Genetic and phenotypic associations of frailty with cardiovascular indicators and behavioral characteristics.

INTRODUCTION: Frailty Index (FI) is a common measure of frailty, which has been advocated as a routine clinical test by many guidelines. The genetic and phenotypic relationships of FI with cardiovascular indicators (CIs) and behavioral characteristics (BCs) are unclear, which has hampered ability to monitor FI using easily collected data. OBJECTIVES: This study is designed to investigate the genetic and phenotypic associations of frailty with CIs and BCs, and further to construct a model to predict FI. METHOD: Genetic relationships of FI with 288 CIs and 90 BCs were assessed by the cross-trait LD score regression (LDSC) and Mendelian randomization (MR). The phenotypic data of these CIs and BCs were integrated with a machine-learning model to predict FI of individuals in UK-biobank. The relationships of the predicted FI with risks of type 2 diabetes (T2D) and neurodegenerative diseases were tested by the Kaplan-Meier estimator and Cox proportional hazards model. RESULTS: MR revealed putative causal effects of seven CIs and eight BCs on FI. These CIs and BCs were integrated to establish a model for predicting FI. The predicted FI is significantly correlated with the observed FI (Pearson correlation coefficient = 0.660, P-value = 4.96 × 10-62). The prediction model indicated "usual walking pace" contributes the most to prediction. Patients who were predicted with high FI are in significantly higher risk of T2D (HR = 2.635, P < 2 × 10-16) and neurodegenerative diseases (HR = 2.307, P = 1.62 × 10-3) than other patients. CONCLUSION: This study supports associations of FI with CIs and BCs from genetic and phenotypic perspectives. The model that is developed by integrating easily collected CIs and BCs data in predicting FI has the potential to monitor disease risk.

Recent Progress of Protein Tertiary Structure Prediction.

The prediction of three-dimensional (3D) protein structure from amino acid sequences has stood as a significant challenge in computational and structural bioinformatics for decades. Recently, the widespread integration of artificial intelligence (AI) algorithms has substantially expedited advancements in protein structure prediction, yielding numerous significant milestones. In particular, the end-to-end deep learning method AlphaFold2 has facilitated the rise of structure prediction performance to new heights, regularly competitive with experimental structures in the 14th Critical Assessment of Protein Structure Prediction (CASP14). To provide a comprehensive understanding and guide future research in the field of protein structure prediction for researchers, this review describes various methodologies, assessments, and databases in protein structure prediction, including traditionally used protein structure prediction methods, such as template-based modeling (TBM) and template-free modeling (FM) approaches; recently developed deep learning-based methods, such as contact/distance-guided methods, end-to-end folding methods, and protein language model (PLM)-based methods; multi-domain protein structure prediction methods; the CASP experiments and related assessments; and the recently released AlphaFold Protein Structure Database (AlphaFold DB). We discuss their advantages, disadvantages, and application scopes, aiming to provide researchers with insights through which to understand the limitations, contexts, and effective selections of protein structure prediction methods in protein-related fields.

Identifying B-cell epitopes using AlphaFold2 predicted structures and pretrained language model.

MOTIVATION: Identifying the B-cell epitopes is an essential step for guiding rational vaccine development and immunotherapies. Since experimental approaches are expensive and time-consuming, many computational methods have been designed to assist B-cell epitope prediction. However, existing sequence-based methods have limited performance since they only use contextual features of the sequential neighbors while neglecting structural information. RESULTS: Based on the recent breakthrough of AlphaFold2 in protein structure prediction, we propose GraphBepi, a novel graph-based model for accurate B-cell epitope prediction. For one protein, the predicted structure from AlphaFold2 is used to construct the protein graph, where the nodes/residues are encoded by ESM-2 learning representations. The graph is input into the edge-enhanced deep graph neural network (EGNN) to capture the spatial information in the predicted 3D structures. In parallel, a bidirectional long short-term memory neural networks (BiLSTM) are employed to capture long-range dependencies in the sequence. The learned low-dimensional representations by EGNN and BiLSTM are then combined into a multilayer perceptron for predicting B-cell epitopes. Through comprehensive tests on the curated epitope dataset, GraphBepi was shown to outperform the state-of-the-art methods by more than 5.5% and 44.0% in terms of AUC and AUPR, respectively. A web server is freely available at http://bio-web1.nscc-gz.cn/app/graphbepi. AVAILABILITY AND IMPLEMENTATION: The datasets, pre-computed features, source codes, and the trained model are available at https://github.com/biomed-AI/GraphBepi.

