PRRDB 2.0: a comprehensive database of pattern-recognition receptors and their ligands

Advances of computational methods enhance the development of multi-epitope vaccines

Vaccine development is one of the most promising fields, and multi-epitope vaccine, which does not need laborious culture processes, is an attractive alternative to classical vaccines with the advantage of safety, and efficiency. The rapid development of algorithms and the accumulation of immune data have facilitated the advancement of computer-aided vaccine design. Here we systemically reviewed the in silico data and algorithms resource, for different steps of computational vaccine design, including immunogen selection, epitope prediction, vaccine construction, optimization, and evaluation. The performance of different available tools on epitope prediction and immunogenicity evaluation was tested and compared on benchmark datasets. Finally, we discuss the future research direction for the construction of a multiepitope vaccine.

Single-cell view into the role of microbiota shaping host immunity in the larynx

Microbiota play a critical role in the development and training of host innate and adaptive immunity. We present the cellular landscape of the upper airway, specifically the larynx, by establishing a reference single-cell atlas, while dissecting the role of microbiota in cell development and function at single-cell resolution. We highlight the larynx's cellular heterogeneity with the identification of 16 cell types and 34 distinct subclusters. Our data demonstrate that commensal microbiota have extensive impact on the laryngeal immune system by regulating cell differentiation, increasing the expression of genes associated with host defense, and altering gene regulatory networks. We uncover macrophages, innate lymphoid cells, and multiple secretory epithelial cells, whose cell proportions and expressions vary with microbial exposure. These cell types play pivotal roles in maintaining laryngeal and upper airway health and provide specific guidance into understanding the mechanism of immune system regulation by microbiota in laryngeal health and disease.

Transcriptome profiling of immune rejection mechanisms in a porcine vascularized composite allotransplantation model

Background

Vascularized composite allotransplantation (VCA) offers the potential for a biological, functional reconstruction in individuals with limb loss or facial disfigurement. Yet, it faces substantial challenges due to heightened immune rejection rates compared to solid organ transplants. A deep understanding of the genetic and immunological drivers of VCA rejection is essential to improve VCA outcomes.

Methods

Heterotopic porcine hindlimb VCA models were established and followed until reaching the endpoint. Skin and muscle samples were obtained from VCA transplant recipient pigs for histological assessments and RNA sequencing analysis. The rejection groups included recipients with moderate pathological rejection, treated locally with tacrolimus encapsulated in triglycerol-monostearate gel (TGMS-TAC), as well as recipients with severe end-stage rejection presenting evident necrosis. Healthy donor tissue served as controls. Bioinformatics analysis, immunofluorescence, and electron microscopy were utilized to examine gene expression patterns and the expression of immune response markers.

Results

Our comprehensive analyses encompassed differentially expressed genes, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes pathways, spanning various composite tissues including skin and muscle, in comparison to the healthy control group. The analysis revealed a consistency and reproducibility in alignment with the pathological rejection grading. Genes and pathways associated with innate immunity, notably pattern recognition receptors (PRRs), damage-associated molecular patterns (DAMPs), and antigen processing and presentation pathways, exhibited upregulation in the VCA rejection groups compared to the healthy controls. Our investigation identified significant shifts in gene expression related to cytokines, chemokines, complement pathways, and diverse immune cell types, with CD8 T cells and macrophages notably enriched in the VCA rejection tissues. Mechanisms of cell death, such as apoptosis, necroptosis and ferroptosis were observed and coexisted in rejected tissues.

Conclusion

Our study provides insights into the genetic profile of tissue rejection in the porcine VCA model. We comprehensively analyze the molecular landscape of immune rejection mechanisms, from innate immunity activation to critical stages such as antigen recognition, cytotoxic rejection, and cell death. This research advances our understanding of graft rejection mechanisms and offers potential for improving diagnostic and therapeutic strategies to enhance the long-term success of VCA.

