A review on host-pathogen interactions: classification and prediction

Comprehensive assessment of chlorination disinfection on microplastic-associated biofilms

Chlorination on microplastic (MP) biofilms was comprehensively investigated with respect to disinfection efficiency, morphology, and core microbiome. The experiments were performed under various conditions: i) MP particles; polypropylene (PP) and polystyrene (PS), ii) MP biofilms; Escherichia coli for single-species and river water microorganisms for multiple-species, iii) different chlorine concentrations, and iv) different chlorine exposure periods. As a result, chlorination effectively inactivated the MP biofilm microorganisms. The disinfection efficiency increased with increasing the free chlorination concentration and exposure periods for both single- and multiple-species MP biofilms. The multiple-species MP biofilms were inactivated 1.3-6.0 times less than single-species MP biofilms. In addition, the PP-MP biofilms were more vulnerable to chlorination than the PS-MP biofilms. Morphology analysis verified that chlorination detached most MP biofilms, while a small part still remained. Interestingly, chlorination strongly changed the biofilm microbiome on MPs; the relative abundance of some microbes increased after the chlorination, suggesting they could be regarded as chlorine-resistant bacteria. Some potential pathogens were also remained on the MP particles after the chlorination. Notably, chlorination was effective in inactivating the MP biofilms. Further research should be performed to evaluate the impacts of residual MP biofilms on the environment.

The crosstalk between SUMOylation and immune system in host-pathogen interactions

Pathogens can not only cause infectious diseases, immune system diseases, and chronic diseases, but also serve as potential triggers or initiators for certain tumors. They directly or indirectly damage human health and are one of the leading causes of global deaths. Small ubiquitin-like modifier (SUMO) modification, a type of protein post-translational modification (PTM) that occurs when SUMO groups bond covalently to particular lysine residues on substrate proteins, plays a crucial role in both innate and adaptive immunologic responses, as well as pathogen-host immune system crosstalk. SUMOylation participates in the host's defense against pathogens by regulating immune responses, while numerically vast and taxonomically diverse pathogens have evolved to exploit the cellular SUMO modification system to break through innate defenses. Here, we describe the characteristics and multiple functions of SUMOylation as a pivotal PTM mechanism, the tactics employed by various pathogens to counteract the immune system through targeting host SUMOylation, and the character of the SUMOylation system in the fight between pathogens and the host immune system. We have also included a summary of the potential anti-pathogen SUMO enzyme inhibitors. This review serves as a reference for basic research and clinical practice in the diagnosis, prognosis, and treatment of pathogenic microorganism-caused disorders.

Coupling Cell Size Regulation and Proliferation Dynamics of C. glutamicum Reveals Cell Division Based on Surface Area

Single cells actively coordinate growth and division to regulate their size, yet how this size homeostasis at the single-cell level propagates over multiple generations to impact clonal expansion remains fundamentally unexplored. Classical timer models for cell proliferation (where the duration of the cell cycle is an independent variable) predict that the stochastic variation in colony size will increase monotonically over time. In stark contrast, implementing size control according to adder strategy (where on average a fixed size added from cell birth to division) leads to colony size variations that eventually decay to zero. While these results assume a fixed size of the colony-initiating progenitor cell, further analysis reveals that the magnitude of the intercolony variation in population number is sensitive to heterogeneity in the initial cell size. We validate these predictions by tracking the growth of isogenic microcolonies of Corynebacterium glutamicum in microfluidic chambers. Approximating their cell shape to a capsule, we observe that the degree of random variability in cell size is different depending on whether the cell size is quantified as per length, surface area, or volume, but size control remains an adder regardless of these size metrics. A comparison of the observed variability in the colony population with the predictions suggests that proliferation matches better with a cell division based on the cell surface. In summary, our integrated mathematical-experimental approach bridges the paradigms of single-cell size regulation and clonal expansion at the population levels. This innovative approach provides elucidation of the mechanisms of size homeostasis from the stochastic dynamics of colony size for rod-shaped microbes.

Tandem mass tag (TMT) labeling-based quantitative proteomic analysis reveals the cellular protein characteristics of 16HBE cells infected with coxsackievirus A10 and the potential effect of HMGB1 on viral replication

Coxsackievirus A10 (CV-A10) is recognized as one of the most important pathogens associated with hand, foot, and mouth disease (HFMD) in young children under 5 years of age worldwide, and it can lead to fatal neurological complications. However, available commercial vaccines fail to protect against CV-A10. Therefore, there is an urgent need to study new protein targets of CV-A10 and develop novel vaccine-based therapeutic strategies. Advances in proteomics in recent years have enabled a comprehensive understanding of host pathogen interactions. Here, to study CV-A10-host interactions, a global quantitative proteomic analysis was conducted to investigate the molecular characteristics of host cell proteins and identify key host proteins involved in CV-A10 infection. Using tandem mass tagging (TMT)-based mass spectrometry, a total of 6615 host proteins were quantified, with 293 proteins being differentially regulated. To ensure the validity and reliability of the proteomics data, three randomly selected proteins were verified by Western blot analysis, and the results were consistent with the TMT results. Further functional analysis showed that the upregulated and downregulated proteins were associated with diverse biological activities and signaling pathways, such as metabolic processes, biosynthetic processes, the AMPK signaling pathway, the neurotrophin signaling pathway, the MAPK signaling pathway, and the GABAergic synaptic signaling. Moreover, subsequent bioinformatics analysis demonstrated that these differentially expressed proteins contained distinct domains, were localized in different subcellular components, and generated a complex network. Finally, high-mobility group box 1 (HMGB1) might be a key host factor involved in CV-A10 replication. In summary, our findings provide comprehensive insights into the proteomic profile during CV-A10 infection, deepen our understanding of the relationship between CV-A10 and host cells, and establish a proteomic signature for this viral infection. Moreover, the observed effect of HMGB1 on CV-A10 replication suggests that it might be a potential therapeutic target treatment of CV-A10 infection.

