Nucleotide-level distance metrics to quantify alternative splicing implemented in TranD.

Advances in affordable transcriptome sequencing combined with better exon and gene prediction has motivated many to compare transcription across the tree of life. We develop a mathematical framework to calculate complexity and compare transcript models. Structural features, i.e. intron retention (IR), donor/acceptor site variation, alternative exon cassettes, alternative 5'/3' UTRs, are compared and the distance between transcript models is calculated with nucleotide level precision. All metrics are implemented in a PyPi package, TranD and output can be used to summarize splicing patterns for a transcriptome (1GTF) and between transcriptomes (2GTF). TranD output enables quantitative comparisons between: annotations augmented by empirical RNA-seq data and the original transcript models; transcript model prediction tools for longread RNA-seq (e.g. FLAIR versus Isoseq3); alternate annotations for a species (e.g. RefSeq vs Ensembl); and between closely related species. In C. elegans, Z. mays, D. melanogaster, D. simulans and H. sapiens, alternative exons were observed more frequently in combination with an alternative donor/acceptor than alone. Transcript models in RefSeq and Ensembl are linked and both have unique transcript models with empirical support. D. melanogaster and D. simulans, share many transcript models and long-read RNAseq data suggests that both species are under-annotated. We recommend combined references.

SECIMTools: a suite of metabolomics data analysis tools.

BACKGROUND: Metabolomics has the promise to transform the area of personalized medicine with the rapid development of high throughput technology for untargeted analysis of metabolites. Open access, easy to use, analytic tools that are broadly accessible to the biological community need to be developed. While technology used in metabolomics varies, most metabolomics studies have a set of features identified. Galaxy is an open access platform that enables scientists at all levels to interact with big data. Galaxy promotes reproducibility by saving histories and enabling the sharing workflows among scientists. RESULTS: SECIMTools (SouthEast Center for Integrated Metabolomics) is a set of Python applications that are available both as standalone tools and wrapped for use in Galaxy. The suite includes a comprehensive set of quality control metrics (retention time window evaluation and various peak evaluation tools), visualization techniques (hierarchical cluster heatmap, principal component analysis, modular modularity clustering), basic statistical analysis methods (partial least squares - discriminant analysis, analysis of variance, t-test, Kruskal-Wallis non-parametric test), advanced classification methods (random forest, support vector machines), and advanced variable selection tools (least absolute shrinkage and selection operator LASSO and Elastic Net). CONCLUSIONS: SECIMTools leverages the Galaxy platform and enables integrated workflows for metabolomics data analysis made from building blocks designed for easy use and interpretability. Standard data formats and a set of utilities allow arbitrary linkages between tools to encourage novel workflow designs. The Galaxy framework enables future data integration for metabolomics studies with other omics data.

Direct Head-to-Head Evaluation of Recombinant Adeno-associated Viral Vectors Manufactured in Human versus Insect Cells.

The major drawback of the Baculovirus/Sf9 system for recombinant adeno-associated viral (rAAV) manufacturing is that most of the Bac-derived rAAV vector serotypes, with few exceptions, demonstrate altered capsid compositions and lower biological potencies. Here, we describe a new insect cell-based production platform utilizing attenuated Kozak sequence and a leaky ribosome scanning to achieve a serotype-specific modulation of AAV capsid proteins stoichiometry. By way of example, rAAV5 and rAAV9 were produced and comprehensively characterized side by side with HEK293-derived vectors. A mass spectrometry analysis documented a 3-fold increase in both viral protein (VP)1 and VP2 capsid protein content compared with human cell-derived vectors. Furthermore, we conducted an extensive analysis of encapsidated single-stranded viral DNA using next-generation sequencing and show a 6-fold reduction in collaterally packaged contaminating DNA for rAAV5 produced in insect cells. Consequently, the re-designed rAAVs demonstrated significantly higher biological potencies, even in a comparison with HEK293-manufactured rAAVs mediating, in the case of rAAV5, 4-fold higher transduction of brain tissues in mice. Thus, the described system yields rAAV vectors of superior infectivity and higher genetic identity providing a scalable platform for good manufacturing practice (GMP)-grade vector production.

Applying machine learning to predict viral assembly for adeno-associated virus capsid libraries.

Machine learning (ML) can aid in novel discoveries in the field of viral gene therapy. Specifically, big data gathered through next-generation sequencing (NGS) of complex capsid libraries is an especially prominent source of lost potential in data analysis and prediction. Furthermore, adeno-associated virus (AAV)-based capsid libraries are becoming increasingly popular as a tool to select candidates for gene therapy vectors. These higher complexity AAV capsid libraries have previously been created and selected in vivo; however, in silico analysis using ML computer algorithms may augment smarter and more robust libraries for selection. In this study, data of AAV capsid libraries gathered before and after viral assembly are used to train ML algorithms. We found that two ML computer algorithms, artificial neural networks (ANNs), and support vector machines (SVMs), can be trained to predict whether unknown capsid variants may assemble into viable virus-like structures. Using the most accurate models constructed, hypothetical mutation patterns in library construction were simulated to suggest the importance of N495, G546, and I554 in AAV2-derived capsids. Finally, two comparative libraries were generated using ML-derived data to biologically validate these findings and demonstrate the predictive power of ML in vector design.

Two subtilisin-like proteases from soybean.

