Proteomics in non-human primates: utilizing RNA-Seq data to improve protein identification by mass spectrometry in vervet monkeys

Identification of Novel Genes and Proteoforms in Angiostrongylus costaricensis through a Proteogenomic Approach

RNA sequencing (RNA-Seq) and mass-spectrometry-based proteomics data are often integrated in proteogenomic studies to assist in the prediction of eukaryote genome features, such as genes, splicing, single-nucleotide (SNVs), and single-amino-acid variants (SAAVs). Most genomes of parasite nematodes are draft versions that lack transcript- and protein-level information and whose gene annotations rely only on computational predictions. Angiostrongylus costaricensis is a roundworm species that causes an intestinal inflammatory disease, known as abdominal angiostrongyliasis (AA). Currently, there is no drug available that acts directly on this parasite, mostly due to the sparse understanding of its molecular characteristics. The available genome of A. costaricensis, specific to the Costa Rica strain, is a draft version that is not supported by transcript- or protein-level evidence. This study used RNA-Seq and MS/MS data to perform an in-depth annotation of the A. costaricensis genome. Our prediction improved the reference annotation with (a) novel coding and non-coding genes; (b) pieces of evidence of alternative splicing generating new proteoforms; and (c) a list of SNVs between the Brazilian (Crissiumal) and the Costa Rica strain. To the best of our knowledge, this is the first time that a multi-omics approach has been used to improve the genome annotation of A. costaricensis. We hope this improved genome annotation can assist in the future development of drugs, kits, and vaccines to treat, diagnose, and prevent AA caused by either the Brazil strain (Crissiumal) or the Costa Rica strain.

Assessment of label-free quantification and missing value imputation for proteomics in non-human primates

BACKGROUND

Reliable and effective label-free quantification (LFQ) analyses are dependent not only on the method of data acquisition in the mass spectrometer, but also on the downstream data processing, including software tools, query database, data normalization and imputation. In non-human primates (NHP), LFQ is challenging because the query databases for NHP are limited since the genomes of these species are not comprehensively annotated. This invariably results in limited discovery of proteins and associated Post Translational Modifications (PTMs) and a higher fraction of missing data points. While identification of fewer proteins and PTMs due to database limitations can negatively impact uncovering important and meaningful biological information, missing data also limits downstream analyses (e.g., multivariate analyses), decreases statistical power, biases statistical inference, and makes biological interpretation of the data more challenging. In this study we attempted to address both issues: first, we used the MetaMorphues proteomics search engine to counter the limits of NHP query databases and maximize the discovery of proteins and associated PTMs, and second, we evaluated different imputation methods for accurate data inference. We used a generic approach for missing data imputation analysis without distinguising the potential source of missing data (either non-assigned m/z or missing values across runs).

RESULTS

Using the MetaMorpheus proteomics search engine we obtained quantitative data for 1622 proteins and 10,634 peptides including 58 different PTMs (biological, metal and artifacts) across a diverse age range of NHP brain frontal cortex. However, among the 1622 proteins identified, only 293 proteins were quantified across all samples with no missing values, emphasizing the importance of implementing an accurate and statiscaly valid imputation method to fill in missing data. In our imputation analysis we demonstrate that Single Imputation methods that borrow information from correlated proteins such as Generalized Ridge Regression (GRR), Random Forest (RF), local least squares (LLS), and a Bayesian Principal Component Analysis methods (BPCA), are able to estimate missing protein abundance values with great accuracy.

CONCLUSIONS

Overall, this study offers a detailed comparative analysis of LFQ data generated in NHP and proposes strategies for improved LFQ in NHP proteomics data.

Integrated omics analysis reveals sirtuin signaling is central to hepatic response to a high fructose diet

BACKGROUND

Dietary high fructose (HFr) is a known metabolic disruptor contributing to development of obesity and diabetes in Western societies. Initial molecular changes from exposure to HFr on liver metabolism may be essential to understand the perturbations leading to insulin resistance and abnormalities in lipid and carbohydrate metabolism. We studied vervet monkeys (Clorocebus aethiops sabaeus) fed a HFr (n=5) or chow diet (n=5) for 6 weeks, and obtained clinical measures of liver function, blood insulin, cholesterol and triglycerides. In addition, we performed untargeted global transcriptomics, proteomics, and metabolomics analyses on liver biopsies to determine the molecular impact of a HFr diet on coordinated pathways and networks that differed by diet.