Hijacking of GPCRs and RTKs by pathogens

Pathogens exploit multiple cellular and molecular pathways in the host organisms for their entry, survival and dissemination. The cell surface receptors such as G protein-coupled receptors (GPCRs) and receptor tyrosine kinases (RTKs) constitute the targets of many pathogens. This is due to the ubiquitous expression of these two receptor families in the organism and their pivotal role in various cellular and physiological processes. At the molecular level, receptor hijacking implies either direct or indirect interactions between pathogens' effectors or toxins with GPCRs and RTKs at the cell surface thereby interfering with their activation and their downstream signaling pathways inside the host cells. As a result, the pathogens manipulate and redirect GPCR/RTK-mediated signaling pathways and different aspects of cell function for their benefit. The review presents a compilation of the major examples of pathogen infections where GPCRs and RTKs and their related intracellular signaling pathways are targeted. This provides a molecular basis for pathogens hijacking cell signaling and their virulence. Our understanding of such complex host-pathogen interactions at the molecular level will open new opportunities to develop new prophylactic and therapeutic approaches against infections. In this context, the pharmacological targeting of GPCRs and RTKs may be a promising approach.

Pathogen-driven cancers from a structural perspective: Targeting host-pathogen protein-protein interactions

Host-pathogen interactions (HPIs) affect and involve multiple mechanisms in both the pathogen and the host. Pathogen interactions disrupt homeostasis in host cells, with their toxins interfering with host mechanisms, resulting in infections, diseases, and disorders, extending from AIDS and COVID-19, to cancer. Studies of the three-dimensional (3D) structures of host-pathogen complexes aim to understand how pathogens interact with their hosts. They also aim to contribute to the development of rational therapeutics, as well as preventive measures. However, structural studies are fraught with challenges toward these aims. This review describes the state-of-the-art in protein-protein interactions (PPIs) between the host and pathogens from the structural standpoint. It discusses computational aspects of predicting these PPIs, including machine learning (ML) and artificial intelligence (AI)-driven, and overviews available computational methods and their challenges. It concludes with examples of how theoretical computational approaches can result in a therapeutic agent with a potential of being used in the clinics, as well as future directions.

Francisella tularensis Exploits AMPK Activation to Harvest Host-Derived Nutrients Liberated from Host Lipolysis

Francisella tularensis is a zoonotic, facultative intracellular bacterial pathogen that replicates in a variety of cell types during infection. Following entry into the cell and phagosome escape, the bacterium replicates rapidly in the cytoplasm. F. tularensis intracellular growth depends on the availability of metabolizable essential nutrients to support replication. However, the mechanism by which metabolizable nutrients become available to the bacterium in the intracellular environment is not fully understood. We found that F. tularensis-infected cells had significantly smaller and fewer lipid droplets than uninfected cells. Inhibition of triacylglycerol degradation significantly reduced bacterial growth, whereas inhibition of triacylglycerol formation did not reduce bacterial growth, suggesting that triacylglycerols sequestered within lipid droplets are important nutrient sources for F. tularensis. We found that F. tularensis-infected cells had increased activation of lipolysis and the upstream regulatory protein AMP protein kinase (AMPK). These data suggest that F. tularensis exploits AMPK activation and lipid metabolism to use host-derived nutrients. Finally, we found that AMPK activation is correlated with an increased bacterial burden, which suggests that it is a host-mediated response to nutrient starvation that results from increased bacterial replication. Altogether, we conclude that F. tularensis exploits AMPK activation to access nutrients sequestered in lipid droplets, specifically glycerol and fatty acids, to undergo efficient bacterial replication and cause successful infection.

Differential microRNAs expression during ex vivo infection of canine and ovine placental explants with Trypanosoma cruzi and Toxoplasma gondii

Trypanosoma cruzi and Toxoplasma gondii are two zoonotic parasites that constitute significant human and animal health threats, causing a significant economic burden worldwide. Both parasites can be transmitted congenitally, but transmission rates for T. gondii are high, contrary to what has been observed for T. cruzi. The probability of congenital transmission depends on complex interactions between the pathogen and the host, including the modulation of host cell gene expression by miRNAs. During ex vivo infection of canine and ovine placental explants, we evaluated the expression of 3 miRNAs (miR-30e-3p, miR-3074-5p, and miR-127-3p) previously associated with parasitic and placental diseases and modulated by both parasites. In addition, we identified the possible target genes of the miRNAs by using computational prediction tools and performed GO and KEGG enrichment analyses to identify the biological functions and associated pathologies. The three miRNAs are differentially expressed in the canine and ovine placenta in response to T. cruzi and T. gondii. We conclude that the observed differential expression and associated functions might explain, at least partially, the differences in transmission rates and susceptibility to parasite infection in different species.

Host-microbiome protein-protein interactions capture disease-relevant pathways

Background

Host-microbe interactions are crucial for normal physiological and immune system development and are implicated in a variety of diseases, including inflammatory bowel disease (IBD), colorectal cancer (CRC), obesity, and type 2 diabetes (T2D). Despite large-scale case-control studies aimed at identifying microbial taxa or genes involved in pathogeneses, the mechanisms linking them to disease have thus far remained elusive.

Results

To identify potential pathways through which human-associated bacteria impact host health, we leverage publicly-available interspecies protein-protein interaction (PPI) data to find clusters of microbiome-derived proteins with high sequence identity to known human-protein interactors. We observe differential targeting of putative human-interacting bacterial genes in nine independent metagenomic studies, finding evidence that the microbiome broadly targets human proteins involved in immune, oncogenic, apoptotic, and endocrine signaling pathways in relation to IBD, CRC, obesity, and T2D diagnoses.

Conclusions

This host-centric analysis provides a mechanistic hypothesis-generating platform and extensively adds human functional annotation to commensal bacterial proteins.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-022-02643-9.

