Discovery of a novel class of rosmarinic acid derivatives as antibacterial agents: Synthesis, structure-activity relationship and mechanism of action.

Twenty-seven rosmarinic acid derivatives were synthesized, among which compound RA-N8 exhibited the most potent antibacterial ability. The minimum inhibition concentration of RA-N8 against both S. aureus (ATCC 29213) and MRSA (ATCC BAA41 and ATCC 43300) was found to be 6 µg/mL, and RA-N8 killed E. coli (ATCC 25922) at 3 µg/mL in the presence of polymyxin B nonapeptide (PMBN) which increased the permeability of E. coli. RA-N8 exhibited a weak hemolytic effect at the minimum inhibitory concentration. SYTOX Green assay, SEM, and LIVE/DEAD fluorescence staining assay proved that the mode of action of RA-N8 is targeting bacterial cell membranes. Furthermore, no resistance in wildtype S. aureus developed after incubation with RA-N8 for 20 passages. Cytotoxicity studies further demonstrated that RA-N8 is non-toxic to the human normal cell line (HFF1). RA-N8 also exerted potent inhibitory ability against biofilm formation of S. aureus and even collapsed the shaped biofilm.

Real-Time Biosynthetic Reaction Monitoring Informs the Mechanism of Action of Antibiotics.

The rapid spread of drug-resistant pathogens and the declining discovery of new antibiotics have created a global health crisis and heightened interest in the search for novel antibiotics. Beyond their discovery, elucidating mechanisms of action has necessitated new approaches, especially for antibiotics that interact with lipidic substrates and membrane proteins. Here, we develop a methodology for real-time reaction monitoring of the activities of two bacterial membrane phosphatases, UppP and PgpB. We then show how we can inhibit their activities using existing and newly discovered antibiotics such as bacitracin and teixobactin. Additionally, we found that the UppP dimer is stabilized by phosphatidylethanolamine, which, unexpectedly, enhanced the speed of substrate processing. Overall, our results demonstrate the potential of native mass spectrometry for real-time biosynthetic reaction monitoring of membrane enzymes, as well as their in situ inhibition and cofactor binding, to inform the mode of action of emerging antibiotics.

Detection and complete genome sequence analysis of human adenovirus in children with acute diarrhea in Yunnan, China, 2015-2021.

The aims of this study were to determine the distribution and prevalence of gastroenteritis caused by human adenovirus (HAdV) in children in Yunnan province, China, in 2015-2021 and to identify preventive measures that can be taken to reduce morbidity and mortality in children.HAdV is a significant agent of diarrhea in children, but limited data are available regarding the epidemiology and genetic diversity of HAdV in children with diarrhea in Yunnan province, China. A total of 1754 fecal samples were subjected to real-time RT-PCR to detect and quantify HAdV. Positive samples were further analyzed using next-generation sequencing (NGS), and epidemiological data were analyzed as well.1754 patients with diarrhea were enrolled, of which 1041 were male and 713 were female (M:F ratio: 1.46). Seventy-two stool samples out of 1754 (4.10%) were positive for HAdV. The detection rates of all age groups varied from 2.50-4.78%. The highest incidence of HAdV was observed in children under 2 years of age, especially in children 12-24 months-old. From 2015-2021, the annual detection rate ranged from 1.62-12.26%. HAdV was detected throughout the year, but with marked seasonality. Children were most likely to be positive for HAdV in June and November. We detected HAdV in 15.53% (16/103) of samples collected in June and in 8.19% (14/171) of those collected in November. The entire viral genome was successfully sequenced for 13 of the 72 HAdV-positive samples, and 76.92% (10/13) of these were classified as genotype F41 and 23.08% (3/13) were classified as genotype C2.ConclusionsIn Yunnan province, children of all ages are susceptible to HAdV infection, but there has been marked variation in the yearly prevalence. The highest rate of HAdV detection was in June, followed by November. Priority should be given to disease prevention over the development of targeted antiviral therapies, and effective vaccines for preventing HAdV diarrhea are needed. It is also important to establish a surveillance system to collect relevant clinical and epidemiological data quickly in order to assess the potential risk of HAdV infection in children and to identify epidemic strains for the development of effective vaccines.

