Efficient diagnosis of tuberculosis (TB) is met with multiple challenges, calling for a shift of focus from pathogen-centric diagnostics towards identification of host-based multi-marker signatures. Transcriptomics offer a list of differentially expressed genes, but cannot by itself identify the most influential contributors to the disease phenotype. Here, we describe a computational pipeline that adopts an unbiased approach to identify a biomarker signature. Data from RNA sequencing from whole blood samples of TB patients were integrated with a curated genome-wide molecular interaction network, from which we obtain a comprehensive perspective of variations that occur in the host due to TB. We then implement a sensitive network mining method to shortlist gene candidates that are most central to the disease alterations. We then apply a series of filters that include applicability to multiple publicly available datasets as well as additional validation on independent patient samples, and identify a signature comprising 10 genes — FCGR1A, HK3, RAB13, RBBP8, IFI44L, TIMM10, BCL6, SMARCD3, CYP4F3 and SLPI, that can discriminate between TB and healthy controls as well as distinguish TB from latent tuberculosis and HIV in most cases. The signature has the potential to serve as a diagnostic marker of TB.

Tuberculosis remains a major global health challenge worldwide, causing more than a million deaths annually. To determine newer methods for detecting and combating the disease, it is necessary to characterise global host responses to infection. Several high throughput omics studies have provided a rich resource including a list of several genes differentially regulated in tuberculosis. An integrated analysis of these studies is necessary to identify a unified response to the infection. Such data integration is met with several challenges owing to platform dependency, patient heterogeneity, and variability in the extent of infection, resulting in little overlap among different datasets. Network-based approaches offer newer alternatives to integrate and compare diverse data. In this study, we describe a meta-analysis of host’s whole blood transcriptomic profiles that were integrated into a genome-scale protein–protein interaction network to generate response networks in active tuberculosis, and monitor their behaviour over treatment. We report the emergence of a highly active common core in disease, showing partial reversals upon treatment. The core comprises 380 genes in which STAT1, phospholipid scramblase 1 (PLSCR1), C1QB, OAS1, GBP2 and PSMB9 are prominent hubs. This network captures the interplay between several biological processes including pro-inflammatory responses, apoptosis, complement signalling, cytoskeletal rearrangement, and enhanced cytokine and chemokine signalling. The common core is specific to tuberculosis, and was validated on an independent dataset from an Indian cohort. A network-based approach thus enables the identification of common regulators that characterise the molecular response to infection, providing a platform-independent foundation to leverage maximum insights from available clinical data.

Fibrosis is a condition shared by numerous inflammatory diseases. Our incomplete understanding of the molecular mechanisms underlying fibrosis has severely hampered effective drug development. CXCL4 is associated with the onset and extent of fibrosis development in multiple inflammatory and fibrotic diseases. Here, we used monocyte-derived cells as a model system to study the effects of CXCL4 exposure on dendritic cell development by integrating 65 longitudinal and paired whole genome transcriptional and methylation profiles. Using data-driven gene regulatory network analyses, we demonstrate that CXCL4 dramatically alters the trajectory of monocyte differentiation, inducing a novel pro-inflammatory and pro-fibrotic phenotype mediated via key transcriptional regulators including CIITA. Importantly, these pro-inflammatory cells directly trigger a fibrotic cascade by producing extracellular matrix molecules and inducing myofibroblast differentiation. Inhibition of CIITA mimicked CXCL4 in inducing a pro-inflammatory and pro-fibrotic phenotype, validating the relevance of the gene regulatory network. Our study unveils that CXCL4 acts as a key secreted factor driving innate immune training and forming the long-sought link between inflammation and fibrosis.

Together, this study identified a unique miRNA cluster associated with NIU that was related to changes in leukocyte subsets demonstrating systemic changes in epigenetic regulation underlying NIU.

