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Tomlinson, D.

Publications and source records attributed to Tomlinson, D..

3 recordsLinked to original sources
bioRxiv2023

Exploiting the Affimer platform against influenza A virus

Influenza A virus (IAV) is well known for its pandemic potential. While current surveillance and vaccination strategies are highly effective, therapeutic approaches are short-lived due to the high mutation rates of IAV. Currently, monoclonal antibodies (mAbs) have emerged as a promising approach to tackle future IAV pandemics. Additionally, several antibody-like alternatives exist that aim to improve upon mAbs. Affimers, one such alternative, benefit from a short development time, high expression levels in E. coli, and complete animal-free production. Here we exploited the Affimer platform to isolate and produce specific and potent inhibitors of IAV. Starting from a monomeric version of the IAV trimeric hemagglutinin (HA) fusion protein, we isolated 12 Affimers that inhibit IAV H3 subtype infection in vitro. Two of these Affimers were characterised in detail: they exhibited binding affinities to the target H3 HA protein in the nM range and bound specifically to the HA1 head domain. Cryo-EM employing a novel spray approach to prepare cryo-grids allowed us to image HA-Affimer complexes. Combined with functional assays, we determined that the mode of inhibition of these Affimers is based on blocking the interaction of HA to the host-cell receptor - sialic acid. Additionally, these Affimers inhibited IAV strains closely related to the one employed for Affimer isolation. Overall, these results support the use of Affimers as an alternative to existing targeted therapies for IAV and pave the way for their use as diagnostic reagents. ImportanceInfluenza A virus is one of the few viruses that can cause devastating pandemics. Due to the high mutation rates of this virus, annual vaccination is required and antivirals are short-lived. Monoclonal antibodies present a promising approach to tackle influenza virus infections, but are associated with some limitations. To improve on this strategy, we explored the Affimer platform, which are antibody-like, bacterially made proteins. By performing phage-display against a monomeric version of influenza virus fusion protein, an established viral target, we were able to isolate Affimers that inhibit influenza virus infection in vitro. We characterised the mechanism of inhibition of the Affimers by challenging with related influenza virus strains. We additionally characterised an HA-Affimer complex structure, using a novel approach to prepare samples for cryo-electron microscopy. Overall, these results show that Affimers are a promising tool against influenza virus infection.

microbiology↗
bioRxiv2020

Combining Natural Language Processing and Metabarcoding to Reveal Pathogen-Environment Associations

