Proteomics and integrative omic approaches for understanding host-pathogen interactions and infectious diseases

Comprehensive assessment of circulatory miRNAs as potential diagnostic markers in HCV recurrence post liver transplantation

HCV recurrence after liver transplantation is one of the causal agents for graft rejection. This study aims to profile non-invasive biomarkers in patients with HCC who had liver transplants. One hundred participants were categorized into three groups (20 control, 32 recurrent HCV (RHCV), and 48 non-RHCV). The expression of six miRNAs (hsa-miR-124-3p, hsa-miR-155-5p, hsa-miR-205-5p, hsa-miR-499a-5p, hsa-miR-574-3p, and hsa-miR-103a-3p) and two mRNAs IL-1β, STAT1 were quantified. RHCV group has higher levels of hsa-miR-574-3p and hsa-miR-155-5p and lesser levels of hsa-miR-499a-5p than control groups (p = 0.024, 0.0001, 0.002; respectively). RHCV and non-RHCV groups revealed a significant reduction in levels of IL-1β and STAT1 mRNA compared to the control (p = 0.011, 0.014; respectively). According to ROC analysis, miR-155-5p can differentiate among the patients' groups, while miR-574-3p, IL-1β, and STAT1 mRNA can discriminate between RHCV and control groups. In conclusion, RHCV patients have dysregulated expression of five transcripts compared to non-RHCV and control groups.

High throughput methods to study protein-protein interactions during host-pathogen interactions

The ability of a pathogen to survive and cause an infection is often determined by specific interactions between the host and pathogen proteins. Such interactions can be both intra- and extracellular and may define the outcome of an infection. There are a range of innovative biochemical, biophysical and bioinformatic techniques currently available to identify protein-protein interactions (PPI) between the host and the pathogen. However, the complexity and the diversity of host-pathogen PPIs has led to the development of several high throughput (HT) techniques that enable the study of multiple interactions at once and/or screen multiple samples at the same time, in an unbiased manner. We review here the major HT laboratory-based technologies employed for host-bacterial interaction studies.

Proteomic and metabolomic analysis of the serum of patients with tick-borne encephalitis

Tick-borne encephalitis virus (TBEV) is a common virus in Europe and Asia, causing around 10,000 to 10,500 infections annually. It affects the central nervous system and poses threats to public health. However, the exact molecular mechanisms of TBE pathogenesis are not yet fully understood due to the complex interactions between the virus and its host. In this study, a comprehensive analysis was conducted to characterize the serum metabolome and proteome of adult patients infected with TBEV, in comparison to a control group of healthy individuals. Liquid chromatography tandem mass spectrometry (LC-MS) was employed to monitor metabolic and proteomic alternations throughout the progression of the disease, significant physiological changes associated with different stages of the disease were identified. A total of 44 proteins and 115 metabolites exhibited significantly alternations in the sera of patients diagnosed with TBE. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses of these metabolites and proteins revealed differential enrichment of genes associated with the extracellular matrix, complement binding, hemostasis, lipid metabolism, and amino acid metabolism between TBE patients and healthy controls. We gained valuable understanding of the specific metabolites implicated in the host's responses to TBE, establishing a basis for further research on TBE disease. SIGNIFICANCE: The current investigation revealed a comprehensive and systematic differences on TBE using LC-MS platform from human serum samples of TBE patients and healthy individuals providing the immune response to the invasion of TBE.

Tapioca: a platform for predicting de novo protein-protein interactions in dynamic contexts

Protein-protein interactions (PPIs) drive cellular processes and responses to environmental cues, reflecting the cellular state. Here we develop Tapioca, an ensemble machine learning framework for studying global PPIs in dynamic contexts. Tapioca predicts de novo interactions by integrating mass spectrometry interactome data from thermal/ion denaturation or cofractionation workflows with protein properties and tissue-specific functional networks. Focusing on the thermal proximity coaggregation method, we improved the experimental workflow. Finely tuned thermal denaturation afforded increased throughput, while cell lysis optimization enhanced protein detection from different subcellular compartments. The Tapioca workflow was next leveraged to investigate viral infection dynamics. Temporal PPIs were characterized during the reactivation from latency of the oncogenic Kaposi's sarcoma-associated herpesvirus. Together with functional assays, NUCKS was identified as a proviral hub protein, and a broader role was uncovered by integrating PPI networks from alpha- and betaherpesvirus infections. Altogether, Tapioca provides a web-accessible platform for predicting PPIs in dynamic contexts.

Proteomic and Lipidomic Profiling of Calves Experimentally Co-Infected with Influenza D Virus and Mycoplasma bovis: Insights into the Host-Pathogen Interactions

The role of Influenza D virus (IDV) in bovine respiratory disease remains unclear. An in vivo experiment resulted in increased clinical signs, lesions, and pathogen replication in calves co-infected with IDV and Mycoplasma bovis (M. bovis), compared to single-infected calves. The present study aimed to elucidate the host-pathogen interactions and profile the kinetics of lipid mediators in the airways of these calves. Bronchoalveolar lavage (BAL) samples collected at 2 days post-infection (dpi) were used for proteomic analyses by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Additionally, lipidomic analyses were performed by LC-MS/MS on BAL samples collected at 2, 7 and 14 dpi. Whereas M. bovis induced the expression of proteins involved in fibrin formation, IDV co-infection counteracted this coagulation mechanism and downregulated other acute-phase response proteins, such as complement component 4 (C4) and plasminogen (PLG). The reduced inflammatory response against M. bovis likely resulted in increased M. bovis replication and delayed M. bovis clearance, which led to a significantly increased abundance of oxylipids in co-infected calves. The identified induced oxylipids mainly derived from arachidonic acid; were likely oxidized by COX-1, COX-2, and LOX-5; and peaked at 7 dpi. This paper presents the first characterization of BAL proteome and lipid mediator kinetics in response to IDV and M. bovis infection in cattle and raises hypotheses regarding how IDV acts as a co-pathogen in bovine respiratory disease.

Pathogenic Rickettsia spp. as emerging models for bacterial biology

Our understanding of free-living bacterial models like Escherichia coli far outpaces that of obligate intracellular bacteria, which cannot be cultured axenically. All obligate intracellular bacteria are host-associated, and many cause serious human diseases. Their constant exposure to the distinct biochemical niche of the host has driven the evolution of numerous specialized bacteriological and genetic adaptations, as well as innovative molecular mechanisms of infection. Here, we review the history and use of pathogenic Rickettsia species, which cause an array of vector-borne vascular illnesses, as model systems to probe microbial biology. Although many challenges remain in our studies of these organisms, the rich pathogenic and biological diversity of Rickettsia spp. constitutes a unique backdrop to investigate how microbes survive and thrive in host and vector cells. We take a bacterial-focused perspective and highlight emerging insights that relate to new host-pathogen interactions, bacterial physiology, and evolution. The transformation of Rickettsia spp. from pathogens to models demonstrates how recalcitrant microbes may be leveraged in the lab to tap unmined bacterial diversity for new discoveries. Rickettsia spp. hold great promise as model systems not only to understand other obligate intracellular pathogens but also to discover new biology across and beyond bacteria.

Monitoring host-pathogen interactions using chemical proteomics

With the rapid emergence and the dissemination of microbial resistance to conventional chemotherapy, the shortage of novel antimicrobial drugs has raised a global health threat. As molecular interactions between microbial pathogens and their mammalian hosts are crucial to establish virulence, pathogenicity, and infectivity, a detailed understanding of these interactions has the potential to reveal novel therapeutic targets and treatment strategies. Bidirectional molecular communication between microbes and eukaryotes is essential for both pathogenic and commensal organisms to colonise their host. In particular, several devastating pathogens exploit host signalling to adjust the expression of energetically costly virulent behaviours. Chemical proteomics has emerged as a powerful tool to interrogate the protein interaction partners of small molecules and has been successfully applied to advance host-pathogen communication studies. Here, we present recent significant progress made by this approach and provide a perspective for future studies.