Plasmonic Bridge Sensor Enabled by Carbon Nanotubes and Au-Ag Nano-Rambutan for Multifunctional Detection of Biomechanics and Bio/Chemical Molecules.

Wearable, noninvasive, and simultaneous sensing of subtle strains and eccrine molecules on human body is essential for future health monitoring and personalized medicine. However, there is a huge chasm between biomechanics and bio/chemical molecule detections. Here, a wearable plasmonic bridge sensor with multiple abilities to monitor subtle strains and molecules is developed. Hollow Au-Ag nano-rambutans and carbon nanotubes (CNTs) are adsorbed in the nonwoven fabrics (NWFs) conjointly, where the gap between the conducting network of CNTs is bridged by the Au-Ag nano-rambutans during the subtle strain sensing, and the detection sensitivity for stress is improved at least 1 order of magnitude compared to that with the only CNTs. In order to acquire the accurate human action recognition, a machine learning algorithm (support vector machines) based on output biomechanics data is designed. The average accuracy of our plasmonic bridge sensor reaches 89.0% for human action recognition. Moreover, due to the hollow structure and high nanoroughness, the single Au-Ag nano-rambutan particle has strong localized surface plasmon resonance effect and high surface-enhanced Raman scattering (SERS) activity. Based on their unique SERS spectra introduced by the hollow Au-Ag nano-rambutan adsorbed in the NWFs, noninvasive extraction and "fingerprint" recognition of bio/chemical molecules could be realized during the wearable sensing. In sum, the NWFs/CNTs/Au-Ag sensor bridges the barrier between the bodily strain detection and molecule recognition during the wearable sensing. Such integrated and multifunctional sensing strategy for universal biomechanics and bio/chemical molecules means to assess human health to be of importance.

Multiple cells of origin in common with various types of mouse N-Myc acute leukemia.

Little is known regarding whether the cell of origin differs among different leukemia types. To address this fundamental issue, we determined the cell of origin in five distinct types of acute leukemia induced by N-Myc overexpression in mice. CD150+CD48-CD41-CD34-c-Kit+Sca-1+Lin- (KSL) (HSC1) cells, CD150-CD48-CD41-CD34-KSL (HSC2) cells, CD150+CD41+CD34-KSL (HPC1) cells, CD150+CD41+CD34+KSL (HPC2) cells, and CD150-CD41-CD34+KSL (HPC3) cells were purified from the bone marrow of adult C57BL/6 mice, transduced with the N-Myc retrovirus vector, and transplanted into lethally irradiated mice. B-cell acute lymphoblastic leukemia (B-ALL), T-cell acute lymphoblastic leukemia (T-ALL), acute myeloid leukemia (AML), acute undifferentiated leukemia (AUL), and mixed phenotype acute leukemia (MPAL) developed from five populations. RNA sequencing data supported the phenotypical diagnoses of leukemia, except that AUL appeared transcriptionally close to T-ALL. Whole-genome sequencing revealed that retroviral integration sites were irrelevant to the leukemia types and that T-ALL and AML of MPAL shared the same integration site and many gene mutations, suggesting their common origin. Additionally, leukemic stem cells were identified in the KSL cell population, suggesting that the phenotypes of leukemic stem cells are irrelevant to leukemia types. This study provides experimental evidence for the similar and multiple cells of origin in acute leukemia.

Accurate Estimation of Single-Cell Differentiation Potency Based on Network Topology and Gene Ontology Information.

One important task in single-cell analysis is to quantify the differentiation potential of single cells. Though various single-cell potency measures have been proposed, they are based on individual biological sources, thus not robust and reliable. It is still a challenge to combine multiple sources to generate a relatively reliable and robust measure to estimate differentiation. In this paper, we propose a New Centrality measure with Gene ontology information (NCG) to estimate single-cell potency. NCG is designed by combining network topology property with edge clustering coefficient, and gene function information using gene ontology function similarity scores. NCG distinguishes pluripotent cells from non-pluripotent cells with high accuracy, correctly ranks different cell types by their differentiation potency, tracks changes during the differentiation process, and constructs the lineage trajectory from human myoblasts into skeletal muscle cells. These indicate that NCG is a reliable and robust measure to estimate single-cell potency. NCG is anticipated to be a useful tool for identifying novel stem or progenitor cell phenotypes from single-cell RNA-Seq data. The source codes and datasets are available at https://github.com/Xinzhe-Ni/NCG.

Improved estimation of model quality using predicted inter-residue distance.

