Paediatric HIV infection in the ‘omics era: defining transcriptional signatures of viral control and vaccine responses

Artificial Intelligence Applied to in vitro Gene Expression Testing (IVIGET) to Predict Trivalent Inactivated Influenza Vaccine Immunogenicity in HIV Infected Children

The number of patients affected by chronic diseases with special vaccination needs is burgeoning. In this scenario, predictive markers of immunogenicity, as well as signatures of immune responses are typically missing even though it would especially improve the identification of personalized immunization practices in these populations. We aimed to develop a predictive score of immunogenicity to Influenza Trivalent Inactivated Vaccination (TIV) by applying deep machine learning algorithms using transcriptional data from sort-purified lymphocyte subsets after in vitro stimulation. Peripheral blood mononuclear cells (PBMCs) collected before TIV from 23 vertically HIV infected children under ART and virally controlled were stimulated in vitro with p09/H1N1 peptides (stim) or left unstimulated (med). A multiplexed-qPCR for 96 genes was made on fixed numbers of 3 B cell subsets, 3 T cell subsets and total PBMCs. The ability to respond to TIV was assessed through hemagglutination Inhibition Assay (HIV) and ELIspot and patients were classified as Responders (R) and Non Responders (NR). A predictive modeling framework was applied to the data set in order to define genes and conditions with the higher predicted probability able to inform the final score. Twelve NR and 11 R were analyzed for gene expression differences in all subsets and 3 conditions [med, stim or Δ (stim-med)]. Differentially expressed genes between R and NR were selected and tested with the Adaptive Boosting Model to build a prediction score. The score obtained from subsets revealed the best prediction score from 46 genes from 5 different subsets and conditions. Calculating a combined score based on these 5 categories, we achieved a model accuracy of 95.6% and only one misclassified patient. These data show how a predictive bioinformatic model applied to transcriptional analysis deriving from in-vitro stimulated lymphocytes subsets may predict poor or protective vaccination immune response in vulnerable populations, such as HIV-infected individuals. Future studies on larger cohorts are needed to validate such strategy in the context of vaccination trials.

Higher PIK3C2B gene expression of H1N1+ specific B-cells is associated with lower H1N1 immunogenicity after trivalent influenza vaccination in HIV infected children

Perinatally HIV-infected children (PHIV), despite successful antiretroviral therapy, present suboptimal responses to vaccinations compared to healthy-controls (HC). Here we investigated phenotypic and transcriptional signatures of H1N1-specific B-cells (H1N1-Sp) in PHIV, differentially responding to trivalent-influenza-vaccine (TIV), and HC. Patients were categorized in responders (R) and non-responders (NR) according to hemagglutination-inhibition-assay at baseline and 21 days after TIV. No differences in H1N1-Sp frequencies were found between groups. H1N1-Sp transcriptional analysis revealed a distinct signature between PHIV and HC. NR presented higher PIK3C2B and NOD2 expression compared to R, confirmed by downregulation of PIK3C2B in resting-memory of R after H1N1 in-vitro stimulation. In conclusion this study confirms that qualitative rather than quantitative analyses are needed to characterize immune responses in PHIV. These results further suggest that higher PIK3C2B in H1N1-Sp of NR is associated with lower H1N1 immunogenicity and may be targeted by future modulating strategies to improve TIV responses in PHIV.

How to dissect the plasticity of antigen-specific immune response: a tissue perspective

Generation of antigen-specific humoral responses following vaccination or infection requires the maturation and function of highly specialized immune cells in secondary lymphoid organs (SLO), such as lymph nodes or tonsils. Factors that orchestrate the dynamics of these cells are still poorly understood. Currently, experimental approaches that enable a detailed description of the function of the immune system in SLO have been mainly developed and optimized in animal models. Conversely, methodological approaches in humans are mainly based on the use of blood-associated material because of the challenging access to tissues. Indeed, only few studies in humans were able to provide a discrete description of the complex network of cytokines, chemokines and lymphocytes acting in tissues after antigenic challenge. Furthermore, even fewer data are currently available on the interaction occurring within the complex micro-architecture of the SLO. This information is crucial in order to design particular vaccination strategies, especially for patients affected by chronic and immune compromising medical conditions who are under-vaccinated or who respond poorly to immunizations. Analysis of immune cells in different human tissues by high-throughput technologies, able to obtain data ranging from gene signature to protein expression and cell phenotypes, is needed to dissect the peculiarity of each immune cell in a definite human tissue. The main aim of this review is to provide an in-depth description of the current available methodologies, proven evidence and future perspectives in the analysis of immune mechanisms following immunization or infections in SLO.

