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Modern life is essentially homochiral, containing D-sugars in nucleic acid backbones and L-amino acids in proteins. Since coded proteins are theorized to have developed from a prebiotic RNA World, the homochirality of L-amino acids observed in all known life presumably resulted from chiral transfer from a homochiral D-RNA World. This transfer would have been mediated by aminoacyl-RNAs defining the genetic code. Previous work on aminoacyl transfer using tRNA mimics has suggested that aminoacylation using D-RNA may be inherently biased toward reactivity with L-amino acids, implying a deterministic path from a D-RNA World to L-proteins. Using a model system of self-aminoacylating D-ribozymes and epimerizable activated amino acid analogs, we test the chiral selectivity of 15 ribozymes derived from an exhaustive search of sequence space. All of the ribozymes exhibit detectable selectivity, and a substantial fraction react preferentially to produce the D-enantiomer of the product. Furthermore, chiral preference is conserved within sequence families. These results are consistent with the transfer of chiral information from RNA to proteins but do not support an intrinsic bias of D-RNA for L-amino acids. Different aminoacylation structures result in different directions of chiral selectivity, such that L-proteins need not emerge from a D-RNA World. more...

Artificial intelligence and machine learning applications are emerging as transformative technologies in medicine. With greater access to a diverse range of big datasets, researchers are turning to these powerful techniques for data analysis. Machine learning can reveal patterns and interactions between variables in large and complex datasets more accurately and efficiently than traditional statistical methods. Machine learning approaches open new possibilities for studying SLE, a multifactorial, highly heterogeneous and complex disease. Here, we discuss how machine learning methods are rapidly being integrated into the field of SLE research. Recent reports have focused on building prediction models and/or identifying novel biomarkers using both supervised and unsupervised techniques for understanding disease pathogenesis, early diagnosis and prognosis of disease. In this review, we will provide an overview of machine learning techniques to discuss current gaps, challenges and opportunities for SLE studies. External validation of most prediction models is still needed before clinical adoption. Utilisation of deep learning models, access to alternative sources of health data and increased awareness of the ethics, governance and regulations surrounding the use of artificial intelligence in medicine will help propel this exciting field forward. more...

The relationship between genotype and phenotype, or the fitness landscape, is the foundation of genetic engineering and evolution. However, mapping fitness landscapes poses a major technical challenge due to the amount of quantifiable data that is required. Catalytic RNA is a special topic in the study of fitness landscapes due to its relatively small sequence space combined with its importance in synthetic biology. The combination of in vitro selection and high-throughput sequencing has recently provided empirical maps of both complete and local RNA fitness landscapes, but the astronomical size of sequence space limits purely experimental investigations. Next steps are likely to involve data-driven interpolation and extrapolation over sequence space using various machine learning techniques. We discuss recent progress in understanding RNA fitness landscapes, particularly with respect to protocells and machine representations of RNA. The confluence of technical advances may significantly impact synthetic biology in the near future. more...

ConspectusCreating a living system from nonliving matter is a great challenge in chemistry and biophysics. The early history of life can provide inspiration from the idea of the prebiotic RNA World established by ribozymes, in which all genetic and catalytic activities were executed by RNA. Such a system could be much simpler than the interdependent central dogma characterizing life today. At the same time, cooperative systems require a mechanism such as cellular compartmentalization in order to survive and evolve. Minimal cells might therefore consist of simple vesicles enclosing a prebiotic RNA metabolism.The internal volume of a vesicle is a distinctive environment due to its closed boundary, which alters diffusion and available volume for macromolecules and changes effective molecular concentrations, among other considerations. These physical effects are mechanistically distinct from chemical interactions, such as electrostatic repulsion, that might also occur between the membrane boundary and encapsulated contents. Both indirect and direct interactions between the membrane and RNA can give rise to nonintuitive, emergent behaviors in the model protocell system. We have been examining how encapsulation inside membrane vesicles would affect the folding and activity of entrapped RNA.Using biophysical techniques such as FRET, we characterized ribozyme folding and activity inside vesicles. Encapsulation inside model protocells generally promoted RNA folding, consistent with an excluded volume effect, independently of chemical interactions. This energetic stabilization translated into increased ribozyme activity in two different systems that were studied (hairpin ribozyme and self-aminoacylating RNAs). A particularly intriguing finding was that encapsulation could rescue the activity of mutant ribozymes, suggesting that encapsulation could affect not only folding and activity but also evolution. To study this further, we developed a high-throughput sequencing assay to more...

Recent advances in generative models, including large language models (LLMs), vision language models (VLMs), and diffusion models, have accelerated the field of natural language and image processing in medicine and marked a significant paradigm shift in how biomedical models can be developed and deployed. While these models are highly adaptable to new tasks, scaling and evaluating their usage presents new challenges not addressed in previous frameworks. In particular, the ability of these models to produce useful outputs with little to no specialized training data ("zero-" or "few-shot" approaches), as well as the open-ended nature of their outputs, necessitate the development of updated guidelines in using and evaluating these models. In response to gaps in standards and best practices for the development of clinical AI tools identified by US Executive Order 141103 and several emerging national networks for clinical AI evaluation, we begin to formalize some of these guidelines by building on the "Minimum information about clinical artificial intelligence modeling" (MI-CLAIM) checklist. The MI-CLAIM checklist, originally developed in 2020, provided a set of six steps with guidelines on the minimum information necessary to encourage transparent, reproducible research for artificial intelligence (AI) in medicine. Here, we propose modifications to the original checklist that highlight differences in training, evaluation, interpretability, and reproducibility of generative models compared to traditional AI models for clinical research. This updated checklist also seeks to clarify cohort selection reporting and adds additional items on alignment with ethical standards. more...

Prescription contraceptives play a critical role in supporting women's reproductive health. With nearly 50 million women in the United States using contraceptives, understanding the factors that drive contraceptives selection and switching is of significant interest. However, many factors related to medication switching are often only captured in unstructured clinical notes and can be difficult to extract. Here, we evaluate the zero-shot abilities of a recently developed large language model, GPT-4 (via HIPAA-compliant Microsoft Azure API), to identify reasons for switching between classes of contraceptives from the UCSF Information Commons clinical notes dataset. We demonstrate that GPT-4 can accurately extract reasons for contraceptive switching, outperforming baseline BERT-based models with microF1 scores of 0.849 and 0.881 for contraceptive start and stop extraction, respectively. Human evaluation of GPT-4-extracted reasons for switching showed 91.4% accuracy, with minimal hallucinations. Using extracted reasons, we identified patient preference, adverse events, and insurance as key reasons for switching using unsupervised topic modeling approaches. Notably, we also showed using our approach that "weight gain/mood change" and "insurance coverage" are disproportionately found as reasons for contraceptive switching in specific demographic populations. Our code and supplemental data are available at https://github.com/BMiao10/contraceptive-switching. more...

