Biology subjects

Carter, B.

Publications and source records attributed to Carter, B..

17 recordsLinked to original sources

bioRxiv2024

Spatial Phylogenetics with Continuous Data: an Application to California Bryophytes

Spatial phylogenetics is premised on the idea that species are not discrete categorical entities but instead lie on a hierarchical evolutionary continuum that contains rich biological information valuable for quantifying spatial biodiversity patterns. Yet while spatial phylogenetic approaches use quantitative information to represent phylogenetic patterns, most have continued to rely on methods that discard valuable information about spatial patterns by converting continuous variables into binary categories. This includes representing geographic ranges using binary presence-absence data, classifying statistical significance into categories, and quantifying biogeographic gradients into discrete regions. In this paper we show how a full suite of spatial phylogenetic analyses, including analyses of alpha and beta diversity, neo- and paleo-endemism, biogeographic hypothesis testing, and spatial conservation prioritization, can be implemented with "smooth" methods that never remove information content by categorizing continuous data. Our analysis focuses on the bryophytes of California, an understudied group in a global plant biodiversity hotspot. Using a time-calibrated phylogeny and species distribution models for 548 species of mosses and liverworts, we profile the evolutionary diversity, compositional turnover, and conservation value of bryophyte communities across the state. Our results highlight important patterns in the diversity of this key plant group, while our methods can serve as a model for future studies seeking to maximize the information content of spatial phylogenetic analyses.

evolutionary biology↗

bioRxiv2024

Global genomics of Aedes aegypti unveils widespread and novel persistent infectious viruses capable of triggering a small RNA response

The mosquito Aedes aegypti is a prominent vector for arboviruses, but the breadth of mosquito viruses that infects this specie is not fully understood. In the broadest global survey to date of over 200 Ae. aegypti small RNA samples, we detected viral small interfering RNAs (siRNAs) and Piwi interacting RNAs (piRNAs) arising from mosquito viruses. We confirmed that most academic laboratory colonies of Ae. aegypti lack persisting viruses, yet two commercial strains were infected by a novel tombus-like virus. Ae. aegypti from North to South American locations were also teeming with multiple insect viruses, with Anphevirus and a bunyavirus displaying geographical boundaries from the viral small RNA patterns. Asian Ae. aegypti small RNA patterns indicate infections by similar mosquito viruses from the Americas and reveal the first wild example of dengue virus infection generating viral small RNAs. African Ae. aegypti also contained various viral small RNAs including novel viruses only found in these African substrains. Intriguingly, viral long RNA patterns can differ from small RNA patterns, indicative of viral transcripts evading the mosquitoes RNA interference (RNAi) machinery. To determine whether the viruses we discovered via small RNA sequencing were replicating and transmissible, we infected C6/36 and Aag2 cells with Ae. aegypti homogenates. Through blind passaging, we generated cell lines stably infected by these mosquito viruses which then generated abundant viral siRNAs and piRNAs that resemble the native mosquito viral small RNA patterns. This mosquito small RNA genomics approach augments surveillance approaches for emerging infectious diseases.

genomics↗

bioRxiv2023

Can a flower color ancestral polymorphism transcend speciation?

Polymorphisms are common in nature, but they are rarely shared among closely related species. They could originate through convergence, ancestral polymorphism, or introgression. Although shared neutral genomic variation is commonplace, very few examples of shared functional traits exist. The blue-orange petal color polymorphisms in two closely related species, Lysimachia monelli and L. arvensis were investigated with UV-vis reflectance spectra, flavonoid biochemistry, and transcriptome comparisons followed by climate niche analysis. The similarities in reflectance spectra, biochemistry, and transcriptomes suggest a single shift from blue-to-orange shared by both lineages is possible. Transcriptome comparisons reveal two orange-specific genes are directly involved in both blue-orange color polymorphisms: DFR-2 specificity redirects flux from the malvidin to the pelargonidin while BZ1-2 stabilizes the pelargonidin with glucose, producing the orange pelargonidin 3-glucoside. The climate niches for each color morph are the same between the two species for three temperature characteristics but differ for four precipitation variables. We suggest that this persistent flower color polymorphism may represent an ancestrally polymorphic trait that has transcended speciation with some unique ecological effects.

