST) and perform various pathway enrichment analyses. The expression levels of several meta-DEGs, specifically ADAMTS1, PF4, EGR1, and EGF, known as angiogenesis regulators, were analyzed using quantitative reverse transcription polymerase chain reaction (RT-qPCR).

RESULTS

The identified 2,898 meta-DEGs, including 665 downregulated and 669 upregulated genes, were subjected to various analyses. A co-regulatory network comprising 2,859 DEGs, 2,688 microRNAs (miRNAs), and 374 transcription factors (TFs) was constructed, and the top molecules in the network were identified based on degree centrality. Part of the pathway enrichment analysis revealed significant disruption in the angiogenesis regulatory pathways in the FSGS kidney. The RT-qPCR results confirmed an imbalance in angiogenesis pathways by demonstrating the differential expression levels of ADAMTS1 and EGR1, two key angiogenesis regulators, in the FSGS condition.

CONCLUSION

In addition to presenting a consensus list of differentially expressed genes in FSGS, this meta-analysis identified significant distortions in angiogenesis-related pathways and factors in the FSGS kidney. Targeting these factors may offer a viable strategy to impede the progression of FSGS.

Meta-analysis of Transcriptomic Data from Lung Autopsy and Cellular Models of SARS-CoV-2 Infection

Severe COVID-19 is a systemic disorder involving excessive inflammatory response, metabolic dysfunction, multi-organ damage, and several clinical features. Here, we performed a transcriptome meta-analysis investigating genes and molecular mechanisms related to COVID-19 severity and outcomes. First, transcriptomic data of cellular models of SARS-CoV-2 infection were compiled to understand the first response to the infection. Then, transcriptomic data from lung autopsies of patients deceased due to COVID-19 were compiled to analyze altered genes of damaged lung tissue. These analyses were followed by functional enrichment analyses and gene-phenotype association. A biological network was constructed using the disturbed genes in the lung autopsy meta-analysis. Central genes were defined considering closeness and betweenness centrality degrees. A sub-network phenotype-gene interaction analysis was performed. The meta-analysis of cellular models found genes mainly associated with cytokine signaling and other pathogen response pathways. The meta-analysis of lung autopsy tissue found genes associated with coagulopathy, lung fibrosis, multi-organ damage, and long COVID-19. Only genes DNAH9 and FAM216B were found perturbed in both meta-analyses. BLNK, FABP4, GRIA1, ATF3, TREM2, TPPP, TPPP3, FOS, ALB, JUNB, LMNA, ADRB2, PPARG, TNNC1, and EGR1 were identified as central elements among perturbed genes in lung autopsy and were found associated with several clinical features of severe COVID-19. Central elements were suggested as interesting targets to investigate the relation with features of COVID-19 severity, such as coagulopathy, lung fibrosis, and organ damage.

Using ExpressAnalyst for Comprehensive Gene Expression Analysis in Model and Non-Model Organisms

ExpressAnalyst is a web-based platform that enables intuitive, end-to-end transcriptomics and proteomics data analysis. Users can start from FASTQ files, gene/protein abundance tables, or gene/protein lists. ExpressAnalyst will perform read quantification, gene expression table processing and normalization, differential expression analysis, or meta-analysis with complex study designs. The results are presented via various interactive visualizations such as volcano plots, heatmaps, networks, and ridgeline charts, with built-in functional enrichment analysis to allow flexible data exploration and understanding. ExpressAnalyst currently contains built-in support for 29 common organisms. For non-model organisms without good reference genomes, it can perform comprehensive transcriptome profiling directly from RNA-seq reads. These common tasks are covered in 11 Basic Protocols. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: RNA-seq count table uploading, processing, and normalization Basic Protocol 2: Differential expression analysis with linear models Basic Protocol 3: Functional analysis with volcano plot, enrichment network, and ridgeline visualization Basic Protocol 4: Hierarchical clustering analysis of transcriptomics data using interactive heatmaps Basic Protocol 5: Cross-species gene expression analysis based on ortholog mapping results Basic Protocol 6: Proteomics and microarray data processing and normalization Basic Protocol 7: Preparing multiple gene expression tables for meta-analysis Basic Protocol 8: Statistical and functional meta-analysis of gene expression data Basic Protocol 9: Functional analysis of transcriptomics signatures Basic Protocol 10: Dose-response and time-series data analysis Basic Protocol 11: RNA-seq reads processing and quantification with and without reference transcriptomes.

