Biology subjects

Bagnoli, J.

Publications and source records attributed to Bagnoli, J..

3 recordsLinked to original sources

bioRxiv2020

A non-invasive method to generate induced pluripotent stem cells from primate urine

Comparing the molecular and cellular properties among primates is crucial to better understand human evolution and biology. However, it is difficult or ethically even impossible to collect matched tissues from many primates, especially during development. An alternative is to model different cell types and their development using induced pluripotent stem cells (iPSCs). These can be generated from many tissue sources, but non-invasive sampling would decisively broaden the spectrum of non-human primates that can be investigated. Here, we report the generation of primate iPSCs from urine samples. We first validate and optimize the procedure using human urine samples and show that Sendai virus transduction of reprogramming factors into urinary cells efficiently generates integration-free iPSCs, which maintain their pluripotency under feeder-free culture conditions. We demonstrate that this method is also applicable to gorilla and orangutan urinary cells isolated from a non-sterile zoo floor. We characterize the urinary cells, iPSCs and derived neural progenitor cells using karyotyping, immunohistochemistry, differentiation assays and RNA-sequencing. We show that the urine-derived human iPSCs are indistinguishable from well characterized PBMC-derived human iPSCs and that the gorilla and orangutan iPSCs are well comparable to the human iPSCs. In summary, this study introduces a novel and efficient approach to generate iPSCs non-invasively from primate urine. This will allow to extend the zoo of species available for a comparative approach to molecular and cellular phenotypes. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=191 HEIGHT=200 SRC="FIGDIR/small/247619v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@127e69forg.highwire.dtl.DTLVardef@95ebfcorg.highwire.dtl.DTLVardef@b815b2org.highwire.dtl.DTLVardef@1988492_HPS_FORMAT_FIGEXP M_FIG Workflow overview for establishing iPSCs from primate urine (A) We established the protocol for human urine based on a previous description (Zhou 2012). We tested volume, storage and culture conditions for primary cells and compared reprogramming by overexpression of OCT3/4, SOX2, KLF4 and MYC (OSKM) via lipofection of episomal vectors and via transduction of a sendai virus derived vector (SeV). (B) We used the the protocol established in humans and adapted it for unsterile floor-collected samples from non-human primates by adding Normocure to the first passages of primary cell culture and reprogrammed visually healthy and uncontaminated cultures using SeV. (C) Pluripotency of established cultures was verified by marker expression, differentiation capacity and cell type classification using RNA sequencing. C_FIG

cell biology↗

bioRxiv2020

PanGIA: A Metagenomics Analytical Framework for RoutineBiosurveillance and Clinical Pathogen Detection

Metagenomics is emerging as an important tool in biosurveillance, public health, and clinical applications. However, ease-of-use for execution and data analysis remains a barrier-of-entry to the adoption of metagenomics in applied health and forensics settings. In addition, these venues often have more stringent requirements for reporting, accuracy, and precision than the traditional ecological research role of the technology. Here, we present PanGIA (Pan-Genomics for Infectious Agents), a novel bioinformatics analysis platform for hosting, processing, analyzing, and reporting shotgun metagenomics data of complex samples suspected of containing one or more pathogens. PanGIA was developed to address gaps that often preclude clinicians, medical technicians, forensics personnel, or other non-expert end-users from the routine application of metagenomics for pathogen identification. Though primarily designed to detect pathogenic microorganisms within clinical and environmental metagenomics data, PanGIA also serves as an analytical framework for microbial community profiling and comparative metagenomics. To provide statistical confidence in PanGIAs taxonomic assignments, the system provides two independent estimations of probability for species and strain level detection. First, PanGIA integrates coverage data with uniqueness information mapped across each reference genome for a stand-alone determination of confidence for each query sequence at each taxonomy level. Second, if a negative-control sample is provided, PanGIA compares this sample with a corresponding experimental unknown sample and determines a measure of confidence associated with detection above background. An integrated graphical user interface allows interactive interrogation and enables users to summarize multiple sample results by confidence score, normalized read abundance, reference genome linear coverage, depth-of-coverage, RPKM, and other metrics to detect specific organisms-of-interest. Comparison testing of the PanGIA algorithm against a number of recent k-mer, read-mapping, and marker-gene based taxonomy classifiers across various real-world datasets with spiked targets shows superior mean positive predictive value, sensitivity, and specificity. PanGIA can process a five million paired-end read dataset in under 1 hour on commodity computational hardware. The source code and documentation are publicly available at https://github.com/LANL-Bioinformatics/PanGIA or https://github.com/mriglobal/PanGIA. The database for PanGIA can be downloaded from ftp://bioinformatics.mriglobal.org/. The full GUI-based PanGIA analysis environment is available in a Docker container and can be installed from https://hub.docker.com/r/poeli/pangia/.

bioinformatics↗

bioRxiv2020

Vorpal: A Novel RNA Virus Feature-Extraction Algorithm Demonstrated Through Interpretable Genotype-to-Phenotype Linear Models

In the analysis of genomic sequence data, so-called "alignment free" approaches are often selected for their relative speed compared to alignment-based approaches, especially in the application of distance comparisons and taxonomic classification1,2,3,4. These methods are typically reliant on excising K-length substrings of the input sequence, called K-mers5. In the context of machine learning, K-mer based feature vectors have been used in applications ranging from amplicon sequencing classification to predictive modeling for antimicrobial resistance genes6,7,8. This can be seen as an analogy of the "bag-of-words" model successfully employed in natural language processing and computer vision for document and image classification9,10. Feature extraction techniques from natural language processing have previously been analogized to genomics data11; however, the "bag-of-words" approach is brittle in the RNA virus space due to the high intersequence variance and the exact matching requirement of K-mers. To reconcile the simplicity of "bag-of-words" methods with the complications presented by the intrinsic variance of RNA virus space, a method to resolve the fragility of extracted K-mers in a way that faithfully reflects an underlying biological phenomenon was devised. Our algorithm, Vorpal, allows the construction of interpretable linear models with clustered, representative degenerate K-mers as the input vector and, through regularization, sparse predictors of binary phenotypes as the output. Here, we demonstrate the utility of Vorpal by identifying nucleotide-level genomic motif predictors for binary phenotypes in three separate RNA virus clades; human pathogen vs. non-human pathogen in Orthocoronavirinae, hemorrhagic fever causing vs. non-hemorrhagic fever causing in Ebolavirus, and human-host vs. non-human host in Influenza A. The capacity of this approach for in silico identification of hypotheses which can be validated by direct experimentation, as well as identification of genomic targets for preemptive biosurveillance of emerging viruses, is discussed. The code is available for download at https://github.com/mriglobal/vorpal.

bioinformatics↗