Compartmentalized metabolism supports midgestation mammalian development.

Mammalian embryogenesis requires rapid growth and proper metabolic regulation1. Midgestation features increasing oxygen and nutrient availability concomitant with fetal organ development2,3. Understanding how metabolism supports development requires approaches to observe metabolism directly in model organisms in utero. Here we used isotope tracing and metabolomics to identify evolving metabolic programmes in the placenta and embryo during midgestation in mice. These tissues differ metabolically throughout midgestation, but we pinpointed gestational days (GD) 10.5-11.5 as a transition period for both placenta and embryo. Isotope tracing revealed differences in carbohydrate metabolism between the tissues and rapid glucose-dependent purine synthesis, especially in the embryo. Glucose's contribution to the tricarboxylic acid (TCA) cycle rises throughout midgestation in the embryo but not in the placenta. By GD12.5, compartmentalized metabolic programmes are apparent within the embryo, including different nutrient contributions to the TCA cycle in different organs. To contextualize developmental anomalies associated with Mendelian metabolic defects, we analysed mice deficient in LIPT1, the enzyme that activates 2-ketoacid dehydrogenases related to the TCA cycle4,5. LIPT1 deficiency suppresses TCA cycle metabolism during the GD10.5-GD11.5 transition, perturbs brain, heart and erythrocyte development and leads to embryonic demise by GD11.5. These data document individualized metabolic programmes in developing organs in utero.

Using deep neural networks and biological subwords to detect protein S-sulfenylation sites.

Protein S-sulfenylation is one kind of crucial post-translational modifications (PTMs) in which the hydroxyl group covalently binds to the thiol of cysteine. Some recent studies have shown that this modification plays an important role in signaling transduction, transcriptional regulation and apoptosis. To date, the dynamic of sulfenic acids in proteins remains unclear because of its fleeting nature. Identifying S-sulfenylation sites, therefore, could be the key to decipher its mysterious structures and functions, which are important in cell biology and diseases. However, due to the lack of effective methods, scientists in this field tend to be limited in merely a handful of some wet lab techniques that are time-consuming and not cost-effective. Thus, this motivated us to develop an in silico model for detecting S-sulfenylation sites only from protein sequence information. In this study, protein sequences served as natural language sentences comprising biological subwords. The deep neural network was consequentially employed to perform classification. The performance statistics within the independent dataset including sensitivity, specificity, accuracy, Matthews correlation coefficient and area under the curve rates achieved 85.71%, 69.47%, 77.09%, 0.5554 and 0.833, respectively. Our results suggested that the proposed method (fastSulf-DNN) achieved excellent performance in predicting S-sulfenylation sites compared to other well-known tools on a benchmark dataset.

Molecular subtype classification of low-grade gliomas using magnetic resonance imaging-based radiomics and machine learning.

In 2016, the World Health Organization (WHO) updated the glioma classification by incorporating molecular biology parameters, including low-grade glioma (LGG). In the new scheme, LGGs have three molecular subtypes: isocitrate dehydrogenase (IDH)-mutated 1p/19q-codeleted, IDH-mutated 1p/19q-noncodeleted, and IDH-wild type 1p/19q-noncodeleted entities. This work proposes a model prediction of LGG molecular subtypes using magnetic resonance imaging (MRI). MR images were segmented and converted into radiomics features, thereby providing predictive information about the brain tumor classification. With 726 raw features obtained from the feature extraction procedure, we developed a hybrid machine learning-based radiomics by incorporating a genetic algorithm and eXtreme Gradient Boosting (XGBoost) classifier, to ascertain 12 optimal features for tumor classification. To resolve imbalanced data, the synthetic minority oversampling technique (SMOTE) was applied in our study. The XGBoost algorithm outperformed the other algorithms on the training dataset by an accuracy value of 0.885. We continued evaluating the XGBoost model, then achieved an overall accuracy of 0.6905 for the three-subtype classification of LGGs on an external validation dataset. Our model is among just a few to have resolved the three-subtype LGG classification challenge with high accuracy compared with previous studies performing similar work.

Using extreme gradient boosting to identify origin of replication in Saccharomyces cerevisiae via hybrid features.

