Biology subjects

Nguyen, M.

Publications and source records attributed to Nguyen, M..

10 recordsLinked to original sources

bioRxiv2018

The origins and relatedness structure of mixed infections vary with local prevalence of P. falciparum malaria

Individual malaria infections can carry multiple strains of Plasmodium falciparum with varying levels of relatedness. Yet, how local epidemiology affects the properties of such mixed infections remains unclear. Here, we develop an enhanced method for strain deconvolution from genome sequencing data, which estimates the number of strains, their proportions, identity-by-descent (IBD) profiles and individual haplotypes. Applying it to the Pf3k data set, we find that the rate of mixed infection varies from 29% to 63% across countries and that 51% of mixed infections involve more than two strains. Further-more, we estimate that 47% of symptomatic dual infections contain sibling strains likely to have been co-transmitted from a single mosquito, and find evidence of mixed infections propagated over successive infection cycles. Finally, leveraging data from the Malaria Atlas Project, we find that prevalence correlates within Africa, but not Asia, with both the rate of mixed infection and the level of IBD.

epidemiology↗

bioRxiv2018

Using machine learning to predict antimicrobial minimum inhibitory concentrations and associated genomic features for nontyphoidal Salmonella

Nontyphoidal Salmonella species are the leading bacterial cause of food-borne disease in the United States. Whole genome sequences and paired antimicrobial susceptibility data are available for Salmonella strains because of surveillance efforts from public health agencies. In this study, a collection of 5,278 nontyphoidal Salmonella genomes, collected over 15 years in the United States, were used to generate XGBoost-based machine learning models for predicting minimum inhibitory concentrations (MICs) for 15 antibiotics. The MIC prediction models have average accuracies between 95-96% within {+/-} 1 two-fold dilution factor and can predict MICs with no a priori information about the underlying gene content or resistance phenotypes of the strains. By selecting diverse genomes for training sets, we show that highly accurate MIC prediction models can be generated with fewer than 500 genomes. We also show that our approach for predicting MICs is stable over time despite annual fluctuations in antimicrobial resistance gene content in the sampled genomes. Finally, using feature selection, we explore the important genomic regions identified by the models for predicting MICs. To date, this is one of the largest MIC modeling studies to be published. Our strategy for developing whole genome sequence-based models for surveillance and clinical diagnostics can be readily applied to other important human pathogens.

bioinformatics↗

bioRxiv2018

Brief Freezing Steps Lead to Robust Immunofluorescence in Drosophila Larval Brains

Drosophila melanogaster possess complex neuronal networks regulating sophisticated behavioral outputs that aid in studying the molecular mechanisms of neuronal function and neurodegenerative disease. Immunofluorescence (IF) techniques provide a way to visualize the spatiotemporal organization of these networks, permitting observation of their development, functional location, remodeling, and eventually - degradation. However, general immunostaining techniques do not always result in sufficient antibody penetration through the brain, and techniques used to enhance permeability can compromise structural integrity. We have found that freezing larval brains facilitates permeability with no apparent loss of antibody specificity or structural integrity. To demonstrate the advantage of this freezing technique, we compared results to two commonly used permeation methods: Detergent alone (Basic) and proteolytic degradation (Collagenase) techniques.\n\nSummaryHere we compare four different immunofluorescence techniques demonstrating that freezing Drosophila brains results in robust staining of small neurons in the larval brain without compromising structural integrity.

cell biology↗

bioRxiv2018

Impeding DNA Break Repair Enables Oocyte Quality Control

Oocyte quality control culls eggs with defects in meiosis. In mouse, oocyte death is triggered by defects in chromosome synapsis and recombination, which involve repair of programmed DNA double-strand breaks (DSBs) between homologous chromosomes. We show that RNF212, a SUMO ligase required for crossing over, also mediates oocyte quality control. Both physiological apoptosis and wholesale oocytes elimination in meiotic mutants require RNF212. RNF212 sensitizes cells to DSB-induced apoptosis within a narrow window when chromosomes desynapse during the transition into quiescence. Analysis of DNA damage during this transition implies that RNF212 impedes DSB repair. Consistently, RNF212 is required for HORMAD1, a negative regulator of inter-sister recombination, to associate with desynapsing chromosomes. We infer that oocytes impede repair of residual DSBs to retain a \"memory\" of meiotic defects that enables quality control processes. These results define the logic of oocyte quality control and suggest RNF212 variants may influence transmission of defective genomes.

