N-terminal coding sequence (NCS) influences gene expression by impacting the translation initiation rate. The NCS optimization problem is to find an NCS that maximizes gene expression. The problem is important in genetic engineering. However, current methods for NCS optimization such as rational design and statistics-guided approaches are labor-intensive yield only relatively small improvements. This paper introduces a deep learning/synthetic biology codesigned few-shot training workflow for NCS optimization. Our method utilizes k-nearest encoding followed by word2vec to encode the NCS, then performs feature extraction using attention mechanisms, before constructing a time-series network for predicting gene expression intensity, and finally a direct search algorithm identifies the optimal NCS with limited training data. We took green fluorescent protein (GFP) expressed by Bacillus subtilis as a reporting protein of NCSs, and employed the fluorescence enhancement factor as the metric of NCS optimization. Within just six iterative experiments, our model generated an NCS (MLD62) that increased average GFP expression by 5.41-fold, outperforming the state-of-the-art NCS designs. Extending our findings beyond GFP, we showed that our engineered NCS (MLD62) can effectively boost the production of N-acetylneuraminic acid by enhancing the expression of the crucial rate-limiting GNA1 gene, demonstrating its practical utility. We have open-sourced our NCS expression database and experimental procedures for public use.

N-acetylneuraminic acid is an active ingredient in tonic foods and an important additive in foods and biopharmaceuticals. To address the limitations of existing methods of N-acetylneuraminic acid quantification, we developed an immunoassay based on antibodies induced in hens using artificial antigen, showing high sensitivity and specificity with no cross-reactivity with eight N-acetylneuraminic acid analogues. An IgY-based indirect competitive enzyme-linked immunosorbent assay showed a detection range of 1.14 to 70.08 ng/mL and a limit of detection of 0.57 ng/mL. In spiked samples, recoveries by the indirect competitive enzyme-linked immunosorbent assay ranged from 74.05% to 110.87% compared with HPLC (73.01% to 108.8%). Consistency between the indirect competitive enzyme-linked immunosorbent assay and HPLC was satisfactory (R2 = 0.9736), demonstrating this established immunoassay as a rapid and reliable approach for N-acetylneuraminic acid analysis. The assay described in this study provides an important method for the screening of N-acetylneuraminic acid in biological samples and foodstuffs.

BACKGROUND: Spinal muscular atrophy (SMA) is a neurodegenerative disorder. Although prior studies have investigated the metabolomes of SMA in various contexts, there is a gap in research on cerebrospinal fluid (CSF) metabolomics compared to healthy controls. CSF metabolomics can provide insights into central nervous system function and patient outcomes. This study aims to investigate CSF metabolite profiles in untreated SMA patients to enhance our understanding of SMA metabolic dysregulation.
METHODS: This case control study included 15 SMA patients and 14 control subjects. CSF samples were collected, and untargeted metabolomics was conducted to detect metabolites in SMA and control groups.
RESULTS: A total of 118 metabolites abundance were significantly changed between the SMA and control groups. Of those, 27 metabolites with variable importance for the projection (VIP) ≥ 1.5 were identified. The top 5 differential metabolites were N-acetylneuraminic acid (VIP = 2.38, Fold change = 0.43, P = 5.49 × 10-5), 2,3-dihydroxyindole (VIP = 2.33, Fold change = 0.39, P = 1.81 × 10-4), lumichrome (VIP = 2.30, Fold change = 0.48, P = 7.90 × 10-5), arachidic acid (VIP = 2.23, Fold change = 10.79, P = 6.50 × 10-6), and 10-hydroxydecanoic acid (VIP = 2.23, Fold change = 0.60, P = 1.44 × 10-4). Cluster analysis demonstrated that the differentially metabolites predominantly clustered within two main categories: protein and amino acid metabolism, and lipid metabolism.
CONCLUSIONS: The findings highlight the complexity of SMA, with widespread effects on multiple metabolic pathways, particularly in amino acid and lipid metabolism. N-acetylneuraminic acid may be a potential treatment for functional improvement in SMA. The exact mechanisms and potential therapeutic targets associated with metabolic dysregulation in SMA require further investigation.

