Biology subjects

Cole, T. J.

Publications and source records attributed to Cole, T. J..

11 recordsLinked to original sources

bioRxiv2025

Extracellular Vesicle Gene Expression Enables Sensitive Detection of Colorectal Neoplasia

BACKGROUND & AIMSExtracellular vesicles (EVs), including exosomes, are emerging as promising carriers of disease-specific biomarkers due to their molecular cargo reflective of cellular origin. While cell-free DNA (cfDNA) methylation assays have recently been developed for colorectal cancer (CRC) screening and perform well for cancer detection, they show limited sensitivity for advanced adenomas (AA), a key precursor in the CRC pathway. To address this gap, we sought to determine whether other blood-based analytes, specifically EV-derived long and small RNA transcriptomes and EV proteomes, could improve detection of AA and early-stage CRC. In a prospective cohort, we performed a head-to-head comparison of EV transcriptomics, EV proteomics, and their combinations against cfDNA methylation, all measured from the same patient cohort, enabling a direct performance benchmark and identification of the most promising modality for larger-scale CRC screening studies. METHODSWe prospectively collected pre-colonoscopy plasma samples from 220 participants across three clinical sites. EVs were isolated and profiled using long RNA-seq, small RNA-seq, and Olink-based proteomics. cfDNA was analyzed for methylation patterns. Analyses were conducted according to a statistical analysis plan pre-specified before unblinding. Machine learning models were developed under nested cross-validation to evaluate sensitivity for detecting AA and CRC at a fixed specificity of 91%, consistent with clinical screening benchmarks. RESULTSEV-derived gene expression on long RNA demonstrated the highest sensitivity for detecting AA as well as CRC: 54.8% (95% CI, 26.4%-75.6%) for AA and 94.1% (95% CI, 79.2%-100%) for CRC. This outperformed cfDNA methylation (33.6% [95% CI, 9.7%-60.3%] for AA, 81.3% [95% CI, 64.6%- 93.9%] for CRC) and other EV-based modalities. In addition, for small RNA the sensitivities were 42.6% (95% CI, 35.9%-47.4%) for AA, and 78.9% (95% CI, 69.2%-83.0%) for CRC, while for proteomics the sensitivities were 30.0% (95% CI, 13.9%-40.0%) for AA, and 64.2% (95% CI, 43.6%-87.2%) for CRC. Transcriptomic profiles revealed progressive enrichment of hallmarks of cancer pathways, including apoptosis and epithelial-mesenchymal transition, across disease stages. Multiomic integration did not improve performance beyond EV transcriptomics alone. CONCLUSIONSBy directly comparing multiple EV-based and cfDNA analytes within the same patient cohort, we found that EV transcriptomics delivers the strongest diagnostic performance for both AA and CRC. This rigorous benchmarking approach allows clear prioritization of the most promising modality guiding the design of larger validation studies and accelerating development of next-generation, blood-based CRC screening tools.

genomics↗

bioRxiv2025

Salivary Extracellular Vesicle RNA Profiling Reveals Biomarkers for Sjogrens

Sjogrens is a chronic autoimmune disease affecting exocrine glands and is subclassified into SSA-positive (SSA+) and SSA-negative (SSA-) subtypes, with a complex diagnostic journey and an average diagnostic delay of almost 4 years. While SSA+ cases can be detected via serological testing, current assays lack specificity. For SSA-patients, no non-invasive diagnostic tools exist, and diagnosis often requires invasive lip biopsy. A saliva-based liquid biopsy capable of diagnosing both subtypes is therefore of high clinical interest. However, saliva poses challenges due to its abundant oral microbiome, which complicates unbiased biomarker discovery. In this study, we present a novel RNA sequencing workflow that efficiently depletes microbial content, enabling deep profiling of long RNAs within salivary extracellular vesicles (EVs). This approach identified both known and novel RNA biomarkers capable of diagnosing SSA+ and SSA-subtypes with high sensitivity and specificity. Moreover, we uncovered distinct RNA signatures that allow molecular stratification of Sjogrens subtypes. Pathway analysis in SSA+ cases revealed enrichment of immune and glandular pathways consistent with prior tissue-based studies, supporting the utility of salivary EVs as a non-invasive surrogate for tissue biopsy. Importantly, our data provides new molecular insights into the under-characterized SSA-subtype, laying the foundation for future mechanistic studies and facilitating their broader inclusion in clinical trials.

