Dataset Information

Protein G: β-galactosidase fusion protein for multi-modal bioanalytical applications.

ABSTRACT: β-galactosidase (β-gal) is one of the most prevalent markers of gene expression. Its activity can be monitored via optical and fluorescence microscopy, electrochemistry, and many other ways after slight modification using protein engineering. Here, we have constructed a chimeric version that incorporates a streptococcal protein G domain at the N-terminus of β-gal that binds immunoglobins, namely IgG. This protein G: β-galactosidase fusion enables β-gal-based spectrophotometric and electrochemical measurements of IgG. Moreover, our results show linearity over an industrially relevant range. We demonstrate applicability with rapid spectroelectrochemical detection of IgG in several formats including using an electrochemical sensing interface that is rapidly assembled directly onto electrodes for incorporation into biohybrid devices. The fusion protein enables sensitive, linear, and rapid responses, and in our case, makes IgG measurements quite robust and simple, expanding the molecular diagnostics toolkit for biological measurement.

SUBMITTER: Motabar D

PROVIDER: S-EPMC10078426 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Protein G: β-galactosidase fusion protein for multi-modal bioanalytical applications.

Motabar Dana D Wang Sally S Tsao Chen-Yu CY Payne Gregory F GF Bentley William E WE

Biotechnology progress 20220829 6

β-galactosidase (β-gal) is one of the most prevalent markers of gene expression. Its activity can be monitored via optical and fluorescence microscopy, electrochemistry, and many other ways after slight modification using protein engineering. Here, we have constructed a chimeric version that incorporates a streptococcal protein G domain at the N-terminus of β-gal that binds immunoglobins, namely IgG. This protein G: β-galactosidase fusion enables β-gal-based spectrophotometric and electrochemica ...[more]

PMID: 35976745

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Project description:BackgroundThe information between multimodal magnetic resonance imaging (MRI) is complementary. Combining multiple modalities for brain tumor image segmentation can improve segmentation accuracy, which has great significance for disease diagnosis and treatment. However, different degrees of missing modality data often occur in clinical practice, which may lead to serious performance degradation or even failure of brain tumor segmentation methods relying on full-modality sequences to complete the segmentation task. To solve the above problems, this study aimed to design a new deep learning network for incomplete multimodal brain tumor segmentation.MethodsWe propose a novel cross-modal attention fusion-based deep neural network (CMAF-Net) for incomplete multimodal brain tumor segmentation, which is based on a three-dimensional (3D) U-Net architecture with encoding and decoding structure, a 3D Swin block, and a cross-modal attention fusion (CMAF) block. A convolutional encoder is initially used to extract the specific features from different modalities, and an effective 3D Swin block is constructed to model the long-range dependencies to obtain richer information for brain tumor segmentation. Then, a cross-attention based CMAF module is proposed that can deal with different missing modality situations by fusing features between different modalities to learn the shared representations of the tumor regions. Finally, the fused latent representation is decoded to obtain the final segmentation result. Additionally, channel attention module (CAM) and spatial attention module (SAM) are incorporated into the network to further improve the robustness of the model; the CAM to help focus on important feature channels, and the SAM to learn the importance of different spatial regions.ResultsEvaluation experiments on the widely-used BraTS 2018 and BraTS 2020 datasets demonstrated the effectiveness of the proposed CMAF-Net which achieved average Dice scores of 87.9%, 81.8%, and 64.3%, as well as Hausdorff distances of 4.21, 5.35, and 4.02 for whole tumor, tumor core, and enhancing tumor on the BraTS 2020 dataset, respectively, outperforming several state-of-the-art segmentation methods in missing modalities situations.ConclusionsThe experimental results show that the proposed CMAF-Net can achieve accurate brain tumor segmentation in the case of missing modalities with promising application potential.

Project description:GM1-gangliosidosis is a lysosomal disease resulting from a deficiency in the hydrolase β-galactosidase (β-gal) and subsequent accumulation of gangliosides, primarily in neuronal tissue, leading to progressive neurological deterioration and eventually early death. Lysosomal diseases with neurological involvement have limited non-invasive therapies due to the inability of lysosomal enzymes to cross the blood-brain barrier (BBB). A novel fusion enzyme, labeled mTfR-GLB1, was designed to act as a ferry across the BBB by fusing β-gal to the mouse monoclonal antibody against the mouse transferrin receptor and tested in a murine model of GM1-gangliosidosis (β-gal-/-). Twelve hours following a single intravenous dose of mTfR-GLB1 (5.0 mg/kg) into adult β-gal-/- mice showed clearance of enzyme activity in the plasma and an increase in β-gal enzyme activity in the liver and spleen. Long-term efficacy of mTfR-GLB1 was assessed by treating β-gal-/- mice intravenously twice a week with a low (2.5 mg/kg) or high (5.0 mg/kg) dose of mTfR-GLB1 for 17 weeks. Long-term studies showed high dose mice gained weight normally compared to vehicle-treated β-gal-/- mice, which are significantly heavier than heterozygous controls. Behavioral assessment at six months of age using the pole test showed β-gal-/- mice treated with mTfR-GLB1 had improved motor function. Biochemical analysis showed an increase in β-gal enzyme activity in the high dose group from negligible levels to 20% and 11% of heterozygous levels in the liver and spleen, respectively. Together, these data show that mTfR-GLB1 is a catalytically active β-gal fusion enzyme in vivo that is readily taken up into tissues. Despite these indications of bioactivity, behavior tests other than the pole test, including the Barnes maze, inverted screen, and accelerating rotarod, showed limited or no improvement of treated mice compared to β-gal-/- mice receiving vehicle only. Further, administration of mTfR-GLB1 was insufficient to create measurable increases in β-gal enzyme activity in the brain or reduce ganglioside content (biochemically and morphologically).

Dataset Information

Protein G: β-galactosidase fusion protein for multi-modal bioanalytical applications.

Publications

Protein G: β-galactosidase fusion protein for multi-modal bioanalytical applications.

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