MARK2 variants cause autism spectrum disorder via the downregulation of WNT/ß-catenin signaling pathway.

Microtubule affinity-regulating kinase 2 (MARK2) contributes to establishing neuronal polarity and developing dendritic spines. Although large-scale sequencing studies have associated MARK2 variants with autism spectrum disorder (ASD), the clinical features and variant spectrum in affected individuals with MARK2 variants, early developmental phenotypes in mutant human neurons, and the pathogenic mechanism underlying effects on neuronal development have remained unclear. Here, we report 31 individuals with MARK2 variants and presenting with ASD, other neurodevelopmental disorders, and distinctive facial features. Loss-of-function (LoF) variants predominate (81%) in affected individuals, while computational analysis and in vitro expression assay of missense variants supported the effect of MARK2 loss. Using proband-derived and CRISPR-engineered isogenic induced pluripotent stem cells (iPSCs), we show that MARK2 loss leads to early neuronal developmental and functional deficits, including anomalous polarity and dis-organization in neural rosettes, as well as imbalanced proliferation and differentiation in neural progenitor cells (NPCs). Mark2+/- mice showed abnormal cortical formation and partition and ASD-like behavior. Through the use of RNA sequencing (RNA-seq) and lithium treatment, we link MARK2 loss to downregulation of the WNT/ß-catenin signaling pathway and identify lithium as a potential drug for treating MARK2-associated ASD.

Meta-DHGNN: method for CRS-related cytokines analysis in CAR-T therapy based on meta-learning directed heterogeneous graph neural network.

Chimeric antigen receptor T-cell (CAR-T) immunotherapy, a novel approach for treating blood cancer, is associated with the production of cytokine release syndrome (CRS), which poses significant safety concerns for patients. Currently, there is limited knowledge regarding CRS-related cytokines and the intricate relationship between cytokines and cells. Therefore, it is imperative to explore a reliable and efficient computational method to identify cytokines associated with CRS. In this study, we propose Meta-DHGNN, a directed and heterogeneous graph neural network analysis method based on meta-learning. The proposed method integrates both directed and heterogeneous algorithms, while the meta-learning module effectively addresses the issue of limited data availability. This approach enables comprehensive analysis of the cytokine network and accurate prediction of CRS-related cytokines. Firstly, to tackle the challenge posed by small datasets, a pre-training phase is conducted using the meta-learning module. Consequently, the directed algorithm constructs an adjacency matrix that accurately captures potential relationships in a more realistic manner. Ultimately, the heterogeneous algorithm employs meta-photographs and multi-head attention mechanisms to enhance the realism and accuracy of predicting cytokine information associated with positive labels. Our experimental verification on the dataset demonstrates that Meta-DHGNN achieves favorable outcomes. Furthermore, based on the predicted results, we have explored the multifaceted formation mechanism of CRS in CAR-T therapy from various perspectives and identified several cytokines, such as IFNG (IFN-γ), IFNA1, IFNB1, IFNA13, IFNA2, IFNAR1, IFNAR2, IFNGR1 and IFNGR2 that have been relatively overlooked in previous studies but potentially play pivotal roles. The significance of Meta-DHGNN lies in its ability to analyze directed and heterogeneous networks in biology effectively while also facilitating CRS risk prediction in CAR-T therapy.

BertTCR: a Bert-based deep learning framework for predicting cancer-related immune status based on T cell receptor repertoire.

The T cell receptor (TCR) repertoire is pivotal to the human immune system, and understanding its nuances can significantly enhance our ability to forecast cancer-related immune responses. However, existing methods often overlook the intra- and inter-sequence interactions of T cell receptors (TCRs), limiting the development of sequence-based cancer-related immune status predictions. To address this challenge, we propose BertTCR, an innovative deep learning framework designed to predict cancer-related immune status using TCRs. BertTCR combines a pre-trained protein large language model with deep learning architectures, enabling it to extract deeper contextual information from TCRs. Compared to three state-of-the-art sequence-based methods, BertTCR improves the AUC on an external validation set for thyroid cancer detection by 21 percentage points. Additionally, this model was trained on over 2000 publicly available TCR libraries covering 17 types of cancer and healthy samples, and it has been validated on multiple public external datasets for its ability to distinguish cancer patients from healthy individuals. Furthermore, BertTCR can accurately classify various cancer types and healthy individuals. Overall, BertTCR is the advancing method for cancer-related immune status forecasting based on TCRs, offering promising potential for a wide range of immune status prediction tasks.

