Axial - Inventors #6
Surveying great inventors and businesses
Axial partners with great founders and inventors. We invest in early-stage life sciences companies such as Appia Bio, Seranova Bio, Delix Therapeutics, Simcha Therapeutics, among others often when they are no more than an idea. We are fanatical about helping the rare inventor who is compelled to build their own enduring business. If you or someone you know has a great idea or company in life sciences, Axial would be excited to get to know you and possibly invest in your vision and company . We are excited to be in business with you - email us at firstname.lastname@example.org
A set of ideas and observations on inventions and discoveries in life sciences.
The immune system and everything in it.
Mitochondrial Oxidative Damage Underlies Regulatory T Cell Defects in Autoimmunity - https://www.cell.com/cell-metabolism/fulltext/S1550-4131(20)30359-4 - the Verginis Lab at University of Crete (home of the Labyrinth and the Minotaur) used an mouse model for induced autoimmune encephalitis (AE) to discovered that mitochondrial reactive oxygen species (ROS) drives Treg dysfunction, and scavenging ROS rescues Tregs reducing AE symptoms. This paper does a nice job establishing the role of mitochondrial stress in autoimmunity. Most autoimmune diseases are driven by autoreactive B-cells. However, dysfunctional Tregs, particularly in MS and lupus, control onset and severity of these diseases by controlling autoimmunity reactions and self-tolerance. Next steps are:
Is metabolic dysfunction in Tregs a sign of onset of autoimmunity or signal of its onset?
Target discovery in general. In particular, lysosomal genes are differentially expressed in Tregs for autoimmunity patients: LAMP3/5, ACP2, GLMP in MS and CTNS, RILP, LAMP5 in RA. This makes sense since patients with lysosomal storage disorders are at higher risk for autoimmunity.
Using mitochondrial ROS scavengers in combination with IL and Treg medicines to treat autoimmunity
Biochemistry and structural biology
The granddaddy of them all.
Structural basis for the activation and suppression of transposition during evolution of the RAG recombinase - https://www.embopress.org/doi/abs/10.15252/embj.2020105857- out of the Schatz Lab at Yale (who was the lead author on the paper discovering RAG), the group uses cryo-EM to characterize the RAG recombinase. RAG1/2 work together to make DNA double-strand breaks to initiate V(D)J recombination. The paper studied the role of RAG in transposition - transferring DNA from a donor site to a new one. The premise was that RAG evolved from a transposase to a recombinase. So the idea was to use structural studies laying out the principles by which RAG made this transition:
Using RAG1 R848M (switching the conserved arginine residue to the ancestral methionine to recapitulate transposition function), the group relies on cryo-EM to find two structures of RAG mimicking a strand-transfer complex where with recombination signal sequence (RSS) insertion to a target DNA strand
M848 enables base stacking and flipping at the insertion site to stimulate transposition
RAG2 suppresses the transposition activity of this RAG complex
This is a foundation paper to understand how RAG evolved into a recombinase. The complex is incredibly important for V(D)J recombination and immunity in general, and suppressing RAG’s ability to reinsert DNA into the genome prevents a lot of potential harmful outcomes for human health. In general, RAG papers are always fun to read.
Roughly 20 years behind but set up to transform the concept of human.
ELAV and FNE Determine Neuronal Transcript Signatures through EXon-Activated Rescue - https://www.sciencedirect.com/science/article/abs/pii/S1097276520306195?dgcid=author# - the Hilgers Lab at Max Planck discovered in Drosophila that two RNA-binding proteins, ELAV and FNE (the backup gene to ELAV), drive neuron-specific alternative 3’ end processing. Modifying polyadenylation (PA) and the untranslated region (UTR) for mRNAs control their localization and half-life. And changes in PA and the UTR has been associated with a wide-set of diseases. Before this paper, ELAV was known to regulate alternative end processing in flies and mammals. With that premise, the group generated ELAV mutants in flies and compared them to wild-type to identify 174 UTRs dependent on ELAV. Then they used iCLIP to figure out ELAV is bound to these UTRs. But in ELAV knockouts, only about half of the neuronal UTRs were lost suggesting another gene is rescuing ELAV function. The group then generated mutants for genes known to co-localize with ELAV and discovered that FNE rescues ELAV-dependent alternative end processing: when ELAV is knocked out, the FNE gene undergoes alternative splicing to include a 45 nucleotide exon conferring ELAV-like functionality. Really neat discovery.
Cell structure and function.
Rapid and direct control of target protein levels with VHL-recruiting dTAG molecules - https://www.nature.com/articles/s41467-020-18377-w - the Gray Lab at Harvard invented a new degradation tag. Building probes to selectively degrade proteins is useful, but there isn’t chemical matter for each protein in the proteome. The dTAG system that the Gray Lab has been building before this paper relies on two parts: (1) FKBP12F36V expressed in-frame with a given target (2) a dTAG molecule that engages with both FKBP12F36V and CRBN (E3 ligase). This paper extends this work by building a dTAG that engages a different E3 ligase: VHL. Discovering that this next-generation probe has better PK/PD features making it more useful for in vivo studies. The group showed the probes ability to selectively degrade KRAS and the EWS/FLI fusion protein, two pretty important targets in oncology. We’re undergoing a mini-revolution in chemical probes.
Genetics, genomics, and developmental biology
Heredity and variation.
Key Parameters of Tumor Epitope Immunogenicity Revealed Through a Consortium Approach Improve Neoantigen Prediction - https://www.cell.com/cell/fulltext/S0092-8674(20)31156-9 - led by Danny Wells along with Nadie Defranoux at PICI, the paper generates a resource to predict neoantigens (tumor specific targets) called Tumor Neoantigen Selection Alliance (TESLA) and uses the data to predict neoantigens. The model generated does a really good job at filtering out poor targets based on abundance, degree of differential expression, immune stimulation, binding affinity and stability, agretopicity (binding affinity of mutant peptide versus reference) among others. Validation is not by measuring immunogenicity of antigens in patient-matched T-cells. This paper is a useful reference to predict/discover neoantigens for tumors with high mutational burdens.