Significance of understanding the genomics of host-pathogen interaction in limiting antibiotic resistance development: lessons from COVID-19 pandemic

The entire world is facing the stiff challenge of COVID-19 pandemic. To overcome the spread of this highly infectious disease, several short-sighted strategies were adopted such as the use of broad-spectrum antibiotics and antifungals. However, the misuse and/or overuse of antibiotics have accentuated the emergence of the next pandemic: antimicrobial resistance (AMR). It is believed that pathogens while transferring between humans and the environment carry virulence and antibiotic-resistant factors from varied species. It is presumed that all such genetic factors are quantifiable and predictable, a better understanding of which could be a limiting step for the progression of AMR. Herein, we have reviewed how genomics-based understanding of host-pathogen interactions during COVID-19 could reduce the non-judicial use of antibiotics and prevent the eruption of an AMR-based pandemic in future.

Current Technologies Unraveling the Significance of Post-Translational Modifications (PTMs) as Crucial Players in Neurodegeneration

Neurodegenerative disorders, such as Parkinson's disease, Alzheimer's disease, and Huntington's disease, are identified and characterized by the progressive loss of neurons and neuronal dysfunction, resulting in cognitive and motor impairment. Recent research has shown the importance of PTMs, such as phosphorylation, acetylation, methylation, ubiquitination, sumoylation, nitration, truncation, O-GlcNAcylation, and hydroxylation, in the progression of neurodegenerative disorders. PTMs can alter protein structure and function, affecting protein stability, localization, interactions, and enzymatic activity. Aberrant PTMs can lead to protein misfolding and aggregation, impaired degradation, and clearance, and ultimately, to neuronal dysfunction and death. The main objective of this review is to provide an overview of the PTMs involved in neurodegeneration, their underlying mechanisms, methods to isolate PTMs, and the potential therapeutic targets for these disorders. The PTMs discussed in this article include tau phosphorylation, α-synuclein and Huntingtin ubiquitination, histone acetylation and methylation, and RNA modifications. Understanding the role of PTMs in neurodegenerative diseases may provide new therapeutic strategies for these devastating disorders.

Cell Fractionation and the Identification of Host Proteins Involved in Plant-Virus Interactions

Plant viruses depend on host cellular factors for their replication and movement. There are cellular proteins that change their localization and/or expression and have a proviral role or antiviral activity and interact with or target viral proteins. Identification of those proteins and their roles during infection is crucial for understanding plant-virus interactions and to design antiviral resistance in crops. Important host proteins have been identified using approaches such as tag-dependent immunoprecipitation or yeast two hybridization that require cloning individual proteins or the entire virus. However, the number of possible interactions between host and viral proteins is immense. Therefore, an alternative method is needed for proteome-wide identification of host proteins involved in host-virus interactions. Here, we present cell fractionation coupled with mass spectrometry as an option to identify protein-protein interactions between viruses and their hosts. This approach involves separating subcellular organelles using differential and/or gradient centrifugation from virus-free and virus-infected cells (1) followed by comparative analysis of the proteomic profiles obtained for each subcellular organelle via mass spectrometry (2). After biological validation, prospect host proteins with proviral or antiviral roles can be subject to fundamental studies in the context of basic biology to shed light on both virus replication and cellular processes. They can also be targeted via gene editing to develop virus-resistant crops.

Proteomics Applications in Toxoplasma gondii: Unveiling the Host-Parasite Interactions and Therapeutic Target Discovery

Toxoplasma gondii, a protozoan parasite with the ability to infect various warm-blooded vertebrates, including humans, is the causative agent of toxoplasmosis. This infection poses significant risks, leading to severe complications in immunocompromised individuals and potentially affecting the fetus through congenital transmission. A comprehensive understanding of the intricate molecular interactions between T. gondii and its host is pivotal for the development of effective therapeutic strategies. This review emphasizes the crucial role of proteomics in T. gondii research, with a specific focus on host-parasite interactions, post-translational modifications (PTMs), PTM crosstalk, and ongoing efforts in drug discovery. Additionally, we provide an overview of recent advancements in proteomics techniques, encompassing interactome sample preparation methods such as BioID (BirA*-mediated proximity-dependent biotin identification), APEX (ascorbate peroxidase-mediated proximity labeling), and Y2H (yeast two hybrid), as well as various proteomics approaches, including single-cell analysis, DIA (data-independent acquisition), targeted, top-down, and plasma proteomics. Furthermore, we discuss bioinformatics and the integration of proteomics with other omics technologies, highlighting its potential in unraveling the intricate mechanisms of T. gondii pathogenesis and identifying novel therapeutic targets.

A multi-platform approach to identify a blood-based host protein signature for distinguishing between bacterial and viral infections in febrile children (PERFORM): a multi-cohort machine learning study

BACKGROUND

Differentiating between self-resolving viral infections and bacterial infections in children who are febrile is a common challenge, causing difficulties in identifying which individuals require antibiotics. Studying the host response to infection can provide useful insights and can lead to the identification of biomarkers of infection with diagnostic potential. This study aimed to identify host protein biomarkers for future development into an accurate, rapid point-of-care test that can distinguish between bacterial and viral infections, by recruiting children presenting to health-care settings with fever or a history of fever in the previous 72 h.

METHODS

In this multi-cohort machine learning study, patient data were taken from EUCLIDS, the Swiss Pediatric Sepsis study, the GENDRES study, and the PERFORM study, which were all based in Europe. We generated three high-dimensional proteomic datasets (SomaScan and two via liquid chromatography tandem mass spectrometry, referred to as MS-A and MS-B) using targeted and untargeted platforms (SomaScan and liquid chromatography mass spectrometry). Protein biomarkers were then shortlisted using differential abundance analysis, feature selection using forward selection-partial least squares (FS-PLS; 100 iterations), along with a literature search. Identified proteins were tested with Luminex and ELISA and iterative FS-PLS was done again (25 iterations) on the Luminex results alone, and the Luminex and ELISA results together. A sparse protein signature for distinguishing between bacterial and viral infections was identified from the selected proteins. The performance of this signature was finally tested using Luminex assays and by calculating disease risk scores.

FINDINGS

376 children provided serum or plasma samples for use in the discovery of protein biomarkers. 79 serum samples were collected for the generation of the SomaScan dataset, 147 plasma samples for the MS-A dataset, and 150 plasma samples for the MS-B dataset. Differential abundance analysis, and the first round of feature selection using FS-PLS identified 35 protein biomarker candidates, of which 13 had commercial ELISA or Luminex tests available. 16 proteins with ELISA or Luminex tests available were identified by literature review. Further evaluation via Luminex and ELISA and the second round of feature selection using FS-PLS revealed a six-protein signature: three of the included proteins are elevated in bacterial infections (SELE, NGAL, and IFN-γ), and three are elevated in viral infections (IL18, NCAM1, and LG3BP). Performance testing of the signature using Luminex assays revealed area under the receiver operating characteristic curve values between 89·4% and 93·6%.

INTERPRETATION

This study has led to the identification of a protein signature that could be ultimately developed into a blood-based point-of-care diagnostic test for rapidly diagnosing bacterial and viral infections in febrile children. Such a test has the potential to greatly improve care of children who are febrile, ensuring that the correct individuals receive antibiotics.

FUNDING

European Union's Horizon 2020 research and innovation programme, the European Union's Seventh Framework Programme (EUCLIDS), Imperial Biomedical Research Centre of the National Institute for Health Research, the Wellcome Trust and Medical Research Foundation, Instituto de Salud Carlos III, Consorcio Centro de Investigación Biomédica en Red de Enfermedades Respiratorias, Grupos de Refeencia Competitiva, Swiss State Secretariat for Education, Research and Innovation.

