InnateDB: facilitating systems-level analyses of the mammalian innate immune response

Reovirus infection induces transcriptome-wide unique A-to-I editing changes in the murine fibroblasts

The conversion of Adenosine (A) to Inosine (I), by Adenosine Deaminases Acting on RNA or ADARs, is an essential post-transcriptional modification that contributes to proteome diversity and regulation in metazoans including humans. In addition to its transcriptome-regulating role, ADARs also play a major part in immune response to viral infection, where an interferon response activates interferon-stimulated genes, such as ADARp150, in turn dynamically regulating host-virus interactions. A previous report has shown that infection from reoviruses, despite strong activation of ADARp150, does not influence the editing of some of the major known editing targets, while likely editing others, suggesting a potentially nuanced editing pattern that may depend on different factors. However, the results were based on a handful of selected editing sites and did not cover the entire transcriptome. Thus, to determine whether and how reovirus infection specifically affects host ADAR editing patterns, we analyzed a publicly available deep-sequenced RNA-seq dataset, from murine fibroblasts infected with wild-type and mutant reovirus strains that allowed us to examine changes in editing patterns on a transcriptome-wide scale. To the best of our knowledge, this is the first transcriptome-wide report on host editing changes after reovirus infection. Our results demonstrate that reovirus infection induces unique nuanced editing changes in the host, including introducing sites uniquely edited in infected samples. Genes with edited sites are overrepresented in pathways related to immune regulation, cellular signaling, metabolism, and growth. Moreover, a shift in editing targets has also been observed, where the same genes are edited in infection and control conditions but at different sites, or where the editing rate is increased for some and decreased for other differential targets, supporting the hypothesis of dynamic and condition-specific editing by ADARs.

Transient peripheral blood transcriptomic response to ketamine treatment in children with ADNP syndrome

Activity-dependent neuroprotective protein (ADNP) syndrome is a rare neurodevelopmental disorder resulting in intellectual disability, developmental delay and autism spectrum disorder (ASD) and is due to mutations in the ADNP gene. Ketamine treatment has emerged as a promising therapeutic option for ADNP syndrome, showing safety and apparent behavioral improvements in a first open label study. However, the molecular perturbations induced by ketamine remain poorly understood. Here, we investigated the longitudinal effect of ketamine on the blood transcriptome of 10 individuals with ADNP syndrome. Transcriptomic profiling was performed before and at multiple time points after a single low-dose intravenous ketamine infusion (0.5 mg/kg). We show that ketamine triggers immediate and profound gene expression alterations, with specific enrichment of monocyte-related expression patterns. These acute alterations encompass diverse signaling pathways and co-expression networks, implicating upregulation of immune and inflammatory-related processes and down-regulation of RNA processing mechanisms and metabolism. Notably, these changes exhibit a transient nature, returning to baseline levels 24 hours to 1 week after treatment. These findings enhance our understanding of ketamine's molecular effects and lay the groundwork for further research elucidating its specific cellular and molecular targets. Moreover, they contribute to the development of therapeutic strategies for ADNP syndrome and potentially, ASD more broadly.

Identifying Reproducible Transcription Regulator Coexpression Patterns with Single Cell Transcriptomics

The proliferation of single cell transcriptomics has potentiated our ability to unveil patterns that reflect dynamic cellular processes, rather than cell type compositional effects that emerge from bulk tissue samples. In this study, we leverage a broad collection of single cell RNA-seq data to identify the gene partners whose expression is most coordinated with each human and mouse transcription regulator (TR). We assembled 120 human and 103 mouse scRNA-seq datasets from the literature (>28 million cells), constructing a single cell coexpression network for each. We aimed to understand the consistency of TR coexpression profiles across a broad sampling of biological contexts, rather than examine the preservation of context-specific signals. Our workflow therefore explicitly prioritizes the patterns that are most reproducible across cell types. Towards this goal, we characterize the similarity of each TR's coexpression within and across species. We create single cell coexpression rankings for each TR, demonstrating that this aggregated information recovers literature curated targets on par with ChIP-seq data. We then combine the coexpression and ChIP-seq information to identify candidate regulatory interactions supported across methods and species. Finally, we highlight interactions for the important neural TR ASCL1 to demonstrate how our compiled information can be adopted for community use.

Transkingdom Network Analysis (TkNA): a systems framework for inferring causal factors underlying host-microbiota and other multi-omic interactions

We present Transkingdom Network Analysis (TkNA), a unique causal-inference analytical framework that offers a holistic view of biological systems by integrating data from multiple cohorts and diverse omics types. TkNA helps to decipher key players and mechanisms governing host-microbiota (or any multi-omic data) interactions in specific conditions or diseases. TkNA reconstructs a network that represents a statistical model capturing the complex relationships between different omics in the biological system. It identifies robust and reproducible patterns of fold change direction and correlation sign across several cohorts to select differential features and their per-group correlations. The framework then uses causality-sensitive metrics, statistical thresholds and topological criteria to determine the final edges forming the transkingdom network. With the subsequent network's topological features, TkNA identifies nodes controlling a given subnetwork or governing communication between kingdoms and/or subnetworks. The computational time for the millions of correlations necessary for network reconstruction in TkNA typically takes only a few minutes, varying with the study design. Unlike most other multi-omics approaches that find only associations, TkNA focuses on establishing causality while accounting for the complex structure of multi-omic data. It achieves this without requiring huge sample sizes. Moreover, the TkNA protocol is user friendly, requiring minimal installation and basic familiarity with Unix. Researchers can access the TkNA software at https://github.com/CAnBioNet/TkNA/ .

Investigating gene signatures associated with immunity in colon adenocarcinoma to predict the immunotherapy effectiveness using NFM and WGCNA algorithms

Colon adenocarcinoma (COAD), a frequently encountered and highly lethal malignancy of the digestive system, has been the focus of intensive research regarding its prognosis. The intricate immune microenvironment plays a pivotal role in the pathological progression of COAD; nevertheless, the underlying molecular mechanisms remain incompletely understood. This study aims to explore the immune gene expression patterns in COAD, construct a robust prognostic model, and delve into the molecular mechanisms and potential therapeutic targets for COAD liver metastasis, thereby providing critical support for individualized treatment strategies and prognostic evaluation. Initially, we curated a comprehensive dataset by screening 2600 immune-related genes (IRGs) from the ImmPort and InnateDB databases, successfully obtaining a rich data resource. Subsequently, the COAD patient cohort was classified using the non-negative matrix factorization (NMF) algorithm, enabling accurate categorization. Continuing on, utilizing the weighted gene co-expression network analysis (WGCNA) method, we analyzed the top 5000 genes with the smallest p-values among the differentially expressed genes (DEGs) between immune subtypes. Through this rigorous screening process, we identified the gene modules with the strongest correlation to the COAD subpopulation, and the intersection of genes in these modules with DEGs (COAD vs COAD vs Normal colon tissue) is referred to as Differentially Expressed Immune Genes Associated with COAD (DEIGRC). Employing diverse bioinformatics methodologies, we successfully developed a prognostic model (DPM) consisting of six genes derived from the DEIGRC, which was further validated across multiple independent datasets. Not only does this predictive model accurately forecast the prognosis of COAD patients, but it also provides valuable insights for formulating personalized treatment regimens. Within the constructed DPM, we observed a downregulation of CALB2 expression levels in COAD tissues, whereas NOXA1, KDF1, LARS2, GSR, and TIMP1 exhibited upregulated expression levels. These genes likely play indispensable roles in the initiation and progression of COAD and thus represent potential therapeutic targets for patient management. Furthermore, our investigation into the molecular mechanisms and therapeutic targets for COAD liver metastasis revealed associations with relevant processes such as fat digestion and absorption, cancer gene protein polysaccharides, and nitrogen metabolism. Consequently, genes including CAV1, ANXA1, CPS1, EDNRA, and GC emerge as promising candidates as therapeutic targets for COAD liver metastasis, thereby providing crucial insights for future clinical practices and drug development. In summary, this study uncovers the immune gene expression patterns in COAD, establishes a robust prognostic model, and elucidates the molecular mechanisms and potential therapeutic targets for COAD liver metastasis, thereby possessing significant theoretical and clinical implications. These findings are anticipated to offer substantial support for both the treatment and prognosis management of COAD patients.

Inference of drug off-target effects on cellular signaling using interactome-based deep learning

Many diseases emerge from dysregulated cellular signaling, and drugs are often designed to target specific signaling proteins. Off-target effects are, however, common and may ultimately result in failed clinical trials. Here we develop a computer model of the cell's transcriptional response to drugs for improved understanding of their mechanisms of action. The model is based on ensembles of artificial neural networks and simultaneously infers drug-target interactions and their downstream effects on intracellular signaling. With this, it predicts transcription factors' activities, while recovering known drug-target interactions and inferring many new ones, which we validate with an independent dataset. As a case study, we analyze the effects of the drug Lestaurtinib on downstream signaling. Alongside its intended target, FLT3, the model predicts an inhibition of CDK2 that enhances the downregulation of the cell cycle-critical transcription factor FOXM1. Our approach can therefore enhance our understanding of drug signaling for therapeutic design.

