Biology subjects

Thompson, M.

Publications and source records attributed to Thompson, M..

49 records · Page 3Linked to original sources

bioRxiv2020

Leveraging UMLS-driven NLP to enhance identification of influenza predictors derived from electronic medical record data

ObjectiveMultiple clinical prediction rules have been developed, but lack validation. This study aims to identify a set of prediction algorithms for influenza, based on electronic health record (EHR) structured data and clinical notes derived data using Unified Medical Language System (UMLS) driven natural language processing (NLP). Materials and MethodsData were extracted from an enterprise-wide data warehouse for all patients who tested positive for influenza and were seen in ambulatory care between 2009 and 2019 (N = 7,278). A text processing pipeline was used to analyze chart notes for UMLS terms for symptoms of interest to improve data quality completeness. Three models, which step up complexity of the dataset and predictors, were tested with least absolute shrinkage and selection operator (LASSO)-selected parameters to identify predictors for influenza. Receiver operating characteristic (ROC) curves compared test accuracy across the three models. ResultsThree models identified 7, 8, and 10 predictors, and the most complex model performed best. The addition of the UMLS-driven NLP symptoms data improved data quality (false negatives) and increased the number of significant predictors. NLP also increased the strength of the models, as did the addition of two-way predictor interactions. DiscussionThe EHR is a feasible source for offering rapidly accessible datasets for influenza related prediction research that was used to produce a prediction model for influenza. Combining data collected in routine care with data science methods improved a prediction model for influenza, and in the future, could be used to drive diagnostics at the point of care.

bioinformatics↗

bioRxiv2020

Expanding the space of protein geometries by computational design of de novo fold families

Naturally occurring proteins use a limited set of fold topologies, but vary the precise geometries of structural elements to create distinct shapes optimal for function. Here we present a computational design method termed LUCS that mimics natures ability to create families of proteins with the same overall fold but precisely tunable geometries. Through near-exhaustive sampling of loop-helix-loop elements, LUCS generates highly diverse geometries encompassing those found in nature but also surpassing known structure space. Biophysical characterization shows that 17 (38%) out of 45 tested LUCS designs were well folded, including 16 with designed non-native geometries. Four experimentally solved structures closely match the designs. LUCS greatly expands the designable structure space and provides a new paradigm for designing proteins with tunable geometries customizable for novel functions. One Sentence SummaryA computational method to systematically sample loop-helix-loop geometries expands the structure space of designer proteins.

bioengineering↗

bioRxiv2020

BATMAN: fast and accurate integration of single-cell RNA-Seq datasets via minimum-weight matching

Single-cell RNA-Sequencing (scRNA-Seq) is a set of technologies used to profile gene expression at the level of individual cells. Although the throughput of scRNA-Seq experiments is steadily growing in terms of the number of cells, large datasets are not yet commonly used due to prohibitively high costs. Integrating multiple datasets into one can improve power in scRNA-Seq experiments, and efficient integration is very important for downstream analyses such as identifying cell-type-specific eQTLs. State-of-the-art scRNA-Seq integration methods are based on the mutual nearest neighbors paradigm and fail to both correct for batch effects and maintain the local structure of the datasets. In this paper, we propose a novel scRNA-Seq dataset integration method called BATMAN (BATch integration via minimum-weight MAtchiNg). Across multiple simulations and real datasets, we show that our method significantly outperforms state-of-the-art tools with respect to existing metrics for batch effects by up to 80% while retaining cell-to-cell relationships. BATMAN is available at https://github.com/mandricigor/batman.

bioinformatics↗

bioRxiv2019

Taking the sub-lexical route: brain dynamics of reading in the semantic variant of Primary Progressive Aphasia.

