Pharmacological levels of withaferin A (Withania somnifera) trigger clinically relevant anticancer effects specific to triple negative breast cancer cells

Withaferin A (WA) isolated from Withania somnifera (Ashwagandha) has recently become an attractive phytochemical under investigation in various preclinical studies for treatment of different cancer types. In the present study, a comparative pathway-based transcriptome analysis was applied in epithelial-like MCF-7 and triple negative mesenchymal MDA-MB-231 breast cancer cells exposed to different concentrations of WA which can be detected systemically in in vivo experiments. Whereas WA treatment demonstrated attenuation of multiple cancer hallmarks, the withanolide analogue Withanone (WN) did not exert any of the described effects at comparable concentrations. Pathway enrichment analysis revealed that WA targets specific cancer processes related to cell death, cell cycle and proliferation, which could be functionally validated by flow cytometry and real-time cell proliferation assays. WA also strongly decreased MDA-MB-231 invasion as determined by single-cell collagen invasion assay. This was further supported by decreased gene expression of extracellular matrix-degrading proteases (uPA, PLAT, ADAM8), cell adhesion molecules (integrins, laminins), pro-inflammatory mediators of the metastasis-promoting tumor microenvironment (TNFSF12, IL6, ANGPTL2, CSF1R) and concomitant increased expression of the validated breast cancer metastasis suppressor gene (BRMS1). In line with the transcriptional changes, nanomolar concentrations of WA significantly decreased protein levels and corresponding activity of uPA in MDA-MB-231 cell supernatant, further supporting its anti-metastatic properties. Finally, hierarchical clustering analysis of 84 chromatin writer-reader-eraser enzymes revealed that WA treatment of invasive mesenchymal MDA-MB-231 cells reprogrammed their transcription levels more similarly towards the pattern observed in non-invasive MCF-7 cells. In conclusion, taking into account that sub-cytotoxic concentrations of WA target multiple metastatic effectors in therapy-resistant triple negative breast cancer, WA-based therapeutic strategies targeting the uPA pathway hold promise for further (pre)clinical development to defeat aggressive metastatic breast cancer

Epithelial to mesenchymal transition: A challenging playground for translational research. current models and focus on TWIST1 relevance and gastrointestinal cancers

Resembling the development of cancer by multistep carcinogenesis, the evolution towards metastasis involves several passages, from local invasion and intravasation, encompassing surviving anoikis into the circulation, landing at distant sites and therein establishing colonization, possibly followed by the outgrowth of macroscopic lesions. Within this cascade, epithelial to mesenchymal transition (EMT) works as a pleiotropic program enabling cancer cells to overcome local, systemic, and distant barriers against diffusion by replacing traits and functions of the epithelial signature with mesenchymal‐like ones. Along the transition, a full‐blown mesenchymal phenotype may not be accomplished. Rather, the plasticity of the program and its dependency on heterotopic signals implies a pendulum with oscillations towards its reversal, that is mesenchymal to epithelial transition. Cells in intermixed E⇔M states can also display stemness, enabling their replication together with the epithelial reversion next to successful distant colonization. If we aim to include the EMT among the hallmarks of cancer that could modify clinical practice, the gap between the results pursued in basic research by animal models and those achieved in translational research by surrogate biomarkers needs to be filled. We review the knowledge on EMT, derived from models and mechanistic studies as well as from translational studies, with an emphasis on gastrointestinal cancers (GI)

INSIdE NANO : a systems biology framework to contextualize the mechanism-of-action of engineered nanomaterials

Engineered nanomaterials (ENMs) are widely present in our daily lives. Despite the efforts to characterize their mechanism of action in multiple species, their possible implications in human pathologies are still not fully understood. Here we performed an integrated analysis of the effects of ENMs on human health by contextualizing their transcriptional mechanism-of-action with respect to drugs, chemicals and diseases. We built a network of interactions of over 3,000 biological entities and developed a novel computational tool, INSIdE NANO, to infer new knowledge about ENM behavior. We highlight striking association of metal and metal-oxide nanoparticles and major neurodegenerative disorders. Our novel strategy opens possibilities to achieve fast and accurate read-across evaluation of ENMs and other chemicals based on their biosignatures.Peer reviewe

RHO Exchange Factors in the Regulation of Squamous Cell Stemness and Carcinogenesis

