Johnson, Kenneth L.

Abstract Multiple myeloma (MM) bone disease is a significant cause of morbidity but there is a paucity of data on the impact of malignant plasma cells on adjacent trabecular bone within the BM. Here, we characterize the proteome of trabecular bone tissue from BM biopsies of 56 patients with monoclonal gammopathy of undetermined significance (MGUS), smoldering (SMM), newly diagnosed (NDMM), relapsed MM (RMM), and normal controls. Proteins involved in extracellular matrix (ECM) formation and immunity pathways were decreased in SMM and active MM. Among the proteins most decreased were immunoglobulins, type IV collagen, and TIMP3, suggesting increased immunoparesis and decreased ECM remodelling within trabecular bone. Proteins most increased in SMM/MM were APP (enhances osteoclast activity), ENPP1 (enhances bone mineralization), and MZB1 (required for normal plasmablast differentiation). Pathway analyses showed that proteins involved in gamma -carboxylation, a pathway implicated in osteocalcin function, osteoblast differentiation, and normal hematopoiesis, were also overexpressed in SMM/MM. This study is the first comprehensive proteomic atlas of the BM bone proteome in dysproteinemias. We identify new key proteins and pathways for MM bone disease and potentially impaired hematopoiesis, and show for the first time that gamma -carboxylation pathways are increased in the bone tissue of SMM/MM.

AbstractObjectiveTo define the proteomic profile of sporadic late‐onset nemaline myopathy (SLONM) and explore its pathogenesis.MethodsWe performed mass spectrometry on laser‐dissected frozen muscle samples from five patients with SLONM, three of whom with an associated monoclonal protein (MP), and four controls, to determine the proteomic profile of SLONM. Furthermore, we assessed the role of the MP by evaluating the expression of the immunoglobulin light chain variable regions (IGVL).ResultsThere were 294 differentially expressed proteins: 272 upregulated and 22 downregulated. Among the top 100 upregulated proteins, the most common categories were: nuclear or nucleic acid metabolism (24%), extracellular matrix and basal lamina (17%), immune response (13%), and actin dynamics (8%). Downregulated proteins consisted mostly of contractile proteins. Among upregulated proteins, there were 65 with a role related to the immune system, including eight proteins involved in major histocompatibility complex 1 (MHC1) and antigen processing, 15 in MHCII complex and phagocytosis, and 23 in B and/or T‐cell function. Among nine upregulated immunoglobulin proteins, there were two IGVL genes. However, these were also detected in SLONM cases without an MP, with no evidence of clonally dominant immunoglobulin deposition. In muscle sections from SLONM patients, nemaline rods tended to accumulate in atrophic fibers with marked rarefaction of the myofibrils. Increased MHC1 reactivity was present in fibers containing nemaline rods as well as adjacent nonatrophic fibers.ConclusionOur findings suggest that aberrant immune activation is present in SLONM, but do not support a direct causal relationship between the MP and SLONM.

