Systemic cellular migration: The forces driving the directed locomotion movement of cells.

Directional motility is an essential property of cells. Despite its enormous relevance in many fundamental physiological and pathological processes, how cells control their locomotion movements remains an unresolved question. Here, we have addressed the systemic processes driving the directed locomotion of cells. Specifically, we have performed an exhaustive study analyzing the trajectories of 700 individual cells belonging to three different species (Amoeba proteus, Metamoeba leningradensis, and Amoeba borokensis) in four different scenarios: in absence of stimuli, under an electric field (galvanotaxis), in a chemotactic gradient (chemotaxis), and under simultaneous galvanotactic and chemotactic stimuli. All movements were analyzed using advanced quantitative tools. The results show that the trajectories are mainly characterized by coherent integrative responses that operate at the global cellular scale. These systemic migratory movements depend on the cooperative nonlinear interaction of most, if not all, molecular components of cells.

Open datasets and code for multi-scale relations on structure, function and neuro-genetics in the human brain.

The human brain is an extremely complex network of structural and functional connections that operate at multiple spatial and temporal scales. Investigating the relationship between these multi-scale connections is critical to advancing our comprehension of brain function and disorders. However, accurately predicting structural connectivity from its functional counterpart remains a challenging pursuit. One of the major impediments is the lack of public repositories that integrate structural and functional networks at diverse resolutions, in conjunction with modular transcriptomic profiles, which are essential for comprehensive biological interpretation. To mitigate this limitation, our contribution encompasses the provision of an open-access dataset consisting of derivative matrices of functional and structural connectivity across multiple scales, accompanied by code that facilitates the investigation of their interrelations. We also provide additional resources focused on neuro-genetic associations of module-level network metrics, which present promising opportunities to further advance research in the field of network neuroscience, particularly concerning brain disorders.

Cognitive and brain connectivity trajectories in critically ill COVID-19 patients.

BACKGROUND: Multiple Organ failure (MOF) is one of the main causes of admission to the Intensive Care Unit (ICU) of patients infected with COVID-19 and can cause short- and long-term neurological deficits. OBJECTIVE: To compare the cognitive functioning and functional brain connectivity at 6-12 months after discharge in two groups of individuals with MOF, one due to COVID-19 and the other due to another cause (MOF-group), with a group of Healthy Controls (HC). METHODS: Thirty-six participants, 12 from each group, underwent a neuropsychological and neuroimaging assessment at both time-points. Functional connectivity of the resting state networks was compared between COVID-19 and HC while controlling for the effect of MOF. The association between functional connectivity and neuropsychological performance was also investigated. RESULTS: Compared to the HC, COVID-19 group demonstrated hypoconnectivity between the Default Mode Network and Salience Network. This pattern was associated with worse performance on tests of attention and information processing speed, at both time-points. CONCLUSION: The study of the association between cognitive function and brain functional connectivity in COVID-19 allows the understanding of the short- and long-term neurological alterations of this disease and promotes the development of intervention programs to improve the quality of life for this understudied population.

Induction of Translational Readthrough on Protein Tyrosine Phosphatases Targeted by Premature Termination Codon Mutations in Human Disease.

Nonsense mutations generating premature termination codons (PTCs) in various genes are frequently associated with somatic cancer and hereditary human diseases since PTCs commonly generate truncated proteins with defective or altered function. Induced translational readthrough during protein biosynthesis facilitates the incorporation of an amino acid at the position of a PTC, allowing the synthesis of a complete protein. This may evade the pathological effect of the PTC mutation and provide new therapeutic opportunities. Several protein tyrosine phosphatases (PTPs) genes are targeted by PTC in human disease, the tumor suppressor PTEN being the more prominent paradigm. Here, using PTEN and laforin as examples, two PTPs from the dual-specificity phosphatase subfamily, we describe methodologies to analyze in silico the distribution and frequency of pathogenic PTC in PTP genes. We also summarize laboratory protocols and technical notes to study the induced translational readthrough reconstitution of the synthesis of PTP targeted by PTC in association with disease in cellular models.

