Biology subjects

Smith, R.

Publications and source records attributed to Smith, R..

At least 109 records · Page 6Linked to original sources

bioRxiv2020

Epigenomic features related to microglia are associated with attenuated effect of APOE ε4 on Alzheimer's disease risk in humans

INTRODUCTIONNot all APOE {varepsilon}4 carriers who survive to advanced age develop Alzheimers disease (AD); factors attenuating the risk of {varepsilon}4 on AD may exist. METHODSGuided by the top {varepsilon}4-attenuating signals from methylome-wide association analyses (N=572, {varepsilon}4+ and {varepsilon}4-) of neurofibrillary tangles and neuritic plaques, we conducted a meta-analysis for pathological AD within the {varepsilon}4+ subgroups (N=235) across four independent collections of brains. Cortical RNA-seq and microglial morphology measurements were used in functional analyses. RESULTSThree out of the four significant CpG dinucleotides were captured by one principle component (PC1), which interacts with {varepsilon}4 on AD, and is associated with expression of innate immune genes and activated microglia. In {varepsilon}4 carriers, reduction in each unit of PC1 attenuated the odds of AD by 58% (OR=2.39, 95%CI=[1.64,3.46], P=7.08x10-6). DISCUSSIONAn epigenomic factor associated with a reduced proportion of activated microglia appears to attenuate the risk of {varepsilon}4 on AD.

neuroscience↗

bioRxiv2020

Confirmatory evidence that healthy individuals can adaptively adjust prior expectations and interoceptive precision estimates

Theoretical proposals have previously been put forward regarding the computational basis of interoception. Following on this, we recently reported using an active inference approach to 1) quantitatively simulate interoceptive computation, and 2) fit the model to behavior on a cardiac awareness task. In the present work, we attempted to replicate our previous results in an independent group of healthy participants. We provide evidence confirming our previous finding that healthy individuals adaptively adjust prior expectations and interoceptive sensory precision estimates based on task context. This offers further support for the utility of computational approaches to characterizing the dynamics of interoceptive processing.

neuroscience↗

bioRxiv2020

Severe corrosion of carbon steel in oil field produced water can be linked to methanogenic archaea containing a special type of hydrogenase.

Methanogenic archaea have long been implicated in microbially influenced corrosion (MIC) of oil and gas infrastructure, yet a first understanding of the underlying molecular mechanisms has only recently emerged. We surveyed pipeline-associated microbiomes from geographically distinct oil field facilities and found methanogens to account for 0.2 - 9.3% of the sequenced communities. Neither the type nor the abundance of the detected methanogens correlated to the perceived severity of MIC in these pipelines. Using fluids from one pipeline, MIC was reproduced in the laboratory, both under stagnant conditions and in customized corrosion reactors simulating pipeline flow. High corrosion rates (up to 2.43 mm Fe0 yr-1) with macroscopic, localized corrosion features were attributed to lithotrophic, mesophilic microbial activity. Other laboratory tests with the same waters yielded negligible corrosion rates (< 0.08 mm Fe0 yr-1). Recently a novel [NiFe] hydrogenase, from Methanococcus maripaludis strain OS7, was demonstrated to accelerate corrosion. We developed a specific qPCR assay and detected the gene encoding the large subunit of this hydrogenase (labelled micH) in corrosive (> 0.15 mm Fe0 yr-1) biofilms. The micH gene on the other hand was absent in non-corrosive biofilms despite an abundance of methanogens. Reconstruction of a nearly complete Methanococcus maripaludis genome from a highly corrosive mixed biofilm revealed micH and associated genes in near-identical genetic configuration as strain OS7, thereby supporting our hypothesis that the encoded molecular mechanism contributed to corrosion. Lastly, the proposed MIC biomarker was detected in multiple oil fields, indicating a geographically widespread involvement of this [NiFe] hydrogenase in MIC. IMPORTANCEMicroorganisms can deteriorate built environments, which is particularly problematic in the case of pipelines transporting hydrocarbons to industrial end users. MIC is notoriously difficult to detect and monitor and as a consequence, is a particularly difficult corrosion mechanism to manage. Despite the advent of molecular tools and improved microbial monitoring strategies for oil and gas operations, specific underlying MIC mechanisms in pipelines remain largely enigmatic. Emerging mechanistic understanding of methanogenic MIC derived from pure culture work allowed us to develop a qPCR assay that distinguishes technically problematic from benign methanogens in a West African oil field. Detection of the same gene in geographically diverse samples from North America hints at the widespread applicability of this assay. The research presented here offers a step towards a mechanistic understanding of biocorrosion in oil fields and introduces a binary marker for (methanogenic) MIC that can find application in corrosion management programs in industrial settings.

