Labeling Approach Research Articles

Mapping Start Codons of Small Open Reading Frames by N-Terminomics Approach

sORF-encoded peptides (SEPs) refer to proteins encoded by small open reading frames (sORFs) with a length of less than 100 amino acids, which play an important role in various life activities. Analysis of known SEPs showed that using non-canonical initiation codons of SEPs was more common. However, the current analysis of SEP sequences mainly relies on bioinformatics prediction, and most of them use AUG as the start site, which may not be completely correct for SEPs. Chemical labeling was used to systematically analyze the N-terminal sequences of SEPs to accurately define the start sites of SEPs. By comparison, we found that dimethylation and guanidinylation are more efficient than acetylation. The ACN precipitation and heating precipitation performed better in SEP enrichment. As an N-terminal peptide enrichment material, Hexadhexaldehyde was superior to CNBr-activated agarose and NHS-activated agarose. Combining these methods, we identified 128 SEPs with 131 N-terminal sequences. Among them, two-thirds are novel N-terminal sequences, and most of them start from the 11-31st amino acids of the original sequence. Partial novel N-termini were produced by proteolysis or signal peptide removal. Some SEPs' transcription start sites were corrected to be non-AUG start codons. One novel start codon was validated using GFP-tag vectors. These results demonstrated that the chemical labeling approaches would be beneficial for identifying the start codons of sORFs and the real N-terminal of their encoded peptides, which helps better understand the characterization of SEPs.

Demonstration-based learning for few-shot biomedical named entity recognition under machine reading comprehension

Objective: Although deep learning techniques have shown significant achievements, they frequently depend on extensive amounts of hand-labeled data and tend to perform inadequately in few-shot scenarios. The objective of this study is to devise a strategy that can improve the model’s capability to recognize biomedical entities in scenarios of few-shot learning.Methods: By redefining biomedical named entity recognition (BioNER) as a machine reading comprehension (MRC) problem, we propose a demonstration-based learning method to address few-shot BioNER, which involves constructing appropriate task demonstrations. In assessing our proposed method, we compared the proposed method with existing advanced methods using six benchmark datasets, including BC4CHEMD, BC5CDR-Chemical, BC5CDR-Disease, NCBI-Disease, BC2GM, and JNLPBA.Results: We examined the models’ efficacy by reporting F1 scores from both the 25-shot and 50-shot learning experiments. In 25-shot learning, we observed 1.1% improvements in the average F1 scores compared to the baseline method, reaching 61.7%, 84.1%, 69.1%, 70.1%, 50.6%, and 59.9% on six datasets, respectively. In 50-shot learning, we further improved the average F1 scores by 1.0% compared to the baseline method, reaching 73.1%, 86.8%, 76.1%, 75.6%, 61.7%, and 65.4%, respectively.Conclusion: We reported that in the realm of few-shot learning BioNER, MRC-based language models are much more proficient in recognizing biomedical entities compared to the sequence labeling approach. Furthermore, our MRC-language models can compete successfully with fully-supervised learning methodologies that rely heavily on the availability of abundant annotated data. These results highlight possible pathways for future advancements in few-shot BioNER methodologies.

Graph-Based Semi-Supervised Learning with Bipartite Graph for Large-Scale Data and Prediction of Unseen Data

Recently, considerable attention has been directed toward graph-based semi-supervised learning (GSSL) as an effective approach for data labeling. Despite the progress achieved by current methodologies, several limitations persist. Firstly, many studies treat all samples equally in terms of weight and influence, disregarding the potential increased importance of samples near decision boundaries. Secondly, the detection of outlier-labeled data is crucial, as it can significantly impact model performance. Thirdly, existing models often struggle with predicting labels for unseen test data, restricting their utility in practical applications. Lastly, most graph-based algorithms rely on affinity matrices that capture pairwise similarities across all data points, thus limiting their scalability to large-scale databases. In this paper, we propose a novel GSSL algorithm tailored for large-scale databases, leveraging anchor points to mitigate the challenges posed by large affinity matrices. Additionally, our method enhances the influence of nodes near decision boundaries by assigning different weights based on their importance and using a mapping function from feature space to label space. Leveraging this mapping function enables direct label prediction for test samples without requiring iterative learning processes. Experimental evaluations on two extensive datasets (Norb and Covtype) demonstrate that our approach is scalable and outperforms existing GSSL methods in terms of performance metrics.