RAP80 phase separation at DNA double-strand break promotes BRCA1 recruitment.

RAP80 has been characterized as a component of the BRCA1-A complex and is responsible for the recruitment of BRCA1 to DNA double-strand breaks (DSBs). However, we and others found that the recruitment of RAP80 and BRCA1 were not absolutely temporally synchronized, indicating that other mechanisms, apart from physical interaction, might be implicated. Recently, liquid-liquid phase separation (LLPS) has been characterized as a novel mechanism for the organization of key signaling molecules to drive their particular cellular functions. Here, we characterized that RAP80 LLPS at DSB was required for RAP80-mediated BRCA1 recruitment. Both cellular and in vitro experiments showed that RAP80 phase separated at DSB, which was ascribed to a highly disordered region (IDR) at its N-terminal. Meanwhile, the Lys63-linked poly-ubiquitin chains that quickly formed after DSBs occur, strongly enhanced RAP80 phase separation and were responsible for the induction of RAP80 condensation at the DSB site. Most importantly, abolishing the condensation of RAP80 significantly suppressed the formation of BRCA1 foci, encovering a pivotal role of RAP80 condensates in BRCA1 recruitment and radiosensitivity. Together, our study disclosed a new mechanism underlying RAP80-mediated BRCA1 recruitment, which provided new insight into the role of phase separation in DSB repair.

PrCRS: a prediction model of severe CRS in CAR-T therapy based on transfer learning.

BACKGROUND: CAR-T cell therapy represents a novel approach for the treatment of hematologic malignancies and solid tumors. However, its implementation is accompanied by the emergence of potentially life-threatening adverse events known as cytokine release syndrome (CRS). Given the escalating number of patients undergoing CAR-T therapy, there is an urgent need to develop predictive models for severe CRS occurrence to prevent it in advance. Currently, all existing models are based on decision trees whose accuracy is far from meeting our expectations, and there is a lack of deep learning models to predict the occurrence of severe CRS more accurately. RESULTS: We propose PrCRS, a deep learning prediction model based on U-net and Transformer. Given the limited data available for CAR-T patients, we employ transfer learning using data from COVID-19 patients. The comprehensive evaluation demonstrates the superiority of the PrCRS model over other state-of-the-art methods for predicting CRS occurrence. We propose six models to forecast the probability of severe CRS for patients with one, two, and three days in advance. Additionally, we present a strategy to convert the model's output into actual probabilities of severe CRS and provide corresponding predictions. CONCLUSIONS: Based on our findings, PrCRS effectively predicts both the likelihood and timing of severe CRS in patients, thereby facilitating expedited and precise patient assessment, thus making a significant contribution to medical research. There is little research on applying deep learning algorithms to predict CRS, and our study fills this gap. This makes our research more novel and significant. Our code is publicly available at https://github.com/wzy38828201/PrCRS . The website of our prediction platform is: http://prediction.unicar-therapy.com/index-en.html .

ILGBMSH: an interpretable classification model for the shRNA target prediction with ensemble learning algorithm.

Short hairpin RNA (shRNA)-mediated gene silencing is an important technology to achieve RNA interference, in which the design of potent and reliable shRNA molecules plays a crucial role. However, efficient shRNA target selection through biological technology is expensive and time consuming. Hence, it is crucial to develop a more precise and efficient computational method to design potent and reliable shRNA molecules. In this work, we present an interpretable classification model for the shRNA target prediction using the Light Gradient Boosting Machine algorithm called ILGBMSH. Rather than utilizing only the shRNA sequence feature, we extracted 554 biological and deep learning features, which were not considered in previous shRNA prediction research. We evaluated the performance of our model compared with the state-of-the-art shRNA target prediction models. Besides, we investigated the feature explanation from the model's parameters and interpretable method called Shapley Additive Explanations, which provided us with biological insights from the model. We used independent shRNA experiment data from other resources to prove the predictive ability and robustness of our model. Finally, we used our model to design the miR30-shRNA sequences and conducted a gene knockdown experiment. The experimental result was perfectly in correspondence with our expectation with a Pearson's coefficient correlation of 0.985. In summary, the ILGBMSH model can achieve state-of-the-art shRNA prediction performance and give biological insights from the machine learning model parameters.