The ongoing coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), continues to cause significant morbidity and mortality worldwide. Although most infections are mild and a majority of patients recover, some experience severe and often fatal systemic inflammation, cytokine storm, and acute respiratory distress syndrome. The innate immune system of the human body is the first line of defense against SARS-CoV-2, sensing the virus through pattern recognition receptors and activating inflammatory cascades that promote viral clearance. Simultaneously, the virus has evolved numerous strategies to escape detection and surveillance of the immune system for successful replication. An improved understanding of innate immunity and viral evasion strategies will help identify targeted therapies to mitigate disease and improve patient outcome. Here, we report two cellular epigenetic proteins, BRD4 and SIRT5, as anti- and proviral binding partners of SARS-CoV-2 envelope (E) and non-structural protein Nsp14, respectively. We identify bromodomain and extra-terminal (BET) proteins as critical antiviral factors as genetic or chemical inactivation of BRD4 exacerbates viral infection in cells and enhanced mortality in mice. BET inactivation suppresses interferon production induced by SARS-CoV-2, a process phenocopied by the “histone mimetic” E protein, supporting a model where the E protein evolved to antagonize the innate immune system via BET protein inhibition. Conversely, genetic or chemical inactivation of SIRT5 reduces SARS-CoV-2 replication in cells. While SIRT5 interacts with Nsp14 through its catalytic domain, Nsp14 does not appear to be directly deacylated by SIRT5. Depletion of SIRT5 results in higher basal levels of innate immunity and a stronger antiviral response during infection, indicating SIRT5 is a proviral factor necessary for efficient viral replication. Lastly, we compared the humoral immune response more...

Inhibitors of bromodomain and extraterminal domain (BET) proteins are possible anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) prophylactics as they downregulate angiotensin-converting enzyme 2 (ACE2). Here we show that BET proteins should not be inactivated therapeutically because they are critical antiviral factors at the post-entry level. Depletion of BRD3 or BRD4 in cells overexpressing ACE2 exacerbates SARS-CoV-2 infection; the same is observed when cells with endogenous ACE2 expression are treated with BET inhibitors during infection and not before. Viral replication and mortality are also enhanced in BET inhibitor-treated mice overexpressing ACE2. BET inactivation suppresses interferon production induced by SARS-CoV-2, a process phenocopied by the envelope (E) protein previously identified as a possible "histone mimetic." E protein, in an acetylated form, directly binds the second bromodomain of BRD4. Our data support a model where SARS-CoV-2 E protein evolved to antagonize interferon responses via BET protein inhibition; this neutralization should not be further enhanced with BET inhibitor treatment. more...

Inhibitors of bromodomain and extraterminal domain (BET) proteins are possible anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) prophylactics as they downregulate angiotensin-converting enzyme 2 (ACE2). Here we show that BET proteins should not be inactivated therapeutically because they are critical antiviral factors at the post-entry level. Depletion of BRD3 or BRD4 in cells overexpressing ACE2 exacerbates SARS-CoV-2 infection; the same is observed when cells with endogenous ACE2 expression are treated with BET inhibitors during infection and not before. Viral replication and mortality are also enhanced in BET inhibitor-treated mice overexpressing ACE2. BET inactivation suppresses interferon production induced by SARS-CoV-2, a process phenocopied by the envelope (E) protein previously identified as a possible "histone mimetic." E protein, in an acetylated form, directly binds the second bromodomain of BRD4. Our data support a model where SARS-CoV-2 E protein evolved to antagonize interferon responses via BET protein inhibition; this neutralization should not be further enhanced with BET inhibitor treatment. more...

Objectives A novel longitudinal clustering technique was applied to comprehensive autoantibody data from a large, well-characterised, multinational inception systemic lupus erythematosus (SLE) cohort to determine profiles predictive of clinical outcomes. Methods Demographic, clinical and serological data from 805 patients with SLE obtained within 15 months of diagnosis and at 3-year and 5-year follow-up were included. For each visit, sera were assessed for 29 antinuclear antibodies (ANA) immunofluorescence patterns and 20 autoantibodies. K-means clustering on principal component analysis-transformed longitudinal autoantibody profiles identified discrete phenotypic clusters. One-way analysis of variance compared cluster enrolment demographics and clinical outcomes at 10-year follow-up. Cox proportional hazards model estimated the HR for survival adjusting for age of disease onset. Results Cluster 1 (n=137, high frequency of anti-Smith, anti-U1RNP, AC-5 (large nuclear speckled pattern) and high ANA titres) had the highest cumulative disease activity and immunosuppressants/biologics use at year 10. Cluster 2 (n=376, low anti-double stranded DNA (dsDNA) and ANA titres) had the lowest disease activity, frequency of lupus nephritis and immunosuppressants/biologics use. Cluster 3 (n=80, highest frequency of all five antiphospholipid antibodies) had the highest frequency of seizures and hypocomplementaemia. Cluster 4 (n=212) also had high disease activity and was characterised by multiple autoantibody reactivity including to antihistone, anti-dsDNA, antiribosomal P, anti-Sjögren syndrome antigen A or Ro60, anti-Sjögren syndrome antigen B or La, anti-Ro52/Tripartite Motif Protein 21, antiproliferating cell nuclear antigen and anticentromere B). Clusters 1 (adjusted HR 2.60 (95% CI 1.12 to 6.05), p=0.03) and 3 (adjusted HR 2.87 (95% CI 1.22 to 6.74), p=0.02) had lower survival compared with cluster 2. Conclusion Four discrete SLE patient longitudinal autoantibody clusters more...

Objectives A novel longitudinal clustering technique was applied to comprehensive autoantibody data from a large, well-characterised, multinational inception systemic lupus erythematosus (SLE) cohort to determine profiles predictive of clinical outcomes. Methods Demographic, clinical and serological data from 805 patients with SLE obtained within 15 months of diagnosis and at 3-year and 5-year follow-up were included. For each visit, sera were assessed for 29 antinuclear antibodies (ANA) immunofluorescence patterns and 20 autoantibodies. K-means clustering on principal component analysis-transformed longitudinal autoantibody profiles identified discrete phenotypic clusters. One-way analysis of variance compared cluster enrolment demographics and clinical outcomes at 10-year follow-up. Cox proportional hazards model estimated the HR for survival adjusting for age of disease onset. Results Cluster 1 (n=137, high frequency of anti-Smith, anti-U1RNP, AC-5 (large nuclear speckled pattern) and high ANA titres) had the highest cumulative disease activity and immunosuppressants/biologics use at year 10. Cluster 2 (n=376, low anti-double stranded DNA (dsDNA) and ANA titres) had the lowest disease activity, frequency of lupus nephritis and immunosuppressants/biologics use. Cluster 3 (n=80, highest frequency of all five antiphospholipid antibodies) had the highest frequency of seizures and hypocomplementaemia. Cluster 4 (n=212) also had high disease activity and was characterised by multiple autoantibody reactivity including to antihistone, anti-dsDNA, antiribosomal P, anti-Sjögren syndrome antigen A or Ro60, anti-Sjögren syndrome antigen B or La, anti-Ro52/Tripartite Motif Protein 21, antiproliferating cell nuclear antigen and anticentromere B). Clusters 1 (adjusted HR 2.60 (95% CI 1.12 to 6.05), p=0.03) and 3 (adjusted HR 2.87 (95% CI 1.22 to 6.74), p=0.02) had lower survival compared with cluster 2. Conclusion Four discrete SLE patient longitudinal autoantibody clusters more...

