Large enrichments in fatty acid 2H/1H ratios distinguish respiration from aerobic fermentation in yeast Saccharomyces cerevisiae.

Shifts in the hydrogen stable isotopic composition (2H/1H ratio) of lipids relative to water (lipid/water 2H-fractionation) at natural abundances reflect different sources of the central cellular reductant, NADPH, in bacteria. Here, we demonstrate that lipid/water 2H-fractionation (2εfattyacid/water) can also constrain the relative importance of key NADPH pathways in eukaryotes. We used the metabolically flexible yeast Saccharomyces cerevisiae, a microbial model for respiratory and fermentative metabolism in industry and medicine, to investigate 2εfattyacid/water. In chemostats, fatty acids from glycerol-respiring cells were >550‰ 2H-enriched compared to those from cells aerobically fermenting sugars via overflow metabolism, a hallmark feature in cancer. Faster growth decreased 2H/1H ratios, particularly in glycerol-respiring cells by 200‰. Variations in the activities and kinetic isotope effects among NADP+-reducing enzymes indicate cytosolic NADPH supply as the primary control on 2εfattyacid/water. Contributions of cytosolic isocitrate dehydrogenase (cIDH) to NAPDH production drive large 2H-enrichments with substrate metabolism (cIDH is absent during fermentation but contributes up to 20 percent NAPDH during respiration) and slower growth on glycerol (11 percent more NADPH from cIDH). Shifts in NADPH demand associated with cellular lipid abundance explain smaller 2εfattyacid/water variations (<30‰) with growth rate during fermentation. Consistent with these results, tests of murine liver cells had 2H-enriched lipids from slower-growing, healthy respiring cells relative to fast-growing, fermenting hepatocellular carcinoma. Our findings point to the broad potential of lipid 2H/1H ratios as a passive natural tracker of eukaryotic metabolism with applications to distinguish health and disease, complementing studies that rely on complex isotope-tracer addition methods.

iLoc-miRNA: extracellular/intracellular miRNA prediction using deep BiLSTM with attention mechanism.

The location of microRNAs (miRNAs) in cells determines their function in regulation activity. Studies have shown that miRNAs are stable in the extracellular environment that mediates cell-to-cell communication and are located in the intracellular region that responds to cellular stress and environmental stimuli. Though in situ detection techniques of miRNAs have made great contributions to the study of the localization and distribution of miRNAs, miRNA subcellular localization and their role are still in progress. Recently, some machine learning-based algorithms have been designed for miRNA subcellular location prediction, but their performance is still far from satisfactory. Here, we present a new data partitioning strategy that categorizes functionally similar locations for the precise and instructive prediction of miRNA subcellular location in Homo sapiens. To characterize the localization signals, we adopted one-hot encoding with post padding to represent the whole miRNA sequences, and proposed a deep bidirectional long short-term memory with the multi-head self-attention algorithm to model. The algorithm showed high selectivity in distinguishing extracellular miRNAs from intracellular miRNAs. Moreover, a series of motif analyses were performed to explore the mechanism of miRNA subcellular localization. To improve the convenience of the model, a user-friendly web server named iLoc-miRNA was established (http://iLoc-miRNA.lin-group.cn/).

Design powerful predictor for mRNA subcellular location prediction in Homo sapiens.

Messenger RNAs (mRNAs) shoulder special responsibilities that transmit genetic code from DNA to discrete locations in the cytoplasm. The locating process of mRNA might provide spatial and temporal regulation of mRNA and protein functions. The situ hybridization and quantitative transcriptomics analysis could provide detail information about mRNA subcellular localization; however, they are time consuming and expensive. It is highly desired to develop computational tools for timely and effectively predicting mRNA subcellular location. In this work, by using binomial distribution and one-way analysis of variance, the optimal nonamer composition was obtained to represent mRNA sequences. Subsequently, a predictor based on support vector machine was developed to identify the mRNA subcellular localization. In 5-fold cross-validation, results showed that the accuracy is 90.12% for Homo sapiens (H. sapiens). The predictor may provide a reference for the study of mRNA localization mechanisms and mRNA translocation strategies. An online web server was established based on our models, which is available at http://lin-group.cn/server/iLoc-mRNA/.

