CD4+ T cell-dependent and CD4+ T cell-independent cytokine-chemokine network changes in the immune responses of HIV-infected individuals

Distribution modeling quantifies collective TH cell decision circuits in chronic inflammation

Immune responses are tightly regulated by a diverse set of interacting immune cell populations. Alongside decision-making processes such as differentiation into specific effector cell types, immune cells initiate proliferation at the beginning of an inflammation, forming two layers of complexity. Here, we developed a general mathematical framework for the data-driven analysis of collective immune cell dynamics. We identified qualitative and quantitative properties of generic network motifs, and we specified differentiation dynamics by analysis of kinetic transcriptome data. Furthermore, we derived a specific, data-driven mathematical model for T helper 1 versus T follicular helper cell-fate decision dynamics in acute and chronic lymphocytic choriomeningitis virus infections in mice. The model recapitulates important dynamical properties without model fitting and solely by using measured response-time distributions. Model simulations predict different windows of opportunity for perturbation in acute and chronic infection scenarios, with potential implications for optimization of targeted immunotherapy.

Human Variation in DNA Repair, Immune Function, and Cancer Risk

DNA damage constantly threatens genome integrity, and DNA repair deficiency is associated with increased cancer risk. An intuitive and widely accepted explanation for this relationship is that unrepaired DNA damage leads to carcinogenesis due to the accumulation of mutations in somatic cells. But DNA repair also plays key roles in the function of immune cells, and immunodeficiency is an important risk factor for many cancers. Thus, it is possible that emerging links between inter-individual variation in DNA repair capacity and cancer risk are driven, at least in part, by variation in immune function, but this idea is underexplored. In this review we present an overview of the current understanding of the links between cancer risk and both inter-individual variation in DNA repair capacity and inter-individual variation in immune function. We discuss factors that play a role in both types of variability, including age, lifestyle, and environmental exposures. In conclusion, we propose a research paradigm that incorporates functional studies of both genome integrity and the immune system to predict cancer risk and lay the groundwork for personalized prevention.

The PI3K∂-Selective Inhibitor Idelalisib Induces T- and NK-Cell Dysfunction Independently of B-Cell Malignancy-Associated Immunosuppression

B-cell receptors, multiple receptor tyrosine kinases, and downstream effectors are constitutively active in chronic lymphocytic leukemia (CLL) B cells. Activation of these pathways results in resistance to apoptosis and enhanced survival of the leukemic cells. Idelalisib is a highly selective inhibitor of the PI3K p110∂ isoform and is approved for the treatment of CLL in patients with relapsed/refractory disease or in those harboring 17p deletions or tp53 mutations. Despite the initial excitement centered around high response rates in clinical trials of idelalisib, its therapeutic success has been hindered by the incidence of severe opportunistic infections. To examine the potential contribution of idelalisib to the increased risk of infection, we investigated the effects of idelalisib on the immune cell compartments of healthy donors (HDs) and CLL patients. PI3K∂ blockade by idelalisib reduced the expression levels of inhibitory checkpoint molecules in T cells isolated from both HDs and CLL patients. In addition, the presence of idelalisib in cultures significantly decreased T-cell-mediated cytotoxicity and granzyme B secretion, as well as cytokine secretion levels in both cohorts. Furthermore, idelalisib reduced the proliferation and cytotoxicity of HD NK cells. Collectively, our data demonstrate that both human T and NK cells are highly sensitive to PI3K∂ inhibition. Idelalisib interfered with the functions of T and NK cell cells from both HDs and CLL patients. Therefore, idelalisib might contribute to an increased risk of infections regardless of the underlying B-cell malignancy.

Network analysis of the immune state of mice

The mammalian immune system protects individuals from infection and disease. It is a complex system of interacting cells and molecules, which has been studied extensively to investigate its detailed function, principally using laboratory mice. Despite the complexity of the immune system, it is often analysed using a restricted set of immunological parameters. Here we have sought to generate a system-wide view of the murine immune response, which we have done by undertaking a network analysis of 120 immune measures. To date, there has only been limited network analyses of the immune system. Our network analysis identified a relatively low number of communities of immune measure nodes. Some of these communities recapitulate the well-known T helper 1 vs. T helper 2 cytokine polarisation (where ordination analyses failed to do so), which validates the utility of our approach. Other communities we detected show apparently novel juxtapositions of immune nodes. We suggest that the structure of these other communities might represent functional immunological units, which may require further empirical investigation. These results show the utility of network analysis in understanding the functioning of the mammalian immune system.

