Impact of Aging and HIV Infection on the Function of the C-Type Lectin Receptor MINCLE in Monocytes

Dectin-1 stimulation promotes a distinct inflammatory signature in the setting of HIV-infection and aging

Dectin-1 is an innate immune receptor that recognizes and binds β-1, 3/1, 6 glucans on fungi. We evaluated Dectin-1 function in myeloid cells in a cohort of HIV-positive and HIV-negative young and older adults. Stimulation of monocytes with β-D-glucans induced a pro-inflammatory phenotype in monocytes of HIV-infected individuals that was characterized by increased levels of IL-12, TNF-α, and IL-6, with some age-associated cytokine increases also noted. Dendritic cells showed a striking HIV-associated increase in IFN-α production. These increases in cytokine production paralleled increases in Dectin-1 surface expression in both monocytes and dendritic cells that were noted with both HIV and aging. Differential gene expression analysis showed that HIV-positive older adults had a distinct gene signature compared to other cohorts characterized by a robust TNF-α and coagulation response (increased at baseline), a persistent IFN-α and IFN-γ response, and an activated dendritic cell signature/M1 macrophage signature upon Dectin-1 stimulation. Dectin-1 stimulation induced a strong upregulation of MTORC1 signaling in all cohorts, although increased in the HIV-Older cohort (stimulation and baseline). Overall, our study demonstrates that the HIV Aging population has a distinct immune signature in response to Dectin-1 stimulation. This signature may contribute to the pro-inflammatory environment that is associated with HIV and aging.

Myeloid C-Type Lectin Receptors in Tuberculosis and HIV Immunity: Insights Into Co-infection?

C-type lectin receptors (CLRs) are carbohydrate binding pattern recognition receptors (PRRs) which play a central role in host recognition of pathogenic microorganisms. Signaling through CLRs displayed on antigen presenting cells dictates important innate and adaptive immune responses. Several pathogens have evolved mechanisms to exploit the receptors or signaling pathways of the CLR system to gain entry or propagate in host cells. CLR responses to high priority pathogens such as Mycobacterium tuberculosis (Mtb), HIV, Ebola, and others are described and considered potential avenues for therapeutic intervention. Mtb and HIV are the leading causes of death due to infectious disease and have a synergistic relationship that further promotes aggressive disease in co-infected persons. Immune recognition through CLRs and other PRRs are important determinants of disease outcomes for both TB and HIV. Investigations of CLR responses to Mtb and HIV, to date, have primarily focused on single infection outcomes and do not account for the potential effects of co-infection. This review will focus on CLRs recognition of Mtb and HIV motifs. We will describe their respective roles in protective immunity and immune evasion or exploitation, as well as their potential as genetic determinants of disease susceptibility, and as avenues for development of therapeutic interventions. The potential convergence of CLR-driven responses of the innate and adaptive immune systems in the setting of Mtb and HIV co-infection will further be discussed relevant to disease pathogenesis and development of clinical interventions.

A comparison of curated gene sets versus transcriptomics-derived gene signatures for detecting pathway activation in immune cells

BACKGROUND

Despite the significant contribution of transcriptomics to the fields of biological and biomedical research, interpreting long lists of significantly differentially expressed genes remains a challenging step in the analysis process. Gene set enrichment analysis is a standard approach for summarizing differentially expressed genes into pathways or other gene groupings. Here, we explore an alternative approach to utilizing gene sets from curated databases. We examine the method of deriving custom gene sets which may be relevant to a given experiment using reference data sets from previous transcriptomics studies. We call these data-derived gene sets, "gene signatures" for the biological process tested in the previous study. We focus on the feasibility of this approach in analyzing immune-related processes, which are complicated in their nature but play an important role in the medical research.

RESULTS

We evaluate several statistical approaches to detecting the activity of a gene signature in a target data set. We compare the performance of the data-derived gene signature approach with comparable GO term gene sets across all of the statistical tests. A total of 61 differential expression comparisons generated from 26 transcriptome experiments were included in the analysis. These experiments covered eight immunological processes in eight types of leukocytes. The data-derived signatures were used to detect the presence of immunological processes in the test data with modest accuracy (AUC = 0.67). The performance for GO and literature based gene sets was worse (AUC = 0.59). Both approaches were plagued by poor specificity.

CONCLUSIONS

When investigators seek to test specific hypotheses, the data-derived signature approach can perform as well, if not better than standard gene-set based approaches for immunological signatures. Furthermore, the data-derived signatures can be generated in the cases that well-defined gene sets are lacking from pathway databases and also offer the opportunity for defining signatures in a cell-type specific manner. However, neither the data-derived signatures nor standard gene-sets can be demonstrated to reliably provide negative predictions for negative cases. We conclude that the data-derived signature approach is a useful and sometimes necessary tool, but analysts should be weary of false positives.