Sialylation, a crucial post-translational modification of glycoconjugates, entails the attachment of sialic acid (SA) to the terminal glycans of glycoproteins and glycolipids through a tightly regulated enzymatic process involving various enzymes. This review offers a comprehensive exploration of sialylation within the gut, encompassing its involvement in mucosal protection and its impact on disease progression. The sialylation of mucins and epithelial glycoproteins contributes to the integrity of the intestinal mucosal barrier. Furthermore, sialylation regulates immune responses in the gut, shaping interactions among immune cells, as well as their activation and tolerance. Additionally, the gut microbiota and gut-brain axis communication are involved in the role of sialylation in intestinal health. Altered sialylation patterns have been implicated in various intestinal diseases, including inflammatory bowel disease (IBD), colorectal cancer (CRC), and other intestinal disorders. Emerging research underscores sialylation as a promising avenue for diagnostic, prognostic, and therapeutic interventions in intestinal diseases. Potential strategies such as sialic acid supplementation, inhibition of sialidases, immunotherapy targeting sialylated antigens, and modulation of sialyltransferases have been utilized in the treatment of intestinal diseases. Future research directions will focus on elucidating the molecular mechanisms underlying sialylation alterations, identifying sialylation-based biomarkers, and developing targeted interventions for precision medicine approaches.

Sepsis, a life-threatening syndrome, continues to be a significant public health issue worldwide. Sialylation is a hot potential marker that affects the surface of a variety of cells. However, the role of genes related to sialylation and sepsis has not been fully explored. Bulk RNA-seq data sets (GSE66099 and GSE65682) were obtained from the open-access databases GEO. The classification of sepsis samples into subtypes was achieved by employing the R package \"ConsensusClusterPlus\" on the bulk RNA-seq data. Hub genes were discerned through the application of the R package \"limma\" and univariate regression analysis, with the calculation of risk scores carried out using the R package \"survminer\". To identify the best learning method and construct a prognostic model, we used 21 different combinations of machine learning, and C-index ranking results of these combinations have been showed. ROC curves, time-dependent ROC curves, and Kaplan-Meier curves were utilized to evaluate the diagnostic accuracy of the model. The R packages \"ESTIMATE\" and \"GSVA\" were employed to quantify the fractions of immune cell infiltration in each sample. The bulk RNA-seq samples were categorized into two distinct sepsis subtypes utilizing 14 prognosis-related sialylation genes. A total of 20 differentially expressed genes (DEGs) were identified as being associated with the relationship between sepsis and sialylation. The RSF was used to identify key genes with importance scores higher than 0.01. The nine hub genes (SLA2A1, TMCC2, TFRC, RHAG, FKBP1B, KLF1, PILRA, ARL4A, and GYPA) with the importance values greater than 0.01 was selected for constructing the prognostic model. This research offers some understanding of the relationship between sepsis and sialylation. Besides, it contains one predictive model that might develop into diagnostic biomarkers for sepsis.

Epithelial-mesenchymal transition (EMT) is a complex process that plays a critical role in tumor progression. In this study, we present an EMT sensing panel for the classification of cancer cells at different EMT stages. This sensing panel consists of three types of fluorescent probes based on boronic acid-functionalized carbon-nitride nanosheet (BCN) derivatives. The selective response toward different EMT-associated biomarkers, namely, EpCAM, N-cadherin, and sialic acid (SA), was achieved by conjugating the corresponding antibodies to each BCN derivative, whereas the rare-earth-doping ensures simultaneous sensing of the three biomarkers with fluorescent emission of the three probes at different wavelengths. Sensitive sensing of the three biomarkers was achieved at the protein level with LODs reaching 1.35 ng mL-1 for EpCAM, 1.62 ng mL-1 for N-cadherin, and 1.54 ng mL-1 for SA. The selective response of these biomarkers on the cell surface also facilitated sensitive detection of MCF-7 cells and MDA-MB-231 cells with LODs of 2 cells/mL and 2 cells/mL, respectively. Based on the simultaneous sensing of the three biomarkers on cancer cells that underwent different extents of EMT, precise discrimination and classification of cells at various EMT stages were also achieved with an accuracy of 93.3%. This EMT sensing panel provided a versatile tool for monitoring the EMT evolution process and has the potential to be used for the evaluation of the EMT-targeting therapy and metastasis prediction.

