Biology subjects

Brown, C. T.

Publications and source records attributed to Brown, C. T..

At least 19 recordsLinked to original sources

bioRxiv2026

genomeRxiv: a genome-based identification web server that bridges the gap between taxonomy and strain typing

Precise identification depends on highly resolved classification. Therefore, whole genome-based strain typing schemes have been implemented for identification of the most important microbial human pathogens. However, there are no dedicated typing schemes for the majority of prokaryotic human, animal, and plant pathogens, or for non-pathogenic microbes, often limiting their identification to taxonomic classification tools that have the species as the smallest unit. To address this deficiency, we created the genomeRxiv web server which contains over 350,000 genomes and leverages the average nucleotide identity (ANI)-based Life Identification Number (LIN) approach, allowing users to identify prokaryotic genomes in real time from approximately genus-rank to near strain-level resolution. LINs are overlaid with circumscriptions of species and infraspecies classes, integrating taxonomy with strain typing. Metadata and distance trees are visualized to complement inferred phylogenetic relationships with epidemiological and ecological information. A command line tool is provided for users to upload k-mer sketches of unpublished genomes and associated metadata, facilitating epidemiological investigations without the need to share complete DNA sequences. In conclusion, genomeRxiv constitutes a much-needed resource for precise, rapid identification of the many prokaryotes not included in any pathogen-specific typing scheme.

bioinformatics↗

bioRxiv2026

How thermostable direct haemolysin (TDH) diverges from TDH-related haemolysin (TRH)? Reassessing haemolysins in Vibrio parahaemolyticus through functional and structural representation learning

Vibrio parahaemolyticus (Vp) is the major foodborne pathogen transmitted via shellfish products, which has posed significant threats to modern public health and resulted in significant economic damage to the seafood industry. Numerous studies have documented diverse aspects of Vp pathogenicity, among which thermostable direct haemolysin (TDH) and TDH related haemolysin (TRH) are considered as the major virulence biomarkers of Vp. Despite their established roles as key biomarkers, a systematic understanding of the divergence of TDH and TRH across sequence, structure, and function remains limited. In this study, a multi scale analysis of TDH and TRH was performed using publicly available (2131 and 99 records from NCBI and Uniprot database, respectively) amino acid sequence data combined with representation learning and structure prediction. Global alignment of curated TDH and TRH sequences revealed extensive, distributed mutations and clear separation between TDH and TRH at the amino acid level (percentage identity of between TDH and TRH ranging from 56.1 to 67.4%). In contrast, protein language model derived embeddings showed high global functional similarity while preserving distinct clustering patterns, indicating conserved core functionality alongside nuanced divergence echoed with the structural inference by AlphaFold. Importantly, modeling of mutation trajectories demonstrated that the transition from TDH to TRH is driven by accumulated, genome-wide residue changes rather than a small set of key mutations. Together, these results suggested that TDH and TRH represent functionally conserved yet evolutionarily diverged toxins driven by accumulated sequence variation throughout the full-length amino acid sequence. These accumulated point mutations lead to major structural difference: TRH forms an alpha-helical tail that TDH lacks, which suggests that TDH and TRH disrupt host membranes by different mechanisms despite their conserved core function, such as pore-forming and ion flux induction capability. These methods provided unprecedented detailed insights into the functional and structural properties of Vp haemolysin, offering critical information on how multi-dimensional variations in sequence might influence their role in Vp pathogenicity. Insights from this study reinforce the rationale for using Vp strains harboring tdh and trh genes in experimental design for environmental fitness investigation.

microbiology↗

bioRxiv2026

Dysregulated neutrophil extracellular trap formation is a novel driver of clonogenicity and therapeutic vulnerability in CMML