Identification of a circRNA-miRNA-mRNA network to explore the effects of circRNAs on renal injury in systemic lupus erythematosus.

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease. At present, the mechanism of non-coding RNA in renal injury in SLE patients is still unclear. A total of 64 DEcircRNAs, 75 DEmiRNAs, and 249 DEmRNAs were identified. We integrated 10 circRNAs, 10 miRNAs, and 88 target mRNAs into a circRNA-miRNA-mRNA network and obtained 9 hub genes (circ-0000006, miR-766-3p, miR-409-3p, miR-339-3p, miR-331-3p, miR-140-3p, miR-186-5p, miR-149-5p, PSME3). The ROC curve results showed that the diagnostic efficiency of 6 hub miRNA was higher than that of has_circ_0000006 and PSEME3. SsGSEA analysis revealed immune cell composition in SLE and control renal tissues, including 3 types of immune cells up-regulated (gamma delta T cell, effector memory CD4 T cell, central memory CD8 T cell) and 4 types down-regulated (memory B cell, mast cell, macrophage, immature dendritic cell, eosinophil) in SLE patients. In addition, PSME3 was negatively correlated with 3 up-regulated immune cells and positively correlated with 4 down-regulated immune cells in SLE patients. Our study provides a deeper understanding of the circRNA-related competing endogenous RNA regulatory mechanism in the renal injury of systemic lupus erythematosus.

Protocol for semisynthesis of serotonylated histone H3 by rapid protein desulfurization in tandem with native chemical ligation.

Here, we present a protocol of rapid protein desulfurization in tandem with native chemical ligation for facile syntheses of proteins with site-specific modifications. We describe using sodium tetraethylborate (NaBEt4) to carry out this desulfurization in an add-and-done manner under ambient conditions without requirement of inert atmosphere protection, UV irradiation, heating, or exogenous thiol additives. Specifically, we detail the semisynthesis of serotonylated histone H3(H3Q5ser) via one-pot ligation desulfurization. This protocol can be applied to synthesize proteins of interest with homogenous post-translational modifications. For complete information on the generation and use of this protocol, please refer to Sun et al. (2022).1.

VASN promotes colorectal cancer progression by activating the YAP/TAZ and AKT signaling pathways via YAP.

Colorectal cancer (CRC) is one of the most common gastrointestinal malignancies. Vasorin (VASN) has been reported to be critical in tumor development and angiogenesis. However, VASN has not been reported in CRC, and its role is unclear. In this study, VASN expression is upregulated in CRC compared with the normal tissues, and VASN expression positively correlates with N stage and poor overall survival by analysis of different datasets and 32 CRC clinicopathologic samples. Overexpression of VASN significantly promotes CRC cell progression, including proliferation, migration, invasion, and epithelial-mesenchymal transition (EMT), while knockdown of VASN inhibits CRC progression. We found that VASN was associated with the YAP/TAZ and PI3K/AKT pathways by gene set enrichment analysis (GSEA) and gene ontology (GO) analysis. Notably, western blotting, immunofluorescence staining and co-immunofluorescence (co-IP) confirmed that VASN could interact with YAP and activate the YAP/TAZ and PTEN/PI3K/AKT pathways, and knockdown of YAP reversed this effect. Importantly, our findings indicate that VASN interacts with YAP to inhibit YAP phosphorylation and stimulates CRC proliferation, migration, and invasion through activation of the YAP/TAZ-TEAD target gene CTGF and PTEN/PI3K/AKT pathways. Our results also show that knockdown of YAP reverses the cellular phenotype induced by increased VASN. In conclusion, our study reveals that VASN acts as an oncogene to stimulate tumor progression in CRC, providing new insights into the molecular mechanisms of CRC development and representing a possible novel biomarker for CRC.

