Structural host-microbiota interaction networks

When inflammatory stressors dramatically change, disease phenotypes may transform between autoimmune hematopoietic failure and myeloid neoplasms

Aplastic anemia (AA) and hypoplastic myelodysplastic syndrome are paradigms of autoimmune hematopoietic failure (AHF). Myelodysplastic syndrome and acute myeloid leukemia are unequivocal myeloid neoplasms (MNs). Currently, AA is also known to be a clonal hematological disease. Genetic aberrations typically observed in MNs are detected in approximately one-third of AA patients. In AA patients harboring MN-related genetic aberrations, a poor response to immunosuppressive therapy (IST) and an increased risk of transformation to MNs occurring either naturally or after IST are predicted. Approximately 10%-15% of patients with severe AA transform the disease phenotype to MNs following IST, and in some patients, leukemic transformation emerges during or shortly after IST. Phenotypic transformations between AHF and MNs can occur reciprocally. A fraction of advanced MN patients experience an aplastic crisis during which leukemic blasts are repressed. The switch that shapes the disease phenotype is a change in the strength of extramedullary inflammation. Both AHF and MNs have an immune-active bone marrow (BM) environment (BME). In AHF patients, an inflamed BME can be evoked by infiltrated immune cells targeting neoplastic molecules, which contributes to the BM-specific autoimmune impairment. Autoimmune responses in AHF may represent an antileukemic mechanism, and inflammatory stressors strengthen antileukemic immunity, at least in a significant proportion of patients who have MN-related genetic aberrations. During active inflammatory episodes, normal and leukemic hematopoieses are suppressed, which leads to the occurrence of aplastic cytopenia and leukemic cell regression. The successful treatment of underlying infections mitigates inflammatory stress-related antileukemic activities and promotes the penetration of leukemic hematopoiesis. The effect of IST is similar to that of treating underlying infections. Investigating inflammatory stress-powered antileukemic immunity is highly important in theoretical studies and clinical practice, especially given the wide application of immune-activating agents and immune checkpoint inhibitors in the treatment of hematological neoplasms.

Tuberculosis-induced aplastic crisis and atypical lymphocyte expansion in advanced myelodysplastic syndrome: A case report and review of literature

BACKGROUND

Myelodysplastic syndrome (MDS) is caused by malignant proliferation and ineffective hematopoiesis. Oncogenic somatic mutations and increased apoptosis, necroptosis and pyroptosis lead to the accumulation of earlier hematopoietic progenitors and impaired productivity of mature blood cells. An increased percentage of myeloblasts and the presence of unfavorable somatic mutations are signs of leukemic hematopoiesis and indicators of entrance into an advanced stage. Bone marrow cellularity and myeloblasts usually increase with disease progression. However, aplastic crisis occasionally occurs in advanced MDS.

CASE SUMMARY

A 72-year-old male patient was definitively diagnosed with MDS with excess blasts-1 (MDS-EB-1) based on an increase in the percentages of myeloblasts and cluster of differentiation (CD)34+ hematopoietic progenitors and the identification of myeloid neoplasm-associated somatic mutations in bone marrow samples. The patient was treated with hypomethylation therapy and was able to maintain a steady disease state for 2 years. In the treatment process, the advanced MDS patient experienced an episode of progressive pancytopenia and bone marrow aplasia. During the aplastic crisis, the bone marrow was infiltrated with sparsely distributed atypical lymphocytes. Surprisingly, the leukemic cells disappeared. Immunological analysis revealed that the atypical lymphocytes expressed a high frequency of CD3, CD5, CD8, CD16, CD56 and CD57, suggesting the activation of autoimmune cytotoxic T-lymphocytes and natural killer (NK)/NKT cells that suppressed both normal and leukemic hematopoiesis. Elevated serum levels of inflammatory cytokines, including interleukin (IL)-6, interferon-gamma (IFN-γ) and tumor necrosis factor-alpha (TNF-α), confirmed the deranged type I immune responses. This morphological and immunological signature led to the diagnosis of severe aplastic anemia secondary to large granule lymphocyte leukemia. Disseminated tuberculosis was suspected upon radiological examinations in the search for an inflammatory niche. Antituberculosis treatment led to reversion of the aplastic crisis, disappearance of the atypical lymphocytes, increased marrow cellularity and 2 mo of hematological remission, providing strong evidence that disseminated tuberculosis was responsible for the development of the aplastic crisis, the regression of leukemic cells and the activation of CD56+ atypical lymphocytes. Reinstitution of hypomethylation therapy in the following 19 mo allowed the patient to maintain a steady disease state. However, the patient transformed the disease phenotype into acute myeloid leukemia and eventually died of disease progression and an overwhelming infectious episode.

CONCLUSION

Disseminated tuberculosis can induce CD56+ lymphocyte infiltration in the bone marrow and in turn suppress both normal and leukemic hematopoiesis, resulting in the development of aplastic crisis and leukemic cell regression.

Bowel inflammatory presentations on computed tomography in adult patients with severe aplastic anemia during flared inflammatory episodes

BACKGROUND

Patients with severe aplastic anemia (SAA) frequently present with inflammatory episodes, and during flared inflammatory episodes, hematopoietic function is further exacerbated. The gastrointestinal tract is the most common site for infectious and inflammatory diseases, and its structural and functional features confer on it the most potent capacity to affect hematopoietic and immune functions. Computed tomography (CT) is a readily accessible approach to provide highly useful information in detecting morphological changes and guiding further work-ups.

