Biology subjects

Gloor, G. B.

Publications and source records attributed to Gloor, G. B..

14 recordsLinked to original sources

bioRxiv2025

Not every gene is special: one simple rule to control the false discovery rate when analysing high-throughput sequencing data

1Differential expression and differential abundance analyses are commonplace in studies employing high-throughput sequencing approaches; however different tools often fail to return comparable results when applied to the same dataset. Most tools employ various normalisations to attempt to correct for technical variation in the count data. Previously, we demonstrated that these normalisations are often inappropriate due to incorrect assumptions regarding the overall scale (i.e. size) of the biological system in question. In this study, we conducted 100 permutation analyses of 10 RNA-seq datasets to show that scale misspecification results in poor control of the false discovery rate by several commonly-used analysis tools. Moreover, we demonstrate that this can be ameliorated by using a scale model in ALDEx2 or ALDEx3 to account for uncertainty around the size of the system scale. We show that there is an inherent trade-off between satisfactory control of false-discovery rates and sensitivity and that no tool offers both. We also quantify how increasing scale uncertainty affects the difference between groups required for a feature to be reported as differentially expressed. Finally, we provide universal guidance on choosing an appropriate amount of scale uncertainty for any type of analysis. Overall, our work highlights the strengths and pitfalls of commonly used tools for differential expression analyses and highlights the choice between sensitivity and false-discovery rate control that all researchers are making when analysing sequencing data.

bioinformatics↗

bioRxiv2025

PHYCUT: Scalable multiplex CRISPR/Cas9 editing forgenome engineering in the diatom Phaeodactylumtricornutum

Diatoms are globally significant microalgae that contribute [~] 20% of oxygen production and exhibit remarkable metabolic diversity. The marine diatom Phaeodactylum tricornutum has emerged as a promising synthetic biology platform for bioproduction of recombinant proteins, supported by a human-like N-linked glycosylation pathway. However, its (1,3)-linked core fucose is immunogenic in humans and thus limits biopharmaceutical applications. One hurdle to efficient genome engineering in P. tricornutum is the lack of a robust system for simultaneous CRISPR/Cas9 editing at multiple sites. To overcome this limitation, we develop PHYCUT (Phaeodactylum tricornutum Csy4-Cas9 multiplex tool), a versatile plasmid-based CRISPR/Cas9 system that uses the Csy4 endoribonuclease to process multi-guide RNA arrays. To highlight PHYCUT applications, we demonstrate multiplex editing of all three FucT genes responsible for (1,3) fucosylation in P. tricornutum, yielding strains with markedly reduced fucosylation of secreted proteins. PHYCUT enables facile, multiplexed genome engineering in diatoms and provides a foundation for humanizing the P. tricornutum glycosylation pathway to support next-generation algal biotechnology.

synthetic biology↗

bioRxiv2025

PAM adenine methylation and flanking sequence regulate SaCas9 activity in bacteria

Cas9 nucleases are the effectors of the class 2 type II CRISPR system in bacteria and function to restrict invading DNA. They can also be used with single guide RNAs (sgRNAs) as antimicrobials and genome engineering tools in bacteria, yet applications are hindered by an incomplete understanding of Cas9-target interactions. Here, we generate large-scale SaCas9/sgRNA in vivo bacterial activity datasets and train a machine learning model (crisprHAL) to predict SaCas9 activity. The highest predictive performance was found when downstream sequence flanking the canonical NNGRRN PAM motif at positions [+1] and [+2] was included in model training, correlating with high in vivo activity on sites that included T-rich di-nucleotides in the [+1] and [+2] flanking positions. Strikingly, model predictions and experimentally determined activity in pooled sgRNA experiments in Escherichia coli and Citrobacter rodentium showed an [~]10-fold reduced SaCas9 activity at sites with 5'-NNGGAT[C]-3' PAM [+1] sequences. Cleavage assays using plasmid DNA isolated from E. coli inactivated for DNA adenine methyltransferase (DAM) and SaCas9/sgRNA combinations targeting sites with NNGGAT[C] PAM sequences confirmed that adenine methylation impacts SaCas9 cleavage. Moreover, ablation of a GATC DAM site in a PAM sequence enhanced SaCas9 in vitro activity, whereas creation of a DAM site reduced activity, providing a mechanistic link between adenine methylation and SaCas9 activity. Our results show that a general purpose machine learning architecture can provide biologically relevant insights into SaCas9-PAM interactions that can better inform activity predictions for bacterial applications. Avoidance of adenine methylated PAM sites by SaCas9 may be a mechanism of self versus non-self discrimination or reflect an evolutionary adaptation to counter methylation as an anti-restriction strategy by phage or plasmids.

