Discrete-state models identify pathway specific B cell states across diseases and infections at single-cell resolution

Oxygen (O2) regulated pathways modulate B cell activation, migration and proliferation during infection, vaccination, and other diseases. Modeling these pathways in health and disease is critical to understand B cell states and ways to mediate them. To characterize B cells by their activation of O2 regulated pathways we develop pathway specific discrete state models using previously published single-cell RNA-sequencing (scRNA-seq) datasets from isolated B cells. Specifically, Single Cell Boolean Omics Network Invariant-Time Analysis (scBONITA) was used to infer logic gates for known pathway topologies. The simplest inferred set of logic gates that maximized the number of "OR" interactions between genes was used to simulate B cell networks involved in oxygen sensing until they reached steady network states (attractors). By focusing on the attractors that best represented sequenced cells, we identified genes critical in determining pathway specific cellular states that corresponded to diseased and healthy B cell phenotypes. Specifically, we investigate the transendothelial migration, regulation of actin cytoskeleton, HIF1A, and Citrate Cycle pathways. Our analysis revealed attractors that resembled the state of B cell exhaustion in HIV+ patients as well as attractors that promoted anerobic metabolism, angiogenesis, and tumorigenesis in breast cancer patients, which were eliminated after neoadjuvant chemotherapy (NACT). Finally, we investigated the attractors to which the Azimuth-annotated B cells mapped and found that attractors resembling B cells from HIV+ patients encompassed a significantly larger number of atypical memory B cells than HIV- attractors. Meanwhile, attractors resembling B cells from breast cancer patients post NACT encompassed a reduced number of atypical memory B cells compared to pre-NACT attractors.

Pillars of biology: Boolean modeling of gene-regulatory networks

Boolean modeling is a mathematical modeling framework used for defining and studying gene-regulatory networks (GRNs). It serves as a means to develop mechanistic models, offering insights into the trajectories and dynamic properties of GRNs. In this review, I delve into seminal papers published in the Journal of Theoretical Biology that have spearheaded this field. Additionally, I explore the application of these modeling methods in the current era of data-intensive science.

The C. elegans Myc-family of transcription factors coordinate a dynamic adaptive response to dietary restriction

Dietary restriction (DR), the process of decreasing overall food consumption over an extended period of time, has been shown to increase longevity across evolutionarily diverse species and delay the onset of age-associated diseases in humans. In Caenorhabditis elegans , the Myc-family transcription factors (TFs) MXL-2 (Mlx) and MML-1 (MondoA/ChREBP), which function as obligate heterodimers, and PHA-4 (orthologous to forkhead box transcription factor A) are both necessary for the full physiological benefits of DR. However, the adaptive transcriptional response to DR and the role of MML-1::MXL-2 and PHA-4 remains elusive. We identified the transcriptional signature of C. elegans DR, using the eat-2 genetic model, and demonstrate broad changes in metabolic gene expression in eat-2 DR animals, which requires both mxl-2 and pha-4 . While the requirement for these factors in DR gene expression overlaps, we found many of the DR genes exhibit an opposing change in relative gene expression in eat-2;mxl-2 animals compared to wild-type, which was not observed in eat-2 animals with pha-4 loss. We further show functional deficiencies of the mxl-2 loss in DR outside of lifespan, as eat-2;mxl-2 animals exhibit substantially smaller brood sizes and lay a proportion of dead eggs, indicating that MML-1::MXL-2 has a role in maintaining the balance between resource allocation to the soma and to reproduction under conditions of chronic food scarcity. While eat-2 animals do not show a significantly different metabolic rate compared to wild-type, we also find that loss of mxl-2 in DR does not affect the rate of oxygen consumption in young animals. The gene expression signature of eat-2 mutant animals is consistent with optimization of energy utilization and resource allocation, rather than induction of canonical gene expression changes associated with acute metabolic stress -such as induction of autophagy after TORC1 inhibition. Consistently, eat-2 animals are not substantially resistant to stress, providing further support to the idea that chronic DR may benefit healthspan and lifespan through efficient use of limited resources rather than broad upregulation of stress responses, and also indicates that MML-1::MXL-2 and PHA-4 may have different roles in promotion of benefits in response to different pro-longevity stimuli.

Generation and Functional Analysis of Defective Viral Genomes during SARS-CoV-2 Infection

Defective viral genomes (DVGs) have been identified in many RNA viruses as a major factor influencing antiviral immune response and viral pathogenesis. However, the generation and function of DVGs in SARS-CoV-2 infection are less known. In this study, we elucidated DVG generation in SARS-CoV-2 and its relationship with host antiviral immune response. We observed DVGs ubiquitously from transcriptome sequencing (RNA-seq) data sets of in vitro infections and autopsy lung tissues of COVID-19 patients. Four genomic hot spots were identified for DVG recombination, and RNA secondary structures were suggested to mediate DVG formation. Functionally, bulk and single-cell RNA-seq analysis indicated the interferon (IFN) stimulation of SARS-CoV-2 DVGs. We further applied our criteria to the next-generation sequencing (NGS) data set from a published cohort study and observed a significantly higher amount and frequency of DVG in symptomatic patients than those in asymptomatic patients. Finally, we observed exceptionally diverse DVG populations in one immunosuppressive patient up to 140 days after the first positive test of COVID-19, suggesting for the first time an association between DVGs and persistent viral infections in SARS-CoV-2. Together, our findings strongly suggest a critical role of DVGs in modulating host IFN responses and symptom development, calling for further inquiry into the mechanisms of DVG generation and into how DVGs modulate host responses and infection outcome during SARS-CoV-2 infection. IMPORTANCE Defective viral genomes (DVGs) are generated ubiquitously in many RNA viruses, including SARS-CoV-2. Their interference activity to full-length viruses and IFN stimulation provide the potential for them to be used in novel antiviral therapies and vaccine development. SARS-CoV-2 DVGs are generated through the recombination of two discontinuous genomic fragments by viral polymerase complex, and this recombination is also one of the major mechanisms for the emergence of new coronaviruses. Focusing on the generation and function of SARS-CoV-2 DVGs, these studies identify new hot spots for nonhomologous recombination and strongly suggest that the secondary structures within viral genomes mediate the recombination. Furthermore, these studies provide the first evidence for IFN stimulation activity of de novo DVGs during natural SARS-CoV-2 infection. These findings set up the foundation for further mechanism studies of SARS-CoV-2 recombination and provide evidence to harness the immunostimulatory potential of DVGs in the development of a vaccine and antivirals for SARS-CoV-2.

Homeodomain-interacting protein kinase maintains neuronal homeostasis during normal Caenorhabditis elegans aging and systemically regulates longevity from serotonergic and GABAergic neurons

Aging and the age-associated decline of the proteome is determined in part through neuronal control of evolutionarily conserved transcriptional effectors, which safeguard homeostasis under fluctuating metabolic and stress conditions by regulating an expansive proteostatic network. We have discovered the Caenorhabditis elegans homeodomain-interacting protein kinase (HPK-1) acts as a key transcriptional effector to preserve neuronal integrity, function, and proteostasis during aging. Loss of hpk-1 results in drastic dysregulation in expression of neuronal genes, including genes associated with neuronal aging. During normal aging hpk-1 expression increases throughout the nervous system more broadly than any other kinase. Within the aging nervous system, hpk-1 induction overlaps with key longevity transcription factors, which suggests hpk-1 expression mitigates natural age-associated physiological decline. Consistently, pan-neuronal overexpression of hpk-1 extends longevity, preserves proteostasis both within and outside of the nervous system, and improves stress resistance. Neuronal HPK-1 improves proteostasis through kinase activity. HPK-1 functions cell non-autonomously within serotonergic and GABAergic neurons to improve proteostasis in distal tissues by specifically regulating distinct components of the proteostatic network. Increased serotonergic HPK-1 enhances the heat shock response and survival to acute stress. In contrast, GABAergic HPK-1 induces basal autophagy and extends longevity, which requires mxl-2 (MLX), hlh-30 (TFEB), and daf-16 (FOXO). Our work establishes hpk-1 as a key neuronal transcriptional regulator critical for preservation of neuronal function during aging. Further, these data provide novel insight as to how the nervous system partitions acute and chronic adaptive response pathways to delay aging by maintaining organismal homeostasis.

Executable Network Models of Integrated Multiomics Data

Multiomics profiling provides a holistic picture of a condition being examined and captures the complexity of signaling events, beginning from the original cause (environmental or genetic), to downstream functional changes at multiple molecular layers. Pathway enrichment analysis has been used with multiomics data sets to characterize signaling mechanisms. However, technical and biological variability between these layered data limit an integrative computational analyses. We present a Boolean network-based method, multiomics Boolean Omics Network Invariant-Time Analysis (mBONITA), to integrate omics data sets that quantify multiple molecular layers. mBONITA utilizes prior knowledge networks to perform topology-based pathway analysis. In addition, mBONITA identifies genes that are consistently modulated across molecular measurements by combining observed fold-changes and variance, with a measure of node (i.e., gene or protein) influence over signaling, and a measure of the strength of evidence for that gene across data sets. We used mBONITA to integrate multiomics data sets from RAMOS B cells treated with the immunosuppressant drug cyclosporine A under varying O₂ tensions to identify pathways involved in hypoxia-mediated chemotaxis. We compare mBONITA's performance with 6 other pathway analysis methods designed for multiomics data and show that mBONITA identifies a set of pathways with evidence of modulation across all omics layers. mBONITA is freely available at https://github.com/Thakar-Lab/mBONITA.