A determination of pan-pathogen antimicrobials?

While antimicrobial drug development has historically mitigated infectious diseases that are known, COVID-19 revealed a dearth of ‘in-advance’ therapeutics suitable for infections by pathogens that have not yet emerged. Such drugs must exhibit a property that is antithetical to the classical paradigm of antimicrobial development: the ability to treat infections by any pathogen. Characterisation of such ‘pan-pathogen’ antimicrobials requires consolidation of drug repositioning studies, a new and growing field of drug discovery. In this review, a previously-established system for evaluating repositioning studies is used to highlight 4 therapeutics which exhibit pan-pathogen properties, namely azithromycin, ivermectin, niclosamide, and nitazoxanide. Recognition of the pan-pathogen nature of these antimicrobials is the cornerstone of a novel paradigm of antimicrobial development that is not only anticipatory of pandemics and bioterrorist attacks, but cognisant of conserved anti-infective mechanisms within the host-pathogen interactome which are only now beginning to emerge. Ultimately, the discovery of pan-pathogen antimicrobials is concomitantly the discovery of a new class of antivirals, and begets significant implications for pandemic preparedness research in a world after COVID-19.

Beyond the List: Bioagent-Agnostic Signatures Could Enable a More Flexible and Resilient Biodefense Posture Than an Approach Based on Priority Agent Lists Alone

As of 2021, the biothreat policy and research communities organize their efforts around lists of priority agents, which elides consideration of novel pathogens and biotoxins. For example, the Select Agents and Toxins list is composed of agents that historic biological warfare programs had weaponized or that have previously caused great harm during natural outbreaks. Similarly, lists of priority agents promulgated by the World Health Organization and the National Institute of Allergy and Infectious Diseases are composed of previously known pathogens and biotoxins. To fill this gap, we argue that the research/scientific and biodefense/biosecurity communities should categorize agents based on how they impact their hosts to augment current list-based paradigms. Specifically, we propose integrating the results of multi-omics studies to identify bioagent-agnostic signatures (BASs) of disease-namely, patterns of biomarkers that accurately and reproducibly predict the impacts of infection or intoxication without prior knowledge of the causative agent. Here, we highlight three pathways that investigators might exploit as sources of signals to construct BASs and their applicability to this framework. The research community will need to forge robust interdisciplinary teams to surmount substantial experimental, technical, and data analytic challenges that stand in the way of our long-term vision. However, if successful, our functionality-based BAS model could present a means to more effectively surveil for and treat known and novel agents alike.

Machine Learning and Artificial Intelligence for the Prediction of Host-Pathogen Interactions: A Viral Case

The research of interactions between the pathogens and their hosts is key for understanding the biology of infection. Commencing on the level of individual molecules, these interactions define the behavior of infectious agents and the outcomes they elicit. Discovery of host-pathogen interactions (HPIs) conventionally involves a stepwise laborious research process. Yet, amid the global pandemic the urge for rapid discovery acceleration through the novel computational methodologies has become ever so poignant. This review explores the challenges of HPI discovery and investigates the efforts currently undertaken to apply the latest machine learning (ML) and artificial intelligence (AI) methodologies to this field. This includes applications to molecular and genetic data, as well as image and language data. Furthermore, a number of breakthroughs, obstacles, along with prospects of AI for host-pathogen interactions (HPI), are discussed.

Computational predictions for protein sequences of COVID-19 virus via machine learning algorithms

The rapid spread of coronavirus disease (COVID-19) has become a worldwide pandemic and affected more than 15 million patients reported in 27 countries. Therefore, the computational biology carrying this virus that correlates with the human population urgently needs to be understood. In this paper, the classification of the human protein sequences of COVID-19, according to the country, is presented based on machine learning algorithms. The proposed model is based on distinguishing 9238 sequences using three stages, including data preprocessing, data labeling, and classification. In the first stage, data preprocessing's function converts the amino acids of COVID-19 protein sequences into eight groups of numbers based on the amino acids' volume and dipole. It is based on the conjoint triad (CT) method. In the second stage, there are two methods for labeling data from 27 countries from 0 to 26. The first method is based on selecting one number for each country according to the code numbers of countries, while the second method is based on binary elements for each country. According to their countries, machine learning algorithms are used to discover different COVID-19 protein sequences in the last stage. The obtained results demonstrate 100% accuracy, 100% sensitivity, and 90% specificity via the country-based binary labeling method with a linear support vector machine (SVM) classifier. Furthermore, with significant infection data, the USA is more prone to correct classification compared to other countries with fewer data. The unbalanced data for COVID-19 protein sequences is considered a major issue, especially as the US's available data represents 76% of a total of 9238 sequences. The proposed model will act as a prediction tool for the COVID-19 protein sequences in different countries.

Derivatives of Natural Chlorophylls as Agents for Antimicrobial Photodynamic Therapy

The rapid growth of drug-resistant bacteria all over the world has given rise to a major research challenge, namely a search for alternative treatments to which bacteria will be unable to develop resistance. Photodynamic therapy is an approach of this kind. It involves the use of photosensitizers in combination with visible light at a certain wavelength to excite the former and generate reactive oxygen species. Various synthetic heterocyclic compounds are used as photosensitizers. Of these, derivatives of natural chlorophylls have a special place due to their properties. This review deals with the use of such compounds in antimicrobial PDT.

