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Camargo, Jose F.; Bhimji, Alyajahan; Kumar, Deepali; Kaul, Rupert; Pavan, Rhea; Schuh, Andre; Seftel, Matthew; Lipton, Jeffrey H.; Gupta, Vikas; Humar, Atul; Husain, Shahid — 2016-03-31 Invasive mold infections (IMI) are among the most devastating complications following chemotherapy and hematopoietic stem cell transplantation (HSCT), with high mortality rates. Yet, the molecular basis for human susceptibility to invasive aspergillosis (IA) and mucormycosis remain poorly understood. Herein, we aimed to characterize the immune profile of individuals with hematological malignancies (n = 18) who developed IMI during the course of chemotherapy or HSCT, and compared it to that of hematological patients who had no evidence of invasive fungal infection (n = 16). First, we measured the expression of the pattern recognition receptors pentraxin 3, dectin-1, and Toll-like receptors (TLR) 2 and 4 in peripheral blood of chemotherapy and HSCT recipients with IMI. Compared to hematological controls, individuals with IA and mucormycosis had defective expression of dectin-1; in addition, patients with mucormycosis had decreased TLR2 and increased TLR4 expression. Since fungal recognition via dectin-1 favors T helper 17 responses and the latter are highly dependent on activation of the signal transducer and activator of transcription (STAT) 3, we next used phospho-flow cytometry to measure the phosphorylation of the transcription factors STAT1 and STAT3 in response to interferon-gamma (IFN-γ) and interleukin (IL)-6, respectively. While IFN-γ/STAT1 signaling was similar between groups, naïve T cells from patients with IA, but not those with mucormycosis, exhibited reduced responsiveness to IL-6 as measured by STAT3 phosphorylation. Furthermore, IL-6 increased Aspergillus-induced IL-17 production in culture supernatants from healthy and hematological controls but not in patients with IA. Altogether, these observations suggest an important role for dectin-1 and the IL-6/STAT3 pathway in protective immunity against Aspergillus.

Hughes, Sean M.; Levy, Claire N.; Katz, Ronit; Lokken, Erica M.; Anahtar, Melis N.; Hall, Melissa Barousse; Bradley, Frideborg; Castle, Philip E.; Cortez, Valerie; Doncel, Gustavo F.; Fichorova, Raina; Fidel, Paul L.; Fowke, Keith R.; Francis, Suzanna C.; Ghosh, Mimi; Hwang, Loris Y.; Jais, Mariel; Jespers, Vicky; Joag, Vineet; Kaul, Rupert; Kyongo, Jordan; Lahey, Timothy; Li, Huiying; Makinde, Julia; McKinnon, Lyle R.; Moscicki, Anna-Barbara; Novak, Richard M.; Patel, Mickey V.; Sriprasert, Intira; Thurman, Andrea R.; Yegorov, Sergey; Mugo, Nelly Rwamba; Roxby, Alison C.; Micks, Elizabeth; Hladik, Florian — 2022 Additional file 3. Code and data. IPD from co-authors who agreed to share it, results of all analyses presented in the paper, and R code files necessary to reproduce the analysis and figures. https://creativecommons.org/licenses/by/4.0/legalcode