Humoral Immunity Against Aspergillus fumigatus

Aspergillus fumigatus is one the most ubiquitous airborne opportunistic human fungal pathogens. Understanding its interaction with host immune system, composed of cellular and humoral arm, is essential to explain the pathobiology of aspergillosis disease spectrum. While cellular immunity has been well studied, humoral immunity has been poorly acknowledge, although it plays a crucial role in bridging the fungus and immune cells. In this review, we have summarized available data on major players of humoral immunity against A. fumigatus and discussed how they may help to identify at-risk individuals, be used as diagnostic tools or promote alternative therapeutic strategies. Remaining challenges are highlighted and leads are given to guide future research to better grasp the complexity of humoral immune interaction with A. fumigatus.

Mass spectrometry-based proteomics as an emerging tool in clinical laboratories

Mass spectrometry (MS)-based proteomics have been increasingly implemented in various disciplines of laboratory medicine to identify and quantify biomolecules in a variety of biological specimens. MS-based proteomics is continuously expanding and widely applied in biomarker discovery for early detection, prognosis and markers for treatment response prediction and monitoring. Furthermore, making these advanced tests more accessible and affordable will have the greatest healthcare benefit.This review article highlights the new paradigms MS-based clinical proteomics has created in microbiology laboratories, cancer research and diagnosis of metabolic disorders. The technique is preferred over conventional methods in disease detection and therapy monitoring for its combined advantages in multiplexing capacity, remarkable analytical specificity and sensitivity and low turnaround time.Despite the achievements in the development and adoption of a number of MS-based clinical proteomics practices, more are expected to undergo transition from bench to bedside in the near future. The review provides insights from early trials and recent progresses (mainly covering literature from the NCBI database) in the application of proteomics in clinical laboratories.

Photo-ANA enables profiling of host-bacteria protein interactions during infection

Bacterial pathogens rapidly change and adapt their proteome to cope with the environment in host cells and secrete effector proteins to hijack host targets and ensure their survival and proliferation during infection. Excessive host proteins make it difficult to profile pathogens' proteome dynamics by conventional proteomics. It is even more challenging to map pathogen-host protein-protein interactions in real time, given the low abundance of bacterial effectors and weak and transient interactions in which they may be involved. Here we report a method for selectively labeling bacterial proteomes using a bifunctional amino acid, photo-ANA, equipped with a bio-orthogonal handle and a photoreactive warhead, which enables simultaneous analysis of bacterial proteome reprogramming and pathogen-host protein interactions of Salmonella enterica serovar Typhimurium (S. Typhimurium) during infection. Using photo-ANA, we identified FLOT1/2 as host interactors of S. Typhimurium effector PipB2 in late-stage infection and globally profiled the extensive interactions between host proteins and pathogens during infection.

Use of an Integrated Multi-Omics Approach To Identify Molecular Mechanisms and Critical Factors Involved in the Pathogenesis of Leptospira

Leptospirosis, a bacterial zoonosis caused by pathogenic Leptospira spp., is prevalent worldwide and has become a serious threat in recent years. Limited understanding of Leptospira pathogenesis and host response has hampered the development of effective vaccine and diagnostics. Although Leptospira is phagocytosed by innate immune cells, it resists its destruction, and the evading mechanism involved is unclear. In the present study, we used an integrative multi-omics approach to identify the critical molecular factors of Leptospira involved in pathogenesis during interaction with human macrophages. Transcriptomic and proteomic analyses were performed at 24 h postinfection of human macrophages (phorbol-12-myristate-13-acetate differentiated THP-1 cells) with the pathogenic Leptospira interrogans serovar Icterohaemorrhagiae strain RGA (LEPIRGA). Our results identified a total of 1,528 transcripts and 871 proteins that were significantly expressed with an adjusted P value of <0.05. The correlations between the transcriptomic and proteomic data were above average (r = 0.844), suggesting the role of the posttranscriptional processes during host interaction. The conjoint analysis revealed the expression of several virulence-associated proteins such as adhesins, invasins, and secretory and chemotaxis proteins that might be involved in various processes of attachment and invasion and as effectors during pathogenesis in the host. Further, the interaction of bacteria with the host cell (macrophages) was a major factor in the differential expression of these proteins. Finally, eight common differentially expressed RNA-protein pairs, predicted as virulent, outer membrane/extracellular proteins were validated by quantitative PCR. This is the first report using integrated multi-omics approach to identify critical factors involved in Leptospira pathogenesis. Validation of these critical factors may lead to the identification of target antigens for the development of improved diagnostics and vaccines against leptospirosis. IMPORTANCE Leptospirosis is a zoonotic disease of global importance. It is caused by a Gram-negative bacterial spirochete of the genus Leptospira. The current challenge is to detect the infection at early stage for treatment or to develop potent vaccines that can induce cross-protection against various pathogenic serovars. Understanding host-pathogen interactions is important to identify the critical factors involved in pathogenesis and host defense for developing improved vaccines and diagnostics. Utilizing an integrated multi-omics approach, our study provides important insight into the interaction of Leptospira with human macrophages and identifies a few critical factors (such as virulence-associated proteins) involved in pathogenesis. These factors can be exploited for the development of novel tools for the detection, treatment, or prevention of leptospirosis.

Deep Learning of Dual Plasma Fingerprints for High-Performance Infection Classification

Infection classification is the key for choosing the proper treatment plans. Early determination of the causative agents is critical for disease control. Host responses analysis can detect variform and sensitive host inflammatory responses to ascertain the presence and type of the infection. However, traditional host-derived inflammatory indicators are insufficient for clinical infection classification. Fingerprints-based omic analysis has attracted increasing attention globally for analyzing the complex host systemic immune response. A single type of fingerprints is not applicable for infection classification (area under curve (AUC) of 0.550-0.617). Herein, an infection classification platform based on deep learning of dual plasma fingerprints (DPFs-DL) is developed. The DPFs with high reproducibility (coefficient of variation <15%) are obtained at low sample consumption (550 nL native plasma) using inorganic nanoparticle and organic matrix assisted laser desorption/ionization mass spectrometry. A classifier (DPFs-DL) for viral versus bacterial infection discrimination (AUC of 0.775) and coronavirus disease 2019 (COVID-2019) diagnosis (AUC of 0.917) is also built. Furthermore, a metabolic biomarker panel of two differentially regulated metabolites, which may serve as potential biomarkers for COVID-19 management (AUC of 0.677-0.883), is constructed. This study will contribute to the development of precision clinical care for infectious diseases.

Infectious and environmental placental insults: from underlying biological pathways to diagnostics and treatments

Because the placenta is bathed in maternal blood, it is exposed to infectious agents and chemicals that may be present in the mother's circulation. Such exposures, which do not necessarily equate with transmission to the fetus, may primarily cause placental injury, thereby impairing placental function. Recent research has improved our understanding of the mechanisms by which some infectious agents are transmitted to the fetus, as well as the mechanisms underlying their impact on fetal outcomes. However, less is known about the impact of placental infection on placental structure and function, or the mechanisms underlying infection-driven placental pathogenesis. Moreover, recent studies indicate that noninfectious environmental agents accumulate in the placenta, but their impacts on placental function and fetal outcomes are unknown. Critically, diagnosing placental insults during pregnancy is very difficult and currently, this is possible only through postpartum placental examination. Here, with emphasis on humans, we discuss what is known about the impact of infectious and chemical agents on placental physiology and function, particularly in the absence of maternal-fetal transmission, and highlight knowledge gaps with potential implications for diagnosis and intervention against placental pathologies.

Serum metabolomic profiles in BALB/c mice induced by Babesia microti infection

Introduction

The protozoan parasite Babesia microti is the primary cause of human babesiosis. This parasite invades and multiplies inside red blood cells (RBCs), and infections differ significantly based on the age and immune competency of the host. The aim of this study was to investigate the use of serum metabolic profiling to identify systemic metabolic variations between B. microti-infected mice and noninfected controls.

Methods

A serum metabolomics analysis of BALB/c mice that had been intraperitoneally injected with 10⁷ B. microti-infected RBCs was performed. Serum samples from the early infected group (2 days postinfection), the acutely infected group (9 days postinfection), and the noninfected group were collected and evaluated using a liquid chromatography-mass spectrometry (LC-MS) platform. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA) identified metabolomic profiles that differentiated the B. microti-infected and noninfected groups.