Gut microbiota-gonadal axis: the impact of gut microbiota on reproductive functions

The influence of gut microbiota on physiological processes is rapidly gaining attention globally. Despite being under-studied, there are available data demonstrating a gut microbiota-gonadal cross-talk, and the importance of this axis in reproduction. This study reviews the impacts of gut microbiota on reproduction. In addition, the possible mechanisms by which gut microbiota modulates male and female reproduction are presented. Databases, including Embase, Google scholar, Pubmed/Medline, Scopus, and Web of Science, were explored using relevant key words. Findings showed that gut microbiota promotes gonadal functions by modulating the circulating levels of steroid sex hormones, insulin sensitivity, immune system, and gonadal microbiota. Gut microbiota also alters ROS generation and the activation of cytokine accumulation. In conclusion, available data demonstrate the existence of a gut microbiota-gonadal axis, and role of this axis on gonadal functions. However, majority of the data were compelling evidences from animal studies with a great dearth of human data. Therefore, human studies validating the reports of experimental studies using animal models are important.

Hmrbase2: a comprehensive database of hormones and their receptors

PURPOSE

Hormones play a critical role in regulating various physiological processes and any hormonal imbalances can lead to major endocrine disorders. Thus, studying hormones is essential for both the therapeutics and the diagnostics of hormonal diseases. To facilitate this need, we have developed Hmrbase2, a comprehensive platform that provides extensive information on hormones.

METHODS

Hmrbase2 is a web-based database which is an update of a previously published database, Hmrbase ( http://crdd.osdd.net/raghava/hmrbase/ ). We collected a large amount of information on peptide and non-peptide hormones and hormone receptors, this information being sourced from Hmrbase, HMDB, UniProt, HORDB, ENDONET, PubChem, and the medical literature.

RESULTS

Hmrbase2 contains a total of 12,056 entries, which is more than twice the number of entries contained in the previous version Hmrbase. These include 7406, 753, and 3897 entries for peptide hormones, non-peptide hormones, and hormone receptors, respectively, from 803 organisms compared to the 562 organisms in the previous version. The database also hosts 5662 hormone receptor pairs. The source organism, function, and subcellular location are provided for peptide hormones and receptors and properties such as melting point and water solubility is provided for non-peptide hormones. Besides browsing and keyword search, an advanced search option has also been supplied. Additionally, a similarity search module has been incorporated enabling users to run similarity searches against peptide hormone sequences using BLAST and Smith-Waterman.

CONCLUSIONS

To make the database accessible to various users, we designed a user-friendly, responsive website that can be easily used on smartphones, tablets, and desktop computers. The updated database version, Hmrbase2, offers improved data content compared to the previous version. Hmrbase2 is freely available at https://webs.iiitd.edu.in/raghava/hmrbase2 .

Induced Immune Reaction in the Acorn Worm, Saccoglossus kowalevskii, Informs the Evolution of Antiviral Immunity

Evolutionary perspectives on the deployment of immune factors following infection have been shaped by studies on a limited number of biomedical model systems with a heavy emphasis on vertebrate species. Although their contributions to contemporary immunology cannot be understated, a broader phylogenetic perspective is needed to understand the evolution of immune systems across Metazoa. In our study, we leverage differential gene expression analyses to identify genes implicated in the antiviral immune response of the acorn worm hemichordate, Saccoglossus kowalevskii, and place them in the context of immunity evolution within deuterostomes-the animal clade composed of chordates, hemichordates, and echinoderms. Following acute exposure to the synthetic viral double-stranded RNA analog, poly(I:C), we show that S. kowalevskii responds by regulating the transcription of genes associated with canonical innate immunity signaling pathways (e.g., nuclear factor κB and interferon regulatory factor signaling) and metabolic processes (e.g., lipid metabolism), as well as many genes without clear evidence of orthology with those of model species. Aggregated across all experimental time point contrasts, we identify 423 genes that are differentially expressed in response to poly(I:C). We also identify 147 genes with altered temporal patterns of expression in response to immune challenge. By characterizing the molecular toolkit involved in hemichordate antiviral immunity, our findings provide vital evolutionary context for understanding the origins of immune systems within Deuterostomia.

