Targeted design of synthetic enhancers for selected tissues in the Drosophila embryo.

Enhancers control gene expression and have crucial roles in development and homeostasis1-3. However, the targeted de novo design of enhancers with tissue-specific activities has remained challenging. Here we combine deep learning and transfer learning to design tissue-specific enhancers for five tissues in the Drosophila melanogaster embryo: the central nervous system, epidermis, gut, muscle and brain. We first train convolutional neural networks using genome-wide single-cell assay for transposase-accessible chromatin with sequencing (ATAC-seq) datasets and then fine-tune the convolutional neural networks with smaller-scale data from in vivo enhancer activity assays, yielding models with 13% to 76% positive predictive value according to cross-validation. We designed and experimentally assessed 40 synthetic enhancers (8 per tissue) in vivo, of which 31 (78%) were active and 27 (68%) functioned in the target tissue (100% for central nervous system and muscle). The strategy of combining genome-wide and small-scale functional datasets by transfer learning is generally applicable and should enable the design of tissue-, cell type- and cell state-specific enhancers in any system.

Enhancers display constrained sequence flexibility and context-specific modulation of motif function.

The information about when and where each gene is to be expressed is mainly encoded in the DNA sequence of enhancers, sequence elements that comprise binding sites (motifs) for different transcription factors (TFs). Most of the research on enhancer sequences has been focused on TF motif presence, whereas the enhancer syntax, that is, the flexibility of important motif positions and how the sequence context modulates the activity of TF motifs, remains poorly understood. Here, we explore the rules of enhancer syntax by a two-pronged approach in Drosophila melanogaster S2 cells: we (1) replace important TF motifs by all possible 65,536 eight-nucleotide-long sequences and (2) paste eight important TF motif types into 763 positions within 496 enhancers. These complementary strategies reveal that enhancers display constrained sequence flexibility and the context-specific modulation of motif function. Important motifs can be functionally replaced by hundreds of sequences constituting several distinct motif types, but these are only a fraction of all possible sequences and motif types. Moreover, TF motifs contribute with different intrinsic strengths that are strongly modulated by the enhancer sequence context (the flanking sequence, the presence and diversity of other motif types, and the distance between motifs), such that not all motif types can work in all positions. The context-specific modulation of motif function is also a hallmark of human enhancers, as we demonstrate experimentally. Overall, these two general principles of enhancer sequences are important to understand and predict enhancer function during development, evolution, and in disease.

Systematic identification and characterization of repressive domains in Drosophila transcription factors.

All multicellular life relies on differential gene expression, determined by regulatory DNA elements and DNA-binding transcription factors that mediate activation and repression via cofactor recruitment. While activators have been extensively characterized, repressors are less well studied: the identities and properties of their repressive domains (RDs) are typically unknown and the specific co-repressors (CoRs) they recruit have not been determined. Here, we develop a high-throughput, next-generation sequencing-based screening method, repressive-domain (RD)-seq, to systematically identify RDs in complex DNA-fragment libraries. Screening more than 200,000 fragments covering the coding sequences of all transcription-related proteins in Drosophila melanogaster, we identify 195 RDs in known repressors and in proteins not previously associated with repression. Many RDs contain recurrent short peptide motifs, which are conserved between fly and human and are required for RD function, as demonstrated by motif mutagenesis. Moreover, we show that RDs that contain one of five distinct repressive motifs interact with and depend on different CoRs, such as Groucho, CtBP, Sin3A, or Smrter. These findings advance our understanding of repressors, their sequences, and the functional impact of sequence-altering mutations and should provide a valuable resource for further studies.

Allelic expression imbalance of PIK3CA mutations is frequent in breast cancer and prognostically significant.

PIK3CA mutations are the most common in breast cancer, particularly in the estrogen receptor-positive cohort, but the benefit of PI3K inhibitors has had limited success compared with approaches targeting other less common mutations. We found a frequent allelic expression imbalance between the missense mutant and wild-type PIK3CA alleles in breast tumors from the METABRIC (70.2%) and the TCGA (60.1%) projects. When considering the mechanisms controlling allelic expression, 27.7% and 11.8% of tumors showed imbalance due to regulatory variants in cis, in the two studies respectively. Furthermore, preferential expression of the mutant allele due to cis-regulatory variation is associated with poor prognosis in the METABRIC tumors (P = 0.031). Interestingly, ER-, PR-, and HER2+ tumors showed significant preferential expression of the mutated allele in both datasets. Our work provides compelling evidence to support the clinical utility of PIK3CA allelic expression in breast cancer in identifying patients of poorer prognosis, and those with low expression of the mutated allele, who will unlikely benefit from PI3K inhibitors. Furthermore, our work proposes a model of differential regulation of a critical cancer-promoting gene in breast cancer.

DeepSTARR predicts enhancer activity from DNA sequence and enables the de novo design of synthetic enhancers.