AIM

To explore CT imaging presentations of gut inflammatory damage in adult SAA patients during inflammatory episodes.

METHODS

We retrospectively evaluated the abdominal CT imaging presentations of 17 hospitalized adult patients with SAA in search of the inflammatory niche when they presented with systemic inflammatory stress and exacerbated hematopoietic function. In this descriptive manuscript, the characteristic images that suggested the presence of gastrointestinal inflammatory damage and related imaging presentations of individual patients were enumerated, analyzed and described.

RESULTS

All eligible patients with SAA had CT imaging abnormalities that suggested the presence of an impaired intestinal barrier and increased epithelial permeability. The inflammatory damages were concurrently present in the small intestine, the ileocecal region and the large intestines. Some readily identified imaging signs, such as bowel wall thickening with mural stratification (“water holo sign”, “fat holo sign”, intramural gas and subserosal pneumatosis) and mesenteric fat proliferation (fat stranding and “creeping fat sign”), fibrotic bowel wall thickening, “balloon sign”, rugged colonic configuration, heterogeneity in the bowel wall texture, and adhered and clustered small bowel loop (including various patterns of “abdominal cocoon”), occurred at a high incidence, which suggested that the damaged gastrointestinal tract is a common inflammatory niche responsible for the systemic inflammatory stresses and the exacerbated hematopoietic failure in patients with SAA. Particularly, the “fat holo sign” was present in 7 patients, a rugged colonic configuration was present in 10 patients, the adhesive bowel loop was present in 15 patients, and extraintestinal manifestations suggestive of tuberculosis infections were present in 5 patients. According to the imaging features, a suggestive diagnosis of Crohn’s disease was made in 5 patients, ulcerative colitis in 1 patient, chronic periappendiceal abscess in 1 patient, and tuberculosis infection in 5 patients. Other patients were diagnosed with chronic enteroclolitis with acutely aggravated inflammatory damage.

CONCLUSION

Patients with SAA had CT imaging patterns that suggested the presence of active chronic inflammatory conditions and aggravated inflammatory damage during flared inflammatory episodes.

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.

What we know about the relationship between autoimmune thyroid diseases and gut microbiota: a perspective on the role of probiotics on pediatric endocrinology

INTRODUCTION

Autoimmune diseases account for a cumulative overall prevalence of about 3-5% worldwide. Among them, autoimmune thyroid diseases (ATDs) are the most common and comprise two main entities: Hashimoto's thyroiditis (HT) and Graves-Basedow disease (GD). The pathogenesis of ATDs remains not fully elucidated, however the role of microbioma has been proposed. Gut microbiota exert an important influence on the intestinal barrier, nutrient metabolism and immune system development and functions.

EVIDENCE ACQUISITION

In this review, we describe on the main features of ATDs in pediatrics, focusing on the reciprocal influence between gut microbiota, thyroid hormone metabolism and thyroid autoimmunity and consider the role of probiotics and other microbiota-targeted therapies in thyroid diseases with a perspective on pediatric endocrinology.

EVIDENCE SYNTHESIS

Microbiome affects both endogenous and exogenous thyroid hormone metabolism and influences the absorption of minerals important to the thyroid function, which are iodine, selenium, zinc and iron. The alteration of the gut microbiota, with the consequent modifications in the barrier function and the increased gut permeability, seems involved in the development of autoimmune and chronic inflammatory diseases, including ATDs. The supplementation with probiotics showed beneficial effects on the thyroid hormone and thyroid function because this strategy could restore the intestinal eubiosis and the good strain microorganism proliferation.

CONCLUSIONS

Even though the evidence about the interaction between microbiota and ATDs in pediatric patients is limited, the promising results obtained in the adult population, and in other autoimmune disorders affecting children, highlight the need of for further research in the pediatric field.

HMI-PRED 2.0: a biologist-oriented web application for prediction of host-microbe protein-protein interaction by interface mimicry

SUMMARY

HMI-PRED 2.0 is a publicly available web service for the prediction of host-microbe protein-protein interaction by interface mimicry that is intended to be used without extensive computational experience. A microbial protein structure is screened against a database covering the entire available structural space of complexes of known human proteins.

AVAILABILITY AND IMPLEMENTATION

HMI-PRED 2.0 provides user-friendly graphic interfaces for predicting, visualizing and analyzing host-microbe interactions. HMI-PRED 2.0 is available at https://hmipred.org/.

