Adaptive resistance to PI3Kα-selective inhibitor CYH33 is mediated by genomic and transcriptomic alterations in ESCC cells.

Phosphoinositide-3 kinase alpha-specific inhibitors (PI3Kαi) displayed promising potential for the treatment of esophageal squamous cell carcinoma (ESCC) with frequent activation in PI3K signaling. However, acquired resistance is likely to develop and limit the efficacy of PI3Kαi like other targeted therapies. To identify genomic adaptation to PI3Kαi, we applied whole-genome sequencing and detected gene mutation and amplification in four lines of ESCC cells established with adapted resistance to a novel PI3Kαi CYH33. Particularly, HRASG12S mutation was found in KYSE180C cells. Overexpression of HRASG12S in ESCC parental cells rendered resistance to CYH33. By contrast, down-regulation of HRASG12S restored the sensitivity of KYSE180C1 cells to CYH33, and combination of CYH33 and MEK162 displayed synergistic effect against KYSE180C1 cells and xenografts. Furthermore, elevated mTORC1, mitogen-activated protein kinase (MAPK), and c-Myc signaling pathways were found in resistant cells by RNA sequencing and combination of CYH33 and RAD001, MEK162, or OTX015 overcame the resistance to CYH33, which was accompanied with enhanced inhibition on S6, extracellular signal-regulated kinase 1 (ERK), or c-Myc, respectively. Overall, we characterized the adaptations to PI3Kαi in ESCC cells and identified combinatorial regimens that may circumvent resistance.

Analysis of Expression Pattern of snoRNAs in Different Cancer Types with Machine Learning Algorithms.

Small nucleolar RNAs (snoRNAs) are a new type of functional small RNAs involved in the chemical modifications of rRNAs, tRNAs, and small nuclear RNAs. It is reported that they play important roles in tumorigenesis via various regulatory modes. snoRNAs can both participate in the regulation of methylation and pseudouridylation and regulate the expression pattern of their host genes. This research investigated the expression pattern of snoRNAs in eight major cancer types in TCGA via several machine learning algorithms. The expression levels of snoRNAs were first analyzed by a powerful feature selection method, Monte Carlo feature selection (MCFS). A feature list and some informative features were accessed. Then, the incremental feature selection (IFS) was applied to the feature list to extract optimal features/snoRNAs, which can make the support vector machine (SVM) yield best performance. The discriminative snoRNAs included HBII-52-14, HBII-336, SNORD123, HBII-85-29, HBII-420, U3, HBI-43, SNORD116, SNORA73B, SCARNA4, HBII-85-20, etc., on which the SVM can provide a Matthew's correlation coefficient (MCC) of 0.881 for predicting these eight cancer types. On the other hand, the informative features were fed into the Johnson reducer and repeated incremental pruning to produce error reduction (RIPPER) algorithms to generate classification rules, which can clearly show different snoRNAs expression patterns in different cancer types. The analysis results indicated that extracted discriminative snoRNAs can be important for identifying cancer samples in different types and the expression pattern of snoRNAs in different cancer types can be partly uncovered by quantitative recognition rules.

Facile Synthesis of the Magnetic Metal Organic Framework Fe3O4@UiO-66-NH2 for Separation of Strontium.

A magnetic metal organic framework (MMOF) was synthesized and used to separate Sr2+ in aqueous solution. The shape and structure of prepared Fe3O4@UiO-66-NH2 were characterized, and the absorbed concentration of strontium was determined through inductively coupled plasma mass spectrometry. The results indicated that Fe3O4 and UiO-66-NH2 combined through chemical bonding. The experimental adsorption results for separation of Sr2+ in aqueous solution indicated that the adsorption of Sr2+ to Fe3O4@UiO-66-NH2 increased drastically from pH 11 to pH 13. The adsorption isotherm model indicated that the adsorption of Sr2+ conformed to the Freundlich isotherm model (R2 = 0.9919). The MMOF thus inherited the superior qualities of magnetic composites and metal organic frameworks, and can easily be separated under an external magnetic field. This MMOF thus has potential applications as a magnetic adsorbent for low level radionuclide 90Sr.

Identification of Candidate Genes Related to Inflammatory Bowel Disease Using Minimum Redundancy Maximum Relevance, Incremental Feature Selection, and the Shortest-Path Approach.

