Biology subjects

Azodi, C. B.

Publications and source records attributed to Azodi, C. B..

7 recordsLinked to original sources

bioRxiv2021

splatPop: simulating population scale single-cell RNA sequencing data

With improving technology and decreasing costs, single-cell RNA sequencing (scRNA-seq) at the population scale has become more viable, opening up the doors to study functional genomics at the single-cell level. This development has lead to a rush to adapt bulk methods and develop new single-cell-specific methods and tools for computational analysis of these studies. Many single-cell methods have been tested, developed, and benchmarked using simulated data. However, current scRNA-seq simulation frameworks do not allow for the simulation of population-scale scRNA-seq data. Here, we present splatPop, a new Splatter model, for flexible, reproducible, and well documented simulation of population-scale scRNA-seq data with known expression quantitative trait loci (eQTL) effects. The splatPop model also allows for the simulation of complex batch effects, cell group effects, and conditional effects between individuals from different cohorts.

bioinformatics↗

bioRxiv2021

Optimising expression quantitative trait locus mapping workflows for single-cell studies

Single-cell RNA-sequencing (scRNA-seq) has enabled the unbiased, high-throughput quantification of gene expression specific to cell types and states. With the cost of scRNA-seq decreasing and techniques for sample multiplexing improving, population-scale scRNA-seq, and thus single-cell expression quantitative trait locus (sc-eQTL) mapping, is increasingly feasible. Mapping of sc-eQTL provides additional resolution to study the regulatory role of common genetic variants on gene expression across a plethora of cell types and states, and promises to improve our understanding of genetic regulation across tissues in both health and disease. While previously established methods for bulk eQTL mapping can, in principle, be applied to sc-eQTL mapping, there are a number of open questions about how best to process scRNA-seq data and adapt bulk methods to optimise sc-eQTL mapping. Here, we evaluate the role of different normalisation and aggregation strategies, covariate adjustment techniques, and multiple testing correction methods to establish best practice guidelines. We use both real and simulated datasets across single-cell technologies to systematically assess the impact of these different statistical approaches and provide recommendations for future single-cell eQTL studies that can yield up to twice as many eQTL discoveries as default approaches ported from bulk studies.

genetics↗

bioRxiv2020

The cis-regulatory codes of response to combined heat and drought stress in Arabidopsis thaliana

Plants respond to their environment by dynamically modulating gene expression. A powerful approach for understanding how these responses are regulated is to integrate information about cis-regulatory elements (CREs) into models called cis-regulatory codes. Transcriptional response to combined stress is typically not the sum of the responses to the individual stresses. However, cis-regulatory codes underlying combined stress response have not been established. Here we modeled transcriptional response to single and combined heat and drought stress in Arabidopsis thaliana. We grouped genes by their pattern of response (independent, antagonistic, synergistic) and trained machine learning models to predict their response using putative CREs (pCREs) as features (median F-measure = 0.64). We then developed a deep learning approach to integrate additional omics information (sequence conservation, chromatin accessibility, histone modification) into our models, improving performance by 6.2%. While pCREs important for predicting independent and antagonistic responses tended to resemble binding motifs of transcription factors associated with heat and/or drought stress, important synergistic pCREs resembled binding motifs of transcription factors not known to be associated with stress. These findings demonstrate how in silico approaches can improve our understanding of the complex codes regulating response to combined stress and help us identify prime targets for future characterization.

genomics↗

bioRxiv2019

Putative cis-regulatory elements predict iron deficiency responses in Arabidopsis roots

Iron (Fe) is a key cofactor in many cellular redox processes, including respiration and photosynthesis. Plant Fe deficiency (-Fe) activates a complex regulatory network which coordinates root Fe uptake and distribution to sink tissues, while avoiding over-accumulation of Fe and other metals to toxic levels. In Arabidopsis (Arabidopsis thaliana), FIT (FER-LIKE FE DEFICIENCY-INDUCED TRANSCRIPTION FACTOR), a bHLH transcription factor (TF), is required for up-regulation of root Fe acquisition genes. However, other root and shoot -Fe-induced genes involved in Fe allocation and signaling are FIT-independent. The cis-regulatory code, i.e. the cis-regulatory elements (CREs) and their combinations that regulate plant -Fe-responses, remains largely elusive. Using Arabidopsis genome and transcriptome data, we identified over 100 putative CREs (pCREs) that were predictive of -Fe-induced up-regulation of genes in root tissue. We used large-scale in vitro TF binding data, association with FIT-dependent or FIT-independent co-expression clusters, positional bias, and evolutionary conservation to assess pCRE properties and possible functions. In addition to bHLH and MYB TFs, also B3, NAC, bZIP, and TCP TFs might be important regulators for -Fe responses. Our approach uncovered IDE1 (Iron Deficiency-responsive Element 1), a -Fe response CRE in grass species, to be conserved in regulating genes for biosynthesis of Fe-chelating compounds also in Arabidopsis. Our findings provide a comprehensive source of cis-regulatory information for -Fe-responsive genes, that advances our mechanistic understanding and informs future efforts in engineering plants with more efficient Fe uptake or transport systems.\n\nOne sentence summary>100 putative cis-regulatory elements robustly predict Arabidopsis root Fe deficiency-responses in computational models, and shed light on the mechanisms of transcriptional regulation.

