Differential Impact of LPG-and PG-Deficient \u3cem\u3eLeishmania major\u3c/em\u3e Mutants on the Immune Response of Human Dendritic Cells

BACKGROUND: Leishmania major infection induces robust interleukin-12 (IL12) production in human dendritic cells (hDC), ultimately resulting in Th1-mediated immunity and clinical resolution. The surface of Leishmania parasites is covered in a dense glycocalyx consisting of primarily lipophosphoglycan (LPG) and other phosphoglycan-containing molecules (PGs), making these glycoconjugates the likely pathogen-associated molecular patterns (PAMPS) responsible for IL12 induction. METHODOLOGY/PRINCIPAL FINDINGS: Here we explored the role of parasite glycoconjugates on the hDC IL12 response by generating L. major Friedlin V1 mutants defective in LPG alone, (FV1 lpg1-), or generally deficient for all PGs, (FV1 lpg2-). Infection with metacyclic, infective stage, L. major or purified LPG induced high levels of IL12B subunit gene transcripts in hDCs, which was abrogated with FV1 lpg1- infections. In contrast, hDC infections with FV1 lpg2- displayed increased IL12B expression, suggesting other PG-related/LPG2 dependent molecules may act to dampen the immune response. Global transcriptional profiling comparing WT, FV1 lpg1-, FV1 lpg2- infections revealed that FV1 lpg1- mutants entered hDCs in a silent fashion as indicated by repression of gene expression. Transcription factor binding site analysis suggests that LPG recognition by hDCs induces IL-12 in a signaling cascade resulting in Nuclear Factor κ B (NFκB) and Interferon Regulatory Factor (IRF) mediated transcription. CONCLUSIONS/SIGNIFICANCE: These data suggest that L. major LPG is a major PAMP recognized by hDC to induce IL12-mediated protective immunity and that there is a complex interplay between PG-baring Leishmania surface glycoconjugates that result in modulation of host cellular IL12

Profiling of human acquired immunity against the salivary proteins of Phlebotomus papatasi reveals clusters of differential immunoreactivity

Citation: Geraci, Nicholas S., Rami M. Mukbel, Michael T. Kemp, Mariha N. Wadsworth, Emil Lesho, Gwen M. Stayback, Matthew M. Champion, et al. 2014. “Profiling of Human Acquired Immunity Against the Salivary Proteins of Phlebotomus Papatasi Reveals Clusters of Differential Immunoreactivity.” The American Journal of Tropical Medicine and Hygiene 90 (5): 923–38. https://doi.org/10.4269/ajtmh.13-0130.Phlebotomus papatasi sand flies are among the primary vectors of Leishmania major parasites from Morocco to the Indian subcontinent and from southern Europe to central and eastern Africa. Antibody-based immunity to sand fly salivary gland proteins in human populations remains a complex contextual problem that is not yet fully understood. We profiled the immunoreactivities of plasma antibodies to sand fly salivary gland sonicates (SGSs) from 229 human blood donors residing in different regions of sand fly endemicity throughout Jordan and Egypt as well as 69 US military personnel, who were differentially exposed to P. papatasi bites and L. major infections in Iraq. Compared with plasma from control region donors, antibodies were significantly immunoreactive to five salivary proteins (12, 26, 30, 38, and 44 kDa) among Jordanian and Egyptian donors, with immunoglobulin G4 being the dominant anti-SGS isotype. US personnel were significantly immunoreactive to only two salivary proteins (38 and 14 kDa). Using k-means clustering, donors were segregated into four clusters distinguished by unique immunoreactivity profiles to varying combinations of the significantly immunogenic salivary proteins. SGS-induced cellular proliferation was diminished among donors residing in sand fly-endemic regions. These data provide a clearer picture of human immune responses to sand fly vector salivary constituents

Differential impact of LPG-and PG-deficient Leishmania major mutants on the immune response of human dendritic cells

