Assessing Immune Factors in Maternal Milk and Paired Infant Plasma Antibody Binding to Human Rhinoviruses

Before they can produce their own antibodies, newborns are protected from infections by transplacental transfer of maternal IgG antibodies and after birth through breast milk IgA antibodies. Rhinovirus (RV) infections are extremely common in early childhood, and while RV infections often result in only mild upper respiratory illnesses, they can also cause severe lower respiratory illnesses such as bronchiolitis and pneumonia. We used high-density peptide arrays to profile infant and maternal antibody reactivity to capsid and full proteome sequences of three human RVs - A16, B52, and C11. Numerous plasma IgG and breast milk IgA RV epitopes were identified that localized to regions of the RV capsid surface and interior, and also to several non-structural proteins. While most epitopes were bound by both IgG and IgA, there were several instances where isotype-specific and RV-specific binding were observed. We also profiled 62 unique RV-C dominant protein loop sequences characteristic of this species' capsid VP1 protein. Many of these RV-C sites were highly bound by IgG from one-year-old infants, indicating recent or ongoing active infections, or alternatively, a level of cross-reactivity among homologous RV-C sites.

Antibody landscape of C57BL/6 mice cured of B78 melanoma via a combined radiation and immunocytokine immunotherapy regimen

Sera of immune mice that were previously cured of their melanoma through a combined radiation and immunocytokine immunotherapy regimen consisting of 12 Gy of external beam radiation and the intratumoral administration of an immunocytokine (anti-GD2 mAb coupled to IL-2) with long-term immunological memory showed strong antibody-binding against melanoma tumor cell lines via flow cytometric analysis. Using a high-density whole-proteome peptide array (of 6.090.593 unique peptides), we assessed potential protein-targets for antibodies found in immune sera. Sera from 6 of these cured mice were analyzed with this high-density, whole-proteome peptide array to determine specific antibody-binding sites and their linear peptide sequence. We identified thousands of peptides that were targeted by these 6 mice and exhibited strong antibody binding only by immune (after successful cure and rechallenge), not naïve (before tumor implantation) sera and developed a robust method to detect these differentially targeted peptides. Confirmatory studies were done to validate these results using 2 separate systems, a peptide ELISA and a smaller scale peptide array utilizing a slightly different technology. To the best of our knowledge, this is the first study of the full set of germline encoded linear peptide-based proteome epitopes that are recognized by immune sera from mice cured of cancer via radio-immunotherapy. We furthermore found that although the generation of B-cell repertoire in immune development is vastly variable, and numerous epitopes are identified uniquely by immune serum from each of these 6 immune mice evaluated, there are still several epitopes and proteins that are commonly recognized by at least half of the mice studied. This suggests that every mouse has a unique set of antibodies produced in response to the curative therapy, creating an individual "fingerprint." Additionally, certain epitopes and proteins stand out as more immunogenic, as they are recognized by multiple mice in the immune group.

MASH Native: a unified solution for native top-down proteomics data processing

MOTIVATION

Native top-down proteomics (nTDP) integrates native mass spectrometry (nMS) with top-down proteomics (TDP) to provide comprehensive analysis of protein complexes together with proteoform identification and characterization. Despite significant advances in nMS and TDP software developments, a unified and user-friendly software package for analysis of nTDP data remains lacking.

RESULTS

We have developed MASH Native to provide a unified solution for nTDP to process complex datasets with database searching capabilities in a user-friendly interface. MASH Native supports various data formats and incorporates multiple options for deconvolution, database searching, and spectral summing to provide a "one-stop shop" for characterizing both native protein complexes and proteoforms.

AVAILABILITY AND IMPLEMENTATION

The MASH Native app, video tutorials, written tutorials, and additional documentation are freely available for download at https://labs.wisc.edu/gelab/MASH_Explorer/MASHSoftware.php. All data files shown in user tutorials are included with the MASH Native software in the download .zip file.