Chlamydia Trachomatis Infection: Their potential implication in the Etiology of Cervical Cancer

Pathogenic bacterial strains can alter the normal function of cells and induce different levels of inflammatory responses that are connected to the development of different diseases, such as tuberculosis, diarrhea, cancer etc. Chlamydia trachomatis (C. trachomatis) is an intracellular obligate gram-negative bacterium which has been connected with the cervical cancer etiology. Nevertheless, establishment of causality and the underlying mechanisms of carcinogenesis of cervical cancer associated with C. trachomatis remain unclear. Studies reveal the existence of C. trachomatis in cervical cancer patients. The DNA repair pathways including mismatch repair, nucleotide excision, and base excision are vital in the abatement of accumulated mutations that can direct to the process of carcinogenesis. C. trachomatis recruits DDR proteins away from sites of DNA damage and, in this way, impedes the DDR. Therefore, by disturbing host cell-cycle control, chromatin and DDR repair, C. trachomatis makes a situation favorable for malignant transformation. Inflammation originated due to infection directs over production of reactive oxygen species (ROS) and consequent oxidative DNA damage. This review may aid our current understanding of the etiology of cervical cancer in C. trachomatis-infected patients.

Host-Parasite Interaction of Atlantic salmon (Salmo salar) and the Ectoparasite Neoparamoeba perurans in Amoebic Gill Disease

Marine farmed Atlantic salmon (Salmo salar) are susceptible to recurrent amoebic gill disease (AGD) caused by the ectoparasite Neoparamoeba perurans over the growout production cycle. The parasite elicits a highly localized response within the gill epithelium resulting in multifocal mucoid patches at the site of parasite attachment. This host-parasite response drives a complex immune reaction, which remains poorly understood. To generate a model for host-parasite interaction during pathogenesis of AGD in Atlantic salmon the local (gill) and systemic transcriptomic response in the host, and the parasite during AGD pathogenesis was explored. A dual RNA-seq approach together with differential gene expression and system-wide statistical analyses of gene and transcription factor networks was employed. A multi-tissue transcriptomic data set was generated from the gill (including both lesioned and non-lesioned tissue), head kidney and spleen tissues naïve and AGD-affected Atlantic salmon sourced from an in vivo AGD challenge trial. Differential gene expression of the salmon host indicates local and systemic upregulation of defense and immune responses. Two transcription factors, znfOZF-like and znf70-like, and their associated gene networks significantly altered with disease state. The majority of genes in these networks are candidates for mediators of the immune response, cellular proliferation and invasion. These include Aurora kinase B-like, rho guanine nucleotide exchange factor 25-like and protein NDNF-like inhibited. Analysis of the N. perurans transcriptome during AGD pathology compared to in vitro cultured N. perurans trophozoites, as a proxy for wild type trophozoites, identified multiple gene candidates for virulence and indicates a potential master regulatory gene system analogous to the two-component PhoP/Q system. Candidate genes identified are associated with invasion of host tissue, evasion of host defense mechanisms and formation of the mucoid lesion. We generated a novel model for host-parasite interaction during AGD pathogenesis through integration of host and parasite functional profiles. Collectively, this dual transcriptomic study provides novel molecular insights into the pathology of AGD and provides alternative theories for future research in a step towards improved management of AGD.

The Role of Sialic Acids in the Establishment of Infections by Pathogens, With Special Focus on Leishmania

Carbohydrates or glycans are ubiquitous components of the cell surface which play crucial biological and structural roles. Sialic acids (Sias) are nine-carbon atoms sugars usually present as terminal residues of glycoproteins and glycolipids on the cell surface or secreted. They have important roles in cellular communication and also in infection and survival of pathogens. More than 20 pathogens can synthesize or capture Sias from their hosts and incorporate them into their own glycoconjugates and derivatives. Sialylation of pathogens’ glycoconjugates may be crucial for survival inside the host for numerous reasons. The role of Sias in protozoa such as Trypanosoma and Leishmania was demonstrated in previous studies. This review highlights the importance of Sias in several pathogenic infections, focusing on Leishmania. We describe in detail the contributions of Sias, Siglecs (sialic acid binding Ig-like lectins) and Neuraminidase 1 (NEU 1) in the course of Leishmania infection. A detailed view on the structural and functional diversity of Leishmania-related Sias and host-cell receptors will be provided, as well as the results of functional studies performed with different Leishmania species.

Machine Learning in Discovery of New Antivirals and Optimization of Viral Infections Therapy

Nowadays, computational approaches play an important role in the design of new drug-like compounds and optimization of pharmacotherapeutic treatment of diseases. The emerging growth of viral infections, including those caused by the Human Immunodeficiency Virus (HIV), Ebola virus, recently detected coronavirus, and some others, leads to many newly infected people with a high risk of death or severe complications. A huge amount of chemical, biological, clinical data is at the disposal of the researchers. Therefore, there are many opportunities to find the relationships between the particular features of chemical data and the antiviral activity of biologically active compounds based on machine learning approaches. Biological and clinical data can also be used for building models to predict relationships between viral genotype and drug resistance, which might help determine the clinical outcome of treatment. In the current study, we consider machine-learning approaches in the antiviral research carried out during the past decade. We overview in detail the application of machine-learning methods for the design of new potential antiviral agents and vaccines, drug resistance prediction, and analysis of virus-host interactions. Our review also covers the perspectives of using the machine-learning approaches for antiviral research, including Dengue, Ebola viruses, Influenza A, Human Immunodeficiency Virus, coronaviruses, and some others.

Farfantepenaeus gene-encoded antimicrobial peptides: Identification, molecular characterization and gene expression in response to fungal infections

The aim of this study was to identify and characterize, at the molecular and transcriptional levels, sequences encoding the different members of the four families of shrimp antimicrobial peptides (AMPs) in species of the genus Farfantepenaeus. The identification of the AMP sequences was performed by in silico analysis as well as by molecular cloning and nucleotide sequencing. We identified all seven shrimp ALFs (ALF-A to ALF-G), both Type IIa and Type IIb crustins as well as two stylicins (STY1 and STY2) in Farfantepenaeus. Only two genes (PEN1/2 and PEN4) of the four-member penaeidin family (PEN1/2 to PEN5) were found and this is the first report of stylicins as well as of several additional members of ALFs, crustins and penaeidins in species of the genus Farfantepenaeus. All AMP genes have shown to be constitutively transcribed in the shrimp immune cells (hemocytes), except for ALF-G. Finally, the transcriptional profile of the different AMPs was assessed in the hemocytes of F. paulensis (pink shrimp) following an experimental infection with the opportunistic filamentous fungus Fusarium solani. We found that while the expression of ALF-B was induced at 24 h, the STY2 gene was down-regulated at 48 h post-challenge. These results provide evidence of the molecular diversity of AMPs from shrimp of the genus Farfantepenaeus in terms of sequences, biochemical properties and expression profiles in response to infectious diseases.