OBJECTIVES: A novel longitudinal clustering technique was applied to comprehensive autoantibody data from a large, well-characterised, multinational inception systemic lupus erythematosus (SLE) cohort to determine profiles predictive of clinical outcomes. METHODS: Demographic, clinical and serological data from 805 patients with SLE obtained within 15 months of diagnosis and at 3-year and 5-year follow-up were included. For each visit, sera were assessed for 29 antinuclear antibodies (ANA) immunofluorescence patterns and 20 autoantibodies. K-means clustering on principal component analysis-transformed longitudinal autoantibody profiles identified discrete phenotypic clusters. One-way analysis of variance compared cluster enrolment demographics and clinical outcomes at 10-year follow-up. Cox proportional hazards model estimated the HR for survival adjusting for age of disease onset. RESULTS: Cluster 1 (n=137, high frequency of anti-Smith, anti-U1RNP, AC-5 (large nuclear speckled pattern) and high ANA titres) had the highest cumulative disease activity and immunosuppressants/biologics use at year 10. Cluster 2 (n=376, low anti-double stranded DNA (dsDNA) and ANA titres) had the lowest disease activity, frequency of lupus nephritis and immunosuppressants/biologics use. Cluster 3 (n=80, highest frequency of all five antiphospholipid antibodies) had the highest frequency of seizures and hypocomplementaemia. Cluster 4 (n=212) also had high disease activity and was characterised by multiple autoantibody reactivity including to antihistone, anti-dsDNA, antiribosomal P, anti-Sjögren syndrome antigen A or Ro60, anti-Sjögren syndrome antigen B or La, anti-Ro52/Tripartite Motif Protein 21, antiproliferating cell nuclear antigen and anticentromere B). Clusters 1 (adjusted HR 2.60 (95% CI 1.12 to 6.05), p=0.03) and 3 (adjusted HR 2.87 (95% CI 1.22 to 6.74), p=0.02) had lower survival compared with cluster 2. CONCLUSION: Four discrete SLE patient longitudinal autoantibody clusters more...

Despite unprecedented efforts, our therapeutic arsenal against SARS-CoV-2 remains limited. The conserved macrodomain 1 (Mac1) in NSP3 is an enzyme exhibiting ADP-ribosylhydrolase activity and a possible drug target. To determine the role of Mac1 catalytic activity in viral replication, we generated recombinant viruses and replicons encoding a catalytically inactive NSP3 Mac1 domain by mutating a critical asparagine in the active site. While substitution to alanine (N40A) reduced catalytic activity by ~10-fold, mutations to aspartic acid (N40D) reduced activity by ~100-fold relative to wild-type. Importantly, the N40A mutation rendered Mac1 unstable in vitro and lowered expression levels in bacterial and mammalian cells. When incorporated into SARS-CoV-2 molecular clones, the N40D mutant only modestly affected viral fitness in immortalized cell lines, but reduced viral replication in human airway organoids by 10-fold. In mice, the N40D mutant replicated at >1000-fold lower levels compared to the wild-type virus while inducing a robust interferon response; all animals infected with the mutant virus survived infection. Our data validate the critical role of SARS-CoV-2 NSP3 Mac1 catalytic activity in viral replication and as a promising therapeutic target to develop antivirals. more...

Although the SARS-CoV-2 Omicron variant (BA.1) spread rapidly across the world and effectively evaded immune responses, its viral fitness in cell and animal models was reduced. The precise nature of this attenuation remains unknown as generating replication-competent viral genomes is challenging because of the length of the viral genome (~30 kb). Here, we present a plasmid-based viral genome assembly and rescue strategy (pGLUE) that constructs complete infectious viruses or noninfectious subgenomic replicons in a single ligation reaction with >80% efficiency. Fully sequenced replicons and infectious viral stocks can be generated in 1 and 3 weeks, respectively. By testing a series of naturally occurring viruses as well as Delta-Omicron chimeric replicons, we show that Omicron nonstructural protein 6 harbors critical attenuating mutations, which dampen viral RNA replication and reduce lipid droplet consumption. Thus, pGLUE overcomes remaining barriers to broadly study SARS-CoV-2 replication and reveals deficits in nonstructural protein function underlying Omicron attenuation. more...

Viruses targeting mammalian cells can indirectly alter the gut microbiota, potentially compounding their phenotypic effects. Multiple studies have observed a disrupted gut microbiota in severe cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection that require hospitalization. Yet, despite demographic shifts in disease severity resulting in a large and continuing burden of non-hospitalized infections, we still know very little about the impact of mild SARS-CoV-2 infection on the gut microbiota in the outpatient setting. To address this knowledge gap, we longitudinally sampled 14 SARS-CoV-2-positive subjects who remained outpatient and 4 household controls. SARS-CoV-2 cases exhibited a significantly less stable gut microbiota relative to controls. These results were confirmed and extended in the K18-humanized angiotensin-converting enzyme 2 mouse model, which is susceptible to SARS-CoV-2 infection. All of the tested SARS-CoV-2 variants significantly disrupted the mouse gut microbiota, including USA-WA1/2020 (the original variant detected in the USA), Delta, and Omicron. Surprisingly, despite the fact that the Omicron variant caused the least severe symptoms in mice, it destabilized the gut microbiota and led to a significant depletion in Akkermansia muciniphila. Furthermore, exposure of wild-type C57BL/6J mice to SARS-CoV-2 disrupted the gut microbiota in the absence of severe lung pathology.IMPORTANCETaken together, our results demonstrate that even mild cases of SARS-CoV-2 can disrupt gut microbial ecology. Our findings in non-hospitalized individuals are consistent with studies of hospitalized patients, in that reproducible shifts in gut microbial taxonomic abundance in response to SARS-CoV-2 have been difficult to identify. Instead, we report a long-lasting instability in the gut microbiota. Surprisingly, our mouse experiments revealed an impact of the Omicron variant, despite producing the least severe symptoms in genetically susceptible more...

One of the primary global health concerns is the increase in antimicrobial resistance. Polymer chemistry enables the preparation of macromolecules with hydrophobic and cationic side chains that kill bacteria by destabilizing their membranes. In the current study, macromolecules are prepared by radical copolymerization of caffeine methacrylate as the hydrophobic monomer and cationic- or zwitterionic-methacrylate monomers. The synthesized copolymers bearing tert-butyl-protected carboxybetaine as cationic side chains showed antibacterial activity toward Gram-positive bacteria (S. aureus) and Gram-negative bacteria (E. coli). By tuning the hydrophobic content, we prepared copolymers with optimal antibacterial activity against S. aureus, including methicillin-resistant clinical isolates. Moreover, the caffeine-cationic copolymers presented good biocompatibility in a mouse embryonic fibroblast cell line, NIH 3T3, and hemocompatibility with erythrocytes even at high hydrophobic monomer content (30-50%). Therefore, incorporating caffeine and introducing tert-butyl-protected carboxybetaine as a quaternary cation in polymers could be a novel strategy to combat bacteria. more...

A high-risk pregnancy is a pregnancy complicated by factors that can adversely affect the outcomes of the mother or the infant. Health insurers use algorithms to identify members who would benefit from additional clinical support. This work presents the implementation of a real-world ML-based system to assist care managers in identifying pregnant patients at risk of complications. In this retrospective evaluation study, we developed a novel hybrid-ML classifier to predict whether patients are pregnant and trained a standard classifier using claims data from a health insurance company in the US to predict whether a patient will develop pregnancy complications. These models were developed in cooperation with the care management team and integrated into a user interface with explanations for the nurses. The proposed models outperformed commonly used claim codes for the identification of pregnant patients at the expense of a manageable false positive rate. Our risk complication classifier shows that we can accurately triage patients by risk of complication. Our approach and evaluation are guided by human-centric design. In user studies with the nurses, they preferred the proposed models over existing approaches. more...