Genotype and Phenotype Characteristics of 58 Cases of Mitochondrial Epilepsy with Nuclear DNA Mutations in Children.

OBJECTIVE: Identify the genotype and clinical characteristics of mitochondrial epilepsy caused by nDNA mutations in Chinese children and explore the treatment and prognosis of the condition. STUDY DESIGN: This is a retrospective cohort study conducted at a single center, including patients diagnosed with an established nDNA mutation-associated primary mitochondrial disease between October 2012 and March 2023 who also met the practical clinical definition of epilepsy published by the ILAE in 2014. RESULTS: Of the 58 patients identified, 74.1% had an onset before the age of 1 year and 63.8% had seizures as their initial symptom. Developmental and epileptic encephalopathy (DEE) (31%) are the most common phenotypes. The most frequently observed MRI abnormalities include abnormal signal asymmetry in the bilateral basal ganglia and/or brainstem (34.7%), as well as brain atrophy, myelin sheath dysplasia, and corpus callosum dysplasia (32.7%). Of the 40 patients followed, seizure treatment was effective in 18 of the cases, while it was ineffective in 22. The mitochondrial DNA depletion syndrome (MDS) was found to be more difficult to control seizures than other phenotypes (P < 0.05). Additionally, the MDS was associated with a significantly higher mortality rate compared to alternative phenotypes (P < 0.05). CONCLUSIONS: The onset of mitochondrial epilepsy due to nDNA mutations is early and seizures are the most common initial symptom. DEE is the most common phenotype. Characteristic MRI abnormalities in the brain may be helpful in the diagnosis of primary mitochondrial disease. People with MDS typically face challenges in seizure control and have a poor prognosis.

A New Quinazolinone Alkaloid along with Known Compounds with Seed-Germination-Promoting Activity from Rhodiola tibetica Endophytic Fungus Penicillium sp. HJT-A-6.

A new quinazolinone alkaloid named peniquinazolinone A (1), as well as eleven known compounds, 2-(2-hydroxy-3-phenylpropionamido)-N-methylbenzamide (2), viridicatin (3), viridicatol (4), (±)-cyclopeptin (5a/5b), dehydrocyclopeptin (6), cyclopenin (7), cyclopenol (8), methyl-indole-3-carboxylate (9), 2,5-dihydroxyphenyl acetate (10), methyl m-hydroxyphenylacetate (11), and conidiogenone B (12), were isolated from the endophytic Penicillium sp. HJT-A-6. The chemical structures of all the compounds were elucidated by comprehensive spectroscopic analysis, including 1D and 2D NMR and HRESIMS. The absolute configuration at C-13 of peniquinazolinone A (1) was established by applying the modified Mosher's method. Compounds 2, 3, and 7 exhibited an optimal promoting effect on the seed germination of Rhodiola tibetica at a concentration of 0.01 mg/mL, while the optimal concentration for compounds 4 and 9 to promote Rhodiola tibetica seed germination was 0.001 mg/mL. Compound 12 showed optimal seed-germination-promoting activity at a concentration of 0.1 mg/mL. Compared with the positive drug 6-benzyladenine (6-BA), compounds 2, 3, 4, 7, 9, and 12 could extend the seed germination period of Rhodiola tibetica up to the 11th day.

A score-based method of immune status evaluation for healthy individuals with complete blood cell counts.