High sensitivity top-down proteomics captures single muscle cell heterogeneity in large proteoforms

Single-cell proteomics has emerged as a powerful method to characterize cellular phenotypic heterogeneity and the cell-specific functional networks underlying biological processes. However, significant challenges remain in single-cell proteomics for the analysis of proteoforms arising from genetic mutations, alternative splicing, and post-translational modifications. Herein, we have developed a highly sensitive functionally integrated top-down proteomics method for the comprehensive analysis of proteoforms from single cells. We applied this method to single muscle fibers (SMFs) to resolve their heterogeneous functional and proteomic properties at the single-cell level. Notably, we have detected single-cell heterogeneity in large proteoforms (>200 kDa) from the SMFs. Using SMFs obtained from three functionally distinct muscles, we found fiber-to-fiber heterogeneity among the sarcomeric proteoforms which can be related to the functional heterogeneity. Importantly, we detected multiple isoforms of myosin heavy chain (~223 kDa), a motor protein that drives muscle contraction, with high reproducibility to enable the classification of individual fiber types. This study reveals single muscle cell heterogeneity in large proteoforms and establishes a direct relationship between sarcomeric proteoforms and muscle fiber types, highlighting the potential of top-down proteomics for uncovering the molecular underpinnings of cell-to-cell variation in complex systems.

Antibody landscape of C57BL/6 mice cured of B78 melanoma via immunotherapy

Hoefges et al. utilized a whole-proteome peptide array approach to show that C57BL/6 mice develop a large repertoire of antibodies against linear peptide sequences of their melanoma after receiving a curative immunotherapy regimen consisting of radiation and an immunocytokine. Antibodies can play an important role in innate and adaptive immune responses against cancer, and in preventing infectious disease. Flow cytometry analysis of sera of immune mice that were previously cured of their melanoma through a combined immunotherapy regimen with long-term memory showed strong antibody-binding against melanoma tumor cell lines. Using a high-density whole-proteome peptide array, we assessed potential protein-targets for antibodies found in immune sera. Sera from 6 of these cured mice were analyzed with this high-density, whole-proteome peptide array to determine specific antibody-binding sites and their linear peptide sequence. We identified thousands of peptides that were targeted by 2 or more of these 6 mice and exhibited strong antibody binding only by immune, not naive sera. Confirmatory studies were done to validate these results using 2 separate ELISA-based systems. To the best of our knowledge, this is the first study of the "immunome" of protein-based epitopes that are recognized by immune sera from mice cured of cancer via immunotherapy.

Ranking Antibody Binding Epitopes and Proteins Across Samples from Whole Proteome Tiled Linear Peptides

Ultradense peptide binding arrays that can probe millions of linear peptides comprising the entire proteomes or immunomes of human or mouse, or numerous microbes, are powerful tools for studying the abundance of different antibody repertoire in serum samples to understand adaptive immune responses. There are few statistical analysis tools for exploring high-dimensional, significant and reproducible antibody targets for ultradense peptide binding arrays at the linear peptide, epitope (grouping of adjacent peptides), and protein level across multiple samples/subjects (I.e. epitope spread or immunogenic regions within each protein) for understanding the heterogeneity of immune responses. We developed HERON (Hierarchical antibody binding Epitopes and pROteins from liNear peptides), an R package, which allows users to identify immunogenic epitopes using meta-analyses and spatial clustering techniques to explore antibody targets at various resolution and confidence levels, that can be found consistently across a specified number of samples through the entire proteome to study antibody responses for diagnostics or treatment. Our approach estimates significance values at the linear peptide (probe), epitope, and protein level to identify top candidates for validation. We test the performance of predictions on all three levels using correlation between technical replicates and comparison of epitope calls on 2 datasets, which shows HERON's competitiveness in estimating false discovery rates and finding general and sample-level regions of interest for antibody binding. The code is available as an R package downloadable from http://github.com/Ong-Research/HERON.

Conditional gene regulation models demonstrate a pro-proliferative role for growth hormone receptor in prostate cancer

BACKGROUND

Humans with inactivating mutations in growth hormone receptor (GHR) have lower rates of cancer, including prostate cancer. Similarly, mice with inactivating Ghr mutations are protected from prostatic intraepithelial neoplasia in the C3(1)/TAg prostate cancer model. However, gaps in clinical relevance in those models persist. The current study addresses these gaps and the ongoing role of Ghr in prostate cancer using loss-of-function and gain-of-function models.