microbiology↗

bioRxiv2025

Better data for better predictions: data curation improves deep learning for sgRNA/Cas9 prediction

The Cas9 enzyme along with a single guide RNA molecule is a modular tool for genetic engineering and has shown effectiveness as a species-specific anti-microbial. The ability to accurately predict on-target cleavage is critical as activity varies by target. Using the sgRNA nucleotide sequence and an activity score, predictive models have been developed with the best performance resulting from deep learning architectures. Prior work has emphasized robust and novel architectures to improve predictive performance. Here, we explore the impact of a data-centric approach through optimization of the input target site adjacent nucleotide sequence length and the use of data filtering for read counts in the control conditions to improve input data utility. Using the existing crisprHAL architecture, we develop crisprHAL Tev, a best-in-class bacterial SpCas9 prediction model with performance that generalizes between related species and across data types. During this process, we also rebuild two prior E. coli Cas9 datasets, demonstrating the importance of data quality, and resulting in the production of an improved bacterial eSpCas9 prediction model. The crisprHAL models are available through GitHub (https://github.com/tbrowne/crisprHAL).

bioinformatics↗

bioRxiv2024

Vaginal metatranscriptome meta-analysis reveals functional BV subgroups and novel colonisation strategies

The application of -omics technologies to study bacterial vaginosis (BV) has uncovered vast differences in composition and scale between the vaginal microbiomes of healthy and BV patients. Compared to amplicon sequencing and shotgun metagenomic approaches focusing on a single or few species, investigating the transcriptome of the vaginal microbiome at a system-wide level can provide insight into the functions which are actively expressed and differential between states of health and disease. We conducted a meta-analysis of vaginal metatran-scriptomes from three studies, split into exploratory (n = 44) and validation (n = 297) datasets, accounting for the compositional nature of sequencing data and differences in scale between healthy and BV microbiomes. Conducting differential abundance analyses on the exploratory dataset, we identified a multitude of strategies employed by microbes associated with states of health and BV to evade host cationic antimicrobial peptides (CAMPs); putative mechanisms used by BV-associated species to resist and counteract the low vaginal pH; and potential approaches to disrupt vaginal epithelial integrity so as to establish sites for adherence and biofilm formation. Moreover, we identified several distinct functional subgroups within the BV population, distinguished by genes involved in motility, chemotaxis, biofilm formation and co-factor biosynthesis. After defining molecular states of health and BV in the validation dataset using KEGG orthology terms rather than community state types, differential abundance analysis confirmed earlier observations regarding CAMP resistance and compromising epithelial barrier integrity in healthy and BV microbiomes, and also supported the existence of motile vs. non-motile subgroups in the BV population. Our findings highlight a need to focus on functional rather than taxonomic differences when considering the role of microbiomes in disease and identify pathways for further research as potential BV treatment targets.

microbiology↗

bioRxiv2024

Beyond Normalization: Incorporating Scale Uncertainty in Microbiome and Gene Expression Analysis

