Biology subjects

Costa, A. B.

Publications and source records attributed to Costa, A. B..

7 recordsLinked to original sources

bioRxiv2026

Canavanine-based assay for gross chromosomal rearrangements reveals genome instability hotspots and modulating genes in fission yeast

Abstract/SummaryGross chromosomal rearrangements are a hallmark of many diseases and cancers. The study of their biogenesis and the mechanisms underlying their formation is greatly facilitated by the availability of genetic reporter assays in model organisms. We present here a novel GCR assay developed in fission yeast, a highly relevant model for understanding genome instability related to human biology. The reporter employs canavanine counter-selection to detect GCRs within a chromosomal context. Using this assay, we identified natural hotspots for GCRs, including inverted long terminal repeats (IR-LTRs). Structural analysis of GCR events showed that IR-LTR-induced GCRs mainly result in either terminal deletions with adjacent inverted duplications or repair via long-range break-induced replication (BIR). Deleting IR-LTRs reduces the GCR rate and reveals another hotspot driven by BIR between homeologous aldo/keto reductase genes on opposite arms of chromosome I. This is the first evidence that BIR can occur in S. pombe on long tracks reaching up to 600 kb. Besides highlighting genome rearrangement hotspots, the assay also identifies regulators of genome instability in fission yeast. Loss of Nup132, a component of the nuclear pore complex, increases IR-LTRs-induced GCRs, while the budding yeast homolog Nup133 has no effect on the stability of a structurally similar IR. In contrast, disrupting djc9, which encodes a conserved histone H3-H4 binding protein, decreases GCR rates. Overall, this sensitive GCR assay enables the identification of factors that control spontaneous and fragile motif-induced chromosomal instability, including those conserved in humans but lost through evolution in other organisms.

genetics↗

bioRxiv2026

Replication fork remodeling proteins, Smc5/6 and Rtt107, promote palindrome-mediated genome instability

Palindromic sequences are a potent source of genomic instability that can lead to cancer and hereditary diseases in humans. Molecular evidence shows that palindrome instability results from the formation of secondary structures, such as hairpins and cruciforms, which are cleaved by structure-specific nucleases. However, the mechanisms underlying cruciform formation and cleavage at palindromic sequences in eukaryotic cells remain incompletely understood. Here, we describe a pathway for secondary structure formation and chromosomal breakage at palindromes involving DNA helicase Mph1, Rad51 recombinase, Rad54 ATPase DNA strand remodeler, Rtt107 scaffold protein, and the multifunctional Smc5/6 complex. Deletion or mutation of any of these components results in a similar reduction in double-strand breaks at an Alu palindrome and a significant decrease in chromosomal rearrangements. We propose that Mph1, Rad51, and Rad54 work together at stalled replication forks to generate cruciform structures via fork remodeling, while Smc5/6 and Rtt107 mark the cruciforms, indicating an appropriate substrate for nuclease cleavage. As members of this pathway are conserved in humans, the uncovered mechanisms may underlie genomic instability at palindrome sites involved in disease etiology.

molecular biology↗

bioRxiv2026

Efficient protein structure prediction fromcompact computers to datacenters withOpenFold-TRT

We introduce accelerations for deep learning inference with OpenFold and TensorRT that, combined with MMseqs2-GPU on an x86 system with one NVIDIA RTX PRO 6000 Blackwell Server Edition GPU, reach up to 131x faster inference compared to AlphaFold2. ARM-optimizations enable homology search on power efficient DGX Spark, and allow search beyond available GPU RAM on NVIDIA Grace Hopper Superchip at comparable overall folding speed. These accelerations enable high-throughput protein structure inference at no accuracy cost using familiar tools.

bioinformatics↗

bioRxiv2025

Genome modeling and design across all domains of life with Evo 2

All of life encodes information with DNA. While tools for sequencing, synthesis, and editing of genomic code have transformed biological research, intelligently composing new biological systems would also require a deep understanding of the immense complexity encoded by genomes. We introduce Evo 2, a biological foundation model trained on 9.3 trillion DNA base pairs from a highly curated genomic atlas spanning all domains of life. We train Evo 2 with 7B and 40B parameters to have an unprecedented 1 million token context window with single-nucleotide resolution. Evo 2 learns from DNA sequence alone to accurately predict the functional impacts of genetic variation--from noncoding pathogenic mutations to clinically significant BRCA1 variants--without task-specific finetuning. Applying mechanistic interpretability analyses, we reveal that Evo 2 autonomously learns a breadth of biological features, including exon-intron boundaries, transcription factor binding sites, protein structural elements, and prophage genomic regions. Beyond its predictive capabilities, Evo 2 generates mitochondrial, prokaryotic, and eukaryotic sequences at genome scale with greater naturalness and coherence than previous methods. Guiding Evo 2 via inference-time search enables controllable generation of epigenomic structure, for which we demonstrate the first inference-time scaling results in biology. We make Evo 2 fully open, including model parameters, training code, inference code, and the OpenGenome2 dataset, to accelerate the exploration and design of biological complexity.