[EN] The squamous cell carcinoma (SCC) is a major cause of cancer mortality. The carcinogenesis of this tumor is linked to a series of molecular derangements that frequently features the deregulation of RHO GTPase signaling. However, the events and agents underpinning such deregulation are yet to be defined. RHO exchange factors (GEFs), the proteins that catalyze the activation of RHO GTPases, have been traditionally contemplated as potential protumorigenic players in this context, but their large numbers and the lack of appropriate models has precluded the elucidation of their true functions in cancer settings. To address this issue, in this thesis we have focused on VAV2 to spearhead the characterization of RHO GEFs as key mediators of tumorigenesis and, more importantly, as candidate targets for novel SCC-directed therapeutic approaches. Here we demonstrate that VAV2 becomes upregulated in cutaneous and head-and-neck SCCs, where it engages a transcriptional program involved in the induction of stem cell-like regenerative proliferation and undifferentiation. Significantly, we show that VAV2 activity predicts disease outcome and that its inhibition within specific catalytic thresholds provides antitumoral benefits without disturbing organismal homeostasis. This work also exposes non-oncogenic roles for this GEF in the physiological maintenance of the cutaneous squamous epithelium, where it regulates the abundance, activity and responsiveness of hair follicle stem cells through the control of their transcriptomic circuits. Lastly, by extending these studies to the whole family of RHO GEFs, our research shows that VAV2 belongs to a small collection of exchange factors with pivotal roles in either the promotion or impairment of SCC tumorigenesis-associated processes. Taken together, our findings unveil hitherto unknown regulators of SCC fitness whose activity can be harnessed to modulate tumor growth and malignancy

Network models of stochastic processes in cancer

Complex systems which can be modelled as networks are ubiquitous. Well-known examples include social and economic networks, as well as many examples in cell biology such as gene regulatory and protein signalling networks. Many cell biological processes are inherently stochastic and non-stationary, and this is the perspective from which I have developed novel mathematical and computational statistical models, focusing particularly on network models. These models are primarily motivated by cell biological processes relating to DNA methylation and stem cell and cancer biology, but can be generalised to other systems and domains. I have used these and other models to identify and analyse novel DNA-based cancer biomarkers

Genetic predisposition to mosaic Y chromosome loss in blood.

Mosaic loss of chromosome Y (LOY) in circulating white blood cells is the most common form of clonal mosaicism1-5, yet our knowledge of the causes and consequences of this is limited. Here, using a computational approach, we estimate that 20% of the male population represented in the UK Biobank study (n = 205,011) has detectable LOY. We identify 156 autosomal genetic determinants of LOY, which we replicate in 757,114 men of European and Japanese ancestry. These loci highlight genes that are involved in cell-cycle regulation and cancer susceptibility, as well as somatic drivers of tumour growth and targets of cancer therapy. We demonstrate that genetic susceptibility to LOY is associated with non-haematological effects on health in both men and women, which supports the hypothesis that clonal haematopoiesis is a biomarker of genomic instability in other tissues. Single-cell RNA sequencing identifies dysregulated expression of autosomal genes in leukocytes with LOY and provides insights into why clonal expansion of these cells may occur. Collectively, these data highlight the value of studying clonal mosaicism to uncover fundamental mechanisms that underlie cancer and other ageing-related diseases.This research has been conducted using the UK Biobank Resource under application 9905 and 19808. This work was supported by the Medical Research Council [Unit Programme number MC_UU_12015/2]. Full study-specific and individual acknowledgements can be found in the supplementary information

A mouse tissue atlas of small noncoding RNA

Small noncoding RNAs (ncRNAs) play a vital role in a broad range of biological processes both in health and disease. A comprehensive quantitative reference of small ncRNA expression would significantly advance our understanding of ncRNA roles in shaping tissue functions. Here, we systematically profiled the levels of five ncRNA classes (microRNA [miRNA], small nucleolar RNA [snoRNA], small nuclear RNA [snRNA], small Cajal body-specific RNA [scaRNA], and transfer RNA [tRNA] fragments) across 11 mouse tissues by deep sequencing. Using 14 biological replicates spanning both sexes, we identified that ∼30% of small ncRNAs are distributed across the body in a tissue-specific manner with some also being sexually dimorphic. We found that some miRNAs are subject to “arm switching” between healthy tissues and that tRNA fragments are retained within tissues in both a gene- and a tissue-specific manner. Out of 11 profiled tissues, we confirmed that brain contains the largest number of unique small ncRNA transcripts, some of which were previously annotated while others are identified in this study. Furthermore, by combining these findings with single-cell chromatin accessibility (scATAC-seq) data, we were able to connect identified brain-specific ncRNAs with their cell types of origin. These results yield the most comprehensive characterization of specific and ubiquitous small RNAs in individual murine tissues to date, and we expect that these data will be a resource for the further identification of ncRNAs involved in tissue function in health and dysfunction in disease

Minor intron splicing is critical for survival of lethal prostate cancer.

