Reduction of Graft-versus-Host-Disease in NOD.Cg-Prkdcscid Il2rgtm1Wjl/SzJ (NSG) Mice by Cotransplantation of Syngeneic Human Umbilical Cord-Derived Mesenchymal Stromal Cells.

Graft-versus-host disease (GVHD) is one of the major complications following hematopoietic stem cell transplantation, which remains the sole curative therapy for many malignant diseases of the hematopoietic system. The immunomodulatory potential of mesenchymal stromal cells (MSCs) to treat GVHD is currently being tested in various preclinical and clinical trials. Because the results of the preclinical and clinical trials on the use of MSCs to treat GVHD have not been consistent, we analyzed the potential beneficial effects of syngeneic versus allogenic treatment, culture expansion of MSCs, and various MSC cell doses and time points of MSC transplantation in a murine GVHD model. We established the murine GVHD model based on the transplantation of umbilical cord blood-derived hematopoietic stem cells (UC-HSCs) and used this model to assess the therapeutic potential of umbilical cord blood-derived MSCs (UC-MSCs). The use of HSC and MSC populations derived from the same donor allowed us to exclude third-party cells and test the UC-HSCs and UC-MSCs in a matched setting. Moreover, we were able to compare various doses, transplantation time points, and the influence of culture expansion of MSCs on the impact of treatment. This resulted in 16 different treatment groups. The most efficient setting for treatment of UC-HSC-induced GVHD reactions was based on the simultaneous administration of 1 × 106 culture-expanded, syngeneically matched UC-MSCs. This therapy effectively reduced the number of CD8+ T cells in the blood, protected the mice from weight loss, and prolonged their survival until the end of observation period. Taken together, our data show beneficial effects of (1) syngeneic over allogeneic UC-HSCs and UC-MSCs, (2) culture-expanded cells over freshly isolated primary cells, (3) simultaneous over sequential administration, and (4) high doses of UC-MSCs. The animal model of GVHD established here is now available for more detailed studies, including a comparative analysis of the efficacy of MSCs derived from alternative sources, such as adipose tissue and bone marrow.

Systematic identification of cancer-specific MHC-binding peptides with RAVEN.

Immunotherapy can revolutionize anti-cancer therapy if specific targets are available. Immunogenic peptides encoded by cancer-specific genes (CSGs) may enable targeted immunotherapy, even of oligo-mutated cancers, which lack neo-antigens generated by protein-coding missense mutations. Here, we describe an algorithm and user-friendly software named RAVEN (Rich Analysis of Variable gene Expressions in Numerous tissues) that automatizes the systematic and fast identification of CSG-encoded peptides highly affine to Major Histocompatibility Complexes (MHC) starting from transcriptome data. We applied RAVEN to a dataset assembled from 2,678 simultaneously normalized gene expression microarrays comprising 50 tumor entities, with a focus on oligo-mutated pediatric cancers, and 71 normal tissue types. RAVEN performed a transcriptome-wide scan in each cancer entity for gender-specific CSGs, and identified several established CSGs, but also many novel candidates potentially suitable for targeting multiple cancer types. The specific expression of the most promising CSGs was validated in cancer cell lines and in a comprehensive tissue-microarray. Subsequently, RAVEN identified likely immunogenic CSG-encoded peptides by predicting their affinity to MHCs and excluded sequence identity to abundantly expressed proteins by interrogating the UniProt protein-database. The predicted affinity of selected peptides was validated in T2-cell peptide-binding assays in which many showed binding-kinetics like a very immunogenic influenza control peptide. Collectively, we provide an exquisitely curated catalogue of cancer-specific and highly MHC-affine peptides across 50 cancer types, and a freely available software (https://github.com/JSGerke/RAVENsoftware) to easily apply our algorithm to any gene expression dataset. We anticipate that our peptide libraries and software constitute a rich resource to advance anti-cancer immunotherapy.