Identification of key genes and biological regulatory mechanisms in diabetic nephropathy: Meta-analysis of gene expression datasets

BACKGROUND

Diabetic nephropathy (DN) which refers to the cases with biopsy proven kidney lesions, is one of the main complications of diabetes all around the world; however, the underlying biological changes causing DN remain to be understood. Studying the alterations in gene expression profiles could give a holistic view of the molecular pathogenicity of DN and aid to discover key molecules as potential therapeutic targets. Here, we performed a meta-analysis study that included microarray gene expression profiles coming from glomerular samples of DN patients in order to acquire a list of consensus Differentially Expressed Genes (meta-DEGs) correlated with DN.

METHODS

After quality control and normalization steps, five gene expression datasets (GES1009, GSE30528, GSE47183, GSE104948, and GSE93804) were entered into the meta-analysis. The meta-analysis was performed by random effect size method and the meta-DEGs were put through network analysis and different pathway enrichment analyses steps. MiRTarBase and TRRUST databases were utilized to predict the meta-DEGs related miRNAs and transcription factors. A co-regulatory network including DEGs, transcription factors and miRNAs was constructed by Cytoscape, and top molecules were identified based on centrality scores in the network.

RESULTS

The identified meta-DEGs were 1364 DEGs including 665 downregulated and 669 upregulated DEGs. The results of pathway enrichment analysis showed, "immune system", "extracellular matrix organization", "hemostasis", "signal transduction", and "platelet activation" to be the top enriched terms with involvement of the meta-DEGs. After construction of the multilayer regulatory network, several top DEGs (TP53, MYC, BTG2, VEGFA, PTEN, etc.), as well as top miRNAs (miR-335, miR-16, miR-17, miR-20a, and miR-93), and transcription factors (SP1, STAT3, NF-KB1, RELA, E2F1), were introduced as potential therapeutic targets in DN. Among the regulatory molecules, miR-335-5p and SP1 were the most interactive miRNA and transcription factor molecules with the highest degree scores in the constructed network.

CONCLUSION

By performing a meta-analysis of available DN-related transcriptomics datasets, we reached a consensus list of DEGs for this complicated disorder. Further enrichment and network analyses steps revealed the involved pathways in the DN pathogenesis and marked the most potential therapeutic targets in this disease.

Trickle infection with Heligmosomoides polygyrus results in decreased worm burdens but increased intestinal inflammation and scarring

Introduction

Intestinal roundworms cause chronic debilitating disease in animals, including humans. Traditional experimental models of these types of infection use a large single-dose infection. However, in natural settings, hosts are exposed to parasites on a regular basis and when mice are exposed to frequent, smaller doses of Heligmosomoides polygyrus, the parasites are cleared more quickly. Whether this more effective host response has any negative consequences for the host is not known.

Results

Using a trickle model of infection, we found that worm clearance was associated with known resistance-related host responses: increased granuloma and tuft cell numbers, increased levels of granuloma IgG and decreased intestinal transit time, as well as higher serum IgE levels. However, we found that the improved worm clearance was also associated with an inflammatory phenotype in and around the granuloma, increased smooth muscle hypertrophy/hyperplasia, and elevated levels of Adamts gene expression.

Discussion

To our knowledge, we are the first to identify the involvement of this protein family of matrix metalloproteinases (MMPs) in host responses to helminth infections. Our results highlight the delicate balance between parasite clearance and host tissue damage, which both contribute to host pathology. When continually exposed to parasitic worms, improved clearance comes at a cost.