AlphaFold, Artificial Intelligence (AI), and Allostery

AlphaFold has burst into our lives. A powerful algorithm that underscores the strength of biological sequence data and artificial intelligence (AI). AlphaFold has appended projects and research directions. The database it has been creating promises an untold number of applications with vast potential impacts that are still difficult to surmise. AI approaches can revolutionize personalized treatments and usher in better-informed clinical trials. They promise to make giant leaps toward reshaping and revamping drug discovery strategies, selecting and prioritizing combinations of drug targets. Here, we briefly overview AI in structural biology, including in molecular dynamics simulations and prediction of microbiota–human protein–protein interactions. We highlight the advancements accomplished by the deep-learning-powered AlphaFold in protein structure prediction and their powerful impact on the life sciences. At the same time, AlphaFold does not resolve the decades-long protein folding challenge, nor does it identify the folding pathways. The models that AlphaFold provides do not capture conformational mechanisms like frustration and allostery, which are rooted in ensembles, and controlled by their dynamic distributions. Allostery and signaling are properties of populations. AlphaFold also does not generate ensembles of intrinsically disordered proteins and regions, instead describing them by their low structural probabilities. Since AlphaFold generates single ranked structures, rather than conformational ensembles, it cannot elucidate the mechanisms of allosteric activating driver hotspot mutations nor of allosteric drug resistance. However, by capturing key features, deep learning techniques can use the single predicted conformation as the basis for generating a diverse ensemble.

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.

Dynamic, but Not Necessarily Disordered, Human-Virus Interactions Mediated through SLiMs in Viral Proteins

Most viruses have small genomes that encode proteins needed to perform essential enzymatic functions. Across virus families, primary enzyme functions are under functional constraint; however, secondary functions mediated by exposed protein surfaces that promote interactions with the host proteins may be less constrained. Viruses often form transient interactions with host proteins through conformationally flexible interfaces. Exposed flexible amino acid residues are known to evolve rapidly suggesting that secondary functions may generate diverse interaction potentials between viruses within the same viral family. One mechanism of interaction is viral mimicry through short linear motifs (SLiMs) that act as functional signatures in host proteins. Viral SLiMs display specific patterns of adjacent amino acids that resemble their host SLiMs and may occur by chance numerous times in viral proteins due to mutational and selective processes. Through mimicry of SLiMs in the host cell proteome, viruses can interfere with the protein interaction network of the host and utilize the host-cell machinery to their benefit. The overlap between rapidly evolving protein regions and the location of functionally critical SLiMs suggest that these motifs and their functional potential may be rapidly rewired causing variation in pathogenicity, infectivity, and virulence of related viruses. The following review provides an overview of known viral SLiMs with select examples of their role in the life cycle of a virus, and a discussion of the structural properties of experimentally validated SLiMs highlighting that a large portion of known viral SLiMs are devoid of predicted intrinsic disorder based on the viral SLiMs from the ELM database.

Challenges and opportunities in network-based solutions for biological questions

Network biology is useful for modeling complex biological phenomena; it has attracted attention with the advent of novel graph-based machine learning methods. However, biological applications of network methods often suffer from inadequate follow-up. In this perspective, we discuss obstacles for contemporary network approaches—particularly focusing on challenges representing biological concepts, applying machine learning methods, and interpreting and validating computational findings about biology—in an effort to catalyze actionable biological discovery.

Computational Analysis of Domains Vulnerable to HPV-16 E6 Oncoprotein and Corresponding Hot Spot Residues

BACKGROUND

Human Papilloma Virus (HPV) is the primary cause of cancers in cervix, head and neck regions. Oncoprotein E6 of HPV-16, after infecting human body, alters host protein- protein interaction networks. E6 interacts with several proteins, causing the infection to progress into cervical cancer. The molecular basis for these interactions is the presence of short linear peptide motifs on E6 identical to those on human proteins.

METHODS

Motifs of LXXLL and E/DLLL/V-G after identification on E6, were analyzed for their dynamic fluctuations by use of elastic network models. Correlation analysis of amino acid residues of E6 was also performed in specific regions of motifs.

RESULTS

Arginine, Leucine, Glutamine, Threonine and Glutamic acid have been identified as hot spot residues of E6 which can subsequently provide a platform for drug designing and understanding of pathogenesis of cervical cancer. These amino acids play a significant role in stabilizing interactions with host proteins, ultimately causing infections and cancers.

CONCLUSION

Our study validates the role of linear binding motifs of E6 of HPV in interacting with these proteins as an important event in the propagation of HPV in human cells and its transformation into cervical cancer. The study further predicts the domains of protein kinase and armadillo as part of the regions involved in the interaction of E6AP, Paxillin and TNF R1, with viral E6.

Bacterial Vaginosis: Effects on reproduction and its therapeutics

Bacterial Vaginosis (BV) is the most common vaginal infection. A large amount of evidence shows that the anatomical scope of BV's pathogenic effect is far beyond the lower reproductive tract. BV is closely related to adverse reproductive outcomes, which may be due to the infection of the vaginal flora ascending to the upper genital tract. In addition, the incidence of BV is relatively high in infertile women. The vaginal microbiome also plays an important role in women's health and diseases. For most women, the normal vaginal microbiota is dominated by Lactobacillus, which can maintain a healthy vaginal environment by producing lactic acid, H₂O₂ and bacteriocin, etc. BV is characterized by the imbalanced vaginal flora. It changes the acidic environment that is normally dominated by Lactobacillus, and causes an overgrowth of anaerobic and facultative anaerobic bacteria such as Gardnerella vaginalis and Atopobium vaginae. Studies have shown that bacterial infections in the vagina can spread to upper genital tract and cause adverse fertility outcome. Therefore, early diagnosis and therapeutics of symptomatic BV is helpful to improve the outcome of poor fertility.