PRR-HyPred: A two-layer hybrid framework to predict pattern recognition receptors and their families by employing sequence encoded optimal features

Pattern recognition receptors (PRRs) recognize distinct features on the surface of pathogens or damaged cells and play key roles in the innate immune system. PRRs are divided into various families, including Toll-like receptors, retinoic acid-inducible gene-I-like receptors, nucleotide oligomerization domain-like receptors, and C-type lectin receptors. As these are implicated in host health and several diseases, their accurate identification is indispensable for their functional characterization and targeted therapeutic approaches. Here, we construct PRR-HyPred, a novel two-layer hybrid framework in which the first layer predicts whether a given sequence is PRR or non-PRR using a support vector machine, and in the second, the predicted PRR sequence is assigned to a specific family using a random forest-based classifier. Based on a 10-fold cross-validation test, PRR-HyPred achieved 83.4 % accuracy in the first layer and 95 % in the second, with Matthew's correlation coefficient values of 0.639 and 0.816, respectively. This is the first study that can simultaneously predict and classify PRRs into specific families. PRR-HyPred is available as a web portal at https://procarb.org/PRRHyPred/. We hope that it could be a valuable tool for the large-scale prediction and classification of PRRs and subsequently facilitate future studies.

Genetic variation and microbiota in bumble bees cross-infected by different strains of C. bombi

The bumblebee Bombus terrestris is commonly infected by a trypanosomatid gut parasite Crithidia bombi. This system shows a striking degree of genetic specificity where host genotypes are susceptible to different genotypes of parasite. To a degree, variation in host gene expression underlies these differences, however, the effects of standing genetic variation has not yet been explored. Here we report on an extensive experiment where workers of twenty colonies of B. terrestris were each infected by one of twenty strains of C. bombi. To elucidate the host's genetic bases of susceptibility to infection (measured as infection intensity), we used a low-coverage (~2 x) genome-wide association study (GWAS), based on angsd, and a standard high-coverage (~15x) GWAS (with a reduced set from a 8 x 8 interaction matrix, selected from the full set of twenty). The results from the low-coverage approach remained ambiguous. The high-coverage approach suggested potentially relevant genetic variation in cell surface and adhesion processes. In particular, mucin, a surface mucoglycoprotein, potentially affecting parasite binding to the host gut epithelia, emerged as a candidate. Sequencing the gut microbial community of the same bees showed that the abundance of bacterial taxa, such as Gilliamella, Snodgrassella, or Lactobacillus, differed between 'susceptible' and 'resistant' microbiota, in line with earlier studies. Our study suggests that the constitutive microbiota and binding processes at the cell surface are candidates to affect infection intensity after the first response (captured by gene expression) has run its course. We also note that a low-coverage approach may not be powerful enough to analyse such complex traits. Furthermore, testing large interactions matrices (as with the full 20 x 20 combinations) for the effect of interaction terms on infection intensity seems to blur the specific host x parasite interaction effects, likely because the outcome of an infection is a highly non-linear process dominated by variation in individually different pathways of host defence (immune) responses.

Host-pathogen protein-nucleic acid interactions: A comprehensive review

Recognition of pathogen-derived nucleic acids by host cells is an effective host strategy to detect pathogenic invasion and trigger immune responses. In the context of pathogen-specific pharmacology, there is a growing interest in mapping the interactions between pathogen-derived nucleic acids and host proteins. Insight into the principles of the structural and immunological mechanisms underlying such interactions and their roles in host defense is necessary to guide therapeutic intervention. Here, we discuss the newest advances in studies of molecular interactions involving pathogen nucleic acids and host factors, including their drug design, molecular structure and specific patterns. We observed that two groups of nucleic acid recognizing molecules, Toll-like receptors (TLRs) and the cytoplasmic retinoic acid-inducible gene (RIG)-I-like receptors (RLRs) form the backbone of host responses to pathogen nucleic acids, with additional support provided by absent in melanoma 2 (AIM2) and DNA-dependent activator of Interferons (IFNs)-regulatory factors (DAI) like cytosolic activity. We review the structural, immunological, and other biological aspects of these representative groups of molecules, especially in terms of their target specificity and affinity and challenges in leveraging host-pathogen protein-nucleic acid interactions (HP-PNI) in drug discovery.

Multi-Omic Analysis in a Metabolic Syndrome Porcine Model Implicates Arachidonic Acid Metabolism Disorder as a Risk Factor for Atherosclerosis

Background

The diet-induced gut microbiota dysbiosis has been suggested as a major risk factor for atherothrombosis, however, the detailed mechanism linking these conditions is yet to be fully understood.

