Structural Basis for RNA Replication by the SARS-CoV-2 Polymerase.

Nucleotide analog inhibitors, including broad-spectrum remdesivir and favipiravir, have shown promise in in vitro assays and some clinical studies for COVID-19 treatment, this despite an incomplete mechanistic understanding of the viral RNA-dependent RNA polymerase nsp12 drug interactions. Here, we examine the molecular basis of SARS-CoV-2 RNA replication by determining the cryo-EM structures of the stalled pre- and post- translocated polymerase complexes. Compared with the apo complex, the structures show notable structural rearrangements happening to nsp12 and its co-factors nsp7 and nsp8 to accommodate the nucleic acid, whereas there are highly conserved residues in nsp12, positioning the template and primer for an in-line attack on the incoming nucleotide. Furthermore, we investigate the inhibition mechanism of the triphosphate metabolite of remdesivir through structural and kinetic analyses. A transition model from the nsp7-nsp8 hexadecameric primase complex to the nsp12-nsp7-nsp8 polymerase complex is also proposed to provide clues for the understanding of the coronavirus transcription and replication machinery.

SEAOP: a statistical ensemble approach for outlier detection in quantitative proteomics data.

Quality control in quantitative proteomics is a persistent challenge, particularly in identifying and managing outliers. Unsupervised learning models, which rely on data structure rather than predefined labels, offer potential solutions. However, without clear labels, their effectiveness might be compromised. Single models are susceptible to the randomness of parameters and initialization, which can result in a high rate of false positives. Ensemble models, on the other hand, have shown capabilities in effectively mitigating the impacts of such randomness and assisting in accurately detecting true outliers. Therefore, we introduced SEAOP, a Python toolbox that utilizes an ensemble mechanism by integrating multi-round data management and a statistics-based decision pipeline with multiple models. Specifically, SEAOP uses multi-round resampling to create diverse sub-data spaces and employs outlier detection methods to identify candidate outliers in each space. Candidates are then aggregated as confirmed outliers via a chi-square test, adhering to a 95% confidence level, to ensure the precision of the unsupervised approaches. Additionally, SEAOP introduces a visualization strategy, specifically designed to intuitively and effectively display the distribution of both outlier and non-outlier samples. Optimal hyperparameter models of SEAOP for outlier detection were identified by using a gradient-simulated standard dataset and Mann-Kendall trend test. The performance of the SEAOP toolbox was evaluated using three experimental datasets, confirming its reliability and accuracy in handling quantitative proteomics.

pathMap: a path-based mapping tool for long noisy reads with high sensitivity.

With the rapid development of single-molecule sequencing (SMS) technologies, the output read length is continuously increasing. Mapping such reads onto a reference genome is one of the most fundamental tasks in sequence analysis. Mapping sensitivity is becoming a major concern since high sensitivity can detect more aligned regions on the reference and obtain more aligned bases, which are useful for downstream analysis. In this study, we present pathMap, a novel k-mer graph-based mapper that is specifically designed for mapping SMS reads with high sensitivity. By viewing the alignment chain as a path containing as many anchors as possible in the matched k-mer graph, pathMap treats chaining as a path selection problem in the directed graph. pathMap iteratively searches the longest path in the remaining nodes; more candidate chains with high quality can be effectively detected and aligned. Compared to other state-of-the-art mapping methods such as minimap2 and Winnowmap2, experiment results on simulated and real-life datasets demonstrate that pathMap obtains the number of mapped chains at least 11.50% more than its closest competitor and increases the mapping sensitivity by 17.28% and 13.84% of bases over the next-best mapper for Pacific Biosciences and Oxford Nanopore sequencing data, respectively. In addition, pathMap is more robust to sequence errors and more sensitive to species- and strain-specific identification of pathogens using MinION reads.

Nanoparticle Deep-Subwavelength Dynamics Empowered by Optical Meron-Antimeron Topology.

