DysRegSig: an R package for identifying gene dysregulations and building mechanistic signatures in cancer.

SUMMARY: Dysfunctional regulations of gene expression programs relevant to fundamental cell processes can drive carcinogenesis. Therefore, systematically identifying dysregulation events is an effective path for understanding carcinogenesis and provides insightful clues to build predictive signatures with mechanistic interpretability for cancer precision medicine. Here, we implemented a machine learning-based gene dysregulation analysis framework in an R package, DysRegSig, which is capable of exploring gene dysregulations from high-dimensional data and building mechanistic signature based on gene dysregulations. DysRegSig can serve as an easy-to-use tool to facilitate gene dysregulation analysis and follow-up analysis. AVAILABILITY AND IMPLEMENTATION: The source code and user's guide of DysRegSig are freely available at Github: https://github.com/SCBIT-YYLab/DysRegSig. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Application and prospect of cutting-edge information technology in biomedical big data.

In recent years, with the development of various high-throughput omics based biological technologies (BT), biomedical research began to enter the era of big data. In the face of high-dimensional, multi-domain and multi-modal biomedical big data, scientific research requires a new paradigm of data intensive scientific research. The vigorous development of cutting-edge information technologies (IT) such as cloud computing, blockchain and artificial intelligence provides technical means for the practice of this new research paradigm. Here,we describe the application of such cutting-edge information technologies in biomedical big data, and propose a forward-looking prospect for the construction of a new paradigm supporting environment for data intensive scientific research. We expect to establish a new research scheme and new scientific research paradigm integrating BT & IT technology, which can finally promote the great leap forward development of biomedical research.

A novel virtual barcode strategy for accurate panel-wide variant calling in circulating tumor DNA.

BACKGROUND: Hybrid capture-based next-generation sequencing of DNA has been widely applied in the detection of circulating tumor DNA (ctDNA). Various methods have been proposed for ctDNA detection, but low-allelic-fraction (AF) variants are still a great challenge. In addition, no panel-wide calling algorithm is available, which hiders the full usage of ctDNA based 'liquid biopsy'. Thus, we developed the VBCALAVD (Virtual Barcode-based Calling Algorithm for Low Allelic Variant Detection) in silico to overcome these limitations. RESULTS: Based on the understanding of the nature of ctDNA fragmentation, a novel platform-independent virtual barcode strategy was established to eliminate random sequencing errors by clustering sequencing reads into virtual families. Stereotypical mutant-family-level background artifacts were polished by constructing AF distributions. Three additional robust fine-tuning filters were obtained to eliminate stochastic mutant-family-level noises. The performance of our algorithm was validated using cell-free DNA reference standard samples (cfDNA RSDs) and normal healthy cfDNA samples (cfDNA controls). For the RSDs with AFs of 0.1, 0.2, 0.5, 1 and 5%, the mean F1 scores were 0.43 (0.25~0.56), 0.77, 0.92, 0.926 (0.86~1.0) and 0.89 (0.75~1.0), respectively, which indicates that the proposed approach significantly outperforms the published algorithms. Among controls, no false positives were detected. Meanwhile, characteristics of mutant-family-level noise and quantitative determinants of divergence between mutant-family-level noises from controls and RSDs were clearly depicted. CONCLUSIONS: Due to its good performance in the detection of low-AF variants, our algorithm will greatly facilitate the noninvasive panel-wide detection of ctDNA in research and clinical settings. The whole pipeline is available at https://github.com/zhaodalv/VBCALAVD.

Establishment of a hepatocellular carcinoma patient-derived xenograft platform and its application in biomarker identification.