Though statistical normalizations are often used in differential abundance or differential expression analysis to address sample-to-sample variation in sequencing depth, we offer a better alternative. These normalizations often make strong, implicit assumptions about the scale of biological systems (e.g., microbial load). Thus, analyses are susceptible to even slight errors in these assumptions, leading to elevated rates of false positives and false negatives. We introduce scale models as a generalization of normalizations so researchers can model potential errors in assumptions about scale. By incorporating scale models into the popular ALDEx2 software, we enhance the reproducibility of analyses while often drastically decreasing false positive and false negative rates. We design scale models that are guaranteed to reduce false positives compared to equivalent normalizations. At least in the context of ALDEx2, we recommend using scale models over normalizations in all practical situations.

bioinformatics↗

bioRxiv2023

Normalizations are not what you think; Explicit Scale Simulation in ALDEx2

In high-throughput sequencing (HTS) studies, sample-to-sample variation in sequencing depth is driven by technical factors, and not by variation in the scale (e.g., total size, microbial load, or total mRNA expression) of the underlying biological systems. Typically a statistical normalization is used to remove unwanted technical variation in the data or the parameters of the model to enable analyses that are reliant on scale; e.g., differential abundance and differential expression analyses. We recently showed that all normalizations make implicit assumptions about the unmeasured system scale and that errors in these assumptions can dramatically increase false positive and false negative rates. We demonstrated that these errors can be mitigated by accounting for uncertainty about scale using a scale model, which we integrated into the ALDEx2 R package. This article provides new insights into those methods, focusing on the application to transcriptomic analysis. Here we provide transcriptomic case studies demonstrating how scale models, rather than traditional normalizations, can reduce false positive and false negative rates in practice while enhancing the transparency and reproducibility of analyses. We show that these scale models replace the need for dual cutoff approaches often used to address the disconnect between practical and statistical significance. We demonstrate the utility of that scale models built based on known housekeeping genes in complex metatranscriptomic datasets. Thus this work provides example and practical guidance on how to incorporate scale into transcriptomic analysis.

bioinformatics↗

bioRxiv2023

De novo synthesis of a conjugative system from human gut metagenomic data for targeted delivery of Cas9 antimicrobials

Metagenomic sequence represents an untapped source of genetic novelty, particularly for conjugative systems that could be used for plasmid-based delivery of Cas9-derived antimicrobial agents. However, unlocking the functional potential of conjugative systems purely from metagenomic sequence requires the identification of suitable candidate systems as starting scaffolds for de novo DNA synthesis. Here, we developed a bioinformatics approach that searches through the metagenomic trash bin for conjugative systems present on contigs that are typically excluded from common metagenomic analysis pipelines. Using a human metagenomic gut dataset representing 2805 taxonomically distinct units, we identified 1598 contigs containing conjugative systems with a differential distribution in human cohorts. We synthesized de novo an entire Citrobacter spp. conjugative system of 54 kb and containing at least 47 genes, pCitro, and found that pCitro conjugates from Escherichia coli to Citrobacter rodentium with a 30-fold higher frequency than to E. coli, and is compatible with Citrobacter resident plasmids. Mutations in the traV and traY conjugation components of pCitro inhibited conjugation. We showed that pCitro can be re-purposed as an antimicrobial delivery agent by programming it with the TevCas9 nuclease and Citrobacter -specific sgRNAs to kill C. rodentium. Our study reveals a trove of uncharacterized conjugative systems in metagenomic data and describes an experimental framework to animate these large genetic systems as novel target-adapted delivery vectors for Cas9-based editing of bacterial genomes.

synthetic biology↗

bioRxiv2023

Annotating Metagenomically Assembled Bacteriophage from a Unique Ecological System using Protein Structure Prediction and Structure Homology Search