Insights into the Dynamic Fluctuations of the Protein HPV16 E1 and Identification of Motifs by Using Elastic Network Modeling

BACKGROUND

Cancers of cervix, head and neck regions have been found to be associated with Human Papilloma Virus (HPV) infection. E1 protein makes an important papillomavirus replication factor. Among the ORFs of papillomaviruses, the most conserved sequence is that of the E1 ORF. It is the viral helicase with being a member of class of ATPases associated with diverse cellular activities (AAA+) helicases. The interactions of E1 with human DNA and proteins occurs in the presence of short linear peptide motifs on E1 identical to those on human proteins.

METHODS

Different Motifs were identified on HPV16 E1 by using ELMs. Elastic network models were generated by using 3D structures of E1. Their dynamic fluctuations were analyzed on the basis of B factors, correlation analysis and deformation energies.

RESULTS

3 motifs were identified on E1 which can interact with Cdk and Cyclin domains of human proteins. 11 motifs identified on E1 have their CDs of Pkinase on human proteins. LIG_MYND_2 has been identified as involved in stabilizing interaction of E1 with Hsp40 and Hsp70. These motifs and amino acids comprising these motifs play a major role in maintaining interactions with human proteins, ultimately causing infections leading to cancers.

CONCLUSION

Our study identified various motifs on E1 which interact with specific counter domains found in human proteins, already reported having the interactions with E1. We also validated the involvement of these specific motifs containing regions of E1 by modeling elastic networks of E1. These motif involving interactions could be used as drug targets.

ViralLink: An integrated workflow to investigate the effect of SARS-CoV-2 on intracellular signalling and regulatory pathways

The SARS-CoV-2 pandemic of 2020 has mobilised scientists around the globe to research all aspects of the coronavirus virus and its infection. For fruitful and rapid investigation of viral pathomechanisms, a collaborative and interdisciplinary approach is required. Therefore, we have developed ViralLink: a systems biology workflow which reconstructs and analyses networks representing the effect of viruses on intracellular signalling. These networks trace the flow of signal from intracellular viral proteins through their human binding proteins and downstream signalling pathways, ending with transcription factors regulating genes differentially expressed upon viral exposure. In this way, the workflow provides a mechanistic insight from previously identified knowledge of virally infected cells. By default, the workflow is set up to analyse the intracellular effects of SARS-CoV-2, requiring only transcriptomics counts data as input from the user: thus, encouraging and enabling rapid multidisciplinary research. However, the wide-ranging applicability and modularity of the workflow facilitates customisation of viral context, a priori interactions and analysis methods. Through a case study of SARS-CoV-2 infected bronchial/tracheal epithelial cells, we evidence the functionality of the workflow and its ability to identify key pathways and proteins in the cellular response to infection. The application of ViralLink to different viral infections in a context specific manner using different available transcriptomics datasets will uncover key mechanisms in viral pathogenesis.

Transkingdom network analysis provides insight into host-microbiome interactions in Atlantic salmon

BACKGROUND

The Atlantic salmon gut constitutes an intriguing system for studying host-microbiota interactions due to the dramatic environmental change salmon experiences during its life cycle. Yet, little is known about the role of interactions in this system and there is a general deficit in computational methods for integrative analysis of omics data from host-microbiota systems.

METHODS

We developed a pipeline to integrate host RNAseq data and microbial 16S rRNA amplicon sequencing data using weighted correlation network analysis. Networks are first inferred from each dataset separately, followed by module detections and finally robust identification of interactions via comparisons of representative module profiles. Through the use of module profiles, this network-based dimensionality reduction approach provides a holistic view into the discovery of potential host-microbiota symbionts.

RESULTS

We analyzed host gene expression from the gut epithelial tissue and microbial abundances from the salmon gut in a long-term feeding trial spanning the fresh-/salt-water transition and including two feeds resembling the fatty acid compositions available in salt- and fresh-water environments, respectively. We identified several host modules with significant correlations to both microbiota modules and variables such as feed, growth and sex. Although the strongest associations largely coincided with the fresh-/salt-water transition, there was a second layer of correlations associating smaller host modules to both variables and microbiota modules. Hence, we identify extensive reprogramming of the gut epithelial transcriptome and large scale coordinated changes in gut microbiota composition associated with water type as well as evidence of host-microbiota interactions linked to feed.

Roles of host genetics and sperm microbiota in reproductive success in healthy rabbit

Although the effects of sperm microbiota and sperm quality have been described previously, recent studies provide evidence that female genital modifications triggered by seminal components could be of significant importance to identify some disturbances associated with fertility. So, sperm microbiota could play a key role in sperm quality, contributing to fertilisation. To understand how sperm microbiota diversity is influenced by the host genetics, the symbiotic bacteria in four inbred lines raised in the same animal facility and their effects on sperm quality and fertility were analysed. Forty healthy rabbits from four selected Spanish commercial lines were used in this research (three based on litter performance, designated A, V and LP, and one selected for daily body weight gain, called R). Significant variations in the seminal concentration, morphology and some motion parameters were found among inbred lines, but sperm motility and viability were similar among inbred lines. After mating, inbred lines selected for litter size had the same fertility rate, significantly higher than inbred line selected for body weight (82 ± 3.3%, 79 ± 3.5% and 89 ± 4.5% versus 61 ± 3.7%, for the A, V and LP vs R lines, respectively, p < 0.05). Bacteria belonging to Proteobacteria, Firmicutes, Fusobacteria and Bacteroidetes were identified in sperm microbiota. At genus level, the bacterial community composition in the sperm microbiota was influenced by host genetics. A total of 35, 16, 34, and 51 genera were accurately detected in the A, V, LP, and R lines, respectively. Moreover, Enhydrobacter, Ferruginibacter, Myroides Paracoccus, Rheinheimera, Tepidiphilus, Tetradesmus obliquus and Thauera genera were present only in the inbred lines selected for litter size. Moreover, the discriminant analysis revealed Lysinibacillus and Flavobacterium genera as potential biomarkers for fertility. Thus, these two genera may play a key role in fertility. Our results demonstrated the existence of a rabbit inbred line-specific variation in bacterial occurrence in sperm microbiota. Moreover, fertility differentials among inbred lines that were not predicted by routine semen analysis could be partly explained by the symbiotic state of the semen microbiota.

