C. Titus Brown

Objective: Despite most often used in research and medicine, it is unclear how well blood metabolite levels can reflect metabolic changes in tissues. Methods: We used untargeted metabolomic data from plasma, liver, gastrocnemius muscle, kidney, inguinal fat, and gonadal fat tissues from mice treated with lifespan-extending interventions: caloric restriction, rapamycin, canagliflozin, 17-α estradiol, and acarbose. Results: For each tissue, about 37% of the identified metabolites were also found in plasma and used for comparative analyses. We found that plasma metabolites do not primarily reflect the metabolome of a single tissue type. Caloric restriction caused the highest proportion of significantly altered metabolites detected in both organs and blood. Unsaturated triacylglycerols, diacylglycerols, amino acid-derivatives, and sphingomyelins had the most concordant overlaps between four tissues and plasma, followed by carbohydrates with three tissue/plasma intersections. Concordant plasma/tissue changes were found more pronounced in male mice than in female mice. Surprisingly, many compounds that responded similarly to lifespan-extending treatments in tissues and in plasma originated from dietary compounds, specifically, ergothioneine, DHA-containing fats, and diacylglycerides. Ergothioneine, previously associated with healthy aging in humans, showed concordant treatment responses in blood and all tissues except gonadal fat. DHA-containing fats showed consistent regulation between blood and inguinal and gonadal adipose tissue. The kidneys showed coherent trends with blood metabolite levels for LPC 15:0, LPC 17:0, and pinitol. Under treatments with strong life-extending effects, 1,5-anhydro-glucitol blood levels were co-regulated with both liver and kidney levels. Conclusions: Overall, plasma can only serve as limited proxy for tissue metabolism. Yet, several potential blood biomarkers were discovered as concordant in plasma and tissues in lifespan-extending interventions.

The rapid rate of virus discovery renders manual curation by taxonomy experts increasingly impractical, creating a need for reliable software that can reproducibly assign viral contigs to taxa at all fifteen ranks of the virus taxonomy. We led an open community challenge for the computational taxonomic classification of viruses and assembled a dataset of virus sequences combining expert-curated and metagenomic sequences. Seventeen teams contributed a total of thirty-four automated, fully reproducible classification pipelines. Most tools correctly assigned viruses belonging to established species, genera, or families, but viruses that are unclassified at those lower ranks remain challenging. This study provides datasets, open-source software, novel approaches, and recommendations to benchmark computational taxonomic classification of viruses, and support organizing the many viruses discovered in big omics data.

Background: High levels of zinc oxide (ZnO) and copper sulfate are widely used as alternative growth promoters in postweaning pig diet. However, excessive exposure to these metals may drive co-selection for heavy metal (HMR) and antibiotic resistance (AMR). Nursery diets also contain abundant iron to offset the low bioavailability of plant-derived iron, yet how dietary iron influence gut dysbiosis and microbial resistance in postweaning pigs remains unclear. This exploratory study examined the effects of dietary iron and metal-based growth promoters on the fecal resistome of postweaning pigs using shotgun metagenomics and whole-genome sequencing (WGS). Methods: Fifty weanling pigs were stratified and randomly assigned to five dietary treatments for 24 d. Experimental diets included a control diet (Con) containing 25, 139, and 141 mg/kg of Cu, Fe, and Zn, respectively, a low-iron diet (LFe, 19 mg Fe/kg), a high-iron diet (HFe, 1,219 mg Fe/kg), a high-copper diet (HCu, 257 mg Cu/kg), and a high-zinc diet (HZn, 2,631 mg Zn/kg, including 2,490 mg Zn/kg from ZnO). All pigs were orally administered with F18 enterotoxigenic Escherichia coli (ETEC) on d 13–16. Metagenome sequencing were performed on d 24 fecal DNA ( n = 24) to identify HMR genes (BacMet Predicted database) and AMR genes (CARD database). Functional annotation was performed using HUMAnN3. Whole genome sequencing (WGS) was conducted on 120 E. coli isolates from fecal cultures on d 1, 12, and 24, and AMR and virulence genes were identified from contig assemblies using ABRicate. Results: Dietary metal treatments significantly altered β-diversity of HMR genes compared with Con, with HZn differing from both HCu and LFe ( P < 0.05). Fecal iron levels correlated with sodB (ρ = 0.64, P = 0.075), an iron-containing superoxide dismutase , while fecal copper levels correlated with pcoC (ρ = 0.66, P = 0.075), a plasmid-mediated copper resistance gene. Across metagenomes, 172 AMR genes were identified, dominated by glycopeptide and tetracycline resistance. While dietary iron had minimal effects on fecal AMR profile, HZn induced the largest shifts in resistome, including increases of ant(9)-la , conferring aminoglycoside resistance on mobile genetic elements, and adeF , encoding a multidrug efflux pump ( P < 0.05). Functional profiling revealed enrichment of carbohydrate metabolism pathways in HZn group ( P < 0.05). WGS of E. coli isolates showed distinct AMR profiles under HZn on d 24 and distinct virulence profile under LFe on d 12, exhibiting increased prevalence of exotoxin and T3SS genes ( P < 0.05). Conclusion: Dietary iron restriction enhanced E. coli virulence genes, whereas excessive ZnO induced the most pronounced changes in the gut resistome and microbial metabolism, highlighting a risk for AMR co-selection and marked influence on gut microbiota.