Reading aloud requires mapping an orthographic form to a phonological one. The mapping process relies on sub-lexical statistical regularities (e.g., "oo" to |u{square}|) or on learned lexical associations between a specific visual form and a series of sounds (e.g., yacht to /j{square}t/). Computational, neuroimaging, and neuropsychological evidence suggest that sub-lexical, phonological and lexico-semantic processes rely on partially distinct neural substrates: a dorsal (occipito-parietal) and a ventral (occipito-temporal) route, respectively. Here, we investigated the spatiotemporal features of orthography-to-phonology mapping, capitalizing on the time resolution of magnetoencephalography and the unique clinical model offered by patients with semantic variant of Primary Progressive Aphasia (svPPA). Behaviorally, svPPA patients manifest marked lexico-semantic impairments including difficulties in reading words with exceptional orthographic to phonological correspondence (irregular words). Moreover, they present with focal neurodegeneration in the anterior temporal lobe (ATL), affecting primarily the ventral, occipito-temporal, lexical route. Therefore, this clinical population allows for testing of specific hypotheses on the neural implementation of the dualroute model for reading, such as whether damage to one route can be compensated by over-reliance on the other. To this end, we reconstructed and analyzed time-resolved whole-brain activity in 12 svPPA patients and 12 healthy age-matched controls while reading irregular words (e.g., yacht) and pseudowords (e.g., pook). Consistent with previous findings that the dorsal route is involved in sub-lexical, phonological processes, in control participants we observed enhanced neural activity over dorsal occipito-parietal cortices for pseudowords, when compared to irregular words. This activation was manifested in the beta-band (12-30 Hz), ramping up slowly over 500 ms after stimulus onset and peaking at [~]800 ms, around response selection and production. Consistent with our prediction, svPPA patients did not exhibit this temporal pattern of neural activity observed in controls this contrast. Furthermore, a direct comparison of neural activity between patients and controls revealed a dorsal spatiotemporal cluster during irregular word reading. These findings suggest that the sub-lexical/phonological route is involved in processing both irregular and pseudowords in svPPA. Together these results provide further evidence supporting a dual-route model for reading aloud mediated by the interplay between lexico-semantic and sub-lexical/phonological neuro-cognitive systems. When the ventral route is damaged, as in the case of neurodegeneration affecting the ATL, partial compensation appears to be possible by over-recruitment of the slower, serial attention-dependent, dorsal one. Abbreviated SummaryBorghesani et al. investigate brain dynamics during irregular word reading using magnetoencephalographic imaging in patients with semantic variant of primary progressive aphasia. Due to ventral anterior temporal lobe neurodegeneration, patients show greater reliance of dorsal, occipito-parietal brain regions - providing novel evidence for the interplay between ventral and dorsal routes for reading.

neuroscience↗

bioRxiv2019

Chemoinformatic-guided engineering of polyketide synthases

Polyketide synthase (PKS) engineering is an attractive method to generate new molecules such as commodity, fine and specialty chemicals. A significant challenge in PKS design is engineering a partially reductive module to produce a saturated {beta}-carbon through a reductive loop exchange. In this work, we sought to establish that chemoinformatics, a field traditionally used in drug discovery, could provide a viable strategy to reductive loop exchanges. We first introduced a set of donor reductive loops of diverse genetic origin and chemical substrate structures into the first extension module of the lipomycin PKS (LipPKS1). Product titers of these engineered unimodular PKSs correlated with atom pair chemical similarity between the substrate of the donor reductive loops and recipient LipPKS1, reaching a titer of 165 mg/L of short chain fatty acids produced by Streptomyces albus J1074 harboring these engineered PKSs. Expanding this method to larger intermediates requiring bimodular communication, we introduced reductive loops of divergent chemosimilarity into LipPKS2 and determined triketide lactone production. Collectively, we observed a statistically significant correlation between atom pair chemosimilarity and production, establishing a new chemoinformatic method that may aid in the engineering of PKSs to produce desired, unnatural products.

biochemistry↗

bioRxiv2019

Integrated clonal analysis reveals circulating tumor DNA in urine and plasma of glioma patients

Glioma-derived cell-free tumor DNA is challenging to detect using standard liquid biopsy techniques as its levels in body fluids are very low, similar to those in patients with early stage carcinomas. By sequencing cell-free DNA across thousands of clonal and private mutations identified individually in each patients tumor we detected tumor-derived DNA in plasma (10/12, 83%) and urine samples (8/11, 72%) from the majority (7/8, 87.5%) of glioma patients tested.\n\nOne Sentence SummaryCirculating tumor DNA can be detected in the majority of plasma and urine samples from primary brain tumor patients using sequencing guided by mutations detected in multi-region tumor biopsies.