Predicting host taxonomic information from viral genomes: A comparison of feature representations

The rise in metagenomics has led to an exponential growth in virus discovery. However, the majority of these new virus sequences have no assigned host. Current machine learning approaches to predicting virus host interactions have a tendency to focus on nucleotide features, ignoring other representations of genomic information. Here we investigate the predictive potential of features generated from four different ‘levels’ of viral genome representation: nucleotide, amino acid, amino acid properties and protein domains. This more fully exploits the biological information present in the virus genomes. Over a hundred and eighty binary datasets for infecting versus non-infecting viruses at all taxonomic ranks of both eukaryote and prokaryote hosts were compiled. The viral genomes were converted into the four different levels of genome representation and twenty feature sets were generated by extracting k-mer compositions and predicted protein domains. We trained and tested Support Vector Machine, SVM, classifiers to compare the predictive capacity of each of these feature sets for each dataset. Our results show that all levels of genome representation are consistently predictive of host taxonomy and that prediction k-mer composition improves with increasing k-mer length for all k-mer based features. Using a phylogenetically aware holdout method, we demonstrate that the predictive feature sets contain signals reflecting both the evolutionary relationship between the viruses infecting related hosts, and host-mimicry. Our results demonstrate that incorporating a range of complementary features, generated purely from virus genome sequences, leads to improved accuracy for a range of virus host prediction tasks enabling computational assignment of host taxonomic information.

Elucidating the host of a newly identified virus species is an important challenge, with applications from knowing the source species of a newly emerged pathogen to understanding the bacteriophage-host relationships within the microbiome of any of earth’s ecosystems. Current high throughput methods used to identify viruses within biological or environmental samples have resulted in an unprecedented increase in virus discovery. However, for the majority of these virus genomes the host species/taxonomic classification remains unknown. To address this gap in our knowledge there is a need for fast, accurate computational methods for the assignment of putative host taxonomic information. Machine learning is an ideal approach but to maximise predictive accuracy the viral genomes need to be represented in a format (sets of features) that makes the discriminative information available to the machine learning algorithm. Here, we compare different types of features derived from the same viral genomes for their ability to predict host information. Our results demonstrate that all these feature sets are predictive of host taxonomy and when combined have the potential to improve accuracy over the use of individual feature sets across many virus host prediction applications.

MicrobioLink: An Integrated Computational Pipeline to Infer Functional Effects of Microbiome-Host Interactions

Microbiome–host interactions play significant roles in health and in various diseases including autoimmune disorders. Uncovering these inter-kingdom cross-talks propels our understanding of disease pathogenesis and provides useful leads on potential therapeutic targets. Despite the biological significance of microbe–host interactions, there is a big gap in understanding the downstream effects of these interactions on host processes. Computational methods are expected to fill this gap by generating, integrating, and prioritizing predictions—as experimental detection remains challenging due to feasibility issues. Here, we present MicrobioLink, a computational pipeline to integrate predicted interactions between microbial and host proteins together with host molecular networks. Using the concept of network diffusion, MicrobioLink can analyse how microbial proteins in a certain context are influencing cellular processes by modulating gene or protein expression. We demonstrated the applicability of the pipeline using a case study. We used gut metaproteomic data from Crohn’s disease patients and healthy controls to uncover the mechanisms by which the microbial proteins can modulate host genes which belong to biological processes implicated in disease pathogenesis. MicrobioLink, which is agnostic of the microbial protein sources (bacterial, viral, etc.), is freely available on GitHub.

HMI-PRED: A Web Server for Structural Prediction of Host-Microbe Interactions Based on Interface Mimicry

Microbes, commensals, and pathogens, control the numerous functions in the host cells. They can alter host signaling and modulate immune surveillance by interacting with the host proteins. For shedding light on the contribution of microbes to health and disease, it is vital to discern how microbial proteins rewire host signaling and through which host proteins they do this. Host-Microbe Interaction PREDictor (HMI-PRED) is a user-friendly web server for structural prediction of protein-protein interactions (PPIs) between the host and a microbial species, including bacteria, viruses, fungi, and protozoa. HMI-PRED relies on "interface mimicry" through which the microbial proteins hijack host binding surfaces. Given the structure of a microbial protein of interest, HMI-PRED will return structural models of potential host-microbe interaction (HMI) complexes, the list of host endogenous and exogenous PPIs that can be disrupted, and tissue expression of the microbe-targeted host proteins. The server also allows users to upload homology models of microbial proteins. Broadly, it aims at large-scale, efficient identification of HMIs. The prediction results are stored in a repository for community access. HMI-PRED is free and available at https://interactome.ku.edu.tr/hmi.