The evolutionarily conserved minor spliceosome (MiS) is required for protein expression of ∼714 minor intron-containing genes (MIGs) crucial for cell-cycle regulation, DNA repair, and MAP-kinase signaling. We explored the role of MIGs and MiS in cancer, taking prostate cancer (PCa) as an exemplar. Both androgen receptor signaling and elevated levels of U6atac, a MiS small nuclear RNA, regulate MiS activity, which is highest in advanced metastatic PCa. siU6atac-mediated MiS inhibition in PCa in vitro model systems resulted in aberrant minor intron splicing leading to cell-cycle G1 arrest. Small interfering RNA knocking down U6atac was ∼50% more efficient in lowering tumor burden in models of advanced therapy-resistant PCa compared with standard antiandrogen therapy. In lethal PCa, siU6atac disrupted the splicing of a crucial lineage dependency factor, the RE1-silencing factor (REST). Taken together, we have nominated MiS as a vulnerability for lethal PCa and potentially other cancers

Simultaneous host and parasite expression profiling identifies tissue-specific transcriptional programs associated with susceptibility or resistance to experimental cerebral malaria

BACKGROUND: The development and outcome of cerebral malaria (CM) reflects a complex interplay between parasite-expressed virulence factors and host response to infection. The murine CM model, Plasmodium berghei ANKA (PbA), which simulates many of the features of human CM, provides an excellent system to study this host/parasite interface. We designed "combination" microarrays that concurrently detect genome-wide transcripts of both PbA and mouse, and examined parasite and host transcriptional programs during infection of CM-susceptible (C57BL/6) and CM-resistant (BALB/c) mice. RESULTS: Analysis of expression data from brain, lung, liver, and spleen of PbA infected mice showed that both host and parasite gene expression can be examined using a single microarray, and parasite transcripts can be detected within whole organs at a time when peripheral blood parasitemia is low. Parasites display a unique transcriptional signature in each tissue, and lung appears to be a large reservoir for metabolically active parasites. In comparisons of susceptible versus resistant animals, both host and parasite display distinct, organ-specific transcriptional profiles. Differentially expressed mouse genes were related to humoral immune response, complement activation, or cell-cell interactions. PbA displayed differential expression of genes related to biosynthetic activities. CONCLUSION: These data show that host and parasite gene expression profiles can be simultaneously analysed using a single "combination" microarray, and that both the mouse and malaria parasite display distinct tissue- and strain-specific responses during infection. This technology facilitates the dissection of host-pathogen interactions in experimental cerebral malaria and could be extended to other disease models

Network-based analysis of gene expression data

The methods of molecular biology for the quantitative measurement of gene expression have undergone a rapid development in the past two decades. High-throughput assays with the microarray and RNA-seq technology now enable whole-genome studies in which several thousands of genes can be measured at a time. However, this has also imposed serious challenges on data storage and analysis, which are subject of the young, but rapidly developing field of computational biology. To explain observations made on such a large scale requires suitable and accordingly scaled models of gene regulation. Detailed models, as available for single genes, need to be extended and assembled in larger networks of regulatory interactions between genes and gene products. Incorporation of such networks into methods for data analysis is crucial to identify molecular mechanisms that are drivers of the observed expression. As methods for this purpose emerge in parallel to each other and without knowing the standard of truth, results need to be critically checked in a competitive setup and in the context of the available rich literature corpus. This work is centered on and contributes to the following subjects, each of which represents important and distinct research topics in the field of computational biology: (i) construction of realistic gene regulatory network models; (ii) detection of subnetworks that are significantly altered in the data under investigation; and (iii) systematic biological interpretation of detected subnetworks. For the construction of regulatory networks, I review existing methods with a focus on curation and inference approaches. I first describe how literature curation can be used to construct a regulatory network for a specific process, using the well-studied diauxic shift in yeast as an example. In particular, I address the question how a detailed understanding, as available for the regulation of single genes, can be scaled-up to the level of larger systems. I subsequently inspect methods for large-scale network inference showing that they are significantly skewed towards master regulators. A recalibration strategy is introduced and applied, yielding an improved genome-wide regulatory network for yeast. To detect significantly altered subnetworks, I introduce GGEA as a method for network-based enrichment analysis. The key idea is to score regulatory interactions within functional gene sets for consistency with the observed expression. Compared to other recently published methods, GGEA yields results that consistently and coherently align expression changes with known regulation types and that are thus easier to explain. I also suggest and discuss several significant enhancements to the original method that are improving its applicability, outcome and runtime. For the systematic detection and interpretation of subnetworks, I have developed the EnrichmentBrowser software package. It implements several state-of-the-art methods besides GGEA, and allows to combine and explore results across methods. As part of the Bioconductor repository, the package provides a unified access to the different methods and, thus, greatly simplifies the usage for biologists. Extensions to this framework, that support automating of biological interpretation routines, are also presented. In conclusion, this work contributes substantially to the research field of network-based analysis of gene expression data with respect to regulatory network construction, subnetwork detection, and their biological interpretation. This also includes recent developments as well as areas of ongoing research, which are discussed in the context of current and future questions arising from the new generation of genomic data