Quantification Quality Control Emerges as a Crucial Factor to Enhance Single-Cell Proteomics Data Analysis.

Mass spectrometry (MS)-based single-cell proteomics (SCP) provides us the opportunity to unbiasedly explore biological variability within cells without the limitation of antibody availability. This field is rapidly developed with the main focuses on instrument advancement, sample preparation refinement, and signal boosting methods; however, the optimal data processing and analysis are rarely investigated which holds an arduous challenge because of the high proportion of missing values and batch effect. Here, we introduced a quantification quality control to intensify the identification of differentially expressed proteins (DEPs) by considering both within and across SCP data. Combining quantification quality control with isobaric matching between runs (IMBR) and PSM-level normalization, an additional 12% and 19% of proteins and peptides, with more than 90% of proteins/peptides containing valid values, were quantified. Clearly, quantification quality control was able to reduce quantification variations and q-values with the more apparent cell type separations. In addition, we found that PSM-level normalization performed similar to other protein-level normalizations but kept the original data profiles without the additional requirement of data manipulation. In proof of concept of our refined pipeline, six uniquely identified DEPs exhibiting varied fold-changes and playing critical roles for melanoma and monocyte functionalities were selected for validation using immunoblotting. Five out of six validated DEPs showed an identical trend with the SCP dataset, emphasizing the feasibility of combining the IMBR, cell quality control, and PSM-level normalization in SCP analysis, which is beneficial for future SCP studies.

Use of a combined antibacterial synergy approach and the ANNOgesic tool to identify novel targets within the gene networks of multidrug-resistant Klebsiella pneumoniae.

Since the 1980s, the development of new drug classes for the treatment of multidrug-resistant Klebsiella pneumoniae has become limited, highlighting the urgent need for novel antibiotics. To address this challenge, this study aimed to explore the synergistic interactions between chemical compounds and representative antibiotics, such as carbapenem and colistin. The primary objective of this study was not only to mitigate the adverse impact of multidrug-resistant K. pneumoniae on public health but also to establish a sustainable balance among humans, animals, and the environment. Phenotypical measurements were conducted using the broth microdilution technique to determine the drug sensitivity of bacterial strains. Additionally, a genotypical approach was employed, involving traditional RNA sequencing analysis to identify differentially expressed genes and the computational ANNOgesic tool to detect noncoding RNAs. This study revealed the existence of various pathways and regulatory RNA elements that form a functional network. These pathways, characterized by the expression of specific genes, contribute to the combined treatment effect and bacterial survival strategies. The connections between pathways are facilitated by regulatory RNA elements that respond to environmental changes. These findings suggest an adaptive response of bacteria to harsh environmental conditions.IMPORTANCENoncoding RNAs were identified as key players in post-transcriptional regulation. Moreover, this study predicted the presence of novel small regulatory RNAs that interact with target genes, as well as the involvement of riboswitches and RNA thermometers in conjunction with associated genes. These findings will contribute to the discovery of potential antimicrobial therapeutic candidates. Overall, this study offers valuable insights into the synergistic effects of chemical compounds and antibiotics, highlighting the role of regulatory RNA elements in bacterial response, and survival strategies. The identification of novel noncoding RNAs and their interactions with target genes, riboswitches, and RNA thermometers holds promise for the development of antimicrobial therapies.

Discovering Novel Bacterial Small RNA by RNA-seq Analysis Toolkit ANNOgesic.

ANNOgesic is an RNA-seq analysis pipeline that can detect sRNAs and many other genomic features in bacteria and archaea. In addition to listing sRNA candidates, ANNOgesic also generates various formats of data files for visual examination and downstream experimental design. Based on validations from previous studies, the sRNA predictions are accurate and reliable. In this chapter, we outline the sRNA detection algorithm, important parameters used, step-by-step execution, and data interpretation with a B. pertussis study as an example. Following those procedures, novel sRNA can be revealed by ANNOgesic.

Benchmarking differential expression, imputation and quantification methods for proteomics data.