genomics↗

bioRxiv2019

Winner(s)-take-all: nonlinear amplification of DNA-encoded library

Information processing functions are essential for biological organisms to perceive and react to their complex enviornment, as well as for human to analyze and rationalize them. While our brain has an extraordinary power to process complex information, winner(s)-take-all computation is one of the simplest models of lateral inhibition and competition among biological neurons. It has been implemented as DNA-based neural networks, for example, to mimic pattern recognition. However, the utility of DNA-based computation in information processing for real biotechnological applications remains to be demonstrated. In this paper, we developed a winner(s)-take-all method for non-linear amplification of mixtures of DNA sequences. Unlike conventional biological experiments, selected species were not directly subjected to analysis. Instead, parallel computation among myriad of different DNA sequences was carried out with a neural network-inspired winner-take-all function, to reduce the information complexity. The method could be used for various oligonucleotide-encoded libraries, as we have demonstrated its application in decoding and data analysis for selection experiment with DNA-encoded chemical library against protein target.

bioengineering↗

bioRxiv2019

Growth behavior and glyphosate resistance level in 10 biotypes of Echinochloa colona in Australia

Recently, poor control of Echinochloa colona with glyphosate has been reported in no-till agriculture systems of the northern grain region (NGR) of Australia. Two experiments were conducted using 10 biotypes of E. colona selected from the NGR of Australia to understand differences in their growth behavior and resistance pattern. Growth studies revealed that these biotypes differed in plant height (53-70 cm plant-1), tiller production (30-52 tillers plant-1), leaf production (124-186 leaves plant-1) and seed head production (37-65 seed heads plant-1). Days taken to seed heads and shoot biomass in these biotypes ranged between 40-48 d and 21-27 g plant-1, respectively. Seed production in these biotypes ranged between 5380 and 10244 seeds plant-1; lowest for biotype B17/25 and highest for biotype B17/13. Correlation studies revealed that seed number plant-1 had a positive correlation with plant height (r = 0.67), tiller number plant-1 (r = 0.89), leaf number plant-1 (r = 0.73), seed heads plant-1 (r = 0.78), seed head weight (r = 0.79), shoot biomass (r = 0.77) and root biomass (r = 0.46). The glyphosate dose-response study showed a wide range of responses in these biotypes and the glyphosate dose required to reduce 50% biomass (GR50 values) was estimated between 217 to 2159 g a.e. glyphosate ha-1. GR50 values of biotypes B17/16, B 17/34 and B17/35 were 719, 2159 and 884 g ha-1, respectively, making them 3, 10 and 4-fold resistant to glyphosate compared with the susceptible biotype B17/37. Growth behavior and seed production potential in these biotypes had no correlation with the resistance index. These results suggest that some biotypes of E. colona are highly problematic; for example, biotype B17/34 was not only highly glyphosate-resistant, but also produced a high seed number (9300 seeds plant-1). This study demonstrated that there is a possibility of great risk with the increased use of glyphosate for managing E. colona in the NGR of Australia. The results warrant integrated weed management strategies and improved stewardship guidelines are required for managing glyphosate-resistant biotypes of E. colona and to restrict further movement of resistant biotypes to other regions of Australia.

ecology↗

bioRxiv2019

Host engineering for improved valerolactam production in Pseudomonas putida

Pseudomonas putida is a promising bacterial chassis for metabolic engineering given its ability to metabolize a wide array of carbon sources, especially aromatic compounds derived from lignin. However, this omnivorous metabolism can also be a hindrance when it can naturally metabolize products produced from engineered pathways. Herein we show that P. putida is able to use valerolactam as a sole carbon source, as well as degrade caprolactam. Lactams represent important nylon precursors, and are produced in quantities exceeding one million tons per year[1]. To better understand this metabolism we use a combination of Random Barcode Transposon Sequencing (RB-TnSeq) and shotgun proteomics to identify the oplBA locus as the likely responsible amide hydrolase that initiates valerolactam catabolism. Deletion of the oplBA genes prevented P. putida from growing on valerolactam, prevented the degradation of valerolactam in rich media, and dramatically reduced caprolactam degradation under the same conditions. Deletion of oplBA, as well as pathways that compete for precursors L-lysine or 5-aminovalerate, increased the titer of valerolactam from undetectable after 48 hours of production to ~90 mg/L. This work may serve as a template to rapidly eliminate undesirable metabolism in non-model hosts in future metabolic engineering efforts.