NeuropsychBrainAge: A biomarker for conversion from mild cognitive impairment to Alzheimer's disease.

INTRODUCTION: BrainAge models based on neuroimaging data have diagnostic classification power but have replicability issues due to site and patient variability. BrainAge models trained on neuropsychological tests could help distinguish stable mild cognitive impairment (sMCI) from progressive MCI (pMCI) to Alzheimer's disease (AD). METHODS: A linear regressor BrainAge model was trained on healthy controls using neuropsychological tests and neuroimaging features separately. The BrainAge delta, predicted age minus chronological age, was used to distinguish between sMCI and pMCI. RESULTS: The cross-validated area under the receiver-operating characteristic (ROC) curve for sMCI versus pMCI was 0.91 for neuropsychological features in contrast to 0.68 for neuroimaging features. The BrainAge delta was correlated with the time to conversion, the time taken for a pMCI subject to convert to AD. DISCUSSION: The BrainAge delta from neuropsychological tests is a good biomarker to distinguish between sMCI and pMCI. Other neurological and psychiatric disorders could be studied using this strategy. Highlights: BrainAge models based on neuropsychological tests outperform models based on neuroimaging features when distinguishing between stable mild cognitive impairment (sMCI) from progressive MCI (pMCI) to Alzheimer's disease (AD).The combination of neuropsychological tests with neuroimaging features does not lead to an improvement in sMCI versus pMCI classification compared to using neuropsychological tests on their own.BrainAge delta of both neuroimaging and neuropsychological models was correlated with the time to conversion, the time taken for a pMCI subject to convert to AD.

COVID-19 vaccines reduce mortality in hospitalized patients with oxygen requirements: Differences between vaccine subtypes. A multicontinental cohort study.

The aim of this study was to analyze whether the coronavirus disease 2019 (COVID-19) vaccine reduces mortality in patients with moderate or severe COVID-19 disease requiring oxygen therapy. A retrospective cohort study, with data from 148 hospitals in both Spain (111 hospitals) and Argentina (37 hospitals), was conducted. We evaluated hospitalized patients for COVID-19 older than 18 years with oxygen requirements. Vaccine protection against death was assessed through a multivariable logistic regression and propensity score matching. We also performed a subgroup analysis according to vaccine type. The adjusted model was used to determine the population attributable risk. Between January 2020 and May 2022, we evaluated 21,479 COVID-19 hospitalized patients with oxygen requirements. Of these, 338 (1.5%) patients received a single dose of the COVID-19 vaccine and 379 (1.8%) were fully vaccinated. In vaccinated patients, mortality was 20.9% (95% confidence interval [CI]: 17.9-24), compared to 19.5% (95% CI: 19-20) in unvaccinated patients, resulting in a crude odds ratio (OR) of 1.07 (95% CI: 0.89-1.29; p = 0.41). However, after considering the multiple comorbidities in the vaccinated group, the adjusted OR was 0.73 (95% CI: 0.56-0.95; p = 0.02) with a population attributable risk reduction of 4.3% (95% CI: 1-5). The higher risk reduction for mortality was with messenger RNA (mRNA) BNT162b2 (Pfizer) (OR 0.37; 95% CI: 0.23-0.59; p < 0.01), ChAdOx1 nCoV-19 (AstraZeneca) (OR 0.42; 95% CI: 0.20-0.86; p = 0.02), and mRNA-1273 (Moderna) (OR 0.68; 95% CI: 0.41-1.12; p = 0.13), and lower with Gam-COVID-Vac (Sputnik) (OR 0.93; 95% CI: 0.6-1.45; p = 0.76). COVID-19 vaccines significantly reduce the probability of death in patients suffering from a moderate or severe disease (oxygen therapy).

The Neurogenetics of Functional Connectivity Alterations in Autism: Insights From Subtyping in 657 Individuals.

