Search

BackgroundBrucellosis, as a serious zoonotic infectious disease, has been recognized as a re-emerging disease in the developing countries worldwide. In china, the incidence of brucellosis is increasing each year, seriously threatening the health of humans as well as animal populations. Despite a quite number of diagnostic methods currently being used for brucellosis, innovative technologies are still needed for its rapid and accurate diagnosis, especially in area where traditional diagnostic is unavailable.Methodology/principal findingsIn this study, a total of 22 B cell linear epitopes were predicted from five Brucella outer membrane proteins (OMPs) using an immunoinformatic approach. These epitopes were then chemically synthesized, and with the method of indirect ELISA (iELISA), each of them displayed a certain degree of capability in identifying human brucellosis positive sera. Subsequently, a fusion protein consisting of the 22 predicted epitopes was prokaryotically expressed and used as diagnostic antigen in a newly established brucellosis testing method, nano-ZnO modified paper-based ELISA (nano-p-ELISA). According to the verifying test using a collection of sera collected from brucellosis and non-brucellosis patients, the sensitivity and specificity of multiepitope based nano-p-ELISA were 92.38% and 98.35% respectively. The positive predictive value was 98.26% and the negative predictive value was 91.67%. The multiepitope based fusion protein also displayed significantly higher specificity than Brucella lipopolysaccharide (LPS) antigen.ConclusionsB cell epitopes are important candidates for serologically testing brucellosis. Multiepitope fusion protein based nano-p-ELISA displayed significantly sensitivity and specificity compared to Brucella LPS antigen. The strategy applied in this study will be helpful to develop rapid and accurate diagnostic method for brucellosis in human as well as animal populations.

Point-of-care serological and direct antigen testing offers actionable insights for diagnosing challenging illnesses, empowering distributed health systems. Here, we report a POC-compatible serologic test for Lyme disease (LD), leveraging synthetic peptides specific to LD antibodies and a paper-based platform for rapid, and cost-effective diagnosis. Antigenic epitopes conserved across Borrelia burgdorferi genospecies, targeted by IgG and IgM antibodies, are selected to develop a multiplexed panel for detection of LD antibodies from patient sera. Multiple peptide epitopes, when combined synergistically with a machine learning-based diagnostic model achieve high sensitivity without sacrificing specificity. Blinded validation with 15 LD-positive and 15 negative samples shows 95.5% sensitivity and 100% specificity. Blind testing with the CDCs LD repository samples confirms the test accuracy, matching lab-based two-tier results, correctly differentiating between LD and look-alike diseases. This LD diagnostic test could potentially replace the cumbersome two-tier testing, improving diagnosis and enabling earlier treatment while facilitating immune monitoring and surveillance.

Comprehensive understanding of the serological response to SARS-CoV-2 infection is important for both pathophysiologic insight and diagnostic development. Here, we generate a pan-human coronavirus programmable phage display assay to perform proteome-wide profiling of coronavirus antigens enriched by 98 COVID-19 patient sera. Next, we use ReScan, a method to efficiently sequester phage expressing the most immunogenic peptides and print them onto paper-based microarrays using acoustic liquid handling, which isolates and identifies nine candidate antigens, eight of which are derived from the two proteins used for SARS-CoV-2 serologic assays: spike and nucleocapsid proteins. After deployment in a high-throughput assay amenable to clinical lab settings, these antigens show improved specificity over a whole protein panel. This proof-of-concept study demonstrates that ReScan will have broad applicability for other emerging infectious diseases or autoimmune diseases that lack a valid biomarker, enabling a seamless pipeline from antigen discovery to diagnostic using one recombinant protein source.

Human pluripotent stem cells (hPSCs) have the intrinsic ability to self-organize into complex multicellular organoids that recapitulate many aspects of tissue development. However, robustly directing morphogenesis of hPSC-derived organoids requires novel approaches to accurately control self-directed pattern formation. Here, we combined genetic engineering with computational modeling, machine learning, and mathematical pattern optimization to create a data-driven approach to control hPSC self-organization by knock down of genes previously shown to affect stem cell colony organization, CDH1 and ROCK1. Computational replication of the in vitro system in silico using an extended cellular Potts model enabled machine learning-driven optimization of parameters that yielded emergence of desired patterns. Furthermore, in vitro the predicted experimental parameters quantitatively recapitulated the in silico patterns. These results demonstrate that morphogenic dynamics can be accurately predicted through model-driven exploration of hPSC behaviors via machine learning, thereby enabling spatial control of multicellular patterning to engineer human organoids and tissues. A record of this paper's Transparent Peer Review process is included in the Supplemental Information.