Results

Our results confirm that the serum metabolome is significantly influenced by acute B. microti infection and show that infection results in dysregulation of metabolic pathways and perturbation of metabolites. Acutely infected mice displayed perturbations in metabolites associated with taurine and hypotaurine metabolism, histidine metabolism, and arachidonic acid metabolism. Taurocholic acid, anserine, and arachidonic acid may be potential candidates as serological biomarkers for diagnosing B. microti infection at the acute stage. These metabolites could be further examined for their role in disease complexity.

Discussion

Our findings demonstrate that the acute stage of B. microti infection induces abnormalities in the metabolites present in mouse serum and provide new insight into the mechanisms involved in systemic metabolic changes that occur during B. microti infection.

Integrative transcriptome analysis of SARS-CoV-2 human-infected cells combined with deep learning algorithms identifies two potential cellular targets for the treatment of coronavirus disease

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) quickly spread worldwide, leading coronavirus disease 2019 (COVID-19) to hit pandemic level less than 4 months after the first official cases. Hence, the search for drugs and vaccines that could prevent or treat infections by SARS-CoV-2 began, intending to reduce a possible collapse of health systems. After 2 years, efforts to find therapies to treat COVID-19 continue. However, there is still much to be understood about the virus' pathology. Tools such as transcriptomics have been used to understand the impact of SARS-CoV-2 on different cells isolated from various tissues, leaving datasets in the databases that integrate genes and differentially expressed pathways during SARS-CoV-2 infection. After retrieving transcriptome datasets from different human cells infected with SARS-CoV-2 available in the database, we performed an integrative analysis associated with deep learning algorithms to determine differentially expressed targets mainly after infection. The targets found represented a fructose transporter (GLUT5) and a component of proteasome 26s. These targets were then molecularly modeled, followed by molecular docking that identified potential inhibitors for both structures. Once the inhibition of structures that have the expression increased by the virus can represent a strategy for reducing the viral replication by selecting infected cells, associating these bioinformatics tools, therefore, can be helpful in the screening of molecules being tested for new uses, saving financial resources, time, and making a personalized screening for each infectious disease.

HPIPred: Host-pathogen interactome prediction with phenotypic scoring

Protein-protein interactions (PPIs) are involved in most cellular processes. Unfortunately, current knowledge of host-pathogen interactomes is still very limited. Experimental methods used to detect PPIs have several limitations, including increasing complexity and economic cost in large-scale screenings. Hence, computational methods are commonly used to support experimental data, although they generally suffer from high false-positive rates. To address this issue, we have created HPIPred, a host-pathogen PPI prediction tool based on numerical encoding of physicochemical properties. Unlike other available methods, HPIPred integrates phenotypic data to prioritize biologically meaningful results. We used HPIPred to screen the entire Homo sapiens and Pseudomonas aeruginosa PAO1 proteomes to generate a host-pathogen interactome with 763 interactions displaying a highly connected network topology. Our predictive model can be used to prioritize protein-protein interactions as potential targets for antibacterial drug development. Available at: https://github.com/SysBioUAB/hpi_predictor.

Observing protein degradation in solution by the PAN-20S proteasome complex: Astate-of-the-art example of bio-macromolecular TR-SANS

In the present book chapter we illustrate the state-of-the-art of time-resolved small-angle neutron scattering (TR-SANS) by a concrete example of a dynamic bio-macromolecular system, i.e., regulated protein degradation by the archaeal PAN-20S proteasome complex. We present the specific and unique structural information that can be obtained by this approach, in combination with bio-macromolecular deuteration and online spectrophotometric measurements of a fluorescent substrate (GFP). The complementarity with atomic-resolution structural biology techniques (SAXS, NMR, crystallography and cryo-EM) and with the advent of atomic structure prediction are discussed, as well as the respective limitations and future perspectives.

The role of proteomics and metabolomics in severe infections

PURPOSE OF REVIEW

Severe infections are a common cause of ICU admission, with a high morbidity and mortality. Omics, namely proteomics and metabolomics, aim to identify, characterize, and quantify biological molecules to achieve a systems-level understanding of disease. The aim of this review is to provide a clear overview of the current evidence of the role of proteomics and metabolomics in severe infections.

RECENT FINDINGS

Proteomics and metabolomics are technologies that are being used to explore new markers of diagnosis and prognosis, clarify mechanisms of disease, and consequently discover potential targets of therapy and finally of a better disease phenotyping. These technologies are starting to be used but not yet in clinical use.

SUMMARY

Our traditional way of approaching the disease as sepsis is believing that a process can be broken into its parts and that the whole can be explained by the sum of each part. This approach is highly reductionist and does not take the system complexity nor the nonlinear dynamics of the processes. Proteomics and metabolomics allow the analysis of several proteins and metabolites simultaneously, thereby generating diagnostic and prognostic signatures. An exciting future prospect for proteomics and metabolomics is their employment towards precision medicine.

Skin-to-blood pH shift triggers metabolome and proteome global remodelling in Staphylococcus epidermidis

Staphylococcus epidermidis is one of the most common bacteria of the human skin microbiota. Despite its role as a commensal, S. epidermidis has emerged as an opportunistic pathogen, associated with 80% of medical devices related infections. Moreover, these bacteria are extremely difficult to treat due to their ability to form biofilms and accumulate resistance to almost all classes of antimicrobials. Thus new preventive and therapeutic strategies are urgently needed. However, the molecular mechanisms associated with S. epidermidis colonisation and disease are still poorly understood. A deeper understanding of the metabolic and cellular processes associated with response to environmental factors characteristic of SE ecological niches in health and disease might provide new clues on colonisation and disease processes. Here we studied the impact of pH conditions, mimicking the skin pH (5.5) and blood pH (7.4), in a S. epidermidis commensal strain by means of next-generation proteomics and 1H NMR-based metabolomics. Moreover, we evaluated the metabolic changes occurring during a sudden pH change, simulating the skin barrier break produced by a catheter. We found that exposure of S. epidermidis to skin pH induced oxidative phosphorylation and biosynthesis of peptidoglycan, lipoteichoic acids and betaine. In contrast, at blood pH, the bacterial assimilation of monosaccharides and its oxidation by glycolysis and fermentation was promoted. Additionally, several proteins related to virulence and immune evasion, namely extracellular proteases and membrane iron transporters were more abundant at blood pH. In the situation of an abrupt skin-to-blood pH shift we observed the decrease in the osmolyte betaine and changes in the levels of several metabolites and proteins involved in cellular redoxl homeostasis. Our results suggest that at the skin pH S. epidermidis cells are metabolically more active and adhesion is promoted, while at blood pH, metabolism is tuned down and cells have a more virulent profile. pH increase during commensal-to-pathogen conversion appears to be a critical environmental signal to the remodelling of the S. epidermidis metabolism toward a more pathogenic state. Targeting S. epidermidis proteins induced by pH 7.4 and promoting the acidification of the medical device surface or surrounding environment might be new strategies to treat and prevent S. epidermidis infections.

Many ways, one microorganism: Several approaches to study Malassezia in interactions with model hosts

Malassezia, a lipophilic and lipid-dependent yeast, is a microorganism of current interest to mycobiologists because of its role as a commensal or pathogen in health conditions such as dermatological diseases, fungemia, and, as discovered recently, cancer and certain neurological disorders. Various novel approaches in the study of Malassezia have led to increased knowledge of the cellular and molecular mechanisms of this yeast. However, additional efforts are needed for more comprehensive understanding of the behavior of Malassezia in interactions with the host. This article reviews advances useful in the experimental field for Malassezia.

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.

