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Biology subjects

Ayala, E.

Publications and source records attributed to Ayala, E..

2 recordsLinked to original sources

miR-203 controls timing of developmental transitions during early preimplantation embryogenesis

Commonly expressed at developmental transitions, microRNAs operate as fine tuners of gene expression to facilitate cell fate acquisition and lineage segregation. Nevertheless, how they might regulate the earliest developmental transitions in early mammalian embryogenesis remains obscure. Here, in a strictly in vivo approach based on novel genetically-engineered mouse models and single-cell RNA sequencing, we identify miR-203 as a critical regulator of timing and cell fate restriction within the totipotency to pluripotency transition in mouse embryos. Genetically engineered mouse models show that loss of miR-203 slows down developmental timing during preimplantation leading to the accumulation of embryos with high expression of totipotency-associated markers, including MERVL endogenous retroviral elements. A new embryonic reporter (eE-Reporter) transgenic mouse carrying MERVL-Tomato and Sox2-GFP transgenes showed that lack of miR-203 leads to sustained expression of MERVL and reduced Sox2 expression in preimplantation developmental stages. A combination of single-cell transcriptional studies and epigenetic analyses identified the central coactivator and histone acetyltransferase P300 as a major miR-203 target at the totipotency to pluripotency transition in vivo. By fine tuning P300 levels, miR-203 carves the epigenetic rewiring process needed for this developmental transition, allowing a timely and correctly paced development.

developmental biology↗

An integrated technology for quantitative wide mutational scanning of human antibody Fab libraries

Antibodies are engineerable quantities in medicine. Learning antibody molecular recognition would enable the in silico design of high affinity binders against nearly any proteinaceous surface. Yet, publicly available experiment antibody sequence-binding datasets may not contain the mutagenic, antigenic, or antibody sequence diversity necessary for deep learning approaches to capture molecular recognition. In part, this is because limited experimental platforms exist for assessing quantitative and simultaneous sequence-function relationships for multiple antibodies. Here we present MAGMA-seq, an integrated technology that combines multiple antigens and multiple antibodies and determines quantitative biophysical parameters using deep sequencing. We demonstrate MAGMA-seq on two pooled libraries comprising mutants of ten different human antibodies spanning light chain gene usage, CDR H3 length, and antigenic targets. We demonstrate the comprehensive mapping of potential antibody development pathways, sequence-binding relationships for multiple antibodies simultaneously, and identification of paratope sequence determinants for binding recognition for broadly neutralizing antibodies (bnAbs). MAGMA-seq enables rapid and scalable antibody engineering of multiple lead candidates because it can measure binding for mutants of many given parental antibodies in a single experiment.

biochemistry↗