Preparation of noninfectious scRNAseq samples from SARS-CoV-2-infected epithelial cells

Coronavirus disease (COVID-19) is an infectious disease caused by the SARS coronavirus 2 (SARS-CoV-2) virus. Direct assessment, detection, and quantitative analysis using high throughput methods like single-cell RNA sequencing (scRNAseq) is imperative to understanding the host response to SARS-CoV-2. One barrier to studying SARS-CoV-2 in the laboratory setting is the requirement to process virus-infected cell cultures, and potentially infectious materials derived therefrom, under Biosafety Level 3 (BSL-3) containment. However, there are only 190 BSL3 laboratory facilities registered with the U.S. Federal Select Agent Program, as of 2020, and only a subset of these are outfitted with the equipment needed to perform high-throughput molecular assays. Here, we describe a method for preparing non-hazardous RNA samples from SARS-CoV-2 infected cells, that enables scRNAseq analyses to be conducted safely in a BSL2 facility-thereby making molecular assays of SARS-CoV-2 cells accessible to a much larger community of researchers. Briefly, we infected African green monkey kidney epithelial cells (Vero-E6) with SARS-CoV-2 for 96 hours, trypsin-dissociated the cells, and inactivated them with methanol-acetone in a single-cell suspension. Fixed cells were tested for the presence of infectious SARS-CoV-2 virions using the Tissue Culture Infectious Dose Assay (TCID50), and also tested for viability using flow cytometry. We then tested the dissociation and methanol-acetone inactivation method on primary human lung epithelial cells that had been differentiated on an air-liquid interface. Finally, we performed scRNAseq quality control analysis on the resulting cell populations to evaluate the effects of our virus inactivation and sample preparation protocol on the quality of the cDNA produced. We found that methanol-acetone inactivated SARS-CoV-2, fixed the lung epithelial cells, and could be used to obtain noninfectious, high-quality cDNA libraries. This methodology makes investigating SARS-CoV-2, and related high-containment RNA viruses at a single-cell level more accessible to an expanded community of researchers.

Homeodomain-interacting protein kinase maintains neuronal homeostasis during normal Caenorhabditis elegans aging and systemically regulates longevity from serotonergic and GABAergic neurons

Aging and the age-associated decline of the proteome is determined in part through neuronal control of evolutionarily conserved transcriptional effectors, which safeguard homeostasis under fluctuating metabolic and stress conditions by regulating an expansive proteostatic network. We have discovered the Caenorhabditis elegans h omeodomain-interacting p rotein k inase (HPK-1) acts as a key transcriptional effector to preserve neuronal integrity, function, and proteostasis during aging. Loss of hpk-1 results in drastic dysregulation in expression of neuronal genes, including genes associated with neuronal aging. During normal aging hpk-1 expression increases throughout the nervous system more broadly than any other kinase. Within the aging nervous system, hpk-1 is co-expressed with key longevity transcription factors, including daf-16 (FOXO), hlh-30 (TFEB), skn-1 (Nrf2), and hif-1 , which suggests hpk-1 expression mitigates natural age-associated physiological decline. Consistently, pan-neuronal overexpression of hpk-1 extends longevity, preserves proteostasis both within and outside of the nervous system, and improves stress resistance. Neuronal HPK-1 improves proteostasis through kinase activity. HPK-1 functions cell non-autonomously within serotonergic and GABAergic neurons to improve proteostasis in distal tissues by specifically regulating distinct components of the proteostatic network. Increased serotonergic HPK-1 enhances the heat shock response and survival to acute stress. In contrast, GABAergic HPK-1 induces basal autophagy and extends longevity. Our work establishes hpk-1 as a key neuronal transcriptional regulator critical for preservation of neuronal function during aging. Further, these data provide novel insight as to how the nervous system partitions acute and chronic adaptive response pathways to delay aging by maintaining organismal homeostasis.

Baseline host determinants of robust human HIV-1 vaccine-induced immune responses: A meta-analysis of 26 vaccine regimens

BACKGROUND

The identification of baseline host determinants that associate with robust HIV-1 vaccine-induced immune responses could aid HIV-1 vaccine development. We aimed to assess both the collective and relative performance of baseline characteristics in classifying individual participants in nine different Phase 1-2 HIV-1 vaccine clinical trials (26 vaccine regimens, conducted in Africa and in the Americas) as High HIV-1 vaccine responders.

METHODS

This was a meta-analysis of individual participant data, with studies chosen based on participant-level (vs. study-level summary) data availability within the HIV-1 Vaccine Trials Network. We assessed the performance of 25 baseline characteristics (demographics, safety haematological measurements, vital signs, assay background measurements) and estimated the relative importance of each characteristic in classifying 831 participants as High (defined as within the top 25th percentile among positive responders or above the assay upper limit of quantification) versus Non-High responders. Immune response outcomes included HIV-1-specific serum IgG binding antibodies and Env-specific CD4+ T-cell responses assessed two weeks post-last dose, all measured at central HVTN laboratories. Three variable importance approaches based on SuperLearner ensemble machine learning were considered.

FINDINGS

Overall, 30.1%, 50.5%, 36.2%, and 13.9% of participants were categorized as High responders for gp120 IgG, gp140 IgG, gp41 IgG, and Env-specific CD4+ T-cell vaccine-induced responses, respectively. When including all baseline characteristics, moderate performance was achieved for the classification of High responder status for the binding antibody responses, with cross-validated areas under the ROC curve (CV-AUC) of 0.72 (95% CI: 0.68, 0.76) for gp120 IgG, 0.73 (0.69, 0.76) for gp140 IgG, and 0.67 (95% CI: 0.63, 0.72) for gp41 IgG. In contrast, the collection of all baseline characteristics yielded little improvement over chance for predicting High Env-specific CD4+ T-cell responses [CV-AUC: 0.53 (0.48, 0.58)]. While estimated variable importance patterns differed across the three approaches, female sex assigned at birth, lower height, and higher total white blood cell count emerged as significant predictors of High responder status across multiple immune response outcomes using Approach 1. Of these three baseline variables, total white blood cell count ranked highly across all three approaches for predicting vaccine-induced gp41 and gp140 High responder status.

INTERPRETATION

The identified features should be studied further in pursuit of intervention strategies to improve vaccine responses and may be adjusted for in analyses of immune response data to enhance statistical power.

FUNDING

National Institute of Allergy and Infectious Diseases (UM1AI068635 to YH, UM1AI068614 to GDT, UM1AI068618 to MJM, and UM1 AI069511 to MCK), the Duke CFAR P30 AI064518 to GDT, and National Institute of Dental and Craniofacial Research (R01DE027245 to JJK). This work was also supported by the Bill and Melinda Gates Foundation. The content is solely the responsibility of the authors and does not necessarily represent the official views of any of the funding sources.

Generation and functional analysis of defective viral genomes during SARS-CoV-2 infection.. [PMID: 36172120 PMCID: PMC9516852 DOI: 10.1101/2022.09.22.509123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Defective viral genomes (DVGs) have been identified in many RNA viruses as a major factor influencing antiviral immune response and viral pathogenesis. However, the generation and function of DVGs in SARS-CoV-2 infection are less known. In this study, we elucidated DVG generation in SARS-CoV-2 and its relationship with host antiviral immune response. We observed DVGs ubiquitously from RNA-seq datasets of in vitro infections and autopsy lung tissues of COVID-19 patients. Four genomic hotspots were identified for DVG recombination and RNA secondary structures were suggested to mediate DVG formation. Functionally, bulk and single cell RNA-seq analysis indicated the IFN stimulation of SARS-CoV-2 DVGs. We further applied our criteria to the NGS dataset from a published cohort study and observed significantly higher DVG amount and frequency in symptomatic patients than that in asymptomatic patients. Finally, we observed unusually high DVG frequency in one immunosuppressive patient up to 140 days after admitted to hospital due to COVID-19, first-time suggesting an association between DVGs and persistent viral infections in SARS-CoV-2. Together, our findings strongly suggest a critical role of DVGs in modulating host IFN responses and symptom development, calling for further inquiry into the mechanisms of DVG generation and how DVGs modulate host responses and infection outcome during SARS-CoV-2 infection.

