SDEAP: a splice graph based differential transcript expression analysis tool for population data

MotivationDifferential transcript expression (DTE) analysis without predefined conditions is critical to biological studies. For example, it can be used to discover biomarkers to classify cancer samples into previously unknown subtypes such that better diagnosis and therapy methods can be developed for the subtypes. Although several DTE tools for population data, i.e. data without known biological conditions, have been published, these tools either assume binary conditions in the input population or require the number of conditions as a part of the input. Fixing the number of conditions to binary is unrealistic and may distort the results of a DTE analysis. Estimating the correct number of conditions in a population could also be challenging for a routine user. Moreover, the existing tools only provide differential usages of exons, which may be insufficient to interpret the patterns of alternative splicing across samples and restrains the applications of the tools from many biology studies.ResultsWe propose a novel DTE analysis algorithm, called SDEAP, that estimates the number of conditions directly from the input samples using a Dirichlet mixture model and discovers alternative splicing events using a new graph modular decomposition algorithm. By taking advantage of the above technical improvement, SDEAP was able to outperform the other DTE analysis methods in our extensive experiments on simulated data and real data with qPCR validation. The prediction of SDEAP also allowed us to classify the samples of cancer subtypes and cell-cycle phases more accurately.Availability and implementationSDEAP is publicly available for free at https://github.com/ewyang089/SDEAP/wiki CONTACT: [email protected]; [email protected] information: Supplementary data are available at Bioinformatics online

Conversations with Documents. An Exploration of Document-Centered Assistance

The role of conversational assistants has become more prevalent in helping people increase their productivity. Document-centered assistance, for example to help an individual quickly review a document, has seen less significant progress, even though it has the potential to tremendously increase a user's productivity. This type of document-centered assistance is the focus of this paper. Our contributions are three-fold: (1) We first present a survey to understand the space of document-centered assistance and the capabilities people expect in this scenario. (2) We investigate the types of queries that users will pose while seeking assistance with documents, and show that document-centered questions form the majority of these queries. (3) We present a set of initial machine learned models that show that (a) we can accurately detect document-centered questions, and (b) we can build reasonably accurate models for answering such questions. These positive results are encouraging, and suggest that even greater results may be attained with continued study of this interesting and novel problem space. Our findings have implications for the design of intelligent systems to support task completion via natural interactions with documents.Comment: Accepted as full paper at CHIIR 2020; 9 pages + Appendi

Preparation and Characterization of Stimuli-Responsive Magnetic Nanoparticles

In this work, the main attention was focused on the synthesis of stimuli-responsive magnetic nanoparticles (SR-MNPs) and the influence of glutathione concentration on its cleavage efficiency. Magnetic nanoparticles (MNPs) were first modified with activated pyridyldithio. Then, MNPs modified with activated pyridyldithio (MNPs-PDT) were conjugated with 2, 4-diamino-6-mercaptopyrimidine (DMP) to form SR-MNPs via stimuli-responsive disulfide linkage. Fourier transform infrared spectra (FTIR), transmission electron microscopy (TEM), and X-ray photoelectron spectroscopy (XPS) were used to characterize MNPs-PDT. The disulfide linkage can be cleaved by reduced glutathione (GHS). The concentration of glutathione plays an important role in controlling the cleaved efficiency. The optimum concentration of GHS to release DMP is in the millimolar range. These results had provided an important insight into the design of new MNPs for biomedicine applications, such as drug delivery and bio-separation

Small Polarons in Transition Metal Oxides

The formation of polarons is a pervasive phenomenon in transition metal oxide compounds, with a strong impact on the physical properties and functionalities of the hosting materials. In its original formulation the polaron problem considers a single charge carrier in a polar crystal interacting with its surrounding lattice. Depending on the spatial extension of the polaron quasiparticle, originating from the coupling between the excess charge and the phonon field, one speaks of small or large polarons. This chapter discusses the modeling of small polarons in real materials, with a particular focus on the archetypal polaron material TiO2. After an introductory part, surveying the fundamental theoretical and experimental aspects of the physics of polarons, the chapter examines how to model small polarons using first principles schemes in order to predict, understand and interpret a variety of polaron properties in bulk phases and surfaces. Following the spirit of this handbook, different types of computational procedures and prescriptions are presented with specific instructions on the setup required to model polaron effects.Comment: 36 pages, 12 figure

Rationalization and Design of the Complementarity Determining Region Sequences in an Antibody-Antigen Recognition Interface

