Positive Selection of Anti–Thy-1 Autoreactive B-1 Cells and Natural Serum Autoantibody Production Independent from Bone Marrow B Cell Development

A natural serum autoantibody specific for the Thy-1 glycoprotein (anti–Thy-1 autoantibody [ATA]) is produced by B-1 cells that are positively selected by self-antigen. Here, using ATAμκ transgenic mice we show that cells with this B cell receptor are negatively selected during bone marrow (BM) development. In a Thy-1 null environment, BM ATA B cells progress to a normal follicular stage in spleen. However, in a self-antigen–positive environment, development is arrested at an immature stage in the spleen, concomitant with induction of CD5. Such cells are tolerant and short-lived, different from B-1. Nonetheless, ATA-positive selection was evident by self-antigen–dependent high serum ATA production, comprising ∼90% of serum immunoglobulin M in ATAμκ mice. Splenectomy did not eliminate ATA production and transfer of tolerant splenic B cells did not induce it. These findings demonstrate that B-1 positive selection, resulting in the production of natural serum ATA, arises independently from the major pathway of BM B cell development and selection

Impact of COVID-19 on cardiovascular testing in the United States versus the rest of the world

Objectives: This study sought to quantify and compare the decline in volumes of cardiovascular procedures between the United States and non-US institutions during the early phase of the coronavirus disease-2019 (COVID-19) pandemic. Background: The COVID-19 pandemic has disrupted the care of many non-COVID-19 illnesses. Reductions in diagnostic cardiovascular testing around the world have led to concerns over the implications of reduced testing for cardiovascular disease (CVD) morbidity and mortality. Methods: Data were submitted to the INCAPS-COVID (International Atomic Energy Agency Non-Invasive Cardiology Protocols Study of COVID-19), a multinational registry comprising 909 institutions in 108 countries (including 155 facilities in 40 U.S. states), assessing the impact of the COVID-19 pandemic on volumes of diagnostic cardiovascular procedures. Data were obtained for April 2020 and compared with volumes of baseline procedures from March 2019. We compared laboratory characteristics, practices, and procedure volumes between U.S. and non-U.S. facilities and between U.S. geographic regions and identified factors associated with volume reduction in the United States. Results: Reductions in the volumes of procedures in the United States were similar to those in non-U.S. facilities (68% vs. 63%, respectively; p = 0.237), although U.S. facilities reported greater reductions in invasive coronary angiography (69% vs. 53%, respectively; p < 0.001). Significantly more U.S. facilities reported increased use of telehealth and patient screening measures than non-U.S. facilities, such as temperature checks, symptom screenings, and COVID-19 testing. Reductions in volumes of procedures differed between U.S. regions, with larger declines observed in the Northeast (76%) and Midwest (74%) than in the South (62%) and West (44%). Prevalence of COVID-19, staff redeployments, outpatient centers, and urban centers were associated with greater reductions in volume in U.S. facilities in a multivariable analysis. Conclusions: We observed marked reductions in U.S. cardiovascular testing in the early phase of the pandemic and significant variability between U.S. regions. The association between reductions of volumes and COVID-19 prevalence in the United States highlighted the need for proactive efforts to maintain access to cardiovascular testing in areas most affected by outbreaks of COVID-19 infection

Tightly Regulated Expression of <i>Autographa californica</i> Multicapsid Nucleopolyhedrovirus Immediate Early Genes Emerges from Their Interactions and Possible Collective Behaviors

<div><p>To infect their hosts, DNA viruses must successfully initiate the expression of viral genes that control subsequent viral gene expression and manipulate the host environment. Viral genes that are immediately expressed upon infection play critical roles in the early infection process. In this study, we investigated the expression and regulation of five canonical regulatory immediate-early (IE) genes of <i>Autographa californica</i> multicapsid nucleopolyhedrovirus: <i>ie0</i>, <i>ie1</i>, <i>ie2</i>, <i>me53</i>, and <i>pe38</i>. A systematic transient gene-expression analysis revealed that these IE genes are generally transactivators, suggesting the existence of a highly interactive regulatory network. A genetic analysis using gene knockout viruses demonstrated that the expression of these IE genes was tolerant to the single deletions of activator IE genes in the early stage of infection. A network graph analysis on the regulatory relationships observed in the transient expression analysis suggested that the robustness of IE gene expression is due to the organization of the IE gene regulatory network and how each IE gene is activated. However, some regulatory relationships detected by the genetic analysis were contradictory to those observed in the transient expression analysis, especially for IE0-mediated regulation. Statistical modeling, combined with genetic analysis using knockout alleles for <i>ie0</i> and <i>ie1</i>, showed that the repressor function of <i>ie0</i> was due to the interaction between <i>ie0</i> and <i>ie1</i>, not <i>ie0</i> itself. Taken together, these systematic approaches provided insight into the topology and nature of the IE gene regulatory network.</p></div