Computational modelling in health and disease: highlights of the 6th annual SysMod meeting

Summary

The Community of Special Interest (COSI) in Computational Modelling of Biological Systems (SysMod) brings together interdisciplinary scientists interested in combining data-driven computational modelling, multi-scale mechanistic frameworks, large-scale -omics data and bioinformatics. SysMod’s main activity is an annual meeting at the Intelligent Systems for Molecular Biology (ISMB) conference, a meeting for computer scientists, biologists, mathematicians, engineers and computational and systems biologists. The 2021 SysMod meeting was conducted virtually due to the ongoing COVID-19 pandemic (coronavirus disease 2019). During the 2-day meeting, the development of computational tools, approaches and predictive models was discussed, along with their application to biological systems, emphasizing disease mechanisms. This report summarizes the meeting.

Availability and implementation

All resources and further information are freely accessible at https://sysmod.info.

Executable models of immune signaling pathways in HIV-associated atherosclerosis

Atherosclerosis (AS)-associated cardiovascular disease is an important cause of mortality in an aging population of people living with HIV (PLWH). This elevated risk has been attributed to viral infection, anti-retroviral therapy, chronic inflammation, and lifestyle factors. However, the rates at which PLWH develop AS vary even after controlling for length of infection, treatment duration, and for lifestyle factors. To investigate the molecular signaling underlying this variation, we sequenced 9368 peripheral blood mononuclear cells (PBMCs) from eight PLWH, four of whom have atherosclerosis (AS+). Additionally, a publicly available dataset of PBMCs from persons before and after HIV infection was used to investigate the effect of acute HIV infection. To characterize dysregulation of pathways rather than just measuring enrichment, we developed the single-cell Boolean Omics Network Invariant Time Analysis (scBONITA) algorithm. scBONITA infers executable dynamic pathway models and performs a perturbation analysis to identify high impact genes. These dynamic models are used for pathway analysis and to map sequenced cells to characteristic signaling states (attractor analysis). scBONITA revealed that lipid signaling regulates cell migration into the vascular endothelium in AS+ PLWH. Pathways implicated included AGE-RAGE and PI3K-AKT signaling in CD8+ T cells, and glucagon and cAMP signaling pathways in monocytes. Attractor analysis with scBONITA facilitated the pathway-based characterization of cellular states in CD8+ T cells and monocytes. In this manner, we identify critical cell-type specific molecular mechanisms underlying HIV-associated atherosclerosis using a novel computational method.

Cyclosporine A Modulates LSP1 Protein Levels in Human B Cells to Attenuate B Cell Migration at Low O2 Levels

Coordinated migration of B cells within and between secondary lymphoid tissues is required for robust antibody responses to infection or vaccination. Secondary lymphoid tissues normally expose B cells to a low O2 (hypoxic) environment. Recently, we have shown that human B cell migration is modulated by an O2-dependent molecular switch, centrally controlled by the hypoxia-induced (transcription) factor-1α (HIF1A), which can be disrupted by the immunosuppressive calcineurin inhibitor, cyclosporine A (CyA). However, the mechanisms by which low O2 environments attenuate B cell migration remain poorly defined. Proteomics analysis has linked CXCR4 chemokine receptor signaling to cytoskeletal rearrangement. We now hypothesize that the pathways linking the O2 sensing molecular switch to chemokine receptor signaling and cytoskeletal rearrangement would likely contain phosphorylation events, which are typically missed in traditional transcriptomic and/or proteomic analyses. Hence, we have performed a comprehensive phosphoproteomics analysis of human B cells treated with CyA after engagement of the chemokine receptor CXCR4 with CXCL12. Statistical analysis of the separate and synergistic effects of CyA and CXCL12 revealed 116 proteins whose abundance is driven by a synergistic interaction between CyA and CXCL12. Further, we used our previously described algorithm BONITA to reveal a critical role for Lymphocyte Specific Protein 1 (LSP1) in cytoskeletal rearrangement. LSP1 is known to modulate neutrophil migration. Validating these modeling results, we show experimentally that LSP1 levels in B cells increase with low O2 exposure, and CyA treatment results in decreased LSP1 protein levels. This correlates with the increased chemotactic activity observed after CyA treatment. Lastly, we directly link LSP1 levels to chemotactic capacity, as shRNA knock-down of LSP1 results in significantly increased B cell chemotaxis at low O2 levels. These results directly link CyA to LSP1-dependent cytoskeletal regulation, demonstrating a previously unrecognized mechanism by which CyA modulates human B cell migration. Data are available via ProteomeXchange with identifier PXD036167.

Biomarkers of Development of Immunity and Allergic Diseases in Farming and Non-farming Lifestyle Infants: Design, Methods and 1 Year Outcomes in the "Zooming in to Old Order Mennonites" Birth Cohort Study

Traditional farming lifestyle has been shown to be protective against asthma and allergic diseases. The individual factors that appear to be associated with this "farm-life effect" include consumption of unpasteurized farm milk and exposure to farm animals and stables. However, the biomarkers of the protective immunity and those associated with early development of allergic diseases in infancy remain unclear. The "Zooming in to Old Order Mennonites (ZOOM)" study was designed to assess the differences in the lifestyle and the development of the microbiome, systemic and mucosal immunity between infants born to traditional farming lifestyle at low risk for allergic diseases and those born to urban/suburban atopic families with a high risk for allergic diseases in order to identify biomarkers of development of allergic diseases in infancy. 190 mothers and their infants born to Old Order Mennonite population protected from or in Rochester families at high risk for allergic diseases were recruited before birth from the Finger Lakes Region of New York State. Questionnaires and samples are collected from mothers during pregnancy and after delivery and from infants at birth and at 1-2 weeks, 6 weeks, 6, 12, 18, and 24 months, with 3-, 4-, and 5-year follow-up ongoing. Samples collected include maternal blood, stool, saliva, nasal and skin swabs and urine during pregnancy; breast milk postnatally; infant blood, stool, saliva, nasal and skin swabs. Signs and symptoms of allergic diseases are assessed at every visit and serum specific IgE is measured at 1 and 2 years of age. Allergic diseases are diagnosed by clinical history, exam, and sensitization by skin prick test and/or serum specific IgE. By the end of the first year of life, the prevalence of food allergy and atopic dermatitis were higher in ROC infants compared to the rates observed in OOM infants as was the number of infants sensitized to foods. These studies of immune system development in a population protected from and in those at risk for allergic diseases will provide critical new knowledge about the development of the mucosal and systemic immunity and lay the groundwork for future studies of prevention of allergic diseases.

The Replica Set Method is a Robust, Accurate, and High-Throughput Approach for Assessing and Comparing Lifespan in C. elegans Experiments

The advent of feeding based RNAi in Caenorhabditis elegans led to an era of gene discovery in aging research. Hundreds of gerogenes were discovered, and many are evolutionarily conserved, raising the exciting possibility that the underlying genetic basis for healthy aging in higher vertebrates could be quickly deciphered. Yet, the majority of putative gerogenes have still only been cursorily characterized, highlighting the need for high-throughput, quantitative assessments of changes in aging. A widely used surrogate measure of aging is lifespan. The traditional way to measure mortality in C. elegans tracks the deaths of individual animals over time within a relatively small population. This traditional method provides straightforward, direct measurements of median and maximum lifespan for the sampled population. However, this method is time consuming, often underpowered, and involves repeated handling of a set of animals over time, which in turn can introduce contamination or possibly damage increasingly fragile, aged animals. We have previously developed an alternative “Replica Set” methodology, which minimizes handling and increases throughput by at least an order of magnitude. The Replica Set method allows changes in lifespan to be measured for over one hundred feeding-based RNAi clones by one investigator in a single experiment- facilitating the generation of large quantitative phenotypic datasets, a prerequisite for development of biological models at a systems level. Here, we demonstrate through analysis of lifespan experiments simulated in silico that the Replica Set method is at least as precise and accurate as the traditional method in evaluating and estimating lifespan, and requires many fewer total animal observations across the course of an experiment. Furthermore, we show that the traditional approach to lifespan experiments is more vulnerable than the Replica Set method to experimental and measurement error. We find no compromise in statistical power for Replica Set experiments, even for moderate effect sizes, or when simulated experimental errors are introduced. We compare and contrast the statistical analysis of data generated by the two approaches, and highlight pitfalls common with the traditional methodology. Collectively, our analysis provides a standard of measure for each method across comparable parameters, which will be invaluable in both experimental design and evaluation of published data for lifespan studies.

A systems genomics approach uncovers molecular associates of RSV severity

Respiratory syncytial virus (RSV) infection results in millions of hospitalizations and thousands of deaths each year. Variations in the adaptive and innate immune response appear to be associated with RSV severity. To investigate the host response to RSV infection in infants, we performed a systems-level study of RSV pathophysiology, incorporating high-throughput measurements of the peripheral innate and adaptive immune systems and the airway epithelium and microbiota. We implemented a novel multi-omic data integration method based on multilayered principal component analysis, penalized regression, and feature weight back-propagation, which enabled us to identify cellular pathways associated with RSV severity. In both airway and immune cells, we found an association between RSV severity and activation of pathways controlling Th17 and acute phase response signaling, as well as inhibition of B cell receptor signaling. Dysregulation of both the humoral and mucosal response to RSV may play a critical role in determining illness severity.