Optical meron is a type of nonplanar topological texture mainly observed in surface plasmon polaritons and highly symmetric points of photonic crystals in the reciprocal space. Here, we report Poynting-vector merons formed at the real space of a photonic crystal for a Γ-point illumination. Optical merons can be utilized for subwavelength-resolution manipulation of nanoparticles, resembling a topological Hall effect on electrons via magnetic merons. In particular, staggered merons and antimerons impose strong radiation pressure on large gold nanoparticles (AuNPs), while focused hot spots in antimerons generate dominant optical gradient forces on small AuNPs. Synergistically, differently sized AuNPs in a still environment can be trapped or orbit in opposite directions, mimicking a coupled galaxy system. They can also be separated with a 10 nm precision when applying a flow velocity of >1 mm/s. Our study unravels a novel way to exploit topological textures for optical manipulation with deep-subwavelength precision and switchable topology in a lossless environment.

Integrative analysis of single-cell embryo data reveals transcriptome signatures for the human pre-implantation inner cell mass.

As the source of embryonic stem cells (ESCs), inner cell mass (ICM) can form all tissues of the embryo proper, however, its role in early human lineage specification remains controversial. Although a stepwise differentiation model has been proposed suggesting the existence of ICM as a distinct developmental stage, the underlying molecular mechanism remains unclear. In the present study, we perform an integrated analysis on the public human preimplantation embryonic single-cell transcriptomic data and apply a trajectory inference algorithm to measure the cell plasticity. In our results, ICM population can be clearly discriminated on the dimension-reduced graph and confirmed by compelling evidences, thus validating the two-step hypothesis of lineage commitment. According to the branch probabilities and differentiation potential, we determine the precise time points for two lineage segregations. Further analysis on gene expression dynamics and regulatory network indicates that transcription factors including GSC, PRDM1, and SPIC may underlie the decisions of ICM fate. In addition, new human ICM marker genes, such as EPHA4 and CCR8 are discovered and validated by immunofluorescence. Given the potential clinical applications of ESCs, our analysis provides a further understanding of human ICM cells and facilitates the exploration of more unique characteristics in early human development.

Hypomorphic and hypermorphic mouse models of Fsip2 indicate its dosage-dependent roles in sperm tail and acrosome formation.

Loss-of-function mutations in multiple morphological abnormalities of the sperm flagella (MMAF)-associated genes lead to decreased sperm motility and impaired male fertility. As an MMAF gene, the function of fibrous sheath-interacting protein 2 (FSIP2) remains largely unknown. In this work, we identified a homozygous truncating mutation of FSIP2 in an infertile patient. Accordingly, we constructed a knock-in (KI) mouse model with this mutation. In parallel, we established an Fsip2 overexpression (OE) mouse model. Remarkably, KI mice presented with the typical MMAF phenotype, whereas OE mice showed no gross anomaly except for sperm tails with increased length. Single-cell RNA sequencing of the testes uncovered altered expression of genes related to sperm flagellum, acrosomal vesicle and spermatid development. We confirmed the expression of Fsip2 at the acrosome and the physical interaction of this gene with Acrv1, an acrosomal marker. Proteomic analysis of the testes revealed changes in proteins sited at the fibrous sheath, mitochondrial sheath and acrosomal vesicle. We also pinpointed the crucial motifs of Fsip2 that are evolutionarily conserved in species with internal fertilization. Thus, this work reveals the dosage-dependent roles of Fsip2 in sperm tail and acrosome formation.

MLRDFM: a multi-view Laplacian regularized DeepFM model for predicting miRNA-disease associations.

MOTIVATION: MicroRNAs (miRNAs), as critical regulators, are involved in various fundamental and vital biological processes, and their abnormalities are closely related to human diseases. Predicting disease-related miRNAs is beneficial to uncovering new biomarkers for the prevention, detection, prognosis, diagnosis and treatment of complex diseases. RESULTS: In this study, we propose a multi-view Laplacian regularized deep factorization machine (DeepFM) model, MLRDFM, to predict novel miRNA-disease associations while improving the standard DeepFM. Specifically, MLRDFM improves DeepFM from two aspects: first, MLRDFM takes the relationships among items into consideration by regularizing their embedding features via their similarity-based Laplacians. In this study, miRNA Laplacian regularization integrates four types of miRNA similarity, while disease Laplacian regularization integrates two types of disease similarity. Second, to judiciously train our model, Laplacian eigenmaps are utilized to initialize the weights in the dense embedding layer. The experimental results on the latest HMDD v3.2 dataset show that MLRDFM improves the performance and reduces the overfitting phenomenon of DeepFM. Besides, MLRDFM is greatly superior to the state-of-the-art models in miRNA-disease association prediction in terms of different evaluation metrics with the 5-fold cross-validation. Furthermore, case studies further demonstrate the effectiveness of MLRDFM.

