High-Throughput Drug Discovery for a Rare Neurological Disorder: Uncovering a Novel Therapeutic Opportunity for the 19q12 Autism Spectrum Disorder

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Bellucci, Sabine Topka, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6298254/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted 14 You are reading this latest preprint version Abstract Discovering new and viable therapies for genetic diseases is a time consuming and cost intensive process. This is even more challenging for rare disorders that affect a small fraction of the population and do not incentivise research and development in the traditional drug discovery lifecycle. There is therefore a need for scalable and efficient solutions for discovering novel therapies for these rare disorders. In this study, we highlight how a high-throughput drug discovery engine was utilized to uncover drugs at scale that normalized the signature for a rare neurological neurodevelopmental disease, 19q12 autism spectrum disorder associated with deficiencies in ZNF536 and TSHZ3. We do this by uncovering and understanding the full 360 o view of the biological impact of the drug in a relevant cell line via transcriptomics. This platform, in combination with a statistically viable n-of-1 trial, was able to quickly uncover and utilize a novel therapeutic potential of a previously approved drug for a patient living with from 19q12 autism spectrum disorder. Biological sciences/Drug discovery/Biomarkers Biological sciences/Drug discovery/Drug screening/High throughput screening Drug-discovery high-throughput sequencing 19q12 Entrectinib neurodevelopmental rare disease disease signature RNA-seq DRUG-seq transcriptomics rare neurological ZNF536 TSHZ3 autism precision medicine n-of-1 trial Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Introduction A rare disease or disorder is defined as such if it affects < 200,000 individuals in the US at any given time 1 . There are over 7000 rare diseases and while these diseases are individually uncommon, aggregated they affect over 30 million people in the US 1 . Rare disorders pose a challenging issue for the drug discovery community. While molecular genetics has helped uncover the cause in many cases, with limited resources available, research and development of viable therapies for these diseases has become exceedingly difficult. Therefore, pipelines focused on discovering new opportunities at speed and scale for available FDA approved small molecules coupled with robust, statistically viable n-of-1 trials that can establish efficacy and support regulatory approval of these therapies have the potential to be highly successful and lower cost for patients. One such rare genetic disorder is a form of autism spectrum disorder (ASD) caused by structural variations in the 19q12 band of chromosome 19. The first known research of ASD associated with 19q12 was published in 2016 2,3 but known cases had been recorded before 2016 4–6 . These structural variations lead to multiple gene-level changes in the q12 band of chromosome 19 including the genes ZNF536 and TSHZ3. ZNF536 and TSHZ3 encode zinc-finger proteins that act as regulators of transcription of other genes along with a myriad of other diverse functions 7 . Both ZNF536 and TSHZ3 have been implicated in a network of complex biological processes that drive neurogenesis and neuronal differentiation of progenitors to neurons during development 2 , 8 . The down-regulation of these proteins was observed in patient-derived neurons along with the down-regulation of a network of other transcription factors and biological pathways implicated in neurogenesis and neuron differentiation. Together these results suggest that 19q12 ASD causes aberrant neurogenesis leading to disruption of synaptic connectivity of these cultured neurons 9 , 10 . Here, we demonstrate an end-to-end pipeline that can be used to profile hundreds of FDA-approved small molecules at scale using low-depth RNA sequencing as described in the DRUG-seq protocol 11 , 12 . In recent years RNA-seq has evolved into a powerful tool for profiling the cellular response to various perturbants at comparatively lower cost. RNA sequencing provides a genome-wide snapshot of the transcriptomic state, thereby allowing more granular observation and monitoring of the pharmacodynamic response 13 . Because of the large amount of information uncovered about the cell response to a molecule, this procedure is well suited for discovering novel repurposing opportunities 14 . Using the disease signature developed from profiling patient neurons, along with profiles of hundreds of FDA-approved drugs generated from DRUG-seq, we identified a potential therapeutic opportunity for 19q12 ASD. This drug was further tested in an n-of-1 trial in a patient and drug response was tracked using RNA-seq of whole blood collected from the patient over a period of 6 months. The RNA-seq results showed improvement of disease causing pathways and clinicians associated with the patient also reported improvements in various clinical endpoints. Results Identification of root cause genes in patient with 19q12 ASD A 3 year old female diagnosed with autism spectrum disorder (ASD) and global developmental delay was enrolled in an approved research protocol (WCG IRB protocol #2001-01) and provided whole blood samples for genetic characterization and cell model development. Clinical SNP microarray testing performed on the patient and both parents had revealed multiple regions of de-novo copy number variation, including both gains and losses on the q-arm of chromosome 19 in the affected child. Additional genetic evaluations were performed on a research basis to further investigate the 19q12 copy number variation (CNV) and identify causative gene candidates which may be related to her clinical presentation. This included Optical Genome Mapping to further characterize the large structural variations in 19q12; long-read Whole Genome Sequencing (10X Genomics Linked Reads) to identify breakpoints and sequence potentially involved genes; and RNA sequencing of lymphocytes to evaluate relative gene expression. Based on this data (Supplementary Information), two candidate disease-relevant genes were identified: ZNF536 and TSHZ3. Both genes were affected differently, a deletion in the region chr19:31138482–31266070 included TSHZ3 deletion (chr19:31149979–31349436) and the region encoding ZNF536 was both inverted and duplicated. Both TSHZ3 deletion as well as ZNF536 duplication have been previously implicated in a form of autism spectrum disorder 2 , 3 . To further validate the transcriptional effect of the observed 19q12 alterations, induced pluripotent stem cells (iPSCs) were generated from both healthy parent and affected child. The iPSC lines were then differentiated to produce neural progenitor cells (NPCs) as described in the Methods. Both these cell lines displayed a morphology characteristic of neural progenitors as assessed by immunocytochemical (ICC) staining (Supplementary Fig. S4) for the expression of Ki-67, a marker for proliferation. The expression of MKI67 decreased as a function of neuronal maturation day in both parent (Fig. 1 A) and patient (Fig. 1 B) lines, showing the evolution of progenitors to a mature neuronal population. The NPC lines were matured into neurons as described for 20 days and gene expression was profiled at multiple time points during maturation using RNA sequencing. Differential expression analysis comparing patient to control parent line showed significant down-regulation of both ZNF536 (q-value corrected p-value < 0.0001) and TSHZ3 (q-value < 0.1) in affected child derived cells starting at the NPC stage and persisting at all neuronal timepoints sequenced (Fig. 2 A). The down-regulation of ZNF536 is correlated (Pearson correlation coefficient = -0.81, p-value = 0.073) with maturation time point in derived neurons, such that ZNF536 down-regulation increased as neurons matured. The down-regulation of TSHZ3 and ZNF536 was further validated by qPCR at the NPC stage and showed significant (calculated using Student’s T-test between parent and patient) down-regulation in NPCs from the affected child when compared to parent NPCs (ZNF536 p-value = 0.015, TSHZ3 p-value = 8.066E-05) (Fig. 2 B). ZNF536 is a novel zinc-finger protein that is highly expressed in the brain and plays an important role in neuronal differentiation during early brain development. Repression of ZNF536 has been shown to cause aberrant enhancement of retinoic acid induced neuronal differentiation in p19 cells 8 . The authors further showed that ZNF536 increased at early stages of neuronal differentiation while later stages displayed constant levels of ZNF536. Data collected from RNA-seq of iPSC derived neurons from affected child and parent control showed similar trends (Fig. 3 A), specifically transcript counts per million (TPM) of ZNF536 increased rapidly from the iPSC to NPC and early neuronal stages (day 7 and day 14), whereas at day 20, ZNF536 levels remained fairly unchanged compared to day 14. TSHZ3 is similarly another zinc-finger protein 2 , and represents a hub gene in a module of co-expressed genes that control early cortical development 15 . Among the other regulatory elements in this module, FOXG1, a brain-specific transcription repressor was observed to be additionally highly significantly (q-value < 1E-23) down-regulated in neurons derived from the affected child compared to the patient iPSC cells (Fig. 3 B). ZNF536 and TSHZ3 lead to aberrant neurogenesis in patient cells In neurons derived from the patient we observed a complex neuronal phenotype compared to the control line. Analysis of gene ontology (GO) revealed that the top pathways commonly associated with these regulatory genes 2 , 8 shared a common theme of being associated with neurogenesis and neuron differentiation (Fig. 4 A). Additionally, a collection of other significant pathways searched by keywords associated with neurogenesis such as neuron differentiation, neurodevelopment, axonogenesis, etc. were also majorly down-regulated starting at the NPC stage showing premature down-regulation of neurogenesis (Supplementary Fig. S5). The p-value for each pathway was calculated using the Fisher method across the patient sampling timepoints (Supplementary Table S2). The lower effect size of these neurogenesis pathways were used to reveal a number of significantly (q-value < 0.1) down-regulated genes in patient-derived neurons compared to the parental control line (Fig. 4 B-C, Supplementary Fig. S6). Of the top significantly down-regulated genes, a large number were regulatory elements involved in neurogenesis and neuronal differentiation; SLIT1 16,17 , NKX6-1 18 , NKX6-2 19 , ZNF536 8 , FOXG1 20 , FOXA1 21 , HOXA2 22 , DLX1 and DLX2 23 etc (Fig. 5 A). Two important signaling pathways associated with cell proliferation, the sonic hedgehog signaling pathway via the gene SHH and regulation of the Notch signaling pathway (Fig. 5 B, top three rows), showed lower effect size in the patient line compared to parental control line 24 , 25 . The Notch signaling pathway is most often co-annotated with neurogenesis pathways indicating that the Notch signaling machinery plays an important role in the formation of new neurons or neuronal proliferation 26 . The down-regulation of widespread regulatory genes involved in neurogenesis as well as signaling pathways that control cell proliferation, implies that the evolution of iPSC to progenitors to matured neurons in the cell line from the affected child is disrupted. In addition to the regulatory genes, we also observed a significant (q-value < 1.5E-38) down-regulation of the gene NTN1. NTN1 or Netrin-1 is implicated in neurodevelopmental processes and inactivation of NTN1 leads to disruption of excitatory synaptic connectivity 27 . The down-regulation of NTN1 is perhaps further evidence of disrupted neurogenesis via aberrant axon development 28 , 29 . Interestingly, while we observed the down-regulation of neurogenesis markers as well as the disruption of the excitatory synaptic plasticity marker gene NTN1, it was also accompanied by significant up-regulation of SLC17A6 (q-value < 5E-11). SLC17A6 (Solute Carrier Family 17 Member 6) encodes the Vesicular Glutamate Transporter 2 (vGLUT2) protein, a key marker gene for excitatory neurons 30 – 32 . An overexpression of vGLUT2 has been previously linked with an autism spectrum disorder due to MEF2C haploinsufficiency leading to overexcitability and synaptic dysfunction 33 . The overexpression of this key glutamatergic/excitatory marker gene was also observed with over-activity of the excitatory neuron specific genes derived previously from single-cell RNA-seq characterization of the human embryonic cerebral cortex 34 (Fig. 5 B, bottom row). Taken together, these results indicate that the neurons derived from the affected-child mature faster, showing premature down-regulation of neurogenesis related transcription factors and pathways that drive cell proliferation such as the sonic hedgehog signaling pathway. This aberrant neurogenesis led to formation of a population of neurons that prematurely and significantly over-expressed glutamatergic neuronal markers such as mGlut2 (SLC17A6) and showed overactive excitatory gene markers. Thus, the mis-regulation of ZNF536 and TSHZ3 during development induces a complex dynamic of gene-level changes that drive and control early neuronal differentiation and eventually lead to changes in synaptic activity in the matured neuronal state. Identification of a disease signature associated