BACKGROUND: With the COVID-19 outbreak, an increasing number of individuals are concerned about their health, particularly their immune status. However, as of now, there is no available algorithm that effectively assesses the immune status of normal, healthy individuals. In response to this, a new score-based method is proposed that utilizes complete blood cell counts (CBC) to provide early warning of disease risks, such as COVID-19. METHODS: First, data on immune-related CBC measurements from 16,715 healthy individuals were collected. Then, a three-platform model was developed to normalize the data, and a Gaussian mixture model was optimized with expectation maximization (EM-GMM) to cluster the immune status of healthy individuals. Based on the results, Random Forest (RF), Light Gradient Boosting Machine (LightGBM) and Extreme Gradient Boosting (XGBoost) were used to determine the correlation of each CBC index with the immune status. Consequently, a weighted sum model was constructed to calculate a continuous immunity score, enabling the evaluation of immune status. RESULTS: The results demonstrated a significant negative correlation between the immunity score and the age of healthy individuals, thereby validating the effectiveness of the proposed method. In addition, a nonlinear polynomial regression model was developed to depict this trend. By comparing an individual's immune status with the reference value corresponding to their age, their immune status can be evaluated. CONCLUSION: In summary, this study has established a novel model for evaluating the immune status of healthy individuals, providing a good approach for early detection of abnormal immune status in healthy individuals. It is helpful in early warning of the risk of infectious diseases and of significant importance.

Population pharmacokinetics of topiramate in Chinese children with epilepsy.

OBJECTIVE: Topiramate, a broad-spectrum antiepileptic drug, exhibits substantial inter-individual variability in both its pharmacokinetics and therapeutic response. The aim of this study was to investigate the influence of patient characteristics and genetic variants on topiramate clearance using population pharmacokinetic (PPK) models in a cohort of Chinese pediatric patients with epilepsy. METHOD: The PPK model was constructed using a nonlinear mixed-effects modeling approach, utilizing a dataset comprising 236 plasma concentrations of topiramate obtained from 181 pediatric patients with epilepsy. A one-compartment model combined with a proportional residual model was employed to characterize the pharmacokinetics of topiramate. Covariate analysis was performed using forward addition and backward elimination to assess the influence of covariates on the model parameters. The model was thoroughly evaluated through goodness-of-fit analysis, bootstrap, visual predictive checks, and normalized prediction distribution errors. Monte Carlo simulations were utilized to devise topiramate dosing strategies. RESULT: In the final PPK models of topiramate, body weight, co-administration with oxcarbazepine, and a combined genotype of GKIR1-UGT (GRIK1 rs2832407, UGT2B7 rs7439366, and UGT1A1 rs4148324) were identified as significant covariates affecting the clearance (CL). The clearance was estimated using the formulas CL (L/h) = 0.44 × (BW/11.7)0.82 × eOXC for the model without genetic variants and CL (L/h) = 0.49 × (BW/11.7)0.81 × eOXC × eGRIK1-UGT for the model incorporating genetic variants. The volume of distribution (Vd) was estimated using the formulas Vd (L) = 6.6 × (BW/11.7). The precision of all estimated parameters was acceptable. Furthermore, the model demonstrated good predictability, exhibiting stability and effectiveness in describing the pharmacokinetics of topiramate. CONCLUSION: The clearance of topiramate in pediatric patients with epilepsy may be subject to the influence of factors such as body weight, co-administration with oxcarbazepine, and genetic polymorphism. In this study, PPK models were developed to better understand and account for these factors, thereby improving the precision and individualization of topiramate therapy in children with epilepsy.

Population pharmacokinetics of oxcarbazepine active metabolite in Chinese children with epilepsy.

