Pramila Tata

Phenotype, Function, and Gene Expression Profiles of Programmed Death-1hi CD8 T Cells in Healthy Human Adults

T cell dysfunction is an important feature of many chronic viral infections. In particular, it was shown that programmed death-1 (PD-1) regulates T cell dysfunction during chronic lymphocytic choriomeningitis virus infection in mice, and PD-1hi cells exhibit an intense exhausted gene signature. These findings were extended to human chronic infections such as HIV, hepatitis C virus, and hepatitis B virus. However, it is not known if PD-1hi cells of healthy humans have the traits of exhausted cells. In this study, we provide a comprehensive description of phenotype, function, and gene expression profiles of PD-1hi versus PD-1lo CD8 T cells in the peripheral blood of healthy human adults as follows: 1) the percentage of naive and memory CD8 T cells varied widely in the peripheral blood cells of healthy humans, and PD-1 was expressed by the memory CD8 T cells; 2) PD-1hi CD8 T cells in healthy humans did not significantly correlate with the PD-1hi exhausted gene signature of HIV-specific human CD8 T cells or chronic lymphocytic choriomeningitis virus-specific CD8 T cells from mice; 3) PD-1 expression did not directly affect the ability of CD8 T cells to secrete cytokines in healthy adults; 4) PD-1 was expressed by the effector memory compared with terminally differentiated effector CD8 T cells; and 5) finally, an interesting inverse relationship between CD45RA and PD-1 expression was observed. In conclusion, our study shows that most PD-1hi CD8 T cells in healthy adult humans are effector memory cells rather than exhausted cells.

Next-Generation Sequencing of Human Mitochondrial Reference Genomes Uncovers High Heteroplasmy Frequency

We describe methods for rapid sequencing of the entire human mitochondrial genome (mtgenome), which involve long-range PCR for specific amplification of the mtgenome, pyrosequencing, quantitative mapping of sequence reads to identify sequence variants and heteroplasmy, as well as de novo sequence assembly. These methods have been used to study 40 publicly available HapMap samples of European (CEU) and African (YRI) ancestry to demonstrate a sequencing error rate <5.63×10−4, nucleotide diversity of 1.6×10−3 for CEU and 3.7×10−3 for YRI, patterns of sequence variation consistent with earlier studies, but a higher rate of heteroplasmy varying between 10% and 50%. These results demonstrate that next-generation sequencing technologies allow interrogation of the mitochondrial genome in greater depth than previously possible which may be of value in biology and medicine.

Origin and differentiation of human memory CD8 T cells after vaccination

The differentiation of human memory CD8 T cells is not well understood. Here we address this issue using the live yellow fever virus (YFV) vaccine, which induces long-term immunity in humans. We used in vivo deuterium labelling to mark CD8 T cells that proliferated in response to the virus and then assessed cellular turnover and longevity by quantifying deuterium dilution kinetics in YFV-specific CD8 T cells using mass spectrometry. This longitudinal analysis showed that the memory pool originates from CD8 T cells that divided extensively during the first two weeks after infection and is maintained by quiescent cells that divide less than once every year (doubling time of over 450 days). Although these long-lived YFV-specific memory CD8 T cells did not express effector molecules, their epigenetic landscape resembled that of effector CD8 T cells. This open chromatin profile at effector genes was maintained in memory CD8 T cells isolated even a decade after vaccination, indicating that these cells retain an epigenetic fingerprint of their effector history and remain poised to respond rapidly upon re-exposure to the pathogen.

Meta-Analyses of Microarray Datasets Identifies ANO1 and FADD as Prognostic Markers of Head and Neck Cancer.

The head and neck squamous cell carcinoma (HNSCC) transcriptome has been profiled extensively, nevertheless, identifying biomarkers that are clinically relevant and thereby with translational benefit, has been a major challenge. The objective of this study was to use a meta-analysis based approach to catalog candidate biomarkers with high potential for clinical application in HNSCC. Data from publically available microarray series (N = 20) profiled using Agilent (4X44K G4112F) and Affymetrix (HGU133A, U133A_2, U133Plus 2) platforms was downloaded and analyzed in a platform/chip-specific manner (GeneSpring software v12.5, Agilent, USA). Principal Component Analysis (PCA) and clustering analysis was carried out iteratively for segregating outliers; 140 normal and 277 tumor samples from 15 series were included in the final analysis. The analyses identified 181 differentially expressed, concordant and statistically significant genes; STRING analysis revealed interactions between 122 of them, with two major gene clusters connected by multiple nodes (MYC, FOS and HSPA4). Validation in the HNSCC-specific database (N = 528) in The Cancer Genome Atlas (TCGA) identified a panel (ECT2, ANO1, TP63, FADD, EXT1, NCBP2) that was altered in 30% of the samples. Validation in treatment naïve (Group I; N = 12) and post treatment (Group II; N = 12) patients identified 8 genes significantly associated with the disease (Area under curve>0.6). Correlation with recurrence/re-recurrence showed ANO1 had highest efficacy (sensitivity: 0.8, specificity: 0.6) to predict failure in Group I. UBE2V2, PLAC8, FADD and TTK showed high sensitivity (1.00) in Group I while UBE2V2 and CRYM were highly sensitive (>0.8) in predicting re-recurrence in Group II. Further, TCGA analysis showed that ANO1 and FADD, located at 11q13, were co-expressed at transcript level and significantly associated with overall and disease-free survival (p<0.05). The meta-analysis approach adopted in this study has identified candidate markers correlated with disease outcome in HNSCC; further validation in a larger cohort of patients will establish their clinical relevance.

