Dual-expression system for blue fluorescent protein optimization

ORP9-PH domain-based fluorescent reporters for visualizing phosphatidylinositol 4-phosphate dynamics in living cells

Fluorescent reporters that visualize phosphatidylinositol 4-phosphate (PI4P) in living cells are indispensable to elucidate the roles of this fundamental lipid in cell physiology. However, currently available PI4P reporters have limitations, such as Golgi-biased localization and low detection sensitivity. Here, we present a series of fluorescent PI4P reporters based on the pleckstrin homology (PH) domain of oxysterol-binding protein-related protein 9 (ORP9). We show that the green fluorescent protein AcGFP1-tagged ORP9-PH domain can be used as a fluorescent PI4P reporter to detect cellular PI4P across its wide distribution at multiple cellular locations, including the plasma membrane (PM), Golgi, endosomes, and lysosomes with high specificity and contrast. We also developed blue, red, and near-infrared fluorescent PI4P reporters suitable for multicolor fluorescence imaging experiments. Finally, we demonstrate the utility of the ORP9-PH domain-based reporter to visualize dynamic changes in the PI4P distribution and level in living cells upon synthetic ER-PM membrane contact manipulation and GPCR stimulation. This work offers a new set of genetically encoded fluorescent PI4P reporters that are practically useful for the study of PI4P biology.

Bacillus subtilis: current and future modification strategies as a protein secreting factory

Bacillus subtilis is regarded as a promising microbial expression system in bioengineering due to its high stress resistance, nontoxic, low codon preference and grow fast. The strain has a relatively efficient expression system, as it has at least three protein secretion pathways and abundant molecular chaperones, which guarantee its expression ability and compatibility. Currently, many proteins are expressed in Bacillus subtilis, and their application prospects are broad. Although Bacillus subtilis has great advantages compared with other prokaryotes related to protein expression and secretion, it still faces deficiencies, such as low wild-type expression, low product activity, and easy gene loss, which limit its large-scale application. Over the years, many researchers have achieved abundant results in the modification of Bacillus subtilis expression systems, especially the optimization of promoters, expression vectors, signal peptides, transport pathways and molecular chaperones. An optimal vector with a suitable promoter strength and other regulatory elements could increase protein synthesis and secretion, increasing industrial profits. This review highlights the research status of optimization strategies related to the expression system of Bacillus subtilis. Moreover, research progress on its application as a food-grade expression system is also presented, along with some future modification and application directions.

Light Control in Microbial Systems

Light is a key environmental component influencing many biological processes, particularly in prokaryotes such as archaea and bacteria. Light control techniques have revolutionized precise manipulation at molecular and cellular levels in recent years. Bacteria, with adaptability and genetic tractability, are promising candidates for light control studies. This review investigates the mechanisms underlying light activation in bacteria and discusses recent advancements focusing on light control methods and techniques for controlling bacteria. We delve into the mechanisms by which bacteria sense and transduce light signals, including engineered photoreceptors and light-sensitive actuators, and various strategies employed to modulate gene expression, protein function, and bacterial motility. Furthermore, we highlight recent developments in light-integrated methods of controlling microbial responses, such as upconversion nanoparticles and optical tweezers, which can enhance the spatial and temporal control of bacteria and open new horizons for biomedical applications.

A short guide on blue fluorescent proteins: limits and perspectives

The advent of the so-called colorful biology era is in line with the discovery of fluorescent proteins (FPs), which can be widely used to detect the intracellular locations of macromolecules or to determine the abundance of metabolites in organelles. The application of multiple FPs that emit different spectra and colors could be implemented to precisely evaluate cellular events. FPs were initially established with the emergence of the green fluorescent protein (GFP) from jellyfish. Red fluorescent proteins (RFPs) from marine anemones and several corals adopt fluorescent chromophores that are similar to GFP. Chromophores of GFP and GFP-like FPs are formed through the oxidative rearrangement of three chromophore-forming residues, thereby limiting their application to only oxidative environments. Alternatively, some proteins can be fluorescent upon their interaction with cellular prosthetic cofactors and, thus, work in aerobic and anaerobic conditions. The modification of an NADPH-dependent blue fluorescent protein (BFP) also expanded its application to the quantization of NADPH in the cellular environment. However, cofactor-dependent BFPs have an intrinsic weakness of poor photostability with a high fluorescent background. This review explores GFP-derived and NADPH-dependent BFPs with a focus on NADPH-dependent BFPs, which might be technically feasible in the near future upon coupling with two-photon fluorescence microscopy or nucleic acid-mimickers. KEY POINTS: • Oxidation-dependent GFP-like BFPs and redox-free NADPH-dependent BFPs • GFPs of weak photostability and intensity with a high fluorescent background • Real-time imaging using mBFP under two-photon fluorescence microscopy.