Transcriptomic meta-analysis reveals unannotated long non-coding RNAs related to the immune response in sheep

Long non-coding RNAs (lncRNAs) are involved in several biological processes, including the immune system response to pathogens and vaccines. The annotation and functional characterization of lncRNAs is more advanced in humans than in livestock species. Here, we take advantage of the increasing number of high-throughput functional experiments deposited in public databases in order to uniformly analyse, profile unannotated lncRNAs and integrate 422 ovine RNA-seq samples from the ovine immune system. We identified 12302 unannotated lncRNA genes with support from independent CAGE-seq and histone modification ChIP-seq assays. Unannotated lncRNAs showed low expression levels and sequence conservation across other mammal species. There were differences in expression levels depending on the genomic location-based lncRNA classification. Differential expression analyses between unstimulated and samples stimulated with pathogen infection or vaccination resulted in hundreds of lncRNAs with changed expression. Gene co-expression analyses revealed immune gene-enriched clusters associated with immune system activation and related to interferon signalling, antiviral response or endoplasmic reticulum stress. Besides, differential co-expression networks were constructed in order to find condition-specific relationships between coding genes and lncRNAs. Overall, using a diverse set of immune system samples and bioinformatic approaches we identify several ovine lncRNAs associated with the response to an external stimulus. These findings help in the improvement of the ovine lncRNA catalogue and provide sheep-specific evidence for the implication in the general immune response for several lncRNAs.

The Biological and Clinical Aspects of a Latent Tuberculosis Infection

Tuberculosis (TB), caused by bacilli from the Mycobacterium tuberculosis complex, remains a serious global public health problem, representing one of the main causes of death from infectious diseases. About one quarter of the world’s population is infected with Mtb and has a latent TB infection (LTBI). According to the World Health Organization (WHO), an LTBI is characterized by a lasting immune response to Mtb antigens without any TB symptoms. Current LTBI diagnoses and treatments are based on this simplified definition, although an LTBI involves a broad range of conditions, including when Mtb remains in the body in a persistent form and the immune response cannot be detected. The study of LTBIs has progressed in recent years; however, many biological and medical aspects of an LTBI are still under discussion. This review focuses on an LTBI as a broad spectrum of states, both of the human body, and of Mtb cells. The problems of phenotypic insusceptibility, diagnoses, chemoprophylaxis, and the necessity of treatment are discussed. We emphasize the complexity of an LTBI diagnosis and its treatment due to its ambiguous nature. We consider alternative ways of differentiating an LTBI from active TB, as well as predicting TB reactivation based on using mycobacterial “latency antigens” for interferon gamma release assay (IGRA) tests and the transcriptomic analysis of human blood cells.

Differential responses of abomasal transcriptome to Haemonchus contortus infection between Haemonchus-selected and Trichostrongylus-selected merino sheep