genomics↗

bioRxiv2025

Deep Profiling of EV Long RNAs Reveals Biofluid-Specific Transcriptomes and Splicing Landscapes

RNA profiling of extracellular vesicles (EVs) from human biofluids has historically been limited to small RNA species, with long RNAs--such as mRNA exons and long non-coding RNAs--remaining largely underexplored. Moreover, the dominance of hematopoietic-derived EVs in complex fluids like plasma has posed significant challenges for detecting low-abundance, tissue-specific transcripts. Here, we establish foundational transcriptomic maps of long RNAs in EVs from plasma, urine, and cerebrospinal fluid (CSF) using ultra-deep whole transcriptome sequencing (WTS), revealing both fluid-specific and shared expression and splicing signatures. We then introduce a targeted RNA capture method that enriches for all protein-coding and long non-coding transcripts, dramatically enhancing sensitivity for gene and splice variant detection. Applying this approach to brain-specific transcripts, we achieve >85-fold enrichment of target gene expression and, on average, 3.1-fold increase in detected splice junctions per gene compared to untargeted WTS. As a proof of concept, we apply this brain-targeted RNA panel to EVs from plasma in a Parkinsons disease cohort of 40 plasma samples and compare its performance to exome sequencing as well as untargeted WTS. This work advances EV transcriptomics into the long RNA domain and establishes a framework for high-sensitivity, noninvasive biomarker profiling across tissues and diseases.

genomics↗

bioRxiv2023

Adapting antibacterial display to identify serum active macrocyclic peptide antibiotics

The lack of available treatments for many antimicrobial resistant infections highlights the critical need for antibiotic discovery innovation. Peptides are an underappreciated antibiotic scaffold because they often suffer from proteolytic instability and toxicity towards human cells, making in vivo use challenging. To investigate sequence factors related to serum activity, we adapt an antibacterial display technology to screen a library of peptide macrocycles for antibacterial potential directly in human serum. We identify dozens of new macrocyclic peptide antibiotic sequences and find that serum activity within our library is influenced by peptide length, cationic charge, and the number of disulfide bonds present. Interestingly, an optimized version of our most active lead peptide permeates the outer membrane of gram-negative bacteria without strong inner membrane disruption and kills bacteria slowly while causing cell elongation. This contrasts with traditional cationic antimicrobial peptides, which kill rapidly via lysis of both bacterial membranes. Notably, this optimized variant is not toxic to mammalian cells and retains its function in vivo, suggesting therapeutic promise. Our results support the use of more physiologically relevant conditions when screening peptides for antimicrobial activity which retain in vivo functionality. SignificanceTraditional methods of natural antibiotic discovery are low throughput and cannot keep pace with the development of antimicrobial resistance. Synthetic peptide display technologies offer a high-throughput means of screening drug candidates, but rarely consider functionality beyond simple target binding and do not consider retention of function in vivo. Here, we adapt a function-based, antibacterial display technology to screen a large library of peptide macrocycles directly for bacterial growth inhibition in human serum. This screen identifies an optimized non-toxic macrocyclic peptide antibiotic retaining in vivo function, suggesting this advancement could increase clinical antibiotic discovery efficiency.

microbiology↗

bioRxiv2023

Sequence and structure based deep learning models represent different aspects of protein biochemistry