High circulating SDF-1and MCP-1 levels and genetic variations in CXCL12, CCL2 and CCR5: Prognostic signature of immune recovery status in treated HIV-positive patients

Background

The underlying mechanisms of incomplete immune reconstitution in treated HIV-positive patients are very complex and may be multifactorial, but perturbation of chemokine secretion could play a key role in CD4⁺T-cell turnover.

Methods

We evaluated the circulating baseline and 48-week follow-up concentrations of SDF-1/CXCL12, fractalkine/CX3CL1, MCP-1/CCL2, MIP-α/CCL3, MIP-β/CCL4 and RANTES/CCL5, and we estimated their association with CXCL12, CX3CR1, CCR2, CCL5 and CCR5 single nucleotide polymorphisms (SNPs) to investigate multiple chemokine-chemokine receptor signatures associated with immune dysregulation preceding poor immune recovery.

Findings

The circulating concentrations and gene expression patterns of SDF-1/CXCL12 (CXCL12 rs1801157) and MCP-1/CCL2 (CCR2 rs1799864_814) were associated with immune recovery status. CCR2 rs1799864_814 and CCR5 rs333_814 (Δ32) determine the baseline plasma RANTES and MIP-α concentrations, respectively, in participants with poor immune response.

Interpretation

SDF-1/CXCL12 and MCP-1/CCL2 could be considered prognostic markers of immune failure despite suppressive antiretroviral therapy. The strong linkage disequilibrium (LD) between CCR2 rs1799864_814 and CCR5 rs1800024 indicated that the alleles of each gene are inherited together more often than would be expected by chance.

Funding

This work was supported by Fondo de Investigacion Sanitaria and SPANISH AIDS Research Network (ISCIII-FEDER); AGAUR and Gilead Fellowship. FV and YMP are supported by grants from the Programa de Intensificación (ISCIII) and Servicio Andaluz de Salud, respectively. JVG,EY and LR are supported by the 10.13039/501100004587Instituto de Salud Carlos III (ISCIII). AR is supported by Departament de Salut, Generalitat de Catalunya and by the Instituto de Salud Carlos III (ISCIII).

Robust latent-variable interpretation of in vivo regression models by nested resampling

Simple multilinear methods, such as partial least squares regression (PLSR), are effective at interrelating dynamic, multivariate datasets of cell-molecular biology through high-dimensional arrays. However, data collected in vivo are more difficult, because animal-to-animal variability is often high, and each time-point measured is usually a terminal endpoint for that animal. Observations are further complicated by the nesting of cells within tissues or tissue sections, which themselves are nested within animals. Here, we introduce principled resampling strategies that preserve the tissue-animal hierarchy of individual replicates and compute the uncertainty of multidimensional decompositions applied to global averages. Using molecular-phenotypic data from the mouse aorta and colon, we find that interpretation of decomposed latent variables (LVs) changes when PLSR models are resampled. Lagging LVs, which statistically improve global-average models, are unstable in resampled iterations that preserve nesting relationships, arguing that these LVs should not be mined for biological insight. Interestingly, resampling is less discriminatory for multidimensional regressions of in vitro data, where replicate-to-replicate variance is sufficiently low. Our work illustrates the challenges and opportunities in translating systems-biology approaches from cultured cells to living organisms. Nested resampling adds a straightforward quality-control step for interpreting the robustness of in vivo regression models.

Adipose-derived stem cell-secreted factors promote early stage follicle development in a biomimetic matrix

Development of primary follicles in vitro benefits from a three-dimensional matrix that is enriched with paracrine factors secreted from feeder cells and mimics the in vivo environment. In this study, we investigated the role of paracrine signaling from adipose-derived stem cells (ADSCs) in supporting primary follicle development in a biomimetic poly(ethylene glycol) (PEG)-based matrix. Follicles co-cultured with ADSCs and follicles cultured in conditioned medium from ADSCs encapsulated in gels (3D CM) exhibited significantly (p < 0.01 and p = 0.09, respectively) improved survival compared to follicles cultured in conditioned medium collected from ADSCs cultured in flasks (2D CM) and follicles cultured without paracrine support. The gene expression of ADSCs suggested that the stem cells maintained their multipotency in the 3D PEG environment over the culture period, regardless of the presence of the follicles, while under 2D conditions the multipotency markers were downregulated. The differences in cytokine signatures of follicles exposed to 3D and 2D ADSC paracrine factors suggest that early cytokine interactions are key for follicle survival. Taken together, the biomimetic PEG scaffold provides a three-dimensional, in vivo-like environment to induce ADSCs to secrete factors which promote early stage ovarian follicle development and survival.