Identification of adaptor proteins using the ANOVA feature selection technique.

The adaptor proteins play a crucially important role in regulating lymphocyte activation. Rapid and efficient identification of adaptor proteins is essential for understanding their functions. However, biochemical methods require not only expensive experimental costs, but also long experiment cycles and more personnel. Therefore, a computational method that could accurately identify adaptor proteins is urgently needed. To solve this issue, we developed a classifier that combined the support vector machine (SVM) with the composition of k-Spaced Amino Acid Pairs (CKSAAP) and the amino acid composition (AAC) to identify adaptor proteins. Analysis of variance (ANOVA) was used to select the optimized features which could generate the maximum prediction performance. By examining the proposed model on independent data, we found that the 447 optimized features could achieve an accuracy of 92.39% with an AUC of 0.9766, demonstrating the powerful capabilities of our model. We hope that the proposed model could provide more clues for studying adaptor proteins.

DM3Loc: multi-label mRNA subcellular localization prediction and analysis based on multi-head self-attention mechanism.

Subcellular localization of messenger RNAs (mRNAs), as a prevalent mechanism, gives precise and efficient control for the translation process. There is mounting evidence for the important roles of this process in a variety of cellular events. Computational methods for mRNA subcellular localization prediction provide a useful approach for studying mRNA functions. However, few computational methods were designed for mRNA subcellular localization prediction and their performance have room for improvement. Especially, there is still no available tool to predict for mRNAs that have multiple localization annotations. In this paper, we propose a multi-head self-attention method, DM3Loc, for multi-label mRNA subcellular localization prediction. Evaluation results show that DM3Loc outperforms existing methods and tools in general. Furthermore, DM3Loc has the interpretation ability to analyze RNA-binding protein motifs and key signals on mRNAs for subcellular localization. Our analyses found hundreds of instances of mRNA isoform-specific subcellular localizations and many significantly enriched gene functions for mRNAs in different subcellular localizations.

A small molecule G6PD inhibitor reveals immune dependence on pentose phosphate pathway.

Glucose is catabolized by two fundamental pathways, glycolysis to make ATP and the oxidative pentose phosphate pathway to make reduced nicotinamide adenine dinucleotide phosphate (NADPH). The first step of the oxidative pentose phosphate pathway is catalyzed by the enzyme glucose-6-phosphate dehydrogenase (G6PD). Here we develop metabolite reporter and deuterium tracer assays to monitor cellular G6PD activity. Using these, we show that the most widely cited G6PD antagonist, dehydroepiandosterone, does not robustly inhibit G6PD in cells. We then identify a small molecule (G6PDi-1) that more effectively inhibits G6PD. Across a range of cultured cells, G6PDi-1 depletes NADPH most strongly in lymphocytes. In T cells but not macrophages, G6PDi-1 markedly decreases inflammatory cytokine production. In neutrophils, it suppresses respiratory burst. Thus, we provide a cell-active small molecule tool for oxidative pentose phosphate pathway inhibition, and use it to identify G6PD as a pharmacological target for modulating immune response.

iTerm-PseKNC: a sequence-based tool for predicting bacterial transcriptional terminators.