Deep learning models are seeing increased use as methods to predict mutational effects or allowed mutations in proteins. The models commonly used for these purposes include large language models (LLMs) and 3D Convolutional Neural Networks (CNNs). These two model types have very different architectures and are commonly trained on different representations of proteins. LLMs make use of the transformer architecture and are trained purely on protein sequences whereas 3D CNNs are trained on voxelized representations of local protein structure. While comparable overall prediction accuracies have been reported for both types of models, it is not known to what extent these models make comparable specific predictions and/or generalize protein biochemistry in similar ways. Here, we perform a systematic comparison of two LLMs and two structure-based models (CNNs) and show that the different model types have distinct strengths and weaknesses. The overall prediction accuracies are largely uncorrelated between the sequence- and structure-based models. Overall, the two structure-based models are better at predicting buried aliphatic and hydrophobic residues whereas the two LLMs are better at predicting solvent-exposed polar and charged amino acids. Finally, we find that a combined model that takes the individual model predictions as input can leverage these individual model strengths and results in significantly improved overall prediction accuracy.

biochemistry↗

bioRxiv2022

Designing β-hairpin peptide macrocycles for antibiotic potential

Peptide macrocycles are a rapidly emerging new class of therapeutic, yet the design of their structure and activity remains challenging. This is especially true for those with {beta}-hairpin structure due to weak folding properties and a propensity for aggregation. Here we use proteomic analysis and common antimicrobial features to design a large peptide library with macrocyclic {beta}-hairpin structure. Using an activity-driven high-throughput screen we identify dozens of peptides killing bacteria through selective membrane disruption and analyze their biochemical features via machine learning. Active peptides contain a unique constrained structure and are highly enriched for cationic charge with arginine in their turn region. Our results provide a synthetic strategy for structured macrocyclic peptide design and discovery, while also elucidating characteristics important for {beta}-hairpin antimicrobial peptide activity. Brief SummaryWe design, screen, and computationally analyze a synthetic macrocyclic {beta}-hairpin peptide library for antibiotic potential.

biochemistry↗

bioRxiv2021

Killer Knots: Molecular evolution of Inhibitor Cystine Knot toxins in wandering spiders (Araneae: Ctenidae)

Venom expressed by the nearly 50,000 species of spiders on Earth largely remains an untapped reservoir of a diverse array of biomolecules with potential for pharmacological and agricultural applications. A large fraction of the noxious components of spider venoms are a functionally diverse family of structurally related polypeptides with an inhibitor cystine knot (ICK) motif. The cysteine-rich nature of these toxins makes structural elucidation difficult, and most studies have focused on venom components from the small handful of medically relevant spider species such as the highly aggressive Brazilian wandering spider Phoneutria nigriventer. To alleviate difficulties associated with the study of ICK toxins in spiders, we devised a comprehensive approach to explore the evolutionary patterns that have shaped ICK functional diversification using venom gland transcriptomes and proteomes from phylogenetically distinct lineages of wandering spiders and their close relatives. We identified 626 unique ICK toxins belonging to seven topological elaborations. Phylogenetic tests of episodic diversification revealed distinct regions between cysteine residues that demonstrated differential evidence of positive or negative selection, which may have structural implications towards the specificity and efficacy of these toxins. Increased taxon sampling and whole genome sequencing will provide invaluable insights to further understand the evolutionary processes that have given rise to this diverse class of toxins.

evolutionary biology↗

bioRxiv2020

Ultraconserved elements help resolve the phylogeny of an ancient radiation of venomous flies (Diptera: Asilidae)

