Borrelia burgdorferi stimulation of chemokine secretion by cells of monocyte lineage in patients with Lyme arthritis

Introduction: Joint fluid in patients with Lyme arthritis often contains high levels of CCL4 and CCL2, which are chemoattractants for monocytes and some T cells, and CXCL9 and CXCL10, which are chemoattractants for CD4+ and CD8+ T effector cells. These chemokines are produced primarily by cells of monocyte lineage in TH1-type immune responses. Our goal was to begin to learn how infection with Borrelia burgdorferi leads to the secretion of these chemokines, using patient cell samples. We hypothesized that B. burgdorferi stimulates chemokine secretion from monocytes/macrophages in multiple ways, thereby linking innate and adaptive immune responses. Methods: Peripheral blood mononuclear cells (PBMC) from 24 Lyme arthritis patients were stimulated with B. burgdorferi, interferon (IFN)-γ, or both, and the levels of CCL4, CCL2, CXCL9 and CXCL10 were measured in culture supernatants. CD14+ monocytes/macrophages from PBMC and synovial fluid mononuclear cells (SFMC) were stimulated in the same way, using available samples. CXCR3, the receptor for CXCL9 and CXCL10, and CCR5, the receptor for CCL4, were assessed on T cells from PBMC and SFMC. Results: In patients with Lyme arthritis, B. burgdorferi but not IFN-γ induced PBMC to secrete CCL4 and CCL2, and B. burgdorferi and IFN-γ each stimulated the production of CXCL9 and CXCL10. However, with the CD14+ cell fraction, B. burgdorferi alone stimulated the secretion of CCL4; B. burgdorferi and IFN-γ together induced CCL2 secretion, and IFN-γ alone stimulated the secretion of CXCL9 and CXCL10. The percentage of T cells expressing CXCR3 or CCR5 was significantly greater in SFMC than PBMC, confirming that $T_H1$ effector cells were recruited to inflamed joints. However, when stimulated with B. burgdorferi or IFN-γ, SFMC and PBMC responded similarly. Conclusions: B. burgdorferi stimulates PBMC or CD14+ monocytes/macrophages directly to secrete CCL4, but spirochetal stimulation of other intermediate cells, which are present in PBMC, is required to induce CD14+ cells to secrete CCL2, CXCL9 and CXCL10. We conclude that B. burgdorferi stimulates monocytes/macrophages directly and indirectly to guide innate and adaptive immune responses in patients with Lyme arthritis

MIF is a common genetic determinant of COVID-19 symptomatic infection and severity

Genetic predisposition to coronavirus disease 2019 (COVID-19) may contribute to its morbidity and mortality. Because cytokines play an important role in multiple phases of infection, we examined whether commonly occurring, functional polymorphisms in macrophage migration inhibitory factor (MIF) are associated with COVID-19 infection or disease severity.This work was supported by National Institute of Health (NIH) [1R01-AR078334 (RB), 5R01-AI51306 (RB), R01-HL155948 (MS, RB), 1R01AG056728 (IK), T32AR07107 (JPY) and KL2 TR001862 (JJS)]; the European Commission (DB) – NextGenerationEU (Regulation EU 2020/2094) through CSIC's Global Health Platform (PTI Salud Global), and Junta de Castilla y León (Programa Estratégico Instituto de Biología y Genética Molecular (IBGM), Junta de Castilla y León (CCVC8485); and the National Natural Science Foundation of China [#81901669 (WF)].Peer reviewe

Localization of BmpA on the Exposed Outer Membrane of Borrelia burgdorferi by Monospecific Anti-Recombinant BmpA Rabbit Antibodies

BmpA (P39) is an immunodominant chromosomally encoded Borrelia burgdorferi protein. The potential strong cross-reactivity of anti-BmpA antibodies with the other members of this paralogous protein family and the previous use of antibodies whose reactivity to the other Bmp proteins was uncharacterized have resulted in continued controversy over its localization in B. burgdorferi. In an effort to provide a definitive demonstration of the localization of BmpA, rabbit antibodies raised to recombinant BmpA (rBmpA) were rendered monospecific by absorption with rBmpB. This reagent did not react with rBmpB, rBmpC, or rBmpD in dot immunobinding, detected only a single 39-kDa band and a single 39-kDa, pI 5.0 spot on one- and two-dimensional immunoblots of B. burgdorferi lysates, respectively, and immunoprecipitated a single 39-kDa protein from these lysates. It detected BmpA in the Triton X-114-soluble and -insoluble fractions of B. burgdorferi, suggesting association with both inner and outer bacterial cell membranes. Treatment of intact B. burgdorferi with proteinase K partially digested BmpA, consistent with a limited surface exposure on the outer bacterial membrane, a suggestion confirmed by immunofluorescence of unfixed B. burgdorferi cultured in vitro and in vivo. Anti-rBmpA antibody was bacteriostatic for B. burgdorferi B31 in culture, again suggesting localization of BmpA on the exposed spirochetal outer surface. Surface localization of BmpA, growth inhibition by anti-rBmpA antibodies, and the previously reported conservation of bmpA in different B. burgdorferi sensu lato strains may indicate that BmpA plays an essential role in B. burgdorferi biology

