Challenges and solutions in proteomics

Prenatal exposures to endocrine disrupting chemicals: The role of multi-omics in understanding toxicity

Endocrine disrupting chemicals (EDCs) are a diverse group of toxicants detected in populations globally. Prenatal EDC exposures impact birth and childhood outcomes. EDCs work through persistent changes at the molecular, cellular, and organ level. Molecular and biochemical signals or 'omics' can be measured at various functional levels - including the epigenome, transcriptome, proteome, metabolome, and the microbiome. In this narrative review, we introduce each omics and give examples of associations with prenatal EDC exposures. There is substantial research on epigenomic modifications in offspring exposed to EDCs during gestation, and a growing number of studies evaluating the transcriptome, proteome, metabolome, or microbiome in response to these exposures. Multi-omics, integrating data across omics layers, may improve understanding of disrupted function pathways related to early life exposures. We highlight several data integration methods to consider in multi-omics studies. Information from multi-omics can improve understanding of the biological processes and mechanisms underlying prenatal EDC toxicity.

Advances in High Throughput Proteomics Profiling in Establishing Potential Biomarkers for Gastrointestinal Cancer

Gastrointestinal cancers (GICs) remain the most diagnosed cancers and accounted for the highest cancer-related death globally. The prognosis and treatment outcomes of many GICs are poor because most of the cases are diagnosed in advanced metastatic stages. This is primarily attributed to the deficiency of effective and reliable early diagnostic biomarkers. The existing biomarkers for GICs diagnosis exhibited inadequate specificity and sensitivity. To improve the early diagnosis of GICs, biomarkers with higher specificity and sensitivity are warranted. Proteomics study and its functional analysis focus on elucidating physiological and biological functions of unknown or annotated proteins and deciphering cellular mechanisms at molecular levels. In addition, quantitative analysis of translational proteomics is a promising approach in enhancing the early identification and proper management of GICs. In this review, we focus on the advances in mass spectrometry along with the quantitative and functional analysis of proteomics data that contributes to the establishment of biomarkers for GICs including, colorectal, gastric, hepatocellular, pancreatic, and esophageal cancer. We also discuss the future challenges in the validation of proteomics-based biomarkers for their translation into clinics.

Convergent Canonical Pathways in Autism Spectrum Disorder from Proteomic, Transcriptomic and DNA Methylation Data

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder with extensive genetic and aetiological heterogeneity. While the underlying molecular mechanisms involved remain unclear, significant progress has been facilitated by recent advances in high-throughput transcriptomic, epigenomic and proteomic technologies. Here, we review recently published ASD proteomic data and compare proteomic functional enrichment signatures with those of transcriptomic and epigenomic data. We identify canonical pathways that are consistently implicated in ASD molecular data and find an enrichment of pathways involved in mitochondrial metabolism and neurogenesis. We identify a subset of differentially expressed proteins that are supported by ASD transcriptomic and DNA methylation data. Furthermore, these differentially expressed proteins are enriched for disease phenotype pathways associated with ASD aetiology. These proteins converge on protein–protein interaction networks that regulate cell proliferation and differentiation, metabolism, and inflammation, which demonstrates a link between canonical pathways, biological processes and the ASD phenotype. This review highlights how proteomics can uncover potential molecular mechanisms to explain a link between mitochondrial dysfunction and neurodevelopmental pathology.

Deciphering plant-microbe crosstalk through proteomics studies

Proteomic approaches are being used to elucidate a better discretion of interactions occurring between host, pathogen, and/or beneficial microorganisms at the molecular level. Application of proteomic techniques, unravel pathogenicity, stress-related, and antioxidant proteins expressed amid plant-microbe interactions and good information have been generated. It is being perceived that a fine regulation of protein expression takes place for effective pathogen recognition, induction of resistance, and maintenance of host integrity. However, our knowledge of molecular plant-microbe interactions is still incomplete and inconsequential. This review aims to provide insight into numerous ways used for proteomic investigation including peptide/protein identification, separation, and quantification during host defense response. Here, we highlight the current progress in proteomics of defense responses elicited by bacterial, fungal, and viral pathogens in plants along with which the proteome level changes induced by beneficial microorganisms are also discussed.

Metaproteomics analysis reveals the adaptation process for the chicken gut microbiota

The animal gastrointestinal tract houses a large microbial community, the gut microbiota, that confers many benefits to its host, such as protection from pathogens and provision of essential metabolites. Metagenomic approaches have defined the chicken fecal microbiota in other studies, but here, we wished to assess the correlation between the metagenome and the bacterial proteome in order to better understand the healthy chicken gut microbiota. Here, we performed high-throughput sequencing of 16S rRNA gene amplicons and metaproteomics analysis of fecal samples to determine microbial gut composition and protein expression. 16 rRNA gene sequencing analysis identified Clostridiales, Bacteroidaceae, and Lactobacillaceae species as the most abundant species in the gut. For metaproteomics analysis, peptides were generated by using the Fasp method and subsequently fractionated by strong anion exchanges. Metaproteomics analysis identified 3,673 proteins. Among the most frequently identified proteins, 380 proteins belonged to Lactobacillus spp., 155 belonged to Clostridium spp., and 66 belonged to Streptococcus spp. The most frequently identified proteins were heat shock chaperones, including 349 GroEL proteins, from many bacterial species, whereas the most abundant enzymes were pyruvate kinases, as judged by the number of peptides identified per protein (spectral counting). Gene ontology and KEGG pathway analyses revealed the functions and locations of the identified proteins. The findings of both metaproteomics and 16S rRNA sequencing analyses are discussed.