Emergent long read sequencing technologies such as Oxfords Nanopore platform are invaluable in constructing high quality and complete genomes from a metagenome, and are needed investigate unique ecosystems on a genetic level. However, generating informative functional annotations from sequences which are highly divergent to existing nucleotide and protein sequence databases is a major challenge. In this study, we present wet and dry lab techniques which allowed us to generate 5432 high quality sub-genomic sized metagenomic circular contigs from 10 samples of microbial communities. This unique ecological system exists in an environment enriched with naphthenic acid (NA), which is a major toxic byproduct in crude oil refining and the major carbon source to this community. Annotation by sequence homology alone was insufficient to characterize the community, so as proof of principle we took a subset of 227 putative bacteriophage and greatly improved our existing annotations by predicting the structures of hypothetical proteins with ColabFold and using structural homology searching with Foldseek. The proportion of proteins for each bacteriophage that were highly similar to known proteins increased from approximately 10% to about 50%, while the number of annotations with KEGG or GO terms increased from essentially 0% to 15%. Therefore, protein structure prediction and homology searches can produce more informative annotations for microbes in unique ecological systems. The characterization of novel microbial ecosystems involved in the bioremediation of crude oil-process-affected wastewater can be greatly improved and this method opens the door to the discovery of novel NA degrading pathways. IMPORTANCEFunctional annotation of metagenomic assembled sequences from novel or unique microbial communities is challenging when the sequences are highly dissimilar to organisms or proteins in the known databases. This is a major obstacle for researchers attempting to characterize the functional capabilities of unique ecosystems. In this study, we demonstrate that including protein structure prediction and homology search based methods vastly improves the annotation of predicted genes identified in novel putative bacteriophage in a bacterial community that degrades naphthenic acids the major toxic component of oil refinery wastewater. This method can be extended to similar genomics studies of unique, uncharacterized ecosystems, to improve their annotations. Please read the Instructions to Authors carefully, or browse the FAQs for further details.

genomics↗

bioRxiv2023

A generalizable Cas9/sgRNA prediction model using machine transfer learning with small high-quality datasets

The CRISPR/Cas9 nuclease from Streptococcus pyogenes (SpCas9) can be used with single guide RNAs (sgRNAs) as a sequence-specific antimicrobial agent and as a genome-engineering tool. However, current bacterial sgRNA activity models poorly predict SpCas9/sgRNA activity and are not generalizable, possibly because the underlying datasets used to train the models do not accurately measure SpCas9/sgRNA cleavage activity and cannot distinguish cleavage activity from toxicity. We solved this problem by using a two-plasmid positive selection system to generate high-quality biologically-relevant data that more accurately reports on SpCas9/sgRNA cleavage activity and that separates activity from toxicity. We developed a new machine transfer learning architecture (crisprHAL) that can be trained on existing datasets and that shows marked improvements in sgRNA activity prediction accuracy when transfer learning is used with small amounts of high-quality data. The crisprHAL model recapitulates known SpCas9/sgRNA-target DNA interactions and provides a pathway to a generalizable sgRNA bacterial activity prediction tool.

bioinformatics↗

bioRxiv2021

Telomere-to-telomere genome assembly of Phaeodactylum tricornutum

Phaeodactylum tricornutum is a marine diatom with a growing genetic toolbox available and is being used in many synthetic biology applications. While most of the genome has been assembled, the currently available genome assembly is not a completed telomere-to-telomere assembly. Here, we used Oxford Nanopore long reads to build a telomere-to-telomere genome for Phaeodactylum tricornutum. We developed a graph-based approach to extract all unique telomeres, and used this information to manually correct assembly errors. In total, we found 25 nuclear chromosomes that comprise all previously assembled fragments, in addition to the chloroplast and mitochondrial genomes. We found that chromosome 19 has filtered long-read coverage and a quality estimate that suggests significantly less haplotype sequence variation than the other chromosomes. This work improves upon the previous genome assembly and provides new opportunities for genetic engineering of this species, including creating designer synthetic chromosomes.

genomics↗

bioRxiv2021

Separation of cohorts on the basis of bacterial type IV conjugation systems identified from metagenomic assemblies