The IE0–IE1 interaction contributed to the expression of other IE genes in a distinct manner.

<p>(A) IE mRNA abundance in <i>ie0</i>, <i>ie1</i> single, and <i>ie0</i>/<i>ie1</i> double knockout viruses 6 hours post-transfection. Error bars are standard errors of the estimated means. Asterisks indicate significant regulatory functions: **, <i>q</i>-value < 0.05. The <i>q</i>-value is the <i>p</i>-value adjusted using the False Discovery Rate. (B) Values used for model fitting to estimate the contribution of IE0 alone, IE1 alone, the IE0–IE1 interaction to the expression of other IE genes, and the rest. Top, real-time qPCR data for the expression of <i>ie2</i>, as an example. The mean expression levels of <i>ie2</i> were estimated using a linear model, with the viral genotypes indicated below. Letters C, D, and E in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0119580#pone.0119580.g005" target="_blank">Fig. 5C, D, and E</a> refer to the letters C (control), D (Δie0), and E (Δie0/ie1) above the columns in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0119580#pone.0119580.g005" target="_blank">Fig. 5B</a>. Bottom, the values assigned to the variables in the model for the single gene and interaction effects. The values (0 or 1) indicate presence or absence of the genes or interaction (indicated by the row label) in the genotype (indicated by the column below the matrix). The values were used to fit the linear model shown in F. (C, D, F) Schematic representations of the decomposition of the contribution of single genes and the IE0–IE1 interaction to IE gene expression. Red and blue bars indicate the positive and negative contribution of each gene or interaction to IE gene expression, respectively. (C) The wild-type virus had a complete set of genes. (D) The <i>ie0</i> knockout virus lacked the contribution of IE0 to IE gene expression, and naturally lacked the IE0–IE1 interaction. (E) <i>ie0</i>/<i>ie1</i> knockout virus lacks IE0, IE1, and the IE0–IE1 interaction. (F) The linear model used to estimate the contribution of single gene and interaction effects. The values (0 or 1) shown in B were assigned to the gene and interaction variables (IE0, IE1, IE0:IE1, ME53, PE38, and REST). (G) Estimated contribution of IE0, IE1, and the IE0–IE1 interaction to the other IE genes. Note that the height of the bars indicates the absolute values of the estimated contributions. Red and blue bars indicate the positive and negative contribution of each gene or interaction to IE gene expression, respectively. Error bars are standard errors of the estimated means. (H) Transient expression analysis to confirm the effects of the IE0–IE1 interaction on the expression of <i>ie2</i> and <i>pe38</i>. The <i>ie2</i> or <i>pe38</i> promoter plasmids were co-transfected with the plasmid(s) expressing the IE gene(s), indicated as “Driver IE gene” in the column. The estimated mean values of the IE gene cassettes were adjusted by the value of the corresponding pGEM-transfected samples. The reported estimated mean values are the sums of the estimated effect size of the basal promoter activity and the IE gene on the promoter. Estimation of the values was performed using the mixed linear model. Error bars are standard errors of the estimated means for the IE gene effects. Two sets of experiments to estimate the random variation in a biological replicate were done (These replicates were defined as “set” and “replicate” in Materials and Methods, respectively). Luciferase activity was measured in transfected cells at 48 hours post-transfection. Error bars are standard errors of the estimated means.</p

Study and construct designs.