Current status and future perspectives of computational studies on human-virus protein-protein interactions

The protein-protein interactions (PPIs) between human and viruses mediate viral infection and host immunity processes. Therefore, the study of human-virus PPIs can help us understand the principles of human-virus relationships and can thus guide the development of highly effective drugs to break the transmission of viral infectious diseases. Recent years have witnessed the rapid accumulation of experimentally identified human-virus PPI data, which provides an unprecedented opportunity for bioinformatics studies revolving around human-virus PPIs. In this article, we provide a comprehensive overview of computational studies on human-virus PPIs, especially focusing on the method development for human-virus PPI predictions. We briefly introduce the experimental detection methods and existing database resources of human-virus PPIs, and then discuss the research progress in the development of computational prediction methods. In particular, we elaborate the machine learning-based prediction methods and highlight the need to embrace state-of-the-art deep-learning algorithms and new feature engineering techniques (e.g. the protein embedding technique derived from natural language processing). To further advance the understanding in this research topic, we also outline the practical applications of the human-virus interactome in fundamental biological discovery and new antiviral therapy development.

Fish Pathology Research and Diagnosis in Aquaculture of Farmed Fish; a Proteomics Perspective

One of the main constraints in aquaculture production is farmed fish vulnerability to diseases due to husbandry practices or external factors like pollution, climate changes, or even the alterations in the dynamic of product transactions in this industry. It is though important to better understand and characterize the intervenients in the process of a disease outbreak as these lead to huge economical losses in aquaculture industries. High-throughput technologies like proteomics can be an important characterization tool especially in pathogen identification and the virulence mechanisms related to host-pathogen interactions on disease research and diagnostics that will help to control, prevent, and treat diseases in farmed fish. Proteomics important role is also maximized by its holistic approach to understanding pathogenesis processes and fish responses to external factors like stress or temperature making it one of the most promising tools for fish pathology research.

Trypanosoma cruzi and Toxoplasma gondii Induce a Differential MicroRNA Profile in Human Placental Explants

Trypanosoma cruzi and Toxoplasma gondii are two parasites than can be transmitted from mother to child through the placenta. However, congenital transmission rates are low for T. cruzi and high for T. gondii. Infection success or failure depends on complex parasite-host interactions in which parasites can alter host gene expression by modulating non-coding RNAs such as miRNAs. As of yet, there are no reports on altered miRNA expression in placental tissue in response to either parasite. Therefore, we infected human placental explants ex vivo by cultivation with either T. cruzi or T. gondii for 2 h. We then analyzed the miRNA expression profiles of both types of infected tissue by miRNA sequencing and quantitative PCR, sequence-based miRNA target prediction, pathway functional enrichment, and upstream regulator analysis of differentially expressed genes targeted by differentially expressed miRNAs. Both parasites induced specific miRNA profiles. GO analysis revealed that the in silico predicted targets of the differentially expressed miRNAs regulated different cellular processes involved in development and immunity, and most of the identified KEGG pathways were related to chronic diseases and infection. Considering that the differentially expressed miRNAs identified here modulated crucial host cellular targets that participate in determining the success of infection, these miRNAs might explain the differing congenital transmission rates between the two parasites. Molecules of the different pathways that are regulated by miRNAs and modulated during infection, as well as the miRNAs themselves, may be potential targets for the therapeutic control of either congenital Chagas disease or toxoplasmosis.

Immunoactive photosensitizers had photodynamic immunostimulatory and immunomodulatory effects on mammalian macrophages

Photodynamic compounds have great potential in biological applications. Their controlled and localized activation with specific wavelength of light provides opportunities to potentially evade the side effects of today's cancer therapies. Biologically compatible photosensitizers can be used in therapy against cancer, infections as well as inflammatory and immune disorders. In this study, we examined chlorophyll derivatives for anti-microbial, immunostimulatory and immunomodulatory activities. Under dark conditions, these chlorophyll derivatives had strong anti-microbial activities on gram positive S.aureus and gram negative E.coli. Photo activation of the chlorophyll derivatives did not alter their anti-microbial activities on gram negative or gram positive bacteria. In order to examine how these anti-microbial chlorophyll derivatives might effect immune reaction of macrophages, they were tested on mammalian macrophages. They had immunostimulatory activities on them in the dark conditions since they led to increased TNF and IL6 cytokine production even in the absence of stimulants lipopolysaccharide (LPS) and lipoteichoic acid (LTA). Photo-activation of the compounds led to decrease in pro-inflammatory cytokines, TNF and IL6, production by LPS or LTA activated macrophages. Therefore, these molecules can be used to regulate the immune response in the patients with bacterial infection while leading to death of bacteria. Light induced activation of the compounds could enable localized and controlled activation of their anti-inflammatory effects.

Machine learning techniques for sequence-based prediction of viral-host interactions between SARS-CoV-2 and human proteins

BACKGROUND

COVID-19 (Coronavirus Disease-19), a disease caused by the SARS-CoV-2 virus, has been declared as a pandemic by the World Health Organization on March 11, 2020. Over 15 million people have already been affected worldwide by COVID-19, resulting in more than 0.6 million deaths. Protein-protein interactions (PPIs) play a key role in the cellular process of SARS-CoV-2 virus infection in the human body. Recently a study has reported some SARS-CoV-2 proteins that interact with several human proteins while many potential interactions remain to be identified.