DNA replication is a fundamental task that plays a crucial role in the propagation of all living things on earth. Hence, the accurate identification of its origin could be the key to giving an insightful understanding of the regulatory mechanism of gene expression. Indeed, with the robust development of computational techniques and the abundant biological sequencing data, it has become possible for scientists to identify the origin of replication accurately and promptly. This growing concern has drawn a lot of attention among experts in this field. However, to gain better outcomes, more work is required. Therefore, this study is designed to explore the combination of state-of-the-art features and extreme gradient boosting learning system in classifying DNA sequences. Our hybrid approach is able to identify the origin of DNA replication with achieved sensitivity of 85.19%, specificity of 93.83%, accuracy of 89.51%, and MCC of 0.7931. Evidence is presented to show that our proposed method is superior to the state-of-the-art methods on the same benchmark dataset. Moreover, the research results represent a further step towards developing the prediction models for DNA replication in particular and DNA sequences in general.

A Computational Framework Based on Ensemble Deep Neural Networks for Essential Genes Identification.

Essential genes contain key information of genomes that could be the key to a comprehensive understanding of life and evolution. Because of their importance, studies of essential genes have been considered a crucial problem in computational biology. Computational methods for identifying essential genes have become increasingly popular to reduce the cost and time-consumption of traditional experiments. A few models have addressed this problem, but performance is still not satisfactory because of high dimensional features and the use of traditional machine learning algorithms. Thus, there is a need to create a novel model to improve the predictive performance of this problem from DNA sequence features. This study took advantage of a natural language processing (NLP) model in learning biological sequences by treating them as natural language words. To learn the NLP features, a supervised learning model was consequentially employed by an ensemble deep neural network. Our proposed method could identify essential genes with sensitivity, specificity, accuracy, Matthews correlation coefficient (MCC), and area under the receiver operating characteristic curve (AUC) values of 60.2%, 84.6%, 76.3%, 0.449, and 0.814, respectively. The overall performance outperformed the single models without ensemble, as well as the state-of-the-art predictors on the same benchmark dataset. This indicated the effectiveness of the proposed method in determining essential genes, in particular, and other sequencing problems, in general.

Unitig-centered pan-genome machine learning approach for predicting antibiotic resistance and discovering novel resistance genes in bacterial strains.

In current genomic research, the widely used methods for predicting antimicrobial resistance (AMR) often rely on prior knowledge of known AMR genes or reference genomes. However, these methods have limitations, potentially resulting in imprecise predictions owing to incomplete coverage of AMR mechanisms and genetic variations. To overcome these limitations, we propose a pan-genome-based machine learning approach to advance our understanding of AMR gene repertoires and uncover possible feature sets for precise AMR classification. By building compacted de Brujin graphs (cDBGs) from thousands of genomes and collecting the presence/absence patterns of unique sequences (unitigs) for Pseudomonas aeruginosa, we determined that using machine learning models on unitig-centered pan-genomes showed significant promise for accurately predicting the antibiotic resistance or susceptibility of microbial strains. Applying a feature-selection-based machine learning algorithm led to satisfactory predictive performance for the training dataset (with an area under the receiver operating characteristic curve (AUC) of > 0.929) and an independent validation dataset (AUC, approximately 0.77). Furthermore, the selected unitigs revealed previously unidentified resistance genes, allowing for the expansion of the resistance gene repertoire to those that have not previously been described in the literature on antibiotic resistance. These results demonstrate that our proposed unitig-based pan-genome feature set was effective in constructing machine learning predictors that could accurately identify AMR pathogens. Gene sets extracted using this approach may offer valuable insights into expanding known AMR genes and forming new hypotheses to uncover the underlying mechanisms of bacterial AMR.

Electron transport chain inhibition increases cellular dependence on purine transport and salvage.