Using a method optimized in hepatocellular carcinoma (HCC), we established patient-derived xenograft (PDX) models with an increased take rate (42.2%) and demonstrated that FBS +10% dimethyl sulfoxide exhibited the highest tumor take rate efficacy. Among 254 HCC patients, 103 stably transplantable xenograft lines that could be serially passaged, cryopreserved and revived were established. These lines maintained the diversity of HCC and the essential features of the original specimens at the histological, transcriptome, proteomic and genomic levels. Tumor engraftment was associated with lack of encapsulation, poor tumor differentiation, large size and overexpression of cancer stem cell biomarkers, and was an independent predictor for overall survival and tumor recurrence after resection. To confirm the preclinical value of the PDX model in HCC treatment, several antitumor agents were tested in 16 selected PDX models. The results revealed a high degree of pharmacologic heterogeneity in the cohort, as well as heterogeneity to different agents in the same individual. The sorafenib responses observed between HCC patients and the corresponding PDXs were also consistent. After molecular characterization of the PDX models, we explored the predictive markers for sorafenib response and found that mitogen-activated protein kinase kinase kinase 1 (MAP3K1) might play an important role in sorafenib resistance and sorafenib response is impaired in patients with MAP3K1 downexpression. Our results indicated that PDX models could accurately reproduce patient tumors biology and could aid in the discovery of new treatments to advance in precision medicine.

DRAP: a toolbox for drug response analysis and visualization tailored for preclinical drug testing on patient-derived xenograft models.

BACKGROUND: One of the key reasons for the high failure rate of new agents and low therapeutic benefit of approved treatments is the lack of preclinical models that mirror the biology of human tumors. At present, the optimal cancer model for drug response study to date is patient-derived xenograft (PDX) models. PDX recaptures both inter- and intra-tumor heterogeneity inherent in human cancer, which represent a valuable platform for preclinical drug testing and personalized medicine applications. Building efficient drug response analysis tools is critical but far from adequate for the PDX platform. RESULTS: In this work, we first classified the emerging PDX preclinical trial designs into four patterns based on the number of tumors, arms, and animal repeats in every arm. Then we developed an R package, DRAP, which implements Drug Response Analyses on PDX platform separately for the four patterns, involving data visualization, data analysis and conclusion presentation. The data analysis module offers statistical analysis methods to assess difference of tumor volume between arms, tumor growth inhibition (TGI) rate calculation to quantify drug response, and drug response level analysis to label the drug response at animal level. In the end, we applied DRAP in two case studies through which the functions and usage of DRAP were illustrated. CONCLUSION: DRAP is the first integrated toolbox for drug response analysis and visualization tailored for PDX platform. It would greatly promote the application of PDXs in drug development and personalized cancer treatments.

A novel mutation KCNQ1p.Thr312del is responsible for long QT syndrome type 1.

Patients with high-risk long QT syndrome (LQTS) mutations may experience life-threatening cardiac events. The present study sought to characterize a novel pathogenic mutation, KCNQ1p.Thr312del, in a Chinese LQT1 family. Clinical and genetic analyses were performed to identify this novel causative gene mutation in this LQTS family. Autosomal dominant inheritance of KCNQ1p.T312del was demonstrated in the three-generation pedigree. All mutation carriers presented with prolonged QT intervals and experienced recurrent syncope during exercise or emotional stress. The functional consequences of the mutant channel were investigated by computer homology modeling as well as whole-cell patch-clamp, western-blot and co-immunoprecipitation techniques using transfected mammalian cells. T312 is in the selectivity filter (SF) of the pore region of the KCNQ1-encoded channel. Homology modeling suggested that secondary structure was altered in the mutant SF compared with the wild-type (WT) SF. There were no significant differences in Kv7.1 expression, membrane trafficking or physical interactions with KCNE1-encoded subunits between the WT and mutant transfected channels. However, the KCNQ1p.T312del channels expressed in transfected cells were non-functional in the absence or presence of auxiliary KCNE1-subunits. Dominant-negative suppression of current density and decelerated activation kinetics were observed in cells expressing KCNQ1WT and KCNQ1p.T312del combined with KCNE1 (KCNQ1WT/p.T312del + KCNE1 channels). Those electrophysiological characteristics underlie the pathogenesis of this novel mutation and also suggest a high risk of cardiac events in patients carrying KCNQ1p.T312del. Although protein kinase A-dependent current increase was preserved, a significant suppression of rate-dependent current facilitation was noted in the KCNQ1WT/p.T312del + KCNE1 channels compared to the WT channels during 1- and 2-Hz stimulation, which was consistent with the patients' phenotype being triggered by exercise. Overall, KCNQ1p.Thr312del induces a loss of function in channel electrophysiology, and it is a high-risk mutation responsible for LQT1.

