Biology subjects

Weller, J.

Publications and source records attributed to Weller, J..

13 recordsLinked to original sources

bioRxiv2026

RNA-seq variants reveal distinct patterns in the aging epitranscriptome: an in-depth analysis of age-matched Alzheimer's Disease patients and a cognitively normal cohort

BackgroundPost-transcriptional modifications are those made to the RNA transcript, which can modulate RNA stability and function. Despite robust investigation of the genome, transcriptome, and proteome, little is known about post-transcriptional modifications during normal aging or Alzheimers disease (AD) pathogenesis. Several studies have shown epitranscriptomic changes in AD brains for certain modification types, establishing epitranscriptomic links to the disease; however, the complete set of post-transcriptional modifications have not been assessed in the context of AD. Furthermore, it is not understood which genes or pathways are under epitranscriptomic regulation, how conserved and sporadic modifications are distributed, or which conserved sites are differentially modified in diseased brains. Therefore, there is a need for a more complete analysis to describe the full landscape of the epitranscriptome in AD, helping to bridge the knowledge gap between post-transcriptional modifications and the molecular etiology of AD. MethodsWe designed and implemented a novel bioinformatics pipeline for complex epitranscriptome-wide analysis of potential RNA modification sites in sample-matched, whole-genome sequencing-filtered variant calls from RNA sequencing data. Using parametric and non-parametric tests, we tested differences in patterns for all detectable variant calls between postmortem brains of AD and cognitively normal, aged individuals. ResultsWe identified 544 genes with hyper-modified transcripts in AD samples compared with cognitively normal controls, a notable observation being high enrichment of genes in the "Kaposis sarcoma-associated herpesvirus" pathway. We also identified patterns of recurring and sporadic modification sites that differed complementarily between disease and non-disease conditions. We found 17 genes (33 total sites) that were differentially modified between conditions including several sites found exclusively in the AD epitranscriptome. ConclusionsThese findings provide a more complete profile of the potential molecular underpinnings which differentiate AD brains from their non-diseased, aged counterparts and reveal patterns and modification sites which can be further investigated for how they contribute to the network of molecular interactions underlying AD. These elements are likely to be valuable candidates for investigations that aim to further the search for biomarkers and therapeutic targets.

neuroscience↗

bioRxiv2025

Generating long deletions across the genome with pooled paired prime editing screens

Engineered deletions are a powerful probe for studying genome architecture, function, and regulation. Yet, the lack of effective methods to create them in large numbers and at multi-kilobase scale has rendered interrogating all the gigabases of human DNA elusive. Prime editing based approaches are promising contenders to fill this gap, but have not been adopted for large-scale pooled screening so far. Here, we performed the first high-throughput paired prime editing deletion screens. To establish the technology, we generated 3,802 deletions of up to 1.2 Mb in a reporter locus in two human cell lines, and identified key determinants of paired prime editing efficiency to be deletion length, predicted pegRNA efficacy, and contact frequency. We used these learnings to generate therapeutically important deletions at rates that are expected to give clinical benefits for retinitis pigmentosa and Duchenne muscular dystrophy. Combining several technological advances, we then performed the first pooled paired prime editing screen to delete over 10,471 sequences of median length 1.1 kb that target 149 genes across the genome. Our screens were high quality, recovering 75% of positive controls. We discovered 462 non-coding regions with fitness effects, and validated deletions with impact as well as tolerated ones in regions with functional annotations. Our screens are the largest pooled characterization of precise deletions to date, demonstrating that prime deletion screens can perturb the genome at scale, and highlighting that most non-coding DNA around essential genes is dispensable for cell growth in one condition.

genomics↗

bioRxiv2025

Resolution of a human super-enhancer by targeted genome randomisation

Human gene expression is controlled from distance via enhancers, which can form longer super-enhancer-regions of intense regulatory activity. Whether super-enhancers constitute a separate regulatory paradigm remains unclear, largely due to the difficulty of dissecting the contributions and interactions of individual elements within their natural chromosomal context. To address this challenge, we developed enhancer scrambling, a high-throughput strategy to generate stochastic inversions and deletions of targeted enhancer regions by combining CRISPR prime editing insertion of symmetrical loxP sites with Cre recombinase-induced rearrangements. We applied our approach to dissect a distal super-enhancer of the OTX2 gene, generating up to 134 alternative regulatory configurations in a single experiment, and establishing how they drive gene expression and chromatin accessibility, as well as the individual contributions of its elements to this activity. Surprisingly, the presence of the sequence containing a single DNase I hypersensitive site predominantly controls OTX2 expression. Our findings highlight that enhancer-driven regulation of some highly expressed, cell-type-specific genes can rely on an individual element within a cluster of non-interacting, dispensable components, and suggest a simple functional core to a subset of super-enhancers. The targeted randomisation method to scramble enhancers can scale to resolve many super-enhancers and human gene regulatory landscapes.

