Biology subjects

Miller, I. J.

Publications and source records attributed to Miller, I. J..

7 recordsLinked to original sources

bioRxiv2026

Rapid Peptide Mapping of Monoclonal Antibodies with Direct Infusion Mass Spectrometry

Peptide mapping is a critical tool for characterizing biotherapeutic proteins and is essential for the development of monoclonal antibody drugs. Here we describe a new direct infusion technology that streamlines peptide mapping data collection and analysis, accelerating the method by up to 100-fold. This method, which we term RaPiD-mAb-MS, combines high-throughput plate-based sample preparation with direct infusion mass spectrometry analysis. RaPiD-mAb-MS allows analysis of 96 samples within [~] 1.5 to 2 hours, routinely achieves >95% sequence coverage, and has been successfully applied to 28 unique antibodies and over 2,000 samples. Here we demonstrate that RaPiD-mAb-MS detects and quantifies oxidation, deamidation, isomerization, glycosylation, and sequence variants with results comparable to conventional LC-MS based methods in a fraction of the time. Further, by eliminating chromatography, data analysis is greatly streamlined and simplified. By allowing for the collection of [~] 1,000 peptide maps per day, RaPiD-mAb-MS is positioned to accelerate all phases of antibody-based drug discovery & development and sets the stage for collection of massive datasets that would allow artificial intelligent prediction of optimal antibody variants and formulations.

biochemistry↗

bioRxiv2024

Deep Learning Predicts Non-Normal Peptide FAIMS Mobility Distributions Directly from Sequence

Peptide ion mobility adds an extra dimension of separation to mass spectrometry-based proteomics. The ability to accurately predict peptide ion mobility would be useful to expedite assay development and to discriminate true answers in database search. There are methods to accurately predict peptide ion mobility through drift tube devices, but methods to predict mobility through high-field asymmetric waveform ion mobility (FAIMS) are underexplored. Here, we successfully model peptide ions FAIMS mobility using a multi-label multi-output classification scheme to account for non-normal transmission distributions. We trained two models from over 100,000 human peptide precursors: a random forest and a long-term short-term memory (LSTM) neural network. Both models had different strengths, and the ensemble average of model predictions produced higher F2 score than either model alone. Finally, we explore cases where the models make mistakes and demonstrate predictive performance of F2=0.66 (AUROC=0.928) on a new test dataset of nearly 40,000 different E. coli peptide ions. The deep learning model is easily accessible via https://faims.xods.org.

bioinformatics↗

bioRxiv2023

Elevated Expression of srp RiPPs Across Bacterial Phyla in Marine Sponges

We investigated transcriptional activity, at a genome-resolved level, of bacterial communities in two S. officinalis and two I. felix sponges, both of which are considered high microbial abundance (HMA) sponges that harbor similar bacterial communities. Overlap of metatranscriptional data on genome-resolved metagenomic data showed that genome bins classified within the Chloroflexota and Poribacteria phyla were the most abundant and transcriptionally active. However, abundant bins in general were not the most transcriptionally active, instead less abundant bins of the same phyla were most active indicating that numerical dominance does not imply transcriptional dominance. We found that although some primary metabolic functions appeared upregulated, they were not obviously attributable to any particular bacterial species. However, assessment of transcription levels of biosynthetic gene clusters (BGCs) encoding secondary metabolites revealed a high transcription of ribosomally synthesized and post-translationally modified peptides (RiPPs) in genome bins across diverse bacterial phyla, most of which were likely srp RiPPs encoding brominated azol(in)e-containing compounds. However, the ecological role of these compounds remains elusive. IMPORTANCEMarine sponges and bacteria have formed close associations over several million years with many bacteria performing specialized functions within their sponge host. Previous studies have either assessed the genomes of a given sponge or the expression profile for a sponge holobiont as a whole. Here, we present the first genome-resolved transcriptomic study which gives us a snapshot of the transcriptional activity of individual bacteria in the context of four different sponge holobionts. Using this approach we found that the numerically dominant bacteria were not the most transcriptionally active and that relatively high expression of a ubiquitous biosynthetic gene cluster was evident in many different bacterial phyla in all four sponge samples.