Cryptococcus neoformans is responsible for life-threatening infections that primarily affect immunocompromised individuals and has an estimated worldwide burden of 220,000 new cases each year--with 180,000 resulting deaths--mostly in sub-Saharan Africa. Surprisingly, little is known about the ecological niches occupied by C. neoformans in nature. To expand our understanding of the distribution and ecological associations of this pathogen we implement a Natural Language Processing approach to better describe the niche of C. neoformans. We use a Latent Dirichlet Allocation model to de novo topic model sets of metagenetic research articles written about varied subjects which either explicitly mention, inadvertently find, or fail to find C. neoformans. These articles are all linked to NCBI Sequence Read Archive datasets of 18S ribosomal RNA and/or Internal Transcribed Spacer gene-regions. The number of topics was determined based on the model coherence score, and articles were assigned to the created topics via a Machine Learning approach with a Random Forest algorithm. Our analysis provides support for a previously suggested linkage between C. neoformans and soils associated with decomposing wood. Our approach, using a search of single-locus metagenetic data, gathering papers connected to the datasets, de novo determination of topics, the number of topics, and assignment of articles to the topics, illustrates how such an analysis pipeline can harness large-scale datasets that are published/available but not necessarily fully analyzed, or whose metadata is not harmonized with other studies. Our approach can be applied to a variety of systems to assert potential evidence of environmental associations. Author SummaryOur finding that C. neoformans is associated with decomposing wood is reinforced by the general literature on C. neoformans and its close congeneric relatives and warrants further investigation. This work demonstrates the potential utility of pairing Natural Language Processing (NLP) with single-locus metagenetic data for the study of Neglected Tropical Diseases. We present a novel method to study the ecological niches of rare pathogens that leverages the immense amount of data available to researchers in the NCBI Sequence Read Archive (SRA)combined with a text-mining analysis based on Natural Language Processing. We demonstrate that text processing, noun identification, and verb identification can play an important role in analyzing a large corpus of documents together with metagenetic data. Forging this connection requires access to all of the available ecological 18S ribosomal RNA and Internal Transcribed Spacer NCBI SRA datasets. These datasets use metabarcoding to query taxonomic diversity in eukaryotic organisms, and in the case of the Internal Transcribed Spacer, they specifically target Fungi. The presence of specific species is inferred when diagnostic 18S or ITS gene region sequences are found in the SRA data. We searched for C. neoformans in all 18S and ITS datasets available and gathered all associated journal articles that either cite the SRA data accessions or are cited in the SRA data accessions. Published metagenetic data often have associated metadata including: latitude and longitude, temperature, and other physical characteristics describing the conditions in which the metagenetic sample was collected. These metadata are not always be presented in consistent formats, so harmonizing study methods may be needed to appropriately compare metagenetic data as commonly required in metanalysis studies. We present an analysis which takes as input articles associated with SRA datasets that were found to contain evidence of C. neoformans. We apply NLP methods to this corpus of articles to describe the niche of C. neoformans. Our results reinforce the current understanding of C. neoformanss niche, indicating the pertinence of employing a NLP analysis to identify the niche of an organism. This approach could further the description of virtually any other organism that routinely appears in metagenetic surveys, especially pathogens, whose ecological niches are unknown or poorly understood. Optional Striking Image O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=150 SRC="FIGDIR/small/280578v1_ufig1.gif" ALT="Figure 1"> View larger version (66K): org.highwire.dtl.DTLVardef@135ce50org.highwire.dtl.DTLVardef@17bf52borg.highwire.dtl.DTLVardef@11cf682org.highwire.dtl.DTLVardef@7be6ea_HPS_FORMAT_FIGEXP M_FIG C_FIG Cryptococcus neoformans cells budding. Image Provided Courtesy of Felipe H. Santiago-Tirado, colored by Kristina Davis, CC-BY 4.0

bioinformatics↗
bioRxiv2017

Transmural unipolar electrogram morphology is achieved within 7s at the posterior left atrial wall during pulmonary vein isolation: VISITAG™ Module-based lesion assessment during radiofrequency ablation

AimsTo assess the occurrence of a histologically validated measure of transmural (TM) atrial ablation - pure R unipolar electrogram (UE) morphology change - at first-ablated left atrial posterior wall (LAPW) sites during contact force (CF)-guided pulmonary vein isolation (PVI).\n\nMethods and resultsExported VISITAG Module and CARTOREPLAY (Biosense Webster Inc.) UE morphology data was retrospectively analysed in 23 consecutive patients undergoing PVI under general anaesthesia. PVI without spontaneous / dormant recovery was achieved in all, employing 16.3[3.2] minutes (mean [SD]) of temperature-controlled RF at 30W. All first-ablated LAPW sites demonstrated RS UE morphology pre-ablation, with RF-induced pure R UE morphology change in 98%. Time to pure R UE morphology was significantly shorter at left-sided LAPW sites (4.9[2.1] s versus 6.7[2.5] s; p=0.02), with significantly greater impedance drop (median 13.5{Omega} versus 9.9{Omega}; p=0.003). Importantly, neither the first-site RF duration (14.9 versus 15.0s) nor the maximum ablation catheter tip distance moved (during RF) were significantly different, yet the mean CF was significantly higher at right-sided sites (16.5g versus 11.2g; p=0.002). Concurrent impedance and objectively annotated bipolar electrogram (BE) data demonstrated ~6-8{Omega} impedance drop and ~30% BE decrease at the time of first pure R UE morphology change.\n\nConclusionUsing objective ablation site annotation, TM UE morphology change was typically achieved within 7s at the LAPW, with significantly greater ablative effect evident at left-sided sites. The methodology described in this report represents a novel and scientifically more rigorous foundation towards future research into the biological effects of RF ablation in vivo.\n\nCondensed abstractThrough appropriate use of the VISITAG Module and CARTOREPLAY, unipolar electrogram morphology change indicative of histologically confirmed transmural atrial ablation in animal models, was proven to occur typically within 7s, during first-site contact force-guided ablation at the left atrial posterior wall during pulmonary vein isolation.

physiology↗