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SARS-CoV-2 uses ACE2 and TMPRSS2 to gain entry into the cell. However, recent studies have shown that SARS-CoV-2 may use additional host factors that are required for the viral lifecycle. Here we used publicly available datasets, CoV-associated genes, and machine learning algorithms to explore the SARS-CoV-2 interaction landscape in different tissues. We found that in general a small fraction of cells express ACE2 in the different tissues, including nasal, bronchi, and lungs. We show that a small fraction of immune cells (including T cells, macrophages, dendritic cells) found in tissues also express ACE2. We show that healthy circulating immune cells do not express ACE2 and TMPRSS2. However, a small fraction of circulating immune cells (including dendritic cells, monocytes, T cells) in the PBMC of COVID-19 patients express ACE2 and TMPRSS2. Additionally, we found that a large spectrum of cells (in tissues and circulation) in both healthy and COVID-19-positive patients were significantly enriched for SARS-CoV-2 factors, such as those associated with RHOA and RAB GTPases, mRNA translation proteins, COPI- and COPII-mediated transport, and integrins. Thus, we propose that further research is needed to explore if SARS-CoV-2 can directly infect tissue and circulating immune cells to better understand the virus’ mechanism of action.

SARS-CoV-2 uses ACE2 and TMPRSS2 to gain entry into the cell. However, recent studies have shown that SARS-CoV-2 may use additional host factors that are required for the viral lifecycle. Here we used publicly available datasets, CoV associated genes and machine learning algorithms to explore the SARS-CoV-2 interaction landscape in different tissues. We find that in general a small fraction of cells expresses ACE2 in the different tissues including nasal, bronchi and lungs. We show that a small fraction of immune cells (including T-cells, macrophages, dendritic cells) found in tissues also express ACE2. We show that healthy circulating immune cells do not express ACE2 and TMPRSS2. However, a small fraction of circulating immune cells (including dendritic cells, monocytes, T-cells) in the PBMC of COVID-19 patients express ACE2 and TMPRSS2. Additionally, we found that a large spectrum of cells (in circulation and periphery) in both healthy and COVID-19 positive patients were significantly enriched for SARS-CoV-2 factors. Thus, we propose that further research is needed to explore if SARS-CoV-2 can directly infect peripheral immune cells to better understand the virus’ mechanism of action.

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., Highlights • An integrated systems biology approach has been adopted to study the host response to tuberculosis. • A multi-gene host biomarker signature is identified for detecting pulmonary tuberculosis from patient blood samples • The signature discriminates TB from HIV and latent-TB and can serve as an adjuvant tool in confirming TB diagnosis Host factors that are altered significantly due to tuberculosis are investigated, with an aim to identify a biomarker panel. A network approach provides a genome-wide view of the molecular interactions, analogous to a road network of a city. By comparing networks between healthy and TB samples, we identify the set of variations in a systematic fashion, analogous to identifying all major variations in the traffic flow in a city between two time points. We then apply a series of filters to identify the most discriminating genes among them. The 10-gene signature is seen to be characteristic of TB.

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.

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 systemic sclerosis, a prototypic inflammatory and fibrotic disease. Here, we integrated 65 paired longitudinal transcriptional and methylation profiles from monocyte-derived cells with/without CXCL4 exposure. 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. CXCL4 exposed monocyte-derived cells directly trigger a fibrotic cascade by producing ECM 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 CXCL4 as a key secreted factor driving innate immune training and forming the long-sought link between inflammation and fibrosis.

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., GM‐CSF released during ALI stimulates neutrophil recruitment, induces down‐regulation of GM‐CSFR, and GM‐CSF consumption by neutrophils, yet sustained anti‐apoptotic signals require continued GM‐CSF stimulation.

Systemic sclerosis (SSc), systemic lupus erythematosus (SLE) and primary Sjogrens 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.

Purpose The cause of noninfectious uveitis (NIU) is poorly understood but is considered to be mediated by a complex interplay between genetic, environmental, and-relatively unexplored-epigenetic factors. MicroRNAs (miRNAs) are noncoding small RNAs that are important epigenetic regulators implicated in pathologic signaling. Therefore, we mapped the circulating miRNA-ome of NIU patients and studied miRNA perturbations within the broader context of the immune system. Methods We designed a strategy to robustly identify changes in the miRNA profiles of two independent cohorts totaling 54 untreated patients with active and eye-restricted disease and 26 age-matched controls. High-resolution miRNA-ome data were obtained by TaqMan OpenArray technology and subsequent RT-qPCR. Flow cytometry data, and proteomic data spanning the cellular immune system, were used to map the uveitis-miRNA signature to changes in the composition of specific leukocyte subsets in blood. Results Using stringent selection criteria, we identified and independently validated an miRNA cluster that is associated with NIU. Pathway enrichment analysis for genes targeted by this cluster revealed significant enrichment for the PI3K/Akt, MAPK, FOXO, and VEGF signaling pathways, and photoreceptor development. In addition, unsupervised multidomain analyses linked the presence of the uveitis-associated miRNA cluster to a different composition of leukocyte subsets, more specifically, CD16+CD11c+HLA-DR- cells. Conclusions 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.

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., Tuberculosis: an underlying common-core host response network Patients suffering from tuberculosis (TB) show a high extent of variations in their gene expression patterns. Such heterogeneity poses major road blocks to our understanding of how hosts respond to the disease. A number of studies have profiled transcriptomes of human blood samples from TB patients, but a meta-analysis indicates that very few changes are consistently seen. The problem, to a large extent, lies with the way large data is analysed. We have used a genome-wide network approach to characterise the host response and have identified a common-core in the TB response networks of different patients, indicating the presence of unified host response mechanisms. This core network provides a comprehensive view into the most significant regulators of the infection-mediated biological processes across patients from different populations, and it shows partial reversals upon treatment.