Transcript-Level Biomarkers of Early Lung Carcinogenesis in Bronchial Lesions.

Premalignant lesions within the bronchial epithelium signify the initial phases of squamous cell lung carcinoma, posing challenges for detection via conventional methods. Instead of focusing solely on gene expression, in this study, we explore transcriptomic alterations linked to lesion progression, with an emphasis on protein-coding transcripts. We reanalyzed a publicly available RNA-Seq dataset on airway epithelial cells from 82 smokers with and without premalignant lesions. Transcript and gene abundance were quantified using kallisto, while differential expression and transcript usage analysis was performed utilizing sleuth and RATs packages. Functional characterization involved overrepresentation analysis via clusterProfiler, weighted coexpression network analysis (WGCNA), and network analysis via Enrichr-KG. We detected 5906 differentially expressed transcripts and 4626 genes, exhibiting significant enrichment within pathways associated with oxidative phosphorylation and mitochondrial function. Remarkably, transcript-level WGCNA revealed a single module correlated with dysplasia status, notably enriched in cilium-related biological processes. Notable hub transcripts included RABL2B (ENST00000395590), DNAH1 (ENST00000420323), EFHC1 (ENST00000635996), and VWA3A (ENST00000563389) along with transcription factors such as FOXJ1 and ZNF474 as potential regulators. Our findings underscore the value of transcript-level analysis in uncovering novel insights into premalignant bronchial lesion biology, including identification of potential biomarkers associated with early lung carcinogenesis.

Multiomics Picture of Obesity in Young Adults.

Obesity is a socially significant disease that is characterized by a disproportionate accumulation of fat. It is also associated with chronic inflammation, cancer, diabetes, and other comorbidities. Investigating biomarkers and pathological processes linked to obesity is especially vital for young individuals, given their increased potential for lifestyle modifications. By comparing the genetic, proteomic, and metabolomic profiles of individuals categorized as underweight, normal, overweight, and obese, we aimed to determine which omics layer most accurately reflects the phenotypic changes in an organism that result from obesity. We profiled blood plasma samples by employing three omics methodologies. The untargeted GC×GC-MS metabolomics approach identified 313 metabolites. To augment the metabolomic dataset, we integrated a label-free HPLC-MS/MS proteomics method, leading to the identification of 708 proteins. The genomic layer encompassed the genotyping of 647,250 SNPs. Utilizing omics data, we trained sparse Partial Least Squares models to predict body mass index. Molecular features exhibiting frequently non-zero coefficients were selected as potential biomarkers, and we further explored enriched biological pathways. Proteomics was the most effective in single-omics analyses, with a median absolute error (MAE) of 5.44 ± 0.31 kg/m2, incorporating an average of 24 proteins per model. Metabolomics showed slightly lower performance (MAE = 6.06 ± 0.33 kg/m2), followed by genomics (MAE = 6.20 ± 0.34 kg/m2). As expected, multiomic models demonstrated better accuracy, particularly the combination of proteomics and metabolomics (MAE = 4.77 ± 0.33 kg/m2), while including genomics data did not enhance the results. This manuscript is the first multiomics study of obesity in a gender-balanced cohort of young adults profiled by genomic, proteomic, and metabolomic methods. The comprehensive approach provides novel insights into the molecular mechanisms of obesity, opening avenues for more targeted interventions.

Massive Proteogenomic Reanalysis of Publicly Available Proteomic Datasets of Human Tissues in Search for Protein Recoding via Adenosine-to-Inosine RNA Editing.

The proteogenomic search pipeline developed in this work has been applied for reanalysis of 40 publicly available shotgun proteomic datasets from various human tissues comprising more than 8000 individual LC-MS/MS runs, of which 5442 .raw data files were processed in total. This reanalysis was focused on searching for ADAR-mediated RNA editing events, their clustering across samples of different origins, and classification. In total, 33 recoded protein sites were identified in 21 datasets. Of those, 18 sites were detected in at least two datasets, representing the core human protein editome. In agreement with prior artworks, neural and cancer tissues were found to be enriched with recoded proteins. Quantitative analysis indicated that recoding the rate of specific sites did not directly depend on the levels of ADAR enzymes or targeted proteins themselves, rather it was governed by differential and yet undescribed regulation of interaction of enzymes with mRNA. Nine recoding sites conservative between humans and rodents were validated by targeted proteomics using stable isotope standards in the murine brain cortex and cerebellum, and an additional one was validated in human cerebrospinal fluid. In addition to previous data of the same type from cancer proteomes, we provide a comprehensive catalog of recoding events caused by ADAR RNA editing in the human proteome.