PD-L1/PD-L1 signalling promotes colorectal cancer cell migration ability through RAS/MEK/ERK.

Programmed death ligand 1 (PD-L1) is widely known as an immune checkpoint, and immunotherapy through the inhibition of checkpoint molecules has become an important component in the successful treatment of tumours via programmed death 1 (PD-1)/PD-L1 signalling pathways. However, its biological functions and expression profile in colorectal cancer (CRC) are elusive. We previously found that PD-L1 can bind to PD-L1 and cause cell detachment. However, the detailed molecular mechanisms of how PD-L1 binds to PD-L1 and how it transmits signals to the cell remain unclear. In this study, we disclosed that PD-L1 expression was dramatically upregulated in CRC compared to normal tissues. Ectopic expression of PD-L1 inhibits cell adhesive capacity and promotes cell migration in CRC cell lines, while silencing PD-L1 had the opposite effects and suppressed invasion and proliferation. Mechanistically, PD-L1 was found to promote epithelial-mesenchymal transition (EMT) through the ERK signalling molecule pathway and interacted with the 1-86 aa fragment of KRAS to transduce signals. Collectively, our study demonstrated the role of PD-L1 after binding to PD-L1 in CRC, thereby providing a new theoretical basis for further improving immunotherapy with anti-PD-L1 antibodies.

Overexpression of PD-L1 causes germ cells to slough from mouse seminiferous tubules via the PD-L1/PD-L1 interaction.

Spermatogenesis is a cyclical process in which different generations of spermatids undergo a series of developmental steps at a fixed time and finally produce spermatids. Here, we report that overexpression of PD-L1 (B7 homolog1) in the testis causes sperm developmental disorders and infertility in male mice, with severe malformation and sloughing during spermatid development, characterized by disorganized and collapsed seminiferous epithelium structure. PD-L1 needs to be simultaneously expressed on Sertoli cells and spermatogonia to cause spermatogenesis failure. After that, we excluded the influence of factors such as the PD-L1 receptor and humoral regulation, confirming that PD-L1 has an intrinsic function to interact with PD-L1. Studies have shown that PD-L1 not only serves as a ligand but also plays a receptor-like role in signal transduction. PD-L1 interacts with PD-L1 to affect the adhesive function of germ cells, causing malformation and spermatid sloughing. Taken together, these results indicate that PD-L1 can interact with PD-L1 to cause germ cell detachment and male infertility.

Spinster homolog 2 in cancers, its functions and mechanisms.

Spinster homolog 2 (SPNS2) is a multi-transmembrane transporter, widely located in the cell membrane and organelle membranes. It transports sphingosine-1-phosphate (S1P) into the extracellular space and the circulatory system, thus alters the concentration and the distribution of S1P, sphingosine-1-phosphate receptor (S1PRs) and S1P related enzymes, meaning that it exerts its functions via S1P signaling pathways. Studies also show that ectopic SPNS2 mediates parts of the physiological process of the cells. As of now, SPNS2 has been reported to participate in physiological processes such as angiogenesis, embryonic development, immune response and metabolisms. It is also associated with the transformation from inflammation to cancer as well as the proliferation and metastasis of cancer cells. In this review, we summarize the functions and the mechanisms of SPNS2 in the pathogenesis of cancer to provide new insights for the diagnosis and the treatments of cancer.

Corner Detection Based Automatic Segmentation of Bioresorbable Vascular Scaffold Struts in IVOCT Images.