Whole-exome mutational and transcriptional landscapes of combined hepatocellular cholangiocarcinoma and intrahepatic cholangiocarcinoma reveal molecular diversity.

BACKGROUND: Primary liver cancer (PLC) is the third largest contributor to cancer mortality in the world. PLC is a heterogeneous disease that encompasses several biologically distinct subtypes including hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC) and combined hepatocellular-cholangiocarcinoma (CHC). CHC is a distinct, albeit rare, subtype of PLC and is comprised of cells with histopathological features of both HCC and ICC. Several studies have focused on the mutation and expression landscapes of HCC and ICC. However, studies of CHC were rare. OBJECTIVE: The aim of the current study was to identify genetic and gene expression alterations in the carcinogenesis and development of CHC and ICC in the Chinese population. Unraveling both similar and differing patterns among these subtypes may help to identify personalized medicine approaches that could improve patient survival. METHODS: Whole genome sequencing (WGS), whole exome sequencing (WES) and RNA-seq were performed on 10 ICC and 10 CHC samples, matched with adjacent non-tumor liver tissue specimens. Comparative analysis was performed using HCC datasets from The Cancer Genome Atlas (TCGA). RESULTS: Mutational and transcriptional landscapes of CHC and ICC were clearly delineated. TP53 and CTNNB1 were identified as exhibiting mutations in CHC. ARID1A, PBRM1, and IDH1 were frequently mutated in ICC. RYR3, FBN2, and KCNN3 are associated with cell migration and metastasis and might be driver genes in CHC. KCNN3 was identified as also exhibiting mutations in ICC. The ECM-receptor interaction pathway associated fibrogenic hepatic progenitor cell differentiation and liver fibrosis may play an important role in carcinogenesis of PLC. Chromatin remodeling and chromosome organization are key processes in carcinogenesis and development in PLC. P53 related pathways showed alterations in CHC and HCC. Inflammation may be a key factor involved in ICC carcinogenesis. CONCLUSION: CHC and ICC are different subtypes of PLC. This study discusses predominantly the molecular genetic details of PLC subtypes and highlights the need for an accurate diagnosis and treatment of specific PLC subtypes to optimize patient management.

PDXliver: a database of liver cancer patient derived xenograft mouse models.

BACKGROUND: Liver cancer is the second leading cause of cancer-related deaths and characterized by heterogeneity and drug resistance. Patient-derived xenograft (PDX) models have been widely used in cancer research because they reproduce the characteristics of original tumors. However, the current studies of liver cancer PDX mice are scattered and the number of available PDX models are too small to represent the heterogeneity of liver cancer patients. To improve this situation and to complement available PDX models related resources, here we constructed a comprehensive database, PDXliver, to integrate and analyze liver cancer PDX models. DESCRIPTION: Currently, PDXliver contains 116 PDX models from Chinese liver cancer patients, 51 of them were established by the in-house PDX platform and others were curated from the public literatures. These models are annotated with complete information, including clinical characteristics of patients, genome-wide expression profiles, germline variations, somatic mutations and copy number alterations. Analysis of expression subtypes and mutated genes show that PDXliver represents the diversity of human patients. Another feature of PDXliver is storing drug response data of PDX mice, which makes it possible to explore the association between molecular profiles and drug sensitivity. All data can be accessed via the Browse and Search pages. Additionally, two tools are provided to interactively visualize the omics data of selected PDXs or to compare two groups of PDXs. CONCLUSION: As far as we known, PDXliver is the first public database of liver cancer PDX models. We hope that this comprehensive resource will accelerate the utility of PDX models and facilitate liver cancer research. The PDXliver database is freely available online at: http://www.picb.ac.cn/PDXliver/.

The mTOR inhibitor AZD8055 overcomes tamoxifen resistance in breast cancer cells by down-regulating HSPB8.

