Project description:Emerging and neglected pathogens pose challenges as their biology is frequently poorly understood, and genetic tools often do not exist to manipulate them. Organism agnostic sequencing technologies offer a promising approach to understand the molecular processes underlying these diseases. Here we apply dual RNA-seq to Orientia tsutsugamushi (Ot), the obligate intracellular causative agent of the vector-borne human disease scrub typhus. Half the Ot genome is composed of repetitive DNA, and there is minimal collinearity in gene order between strains. Integrating RNA-seq, comparative genomics, proteomics, and machine learning, we investigated the transcriptional architecture of Ot, including operon structure and non-coding RNAs, and found evidence for wide-spread post-transcriptional antisense regulation. We compared the host response to two clinical isolates and identified distinct immune response networks that are up-regulated in response to each strain, leading to predictions of relative virulence which were confirmed in a mouse infection model. Thus, dual RNA-seq can reveal the biology and host-pathogen interactions of a poorly characterized and genetically intractable organism such as Ot.
Project description:In this study, differential genes and proteins in cryptorchidism and normal testis were screened by high-throughput transcriptome sequencing (RNA-seq) and tmt-based proteomics techniques, and the potential factors of cryptorchidism were analyzed.
Project description:The dual functional lncRNAs have been intensively studied and identified to be involved in various fundamental cellular processes recently. It is essential to understand in which context when a dual functional lncRNA serves as a non-coding RNA or a template for coding peptide, particularly in some pathological conditions. However, apart from time consuming and cell type specific experiments, there is virtually no in-silico method for predicting the identity of dual functional lncRNAs. Here, we developed a deep-learning model with multi-head self-attention mechanism, LncReader, to identify dual functional lncRNAs based on their sequence, physicochemical and secondary structural features. Our data demonstrated that LncReader showed multiple advantage compared to various classical machine learning methods. Moreover, to obtain independent in-house datasets for robust testing, mass spectrometry proteomics combined with RNA-seq were applied in four leukemia cell lines. Remarkably, LncReader achieved the best performance among all these datasets. Therefore, LncReader provides a sophisticated and practical tool that enables fast dual functional lncRNAs identification.
Project description:mTOR activation has been known to affect protein synthesis. To identify molecular signatures in transcriptome that could enhance protein synthesis, RNA-seq and quantitative proteomics studies were conducted using WT and TSC1 null MEFs. In this study, we found that the activation of mTOR leads to genome-wide 3'UTR shortening in mRNAs by alternative polyadenylation and activates ubiquitin-mediated proteolysis. The accession number for the RNA-seq data in this study is SRP056624.
Project description:The first GSSM of V. vinifera was reconstructed (MODEL2408120001). Tissue-specific models for stem, leaf, and berry of the Cabernet Sauvignon cultivar were generated from the original model, through the integration of RNA-Seq data. These models have been merged into diel multi-tissue models to study the interactions between tissues at light and dark phases.