Two subtilisin-like proteases (SLP) were identified in soybean (Glycine max [L.] Merr.). The first, SLP-1, was localized in seed coats early in seed development, but became undetectable with anti-SLP-1 antibodies as seed fill progressed. A partial purification of SLP-1 was achieved using a two step chromatographic procedure. NH2-terminal sequence analysis of the partially purified enzyme permitted primers to be designed that were used to amplify cDNA encoding SLP-1. A genomic clone encoding SLP-1 was also obtained. Characterization of the cDNA and partially purified SLP-1 revealed the initial translation product was an 82 694 MW precursor. After removal of a signal peptide, the mature protein was formed by removal of an NH2-terminal propeptide. A COOH-terminal peptide also appeared to be removed from some of the protease molecules. DNA blot analysis suggested that at least one additional SLP gene was present in soybean. The second gene, SLP-2, was subsequently cloned and characterized. Although the coding regions for SLP-1 and SLP-2 were homologous, their promoters were quite divergent. RT-PCR revealed that SLP-2 message was found in the mature plant and in cotyledons of germinating seeds. Although SLP-2 mRNA could be identified in developing seeds, the message was at least an order of magnitude less abundant than that for SLP-1, and it was mis-spliced such that a chain termination event would preclude obtaining a product. As with SLPs from other organisms, the functions of the soybean proteases are unknown. However, SLP-1 is one of only a few proteins from soybean seed coats that have been described.

Cryptococcal genotype influences immunologic response and human clinical outcome after meningitis.

UNLABELLED: In sub-Saharan Africa, cryptococcal meningitis (CM) continues to be a predominant cause of AIDS-related mortality. Understanding virulence and improving clinical treatments remain important. To characterize the role of the fungal strain genotype in clinical disease, we analyzed 140 Cryptococcus isolates from 111 Ugandans with AIDS and CM. Isolates consisted of 107 nonredundant Cryptococcus neoformans var. grubii strains and 8 C. neoformans var. grubii/neoformans hybrid strains. Multilocus sequence typing (MLST) was used to characterize genotypes, yielding 15 sequence types and 4 clonal clusters. The largest clonal cluster consisted of 74 isolates. The results of Burst and phylogenetic analysis suggested that the C. neoformans var. grubii strains could be separated into three nonredundant evolutionary groups (Burst group 1 to group 3). Patient mortality was differentially associated with the different evolutionary groups (P = 0.04), with the highest mortality observed among Burst group 1, Burst group 2, and hybrid strains. Compared to Burst group 3 strains, Burst group 1 strains were associated with higher mortality (P = 0.02), exhibited increased capsule shedding (P = 0.02), and elicited a more pronounced Th(2) response during ex vivo cytokine release assays with strain-specific capsule stimulation (P = 0.02). The results of these analyses suggest that cryptococcal strain variation can be an important determinant of human immune responses and mortality. IMPORTANCE: Cryptococcus neoformans is a common life-threatening human fungal pathogen that is responsible for an estimated 1 million cases of meningitis in HIV-infected patients annually. Virulence factors that are important in human disease have been identified, yet the impacts of the fungal strain genotype on virulence and outcomes of human infection remain poorly understood. Using an analysis of strain variation based on in vitro assays and clinical data from Ugandans living with AIDS and cryptococcal infection, we report that strain genotype predicts the type of immune response and mortality risk. These studies suggest that knowledge of the strain genotype during human infections could be used to predict disease outcomes and lead to improved treatment approaches aimed at targeting the specific combination of pathogen virulence and host response.

Effect of sodium and acetate ions on 8-hydroxyguanine formation in irradiated aqueous solutions of DNA and 2'-deoxyguanosine 5'-monophosphate.

PURPOSE: The aim of this work was to study the combined effect of sodium and acetate ions on the radiation yield of 8-hydroxyguanine (8-OHG), one of the major DNA base lesions induced by free radicals. MATERIALS AND METHODS: Aqueous solutions of DNA and 2'-deoxyguanosine 5'-monophosphate (dGMP) with various concentrations of sodium acetate and sodium perchlorate were γ-irradiated, enzymatically digested and analyzed by high-performance liquid chromatography (HPLC) methods. RESULTS: It was found that both salts decrease the 8-OHG radiation yield in the concentration range studied for both DNA and dGMP, except in the case of dGMP wherein an increase in yield occurs in the concentration range from 0.1-1 mM. The dependence of the 8-hydroxy-2'-deoxyguanosine radiation yield on the concentration of both sodium acetate and sodium perchlorate have different shapes and have steeper slopes for the DNA compared with the dGMP solutions. CONCLUSIONS: The observed decrease in the radiation yield of 8-OHG with increasing concentrations of sodium acetate is consistent with the hypothesis that sodium acetate produces two concentration-dependent effects in the DNA solutions: (1) A conformational change in the DNA caused by Na(+) counterions; and (2) free radical reactions related to the radiolysis of acetate ion.

Non-linear effects in the formation of DNA damage in medaka fish fibroblast cells caused by combined action of cadmium and ionizing radiation.

Ionizing radiation-induced formation of genomic DNA damage can be modulated by nearby chemical species such as heavy metal ions, which can lead to non-linear dose response. To investigate this phenomenon, we studied cell survival and formation of 8-hydroxyguanine (8-OHG) base modifications and double strand breaks (DSB) caused by combined action of cadmium (Cd) and gamma radiation in cultured medaka fish (Oryzias latipes) fibroblast cells. Our data show that the introduction of Cd leads to a significant decrease in the fraction of surviving cells and to increased sensitivity of cells to ionizing radiation (IR). Cd also appears to cause non-linear increases in radiation-induced yields of 8-OHG and DSB as dose-yield plots of these lesions exhibit non-linear S-shaped curves with a sharp increase in the yields of lesions in the 10-20 microM range of Cd concentrations. The combined action of ionizing radiation and Cd leads to increased DNA damage formation compared to the effects of the individual stressors. These results are consistent with a hypothesis that the presence of Cd modulates the efficiency of DNA repair systems thus causing increases in radiation-induced DNA damage formation and decreases in cell survival.