Pathogenic E. coli Extracts Nutrients from Infected Host Cells Utilizing Injectisome Components.

Microbiota and intestinal epithelium restrict pathogen growth by rapid nutrient consumption. We investigated how pathogens circumvent this obstacle to colonize the host. Utilizing enteropathogenic E. coli (EPEC), we show that host-attached bacteria obtain nutrients from infected host cell in a process we termed host nutrient extraction (HNE). We identified an inner-membrane protein complex, henceforth termed CORE, as necessary and sufficient for HNE. The CORE is a key component of the EPEC injectisome, however, here we show that it supports the formation of an alternative structure, composed of membranous nanotubes, protruding from the EPEC surface to directly contact the host. The injectisome and flagellum are evolutionarily related, both containing conserved COREs. Remarkably, CORE complexes of diverse ancestries, including distant flagellar COREs, could rescue HNE capacity of EPEC lacking its native CORE. Our results support the notion that HNE is a widespread virulence strategy, enabling pathogens to thrive in competitive niches.

Metabolite Measurement: Pitfalls to Avoid and Practices to Follow.

Metabolites are the small biological molecules involved in energy conversion and biosynthesis. Studying metabolism is inherently challenging due to metabolites' reactivity, structural diversity, and broad concentration range. Herein, we review the common pitfalls encountered in metabolomics and provide concrete guidelines for obtaining accurate metabolite measurements, focusing on water-soluble primary metabolites. We show how seemingly straightforward sample preparation methods can introduce systematic errors (e.g., owing to interconversion among metabolites) and how proper selection of quenching solvent (e.g., acidic acetonitrile:methanol:water) can mitigate such problems. We discuss the specific strengths, pitfalls, and best practices for each common analytical platform: liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), nuclear magnetic resonance (NMR), and enzyme assays. Together this information provides a pragmatic knowledge base for carrying out biologically informative metabolite measurements.

TMUB1 is an endoplasmic reticulum-resident escortase that promotes the p97-mediated extraction of membrane proteins for degradation.

Membrane protein clients of endoplasmic reticulum (ER)-associated degradation must be retrotranslocated from the ER membrane by the AAA-ATPase p97 for proteasomal degradation. Before direct engagement with p97, client transmembrane domains (TMDs) that have partially or fully crossed the membrane must be constantly shielded to avoid non-native interactions. How client TMDs are seamlessly escorted from the membrane to p97 is unknown. Here, we identified ER-anchored TMUB1 as a TMD-specific escortase. TMUB1 interacts with the TMD of clients within the membrane and holds ∼10-14 residues of a hydrophobic sequence that is exposed out of membrane, using its transmembrane and cytosolic regions, respectively. The ubiquitin-like domain of TMUB1 recruits p97, which can pull client TMDs from bound TMUB1 into the cytosol. The disruption of TMUB1 escortase activity impairs retrotranslocation and stabilizes retrotranslocating intermediates of client proteins within the ER membrane. Thus, TMUB1 promotes TMD segregation by safeguarding the TMD movement from the membrane to p97.

Metastable precipitation and ion-extractant transport in liquid-liquid separations of trivalent elements.

The extractant-assisted transport of metal ions from aqueous to organic environments by liquid-liquid extraction has been widely used to separate and recover critical elements on an industrial scale. While current efforts focus on designing better extractants and optimizing process conditions, the mechanism that underlies ionic transport remains poorly understood. Here, we report a nonequilibrium process in the bulk aqueous phase that influences interfacial ion transport: the formation of metastable ion-extractant precipitates away from the liquid-liquid interface, separated from it by a depletion region without precipitates. Although the precipitate is soluble in the organic phase, the depletion region separates the two and ions are sequestered in a long-lived metastable state. Since precipitation removes extractants from the aqueous phase, even extractants that are sparingly soluble in water will continue to be withdrawn from the organic phase to feed the aqueous precipitation process. Solute concentrations in both phases and the aqueous pH influence the temporal evolution of the process and ionic partitioning between the precipitate and organic phase. Aqueous ion-extractant precipitation during liquid-liquid extraction provides a reaction path that can influence the extraction kinetics, which plays an important role in designing advanced processes to separate rare earths and other minerals.

Surveying biomedical relation extraction: a critical examination of current datasets and the proposal of a new resource.

