RNA epitranscriptomics dysregulation: A major determinant for significantly increased risk of ASD pathogenesis.

Autism spectrum disorders (ASDs) are perhaps the most severe, intractable and challenging child psychiatric disorders. They are complex, pervasive and highly heterogeneous and depend on multifactorial neurodevelopmental conditions. Although the pathogenesis of autism remains unclear, it revolves around altered neurodevelopmental patterns and their implications for brain function, although these cannot be specifically linked to symptoms. While these affect neuronal migration and connectivity, little is known about the processes that lead to the disruption of specific laminar excitatory and inhibitory cortical circuits, a key feature of ASD. It is evident that ASD has multiple underlying causes and this multigenic condition has been considered to also dependent on epigenetic effects, although the exact nature of the factors that could be involved remains unclear. However, besides the possibility for differential epigenetic markings directly affecting the relative expression levels of individual genes or groups of genes, there are at least three mRNA epitranscriptomic mechanisms, which function cooperatively and could, in association with both genotypes and environmental conditions, alter spatiotemporal proteins expression patterns during brain development, at both quantitative and qualitative levels, in a tissue-specific, and context-dependent manner. As we have already postulated, sudden changes in environmental conditions, such as those conferred by maternal inflammation/immune activation, influence RNA epitranscriptomic mechanisms, with the combination of these processes altering fetal brain development. Herein, we explore the postulate whereby, in ASD pathogenesis, RNA epitranscriptomics might take precedence over epigenetic modifications. RNA epitranscriptomics affects real-time differential expression of receptor and channel proteins isoforms, playing a prominent role in central nervous system (CNS) development and functions, but also RNAi which, in turn, impact the spatiotemporal expression of receptors, channels and regulatory proteins irrespective of isoforms. Slight dysregulations in few early components of brain development, could, depending upon their extent, snowball into a huge variety of pathological cerebral alterations a few years after birth. This may very well explain the enormous genetic, neuropathological and symptomatic heterogeneities that are systematically associated with ASD and psychiatric disorders at large.

Autism spectrum disorders pathogenesis: Toward a comprehensive model based on neuroanatomic and neurodevelopment considerations.

Autism spectrum disorder (ASD) involves alterations in neural connectivity affecting cortical network organization and excitation to inhibition ratio. It is characterized by an early increase in brain volume mediated by abnormal cortical overgrowth patterns and by increases in size, spine density, and neuron population in the amygdala and surrounding nuclei. Neuronal expansion is followed by a rapid decline from adolescence to middle age. Since no known neurobiological mechanism in human postnatal life is capable of generating large excesses of frontocortical neurons, this likely occurs due to a dysregulation of layer formation and layer-specific neuronal migration during key early stages of prenatal cerebral cortex development. This leads to the dysregulation of post-natal synaptic pruning and results in a huge variety of forms and degrees of signal-over-noise discrimination losses, accounting for ASD clinical heterogeneities, including autonomic nervous system abnormalities and comorbidities. We postulate that sudden changes in environmental conditions linked to serotonin/kynurenine supply to the developing fetus, throughout the critical GW7 - GW20 (Gestational Week) developmental window, are likely to promote ASD pathogenesis during fetal brain development. This appears to be driven by discrete alterations in differentiation and patterning mechanisms arising from in utero RNA editing, favoring vulnerability outcomes over plasticity outcomes. This paper attempts to provide a comprehensive model of the pathogenesis and progression of ASD neurodevelopmental disorders.

Autonomic Nervous System Neuroanatomical Alterations Could Provoke and Maintain Gastrointestinal Dysbiosis in Autism Spectrum Disorder (ASD): A Novel Microbiome-Host Interaction Mechanistic Hypothesis.