evolutionary biology↗

bioRxiv2023

Machine learning model interpretations explain T cell receptor binding

T cells mediate immune responses against pathogens and cancer through T cell receptors (TCRs) that recognize foreign peptides presented on the cell surface by Major Histocompatibility Complex (MHC) proteins. TCRs carry enormous diversity and differ across individuals, and mechanisms that determine TCR-pMHC binding are poorly understood. The ability to predict TCR-pMHC interactions would accelerate development of cellular therapeutics to design TCRs that specifically bind to a target of interest. We designed a randomized library of 196 TCR CDR3{beta} sequences and experimentally evaluated their affinities for the Tax-A02 peptide-MHC target. We trained ML models that predict TCR binding to Tax-A02 from TCR sequence and used model interpretation to identify TCR sequence features associated with binding. We found these features accurately mirror the true sequence features in our experimental data.

immunology↗

bioRxiv2022

Epigenetic silencing by SMYD3 represses tumor intrinsic interferon response in HPV-negative squamous cell carcinoma of the head and neck.

Cancers often display immune escape, but the mechanisms and potential for reversibility are incompletely understood. Epigenetic dysregulation has been implicated in the immune escape of various cancer types. We have identified the epigenetic modifier SET and MYND-domain containing protein 3 (SMYD3) as a mediator of immune escape in human papilloma virus (HPV)- negative head and neck squamous cell carcinoma (HNSCC), an aggressive disease with poor prognosis and low response to immunotherapy with pembrolizumab, a programmed-death-1 (PD-1) targeting antibody. SMYD3 loss increased the sensitivity of HNSCC cancer cells to IFN-{beta}, resulting in upregulation of type I IFN response and antigen presentation machinery genes. We found that SMYD3 regulates the transcription of Ubiquitin-Like PHD And RING Finger Domain- Containing Protein 1 (UHRF1), a key epigenetic reader of trimethylated lysine 9 on histone H3 (H3K9me3), which binds to H3K9me3-enriched promoters of key immune-related genes and silences their expression. SMYD3 further maintains the repression of immune-related genes through the deposition of H4K20me3 within the gene body regions of these genes. In an anti-PD-1 immune checkpoint resistant syngeneic mouse model of HPV-negative HNSCC, Smyd3 depletion induced influx of CD8+ T-cells, upregulated PD-L1 and MHC class I molecules, and increased sensitivity to anti-PD-1 therapy. SMYD3 overexpression was associated with decreased CD8 T-cell infiltration in tumor samples from patients with HPV-negative HNSCC, and was associated with poor response to pembrolizumab. Overall, these data highlight a previously unreported function of SMYD3 as a master epigenetic regulator of anti-tumor immune response in HPV-negative HNSCC and provide a rationale for translational approaches combining SMYD3 depletion strategies with checkpoint blockade to overcome anti-PD-1 resistance in this devastating disease.

cancer biology↗

bioRxiv2022

A pan-variant mRNA-LNP T cell vaccine protects HLA transgenic mice from mortality after infection with SARS-CoV-2 Beta

Clinically licensed COVID-19 vaccines ameliorate viral infection by inducing vaccinee production of neutralizing antibodies that bind to the SARS-CoV-2 Spike protein to inhibit viral cellular entry (Walsh et al., 2020; Baden et al., 2021), however the clinical effectiveness of these vaccines is transitory as viral variants arise that escape antibody neutralization (Tregoning et al., 2021; Willett et al., 2022). Vaccines that solely rely upon a T cell response to combat viral infection could be transformational because they can be based on highly conserved short peptide epitopes that hold the potential for pan-variant immunity, but a mRNA-LNP T cell vaccine has not been shown to be sufficient for effective antiviral prophylaxis. Here we show that a mRNA-LNP vaccine based on highly conserved short peptide epitopes activates a CD8+ and CD4+ T cell response that prevents mortality in HLA-A*02:01 transgenic mice infected with the SARS-CoV-2 Beta variant of concern (B.1.351). In mice vaccinated with the T cell vaccine, 24% of the nucleated cells in lung were CD8+ T cells on day 7 post infection. This was 5.5 times more CD8+ T cell infiltration of the lungs in response to infection compared to the Pfizer-BioNTech Comirnaty(R) vaccine. Between days 2 and 7 post infection, the number of CD8+ T cells in the lung increased in mice vaccinated with the T cell vaccine and decreased in mice vaccinated with Comirnaty(R). The T cell vaccine did not produce neutralizing antibodies, and thus our results demonstrate that SARS-CoV-2 viral infection can be controlled by a T cell response alone. Our results suggest that further study is merited for pan-variant T cell vaccines, and that T cell vaccines may be relevant for individuals that cannot produce neutralizing antibodies or to help mitigate Long COVID.