with 19q12 ASD While 19q12 autism spectrum disorder is a brain-specific disorder, generating disease specific neurons is both expensive and time consuming. To overcome this, we generated a disease signature focused mainly on biological pathways as targets that overcomes cell type specificity of specific genes, for example both ZNF536 and TSHZ3 are highly brain specific. The reliance of pathways on gene sets ensures that it is able to capture representative activity even in the absence of a subset of tissue specific genes. Additionally, the use of disease signatures as a target enables a more system-wide comprehensive understanding of the molecular changes in the disease for considerable time and cost savings as well as a strategy to generate biomarkers for the disease that can be assessed in the patient during treatment 35 . The pathways identified above showed overwhelmingly that neurogenesis in the patient with 19q12 autism spectrum disorder was accelerated. However, since most neurogenesis happens prenatally, these pathways may not be representative of current neurodevelopmental status in the patient 36 – 38 . We therefore focused on two other aspects of functional pathways in the patient associated with the gene regulations observed in the last section. We first focused on pathways associated with the transporter gene SLC17A6. Since SLC17A6 is an important marker gene for glutamatergic neurons, its mis-regulation indicates a key disease-specific marker for 19q12 ASD. Pathway enrichment analysis revealed that SLC17A6 was a top ranked gene for pathways associated with neurotransmitters including anion (glutamate) transport as well as glutamatergic synaptic transmission and sensory system development (Fig. 6 A). The protein vGLUT2 encoded by SLC17A6 is known to play an important role in synaptic transmission and plasticity by filling vesicles with the neurotransmitter glutamate 39 , 40 . SLC17A6 imbalance has also been implicated in some sensory processing impairments in SLC17A6 KO mice 40 . A majority of all significant pathways associated with SLC17A6 were enriched in patient neurons compared to control during neuronal differentiation, similar to up-regulation of the gene SLC17A6 observed in the patient (Fig. 6 A, supplementary Fig. S7A, supplementary table S3). Neurons in the brain function via the formation and transmission of synapses between each other to perform a wide variety of complex tasks 41 . Since synapses are the backbone of healthy neuronal function, it can be expected that the neurons formed from the aberrant neurogenesis observed in the 19q12 patient neurons also lead to some impaired synaptic behavior. Moreover, excitatory synaptic transmission is one of the key functions of SLC17A6, a key marker gene in excitatory neurons. We, therefore, looked at synaptic pathways significantly regulated in the patient neurons compared to the control (Fig. 6 B, supplementary Fig. S7B, supplementary table S4). Similar to the results observed with the pathways associated with SLC17A6, a majority of the synaptic pathways also showed higher activity in the patient neurons. This increased synaptic transmission activity agrees with previously published results showing that increase in vGLUT2 (SLC17A6) eventually leads to enhanced excitatory neurotransmission in mice with MEF2C haploinsufficiency 33 . The two identified pathway classes; related to synapse formation and neurotransmission (Fig. 6 ); impaired in 19q12 therefore, highlight the mis-regulated neuronal function in the patient. Combined, these pathways can then be utilized as the disease signature for 19q12 ASD (Table 1 ). Contrary to neurogenesis, evolution of synaptic plasticity occurs well into adulthood and has also been implicated in autism spectrum disorder (ASD), and is therefore relevant to the disease biology 42 – 44 . Table 1 Table showing the two main classes of pathways identified in the disease signature for 19q12 ASD, the average overall direction of regulation and the number of individual pathways constituting each class. Pathway Class Overall direction of regulation in neurons Number of constituent pathways Neurotransmitter transport Up 13 Synaptic transmission/plasticity Up 17 Low dose Entrectinib reverses disease signature in a high throughput drug screen Using a modified DRUG-seq protocol as described in the Methods section, a collection of bioactive, FDA approved molecules were profiled in NGN2-derived glutamatergic neurons. Neurons were matured for 15 days on a 384-well plate followed by treatment with each respective drug in 10-point concentration responses for 72 hours and sequenced to profile the transcriptomic response to each respective compound-concentration. Parallel to sequencing, a high content imaging assay was used to generate phenotypic profiles of the neurons under drug treatment (supplementary information). Of the small molecules profiled is Entrectinib, an FDA approved potent inhibitor of tropomyosin receptor kinases (TRKs) and ROS1. Entrectinib is approved for use in treating neurotrophic tyrosine receptor kinase gene (NTRK)-fusion positive solid tumors 45 . Cytotoxicity of Entrectinib during the screen was assessed using the lactate dehydrogenase (LDH) assay as well as from imaging metrics (Supplementary Fig. S8). Entrectinib was only mildly cytotoxic to the cells at concentrations between 14 nM to 3.33 µM and caused almost complete cell death at concentrations above that. No cytotoxicity was recorded at concentrations below 14 nM. Transcriptomic response of NGN2-derived glutamatergic neurons to low-dose Entrectinib treatment (Entrectinib concentration ≤ 14 nM) showed reversal of signature pathways that were mis-regulated in neurons derived from patient. The extent of reversal of signature pathways was estimated using a cosine correlation coefficient of the response in patient-derived neurons vs. control and the response in the screened glutamatergic neuron line under Entrectinib treatment (Fig. 7 A-B). A cosine correlation measures the similarity between two vectors in space such that a negative value implies opposing vectors and vice versa. The negative correlation shows that Entrectinib’s effect on the signature pathways is opposite of the observed activity in patient neurons and therefore could be used to normalize pathway activity in 19q12 ASD. Entrectinib’s effect on pathways was also dose-dependent such that the magnitude of correlation increased with increase in dose up to 14 nM. At concentrations > 14 nM, with the onset of cytotoxicity, the disease signature reversal was lost (Supplementary Fig. S9). These concentrations that conferred therapeutic reversal of disease signature pathways in the in vitro neuronal model were much lower than the steady state plasma concentration of 2 µM observed in patients being prescribed the FDA approved dosage for the treatment of cancer 46 . Pathways associated with neurotransmitter transport and synapse formation and transmission were all regulated in the opposite direction from what was observed in patient neurons (Supplementary Fig. S10, Supplementary tables S5 and S6). Effect of low dose Entrectinib on patient transcriptomic profile from whole blood RNAseq The patient identified with 19q12 associated autism spectrum disorder was prescribed low-dose (significantly lower than that approved therapeutically for pediatric cancer) Entrectinib under the supervision of a physician (Table 2 ). Table 2 Dosing schedule of Entrectinib in patient. Start date End date Dose Frequency 2023-12-03 2024-02-05 200 mg Every 4 days 2024-02-06 2024-06-07 400 mg Every 5 days 2024-06-08 2024-08-15 400 mg Every 5 days, with an additional dose 2 days after each regular dose 2024-08-16 Ongoing 400 mg Every 5 days Blood was collected during treatment and whole blood RNA-seq was performed to track therapeutic effect of Entrectinib in the patient. A pre-dose blood collection was performed before treatment started and two post-dose collections at approximately 3 and 6 months after treatment started were performed 24 hours after drug intake (Table 3 ). Entrectinib dose was doubled between the first and second post-dose collection. In addition to patient blood, a blood sample was also collected from an unaffected age-matched sibling to serve as a control. Table 3 Dates of the pre-dose and the 2 post-dose blood collections used for whole blood RNA sequencing to probe the effect of Entrectinib in patient. Collection date Pre-dose collection 2023-11-08 Post-dose collection 1 2024-03-13 Post-dose collection 2 2024-07-11 In line with Entrectinib’s known mechanism of action we observed a small but significant (q-value < 0.0005) down-regulation of its target gene NTRK2 45 (Fig. 8 A). This validated that marker genes of the pharmacodynamic effect of Entrectinib were captured in whole blood RNA-seq from patient. Interestingly, pathways normally inhibited under approved dosage of Entrectinib; MAPK/ERK signaling, signaling by phospholipase-𝜸 and PI3K/AKT signaling 47 , 48 ; are activated in the patient (Fig. 8 A). In a study of Entrectinib-resistant cancer lines a similar biological activity was observed that is opposite of its intended outcome, with increased PI3K/AKT and MAPK/ERK signaling conferring resistance towards Entrectinib 49 , 50 . This phenomenon, when a drug causes an effect that is the opposite of its intended outcome, is known as the paradoxical drug effect 51 . Various other studies have demonstrated similar paradoxical effects for other drugs at low concentrations compared to higher FDA-approved concentrations 52 – 55 . We therefore hypothesized that the effect observed in the patient is a compensatory mechanism specific to the low dosage conferring resistance-like activity unlike its anti-cancer activity observed at higher doses 53 , 55 . This activity is inversely related to dosage, such that pathway activity decreases at higher doses (at month 6) (Fig. 8 A, orange bars). The leading causative genes associated with the two classes of dysregulated pathways in the patient were examined pre-treatment in the blood of the patient compared to a sibling control using whole blood RNA sequencing. Whole blood RNA sequencing provides insight on how a drug changes the transcriptome in a relatively cheap and non-invasive manner, allowing us to track the effect of drug in the patient pre- and post-treatment in real time 56 . Fold changes were calculated for the sibling control and each of the two post-treated samples compared to an untreated blood collection. A cosine correlation coefficient was calculated for the fold changes of the leading genes in the disease pathways between the sibling control and the post-treatment samples to assess if Entrectinib showed reversal in the patient similar to its activity in the neuronal model (Fig. 8 B). Cosine correlation between sibling and treated samples increased with treatment time showing normalization of gene expression profile in the patient towards sibling control levels with Entrectinib treatment. Unlike the pharmacodynamic effect shown in Fig. 8 A, a significant dose-specific decrease was not observed for the cosine correlation. In the patient pre-treatment blood samples, a large number of the identified genes from the disease signature pathways were down-regulated compared to the sibling control (Fig. 9 A). Both 3 and 6 month post-treated samples showed that Entrectinib reversed the expression pattern of these genes (Fig. 9 B-C). Additionally, some genes that were up-regulated at the lower-dosage (3 months post-treatment) were down-regulated at the higher dosage, further emphasizing the anti-correlation of therapeutic effect to dosage. These results indicate that Entrectinib at low dose in the patient was able to reverse the disease phenotype associated with 19q12 ASD. Rescue of disease phenotype via increased expression of RARA A key regulator of synaptic plasticity that emerged from this study is the signaling protein retinoic acid receptor alpha encoded by the RARA gene. RARA has varied roles in the formation of neurons during embryonic development and later in neuronal function 57 – 59 . During brain development, RARA and ZNF536 are closely associated in function; while RARA promotes neuronal differentiation via transcription of neuron-specific marker genes, ZNF536 negatively regulates this by inhibiting RARA activity 8 . This is in line with observations in patient derived neurons with depleted ZNF536, that showed accelerated differentiation into excitatory neurons and premature over-expression of excitatory marker genes. Post-development, the role of RARA is closely associated with maintaining homeostasis in synaptic plasticity: (1) by activating BDNF synthesis and driving BDNF-TrkB signaling to achieve presynaptic homeostatic plasticity and (2) promoting translation of AMPA receptors to achieve postsynaptic homeostatic plasticity 59 , 60 . RARA is expressed uniformly across tissue types and can be easily tracked in RNA-seq of whole blood. RARA was found to be significantly (q-value < 1E-8) down-regulated in the patient compared to a sibling control (Fig. 10 A-B). Administration of Entrectinib normalized RARA levels in patient blood and was also accompanied by normalization of retinoic acid receptor signaling activity relative to the sibling control (Fig. 10 A-C). Entrectinib treatment was also accompanied by normalization of AMPA and BDNF signaling pathways (Supplementary Fig. S11). RARA up-regulation, therefore, could be instrumental in rescuing the dysregulated genes associated with 19q12 ASD in the patient. Clinical Outcomes: Observed Patient Improvements Following Treatment