Michael S. Rooney

Dynamic profiling of the protein life cycle in response to pathogens

How the immune system readies for battle Although gene expression is tightly controlled at both the RNA and protein levels, the quantitative contribution of each step, especially during dynamic responses, remains largely unknown. Indeed, there has been much debate whether changes in RNA level contribute substantially to protein-level regulation. Jovanovic et al. built a genome-scale model of the temporal dynamics of differential protein expression during the stimulation of immunological dendritic cells (see the Perspective by Li and Biggin). Newly stimulated functions involved the up-regulation of specific RNAs and concomitant increases in the levels of the proteins they encode, whereas housekeeping functions were regulated posttranscriptionally at the protein level. Science, this issue 10.1126/science.1259038; see also p. 1066 Levels of “housekeeping” proteins are maintained directly, but those of immune response proteins depend on more transcription. [Also see Perspective by Li and Biggin] INTRODUCTION Mammalian gene expression is tightly controlled through the interplay between the RNA and protein life cycles. Although studies of individual genes have shown that regulation of each of these processes is important for correct protein expression, the quantitative contribution of each step to changes in protein expression levels remains largely unknown and much debated. Many studies have attempted to address this question in the context of steady-state protein levels, and comparing steady-state RNA and protein abundances has indicated a considerable discrepancy between RNA and protein levels. In contrast, only a few studies have attempted to shed light on how changes in each of these processes determine differential protein expression—either relative (ratios) or absolute (differences)—during dynamic responses, and only one recent report has attempted to quantitate each process. Understanding these contributions to a dynamic response on a systems scale is essential both for deciphering how cells deploy regulatory processes to accomplish physiological changes and for discovering key molecular regulators controlling each process. RATIONALE We developed an integrated experimental and computational strategy to quantitatively assess how protein levels are maintained in the context of a dynamic response and applied it to the model response of mouse immune bone marrow–derived dendritic cells (DCs) to stimulation with lipopolysaccharide (LPS). We used a modified pulsed-SILAC (stable isotope labeling with amino acids in cell culture) approach to track newly synthesized and previously labeled proteins over the first 12 hours of the response. In addition, we independently measured replicate RNA-sequencing profiles under the same conditions. We devised a computational strategy to infer per-mRNA translation rates and protein degradation rates at each time point from the temporal transcriptional profiles and pulsed-SILAC proteomics data. This allowed us to build a genome-scale quantitative model of the temporal dynamics of differential protein expression in DCs responding to LPS. RESULTS We found that before stimulation, mRNA levels contribute to overall protein expression levels more than double the combined contribution of protein translation and degradation rates. Upon LPS stimulation, changes in mRNA abundance play an even more dominant role in dynamic changes in protein levels, especially in immune response genes. Nevertheless, several protein modules—especially the preexisting proteome of proteins performing basic cellular functions—are predominantly regulated in stimulated cells at the level of protein translation or degradation, accounting for over half of the absolute change in protein molecules in the cell. In particular, despite the repression of their transcripts, the level of many proteins in the translational machinery is up-regulated upon LPS stimulation because of significantly increased translation rates, and elevated protein degradation of mitochondrial proteins plays a central role in remodeling cellular energy metabolism. CONCLUSIONS Our results support a model in which the induction of novel cellular functions is primarily driven through transcriptional changes, whereas regulation of protein production or degradation updates the levels of preexisting functions as required for an activated state. Our approach for building quantitative genome-scale models of the temporal dynamics of protein expression is broadly applicable to other dynamic systems. Dynamic protein expression regulation in dendritic cells upon stimulation with LPS. We developed an integrated experimental and computational strategy to quantitatively assess how protein levels are maintained in the context of a dynamic response. Our results support a model in which the induction of novel cellular functions is primarily driven through transcriptional changes, whereas regulation of protein production or degradation updates the levels of preexisting functions. Protein expression is regulated by the production and degradation of messenger RNAs (mRNAs) and proteins, but their specific relationships remain unknown. We combine measurements of protein production and degradation and mRNA dynamics so as to build a quantitative genomic model of the differential regulation of gene expression in lipopolysaccharide-stimulated mouse dendritic cells. Changes in mRNA abundance play a dominant role in determining most dynamic fold changes in protein levels. Conversely, the preexisting proteome of proteins performing basic cellular functions is remodeled primarily through changes in protein production or degradation, accounting for more than half of the absolute change in protein molecules in the cell. Thus, the proteome is regulated by transcriptional induction for newly activated cellular functions and by protein life-cycle changes for remodeling of preexisting functions.

