Genomic and Transcriptomic Studies on Non-Model Organisms

As the advance in high-throughput sequencing enables the generation of large volumes of genomic information, it provides researchers the opportunity to study non-model organisms even in the absence of a fully sequenced genome. The hugely advantageous progress calls for powerful sequencing assembly algorithms as these technologies also raise challenging assembly problems: (1) Some RNA products are highly expressed but others may have much lower expression level. (2) Data cannot easily be represented as linear structure, due to post-transcriptional modification like alternative splicing. (3) Conserved sequences in domains in gene families can result in assembly errors, (4) Sequencing errors due to technique limitations. Useful assembly algorithms are required to overcome the difficulties above. In these studies, there is often a need to identify similar transcripts in non-model organisms to transcripts found in related organisms. The traditional approach to address this problem is to perform de novo transcriptome assemblies to obtain predicted transcripts for these organisms and then employ similarity comparison algorithms to identify them. I observe it is possible to obtain a more complete set of similar transcripts from transcriptome assembly by making use of evolutionary information. I apply new algorithms to study non-model organisms which play an important role in applied biology. Moreover, improvement of sequencing technologies and application of current algorithms also help to study interkingdom signals between blow flies and bacteria community. With current computational tools, I annotate genomes of Proteus mirabilis and Providencia stuartii, which play an important role in bacteria-insect interaction. The study shows significant features of these strains isolated, which provides useful information to develop and test hypothesis in related interactions in insects and bacteria

Tissue-specific usage of transposable element-derived promoters in mouse development

BACKGROUND: Transposable elements (TEs) are a significant component of eukaryotic genomes and play essential roles in genome evolution. Mounting evidence indicates that TEs are highly transcribed in early embryo development and contribute to distinct biological functions and tissue morphology. RESULTS: We examine the epigenetic dynamics of mouse TEs during the development of five tissues: intestine, liver, lung, stomach, and kidney. We found that TEs are associated with over 20% of open chromatin regions during development. Close to half of these accessible TEs are only activated in a single tissue and a specific developmental stage. Most accessible TEs are rodent-specific. Across these five tissues, 453 accessible TEs are found to create the transcription start sites of downstream genes in mouse, including 117 protein-coding genes and 144 lincRNA genes, 93.7% of which are mouse-specific. Species-specific TE-derived transcription start sites are found to drive the expression of tissue-specific genes and change their tissue-specific expression patterns during evolution. CONCLUSION: Our results suggest that TE insertions increase the regulatory potential of the genome, and some TEs have been domesticated to become a crucial component of gene and regulate tissue-specific expression during mouse tissue development

Heuristic pairwise alignment of de Bruijn graphs to facilitate simultaneous transcript discovery in related organisms from RNA-Seq data

BACKGROUND: The advance of high-throughput sequencing has made it possible to obtain new transcriptomes and study splicing mechanisms in non-model organisms. In these studies, there is often a need to investigate the transcriptomes of two related organisms at the same time in order to find the similarities and differences between them. The traditional approach to address this problem is to perform de novo transcriptome assemblies to obtain predicted transcripts for these organisms independently and then employ similarity comparison algorithms to study them. RESULTS: Instead of obtaining predicted transcripts for these organisms separately from the intermediate de Bruijn graph structures employed by de novo transcriptome assembly algorithms, we develop an algorithm to allow direct comparisons between paths in two de Bruijn graphs by first enumerating short paths in both graphs, and iteratively extending paths in one graph that have high similarity to paths in the other graph to obtain longer corresponding paths between the two graphs. These paths represent predicted transcripts that are present in both organisms. We show that our algorithm recovers significantly more shared transcripts than traditional approaches by applying it to simultaneously recover transcripts in mouse against rat and in mouse against human from publicly available RNA-Seq libraries. Our strategy utilizes sequence similarity information within the paths that is often more reliable than coverage information. CONCLUSIONS: Our approach generalizes the pairwise sequence alignment problem to allow the input to be non-linear structures, and provides a heuristic to reliably recover similar paths from the two structures. Our algorithm allows detailed investigation of the similarities and differences in alternative splicing between the two organisms at both the sequence and structure levels, even in the absence of reference transcriptomes or a closely related model organism

A genome-wide CRISPR-Cas9 knockout screen identifies essential and growth-restricting genes in human trophoblast stem cells

The recent derivation of human trophoblast stem cells (hTSCs) provides a scalable in vitro model system of human placental development, but the molecular regulators of hTSC identity have not been systematically explored thus far. Here, we utilize a genome-wide CRISPR-Cas9 knockout screen to comprehensively identify essential and growth-restricting genes in hTSCs. By cross-referencing our data to those from similar genetic screens performed in other cell types, as well as gene expression data from early human embryos, we define hTSC-specific and -enriched regulators. These include both well-established and previously uncharacterized trophoblast regulators, such as ARID3A, GATA2, and TEAD1 (essential), and GCM1, PTPN14, and TET2 (growth-restricting). Integrated analysis of chromatin accessibility, gene expression, and genome-wide location data reveals that the transcription factor TEAD1 regulates the expression of many trophoblast regulators in hTSCs. In the absence of TEAD1, hTSCs fail to complete faithful differentiation into extravillous trophoblast (EVT) cells and instead show a bias towards syncytiotrophoblast (STB) differentiation, thus indicating that this transcription factor safeguards the bipotent lineage potential of hTSCs. Overall, our study provides a valuable resource for dissecting the molecular regulation of human placental development and diseases

Microbial proteases and their applications

Proteases (proteinases or peptidases) are a class of hydrolases that cleave peptide chains in proteins. Endopeptidases are a type of protease that hydrolyze the internal peptide bonds of proteins, forming shorter peptides; exopeptidases hydrolyze the terminal peptide bonds from the C-terminal or N-terminal, forming free amino acids. Microbial proteases are a popular instrument in many industrial applications. In this review, the classification, detection, identification, and sources of microbial proteases are systematically introduced, as well as their applications in food, detergents, waste treatment, and biotechnology processes in the industry fields. In addition, recent studies on techniques used to express heterologous microbial proteases are summarized to describe the process of studying proteases. Finally, future developmental trends for microbial proteases are discussed