Hughes, Sean M.; Levy, Claire N.; Katz, Ronit; Lokken, Erica M.; Anahtar, Melis N.; Hall, Melissa Barousse; Bradley, Frideborg; Castle, Philip E.; Cortez, Valerie; Doncel, Gustavo F.; Fichorova, Raina; Fidel, Paul L.; Fowke, Keith R.; Francis, Suzanna C.; Ghosh, Mimi; Hwang, Loris Y.; Jais, Mariel; Jespers, Vicky; Joag, Vineet; Kaul, Rupert; Kyongo, Jordan; Lahey, Timothy; Li, Huiying; Makinde, Julia; McKinnon, Lyle R.; Moscicki, Anna-Barbara; Novak, Richard M.; Patel, Mickey V.; Sriprasert, Intira; Thurman, Andrea R.; Yegorov, Sergey; Mugo, Nelly Rwamba; Roxby, Alison C.; Micks, Elizabeth; Hladik, Florian — 2022 Additional file 5: Figure S1. Assessment of publication bias. A Funnel plots. Symbols show the effect of the menstrual cycle (x-axis) and the standard error of that effect (y-axis, reversed). Each symbol shows an individual study. Vertical solid line shows no effect. Vertical dashed line shows the meta-estimate of effect. Diagonal dashed lines enclose the region expected to include 95% of studies based on the estimated meta-effect and the standard errors. B Results of Egger’s tests for publication bias. Figure S2. Periovulatory meta-analyses. A The log2 difference between periovulatory and follicular phases (log2-pg/mL of the follicular phase minus log2-pg/mL of the periovulatory phase). For TGF-β1, the error bars for one study and the meta-estimate extend off-scale. B The log2 difference between periovulatory and luteal phases (log2-pg/mL of the luteal phase minus log2-pg/mL of the periovulatory phase). For IL-10, the error bars for one study extend off-scale. Each row represents a different immune mediator, with the symbols showing the mean and the lines showing the 95% confidence intervals. Gray symbols indicate individual studies and black the meta-estimates as determined by inverse-variance pooling random effects models. Black filled symbols indicate p < 0.05 while white filled symbols indicate p > 0.05. Positive numbers indicate higher during the follicular or luteal phase, while negative numbers indicate higher during the periovulatory phase. Fig S3. Subgroup analysis: Does the effect of menstrual cycle differ by assay method, geographical region, or method of determining menstrual phase? A Meta-analyses, comparing all studies (black circles) to studies grouped by assay method (ELISA: blue squares; MSD: yellow triangles; Luminex: green diamonds). B Meta-analyses, comparing all studies (black circles) to studies grouped by geographical region of sample origin (Africa: blue diamonds; Europe: red squares; North America: green triangles). C Meta-analyses, comparing all studies (black circles) to studies grouped by method of menstrual cycle phasing (Days since LMP: orange squares; Progesterone: pale purple diamonds; Progesterone plus LH: dark purple triangles). Figure S4. Secondary outcomes: Method of determining menstrual cycle phase and normalization to total protein. A The standard errors of the effect sizes for the difference between menstrual cycle phases, with phases determined by days since last menstrual period (“LMP”) or serum progesterone (“Prog”). Each symbol represents an immune factor, with lines connecting the same immune factor. B The standard errors of the effect sizes for the difference between menstrual cycle phases as determined using raw concentration measurements (pg/mL) and concentrations normalized to total protein (pg/pg total protein). Each symbol represents an immune factor, with lines connecting the same immune factor. Table S1. Summary of immune mediators measured in single studies. Table S2. Summary of follicular vs. periovulatory meta-analyses. Table S3. Summary of luteal vs. periovulatory meta-analyses. Table S4. Covariates adjusted for in multivariate analysis of each study. https://creativecommons.org/licenses/by/4.0/legalcode

Armstrong, Eric; Hemmerling, Anke; Miller, Steve; Huibner, Sanja; Kulikova, Maria; Crawford, Emily; Castañeda, Gloria R.; Coburn, Bryan; Cohen, Craig R.; Kaul, Rupert — 2024 Supplementary Material 2. https://creativecommons.org/licenses/by/4.0/legalcode

Armstrong, Eric; Hemmerling, Anke; Miller, Steve; Huibner, Sanja; Kulikova, Maria; Crawford, Emily; Castañeda, Gloria R.; Coburn, Bryan; Cohen, Craig R.; Kaul, Rupert — 2024 Supplementary Material 3. https://creativecommons.org/licenses/by/4.0/legalcode

Armstrong, Eric; Liu, Rachel; Pollock, James; Huibner, Sanja; Udayakumar, Suji; Irungu, Erastus; Ngurukiri, Pauline; Muthoga, Peter; Adhiambo, Wendy; Yegorov, Sergey; Kimani, Joshua; Beattie, Tara; Coburn, Bryan; Kaul, Rupert — 2025 Supplementary Material 1: Supplementary methods. Figure S1: Variation in total bacterial load and vaginal soluble immune factors across CST-IV subgroups. Figure S2: BV-associated bacteria drive the association between total bacterial load and immune factors within CST-III. Figure S3: Genital immune milieu cluster tightly with vaginal microbiota composition. Figure S4: Genital immune milieu is closely tied to vaginal microbiota composition in an independent, Uganda-based confirmatory cohort. Table S1: Association between vaginal CST and sociodemographic variables. Table S2: The absolute abundance of BV-associated bacteria, including G. vaginalis and F. vaginae, but not L. iners, were positively associated with sE-cad and IL-1α. Table S3: Nugent scores of women misclassified by the logistic regression model predicting Nugent BV with bacterial load in the SWOP cohort. Table S4: CST subgroups of women misclassified by the logistic regression model predicting molecular BV with bacterial load in the SWOP cohort. Table S5: Comparison of linear regression models predicting soluble immune factors with different vaginal microbiota characterization metrics. Table S6: Comparison of sociodemographic characteristics based on availability of complete immune data in the SWOP cohort. Table S7: Association between PAM immune cluster and sociodemographic variables. Table S8: Sociodemographic factors for Uganda-based confirmatory cohort. N = 61. Table S9: Nugent scores of women misclassified by the logistic regression model predicting Nugent BV with bacterial load in the Uganda-based confirmatory cohort. Table S10: Comparison of linear regression models predicting soluble immune factors with different vaginal microbiota characterization metrics in the confirmatory Uganda-based confirmatory cohort. Table S11: Primer and probe sequences for qPCR assays quantifying total bacterial load. https://creativecommons.org/licenses/by/4.0/legalcode