Haemonchus contortus is the most prevalent and pathogenic gastrointestinal nematode infecting sheep and goats. The two CSIRO sheep resource flocks, the Haemonchus-selected flock (HSF) and Trichostrongylus-selected flock (TSF) were developed for research on host resistance or susceptibility to gastrointestinal nematode infection. A recent study focused on the gene expression differences between resistant and susceptible sheep within each flock, with lymphatic and gastrointestinal tissues. To identify features in the host transcriptome and understand the molecular differences underlying host resistance to H. contortus between flocks with different selective breeding and genetic backgrounds, we compared the abomasal transcriptomic responses of the resistant or susceptible animals between HSF and TSF flocks. A total of 11 and 903 differentially expressed genes were identified in the innate infection treatment in HSF and TSF flocks between resistant and susceptible sheep respectively, while 52 and 485 genes were identified to be differentially expressed in the acquired infection treatment, respectively. Among them, 294 genes had significantly different gene expression levels between HSF and TSF flock animals within the susceptible sheep by both the innate and acquired infections. Moreover, similar expression patterns of the 294 genes were observed, with 273 genes more highly expressed in HSF and 21 more highly expressed in the TSF within the abomasal transcriptome of the susceptible animals. Gene ontology enrichment of the differentially expressed genes identified in this study predicted the likely differing function between the two flock's susceptible lines in response to H. contortus infection. Nineteen pathways were significantly enriched in both the innate and adaptive immune responses in susceptible animals, which indicated that these pathways likely contribute to the host resistance development to H. contortus infection in susceptible sheep. Biological networks built for the set of genes differentially abundant in susceptible animals identified hub genes of PRKG1, PRKACB, PRKACA, and ITGB1 for the innate immune response, and CALM2, MYL1, COL1A1, ITGB1 and ITGB3 for the adaptive immune response, respectively. Our results offered a quantitative snapshot of host transcriptomic changes induced by H. contortus infection between flocks with different selective breeding and genetic backgrounds and provided novel insights into molecular mechanisms of host resistance.

The composition of lung microbiome in lung cancer: a systematic review and meta-analysis

Background

Although recent studies have indicated that imbalance in the respiratory microbiome composition is linked to several chronic respiratory diseases, the association between the lung microbiome and lung cancer has not been extensively studied. Conflicting reports of individual studies on respiratory microbiome alterations in lung cancer complicate the matter for specifying how the lung microbiome is linked to lung cancer. Consequently, as the first meta-analysis on this topic, we integrate publicly available 16S rRNA gene sequence data on lung tissue samples of lung cancer patients to identify bacterial taxa which differ consistently between case and control groups.

Results

The findings of the current study suggest that the relative abundance of several bacterial taxa including Actinobacteria phylum, Corynebacteriaceae and Halomonadaceae families, and Corynebacterium, Lachnoanaerobaculum, and Halomonas genera is significantly decreased (p < 0.05) in lung tumor tissues of lung cancer patients in comparison with tumor-adjacent normal tissues.

Conclusions

Despite the underlying need for scrutinizing the findings further, the present study lays the groundwork for future research and adds to our limited understanding of the key role of the lung microbiome and its complex interaction with lung cancer. More data on demographic factors and tumor tissue types would help establish a greater degree of accuracy in characterizing the lung microbial community which accords with subtypes and stages of the disease and fully capturing the changes of the lung microbiome in lung cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-021-02375-z.

Modulation of the mTOR pathway plays a central role in dendritic cell functions after Echinococcus granulosus antigen recognition

Immune evasion is a hallmark of persistent echinococcal infection, comprising modulation of innate immune cells and antigen-specific T cell responses. However, recognition of Echinococcus granulosus by dendritic cells (DCs) is a key determinant of the host's response to this parasite. Given that mTOR signaling pathway has been described as a regulator linking metabolism and immune function in DCs, we reported for the first time in these cells, global translation levels, antigen uptake, phenotype, cytokine transcriptional levels, and splenocyte priming activity upon recognition of the hydatid fluid (HF) and the highly glycosylated laminar layer (LL). We found that LL induced a slight up-regulation of CD86 and MHC II in DCs and also stimulated the production of IL-6 and TNF-α. By contrast, HF did not increase the expression of any co-stimulatory molecules, but also down-modulated CD40 and stimulated the expression of the anti-inflammatory cytokine IL-10. Both parasitic antigens promoted protein synthesis through mTOR activation. The use of rapamycin decreased the expression of the cytokines tested, empowered the down-modulation of CD40 and also reduced splenocyte proliferation. Finally, we showed that E. granulosus antigens increase the amounts of LC3-positive structures in DCs which play critical roles in the presentation of these antigens to T cells.