Sample preparation method considerations for integrated transcriptomic and proteomic analysis of tumors

Sample processing protocols that enable compatible recovery of differentially expressed transcripts and proteins are necessary for integration of the multiomics data applied in the analysis of tumors. In this pilot study, we compared two different isolation methods for extracting RNA and protein from laryngopharyngeal tumor tissues and the corresponding adjacent normal sections. In Method 1, RNA and protein were isolated from a single tissue section sequentially and in Method 2, the extraction was carried out using two different sections and two independent and parallel protocols for RNA and protein. RNA and protein from both methods were subjected to RNA‐seq and iTRAQ‐based LC‐MS/MS analysis, respectively. Analysis of data revealed that a higher number of differentially expressed transcripts and proteins were concordant in their regulation trends in Method 1 as compared to Method 2. Cross‐method comparison of concordant entities revealed that RNA and protein extraction from the same tissue section (Method 1) recovered more concordant entities that are missed in the other extraction method (Method 2) indicating heterogeneity in distribution of these entities in different tissue sections. Method 1 could thus be the method of choice for integrated analysis of transcriptome and proteome data.

Biologic Basis of Personalized Therapy in Head and Neck Squamous Cell Carcinoma

Head and neck squamous cell carcinoma (HNSCC) is the 7th most common cancer worldwide, with more than 500,000 new cases each year [1]. HNSCCs are associated with a 5-year overall survival of approximately 50 % which has remained largely unchanged [2]. Cetuximab is the only FDA-approved targeted therapy for HNSCC and there are no clinically used biomarkers for HNSCC. Personalized therapy, treatment management customized to individual patient profile, is now being investigated with greater emphasis on the molecular profile of the patients, in addition to their clinical and pathological status. With the stagnant 5-year overall survival, this shift toward molecular integration is definitely warranted. Molecular profiling has the ability to categorize the patients based on effective treatment modality, treatment response and susceptibility to develop metastasis, thereby delineating the prognosis more accurately. Response assessment further enables selection of drugs based on the status of the targeted pathways, hence ensuring better treatment outcome. Nevertheless, the success of this approach is dependent on the selection of appropriate biomarkers. Differences in the biology of cancers in terms of characteristics such as site, tissue of origin, and etiology make it mandatory to catalog the changes that are specific to each cancer as a step toward the personalized medicine approach. The advent of high-throughput techniques has enabled a global view of the molecular changes that occur at every level and hence are arguably the best option to understand and identify the probable clinically relevant and targetable pathways in HNSCC.

An integrated transcriptomics and proteomics study of Head and Neck Squamous Cell Carcinoma – methodological and analytical considerations.

High throughput molecular profiling and integrated data analysis with tumor tissues require overcoming challenges like tumor heterogeneity and tissue paucity. This study is an attempt to understand and optimize various steps during tissue processing and in establishing pipelines essential for integrated analysis. Towards this effort, we subjected laryngo-pharyngeal primary tumors and the corresponding adjacent normal tissues (n=2) to two RNA and protein isolation methods, one wherein RNA and protein were isolated from the same tissue sequentially (Method 1) and second, wherein the extraction was carried out using two independent methods (Method 2). RNA and protein from both methods were subjected to RNA-seq and iTRAQ based LC-MS/MS analysis. Transcript and peptide identification and quantification was followed by both individual-ome and integrated data analysis. As a result of this analysis, we identified a higher number of total, as well as differentially expressed (DE) transcripts (1329 vs 1134) and proteins (799 vs 408) with fold change ≥ 2.0, in Method 1. Among these, 173 and 86 entities were identified by both transcriptome and proteome analysis in Method 1 and 2, respectively, with higher concordance in the regulation trends observed in the former. The significant cancer related pathways enriched with the individual DE transcript or protein data were similar in both the methods. However, the entities mapping to them were different, allowing enhanced view of the pathways identified after integration of the data and subsequent mapping. The concordant DE transcripts and proteins also revealed key molecules of the pathways with important roles in cancer development. This study thus demonstrates that sequential extraction of the RNA and proteins from the same tissue allows for better profiling of differentially expressed entities and a more accurate integrated data analysis. Author Contributions ARB, MKG, PK and SK contributed final data analysis. KD and JN were involved in the RNASeq experiments while MKG, SHV LB and SC were involved in the iTRAQ MS/MS analysis. RBR and HV contributed towards the standardization of sample collection and processing, and were also involved in obtaining clinical information of the patients along with SD. VK and MAK were involved in study design, providing clinical insights into the analysis and in critical assessment of the manuscript. ARB, MKG and PK were involved in manuscript preparation. AS, PT, BP, MAK and RS were involved in the establishing the study design, overall monitoring of the experimental results and manuscript preparation. PT, MAK, BP and RS are the lead investigators of the project. Significance of the study The study highlights the need to optimize tissue processing and analytical pipelines to enable accurate integrated analysis of high throughput omics data; a sequential extraction of RNA and protein entities and subsequent integrated analysis was identified to provide a better representation of the molecular profile in terms concordant entities and pathways.