Transient peripheral blood transcriptomic response to ketamine treatment in children with ADNP syndrome

Activity-dependent neuroprotective protein (ADNP) syndrome is a rare neurodevelopmental disorder resulting in intellectual disability, developmental delay and autism spectrum disorder (ASD) and is due to mutations in the ADNP gene. Ketamine treatment has emerged as a promising therapeutic option for ADNP syndrome, showing safety and apparent behavioral improvements in a first open label study. However, the molecular perturbations induced by ketamine remain poorly understood. Here, we investigated the longitudinal effect of ketamine on the blood transcriptome of 10 individuals with ADNP syndrome. Transcriptomic profiling was performed before and at multiple time points after a single low-dose intravenous ketamine infusion (0.5mg/kg). We show that ketamine triggers immediate and profound gene expression alterations, with specific enrichment of monocyte-related expression patterns. These acute alterations encompass diverse signaling pathways and co-expression networks, implicating up-regulation of immune and inflammatory-related processes and down-regulation of RNA processing mechanisms and metabolism. Notably, these changes exhibit a transient nature, returning to baseline levels 24 hours to 1 week after treatment. These findings enhance our understanding of ketamine's molecular effects and lay the groundwork for further research elucidating its specific cellular and molecular targets. Moreover, they contribute to the development of therapeutic strategies for ADNP syndrome and potentially, ASD more broadly.

Using the power of innate immunoprofiling to understand vaccine design, infection, and immunity

In the field of immunology, a systems biology approach is crucial to understanding the immune response to infection and vaccination considering the complex interplay between genetic, epigenetic, and environmental factors. Significant progress has been made in understanding the innate immune response, including cell players and critical signaling pathways, but many questions remain unanswered, including how the innate immune response dictates host/pathogen responses and responses to vaccines. To complicate things further, it is becoming increasingly clear that the innate immune response is not a linear pathway but is formed from complex networks and interactions. To further our understanding of the crosstalk and complexities, systems-level analyses and expanded experimental technologies are now needed. In this review, we discuss the most recent immunoprofiling techniques and discuss systems approaches to studying the global innate immune landscape which will inform on the development of personalized medicine and innovative vaccine strategies.

Predicting congenital renal tract malformation genes using machine learning

Congenital renal tract malformations (RTMs) are the major cause of severe kidney failure in children. Studies to date have identified defined genetic causes for only a minority of human RTMs. While some RTMs may be caused by poorly defined environmental perturbations affecting organogenesis, it is likely that numerous causative genetic variants have yet to be identified. Unfortunately, the speed of discovering further genetic causes for RTMs is limited by challenges in prioritising candidate genes harbouring sequence variants. Here, we exploited the computer-based artificial intelligence methodology of supervised machine learning to identify genes with a high probability of being involved in renal development. These genes, when mutated, are promising candidates for causing RTMs. With this methodology, the machine learning classifier determines which attributes are common to renal development genes and identifies genes possessing these attributes. Here we report the validation of an RTM gene classifier and provide predictions of the RTM association status for all protein-coding genes in the mouse genome. Overall, our predictions, whilst not definitive, can inform the prioritisation of genes when evaluating patient sequence data for genetic diagnosis. This knowledge of renal developmental genes will accelerate the processes of reaching a genetic diagnosis for patients born with RTMs.

Designing a Novel Monitoring Approach for the Effects of Space Travel on Astronauts' Health

Space exploration and extraterrestrial civilization have fascinated humankind since the earliest days of human history. It was only in the last century that humankind finally began taking significant steps towards these goals by sending astronauts into space, landing on the moon, and building the International Space Station. However, space voyage is very challenging and dangerous, and astronauts are under constant space radiation and microgravity. It has been shown that astronauts are at a high risk of developing a broad range of diseases/disorders. Thus, it is critical to develop a rapid and effective assay to monitor astronauts' health in space. In this study, gene expression and correlation patterns were analyzed for 10 astronauts (8 male and 2 female) using the publicly available microarray dataset E-GEOD-74708. We identified 218 differentially expressed genes between In-flight and Pre-flight and noticed that space travel decreased genome regulation and gene correlations across the entire genome, as well as individual signaling pathways. Furthermore, we systematically developed a shortlist of 32 genes that could be used to monitor astronauts' health during space travel. Further studies, including microgravity experiments, are warranted to optimize and validate the proposed assay.

Dual RNA sequencing of group B Streptococcus-infected human monocytes reveals new insights into host-pathogen interactions and bacterial evasion of phagocytosis

Streptococcus agalactiae, also known as Group B Streptococcus (GBS) is a frequent cause of infections, including bacteraemia and other acute diseases in adults and immunocompromised individuals. We developed a novel system to study GBS within human monocytes to define the co-transcriptome of intracellular GBS (iGBS) and host cells simultaneously using dual RNA-sequencing (RNA-seq) to better define how this pathogen responds to host cells. Using human U937 monocytes and genome-sequenced GBS reference strain 874,391 in antibiotic protection assays we validated a system for dual-RNA seq based on measures of GBS and monocyte viability to ensure that the bacterial and host cell co-transcriptome reflected mainly intracellular (iGBS) rather than extracellular GBS. Elucidation of the co-transcriptome revealed 1119 dysregulated transcripts in iGBS with most genes, including several that encode virulence factors (e.g., scpB, hvgA, ribD, pil2b) exhibiting activation by upregulated expression. Infection with iGBS resulted in significant remodelling of the monocyte transcriptome, with 7587 transcripts differentially expressed including 7040 up-regulated and 547 down-regulated. qPCR confirmed that the most strongly activated genes included sht, encoding Streptococcal Histidine Triad Protein. An isogenic GBS mutant strain deficient in sht revealed a significant effect of this gene on phagocytosis of GBS and survival of the bacteria during systemic infection in mice. Identification of a novel contribution of sht to GBS virulence shows the co-transcriptome responses elucidated in GBS-infected monocytes help to shape the host-pathogen interaction and establish a role for sht in the response of the bacteria to phagocytic uptake. This study provides comprehension of concurrent transcriptional responses that occur in GBS and human monocytes that shape the host-pathogen interaction.

Pathway expression analysis

This paper introduces a pathway expression framework as an approach for constructing derived biomarkers. The pathway expression framework incorporates the biological connections of genes leading to a biologically relevant model. Using this framework, we distinguish between shedding subjects post-infection and all subjects pre-infection in human blood transcriptomic samples challenged with various respiratory viruses: H1N1, H3N2, HRV (Human Rhinoviruses), and RSV (Respiratory Syncytial Virus). Additionally, pathway expression data is used for selecting discriminatory pathways from these experiments. The classification results and selected pathways are benchmarked against standard gene expression based classification and pathway ranking methodologies. We find that using the pathway expression data along with selected pathways, which have minimal overlap with high ranking pathways found by traditional methods, improves classification rates across experiments.

New metabolic signature for Chagas disease reveals sex steroid perturbation in humans and mice

The causative agent of Chagas disease (CD), Trypanosoma cruzi, claims thousands of lives each year. Current diagnostic tools are insufficient to ensure parasitological detection in chronically infected patients has been achieved. A host-derived metabolic signature able to distinguish CD patients from uninfected individuals and assess antiparasitic treatment efficiency is introduced. Serum samples were collected from chronic CD patients, prior to and three years after treatment, and subjected to untargeted metabolomics analysis against demographically matched CD-negative controls. Five metabolites were confirmed by high-resolution tandem mass spectrometry. Several database matches for sex steroids were significantly altered in CD patients. A murine experiment corroborated sex steroid perturbation in T. cruzi-infected mice, particularly in male animals. Proteomics analysis also found increased steroidogenesis in the testes of infected mice. Metabolic alterations identified in this study shed light on the pathogenesis and provide the basis for developing novel assays for the diagnosis and screening of CD patients.

Dendritic Cells or Macrophages? The Microenvironment of Human Clear Cell Renal Cell Carcinoma Imprints a Mosaic Myeloid Subtype Associated with Patient Survival

Since their initial description by Elie Metchnikoff, phagocytes have sparked interest in a variety of biologic disciplines. These important cells perform central functions in tissue repair and immune activation as well as tolerance. Myeloid cells can be immunoinhibitory, particularly in the tumor microenvironment, where their presence is generally associated with poor patient prognosis. These cells are highly adaptable and plastic, and can be modulated to perform desired functions such as antitumor activity, if key programming molecules can be identified. Human clear cell renal cell carcinoma (ccRCC) is considered immunogenic; yet checkpoint blockades that target T cell dysfunction have shown limited clinical efficacy, suggesting additional layers of immunoinhibition. We previously described "enriched-in-renal cell carcinoma" (erc) DCs that were often found in tight contact with dysfunctional T cells. Using transcriptional profiling and flow cytometry, we describe here that ercDCs represent a mosaic cell type within the macrophage continuum co-expressing M1 and M2 markers. The polarization state reflects tissue-specific signals that are characteristic of RCC and renal tissue homeostasis. ErcDCs are tissue-resident with increasing prevalence related to tumor grade. Accordingly, a high ercDC score predicted poor patient survival. Within the profile, therapeutic targets (VSIG4, NRP1, GPNMB) were identified with promise to improve immunotherapy.