Enhancer sequences control gene expression and comprise binding sites (motifs) for different transcription factors (TFs). Despite extensive genetic and computational studies, the relationship between DNA sequence and regulatory activity is poorly understood, and de novo enhancer design has been challenging. Here, we built a deep-learning model, DeepSTARR, to quantitatively predict the activities of thousands of developmental and housekeeping enhancers directly from DNA sequence in Drosophila melanogaster S2 cells. The model learned relevant TF motifs and higher-order syntax rules, including functionally nonequivalent instances of the same TF motif that are determined by motif-flanking sequence and intermotif distances. We validated these rules experimentally and demonstrated that they can be generalized to humans by testing more than 40,000 wildtype and mutant Drosophila and human enhancers. Finally, we designed and functionally validated synthetic enhancers with desired activities de novo.

Allosteric Antagonist Modulation of TRPV2 by Piperlongumine Impairs Glioblastoma Progression.

The use of computational tools to identify biological targets of natural products with anticancer properties and unknown modes of action is gaining momentum. We employed self-organizing maps to deconvolute the phenotypic effects of piperlongumine (PL) and establish a link to modulation of the human transient receptor potential vanilloid 2 (hTRPV2) channel. The structure of the PL-bound full-length rat TRPV2 channel was determined by cryo-EM. PL binds to a transient allosteric pocket responsible for a new mode of anticancer activity against glioblastoma (GBM) in which hTRPV2 is overexpressed. Calcium imaging experiments revealed the importance of Arg539 and Thr522 residues on the antagonistic effect of PL and calcium influx modulation of the TRPV2 channel. Downregulation of hTRPV2 reduces sensitivity to PL and decreases ROS production. Analysis of GBM patient samples associates hTRPV2 overexpression with tumor grade, disease progression, and poor prognosis. Extensive tumor abrogation and long term survival was achieved in two murine models of orthotopic GBM by formulating PL in an implantable scaffold/hydrogel for sustained local therapy. Furthermore, in primary tumor samples derived from GBM patients, we observed a selective reduction of malignant cells in response to PL ex vivo. Our results establish a broadly applicable strategy, leveraging data-motivated research hypotheses for the discovery of novel means tackling cancer.

Expression of receptor activator of NFkB (RANK) drives stemness and resistance to therapy in ER+HER2- breast cancer.

The role of RANKL-RANK pathway in progesterone-driven mammary carcinogenesis and triple negative breast cancer tumorigenesis has been well characterized. However, and despite evidences of the existence of RANK-positive hormone receptor (HR)-positive breast tumors, the implication of RANK expression in HR-positive breast cancers has not been addressed before. Here, we report that RANK pathway affects the expression of cell cycle regulators and decreases sensitivity to fulvestrant of estrogen receptor (ER)-positive (ER+)/HER2- breast cancer cells, MCF-7 and T47D. Moreover, RANK overexpressing cells had a staminal and mesenchymal phenotype, with decreased proliferation rate and decreased susceptibility to chemotherapy, but were more invasive in vivo. In silico analysis of the transcriptome of human breast tumors, confirmed the association between RANK expression and stem cell and mesenchymal markers in ER+HER2- tumors. Importantly, exposure of ER+HER2- cells to continuous RANK pathway activation by exogenous RANKL, in vitro and in vivo, induced a negative feedback effect, independent of RANK levels, leading to the downregulation of HR and increased resistance to hormone therapy. These results suggest that ER+HER2- RANK-positive cells may constitute an important reservoir of slow cycling, therapy-resistance cancer cells; and that RANK pathway activation is deleterious in all ER+HER2- breast cancer cells, independently of RANK levels.

Dissecting celastrol with machine learning to unveil dark pharmacology.

By coalescing bespoke machine learning and bioinformatics analyses with cell-based assays, we unveil the pharmacology of celastrol. Celastrol is a direct modulator of the progesterone and cannabinoid receptors, and its effects correlate with the antiproliferative activity. We demonstrate how in silico methods may drive systems biology studies for natural products.

Roadmap of DNA methylation in breast cancer identifies novel prognostic biomarkers.

