Biology subjects

Cieza, B.

Publications and source records attributed to Cieza, B..

4 recordsLinked to original sources

bioRxiv2025

SAGA (Simplified Association Genomewide Analyses): a user-friendly Pipeline to Democratize Genome-Wide Association Studies

Genome-wide association studies (GWAS) have enabled clinicians and researchers to identify genetic variants linked to complex traits and diseases(1-3). However, GWAS still face several challenges, particularly regarding accessibility and reproducibility (4-6). Conducting these analyses often requires substantial bioinformatics expertise for data preprocessing, software installation, and scripting(7-10). We then developed SAGA ("Simplified Association Genome-wide Analyses"), a BASH-based, open-source, fully automated pipeline that integrates three widely adopted tools--PLINK(11), GMMAT(12), and SAIGE(13)--for accessible, robust, and reproducible GWAS. After installation, users simply need to provide genotype and phenotype files in standard formats. The pipeline automates preprocessing, association testing, and visualization, outputting summary statistics, Manhattan plots, and quantile-quantile plots. SAGA enables robust GWAS for users without scripting experience, expanding access to complex genetic analyses.

bioinformatics↗

bioRxiv2024

MU-BRAIN: MUltiethnic Brain Rna-seq for Alzheimer INitiative

Alzheimers Disease (AD) exhibits a complex molecular and phenotypic profile. Investigating gene expression plays a crucial role in unraveling the diseases etiology and progression. Transcriptome data across ethnic groups lack, negatively impacting equity in intervention and research. We employed 565 brains across six U.S. brain banks (n=399 non-Hispanic Whites, n=113 Hispanics, n=12 African Americans) to generated bulk RNA sequencing from prefrontal cortex. We sought to identify cross-ancestry and ancestry-specific differentially expressed genes (DEG) across Braak stages, adjusting for sex, age at death, and RNA quality metrics. We further validated our findings using the Religious Orders Study/Memory Aging Project brains (ROS/MAP; n=1,095) and performed metanalysis (n=1,660). We conducted Gene Set and Variation and Enrichment analysis (GSVA; GSEA). We employed a machine-learning approach for phenotype prediction and gene prioritization to construct a polytranscriptomics risk score (PTRS) splitting our sample into training and testing sub-samples, either randomly or by ethnicity ("ancestry-agnostic" and "ancestry-aware", respectively). Lastly, we validated top DEG using single-nucleus RNA sequencing (snRNAseq) data. We identified several DEG associated with Braak staging: AD-known genes VGF (Padj =3.78E- 07) and ADAMTS2 (Pad j=1.21E-04) were consistently differentially expressed across statistical models, ethnicities, and replicated in ROS/MAP. Genes from the heat shock protein (HSP) family, e.g. HSPB7 (Padj =3.78E-07), were the top differentially expressed genes and replicated in ROS/MAP. Ethnic-stratified analyses prioritized TNFSF14 and SPOCD1 as top Hispanics DEG. GSEA highlighted "Alzheimer disease" (Padj =4.24E-06) and "TYROBP causal network in microglia" (Padj =1.68E-08) pathways. Up- and down-regulated genes were enriched in several pathways (e.g. "Immune response activation signal pathways", "Vesicle-mediated transport in synapse", "cognition"). Ancestry-agnostic and ancestry-aware PTRS effectively classified brains (AUC=0.77 and 0.73 respectively) and replicated in ROS/MAP. snRNAseq validated prioritized genes, including VGF (downregulated in neurons; Padj=1.1 E-07). This is the largest diverse AD transcriptome in post-mortem brain tissue, to our knowledge. We identified perturbated genes, pathways and network expressions in AD brains resulting in cross- ethnic and ethnic-specific findings, ultimately highlighting the diversity within AD pathogenesis. The latter underscores the need for an integrative and personalized approach in AD studies.

genetics↗

bioRxiv2020

Deciphering the mechanical code of genome and epigenome

Sequence features have long been known to influence the local mechanical properties and shapes of DNA. However, a mechanical code (i.e. a comprehensive mapping between DNA sequence and mechanical properties), if it exists, has been difficult to experimentally determine because direct means of measuring the mechanical properties of DNA are typically limited in throughput. Here we use Loop-seq - a recently developed technique to measure the intrinsic cyclizabilities (a proxy for bendability) of DNA fragments in genomic-scale throughput - to characterize the mechanical code. We tabulate how DNA sequence features (distribution patterns of all possible dinucleotides and dinucleotide pairs) influence intrinsic cyclizability, and build a linear model to predict intrinsic cyclizability from sequence. Using our model, we predict that DNA mechanical landscape shapes nucleosome organization around the promoters of various organisms and at the binding site of the transcription factor CTCF, and that hyperperiodic DNA in C. elegans leads to globally curved DNA segments. By performing loop-seq on random libraries in the presence or absence of CpG methylation, we show that CpG methylation leads to global stiffening of DNA in a wide sequence context, and predict based on our model that CpG methylation widely changes the mechanical landscape around mouse promoters. It suggests how epigenetic modifications of DNA might alter gene expression and mediate cellular adaptation by affecting critical processes around promoters that require mechanical deformations of DNA, such as nucleosome organization and transcription initiation. Finally, we show that the genetic code and the mechanical code are linked: sequence-dependent mechanical properties of coding DNA constrains the amino acid sequence despite the degeneracy in the genetic code. Our measurements explain why the pattern of nucleosome organization along genes influences the distribution of amino acids in the translated polypeptide.

biophysics↗

bioRxiv2020

Measuring DNA mechanics on the genome scale

Mechanical deformations of DNA such as bending are ubiquitous and implicated in diverse cellular functions1. However, the lack of high-throughput tools to directly measure the mechanical properties of DNA limits our understanding of whether and how DNA sequences modulate DNA mechanics and associated chromatin transactions genome-wide. We developed an assay called loop-seq to measure the intrinsic cyclizability of DNA - a proxy for DNA bendability - in high throughput. We measured the intrinsic cyclizabilities of 270,806 50 bp DNA fragments that span the entire length of S. cerevisiae chromosome V and other genomic regions, and also include random sequences. We discovered sequence-encoded regions of unusually low bendability upstream of Transcription Start Sites (TSSs). These regions disfavor the sharp DNA bending required for nucleosome formation and are co-centric with known Nucleosome Depleted Regions (NDRs). We show biochemically that low bendability of linker DNA located about 40 bp away from a nucleosome edge inhibits nucleosome sliding into the linker by the chromatin remodeler INO80. The observation explains how INO80 can create promoter-proximal nucleosomal arrays in the absence of any other factors2 by reading the DNA mechanical landscape. We show that chromosome wide, nucleosomes are characterized by high DNA bendability near dyads and low bendability near the linkers. This contrast increases for nucleosomes deeper into gene bodies, suggesting that DNA mechanics plays a previously unappreciated role in organizing nucleosomes far from the TSS, where nucleosome remodelers predominate. Importantly, random substitution of synonymous codons does not preserve this contrast, suggesting that the evolution of codon choice has been impacted by selective pressure to preserve sequence-encoded mechanical modulations along genes. We also provide evidence that transcription through the TSS-proximal nucleosomes is impacted by local DNA mechanics. Overall, this first genome-scale map of DNA mechanics hints at a mechanical code with broad functional implications.

biophysics↗