Identification of disease genes is a hot topic in biomedicine and genomics. However, it is a challenging problem because of the complexity of diseases. Inflammatory bowel disease (IBD) is an idiopathic disease caused by a dysregulated immune response to host intestinal microflora. It has been proven to be associated with the development of intestinal malignancies. Although the specific pathological characteristics and genetic background of IBD have been partially revealed, it is still an overdetermined disease and the blueprint of all genetic variants still needs to be improved. In this study, a novel computational method was built to identify genes related to IBD. Samples from two subtypes of IBD (ulcerative colitis and Crohn's disease) and normal samples were employed. By analyzing the gene expression profiles of these samples using minimum redundancy maximum relevance and incremental feature selection, 21 genes were obtained that could effectively distinguish samples from the two subtypes of IBD and the normal samples. Then, the shortest-path approach was used to search for an additional 20 genes in a large network constructed using protein-protein interactions based on the above-mentioned 21 genes. Analyses of the 41 genes obtained indicate that they are closely associated with this disease.

Mining for Candidate Genes Related to Pancreatic Cancer Using Protein-Protein Interactions and a Shortest Path Approach.

Pancreatic cancer (PC) is a highly malignant tumor derived from pancreas tissue and is one of the leading causes of death from cancer. Its molecular mechanism has been partially revealed by validating its oncogenes and tumor suppressor genes; however, the available data remain insufficient for medical workers to design effective treatments. Large-scale identification of PC-related genes can promote studies on PC. In this study, we propose a computational method for mining new candidate PC-related genes. A large network was constructed using protein-protein interaction information, and a shortest path approach was applied to mine new candidate genes based on validated PC-related genes. In addition, a permutation test was adopted to further select key candidate genes. Finally, for all discovered candidate genes, the likelihood that the genes are novel PC-related genes is discussed based on their currently known functions.

Determination of Trace Elements in Edible Nuts in the Beijing Market by ICP-M.

Nuts have received increased attention from the public in recent years as important sources of some essential elements, and information on the levels of elements in edible nuts is useful to consumers. Determination of the elemental distributions in nuts is not only necessary in evaluating the total dietary intake of the essential elements, but also useful in detecting heavy metal contamination in food. The aim of this study was to determine the mineral contents in edible nuts, and to assess the food safety of nuts in the Beijing market. Levels of Li, Cr, Mn, Co, Cu, Zn, As, Se, Rb, Sr, Mo, Cd, Cs, Ba, Pb, Th, and U in 11 types of edible nuts and seeds (macadamia nuts, lotus nuts, pistachios, sunflower seeds, pine nuts, almonds, walnuts, chestnuts, hazelnuts, cashews, and ginkgo nuts) as well as raisins were determined by inductively coupled plasma mass spectrometry (ICP-MS). The accuracy of the method was validated using standard reference materials GBW10014 (cabbage) and GBW10016 (tea). Our results provide useful information for evaluating the levels of trace elements in edible nuts in the Beijing market, will be helpful for improving food safety, and will aid in better protecting consumer interests.

Classifying ten types of major cancers based on reverse phase protein array profiles.

Gathering vast data sets of cancer genomes requires more efficient and autonomous procedures to classify cancer types and to discover a few essential genes to distinguish different cancers. Because protein expression is more stable than gene expression, we chose reverse phase protein array (RPPA) data, a powerful and robust antibody-based high-throughput approach for targeted proteomics, to perform our research. In this study, we proposed a computational framework to classify the patient samples into ten major cancer types based on the RPPA data using the SMO (Sequential minimal optimization) method. A careful feature selection procedure was employed to select 23 important proteins from the total of 187 proteins by mRMR (minimum Redundancy Maximum Relevance Feature Selection) and IFS (Incremental Feature Selection) on the training set. By using the 23 proteins, we successfully classified the ten cancer types with an MCC (Matthews Correlation Coefficient) of 0.904 on the training set, evaluated by 10-fold cross-validation, and an MCC of 0.936 on an independent test set. Further analysis of these 23 proteins was performed. Most of these proteins can present the hallmarks of cancer; Chk2, for example, plays an important role in the proliferation of cancer cells. Our analysis of these 23 proteins lends credence to the importance of these genes as indicators of cancer classification. We also believe our methods and findings may shed light on the discoveries of specific biomarkers of different types of cancers.

A method to distinguish between lysine acetylation and lysine ubiquitination with feature selection and analysis.

