Elucidating the dynamic remodelling of Escherichia coli interactome in different growth conditions using multiplex co-fractionation MS (mCF-MS)

Most proteins function by forming complexes within a dynamic interconnected network that underlies various biological mechanisms. To systematically investigate such interactomes, high-throughput techniques, including CF-MS, have been developed to capture, identify, and quantify protein-protein interactions (PPIs) on a large scale. Compared to other techniques, CF-MS allows the global identification and quantification of native protein complexes in one setting, without genetic manipulation. Furthermore, quantitative CF-MS can potentially elucidate the distribution of a protein in multiple co-elution features, informing the stoichiometries and dynamics of a target protein complex. In this issue, Youssef et al. (Proteomics 2023, 00, e2200404) combined multiplex CF-MS and a new algorithm to study the dynamics of the PPI network for Escherichia coli grown under ten different conditions. Although the results demonstrated that most proteins remained stable, the authors were able to detect disrupted interactions that were growth condition specific. Further bioinformatics analyses also revealed the biophysical properties and structural patterns that govern such a response.     

Drug response prediction using graph representation learning and Laplacian feature selection

Background

Essential proteins are indispensable to the development and survival of cells. The identification of essential proteins not only is helpful for the understanding of the minimal requirements for cell survival, but also has practical significance in disease diagnosis, drug design and medical treatment. With the rapidly amassing of protein–protein interaction (PPI) data, computationally identifying essential proteins from protein–protein interaction networks (PINs) becomes more and more popular. Up to now, a number of various approaches for essential protein identification based on PINs have been developed.

Results

In this paper, we propose a new and effective approach called iMEPP to identify essential proteins from PINs by fusing multiple types of biological data and applying the influence maximization mechanism to the PINs. Concretely, we first integrate PPI data, gene expression data and Gene Ontology to construct weighted PINs, to alleviate the impact of high false-positives in the raw PPI data. Then, we define the influence scores of nodes in PINs with both orthological data and PIN topological information. Finally, we develop an influence discount algorithm to identify essential proteins based on the influence maximization mechanism.

Conclusions

We applied our method to identifying essential proteins from saccharomyces cerevisiae PIN. Experiments show that our iMEPP method outperforms the existing methods, which validates its effectiveness and advantage.

     

Analytical Guidelines for co-fractionation Mass Spectrometry Obtained through Global Profiling of Gold Standard Saccharomyces cerevisiae Protein Complexes

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Highlights

• •Guidelines for studying protein complexes via co-fractionation mass spectrometry.
• •A novel procedure for profiling gold standard protein complexes in CF-MS data.
• •Recommendations for efficient CF-MS fractionation collection.
• •Scoring metric recommendations for precise and sensitive CF-MS data analysis.

     

Combining Rapid Data Independent Acquisition and CRISPR Gene Deletion for Studying Potential Protein Functions: A Case of HMGN1

CRISPR‐Cas gene editing holds substantial promise in many biomedical disciplines and basic research. Due to the important functional implications of non‐histone chromosomal protein HMG‐14 (HMGN1) in regulating chromatin structure and tumor immunity, gene knockout of HMGN1 is performed by CRISPR in cancer cells and the following proteomic regulation events are studied. In particular, DIA mass spectrometry (DIA‐MS) is utilized, and more than 6200 proteins (protein‐ FDR 1%) and more than 82 000 peptide precursors are reproducibly measured in the single MS shots of 2 h. HMGN1 protein deletion is confidently verified by DIA‐MS in all of the clone‐ and dish‐ replicates following CRISPR. Statistical analysis reveals 147 proteins change their expressions significantly after HMGN1 knockout. Functional annotation and enrichment analysis indicate the deletion of HMGN1 induces histone inactivation, various stress pathways, remodeling of extracellular proteomes, cell proliferation, as well as immune regulation processes such as complement and coagulation cascade and interferon alpha/ gamma response in cancer cells. These results shed new lights on the cellular functions of HMGN1. It is suggested that DIA‐MS can be reliably used as a rapid, robust, and cost‐effective proteomic‐screening tool to assess the outcome of the CRISPR experiments.     

Engineering responsiveness to cell culture stresses: growth arrest and DNA damage gene 153 (GADD153) and the unfolded protein response (UPR) in NS0 myeloma cells

The successful movement of a newly synthesized protein through the endoplasmic reticulum (ER) and associated membranous compartments is dependent on appropriate recognition by complex processing systems. Failure to perceive appropriately processed or modified intermediates in the pathway can initiate a series of cellular signaling events (ER stress or unfolded protein response, UPR) that can lead to cell apoptosis and loss of biomass in culture processes. We have shown that expression of growth arrest and DNA damage gene 153 (GADD153) is associated with recognition of damaged or mis-processed proteins within the secretory processes of CHO and NS0 myeloma cells. To directly characterize the roles of GADD153 in UPR-directed apoptosis, we have generated stable clones of NS0 myeloma cells with elevated (constitutive and inducible) and deleted GADD153 expression. Although GADD153 is a robust indicator of the onset of ER stress or the UPR, GADD153 expression alone is not sufficient to provoke NS0 myeloma apoptosis and it is not required for apoptosis to occur.     