<p>(A) Study design. <i>ie0</i>, <i>ie1</i>, <i>ie2</i>, <i>me53</i>, and <i>pe38</i> were selected because they are validated or inferred regulators of IE gene expression. 1, Plasmids for transient expression analysis were constructed from the AcMNPV-C6 genome. Transient expression analyses to measure the IE gene promoter activities were performed in all combinations of the five IE gene expression cassettes and six reporter plasmids (five containing IE gene promoters and one empty vector). The results obtained from this were used in a network analysis (2). 3, Reverse genetic analyses were performed using bacmids that each lacked one of the 5 IE genes. IE gene transcription in the knockout viruses was quantified using quantitative real-time PCR. 4, The results obtained in the transient expression analysis and the genetic analysis were compared and used to build and test hypotheses. (B and C) The designs of constructs used for transient expression analysis. (B) The reporter plasmids. Approximately 500 bp of the sequence upstream of the IE ORFs were used as the IE gene promoters in this study. (C) IE gene expression plasmids. The IE gene ORFs and the upstream sequences used in the reporter plasmids were inserted into a pGEM-Teasy vector carrying a fibroin poly(A) signal. (D) Schematic representation of the generation of IE gene knockout viruses. The entire IE gene ORFs, from the initiation codons to the termination codons, were replaced with an ampicillin resistance gene. (E) The structure of immediate early ie0 transcript and the genomic region used to knock out <i>ie0</i>. Immediate early ie0 transcript consists of two exons. Δ<i>ie0</i> bacmid was generated by replacing a part of <i>Orf141</i> ORF with an ampicillin resistance gene.</p

IE gene regulatory functions observed in the reverse genetic analysis.

<p>The abundance of IE mRNA in cells transfected with each IE gene knockout virus 6 hours post-transfection was quantified using real-time PCR. (A) Comparison of IE gene expression level in each knockout virus. The estimated mean copy numbers of each transcript (indicated by the label on the panel) with each genotype (indicated by the columns below the panel) represented as heights of the bars (the vertical axis). The values are the sums of the estimated steady-state expression level in the control virus and the estimated genotype effect of the knockout virus. Error bars are standard errors of the estimated copy numbers. These values were estimated by fitting a mixed linear model. Asterisks indicate significant regulatory functions: *, <i>q</i>-value < 0.1; **, <i>q</i>-value < 0.05. The <i>q</i>-value is the <i>p</i>-value adjusted using the False Discovery Rate. The reported values are calculated from three technical replicates and three biological replicates. (B) Comparison of IE gene regulatory activity on IE gene transcription. The estimated genotype effect on the copy number of each transcript (indicated by the columns below the panel) used in (A) multiplied by -1, represented as the estimated regulatory activity of the wild-type viral gene. The vertical axis indicates quantity of regulatory activity and direction of the regulation. (C) Comparison of IE gene regulatory functions in the genetic analysis using knockout viruses to those in the transient expression analysis using the reporter assay. The values and statistics are shown in Figs. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0119580#pone.0119580.g002" target="_blank">2B</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0119580#pone.0119580.g004" target="_blank">4B</a> for the “Reporter assay” and “Genetics”, respectively. The logarithmic bases of the values for the transient expression analysis and the genetic analysis are 10 and 2, respectively. Shaded cells indicate transcripts not measured because the genes were removed from the genome. Bold boundaries indicate significant regulatory relationships (<i>q</i>-value < 0.1). Asterisks indicate a genotype–phenotype paradox, in which a perturbation to a gene with an activator function or no regulatory function in the transient expression analysis resulted in enhanced target gene expression in the genetic analysis.</p

Network analysis of IE gene regulatory functions.

<p>(A) A network graph of the IE gene regulatory functions observed in the transient expression analysis. Orange and blue links indicate positive and negative regulatory functions, respectively. The width of the links corresponds proportionally to the absolute values of the ratio values shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0119580#pone.0119580.g002" target="_blank">Fig. 2B</a>, representing the strength of the regulatory function. (B) The number of links to each IE gene in our network model. “In” and “out” represent incoming links to and outgoing links from the IE genes as indicated below. (C) Normalized betweenness of IE genes in our IE gene network model. (D) Possible path redundancy in the IE gene regulatory network. The simulation of genetic perturbations was performed by calculating the shortest path length following removal of one IE gene from the model. Crossed cells indicate combinations of input and output IE genes in which the shortest path length could not be computed. Shaded cells indicate the isolation of the output IE gene due to the removal of IE genes connected with it.</p