Biology subjects

Gutierrez, C. S.

Publications and source records attributed to Gutierrez, C. S..

3 recordsLinked to original sources

bioRxiv2025

Simulating a for-loop in the human genome: Design and evaluation of recombinase genetic programs that count to three.

Pluripotent cells specialize into numerous cell types by receiving external signals, making fate decisions, and executing differentiation functions - a paradigm similar to computer algorithms. While advances in biosensor design have enabled cells to respond to diverse stimuli, the ability to maintain a synthetic memory of the cells experiences that then informs its behaviors remains elusive. Here, we developed a system for cellular memory-driven behaviors by simulating a "for-loop" that counts to three using recombinase STepwise gene Expression Programs (STEPs). STEPs were genomically integrated into human cells, genotyped through targeted nanopore sequencing, and evaluated for function through changes in fluorescent reporter expression. While all STEPs were capable of heritable memory and sequential gene expression, the STEP design using tyrosine recombinases for successive excisions (TRex) significantly outperformed the others tested. We then used live cell imaging to track TRex cells as they incremented from count zero to three and observed the successive emergence of four cell states from an initially homogeneous population. The STEPs framework provides biological memory and conditional expression capabilities that, coupled with input mechanisms such as biosensors, can start to approach a programming language for biology.

synthetic biology↗

bioRxiv2025

Pseudouridine residues as substrates for serum ribonucleases

In clinical uses, RNA must maintain its integrity in serum that contains ribonucleases (RNases), especially RNase 1, which is a human homolog of RNase A. These omnipresent enzymes catalyze the cleavage of the P-O5'' bond on the 3' side of pyrimidine residues. Pseudouridine ({Psi}) is the most abundant modified nucleoside in natural RNA. The substitution of uridine (U) with {Psi} or N1-methylpseudouridine (m1{Psi}) reduces the immunogenicity of mRNA and increases ribosomal translation, and these modified nucleosides are key components of RNA-based vaccines. Here, we assessed the ability of RNase A and RNase 1 to catalyze the cleavage of the P-O5'' bond on the 3' side of {Psi} and m1{Psi}. We find that these enzymes catalyze the cleavage of UpA up to 10-fold more efficiently than the cleavage of {Psi}pA or m1{Psi}pA. X-ray crystallography of enzyme-bound nucleoside 2',3'-cyclic vanadate complexes and molecular dynamics simulations of enzyme{middle dot}dinucleotide complexes show that U, {Psi}, and m1{Psi} bind to RNase A and RNase 1 in a similar manner. Quantum chemistry calculations suggested that the higher reactivity of UpA is intrinsic, arising from an inductive effect that decreases the pKa of the 2'-hydroxy group of U and enhances its nucleophilicity toward the P-O5'' bond. Experimentally, we found that UpA does indeed undergo spontaneous hydrolysis faster than does m1{Psi}pA. Our findings inform the continuing development of RNA-based vaccines and therapeutic agents.

biochemistry↗

bioRxiv2024

Sitetack: A Deep Learning Model that Improves PTM Predictionby Using Known PTMs

Post-translational modifications (PTMs) increase the diversity of the proteome and are vital to organismal life and therapeutic strategies. Deep learning has been used to predict PTM locations. Still, limitations in datasets and their analyses compromise success. Here we evaluate the use of known PTM sites in prediction via sequence-based deep learning algorithms. Specifically, PTM locations were encoded as a separate amino acid before sequences were encoded via word embedding and passed into a convolutional neural network that predicts the probability of a modification at a given site. Without labeling known PTMs, our model is on par with others. With labeling, however, we improved significantly upon extant models. Moreover, knowing PTM locations can increase the predictability of a different PTM. Our findings highlight the importance of PTMs for the installation of additional PTMs. We anticipate that including known PTM locations will enhance the performance of other proteomic machine learning algorithms.

bioinformatics↗