Methods

We established a long-term excessive-energy diet-induced metabolic syndrome (MetS) inbred Wuzhishan minipig model, which is characterized by its genetic stability, small size, and human-like physiology. The metabolic parameters, atherosclerotic lesions, gut microbiome, and host transcriptome were analyzed. Metabolomics profiling revealed a linkage between gut microbiota and atherothrombosis.

Results

We showed that white atheromatous plaque was clearly visible on abdominal aorta in the MetS model. Furthermore, using metagenome and metatranscriptome sequencing, we discovered that the long-term excessive energy intake altered the local intestinal microbiota composition and transcriptional profile, which was most dramatically illustrated by the reduced abundance of SCFAs-producing bacteria including Bacteroides, Lachnospiraceae, and Ruminococcaceae in the MetS model. Liver and abdominal aorta transcriptomes in the MetS model indicate that the diet-induced gut microbiota dysbiosis activated host chronic inflammatory responses and significantly upregulated the expression of genes related to arachidonic acid-dependent signaling pathways. Notably, metabolomics profiling further revealed an intimate linkage between arachidonic acid metabolism and atherothrombosis in the host-gut microbial metabolism axis.

Conclusions

These findings provide new insights into the relationship between atherothrombosis and regulation of gut microbiota via host metabolomes and will be of potential value for the treatment of cardiovascular diseases in MetS.

Immunoinformatic Approaches for Vaccine Designing for Pathogens with Unclear Pathogenesis

Designing a vaccine against a pathogen has been the toughest challenge to fight against any infectious diseases. To overcome this problem, use of artificial neural network with immuno-informatics is emerging as a front runner solution. For a successful designing of a potent vaccine, prediction of T-cell/B-cell epitopes, antigen processing and presentation analysis, antigenic potential analysis of epitopes, usages of linkers, population coverage, codon optimization, allergenicity assessment, toxicity prediction of construct, and finally protein-peptide docking for stability of vaccine are important steps. To achieve this, several bioinformatics software, tools and online web servers have been developed for each application, which have their own advantages and limitations. Scientists must evaluate these parameters and should take the decision to apply more suitable and precise servers for each analysis and prediction based on their accuracy, suitability, and robustness.

In-Silico Tool for Predicting, Scanning, and Designing Defensins

Defensins are host defense peptides present in nearly all living species, which play a crucial role in innate immunity. These peptides provide protection to the host, either by killing microbes directly or indirectly by activating the immune system. In the era of antibiotic resistance, there is a need to develop a fast and accurate method for predicting defensins. In this study, a systematic attempt has been made to develop models for predicting defensins from available information on defensins. We created a dataset of defensins and non-defensins called the main dataset that contains 1,036 defensins and 1,035 AMPs (antimicrobial peptides, or non-defensins) to understand the difference between defensins and AMPs. Our analysis indicates that certain residues like Cys, Arg, and Tyr are more abundant in defensins in comparison to AMPs. We developed machine learning technique-based models on the main dataset using a wide range of peptide features. Our SVM (support vector machine)-based model discriminates defensins and AMPs with MCC of 0.88 and AUC of 0.98 on the validation set of the main dataset. In addition, we created an alternate dataset that consists of 1,036 defensins and 1,054 non-defensins obtained from Swiss-Prot. Models were also developed on the alternate dataset to predict defensins. Our SVM-based model achieved maximum MCC of 0.96 with AUC of 0.99 on the validation set of the alternate dataset. All models were trained, tested, and validated using standard protocols. Finally, we developed a web-based service "DefPred" to predict defensins, scan defensins in proteins, and design the best defensins from their analogs. The stand-alone software and web server of DefPred are available at https://webs.iiitd.edu.in/raghava/defpred.

