Database Search Engines: Paradigms, Challenges and Solutions

Whole genome analysis for plant growth promotion profiling of Pantoea agglomerans CPHN2, a non-rhizobial nodule endophyte

Reduced agricultural production as well as issues like nutrient-depleted soils, eutrophication, and groundwater contamination have drawn attention to the use of endophyte-based bioformulations to restore soil fertility. Pantoea agglomerans CPHN2, a non-rhizobial nodule endophyte isolated from Cicer arietinum, exhibited a variety of plant growth-promoting traits. In this study, we used NextSeq500 technology to analyze whole-genome sequence information of this plant growth-promoting endophytic bacteria. The genome of P. agglomerans CPHN2 has a length of 4,839,532 bp and a G + C content of 55.2%. The whole genome comprises three different genomic fractions, comprising one circular chromosome and two circular plasmids. A comparative analysis between P. agglomerans CPHN2 and 10 genetically similar strains was performed using a bacterial pan-genome pipeline. All the predicted and annotated gene sequences for plant growth promotions (PGPs), such as phosphate solubilization, siderophore synthesis, nitrogen metabolism, and indole-3-acetic acid (IAA) of P. agglomerans CPHN2, were identified. The whole-genome analysis of P. agglomerans CPHN2 provides an insight into the mechanisms underlying PGP by endophytes and its potential applications as a biofertilizer.

A comprehensive LFQ benchmark dataset on modern day acquisition strategies in proteomics

In the last decade, a revolution in liquid chromatography-mass spectrometry (LC-MS) based proteomics was unfolded with the introduction of dozens of novel instruments that incorporate additional data dimensions through innovative acquisition methodologies, in turn inspiring specialized data analysis pipelines. Simultaneously, a growing number of proteomics datasets have been made publicly available through data repositories such as ProteomeXchange, Zenodo and Skyline Panorama. However, developing algorithms to mine this data and assessing the performance on different platforms is currently hampered by the lack of a single benchmark experimental design. Therefore, we acquired a hybrid proteome mixture on different instrument platforms and in all currently available families of data acquisition. Here, we present a comprehensive Data-Dependent and Data-Independent Acquisition (DDA/DIA) dataset acquired using several of the most commonly used current day instrumental platforms. The dataset consists of over 700 LC-MS runs, including adequate replicates allowing robust statistics and covering over nearly 10 different data formats, including scanning quadrupole and ion mobility enabled acquisitions. Datasets are available via ProteomeXchange (PXD028735).

Measurement(s)	Digital Data Repository
Technology Type(s)	Digital Data Repository

Recent advances in mass-spectrometry based proteomics software, tools and databases

The field of proteomics immensely depends on data generation and data analysis which are thoroughly supported by software and databases. There has been a massive advancement in mass spectrometry-based proteomics over the last 10 years which has compelled the scientific community to upgrade or develop algorithms, tools, and repository databases in the field of proteomics. Several standalone software, and comprehensive databases have aided the establishment of integrated omics pipeline and meta-analysis workflow which has contributed to understand the disease pathobiology, biomarker discovery and predicting new therapeutic modalities. For shotgun proteomics where Data Dependent Acquisition is performed, several user-friendly software are developed that can analyse the pre-processed data to provide mechanistic insights of the disease. Likewise, in Data Independent Acquisition, pipelines are emerged which can accomplish the task from building the spectral library to identify the therapeutic targets. Furthermore, in the age of big data analysis the implications of machine learning and cloud computing are appending robustness, rapidness and in-depth proteomics data analysis. The current review talks about the recent advancement, and development of software, tools, and database in the field of mass-spectrometry based proteomics.

Bacterial endophytes: Molecular interactions with their hosts

Plant growth promotion has been found associated with plants on the surface (epiphytic), inside (endophytic), or close to the plant roots (rhizospheric). Endophytic bacteria mainly have been researched for their beneficial activities in terms of nutrient availability, plant growth hormones, and control of soil-borne and systemic pathogens. Molecular communications leading to these interactions between plants and endophytic bacteria are now being unrevealed using multidisciplinary approaches with advanced techniques such as metagenomics, metaproteomics, metatranscriptomics, metaproteogenomic, microRNAs, microarray, chips as well as the comparison of complete genome sequences. More than 400 genes in both the genomes of host plant and bacterial endophyte are up- or downregulated for the establishment of endophytism and plant growth-promoting activity. The involvement of more than 20 genes for endophytism, about 50 genes for direct plant growth promotion, about 25 genes for biocontrol activity, and about 10 genes for mitigation of different stresses has been identified in various bacterial endophytes. This review summarizes the progress that has been made in recent years by these modern techniques and approaches.

The Power of Three in Cannabis Shotgun Proteomics: Proteases, Databases and Search Engines

Cannabis research has taken off since the relaxation of legislation, yet proteomics is still lagging. In 2019, we published three proteomics methods aimed at optimizing protein extraction, protein digestion for bottom-up and middle-down proteomics, as well as the analysis of intact proteins for top-down proteomics. The database of Cannabis sativa proteins used in these studies was retrieved from UniProt, the reference repositories for proteins, which is incomplete and therefore underrepresents the genetic diversity of this non-model species. In this fourth study, we remedy this shortcoming by searching larger databases from various sources. We also compare two search engines, the oldest, SEQUEST, and the most popular, Mascot. This shotgun proteomics experiment also utilizes the power of parallel digestions with orthogonal proteases of increasing selectivity, namely chymotrypsin, trypsin/Lys-C and Asp-N. Our results show that the larger the database the greater the list of accessions identified but the longer the duration of the search. Using orthogonal proteases and different search algorithms increases the total number of proteins identified, most of them common despite differing proteases and algorithms, but many of them unique as well.

