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Elderly individuals have the highest burden of disease from influenza infection but also the lowest immune response to influenza vaccination. A better understanding of the host response to influenza vaccination in the elderly is therefore urgently needed. We conducted a biphasic prospective, population-based study from Dec. 2014 to May 2015 (pilot study) and Sept. 2015 to May 2016 (main study). Individuals 65–80 y of age were randomly selected from the residents' registration office in Hannover, Germany, for the pilot (n = 34) and main study (n = 200). The pilot study tested recruitment for study arms featuring 2, 4, or 5 visits/blood draws. The 5-visit (day 0, 1/3, 7, 21, 70 with respect to vaccination) study arm was selected for the main study. Both studies featured vaccination with Fluad™ (Novartis, Italy), a detailed medical history, a physical exam, recording of adverse events, completion of a questionnaire on common infections and an end-of-study questionnaire, and blood samples. Response rates in the pilot and main studies were 3.7% and 4.0%, respectively. Willingness to participate did not differ among the study arms (Fisher's exact test, p = 0.44). In both studies, there were no losses to follow-up. Compliance with study visits, blood sampling and completion of the questionnaires was very high (100%, >97%, 100%, respectively), as were participants' acceptance of and satisfaction with both phases of the study. The low response rates indicate the need for optimized recruitment strategies if the study population is to be representative of the general population. Nonetheless, the complex prospective study design proved to be highly feasible.

ABSTRACT In tuberculosis (TB), as in other infectious diseases, studies of small noncoding RNAs (sncRNA) in peripheral blood have focused on microRNAs (miRNAs) but have neglected the other major sncRNA classes in spite of their potential functions in host gene regulation. Using RNA sequencing of whole blood, we have therefore determined expression of miRNA, PIWI-interacting RNA (piRNA), small nucleolar RNA (snoRNA), and small nuclear RNA (snRNA) in patients with TB (n = 8), latent TB infection (LTBI; n = 21), and treated LTBI (LTBItt; n = 6) and in uninfected exposed controls (ExC; n = 14). As expected, sncRNA reprogramming was greater in TB than in LTBI, with the greatest changes seen in miRNA populations. However, substantial dynamics were also evident in piRNA and snoRNA populations. One miRNA and 2 piRNAs were identified as moderately accurate (area under the curve [AUC] = 0.70 to 0.74) biomarkers for LTBI, as were 1 miRNA, 1 piRNA, and 2 snoRNAs (AUC = 0.79 to 0.91) for accomplished LTBI treatment. Logistic regression identified the combination of 4 sncRNA (let-7a-5p, miR-589-5p, miR-196b-5p, and SNORD104) as a highly sensitive (100%) classifier to discriminate TB from all non-TB groups. Notably, it reclassified 8 presumed LTBI cases as TB cases, 5 of which turned out to have features of Mycobacterium tuberculosis infection on chest radiographs. SNORD104 expression decreased during M. tuberculosis infection of primary human peripheral blood mononuclear cells (PBMC) and M2-like (P = 0.03) but not M1-like (P = 0.31) macrophages, suggesting that its downregulation in peripheral blood in TB is biologically relevant. Taken together, the results demonstrate that snoRNA and piRNA should be considered in addition to miRNA as biomarkers and pathogenesis factors in the various stages of TB. IMPORTANCE Tuberculosis is the infectious disease with the worldwide largest disease burden and there remains a great need for better diagnostic biomarkers to detect latent and active M. tuberculosis infection. RNA molecules hold great promise in this regard, as their levels of expression may differ considerably between infected and uninfected subjects. We have measured expression changes in the four major classes of small noncoding RNAs in blood samples from patients with different stages of TB infection. We found that, in addition to miRNAs (which are known to be highly regulated in blood cells from TB patients), expression of piRNA and snoRNA is greatly altered in both latent and active TB, yielding promising biomarkers. Even though the functions of many sncRNA other than miRNA are still poorly understood, our results strongly suggest that at least piRNA and snoRNA populations may represent hitherto underappreciated players in the different stages of TB infection.

Background: Group A streptococci may induce lymphopenia, but the value of lymphocyte loss as early biomarkers for systemic spread and severe infection has not been examined systematically.Methods: We evaluated peripheral blood cell indices as biomarkers for severity and spread of infection in a mouse model of Streptococcus pyogenes skin infection, using two isolates of greatly differing virulence. Internal organs were examined histologically.Results: After subcutaneous inoculation, strain AP1 disseminated rapidly to peripheral blood and internal organs, causing frank sepsis. In contrast, seeding of internal organs by 5448 was mild, this strain could not be isolated from blood, and infection remained mostly localized to skin. Histopathologic examination of liver revealed microvesicular fatty change (steatosis) in AP1 infection, and examination of spleen showed elevated apoptosis and blurring of the white pulp/red pulp border late (40 h post infection) in AP1 infection. Both strains caused profound lymphopenia, but lymphocyte loss was more rapid early in AP1 infection, and lymphocyte count at 6 h post infection was the most accurate early marker for AP1 infection (area under the receiver operator curve [AUC] = 0.93), followed by the granulocyte/lymphocyte ratio (AUC = 0.89).Conclusions: The results suggest that virulence of S. pyogenes correlates with the degree of early lymphopenia and underscore the value of peripheral blood indices to predict severity of bacterial infections in mice. Early lymphopenia and elevated granulocyte/lymphocyte ratio merit further investigation as biomarkers for systemic spread of S. pyogenes skin infections in humans and, possibly, related pyogenic streptococci in humans and animals.