BACKGROUND: There is little consensus and controversial evidence on anatomical alterations in the brains of people with autism spectrum disorder (ASD), due in part to the large heterogeneity present in ASD, which in turn is a major drawback for developing therapies. One strategy to characterize this heterogeneity in ASD is to cluster large-scale functional brain connectivity profiles. METHODS: A subtyping approach based on consensus clustering of functional brain connectivity patterns was applied to a population of 657 autistic individuals with quality-assured neuroimaging data. We then used high-resolution gene transcriptomic data to characterize the molecular mechanism behind each subtype by performing enrichment analysis of the set of genes showing a high spatial similarity with the profiles of functional connectivity alterations between each subtype and a group of typically developing control participants. RESULTS: Two major stable subtypes were found: subtype 1 exhibited hypoconnectivity (less average connectivity than typically developing control participants) and subtype 2, hyperconnectivity. The 2 subtypes did not differ in structural imaging metrics in any of the analyzed regions (68 cortical and 14 subcortical) or in any of the behavioral scores (including IQ, Autism Diagnostic Interview, and Autism Diagnostic Observation Schedule). Finally, only subtype 2, comprising about 43% of ASD participants, led to significant enrichments after multiple testing corrections. Notably, the dominant enrichment corresponded to excitation/inhibition imbalance, a leading well-known primary mechanism in the pathophysiology of ASD. CONCLUSIONS: Our results support a link between excitation/inhibition imbalance and functional connectivity alterations, but only in one ASD subtype, overall characterized by brain hyperconnectivity and major alterations in somatomotor and default mode networks.

One-year prediction of cognitive decline following cognitive-stimulation from real-world data.

Clinical evidence based on real-world data (RWD) is accumulating exponentially providing larger sample sizes available, which demand novel methods to deal with the enhanced heterogeneity of the data. Here, we used RWD to assess the prediction of cognitive decline in a large heterogeneous sample of participants being enrolled with cognitive stimulation, a phenomenon that is of great interest to clinicians but that is riddled with difficulties and limitations. More precisely, from a multitude of neuropsychological Training Materials (TMs), we asked whether was possible to accurately predict an individual's cognitive decline one year after being tested. In particular, we performed longitudinal modelling of the scores obtained from 215 different tests, grouped into 29 cognitive domains, a total of 124,610 instances from 7902 participants (40% male, 46% female, 14% not indicated), each performing an average of 16 tests. Employing a machine learning approach based on ROC analysis and cross-validation techniques to overcome overfitting, we show that different TMs belonging to several cognitive domains can accurately predict cognitive decline, while other domains perform poorly, suggesting that the ability to predict decline one year later is not specific to any particular domain, but is rather widely distributed across domains. Moreover, when addressing the same problem between individuals with a common diagnosed label, we found that some domains had more accurate classification for conditions such as Parkinson's disease and Down syndrome, whereas they are less accurate for Alzheimer's disease or multiple sclerosis. Future research should combine similar approaches to ours with standard neuropsychological measurements to enhance interpretability and the possibility of generalizing across different cohorts.

Multimodal and multidomain lesion network mapping enhances prediction of sensorimotor behavior in stroke patients.

Beyond the characteristics of a brain lesion, such as its etiology, size or location, lesion network mapping (LNM) has shown that similar symptoms after a lesion reflects similar dis-connectivity patterns, thereby linking symptoms to brain networks. Here, we extend LNM by using a multimodal strategy, combining functional and structural networks from 1000 healthy participants in the Human Connectome Project. We apply multimodal LNM to a cohort of 54 stroke patients with the aim of predicting sensorimotor behavior, as assessed through a combination of motor and sensory tests. Results are two-fold. First, multimodal LNM reveals that the functional modality contributes more than the structural one in the prediction of sensorimotor behavior. Second, when looking at each modality individually, the performance of the structural networks strongly depended on whether sensorimotor performance was corrected for lesion size, thereby eliminating the effect that larger lesions generally produce more severe sensorimotor impairment. In contrast, functional networks provided similar performance regardless of whether or not the effect of lesion size was removed. Overall, these results support the extension of LNM to its multimodal form, highlighting the synergistic and additive nature of different types of network modalities, and their corresponding influence on behavioral performance after brain injury.

Brain Mapping of Behavioral Domains Using Multi-Scale Networks and Canonical Correlation Analysis.

