Testing the role of SOX15 in human primordial germ cell fate.

Background: Potentially novel regulators of early human germline development have been identified recently, including SOX15 and SOX17, both of which show specific expression in human primordial germ cells. SOX17 is now known to be a critical specifier of human germ cell identity. There have been suggestions, as yet without evidence, that SOX15 might also play a prominent role. The early human germline is inaccessible for direct study, but an in vitro model of human primordial germ cell-like cell (hPGCLC) specification from human embryonic stem cells (hESCs) has been developed. This enables mechanistic study of human germ cell specification using genetic tools to manipulate the levels of SOX15 and SOX17 proteins to explore their roles in hPGCLC specification. Methods: SOX15 and SOX17 proteins were depleted during hPGCLC specification from hESCs using the auxin-inducible degron system, combined with a fluorescent reporter for tracking protein levels. Additionally, SOX15 protein was overexpressed using the ProteoTuner system. Protein-level expression changes were confirmed by immunofluorescence. The impact on hPGCLC specification efficiency was determined by flow cytometry at various time points. qPCR experiments were performed to determine some transcriptional effects of SOX15 perturbations. Results: We observed specific SOX15 expression in hPGCLCs by using immunofluorescence and flow cytometry analysis. Depletion of SOX15 had no significant effect on hPGCLC specification efficiency on day 4 after induction, but there was a significant and progressive decrease in hPGCLCs on days 6 and 8. By contrast, depletion of SOX17 completely abrogated hPGCLC specification. Furthermore, SOX15 overexpression resulted in a significant increase in hPGCLC fraction on day 8. qPCR analysis revealed a possible role for the germ cell and pluripotency regulator PRDM14 in compensating for changes to SOX15 protein levels. Conclusions: SOX17 is essential for hPGCLC specification, yet SOX15 is dispensable. However, SOX15 may have a role in maintaining germ cell identity

A critical role of PRDM14 in human primordial germ cell fate revealed by inducible degrons

Funder: Wellcome Trust (Wellcome); doi: https://doi.org/10.13039/100004440Funder: University of Cambridge | Churchill College, University of Cambridge; doi: https://doi.org/10.13039/501100000742Abstract: PRDM14 is a crucial regulator of mouse primordial germ cells (mPGCs), epigenetic reprogramming and pluripotency, but its role in the evolutionarily divergent regulatory network of human PGCs (hPGCs) remains unclear. Besides, a previous knockdown study indicated that PRDM14 might be dispensable for human germ cell fate. Here, we decided to use inducible degrons for a more rapid and comprehensive PRDM14 depletion. We show that PRDM14 loss results in significantly reduced specification efficiency and an aberrant transcriptome of hPGC-like cells (hPGCLCs) obtained in vitro from human embryonic stem cells (hESCs). Chromatin immunoprecipitation and transcriptomic analyses suggest that PRDM14 cooperates with TFAP2C and BLIMP1 to upregulate germ cell and pluripotency genes, while repressing WNT signalling and somatic markers. Notably, PRDM14 targets are not conserved between mouse and human, emphasising the divergent molecular mechanisms of PGC specification. The effectiveness of degrons for acute protein depletion is widely applicable in various developmental contexts

Applying support-vector machine learning algorithms toward predicting host-guest interactions with cucurbit[7]uril.