Comparisons of long-read and short-read (meta)genome assemblies typically show that short-read sequence assemblies are less error-prone, but struggle to assemble complicated genome regions (e.g. repeats) compared to long-read sequence assemblies. Accurate metagenome assembly is especially challenging in diverse environments, such as soil, and long-read sequencing has been shown to improve assembly. Here, we use metagenomic data with paired long-read and short-read sequences to identify specific factors that impact genome assembly and assess their relative importance in a natural soil community. Our analysis suggests that low coverage and high sequence diversity are the two main factors leading to misassemblies in short-read data, and many of these “missed” regions tend to be variable parts of the genome, such as integrated viruses or defense system islands. Taken together, our results demonstrate that short-read metagenomes can possibly underestimate the diversity of these genome regions and that long-read sequencing can complement short-read metagenomes by improving assembly contiguity and the recovery of variable regions.

Background: In the rapidly evolving landscape of veterinary healthcare, integrating machine learning (ML) clinical decision-making tools with electronic health records (EHRs) promises to improve diagnostic accuracy and patient care. However, the seamless integration of ML classifiers into existing EHR systems in veterinary medicine is often hindered by the inherent rigidity of these systems or by the limited availability of IT resources to implement the modifications necessary for ML compatibility. Results: Anna is a standalone analytics platform that can host ML classifiers and interfaces with EHR systems to provide classifier predictions for laboratory data in real-time. Following a request from the EHR system, Anna retrieves patient-specific data from the EHR system, merges diagnostic test results based on user-defined temporal criteria and returns predictions for all available classifiers for display in real-time. Anna was developed in Python and is freely available. Because Anna is a stand-alone platform, it does not require substantial modifications to the existing EHR, allowing for easy integration into existing computing infrastructure. To demonstrate Anna’s versatility, we implemented three previously published ML classifiers to predict a diagnosis of hypoadrenocorticism, leptospirosis, or a portosystemic shunt in dogs. Conclusion: Anna is an open-source tool designed to improve the accessibility of ML classifiers for the veterinary community. Its flexible architecture supports the integration of classifiers developed in various programming languages and with diverse environment requirements. Anna facilitates rapid prototyping, enabling researchers and developers to deploy ML classifiers quickly without modifications to the existing EHR system. Anna could drive broader adoption of ML in veterinary practices, ultimately enhancing diagnostic capabilities and patient outcomes.

Understanding genetic variation is necessary to unravel the complexities of evolution and diverse traits across species. Short genetic variants (<50 bp in length) represent key genomic variations that play a crucial role in shaping the genetic landscape of populations. Human short genetic variant databases are often considered the gold standard for variant repositories, and this review compares them with variant databases created for various animal species. This review examines the methodologies, data integration, and various applications that differ or align between human and animal databases. The goal is to identify challenges in leveraging genetic variation across species, recommend strategies for overcoming these challenges, and suggest future directions for research and database development.

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.