Mitochondria house many metabolic pathways required for homeostasis and growth. To explore how human cells respond to mitochondrial dysfunction, we performed metabolomics in fibroblasts from patients with various mitochondrial disorders and cancer cells with electron transport chain (ETC) blockade. These analyses revealed extensive perturbations in purine metabolism, and stable isotope tracing demonstrated that ETC defects suppress de novo purine synthesis while enhancing purine salvage. In human lung cancer, tumors with markers of low oxidative mitochondrial metabolism exhibit enhanced expression of the salvage enzyme hypoxanthine phosphoribosyl transferase 1 (HPRT1) and high levels of the HPRT1 product inosine monophosphate. Mechanistically, ETC blockade activates the pentose phosphate pathway, providing phosphoribosyl diphosphate to drive purine salvage supplied by uptake of extracellular bases. Blocking HPRT1 sensitizes cancer cells to ETC inhibition. These findings demonstrate how cells remodel purine metabolism upon ETC blockade and uncover a new metabolic vulnerability in tumors with low respiration.

Dosiomics-Based Prediction of Radiation-Induced Valvulopathy after Childhood Cancer.

Valvular Heart Disease (VHD) is a known late complication of radiotherapy for childhood cancer (CC), and identifying high-risk survivors correctly remains a challenge. This paper focuses on the distribution of the radiation dose absorbed by heart tissues. We propose that a dosiomics signature could provide insight into the spatial characteristics of the heart dose associated with a VHD, beyond the already-established risk induced by high doses. We analyzed data from the 7670 survivors of the French Childhood Cancer Survivors' Study (FCCSS), 3902 of whom were treated with radiotherapy. In all, 63 (1.6%) survivors that had been treated with radiotherapy experienced a VHD, and 57 of them had heterogeneous heart doses. From the heart-dose distribution of each survivor, we extracted 93 first-order and spatial dosiomics features. We trained random forest algorithms adapted for imbalanced classification and evaluated their predictive performance compared to the performance of standard mean heart dose (MHD)-based models. Sensitivity analyses were also conducted for sub-populations of survivors with spatially heterogeneous heart doses. Our results suggest that MHD and dosiomics-based models performed equally well globally in our cohort and that, when considering the sub-population having received a spatially heterogeneous dose distribution, the predictive capability of the models is significantly improved by the use of the dosiomics features. If these findings are further validated, the dosiomics signature may be incorporated into machine learning algorithms for radiation-induced VHD risk assessment and, in turn, into the personalized refinement of follow-up guidelines.

Comprehensive isotopomer analysis of glutamate and aspartate in small tissue samples.

Stable isotopes are powerful tools to assess metabolism. 13C labeling is detected using nuclear magnetic resonance (NMR) spectroscopy or mass spectrometry (MS). MS has excellent sensitivity but generally cannot discriminate among different 13C positions (isotopomers), whereas NMR is less sensitive but reports some isotopomers. Here, we develop an MS method that reports all 16 aspartate and 32 glutamate isotopomers while requiring less than 1% of the sample used for NMR. This method discriminates between pathways that result in the same number of 13C labels in aspartate and glutamate, providing enhanced specificity over conventional MS. We demonstrate regional metabolic heterogeneity within human tumors, document the impact of fumarate hydratase (FH) deficiency in human renal cancers, and investigate the contributions of tricarboxylic acid (TCA) cycle turnover and CO2 recycling to isotope labeling in vivo. This method can accompany NMR or standard MS to provide outstanding sensitivity in isotope-labeling experiments, particularly in vivo.

Electron transport chain inhibition increases cellular dependence on purine transport and salvage.

Cancer cells reprogram their metabolism to support cell growth and proliferation in harsh environments. While many studies have documented the importance of mitochondrial oxidative phosphorylation (OXPHOS) in tumor growth, some cancer cells experience conditions of reduced OXPHOS in vivo and induce alternative metabolic pathways to compensate. To assess how human cells respond to mitochondrial dysfunction, we performed metabolomics in fibroblasts and plasma from patients with inborn errors of mitochondrial metabolism, and in cancer cells subjected to inhibition of the electron transport chain (ETC). All these analyses revealed extensive perturbations in purine-related metabolites; in non-small cell lung cancer (NSCLC) cells, ETC blockade led to purine metabolite accumulation arising from a reduced cytosolic NAD + /NADH ratio (NADH reductive stress). Stable isotope tracing demonstrated that ETC deficiency suppressed de novo purine nucleotide synthesis while enhancing purine salvage. Analysis of NSCLC patients infused with [U- 13 C]glucose revealed that tumors with markers of low oxidative mitochondrial metabolism exhibited high expression of the purine salvage enzyme HPRT1 and abundant levels of the HPRT1 product inosine monophosphate (IMP). ETC blockade also induced production of ribose-5' phosphate (R5P) by the pentose phosphate pathway (PPP) and import of purine nucleobases. Blocking either HPRT1 or nucleoside transporters sensitized cancer cells to ETC inhibition, and overexpressing nucleoside transporters was sufficient to drive growth of NSCLC xenografts. Collectively, this study mechanistically delineates how cells compensate for suppressed purine metabolism in response to ETC blockade, and uncovers a new metabolic vulnerability in tumors experiencing NADH excess.

