Biology subjects

Walker, J. A.

Publications and source records attributed to Walker, J. A..

26 records · Page 2Linked to original sources

bioRxiv2019

Neurofibromin regulates metabolic rate via neuronal mechanisms in Drosophila

Neurofibromatosis type 1 (NF1) is a genetic disorder predisposing patients to a range of features, the most characteristic of which include areas of abnormal skin pigmentation and benign tumors associated with peripheral nerves, termed neurofibromas. Less common, but more serious symptoms also include malignant peripheral nerve sheath tumors, other malignancies, and learning disabilities. The NF1 gene encodes neurofibromin, a large protein that functions as a negative regulator of Ras signaling and mediates pleiotropic cellular and organismal function. Recent evidence suggests NF1 may regulate metabolism, though the mechanisms are unknown. Here we show that the Drosophila ortholog of NF1, dNf1 regulates metabolic homeostasis in fruit flies by functioning within a discrete brain circuit. Loss of dNf1 increases metabolic rate and feeding, enhances starvation susceptibility, and decreases lipid stores while increasing lipid turnover rate. The increase in metabolic rate is independent of locomotor activity (grooming), and maps to a subset of neurons in the ventral nervous system. The feeding and metabolic rate effects are due to loss of dNf1 in the same set of neurons, suggesting that increased feeding may be a compensatory effect driven by the increase in metabolic rate and lipid turnover. Finally, we show that the Ras GAP-related domain of neurofibromin is required for normal metabolism, demonstrating that Ras signaling downstream of dNf1 mediates the metabolic effects. These data demonstrate that dNf1 regulates metabolic rate via neuronal mechanisms, suggest that cellular and systemic metabolic alterations may represent a pathophysiological mechanism in NF1, and provide a platform for investigating the cellular role of neurofibromin in metabolic homeostasis.

neuroscience↗

bioRxiv2019

Social behavioral deficits in NF1 emerge from peripheral chemosensory neuron dysfunction.

Neurofibromatosis type 1 (NF1) is a neurodevelopmental disorder commonly associated with social and communicative disabilities. The cellular and circuit mechanisms by which loss of neurofibromin 1 (Nf1) function results in social deficits are unknown. Here, we identify social behavioral dysregulation with loss of Nf1 in Drosophila. These deficits map to primary dysfunction of a small group of peripheral sensory neurons, rather than central brain circuits. Specifically, Nf1 regulation of Ras signaling in adult, Ppk23+ chemosensory cells is required for normal social behaviors in flies. Loss of Nf1 results in attenuated ppk23+ neuronal activity in response to pheromonal cues, and circuit-specific manipulation of Nf1 expression or neuronal activity in ppk23+ neurons rescues social deficits. Unexpectedly, this disrupted sensory processing gives rise to persistent changes in behavior lasting beyond the social interaction, indicating a sustained effect of an acute sensory misperception. Together our data identify a specific circuit mechanism through which Nf1 acts to regulate social behaviors, and suggest social deficits in NF1 arise from propagation of sensory misinformation.

neuroscience↗

bioRxiv2019

Rora regulates activated T helper cells during inflammation

The transcription factor Rora has been shown to be important for the development of ILC2 and the regulation of ILC3, macrophages and Treg cells. Here we investigate the role of Rora across CD4+ T cells, both in vitro as well as in the context of several in vivo type 2 infection models. We dissect the function of Rora using overexpression and a CD4-conditional Rora-knockout mouse, as well as a RORA-reporter mouse. We establish the importance of Rora in CD4+ T cells for controlling lung inflammation induced by Nippostrongylus brasiliensis infection, and have measured the effect on downstream genes using RNA-seq. Using a systematic stimulation screen of CD4+ T cells, coupled with RNA-seq, we identify upstream regulators of Rora, most importantly IL-33 and CCL7. Our data suggest that Rora is a negative regulator of the immune system, possibly through several downstream pathways, and is under control of the local microenvironment.

immunology↗

bioRxiv2019

HTT is a repressor of ABL activity required for APP induced axonal growth

ABL tyrosine kinase activity controls several aspects of development including axon patterning. Amyloid precursor protein (APP) is linked to Alzheimers disease and previous work established that ABL is a downstream effector in an Appl, the Drosophila App ortholog, signaling pathway which modulates axon outgrowth in the mushroom bodies (MBs), the fly memory center. Here we show that Abl is required for the MB neuron axonal growth. Importantly, both Abl overexpression and lack of expression produce a similar phenotype in the MBs indicating the necessity of tightly regulating ABL activity. We find that the fly huntingtin protein (HTT), the homolog of the protein involved in Huntingtons disease, behaves genetically as a repressor of ABL activity. Supporting this, FRET-based measurements of in vivo ABL activity in the MBs reveal a clear increase in its activity when HTT levels are reduced. Thus, in addition to its many other reported roles, HTT acts as a negative regulator of ABL activity, at least in the MBs, to maintain its appropriate physiological levels necessary for axon growth.