immunology↗

bioRxiv2022

Early Signaling Events in Renal Compensatory Hypertrophy Revealed by Multi-Omics

Loss of a kidney results in compensatory growth of the remaining kidney, a phenomenon of considerable clinical importance. However, the mechanisms involved are largely unknown. Here, we used a multi-omic approach in a mouse unilateral nephrectomy model to identify signaling processes associated with compensatory hypertrophy of the renal proximal tubule. Morphometry applied to microdissected proximal tubules showed that growth of the proximal tubule involves a marked, rapid increase in cell volume rather than cell number. Measurements of DNA accessibility (ATAC-seq), transcriptome (RNA-seq) and proteome (quantitative protein mass spectrometry) independently identified patterns of change that are indicative of activation of the lipid-regulated transcription factor, PPAR. Activation of PPAR by fenofibrate administration increased proximal tubule cell size, while genetic deletion of PPAR in mice decreased it. The results indicate that PPAR is an important determinant of proximal tubule cell size and is a likely mediator of compensatory proximal tubule hypertrophy.

systems biology↗

bioRxiv2022

Validity Assessment of Michigan's Proposed qPCR Threshold Value for Rapid Water-Quality Monitoring of E. coli Contamination

Michigans water-quality standards specify that E. coli concentrations at bathing beaches must not exceed 300 E. coli per 100 mL, as determined by the geometric mean of culture-based concentrations in three or more representative samples from a given beach on a given day. Culture-based analyses require 18-24 h to complete, so results are not available for issuing beach notifications (advisories or closings) until the day following collection. This one-day delay is problematic because E. coli concentrations at beaches can change markedly from one day to the next. qPCR-based E. coli concentrations, by contrast, can be obtained in only 3-4 h, making same-day beach notifications possible. Michigan has proposed a qPCR threshold value (qTV) for E. coli of 1.863 log10 gene copies per reaction as a potential equivalent value to the state standard, based on statistical analyses of a set of training data from 2016-2018. The main purpose of the present study is to assess the validity of the proposed qTV by determining whether the implied qPCR-based beach notification decisions agree well with culture-based decisions on two sets of test data (from 2016-2018 and 2019-2020), and whether performance of the proposed threshold is similar on the test and training data. The results show that performance of the proposed qTV on both sets of test data was consistently good (e.g., 95% agreement with culture-based beach notification decisions during 2019-2020) and was at least as good as its performance on the training data set, supporting its use as an equivalent value to the state standard.

ecology↗

bioRxiv2021

Preliminary Immunogenicity of a Pan-COVID-19 T Cell Vaccine in HLA-A*02:01 Mice

New strains of SARS-CoV-2 have emerged, including B.1.351 and P.1, that demonstrate increased transmissibility and the potential of rendering current SARS-CoV-2 vaccines less effective. A concern is that existing SARS-CoV-2 spike subunit vaccines produce neutralizing antibodies to three dimensional spike epitopes that are subject to change during viral drift. Here we provide an initial report on the hypothesis that adaptive T cell based immunity may provide a path for a pan-COVID-19 vaccine that is resilient to viral drift. T cell based adaptive immunity can be based on short peptide sequences selected from the viral proteome that are less subject to drift, and can utilize multiple such epitopes to provide redundancy in the event of drift. We find that SARS-CoV-2 peptides contained in a mRNA-LNP T cell vaccine for SARS-CoV-2 are immunogenic in mice transgenic for the human HLA-A*02:01 gene. We plan to test the efficacy of this vaccine with SARS-CoV-2 B.1.351 challenge trials with HLA-A*02:01 mice.

