Characterization of natural peptides in Pheretima by integrating proteogenomics and label-free peptidomics

Pheretima,also called“earthworms”,is a well-known animal-derived traditional Chinese medicine that is extensively used in over 50 Chinese patent medicines(CPMs)in Chinese Pharmacopoeia(2020 edition).However,its zoological origin is unclear,both in the herbal market and CPMs.In this study,a strategy for integrating in-house annotated protein databases constructed from close evolutionary relationship-sourced RNA sequencing data from public archival resources and various sequencing algorithms(restricted search,open search,and de novo)was developed to characterize the phenotype of natural peptides of three major commercial species of Pheretima,including Pheretima aspergillum(PA),Pheretima vulgaris(PV),and Metaphire magna(MM).We identified 10,477 natural peptides in the PA,7,451 in PV,and 5,896 in MM samples.Five specific signature peptides were screened and then validated using synthetic peptides;these demonstrated robust specificity for the authentication of PA,PV,and MM.Finally,all marker peptides were successfully applied to identify the zoological origins of Brain Heart capsules and Xiaohuoluo pills,revealing the inconsistent Pheretima species used in these CPMs.In conclusion,our integrated strategy could be used for the in-depth characterization of natural peptides of other animal-derived traditional Chinese medicines,especially non-model species with poorly annotated protein databases.

Proteogenomics for pediatric brain cancer

Pediatric central nervous system tumors are the most common tumors in children,it constitute 15%–20%of all malignancies in children and are the leading cause of cancer related deaths in children.Proteogenomics is an emerging field of biological research that utilizes a combination of proteomics,genomics,and transcriptomics to aid in the discovery and identification of biomarkers for diagnosis and therapeutic purposes.Integrative proteogenomics analysis of pediatric tumors identified underlying biological processes and potential treatments as well as the functional effects of somatic mutations and copy number variation driving tumorigenesis.

Improving the genome and proteome annotations of the marine model diatom Thalassiosira pseudonana using a proteogenomics strategy

Diatoms are unicellular eukaryotic phytoplankton that account for approximately 20%of global carbon fixation and 40%of marine primary productivity;thus,they are essential for global carbon biogeochemical cycling and climate.The availability of ten diatom genome sequences has facilitated evolutionary,biological and ecological research over the past decade;however,a complimentary map of the diatom proteome with direct measurements of proteins and peptides is still lacking.Here,we present a proteome map of the model marine diatom Thalassiosira pseudonana using high-resolution mass spectrometry combined with a proteogenomic strategy.In-depth proteomic profiling of three different growth phases and three nutrient-deficient samples identified 9526 proteins,accounting for~81%of the predicted protein-coding genes.Proteogenomic analysis identified 1235 novel genes,975 revised genes,104 splice variants and 234 single amino acid variants.Furthermore,our quantitative proteomic analysis experimentally demonstrated that a considerable number of novel genes were differentially translated under different nutrient conditions.These findings substantially improve the genome annotation of T.pseudonana and provide insights into new biological functions of diatoms.This relatively comprehensive diatom proteome catalog will complement available diatom genome and transcriptome data to advance biological and ecological research of marine diatoms.

A large-scale proteogenomic atlas of pear

Pear is an important fruit tree that is widely distributed around the world.The first pear genome map was reported from our laboratory approximately 10 years ago.To further study global protein expression patterns in pear,we generated pear proteome data based on 24 major tissues.The tissue-resolved profiles provided evidence of the expression of 17953 proteins.We identified 4294 new coding events and improved the pear genome annotation via the proteogenomic strategy based on 18090 peptide spectra with peptide spectrum matches>1.Among the eight randomly selected new short coding open reading frames that were expressed in the style,four promoted and one inhibited the growth of pear pollen tubes.Based on gene coexpression module analysis,we explored the key genes associated with important agronomic traits,such as stone cell formation in fruits.The network regulating the synthesis of lignin,a major component of stone cells,was reconstructed,and receptor-like kinases were implicated as core factors in this regulatory network.Moreover,we constructed the online database PearEXP(http://www.peardb.org.cn)to enable access to the pear proteogenomic resources.This study provides a paradigm for in-depth proteogenomic studies of woody plants.

