Disruptions in global network segregation and integration in adolescents and young adults with fetal alcohol spectrum disorder.

BACKGROUND: Fetal alcohol spectrum disorder (FASD) is a significant public health problem that is associated with a broad range of physical, neurocognitive, and behavioral effects resulting from prenatal alcohol exposure (PAE). Magnetic resonance imaging (MRI) has been an important tool for advancing our knowledge of abnormal brain structure and function in individuals with FASD. However, whereas only a small number of studies have applied graph theory-based network analysis to resting-state functional MRI (fMRI) data in individuals with FASD additional research in this area is needed. METHODS: Resting-state fMRI data were collected from adolescent and young adult participants (ages 12-22) with fetal alcohol syndrome (FAS) or alcohol-related neurodevelopmental disorder (ARND) and neurotypically developing controls (CNTRL) from previous studies. Group independent components analysis (gICA) was applied to fMRI data to extract components representing functional brain networks. Functional network connectivity (FNC), measured by Pearson correlation of the average independent component (IC) time series, was analyzed under a graph theory framework to compare network modularity, the average clustering coefficient, characteristic path length, and global efficiency between groups. Cognitive intelligence, measured by the Wechsler Abbreviated Scale of Intelligence (WASI), was compared and correlated to global network measures. RESULTS: Group comparisons revealed significant differences in the average clustering coefficient, characteristic path length, and global efficiency. Modularity was not significantly different between groups. The FAS and ARND groups scored significantly lower than the CNTRL group on Full Scale IQ (FS-IQ) and the Vocabulary subtest, but not the Matrix Reasoning subtest. No significant associations between intelligence and graph theory measures were detected. CONCLUSION: Our results partially agree with previous studies examining global graph theory metrics in children and adolescents with FASD and suggest that the exposure to alcohol during prenatal development leads to disruptions in aspects of functional network segregation and integration.

Integrating Electrochemical Immunosensing and Cell Adhesion Technologies for Cancer Cell Detection and Enumeration.

We have successfully integrated techniques for controlling cell adhesion and performing electrochemical differential pulse voltammetry (DPV) through the use of digitally controlled microfluidics and patterned transparent indium tin oxide electrode arrays to enable rapid and sensitive enumeration of cancer cells in a scalable microscale format. This integrated approach leverages a dual-working electrode (WE) surface to improve the specificity of the detection system. Here, one of the WE surfaces is functionalized with anti-Melanocortin 1 Receptor antibodies specific to melanoma cancer cells, while the other WE acts as a control (i.e., without antibody), for detecting non-specific interactions between cells and the electrode. The method is described and shown to provide effective detection of melanoma cells at concentrations ranging between 25 to 300 cells per 20 µL sample volume after a 5 min incubation and 15 s of DPV measurements. The estimated limit of detection was ~17 cells. The sensitivity and specificity of the assay were quantified using addition of large fractions of non-target cells and resulted in a detection reproducibility of ~97%. The proposed approach demonstrates a unique integration of electrochemical sensing and microfluidic cell adhesion technologies with multiple advantages such as label-free detection, short detection times, and low sample volumes. Next steps for this platform include testing with patient samples and use of other cell-surface biomarkers for detection and enumeration of circulating tumor cells in prostate, breast, and colon cancer.

Moderate Prenatal Alcohol Exposure Alters Functional Connectivity in the Adult Rat Brain.

BACKGROUND: Past studies of moderate prenatal alcohol exposure (PAE) have focused on specific brain regions, neurotransmitter systems, and behaviors. However, the effects of PAE on brain function and behavior are complex and not limited to discrete brain regions. Thus, there is a critical need to understand the global effects of moderate PAE on neural function. A primary aim of this research was to explore the functional relationships in neural activity of spatially distinct areas by applying a widely used computational algorithm-group-independent component analysis (gICA)-to resting-state functional magnetic resonance imaging data from rats exposed to either an alcohol or saccharin control solution via maternal consumption during pregnancy. METHODS: Long-Evans rat dams consumed either 5% (v/v) alcohol or a saccharin control solution throughout gestation. Adult offspring from each prenatal treatment group were anesthetized for functional, structural, and perfusion magnetic resonance-based image acquisition sequences. gICA was applied to the functional data to extract components. To determine connectivity, component time-course correlations were computed and compared. Additionally, spectral power analyses were utilized as an additional measure of functional connectivity. Finally, blood perfusion-assessed by arterial spin labeling-and whole-brain volumetric analyses were evaluated. RESULTS: Analyses revealed 17 components in several brain regions such as the cortex, hippocampus, and thalamus. PAE was associated with reductions in coordinated activity between components, especially in males. PAE was also associated with reductions in low-frequency spectral power, an effect that was more robust in females. Brain volumetric analyses revealed sex-dependent reductions in females while blood flow analyses revealed sex-dependent reductions in males. CONCLUSIONS: Moderate PAE leads to persistent changes in functional connectivity in the absence of whole-brain volume or blood flow measures. Future studies will investigate the relationships between alterations in functional network connectivity and behavior.

Antigen-Dependent Inducible T-Cell Reporter System for PET Imaging of Breast Cancer and Glioblastoma.