Data analysis is a critical part of quantitative proteomics studies in interpreting biological questions. Numerous computational tools for protein quantification, imputation and differential expression (DE) analysis were generated in the past decade and the search for optimal tools is still going on. Moreover, due to the rapid development of RNA sequencing (RNA-seq) technology, a vast number of DE analysis methods were created for that purpose. The applicability of these newly developed RNA-seq-oriented tools to proteomics data remains in doubt. In order to benchmark these analysis methods, a proteomics dataset consisting of proteins derived from humans, yeast and drosophila, in defined ratios, was generated in this study. Based on this dataset, DE analysis tools, including microarray- and RNA-seq-based ones, imputation algorithms and protein quantification methods were compared and benchmarked. Furthermore, applying these approaches to two public datasets showed that RNA-seq-based DE tools achieved higher accuracy (ACC) in identifying DEPs. This study provides useful guidelines for analyzing quantitative proteomics datasets. All the methods used in this study were integrated into the Perseus software, version 2.0.3.0, which is available at https://www.maxquant.org/perseus.

Identification of BvgA-Dependent and BvgA-Independent Small RNAs (sRNAs) in Bordetella pertussis Using the Prokaryotic sRNA Prediction Toolkit ANNOgesic.

Noncoding small RNAs (sRNAs) are crucial for the posttranscriptional regulation of gene expression in all organisms and are known to be involved in the regulation of bacterial virulence. In the human pathogen Bordetella pertussis, which causes whooping cough, virulence is controlled primarily by the master two-component system BvgA (response regulator)/BvgS (sensor kinase). In this system, BvgA is phosphorylated (Bvg+ mode) or nonphosphorylated (Bvg- mode), with global transcriptional differences between the two. B. pertussis also carries the bacterial sRNA chaperone Hfq, which has previously been shown to be required for virulence. Here, we conducted transcriptomic analyses to identify possible B. pertussis sRNAs and to determine their BvgAS dependence using transcriptome sequencing (RNA-seq) and the prokaryotic sRNA prediction program ANNOgesic. We identified 143 possible candidates (25 Bvg+ mode specific and 53 Bvg- mode specific), of which 90 were previously unreported. Northern blot analyses confirmed all of the 10 ANNOgesic candidates that we tested. Homology searches demonstrated that 9 of the confirmed sRNAs are highly conserved among B. pertussis, Bordetella parapertussis, and Bordetella bronchiseptica, with one that also has homologues in other species of the Alcaligenaceae family. Using coimmunoprecipitation with a B. pertussis FLAG-tagged Hfq, we demonstrated that 3 of the sRNAs interact directly with Hfq, which is the first identification of sRNA binding to B. pertussis Hfq. Our study demonstrates that ANNOgesic is a highly useful tool for the identification of sRNAs in this system and that its combination with molecular techniques is a successful way to identify various BvgAS-dependent and Hfq-binding sRNAs. IMPORTANCE Noncoding small RNAs (sRNAs) are crucial for posttranscriptional regulation of gene expression in all organisms and are known to be involved in the regulation of bacterial virulence. We have investigated the presence of sRNAs in the obligate human pathogen B. pertussis, using transcriptome sequencing (RNA-seq) and the recently developed prokaryotic sRNA search program ANNOgesic. This analysis has identified 143 sRNA candidates (90 previously unreported). We have classified their dependence on the B. pertussis two-component system required for virulence, namely, BvgAS, based on their expression in the presence/absence of the phosphorylated response regulator BvgA, confirmed several by Northern analyses, and demonstrated that 3 bind directly to B. pertussis Hfq, the RNA chaperone involved in mediating sRNA effects. Our study demonstrates the utility of combining RNA-seq, ANNOgesic, and molecular techniques to identify various BvgAS-dependent and Hfq-binding sRNAs, which may unveil the roles of sRNAs in pertussis pathogenesis.

Expanding the Perseus Software for Omics Data Analysis With Custom Plugins.