An Integrated Multi-Disciplinary Perspectivefor Addressing Challenges of the Human Gut Microbiome

Our understanding of the human gut microbiome has grown exponentially. Advances in genome sequencing technologies and metagenomics analysis have enabled researchers to study microbial communities and their potential function within the context of a range of human gut related diseases and disorders. However, up until recently, much of this research has focused on characterizing the gut microbiological community structure and understanding its potential through system wide (meta) genomic and transcriptomic-based studies. Thus far, the functional output of these microbiomes, in terms of protein and metabolite expression, and within the broader context of host-gut microbiome interactions, has been limited. Furthermore, these studies highlight our need to address the issues of individual variation, and of samples as proxies. Here we provide a perspective review of the recent literature that focuses on the challenges of exploring the human gut microbiome, with a strong focus on an integrated perspective applied to these themes. In doing so, we contextualize the experimental and technical challenges of undertaking such studies and provide a framework for capitalizing on the breadth of insight such approaches afford. An integrated perspective of the human gut microbiome and the linkages to human health will pave the way forward for delivering against the objectives of precision medicine, which is targeted to specific individuals and addresses the issues and mechanisms in situ.

Gut-host Crosstalk: Methodological and Computational Challenges

Understanding how health-promoting microbiota are established and their beneficial interactions with the host is of critical biomedical importance. The current high throughput data acquisition technologies allow for integrating components of the gut ecosystem. The richness of data types and large number of measured variables involved underscores the critical importance of the appropriate choice of analytical and computational methods that can be used to model this complex ecosystem. This review outlines currently used approaches to perform analyses of data obtained as a result of interrogation of the gut-microbiota ecosystem and the challenges associated with these methodological and computational efforts. The problem of large dimensionality versus small numbers of samples is explained with discussions of clustering, dimensionality reduction, and statistical testing. Predictive modeling and data integration specific to the gut ecosystem are also discussed.

Potential Elimination of Human Gut Resistome by Exploiting the Benefits of Functional Foods

Recent advances in technology over the last decades have strived to elucidate the diverse and abundant ecosystem of the human microbiome. The intestinal microbiota represents a densely inhabited environment that offers a plethora of beneficial effects to the host's wellbeing. On the other hand, it can serve as a potential reservoir of Multi-Drug Resistant (MDR) bacteria and their antibiotic-resistant genes (ARgenes), which comprise the "gut resistome." ARgenes, like antibiotics, have been omnipresent in the environment for billions of years. In the context of the gut microbiome, these genes may conflate into exogenous MDR or emerge in commensals due to mutations or gene transfers. It is currently generally accepted that Antimicrobial Resistance (AMR) poses a serious threat to public health worldwide. It is of paramount importance that researchers focus on, amongst other parameters, elaborating strategies to manage the gut resistome, particularly focusing on the diminution of AMR. Potential interventions in the gut microbiome field by Fecal Microbiota Transplant (FMT) or functional foods are newly emerged candidates for the uprooting of MDR strains and restoring dysbiosis and resilience. Probiotic nutrition is thought to diminish gut colonization from pathobionts. Yet only a few studies have explored the effects of antibiotics use on the reservoir of AR genes and the demanding time for return to normal by gut microbiota-targeted strategies. Regular administration of probiotic bacteria has recently been linked to restoration of the gut ecosystem and decrease of the gut resistome and AR genes carriers. This review summarizes the latest information about the intestinal resistome and the intriguing methods of fighting against AMR through probiotic-based methods and gut microbial shifts that have been proposed. This study contains some key messages: (1) AMR currently poses a lethal threat to global health, and it is pivotal for the scientific community to do its utmost in fighting against it; (2) human gut microbiome research, within the last decade especially, seems to be preoccupied with the interface of numerous diseases and identifying a potential target for a variety of interventions; (3) the gut resistome, comprised of AR genesis, presents very early on in life and is prone to shifts due to the use of antibiotics or dietary supplements; and (4) future strategies involving functional foods seem promising for the battle against AMR through intestinal resistome diminution.

Trends in Symbiont-Induced Host Cellular Differentiation

Bacteria participate in a wide diversity of symbiotic associations with eukaryotic hosts that require precise interactions for bacterial recognition and persistence. Most commonly, host-associated bacteria interfere with host gene expression to modulate the immune response to the infection. However, many of these bacteria also interfere with host cellular differentiation pathways to create a hospitable niche, resulting in the formation of novel cell types, tissues, and organs. In both of these situations, bacterial symbionts must interact with eukaryotic regulatory pathways. Here, we detail what is known about how bacterial symbionts, from pathogens to mutualists, control host cellular differentiation across the central dogma, from epigenetic chromatin modifications, to transcription and mRNA processing, to translation and protein modifications. We identify four main trends from this survey. First, mechanisms for controlling host gene expression appear to evolve from symbionts co-opting cross-talk between host signaling pathways. Second, symbiont regulatory capacity is constrained by the processes that drive reductive genome evolution in host-associated bacteria. Third, the regulatory mechanisms symbionts exhibit correlate with the cost/benefit nature of the association. And, fourth, symbiont mechanisms for interacting with host genetic regulatory elements are not bound by native bacterial capabilities. Using this knowledge, we explore how the ubiquitous intracellular Wolbachia symbiont of arthropods and nematodes may modulate host cellular differentiation to manipulate host reproduction. Our survey of the literature on how infection alters gene expression in Wolbachia and its hosts revealed that, despite their intermediate-sized genomes, different strains appear capable of a wide diversity of regulatory manipulations. Given this and Wolbachia's diversity of phenotypes and eukaryotic-like proteins, we expect that many symbiont-induced host differentiation mechanisms will be discovered in this system.