Natural language processing (NLP) has become an essential technique in various fields, offering a wide range of possibilities for analyzing data and developing diverse NLP tasks. In the biomedical domain, understanding the complex relationships between compounds and proteins is critical, especially in the context of signal transduction and biochemical pathways. Among these relationships, protein-protein interactions (PPIs) are of particular interest, given their potential to trigger a variety of biological reactions. To improve the ability to predict PPI events, we propose the protein event detection dataset (PEDD), which comprises 6823 abstracts, 39 488 sentences and 182 937 gene pairs. Our PEDD dataset has been utilized in the AI CUP Biomedical Paper Analysis competition, where systems are challenged to predict 12 different relation types. In this paper, we review the state-of-the-art relation extraction research and provide an overview of the PEDD's compilation process. Furthermore, we present the results of the PPI extraction competition and evaluate several language models' performances on the PEDD. This paper's outcomes will provide a valuable roadmap for future studies on protein event detection in NLP. By addressing this critical challenge, we hope to enable breakthroughs in drug discovery and enhance our understanding of the molecular mechanisms underlying various diseases.

Lactylation prediction models based on protein sequence and structural feature fusion.

Lysine lactylation (Kla) is a newly discovered posttranslational modification that is involved in important life activities, such as glycolysis-related cell function, macrophage polarization and nervous system regulation, and has received widespread attention due to the Warburg effect in tumor cells. In this work, we first design a natural language processing method to automatically extract the 3D structural features of Kla sites, avoiding potential biases caused by manually designed structural features. Then, we establish two Kla prediction frameworks, Attention-based feature fusion Kla model (ABFF-Kla) and EBFF-Kla, to integrate the sequence features and the structure features based on the attention layer and embedding layer, respectively. The results indicate that ABFF-Kla and Embedding-based feature fusion Kla model (EBFF-Kla), which fuse features from protein sequences and spatial structures, have better predictive performance than that of models that use only sequence features. Our work provides an approach for the automatic extraction of protein structural features, as well as a flexible framework for Kla prediction. The source code and the training data of the ABFF-Kla and the EBFF-Kla are publicly deposited at: https://github.com/ispotato/Lactylation_model.

A comprehensive benchmarking of machine learning algorithms and dimensionality reduction methods for drug sensitivity prediction.

A major challenge of precision oncology is the identification and prioritization of suitable treatment options based on molecular biomarkers of the considered tumor. In pursuit of this goal, large cancer cell line panels have successfully been studied to elucidate the relationship between cellular features and treatment response. Due to the high dimensionality of these datasets, machine learning (ML) is commonly used for their analysis. However, choosing a suitable algorithm and set of input features can be challenging. We performed a comprehensive benchmarking of ML methods and dimension reduction (DR) techniques for predicting drug response metrics. Using the Genomics of Drug Sensitivity in Cancer cell line panel, we trained random forests, neural networks, boosting trees and elastic nets for 179 anti-cancer compounds with feature sets derived from nine DR approaches. We compare the results regarding statistical performance, runtime and interpretability. Additionally, we provide strategies for assessing model performance compared with a simple baseline model and measuring the trade-off between models of different complexity. Lastly, we show that complex ML models benefit from using an optimized DR strategy, and that standard models-even when using considerably fewer features-can still be superior in performance.

Lignin with controlled structural properties by N-heterocycle-based deep eutectic solvent extraction.

The complex and heterogeneous nature of the lignin macromolecule has presented a lasting barrier to its utilization. To achieve high lignin yield, the technical lignin extraction process usually severely modifies and condenses the native structure of lignin, which is a critical drawback for its utilization in conversion processes. In addition, there is no method capable of separating lignin from plant biomass with controlled structural properties. Here, we developed an N-heterocycle-based deep eutectic solvent formed between lactic acid and pyrazole (La-Py DES) with a binary hydrogen bonding functionality resulting in a high affinity toward lignin. Up to 93.7% of lignin was extracted from wheat straw biomass at varying conditions from 90 °C to 145 °C. Through careful selection of treatment conditions as well as lactic acid to pyrazole ratios, lignin with controlled levels of ether linkage content, hydroxyl group content, and average molecular weight can be generated. Under mild extraction conditions (90 °C to 120 °C), light-colored native-like lignin can be produced with up to 80% yield, whereas ether linkage-free lignin with low polydispersity can be obtained at 145 °C. Overall, this study offers a new strategy for native lignin extraction and generating lignin with controlled structural properties.

A comprehensive review on knowledge graphs for complex diseases.

In recent years, knowledge graphs (KGs) have gained a great deal of popularity as a tool for storing relationships between entities and for performing higher level reasoning. KGs in biomedicine and clinical practice aim to provide an elegant solution for diagnosing and treating complex diseases more efficiently and flexibly. Here, we provide a systematic review to characterize the state-of-the-art of KGs in the area of complex disease research. We cover the following topics: (1) knowledge sources, (2) entity extraction methods, (3) relation extraction methods and (4) the application of KGs in complex diseases. As a result, we offer a complete picture of the domain. Finally, we discuss the challenges in the field by identifying gaps and opportunities for further research and propose potential research directions of KGs for complex disease diagnosis and treatment.