The Perseus software provides a comprehensive framework for the statistical analysis of large-scale quantitative proteomics data, also in combination with other omics dimensions. Rapid developments in proteomics technology and the ever-growing diversity of biological studies increasingly require the flexibility to incorporate computational methods designed by the user. Here, we present the new functionality of Perseus to integrate self-made plugins written in C#, R, or Python. The user-written codes will be fully integrated into the Perseus data analysis workflow as custom activities. This also makes language-specific R and Python libraries from CRAN (cran.r-project.org), Bioconductor (bioconductor.org), PyPI (pypi.org), and Anaconda (anaconda.org) accessible in Perseus. The different available approaches are explained in detail in this article. To facilitate the distribution of user-developed plugins among users, we have created a plugin repository for community sharing and filled it with the examples provided in this article and a collection of already existing and more extensive plugins. © 2020 The Authors. Basic Protocol 1: Basic steps for R plugins Support Protocol 1: R plugins with additional arguments Basic Protocol 2: Basic steps for python plugins Support Protocol 2: Python plugins with additional arguments Basic Protocol 3: Basic steps and construction of C# plugins Basic Protocol 4: Basic steps of construction and connection for R plugins with C# interface Support Protocol 4: Advanced example of R Plugin with C# interface: UMAP Basic Protocol 5: Basic steps of construction and connection for python plugins with C# interface Support Protocol 5: Advanced example of python plugin with C# interface: UMAP Support Protocol 6: A basic workflow for the analysis of label-free quantification proteomics data using perseus.

Isobaric Matching between Runs and Novel PSM-Level Normalization in MaxQuant Strongly Improve Reporter Ion-Based Quantification.

Isobaric labeling has the promise of combining high sample multiplexing with precise quantification. However, normalization issues and the missing value problem of complete n-plexes hamper quantification across more than one n-plex. Here, we introduce two novel algorithms implemented in MaxQuant that substantially improve the data analysis with multiple n-plexes. First, isobaric matching between runs makes use of the three-dimensional MS1 features to transfer identifications from identified to unidentified MS/MS spectra between liquid chromatography-mass spectrometry runs in order to utilize reporter ion intensities in unidentified spectra for quantification. On typical datasets, we observe a significant gain in MS/MS spectra that can be used for quantification. Second, we introduce a novel PSM-level normalization, applicable to data with and without the common reference channel. It is a weighted median-based method, in which the weights reflect the number of ions that were used for fragmentation. On a typical dataset, we observe complete removal of batch effects and dominance of the biological sample grouping after normalization. Furthermore, we provide many novel processing and normalization options in Perseus, the companion software for the downstream analysis of quantitative proteomics results. All novel tools and algorithms are available with the regular MaxQuant and Perseus releases, which are downloadable at http://maxquant.org.

ANNOgesic: a Swiss army knife for the RNA-seq based annotation of bacterial/archaeal genomes.

To understand the gene regulation of an organism of interest, a comprehensive genome annotation is essential. While some features, such as coding sequences, can be computationally predicted with high accuracy based purely on the genomic sequence, others, such as promoter elements or noncoding RNAs, are harder to detect. RNA sequencing (RNA-seq) has proven to be an efficient method to identify these genomic features and to improve genome annotations. However, processing and integrating RNA-seq data in order to generate high-resolution annotations is challenging, time consuming, and requires numerous steps. We have constructed a powerful and modular tool called ANNOgesic that provides the required analyses and simplifies RNA-seq-based bacterial and archaeal genome annotation. It can integrate data from conventional RNA-seq and differential RNA-seq and predicts and annotates numerous features, including small noncoding RNAs, with high precision. The software is available under an open source license (ISCL) at https://pypi.org/project/ANNOgesic/.

An RpoHI-Dependent Response Promotes Outgrowth after Extended Stationary Phase in the Alphaproteobacterium Rhodobacter sphaeroides.