METHODS

Conditional Ghr inactivation was achieved in the C3(1)/TAg model by employing a tamoxifen-inducible Cre and a prostate-specific Cre. In parallel, a transgenic GH antagonist was also used. Pathology, proliferation, and gene expression of 6-month old mouse prostates were assessed. Analysis of The Cancer Genome Atlas data was conducted to identify GHR overexpression in a subset of human prostate cancers. Ghr overexpression was modeled in PTEN-P2 and TRAMP-C2 mouse prostate cancer cells using stable transfectants. The growth, proliferation, and gene expression effects of Ghr overexpression was assessed in vitro and in vivo.

RESULTS

Loss-of-function for Ghr globally or in prostatic epithelial cells reduced proliferation and stratification of the prostatic epithelium in the C3(1)/TAg model. Genes and gene sets involved in the immune system and tumorigenesis, for example, were dysregulated upon global Ghr disruption. Analysis of The Cancer Genome Atlas revealed higher GHR expression in human prostate cancers with ERG-fusion genes or ETV1-fusion genes. Modeling the GHR overexpression observed in these human prostate cancers by overexpressing Ghr in mouse prostate cancer cells with mutant Pten or T-antigen driver genes increased proliferation of prostate cancer cells in vitro and in vivo. Ghr overexpression regulated the expression of multiple genes oppositely to Ghr loss-of-function models.

CONCLUSIONS

Loss-of-function and gain-of-function Ghr models, including prostatic epithelial cell specific alterations in Ghr, altered proliferation, and gene expression. These data suggest that changes in GHR activity in human prostatic epithelial cells play a role in proliferation and gene regulation in prostate cancer, suggesting the potential for disrupting GH signaling, for example by the FDA approved GH antagonist pegvisomant, may be beneficial in treating prostate cancer.

Inflammatory CD4/CD8 double-positive human T cells arise from reactive CD8 T cells and are sufficient to mediate GVHD pathology

An important paradigm in allogeneic hematopoietic cell transplantations (allo-HCTs) is the prevention of graft-versus-host disease (GVHD) while preserving the graft-versus-leukemia (GVL) activity of donor T cells. From an observational clinical study of adult allo-HCT recipients, we identified a CD4+/CD8+ double-positive T cell (DPT) population, not present in starting grafts, whose presence was predictive of ≥ grade 2 GVHD. Using an established xenogeneic transplant model, we reveal that the DPT population develops from antigen-stimulated CD8 T cells, which become transcriptionally, metabolically, and phenotypically distinct from single-positive CD4 and CD8 T cells. Isolated DPTs were sufficient to mediate xeno-GVHD pathology when retransplanted into naïve mice but provided no survival benefit when mice were challenged with a human B-ALL cell line. Overall, this study reveals human DPTs as a T cell population directly involved with GVHD pathology.

MASH Native: A Unified Solution for Native Top-Down Proteomics Data Processing

Native top-down proteomics (nTDP) integrates native mass spectrometry (nMS) with top-down proteomics (TDP) to provide comprehensive analysis of protein complexes together with proteoform identification and characterization. Despite significant advances in nMS and TDP software developments, a unified and user-friendly software package for analysis of nTDP data remains lacking. Herein, we have developed MASH Native to provide a unified solution for nTDP to process complex datasets with database searching capabilities in a user-friendly interface. MASH Native supports various data formats and incorporates multiple options for deconvolution, database searching, and spectral summing to provide a one-stop shop for characterizing both native protein complexes and proteoforms. The MASH Native app, video tutorials, written tutorials and additional documentation are freely available for download at https://labs.wisc.edu/gelab/MASH_Explorer/MASHNativeSoftware.php . All data files shown in user tutorials are included with the MASH Native software in the download .zip file.

Abstract 1377: Endogenous antibodies recognize multiple proteins on B78 melanoma in mice cured via immunotherapy

Introduction: Antibodies can play an important role in innate and adaptive immune responses against cancer. By using a high-density whole mouse proteome peptide array, we were able to assess potential protein-targets for antibodies present in mice cured of their melanoma by a combination immunotherapy regimen of 12Gy local radiation and intratumoral administration of a tumor-specific immunocytokine (anti-GD2 antibody linked to IL-2). This regimen can induce an “in situ vaccine” effect (ISV) enabling mice to be cured of their tumors with long-term immune memory.