Weighted Gene Co-Expression Network Analysis Identifies Key Modules and Hub Genes Associated with Mycobacterial Infection of Human Macrophages

Tuberculosis (TB) is still a leading cause of death worldwide. Treatments remain unsatisfactory due to an incomplete understanding of the underlying host–pathogen interactions during infection. In the present study, weighted gene co-expression network analysis (WGCNA) was conducted to identify key macrophage modules and hub genes associated with mycobacterial infection. WGCNA was performed combining our own transcriptomic results using Mycobacterium aurum-infected human monocytic macrophages (THP1) with publicly accessible datasets obtained from three types of macrophages infected with seven different mycobacterial strains in various one-to-one combinations. A hierarchical clustering tree of 11,533 genes was built from 198 samples, and 47 distinct modules were revealed. We identified a module, consisting of 226 genes, which represented the common response of host macrophages to different mycobacterial infections that showed significant enrichment in innate immune stimulation, bacterial pattern recognition, and leukocyte chemotaxis. Moreover, by network analysis applied to the 74 genes with the best correlation with mycobacteria infection, we identified the top 10 hub-connecting genes: NAMPT, IRAK2, SOCS3, PTGS2, CCL20, IL1B, ZC3H12A, ABTB2, GFPT2, and ELOVL7. Interestingly, apart from the well-known Toll-like receptor and inflammation-associated genes, other genes may serve as novel TB diagnosis markers and potential therapeutic targets.

Comprehensive Meta-Analysis of COVID-19 Global Metabolomics Datasets

The novel coronavirus SARS-CoV-2 has spread across the world since 2019, causing a global pandemic. The pathogenesis of the viral infection and the associated clinical presentations depend primarily on host factors such as age and immunity, rather than the viral load or its genetic variations. A growing number of omics studies have been conducted to characterize the host immune and metabolic responses underlying the disease progression. Meta-analyses of these datasets have great potential to identify robust molecular signatures to inform clinical care and to facilitate therapeutics development. In this study, we performed a comprehensive meta-analysis of publicly available global metabolomics datasets obtained from three countries (United States, China and Brazil). To overcome high heterogeneity inherent in these datasets, we have (a) implemented a computational pipeline to perform consistent raw spectra processing; (b) conducted meta-analyses at pathway levels instead of individual feature levels; and (c) performed visual data mining on consistent patterns of change between disease severities for individual studies. Our analyses have yielded several key metabolic signatures characterizing disease progression and clinical outcomes. Their biological interpretations were discussed within the context of the current literature. To the best of our knowledge, this is the first comprehensive meta-analysis of global metabolomics datasets of COVID-19.

Multi-omic meta-analysis identifies functional signatures of airway microbiome in chronic obstructive pulmonary disease

The interaction between airway microbiome and host in chronic obstructive pulmonary disease (COPD) is poorly understood. Here we used a multi-omic meta-analysis approach to characterize the functional signature of airway microbiome in COPD. We retrieved all public COPD sputum microbiome datasets, totaling 1640 samples from 16S rRNA gene datasets and 26 samples from metagenomic datasets from across the world. We identified microbial taxonomic shifts using random effect meta-analysis and established a global classifier for COPD using 12 microbial genera. We inferred the metabolic potentials for the airway microbiome, established their molecular links to host targets, and explored their effects in a separate meta-analysis on 1340 public human airway transcriptome samples for COPD. 29.6% of differentially expressed human pathways were predicted to be targeted by microbiome metabolism. For inferred metabolite-host interactions, the flux of disease-modifying metabolites as predicted from host transcriptome was generally concordant with their predicted metabolic turnover in microbiome, suggesting a synergistic response between microbiome and host in COPD. The meta-analysis results were further validated by a pilot multi-omic study on 18 COPD patients and 10 controls, in which airway metagenome, metabolome, and host transcriptome were simultaneously characterized. 69.9% of the proposed "microbiome-metabolite-host" interaction links were validated in the independent multi-omic data. Butyrate, homocysteine, and palmitate were the microbial metabolites showing strongest interactions with COPD-associated host genes. Our meta-analysis uncovered functional properties of airway microbiome that interacted with COPD host gene signatures, and demonstrated the possibility of leveraging public multi-omic data to interrogate disease biology.