Real-Time Lithology Prediction at the Bit Using Machine Learning

Real-time drilling analysis requires knowledge of lithology at the drill bit. However, logging-while-drilling (LWD) sensors in the bottom hole assembly (BHA) are usually positioned 2–50 m (7–164 ft) above the bit (called the sensor offset), leading to a delay in real-time drilling analysis. The current industry solution to overcome this delay involves stopping drilling to perform a bottoms-up circulation for cuttings evaluation—a process that is both time-consuming and costly. To address this issue, our study evaluates three methodologies for real-time lithology prediction at the bit using drilling and petrophysical parameters. The first method employs a petrophysical approach, which involves using bulk density and neutron porosity predicted at the bit. The second method combines unsupervised and supervised machine learning (ML) for prediction. The third method employs classification algorithms on manually labeled lithology data from mud log reports, a novel approach used in this work. Our results show varying degrees of success: the bulk density versus neutron porosity cross-plot method achieved an accuracy of 58% with blind-well test data; the ML approach improved accuracy to 66%; and the Random Forest (RF) classification with manual labeling significantly increased accuracy to 86%. This comparative analysis of three different methodologies for lithology prediction has not been previously explored in the literature. While clustering and classification methods have been regarded as the most effective, our study demonstrates that they do not always yield the best result. These findings demonstrate that ML models, particularly the manual labeling approach, substantially outperform the petrophysical method. This new algorithm, designed for real-time applications, uses selected input parameters to effectively minimize problems associated with the sensor offset of LWD tools. It rapidly adapts to changes, offering a quicker and more cost-effective interpretation of lithology. This eliminates the need for time-consuming bottoms-up circulation to evaluate cuttings. Ultimately, this approach enhances drilling efficiency and significantly improves the accuracy of lithology prediction, notably in identifying interbedded geological layers.

Imaging and proteomics toolkits for studying organelle contact sites.

Organelle contact sites are regions where two heterologous membranes are juxtaposed by molecular tethering complexes. These contact sites are important in inter-organelle communication and cellular functional integration. However, visualizing these minute foci and identifying contact site proteomes have been challenging. In recent years, fluorescence-based methods have been developed to visualize the dynamic physical interaction of organelles while proximity labeling approaches facilitate the profiling of proteomes at contact sites. In this review, we explain the design principle for these contact site reporters: a dual-organelle interaction mechanism based on how endogenous tethers and/or tethering complexes localize to contact sites. We classify the contact site reporters into three categories: (i) single-protein systems, (ii) two-component systems with activated reporter signal upon organelle proximity, and (iii) reporters for contact site proteomes. We also highlight advanced imaging analysis with high temporal-spatial resolution and the use of machine-learning algorithms for detecting contact sites.

RAF-AG: Report analysis framework for attack path generation

Information sharing is a key practice in cybersecurity for coping with the ever-changing cyberattacks that are targeting computer systems. Thus, when cyber incidents happen, cyber threat intelligence (CTI) reports are prepared and shared among cybersecurity practitioners to help them get up-to-date information about those incidents. However, reading and analyzing the report text to comprehend the included information is a cumbersome process. Although techniques based on deep learning were proposed to speed up report analysis in order to obtain the enclosed essential information, such as attack path, training data insufficiency makes these methods inefficient in practical circumstances.This paper presents RAF-AG, a report analysis framework for attack path generation. To analyze CTI reports, RAF-AG utilizes the sentence dependency tree for entity and relation extraction, and a weak supervision approach for entity labeling. This is followed by graph building and graph alignment for generating the attack paths. Our approach resolves the data insufficiency problem in the cybersecurity domain by lowering the need for expert involvement. We evaluated RAF-AG by comparing the generated attack paths with those produced by AttacKG, a state-of-the-art automatic report analysis framework. RAF-AG was able to identify cyberattack steps by matching their appearance order inside the report, and link them with techniques from the MITRE ATT&CK knowledge base with an improved F1 score compared to AttacKG (0.708 versus 0.393).

Factors to consider before choosing EV labeling method for fluorescence-based techniques.