Integrative Functional Genomic Analysis in Multiplex Autism Families from Kazakhstan

The study of extended pedigrees containing autism spectrum disorder- (ASD-) related broader autism phenotypes (BAP) offers a promising approach to the search for ASD candidate variants. Here, a total of 650,000 genetic markers were tested in four Kazakhstani multiplex families with ASD and BAP to obtain data on de novo mutations (DNMs), common, and rare inherited variants that may contribute to the genetic risk for developing autistic traits. The variants were analyzed in the context of gene networks and pathways. Several previously well-described enriched pathways were identified, including ion channel activity, regulation of synaptic function, and membrane depolarization. Perhaps these pathways are crucial not only for the development of ASD but also for ВАР. The results also point to several additional biological pathways (circadian entrainment, NCAM and BTN family interactions, and interaction between L1 and Ankyrins) and hub genes (CFTR, NOD2, PPP2R2B, and TTR). The obtained results suggest that further exploration of PPI networks combining ASD and BAP risk genes can be used to identify novel or overlooked ASD molecular mechanisms.

mSep: investigating physiological and immune-metabolic biomarkers in septic and healthy pregnant women to predict feto-maternal immune health - a prospective observational cohort study protocol

INTRODUCTION

Maternal sepsis remains a leading cause of death in pregnancy. Physiological adaptations to pregnancy obscure early signs of sepsis and can result in delays in recognition and treatment. Identifying biomarkers that can reliably diagnose sepsis will reduce morbidity and mortality and antibiotic overuse. We have previously identified an immune-metabolic biomarker network comprising three pathways with a >99% accuracy for detecting bacterial neonatal sepsis. In this prospective study, we will describe physiological parameters and novel biomarkers in two cohorts-healthy pregnant women and pregnant women with suspected sepsis-with the aim of mapping pathophysiological drivers and evaluating predictive biomarkers for diagnosing maternal sepsis.

METHODS AND ANALYSIS

Women aged over 18 with an ultrasound-confirmed pregnancy will be recruited to a pilot and two main study cohorts. The pilot will involve blood sample collection from 30 pregnant women undergoing an elective caesarean section. Cohort A will follow 100 healthy pregnant women throughout their pregnancy journey, with collection of blood samples from participants at routine time points in their pregnancy: week 12 'booking', week 28 and during labour. Cohort B will follow 100 pregnant women who present with suspected sepsis in pregnancy or labour and will have at least two blood samples taken during their care pathway. Study blood samples will be collected during routine clinical blood sampling. Detailed medical history and physiological parameters at the time of blood sampling will be recorded, along with the results of routine biochemical tests, including C reactive protein, lactate and white blood cell count. In addition, study blood samples will be processed and analysed for transcriptomic, lipidomic and metabolomic analyses and both qualitative and functional immunophenotyping.

ETHICS AND DISSEMINATION

Ethical approval has been obtained from the Wales Research Ethics Committee 2 (SPON1752-19, 30 October 2019).

TRIAL REGISTRATION NUMBER

NCT05023954.

Transcriptomic Responses of Human Retinal Vascular Endothelial Cells to Inflammatory Cytokines

Purpose

Molecular profiling of human retinal endothelial cells provides opportunities to understand the roles of this cell population in maintenance of the blood-ocular barrier, and its involvements in diverse retinal vasculopathies. We aimed to generate a transcriptome of human retinal endothelial cells in the unstimulated state, and following treatment with inflammatory cytokines linked to cell dysfunction.

Methods

Endothelial cells were isolated from retinae of five human cadaveric donors, and treated for 60 minutes and 24 hours with interleukin-1β or tumor necrosis factor-α, or exposed to medium alone for the same intervals. Expression of intercellular adhesion molecule-1 was measured by RT-qPCR to confirm cytokine-induced activation of the cells. RNA was sequenced on the Illumina NovaSeq 6000 platform. Reads were aligned to the human GRCh38 genome, and reads that aligned to Ensembl-annotated genes were counted. Quality control of sequencing was performed with FastQC, and sequences were classified by Kraken.

Results

A human retinal endothelial cell RNA-sequencing dataset with mean of 99% reads aligned to the human genome was produced as raw RNA sequence data (FASTQ files) and processed read data (XLSX files). Multidimensional scaling analysis showed a strong donor effect, which was readily controlled by ComBat.

Conclusions

Our dataset may be useful for human retinal endothelial cell transcriptomic assemblies, functional gene annotating and/or gene expression and enrichment analyses, as well as cross-dataset harmonization.

Translational Relevance

The molecular profile of the human retinal endothelium is a source of candidate biologic targets for retinal vasculopathies.

ImitateDB: A database for domain and motif mimicry incorporating host and pathogen protein interactions

Molecular mimicry of host proteins by pathogens constitutes a strategy to hijack the host pathways. At present, there is no dedicated resource for mimicked domains and motifs in the host-pathogen interactome. In this work, the experimental host-pathogen (HP) and host-host (HH) protein-protein interactions (PPIs) were collated. The domains and motifs of these proteins were annotated using CD Search and ScanProsite, respectively. Host and pathogen proteins with a shared host interactor and similar domain/motif constitute a mimicry pair exhibiting global structural similarity (domain mimicry pair; DMP) or local sequence motif similarity (motif mimicry pair; MMP). Mimicry pairs are likely to be co-expressed and co-localized. 1,97,607 DMPs and 32,67,568 MMPs were identified in 49,265 experimental HP-PPIs and organized in a web-based resource, ImitateDB ( http://imitatedb.sblab-nsit.net ) that can be easily queried. The results are externally integrated using hyperlinked domain PSSM ID, motif ID, protein ID and PubMed ID. Kinase, UL36, Smc and DEXDc were frequent DMP domains whereas protein kinase C phosphorylation, casein kinase 2 phosphorylation, glycosylation and myristoylation sites were frequent MMP motifs. Novel DMP domains SANT, Tudor, PhoX and MMP motif microbody C-terminal targeting signal, cornichon signature and lipocalin signature were proposed. ImitateDB is a novel resource for identifying mimicry in interacting host and pathogen proteins.

Identification and validation of EMT-immune-related prognostic biomarkers CDKN2A, CMTM8 and ILK in colon cancer

Colon cancer (CC) is a disease with high incidence and mortality rate. The interaction between epithelial-mesenchymal transition (EMT) and immune status has important clinical significance. We aim to identify EMT-immune-related prognostic biomarkers in colon cancer. The GEO2R and GEPIA 2.0 were utilized to calculate the differential expression genes between CC and normal mucosa. Immport, InnateDB and EMTome databases were used to define EMT-immune-related genes. We conducted batch prognostic analysis by TCGA data. The expression patterns were verified by multiple datasets and lab experiments. GEPIA 2.0 and TIMER 2.0 were utilized to analyze the correlation of the hub genes with EMT markers and immune infiltration. GeneMANIA, STRING, and Metascape were used for co-expression and pathway enrichment analysis. Finally, we established a signature by the method of multivariate Cox regression analysis. CDKN2A, CMTM8 and ILK were filtered out as prognostic genes. CDKN2A and CMTM8 were up-regulated, while ILK was down-regulated in CC. CDKN2A was positively correlated with infiltration of macrophages, Th2 cells, Treg cells, and negatively correlated with NK cells. CMTM8 was negatively correlated with CD8+ T cells, dendritic cells, and NK cells. ILK was positively correlated with CD8+ T cells and dendritic cells. Moreover, CDKN2A, CMTM8 and ILK were significantly correlated with EMT markers. The three genes could participate in the TGF-β pathway. The prognosis model established by the three hub genes was an independent prognosis factor, which can better predict the prognosis. CDKN2A, CMTM8 and ILK are promising prognostic biomarkers and may be potential therapeutic targets in colon cancer.

Analysis of the immune landscape in virus-induced cancers using a novel integrative mechanism discovery approach

Background

The mechanisms of carcinogenesis from viral infections are extraordinarily complex and not well understood. Traditional methods of analyzing RNA-sequencing data may not be sufficient for unraveling complicated interactions between viruses and host cells. Using RNA and DNA-sequencing data from The Cancer Genome Atlas (TCGA), we aim to explore whether virus-induced tumors exhibit similar immune-associated (IA) dysregulations using a new algorithm we developed that focuses on the most important biological mechanisms involved in virus-induced cancers. Differential expression, survival correlation, and clinical variable correlations were used to identify the most clinically relevant IA genes dysregulated in 5 virus-induced cancers (HPV-induced head and neck squamous cell carcinoma, HPV-induced cervical cancer, EBV-induced stomach cancer, HBV-induced liver cancer, and HCV-induced liver cancer) after which a mechanistic approach was adopted to identify pathways implicated in IA gene dysregulation.