Mitochondrial metabolism in primary and metastatic human kidney cancers.

Most kidney cancers display evidence of metabolic dysfunction1-4 but how this relates to cancer progression in humans is unknown. We used a multidisciplinary approach to infuse 13C-labeled nutrients during surgical tumour resection in over 70 patients with kidney cancer. Labeling from [U-13C]glucose varies across cancer subtypes, indicating that the kidney environment alone cannot account for all metabolic reprogramming in these tumours. Compared to the adjacent kidney, clear cell renal cell carcinomas (ccRCC) display suppressed labelling of tricarboxylic acid (TCA) cycle intermediates in vivo and in organotypic slices cultured ex vivo, indicating that suppressed labeling is tissue intrinsic. Infusions of [1,2-13C]acetate and [U-13C]glutamine in patients, coupled with respiratory flux of mitochondria isolated from kidney and tumour tissue, reveal primary defects in mitochondrial function in human ccRCC. However, ccRCC metastases unexpectedly have enhanced labeling of TCA cycle intermediates compared to primary ccRCCs, indicating a divergent metabolic program during ccRCC metastasis in patients. In mice, stimulating respiration in ccRCC cells is sufficient to promote metastatic colonization. Altogether, these findings indicate that metabolic properties evolve during human kidney cancer progression, and suggest that mitochondrial respiration may be limiting for ccRCC metastasis but not for ccRCC growth at the site of origin.

Persea Americana Mill seed extracts: Understanding insights into the antioxidant and antityrosinase activities and effects on preserving qualities of whiteleg shrimp (Litopenaus vannamei) during refrigerated storage.

An ethanol extract of avocado seed (TN-1) and six smaller fractions (PD-1 to PD-6) were prepared. Most of the extracts exhibited scavenging DPPH radical, reducing Fe3+ to Fe2+, and inhibiting polyphenoloxidase, consistently matching with their high polyphenolic content (p < 0.05). Most of the 47 compounds identified from TN-1 were classified into phenolic acid, condensed tannin, flavonoid, fatty acids, and alkaloids. Two extracts TN-1 and PD-2 (0.025%, w/v) were used to treat white-leg shrimp and the quality changes were evaluated compared to those treated with sodium metabisulfite (1.25%, w/v) and controls (without treatment) during 8-day storage at 2 °C. Changes in melanosis scores, lipid peroxidation, pHs, microorganisms, and nutrient in shrimps treated with the extracts were comparable to or even much better than others. These results promise a potential use of avocado seed extract as a cost-effective, eco-friendly, and effective alternative to commercial additives in shrimp storage.

A Radiomics-Based Machine Learning Model for Prediction of Tumor Mutational Burden in Lower-Grade Gliomas.

Glioma is a Center Nervous System (CNS) neoplasm that arises from the glial cells. In a new scheme category of the World Health Organization 2016, lower-grade gliomas (LGGs) are grade II and III gliomas. Following the discovery of suppression of negative immune regulation, immunotherapy is a promising effective treatment method for lower-grade glioma patients. However, the therapy is not effective for all types of LGGs, and tumor mutational burden (TMB) has been shown to be a potential biomarker for the susceptibility and prognosis of immunotherapy in lower-grade glioma patients. Hence, predicting TMB benefits brain cancer patients. In this study, we investigated the correlation between MRI (magnetic resonance imaging)-based radiomic features and TMB in LGG by applying machine learning methods. Six machine learning classifiers were examined on the features extracted from the genetic algorithm. Subsequently, a light gradient boosting machine (LightGBM) succeeded in selecting 11 radiomics signatures for TMB classification. Our LightGBM model resulted in high accuracy of 0.7936, and reached a balance between sensitivity and specificity, achieving 0.76 and 0.8107, respectively. To our knowledge, our study represents the best model for classification of TMB in LGG patients at present.

