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.

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.

Empirical comparison and recent advances of computational prediction of hormone binding proteins using machine learning methods.

Hormone binding proteins (HBPs) belong to the group of soluble carrier proteins. These proteins selectively and non-covalently interact with hormones and promote growth hormone signaling in human and other animals. The HBPs are useful in many medical and commercial fields. Thus, the identification of HBPs is very important because it can help to discover more details about hormone binding proteins. Meanwhile, the experimental methods are time-consuming and expensive for hormone binding proteins recognition. Computational prediction methods have played significant roles in the correct recognition of hormone binding proteins with the use of sequence information and ML algorithms. In this review, we compared and assessed the implementation of ML-based tools in recognition of HBPs in a unique way. We hope that this study will give enough awareness and knowledge for research on HBPs.

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.

A cost-effective machine learning-based method for preeclampsia risk assessment and driver genes discovery.

BACKGROUND: The placenta, as a unique exchange organ between mother and fetus, is essential for successful human pregnancy and fetal health. Preeclampsia (PE) caused by placental dysfunction contributes to both maternal and infant morbidity and mortality. Accurate identification of PE patients plays a vital role in the formulation of treatment plans. However, the traditional clinical methods of PE have a high misdiagnosis rate. RESULTS: Here, we first designed a computational biology method that used single-cell transcriptome (scRNA-seq) of healthy pregnancy (38 wk) and early-onset PE (28-32 wk) to identify pathological cell subpopulations and predict PE risk. Based on machine learning methods and feature selection techniques, we observed that the Tuning ReliefF (TURF) score hybrid with XGBoost (TURF_XGB) achieved optimal performance, with 92.61% accuracy and 92.46% recall for classifying nine cell subpopulations of healthy placentas. Biological landscapes of placenta heterogeneity could be mapped by the 110 marker genes screened by TURF_XGB, which revealed the superiority of the TURF feature mining. Moreover, we processed the PE dataset with LASSO to obtain 497 biomarkers. Integration analysis of the above two gene sets revealed that dendritic cells were closely associated with early-onset PE, and C1QB and C1QC might drive preeclampsia by mediating inflammation. In addition, an ensemble model-based risk stratification card was developed to classify preeclampsia patients, and its area under the receiver operating characteristic curve (AUC) could reach 0.99. For broader accessibility, we designed an accessible online web server ( http://bioinfor.imu.edu.cn/placenta ). CONCLUSION: Single-cell transcriptome-based preeclampsia risk assessment using an ensemble machine learning framework is a valuable asset for clinical decision-making. C1QB and C1QC may be involved in the development and progression of early-onset PE by affecting the complement and coagulation cascades pathway that mediate inflammation, which has important implications for better understanding the pathogenesis of PE.

AcrPred: A hybrid optimization with enumerated machine learning algorithm to predict Anti-CRISPR proteins.

CRISPR-Cas, as a tool for gene editing, has received extensive attention in recent years. Anti-CRISPR (Acr) proteins can inactivate the CRISPR-Cas defense system during interference phase, and can be used as a potential tool for the regulation of gene editing. In-depth study of Anti-CRISPR proteins is of great significance for the implementation of gene editing. In this study, we developed a high-accuracy prediction model based on two-step model fusion strategy, called AcrPred, which could produce an AUC of 0.952 with independent dataset validation. To further validate the proposed model, we compared with published tools and correctly identified 9 of 10 new Acr proteins, indicating the strong generalization ability of our model. Finally, for the convenience of related wet-experimental researchers, a user-friendly web-server AcrPred (Anti-CRISPR proteins Prediction) was established at http://lin-group.cn/server/AcrPred, by which users can easily identify potential Anti-CRISPR proteins.

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.

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/).

IBPred: A sequence-based predictor for identifying ion binding protein in phage.

Ion binding proteins (IBPs) can selectively and non-covalently interact with ions. IBPs in phages also play an important role in biological processes. Therefore, accurate identification of IBPs is necessary for understanding their biological functions and molecular mechanisms that involve binding to ions. Since molecular biology experimental methods are still labor-intensive and cost-ineffective in identifying IBPs, it is helpful to develop computational methods to identify IBPs quickly and efficiently. In this work, a random forest (RF)-based model was constructed to quickly identify IBPs. Based on the protein sequence information and residues' physicochemical properties, the dipeptide composition combined with the physicochemical correlation between two residues were proposed for the extraction of features. A feature selection technique called analysis of variance (ANOVA) was used to exclude redundant information. By comparing with other classified methods, we demonstrated that our method could identify IBPs accurately. Based on the model, a Python package named IBPred was built with the source code which can be accessed at https://github.com/ShishiYuan/IBPred.

Survival comparison between postoperative and preoperative radiotherapy for stage I-III non-inflammatory breast cancer.

To compare the survival benefit between preoperative and postoperative radiotherapy for stage I-III non-inflammatory breast cancer patients, we conducted a retrospective cohort study using surveillance, epidemiology and end results databases. Our study recruited patients who had been diagnosed with stage I-III breast cancer and underwent surgery and radiotherapy. The overall survival was calculated by Kaplan-Meier method. Cox risk model was used to determine the impact of radiotherapy according to stage, molecular subtype and other risk factors. Propensity score matching was used to balance measurable confounding factors. Of all the 411,279 enrolled patients varying from 1975 to 2016, 1712 patients received preoperative radiotherapy, and 409,567 patients received postoperative radiotherapy. Compared with the postoperative radiotherapy group, the preoperative radiotherapy group showed significantly higher risks of overall mortality and breast cancer-specific mortality. Survival differences in treatment sequences were correlated with stage, molecular subtypes and other risk factors. According to the results of this study, preoperative radiotherapy did not show a survival advantage, and postoperative radiotherapy is still the primary treatment. However, preoperative radiotherapy also has some theoretical advantages, such as phase reduction and recurrence reduction. Therefore, it is still worthy of further exploration.

iThermo: A Sequence-Based Model for Identifying Thermophilic Proteins Using a Multi-Feature Fusion Strategy.

