Bioinformatic Analysis of Temporal and Spatial Proteome Alternations During Infections

Recent Advancements in Subcellular Proteomics: Growing Impact of Organellar Protein Niches on the Understanding of Cell Biology

The mammalian cell is a complex entity, with membrane-bound and membrane-less organelles playing vital roles in regulating cellular homeostasis. Organellar protein niches drive discrete biological processes and cell functions, thus maintaining cell equilibrium. Cellular processes such as signaling, growth, proliferation, motility, and programmed cell death require dynamic protein movements between cell compartments. Aberrant protein localization is associated with a wide range of diseases. Therefore, analyzing the subcellular proteome of the cell can provide a comprehensive overview of cellular biology. With recent advancements in mass spectrometry, imaging technology, computational tools, and deep machine learning algorithms, studies pertaining to subcellular protein localization and their dynamic distributions are gaining momentum. These studies reveal changing interaction networks because of "moonlighting proteins" and serve as a discovery tool for disease network mechanisms. Consequently, this review aims to provide a comprehensive repository for recent advancements in subcellular proteomics subcontexting methods, challenges, and future perspectives for method developers. In summary, subcellular proteomics is crucial to the understanding of the fundamental cellular mechanisms and the associated diseases.

Lipid exchange at ER-trans-Golgi contact sites governs polarized cargo sorting

Oxysterol binding protein (OSBP) extracts cholesterol from the ER to deliver it to the TGN via counter exchange and subsequent hydrolysis of the phosphoinositide PI(4)P. Here, we show that this pathway is essential in polarized epithelial cells where it contributes not only to the proper subcellular distribution of cholesterol but also to the trans-Golgi sorting and trafficking of numerous plasma membrane cargo proteins with apical or basolateral localization. Reducing the expression of OSBP, blocking its activity, or inhibiting a PI4Kinase that fuels OSBP with PI(4)P abolishes the epithelial phenotype. Waves of cargo enrichment in the TGN in phase with OSBP and PI(4)P dynamics suggest that OSBP promotes the formation of lipid gradients along the TGN, which helps cargo sorting. During their transient passage through the trans-Golgi, polarized plasma membrane proteins get close to OSBP but fail to be sorted when OSBP is silenced. Thus, OSBP lipid exchange activity is decisive for polarized cargo sorting and distribution in epithelial cells.

DeepSP: A Deep Learning Framework for Spatial Proteomics

The study of protein subcellular localization (PSL) is a fundamental step toward understanding the mechanism of protein function. The recent development of mass spectrometry (MS)-based spatial proteomics to quantify the distribution of proteins across subcellular fractions provides us a high-throughput approach to predict unknown PSLs based on known PSLs. However, the accuracy of PSL annotations in spatial proteomics is limited by the performance of existing PSL predictors based on traditional machine learning algorithms. In this study, we present a novel deep learning framework named DeepSP for PSL prediction of an MS-based spatial proteomics data set. DeepSP constructs the new feature map of a difference matrix by capturing detailed changes between different subcellular fractions of protein occupancy profiles and uses the convolutional block attention module to improve the prediction performance of PSL. DeepSP achieved significant improvement in accuracy and robustness for PSL prediction in independent test sets and unknown PSL prediction compared to current state-of-the-art machine learning predictors. As an efficient and robust framework for PSL prediction, DeepSP is expected to facilitate spatial proteomics studies and contributes to the elucidation of protein functions and the regulation of biological processes.

Role of NF-κB in lead exposure-induced activation of astrocytes based on bioinformatics analysis of hippocampal proteomics

Lead (Pb), as a heavy metal, is used in batteries, ceramics, paint, pipes, certain ceramics, e-waste recycling, etc. Chronic Pb exposure can result in the inflammation of the central nervous system, as well as neurobehavioral changes. Both glial cells and neurons are involved in central nervous injury following Pb exposure. However, significant cellular events and their key regulators following Pb exposure remain to be elucidated. In this study, rats were randomly exposed to 250 or 500 mg/L PbAc for 9 weeks. Hippocampal proteomics was performed using isobaric tags for relative absolute quantification. Bioinformatics analysis was used to identify 301 and 267 differentially expressed proteins-which were involved in biological processes, including glial cell activation, neural nucleus development, and mRNA processing-in the low and high Pb exposure groups, respectively. Gene Set Enrichment Analysis showed that astrocyte activation was identified as a significant cellular event occurring in the low- or high-dose Pb exposure group. Subsequently, in vivo and in vitro models of Pb exposure were established to confirm astrocyte activation. As a result, glial fibrillary acidic protein expression in astrocytes was much higher in the Pb exposure group. Moreover, the mRNA expression of neurotoxic reactive astrocyte genes was much higher than that of the control group. The analysis of transcription factors indicated that NF-κB was screened as the top transcription factor, which might regulate astrocyte activation following Pb exposure in the rat hippocampus. The data also showed that the inhibition of NF-κB transcription suppressed astrocyte activation following Pb exposure. Overall, astrocyte activation was one of the significant cellular events following Pb exposure in the rat hippocampus, which was regulated by the NF-κB transcription factor, suggesting that inhibiting astrocyte activation may be a potential target for the prevention of Pb neurotoxicity.