Exploiting Multi-Omics Profiling and Systems Biology to Investigate Functions of TOMM34.

Although modern biology is now in the post-genomic era with vastly increased access to high-quality data, the set of human genes with a known function remains far from complete. This is especially true for hundreds of mitochondria-associated genes, which are under-characterized and lack clear functional annotation. However, with the advent of multi-omics profiling methods coupled with systems biology algorithms, the cellular role of many such genes can be elucidated. Here, we report genes and pathways associated with TOMM34, Translocase of Outer Mitochondrial Membrane, which plays role in the mitochondrial protein import as a part of cytosolic complex together with Hsp70/Hsp90 and is upregulated in various cancers. We identified genes, proteins, and metabolites altered in TOMM34-/- HepG2 cells. To our knowledge, this is the first attempt to study the functional capacity of TOMM34 using a multi-omics strategy. We demonstrate that TOMM34 affects various processes including oxidative phosphorylation, citric acid cycle, metabolism of purine, and several amino acids. Besides the analysis of already known pathways, we utilized de novo network enrichment algorithm to extract novel perturbed subnetworks, thus obtaining evidence that TOMM34 potentially plays role in several other cellular processes, including NOTCH-, MAPK-, and STAT3-signaling. Collectively, our findings provide new insights into TOMM34's cellular functions.

Multiomic Profiling Identified EGF Receptor Signaling as a Potential Inhibitor of Type I Interferon Response in Models of Oncolytic Therapy by Vesicular Stomatitis Virus.

Cancer cell lines responded differentially to type I interferon treatment in models of oncolytic therapy using vesicular stomatitis virus (VSV). Two opposite cases were considered in this study, glioblastoma DBTRG-05MG and osteosarcoma HOS cell lines exhibiting resistance and sensitivity to VSV after the treatment, respectively. Type I interferon responses were compared for these cell lines by integrative analysis of the transcriptome, proteome, and RNA editome to identify molecular factors determining differential effects observed. Adenosine-to-inosine RNA editing was equally induced in both cell lines. However, transcriptome analysis showed that the number of differentially expressed genes was much higher in DBTRG-05MG with a specific enrichment in inflammatory proteins. Further, it was found that two genes, EGFR and HER2, were overexpressed in HOS cells compared with DBTRG-05MG, supporting recent reports that EGF receptor signaling attenuates interferon responses via HER2 co-receptor activity. Accordingly, combined treatment of cells with EGF receptor inhibitors such as gefitinib and type I interferon increases the resistance of sensitive cell lines to VSV. Moreover, sensitive cell lines had increased levels of HER2 protein compared with non-sensitive DBTRG-05MG. Presumably, the level of this protein expression in tumor cells might be a predictive biomarker of their resistance to oncolytic viral therapy.

Cysteine alkylation methods in shotgun proteomics and their possible effects on methionine residues.

In order to optimize sample preparation for shotgun proteomics, we compared four cysteine alkylating agents: iodoacetamide, chloroacetamide, 4-vinylpyridine and methyl methanethiosulfonate, and estimated their effects on the results of proteome analysis. Because alkylation may result in methionine modification in vitro, proteomics data were searched for methionine to isothreonine conversions, which may mimic genomic methionine to threonine substitutions found in proteogenomic analyses. We found that chloroacetamide was superior to the other reagents in terms of the number of identified peptides and undesirable off-site reactions. Among the reagents evaluated, iodoacetamide increased the rate of methionine-to-isothreonine conversion, especially if the sample was prepared in gel. The presence of proline following methionine in a protein sequence increased the modification rate as well. Generally, the methionine-to-isothreonine conversion events were relatively rare, but should be taken into account in proteogenomic studies when searching for single nucleotide polymorphism events at the protein level. Additionally, we have evaluated other methionine modifications, such as oxidation and carbamidomethylation. We found that carbamidomethylation may affect up to 80% of peptides containing methionine under the condition of iodoacetamide alkylation. In this case, carbamidomethylation of methionine is more common than oxidation and should be accounted for as a variable modification during proteomic search. SIGNIFICANCE: One of the most trending questions in bottom-up proteomics is the depth of proteome profiling, in other words, the coverage of proteins by identified tryptic peptides. In proteogenomics, where the identification of a single peptide, e.g. bearing an amino acid substitution, may be of interest, high sequence coverage is especially important. Chemical modifications during sample preparation may mimic biologically significant coding mutations at the proteome level. A typical example of such modification is methionine to isothreonine conversion during alkylation, which mimics methionine to threonine substitution in protein sequences due to respective genomic mutations. Therefore, the studies on the proper selection of alkylating reagents which balance the cysteine alkylation efficiency and the extent of methionine conversion upon conventional proteomic sample preparation workflow are crucial for the outcome of proteogenomic analyses and should present a general interest for the proteomic community.

