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3,4-Ethylenedioxythiophene Hydrogels: Relating Structure and Charge Transport in Supramolecular Gels
Ionic charge transport is a ubiquitous language of communication in biological systems. As such, bioengineering is in constant need of innovative, soft, and biocompatible materials that facilitate ionic conduction. Low molecular weight gelators (LMWGs) are complex self-assembled materials that have received increasing attention in recent years. Beyond their biocompatible, self-healing, and stimuli responsive facets, LMWGs can be viewed as a “solid” electrolyte solution. In this work, we investigate 3,4-ethylenedioxythiophene (EDOT) as a capping group for a small peptide library, which we use as a system to understand the relationship between modes of assembly and charge transport in supramolecular gels. Through a combination of techniques including small-angle neutron scattering (SANS), NMR-based Van’t Hoff analysis, atomic force microscopy (AFM), rheology, four-point probe, and electrochemical impedance spectroscopy (EIS), we found that modifications to the peptide sequence result in distinct assembly pathways, thermodynamic parameters, mechanical properties, and ionic conductivities. Four-point probe conductivity measurements and electrochemical impedance spectroscopy suggest that ionic conductivity is approximately doubled by programmable gel assemblies with hollow cylinder morphologies relative to gels containing solid fibers or a control electrolyte. More broadly, it is hoped this work will serve as a platform for those working on charge transport of aqueous soft materials in general.
Interrogation of the human cortical peptidome uncovers cell-type specific signatures of cognitive resilience against Alzheimer's disease.
Alzheimer's disease (AD) is characterised by age-related cognitive decline. Brain accumulation of amyloid-β plaques and tau tangles is required for a neuropathological AD diagnosis, yet up to one-third of AD-pathology positive community-dwelling elderly adults experience no symptoms of cognitive decline during life. Conversely, some exhibit chronic cognitive impairment in absence of measurable neuropathology, prompting interest into cognitive resilience-retained cognition despite significant neuropathology-and cognitive frailty-impaired cognition despite low neuropathology. Synapse loss is widespread within the AD-dementia, but not AD-resilient, brain. Recent evidence points towards critical roles for synaptic proteins, such as neurosecretory VGF, in cognitive resilience. However, VGF and related proteins often signal as peptide derivatives. Here, nontryptic peptidomic mass spectrometry was performed on 102 post-mortem cortical samples from individuals across cognitive and neuropathological spectra. Neuropeptide signalling proteoforms derived from VGF, somatostatin (SST) and protachykinin-1 (TAC1) showed higher abundance in AD-resilient than AD-dementia brain, whereas signalling proteoforms of cholecystokinin (CCK) and chromogranin (CHG) A/B and multiple cytoskeletal molecules were enriched in frail vs control brain. Integrating our data with publicly available single nuclear RNA sequencing (snRNA-seq) showed enrichment of cognition-related genes in defined cell-types with established links to cognitive resilience, including SST interneurons and excitatory intratelencephalic cells.
Current Research in Neurobiology, an experimental platform for innovation.
Welcome to Current Research in Neurobiology (CRNEUR), the gold open access, sibling journal to Current Opinion in Neurobiology, a journal for timely original research in neuroscience. At its very core, CRNEUR is a journal for creativity and innovation in science and publishing. As a journal, we ambitiously aim for CRNEUR to be a vehicle for what many of us envisioned an academic journal could be. Empowered by our commitment to fairness and transparency-to hold ourselves and others to a higher standard-here we describe our ambitions for innovation going forward. We need your help in this process and welcome your views via this survey (https://www.surveymonkey.co.uk/r/5LHWTML) and on social media (to start or join a discussion please use the hashtag #CRNEUR).
Phenotypic screen of sixty-eight colorectal cancer cell lines identifies CEACAM6 and CEACAM5 as markers of acid resistance.
Elevated cancer metabolism releases lactic acid and CO2 into the under-perfused tumor microenvironment, resulting in extracellular acidosis. The surviving cancer cells must adapt to this selection pressure; thus, targeting tumor acidosis is a rational therapeutic strategy to manage tumor growth. However, none of the major approved treatments are based explicitly on disrupting acid handling, signaling, or adaptations, possibly because the distinction between acid-sensitive and acid-resistant phenotypes is not clear. Here, we report pH-related phenotypes of sixty-eight colorectal cancer (CRC) cell lines by measuring i) extracellular acidification as a readout of acid production by fermentative metabolism and ii) growth of cell biomass over a range of extracellular pH (pHe) levels as a measure of the acid sensitivity of proliferation. Based on these measurements, CRC cell lines were grouped along two dimensions as "acid-sensitive"/"acid-resistant" versus "low metabolic acid production"/"high metabolic acid production." Strikingly, acid resistance was associated with the expression of CEACAM6 and CEACAM5 genes coding for two related cell-adhesion molecules, and among pH-regulating genes, of CA12. CEACAM5/6 protein levels were strongly induced by acidity, with a further induction under hypoxia in a subset of CRC lines. Lack of CEACAM6 (but not of CEACAM5) reduced cell growth and their ability to differentiate. Finally, CEACAM6 levels were strongly increased in human colorectal cancers from stage II and III patients, compared to matched samples from adjacent normal tissues. Thus, CEACAM6 is a marker of acid-resistant clones in colorectal cancer and a potential motif for targeting therapies to acidic regions within the tumors.
