Engineering topics
Publications and source records attributed to Velappan, Nileena.
A novel multidimensional scan technique combining MS 1 and MS/MS scans greatly enhances bacterial differentiation from low resolution data for clinical and field-forward applications.
Monoclonal antibodies that specifically bind the F1 antigen of Yersinia pestis with high affinity are described. Use of the monoclonal antibodies for the detection of Y. pestis infection and the diagnosis of plague are also described. Immunoconjugates of the monoclonal antibodies and a radionuclide can also be used for the treatment of a Y. pestis infection.
Antibodies are important reagents for research, diagnostics, and therapeutics. Many examples of chimeric proteins combining the specific target recognition of antibodies with complementing functionalities such as fluorescence, toxicity or enzymatic activity have been described. However, antibodies selected solely on the basis of their binding specificities are not necessarily ideal candidates for the construction of chimeras. Here, we describe a high throughput method based on yeast display to directly select antibodies most suitable for conversion to fluorescent chimera. A library of scFv binders was converted to a fluorescent chimeric form, by cloning thermal green protein into the linker between VH and VL, and directly selecting for both binding and fluorescent functionality. This allowed us to directly identify antibodies functional in the single chain TGP format, that manifest higher protein expression, easier protein purification, and one-step binding assays.
Cytotoxicity was induced in cells mimicking SARS-CoV-2 infection by a 225 Ac-radiolabeled antibody that targets the RBD2 of the spike protein.
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Black swan events in infectious disease describe rare but devastatingly large outbreaks. While experts are skeptical that such events are predictable, it might be possible to identify the warning signs of a black swan event. Specifically, following the initiation of an outbreak, key differentiating features could serve as alerts. Such features could be derived from meta-analyses of large outbreaks for multiple infectious diseases. We hypothesized there may be common features among the pathogen, environment, and host epidemiological triad that characterize an infectious disease black swan event. Using Los Alamos National Laboratory’s tool, Analytics for Investigation of Disease Outbreaks, we investigated historical disease outbreak information and anomalous events for several infectious diseases. By studying 32 different infectious diseases and global outbreaks, we observed that in the past 20–30 years, there have been potential black swan events in the majority of infectious diseases analyzed. Importantly, these potential black swan events cannot be attributed to the first introduction of the disease to a susceptible host population. This paper describes our observations and perspectives and illustrates the value of broad analysis of data across the infectious disease realm, providing insights that may not be possible when we focus on singular infectious agents or diseases. Data analytics could be developed to warn health authorities at the beginning of an outbreak of an impending black swan event. Such tools could complement traditional epidemiological modeling to help forecast future large outbreaks and facilitate timely warning and effective, targeted resource allocation for mitigation efforts.
Autophagy is a critical mechanism deployed by eukaryotic cells in response to stress, including viral infection, to boost the innate antimicrobial responses. However, an increasing number of pathogens hijack the autophagic machinery to facilitate their own replication. Influenza A virus (IAV), responsible for several global pandemics, has an intricate dependence on autophagy for successful replication in mammalian cells. To elucidate key chokepoints in the host stress responses facilitating IAV replication, we constructed a meta-transcriptome of IAV and host gene expression dynamics during early (1–3 hpi), mid (4–6 hpi), and late (8–12 hpi) stages of the viral replication cycle at two multiplicities of infection (MOI): 1 and 5. We supplemented the global transcriptome study with phosphoproteomic analysis of stress-activated protein kinase (SAPK/JNK) signaling in lung carcinoma (predominantly used as an in vitro model of IAV replication) and normal human bronchial epithelial cells. We report significant differences in the activation profiles of autophagy regulating genes upon IAV infection at the two MOI as well as divergent dependence on ULK1 signaling within the normal and cancer cells. Regardless of the cell model, JNK-Thr187 signaling was crucial for the production of infectious viral particles.