A well-designed fluorescence-based analysis of extracellular vesicles (EV) can provide insights into the size, morphology, and biological function of EVs, which can be used in medical applications. Fluorescent nanoparticle tracking analysis with appropriate controls can provide reliable data for size and concentration measurements, while nanoscale flow cytometry is the most appropriate tool for characterizing molecular cargoes. Label selection is a crucial element in all fluorescence methods. The most comprehensive data can be obtained if several labeling approaches for a given marker are used, as they would provide complementary information about EV populations and interactions with the cells. In all EV-related experiments, the influence of lipoproteins and protein corona on the results should be considered. By reviewing and considering all the factors affecting EV labeling methods used in fluorescence-based techniques, we can assert that the data will provide as accurate as possible information about true EV biology and offer precise, clinically applicable information for future EV-based diagnostic or therapeutic applications.

Quantification of persulfidation on specific proteins: are we nearly there yet?

Hydrogen sulfide (H2S) played a pivotal role in the early evolution of life on Earth before the predominance of atmospheric oxygen. The legacy of a persistent role for H2S in life's processes recently emerged through its discovery in modern biochemistry as an endogenous cellular signalling modulator involved in numerous biological processes. One major mechanism through which H2S signals is protein cysteine persulfidation, an oxidative post-translational modification. In recent years, chemoproteomic technologies have been developed to allow the global scanning of protein persulfidation targets in mammalian cells and tissues, providing a powerful tool to elucidate the broader impact of altered H2S in organismal physiological health and human disease states. While hundreds of proteins were confirmed to be persulfidated by global persulfidome methodologies, the targeting of specific proteins of interest and the investigation of further mechanistic studies are still underdeveloped due to a lack of stringent specificity of the methods and the inherent instability of persulfides. This review provides an overview of the processes of endogenous H2S production, oxidation, and signalling and highlights the application and limitations of current persulfidation labelling approaches for investigation of this important evolutionarily conserved biological switch for protein function.

Chemoenzymatic Labeling, Detection and Profiling of Core Fucosylation in Live Cells.

Core fucosylation, the attachment of an α-1,6-linked-fucose to the N-glycan core pentasaccharide, is an abundant protein modification that plays critical roles in various biological processes such as cell signaling, B cell development, antibody-dependent cellular cytotoxicity, and oncogenesis. However, the tools currently used to detect core fucosylation suffer from poor specificity, exhibiting cross-reactivity against all types of fucosylation. Herein we report the development of a new chemoenzymatic strategy for the rapid and selective detection of core fucosylated glycans. This approach employs a galactosyltransferase enzyme identified fromCaenorhabditis elegansthat specifically transfers an azido-appended galactose residue onto core fucose via a β-1,4 glycosidic linkage. We demonstrate that the approach exhibits superior specificity toward core fucose on a variety of complex N-glycans. The method enables detection of core fucosylated glycoproteins from complex cell lysates, as well as on live cell surfaces, and it can be integrated into a diagnostic platform to profile protein-specific core fucosylation levels. This chemoenzymatic labeling approach offers a new strategy for the identification of disease biomarkers and will allow researchers to further characterize the fundamental role of this important glycan in normal and disease physiology.

Conjugation of primary amine groups in targeted proteomics.

Primary amines, in the form of unmodified N-terminus of peptide/protein and unmodified lysine residue, are perhaps the most important functional groups that can serve as the starting points in proteomic analysis, especially via mass spectrometry-based approaches. A variety of multifunctional probes that conjugate primary amine groups through covalent bonds have been developed and employed to facilitate protein/protein complex characterization, including identification, quantification, structure and localization elucidation, protein-protein interaction investigation, and so forth. As an integral part of more accurate peptide quantification in targeted proteomics, isobaric stable isotope-coded primary amine labeling approaches eventually facilitated protein/peptide characterization at the single-cell level, paving the way for single-cell proteomics. The development and advances in the field can be reviewed in terms of key components of a multifunctional probe: functional groups and chemistry for primary amine conjugation; hetero-bifunctional moiety for separation/enrichment of conjugated protein/protein complex; and functionalized linker/spacer. Perspectives are primarily focused on optimizing primary amine conjugation under physiological conditions to improve characterization of native proteins, especially those associated with the surface of living cells/microorganisms.

Endogenous cell membrane interactome mapping for the GLP-1 receptor in different cell types.