Many statistical methods have been developed for the analysis of microbial community profiles, but due to the complexity of typical microbiome measurements, inference of interactions between microbial features remains challenging. We develop a Bayesian zero-inflated rounded log-normal kernel method to model interaction between microbial features in a community using multivariate count data in the presence of covariates and excess zeros. The model carefully constructs the interaction structure by imposing joint sparsity on the covariance matrix of the kernel and obtains a reliable estimate of the structure with a small sample size. The model also includes zero inflation to account for excess zeros observed in data and infers differential abundance of microbial features associated with covariates through log-linear regression. We provide simulation studies and real data analysis examples to demonstrate the developed model. Comparison of the model to a simpler model and popular alternatives in simulation studies shows that, in addition to an added and important insight on the feature interaction, it yields superior parameter estimates and model fit in various settings. more...

Ethical frameworks for the use of natural language processing (NLP) are urgently needed to shape how large language models (LLMs) and similar tools are used for healthcare applications. Healthcare faces existing challenges including the balance of power in clinician-patient relationships, systemic health disparities, historical injustices, and economic constraints. Drawing directly from the voices of those most affected, and focusing on a case study of a specific healthcare setting, we propose a set of guiding principles for the use of NLP in maternal healthcare. We led an interactive session centered on an LLM-based chatbot demonstration during a full-day workshop with 39 participants, and additionally surveyed 30 healthcare workers and 30 birthing people about their values, needs, and perceptions of NLP tools in the context of maternal health. We conducted quantitative and qualitative analyses of the survey results and interactive discussions to consolidate our findings into a set of guiding principles. We propose nine principles for ethical use of NLP for maternal healthcare, grouped into three themes: (i) recognizing contextual significance (ii) holistic measurements, and (iii) who/what is valued. For each principle, we describe its underlying rationale and provide practical advice. This set of principles can provide a methodological pattern for other researchers and serve as a resource to practitioners working on maternal health and other healthcare fields to emphasize the importance of technical nuance, historical context, and inclusive design when developing NLP technologies for clinical use. more...

N-acyl-ethanolamine (NAEs) serve as key endogenous lipid mediators as revealed by manipulation of fatty acid amide hydrolase (FAAH), the primary enzyme responsible for metabolizing NAEs. Preclinical studies focused on FAAH or NAE receptors indicate an important role for NAE signaling in nociception and affective behaviors. However, there is limited information on the role of NAE biosynthesis in these same behavioral paradigms. Biosynthesis of NAEs has been attributed largely to the enzyme N-acylphosphatidylethanolamine Phospholipase D (NAPE-PLD), one of three pathways capable of producing these bioactive lipids in the brain. In this report, we demonstrate that Nape-pld knockout (KO) mice displayed reduced sucrose preference and consumption, but other baseline anxiety-like or depression-like behaviors were unaltered. Additionally, we observed sex-dependent responses in thermal nociception and other baseline measures in wildtype (WT) mice that were absent in Nape-pld KO mice. In the Complete Freund's Adjuvant (CFA) model of inflammatory arthritis, WT mice exhibited sex-dependent changes in paw edema that were lost in Nape-pld KO mice. However, there was no effect of Nape-pld deletion on arthritic pain-like behaviors (grip force deficit and tactile allodynia) in either sex, indicating that while NAPE-PLD may alter local inflammation, it does not contribute to pain-like behaviors associated with inflammatory arthritis. Collectively, these findings indicate that chronic and systemic NAPE-PLD inactivation will likely be well-tolerated, warranting further pharmacological evaluation of this target in other disease indications. more...

The drug development pipeline for a new compound can last 10-20 years and cost over 10 billion. Drug repurposing offers a more time- and cost-effective alternative. Computational approaches based on biomedical knowledge graph representations have recently yielded new drug repurposing hypotheses. In this study, we present a novel, disease-specific hypergraph representation learning technique to derive contextual embeddings of biological pathways of various lengths but that all start at any given drug and all end at the disease of interest. Further, we extend this method to multi-disease hypergraphs. To determine the repurposing potential of each of the 1,522 drugs, we derive drug-specific distributions of cosine similarity values and ultimately consider the median for ranking. Cosine similarity values are computed between (1) all biological pathways starting at the considered drug and ending at the disease of interest and (2) all biological pathways starting at drugs currently prescribed against that disease and ending at the disease of interest. We illustrate our approach with Alzheimer's disease (AD) and two of its risk factors: hypertension (HTN) and type 2 diabetes (T2D). We compare each drug's rank across four hypergraph settings (single- or multi-disease): AD only, AD + HTN, AD + T2D, and AD + HTN + T2D. Notably, our framework led to the identification of two promising drugs whose repurposing potential was significantly higher in hypergraphs combining two diseases: dapagliflozin (antidiabetic; moved up, from top 32$\%$ to top 7$\%$, across all considered drugs) and debrisoquine (antihypertensive; moved up, from top 76$\%$ to top 23$\%$). Our approach serves as a hypothesis generation tool, to be paired with a validation pipeline relying on laboratory experiments and semi-automated parsing of the biomedical literature., Comment: Extended Abstract presented at Machine Learning for Health (ML4H) symposium 2023, December 10th, 2023, New Orleans, United States, 9 pages more...

High-throughput sequencing (HTS) can identify unique DNA sequences and quantify their abundances from mixed DNA pools. HTS-based assays can profile complex biological or chemical systems with entities that can convert to unique DNA sequences. Computational models are also developed to analyze these HTS data at a larger scale. However, such data contain unique analytical challenges, including discrete counts, relative measurement, and small sample size. Careful assessments of these computational tools are required for robust interpretations of results.In this dissertation, we investigated the computational challenges, proposed and assess the solutions for two applications of HTS-based assays. In the first work, we proposed k-Seq, a kinetic assay to measure the activity of self-aminoacylation ribozymes (catalytic RNA). Characterizing the kinetics for different molecules in a heterogeneous pool is challenging as their abundance and activities can vary in several orders of magnitude. We explored different designs of experiments and identified critical factors affecting the estimation of kinetic coefficients in the pseudo-first-order kinetic model for these ribozymes. Using bootstrapping, we robustly quantified the uncertainty of estimation for individual sequences and determined the minimum sequencing counts required for reliable estimations. Combining the improved experimental design and new analytical tools, we robustly quantified the kinetics for 10^5 different ribozymes.In the second work, we constructed the correlation networks between microorganisms from metagenomic data and studied the structure of a human skin microbiome in patients with chronic wounds. We designed a variation of Gaussian graphical models to capture the direct correlations between the abundances of bacteria and viruses while accounting for the structure and limitations in the data. To minimize the discovery of false correlations from the small noisy dataset, we applied a two-step model selection more...

SARS-CoV-2 Delta and Omicron are globally relevant variants of concern. Although individuals infected with Delta are at risk of developing severe lung disease, infection with Omicron often causes milder symptoms, especially in vaccinated individuals1,2. The question arises of whether widespread Omicron infections could lead to future cross-variant protection, accelerating the end of the pandemic. Here we show that without vaccination, infection with Omicron induces a limited humoral immune response in mice and humans. Sera from mice overexpressing the human ACE2 receptor and infected with Omicron neutralize only Omicron, but not other variants of concern, whereas broader cross-variant neutralization was observed after WA1 and Delta infections. Unlike WA1 and Delta, Omicron replicates to low levels in the lungs and brains of infected animals, leading to mild disease with reduced expression of pro-inflammatory cytokines and diminished activation of lung-resident T cells. Sera from individuals who were unvaccinated and infected with Omicron show the same limited neutralization of only Omicron itself. By contrast, Omicron breakthrough infections induce overall higher neutralization titres against all variants of concern. Our results demonstrate that Omicron infection enhances pre-existing immunity elicited by vaccines but, on its own, may not confer broad protection against non-Omicron variants in unvaccinated individuals. more...