A novel method to predict protein-protein interactions based on the information of protein-protein interaction networks and protein sequence

Protein-protein interactions (PPIs) are crucial to most biochemical processes in human beings. Although many human PPIs have been identified by experiments, the number is still limited compared to the available protein sequences of human organisms. Recently, many computational methods have been proposed to facilitate the recognition of novel human PPIs. However the existing methods only concentrated on the information of individual PPI, while the systematic characteristic of protein-protein interaction networks (PINs) was ignored. In this study, a new method was proposed by combining the global information of PINs and protein sequence information. Random forest (RF) algorithm was implemented to develop the prediction model, and a high accuracy of 91.88% was obtained. Furthermore, the RF model was tested using three independent datasets with good performances, suggesting that our method is a useful tool for identification of PPIs and investigation into PINs as well.     

New targeted approaches for the quantification of data‐independent acquisition mass spectrometry

The use of data‐independent acquisition (DIA) approaches for the reproducible and precise quantification of complex protein samples has increased in the last years. The protein information arising from DIA analysis is stored in digital protein maps (DIA maps) that can be interrogated in a targeted way by using ad hoc or publically available peptide spectral libraries generated on the same sample species as for the generation of the DIA maps. The restricted availability of certain difficult‐to‐obtain human tissues (i.e., brain) together with the caveats of using spectral libraries generated under variable experimental conditions limits the potential of DIA. Therefore, DIA workflows would benefit from high‐quality and extended spectral libraries that could be generated without the need of using valuable samples for library production. We describe here two new targeted approaches, using either classical data‐dependent acquisition repositories (not specifically built for DIA) or ad hoc mouse spectral libraries, which enable the profiling of human brain DIA data set. The comparison of our results to both the most extended publically available human spectral library and to a state‐of‐the‐art untargeted method supports the use of these new strategies to improve future DIA profiling efforts.     

An Immuno-informatics driven Epitope study from the molecular interaction of JEV non-structural (NS) proteins with Ribophorin (RPN)

Japanese encephalitis (JE) is an acute viral infection of the central nervous system where the JE virus infects the lumen of the endoplasmic reticulum (ER) and rapidly accumulates substantial amount of seven different nonstructural proteins (NS). These NS proteins tend to bind on a glycoprotein receptor, ribophorin (RPN) resulting in the malfunctioning of ER in host cells, subsequently triggering an unfolded protein response. Therefore, it is of interest to predict the best possible antigenic determinants in the NS protein capable of eliciting immune response as a strategy to combat JE. Hence, it is our interest to explore the most potent NS protein among all showing the best possible molecular interaction with the RPN receptor present on ER. However, the structures of these NS protein and RPN are currently unknown. Thus, we modeled their structures using the established homology modeling techniques in the MODELLER 9v10 software. The molecular docking of NS proteins with RPN was subsequently completed using the Discovery Studio 2.5 software suite. The docked conformations of RPN with NS were further analyzed and its graphical interpretations were presented for identifying the most potential NS protein for efficient epitope activity. Further, the B cell epitopes were mapped using BCPred and the predicted epitope regions are documented. The data presented in this report provides useful insights towards the design and development of potential epitopes to generate a vaccine candidate against JEV.     

Detecting protein complexes from active protein interaction networks constructed with dynamic gene expression profiles

Background

Protein interaction networks (PINs) are known to be useful to detect protein complexes. However, most available PINs are static, which cannot reflect the dynamic changes in real networks. At present, some researchers have tried to construct dynamic networks by incorporating time-course (dynamic) gene expression data with PINs. However, the inevitable background noise exists in the gene expression array, which could degrade the quality of dynamic networkds. Therefore, it is needed to filter out contaminated gene expression data before further data integration and analysis.

Results

Firstly, we adopt a dynamic model-based method to filter noisy data from dynamic expression profiles. Then a new method is proposed for identifying active proteins from dynamic gene expression profiles. An active protein at a time point is defined as the protein the expression level of whose corresponding gene at that time point is higher than a threshold determined by a standard variance involved threshold function. Furthermore, a noise-filtered active protein interaction network (NF-APIN) is constructed. To demonstrate the efficiency of our method, we detect protein complexes from the NF-APIN, compared with those from other dynamic PINs.