microbiology↗

bioRxiv2020

Aspirin inhibition of prostaglandin synthesis impairs egg development across mosquito taxa

Several endocrine signals are known to mediate mosquito egg development including insulin-like peptide, 20-hydroxyecdysone, and juvenile hormone. The objective of this study was to determine the effects of prostaglandin E2 (PGE2) as an additional mediator of oogenesis in the mosquitoes, Aedes albopictus and Anopheles gambiae. The injection of aspirin (an inhibitor of cyclooxygenase) shortly after blood-feeding significantly inhibited egg development at choriogenesis in a dose-dependent manner in Ae. albopictus. Moreover, oral administration of aspirin to An. albopictus and An. gambiae also inhibited egg production. The aspirin treatment suppressed expression of the genes (Yellow-g and Yellow-g2) associated with exochorion darkening and led to the production of a malformed egg shell in Ae. albopictus. These inhibitory effects of aspirin on egg development were rescued by the addition of PGE2, confirming the specificity of aspirin in inhibiting prostaglandin production. To validate these results, we identified a putative PGE2 receptor (Aa-PGE2R) in Ae. albopictus. Aa-PGE2R expression was highly inducible in adult ovary after blood-feeding. RNA interference of Aa-PGE2R expression resulted in the significant suppression of choriogenesis similar to aspirin treatment, where the addition of PGE2 to Aa-PGE2R-silenced females failed to rescue egg production. Together, these results suggest that PG synthesis and signaling are required for egg development across diverse mosquito taxa. Author SummaryProgstaglandins (PGs) play crucial roles in mediating various physiological processes in insects. Aspirin (ASP) inhibits PG biosynthesis and has been used as an anti-inflammatory drug. ASP injection or feeding to mosquitoes of Aedes albopictus or Anopheles gambiae significantly inhibits egg production at chorion formation. This led to significant reduction in fecundity and egg hatchability. PG signal is interrupted by RNA interference (RNAi) of PGE2 receptor. The RNAi treatment also gave a similar damage to females in egg production as seen in ASP treatment. Thus, PG signal is required for egg production of these mosquitoes. Data Availability StatementAll relevant data are within the manuscript and its Supporting Information file.

developmental biology↗

bioRxiv2020

Histone H3.3 Hira chaperone complex contributes to zygote formation in mice and humans.

Elucidating the underlining mechanisms underpinning successful fertilisation is imperative in optimising IVF treatments, and may lead to a specific diagnosis and therefore potential treatment for some infertile couples. One of the critical steps involves paternal chromatin reprogramming, in which compacted sperm chromatin packed by protamines is removed by oocyte factors and new histones, including histone H3.3, are incorporated. This step is critical for the formation of the male pronucleus, without which the zygote contains only 1 pronucleus (1PN), in contrast to normally fertilised zygotes with two-pronuclei (2PN). 1PN zygotes are a frequently observed phenomenon in IVF treatments, therefore aberrant mechanism of action controlling paternal chromatin repackaging may be an important cause of abnormal fertilisation. Hira is the main H3.3 chaperone that governs this protamine-to-histone exchange. In this study, we investigated the maternal functions of two other molecules of the Hira complex, Cabin1 and Ubn1 in the mouse. Loss-of-function Cabin1 and Ubn1 mouse models were developed: their zygotes displayed abnormal 1PN zygote phenotypes, similar to the phenotype of Hira mutants. We then studied human 1PN zygotes, and found that the Hira complex was absent in 1PN zygotes which were lacking the male pronucleus. This result confirms that the role of the Hira complex in male pronucleus formation has coherence from mice to humans. Furthermore, rescue experiments showed that the abnormal 1PN phenotype derived from Hira mutants could be resolved by overexpression of Hira in the mouse oocytes. In summary, we have provided evidence of the role of Hira complex in regulating male pronucleus formation in both mice and humans, that both Cabin1 and Ubn1 components of the Hira complex are equally essential for male pronucleus formation, and that this can be rescued. We present a proof-of-concept experiment that could potentially lead to a personalised IVF therapy for oocyte defects.

