Cell surface-specific N-glycan profiling in breast cancer

Aberrant changes in specific glycans have been shown to be associated with immunosurveillance, tumorigenesis, tumor progression and metastasis. In this study, the N-glycan profiling of membrane proteins from human breast cancer cell lines and tissues was detected using modified DNA sequencer-assisted fluorophore-assisted carbohydrate electrophoresis (DSA-FACE). The N-glycan profiles of membrane proteins were analyzed from 7 breast cancer cell lines and MCF 10A, as well as from 100 pairs of breast cancer and corresponding adjacent tissues. The results showed that, compared with the matched adjacent normal tissue samples, two biantennary N-glycans (NA2 and NA2FB) were significantly decreased (p <0.0001) in the breast cancer tissue samples, while the triantennary glycan (NA3FB) and a high-mannose glycan (M8) were dramatically increased (p = 0.001 and p <0.0001, respectively). Moreover, the alterations in these specific N-glycans occurred through the oncogenesis and progression of breast cancer. These results suggested that the modified method based on DSA-FACE is a high-throughput detection technology that is suited for analyzing cell surface N-glycans. These cell surface-specific N-glycans may be helpful in recognizing the mechanisms of tumor cell immunologic escape and could be potential targets for new breast cancer drugs

2D separation and profiling of complex oligosaccharide mixtures

Oligosaccharides present in various natural sources can contain different linkages, susbstitutions and building blocks with the same mass. Oligosaccharide mixtures often contain these isomeric and isobaric structures, which causes their complexity. These components are difficult to separate and analyze with conventional analytical methods. The technique described below was developed to obtain a higher resolution

High-throughput generation of product profiles for arabinoxylan-active enzymes from metagenomes

Metagenomics is an exciting alternative to seek carbohydrate-active enzymes from a range of sources. Typically, metagenomics reveals dozens of putative catalysts that require functional characterization for further application in industrial processes. High-throughput screening methods compatible with adequate natural substrates are crucial for an accurate functional elucidation of substrate preferences. Based on DNA sequencer-aided fluorophore-assisted carbohydrate electrophoresis (DSA-FACE) analysis of enzymatic-reaction products, we generated product profiles to consequently infer substrate cleavage positions, resulting in the generation of enzymatic-degradation maps. Product profiles were produced in high throughput for arabinoxylan (AX)-active enzymes belonging to the glycoside hydrolase families GH43 (subfamilies 2 [MG432], 7 [MG437], and 28 [MG4328]) and GH8 (MG8) starting from 12 (arabino)xylo-oligosaccharides. These enzymes were discovered through functional metagenomic studies of feces from the North American beaver (Castor canadensis). This work shows how enzyme loading alters the product profiles of all enzymes studied and gives insight into AX degradation patterns, revealing sequential substrate preferences of AX-active enzymes. IMPORTANCE: Arabinoxylan is mainly found in the hemicellulosic fractions of rice straw, corn cobs, and rice husk. Converting arabinoxylan into (arabino)xylooligosaccharides as added-value products that can be applied in food, feed, and cosmetics presents a sustainable and economic alternative for the biorefinery industries. Efficient and profitable AX degradation requires a set of enzymes with particular characteristics. Therefore, enzyme discovery and the study of substrate preferences are of utmost importance. Beavers, as consumers of woody biomass, are a promising source of a repertoire of enzymes able to deconstruct hemicelluloses into soluble oligosaccharides. High-throughput analysis of the oligosaccharide profiles produced by these enzymes will assist in the selection of the most appropriate enzymes for the biorefinery