Investigating host-bacterial interactions among enteric pathogens

Background

In 2017, World Health Organization (WHO) published a catalogue of 12 families of antibiotic-resistant “priority pathogens” that are posing the greatest threats to human health. Six of these dreaded pathogens are known to infect the human gastrointestinal system. In addition to causing gastrointestinal and systemic infections, these pathogens can also affect the composition of other microbes constituting the healthy gut microbiome. Such aberrations in gut microbiome can significantly affect human physiology and immunity. Identifying the virulence mechanisms of these enteric pathogens are likely to help in developing newer therapeutic strategies to counter them.

Results

Using our previously published in silico approach, we have evaluated (and compared) Host-Pathogen Protein-Protein Interaction (HPI) profiles of four groups of enteric pathogens, namely, different species of Escherichia, Shigella, Salmonella and Vibrio. Results indicate that in spite of genus/ species specific variations, most enteric pathogens possess a common repertoire of HPIs. This core set of HPIs are probably responsible for the survival of these pathogen in the harsh nutrient-limiting environment within the gut. Certain genus/ species specific HPIs were also observed.

Conslusions

The identified bacterial proteins involved in the core set of HPIs are expected to be helpful in understanding the pathogenesis of these dreaded gut pathogens in greater detail. Possible role of genus/ species specific variations in the HPI profiles in the virulence of these pathogens are also discussed. The obtained results are likely to provide an opportunity for development of novel therapeutic strategies against the most dreaded gut pathogens.

Oncoviruses Can Drive Cancer by Rewiring Signaling Pathways Through Interface Mimicry

Oncoviruses rewire host pathways to subvert host immunity and promote their survival and proliferation. However, exactly how is challenging to understand. Here, by employing the first and to date only interface-based host-microbe interaction (HMI) prediction method, we explore a pivotal strategy oncoviruses use to drive cancer: mimicking binding surfaces-interfaces-of human proteins. We show that oncoviruses can target key human network proteins and transform cells by acquisition of cancer hallmarks. Experimental large-scale mapping of HMIs is difficult and individual HMIs do not permit in-depth grasp of tumorigenic virulence mechanisms. Our computational approach is tractable and 3D structural HMI models can help elucidate pathogenesis mechanisms and facilitate drug design. We observe that many host proteins are unique targets for certain oncoviruses, whereas others are common to several, suggesting similar infectious strategies. A rough estimation of our false discovery rate based on the tissue expression of oncovirus-targeted human proteins is 25%.

The microbiome in autoimmune diseases

The microbiome is represented by microorganisms which live in a symbiotic way with the mammalian. Microorganisms have the ability to influence different physiological aspects such as the immune system, metabolism and behaviour. In recent years, several studies have highlighted the role of the microbiome in the pathogenesis of autoimmune diseases. Notably, in systemic lupus erythematosus an alteration of the intestinal flora (lower Firmicutes/Bacteroidetes ratio) has been described. Conversely, changes to the gut commensal and periodontal disease have been proposed as important factors in the pathogenesis of rheumatoid arthritis. At the same time, other autoimmune diseases (i.e. systemic sclerosis, Sjögren's syndrome and anti-phospholipid syndrome) also share modifications of the microbiome in the intestinal tract and oral flora. Herein, we describe the role of the microbiome in the maintenance homeostasis of the immune system and then the alterations of the microorganisms that occur in systemic autoimmune diseases. Finally, we will consider the use of probiotics and faecal transplantation as novel therapeutic targets.

Computational Structural Biology: Successes, Future Directions, and Challenges

Computational biology has made powerful advances. Among these, trends in human health have been uncovered through heterogeneous 'big data' integration, and disease-associated genes were identified and classified. Along a different front, the dynamic organization of chromatin is being elucidated to gain insight into the fundamental question of genome regulation. Powerful conformational sampling methods have also been developed to yield a detailed molecular view of cellular processes. when combining these methods with the advancements in the modeling of supramolecular assemblies, including those at the membrane, we are finally able to get a glimpse into how cells' actions are regulated. Perhaps most intriguingly, a major thrust is on to decipher the mystery of how the brain is coded. Here, we aim to provide a broad, yet concise, sketch of modern aspects of computational biology, with a special focus on computational structural biology. We attempt to forecast the areas that computational structural biology will embrace in the future and the challenges that it may face. We skirt details, highlight successes, note failures, and map directions.

