Biology subjects

Kelly, M.

Publications and source records attributed to Kelly, M..

6 recordsLinked to original sources

bioRxiv2018

Point of care influenza testing using Alere-i Influenza A & B assay: a practical assessment

We assessed the utility of the Alere i Influenza A & B point of care influenza test (Ai-POCIT) with laboratory testing using RT-PCR. 270 adult hospital patients had both Ai-POCIT and laboratory influenza tests conducted on the same sample. Overall, 30% and 32% influenza tests were positive by Ai-POCIT and RT-PCR, respectively. The sensitivity of the Ai-POCIT for influenza A, influenza B and any influenza were 93%, 100%, and 95%, respectively. Specificity was 100% for both viruses, but an 11% test failure rate indicates the need for better training of users. We believe that the use of nasopharyngeal (NP) swabs resulted in the observed high performance of the Ai-POCIT in comparison to other published studies. Ai-POCIT was regarded as very useful by front line clinical staff for clinical decision making and acute bed management.

microbiology↗

bioRxiv2018

CRISPR delivery particles for developing therapeutic strategies in metabolic disease

RNA-guided engineered nucleases derived from a prokaryotic adaptive immune system known as CRISPR-Cas represent a promising platform for gene deletion and editing. As a therapeutic approach, direct delivery of Cas9 protein and guide RNA could circumvent the safety problems associated with plasmid delivery and therefore represents an attractive tool for genome engineering. Gene deletion or editing in adipose tissue to enhance its energy expenditure, fat oxidation and secretion of bioactive factors through a \"browning\" process presents a potential therapeutic strategy to alleviate metabolic disease. Here, we developed novel CRISPR delivery particles, denoted CriPs, composed of nano-size complexes of Cas9 protein and single guide (sg)RNA, coated with an amphipathic peptide called Endo-Porter that mediates entry into cells. Efficient CRISPR-Cas9 mediated gene deletion of ectopically expressed Green fluorescent protein (GFP) by CriPs was achieved in multiple cell types including a macrophage cell line, primary macrophages and primary pre-adipocytes. Significant GFP loss was also observed in peritoneal exudate cells with minimum systemic toxicity in GFP expressing mice following intraperitoneal injection of CriPs containing sgRNA targeting Gfp. Furthermore, the disruption of the Nrip1 gene in white adipocytes by CriPs enhanced adipocyte \"browning\" with a marked increase of UCP1 expression. Deletion of Nrip1 by CriPs did not produce detectable off-target effects. Thus CriPs represent a novel CRISPR delivery system for Cas9 and sgRNA that is effective for ablating targeted gene products in cultured cells and in vivo, and provide a potential therapeutic strategy for metabolic disease.

bioengineering↗

bioRxiv2018

Neuronal Modulation of Brown Adipose Activity Through Perturbation of White Adipocyte Lipogenesis

White adipose tissue (WAT) secretes factors to communicate with other metabolic organs to maintain energy homeostasis. We previously reported that perturbation of adipocyte de novo lipogenesis (DNL) by deletion of fatty acid synthase (FASN) causes expansion of sympathetic neurons within white adipose tissue (WAT) and the appearance of \"beige\" adipocytes. Here we report evidence that white adipocyte DNL activity is also coupled to neuronal regulation and thermogenesis in brown adipose tissue (BAT). Induced deletion of FASN in all adipocytes in mature mice (iAdFASNKO) enhanced sympathetic innervation and neuronal activity as well as UCP1 expression in both WAT and BAT. In contrast, selective ablation of FASN in brown adipocytes of mice (iUCP1FASNKO) failed to modulate sympathetic innervation and the thermogenic program in BAT. Surprisingly, DNL in brown adipocytes was also dispensable in maintaining euthermia when UCP1FASNKO mice were cold-exposed. These results indicate that DNL in white adipocytes influences long distance signaling to BAT, which can modify BAT sympathetic innervation and expression of genes involved in thermogenesis.

cell biology↗

bioRxiv2018

Spatial Mark-Resight for Categorically MarkedPopulations with an Application to GeneticCapture-Recapture

