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Open Access Publications from the University of California

Scholarly Works (26 results)

  • Thesis
  • Peer Reviewed
UCLA Electronic Theses and Dissertations (2018)

The human innate immune system consists of several components, which include antimicrobial peptides, pattern-recognition receptors, cytokines, and immune cells. In this dissertation, we explore unifying themes underlying the antimicrobial, membrane remodeling, and immunomodulatory behaviors of antimicrobial peptides and related molecules, and their interactions with microbial and mammalian cells. We utilize machine learning on antimicrobial peptides to examine the physicochemical parameters characteristic of membrane curvature generation, and develop a search tool to discover hidden antimicrobial and membrane-remodeling activity in new and existing taxonomies of multifunctional peptides and proteins, including mitochondrial fission proteins, histones, and neuropeptides. Using structural characterization and calibrating immune cell stimulation experiments, we outline molecular rules for antimicrobial peptide-mediated immunomodulation via ligand clustering of nucleic acids. Antimicrobial peptides condense naked DNA, nucleosomal DNA, and dsRNA into nanocrystalline immunocomplexes, which drastically amplify inflammation via multivalent binding to Toll-like receptors in immune cells. This work has broad implications for the deterministic control of inflammation in the contexts of infection, chronic inflammation, and autoimmune disease.

    Cover page: Discovery and Design of Multifunctional Membrane-Active and Immunomodulatory Peptides and Proteins
    • Article
    • Peer Reviewed
    UCLA Previously Published Works (2020)
    The coronavirus disease 2019 (COVID-19) pandemic is a public health crisis that has quickly overwhelmed our healthcare system. It has led to significant shortages in personal protective equipment (PPE), ventilators, and intensive care unit beds across the nation. As the initial entry point for patients with suspected COVID illness, emergency departments (ED) have had to adapt quickly to prioritize the safety of patients and providers while still delivering optimal, timely patient care. COVID-19 has presented many challenges for the ED that also extend to all inpatient services. Some of these key challenges are the fundamental tasks of communicating with patients in respiratory isolation while minimizing PPE usage and enabling all patients who have been affected by hospitals' visitor restrictions to connect with their families. We discuss the design principles behind implementing a robust in-hospital telehealth system for patient-provider and patient-family communication, provide a review of the strengths and weaknesses of potential videoconferencing options, and deliver concise, step-by-step guides for setting up a secure, low-cost, user-friendly solution that can be rapidly deployed.
      Cover page: Telehealth Solutions for In-hospital Communication with Patients Under Isolation During COVID-19Creative Commons 'BY' version 4.0 license
      • Article
      • Peer Reviewed
      UCLA Previously Published Works (2018)
      Antimicrobial peptides are a class of membrane-active peptides that form a critical component of innate host immunity and possess a diversity of sequence and structure. Machine learning approaches have been profitably employed to efficiently screen sequence space and guide experiment towards promising candidates with high putative activity. In this mini-review, we provide an introduction to antimicrobial peptides and summarize recent advances in machine learning-enabled antimicrobial peptide discovery and design with a focus on a recent work Lee et al. Proc. Natl. Acad. Sci. USA 2016;113(48):13588-13593. This study reports the development of a support vector machine classifier to aid in the design of membrane active peptides. We use this model to discover membrane activity as a multiplexed function in diverse peptide families and provide interpretable understanding of the physicochemical properties and mechanisms governing membrane activity. Experimental validation of the classifier reveals it to have learned membrane activity as a unifying signature of antimicrobial peptides with diverse modes of action. Some of the discriminating rules by which it performs classification are in line with existing "human learned" understanding, but it also unveils new previously unknown determinants and multidimensional couplings governing membrane activity. Integrating machine learning with targeted experimentation can guide both antimicrobial peptide discovery and design and new understanding of the properties and mechanisms underpinning their modes of action.
        Cover page: Machine learning-enabled discovery and design of membrane-active peptides
        • Article
        • Peer Reviewed
        Western Journal of Emergency Medicine: Integrating Emergency Care with Population Health, Volume 21, Issue 4 (2020)

        The coronavirus disease 2019 (COVID-19) pandemic is a public health crisis that has quickly overwhelmed our healthcare system. It has led to significant shortages in personal protective equipment (PPE), ventilators, and intensive care unit beds across the nation. As the initial entry point for patients with suspected COVID illness, emergency departments (ED) have had to adapt quickly to prioritize the safety of patients and providers while still delivering optimal, timely patient care. COVID-19 has presented many challenges for the ED that also extend to all inpatient services. Some of these key challenges are the fundamental tasks of communicating with patients in respiratory isolation while minimizing PPE usage and enabling all patients who have been affected by hospitals’ visitor restrictions to connect with their families. We discuss the design principles behind implementing a robust in-hospital telehealth system for patient-provider and patient-family communication, provide a review of the strengths and weaknesses of potential videoconferencing options, and deliver concise, step-by-step guides for setting up a secure, low-cost, user-friendly solution that can be rapidly deployed.