METHOD

In this article, various machine learning models are built to predict the PPIs between the virus and human proteins that are further validated using biological experiments. The classification models are prepared based on different sequence-based features of human proteins like amino acid composition, pseudo amino acid composition, and conjoint triad.

RESULT

We have built an ensemble voting classifier using SVMRadial, SVMPolynomial, and Random Forest technique that gives a greater accuracy, precision, specificity, recall, and F1 score compared to all other models used in the work. A total of 1326 potential human target proteins of SARS-CoV-2 have been predicted by the proposed ensemble model and validated using gene ontology and KEGG pathway enrichment analysis. Several repurposable drugs targeting the predicted interactions are also reported.

CONCLUSION

This study may encourage the identification of potential targets for more effective anti-COVID drug discovery.

Systematic evaluation of machine learning methods for identifying human-pathogen protein-protein interactions

In recent years, high-throughput experimental techniques have significantly enhanced the accuracy and coverage of protein-protein interaction identification, including human-pathogen protein-protein interactions (HP-PPIs). Despite this progress, experimental methods are, in general, expensive in terms of both time and labour costs, especially considering that there are enormous amounts of potential protein-interacting partners. Developing computational methods to predict interactions between human and bacteria pathogen has thus become critical and meaningful, in both facilitating the detection of interactions and mining incomplete interaction maps. In this paper, we present a systematic evaluation of machine learning-based computational methods for human-bacterium protein-protein interactions (HB-PPIs). We first reviewed a vast number of publicly available databases of HP-PPIs and then critically evaluate the availability of these databases. Benefitting from its well-structured nature, we subsequently preprocess the data and identified six bacterium pathogens that could be used to study bacterium subjects in which a human was the host. Additionally, we thoroughly reviewed the literature on 'host-pathogen interactions' whereby existing models were summarized that we used to jointly study the impact of different feature representation algorithms and evaluate the performance of existing machine learning computational models. Owing to the abundance of sequence information and the limited scale of other protein-related information, we adopted the primary protocol from the literature and dedicated our analysis to a comprehensive assessment of sequence information and machine learning models. A systematic evaluation of machine learning models and a wide range of feature representation algorithms based on sequence information are presented as a comparison survey towards the prediction performance evaluation of HB-PPIs.

ASAPP: Architectural Similarity-Based Automated Pathway Prediction System and Its Application in Host-Pathogen Interactions

The significance of metabolic pathway prediction is to envision the viable unknown transformations that can occur provided the appropriate enzymes are present. It can facilitate the prediction of the consequences of host-pathogen interactions. In this article, we have proposed a new algorithm Architectural Similarity-based Automated Pathway Prediction (ASAPP) to predict metabolic pathways based on the structural similarity among the metabolites. ASAPP takes two-dimensional structure and molecular weight of metabolites as input, and generates a list of probable transformations without the knowledge of any externally established reactions, with an accuracy of 85.09 percent. ASAPP has also been applied to predict the outcome of pathogen liberated toxins on the carbohydrate and lipid pathways of the hosts. We have analyzed the disruption of host pathways in the presence of toxins, and have found that some metabolites in Glycolysis and the TCA cycle have a high chance of being the breakpoints in the pathway. The tool is available at http://asapp.droppages.com/.

Comparative ex vivo infection with Trypanosoma cruzi and Toxoplasma gondii of human, canine and ovine placenta: Analysis of tissue damage and infection efficiency

Trypanosoma cruzi, the causative agent of Chagas disease, and Toxoplasma gondii, which is responsible for Toxoplasmosis, are two parasites that cause significant protozoan zoonoses and consequently important economic losses in human, companion animals and livestock. For the congenital transmission to occur, both parasites must cross the barrier present in the mammalian placenta, which differs between species. Particularly, hemochorial, endotheliochorial and epitheliochorial placental barriers are present, respectively, in human, dog and sheep. The type of placental barrier has been associated with the probability of transmission of pathogens. In this study, we used experimental placental ex vivo infection models of T. cruzi and T. gondii in the above-mentioned mammals in order to study tissue alterations and to compare infection efficiency. Here, we infected placental term explants from human, dog and sheep and analyzed tissue damage by standard histological and histochemical methods. Comparative infection efficiency was determined by quantitative PCR. Both parasites are able to infect the different placental explants; however, more T. gondii parasites were detected, and T. gondii causes a more severe tissue damage in human and canine explants than T. cruzi. The histopathological changes observed in ovine placenta explants were similar in presence of both parasites. We conclude that the infection efficiency of T. gondii is higher, compared to T. cruzi, during the ex vivo infection of human, canine and ovine placental explants. In addition, the ex vivo infection of mammalian placental explants constitutes an interesting experimental approach to study part of the infection mechanisms as well as host responses during congenital infection of both parasites.

Using Omics Technologies and Systems Biology to Identify Epitope Targets for the Development of Monoclonal Antibodies Against Antibiotic-Resistant Bacteria

Over the past few decades, antimicrobial resistance has emerged as an important threat to public health due to the global dissemination of multidrug-resistant strains from several bacterial species. This worrisome trend, in addition to the paucity of new antibiotics with novel mechanisms of action in the development pipeline, warrants the development of non-antimicrobial approaches to combating infection caused by these isolates. Monoclonal antibodies (mAbs) have emerged as highly effective molecules for the treatment of multiple diseases. However, in spite of the fact that antibodies play an important role in protective immunity against bacteria, only three mAb therapies have been approved for clinical use in the treatment of bacterial infections. In the present review, we briefly outline the therapeutic potential of mAbs in the treatment of bacterial diseases and discuss how their development can be facilitated when assisted by “omics” technologies and interpreted under a systems biology paradigm. Specifically, methods employing large genomic, transcriptomic, structural, and proteomic datasets allow for the rational identification of epitopes. Ideally, these include those that are present in the majority of circulating isolates, highly conserved at the amino acid level, surface-exposed, located on antigens essential for virulence, and expressed during critical stages of infection. Therefore, these knowledge-based approaches can contribute to the identification of high-value epitopes for the development of effective mAbs against challenging bacterial clones.

