We still know very little about how the human immune system responds to SARS-CoV-2. Here we construct a SARS-CoV-2 proteome microarray containing 18 out of the 28 predicted proteins and apply it to ...the characterization of the IgG and IgM antibodies responses in the sera from 29 convalescent patients. We find that all these patients had IgG and IgM antibodies that specifically bind SARS-CoV-2 proteins, particularly the N protein and S1 protein. Besides these proteins, significant antibody responses to ORF9b and NSP5 are also identified. We show that the S1 specific IgG signal positively correlates with age and the level of lactate dehydrogenase (LDH) and negatively correlates with lymphocyte percentage. Overall, this study presents a systemic view of the SARS-CoV-2 specific IgG and IgM responses and provides insights to aid the development of effective diagnostic, therapeutic and vaccination strategies.
Background
Accurate glioma grading plays an important role in the clinical management of patients and is also the basis of molecular stratification nowadays.
Purpose/Hypothesis
To verify the ...superiority of radiomics features extracted from multiparametric MRI to glioma grading and evaluate the grading potential of different MRI sequences or parametric maps.
Study Type
Retrospective; radiomics.
Population
A total of 153 patients including 42, 33, and 78 patients with Grades II, III, and IV gliomas, respectively.
Field Strength/Sequence
3.0T MRI/T1‐weighted images before and after contrast‐enhanced, T2‐weighted, multi‐b‐value diffusion‐weighted and 3D arterial spin labeling images.
Assessment
After multiparametric MRI preprocessing, high‐throughput features were derived from patients' volumes of interests (VOIs). The support vector machine‐based recursive feature elimination was adopted to find the optimal features for low‐grade glioma (LGG) vs. high‐grade glioma (HGG), and Grade III vs. IV glioma classification tasks. Then support vector machine (SVM) classifiers were established using the optimal features. The accuracy and area under the curve (AUC) was used to assess the grading efficiency.
Statistical Tests
Student's t‐test or a chi‐square test were applied on different clinical characteristics to confirm whether intergroup significant differences exist.
Results
Patients' ages between LGG and HGG groups were significantly different (P < 0.01). For each patient, 420 texture and 90 histogram parameters were derived from 10 VOIs of multiparametric MRI. SVM models were established using 30 and 28 optimal features for classifying LGGs from HGGs and grades III from IV, respectively. The accuracies/AUCs were 96.8%/0.987 for classifying LGGs from HGGs, and 98.1%/0.992 for classifying grades III from IV, which were more promising than using histogram parameters or using the single sequence MRI.
Data Conclusion
Texture features were more effective for noninvasively grading gliomas than histogram parameters. The combined application of multiparametric MRI provided a higher grading efficiency. The proposed radiomic strategy could facilitate clinical decision‐making for patients with varied glioma grades.
Level of Evidence: 3
Technical Efficacy: Stage 2
J. Magn. Reson. Imaging 2018;48:1518–1528
The collagenases of Vibrio species, many of which are pathogens, have been regarded as an important virulence factor. However, there is little information on the structure and collagenolytic ...mechanism of Vibrio collagenase. Here, we report the crystal structure of the collagenase module (CM) of Vibrio collagenase VhaC and the conformation of VhaC in solution. Structural and biochemical analyses and molecular dynamics studies reveal that triple-helical collagen is initially recognized by the activator domain, followed by subsequent cleavage by the peptidase domain along with the closing movement of CM. This is different from the peptidolytic mode or the proposed collagenolysis of Clostridium collagenase. We propose a model for the integrated collagenolytic mechanism of VhaC, integrating the functions of VhaC accessory domains and its collagen degradation pattern. This study provides insight into the mechanism of bacterial collagenolysis and helps in structure-based drug design targeting of the Vibrio collagenase.
Predator-prey interactions play important roles in the cycling of marine organic matter. Here we show that a Gram-negative bacterium isolated from marine sediments (Pseudoalteromonas sp. strain ...CF6-2) can kill Gram-positive bacteria of diverse peptidoglycan (PG) chemotypes by secreting the metalloprotease pseudoalterin. Secretion of the enzyme requires a Type II secretion system. Pseudoalterin binds to the glycan strands of Gram positive bacterial PG and degrades the PG peptide chains, leading to cell death. The released nutrients, including PG-derived D-amino acids, can then be utilized by strain CF6-2 for growth. Pseudoalterin synthesis is induced by PG degradation products such as glycine and glycine-rich oligopeptides. Genes encoding putative pseudoalterin-like proteins are found in many other marine bacteria. This study reveals a new microbial interaction in the ocean.
To fully decipher the immunogenicity of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Spike protein, it is essential to assess which part is highly immunogenic in a systematic way. ...We generate a linear epitope landscape of the Spike protein by analyzing the serum immunoglobulin G (IgG) response of 1,051 coronavirus disease 2019 (COVID-19) patients with a peptide microarray. We reveal two regions rich in linear epitopes, i.e., C-terminal domain (CTD) and a region close to the S2′ cleavage site and fusion peptide. Unexpectedly, we find that the receptor binding domain (RBD) lacks linear epitope. We reveal that the number of responsive peptides is highly variable among patients and correlates with disease severity. Some peptides are moderately associated with severity and clinical outcome. By immunizing mice, we obtain linear-epitope-specific antibodies; however, no significant neutralizing activity against the authentic virus is observed for these antibodies. This landscape will facilitate our understanding of SARS-CoV-2-specific humoral responses and might be useful for vaccine refinement.