RESULTS

We show that integration of omics data sets improved statistical significance for some pathways and networks, and decreased significance for others, suggesting that multiple omics datasets enhance confidence in relevant pathway and network identification. Specifically, we found that sirtuin signaling and a peroxisome proliferator activated receptor alpha (PPARA) regulatory network were significantly altered in hepatic response to HFr. Integration of metabolomics and miRNAs data further strengthened our findings.

CONCLUSIONS

Our integrated analysis of three types of omics data with pathway and regulatory network analysis demonstrates the usefulness of this approach for discovery of molecular networks central to a biological response. In addition, metabolites aspartic acid and docosahexaenoic acid (DHA), protein ATG3, and genes ATG7, and HMGCS2 link sirtuin signaling and the PPARA network suggesting molecular mechanisms for altered hepatic gluconeogenesis from consumption of a HFr diet.

Proteomic variations of esophageal squamous cell carcinoma revealed by combining RNA-seq proteogenomics and G-PTM search strategy

BACKGROUND

Cancer that arises from epithelial cells of the esophagus is called esophagus squamous cell carcinoma (ESCC) and is mostly observed in developing nations. Evaluation of cancer genomes and its regulation into proteins plays a predominant role in understanding the cancer progressions. Mass-spectrometry-based proteomics is a consequential tool to estimate proteomic variation and posttranslational modifications (PTMs) from standard protein databases. Post-translational modifications play a crucial role in protein folding and PTMs can be accounted for as a biological signal to interpret the structural changes and transition order of proteins. Functional validation of cancer-related mutations can explain the effects of mutations on genes and the identification of Oncogenes and tumor suppressor genes. Therefore, we present a study on protein variations to interpret the structural changes and transition order of proteins in ESCC carcinogenesis.

METHODOLOGY

We are using a bottom-up proteomics approach with Galaxy-P framework and RNA sequence data analysis to generate the sample-specific databases containing details of RNA splicing and variant peptides. Once the database generated with information on variable modification, only the curated PTMs at specific positions are considered to perform spectral matching. Proteogenomics mapping was performed to identify protein variations in ESCC.

RESULTS

RNA-sequence proteogenomics with G-PTM (Global Post-Translational Modification) searching strategy has revealed proteomic events including several peptides that contain single amino acid variations, novel splice junction peptides and posttranslationally modified peptides. Proteogenomic mapping exhibited the splice junction peptides mapped predominantly for Malic enzyme exon type (ME-3) and MCM7 protein-coding genes that promote cancer progression, found to be exhibited in ESCC samples. Approximately 25 ± types of PTM modifications were recorded, and Protein Phosphorylation was largely noted.

CONCLUSION

ESCC cancer prognosis at the molecular level enables a better understanding of cancer carcinogenesis and protein modifications can be used as potential biomarkers.

Prime time for primate functional genomics

Functional genomics research is continually improving our understanding of genotype-phenotype relationships in humans, and comparative genomics perspectives can provide additional insight into the evolutionary histories of such relationships. To specifically identify conservation or species-specific divergence in humans, we must look to our closest extant evolutionary relatives. Primate functional genomics research has been steadily advancing and expanding, in spite of several limitations and challenges that this field faces. New technologies and cheaper sequencing provide a unique opportunity to enhance and expand primate comparative studies, and we outline possible paths going forward. The potential human-specific insights that can be gained from primate functional genomics research are substantial, and we propose that now is a prime time to expand such endeavors.