neuroscience↗

bioRxiv2019

Small-molecule control of super-Mendelian inheritance in gene drives

By surpassing the 50% inheritance limit of Mendels law of independent assortment, CRISPR-based gene drives have the potential to fight vector-borne diseases or suppress crop pests. However, contemporary gene drives could spread unchecked, posing safety concerns that limit their use in both laboratory and field settings. Current technologies also lack chemical control strategies, which could be applied in the field for dose, spatial and temporal control of gene drives. We describe in Drosophila the first gene-drive system controlled by an engineered Cas9 and a synthetic, orally-available small molecule.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=51 SRC=\"FIGDIR/small/665620v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (15K):\norg.highwire.dtl.DTLVardef@1aa8522org.highwire.dtl.DTLVardef@a99c8org.highwire.dtl.DTLVardef@1f63636org.highwire.dtl.DTLVardef@1613408_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical AbstractC_FLOATNO C_FIG

genetics↗

bioRxiv2018

Birds invest wingbeats to keep a steady head and reap the ultimate benefits of flocking

Flapping flight is the most energetically demanding form of sustained forwards locomotion that vertebrates perform. Flock dynamics therefore have significant implications for energy expenditure. Despite this, no studies have quantified the biomechanical consequences of flying in a cluster flock relative to flying solo. Here, we compared the flight characteristics of homing pigeons (Columba livia) flying solo and in pairs, using high-precision 5 Hz GPS and 200 Hz tri-axial accelerometer biologgers. Paired flight increased route accuracy by ~7%, but, was accompanied by an increase in wingbeat frequency of ~18%. As expected, paired individuals benefitted from improved homing route accuracy, which reduced flight distance by ~7% and time by ~9%. However, realising these navigational gains involved substantial changes in flight kinematics and energetics. Both individuals in a pair increased their wingbeat frequency by c.18%, by decreasing the duration of their upstroke. This sharp increase in wingbeat frequency caused just a 3% increase in airspeed, but reduced the oscillatory displacement of the body by ~22%, which we hypothesise relates to an increased requirement for visual stability and manoeuvrability when flocking. Overall, the shorter flight distances and increased wingbeat frequency in a pair resulted in a net increase in the aerodynamic cost of returning home, which we estimate was ~14%. Our results demonstrate that flocking costs have been underestimated by an order of magnitude and force reinterpretation of their mechanistic origin. We show that, for pigeons, two heads are better than one, but keeping a steady head necessitates energetically costly kinematics.

animal behavior and cognition↗

bioRxiv2018

Show the model - A Harrell plot combines a forest plot of effects and uncertainty with a dot plot of the data

Several high profile editorials and articles have advocated replacing the ubiquitous, but not especially informative, bar plot with dot plots or box plots which show the data or some summary of the data distribution. I argue that the biggest deficiency of bar plots (and other mean-and-error plots) is that they fail to directly communicate modeled effects and uncertainty, and dot and box plots do not address this deficiency. A Harrell plot combines a forest plot of modeled treatment effects and uncertainty, a dot plot of raw data, and a box plot of the distribution of the raw data into a single, two-part graph. A Harrell plot encourages best practices such as communication of the distribution of the data and a focus on effect size and uncertainty, while discouraging poor practices such as hiding distributions and focusing on p-values.

scientific communication and education↗

bioRxiv2017

A Defense Of Model Averaging

Model-averaged partial regression coefficients have been criticized for averaging over a set of models with coefficients that have different meanings from model to model. This criticism arises because statisticians since Fisher believe that the meaning of a coefficient in a regression model arises from probabilistic conditioning (P (Y |X)) and that coefficients are mere descriptors of conditional association (or \"differences in conditional means\"). Because this association parameter is conditional on a specific set of covariates, the parameter for a predictor varies from model to model. The coefficients in many applied regression models, however, take their meaning from causal conditioning (P (Y |do(X))) and these coefficients estimate causal effect parameters (or simply, causal effects or Average Treatment Effects). Causal effect parameters are also differences in conditional expectations, but the event conditioned on is not the set of covariates in a regression model but a hypothetical intervention. Because an effect parameter for a predictor takes its meaning from causal and not probabilistic conditioning, it is the same from model to model, and an averaged coefficient has a straightforward interpretation as an estimate of a causal effect. But, because the effect parameter is the same from model to model, the estimates of the parameter will generally be biased. By contrast, with probabilistic conditioning, the coefficients are consistent estimates of their parameter in every model. Confounding and omitted variable bias, which are central to explanatory modeling, are meaningless in regression modeling as mere description.

ecology↗