Biomolecule and Bioentity Interaction Databases in Systems Biology: A Comprehensive Review

Technological advances in high-throughput techniques have resulted in tremendous growth of complex biological datasets providing evidence regarding various biomolecular interactions. To cope with this data flood, computational approaches, web services, and databases have been implemented to deal with issues such as data integration, visualization, exploration, organization, scalability, and complexity. Nevertheless, as the number of such sets increases, it is becoming more and more difficult for an end user to know what the scope and focus of each repository is and how redundant the information between them is. Several repositories have a more general scope, while others focus on specialized aspects, such as specific organisms or biological systems. Unfortunately, many of these databases are self-contained or poorly documented and maintained. For a clearer view, in this article we provide a comprehensive categorization, comparison and evaluation of such repositories for different bioentity interaction types. We discuss most of the publicly available services based on their content, sources of information, data representation methods, user-friendliness, scope and interconnectivity, and we comment on their strengths and weaknesses. We aim for this review to reach a broad readership varying from biomedical beginners to experts and serve as a reference article in the field of Network Biology.

The Gut Microbiota-Derived Immune Response in Chronic Liver Disease

In chronic liver disease, the causative factor is important; however, recently, the intestinal microbiome has been associated with the progression of chronic liver disease and the occurrence of side effects. The immune system is affected by the metabolites of the microbiome, and diet is the primary regulator of the microbiota composition and function in the gut–liver axis. These metabolites can be used as therapeutic material, and postbiotics, in the future, can increase or decrease human immunity by modulating inflammation and immune reactions. Therefore, the excessive intake of nutrients and the lack of nutrition have important effects on immunity and inflammation. Evidence has been published indicating that microbiome-induced chronic inflammation and the consequent immune dysregulation affect the development of chronic liver disease. In this research paper, we discuss the overall trend of microbiome-derived substances related to immunity and the future research directions.

SARS-CoV-2 Infected Pediatric Cerebral Cortical Neurons: Transcriptomic Analysis and Potential Role of Toll-like Receptors in Pathogenesis

Different mechanisms were proposed as responsible for COVID-19 neurological symptoms but a clear one has not been established yet. In this work we aimed to study SARS-CoV-2 capacity to infect pediatric human cortical neuronal HCN-2 cells, studying the changes in the transcriptomic profile by next generation sequencing. SARS-CoV-2 was able to replicate in HCN-2 cells, that did not express ACE2, confirmed also with Western blot, and TMPRSS2. Looking for pattern recognition receptor expression, we found the deregulation of scavenger receptors, such as SR-B1, and the downregulation of genes encoding for Nod-like receptors. On the other hand, TLR1, TLR4 and TLR6 encoding for Toll-like receptors (TLRs) were upregulated. We also found the upregulation of genes encoding for ERK, JNK, NF-κB and Caspase 8 in our transcriptomic analysis. Regarding the expression of known receptors for viral RNA, only RIG-1 showed an increased expression; downstream RIG-1, the genes encoding for TRAF3, IKKε and IRF3 were downregulated. We also found the upregulation of genes encoding for chemokines and accordingly we found an increase in cytokine/chemokine levels in the medium. According to our results, it is possible to speculate that additionally to ACE2 and TMPRSS2, also other receptors may interact with SARS-CoV-2 proteins and mediate its entry or pathogenesis in pediatric cortical neurons infected with SARS-CoV-2. In particular, TLRs signaling could be crucial for the neurological involvement related to SARS-CoV-2 infection.

Molecular Mechanisms of the Toll-Like Receptor, STING, MAVS, Inflammasome, and Interferon Pathways

Pattern recognition receptors (PRRs) form the front line of defense against pathogens. Many of the molecular mechanisms that facilitate PRR signaling have been characterized in detail, which is critical for the development of accurate PRR pathway models at the molecular interaction level. These models could support the development of therapeutics for numerous diseases, including sepsis and COVID-19. This review describes the molecular mechanisms of the principal signaling interactions of the Toll-like receptor, STING, MAVS, and inflammasome pathways. A detailed molecular mechanism network is included as Data Set S1 in the supplemental material.

Prognostic Biomarker-Based Identification of Drugs for Managing the Treatment of Endometrial Cancer

INTRODUCTION

Uterine corpus endometrial carcinoma (UCEC) causes thousands of deaths per year. To improve the overall survival of patients with UCEC, there is a need to identify prognostic biomarkers and potential drugs.

OBJECTIVES

The aim of this study was twofold: the identification of prognostic gene signatures from expression profiles of pattern recognition receptor (PRR) genes and identification of the most effective existing drugs using the prognostic gene signature.