Meta-Analysis of Expression Profiling Data Indicates Need for Combinatorial Biomarkers in Pediatric Ulcerative Colitis

BACKGROUND

Unbiased studies using different genome-wide methods have identified a great number of candidate biomarkers for diagnosis and treatment response in pediatric ulcerative colitis (UC). However, clinical translation has been proven difficult. Here, we hypothesized that one reason could be differences between inflammatory responses in an inflamed gut and in peripheral blood cells.

METHODS

We performed meta-analysis of gene expression microarray data from intestinal biopsies and whole blood cells (WBC) from pediatric patients with UC and healthy controls in order to identify overlapping pathways, predicted upstream regulators, and potential biomarkers.

RESULTS

Analyses of profiling datasets from colonic biopsies showed good agreement between different studies regarding pathways and predicted upstream regulators. The most activated predicted upstream regulators included TNF, which is known to have a key pathogenic and therapeutic role in pediatric UC. Despite this, the expression levels of TNF were increased in neither colonic biopsies nor WBC. A potential explanation was increased expression of TNFR2, one of the membrane-bound receptors of TNF in the inflamed colon. Further analyses showed a similar pattern of complex relations between the expression levels of the regulators and their receptors. We also found limited overlap between pathways and predicted upstream regulators in colonic biopsies and WBC. An extended search including all differentially expressed genes that overlapped between colonic biopsies and WBC only resulted in identification of three potential biomarkers involved in the regulation of intestinal inflammation. However, two had been previously proposed in adult inflammatory bowel diseases (IBD), namely, MMP9 and PROK2.

CONCLUSIONS

Our findings indicate that biomarker identification in pediatric UC is complicated by the involvement of multiple pathways, each of which includes many different types of genes in the blood or inflamed intestine. Therefore, further studies for identification of combinatorial biomarkers are warranted. Our study may provide candidate biomarkers for such studies.

OMIC Technologies and Vaccine Development: From the Identification of Vulnerable Individuals to the Formulation of Invulnerable Vaccines

Routine vaccination is among the most effective clinical interventions to prevent diseases as it is estimated to save over 3 million lives every year. However, the full potential of global immunization programs is not realised because population coverage is still suboptimal. This is also due to the inadequate immune response and paucity of informative correlates of protection upon immunization of vulnerable individuals such as newborns, preterm infants, pregnant women, and elderly individuals as well as those patients affected by chronic and immune compromising medical conditions. In addition, these groups are undervaccinated for a number of reasons, including lack of awareness of vaccine-preventable diseases and uncertainty or misconceptions about the safety and efficacy of vaccination by parents and healthcare providers. The presence of these nonresponders/undervaccinated individuals represents a major health and economic burden to society, which will become particularly difficult to address in settings with limited public resources. This review describes innovative and experimental approaches that can help identify specific genomic profiles defining nonresponder individuals for whom specific interventions might be needed. We will provide examples that show how such information can be useful to identify novel biomarkers of safety and immunogenicity for future vaccine trials. Finally, we will discuss how system biology “OMICs” data can be used to design bioinformatic tools to predict the vaccination outcome providing genetic and molecular “signatures” of protective immune response. This strategy may soon enable identification of signatures highly predictive of vaccine safety, immunogenicity, and efficacy/protection thereby informing personalized vaccine interventions in vulnerable populations.

Molecular Signatures of a TLR4 Agonist-Adjuvanted HIV-1 Vaccine Candidate in Humans

Systems biology approaches have recently provided new insights into the mechanisms of action of human vaccines and adjuvants. Here, we investigated early transcriptional signatures induced in whole blood of healthy subjects following vaccination with a recombinant HIV-1 envelope glycoprotein subunit CN54gp140 adjuvanted with the TLR4 agonist glucopyranosyl lipid adjuvant-aqueous formulation (GLA-AF) and correlated signatures to CN54gp140-specific serum antibody responses. Fourteen healthy volunteers aged 18–45 years were immunized intramuscularly three times at 1-month intervals and whole blood samples were collected at baseline, 6 h, and 1, 3, and 7 days post first immunization. Subtle changes in the transcriptomic profiles were observed following immunization, ranging from over 300 differentially expressed genes (DEGs) at day 1 to nearly 100 DEGs at day 7 following immunization. Functional pathway analysis revealed blood transcription modules (BTMs) related to general cell cycle activation, and innate immune cell activation at early time points, as well as BTMs related to T cells and B cell activation at the later time points post-immunization. Diverse CN54gp140-specific serum antibody responses of the subjects enabled their categorization into high or low responders, at early (<1 month) and late (up to 6 months) time points post vaccination. BTM analyses revealed repression of modules enriched in NK cells, and the mitochondrial electron chain, in individuals with high or sustained antigen-specific antibody responses. However, low responders showed an enhancement of BTMs associated with enrichment in myeloid cells and monocytes as well as integrin cell surface interactions. Flow cytometry analysis of peripheral blood mononuclear cells obtained from the subjects revealed an enhanced frequency of CD56^dim NK cells in the majority of vaccines 14 days after vaccination as compared with the baseline. These results emphasize the utility of a systems biology approach to enhance our understanding on the mechanisms of action of TLR4 adjuvanted human vaccines.

