Drug Target for Fragile X Syndrome Identified Through Preclinical Study

UCLA Health researchers have identified a potential drug target for treating fragile X syndrome (FXS), the most common genetic cause of intellectual disability and autism that affects roughly one in 2,000 boys.

Fragile X syndrome is caused by a mutation in a single gene, FMR1, that results in the loss of a protein critical for normal brain development and function. Headed by Carlos Portera-Cailliau, MD, PhD, professor of neurology at UCLA and member of the UCLA Brain Research Institute, the researchers, the team’s work in genetically engineered mice lacking the Fmr1 gene identified the synaptic protein EPAC2 as a potential therapeutic target for fragile X syndrome. Their study showed that blocking EPAC2 in the fragile X mouse model restored abnormal patterns of brain activity and improved several FXS-associated behavioral symptoms.

Pertera-Cailliau is senior and corresponding author of the researchers published paper in Neuron, titled “Translatome profiling reveals opposing alterations in inhibitory and excitatory neurons of fragile X mice and identifies EPAC2 as a therapeutic target.”

Fragile X syndrome is a prototypical neurodevelopmental disorder (NDD) characterized by intellectual disability, social anxiety, atypical sensory processing characterized heightened sensitivity to sensory input such as sound and touch, and a higher risk of seizures. Many also meet the criteria for an autism spectrum disorder diagnosis. “Symptoms of fragile X syndrome (FXS), the leading monogenic cause of intellectual disability and autism, are thought to arise from an excitation/inhibition (E/I) imbalance,” the authors stated.

FXS is caused by mutations in the FMR1 gene, resulting in near complete loss of the fragile X messenger ribonucleoprotein (FMRP), an RNA-binding protein in neurons that plays different roles in cell compartments including the nucleus, axons and dendrites, including regulating mRNA translation at synapses, they explained. As it is caused by a change in a single gene, fragile X syndrome has long been considered a promising candidate for targeted therapies yet clinical trials to date have not produced an effective treatment. “Since the discovery of the genetic basis of FXS in 1991, several clinical trials have been undertaken—without success—and no specific treatments for FXS are currently available,” the investigators continued. “Thus, there is an urgent need to rethink therapeutic strategies for FXS.”

For their newly reported study the researchers used genetically engineered knockout (KO) mice that lack Fmr1 to simulate fragile X syndrome. Using genetic sequencing, they found that levels of the gene EPAC2 were increased in the brain of fragile X mice. This was of potential interest as a target for therapy because the gene’s protein, EPAC2, is localized to synapses and is known to be important for learning and memory.

The researchers then demonstrated that blocking EPAC2 in the fragile X mouse model, either genetically, or using an EPAC2 inhibitor compound, restored cortical circuit function and improved multiple behavioral symptoms associated with fragile X syndrome, including heightened sensitivity to touch, difficulties with social interaction and their susceptibility for seizures. “Perhaps the most exciting result is that treatment with an EPAC2 antagonist can rescue several behavioral phenotypes in Fmr1 KO mice,” the authors stated.

“EPAC2 emerged as an attractive target because it was consistently altered across multiple types of brain cells in our analysis,” said the study’s first author Anand Suresh, PhD, a post-doctoral fellow in the laboratory of Portera-Cailliau. “When we blocked it, either genetically or with a drug compound, we saw meaningful improvements in both brain circuit function and behavior.”

EPAC2 is expressed almost exclusively in the brain, which means drugs targeting it are less likely to cause unwanted effects elsewhere in the body. Suresh said this is an important consideration as researchers continue preclinical studies. “This bodes well for future preclinical trials and safety studies in humans, as compounds that target EPAC2 should not have off-target effects,” the authors stated in their report.

For their study the UCLA investigators used an RNA sequencing technique to examine gene activity separately in two major classes of brain cells: those that excite and those that inhibit neural activity. Fragile X syndrome is thought to arise from an imbalance between these two systems. The analysis revealed striking differences in how the genetic mutation underlying Fragile X syndrome affects each cell type but also identified a small set of genes, including the one that encodes EPAC2, that were dysregulated in both.

The researchers also found that EPAC2 levels appear to rise gradually as the brain matures, suggesting it may be a particularly relevant target for older children and adults with Fragile X syndrome, rather than only in early development. They concluded, “Our results should encourage the development of novel EPAC2 inhibitors for the treatment of FXS. More generally, our study exemplifies how transcriptomic approaches in animal models of neuropsychiatric conditions can be used to prioritize potential novel therapeutic targets.”