Synergy of Paracrine Signaling During Early-Stage Mouse Ovarian Follicle Development In Vitro

Introduction

Paracrine signals, such as soluble cytokines and extracellular matrix cues, are essential for the survival and development of multicellular ovarian follicles. While it is well established that hydrogel-based culture systems successfully support the growth of late-stage follicles for fertility preservation, growing small, early-stage ovarian follicles still proves to be challenging. We hypothesized that paracrine factors secreted from neighboring follicles may be crucial for improving the survival of early-stage follicles in vitro.

Methods

To test our hypothesis, we investigated the bi-directional crosstalk of the paracrine signals, such as cell-secreted cytokines, sex hormones and transcription factors (TFs), in follicles encapsulated and cultured for 12 days in alginate in groups of five (5×) and ten (10×).

Results

The differential profiles of TF activity and secretome during folliculogenesis were analyzed using TRanscriptional Activity CEllular aRray (TRACER) and data-driven multivariate modeling approach. The mechano- and oxygen-responsive TFs, NF-κB and HIF1, exhibited a unique upregulation signature in 10× follicles. Consistently, levels of proangiogenic factors, such as VEGF-A and angiopoietin-2, were significantly higher in 10× follicles than those in 5× follicles, reaching 269.77 and 242.82 pg/mL on the last day of culture. The analysis of TRACER and secreted cytokines also revealed critical early interactions between cytokines and TFs, correlating with the observed phenotypical and functional differences between conditions.

Conclusions

We identified unique signatures of synergism during successful early-stage ovarian follicle development. These findings bring us closer to understanding of mechanisms underlying the downstream effects of interactions between the extracellular microenvironment and early-stage folliculogenesis in vitro.

Advancing systems immunology through data-driven statistical analysis

Systems biology provides an effective approach to decipher, predict, and ultimately manipulate the complex and inter-connected networks that regulate the immune system. Advances in high-throughput, multiplexed experimental techniques have increased the availability of proteomic and transcriptomic immunological datasets, and as a result, have also accelerated the development of new data-driven computational algorithms to extract biological insight from these data. This review highlights how data-driven statistical models have been used to characterize immune cell subsets and their functions, to map the signaling and intercellular networks that regulate immune responses, and to connect immune cell states to disease outcomes to generate hypotheses for novel therapeutic strategies. We focus on recent advances in evaluating immune cell responses following viral infection and in the tumor microenvironment, which hold promise for improving vaccines, antiviral and cancer immunotherapy.

Prospects from systems serology research

Antibodies are highly functional glycoproteins capable of providing immune protection through multiple mechanisms, including direct pathogen neutralization and the engagement of their Fc portions with surrounding effector immune cells that induce anti-pathogenic responses. Small modifications to multiple antibody biophysical features induced by vaccines can significantly alter functional immune outcomes, though it is difficult to predict which combinations confer protective immunity. In order to give insight into the highly complex and dynamic processes that drive an effective humoral immune response, here we discuss recent applications of 'Systems Serology', a new approach that uses data-driven (also called 'machine learning') computational analysis and high-throughput experimental data to infer networks of important antibody features associated with protective humoral immunity and/or Fc functional activity. This approach offers the ability to understand humoral immunity beyond single correlates of protection, assessing the relative importance of multiple biophysical modifications to antibody features with multivariate computational approaches. Systems Serology has the exciting potential to help identify novel correlates of protection from infection and may generate a more comprehensive understanding of the mechanisms behind protection, including key relationships between specific Fc functions and antibody biophysical features (e.g. antigen recognition, isotype, subclass and/or glycosylation events). Reviewed here are some of the experimental and computational technologies available for Systems Serology research and evidence that the application has broad relevance to multiple different infectious diseases including viruses, bacteria, fungi and parasites.