Different Spectral Domain Transformation for Land Cover Classification Using Convolutional Neural Networks with Multi-Temporal Satellite Imagery

This study compares some different types of spectral domain transformations for convolutional neural network (CNN)-based land cover classification. A novel approach was proposed, which transforms one-dimensional (1-D) spectral vectors into two-dimensional (2-D) features: Polygon graph images (CNN-Polygon) and 2-D matrices (CNN-Matrix). The motivations of this study are that (1) the shape of the converted 2-D images is more intuitive for human eyes to interpret when compared to 1-D spectral input; and (2) CNNs are highly specialized and may be able to similarly utilize this information for land cover classification. Four seasonal Landsat 8 images over three study areas-Lake Tapps, Washington, Concord, New Hampshire, USA, and Gwangju, Korea-were used to evaluate the proposed approach for nine land cover classes compared to several other methods: Random forest (RF), support vector machine (SVM), 1-D CNN, and patch-based CNN. Oversampling and undersampling approaches were conducted to examine the effect of the sample size on the model performance. The CNN-Polygon had better performance than the other methods, with overall accuracies of about 93%-95 % for both Concord and Lake Tapps and 80%-84% for Gwangju. The CNN-Polygon particularly performed well when the training sample size was small, less than 200 per class, while the CNN-Matrix resulted in similar or higher performance as sample sizes became larger. The contributing input variables to the models were carefully analyzed through sensitivity analysis based on occlusion maps and accuracy decreases. Our result showed that a more visually intuitive representation of input features for CNN-based classification models yielded higher performance, especially when the training sample size was small. This implies that the proposed graph-based CNNs would be useful for land cover classification where reference data are limited.Y

Different Spectral Domain Transformation for Land Cover Classification Using Convolutional Neural Networks with Multi-Temporal Satellite Imagery

This study compares some different types of spectral domain transformations for convolutional neural network (CNN)-based land cover classification. A novel approach was proposed, which transforms one-dimensional (1-D) spectral vectors into two-dimensional (2-D) features: Polygon graph images (CNN-Polygon) and 2-D matrices (CNN-Matrix). The motivations of this study are that (1) the shape of the converted 2-D images is more intuitive for human eyes to interpret when compared to 1-D spectral input; and (2) CNNs are highly specialized and may be able to similarly utilize this information for land cover classification. Four seasonal Landsat 8 images over three study areas-Lake Tapps, Washington, Concord, New Hampshire, USA, and Gwangju, Korea-were used to evaluate the proposed approach for nine land cover classes compared to several other methods: Random forest (RF), support vector machine (SVM), 1-D CNN, and patch-based CNN. Oversampling and undersampling approaches were conducted to examine the effect of the sample size on the model performance. The CNN-Polygon had better performance than the other methods, with overall accuracies of about 93%-95 % for both Concord and Lake Tapps and 80%-84% for Gwangju. The CNN-Polygon particularly performed well when the training sample size was small, less than 200 per class, while the CNN-Matrix resulted in similar or higher performance as sample sizes became larger. The contributing input variables to the models were carefully analyzed through sensitivity analysis based on occlusion maps and accuracy decreases. Our result showed that a more visually intuitive representation of input features for CNN-based classification models yielded higher performance, especially when the training sample size was small. This implies that the proposed graph-based CNNs would be useful for land cover classification where reference data are limited

Convolutional Neural Network-Based Land Cover Classification Using 2-D Spectral Reflectance Curve Graphs With Multitemporal Satellite Imagery

Researchers constantly seek more efficient detection techniques to better utilize enhanced image resolution in accurately detecting and monitoring land cover. Recently, convolutional neural networks (CNNs) have shown high performances comparable to or even better than widely used and adopted machine learning techniques. The aim of this study is to investigate the application of CNNs for land cover classification by using two-dimensional (2-D) spectral curve graphs from multispectral satellite images. The land cover classification was conducted in Concord, New Hampshire, USA, and South Korea by using multispectral images acquired from 30-m Landsat-8 and 500-m Geostationary Ocean Color Imager images. For the construction of input data specific to CNNs, two seasons (winter and summer) of multispectral bands were transformed into 2-D spectral curve graphs for each class. Land cover classification results of CNNs were compared with the results of support vector machines (SVMs) and random forest (RFs). The CNNs model showed higher performance than RFs and SVMs in both study sites. The examination of land cover classification maps demonstrates a good agreement with reference maps, Google Earth images, and existing global scale land cover map, especially for croplands. Using the spectral curve graph could incorporate the phenological cycles on classifying the land cover types. This study shows that the use of a new transformation of spectral bands into a 2-D form for application in CNNs can improve land cover classification performance

