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Biology subjects

Soler, S.

Publications and source records attributed to Soler, S..

2 recordsLinked to original sources

RIG-I activation primes and trains innate antiviral immune memory

Adaptive processes of the innate immune system, known as trained immunity (TI), are critical to human health and disease, yet they have not been systematically investigated downstream of antiviral sensing. Here, we elucidate the potential of the antiviral cytosolic RNA receptor retinoic acid-inducible gene I (RIG-I) to train, prime and tolerize the innate immune system. Using a specific RIG-I agonist, we observed that repetitive stimulation enhanced interferon-stimulated gene (ISG) and pro-inflammatory cytokine induction in human primary monocytes, epithelial cells and fibroblasts and afforded non-specific antiviral protection. RNA sequencing revealed broad, cell type-specific transcriptional changes, indicative of priming of ISGs and training of the NF{kappa}B pathway, without measurable tolerization, while ATAC sequencing in monocytes demonstrated chromatin remodeling and enhanced accessibility of key transcription factor-binding motifs such as STAT1. Moreover, while STAT1 signaling was critically required, it was not sufficient to recapitulate RIG-I induced TI. Altogether, our data demonstrate that RIG-I-mediated TI promotes an immunologically alert state with important implications for host defense and the application of RIG-I ligands in anti-infective and anti-tumoral therapies. One Sentence SummaryRIG-I activation trains and primes innate immune response at the cellular level, affording non-specific immune protection by immune and non-immune cells.

immunology↗

Computer generation of fruit shapes from DNA sequence

The generation of realistic plant and animal images from marker information could be a main contribution of artificial intelligence to genetics and breeding. Since morphological traits are highly variable and highly heritable, this must be possible. However, a suitable algorithm has not been proposed yet. This paper is a proof of concept demonstrating the feasibility of this proposal using decoders, a class of deep learning architecture. We apply it to Cucurbitaceae, perhaps the family harboring the largest variability in fruit shape in the plant kingdom, and to tomato, a species with high morphological diversity also. We generate Cucurbitaceae shapes assuming a hypothetical, but plausible, evolutive path along observed fruit shapes of C. melo. In tomato, we used 353 images from 129 crosses between 25 maternal and 7 paternal lines for which genotype data were available. In both instances, a simple decoder was able to recover expected shapes with large accuracy. For the tomato pedigree, we also show that the algorithm can be trained to generate offspring images from their parents shapes, bypassing genotype information. Data and code are available at https://github.com/miguelperezenciso/dna2image.

bioinformatics↗