Ipilimumab for Patients with Relapse after Allogeneic Transplantation

BACKGROUND Loss of donor-mediated immune antitumor activity after allogeneic hematopoietic stem-cell transplantation (HSCT) permits relapse of hematologic cancers. We hypothesized that immune checkpoint blockade established by targeting cytotoxic T-lymphocyte-associated protein 4 with ipilimumab could restore antitumor reactivity through a graft-versus-tumor effect. METHODS We conducted a phase 1/1b multicenter, investigator-initiated study to determine the safety and efficacy of ipilimumab in patients with relapsed hematologic cancer after allogeneic HSCT. Patients received induction therapy with ipilimumab at a dose of 3 or 10 mg per kilogram of body weight every 3 weeks for a total of 4 doses, with additional doses every 12 weeks for up to 60 weeks in patients who had a clinical benefit. RESULTS A total of 28 patients were enrolled. Immune-related adverse events, including one death, were observed in 6 patients (21%), and graft-versus-host disease (GVHD) that precluded further administration of ipilimumab was observed in 4 patients (14%). No responses that met formal response criteria occurred in patients who received a dose of 3 mg per kilogram. Among 22 patients who received a dose of 10 mg per kilogram, 5 (23%) had a complete response, 2 (9%) had a partial response, and 6 (27%) had decreased tumor burden. Complete responses occurred in 4 patients with extramedullary acute myeloid leukemia and 1 patient with the myelodysplastic syndrome developing into acute myeloid leukemia. Four patients had a durable response for more than 1 year. Responses were associated with in situ infiltration of cytotoxic CD8+ T cells, decreased activation of regulatory T cells, and expansion of subpopulations of effector T cells in the blood. CONCLUSIONS Our early-phase data showed that administration of ipilimumab was feasible in patients with recurrent hematologic cancers after allogeneic HSCT, although immune-mediated toxic effects and GVHD occurred. Durable responses were observed in association with several histologic subtypes of these cancers, including extramedullary acute myeloid leukemia. (Funded by the National Institutes of Health and others; ClinicalTrials.gov number, NCT01822509.).

High-Resolution Sequencing and Modeling Identifies Distinct Dynamic RNA Regulatory Strategies

Cells control dynamic transitions in transcript levels by regulating transcription, processing, and/or degradation through an integrated regulatory strategy. Here, we combine RNA metabolic labeling, rRNA-depleted RNA-seq, and DRiLL, a novel computational framework, to quantify the level; editing sites; and transcription, processing, and degradation rates of each transcript at a splice junction resolution during the LPS response of mouse dendritic cells. Four key regulatory strategies, dominated by RNA transcription changes, generate most temporal gene expression patterns. Noncanonical strategies that also employ dynamic posttranscriptional regulation control only a minority of genes, but provide unique signal processing features. We validate Tristetraprolin (TTP) as a major regulator of RNA degradation in one noncanonical strategy. Applying DRiLL to the regulation of noncoding RNAs and to zebrafish embryogenesis demonstrates its broad utility. Our study provides a new quantitative approach to discover transcriptional and posttranscriptional events that control dynamic changes in transcript levels using RNA sequencing data.

Mass Spectrometry Profiling of HLA-Associated Peptidomes in Mono-allelic Cells Enables More Accurate Epitope Prediction

SUMMARY Identification of human leukocyte antigen (HLA)‐bound peptides by liquid chromatography‐tandem mass spectrometry (LC‐MS/MS) is poised to provide a deep understanding of rules underlying antigen presentation. However, a key obstacle is the ambiguity that arises from the co‐expression of multiple HLA alleles. Here, we have implemented a scalable mono‐allelic strategy for profiling the HLA peptidome. By using cell lines expressing a single HLA allele, optimizing immunopurifications, and developing an application‐specific spectral search algorithm, we identified thousands of peptides bound to 16 different HLA class I alleles. These data enabled the discovery of subdominant binding motifs and an integrative analysis quantifying the contribution of factors critical to epitope presentation, such as protein cleavage and gene expression. We trained neural‐network prediction algorithms with our large dataset (>24,000 peptides) and outperformed algorithms trained on datasets of peptides with measured affinities. We thus demonstrate a strategy for systematically learning the rules of endogenous antigen presentation. Graphical Abstract Figure. No Caption available. Highlights24,000 HLA class I peptides were identified through a scalable MS‐based pipeline.Mono‐allelic data revealed binding motifs that were validated biochemically.Comprehensive analyses provide an updated portrait of antigen processing rules.Neural networks were trained for 16 alleles and outperform standard by 2‐fold. &NA; HLA class I binding prediction has traditionally been based on biochemical binding experiments. Abelin and colleagues present an LC‐MS/MS‐based workflow and analytical framework that greatly accelerates gains in prediction performance. Key advances include the discovery of sequence motifs and improved quantification of the roles of gene expression and proteasomal processing.