Protein-protein interactions are critical determinants in biological systems. Engineered proteins binding to specific areas on protein surfaces could lead to therapeutics or diagnostics for treating diseases in humans. But designing epitope-specific protein-protein interactions with computational atomistic interaction free energy remains a difficult challenge. Here we show that, with the antibody-VEGF (vascular endothelial growth factor) interaction as a model system, the experimentally observed amino acid preferences in the antibody-antigen interface can be rationalized with 3-dimensional distributions of interacting atoms derived from the database of protein structures. Machine learning models established on the rationalization can be generalized to design amino acid preferences in antibody-antigen interfaces, for which the experimental validations are tractable with current high throughput synthetic antibody display technologies. Leave-one-out cross validation on the benchmark system yielded the accuracy, precision, recall (sensitivity) and specificity of the overall binary predictions to be 0.69, 0.45, 0.63, and 0.71 respectively, and the overall Matthews correlation coefficient of the 20 amino acid types in the 24 interface CDR positions was 0.312. The structure-based computational antibody design methodology was further tested with other antibodies binding to VEGF. The results indicate that the methodology could provide alternatives to the current antibody technologies based on animal immune systems in engineering therapeutic and diagnostic antibodies against predetermined antigen epitopes

Novel Quinazolinone MJ-29 Triggers Endoplasmic Reticulum Stress and Intrinsic Apoptosis in Murine Leukemia WEHI-3 Cells and Inhibits Leukemic Mice

The present study was to explore the biological responses of the newly compound, MJ-29 in murine myelomonocytic leukemia WEHI-3 cells in vitro and in vivo fates. We focused on the in vitro effects of MJ-29 on ER stress and mitochondria-dependent apoptotic death in WEHI-3 cells, and to hypothesize that MJ-29 might fully impair the orthotopic leukemic mice. Our results indicated that a concentration-dependent decrease of cell viability was shown in MJ-29-treated cells. DNA content was examined utilizing flow cytometry, whereas apoptotic populations were determined using annexin V/PI, DAPI staining and TUNEL assay. Increasing vital factors of mitochondrial dysfunction by MJ-29 were further investigated. Thus, MJ-29-provaked apoptosis of WEHI-3 cells is mediated through the intrinsic pathway. Importantly, intracellular Ca2+ release and ER stress-associated signaling also contributed to MJ-29-triggered cell apoptosis. We found that MJ-29 stimulated the protein levels of calpain 1, CHOP and p-eIF2α pathways in WEHI-3 cells. In in vivo experiments, intraperitoneal administration of MJ-29 significantly improved the total survival rate, enhanced body weight and attenuated enlarged spleen and liver tissues in leukemic mice. The infiltration of immature myeloblastic cells into splenic red pulp was reduced in MJ-29-treated leukemic mice. Moreover, MJ-29 increased the differentiations of T and B cells but decreased that of macrophages and monocytes. Additionally, MJ-29-stimulated immune responses might be involved in anti-leukemic activity in vivo. Based on these observations, MJ-29 suppresses WEHI-3 cells in vitro and in vivo, and it is proposed that this potent and selective agent could be a new chemotherapeutic candidate for anti-leukemia in the future

Small Molecule Amiloride Modulates Oncogenic RNA Alternative Splicing to Devitalize Human Cancer Cells

Alternative splicing involves differential exon selection of a gene transcript to generate mRNA and protein isoforms with structural and functional diversity. Abnormal alternative splicing has been shown to be associated with malignant phenotypes of cancer cells, such as chemo-resistance and invasive activity. Screening small molecules and drugs for modulating RNA splicing in human hepatocellular carcinoma cell line Huh-7, we discovered that amiloride, distinct from four pH-affecting amiloride analogues, could “normalize” the splicing of BCL-X, HIPK3 and RON/MISTR1 transcripts. Our proteomic analyses of amiloride-treated cells detected hypo-phosphorylation of splicing factor SF2/ASF, and decreased levels of SRp20 and two un-identified SR proteins. We further observed decreased phosphorylation of AKT, ERK1/2 and PP1, and increased phosphorylation of p38 and JNK, suggesting that amiloride treatment down-regulates kinases and up-regulates phosphatases in the signal pathways known to affect splicing factor protein phosphorylation. These amiloride effects of “normalized” oncogenic RNA splicing and splicing factor hypo-phosphorylation were both abrogated by pre-treatment with a PP1 inhibitor. Global exon array of amiloride-treated Huh-7 cells detected splicing pattern changes involving 584 exons in 551 gene transcripts, many of which encode proteins playing key roles in ion transport, cellular matrix formation, cytoskeleton remodeling, and genome maintenance. Cellular functional analyses revealed subsequent invasion and migration defects, cell cycle disruption, cytokinesis impairment, and lethal DNA degradation in amiloride-treated Huh-7 cells. Other human solid tumor and leukemic cells, but not a few normal cells, showed similar amiloride-altered RNA splicing with devitalized consequence. This study thus provides mechanistic underpinnings for exploiting small molecule modulation of RNA splicing for cancer therapeutics