Ewing sarcoma partial regression without GvHD by chondromodulin-I/HLA-A*02:01-specific allorestricted T cell receptor transgenic T cells.

Background: Chondromodulin-I (CHM1) sustains malignancy in Ewing sarcoma (ES). Refractory ES carries a dismal prognosis and patients with bone marrow (BM) metastases do not survive irrespective of therapy. We assessed HLA-A*02:01/CHM1-specific allorestricted T cell receptor (TCR) wild-type and transgenic cytotoxic (CD8+) T cells against ES. Patients and Methods: Three refractory HLA-A2+ ES patients were treated with HLA-A*02:01/peptide-specific allorepertoire-derived (i.e., allorestricted) CD8+ T cells. Patient #1 received up to 4.8 × 105/kg body weight HLA-A*02:01- allorestricted donor-derived wild-type CD8+ T cells. Patient #2 received up to 8.2 × 106/kg HLA-A*02:01- donor-derived and patient #3 up to 6 × 106/kg autologous allorestricted TCR transgenic CD8+ T cells. All patients were treated with the same TCR complementary determining region 3 allorecognition sequence for CHM1 peptide 319 (CHM1319). Results: HLA-A*02:01/CHM1319-specific allorestricted CD8+ T cells showed specific in vitro lysis of all patient-derived ES cell lines. Therapy was well tolerated and did not cause graft versus host disease (GvHD). Patients #1 and #3 showed slow progression, whereas patient #2, while having BM involvement, showed partial metastatic regression associated with T cell homing to involved lesions. CHM1319 TCR transgenic T cells could be tracked in his BM for weeks. Conclusions: CHM1319-TCR transgenic T cells home to affected BM and may cause partial disease regression. HLA-A*02:01/antigen-specific allorestricted T cells proliferate in vivo without causing GvHD.

Pappalysin-1 T cell receptor transgenic allo-restricted T cells kill Ewing sarcoma in vitro and in vivo.

Pregnancy-associated plasma protein-A (PAPPA), also known as pappalysin, is a member of the insulin-like growth factor (IGF) family. PAPPA acts as a protease, cleaving IGF inhibitors, i.e., IGF binding proteins (IGFBPs), thereby setting free IGFs. The insulin/IGF-axis is involved in cancer in general and in Ewing sarcoma (ES) in particular. ES is a highly malignant bone tumor characterized by early metastatic spread. PAPPA is associated with various cancers. It is overexpressed and required for proliferation in ES. PAPPA also stimulates normal bone growth. We isolated HLA-A*02:01+/peptide-restricted T cells from A*02:01- healthy donors directed against PAPPA, generated by priming with A*02:01+ PAPPA peptide loaded dendritic cells. After TCR identification, retrovirally TCR transduced CD8+ T cells were assessed for their in vitro specificity and in vivo efficacy in human ES bearing Rag2-/-γc-/- mice. Engraftment in mice and tumor infiltration of TCR transgenic T cells in the mice was evaluated. The TCR transgenic T cell clone PAPPA-2G6 demonstrated specific reactivity toward HLA-A*02:01+/PAPPA+ ES cell lines. We furthermore detected circulating TCR transgenic T cells in the blood in Rag2-/-γc-/- mice and in vivo engraftment in bone marrow. Tumor growth in mice with xenografted ES was significantly reduced after treatment with PAPPA-2G6 TCR transgenic T cells in contrast to controls. Tumors of treated mice revealed tumor-infiltrating PAPPA-2G6 TCR transgenic T cells. In summary, we demonstrate that PAPPA is a first-rate target for TCR-based immunotherapy of ES.

Lysosome-associated membrane glycoprotein 1 predicts fratricide amongst T cell receptor transgenic CD8+ T cells directed against tumor-associated antigens.