Oxcarbazepine (OXC) is an antiepileptic drug whose efficacy is largely attributed to its monohydroxy derivative metabolite (MHD). Nevertheless, there exists significant inter-individual variability in both the pharmacokinetics and therapeutic response of this drug. The objective of this study is to explore the impact of patients' characteristics and genetic variants on MHD clearance in a population pharmacokinetic (PPK) model of Chinese pediatric patients with epilepsy. The PPK model was developed using a nonlinear mixed effects modeling method based on 231 MHD plasma concentrations obtained from 185 children with epilepsy. The one-compartment model and combined residual model were established to describe the pharmacokinetics of MHD. Forward addition and backward elimination were employed to evaluate the impact of covariates on the model parameters. The model was evaluated using goodness-of-fit, bootstrap, visual predictive checks, and normalized prediction distribution errors. In the two final PPK models, age, estimated glomerular filtration rate (eGFR), and a combined genotype of six variants (rs1045642, rs2032582, rs7668282, rs2396185, rs2304016, rs1128503) were found to significantly reduce inter-individual variability for MHD clearance. The inter-individual clearance equals to 1.38 × (Age/4.74)0.29 × (eGFR/128.66)0.25 × eθABCB-UGT-SCN-INSR for genetic variants included model and 1.30 × (Age/4.74)0.30 × (eGFR/128.66)0.23 for model without genetic variants. The precision of all parameters was deemed acceptable, and the model exhibited good predictability while remaining stable and effective.    Conclusion: Age, eGFR, and genotype may play a significant role in MHD clearance in children with epilepsy. The developed PPK models hold potential utility in facilitating oxcarbazepine dose adjustment in pediatric patients. What is Known: • The adjustment of the oxcarbazepine regimen remains difficult due to the considerable inter- and intra-individual variability of oxcarbazepine pharmacokinetics. • Body weight and co-administration with enzyme-inducing antiepileptic drugs emerge as the most influential factors contributing to the pharmacokinetics of MHD. What is New: • A positive correlation was observed between eGFR and the clearance of MHD in pediatric patients with epilepsy. • We explored the influence of genetic polymorphisms on MHD clearance and identified a combined genotype (ABCB-UGT-SCN-INSR) that exhibited a significant association with MHD concentration.

Methods for predicting single-cell miRNA in breast cancer.

It has been demonstrated that miRNAs are involved in many biological processes including cell proliferation and differentiation, apoptosis, and stress responses. Although single-cell RNA sequencing technology is prevailing nowadays, it still remains challenging in quantifying miRNA at the single-cell level. Herein, we present the computational methods to infer the single-cell miRNA expression level using its target gene abundances. Firstly, we developed an enrichment-based approach in estimating miRNA expression considering miRNA-mRNA regulation information and miRNA-mRNA correlation signal captured from existing TCGA datasets. Further efforts were made to infer the miRNA expression with machine learning models. The methods were applied to compare the accuracy and robustness with the simulated single-cell data. Finally, we applied the method in single-cell RNA-seq triple negative breast cancer (TNBC) patients to further discover miRNA marker at the single-cell level for the malignant cells. Our tool is available online at: https://github.com/ChengkuiZhao/Single-cell-miRNA-prediction.

Distance correlation application to gene co-expression network analysis.

BACKGROUND: To construct gene co-expression networks, it is necessary to evaluate the correlation between different gene expression profiles. However, commonly used correlation metrics, including both linear (such as Pearson's correlation) and monotonic (such as Spearman's correlation) dependence metrics, are not enough to observe the nature of real biological systems. Hence, introducing a more informative correlation metric when constructing gene co-expression networks is still an interesting topic. RESULTS: In this paper, we test distance correlation, a correlation metric integrating both linear and non-linear dependence, with other three typical metrics (Pearson's correlation, Spearman's correlation, and maximal information coefficient) on four different arrays (macrophage and liver) and RNA-seq (cervical cancer and pancreatic cancer) datasets. Among all the metrics, distance correlation is distribution free and can provide better performance on complex relationships and anti-outlier. Furthermore, distance correlation is applied to Weighted Gene Co-expression Network Analysis (WGCNA) for constructing a gene co-expression network analysis method which we named Distance Correlation-based Weighted Gene Co-expression Network Analysis (DC-WGCNA). Compared with traditional WGCNA, DC-WGCNA can enhance the result of enrichment analysis and improve the module stability. CONCLUSIONS: Distance correlation is better at revealing complex biological relationships between gene profiles compared with other correlation metrics, which contribute to more meaningful modules when analyzing gene co-expression networks. However, due to the high time complexity of distance correlation, the implementation requires more computer memory.

Investigation of CRS-associated cytokines in CAR-T therapy with meta-GNN and pathway crosstalk.