Systems of catalytic RNAs presumably gave rise to important evolutionary innovations, such as the genetic code. Such systems may exhibit particular tolerance to errors (error minimization) as well as coding specificity. While often assumed to result from natural selection, error minimization may instead be an emergent by-product. In an RNA world, a system of self-aminoacylating ribozymes could enforce the mapping of amino acids to anticodons. We measured the activity of thousands of ribozyme mutants on alternative substrates (activated analogs for tryptophan, phenylalanine, leucine, isoleucine, valine, and methionine). Related ribozymes exhibited shared preferences for substrates, indicating that adoption of additional amino acids by existing ribozymes would itself lead to error minimization. Furthermore, ribozyme activity was positively correlated with specificity, indicating that selection for increased activity would also lead to increased specificity. These results demonstrate that by-products of ribozyme evolution could lead to adaptive value in specificity and error tolerance. more...

How did life originate on Earth? How might it have happened differently? The answers to these fundamental questions will inform efforts to develop useful synthetic organisms and empower the search for extraterrestrial life. The “RNA World” hypothesis posits that ribonucleic acid (RNA) once played the roles of both information carrier and catalyst in primitive living systems. In the transition from the RNA World to DNA-based heredity and catalysis by left-handed (L-)proteins, a likely essential step would have been the emergence of substrate-specific self-aminoacylating RNA enzymes (ribozymes) functioning in an early translation system. How these early self-replicating systems evolved toward modern cells, and what other outcomes might have been possible, are open questions. Evolution is conceptualized as a biased random walk through genetic space, in which each genotype is associated with a fitness (the “fitness landscape”). Mapping the fitness landscape for a given genetic element enables prediction of evolutionary paths. A recently developed method maps fitness landscapes for self-aminoacylating ribozymes by selection from a random library using prebiotically plausible activated amino acid analogs. In this work, we map the landscapes for self-aminoacylating ribozymes under variable environmental conditions and assess the effect of the environment on the topography and connectivity of fitness landscapes. We also evaluate the stereoselectivity of self-aminoacylating ribozymes to determine whether life’s transition to homochiral L-proteins was deterministic, or whether a right-handed (D-)protein world was possible. We find that a dynamic environment improves the connectivity of fitness landscapes, increasing the number of evolutionary paths available and enhancing evolution’s ability to optimize function. We also find that self-aminoacylating ribozymes can be either L- or D-selective, suggesting alternate possible outcomes for the chirality of life. These results repr more...

High-throughput sequencing (HTS) can identify unique DNA sequences and quantify their abundances from mixed DNA pools. HTS-based assays can profile complex biological or chemical systems with entities that can convert to unique DNA sequences. Computational models are also developed to analyze these HTS data at a larger scale. However, such data contain unique analytical challenges, including discrete counts, relative measurement, and small sample size. Careful assessments of these computational tools are required for robust interpretations of results.In this dissertation, we investigated the computational challenges, proposed and assess the solutions for two applications of HTS-based assays. In the first work, we proposed k-Seq, a kinetic assay to measure the activity of self-aminoacylation ribozymes (catalytic RNA). Characterizing the kinetics for different molecules in a heterogeneous pool is challenging as their abundance and activities can vary in several orders of magnitude. We explored different designs of experiments and identified critical factors affecting the estimation of kinetic coefficients in the pseudo-first-order kinetic model for these ribozymes. Using bootstrapping, we robustly quantified the uncertainty of estimation for individual sequences and determined the minimum sequencing counts required for reliable estimations. Combining the improved experimental design and new analytical tools, we robustly quantified the kinetics for 10^5 different ribozymes.In the second work, we constructed the correlation networks between microorganisms from metagenomic data and studied the structure of a human skin microbiome in patients with chronic wounds. We designed a variation of Gaussian graphical models to capture the direct correlations between the abundances of bacteria and viruses while accounting for the structure and limitations in the data. To minimize the discovery of false correlations from the small noisy dataset, we applied a two-step model selection more...

How did life originate on Earth? How might it have happened differently? The answers to these fundamental questions will inform efforts to develop useful synthetic organisms and empower the search for extraterrestrial life. The “RNA World” hypothesis posits that ribonucleic acid (RNA) once played the roles of both information carrier and catalyst in primitive living systems. In the transition from the RNA World to DNA-based heredity and catalysis by left-handed (L-)proteins, a likely essential step would have been the emergence of substrate-specific self-aminoacylating RNA enzymes (ribozymes) functioning in an early translation system. How these early self-replicating systems evolved toward modern cells, and what other outcomes might have been possible, are open questions. Evolution is conceptualized as a biased random walk through genetic space, in which each genotype is associated with a fitness (the “fitness landscape”). Mapping the fitness landscape for a given genetic element enables prediction of evolutionary paths. A recently developed method maps fitness landscapes for self-aminoacylating ribozymes by selection from a random library using prebiotically plausible activated amino acid analogs. In this work, we map the landscapes for self-aminoacylating ribozymes under variable environmental conditions and assess the effect of the environment on the topography and connectivity of fitness landscapes. We also evaluate the stereoselectivity of self-aminoacylating ribozymes to determine whether life’s transition to homochiral L-proteins was deterministic, or whether a right-handed (D-)protein world was possible. We find that a dynamic environment improves the connectivity of fitness landscapes, increasing the number of evolutionary paths available and enhancing evolution’s ability to optimize function. We also find that self-aminoacylating ribozymes can be either L- or D-selective, suggesting alternate possible outcomes for the chirality of life. These results repr more...

Systems of catalytic RNAs presumably gave rise to important evolutionary innovations, such as the genetic code. Such systems may exhibit particular tolerance to errors (error minimization) as well as coding specificity. While often assumed to result from natural selection, error minimization may instead be an emergent by-product. In an RNA world, a system of self-aminoacylating ribozymes could enforce the mapping of amino acids to anticodons. We measured the activity of thousands of ribozyme mutants on alternative substrates (activated analogs for tryptophan, phenylalanine, leucine, isoleucine, valine, and methionine). Related ribozymes exhibited shared preferences for substrates, indicating that adoption of additional amino acids by existing ribozymes would itself lead to error minimization. Furthermore, ribozyme activity was positively correlated with specificity, indicating that selection for increased activity would also lead to increased specificity. These results demonstrate that by-products of ribozyme evolution could lead to adaptive value in specificity and error tolerance. more...

SARS-CoV-2 Delta and Omicron are globally relevant variants of concern. Although individuals infected with Delta are at risk of developing severe lung disease, infection with Omicron often causes milder symptoms, especially in vaccinated individuals1,2. The question arises of whether widespread Omicron infections could lead to future cross-variant protection, accelerating the end of the pandemic. Here we show that without vaccination, infection with Omicron induces a limited humoral immune response in mice and humans. Sera from mice overexpressing the human ACE2 receptor and infected with Omicron neutralize only Omicron, but not other variants of concern, whereas broader cross-variant neutralization was observed after WA1 and Delta infections. Unlike WA1 and Delta, Omicron replicates to low levels in the lungs and brains of infected animals, leading to mild disease with reduced expression of pro-inflammatory cytokines and diminished activation of lung-resident T cells. Sera from individuals who were unvaccinated and infected with Omicron show the same limited neutralization of only Omicron itself. By contrast, Omicron breakthrough infections induce overall higher neutralization titres against all variants of concern. Our results demonstrate that Omicron infection enhances pre-existing immunity elicited by vaccines but, on its own, may not confer broad protection against non-Omicron variants in unvaccinated individuals. more...