This paper presents a novel approach to understanding the localized molecular responses to respiratory syncytial virus (RSV) and the system-level correlates of clinical outcomes. To do this, we developed a novel statistical method able to integrate high dimensional molecular data characterizing the host airway microbota and immune and nasal gene expression. We show that this integrative approach facilitates superior performance in estimating clinical outcome as opposed to any single data type. Using this approach, we identified both cell type-specific and shared biomarkers and regulatory pathways associated with RSV severity. Specifically, we identified an association between RSV severity, activation of pathways controlling Th17, and inhibition of B cell receptor signaling, which were present in both the site of infection airway and in peripheral immune cells. These results can guide future efforts to identify biomarkers for identifying or predicting illness severity following infant RSV infection. They may also be useful as biomarkers to inform the efficacy of future interventions (e.g., therapies) or preventative measures to suppress the rate of severe disease (e.g., vaccines).

Infant gut microbiome is enriched with Bifidobacterium longum ssp. infantis in Old Order Mennonites with traditional farming lifestyle

BACKGROUND

Growing up on traditional, single-family farms is associated with protection against asthma in school age, but the mechanisms against early manifestations of atopic disease are largely unknown. We sought determine the gut microbiome and metabolome composition in rural Old Order Mennonite (OOM) infants at low risk and Rochester, NY urban/suburban infants at high risk for atopic diseases.

METHODS

In a cohort of 65 OOM and 39 Rochester mother-infant pairs, 101 infant stool and 61 human milk samples were assessed by 16S rRNA gene sequencing for microbiome composition and qPCR to quantify Bifidobacterium spp. and B. longum ssp. infantis (B. infantis), a consumer of human milk oligosaccharides (HMOs). Fatty acids (FAs) were analyzed in 34 stool and human 24 milk samples. Diagnoses and symptoms of atopic diseases by 3 years of age were assessed by telephone.

RESULTS

At a median age of 2 months, stool was enriched with Bifidobacteriaceae, Clostridiaceae, and Aerococcaceae in the OOM compared with Rochester infants. B. infantis was more abundant (p < .001) and prevalent, detected in 70% of OOM compared with 21% of Rochester infants (p < .001). Stool colonized with B. infantis had higher levels of lactate and several medium- to long/odd-chain FAs. In contrast, paired human milk was enriched with a distinct set of FAs including butyrate. Atopic diseases were reported in 6.5% of OOM and 35% of Rochester children (p < .001).

CONCLUSION

A high rate of B. infantis colonization, similar to that seen in developing countries, is found in the OOM at low risk for atopic diseases.

WikiNetworks: translating manually created biological pathways for topological analysis

SUMMARY

WikiPathways is a database of 2979 biological pathways across 31 species created using the drawing software PathVisio. Many of these pathways are not directly usable for network-based topological analyses due to differences in curation styles and drawings. We developed the WikiNetworks package to standardize and construct directed networks by combining geometric information and manual annotations from WikiPathways. WikiNetworks performs significantly better than existing tools. This enables the use of high-quality WikiPathways resource for network-based topological analysis of high-throughput data.

AVAILABILITY AND IMPLEMENTATION

WikiNetworks is written in Python3 and is available on github.com/Thakar-Lab/wikinetworks and on PyPI.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Traditional Farming Lifestyle in Old Older Mennonites Modulates Human Milk Composition

Background

In addition to farming exposures in childhood, maternal farming exposures provide strong protection against allergic disease in their children; however, the effect of farming lifestyle on human milk (HM) composition is unknown.

Objective

This study aims to characterize the maternal immune effects of Old Order Mennonite (OOM) traditional farming lifestyle when compared with Rochester (ROC) families at higher risk for asthma and allergic diseases using HM as a proxy.

Methods

HM samples collected at median 2 months of lactation from 52 OOM and 29 ROC mothers were assayed for IgA₁ and IgA₂ antibodies, cytokines, endotoxin, HM oligosaccharides (HMOs), and targeted fatty acid (FA) metabolites. Development of early childhood atopic diseases in children by 3 years of age was assessed. In addition to group comparisons, systems level network analysis was performed to identify communities of multiple HM factors in ROC and OOM lifestyle.

Results

HM contains IgA₁ and IgA₂ antibodies broadly recognizing food, inhalant, and bacterial antigens. OOM HM has significantly higher levels of IgA to peanut, ovalbumin, dust mites, and Streptococcus equii as well TGF-β2, and IFN-λ3. A strong correlation occurred between maternal antibiotic use and levels of several HMOs. Path-based analysis of HMOs shows lower activity in the path involving lactoneohexaose (LNH) in the OOM as well as higher levels of lacto-N-neotetraose (LNnT) and two long-chain FAs C-18OH (stearic acid) and C-23OH (tricosanoic acid) compared with Rochester HM. OOM and Rochester milk formed five different clusters, e.g., butyrate production was associated with Prevotellaceae, Veillonellaceae, and Micrococcaceae cluster. Development of atopic disease in early childhood was more common in Rochester and associated with lower levels of total IgA, IgA₂ to dust mite, as well as of TSLP.

Conclusion

Traditional, agrarian lifestyle, and antibiotic use are strong regulators of maternally derived immune and metabolic factors, which may have downstream implications for postnatal developmental programming of infant's gut microbiome and immune system.

SysMod: the ISCB community for data-driven computational modelling and multi-scale analysis of biological systems

Computational models of biological systems can exploit a broad range of rapidly developing approaches, including novel experimental approaches, bioinformatics data analysis, emerging modelling paradigms, data standards and algorithms. A discussion about the most recent advances among experts from various domains is crucial to foster data-driven computational modelling and its growing use in assessing and predicting the behaviour of biological systems. Intending to encourage the development of tools, approaches and predictive models, and to deepen our understanding of biological systems, the Community of Special Interest (COSI) was launched in Computational Modelling of Biological Systems (SysMod) in 2016. SysMod’s main activity is an annual meeting at the Intelligent Systems for Molecular Biology (ISMB) conference, which brings together computer scientists, biologists, mathematicians, engineers, computational and systems biologists. In the five years since its inception, SysMod has evolved into a dynamic and expanding community, as the increasing number of contributions and participants illustrate. SysMod maintains several online resources to facilitate interaction among the community members, including an online forum, a calendar of relevant meetings and a YouTube channel with talks and lectures of interest for the modelling community. For more than half a decade, the growing interest in computational systems modelling and multi-scale data integration has inspired and supported the SysMod community. Its members get progressively more involved and actively contribute to the annual COSI meeting and several related community workshops and meetings, focusing on specific topics, including particular techniques for computational modelling or standardisation efforts.

Molecular characterization of atherosclerosis in HIV positive persons

People living with HIV are at higher risk of atherosclerosis (AS). The pathogenesis of this risk is not fully understood. To assess the regulatory networks involved in AS we sequenced mRNA of the peripheral blood mononuclear cells (PBMCs) and measured cytokine and chemokine levels in the plasma of 13 persons living with HIV and 12 matched HIV-negative persons with and without AS. microRNAs (miRNAs) are known to play a role in HIV infection and may modulate gene regulation to drive AS. Hence, we further assessed miRNA expression in PBMCs of a subset of 12 HIV+ people with and without atherosclerosis. We identified 12 miRNAs differentially expressed between HIV+ AS+ and HIV+ , and validated 5 of those by RT-qPCR. While a few of these miRNAs have been implicated in HIV and atherosclerosis, others are novel. Integrating miRNA measurements with mRNA, we identified 27 target genes including SLC4A7, a critical sodium and bicarbonate transporter, that are potentially dysregulated during atherosclerosis. Additionally, we uncovered that levels of plasma cytokines were associated with transcription factor activity and miRNA expression in PBMCs. For example, BACH2 activity was associated with IL-1β, IL-15, and MIP-1α. IP10 and TNFα levels were associated with miR-124-3p. Finally, integration of all data types into a single network revealed increased importance of miRNAs in network regulation of the HIV+ group in contrast with increased importance of cytokines in the HIV+ AS+ group.

Impact of vaccine type on HIV-1 vaccine elicited antibody durability and B cell gene signature

Efficacious HIV-1 vaccination requires elicitation of long-lived antibody responses. However, our understanding of how different vaccine types elicit durable antibody responses is lacking. To assess the impact of vaccine type on antibody responses, we measured IgG isotypes against four consensus HIV antigens from 2 weeks to 10 years post HIV-1 vaccination and used mixed effects models to estimate half-life of responses in four human clinical trials. Compared to protein-boosted regimens, half-lives of gp120-specific antibodies were longer but peak magnitudes were lower in Modified Vaccinia Ankara (MVA)-boosted regimens. Furthermore, gp120-specific B cell transcriptomics from MVA-boosted and protein-boosted vaccines revealed a distinct signature at a peak (2 weeks after last vaccination) including CD19, CD40, and FCRL2-5 activation along with increased B cell receptor signaling. Additional analysis revealed contributions of RIG-I-like receptor pathway and genes such as SMAD5 and IL-32 to antibody durability. Thus, this study provides novel insights into vaccine induced antibody durability and B-cell receptor signaling.