Glycans, particularly sialic acids (SAs), play crucial roles in diverse biological processes. Despite their significance, analyzing specific glycans, such as sialic acids, on individual small extracellular vesicles (sEVs) has remained challenging due to the limited glycan capacity and substantial heterogeneity of sEVs. To tackle this issue, we introduce a chemical modification method of surface SAs on sEVs named PALEV-nFCM, which involves periodate oxidation and aniline-catalyzed oxime ligation (PAL), in conjunction with single-particle analysis using a laboratory-built nano-flow cytometer (nFCM). The specificity of the PALEV labeling method was validated using SA-decorated liposomes, enzymatic removal of terminal SA residues, lectin preblocking, and cellular treatment with an endogenous sialyltransferase inhibitor. Comprehensive mapping of SA distributions was conducted for sEVs derived from different sources, including conditioned cell culture medium (CCCM) of various cell lines, human saliva, and human red blood cells (RBCs). Notably, treatment with the calcium ionophore substantially increases the population of SA-positive RBC sEVs and enhances the SA content on individual RBC sEVs as well. nFCM provides a sensitive and versatile platform for mapping SAs of individual sEVs, which could significantly contribute to resolving the heterogeneity of sEVs and advancing the understanding of their glycosignature.

Tumors have always been a major public health concern worldwide, and attempts to look for effective treatments have never ceased. Sialic acid is known to be a crucial element for tumor development and its receptors are highly expressed on tumor-associated immune cells, which perform significant roles in establishing the immunosuppressive tumor microenvironment and further boosting tumorigenesis, progression, and metastasis. Obviously, it is essential to consider sophisticated crosstalk between tumors, the immune system, and preparations, and understand the links between pharmaceutics and immunology. Sialic acid-based chemoimmunotherapy enables active targeting drug delivery via mediating the recognition between the sialic acid-modified nano-drug delivery system represented by liposomes and sialic acid-binding receptors on tumor-associated immune cells, which inhibit their activity and utilize their homing ability to deliver drugs. Such a \"Trojan horse\" strategy has remarkably improved the shortcomings of traditional passive targeting treatments, unexpectedly promoted tumor shedding, and persistently induced robust immunological memory, thus highlighting its prospective application potential for targeting various tumors. Herein, we review recent advances in sialic acid-based active targeting chemoimmunotherapy to promote tumor shedding, summarize the current viewpoints on the tumor shedding mechanism, especially the formation of durable immunological memory, and analyze the challenges and opportunities of this attractive approach.

The gold standard of milk is human milk, not cow milk. The present study expects to explored the comprehensive nutritional value of different kinds of milk and the differences between them through multi-omics analysis and found functional components that are more similar to human milk. This study employed untargeted LC-MS/MS metabolomics, untargeted LC-MS/MS lipidomics, and 4D label-free proteomics analysis techniques. The findings revealed substantial disparities in metabolites, lipids, and proteins among the five types of milk. Notably, pig milk exhibited a remarkable abundance of N-acetylneuraminic acid (Neu5Ac) and specific polar lipids. Yak milk stood out with significantly elevated levels of creatine and lipoprotein lipase (LPL) compared to other species. Buffalo milk boasted the highest concentrations of L-isoleucine, echinocystic acid, and alkaline phosphatase, tissue-nonspecific isozyme (ALPL). The concentrations of iminostilbene and osteopontin (OPN) were higher in cow milk.

Bacterial nonulosonic acids (NulOs), which feature a nine-carbon backbone, are associated with the biological functions of bacterial glycans. Here, an orthogonally protected 5-amino-7-azido-3,5,7,9-tetradeoxy-d-glycero-l-gluco-2-nonulosonic acid related to Fusobacterium nucleatum ATCC 23726 NulO was synthesized from N-acetylneuraminic acid with sequential performance of C5,7 azidation, C9 deoxygenation, C4 epimerization, and N5,7 differentiation. The C5 azido group in the obtained 5,7-diazido-NulO can be regioselectively reduced to differentiate the two amino groups.