Simultaneous mapping of multiple behavioral domains into brain networks remains a major challenge. Here, we shed some light on this problem by employing a combination of machine learning, structural and functional brain networks at different spatial resolutions (also known as scales), together with performance scores across multiple neurobehavioral domains, including sensation, motor skills, and cognition. Provided by the Human Connectome Project, we make use of three cohorts: 640 participants for model training, 160 subjects for validation, and 200 subjects for model performance testing thus enhancing prediction generalization. Our modeling consists of two main stages, namely dimensionality reduction in brain network features at multiple scales, followed by canonical correlation analysis, which determines an optimal linear combination of connectivity features to predict multiple behavioral performance scores. To assess the differences in the predictive power of each modality, we separately applied three different strategies: structural unimodal, functional unimodal, and multimodal, that is, structural in combination with functional features of the brain network. Our results show that the multimodal association outperforms any of the unimodal analyses. Then, to answer which human brain structures were most involved in predicting multiple behavioral scores, we simulated different synthetic scenarios in which in each case we completely deleted a brain structure or a complete resting state network, and recalculated performance in its absence. In deletions, we found critical structures to affect performance when predicting single behavioral domains, but this occurred in a lesser manner for prediction of multi-domain behavior. Overall, our results confirm that although there are synergistic contributions between brain structure and function that enhance behavioral prediction, brain networks may also be mutually redundant in predicting multidomain behavior, such that even after deletion of a structure, the connectivity of the others can compensate for its lack in predicting behavior.

Heart-brain synchronization breakdown in Parkinson's disease.

Heart rate variability (HRV) abnormalities are potential early biomarkers in Parkinson's disease (PD) but their relationship with central autonomic network (CAN) activity is not fully understood. We analyzed the synchronization between HRV and brain activity in 31 PD patients and 21 age-matched healthy controls using blood-oxygen-level-dependent (BOLD) signals from resting-state functional brain MRI and HRV metrics from finger plethysmography recorded for 7.40 min. We additionally quantified autonomic symptoms (SCOPA-AUT) and objective autonomic cardiovascular parameters (blood pressure and heart rate) during deep breathing, Valsalva, and head-up tilt, which were used to classify the clinical severity of dysautonomia. We evaluated HRV and BOLD signals synchronization (HRV-BOLD-sync) with Pearson lagged cross-correlations and Fisher's statistics for combining window-length-dependent HRV-BOLD-Sync Maps and assessed their association with clinical dysautonomia. HRV-BOLD-sync was lower significantly in PD than in controls in various brain regions within CAN or in networks involved in autonomic modulation. Moreover, heart-brain synchronization index (HBSI), which quantifies heart-brain synchronization at a single-subject level, showed an inverse exposure-response relationship with dysautonomia severity, finding the lowest HBSI in patients with severe dysautonomia, followed by moderate, mild, and, lastly, controls. Importantly, HBSI was associated in PD, but not in controls, with Valsalva pressure recovery time (sympathetic), deep breathing E/I ratio (cardiovagal), and SCOPA-AUT. Our findings support the existence of heart-brain de-synchronization in PD with an impact on clinically relevant autonomic outcomes.

White matter integrity changes and neurocognitive functioning in adult-late onset DM1: a follow-up DTI study.

Myotonic Dystrophy Type 1 (DM1) is a multisystemic disease that affects gray and white matter (WM) tissues. WM changes in DM1 include increased hyperintensities and altered tract integrity distributed in a widespread manner. However, the precise temporal and spatial progression of the changes are yet undetermined. MRI data were acquired from 8 adult- and late-onset DM1 patients and 10 healthy controls (HC) at two different timepoints over 9.06 years. Fractional anisotropy (FA) and mean diffusivity (MD) variations were assessed with Tract-Based Spatial Statistics. Transversal and longitudinal intra- and intergroup analyses were conducted, along with correlation analyses with clinical and neuropsychological data. At baseline, reduced FA and increased MD values were found in patients in the uncinate, anterior-thalamic, fronto-occipital, and longitudinal tracts. At follow-up, the WM disconnection was shown to have spread from the frontal part to the rest of the tracts in the brain. Furthermore, WM lesion burden was negatively correlated with FA values, while visuo-construction and intellectual functioning were positively correlated with global and regional FA values at follow-up. DM1 patients showed a pronounced WM integrity loss over time compared to HC, with a neurodegeneration pattern that suggests a progressive anterior-posterior disconnection. The visuo-construction domain stands out as the most sensitive neuropsychological measure for WM microstructural impairment.