AIM: Autologous as well as allogeneic CD8+ T cells transduced with tumor antigen specific T cell receptors (TCR) may cause significant tumor lysis upon adoptive transfer. Besides unpredictable life-threatening off-target effects, these TCRs may unexpectedly commit fratricide. We hypothesized lysosome-associated membrane glycoprotein 1 (LAMP1, CD107a) to be a marker for fratricide in TCR transgenic CD8+ T cells. METHODS: We identified HLA-A*02:01/peptide-restricted T cells directed against ADRB3295. After TCR identification, we generated HLA-A*02:01/peptide restricted TCR transgenic T cells by retroviral transduction and tested T cell expansion rates as well as A*02:01/peptide recognition and ES killing in ELISpot and xCELLigence assays. Expansion arrest was analyzed via Annexin and CD107a staining. Results were compared to CHM1319-TCR transgenic T cells. RESULTS: Beta-3-adrenergic receptor (ADRB3) as well as chondromodulin-1 (CHM1) are over-expressed in Ewing Sarcoma (ES) but not on T cells. TCR transgenic T cells demonstrated HLA-A*02:01/ADRB3295 mediated ES recognition and killing in ELISpot and xCELLigence assays. 24h after TCR transduction, CD107a expression correlated with low expansion rates due to apoptosis of ADRB3 specific T cells in contrast to CHM1 specific transgenic T cells. Amino-acid exchange scans clearly indicated the cross-reactive potential of HLA-A*02:01/ADRB3295- and HLA-A*02:01/CHM1319-TCR transgenic T cells. Comparison of peptide motive binding affinities revealed extended fratricide among ADRB3295 specific TCR transgenic T cells in contrast to CHM1319. CONCLUSION: Amino-acid exchange scans alone predict TCR cross-reactivity with little specificity and thus require additional assessment of potentially cross-reactive HLA-A*02:01 binding candidates. CD107a positivity is a marker for fratricide of CD8+ TCR transgenic T cells.

Human HLA-A*02:01/CHM1+ allo-restricted T cell receptor transgenic CD8+ T cells specifically inhibit Ewing sarcoma growth in vitro and in vivo.

The endochondral bone protein Chondromodulin-I (CHM1) provides oncogene addiction in Ewing sarcoma (ES). We pre-clinically tested the targetability of CHM1 by TCR transgenic, allo-restricted, peptide specific T cells to treat ES. We previously generated allo-restricted wildtype CD8+ T cells directed against the ES specific antigen CHM1319 causing specific responses against ES. However, utilization of these cells in current therapy protocols is hampered due to high complexity in production, relatively low cell numbers, and rapid T cell exhaustion.In order to provide off-the-shelf products in the future, we successfully generated HLA-A*02:01-restricted T cell receptor (TCR) transgenic T cells directed against CHM1319 by retroviral transduction.After short-term expansion a 100% purified CHM1319-TCR-transgenic T cell population expressed a CD62L+/CD45RO and CD62L+/CD45RA+ phenotype. These cells displayed specific in vitro IFNg and granzyme B release in co-culture with HLA-A*02:01+ ES cell lines expressing CHM1. When co-injected with ES cells in Rag2-/-É£c-/- mice, CHM1-specific TCR-transgenic T cells significantly inhibited the formation of lung and liver metastases in contrast to control mice. Lungs and livers of representative mice displayed CD8+ T cell infiltration in the presence (control group treated with unspecific T cells) and in the absence (study group) of metastatic disease, respectively. Furthermore, mice receiving unspecific T cells showed signs of graft-versus-host-disease in contrast to all mice, receiving CHM1319-TCR-transgenic T cells.CHM1319 specific TCR-transgenic T cells were successfully generated causing anti-ES responses in vitro and in vivo. In the future, CHM1319-TCR-transgenic T cells may control minimal residual disease rendering donor lymphocyte infusions more efficacious and less toxic.

Immunotherapeutic targeting of membrane Hsp70-expressing tumors using recombinant human granzyme B.