The influence of cortical activity on perception depends on behavioral state and sensory context
The mechanistic link between neural circuit activity and behavior remains unclear. While manipulating cortical activity can bias certain behaviors and elicit artificial percepts, some tasks can still be solved when cortex is silenced or removed. Here, mice were trained to perform a visual detection task during which we selectively targeted groups of visually responsive and co-tuned neurons in L2/3 of primary visual cortex (V1) for two-photon photostimulation. The influence of photostimulation was conditional on two key factors: the behavioral state of the animal and the contrast of the visual stimulus. The detection of low-contrast stimuli was enhanced by photostimulation, while the detection of high-contrast stimuli was suppressed, but crucially, only when mice were highly engaged in the task. When mice were less engaged, our manipulations of cortical activity had no effect on behavior. The behavioral changes were linked to specific changes in neuronal activity. The responses of non-photostimulated neurons in the local network were also conditional on two factors: their functional similarity to the photostimulated neurons and the contrast of the visual stimulus. Functionally similar neurons were increasingly suppressed by photostimulation with increasing visual stimulus contrast, correlating with the change in behavior. Our results show that the influence of cortical activity on perception is not fixed, but dynamically and contextually modulated by behavioral state, ongoing activity and the routing of information through specific circuits.
CRISPRi: a way to integrate iPSC-derived neuronal models
The genetic landscape of neurodegenerative diseases encompasses genes affecting multiple cellular pathways which exert effects in an array of neuronal and glial cell-types. Deconvolution of the roles of genes implicated in disease and the effects of disease-associated variants remains a vital step in the understanding of neurodegeneration and the development of therapeutics. Disease modelling using patient induced pluripotent stem cells (iPSCs) has enabled the generation of key cell-types associated with disease whilst maintaining the genomic variants that predispose to neurodegeneration. The use of CRISPR interference (CRISPRi), alongside other CRISPR-perturbations, allows the modelling of the effects of these disease-associated variants or identifying genes which modify disease phenotypes. This review summarises the current applications of CRISPRi in iPSC-derived neuronal models, such as fluorescence-activated cell sorting (FACS)-based screens, and discusses the future opportunities for disease modelling, identification of disease risk modifiers and target/drug discovery in neurodegeneration.
Hierarchical temporal prediction captures motion processing from retina to higher visual cortex
Visual neurons respond selectively to specific features that become increasingly complex in their form and dynamics from the eyes to the cortex. Retinal neurons prefer localized flashing spots of light, primary visual cortical (V1) neurons moving bars, and those in higher cortical areas, such as middle temporal (MT) cortex, favor complex features like moving textures. Whether there are general computational principles behind this diversity of response properties remains unclear. To date, no single normative model has been able to account for the hierarchy of tuning to dynamic inputs along the visual pathway. Here we show that hierarchical application of temporal prediction - representing features that efficiently predict future sensory input from past sensory input - can explain how neuronal tuning properties, particularly those relating to motion, change from retina to higher visual cortex. This suggests that the brain may not have evolved to efficiently represent all incoming information, as implied by some leading theories. Instead, the selective representation of sensory inputs that help in predicting the future may be a general neural coding principle, which when applied hierarchically extracts temporally-structured features that depend on increasingly high-level statistics of the sensory input.
Simple spectral transformations capture the contribution of peripheral processing to cortical responses to natural sounds
Processing in the sensory periphery involves various mechanisms that enable the detection and discrimination of sensory information. Despite their biological complexity, could these processing steps sub-serve a relatively simple transformation of sensory inputs, which are then transmitted to the CNS? Here we explored both biologically-detailed and very simple models of the auditory periphery to find the appropriate input to a phenomenological model of auditory cortical responses to natural sounds. We examined a range of cochlear models, from those involving detailed biophysical characteristics of the cochlea and auditory nerve to very pared-down spectrogram-like approximations of the information processing in these structures. We tested the capacity of these models to predict the time-course of single-unit neural responses recorded in the ferret primary auditory cortex, when combined with a linear non-linear encoding model. We show that a simple model based on a log-spaced, log-scaled power spectrogram with Hill-function compression performs as well as biophysically-detailed models of the cochlea and the auditory nerve. These findings emphasize the value of using appropriate simple models of the periphery when building encoding models of sensory processing in the brain, and imply that the complex properties of the auditory periphery may together result in a simpler than expected functional transformation of the inputs.