Chronic myelomonocytic leukemia (CMML) is an aggressive hematologic malignancy characterized by excess inflammatory signaling and clonal myeloproliferation. The relative contribution of neutrophils (PMNs) to the inflammatory milieu in CMML is poorly understood. In this study we sought to understand whether neutrophil extracellular trap (NET) formation, a key mediator of neutrophilic inflammation, is dysregulated in CMML and can be therapeutically targeted with a novel peptide inhibitor of NETosis called neonatal NET-inhibitory factor (nNIF). Here, we demonstrate that baseline NET formation is aberrantly increased in primary CMML PMNs transcriptionally primed for NETosis, and that soluble factors produced during CMML NET formation promote clonogenicity in CMML CD34+ hematopoietic cells matched to the same patient. Further, we show that nNIF and clinical agents under investigation in CMML effectively inhibit NETosis, warranting further study of NET inhibitory agents in this rare disease with limited treatment options.

cancer biology↗

bioRxiv2026

Compact adenine base editors to enable therapeutic rescue of Duchenne muscular dystrophy

Adenine base editors (ABEs) have emerged as a powerful gene-editing technology enabling precise and programmable adenine-to-guanine substitutions across the genome. However, their translation into in vivo therapeutics is limited by delivery challenges, as their size exceeds the packaging capacity of adeno-associated virus (AAV). Here, we report the discovery, structural characterization, and engineering of two compact, highly active ABEs built on novel deaminases and Cas9d nucleases, enabling all-in-one, single-vector AAV delivery. Applying these compact ABEs to Duchenne muscular dystrophy (DMD), we demonstrate efficient disruption of conserved splice-acceptor sites at dystrophin exons 45 and 51 in human skeletal muscle cells, enabling therapeutically relevant exon skipping. Together, these ABEs help expand the therapeutic reach of base editing towards diverse tissue types and disease targets.

bioengineering↗

bioRxiv2025

Comparative characterization of Cas12f orthologs reveals mechanistic features underlying enhanced genome editing efficiency

Miniature CRISPR-Cas12f nucleases are attractive candidates for therapeutic genome editing owing to their compact size and compatibility with adeno-associated virus (AAV) delivery. However, editing efficiencies in mammalian cells are lower than those of larger systems such as Cas12a and SpCas9. The extensive phylogenetic diversity of Cas12f suggests unexplored mechanistic variation with the potential for optimization. Here, we characterize a naturally occurring Cas12f ortholog discovered through metagenomics, Cas12f-MG119-28, which supports robust genome editing in human cells. Through structural, biochemical, and kinetic analyses, we compare Cas12f-MG119-28 with two recently described orthologs, Oscillibacter sp. Cas12f (OsCas12f) and Ruminiclostridium herbifermentans Cas12f (RhCas12f). These orthologs present divergent architectures and regulatory features governing PAM recognition, gRNA binding, dimerization, and DNA cleavage. Notably, Cas12f-MG119-28 achieves efficient R-loop formation via a stable dimer interface and a naturally optimized guide RNA. These discoveries elucidate key mechanistic determinants of Cas12f activity and may offer a framework for engineering compact genome editors with therapeutic potential.

biochemistry↗

bioRxiv2025

Pathogenic potential prediction of Vibrio parahaemolyticus by using pangenome data with high performance machine learning algorithms

The presence of Vibrio parahaemolyticus (Vp) at various stages of seafood production has adversely affected public health and threatened the sustainability of the industry. To address the critical public health threats posed by this prevalent seafood-borne pathogen, this research applied advanced machine learning (ML) and deep learning (DL) algorithms to predict the pathogenic potential of Vp using pangenome data. Utilizing comprehensive pangenomic assemblies and sophisticated ML/DL models, this study achieved robust and precise pathogenic potential prediction of Vp based on source attribution, which provides a novel reliable diagnostic tool facilitating conventional serotyping and virulence gene combination approaches. Based on results, non-core regions in Vp pangenome exhibited useful signals ML models can utilize in pathogenic potential determination process. Tree-based ensemble learning methods (Random Forest and Gradient Boosting Trees) have shown the distinguished performance with AUC score 0.97 based on selected pangenome matrix. Furthermore, Convolutional Neural Network successfully predicted the pathogenic potential of isolates with slightly better performance with AUC score 0.98 based on full pangenome. Critical biological insights revealing critical pathogenic potential-associated genes were retrieved from established ML/DL models: the gene feature weight analysis from Random Forest revealed the importance of accessory genes during Vp evolution (similarly highlighted by Gram-cam analysis of Convolutional Neural Network), which provided potential guidance for future research direction.

