Analysis of the human monocyte-derived macrophage transcriptome and response to lipopolysaccharide provides new insights into genetic aetiology of inflammatory bowel disease

The FANTOM5 consortium utilised cap analysis of gene expression (CAGE) to provide an unprecedented insight into transcriptional regulation in human cells and tissues. In the current study, we have used CAGE-based transcriptional profiling on an extended dense time course of the response of human monocyte-derived macrophages grown in macrophage colony-stimulating factor (CSF1) to bacterial lipopolysaccharide (LPS). We propose that this system provides a model for the differentiation and adaptation of monocytes entering the intestinal lamina propria. The response to LPS is shown to be a cascade of successive waves of transient gene expression extending over at least 48 hours, with hundreds of positive and negative regulatory loops. Promoter analysis using motif activity response analysis (MARA) identified some of the transcription factors likely to be responsible for the temporal profile of transcriptional activation. Each LPS-inducible locus was associated with multiple inducible enhancers, and in each case, transient eRNA transcription at multiple sites detected by CAGE preceded the appearance of promoter-associated transcripts. LPS-inducible long non-coding RNAs were commonly associated with clusters of inducible enhancers. We used these data to re-examine the hundreds of loci associated with susceptibility to inflammatory bowel disease (IBD) in genome-wide association studies. Loci associated with IBD were strongly and specifically (relative to rheumatoid arthritis and unrelated traits) enriched for promoters that were regulated in monocyte differentiation or activation. Amongst previously-identified IBD susceptibility loci, the vast majority contained at least one promoter that was regulated in CSF1-dependent monocyte-macrophage transitions and/or in response to LPS. On this basis, we concluded that IBD loci are strongly-enriched for monocyte-specific genes, and identified at least 134 additional candidate genes associated with IBD susceptibility from reanalysis of published GWA studies. We propose that dysregulation of monocyte adaptation to the environment of the gastrointestinal mucosa is the key process leading to inflammatory bowel disease

Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network

Using the Cap Analysis of Gene Expression (CAGE) technology, the FANTOM5 consortium provided one of the most comprehensive maps of transcription start sites (TSSs) in several species. Strikingly, ~72% of them could not be assigned to a specific gene and initiate at unconventional regions, outside promoters or enhancers. Here, we probe these unassigned TSSs and show that, in all species studied, a significant fraction of CAGE peaks initiate at microsatellites, also called short tandem repeats (STRs). To confirm this transcription, we develop Cap Trap RNA-seq, a technology which combines cap trapping and long read MinION sequencing. We train sequence-based deep learning models able to predict CAGE signal at STRs with high accuracy. These models unveil the importance of STR surrounding sequences not only to distinguish STR classes, but also to predict the level of transcription initiation. Importantly, genetic variants linked to human diseases are preferentially found at STRs with high transcription initiation level, supporting the biological and clinical relevance of transcription initiation at STRs. Together, our results extend the repertoire of non-coding transcription associated with DNA tandem repeats and complexify STR polymorphism

Applying machine learning for integration of multi-modal genomics data and imaging data to quantify heterogeneity in tumour tissues

With rapid advances in experimental instruments and protocols, imaging and sequencing data are being generated at an unprecedented rate contributing significantly to the current and coming big biomedical data. Meanwhile, unprecedented advances in computational infrastructure and analysis algorithms are realizing image-based digital diagnosis not only in radiology and cardiology but also oncology and other diseases. Machine learning methods, especially deep learning techniques, are already and broadly implemented in diverse technological and industrial sectors, but their applications in healthcare are just starting. Uniquely in biomedical research, a vast potential exists to integrate genomics data with histopathological imaging data. The integration has the potential to extend the pathologist's limits and boundaries, which may create breakthroughs in diagnosis, treatment, and monitoring at molecular and tissue levels. Moreover, the applications of genomics data are realizing the potential for personalized medicine, making diagnosis, treatment, monitoring, and prognosis more accurate. In this chapter, we discuss machine learning methods readily available for digital pathology applications, new prospects of integrating spatial genomics data on tissues with tissue morphology, and frontier approaches to combining genomics data with pathological imaging data. We present perspectives on how artificial intelligence can be synergized with molecular genomics and imaging to make breakthroughs in biomedical and translational research for computer-aided applications

Combined inhibition of XIAP and BCL2 drives maximal therapeutic efficacy in genetically diverse aggressive acute myeloid leukemia

Aggressive therapy-resistant and refractory acute myeloid leukemia (AML) has an extremely poor outcome. By analyzing a large number of genetically complex and diverse, primary high-risk poor-outcome human AML samples, we identified specific pathways of therapeutic vulnerability. Through drug screens followed by extensive in vivo validation and genomic analyses, we found inhibition of cytosolic and mitochondrial anti-apoptotic proteins XIAP, BCL2 and MCL1, and a key regulator of mitosis, AURKB, as a vulnerability hub based on patient-specific genetic aberrations and transcriptional signatures. Combinatorial therapeutic inhibition of XIAP with an additional patient-specific vulnerability eliminated established AML in vivo in patient-derived xenografts (PDXs) bearing diverse genetic aberrations, with no signs of recurrence during off-treatment follow-up. By integrating genomic profiling and drug-sensitivity testing, this work provides a platform for a precision-medicine approach for treating aggressive AML with high unmet need