Lifeboat Foundation

The CRISPR-Cas9 system has revolutionised gene-editing, but cutting DNA isn’t all it can do. From turning gene expression on and off to fluorescently tagging particular sequences, this animation explores some of the exciting possibilities of CRISPR.

Download a poster on ‘The expanding CRISPR toolbox’ here: https://www.nature.com/posters/crispr…

Continue reading “CRISPR: Gene editing and beyond” »

Researchers at Columbia University College of Dental Medicine have developed an exosome-based platform, “safeEXO-Cas,” that significantly enhances the delivery of CRISPR/Cas9 genome editing components to specific cells and tissues.

New rules on NGTs — here’s what it means.

During her chemistry Nobel Prize lecture in 2018, Frances Arnold said, “Today we can for all practical purposes read, write and edit any sequence of DNA, but we cannot compose it.” That isn’t true anymore.

Advances in gene editing technology have led to the first successful transplant of a pig kidney into a human.

Joint research led by Yutaro Shuto, Ryoya Nakagawa, and Osamu Nureki of the University of Tokyo determined the spatial structure of various processes of a novel gene-editing tool called “prime editor.” Functional analysis based on these structures also revealed how a “prime editor” could achieve reverse transcription, synthesizing DNA from RNA, without “cutting” both strands of the double helix. Clarifying these molecular mechanisms contributes greatly to designing gene-editing tools accurate enough for gene therapy treatments. The findings were published in the journal Nature.

The 2020 Nobel Prize in Chemistry was awarded to Jennifer Doudna and Emmanuelle Charpentier for developing a groundbreaking yet simple way to edit DNA, the “blueprint” of living organisms. While their discovery opened new avenues for research, the accuracy of the method and safety concerns about “cutting” both strands of DNA limited its use for gene therapy treatments. As such, research has been underway to develop tools that do not have these drawbacks.

The prime editing system is one such tool, a molecule complex consisting of two components. One component is the prime editor, which combines a SpCas9 protein, used in the first CRISPR-Cas gene editing technology, and a reverse transcriptase, an enzyme that transcribes RNA into DNA. The second component is the prime editing guide RNA (pegRNA), a modified guide RNA that identifies the target sequence within the DNA and encodes the desired edit. In this complex, the prime editor works like a “word processor,” accurately replacing genomic information. The tool has already been successfully implemented in living cells of organisms such as plants, zebrafish, and mice. However, precisely how this molecule complex executes each step of the editing process has not been clear, mostly due to a lack of information on its spatial structure.

It’s estimated that almost half of the world’s population — about 3.7 billion people under the age of 50 — are infected with (HSV-1), which can cause oral herpes. About half a billion people between the ages of 15 and 49 are infected with herpes simplex virus-2 (HSV-2), the cause of genital herpes. There are therapeutics that can eliminate some symptoms of herpes, like blisters, but there is no cure for the infection, and those who are infected can spread the virus to others. Studies have suggested that HSV-1 may increase the risk of dementia, and HSV-2 raises the risk of HIV infection.

Scientists have now developed a gene therapy that can eliminate as much as 90 percent of oral herpes and 97 percent of genital herpes infections in pre-clinical mouse models. The gene therapy also reduced the level of virus that was released from an individual in a mouse model of the infection. These reductions took about one month to be completed, and more of the virus seemed to be eliminated over time. The work has been reported in Nature Communications.

Conductive aerogels have gained significant research interests due to their ultralight characteristics, adjustable mechanical properties, and outstanding electrical performance^1,2,3,4,5,6. These attributes make them desirable for a range of applications, spanning from pressure sensors^7,8,9,10 to electromagnetic interference shielding^11,12,13, thermal insulation^14,15,16, and wearable heaters^17,18,19. Conventional methods for the fabrication of conductive aerogels involve the preparation of aqueous mixtures of various building blocks, followed by a freeze-drying process^20,21,22,23. Key building blocks include conductive nanomaterials like carbon nanotubes, graphene, Ti₃C₂T_x MXene nanosheets^{24,25,26,27,28,29,30}, functional fillers like cellulose nanofibers (CNFs), silk nanofibrils, and chitosan^{29,31,32,33,34}, polymeric binders like gelatin^25,26, and crosslinking agents that include glutaraldehyde (GA) and metal ions^30,35,36,37. By adjusting the proportions of these building blocks, one can fine-tune the end properties of the conductive aerogels, such as electrical conductivities and compression resilience^38,39,40,41. However, the correlations between compositions, structures, and properties within conductive aerogels are complex and remain largely unexplored^{42,43,44,45,46,47}. Therefore, to produce a conductive aerogel with user-designated mechanical and electrical properties, labor-intensive and iterative optimization experiments are often required to identify the optimal set of fabrication parameters. Creating a predictive model that can automatically recommend the ideal parameter set for a conductive aerogel with programmable properties would greatly expedite the development process⁴⁸.

Machine learning (ML) is a subset of artificial intelligence (AI) that builds models for predictions or recommendations^49,50,51. AI/ML methodologies serve as an effective toolbox to unravel intricate correlations within the parameter space with multiple degrees of freedom (DOFs)^50,52,53. The AI/ML adoption in materials science research has surged, particularly in the fields with available simulation programs and high-throughput analytical tools that generate vast amounts of data in shared and open databases⁵⁴, including gene editing^55,56, battery electrolyte optimization^57,58, and catalyst discovery^59,60. However, building a prediction model for conductive aerogels encounters significant challenges, primarily due to the lack of high-quality data points. One major root cause is the lack of standardized fabrication protocols for conductive aerogels, and different research laboratories adopt various building blocks^35,40,46. Additionally, recent studies on conductive aerogels focus on optimizing a single property, such as electrical conductivity or compressive strength, and the complex correlations between these attributes are often neglected to understand^{37,42,61,62,63,64}. Moreover, as the fabrication of conductive aerogels is labor-intensive and time-consuming, the acquisition rate of training data points is highly limited, posing difficulties in constructing an accurate prediction model capable of predicting multiple characteristics.

Herein, we developed an integrated platform that combines the capabilities of collaborative robots with AI/ML predictions to accelerate the design of conductive aerogels with programmable mechanical and electrical properties (see Supplementary Fig. 1 for the robot–human teaming workflow). Based on specific property requirements, the robots/ML-integrated platform was able to automatically suggest a tailored parameter set for the fabrication of conductive aerogels, without the need for conducting iterative optimization experiments. To produce various conductive aerogels, four building blocks were selected, including MXene nanosheets, CNFs, gelatin, and GA crosslinker (see Supplementary Note 1 and Supplementary Fig. 2 for the selection rationale and model expansion strategy). Initially, an automated pipetting robot (i.e., OT-2 robot) was operated to prepare 264 mixtures with varying MXene/CNF/gelatin ratios and mixture loadings (i.e.

Synthetic biology has been game-changing and with generative artificial intelligence, generative biology holds immense potential; let’s just speed it up.