microbiology↗

bioRxiv2024

DNA targeting by compact Cas9d and its resurrected ancestor

The type II-A CRISPR effector SpCas9 has gained widespread popularity as an efficient and programmable genome editing tool. However, much remains to be known about novel compact variants that may overcome some limitations of current systems1,2. Recently, alternative CRISPR-Cas systems with highly compact nucleases capable of genome editing in mammalian cells have been discovered through metagenomic analysis of uncultivated microbes, including Cas9d (a type II-D CRISPR-Cas effector)3. Here, we report the cryo-EM structures of a Cas9d nuclease (747 amino acids in length) in multiple functional states, revealing a stepwise process of DNA targeting involving a conformational switch in a REC2 domain insertion. Our structures provide insights into the intricately folded guide RNA which acts as a structural scaffold to anchor small, flexible protein domains and facilitate DNA target recognition. We find that the sgRNA can be truncated by up to [~]25% yet still retain activity in vivo. We also show that despite preferentially targeting an NGG PAM, Cas9d exhibits a unique mechanism for PAM recognition. Finally, we identify the first Cas9d smaller than 800 amino acids exhibiting robust nuclease activity in mammalian cells. Using ancestral sequence reconstruction, we demonstrate that it is possible to generate compact nucleases capable of efficient genome editing by expanding the diversity of Cas9d families. Collectively, our results provide mechanistic insights into the evolution and DNA targeting of diverse type II CRISPR-Cas systems, providing a molecular blueprint for future rational re-engineering of minimal RNA-guided DNA nucleases.

biochemistry↗

bioRxiv2022

Sourmash Branchwater Enables Lightweight Petabyte-Scale Sequence Search

We introduce branchwater, a flexible and fast petabase-scale search for the 767,000 public metagenomes presently in the NCBI Sequence Read Archive. Our search is based on the FracMinHash k-mer sketching technique and can search all public metagenomes with 1000 query genomes in approximately 36 hours using 50 GB of RAM and 32 threads. Branchwater is a Rust-based multithreading front-end built on top of the sourmash library. We provide biological use cases, examine performance, and discuss design and performance considerations.

bioinformatics↗

bioRxiv2022

Biogeographic Distribution of Five Antarctic Cyanobacteria Using Large-Scale k-mer Searching with sourmash branchwater

Cyanobacteria form diverse communities and are important primary producers in Antarctic freshwater environments, but their geographic distribution patterns in Antarctica and globally are still unresolved. There are however few genomes of cultured cyanobacteria from Antarctica available and therefore metagenome-assembled genomes (MAGs) from Antarctic cyanobacteria microbial mats provide an opportunity to explore distribution of uncultured taxa. These MAGs also allow comparison with metagenomes of cyanobacteria enriched communities from a range of habitats, geographic locations, and climates. However, most MAGs do not contain 16S rRNA gene sequences, making a 16S rRNA gene-based biogeography comparison difficult. An alternative technique is to use large-scale k-mer searching to find genomes of interest in public metagenomes. This paper presents the results of k-mer based searches for 5 Antarctic cyanobacteria MAGs from Lakes Fryxell and Lake Vanda, assigned the names Phormidium pseudopriestleyi, a Microcoleus, a Leptolyngbya, a Pseudanabaena, and a Neosynechococcus (Lumian et al., 2021, Lumian et al., 2022, in prep.) in 498,942 unassembled metagenomes from the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA). The Microcoleus MAG was found in a wide variety of environments, P. pseudopriestleyi was found in environments with challenging conditions, the Neosynechococcus was only found in Antarctica, and the Leptolyngbya and Pseudanabaena MAGs were found in Antarctic and other cold environments. The findings based on metagenome matches and global comparisons suggest that these Antarctic cyanobacteria have distinct distribution patterns ranging from locally restricted to global distribution across the cold biosphere and other climatic zones.

microbiology↗

bioRxiv2022

Meta-analysis of metagenomes via machine learning and assembly graphs reveals strain switches in Crohn's disease