Results

Our results revealed that IA dysregulations vary with the cancer type and the virus type, but cytokine signaling pathways are dysregulated in all virus-induced cancers. Furthermore, we also found that important similarities exist between all 5 virus-induced cancers in dysregulated clinically relevant oncogenic signatures and IA pathways. Finally, we also discovered potential mechanisms for genomic alterations to induce IA gene dysregulations using our algorithm.

Conclusions

Our study offers a new approach to mechanism identification through integrating functional annotations and large-scale sequencing data, which may be invaluable to the discovery of new immunotherapy targets for virus-induced cancers.

Experiment level curation of transcriptional regulatory interactions in neurodevelopment

To facilitate the development of large-scale transcriptional regulatory networks (TRNs) that may enable in-silico analyses of disease mechanisms, a reliable catalogue of experimentally verified direct transcriptional regulatory interactions (DTRIs) is needed for training and validation. There has been a long history of using low-throughput experiments to validate single DTRIs. Therefore, we reason that a reliable set of DTRIs could be produced by curating the published literature for such evidence. In our survey of previous curation efforts, we identified the lack of details about the quantity and the types of experimental evidence to be a major gap, despite the theoretical importance of such details for the identification of bona fide DTRIs. We developed a curation protocol to inspect the published literature for support of DTRIs at the experiment level, focusing on genes important to the development of the mammalian nervous system. We sought to record three types of low-throughput experiments: Transcription factor (TF) perturbation, TF-DNA binding, and TF-reporter assays. Using this protocol, we examined a total of 1,310 papers to assemble a collection of 1,499 unique DTRIs, involving 251 TFs and 825 target genes, many of which were not reported in any other DTRI resource. The majority of DTRIs (965; 64%) were supported by two or more types of experimental evidence and 27% were supported by all three. Of the DTRIs with all three types of evidence, 170 had been tested using primary tissues or cells and 44 had been tested directly in the central nervous system. We used our resource to document research biases among reports towards a small number of well-studied TFs. To demonstrate a use case for this resource, we compared our curation to a previously published high-throughput perturbation screen and found significant enrichment of the curated targets among genes differentially expressed in the developing brain in response to Pax6 deletion. This study demonstrates a proof-of-concept for the assembly of a high resolution DTRI resource to support the development of large-scale TRNs.

The capacity to computationally reconstruct gene regulatory networks using large-scale biological data is currently limited by the absence of a high confidence set of one-to-one regulatory interactions. Given the lengthy history of using small scale experimental assays to investigate individual interactions, we reason that a reliable collection of gene regulatory interactions could be compiled by systematically inspecting the published literature. To this end, we developed a curation protocol to examine and record evidence of regulatory interactions at the individual experiment level. Focusing on the area of brain development, we applied our pipeline to 1,310 publications. We identified 3,601 individual experiments, providing detailed information about 1,499 regulatory interactions. Many of these interactions have verified activity specifically in the embryonic brain. By capturing reports of regulatory interactions at this level of detail, we equip the users with more granular information than other similar resources, enabling more informed assessments of reliability.

Proteomics reveals disturbances in the immune response and energy metabolism of monocytes from patients with septic shock

Sepsis results from a dyshomeostatic response to infection, which may lead to hyper or hypoimmune states. Monocytes are central regulators of the inflammatory response, but our understanding of their role in the genesis and resolution of sepsis is still limited. Here, we report a comprehensive exploration of monocyte molecular responses in a cohort of patients with septic shock via proteomic profiling. The acute stage of septic shock was associated with an impaired inflammatory phenotype, indicated by the down-regulation of MHC class II molecules and proinflammatory cytokine pathways. Simultaneously, there was an up-regulation of glycolysis enzymes and a decrease in proteins related to the citric acid cycle and oxidative phosphorylation. On the other hand, the restoration of immunocompetence was the hallmark of recovering patients, in which an upregulation of interferon signaling pathways was a notable feature. Our results provide insights into the immunopathology of sepsis and propose that, pending future studies, immunometabolism pathway components could serve as therapeutic targets in septic patients.

Differential gene expression analysis of corneal endothelium indicates involvement of phagocytic activity in Fuchs' endothelial corneal dystrophy

Fuchs' endothelial corneal dystrophy (FECD) is a progressive vision impairing disease caused by thickening of Descemet's membrane and gradual degeneration and loss of corneal endothelial cells. The aim of this study was to identify differentially expressed genes between FECD-affected and unaffected corneal endothelium to gain insight into the pathophysiological mechanisms underlying this disease. Microarray gene expression analysis was performed on total RNA from FECD-affected and unaffected corneal endothelium-Descemet's membrane (CE-DM) specimens using the Illumina HumanHT-12 v4.0 expression array. RNA from pools of FECD-affected (n = 3 per pool) and individual unaffected (n = 3) specimens was used for comparison. Altered expression of a sub-set of differentially expressed genes was validated by quantitative reverse transcription-polymerase chain reaction (qRT-PCR) in independent specimens. Bioinformatics analysis was performed using InnateDB to reveal functional relationships among the differentially expressed genes and molecular pathways involved in the disease. A total of 16,513 genes were found expressed in the corneal endothelium of which 142 genes were differentially expressed between FECD-affected and unaffected endothelium (log₂ fold-change ≥1.5, corrected p-value ≤0.05). Most of the genes were up-regulated (126) and a small proportion down-regulated (16) in affected corneal endothelium. Of the twelve genes prioritised for validation, differential expression of 10 genes, including those ranked 57th and 81st by significance validated by qRT-PCR (8 up-regulated and 2 downregulated, corrected p ≤ 0.05), one gene showed a trend for up-regulation in affected endothelium, consistent with the microarray analysis and another was up-regulated in an independent study indicating robustness of the differential expression dataset. Bioinformatic analysis revealed significant over-representation of differentially expressed genes in extracellular matrix reorganisation, cellular remodelling, immune response, and inflammation. Network analysis showed functional inter-relatedness of the majority of the dysregulated genes and revealed known direct functional relationships between 20 of the genes; many of these genes have roles in macrophage differentiation, phagocytosis and inflammation. This is the second report of microarray gene expression analysis in FECD. This study revealed a set of highly dysregulated genes in the corneal endothelium in FECD. More than a third of the dysregulated genes in the disease have been discovered for the first time and thus are novel. The dysregulated genes strongly suggest the presence of phagocytic cells, most likely immune cells, and inflammation in corneal endothelium in the disease. This study provides a molecular framework for delineating the mechanisms underlying these cellular processes in FECD.

Zika Virus Infection of Human Iris Pigment Epithelial Cells

During recent Zika epidemics, adults infected with Zika virus (ZIKV) have developed organ-specific inflammatory complications. The most serious Zika-associated inflammatory eye disease is uveitis, which is commonly anterior in type, affecting both eyes and responding to corticosteroid eye drops. Mechanisms of Zika-associated anterior uveitis are unknown, but ZIKV has been identified in the aqueous humor of affected individuals. The iris pigment epithelium is a target cell population in viral anterior uveitis, and it acts to maintain immune privilege within the anterior eye. Interactions between ZIKV and human iris pigment epithelial cells were investigated with infectivity assays and RNA-sequencing. Primary cell isolates were prepared from eyes of 20 cadaveric donors, and infected for 24 hours with PRVABC59 strain ZIKV or incubated uninfected as control. Cytoimmunofluorescence, RT-qPCR on total cellular RNA, and focus-forming assays of culture supernatant showed cell isolates were permissive to infection, and supported replication and release of infectious ZIKV. To explore molecular responses of cell isolates to ZIKV infection at the whole transcriptome level, RNA was sequenced on the Illumina NextSeq 500 platform, and results were aligned to the human GRCh38 genome. Multidimensional scaling showed clear separation between transcriptomes of infected and uninfected cell isolates. Differential expression analysis indicated a vigorous molecular response of the cell to ZIKV: 7,935 genes were differentially expressed between ZIKV-infected and uninfected cells (FDR < 0.05), and 99% of 613 genes that changed at least two-fold were up-regulated. Reactome and KEGG pathway and Gene Ontology enrichment analyses indicated strong activation of viral recognition and defense, in addition to biosynthesis processes. A CHAT network included 6275 molecular nodes and 24 contextual hubs in the cell response to ZIKV infection. Receptor-interacting serine/threonine kinase 1 (RIPK1) was the most significantly connected contextual hub. Correlation of gene expression with read counts assigned to the ZIKV genome identified a negative correlation between interferon signaling and viral load across isolates. This work represents the first investigation of mechanisms of Zika-associated anterior uveitis using an in vitro human cell model. The results suggest the iris pigment epithelium mounts a molecular response that limits intraocular pathology in most individuals.