MOTIVATION: Transcription termination is an important regulatory step of gene expression. If there is no terminator in gene, transcription could not stop, which will result in abnormal gene expression. Detecting such terminators can determine the operon structure in bacterial organisms and improve genome annotation. Thus, accurate identification of transcriptional terminators is essential and extremely important in the research of transcription regulations. RESULTS: In this study, we developed a new predictor called 'iTerm-PseKNC' based on support vector machine to identify transcription terminators. The binomial distribution approach was used to pick out the optimal feature subset derived from pseudo k-tuple nucleotide composition (PseKNC). The 5-fold cross-validation test results showed that our proposed method achieved an accuracy of 95%. To further evaluate the generalization ability of 'iTerm-PseKNC', the model was examined on independent datasets which are experimentally confirmed Rho-independent terminators in Escherichia coli and Bacillus subtilis genomes. As a result, all the terminators in E. coli and 87.5% of the terminators in B. subtilis were correctly identified, suggesting that the proposed model could become a powerful tool for bacterial terminator recognition. AVAILABILITY AND IMPLEMENTATION: For the convenience of most of wet-experimental researchers, the web-server for 'iTerm-PseKNC' was established at http://lin-group.cn/server/iTerm-PseKNC/, by which users can easily obtain their desired result without the need to go through the detailed mathematical equations involved.

iLoc-lncRNA: predict the subcellular location of lncRNAs by incorporating octamer composition into general PseKNC.

Motivation: Long non-coding RNAs (lncRNAs) are a class of RNA molecules with more than 200 nucleotides. They have important functions in cell development and metabolism, such as genetic markers, genome rearrangements, chromatin modifications, cell cycle regulation, transcription and translation. Their functions are generally closely related to their localization in the cell. Therefore, knowledge about their subcellular locations can provide very useful clues or preliminary insight into their biological functions. Although biochemical experiments could determine the localization of lncRNAs in a cell, they are both time-consuming and expensive. Therefore, it is highly desirable to develop bioinformatics tools for fast and effective identification of their subcellular locations. Results: We developed a sequence-based bioinformatics tool called 'iLoc-lncRNA' to predict the subcellular locations of LncRNAs by incorporating the 8-tuple nucleotide features into the general PseKNC (Pseudo K-tuple Nucleotide Composition) via the binomial distribution approach. Rigorous jackknife tests have shown that the overall accuracy achieved by the new predictor on a stringent benchmark dataset is 86.72%, which is over 20% higher than that by the existing state-of-the-art predictor evaluated on the same tests. Availability and implementation: A user-friendly webserver has been established at http://lin-group.cn/server/iLoc-LncRNA, by which users can easily obtain their desired results. Supplementary information: Supplementary data are available at Bioinformatics online.

A novel lncRNA-focus expression signature for survival prediction in endometrial carcinoma.

BACKGROUND: Endometrial cancer (UCEC) is a complex malignant tumor characterized by both genetic level and clinical trial. Patients with UCEC exhibit the similar clinical features, however, they have distinct outcomes due to molecular heterogeneity. The aim of this study was to access the prognostic value of long non-coding RNAs (lncRNAs) in UCEC patients and to identify potential lncRNA signature for predicting patients' survival and improving patient-tailored treatment. METHODS: We performed a comprehensive genome-wide analysis of lncRNA expression profiles and clinical data in a large cohort of 301 UCEC patients. UCEC patients were randomly divided into the discovery cohort (n = 150) and validation cohort (n = 151). A novel lncRNA-focus expression signature was identified in the discovery cohort, and independently accessed in the validation cohort. Additionally, the lncRNA signature was evaluated by multivariable Cox regression and stratification analysis as well as functional enrichment analysis. RESULTS: We detected a novel lncRNA-focus expression signature (LFES) consisting of 11 lncRNAs that were associated with survival based on risk scoring strategy in UCEC. The risk score based on the LFES was able to separate patients of discovery cohort into high-risk and low-risk groups with significantly different overall survival and progression-free survival, and has been successfully confirmed in the validation cohort. Furthermore, the LFES is an independent prognostic predictor of survival and demonstrates superior prognostic performance compared with the clinical covariates for predicting 5-year survival (AUC = 0.887). Functional analysis has linked the expression of prognostic lncRNAs to well-known tumor suppressor or ontogenetic pathways in endometrial carcinogenesis. CONCLUSIONS: Our study revealed a novel 11-lncRNA signature to predict survival of UCEC patient. This lncRNA signature may be a valuable and alternative marker for risk evaluation to aid patient-tailored treatment and improve the outcome of patients with UCEC.

