scholarly journals A brain atlas of synapse protein lifetime across the mouse lifespan.

2021 ◽  
Author(s):  
Edita Bulovaite ◽  
Zhen Qiu ◽  
Maximillian Kratschke ◽  
Adrianna Zgraj ◽  
David Fricker ◽  
...  

Protein turnover is required for synapse maintenance and remodelling and may impact memory duration. We quantified the lifetime of postsynaptic protein PSD95 in individual excitatory synapses across the mouse brain and lifespan, generating the Protein Lifetime Synaptome Atlas. Excitatory synapses have a wide range of protein lifetimes that may extend from a few hours to several months, with distinct spatial distributions in dendrites, neuron types and brain regions. Short protein lifetime (SPL) synapses are enriched in developing animals and in regions controlling innate behaviors, whereas long protein lifetime (LPL) synapses accumulate during development, are enriched in the cortex and CA1 where memories are stored, and are preferentially preserved in old age. The protein lifetime synaptome architecture is disrupted in an autism model, with synapse protein lifetime increased throughout the brain. These findings add a further layer to synapse diversity in the brain and enrich prevailing concepts in behavior, development, ageing and brain repair.

2020 ◽  
Vol 4 (Supplement_1) ◽  
Author(s):  
Bianca S Bono ◽  
Persephone A Miller ◽  
Nikita K Koziel Ly ◽  
Melissa J Chee

Abstract Fibroblast growth factor 21 (FGF21) has emerged as a critical endocrine factor for understanding the neurobiology of obesity, such as by the regulation thermogenesis, food preference, and metabolism, as well as for neuroprotection in Alzheimer’s disease and traumatic brain injury. FGF21 is synthesized primarily by the liver and pancreas then crosses the blood brain barrier to exert its effects in the brain. However, the sites of FGF21 action in the brain is not well-defined. FGF21 action requires the activation of FGF receptor 1c as well as its obligate co-receptor beta klotho (KLB). In order to determine the sites of FGF21 action, we mapped the distribution of Klb mRNA by in situ hybridization using RNAscope technology. We labeled Klb distribution throughout the rostrocaudal axis of male wildtype mice by amplifying Klb hybridization using tyramine signal amplification and visualizing Klb hybridization using Cyanine 3 fluorescence. The resulting Klb signal appears as punctate red “dots,” and each Klb neuron may express low (1–4 dots), medium (5–9 dots), or high levels (10+ dots) of Klb hybridization. We then mapped individual Klb expressing neuron to the atlas plates provided by the Allen Brain Atlas in order to determine Klb distribution within the substructures of each brain region, which are defined by Nissl-based parcellations of cytoarchitectural boundaries. The distribution of Klb mRNA is widespread throughout the brain, and the brain regions analyzed thus far point to notable expression in the hypothalamus, amygdala, hippocampus, and the cerebral cortex. The highest expression of Klb was localized to the suprachiasmatic nucleus in the hypothalamus, which contained low and medium Klb-expressing neurons in the lateral hypothalamic area and ventromedial hypothalamic nucleus while low expressing Klb neurons were seen in the paraventricular and dorsmedial hypothalamic nucleus. Hippocampal Klb expression was limited to the dorsal region and largely restricted to the pyramidal cell layer of the dentate gyrus, CA3, CA2, and CA1 but at low levels only. In the amygdala, low and medium Klb expressing cells were seen in lateral amygdala nucleus while low levels were observed in the basolateral amygdala nucleus. Cortical Klb expression analyzed thus far included low Klb-expressing neurons in the olfactory areas, including layers 2 and 3 of piriform cortex and nucleus of the lateral olfactory tract. These findings are consistent with the known roles of FGF21 in the central regulation of energy balance, but also implicates potentially wide-ranging effects of FGF21 such as in executive functions.


2018 ◽  
Author(s):  
Amitabha Bose ◽  
Áine Byrne ◽  
John Rinzel

AbstractWhen listening to music, humans can easily identify and move to the beat. Numerous experimental studies have identified brain regions that may be involved with beat perception and representation. Several theoretical and algorithmic approaches have been proposed to account for this ability. Related to, but different from the issue of how we perceive a beat, is the question of how we learn to generate and hold a beat. In this paper, we introduce a neuronal framework for a beat generator that is capable of learning isochronous rhythms over a range of frequencies that are relevant to music and speech. Our approach combines ideas from error-correction and entrainment models to investigate the dynamics of how a biophysically-based neuronal network model synchronizes its period and phase to match that of an external stimulus. The model makes novel use of on-going faster gamma rhythms to form a set of discrete clocks that provide estimates, but not exact information, of how well the beat generator spike times match those of a stimulus sequence. The beat generator is endowed with plasticity allowing it to quickly learn and thereby adjust its spike times to achieve synchronization. Our model makes generalizable predictions about the existence of asymmetries in the synchronization process, as well as specific predictions about resynchronization times after changes in stimulus tempo or phase. Analysis of the model demonstrates that accurate rhythmic time keeping can be achieved over a range of frequencies relevant to music, in a manner that is robust to changes in parameters and to the presence of noise.Author summaryMusic is integral to human experience and is appreciated across a wide range of cultures. Although many features distinguish different musical traditions, rhythm is central to nearly all. Most humans can detect and move along to the beat through finger or foot tapping, hand clapping or other bodily movements. But many people have a hard time “keeping a beat”, or say they have “no sense of rhythm”. There appears to be a disconnect between our ability to perceive a beat versus our ability to produce a beat, as a drummer would do as part of a musical group. Producing a beat requires beat generation, the process by which we learn how to keep track of the specific time intervals between beats, as well as executing the motor movement needed to produce the sound associated with a beat. In this paper, we begin to explore neural mechanisms that may be responsible for our ability to generate and keep a beat. We develop a computational model that includes different neurons and shows how they cooperate to learn a beat and keep it, even after the stimulus is removed, across a range of frequencies relevant to music. Our dynamical systems model leads to predictions for how the brain may react when learning a beat. Our findings and techniques should be widely applicable to those interested in understanding how the brain processes time, particularly in the context of music.


