Comparative analyses of the Smith-Magenis syndrome protein RAI1 in mice and common marmoset monkeys.

Retinoic acid-induced 1 (RAI1) encodes a transcriptional regulator critical for brain development and function. RAI1 haploinsufficiency in humans causes a syndromic autism spectrum disorder known as Smith-Magenis syndrome (SMS). The neuroanatomical distribution of RAI1 has not been quantitatively analyzed during the development of the prefrontal cortex, a brain region critical for cognitive function and social behaviors and commonly implicated in autism spectrum disorders, including SMS. Here, we performed comparative analyses to uncover the evolutionarily convergent and divergent expression profiles of RAI1 in major cell types during prefrontal cortex maturation in common marmoset monkeys (Callithrix jacchus) and mice (Mus musculus). We found that while RAI1 in both species is enriched in neurons, the percentage of excitatory neurons that express RAI1 is higher in newborn mice than in newborn marmosets. By contrast, RAI1 shows similar neural distribution in adult marmosets and adult mice. In marmosets, RAI1 is expressed in several primate-specific cell types, including intralaminar astrocytes and MEIS2-expressing prefrontal GABAergic neurons. At the molecular level, we discovered that RAI1 forms a protein complex with transcription factor 20 (TCF20), PHD finger protein 14 (PHF14), and high mobility group 20A (HMG20A) in the marmoset brain. In vitro assays in human cells revealed that TCF20 regulates RAI1 protein abundance. This work demonstrates that RAI1 expression and protein interactions are largely conserved but with some unique expression in primate-specific cells. The results also suggest that altered RAI1 abundance could contribute to disease features in disorders caused by TCF20 dosage imbalance.

Smith-Magenis syndrome protein RAI1 regulates body weight homeostasis through hypothalamic BDNF-producing neurons and neurotrophin downstream signalling.

Retinoic acid-induced 1 (RAI1) haploinsufficiency causes Smith-Magenis syndrome (SMS), a genetic disorder with symptoms including hyperphagia, hyperlipidemia, severe obesity, and autism phenotypes. RAI1 is a transcriptional regulator with a pan-neural expression pattern and hundreds of downstream targets. The mechanisms linking neural Rai1 to body weight regulation remain unclear. Here we find that hypothalamic brain-derived neurotrophic factor (BDNF) and its downstream signalling are disrupted in SMS (Rai1+/-) mice. Selective Rai1 loss from all BDNF-producing cells or from BDNF-producing neurons in the paraventricular nucleus of the hypothalamus (PVH) induced obesity in mice. Electrophysiological recordings revealed that Rai1 ablation decreased the intrinsic excitability of PVHBDNF neurons. Chronic treatment of SMS mice with LM22A-4 engages neurotrophin downstream signalling and delayed obesity onset. This treatment also partially rescued disrupted lipid profiles, insulin intolerance, and stereotypical repetitive behaviour in SMS mice. These data argue that RAI1 regulates body weight and metabolic function through hypothalamic BDNF-producing neurons and that targeting neurotrophin downstream signalling might improve associated SMS phenotypes.

Genetic diversity, cholesterol reduction, and presence of conserved bile salt hydrolase gene in probiotic strains from human milk.

Probiotics are known to possess strain- and species-specific functional properties, of which hypocholesteremia is of major interest. Bile salt hydrolase (BSH) activity is one of the key mechanisms involved in the hypocholesterolemic effect. The study was designed to genetically characterize probiotics obtained from human milk on the basis of simple sequence repeat (SSR), isolate potent hypocholesterolemic strains, and detect BSH activity, deconjugation of bile salts, and bsh polymorphism. This study, for the first time, linked genetic diversity with cholesterol reduction potential and proved the presence of conserved bsh of Levilactobacillus brevis in genetically diverse species. The strains displayed 2.78%-42.23% cholesterol reduction, which was not influenced by prebiotics. In this study, data obtained from SSR markers indicated 93.3% diversity, and based on cluster analysis, they were distributed into XI clades; out of five potent cholesterol-reducing strains, three belonged to clade I. The strains could deconjugate both sodium glycocholate and sodium taurocholate, but we preferred using sodium glycocholate. The variation in cholesterol reduction potential and BSH activity pointed toward the presence of more than one bsh in the strains. Weissella confusa MW051433 displayed highest cholesterol reduction (42.23%) and specific BSH activity (2.64 U ml -1). Search for other bsh and in vivo assessments of cholesterol reduction by W. confusa MW051433 have been proposed.