The GLP-1 receptor, one of the most successful drug targets for the treatment of type 2 diabetes and obesity, is known to engage multiple intracellular signaling proteins. However, it remains less explored how the receptor interacts with proteins on the cell membrane. Here, we present a ligand-based proximity labeling approach to interrogate the native cell membrane interactome for the GLP-1 receptor after agonist simulation. Our study identified several unreported putative cell membrane interactors for the endogenous receptor in either a pancreatic β cell line or a neuronal cell line. We further uncovered new regulators of GLP-1 receptor-mediated signaling and insulinotropic responses in β cells. Additionally, we obtained a time-resolved cell membrane interactome map for the receptor in β cells. Therefore, our study provides a new approach that is generalizable to map endogenous cell membrane interactomes for G-protein-coupled receptors to decipher the molecular basis of their cell-type-specific functional regulation.

Discovering Consumer Behavior Towards Back-of-Pack Nutrition Labels: A Systematic Literature Review

This systematic literature review aims to examine the impact of back-of-pack (BOP) labels on food manufacturers' practices in the field of consumer behaviour research. The review comprehensively analyses a wide range of articles spanning over two decades to provide an up-to-date and comprehensive analysis of the subject matter. It focuses specifically on how BOP labels affect consumers, food manufacturers' behaviors and practices. The findings highlight that BOP labels conveying intuitive information effectively prompt product reformulation, particularly in reducing unhealthy nutrients such as sodium, sugar, and calories. Voluntary BOP labeling has limited uptake and is often applied to already healthier products. Consumers and food producers' response varies based on label design and enforcement type, suggesting strategic labeling of healthier choices. The review provides valuable insights for future public health research and policymaking efforts, emphasizing the importance of mandatory policies and specific guidance in BOP labels. This research brings novelty by comprehensively examining the impact of back-of-pack (BOP) labeling on consumers and food manufacturers' practices. The findings contribute to the literature by highlighting the differential effects of mandatory and voluntary BOP labeling approaches and offering insights into label design and enforcement types. As per the researcher knowledge there is no available systematic literature review (SLR) specifically focusing on BOP labeling in recent years. Future research should explore the long-term impacts of mandatory versus voluntary BOP labeling on consumer dietary habits and food manufacturers' product reformulation strategies.

µMap proximity labeling in living cells reveals stress granule disassembly mechanisms.

Phase-separated condensates are membrane-less intracellular structures comprising dynamic protein interactions that organize essential biological processes. Understanding the composition and dynamics of these organelles advances our knowledge of cellular behaviors and disease pathologies related to granule dysregulation. In this study, we apply microenvironment mapping with a HaloTag-based platform (HaloMap) to characterize intracellular stress granule dynamics in living cells. After validating the robustness and sensitivity of this approach, we then profile the stress granule proteome throughout the formation and disassembly and under pharmacological perturbation. These experiments reveal several ubiquitin-related modulators, including the HECT (homologous to E6AP C terminus) E3 ligases ITCH and NEDD4L, as well as the ubiquitin receptor toll-interacting protein TOLLIP, as key mediators of granule disassembly. In addition, we identify an autophagy-related pathway that promotes granule clearance. Collectively, this work establishes a general photoproximity labeling approach for unraveling intracellular protein interactomes and uncovers previously unexplored regulatory mechanisms of stress granule dynamics.

Metabolic labeling based methylome profiling enables functional dissection of histidine methylation in C3H1 zinc fingers

Protein methylation is a functionally important post-translational modification that occurs on diverse amino acid residues. The current proteomics approaches are inefficient to discover the methylation on residues other than Arg and Lys, which hinders the deep understanding of the functional role of rare protein methylation. Herein, we present a methyl-specific metabolic labeling approach for global methylome mapping, which enable the acquisition of methylome dataset covering diverse methylation types. Interestingly, of the identified methylation events, His methylation is found to be preferably occurred in C3H1 zinc fingers (ZFs). These His methylation events are determined to be Nπ specific and catalyzed by CARNMT1. The His methylation is found to stabilize the structure of ZFs. U2AF1 is used as a proof-of-concept to highlight the functional importance of His methylation in ZFs in RNA binding and RNA metabolism. The results of this study enable novel understanding of how protein methylation regulates cellular processes.