Author(s):  
Spase Petkoski ◽  
Viktor K. Jirsa

The timing of activity across brain regions can be described by its phases for oscillatory processes, and is of crucial importance for brain functioning. The structure of the brain constrains its dynamics through the delays due to propagation and the strengths of the white matter tracts. We use self-sustained delay-coupled, non-isochronous, nonlinearly damped and chaotic oscillators to study how spatio-temporal organization of the brain governs phase lags between the coherent activity of its regions. In silico results for the brain network model demonstrate a robust switching from in- to anti-phase synchronization by increasing the frequency, with a consistent lagging of the stronger connected regions. Relative phases are well predicted by an earlier analysis of Kuramoto oscillators, confirming the spatial heterogeneity of time delays as a crucial mechanism in shaping the functional brain architecture. Increased frequency and coupling are also shown to distort the oscillators by decreasing their amplitude, and stronger regions have lower, but more synchronized activity. These results indicate specific features in the phase relationships within the brain that need to hold for a wide range of local oscillatory dynamics, given that the time delays of the connectome are proportional to the lengths of the structural pathways. This article is part of the theme issue ‘Nonlinear dynamics of delay systems’.


2020 ◽  
Author(s):  
Adam J. Funk ◽  
Guillaume Labilloy ◽  
James Reigle ◽  
Rawan Alnafisah ◽  
Michael R. Heaven ◽  
...  

The overarching goal of this exploratory study is to link subcellular microdomain specific protein-protein interactomes with big data analytics. We isolated postsynaptic density-95 (PSD-95) complexes from four human brain regions and compared their protein interactomes using multiple bioinformatics techniques. We demonstrate that human brain regions have unique postsynaptic protein signatures that may be used to interrogate perturbagen databases. Assessment of our hippocampal signature using the iLINCS database yielded several compounds with recently characterized “off target” effects on protein-protein interactions in the posynaptic density compartment.


2018 ◽  
Author(s):  
Philip Shamash ◽  
Matteo Carandini ◽  
Kenneth D Harris ◽  
Nicholas A Steinmetz

It is now possible to record from hundreds of neurons across multiple brain regions in a single electrophysiology experiment. An essential step in the ensuing data analysis is to assign recorded neurons to the correct brain regions. Brain regions are typically identified after the recordings by comparing images of brain slices to a reference atlas by eye. This introduces error, in particular when slices are not cut at a perfectly coronal angle or when electrode tracks span multiple slices. Here we introduce SHARP-Track, a tool to localize regions of interest and plot the brain regions they pass through. SHARP-Track offers a MATLAB user interface to explore the Allen Mouse Brain Atlas, register asymmetric slice images to the atlas using manual input, and interactively analyze electrode tracks. We find that it reduces error compared to localizing electrodes in a reference atlas by eye. See github.com/cortex-lab/allenCCF for the software and wiki.


2017 ◽  
Vol 41 (S1) ◽  
pp. S631-S631
Author(s):  
A. Carvalho ◽  
J. Felgueiras ◽  
T. Abreu ◽  
C. Freitas ◽  
J. Silva

ObjectivesSchizophrenia is a debilitating psychiatric disorder which places a significant emotional and economic strain on the individual and society-at-large. Unfortunately, currently available therapeutic strategies do not provide adequate relief and some patients are treatment-resistant. Therefore there is urgent need for the development of mechanistically different and less side effect prone antipsychotic compounds. Recently, the endocannabinoid system has emerged as a potential therapeutic target for pharmacotherapy that is involved in a wide range of disorders, including schizophrenia. Modulation of this system by the main psychoactive component in cannabis, Δ9tetrahydrocannabinol (THC), induces acute psychotic effects and cognitive impairment. However, the non-psychotropic, plant-derived cannabinoid agent cannabidiol shows great promise for the treatment of psychosis, and is associated with fewer extrapyramidal side effects than conventional antipsychotic drugs.MethodsThe aim of this review is to analyse the involvement of the endocannabinoid system in schizophrenia and the potential role of cannabidiol in its treatment.Results and conclusionsThere is still considerable uncertainty about the mechanism of action of cannabidiol as well as the brain regions which are thought to mediate its putative antipsychotic effect. Further data is warrant before this novel therapy can be introduced into clinical practice.Disclosure of interestThe authors have not supplied their declaration of competing interest


Science ◽  
2020 ◽  
pp. eaba3163 ◽  
Author(s):  
Mélissa Cizeron ◽  
Zhen Qiu ◽  
Babis Koniaris ◽  
Ragini Gokhale ◽  
Noboru H. Komiyama ◽  
...  