Eleven Labs within the US Department of Energy (DOE), National Virtual Biotechnology Laboratory (NVBL), came together as a team to address significant R&D gaps in COVID-19 testing. Beginning in March 2020, the NVBL COVID Testing Team developed an R&D agenda, worked with DOE and other agencies to set priorities, and collaborated to deliver timely results. Priority was given to quick implementation as well as development of novel capabilities for immediate and evolving pandemic needs without placing additional burden on operational performers. Priority elements capitalized on DOE National Laboratory strengths and expertise. The Team delivered: testing and evaluation that enabled decisions on testing options, forwardleaning approaches to prepare for future scale-up needs, and models and experiments that supported prioritization of diagnostic and therapeutic candidates.
Background: Currently, the identification of infectious disease re-emergence is performed without describing specific quantitative criteria that can be used to identify re-emergence events consistently. This practice may lead to ineffective mitigation. In addition, identification of factors contributing to local disease re-emergence and assessment of global disease re-emergence require access to data about disease incidence and a large number of factors at the local level for the entire world. This paper presents Re-emerging Disease Alert (RED Alert), a web-based tool designed to help public health officials detect and understand infectious disease re-emergence. Objective: Our objective is to bring together a variety of disease-related data and analytics needed to help public health analysts answer the following 3 primary questions for detecting and understanding disease re-emergence: Is there a potential disease re-emergence at the local (country) level? What are the potential contributing factors for this re-emergence? Is there a potential for global re-emergence? Methods: We collected and cleaned disease-related data (eg, case counts, vaccination rates, and indicators related to disease transmission) from several data sources including the World Health Organization (WHO), Pan American Health Organization (PAHO), World Bank, and Gideon. We combined these data with machine learning and visual analytics into a tool called RED Alert to detect re-emergence for the following 4 diseases: measles, cholera, dengue, and yellow fever. We evaluated the performance of the machine learning models for re-emergence detection and reviewed the output of the tool through a number of case studies. Results: Our supervised learning models were able to identify 82%-90% of the local re-emergence events, although with 18%-31% (except 46% for dengue) false positives. This is consistent with our goal of identifying all possible re-emergences while allowing some false positives. The review of the web-based tool through case studies showed that local re-emergence detection was possible and that the tool provided actionable information about potential factors contributing to the local disease re-emergence and trends in global disease re-emergence. Conclusions: To the best of our knowledge, this is the first tool that focuses specifically on disease re-emergence and addresses the important challenges mentioned above.
While natural protein–protein interactions have evolved to be induced by complex stimuli, rational design of interactions that can be switched-on-demand still remain challenging in the protein design world. Here, we demonstrate that a computationally redesigned natural interface for improved binding affinity could further be mutated to adopt a pH switchable interaction. The redesigned interface of Protein G/human IgG Fc domain (referred to as PrG/hIgG), when incorporated with histidine and glutamic acid on PrG (PrG-EHHE), showed a switch in binding affinity by 50-fold when the pH was altered from mild acidic to mild basic. The wild-type (WT) interface showed a negligible switch. The overall binding affinity under mild acidic pH for PrG-EHHE outperformed the wild-type PrG (PrG-WT) interaction. Overall, we find that the new reagent PrG-EHHE can be revolutionary in IgG purification, since the standard method of using an extreme acidic pH for elution can be circumvented.
