The real danger in bio is smallpox resurrection - possible via copying a decade old paper, an order from Twist, and maybe help from a GPT 3.5 tier model. Fable lockout is security theatre.

It’s nearly impossible to beat viral evolution. But simple horizontal transfer of naturally evolved traits is trivial to try, can create surprising results. Sometimes all it takes is swapping in that interesting new pangolin RBD, maybe a well placed furin cleavage site…

I made this figure. Having looked at many thousands of similar plots - this result is very striking. Will attempt to explain…. Daratumumab (anti-CD38) has partially reset autoantibody repertoires in this trial. The cohort is small, but the reset effect occurs more in responders than non-responders. What you typically see at paired timepoints looks like the “untreated” patients at bottom. Very tight and reproducible autoantibody repertoires across time. We and others have published on this. It is hard to get autoantibody repertoires to change (Bodansky et al 2024 shows BCMA CAR-T can do it, but not anti-CD20 - a B cell depletion that does not reset long lived plasma cells). The plot is an intra-patient z-score scatter of raw HuProt protein microarray IgG autoantibody data before treatment, and then one year later. Each dot is a unique human protein. Most of the autoantibody events are private to individual patients. You can quantify these events to call “hits” with a simple binary cutoff (z>3). Colored are unique antibody hits for patients in each sample pair. Blue occur ONLY at baseline, red pass threshold in both samples, yellow only post-treatment). You’ll note that all patients preserve a dominant autoantibody signature (red diagonal) demonstrating that the autoantibody reset is not perfect - the most dominant clones hold out in most patients. You’ll note in the responder scatters “blue blobs” - these are the dozens of autoantibodies that go-away post therapy. You’ll note this blob looks a bit larger in the responders. You can quantify the amount of this reset with a sort of Venn diagram ratio of target overlaps called a jaccard similarity. The untreated patients are about a 0.8/1 meaning nearly all the dots are red and all autoantibodies are shared at both timepoints by a simple binary categorization. I’ve seen that same ratio in thousands of other pre vs post timepoint autoantibody samples. And thus, I find changes in that ratio quite striking! The treated non-responders have a median jaccard ratio about 0.5/1 and the responders about 0.3/1 which are the largest changes I’ve ever seen in HuProt data from the same individuals longitudinally. The amount of autoantibody hits that disappear also correlate with magnitude of patient improvement (SF 36PF scores, another slide from talk). The identities of the autoantibodies which disappear most is somewhat shared between the responders and also quite interesting- but will be up to the authors to reveal those results. Hard to be too certain about mechanistic autoantibodies as biomarkers in any study this small.

"Physical exposome... accelerated structural brain aging" Chemical alterations can disrupt directly - but damaged proteins are also *neoantigens* that make brains appear different to immunity. And so - is exposure-induced brain aging direct -damage-induced autoimmune? Both?

Did the daratumumab *just* destroy the myeloma - or did it *also* knock out a T-cell suppressing autoantibody? Daratumumab depletes long-lived plasma cells via CD38 & potentially-harmful immune-altering autoantibodies - not just the cancer! @casanova_lab