Predicting drug-drug interactions by graph convolutional network with multi-kernel.

Drug repositioning is proposed to find novel usages for existing drugs. Among many types of drug repositioning approaches, predicting drug-drug interactions (DDIs) helps explore the pharmacological functions of drugs and achieves potential drugs for novel treatments. A number of models have been applied to predict DDIs. The DDI network, which is constructed from the known DDIs, is a common part in many of the existing methods. However, the functions of DDIs are different, and thus integrating them in a single DDI graph may overlook some useful information. We propose a graph convolutional network with multi-kernel (GCNMK) to predict potential DDIs. GCNMK adopts two DDI graph kernels for the graph convolutional layers, namely, increased DDI graph consisting of 'increase'-related DDIs and decreased DDI graph consisting of 'decrease'-related DDIs. The learned drug features are fed into a block with three fully connected layers for the DDI prediction. We compare various types of drug features, whereas the target feature of drugs outperforms all other types of features and their concatenated features. In comparison with three different DDI prediction methods, our proposed GCNMK achieves the best performance in terms of area under receiver operating characteristic curve and area under precision-recall curve. In case studies, we identify the top 20 potential DDIs from all unknown DDIs, and the top 10 potential DDIs from the unknown DDIs among breast, colorectal and lung neoplasms-related drugs. Most of them have evidence to support the existence of their interactions. fangxiang.wu@usask.ca.

HyMM: hybrid method for disease-gene prediction by integrating multiscale module structure.

MOTIVATION: Identifying disease-related genes is an important issue in computational biology. Module structure widely exists in biomolecule networks, and complex diseases are usually thought to be caused by perturbations of local neighborhoods in the networks, which can provide useful insights for the study of disease-related genes. However, the mining and effective utilization of the module structure is still challenging in such issues as a disease gene prediction. RESULTS: We propose a hybrid disease-gene prediction method integrating multiscale module structure (HyMM), which can utilize multiscale information from local to global structure to more effectively predict disease-related genes. HyMM extracts module partitions from local to global scales by multiscale modularity optimization with exponential sampling, and estimates the disease relatedness of genes in partitions by the abundance of disease-related genes within modules. Then, a probabilistic model for integration of gene rankings is designed in order to integrate multiple predictions derived from multiscale module partitions and network propagation, and a parameter estimation strategy based on functional information is proposed to further enhance HyMM's predictive power. By a series of experiments, we reveal the importance of module partitions at different scales, and verify the stable and good performance of HyMM compared with eight other state-of-the-arts and its further performance improvement derived from the parameter estimation. CONCLUSIONS: The results confirm that HyMM is an effective framework for integrating multiscale module structure to enhance the ability to predict disease-related genes, which may provide useful insights for the study of the multiscale module structure and its application in such issues as a disease-gene prediction.

Biomedical data, computational methods and tools for evaluating disease-disease associations.

In recent decades, exploring potential relationships between diseases has been an active research field. With the rapid accumulation of disease-related biomedical data, a lot of computational methods and tools/platforms have been developed to reveal intrinsic relationship between diseases, which can provide useful insights to the study of complex diseases, e.g. understanding molecular mechanisms of diseases and discovering new treatment of diseases. Human complex diseases involve both external phenotypic abnormalities and complex internal molecular mechanisms in organisms. Computational methods with different types of biomedical data from phenotype to genotype can evaluate disease-disease associations at different levels, providing a comprehensive perspective for understanding diseases. In this review, available biomedical data and databases for evaluating disease-disease associations are first summarized. Then, existing computational methods for disease-disease associations are reviewed and classified into five groups in terms of the usages of biomedical data, including disease semantic-based, phenotype-based, function-based, representation learning-based and text mining-based methods. Further, we summarize software tools/platforms for computation and analysis of disease-disease associations. Finally, we give a discussion and summary on the research of disease-disease associations. This review provides a systematic overview for current disease association research, which could promote the development and applications of computational methods and tools/platforms for disease-disease associations.

DeepLncLoc: a deep learning framework for long non-coding RNA subcellular localization prediction based on subsequence embedding.

