A comprehensive analysis of the circRNA-miRNA-mRNA network in osteocyte-like cell associated with Mycobacterium leprae infection.

BACKGROUND: Bone formation and loss are the characteristic clinical manifestations of leprosy, but the mechanisms underlying the bone remodeling with Mycobacterium leprae (M. leprae) infection are unclear. METHODOLOGY/PRINCIPAL FINDINGS: Osteocytes may have a role through regulating the differentiation of osteogenic lineages. To investigate osteocyte-related mechanisms in leprosy, we treated osteocyte-like cell with N-glycosylated muramyl dipeptide (N.g MDP). RNA-seq analysis showed 724 differentially expressed messenger RNAs (mRNAs) and 724 differentially expressed circular RNA (circRNAs). Of these, we filtered through eight osteogenic-related differentially expressed genes, according to the characteristic of competing endogenous RNA, PubMed databases, and bioinformatic analysis, including TargetScan, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes. Based on these results, we built a circRNA-microRNA (miRNA)-mRNA triple network. Quantitative reverse-transcription polymerase chain reaction and western blots analyses confirmed decreased Clock expression in osteocyte-like cell, while increased in bone mesenchymal stem cells (BMSCs), implicating a crucial factor in osteogenic differentiation. Immunohistochemistry showed obviously increased expression of CLOCK protein in BMSCs and osteoblasts in N.g MDP-treated mice, but decreased expression in osteocytes. CONCLUSIONS/SIGNIFICANCE: This analytical method provided a basis for the relationship between N.g MDP and remodeling in osteocytes, and the circRNA-miRNA-mRNA triple network may offer a new target for leprosy therapeutics.

Blood RNA signature RISK4LEP predicts leprosy years before clinical onset.

BACKGROUND: Leprosy, a chronic infectious disease caused by Mycobacterium leprae, is often late- or misdiagnosed leading to irreversible disabilities. Blood transcriptomic biomarkers that prospectively predict those who progress to leprosy (progressors) would allow early diagnosis, better treatment outcomes and facilitate interventions aimed at stopping bacterial transmission. To identify potential risk signatures of leprosy, we collected whole blood of household contacts (HC, n=5,352) of leprosy patients, including individuals who were diagnosed with leprosy 4-61 months after sample collection. METHODS: We investigated differential gene expression (DGE) by RNA-Seq between progressors before presence of symptoms (n=40) and HC (n=40), as well as longitudinal DGE within each progressor. A prospective leprosy signature was identified using a machine learning approach (Random Forest) and validated using reverse transcription quantitative PCR (RT-qPCR). FINDINGS: Although no significant intra-individual longitudinal variation within leprosy progressors was identified, 1,613 genes were differentially expressed in progressors before diagnosis compared to HC. We identified a 13-gene prospective risk signature with an Area Under the Curve (AUC) of 95.2%. Validation of this RNA-Seq signature in an additional set of progressors (n=43) and HC (n=43) by RT-qPCR, resulted in a final 4-gene signature, designated RISK4LEP (MT-ND2, REX1BD, TPGS1, UBC) (AUC=86.4%). INTERPRETATION: This study identifies for the first time a prospective transcriptional risk signature in blood predicting development of leprosy 4 to 61 months before clinical diagnosis. Assessment of this signature in contacts of leprosy patients can function as an adjunct diagnostic tool to target implementation of interventions to restrain leprosy development. FUNDING: This study was supported by R2STOP Research grant, the Order of Malta-Grants-for-Leprosy-Research, the Q.M. Gastmann-Wichers Foundation and the Leprosy Research Initiative (LRI) together with the Turing Foundation (ILEP# 702.02.73 and # 703.15.07).

Revisiting the expression signature of pks15/1 unveils regulatory patterns controlling phenolphtiocerol and phenolglycolipid production in pathogenic mycobacteria.