Microbial strains have closely related genomes but may have different phenotypes in the same environment. Shotgun metagenomic sequencing can capture the genomes of all strains present in a community but strain-resolved analysis from shotgun sequencing alone remains difficult. We developed an approach to identify and interrogate strain-level differences in groups of metagenomes. We use this approach to perform a meta-analysis of stool microbiomes from individuals with and without inflammatory bowel disease (IBD; Crohns disease, ulcerative colitis; n = 605), a disease for which there are not specific microbial biomarkers but some evidence that microbial strain variation may stratify by disease state. We first developed a machine learning classifier based on compressed representations of complete metagenomes (FracMinHash sketches) and identified genomes that correlate with IBD subtype. To rescue variation that may not have been present in the genomes, we then used assembly graph genome queries to recover strain variation for correlated genomes. Lastly, we developed a novel differential abundance framework that works directly on the assembly graph to uncover all sequence variants correlated with IBD. We refer to this approach as dominating set differential abundance analysis and have implemented it in the spacegraphcats software package. Using this approach, we identified five bacterial strains that are associated with Crohns disease. Our method captures variation within the entire sequencing data set, allowing for discovery of previously hidden disease associations.

bioinformatics↗

bioRxiv2022

Protein k-mers enable assembly-free microbial metapangenomics

An estimated 2 billion species of microbes exist on Earth with orders of magnitude more strains. Microbial pangenomes are created by aggregating all genomes of a single clade and reflect the metabolic diversity of groups of organisms. As de novo metagenome analysis techniques have matured and reference genome databases have expanded, metapangenome analysis has risen in popularity as a tool to organize the functional potential of organisms in relation to the environment from which those organisms were sampled. However, the reliance on assembly and binning or on reference databases often leaves substantial portions of metagenomes unanalyzed, thereby underestimating the functional potential of a community. To address this challenge, we present a method for metapangenomics that relies on amino acid k-mers (kaa-mers) and metagenome assembly graph queries. To enable this method, we first show that kaa-mers estimate pangenome characteristics and that open reading frames can be accurately predicted from short shotgun sequencing reads using the previously developed tool orpheum. These techniques enable pangenomics to be performed directly on short sequencing reads. To enable metapangenome analysis, we combine these approaches with compact de Bruijn assembly graph queries to directly generate sets of sequencing reads for a specific species from a metagenome. When applied to stool metagenomes from an individual receiving antibiotics over time, we show that these approaches identify strain fluctuations that coincide with antibiotic exposure.

bioinformatics↗

bioRxiv2022

Candidatus Nealsonbacteria (OD1) in a methanogenic benzene-degrading enrichment culture is likely an ectosymbiotic biomass recycler

The Candidate Phyla Radiation (CPR) is a very large group of bacteria with no pure culture representatives, first discovered by metagenomic analyses. Within the CPR, candidate phylum Parcubacteria (previously referred to as OD1) within the candidate superphylum Patescibacteria is prevalent in anoxic sediments and groundwater. Previously, we had identified a specific member of the Parcubacteria (referred to as DGGOD1a) as an important member of a methanogenic benzene-degrading consortium. Phylogenetic analyses herein place DGGOD1a within the Candidate clade Nealsonbacteria. Because of its persistence over many years, we hypothesized that Ca. Nealsonbacteria DGGOD1a must serve an important role in sustaining anaerobic benzene metabolism in the consortium. To try to identify its growth substrate, we amended the culture with a variety of defined compounds (pyruvate, acetate, hydrogen, DNA, phospholipid), as well as crude culture lysate and three subfractions thereof. We observed the greatest (10 fold) increase in the absolute abundance of Ca. Nealsonbacteria DGGOD1a only when the consortium was amended with crude cell lysate. These results implicate Ca. Nealsonbacteria in biomass recycling. Fluorescent in situ hybridization and cryogenic transmission electron microscope images revealed that Ca. Nealsonbacteria DGGOD1a cells were attached to larger archaeal Methanothrix cells. This apparent epibiont lifestyle was supported by metabolic predictions from a manually curated complete genome. This is one of the first examples of bacterial-archaeal episymbiosis and may be a feature of other Ca. Nealsonbacteria found in anoxic environments.