Synapses connect neurons together to form the circuits of the brain and their molecular composition controls innate and learned behavior. We have analyzed the molecular and morphological diversity of five billion excitatory synapses at single-synapse resolution across the mouse brain from birth to old age. A continuum of changes alters synapse composition in all brain regions across the lifespan. Expansion in synapse diversity produces differentiation of brain regions until early adulthood and compositional changes cause dedifferentiation in old age. The spatiotemporal synaptome architecture of the brain potentially accounts for lifespan transitions in intellectual ability, memory, and susceptibility to behavioral disorders.


Science ◽  
2020 ◽  
Vol 367 (6482) ◽  
pp. eaay5947 ◽  
Author(s):  
Evelina Sjöstedt ◽  
Wen Zhong ◽  
Linn Fagerberg ◽  
Max Karlsson ◽  
Nicholas Mitsios ◽  
...  

The brain, with its diverse physiology and intricate cellular organization, is the most complex organ of the mammalian body. To expand our basic understanding of the neurobiology of the brain and its diseases, we performed a comprehensive molecular dissection of 10 major brain regions and multiple subregions using a variety of transcriptomics methods and antibody-based mapping. This analysis was carried out in the human, pig, and mouse brain to allow the identification of regional expression profiles, as well as to study similarities and differences in expression levels between the three species. The resulting data have been made available in an open-access Brain Atlas resource, part of the Human Protein Atlas, to allow exploration and comparison of the expression of individual protein-coding genes in various parts of the mammalian brain.


2021 ◽  
Author(s):  
Daniel Martins ◽  
Alessio Giacomel ◽  
Steven CR Williams ◽  
Federico E Turkheimer ◽  
Ottavia Dipasquale ◽  
...  

The expansion of neuroimaging techniques over the last decades has opened a wide range of new possibilities to characterize brain dysfunction in several neurological and psychiatric disorders. However, the lack of specificity of most of these techniques, such as magnetic resonance imaging (MRI)-derived measures, to the underlying molecular and cellular properties of the brain tissue poses limitations to the amount of information one can extract to inform precise models of brain disease. The integration of transcriptomic and neuroimaging data, known as 'imaging transcriptomics', has recently emerged as an indirect way forward to test and/or generate hypotheses about potential cellular and transcriptomic pathways that might underly specific changes in neuroimaging MRI biomarkers. However, the validity of this approach is yet to be examined in-depth. Here, we sought to bridge this gap by performing imaging transcriptomic analyses of the regional distribution of well-known molecular markers, assessed by positron emission tomography (PET), in the healthy human brain. We focused on tracers spanning different elements of the biology of the brain, including neuroreceptors, synaptic proteins, metabolism, and glia. Using transcriptome-wide data from the Allen Brain Atlas, we applied partial least square regression to rank genes according to their level of spatial alignment with the regional distribution of these neuroimaging markers in the brain. Then, we performed gene set enrichment analyses to explore the enrichment for specific biological and cell-type pathways among the genes most strongly associated with each neuroimaging marker. Overall, our findings show that imaging transcriptomics can recover plausible transcriptomic and cellular correlates of the regional distribution of benchmark molecular imaging markers, independently of the type of parcellation used to map gene expression and neuroimaging data. Our data support the plausibility and robustness of imaging transcriptomics as an indirect approach for bridging gene expression, cells and macroscopical neuroimaging and improving our understanding of the biological pathways underlying regional variability in neuroimaging features


2020 ◽  
Author(s):  
Kosuke Motoki ◽  
Shinsuke Suzuki

Subjective value for food rewards guide our dietary choices. There is growing evidence that value signals are constructed in the brain by integrating multiple types of information about flavour, taste, and nutritional attributes of the foods. However, much less is known about the influence of food-extrinsic factors such as labels, brands, prices, and packaging designs. In this mini review, we outline recent findings in decision neuroscience, consumer psychology, and food science with regard to the effect of extrinsic factors on food value computations in the human brain. To date, studies have demonstrated that, while the integrated value signal is encoded in the ventromedial prefrontal cortex, information on the extrinsic factors of the food is encoded in diverse brain regions previously implicated in a wide range of functions: cognitive control, memory, emotion and reward processing. We suggest that a comprehensive understanding of food valuation requires elucidation of the mechanisms behind integrating extrinsic factors in the brain to compute an overall subjective value signal.


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