CG-SLENP: A Chemical Genetics Strategy To Selectively Label Existing Proteins and Newly Synthesized Proteins.

Protein synthesis and subsequent delivery to the target locations in cells are essential for their proper functions. Methods to label and distinguish newly synthesized proteins from existing ones are critical to assess their differential properties, but such methods are lacking. We describe the first chemical genetics-based approach for selective labeling of existing and newly synthesized proteins that we termed as CG-SLENP. Using HaloTag in-frame fusion with lamin A (LA), we demonstrate that the two pools of proteins can be selectively labeled using CG-SLENP in living cells. We further employ our recently developed selective small molecule ligand LBL1 for LA to probe the potential differences between newly synthesized and existing LA. Our results show that LBL1 can differentially modulate these two pools of LA. These results indicate that the assembly states of newly synthesized LA are distinct from existing LA in living cells. The CG-SLENP method is potentially generalizable to study any cellular proteins.

One N-glycan regulates natural killer cell antibody-dependent cell-mediated cytotoxicity and modulates Fc γ receptor IIIa / CD16a structure.

Both endogenous antibodies and a subset of antibody therapeutics engage Fc gamma receptor (FcγR)IIIa / CD16a to stimulate a protective immune response. Increasing the FcγRIIIa/IgG1 interaction improves the immune response and thus represents a strategy to improve therapeutic efficacy. FcγRIIIa is a heavily glycosylated receptor and glycan composition affects antibody-binding affinity. Though our laboratory previously demonstrated that natural killer (NK) cell N-glycan composition affected the potency of one key protective mechanism, antibody-dependent cell-mediated cytotoxicity (ADCC), it was unclear if this effect was due to FcγRIIIa glycosylation. Furthermore, the structural mechanism linking glycan composition to affinity and cellular activation remained undescribed. To define the role of individual amino acid and N-glycan residues we measured affinity using multiple FcγRIIIa glycoforms. We observed stepwise affinity increases with each glycan truncation step with the most severely truncated glycoform displaying the highest affinity. Removing the N162 glycan demonstrated its predominant role in regulating antibody-binding affinity, in contrast to four other FcγRIIIa N-glycans. We next evaluated the impact of the N162 glycan on NK cell ADCC. NK cells expressing the FcγRIIIa V158 allotype exhibited increased ADCC following kifunensine treatment to limit N-glycan processing. Notably, an increase was not observed with cells expressing the FcγRIIIa V158 S164A variant that lacks N162 glycosylation, indicating the N162 glycan is required for increased NK cell ADCC. To gain structural insight into the mechanisms of N162 regulation, we applied a novel protein isotope labeling approach in combination with solution NMR spectroscopy. FG loop residues proximal to the N162 glycosylation site showed large chemical shift perturbations following glycan truncation. These data support a model for the regulation of FcγRIIIa affinity and NK cell ADCC whereby composition of the N162 glycan stabilizes the FG loop and thus the antibody-binding site.

DEAD-box RNA helicase RH20 positively regulates RNAi-based antiviral immunity in plants by associating with SGS3/RDR6 bodies.

RNA silencing plays a crucial role in defending against viral infections in diverse eukaryotic hosts. Despite extensive studies on core components of the antiviral RNAi pathway such as DCLs, AGOs and RDRs proteins, host factors involved in antiviral RNAi remain incompletely understood. In this study, we employed the proximity labelling approach to identify the host factors required for antiviral RNAi in Nicotiana benthamiana. Using the barley stripe mosaic virus (BSMV)-encoded γb, a viral suppressor of RNA silencing (VSR), as the bait protein, we identified the DEAD-box RNA helicase RH20, a broadly conserved protein in plants and animals with a homologous human protein known as DDX5. We demonstrated the interaction between RH20 and BSMV γb. Knockdown or knockout of RH20 attenuates the accumulation of viral small interfering RNAs, leading to increased susceptibility to BSMV, while overexpression of RH20 enhances resistance to BSMV, a process requiring the cytoplasmic localization and RNA-binding activity of RH20. In addition to BSMV, RH20 also negatively regulates the infection of several other positive-sense RNA viruses, suggesting the broad-spectrum antiviral activity of RH20. Mechanistic analysis revealed the colocalization and interaction of RH20 with SGS3/RDR6, and disruption of either SGS3 or RDR6 undermines the antiviral function of RH20, suggesting RH20 as a new component of the SGS3/RDR6 bodies. As a counter-defence, BSMV γb VSR subverts the RH20-mediated antiviral defence by interfering with the RH20-SGS3 interaction. Our results uncover RH20 as a new positive regulator of antiviral RNAi and provide new potential targets for controlling plant viral diseases.