BACKGROUND: Breast cancer is a highly heterogeneous disease resulting in diverse clinical behaviours and therapeutic responses. DNA methylation is a major epigenetic alteration that is commonly perturbed in cancers. The aim of this study is to characterize the relationship between DNA methylation and aberrant gene expression in breast cancer. METHODS: We analysed DNA methylation and gene expression profiles from breast cancer tissue and matched normal tissue in The Cancer Genome Atlas (TCGA). Genome-wide differential methylation analysis and methylation-gene expression correlation was performed. Gene expression changes were subsequently validated in the METABRIC dataset. The Oncoscore tool was used to identify genes that had previously been associated with cancer in the literature. A subset of genes that had not previously been studied in cancer was chosen for further analysis. RESULTS: We identified 368 CpGs that were differentially methylated between tumor and normal breast tissue (Ƨ > 0.4). Hypermethylated CpGs were overrepresented in tumor tissue and were found predominantly (56%) in upstream promoter regions. Conversely, hypomethylated CpG sites were found primarily in the gene body (66%). Expression analysis revealed that 209 of the differentially-methylated CpGs were located in 169 genes that were differently expressed between normal and breast tumor tissue. Methylation-expression correlations were predominantly negative (70%) for promoter CpG sites and positive (74%) for gene body CpG sites. Among these differentially-methylated and differentially-expressed genes, we identified 7 that had not previously been studied in any form of cancer. Three of these, TDRD10, PRAC2 and TMEM132C, contained CpG sites that showed diagnostic and prognostic value in breast cancer, particularly in estrogen-receptor (ER)-positive samples. A pan-cancer analysis confirmed differential expression of these genes together with diagnostic and prognostic value of their respective CpG sites in multiple cancer types. CONCLUSION: We have identified 368 DNA methylation changes that characterize breast cancer tumor tissue, of which 209 are associated with genes that are differentially-expressed in the same samples. Novel DNA methylation markers were identified, of which cg12374721 (PRAC2), cg18081940 (TDRD10) and cg04475027 (TMEM132C) show promise as diagnostic and prognostic markers in breast cancer as well as other cancer types.

Natural product-drug conjugates for modulation of TRPV1-expressing tumors.

We report the design, synthesis and biological evaluation of natural product-drug conjugates for treatment of prostate cancers over-expressing the transient receptor potential vanilloid 1 (TRPV1) channel. We validate the relevance of TRPV1 as a target in prostate cancer patients by using a bioinformatics approach and provide proof-of-concept for the drug delivery strategy through bioorthogonal chemistry and stability assays under simulated physiological conditions. In cell-based assays, the constructs displayed modest activity. Moreover, we serendipitously discover that a stoichiometric combination of a TRPV1 agonist with a small, positively charged cytotoxic may provide new research avenues in personalized medicines for prostate cancer.

Pan-cancer association of a centrosome amplification gene expression signature with genomic alterations and clinical outcome.

Centrosome amplification (CA) is a common feature of human tumours and a promising target for cancer therapy. However, CA's pan-cancer prevalence, molecular role in tumourigenesis and therapeutic value in the clinical setting are still largely unexplored. Here, we used a transcriptomic signature (CA20) to characterise the landscape of CA-associated gene expression in 9,721 tumours from The Cancer Genome Atlas (TCGA). CA20 is upregulated in cancer and associated with distinct clinical and molecular features of breast cancer, consistently with our experimental CA quantification in patient samples. Moreover, we show that CA20 upregulation is positively associated with genomic instability, alteration of specific chromosomal arms and C>T mutations, and we propose novel molecular players associated with CA in cancer. Finally, high CA20 is associated with poor prognosis and, by integrating drug sensitivity with drug perturbation profiles in cell lines, we identify candidate compounds for selectively targeting cancer cells exhibiting transcriptomic evidence for CA.

Decoding a cancer-relevant splicing decision in the RON proto-oncogene using high-throughput mutagenesis.

Mutations causing aberrant splicing are frequently implicated in human diseases including cancer. Here, we establish a high-throughput screen of randomly mutated minigenes to decode the cis-regulatory landscape that determines alternative splicing of exon 11 in the proto-oncogene MST1R (RON). Mathematical modelling of splicing kinetics enables us to identify more than 1000 mutations affecting RON exon 11 skipping, which corresponds to the pathological isoform RON∆165. Importantly, the effects correlate with RON alternative splicing in cancer patients bearing the same mutations. Moreover, we highlight heterogeneous nuclear ribonucleoprotein H (HNRNPH) as a key regulator of RON splicing in healthy tissues and cancer. Using iCLIP and synergy analysis, we pinpoint the functionally most relevant HNRNPH binding sites and demonstrate how cooperative HNRNPH binding facilitates a splicing switch of RON exon 11. Our results thereby offer insights into splicing regulation and the impact of mutations on alternative splicing in cancer.

Over-elongation of centrioles in cancer promotes centriole amplification and chromosome missegregation.

Centrosomes are the major microtubule organising centres of animal cells. Deregulation in their number occurs in cancer and was shown to trigger tumorigenesis in mice. However, the incidence, consequence and origins of this abnormality are poorly understood. Here, we screened the NCI-60 panel of human cancer cell lines to systematically analyse centriole number and structure. Our screen shows that centriole amplification is widespread in cancer cell lines and highly prevalent in aggressive breast carcinomas. Moreover, we identify another recurrent feature of cancer cells: centriole size deregulation. Further experiments demonstrate that severe centriole over-elongation can promote amplification through both centriole fragmentation and ectopic procentriole formation. Furthermore, we show that overly long centrioles form over-active centrosomes that nucleate more microtubules, a known cause of invasiveness, and perturb chromosome segregation. Our screen establishes centriole amplification and size deregulation as recurrent features of cancer cells and identifies novel causes and consequences of those abnormalities.