rAAV-CRISPRa therapy corrects Rai1 haploinsufficiency and rescues selective disease features in Smith-Magenis syndrome mice.

Haploinsufficiency in retinoic acid induced 1 (RAI1) causes Smith-Magenis syndrome (SMS), a severe neurodevelopmental disorder characterized by neurocognitive deficits and obesity. Currently, curative treatments for SMS do not exist. Here, we take a recombinant adeno-associated virus (rAAV)-clustered regularly interspaced short palindromic repeats activation (CRISPRa) approach to increase expression of the remaining intact Rai1 allele. Building upon our previous work that found the paraventricular nucleus of hypothalamus plays a central role in SMS pathogenesis, we performed paraventricular nucleus of hypothalamus-specific rAAV-CRISPRa therapy by increasing endogenous Rai1 expression in SMS (Rai1±) mice. We found that rAAV-CRISPRa therapy rescues excessive repetitive behavior, delays the onset of obesity, and partially reduces hyperphagia in SMS mice. Our work provides evidence that rAAV-CRISPRa therapy during early adolescence can boost the expression of healthy Rai1 allele and modify disease progression in a mouse model of Smith-Magenis syndrome.

Risk prediction of pancreatic cancer using AI analysis of pancreatic subregions in computed tomography images.

Early detection of Pancreatic Ductal Adenocarcinoma (PDAC) is complicated as PDAC remains asymptomatic until cancer advances to late stages when treatment is mostly ineffective. Stratifying the risk of developing PDAC can improve early detection as subsequent screening of high-risk individuals through specialized surveillance systems reduces the chance of misdiagnosis at the initial stage of cancer. Risk stratification is however challenging as PDAC lacks specific predictive biomarkers. Studies reported that the pancreas undergoes local morphological changes in response to underlying biological evolution associated with PDAC development. Accurate identification of these changes can help stratify the risk of PDAC. In this retrospective study, an extensive radiomic analysis of the precancerous pancreatic subregions was performed using abdominal Computed Tomography (CT) scans. The analysis was performed using 324 pancreatic subregions identified in 108 contrast-enhanced abdominal CT scans with equal proportion from healthy control, pre-diagnostic, and diagnostic groups. In a pairwise feature analysis, several textural features were found potentially predictive of PDAC. A machine learning classifier was then trained to perform risk prediction of PDAC by automatically classifying the CT scans into healthy control (low-risk) and pre-diagnostic (high-risk) classes and specifying the subregion(s) likely to develop a tumor. The proposed model was trained on CT scans from multiple phases. Whereas using 42 CT scans from the venous phase, model validation was performed which resulted in ~89.3% classification accuracy on average, with sensitivity and specificity reaching 86% and 93%, respectively, for predicting the development of PDAC (i.e., high-risk). To our knowledge, this is the first model that unveiled microlevel precancerous changes across pancreatic subregions and quantified the risk of developing PDAC. The model demonstrated improved prediction by 3.3% in comparison to the state-of-the-art method that considers the global (whole pancreas) features for PDAC prediction.

Segmentation of Pancreatic Subregions in Computed Tomography Images.