METHODS

This study was based on the expression profile of PRR genes of 541 patients with UCEC obtained from The Cancer Genome Atlas. Key prognostic signatures were identified using various approaches, including survival analysis, network, and clustering. Hub genes were identified by constructing a co-expression network. Representative genes were identified using k-means and k-medoids-based clustering. Univariate Cox proportional hazard (PH) analysis was used to identify survival-associated genes. 'cmap2' was used to identify potential drugs that can suppress/enhance the expression of prognostic genes.

RESULTS

Models were developed using hub genes and achieved a maximum hazard ratio (HR) of 1.37 (p = 0.294). Then, a clustering-based model was developed using seven genes (HR 9.14; p = 1.49 × 10^-12). Finally, a nine gene-based risk stratification model was developed (CLEC1B, CLEC3A, IRF7, CTSB, FCN1, RIPK2, NLRP10, NLRP9, and SARM1) and achieved HR 10.70; p = 1.1 × 10^-12. The performance of this model improved significantly in combination with the clinical stage and achieved HR 15.23; p = 2.21 × 10^-7. We also developed a model for predicting high-risk patients (survival ≤ 4.3 years) and achieved an area under the receiver operating characteristic curve (AUROC) of 0.86.

CONCLUSION

We identified potential immunotherapeutic agents based on prognostic gene signature: hexamethonium bromide and isoflupredone. Several novel candidate drugs were suggested, including human interferon-α-2b, paclitaxel, imiquimod, MESO-DAP1, and mifamurtide. These biomolecules and repurposed drugs may be utilised for prognosis and treatment for better survival.

For Better or for Worse: A Look Into Neutrophils in Traumatic Spinal Cord Injury

Neutrophils are short-lived cells of the innate immune system and the first line of defense at the site of an infection and tissue injury. Pattern recognition receptors on neutrophils recognize pathogen-associated molecular patterns or danger-associated molecular patterns, which recruit them to the destined site. Neutrophils are professional phagocytes with efficient granular constituents that aid in the neutralization of pathogens. In addition to phagocytosis and degranulation, neutrophils are proficient in creating neutrophil extracellular traps (NETs) that immobilize pathogens to prevent their spread. Because of the cytotoxicity of the associated granular proteins within NETs, the microbes can be directly killed once immobilized by the NETs. The role of neutrophils in infection is well studied; however, there is less emphasis placed on the role of neutrophils in tissue injury, such as traumatic spinal cord injury. Upon the initial mechanical injury, the innate immune system is activated in response to the molecules produced by the resident cells of the injured spinal cord initiating the inflammatory cascade. This review provides an overview of the essential role of neutrophils and explores the contribution of neutrophils to the pathologic changes in the injured spinal cord.

An overview of in silico vaccine design against different pathogens and cancer

INTRODUCTION

Due to overcome the hardness of the vaccine design, computational vaccinology is emerging widely. Prediction of T cell and B cell epitopes, antigen processing analysis, antigenicity analysis, population coverage, conservancy analysis, allergenicity assessment, toxicity prediction, and protein-peptide docking are important steps in the process of designing and developing potent vaccines against various viruses and cancers. In order to perform all of the analyses, several bioinformatics tools and online web servers have been developed. Scientists must take the decision to apply more suitable and precise servers for each part based on their accuracy.

AREAS COVERED

In this review, a wide-range list of different bioinformatics tools and online web servers has been provided. Moreover, some studies were proposed to show the importance of various bioinformatics tools for predicting and developing efficient vaccines against different pathogens including viruses, bacteria, parasites, and fungi as well as cancer.

EXPERT OPINION

Immunoinformatics is the best way to find potential vaccine candidates against different pathogens. Thus, the selection of the most accurate tools is necessary to predict and develop potent preventive and therapeutic vaccines. To further evaluation of the computational and in silico vaccine design, in vitro/in vivo analyses are required to develop vaccine candidates.