Proteomic Approaches to Unravel Mechanisms of Antibiotic Resistance and Immune Evasion of Bacterial Pathogens

The profound effects of and distress caused by the global COVID-19 pandemic highlighted what has been known in the health sciences a long time ago: that bacteria, fungi, viruses, and parasites continue to present a major threat to human health. Infectious diseases remain the leading cause of death worldwide, with antibiotic resistance increasing exponentially due to a lack of new treatments. In addition to this, many pathogens share the common trait of having the ability to modulate, and escape from, the host immune response. The challenge in medical microbiology is to develop and apply new experimental approaches that allow for the identification of both the microbe and its drug susceptibility profile in a time-sensitive manner, as well as to elucidate their molecular mechanisms of survival and immunomodulation. Over the last three decades, proteomics has contributed to a better understanding of the underlying molecular mechanisms responsible for microbial drug resistance and pathogenicity. Proteomics has gained new momentum as a result of recent advances in mass spectrometry. Indeed, mass spectrometry-based biomedical research has been made possible thanks to technological advances in instrumentation capability and the continuous improvement of sample processing and workflows. For example, high-throughput applications such as SWATH or Trapped ion mobility enable the identification of thousands of proteins in a matter of minutes. This type of rapid, in-depth analysis, combined with other advanced, supportive applications such as data processing and artificial intelligence, presents a unique opportunity to translate knowledge-based findings into measurable impacts like new antimicrobial biomarkers and drug targets. In relation to the Research Topic “Proteomic Approaches to Unravel Mechanisms of Resistance and Immune Evasion of Bacterial Pathogens,” this review specifically seeks to highlight the synergies between the powerful fields of modern proteomics and microbiology, as well as bridging translational opportunities from biomedical research to clinical practice.

Deep Learning-Powered Prediction of Human-Virus Protein-Protein Interactions

Identifying human-virus protein-protein interactions (PPIs) is an essential step for understanding viral infection mechanisms and antiviral response of the human host. Recent advances in high-throughput experimental techniques enable the significant accumulation of human-virus PPI data, which have further fueled the development of machine learning-based human-virus PPI prediction methods. Emerging as a very promising method to predict human-virus PPIs, deep learning shows the powerful ability to integrate large-scale datasets, learn complex sequence-structure relationships of proteins and convert the learned patterns into final prediction models with high accuracy. Focusing on the recent progresses of deep learning-powered human-virus PPI predictions, we review technical details of these newly developed methods, including dataset preparation, deep learning architectures, feature engineering, and performance assessment. Moreover, we discuss the current challenges and potential solutions and provide future perspectives of human-virus PPI prediction in the coming post-AlphaFold2 era.

Phosphorylation regulation of cardiac proteins in Babesia microti infected mice in an effort to restore heart function

Background

Babesia is a common protozoan parasite that infects red blood cells. In mice infected with Babesia microti, the red blood cells were lysed, resulting in decreased oxygen-carrying capacity. To compensate for low blood oxygen levels, stress on the heart was greatly increased. Babesiosis induces a variety of pathologies; meanwhile, heart tissues initiate self-repair responses to babesiosis-induced tissue damage to restore heart function.

Methods

To discover the molecular mechanisms of the damage and self-repair in the heart after B. microti infection in mice, we investigated the changes in protein expression and phosphorylation modification levels in heart tissues at 0, 5, 8, 11, and 19 days post-infection using data-independent acquisition (DIA) quantitative proteomics.

Results

The numbers of global proteins we identified were 1934, 1966, 1984, 1989, and 1955 and of phosphopeptides were 5118, 5133, 5130, 5133, and 5140 at 0, 5, 8, 11, and 19 days, respectively, in heart cells after infection with B. microti. The results showed that after B. microti infection the differentially expressed proteins in mice mainly include fibrinogen α (Fgα), fibrinogen β (Fgβ), Serpina1b, Serpina1c, cathepsin Z, cytochrome c oxidases (COXs), RPS11, and RPS20. The proteins with phosphorylation changes mainly include 20-kDa light chain of myosin II (MLC20), myosin light chain kinase (MLCK), mitogen-activated protein kinase 14 (MAPK14), and Akt1. These proteins were mainly involved in coagulation processes, cell apoptosis, oxidative phosphorylation, and ribosomes.

Conclusions

The coagulation cascade-related proteins, apoptosis-related proteins, oxidative phosphorylation-related proteins, and other types of proteins are all involved in the damage and self-repair process in the heart after B. microti infection. These results offer a wealth of new targets for further exploration into the causes of heart disease induced by Babesia infection and are of great significance for novel drug development and new opportunities for targeted therapies.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s13071-022-05233-7.

Recent Advances in Surface Plasmon Resonance Sensors for Sensitive Optical Detection of Pathogens

The advancement of science and technology has led to the recent development of highly sensitive pathogen biosensing techniques. The effective treatment of pathogen infections requires sensing technologies to not only be sensitive but also render results in real-time. This review thus summarises the recent advances in optical surface plasmon resonance (SPR) sensor technology, which possesses the aforementioned advantages. Specifically, this technology allows for the detection of specific pathogens by applying nano-sized materials. This review focuses on various nanomaterials that are used to ensure the performance and high selectivity of SPR sensors. This review will undoubtedly accelerate the development of optical biosensing technology, thus allowing for real-time diagnosis and the timely delivery of appropriate treatments as well as preventing the spread of highly contagious pathogens.

Dynamics of huntingtin protein interactions in the striatum identifies candidate modifiers of Huntington disease

Huntington disease (HD) is a monogenic neurodegenerative disorder with one causative gene, huntingtin (HTT). Yet, HD pathobiology is multifactorial, suggesting that cellular factors influence disease progression. Here, we define HTT protein-protein interactions (PPIs) perturbed by the mutant protein with expanded polyglutamine in the mouse striatum, a brain region with selective HD vulnerability. Using metabolically labeled tissues and immunoaffinity purification-mass spectrometry, we establish that polyglutamine-dependent modulation of HTT PPI abundances and relative stability starts at an early stage of pathogenesis in a Q140 HD mouse model. We identify direct and indirect PPIs that are also genetic disease modifiers using in-cell two-hybrid and behavioral assays in HD human cell and Drosophila models, respectively. Validated, disease-relevant mHTT-dependent interactions encompass mediators of synaptic neurotransmission (SNAREs and glutamate receptors) and lysosomal acidification (V-ATPase). Our study provides a resource for understanding mHTT-dependent dysfunction in cortico-striatal cellular networks, partly through impaired synaptic communication and endosomal-lysosomal system. A record of this paper's Transparent Peer Review process is included in the supplemental information.

Advances in understanding of the innate immune response to human norovirus infection using organoid models

Norovirus is the leading cause of epidemic and endemic acute gastroenteritis worldwide and the most frequent cause of foodborne illness in the United States. There is no specific treatment for norovirus infections and therapeutic interventions are based on alleviating symptoms and limiting viral transmission. The immune response to norovirus is not completely understood and mechanistic studies have been hindered by lack of a robust cell culture system. In recent years, the human intestinal enteroid/human intestinal organoid system (HIE/HIO) has enabled successful human norovirus replication. Cells derived from HIE have also successfully been subjected to genetic manipulation using viral vectors as well as CRISPR/Cas9 technology, thereby allowing studies to identify antiviral signaling pathways important in controlling norovirus infection. RNA sequencing using HIE cells has been used to investigate the transcriptional landscape during norovirus infection and to identify antiviral genes important in infection. Other cell culture platforms such as the microfluidics-based gut-on-chip technology in combination with the HIE/HIO system also have the potential to address fundamental questions on innate immunity to human norovirus. In this review, we highlight the recent advances in understanding the innate immune response to human norovirus infections in the HIE system, including the application of advanced molecular technologies that have become available in recent years such as the CRISPR/Cas9 and RNA sequencing, as well as the potential application of single cell transcriptomics, viral proteomics, and gut-on-a-chip technology to further elucidate innate immunity to norovirus.