Impact of Host Genetics and Biological Response Modifiers on Respiratory Tract Infections

Host susceptibility to respiratory tract infections (RTI) is dependent on both genetic and acquired risk factors. Repeated bacterial and viral RTI, such as pneumonia from encapsulated microorganisms, respiratory tract infections related to respiratory syncytial virus or influenza, and even the development of bronchiectasis and asthma, are often reported as the first symptom of primary immunodeficiencies. In the same way, neutropenia is a well-known risk factor for invasive aspergillosis, as well as lymphopenia for Pneumocystis, and mycobacterial infections. However, in the last decades a better knowledge of immune signaling networks and the introduction of next generation sequencing have increased the number and diversity of known inborn errors of immunity. On the other hand, the use of monoclonal antibodies targeting cytokines, such as tumor necrosis factor alpha has revealed new risk groups for infections, such as tuberculosis. The use of biological response modifiers has spread to almost all medical specialties, including inflammatory diseases and neoplasia, and are being used to target different signaling networks that may mirror some of the known immune deficiencies. From a clinical perspective, the individual contribution of genetics, and/or targeted treatments, to immune dysregulation is difficult to assess. The aim of this article is to review the known and newly described mechanisms of impaired immune signaling that predispose to RTI, including new insights into host genetics and the impact of biological response modifiers, and to summarize clinical recommendations regarding vaccines and prophylactic treatments in order to prevent infections.

Understanding Human-Virus Protein-Protein Interactions Using a Human Protein Complex-Based Analysis Framework

Although human protein complexes have been reported to be directly related to viral infection, previous studies have not systematically investigated human-virus PPIs from the perspective of human protein complexes. To the best of our knowledge, we have presented here the most comprehensive and in-depth analysis of human-virus PPIs in the context of VTCs. Our findings confirm that human protein complexes are heavily involved in viral infection. The observed preferences of virally targeted subunits within complexes reflect the mechanisms used by viruses to manipulate host protein complexes. The identified periodic expression patterns of the VTCs and the corresponding candidates could increase our understanding of how viruses manipulate the host cell cycle. Finally, our proposed conceptual application framework of VTCs and the developed VTcomplex could provide new hints to develop antiviral drugs for the clinical treatment of viral infections.

Computational analysis of human-virus protein-protein interaction (PPI) data is an effective way toward systems understanding the molecular mechanism of viral infection. Previous work has mainly focused on characterizing the global properties of viral targets within the entire human PPI network. In comparison, how viruses manipulate host local networks (e.g., human protein complexes) has been rarely addressed from a computational perspective. By mainly integrating information about human-virus PPIs, human protein complexes, and gene expression profiles, we performed a large-scale analysis of virally targeted complexes (VTCs) related to five common human-pathogenic viruses, including influenza A virus subtype H1N1, human immunodeficiency virus type 1, Epstein-Barr virus, human papillomavirus, and hepatitis C virus. We found that viral targets are enriched within human protein complexes. We observed in the context of VTCs that viral targets tended to have a high within-complex degree and to be scaffold and housekeeping proteins. Complexes that are essential for viral propagation were simultaneously targeted by multiple viruses. We characterized the periodic expression patterns of VTCs and provided the corresponding candidates that may be involved in the manipulation of the host cell cycle. As a potential application of the current analysis, we proposed a VTC-based antiviral drug target discovery strategy. Finally, we developed an online VTC-related platform known as VTcomplex (http://zzdlab.com/vtcomplex/index.php or http://systbio.cau.edu.cn/vtcomplex/index.php). We hope that the current analysis can provide new insights into the global landscape of human-virus PPIs at the VTC level and that the developed VTcomplex will become a vital resource for the community.

IMPORTANCE Although human protein complexes have been reported to be directly related to viral infection, previous studies have not systematically investigated human-virus PPIs from the perspective of human protein complexes. To the best of our knowledge, we have presented here the most comprehensive and in-depth analysis of human-virus PPIs in the context of VTCs. Our findings confirm that human protein complexes are heavily involved in viral infection. The observed preferences of virally targeted subunits within complexes reflect the mechanisms used by viruses to manipulate host protein complexes. The identified periodic expression patterns of the VTCs and the corresponding candidates could increase our understanding of how viruses manipulate the host cell cycle. Finally, our proposed conceptual application framework of VTCs and the developed VTcomplex could provide new hints to develop antiviral drugs for the clinical treatment of viral infections.

Differential infectivity of two Trypanosoma cruzi strains in placental cells and tissue

Congenital Chagas disease, caused by Trypanosoma cruzi (T. cruzi), has become epidemiologically relevant. The probability of congenital transmission depends on the maternal and developing fetal/newborn immune responses, placental factors and importantly, the virulence of the parasite. It has been proposed, that different genotypes of T. cruzi and their associated pathogenicity, virulence and tissue tropism may play an important role in congenital infection. Since there is no laboratory or animal model that recapitulates the complexities of vertical transmission in humans, here we studied parasite infectivity in human placental explants (HPE) as well as in the human trophoblast-derived cell line BeWo of the Y(DTU II) and the VD (TcVI) T. cruzi strains; the latter was isolated from a human case of congenital infection. Our results show that the VD strain is more infective and pathogenic than the Y strain, as demonstrated by qPCR and cell counting as well as by histopathological analysis. The present study constitutes the first approach to study the relationship between parasite two parasite strains from different genotypes and the infection efficiency in human placenta.