Background: Long-read sequencing (LRS) enables high-quality structural variant (SV) discovery. SV genotypers utilize these precise call sets to improve the recall and precision of genotyping in short-read sequencing (SRS) samples. With the extensive growth in publicly available SRS datasets, it is now possible to calculate accurate population allele frequencies of SVs. However, reprocessing hundreds of terabytes of raw SRS data to genotype new variants is impractical for population-scale studies, a computational challenge known as the N+1 problem (i.e., the challenge of re-genotyping an entire cohort for one additional variant). Overcoming this computational bottleneck is essential for analyzing new SVs from the growing number of pangenomes, public genomic databases, and pathogenic variant discovery studies. Results: We propose the Great Genotyper, a population-scale genotyping workflow to address the N+1 problem. Applied to a human dataset, the workflow begins by preprocessing 4.2k short-read samples of a total of 183 TB raw data to create an 867-GB Counting Colored de Bruijn Graph (CCDG). The Great Genotyper uses this CCDG to genotype a list of phased or unphased variants, leveraging the CCDG population information to increase both precision and recall. The Great Genotyper offers the same accuracy as the state-of-the-art genotypers while achieving unprecedented performance. It took about 100 hours to genotype 4.5M variants across the 4.2k samples and calculate their population allele frequencies using 1 server with 32 cores and 145 GB of memory. The Great Genotyper opens the door to new ways to study SVs. For example, using the premade index, we demonstrate the Great Genotyper's application in finding pathogenic variants by calculating accurate allele frequency for novel SVs. Also, we used it to create a 4k reference panel by genotyping variants from the Human Pangenome Reference Consortium (HPRC). The new reference panel allows for SV imputation from genotyping microarrays. Moreover, we genotype the human GWAS Catalog and merge its variants with the 4k reference panel. We show 6,253 events of high linkage between the HPRC’s SVs and nearby GWAS single-nucleotide polymorphisms, which can help in interpreting the effect of these SVs on gene functions. This analysis uncovers the detailed haplotype structure of the human fibrinogen locus and revives the pathogenic association of a 28-bp insertion in the FGA gene with thromboembolic disorders. Conclusion: The Great Genotyper solves the N+1 problem for population-scale genotyping of small and structural variants, offering both high accuracy and efficiency. Its ability to rapidly re-genotype large cohorts paves the road for several new studies of SVs.

In the rapidly evolving landscape of veterinary healthcare, integrating machine learning (ML) clinical decision-making tools with electronic health records (EHRs) promises to improve diagnostic accuracy and patient care. However, the seamless integration of ML classifiers into existing EHRs in veterinary medicine is frequently hindered by the rigidity of EHR systems or the limited availability of IT resources. To address this shortcoming, we present Anna, a freely-available software solution that provides ML classifier results for EHR laboratory data in real-time.

1 Long-read sequencing (LRS) enables variant calling of high-quality structural variants (SVs). Genotypers of SVs utilize these precise call sets to increase the recall and precision of genotyping in short-read sequencing (SRS) samples. With the extensive growth in availabilty of SRS datasets in recent years, we should be able to calculate accurate population allele frequencies of SV. However, reprocessing hundreds of terabytes of raw SRS data to genotype new variants is impractical for population-scale studies, a computational challenge known as the N+1 problem. Solving this computational bottleneck is necessary to analyze new SVs from the growing number of pangenomes in many species, public genomic databases, and pathogenic variant discovery studies. To address the N+1 problem, we propose The Great Genotyper, a population genotyping workflow. Applied to a human dataset, the workflow begins by preprocessing 4.2K short-read samples of a total of 183TB raw data to create an 867GB Counting Colored De Bruijn Graph (CCDG). The Great Genotyper uses this CCDG to genotype a list of phased or unphased variants, leveraging the CCDG population information to increase both precision and recall. The Great Genotyper offers the same accuracy as the state-of-the-art genotypers with the addition of unprecedented performance. It took 100 hours to genotype 4.5M variants in the 4.2K samples using one server with 32 cores and 145GB of memory. A similar task would take months or even years using single-sample genotypers. The Great Genotyper opens the door to new ways to study SVs. We demonstrate its application in finding pathogenic variants by calculating accurate allele frequency for novel SVs. Also, a premade index is used to create a 4K reference panel by genotyping variants from the Human Pangenome Reference Consortium (HPRC). The new reference panel allows for SV imputation from genotyping microarrays. Moreover, we genotype the GWAS catalog and merge its variants with the 4K reference panel. We show 6.2K events of high linkage between the HPRC’s SVs and nearby GWAS SNPs, which can help in interpreting the effect of these SVs on gene functions. This analysis uncovers the detailed haplotype structure of the human fibrinogen locus and revives the pathogenic association of a 28 bp insertion in the FGA gene with thromboembolic disorders.