The accurate segmentation of pancreatic subregions (head, body, and tail) in CT images provides an opportunity to examine the local morphological and textural changes in the pancreas. Quantifying such changes aids in understanding the spatial heterogeneity of the pancreas and assists in the diagnosis and treatment planning of pancreatic cancer. Manual outlining of pancreatic subregions is tedious, time-consuming, and prone to subjective inconsistency. This paper presents a multistage anatomy-guided framework for accurate and automatic 3D segmentation of pancreatic subregions in CT images. Using the delineated pancreas, two soft-label maps were estimated for subregional segmentation-one by training a fully supervised naïve Bayes model that considers the length and volumetric proportions of each subregional structure based on their anatomical arrangement, and the other by using the conventional deep learning U-Net architecture for 3D segmentation. The U-Net model then estimates the joint probability of the two maps and performs optimal segmentation of subregions. Model performance was assessed using three datasets of contrast-enhanced abdominal CT scans: one public NIH dataset of the healthy pancreas, and two datasets D1 and D2 (one for each of pre-cancerous and cancerous pancreas). The model demonstrated excellent performance during the multifold cross-validation using the NIH dataset, and external validation using D1 and D2. To the best of our knowledge, this is the first automated model for the segmentation of pancreatic subregions in CT images. A dataset consisting of reference anatomical labels for subregions in all images of the NIH dataset is also established.

Artificial intelligence and imaging for risk prediction of pancreatic cancer: a narrative review.

OBJECTIVE: To emphasize the importance of pancreatic imaging and the application of artificial intelligence (AI) for enhanced risk prediction of pancreatic ductal adenocarcinoma (PDAC). BACKGROUND: Detecting PDAC at the early stage is challenging as the disease either remains asymptomatic or presents nonspecific symptoms. Risk prediction of PDAC is an efficient strategy as subsequent targeted screening can assist in diagnosing cancer at the early stage even before the symptoms appear. However, the lack of specific clinical and epidemiological predictors of PDAC makes prediction a highly challenging task. Detecting precursor changes in the pancreas can potentially assist in the risk prediction of PDAC as the precancerous pancreas evolves through biological adaptations-presented as morphological and textural changes on abdominal imaging. However, such microlevel "clues" usually remain unnoticed or unappreciated, partly due to the unavailability of tools to detect and interpret such complex measurements, making the risk prediction of PDAC an unresolved problem. METHODS: This review study highlights the limitations of the current risk prediction models of PDAC and the importance of abdominal imaging for predicting PDAC. A suggestive narrative is made as to how recent AI tools can assist in extracting precise measurements of biomarkers, detecting early signs and precancerous abnormalities, quantifying tissue characteristics, and revealing complex features potentially indicative of future incidence of pancreatic cancer (PC) using abdominal imaging. With the help of peer examples of other cancers, a case is built about the application of AI in utilizing image features of the pancreas to enhance risk prediction of PDAC. Furthermore, the challenges of AI applications including insufficient data for model training, risk of data privacy violation, inconsistent data labeling, and limited computational resources, and their potential solutions are also discussed. CONCLUSIONS: The recent advancement in the domain of AI is a potential opportunity to utilize automated tools for the identification of imaging-based indicators of PDAC and perform enhanced risk prediction of cancer. With this awareness and motivation, better management of PDAC has expected.

Temporal dissection of Rai1 function reveals brain-derived neurotrophic factor as a potential therapeutic target for Smith-Magenis syndrome.

Haploinsufficiency of retinoic acid-induced 1 (RAI1) is responsible for Smith-Magenis syndrome (SMS), a childhood neurodevelopmental disorder associated with hyperphagia, obesity and autistic features. We previously showed that constitutive inactivation of one or both copies of Rai1 in the germline or developing brain induces SMS-like neurobehavioral deficits and obesity in mice. By contrast, the postnatal function of Rai1 is unclear. Here, we globally deleted one or both copies of Rai1 during two postnatal developmental windows by generating an inducible Rai1 knockout mouse model. We found that delayed Rai1 deletion at 3 or 8 weeks of age had no effect on neurobehavioral functions but resulted in adult-onset obesity and decreased expression of brain-derived neurotrophic factor (Bdnf) in the hypothalamus. Remarkably, genetic overexpression of human Bdnf in Rai1 heterozygous mice reversed SMS-like obesity, hyperphagia, metabolic syndrome-like features and hyposociability. Increasing Bdnf signaling in the paraventricular nucleus of the hypothalamus or the ventromedial nucleus of the hypothalamus was sufficient to mediate the anti-obesity effect. Our work identifies the function of Rai1 in different temporal windows after birth and provides in vivo evidence that increasing Bdnf signaling is therapeutically effective in a preclinical mouse model of SMS.