Tamoxifen, an important endocrine therapeutic agent, is widely used for the treatment of estrogen receptor positive (ER+) breast cancer. However, de novo or acquired resistance prevents patients from benefitting from endocrine approaches and necessitates alternative treatments. In this study, we report that small heat protein beta-8 (HSPB8) may serve as an important molecule in tamoxifen resistance. HSPB8 expression is enhanced in MCF-7 cells resistant to tamoxifen (MCF-7/R) compared to parent cells. Moreover, high expression of HSPB8 associates with poor prognosis in ER+ breast cancer patients but not in patients without classification. Stimulating ER signaling by heterogeneous expression of ERa or 17ß-estradiol promotes HSPB8 expression and reduces the cell population in G1 phase. In contrast, blockage of ER signaling by tamoxifen down-regulates the expression of HSPB8. In addition, knocking down HSPB8 by specific siRNAs induces significant cell cycle arrest at G1 phase. AZD8055 was found to be more potent against the proliferation of MCF-7/R cells than that of parent cells, which was associated with down-regulation of HSPB8. We found that the anti-proliferative activity of AZD8055 was positively correlated with the HSPB8 expression level in ER+ breast cancer cells. Thus, AZD8055 was able to overcome tamoxifen resistance in breast cancer cells, and the expression of HSPB8 may predict the efficacy of AZD8055 in ER+ breast cancer. This hypothesis deserves further investigation.

DSviaDRM: an R package for estimating disease similarity via dysfunctional regulation mechanism.

UNLABELLED: Elucidation of human disease similarities has provided new insights into etiology, disease classification and drug repositioning. Since dysfunctional regulation would be manifested as the decoupling of expression correlation, disease similarity (DS) in terms of dysfunctional regulation mechanism (DRM) could be estimated by using a differential coexpression based approach, which is described in a companion paper. Due to the lack of tools for estimating DS from the viewpoint of DRM in public domain, we implemented an R package 'DSviaDRM' to identify significant DS via DRM based on transcriptomic data. DSviaDRM contains five easy-to-use functions, DCEA, DCpathway, DS, comDCGL and comDCGLplot, for identifying disease relationships and showing common differential regulation information shared by similar diseases. AVAILABILITY AND IMPLEMENTATION: DSviaDRM is available as an R package, with a user's guide and source code, at http://cran.r-project.org/web/packages/DSviaDRM/index.html. CONTACT: yyli@scbit.org or yxli@scbit.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Rig-I regulates NF-κB activity through binding to Nf-κb1 3'-UTR mRNA.

Retinoic acid inducible gene I (RIG-I) senses viral RNAs and triggers innate antiviral responses through induction of type I IFNs and inflammatory cytokines. However, whether RIG-I interacts with host cellular RNA remains undetermined. Here we report that Rig-I interacts with multiple cellular mRNAs, especially Nf-κb1. Rig-I is required for NF-κB activity via regulating Nf-κb1 expression at posttranscriptional levels. It interacts with the multiple binding sites within 3'-UTR of Nf-κb1 mRNA. Further analyses reveal that three distinct tandem motifs enriched in the 3'-UTR fragments can be recognized by Rig-I. The 3'-UTR binding with Rig-I plays a critical role in normal translation of Nf-κb1 by recruiting the ribosomal proteins [ribosomal protein L13 (Rpl13) and Rpl8] and rRNAs (18S and 28S). Down-regulation of Rig-I or Rpl13 significantly reduces Nf-κb1 and 3'-UTR-mediated luciferase expression levels. These findings indicate that Rig-I functions as a positive regulator for NF-κB signaling and is involved in multiple biological processes in addition to host antivirus immunity.

Global transcriptome analysis profiles metabolic pathways in traditional herb Astragalus membranaceus Bge. var. mongolicus (Bge.) Hsiao.