Conclusion

A dynamic model based method can effectively filter out noises in dynamic gene expression data. Our method to compute a threshold for determining the active time points of noise-filtered genes can make the dynamic construction more accuracy and provide a high quality framework for network analysis, such as protein complex prediction.

     

A novel dynamic impact approach (DIA) for functional analysis of time-course omics studies: validation using the bovine mammary transcriptome

The overrepresented approach (ORA) is the most widely-accepted method for functional analysis of microarray datasets. The ORA is computationally-efficient and robust; however, it suffers from the inability of comparing results from multiple gene lists particularly with time-course experiments or those involving multiple treatments. To overcome such limitation a novel method termed Dynamic Impact Approach (DIA) is proposed. The DIA provides an estimate of the biological impact of the experimental conditions and the direction of the impact. The impact is obtained by combining the proportion of differentially expressed genes (DEG) with the log2 mean fold change and mean -log P-value of genes associated with the biological term. The direction of the impact is calculated as the difference of the impact of up-regulated DEG and down-regulated DEG associated with the biological term. The DIA was validated using microarray data from a time-course experiment of bovine mammary gland across the lactation cycle. Several annotation databases were analyzed with DIA and compared to the same analysis performed by the ORA. The DIA highlighted that during lactation both BTA6 and BTA14 were the most impacted chromosomes; among Uniprot tissues those related with lactating mammary gland were the most positively-impacted; within KEGG pathways 'Galactose metabolism' and several metabolism categories related to lipid synthesis were among the most impacted and induced; within Gene Ontology "lactose biosynthesis" among Biological processes and "Lactose synthase activity" and "Stearoyl-CoA 9-desaturase activity" among Molecular processes were the most impacted and induced. With the exception of the terms 'Milk', 'Milk protein' and 'Mammary gland' among Uniprot tissues and SP_PIR_Keyword, the use of ORA failed to capture as significantly-enriched (i.e., biologically relevant) any term known to be associated with lactating mammary gland. Results indicate the DIA is a biologically-sound approach for analysis of time-course experiments. This tool represents an alternative to ORA for functional analysis.     

昆虫蜕皮激素受体及其类似物的杀虫机制研究进展

昆虫的蜕皮、变态和繁殖受到蜕皮激素的严格调控。蜕皮激素作用靶标由蜕皮激素受体（ecdysteroid receptor, EcR）和超气门蛋白（ultraspiracle protein, USP）组成,蜕皮激素与EcR/USP作用启动蜕皮级联反应过程。昆虫EcR具有种类或类群的特异性,研究其结构、功能和调控机理在开发环境友好型新药剂和基因调控开关等方面具有重要指导作用。该文介绍了昆虫EcR的结构和功能特点,蜕皮激素及其类似物与EcR/USP的分子作用方式,以及基于EcR/USP的新杀虫剂创制和基因调控开关设计等方面的重要进展。     

A multiple-target mRNA-LNP vaccine induces protective immunity against experimental multi-serotype DENV in mice

Dengue virus (DENV) is a mosquito-borne virus with a rapid spread to humans, causing mild to potentially fatal illness in hundreds of millions of people each year. Due to the large number of serotypes of the virus, there remains an unmet need to develop protective vaccines for a broad spectrum of the virus. Here, we constructed a modified mRNA vaccine containing envelope domain III (E-DIII) and non-structural protein 1 (NS1) coated with lipid nanoparticles. This multi-target vaccine induced a robust antiviral immune response and increased neutralizing antibody titers that blocked all four types of DENV infection in vitro without significant antibody-dependent enhancement (ADE). In addition, there was more bias for Th1 than Th2 in the exact E-DIII and NS1-specific T cell responses after a single injection. Importantly, intramuscular immunization limited DENV transmission in vivo and eliminated vascular leakage. Our findings highlight that chimeric allogeneic structural and non-structural proteins can be effective targets for DENV vaccine and that they can prevent the further development of congenital DENV syndrome.     

Integrated tandem affinity protein purification using the polyhistidine plus extra 4 amino acids (HiP4) tag system

Peptide tag systems are a robust biophysical and biochemical method that is widely used for protein detection and purification. Here, we developed a novel tag system termed “HiP4” (histidine plus four amino acids) whose epitope sequence comprises only seven amino acids (HHHDYDI) that partially overlap with the conventional 6x histidine tag (6xHis-tag). We produced a monoclonal antibody against the HiP4 tag that can be used in multiple immunoassays with high specificity and affinity. Using this system, we developed a tandem affinity purification (TAP) and mass spectrometry (TAP-MS) system for comprehensive protein interactome analysis. The integrated use of nickel bead purification followed by HiP4 tag immunoprecipitation made it possible to reduce nonspecific binding and improve selectivity, leading to the recovery of previously unrecognized proteins that interact with hepatitis B virus X (HBx) protein or TAR DNA-binding protein 43 (TARDBP or TDP-43). Our results indicate that this system may be viable as a simple and powerful tool for TAP-MS that can achieve low background and high selectivity in comprehensive protein–protein interaction analyses.     

