BACKGROUND AND CLINICAL ASPECTS Autism Spectrum Disorder (ASD) is a multi-faceted, pervasive, neurodevelopmental disorder that tends to reveal itself at around 18-36 months of age (Brian et al., 2008). Unfortunately, no clear etiology for the disease exists. Among the plethora of symptoms that are associated with it, the more common ones include; pragmatic aspects in language development (Matson et al., 2010), an absence in symbolic play (Thiemann-Bourque et al., 2012), an impairment in their ability to socialize (Kasari and Patterson, 2012), and the presence of repetitive or ritualistic behaviors (Greaves et al., 2006). Furthermore, ASD has been shown to associate itself frequently with comorbidities such as epilepsy (Besag, 2018), intellectual disabilities (Mpaka et al., 2016), immune dysfunction (Hughes et al., 2018), hyperactivity (Lyall et al., 2017), and gastrointestinal disorders (Chaidez et al., 2014). Furthermore, studies have aimed to understand whether genetic damage, or mediated epigenetic factors contribute to the existence of these symptoms. Consequently, a lack of common consensus has driven researchers to understand the fundamentals of the disorder and to further invest in developing better diagnostic criteria.

Autism spectrum disorder (ASD) is a multifaced neurodevelopmental disorder that becomes apparent during early childhood development. The complexity of ASD makes clinically diagnosing the condition difficult. Consequently, by identifying the biomarkers associated with ASD severity and combining them with clinical diagnosis, one may better factionalize within the spectrum and devise more targeted therapeutic strategies. Currently, there are no reliable biomarkers that can be used for precise ASD diagnosis. Consequently, our pilot experimental cohort was subdivided into three groups: healthy controls, individuals those that express severe symptoms of ASD, and individuals that exhibit mild symptoms of ASD. Using next-generation sequencing, we were able to identify several circulating non-coding RNAs (cir-ncRNAs) in plasma. To the best of our knowledge, this study is the first to show that miRNAs, piRNAs, snoRNAs, Y-RNAs, tRNAs, and lncRNAs are stably expressed in plasma. Our data identify cir-ncRNAs that are specific to ASD. Furthermore, several of the identified cir-ncRNAs were explicitly associated with either the severe or mild groups. Hence, our findings suggest that cir-ncRNAs have the potential to be utilized as objective diagnostic biomarkers and clinical targets.

Although many factors have been identified and used to enhance the iPSC reprogramming process, its efficiency remains quite low. In addition, reprogramming efficacy has been evidenced to be affected by disease mutations that are present in patient samples. In this study, using RNA-seq platform we have identified and validated the differential gene expression of five transcription factors (TFs) (GBX2, NANOGP8, SP8, PEG3, and ZIC1) that were associated with a remarkable increase in the number of iPSC colonies generated from a patient with Parkinson's disease. We have applied different bioinformatics tools (Gene ontology, protein–protein interaction, and signaling pathways analyses) to investigate the possible roles of these TFs in pluripotency and developmental process. Interestingly, GBX2, NANOGP8, SP8, PEG3, and ZIC1 were found to play a role in maintaining pluripotency, regulating self-renewal stages, and interacting with other factors that are involved in pluripotency regulation including OCT4, SOX2, NANOG, and KLF4. Therefore, the TFs identified in this study could be used as additional transcription factors that enhance reprogramming efficiency to boost iPSC generation technology.