Within-Neighborhood Patterns and Sources of Particle Pollution: Mobile Monitoring and Geographic Information System Analysis in Four Communities in Accra, Ghana

Background Sources of air pollution in developing country cities include transportation and industrial pollution, biomass and coal fuel use, and resuspended dust from unpaved roads. Objectives Our goal was to understand within-neighborhood spatial variability of particulate matter (PM) in communities of varying socioeconomic status (SES) in Accra, Ghana, and to quantify the effects of nearby sources on local PM concentration. Methods We conducted 1 week of morning and afternoon mobile and stationary air pollution measurements in four study neighborhoods. PM with aerodynamic diameters ≤ 2.5 μm (PM2.5) and ≤ 10 μm (PM10) was measured continuously, with matched global positioning system coordinates; detailed data on local sources were collected at periodic stops. The effects of nearby sources on local PM were estimated using linear mixed-effects models. Results In our measurement campaign, the geometric means of PM2.5 and PM10 along the mobile monitoring path were 21 and 49 μg/m3, respectively, in the neighborhood with highest SES and 39 and 96 μg/m3, respectively, in the neighborhood with lowest SES and highest population density. PM2.5 and PM10 were as high as 200 and 400 μg/m3, respectively, in some segments of the path. After adjusting for other factors, the factors that had the largest effects on local PM pollution were nearby wood and charcoal stoves, congested and heavy traffic, loose dirt road surface, and trash burning. Conclusions Biomass fuels, transportation, and unpaved roads may be important determinants of local PM variation in Accra neighborhoods. If confirmed by additional or supporting data, the results demonstrate the need for effective and equitable interventions and policies that reduce the impacts of traffic and biomass pollution.

Resistance to checkpoint blockade therapy through inactivation of antigen presentation

Treatment with immune checkpoint blockade (CPB) therapies often leads to prolonged responses in patients with metastatic melanoma, but the common mechanisms of primary and acquired resistance to these agents remain incompletely characterized and have yet to be validated in large cohorts. By analyzing longitudinal tumor biopsies from 17 metastatic melanoma patients treated with CPB therapies, we observed point mutations, deletions or loss of heterozygosity (LOH) in beta-2-microglobulin (B2M), an essential component of MHC class I antigen presentation, in 29.4% of patients with progressing disease. In two independent cohorts of melanoma patients treated with anti-CTLA4 and anti-PD1, respectively, we find that B2M LOH is enriched threefold in non-responders (~30%) compared to responders (~10%) and associated with poorer overall survival. Loss of both copies of B2M is found only in non-responders. B2M loss is likely a common mechanism of resistance to therapies targeting CTLA4 or PD1.Resistance to immune-checkpoint blockade often occurs in treated patients. Here, the authors demonstrate that B2M loss is a mechanism of primary and acquired resistance to therapies targeting CTLA4 or PD-1 in melanoma patients.

Spatial and temporal patterns of particulate matter sources and pollution in four communities in Accra, Ghana

Sources of air pollution in developing country cities include transportation and industrial pollution, biomass fuel use, and re-suspended dust from unpaved roads. We examined the spatial patterns of particulate matter (PM) and its sources in four neighborhoods of varying socioeconomic status (SES) in Accra. PM data were from 1 week of morning and afternoon mobile and stationary air pollution measurements in each of the study neighborhoods. PM(2.5) and PM(10) were measured continuously, with matched GPS coordinates. Data on biomass fuel use were from the Ghana 2000 population and housing census and from a census of wood and charcoal stoves along the mobile monitoring paths. We analyzed the associations of PM with sources using a mixed-effects regression model accounting for temporal and spatial autocorrelation. After adjusting for other factors, the density of wood stoves, fish smoking, and trash burning along the mobile monitoring path as well as road capacity and surface were associated with higher PM(2.5). Road capacity and road surface variables were also associated with PM(10), but the association with biomass sources was weak or absent. While wood stoves and fish smoking were significant sources of air pollution, addressing them would require financial and physical access to alternative fuels for low-income households and communities.