developmental biology↗

bioRxiv2020

The H3.3 chaperone Hira complex orchestrates oocyte developmental competence

Reproductive success relies on a healthy oocyte competent for fertilisation and capable of sustaining early embryo development. By the end of oogenesis, the oocyte is characterised by a transcriptionally silenced state, but the significance of this state and how it is achieved remains poorly understood. Histone H3.3, one of the H3 variants, has unique functions in chromatin structure and gene expression that are cell cycle-independent. We report here a comprehensive characterisation of the roles of the subunits of the Hira complex (i.e. Hira, Cabin1 and Ubn1), which is primarily responsible for H3.3 deposition during mouse oocyte development. Loss-of-function of any component of the Hira complex led to early embryogenesis failure. Transcriptome and nascent RNA analyses revealed that mutant oocytes fail to silence global transcription. Hira complex mutants are unable to establish the H3K4me3 and H3K9me3 repressive marks, resulting in aberrant chromatin accessibility. Among the misregulated genes in mutant oocytes is Zscan4, a 2-cell specific gene that is involved in zygote genome activation. Overexpression of Zscan4 recapitulates the phenotypes of Hira mutants, illustrating that temporal and spatial expression of Zscan4 is fine-tuned at the oocyte-to-embryo transition. Thus, the H3.3 chaperone Hira complex has a maternal effect function in oocyte developmental competence and early embryogenesis by modulating chromatin condensation and transcriptional quiescence.

developmental biology↗

bioRxiv2020

Efficacy of a novel iodine complex solution, CupriDyne, in inactivating SARS-CoV-2

The coronavirus known as SARS-CoV-2, which causes COVID-19 disease, is presently responsible for a global pandemic wherein more than 3.5 million people have been infected and more than 250,000 killed to-date. There is currently no vaccine for COVID-19, leaving governments and public health agencies with little defense against the virus aside from advising or enforcing best practices for virus transmission prevention, which include hand-washing, physical distancing, use of face covers, and use of effective disinfectants. In this study, a novel iodine complex called CupriDyne(R) was assessed for its ability to inactivate SARS-CoV-2. CupriDyne was shown to be effective in inactivating the virus in a time-dependent manner, reducing virus titers by 99% (2 logs) after 30 minutes, and reducing virus titers to below the detection limit after 60 minutes. The novel iodine complex tested herein offers a safe and gentle alternative to conventional disinfectants for use on indoor and outdoor surfaces.

microbiology↗

bioRxiv2020

Recalibrating the Epigenetic Clock: Implications for Assessing Biological Age in the Human Cortex

Human DNA-methylation data have been used to develop biomarkers of ageing - referred to epigenetic clocks - that have been widely used to identify differences between chronological age and biological age in health and disease including neurodegeneration, dementia and other brain phenotypes. Existing DNA methylation clocks are highly accurate in blood but are less precise when used in older samples or on brain tissue. We aimed to develop a novel epigenetic clock that performs optimally in human cortex tissue and has the potential to identify phenotypes associated with biological ageing in the brain. We generated an extensive dataset of human cortex DNA methylation data spanning the life-course (n = 1,397, ages = 1 to 104 years). This dataset was split into training and testing samples (training: n = 1,047; testing: n = 350). DNA methylation age estimators were derived using a transformed version of chronological age on DNA methylation at specific sites using elastic net regression, a supervised machine learning method. The cortical clock was subsequently validated in a novel human cortex dataset (n = 1,221, ages = 41 to 104 years) and tested for specificity in a large whole blood dataset (n = 1,175, ages = 28 to 98 years). We identified a set of 347 DNA methylation sites that, in combination optimally predict age in the human cortex. The sum of DNA methylation levels at these sites weighted by their regression coefficients provide the cortical DNA methylation clock age estimate. The novel clock dramatically out-performed previously reported clocks in additional cortical datasets. Our findings suggest that previous associations between predicted DNA methylation age and neurodegenerative phenotypes might represent false positives resulting from clocks not robustly calibrated to the tissue being tested and for phenotypes that become manifest in older ages. The age distribution and tissue type of samples included in training datasets need to be considered when building and applying epigenetic clock algorithms to human epidemiological or disease cohorts.