Approximately 2 months after dosage was doubled, it was dropped back to 400 mg every 5 days, as the parents reported regression in positive improvement observed at lower dose (Table 3 ). This reversal at higher dose is expected for a low-concentration specific paradoxical drug response, as in this case with Entrectinib. To monitor for cognitive, behavioral, and developmental changes, the patient underwent virtual neuropsychology evaluations with a trained provider. Nineteen months after the pre-treatment evaluation (see methods), the clinician reported that the patient presented to be calmer, had dramatically improved communication with caregivers, and did not exhibit any frustration-related disruptive behaviors that had been observed during the baseline evaluation. This was in alignment with the observed reduction in challenging behaviors post-treatment by the parents. Quantitatively, the patient’s DAYC-2 overall cognitive score was consistent with a 23 month old child, which was increased from the 17 month old cognitive level that was noted at the baseline evaluation. The patient’s cognitive developmental level had plateaued around that of a 2 year old child, however a total of 6 months of progress within 2 years is noteworthy compared to 17 months of progress made within her first 5 years, suggesting at least some speed up in cognitive development post-treatment. Significant qualitative improvements were also demonstrated by caregiver reported Vineland measures (Fig. 11 ). Raw scores for communication all showed growth, with remarkable improvement in written scores that nearly doubled post-treatment, as well as in daily living skills. For example, at the follow-up evaluation the patient was reportedly toilet trained, which had been a major challenge for caregivers. Additionally, the patient made an extraordinary 6 months worth of progress in her personal domain. Caregivers also reported changes in the child’s ability to navigate and function within the community and although assistance and supervision were still required, there was notable progress reported in her ability to communicate her needs and tolerate frustration. Socially, the patient made meaningful qualitative progress, i.e. engaging with other children outside of her parental circle. The patient was also evaluated routinely at her school by both special education teachers and a speech therapist who evaluated her on her ability to follow two-step directions as well as conversationality. Both these skills measure cognitive development and functional skills 61 . The evaluation began at the end of August just after the dose was decreased. Within a few months, both teachers and the speech therapist reported marked improvement in the patient’s ability to follow two-step directions and conversationality. Discussion Recently, more accessible and cheaper sequencing technologies have allowed much deeper biological insight into systems of interest than was previously possible. RNA sequencing, for example, has enabled the probing of the whole transcriptome in a single shot under different types of perturbations. Development of high-throughput RNA-sequencing technologies like DRUG-seq can be leveraged to scale up drug discovery, focused on discovering novel mechanisms of action and targets for known and novel small molecules. This is uniquely suited but not limited to the challenge of drug discovery for rare diseases. Research and development resources for individual rare diseases are very limited due to the small population sizes and the massive cost of discovering new therapeutics in the traditional pharmaceutical development paradigm. To circumvent these challenges, we have developed a drug discovery platform focused on speed and scale while remaining statistically rigorous, using high-throughput RNA sequencing. We demonstrate how RNA-seq can be employed end-to-end; to generate target hypotheses, novel therapeutic opportunities for those targets and finally be used to track appropriate therapeutic endpoints post-treatment in a patient. A patient, aged 7 years, diagnosed with a complex neurodevelopmental genetic disorder resulting in autism spectrum disorder and global developmental delay first diagnosed at 3 years old, was enrolled in an IRB approved research protocol to find new opportunities for available FDA approved drugs. Through whole genome sequencing and optical genome mapping, the genetic causes were narrowed down to rearrangements and deletions in the q12 arm of chromosome 19. Two root cause genes, ZNF536 and TSHZ3, were identified, both involved in neurogenesis during embryonic development. We discovered by comparing neurons derived from patient iPSCs to unaffected familial control that both identified root cause genes were down-regulated during neuronal maturation in the patient-derived neurons. We further discovered that the neurons derived from the patient iPSCs showed a complex down-stream phenotype with over-activity of synaptic transmission and glutamatergic neurotransmitter transport pathways. Using this disease signature we identified a drug, Entrectinib, in a high-throughput drug screen in glutamatergic neurons that generated an opposing signature at low concentrations. The patient was prescribed low-dose Entrectinib under the supervision of a physician. The progress in the patient was tracked using whole blood RNA-sequencing pre- and post-treatment along with a control sample from an unaffected sibling. Pre-treatment, the genes involved in the disease signature pathways were dysregulated compared to the control sample but normalized post-treatment within 6 months to levels in the control sample. The re-regulation of synaptic plasticity and neurotransmitter transport potentially occurs via up-regulation of the gene RARA post treatment with Entrectinib. RARA, a key regulator of maintaining synaptic homeostasis, was down-regulated in the patient pre-treatment and is also closely associated with the root cause gene ZNF536. Entrectinib up-regulated RARA in the patient comparable to levels in the unaffected sibling. This study reveals a potential path to approaching discovery of novel therapeutic opportunities by leveraging high-throughput RNA-seq to profile drugs at scale in a model system and identifying these opportunities based on normalization of a disease signature as target and tying in a statistically-valid n-of-1 trial to speedily and safely assess the drug in a patient population. Such an approach provides a pipeline that can be utilized at scale for any disease for uncovering novel therapeutic opportunities for considerable cost-savings. Methods Patient Cell Model Generation iPSC generation All patient cells described in this section were collected with consent from the participant or the legal guardian of the participant in accordance with protocols reviewed and approved by the WGC Institutional Review Board (IRB) (WCG IRB protocol #2001-01). Patient fibroblasts were collected via skin punch biopsy performed by a physician. The samples were then sent to Sampled (Piscataway, NJ) for isolation and cryopreservation of fibroblasts. Induced pluripotent stem cells (iPSCs) were generated from two patient-derived fibroblast lines (JP0000009 and JP0000013) reprogrammed using the StemRNA 3rd Generation Reprogramming kit (REPROCELL Inc). 6 clones from each cell line were picked and expanded; 3 clones for each cell line (JP9.4, JP9.5, JP9.6, JP13.1, JP13.2 and JP13.3) were characterized via G-banding for karyotyping and immunofluorescent staining of two pluripotency markers. NPC generation and expansion (Columbia University, Corneo Barbara) Neural progenitor cells (NPCs) were generated from (iPSCs from both healthy parent and affected child using a modified 2D cortical glutamatergic protocol based on the STEMdiff neural system (STEMdiff™ SMADi Neural Induction Kit, Stem Cell Technologies). iPSCs were plated in a 96-well spheroid plate and treated with neural induction media containing SMAD inhibitors (SMADi) and basic fibroblast growth factor (bFGF, 5ng/ml). On day 5, embryoid bodies (EBs) were replated onto Matrigel-coated plates. Over the next several days, media was gradually switched to N2/B27 media without Vitamin A ((DMEM/F12 (Gibco 11330032), Neurobasal medium (Gibco 21103-049), N2-B supplement (Stem Cell Technologies), B27 supplement without Vitamin A (Gibco 12587-010), Glutamine (Gibco), Non-Essential Amino Acids (Gibco 11140-050), and beta-Mercaptoethanol (Gibco)), supplemented with SMADi (LDN193189 (250nM), SB431542 (10µM)), and ascorbic acid (200µM). Neural rosettes formed and were selectively replated on day 12 onto Matrigel-coated plates following trypsinization. Cells were cultured for another 4 days in N2/B27 media without Vitamin A and cryopreserved on day 16. Neural progenitor cells (NPCs) were thawed and expanded in T75 flasks coated with poly-l-ornithine and laminin, using STEMdiff™ Neural Progenitor Medium (Stem Cell Technologies). Cells were maintained with full media changes every 2–3 days until they reached 70–80% confluence for further passaging or experimentation. Neuron maturation NPCs were differentiated into neurons using BrainPhys™ Neuronal Medium (StemCell Technologies). NPCs were plated on poly-l-ornithine and laminin-coated plates at a density of 1.25 x 10 5 cells/cm². The medium was switched to BrainPhys™ medium supplemented with N2, Brain-Derived Neurotrophic Factor (BDNF, 20 ng/mL), Glial-Derived Neurotrophic Factor (GDNF, 20 ng/mL), dibutyryl-cAMP (500 µM), and ascorbic acid (200 µM). Media changes were performed every 2–3 days, and cells were maintained for 14–20 days to promote neuronal maturation. Imaging Immunocytochemistry was performed on 3 96-well PerkinElmer Phenoplates seeded with neurons. Neuronal induction was performed by seeding NPCs in the 3 plates with BrainPhys Complete Neuronal media (StemCell, cat# 05790). All plates were split between parent and patient cell lines, and further split into two seeding densities, 30k and 40k cells/well. Plates were fixed with 4% Paraformaldehyde (Thermo Scientific, cat# 043368-9M) for 15 minutes followed by 3x washes with DPBS (Gibco cat# 14190144) on days 1, 7 and 14. Cells were permeabilized with 0.25% TritonX-100 solution (Thermo Scientific, cat# 85111) diluted in DPBS for 15 minutes prior to blocking and staining. All plates were stained with DAPI and the proliferation marker Ki67 (Thermo Scientific, cat# MA5-14520). High Protein Blocking Buffer (eBioscience, Thermo Scientific cat# 00-4952-54) was added prior to primary antibody staining for 45 minutes, followed by primary antibody solution diluted (1:500) in blocking buffer and incubated overnight at 4°C. The following morning, the primary antibody solution was removed and 2 washes were performed with DPBS. Secondary antibody (Thermo Scientific) solution was diluted in Low Protein Blocking Buffer (eBioscience, Thermo Scientific cat# 00-4953-54, 1:2000 dilution) and incubated at room temperature for 1 hour protected from light. Two washes with DPBS were performed, followed by a brief incubation with DAPI (Thermo Scientific cat# D1306, 1:10,000 dilution) for 5 minutes at room temperature. A final DPBS wash was performed followed by a wash with sterile distilled water. Plates were imaged using the PerkinElmer Opera Phenix confocal microscope at 20x magnification. Analysis was performed in PerkinElmer Harmony Image Analysis software via integration of manually trained cell masks to determine live/dead cell status through fluorescence intensity, nuclear shape, size and roundness. The program was trained on 5 distinct, randomly selected well sections. Live cell fraction (LCF) was then calculated by determining the proportion of live to dead nuclei. Ki67 + nuclei were identified, quantified and compared to overall nuclei number for each timepoint. Sequencing RNA extraction was performed using Qiagen RNeasy Plus Mini Kit (Qiagen, Cat: 74134). RNA quality and purity was checked by testing 2uL of RNA on a High Sensitivity RNA screentape (Agilent Tapestation). RNA concentration was quantified using a Qubit 4 Fluorometer (Invitrogen). Bulk RNA libraries were prepared from 200ng of RNA input using Illumina Stranded Total RNA Prep with Ribo-Zero Plus kit. RNA library quality was checked by running 1uL of each library on a High Sensitivity D1000 screentape (Agilent Tapestation), and concentration was quantified by qPCR using a KAPA Universal Illumina library quantification kit (Roche). Samples were sequenced in batches of 8 or less, on a NextSeq 1000 using the standard loading protocol for a P2 200 cycle sequencing cartridge. DRUG-seq NGN2-driven Neuronal Model 10 cm dishes for NGN2-induced neural differentiation were coated with Matrigel according to the manufacturer's manual and incubated overnight at 37°C. On the day of induction, iPSCs were removed using Accutase (StemCell, Cat# 07920), centrifuged at 300 xg for 5 mins and resuspended in NGN2 Neural Induction Media containing 10 µM Y-27632 (StemCell, Cat# 72304). NGN2 Neural Induction Media was made using a base of Knockout DMEM/F12 (Gibco, Cat# 12660012) with the addition of 1x N2 Supplement-B (StemCell, Cat# 07156), 1x MEM Non-Essential Amino Acids (Gibco, Cat# 11140050), 1ug/mL Natural Mouse Laminin (1 mg/mL stock Life Technologies, Cat# 23017-015), 10 ng/mL NT-3 in sterile distilled water (StemCell, Cat# 78074), 10 ng/mL BDNF in sterile distilled water (StemCell, Cat# 78133) and 2 µg/mL doxycycline (stock concentration 20 mg/mL, Fisher Scientific, Cat# AAJ67043AD). Cells were counted using Trypan Blue (Invitrogen, Cat# T10282) on the Invitrogen Countess and plated at 5X10 6 cells/10 cm