Urine Metabolomic Profiling and Machine Learning in Autism Spectrum Disorder Diagnosis: Toward Precision Treatment.

Background: Autism spectrum disorder (ASD) diagnosis traditionally relies on behavioral assessments, which can be subjective and often lead to delayed identification. Recent advances in metabolomics and machine learning offer promising alternatives for more objective and precise diagnostic approaches.

Methods: First-morning urine samples were collected from 52 children (32 with ASD and 20 neurotypical controls), aged 5.

ADNP is essential for sex-dependent hippocampal neurogenesis, through male unfolded protein response and female mitochondrial gene regulation.

Essential for brain formation and protective against tauopathy, activity-dependent neuroprotective protein (ADNP) is critical for neurogenesis and cognitive functions, while regulating steroid hormone biogenesis. As such, de novo mutations in ADNP lead to syndromic autism and somatic ADNP mutations parallel Alzheimer's disease progression. Furthermore, clinical trials with the ADNP fragment NAP (the investigational drug davunetide) showed efficacy in women suffering from the tauopathy progressive supranuclear palsy and differentially boosted memory in men (spatial) and women (verbal), exhibiting prodromal Alzheimer's disease.

Sex-Specific ADNP/NAP (Davunetide) Regulation of Cocaine-Induced Plasticity.

Cocaine use disorder (CUD) is a chronic neuropsychiatric disorder estimated to effect 1-3% of the population. Activity-dependent neuroprotective protein (ADNP) is essential for brain development and functioning, shown to be protective in fetal alcohol syndrome and to regulate alcohol consumption in adult mice. The goal of this study was to characterize the role of ADNP, and its active peptide NAP (NAPVSIPQ), which is also known as davunetide (investigational drug) in mediating cocaine-induced neuroadaptations.

Longitudinal Genotype-Phenotype (Vineland Questionnaire) Characterization of 15 ADNP Syndrome Cases Highlights Mutated Protein Length and Structural Characteristics Correlation with Communicative Abilities Accentuated in Males.

Activity-dependent neuroprotective protein (ADNP) is essential for neurodevelopment and de novo mutations in ADNP cause the ADNP syndrome. From brain pathologies point of view, tauopathy has been demonstrated at a young age, implying stunted development coupled with early/accelerated neurodegeneration. Given potential genotype-phenotype differences and age-dependency, we have assessed here a cohort of 15 individuals (1-27-year-old), using 1-3 longitudinal parent (caretaker) interview/s (Vineland 3 questionnaire) over several years.

Understanding Type 2 Diabetes Mellitus Risk Parameters through Intermittent Fasting: A Machine Learning Approach.

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder characterized by elevated blood glucose levels. Despite the availability of pharmacological treatments, dietary plans, and exercise regimens, T2DM remains a significant global cause of mortality. As a result, there is an increasing interest in exploring lifestyle interventions, such as intermittent fasting (IF).

NAP (Davunetide): The Neuroprotective ADNP Drug Candidate Penetrates Cell Nuclei Explaining Pleiotropic Mechanisms.

(1) Background: Recently, we showed aberrant nuclear/cytoplasmic boundaries/activity-dependent neuroprotective protein (ADNP) distribution in ADNP-mutated cells. This malformation was corrected upon neuronal differentiation by the ADNP-derived fragment drug candidate NAP (davunetide). Here, we investigated the mechanism of NAP nuclear protection.

A novel davunetide (NAPVSIPQQ to NAPVSIPQE) point mutation in activity-dependent neuroprotective protein (ADNP) causes a mild developmental syndrome.

NAP (NAPVSIPQ, drug candidate name, davunetide) is the neuroprotective fragment of activity-dependent neuroprotective protein (ADNP). Recent studies identified NAPVSIP as a Src homology 3 (SH3) domain-ligand association site, responsible for controlling signalling pathways regulating the cytoskeleton. Furthermore, the SIP motif in NAP/ADNP was identified as crucial for direct microtubule end-binding protein interaction facilitating microtubule dynamics and Tau microtubule interaction, at the microtubule end-binding protein site EB1 and EB3.

SH3- and actin-binding domains connect ADNP and SHANK3, revealing a fundamental shared mechanism underlying autism.

De novo heterozygous mutations in activity-dependent neuroprotective protein (ADNP) cause autistic ADNP syndrome. ADNP mutations impair microtubule (MT) function, essential for synaptic activity. The ADNP MT-associating fragment NAPVSIPQ (called NAP) contains an MT end-binding protein interacting domain, SxIP (mimicking the active-peptide, SKIP).

OnTheFly: a database of Drosophila melanogaster transcription factors and their binding sites.

We present OnTheFly (http://bhapp.c2b2.columbia.

Predicting nucleic acid binding interfaces from structural models of proteins.

The function of DNA- and RNA-binding proteins can be inferred from the characterization and accurate prediction of their binding interfaces. However, the main pitfall of various structure-based methods for predicting nucleic acid binding function is that they are all limited to a relatively small number of proteins for which high-resolution three-dimensional structures are available. In this study, we developed a pipeline for extracting functional electrostatic patches from surfaces of protein structural models, obtained using the I-TASSER protein structure predictor.

From face to interface recognition: a differential geometric approach to distinguish DNA from RNA binding surfaces.

Protein nucleic acid interactions play a critical role in all steps of the gene expression pathway. Nucleic acid (NA) binding proteins interact with their partners, DNA or RNA, via distinct regions on their surface that are characterized by an ensemble of chemical, physical and geometrical properties. In this study, we introduce a novel methodology based on differential geometry, commonly used in face recognition, to characterize and predict NA binding surfaces on proteins.

Classifying RNA-binding proteins based on electrostatic properties.

Protein structure can provide new insight into the biological function of a protein and can enable the design of better experiments to learn its biological roles. Moreover, deciphering the interactions of a protein with other molecules can contribute to the understanding of the protein's function within cellular processes. In this study, we apply a machine learning approach for classifying RNA-binding proteins based on their three-dimensional structures.

Patch Finder Plus (PFplus): a web server for extracting and displaying positive electrostatic patches on protein surfaces.

Positively charged electrostatic patches on protein surfaces are usually indicative of nucleic acid binding interfaces. Interestingly, many proteins which are not involved in nucleic acid binding possess large positive patches on their surface as well. In some cases, the positive patches on the protein are related to other functional properties of the protein family.