TMKit: a Python interface for computational analysis of transmembrane proteins.

Transmembrane proteins are receptors, enzymes, transporters and ion channels that are instrumental in regulating a variety of cellular activities, such as signal transduction and cell communication. Despite tremendous progress in computational capacities to support protein research, there is still a significant gap in the availability of specialized computational analysis toolkits for transmembrane protein research. Here, we introduce TMKit, an open-source Python programming interface that is modular, scalable and specifically designed for processing transmembrane protein data. TMKit is a one-stop computational analysis tool for transmembrane proteins, enabling users to perform database wrangling, engineer features at the mutational, domain and topological levels, and visualize protein-protein interaction interfaces. In addition, TMKit includes seqNetRR, a high-performance computing library that allows customized construction of a large number of residue connections. This library is particularly well suited for assigning correlation matrix-based features at a fast speed. TMKit should serve as a useful tool for researchers in assisting the study of transmembrane protein sequences and structures. TMKit is publicly available through https://github.com/2003100127/tmkit and https://tmkit-guide.herokuapp.com/doc/overview.

IK-DDI: a novel framework based on instance position embedding and key external text for DDI extraction.

Determining drug-drug interactions (DDIs) is an important part of pharmacovigilance and has a vital impact on public health. Compared with drug trials, obtaining DDI information from scientific articles is a faster and lower cost but still a highly credible approach. However, current DDI text extraction methods consider the instances generated from articles to be independent and ignore the potential connections between different instances in the same article or sentence. Effective use of external text data could improve prediction accuracy, but existing methods cannot extract key information from external data accurately and reasonably, resulting in low utilization of external data. In this study, we propose a DDI extraction framework, instance position embedding and key external text for DDI (IK-DDI), which adopts instance position embedding and key external text to extract DDI information. The proposed framework integrates the article-level and sentence-level position information of the instances into the model to strengthen the connections between instances generated from the same article or sentence. Moreover, we introduce a comprehensive similarity-matching method that uses string and word sense similarity to improve the matching accuracy between the target drug and external text. Furthermore, the key sentence search method is used to obtain key information from external data. Therefore, IK-DDI can make full use of the connection between instances and the information contained in external text data to improve the efficiency of DDI extraction. Experimental results show that IK-DDI outperforms existing methods on both macro-averaged and micro-averaged metrics, which suggests our method provides complete framework that can be used to extract relationships between biomedical entities and process external text data.

A feature extraction free approach for protein interactome inference from co-elution data.

Protein complexes are key functional units in cellular processes. High-throughput techniques, such as co-fractionation coupled with mass spectrometry (CF-MS), have advanced protein complex studies by enabling global interactome inference. However, dealing with complex fractionation characteristics to define true interactions is not a simple task, since CF-MS is prone to false positives due to the co-elution of non-interacting proteins by chance. Several computational methods have been designed to analyze CF-MS data and construct probabilistic protein-protein interaction (PPI) networks. Current methods usually first infer PPIs based on handcrafted CF-MS features, and then use clustering algorithms to form potential protein complexes. While powerful, these methods suffer from the potential bias of handcrafted features and severely imbalanced data distribution. However, the handcrafted features based on domain knowledge might introduce bias, and current methods also tend to overfit due to the severely imbalanced PPI data. To address these issues, we present a balanced end-to-end learning architecture, Software for Prediction of Interactome with Feature-extraction Free Elution Data (SPIFFED), to integrate feature representation from raw CF-MS data and interactome prediction by convolutional neural network. SPIFFED outperforms the state-of-the-art methods in predicting PPIs under the conventional imbalanced training. When trained with balanced data, SPIFFED had greatly improved sensitivity for true PPIs. Moreover, the ensemble SPIFFED model provides different voting schemes to integrate predicted PPIs from multiple CF-MS data. Using the clustering software (i.e. ClusterONE), SPIFFED allows users to infer high-confidence protein complexes depending on the CF-MS experimental designs. The source code of SPIFFED is freely available at: https://github.com/bio-it-station/SPIFFED.

DNA-MP: a generalized DNA modifications predictor for multiple species based on powerful sequence encoding method.