Dosage-sensitive genes in autism spectrum disorders: From neurobiology to therapy.

Autism spectrum disorders (ASDs) are a group of heterogenous neurodevelopmental disorders affecting 1 in 59 children. Syndromic ASDs are commonly associated with chromosomal rearrangements or dosage imbalance involving a single gene. Many of these genes are dosage-sensitive and regulate transcription, protein homeostasis, and synaptic function in the brain. Despite vastly different molecular perturbations, syndromic ASDs share core symptoms including social dysfunction and repetitive behavior. However, each ASD subtype has a unique pathogenic mechanism and combination of comorbidities that require individual attention. We have learned a great deal about how these dosage-sensitive genes control brain development and behaviors from genetically-engineered mice. Here we describe the clinical features of eight monogenic neurodevelopmental disorders caused by dosage imbalance of four genes, as well as recent advances in using genetic mouse models to understand their pathogenic mechanisms and develop intervention strategies. We propose that applying newly developed quantitative molecular and neuroscience technologies will advance our understanding of the unique neurobiology of each disorder and enable the development of personalized therapy.

Antimicrobial susceptibility of organisms isolated from sputum culture of Karachi, Pakistan.

Our study aims to contribute to developing antibiotics prescription guidelines at a national and a regional level directed by the antibiotics susceptibility patterns and rapidly emerging resistant organisms. This study is designed to observe the antimicrobial susceptibility in sputum culture isolates and drug resistance patterns against various antimicrobials. This was a retrospective cohort study; data was collected from two laboratories from 1st Jan to 15 July 2007. All laboratory reports were analyzed using SPSS version 19.0.The sputum culture was found positive for microbial growth in 217 reports out of 864 total (25.11%). The leading organisms were 25.8% Klebsiella pneumoniae, 23.5% Streptococcus pneumoniae, 18% Pseudomonas aeruginosa and 12.9% Staphylococcus aureus. S.pneumoniae and S.aureus were sensitive to the combination of beta lactam antibiotics and anti-beta lactamase while K.pneumoniae and P. aureginosa were susceptible to fluoroquinolones, macrolides and aminoglycosides. The total yield of sputum culture was 25.11%. The gram positive cocci which were isolated were mostly resistant to beta lactam antibiotics alone i.e. only 45% of S. pneumoniae and 33% of S. aureus were sensitive to Amoxicillin alone. Sensitivity to Co-amoxiclav was still high. 90% and 85% of S. pneumonia and S. aureus respectively were sensitive to Co-amoxiclav. The P. aeruginosa resistance to Amikacin in our study is 10%.

Resveratrol and Alzheimer's Disease: Mechanistic Insights.

Alzheimer's disease (AD) is the leading cause of dementia in the elderly and is characterized by progressive cognitive and memory deficits. The pathological hallmarks of AD include extracellular senile plaques and intracellular neurofibrillary tangles. Although several mechanisms have been used to explain the underlying pathogenesis of AD, current treatment regimens remain inadequate. The neuroprotective effects of the polyphenolic stilbene resveratrol (3,5,4'-trihydroxy-trans-stilbene) have been investigated in several in vitro and in vivo models of AD. The current review discusses the multiple potential mechanisms of action of resveratrol on the pathobiology of AD. Moreover, due to the limited pharmacokinetic parameters of resveratrol, multiple strategies aimed at increasing the bioavailability of resveratrol have also been addressed.