microbiology↗

bioRxiv2022

Evaluation of taxonomic profiling methods for long-read shotgun metagenomic sequencing datasets

BackgroundLong-read shotgun metagenomic sequencing is gaining in popularity and offers many advantages over short-read sequencing. The higher information content in long reads is useful for a variety of metagenomics analyses, including taxonomic classification and profiling. The development of long-read specific tools for taxonomic classification is accelerating, yet there is a lack of information regarding their relative performance. Here, we perform a critical benchmarking study using 11 methods, including five methods designed specifically for long reads. We applied these tools to several mock community datasets generated using Pacific Biosciences (PacBio) HiFi or Oxford Nanopore Technology (ONT) sequencing, and evaluated their performance based on read utilization, detection metrics, and relative abundance estimates. ResultsOur results show that long-read classifiers generally performed best. Several short-read classification and profiling methods produced many false positives (particularly at lower abundances), required heavy filtering to achieve acceptable precision (at the cost of reduced recall), and produced inaccurate abundance estimates. By contrast, two long-read methods (BugSeq, MEGAN-LR & DIAMOND) and one generalized method (sourmash) displayed high precision and recall without any filtering required. Furthermore, in the PacBio HiFi datasets these methods detected all species down to the 0.1% abundance level with high precision. Some long-read methods, such as MetaMaps and MMseqs2, required moderate filtering to reduce false positives to resemble the precision and recall of the top-performing methods. We found read quality affected performance for methods relying on protein prediction or exact k-mer matching, and these methods performed better with PacBio HiFi datasets. We also found that long-read datasets with a large proportion of shorter reads (<2kb length) resulted in lower precision and worse abundance estimates, relative to length-filtered datasets. Finally, for classification methods, we found that the long-read datasets produced significantly better results than short-read datasets, demonstrating clear advantages for long-read metagenomic sequencing. ConclusionsOur critical assessment of available methods provides best-practice recommendations for current research using long reads and establishes a baseline for future benchmarking studies.

bioinformatics↗

bioRxiv2022

Lightweight compositional analysis of metagenomes with FracMinHash and minimum metagenome covers

The identification of reference genomes and taxonomic labels from metagenome data underlies many microbiome studies. Here we describe two algorithms for compositional analysis of metagenome sequencing data. We first investigate the FracMinHash sketching technique, a derivative of modulo hash that supports Jaccard containment estimation between sets of different sizes. We implement FracMinHash in the sourmash software, evaluate its accuracy, and demonstrate large-scale containment searches of metagenomes using 700,000 microbial reference genomes. We next frame shotgun metagenome compositional analysis as the problem of finding a minimum collection of reference genomes that "cover" the known k-mers in a metagenome, a minimum set cover problem. We implement a greedy approximate solution using FracMinHash sketches, and evaluate its accuracy for taxonomic assignment using a CAMI community benchmark. Finally, we show that the minimum metagenome cover can be used to guide the selection of reference genomes for read mapping. sourmash is available as open source software under the BSD 3-Clause license at github.com/dib-lab/sourmash/.

bioinformatics↗

bioRxiv2021

Making Common Fund data more Findable: Catalyzing a Data Ecosystem

The Common Fund Data Ecosystem (CFDE) has created a flexible system of data federation that enables users to discover datasets from across the U.S. National Institutes of Health Common Fund without requiring that data owners move, reformat, or rehost those data. The CFDEs federation system is centered on a catalog that ingests metadata from individual Common Fund Programs Data Coordination Centers (DCCs) into a uniform metadata model that can then be indexed and searched from a centralized portal. This uniform Crosscut Metadata Model (C2M2) supports the wide variety of data types and metadata terms used by the individual DCCs and is designed to enable easy expansion to accommodate new data types. We describe its use to ingest and index data from ten DCCs.