Identifying kinase targets of PPARγ in human breast cancer

Breast cancer is the most common cancer in women. Despite advances in screening women for genetic predisposition to breast cancer and risk stratification, a majority of women carriers remain undetected until they become affected. Thus, there is a need to develop a cost-effective, rapid, sensitive and non-invasive early-stage diagnostic method. Kinases are involved in all fundamental cellular processes and mutations in kinases have been reported as drivers of cancer. PPARγ is a ligand-activated transcription factor that plays important roles in cell proliferation and metabolism. However, the complete set of kinases modulated by PPARγ is still unknown. In this study, we identified human kinases that are potential PPARγ targets and evaluated their differential expression and gene pair correlations in human breast cancer patient dataset TCGA-BRCA. We further confirmed the findings in human breast cancer cell lines MCF7 and SK-BR-3 using a kinome array. We observed that gene pair correlations are lost in tumours as compared to healthy controls and could be used as a supplement strategy for diagnosis and prognosis of breast cancer.

Characterization of Small-Molecule-Induced Changes in Parkinson's-Related Trafficking via the Nedd4 Ubiquitin Signaling Cascade

The benzdiimidazole NAB2 rescues α-synuclein-associated trafficking defects associated with early onset Parkinson's disease in a Nedd4-dependent manner. Despite identification of E3 ubiquitin ligase Nedd4 as a putative target of NAB2, its molecular mechanism of action has not been elucidated. As such, the effect of NAB2 on Nedd4 activity and specificity was interrogated through biochemical, biophysical, and proteomic analyses. NAB2 was found to bind Nedd4 (K_D^app = 42 nM), but this binding is side chain mediated and does not alter its conformation or ubiquitination kinetics in vitro. Nedd4 co-localizes with trafficking organelles, and NAB2 exposure did not alter its co-localization. Ubiquitin enrichment coupled proteomics revealed that NAB2 stimulates ubiquitination of trafficking-associated proteins, most likely through modulating the substrate specificity of Nedd4, providing a putative protein network involved in the NAB2 mechanism and revealing trafficking scaffold protein TFG as a Nedd4 substrate.

Dysregulated Host Responses Underlie 2009 Pandemic Influenza-Methicillin Resistant Staphylococcus aureus Coinfection Pathogenesis at the Alveolar-Capillary Barrier

Influenza viruses are a continual public health concern resulting in 3-5 million severe infections annually despite intense vaccination campaigns and messaging. Secondary bacterial infections, including Staphylococcus aureus, result in increased morbidity and mortality during seasonal epidemics and pandemics. While coinfections can result in deleterious pathologic consequences, including alveolar-capillary barrier disruption, the underlying mechanisms are poorly understood. We have characterized host- and pathogen-centric mechanisms contributing to influenza-bacterial coinfections in a primary cell coculture model of the alveolar-capillary barrier. Using 2009 pandemic influenza (pH1N1) and methicillin-resistant S. aureus (MRSA), we demonstrate that coinfection resulted in dysregulated barrier function. Preinfection with pH1N1 resulted in modulation of adhesion- and invasion-associated MRSA virulence factors during lag phase bacterial replication. Host response modulation in coinfected alveolar epithelial cells were primarily related to TLR- and inflammatory response-mediated cell signaling events. While less extensive in cocultured endothelial cells, coinfection resulted in changes to cellular stress response- and TLR-related signaling events. Analysis of cytokine expression suggested that cytokine secretion might play an important role in coinfection pathogenesis. Taken together, we demonstrate that coinfection pathogenesis is related to complex host- and pathogen-mediated events impacting both epithelial and endothelial cell regulation at the alveolar-capillary barrier.

Survival Strategies of Pathogenic Candida Species in Human Blood Show Independent and Specific Adaptations

To ensure their survival, pathogens have to adapt immediately to new environments in their hosts, for example, during the transition from the gut to the bloodstream. Here, we investigated the basis of this adaptation in a group of fungal species which are among the most common causes of hospital-acquired infections, the Candida species. On the basis of a human whole-blood infection model, we studied which genes and processes are active over the course of an infection in both the host and four different Candida pathogens. Remarkably, we found that, while the human host response during the early phase of infection is predominantly uniform, the pathogens pursue largely individual strategies and each one regulates genes involved in largely disparate processes in the blood. Our results reveal that C. albicans, C. glabrata, C. parapsilosis, and C. tropicalis all have developed individual strategies for survival in the host. This indicates that their pathogenicity in humans has evolved several times independently and that genes which are central for survival in the host for one species may be irrelevant in another.

Only four species, Candida albicans, C. glabrata, C. parapsilosis, and C. tropicalis, together account for about 90% of all Candida bloodstream infections and are among the most common causes of invasive fungal infections of humans. However, virulence potential varies among these species, and the phylogenetic tree reveals that their pathogenicity may have emerged several times independently during evolution. We therefore tested these four species in a human whole-blood infection model to determine, via comprehensive dual-species RNA-sequencing analyses, which fungal infection strategies are conserved and which are recent evolutionary developments. The ex vivo infection progressed from initial immune cell interactions to nearly complete killing of all fungal cells. During the course of infection, we characterized important parameters of pathogen-host interactions, such as fungal survival, types of interacting immune cells, and cytokine release. On the transcriptional level, we obtained a predominantly uniform and species-independent human response governed by a strong upregulation of proinflammatory processes, which was downregulated at later time points after most of the fungal cells were killed. In stark contrast, we observed that the different fungal species pursued predominantly individual strategies and showed significantly different global transcriptome patterns. Among other findings, our functional analyses revealed that the fungal species relied on different metabolic pathways and virulence factors to survive the host-imposed stress. These data show that adaptation of Candida species as a response to the host is not a phylogenetic trait, but rather has likely evolved independently as a prerequisite to cause human infections.

Salmonella enterica serovar Typhimurium inhibits the innate immune response and promotes apoptosis in a ribosomal/TRP53-dependent manner in swine neutrophils

Neutrophils are the first barriers for resisting the invasion, proliferation, and damage caused by Salmonella Typhimurium. However, the mechanisms that control this resistance are not completely understood. In this study, we established an in vitro Salmonella infection model in porcine neutrophils, and analyzed the cellular transcriptome by deep sequencing and flow cytometry. The results showed that ribosomal gene transcription was inhibited, and two of these genes, RPL39 and RPL9, were related to TRP53 activation. Furthermore, several important innate immunity genes were also inhibited. Knock-down of RPL39 and RPL9 by siRNA caused an approximate fourfold up-regulation of TRP53. Knock-down of RPL39 and RPL9 also resulted in a significant down-regulation of IFNG and TNF, indicating an inhibition of the innate immune response. Silencing of RPL39 and RPL9 also resulted in the up-regulation of FAS, RB1, CASP6, and GADD45A, which play roles in cell cycle arrest and apoptosis. Neutrophils were either first treated with RPL39 siRNA, RPL9 siRNA, TRP53 activator, or TRP53 inhibitor, and then infected with Salmonella. Knock-down of RPL39 and RPL9, or treatment with TRP53 activator, can increase the intracellular proliferation of Salmonella in neutrophils. We speculate that much of the Salmonella virulence can be attributed to the enhancement of cell cycle arrest and the inhibition of the innate immune response, which allows the bacteria to successfully proliferate intracellularly.

MDA-MB-231 Breast Cancer Cells Resistant to Pleurocidin-Family Lytic Peptides Are Chemosensitive and Exhibit Reduced Tumor-Forming Capacity

Direct-acting anticancer (DAA) peptides are cytolytic peptides that show promise as novel anticancer agents. DAA peptides bind to anionic molecules that are abundant on cancer cells relative to normal healthy cells, which results in preferential killing of cancer cells. Due to the mechanism by which DAA peptides kill cancer cells, it was thought that resistance would be difficult to achieve. Here, we describe the generation and characterization of two MDA-MB-231 breast cancer cell-line variants with reduced susceptibility to pleurocidin-family and mastoparan DAA peptides. Peptide resistance correlated with deficiencies in peptide binding to cell-surface structures, suggesting that resistance was due to altered composition of the cell membrane. Peptide-resistant MDA-MB-231 cells were phenotypically distinct yet remained susceptible to chemotherapy. Surprisingly, neither of the peptide-resistant breast cancer cell lines was able to establish tumors in immune-deficient mice. Histological analysis and RNA sequencing suggested that tumorigenicity was impacted by alternations in angiogenesis and extracellular matrix composition in the peptide-resistant MDA-MB-231 variants. Collectively, these data further support the therapeutic potential of DAA peptides as adjunctive treatments for cancer.