The post Drug Target for Fragile X Syndrome Identified Through Preclinical Study appeared first on GEN – Genetic Engineering and Biotechnology News.

AI Model Predicts Alzheimer’s Progression from a Single MRI Scan

Researchers at the University of California, San Francisco (UCSF) have developed an artificial intelligence model capable of predicting cognitive impairment and Alzheimer’s disease progression using only a single baseline MRI scan and basic demographic information. The approach, published in Nature Aging, could help make early Alzheimer’s assessment faster, more accessible, and less dependent on costly specialized testing.

Alzheimer’s diagnosis remains complex and resource-intensive

Alzheimer’s disease accounts for approximately 60% to 70% of dementia cases worldwide. Although structural brain changes and cognitive decline are hallmarks of the disease, accurately forecasting who will develop progressive impairment remains difficult.

Current diagnostic workflows often rely on multiple complementary techniques, including PET imaging, cerebrospinal fluid or blood biomarkers, genetic testing, and comprehensive neuropsychological assessments. While effective, these approaches can be expensive, time-consuming, and inaccessible in many healthcare settings.

MRI scans are among the most widely available clinical imaging tools for neurological assessment, but MRI data alone has historically struggled to capture the complexity and heterogeneity of Alzheimer’s disease progression when used in conventional AI frameworks.

To address this challenge, the UCSF team designed a multitask deep learning framework that combines domain-specific imaging knowledge with advanced machine learning methods to predict cognitive outcomes directly from structural MRI scans.

AI framework predicts cognition without invasive testing

Unlike many earlier Alzheimer’s prediction models, the new system does not require longitudinal imaging data, baseline cognitive testing, PET scans, or molecular biomarker analysis.

The researchers instead focused on extracting clinically meaningful information from a single baseline MRI scan. The framework was trained to perform several related tasks simultaneously, including tissue segmentation, Alzheimer’s diagnosis prediction, and estimation of both present and future cognitive performance.

A key innovation of the study was the development of a specialized image model that segments brain tissue into gray matter, white matter, and cerebrospinal fluid before generating cognitive predictions. According to the authors, this task-specific segmentation step allowed the model to learn biologically relevant spatial brain features more effectively than standard transfer-learning approaches.

Senior study author Ashish Raj, PhD, professor of radiology and biomedical imaging at UCSF, said the goal was to create a system that could be realistically implemented in routine clinical environments.

“Unlike previous approaches, our model does not require baseline cognitive assessment, specialized image pipelines, expensive PET scans, genetic analysis, or fluid proteomics, making it a fast, accurate, and easily implementable tool for most clinical settings,” Raj said in a statement.

Large imaging datasets improved robustness and generalizability

To train and validate the framework, the researchers used imaging and clinical data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), including MRI scans, demographic information, diagnoses, and cognitive assessments.

The team also incorporated MRI data from the Human Connectome Project Young Adult cohort, which contains scans from healthy younger adults with minimal age-related brain atrophy. According to the authors, exposing the model to healthy brain anatomy improved its ability to distinguish pathological neurodegeneration from normal aging.

An external validation cohort from the Dallas Lifespan Brain Study was additionally used to test the generalizability of the framework across independent datasets.

The researchers reported that the multitask framework outperformed existing AI methods, including standard transfer-learning approaches, in predicting clinically relevant outcomes. The model generated accurate predictions for Alzheimer’s diagnosis, tissue segmentation, current cognitive function, and future cognitive decline using only baseline MRI data.

The study also reported improvements in computational efficiency and processing speed compared with more complex MRI morphometry pipelines commonly used in neuroimaging research.

First author Daren Ma, MSc, a machine learning specialist in the Raj Lab at UCSF, said the framework could help clinicians identify at-risk patients earlier and streamline referrals for advanced neurological evaluation.

“We reported meaningful gains in speed and performance over other pipelines, which could prove valuable in developing a quick clinical prediction of cognitive impairment prior to referring the patient to a more advanced imaging lab and/or a full neuroradiology report,” Ma said.

Potential implications beyond Alzheimer’s disease

The researchers believe the framework could eventually be adapted for other neurodegenerative disorders characterized by structural brain changes and progressive cognitive decline.