Machine learning is a valuable tool in the development of chemical technologies but its applications into supramolecular chemistry have been limited. Here, the utility of kernel-based support vector machine learning using density functional theory calculations as training data is evaluated when used to predict equilibrium binding coefficients of small molecules with cucurbit[7]uril (CB[7]). We find that utilising SVMs may confer some predictive ability. This algorithm was then used to predict the binding of drugs TAK-580 and selumetinib. The algorithm did predict strong binding for TAK-580 and poor binding for selumetinib, and these results were experimentally validated. It was discovered that the larger homologue cucurbit[8]uril (CB[8]) is partial to selumetinib, suggesting an opportunity for tunable release by introducing different concentrations of CB[7] or CB[8] into a hydrogel depot. We qualitatively demonstrated that these drugs may have utility in combination against gliomas. Finally, mass transfer simulations show CB[7] can independently tune the release of TAK-580 without affecting selumetinib. This work gives specific evidence that a machine learning approach to recognition of small molecules by macrocycles has merit and reinforces the view that machine learning may prove valuable in the development of drug delivery systems and supramolecular chemistry more broadly.A.T. and M.P.S. thank The Winston Churchill Foundation of the United States. A.T. thanks the National Science Foundation graduate research fellowship, the MIT Chemical Engineering first year fellowship, and the Churchill College post-graduate grant program. G.W. thanks the Leverhulme Trust (project: ‘Natural material innovation for sustainable living’). V.K.R. thanks the Swiss National Science Foundation (P2EZP2_168784). O.A.S. acknowledges EPSRC Programme grant Nano-Optics to controlled Nano- Chemistry (NOtCH, EP/L027151/1) for funding

A critical role of PRDM14 in human primordial germ cell fate revealed by inducible degrons

Funder: Wellcome Trust (Wellcome); doi: https://doi.org/10.13039/100004440Funder: University of Cambridge | Churchill College, University of Cambridge; doi: https://doi.org/10.13039/501100000742Abstract: PRDM14 is a crucial regulator of mouse primordial germ cells (mPGCs), epigenetic reprogramming and pluripotency, but its role in the evolutionarily divergent regulatory network of human PGCs (hPGCs) remains unclear. Besides, a previous knockdown study indicated that PRDM14 might be dispensable for human germ cell fate. Here, we decided to use inducible degrons for a more rapid and comprehensive PRDM14 depletion. We show that PRDM14 loss results in significantly reduced specification efficiency and an aberrant transcriptome of hPGC-like cells (hPGCLCs) obtained in vitro from human embryonic stem cells (hESCs). Chromatin immunoprecipitation and transcriptomic analyses suggest that PRDM14 cooperates with TFAP2C and BLIMP1 to upregulate germ cell and pluripotency genes, while repressing WNT signalling and somatic markers. Notably, PRDM14 targets are not conserved between mouse and human, emphasising the divergent molecular mechanisms of PGC specification. The effectiveness of degrons for acute protein depletion is widely applicable in various developmental contexts

Investigating the impact of cannabis

The psychoactive component of cannabis, ∆9-THC, affects cell growth and metabolism in early embryonic cell types in mice

A critical role of PRDM14 in human primordial germ cell fate revealed by inducible degrons.

PRDM14 is a crucial regulator of mouse primordial germ cells (mPGCs), epigenetic reprogramming and pluripotency, but its role in the evolutionarily divergent regulatory network of human PGCs (hPGCs) remains unclear. Besides, a previous knockdown study indicated that PRDM14 might be dispensable for human germ cell fate. Here, we decided to use inducible degrons for a more rapid and comprehensive PRDM14 depletion. We show that PRDM14 loss results in significantly reduced specification efficiency and an aberrant transcriptome of hPGC-like cells (hPGCLCs) obtained in vitro from human embryonic stem cells (hESCs). Chromatin immunoprecipitation and transcriptomic analyses suggest that PRDM14 cooperates with TFAP2C and BLIMP1 to upregulate germ cell and pluripotency genes, while repressing WNT signalling and somatic markers. Notably, PRDM14 targets are not conserved between mouse and human, emphasising the divergent molecular mechanisms of PGC specification. The effectiveness of degrons for acute protein depletion is widely applicable in various developmental contexts

Research data supporting "Applying Support-Vector Machine Learning Algorithms towards Predicting Host-Guest Interactions with Cucurbit[7]uril"

DFT simulations were run using Gaussian 09 on organic small molecules that are known guests to cucurbit[7]uril. Controls are also included. This data supports the manuscript entitled "Applying Support-Vector Machine Learning Algorithms towards Predicting Host-Guest Interactions with Cucurbit[7]uril"

Predicting Macrocyclic Molecular Recognition with Machine Learning

DFT, NMR, ITC, and cell confluence data are used to generate predictive algorithms of supramolecular binding to cucurbit[7]uril and experimentally validate these predictions