Publications

Bacteriophages are the most prolific organisms on Earth, yet many of their genomes and assemblies from metagenomic sources lack protein sequences with identified functions. While most bacteriophage proteins are structural proteins, categorized as Phage Virion Proteins (PVPs), a considerable number remain unclassified. Complicating matters further, traditional lab-based methods for PVP identification can be tedious. To expedite the process of identifying PVPs, machine-learning models are increasingly being employed. Existing tools have developed models for predicting PVPs from protein sequences as input. However, none of these efforts have built software allowing for both genomic and metagenomic data as input. In addition, there is currently no framework available for easily curating data and creating new types of machine learning models. In response, we introduce PhageScanner, an open-source platform that streamlines data collection for genomic and metagenomic datasets, model training and testing, and includes a prediction pipeline for annotating genomic and metagenomic data. PhageScanner also features a graphical user interface (GUI) for visualizing annotations on genomic and metagenomic data. We further introduce a BLAST-based classifier that outperforms ML-based models and an efficient Long Short-Term Memory (LSTM) classifier. We then showcase the capabilities of PhageScanner by predicting PVPs in six previously uncharacterized bacteriophage genomes. In addition, we create a new model that predicts phage-encoded toxins within bacteriophage genomes, thus displaying the utility of the framework.

Objective: Recent advancements demonstrate the significant role of digital microfluidics in automating laboratory work with DNA and on-site viral testing. However, since commercially available instruments are limited to droplet manipulation, our work addresses the need for accelerated integration of other components, such as temperature control, that can expand the application domain. Methods: We developed PhageBox—an accessible device that can be used as a biochip extension. At hardware level, PhageBox integrates temperature and electromagnetic control modules. At software level, PhageBox is controlled by embedded software containing a unique model for bio-protocol programming, and a graphical user interface for visual device feedback and operation. Results: To evaluate PhageBox's efficacy for biomedical applications, we performed functional testing. We validated the temperature control using thermography, obtaining a range of ±0.2°C. The electromagnets produced a magnetic force of 15 milliTesla, demonstrating precise immobilization of magnetic beads. We show the potential of PhageBox for bacteriophage research through three initial protocols: a universal framework for PCR, T7 bacteriophage restriction enzyme digestion, and concentrating ϕX174 RF genomic DNA. Conclusion: Our work presents an open-source hardware and software extension for digital microfluidics devices. This extension integrates temperature and electromagnetic modules, demonstrating efficacy in biomedical applications and potential for bacteriophage research. Significance: We developed PhageBox to be accessible: the components are off-the-shelf at a low cost (≤$200), and the hardware designs and software code are open-source. With the long aim of ensuring reproducibility and accelerating collaboration, we also provide a DIY-build document.

Even though bacteriophages are the most plentiful organisms on Earth, many of their genomes and assemblies from metagenomic sources lack protein sequences with identified functions. Most proteins in bacteriophages are structural, known as Phage Virion Proteins (PVPs), but a considerable number remain unclassified. Complicating matters further, conventional lab-based methods for PVP identification are time-consuming and tedious. To expedite the process of identifying PVPs, machine-learning models are increasingly being employed. While existing tools have developed models for predicting PVPs from protein sequences as input, none of these efforts have built software allowing for genomic and metagenomic as input. In addition, there isn't a framework available for easily curating data and creating new types of models. In response, we introduce PhageScanner, an open-source platform that streamlines data collection, model training and testing, and includes a prediction pipeline for annotating genomic and metagenomic data. PhageScanner also features a graphical user interface (GUI) for visualizing annotations on genomic and metagenomic data. We also introduce a BLAST-based classifier that outperforms ML-based models (achieving an F1 score of 94% for multiclass PVP detection and 97% for binary PVP detection) and an efficient Long Short-Term Memory (LSTM) classifier. We showcase the capabilities of PhageScanner by predicting PVPs in six previously uncharacterized bacteriophage genomes. In addition, showing the utility of the framework, we create a new model that predicts phage-encoded toxins within bacteriophage genomes.

PhD dissertation on automating bacteriophage research.

The COVID-19 pandemic has emphasized the importance of accurate detection of known and emerging pathogens. However, robust characterization of pathogenic sequences remains an open challenge. To address this need we developed SeqScreen, which accurately characterizes short nucleotide sequences using taxonomic and functional labels and a customized set of curated Functions of Sequences of Concern (FunSoCs) specific to microbial pathogenesis. We show our ensemble machine learning model can label protein-coding sequences with FunSoCs with high recall and precision. SeqScreen is a step towards a novel paradigm of functionally informed synthetic DNA screening and pathogen characterization, available for download at www.gitlab.com/treangenlab/seqscreen.

Background: Activation of the aryl hydrocarbon receptor (AhR) can alter diurnal rhythms including those for innate lymphoid cell numbers, cytokine and hormone levels, and feeding behaviors. Because immune responses and antibody levels are modulated by exposure to AhR agonists, we hypothesized that some of the variation previously reported for the effects of AhR activation on fecal secretory immunoglobulin A (sIgA) levels could be explained by dysregulation of the diurnal sIgA rhythm. Methods: C57Bl/6 J mice were exposed to peanut oil or 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD, 10 or 40 μg/Kg) and fecal sIgA levels were determined in samples collected every 4 h over 4 days. Results: Fecal sIgA concentrations were not significantly different between light and dark phases of the photoperiod in either male or female mice, and there were no significant circadian rhythms observed, but TCDD exposure significantly altered both fecal mesor sIgA and serum IgA concentrations, in parallel, in male (increased) and female (biphasic) mice. Conclusions: AhR activation can contribute to the regulation of steady state IgA/sIgA concentrations.