Modulation Signal Recognition Based on Information Entropy and Ensemble Learning.

In this paper, information entropy and ensemble learning based signal recognition theory and algorithms have been proposed. We have extracted 16 kinds of entropy features out of 9 types of modulated signals. The types of information entropy used are numerous, including Rényi entropy and energy entropy based on S Transform and Generalized S Transform. We have used three feature selection algorithms, including sequence forward selection (SFS), sequence forward floating selection (SFFS) and RELIEF-F to select the optimal feature subset from 16 entropy features. We use five classifiers, including k-nearest neighbor (KNN), support vector machine (SVM), Adaboost, Gradient Boosting Decision Tree (GBDT) and eXtreme Gradient Boosting (XGBoost) to classify the original feature set and the feature subsets selected by different feature selection algorithms. The simulation results show that the feature subsets selected by SFS and SFFS algorithms are the best, with a 48% increase in recognition rate over the original feature set when using KNN classifier and a 34% increase when using SVM classifier. For the other three classifiers, the original feature set can achieve the best recognition performance. The XGBoost classifier has the best recognition performance, the overall recognition rate is 97.74% and the recognition rate can reach 82% when the signal to noise ratio (SNR) is -10 dB.

Chemical Basis for Deuterium Labeling of Fat and NADPH.

Much understanding of metabolism is based on monitoring chemical reactions in cells with isotope tracers. For this purpose, 13C is well suited due to its stable incorporation into biomolecules and minimal kinetic isotope effect. For redox reactions, deuterium tracing can provide additional information. To date, studies examining NADPH production with deuterated carbon sources have failed to account for roughly half of NADPH's redox active hydrogen. We show the missing hydrogen is the result of enzyme-catalyzed H-D exchange between water and NADPH. Though isolated NADPH does not undergo H-D exchange with water, such exchange is catalyzed by Flavin enzymes and occurs rapidly in cells. Correction for H-D exchange is required for accurate assessment of biological sources of NADPH's high energy electrons. Deuterated water (D2O) is frequently used to monitor fat synthesis in vivo, but the chemical pathway of the deuterons into fat remains unclear. We show D2O labels fatty acids primarily via NADPH. Knowledge of this route enables calculation, without any fitting parameters, of the mass isotopomer distributions of fatty acids from cells grown in D2O. Thus, knowledge of enzyme-catalyzed H-D exchange between water and NADPH enables accurate interpretation of deuterium tracing studies of redox cofactor and fatty acid metabolism.

Palladium-Triggered Chemical Rescue of Intracellular Proteins via Genetically Encoded Allene-Caged Tyrosine.

Chemical de-caging has emerged as an attractive strategy for gain-of-function study of proteins via small-molecule reagents. The previously reported chemical de-caging reactions have been largely centered on liberating the side chain of lysine on a given protein. Herein, we developed an allene-based caging moiety and the corresponding palladium de-caging reagents for chemical rescue of tyrosine (Tyr) activity on intracellular proteins. This bioorthogonal de-caging pair has been successfully applied to unmask enzymatic Tyr sites (e.g., Y671 on Taq polymerase and Y728 on Anthrax lethal factor) as well as the post-translational Tyr modification site (Y416 on Src kinase) in vitro and in living cells. Our strategy provides a general platform for chemical rescue of Tyr-dependent protein activity inside cells.

Chemical remodeling of cell-surface sialic acids through a palladium-triggered bioorthogonal elimination reaction.