The structure of anticorrelated networks in the human brain.

During the performance of a specific task--or at rest--, the activity of different brain regions shares statistical dependencies that reflect functional connections. While these relationships have been studied intensely for positively correlated networks, considerably less attention has been paid to negatively correlated networks, a. k.a. anticorrelated networks (ACNs). Although the most celebrated of all ACNs is the default mode network (DMN), and has even been extensively studied in health and disease, for systematically all ACNs other than DMN, there is no comprehensive study yet. Here, we have addressed this issue by making use of three neuroimaging data sets: one of N = 192 healthy young adults to fully describe ACN, another of N = 40 subjects to compare ACN between two groups of young and old participants, and another of N = 1,000 subjects from the Human Connectome Project to evaluate the association between ACN and cognitive scores. We first provide a comprehensive description of the anatomical composition of all ACNs, each of which participated in distinct resting-state networks (RSNs). In terms of participation ranking, from highest to the lowest, the major anticorrelated brain areas are the precuneus, the anterior supramarginal gyrus and the central opercular cortex. Next, by evaluating a more detailed structure of ACN, we show it is possible to find significant differences in ACN between specific conditions, in particular, by comparing groups of young and old participants. Our main finding is that of increased anticorrelation for cerebellar interactions in older subjects. Finally, in the voxel-level association study with cognitive scores, we show that ACN has multiple clusters of significance, clusters that are different from those obtained from positive correlated networks, indicating a functional cognitive meaning of ACN. Overall, our results give special relevance to ACN and suggest their use to disentangle unknown alterations in certain conditions, as could occur in early-onset neurodegenerative diseases or in some psychiatric conditions.

Transcriptional signatures of synaptic vesicle genes define myotonic dystrophy type I neurodegeneration.

AIM: To delineate the neurogenetic profiles of brain degeneration patterns in myotonic dystrophy type I (DM1). METHODS: In two cohorts of DM1 patients, brain maps of volume loss (VL) and neuropsychological deficits (NDs) were intersected to large-scale transcriptome maps provided by the Allen Human Brain Atlas (AHBA). For validation, neuropathological and RNA analyses were performed in a small series of DM1 brain samples. RESULTS: Twofold: (1) From a list of preselected hypothesis-driven genes, confirmatory analyses found that three genes play a major role in brain degeneration: dystrophin (DMD), alpha-synuclein (SNCA) and the microtubule-associated protein tau (MAPT). Neuropathological analyses confirmed a highly heterogeneous Tau-pathology in DM1, different to the one in Alzheimer's disease. (2) Exploratory analyses revealed gene clusters enriched for key biological processes in the central nervous system, such as synaptic vesicle recycling, localization, endocytosis and exocytosis, and the serotonin and dopamine neurotransmitter pathways. RNA analyses confirmed synaptic vesicle dysfunction. CONCLUSIONS: The combination of large-scale transcriptome interactions with brain imaging and cognitive function sheds light on the neurobiological mechanisms of brain degeneration in DM1 that might help define future therapeutic strategies and research into this condition.

Individual-based morphological brain network organization and its association with autistic symptoms in young children with autism spectrum disorder.

