Aberrant RNA Processing in a Neurodegenerative Disease: the Cause for Absent EAAT2, a Glutamate Transporter, in Amyotrophic Lateral Sclerosis

AbstractAmyotrophic lateral sclerosis (ALS) is a neurodegenerative disease that is characterized by selective upper and lower motor neuron degeneration, the pathogenesis of which is unknown. About 60%–70% of sporadic ALS patients have a 30%–95% loss of the astroglial glutamate transporter EAAT2 (excitatory amino acid transporter 2) protein in motor cortex and spinal cord. Loss of EAAT2 leads to increased extracellular glutamate and excitotoxic neuronal degeneration. Multiple abnormal EAAT2 mRNAs, including intron-retention and exon-skipping, have now been identified from the affected areas of ALS patients. The aberrant mRNAs were highly abundant and were found only in neuropathologically affected areas of ALS patients but not in other brain regions. They were found in 65% of sporadic ALS patients but were not found in nonneurologic disease or other disease controls. They were also detectable in the cerebrospinal fluid (CSF) of living ALS patients, early in the disease. In vitro expression studies suggest that proteins translated from these aberrant mRNAs may undergo rapid degradation and/or produce a dominant negative effect on normal EAAT2 resulting in loss of protein and activity. These findings suggest that the loss of EAAT2 in ALS is due to aberrant mRNA and that these aberrant mRNAs could result from RNA processing errors. Aberrant RNA processing could be important in the pathophysiology of neurodegenerative disease and in excitotoxicity. The presence of these mRNA species in ALS CSF may have diagnostic utility

Improving fairness for spoken language understanding in atypical speech with Text-to-Speech

Spoken language understanding (SLU) systems often exhibit suboptimal performance in processing atypical speech, typically caused by neurological conditions and motor impairments. Recent advancements in Text-to-Speech (TTS) synthesis-based augmentation for more fair SLU have struggled to accurately capture the unique vocal characteristics of atypical speakers, largely due to insufficient data. To address this issue, we present a novel data augmentation method for atypical speakers by finetuning a TTS model, called Aty-TTS. Aty-TTS models speaker and atypical characteristics via knowledge transferring from a voice conversion model. Then, we use the augmented data to train SLU models adapted to atypical speech. To train these data augmentation models and evaluate the resulting SLU systems, we have collected a new atypical speech dataset containing intent annotation. Both objective and subjective assessments validate that Aty-TTS is capable of generating high-quality atypical speech. Furthermore, it serves as an effective data augmentation strategy, contributing to more fair SLU systems that can better accommodate individuals with atypical speech patterns.Comment: Accepted at SyntheticData4ML 2023 Ora

Amyotrophic lateral sclerosis–specific quality of life–short form (ALSSQOL‐SF): A brief, reliable, and valid version of the ALSSQOL‐R

Introduction: The Amyotrophic Lateral Sclerosis (ALS)‐Specific Quality of Life instrument and its revised version (ALSSQOL and ALSSQOL‐R) have strong psychometric properties, and have demonstrated research and clinical utility. In this study we aimed to develop a short form (ALSSQOL‐SF) suitable for limited clinic time and patient stamina. Methods: The ALSSQOL‐SF was created using Item Response Theory and confirmatory factor analysis on 389 patients. A cross‐validation sample of 162 patients assessed convergent, divergent, and construct validity of the ALSSQOL‐SF compared with psychosocial and physical functioning measures. Results: The ALSSQOL‐SF consisted of 20 items. Compared with the ALSSQOL‐R, optimal precision was retained, and completion time was reduced from 15–25 minutes to 2–4 minutes. Psychometric properties for the ALSSQOL‐SF and its subscales were strong. Discussion: The ALSSQOL‐SF is a disease‐specific global QOL instrument that has a short administration time suitable for clinical use, and can provide clinically useful, valid information about persons with ALS. Muscle Nerve 58: 646–654, 2018Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146574/1/mus26203_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146574/2/mus26203.pd

A high-resolution map of human evolutionary constraint using 29 mammals.

The comparison of related genomes has emerged as a powerful lens for genome interpretation. Here we report the sequencing and comparative analysis of 29 eutherian genomes. We confirm that at least 5.5% of the human genome has undergone purifying selection, and locate constrained elements covering ∼4.2% of the genome. We use evolutionary signatures and comparisons with experimental data sets to suggest candidate functions for ∼60% of constrained bases. These elements reveal a small number of new coding exons, candidate stop codon readthrough events and over 10,000 regions of overlapping synonymous constraint within protein-coding exons. We find 220 candidate RNA structural families, and nearly a million elements overlapping potential promoter, enhancer and insulator regions. We report specific amino acid residues that have undergone positive selection, 280,000 non-coding elements exapted from mobile elements and more than 1,000 primate- and human-accelerated elements. Overlap with disease-associated variants indicates that our findings will be relevant for studies of human biology, health and disease

A click-based electrocorticographic brain-computer interface enables long-term high-performance switch-scan spelling