synthetic biology↗

bioRxiv2019

Computational design of a modular protein sense/response system

Sensing and responding to signals is a fundamental ability of living systems, but despite remarkable progress in computational design of new protein structures, there is no general approach for engineering arbitrary new protein sensors. Here we describe a generalizable computational strategy for designing sensor/actuator proteins by building binding sites de novo into heterodimeric protein-protein interfaces and coupling ligand sensing to modular actuation via split reporters. Using this approach, we designed protein sensors that respond to farnesyl pyrophosphate, a metabolic intermediate in the production of valuable compounds. The sensors are functional in vitro and in cells, and the crystal structure of the engineered binding site matches the design model with atomic accuracy. Our computational design strategy opens broad avenues to link biological outputs to new signals.\n\nOne Sentence SummaryAn engineering strategy to design modular synthetic signaling systems that respond to new small molecule inputs.

biophysics↗

bioRxiv2019

Distinguishing biological from technical sources of variation by leveraging multiple methylation datasets

DNA methylation remains one of the most widely studied epigenetic markers. One of the major challenges in population studies of methylation is the presence of global methylation effects that may mask local signals. Such global effects may be due to either technical effects (e.g., batch effects) or biological effects (e.g., cell-type composition, genetics). Many methods have been developed for the detection of such global effects, typically in the context of epigenome-wide association studies. However, current unsupervised methods do not distinguish between biological and technical effects, resulting in a loss of highly relevant information. Though supervised methods can be used to estimate known biological effects, it remains difficult to identify and estimate unknown biological effects that globally affect the methylome. Here, we propose CONFINED, a reference-free method based on sparse canonical correlation analysis that captures replicable sources of variation--such as age, sex, and cell-type composition--across multiple methylation datasets and distinguishes them from dataset-specific sources of variability (e.g., technical effects). Consequently, we demonstrate through simulated and real data that by leveraging multiple datasets simultaneously, our approach captures several replicable sources of biological variation better than previous reference-free methods and is considerably more robust to technical noise than previous reference-free methods. CONFINED is available as an R package as detailed at https://github.com/cozygene/CONFINED.

bioinformatics↗

bioRxiv2018

New inhibitors of Mycobacterium tuberculosis identified using systems chemical biology

New antibiotics are needed to combat rising resistance, with new Mycobacterium tuberculosis (Mtb) drugs of highest priority. Conventional whole-cell and biochemical antibiotic screens have failed. We developed a novel strategy termed PROSPECT (PRimary screening Of Strains to Prioritize Expanded Chemistry and Targets) in which we screen compounds against pools of strains depleted for essential bacterial targets. We engineered strains targeting 474 Mtb essential genes and screened pools of 100-150 strains against activity-enriched and unbiased compounds libraries, measuring > 8.5-million chemical-genetic interactions. Primary screens identified >10-fold more hits than screening wild-type Mtb alone, with chemical-genetic interactions providing immediate, direct target insight. We identified > 40 novel compounds targeting DNA gyrase, cell wall, tryptophan, folate biosynthesis, and RNA polymerase, as well as inhibitors of a novel target EfpA. Chemical optimization yielded EfpA inhibitors with potent wild-type activity, thus demonstrating PROSPECTs ability to yield inhibitors against novel targets which would have eluded conventional drug discovery.

microbiology↗

bioRxiv2017

Capture and Purification of Plant Genomic DNA on a Readily-available Cellulose Matrix

Whatman FTA (R)Cards are a fast and efficient method for capturing and storing nucleic acids but are cost-prohibitive for some researchers. We developed a method that substitutes readily-available cellulose-based paper and homemade washing buffer for commercial FTA (R)Cards and FTA (R)Purification Reagent. This method is suitable for long-term storage of DNA from many plant species, including Arabidopsis thaliana, prior to purification and PCR.\n\nMethod SummaryHere we report a low-cost method for long-term storage of plant genomic DNA on a readily available cellulose matrix.

molecular biology↗