genomics↗

bioRxiv2020

Meta-analysis of epigenome-wide association studies in Alzheime's disease highlights 220 differentially methylated loci across cortex

Epigenome-wide association studies of Alzheimers disease have highlighted neuropathology-associated DNA methylation differences, although existing studies have been limited in sample size and utilized different brain regions. Here, we combine data from six DNA methylomic studies of Alzheimers disease (N=1,453 unique individuals) to identify differential methylation associated with Braak stage in different brain regions and across cortex. We identify 236 CpGs in the prefrontal cortex, 95 CpGs in the temporal gyrus and ten CpGs in the entorhinal cortex at Bonferroni significance, with none in the cerebellum. Our cross-cortex meta-analysis (N=1,408 donors) identifies 220 CpGs associated with neuropathology, annotated to 121 genes, of which 84 genes have not been previously reported at this significance threshold. We have replicated our findings using two further DNA methylomic datasets consisting of a further > 600 unique donors. The meta-analysis summary statistics are available in our online data resource (www.epigenomicslab.com/ad-meta-analysis/).

genomics↗

bioRxiv2020

Crosstalk between invadopodia and the extracellular matrix

The scaffold protein Tks5 is required for invadopodia-mediated cancer invasion both in vitro and in vivo. We have previously also revealed a role for Tks5 in tumor cell growth using three-dimensional (3D) culture model systems and mouse transplantation experiments. Here we use both 3D and high-density fibrillar collagen (HDFC) culture to demonstrate that native type I collagen, but not a form lacking the telopeptides, stimulated Tks5-dependent growth, which was dependent on the DDR collagen receptors. We used microenvironmental microarray (MEMA) technology to determine that laminin, collagen I, fibronectin and tropoelastin also stimulated invadopodia formation. A Tks5-specific monoclonal antibody revealed its expression both on microtubules and at invadopodia. High- and super-resolution microscopy of cells in and on collagen was then used to place Tks5 at the base of invadopodia, separated from much of the actin and cortactin, but coincident with both matrix metalloprotease and cathepsin proteolytic activity. Inhibition of the Src family kinases, cathepsins or metalloproteases all reduced invadopodia length but each had distinct effects on Tks5 localization. These studies highlight the crosstalk between invadopodia and extracellular matrix components, and reveal the invadopodium to be a spatially complex structure.

cancer biology↗

bioRxiv2020

The Predictive Global Neuronal Workspace: A Formal Active Inference Model of Visual Consciousness

The global neuronal workspace (GNW) model has inspired over two decades of hypothesis driven research on the neural basis consciousness. However, recent studies have reported findings that are at odds with empirical predictions of the model. Further, the macro-anatomical focus of current GNW research has limited the specificity of predictions afforded by the model. In this paper we present a neurocomputational model - based on Active Inference - that captures central architectural elements of the GNW and is able to address these limitations. The resulting predictive global workspace casts neuronal dynamics as approximating Bayesian inference, allowing precise, testable predictions at both the behavioural and neural levels of description. We report simulations demonstrating the models ability to reproduce: 1) the electrophysiological and behaviour results observed in previous studies of inattentional blindness; and 2) the previously introduced four-way taxonomy predicted by the GNW, which describes the relationship between consciousness, attention, and sensory signal strength. We then illustrate how our model can reconcile/explain (apparently) conflicting findings, extend the GNW taxonomy to include the influence of prior expectations, and inspire novel paradigms to test associated behavioural and neural predictions.

neuroscience↗

bioRxiv2020

Mechanochemical modelling of dorsal closure reveals emergent cell behaviour in tissue morphogenesis