        • 3 supplemental files
        Cover page: Telehealth Solutions for In-hospital Communication with Patients Under Isolation During COVID-19Creative Commons 'BY' version 4.0 license
        • Article
        • Peer Reviewed
        UCLA Previously Published Works (2019)
        Antimicrobial peptides (AMPs) are typically thought of as molecular hole punchers that directly kill pathogens by membrane permeation. However, recent work has shown that AMPs are pleiotropic, multifunctional molecules that can strongly modulate immune responses. In this review, we provide a historical overview of the immunomodulatory properties of natural and synthetic antimicrobial peptides, with a special focus on human cathelicidin and defensins. We also summarize the various mechanisms of AMP immune modulation and outline key structural rules underlying the recently-discovered phenomenon of AMP-mediated Toll-like receptor (TLR) signaling. In particular, we describe several complementary studies demonstrating how AMPs self-assemble with nucleic acids to form nanocrystalline complexes that amplify TLR-mediated inflammation. In a broader scope, we discuss how this new conceptual framework allows for the prediction of immunomodulatory behavior in AMPs, how the discovery of hidden antimicrobial activity in known immune signaling proteins can inform these predictions, and how these findings reshape our understanding of AMPs in normal host defense and autoimmune disease.
          Cover page: Modulation of toll-like receptor signaling by antimicrobial peptides
          • Article
          • Peer Reviewed
          UCLA Previously Published Works (2020)

          Background

          The Warkentin 4-T scoring system for determining the pretest probability of heparin-induced thrombocytopenia (HIT) has been shown to be inaccurate in the ICU and does not take into account body mass index (BMI).

          Methods

          Prospectively collected data on patients in the surgical and cardiac ICU between January 2007 and February 2016 who were presumed to have HIT by clinical suspicion were reviewed. Patients were categorized into 3 BMI groups and assigned scores: Normal weight, overweight, and obese. Multivariate analyses were used to identify independent predictors of HIT.

          Results

          A total of 523 patients met inclusion criteria. Multivariate analysis showed that only BMI, Timing, and oTher variables were independently associated with HIT. This new 3-T model was better than a five-component model consisting of the entire 4-T scoring system plus BMI (AUC = 0.791).

          Conclusions

          Incorporating patient 'T'hickness into a pretest probability model along with platelet 'T'iming and the exclusion of o'T'her causes of thrombocytopenia yields a simplified "3-T" scoring system that has increased predictive accuracy in the ICU.
            Cover page: Improved prediction of HIT in the SICU using an improved model of the Warkentin 4-T system: 3-T.
            • Article
            • Peer Reviewed
            UC Davis Previously Published Works (2017)

            Background

            Identifying orthologous genes is an initial step required for phylogenetics, and it is also a common strategy employed in functional genetics to find candidates for functionally equivalent genes across multiple species. At the same time, in silico orthology prediction tools often require large computational resources only available on computing clusters. Here we present OrthoReD, an open-source orthology prediction tool with accuracy comparable to published tools that requires only a desktop computer. The low computational resource requirement of OrthoReD is achieved by repeating orthology searches on one gene of interest at a time, thereby generating a reduced dataset to limit the scope of orthology search for each gene of interest.

            Results

            The output of OrthoReD was highly similar to the outputs of two other published orthology prediction tools, OrthologID and/or OrthoDB, for the three dataset tested, which represented three phyla with different ranges of species diversity and different number of genomes included. Median CPU time for ortholog prediction per gene by OrthoReD executed on a desktop computer was <15 min even for the largest dataset tested, which included all coding sequences of 100 bacterial species.