Lysine acetylation and ubiquitination are two primary post-translational modifications (PTMs) in most eukaryotic proteins. Lysine residues are targets for both types of PTMs, resulting in different cellular roles. With the increasing availability of protein sequences and PTM data, it is challenging to distinguish the two types of PTMs on lysine residues. Experimental approaches are often laborious and time consuming. There is an urgent need for computational tools to distinguish between lysine acetylation and ubiquitination. In this study, we developed a novel method, called DAUFSA (distinguish between lysine acetylation and lysine ubiquitination with feature selection and analysis), to discriminate ubiquitinated and acetylated lysine residues. The method incorporated several types of features: PSSM (position-specific scoring matrix) conservation scores, amino acid factors, secondary structures, solvent accessibilities, and disorder scores. By using the mRMR (maximum relevance minimum redundancy) method and the IFS (incremental feature selection) method, an optimal feature set containing 290 features was selected from all incorporated features. A dagging-based classifier constructed by the optimal features achieved a classification accuracy of 69.53%, with an MCC of .3853. An optimal feature set analysis showed that the PSSM conservation score features and the amino acid factor features were the most important attributes, suggesting differences between acetylation and ubiquitination. Our study results also supported previous findings that different motifs were employed by acetylation and ubiquitination. The feature differences between the two modifications revealed in this study are worthy of experimental validation and further investigation.

Identifying chemicals with potential therapy of HIV based on protein-protein and protein-chemical interaction network.

Acquired immune deficiency syndrome (AIDS) is a severe infectious disease that causes a large number of deaths every year. Traditional anti-AIDS drugs directly targeting the HIV-1 encoded enzymes including reverse transcriptase (RT), protease (PR) and integrase (IN) usually suffer from drug resistance after a period of treatment and serious side effects. In recent years, the emergence of numerous useful information of protein-protein interactions (PPI) in the HIV life cycle and related inhibitors makes PPI a new way for antiviral drug intervention. In this study, we identified 26 core human proteins involved in PPI between HIV-1 and host, that have great potential for HIV therapy. In addition, 280 chemicals that interact with three HIV drugs targeting human proteins can also interact with these 26 core proteins. All these indicate that our method as presented in this paper is quite promising. The method may become a useful tool, or at least plays a complementary role to the existing method, for identifying novel anti-HIV drugs.

Identification of lung-cancer-related genes with the shortest path approach in a protein-protein interaction network.

Lung cancer is one of the leading causes of cancer mortality worldwide. The main types of lung cancer are small cell lung cancer (SCLC) and nonsmall cell lung cancer (NSCLC). In this work, a computational method was proposed for identifying lung-cancer-related genes with a shortest path approach in a protein-protein interaction (PPI) network. Based on the PPI data from STRING, a weighted PPI network was constructed. 54 NSCLC- and 84 SCLC-related genes were retrieved from associated KEGG pathways. Then the shortest paths between each pair of these 54 NSCLC genes and 84 SCLC genes were obtained with Dijkstra's algorithm. Finally, all the genes on the shortest paths were extracted, and 25 and 38 shortest genes with a permutation P value less than 0.05 for NSCLC and SCLC were selected for further analysis. Some of the shortest path genes have been reported to be related to lung cancer. Intriguingly, the candidate genes we identified from the PPI network contained more cancer genes than those identified from the gene expression profiles. Furthermore, these genes possessed more functional similarity with the known cancer genes than those identified from the gene expression profiles. This study proved the efficiency of the proposed method and showed promising results.

Deciphering the effects of gene deletion on yeast longevity using network and machine learning approaches.

Longevity is one of the most basic and one of the most essential properties of all living organisms. Identification of genes that regulate longevity would increase understanding of the mechanisms of aging, so as to help facilitate anti-aging intervention and extend the life span. In this study, based on the network features and the biochemical/physicochemical features of the deletion network and deletion genes, as well as their functional features, a two-layer model was developed for predicting the deletion effects on yeast longevity. The first stage of our prediction approach was to identify whether the deletion of one gene would change the life span of yeast; if it did, the second stage of our procedure would automatically proceed to predict whether the deletion of one gene would increase or decrease the life span. It was observed by analyzing the predicted results that the functional features (such as mitochondrial function and chromatin silencing), the network features (such as the edge density and edge weight density of the deletion network), and the local centrality of deletion gene, would have important impact for predicting the deletion effects on longevity. It is anticipated that our model may become a useful tool for studying longevity from the angle of genes and networks. Moreover, it has not escaped our notice that, after some modification, the current model can also be used to study many other phenotype prediction problems from the angle of systems biology.

Selection of reprogramming factors of induced pluripotent stem cells based on the protein interaction network and functional profiles.