Convolutional Neural Network-Based Land Cover Classification Using 2-D Spectral Reflectance Curve Graphs With Multitemporal Satellite Imagery

Researchers constantly seek more efficient detection techniques to better utilize enhanced image resolution in accurately detecting and monitoring land cover. Recently, convolutional neural networks (CNNs) have shown high performances comparable to or even better than widely used and adopted machine learning techniques. The aim of this study is to investigate the application of CNNs for land cover classification by using two-dimensional (2-D) spectral curve graphs from multispectral satellite images. The land cover classification was conducted in Concord, New Hampshire, USA, and South Korea by using multispectral images acquired from 30-m Landsat-8 and 500-m Geostationary Ocean Color Imager images. For the construction of input data specific to CNNs, two seasons (winter and summer) of multispectral bands were transformed into 2-D spectral curve graphs for each class. Land cover classification results of CNNs were compared with the results of support vector machines (SVMs) and random forest (RFs). The CNNs model showed higher performance than RFs and SVMs in both study sites. The examination of land cover classification maps demonstrates a good agreement with reference maps, Google Earth images, and existing global scale land cover map, especially for croplands. Using the spectral curve graph could incorporate the phenological cycles on classifying the land cover types. This study shows that the use of a new transformation of spectral bands into a 2-D form for application in CNNs can improve land cover classification performance.N

Chemokine Signatures in the Skin Disorders of Lyme Borreliosis in Europe: Predominance of CXCL9 and CXCL10 in Erythema Migrans and Acrodermatitis and CXCL13 in Lymphocytoma▿

The three skin disorders of Lyme borreliosis in Europe include erythema migrans, an acute, self-limited lesion; borrelial lymphocytoma, a subacute lesion; and acrodermatitis chronica atrophicans, a chronic lesion. Using quantitative reverse transcription-PCR, we determined mRNA expression of selected chemokines, cytokines, and leukocyte markers in skin samples from 100 patients with erythema migrans, borrelial lymphocytoma, or acrodermatitis chronica atrophicans and from 25 control subjects. Chemokine patterns in lesional skin in each of the three skin disorders included low but significant mRNA levels of the neutrophil chemoattractant CXCL1 and the dendritic cell chemoattractant CCL20 and intermediate levels of the macrophage chemoattractant CCL2. Erythema migrans and particularly acrodermatitis lesions had high mRNA expression of the T-cell-active chemokines CXCL9 and CXCL10 and low levels of the B-cell-active chemokine CXCL13, whereas lymphocytoma lesions had high levels of CXCL13 and lower levels of CXCL9 and CXCL10. This pattern of chemokine expression was consistent with leukocyte marker mRNA in lesional skin. Moreover, using immunohistologic methods, CD3+ T cells and CXCL9 were visualized in erythema migrans and acrodermatitis lesions, and CD20+ B cells and CXCL13 were seen in lymphocytoma lesions. Thus, erythema migrans and acrodermatitis chronica atrophicans have high levels of the T-cell-active chemokines CXCL9 and CXCL10, whereas borrelial lymphocytoma has high levels of the B-cell-active chemokine CXCL13

Salmonella enterica Serovar Typhi O:1,9,12 Polysaccharide-Protein Conjugate as a Diagnostic Tool for Typhoid Fever

Serologic tests play an important role in diagnosis of typhoid fever. In an effort to develop a more defined reagent for these tests, purified Salmonella enterica serovar Typhi (ST) O:1,9,12 polysaccharide was conjugated to human serum albumin (HSA), and the conjugate was purified chromatographically to yield a reagent with 2 moles ST O polysaccharide per mole HSA. In 40 patients with bacteriologically confirmed typhoid fever, significant dot immunobinding titers (≥20,000) were present in 28 (70%) tested with 100 ng of ST O antigen-HSA (ST O-HSA) conjugate, in 38 (95%) tested with 100 ng of ST lipopolysaccharide, and in 16 (40%) tested with purified unconjugated ST O chains. In sera from 22 patients with other nontyphoid fevers, 2 (9.1%) had such reactivities with 100 ng of ST O-HSA, 1 (4.5%) had such reactivity with 100 ng of ST lipopolysaccharide (4.5%), and none reacted with 100 ng of unconjugated ST O chains. None of the 17 healthy-control sera reacted significantly with any of the ST reagents. None of the patient or control sera reacted with unconjugated HSA. The sensitivity of dot immunobinding for typhoid fever was 70% with 100 ng of ST O-HSA, somewhat lower than that with 100 ng of ST lipopolysaccharide (95%) but similar to that of the Widal H agglutination test with a ≥1/160 cutoff (74%). Specificities of these tests were 91%, 95%, and 86%, respectively. These preliminary results suggest that ST O polysaccharide-protein conjugates could provide a nontoxic, easily quality-controlled synthetic reagent for analysis of human immune responses to ST as well as for the development of new diagnostics and vaccines for typhoid fever