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The developing fetus and neonate are highly sensitive to maternal environment. Besides the well-documented effects of maternal stress, nutrition and infections, maternal mutations, by altering the fetal, perinatal and/or early postnatal environment, can impact the behavior of genetically normal offspring. Mutation/premutation in the X linked FMR1 (encoding the translational regulator FMRP) in females, although primarily responsible for causing fragile X syndrome (FXS) in their children, may also elicit such maternal effects. We demonstrated in mice that a deficit in maternal FMRP results in hyperactivity in the genetically normal offspring. To test if maternal FMRP has a broader intergenerational effect, we measured social behavior, a core dimension of neurodevelopmental disorders, in males of FMRP-deficient dams. We found that male offspring of Fmr1(+/−) mothers, independent of their own Fmr1 genotype, exhibit increased approach and reduced avoidance toward conspecific strangers, reminiscent of “indiscriminate friendliness”, or the lack of stranger anxiety, diagnosed in neglected children and in patients with Asperger’s and Williams syndrome. Furthermore, social interaction failed to activate mesolimbic/amygdala regions, encoding social aversion, in these mice, providing a neurobiological basis for the behavioral abnormality. This work identifies a novel role for FMRP that extends its function beyond the well-established genetic function into intergenerational non-genetic inheritance/programming of social behavior and the corresponding neuronal circuit. As FXS premutation and some psychiatric conditions that can be associated with reduced FMRP expression are far more prevalent in mothers than full FMR1 mutation, our findings potentially broaden the significance of FMRP-dependent programming of social behavior beyond the FXS population.

Na+-coupled solute transport is crucial for the uptake of nutrients and metabolic precursors, such as myo-inositol, an important osmolyte and precursor for various cell signaling molecules. We found that various solute transporters and potassium channel subunits formed complexes and reciprocally regulated each other in vitro and in vivo. Global metabolite profiling revealed that mice lacking KCNE2, a K+ channel β subunit, showed a reduction in myo-inositol concentration in cerebrospinal fluid (CSF) but not in serum. Increased behavioral responsiveness to stress and seizure susceptibility in Kcne2-/- mice were alleviated by injections of myo-inositol. Suspecting a defect in myo-inositol transport, we found that KCNE2 and KCNQ1, a voltage-gated potassium channel a subunit, colocalized and coimmunoprecipitated with SMIT1, a Na+-coupled myo-inositol transporter, in the choroid plexus epithelium. Heterologous coexpression demonstrated that myo-inositol transport by SMIT1 was augmented by coexpression of KCNQ1 but was inhibited by coexpression of both KCNQ1 and KCNE2, which form a constitutively active, heteromeric K+ channel. SMIT1 and the related transporter SMIT2 were also inhibited by a constitutively active mutant form of KCNQ1. The activities of KCNQ1 and KCNQ1-KCNE2 were augmented by SMIT1 and the glucose transporter SGLT1 but were suppressed by SMIT2. Channel-transporter signaling complexes may be a widespread mechanism to facilitate solute transport and electrochemical crosstalk.

Fused in sarcoma (FUS) and TAR DNA-binding protein 43 (TDP-43) are RNA-binding proteins pathogenetically linked to amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD), but it is not known if they regulate the same transcripts. We addressed this question using crosslinking and immunoprecipitation (iCLIP) in mouse brain, which showed that FUS binds along the whole length of the nascent RNA with limited sequence specificity to GGU and related motifs. A saw-tooth binding pattern in long genes demonstrated that FUS remains bound to pre-mRNAs until splicing is completed. Analysis of FUS(-/-) brain demonstrated a role for FUS in alternative splicing, with increased crosslinking of FUS in introns around the repressed exons. We did not observe a significant overlap in the RNA binding sites or the exons regulated by FUS and TDP-43. Nevertheless, we found that both proteins regulate genes that function in neuronal development.

The sequencing of the human genome and the genomes of several model organisms is the first step toward the long-term objective of genetic research: the identification of all genes, and the discovery of their functions and mutual interactions. This article presents a methodology and a computer program called GenePath to support the discovery of gene function. GenePath uses mutant data and available genetic knowledge to identify potential genetic pathways. Several pilot applications based on experimental results from Dictyostelium and C. elegans confirmed the usefulness of the proposed schema. Our results suggest that GenePath is a valuable tool that can be used as an intelligent assistant to support genetic reasoning.

16 1 1 2 ID: 10225343 Accession Number: 10225343. Language: English. Date Revised: 20041117. Date Created: 19990723. Date Completed: 19990723. Update Code: 20161213. Publication Type: Editorial. Journal ID: 8915031. Publication Model: Print. Cited Medium: Print. NLM ISO Abbr: Artif Intell Med. Linking ISSN: 09333657. Subset: IM Date of Electronic Publication: 19990501. Current Imprints: Publication: Amsterdam : Elsevier Science Publishing Original Imprints: Publication: Tecklenburg, Federal Republic of Germany : Burgverlag, c1989

Orange is a machine learning and data mining suite for data analysis through Python scripting and visual programming. Here we report on the scripting part, which features interactive data analysis and component-based assembly of data mining procedures. In the selection and design of components, we focus on the flexibility of their reuse: our principal intention is to let the user write simple and clear scripts in Python, which build upon C++ implementations of computationally-intensive tasks. Orange is intended both for experienced users and programmers, as well as for students of data mining.

BACKGROUND: Personalized, precision, P4, or stratified medicine is understood as a medical approach in which patients are stratified based on their disease subtype, risk, prognosis, or treatment response using specialized diagnostic tests. The key idea is to base medical decisions on individual patient characteristics, including molecular and behavioral biomarkers, rather than on population averages. Personalized medicine is deeply connected to and dependent on data science, specifically machine learning (often named Artificial Intelligence in the mainstream media). While during recent years there has been a lot of enthusiasm about the potential of 'big data' and machine learning-based solutions, there exist only few examples that impact current clinical practice. The lack of impact on clinical practice can largely be attributed to insufficient performance of predictive models, difficulties to interpret complex model predictions, and lack of validation via prospective clinical trials that demonstrate a clear benefit compared to the standard of care. In this paper, we review the potential of state-of-the-art data science approaches for personalized medicine, discuss open challenges, and highlight directions that may help to overcome them in the future. CONCLUSIONS: There is a need for an interdisciplinary effort, including data scientists, physicians, patient advocates, regulatory agencies, and health insurance organizations. Partially unrealistic expectations and concerns about data science-based solutions need to be better managed. In parallel, computational methods must advance more to provide direct benefit to clinical practice.