genomics↗

bioRxiv2024

Targetable Treatment Resistance in Thyroid Cancer with Clonal Hematopoiesis

Anaplastic thyroid cancer (ATC) is a clinically aggressive malignancy with a dismal prognosis. Combined BRAF/MEK inhibition offers significant therapeutic benefit in patients with BRAFV600E-mutant ATCs. However, relapses are common and overall survival remains poor. Compared with differentiated thyroid cancer, a hallmark of ATCs is significant infiltration with myeloid cells, particularly macrophages. ATCs are most common in the aging population, which also has an increased incidence of TET2-mutant clonal hematopoiesis (CH). CH-mutant macrophages have been shown to accelerate CH-associated pathophysiology including atherosclerosis. However, the clinical and mechanistic contribution of CH-mutant clones to solid tumour biology, prognosis and therapeutic response has not been elucidated. Here we show that TET2-mutant CH is enriched in the tumour microenvironment of patients with solid tumours and associated with adverse prognosis in ATC patients. We find that Tet2-mutant macrophages selectively infiltrate mouse BrafV600E-mutant ATC and that their overexpression of Tgf{beta}-family ligands mediates resistance to BRAF/MEK inhibition. Importantly, inhibition of Tgf{beta} signaling restores sensitivity to MAPK pathway inhibition, opening a path for synergistic strategies to improve outcomes of patients with ATCs and concurrent CH.

cancer biology↗

bioRxiv2023

ProT-VAE: Protein Transformer Variational AutoEncoder for Functional Protein Design

The data-driven design of protein sequences with desired function is challenged by the absence of good theoretical models for the sequence-function mapping and the vast size of protein sequence space. Deep generative models have demonstrated success in learning the sequence to function relationship over natural training data and sampling from this distribution to design synthetic sequences with engineered functionality. We introduce a deep generative model termed the Protein Transformer Variational AutoEncoder (ProT-VAE) that furnishes an accurate, generative, fast, and transferable model of the sequence-function relationship for data-driven protein engineering by blending the merits of variational autoencoders to learn interpretable, low-dimensional latent embeddings and fully generative decoding for conditional sequence design with the expressive, alignment-free featurization offered by transformers. The model sandwiches a lightweight, task-specific variational autoencoder between generic, pre-trained transformer encoder and decoder stacks to admit alignment-free training in an unsupervised or semi-supervised fashion, and interpretable low-dimensional latent spaces that facilitate understanding, optimization, and generative design of functional synthetic sequences. We implement the model using NVIDIAs BioNeMo framework and validate its performance in retrospective functional prediction and prospective design of novel protein sequences subjected to experimental synthesis and testing. The ProT-VAE latent space exposes ancestral and functional relationships that enable conditional generation of novel sequences with high functionality and substantial sequence diversity. We anticipate that the model can offer an extensible and generic platform for machine learning-guided directed evolution campaigns for the data-driven design of novel synthetic proteins with "super-natural" function.

synthetic biology↗

bioRxiv2022

Deletion of can1/cat1 genes and expression of a dominant any1 mutation establish an effective canavanine selection in fission yeast

Positive and counter-selectable markers have been successfully integrated as a part of numerous genetic assays in many model organisms. In this study, we investigate the mechanism of resistance to arginine analog canavanine and its applicability for genetic selection in Schizosaccharomyces pombe. Deletion of both arginine permease genes cat1 and can1 provides strong drug resistance, while the single can1 deletion does not have impact on canavanine resistance. Surprisingly, the widely used can1-1 allele does not match to the can1 gene but rather corresponds to the any1-523C>T allele. The strong canavanine-resistance conferred by this allele arises from an inability to deposit basic amino acid transporters on the cellular membrane. any1-523C>T leads to reduced post-translational modifications of Any1 regulated by the Tor2 kinase. We also demonstrate that any1-523C>T is a dominate allele. Our results uncover the mechanisms of canavanine-resistance in fission yeast and open the opportunity of using cat1, can1 and any1 mutant alleles in genetic assays.

genetics↗