Accurate prediction of deoxyribonucleic acid (DNA) modifications is essential to explore and discern the process of cell differentiation, gene expression and epigenetic regulation. Several computational approaches have been proposed for particular type-specific DNA modification prediction. Two recent generalized computational predictors are capable of detecting three different types of DNA modifications; however, type-specific and generalized modifications predictors produce limited performance across multiple species mainly due to the use of ineffective sequence encoding methods. The paper in hand presents a generalized computational approach "DNA-MP" that is competent to more precisely predict three different DNA modifications across multiple species. Proposed DNA-MP approach makes use of a powerful encoding method "position specific nucleotides occurrence based 117 on modification and non-modification class densities normalized difference" (POCD-ND) to generate the statistical representations of DNA sequences and a deep forest classifier for modifications prediction. POCD-ND encoder generates statistical representations by extracting position specific distributional information of nucleotides in the DNA sequences. We perform a comprehensive intrinsic and extrinsic evaluation of the proposed encoder and compare its performance with 32 most widely used encoding methods on $17$ benchmark DNA modifications prediction datasets of $12$ different species using $10$ different machine learning classifiers. Overall, with all classifiers, the proposed POCD-ND encoder outperforms existing $32$ different encoders. Furthermore, combinedly over 5-fold cross validation benchmark datasets and independent test sets, proposed DNA-MP predictor outperforms state-of-the-art type-specific and generalized modifications predictors by an average accuracy of 7% across 4mc datasets, 1.35% across 5hmc datasets and 10% for 6ma datasets. To facilitate the scientific community, the DNA-MP web application is available at https://sds_genetic_analysis.opendfki.de/DNA_Modifications/.

Pipelined biomedical event extraction rivaling joint learning.

Biomedical event extraction is an information extraction task to obtain events from biomedical text, whose targets include the type, the trigger, and the respective arguments involved in an event. Traditional biomedical event extraction usually adopts a pipelined approach, which contains trigger identification, argument role recognition, and finally event construction either using specific rules or by machine learning. In this paper, we propose an n-ary relation extraction method based on the BERT pre-training model to construct Binding events, in order to capture the semantic information about an event's context and its participants. The experimental results show that our method achieves promising results on the GE11 and GE13 corpora of the BioNLP shared task with F1 scores of 63.14% and 59.40%, respectively. It demonstrates that by significantly improving the performance of Binding events, the overall performance of the pipelined event extraction approach or even exceeds those of current joint learning methods.

AlpaPICO: Extraction of PICO frames from clinical trial documents using LLMs.

In recent years, there has been a surge in the publication of clinical trial reports, making it challenging to conduct systematic reviews. Automatically extracting Population, Intervention, Comparator, and Outcome (PICO) from clinical trial studies can alleviate the traditionally time-consuming process of manually scrutinizing systematic reviews. Existing approaches of PICO frame extraction involves supervised approach that relies on the existence of manually annotated data points in the form of BIO label tagging. Recent approaches, such as In-Context Learning (ICL), which has been shown to be effective for a number of downstream NLP tasks, require the use of labeled examples. In this work, we adopt ICL strategy by employing the pretrained knowledge of Large Language Models (LLMs), gathered during the pretraining phase of an LLM, to automatically extract the PICO-related terminologies from clinical trial documents in unsupervised set up to bypass the availability of large number of annotated data instances. Additionally, to showcase the highest effectiveness of LLM in oracle scenario where large number of annotated samples are available, we adopt the instruction tuning strategy by employing Low Rank Adaptation (LORA) to conduct the training of gigantic model in low resource environment for the PICO frame extraction task. More specifically, both of the proposed frameworks utilize AlpaCare as base LLM which employs both few-shot in-context learning and instruction tuning techniques to extract PICO-related terms from the clinical trial reports. We applied these approaches to the widely used coarse-grained datasets such as EBM-NLP, EBM-COMET and fine-grained datasets such as EBM-NLPrev and EBM-NLPh. Our empirical results show that our proposed ICL-based framework produces comparable results on all the version of EBM-NLP datasets and the proposed instruction tuned version of our framework produces state-of-the-art results on all the different EBM-NLP datasets. Our project is available at https://github.com/shrimonmuke0202/AlpaPICO.git.

Predicting lysine methylation sites using a convolutional neural network.

Protein lysine methylation is a particular type of post translational modification that plays an important role in both histone and non-histone function regulation in proteins. Deregulation caused by lysine methyltransferases has been identified as the cause of several diseases including cancer as well as both mental and developmental disorders. Identifying lysine methylation sites is a critical step in both early diagnosis and drug design. This study proposes a new Machine Learning method called CNN-Meth for predicting lysine methylation sites using a convolutional neural network (CNN). Our model is trained using evolutionary, structural, and physicochemical-based presentation along with binary encoding. Unlike previous studies, instead of extracting handcrafted features, we use CNN to automatically extract features from different presentations of amino acids to avoid information loss. Automated feature extraction from these representations of amino acids as well as CNN as a classifier have never been used for this problem. Our results demonstrate that CNN-Meth can significantly outperform previous methods for predicting methylation sites. It achieves 96.0%, 85.1%, 96.4%, and 0.65 in terms of Accuracy, Sensitivity, Specificity, and Matthew's Correlation Coefficient (MCC), respectively. CNN-Meth and its source code are publicly available at https://github.com/MLBC-lab/CNN-Meth.