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•A linear epitope landscape of the SARS-CoV-2 Spike from 1,051 COVID-19 patients•Responsive epitopes are highly variable among patients and correlate with severity•The RBD lacks linear epitopes, but two other regions are rich in linear epitopes•Little neutralization activity is observed for the linear-epitope-elicited antibodies
Li et al. construct a B cell linear epitope landscape of SARS-CoV-2 Spike protein, based on a large cohort of COVID-19 patients. The epitope responses were related to disease severity and outcome but mainly elicit non-neutralizing antibodies.
Background
The missing asymptomatic COVID‐19 infections have been overlooked because of the imperfect sensitivity of the nucleic acid testing (NAT). Globally understanding the humoral immunity in ...asymptomatic carriers will provide scientific knowledge for developing serological tests, improving early identification, and implementing more rational control strategies against the pandemic.
Measure
Utilizing both NAT and commercial kits for serum IgM and IgG antibodies, we extensively screened 11 766 epidemiologically suspected individuals on enrollment and 63 asymptomatic individuals were detected and recruited. Sixty‐three healthy individuals and 51 mild patients without any preexisting conditions were set as controls. Serum IgM and IgG profiles were further probed using a SARS‐CoV‐2 proteome microarray, and neutralizing antibody was detected by a pseudotyped virus neutralization assay system. The dynamics of antibodies were analyzed with exposure time or symptoms onset.
Results
A combination test of NAT and serological testing for IgM antibody discovered 55.5% of the total of 63 asymptomatic infections, which significantly raises the detection sensitivity when compared with the NAT alone (19%). Serum proteome microarray analysis demonstrated that asymptomatics mainly produced IgM and IgG antibodies against S1 and N proteins out of 20 proteins of SARS‐CoV‐2. Different from strong and persistent N‐specific antibodies, S1‐specific IgM responses, which evolved in asymptomatic individuals as early as the seventh day after exposure, peaked on days from 17 days to 25 days, and then disappeared in two months, might be used as an early diagnostic biomarker. 11.8% (6/51) mild patients and 38.1% (24/63) asymptomatic individuals did not produce neutralizing antibody. In particular, neutralizing antibody in asymptomatics gradually vanished in two months.
Conclusion
Our findings might have important implications for the definition of asymptomatic COVID‐19 infections, diagnosis, serological survey, public health, and immunization strategies.
The combination of NAT and serological testing for IgM antibody significantly improves the detection sensitivity of asymptomatic COVID‐19 infections, compared with NAT alone. S1‐specific IgM antibody response with rapid emergence and disappearance might be helpful to assist NAT for early identification of infectious individuals. A majority of asymptomatics induce very low levels of neutralizing antibody that disappear in two months. Abbreviations: NAT, nucleic acid testing; FI, fluorescence intensity; NT50, half‐maximal neutralizing titer.
Nitrogen is one of the most important nutrients needed for plants and algae to survive, and the photosynthetic ability of algae is related to nitrogen abundance. Red algae are unique photosynthetic ...eukaryotic organisms in the evolution of algae, as they contain phycobilisomes (PBSs) on their thylakoid membranes. In this report, the in vivo chlorophyll (Chl) a fluorescence kinetics of nitrogen-starved Porphyridium cruentum were analyzed to determine the effects of nitrogen deficiency on photosynthetic performance using a multi-color pulse amplitude modulation (PAM) chlorophyll fluorometer. Due to nitrogen starvation, the photochemical efficiency of PSII and the activity of PSII reaction centers (RCs) decreased, and photoinhibition of PSII occurred. The water-splitting system on the donor side of PSII was seriously impacted by nitrogen deficiency, leading to the inactivation of the oxygen-evolving complex (OEC) and decreased light energy conversion efficiency. In nitrogen-starved cells, a higher proportion of energy was used for photochemical reactions, and thermal dissipation was reduced, as shown by qP and qN. The ability of nitrogen-starved cells to tolerate and resist high photon flux densities was weakened. Our results showed that the photosynthetic performance of P. cruentum was severely impacted by nitrogen deficiency.
Accurate glioma grading before surgery is of the utmost importance in treatment planning and prognosis prediction. But previous studies on magnetic resonance imaging (MRI) images were not effective ...enough. According to the remarkable performance of convolutional neural network (CNN) in medical domain, we hypothesized that a deep learning algorithm can achieve high accuracy in distinguishing the World Health Organization (WHO) low grade and high grade gliomas.
One hundred and thirteen glioma patients were retrospectively included. Tumor images were segmented with a rectangular region of interest (ROI), which contained about 80% of the tumor. Then, 20% data were randomly selected and leaved out at patient-level as test dataset. AlexNet and GoogLeNet were both trained from scratch and fine-tuned from models that pre-trained on the large scale natural image database, ImageNet, to magnetic resonance images. The classification task was evaluated with five-fold cross-validation (CV) on patient-level split.
The performance measures, including validation accuracy, test accuracy and test area under curve (AUC), averaged from five-fold CV of GoogLeNet which trained from scratch were 0.867, 0.909, and 0.939, respectively. With transfer learning and fine-tuning, better performances were obtained for both AlexNet and GoogLeNet, especially for AlexNet. Meanwhile, GoogLeNet performed better than AlexNet no matter trained from scratch or learned from pre-trained model.
In conclusion, we demonstrated that the application of CNN, especially trained with transfer learning and fine-tuning, to preoperative glioma grading improves the performance, compared with either the performance of traditional machine learning method based on hand-crafted features, or even the CNNs trained from scratch.
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