Host-parasite interaction: changes in human placental gene expression induced by Trypanosoma cruzi

Background

Chagas disease is caused by Trypanosoma cruzi, a parasite endemic to Latin America. Most infections occur in children by vector or congenital transmission. Trypanosoma cruzi establishes a complexity of specific molecular parasite-host cell interactions to invade the host. However, most studies have been mainly focused on the interaction between the parasite and different cell types, but not on the infection and invasion on a tissue level. During congenital transmission, T. cruzi must cross the placental barrier, composed of epithelial and connective tissues, in order to infect the developing fetus. Here we aimed to study the global changes of transcriptome in the placental tissue after a T. cruzi challenge.

Results

Strong changes in gene expression profiling were found in the different experimental conditions, involving the reprogramming of gene expression in genes involved in the innate immune response.

Conclusions

Trypanosoma cruzi induces strong changes in genes involved in a wide range of pathways, especially those involved in immune response against infections.

Electronic supplementary material

The online version of this article (10.1186/s13071-018-2988-0) contains supplementary material, which is available to authorized users.

Novel approach to study gastropod-mediated innate immune reactions against metastrongyloid parasites

The anthropozoonotic metastrongyloid nematodes Angiostrongylus cantonensis and Angiostrongylus costaricensis, as well as Angiostrongylus vasorum, Crenosoma vulpis, Aelurostrongylus abstrusus and Troglostrongylus brevior are currently considered as emerging gastropod-borne parasites and have gained growing scientific attention in the last years. However, the knowledge on invertebrate immune responses and on how metastrongyloid larvae are attacked by gastropod immune cells is still limited. This work aims to describe an in vitro system to investigate haemocyte-derived innate immune responses of terrestrial gastropods induced by vital axenic metastrongyloid larvae. We also provide protocols on slug/snail management and breeding under standardized climate conditions (circadian cycle, temperature and humidity) for the generation of parasite-free F0 stages which are essential for immune-related investigations. Adult slug species (Arion lusitanicus, Limax maximus) and giant snails (Achatina fulica) were maintained in fully automated climate chambers until mating and production of fertilized eggs. Newly hatched F0 juvenile specimens were kept under parasite-free conditions before experimental use. An improved protocol for gastropod haemolymph collection and haemocyte isolation was established. Giemsa-stained haemolymph preparations showed adequate haemocyte isolation in all three gastropod species. Additionally, a protocol for the production of axenic first and third stage larvae (L1, L3) was established. Haemocyte functionality was tested in haemocyte-nematode-co-cultures. Scanning electron microscopy (SEM) and light microscopy analyses revealed that gastropod-derived haemocytes formed clusters as well as DNA-rich extracellular aggregates catching larvae and decreasing their motility. These data confirm the usefulness of the presented methods to study haemocyte-mediated gastropod immune responses to better understand the complex biology of gastropod-borne diseases.

Systems Biology Modeling to Study Pathogen-Host Interactions

Pathogen-host interactions (PHIs) underlie the process of infection. The systems biology view of the whole PHI system is superior to the investigation of the pathogen or host separately in understanding the infection mechanisms. Especially, the identification of host-oriented drug targets for the next-generation anti-infection therapeutics requires the properties of the host factors targeted by pathogens. Here, we provide an outline of computational analysis of PHI networks, focusing on the properties of the pathogen-targeted host proteins. We also provide information about the available PHI data and the related Web-based resources.

Toll-like receptor-2 mediates local innate immune response against Trypanosoma cruzi in ex vivo infected human placental chorionic villi explants

BACKGROUND

Congenital Chagas disease is caused by the protozoan parasite Trypanosoma cruzi that must cross the placental barrier during transmission. The trophoblast constitutes the first tissue in contact with the maternal-blood circulating parasite. Importantly, the congenital transmission rates are low, suggesting the presence of local placental defense mechanisms. On the other hand, the placenta is considered an immune regulatory organ since it acts as a modulator of fetal and maternal immune responses. We have previously proposed that local placental factors, such as the epithelial turnover of the trophoblast and the innate immune response initiated by Toll-like receptors (TLRs), might prevent parasite infection and congenital transmission. Here, we studied in an ex vivo infected human placental chorionic villi explant HPCVE model, the relationship between TLR-2 activation in response to T. cruzi trypomastigotes, the secreted profile of cytokines, the integrity of the placental barrier and the expression of trophoblast turnover markers.

RESULTS

TLR-2 inhibition increases the parasite induced histopathological damage, prevents secretion of IL-6 and IL-10, decreases expression of PCNA (proliferation marker) and of β-hCG (differentiation marker) while increasing caspase 3 activity (cell death marker).

CONCLUSION

Our results suggest that TLR-2 is not only involved in the local secretion of cytokines but also regulates, at least partially, the trophoblast turnover.

Ex vivo infection of human placental chorionic villi explants with Trypanosoma cruzi and Toxoplasma gondii induces different Toll-like receptor expression and cytokine/chemokine profiles

PROBLEM

Trypanosoma cruzi and Toxoplasma gondii present, respectively, low and high congenital transmission rates. The placenta as an immune regulatory organ expresses TLRs, leading to the secretion of cytokines. Both parasites are recognized by TLR-2, TLR-4, and TLR-9. Here, we studied if the parasites induce differences in TLR protein expression, cytokine profiles, and whether receptor inhibition is related to parasite infection.

METHOD OF STUDY

Placental tissue explants were infected ex vivo with each parasite, TLRs protein expression, cytokine profile and parasite infection were determined by Western blotting, ELISA and qPCR.

RESULTS

Trypanosoma cruzi and Toxoplasma gondii infection is related to TLR-2 and TLR-4/TLR-9, respectively. Trypanosoma cruzi elicits an increase in TNF-α, IL-1β, IL-6, IL-8 and IL-10 cytokine secretion whereas T. gondii only increases the secretion of IL-8.

CONCLUSION

The susceptibility of the placenta to each parasite is mediated partially by the innate immune response.