Integrating bioelectronics with cell-based synthetic biology
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The transcriptomic architecture of common cancers reflects synthetic lethal interactions
To maintain cell fitness, deleterious genetic alterations are buffered by compensatory changes in additional genes. In cancer, buffering processes could be targeted by synthetic lethality. However, despite the large-scale identification of synthetic lethal effects in preclinical models, evidence that these operate clinically is limited. This impedes the application of synthetic lethal approaches. By integrating molecular profiling data from >9,000 cancers with synthetic lethal screens, we show that transcriptomic buffering of tumor suppressor gene (TSG) loss by hyperexpression of synthetic lethal partners is a common phenomenon, extending to multiple TSGs and histotypes. Transcriptomic buffering is also notable in cancers that phenocopy TSG loss, such as BRCAness cancers, where expression of BRCA1/2 synthetic lethal genes correlates with clinical outcome. Synthetic lethal genes that exhibit transcriptomic buffering also represent more robust synthetic lethal effects. These observations have implications for understanding how tumor cells tolerate TSG loss, in part explain transcriptomic architectures in cancer and provide insight into target selection.
Modelling and design of transcriptional enhancers
Transcriptional enhancers are the genomic elements that contain critical information for the regulation of gene expression. This information is encoded through precisely arranged transcription factor-binding sites. Genomic sequence-to-function models, computational models that take DNA sequences as input and predict gene regulatory features, have become essential for unravelling the complex combinatorial rules that govern cell-type-specific activities of enhancers. These models function as biological ‘oracles’, capable of accurately predicting the activity of novel DNA sequences. By leveraging these oracles, DNA sequences can be optimized towards designed synthetic enhancers with tailored cell-type-specific or cell-state-specific activities. In parallel, generative artificial intelligence is rapidly advancing in genomics and enhancer design. Synthetic enhancers hold great promise for a wide range of biomedical applications, from facilitating fundamental research to enabling gene therapies.
Deep learning-based image analysis in muscle histopathology using photo-realistic synthetic data
Artificial intelligence (AI), specifically Deep learning (DL), has revolutionized biomedical image analysis, but its efficacy is limited by the need for representative, high-quality large datasets with manual annotations. While latest research on synthetic data using AI-based generative models has shown promising results to tackle this problem, several challenges such as lack of interpretability and need for vast amounts of real data remain. This study aims to introduce a new approach—SYNTA—for the generation of photo-realistic synthetic biomedical image data to address the challenges associated with state-of-the art generative models and DL-based image analysis.
Inhibition of GSK3β is synthetic lethal with FHIT loss in lung cancer by blocking homologous recombination repair
FHIT is a fragile site tumor suppressor that is primarily inactivated upon tobacco smoking. FHIT loss is frequently observed in lung cancer, making it an important biomarker for the development of targeted therapy for lung cancer. Here, we report that inhibitors of glycogen synthase kinase 3 beta (GSK3β) and the homologous recombination DNA repair (HRR) pathway are synthetic lethal with FHIT loss in lung cancer. Pharmacological inhibition or siRNA depletion of GSK3β selectively suppressed the growth of FHIT-deficient lung cancer tumors in vitro and in animal models. We further showed that FHIT inactivation leads to the activation of DNA damage repair pathways, including the HRR and NHEJ pathways, in lung cancer cells. Conversely, FHIT-deficient cells are highly dependent on HRR for survival under DNA damage stress. The inhibition of GSK3β in FHIT-deficient cells suppressed the ATR/BRCA1/RAD51 axis in HRR signaling via two distinct pathways and suppressed DNA double-strand break repair, leading to the accumulation of DNA damage and apoptosis. Small molecule inhibitors of HRR, but not NHEJ or PARP, induced synthetic lethality in FHIT-deficient lung cancer cells. The findings of this study suggest that the GSK3β and HRR pathways are potential drug targets in lung cancer patients with FHIT loss.
Coupling of cell shape, matrix and tissue dynamics ensures embryonic patterning robustness
Tissue patterning coordinates morphogenesis, cell dynamics and fate specification. Understanding how precision in patterning is robustly achieved despite inherent developmental variability during mammalian embryogenesis remains a challenge. Here, based on cell dynamics quantification and simulation, we show how salt-and-pepper epiblast and primitive endoderm (PrE) cells pattern the inner cell mass of mouse blastocysts. Coupling cell fate and dynamics, PrE cells form apical polarity-dependent actin protrusions required for RAC1-dependent migration towards the surface of the fluid cavity, where PrE cells are trapped due to decreased tension. Concomitantly, PrE cells deposit an extracellular matrix gradient, presumably breaking the tissue-level symmetry and collectively guiding their own migration. Tissue size perturbations of mouse embryos and their comparison with monkey and human blastocysts further demonstrate that the fixed proportion of PrE/epiblast cells is optimal with respect to embryo size and tissue geometry and, despite variability, ensures patterning robustness during early mammalian development.
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