Interface-Based Structural Prediction of Novel Host-Pathogen Interactions

About 20% of the cancer incidences worldwide have been estimated to be associated with infections. However, the molecular mechanisms of exactly how they contribute to host tumorigenesis are still unknown. To evade host defense, pathogens hijack host proteins at different levels: sequence, structure, motif, and binding surface, i.e., interface. Interface similarity allows pathogen proteins to compete with host counterparts to bind to a target protein, rewire physiological signaling, and result in persistent infections, as well as cancer. Identification of host-pathogen interactions (HPIs)-along with their structural details at atomic resolution-may provide mechanistic insight into pathogen-driven cancers and innovate therapeutic intervention. HPI data including structural details is scarce and large-scale experimental detection is challenging. Therefore, there is an urgent and mounting need for efficient and robust computational approaches to predict HPIs and their complex (bound) structures. In this chapter, we review the first and currently only interface-based computational approach to identify novel HPIs. The concept of interface mimicry promises to identify more HPIs than complete sequence or structural similarity. We illustrate this concept with a case study on Kaposi's sarcoma herpesvirus (KSHV) to elucidate how it subverts host immunity and helps contribute to malignant transformation of the host cells.

On an algorithmic definition for the components of the minimal cell

Living cells are highly complex systems comprising a multitude of elements that are engaged in the many convoluted processes observed during the cell cycle. However, not all elements and processes are essential for cell survival and reproduction under steady-state environmental conditions. To distinguish between essential from expendable cell components and thus define the ‘minimal cell’ and the corresponding ‘minimal genome’, we postulate that the synthesis of all cell elements can be represented as a finite set of binary operators, and within this framework we show that cell elements that depend on their previous existence to be synthesized are those that are essential for cell survival. An algorithm to distinguish essential cell elements is presented and demonstrated within an interactome. Data and functions implementing the algorithm are given as supporting information. We expect that this algorithmic approach will lead to the determination of the complete interactome of the minimal cell, which could then be experimentally validated. The assumptions behind this hypothesis as well as its consequences for experimental and theoretical biology are discussed.

Structure-based Methods for Binding Mode and Binding Affinity Prediction for Peptide-MHC Complexes

Understanding the mechanisms involved in the activation of an immune response is essential to many fields in human health, including vaccine development and personalized cancer immunotherapy. A central step in the activation of the adaptive immune response is the recognition, by T-cell lymphocytes, of peptides displayed by a special type of receptor known as Major Histocompatibility Complex (MHC). Considering the key role of MHC receptors in T-cell activation, the computational prediction of peptide binding to MHC has been an important goal for many immunological applications. Sequence- based methods have become the gold standard for peptide-MHC binding affinity prediction, but structure-based methods are expected to provide more general predictions (i.e., predictions applicable to all types of MHC receptors). In addition, structural modeling of peptide-MHC complexes has the potential to uncover yet unknown drivers of T-cell activation, thus allowing for the development of better and safer therapies. In this review, we discuss the use of computational methods for the structural modeling of peptide-MHC complexes (i.e., binding mode prediction) and for the structure-based prediction of binding affinity.

Foodomics for human health: current status and perspectives

INTRODUCTION

In the post-genomic era, the opportunity to combine and integrate cutting-edge analytical platforms and data processing systems allowed the birth of foodomics, 'a discipline that studies the Food and Nutrition domains through the application of advanced omics technologies to improve consumer's well-being, health, and confidence'. Since then, this discipline has rapidly evolved and researchers are now facing the daunting tasks to meet consumers' needs in terms of food traceability, sustainability, quality, safety and integrity. Most importantly, today it is imperative to provide solid evidence of the mechanisms through which food can promote human health and well-being. Areas covered: In this review, the complex relationships connecting food, nutrition and human health will be discussed, with emphasis on the relapses for the development of functional foods and nutraceuticals, personalized nutrition approaches, and the study of the interplay among gut microbiota, diet and health/diseases. Expert commentary: Evidence has been provided supporting the role of various omic platforms in studying the health-promoting effects of food and customized dietary interventions. However, although associated to major analytical challenges, only the proper integration of multi-omics studies and the implementation of bioinformatics tools and databases will help translate findings from clinical practice into effective personalized treatment strategies.

Prediction of Host-Pathogen Interactions for Helicobacter pylori by Interface Mimicry and Implications to Gastric Cancer

There is a strong correlation between some pathogens and certain cancer types. One example is Helicobacter pylori and gastric cancer. Exactly how they contribute to host tumorigenesis is, however, a mystery. Pathogens often interact with the host through proteins. To subvert defense, they may mimic host proteins at the sequence, structure, motif, or interface levels. Interface similarity permits pathogen proteins to compete with those of the host for a target protein and thereby alter the host signaling. Detection of host-pathogen interactions (HPIs) and mapping the re-wired superorganism HPI network-with structural details-can provide unprecedented clues to the underlying mechanisms and help therapeutics. Here, we describe the first computational approach exploiting solely interface mimicry to model potential HPIs. Interface mimicry can identify more HPIs than sequence or complete structural similarity since it appears more common than the other mimicry types. We illustrate the usefulness of this concept by modeling HPIs of H. pylori to understand how they modulate host immunity, persist lifelong, and contribute to tumorigenesis. H. pylori proteins interfere with multiple host pathways as they target several host hub proteins. Our results help illuminate the structural basis of resistance to apoptosis, immune evasion, and loss of cell junctions seen in H. pylori-infected host cells.