The 2009 pandemic influenza A virus (IAV) H1N1 strain (H1N1pdm09) has widely spread and is circulating in humans and swine together with other human and avian IAVs. This fact raises the concern that reassortment between H1N1pdm09 and co-circulating viruses might lead to an increase of H1N1pdm09 pathogenicity in different susceptible host species. Herein, we explored the potential of different NS segments to enhance the replication dynamics, pathogenicity and host range of H1N1pdm09 strain A/Giessen/06/09 (Gi-wt). The NS segments were derived from (i) human H1N1- and H3N2 IAVs, (ii) highly pathogenic- (H5- or H7-subtypes) or (iii) low pathogenic avian influenza viruses (H7- or H9-subtypes). A significant increase of growth kinetics in A549 (human lung epithelia) and NPTr (porcine tracheal epithelia) cells was only noticed in vitro for the reassortant Gi-NS-PR8 carrying the NS segment of the 1918-descendent A/Puerto Rico/8/34 (PR8-wt, H1N1), whereas all other reassortants showed either reduced or comparable replication efficiencies. Analysis using ex vivo tracheal organ cultures of turkeys (TOC-Tu), a species susceptible to IAV H1N1 infection, demonstrated increased replication of Gi-NS-PR8 compared to Gi-wt. Also, Gi-NS-PR8 induced a markedly higher expression of immunoregulatory and pro-inflammatory cytokines, chemokines and interferon-stimulated genes in A549 cells, THP-1-derived macrophages (dHTP) and TOC-Tu. In vivo, Gi-NS-PR8 induced an earlier onset of mortality than Gi-wt in mice, whereas, 6-week-old chickens were found to be resistant to both viruses. These data suggest that the specific characteristics of the PR8 NS segments can impact on replication, virus induced cellular immune responses and pathogenicity of the H1N1pdm09 in different avian and mammalian host species.

Understanding the mechanisms regulating cell cycle, proliferation and potency of pluripotent stem cells guarantees their safe use in the clinic. Embryonic stem cells (ESCs) present a fast cell cycle with a short G1 phase. This is due to the lack of expression of cell cycle inhibitors, which ultimately determines naïve pluripotency by holding back differentiation. The canonical Wnt/β-catenin pathway controls mESC pluripotency via the Wnt-effector Tcf3. However, if the activity of the Wnt/β-catenin controls the cell cycle of mESCs remains unknown. Here we show that the Wnt-effector Tcf1 is recruited to and triggers transcription of the Ink4/Arf tumor suppressor locus. Thereby, the activation of the Wnt pathway, a known mitogenic pathway in somatic tissues, restores G1 phase and drastically reduces proliferation of mESCs without perturbing pluripotency. Tcf1, but not Tcf3, is recruited to a palindromic motif enriched in the promoter of cell cycle repressor genes, such as p15Ink4b, p16Ink4a and p19Arf, which mediate the Wnt-dependent anti-proliferative effect in mESCs. Consistently, ablation of β-catenin or Tcf1 expression impairs Wnt-dependent cell cycle regulation. All together, here we showed that Wnt signaling controls mESC pluripotency and proliferation through non-overlapping functions of distinct Tcf factors.

Immunoresponsive gene 1 (IRG1) is one of the highest induced genes in macrophages under pro-inflammatory conditions. Its function has been recently described: it codes for immune-responsive gene 1 protein/cis-aconitic acid decarboxylase (IRG1/CAD), an enzyme catalysing the production of itaconic acid from cis-aconitic acid, a tricarboxylic acid (TCA) cycle intermediate. Itaconic acid possesses specific antimicrobial properties inhibiting isocitrate lyase, the first enzyme of the glyoxylate shunt, an anaplerotic pathway that bypasses the TCA cycle and enables bacteria to survive on limited carbon conditions. To elucidate the mechanisms underlying itaconic acid production through IRG1 induction in macrophages, we examined the transcriptional regulation of IRG1. To this end, we studied IRG1 expression in human immune cells under different inflammatory stimuli, such as TNFα and IFNγ, in addition to lipopolysaccharides. Under these conditions, as previously shown in mouse macrophages, IRG1/CAD accumulates in mitochondria. Furthermore, using literature information and transcription factor prediction models, we re-constructed raw gene regulatory networks (GRNs) for IRG1 in mouse and human macrophages. We further implemented a contextualization algorithm that relies on genome-wide gene expression data to infer putative cell type-specific gene regulatory interactions in mouse and human macrophages, which allowed us to predict potential transcriptional regulators of IRG1. Among the computationally identified regulators, siRNA-mediated gene silencing of interferon regulatory factor 1 (IRF1) in macrophages significantly decreased the expression of IRG1/CAD at the gene and protein level, which correlated with a reduced production of itaconic acid. Using a synergistic approach of both computational and experimental methods, we here shed more light on the transcriptional machinery of IRG1 expression and could pave the way to therapeutic approaches targeting itaconic acid levels.

