Applying proteomic technology to clinical virology

Proteomics-based diagnostic peptide discovery for severe fever with thrombocytopenia syndrome virus in patients

BACKGROUND

Severe fever with thrombocytopenia syndrome (SFTS) virus is an emerging infectious virus which causes severe hemorrhage, thrombocytopenia, and leukopenia, with a high fatality rate. Since there is no approved therapeutics or vaccines for SFTS, early diagnosis is essential to manage this infectious disease.

METHODS

Here, we tried to detect SFTS virus in serum samples from SFTS patients by proteomic analysis. Firstly, in order to obtain the reference MS/MS spectral data of SFTS virus, medium from infected Vero cell culture was used for shotgun proteomic analysis. Then, tryptic peptides in sera from SFTS patients were confirmed by comparative analysis with the reference MS/MS spectral data of SFTS virus.

RESULTS

Proteomic analysis of culture medium successfully discovered tryptic peptides from all the five antigen proteins of SFTS virus. The comparative spectral analysis of sera of SFTS patients revealed that the N-terminal tryptic peptide of the nucleocapsid (N) protein is the major epitope of SFTS virus detected in the patient samples. The prevalence of the peptides was strongly correlated with the viral load in the clinical samples.

CONCLUSIONS

Proteomic analysis of SFTS patient samples revealed that nucleocapsid (N) protein is the major antigen proteins in sera of SFTS patients and N-terminal tryptic peptide of the N protein might be a useful proteomic target for direct detection of SFTS virus. These findings suggest that proteomic analysis could be an alternative tool for detection of pathogens in clinical samples and diagnosis of infectious diseases.

Nasopharyngeal metabolomics and machine learning approach for the diagnosis of influenza

Background

Respiratory virus infections are significant causes of morbidity and mortality, and may induce host metabolite alterations by infecting respiratory epithelial cells. We investigated the use of liquid chromatography quadrupole time-of-flight mass spectrometry (LC/Q-TOF) combined with machine learning for the diagnosis of influenza infection.

Methods

We analyzed nasopharyngeal swab samples by LC/Q-TOF to identify distinct metabolic signatures for diagnosis of acute illness. Machine learning models were performed for classification, followed by Shapley additive explanation (SHAP) analysis to analyze feature importance and for biomarker discovery.

Findings

A total of 236 samples were tested in the discovery phase by LC/Q-TOF, including 118 positive samples (40 influenza A 2009 H1N1, 39 influenza H3 and 39 influenza B) as well as 118 age and sex-matched negative controls with acute respiratory illness. Analysis showed an area under the receiver operating characteristic curve (AUC) of 1.00 (95% confidence interval [95% CI] 0.99, 1.00), sensitivity of 1.00 (95% CI 0.86, 1.00) and specificity of 0.96 (95% CI 0.81, 0.99). The metabolite most strongly associated with differential classification was pyroglutamic acid. Independent validation of a biomarker signature based on the top 20 differentiating ion features was performed in a prospective cohort of 96 symptomatic individuals including 48 positive samples (24 influenza A 2009 H1N1, 5 influenza H3 and 19 influenza B) and 48 negative samples. Testing performed using a clinically-applicable targeted approach, liquid chromatography triple quadrupole mass spectrometry, showed an AUC of 1.00 (95% CI 0.998, 1.00), sensitivity of 0.94 (95% CI 0.83, 0.98), and specificity of 1.00 (95% CI 0.93, 1.00). Limitations include lack of sample suitability assessment, and need to validate these findings in additional patient populations.

Interpretation

This metabolomic approach has potential for diagnostic applications in infectious diseases testing, including other respiratory viruses, and may eventually be adapted for point-of-care testing.

Coronavirus: proteomics analysis of chicken kidney tissue infected with variant 2 (IS-1494)-like avian infectious bronchitis virus

Avian infectious bronchitis virus is one of the most important gammacoronaviruses, which causes a highly contagious disease. In this study, we investigated changes in the proteome of kidney tissue of specific-pathogen-free (SPF) chickens that were infected with an isolate of the nephrotropic variant 2 genotype (IS/1494/06) of avian coronavirus. Twenty 1-day-old SPF White Leghorn chickens were randomly divided into two groups, each comprising 10 chickens, which were kept in separate positive-pressure isolators. Chickens in group A served as a virus-free control group up to the end of the experiment, whereas chickens in group B were inoculated with 0.1 ml of 10^4.5 EID₅₀ of the IBV/chicken/Iran/UTIVO-C/2014 isolate of IBV, and kidney tissue samples were collected at 2 and 7 days post-inoculation (dpi) from both groups. Sequencing of five protein spots at 2 dpi and 22 spots at 7 dpi that showed differential expression by two-dimensional electrophoresis (2DE) along with fold change greater than 2 was done by MS-MALDI/TOF/TOF. Furthermore, the corresponding protein-protein interaction (PPI) networks at 2 and 7 dpi were identified to develop a detailed understanding of the mechanism of molecular pathogenesis. Topological graph analysis of this undirected PPI network revealed the effect of 10 genes in the 2 dpi PPI network and nine genes in the 7 dpi PPI network during virus pathogenesis. Proteins that were found by 2DE analysis and MS/TOF-TOF mass spectrometry to be down- or upregulated were subjected to PPI network analysis to identify interactions with other cellular components. The results show that cellular metabolism was altered due to viral infection. Additionally, multifunctional heat shock proteins with a significant role in host cell survival may be employed circuitously by the virus to reach its target. The data from this study suggest that the process of pathogenesis that occurs during avian coronavirus infection involves the regulation of vital cellular processes and the gradual disruption of critical cellular functions.