bioinformatics↗

bioRxiv2021

Single-cell transcriptomics for the 99.9% of species without reference genomes

Single-cell RNA-seq (scRNA-seq) is a powerful tool for cell type identification but is not readily applicable to organisms without well-annotated reference genomes. Of the approximately 10 million animal species predicted to exist on Earth, >99.9% do not have any submitted genome assembly. To enable scRNA-seq for the vast majority of animals on the planet, here we introduce the concept of "k-mer homology," combining biochemical synonyms in degenerate protein alphabets with uniform data subsampling via MinHash into a pipeline called Kmermaid. Implementing this pipeline enables direct detection of similar cell types across species from transcriptomic data without the need for a reference genome. Underpinning Kmermaid is the tool Orpheum, a memory-efficient method for extracting high-confidence protein-coding sequences from RNA-seq data. After validating Kmermaid using datasets from human and mouse lung, we applied Kmermaid to the Chinese horseshoe bat (Rhinolophus sinicus), where we propagated cellular compartment labels at high fidelity. Our pipeline provides a high-throughput tool that enables analyses of transcriptomic data across divergent species transcriptomes in a genome- and gene annotation-agnostic manner. Thus, the combination of Kmermaid and Orpheum identifies cell type-specific sequences that may be missing from genome annotations and empowers molecular cellular phenotyping for novel model organisms and species.

bioinformatics↗

bioRxiv2021

Mapping global shifts in Saccharomyces cerevisiae gene expression across asynchronous time trajectories with diffusion maps

Grapes grown in a particular geographic region often produce wines with consistent characteristics, suggesting there are site-specific factors driving recurrent fermentation outcomes. However, our understanding of the relationship between site-specific factors, microbial metabolism, and wine fermentation outcomes are not well understood. Here, we used differences in Saccharomyces cerevisiae gene expression as a biosensor for differences among Pinot noir fermentations from 15 vineyard sites. We profiled time series gene expression patterns of primary fermentations, but fermentations proceeded at different rates, making analyzes of these data with conventional differential expression tools difficult. This led us to develop a novel approach that combines diffusion mapping with continuous differential expression analysis. Using this method, we identified vineyard specific deviations in gene expression, including changes in gene expression correlated with the activity of the non-Saccharomyces yeast Hanseniaspora uvarum, as well as with initial nitrogen concentrations in grape musts. These results highlight novel relationships between site-specific variables and Saccharomyces cerevisiae gene expression that are linked to repeated wine fermentation outcomes. In addition, we demonstrate that our analysis approach can extract biologically relevant gene expression patterns in other contexts (e.g., hypoxic response of Saccharomyces cerevisiae), indicating that this approach offers a general method for investigating asynchronous time series gene expression data. ImportanceWhile it is generally accepted that foods, in particular wine, possess sensory characteristics associated with or derived from their place of origin, we lack knowledge of the biotic and abiotic factors central to this phenomenon. We have used Saccharomyces cerevisiae gene expression as a biosensor to capture differences in fermentations of Pinot noir grapes from 15 vineyards across two vintages. We find that gene expression by non-Saccharomyces yeasts and initial nitrogen content in the grape must correlates with differences in gene expression among fermentations from these vintages. These findings highlight important relationships between site-specific variables and gene expression that can be used to understand, or possibly modify, wine fermentation outcomes. Our work also provides a novel analysis method for investigating asynchronous gene expression data sets that is able to reveal both global shifts and subtle differences in gene expression due to varied cell - environment interactions.

microbiology↗

bioRxiv2020

MQF and buffered MQF: Quotient filters for efficient storage of k-mers with their counts and metadata

BackgroundSpecialized data structures are required for online algorithms to efficiently handle large sequencing datasets. The counting quotient filter (CQF), a compact hashtable, can efficiently store k-mers with a skewed distribution. ResultHere, we present the mixed-counters quotient filter (MQF) as a new variant of the CQF with novel counting and labeling systems. The new counting system adapts to a wider range of data distributions for increased space efficiency and is faster than the CQF for insertions and queries in most of the tested scenarios. A buffered version of the MQF can offload storage to disk, trading speed of insertions and queries for a significant memory reduction. The labeling system provides a flexible framework for assigning labels to member items while maintaining good data locality and a concise memory representation. These labels serve as a minimal perfect hash function but are ~10 fold faster than BBhash, with no need to re-analyze the original data for further insertions or deletions. ConclusionThe MQF is a flexible and efficient data structure that extends our ability to work with high throughput sequencing data.

bioinformatics↗