The estimation of animal population density is a fundamental goal in wildlife ecology and management, commonly met using mark recapture or spatial mark recapture (SCR) study designs and statistical methods. Mark-recapture methods require the identification of individuals; however, for many species and sampling methods, particularly noninvasive methods, no individuals or only a subset of individuals are individually identifiable. The unmarked SCR model, theoretically, can estimate the density of unmarked populations; however, it produces biased and imprecise density estimates in many sampling scenarios typically encountered. Spatial mark-resight (SMR) models extend the unmarked SCR model in three ways: 1) by introducing a subset of individuals that are marked and individually identifiable, 2) introducing the possibility of individual-linked telemetry data, and 3) introducing the possibility that the capture-recapture data from the survey used to deploy the marks can be used in a joint model, all improving the reliability of density estimates. The categorical spatial partial identity model (SPIM) improves the reliability of density estimates over unmarked SCR along another dimension, by adding categorical identity covariates that improve the probabilistic association of the latent identity samples. Here, we combine these two models into a \"categorical SMR\" model to exploit the benefits of both models simultaneously. We demonstrate using simulations that SMR alone can produce biased and imprecise density estimates with sparse data and/or when few individuals are marked. Then, using a fisher (Pekania pennanti) genetic capture-recapture data set, we show how categorical identity covariates, marked individuals, telemetry data, and jointly modeling the capture survey used to deploy marks with the resighting survey all combine to improve inference over the unmarked SCR model. As previously seen in an application of the categorical SPIM to a real-world data set, the fisher data set demonstrates that individual heterogeneity in detection function parameters, especially the spatial scale parameter{sigma} , introduces positive bias into latent identity SCR models (e.g., unmarked SCR, SMR), but the categorical SMR model provides more tools to reduce this positive bias than SMR or the categorical SPIM alone. We introduce the possibility of detection functions that vary by identity category level, which will remove individual heterogeneity in detection function parameters than is explained by categorical covariates, such as individual sex. Finally, we provide efficient SMR algorithms that accommodate all SMR sample types, interspersed marking and sighting periods, and any number of identity covariates using the 2-dimensional individual by trap data in conjunction with precomputed constraint matrices, rather than the 3-dimensional individual by trap by occasion data used in SMR algorithms to date.

ecology↗

bioRxiv2018

Spatial Capture-Recapture for Categorically Marked Populations with An Application to Genetic Capture-Recapture

Recently introduced unmarked spatial capture-recapture (SCR), spatial mark-resight (SMR), and 2-flank spatial partial identity models (SPIM) extend the domain of SCR to populations or observation systems that do not always allow for individual identity to be determined with certainty. For example, some species do not have natural marks that can reliably produce individual identities from photographs, and some methods of observation produce partial identity samples as is the case with remote cameras that sometimes produce single flank photographs. These models share the feature that they probabilistically resolve the uncertainty in individual identity using the spatial location where samples were collected. Spatial location is informative of individual identity in spatially structured populations with home range sizes smaller than the extent of the trapping array because a latent identity sample is more likely to have been produced by an individual living near the trap where it was recorded than an individual living further away from the trap. Further, the level of information about individual identity that a spatial location contains is determined by two key ecological concepts, population density and home range size. The number of individuals that could have produced a latent or partial identity sample increases as density and home range size increase because more individual home ranges will overlap any given trap. We show this uncertainty can be quantified using a metric describing the expected magnitude of uncertainty in individual identity for any given population density and home range size, the Identity Diversity Index (IDI). We then show that the performance of latent and partial identity SCR models varies as a function of this index and produces imprecise and biased estimates in many high IDI scenarios when data are sparse. We then extend the unmarked SCR model to incorporate partially identifying covariates which reduce the level of uncertainty in individual identity, increasing the reliability and precision of density estimates, and allowing reliable density estimation in scenarios with higher IDI values and with more sparse data. We illustrate the performance of this \"categorical SPIM\" via simulations and by applying it to a black bear data set using microsatellite loci as categorical covariates, where we reproduce the full data set estimates with only slightly less precision using fewer loci than necessary for confident individual identification. The categorical SPIM offers an alternative to using probability of identity criteria for classifying genotypes as unique, shifting the \"shadow effect\", where more than one individual in the population has the same genotype, from a source of bias to a source of uncertainty. We discuss the difficulties that real world data sets pose for latent identity SCR methods, most importantly, individual heterogeneity in detection function parameters, and argue that the addition of partial identity information reduces these concerns. We then discuss how the categorical SPIM can be applied to other wildlife sampling scenarios such as remote camera surveys, where natural or researcher-applied partial marks can be observed in photographs. Finally, we discuss how the categorical SPIM can be added to SMR, 2-flank SPIM, or other future latent identity SCR models.

ecology↗

bioRxiv2017

Molecular map of GNAO1-related disease phenotypes and reactions to treatment

The GNAO1 gene codes for the most commonly expressed G protein in the central nervous system. Pathogenic GNAO1 variants result in early-onset neurological phenotypes, sometimes with distinct epilepsy or movement disorder, and sometimes with both mani-festations in the same patient. The existing extensive knowledge about G-protein coupled receptor (GPCR) signaling provides the input needed to describe quantitatively how mutations modify the GPCR signal. This in turn allows rational interpretation of distinct phenotypes arising from mutations in GNAO1. In this work we outline a model that enables understanding of clinical phenotypes at a molecular level. The mutations affecting the catalytic pocket of GNAO1, we show, result in the improper withdrawal of the signal, and give rise to epileptic phenotypes (EPs). The converse is not true - some pure EPs are caused by mutations with no obvious impact on catalysis. Mutations close to the interface with GNAO1s downstream effector block the signal propagation in that direction, and manifest as a movement disorder phenotype without epilepsy. Quantifying the reported reaction to therapy highlights the tendency of the latter group to be unresponsive to the therapies currently in use. We argue, however, that the majority of clinically described mutations can impact several aspects of GNAO1 function at once, resulting in the continuum of phenotypes observed in patients. The reasoning based on GNAO1 signaling model provides a precision medicine paradigm to aid clinicians in selecting effective categories of medication, and in addition, can suggest pragmatic targets for future therapies.

molecular biology↗