Tissue morphogenesis integrates cell type-specific biochemistry and architecture, cellular force generation and mechanisms coordinating forces amongst neighbouring cells and tissues. We use finite element-based modelling to explore the interconnections at these multiple biological scales in embryonic dorsal closure, where pulsed actomyosin contractility in adjacent Amnioserosa (AS) cells powers the closure of an epidermis opening. Based on our in vivo observations, the model implements F-actin nucleation periodicity that is independent of MyoII activity. Our model reveals conditions, where depleting MyoII activity nevertheless indirectly affects oscillatory F-actin behaviour, without the need for biochemical feedback. In addition, it questions the previously proposed role of Dpp-mediated regulation of the patterned actomyosin dynamics in the AS tissue, suggesting them to be emergent. Tissue-specific Dpp interference supports the models prediction. The model further predicts that the mechanical properties of the surrounding epidermis tissue feed back on the shaping and patterning of the AS tissue. Finally, our models parameter space reproduces mutant phenotypes and provides predictions for their underlying cause. Our modelling approach thus reveals several unappreciated mechanistic properties of tissue morphogenesis.

systems biology↗

bioRxiv2019

XFlow: An algorithm for extracting ion chromatograms

Mass spectrometry is a fundamental tool for modern proteomics. The increasing availability of mass spectrometry data paired with the increasing sensitivity and fidelity of the instruments necessitates new and more potent analytical methods. To that end, we have created and present XFlow, a feature detection algorithm for extracting ion chromatograms from MS1 LC-MS data. XFlow is a parameter-free procedurally agnostic feature detection algorithm that utilizes the latent properties of ion chromatograms to resolve them from the surrounding noise present in MS1 data. XFlow is designed to function on either profile or centroided data across different resolutions and instruments. This broad applicability lends XFlow strong utility as a one-size-fits-all method for MS1 analysis or target acquisition for MS2. XFlow is written in Java and packaged with JS-MS, an open-source mass spectrometry analysis toolkit.

bioinformatics↗

bioRxiv2019

An Active Inference Approach to Dissecting Reasons for Non-Adherence to Antidepressants

BackgroundAntidepressant medication adherence is among the most important problems in health care worldwide. Interventions designed to increase adherence have largely failed, pointing towards a critical need to better understand the underlying decision-making processes that contribute to adherence. A computational decision-making model that integrates empirical data with a fundamental action selection principle could be pragmatically useful in 1) making individual level predictions about adherence, and 2) providing an explanatory framework that improves our understanding of non-adherence. MethodsHere we formulate a partially observable Markov decision process model based on the active inference framework that can simulate several processes that plausibly influence adherence decisions. ResultsUsing model simulations of the day-to-day decisions to take a prescribed selective serotonin reuptake inhibitor (SSRI), we show that several distinct parameters in the model can influence adherence decisions in predictable ways. These parameters include differences in policy depth (i.e., how far into the future one considers when deciding), decision uncertainty, beliefs about the predictability (stochasticity) of symptoms, beliefs about the magnitude and time course of symptom reductions and side effects, and the strength of medication-taking habits that one has acquired. ConclusionsClarifying these influential factors will be an important first step toward empirically determining which are contributing to non-adherence to antidepressants in individual patients. The model can also be seamlessly extended to simulate adherence to other medications (by incorporating the known symptom reduction and side effect trajectories of those medications), with the potential promise of identifying which medications may be best suited for different patients.

neuroscience↗

bioRxiv2019

Innovations in Primate Interneuron Repertoire

Primates and rodents, which descended from a common ancestor more than 90 million years ago, exhibit profound differences in behavior and cognitive capacity. Modifications, specializations, and innovations to brain cell types may have occurred along each lineage. We used Drop-seq to profile RNA expression in more than 184,000 individual telencephalic interneurons from humans, macaques, marmosets, and mice. Conserved interneuron types varied significantly in abundance and RNA expression between mice and primates, but varied much more modestly among primates. In adult primates, the expression patterns of dozens of genes exhibited spatial expression gradients among neocortical interneurons, suggesting that adult neocortical interneurons are imprinted by their local cortical context. In addition, we found that an interneuron type previously associated with the mouse hippocampus--the \"ivy cell\", which has neurogliaform characteristics--has become abundant across the neocortex of humans, macaques, and marmosets. The most striking innovation was subcortical: we identified an abundant striatal interneuron type in primates that had no molecularly homologous cell population in mouse striatum, cortex, thalamus, or hippocampus. These interneurons, which expressed a unique combination of transcription factors, receptors, and neuropeptides, including the neuropeptide TAC3, constituted almost 30% of striatal interneurons in marmosets and humans. Understanding how gene and cell-type attributes changed or persisted over the evolutionary divergence of primates and rodents will guide the choice of models for human brain disorders and mutations and help to identify the cellular substrates of expanded cognition in humans and other primates.