Integrated Microarray and RNAseq Transcriptomic Analysis of Retinal Pigment Epithelium/Choroid in Age-Related Macular Degeneration

We report for the first time an integrated transcriptomic analysis of RPE/choroid dysfunction in AMD (mixed stages) based on combining data from publicly available microarray (GSE29801) and RNAseq (GSE135092) datasets aimed at increasing the ability and power of detection of differentially expressed genes and AMD-associated pathways. The analysis approach employed an integrating quantitative method designed to eliminate bias among different transcriptomic studies. The analysis highlighted 764 meta-genes (366 downregulated and 398 upregulated) in macular AMD RPE/choroid and 445 meta-genes (244 downregulated and 201 upregulated) in non-macular AMD RPE/choroid. Of these, 731 genes were newly detected as differentially expressed (DE) genes in macular AMD RPE/choroid and 434 genes in non-macular AMD RPE/choroid compared with controls. Over-representation analysis of KEGG pathways associated with these DE genes mapped revealed two most significantly associated biological processes in macular RPE/choroid in AMD, namely the neuroactive ligand-receptor interaction pathway (represented by 30 DE genes) and the extracellular matrix-receptor interaction signaling pathway (represented by 12 DE genes). Furthermore, protein-protein interaction (PPI) network identified two central hub genes involved in the control of cell proliferation/differentiation processes, HDAC1 and CDK1. Overall, the analysis provided novel insights for broadening the exploration of AMD pathogenesis by extending the number of molecular determinants and functional pathways that underpin AMD-associated RPE/choroid dysfunction.

OmicsNet: a web-based tool for creation and visual analysis of biological networks in 3D space

Biological networks play increasingly important roles in omics data integration and systems biology. Over the past decade, many excellent tools have been developed to support creation, analysis and visualization of biological networks. However, important limitations remain: most tools are standalone programs, the majority of them focus on protein-protein interaction (PPI) or metabolic networks, and visualizations often suffer from 'hairball' effects when networks become large. To help address these limitations, we developed OmicsNet - a novel web-based tool that allows users to easily create different types of molecular interaction networks and visually explore them in a three-dimensional (3D) space. Users can upload one or multiple lists of molecules of interest (genes/proteins, microRNAs, transcription factors or metabolites) to create and merge different types of biological networks. The 3D network visualization system was implemented using the powerful Web Graphics Library (WebGL) technology that works natively in most major browsers. OmicsNet supports force-directed layout, multi-layered perspective layout, as well as spherical layout to help visualize and navigate complex networks. A rich set of functions have been implemented to allow users to perform coloring, shading, topology analysis, and enrichment analysis. OmicsNet is freely available at http://www.omicsnet.ca.

The Untapped Pharmacopeic Potential of Helminths

The dramatic rise in immunological disorders that occurs with socioeconomic development is associated with alterations in microbial colonization and reduced exposure to helminths. Excretory-secretory (E/S) helminth products contain a mixture of proteins and low-molecular-weight molecules representing the primary interface between parasite and host. Research has shown great pharmacopeic potential for helminth-derived products in animal disease models and even in clinical trials. Although in its infancy, the translation of worm-derived products into therapeutics is highly promising. Here, we focus on important key aspects in the development of immunomodulatory drugs, also highlighting novel approaches that hold great promise for future development of innovative research strategies.

