Rileynguyen7463

Cell engineering is commonly limited to the serial manipulation of a single gene or locus. The recently discovered CRISPR-associated transposases (CASTs) could manipulate multiple sets of genes to achieve predetermined cell diversity, with orthogonal CASTs being able to manipulate them in parallel. Here, a novel CAST from Pseudoalteromonas translucida KMM520 (PtrCAST) was characterized without a protospacer adjacent motif (PAM) preference which can achieve a high insertion efficiency for larger cargo and multiplexed transposition and tolerate mismatches out of 4-nucleotide seed sequence. More importantly, PtrCAST operates orthogonally with CAST from Vibrio cholerae Tn6677 (VchCAST), though both belonging to type I-F3. The two CASTs were exclusively active on their respective mini-Tn substrate with their respective crRNAs that target the corresponding 5 and 2 loci in one Escherichia coli cell. The multiplexed orthogonal MUCICAT (MUlticopy Chromosomal Integration using CRISPR-Associated Transposases) is a powerful tool for cell programming and appears promising with applications in synthetic biology.

Whole genome sequencing of patient populations is identifying thousands of new variants in UnTranslated Regions(UTRs). While the consequences of UTR mutations are not as easily predicted from primary sequence as coding mutations are, there are some known features of UTRs that modulate their function. utr.annotation is an R package that can be used to annotate potential deleterious variants in the UTR regions for both human and mouse species. Given a CSV or VCF format variant file, utr.annotation provides information of each variant on whether and how it alters known translational regulators including upstream Open Reading Frames (uORFs), upstream Kozak sequences, polyA signals, Kozak sequences at the annotated translation start site, start codons, and stop codons, conservation scores in the variant position, and whether and how it changes ribosome loading based on a model derived from empirical data.

utr.annotation is freely available on Bitbucket (https//bitbucket.org/jdlabteam/utr.annotation/src/master/) and CRAN (https//cran.r-project.org/web/packages/utr.annotation/index.html).

Supplementary data are available at https//wustl.box.com/s/yye99bryfin89nav45gv91l5k35fxo7z.

Supplementary data are available at https//wustl.box.com/s/yye99bryfin89nav45gv91l5k35fxo7z.

Quantitative interpretation of single-molecule FRET experiments requires a model of the dye dynamics to link experimental energy transfer efficiencies to distances between atom positions. We have developed FRETraj, a Python module to predict FRET distributions based on accessible-contact volumes (ACV) and simulated photon statistics. FRETraj helps to identify optimal fluorophore positions on a biomolecule of interest by rapidly evaluating donor-acceptor distances. FRETraj is scalable and fully integrated into PyMOL and the Jupyter ecosystem. Here we describe the conformational dynamics of a DNA hairpin by computing multiple ACVs along a molecular dynamics trajectory and compare the predicted FRET distribution with single-molecule experiments. FRET-assisted modeling will accelerate the analysis of structural ensembles in particular dynamic, non-coding RNAs and transient protein-nucleic acid complexes.

FRETraj is implemented as a cross-platform Python package available under the GPL-3.0 on Github (https//github.com/RNA-FRETools/fretraj) and is documented at https//RNA-FRETools.github.io/fretraj.

Supplementary data are available at Bioinformatics online.

Supplementary data are available at Bioinformatics online.

Mitochondrial DNA (mtDNA) abnormalities were previously found to be causative in the pathogenesis of various diseases. Here, comprehensive mitochondrial and nuclear sequence and transcript analyses, along with analyses of the methylation aspects of nuclear genes related to mitochondrial function, were performed in patients with keratoconus (KTCN) to evaluate their contribution to the KTCN pathogenesis.

Blood mtDNA of 42 KTCN and 51 non-KTCN individuals was Sanger sequenced and analyzed along with the previously obtained corneal RNA-sequencing data of 20 KTCN and 21 non-KTCN individuals. Selleck Poly(vinyl alcohol) In addition, the expression and methylation of mtDNA genes and 1223 mitochondria-related nuclear genes were evaluated.

The mtDNA sequence alterations detected in blood coincided with variants identified in transcripts of the matched corneal tissues. In KTCN corneas, 97 mitochondria-related genes were deregulated, including TGFB1, P4HB, and BCL2, which are involved in the extracellular matrix (ECM) organization, collagen predict the progression of corneal changes in KTCN.

To develop a method to label proliferating corneal endothelial cells (ECs) in rabbits in vivo and track their migration over time.

We compared intraperitoneal (IP) and intracameral (IC) administration of 5-ethynyl-2'-deoxyuridine (EdU) in two experiments (1) six rabbits received IP or IC EdU. Blood and aqueous humor (AH) samples were incubated with HL-60 cells. Flow cytometry detected the EdU incorporation, representing the bioavailability of EdU. (2) In vivo EdU labeling was investigated in pulse-chase study 48 rabbits received EdU IP or IC. The corneas were flat-mounted after 1, 2, 5, or 40 days and imaged using fluorescence microscopy. EdU+ and Ki67+ ECs were quantified and their distance from the peripheral endothelial edge was measured.