Development of the "Applied Proteomics" Concept for Biotechnology Applications in Microalgae: Example of the Proteome Data in Nannochloropsis gaditana

Most of the marine ecosystems on our planet are still unknown. Among these ecosystems, microalgae act as a baseline due to their role as primary producers. The estimated millions of species of these microorganisms represent an almost infinite source of potentially active biocomponents offering unlimited biotechnology applications. This review considers current research in microalgae using the "omics" approach, which today is probably the most important biotechnology tool. These techniques enable us to obtain a large volume of data from a single experiment. The specific focus of this review is proteomics as a technique capable of generating a large volume of interesting information in a single proteomics assay, and particularly the concept of applied proteomics. As an example, this concept has been applied to the study of Nannochloropsis gaditana, in which proteomics data generated are transformed into information of high commercial value by identifying proteins with direct applications in the biomedical and agri-food fields, such as the protein designated UCA01 which presents antitumor activity, obtained from N. gaditana.

Proteomics in support of immunotherapy: contribution to model-based precision medicine

INTRODUCTION

Proteomics encompasses a wide and expanding range of methods to identify, characterize, and quantify thousands of proteins from a variety of biological samples, including blood samples, tumors, and tissues. Such methods are supportive of various forms of immunotherapy applied to chronic conditions such as allergies, autoimmune diseases, cancers, and infectious diseases.

AREAS COVERED

In support of immunotherapy, proteomics based on mass spectrometry has multiple specific applications related to (i) disease modeling and patient stratification, (ii) antigen/ autoantigen/neoantigen/ allergen identification, (iii) characterization of proteins and monoclonal antibodies used for immunotherapeutic or diagnostic purposes, (iv) identification of biomarkers and companion diagnostics and (v) monitoring by immunoproteomics of immune responses elicited in the course of the disease or following immunotherapy.

EXPERT OPINION

Proteomics contributes as an enabling technology to an evolution of immunotherapy toward a precision medicine approach aiming to better tailor treatments to patients' specificities in multiple disease areas. This trend is favored by a better understanding through multi-omics profiling of both the patient's characteristics, his/her immune status as well as of the features of the immunotherapeutic drug.

Spotted Fever Group Rickettsia Trigger Species-Specific Alterations in Macrophage Proteome Signatures with Different Impacts in Host Innate Inflammatory Responses

The molecular details underlying differences in pathogenicity between Rickettsia species remain to be fully understood. Evidence points to macrophage permissiveness as a key mechanism in rickettsial virulence. Different studies have shown that several rickettsial species responsible for mild forms of rickettsioses can also escape macrophage-mediated killing mechanisms and establish a replicative niche within these cells. However, their manipulative capacity with respect to host cellular processes is far from being understood. A deeper understanding of the interplay between mildly pathogenic rickettsiae and macrophages and the commonalities and specificities of host responses to infection would illuminate differences in immune evasion mechanisms and pathogenicity. We used quantitative proteomics by sequential windowed data independent acquisition of the total high-resolution mass spectra with tandem mass spectrometry (SWATH-MS/MS) to profile alterations resulting from infection of THP-1 macrophages with three mildly pathogenic rickettsiae: Rickettsia parkeri, Rickettsia africae, and Rickettsia massiliae, all successfully proliferating in these cells. We show that all three species trigger different proteome signatures. Our results reveal a significant impact of infection on proteins categorized as type I interferon responses, which here included several components of the retinoic acid-inducible gene I (RIG-1)-like signaling pathway, mRNA splicing, and protein translation. Moreover, significant differences in protein content between infection conditions provide evidence for species-specific induced alterations. Indeed, we confirm distinct impacts on host inflammatory responses between species during infection, demonstrating that these species trigger different levels of beta interferon (IFN-β), differences in the bioavailability of the proinflammatory cytokine interleukin 1β (IL-1β), and differences in triggering of pyroptotic events. This work reveals novel aspects and exciting nuances of macrophage-Rickettsia interactions, adding additional layers of complexity between Rickettsia and host cells' constant arms race for survival. IMPORTANCE The incidence of diseases caused by Rickettsia has been increasing over the years. It has long been known that rickettsioses comprise diseases with a continuous spectrum of severity. There are highly pathogenic species causing diseases that are life threatening if untreated, others causing mild forms of the disease, and a third group for which no pathogenicity to humans has been described. These marked differences likely reflect distinct capacities for manipulation of host cell processes, with macrophage permissiveness emerging as a key virulence trait. However, what defines pathogenicity attributes among rickettsial species is far from being resolved. We demonstrate that the mildly pathogenic Rickettsia parkeri, Rickettsia africae, and Rickettsia massiliae, all successfully proliferating in macrophages, trigger different proteome signatures in these cells and differentially impact critical components of innate immune responses by inducing different levels of beta interferon (IFN-β) and interleukin 1β (IL-1β) and different timing of pyroptotic events during infection. Our work reveals novel nuances in rickettsia-macrophage interactions, offering new clues to understand Rickettsia pathogenicity.

Host directed therapies: COVID-19 and beyond

The global spread of SARS-CoV-2 has necessitated the development of novel, safe and effective therapeutic agents against this virus to stop the pandemic, however the development of novel antivirals may take years, hence, the best alternative available, is to repurpose the existing antiviral drugs with known safety profile in humans. After more than one year into this pandemic, global efforts have yielded the fruits and with the launch of many vaccines in the market, the world is inching towards the end of this pandemic, nonetheless, future pandemics of this magnitude or even greater cannot be denied. The preparedness against viruses of unknown origin should be maintained and the broad-spectrum antivirals with activity against range of viruses should be developed to curb future viral pandemics. The majority of antivirals developed till date are pathogen specific agents, which target critical viral pathways and lack broad spectrum activity required to target wide range of viruses. The surge in drug resistance among pathogens has rendered a compelling need to shift our focus towards host directed factors in the treatment of infectious diseases. This gains special relevance in the case of viral infections, where the pathogen encodes a handful of genes and predominantly depends on host factors for their propagation and persistence. Therefore, future antiviral drug development should focus more on targeting molecules of host pathways that are often hijacked by many viruses. Such cellular proteins of host pathways offer attractive targets for the development of broad-spectrum anticipatory antivirals. In the present article, we have reviewed the host directed therapies (HDTs) effective against viral infections with a special focus on COVID-19. This article also discusses the strategies involved in identifying novel host targets and subsequent development of broad spectrum HDTs.

Ultrastructural insights into the replication cycle of salmon pancreas disease virus (SPDV) using salmon cardiac primary cultures (SCPCs)

Salmon pancreas disease virus (SPDV) has been affecting the salmon farming industry for over 30 years, but despite the substantial amount of studies, there are still a number of recognized knowledge gaps, for example in the transmission of the virus. In this work, an ultrastructural morphological approach was used to describe observations after infection by SPDV of an ex vivo cardiac model generated from Atlantic salmon embryos. The observations in this study and those available on previous ultrastructural work on SPDV are compared and contrasted with the current knowledge on terrestrial mammalian and insect alphaviral replication cycles, which is deeper than that of SPDV both morphologically and mechanistically. Despite their limitations, morphological descriptions remain an excellent way to generate novel hypotheses, and this has been the aim of this work. This study has used a target host, ex vivo model and resulted in some previously undescribed features, including filopodial membrane projections, cytoplasmic stress granules or putative intracytoplasmic budding. The latter suggests a new hypothesis that warrants further mechanistic research: SPDV in salmon may have retained the capacity for non-cytolytic (persistent) infections by intracellular budding, similar to that noted in arthropod vectors of other alphaviruses. In the notable absence of a known intermediate host for SPDV, the presence of this pattern suggests that both cytopathic and persistent infections may coexist in the same host. It is our hope that the ultrastructural comparison presented here stimulates new research that brings the knowledge on SPDV replication cycle up to a similar level to that of terrestrial alphaviruses.