dish. Daily media changes were performed on days 2–3 with NGN2 Neural Induction Medium. One day prior to replating cells for neuronal maturation, two 384-well PerkinElmer Pheno Plates were coated with 0.1mg/mL Poly-D-Lysine (Gibco, cat# A3890401) at 37°C overnight. The next morning, the plates were washed 3x with sterile water and dried under a BSC with the fan turned on. At least 2 hours prior to replating, 10 µg/mL Natural Mouse Laminin (Gibco, Cat# 23017015) diluted in DMEM/F12 (Gibco, Cat#11320033) was added to the plates and returned to a 37°C incubator. On day 4, cells were replated at 20000 cells/well in 384-well plates (PerkinElmer Phenoplates) for neuronal maturation in NGN2 Neuronal Medium containing 2 µg/mL doxycycline (Fisher Scientific, Cat# AAJ67043AD) and 5 µM of U-FdU. NGN2 Neuronal Medium was made using 1:1 DMEM/F12 (Gibco, Cat#11320033) and Neurobasal-A Medium (Gibco, Cat# 10888022) as the base with the addition of 1x MEM Non-Essential Amino Acids (Gibco, Cat# 11140050), 0.5x GlutaMAX Supplement (Gibco, Cat# 35050061), 1x N2 Supplement-B (StemCell, Cat# 07156), 1x B-27 Plus Supplement (Gibco, Cat# A3582801), 10 ng/mL NT-3 (StemCell, Cat# 78074), 10ng/mL BDNF (StemCell, Cat# 78133) and 1 µg/mL Natural Mouse Laminin (Gibco, Cat# 23017015). Half media changes were performed every 3–4 days from Day 7-Day 15 using NGN2 Neuronal Medium. Neurons were imaged using an Incucyte for quality control purposes prior to drug treatment using a basic confluence analysis program, followed by a Neurotrack program to identify cell body clusters and neurites. Drug Treatment Neurons were treated with a selection of FDA-approved small molecule compounds added at one-third decreasing dose for 10 doses: 10µM, 3.33µM, 1.11µM, 0.37µM, 0.12µM, 40nM, 14nM, 5nM, 2nM, and 0.5nM in quadruplicate. Additionally, a total of 16 wells were treated with DMSO as control on each 384-well plate. Compounds and DMSO controls were dispensed directly into the 384-well cell culture plates using an Echo 650 Liquid Handler (Beckman Coulter). Cells were held at 37°C for 72 hours following drug treatment. Following drug treatment, one of the two plates was used for RNA sequencing and the second was used for live cell imaging (See Supplementary Methods). Sequencing Libraries were prepared using a customized version of the DRUG-seq protocol 11 , 12 . Briefly, cell culture media was aspirated using a STARlet liquid handler (Hamilton) and lysis buffer was dispensed directly into each well with a Multidrop Combi Reagent Dispenser (ThermoFisher Scientific) and placed on a shaker for 15 minutes at 900 rpm. Cell lysate was transferred into 384-well PCR plates and reverse transcription (RT) mix was dispensed into each well. Using an Echo 650 Liquid Handler (Beckman Coulter), barcoded RT primers were dispensed into each well. Plates were incubated at 42°C for 2 hours. Following RT, each well was pooled into a single tube and purified using DNA Clean & Concentrator-100 (Zymo Research) followed by concentration with Agencourt RNAClean XP beads (Beckman Coulter). Single-stranded DNA was removed by incubating cDNA with Exonuclease I (New England Biolabs) followed by pre-amplification of the cDNA with DRUG-seq PCR primers. cDNA was fragmented and tagged with Illumina sequencing adapters using Tagmentation DNA Enzyme (TDE1). A final PCR was performed to amplify the libraries and add P7, P5, and index sequences. Libraries were sequenced on a NovaSeq 6000 (Illumina). Whole blood RNA-sequencing Patient n-of-1 trial and data collection was performed in accordance with protocols reviewed and approved by the WGC Institutional Review Board (IRB) (WCG IRB protocol #2001-01). All data was collected with consent from the legal guardians of the patient. Blood samples were collected from a 7 year old female patient diagnosed with 19q12 autism spectrum disorder caused by interruptions in q12 region of chromosome 19 affecting genes ZNF536 and TSHZ3. Whole blood samples were collected approximately 3 and 6 months after treatment started with Entrectinib along with a pre-treatment sample (Table 3 ). Blood samples were collected < 24 hours after dosing. Blood was collected in PAXgene tubes and shipped at room temperature to the sequencing facility (Genewiz, NJ) for processing within 24 hours for RNA sequencing. The RNA was extracted using the PAXgene RNA extraction kit. Barcoded libraries for RNA-seq were prepared with 100 ng of RNA using NEBNext Ultra II RNA Library Prep Kit (NEB, Ipswich, MA) for Illumina with QIAGEN QIAseq FastSelect -rRNA HMR Kit (QIAGEN, Germany) per manufacturer’s protocol. Paired-end sequencing (150 bp x 2) was performed on NovaSeq X Plus sequencers (Illumina, San Diego, CA). Data Analysis Bulk RNA-seq Bulk RNA-seq paired fastq files were aligned using Kallisto to the human GRCh38 assembly 62 . Differential expression analysis (DEA) was performed using the R Sleuth package to obtain fold change values by comparing a test group to a control group 63 . Pathway analysis was performed based on fold change values from Sleuth for a control and test comparison using Gene Set Enrichment Analysis (GSEA) using the R ssGSEA package 64 . DRUG-seq The fastq file for the compound treated 384-well plate was aligned and demultiplexed into wells using Kallisto and Bustools for fast quantification of transcripts 62 , 65 . Transcript-level counts were collapsed into gene-level counts for further analysis. Reads were mapped to the human GRCh38 assembly and transcript to gene mapping was performed using gtf version 107. Gene-level fold changes were calculated using pyDESeq2 for differential expression analysis (DEA) comparing wells treated with Entrectinib pairwise at each concentration to DMSO-vehicle control wells 66 , 67 . Prior to the DEA, each quadruplicate set of sample wells at each concentration and the 16 DMSO control wells were checked for quality. Wells with poor sequencing quality due to low sequencing depth (for example, due to cytotoxicity) were removed. Additionally, any DMSO wells that were dissimilar to the ensemble distribution were removed (see supplementary information methods). This sought to exclude any “bad” DMSO wells that deviated from the population and could artificially inflate the number of DE genes. Finally, a number of genes were also removed from the DEA analysis that were detected sparsely throughout the passing DMSO and compound-treated wells (see supplementary information methods). Pathway analysis was performed using the fold change values from pyDESeq2 using gene set enrichment analysis (GSEA) 64 . To accommodate multiple concentrations a modified GSEA approach was utilized. Briefly, gene-level fold changes were ranked for a full concentration series for Entrectinib. An enrichment score was then calculated for each gene-concentration combination using published procedures for GSEA. A combined p-value for fold changes and pathway enrichment scores was calculated using the Fisher test for the individual p-values across multiple timepoints and concentrations. False discovery rate corrected p-values (q-values) are reported where log2(Fold Change) values for individual genes are shown. Clinical evaluation The patient had two neuro-psychology evaluations, one on 6-18-2023 and another on 1-24-2025. The patient was ~ 5 years 11 months at her first evaluation and 7 years 7 months during the second. Both evaluations were additionally performed at the same time of day to minimize any differences due to time of testing. At the evaluations, two tests were administered, the DAYC-2 (Developmental Assessment of Young Children, Second Edition) for cognitive functioning and caregiver reported Vineland Adaptive Behavior Scales for adaptive functioning. The DAYC-2 is a measure of developmental functioning in young children. Information is obtained through direct observation/assessment as well as caregiver report. It helps identify developmental delays, determine strengths, and track progress over time. To understand functional skills Vineland Adaptive Behavior Scales-3 was administered. This is a measure based on parental report and is a standardized measure of adaptive behavior–the things that people do to function in their everyday lives. In addition to the aforementioned physician-performed evaluations, daily evaluations were also conducted by trained teaching staff at the patient’s school. Declarations Acknowledgements We would like to thank the patient’s family, physicians and caregivers for graciously being a part of the study in donating samples and sharing research findings. We would specifically like to thank Dr. David Clayton, M.D., at The Child and Family Counseling Group (VA, USA), for directing patient care. Finally we would like to thank philanthropic contributions for funding the research into rare diseases. Author contributions D. G., U.H., S.T., J.B., N.P., and C.M. contributed to study design. D.G., K.D., and N.P. analyzed the data. 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Biotechnol. 34 (5), 525–527. https://doi.org/10.1038/nbt.3519 (2016). Pimentel, H., Bray, N. L., Puente, S., Melsted, P. & Pachter, L. Differential Analysis of RNA-Seq Incorporating Quantification Uncertainty. Nat. Methods . 14 (7), 687–690. https://doi.org/10.1038/nmeth.4324 (2017). Subramanian, A. et al. Gene Set Enrichment Analysis: A Knowledge-Based Approach for Interpreting Genome-Wide Expression Profiles. Proc. Natl. Acad. Sci. 102 (43), 15545–15550. (2005). https://doi.org/10.1073/pnas.0506580102 Sullivan, D. K. et al. (eds) (Joseph);;;;; Kallisto, Bustools, and Kb-Python for Quantifying Bulk, Single-Cell, and Single-Nucleus RNA-Seq. bioRxiv 2024, 11.21.568164. (2023). https://doi.org/10.1101/2023.11.21.568164 Muzellec, B., Teleńczuk, M., Cabeli, V. & Andreux, M. PyDESeq2: A Python Package for Bulk RNA-Seq Differential Expression Analysis. Bioinforma Oxf. Engl. 39 (9), btad547. https://doi.org/10.1093/bioinformatics/btad547 (2023). Love, M. I., Huber, W. & Anders, S. Moderated Estimation of Fold Change and Dispersion for RNA-Seq Data with DESeq2. Genome Biol. 15 (12), 550. https://doi.org/10.1186/s13059-014-0550–8 (2014). Additional Declarations No competing interests reported. Supplementary Files Supplementary.docx Cite Share Download PDF Status: Published Journal Publication published 25 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 09 Sep, 2025 Reviews received at journal 28 Aug, 2025 Reviewers agreed at journal 28 Aug, 2025 Reviews received at journal 17 Aug, 2025 Reviewers agreed at journal 17 Aug, 2025 Reviewers agreed at journal 05 Jul, 2025 Reviews received at journal 07 May, 2025 Reviewers agreed at journal 28 Apr, 2025 Reviewers agreed at journal 11 Apr, 2025 Reviewers invited by journal 11 Apr, 2025 Editor assigned by journal 11 Apr, 2025 Editor invited by journal 07 Apr, 2025 Submission checks completed at journal 04 Apr, 2025 First submitted to journal 24 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6298254","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":453160552,"identity":"f5ccbac8-0180-45ec-8159-b45289761458","order_by":0,"name":"Drishti Guin","email":"","orcid":"","institution":"Transcripta Bio","correspondingAuthor":false,"prefix":"","firstName":"Drishti","middleName":"","lastName":"Guin","suffix":""},{"id":453160553,"identity":"e5e8a5c4-feb5-4820-b7f8-826225dec237","order_by":1,"name":"Ursula Haditsch","email":"","orcid":"","institution":"Transcripta Bio","correspondingAuthor":false,"prefix":"","firstName":"Ursula","middleName":"","lastName":"Haditsch","suffix":""},{"id":453160554,"identity":"339e8100-4f1a-4787-a6b3-deb5b4c858d5","order_by":2,"name":"Joseph J. Bellucci","email":"","orcid":"","institution":"Transcripta Bio","correspondingAuthor":false,"prefix":"","firstName":"Joseph","middleName":"J.","lastName":"Bellucci","suffix":""},{"id":453160555,"identity":"5100f3a0-88c4-40ea-a2f5-0caa83f19443","order_by":3,"name":"Sabine Topka","email":"","orcid":"","institution":"Transcripta Bio","correspondingAuthor":false,"prefix":"","firstName":"Sabine","middleName":"","lastName":"Topka","suffix":""},{"id":453160556,"identity":"bc6a1658-8ebb-47a6-b8c6-7131c19eac84","order_by":4,"name":"Katherine E. Dyer","email":"","orcid":"","institution":"Transcripta Bio","correspondingAuthor":false,"prefix":"","firstName":"Katherine","middleName":"E.","lastName":"Dyer","suffix":""},{"id":453160557,"identity":"91451295-7a06-4e39-bb32-b99c2b9c2385","order_by":5,"name":"Gabriel A. Rivera Del Toro","email":"","orcid":"","institution":"Transcripta Bio","correspondingAuthor":false,"prefix":"","firstName":"Gabriel","middleName":"A. Rivera Del","lastName":"Toro","suffix":""},{"id":453160558,"identity":"2a53c1fc-8cc0-4f8d-9599-441450c9929d","order_by":6,"name":"Michael R. Blanco","email":"","orcid":"","institution":"Transcripta Bio","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"R.","lastName":"Blanco","suffix":""},{"id":453160559,"identity":"117c69be-bf01-45a5-9e21-d955a151d94e","order_by":7,"name":"Natalie F. Downs","email":"","orcid":"","institution":"Transcripta Bio","correspondingAuthor":false,"prefix":"","firstName":"Natalie","middleName":"F.","lastName":"Downs","suffix":""},{"id":453160560,"identity":"cd76e289-9257-4ea9-9d5c-67986ddf067f","order_by":8,"name":"Nicole Perfito","email":"","orcid":"","institution":"Transcripta Bio","correspondingAuthor":false,"prefix":"","firstName":"Nicole","middleName":"","lastName":"Perfito","suffix":""},{"id":453160561,"identity":"9e1e7a0a-c355-4d14-9982-d65aaae508ac","order_by":9,"name":"Christopher