Conjugation enables the exchange of genetic elements throughout environments, including the human gut microbiome. Conjugative elements can carry and transfer clinically relevant metabolic pathways which makes precise identification of these systems in metagenomic samples clinically important. Here, we outline two distinct methods to identify conjugative systems in the human gut microbiome. We first show that conjugative systems exhibit strong population and age-level stratification. Additionally, we find that the total relative abundance of all conjugative systems present in a sample is not an informative metric to use, regardless of the method of identifying the systems. Finally, we demonstrate that the majority of assembled conjugative systems are not included within metagenomic bins, and that only a small proportion of the binned conjugative systems are included in "high-quality" metagenomic bins. Our findings highlight that conjugative systems differ between general North Americans and a cohort of North American pre-term infants, revealing a potential use as an age-related biomarker. Furthermore, conjugative systems can distinguish between other geographical-based cohorts. Our findings emphasize the need to identify and analyze conjugative systems outside of standard metagenomic binning pipelines. ImportanceThe human gut microbiome is increasingly being associated with human health outcomes through shotgun metagenomic sequencing. The usual approach of metagenomic-level analyses is to bin assembled sequences into approximations of bacterial genomes and perform further investigations on the resultant bins. Here, we show that type IV conjugative systems differ between age and geographically-based cohorts and that these systems are systematically excluded by binning algorithms. We suggest that analysis of type IV conjugative systems should be added to the current metagenomic analysis approaches as they contain much information that could explain differences between cohorts beyond those we investigated.

bioinformatics↗

bioRxiv2020

Complete and validated genomes from a metagenome

0.1The assembly and binning of metagenomically-assembled genomes (MAGs) using Illumina sequencing has improved the genomic characterization of unculturable communities. However, short-read-only metagenomic assemblies rarely result in completed genomes because of the difficulty assembling repetitive regions. Here, we present a strategy to complete and validate multiple MAGs from a bacterial community using a combination of short and ultra long reads (N50 > 25 kb). Our strategy is to perform an initial long read-only metagenomic assembly using metaFlye, followed by multiple rounds of polishing using both long and short reads. To validate the genomes, we verified that longs reads spanned the regions that were not supported by uniquely mapped paired-end Illumina sequences. We obtained multiple complete genomes from a naphthenic acid-degrading community, including one from the recently proposed Candidate Phyla Radiation. The majority of the population is represented by the assembled genomes; recruiting 63.77 % of Nanopore reads, and 64.38 % of Illumina reads. The pipeline we developed will enable researchers to validate genomes from metagenomic assemblies, increasing the quality of metagenomically assembled genomes through additional scrutiny.

bioinformatics↗

bioRxiv2019

The Impact of Uremia and Intestinal Dysbiosis on Hepatic Drug Metabolism in a Rat Model of Progressive Chronic Kidney Disease

Nonrenal clearance pathways such as drug metabolism are decreased in chronic kidney disease (CKD). Although the mechanism remains elusive, uremic toxin retention and an altered gut microbiota are suspected to influence cytochrome P450s (CYPs) contributing to the unpredictable pharmacokinetics in patients with CKD. We characterized dysbiosis and uremia in CKD to elucidate associations between CYP expression and CKD progression. Rats fed control or CKD-inducing adenine diet were subsequently studied at five time points over 42 days. CYP expression and activity were compared to alterations in the 1) plasma and liver metabolome and 2) gut bacterial microbiota. CYP3A2 and CYP2C11 were downregulated in CKD by [≥]76% (p<0.001) concurrently with or slightly prior to CKD onset as defined by serum creatinine. Metabolite profiles were altered prior to changes in the gut microbiota, and gut-derived uremic toxins including indoxyl sulfate, phenyl sulfate and 4-ethylphenyl sulfate correlated with CYP3A2 or CYP2C11 expression. Bacterial genera Turicibacter and Parabacteroides were identified as being characteristic of CKD. In conclusion, CYP3A2 and CYP2C11 are downregulated before dysbiosis and correlate with select uremic toxins.

pharmacology and toxicology↗