EdU was bioavailable in the AH up to 4 hours after IC injection. No EdU was detected in the blood or the AH after IP injection. High quality EdU labeling of EC was obtained only after IC injection, achieving 2047 ± 702 labeled ECs. Proliferating ECs were located exclusively in the periphery within 1458 ± 146 µm from the endothelial edge. After 40 days, 1490 ± 397 label-retaining ECs (LRCs) were detected, reaching 2219 ± 141 µm from the edge, indicating that LRCs migrated centripetally.

IC EdU injection enables the labeling and tracking of proliferating ECs. LRCs seem to be involved in endothelial homeostasis, yet it remains to be investigated whether they represent endothelial progenitor cells.

EdU labeling in animal models can aid the search for progenitor cells and the development of cell therapy for corneal endothelial dysfunction.

EdU labeling in animal models can aid the search for progenitor cells and the development of cell therapy for corneal endothelial dysfunction.

Combining the results of different experiments to exhibit complex patterns or to improve statistical power is a typical aim of data integration. The starting point of the statistical analysis often comes as sets of p-values resulting from previous analyses, that need to be combined in a flexible way to explore complex hypotheses, while guaranteeing a low proportion of false discoveries.

We introduce the generic concept of composed hypothesis, which corresponds to an arbitrary complex combination of simple hypotheses. We rephrase the problem of testing a composed hypothesis as a classification task, and show that finding items for which the composed null hypothesis is rejected boils down to fitting a mixture model and classify the items according to their posterior probabilities. We show that inference can be efficiently performed and provide a thorough classification rule to control for type I error. The performance and the usefulness of the approach are illustrated on simulations and on two different applications. The method is scalable, does not require any parameter tuning, and provided valuable biological insight on the considered application cases.

The QCH methodology is implemented in the qch R package hosted on CRAN.

The QCH methodology is implemented in the qch R package hosted on CRAN.

Metabolomics studies aim at reporting a metabolic signature (list of metabolites) related to a particular experimental condition. These signatures are instrumental in the identification of biomarkers or classification of individuals, however their biological and physiological interpretation remains a challenge. To support this task, we introduce FORUM a Knowledge Graph (KG) providing a semantic representation of relations between chemicals and biomedical concepts, built from a federation of life science databases and scientific literature repositories.

The use of a Semantic Web framework on biological data allows us to apply ontological based reasoning to infer new relations between entities. We show that these new relations provide different levels of abstraction and could open the path to new hypotheses. We estimate the statistical relevance of each extracted relation, explicit or inferred, using an enrichment analysis, and instantiate them as new knowledge in the KG to support results interpretation/further inquiries.

A web interface to browse and download the extracted relations, as well as a SPARQL endpoint to directly probe the whole FORUM knowledge graph, are available at https//forum-webapp.semantic-metabolomics.fr. The code needed to reproduce the triplestore is available at https//github.com/eMetaboHUB/Forum-DiseasesChem.

Supplementary data are available at Bioinformatics online.

Supplementary data are available at Bioinformatics online.

Image-based experiments can yield many thousands of individual measurements describing each object of interest, such as cells in microscopy screens. CellProfiler Analyst is a free, open-source software package designed for the exploration of quantitative image-derived data and the training of machine learning classifiers with an intuitive user interface. We have now released CellProfiler Analyst 3.0, which in addition to enhanced performance adds support for neural network classifiers, identifying rare object subsets, and direct transfer of objects of interest from visualisation tools into the Classifier tool for use as training data. This release also increases interoperability with the recently released CellProfiler 4, making it easier for users to detect and measure particular classes of objects in their analyses.

CellProfiler Analyst binaries for Windows and MacOS are freely available for download at https//cellprofileranalyst.org/. Source code is implemented in Python 3 and is available at https//github.com/CellProfiler/CellProfiler-Analyst/. A sample data set is available at https//cellprofileranalyst.org/examples, based on images freely available from the Broad Bioimage Benchmark Collection (BBBC).

CellProfiler Analyst binaries for Windows and MacOS are freely available for download at https//cellprofileranalyst.org/. Source code is implemented in Python 3 and is available at https//github.com/CellProfiler/CellProfiler-Analyst/. A sample data set is available at https//cellprofileranalyst.org/examples, based on images freely available from the Broad Bioimage Benchmark Collection (BBBC).The SCHOLAR-1 international retrospective study highlighted poor clinical outcomes and survival among patients with refractory large B-cell lymphoma (LBCL) treated with conventional chemotherapy. Axicabtagene ciloleucel (axi-cel), an autologous anti-CD19 chimeric antigen receptor T-cell therapy, demonstrated durable responses in patients with refractory LBCL in the pivotal phase 1/2 ZUMA-1 study (NCT02348216). Here, we compared SCHOLAR-1 with the 2 year outcomes of ZUMA-1. Prior to comparison of clinical outcomes, propensity scoring (based on a broad set of prognostic covariates) was used to create balance between ZUMA-1 and SCHOLAR-1 patients. In the pivotal phase 2 portion of ZUMA-1, 101 patients received axi-cel and were evaluable for response and survival. In SCHOLAR-1, 434 and 424 patients were evaluable for response and survival, respectively. ZUMA-1 patients were more heavily pretreated than SCHOLAR-1 patients. The median follow-up was 27.1 months in ZUMA-1. The objective response rate and complete response rate were 83% and 54% in ZUMA 1 vs 34% and 12% in SCHOLAR-1, respectively.