BACKGROUND: Chimeric antigen receptor T-cell (CAR-T) therapy is a new and efficient cellular immunotherapy. The therapy shows significant efficacy, but also has serious side effects, collectively known as cytokine release syndrome (CRS). At present, some CRS-related cytokines and their roles in CAR-T therapy have been confirmed by experimental studies. However, the mechanism of CRS remains to be fully understood. METHODS: Based on big data for human protein interactions and meta-learning graph neural network, we employed known CRS-related cytokines to comprehensively investigate the CRS associated cytokines in CAR-T therapy through protein interactions. Subsequently, the clinical data for 119 patients who received CAR-T therapy were examined to validate our prediction results. Finally, we systematically explored the roles of the predicted cytokines in CRS occurrence by protein interaction network analysis, functional enrichment analysis, and pathway crosstalk analysis. RESULTS: We identified some novel cytokines that would play important roles in biological process of CRS, and investigated the biological mechanism of CRS from the perspective of functional analysis. CONCLUSIONS: 128 cytokines and related molecules had been found to be closely related to CRS in CAR-T therapy, where several important ones such as IL6, IFN-γ, TNF-α, ICAM-1, VCAM-1 and VEGFA were highlighted, which can be the key factors to predict CRS.

Phytochemical, Chemotaxonomic and Bioinformatics Study on Laportea bulbifera (Urticaceae).

Phytochemical investigation of the aerial part of Laportea bulbifera (Siebold & Zucc.) Wedd. (L. bulbifera) showed the isolation of seventeen compounds, including five flavonoids (1-4 and 6), one terpenoid (5), five phenolic acids (7-11), one coumarin (12), two steroids (13-14), and three alkaloids (15-17). Structure elucidations of these compounds were performed on the basis of extensive NMR experiments and compared with the published data in the references. It is remarkable that compounds (3-5) were firstly isolated from the Urticaceae family, compounds (3-8, 11 and 15-17) were firstly obtained from genus Laportea. Furthermore, the result of the chemotaxonomic significance discussion showed that compounds (2-4) may can be served as compound fingerprints to distinguish between species of L. bulbifera and genus Urtica, and what' more, we proposed a bold conjecture that isoflavones can distinguish between species of L. bulbifera and genus Urtica. At the same time, the molecular docking method was used to evaluate the inhibitory effect of these compounds on human steroid 5α-reductase 2 (SRD5α2). The results showed that compounds (1-4 and 6) had better expected effects than the positive drug finasteride can by effectively binding to the active sites of SRD5α2. This study assisted in the future phytochemical and chemotaxonomic research on genus Laportea. Simultaneously, this research provided the theoretical evidence for the application of L. bulbifera in treating benign prostatic hyperplasia (BPH).

TPSC: a module detection method based on topology potential and spectral clustering in weighted networks and its application in gene co-expression module discovery.

BACKGROUND: Gene co-expression networks are widely studied in the biomedical field, with algorithms such as WGCNA and lmQCM having been developed to detect co-expressed modules. However, these algorithms have limitations such as insufficient granularity and unbalanced module size, which prevent full acquisition of knowledge from data mining. In addition, it is difficult to incorporate prior knowledge in current co-expression module detection algorithms. RESULTS: In this paper, we propose a novel module detection algorithm based on topology potential and spectral clustering algorithm to detect co-expressed modules in gene co-expression networks. By testing on TCGA data, our novel method can provide more complete coverage of genes, more balanced module size and finer granularity than current methods in detecting modules with significant overall survival difference. In addition, the proposed algorithm can identify modules by incorporating prior knowledge. CONCLUSION: In summary, we developed a method to obtain as much as possible information from networks with increased input coverage and the ability to detect more size-balanced and granular modules. In addition, our method can integrate data from different sources. Our proposed method performs better than current methods with complete coverage of input genes and finer granularity. Moreover, this method is designed not only for gene co-expression networks but can also be applied to any general fully connected weighted network.

Prediction of functional microexons by transfer learning.