            Conclusions

            With high-throughput sequencing, unprecedented numbers of genes from non-model organisms are available with increasing need for clear information about their orthologies and/or functional equivalents in model organisms. OrthoReD is not only fast and accurate as an orthology prediction tool, but also gives researchers flexibility in the number of genes analyzed at a time, without requiring a high-performance computing cluster.
              Cover page: OrthoReD: a rapid and accurate orthology prediction tool with low computational requirement.
              • Article
              • Peer Reviewed
              UC San Diego Previously Published Works (2017)
              Double-stranded RNA (dsRNA) induces production of pro-inflammatory cytokines in normal human epidermal keratinocytes (NHEK) by specific binding to endosomal Toll-like receptor-3 (TLR3). Recently, it has been shown that hyperactivation of TLR3 in psoriatic keratinocytes by dsRNA can occur in the presence of human antimicrobial peptide (AMP) LL37. Here, we combine synchrotron X-ray scattering, microscopy, computer simulations, and measurements of NHEK cytokine production to elucidate a previously unanticipated form of specific molecular pattern recognition. LL37 and similar α-helical AMPs can form pro-inflammatory nanocrystalline complexes with dsRNA that are recognized by TLR3 differently than dsRNA alone. dsRNA complexes that activate IL-6 production in NHEK and those that do not are both able to enter cells and co-localize with TLR3. However, the crystallinity of these AMP-dsRNA complexes, specifically the geometric spacing between parallel dsRNA and the repeat number of ordered dsRNA, strongly influences the level of TLR3 activation. Crystalline complexes that present dsRNA at a spacing that matches with the steric size of TLR3 can recruit and engage multiple TLR3 receptors, driving receptor clustering and immune amplification, whereas crystalline complexes that exhibit poor steric matching do not. Reverse-transcription quantitative PCR of IL-6 during siRNA knockdown of TLR3 confirms that cytokine production is due to TLR3: High levels of IL-6 transcription are observed for sterically matched complexes without TLR3 knockdown, whereas such activity is abrogated with TLR3 knockdown.
                Cover page: Crystallinity of Double-Stranded RNA-Antimicrobial Peptide Complexes Modulates Toll-Like Receptor 3‑Mediated Inflammation
                • Article
                • Peer Reviewed
                UCLA Previously Published Works (2020)
                Purpose: Apremilast is a phosphodiesterase-4 inhibitor FDA approved for psoriatic arthritis and moderate to severe plaque psoriasis. In recent years, multiple studies have suggested other potential uses for apremilast in dermatology. A summary of these various studies will be a valuable aid to dermatologists considering apremilast for an alternative indication.Materials and methods: The PubMed/MEDLINE and ClinicalTrials.gov databases were queried with the term 'apremilast,' with results manually screened to identify published data on off-label uses of apremilast. The article was structured by the quality of evidence available.Results: Apremilast use in dermatology beyond plaque psoriasis and psoriatic arthritis is frequently described in the literature, with a mixture of positive and negative results. Randomized controlled data is available for Behçet's disease, hidradenitis suppurativa, nail/scalp/palmoplantar psoriasis, alopecia areata, and atopic dermatitis.Conclusion: The relatively safe adverse effect profile of apremilast and its broad immunomodulatory characteristics may make it a promising option in the future for patients with difficult to treat diseases in dermatology, refractory to first line therapies, but further studies will be necessary to clarify its role.
                  Cover page: Off-label studies on apremilast in dermatology: a review
                  • Article
                  • Peer Reviewed
                  UCLA Previously Published Works (2016)
                  There are some ∼1,100 known antimicrobial peptides (AMPs), which permeabilize microbial membranes but have diverse sequences. Here, we develop a support vector machine (SVM)-based classifier to investigate ⍺-helical AMPs and the interrelated nature of their functional commonality and sequence homology. SVM is used to search the undiscovered peptide sequence space and identify Pareto-optimal candidates that simultaneously maximize the distance σ from the SVM hyperplane (thus maximize its "antimicrobialness") and its ⍺-helicity, but minimize mutational distance to known AMPs. By calibrating SVM machine learning results with killing assays and small-angle X-ray scattering (SAXS), we find that the SVM metric σ correlates not with a peptide's minimum inhibitory concentration (MIC), but rather its ability to generate negative Gaussian membrane curvature. This surprising result provides a topological basis for membrane activity common to AMPs. Moreover, we highlight an important distinction between the maximal recognizability of a sequence to a trained AMP classifier (its ability to generate membrane curvature) and its maximal antimicrobial efficacy. As mutational distances are increased from known AMPs, we find AMP-like sequences that are increasingly difficult for nature to discover via simple mutation. Using the sequence map as a discovery tool, we find a unexpectedly diverse taxonomy of sequences that are just as membrane-active as known AMPs, but with a broad range of primary functions distinct from AMP functions, including endogenous neuropeptides, viral fusion proteins, topogenic peptides, and amyloids. The SVM classifier is useful as a general detector of membrane activity in peptide sequences.
                    Cover page: Mapping membrane activity in undiscovered peptide sequence space using machine learning
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