Tolerogenic Transcriptional Signatures of Steady-State and Pathogen-Induced Dendritic Cells

Dendritic cells (DCs) are key directors of tolerogenic and immunogenic immune responses. During the steady state, DCs maintain T cell tolerance to self-antigens by multiple mechanisms including inducing anergy, deletion, and Treg activity. All of these mechanisms help to prevent autoimmune diseases or other hyperreactivities. Different DC subsets contribute to pathogen recognition by expression of different subsets of pattern recognition receptors, including Toll-like receptors or C-type lectins. In addition to the triggering of immune responses in infected hosts, most pathogens have evolved mechanisms for evasion of targeted responses. One such strategy is characterized by adopting the host’s T cell tolerance mechanisms. Understanding these tolerogenic mechanisms is of utmost importance for therapeutic approaches to treat immune pathologies, tumors and infections. Transcriptional profiling has developed into a potent tool for DC subset identification. Here, we review and compile pathogen-induced tolerogenic transcriptional signatures from mRNA profiling data of currently available bacterial- or helminth-induced transcriptional signatures. We compare them with signatures of tolerogenic steady-state DC subtypes to identify common and divergent strategies of pathogen induced immune evasion. Candidate molecules are discussed in detail. Our analysis provides further insights into tolerogenic DC signatures and their exploitation by different pathogens.

Meta-analysis of human gene expression in response to Mycobacterium tuberculosis infection reveals potential therapeutic targets

Background

With the global emergence of multi-drug resistant strains of Mycobacterium tuberculosis, new strategies to treat tuberculosis are urgently needed such as therapeutics targeting potential human host factors.

Results

Here we performed a statistical meta-analysis of human gene expression in response to both latent and active pulmonary tuberculosis infections from nine published datasets. We found 1655 genes that were significantly differentially expressed during active tuberculosis infection. In contrast, no gene was significant for latent tuberculosis. Pathway enrichment analysis identified 90 significant canonical human pathways, including several pathways more commonly related to non-infectious diseases such as the LRRK2 pathway in Parkinson’s disease, and PD-1/PD-L1 signaling pathway important for new immuno-oncology therapies. The analysis of human genome-wide association studies datasets revealed tuberculosis-associated genetic variants proximal to several genes in major histocompatibility complex for antigen presentation. We propose several new targets and drug-repurposing opportunities including intravenous immunoglobulin, ion-channel blockers and cancer immuno-therapeutics for development as combination therapeutics with anti-mycobacterial agents.

Conclusions

Our meta-analysis provides novel insights into host genes and pathways important for tuberculosis and brings forth potential drug repurposing opportunities for host-directed therapies.

Electronic supplementary material

The online version of this article (doi: 10.1186/s12918-017-0524-z) contains supplementary material, which is available to authorized users.

Life History, Systematics and Evolution of the Diplostomoidea Poirier, 1886: Progress, Promises and Challenges Emerging From Molecular Studies

Members of the Diplostomoidea mature in amniotes and employ vertebrates, annelids and molluscs as second intermediate hosts. Diplostomoid life cycles generally follow a three-host pattern typical of digeneans, but novelties have arisen in some species, including obligate four-host life cycles, vertical transmission, and intracellular parasitism. In this review, we summarize the basic biology of diplostomoids with reference to molecular studies, and present challenges, gaps and areas where molecular data could address long-standing questions. Our analysis of published studies revealed that most molecular surveys find more diplostomoid species than expected, but this tendency is influenced by how much effort goes into examining specimens morphologically and the number of sequenced worms. To date, molecular work has concentrated disproportionately on intraspecific or species-level diversity of larval stages in the Diplostomidae in temperate northern regions. Although the higher taxonomy of the superfamily is recognized to be in need of revision, little molecular work has been conducted at this level. Our phylogenetic analysis indicates several families and subfamilies require reconsideration, and that larval morphotypes are more reflective of evolutionary relationships than definitive hosts. The host associations of adult diplostomoids result from host-switching processes, whereas molecular surveys indicate that larval diplostomoid metacercariae have narrow ranges of second intermediate hosts, consistent with coevolution. Molecular data are often used to link diplostomoid developmental stages, and we provide data from adult Neodiplostomum and Mesoophorodiplostomum that correct earlier misidentifications of their larval stages and propose alternatives to collecting definitive hosts.