Msp1 Is a Membrane Protein Dislocase for Tail-Anchored Proteins.

Mislocalized tail-anchored (TA) proteins of the outer mitochondrial membrane are cleared by a newly identified quality control pathway involving the conserved eukaryotic protein Msp1 (ATAD1 in humans). Msp1 is a transmembrane AAA-ATPase, but its role in TA protein clearance is not known. Here, using purified components reconstituted into proteoliposomes, we show that Msp1 is both necessary and sufficient to drive the ATP-dependent extraction of TA proteins from the membrane. A crystal structure of the Msp1 cytosolic region modeled into a ring hexamer suggests that active Msp1 contains a conserved membrane-facing surface adjacent to a central pore. Structure-guided mutagenesis of the pore residues shows that they are critical for TA protein extraction in vitro and for functional complementation of an msp1 deletion in yeast. Together, these data provide a molecular framework for Msp1-dependent extraction of mislocalized TA proteins from the outer mitochondrial membrane.

Application of Liquid-Liquid Extraction for N-terminal Myristoylation Proteomics.

Proteins can be modified by lipids in various ways, for example, by myristoylation, palmitoylation, farnesylation, and geranylgeranylation-these processes are collectively referred to as lipidation. Current chemical proteomics using alkyne lipids has enabled the identification of lipidated protein candidates but does not identify endogenous lipidation sites and is not readily applicable to in vivo systems. Here, we introduce a proteomic methodology for global analysis of endogenous protein N-terminal myristoylation sites that combines liquid-liquid extraction of hydrophobic lipidated peptides with liquid chromatography-tandem mass spectrometry using a gradient program of acetonitrile in the high concentration range. We applied this method to explore myristoylation sites in HeLa cells and identified a total of 75 protein N-terminal myristoylation sites, which is more than the number of high-confidence myristoylated proteins identified by myristic acid analog-based chemical proteomics. Isolation of myristoylated peptides from HeLa digests prepared with different proteases enabled the identification of different myristoylated sites, extending the coverage of N-myristoylome. Finally, we analyzed in vivo myristoylation sites in mouse tissues and found that the lipidation profile is tissue-specific. This simple method (not requiring chemical labeling or affinity purification) should be a promising tool for global profiling of protein N-terminal myristoylation.

Exploring the ancient chemistry of mercury.

This paper explores the chemistry of mercury as described in ancient alchemical literature. Alchemy's focus on the knowledge and manipulation of natural substances is not so different from modern chemistry's purposes. The great divide between the two is marked by the way of conceptualizing and recording their practices. Our interdisciplinary research group, composed of chemists and historians of science, has set off to explore the cold and hot extraction of mercury from cinnabar. The ancient written records have been perused in order to devise laboratory experiments that could shed light on the material reality behind the alchemical narratives and interpret textual details in a unique perspective. In this way, it became possible to translate the technical lore of ancient alchemy into the modern language of chemistry. Thanks to the replication of alchemical practices, chemistry can regain its centuries-long history that has fallen into oblivion.

Defining the challenges of Li extraction with olivine host: The roles of competitor and spectator ions.

The lithium supply issue mainly lies in the inability of current mining methods to access lithium sources of dilute concentrations and complex chemistry. Electrochemical intercalation has emerged as a highly selective method for lithium extraction; however, limited source compositions have been studied, which is insufficient to predict its applicability to the wide range of unconventional water sources (UWS). This work addresses the feasibility and identifies the challenges of Li extraction by electrochemical intercalation from UWS, by answering three questions: 1) Is there enough Li in UWS? 2) How would the solution compositions affect the competition of Li+ to major ions (Na+/Mg2+/K+/Ca2+)? 3) Does the complex solution composition affect the electrode stability? Using one-dimensional olivine FePO4 as the model electrode, we show the complicated roles of major ions. Na+ acts as the competitor ion for host storage sites. The competition from Na+ grants Mg2+ and Ca2+ being only the spectator ions. However, Mg2+ and Ca2+ can significantly affect the charge transfer of Li+ and Na+, therefore affecting the Li selectivity. We point to improving the selectivity of Li+ to Na+ as the key challenge for broadening the minable UWS using the olivine host.