<div><p>Background</p><p><i>Leishmania major</i> infection induces robust interleukin-12 (IL12) production in human dendritic cells (hDC), ultimately resulting in Th1-mediated immunity and clinical resolution. The surface of <i>Leishmania</i> parasites is covered in a dense glycocalyx consisting of primarily lipophosphoglycan (LPG) and other phosphoglycan-containing molecules (PGs), making these glycoconjugates the likely pathogen-associated molecular patterns (PAMPS) responsible for IL12 induction.</p><p>Methodology/Principal Findings</p><p>Here we explored the role of parasite glycoconjugates on the hDC IL12 response by generating <i>L</i>. <i>major</i> Friedlin V1 mutants defective in LPG alone, (FV1 <i>lpg1-</i>), or generally deficient for all PGs, (FV1 <i>lpg2-</i>). Infection with metacyclic, infective stage, <i>L</i>. <i>major</i> or purified LPG induced high levels of <i>IL12B</i> subunit gene transcripts in hDCs, which was abrogated with FV1 <i>lpg1-</i> infections. In contrast, hDC infections with FV1 <i>lpg2-</i> displayed increased <i>IL12B</i> expression, suggesting other PG-related/<i>LPG2</i> dependent molecules may act to dampen the immune response. Global transcriptional profiling comparing WT, FV1 <i>lpg1-</i>, FV1 <i>lpg2-</i> infections revealed that FV1 <i>lpg1-</i> mutants entered hDCs in a silent fashion as indicated by repression of gene expression. Transcription factor binding site analysis suggests that LPG recognition by hDCs induces IL-12 in a signaling cascade resulting in Nuclear Factor κ B (NFκB) and Interferon Regulatory Factor (IRF) mediated transcription.</p><p>Conclusions/Significance</p><p>These data suggest that <i>L</i>. <i>major</i> LPG is a major PAMP recognized by hDC to induce IL12-mediated protective immunity and that there is a complex interplay between PG-baring <i>Leishmania</i> surface glycoconjugates that result in modulation of host cellular IL12.</p></div

Integrative Multiomics to Dissect the Lung Transcriptional Landscape of Pulmonary Arterial Hypertension

Pulmonary arterial hypertension (PAH) remains an incurable and often fatal disease despite currently available therapies. Multiomics systems biology analysis can shed new light on PAH pathobiology and inform translational research efforts. Using RNA sequencing on the largest PAH lung biobank to date (96 disease and 52 control), we aim to identify gene co-expression network modules associated with PAH and potential therapeutic targets. Co-expression network analysis was performed to identify modules of co-expressed genes which were then assessed for and prioritized by importance in PAH, regulatory role, and therapeutic potential via integration with clinicopathologic data, human genome-wide association studies (GWAS) of PAH, lung Bayesian regulatory networks, single-cell RNA-sequencing data, and pharmacotranscriptomic profiles. We identified a co-expression module of 266 genes, called the pink module, which may be a response to the underlying disease process to counteract disease progression in PAH. This module was associated not only with PAH severity such as increased PVR and intimal thickness, but also with compensated PAH such as lower number of hospitalizations, WHO functional class and NT-proBNP. GWAS integration demonstrated the pink module is enriched for PAH-associated genetic variation in multiple cohorts. Regulatory network analysis revealed that BMPR2 regulates the main target of FDA-approved riociguat, GUCY1A2, in the pink module. Analysis of pathway enrichment and pink hub genes (i.e. ANTXR1 and SFRP4) suggests the pink module inhibits Wnt signaling and epithelial-mesenchymal transition. Cell type deconvolution showed the pink module correlates with higher vascular cell fractions (i.e. myofibroblasts). A pharmacotranscriptomic screen discovered ubiquitin-specific peptidases (USPs) as potential therapeutic targets to mimic the pink module signature. Our multiomics integrative study uncovered a novel gene subnetwork associated with clinicopathologic severity, genetic risk, specific vascular cell types, and new therapeutic targets in PAH. Future studies are warranted to investigate the role and therapeutic potential of the pink module and targeting USPs in PAH

Electric dipole moments and the search for new physics

Static electric dipole moments of nondegenerate systems probe mass scales for physics beyond the Standard Model well beyond those reached directly at high energy colliders. Discrimination between different physics models, however, requires complementary searches in atomic-molecular-and-optical, nuclear and particle physics. In this report, we discuss the current status and prospects in the near future for a compelling suite of such experiments, along with developments needed in the encompassing theoretical framework.Comment: Contribution to Snowmass 2021; updated with community edits and endorsement

Insect Biochemistry and Molecular Biology

The suborder Ixodida includes many tick species of medical and veterinary importance, but little is known about the genomic characteristics of these ticks. We report the first study to determine genome size in two species of Argasidae (soft ticks) and seven species of Ixodidae (hard ticks) using flow cytometry analysis of fluorescent stained nuclei. Our results indicate a large haploid genome size (1C41000 Mbp) for all Ixodida with a mean of 1281 Mbp (1.3170.07 pg) for the Argasidae and 2671 Mbp (2.7370.04 pg) for the Ixodidae. The haploid genome size of Ixodes scapularis was determined to be 2262 Mbp. We observed inter- and intra-familial variation in genome size as well as variation between strains of the same species. We explore the implications of these results for tick genome evolution and tick genomics research. r 2007 Published by Elsevier Ltd

Identification of alterations in macrophage activation associated with disease activity in systemic lupus erythematosus.