Potential future applications include Parkinson’s disease, amyotrophic lateral sclerosis (ALS), and Huntington’s disease. The ability to estimate cognitive impairment using minimal baseline data may also prove useful in community healthcare settings where access to specialist neuropsychological testing is limited.

In addition, the model may have implications for clinical trial design. Identifying likely disease progressors early could help reduce trial size requirements and improve patient selection for studies evaluating disease-modifying therapies.

“The ability to correctly predict progressors from non-progressors using only baseline data can dramatically reduce sample sizes and cost,” Raj said.

The authors emphasized, however, that further validation will be necessary before the model can be broadly implemented in routine clinical practice. Future iterations of the framework may incorporate additional clinical measurements where available, including longitudinal MRI imaging, PET scans, genetics, and blood or cerebrospinal fluid biomarkers.

The study highlights the growing role of AI-driven imaging analysis in neurology and suggests that clinically accessible tools such as MRI may eventually support earlier and more scalable prediction of Alzheimer’s disease progression.

The post AI Model Predicts Alzheimer’s Progression from a Single MRI Scan appeared first on Inside Precision Medicine.

Amelioration of tic disorder by Jujuboside A via gut microbiota remodeling and intestinal 5-HT signaling

BackgroundTic disorder (TD) is a common chronic neuropsychiatric condition manifesting during childhood and adolescence. Jujuboside A (JuA) may alleviate TD symptoms; however, the mechanisms underlying its therapeutic effects remain unclear.MethodsWe established a rat model of TD and used histological techniques to evaluate the effects of JuA on pathological changes. We also measured 5-hydroxytryptamine (5-HT) and 5-hydroxyindoleacetic acid (5-HIAA) levels and assessed tryptophan hydroxylase 1 (TPH1) mRNA expression. Finally, we analyzed the gut microbiota composition in fecal samples using 16S rRNA metagenomic sequencing.ResultsJuA administration alleviated pathological changes in rats with TD, increased 5-HT and 5-HIAA levels, and upregulated TPH1 mRNA expression. Compared with no treatment, JuA treatment increased the proportion of Bacteroidia, Muribaculaceae, Bacteroidales, and Bacteroidota, while reducing that of Bacilli, Lactobacillaceae, Lactobacillus, Lactobacillales, and Firmicutes.ConclusionThese findings indicate that JuA mitigates TD progression, potentially by remodeling the gut microbiota and regulating 5-HT levels.

Neurocognitive function among individuals with problematic social media use

BackgroundWith the development of technology and the internet, social networks gained momentum quickly and play a central role in daily activities. Despite this, there is a public health concern over excessive or problematic social media use. There is also a debate whether excessive social media use should be considered as a behavioral addiction characterized by impulsivity or an impulse control disorder characterized by compulsivity. The goal of this study is to use neurocognitive tasks to investigate impulsivity and compulsivity among excessive social media users compared with non-excessive users.MethodThe study included 79 participants (age range 18 to 37), divided into two groups: 34 participants who excessively use social media (Mean Age = 23.03, SD = 2.71) and 45 participants who do not excessively use social media (Mean Age = 25.47, SD = 4.3). Participants filled out a demographic questionnaire, questionnaires on social media use, impulsivity, compulsivity, anxiety, and depression. They performed computerized cognitive tasks: GO/NO-GO (with Facebook and traffic sign pictures), Experimental Delay Discounting (EDT), and the Wisconsin Card Sorting Test (WCST).ResultsExcessive users of social media exhibited a lower ability to delay gratification on the EDT, indicating impulsivity. They made fewer non-perseverative errors on the WCST, which indicated high flexibility and test shifting, which is a contradicting evidence for compulsivity. Furthermore, on the GO/NO-GO task, individuals who excessively use social media made more omission errors in response to the “Facebook” sign compared to traffic signs (GO condition), indicating impaired selective attention. Finally, they also showed higher subjective ratings of anxiety, depression, impulsivity, and compulsivity.DiscussionThe results of this study provide evidence for impulsivity indicated by delay discounting tendency, which supports the behavioral addiction model, impaired selection attention and lack of evidence for compulsivity in excessive social media users. Further research on neurocognitive function in excessive social media users is required in order to determine whether it should be considered a behavioral addiction or an impulse control disorder.