Abstract Background The lymphoma proteome is the phenotypic representation of the underlying genetic and epigenetic makeup of each individual patient's tumor. The proteome is rich in druggable targets and offers a unique opportunity for the hematologist to personalize therapy. We developed a deep proteomic profiling method using ion exchange fractionation and tandem mass spectrometry. In this pilot study, we applied this method to detect differentially expressed proteins in anaplastic large cell lymphoma (ALCL) cell lines with previously known (ALK-positive) and unknown (ALK-negative) therapeutic targets. We then asked whether integrative informatic analysis of these data could be used to predict drug sensitivity in each of the cell lines. Methods To examine reproducibility of our method, proteins were extracted independently from 4 pellets each of FE-PD (ALK-negative) and Karpas 299 (ALK-positive) ALCL cells, reduced with dithiothreitol, alkylated with iodoacetamide, and digested with trypsin. Resulting peptides were separated into 6 fractions using strong anion exchange (SAX) chromatography. Peptides in each fraction were analyzed via shotgun proteomics on a QExactive mass spectrometer. Peptide mass spectra (MS/MS) were matched against a RefSeq human protein sequence database using MyriMatch software. Reversed sequences were added to the database to measure identification false discovery rates (FDRs). IDPicker filtered the peptide identifications at 2% FDR. Proteins with at least two unique peptide identifications and five MS/MS matches were considered to be present in the sample. Filtered protein identifications and corresponding spectral counts were used as input to QuasiTel software, which was configured to use proteins with at least one spectrum per biological replicate. Proteins that were significantly differentially expressed (quasi p-value < 0.05) with an absolute log2 fold-change of at least 0.5 fold were loaded into the Ingenuity Pathway Analysis (IPA) software, and a master list of drugs and corresponding gene targets was assembled using PharmGKB database and the Drug-Gene Interaction Database (DGID). The resulting drug-gene target list was merged with the differentially expressed protein identifications. Candidate targets were validated by Western blot and candidate drugs were assessed in viability assays. Results The SAX-LC-MS/MS method identified 10,111 proteins from all replicate analyses of FE-PD and Karpas 299 samples, and 93% of the identified proteome was detected in all 4 replicate analyses. The detected proteome was well represented by key transcription factors, phophatases, kinases, translation regulators and transmembrane regulators. There were 1369 proteins differentially expressed between the 2 cell lines, 709 up regulated in Karpas 299 cell line and 673 up regulated in FE-PD. Differentially expressed proteins also showed consistent expression across the biological replicates (Figure 1). Our integrated approach to identify candidate targets and drugs “rediscovered” ALK in Karpas299 and unexpectedly identified relative overexpression of the IL2-IL2RA-STAT5A-STAT5B network in ALK-negative FE-PD cells. Western blot confirmed these findings. As expected, 50-100 nM crizotinib (ALK inhibitor) decreased Karpas 299 viability (p =0.016) but had no effect on FE-PD (Figure 2). In contrast, 50-100 nM of the experimental STAT5 inhibitor 573108 (EMD Millipore) decreased FE-PD viability (p=0.002) but had no effect on Karpas 299 (Figure 2). Conclusion The lymphoma proteome is complex with 10,000 proteins and contains druggable targets that can be reproducibly identified using SAX-LC-MS/MS. These targets vary across samples and integrated informatic analysis can predict target-drug combinations that have efficacy in an experimental model. These data suggest that SAX-LC-MS/MS could be used to personalize treatment regimens in lymphoma patients. Figure 1: Spectral counts of 1369 differentially expressed proteins between Karpas299 and FE-PD cell lines were normalized and plotted in a heat map. Red color indicates down regulation and yellow color indicates up regulation. Disclosures: No relevant conflicts of interest to declare.

Abstract Motivation: Using stable isotopes in global proteome scans, labeled molecules from one sample are pooled with unlabeled molecules from another sample and subsequently subjected to mass-spectral analysis. Stable-isotope methodologies make use of the fact that identical molecules of different stable-isotope compositions are differentiated in a mass spectrometer and are represented in a mass spectrum as distinct isotopic clusters with a known mass shift. We describe two multivariable linear regression models for 16O/18O stable-isotope labeled data that jointly model pairs of resolved isotopic clusters from the same peptide and quantify the abundance present in each of the two biological samples while concurrently accounting for peptide-specific incorporation rates of the heavy isotope. The abundance measure for each peptide from the two biological samples is then used in down-stream statistical analyses, e.g. differential expression analysis. Because the multivariable regression models are able to correct for the abundance of the labeled peptide that appear as an unlabeled peptide due to the inability to exchange the natural C-terminal oxygen for the heavy isotope, they are particularly advantageous for a two-step digestion/labeling procedure. We discuss how estimates from the regression model are used to quantify the variability of the estimated abundance measures for the paired samples. Although discussed in the context of 16O/18O stable-isotope labeled data, the multivariable regression models are generalizable to other stable-isotope labeled technologies. Contact: eckel@mayo.edu

ABSTRACTPreviously, we identified a naturally processed and presented measles virus (MV) 19-amino-acid peptide, ASDVETAEGGEIHELLRLQ (MV-P), derived from the phosphoprotein and eluted from the human leukocyte antigen (HLA) class II molecule by using mass spectrometry. We report here the identification of a 14-amino-acid peptide, SAGKVSSTLASELG, derived from the MV nucleoprotein (MV-N) bound to HLA-DRB1*0301. Peripheral blood mononuclear cells (PBMC) from 281 previously vaccinated measles-mumps-rubella II (MMR-II) subjects (HLA discordant) were studied for peptide recognition by T cells. Significant gamma interferon (IFN-γ) responses to MV-P and MV-N peptides were observed in 55.9 and 15.3% of subjects, respectively. MV-P- and MV-N-specific interleukin-4 (IL-4) responses were detected in 19.2 and 23.1%, respectively, of PBMC samples. Peptide-specific cytokine responses and HLA-DRB1 allele associations revealed that, for the MV-P peptide, the allele with the strongest association with both IFN-γ (P= 0.02) and IL-4 (P= 0.03) secretion was DRB1*0301. For MV-N, the allele with the strongest association with IFN-γ secretion was DRB1*1501 (P= 0.04), and the alleles with the strongest associations with IL-4 secretion were DRB1*1103 and DRB1*1303 (P= 0.01). These results indicate that HLA class II MV proteins can be processed, presented, and identified, and the ability to generate cell-mediated immune responses can be demonstrated. This information is promising for new vaccine design strategies with peptide-based vaccines.