Characterization of platelet signature on monocytes isolated from HIV infected individuals

Background

Higher levels of platelet-monocyte complexes (PMCs) are detected in human immunodeficiency virus (HIV)-infected individuals. Such complexes are implicated in expansion of monocytes exhibiting inflammatory phenotype, and their translocation into tissues, thus promoting development of cerebrovascular and cardiovascular disorders. Here we have recruited small sub-set of HIV-infected individuals to investigate how platelets cause epigenetic changes in monocytes of HIV-infected individuals. We posit that the formation of PMCs facilitates monocyte differentiation via exchange of biomaterial between activated platelets and monocytes during HIV-infection.

Methods

We performed ATAC-seq to measure changes in chromatin accessibility from two types of monocytes, PMC-derived or non-PMC-derived, isolated from HIV-infected persons.

Results

Using ATAC-Seq data, we were able to detect distinct changes in chromatin accessibility patterns within the genomic regions of proinflammatory mediators in PMC-derived monocytes. Chromatin accessibility of inflammatory genes was dramatically increased in PMC-derived monocytes. This suggested to us a possibility of active exchange of molecules that causes such alterations in monocytes.

Conclusions

Increases in chromatin accessibility of pro-inflammatory genes occur in the PMC-derived monocytes in persons with HIV. This change is likely due to the transfer of platelet antigens to monocytes via cell-cell interaction, suggesting a novel mechanism through which platelets trigger inflammatory response in monocytes. Thus, our initial findings pave a way that has a strong diagnostic as well as therapeutic value.

Cyclosporine a directly affects human and mouse b cell migration in vitro by disrupting a hIF-1 αdependent, o2 sensing, molecular switch

BACKGROUND

Hypoxia is a potent molecular signal for cellular metabolism, mitochondrial function, and migration. Conditions of low oxygen tension trigger regulatory cascades mediated via the highly conserved HIF-1 α post-translational modification system. In the adaptive immune response, B cells (Bc) are activated and differentiate under hypoxic conditions within lymph node germinal centers, and subsequently migrate to other compartments. During migration, they traverse through changing oxygen levels, ranging from 1-5% in the lymph node to 5-13% in the peripheral blood. Interestingly, the calcineurin inhibitor cyclosporine A is known to stimulate prolyl hydroxylase activity, resulting in HIF-1 α destabilization and may alter Bc responses directly. Over 60% of patients taking calcineurin immunosuppressant medications have hypo-gammaglobulinemia and poor vaccine responses, putting them at high risk of infection with significantly increased morbidity and mortality.

RESULTS

We demonstrate that O 2 tension is a previously unrecognized Bc regulatory switch, altering CXCR4 and CXCR5 chemokine receptor signaling in activated Bc through HIF-1 α expression, and controlling critical aspects of Bc migration. Our data demonstrate that calcineurin inhibition hinders this O 2 regulatory switch in primary human Bc.

CONCLUSION

This previously unrecognized effect of calcineurin inhibition directly on human Bc has significant and direct clinical implications.

Network Analysis of Large-Scale Data and Its Application to Immunology

Diseases and infections elicit a multilayered immune response which consists of molecular and cellular interaction cascades. Recent advances in high-throughput technologies have facilitated multiparameter investigation of immune cells involved in human immune responses. These multiparameter investigations generate large-scale datasets and advanced computational techniques are required to gain useful information from them. Networks or graphs offer a practical way to represent complex information and develop advanced algorithms to unveil the underlying mechanisms. Here we discuss ways to assemble and analyze networks using genome-wide transcriptional profiles. Additionally, we discuss ways to integrate information available in primary literature and databases with the networks assembled using large-scale datasets. Finally, we describe ways in which network analysis offers insights into human immune responses.

IL-1β-driven amyloid plaque clearance is associated with an expansion of transcriptionally reprogrammed microglia

BACKGROUND

Neuroinflammation is thought to contribute to the pathogenesis of Alzheimer's disease (AD), yet numerous studies have demonstrated a beneficial role for neuroinflammation in amyloid plaque clearance. We have previously shown that sustained expression of IL-1β in the hippocampus of APP/PS1 mice decreases amyloid plaque burden independent of recruited CCR2+ myeloid cells, suggesting resident microglia as the main phagocytic effectors of IL-1β-induced plaque clearance. To date, however, the mechanisms of IL-1β-induced plaque clearance remain poorly understood.

METHODS

To determine whether microglia are involved in IL-1β-induced plaque clearance, APP/PS1 mice induced to express mature human IL-1β in the hippocampus via adenoviral transduction were treated with the Aβ fluorescent probe methoxy-X04 (MX04) and microglial internalization of fibrillar Aβ (fAβ) was analyzed by flow cytometry and immunohistochemistry. To assess microglial proliferation, APP/PS1 mice transduced with IL-1β or control were injected intraperitoneally with BrdU and hippocampal tissue was analyzed by flow cytometry. RNAseq analysis was conducted on microglia FACS sorted from the hippocampus of control or IL-1β-treated APP/PS1 mice. These microglia were also sorted based on MX04 labeling (MX04+ and MX04- microglia).

RESULTS

Resident microglia (CD45loCD11b+) constituted > 70% of the MX04+ cells in both Phe- and IL-1β-treated conditions, and < 15% of MX04+ cells were recruited myeloid cells (CD45hiCD11b+). However, IL-1β treatment did not augment the percentage of MX04+ microglia nor the quantity of fAβ internalized by individual microglia. Instead, IL-1β increased the total number of MX04+ microglia in the hippocampus due to IL-1β-induced proliferation. In addition, transcriptomic analyses revealed that IL-1β treatment was associated with large-scale changes in the expression of genes related to immune responses, proliferation, and cytokine signaling.

CONCLUSIONS

These studies show that IL-1β overexpression early in amyloid pathogenesis induces a change in the microglial gene expression profile and an expansion of microglial cells that facilitates Aβ plaque clearance.

Machine Learning Approach for Predicting Past Environmental Exposures From Molecular Profiling of Post-Exposure Human Serum Samples

OBJECTIVE

To develop an approach for a retrospective analysis of post-exposure serum samples using diverse molecular profiles.

METHODS

The 236 molecular profiles from 800 de-identified human serum samples from the Department of Defense Serum Repository were classified as smokers or non-smokers based on direct measurement of serum cotinine levels. A machine-learning pipeline was used to classify smokers and non-smokers from their molecular profiles.

RESULTS

The refined supervised support vector machines with recursive feature elimination predicted smokers and non-smokers with 78% accuracy on the independent held-out set. Several of the identified classifiers of smoking status have previously been reported and four additional miRNAs were validated with experimental tobacco smoke exposure in mice, supporting the computational approach.

CONCLUSIONS

We developed and validated a pipeline that shows retrospective analysis of post-exposure serum samples can identify environmental exposures.

Exposure to Heptachlorodibenzo-p-dioxin (HpCDD) Regulates microRNA Expression in Human Lung Fibroblasts

OBJECTIVE

Benzo(ghi)perylene (BghiP) and 1,2,3,4,6,7,8-Heptachlorodibenzo-p-dioxin (HpCDD) were elevated in serum from personnel deployed to sites with open burn pits. Here, we investigated the ability of BghiP and HpCDD to regulate microRNA (miRNA) expression through the aryl hydrocarbon receptor (AHR).

METHODS

Human lung fibroblasts (HLFs) were exposed to BghiP and HpCDD. AHR activity was measured by reporter assay and gene expression. Deployment related miRNA were measured by quantitative polymerase chain reaction. AHR expression was depleted using siRNA.

RESULTS

BghiP displayed weak AHR agonist activity. HpCDD induced AHR activity in a dose-dependent manner. Let-7d-5p, miR-103-3p, miR-107, and miR-144-3p levels were significantly altered by HpCDD. AHR knockdown attenuated these effects.

CONCLUSIONS

These studies reveal that miRNAs previously identified in sera from personnel deployed to sites with open burn pits are altered by HpCDD exposure in HLFs.

Aims, Study Design, and Enrollment Results From the Assessing Predictors of Infant Respiratory Syncytial Virus Effects and Severity Study

Background

The majority of infants hospitalized with primary respiratory syncytial virus (RSV) infection have no obvious risk factors for severe disease.

Objective

The aim of this study (Assessing Predictors of Infant RSV Effects and Severity, AsPIRES) was to identify factors associated with severe disease in full-term healthy infants younger than 10 months with primary RSV infection.