One of the most important and exclusive characteristics of mycobacteria is their cell wall. Amongst its constituent components are two related families of glycosylated lipids, diphthioceranates and phthiocerol dimycocerosate (PDIM) and its variant phenolic glycolipids (PGL). PGL have been associated with cell wall impermeability, phagocytosis, defence against nitrosative and oxidative stress and, intriguingly, biofilm formation. In bacteria from the Mycobacterium tuberculosis complex (MTBC), the biosynthetic pathway of the phenolphthiocerol moiety of PGL depends upon the expression of several genes encoding type I polyketide synthases (PKS), namely ppsA-E and pks15/1 which constitute the PDIM + PGL locus, and that are highly conserved in PDIM/PGL-producing strains. Consensus has not been achieved regarding the genetic organization of pks15/1 locus and knowledge is lacking on its transcriptional signature. Here we explore publicly available datasets of transcriptome data (RNA-seq) from more than 100 MTBC experiments in 40 growth conditions to outline the transcriptional structure and signature of pks15/1, using a differential expression approach to infer the regulatory patterns involving these and related genes. We show that pks1 expression is highly correlated with fadD22, Rv2949c, lppX, fadD29 and, also, pks6 and pks12, with the first three putatively integrating into a polycistronic structure. We evidence dynamic transcriptional heterogeneity within the genes involved in phenolphtiocerol and phenolic glycolipid production, most exhibiting up-regulation upon acidic pH and antibiotic exposure and down-regulation under hypoxia, dormancy, and low/high iron concentration. We finally propose a model based on transcriptome data in which σD positively regulates pks1, pks15 and fadD22, while σB and σE factors exert negative regulation at an upper level.

Cell-type deconvolution with immune pathways identifies gene networks of host defense and immunopathology in leprosy.

Transcriptome profiles derived from the site of human disease have led to the identification of genes that contribute to pathogenesis, yet the complex mixture of cell types in these lesions has been an obstacle for defining specific mechanisms. Leprosy provides an outstanding model to study host defense and pathogenesis in a human infectious disease, given its clinical spectrum, which interrelates with the host immunologic and pathologic responses. Here, we investigated gene expression profiles derived from skin lesions for each clinical subtype of leprosy, analyzing gene coexpression modules by cell-type deconvolution. In lesions from tuberculoid leprosy patients, those with the self-limited form of the disease, dendritic cells were linked with MMP12 as part of a tissue remodeling network that contributes to granuloma formation. In lesions from lepromatous leprosy patients, those with disseminated disease, macrophages were linked with a gene network that programs phagocytosis. In erythema nodosum leprosum, neutrophil and endothelial cell gene networks were identified as part of the vasculitis that results in tissue injury. The present integrated computational approach provides a systems approach toward identifying cell-defined functional networks that contribute to host defense and immunopathology at the site of human infectious disease.

A powerful score-based statistical test for group difference in weighted biological networks.

BACKGROUND: Complex disease is largely determined by a number of biomolecules interwoven into networks, rather than a single biomolecule. A key but inadequately addressed issue is how to test possible differences of the networks between two groups. Group-level comparison of network properties may shed light on underlying disease mechanisms and benefit the design of drug targets for complex diseases. We therefore proposed a powerful score-based statistic to detect group difference in weighted networks, which simultaneously capture the vertex changes and edge changes. RESULTS: Simulation studies indicated that the proposed network difference measure (NetDifM) was stable and outperformed other methods existed, under various sample sizes and network topology structure. One application to real data about GWAS of leprosy successfully identified the specific gene interaction network contributing to leprosy. For additional gene expression data of ovarian cancer, two candidate subnetworks, PI3K-AKT and Notch signaling pathways, were considered and identified respectively. CONCLUSIONS: The proposed method, accounting for the vertex changes and edge changes simultaneously, is valid and powerful to capture the group difference of biological networks.

Integrative analyses of leprosy susceptibility genes indicate a common autoimmune profile.