Proteome-Wide Analysis of ADAR-Mediated Messenger RNA Editing during Fruit Fly Ontogeny.

Adenosine-to-inosine RNA editing is an enzymatic post-transcriptional modification which modulates immunity and neural transmission in multicellular organisms. In particular, it involves editing of mRNA codons with the resulting amino acid substitutions. We identified such sites for developmental proteomes of Drosophila melanogaster at the protein level using available data for 15 stages of fruit fly development from egg to imago and 14 time points of embryogenesis. In total, 40 sites were obtained, each belonging to a unique protein, including four sites related to embryogenesis. The interactome analysis has revealed that the majority of the editing-recoded proteins were associated with synaptic vesicle trafficking and actomyosin organization. Quantitation data analysis suggested the existence of a phase-specific RNA editing regulation with yet unknown mechanisms. These findings supported the transcriptome analysis results, which showed that a burst in the RNA editing occurs during insect metamorphosis from pupa to imago. Finally, targeted proteomic analysis was performed to quantify editing-recoded and genomically encoded versions of five proteins in brains of larvae, pupae, and imago insects, which showed a clear tendency toward an increase in the editing rate for each of them. These results will allow a better understanding of the protein role in physiological effects of RNA editing.

Adenosine-to-Inosine RNA Editing in Mouse and Human Brain Proteomes.

Proteogenomics is based on the use of customized genome or RNA sequencing databases for interrogation of shotgun proteomics data in search for proteome-level evidence of genome variations or RNA editing. In this work, the products of adenosine-to-inosine RNA editing in human and murine brain proteomes are identified using publicly available brain proteome LC-MS/MS datasets and an RNA editome database compiled from several sources. After filtering of false-positive results, 20 and 37 sites of editing in proteins belonging to 14 and 32 genes are identified for murine and human brain proteomes, respectively. Eight sites of editing identified with high spectral counts overlapped between human and mouse brain samples. Some of these sites have been previously reported using orthogonal methods, such as α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) glutamate receptors, CYFIP2, coatomer alpha. Also, differential editing between neurons and microglia is demonstrated in this work for some of the proteins from primary murine brain cell cultures. Because many edited sites are still not characterized functionally at the protein level, the results provide a necessary background for their further analysis in normal and diseased cells and tissues using targeted proteomic approaches.

Proteogenomics of Malignant Melanoma Cell Lines: The Effect of Stringency of Exome Data Filtering on Variant Peptide Identification in Shotgun Proteomics.

The identification of genetically encoded variants at the proteome level is an important problem in cancer proteogenomics. The generation of customized protein databases from DNA or RNA sequencing data is a crucial stage of the identification workflow. Genomic data filtering applied at this stage may significantly modify variant search results, yet its effect is generally left out of the scope of proteogenomic studies. In this work, we focused on this impact using data of exome sequencing and LC-MS/MS analyses of six replicates for eight melanoma cell lines processed by a proteogenomics workflow. The main objectives were identifying variant peptides and revealing the role of the genomic data filtering in the variant identification. A series of six confidence thresholds for single nucleotide polymorphisms and indels from the exome data were applied to generate customized sequence databases of different stringency. In the searches against unfiltered databases, between 100 and 160 variant peptides were identified for each of the cell lines using X!Tandem and MS-GF+ search engines. The recovery rate for variant peptides was ∼1%, which is approximately three times lower than that of the wild-type peptides. Using unfiltered genomic databases for variant searches resulted in higher sensitivity and selectivity of the proteogenomic workflow and positively affected the ability to distinguish the cell lines based on variant peptide signatures.

Post-translational modifications of FDA-approved plasma biomarkers in glioblastoma samples.