Long non-coding RNAs (lncRNAs) are a class of RNA molecules with more than 200 nucleotides. A growing amount of evidence reveals that subcellular localization of lncRNAs can provide valuable insights into their biological functions. Existing computational methods for predicting lncRNA subcellular localization use k-mer features to encode lncRNA sequences. However, the sequence order information is lost by using only k-mer features. We proposed a deep learning framework, DeepLncLoc, to predict lncRNA subcellular localization. In DeepLncLoc, we introduced a new subsequence embedding method that keeps the order information of lncRNA sequences. The subsequence embedding method first divides a sequence into some consecutive subsequences and then extracts the patterns of each subsequence, last combines these patterns to obtain a complete representation of the lncRNA sequence. After that, a text convolutional neural network is employed to learn high-level features and perform the prediction task. Compared with traditional machine learning models, popular representation methods and existing predictors, DeepLncLoc achieved better performance, which shows that DeepLncLoc could effectively predict lncRNA subcellular localization. Our study not only presented a novel computational model for predicting lncRNA subcellular localization but also introduced a new subsequence embedding method which is expected to be applied in other sequence-based prediction tasks. The DeepLncLoc web server is freely accessible at http://bioinformatics.csu.edu.cn/DeepLncLoc/, and source code and datasets can be downloaded from https://github.com/CSUBioGroup/DeepLncLoc.

An integrated brain-specific network identifies genes associated with neuropathologic and clinical traits of Alzheimer's disease.

Alzheimer's disease (AD) has a strong genetic predisposition. However, its risk genes remain incompletely identified. We developed an Alzheimer's brain gene network-based approach to predict AD-associated genes by leveraging the functional pattern of known AD-associated genes. Our constructed network outperformed existing networks in predicting AD genes. We then systematically validated the predictions using independent genetic, transcriptomic, proteomic data, neuropathological and clinical data. First, top-ranked genes were enriched in AD-associated pathways. Second, using external gene expression data from the Mount Sinai Brain Bank study, we found that the top-ranked genes were significantly associated with neuropathological and clinical traits, including the Consortium to Establish a Registry for Alzheimer's Disease score, Braak stage score and clinical dementia rating. The analysis of Alzheimer's brain single-cell RNA-seq data revealed cell-type-specific association of predicted genes with early pathology of AD. Third, by interrogating proteomic data in the Religious Orders Study and Memory and Aging Project and Baltimore Longitudinal Study of Aging studies, we observed a significant association of protein expression level with cognitive function and AD clinical severity. The network, method and predictions could become a valuable resource to advance the identification of risk genes for AD.

DeepCellEss: cell line-specific essential protein prediction with attention-based interpretable deep learning.

MOTIVATION: Protein essentiality is usually accepted to be a conditional trait and strongly affected by cellular environments. However, existing computational methods often do not take such characteristics into account, preferring to incorporate all available data and train a general model for all cell lines. In addition, the lack of model interpretability limits further exploration and analysis of essential protein predictions. RESULTS: In this study, we proposed DeepCellEss, a sequence-based interpretable deep learning framework for cell line-specific essential protein predictions. DeepCellEss utilizes a convolutional neural network and bidirectional long short-term memory to learn short- and long-range latent information from protein sequences. Further, a multi-head self-attention mechanism is used to provide residue-level model interpretability. For model construction, we collected extremely large-scale benchmark datasets across 323 cell lines. Extensive computational experiments demonstrate that DeepCellEss yields effective prediction performance for different cell lines and outperforms existing sequence-based methods as well as network-based centrality measures. Finally, we conducted some case studies to illustrate the necessity of considering specific cell lines and the superiority of DeepCellEss. We believe that DeepCellEss can serve as a useful tool for predicting essential proteins across different cell lines. AVAILABILITY AND IMPLEMENTATION: The DeepCellEss web server is available at http://csuligroup.com:8000/DeepCellEss. The source code and data underlying this study can be obtained from https://github.com/CSUBioGroup/DeepCellEss. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

invMap: a sensitive mapping tool for long noisy reads with inversion structural variants.

