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      Cortical hyperexcitability in mouse models and patients with amyotrophic lateral sclerosis is linked to noradrenaline deficiency

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      Science Translational Medicine
      American Association for the Advancement of Science (AAAS)

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disease, characterized by the death of upper (UMN) and lower motor neurons (LMN) in the motor cortex, brainstem, and spinal cord. Despite decades of research, ALS remains incurable, challenging to diagnose, and of extremely rapid progression. A unifying feature of sporadic and familial forms of ALS is cortical hyperexcitability, which precedes symptom onset, negatively correlates with survival, and is sufficient to trigger neurodegeneration in rodents. Using electrocorticography in the Sod1 G86R and Fus Δ NLS/+ ALS mouse models and standard electroencephalography recordings in patients with sporadic ALS, we demonstrate a deficit in theta-gamma phase-amplitude coupling (PAC) in ALS. In mice, PAC deficits started before symptom onset, and in patients, PAC deficits correlated with the rate of disease progression. Using mass spectrometry analyses of CNS neuropeptides, we identified a presymptomatic reduction of noradrenaline (NA) in the motor cortex of ALS mouse models, further validated by in vivo two-photon imaging in behaving SOD1 G93A and Fus Δ NLS/+ mice, that revealed pronounced reduction of locomotion-associated NA release. NA deficits were also detected in postmortem tissues from patients with ALS, along with transcriptomic alterations of noradrenergic signaling pathways. Pharmacological ablation of noradrenergic neurons with DSP-4 reduced theta-gamma PAC in wild-type mice and administration of a synthetic precursor of NA augmented theta-gamma PAC in ALS mice. Our findings suggest theta-gamma PAC as means to assess and monitor cortical dysfunction in ALS and warrant further investigation of the NA system as a potential therapeutic target.

          Abstract

          Cortical dysfunction in ALS manifests in altered theta-gamma coupling that correlates with disease progression and relies on noradrenergic deficits.

          Editor’s summary

          Cortical hyperexcitability in amyotrophic lateral sclerosis (ALS) is commonly assessed by transcranial magnetic stimulation combined with electromyogram recordings, but this approach requires functional nerve-to-muscle connections. Scekic-Zahirovic et al. show that cortical hyperexcitability was associated with early deficits in theta-gamma phase-amplitude coupling (PAC) and decreased noradrenaline (NA) in the motor cortex of patients with ALS and mouse models. PAC deficits could be ameliorated by administration of a synthetic NA precursor in mice. These results suggest that PAC could serve as an alternative, muscle-independent readout for cortical hyperexcitability in ALS and warrant further studies into NA signaling as a potential molecular culprit driving this hyperexcitability. —Daniela Neuhofer

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          Most cited references93

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          Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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            EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis

            We have developed a toolbox and graphic user interface, EEGLAB, running under the crossplatform MATLAB environment (The Mathworks, Inc.) for processing collections of single-trial and/or averaged EEG data of any number of channels. Available functions include EEG data, channel and event information importing, data visualization (scrolling, scalp map and dipole model plotting, plus multi-trial ERP-image plots), preprocessing (including artifact rejection, filtering, epoch selection, and averaging), independent component analysis (ICA) and time/frequency decompositions including channel and component cross-coherence supported by bootstrap statistical methods based on data resampling. EEGLAB functions are organized into three layers. Top-layer functions allow users to interact with the data through the graphic interface without needing to use MATLAB syntax. Menu options allow users to tune the behavior of EEGLAB to available memory. Middle-layer functions allow users to customize data processing using command history and interactive 'pop' functions. Experienced MATLAB users can use EEGLAB data structures and stand-alone signal processing functions to write custom and/or batch analysis scripts. Extensive function help and tutorial information are included. A 'plug-in' facility allows easy incorporation of new EEG modules into the main menu. EEGLAB is freely available (http://www.sccn.ucsd.edu/eeglab/) under the GNU public license for noncommercial use and open source development, together with sample data, user tutorial and extensive documentation.
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              Mechanisms of gamma oscillations.

              Gamma rhythms are commonly observed in many brain regions during both waking and sleep states, yet their functions and mechanisms remain a matter of debate. Here we review the cellular and synaptic mechanisms underlying gamma oscillations and outline empirical questions and controversial conceptual issues. Our main points are as follows: First, gamma-band rhythmogenesis is inextricably tied to perisomatic inhibition. Second, gamma oscillations are short-lived and typically emerge from the coordinated interaction of excitation and inhibition, which can be detected as local field potentials. Third, gamma rhythm typically concurs with irregular firing of single neurons, and the network frequency of gamma oscillations varies extensively depending on the underlying mechanism. To document gamma oscillations, efforts should be made to distinguish them from mere increases of gamma-band power and/or increased spiking activity. Fourth, the magnitude of gamma oscillation is modulated by slower rhythms. Such cross-frequency coupling may serve to couple active patches of cortical circuits. Because of their ubiquitous nature and strong correlation with the "operational modes" of local circuits, gamma oscillations continue to provide important clues about neuronal population dynamics in health and disease.
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                Journal
                Science Translational Medicine
                Sci. Transl. Med.
                American Association for the Advancement of Science (AAAS)
                1946-6234
                1946-6242
                March 13 2024
                March 13 2024
                : 16
                : 738
                Affiliations
                [1 ]Université de Strasbourg, Inserm UMRS 1329, Strasbourg Translational Neuroscience and Psychiatry (STEP), Centre de Recherche en Biomédecine de Strasbourg, 67000 Strasbourg, France.
                [2 ]Sorbonne Université, Inserm, CNRS, Laboratoire d'Imagerie Biomédicale, LIB, 75006 Paris, France.
                [3 ]Institute of Clinical Neuroimmunology, Klinikum der Universität München, Ludwig–Maximilians University Munich, 82152 Martinsried, Germany.
                [4 ]Biomedical Center, Ludwig–Maximilians University Munich, 82152 Martinsried, Germany.
                [5 ]Laboratoire de Neurosciences Cognitives et Adaptatives, CNRS UMR7364, Université de Strasbourg, 67000 Strasbourg, France.
                [6 ]CNRS UPR3212, SMPMS-INCI, Mass Spectrometry Facilities, Institut des Neurosciences Cellulaires et Intégratives, Centre National de la Recherche Scientifique and University of Strasbourg, 67 000 Strasbourg, France.
                [7 ]Inserm UMS 38, Centre de Recherche en Biomédecine de Strasbourg, Faculté de Médecine, Université de Strasbourg, 67000 Strasbourg, France.
                [8 ]Neurologie, AP-HP, Hôpital Pitié-Salpêtrière, 75013-Paris, France.
                [9 ]Munich Cluster for Systems Neurology (SyNergy), 81377 Munich, Germany.
                [10 ]Medical Faculty, University of Cologne and Department of Neurology, University Hospital of Cologne, Cologne, 50937 Germany.
                Article
                10.1126/scitranslmed.adg3665
                468f4aa9-7618-482c-945d-702615630eee
                © 2024

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