Systems analysis of latent HIV reversal reveals altered stress kinase signaling and increased cell death in infected T cells

Viral latency remains the most significant obstacle to HIV eradication. Clinical strategies aim to purge the latent CD4+ T cell reservoir by activating viral expression to induce death, but are undercut by the inability to target latently infected cells. Here we explored the acute signaling response of latent HIV-infected CD4+ T cells to identify dynamic phosphorylation signatures that could be targeted for therapy. Stimulation with CD3/CD28, PMA/ionomycin, or latency reversing agents prostratin and SAHA, yielded increased phosphorylation of IκBα, ERK, p38, and JNK in HIV-infected cells across two in vitro latency models. Both latent infection and viral protein expression contributed to changes in perturbation-induced signaling. Data-driven statistical models calculated from the phosphorylation signatures successfully classified infected and uninfected cells and further identified signals that were functionally important for regulating cell death. Specifically, the stress kinase pathways p38 and JNK were modified in latently infected cells, and activation of p38 and JNK signaling by anisomycin resulted in increased cell death independent of HIV reactivation. Our findings suggest that altered phosphorylation signatures in infected T cells provide a novel strategy to more selectively target the latent reservoir to enhance eradication efforts.

Genital-Systemic Chemokine Gradients and the Risk of HIV Acquisition in Women

Supplemental Digital Content is Available in the Text.

Background:

Mucosal and systemic immune mediators have been independently associated with HIV acquisition risk, but the relationship between compartments remains unclear.

Methods:

To address this, the concentrations of 12 cytokines were compared in matched plasma and cervicovaginal lavages (CVLs) from 57 HIV-positive women before their acquisition of HIV (cases) and 50 women who remained uninfected (controls) during the CAPRISA 004 trial.

Results:

Although genital IP-10 concentrations were significantly higher in cases, plasma IP-10 concentrations were inversely associated with HIV risk. Comparing differences in mucosal and systemic cytokine concentrations between cases and controls, mucosa-biased gradients indicating higher cervicovaginal lavage relative to plasma concentrations were observed for all 5 chemokines in the panel. Four were significantly associated with HIV acquisition, including IP-10 (odds ratio [OR] 1.73, 95% confidence interval [CI]: 1.27 to 2.36), macrophage inflammatory protein–1β (OR 1.72, 95% CI: 1.23 to 2.40), interleukin (IL)-8 (OR 1.50, 95% CI: 1.09 to 2.05), and monocyte chemotactic protein-1 (OR 1.36, 95% CI: 1.01 to 1.83). None of the other 7 cytokines tested predicted HIV risk. Decision tree analyses confirmed this association, with gradients of IP-10, IL-8, and granulocyte-macrophage colony-stimulating factor concentrations correctly classifying 77% of HIV outcomes.

Conclusions:

Our findings suggest that mucosa-biased gradients of IP-10, macrophage inflammatory protein–1β, IL-8, and monocyte chemotactic protein-1 are associated with an increased risk of HIV infection.

Systems analysis of dynamic transcription factor activity identifies targets for treatment in Olaparib resistant cancer cells

The development of resistance to targeted therapeutics is a challenging issue for the treatment of cancer. Cancers that have mutations in BRCA, a DNA repair protein, have been treated with poly(ADP-ribose) polymerase (PARP) inhibitors, which target a second DNA repair mechanism with the aim of inducing synthetic lethality. While these inhibitors have shown promise clinically, the development of resistance can limit their effectiveness as a therapy. This study investigated mechanisms of resistance in BRCA-mutated cancer cells (HCC1937) to Olaparib (AZD2281) using TRACER, a technique for measuring dynamics of transcription factor (TF) activity in living cells. TF activity was monitored in the parental HCC1937 cell line and two distinct resistant cell lines, one with restored wild-type BRCA1 and one with acquired resistance independent of BRCA1 for 48 h during treatment with Olaparib. Partial least squares discriminant analysis (PLSDA) was used to categorize the three cell types based on TF activity, and network analysis was used to investigate the mechanism of early response to Olaparib in the study cells. NOTCH signaling was identified as a common pathway linked to resistance in both Olaparib-resistant cell types. Western blotting confirmed upregulation of NOTCH protein, and sensitivity to Olaparib was restored through co-treatment with a gamma secretase inhibitor. The identification of NOTCH signaling as a common pathway contributing to PARP inhibitor resistance by TRACER indicates the efficacy of transcription factor dynamics in identifying targets for intervention in treatment-resistant cancer and provides a new method for determining effective strategies for directed chemotherapy. Biotechnol. Bioeng. 2017;114: 2085-2095. © 2017 Wiley Periodicals, Inc.