Systems Biology Approaches to Understanding the Human Immune System

Systems biology is an approach to interrogate complex biological systems through large-scale quantification of numerous biomolecules. The immune system involves >1,500 genes/proteins in many interconnected pathways and processes, and a systems-level approach is critical in broadening our understanding of the immune response to vaccination. Changes in molecular pathways can be detected using high-throughput omics datasets (e.g., transcriptomics, proteomics, and metabolomics) by using methods such as pathway enrichment, network analysis, machine learning, etc. Importantly, integration of multiple omic datasets is becoming key to revealing novel biological insights. In this perspective article, we highlight the use of protein-protein interaction (PPI) networks as a multi-omics integration approach to unravel information flow and mechanisms during complex biological events, with a focus on the immune system. This involves a combination of tools, including: InnateDB, a database of curated interactions between genes and protein products involved in the innate immunity; NetworkAnalyst, a visualization and analysis platform for InnateDB interactions; and MetaBridge, a tool to integrate metabolite data into PPI networks. The application of these systems techniques is demonstrated for a variety of biological questions, including: the developmental trajectory of neonates during the first week of life, mechanisms in host-pathogen interaction, disease prognosis, biomarker discovery, and drug discovery and repurposing. Overall, systems biology analyses of omics data have been applied to a variety of immunology-related questions, and here we demonstrate the numerous ways in which PPI network analysis can be a powerful tool in contributing to our understanding of the immune system and the study of vaccines.

Identification of common key genes and pathways between type 1 diabetes and multiple sclerosis using transcriptome and interactome analysis

PURPOSE

Type 1 diabetes (T1D) and multiple sclerosis (MS) are classified as T cell-mediated autoimmune diseases. Although convergent evidence proposed common genetic architecture for autoimmune diseases, it remains a challenge to identify them. This study aimed to determine common gene signature and pathways in T1D and MS via systems biology approach.

METHODS

Gene expression profiles of peripheral blood mononuclear cells (PBMCs) and pancreatic-β cells in T1D as well as PBMCs and cerebrospinal fluid (CSF) in MS were analyzed in our previous published data, and differential expressed genes were integrated with protein-protein interactions data to construct Query-Query PPI (QQPPI) networks. In this study, QQPPI networks were further analyzed to investigate more central genes, functional modules and complexes shared in T1D and MS progression. Lastly, the interaction of common genes with drugs was also explored.

RESULTS

Several cytokines such as IL-23A, IL-32, IL-34, and IL-37 tend to be differentially expressed in both diseases. In addition, PSMA1, MYC, SRPK1, YBX1, HNRNPM, NF-κB2, IKBKE, RAC1, FN1, ARRB2, ESR1, HSP90AB1, and PPP1CA were common high central genes in QQPPI networks corresponding to each disease. Proteasome, spliceosome, immune responses, apoptosis, cellular communication/signaling transduction mechanism, interaction with environment, and activity of intercellular mediators were shared biological processes in T1D and MS. Finally, azathioprine, melatonin, resveratrol, and geldanamycin identified as prioritized drugs for the treatment of patients with T1D and MS.

CONCLUSIONS

This study represented novel key genes and pathways shared between T1D and MS, which may facilitate the identification of potential therapeutic targets in these diseases.

Kinome Analysis of Honeybee (Apis mellifera L.) Dark-Eyed Pupae Identifies Biomarkers and Mechanisms of Tolerance to Varroa Mite Infestation

The mite Varroa destructor is a serious threat to honeybee populations. Selective breeding for Varroa mite tolerance could be accelerated by biomarkers within individual bees that could be applied to evaluate a colony phenotype. Previously, we demonstrated differences in kinase-mediated signaling between bees from colonies of extreme phenotypes of mite susceptibility. We expand these findings by defining a panel of 19 phosphorylation events that differ significantly between individual pupae from multiple colonies with distinct Varroa mite tolerant phenotypes. The predictive capacity of these biomarkers was evaluated by analyzing uninfested pupae from eight colonies representing a spectrum of mite tolerance. The pool of biomarkers effectively discriminated individual pupae on the basis of colony susceptibility to mite infestation. Kinome analysis of uninfested pupae from mite tolerant colonies highlighted an increased innate immune response capacity. The implication that differences in innate immunity contribute to mite susceptibility is supported by the observation that induction of innate immune signaling responses to infestation is compromised in pupae of the susceptible colonies. Collectively, biomarkers within individual pupae that are predictive of the susceptibility of colonies to mite infestation could provide a molecular tool for selective breeding of tolerant colonies.

Prospective isolation of nonhematopoietic cells of the niche and their differential molecular interactions with HSCs

A major limitation preventing in vivo modulation of hematopoietic stem cells (HSCs) is the incomplete understanding of the cellular and molecular support of the microenvironment in regulating HSC fate decisions. Consequently, murine HSCs cannot be generated, maintained, or expanded in culture over extended periods of time. A significantly improved understanding of the bone marrow niche environment and its molecular interactions with HSCs is pivotal to overcoming this challenge. We here prospectively isolated all major nonhematopoietic cellular niche components and cross-correlate them in detail with niche cells defined by lineage marking or tracing. Compiling an extensive database of soluble and membrane-bound ligand-receptor interactions, we developed a computational method to infer potential cell-to-cell interactions based on transcriptome data of sorter-purified niche cells and hematopoietic stem and progenitor cell subpopulations. Thus, we establish a compendium of the molecular communication between defined niche components and HSCs. Our analysis suggests an important role for cytokine antagonists in the regulation of HSC functions.

Systems biology and big data in asthma and allergy: recent discoveries and emerging challenges

Asthma is a common condition caused by immune and respiratory dysfunction, and it is often linked to allergy. A systems perspective may prove helpful in unravelling the complexity of asthma and allergy. Our aim is to give an overview of systems biology approaches used in allergy and asthma research. Specifically, we describe recent "omic"-level findings, and examine how these findings have been systematically integrated to generate further insight.Current research suggests that allergy is driven by genetic and epigenetic factors, in concert with environmental factors such as microbiome and diet, leading to early-life disturbance in immunological development and disruption of balance within key immuno-inflammatory pathways. Variation in inherited susceptibility and exposures causes heterogeneity in manifestations of asthma and other allergic diseases. Machine learning approaches are being used to explore this heterogeneity, and to probe the pathophysiological patterns or "endotypes" that correlate with subphenotypes of asthma and allergy. Mathematical models are being built based on genomic, transcriptomic and proteomic data to predict or discriminate disease phenotypes, and to describe the biomolecular networks behind asthma.The use of systems biology in allergy and asthma research is rapidly growing, and has so far yielded fruitful results. However, the scale and multidisciplinary nature of this research means that it is accompanied by new challenges. Ultimately, it is hoped that systems medicine, with its integration of omics data into clinical practice, can pave the way to more precise, personalised and effective management of asthma.

Rapid Bladder Interleukin-10 Synthesis in Response to Uropathogenic Escherichia coli Is Part of a Defense Strategy Triggered by the Major Bacterial Flagellar Filament FliC and Contingent on TLR5

Interleukin-10 is part of the immune response to urinary tract infection (UTI) due to E. coli, and it is important in the early control of infection in the bladder. Defining the mechanism of engagement of the immune system by the bacteria that enables the protective IL-10 response is critical to exploring how we might exploit this mechanism for new infection control strategies. In this study, we reveal part of the bacterial flagellar apparatus (FliC) is an important component that is sensed by and responsible for induction of IL-10 in the response to UPEC. We show this response occurs in a TLR5-dependent manner. Using infection prevention and control trials in mice infected with E. coli, this study also provides evidence that purified FliC might be of value in novel approaches for the treatment of UTI or in preventing infection by exploiting the FliC-triggered bladder transcriptome.

Urinary tract infection (UTI) caused by uropathogenic Escherichia coli (UPEC) engages interleukin-10 (IL-10) as an early innate immune response to regulate inflammation and promote the control of bladder infection. However, the mechanism of engagement of innate immunity by UPEC that leads to elicitation of IL-10 in the bladder is unknown. Here, we identify the major UPEC flagellar filament, FliC, as a key bacterial component sensed by the bladder innate immune system responsible for the induction of IL-10 synthesis. IL-10 responses of human as well as mouse bladder epithelial cell-monocyte cocultures were triggered by flagella of three major UPEC representative strains, CFT073, UTI89, and EC958. FliC purified to homogeneity induced IL-10 in vitro and in vivo as well as other functionally related cytokines, including IL-6. The genome-wide innate immunological context of FliC-induced IL-10 in the bladder was defined using RNA sequencing that revealed a network of transcriptional and antibacterial defenses comprising 1,400 genes that were induced by FliC. Of the FliC-responsive bladder transcriptome, altered expression of il10 and 808 additional genes were dependent on Toll-like receptor 5 (TLR5), according to analysis of TLR5-deficient mice. Examination of the potential of FliC and associated innate immune signature in the bladder to boost host defense, based on prophylactic or therapeutic administration to mice, revealed significant benefits for the control of UPEC. We conclude that detection of FliC through TLR5 triggers rapid IL-10 synthesis in the bladder, and FliC represents a potential immune modulator that might offer benefit for the treatment or prevention of UPEC UTI.