During the origin of life, encapsulation of RNA inside vesicles is believed to have been a defining feature of the earliest cells (protocells). The confined biophysical environment provided by membrane encapsulation differs from that of bulk solution and has been shown to increase activity as well as evolutionary rate for functional RNA. However, the structural basis of the effect on RNA has not been clear. Here, we studied how encapsulation of the hairpin ribozyme inside model protocells affects ribozyme kinetics, ribozyme folding into the active conformation, and cleavage and ligation activities. We further examined the effect of encapsulation on the folding of a stem-loop RNA structure and on the formation of a triplex structure in a pH-sensitive DNA switch. The results indicate that encapsulation promotes RNA-RNA association, both intermolecular and intramolecular, and also stabilizes tertiary folding, including the docked conformation characteristic of the active hairpin ribozyme and the triplex structure. The effects of encapsulation were sufficient to rescue the activity of folding-deficient mutants of the hairpin ribozyme. Stabilization of multiple modes of nucleic acid folding and interaction thus enhanced the activity of encapsulated nucleic acids. Increased association between RNA molecules may facilitate the formation of more complex structures and cooperative interactions. These effects could promote the emergence of biological functions in an "RNA world" and may have utility in the construction of minimal synthetic cells. more...

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant contains extensive sequence changes relative to the earlier-arising B.1, B.1.1, and Delta SARS-CoV-2 variants that have unknown effects on viral infectivity and response to existing vaccines. Using SARS-CoV-2 virus-like particles (VLPs), we examined mutations in all four structural proteins and found that Omicron and Delta showed 4.6-fold higher luciferase delivery overall relative to the ancestral B.1 lineage, a property conferred mostly by enhancements in the S and N proteins, while mutations in M and E were mostly detrimental to assembly. Thirty-eight antisera samples from individuals vaccinated with Pfizer/BioNTech, Moderna, or Johnson & Johnson vaccines and convalescent sera from unvaccinated COVID-19 survivors had 15-fold lower efficacy to prevent cell transduction by VLPs containing the Omicron mutations relative to the ancestral B.1 spike protein. A third dose of Pfizer vaccine elicited substantially higher neutralization titers against Omicron, resulting in detectable neutralizing antibodies in eight out of eight subjects compared to one out of eight preboosting. Furthermore, the monoclonal antibody therapeutics casirivimab and imdevimab had robust neutralization activity against B.1 and Delta VLPs but no detectable neutralization of Omicron VLPs, while newly authorized bebtelovimab maintained robust neutralization across variants. Our results suggest that Omicron has similar assembly efficiency and cell entry compared to Delta and that its rapid spread is due mostly to reduced neutralization in sera from previously vaccinated subjects. In addition, most currently available monoclonal antibodies will not be useful in treating Omicron-infected patients with the exception of bebtelovimab. more...

SARS-CoV-2 non-structural protein Nsp14 is a highly conserved enzyme necessary for viral replication. Nsp14 forms a stable complex with non-structural protein Nsp10 and exhibits exoribonuclease and N7-methyltransferase activities. Protein-interactome studies identified human sirtuin 5 (SIRT5) as a putative binding partner of Nsp14. SIRT5 is an NAD-dependent protein deacylase critical for cellular metabolism that removes succinyl and malonyl groups from lysine residues. Here we investigated the nature of this interaction and the role of SIRT5 during SARS-CoV-2 infection. We showed that SIRT5 interacts with Nsp14, but not with Nsp10, suggesting that SIRT5 and Nsp10 are parts of separate complexes. We found that SIRT5 catalytic domain is necessary for the interaction with Nsp14, but that Nsp14 does not appear to be directly deacylated by SIRT5. Furthermore, knock-out of SIRT5 or treatment with specific SIRT5 inhibitors reduced SARS-CoV-2 viral levels in cell-culture experiments. SIRT5 knock-out cells expressed higher basal levels of innate immunity markers and mounted a stronger antiviral response, independently of the Mitochondrial Antiviral Signaling Protein MAVS. Our results indicate that SIRT5 is a proviral factor necessary for efficient viral replication, which opens novel avenues for therapeutic interventions. more...

A collection of the extended abstracts that were presented at the 2nd Machine Learning for Health symposium (ML4H 2022), which was held both virtually and in person on November 28, 2022, in New Orleans, Louisiana, USA. Machine Learning for Health (ML4H) is a longstanding venue for research into machine learning for health, including both theoretical works and applied works. ML4H 2022 featured two submission tracks: a proceedings track, which encompassed full-length submissions of technically mature and rigorous work, and an extended abstract track, which would accept less mature, but innovative research for discussion. All the manuscripts submitted to ML4H Symposium underwent a double-blind peer-review process. Extended abstracts included in this collection describe innovative machine learning research focused on relevant problems in health and biomedicine. more...

Objectives To describe the imaging findings of intimate partner violence (IPV)–related injury and to evaluate the role of longitudinal imaging review in detecting IPV. Methods Radiology studies were reviewed in chronological order and IPV-related injuries were recorded among 400 victims of any type of abuse (group 1) and 288 of physical abuse (group 2) from January 2013 to June 2018. The likelihood of IPV was assessed as low/moderate/high based on the review of (1) current and prior anatomically related studies only and (2) longitudinal imaging history consisting of all prior studies. The first radiological study date with moderate/high suspicion was compared to the self-reported date by the victim. Results A total of 135 victims (33.8%) in group 1 and 144 victims (50%) in group 2 demonstrated IPV-related injuries. Musculoskeletal injury was most common (58.2% and 44.5% in groups 1 and 2, respectively; most commonly lower/upper extremity fractures), followed by neurologic injury (20.9% and 32.9% in groups 1 and 2, respectively; most commonly facial injury). With longitudinal imaging history, radiologists were able to identify IPV in 31% of group 1 and 46.5% of group 2 patients. Amongst these patients, earlier identification by radiologists was provided compared to the self-reported date in 62.3% of group 1 (median, 64 months) and in 52.6% of group 2 (median, 69.3 months). Conclusions Musculoskeletal and neurological injuries were the most common IPV-related injuries. Knowledge of common injuries and longitudinal imaging history may help IPV identification when victims are not forthcoming. Key Points • Musculoskeletal injuries were the most common type of IPV-related injury, followed by neurological injuries. • With longitudinal imaging history, radiologists were able to better raise the suspicion of IPV compared to the selective review of anatomically related studies only. • With longitudinal imaging history, radiologists were able to identify IPV earlier t more...