PurposePathologic corneal neovascularization is a major cause of blindness worldwide, and treatment options are currently limited. VEGF is one of the critical mediators of corneal neovascularization but current anti-VEGF therapies have produced limited results in the cornea. Thus, additional therapeutic agents are needed to enhance the antiangiogenic arsenal. Our group previously demonstrated epithelial membrane protein-2 (EMP2) involvement in pathologic angiogenesis in multiple cancer models including breast cancer and glioblastoma. In this paper, we investigate the efficacy of anti-EMP2 immunotherapy in the prevention of corneal neovascularization.MethodsAn in vivo murine cornea alkali burn model was used to study pathologic neovascularization. A unilateral corneal burn was induced using NaOH, and subconjunctival injection of either anti-EMP2 antibody, control antibody, or sterile saline was performed after corneal burn. Neovascularization was clinically scored at 7 days postalkali burn, and eyes were enucleated for histologic analysis and immunostaining including VEGF, CD31, and CD34 expression.ResultsAnti-EMP2 antibody, compared to control antibody or vehicle, significantly reduced neovascularization as measured by clinical score and central cornea thickness, as well as by histologic reduction of neovascularization, decreased CD34 staining, and decreased CD31 staining. Incubation of corneal limbal cells in vitro with anti-EMP2 blocking antibody significantly decreased EMP2 expression, VEGF expression and secretion, and cell migration.ConclusionsThis work demonstrates the effectiveness of EMP2 as a novel target in pathologic corneal neovascularization in an animal model and supports additional investigation into EMP2 antibody blockade as a potential new therapeutic option.

BackgroundComparing phenotypes of heterogeneous cell populations from multiple biological conditions is at the heart of scientific discovery based on flow cytometry (FC). When the biological signal is measured by the average expression of a biomarker, standard statistical methods require that variance be approximately stabilized in populations to be compared. Since the mean and variance of a cell population are often correlated in fluorescence-based FC measurements, a preprocessing step is needed to stabilize the within-population variances.ResultsWe present a variance-stabilization algorithm, called flowVS, that removes the mean-variance correlations from cell populations identified in each fluorescence channel. flowVS transforms each channel from all samples of a data set by the inverse hyperbolic sine (asinh) transformation. For each channel, the parameters of the transformation are optimally selected by Bartlett's likelihood-ratio test so that the populations attain homogeneous variances. The optimum parameters are then used to transform the corresponding channels in every sample. flowVS is therefore an explicit variance-stabilization method that stabilizes within-population variances in each channel by evaluating the homoskedasticity of clusters with a likelihood-ratio test. With two publicly available datasets, we show that flowVS removes the mean-variance dependence from raw FC data and makes the within-population variance relatively homogeneous. We demonstrate that alternative transformation techniques such as flowTrans, flowScape, logicle, and FCSTrans might not stabilize variance. Besides flow cytometry, flowVS can also be applied to stabilize variance in microarray data. With a publicly available data set we demonstrate that flowVS performs as well as the VSN software, a state-of-the-art approach developed for microarrays.ConclusionsThe homogeneity of variance in cell populations across FC samples is desirable when extracting features uniformly and comparing cell populations with different levels of marker expressions. The newly developed flowVS algorithm solves the variance-stabilization problem in FC and microarrays by optimally transforming data with the help of Bartlett's likelihood-ratio test. On two publicly available FC datasets, flowVS stabilizes within-population variances more evenly than the available transformation and normalization techniques. flowVS-based variance stabilization can help in performing comparison and alignment of phenotypically identical cell populations across different samples. flowVS and the datasets used in this paper are publicly available in Bioconductor.