Individual-based morphological brain networks built from T1-weighted magnetic resonance imaging (MRI) reflect synchronous maturation intensities between anatomical regions at the individual level. Autism spectrum disorder (ASD) is a socio-cognitive and neurodevelopmental disorder with high neuroanatomical heterogeneity, but the specific patterns of morphological networks in ASD remain largely unexplored at the individual level. In this study, individual-based morphological networks were constructed by using high-resolution structural MRI data from 40 young children with ASD (age range: 2-8 years) and 38 age-, gender-, and handedness-matched typically developing children (TDC). Measurements were recorded as threefold. Results showed that compared with TDC, young children with ASD exhibited lower values of small-worldness (i.e., σ) of individual-level morphological brain networks, increased morphological connectivity in cortico-striatum-thalamic-cortical (CSTC) circuitry, and decreased morphological connectivity in the cortico-cortical network. In addition, morphological connectivity abnormalities can predict the severity of social communication deficits in young children with ASD, thus confirming an associational impact at the behavioral level. These findings suggest that the morphological brain network in the autistic developmental brain is inefficient in segregating and distributing information. The results also highlight the crucial role of abnormal morphological connectivity patterns in the socio-cognitive deficits of ASD and support the possible use of the aberrant developmental patterns of morphological brain networks in revealing new clinically-relevant biomarkers for ASD.

A Controlled Thermoalgesic Stimulation Device for Exploring Novel Pain Perception Biomarkers.

OBJECTIVE: To develop a new device for identifying physiological markers of pain perception by reading the brain's electrical activity and hemodynamic interactions while applying thermoalgesic stimulation. METHODS: We designed a compact prototype that generates well-controlled thermal stimuli using a computer-driven Peltier cell while simultaneously capturing electroencephalography (EEG) and photoplethysmography (PPG) signals. The study was performed on 35 healthy subjects (mean age 30.46 years, SD 4.93 years; 20 males, 15 females). We first determined the heat pain threshold (HPT) for each subject, defined as the maximum temperature that the subject can withstand when the Peltier cell gradually increased the temperature. Next, we defined the painful condition as the one occurring at temperature equal to 90% of the HPT, comparing this to the no-pain state (control) in the absence of thermoalgesic stimulation. RESULTS: Both the one-dimensional and the two-dimensional spectral entropy (SE) obtained from both the EEG and PPG signals differentiated the condition of pain. In particular, the SE for PPG was significantly reduced in association with pain, while the SE for EEG increased slightly. Moreover, significant discrimination occurred within a specific range of frequencies, 26-30 Hz for EEG and about 5-10 Hz for PPG. CONCLUSION: Hemodynamics, brain dynamics and their interactions can discriminate thermal pain perception. SIGNIFICANCE: The possibility of monitoring on-line variations in thermal pain perception using a similar device and algorithms may be of interest to study different pathologies that affect the peripheral nervous system, such as small fiber neuropathies, fibromyalgia or painful diabetic neuropathy.

High-Order Interdependencies in the Aging Brain.

Background: Brain interdependencies can be studied from either a structural/anatomical perspective ("structural connectivity") or by considering statistical interdependencies ("functional connectivity" [FC]). Interestingly, while structural connectivity is by definition pairwise (white-matter fibers project from one region to another), FC is not. However, most FC analyses only focus on pairwise statistics and they neglect higher order interactions. A promising tool to study high-order interdependencies is the recently proposed O-Information, which can quantify the intrinsic statistical synergy and the redundancy in groups of three or more interacting variables. Methods: We analyzed functional magnetic resonance imaging (fMRI) data obtained at rest from 164 healthy subjects with ages ranging in 10 to 80 years and used O-Information to investigate how high-order statistical interdependencies are affected by age. Results: Older participants (from 60 to 80 years old) exhibited a higher predominance of redundant dependencies compared with younger participants, an effect that seems to be pervasive as it is evident for all orders of interaction. In addition, while there is strong heterogeneity across brain regions, we found a "redundancy core" constituted by the prefrontal and motor cortices in which redundancy was evident at all the interaction orders studied. Discussion: High-order interdependencies in fMRI data reveal a dominant redundancy in functions such as working memory, executive, and motor functions. Our methodology can be used for a broad range of applications, and the corresponding code is freely available. Impact statement Past research has showcased multiple changes to the brain's structural and functional properties caused by aging. Here we expand prior work through recent advancements in multivariate information theory, which provide richer and more theoretically principled analyses than existing alternatives. We show that the brains of older participants contain more redundant information at multiple spatial scales-that is, activation in different brain regions is less diverse, compared with younger participants-and identify a "redundancy core" constituted by prefrontal and motor cortices, which might explained impaired performance in the old population in functions such as working memory and executive control.

