A Gene Network Perspective on Axonal Regeneration

The regenerative capacity of injured neurons in the central nervous system is limited due to the absence of a robust neuron-intrinsic injury-induced gene response that supports axon regeneration. In peripheral neurons axotomy induces a large cohort of regeneration-associated genes (RAGs). The forced expression of some of these RAGs in injured neurons has some beneficial effect on axon regeneration, but the reported effects are rather small. Transcription factors (TFs) provide a promising class of RAGs. TFs are hubs in the regeneration-associated gene network, and potentially control the coordinate expression of many RAGs simultaneously. Here we discuss the use of combined experimental and computational methods to identify novel regeneration-associated TFs with a key role in initiating and maintaining the RAG-response in injured neurons. We propose that a relatively small number of hub TFs with multiple functional connections in the RAG network might provide attractive new targets for gene-based and/or pharmacological approaches to promote axon regeneration in the central nervous system

Somatic TARDBP variants as a cause of semantic dementia

The aetiology of late-onset neurodegenerative diseases is largely unknown. Here we investigated whether de novo somatic variants for semantic dementia can be detected, thereby arguing for a more general role of somatic variants in neurodegenerative disease. Semantic dementia is characterized by a non-familial occurrence, early onset (<65 years), focal temporal atrophy and TDP-43 pathology. To test whether somatic variants in neural progenitor cells during brain development might lead to semantic dementia, we compared deep exome sequencing data of DNA derived from brain and blood of 16 semantic dementia cases. Somatic variants observed in brain tissue and absent in blood were validated using amplicon sequencing and digital PCR. We identified two variants in exon one of the TARDBP gene (L41F and R42H) at low level (1-3%) in cortical regions and in dentate gyrus in two semantic dementia brains, respectively. The pathogenicity of both variants is supported by demonstrating impaired splicing regulation of TDP-43 and by altered subcellular localization of the mutant TDP-43 protein. These findings indicate that somatic variants may cause semantic dementia as a non-hereditary neurodegenerative disease, which might be exemplary for other late-onset neurodegenerative disorders

FADS2 Genetic Variance in Combination with Fatty Acid Intake Might Alter Composition of the Fatty Acids in Brain

Multiple lines of evidence suggest that fatty acids (FA) play an important role in cognitive function. However, little is known about the functional genetic pathways involved in cognition. The main goals of this study were to replicate previously reported interaction effects between breast feeding (BF) and FA desaturase (FADS) genetic variation on IQ and to investigate the possible mechanisms by which these variants might moderate BF effect, focusing on brain expression. Using a sample of 534 twins, we observed a trend in the moderation of BF effects on IQ by FADS2 variation. In addition, we made use of publicly available gene expression databases from both humans (193) and mice (93) and showed that FADS2 variants also correlate with FADS1 brain expression (P-value<1.1E-03). Our results provide novel clues for the understanding of the genetic mechanisms regulating FA brain expression and improve the current knowledge of the FADS moderation effect on cognition

LLM3D: a log-linear modeling-based method to predict functional gene regulatory interactions from genome-wide expression data

All cellular processes are regulated by condition-specific and time-dependent interactions between transcription factors and their target genes. While in simple organisms, e.g. bacteria and yeast, a large amount of experimental data is available to support functional transcription regulatory interactions, in mammalian systems reconstruction of gene regulatory networks still heavily depends on the accurate prediction of transcription factor binding sites. Here, we present a new method, log-linear modeling of 3D contingency tables (LLM3D), to predict functional transcription factor binding sites. LLM3D combines gene expression data, gene ontology annotation and computationally predicted transcription factor binding sites in a single statistical analysis, and offers a methodological improvement over existing enrichment-based methods. We show that LLM3D successfully identifies novel transcriptional regulators of the yeast metabolic cycle, and correctly predicts key regulators of mouse embryonic stem cell self-renewal more accurately than existing enrichment-based methods. Moreover, in a clinically relevant in vivo injury model of mammalian neurons, LLM3D identified peroxisome proliferator-activated receptor γ (PPARγ) as a neuron-intrinsic transcriptional regulator of regenerative axon growth. In conclusion, LLM3D provides a significant improvement over existing methods in predicting functional transcription regulatory interactions in the absence of experimental transcription factor binding data