GM‐CSF is important in regulating acute, persistent neutrophilic inflammation in certain settings, including lung injury. Ligand binding induces rapid internalization of the GM‐CSF receptor (GM‐CSFRα) complex, a process essential for signaling. Whereas GM‐CSF controls many aspects of neutrophil biology, regulation of GM‐CSFRα expression is poorly understood, particularly the role of GM‐CSFRα in ligand clearance and whether signaling is sustained despite major down‐regulation of GM‐CSFRα surface expression. We established a quantitative assay of GM‐CSFRα surface expression and used this, together with selective anti‐GM‐CSFR antibodies, to define GM‐CSFRα kinetics in human neutrophils, and in murine blood and alveolar neutrophils in a lung injury model. Despite rapid sustained ligand‐induced GM‐CSFRα loss from the neutrophil surface, which persisted even following ligand removal, pro‐survival effects of GM‐CSF required ongoing ligand‐receptor interaction. Neutrophils recruited to the lungs following LPS challenge showed initially high mGM‐CSFRα expression, which along with mGM‐CSFRβ declined over 24 hr; this was associated with a transient increase in bronchoalveolar lavage fluid (BALF) mGM‐CSF concentration. Treating mice in an LPS challenge model with CAM‐3003, an anti‐mGM‐CSFRα mAb, inhibited inflammatory cell influx into the lung and maintained the level of BALF mGM‐CSF. Consistent with neutrophil consumption of GM‐CSF, human neutrophils depleted exogenous GM‐CSF, independent of protease activity. These data show that loss of membrane GM‐CSFRα following GM‐CSF exposure does not preclude sustained GM‐CSF/GM‐CSFRα signaling and that this receptor plays a key role in ligand clearance. Hence neutrophilic activation via GM‐CSFR may play an important role in neutrophilic lung inflammation even in the absence of high GM‐CSF levels or GM‐CSFRα expression.

8Immune system is crucial for the development and progression of immune-mediated and non-9

Systemic sclerosis (SSc), systemic lupus erythematosus (SLE) and primary Sjögrens syndrome (pSS) are clinically distinct systemic autoimmune diseases (SADs) that share molecular pathways. We quantified the frequency of circulating immune‐cells in 169 patients with these SADs and 44 healty controls (HC) using mass‐cytometry and assessed the diagnostic value of these results. Alterations in the frequency of immune‐cell subsets were present in all SADs compared to HC. Most alterations, including a decrease of CD56hi NK‐cells in SSc and IgM+ Bcells in pSS, were disease specific; only a reduced frequency of plasmacytoid dendritic cells was common between all SADs Strikingly, hierarchical clustering of SSc patients identified 4 clusters associated with different clinical phenotypes, and 9 of the 12 cell subset‐alterations in SSc were also present during the preclinical‐phase of the disease. Additionally, we found a strong association between the use of prednisone and alterations in B‐cell subsets. Although differences in immune‐cell frequencies between these SADs are apparent, the discriminative value thereof is too low for diagnostic purposes. Within each disease, mass cytometry analyses revealed distinct patterns between endophenotypes. Given the lack of tools enabling early diagnosis of SSc, our results justify further research into the value of cellular phenotyping as a diagnostic aid.

ObjectiveDevelopment and progression of immune-mediated inflammatory diseases (IMIDs) involve intricate dysregulation of the disease-associated genes (DAGs) and their expressing immune cells. Identifying the crucial disease-associated cells (DACs) in IMIDs has been challenging due to the underlying complex molecular mechanism.MethodsUsing transcriptome profiles of 40 different immune cells, unsupervised machine learning, and disease-gene networks, we constructed the Disease-gene IMmune cell Expression (DIME) network and identified top DACs and DAGs of 12 phenotypically different IMIDs. We compared the DIME networks of IMIDs to identify common pathways between them. We used the common pathways and publicly available drug-gene network to identify promising drug repurposing targets.ResultsWe found CD4+Treg, CD4+Th1, and NK cells as top DACs in inflammatory arthritis such as ankylosing spondylitis (AS), psoriatic arthritis, and rheumatoid arthritis (RA); neutrophils, granulocytes, and BDCA1+CD14+ cells in systemic lupus erythematosus and systemic scleroderma; ILC2, CD4+Th1, CD4+Treg, and NK cells in the inflammatory bowel diseases (IBDs). We identified lymphoid cells (CD4+Th1, CD4+Treg, and NK) and their associated pathways to be important in HLA-B27 type diseases (psoriasis, AS, and IBDs) and in primary-joint-inflammation-based inflammatory arthritis (AS and RA). Based on the common cellular mechanisms, we identified lifitegrast as a potential drug repurposing candidate for Crohn’s disease and other IMIDs.ConclusionsExisting methods are inadequate in capturing the intricate involvement of the crucial genes and cell types essential to IMIDs. Our approach identified the key DACs, DAGs, common mechanisms between IMIDs, and proposed potential drug repurposing targets using the DIME network. To extend our method to other diseases, we built the DIME tool (https://bitbucket.org/systemsimmunology/dime/) to help scientists uncover the etiology of complex and rare diseases to further drug development by better-determining drug targets, thereby mitigating the risk of failure in late clinical development.