Conclusions of the presentation were: The scTGPs are recombinant intrinsically fluorescent antibodies, ready for use in traditional fluorescent antibody based assays; The scFPs can be constructed as vL-FP-vH format, TGP an extremely stable monomeric fluorescent protein is suitable FP; The scFPs can be effectively displayed on yeast, where they function similarly to their corresponding scFv counterparts, recognize specific antigen similar affinity; Functional expression levels of scFPs in the bacterial cytoplasm are relatively high and can rescue some scFvs otherwise expressed at low levels in the periplasm; Straightforward assessment of expression levels and monitoring of protein purification steps; Antigen recognition in one step binding assays, novel assays such as epitope binning TGP is a monomeric fluorescent protein amenable to crystallization. These features provide unique and valuable characteristics to scTGP molecules for their use in protein chemistry; and, We present unique antibody format with wide variety of use in research and clinical setting
In vitro display technologies based on phage and yeast have a successful history of selecting single-chain variable fragment (scFv) antibodies against various targets. However, single-chain antibodies are often unstable and poorly expressed in Escherichia coli. Here, we explore the feasibility of converting scFv antibodies to an intrinsically fluorescent format by inserting the monomeric, stable fluorescent protein named thermal green, between the light- and heavy-chain variable regions. Our results show that the scTGP format maintains the affinity and specificity of the antibodies, improves expression levels, allows one-step fluorescent assay for detection of binding and is a suitable reagent for epitope binning. We also report the crystal structure of an scTGP construct that recognizes phosphorylated tyrosine on FcεR1 receptor of the allergy pathway.
Goal: to develop multiple high affinity/specificity antibodies for diagnostic and therapeutic use.
Yersinia pestis is a category A infective agent that causes bubonic, septicemic, and pneumonic plague. Notably, the acquisition of antimicrobial or multidrug resistance through natural or purposed means qualifies Y. pestis as a potential biothreat agent. Therefore, high-quality antibodies designed for accurate and sensitive Y. pestis diagnostics, and therapeutics potentiating or replacing traditional antibiotics are of utmost need for national security and public health preparedness. Here, we describe a set of human monoclonal immunoglobulins (IgG1s) targeting Y. pestis fraction 1 (F1) antigen, previously derived from in vitro evolution of a phage-display library of single-chain antibodies (scFv). We extensively characterized these antibodies and their effect on bacterial and mammalian cells via: ELISA, flow cytometry, mass spectrometry, spectroscopy, and various metabolic assays. Two of our anti-F1 IgG (αF1Ig 2 and αF1Ig 8) stood out for high production yield, specificity, and stability. These two antibodies were additionally attractive in that they displayed picomolar affinity, did not compete when binding Y. pestis, and retained immunoreactivity upon chemical derivatization. Most importantly, these antibodies detected < 1,000 Y. pestis cells in sandwich ELISA, did not harm respiratory epithelial cells, induced Y. pestis agglutination at low concentration (350 nM), and caused apparent reduction in cell growth when radiolabeled at a nonagglutinating concentration (34 nM). These antibodies are amenable to the development of accurate and sensitive diagnostics and immuno/radioimmunotherapeutics.
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Persister cells are genetically identical variants in a bacterial population that have phenotypically modified their physiology to survive environmental stress. In bacterial pathogens, persisters are able to survive antibiotic treatment and reinfect patients in a frustrating cycle of chronic infection. To better define core persistence mechanisms for therapeutics development, we performed transcriptomics analyses of Burkholderia thailandensis populations enriched for persisters via three methods: flow sorting for low proton motive force, meropenem treatment, and culture aging. Although the three persister-enriched populations generally displayed divergent gene expression profiles that reflect the multimechanistic nature of stress adaptations, there were several common gene pathways activated in two or all three populations. These include polyketide and nonribosomal peptide synthesis, Clp proteases, mobile elements, enzymes involved in lipid metabolism, and ATP binding cassette (ABC) transporter systems. In particular, identification of genes that encode polyketide synthases (PKSs) and fatty acid catabolism factors indicates that generation of secondary metabolites, natural products, and complex lipids could be part of the metabolic program that governs the persistence state. We also found that loss-of-function mutations in the PKS-encoding gene locus BTH_I2366, which plays a role in biosynthesis of histone deacetylase (HDAC) inhibitors, resulted in increased sensitivity to antibiotics targeting DNA replication. Furthermore, treatment of multiple bacterial pathogens with a fatty acid synthesis inhibitor, CP-640186, potentiated the efficacy of meropenem against the persister populations. Altogether, our results suggest that bacterial persisters may exhibit an outwardly dormant physiology but maintain active metabolic processes that are required to maintain persistence.