immunology↗

bioRxiv2020

Machine learning optimization of peptides for presentation by class II MHCs

T cells play a critical role in normal immune responses to pathogens and cancer and can be targeted to MHC-presented antigens via interventions such as peptide vaccines. Here, we present a machine learning method to optimize the presentation of peptides by class II MHCs by modifying the peptides anchor residues. Our method first learns a model of peptide affinity for a class II MHC using an ensemble of deep residual networks, and then uses the model to propose anchor residue changes to improve peptide affinity. We use a high throughput yeast display assay to show that anchor residue optimization successfully improved peptide binding.

immunology↗

bioRxiv2020

Predicted Cellular Immunity Population Coverage Gaps for SARS-CoV-2 Subunit Vaccines and their Augmentation by Compact Joint Sets

Subunit vaccines induce immunity to a pathogen by presenting a component of the pathogen and thus inherently limit the representation of pathogen peptides for cellular immunity based memory. We find that SARS-CoV-2 subunit peptides may not be robustly displayed by the Major Histocompatibility Complex (MHC) molecules in certain individuals. We introduce an augmentation strategy for subunit vaccines that adds a small number of SARS-CoV-2 peptides to a vaccine to improve the population coverage of pathogen peptide display. Our population coverage estimates integrate clinical data on peptide immunogenicity in convalescent COVID-19 patients and machine learning predictions. We evaluate the population coverage of 9 different subunits of SARS-CoV-2, including 5 functional domains and 4 full proteins, and augment each of them to fill a predicted coverage gap.

immunology↗

bioRxiv2020

Robust computational design and evaluation of peptide vaccines for cellular immunity with application to SARS-CoV-2

We present a combinatorial machine learning method to evaluate and optimize peptide vaccine formulations, and we find for SARS-CoV-2 that it provides superior predicted display of viral epitopes by MHC class I and MHC class II molecules over populations when compared to other candidate vaccines. Our method is robust to idiosyncratic errors in the prediction of MHC peptide display and considers target population HLA haplotype frequencies during optimization. To minimize clinical development time our methods validate vaccines with multiple peptide presentation algorithms to increase the probability that a vaccine will be effective. We optimize an objective function that is based on the presentation likelihood of a diverse set of vaccine peptides conditioned on a target population HLA haplotype distribution and expected epitope drift. We produce separate peptide formulations for MHC class I loci (HLA-A, HLA-B, and HLA-C) and class II loci (HLA-DP, HLA-DQ, and HLA-DR) to permit signal sequence based cell compartment targeting using nucleic acid based vaccine platforms. Our SARS-CoV-2 MHC class I vaccine formulations provide 93.21% predicted population coverage with at least five vaccine peptide-HLA hits on average in an individual ([≥] 1 peptide 99.91%) with all vaccine peptides perfectly conserved across 4,690 geographically sampled SARS-CoV-2 genomes. Our MHC class II vaccine formulations provide 90.17% predicted coverage with at least five vaccine peptide-HLA hits on average in an individual with all peptides having observed mutation probability [≤] 0.001. We evaluate 29 previously published peptide vaccine designs with our evaluation tool with the requirement of having at least five vaccine peptide-HLA hits per individual, and they have a predicted maximum of 58.51% MHC class I coverage and 71.65% MHC class II coverage given haplotype based analysis. We provide an open source implementation of our design methods (OptiVax), vaccine evaluation tool (EvalVax), as well as the data used in our design efforts.

immunology↗

bioRxiv2019

Antibody Complementarity Determining Region Design Using High-Capacity Machine Learning

The precise targeting of antibodies and other protein therapeutics is required for their proper function and the elimination of deleterious off-target effects. Often the molecular structure of a therapeutic target is unknown and randomized methods are used to design antibodies without a model that relates antibody sequence to desired properties. Here we present a machine learning method that can design human Immunoglobulin G (IgG) antibodies with target affinities that are superior to candidates from phage display panning experiments within a limited design budget. We also demonstrate that machine learning can improve target-specificity by the modular composition of models from different experimental campaigns, enabling a new integrative approach to improving target specificity. Our results suggest a new path for the discovery of therapeutic molecules by demonstrating that predictive and differentiable models of antibody binding can be learned from high-throughput experimental data without the need for target structural data.\n\nSignificanceAntibody based therapeutics must meet both affinity and specificity metrics, and existing in vitro methods for meeting these metrics are based upon randomization and empirical testing. We demonstrate that with sufficient target-specific training data machine learning can suggest novel antibody variable domain sequences that are superior to those observed during training. Our machine learning method does not require any target structural information. We further show that data from disparate antibody campaigns can be combined by machine learning to improve antibody specificity.