Thermophilic proteins have important application value in biotechnology and industrial processes. The correct identification of thermophilic proteins provides important information for the application of these proteins in engineering. The identification method of thermophilic proteins based on biochemistry is laborious, time-consuming, and high cost. Therefore, there is an urgent need for a fast and accurate method to identify thermophilic proteins. Considering this urgency, we constructed a reliable benchmark dataset containing 1,368 thermophilic and 1,443 non-thermophilic proteins. A multi-layer perceptron (MLP) model based on a multi-feature fusion strategy was proposed to discriminate thermophilic proteins from non-thermophilic proteins. On independent data set, the proposed model could achieve an accuracy of 96.26%, which demonstrates that the model has a good application prospect. In order to use the model conveniently, a user-friendly software package called iThermo was established and can be freely accessed at http://lin-group.cn/server/iThermo/index.html. The high accuracy of the model and the practicability of the developed software package indicate that this study can accelerate the discovery and engineering application of thermally stable proteins.

Comprehensive Prediction of Lipocalin Proteins Using Artificial Intelligence Strategy.

BACKGROUND: Lipocalin belongs to the calcyin family, and its sequence length is generally between 165 and 200 residues. They are mainly stable and multifunctional extracellular proteins. Lipocalin plays an important role in several stress responses and allergic inflammations. Because the accurate identification of lipocalins could provide significant evidences for the study of their function, it is necessary to develop a machine learning-based model to recognize lipocalin. METHODS: In this study, we constructed a prediction model to identify lipocalin. Their sequences were encoded by six types of features, namely amino acid composition (AAC), composition of k-spaced amino acid pairs (CKSAAP), pseudo amino acid composition (PseAAC), Geary correlation (GD), normalized Moreau-Broto autocorrelation (NMBroto) and composition/transition/distribution (CTD). Subsequently, these features were optimized by using feature selection techniques. A classifier based on random forest was trained according to the optimal features. RESULTS: The results of 10-fold cross-validation showed that our computational model would classify lipocalins with accuracy of 95.03% and area under the curve of 0.987. On the independent dataset, our computational model could produce the accuracy of 89.90% which was 4.17% higher than the existing model. CONCLUSIONS: In this work, we developed an advanced computational model to discriminate lipocalin proteins from non-lipocalin proteins. In the proposed model, protein sequences were encoded by six descriptors. Then, feature selection was performed to pick out the best features which could produce the maximum accuracy. On the basis of the best feature subset, the RF-based classifier can obtained the best prediction results.

Identification of Helicobacter pylori Membrane Proteins Using Sequence-Based Features.

Helicobacter pylori (H. pylori) is the most common risk factor for gastric cancer worldwide. The membrane proteins of the H. pylori are involved in bacterial adherence and play a vital role in the field of drug discovery. Thus, an accurate and cost-effective computational model is needed to predict the uncharacterized membrane proteins of H. pylori. In this study, a reliable benchmark dataset consisted of 114 membrane and 219 nonmembrane proteins was constructed based on UniProt. A support vector machine- (SVM-) based model was developed for discriminating H. pylori membrane proteins from nonmembrane proteins by using sequence information. Cross-validation showed that our method achieved good performance with an accuracy of 91.29%. It is anticipated that the proposed model will be useful for the annotation of H. pylori membrane proteins and the development of new anti-H. pylori agents.

A Survey for Predicting ATP Binding Residues of Proteins Using Machine Learning Methods.

Protein-ligand interactions are necessary for majority protein functions. Adenosine- 5'-triphosphate (ATP) is one such ligand that plays vital role as a coenzyme in providing energy for cellular activities, catalyzing biological reaction and signaling. Knowing ATP binding residues of proteins is helpful for annotation of protein function and drug design. However, due to the huge amounts of protein sequences influx into databases in the post-genome era, experimentally identifying ATP binding residues is costineffective and time-consuming. To address this problem, computational methods have been developed to predict ATP binding residues. In this review, we briefly summarized the application of machine learning methods in detecting ATP binding residues of proteins. We expect this review will be helpful for further research.

Serine catabolism generates liver NADPH and supports hepatic lipogenesis.

Carbohydrate can be converted into fat by de novo lipogenesis, a process upregulated in fatty liver disease. Chemically, de novo lipogenesis involves polymerization and reduction of acetyl-CoA, using NADPH as the electron donor. The feedstocks used to generate acetyl-CoA and NADPH in lipogenic tissues remain, however, unclear. Here we show using stable isotope tracing in mice that de novo lipogenesis in adipose is supported by glucose and its catabolism via the pentose phosphate pathway to make NADPH. The liver, in contrast, derives acetyl-CoA for lipogenesis from acetate and lactate, and NADPH from folate-mediated serine catabolism. Such NADPH generation involves the cytosolic serine pathway in liver running in the opposite direction to that observed in most tissues and tumours, with NADPH made by the SHMT1-MTHFD1-ALDH1L1 reaction sequence. SHMT inhibition decreases hepatic lipogenesis. Thus, liver folate metabolism is distinctively wired to support cytosolic NADPH production and lipogenesis. More generally, while the same enzymes are involved in fat synthesis in liver and adipose, different substrates are used, opening the door to tissue-specific pharmacological interventions.