Single domain heavychain binders (nanobodies) obtained from camelid antibody libraries hold a great promise for immunoassay development. However, there is no simple method to select the most valuable nanobodies from the crowd of positive clones obtained after the initial screening. In this paper, we describe a novel nanobody-based platform that allows comparison of the reactivity of hundreds of clones with the labeled antigen, and identifies the best nanobody pairs for two-site immunoassay development. The output clones are biotinylated in vivo in 96-well culture blocks and then used to saturate the biotin binding capacity of avidin coated wells. This standardizes the amount of captured antibody allowing their sorting by ranking their reactivity with the labeled antigen. Using human soluble epoxide hydrolase (sEH) as a model antigen, we were able to classify 96 clones in four families and confirm this classification by sequencing. This provided a criterion to select a restricted panel of five capturing antibodies and to test each of them against the rest of the 96 clones. The method constitutes a powerful tool for epitope binning, and in our case allowed development of a sandwich ELISA for sEH with a detection limit of 63 pg/mL and four log dynamic range, which performed with excellent recovery in different tissue extracts. This strategy provides a systematic way to test nanobody pairwise combinations and would have a broad utility for the development of highly sensitive sandwich immunoassays.

Previous studies from this laboratory have suggested a role for cell-mediated immunity in host defense against Legionella pneumophila. In this paper, cell-mediated immunity to L. pneumophila in patients recovered from Legionnaires' disease was studied by examining patient mononuclear cell responses to L. pneumophila antigens. Patient mononuclear cells were assayed both for their capacity to respond to L. pneumophila antigens with the production of cytokines that activate monocytes, as measured by monocyte inhibition of L. pneumophila multiplication, and for their capacity to respond with proliferation, as measured by [(3)H]thymidine incorporation. Patient mononuclear cells incubated with formalin-killed L. pneumophila generated cytokines (supernatants) that were capable of activating in vitro freshly explanted monocytes from a person without historical or serological evidence of L. pneumophila infection (nonpatient). Such activated nonpatient monocytes inhibited the intracellular multiplication of L. pneumophila, and the degree of inhibition was proportional to the concentration of supernatant added. Patient mononuclear cells incubated with 5 x 10(6) - 5 x 10(8) formalin-killed L. pneumophila/ml for 4 d produced maximally potent supernatants; supernatants generated in flat-bottomed wells were equivalent in potency to supernatants generated in cone-shaped wells. Patient L. pneumophila-induced mononuclear cell supernatants were less potent than patient concanavalin A-induced mononuclear cell supernatants. Patient mononuclear cells also responded to formalin-killed L. pneumophila with proliferation (lymphoproliferation). Patient mononuclear cells responded more strongly to L. pneumophila antigens than mononuclear cells of age- and sex-matched nonpatients, as measured by both assays; responses to concanavalin A were comparable. Mononuclear cells from patients recovered from Legionnaires' disease responded more strongly to L. pneumophila than to Mycobacterium leprae antigens, whereas mononuclear cells from patients with tuberculoid leprosy responded more strongly to M. leprae antigens. These findings indicate that cell-mediated immunity to L. pneumophila develops in patients with Legionnaires' disease and, taken together with previously reported findings, that cell-mediated immunity plays a major role in host defense against L. pneumophila. The monocyte activation assay described in this paper has general applicability for the study of monocyte and mononuclear cell effector functions in selected patients. The assay may be used to study (a) the capacity of a patient's monocytes to be activated to an antimicrobial potential by a standard preparation of cytokines and (b) the capacity of a patient's mononuclear cells to generate such monocyte-activating cytokines in response to a mitogen or antigen.

Pharmacological focal adhesion kinase (FAK) inhibition prevents tumor growth and metastasis, via actions on both tumor and stromal cells. In this paper, we show that vascular endothelial cadherin (VEC) tyrosine (Y) 658 is a target of FAK in tumor-associated endothelial cells (ECs). Conditional kinase-dead FAK knockin within ECs inhibited recombinant vascular endothelial growth factor (VEGF-A) and tumor-induced VEC-Y658 phosphorylation in vivo. Adherence of VEGF-expressing tumor cells to ECs triggered FAK-dependent VEC-Y658 phosphorylation. Both FAK inhibition and VEC-Y658F mutation within ECs prevented VEGF-initiated paracellular permeability and tumor cell transmigration across EC barriers. In mice, EC FAK inhibition prevented VEGF-dependent tumor cell extravasation and melanoma dermal to lung metastasis without affecting primary tumor growth. As pharmacological c-Src or FAK inhibition prevents VEGF-stimulated c-Src and FAK translocation to EC adherens junctions, but FAK inhibition does not alter c-Src activation, our experiments identify EC FAK as a key intermediate between c-Src and the regulation of EC barrier function controlling tumor metastasis.