Unraveling Protein Interactions between the Temperate Virus Bam35 and Its Bacillus Host Using an Integrative Yeast Two Hybrid-High Throughput Sequencing Approach

Bacillus virus Bam35 is the model Betatectivirus and member of the family Tectiviridae, which is composed of tailless, icosahedral, and membrane-containing bacteriophages. Interest in these viruses has greatly increased in recent years as they are thought to be an evolutionary link between diverse groups of prokaryotic and eukaryotic viruses. Additionally, betatectiviruses infect bacteria of the Bacillus cereus group, which are known for their applications in industry and notorious since it contains many pathogens. Here, we present the first protein–protein interactions (PPIs) network for a tectivirus–host system by studying the Bam35–Bacillus thuringiensis model using a novel approach that integrates the traditional yeast two-hybrid system and high-throughput sequencing (Y2H-HTS). We generated and thoroughly analyzed a genomic library of Bam35′s host B. thuringiensis HER1410 and screened interactions with all the viral proteins using different combinations of bait–prey couples. Initial analysis of the raw data enabled the identification of over 4000 candidate interactions, which were sequentially filtered to produce 182 high-confidence interactions that were defined as part of the core virus–host interactome. Overall, host metabolism proteins and peptidases were particularly enriched within the detected interactions, distinguishing this host–phage system from the other reported host–phage PPIs. Our approach also suggested biological roles for several Bam35 proteins of unknown function, including the membrane structural protein P25, which may be a viral hub with a role in host membrane modification during viral particle morphogenesis. This work resulted in a better understanding of the Bam35–B. thuringiensis interaction at the molecular level and holds great potential for the generalization of the Y2H-HTS approach for other virus–host models.

Mass spectrometry-based proteomics in basic and translational research of SARS-CoV-2 coronavirus and its emerging mutants

Molecular diagnostics of the coronavirus disease of 2019 (COVID-19) now mainly relies on the measurements of viral RNA by RT-PCR, or detection of anti-viral antibodies by immunoassays. In this review, we discussed the perspectives of mass spectrometry-based proteomics as an analytical technique to identify and quantify proteins of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and to enable basic research and clinical studies on COVID-19. While RT-PCR and RNA sequencing are indisputably powerful techniques for the detection of SARS-CoV-2 and identification of the emerging mutations, proteomics may provide confirmatory diagnostic information and complimentary biological knowledge on protein abundance, post-translational modifications, protein-protein interactions, and the functional impact of the emerging mutations. Pending advances in sensitivity and throughput of mass spectrometry and liquid chromatography, shotgun and targeted proteomic assays may find their niche for the differential quantification of viral proteins in clinical and environmental samples. Targeted proteomic assays in combination with immunoaffinity enrichments also provide orthogonal tools to evaluate cross-reactivity of serology tests and facilitate development of tests with the nearly perfect diagnostic specificity, this enabling reliable testing of broader populations for the acquired immunity. The coronavirus pandemic of 2019-2021 is another reminder that the future global pandemics may be inevitable, but their impact could be mitigated with the novel tools and assays, such as mass spectrometry-based proteomics, to enable continuous monitoring of emerging viruses, and to facilitate rapid response to novel infectious diseases.

Methods for Proteomic Analyses of Mycobacteria

The use of proteomic technologies to characterize and study the proteome of mycobacteria has provided important information in terms of function, diversity, protein-protein interactions, and host-pathogen interactions in Mycobacterium spp. There are many different mass spectrometry methodologies that can be applied to proteomics studies of mycobacteria and microorganisms in general. Sample processing and appropriate study design are critical to generating high-quality data regardless of the mass spectrometry method applied. Appropriate study design relies on statistical rigor and data curation using bioinformatics approaches that are widely applicable regardless of the organism or system studied. Sample processing, on the other hand, is often a niched process specific to the physiology of the organism or system under investigation. Therefore, in this chapter, we will provide protocols for processing mycobacterial protein samples for the specific application of Top-down and Bottom-up proteomic analyses.

The pentose phosphate pathway constitutes a major metabolic hub in pathogenic Francisella

Metabolic pathways are now considered as intrinsic virulence attributes of pathogenic bacteria and thus represent potential targets for antibacterial strategies. Here we focused on the role of the pentose phosphate pathway (PPP) and its connections with other metabolic pathways in the pathophysiology of Francisella novicida. The involvement of the PPP in the intracellular life cycle of Francisella was first demonstrated by studying PPP inactivating mutants. Indeed, we observed that inactivation of the tktA, rpiA or rpe genes severely impaired intramacrophage multiplication during the first 24 hours. However, time-lapse video microscopy demonstrated that rpiA and rpe mutants were able to resume late intracellular multiplication. To better understand the links between PPP and other metabolic networks in the bacterium, we also performed an extensive proteo-metabolomic analysis of these mutants. We show that the PPP constitutes a major bacterial metabolic hub with multiple connections to glycolysis, the tricarboxylic acid cycle and other pathways, such as fatty acid degradation and sulfur metabolism. Altogether our study highlights how PPP plays a key role in the pathogenesis and growth of Francisella in its intracellular niche.

Quantitative Proteomic Profiling of Murine Ocular Tissue and the Extracellular Environment

Mass spectrometry-based proteomics provides a robust and reliable method for detecting and quantifying changes in protein abundance among samples, including cells, tissues, organs, and supernatants. Physical damage or inflammation can compromise the ocular surface permitting colonization by bacterial pathogens, commonly Pseudomonas aeruginosa, and the formation of biofilms. The interplay between P. aeruginosa and the immune system at the site of infection defines the host's ability to defend against bacterial invasion and promote clearance of infection. Profiling of the ocular tissue following infection describes the nature of the host innate immune response and specifically the presence and abundance of neutrophil-associated proteins to neutralize the bacterial biofilm. Moreover, detection of unique proteins produced by P. aeruginosa enable identification of the bacterial species and may serve as a diagnostic approach in a clinical setting. Given the emergence and prevalence of antimicrobial resistant bacterial strains, the ability to rapidly diagnose a bacterial infection promoting quick and accurate treatment will reduce selective pressure towards resistance. Furthermore, the ability to define differences in the host immune response towards bacterial invasion enhances our understanding of innate immune system regulation at the ocular surface. Here, we describe murine ocular infection and sample collection, as well as outline protocols for protein extraction and mass spectrometry profiling from corneal tissue and extracellular environment (eye wash) samples. © 2020 Wiley Periodicals LLC. Basic Protocol 1: Murine model of ocular infection Basic Protocol 2: Murine model sample collection Basic Protocol 3: Protein extraction from eye wash Basic Protocol 4: Protein extraction from corneal tissue Basic Protocol 5: Mass spectrometry-based proteomics and bioinformatics from eye wash and corneal tissue samples.

Bioinformatic Analysis of Temporal and Spatial Proteome Alternations During Infections

Microbial pathogens have evolved numerous mechanisms to hijack host's systems, thus causing disease. This is mediated by alterations in the combined host-pathogen proteome in time and space. Mass spectrometry-based proteomics approaches have been developed and tailored to map disease progression. The result is complex multidimensional data that pose numerous analytic challenges for downstream interpretation. However, a systematic review of approaches for the downstream analysis of such data has been lacking in the field. In this review, we detail the steps of a typical temporal and spatial analysis, including data pre-processing steps (i.e., quality control, data normalization, the imputation of missing values, and dimensionality reduction), different statistical and machine learning approaches, validation, interpretation, and the extraction of biological information from mass spectrometry data. We also discuss current best practices for these steps based on a collection of independent studies to guide users in selecting the most suitable strategies for their dataset and analysis objectives. Moreover, we also compiled the list of commonly used R software packages for each step of the analysis. These could be easily integrated into one's analysis pipeline. Furthermore, we guide readers through various analysis steps by applying these workflows to mock and host-pathogen interaction data from public datasets. The workflows presented in this review will serve as an introduction for data analysis novices, while also helping established users update their data analysis pipelines. We conclude the review by discussing future directions and developments in temporal and spatial proteomics and data analysis approaches. Data analysis codes, prepared for this review are available from https://github.com/BabuLab-UofR/TempSpac, where guidelines and sample datasets are also offered for testing purposes.