synthetic biology↗

bioRxiv2024

Linking phenology, harvest index and genetics to improve chickpea grain yield

Phenology is critical to crop adaptation. We grew 24 chickpea genotypes in 12 environments to analyse: the environmental and genotypic drivers of phenology; associations between phenology and yield; and phenotypes associated with allelic variants of three flowering related candidate loci: CaELF3a; a cluster of three FT genes on chromosome 3; and a region on chromosome 4 with an orthologue of the floral promoter GIGANTEA. A simple model with 3 genotype-specific parameters explained the differences in flowering response to daylength. Environmental factors causing flower abortion, such as low temperature and radiation and high humidity, led to a longer flowering-to-podding interval. Late podding associated with poor partition to grain, limiting yield in favourable environments. Sonali, carrying the early allele of Caelf3a (elf3a), was generally the earliest to set pod, had low biomass but the highest harvest index. Genotypes combining the early variants of GIGANTEA and FT orthologues FTdel, where a deletion in the intergenic region of FTa1-FTa2 was associated with slow development, usually featured early reproduction and high harvest index, returning high yield in favourable environments. We emphasise the importance of pod set, rather than flowering, as a target for breeding, agronomic, and modelling applications. HighlightThis paper analyses the environmental and genetic controls of chickpea phenology and its effects on grain yield, in a multi-environment trial including 24 genotypes with varying combinations of flowering related genes.

plant biology↗

bioRxiv2024

Laminin Receptor Characterization in Acute Myeloid Leukemia: Integrin α7β1 Defines non-Leukemic Stem Cells with Migratory Potential

Interactions with the bone marrow (BM) niche are crucial for promoting self-renewal and survival of acute myeloid leukemia (AML) cells. Consequently, AML cells express a variety of surface receptors to engage with BM niche cells and extracellular matrix proteins, including laminins. Despite the association of laminin receptor expression with stemness in healthy hematopoiesis, the role of laminin receptors in AML remains poorly understood. In this study, we present a comprehensive examination of the laminin receptors integrin 3{beta}1, 6{beta}1, 7{beta}1 and basal cell adhesion molecule (BCAM) in AML. We demonstrate that high mRNA expression of all four laminin receptors correlates with poor overall survival. Notably, integrin 6 and 7 display the highest cell surface presentation among the examined laminin receptors and are higher expressed on AML cells compared to healthy controls. Moreover, our results indicate that integrin 7 expression allows to distinguish between leukemic stem cells (LSC) and non-LSC populations. Specifically, integrin 7 appears to mark non-LSC with enhanced migratory potential. Together, our results confirm the association of high laminin receptor expression with poor prognosis and establish integrin 7 as marker of high migratory non-LSC. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=128 SRC="FIGDIR/small/587290v1_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@bdcc20org.highwire.dtl.DTLVardef@a45fd2org.highwire.dtl.DTLVardef@18f058forg.highwire.dtl.DTLVardef@b613fc_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2024

Massively parallel combination screen reveals small molecule sensitization of antibiotic-resistant Gram-negative ESKAPE pathogens

Antibiotic resistance, especially in multidrug-resistant ESKAPE pathogens, remains a worldwide problem. Combination antimicrobial therapies may be an important strategy to overcome resistance and broaden the spectrum of existing antibiotics. However, this strategy is limited by the ability to efficiently screen large combinatorial chemical spaces. Here, we deployed a high-throughput combinatorial screening platform, DropArray, to evaluate the interactions of over 30,000 compounds with up to 22 antibiotics and 6 strains of Gram-negative ESKAPE pathogens, totaling to over 1.3 million unique strain-antibiotic-compound combinations. In this dataset, compounds more frequently exhibited synergy with known antibiotics than single-agent activity. We identified a compound, P2-56, and developed a more potent analog, P2-56-3, which potentiated rifampin (RIF) activity against Acinetobacter baumannii and Klebsiella pneumoniae. Using phenotypic assays, we showed P2-56-3 disrupts the outer membrane of A. baumannii. To identify pathways involved in the mechanism of synergy between P2-56-3 and RIF, we performed genetic screens in A. baumannii. CRISPRi-induced partial depletion of lipooligosaccharide transport genes (lptA-D, lptFG) resulted in hypersensitivity to P2-56-3/RIF treatment, demonstrating the genetic dependency of P2-56-3 activity and RIF sensitization on lpt genes in A. baumannii. Consistent with outer membrane homeostasis being an important determinant of P2-56-3/RIF tolerance, knockout of maintenance of lipid asymmetry complex genes and overexpression of certain resistance-nodulation-division efflux pumps - a phenotype associated with multidrug-resistance - resulted in hypersensitivity to P2-56-3. These findings demonstrate the immense scale of phenotypic antibiotic combination screens using DropArray and the potential for such approaches to discover new small molecule synergies against multidrug-resistant ESKAPE strains. Significance StatementThere is an unmet need for new antibiotic therapies effective against the multidrug-resistant, Gram-negative ESKAPE pathogens. Combination therapies have the potential to overcome resistance and broaden the spectrum of existing antibiotics. In this study, we use DropArray, a massively parallel combinatorial screening tool, to assay more than 1.3 million combinations of small molecules against the Gram-negative ESKAPE pathogens, Acinetobacter baumannii, Klebsiella pneumoniae, and Pseudomonas aeruginosa. We discovered a synthetic small molecule potentiator, P2-56, of the antibiotic rifampin effective in A. baumannii and K. pneumoniae. We generated P2-56-3, a more potent derivative of P2-56, and found that it likely potentiates rifampin by compromising the outer membrane integrity. Our study demonstrates a high-throughput strategy for identifying antibiotic potentiators against multidrug-resistant bacteria.