Perturbation of B Cell Gene Expression Persists in HIV-Infected Children Despite Effective Antiretroviral Therapy and Predicts H1N1 Response

Despite effective antiretroviral therapy (ART), HIV-infected individuals with apparently similar clinical and immunological characteristics can vary in responsiveness to vaccinations. However, molecular mechanisms responsible for such impairment, as well as biomarkers able to predict vaccine responsiveness in HIV-infected children, remain unknown. Following the hypothesis that a B cell qualitative impairment persists in HIV-infected children (HIV) despite effective ART and phenotypic B cell immune reconstitution, the aim of the current study was to investigate B cell gene expression of HIV compared to age-matched healthy controls (HCs) and to determine whether distinct gene expression patterns could predict the ability to respond to influenza vaccine. To do so, we analyzed prevaccination transcriptional levels of a 96-gene panel in equal numbers of sort-purified B cell subsets (SPBS) isolated from peripheral blood mononuclear cells using multiplexed RT-PCR. Immune responses to H1N1 antigen were determined by hemaglutination inhibition and memory B cell ELISpot assays following trivalent-inactivated influenza vaccination (TIV) for all study participants. Although there were no differences in terms of cell frequencies of SPBS between HIV and HC, the groups were distinguishable based upon gene expression analyses. Indeed, a 28-gene signature, characterized by higher expression of genes involved in the inflammatory response and immune activation was observed in activated memory B cells (CD27⁺CD21⁻) from HIV when compared to HC despite long-term viral control (>24 months). Further analysis, taking into account H1N1 responses after TIV in HIV participants, revealed that a 25-gene signature in resting memory (RM) B cells (CD27⁺CD21⁺) was able to distinguish vaccine responders from non-responders (NR). In fact, prevaccination RM B cells of responders showed a higher expression of gene sets involved in B cell adaptive immune responses (APRIL, BTK, BLIMP1) and BCR signaling (MTOR, FYN, CD86) when compared to NR. Overall, these data suggest that a perturbation at a transcriptional level in the B cell compartment persists despite stable virus control achieved through ART in HIV-infected children. Additionally, the present study demonstrates the potential utility of transcriptional evaluation of RM B cells before vaccination for identifying predictive correlates of vaccine responses in this population.

Induction of IL21 in Peripheral T Follicular Helper Cells Is an Indicator of Influenza Vaccine Response in a Previously Vaccinated HIV-Infected Pediatric Cohort

HIV-infected patients of all ages frequently underperform in response to seasonal influenza vaccination, despite virologic control of HIV. The molecular mechanisms governing this impairment, as well as predictive biomarkers for responsiveness, remain unknown. This study was performed in samples obtained prevaccination (T0) from HIV-infected children who received the 2012-2013 seasonal influenza vaccine. Response status was determined based on established criterion for hemagglutination inhibition titer; participants with a hemagglutination titer ≥1:40 plus a ≥4-fold increase over T0 at 3 wk postvaccination were designated as responders. All children had a history of prior influenza vaccinations. At T0, the frequencies of CD4 T cell subsets, including peripheral T follicular helper (pTfh) cells, which provide help to B cells for developing into Ab-secreting cells, were similar between responders and nonresponders. However, in response to in vitro stimulation with influenza A/California/7/2009 (H1N1) Ag, differential gene expression related to pTfh cell function was observed by Fluidigm high-density RT-PCR between responders and nonresponders. In responders, H1N1 stimulation at T0 also resulted in CXCR5 induction (mRNA and protein) in CD4 T cells and IL21 gene induction in pTfh cells that were strongly associated with H1N1-specific B cell responses postvaccination. In contrast, CD4 T cells of nonresponders exhibited increased expression of IL2 and STAT5 genes, which are known to antagonize peripheral Tfh cell function. These results suggest that the quality of pTfh cells at the time of immunization is important for influenza vaccine responses and provide a rationale for targeted, ex vivo Ag-driven molecular profiling of purified immune cells to detect predictive biomarkers of the vaccine response.