IMPORTANCE Interleukin-10 is part of the immune response to urinary tract infection (UTI) due to E. coli, and it is important in the early control of infection in the bladder. Defining the mechanism of engagement of the immune system by the bacteria that enables the protective IL-10 response is critical to exploring how we might exploit this mechanism for new infection control strategies. In this study, we reveal part of the bacterial flagellar apparatus (FliC) is an important component that is sensed by and responsible for induction of IL-10 in the response to UPEC. We show this response occurs in a TLR5-dependent manner. Using infection prevention and control trials in mice infected with E. coli, this study also provides evidence that purified FliC might be of value in novel approaches for the treatment of UTI or in preventing infection by exploiting the FliC-triggered bladder transcriptome.

Immunoinformatics: In Silico Approaches and Computational Design of a Multi-epitope, Immunogenic Protein

Immunoinformatics is a new critical field with several tools and databases that conduct the eyesight of experimental selection and facilitate analysis of the great amount of immunologic data obtained from experimental researches and helps to design and introducing new hypothesis. Given these visages, immunoinformatics seems to be the way that develop and progress the immunological research. Bioinformatics methods and applications are successfully employed in vaccine informatics to assist different sites of the preclinical, clinical, and post-licensure vaccine enterprises. On the other hand, the progression of molecular biology and immunology caused epitope vaccines have become the focus of research on molecular vaccines. Moreover, reverse vaccinology could improve vaccine production and vaccination protocols by in silico prediction of protein-vaccine candidates from genome sequences. B- and T-cell immune epitopes could be predicted by immunoinformatics algorithms and computational methods to improve the vaccine design, protective immunity analysis, assessment of vaccine safety and efficacy, and immunization modeling. This review aims to discuss the power of computational approaches in vaccine design and their relevance to the development of effective vaccines. Furthermore, the various divisions of this field and available tools in each item are introduced and reviewed.

Ibrutinib as a potential therapeutic option for HER2 overexpressing breast cancer - the role of STAT3 and p21

Treatment response rates to current anticancer therapies for HER2 overexpressing breast cancer are limited and are associated with severe adverse drug reactions. Tyrosine kinases perform crucial roles in cellular processes by mediating cell signalling cascades. Ibrutinib is a recently approved Tyrosine Kinase Inhibitor (TKI) that has been shown be an effective therapeutic option for HER2 overexpressing breast cancer. The molecular mechanisms, pathways, or genes that are modulated by ibrutinib and the mechanism of action of ibrutinib in HER2 overexpressing breast cancer remain obscure. In this study, we have performed a kinome array analysis of ibrutinib treatment in two HER2 overexpressing breast cancer cell lines. Our analysis shows that ibrutinib induces changes in nuclear morphology and causes apoptosis via caspase-dependent extrinsic apoptosis pathway with the activation of caspases-8, caspase-3, and cleavage of PARP1. We further show that phosphorylated STAT3^Y705 is upregulated and phosphorylated p21^T145 is downregulated upon ibrutinib treatment. We propose that STAT3 upregulation is a passive response as a result of induction of DNA damage and downregulation of phosphorylated p21 is promoting cell cycle arrest and apoptosis in the two HER2 overexpressing cell lines. These results suggest that inhibitors of STAT3 phosphorylation may be potential options for combination therapy to help increase the efficacy of ibrutinib against HER2-overexpressing tumors.

Reduced expression of apolipoprotein E and immunoglobulin heavy constant gamma 1 proteins in Fuchs endothelial corneal dystrophy

BACKGROUND

Fuchs endothelial corneal dystrophy (FECD) is a progressive and potentially a sight threatening disease, and a common indication for corneal grafting in the elderly. Aberrant thickening of Descemet's membrane, formation of microscopic excrescences (guttae) and gradual loss of corneal endothelial cells are the hallmarks of the disease. The aim of this study was to identify differentially abundant proteins between FECD-affected and unaffected Descemet's membrane.

METHODS

Label-free quantitative proteomics using nanoscale ultra-performance liquid chromatography-mass spectrometry (nUPLC-MS^E ) was employed on affected and unaffected Descemet's membrane extracts, and interesting findings were further investigated using quantitative reverse transcription-polymerase chain reaction and immunohistochemical techniques.

RESULTS

Quantitative proteomics revealed significantly lower abundance of apolipoprotein E (APOE) and immunoglobulin heavy constant gamma 1 protein (IGHG1) in affected Descemet's membrane. The difference in the distribution of APOE between affected and unaffected Descemet's membrane and of IGHG1 detected by immunohistochemistry support their down-regulation in the disease. Comparative gene expression analysis showed significantly lower APOE mRNA levels in FECD-affected than unaffected corneal endothelium. IGHG1 gene is expressed at extremely low levels in the corneal endothelium, precluding relative expression analysis.

CONCLUSIONS

This is the first study to report comparative proteomics of Descemet's membrane tissue, and implicates dysregulation of APOE and IGHG1 proteins in the pathogenesis of Fuchs endothelial corneal dystrophy.

Transcriptional and metabolic rewiring of colorectal cancer cells expressing the oncogenic KRASG13D mutation

Background

Activating mutations in KRAS frequently occur in colorectal cancer (CRC) patients, leading to resistance to EGFR-targeted therapies.

Methods

To better understand the cellular reprogramming which occurs in mutant KRAS cells, we have undertaken a systems-level analysis of four CRC cell lines which express either wild type (wt) KRAS or the oncogenic KRAS^G13D allele (mtKRAS).

Results

RNAseq revealed that genes involved in ribosome biogenesis, mRNA translation and metabolism were significantly upregulated in mtKRAS cells. Consistent with the transcriptional data, protein synthesis and cell proliferation were significantly higher in the mtKRAS cells. Targeted metabolomics analysis also confirmed the metabolic reprogramming in mtKRAS cells. Interestingly, mtKRAS cells were highly transcriptionally responsive to EGFR activation by TGFα stimulation, which was associated with an unexpected downregulation of genes involved in a range of anabolic processes. While TGFα treatment strongly activated protein synthesis in wtKRAS cells, protein synthesis was not activated above basal levels in the TGFα-treated mtKRAS cells. This was likely due to the defective activation of the mTORC1 and other pathways by TGFα in mtKRAS cells, which was associated with impaired activation of PKB signalling and a transient induction of AMPK signalling.

Conclusions

We have found that mtKRAS cells are substantially rewired at the transcriptional, translational and metabolic levels and that this rewiring may reveal new vulnerabilities in oncogenic KRAS CRC cells that could be exploited in future.

MorCVD: A Unified Database for Host-Pathogen Protein-Protein Interactions of Cardiovascular Diseases Related to Microbes

Microbe induced cardiovascular diseases (CVDs) are less studied at present. Host-pathogen interactions (HPIs) between human proteins and microbial proteins associated with CVD can be found dispersed in existing molecular interaction databases. MorCVD database is a curated resource that combines 23,377 protein interactions between human host and 432 unique pathogens involved in CVDs in a single intuitive web application. It covers endocarditis, myocarditis, pericarditis and 16 other microbe induced CVDs. The HPI information has been compiled, curated, and presented in a freely accessible web interface (http://morcvd.sblab-nsit.net/About). Apart from organization, enrichment of the HPI data was done by adding hyperlinked protein ID, PubMed, gene ontology records. For each protein in the database, drug target and interactors (same as well as different species) information has been provided. The database can be searched by disease, protein ID, pathogen name or interaction detection method. Interactions detected by more than one method can also be listed. The information can be presented in tabular form or downloaded. A comprehensive help file has been developed to explain the various options available. Hence, MorCVD acts as a unified resource for retrieval of HPI data for researchers in CVD and microbiology.

Characterization of Host and Bacterial Contributions to Lung Barrier Dysfunction Following Co-infection with 2009 Pandemic Influenza and Methicillin Resistant Staphylococcus aureus

Influenza viruses are a threat to global public health resulting in ~500,000 deaths each year. Despite an intensive vaccination program, influenza infections remain a recurrent, yet unsolved public health problem. Secondary bacterial infections frequently complicate influenza infections during seasonal outbreaks and pandemics, resulting in increased morbidity and mortality. Staphylococcus aureus, including methicillin-resistant S. aureus (MRSA), is frequently associated with these co-infections, including the 2009 influenza pandemic. Damage to alveolar epithelium is a major contributor to severe influenza-bacterial co-infections and can result in gas exchange abnormalities, fluid leakage, and respiratory insufficiency. These deleterious manifestations likely involve both pathogen- and host-mediated mechanisms. However, there is a paucity of information regarding the mechanisms (pathogen- and/or host-mediated) underlying influenza-bacterial co-infection pathogenesis. To address this, we characterized the contributions of viral-, bacterial-, and host-mediated factors to the altered structure and function of alveolar epithelial cells during co-infection with a focus on the 2009 pandemic influenza (pdm2009) and MRSA. Here, we characterized pdm2009 and MRSA replication kinetics, temporal host kinome responses, modulation of MRSA virulence factors, and disruption of alveolar barrier integrity in response to pdm2009-MRSA co-infection. Our results suggest that alveolar barrier disruption during co-infection is mediated primarily through host response dysregulation, resulting in loss of alveolar barrier integrity.