Methods: Immunocompetent C57/BL6 mice bearing large B78 (GD2-expressing) melanoma tumors were treated with ISV. Sera were collected at different timepoints (before tumor implantation, after implantation before treatment, after completion of treatment, after the mouse was rendered tumor-free and after a rejected rechallenge). Tumor cell surface binding of these serum samples was assessed via flow cytometry and showed a strong increase in binding of sample obtained following immunotherapy, and after rejection of a tumor rechallenge. Binding was assessed against the original tumor line, B78, as well as B16 (parental to B78 without GD2 expression).We used the naïve and immune sera on a Nimble Therapeutics whole mouse proteome peptide array to determine specific antibody-binding sites. Whole proteome data was analyzed using a newly developed algorithm which scans adjacent peptides to detect differential binding between naïve and immune samples.

Results: Multiple proteins were selectively identified by immune sera which were not well detected by sera from naïve mice. Hundreds of proteins were targeted by 3 or more of 6 mice and exhibited strong antibody binding only by immune sera. Some proteins were recognized via the same binding epitopes, others via different epitopes in separate mice. Some of the whole protein peptide array results were validated via JPT multi-well peptide array and some via independent peptide ELISA. Results from Nimble peptide array and ELISA validated well.

Conclusions: We were able to detect antibody in immune sera binding selectively to specific linear epitopes and identify proteins of interest for further investigation as potential targets for antibody-based or cellular therapies. Results from our whole proteome peptide array were validated in separate assays via peptide ELISA. We are also exploring if some of the identified tumor-specific endogenous antibodies could be used as biomarkers to predict response to our ISV regimen and potentially other immunotherapy treatments.

Citation Format: Anna Hoefges, Sean J. McIlwain, Amy K. Erbe, Trang Q. Le, Kaitlin Tetreault, Nicholas Mathers, Alexander L. Rakhmilevich, Jacquelyn A. Hank, Jigar Patel, Brad Garcia, Zachary S. Morris, Irene M. Ong, Paul M. Sondel. Endogenous antibodies recognize multiple proteins on B78 melanoma in mice cured via immunotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1377.

The landscape of antibody binding in SARS-CoV-2 infection

The search for potential antibody-based diagnostics, vaccines, and therapeutics for pandemic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has focused almost exclusively on the spike (S) and nucleocapsid (N) proteins. Coronavirus membrane (M), ORF3a, and ORF8 proteins are humoral immunogens in other coronaviruses (CoVs) but remain largely uninvestigated for SARS-CoV-2. Here, we use ultradense peptide microarray mapping to show that SARS-CoV-2 infection induces robust antibody responses to epitopes throughout the SARS-CoV-2 proteome, particularly in M, in which 1 epitope achieved excellent diagnostic accuracy. We map 79 B cell epitopes throughout the SARS-CoV-2 proteome and demonstrate that antibodies that develop in response to SARS-CoV-2 infection bind homologous peptide sequences in the 6 other known human CoVs. We also confirm reactivity against 4 of our top-ranking epitopes by enzyme-linked immunosorbent assay (ELISA). Illness severity correlated with increased reactivity to 9 SARS-CoV-2 epitopes in S, M, N, and ORF3a in our population. Our results demonstrate previously unknown, highly reactive B cell epitopes throughout the full proteome of SARS-CoV-2 and other CoV proteins.