M. Moxham","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYDACCQaGAwwVMJ4BSISxgQgtZwzgWiSI0sLA2GaAxJcg4C7+2b0PD/6c9yeff3b7w88VBXfq+Gc3Nz5gqLGJxmnJneMGh3m3GVjOuHPGWPKMwTMJiTsHmw0YjqXl4nKegUQaw2HGbQYGDDdy2BgbDA5LMNxIbAN65zBeLQd/zjEwkL+R/gysRZ4YLQd4GwwMDG4kmIG1GBDSInED6DCeY8YGhjdyjCUbDJ5JbgT5JQGPX/hnpDF//FEjZyB3I/3hx4Y/d/jlbrc/fPChxganFnRwAEIlEKkcScsoGAWjYBSMAiQAAGhlXTe+wQpKAAAAAElFTkSuQmCC","orcid":"","institution":"Transcripta Bio","correspondingAuthor":true,"prefix":"","firstName":"Christopher","middleName":"M.","lastName":"Moxham","suffix":""}],"badges":[],"createdAt":"2025-03-24 20:53:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6298254/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6298254/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-26015-y","type":"published","date":"2025-11-25T15:56:52+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82558100,"identity":"cd4527aa-1593-4e20-af11-b67d72f40b0a","added_by":"auto","created_at":"2025-05-13 01:20:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":284017,"visible":true,"origin":"","legend":"\u003cp\u003eExpression of MKI67 as a function of neuronal maturation timepoint. Bars showing decrease in total MKI67+ nuclei relative to total live nuclei in (A) the parent and (B) patient lines, respectively. Significance testing was performed using one-way ANOVA testing with Tukey’s multiple comparisons test. ** represents significance \u0026lt; 0.01, and **** represents significance \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6298254/v1/63ac68351ad582abb2205b54.png"},{"id":82558102,"identity":"6441bac3-31b1-47ae-b454-a6c926f1c248","added_by":"auto","created_at":"2025-05-13 01:20:33","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":150033,"visible":true,"origin":"","legend":"\u003cp\u003eExpression levels of ZNF536 and TSHZ3 in neurons derived from patient compared to parent. Error bars show standard error in measurements. (A) Log\u003csub\u003e2\u003c/sub\u003e(Fold change) showing down-regulation of ZNF536 (purple) and TSHZ3 (orange) in patient derived cells compared to parent at various neuronal maturation stages from iPSC to mature 20 day neurons using bulk RNA-seq. Hatched and solid bars show non-significant and significant fold changes, respectively. (B) Comparison of transcript level showing down-regulation of ZNF536 and TSHZ3 in patient (light green) and parental control (cyan) neural progenitor cells using qPCR. ZNF536 and TSHZ3 levels in patient are reduced by 40% (p-value = 0.015)\u0026nbsp; and 48% (p-value = 8.07E-05), respectively. Both down-regulations are statistically significant, p-values calculated using T-test.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6298254/v1/91da57e63c58a5d5c89ec631.jpeg"},{"id":82559505,"identity":"5b2d6e52-7fb6-4542-930d-21add13e92c3","added_by":"auto","created_at":"2025-05-13 01:28:33","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":146605,"visible":true,"origin":"","legend":"\u003cp\u003eCo-regulation of transcription factors ZNF536 and FOXG1 as a function of neuron maturation stage (iPSC, NPC, day 7, day 14 and day 20 neurons). Error bars show standard error in measurements. (A) Transcripts per million of ZNF536 in patient-derived (orange) and parental control (purple) lines. (B) Log\u003csub\u003e2\u003c/sub\u003e(Fold Change) showing down-regulation of FOXG1 in a patient-derived line compared to a parental control. Hatched and solid bars show non-significant and significant fold changes, respectively.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6298254/v1/d34abc8692238d9c7594d9f9.jpeg"},{"id":82558106,"identity":"9110b794-7820-461a-aa72-46ca0773d3bd","added_by":"auto","created_at":"2025-05-13 01:20:34","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":167563,"visible":true,"origin":"","legend":"\u003cp\u003eRegulation of biologically relevant pathways associated with ZNF536 and TSHZ3 and key genes in respective pathways. (A) Normalized enrichment scores as a function of neuron maturation stage showing the relatively lower activity of neurogenesis pathways in patient-derived \u003cem\u003evs.\u003c/em\u003e a parental control line. The individual q-values for each datapoint is shown by marker size. The combined p-value for each pathway across the timepoints was calculated using the Fisher Test and all pathways were significant (p-value \u0026lt; 0.0001). (B-C) Volcano plots showing regulation of key genes in pathways from panel A in patient-derived (B) neural progenitor cells and (C) day 20 neurons compared to a parental control line. Purple and orange points show non-significant and significant fold changes, respectively.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6298254/v1/91301c5f65ae8694ba146977.jpeg"},{"id":82558121,"identity":"61ec47b8-2f53-4166-abb9-7fe885705251","added_by":"auto","created_at":"2025-05-13 01:20:34","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":260535,"visible":true,"origin":"","legend":"\u003cp\u003eTranscriptomic comparison of patient-derived neurons to healthy control. Heatmaps showing (A) Log\u003csub\u003e2\u003c/sub\u003e(Fold change) of key transcription factors and (B) Normalized enrichment score of key signaling and excitatory neuronal pathways in patient-derived \u003cem\u003evs.\u003c/em\u003e a parental control line as a function of neuron maturation stage (iPSC, NPC, day 7, day 14 and day 20 neurons). Inset numbers show magnitude of regulation (Log\u003csub\u003e2\u003c/sub\u003e(Fold change) and normalized enrichment score). Redder and greener colors show lower and higher activity, respectively, in patient-derived compared to a parental control line.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6298254/v1/30e68c9a0a440b7227f5d470.jpeg"},{"id":82558110,"identity":"62c844bd-21bf-4a3c-91a1-d292f9c775ec","added_by":"auto","created_at":"2025-05-13 01:20:34","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":345355,"visible":true,"origin":"","legend":"\u003cp\u003eNormalized enrichment scores of relevant pathway sets associated with disease signature of 19q12 autism spectrum disorder averaged across differentiation timepoints. Redder and greener colors show lower and higher activity, respectively, in patient-derived compared to a parental control line for (A) neurotransmitter export and trafficking associated with key glutamatergic marker gene SLC17A6 and (B) synapse transmission and organization. A combined p-value for each pathway across timepoints was calculated using the Fisher Test for the individual p-values. Pathways associated with SLC17A6 and synapse transmission all have p-values \u0026lt; 0.05 and bubble sizes correspond to these p-values.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6298254/v1/7fb1123d540bee2d71e93154.jpeg"},{"id":82558128,"identity":"c8b8e773-3d78-4f67-9fb7-ae012c8f49e6","added_by":"auto","created_at":"2025-05-13 01:20:34","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":158631,"visible":true,"origin":"","legend":"\u003cp\u003eActivity of Entrectinib on NGN2-derived wildtype neurons. (A) Cosine correlation coefficient calculated using the normalized enrichment scores. Negative correlations imply reversal of disease signature. The correlation p-value is the significance of the response as a function of concentration, calculated by fitting the correlation as a function of the compound concentration using a linear model. (B) average enrichment score of each class of signature pathways in table 1 for patient-derived day 20 neurons (labeled: patient neurons) compared to a parental control line or NGN2-derived neurons treated with Entrectinib at various concentrations compared to DMSO vehicle control. The p-value for the response of each pathway set to treatment concentration is calculated by fitting the response as a function of concentration using a linear model. Greener colors indicate pathways enriched and red colors indicate depletion. Marker sizes correspond to p-values for each pathway set calculated by combining individual pathway p-values using Fisher test.\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6298254/v1/f53c49b87321938ee18a5605.jpeg"},{"id":82558126,"identity":"d3f4b62f-aa5b-4d06-bd2c-c7dcb1c6da0c","added_by":"auto","created_at":"2025-05-13 01:20:34","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":148546,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of Entrectinib treatment in a patient characterized using whole blood RNA-sequencing post 3 (purple) and 6 (orange) months on Entrectinib. (A) Log\u003csub\u003e2\u003c/sub\u003e(Fold change) of target gene NTRK2 and target pathways of Entrectinib identified in patient blood. Error bars show standard error in measurement. Hatched and solid bars signify non-significant and significant fold changes, respectively. (B) Cosine correlation coefficient calculated using the log\u003csub\u003e2\u003c/sub\u003e(Fold change) between sibling and the post-treated gene fold changes compared to the pre-treated patient whole blood sample.\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6298254/v1/260865cfc6a7ff6e0059e795.jpeg"},{"id":82560725,"identity":"de581ea5-0e85-4654-bd2c-2c1db2b0ee79","added_by":"auto","created_at":"2025-05-13 01:36:34","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":127991,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano plots showing regulation of genes in disease signature pathways in (A) patient pre-treated \u003cem\u003evs.\u003c/em\u003e sibling control, (B) patient 3 month post-treated \u003cem\u003evs.\u003c/em\u003epre-treated and (C) patient 6 month post-treated \u003cem\u003evs.\u003c/em\u003e pre-treated. Purple and orange points show non-significant and significant fold changes, respectively.\u003c/p\u003e","description":"","filename":"floatimage9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6298254/v1/3b2a184941bce4d9ed9e2ccd.jpeg"},{"id":82559504,"identity":"5af7d4f2-a3fd-4a25-8237-8b017e330aef","added_by":"auto","created_at":"2025-05-13 01:28:33","extension":"jpeg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":112308,"visible":true,"origin":"","legend":"\u003cp\u003eRARA expression profile in RNA-seq of whole blood from patient and unaffected sibling control pre- and post-treatment with Entrectinib. Entrectinib normalized both RARA levels and retinoic acid signaling activity in the patient when compared to a sibling control. (A) RARA TPM in sibling (yellow), patient pre-treatment (magenta), 3 and 6 months post-treatment (purple and salmon, respectively). (B) Heatmap showing change in RARA expression in a pre-treated sample compared to sibling and post-treatment samples compared to pre-treated. All fold changes are significant with q-values \u0026lt; 1E-6. (C) Activity of pathways associated with retinoic acid (RA) signaling in a pre-treated sample compared to sibling and post-treatment samples compared to pre-treated.\u003c/p\u003e","description":"","filename":"floatimage10.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6298254/v1/8feead7d10ff39e08d18abb2.jpeg"},{"id":82558117,"identity":"9fa69d33-23c6-40ee-9f12-aead59c58cbf","added_by":"auto","created_at":"2025-05-13 01:20:34","extension":"jpeg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":154731,"visible":true,"origin":"","legend":"\u003cp\u003eRaw scores for physician reported Vineland Adaptive Behavior Scales (Version 3) sub-domains pre-treatment for evaluation conducted in 2023 (pink) and post-treatment for evaluation conducted in 2025 (orange).\u003c/p\u003e","description":"","filename":"floatimage11.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6298254/v1/ba94e955402a3b4cfcef8904.jpeg"},{"id":97178755,"identity":"21bf5254-ba39-4294-a494-d0eb2698f295","added_by":"auto","created_at":"2025-12-01 16:13:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2914633,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6298254/v1/2400ebaf-f217-466c-97b8-4d763c74d8ee.pdf"},{"id":82558115,"identity":"48bb5a0d-69d6-42ae-80bb-49569c9e7930","added_by":"auto","created_at":"2025-05-13 01:20:34","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":7110667,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-6298254/v1/7f8921bae14f8b70e759b884.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"High-Throughput Drug Discovery for a Rare Neurological Disorder: Uncovering a Novel Therapeutic Opportunity for the 19q12 Autism Spectrum Disorder","fulltext":[{"header":"Introduction","content":"\u003cp\u003eA rare disease or disorder is defined as such if it affects\u0026thinsp;\u0026lt;\u0026thinsp;200,000 individuals in the US at any given time \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. There are over 7000 rare diseases and while these diseases are individually uncommon, aggregated they affect over 30\u0026nbsp;million people in the US \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Rare disorders pose a challenging issue for the drug discovery community. While molecular genetics has helped uncover the cause in many cases, with limited resources available, research and development of viable therapies for these diseases has become exceedingly difficult. Therefore, pipelines focused on discovering new opportunities at speed and scale for available FDA approved small molecules coupled with robust, statistically viable n-of-1 trials that can establish efficacy and support regulatory approval of these therapies have the potential to be highly successful and lower cost for patients.\u003c/p\u003e \u003cp\u003eOne such rare genetic disorder is a form of autism spectrum disorder (ASD) caused by structural variations in the 19q12 band of chromosome 19. The first known research of ASD associated with 19q12 was published in 2016 \u003csup\u003e2,3\u003c/sup\u003e but known cases had been recorded before 2016 \u003csup\u003e4\u0026ndash;6\u003c/sup\u003e. These structural variations lead to multiple gene-level changes in the q12 band of chromosome 19 including the genes ZNF536 and TSHZ3.