Project description:The purpose is to obtain samples for mRNA and miRNA analysis with and without interferon α treatment; in parallel, to obtain cell pellets to forward to PNNL for use in top-down proteomics pilot experiments
Project description:The purpose is to obtain samples for mRNA and miRNA analysis with and without interferon α treatment; in parallel, to obtain cell pellets to forward to PNNL for use in top-down proteomics pilot experiments
Project description:We performed transcriptime analysis (RNA-seq) in the stony coral Stylophora pistillata treated with different nucleotide messengers produced by cGLRs.
Project description:We performed transcriptime analysis (RNA-seq) in the stony coral Crassostrea virginica treated with different nucleotide messengers produced by cGLRs.
Project description:Protamines replace histones as the main nuclear protein in sperm cells having a crucial role in compacting the paternal genome. Human spermatozoa contain the protamine 1 (P1) and the family of protamine 2 (P2) proteins. Alterations in protamine PTMs or on the P1/P2 ratio could be associated with male infertility. Top-down proteomics enables large-scale analysis of intact proteforms derived from alternative splicing, missense or nonsense genetic variants or PTMs. In contrast to current gold standard techniques, top-down allows a more in-depth analysis of protamine PTMs and proteoforms, opening up new perspectives to unravel their impact on male fertility. We analyzed two normozoospermic semen samples by top-down and discussed in detail the difficulties we found in the data analysis and the suggested solutions, as this step is one of the current bottlenecks in top-down proteomics with currently available bioinformatic tools. Our strategy for the data analysis combines two different software, ProSight PD (PS) and TopPIC suite (TP), with a clustering algorithm to decipher protamine proteoforms. We identified up to 32 protamine proteoforms at different levels of characterization. This in-depth analysis of the protamine proteoform landscape of an individual boosts personalized diagnosis of male infertility.
Project description:A mutualistic relationship between reef-building corals and endosymbiotic algae (Symbiodinium spp.) forms the basis for the existence of coral reefs. Genotyping tools for Symbiodinium spp. have added a new level of complexity to studies concerning cnidarian growth, nutrient acquisition, and stress. For example, the response of the coral holobiont to thermal stress is connected to the host-Symbiodinium genotypic combination, as different partnerships can have different bleaching susceptibilities. If, and to what extent, differences in algal symbiont clade contents can exert effects on the coral host transcriptome is currently unknown. In this study, we monitored algal physiological parameters and profiled the coral host transcriptional responses in acclimated, thermally stressed, and recovered coral fragments using a custom cDNA gene expression microarray. Combining these analyses with results from algal and host genotyping revealed a striking symbiont effect on both the acclimated coral host transcriptome and the magnitude of the thermal stress response. This is the first study that links coral host transcriptomic patterns to the clade content of their algal symbiont community. Our data provide a critical step to elucidating the molecular basis of the apparent variability seen among different coral-algal partnerships.
Project description:Purpose: There is a dearth of knowledge regarding the molecular pathology of growth anomaly in corals. We investigated the gene expression profile of Montipora capitata metatranscriptomes from healthy and diseased (growth anomaly) coral colonies to elucidate differentially expressed genes. Methods: mRNA profiles of coral tissue (including symbionts) were generated from three different tissue states: healthy, affected and unaffected. Healthy tissue was collected from coral colonies not affected by growth anomaly. Affected tissue was collected from coral growth anomaly lesions. Unaffected tissue was collected from coral colonies affected by growth anomaly.
Project description:The surprising observation that virtually the entire human genome is transcribed means we know very little about the function of many emerging classes of RNAs, except their astounding diversity. Traditional RNA function prediction methods rely on sequence or alignment information, which are limited in their ability to classify classes of non-coding RNAs (ncRNAs). To address this, we developed CoRAL, a machine learning-based approach for classification of RNA molecules. CoRAL uses biologically interpretable features including fragment length, cleavage specificity, and antisense transcription to distinguish between different ncRNA classes. We evaluated CoRAL using genome-wide small RNA sequencing (smRNA-seq) datasets from two human tissue types (brain and skin [GSE31037]), and were able to classify six different types of RNA transcripts with 79~80% accuracy in cross-validation experiments, and with 71~73% accuracy when CoRAL uses one tissue type for training and the other as validation. Analysis by CoRAL revealed that long intergenic ncRNAs, small cytoplasmic RNAs, and small nuclear RNAs show more tissue specificity, while microRNAs, small nucleolar, and transposon-derived RNAs are highly discernible and consistent across the two tissue types. The ability to consistently annotate loci across tissue types demonstrates the potential of CoRAL to characterize ncRNAs using smRNA-seq data in less characterized organisms.