Liquid chromatography-tandem mass spectrometry was used to analyze plasma proteins of volunteers (control) and patients with glioblastoma multiform (GBM). A database search was pre-set with a variable post-translational modification (PTM): phosphorylation, acetylation or ubiquitination. There were no significant differences between the control and the GBM groups regarding the number of protein identifications, sequence coverage or number of PTMs. However, in GBM plasma, we unambiguously observed a decreased fraction in post-translationally modified peptides identified with high quality. The disease-specific PTM patterns were extracted and mapped to the set of FDA-approved plasma protein markers. Decreases of 46% and 24% in the number of acetylated and ubiquitinated peptides, respectively, were observed in the GBM samples. Significance of capturing disease-associated patterns of protein modifications was envisaged.

Threonine versus isothreonine in synthetic peptides analyzed by high-resolution liquid chromatography/tandem mass spectrometry.

RATIONALE: One of the problems in proteogenomic research aimed at identification of variant peptides is the presence of peptides with amino acid isomers of different origin in the analyzed samples. Among the most challenging examples are peptides with threonine and isothreonine (homoserine) in their sequences. Indeed, the latter residue may appear in vitro as a methionine substitution during sample preparation for shotgun proteome analysis. Yet, this substitution of Met to isoThr is not encoded genetically and should be unambiguously distinguished from, e.g., point mutations in proteins that result in Met conversion to Thr. METHODS: In this work we compared tandem mass (MS/MS) spectra produced by an Orbitrap mass spectrometer of Thr- and isoThr-containing tryptic peptides and found a distinctive feature in their collisionally activated fragmentation patterns. RESULTS: Up to 84% of MS/MS spectra for peptides containing isoThr residues have been positively specified. We also studied the differences in retention times for peptides containing Thr isoforms that can be further used for their distinction. CONCLUSIONS: Threonine can be distinguished from isothreonine by its retention time and HCD fragmentation pattern, specifically relative intensity of the bn - product ion, which can be further used in proteomic research. Copyright © 2016 John Wiley & Sons, Ltd.

Methionine to isothreonine conversion as a source of false discovery identifications of genetically encoded variants in proteogenomics.

Searching deep proteome data for 9 NCI-60 cancer cell lines obtained earlier by Moghaddas Gholami et al. (Cell Reports, 2013) against a database from cancer genomes returned a variant tryptic peptide fragment 57-72 of molecular chaperone HSC70, in which methionine residue at 61 position is replaced by threonine, or isothreonine (homoserine), residue. However, no traces of the corresponding genetic alteration were found in the cell line genomes reported by Abaan et al. (Cancer Research, 2013). Studying on the background of this modification led us to conclude that a conversion of methionine into isothreonine resulted from iodoacetamide treatment of the probe during a sample preparation step. We found that up to 10% of methionine containing peptides experienced the above conversion for the datasets under study. The artifact was confirmed by model experiment with bovine albumin, where three of four methionine residues were partly converted to isothreonine by conventional iodoacetamide treatment. This experimental side reaction has to be taken into account when searching for genetically encoded peptide variants in the proteogenomics studies. BIOLOGICAL SIGNIFICANCE: A lot of effort is currently put into proteogenomics of cancer. Studies detect non-synonymous cancer mutations at protein level by search of high-throughput LC-MS/MS data against customized genomic databases. In such studies, much attention is paid to potential false positive identifications. Here we describe one possible cause of such false identifications, an artifact of sample preparation which mimics methionine to threonine nucleic acid-encoded variant. The methionine to isothreonine conversion should be taken into consideration for correct interpretation of proteogenomic data.

Exome-driven characterization of the cancer cell lines at the proteome level: the NCI-60 case study.

Cancer genome deviates significantly from the reference human genome, and thus a search against standard genome databases in cancer cell proteomics fails to identify cancer-specific protein variants. The goal of this Article is to combine high-throughput exome data [Abaan et al. Cancer Res. 2013] and shotgun proteomics analysis [Modhaddas Gholami et al. Cell Rep. 2013] for cancer cell lines from NCI-60 panel to demonstrate further that the cell lines can be effectively recognized using identified variant peptides. To achieve this goal, we generated a database containing mutant protein sequences of NCI-60 panel of cell lines. The proteome data were searched using Mascot and X!Tandem search engines against databases of both reference and mutant protein sequences. The identification quality was further controlled by calculating a fraction of variant peptides encoded by the own exome sequence for each cell line. We found that up to 92.2% peptides identified by both search engines are encoded by the own exome. Further, we used the identified variant peptides for cell line recognition. The results of the study demonstrate that proteome data supported by exome sequence information can be effectively used for distinguishing between different types of cancer cell lines.