YTnC2, an improved genetically encoded green calcium indicator based on toadfish troponin C

Genetically encoded calcium indicators based on truncated troponin C are attractive probes for calcium imaging due to their relatively small molecular size and twofold reduced calcium ion buffering. However, the best-suited members of this family, YTnC and cNTnC, suffer from low molecular brightness, limited dynamic range, and/or poor sensitivity to calcium transients in neurons. To overcome these limitations, we developed an enhanced version of YTnC, named YTnC2. Compared with YTnC, YTnC2 had 5.7-fold higher molecular brightness and 6.4-fold increased dynamic range in vitro. YTnC2 was successfully used to reveal calcium transients in the cytosol and in the lumen of mitochondria of both mammalian cells and cultured neurons. Finally, we obtained and analyzed the crystal structure of the fluorescent domain of the YTnC2 mutant.

Quantitative assessment of near-infrared fluorescent proteins

Recent progress in fluorescent protein development has generated a large diversity of near-infrared fluorescent proteins (NIR FPs), which are rapidly becoming popular probes for a variety of imaging applications. However, the diversity of NIR FPs poses a challenge for end-users in choosing the optimal one for a given application. Here we conducted a systematic and quantitative assessment of intracellular brightness, photostability, oligomeric state, chemical stability and cytotoxicity of 22 NIR FPs in cultured mammalian cells and primary mouse neurons and identified a set of top-performing FPs including emiRFP670, miRFP680, miRFP713 and miRFP720, which can cover a majority of imaging applications. The top-performing proteins were further validated for in vivo imaging of neurons in Caenorhabditis elegans, zebrafish, and mice as well as in mice liver. We also assessed the applicability of the selected NIR FPs for multicolor imaging of fusions, expansion microscopy and two-photon imaging.

Application of Entity-BERT model based on neuroscience and brain-like cognition in electronic medical record entity recognition

Introduction

In the medical field, electronic medical records contain a large amount of textual information, and the unstructured nature of this information makes data extraction and analysis challenging. Therefore, automatic extraction of entity information from electronic medical records has become a significant issue in the healthcare domain.

Methods

To address this problem, this paper proposes a deep learning-based entity information extraction model called Entity-BERT. The model aims to leverage the powerful feature extraction capabilities of deep learning and the pre-training language representation learning of BERT(Bidirectional Encoder Representations from Transformers), enabling it to automatically learn and recognize various entity types in medical electronic records, including medical terminologies, disease names, drug information, and more, providing more effective support for medical research and clinical practices. The Entity-BERT model utilizes a multi-layer neural network and cross-attention mechanism to process and fuse information at different levels and types, resembling the hierarchical and distributed processing of the human brain. Additionally, the model employs pre-trained language and sequence models to process and learn textual data, sharing similarities with the language processing and semantic understanding of the human brain. Furthermore, the Entity-BERT model can capture contextual information and long-term dependencies, combining the cross-attention mechanism to handle the complex and diverse language expressions in electronic medical records, resembling the information processing method of the human brain in many aspects. Additionally, exploring how to utilize competitive learning, adaptive regulation, and synaptic plasticity to optimize the model's prediction results, automatically adjust its parameters, and achieve adaptive learning and dynamic adjustments from the perspective of neuroscience and brain-like cognition is of interest.

Results and discussion

Experimental results demonstrate that the Entity-BERT model achieves outstanding performance in entity recognition tasks within electronic medical records, surpassing other existing entity recognition models. This research not only provides more efficient and accurate natural language processing technology for the medical and health field but also introduces new ideas and directions for the design and optimization of deep learning models.

Chaotic medical image encryption method using attention mechanism fusion ResNet model

Introduction

With the rapid advancement of artificial intelligence (AI) technology, the protection of patient medical image privacy and security has become a critical concern in current research on image privacy protection. However, traditional methods for encrypting medical images have faced criticism due to their limited flexibility and inadequate security. To overcome these limitations, this study proposes a novel chaotic medical image encryption method, called AT-ResNet-CM, which incorporates the attention mechanism fused with the ResNet model.