The landscape of antibody binding in SARS-CoV-2 infection

The search for potential antibody-based diagnostics, vaccines, and therapeutics for pandemic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has focused almost exclusively on the spike (S) and nucleocapsid (N) proteins. Coronavirus membrane (M), ORF3a, and ORF8 proteins are humoral immunogens in other coronaviruses (CoVs) but remain largely uninvestigated for SARS-CoV-2. Here we use ultradense peptide microarray mapping to show that SARS-CoV-2 infection induces robust antibody responses to epitopes throughout the SARS-CoV-2 proteome, particularly in M, in which one epitope achieved excellent diagnostic accuracy. We map 79 B cell epitopes throughout the SARS-CoV-2 proteome and demonstrate that antibodies that develop in response to SARS-CoV-2 infection bind homologous peptide sequences in the six other known human CoVs. We also confirm reactivity against four of our top-ranking epitopes by enzyme-linked immunosorbent assay (ELISA). Illness severity correlated with increased reactivity to nine SARS-CoV-2 epitopes in S, M, N, and ORF3a in our population. Our results demonstrate previously unknown, highly reactive B cell epitopes throughout the full proteome of SARS-CoV-2 and other CoV proteins.

Functionally Integrated Top-Down Proteomics for Standardized Assessment of Human Induced Pluripotent Stem Cell-Derived Engineered Cardiac Tissues

Three-dimensional (3D) human induced pluripotent stem cell-derived engineered cardiac tissues (hiPSC-ECTs) have emerged as a promising alternative to two-dimensional hiPSC-cardiomyocyte monolayer systems because hiPSC-ECTs are a closer representation of endogenous cardiac tissues and more faithfully reflect the relevant cardiac pathophysiology. The ability to perform functional and molecular assessments using the same hiPSC-ECT construct would allow for more reliable correlation between observed functional performance and underlying molecular events, and thus is critically needed. Herein, for the first time, we have established an integrated method that permits sequential assessment of functional properties and top-down proteomics from the same single hiPSC-ECT construct. We quantitatively determined the differences in isometric twitch force and the sarcomeric proteoforms between two groups of hiPSC-ECTs that differed in the duration of time of 3D-ECT culture. Importantly, by using this integrated method we discovered a new and strong correlation between the measured contractile parameters and the phosphorylation levels of alpha-tropomyosin between the two groups of hiPSC-ECTs. The integration of functional assessments together with molecular characterization by top-down proteomics in the same hiPSC-ECT construct enables a holistic analysis of hiPSC-ECTs to accelerate their applications in disease modeling, cardiotoxicity, and drug discovery. Data are available via ProteomeXchange with identifier PXD022814.

MASH Explorer: A Universal Software Environment for Top-Down Proteomics

Top-down mass spectrometry (MS)-based proteomics enable a comprehensive analysis of proteoforms with molecular specificity to achieve a proteome-wide understanding of protein functions. However, the lack of a universal software for top-down proteomics is becoming increasingly recognized as a major barrier, especially for newcomers. Here, we have developed MASH Explorer, a universal, comprehensive, and user-friendly software environment for top-down proteomics. MASH Explorer integrates multiple spectral deconvolution and database search algorithms into a single, universal platform which can process top-down proteomics data from various vendor formats, for the first time. It addresses the urgent need in the rapidly growing top-down proteomics community and is freely available to all users worldwide. With the critical need and tremendous support from the community, we envision that this MASH Explorer software package will play an integral role in advancing top-down proteomics to realize its full potential for biomedical research.

Enhancing Top-Down Proteomics Data Analysis by Combining Deconvolution Results through a Machine Learning Strategy

Top-down mass spectrometry (MS) is a powerful tool for the identification and comprehensive characterization of proteoforms arising from alternative splicing, sequence variation, and post-translational modifications. However, the complex data set generated from top-down MS experiments requires multiple sequential data processing steps to successfully interpret the data for identifying and characterizing proteoforms. One critical step is the deconvolution of the complex isotopic distribution that arises from naturally occurring isotopes. Multiple algorithms are currently available to deconvolute top-down mass spectra, resulting in different deconvoluted peak lists with varied accuracy compared to true positive annotations. In this study, we have designed a machine learning strategy that can process and combine the peak lists from different deconvolution results. By optimizing clustering results, deconvolution results from THRASH, TopFD, MS-Deconv, and SNAP algorithms were combined into consensus peak lists at various thresholds using either a simple voting ensemble method or a random forest machine learning algorithm. For the random forest algorithm, which had better predictive performance, the consensus peak lists on average could achieve a recall value (true positive rate) of 0.60 and a precision value (positive predictive value) of 0.78. It outperforms the single best algorithm, which achieved a recall value of only 0.47 and a precision value of 0.58. This machine learning strategy enhanced the accuracy and confidence in protein identification during database searches by accelerating the detection of true positive peaks while filtering out false positive peaks. Thus, this method shows promise in enhancing proteoform identification and characterization for high-throughput data analysis in top-down proteomics.

