Biology subjects

Yates, T.

Publications and source records attributed to Yates, T..

4 recordsLinked to original sources

bioRxiv2021

Mutating novel interaction sites in NRP1 reduces SARS-CoV-2 spike protein internalization

The global pandemic of Coronavirus disease 2019 caused by severe acute respiratory syndrome coronavirus 2 has become a severe global health problem because of its rapid spread(1). Both angiotensin-converting enzyme 2 and neuropilin 1 provide initial viral binding sites for SARS-CoV-2 (2, 3). Here, we show that three cysteine residues located in a1/a2 and b1 domains of neuropilin 1 are necessary for SARS-CoV-2 spike protein internalization in human cells. Mutating cysteines C82, C104 and C147 altered neuropilin 1 stability and binding ability as well as cellular internalization and lysosomal translocation of the spike protein. This resulted in up to 4 times reduction in spike protein load in cells for the original, alpha and delta SARS-CoV-2 variants even in the presence of the endogenous angiotensin-converting enzyme 2 receptor. Transcriptome analysis of cells transfected with mutated NRP1 revealed significantly reduced expression of genes involved in viral infection and replication, including eight members of the ribosomal protein L, ten members of ribosomal protein S and five members of the proteasome {beta} subunit family proteins. We also observed higher expression of genes involved in suppression of inflammation and endoplasmic reticulum associated degradation. These observations suggest that these cysteines offer viable targets for therapies against COVID-19.

genetics↗

bioRxiv2021

Perceptual clustering in auditory streaming

Perception is dependent on the ability to separate stimuli from different objects and causes in order to perform inference and further processing. We have models of how the human brain can perform such causal inference for simple binary stimuli (e.g., auditory and visual), but the complexity of the models increases dramatically with more than two stimuli. To characterize human perception with more complex stimuli, we developed a Bayesian inference model that takes into account a potentially unlimited number of stimulus sources: it is general enough to factor in any discrete sequential cues from any modality. Because the model employs a non-parametric prior, increased signal complexity does not necessitate the addition of more parameters. The model not only predicts the number of possible sources, but also specifies the source with which each signal is associated. As a test case, we demonstrate that such a model can explain several phenomena in the auditory stream perception literature, that it provides an excellent fit to experimental data, and that it makes novel predictions that we experimentally confirm. These findings have implications not just for human auditory temporal perception, but for a wide range of perceptual phenomena across unisensory and multisensory stimuli. Author summaryPerceiving the world requires humans to organize perceptual stimuli according to the likely sources that generated them, requiring inference about these sources and their relationship with the generated stimuli. As an example, the sound of two different species of song birds should be segregated in order to better identify them. In this paper we utilize ideas from statistics to propose a way for the mind to do the allocation of stimuli to sources, that is not restricted by the number of stimuli, and that instead combines information dynamically as the complexity grows. We show that this not only qualitatively explains known phenomena in auditory perception, but can also quantitatively explain behavior, and leads to an experimental prediction that we confirm.

neuroscience↗

bioRxiv2019

A data-driven, meaningful, easy to interpret, population-independent accelerometer outcome variable for global surveillance

BackgroundAccelerometer-driven physical activity guidelines are not available, likely due to the lack of consensus on meaningful and interpretable accelerometer outcomes. The aim of this paper is to demonstrate how a data-driven accelerometer metric, the acceleration above which a persons most active minutes are accumulated, can a) quantify the prevalence of meeting current physical activity guidelines for global surveillance and b) moving forward, could inform accelerometer-driven physical activity guidelines. Unlike cut-point methods, the metric is population-independent (e.g. age) and comparable across datasets.\n\nMethodsSecondary data analyses were carried out on five datasets using wrist-worn accelerometers: children (N=145), adolescent girls (N=1669), office workers (N=114), pre- (N=1218) and post- (N=1316) menopausal women, and adults with type 2 diabetes (N=475). Open-source software (GGIR) was used to generate the magnitude of acceleration above which a persons most active 60, 30 and 2 minutes are accumulated: M60ACC; M30ACC and M2ACC, respectively.\n\nResultsThe proportion of participants with M60ACC (children) and M30ACC (adults) values higher than accelerations indicative of brisk walking (i.e., moderate-to-vigorous physical activity) ranged from 17-68% in children and 15%-81% in adults, tending to decline with age. The proportion of pre-and post-menopausal women with M2ACC values indicative of running and thus meeting recently presented thresholds for bone health ranged from 6-13%.\n\nConclusionThese metrics can be used for global surveillance of physical activity, including assessing prevalence of meeting the current physical activity guidelines, across the lifespan. Translation of acceleration magnitudes into indicative activities provides a public health friendly interpretation of results. As accelerometer and corresponding health data accumulate it will be possible to interpret the metrics relative to age- and sex-specific norms and derive evidence-based physical activity guidelines directly from accelerometer data for use in future global surveillance. This is where the key advantages of these metrics lie.

epidemiology↗

bioRxiv2017

A Non-Parametric Bayesian Prior For Causal Inference Of Auditory Streaming

Human perceptual grouping of sequential auditory cues has traditionally been modeled using a mechanistic approach. The problem however is essentially one of source inference - a problem that has recently been tackled using statistical Bayesian models in visual and auditory-visual modalities. Usually the models are restricted to performing inference over just one or two possible sources, but human perceptual systems have to deal with much more complex scenarios. To characterize human perception we have developed a Bayesian inference model that allows an unlimited number of signal sources to be considered: it is general enough to allow any discrete sequential cues, from any modality. The model uses a non-parametric prior, hence increased complexity of the signal does not necessitate more parameters. The model not only determines the most likely number of sources, but also specifies the source that each signal is associated with. The model gives an excellent fit to data from an auditory stream segregation experiment in which the pitch and presentation rate of pure tones determined the perceived number of sources.

neuroscience↗