Post-translational modifications, such as the phosphorylation of tyrosines, are often the initiation step for intracellular signaling cascades. Pan-reactive antibodies against modified amino acids (e.g., anti-phosphotyrosine), which are often used to assay these changes, require isolation of the specific protein prior to analysis and do not identify the specific residue that has been modified (in the case that multiple amino acids have been modified). Phosphorylation state-specific antibodies (PSSAs) developed to recognize post-translational modifications within a specific amino acid sequence can be used to study the timeline of modifications during a signal cascade. We utilized the FcεRI receptor as a model system to develop and characterize high-affinity PSSAs using phage and yeast display technologies. We determined three β-subunit antibodies that recognized: 1) phosphorylation of tyrosines Y 218 or Y 224 ; 2) phosphorylation of the Y 228 tyrosine; and 3) phosphorylation of all three tyrosines. We used these antibodies to study the receptor activation timeline of FcεR1 in rat basophilic leukemia cells (RBL-2H3) upon stimulation with DNP 24 -BSA. We also selected an antibody recognizing the N-terminal phosphorylation site of the γ-subunit (Y 65 ) of the receptor and applied this antibody to evaluate receptor activation. Recognition patterns of these antibodies show different timelines for phosphorylation of tyrosines in both β and γ subunits. Our methodology provides a strategy to select antibodies specific to post-translational modifications and provides new reagents to study mast cell activation by the high-affinity IgE receptor, FcεRI.
Background: Information from historical infectious disease outbreaks provides real-world data about outbreaks and their impacts on affected populations. These data can be used to develop a picture of an unfolding outbreak in its early stages, when incoming information is sparse and isolated, to identify effective control measures and guide their implementation. Objective: This study aimed to develop a publicly accessible Web-based visual analytic called Analytics for the Investigation of Disease Outbreaks (AIDO) that uses historical disease outbreak information for decision support and situational awareness of an unfolding outbreak. Methods: We developed an algorithm to allow the matching of unfolding outbreak data to a representative library of historical outbreaks. This process provides epidemiological clues that facilitate a user’s understanding of an unfolding outbreak and facilitates informed decisions about mitigation actions. Disease-specific properties to build a complete picture of the unfolding event were identified through a data-driven approach. A method of analogs approach was used to develop a short-term forecasting feature in the analytic. The 4 major steps involved in developing this tool were (1) collection of historic outbreak data and preparation of the representative library, (2) development of AIDO algorithms, (3) development of user interface and associated visuals, and (4) verification and validation. Results: The tool currently includes representative historical outbreaks for 39 infectious diseases with over 600 diverse outbreaks. We identified 27 different properties categorized into 3 broad domains (population, location, and disease) that were used to evaluate outbreaks across all diseases for their effect on case count and duration of an outbreak. Statistical analyses revealed disease-specific properties from this set that were included in the disease-specific similarity algorithm. Although there were some similarities across diseases, we found that statistically important properties tend to vary, even between similar diseases. This may be because of our emphasis on including diverse representative outbreak presentations in our libraries. AIDO algorithm evaluations (similarity algorithm and short-term forecasting) were conducted using 4 case studies and we have shown details for the Q fever outbreak in Bilbao, Spain (2014), using data from the early stages of the outbreak. Using data from only the initial 2 weeks, AIDO identified historical outbreaks that were very similar in terms of their epidemiological picture (case count, duration, source of exposure, and urban setting). The short-term forecasting algorithm accurately predicted case count and duration for the unfolding outbreak. Conclusions: AIDO is a decision support tool that facilitates increased situational awareness during an unfolding outbreak and enables informed decisions on mitigation strategies. AIDO analytics are available to epidemiologists across the globe with access to internet, at no cost. In this study, we presented a new approach to applying historical outbreak data to provide actionable information during the early stages of an unfolding infectious disease outbreak.