Systemic lupus erythematosus (SLE) is characterized by abnormalities in B cell and T cell function, but the role of disturbances in the activation status of macrophages (Mϕ) has not been well described in human patients. To address this, gene expression profiles from isolated lymphoid and myeloid populations were analyzed to identify differentially expressed (DE) genes between healthy controls and patients with either inactive or active SLE. While hundreds of DE genes were identified in B and T cells of active SLE patients, there were no DE genes found in B or T cells from patients with inactive SLE compared to healthy controls. In contrast, large numbers of DE genes were found in myeloid cells (MC) from both active and inactive SLE patients. Among the DE genes were several known to play roles in Mϕ activation and polarization, including the M1 genes STAT1 and SOCS3 and the M2 genes STAT3, STAT6, and CD163. M1-associated genes were far more frequent in data sets from active versus inactive SLE patients. To characterize the relationship between Mϕ activation and disease activity in greater detail, weighted gene co-expression network analysis (WGCNA) was used to identify modules of genes associated with clinical activity in SLE patients. Among these were disease activity-correlated modules containing activation signatures of predominantly M1-associated genes. No disease activity-correlated modules were enriched in M2-associated genes. Pathway and upstream regulator analysis of DE genes from both active and inactive SLE MC were cross-referenced with high-scoring hits from the drug discovery Library of Integrated Network-based Cellular Signatures (LINCS) to identify new strategies to treat both stages of SLE. A machine learning approach employing MC gene modules and a generalized linear model was able to predict the disease activity status in unrelated gene expression data sets. In summary, altered MC gene expression is characteristic of both active and inactive SLE. However, disease activity is associated with an alteration in the activation of MC, with a bias toward the M1 proinflammatory phenotype. These data suggest that while hyperactivity of B cells and T cells is associated with active SLE, MC potentially direct flare-ups and remission by altering their activation status toward the M1 state

Machine learning approaches to predict lupus disease activity from gene expression data

The integration of gene expression data to predict systemic lupus erythematosus (SLE) disease activity is a significant challenge because of the high degree of heterogeneity among patients and study cohorts, especially those collected on different microarray platforms. Here we deployed machine learning approaches to integrate gene expression data from three SLE data sets and used it to classify patients as having active or inactive disease as characterized by standard clinical composite outcome measures. Both raw whole blood gene expression data and informative gene modules generated by Weighted Gene Co-expression Network Analysis from purified leukocyte populations were employed with various classification algorithms. Classifiers were evaluated by 10-fold cross-validation across three combined data sets or by training and testing in independent data sets, the latter of which amplified the effects of technical variation. A random forest classifier achieved a peak classification accuracy of 83 percent under 10-fold cross-validation, but its performance could be severely affected by technical variation among data sets. The use of gene modules rather than raw gene expression was more robust, achieving classification accuracies of approximately 70 percent regardless of how the training and testing sets were formed. Fine-tuning the algorithms and parameter sets may generate sufficient accuracy to be informative as a standalone estimate of disease activity

Machine learning approaches to predict lupus disease activity from gene expression data.

The integration of gene expression data to predict systemic lupus erythematosus (SLE) disease activity is a significant challenge because of the high degree of heterogeneity among patients and study cohorts, especially those collected on different microarray platforms. Here we deployed machine learning approaches to integrate gene expression data from three SLE data sets and used it to classify patients as having active or inactive disease as characterized by standard clinical composite outcome measures. Both raw whole blood gene expression data and informative gene modules generated by Weighted Gene Co-expression Network Analysis from purified leukocyte populations were employed with various classification algorithms. Classifiers were evaluated by 10-fold cross-validation across three combined data sets or by training and testing in independent data sets, the latter of which amplified the effects of technical variation. A random forest classifier achieved a peak classification accuracy of 83 percent under 10-fold cross-validation, but its performance could be severely affected by technical variation among data sets. The use of gene modules rather than raw gene expression was more robust, achieving classification accuracies of approximately 70 percent regardless of how the training and testing sets were formed. Fine-tuning the algorithms and parameter sets may generate sufficient accuracy to be informative as a standalone estimate of disease activity