immunology↗

bioRxiv2019

Critiquing Protein Family Classification Models Using Sufficient Input Subsets

In many application domains, neural networks are highly accurate and have been deployed at large scale. However, users often do not have good tools for understanding how these models arrive at their predictions. This has hindered adoption in fields such as the life and medical sciences, where researchers require that models base their decisions on underlying biological phenomena rather than peculiarities of the dataset introduced. In response, we propose a set of methods for critiquing deep learning models and demonstrate their application for protein family classification, a task for which high-accuracy models have considerable potential impact. Our methods extend the sufficient input subsets technique, which we use to identify subsets of features (SIS) in each protein sequence that are alone sufficient for classification. Our suite of tools analyzes these subsets to shed light on the decision-making criteria employed by models trained on this task. These tools expose that while deep models may perform classification for biologically-relevant reasons, their behavior varies considerably across choice of network architecture and parameter initialization. While the techniques that we develop are specific to the protein sequence classification task, the approach taken generalizes to a broad set of scientific contexts in which model interpretability is essential.

bioinformatics↗

bioRxiv2019

Using Deep Learning to Annotate the Protein Universe

Understanding the relationship between amino acid sequence and protein function is a long-standing problem in molecular biology with far-reaching scientific implications. Despite six decades of progress, state-of-the-art techniques cannot annotate 1/3 of microbial protein sequences, hampering our ability to exploit sequences collected from diverse organisms. In this paper, we explore an alternative methodology based on deep learning that learns the relationship between unaligned amino acid sequences and their functional annotations across all 17929 families of the Pfam database. Using the Pfam seed sequences we establish rigorous benchmark assessments that use both random and clustered data splits to control for potentially confounding sequence similarities between train and test sequences. Using Pfam full, we report convolutional networks that are significantly more accurate and computationally efficient than BLASTp, while learning sequence features such as structural disorder and transmembrane helices. Our model co-locates sequences from unseen families in embedding space, allowing sequences from novel families to be accurately annotated. These results suggest deep learning models will be a core component of future protein function prediction tools.

bioinformatics↗

bioRxiv2019

Mapping Histone Modifications in Low Cell Number and Single Cells Using Antibody-guided Chromatin Tagmentation (ACT-seq)

Modern next-generation sequencing-based methods have empowered researchers to assay the epigenetic states of individual cells. Existing techniques for profiling epigenetic marks in single cells often require the use and optimization of time-intensive procedures such as drop fluidics, chromatin fragmentation, and end repair. Here we describe ACT-seq, a novel and streamlined method for mapping genome-wide distributions of histone tail modifications, histone variants, and chromatin-binding proteins in a small number of or single cells. ACT-seq utilizes a fusion of Tn5 transposase to Protein A that is targeted to chromatin by a specific antibody, allowing chromatin fragmentation and sequence tag insertion specifically at genomic sites presenting the relevant antigen. The Tn5 transposase enables the use of an index multiplexing strategy (iACT-seq), which enables construction of thousands of single-cell libraries in one day by a single researcher without the need for drop-based fluidics or visual sorting. We conclude that ACT-seq present an attractive alternative to existing techniques for mapping epigenetic marks in single cells.

genomics↗

bioRxiv2018

Comparative efficacy and acceptability of non-surgical brain stimulation for the acute treatment of adult major depressive episodes: A systematic review and network meta-analysis of 113 randomised clinical trials