Greenwashing in food labelling: Consumer deception by claims of climate neutrality and the importance of an interpretative labelling approach

Approaches to climate labelling are gaining significant importance in food marketing. Labels that are based on carbon offsets are becoming increasingly popular. However, offsetting-labels have been criticized as misleading and are subject to accusations of greenwashing. The proposal of the EU Green Claims Directive therefore requires companies to substantiate claims. This study employs a consumer survey (n = 2,109) using a between-subjects and within-subjects design to explore how German participants evaluate the climate impact of six food products (ranging from low to very high) through five distinct climate labels: (1) ‘climate-neutral’ (without any declaration), (2) ‘climate-compensated’, (3) ‘climate-neutral and CO2-compensated product’ (declaration according to the proposed EU Green Claims Directive), (4) informative labels indicating the actual climate impact as a numeric carbon footprint (kg CO2eq/kg of food) or (5) as an interpretative traffic light-like label. Except for the numeric indication of the carbon footprint (4) and the traffic light (5), all climate labels significantly skewed perceptions of a food’s climate impact positively, compared to the control group without any label. The effect was sometimes even stronger for highly involved consumers. In contrast, the interpretative traffic light climate label helps to correctly assess the climate impact. In summary, green claims such as ‘climate-neutral’ can be misleading by fostering a false perception of a food’s climate impact, even when the compensatory character is explained or justified close to the claim. This challenges the approach of the draft European Green Claims Directive, which posits that additional information (‘substantiation’) is sufficient to avoid misconceptions.

A ubiquitin-specific, proximity-based labeling approach for the identification of ubiquitin ligase substrates.

Over 600 E3 ligases in humans execute ubiquitination of specific target proteins in a spatiotemporal manner to elicit desired signaling effects. Here, we developed a ubiquitin-specific proximity-based labeling method to selectively biotinylate substrates of a given ubiquitin ligase. By fusing the biotin ligase BirA and an Avi-tag variant to the candidate E3 ligase and ubiquitin, respectively, we were able to specifically enrich bona fide substrates of a ligase using a one-step streptavidin pulldown under denaturing conditions. We applied our method, which we named Ub-POD, to the really interesting new gene (RING) E3 ligase RAD18 and identified proliferating cell nuclear antigen and several other critical players in the DNA damage repair pathway. Furthermore, we successfully applied Ub-POD to the RING ubiquitin ligase tumor necrosis factor receptor-associated factor 6 and a U-box-type E3 ubiquitin ligase carboxyl terminus of Hsc70-interacting protein. We anticipate that our method could be widely adapted to all classes of ubiquitin ligases to identify substrates.

A natural small molecule alleviates liver fibrosis by targeting apolipoprotein L2.

Liver fibrosis is an urgent clinical problem without effective therapies. Here we conducted a high-content screening on a natural Euphorbiaceae diterpenoid library to identify a potent anti-liver fibrosis lead, 12-deoxyphorbol 13-palmitate (DP). Leveraging a photo-affinity labeling approach, apolipoprotein L2 (APOL2), an endoplasmic reticulum (ER)-rich protein, was identified as the direct target of DP. Mechanistically, APOL2 is induced in activated hepatic stellate cells upon transforming growth factor-β1 (TGF-β1) stimulation, which then binds to sarcoplasmic/ER calcium ATPase 2 (SERCA2) to trigger ER stress and elevate its downstream protein kinase R-like ER kinase (PERK)-hairy and enhancer of split 1 (HES1) axis, ultimately promoting liver fibrosis. As a result, targeting APOL2 by DP or ablation of APOL2 significantly impairs APOL2-SERCA2-PERK-HES1 signaling and mitigates fibrosis progression. Our findings not only define APOL2 as a novel therapeutic target for liver fibrosis but also highlight DP as a promising lead for treatment of this symptom.