Objectives To describe the imaging findings of intimate partner violence (IPV)–related injury and to evaluate the role of longitudinal imaging review in detecting IPV. Methods Radiology studies were reviewed in chronological order and IPV-related injuries were recorded among 400 victims of any type of abuse (group 1) and 288 of physical abuse (group 2) from January 2013 to June 2018. The likelihood of IPV was assessed as low/moderate/high based on the review of (1) current and prior anatomically related studies only and (2) longitudinal imaging history consisting of all prior studies. The first radiological study date with moderate/high suspicion was compared to the self-reported date by the victim. Results A total of 135 victims (33.8%) in group 1 and 144 victims (50%) in group 2 demonstrated IPV-related injuries. Musculoskeletal injury was most common (58.2% and 44.5% in groups 1 and 2, respectively; most commonly lower/upper extremity fractures), followed by neurologic injury (20.9% and 32.9% in groups 1 and 2, respectively; most commonly facial injury). With longitudinal imaging history, radiologists were able to identify IPV in 31% of group 1 and 46.5% of group 2 patients. Amongst these patients, earlier identification by radiologists was provided compared to the self-reported date in 62.3% of group 1 (median, 64 months) and in 52.6% of group 2 (median, 69.3 months). Conclusions Musculoskeletal and neurological injuries were the most common IPV-related injuries. Knowledge of common injuries and longitudinal imaging history may help IPV identification when victims are not forthcoming. Key Points • Musculoskeletal injuries were the most common type of IPV-related injury, followed by neurological injuries. • With longitudinal imaging history, radiologists were able to better raise the suspicion of IPV compared to the selective review of anatomically related studies only. • With longitudinal imaging history, radiologists were able to identify IPV earlier t more...

Research has revealed the positive effects of flipped classroom approaches on students’ learning engagement and performance compared with conventional lecture-based classrooms. However, because of a lack of out-of-class learning support, many students fail to comprehensively prepare the provided lecture materials before class. One promising solution to this problem is recommendation systems in the educational area, which have been instrumental in helping learners identify useful and relevant lecture materials that satisfy their learning needs. Thus, in this study, we propose a summarized lecture material recommendation system, which is integrated into an e-book reading system as an enhancement of the flipped classroom approach. This system helps students identify pages that contain essential knowledge that must be thoroughly studied before class. The proposed system was constructed on the basis of our previous work. In this study, a quasi-experiment was conducted in a graduate course that implemented the flipped classroom model: experimental group students learned with the proposed system, whereas the control group students had no access to the additional features. The findings of this study suggest that students who learn with the proposed recommendation system significantly outperform those who learn without the system in a flipped classroom in terms of their learning outcomes and engagement in preclass preparation. more...

Functional biomolecules, such as RNA, encapsulated inside a protocellular membrane are believed to have comprised a very early, critical stage in the evolution of life, since membrane vesicles allow selective permeability and create a unit of selection enabling cooperative phenotypes. The biophysical environment inside a protocell would differ fundamentally from bulk solution due to the microscopic confinement. However, the effect of the encapsulated environment on ribozyme evolution has not been previously studied experimentally. Here, we examine the effect of encapsulation inside model protocells on the self-aminoacylation activity of tens of thousands of RNA sequences using a high-throughput sequencing assay. We find that encapsulation of these ribozymes generally increases their activity, giving encapsulated sequences an advantage over nonencapsulated sequences in an amphiphile-rich environment. In addition, highly active ribozymes benefit disproportionately more from encapsulation. The asymmetry in fitness gain broadens the distribution of fitness in the system. Consistent with Fisher's fundamental theorem of natural selection, encapsulation therefore leads to faster adaptation when the RNAs are encapsulated inside a protocell during in vitro selection. Thus, protocells would not only provide a compartmentalization function but also promote activity and evolutionary adaptation during the origin of life. more...

Understanding molecular evolution can reveal a great deal about the past, present, and future of biological systems. The evolution of catalytic RNA is of particular interest because of its potential role in an ‘RNA World’ at the origin of life. Two crucial aspects in the evolution of biomolecules are optimization on the fitness landscape and co-option for new functions. The fitness landscape describes a function of fitness in the space of all possible sequences. Molecules evolve through a random walk on the fitness landscape, with a bias toward climbing peaks. In addition, the ability of enzymes, including ribozymes, to catalyze side reactions is believed to be essential to the evolution of novel biochemical activities. It has been speculated that the earliest ribozymes were low in activity but high in promiscuity, which then gave rise to specialized descendants with higher activity and specificity. One particularly essential activity for the origin of life would be the reaction of ribozymes with activated amino acids to form aminoacyl-RNAs, with co-option of these aminoacyl-RNAs leading to genetic code expansion. In this work, self-aminoacylating ribozymes were identified through in vitro selection from full coverage of sequence space and characterized using a massively parallel kinetic assay. Three major sequence motifs were identified on the landscape and analysis of evolutionary pathways revealed that, while local optimization within a ribozyme family would be possible, optimization of activity over the entire landscape would be frustrated by large valleys of low activity. The sequence motifs associated with each peak represent different solutions for catalysis, so the inability to traverse the landscape globally corresponds to an inability to restructure the ribozyme without losing activity. In addition, five families representing the three sequence motifs were further investigated to measure their activity with alternative substrates. Ribozymes in each family more...

Various self-cleaving ribozymes appearing in nature catalyze the sequence-specific intramolecular cleavage of RNA and can be engineered to catalyze cleavage of appropriate substrates in an intermolecular fashion, thus acting as true catalysts. The mechanisms of the small, self-cleaving ribozymes have been extensively studied and reviewed previously. Self-cleaving ribozymes can possess high catalytic activity and high substrate specificity; however, substrate specificity is also engineerable within the constraints of the ribozyme structure. While these ribozymes share a common fundamental catalytic mechanism, each ribozyme family has a unique overall architecture and active site organization, indicating that several distinct structures yield this chemical activity. The multitude of catalytic structures, combined with some flexibility in substrate specificity within each family, suggests that such catalytic RNAs, taken together, could access a wide variety of substrates. Here, we give an overview of 10 classes of self-cleaving ribozymes and capture what is understood about their substrate specificity and synthetic applications. Evolution of these ribozymes in an RNA world might be characterized by the emergence of a new ribozyme family followed by rapid adaptation or diversification for specific substrates. more...

Bacterial infections impose some of the largest burdens on human health. Sepsis, typically induced by bacterial infections, accounts for more than 50% of hospital deaths in the United States. With an increase in bacteria resistant to antibiotics, developing tools that can detect bacteria and treat both bacterial contamination and infections is of paramount importance. Bacteriophage (phage) are bacterial viruses that are non-toxic to humans. They have evolved various mechanisms to target and damage host bacterial cells. Phages are easy to engineer and could be used as an additional instrument in the antimicrobial tool box to detect and treat bacterial infections. Through bioengineering phage we exploit their cell targeting capabilities and use them as a tunable scaffold for bacterial detection and therapeutic applications. The highly modular nature of phage allows for the detection of specific bacterial species ex vivo as well as in vivo. For rapid, inexpensive, and selective detection of bacterial pathogens in complex samples we use aggregation of gold nanoparticles induced by the enriched thiols of the phage that undergo a colorimetric change in the presence of target bacteria. For therapeutics we have conjugated gold nanorods to the virions that then ablate target bacteria cells after irradiating the gold nanorods with near-infrared light. For in vivo applications virions loaded with MR contrast agent manganese have been shown to selectively detect target bacterial strains in a murine model. This diagnostic strategy integrates the highly specific targeting properties of bacteriophage with the MR contrast agents to create a modular platform for detecting target bacterial cells. more...