Induced pluripotent stem cells have displayed great potential in disease investigation and drug development applications. However, selection of reprogramming factors in each cell type or disease state is both expensive and time consuming. To deal with this kind of situation, a fast computational framework was developed by optimize the reprogramming factors via the protein interaction network and gene functional profiles. It can be used to select reprogramming factors from millions of possibilities. It is anticipated that the novel approach will become a very useful tool for both basic research and drug development.

Nucleosome positioning based on the sequence word composition.

The DNA of all eukaryotic organisms is packaged into nucleosomes (a basic repeating unit of chromatin). A nucleosome consists of histone octamer wrapped by core DNA and linker histone H1 associated with linker DNA. It has profound effects on all DNA-dependent processes by affecting sequence accessibility. Understanding the factors that influence nucleosome positioning has great help to the study of genomic control mechanism. Among many determinants, the inherent DNA sequence has been suggested to have a dominant role in nucleosome positioning in vivo. Here, we used the method of minimum redundancy maximum relevance (mRMR) feature selection and the nearest neighbor algorithm (NNA) combined with the incremental feature selection (IFS) method to identify the most important sequence features that either favor or inhibit nucleosome positioning. We analyzed the words of 53,021 nucleosome DNA sequences and 50,299 linker DNA sequences of Saccharomyces cerevisiae. 32 important features were abstracted from 5,460 features, and the overall prediction accuracy through jackknife cross-validation test was 76.5%. Our results support that sequence-dependent DNA flexibility plays an important role in positioning nucleosome core particles and that genome sequence facilitates the rapid nucleosome reassembly instead of nucleosome depletion. Besides, our results suggest that there exist some additional features playing a considerable role in discriminating nucleosome forming and inhibiting sequences. These results confirmed that the underlying DNA sequence plays a major role in nucleosome positioning.

Classification of transcription factors using protein primary structure.

The transcription factor (TF) is a protein that binds DNA at specific site to help regulate the transcription from DNA to RNA. The mechanism of transcriptional regulatory can be much better understood if the category of transcription factors is known. We introduce a system which can automatically categorize transcription factors using their primary structures. A feature analysis strategy called "mRMR" (Minimum Redundancy, Maximum Relevance) is used to analyze the contribution of the TF properties towards the TF classification. mRMR is coupled with forward feature selection to choose an optimized feature subset for the classification. TF properties are composed of the amino acid composition and the physiochemical characters of the proteins. These properties will generate over a hundred features/parameters. We put all the features/parameters into a classifier, called NNA (nearest neighbor algorithm), for the classification. The classification accuracy is 93.81%, evaluated by a Jackknife test. Feature analysis using mRMR algorithm shows that secondary structure, amino acid composition and hydrophobicity are the most relevant features for classification. A free online classifier is available at http://app3.biosino.org/132dvc/tf/.

Temporal and parental-specific expression of imprinted genes in a newly derived Chinese human embryonic stem cell line and embryoid bodies.

Although the study of imprinted genes in human development is very important, little is known about their expression and regulation in the early differentiation of human tissues due to lack of an appropriate model. In this study, a Chinese human embryonic stem (hES) cell line, SHhES1, was derived and fully characterized. Expression profiles of human imprinted genes were determined by Affymetrix Oligo micro-array in undifferentiated SHhES1 cells and SHhES1-derived embryoid bodies (EBs) at day 3, 8, 13 and 18. Thirty-two known human imprinted genes were detected in undifferentiated ES cells. Significantly, differential expression was found in nine genes at different stages of EB formation. Expression profile changes were confirmed by quantitative real-time reverse transcriptase-polymerase chain reaction in SHhES1 cells as well as in another independently derived hES cell line, HUES-7. In addition, the monoallelic expressions of four imprinted genes were examined in three different passages of undifferentiated ES cells and EBs of both hES cell lines. The monoallelic expressions of imprinted genes, H19, PEG10, NDNL1 and KCNQ1 were maintained in both undifferentiated hES cells and derived EBs. More importantly, with the availability of maternal peripheral blood lymphocyte sample, we demonstrated that the maternal expression of KCNQ1 and the paternal expression of NDNL1 and PEG10 were maintained in SHhES1 cells. These data provide the first demonstration that the parental-specific expression of imprinted genes is stable in EBs after extensive differentiation, also indicating that in vitro fertilization protocol does not disrupt the parental monoallelic expression of the imprinted genes examined.

Simple sequence repeat-based consensus linkage map of Bombyx mori.