Regulators of Salmonella-host interaction identified by peripheral blood transcriptome profiling: roles of TGFB1 and TRP53 in intracellular Salmonella replication in pigs

Peripheral blood transcriptome is an important intermediate data source for investigating the mechanism of Salmonella invasion, proliferation, and transmission. We challenged 4-week old piglets with Salmonella enterica serovar Typhimurium LT2 and investigated the peripheral blood gene expression profile before treatment (d0) and at 2 and 7 days post-inoculation (dpi) using deep sequencing. Regulator pathways were first predicted in silico and validated by wet-lab experiments. In total, 1255, 765, and 853 genes were differentially expressed between 2 dpi/d0, 7 dpi/d0, and 7 dpi/2 dpi, respectively. Additionally, 1333 genes showed a time effect during the investigated Salmonella infection period. Clustering analysis showed that the differentially expressed genes fell into six distinct expression clusters. Pathway annotation of these gene clusters showed that the innate immune system was first significantly upregulated at 2 dpi and then attenuated at 7 dpi. Toll-like receptor cascades, MyD88 cascade, phagosome pathway, cytokine signaling pathway, and lysosome pathway showed a similar expression pattern. Interestingly, we found that the ribosome pathway was significantly inhibited at 2 and 7 dpi. Gene expression regulation network enrichment analysis identified several candidate factors controlling the expression clusters. Further in vitro study showed that TGFB1 can inhibit Salmonella replication whereas TRP53 can promote Salmonella replication in porcine peripheral blood mononuclear cells and murine macrophages. These results provide new insights into the molecular mechanism of Salmonella-host interactions and clues for the genetic improvement of Salmonella infection resistance in pigs.

Up-regulation of fibroblast growth factor receptor 1 due to prenatal tobacco exposure can lead to developmental defects in new born

Introduction: Tobacco-smoking is one of the most important risk factor for preterm delivery, pregnancy loss, low birth weight, and fetal growth restriction. It is estimated that approximately 30% of growth-restricted neonates could be independently associated with maternal smoking.Methods: In this study, gene expression profile, GSE11798, was chosen from GEO database with an aim to perceive change in gene expression signature in new born due to maternal smoking. Enrichment analysis was performed to annotate differentially expressed genes (DEGs) through gene ontology and pathway analysis using DAVID. Protein-protein interactions and module detection of these DEGs were carried out using cytoscape v3.6.0. Thirty umbilical cord tissue samples from 15 smokers and 15 non-smokers pregnant women were included in this analysis.Results: Twenty-six differentially expressed genes (DEGs) between two groups were selected using GEO2R tool. The DEGs were observed to be participating in biological processes/pathways related to growth releasing hormone, angiogenesis, embryonic skeletal, and cardiac development. Fibroblast growth factor receptor-1 (FGFR1) was identified to be the hub node with 348 interacting partners, which regulates transcription, cell growth, differentiation, and apoptosis. The up-regulation of FGFR1 in umbilical cord tissue may lead to reproductive and developmental complications such as encephalocraniocutaneous lipomatosis, osteoglophonic dysplasia, and Pfeiffer syndrome in new-borns.Conclusion: The findings manifests the possibility of overcoming these adverse health effects in new born through FGFR1 modulating treatments during pregnancy.

Systems vaccinology and big data in the vaccine development chain

Systems vaccinology has proven a fascinating development in the last decade. Where traditionally vaccine development has been dominated by trial and error, systems vaccinology is a tool that provides novel and comprehensive understanding if properly used. Data sets retrieved from systems‐based studies endorse rational design and effective development of safe and efficacious vaccines. In this review we first describe different omics‐techniques that form the pillars of systems vaccinology. In the second part, the application of systems vaccinology in the different stages of vaccine development is described. Overall, this review shows that systems vaccinology has become an important tool anywhere in the vaccine development chain.

Bioinformatics Approaches to Predict Drug Responses from Genomic Sequencing

Fulfilling the promises of precision medicine will depend on our ability to create patient-specific treatment regimens. Therefore, being able to translate genomic sequencing into predicting how a patient will respond to a given drug is critical. In this chapter, we review common bioinformatics approaches that aim to use sequencing data to predict sample-specific drug susceptibility. First, we explain the importance of customized drug regimens to the future of medical care. Second, we discuss the different public databases and community efforts that can be leveraged to develop new methods for identifying new predictive biomarkers. Third, we cover the basic methods that are currently used to identify markers or signatures of drug response, without any prior knowledge of the drug's mechanism of action. We further discuss how one can integrate knowledge about drug targets, mechanisms, and predictive markers to better estimate drug response in a diverse set of samples. We begin this section with a primer on popular methods to identify targets and mechanism of action for new small molecules. This discussion also includes a set of computational methods that incorporate other drug features, which do not relate to drug-induced genetic changes or sequencing data such as drug structures, side-effects, and efficacy profiles. Those additional drug properties can aid in gaining higher accuracy for the identification of drug target and mechanism of action. We then progress to discuss using these targets in combination with disease-specific expression patterns, known pathways, and genetic interaction networks to aid drug choice. Finally, we conclude this chapter with a general overview of machine learning methods that can integrate multiple pieces of sequencing data along with prior drug or biological knowledge to drastically improve response prediction.

Context-enriched interactome powered by proteomics helps the identification of novel regulators of macrophage activation

The role of pro-inflammatory macrophage activation in cardiovascular disease (CVD) is a complex one amenable to network approaches. While an indispensible tool for elucidating the molecular underpinnings of complex diseases including CVD, the interactome is limited in its utility as it is not specific to any cell type, experimental condition or disease state. We introduced context-specificity to the interactome by combining it with co-abundance networks derived from unbiased proteomics measurements from activated macrophage-like cells. Each macrophage phenotype contributed to certain regions of the interactome. Using a network proximity-based prioritization method on the combined network, we predicted potential regulators of macrophage activation. Prediction performance significantly increased with the addition of co-abundance edges, and the prioritized candidates captured inflammation, immunity and CVD signatures. Integrating the novel network topology with transcriptomics and proteomics revealed top candidate drivers of inflammation. In vitro loss-of-function experiments demonstrated the regulatory role of these proteins in pro-inflammatory signaling.

When human cells or tissues are injured, the body triggers a response known as inflammation to repair the damage and protect itself from further harm. However, if the same issue keeps recurring, the tissues become inflamed for longer periods of time, which may ultimately lead to health problems. This is what could be happening in cardiovascular diseases, where long-term inflammation could damage the heart and blood vessels.

Many different proteins interact with each other to control inflammation; gaining an insight into the nature of these interactions could help to pinpoint the role of each molecular actor. Researchers have used a combination of unbiased, large-scale experimental and computational approaches to develop the interactome, a map of the known interactions between all proteins in humans. However, interactions between proteins can change between cell types, or during disease. Here, Halu et al. aimed to refine the human interactome and identify new proteins involved in inflammation, especially in the context of cardiovascular disease.

Cells called macrophages produce signals that trigger inflammation whey they detect damage in other cells or tissues. The experiments used a technique called proteomics to measure the amounts of all the proteins in human macrophages. Combining these data with the human interactome made it possible to predict new links between proteins known to have a role in inflammation and other proteins in the interactome. Further analysis using other sets of data from macrophages helped identify two new candidate proteins – GBP1 and WARS – that may promote inflammation. Halu et al. then used a genetic approach to deactivate the genes and decrease the levels of these two proteins in macrophages, which caused the signals that encourage inflammation to drop.

These findings suggest that GBP1 and WARS regulate the activity of macrophages to promote inflammation. The two proteins could therefore be used as drug targets to treat cardiovascular diseases and other disorders linked to inflammation, but further studies will be needed to precisely dissect how GBP1 and WARS work in humans.

Increased expression of microRNAs, miR-20a and miR-326 in PBMCs of patients with type 1 diabetes

Type 1 diabetes (T1D) is an autoimmune disorder which is characterized by autoimmune attack on β cells of pancreas and lack of insulin. The involvement of microRNAs (miRNAs) in the development of immune system and their differential expression in various autoimmune diseases including T1D have been well established. In this study, the association between expression levels of miR-20a, miR-326 and T1D were evaluated. The expression levels of miR-20a and miR-326 were measured in the PBMCs of 21 T1D patients and 16 healthy controls using qPCR method. In silico analysis was also performed on targetome of miR-20a and miR-326. Both miR-20a (p value: 0.015) and miR-326 (p value: 0.005) were upregulated in the PBMCs of T1D patients compared to healthy controls. Furthermore, different dysregulated miR326-mRNA and miR20a-mRNA interactions were also suggested using integrative computational analysis. The expression level of miR-20a and miR-326 indicates significant association with T1D which suggests the possible regulatory effects of these non-coding RNAs in T1D.