Recent progress in predicting protein sub-subcellular locations

In the last two decades, the number of the known protein sequences increased very rapidly. However, a knowledge of protein function only exists for a small portion of these sequences. Since the experimental approaches for determining protein functions are costly and time consuming, in silico methods have been introduced to bridge the gap between knowledge of protein sequences and their functions. Knowing the subcellular location of a protein is considered to be a critical step in understanding its biological functions. Many efforts have been undertaken to predict the protein subcellular locations in silico. With the accumulation of available data, the substructures of some subcellular organelles, such as the cell nucleus, mitochondria and chloroplasts, have been taken into consideration by several studies in recent years. These studies create a new research topic, namely 'protein sub-subcellular location prediction', which goes one level deeper than classic protein subcellular location prediction.

The oligodeoxynucleotide sequences corresponding to never-expressed peptide motifs are mainly located in the non-coding strand

BACKGROUND

We study the usage of specific peptide platforms in protein composition. Using the pentapeptide as a unit of length, we find that in the universal proteome many pentapeptides are heavily repeated (even thousands of times), whereas some are quite rare, and a small number do not appear at all. To understand the physico-chemical-biological basis underlying peptide usage at the proteomic level, in this study we analyse the energetic costs for the synthesis of rare and never-expressed versus frequent pentapeptides. In addition, we explore residue bulkiness, hydrophobicity, and codon number as factors able to modulate specific peptide frequencies. Then, the possible influence of amino acid composition is investigated in zero- and high-frequency pentapeptide sets by analysing the frequencies of the corresponding inverse-sequence pentapeptides. As a final step, we analyse the pentadecamer oligodeoxynucleotide sequences corresponding to the never-expressed pentapeptides.

RESULTS

We find that only DNA context-dependent constraints (such as oligodeoxynucleotide sequence location in the minus strand, introns, pseudogenes, frameshifts, etc.) provide a coherent mechanistic platform to explain the occurrence of never-expressed versus frequent pentapeptides in the protein world.

CONCLUSIONS

This study is of importance in cell biology. Indeed, the rarity (or lack of expression) of specific 5-mer peptide modules implies the rarity (or lack of expression) of the corresponding n-mer peptide sequences (with n < 5), so possibly modulating protein compositional trends. Moreover the data might further our understanding of the role exerted by rare pentapeptide modules as critical biological effectors in protein-protein interactions.

Protein Bioinformatics Infrastructure for the Integration and Analysis of Multiple High-Throughput "omics" Data

High-throughput “omics” technologies bring new opportunities for biological and biomedical researchers to ask complex questions and gain new scientific insights. However, the voluminous, complex, and context-dependent data being maintained in heterogeneous and distributed environments plus the lack of well-defined data standard and standardized nomenclature imposes a major challenge which requires advanced computational methods and bioinformatics infrastructures for integration, mining, visualization, and comparative analysis to facilitate data-driven hypothesis generation and biological knowledge discovery. In this paper, we present the challenges in high-throughput “omics” data integration and analysis, introduce a protein-centric approach for systems integration of large and heterogeneous high-throughput “omics” data including microarray, mass spectrometry, protein sequence, protein structure, and protein interaction data, and use scientific case study to illustrate how one can use varied “omics” data from different laboratories to make useful connections that could lead to new biological knowledge.

UniRef: comprehensive and non-redundant UniProt reference clusters

MOTIVATION

Redundant protein sequences in biological databases hinder sequence similarity searches and make interpretation of search results difficult. Clustering of protein sequence space based on sequence similarity helps organize all sequences into manageable datasets and reduces sampling bias and overrepresentation of sequences.

RESULTS

The UniRef (UniProt Reference Clusters) provide clustered sets of sequences from the UniProt Knowledgebase (UniProtKB) and selected UniProt Archive records to obtain complete coverage of sequence space at several resolutions while hiding redundant sequences. Currently covering >4 million source sequences, the UniRef100 database combines identical sequences and subfragments from any source organism into a single UniRef entry. UniRef90 and UniRef50 are built by clustering UniRef100 sequences at the 90 or 50% sequence identity levels. UniRef100, UniRef90 and UniRef50 yield a database size reduction of approximately 10, 40 and 70%, respectively, from the source sequence set. The reduced redundancy increases the speed of similarity searches and improves detection of distant relationships. UniRef entries contain summary cluster and membership information, including the sequence of a representative protein, member count and common taxonomy of the cluster, the accession numbers of all the merged entries and links to rich functional annotation in UniProtKB to facilitate biological discovery. UniRef has already been applied to broad research areas ranging from genome annotation to proteomics data analysis.

AVAILABILITY

UniRef is updated biweekly and is available for online search and retrieval at http://www.uniprot.org, as well as for download at ftp://ftp.uniprot.org/pub/databases/uniprot/uniref.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.