Dysbiosis secondary to environmental factors, including dietary patterns, antibiotics use, pollution exposure, and other lifestyle factors, has been associated to many non-infective chronic inflammatory diseases. Autism spectrum disorder (ASD) is related to maternal inflammation, although there is no conclusive evidence that affected individuals suffer from systemic low-grade inflammation as in many psychological and psychiatric diseases. However, neuro-inflammation and neuro-immune abnormalities are observed within ASD-affected individuals. Rebalancing human gut microbiota to treat disease has been widely investigated with inconclusive and contradictory findings. These observations strongly suggest that the forms of dysbiosis encountered in ASD-affected individuals could also originate from autonomic nervous system (ANS) functioning abnormalities, a common neuro-anatomical alteration underlying ASD. According to this hypothesis, overactivation of the sympathetic branch of the ANS, due to the fact of an ASD-specific parasympathetic activity deficit, induces deregulation of the gut-brain axis, attenuating intestinal immune and osmotic homeostasis. This sets-up a dysbiotic state, that gives rise to immune and osmotic dysregulation, maintaining dysbiosis in a vicious cycle. Here, we explore the mechanisms whereby ANS imbalances could lead to alterations in intestinal microbiome-host interactions that may contribute to the severity of ASD by maintaining the brain-gut axis pathways in a dysregulated state.

Genome, Environment, Microbiome and Metabolome in Autism (GEMMA) Study Design: Biomarkers Identification for Precision Treatment and Primary Prevention of Autism Spectrum Disorders by an Integrated Multi-Omics Systems Biology Approach.

Autism Spectrum Disorder (ASD) affects approximately 1 child in 54, with a 35-fold increase since 1960. Selected studies suggest that part of the recent increase in prevalence is likely attributable to an improved awareness and recognition, and changes in clinical practice or service availability. However, this is not sufficient to explain this epidemiological phenomenon. Research points to a possible link between ASD and intestinal microbiota because many children with ASD display gastro-intestinal problems. Current large-scale datasets of ASD are limited in their ability to provide mechanistic insight into ASD because they are predominantly cross-sectional studies that do not allow evaluation of perspective associations between early life microbiota composition/function and later ASD diagnoses. Here we describe GEMMA (Genome, Environment, Microbiome and Metabolome in Autism), a prospective study supported by the European Commission, that follows at-risk infants from birth to identify potential biomarker predictors of ASD development followed by validation on large multi-omics datasets. The project includes clinical (observational and interventional trials) and pre-clinical studies in humanized murine models (fecal transfer from ASD probands) and in vitro colon models. This will support the progress of a microbiome-wide association study (of human participants) to identify prognostic microbiome signatures and metabolic pathways underlying mechanisms for ASD progression and severity and potential treatment response.

How scientific literature analysis yields innovative therapeutic hypothesis through integrative iterations.

It is becoming generally accepted that the current diagnostic system often guarantees, rather than diminishes, disease heterogeneity. In effects, syndrome-dominated conceptual thinking has become a barrier to understanding the biological causes of complex, multifactorial diseases characterized by clinical and therapeutic heterogeneity. Furthermore, not only is the flood of currently available medical and biological information highly heterogeneous, it is also often conflicting. Together with the entire absence of functional models of pathogenesis and pathological evolution of complex diseases, this leads to a situation where illness activity cannot be coherently approached and where therapeutic developments become highly problematic. Acquisition of the necessary knowledge can be obtained, in parts, using in silico models produced through analytical approaches and processes collectively known as `Systems Biology'. However, without analytical approaches that specifically incorporate the facts that all that is called `information' is not necessarily useful nor utilisable and that all information should be considered as a priori suspect, modelling attempts will fail because of the much too numerous conflicting and, although correct in molecular terms, physiologically invalid reports. In the present essay, we suggest means whereby this body of problems could be functionally attacked and describe new analytical approaches that have demonstrated their efficacy in alleviating these difficulties.

[Production and implementation of predictive biological models]. / Modélisation intégrative prédictive et biologie expérimentale: un processus synergique remarquablement efficace au service de la recherche médicale.

The production and implementation of methodologies allowing the construction of coherent, precise and trustworthy predictive biological models has become an inescapable necessity. The financial stakes -attached to this reality are very high indeed, be it in the public or the private domains (health, research, pharma and foodstuffs industries, environment, etc.). Modelling biological systems is widely presented as a problem in computational sciences. While certainly very true for low complexity, practically continuous systems, this view cannot be upheld in the case of discontinuous, hyper-complex systems such as living entities. In these domains, modelling becomes a problem in biology assisted by computational sciences and certainly not the obverse. The following article will attempt to demonstrate why it is so, using concrete examples.