MMADHC premature termination codons in the pathogenesis of cobalamin D disorder: Potential of translational readthrough reconstitution.

Mutations in the MMADHC gene cause cobalamin D disorder (cblD), an autosomal recessive inborn disease with defects in intracellular cobalamin (cbl, vitamin B12) metabolism. CblD patients present methylmalonic aciduria (MMA), homocystinuria (HC), or combined MMA/HC, and usually suffer developmental delay and cognitive deficits. The most frequent MMADHC genetic alterations associated with disease generate MMADHC truncated proteins, in many cases due to mutations that create premature termination codons (PTC). In this study, we have performed a comprehensive and global characterization of MMADHC protein variants generated by all annotated MMADHC PTC mutations in cblD patients, and analyzed the potential of inducible translational PTC readthrough to reconstitute MMADHC biosynthesis. MMADHC protein truncation caused by disease-associated PTC differentially affected the alternative usage of translation initiation sites, protein abundance, and subcellular localization of MMADHC. Aminoglycoside compounds induced translational PTC readthrough of MMADHC truncated variants, allowing the biosynthesis of full-length MMADHC in a PTC-specific manner. Our results suggest that translational PTC readthrough-based interventions could complement current therapies for cblD patients carrying specific MMADHC PTC mutations.

A global analysis of the reconstitution of PTEN function by translational readthrough of PTEN pathogenic premature termination codons.

The PTEN tumor suppressor gene is mutated with high incidence in tumors and in the germline of patients with cancer predisposition or with macrocephaly associated with autism. PTEN nonsense mutations generating premature termination codons (PTC) and producing nonfunctional truncated PTEN proteins are frequent in association with human disease. However, there are no studies addressing the restoration of full-length PTEN proteins from the PTC-mutated PTEN gene by translational readthrough. Here, we have performed a global translational and functional readthrough analysis of the complete collection of PTEN PTC somatic or hereditary mutations found in tumors or in the germline of patients (disease-associated PTEN PTCome), and we set standards for the analysis of the potential of readthrough functional reconstitution in disease-relevant genes. Our analysis indicates that prevalent pathogenic PTEN PTC mutations are susceptible to PTEN functional restoration in response to readthrough-inducing compounds. Comprehensive readthrough analyses of disease-associated PTComes will be valuable tools for the implementation of readthrough-based precision interventions in specific groups of patients.

Neurodegeneration trajectory in pediatric and adult/late DM1: A follow-up MRI study across a decade.

OBJECTIVE: To characterize the progression of brain structural abnormalities in adults with pediatric and adult/late onset DM1, as well as to examine the potential predictive markers of such progression. METHODS: 21 DM1 patients (pediatric onset: N = 9; adult/late onset: N = 12) and 18 healthy controls (HC) were assessed longitudinally over 9.17 years through brain MRI. Additionally, patients underwent neuropsychological, genetic, and muscular impairment assessment. Inter-group comparisons of total and voxel-level regional brain volume were conducted through Voxel Based Morphometry (VBM); cross-sectionally and longitudinally, analyzing the associations between brain changes and demographic, clinical, and cognitive outcomes. RESULTS: The percentage of GM loss did not significantly differ in any of the groups compared with HC and when assessed independently, adult/late DM1 patients and their HC group suffered a significant loss in WM volume. Regional VBM analyses revealed subcortical GM damage in both DM1 groups, evolving to frontal regions in the pediatric onset patients. Muscular impairment and the outcomes of certain neuropsychological tests were significantly associated with follow-up GM damage, while visuoconstruction, attention, and executive function tests showed sensitivity to WM degeneration over time. INTERPRETATION: Distinct patterns of brain atrophy and its progression over time in pediatric and adult/late onset DM1 patients are suggested. Results indicate a possible neurodevelopmental origin of the brain abnormalities in DM1, along with the possible existence of an additional neurodegenerative process. Fronto-subcortical networks appear to be involved in the disease progression at young adulthood in pediatric onset DM1 patients. The involvement of a multimodal integration network in DM1 is discussed.