Thousands of new antigens are recognized in mice via endogenous antibodies after being cured of a B78 melanoma via immunotherapy

Antibodies can play an important role in innate and adaptive immune responses against cancer. Using a high-density peptide array, we assessed potential protein-targets for antibodies activated in mice cured of their melanoma through a combined immunotherapy regimen. Our goal was to determine the linear peptide sequences recognized by anti-tumor antibodies produced in mice cured of their melanoma with immunotherapy.

Mice with GD2-expressing syngeneic B78 melanoma were treated with a combination immunotherapy capable of inducing an “in situ vaccine” effect (ISV), enabling mice to be cured of their tumors with long-term immune memory. Naïve and immune sera were collected from these mice. Using flow cytometry, immune sera showed strong antibody-binding against B16 (parental cell line of B78 without GD2 expression). These sera were then used on a Nimble Therapeutics’ peptide-array (either whole proteome or a curated list of ~650 proteins) to determine specific antibody-binding sites, and data were analyzed using a probabilistic model. Using the “curated list” peptide array, proteins were selected if the protein was bound in immune sera but not bound in the sera from naïve or non-responding tumor-bearing mice. When focusing on the whole mouse proteome data, thousands of peptides were targeted by 2 or more mice and exhibited strong antibody binding only by immune sera.

We are continuing to refine our analytical methods and are further investigating all identified proteins. These peptides may be new targets for antibody-based or cellular therapies and some of the tumor-specific endogenous antibodies that we have identified may be used as biomarkers to predict response to our ISV regimen and potentially other immunotherapy treatments.

An Unbiased Proteomics Method to Assess the Maturation of Human Pluripotent Stem Cell-Derived Cardiomyocytes

RATIONALE

Human pluripotent stem cell (hPSC)-derived cardiomyocytes exhibit the properties of fetal cardiomyocytes, which limits their applications. Various methods have been used to promote maturation of hPSC-cardiomyocytes; however, there is a lack of an unbiased and comprehensive method for accurate assessment of the maturity of hPSC-cardiomyocytes.

OBJECTIVE

We aim to develop an unbiased proteomics strategy integrating high-throughput top-down targeted proteomics and bottom-up global proteomics for the accurate and comprehensive assessment of hPSC-cardiomyocyte maturation.

METHODS AND RESULTS

Utilizing hPSC-cardiomyocytes from early- and late-stage 2-dimensional monolayer culture and 3-dimensional engineered cardiac tissue, we demonstrated the high reproducibility and reliability of a top-down proteomics method, which enabled simultaneous quantification of contractile protein isoform expression and associated post-translational modifications. This method allowed for the detection of known maturation-associated contractile protein alterations and, for the first time, identified contractile protein post-translational modifications as promising new markers of hPSC-cardiomyocytes maturation. Most notably, decreased phosphorylation of α-tropomyosin was found to be associated with hPSC-cardiomyocyte maturation. By employing a bottom-up global proteomics strategy, we identified candidate maturation-associated markers important for sarcomere organization, cardiac excitability, and Ca²⁺ homeostasis. In particular, upregulation of myomesin 1 and transmembrane 65 was associated with hPSC-cardiomyocyte maturation and validated in cardiac development, making these promising markers for assessing maturity of hPSC-cardiomyocytes. We have further validated α-actinin isoforms, phospholamban, dystrophin, αB-crystallin, and calsequestrin 2 as novel maturation-associated markers, in the developing mouse cardiac ventricles.

CONCLUSIONS

We established an unbiased proteomics method that can provide accurate and specific assessment of the maturity of hPSC-cardiomyocytes and identified new markers of maturation. Furthermore, this integrated proteomics strategy laid a strong foundation for uncovering the molecular pathways involved in cardiac development and disease using hPSC-cardiomyocytes.