We herein report a chemical decaging strategy for the in situ generation of neuramic acid (Neu), a unique type of sialic acid, on live cells by the use of a palladium-mediated bioorthogonal elimination reaction. Palladium nanoparticles (Pd NPs) were found to be a highly efficient and biocompatible depropargylation catalyst for the direct conversion of metabolically incorporated N-(propargyloxycarbonyl)neuramic acid (Neu5Proc) into Neu on cell-surface glycans. This conversion chemically mimics the enzymatic de-N-acetylation of N-acetylneuramic acid (Neu5Ac), a proposed mechanism for the natural occurrence of Neu on cell-surface glycans. The bioorthogonal elimination was also exploited for the manipulation of cell-surface charge by unmasking the free amine at C5 to neutralize the negatively charged carboxyl group at C1 of sialic acids.

ANRIL inhibits p15(INK4b) through the TGFß1 signaling pathway in human esophageal squamous cell carcinoma.

The INK4b-ARF-INK4a gene cluster encodes three tumor suppressors: p15(INK4b), p14(ARF), and p16(INK4a). Antisense non-coding RNA in the INK4 locus (ANRIL) is transcribed in the opposite direction from this gene cluster. Recent studies suggest that ANRIL represses the expression of p15(INK4b), p14(ARF), and p16(INK4a); however, the underlying mechanism is unclear. In this study, the expressions of ANRIL in human esophageal squamous cell carcinoma (ESCC) tissues and matched adjacent non-tumor tissues were examined by quantitative real-time polymerase chain reaction. Compared with matched adjacent non-tumor tissues, the expression levels of ANRIL in ESCC tissues were significantly increased. Furthermore, inhibition of ANRIL was found to increase the expression of p15(INK4b) and transforming growth factor ß1 (TGFß1) and depletion of ANRIL in ESCC cell lines may inhibit cellular proliferation. Thus, our findings suggest a significant role of ANRIL in the occurrence and development of ESCC through TGFß1 signaling pathways.

A BERT-based model for the prediction of lncRNA subcellular localization in Homo sapiens.

Understanding the subcellular localization of lncRNAs is crucial for comprehending their regulation activities. The conventional detection of lncRNA subcellular location usually uses in situ detection techniques, which are resource intensive. Some machine learning-based algorithms have been proposed for lncRNA subcellular location prediction in mammals. However, due to the low level of conservation of lncRNA sequence, the performance of cross-species models remains unsatisfactory. In this study, we curated a novel dataset containing subcellular location information of lncRNAs in Homo sapiens. Subsequently, based on the BERT pre-trained language algorithm, we developed a model for lncRNA subcellular location prediction. Our model achieved a micro-average area under the receiver operating characteristic (AUROC) of 0.791 on the training set and an AUROC of 0.700 on the testing nucleus set. Additionally, we conducted cross-species validation and motif discovery to further investigate underlying patterns. In summary, our study provides valuable guidance and computational analysis tools for exploring the mechanisms of lncRNA subcellular localization and the dynamic spatial changes of RNA in abnormal physiological states.

Excavation of gene markers associated with pancreatic ductal adenocarcinoma based on interrelationships of gene expression.

Pancreatic ductal adenocarcinoma (PDAC) accounts for 95% of all pancreatic cancer cases, posing grave challenges to its diagnosis and treatment. Timely diagnosis is pivotal for improving patient survival, necessitating the discovery of precise biomarkers. An innovative approach was introduced to identify gene markers for precision PDAC detection. The core idea of our method is to discover gene pairs that display consistent opposite relative expression and differential co-expression patterns between PDAC and normal samples. Reversal gene pair analysis and differential partial correlation analysis were performed to determine reversal differential partial correlation (RDC) gene pairs. Using incremental feature selection, the authors refined the selected gene set and constructed a machine-learning model for PDAC recognition. As a result, the approach identified 10 RDC gene pairs. And the model could achieve a remarkable accuracy of 96.1% during cross-validation, surpassing gene expression-based models. The experiment on independent validation data confirmed the model's performance. Enrichment analysis revealed the involvement of these genes in essential biological processes and shed light on their potential roles in PDAC pathogenesis. Overall, the findings highlight the potential of these 10 RDC gene pairs as effective diagnostic markers for early PDAC detection, bringing hope for improving patient prognosis and survival.