Methods

RSV infected infants were enrolled from 3 cohorts during consecutive winters from August 2012 to April 2016 in Rochester, New York. A birth cohort was prospectively enrolled and followed through their first winter for development of RSV infection. An outpatient supplemental cohort was enrolled in the emergency department or pediatric offices, and a hospital cohort was enrolled on admission with RSV infection. RSV was diagnosed by reverse transcriptase-polymerase chain reaction. Demographic and clinical data were recorded and samples collected for assays: buccal swab (cytomegalovirus polymerase chain reaction, PCR), nasal swab (RSV qualitative PCR, complete viral gene sequence, 16S ribosomal ribonucleic acid [RNA] amplicon microbiota analysis), nasal wash (chemokine and cytokine assays), nasal brush (nasal respiratory epithelial cell gene expression using RNA sequencing [RNAseq]), and 2 to 3 ml of heparinized blood (flow cytometry, RNAseq analysis of purified cluster of differentiation [CD]4+, CD8+, B cells and natural killer cells, and RSV-specific antibody). Cord blood (RSV-specific antibody) was also collected for the birth cohort. Univariate and multivariate logistic regression will be used for analysis of data using a continuous Global Respiratory Severity Score (GRSS) as the outcome variable. Novel statistical methods will be developed for integration of the large complex datasets.

Results

A total of 453 infants were enrolled into the 3 cohorts; 226 in the birth cohort, 60 in the supplemental cohort, and 78 in the hospital cohort. A total of 126 birth cohort infants remained in the study and were evaluated for 150 respiratory illnesses. Of the 60 RSV positive infants in the supplemental cohort, 42 completed the study, whereas all 78 of the RSV positive hospital cohort infants completed the study. A GRSS was calculated for each RSV-infected infant and is being used to analyze each of the complex datasets by correlation with disease severity in univariate and multivariate methods.

Conclusions

The AsPIRES study will provide insights into the complex pathogenesis of RSV infection in healthy full-term infants with primary RSV infection. The analysis will allow assessment of multiple factors potentially influencing the severity of RSV infection including the level of RSV specific antibodies, the innate immune response of nasal epithelial cells, the adaptive response by various lymphocyte subsets, the resident airway microbiota, and viral factors. Results of this study will inform disease interventions such as vaccines and antiviral therapies.

IL-33 enhances the kinetics and quality of the antibody response to a DNA and protein-based HIV-1 Env vaccine

Induction of a sustained and broad antibody (Ab) response is a major goal in developing a protective HIV-1 vaccine. DNA priming alone shows reduced levels of immunogenicity; however, when combined with protein boosting is an attractive vaccination strategy for induction of humoral responses. Using the VC10014 DNA and protein-based vaccine consisting of HIV-1 envelope (Env) gp160 plasmids and trimeric gp140 proteins derived from an HIV-1 clade B infected subject who developed broadly neutralizing serum Abs, and which has been previously demonstrated to induce Tier 2 heterologous neutralizing Abs in rhesus macaques, we evaluated whether MPLA and IL-33 when administered during the DNA priming phase enhances the humoral response in mice. The addition of IL-33 during the gp160 DNA priming phase resulted in high titer gp120-specific plasma IgG after the first immunization. The IL-33 treated mice had higher plasma IgG Ab avidity, breadth, and durability after DNA and protein co-immunization with alum adjuvant as compared to MPLA and alum only treated mice. IL-33 was also associated with a significant IgM Env-specific response and expansion of peritoneal and splenic B-1b B cells. These results indicate that DNA priming in the presence of exogenous IL-33 qualitatively alters the HIV-1 Env-specific humoral response, improving the kinetics and breadth of potentially protective Ab.

The Replica Set Method: A High-throughput Approach to Quantitatively Measure Caenorhabditis elegans Lifespan

The Replica Set method is an approach to quantitatively measure lifespan or survival of Caenorhabditis elegans nematodes in a high-throughput manner, thus allowing a single investigator to screen more treatments or conditions over the same amount of time without loss of data quality. The method requires common equipment found in most laboratories working with C. elegans and is thus simple to adopt. The approach centers on assaying independent samples of a population at each observation point, rather than a single sample over time as with traditional longitudinal methods. Scoring entails adding liquid to the wells of a multi-well plate, which stimulates C. elegans to move and facilitates quantifying changes in healthspan. Other major benefits of the Replica Set method include reduced exposure of agar surfaces to airborne contaminants (e.g. mold or fungus), minimal handling of animals, and robustness to sporadic mis-scoring (such as calling an animal as dead when it is still alive). To appropriately analyze and visualize the data from a Replica Set style experiment, a custom software tool was also developed. Current capabilities of the software include plotting of survival curves for both Replica Set and traditional (Kaplan-Meier) experiments, as well as statistical analysis for Replica Set. The protocols provided here describe the traditional experimental approach and the Replica Set method, as well as an overview of the corresponding data analysis.

FUNNEL-GSEA: FUNctioNal ELastic-net regression in time-course gene set enrichment analysis

Motivation

Gene set enrichment analyses (GSEAs) are widely used in genomic research to identify underlying biological mechanisms (defined by the gene sets), such as Gene Ontology terms and molecular pathways. There are two caveats in the currently available methods: (i) they are typically designed for group comparisons or regression analyses, which do not utilize temporal information efficiently in time-series of transcriptomics measurements; and (ii) genes overlapping in multiple molecular pathways are considered multiple times in hypothesis testing.

Results

We propose an inferential framework for GSEA based on functional data analysis, which utilizes the temporal information based on functional principal component analysis, and disentangles the effects of overlapping genes by a functional extension of the elastic-net regression. Furthermore, the hypothesis testing for the gene sets is performed by an extension of Mann-Whitney U test which is based on weighted rank sums computed from correlated observations. By using both simulated datasets and a large-scale time-course gene expression data on human influenza infection, we demonstrate that our method has uniformly better receiver operating characteristic curves, and identifies more pathways relevant to immune-response to human influenza infection than the competing approaches.

Availability and Implementation

The methods are implemented in R package FUNNEL, freely and publicly available at: https://github.com/yunzhang813/FUNNEL-GSEA-R-Package.

Supplementary information

Supplementary data are available at Bioinformatics online.

Developing Network Models of Multiscale Host Responses Involved in Infections and Diseases

Complex interactions involved in host response to infections and diseases require advanced analytical tools to infer drivers of the response in order to develop strategies for intervention. This chapter discusses approaches to assemble interactions ranging from molecular to cellular levels and their analysis to investigate the cross talk between immune pathways. Particularly, construction of immune networks by either data-driven or literature-driven methods is explained. Next, graph theoretic approaches for probing static network properties as well as visualization of networks are discussed. Finally, development of Boolean models for simulation of network dynamics to investigate cross talk and emergent properties are considered along with Boolean-like models that may compensate for some of the limitations encountered in Boolean simulations. In conclusion, the chapter will allow readers to construct and analyze multiscale networks involved in immune responses.

Association of Dynamic Changes in the CD4 T-Cell Transcriptome With Disease Severity During Primary Respiratory Syncytial Virus Infection in Young Infants

Background

Nearly all children are infected with respiratory syncytial virus (RSV) within the first 2 years of life, with a minority developing severe disease (1%-3% hospitalized). We hypothesized that an assessment of the adaptive immune system, using CD4+ T-lymphocyte transcriptomics, would identify gene expression correlates of disease severity.

Methods

Infants infected with RSV representing extremes of clinical severity were studied. Mild illness (n = 23) was defined as a respiratory rate (RR) < 55 and room air oxygen saturation (SaO2) ≥ 97%, and severe illness (n = 23) was defined as RR ≥ 65 and SaO2 ≤ 92%. RNA from fresh, sort-purified CD4+ T cells was assessed by RNA sequencing.

Results

Gestational age, age at illness onset, exposure to environmental tobacco smoke, bacterial colonization, and breastfeeding were associated (adjusted P < .05) with disease severity. RNA sequencing analysis reliably measured approximately 60% of the genome. Severity of RSV illness had the greatest effect size upon CD4 T-cell gene expression. Pathway analysis identified correlates of severity, including JAK/STAT, prolactin, and interleukin 9 signaling. We also identified genes and pathways associated with timing of symptoms and RSV group (A/B).

Conclusions

These data suggest fundamental changes in adaptive immune cell phenotypes may be associated with RSV clinical severity.

Frontline Science: c-Myc regulates P-selectin glycoprotein ligand-1 expression in monocytes during HIV-1 infection

Leukocyte extravasation is a crucial feature of the normal immune response to disease and infection and is implicated in various pathologies during chronic inflammatory disease. P-Selectin glycoprotein ligand-1 (PSGL-1) is critical for leukocyte extravasation; however, despite extensive study, it remains unclear how its expression is regulated, which in turn, impedes a more precise understanding of how its expression level affects transmigration. To investigate the regulation of PSGL-1, 60 subjects, with or without HIV infection, were recruited and PSGL-1 expression in monocytes was measured. PSGL-1 was found to be up-regulated on leukocytes from HIV-infected individuals, and the physiologically relevant mediators soluble CD40 ligand (sCD40L) and glutamate were able to induce PSGL-1 transcription in human monocytes ex vivo. HIV-1 induced PSGL-1 induction, and its dependence on CD40L was validated further by use of the mouse-tropic HIV (EcoHIV) mouse model of HIV infection in C57BL/6 and CD40L knockout (KO) mice. To investigate crosstalk between the signaling cascades induced by CD40L and glutamate that lead to PSGL-1 induction, a network-based, discrete dynamic model was developed. The model reveals the MAPK pathway and oxidative stress as critical mediators of crosstalk between CD40L and glutamate-induced pathways. Importantly, the model predicted induction of the c-Myc transcription factor upon cotreatment, which was validated using transcriptomic data and pharmacologic inhibition of c-Myc. This study suggests a novel systems serology approach for translational research and reveals a mechanism for PSGL-1 transcriptional regulation, which might be leveraged to identify novel targets for therapeutic intervention.