Improving MGMT methylation status prediction of glioblastoma through optimizing radiomics features using genetic algorithm-based machine learning approach.

O6-Methylguanine-DNA-methyltransferase (MGMT) promoter methylation was shown in many studies to be an important predictive biomarker for temozolomide (TMZ) resistance and poor progression-free survival in glioblastoma multiforme (GBM) patients. However, identifying the MGMT methylation status using molecular techniques remains challenging due to technical limitations, such as the inability to obtain tumor specimens, high prices for detection, and the high complexity of intralesional heterogeneity. To overcome these difficulties, we aimed to test the feasibility of using a novel radiomics-based machine learning (ML) model to preoperatively and noninvasively predict the MGMT methylation status. In this study, radiomics features extracted from multimodal images of GBM patients with annotated MGMT methylation status were downloaded from The Cancer Imaging Archive (TCIA) public database for retrospective analysis. The radiomics features extracted from multimodal images from magnetic resonance imaging (MRI) had undergone a two-stage feature selection method, including an eXtreme Gradient Boosting (XGBoost) feature selection model followed by a genetic algorithm (GA)-based wrapper model for extracting the most meaningful radiomics features for predictive purposes. The cross-validation results suggested that the GA-based wrapper model achieved the high performance with a sensitivity of 0.894, specificity of 0.966, and accuracy of 0.925 for predicting the MGMT methylation status in GBM. Application of the extracted GBM radiomics features on a low-grade glioma (LGG) dataset also achieved a sensitivity 0.780, specificity 0.620, and accuracy 0.750, indicating the potential of the selected radiomics features to be applied more widely on both low- and high-grade gliomas. The performance indicated that our model may potentially confer significant improvements in prognosis and treatment responses in GBM patients.

In vivo isotope tracing reveals a requirement for the electron transport chain in glucose and glutamine metabolism by tumors.

In mice and humans with cancer, intravenous 13C-glucose infusion results in 13C labeling of tumor tricarboxylic acid (TCA) cycle intermediates, indicating that pyruvate oxidation in the TCA cycle occurs in tumors. The TCA cycle is usually coupled to the electron transport chain (ETC) because NADH generated by the cycle is reoxidized to NAD+ by the ETC. However, 13C labeling does not directly report ETC activity, and other pathways can oxidize NADH, so the ETC's role in these labeling patterns is unverified. We examined the impact of the ETC complex I inhibitor IACS-010759 on tumor 13C labeling. IACS-010759 suppresses TCA cycle labeling from glucose or lactate and increases labeling from glutamine. Cancer cells expressing yeast NADH dehydrogenase-1, which recycles NADH to NAD+ independently of complex I, display normalized labeling when complex I is inhibited, indicating that cancer cell ETC activity regulates TCA cycle metabolism and 13C labeling from multiple nutrients.

A sequence-based prediction of Kruppel-like factors proteins using XGBoost and optimized features.

Krüppel-like factors (KLF) refer to a group of conserved zinc finger-containing transcription factors that are involved in various physiological and biological processes, including cell proliferation, differentiation, development, and apoptosis. Some bioinformatics methods such as sequence similarity searches, multiple sequence alignment, phylogenetic reconstruction, and gene synteny analysis have also been proposed to broaden our knowledge of KLF proteins. In this study, we proposed a novel computational approach by using machine learning on features calculated from primary sequences. To detail, our XGBoost-based model is efficient in identifying KLF proteins, with accuracy of 96.4% and MCC of 0.704. It also holds a promising performance when testing our model on an independent dataset. Therefore, our model could serve as an useful tool to identify new KLF proteins and provide necessary information for biologists and researchers in KLF proteins. Our machine learning source codes as well as datasets are freely available at https://github.com/khanhlee/KLF-XGB.

Radiomics-based machine learning model for efficiently classifying transcriptome subtypes in glioblastoma patients from MRI.