Conserved autosomal syntenic group on mouse (MMU) chromosome 15 and human (HSA) chromosome 22: assignment of a gene for arylsulfatase A to MMU 15 and regional mapping of DIA1, ARSA, and ACO2 on HSA 22

We have utilized a panel of Chinese hamster x mouse somatic cell hybrids segregating mouse chromosomes to assign a gene for arylsulfatase A (ARSA) to mouse chromosome 15. Considering our previous assignment of a gene for diaphorase-1 (DIA1) to the same mouse chromosome, we have evidence for another syntenic relationship that has been conserved, since the homologous loci for human ARSA and DIA1 are both located on human chromosome 22. Because MMU 15 and HSA 22 are quite dissimilar in size and banding patterns, we have attempted to identify the conserved portion by regional mapping of human DIA1 and ARSA using somatic cell hybrids segregating a human chromosome translocation t(15;22)(q14;q13.31). The results assign human DIA1 and ARSA to the distal sub-band of 22q13 (region 22q13.31 leads to qter). The locus for mitochondrial aconitase (ACO2) has been separated by the breakpoint from DIA1 and ARSA and is located more proximally.     

基于数据非依赖采集的蛋白质组质谱数据解析方法研究进展

数据非依赖采集（DIA）是蛋白质组学领域近年来快速发展的质谱采集技术，其通过无偏碎裂隔离窗口内的所有母离子采集二级谱图，理论上可实现蛋白质样品的深度覆盖，同时具有高通量、高重现性和高灵敏度的优点。现有的DIA数据采集方法可以分为全窗口碎裂方法、隔离窗口序列碎裂方法和四维DIA数据采集方法（4D-DIA）3大类。针对DIA数据的不同特点，主要数据解析方法包括谱库搜索方法、蛋白质序列库直接搜索方法、伪二级谱图鉴定方法和从头测序方法4大类。解析得到的肽段鉴定结果需要进行可信度评估，包括使用机器学习方法的重排序和对报告结果集合的假发现率估计两个步骤，实现对数据解析结果的质控。本文对DIA数据的采集方法、数据解析方法及软件和鉴定结果可信度评估方法进行了整理和综述，并展望了未来的发展方向。     

A gas phase fractionation acquisition scheme integrating ion mobility for rapid diaPASEF library generation

Data independent acquisition (DIA/SWATH) MS is a primary strategy in quantitative proteomics. diaPASEF is a recent adaptation using trapped ion mobility spectrometry (TIMS) to improve selectivity/sensitivity. Complex DIA spectra are typically analyzed with reference to spectral libraries. The best-established method for generating libraries uses offline fractionation to increase depth of coverage. More recently strategies for spectral library generation based on gas phase fractionation (GPF), where a representative sample is injected serially using narrow DIA windows that cover different mass ranges of the complete precursor space, have been introduced that performed comparably to deep offline fractionation-based libraries. We investigated whether an analogous GPF-based approach that accounts for the ion mobility (IM) dimension is useful for the analysis of diaPASEF data. We developed a rapid library generation approach using an IM-GPF acquisition scheme in the m/z versus 1/K0 space requiring seven injections of a representative sample and compared this with libraries generated by direct deconvolution-based analysis of diaPASEF data or by deep offline fractionation. We found that library generation by IM-GPF outperformed direct library generation from diaPASEF and had performance approaching that of the deep library. This establishes the IM-GPF scheme as a pragmatic approach to rapid library generation for analysis of diaPASEF data.     

Cleavage of recombinant proteins at poly-His sequences by Co(II) and Cu(II)

Improved ways to cleave peptide chains at engineered sites easily and specifically would form useful tools for biochemical research. Uses of such methods include the activation or inactivation of enzymes or the removal of tags for enhancement of recombinant protein expression or tags used for purification of recombinant proteins. In this work we show by gel electrophoresis and mass spectroscopy that salts of Co(II) and Cu(II) can be used to cleave fusion proteins specifically at sites where sequences of His residues have been introduced by protein engineering. The His residues could be either consecutive or spaced with other amino acids in between. The cleavage reaction required the presence of low concentrations of ascorbate and in the case of Cu(II) also hydrogen peroxide. The amount of metal ions required for cleavage was very low; in the case of Cu(II) only one to two molar equivalents of Cu(II) to protein was required. In the case of Co(II), 10 molar equivalents gave optimal cleavage. The reaction occurred within minutes, at a wide pH range, and efficiently at temperatures ranging from 0 degrees C to 70 degrees C. The work described here can also have implications for understanding protein stability in vitro and in vivo.