Under unfavorable growth conditions, bacteria enter stationary phase and can maintain cell viability over prolonged periods with no increase in cell number. To obtain insights into the regulatory mechanisms that allow bacteria to resume growth when conditions become favorable again (outgrowth), we performed global transcriptome analyses at different stages of growth for the alphaproteobacterium Rhodobacter sphaeroides The majority of genes were not differentially expressed across growth phases. After a short stationary phase (about 20 h after growth starts to slow down), only 7% of the genes showed altered expression (fold change of >1.6 or less than -1.6, corresponding to a log2 fold change of >0.65 or less than -0.65, respectively) compared to expression at exponential phase. Outgrowth induced a distinct response in gene expression which was strongly influenced by the length of the preceding stationary phase. After a long stationary phase (about 64 h after growth starts to slow down), a much larger number of genes (15.1%) was induced in outgrowth than after a short stationary phase (1.7%). Many of those genes are known members of the RpoHI/RpoHII regulons and have established functions in stress responses. A main effect of RpoHI on the transcriptome in outgrowth after a long stationary phase was confirmed. Growth experiments with mutant strains further support an important function in outgrowth after prolonged stationary phase for the RpoHI and RpoHII sigma factors.IMPORTANCE In natural environments, the growth of bacteria is limited mostly by lack of nutrients or other unfavorable conditions. It is important for bacterial populations to efficiently resume growth after being in stationary phase, which may last for long periods. Most previous studies on growth-phase-dependent gene expression did not address outgrowth after stationary phase. This study on growth-phase-dependent gene regulation in a model alphaproteobacterium reveals, for the first time, that the length of the stationary phase strongly impacts the transcriptome during outgrowth. The alternative sigma factors RpoHI and RpoHII, which are important regulators of stress responses in alphaproteobacteria, play a major role during outgrowth following prolonged stationary phase. These findings provide the first insight into the regulatory mechanisms enabling efficient outgrowth.

Effect of Shear Stress on Pseudomonas aeruginosa Isolated from the Cystic Fibrosis Lung.

UNLABELLED: Chronic colonization of the lungs by Pseudomonas aeruginosa is one of the major causes of morbidity and mortality in cystic fibrosis (CF) patients. To gain insights into the characteristic biofilm phenotype of P. aeruginosa in the CF lungs, mimicking the CF lung environment is critical. We previously showed that growth of the non-CF-adapted P. aeruginosa PAO1 strain in a rotating wall vessel, a device that simulates the low fluid shear (LS) conditions present in the CF lung, leads to the formation of in-suspension, self-aggregating biofilms. In the present study, we determined the phenotypic and transcriptomic changes associated with the growth of a highly adapted, transmissible P. aeruginosa CF strain in artificial sputum medium under LS conditions. Robust self-aggregating biofilms were observed only under LS conditions. Growth under LS conditions resulted in the upregulation of genes involved in stress response, alginate biosynthesis, denitrification, glycine betaine biosynthesis, glycerol metabolism, and cell shape maintenance, while genes involved in phenazine biosynthesis, type VI secretion, and multidrug efflux were downregulated. In addition, a number of small RNAs appeared to be involved in the response to shear stress. Finally, quorum sensing was found to be slightly but significantly affected by shear stress, resulting in higher production of autoinducer molecules during growth under high fluid shear (HS) conditions. In summary, our study revealed a way to modulate the behavior of a highly adapted P. aeruginosa CF strain by means of introducing shear stress, driving it from a biofilm lifestyle to a more planktonic lifestyle. IMPORTANCE: Biofilm formation by Pseudomonas aeruginosa is one of the hallmarks of chronic cystic fibrosis (CF) lung infections. The biofilm matrix protects this bacterium from antibiotics as well as from the immune system. Hence, the prevention or reversion of biofilm formation is believed to have a great impact on treatment of chronic P. aeruginosa CF lung infections. In the present study, we showed that it is possible to modulate the behavior of a highly adapted transmissible P. aeruginosa CF isolate at both the transcriptomic and phenotypic levels by introducing shear stress in a CF-like environment, driving it from a biofilm to a planktonic lifestyle. Consequently, the results obtained in this study are of great importance with regard to therapeutic applications that introduce shear stress in the lungs of CF patients.

Local packing density is the main structural determinant of the rate of protein sequence evolution at site level.