We established a genetic linkage map employing 518 simple sequence repeat (SSR, or microsatellite) markers for Bombyx mori (silkworm), the economically and culturally important lepidopteran insect, as part of an international genomics program. A survey of six representative silkworm strains using 2,500 (CA)n- and (CT)n-based SSR markers revealed 17-24% polymorphism, indicating a high degree of homozygosity resulting from a long history of inbreeding. Twenty-nine SSR linkage groups were established in well characterized Dazao and C108 strains based on genotyping of 189 backcross progeny derived from an F(1) male mated with a C108 female. The clustering was further focused to 28 groups by genotyping 22 backcross progeny derived from an F(1) female mated with a C108 male. This set of SSR linkage groups was further assigned to the 28 chromosomes (established linkage groups) of silkworm aided by visible mutations and cleaved amplified polymorphic sequence markers developed from previously mapped genes, cDNA sequences, and cloned random amplified polymorphic DNAs. By integrating a visible mutation p (plain, larval marking) and 29 well conserved genes of insects onto this SSR-based linkage map, a second generation consensus silkworm genetic map with a range of 7-40 markers per linkage group and a total map length of approximately 3431.9 cM was constructed and its high efficiency for genotyping and potential application for synteny studies of Lepidoptera and other insects was demonstrated.

[IRF6 gene mutation analysis in a van Der Woude syndrome family in Henan province].

PURPOSE: To investigate IRF6 gene mutation in a van Der Woude syndrome (VWS) family in Henan province. METHODS: PCR and DNA sequencing was employed to detect the mutation of IRF6.Secondary construction transformation analysis was performed using PIX-Protein Identification software. RESULTS: A CGC>TGC(r.279c-->t) transversion of IRF6 was identified in condon 6, showing complete segregation with the disease phenotypes and was resulting in changes of the secondary constructure of IRF6. CONCLUSION: VWS is caused by mutations in IRF6 gene, and IRF6 is closely related to the development of lip, palate and tooth.

[GNU Pattern: open source pattern hunter for biological sequences based on SPLASH algorithm].

OBJECTIVE: To construct a high performance open source software engine based on IBM SPLASH algorithm for later research on pattern discovery. METHODS: Gpat, which is based on SPLASH algorithm, was developed by using open source software. RESULTS: GNU Pattern (Gpat) software was developped, which efficiently implemented the core part of SPLASH algorithm. Full source code of Gpat was also available for other researchers to modify the program under the GNU license. CONCLUSION: Gpat is a successful implementation of SPLASH algorithm and can be used as a basic framework for later research on pattern recognition in biological sequences.

Embryonic stem cells generated by nuclear transfer of human somatic nuclei into rabbit oocytes.

To solve the problem of immune incompatibility, nuclear transplantation has been envisaged as a means to produce cells or tissues for human autologous transplantation. Here we have derived embryonic stem cells by the transfer of human somatic nuclei into rabbit oocytes. The number of blastocysts that developed from the fused nuclear transfer was comparable among nuclear donors at ages of 5, 42, 52 and 60 years, and nuclear transfer (NT) embryonic stem cells (ntES cells) were subsequently derived from each of the four age groups. These results suggest that human somatic nuclei can form ntES cells independent of the age of the donor. The derived ntES cells are human based on karyotype, isogenicity, in situ hybridization, PCR and immunocytochemistry with probes that distinguish between the various species. The ntES cells maintain the capability of sustained growth in an undifferentiated state, and form embryoid bodies, which, on further induction, give rise to cell types such as neuron and muscle, as well as mixed cell populations that express markers representative of all three germ layers. Thus, ntES cells derived from human somatic cells by NT to rabbit eggs retain phenotypes similar to those of conventional human ES cells, including the ability to undergo multilineage cellular differentiation.

Mutation analysis of 20 SARS virus genome sequences: evidence for negative selection in replicase ORF1b and spike gene.

AIM: Recently, more SARS-CoV virus genome sequences are released to the GenBank database. The aim of this study is to reveal the evolution forces of SARS-CoV virus by analyzing the nucleotide mutations in these sequences. METHODS: We obtained 20 SARS-CoV virus genome sequences from NCBI database, and calculated the ratio of non-synonymous nucleotide substitution per non-synonymous site (Ka) and synonymous nucleotide substitution per synonymous site (Ks) for SARS-CoV virus genes. RESULTS: The Ka/Ks ratios for replicase polyprotein ORF1a, ORF1b, and spike protein gene are 1.09 (P=0.6501), 0.38 (P=0.0074), 0.65 (P=0.0685) respectively. CONCLUSION: SARS-CoV virus replicase polyprotein ORF1b is undergoing negative selection; negative selection force is also probably operating on spike protein gene. These results provide basis for future developing a new drug and vaccine against SARS.