genetics↗

bioRxiv2017

Shared understanding is correlated with shared neural responses in the default mode network

Humans have a striking ability to infer meaning from even the sparsest and most abstract forms of narratives. At the same time, flexibility in the form of a narrative is matched by inherent ambiguity in the interpretation of it. How does the brain represent subtle, idiosyncratic differences in the interpretation of abstract and ambiguous narratives? In this fMRI study, we scanned subjects watching a 7-min original animation that depicts a complex narrative through the movement of simple geometric shapes. We additionally scanned two separate groups listening to concrete verbal descriptions of either the social narrative or the physical properties of the movie. After scanning, all subjects freely recalled their interpretation of the stimulus. Using an intersubject representational similarity analysis, we compared the similarity of narrative interpretation across subjects, as measured using text analysis, with the similarity of neural responses, as measured using intersubject correlation (ISC). We found that the more similar two peoples interpretations of the abstract shape movie were, the more similar their neural responses were in the default mode network (DMN). Moreover, these shared responses were modality invariant: despite vast differences in stimulus properties, we found that the shapes movie and the verbal interpretation of the movie elicited shared responses in linguistic areas and a subset of the DMN when subjects shared interpretations. Together, these results suggest that the DMN is not only sensitive to subtle individual differences in narrative interpretation, but also resilient to large differences in the modality of the narrative.\n\nSignificance statementThe same narrative can be both communicated in different ways and interpreted in different ways. How are subtle, idiosyncratic differences in the interpretation of complex narratives presented in different forms represented in the brain? In this fMRI study, we show that the more similarly two people interpreted an ambiguous animation composed of moving shapes, the more similar their neural responses were in the Default Mode Network. In addition, by presenting the same narrative in a different form, we found shared responses across modalities when subjects shared interpretations despite the vast differences in modality of the stimuli. Together, these results suggest that the DMN is at once sensitive to individual differences in narrative interpretation and resilient to differences narrative modality.

neuroscience↗

bioRxiv2017

Developing an in silico minimum inhibitory concentration panel test for Klebsiella pneumoniae

Antimicrobial resistant infections are a serious public health threat worldwide. Whole genome sequencing approaches to apidly identify pathogens and predict antibiotic resistance phenotypes are becoming more feasible and may offer a way to reduce clinical test turnaround times compared to conventional culture-based methods, and in turn, improve patient outcomes. In this study, we use whole genome sequence data from 1668 clinical isolates of Klebsiella pneumoniae to develop a XGBoost-based machine learning model that accurately predicts minimum inhibitory concentrations (MICs) for 20 antibiotics. The overall accuracy of the model, within {+/-}1 two-fold dilution factor, is 92%. Individual accuracies are[≥]90% for 15/20 antibiotics. We show that the MICs predicted by the model correlate with known antimicrobial resistance genes. Importantly, the genome-wide approach described in this study offers a way to predict MICs for isolates without knowledge of the underlying gene content. This study shows that machine learning can be used to build a complete in silico MIC prediction panel for K. pneumoniae and provides a framework for building MIC prediction models for other pathogenic bacteria.

bioinformatics↗

bioRxiv2017

Multiplexing droplet-based single cell RNA-sequencing using natural genetic barcodes

Droplet-based single-cell RNA-sequencing (dscRNA-seq) has enabled rapid, massively parallel profiling of transcriptomes from tens of thousands of cells. Multiplexing samples for single cell capture and library preparation in dscRNA-seq would enable cost-effective designs of differential expression and genetic studies while avoiding technical batch effects, but its implementation remains challenging. Here, we introduce an in-silico algorithm demuxlet that harnesses natural genetic variation to discover the sample identity of each cell and identify droplets containing two cells. These capabilities enable multiplexed dscRNA-seq experiments where cells from unrelated individuals are pooled and captured at higher throughput than standard workflows. To demonstrate the performance of demuxlet, we sequenced 3 pools of peripheral blood mononuclear cells (PBMCs) from 8 lupus patients. Given genotyping data for each individual, demuxlet correctly recovered the sample identity of > 99% of singlets, and identified doublets at rates consistent with previous estimates. In PBMCs, we demonstrate the utility of multiplexed dscRNA-seq in two applications: characterizing cell type specificity and inter-individual variability of cytokine response from 8 lupus patients and mapping genetic variants associated with cell type specific gene expression from 23 donors. Demuxlet is fast, accurate, scalable and could be extended to other single cell datasets that incorporate natural or synthetic DNA barcodes.