Face Recognition with Attention Mechanisms

Face recognition has been widely used in people’s daily lives due to its contactless process and high accuracy. Existing works can be divided into two categories: global and local approaches. The mainstream global approaches usually extract features on whole faces. However, global faces tend to suffer from dramatic appearance changes under the scenarios of large pose variations, heavy occlusions, and so on. On the other hand, since some local patches may remain similar, they can play an important role in such scenarios. Existing local approaches mainly rely on cropping local patches around facial landmarks and then extracting corresponding local representations. However, facial landmark detection may be inaccurate or even fail, which would limit their applications. To address this issue, attention mechanisms are applied to automatically locate discriminative facial parts, while suppressing noisy parts. Following this motivation, several models are proposed, including: the Local multi-Scale Convolutional Neural Networks (LS-CNN), Hierarchical Pyramid Diverse Attention (HPDA) networks, Contrastive Quality-aware Attentions (CQA-Face), Diverse and Sparse Attentions (DSA-Face), and Attention Augmented Networks (AAN-Face). Firstly, a novel spatial attention (local aggregation networks, LANet) is proposed to adaptively locate useful facial parts. Meanwhile, different facial parts may appear at different scales due to pose variations and expression changes. In order to solve this issue, LS-CNN are proposed to extract discriminative local information at different scales. Secondly, it is observed that some important facial parts may be neglected, if without a proper guidance. Besides, hierarchical features from different layers are not fully exploited which can contain rich low-level and high-level information. To overcome these two issues, HPDA are proposed. Specifically, a diverse learning is proposed to enlarge the Euclidean distances between each two spatial attention maps, locating diverse facial parts. Besides, hierarchical bilinear pooling is adopted to effectively combine features from different layers. Thirdly, despite the decent performance of the HPDA, the Euclidean distance may not be flexible enough to control the distances between each two attention maps. Further, it is also important to assign different quality scores for various local patches because various facial parts contain information with various importance, especially for faces with heavy occlusions, large pose variations, or quality changes. The CQA-Face is proposed which mainly consists of the contrastive attention learning and quality-aware networks where the former proposes a better distance function to enlarge the distances between each two attention maps and the latter applies a graph convolutional network to effectively learn the relations among different facial parts, assigning higher quality scores for important patches and smaller values for less useful ones. Fourthly, the attention subset problem may occur where some attention maps are subsets of other attention maps. Consequently, the learned facial parts are not diverse enough to cover every facial detail, leading to inferior results. In our DSA-Face model, a new pairwise self-constrastive attention is proposed which considers the complement of subset attention maps in the loss function to address the aforementioned attention subset problem. Moreover, a attention sparsity loss is proposed to suppress the responses around noisy image regions, especially for masked faces. Lastly, in existing popular face datasets, some characteristics of facial images (e.g. frontal faces) are over-represented, while some characteristics (e.g. profile faces) are under-represented. In AAN-Face model, attention erasing is proposed to simulate various occlusion levels. Besides, attention center loss is proposed to control the responses on each attention map, guiding it to focus on the similar facial part. Our works have greatly improved the performance of cross-pose, cross-quality, cross-age, cross-modality, and masked face matching tasks

The impact of elevation of total bilirubin level and etiology of the liver disease on serum N-glycosylation patterns in mice and men

The GlycoFibroTest and GlycoCirrhoTest are noninvasive alternatives for liver biopsy that can be used as a follow-up tool for fibrosis patients and to diagnose cirrhotic patients, respectively. These tests are based on the altered N-glycosylation of total serum protein. Our aim was to investigate the impact of etiology on the alteration of N-glycosylation and whether other characteristics of liver patients could have an influence on N-glycosylation. In human liver patients, no specific alteration could be found to make a distinction according to etiological factor, although alcoholic patients had a significant higher mean value for the GlycoCirrhoTest. Undergalactosylation did not show a significantly different quantitative alteration in the cirrhotic and non-cirrhotic population of all etiologies. Importantly, patients with an elevation of total bilirubin level (>2 mg/dl) had a strong increase of glycans modified with alpha 1-6 fucose. The fucosylation index was therefore significantly higher in fibrosis/cirrhosis and hepatocellular carcinoma patients with elevated total bilirubin levels irrespective of etiology. Furthermore, in a multiple linear regression analysis, only markers for cholestasis significantly correlated with the fucosylation index. In mouse models of chronic liver disease, the fucosylation index was uniquely significantly increased in mice that were induced with a common bile duct ligation. Mice that were chronically injected with CCl4 did not show this increase. Apart from this difference, common changes characteristic to fibrosis development in mice were observed. Finally, mice induced with a partial portal vein ligation did not show biological relevant changes indicating that portal hypertension does not contribute to the alteration of N-glycosylation

Specific N-glycans of Hepatocellular Carcinoma Cell Surface and the Abnormal Increase of Core-α-1, 6-fucosylated Triantennary Glycan via N-acetylglucosaminyltransferases-IVa Regulation

Glycosylation alterations of cell surface proteins are often observed during the progression of malignancies. The specific cell surface N-glycans were profiled in hepatocellular carcinoma (HCC) with clinical tissues (88 tumor and adjacent normal tissues) and the corresponding serum samples of HCC patients. The level of core-α-1,6-fucosylated triantennary glycan (NA3Fb) increased both on the cell surface and in the serum samples of HCC patients (p \u3c 0.01). Additionally, the change of NA3Fb was not influenced by Hepatitis B virus (HBV)and cirrhosis. Furthermore, the mRNA and protein expression of N-acetylglucosaminyltransferase IVa (GnT-IVa), which was related to the synthesis of the NA3Fb, was substantially increased in HCC tissues. Knockdown of GnT-IVa leads to a decreased level of NA3Fb and decreased ability of invasion and migration in HCC cells. NA3Fb can be regarded as a specific cell surface N-glycan of HCC. The high expression of GnT-IVa is the cause of the abnormal increase of NA3Fb on the HCC cell surface, which regulates cell migration. This study demonstrated the specific N-glycans of the cell surface and the mechanisms of altered glycoform related with HCC. These findings lead to better understanding of the function of glycan and glycosyltransferase in the tumorigenesis, progression and metastasis of HCC