APIR:Aggregating Universal Proteomics Database Search Algorithms for Peptide Identification with FDR Control

Advances in mass spectrometry(MS)have enabled high-throughput analysis of proteomes in biological systems.The state-of-the-art MS data analysis relies on database search algorithms to quantify proteins by identifying peptide–spectrum matches(PSMs),which convert mass spectra to peptide sequences.Different database search algorithms use distinct search strategies and thus may identify unique PSMs.However,no existing approaches can aggregate all user-specified database search algorithms with a guaranteed increase in the number of identified peptides and a control on the false discovery rate(FDR).To fill in this gap,we proposed a statistical framework,Aggregation of Peptide Identification Results(APIR),that is universally compatible with all database search algorithms.Notably,under an FDR threshold,APIR is guaranteed to identify at least as many,if not more,peptides as individual database search algorithms do.Evaluation of APIR on a complex proteomics standard dataset showed that APIR outpowers individual database search algorithms and empirically controls the FDR.Real data studies showed that APIR can identify disease-related proteins and post-translational modifications missed by some individual database search algorithms.The APIR framework is easily extendable to aggregating discoveries made by multiple algorithms in other high-throughput biomedical data analysis,e.g.,differential gene expression analysis on RNA sequencing data.The APIR R package is available at https://github.com/yiling0210/APIR.

A comprehensive study of the proteins involved in salinity stress response in roots and shoots of the FL478 genotype of rice(Oryza sativa L. ssp. indica)

Rice,a major staple,is the most salt-sensitive cereal.High salinity triggers several adaptive responses in rice to cope with osmotic and ionic stress at the physiological,cellular,and molecular levels.A major QTL for salinity tolerance,named Saltol,is present on chromosome 1 of Indian landraces such as Pokkali and Nona Bokra.The early proteomic and physiological responses to salinity in roots and shoots of FL478,an inbred rice line harboring the Saltol QTL,were characterized.Plantlets were cultured in hydroponic cultures with 100 mmol L^(-1) Na Cl and evaluated at 6,24,and 48 h.At the physiological level,root length significantly increased at 48 h,whereas shoot length was reduced.The Na^(+)/K^(+) ratio was maintained at lower levels in shoots than in roots,suggesting that roots play a protective role.More than 2000 proteins were detected in both tissues.Roots showed a faster and more coordinated proteomic response than shoots,evident after only 6 h of treatment.These responses showed clear correspondence with those of proteins involved in transcription and translation.Maintenance of mitochondrial activity and amino acid metabolism in roots,and activation of stress-responsive proteins such as dehydrins and PLAT in shoots,may play a key role during the response of the plant to salinity stress.Proteomic and physiological responses showed that roots respond in a more highly adaptive manner than shoots to salinity stress,suggesting that this tissue is critical to the tolerance observed in cultivars harboring Saltol.

Proteome changes of porcine follicular fluid during follicle development

Background:Ovarian follicular fluid influences follicle and oocyte growth,but the fluctuation of its protein content during folliculogenesis has not been comprehensively analyzed.Here we used a shotgun approach and bioinformatics analyses to investigate and compare the proteomes of porcine follicular fluid(pFF)obtained from small(<4 mm),medium(4–6 mm)and large(>6–12 mm)follicles.Results:Follicular fluid samples containing highest estrogen levels were selected as non-atretic from small(SNA:26.1±15 ng/mL),medium(MNA:162±54 ng/mL),and large(LNA:290±37 ng/mL)follicles for proteomic analyses.We detected 1627,1699,and 1756 proteins in SNA,MNA,and LNA samples,respectively.Nearly 60–63%of total proteins were specific to each sample,11–13%were shared in pairwise comparisons,and 247 proteins were shared among all samples.Functional categorization indicated comparable gene ontology(GO)terms distribution per cellular component,molecular function,and biological process categories across samples;however,the ranking of highly significantly enriched GO terms per category revealed differences between samples.The patterns of proteinto-protein interactions varied throughout follicle development,and proteins such as serine protease inhibitor,clade E(SERPINE);plasminogen activator,urokinase(PLAU);and plasminogen activator,urokinase receptor(PLAUR)appeared stage-specific to SNA,MNA,and LNA,respectively.The“complement and coagulation cascades”was the common major pathway.Besides,properdin and fibulin-1 were abundant proteins that appeared absent in LNA samples.Conclusion:This study provides extensive and functional analyses of the pFF proteome changes during folliculogenesis and offers the potential for novel biomarker discovery in pFF for oocyte quality assessment.

Recent progress in mass spectrometry proteomics for biomedical research

Proteins are the key players in many cellular processes. Their composition, trafficking, and interactions underlie the dynamic processes of life. Furthermore, diseases are frequently accompanied by malfunction of proteins at multiple levels. Understanding how biological processes are regulated at the protein level is critically important to understanding the molecular basis for diseases and often shed light on disease prevention, diagnosis, and treatment. With rapid advances in mass spectrometry(MS)instruments and experimental methodologies, MS-based proteomics has become a reliable and essential tool for elucidating biological processes at the protein level. Over the past decade, we have witnessed great expansion of knowledge of human diseases with the application of MS-based proteomic technologies, which has led to many exciting discoveries. Herein we review the recent progress in MS-based proteomics in biomedical research, including that in establishing disease-related proteomes and interactomes. We also discuss how this progress will benefit biomedical research and clinical diagnosis and treatment of disease.

In silico Proteome-wide Amino aCid and Elemental Composition (PACE) Analysis of Expression Proteomics Data Provides A Fingerprint of Dominant Metabolic Processes

Proteome-wide Amino aCid and Elemental composition （PACE） analysis is a novel and informative way of interrogating the proteome. The PACE approach consists of in silico decompo- sition of proteins detected and quantified in a proteomics experiment into 20 amino acids and five elements （C, H, N, O and S）, with protein abundances converted to relative abundances of amino acids and elements. The method is robust and very sensitive; it provides statistically reliable differ- entiation between very similar proteomes. In addition, PACE provides novel insights into prote- ome-wide metabolic processes, occurring, e.g., during cell starvation. For instance, both Escherichia coli and Synechocystis down-regulate sulfur-rich proteins upon sulfur deprivation, but E. coli preferentially down-regulates cysteine-rich proteins while Synechocystis mainly down- regulates methionine-rich proteins. Due to its relative simplicity, flexibility, generality and wide applicability, PACE analysis has the potential of becoming a standard analytical tool in proteomics.