BACKGROUND: Microexons are a particular kind of exon of less than 30 nucleotides in length. More than 60% of annotated human microexons were found to have high levels of sequence conservation, suggesting their potential functions. There is thus a need to develop a method for predicting functional microexons. RESULTS: Given the lack of a publicly available functional label for microexons, we employed a transfer learning skill called Transfer Component Analysis (TCA) to transfer the knowledge obtained from feature mapping for the prediction of functional microexons. To provide reference knowledge, microindels were chosen because of their similarities to microexons. Then, Support Vector Machine (SVM) was used to train a classification model in the newly built feature space for the functional microindels. With the trained model, functional microexons were predicted. We also built a tool based on this model to predict other functional microexons. We then used this tool to predict a total of 19 functional microexons reported in the literature. This approach successfully predicted 16 out of 19 samples, giving accuracy greater than 80%. CONCLUSIONS: In this study, we proposed a method for predicting functional microexons and applied it, with the predictive results being largely consistent with records in the literature.

Evaluation of bottom-up and top-down mass spectrum identifications with different customized protein sequences databases.

MOTIVATION: Generally, bottom-up and top-down are two complementary approaches for proteoforms identification. The inference of proteoforms relies on searching mass spectra against an accurate proteoform sequence database. A customized protein sequence database derived by RNA-Seq data can be used to better identify the proteoform existed in a studied species. However, the quality of sequences in customized databases which constructed by different strategies affect the performances of mass spectrometry (MS) identification. Additionally, performances of identifications between bottom-up and top-down using customized databases are also needed to be evaluated. RESULTS: Three customized databases were constructed with different strategies separately. Two of them were based on translating assembled transcripts with or without genomic annotation, and the third one is a variant-extending protein database. By testing with bottom-up and top-down MS data separately, a variant-extending protein database could identify not only the most number of spectra but also the alleles expressed at the same time in diploid cells. An assembled database could identify the spectrum missed in reference database and amino acid (AA) alterations existed in studied species. AVAILABILITY AND IMPLEMENTATION: Experimental results demonstrated that the proteoform sequences in an annotated database are more suitable for identifying AA alterations and peptide sequences missed in reference database. An unannotated database instead of a reference proteome database gets an enough high sensitivity of identifying mass spectra. The variant-extending reference database is the most sensitive to identify mass spectra and single AA variants. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Polyketone metabolites isolated from Rhodiola tibetica endohytic fungus Alternaria sp. HJT-Y7 and their SARS-CoV-2 virus inhibitory activitives.

Six new polyketone metabolites, compounds (1-6) and seven known polyketone compounds (7-13) were isolated from Rhodiola tibetica endophytic fungus Alternaria sp. The structural elucidation of five new polyketone metabolites were elucidated on the basis of spectroscopic including 2D NMR and HRMS and spectrometric analysis. Inhibition rate evaluation revealed that compounds 1(EC50 = 0.02 mM), 3(EC50 = 0.3 mM), 6(EC50 = 0.07 mM), 8(EC50 = 0.1 mM) and 9(EC50 = 0.04 mM) had inhibitory effect on the SARS-CoV-2 virus.

Propensity score-adjusted three-component mixture model for drug-drug interaction data mining in FDA Adverse Event Reporting System.

With increasing trend of polypharmacy, drug-drug interaction (DDI)-induced adverse drug events (ADEs) are considered as a major challenge for clinical practice. As premarketing clinical trials usually have stringent inclusion/exclusion criteria, limited comedication data capture and often times small sample size have limited values in study DDIs. On the other hand, ADE reports collected by spontaneous reporting system (SRS) become an important source for DDI studies. There are two major challenges in detecting DDI signals from SRS: confounding bias and false positive rate. In this article, we propose a novel approach, propensity score-adjusted three-component mixture model (PS-3CMM). This model can simultaneously adjust for confounding bias and estimate false discovery rate for all drug-drug-ADE combinations in FDA Adverse Event Reporting System (FAERS), which is a preeminent SRS database. In simulation studies, PS-3CMM performs better in detecting true DDIs comparing to the existing approach. It is more sensitive in selecting the DDI signals that have nonpositive individual drug relative ADE risk (NPIRR). The application of PS-3CMM is illustrated in analyzing the FAERS database. Compared to the existing approaches, PS-3CMM prioritizes DDI signals differently. PS-3CMM gives high priorities to DDI signals that have NPIRR. Both simulation studies and FAERS data analysis conclude that our new PS-3CMM is a new method that is complement to the existing DDI signal detection methods.