The construction and application of biological network models is an approach that offers a holistic way to understand biological processes involved in disease. Chronic obstructive pulmonary disease (COPD) is a progressive inflammatory disease of the airways for which therapeutic options currently are limited after diagnosis, even in its earliest stage. COPD network models are important tools to better understand the biological components and processes underlying initial disease development. With the increasing amounts of literature that are now available, crowdsourcing approaches offer new forms of collaboration for researchers to review biological findings, which can be applied to the construction and verification of complex biological networks. We report the construction of 50 biological network models relevant to lung biology and early COPD using an integrative systems biology and collaborative crowd-verification approach. By combining traditional literature curation with a data-driven approach that predicts molecular activities from transcriptomics data, we constructed an initial COPD network model set based on a previously published non-diseased lung-relevant model set. The crowd was given the opportunity to enhance and refine the networks on a website (https://bionet.sbvimprover.com/) and to add mechanistic detail, as well as critically review existing evidence and evidence added by other users, so as to enhance the accuracy of the biological representation of the processes captured in the networks. Finally, scientists and experts in the field discussed and refined the networks during an in-person jamboree meeting. Here, we describe examples of the changes made to three of these networks: Neutrophil Signaling, Macrophage Signaling, and Th1-Th2 Signaling. We describe an innovative approach to biological network construction that combines literature and data mining and a crowdsourcing approach to generate a comprehensive set of COPD-relevant models that can be used to help understand the mechanisms related to lung pathobiology. Registered users of the website can freely browse and download the networks.

The construction and application of biological network models is an approach that offers a holistic way to understand biological processes involved in disease. Chronic obstructive pulmonary disease (COPD) is a progressive inflammatory disease of the airways for which therapeutic options currently are limited after diagnosis, even in its earliest stage. COPD network models are important tools to better understand the biological components and processes underlying initial disease development. With the increasing amounts of literature that are now available, crowdsourcing approaches offer new forms of collaboration for researchers to review biological findings, which can be applied to the construction and verification of complex biological networks. We report the construction of 50 biological network models relevant to lung biology and early COPD using an integrative systems biology and collaborative crowd-verification approach. By combining traditional literature curation with a data-driven approach that predicts molecular activities from transcriptomics data, we constructed an initial COPD network model set based on a previously published non-diseased lung-relevant model set. The crowd was given the opportunity to enhance and refine the networks on a website ( https://bionet.sbvimprover.com/) and to add mechanistic detail, as well as critically review existing evidence and evidence added by other users, so as to enhance the accuracy of the biological representation of the processes captured in the networks. Finally, scientists and experts in the field discussed and refined the networks during an in-person jamboree meeting. Here, we describe examples of the changes made to three of these networks: Neutrophil Signaling, Macrophage Signaling, and Th1-Th2 Signaling. We describe an innovative approach to biological network construction that combines literature and data mining and a crowdsourcing approach to generate a comprehensive set of COPD-relevant models that can be used to help understand the mechanisms related to lung pathobiology. Registered users of the website can freely browse and download the networks.

The construction and application of biological network models is an approach that offers a holistic way to understand biological processes involved in disease. Chronic obstructive pulmonary disease (COPD) is a progressive inflammatory disease of the airways for which therapeutic options currently are limited after diagnosis, even in its earliest stage. COPD network models are important tools to better understand the biological components and processes underlying initial disease development. With the increasing amounts of literature that are now available, crowdsourcing approaches offer new forms of collaboration for researchers to review biological findings, which can be applied to the construction and verification of complex biological networks. We report the construction of 50 biological network models relevant to lung biology and early COPD using an integrative systems biology and collaborative crowd-verification approach. By combining traditional literature curation with a data-driven approach that predicts molecular activities from transcriptomics data, we constructed an initial COPD network model set based on a previously published non-diseased lung-relevant model set. The crowd was given the opportunity to enhance and refine the networks on a website ( https://bionet.sbvimprover.com/) and to add mechanistic detail, as well as critically review existing evidence and evidence added by other users, so as to enhance the accuracy of the biological representation of the processes captured in the networks. Finally, scientists and experts in the field discussed and refined the networks during an in-person jamboree meeting. Here, we describe examples of the changes made to three of these networks: Neutrophil Signaling, Macrophage Signaling, and Th1-Th2 Signaling. We describe an innovative approach to biological network construction that combines literature and data mining and a crowdsourcing approach to generate a comprehensive set of COPD-relevant models that can be used to help understand the mechanisms related to lung pathobiology. Registered users of the website can freely browse and download the networks.