BACKGROUND: Brain-computer interfaces (BCIs) can restore communication in movement- and/or speech-impaired individuals by enabling neural control of computer typing applications. Single command "click" decoders provide a basic yet highly functional capability. METHODS: We sought to test the performance and long-term stability of click-decoding using a chronically implanted high density electrocorticographic (ECoG) BCI with coverage of the sensorimotor cortex in a human clinical trial participant (ClinicalTrials.gov, NCT03567213) with amyotrophic lateral sclerosis (ALS). We trained the participant's click decoder using a small amount of training data (< 44 minutes across four days) collected up to 21 days prior to BCI use, and then tested it over a period of 90 days without any retraining or updating. RESULTS: Using this click decoder to navigate a switch-scanning spelling interface, the study participant was able to maintain a median spelling rate of 10.2 characters per min. Though a transient reduction in signal power modulation interrupted testing with this fixed model, a new click decoder achieved comparable performance despite being trained with even less data (< 15 min, within one day). CONCLUSION: These results demonstrate that a click decoder can be trained with a small ECoG dataset while retaining robust performance for extended periods, providing functional text-based communication to BCI users

Online speech synthesis using a chronically implanted brain-computer interface in an individual with ALS

Recent studies have shown that speech can be reconstructed and synthesized using only brain activity recorded with intracranial electrodes, but until now this has only been done using retrospective analyses of recordings from able-bodied patients temporarily implanted with electrodes for epilepsy surgery. Here, we report online synthesis of intelligible words using a chronically implanted brain-computer interface (BCI) in a clinical trial participant (ClinicalTrials.gov, NCT03567213) with dysarthria due to amyotrophic lateral sclerosis (ALS). We demonstrate a reliable BCI that synthesizes commands freely chosen and spoken by the user from a vocabulary of 6 keywords originally designed to allow intuitive selection of items on a communication board. Our results show for the first time that a speech-impaired individual with ALS can use a chronically implanted BCI to reliably produce synthesized words that are intelligible to human listeners while preserving the participants voice profile

Online speech synthesis using a chronically implanted brain–computer interface in an individual with ALS

Brain–computer interfaces (BCIs) that reconstruct and synthesize speech using brain activity recorded with intracranial electrodes may pave the way toward novel communication interfaces for people who have lost their ability to speak, or who are at high risk of losing this ability, due to neurological disorders. Here, we report online synthesis of intelligible words using a chronically implanted brain-computer interface (BCI) in a man with impaired articulation due to ALS, participating in a clinical trial (ClinicalTrials.gov, NCT03567213) exploring different strategies for BCI communication. The 3-stage approach reported here relies on recurrent neural networks to identify, decode and synthesize speech from electrocorticographic (ECoG) signals acquired across motor, premotor and somatosensory cortices. We demonstrate a reliable BCI that synthesizes commands freely chosen and spoken by the participant from a vocabulary of 6 keywords previously used for decoding commands to control a communication board. Evaluation of the intelligibility of the synthesized speech indicates that 80% of the words can be correctly recognized by human listeners. Our results show that a speech-impaired individual with ALS can use a chronically implanted BCI to reliably produce synthesized words while preserving the participant’s voice profile, and provide further evidence for the stability of ECoG for speech-based BCIs

Stable Decoding from a Speech BCI Enables Control for an Individual with ALS without Recalibration for 3 Months

Brain-computer interfaces (BCIs) can be used to control assistive devices by patients with neurological disorders like amyotrophic lateral sclerosis (ALS) that limit speech and movement. For assistive control, it is desirable for BCI systems to be accurate and reliable, preferably with minimal setup time. In this study, a participant with severe dysarthria due to ALS operates computer applications with six intuitive speech commands via a chronic electrocorticographic (ECoG) implant over the ventral sensorimotor cortex. Speech commands are accurately detected and decoded (median accuracy: 90.59%) throughout a 3-month study period without model retraining or recalibration. Use of the BCI does not require exogenous timing cues, enabling the participant to issue self-paced commands at will. These results demonstrate that a chronically implanted ECoG-based speech BCI can reliably control assistive devices over long time periods with only initial model training and calibration, supporting the feasibility of unassisted home use

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Use of noninvasive ventilation in patients with amyotrophic lateral sclerosis

INTRODUCTION: Noninvasive positive pressure ventilation (NIPPV) is associated with improved survival in amyotrophic lateral sclerosis (ALS) and has been widely recommended. The extent of NIPPV use in ALS patients and the factors associated with its use have not been studied. METHODS: A cross-sectional study using the ALS Patient Care Database. Analyses were performed to assess the association of patient and care characteristics with use of ventilatory support. RESULTS: 1458 patients were studied. 15.6% used NIPPV and 2.1% used invasive mechanical ventilation. Patients who used NIPPV were significantly more likely to be male and have higher income than those who did not. They were also more likely to have a gastrostomy tube, lower vital capacity, more severe disease, bulbar involvement and poorer general health status as measured by the SF-12 and Sickness Impact Profile. Multivariate analysis revealed that lower FVC, higher income and use of gastrostomy tube were independently associated with use of NIPPV. CONCLUSIONS: NIPPV is used more than seven times as frequently as invasive ventilation in ALS patients. Patients who use NIPPV have more severe disease than those who do not use any respiratory intervention. Patients with lower income are less likely to use NIPPV, which raises concerns about disparities in the care of patients with ALS