\u003c/p\u003e \u003cp\u003eZNF536 and TSHZ3 encode zinc-finger proteins that act as regulators of transcription of other genes along with a myriad of other diverse functions \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Both ZNF536 and TSHZ3 have been implicated in a network of complex biological processes that drive neurogenesis and neuronal differentiation of progenitors to neurons during development \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. The down-regulation of these proteins was observed in patient-derived neurons along with the down-regulation of a network of other transcription factors and biological pathways implicated in neurogenesis and neuron differentiation. Together these results suggest that 19q12 ASD causes aberrant neurogenesis leading to disruption of synaptic connectivity of these cultured neurons \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHere, we demonstrate an end-to-end pipeline that can be used to profile hundreds of FDA-approved small molecules at scale using low-depth RNA sequencing as described in the DRUG-seq protocol \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. In recent years RNA-seq has evolved into a powerful tool for profiling the cellular response to various perturbants at comparatively lower cost. RNA sequencing provides a genome-wide snapshot of the transcriptomic state, thereby allowing more granular observation and monitoring of the pharmacodynamic response \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Because of the large amount of information uncovered about the cell response to a molecule, this procedure is well suited for discovering novel repurposing opportunities \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eUsing the disease signature developed from profiling patient neurons, along with profiles of hundreds of FDA-approved drugs generated from DRUG-seq, we identified a potential therapeutic opportunity for 19q12 ASD. This drug was further tested in an n-of-1 trial in a patient and drug response was tracked using RNA-seq of whole blood collected from the patient over a period of 6 months. The RNA-seq results showed improvement of disease causing pathways and clinicians associated with the patient also reported improvements in various clinical endpoints.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eIdentification of root cause genes in patient with 19q12 ASD\u003c/p\u003e \u003cp\u003eA 3 year old female diagnosed with autism spectrum disorder (ASD) and global developmental delay was enrolled in an approved research protocol (WCG IRB protocol #2001-01) and provided whole blood samples for genetic characterization and cell model development. Clinical SNP microarray testing performed on the patient and both parents had revealed multiple regions of de-novo copy number variation, including both gains and losses on the q-arm of chromosome 19 in the affected child. Additional genetic evaluations were performed on a research basis to further investigate the 19q12 copy number variation (CNV) and identify causative gene candidates which may be related to her clinical presentation. This included Optical Genome Mapping to further characterize the large structural variations in 19q12; long-read Whole Genome Sequencing (10X Genomics Linked Reads) to identify breakpoints and sequence potentially involved genes; and RNA sequencing of lymphocytes to evaluate relative gene expression. Based on this data (Supplementary Information), two candidate disease-relevant genes were identified: ZNF536 and TSHZ3. Both genes were affected differently, a deletion in the region chr19:31138482\u0026ndash;31266070 included TSHZ3 deletion (chr19:31149979\u0026ndash;31349436) and the region encoding ZNF536 was both inverted and duplicated. Both TSHZ3 deletion as well as ZNF536 duplication have been previously implicated in a form of autism spectrum disorder \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo further validate the transcriptional effect of the observed 19q12 alterations, induced pluripotent stem cells (iPSCs) were generated from both healthy parent and affected child. The iPSC lines were then differentiated to produce neural progenitor cells (NPCs) as described in the Methods. Both these cell lines displayed a morphology characteristic of neural progenitors as assessed by immunocytochemical (ICC) staining (Supplementary Fig. S4) for the expression of Ki-67, a marker for proliferation. The expression of MKI67 decreased as a function of neuronal maturation day in both parent (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA) and patient (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB) lines, showing the evolution of progenitors to a mature neuronal population.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe NPC lines were matured into neurons as described for 20 days and gene expression was profiled at multiple time points during maturation using RNA sequencing. Differential expression analysis comparing patient to control parent line showed significant down-regulation of both ZNF536 (q-value corrected p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and TSHZ3 (q-value\u0026thinsp;\u0026lt;\u0026thinsp;0.1) in affected child derived cells starting at the NPC stage and persisting at all neuronal timepoints sequenced (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The down-regulation of ZNF536 is correlated (Pearson correlation coefficient = -0.81, p-value\u0026thinsp;=\u0026thinsp;0.073) with maturation time point in derived neurons, such that ZNF536 down-regulation increased as neurons matured.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe down-regulation of TSHZ3 and ZNF536 was further validated by qPCR at the NPC stage and showed significant (calculated using Student\u0026rsquo;s T-test between parent and patient) down-regulation in NPCs from the affected child when compared to parent NPCs (ZNF536 p-value\u0026thinsp;=\u0026thinsp;0.015, TSHZ3 p-value\u0026thinsp;=\u0026thinsp;8.066E-05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eZNF536 is a novel zinc-finger protein that is highly expressed in the brain and plays an important role in neuronal differentiation during early brain development. Repression of ZNF536 has been shown to cause aberrant enhancement of retinoic acid induced neuronal differentiation in p19 cells \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. The authors further showed that ZNF536 increased at early stages of neuronal differentiation while later stages displayed constant levels of ZNF536.\u003c/p\u003e \u003cp\u003eData collected from RNA-seq of iPSC derived neurons from affected child and parent control showed similar trends (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), specifically transcript counts per million (TPM) of ZNF536 increased rapidly from the iPSC to NPC and early neuronal stages (day 7 and day 14), whereas at day 20, ZNF536 levels remained fairly unchanged compared to day 14.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTSHZ3 is similarly another zinc-finger protein \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, and represents a hub gene in a module of co-expressed genes that control early cortical development \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Among the other regulatory elements in this module, FOXG1, a brain-specific transcription repressor was observed to be additionally highly significantly (q-value\u0026thinsp;\u0026lt;\u0026thinsp;1E-23) down-regulated in neurons derived from the affected child compared to the patient iPSC cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eZNF536 and TSHZ3 lead to aberrant neurogenesis in patient cells\u003c/p\u003e \u003cp\u003eIn neurons derived from the patient we observed a complex neuronal phenotype compared to the control line. Analysis of gene ontology (GO) revealed that the top pathways commonly associated with these regulatory genes \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e shared a common theme of being associated with neurogenesis and neuron differentiation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Additionally, a collection of other significant pathways searched by keywords associated with neurogenesis such as neuron differentiation, neurodevelopment, axonogenesis, etc. were also majorly down-regulated starting at the NPC stage showing premature down-regulation of neurogenesis (Supplementary Fig. S5). The p-value for each pathway was calculated using the Fisher method across the patient sampling timepoints (Supplementary Table S2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe lower effect size of these neurogenesis pathways were used to reveal a number of significantly (q-value\u0026thinsp;\u0026lt;\u0026thinsp;0.1) down-regulated genes in patient-derived neurons compared to the parental control line (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB-C, Supplementary Fig. S6). Of the top significantly down-regulated genes, a large number were regulatory elements involved in neurogenesis and neuronal differentiation; SLIT1 \u003csup\u003e16,17\u003c/sup\u003e, NKX6-1 \u003csup\u003e18\u003c/sup\u003e, NKX6-2 \u003csup\u003e19\u003c/sup\u003e, ZNF536 \u003csup\u003e8\u003c/sup\u003e, FOXG1 \u003csup\u003e20\u003c/sup\u003e, FOXA1 \u003csup\u003e21\u003c/sup\u003e, HOXA2 \u003csup\u003e22\u003c/sup\u003e, DLX1 and DLX2 \u003csup\u003e23\u003c/sup\u003e etc (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTwo important signaling pathways associated with cell proliferation, the sonic hedgehog signaling pathway via the gene SHH and regulation of the Notch signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, top three rows), showed lower effect size in the patient line compared to parental control line \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. The Notch signaling pathway is most often co-annotated with neurogenesis pathways indicating that the Notch signaling machinery plays an important role in the formation of new neurons or neuronal proliferation \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe down-regulation of widespread regulatory genes involved in neurogenesis as well as signaling pathways that control cell proliferation, implies that the evolution of iPSC to progenitors to matured neurons in the cell line from the affected child is disrupted.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn addition to the regulatory genes, we also observed a significant (q-value\u0026thinsp;\u0026lt;\u0026thinsp;1.5E-38) down-regulation of the gene NTN1. NTN1 or Netrin-1 is implicated in neurodevelopmental processes and inactivation of NTN1 leads to disruption of excitatory synaptic connectivity \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. The down-regulation of NTN1 is perhaps further evidence of disrupted neurogenesis via aberrant axon development \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eInterestingly, while we observed the down-regulation of neurogenesis markers as well as the disruption of the excitatory synaptic plasticity marker gene NTN1, it was also accompanied by significant up-regulation of SLC17A6 (q-value\u0026thinsp;\u0026lt;\u0026thinsp;5E-11). SLC17A6 (Solute Carrier Family 17 Member 6) encodes the Vesicular Glutamate Transporter 2 (vGLUT2) protein, a key marker gene for excitatory neurons \u003csup\u003e\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. An overexpression of vGLUT2 has been previously linked with an autism spectrum disorder due to MEF2C haploinsufficiency leading to overexcitability and synaptic dysfunction \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. The overexpression of this key glutamatergic/excitatory marker gene was also observed with over-activity of the excitatory neuron specific genes derived previously from single-cell RNA-seq characterization of the human embryonic cerebral cortex \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, bottom row).\u003c/p\u003e \u003cp\u003eTaken together, these results indicate that the neurons derived from the affected-child mature faster, showing premature down-regulation of neurogenesis related transcription factors and pathways that drive cell proliferation such as the sonic hedgehog signaling pathway. This aberrant neurogenesis led to formation of a population of neurons that prematurely and significantly over-expressed glutamatergic neuronal markers such as mGlut2 (SLC17A6) and showed overactive excitatory gene markers.\u003c/p\u003e \u003cp\u003eThus, the mis-regulation of ZNF536 and TSHZ3 during development induces a complex dynamic of gene-level changes that drive and control early neuronal differentiation and eventually lead to changes in synaptic activity in the matured neuronal state.\u003c/p\u003e \u003cp\u003eIdentification of a disease signature associated with 19q12 ASD\u003c/p\u003e \u003cp\u003eWhile 19q12 autism spectrum disorder is a brain-specific disorder, generating disease specific neurons is both expensive and time consuming. To overcome this, we generated a disease signature focused mainly on biological pathways as targets that overcomes cell type specificity of specific genes, for example both ZNF536 and TSHZ3 are highly brain specific.