neuroscience↗

bioRxiv2019

Neurocomputational mechanisms underlying emotional awareness: insights afforded by deep active inference and their potential clinical relevance

Emotional awareness (EA) is recognized as clinically relevant to the vulnerability to, and maintenance of, psychiatric disorders. However, the neurocomputational processes that underwrite individual variations remain unclear. In this paper, we describe a deep (active) inference model that reproduces the cognitive-emotional processes and self-report behaviors associated with EA. We then present simulations to illustrate (seven) distinct mechanisms that (either alone or in combination) can produce phenomena - such as somatic misattribution, coarse-grained emotion conceptualization, and constrained reflective capacity - characteristic of low EA. Our simulations suggest that the clinical phenotype of impoverished EA can be reproduced by dissociable computational processes. The possibility that different processes are at work in different individuals suggests that they may benefit from distinct clinical interventions. As active inference makes particular predictions about the underlying neurobiology of such aberrant inference, we also discuss how this type of modelling could be used to design neuroimaging tasks to test predictions and identify which processes operate in different individuals - and provide a principled basis for personalized precision medicine.

neuroscience↗

bioRxiv2019

Simulating emotions: An active inference model of emotional state inference and emotion concept learning

The ability to conceptualize and understand ones own affective states and responses - or "emotional awareness" (EA) - is reduced in multiple psychiatric populations; it is also positively correlated with a range of adaptive cognitive and emotional traits. While a growing body of work has investigated the neurocognitive basis of EA, the neurocomputational processes underlying this ability have received limited attention. Here, we present a formal Active Inference (AI) model of emotion conceptualization that can simulate the neurocomputational (Bayesian) processes associated with learning about emotion concepts and inferring the emotions one is feeling in a given moment. We validate the model and inherent constructs by showing (i) it can successfully acquire a repertoire of emotion concepts in its "childhood", as well as (ii) acquire new emotion concepts in synthetic "adulthood," and (iii) that these learning processes depend on early experiences, environmental stability, and habitual patterns of selective attention. These results offer a proof of principle that cognitive-emotional processes can be modeled formally, and highlight the potential for both theoretical and empirical extensions of this line of research on emotion and emotional disorders.

neuroscience↗

bioRxiv2019

An active inference model of concept learning

Within computational neuroscience, the algorithmic and neural basis of structure learning remains poorly understood. Concept learning is one primary example, which requires both a type of internal model expansion process (adding novel hidden states that explain new observations), and a model reduction process (merging different states into one underlying cause and thus reducing model complexity via meta-learning). Although various algorithmic models of concept learning have been proposed within machine learning and cognitive science, many are limited to various degrees by an inability to generalize, the need for very large amounts of training data, and/or insufficiently established biological plausibility. Using concept learning as an example case, we introduce a novel approach for modeling structure learning - and specifically state-space expansion and reduction - within the active inference framework and its accompanying neural process theory. Our aim is to demonstrate its potential to facilitate a novel line of active inference research in this area. The approach we lay out is based on the idea that a generative model can be equipped with extra (hidden state or cause) slots that can be engaged when an agent learns about novel concepts. This can be combined with a Bayesian model reduction process, in which any concept learning - associated with these slots - can be reset in favor of a simpler model with higher model evidence. We use simulations to illustrate this models ability to add new concepts to its state space (with relatively few observations) and increase the granularity of the concepts it currently possesses. We also simulate the predicted neural basis of these processes. We further show that it can accomplish a simple form of one-shot generalization to new stimuli. Although deliberately simple, these simulation results highlight ways in which active inference could offer useful resources in developing neurocomputational models of structure learning. They provide a template for how future active inference research could apply this approach to real-world structure learning problems and assess the added utility it may offer.

neuroscience↗