Demystifying the cytokine network: Mathematical models point the way

Cytokines provide the means by which immune cells communicate with each other and with parenchymal cells. There are over one hundred cytokines and many exist in families that share receptor components and signal transduction pathways, creating complex networks. Reductionist approaches to understanding the role of specific cytokines, through the use of gene-targeted mice, have revealed further complexity in the form of redundancy and pleiotropy in cytokine function. Creating an understanding of the complex interactions between cytokines and their target cells is challenging experimentally. Mathematical and computational modeling provides a robust set of tools by which complex interactions between cytokines can be studied and analyzed, in the process creating novel insights that can be further tested experimentally. This review will discuss and provide examples of the different modeling approaches that have been used to increase our understanding of cytokine networks. This includes discussion of knowledge-based and data-driven modeling approaches and the recent advance in single-cell analysis. The use of modeling to optimize cytokine-based therapies will also be discussed.

Synergistic Communication between CD4+ T Cells and Monocytes Impacts the Cytokine Environment

Physiological cytokine environments arise from factors produced by diverse cell types in coordinated concert. Understanding the contributions of each cell type in the context of cell-cell communication is important for effectively designing disease modifying interventions. Here, we present multi-plexed measurement of 48 cytokines from a coculture system of primary human CD4+ T cells and monocytes across a spectrum of stimuli and for a range of relative T cell/monocyte compositions, coupled with corresponding measurements from PBMCs and plasma from the same donors. Computational analysis of the resulting data-sets elucidated communication-independent and communication-dependent contributions, including both positive and negative synergies. We find that cytokines in cell supernatants were uncorrelated to those found in plasma. Additionally, as an example of positive synergy, production levels of CXCR3 cytokines IP-10 and MIG, depend non-linearly on both IFNγ and TNFα levels in cross-talk between T cells and monocytes. Overall, this work demonstrates that communication between cell types can significantly impact the consequent cytokine environment, emphasizing the value of mixed cell population studies.

Protein biomarkers discriminate Leishmania major-infected and non-infected individuals in areas endemic for cutaneous leishmaniasis

Background

A successful host immune response to infection is dependent upon both innate and adaptive immune effector mechanisms. Cutaneous leishmaniasis results in an adaptive Th1 CD4⁺ T cell response that efficiently clears the parasite, but may also result in scaring. However the role of innate mechanisms during parasite clearance remains less well defined.

Methods

We examined a unique cohort of individuals, living in a Leishmania major endemic region, that were stratified among 3 distinct clinical groups in a cross-sectional study. Specifically, patients were classified either as healed (n = 17), asymptomatic (23), or naïve to infection (18) based upon the classical Leishmanin Skin Test (LST) and the presence or absence of scars. Utilizing a multiplexed immunoassay approach we characterized the induced cytokine and chemokine response to L. major.

Results

A subset of innate immune molecules was induced in all groups. By contrast, T cell-associated cytokines were largely induced in exposed groups as compared to L. major-infection naïve individuals. Two exceptions were IL-17A and IL-12p70, induced and not induced, respectively, in naïve individuals. In addition, GM-CSF was more strongly induced in healed patients as compared to the other two groups. Surprisingly an IL-13 response was the best cytokine for classifying previously infected donors.

Conclusions

Exploratory data analysis, utilizing principle component analysis (PCA), revealed distinct patient clusters of the healed and naïve groups based on the most differentially induced proteins. Asymptomatic previously infected individuals were more difficult to assign to a particular cluster based on these induced proteins. Analysis of these proteins may enable the identification of biomarkers associated with disease, leading to a better understanding of the protective mechanisms of immune response against leishmaniasis.

Electronic supplementary material

The online version of this article (doi:10.1186/s12879-016-1458-6) contains supplementary material, which is available to authorized users.