BACKGROUND: Astragalus membranaceus Bge. var. mongolicus (Bge.) Hsiao (A. mongolicus, family Leguminosae) is one of the most important traditional Chinese herbs. Among many secondary metabolites it produces, the effective bioactive constituents include isoflavonoids and triterpene saponins. The genomic resources regarding the biosynthesis of these metabolites in A. mongolicus are limited. Although roots are the primary material harvested for medical use, the biosynthesis of the bioactive compounds and its regulation in A. mongolicus are not well understood. Therefore, a global transcriptome analysis on A. mongolicus tissues was performed to identify the genes essential for the metabolism and to profile their expression patterns in greater details. RESULTS: RNA-sequencing was performed for three different A. mongolicus tissues: leaf, stem, and root, using the Illumina Hiseq2000 platform. A total of 159.5 million raw sequence reads were generated, and assembled into 186,324 unigenes with an N50 of 1,524bp. Among them, 129,966 unigenes (~69.7%) were annotated using four public databases (Swiss-Prot, TrEMBL, CDD, Pfam), and 90,202, 63,946, and 78,326 unigenes were found to express in leaves, roots, and stems, respectively. A total of 8,025 transcription factors (TFs) were identified, in which the four largest families, bHLH, MYB, C3H, and WRKY, were implicated in regulation of tissue development, metabolisms, stress response, etc. Unigenes associated with secondary metabolism, especially those with isolavonoids and triterpene saponins biosynthesis were characterized and profiled. Most genes involved in the isoflavonoids biosynthesis had the lowest expression in the leaves, and the highest in the stems. For triterpene saponin biosynthesis, we found the genes in MVA and non-MVA pathways were differentially expressed among three examined tissues, indicating the parallel but compartmentally separated biosynthesis pathways of IPP and DMAPP in A. mongolicus. The first committed enzyme in triterpene saponin biosynthesis from A. mongolicus, cycloartenol synthase (AmCAS), which belongs to the oxidosqualene cyclase family, was cloned by us to study the astragalosides biosynthesis. Further co-expression analysis indicated the candidate CYP450s and glycosyltransferases (GTs) in the cascade of triterpene saponins biosynthesis. The presence of the large CYP450 families in A. mongolicus was further compared with those from Medicago truncatula and Arabidopsis thaliana, and the diversity and phylegenetic relationships of the CYP450 families were established. CONCLUSION: A transcriptome study was performed for A. mongolicus tissues to construct and profile their metabolic pathways, especially for the important bioactive molecules. The results revealed a comprehensive profile for metabolic activities among tissues, pointing to the equal importance of leaf, stem, and root in A. mongolicus for the production of bioactive compounds. This work provides valuable resources for bioengineering and in vitro synthesis of the natural compounds for medical research and for potential drug development.

π-π Stacking mediated drug-drug interactions in human CYP2E1.

Because of having many low molecular mass substrates, CYP2E1 is of particular interests to the pharmaceutical industry. Many evidences showed that this enzyme can adopt multiple substrates to significantly reduce the oxidation rate of the substrates. The detailed mechanism for this observation is still unclear. In the current study, we employed GPU-accelerated molecular dynamics simulations to study the multiple-binding mode of human CYP2E1, with an aim of offering a mechanistic explanation for the unexplained multiple-substrate binding. Our results showed that Thr303 and Phe478 were key factors for the substrate recognition and multiple-substrate binding. The former can form a significant hydrogen bond to recognize and position the substrate in the productive binding orientation in the active site. The latter acted as a mediator for the substrate communications via π-π stacking interactions. In the multiple-binding mode, the aforementioned π-π stacking interactions formed by the aromatic rings of both substrates and Phe478 drove the first substrate far away from the catalytic center, orienting in an additional binding position and going against the substrate metabolism. All these findings could give atomic insights into the detailed mechanism for the multiple-substrate binding in human CYP2E1, providing useful information for the drug metabolism mechanism and personalized use of clinical drugs.

Systems perspectives on erythromycin biosynthesis by comparative genomic and transcriptomic analyses of S. erythraea E3 and NRRL23338 strains.

BACKGROUND: S. erythraea is a Gram-positive filamentous bacterium used for the industrial-scale production of erythromycin A which is of high clinical importance. In this work, we sequenced the whole genome of a high-producing strain (E3) obtained by random mutagenesis and screening from the wild-type strain NRRL23338, and examined time-series expression profiles of both E3 and NRRL23338. Based on the genomic data and transcriptpmic data of these two strains, we carried out comparative analysis of high-producing strain and wild-type strain at both the genomic level and the transcriptomic level. RESULTS: We observed a large number of genetic variants including 60 insertions, 46 deletions and 584 single nucleotide variations (SNV) in E3 in comparison with NRRL23338, and the analysis of time series transcriptomic data indicated that the genes involved in erythromycin biosynthesis and feeder pathways were significantly up-regulated during the 60 hours time-course. According to our data, BldD, a previously identified ery cluster regulator, did not show any positive correlations with the expression of ery cluster, suggesting the existence of alternative regulation mechanisms of erythromycin synthesis in S. erythraea. Several potential regulators were then proposed by integration analysis of genomic and transcriptomic data. CONCLUSION: This is a demonstration of the functional comparative genomics between an industrial S. erythraea strain and the wild-type strain. These findings help to understand the global regulation mechanisms of erythromycin biosynthesis in S. erythraea, providing useful clues for genetic and metabolic engineering in the future.