bioinformatics↗

bioRxiv2017

The butterfly effect: amplification of local changes along the temporal processing hierarchy

Changing just a few words in a story can induce a substantial change in the overall narrative. How does the brain accumulate and process local and sparse changes, creating a unique situation model of the story, over the course of a real-life narrative? Recently, we mapped a hierarchy of processing timescales in the brain: from early sensory areas that integrate information over 10s-100s ms, to high-order areas that integrate information over many seconds to minutes. Based on this hierarchy, we hypothesize that early sensory areas would be sensitive to local changes in word use, but that there will be increasingly divergent neural responses along the processing hierarchy as higher-order areas accumulate and amplify these local changes. To test this hypothesis, we created two structurally related but interpretively distinct narratives by changing some individual words. We found that the neural response distance between the stories was amplified as story information is transferred from low-level regions (e.g. early auditory cortex) to high-level regions (e.g precuneus and prefrontal cortex) and that the neural difference between stories is highly correlated with an areas ability to integrate information over time. Our results suggest a neural mechanism by which two similar situations become easy to distinguish.

neuroscience↗

bioRxiv2016

Cleanliness in context: reconciling hygiene with a modern microbial perspective

The concept of hygiene is rooted in the relationship between cleanliness and the maintenance of good health. Since the widespread acceptance of the germ theory of disease, hygiene has become increasingly conflated with that of sterilization. Recent research on microbial ecology is demonstrating that humans have intimate and evolutionarily significant relationships with a diverse assemblage of microorganisms (our microbiota). Human skin is home to a diverse, skin habitat specific community of microorganisms; this includes members that exist across the ecological spectrum from pathogen through commensal to mutualist. Most evidence suggests that the skin microbiota is likely of direct benefit to the host, and only rarely exhibits pathogenicity. This complex ecological context suggests that the conception of hygiene as a unilateral reduction or removal of microbes has outlived its usefulness. As such, we suggest the explicit definition of hygiene as those actions and practices that reduce the spread or transmission of pathogenic microorganisms, and thus reduce the incidence of disease. To examine the implications of this definition, we review the literature related to hand drying as an aspect of hand hygienic practice. Research on hand drying generally focuses on hygienic efficacy, a concept not typically defined explicitly, but nearly always including alterations to bulk microbial load. The corresponding literature is differentiable into two divisions: research supporting the use of forced air dryers, which typically includes effectiveness of drying as an aspect of hygienic efficacy; and research supporting the use of paper towels, which typically includes risk of aerosolized spread of microbes from hands as an aspect of hygienic efficacy. Utilizing a definition of hygiene that explicitly relies on reduction in disease spread rather than alterations to bulk microbial load would address concerns raised on both sides of the debate. Future research should take advantage of cultivation-independent techniques, working to bridge the gap between the two existing divisions of research by using health outcomes (such as the spread of disease) as dependent variables, taking into account the microbial community context of the skin microbiota, and focusing on understanding the relative contribution of bioaerosols and residual moisture to the risk of disease transmission.

microbiology↗

bioRxiv2016

Neuronal synchrony and the relation between the BOLD response and the local field potential.

The most widespread measures of human brain activity are the blood oxygen level dependent (BOLD) signal and surface field potential. Prior studies report a variety of relationships between these signals. To develop an understanding of how to interpret these signals and the relationship between them, we developed a model of (a) neuronal population responses, and (b) transformations from neuronal responses into the fMRI BOLD signal and electrocorticographic (ECoG) field potential. Rather than seeking a transformation between the two measures directly, this approach interprets each measure with respect to the underlying neuronal population responses. This model accounts for the relationship between BOLD and ECoG data from human visual cortex in V1-V3, with the model predictions and data matching in three ways: Across stimuli, the BOLD amplitude and ECoG broadband power were positively correlated, the BOLD amplitude and alpha power (8-13 Hz) were negatively correlated, and the BOLD amplitude and narrowband gamma power (30-80 Hz) were uncorrelated. The two measures provide complementary information about human brain activity and we infer that features of the field potential that are uncorrelated with BOLD arise largely from changes in synchrony, rather than level, of neuronal activity.

neuroscience↗