The COVID-19 pandemic has sparked an urgent need to uncover the underlying biology of this devastating disease. Though RNA viruses mutate more rapidly than DNA viruses, there are a relatively small number of single nucleotide polymorphisms (SNPs) that differentiate the main SARS-CoV-2 lineages that have spread throughout the world. In this study, we investigated 129 RNA-seq data sets and 6928 consensus genomes to contrast the intra-host and inter-host diversity of SARS-CoV-2. Our analyses yielded three major observations. First, the mutational profile of SARS-CoV-2 highlights intra-host single nucleotide variant (iSNV) and SNP similarity, albeit with differences in C > U changes. Second, iSNV and SNP patterns in SARS-CoV-2 are more similar to MERS-CoV than SARS-CoV-1. Third, a significant fraction of insertions and deletions contribute to the genetic diversity of SARS-CoV-2. Altogether, our findings provide insight into SARS-CoV-2 genomic diversity, inform the design of detection tests, and highlight the potential of iSNVs for tracking the transmission of SARS-CoV-2.

In October 2020, 62 scientists from nine nations worked together remotely in the Second Baylor College of Medicine & DNAnexus hackathon, focusing on different related topics on Structural Variation, Pan-genomes, and SARS-CoV-2 related research. The overarching focus was to assess the current status of the field and identify the remaining challenges, and furthermore, how to combine the strengths of the different interests to drive research and method development forward. Over the four days, eight groups each designed and developed new open-source methods to improve the identification and analysis of variations among species, including humans and SARS-CoV-2. These included improvements in SV calling, genotyping, annotations and filtering, together with advancements in benchmarking existing methods. Furthermore, groups focused on the diversity of SARS-CoV-2. Daily discussion summaries and methods are available publicly at https://github.com/collaborativebioinformatics, providing valuable insights for both participants and the research community.

The wide prevalence and regulated expression of long noncoding RNAs (lncRNAs) highlight their functional roles, but the molecular basis for their activities and structure-function relationships remains to be investigated, with few exceptions. Among the relatively few lncRNAs conserved over significant evolutionary distances is the long intergenic noncoding RNA (lincRNA) Cyrano (orthologous to human OIP5-AS1), which contains a region of 300 highly conserved nucleotides within tetrapods, which in turn contains a functional stretch of 26 nt of deep conservation. This region binds to and facilitates the degradation of the microRNA miR-7, a short ncRNA with multiple cellular functions, including modulation of oncogenic expression. We probed the secondary structure of Cyrano in vitro and in cells using chemical and enzymatic probing, and validated the results using comparative sequence analysis. At the center of the functional core of Cyrano is a cloverleaf structure maintained over the >400 million years of divergent evolution that separates fish and primates. This strikingly conserved motif provides interaction sites for several RNA-binding proteins and masks a conserved recognition site for miR-7. Conservation in this region strongly suggests that the function of Cyrano depends on the formation of this RNA structure, which could modulate the rate and efficiency of degradation of miR-7.

Specialized translation initiation is a novel form of regulation of protein synthesis, whereby RNA structures within the 5'-UTR regulate translation rates of specific mRNAs. Similar to internal ribosome entry sites (IRESs), specialized translation initiation requires the recruitment of eukaryotic initiation factor 3 (eIF3), but also requires cap recognition by eIF3d, a new 5'-m7GTP recognizing protein. How these RNA structures mediate eIF3 recruitment to affect translation of specific mRNAs remains unclear. Here, we report the nuclear magnetic resonance (NMR) structure of a stem-loop within the c-JUN 5' UTR recognized by eIF3 and essential for specialized translation initiation of this well-known oncogene. The structure exhibits similarity to eIF3 recognizing motifs found in hepatitis C virus (HCV)-like IRESs, suggesting mechanistic similarities. This work establishes the RNA structural features involved in c-JUN specialized translation initiation and provides a basis to search for small molecule inhibitors of aberrant expression of the proto-oncogenic c-JUN.

Master's thesis on computational screening of nucleic acids.

Rapid advancements in synthetic biology and nucleic acid synthesis, in particular concerns about its intentional or accidental misuse, call for more sophisticated screening tools to identify genes of interest within short sequence fragments. One major gap in predicting genes of concern is the inadequacy of current tools and ontologies to describe the specific biological processes of pathogenic proteins. The objective of this work is to design software that sensitively assigns taxonomic classifications, functional annotations, and biological processes of interest to short nucleotide sequences of unknown origin (50bp-1,000bp). The overarching goal is to perform sensitive characterization of short sequences and highlight specific pathogenic biological processes of interest (BPoIs). The SeqScreen software executes these tasks in analytical workflows with Nextflow and outputs results in a tab-delimited report. Local and global alignments differentiate hits to taxonomically-related sequences from similar but unrelated sequences, and an ensemble approach leverages multiple tools and databases to assign a variety of functional terms to each query sequence. Final biological process assessments are made from the predicted functional annotations, which leverage information in pre-existing databases, as well as new custom biocurations. Machine learning models predict each biological process of interest on large protein databases before incorporation into the SeqScreen framework to streamline computational efficiency, ensure reproducible results, allow for version control, and facilitate the review of the automated predictions by expert biocurators. The SeqScreen source code is available at https://gitlab.com/treangenlab/seqscreen.

Undergraduate thesis on immunohistochemical analysis.