Meta-analysis of cell- specific transcriptomic data using fuzzy c-means clustering discovers versatile viral responsive genes

BACKGROUND

Despite advances in the gene-set enrichment analysis methods; inadequate definitions of gene-sets cause a major limitation in the discovery of novel biological processes from the transcriptomic datasets. Typically, gene-sets are obtained from publicly available pathway databases, which contain generalized definitions frequently derived by manual curation. Recently unsupervised clustering algorithms have been proposed to identify gene-sets from transcriptomics datasets deposited in public domain. These data-driven definitions of the gene-sets can be context-specific revealing novel biological mechanisms. However, the previously proposed algorithms for identification of data-driven gene-sets are based on hard clustering which do not allow overlap across clusters, a characteristic that is predominantly observed across biological pathways.

RESULTS

We developed a pipeline using fuzzy-C-means (FCM) soft clustering approach to identify gene-sets which recapitulates topological characteristics of biological pathways. Specifically, we apply our pipeline to derive gene-sets from transcriptomic data measuring response of monocyte derived dendritic cells and A549 epithelial cells to influenza infections. Our approach apply Ward's method for the selection of initial conditions, optimize parameters of FCM algorithm for human cell-specific transcriptomic data and identify robust gene-sets along with versatile viral responsive genes.

CONCLUSION

We validate our gene-sets and demonstrate that by identifying genes associated with multiple gene-sets, FCM clustering algorithm significantly improves interpretation of transcriptomic data facilitating investigation of novel biological processes by leveraging on transcriptomic data available in the public domain. We develop an interactive 'Fuzzy Inference of Gene-sets (FIGS)' package (GitHub: https://github.com/Thakar-Lab/FIGS ) to facilitate use of of pipeline. Future extension of FIGS across different immune cell-types will improve mechanistic investigation followed by high-throughput omics studies.

IL-33 enhances the induction, durability, and breadth of the antibody response to a DNA/protein-based HIV Env vaccine

Induction of a sustained and broad antibody (Ab) response is a major goal in developing a protective HIV vaccine. DNA is an attractive priming strategy when combined with protein or viral vector boosting; however, DNA priming alone fails to induce a substantial Ab response, limiting its potential as a stand-alone immunogen. Using the VC10014 DNA/protein vaccine consisting of gp160 plasmids and gp140 proteins derived from an HIV clade B infected subject who developed broadly neutralizing serum Abs, and which has been previously shown to induce Tier 2 heterologous neutralizing Abs in rhesus macaques, we screened a panel of factors in C57BL/6 mice for their ability to enhance the Env-specific Ab response. IL-33, an alarmin, emerged as the lead candidate. The addition of recombinant IL-33 during the gp160 DNA priming phase dramatically changed the kinetics of the Ab response, inducing serum gp120-specific IgG (&gt;1:12,500 titer) after a single DNA immunization, which was not detectable in mice that did not receive IL-33 (p&lt;0.0001). Then following boosting with DNA/protein coimmunization in Alum, gp120-specific IgG levels were extraordinarily durable, remaining stable even at 6 weeks after final immunization compared to mice not receiving IL-33 (p&lt;0.0001), in which they rapidly declined. IL-33 also increased the cross-clade breadth and avidity of the serum gp120-specific IgG response. Analysis of the Env-specific B cell immunoglobulin repertoire and the IL-33 mechanism of action is ongoing. These results demonstrate the profound effect that priming conditions can have on the quality of the Env-specific antibody response.

Optimized logic rules reveal interferon-γ-induced modes regulated by histone deacetylases and protein tyrosine phosphatases

The pro-inflammatory cytokine interferon-γ (IFN-γ) is critical for activating innate and adaptive immunity against tumours and intracellular pathogens. Interferon-γ is secreted at the fetal-maternal interface in pregnant women and mice. The outer layer of the placenta in contact with maternal blood is composed of semi-allogeneic trophoblast cells, which constitute the fetal component of the fetal-maternal interface. The simultaneous presence of pro-inflammatory IFN-γ and trophoblast cells at the fetal-maternal interface appears to represent an immunological paradox, for trophoblastic responses to IFN-γ could potentially lead to activation of maternal immunity and subsequent attack of the placenta. However, our previous studies demonstrate that IFN-γ responsive gene (IRG) expression is negatively regulated in human and mouse trophoblast cells. In human cytotrophoblast and trophoblast-derived choriocarcinoma cells, janus kinase signalling is blocked by protein tyrosine phosphatases (PTPs), whereas in mouse trophoblast, histone deacetylases (HDACs) inhibit IRG expression. Here, we used genome-wide transcriptional profiling to investigate the collective roles of PTPs and HDACs on regulation of IRG expression in human choriocarcinoma cells. Logic-rules were optimized to derive regulatory modes governing gene expression patterns observed upon different combinations of treatment with PTP and HDAC inhibitors. The results demonstrate that IRGs can be divided into several categories in human choriocarcinoma cells, each of which is subject to distinct mechanisms of repression. Hence, the regulatory modes identified in this study suggest that human trophoblast and choriocarcinoma cells may evade the potentially deleterious consequences of exposure to IFN-γ by using several overlapping mechanisms to block IRG expression.

Evaluating side effects of nanoparticle-mediated siRNA delivery to mesenchymal stem cells using next generation sequencing and enrichment analysis

RNA interference has immense potential to modulate cell functions. However, effective delivery of small interfering RNA (siRNA) while avoiding deleterious side effects has proven challenging. This study investigates both intended and unintended effects of diblock copolymer nanoparticle (NP) delivery of siRNA delivery to human mesenchymal stem cells (hMSC). Specifically, siRNA delivery was investigated at a range of NP‐siRNA:hMSC ratios with a focus on the effects of NP‐siRNA treatment on hMSC functions. Additionally, next generation RNA sequencing (RNAseq) was used with enrichment analysis to observe side effects in hMSC gene expression. Results show NP‐siRNA delivery is negatively correlated with hMSC density. However, higher NP‐siRNA:hMSC ratios increased cytotoxicity and decreased metabolic activity. hMSC proliferation was largely unaffected by NP‐siRNA treatment, except for a threefold reduction in hMSCs seeded at 4,000 cells/cm². Flow cytometry reveals that apoptosis is a function of NP‐siRNA treatment time and seeding density; ∼14% of the treated hMSCs seeded at 8,000 cells/cm² were annexin V⁺‐siRNA⁺ 24 hr after treatment, while 11% of the treated population was annexin V⁺‐siRNA⁻. RNAseq shows that NP‐siRNA treatment results in transcriptomic changes in hMSCs, while pathway analysis shows upregulation of apoptosis signaling and downregulation of metabolism, cell cycle, and DNA replication pathways, as corroborated by apoptosis, metabolism, and proliferation assays. Additionally, multiple innate immune signaling pathways such as toll‐like receptor, RIG‐I‐like receptor, and nuclear factor‐κB signaling pathways are upregulated. Furthermore, and consistent with traditional siRNA immune activation, cytokine–cytokine receptor signaling was also upregulated. Overall, this study provides insight into NP‐siRNA:hMSC ratios that are favorable for siRNA delivery. Moreover, NP‐siRNA delivery results in side effects across the hMSC transcriptome that suggest activation of the innate immunity that could alter MSC functions associated with their therapeutic potential.

Novel Sequence-Based Mapping of Recently Emerging H5NX Influenza Viruses Reveals Pandemic Vaccine Candidates

Recently, an avian influenza virus, H5NX subclade 2.3.4.4, emerged and spread to North America. This subclade has frequently reassorted, leading to multiple novel viruses capable of human infection. Four cases of human infections, three leading to death, have already occurred. Existing vaccine strains do not protect against these new viruses, raising a need to identify new vaccine candidate strains. We have developed a novel sequence-based mapping (SBM) tool capable of visualizing complex protein sequence data sets using a single intuitive map. We applied SBM on the complete set of avian H5 viruses in order to better understand hemagglutinin protein variance amongst H5 viruses and identify any patterns associated with this variation. The analysis successfully identified the original reassortments that lead to the emergence of this new subclade of H5 viruses, as well as their known unusual ability to re-assort among neuraminidase subtypes. In addition, our analysis revealed distinct clusters of 2.3.4.4 variants that would not be covered by existing strains in the H5 vaccine stockpile. The results suggest that our method may be useful for pandemic candidate vaccine virus selection.