Robber flies or assassin flies (Diptera: Asilidae) are a diverse family of venomous predators. The most recent classification organizes Asilidae into 14 subfamilies based on a comprehensive morphological phylogeny, but many of these have not been supported in a subsequent molecular study using traditional molecular markers. To address questions of monophyly in Asilidae, we leveraged the recently developed Diptera-wide UCE baitset to compile seven datasets comprising 151 robber flies and 146 - 2,508 loci, varying in the extent of missing data. We also studied the behavior of different nodal support metrics, as the non-parametric bootstrap is known to perform poorly with large genomic datasets. Our ML phylogeny was fully resolved and well-supported, but partially incongruent with the coalescent phylogeny. Further examination of the datasets suggested the possibility that GC bias had influenced gene tree inference and subsequent species tree analysis. The subfamilies Brachyrhopalinae, Dasypogoninae, Dioctriinae, Stenopogoninae, Tillobromatinae, Trigonomiminae, and Willistonininae were not recovered as monophyletic in either analysis, consistent with a previous molecular study. The inter-subfamily relationships are summarized as follows: Laphriinae and Dioctriinae (in part) are successively sister to the remaining subfamilies, which form two clades; the first consists of a grade of Stenopogoninae (in part), Willistonininae (in part), Bathypogoninae+Phellinae, Stichopogoninae, Leptogastrinae, Ommatiinae, and Asilinae; the second clade consists of a thoroughly paraphyletic assemblage of genera from Dioctriinae (in part), Trigonomiminae, Stenopogoninae (in part), Tillobromatinae, Brachyrhopalinae, and Dasypogoninae. We find that nodal support does not significantly vary with missing data. Furthermore, the bootstrap appears to overestimate nodal support, as has been reported from many recent studies. Gene concordance and site concordance factors seem to perform better, but may actually underestimate support. We instead recommend quartet concordance as a more appropriate estimator of nodal support. Our comprehensive phylogeny demonstrates that the higher classification of Asilidae is far from settled, and it will provide a much-needed foundation for a thorough revision of the subfamily classification.

evolutionary biology↗

bioRxiv2020

Pick Your Poison: Molecular Evolution of Venom Proteins in Asilidae (Insecta: Diptera)

Robber flies are an understudied family of venomous, predatory Diptera. With the recent characterization of venom from three asilid species, it is possible for the first time to study the molecular evolution of venom genes in this unique lineage. To accomplish this, a novel whole-body transcriptome of Eudioctria media was combined with 10 other publicly available asiloid thoracic or salivary gland transcriptomes to identify putative venom gene families and assess evidence of pervasive positive selection. A total of 348 gene families of sufficient size were analyzed, and 33 of these were predicted to contain venom genes. We recovered 151 families containing homologs to previously described venoms, and 40 of these were uniquely gained in Asilidae. Our gene family clustering suggests that many asilidin venom gene families are not natural groupings as originally delimited. Additionally, robber-fly venoms have relatively few sites under positive selection, consistent with the hypothesis that the venom of older lineages are dominated by negative selection acting to maintain toxic function.

evolutionary biology↗

bioRxiv2018

Low-frequency variation in TP53 has large effects on head circumference and intracranial volume

Cranial growth and development affects the closely related traits of head circumference (HC) and intracranial volume (ICV). Here we model the developmental genetic architecture of HC, showing this is genetically stable and correlated with genetic determinants of ICV. Investigating up to 46,000 children and adults of European descent, we identify association with final HC and/or final ICV+HC at 9 novel common and low-frequency loci, illustrating that genetic variation from a wide allele frequency spectrum contributes to cranial growth. The largest effects are reported for low-frequency variants within TP53, with 0.5 cm wider heads in increaser-allele carriers versus non-carriers during mid-childhood.

genetics↗

bioRxiv2017

FUSTr: a tool to find gene Families Under Selection in Transcriptomes

FUSTr is a tool for finding genes in transcriptomic datasets under strong positive selection that automatically detects isoform designation patterns in transcriptome assemblies to maximize phylogenetic independence in downstream analysis. When applied to previously studied spider toxin families as well as simulated data, FUSTr successfully grouped coding sequences into proper gene families as well as correctly identified those under strong positive selection. FUSTr provides a tool capable of utilizing multi-processor high-performance computational facilities and is scalable for large transcriptomic biodiversity datasets.\n\nAvailabilityFUSTr is freely available under a GNU license and can be downloaded at https://github.com/tijeco/FUSTr.\n\nContactcoleti16@students.ecu.edu

bioinformatics↗