Applying of hierarchical clustering to analysis of protein patterns in the human cancer-associated liver.

BACKGROUND: There are two ways that statistical methods can learn from biomedical data. One way is to learn classifiers to identify diseases and to predict outcomes using the training dataset with established diagnosis for each sample. When the training dataset is not available the task can be to mine for presence of meaningful groups (clusters) of samples and to explore underlying data structure (unsupervised learning). RESULTS: We investigated the proteomic profiles of the cytosolic fraction of human liver samples using two-dimensional electrophoresis (2DE). Samples were resected upon surgical treatment of hepatic metastases in colorectal cancer. Unsupervised hierarchical clustering of 2DE gel images (n = 18) revealed a pair of clusters, containing 11 and 7 samples. Previously we used the same specimens to measure biochemical profiles based on cytochrome P450-dependent enzymatic activities and also found that samples were clearly divided into two well-separated groups by cluster analysis. It turned out that groups by enzyme activity almost perfectly match to the groups identified from proteomic data. Of the 271 reproducible spots on our 2DE gels, we selected 15 to distinguish the human liver cytosolic clusters. Using MALDI-TOF peptide mass fingerprinting, we identified 12 proteins for the selected spots, including known cancer-associated species. CONCLUSIONS/SIGNIFICANCE: Our results highlight the importance of hierarchical cluster analysis of proteomic data, and showed concordance between results of biochemical and proteomic approaches. Grouping of the human liver samples and/or patients into differing clusters may provide insights into possible molecular mechanism of drug metabolism and creates a rationale for personalized treatment.

Proteome and cytokine serum profiling to diagnose a mycosis fungoides.

PURPOSE: The aim of this study was to estimate a possibility of mycosis fungoides (MF) diagnostics based on protein profiling in blood serum. EXPERIMENTAL DESIGN: We obtained and analysed samples of blood serum from 23 patients with MF, and 29 psoriasis patients and 22 healthy donors as controls. Protein profiling was carried out using SELDI TOF MS SELDI-TOF and also profiling of 27 cytokines with multiplex immunoassay technology was implemented. RESULTS: MS data analysis of sera did not give satisfactory statistical discrimination between the groups. Antibody-based cytokine profiling revealed a number of cytokines with a change in their concentrations in both MF and psoriasis (IL-1Ra, IL-4, G-CSF). The C-X-C motif chemokine 10 (IP-10, CXCL10) cytokine had a significantly increased concentration (p<0,001) in samples from MF patients as compared with the other groups. CONCLUSIONS AND CLINICAL RELEVANCE: IP-10 may be considered as a promising biomarker for the differentiation between MF and other skin conditions.

Acute phase serum amyloid A in ovarian cancer as an important component of proteome diagnostic profiling.

In the context of serum amyloid A (SAA) identification as ovarian cancer marker derived by SELDI-MS, its serum levels were measured by immunoassay in different stages of ovarian cancer, in benign gynecological tumors, and in healthy controls. In addition, SELDI-TOF-MS spectra were obtained by protocol optimized for the SAA peak intensity. SELDI data on small proteins (5.5-17.5 kDa) and SAA immunoassay data were combined with cancer antigen (CA)125 data in order to study the classification accuracy between cancer and noncancer by support vector machine (SVM), logistic regression, and top scoring pair classifiers. Although an addition of SAA immunoassay data to CA125 data did not significantly improve cancer/noncancer discrimination, SVM applied to combined biomarker data (CA125 and SAA immunoassay variables plus 48 SELDI peak variables) yielded the best classification rate (accuracy 95.2% vs. 86.2% for CA125 alone). Notably, most of discriminatory peaks selected by the classifiers have significant correlation with the major known peaks of SAA (11.7 kDa) and transthyretin (13.9 kDa). Acute phase serum amyloid A (A-SAA) was proved to be an important member of cancer discriminatory protein profile. Among the eight known ovarian cancer SELDI profile components, A-SAA is the most relevant to molecular pathogenesis of cancer and it has the highest degree of up-regulation in disease.