The Role of Mass Spectrometry and Proteogenomics in the Advancement of HLA Epitope Prediction

A challenge in developing personalized cancer immunotherapies is the prediction of putative cancer‐specific antigens. Currently, predictive algorithms are used to infer binding of peptides to human leukocyte antigen (HLA) heterodimers to aid in the selection of putative epitope targets. One drawback of current epitope prediction algorithms is that they are trained on datasets containing biochemical HLA‐peptide binding data that may not completely capture the rules associated with endogenous processing and presentation. The field of MS has made great improvements in instrumentation speed and sensitivity, chromatographic resolution, and proteogenomic database search strategies to facilitate the identification of HLA‐ligands from a variety of cell types and tumor tissues. As such, these advances have enabled MS profiling of HLA‐binding peptides to be a tractable, orthogonal approach to lower throughput biochemical assays for generating comprehensive datasets to train epitope prediction algorithms. In this review, we will highlight the progress made in the field of HLA‐ligand profiling enabled by MS and its impact on current and future epitope prediction strategies.

Abstract LB-179: Next-generation epitope prediction using mass spectrometry and integrative genomics

Personalized neoantigen therapies for cancer require accurate epitope selection. However, most Class I prediction algorithms in common use today are based on biochemical binding assays that are difficult to scale and do not address processing steps upstream of peptide-MHC binding. Here we present an alternative approach based on the LC-MS/MS identification of MHC Class I-bound peptides. While MS-based profiling is not new, we optimized the system for rule learning by focusing on cell lines expressing only a single HLA-A or HLA-B allele and by collecting parallel transcriptomic and proteomic measurements. Identifying over 24,000 peptides across 16 individual alleles, we were able to discover novel binding motifs, which were validated biochemically, and develop novel neural network algorithms. Furthermore, we systematically interrogated processing rules - discovering a novel motif conserved across multiple cell types - and developed a principled framework for integrating epitope cleavability, expression, and MHC binding potential into an overall ranking. Validating on external datasets, we saw a doubling in positive predictive value with respect to standard approaches. We thus demonstrate a scalable strategy for systematically learning the rules of endogenous antigen presentation that can be deployed for the optimal selection of patient-specific cancer neoantigens. Citation Format: Michael S. Rooney, Jenn Abelin, Siranush Sarkizova, Derin Keskin, Christine Hartigan, Wandi Zhang, John Sidney, William Lane, Jonathan Stevens, Guang L. Zhang, Karl Clauser, Nir Hacohen, Steve Carr, Cathy Wu. Next-generation epitope prediction using mass spectrometry and integrative genomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr LB-179. doi:10.1158/1538-7445.AM2017-LB-179

Abstract B089: High-throughput profiling of HLA allele-specific peptides by MS for improved epitope prediction

Cancer mutations yield neo-antigens, which are instrumental to immune-mediated recognition and control of cancer. Vaccine-based therapies targeting neo-antigens will require accurate prediction of which mutations yield peptides presented on polymorphic HLA class I. While in vitro methods have produced increasingly accurate predictors of peptide:MHC binding, there remains a need to define rules for endogenous antigen presentation. Here, we use rapid, high-resolution liquid chromatography mass spectrometry (LC-MS/MS) to identify >24,000 peptides associated with 16 HLA alleles in B cell lines that each express a single HLA allele. The elution of peptides from single HLA alleles allowed us to develop improved rules for endogenous peptide presentation based on the physicochemical properties of binding peptides, patterns of peptide cleavage and abundance of cognate transcripts. Finally, we trained models that integrated MS-derived peptide data and gene expression and demonstrate improved prediction of endogenous peptide presentation in independent datasets. Our strategy thus improves the performance of current predictive algorithms and provides a rapid and scalable method to generate rules for the massive and diverse set of human HLA alleles. Citation Format: Michael S. Rooney, Jennifer G. Abelin, Derin B. Keskin, Siranush Sarkizova, Christina Hartigan, Wandi Zhang, John Sidney, Jonathan Stevens, William J. Lane, Guang L. Zhang, Karl R. Clauser, Nir Hacohen, Steven A. Carr, Catherine J. Wu. High-throughput profiling of HLA allele-specific peptides by MS for improved epitope prediction [abstract]. In: Proceedings of the Second CRI-CIMT-EATI-AACR International Cancer Immunotherapy Conference: Translating Science into Survival; 2016 Sept 25-28; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2016;4(11 Suppl):Abstract nr B089.