\u003c/p\u003e \u003cp\u003eThe reliance of pathways on gene sets ensures that it is able to capture representative activity even in the absence of a subset of tissue specific genes. Additionally, the use of disease signatures as a target enables a more system-wide comprehensive understanding of the molecular changes in the disease for considerable time and cost savings as well as a strategy to generate biomarkers for the disease that can be assessed in the patient during treatment \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe pathways identified above showed overwhelmingly that neurogenesis in the patient with 19q12 autism spectrum disorder was accelerated. However, since most neurogenesis happens prenatally, these pathways may not be representative of current neurodevelopmental status in the patient \u003csup\u003e\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. We therefore focused on two other aspects of functional pathways in the patient associated with the gene regulations observed in the last section.\u003c/p\u003e \u003cp\u003eWe first focused on pathways associated with the transporter gene SLC17A6. Since SLC17A6 is an important marker gene for glutamatergic neurons, its mis-regulation indicates a key disease-specific marker for 19q12 ASD. Pathway enrichment analysis revealed that SLC17A6 was a top ranked gene for pathways associated with neurotransmitters including anion (glutamate) transport as well as glutamatergic synaptic transmission and sensory system development (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). The protein vGLUT2 encoded by SLC17A6 is known to play an important role in synaptic transmission and plasticity by filling vesicles with the neurotransmitter glutamate \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. SLC17A6 imbalance has also been implicated in some sensory processing impairments in SLC17A6 KO mice \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. A majority of all significant pathways associated with SLC17A6 were enriched in patient neurons compared to control during neuronal differentiation, similar to up-regulation of the gene SLC17A6 observed in the patient (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, supplementary Fig. S7A, supplementary table S3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNeurons in the brain function via the formation and transmission of synapses between each other to perform a wide variety of complex tasks \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Since synapses are the backbone of healthy neuronal function, it can be expected that the neurons formed from the aberrant neurogenesis observed in the 19q12 patient neurons also lead to some impaired synaptic behavior. Moreover, excitatory synaptic transmission is one of the key functions of SLC17A6, a key marker gene in excitatory neurons. We, therefore, looked at synaptic pathways significantly regulated in the patient neurons compared to the control (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB, supplementary Fig. S7B, supplementary table S4). Similar to the results observed with the pathways associated with SLC17A6, a majority of the synaptic pathways also showed higher activity in the patient neurons. This increased synaptic transmission activity agrees with previously published results showing that increase in vGLUT2 (SLC17A6) eventually leads to enhanced excitatory neurotransmission in mice with MEF2C haploinsufficiency \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe two identified pathway classes; related to synapse formation and neurotransmission (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e); impaired in 19q12 therefore, highlight the mis-regulated neuronal function in the patient. Combined, these pathways can then be utilized as the disease signature for 19q12 ASD (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Contrary to neurogenesis, evolution of synaptic plasticity occurs well into adulthood and has also been implicated in autism spectrum disorder (ASD), and is therefore relevant to the disease biology \u003csup\u003e\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTable showing the two main classes of pathways identified in the disease signature for 19q12 ASD, the average overall direction of regulation and the number of individual pathways constituting each class.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathway Class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall direction of regulation in neurons\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of constituent pathways\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeurotransmitter transport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSynaptic transmission/plasticity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eLow dose Entrectinib reverses disease signature in a high throughput drug screen\u003c/p\u003e \u003cp\u003eUsing a modified DRUG-seq protocol as described in the Methods section, a collection of bioactive, FDA approved molecules were profiled in NGN2-derived glutamatergic neurons. Neurons were matured for 15 days on a 384-well plate followed by treatment with each respective drug in 10-point concentration responses for 72 hours and sequenced to profile the transcriptomic response to each respective compound-concentration. Parallel to sequencing, a high content imaging assay was used to generate phenotypic profiles of the neurons under drug treatment (supplementary information).\u003c/p\u003e \u003cp\u003eOf the small molecules profiled is Entrectinib, an FDA approved potent inhibitor of tropomyosin receptor kinases (TRKs) and ROS1. Entrectinib is approved for use in treating neurotrophic tyrosine receptor kinase gene (NTRK)-fusion positive solid tumors \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCytotoxicity of Entrectinib during the screen was assessed using the lactate dehydrogenase (LDH) assay as well as from imaging metrics (Supplementary Fig. S8). Entrectinib was only mildly cytotoxic to the cells at concentrations between 14 nM to 3.33 \u0026micro;M and caused almost complete cell death at concentrations above that. No cytotoxicity was recorded at concentrations below 14 nM. Transcriptomic response of NGN2-derived glutamatergic neurons to low-dose Entrectinib treatment (Entrectinib concentration\u0026thinsp;\u0026le;\u0026thinsp;14 nM) showed reversal of signature pathways that were mis-regulated in neurons derived from patient. The extent of reversal of signature pathways was estimated using a cosine correlation coefficient of the response in patient-derived neurons \u003cem\u003evs.\u003c/em\u003e control and the response in the screened glutamatergic neuron line under Entrectinib treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA-B). A cosine correlation measures the similarity between two vectors in space such that a negative value implies opposing vectors and vice versa.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe negative correlation shows that Entrectinib\u0026rsquo;s effect on the signature pathways is opposite of the observed activity in patient neurons and therefore could be used to normalize pathway activity in 19q12 ASD. Entrectinib\u0026rsquo;s effect on pathways was also dose-dependent such that the magnitude of correlation increased with increase in dose up to 14 nM. At concentrations\u0026thinsp;\u0026gt;\u0026thinsp;14 nM, with the onset of cytotoxicity, the disease signature reversal was lost (Supplementary Fig. S9). These concentrations that conferred therapeutic reversal of disease signature pathways in the \u003cem\u003ein vitro\u003c/em\u003e neuronal model were much lower than the steady state plasma concentration of 2 \u0026micro;M observed in patients being prescribed the FDA approved dosage for the treatment of cancer \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePathways associated with neurotransmitter transport and synapse formation and transmission were all regulated in the opposite direction from what was observed in patient neurons (Supplementary Fig. S10, Supplementary tables S5 and S6).\u003c/p\u003e \u003cp\u003eEffect of low dose Entrectinib on patient transcriptomic profile from whole blood RNAseq\u003c/p\u003e \u003cp\u003eThe patient identified with 19q12 associated autism spectrum disorder was prescribed low-dose (significantly lower than that approved therapeutically for pediatric cancer) Entrectinib under the supervision of a physician (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDosing schedule of Entrectinib in patient.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStart date\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnd date\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDose\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2023-12-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2024-02-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e200 mg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEvery 4 days\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2024-02-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2024-06-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e400 mg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEvery 5 days\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2024-06-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2024-08-15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e400 mg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEvery 5 days, with an additional dose 2 days after each regular dose\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2024-08-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOngoing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e400 mg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEvery 5 days\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBlood was collected during treatment and whole blood RNA-seq was performed to track therapeutic effect of Entrectinib in the patient. A pre-dose blood collection was performed before treatment started and two post-dose collections at approximately 3 and 6 months after treatment started were performed 24 hours after drug intake (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Entrectinib dose was doubled between the first and second post-dose collection. In addition to patient blood, a blood sample was also collected from an unaffected age-matched sibling to serve as a control.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDates of the pre-dose and the 2 post-dose blood collections used for whole blood RNA sequencing to probe the effect of Entrectinib in patient.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCollection date\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePre-dose collection\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e2023-11-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePost-dose collection 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e2024-03-13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePost-dose collection 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e2024-07-11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn line with Entrectinib\u0026rsquo;s known mechanism of action we observed a small but significant (q-value\u0026thinsp;\u0026lt;\u0026thinsp;0.0005) down-regulation of its target gene NTRK2 \u003csup\u003e45\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). This validated that marker genes of the pharmacodynamic effect of Entrectinib were captured in whole blood RNA-seq from patient. Interestingly, pathways normally inhibited under approved dosage of Entrectinib; MAPK/ERK signaling, signaling by phospholipase-\u0026#120632; and PI3K/AKT signaling \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e; are activated in the patient (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn a study of Entrectinib-resistant cancer lines a similar biological activity was observed that is opposite of its intended outcome, with increased PI3K/AKT and MAPK/ERK signaling conferring resistance towards Entrectinib \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. This phenomenon, when a drug causes an effect that is the opposite of its intended outcome, is known as the paradoxical drug effect \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Various other studies have demonstrated similar paradoxical effects for other drugs at low concentrations compared to higher FDA-approved concentrations \u003csup\u003e\u003cspan additionalcitationids=\"CR53 CR54\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. We therefore hypothesized that the effect observed in the patient is a compensatory mechanism specific to the low dosage conferring resistance-like activity unlike its anti-cancer activity observed at higher doses \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. This activity is inversely related to dosage, such that pathway activity decreases at higher doses (at month 6) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA, orange bars).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe leading causative genes associated with the two classes of dysregulated pathways in the patient were examined pre-treatment in the blood of the patient compared to a sibling control using whole blood RNA sequencing. Whole blood RNA sequencing provides insight on how a drug changes the transcriptome in a relatively cheap and non-invasive manner, allowing us to track the effect of drug in the patient pre- and post-treatment in real time \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFold changes were calculated for the sibling control and each of the two post-treated samples compared to an untreated blood collection. A cosine correlation coefficient was calculated for the fold changes of the leading genes in the disease pathways between the sibling control and the post-treatment samples to assess if Entrectinib showed reversal in the patient similar to its activity in the neuronal model (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). Cosine correlation between sibling and treated samples increased with treatment time showing normalization of gene expression profile in the patient towards sibling control levels with Entrectinib treatment. Unlike the pharmacodynamic effect shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA, a significant dose-specific decrease was not observed for the cosine correlation.