ecology↗

bioRxiv2021

Systems genetics uncovers microbe-lipid-host connections in the murine gut

The molecular bases of how host genetic variation impact gut microbiome remain largely unknown. Here, we used a genetically diverse mouse population and systems genetics strategies to identify interactions between molecular phenotypes, including microbial functions, intestinal transcripts and cecal lipids that influence microbe-host dynamics. Quantitative trait loci (QTL) analysis identified genomic regions associated with variations in bacterial taxa, bacterial functions, including motility, sporulation and lipopolysaccharide production, and levels of bacterial- and host-derived lipids. We found overlapping QTL for the abundance of Akkermansia muciniphila and cecal levels of ornithine lipids (OL). Follow-up studies revealed that A. muciniphila is a major source of these lipids in the gut, provided evidence that OL have immunomodulatory effects and identified intestinal transcripts co-regulated with these traits. Collectively, these results suggest that OL are key players in A. muciniphila-host interactions and support the role of host genetics as a determinant of responses to gut microbes.

genetics↗

bioRxiv2019

Real time health monitoring through urine metabolomics

Current healthcare practices are reactive and based on limited physiological information collected months or years apart. By enabling patients and healthy consumers access to continuous measurements of health, wearable devices and digital medicine stand to realize highly personalized and preventative care. However, most current digital technologies provide information on a limited set of physiological traits, such as heart rate and step count, which alone offer little insight into the etiology of most diseases. Here we propose to integrate data from biohealth smartphone applications with continuous metabolic phenotypes derived from urine metabolites. This combination of molecular phenotypes with quantitative measurements of lifestyle reflect the biological consequences of human behavior in real time. We present data from an observational study involving two healthy subjects and discuss the challenges, opportunities, and implications of integrating this new layer of physiological information into digital medicine. Though our dataset is limited to two subjects, our analysis (also available through an interactive web-based visualization tool) provides an initial framework to monitor lifestyle factors, such as nutrition, drug metabolism, exercise, and sleep using urine metabolites.

biochemistry↗

bioRxiv2018

Learning Molecule Drug Function from Structure Representations with Deep Neural Networks or Random Forests

Empirical testing of chemicals for desirable properties, such as drug efficacy, toxicity, solubility, and thermal conductivity, costs many billions of dollars every year. Further, the choice of molecules for testing relies on expert knowledge and intuition in the relevant domain of application. The ability to predict the action of a molecule in silico would greatly increase the speed and decrease the cost of prioritizing molecules with desirable function for experimental testing. We explore the use of molecule structures to predict their drug classification without any explicit biological model (e.g. protein structure or cell system). Two approaches were compared: (1) a two-dimensional image representation of molecule structure with transfer learning from a pre-trained convolutional neural network (CNN), and (2) Morgan molecular fingerprints (MFP) with a random forest (RF). Both methods using only molecule information as input achieved significantly better accuracy than previous work that used transcriptomic measurements of molecular effects. Because these data suggest that much of a molecules function is encoded by its chemical structure, we explore which general molecular properties are drug class-specific and how misclassification of structures might provide drug repurposing opportunities.

bioinformatics↗

bioRxiv2018

Autometa: Automated extraction of microbial genomes from individual shotgun metagenomes

MotivationShotgun metagenomics is a powerful, high-resolution technique enabling the study of microbial communities in situ. However, species-level resolution is only achieved after a process of \"binning\" where contigs predicted to originate from the same genome are clustered. Such culture-independent sequencing frequently unearths novel microbes, and so various methods have been devised for reference-free binning. Existing methods, however, suffer from: (1) reliance on human pattern recognition, which is inherently unscalable; (2) requirement for multiple co-assembled metagenomes, which degrades assembly quality due to strain variance; and (3) assumption of prior host genome removal not feasible for non-model hosts. We therefore devised a fully-automated pipeline, termed \"Autometa,\" to address these issues. Results: Autometa implements a method for taxonomic partitioning of contigs based on predicted protein homology, and this was shown to vastly improve binning in host-associated and complex metagenomes. Autometas method of automated clustering, based on Barnes-Hut Stochastic Neighbor Embedding (BH-tSNE) and DBSCAN, was shown to be highly scalable, outperforming other binning pipelines in complex simulated datasets.\n\nAvailability and implementationAutometa is freely available at https://bitbucket.org/jasonckwan/autometa and as a docker image at https://hub.docker.com/r/jasonkwan/autometa under the GNU Affero General Public License 3 (AGPL 3).\n\nContactjason.kwan@wisc.edu\n\nSupplementary informationSupplementary data are available attached to this article at https://biorxiv.org

bioinformatics↗