Empowering Shotgun Mass Spectrometry with 2DE: A HepG2 Study

One of the major goals of the Chromosome-Centric Human Proteome Project (C-HPP) is to catalog and annotate a myriad of heterogeneous proteoforms, produced by ca. 20 thousand genes. To achieve a detailed and personalized understanding into proteomes, we suggest using a customized RNA-seq library of potential proteoforms, which includes aberrant variants specific to certain biological samples. Two-dimensional electrophoresis coupled with high-performance liquid chromatography allowed us to downgrade the difficulty of biological mixing following shotgun mass spectrometry. To benchmark the proposed pipeline, we examined heterogeneity of the HepG2 hepatoblastoma cell line proteome. Data are available via ProteomeXchange with identifier PXD018450.

[Modification of cysteine residues for mass spectrometry-based proteomic analysis: facts and artifacts]

Mass spectrometric proteomic analysis at the sample preparation stage involves the artificial reduction of disulfide bonds in proteins formed between cysteine residues. Such bonds, when preserved in their native state, complicate subsequent enzymatic hydrolysis and interpretation of the research results. To prevent the re-formation of the disulfide bonds, cysteine residues are protected by special groups, most often by alkylation. In this review, we consider the methods used to modify cysteine residues during sample preparation, as well as possible artifacts of this stage. Particularly, adverse reactions of the alkylating agents with other amino acid residues are described. The most common alkylating compound used to protect cysteine residues in mass spectrometric proteomic analysis is iodoacetamide. However, an analysis of the literature in this area indicates that this reagent causes more adverse reactions than other agents used, such as chloroacetamide and acrylamide. The latter can be recommended for wider use. In the review we also discuss the features of the cysteine residue modifications and their influence on the efficiency of the search for post-translational modifications and protein products of single nucleotide substitutions.

Precision Profiling of the Cardiovascular Post-Translationally Modified Proteome: Where There Is a Will, There Is a Way

There is an exponential increase in biological complexity as initial gene transcripts are spliced, translated into amino acid sequence, and post-translationally modified. Each protein can exist as multiple chemical or sequence-specific proteoforms, and each has the potential to be a critical mediator of a physiological or pathophysiological signaling cascade. Here, we provide an overview of how different proteoforms come about in biological systems and how they are most commonly measured using mass spectrometry-based proteomics and bioinformatics. Our goal is to present this information at a level accessible to every scientist interested in mass spectrometry and its application to proteome profiling. We will specifically discuss recent data linking various protein post-translational modifications to cardiovascular disease and conclude with a discussion for enablement and democratization of proteomics across the cardiovascular and scientific community. The aim is to inform and inspire the readership to explore a larger breadth of proteoform, particularity post-translational modifications, related to their particular areas of expertise in cardiovascular physiology.

Towards an Internet of Science

Big data and complex analysis workflows (pipelines) are common issues in data driven science such as bioinformatics. Large amounts of computational tools are available for data analysis. Additionally, many workflow management systems to piece together such tools into data analysis pipelines have been developed. For example, more than 50 computational tools for read mapping are available representing a large amount of duplicated effort. Furthermore, it is unclear whether these tools are correct and only a few have a user base large enough to have encountered and reported most of the potential problems. Bringing together many largely untested tools in a computational pipeline must lead to unpredictable results. Yet, this is the current state. While presently data analysis is performed on personal computers/workstations/clusters, the future will see development and analysis shift to the cloud. None of the workflow management systems is ready for this transition. This presents the opportunity to build a new system, which will overcome current duplications of effort, introduce proper testing, allow for development and analysis in public and private clouds, and include reporting features leading to interactive documents.

Understanding Camellia sinensis using Omics Technologies along with Endophytic Bacteria and Environmental Roles on Metabolism: A Review

Camellia sinensis is the most consumed beverage worldwide. It contains a wide variety of secondary metabolites, such as alkaloids, saponins, tannins, catechins, and polyphenols, generated through a condensation reaction of cinnamic acid with three malonyl-CoA groups. In addition to the metabolic processes occurring within this plant, there are also some plant-associated bacterial endophytes. These bacteria reside in the living tissues of the host plants without causing any harmful effect to them, thereby stimulating secondary metabolite production with a diverse range of biological effects. Omics technologies reveal understanding of the biological phenomena of transcriptomics, proteomics, and metabolomics. In this sense, the present review aims to provide a comprehensive review of various methods used to identify distinct plant compounds, namely transcriptomic, proteomic, and metabolomic analysis. The role of endophytic bacteria in C. sinensis metabolism, and C. sinensis antioxidant and antimicrobial effects, are also carefully highlighted.

Proteomic Cinderella: Customized analysis of bulky MS/MS data in one night

Proteomic challenges, stirred up by the advent of high-throughput technologies, produce large amount of MS data. Nowadays, the routine manual search does not satisfy the "speed" of modern science any longer. In our work, the necessity of single-thread analysis of bulky data emerged during interpretation of HepG2 proteome profiling results for proteoforms searching. We compared the contribution of each of the eight search engines (X!Tandem, MS-GF[Formula: see text], MS Amanda, MyriMatch, Comet, Tide, Andromeda, and OMSSA) integrated in an open-source graphical user interface SearchGUI ( http://searchgui.googlecode.com ) into total result of proteoforms identification and optimized set of engines working simultaneously. We also compared the results of our search combination with Mascot results using protein kit UPS2, containing 48 human proteins. We selected combination of X!Tandem, MS-GF[Formula: see text] and OMMSA as the most time-efficient and productive combination of search. We added homemade java-script to automatize pipeline from file picking to report generation. These settings resulted in rise of the efficiency of our customized pipeline unobtainable by manual scouting: the analysis of 192 files searched against human proteome (42153 entries) downloaded from UniProt took 11[Formula: see text]h.