Bioresorbable Vascular scaffold (BVS) is a promising type of stent in percutaneous coronary intervention. Struts apposition assessment is important to ensure the safety of implanted BVS. Currently, BVS struts apposition analysis in IVOCT images still depends on manual delineation of struts, which is labor intensive and time consuming. Automatic struts segmentation is highly desired to simplify and speed up quantitative analysis. However, it is difficult to segment struts accurately based on the contour, due to the influence of fractures inside strut and blood artifacts around strut. In this paper, a novel framework of automatic struts segmentation based on four corners is introduced, in which priori knowledge is utilized that struts have obvious feature of box-shape. Firstly, a cascaded AdaBoost classifier based on enriched haar-like features is trained to detect struts corners. Then, segmentation result can be obtained based on the four detected corners of each strut. Tested on five pullbacks consisting of 483 images with strut, our novel method achieved an average Dice's coefficient of 0.82 for strut segmentation areas. It concludes that our method can segment struts accurately and robustly. Furthermore, automatic struts malapposition analysis in clinical practice is feasible based on the segmentation results.

Automatic analysis of bioresorbable vascular scaffolds in intravascular optical coherence tomography images.

The bioresorbable vascular scaffold (BVS) is a new generation of bioresorbable scaffold (BRS) for the treatment of coronary artery disease. A potential challenge of BVS is malapposition, which may possibly lead to late stent thrombosis. It is therefore important to conduct malapposition analysis right after stenting. Since an intravascular optical coherence tomography (IVOCT) image sequence contains thousands of BVS struts, manual analysis is labor intensive and time consuming. Computer-based automatic analysis is an alternative, but faces some difficulties due to the interference of blood artifacts and the uncertainty of the struts number, position and size. In this paper, we propose a novel framework for a struts malapposition analysis that breaks down the problem into two steps. Firstly, struts are detected by a cascade classifier trained by AdaBoost and a region of interest (ROI) is determined for each strut to completely contain it. Then, strut boundaries are segmented within ROIs through dynamic programming. Based on the segmentation result, malapposition analysis is conducted automatically. Tested on 7 pullbacks labeled by an expert, our method correctly detected 91.5% of 5821 BVS struts with 12.1% false positives. The average segmentation Dice coefficient for correctly detected struts was 0.81. The time consumption for a pullback is 15 sec on average. We conclude that our method is accurate and efficient for BVS strut detection and segmentation, and enables automatic BVS malapposition analysis in IVOCT images.

Automatic Side Branch Ostium Detection and Main Vascular Segmentation in Intravascular Optical Coherence Tomography Images.

Intravascular optical coherence tomography is the state-of-the-art imaging modality in percutaneous coronary intervention planning and evaluation, in which side branch ostium and main vascular measurements play critical roles. However, manual measurement is time consuming and labor intensive. In this paper, we propose a fully automatic method for side branch ostium detection and main vascular segmentation to make up manual deficiency. In our method, side branch ostium points are first detected and subsequently used to divide the lumen contour into side branch and main vascular regions. Based on the division, main vascular contour is then smoothly fitted for segmentation. In side branch ostium detection, our algorithm creatively converts the definition of curvature into the calculation of the signed included angles in global view, and originally applies a differential filter to highlight the feature of side branch ostium points. A total of 4618 images from 22 pullback runs were used to evaluate the performance of the presented method. The validation results of side branch detection were TPR = 82.8 $\%$, TNR = 98.7$\%$ , PPV = 86.8$\%$, NPV = 98.7$\%$. The average ostial distance error (ODE) was 0.22 mm, and the DSC of main vascular segmentation was 0.96. In conclusion, the qualitative and quantitative evaluation indicated that the presented method is effective and accurate.

Automatic Lumen Segmentation in Intravascular Optical Coherence Tomography Images Using Level Set.

Automatic lumen segmentation from intravascular optical coherence tomography (IVOCT) images is an important and fundamental work for diagnosis and treatment of coronary artery disease. However, it is a very challenging task due to irregular lumen caused by unstable plaque and bifurcation vessel, guide wire shadow, and blood artifacts. To address these problems, this paper presents a novel automatic level set based segmentation algorithm which is very competent for irregular lumen challenge. Before applying the level set model, a narrow image smooth filter is proposed to reduce the effect of artifacts and prevent the leakage of level set meanwhile. Moreover, a divide-and-conquer strategy is proposed to deal with the guide wire shadow. With our proposed method, the influence of irregular lumen, guide wire shadow, and blood artifacts can be appreciably reduced. Finally, the experimental results showed that the proposed method is robust and accurate by evaluating 880 images from 5 different patients and the average DSC value was 98.1% ± 1.1%.