Humoral Dysregulation Associated with Increased Systemic Inflammation among Injection Heroin Users

Background

Injection drug use is a growing major public health concern. Injection drug users (IDUs) have a higher incidence of co-morbidities including HIV, Hepatitis, and other infections. An effective humoral response is critical for optimal homeostasis and protection from infection; however, the impact of injection heroin use on humoral immunity is poorly understood. We hypothesized that IDUs have altered B cell and antibody profiles.

Methods and Findings

A comprehensive systems biology-based cross-sectional assessment of 130 peripheral blood B cell flow cytometry- and plasma- based features was performed on HIV-/Hepatitis C-, active heroin IDUs who participated in a syringe exchange program (n = 19) and healthy control subjects (n = 19). The IDU group had substantial polydrug use, with 89% reporting cocaine injection within the preceding month. IDUs exhibited a significant, 2-fold increase in total B cells compared to healthy subjects, which was associated with increased activated B cell subsets. Although plasma total IgG titers were similar between groups, IDUs had significantly higher IgG3 and IgG4, suggestive of chronic B cell activation. Total IgM was also increased in IDUs, as well as HIV Envelope-specific IgM, suggestive of increased HIV exposure. IDUs exhibited numerous features suggestive of systemic inflammation, including significantly increased plasma sCD40L, TNF-α, TGF-α, IL-8, and ceramide metabolites. Machine learning multivariate analysis distilled a set of 10 features that classified samples based on group with absolute accuracy.

Conclusions

These results demonstrate broad alterations in the steady-state humoral profile of IDUs that are associated with increased systemic inflammation. Such dysregulation may impact the ability of IDUs to generate optimal responses to vaccination and infection, or lead to increased risk for inflammation-related co-morbidities, and should be considered when developing immune-based interventions for this growing population.

Competitive SWIFT cluster templates enhance detection of aging changes

Clustering‐based algorithms for automated analysis of flow cytometry datasets have achieved more efficient and objective analysis than manual processing. Clustering organizes flow cytometry data into subpopulations with substantially homogenous characteristics but does not directly address the important problem of identifying the salient differences in subpopulations between subjects and groups. Here, we address this problem by augmenting SWIFT—a mixture model based clustering algorithm reported previously. First, we show that SWIFT clustering using a “template” mixture model, in which all subpopulations are represented, identifies small differences in cell numbers per subpopulation between samples. Second, we demonstrate that resolution of inter‐sample differences is increased by “competition” wherein a joint model is formed by combining the mixture model templates obtained from different groups. In the joint model, clusters from individual groups compete for the assignment of cells, sharpening differences between samples, particularly differences representing subpopulation shifts that are masked under clustering with a single template model. The benefit of competition was demonstrated first with a semisynthetic dataset obtained by deliberately shifting a known subpopulation within an actual flow cytometry sample. Single templates correctly identified changes in the number of cells in the subpopulation, but only the competition method detected small changes in median fluorescence. In further validation studies, competition identified a larger number of significantly altered subpopulations between young and elderly subjects. This enrichment was specific, because competition between templates from consensus male and female samples did not improve the detection of age‐related differences. Several changes between the young and elderly identified by SWIFT template competition were consistent with known alterations in the elderly, and additional altered subpopulations were also identified. Alternative algorithms detected far fewer significantly altered clusters. Thus SWIFT template competition is a powerful approach to sharpen comparisons between selected groups in flow cytometry datasets. © 2015 The Authors. Published Wiley Periodicals Inc.

Diversity in Compartmental Dynamics of Gene Regulatory Networks: The Immune Response in Primary Influenza A Infection in Mice

Current approaches to study transcriptional profiles post influenza infection typically rely on tissue sampling from one or two sites at a few time points, such as spleen and lung in murine models. In this study, we infected female C57/BL6 mice intranasally with mouse-adapted H3N2/Hong Kong/X31 avian influenza A virus, and then analyzed the gene expression profiles in four different compartments (blood, lung, mediastinal lymph nodes, and spleen) over 11 consecutive days post infection. These data were analyzed by an advanced statistical procedure based on ordinary differential equation (ODE) modeling. Vastly different lists of significant genes were identified by the same statistical procedure in each compartment. Only 11 of them are significant in all four compartments. We classified significant genes in each compartment into co-expressed modules based on temporal expression patterns. We then performed functional enrichment analysis on these co-expression modules and identified significant pathway and functional motifs. Finally, we used an ODE based model to reconstruct gene regulatory network (GRN) for each compartment and studied their network properties.

Comparative analysis of anti-viral transcriptomics reveals novel effects of influenza immune antagonism

BACKGROUND

Comparative analysis of genome-wide expression profiles are increasingly being used to study virus-specific host interactions. In order to gain mechanistic insights, gene expression profiles can be combined with information on DNA-binding sites of transcription factors to detect transcription factor activity (by analysis of target gene sets) during viral infections. Here, we apply this approach to study mechanisms of immune antagonism elicited by Influenza A virus (New Caledonia/20/1999) by comparing the transcriptional response with the non-pathogenic Newcastle disease virus (NDV), which lacks human immune antagonism.

RESULTS

Existing gene set approaches do not quantify activity in a way that can be statistically compared between responses. We thus developed a new method for Bayesian Estimation of Transcription factor Activity (BETA) that allows for such quantification and comparative analysis across multiple responses. BETA predicted decreased ISGF3 activity during influenza A infection of human dendritic cells (reflected in lower expression of Interferon Stimulated Genes, ISGs). This prediction was confirmed through a combination of mathematical modeling and experiments at different multiplicities of infection to show that ISGs were specifically blocked in infected cells. Suppression of the transcription factor SATB1 was also predicted as a novel effect of influenza-mediated immune antagonism, and validated experimentally.

CONCLUSIONS

Comparative analysis of genome-wide transcriptional profiles can reveal new effects of viral immune antagonism. We have developed a computational framework (BETA) that enables quantitative comparative analysis of transcription factor activities. This method will aid future studies to identify mechanistic differences in the host-pathogen interactions. Application of BETA to genome-wide transcriptional profiling data from human DCs identified SATB1 as a novel effect of influenza antagonism.

GESPA: classifying nsSNPs to predict disease association

BACKGROUND

Non-synonymous single nucleotide polymorphisms (nsSNPs) are the most common DNA sequence variation associated with disease in humans. Thus determining the clinical significance of each nsSNP is of great importance. Potential detrimental nsSNPs may be identified by genetic association studies or by functional analysis in the laboratory, both of which are expensive and time consuming. Existing computational methods lack accuracy and features to facilitate nsSNP classification for clinical use. We developed the GESPA (GEnomic Single nucleotide Polymorphism Analyzer) program to predict the pathogenicity and disease phenotype of nsSNPs.

RESULTS

GESPA is a user-friendly software package for classifying disease association of nsSNPs. It allows flexibility in acceptable input formats and predicts the pathogenicity of a given nsSNP by assessing the conservation of amino acids in orthologs and paralogs and supplementing this information with data from medical literature. The development and testing of GESPA was performed using the humsavar, ClinVar and humvar datasets. Additionally, GESPA also predicts the disease phenotype associated with a nsSNP with high accuracy, a feature unavailable in existing software. GESPA's overall accuracy exceeds existing computational methods for predicting nsSNP pathogenicity. The usability of GESPA is enhanced by fast SQL-based cloud storage and retrieval of data.

CONCLUSIONS

GESPA is a novel bioinformatics tool to determine the pathogenicity and phenotypes of nsSNPs. We anticipate that GESPA will become a useful clinical framework for predicting the disease association of nsSNPs. The program, executable jar file, source code, GPL 3.0 license, user guide, and test data with instructions are available at http://sourceforge.net/projects/gespa.

Aging-dependent alterations in gene expression and a mitochondrial signature of responsiveness to human influenza vaccination

To elucidate gene expression pathways underlying age-associated impairment in influenza vaccine response, we screened young (age 21-30) and older (age ≥65) adults receiving influenza vaccine in two consecutive seasons and identified those with strong or absent response to vaccine, including a subset of older adults meeting criteria for frailty. PBMCs obtained prior to vaccination (Day 0) and at day 2 or 4, day 7 and day 28 post-vaccine were subjected to gene expression microarray analysis. We defined a response signature and also detected induction of a type I interferon response at day 2 and a plasma cell signature at day 7 post-vaccine in young responders. The response signature was dysregulated in older adults, with the plasma cell signature induced at day 2, and was never induced in frail subjects (who were all non-responders). We also identified a mitochondrial signature in young vaccine responders containing genes mediating mitochondrial biogenesis and oxidative phosphorylation that was consistent in two different vaccine seasons and verified by analyses of mitochondrial content and protein expression. These results represent the first genome-wide transcriptional profiling analysis of age-associated dynamics following influenza vaccination, and implicate changes in mitochondrial biogenesis and function as a critical factor in human vaccine responsiveness.