Structural basis for the mutation-induced dysfunction of human CYP2J2: a computational study.

Arachidonic acid is an essential fatty acid in cells, acting as a key inflammatory intermediate in inflammatory reactions. In cardiac tissues, CYP2J2 can adopt arachidonic acid as a major substrate to produce epoxyeicosatrienoic acids (EETs), which can protect endothelial cells from ischemic or hypoxic injuries and have been implicated in the pathogenesis of coronary artery disease and hypertension. However, some CYP2J2 polymorphisms, i.e., T143A and N404Y, significantly reduce the metabolism of arachidonic acid. Lacking experimental structural data for CYP2J2, the detailed mechanism for the mutation-induced dysfunction in the metabolism of arachidonic acid is still unknown. In the current study, three-dimensional structural models of the wild-type CYP2J2 and two mutants (T143A and N404Y) were constructed by a coordinate reconstruction approach and ab initio modeling using CYP2R1 as a template. The structural analysis of the computational models showed that the wild-type CYP2J2 exhibited a typical CYP fold with 12 alpha-helices and three beta-sheets on one side and with the heme group buried deeply inside the core. Due to the small and hydrophobic side-chain, T143A mutation could destabilize the C helix, further placing the water access channel in a closed state to prevent the escape of the produced water molecules during the catalytic processes. N404Y mutation could reposition the side-chain of Leu(378), making it no longer form a hydrogen bond with the carboxyl group of arachidonic acid. However, this hydrogen bond was essential for substrate recognition and positioning in a correct orientation.

Large scale phosphoproteome profiles comprehensive features of mouse embryonic stem cells.

Embryonic stem cells are pluripotent and capable of unlimited self-renewal. Elucidation of the underlying molecular mechanism may contribute to the advancement of cell-based regenerative medicine. In the present work, we performed a large scale analysis of the phosphoproteome in mouse embryonic stem (mES) cells. Using multiplex strategies, we detected 4581 proteins and 3970 high confidence distinct phosphosites in 1642 phosphoproteins. Notably, 22 prominent phosphorylated stem cell marker proteins with 39 novel phosphosites were identified for the first time by mass spectrometry, including phosphorylation sites in NANOG (Ser-65) and RE1 silencing transcription factor (Ser-950 and Thr-953). Quantitative profiles of NANOG peptides obtained during the differentiation of mES cells revealed that the abundance of phosphopeptides and non-phosphopeptides decreased with different trends. To our knowledge, this study presents the largest global characterization of phosphorylation in mES cells. Compared with a study of ultimately differentiated tissue cells, a bioinformatics analysis of the phosphorylation data set revealed a consistent phosphorylation motif in human and mouse ES cells. Moreover, investigations into phosphorylation conservation suggested that phosphoproteins were more conserved in the undifferentiated ES cell state than in the ultimately differentiated tissue cell state. However, the opposite conclusion was drawn from this conservation comparison with phosphosites. Overall, this work provides an overview of phosphorylation in mES cells and is a valuable resource for the future understanding of basic biology in mES cells.

DNMT3A governs tyrosine kinase inhibitors responses through IAPs and in a cell senescence-dependent manner in non-small cell lung cancer.