For the past several decades, chimeric antigen receptor T-cell therapies have shown promise in the treatment of cancers. These treatments would greatly benefit from companion imaging biomarkers to follow the trafficking of T cells in vivo. Methods: Using synthetic biology, we engineered T cells with a chimeric receptor synthetic intramembrane proteolysis receptor (SNIPR) that induces overexpression of an exogenous reporter gene cassette on recognition of specific tumor markers. We then applied a SNIPR-based PET reporter system to 2 cancer-relevant antigens, human epidermal growth factor receptor 2 (HER2) and epidermal growth factor receptor variant III (EGFRvIII), commonly expressed in breast and glial tumors, respectively. Results: Antigen-specific reporter induction of the SNIPR PET T cells was confirmed in vitro using green fluorescent protein fluorescence, luciferase luminescence, and the HSV-TK PET reporter with 9-(4-18F-fluoro-3-[hydroxymethyl]butyl)guanine ([18F]FHBG). T cells associated with their target antigens were successfully imaged using PET in dual-xenograft HER2+/HER2- and EGFRvIII+/EGFRvIII- animal models, with more than 10-fold higher [18F]FHBG signals seen in antigen-expressing tumors versus the corresponding controls. Conclusion: The main innovation found in this work was PET detection of T cells via specific antigen-induced signals, in contrast to reporter systems relying on constitutive gene expression.

EPAC Regulates Melanoma Growth by Stimulating mTORC1 Signaling and Loss of EPAC Signaling Dependence Correlates with Melanoma Progression.

Exchange proteins directly activated by cAMP (EPAC) belong to a family of RAP guanine nucleotide exchange factors (RAPGEF). EPAC1/2 (RAPGEF3/4) activates RAP1 and the alternative cAMP signaling pathway. We previously showed that the differential growth response of primary and metastatic melanoma cells to cAMP is mediated by EPAC. However, the mechanisms responsible for this differential response to EPAC signaling are not understood. In this study, we show that pharmacologic inhibition or siRNA-mediated knockdown of EPAC selectively inhibits the growth and survival of primary melanoma cells by downregulation of cell-cycle proteins and inhibiting the cell-cycle progression independent of ERK1/2 phosphorylation. EPAC inhibition results in upregulation of AKT phosphorylation but a downregulation of mTORC1 activity and its downstream effectors. We also show that EPAC regulates both glycolysis and oxidative phosphorylation, and production of mitochondrial reactive oxygen species, preferentially in primary melanoma cells. Employing a series of genetically matched primary and lymph node metastatic (LNM) melanoma cells, and distant organ metastatic melanoma cells, we show that the LNM and metastatic melanoma cells become progressively less responsive and refractory to EPAC inhibition suggesting loss of dependency on EPAC signaling correlates with melanoma progression. Analysis of The Cancer Genome Atlas dataset showed that lower RAPGEF3, RAPGEF4 mRNA expression in primary tumor is a predictor of better disease-free survival of patients diagnosed with primary melanoma suggesting that EPAC signaling facilitates tumor progression and EPAC is a useful prognostic marker. These data highlight EPAC signaling as a potential target for prevention of melanoma progression. IMPLICATIONS: This study establishes loss of dependency on EPAC-mTORC1 signaling as hallmark of primary melanoma evolution and targeting this escape mechanism is a promising strategy for metastatic melanoma.

Detection of prenatal alcohol exposure using machine learning classification of resting-state functional network connectivity data.

Fetal Alcohol Spectrum Disorder (FASD), a wide range of physical and neurobehavioral abnormalities associated with prenatal alcohol exposure (PAE), is recognized as a significant public health concern. Advancements in the diagnosis of FASD have been hindered by a lack of consensus in diagnostic criteria and limited use of objective biomarkers. Previous research from our group utilized resting-state functional magnetic resonance imaging (fMRI) to measure functional network connectivity (FNC), which revealed several sex- and region-dependent alterations in FNC as a result of moderate PAE relative to controls. Considering that FNC is sensitive to moderate PAE, this study explored the use of FNC data and machine learning methods to detect PAE among a sample of rodents exposed to alcohol prenatally and controls. We utilized previously acquired resting state fMRI data collected from adult rats exposed to moderate levels of prenatal alcohol (PAE) or a saccharin control solution (SAC) to assess FNC of resting state networks extracted by spatial group independent component analysis (GICA). FNC data were subjected to binary classification using support vector machine (SVM) -based algorithms and leave-one-out-cross validation (LOOCV) in an aggregated sample of males and females (n = 48; 12 male PAE, 12 female PAE, 12 male SAC, 12 female SAC), a males-only sample (n = 24; 12 PAE, 12 SAC), and a females-only sample (n = 24; 12 PAE, 12 SAC). Results revealed that a quadratic SVM (QSVM) kernel was significantly effective for PAE detection in females. QSVM kernel-based classification resulted in accuracy rates of 62.5% for all animals, 58.3% for males, and 79.2% for females. Additionally, qualitative evaluation of QSVM weights implicates an overarching theme of several hippocampal and cortical networks in contributing to the formation of correct classification decisions by QSVM. Our results suggest that binary classification using QSVM and adult female FNC data is a potential candidate for the translational development of novel and non-invasive techniques for the identification of FASD.

Highly selective recovery of medium chain carboxylates from co-fermented organic wastes using anion exchange with carbon dioxide expanded methanol desorption.

The aim of this work was to recover a mixture of carboxylates ranging from 2 to 7 carbon atoms using a strong anion exchange resin, followed by desorption with CO2-expanded methanol. Medium chain carboxylates hexanoate and heptanoate adsorbed better than acetate, and the corresponding medium chain carboxylic acids desorbed easier than acetic acid. Consequently, hexanoate and heptanoate were concentrated up to 14.6 and 20.7 times, respectively. These findings will enable effective separation and purification of the produced carboxylic acids. Notably, the presence of inorganic ions in the sample, such as chloride, decreased the adsorption affinity compared to a synthetic mixture only of carboxylates.