Genome Annotation of a Model Diatom Phaeodactylum tricornutum Using an Integrated Proteogenomic Pipeline

Diatoms comprise a diverse and ecologically important group of eukaryotic phytoplankton that signifi- cantly contributes to marine primary production and global carbon cycling. Phaeodactylum tricornutum is commonly used as a model organism for studying diatom biology. Although its genome was sequenced in 2008, a high-quality genome annotation is still not available for this diatom. Here we report the develop- ment of an integrated proteogenomic pipeline and its application for improved annotation of P. tricornutum genome using mass spectrometry （MS）-based proteomics data. Our proteogenomic analysis unambigu- ously identified approximately 8300 genes and revealed 606 novel proteins, 506 revised genes, 94 splice variants, 58 single amino acid variants, and a holistic view of post-translational modifications in P. tricor- nutum. We experimentally confirmed a subset of novel events and obtained MS evidence for more than 200 micropeptides in P. tricornutum. These findings expand the genomic landscape of P. tricornutum and provide a rich resource for the study of diatom biology. The proteogenomic pipeline we developed in this study is applicable to any sequenced eukaryote and thus represents a significant contribution to the toolset for eukaryotic proteogenomic analysis. The pipeline and its source code are freely available at https：//sourceforge.net/projects/gapeproteogeno mic.

Biomarker for monitoring heavy metal pollution in aquatic environment:An overview toward molecular perspectives

Water pollution is a significant problem in almost all parts of the world.The complexity of anthropogenic activities along the watershed seems to lead the river to function as a giant disposal container.The river is under threat of degradation,mainly due to heavy metal pollution from anthropogenic actions.Heavy metals become harmful if they pollute waters since they are accumulative,toxic,and carcinogenic in water bodies and biota.Various biomarkers to evaluate heavy metal contamination in several aquatic organisms have been widely reported.The use of molecular biomarkers become more popular in the last years and still lead for future prospect.Proteomics and genomics with bioinformatics approaches have been expanded with technological methods through DNA and RNA sequencing and mass spectrometry based proteomics.Therefore,this article aims to review studies using biomarker approaches in many aquatic organisms.This review is expected to reference and encourage future biomarker research,especially for monitoring heavy metal pollution in rivers.

Characterizing functional consequences of DNA copy number alterations in breast and ovarian tumors by spaceMap

We propose a novel conditional graphical model -- spaceMap -- to construct gene regulatory networks from multiple types of high dimensional omic profiles. A motivating application is to characterize the perturbation of DNA copy number alterations （CNAs） on downstream protein levels in tumors. Through a penalized multivariate regression framework, spaceMap jointly models high dimensional protein levels as responses and high dimensional CNAs as predictors. In this setup, spaceMap infers an undirected network among proteins together with a directed network encoding how CNAs perturb the protein network, spaceMap can be applied to learn other types of regulatory relationships from high dimensional molecular profiles, especially those exhibiting hub structures. Simulation studies show spaceMap has greater power in detecting regulatory relationships over competing methods. Additionally, spaceMap includes a network analysis toolkit for biological interpretation of inferred networks. We applies spaceMap to the CNAs, gene expression and proteomics data sets from CPTAC-TCGA breast （n = 77） and ovarian （n = 174） cancer studies. Each cancer exhibits disruption of＇ion transmembrane transport＇ and ＇regulation from RNA polymerase lI promoter＇ by CNA events unique to each cancer. Moreover, using protein levels as a response yields a more functionally-enriched network than using RNA expressions in both cancer types. The network results also help to pinpoint crucial cancer genes and provide insights on the functional consequences of important CNA in breast and ovarian cancers.