microbiology↗

bioRxiv2024

Randomizing the human genome by engineering recombination between repeat elements

While protein-coding genes are characterized increasingly well, 99% of the human genome is non-coding and poorly understood. This gap is due to a lack of tools for engineering variants that affect sequence to the necessary extent. To bridge this gap, we have developed a toolbox to create deletions, inversions, translocations, and extrachromosomal circular DNA at scale by highly multiplexed insertion of recombinase recognition sites into repetitive sequences with CRISPR prime editing. Using this strategy, we derived stable human cell lines with several thousand clonal insertions, the highest number of novel sequences inserted into single human genomes. Subsequent recombinase induction generated an average of more than one hundred megabase-sized rearrangements per cell, and thousands across the whole population. The ability to detect rearrangements as they are generated and to track their abundance over time allowed us to measure the selection pressures acting on different types of structural changes. We observed a consolidation towards shorter variants that preferentially delete growth-inhibiting genes and a depletion of translocations. We isolated and characterized 21 clones with multiple recombinase-induced rearrangements. These included viable haploid clones with deletions that span hundreds of kilobases as well as triploid HEK293T clones with aneuploidies and fold back chromosomes. We mapped the impact of these genetic changes on gene expression to decipher how structural variants affect gene regulation. The genome scrambling strategy developed here makes it possible to delete megabases of sequence, move sequences between and within chromosomes, and implant regulatory elements into new contexts which will shed light on the genome organization principles of humans and other species.

genomics↗

bioRxiv2022

Focusing the GWAS Lens on days to flower using latent variable phenotypes derived from global multi-environment trials

Adaptation constraints within crop species have resulted in limited genetic diversity in some breeding programs and/or areas where new crops have been introduced, e.g., lentil (Lens culinaris Medik.) in North America. An improved understanding of the underlying genetics involved in phenology-related traits is valuable knowledge to aid breeders in overcoming limitations associated with unadapted germplasm and expanding their genetic diversity by introducing new, exotic material. We used a large, 18 site-year, multi-environment dataset, phenotyped for phenology-related traits across nine locations and over three years, along with accompanying latent variable phenotypes derived from a photothermal model and principal component analysis (PCA) of days from sowing to flower (DTF) data for a lentil diversity panel (324 accessions) which has also been genotyped with an exome capture array. Genomewide association studies (GWAS) on DTF across multiple environments helped confirm associations with known flowering time genes and identify new quantitative trait loci (QTL), which may contain previously unknown flowering time genes. Additionally, the use of latent variable phenotypes, which can incorporate environmental data such as temperature and photoperiod as both GWAS traits and as covariates, strengthened associations, revealed additional hidden associations, and alluded to potential roles of the associated QTL. Our approach can be replicated with other crop species, and the results from our GWAS serve as a resource for further exploration into the complex nature of phenology-related traits across the major growing environments for cultivated lentil.

genetics↗

bioRxiv2021

Predicting efficiency of writing short sequences into the genome using prime editing

Short sequences can be precisely written into a selected genomic target using prime editing. This ability facilitates protein tagging, correction of pathogenic deletions, and many other exciting applications. However, it remains unclear what types of sequences prime editors can easily insert, and how to choose optimal reagents for a desired outcome. To characterize features that influence insertion efficiency, we designed a library of 2,666 sequences up to 69 nt in length and measured the frequency of their insertion into four genomic sites in three human cell lines, using different prime editor systems. We discover that insertion sequence length, nucleotide composition and secondary structure all affect insertion rates, and that mismatch repair proficiency is a strong determinant for the shortest insertions. Combining the sequence and repair features into a machine learning model, we can predict insertion frequency for new sequences with R = 0.69. The tools we provide allow users to choose optimal constructs for DNA insertion using prime editing.