plant biology↗

bioRxiv2018

Cis-regulatory code for predicting plant cell-type specific high salinity response

Multicellular organisms have diverse cell types with distinct roles in development and responses to the environment. At the transcriptional level, the differences in environmental response between cell types are due to differences in regulatory programs. In plants, although cell-type environmental responses have been examined, details on how these responses are regulated remain spotty. Here, we identify a set of putative cis-regulatory elements (pCREs) enriched in the promoters of genes responsive to high salinity stress in six Arabidopsis thaliana root cell types. Using machine learning with pCREs as predictors, we establish cis-regulatory codes, i.e. models predicting whether a gene is responsive to high salinity for each cell type. These pCRE-based models outperform models utilizing in vitro binding data of 758 A. thaliana transcription factors. Surprisingly, organ pCREs identified based on whole root high salinity response can predict cell-type responses as well as pCREs derived from cell-type data -because organ and cell-type pCREs predict complementary subsets of high salinity response genes. Our findings not only advance our understanding of the regulatory mechanisms of plant spatial transcriptional response through cis-regulatory codes, but also suggest broad applicability of the approach to any species, particularly those with little or no trans regulatory data.

plant biology↗

bioRxiv2018

Evolutionary characteristics of intergenic transcribed regions indicate widespread noisy transcription in the Poaceae

Extensive transcriptional activity occurring in unannotated, intergenic regions of genomes has raised the question whether intergenic transcription represents the activity of novel genes or noisy expression. To address this, we evaluated cross-species and post-duplication sequence and expression conservation of intergenic transcribed regions (ITRs) in four Poaceae species. Most ITR sequences are species-specific. Those found across species tend to be more divergent in expression and have more recent duplicates compared to annotated genes. To assess if ITRs are functional (under selection), machine learning models were established in Oryza sativa (rice) that could distinguish between benchmark functional (phenotype genes) and nonfunctional (pseudogenes) sequences with high accuracy based on 44 evolutionary and biochemical features. Based on the prediction models, 584 rice ITRs (8%) are classified as likely functional that tend to have conserved expression and ancient retained duplicates. However, most ITRs do not exhibit sequence or expression conservation across species or following duplication, consistent with computational predictions that suggest 61% ITRs are not under selection. We outline key evolutionary characteristics that are tightly associated with likely-functional ITRs and provide a framework to identify novel genes to improve genome annotation and move toward connecting genotype to phenotype in crop and model systems.

genomics↗

bioRxiv2017

Asymmetric evolution of the transcription profiles and cis-regulatory sites contributes to the retention of transcription factor duplicates

Transcription factors (TFs) play a key role in regulating plant development and response to environmental stimuli. While most genes revert to single copy after a duplication event, transcription factors are retained at a significantly higher rate. However, it is unclear why TF duplicates have higher rates of retention relative to other genes. In this study, we compared three types of features (expression, sequence, and conservation) of retained TFs following whole genome duplication (WGD) events to genes with other functions, using Arabidopsis thaliana as a model. We found that gene function groups with higher maximum expression but lower mean expression tended to have higher duplicate retention rate post WGD, though TFs in particular are retained more often than would be expected based on the features examined. Conversely, expression of individual genes was not associated with duplication, but sequence conservation was. Furthermore, we found that the evolution of TF expression patterns and cis-regulatory cites favors the partitioning of ancestral states among the resulting duplicates. In particular, we found that one duplicate retains the majority of ancestral expression and cis-regulatory sites, while the \"non-ancestral\" duplicate was enriched for novel regulatory sites. To investigate how this pattern of partitioning pattern evolved, we modeled the retention of ancestral states in duplicate pairs using a system of differential equations. Our findings indicate that duplicate pairs evolve to a partitioned state more often than away from it, which in combination with accumulation of new regulatory sites in non-ancestral duplicates, suggest that selection favors partitioning via neofunctionalization.\n\nAuthor SummaryGene expression is controlled by regulatory proteins known as transcription factors. These factors control how an organism develops and responds to its environment. The evolution of transcription factor functions also contributes to the emergence of new species and crop domestication. In plants, new transcription factors mainly arise due to polyploidy, multiplication of the genome. Although most duplicated copies are lost following a genome duplication event, transcription factors are exceptional because they are often kept. Furthermore, we found that transcription factor duplicates that tend to diverge in how they are expressed and regulated in an unusual way where one copy mirrors the original, pre-duplication functional states of the ancestral gene, while the other loses the ancestral status and instead accumulates novel regulatory sites. Our results suggest these duplicate transcription factors may have been kept because one copy preserve ancestral function while the other has evolved new ones.

evolutionary biology↗