Background: The pathogenesis of Alzheimer's disease is associated with dysregulation at different levels from transcriptome to cellular functioning. Such complexity necessitates investigations of disease etiology to be carried out considering multiple aspects of the disease and the use of independent strategies. The established works more emphasized on the structural organization of gene regulatory network while neglecting the internal regulation changes. Methods: Applying a strategy different from popularly used co-expression network analysis, this study investigated the transcriptional dysregulations during the transition from normal to disease states. Results: Ninety- seven genes were predicted as dysregulated genes, which were also associated with clinical outcomes of Alzheimer's disease. Both the co-expression and differential co-expression analysis suggested these genes to be interconnected as a core network and that their regulations were strengthened during the transition to disease states. Functional studies suggested the dysregulated genes to be associated with aging and synaptic function. Further, we checked the conservation of the gene co-expression and found that human and mouse brain might have divergent transcriptional co-regulation even when they had conserved gene expression profiles. Conclusion: Overall, our study reveals a core network of transcriptional dysregulation associated with the progression of Alzheimer's disease by affecting the aging and synaptic functions related genes; the gene regulation is not conserved in the human and mouse brains.

PacBio sequencing is a powerful approach to study DNA or RNA sequences in a longer scope. It is especially useful in exploring the complex structural variants generated by random integration or multiple rearrangement of endogenous or exogenous sequences. Here, we present a tool, TSD, for complex structural variant discovery using PacBio targeted sequencing data. It allows researchers to identify and visualize the genomic structures of targeted sequences by unlimited splitting, alignment and assembly of long PacBio reads. Application to the sequencing data derived from an HBV integrated human cell line(PLC/PRF/5) indicated that TSD could recover the full profile of HBV integration events, especially for the regions with the complex human-HBV genome integrations and multiple HBV rearrangements. Compared to other long read analysis tools, TSD showed a better performance for detecting complex genomic structural variants. TSD is publicly available at: https://github.com/menggf/tsd.

Genetically modified organisms are widely used in lifescience research, agriculture and in commercial products. However, in most cases, the genetic modification in the host genome is often less well characterized with respect to integration location, copy number and host gene expression. The application of next generation sequencing technologies has enabled the characterization of transgene events but still limited by the lack of computational tools. We present a one-stop R tool, transgeneR, as a general computational tool for discovering transgene integration and rearrangement in the host genome. It especially considers the properties of transgene events, such as the homologous transgene sequences, complex genetic structure and multiple copies of transgene insertion. Using this tool, we have successfully mapped the chromosomal transgene integration loci and transgene rearrangements in an artificially simulated MAPT transgene mice genome as well as in a newly generated human tau (MAPT, 0N4R) transgene mice. When unbiased sequencing data such as the whole genome sequencing data, were provided as input, transgeneR integrated multiple information, including integration location, direction, split- and nonsplit-reads, to predict the transgene fragments and their copy number. Overall, our initial evaluation indicates that the transgeneR package is an effective tool for the identification and characterization of transgene integration and rearrangements events, especially in transgene genome with complex genetic structure. TransgeneR is publicly available: https://github.com/menggf/transgeneR

The progress of cancer genome sequencing projects yields unprecedented information of mutations for numerous patients. However, the complexity of mutation profiles of cancer patients hinders the further understanding to mechanisms of oncogenesis. One basic question is how to find mutations with functional impacts. In this work, we introduce a computational method to predict functional somatic mutations of each patient by integrating mutation recurrence with expression profile similarity. With this method, the functional mutations are determined by checking the mutation enrichment among a group of patients with similar expression profiles. We applied this method to three cancer types and identified the functional mutations. Comparison of the predictions for three cancer types suggested that most of the functional mutations were cancer-type-specific with one exception to p53. By checking predicted results, we found that our method effectively filtered non-functional mutations resulting from large protein sizes. In addition, this method can also perform functional annotation to each patient to describe their association with signalling pathways or biological processes. In breast cancer, we predicted “cell adhesion” and other terms to be significantly associated with oncogenesis.

Epigenetic dysregulation has been widely reported in patients of Alzheimer’s disease (AD) and epigenetic drugs are gaining particular interest as a potential candidate therapy target. However, it is less clear how epigenetic dysregulation contributes to AD development. In this work, we performed regulatory divergence analysis using large-scale AD brain RNA-seq data and reported a widespread existence of regulatory degeneration among AD patients. It seems that transcription factor (TF)-mediated regulations get weakened or lost during AD development, resulting in disruption of normal neuronal function, especially including protein degradation, neuroinflammation, mitochondria and synaptic dysfunction. The regulatory degeneration burden (RDB) is well correlated with the detrimental clinical manifestations of AD patients. Studies of epigenetic marks, including histone modification, open chromatin accessibility and three TF binding sites supported the existence of regulatory degeneration. It suggested that epigenetic dysregulation contributed to regulatory degeneration, which also explained the consequence of epigenetic dysregulation. Among the epigenetic regulators, HDAC1 was proposed as a potential participator in such a process. Overall, our computational analysis suggested a novel causal mechanism of AD development and proposed HDAC1 as a drug target to treat AD.

Alzheimer’s disease (AD) is extremely complex for both causal mechanism and clinical manifestation, requiring efforts to uncover its diversity and the corresponding mechanisms. Here, we applied a modelling analysis to investigate the regulation divergence among a large-scale cohort of AD patients. We found that transcription regulation tended to get degenerated in AD patients, which contributed to disease development and the detrimental clinical outcomes, mainly by disrupting protein degradation, neuroinflammation, mitochondrial and synaptic functions. To measure the accumulated effects, we came up with a new concept, regulation loss burden, which better correlated with AD related clinical manifestations and the ageing process. The epigenetic studies to multiple active regulation marks also supported a tendency of regulation loss in AD patients. Our finding can lead to a unified model as AD causal mechanism, where AD and its diversity are contributed by accumulated degeneration of transcriptional regulation. The significance of this study is that: (1) it is the first system biology investigation to transcription regulation divergence among AD patients; (2) we observed an accumulated degeneration of transcription regulation, which well correlates with detrimental clinical outcomes; (3) transcriptional degeneration also contributes to the ageing process, where its correlation with ages is up to 0.78.