Heatwave-related variations in psychiatric consultations and admissions: a time-series analysis

BackgroundHeatwaves are becoming increasingly frequent and intense across Europe, posing significant risks to physical and mental health. Emerging evidence suggests that prolonged exposure to high temperatures may exacerbate psychiatric symptoms and increase the demand for acute mental health services.ObjectivesThis study examined the relationship between extreme heat events and psychiatric service utilization in Bolzano, Italy, by analyzing emergency psychiatric consultations and acute psychiatric admissions across three non-consecutive years.MethodsA retrospective observational analysis was conducted using daily psychiatric consultations in the Emergency Department (ED) and daily admissions to acute psychiatric wards from 2013, 2018, and 2023. Meteorological data were obtained from the provincial environmental agency. Time-series analyses employed ARIMA models, incorporating daily minimum and maximum temperatures, tropical nights, and a cumulative heatwave index (n_hot_htwv). Model selection was based on BIC, and the effect of exogenous temperature variables was evaluated through changes in AIC. Residual diagnostics guided the inclusion of weekly seasonal dummy variables.ResultsNon-seasonal ARIMA models with day-of-week dummies provided the best fit for both consultations and admissions. Adding the cumulative heatwave variable (n_hot_htwv) consistently improved model fit across all years, whereas minimum and maximum temperatures alone did not. Heatwave duration emerged as a more sensitive predictor of psychiatric service utilization than isolated temperature peaks. No evidence of yearly seasonality was found, and residual diagnostics supported the robustness of models including weekly dummy variables.ConclusionHeatwaves are associated with increased psychiatric consultations and hospital admissions in Bolzano, with cumulative heat exposure representing a critical determinant. These effects cannot be explained solely by seasonal patterns, suggesting an independent climatic influence. Given the projected rise in heatwave intensity and duration, mental health services should incorporate climate-responsive planning and early-warning strategies.
<![CDATA[Computational phenotyping reveals prodromal psychosis clues, connects biomarkers to symptoms, and shows why stress and cannabis reduction matters.]]>

Personalized Pharmaco-Lifestyle Interventions for Severe Mental Illnesses (LIFETRAIN)

Conditions: Severe Mental Illness; Depression / Major Depressive Disorder; Bipolar Disorder (BD); Schizophrenia

Interventions: Drug: Semaglutide (SEMA); Behavioral: Exercise module; Behavioral: Anti-inflammatory diet module; Behavioral: Sleep intervention module; Behavioral: Social prescribing module; Device: Closed-loop transcranial alternating current stimulation (CL-tACS); Behavioral: Structured lifestyle psychoeducation; Device: Sham CL-tACS

Sponsors: Ludwig-Maximilians – University of Munich

Not yet recruiting

Factors Influencing the Initiation and Continued Engagement of Digital Mental Health Tools Among Adults: Theory of Planned Behavior–Informed Systematic Review

Background: Digital mental health tools (DMHTs) offer scalable support, but engagement varies. Understanding the shapes of initiation and ongoing use is essential for effective design and implementation. Objective: This study aims to synthesize determinants of adults’ initiation and engagement with DMHTs, organized through two lenses: (1) psychological factors aligned with the theory of planned behavior (TPB) and (2) design and access features. Methods: A systematic search of 9 databases (June 2025) identified qualitative and mixed methods primary studies reporting end-users’ experiences with DMHTs. Studies were screened and reported in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Quality appraisal used quality assessment with diverse studies (QuADS). Data were synthesized using a framework-guided thematic approach, mapping findings to TPB constructs and complementary design and access domains. Results: A total of 22 studies met inclusion criteria. Findings clustered into 2 interdependent domains. TPB constructs explained how beliefs, social expectations, and perceived control shaped decisions to start and persist with DMHTs. Design and access features frequently acted through these same pathways, especially by altering perceived behavioral control (PBC), with cost, connectivity, device constraints, and time flexibility affecting feasibility, with content design and privacy shaping perceived value and trust. Perceived fit (goals, cultural or linguistic relevance, and routine alignment) consistently influenced both initiation and continuation. Several features operated bidirectionally; depending on context, the same feature could facilitate or hinder engagement. Conclusions: Engagement with DMHTs is jointly determined by users’ beliefs and the design and access conditions within which tools are offered. Implementation should pursue a dual strategy, strengthening willingness to seek support (addressing attitudes, norms, and perceived control) while engineering low-effort, trustworthy, and context-appropriate experiences. Priorities include equity-focused policies (data costs, devices, and connectivity), transparent data practices, co-design with diverse communities, and consistent, theory-informed outcome measures.
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