Mass spectrometry for the detection of bioterrorism agents: from environmental to clinical applications

In the current context of international conflicts and localized terrorist actions, there is unfortunately a permanent threat of attacks with unconventional warfare agents. Among these, biological agents such as toxins, microorganisms, and viruses deserve particular attention owing to their ease of production and dissemination. Mass spectrometry (MS)-based techniques for the detection and quantification of biological agents have a decisive role to play for countermeasures in a scenario of biological attacks. The application of MS to every field of both organic and macromolecular species has in recent years been revolutionized by the development of soft ionization techniques (MALDI and ESI), and by the continuous development of MS technologies (high resolution, accurate mass HR/AM instruments, novel analyzers, hybrid configurations). New possibilities have emerged for exquisite specific and sensitive detection of biological warfare agents. MS-based strategies for clinical application can now address a wide range of analytical questions mainly including issues related to the complexity of biological samples and their available volume. Multiplexed toxin detection, discovery of new markers through omics approaches, and identification of untargeted microbiological or of novel molecular targets are examples of applications. In this paper, we will present these technological advances along with the novel perspectives offered by omics approaches to clinical detection and follow-up.

Decoding protein networks during virus entry by quantitative proteomics

Virus entry into host cells relies on interactions between viral and host structures including lipids, carbohydrates and proteins. Particularly, protein–protein interactions between viral surface proteins and host proteins as well as secondary host protein–protein interactions play a pivotal role in coordinating virus binding and uptake. These interactions are dynamic and frequently involve multiprotein complexes. In the past decade mass spectrometry based proteomics methods have reached sensitivities and high throughput compatibilities of genomics methods and now allow the reliable quantitation of proteins in complex samples from limited material. As proteomics provides essential information on the biologically active entity namely the protein, including its posttranslational modifications and its interactions with other proteins, it is an indispensable method in the virologist's toolbox. Here we review protein interactions during virus entry and compare classical biochemical methods to study entry with novel technically advanced quantitative proteomics techniques. We highlight the value of quantitative proteomics in mapping functional virus entry networks, discuss the benefits and limitations and illustrate how the methodology will help resolve unsettled questions in virus entry research in the future.

Proteomic approaches to analyzing hepatitis C virus biology

Hepatitis C virus (HCV) is a major cause of liver disease worldwide. Acute infection often progresses to chronicity resulting frequently in fibrosis, cirrhosis, and in rare cases, in the development of hepatocellular carcinoma. Although HCV has proven to be an arduous object of research and has raised important technical challenges, several experimental models have been developed all over the last two decades in order to improve our understanding of the virus life cycle, pathogenesis and virus-host interactions. The recent development of direct acting-agents, leading to considerable progress in treatment of patients, represents the direct outcomes of these achievements. Proteomic approaches have been of critical help to shed light on several aspect of the HCV biology such as virion composition, viral replication, and virus assembly and to unveil diagnostic or prognostic markers of HCV-induced liver disease. Here, we review how proteomic approaches have led to improve our understanding of HCV life cycle and liver disease, thus highlighting the relevance of these approaches for studying the complex interactions between other challenging human viral pathogens and their host.

Proteomic strategies for the discovery of novel diagnostic and therapeutic targets for infectious diseases

Viruses have developed numerous and elegant strategies to manipulate the host cell machinery to establish a productive infectious cycle. The interaction of viral proteins with host proteins plays an important role in infection and pathogenesis, often bypassing traditional host defenses such as the interferon response and apoptosis. Host–viral protein interactions can be studied using a variety of proteomic approaches ranging from genetic and biochemical to large‐scale high‐throughput technologies. Protein interactions between host and viral proteins are greatly influenced by host signal transduction pathways. In this review, we will focus on comparing proteomic information obtained through differing technologies and how their integration can be used to determine the functional aspect of the host response to infection. We will briefly review and evaluate techniques employed to elucidate viral–host interactions with a primary focus on Protein Microarrays (PMA) and Mass Spectrometry (MS) as potential tools in the discovery of novel therapeutic targets. As many potential molecular markers and targets are proteins, proteomic profiling is expected to yield both clearer and more direct answers to functional and pharmacologic questions.

Proteomics technology - a powerful tool for the biomedical scientists

"Proteomics" refers to the systematic analysis of proteins. It complements other "omics" technologies such as genomics and transcriptomics in elucidating the identity of proteins of an organism, and understanding their functions. Proteomics is used in many areas of research such as discovery of markers for diagnosis and vaccine candidates, understanding pathogenic mechanisms, in the study of expression patterns at different time points and in response to different stimuli, and in elucidating functional protein networks. Proteomics analysis involves sample preparation, protein separation, and protein identification. The 'heart' of current proteomics is mass-spectrometry, with LC-MS/MS and MALDI-TOF/TOF being commonly used equipment. However, the high costs of the equipment, software, databases, and the need for skilled personnel limit the wide utilization of this technology in the less developed countries. Therefore, there need to be sharing of facilities, better networking and collaborations among our scientists and laboratories to take advantage of this powerful technology.