Segmentation of White Blood Cell from Acute Lymphoblastic Leukemia Images Using Dual-Threshold Method.

We propose a dual-threshold method based on a strategic combination of RGB and HSV color space for white blood cell (WBC) segmentation. The proposed method consists of three main parts: preprocessing, threshold segmentation, and postprocessing. In the preprocessing part, we get two images for further processing: one contrast-stretched gray image and one H component image from transformed HSV color space. In the threshold segmentation part, a dual-threshold method is proposed for improving the conventional single-threshold approaches and a golden section search method is used for determining the optimal thresholds. For the postprocessing part, mathematical morphology and median filtering are utilized to denoise and remove incomplete WBCs. The proposed method was tested in segmenting the lymphoblasts on a public Acute Lymphoblastic Leukemia (ALL) image dataset. The results show that the performance of the proposed method is better than single-threshold approach independently performed in RGB and HSV color space and the overall single WBC segmentation accuracy reaches 97.85%, showing a good prospect in subsequent lymphoblast classification and ALL diagnosis.

Label image constrained multiatlas selection.

Multiatlas based method is commonly used in medical image segmentation. In multiatlas based image segmentation, atlas selection and combination are considered as two key factors affecting the performance. Recently, manifold learning based atlas selection methods have emerged as very promising methods. However, due to the complexity of prostate structures in raw images, it is difficult to get accurate atlas selection results by only measuring the distance between raw images on the manifolds. Although the distance between the regions to be segmented across images can be readily obtained by the label images, it is infeasible to directly compute the distance between the test image (gray) and the label images (binary). This paper tries to address this problem by proposing a label image constrained atlas selection method, which exploits the label images to constrain the manifold projection of raw images. Analyzing the data point distribution of the selected atlases in the manifold subspace, a novel weight computation method for atlas combination is proposed. Compared with other related existing methods, the experimental results on prostate segmentation from T2w MRI showed that the selected atlases are closer to the target structure and more accurate segmentation were obtained by using our proposed method.

[Analysis of HA and NA Genes of Influenza A H1N1 Virus in Yunnan Province during 2009-2014].

To analyze influenza pathogen spectrum in Yunnan province during 2009-2014 years, and analyze HA and NA genes of influenza A H1N1. Analysis was made on the monitoring date of influenza cases in Yunnan province in recent 6 years, 23 strains of influenza virus of HA and NA gene was sequenced and analyzed by MEGA 5 software to construct phylogenetic tree. 4 times of influenza AH1N1 epidemic peak were monitored from 2009-2014 years in Yunnan Province, as the nucleic acid detection results of influenza A H1N1 accounted for 28.8% of the total. The sequencing result showed that HA and NA gene were divided into 3 groups, one was detected with H275Y mutation strains. Influenza A H1N1 is one of the important subtypes in Yunnan province and their genes have divided into three branches during the period of 2009-2014 years, the vast majority of influenza a H1N1 are still sensitive to neuraminidase inhibitors.

Segmenting images by combining selected atlases on manifold.

Atlas selection and combination are two critical factors affecting the performance of atlas-based segmentation methods. In the existing works, those tasks are completed in the original image space. However, the intrinsic similarity between the images may not be accurately reflected by the Euclidean distance in this high-dimensional space. Thus, the selected atlases may be away from the input image and the generated template by combining those atlases for segmentation can be misleading. In this paper, we propose to select and combine atlases by projecting the images onto a low-dimensional manifold. With this approach, atlases can be selected according to their intrinsic similarity to the patient image. A novel method is also proposed to compute the weights for more efficiently combining the selected atlases to achieve better segmentation performance. The experimental results demonstrated that our proposed method is robust and accurate, especially when the number of training samples becomes large.