MOTIVATION: Protein model quality assessment (QA) is an essential component in protein structure prediction, which aims to estimate the quality of a structure model and/or select the most accurate model out from a pool of structure models, without knowing the native structure. QA remains a challenging task in protein structure prediction. RESULTS: Based on the inter-residue distance predicted by the recent deep learning-based structure prediction algorithm trRosetta, we developed QDistance, a new approach to the estimation of both global and local qualities. QDistance works for both single- and multi-models inputs. We designed several distance-based features to assess the agreement between the predicted and model-derived inter-residue distances. Together with a few widely used features, they are fed into a simple yet powerful linear regression model to infer the global QA scores. The local QA scores for each structure model are predicted based on a comparative analysis with a set of selected reference models. For multi-models input, the reference models are selected from the input based on the predicted global QA scores. For single-model input, the reference models are predicted by trRosetta. With the informative distance-based features, QDistance can predict the global quality with satisfactory accuracy. Benchmark tests on the CASP13 and the CAMEO structure models suggested that QDistance was competitive with other methods. Blind tests in the CASP14 experiments showed that QDistance was robust and ranked among the top predictors. Especially, QDistance was the top 3 local QA method and made the most accurate local QA prediction for unreliable local region. Analysis showed that this superior performance can be attributed to the inclusion of the predicted inter-residue distance. AVAILABILITY AND IMPLEMENTATION: http://yanglab.nankai.edu.cn/QDistance. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Precise estimation of residue relative solvent accessible area from Cα atom distance matrix using a deep learning method.

MOTIVATION: The solvent accessible surface is an essential structural property measure related to the protein structure and protein function. Relative solvent accessible area (RSA) is a standard measure to describe the degree of residue exposure in the protein surface or inside of protein. However, this computation will fail when the residues information is missing. RESULTS: In this article, we proposed a novel method for estimation RSA using the Cα atom distance matrix with the deep learning method (EAGERER). The new method, EAGERER, achieves Pearson correlation coefficients of 0.921-0.928 on two independent test datasets. We empirically demonstrate that EAGERER can yield better Pearson correlation coefficients than existing RSA estimators, such as coordination number, half sphere exposure and SphereCon. To the best of our knowledge, EAGERER represents the first method to estimate the solvent accessible area using limited information with a deep learning model. It could be useful to the protein structure and protein function prediction. AVAILABILITYAND IMPLEMENTATION: The method is free available at https://github.com/cliffgao/EAGERER. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

flDPnn: Accurate intrinsic disorder prediction with putative propensities of disorder functions.

Identification of intrinsic disorder in proteins relies in large part on computational predictors, which demands that their accuracy should be high. Since intrinsic disorder carries out a broad range of cellular functions, it is desirable to couple the disorder and disorder function predictions. We report a computational tool, flDPnn, that provides accurate, fast and comprehensive disorder and disorder function predictions from protein sequences. The recent Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment and results on other test datasets demonstrate that flDPnn offers accurate predictions of disorder, fully disordered proteins and four common disorder functions. These predictions are substantially better than the results of the existing disorder predictors and methods that predict functions of disorder. Ablation tests reveal that the high predictive performance stems from innovative ways used in flDPnn to derive sequence profiles and encode inputs. flDPnn's webserver is available at http://biomine.cs.vcu.edu/servers/flDPnn/.

PCAT-1 facilitates breast cancer progression via binding to RACK1 and enhancing oxygen-independent stability of HIF-1α.

Hypoxia induces a series of cellular adaptive responses that enable promotion of inflammation and cancer development. Hypoxia-inducible factor-1α (HIF-1α) is involved in the hypoxia response and cancer promotion, and it accumulates in hypoxia and is degraded under normoxic conditions. Here we identify prostate cancer associated transcript-1 (PCAT-1) as a hypoxia-inducible long non-coding RNA (lncRNA) that regulates HIF-1α stability, crucial for cancer progression. Extensive analyses of clinical data indicate that PCAT-1 is elevated in breast cancer patients and is associated with pathological grade, tumor size, and poor clinical outcomes. Through gain- and loss-of-function experiments, we find that PCAT-1 promotes hypoxia-associated breast cancer progression including growth, migration, invasion, colony formation, and metabolic regulation. Mechanistically, PCAT-1 directly interacts with the receptor of activated protein C kinase-1 (RACK1) protein and prevents RACK1 from binding to HIF-1α, thus protecting HIF-1α from RACK1-induced oxygen-independent degradation. These findings provide new insight into lncRNA-mediated mechanisms for HIF-1α stability and suggest a novel role of PCAT-1 as a potential therapeutic target for breast cancer.