This dissertation delineates the development of a simplified, reproducible in vitro microbiome model of the human dental plaque, with the specific aims of minimizing procedural complexity and investigating the effects of preservation on a complex laboratory microbiome model. This model is based on a 24-well-plate anaerobic model that uses an enriched, undefined culture media, with complex carbohydrate sources and supplemented by nutritional components required by certain fastidious organisms. Preliminary work with the plaque inoculum from a single host served as a proof-of-concept and foundation for the model. In this stage of the project, the viability, membership and relative abundances of the preliminary model were assessed by fluorescence microscopy and 16S rRNA amplicon sequencing on the Illumina MiSeq platform. Processing and analysis of the sequencing data were performed using the mothur pipeline and phyloseq package in R. Preliminary work validated the cultivation, sequencing, and analytical procedures by showing members and abundances expected of early-stage human oral bacterial communities. These procedures were then adopted in the development of a temporal model with a maximum incubation time of 168 hours. The temporal model was fed on a defined schedules and minimal nutrients. Analysis of this model showed a temporal succession of bacteria that closely follows the order of succession in host oral cavities, as well as developmental differences across different hosts. These results show promise that the temporal model may be modified with periodic re-inoculation and lengthened to capture a broader temporal range and thus a more complex colonization succession in the dental plaque bacterial communities.The combined results from the preliminary and temporal models formed the basis of the preservation experiments, which investigated the feasibility of refrigerating and cryopreserving 72-hour in vitro communities from three hosts for subsequent propagation. Th more...

This dissertation delineates the development of a simplified, reproducible in vitro microbiome model of the human dental plaque, with the specific aims of minimizing procedural complexity and investigating the effects of preservation on a complex laboratory microbiome model. This model is based on a 24-well-plate anaerobic model that uses an enriched, undefined culture media, with complex carbohydrate sources and supplemented by nutritional components required by certain fastidious organisms. Preliminary work with the plaque inoculum from a single host served as a proof-of-concept and foundation for the model. In this stage of the project, the viability, membership and relative abundances of the preliminary model were assessed by fluorescence microscopy and 16S rRNA amplicon sequencing on the Illumina MiSeq platform. Processing and analysis of the sequencing data were performed using the mothur pipeline and phyloseq package in R. Preliminary work validated the cultivation, sequencing, and analytical procedures by showing members and abundances expected of early-stage human oral bacterial communities. These procedures were then adopted in the development of a temporal model with a maximum incubation time of 168 hours. The temporal model was fed on a defined schedules and minimal nutrients. Analysis of this model showed a temporal succession of bacteria that closely follows the order of succession in host oral cavities, as well as developmental differences across different hosts. These results show promise that the temporal model may be modified with periodic re-inoculation and lengthened to capture a broader temporal range and thus a more complex colonization succession in the dental plaque bacterial communities.The combined results from the preliminary and temporal models formed the basis of the preservation experiments, which investigated the feasibility of refrigerating and cryopreserving 72-hour in vitro communities from three hosts for subsequent propagation. Th more...

Bacterial infections impose some of the largest burdens on human health. Sepsis, typically induced by bacterial infections, accounts for more than 50% of hospital deaths in the United States. With an increase in bacteria resistant to antibiotics, developing tools that can detect bacteria and treat both bacterial contamination and infections is of paramount importance. Bacteriophage (phage) are bacterial viruses that are non-toxic to humans. They have evolved various mechanisms to target and damage host bacterial cells. Phages are easy to engineer and could be used as an additional instrument in the antimicrobial tool box to detect and treat bacterial infections. Through bioengineering phage we exploit their cell targeting capabilities and use them as a tunable scaffold for bacterial detection and therapeutic applications. The highly modular nature of phage allows for the detection of specific bacterial species ex vivo as well as in vivo. For rapid, inexpensive, and selective detection of bacterial pathogens in complex samples we use aggregation of gold nanoparticles induced by the enriched thiols of the phage that undergo a colorimetric change in the presence of target bacteria. For therapeutics we have conjugated gold nanorods to the virions that then ablate target bacteria cells after irradiating the gold nanorods with near-infrared light. For in vivo applications virions loaded with MR contrast agent manganese have been shown to selectively detect target bacterial strains in a murine model. This diagnostic strategy integrates the highly specific targeting properties of bacteriophage with the MR contrast agents to create a modular platform for detecting target bacterial cells. more...

Understanding molecular evolution can reveal a great deal about the past, present, and future of biological systems. The evolution of catalytic RNA is of particular interest because of its potential role in an ‘RNA World’ at the origin of life. Two crucial aspects in the evolution of biomolecules are optimization on the fitness landscape and co-option for new functions. The fitness landscape describes a function of fitness in the space of all possible sequences. Molecules evolve through a random walk on the fitness landscape, with a bias toward climbing peaks. In addition, the ability of enzymes, including ribozymes, to catalyze side reactions is believed to be essential to the evolution of novel biochemical activities. It has been speculated that the earliest ribozymes were low in activity but high in promiscuity, which then gave rise to specialized descendants with higher activity and specificity. One particularly essential activity for the origin of life would be the reaction of ribozymes with activated amino acids to form aminoacyl-RNAs, with co-option of these aminoacyl-RNAs leading to genetic code expansion. In this work, self-aminoacylating ribozymes were identified through in vitro selection from full coverage of sequence space and characterized using a massively parallel kinetic assay. Three major sequence motifs were identified on the landscape and analysis of evolutionary pathways revealed that, while local optimization within a ribozyme family would be possible, optimization of activity over the entire landscape would be frustrated by large valleys of low activity. The sequence motifs associated with each peak represent different solutions for catalysis, so the inability to traverse the landscape globally corresponds to an inability to restructure the ribozyme without losing activity. In addition, five families representing the three sequence motifs were further investigated to measure their activity with alternative substrates. Ribozymes in each family more...

Various self-cleaving ribozymes appearing in nature catalyze the sequence-specific intramolecular cleavage of RNA and can be engineered to catalyze cleavage of appropriate substrates in an intermolecular fashion, thus acting as true catalysts. The mechanisms of the small, self-cleaving ribozymes have been extensively studied and reviewed previously. Self-cleaving ribozymes can possess high catalytic activity and high substrate specificity; however, substrate specificity is also engineerable within the constraints of the ribozyme structure. While these ribozymes share a common fundamental catalytic mechanism, each ribozyme family has a unique overall architecture and active site organization, indicating that several distinct structures yield this chemical activity. The multitude of catalytic structures, combined with some flexibility in substrate specificity within each family, suggests that such catalytic RNAs, taken together, could access a wide variety of substrates. Here, we give an overview of 10 classes of self-cleaving ribozymes and capture what is understood about their substrate specificity and synthetic applications. Evolution of these ribozymes in an RNA world might be characterized by the emergence of a new ribozyme family followed by rapid adaptation or diversification for specific substrates. more...

Functional biomolecules, such as RNA, encapsulated inside a protocellular membrane are believed to have comprised a very early, critical stage in the evolution of life, since membrane vesicles allow selective permeability and create a unit of selection enabling cooperative phenotypes. The biophysical environment inside a protocell would differ fundamentally from bulk solution due to the microscopic confinement. However, the effect of the encapsulated environment on ribozyme evolution has not been previously studied experimentally. Here, we examine the effect of encapsulation inside model protocells on the self-aminoacylation activity of tens of thousands of RNA sequences using a high-throughput sequencing assay. We find that encapsulation of these ribozymes generally increases their activity, giving encapsulated sequences an advantage over nonencapsulated sequences in an amphiphile-rich environment. In addition, highly active ribozymes benefit disproportionately more from encapsulation. The asymmetry in fitness gain broadens the distribution of fitness in the system. Consistent with Fisher's fundamental theorem of natural selection, encapsulation therefore leads to faster adaptation when the RNAs are encapsulated inside a protocell during in vitro selection. Thus, protocells would not only provide a compartmentalization function but also promote activity and evolutionary adaptation during the origin of life. more...