TLR Stimulation during T-cell Activation Lowers PD-1 Expression on CD8+ T Cells

Expression of T-cell checkpoint receptors can compromise antitumor immunity. Blockade of these receptors, notably PD-1 and LAG-3, which become expressed during T-cell activation with vaccination, can improve antitumor immunity. We evaluated whether T-cell checkpoint expression could be separated from T-cell activation in the context of innate immune stimulation with TLR agonists. We found that ligands for TLR1/2, TLR7, and TLR9 led to a decrease in expression of PD-1 on antigen-activated CD8⁺ T cells. These effects were mediated by IL12 released by professional antigen-presenting cells. In two separate tumor models, treatment with antitumor vaccines combined with TLR1/2 or TLR7 ligands induced antigen-specific CD8⁺ T cells with lower PD-1 expression and improved antitumor immunity. These findings highlight the role of innate immune activation during effector T-cell development and suggest that at least one mechanism by which specific TLR agonists can be strategically used as vaccine adjuvants is by modulating the expression of PD-1 during CD8⁺ T-cell activation. Cancer Immunol Res; 6(11); 1364-74. ©2018 AACR.

Temperature-sensitive sarcomeric protein post-translational modifications revealed by top-down proteomics

Despite advancements in symptom management for heart failure (HF), this devastating clinical syndrome remains the leading cause of death in the developed world. Studies using animal models have greatly advanced our understanding of the molecular mechanisms underlying HF; however, differences in cardiac physiology and the manifestation of HF between animals, particularly rodents, and humans necessitates the direct interrogation of human heart tissue samples. Nevertheless, an ever-present concern when examining human heart tissue samples is the potential for artefactual changes related to temperature changes during tissue shipment or sample processing. Herein, we examined the effects of temperature on the post-translational modifications (PTMs) of sarcomeric proteins, the proteins responsible for muscle contraction, under conditions mimicking those that might occur during tissue shipment or sample processing. Using a powerful top-down proteomics method, we found that sarcomeric protein PTMs were differentially affected by temperature. Specifically, cardiac troponin I and enigma homolog isoform 2 showed robust increases in phosphorylation when tissue was incubated at either 4 °C or 22 °C. The observed increase is likely due to increased cyclic AMP levels and activation of protein kinase A in the tissue. On the contrary, cardiac troponin T and myosin regulatory light chain phosphorylation decreased when tissue was incubated at 4 °C or 22 °C. Furthermore, significant protein degradation was also observed after incubation at 4 °C or 22 °C. Overall, these results indicate that temperature exerts various effects on sarcomeric protein PTMs and careful tissue handling is critical for studies involving human heart samples. Moreover, these findings highlight the power of top-down proteomics for examining the integrity of cardiac tissue samples.

Complete genome sequence and the expression pattern of plasmids of the model ethanologen Zymomonas mobilis ZM4 and its xylose-utilizing derivatives 8b and 2032

BACKGROUND

Zymomonas mobilis is a natural ethanologen being developed and deployed as an industrial biofuel producer. To date, eight Z. mobilis strains have been completely sequenced and found to contain 2-8 native plasmids. However, systematic verification of predicted Z. mobilis plasmid genes and their contribution to cell fitness has not been hitherto addressed. Moreover, the precise number and identities of plasmids in Z. mobilis model strain ZM4 have been unclear. The lack of functional information about plasmid genes in ZM4 impedes ongoing studies for this model biofuel-producing strain.

RESULTS

In this study, we determined the complete chromosome and plasmid sequences of ZM4 and its engineered xylose-utilizing derivatives 2032 and 8b. Compared to previously published and revised ZM4 chromosome sequences, the ZM4 chromosome sequence reported here contains 65 nucleotide sequence variations as well as a 2400-bp insertion. Four plasmids were identified in all three strains, with 150 plasmid genes predicted in strain ZM4 and 2032, and 153 plasmid genes predicted in strain 8b due to the insertion of heterologous DNA for expanded substrate utilization. Plasmid genes were then annotated using Blast2GO, InterProScan, and systems biology data analyses, and most genes were found to have apparent orthologs in other organisms or identifiable conserved domains. To verify plasmid gene prediction, RNA-Seq was used to map transcripts and also compare relative gene expression under various growth conditions, including anaerobic and aerobic conditions, or growth in different concentrations of biomass hydrolysates. Overall, plasmid genes were more responsive to varying hydrolysate concentrations than to oxygen availability. Additionally, our results indicated that although all plasmids were present in low copy number (about 1-2 per cell), the copy number of some plasmids varied under specific growth conditions or due to heterologous gene insertion.