Background: Non-surgical brain stimulation techniques have been applied as tertiary treatments in major depression. However, the relative efficacy and acceptability of individual protocols is uncertain. Our aim was to estimate the comparative clinical efficacy and acceptability of non-surgical brain stimulation for the acute treatment of major depressive episodes in adults.\n\nMethods: Embase, PubMed/MEDLINE and PsycINFO were searched up until May 8, 2018, supplemented by manual searches of bibliographies of recent reviews and included trials. We included clinical trials with random allocation to electroconvulsive therapy (ECT), repetitive transcranial magnetic stimulation (rTMS), accelerated TMS (aTMS), priming TMS (pTMS), deep TMS (dTMS), theta burst stimulation (TBS), synchronised TMS (sTMS), magnetic seizure therapy (MST) or transcranial direct current stimulation (tDCS) protocols or sham. Data were extracted from published reports and outcomes were synthesised using pairwise and network random-effects meta-analysis. Primary outcomes were response (efficacy) and all-cause discontinuation (acceptability). We computed odds ratios (OR) with 95% confidence intervals (CI). Remission and continuous post-treatment depression severity scores were also examined.\n\nResults: 113 trials (262 treatment arms) randomising 6,750 patients (mean age = 47.9 years; 59% female) with major depressive disorder or bipolar depression met our inclusion criteria. In terms of efficacy, 10 out of 18 treatment protocols were associated with higher response relative to sham in network meta-analysis: bitemporal ECT (OR=8.91, 95%CI 2.57-30.91), high-dose right-unilateral ECT (OR=7.27, 1.90-27.78), pTMS (OR=6.02, 2.21-16.38), MST (OR=5.55, 1.06-28.99), bilateral rTMS (OR=4.92, 2.93-8.25), bilateral TBS (OR=4.44, 1.47-13.41), low-frequency right rTMS (OR=3.65, 2.13-6.24), intermittent TBS (OR=3.20, 1.45-7.08), high-frequency left rTMS (OR=3.17, 2.29-4.37) and tDCS (OR=2.65, 1.55-4.55). Comparing active treatments, bitemporal ECT and high-dose right-unilateral ECT were associated with increased response. All treatment protocols were at least as acceptable as sham treatment.\n\nConclusion: We found that non-surgical brain stimulation techniques constitute viable alternative or add-on treatments for adult patients with major depressive episodes. Our findings also highlight the need to consider other patient and treatment-related factors in addition to antidepressant efficacy and acceptability when making clinical decisions; and emphasize important research priorities in the field of brain stimulation.\n\nO_LSTTreatment abbreviationsC_LSTECT = Electroconvulsive Therapy O_LIBF ECT = bifrontal ECT (1)\nC_LIO_LIBT ECT = bitemporal ECT (2)\nC_LIO_LIRUL ECT= right unilateral ECT O_LIH-RUL = high-dose RUL ECT (3)\nC_LIO_LILM-RUL = low to moderate-dose RUL ECT (4)\nC_LI\nC_LI\nrTMS = repetitive Transcranial Magnetic Stimulation O_LIHF-L rTMS = high-frequency rTMS of the left DLPFC (5)\nC_LIO_LIHF-R rTMS = high-frequency rTMS of the right DLPFC (6)\nC_LIO_LILF-R rTMS = low-frequency rTMS of the right DLPFC (7)\nC_LIO_LILF-L rTMS = low-frequency rTMS of the left DLPFC (8)\nC_LIO_LIBL rTMS = bilateral rTMS of the DLPFC (9)\nC_LI\ndTMS = deep Transcranial Magnetic Stimulation (10)\npTMS = priming Transcranial Magnetic Stimulation (11)\naTMS = accelerated Transcranial Magnetic Stimulation (12)\nsTMS = synchronised Transcranial Magnetic Stimulation (13)\nTBS = Theta Burst Stimulation O_LIiTBS = intermittent TBS of the left DLPFC (14)\nC_LIO_LIcTBS = continuous TBS of the right DLPFC (15)\nC_LIO_LIblTBS = bilateral TBS of the DLPFC (16)\nC_LI\nMST = Magnetic Seizure Therapy (17)\ntDCS = transcranial Direct Current Stimulation (18)\n\n\nKey points\n\nQuestion: What is the clinical efficacy and acceptability of non-surgical brain stimulation protocols for the acute treatment of major depressive episodes in adults?\n\nFindings: In this network meta-analysis, 10 out of 18 treatment protocols were associated with higher response rates relative to sham, most notably bitemporal and high-dose right unilateral electroconvulsive therapy. All treatment protocols were at least as acceptable as sham treatment.\n\nMeaning: Non-surgical brain stimulation techniques constitute viable alternative or add-on treatment strategies for adult patients with major depressive episodes.

neuroscience↗