MOTIVATION: Longer reads produced by PacBio or Oxford Nanopore sequencers could more frequently span the breakpoints of structural variations (SVs) than shorter reads. Therefore, existing long-read mapping methods often generate wrong alignments and variant calls. Compared to deletions and insertions, inversion events are more difficult to be detected since the anchors in inversion regions are nonlinear to those in SV-free regions. To address this issue, this study presents a novel long-read mapping algorithm (named as invMap). RESULTS: For each long noisy read, invMap first locates the aligned region with a specifically designed scoring method for chaining, then checks the remaining anchors in the aligned region to discover potential inversions. We benchmark invMap on simulated datasets across different genomes and sequencing coverages, experimental results demonstrate that invMap is more accurate to locate aligned regions and call SVs for inversions than the competing methods. The real human genome sequencing dataset of NA12878 illustrates that invMap can effectively find more candidate variant calls for inversions than the competing methods. AVAILABILITY AND IMPLEMENTATION: The invMap software is available at https://github.com/zhang134/invMap.git.

Ethylene-mediated stomatal responses to dehydration and rehydration in seed plants.

Ethylene, a plant hormone that significantly influences both plant growth and response to stress, plays a well-established role in stress signaling. However, its impact on stomatal opening and closure during dehydration and rehydration remains relatively unexplored and is still debated. Exogenous ethylene has been proven to induce stomatal closure through a series of signaling pathways, including the accumulation of reactive oxygen species (ROS), subsequent synthesis of nitric oxide (NO) and hydrogen sulfide (H2S), and SLOW ANION CHANNEL-ASSOCIATED 1 (SLAC1) activation. Thus, it has been suggested that ethylene might function to induce stomatal closure synergistically with abscisic acid (ABA). Furthermore, it has also been shown that increased ethylene can inhibit ABA- and jasmonic acid (JA)-induced stomatal closure, thus hindering drought-induced closure during dehydration. Simultaneously, other stresses, such as chilling, ozone pollution and K+ deficiency, inhibit drought and ABA-induced stomatal closure through an ethylene synthesis dependent way. However, ethylene has been shown to take on an opposing role during rehydration, preventing stomatal opening in the absence of ABA through its own signaling pathway. These findings offer novel insights into the function of ethylene in stomatal regulation during dehydration and rehydration, gaining a better understanding of the mechanisms underlying ethylene-induced stomatal movement in seed plants.

All-dielectric absorber based on nano-graphite sheets/ionogels with configurable absorbing band.

With the increasing complexity of the electromagnetic environment, there is a growing demand for the manipulation of electromagnetic waves, leading to the rapid development in configurable microwave absorbers. All-dielectric absorbers offer broadband and high-intensity absorption effects in microwave absorption and shielding. However, they face a significant challenge: their performance is not adjustable once the design is completed. In this study, we propose a solution to this problem by creating all-dielectric absorbers with flexibly configurable absorbing properties. We achieve this by designing a composite material of ionogels/nano-graphite sheets into compressible deformable absorbing units that can be molded into different shapes using 3D printing modes. The plasticity allows us to change the performance of the all-dielectric absorber, including the microwave absorption intensity, absorption peak, frequency bandwidths, and wide-angle absorption performance. With this approach, we can flexibly manipulate electromagnetic waves using all-dielectric absorbers through different plasticity models.

Muscone abrogates breast cancer progression through tumor angiogenic suppression via VEGF/PI3K/Akt/MAPK signaling pathways.

BACKGROUND: Angiogenesis strongly reflects poor breast cancer outcome and an important contributor to breast cancer (BC) metastasis; therefore, anti-angiogenic intervention is a potential tool for cancer treatment. However, currently used antibodies against vascular endothelial growth factor A (VEGFA) or inhibitors that target the VEGFA receptor are not effective due to weak penetration and low efficiency. Herein, we assessed the anti-BC angiogenic role of muscone, a natural bioactive musk constituent, and explored possible anti-cancer mechanisms of this compound. METHODS: CCK-8, EdU, scratch and Transwell assessments were employed to detect the muscone-mediated regulation of breast cancer (BC) and human umbilical vein endothelial cells (HUVECs) proliferation and migration. Tube formation, matrigel plug assay and zebrafish assay were employed for assessment of regulation of tumor angiogenesis by muscone. In vivo xenograft mouse model was constructed to compare microvessel density (MVD), vascular leakage, vascular maturation and function in muscone-treated or untreated mice. RNA sequencing was performed for gene screening, and Western blot verified the effect of the VEGFA-VEGFR2 pathway on BC angiogenic inhibition by muscone. RESULTS: Based on our findings, muscone suppressed BC progression via tumor angiogenic inhibition in cellular and animal models. Functionally, muscone inhibited BC cell proliferation and migration as well as tumor cell-conditioned medium-based endothelial cell proliferation and migration. Muscone exhibited a strong suppressive influence on tumor vasculature in cellular and animal models. It abrogated tumor cell growth in a xenograft BC mouse model and minimized tumor microvessel density and hypoxia, and increased vascular wall cell coverage and perfusion. Regarding the mechanism of action, we found that muscone suppressed phosphorylation of members of the VEGF/PI3K/Akt/MAPK axis, and it worked synergistically with a VEGFR2 inhibitor, an Akt inhibitor, and a MAPK inhibitor to further inhibit tube formation. CONCLUSION: Overall, our results demonstrate that muscone may proficiently suppress tumor angiogenesis via modulation of the VEGF/PI3K/Akt/MAPK axis, facilitating its candidacy as a natural small molecule drug for BC treatment.