Application of Machine Learning in Spatial Proteomics

Spatial proteomics is an interdisciplinary field that investigates the localization and dynamics of proteins, and it has gained extensive attention in recent years, especially the subcellular proteomics. Numerous evidence indicate that the subcellular localization of proteins is associated with various cellular processes and disease progression. Mass spectrometry (MS)-based and imaging-based experimental approaches have been developed to acquire large-scale spatial proteomic data. To allow the reliable analysis of increasingly complex spatial proteomics data, machine learning (ML) methods have been widely used in both MS-based and imaging-based spatial proteomic data analysis pipelines. Here, we comprehensively survey the applications of ML in spatial proteomics from following aspects: (1) data resources for spatial proteome are comprehensively introduced; (2) the roles of different ML algorithms in data analysis pipelines are elaborated; (3) successful applications of spatial proteomics and several analytical tools integrating ML methods are presented; (4) challenges existing in modern ML-based spatial proteomics studies are discussed. This review provides guidelines for researchers seeking to apply ML methods to analyze spatial proteomic data and can facilitate insightful understanding of cell biology as well as the future research in medical and drug discovery communities.

HPiP: an R/Bioconductor package for predicting host-pathogen protein-protein interactions from protein sequences using ensemble machine learning approach

Motivation

Despite arduous and time-consuming experimental efforts, protein-protein interactions (PPIs) for many pathogenic microbes with their human host are still unknown, limiting our understanding of the intricate interactions during infection and the identification of therapeutic targets. Since computational tools offer a promising alternative, we developed an R/Bioconductor package, HPiP (Host-Pathogen Interaction Prediction) software with a series of amino acid sequence property descriptors and an ensemble machine learning classifiers to predict the yet unmapped interactions between pathogen and host proteins.

Results

Using severe acute respiratory syndrome coronavirus 1 (SARS-CoV-1) or the novel SARS-CoV-2 coronavirus-human PPI training sets as a case study, we show that HPiP achieves a good performance with PPI predictions between SARS-CoV-2 and human proteins, which we confirmed experimentally in human monocyte THP-1 cells, and with several quality control metrics. HPiP also exhibited strong performance in accurately predicting the previously reported PPIs when tested against the sequences of pathogenic bacteria, Mycobacterium tuberculosis and human proteins. Collectively, our fully documented HPiP software will hasten the exploration of PPIs for a systems-level understanding of many understudied pathogens and uncover molecular targets for repurposing existing drugs.

Availability and implementation

HPiP is released as an open-source code under the MIT license that is freely available on GitHub (https://github.com/BabuLab-UofR/HPiP) as well as on Bioconductor (http://bioconductor.org/packages/devel/bioc/html/HPiP.html).

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Host cell proteins modulated upon Toxoplasma infection identified using proteomic approaches: a molecular rationale

Toxoplasma gondii is a pathogenic protozoan parasite belonging to the apicomplexan phylum that infects the nucleated cells of warm-blooded hosts leading to an infectious disease known as toxoplasmosis. Apicomplexan parasites such as T. gondii can display different mechanisms to control or manipulate host cells signaling at different levels altering the host subcellular genome and proteome. Indeed, Toxoplasma is able to modulate host cell responses (especially immune responses) during infection to its advantage through both structural and functional changes in the proteome of different infected cells. Consequently, parasites can transform the invaded cells into a suitable environment for its own replication and the induction of infection. Proteomics as an applicable tool can identify such critical proteins involved in pathogen (Toxoplasma)-host cell interactions and consequently clarify the cellular mechanisms that facilitate the entry of pathogens into host cells, and their replication and transmission, as well as the central mechanisms of host defense against pathogens. Accordingly, the current paper reviews several proteins (identified using proteomic approaches) differentially expressed in the proteome of Toxoplasma-infected host cells (macrophages and human foreskin fibroblasts) and tissues (brain and liver) and highlights their plausible functions in the cellular biology of the infected cells. The identification of such modulated proteins and their related cell impact (cell responses/signaling) can provide further information regarding parasite pathogenesis and biology that might lead to a better understanding of therapeutic strategies and novel drug targets.