BACKGROUND: We have previously reported that human recombinant granzyme B (grB) mediates apoptosis in membrane heat shock protein 70 (Hsp70)-positive tumor cells in a perforin-independent manner. METHODOLOGY/PRINCIPAL FINDINGS: Optical imaging of uptake kinetics revealed co-localization of grB with recycling endosomes (Rab9/11) as early as 5 min after internalization, with late endosomes (Rab7) after 30 min, and the lysosomal compartment (LAMP1/2) after 60 to 120 min. Active caspase-3-mediated apoptosis was induced in mouse CT26 monolayer cells and 3D tumor spheroids, but not in normal mouse endothelial cells. Granzyme B selectively reduced the proportion of membrane Hsp70-positive cells in CT26 tumor spheroids. Consecutive i.v. injections of recombinant human grB into mice bearing membrane Hsp70-positive CT26 tumors resulted in significant tumor suppression, and a detailed inspection of normal mouse organs revealed that the administration of anti-tumoral concentrations of grB elicited no clinicopathological changes. CONCLUSIONS/SIGNIFICANCE: These findings support the future clinical evaluation of human grB as a potential adjuvant therapeutic agent, especially for treating immunosuppressed patients that bear membrane Hsp70-positive tumors.

In situ assay of fatty acid ß-oxidation by metabolite profiling following permeabilization of cell membranes.

Quantitative analysis of mitochondrial FA ß-oxidation (FAO) has drawn increasing interest for defining lipid-induced metabolic dysfunctions, such as in obesity-induced insulin resistance, and evaluating pharmacologic strategies to improve ß-oxidation function. The aim was to develop a new assay to quantify ß-oxidation function in intact mitochondria and with a low amount of cell material. Cell membranes of primary human fibroblasts were permeabilized with digitonin prior to a load with FFA substrate. Following 120 min of incubation, the various generated acylcarnitines were extracted from both cells and incubation medium by protein precipitation/desalting and subjected to solid-phase extraction. A panel of 30 acylcarnitines per well was quantified by MS/MS and normalized to citrate synthase activity to analyze mitochondrial metabolite flux. Pretreatment with bezafibrate and etomoxir revealed stimulating and inhibiting regulatory effects on ß-oxidation function, respectively. In addition to the advantage of a much shorter assay time due to in situ permeabilization compared with whole-cell incubation systems, the method allows the detection of multiple acylcarnitines from an only limited amount of intact cells, particularly relevant to the use of primary cells. This novel approach facilitates highly sensitive, simple, and fast monitoring of pharmacological effects on FAO.

Prediction of helix-helix contacts and interacting helices in polytopic membrane proteins using neural networks.

Despite rapidly increasing numbers of available 3D structures, membrane proteins still account for less than 1% of all structures in the Protein Data Bank. Recent high-resolution structures indicate a clearly broader structural diversity of membrane proteins than initially anticipated, motivating the development of reliable structure prediction methods specifically tailored for this class of molecules. One important prediction target capturing all major aspects of a protein's 3D structure is its contact map. Our analysis shows that computational methods trained to predict residue contacts in globular proteins perform poorly when applied to membrane proteins. We have recently published a method to identify interacting alpha-helices in membrane proteins based on the analysis of coevolving residues in predicted transmembrane regions. Here, we present a substantially improved algorithm for the same problem, which uses a newly developed neural network approach to predict helix-helix contacts. In addition to the input features commonly used for contact prediction of soluble proteins, such as windowed residue profiles and residue distance in the sequence, our network also incorporates features that apply to membrane proteins only, such as residue position within the transmembrane segment and its orientation toward the lipophilic environment. The obtained neural network can predict contacts between residues in transmembrane segments with nearly 26% accuracy. It is therefore the first published contact predictor developed specifically for membrane proteins performing with equal accuracy to state-of-the-art contact predictors available for soluble proteins. The predicted helix-helix contacts were employed in a second step to identify interacting helices. For our dataset consisting of 62 membrane proteins of solved structure, we gained an accuracy of 78.1%. Because the reliable prediction of helix interaction patterns is an important step in the classification and prediction of membrane protein folds, our method will be a helpful tool in compiling a structural census of membrane proteins.