CONCLUSIONS

The complete genome of ZM4 and two xylose-utilizing derivatives is reported in this study, with an emphasis on identifying and characterizing plasmid genes. Plasmid gene annotation, validation, expression levels at growth conditions of interest, and contribution to host fitness are reported for the first time.

Directed Evolution Reveals Unexpected Epistatic Interactions That Alter Metabolic Regulation and Enable Anaerobic Xylose Use by Saccharomyces cerevisiae

The inability of native Saccharomyces cerevisiae to convert xylose from plant biomass into biofuels remains a major challenge for the production of renewable bioenergy. Despite extensive knowledge of the regulatory networks controlling carbon metabolism in yeast, little is known about how to reprogram S. cerevisiae to ferment xylose at rates comparable to glucose. Here we combined genome sequencing, proteomic profiling, and metabolomic analyses to identify and characterize the responsible mutations in a series of evolved strains capable of metabolizing xylose aerobically or anaerobically. We report that rapid xylose conversion by engineered and evolved S. cerevisiae strains depends upon epistatic interactions among genes encoding a xylose reductase (GRE3), a component of MAP Kinase (MAPK) signaling (HOG1), a regulator of Protein Kinase A (PKA) signaling (IRA2), and a scaffolding protein for mitochondrial iron-sulfur (Fe-S) cluster biogenesis (ISU1). Interestingly, the mutation in IRA2 only impacted anaerobic xylose consumption and required the loss of ISU1 function, indicating a previously unknown connection between PKA signaling, Fe-S cluster biogenesis, and anaerobiosis. Proteomic and metabolomic comparisons revealed that the xylose-metabolizing mutant strains exhibit altered metabolic pathways relative to the parental strain when grown in xylose. Further analyses revealed that interacting mutations in HOG1 and ISU1 unexpectedly elevated mitochondrial respiratory proteins and enabled rapid aerobic respiration of xylose and other non-fermentable carbon substrates. Our findings suggest a surprising connection between Fe-S cluster biogenesis and signaling that facilitates aerobic respiration and anaerobic fermentation of xylose, underscoring how much remains unknown about the eukaryotic signaling systems that regulate carbon metabolism.

The yeast Saccharomyces cerevisiae is being genetically engineered to produce renewable biofuels from sustainable plant material. Efficient biofuel production from plant material requires conversion of the complex suite of sugars found in plant material, including the five-carbon sugar xylose. Because it does not efficiently metabolize xylose, S. cerevisiae has been engineered with a minimal set of genes that should overcome this problem; however, additional genetic changes are required for optimal fermentative conversion of xylose into biofuel. Despite extensive knowledge of the regulatory networks controlling glucose metabolism, less is known about the regulation of xylose metabolism and how to rewire these networks for effective biofuel production. Here we report genetic mutations that enabled the conversion of xylose into bioethanol by a previously ineffective yeast strain. By comparing altered protein and metabolite abundance within yeast cells containing these mutations, we determined that the mutations synergistically alter metabolic pathways to improve the rate of xylose conversion. One change in a gene with well-characterized aerobic mitochondrial functions was found to play an unexpected role in anaerobic conversion of xylose into ethanol. The results of this work will allow others to rapidly generate yeast strains for the conversion of xylose into biofuels and other products.

Chemical genomic profiling via barcode sequencing to predict compound mode of action

Chemical genomics is an unbiased, whole-cell approach to characterizing novel compounds to determine mode of action and cellular target. Our version of this technique is built upon barcoded deletion mutants of Saccharomyces cerevisiae and has been adapted to a high-throughput methodology using next-generation sequencing. Here we describe the steps to generate a chemical genomic profile from a compound of interest, and how to use this information to predict molecular mechanism and targets of bioactive compounds.