EPIFANY: A Method for Efficient High-Confidence Protein Inference

Accurate protein inference in the presence of shared peptides is still one of the key problems in bottom-up proteomics. Most protein inference tools employing simple heuristic inference strategies are efficient but exhibit reduced accuracy. More advanced probabilistic methods often exhibit better inference quality but tend to be too slow for large data sets. Here, we present a novel protein inference method, EPIFANY, combining a loopy belief propagation algorithm with convolution trees for efficient processing of Bayesian networks. We demonstrate that EPIFANY combines the reliable protein inference of Bayesian methods with significantly shorter runtimes. On the 2016 iPRG protein inference benchmark data, EPIFANY is the only tested method that finds all true-positive proteins at a 5% protein false discovery rate (FDR) without strict prefiltering on the peptide-spectrum match (PSM) level, yielding an increase in identification performance (+10% in the number of true positives and +14% in partial AUC) compared to previous approaches. Even very large data sets with hundreds of thousands of spectra (which are intractable with other Bayesian and some non-Bayesian tools) can be processed with EPIFANY within minutes. The increased inference quality including shared peptides results in better protein inference results and thus increased robustness of the biological hypotheses generated. EPIFANY is available as open-source software for all major platforms at https://OpenMS.de/epifany.

Proteomic Investigation of a Diseased Gorgonian Coral Indicates Disruption of Essential Cell Function and Investment in Inflammatory and Other Immune Processes

As scleractinian coral cover declines in the face of increased frequency in disease outbreaks, future reefs may become dominated by octocorals. Understanding octocoral disease responses and consequences is therefore necessary if we are to gain insight into the future of ecosystem services provided by coral reefs. In Florida, populations of the octocoral Eunicea calyculata infected with Eunicea black disease (EBD) were observed in the field in the fall of 2011. This disease was recognized by a stark, black pigmentation caused by heavy melanization. Histological preparations of E. calyculata infected with EBD demonstrated granular amoebocyte (GA) mobilization, melanin granules in much of the GA population, and the presence of fungal hyphae penetrating coral tissue. Previous transcriptomic analysis also identified immune trade-offs evidenced by increased immune investment at the expense of growth. Our investigation utilized proteogenomic techniques to reveal decreased investment in general cell signaling while increasing energy production for immune responses. Inflammation was also prominent in diseased E. calyculata and sheds light on factors driving the extreme phenotype observed with EBD. With disease outbreaks continuing to increase in frequency, our results highlight new targets within the cnidarian immune system and provide a framework for understanding transcriptomics in the context of an organismal disease phenotype and its protein expression.

Integration of large-scale multi-omic datasets: a protein-centric view

Innovative mass spectrometry-based proteomics has enabled routine measurements of protein abundance, localization, interactions, and modifications, covering unique aspects of gene expression regulation and function. It is now time to move from isolated analyses of these datasets toward true integration of proteomics with other data types to gain insights from the interactions and interdependencies of biomolecules. When combined with genomic or transcriptomic data, proteomics expands genome annotation to identify variant or missing genes. Dynamic proteomic measurements can move analysis from predominantly concentration-based framework to that of synthesis and degradation of proteins. Proteomic data from thousands of cancer patients can foster identification of novel pathogenic mutations via detection of protein sequence changes that lead to dysregulated pathways in various tumors. Such comprehensive efforts can exploit the synergy arising from large and complex datasets to advance virtually every field of biology.

Integrated Omics: Tools, Advances, and Future Approaches

With the rapid adoption of high-throughput omic approaches to analyze biological samples such as genomics, transcriptomics, proteomics, and metabolomics, each analysis can generate tera- to peta-byte sized data files on a daily basis. These data file sizes, together with differences in nomenclature among these data types, make the integration of these multi-dimensional omics data into biologically meaningful context challenging. Variously named as integrated omics, multi-omics, poly-omics, trans-omics, pan-omics, or shortened to just 'omics', the challenges include differences in data cleaning, normalization, biomolecule identification, data dimensionality reduction, biological contextualization, statistical validation, data storage and handling, sharing, and data archiving. The ultimate goal is towards the holistic realization of a 'systems biology' understanding of the biological question in hand. Commonly used approaches in these efforts are currently limited by the 3 i's - integration, interpretation, and insights. Post integration, these very large datasets aim to yield unprecedented views of cellular systems at exquisite resolution for transformative insights into processes, events, and diseases through various computational and informatics frameworks. With the continued reduction in costs and processing time for sample analyses, and increasing types of omics datasets generated such as glycomics, lipidomics, microbiomics, and phenomics, an increasing number of scientists in this interdisciplinary domain of bioinformatics face these challenges. We discuss recent approaches, existing tools, and potential caveats in the integration of omics datasets for development of standardized analytical pipelines that could be adopted by the global omics research community.