molecular biology↗

bioRxiv2021

Predicting base editing outcomes using position-specific sequence determinants

Nucleotide-level control over DNA sequences is poised to power functional genomics studies and lead to new therapeutics. CRISPR/Cas base editors promise to achieve this ability, but the determinants of their activity remain incompletely understood. We measured base editing frequencies in two human cell lines for two cytosine and two adenine base editors at [~]14,000 target sequences. Base editing activity is sequence-biased, with largest effects from nucleotides flanking the target base, and is correlated with measures of Cas9 guide RNA efficiency. Whether a base is edited depends strongly on the combination of its position in the target and the preceding base, with a preceding thymine in both editor types leading to a wider editing window, while a preceding guanine in cytosine editors and preceding adenine in adenine editors to a narrower one. The impact of features on editing rate depends on the position, with guide RNA efficacy mainly influencing bases around the centre of the window, and sequence biases away from it. We use these observations to train a machine learning model to predict editing activity per position for both adenine and cytosine editors, with accuracy ranging from 0.49 to 0.72 between editors, and with better generalization performance across datasets than existing tools. We demonstrate the usefulness of our model by predicting the efficacy of potential disease mutation correcting guides, and find that most of them suffer from more unwanted editing than corrected outcomes. This work unravels the position-specificity of base editing biases, and provides a solution to account for them, thus allowing more efficient planning of base edits in experimental and therapeutic contexts.

genetics↗

bioRxiv2021

Transcriptomics data availability and reusability in the transition from microarray to next-generation sequencing

Over the last two decades, molecular biology has been changed by the introduction of high-throughput technologies. Data sharing requirements have prompted the establishment of persistent data archives. A standardized approach for recording and managing these data was first proposed in the Minimal Information About a Microarray Experiment (MIAME) guidelines. The Minimal Information about a high throughput nucleotide Sequencing Experiment (MINSEQE) proposal was introduced in 2008 as a logical extension of the guidelines to next-generation sequencing (NGS) technologies used for transcriptome analysis. We present a historical snapshot of the data-sharing situation focusing on transcriptomics data from both microarray and RNA-sequencing experiments published between 2009 and 2013, a period during which RNA-seq studies became increasingly popular for transcriptome analysis. We assess how much data from RNA-seq based experiments is actually available in persistent data archives, compared to data derived from microarray based experiments, and evaluate how these types of data differ. Based on this analysis, we provide recommendations to improve RNA-seq data availability, reusability, and reproducibility.

bioinformatics↗

bioRxiv2020

Efficient design of maximally active and specific nucleic acid diagnostics for thousands of viruses

Diagnostics, particularly for rapidly evolving viruses, stand to benefit from a principled, measurement-driven design that harnesses machine learning and vast genomic data--yet the capability for such design has not been previously built. Here, we develop and extensively validate an approach to designing viral diagnostics that applies a learned model within a combinatorial optimization framework. Concentrating on CRISPR-based diagnostics, we screen a library of 19,209 diagnostic-target pairs and train a deep neural network that predicts, from RNA sequence alone, diagnostic signal better than contemporary techniques. Our model then makes it possible to design assays that are maximally sensitive over the spectrum of a viruss genomic variation. We introduce ADAPT (https://adapt.guide), a system for fully-automated design, and use ADAPT to design optimal diagnostics for the 1,933 vertebrate-infecting viral species within 2 hours for most species and 24 hours for all but 3. We experimentally show ADAPTs designs are sensitive and specific down to the lineage level, including against viruses that pose challenges involving genomic variation and specificity. ADAPTs designs exhibit significantly higher fluorescence and permit lower limits of detection, across a viruss entire variation, than the outputs of standard design techniques. Our model-based optimization strategy has applications broadly to viral nucleic acid diagnostics and other sequence-based technologies, and, paired with clinical validation, could enable a critically-needed, proactive resource of assays for surveilling and responding to pathogens.

genomics↗

bioRxiv2020

AutoRELACS: Automated Generation And Analysis Of Ultra-parallel ChIP-seq

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is a method used to profile protein-DNA interactions genome-wide. RELACS (Restriction Enzyme-based Labeling of Chromatin in Situ) is a recently developed ChIP-seq protocol that deploys a chromatin barcoding strategy to enable standardized and high-throughput generation of ChIP-seq data. The manual implementation of RELACS is constrained by human processivity in both data generation and data analysis. To overcome these limitations, we have developed AutoRELACS, an automated implementation of the RELACS protocol using the liquid handler Biomek i7 workstation. We match the unprecedented processivity in data generation allowed by AutoRELACS with the automated computation pipelines offered by snakePipes. In doing so, we build a continuous workflow that streamlines epigenetic profiling, from sample collection to biological interpretation. Here, we show that AutoRELACS successfully automates chromatin barcode integration, and is able to generate high-quality ChIP-seq data comparable with the standards of the manual protocol, also for limited amounts of biological samples.

genomics↗