Quantitative Temporal Viromics

The abundance, localization, modifications, and protein-protein interactions of many host cell and virus proteins can change dynamically throughout the course of any viral infection. Studying these changes is critical for a comprehensive understanding of how viruses replicate and cause disease, as well as for the development of antiviral therapeutics and vaccines. Previously, we developed a mass spectrometry-based technique called quantitative temporal viromics (QTV), which employs isobaric tandem mass tags (TMTs) to allow precise comparative quantification of host and virus proteomes through a whole time course of infection. In this review, we discuss the utility and applications of QTV, exemplified by numerous studies that have since used proteomics with a variety of quantitative techniques to study virus infection through time. Expected final online publication date for the Annual Review of Virology, Volume 8 is September 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Combining of transcriptomic and proteomic data to mine immune-related genes and proteins in the liver of Cynoglossus semilaevis challenged with Vibrio anguillarum

The liver is a multi-functional organ including metabolism, substance synthesis, detoxification, and various immune functions, and its role in immunity has attracted more and more attention. However, research on the liver immune response of fish infected by pathogenic bacteria is currently lacking. In this study, the transcriptomics and proteomics of the liver of Cynoglossus semilaevis infected with Vibrio anguillarum were analyzed. A total of 1470 genes and 497 proteins were differentially expressed in the pairwise comparison of obvious symptoms of infection (HOSG), no obvious symptoms of infection (NOSG) and PBS treatment (CG). Gene ontology and KEGG enrichment pathways analysis showed that differentially expressed genes (DEGs) and differentially expressed proteins (DEPs) were mainly enriched in toll-like receptors (TLRs), complement and coagulation cascades, nucleotide oligomerization domain (NOD)-like receptors (NLRs), mitogen-activated protein kinase (MAPK) and phagosome signaling pathways, which suggested the combined action of the five pathways were significant to enhance the liver immune defense. The combination of transcriptomic and proteomic analysis showed that ITGβ1, C3, C5 and MRC1 were significantly up-regulated, which might play an important role in the liver immune response to the recognition of V. anguillarum, inflammatory response and phagocytosis. The transcriptome and proteome data we obtained provide information on some key genes and proteins for further study of the mechanism of liver immune response.

Revisiting Ehrlichia ruminantium Replication Cycle Using Proteomics: The Host and the Bacterium Perspectives

The Rickettsiales Ehrlichia ruminantium, the causal agent of the fatal tick-borne disease Heartwater, induces severe damage to the vascular endothelium in ruminants. Nevertheless, E. ruminantium-induced pathobiology remains largely unknown. Our work paves the way for understanding this phenomenon by using quantitative proteomic analyses (2D-DIGE-MS/MS, 1DE-nanoLC-MS/MS and biotin-nanoUPLC-MS/MS) of host bovine aorta endothelial cells (BAE) during the in vitro bacterium intracellular replication cycle. We detect 265 bacterial proteins (including virulence factors), at all time-points of the E. ruminantium replication cycle, highlighting a dynamic bacterium–host interaction. We show that E. ruminantium infection modulates the expression of 433 host proteins: 98 being over-expressed, 161 under-expressed, 140 detected only in infected BAE cells and 34 exclusively detected in non-infected cells. Cystoscape integrated data analysis shows that these proteins lead to major changes in host cell immune responses, host cell metabolism and vesicle trafficking, with a clear involvement of inflammation-related proteins in this process. Our findings led to the first model of E. ruminantium infection in host cells in vitro, and we highlight potential biomarkers of E. ruminantium infection in endothelial cells (such as ROCK1, TMEM16K, Albumin and PTPN1), which may be important to further combat Heartwater, namely by developing non-antibiotic-based strategies.

Analysis of Cryptosporidium spp. from clinical samples by matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry

AIM

To purify Cryptosporidium spp. oocysts from clinical stool samples and evaluate using an up-to-date mass spectrometry protocol producing high-quality reference spectra.

METHODS AND RESULTS

A refined purification protocol was developed for oocysts from stools, involving salt flotation and potassium bromide density centrifugation. Purified oocysts were prepared for matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) by formic acid extraction, and the extracts analysed using the Bruker MALDI Biotyper system. Individual spectral markers were identified by their specific mass peaks. Cryptosporidium parvum oocysts (Iowa strain) propagated in vivo, and C.  parvum (n = 2) and Cryptosporidium hominis (n = 1) oocysts from clinical stool samples produced distinct spectra that were considered specific to Cryptosporidium spp. with no evidence of contamination.

CONCLUSIONS

The production of distinct spectra demonstrated the utility of the purification method for oocysts from clinical stool samples and provided reference spectra.

SIGNIFICANCE AND IMPACT OF THE STUDY

The use of MALDI-TOF MS and other mass spectrometry techniques has been limited previously to C.  parvum oocysts propagated in vivo. Appropriate purification of oocysts can achieve sufficient biomass, enabling analysis by MALDI-TOF MS and potentially other mass spectrometry platforms, facilitating peptide and protein discovery and identification of biomarkers from a much wider range of Cryptosporidium spp. from natural infections.

Insights into the molecular basis of tick-borne encephalitis from multiplatform metabolomics

Background

Tick-borne encephalitis virus (TBEV) is the most prevalent arbovirus, with a tentative estimate of 10,000 to 10,500 infections occurring in Europe and Asia every year. Endemic in Northeast China, tick-borne encephalitis (TBE) is emerging as a major threat to public health, local economies and tourism. The complicated array of host physiological changes has hampered elucidation of the molecular mechanisms underlying the pathogenesis of this disease.

Methodology/Principle findings

System-level characterization of the serum metabolome and lipidome of adult TBEV patients and a healthy control group was performed using liquid chromatography tandem mass spectrometry. By tracking metabolic and lipid changes during disease progression, crucial physiological changes that coincided with disease stages could be identified. Twenty-eight metabolites were significantly altered in the sera of TBE patients in our metabolomic analysis, and 14 lipids were significantly altered in our lipidomics study. Among these metabolites, alpha-linolenic acid, azelaic acid, D-glutamine, glucose-1-phosphate, L-glutamic acid, and mannose-6-phosphate were altered compared to the control group, and PC(38:7), PC(28:3;1), TAG(52:6), etc. were altered based on lipidomics. Major perturbed metabolic pathways included amino acid metabolism, lipid and oxidative stress metabolism (lipoprotein biosynthesis, arachidonic acid biosynthesis, leukotriene biosynthesis and sphingolipid metabolism), phospholipid metabolism and triglyceride metabolism. These metabolites were significantly perturbed during disease progression, implying their latent utility as prognostic markers.

Conclusions/Significance

TBEV infection causes distinct temporal changes in the serum metabolome and lipidome, and many metabolites are potentially involved in the acute inflammatory response and immune regulation. Our global analysis revealed anti- and pro-inflammatory processes in the host and changes to the entire metabolic profile. Relationships between metabolites and pathologies were established. This study provides important insight into the pathology of TBE, including its pathology, and lays the foundation for further research into putative markers of TBE disease.

Tick-borne encephalitis virus (TBEV) with extreme contagiousness is a key danger to public health systems in Europe and Asia. To date, little information is obtained about the molecular mechanism underlying infection, and although commercial vaccines against TBEV exist, there is no specific treatment for the disease. Metabolomics and lipidomics offer multiple-visions of metabolome and lipidome sights and help elucidating metabolic to disease phenotype. Serum metabolism and lipidome analysis were performed based on mass spectrometer (MS) platform on a cohort of TBEV patients. About 400 metabolites performed crucial shifts in TBEV patients compared with healthy subjects. This study revealed that in the stage of infection, the host metabolome is tightly regulated, with anti-inflammatory processes modulating pro-inflammatory processes implying the self-limiting phenotype of TBEV and the inherent regulation in humans. The crucial perturbed metabolic pathways contained amino acid metabolism, fatty acid metabolism and phospholipid metabolism. This study provides a powerful and new approach to decipher the interactions between host and virus. These potential metabolites provide high sensitivity and specificity and have the capacity to function as biomarkers for disease surveillance and estimation of therapeutic interventions.