Methods

The proposed method utilizes the ResNet model as the underlying network for constructing the encryption and decryption framework. The ResNet's residual structure and jump connections are employed to effectively extract profound information from medical images and expedite the model's convergence. To enhance security, the output of the ResNet model is encrypted using a logistic chaotic system, introducing randomness and complexity to the encryption process. Additionally, an attention mechanism is introduced to enhance the model's response to the region of interest within the medical image, thereby strengthening the security of the encrypted network.

Results

Experimental simulations and analyses were conducted to evaluate the performance of the proposed approach. The results demonstrate that the proposed method outperforms alternative models in terms of encryption effectiveness, as indicated by a horizontal correlation coefficient of 0.0021 and information entropy of 0.9887. Furthermore, the incorporation of the attention mechanism significantly improves the encryption performance, reducing the horizontal correlation coefficient to 0.0010 and increasing the information entropy to 0.9965. These findings validate the efficacy of the proposed method for medical image encryption tasks, as it offers enhanced security and flexibility compared to existing approaches.

Discussion

In conclusion, the AT-ResNet-CM method presents a promising solution to address the limitations of traditional encryption techniques in protecting patient medical images. By leveraging the attention mechanism fused with the ResNet model, the method achieves improved security and flexibility. The experimental results substantiate the superiority of the proposed method in terms of encryption effectiveness, horizontal correlation coefficient, and information entropy. The proposed method not only addresses the shortcomings of traditional methods but also provides a more robust and reliable approach for safeguarding patient medical image privacy and security.

Enhanced fluorescent imaging of proteins in live yeast cells using fluorescently labeled scFv

Fluorescent labeling of proteins is a widespread approach for the microscopic examination of protein function, expression, and localization in the cell. Here, we present a protocol for the labeling of hemagglutinin (HA)-tagged protein of interest (POI) with the single-chain antibody (scFv) 2E2 fused to different fluorescent proteins (FPs) in Saccharomyces cerevisiae. We describe steps for expressing 2E2-FP, and HA tagging and labeling of POI. We detail in vivo fluorescent imaging of proteins at different cellular compartments and with diverse expression levels. For complete details on the use and execution of this protocol, please refer to Tsirkas et al. (2022).¹.

Fluorescent proteins and genetically encoded biosensors

The genetically encoded fluorescent sensors convert chemical and physical signals into light. They are powerful tools for the visualisation of physiological processes in living cells and freely moving animals. The fluorescent protein is the reporter module of a genetically encoded biosensor. In this study, we first review the history of the fluorescent protein in full emission spectra on a structural basis. Then, we discuss the design of the genetically encoded biosensor. Finally, we briefly review several major types of genetically encoded biosensors that are currently widely used based on their design and molecular targets, which may be useful for the future design of fluorescent biosensors.

Enhanced small green fluorescent proteins as a multisensing platform for biosensor development

Engineered light, oxygen, and voltage (LOV)-based proteins are able to fluoresce without oxygen requirement due to the autocatalytic incorporation of exogenous flavin as a chromophore thus allowing for live cell imaging under hypoxic and anaerobic conditions. They were also discovered to have high sensitivity to transition metal ions and physiological flavin derivatives. These properties make flavin-binding fluorescent proteins (FPs) a perspective platform for biosensor development. However, brightness of currently available flavin-binding FPs is limited compared to GFP-like FPs creating a need for their further enhancement and optimization. In this study, we applied a directed molecular evolution approach to develop a pair of flavin-binding FPs, named miniGFP1 and miniGFP2. The miniGFP proteins are characterized by cyan-green fluorescence with excitation/emission maxima at 450/499 nm and a molecular size of ∼13 kDa. We carried out systematic benchmarking of miniGFPs in Escherichia coli and cultured mammalian cells against spectrally similar FPs including GFP-like FP, bilirubin-binding FP, and bright flavin-binding FPs. The miniGFPs proteins exhibited improved photochemical properties compared to other flavin-binding FPs enabling long-term live cell imaging. We demonstrated the utility of miniGFPs for live cell imaging in bacterial culture under anaerobic conditions and in CHO cells under hypoxia. The miniGFPs’ fluorescence was highly sensitive to Cu(II) ions in solution with K_d values of 67 and 68 nM for miniGFP1 and miniGFP2, respectively. We also observed fluorescence quenching of miniGFPs by the reduced form of Cu(I) suggesting its potential application as an optical indicator for Cu(I) and Cu(II). In addition, miniGFPs showed the ability to selectively bind exogenous flavin mononucleotide demonstrating a potential for utilization as a selective fluorescent flavin indicator. Altogether, miniGFPs can serve as a multisensing platform for fluorescence biosensor development for in vitro and in-cell applications.