An evolutionary and structural characterization of mammalian protein complex organization.

BACKGROUND: We have recently released a comprehensive, manually curated database of mammalian protein complexes called CORUM. Combining CORUM with other resources, we assembled a dataset of over 2700 mammalian complexes. The availability of a rich information resource allows us to search for organizational properties concerning these complexes. RESULTS: As the complexity of a protein complex in terms of the number of unique subunits increases, we observed that the number of such complexes and the mean non-synonymous to synonymous substitution ratio of associated genes tend to decrease. Similarly, as the number of different complexes a given protein participates in increases, the number of such proteins and the substitution ratio of the associated gene also tends to decrease. These observations provide evidence relating natural selection and the organization of mammalian complexes. We also observed greater homogeneity in terms of predicted protein isoelectric points, secondary structure and substitution ratio in annotated versus randomly generated complexes. A large proportion of the protein content and interactions in the complexes could be predicted from known binary protein-protein and domain-domain interactions. In particular, we found that large proteins interact preferentially with much smaller proteins. CONCLUSION: We observed similar trends in yeast and other data. Our results support the existence of conserved relations associated with the mammalian protein complexes.

Prediction of beta-turns and beta-turn types by a novel bidirectional Elman-type recurrent neural network with multiple output layers (MOLEBRNN).

UNLABELLED: Prediction of beta-turns from amino acid sequences has long been recognized as an important problem in structural bioinformatics due to their frequent occurrence as well as their structural and functional significance. Because various structural features of proteins are intercorrelated, secondary structure information has been often employed as an additional input for machine learning algorithms while predicting beta-turns. Here we present a novel bidirectional Elman-type recurrent neural network with multiple output layers (MOLEBRNN) capable of predicting multiple mutually dependent structural motifs and demonstrate its efficiency in recognizing three aspects of protein structure: beta-turns, beta-turn types, and secondary structure. The advantage of our method compared to other predictors is that it does not require any external input except for sequence profiles because interdependencies between different structural features are taken into account implicitly during the learning process. In a sevenfold cross-validation experiment on a standard test dataset our method exhibits the total prediction accuracy of 77.9% and the Mathew's Correlation Coefficient of 0.45, the highest performance reported so far. It also outperforms other known methods in delineating individual turn types. We demonstrate how simultaneous prediction of multiple targets influences prediction performance on single targets. The MOLEBRNN presented here is a generic method applicable in a variety of research fields where multiple mutually depending target classes need to be predicted. AVAILABILITY: http://webclu.bio.wzw.tum.de/predator-web/.

Will my protein crystallize? A sequence-based predictor.

We propose a machine-learning approach to sequence-based prediction of protein crystallizability in which we exploit subtle differences between proteins whose structures were solved by X-ray analysis [or by both X-ray and nuclear magnetic resonance (NMR) spectroscopy] and those proteins whose structures were solved by NMR spectroscopy alone. Because the NMR technique is usually applied on relatively small proteins, sequence length distributions of the X-ray and NMR datasets were adjusted to avoid predictions biased by protein size. As feature space for classification, we used frequencies of mono-, di-, and tripeptides represented by the original 20-letter amino acid alphabet as well as by several reduced alphabets in which amino acids were grouped by their physicochemical and structural properties. The classification algorithm was constructed as a two-layered structure in which the output of primary support vector machine classifiers operating on peptide frequencies was combined by a second-level Naive Bayes classifier. Due to the application of metamethods for cost sensitivity, our method is able to handle real datasets with unbalanced class representation. An overall prediction accuracy of 67% [65% on the positive (crystallizable) and 69% on the negative (noncrystallizable) class] was achieved in a 10-fold cross-validation experiment, indicating that the proposed algorithm may be a valuable tool for more efficient target selection in structural genomics. A Web server for protein crystallizability prediction called SECRET is available at http://webclu.bio.wzw.tum.de:8080/secret.