Patients with non-small cell lung cancer (NSCLC) treated with tyrosine kinase inhibitors (TKIs) inevitably exhibit drug resistance, which diminishes therapeutic effects. Nonetheless, the molecular mechanisms of TKI resistance in NSCLC remain obscure. In this study, data from clinical and TCGA databases revealed an increase in DNMT3A expression, which was correlated with a poor prognosis. Using NSCLC organoid models, we observed that high DNMT3A levels reduced TKI susceptibility of NSCLC cells via upregulating inhibitor of apoptosis proteins (IAPs). Simultaneously, the DNMT3Ahigh subset, which escaped apoptosis, underwent an early senescent-like state in a CDKN1A-dependent manner. Furthermore, the cellular senescence induced by TKIs was observed to be reversible, whereas DNMT3Ahigh cells reacquired their proliferative characteristics in the absence of TKIs, resulting in subsequent tumour recurrence and growth. Notably, the blockade of DNMT3A/IAPs signals enhanced the efficacy of TKIs in DNMT3Ahigh tumour-bearing mice, which represented a promising strategy for the effective treatment of NSCLC.

Partition dataset according to amino acid type improves the prediction of deleterious non-synonymous SNPs.

Many non-synonymous SNPs (nsSNPs) are associated with diseases, and numerous machine learning methods have been applied to train classifiers for sorting disease-associated nsSNPs from neutral ones. The continuously accumulated nsSNP data allows us to further explore better prediction approaches. In this work, we partitioned the training data into 20 subsets according to either original or substituted amino acid type at the nsSNP site. Using support vector machine (SVM), training classification models on each subset resulted in an overall accuracy of 76.3% or 74.9% depending on the two different partition criteria, while training on the whole dataset obtained an accuracy of only 72.6%. Moreover, the dataset was also randomly divided into 20 subsets, but the corresponding accuracy was only 73.2%. Our results demonstrated that partitioning the whole training dataset into subsets properly, i.e., according to the residue type at the nsSNP site, will improve the performance of the trained classifiers significantly, which should be valuable in developing better tools for predicting the disease-association of nsSNPs.

Autoinhibitory mechanism for the mutation-induced impaired FGF9 signaling.

Fibroblast growth factor 9 (FGF9), an important member of the fibroblast growth factor (FGF) family, can bind with high affinity to FGFR3 in a heparin-dependent approach. In humans, the deletions and mutations resulting in dysfunction of the FGF9 signaling can cause human skeletal dysplasia and cancers. A mutation (S99N) in this protein has been identified to be associated with significantly impaired FGF signaling considered as a potential cause of synostoses syndrome. However, the detailed mechanism for this observation still remains unknown. In this study, we used molecular dynamics simulations and free energy calculations to study the interactions of FGF9(WT/S99N), FGFR3c, and heparin, with an aim of providing atomic sights into the detailed mechanism for the impaired FGF signaling caused by the S99N mutation. We found that the S99N mutation has a well-ordered C-terminal structure, which can reduce its homodimerization ability so as to break the monomer-dimer equilibrium in the FGF signaling, which is considered as a key factor to regulate extracellular matrix affinity and tissue diffusion in the FGF signaling pathway. The FGF9(WT) monomer can preferentially form a homodimer owing to its comparatively favorable binding free energy. In contrast, the FGF9(S99N) monomer is preferred to bind with the FGFR3c receptor to form an inactive complex, leading to impair FGF signaling. To support our computational findings, we also performed biochemical experiments, which confirm the computational results mentioned above. The impaired FGF signaling is believed to be a potential cause of human synostoses syndrome, implicating an important role for FGF9 in normal joint development.

The discovery of novel protein-coding features in mouse genome based on mass spectrometry data.

Identifying protein-coding genes in eukaryotic genomes remains a challenge in post-genome era due to the complex gene models. We applied a proteogenomics strategy to detect un-annotated protein-coding regions in mouse genome. High-accuracy tandem mass spectrometry (MS/MS) data from diverse mouse samples were generated by LTQ-Orbitrap mass spectrometer in house. Two searchable diagnostic proteomic datasets were constructed, one with all possible encoding exon junctions, and the other with all putative encoding exons, for the discovery of novel exon splicing events and novel uninterrupted protein-coding regions. Altogether 29,586 unique peptides were identified. Aligning backwards to the mouse genome, the translation of 4471 annotated genes was validated by the known peptides; and 172 genic events were defined in mouse genome by the novel peptides. The approach in the current work can provide substantial evidences for eukaryote genome annotation in encoding genes.