BACKGROUND: Leprosy is an ancient chronic infection in the skin and peripheral nerves caused by Mycobacterium leprae. The development of leprosy depends on genetic background and the immune status of the host. However, there is no systematic view focusing on the biological pathways, interaction networks and overall expression pattern of leprosy-related immune and genetic factors. OBJECTIVES: To identify the hub genes in the center of leprosy genetic network and to provide an insight into immune and genetic factors contributing to leprosy. METHODS: We retrieved all reported leprosy-related genes and performed integrative analyses covering gene expression profiling, pathway analysis, protein-protein interaction network, and evolutionary analyses. RESULTS: A list of 123 differentially expressed leprosy related genes, which were enriched in activation and regulation of immune response, was obtained in our analyses. Cross-disorder analysis showed that the list of leprosy susceptibility genes was largely shared by typical autoimmune diseases such as lupus erythematosus and arthritis, suggesting that similar pathways might be affected in leprosy and autoimmune diseases. Protein-protein interaction (PPI) and positive selection analyses revealed a co-evolution network of leprosy risk genes. CONCLUSIONS: Our analyses showed that leprosy associated genes constituted a co-evolution network and might undergo positive selection driven by M. leprae. We suggested that leprosy may be a kind of autoimmune disease and the development of leprosy is a matter of defect or over-activation of body immunity.

A novel integrative network approach to understand the interplay between cardiovascular disease and other complex disorders.

There is accumulating evidence that the proteins encoded by the genes associated with a common disorder interact with each other, participate in similar pathways and share GO terms. It has been anticipated that the functional modules in a disease related functional linkage network can be integrated with bibliomics to reveal association with other complex disorders. In this study, the cardiovascular disease functional linkage network (CFN) containing 1536 nodes and 3345 interactions was constructed using proteins encoded by 234 genes associated with the disease. Integration of CFN with bibliomics showed that 227 out of 566 functional modules are significantly associated with one or more diseases. Analysis of functional modules revealed the possible regulatory roles of SP1 and CXCL12 in the pathogenesis of cardiovascular disease (CVD) and modulation of their activities may be considered as potential therapeutic tools. The integration of CFN with bibliomics also indicated significant relations of CVD with other complex disorders. In a stratified map the members of 227 functional modules and 58 diseases in 15 disease classes were combined. In this map, leprosy, listeria monocytogenes, myasthenia, hemorrhagic diathesis and Protein S deficiency, which were not previously reported to be associated with CVD, showed significant associations. Several cancers arising from epithelial cells were also found to be linked to other diseases through hub proteins, VEGFA and PTGS2.

Genomewide association study of leprosy.

BACKGROUND: The narrow host range of Mycobacterium leprae and the fact that it is refractory to growth in culture has limited research on and the biologic understanding of leprosy. Host genetic factors are thought to influence susceptibility to infection as well as disease progression. METHODS: We performed a two-stage genomewide association study by genotyping 706 patients and 1225 controls using the Human610-Quad BeadChip (Illumina). We then tested three independent replication sets for an association between the presence of leprosy and 93 single-nucleotide polymorphisms (SNPs) that were most strongly associated with the disease in the genomewide association study. Together, these replication sets comprised 3254 patients and 5955 controls. We also carried out tests of heterogeneity of the associations (or lack thereof) between these 93 SNPs and disease, stratified according to clinical subtype (multibacillary vs. paucibacillary). RESULTS: We observed a significant association (P<1.00x10(-10)) between SNPs in the genes CCDC122, C13orf31, NOD2, TNFSF15, HLA-DR, and RIPK2 and a trend toward an association (P=5.10x10(-5)) with a SNP in LRRK2. The associations between the SNPs in C13orf31, LRRK2, NOD2, and RIPK2 and multibacillary leprosy were stronger than the associations between these SNPs and paucibacillary leprosy. CONCLUSIONS: Variants of genes in the NOD2-mediated signaling pathway (which regulates the innate immune response) are associated with susceptibility to infection with M. leprae.