Formula Diet Alters the Ileal Metagenome and Transcriptome at Weaning and during the Postweaning Period in a Porcine Model

Exclusive breastfeeding impacts the intestinal microbiome and is associated with a better immune function than is seen with milk formula (MF) feeding in infants and yet with mechanisms poorly defined. The porcine model was used to evaluate the impact of MF on ileum microbial communities and gene expression relative to human milk (HM)-fed piglets. Fifty-two Dutch Landrace male piglets were fed an isocaloric diet of either HM (n = 26) or MF (n = 26) from day 2 through day 21 of age and weaned to a solid diet until day 51. Eleven piglets from each group were euthanized at day 21, while the remaining piglets (HM, n = 15; MF, n = 15) were euthanized at day 51 to collect ileal epithelium (EP) scrapings and ileal (IL) tissues. The epithelial mucosa was subjected to shotgun metagenome sequencing, and EP and IL tissues were used for transcriptome analysis. On day 21, transcriptome data revealed that the levels of pathways involved in inflammation and apoptosis were significantly higher in MF piglets than in HM piglets, whereas the levels of tight junctions and pathogen detection systems were lower in MF piglets than in HM piglets. The MF impacts on the small intestine were maintained over the postweaning period (day 51) as indicated by higher levels of Dialister invisus bacteria and higher levels of expression of genes associated with inflammation and apoptosis pathways relative to HM group. The current study demonstrated that MF might impact local intestinal inflammation, apoptosis, and tight junctions and might suppress pathogen recognition in the small intestine compared with HM.IMPORTANCE Exclusive human milk (HM) breastfeeding for the first 6 months of age in infants is recommended to improve health outcomes during early life and beyond. When women are unable to provide sufficient HM, milk formula (MF) is often recommended as a complementary or alternative source of nutrition. Previous studies in piglets demonstrated that MF alters the gut microbiome and induces inflammatory cytokine production. The links between MF feeding, gut microbiome, and inflammation status are unclear due to challenges associated with the collection of intestinal samples from human infants. The current report provides the first insight into MF-microbiome-inflammation connections in the small intestine compared with HM feeding using a porcine model. The present results showed that, compared with HM, MF might impact immune function through the induction of ileal inflammation, apoptosis, and tight junction disruptions and likely compromised immune defense against pathogen detection in the small intestine relative to piglets that were fed HM.

The Gut-liver Axis in Immune Remodeling: New insight into Liver Diseases

The gut microbiota consists of a dynamic multispecies community of bacteria, fungi, archaea, and protozoans, playing a fundamental role in the induction, training, and function of the host immune system. The liver is anatomically and physiologically linked to the gut microbiota via enterohepatic circulation, specifically receiving intestine-derived blood through the portal vein. The gut microbiota is crucial for maintaining immune homeostasis of the gut-liver axis. A shift in gut microbiota composition can result in activation of the mucosal immune response causing homeostasis imbalance. This imbalance results in translocation of bacteria and migration of immune cells to the liver, which is related to inflammation-mediated liver injury and tumor progression. In this review, we outline the role of the gut microbiota in modulating host immunity and summarize novel findings and recent advances in immune-based therapeutics associated with the gut-liver axis. Moving forward, a deep understanding of the microbiome-immune-liver axis will provide insight into the basic mechanisms of gut microbiota dysbiosis affecting liver diseases.

A Hybrid Model for Predicting Pattern Recognition Receptors Using Evolutionary Information

This study describes a method developed for predicting pattern recognition receptors (PRRs), which are an integral part of the immune system. The models developed here were trained and evaluated on the largest possible non-redundant PRRs, obtained from PRRDB 2.0, and non-pattern recognition receptors (Non-PRRs), obtained from Swiss-Prot. Firstly, a similarity-based approach using BLAST was used to predict PRRs and got limited success due to a large number of no-hits. Secondly, machine learning-based models were developed using sequence composition and achieved a maximum MCC of 0.63. In addition to this, models were developed using evolutionary information in the form of PSSM composition and achieved maximum MCC value of 0.66. Finally, we developed hybrid models that combined a similarity-based approach using BLAST and machine learning-based models. Our best model, which combined BLAST and PSSM based model, achieved a maximum MCC value of 0.82 with an AUROC value of 0.95, utilizing the potential of both similarity-based search and machine learning techniques. In order to facilitate the scientific community, we also developed a web server "PRRpred" based on the best model developed in this study (http://webs.iiitd.edu.in/raghava/prrpred/).