Glycine homeostasis requires reverse SHMT flux.

The folate-dependent enzyme serine hydroxymethyltransferase (SHMT) reversibly converts serine into glycine and a tetrahydrofolate-bound one-carbon unit. Such one-carbon unit production plays a critical role in development, the immune system, and cancer. Using rodent models, here we show that the whole-body SHMT flux acts to net consume rather than produce glycine. Pharmacological inhibition of whole-body SHMT1/2 and genetic knockout of liver SHMT2 elevated circulating glycine levels up to eight-fold. Stable-isotope tracing revealed that the liver converts glycine to serine, which is then converted by serine dehydratase into pyruvate and burned in the tricarboxylic acid cycle. In response to diets deficient in serine and glycine, de novo biosynthetic flux was unaltered, but SHMT2- and serine-dehydratase-mediated catabolic flux was lower. Thus, glucose-derived serine synthesis is largely insensitive to systemic demand. Instead, circulating serine and glycine homeostasis is maintained through variable consumption, with liver SHMT2 a major glycine-consuming enzyme.

Recognition of outer membrane proteins using multiple feature fusion.

Introduction: Outer membrane proteins are crucial in maintaining the structural stability and permeability of the outer membrane. Outer membrane proteins exhibit several functions such as antigenicity and strong immunogenicity, which have potential applications in clinical diagnosis and disease prevention. However, wet experiments for studying OMPs are time and capital-intensive, thereby necessitating the use of computational methods for their identification. Methods: In this study, we developed a computational model to predict outer membrane proteins. The non-redundant dataset consists of a positive set of 208 outer membrane proteins and a negative set of 876 non-outer membrane proteins. In this study, we employed the pseudo amino acid composition method to extract feature vectors and subsequently utilized the support vector machine for prediction. Results and Discussion: In the Jackknife cross-validation, the overall accuracy and the area under receiver operating characteristic curve were observed to be 93.19% and 0.966, respectively. These results demonstrate that our model can produce accurate predictions, and could serve as a valuable guide for experimental research on outer membrane proteins.

Computational prediction of promotors in Agrobacterium tumefaciens strain C58 by using the machine learning technique.

Promotors are those genomic regions on the upstream of genes, which are bound by RNA polymerase for starting gene transcription. Because it is the most critical element of gene expression, the recognition of promoters is crucial to understand the regulation of gene expression. This study aimed to develop a machine learning-based model to predict promotors in Agrobacterium tumefaciens (A. tumefaciens) strain C58. In the model, promotor sequences were encoded by three different kinds of feature descriptors, namely, accumulated nucleotide frequency, k-mer nucleotide composition, and binary encodings. The obtained features were optimized by using correlation and the mRMR-based algorithm. These optimized features were inputted into a random forest (RF) classifier to discriminate promotor sequences from non-promotor sequences in A. tumefaciens strain C58. The examination of 10-fold cross-validation showed that the proposed model could yield an overall accuracy of 0.837. This model will provide help for the study of promoters in A. tumefaciens C58 strain.

Prediction of apoptosis protein subcellular location based on amphiphilic pseudo amino acid composition.

Introduction: Apoptosis proteins play an important role in the process of cell apoptosis, which makes the rate of cell proliferation and death reach a relative balance. The function of apoptosis protein is closely related to its subcellular location, it is of great significance to study the subcellular locations of apoptosis proteins. Many efforts in bioinformatics research have been aimed at predicting their subcellular location. However, the subcellular localization of apoptotic proteins needs to be carefully studied. Methods: In this paper, based on amphiphilic pseudo amino acid composition and support vector machine algorithm, a new method was proposed for the prediction of apoptosis proteins\x{2019} subcellular location. Results and Discussion: The method achieved good performance on three data sets. The Jackknife test accuracy of the three data sets reached 90.5%, 93.9% and 84.0%, respectively. Compared with previous methods, the prediction accuracies of APACC_SVM were improved.