Functional and biophysical constraints result in site-dependent patterns of protein sequence variability. It is commonly assumed that the key structural determinant of site-specific rates of evolution is the Relative Solvent Accessibility (RSA). However, a recent study found that amino acid substitution rates correlate better with two Local Packing Density (LPD) measures, the Weighted Contact Number (WCN) and the Contact Number (CN), than with RSA. This work aims at a more thorough assessment. To this end, in addition to substitution rates, we considered four other sequence variability scores, four measures of solvent accessibility (SA), and other CN measures. We compared all properties for each protein of a structurally and functionally diverse representative dataset of monomeric enzymes. We show that the best sequence variability measures take into account phylogenetic tree topology. More importantly, we show that both LPD measures (WCN and CN) correlate better than all of the SA measures, regardless of the sequence variability score used. Moreover, the independent contribution of the best LPD measure is approximately four times larger than that of the best SA measure. This study strongly supports the conclusion that a site's packing density rather than its solvent accessibility is the main structural determinant of its rate of evolution.

Site-specific structural constraints on protein sequence evolutionary divergence: local packing density versus solvent exposure.

Protein sequences evolve under selection pressures imposed by functional and biophysical requirements, resulting in site-dependent rates of amino acid substitution. Relative solvent accessibility (RSA) and local packing density (LPD) have emerged as the best candidates to quantify structural constraint. Recent research assumes that RSA is the main determinant of sequence divergence. However, it is not yet clear which is the best predictor of substitution rates. To address this issue, we compared RSA and LPD with site-specific rates of evolution for a diverse data set of enzymes. In contrast with recent studies, we found that LPD measures correlate better than RSA with evolutionary rate. Moreover, the independent contribution of RSA is minor. Taking into account that LPD is related to backbone flexibility, we put forward the possibility that the rate of evolution of a site is determined by the ease with which the backbone deforms to accommodate mutations.

On the relationship between catalytic residues and their protein contact number.

Due to advances in structural biology, an increasing number of protein structures of unknown function have been deposited in Protein Data Bank (PDB). These proteins are usually characterized by novel structures and sequences. Conventional comparative methodology (such as sequence alignment, structure comparison, or template search) is unable to determine their function. Thus, it is important to identify protein's function directly from its structure, but this is not an easy task. One of the strategies used is to analyze whether there are distinctive structure-derived features associated with functional residues. If so, one may be able to identify the functional residues directly from a single structure. Recently, we have shown that protein weighted contact number is related to atomic thermal fluctuations and can be used to derive motional correlations in proteins. In this report, we analyze the weighted contact-number profiles of both catalytic residues and non-catalytic residues for a dataset of 760 structures. We found that catalytic residues have distinct distributions of weighted contact numbers from those of non-catalytic residues. Using this feature, we are able to effectively differentiate catalytic residues from other residues with a single optimized threshold value. Our method is simple to implement and compares favourably with other more sophisticated methods. In addition, we discuss the physics behind the relationship between catalytic residues and their contact numbers as well as other features (such as residue centrality or B-factors) associated with catalytic residues.

pKNOT: the protein KNOT web server.

Knotted proteins are more commonly observed in recent years due to the enormously growing number of structures in the Protein Data Bank (PDB). Studies show that the knot regions contribute to both ligand binding and enzyme activity in proteins such as the chromophore-binding domain of phytochrome, ketol-acid reductoisomerase or SpoU methyltransferase. However, there are still many misidentified knots published in the literature due to the absence of a convenient web tool available to the general biologists. Here, we present the first web server to detect the knots in proteins as well as provide information on knotted proteins in PDB-the protein KNOT (pKNOT) web server. In pKNOT, users can either input PDB ID or upload protein coordinates in the PDB format. The pKNOT web server will detect the knots in the protein using the Taylor's smoothing algorithm. All the detected knots can be visually inspected using a Java-based 3D graphics viewer. We believe that the pKNOT web server will be useful to both biologists in general and structural biologists in particular.

A simple way to compute protein dynamics without a mechanical model.

We found that in proteins the average atomic fluctuation is linearly related to the square of the atomic distance from the center of mass of the protein. Using this simple relation, we can accurately compute the temperature factors of proteins of a wide range of sizes and folds, and the correlation of the fluctuations in proteins. This simple relation provides a direct link between protein dynamics and the static protein's geometrical shape and offers a simple way to compute protein dynamics without either long time trajectory integration or any matrix operations.