A time-resolved multi-omics atlas of Acanthamoeba castellanii encystment

Encystment is a common stress response of most protists, including free-living amoebae. Cyst formation protects the amoebae from eradication and can increase virulence of the bacteria they harbor. Here, we mapped the global molecular changes that occur in the facultatively pathogenic amoeba Acanthamoeba castellanii during the early steps of the poorly understood process of encystment. By performing transcriptomic, proteomic, and phosphoproteomic experiments during encystment, we identified more than 150,000 previously undescribed transcripts and thousands of protein sequences absent from the reference genome. These results provide molecular details to the regulation of expected biological processes, such as cell proliferation shutdown, and reveal new insights such as a rapid phospho-regulation of sites involved in cytoskeleton remodeling and translation regulation. This work constitutes the first time-resolved molecular atlas of an encysting organism and a useful resource for further investigation of amoebae encystment to allow for a better control of pathogenic amoebae.

The role of micropeptides in biology

Micropeptides are small polypeptides coded by small open-reading frames. Progress in computational biology and the analyses of large-scale transcriptomes and proteomes have revealed that mammalian genomes produce a large number of transcripts encoding micropeptides. Many of these have been previously annotated as long noncoding RNAs. The role of micropeptides in cellular homeostasis maintenance has been demonstrated. This review discusses different types of micropeptides as well as methods to identify them, such as computational approaches, ribosome profiling, and mass spectrometry.

Bioinformatics approaches for improving seminal plasma proteome analysis

Reproduction efficiency of male animals is one of the key factors influencing the sustainability of livestock. Mass spectrometry (MS) based proteomics has become an important tool for studying seminal plasma proteomes. In this review, we summarize bioinformatics analysis strategies for current proteomics approaches, for identifying novel biomarkers of reproductive robustness.

Bayesian Hierarchical Varying-sparsity Regression Models with Application to Cancer Proteogenomics

Identifying patient-specific prognostic biomarkers is of critical importance in developing personalized treatment for clinically and molecularly heterogeneous diseases such as cancer. In this article, we propose a novel regression framework, Bayesian hierarchical varying-sparsity regression (BEHAVIOR) models to select clinically relevant disease markers by integrating proteogenomic (proteomic+genomic) and clinical data. Our methods allow flexible modeling of protein-gene relationships as well as induces sparsity in both protein-gene and protein-survival relationships, to select ge-nomically driven prognostic protein markers at the patient-level. Simulation studies demonstrate the superior performance of BEHAVIOR against competing method in terms of both protein marker selection and survival prediction. We apply BEHAV-IOR to The Cancer Genome Atlas (TCGA) proteogenomic pan-cancer data and find several interesting prognostic proteins and pathways that are shared across multiple cancers and some that exclusively pertain to specific cancers.

Targeted mass spectrometry: An emerging powerful approach to unblock the bottleneck in phosphoproteomics

Following the rapid expansion of the proteomics field, the investigation of post translational modifications (PTM) has become extremely popular changing our perspective of how proteins constantly fine tune cellular functions. Reversible protein phosphorylation plays a pivotal role in virtually all biological processes in the cell and it is one the most characterized PTM up to date. During the last decade, the development of phosphoprotein/phosphopeptide enrichment strategies and mass spectrometry (MS) technology has revolutionized the field of phosphoproteomics discovering thousands of new site-specific phosphorylations and unveiling unprecedented evidence about their modulation under distinct cellular conditions. The field has expanded so rapidly that the use of traditional methods to validate and characterize the biological role of the phosphosites is not feasible any longer. Targeted MS holds great promise for becoming the method of choice to study with high precision and sensitivity already known site-specific phosphorylation events. This review summarizes the contribution of large-scale unbiased MS analyses and highlights the need of targeted MS-based approaches for follow-up investigation. Additionally, the article illustrates the biological relevance of protein phosphorylation by providing examples of disease-related phosphorylation events and emphasizes the benefits of applying targeted MS in clinics for disease diagnosis, prognosis and drug-response evaluation.

Proteogenomic studies on cancer drug resistance: towards biomarker discovery and target identification

INTRODUCTION

Chemoresistance is a major obstacle for current cancer treatment. Proteogenomics is a powerful multi-omics research field that uses customized protein sequence databases generated by genomic and transcriptomic information to identify novel genes (e.g. noncoding, mutation and fusion genes) from mass spectrometry-based proteomic data. By identifying aberrations that are differentially expressed between tumor and normal pairs, this approach can also be applied to validate protein variants in cancer, which may reveal the response to drug treatment. Areas covered: In this review, we will present recent advances in proteogenomic investigations of cancer drug resistance with an emphasis on integrative proteogenomic pipelines and the biomarker discovery which contributes to achieving the goal of using precision/personalized medicine for cancer treatment. Expert commentary: The discovery and comprehensive understanding of potential biomarkers help identify the cohort of patients who may benefit from particular treatments, and will assist real-time clinical decision-making to maximize therapeutic efficacy and minimize adverse effects. With the development of MS-based proteomics and NGS-based sequencing, a growing number of proteogenomic tools are being developed specifically to investigate cancer drug resistance.

Exoproteomics of Pathogens: Analysis of Toxins and Other Virulence Factors by Proteomics

Pathogens are known to release in their environment a large range of toxins and other virulence factors. Their pathogenicity relies on this arsenal of exoproteins and their orchestrated release upon changing environmental conditions. Exoproteomics aims at describing and quantifying the proteins found outside of the cells, thus takes advantage of the most recent methodologies of next-generation proteomics. This approach has been applied with great success to a variety of pathogens increasing the fundamental knowledge on pathogenicity. In this chapter, we describe how the exoproteome should be prepared and handled for high-throughput identification of exoproteins and their quantitation by label-free shotgun proteomics. We also mentioned some bioinformatics tools for extracting information such as toxin similarity search.