BACKGROUND: In the field of glioma, transcriptome subtypes have been considered as an important diagnostic and prognostic biomarker that may help improve the treatment efficacy. However, existing identification methods of transcriptome subtypes are limited due to the relatively long detection period, the unattainability of tumor specimens via biopsy or surgery, and the fleeting nature of intralesional heterogeneity. In search of a superior model over previous ones, this study evaluated the efficiency of eXtreme Gradient Boosting (XGBoost)-based radiomics model to classify transcriptome subtypes in glioblastoma patients. METHODS: This retrospective study retrieved patients from TCGA-GBM and IvyGAP cohorts with pathologically diagnosed glioblastoma, and separated them into different transcriptome subtypes groups. GBM patients were then segmented into three different regions of MRI: enhancement of the tumor core (ET), non-enhancing portion of the tumor core (NET), and peritumoral edema (ED). We subsequently used handcrafted radiomics features (n = 704) from multimodality MRI and two-level feature selection techniques (Spearman correlation and F-score tests) in order to find the features that could be relevant. RESULTS: After the feature selection approach, we identified 13 radiomics features that were the most meaningful ones that can be used to reach the optimal results. With these features, our XGBoost model reached the predictive accuracies of 70.9%, 73.3%, 88.4%, and 88.4% for classical, mesenchymal, neural, and proneural subtypes, respectively. Our model performance has been improved in comparison with the other models as well as previous works on the same dataset. CONCLUSION: The use of XGBoost and two-level feature selection analysis (Spearman correlation and F-score) could be expected as a potential combination for classifying transcriptome subtypes with high performance and might raise public attention for further research on radiomics-based GBM models.

XGBoost Improves Classification of MGMT Promoter Methylation Status in IDH1 Wildtype Glioblastoma.

Approximately 96% of patients with glioblastomas (GBM) have IDH1 wildtype GBMs, characterized by extremely poor prognosis, partly due to resistance to standard temozolomide treatment. O6-Methylguanine-DNA methyltransferase (MGMT) promoter methylation status is a crucial prognostic biomarker for alkylating chemotherapy resistance in patients with GBM. However, MGMT methylation status identification methods, where the tumor tissue is often undersampled, are time consuming and expensive. Currently, presurgical noninvasive imaging methods are used to identify biomarkers to predict MGMT methylation status. We evaluated a novel radiomics-based eXtreme Gradient Boosting (XGBoost) model to identify MGMT promoter methylation status in patients with IDH1 wildtype GBM. This retrospective study enrolled 53 patients with pathologically proven GBM and tested MGMT methylation and IDH1 status. Radiomics features were extracted from multimodality MRI and tested by F-score analysis to identify important features to improve our model. We identified nine radiomics features that reached an area under the curve of 0.896, which outperformed other classifiers reported previously. These features could be important biomarkers for identifying MGMT methylation status in IDH1 wildtype GBM. The combination of radiomics feature extraction and F-core feature selection significantly improved the performance of the XGBoost model, which may have implications for patient stratification and therapeutic strategy in GBM.

Novel regulations of MEF2-A, MEF2-D, and CACNA1S in the functional incompetence of adipose-derived mesenchymal stem cells by induced indoxyl sulfate in chronic kidney disease.

Indoxyl sulfate (IS) is a digestive intermediate product that is a known indicator of chronic kidney disease. Its toxicity has also been suggested to accelerate chronic kidney disease. Recently, mesenchymal stem cells (MSCs) have been confirmed as a potential treatment in kidney regeneration. To determine the universal alteration in gene expression, we combined high-throughput microarray technology and in vitro culture of adipose-derived mesenchymal stem cells at different doses of IS (20, 40, 60 mg/l). We found that indoxyl sulfate has a remarkable interconnection with stem cell and calcium/calmodulin-dependent kinase pathways. In vitro results showed that indoxyl sulfate exerts anti-proliferation and anti-migration effects on ADMSCs. In addition, IS effects lead to increase in apoptotic cells and cells arrested at the G1 phase. Moreover, MEF2-A, MEF2-D and CACNA1S expression significantly decreased after indoxyl sulfate treatment. It can be speculated that following treatment with indoxyl sulfate, the function of ADMSCs is decreased and ADMSCs' ability to support renal tubule regeneration in chronic kidney disease patients may be lower.