Current molecular biology laboratories rely heavily on the purification and manipulation of nucleic acids. Yet, commonly used centrifuge- and column-based protocols require specialised equipment, often use toxic reagents, and are not economically scalable or practical to use in a high-throughput manner. Although it has been known for some time that magnetic beads can provide an elegant answer to these issues, the development of open-source protocols based on beads has been limited. In this article, we provide step-by-step instructions for an easy synthesis of functionalised magnetic beads, and detailed protocols for their use in the high-throughput purification of plasmids, genomic DNA, RNA and total nucleic acid (TNA) from a range of bacterial, animal, plant, environmental and synthetic sources. We also provide a bead-based protocol for bisulfite conversion and size selection of DNA and RNA fragments. Comparison to other methods highlights the capability, versatility, and extreme cost-effectiveness of using magnetic beads. These open-source protocols and the associated webpage (https://bomb.bio) can serve as a platform for further protocol customisation and community engagement.

DNA methylation plays a critical role in the regulation and maintenance of cell-type specific transcriptional programs. Targeted epigenome editing is an emerging technology to specifically regulate cellular gene expression in order to modulate cell phenotypes or dissect the epigenetic mechanisms involved in their control. In this work, we employed a DNA methyltransferase Dnmt3a-Dnmt3L construct fused to the nuclease-inactivated dCas9 programmable targeting domain to introduce DNA methylation into the human genome specifically at the EpCAM, CXCR4 and TFRC gene promoters. We show that targeting of these loci with single gRNAs leads to efficient and widespread methylation of the promoters. Multiplexing of several guide RNAs does not increase the efficiency of methylation. Peaks of targeted methylation were observed around 25 bp upstream and 40 bp downstream of the PAM site, while 20-30 bp of the binding site itself are protected against methylation. Potent methylation is dependent on the multimerization of Dnmt3a/Dnmt3L complexes on the DNA. Furthermore, the introduced methylation causes transcriptional repression of the targeted genes. These new programmable epigenetic editors allow unprecedented control of the DNA methylation status in cells and will lead to further advances in the understanding of epigenetic signaling.

Aberrant promoter DNA hypermethylation is a hallmark of cancer; however, whether this is sufficient to drive cellular transformation is not clear. To investigate this question, we use a CRISPR-dCas9 epigenetic editing tool, where an inactive form of Cas9 is fused to DNA methyltransferase effectors. Using this system, here we show simultaneous de novo DNA methylation of genes commonly methylated in cancer, CDKN2A, RASSF1, HIC1 and PTEN in primary breast cells isolated from healthy human breast tissue. We find that promoter methylation is maintained in this system, even in the absence of the fusion construct, and this prevents cells from engaging senescence arrest. Our data show that the key driver of this phenotype is repression of CDKN2A transcript p16 where myoepithelial cells harbour cancer-like gene expression but do not exhibit anchorage-independent growth. This work demonstrates that hit-and-run epigenetic events can prevent senescence entry, which may facilitate tumour initiation.

Epigenetics is currently one of the hottest topics in basic and biomedical research. However, to date, most of the studies have been descriptive in nature, designed to investigate static distribution of various epigenetic modifications in cells. Even though tremendous amount of information has been collected, we are still far from the complete understanding of epigenetic processes, their dynamics or even their direct effects on local chromatin and we still do not comprehend whether these epigenetic states are the cause or the consequence of the transcriptional profile of the cell. In this review, we try to define the concept of synthetic epigenetics and outline the available genome targeting technologies, which are used for locus-specific editing of epigenetic signals. We report early success stories and the lessons we have learned from them, and provide a guide for their application. Finally, we discuss existing limitations of the available technologies and indicate possible areas for further development.

Radiotherapy, a common component in cancer treatment, can induce adverse effects including fibrosis in co-irradiated tissues. We previously showed that differential DNA methylation at an enhancer of diacylglycerol kinase alpha (DGKA) in normal dermal fibroblasts is associated with radiation-induced fibrosis. After irradiation, the transcription factor EGR1 is induced and binds to the hypomethylated enhancer, leading to increased DGKA and pro-fibrotic marker expression. We now modulated this DGKA induction by targeted epigenomic and genomic editing of the DGKA enhancer and administering epigenetic drugs. Targeted DNA demethylation of the DGKA enhancer in HEK293T cells resulted in enrichment of enhancer-related histone activation marks and radiation-induced DGKA expression. Mutations of the EGR1-binding motifs decreased radiation-induced DGKA expression in BJ fibroblasts and caused dysregulation of multiple fibrosis-related pathways. EZH2 inhibitors (GSK126, EPZ6438) did not change radiation-induced DGKA increase. Bromodomain inhibitors (CBP30, JQ1) suppressed radiation-induced DGKA and pro-fibrotic marker expression. Similar drug effects were observed in donor-derived fibroblasts with low DNA methylation. Overall, epigenomic manipulation of DGKA expression may offer novel options for a personalized treatment to prevent or attenuate radiotherapy-induced fibrosis.

Computational simulation of biological processes can be a valuable tool for accelerating biomedical research, but usually requires extensive domain knowledge and manual adaptation. Large language models (LLMs) such as GPT-4 have proven surprisingly successful for a wide range of tasks. This study provides proof-of-concept for the use of GPT-4 as a versatile simulator of biological systems. We introduce SimulateGPT, a proof-of-concept for knowledge-driven simulation across levels of biological organization through structured prompting of GPT-4. We benchmarked our approach against direct GPT-4 inference in blinded qualitative evaluations by domain experts in four scenarios and in two quantitative scenarios with experimental ground truth. The qualitative scenarios included mouse experiments with known outcomes and treatment decision support in sepsis. The quantitative scenarios included prediction of gene essentiality in cancer cells and progression-free survival in cancer patients. In qualitative experiments, biomedical scientists rated SimulateGPT's predictions favorably over direct GPT-4 inference. In quantitative experiments, SimulateGPT substantially improved classification accuracy for predicting the essentiality of individual genes and increased correlation coefficients and precision in the regression task of predicting progression-free survival. This proof-of-concept study suggests that LLMs may enable a new class of biomedical simulators. Such text-based simulations appear well suited for modeling and understanding complex living systems that are difficult to describe with physics-based first-principles simulations, but for which extensive knowledge is available as written text. Finally, we propose several directions for further development of LLM-based biomedical simulators, including augmentation through web search retrieval, integrated mathematical modeling, and fine-tuning on experimental data.