Three-Dimensional Self-Supported Ge Anode for Advanced Lithium-Ion Batteries.

Three-dimensional (3D) self-supported Ge anode is one of the promising candidates to replace the traditional graphite anode material for high-performance binder-free lithium-ion batteries (LIBs). The enlarged surface area and the shortened ions/electrons transporting distance of the 3D electrode would greatly facilitate the rapid transfer of abundant lithium ions during cycling, thus achieve enhanced energy and power density during cycling. Cycle stability of the 3D self-supported Ge electrode would be improved due to the obtained enough space could effectively accommodate the large volume expansion of the Ge anode. In this review, we first describe the electrochemical properties and Li ions storage mechanism of Ge anode. Moreover, the recent advances in the 3D self-supported Ge anode architectures design are majorly illustrated and discussed. Challenges and prospects of the 3D self-supported Ge electrode are finally provided, which shed light on ways to design more reliable 3D Ge-based electrodes in energy storage systems.

A posterior probability based Bayesian method for single-cell RNA-seq data imputation.

Single-cell RNA-sequencing (scRNA-seq) data suffer from a lot of zeros. Such dropout events impede the downstream data analyses. We propose BayesImpute to infer and impute dropouts from the scRNA-seq data. Using the expression rate and coefficient of variation of the genes within the cell subpopulation, BayesImpute first determines likely dropouts, and then constructs the posterior distribution for each gene and uses the posterior mean to impute dropout values. Some simulated and real experiments show that BayesImpute can effectively identify dropout events and reduce the introduction of false positive signals. Additionally, BayesImpute successfully recovers the true expression levels of missing values, restores the gene-to-gene and cell-to-cell correlation coefficient, and maintains the biological information in bulk RNA-seq data. Furthermore, BayesImpute boosts the clustering and visualization of cell subpopulations and improves the identification of differentially expressed genes. We further demonstrate that, in comparison to other statistical-based imputation methods, BayesImpute is scalable and fast with minimal memory usage.

Development of cancer biomarker heat shock protein 90α certified reference material using two different isotope dilution mass spectrometry techniques.

Heat shock protein 90α (HSP90α) has been regarded as an important indicator for judging tumor metastasis and prognosis due to its significant upregulation in various tumors. Therefore, the accurate quantification of HSP90α is of great significance for clinical diagnosis and therapy of cancers. However, the lack of HSP90α certified reference material (CRM) leads to the accuracy and consistency of quantification methods not being effectively evaluated. Besides, quantitative results without traceability make comparisons between different studies difficult. In this study, an HSP90α solution CRM was developed from the recombinant protein raw material. The recombinant protein is a dimer, and the purity of the CRM candidate reached 96.71%. Both amino acid analysis-isotope dilution mass spectrometry (AAA-IDMS) and unique peptide analysis-isotope dilution mass spectrometry (UPA-IDMS) were performed to measure the content of HSP90α in the solution CRM candidate, and the certified value was assessed to be 66.2 ± 8.8 µg/g. Good homogeneity of the CRM was identified, and the stability examination suggested that the CRM was stable for at least 4 months at - 80 °C and for 7 days at 4 °C. With traceability to SI unit (kg), this CRM has potential to help establish a metrological traceability chain for quantification of HSP90α, which will make the quantification results standardized and comparable regardless of the quantitative methods.