\u003c/p\u003e \u003cp\u003eIn the patient pre-treatment blood samples, a large number of the identified genes from the disease signature pathways were down-regulated compared to the sibling control (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). Both 3 and 6 month post-treated samples showed that Entrectinib reversed the expression pattern of these genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB-C). Additionally, some genes that were up-regulated at the lower-dosage (3 months post-treatment) were down-regulated at the higher dosage, further emphasizing the anti-correlation of therapeutic effect to dosage.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThese results indicate that Entrectinib at low dose in the patient was able to reverse the disease phenotype associated with 19q12 ASD.\u003c/p\u003e \u003cp\u003eRescue of disease phenotype via increased expression of RARA\u003c/p\u003e \u003cp\u003eA key regulator of synaptic plasticity that emerged from this study is the signaling protein retinoic acid receptor alpha encoded by the RARA gene. RARA has varied roles in the formation of neurons during embryonic development and later in neuronal function \u003csup\u003e\u003cspan additionalcitationids=\"CR58\" citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDuring brain development, RARA and ZNF536 are closely associated in function; while RARA promotes neuronal differentiation via transcription of neuron-specific marker genes, ZNF536 negatively regulates this by inhibiting RARA activity \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. This is in line with observations in patient derived neurons with depleted ZNF536, that showed accelerated differentiation into excitatory neurons and premature over-expression of excitatory marker genes.\u003c/p\u003e \u003cp\u003ePost-development, the role of RARA is closely associated with maintaining homeostasis in synaptic plasticity: (1) by activating BDNF synthesis and driving BDNF-TrkB signaling to achieve presynaptic homeostatic plasticity and (2) promoting translation of AMPA receptors to achieve postsynaptic homeostatic plasticity \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. RARA is expressed uniformly across tissue types and can be easily tracked in RNA-seq of whole blood. RARA was found to be significantly (q-value\u0026thinsp;\u0026lt;\u0026thinsp;1E-8) down-regulated in the patient compared to a sibling control (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA-B). Administration of Entrectinib normalized RARA levels in patient blood and was also accompanied by normalization of retinoic acid receptor signaling activity relative to the sibling control (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA-C). Entrectinib treatment was also accompanied by normalization of AMPA and BDNF signaling pathways (Supplementary Fig. S11).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRARA up-regulation, therefore, could be instrumental in rescuing the dysregulated genes associated with 19q12 ASD in the patient.\u003c/p\u003e \u003cp\u003eClinical Outcomes: Observed Patient Improvements Following Treatment\u003c/p\u003e \u003cp\u003eApproximately 2 months after dosage was doubled, it was dropped back to 400 mg every 5 days, as the parents reported regression in positive improvement observed at lower dose (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This reversal at higher dose is expected for a low-concentration specific paradoxical drug response, as in this case with Entrectinib.\u003c/p\u003e \u003cp\u003eTo monitor for cognitive, behavioral, and developmental changes, the patient underwent virtual neuropsychology evaluations with a trained provider. Nineteen months after the pre-treatment evaluation (see methods), the clinician reported that the patient presented to be calmer, had dramatically improved communication with caregivers, and did not exhibit any frustration-related disruptive behaviors that had been observed during the baseline evaluation. This was in alignment with the observed reduction in challenging behaviors post-treatment by the parents.\u003c/p\u003e \u003cp\u003eQuantitatively, the patient\u0026rsquo;s DAYC-2 overall cognitive score was consistent with a 23 month old child, which was increased from the 17 month old cognitive level that was noted at the baseline evaluation. The patient\u0026rsquo;s cognitive developmental level had plateaued around that of a 2 year old child, however a total of 6 months of progress within 2 years is noteworthy compared to 17 months of progress made within her first 5 years, suggesting at least some speed up in cognitive development post-treatment.\u003c/p\u003e \u003cp\u003eSignificant qualitative improvements were also demonstrated by caregiver reported Vineland measures (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e). Raw scores for communication all showed growth, with remarkable improvement in written scores that nearly doubled post-treatment, as well as in daily living skills. For example, at the follow-up evaluation the patient was reportedly toilet trained, which had been a major challenge for caregivers. Additionally, the patient made an extraordinary 6 months worth of progress in her personal domain. Caregivers also reported changes in the child\u0026rsquo;s ability to navigate and function within the community and although assistance and supervision were still required, there was notable progress reported in her ability to communicate her needs and tolerate frustration. Socially, the patient made meaningful qualitative progress, i.e. engaging with other children outside of her parental circle.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe patient was also evaluated routinely at her school by both special education teachers and a speech therapist who evaluated her on her ability to follow two-step directions as well as conversationality. Both these skills measure cognitive development and functional skills \u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. The evaluation began at the end of August just after the dose was decreased. Within a few months, both teachers and the speech therapist reported marked improvement in the patient\u0026rsquo;s ability to follow two-step directions and conversationality.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eRecently, more accessible and cheaper sequencing technologies have allowed much deeper biological insight into systems of interest than was previously possible. RNA sequencing, for example, has enabled the probing of the whole transcriptome in a single shot under different types of perturbations. Development of high-throughput RNA-sequencing technologies like DRUG-seq can be leveraged to scale up drug discovery, focused on discovering novel mechanisms of action and targets for known and novel small molecules. This is uniquely suited but not limited to the challenge of drug discovery for rare diseases. Research and development resources for individual rare diseases are very limited due to the small population sizes and the massive cost of discovering new therapeutics in the traditional pharmaceutical development paradigm.\u003c/p\u003e \u003cp\u003eTo circumvent these challenges, we have developed a drug discovery platform focused on speed and scale while remaining statistically rigorous, using high-throughput RNA sequencing. We demonstrate how RNA-seq can be employed end-to-end; to generate target hypotheses, novel therapeutic opportunities for those targets and finally be used to track appropriate therapeutic endpoints post-treatment in a patient.\u003c/p\u003e \u003cp\u003eA patient, aged 7 years, diagnosed with a complex neurodevelopmental genetic disorder resulting in autism spectrum disorder and global developmental delay first diagnosed at 3 years old, was enrolled in an IRB approved research protocol to find new opportunities for available FDA approved drugs. Through whole genome sequencing and optical genome mapping, the genetic causes were narrowed down to rearrangements and deletions in the q12 arm of chromosome 19. Two root cause genes, ZNF536 and TSHZ3, were identified, both involved in neurogenesis during embryonic development. We discovered by comparing neurons derived from patient iPSCs to unaffected familial control that both identified root cause genes were down-regulated during neuronal maturation in the patient-derived neurons. We further discovered that the neurons derived from the patient iPSCs showed a complex down-stream phenotype with over-activity of synaptic transmission and glutamatergic neurotransmitter transport pathways. Using this disease signature we identified a drug, Entrectinib, in a high-throughput drug screen in glutamatergic neurons that generated an opposing signature at low concentrations.\u003c/p\u003e \u003cp\u003eThe patient was prescribed low-dose Entrectinib under the supervision of a physician. The progress in the patient was tracked using whole blood RNA-sequencing pre- and post-treatment along with a control sample from an unaffected sibling. Pre-treatment, the genes involved in the disease signature pathways were dysregulated compared to the control sample but normalized post-treatment within 6 months to levels in the control sample. The re-regulation of synaptic plasticity and neurotransmitter transport potentially occurs via up-regulation of the gene RARA post treatment with Entrectinib. RARA, a key regulator of maintaining synaptic homeostasis, was down-regulated in the patient pre-treatment and is also closely associated with the root cause gene ZNF536. Entrectinib up-regulated RARA in the patient comparable to levels in the unaffected sibling.\u003c/p\u003e \u003cp\u003eThis study reveals a potential path to approaching discovery of novel therapeutic opportunities by leveraging high-throughput RNA-seq to profile drugs at scale in a model system and identifying these opportunities based on normalization of a disease signature as target and tying in a statistically-valid n-of-1 trial to speedily and safely assess the drug in a patient population. Such an approach provides a pipeline that can be utilized at scale for any disease for uncovering novel therapeutic opportunities for considerable cost-savings.\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003ePatient Cell Model Generation\u003c/p\u003e\n\u003ch3\u003eiPSC generation\u003c/h3\u003e\n\u003cp\u003eAll patient cells described in this section were collected with consent from the participant or the legal guardian of the participant in accordance with protocols reviewed and approved by the WGC Institutional Review Board (IRB) (WCG IRB protocol #2001-01). Patient fibroblasts were collected via skin punch biopsy performed by a physician. The samples were then sent to Sampled (Piscataway, NJ) for isolation and cryopreservation of fibroblasts.\u003c/p\u003e \u003cp\u003eInduced pluripotent stem cells (iPSCs) were generated from two patient-derived fibroblast lines (JP0000009 and JP0000013) reprogrammed using the StemRNA 3rd Generation Reprogramming kit (REPROCELL Inc). 6 clones from each cell line were picked and expanded; 3 clones for each cell line (JP9.4, JP9.5, JP9.6, JP13.1, JP13.2 and JP13.3) were characterized via G-banding for karyotyping and immunofluorescent staining of two pluripotency markers.\u003c/p\u003e\n\u003ch3\u003eNPC generation and expansion (Columbia University, Corneo Barbara)\u003c/h3\u003e\n\u003cp\u003eNeural progenitor cells (NPCs) were generated from (iPSCs from both healthy parent and affected child using a modified 2D cortical glutamatergic protocol based on the STEMdiff neural system (STEMdiff\u0026trade; SMADi Neural Induction Kit, Stem Cell Technologies). iPSCs were plated in a 96-well spheroid plate and treated with neural induction media containing SMAD inhibitors (SMADi) and basic fibroblast growth factor (bFGF, 5ng/ml). On day 5, embryoid bodies (EBs) were replated onto Matrigel-coated plates. Over the next several days, media was gradually switched to N2/B27 media without Vitamin A ((DMEM/F12 (Gibco 11330032), Neurobasal medium (Gibco 21103-049), N2-B supplement (Stem Cell Technologies), B27 supplement without Vitamin A (Gibco 12587-010), Glutamine (Gibco), Non-Essential Amino Acids (Gibco 11140-050), and beta-Mercaptoethanol (Gibco)), supplemented with SMADi (LDN193189 (250nM), SB431542 (10\u0026micro;M)), and ascorbic acid (200\u0026micro;M). Neural rosettes formed and were selectively replated on day 12 onto Matrigel-coated plates following trypsinization. Cells were cultured for another 4 days in N2/B27 media without Vitamin A and cryopreserved on day 16. Neural progenitor cells (NPCs) were thawed and expanded in T75 flasks coated with poly-l-ornithine and laminin, using STEMdiff\u0026trade; Neural Progenitor Medium (Stem Cell Technologies). Cells were maintained with full media changes every 2\u0026ndash;3 days until they reached 70\u0026ndash;80% confluence for further passaging or experimentation.\u003c/p\u003e\n\u003ch3\u003eNeuron maturation\u003c/h3\u003e\n\u003cp\u003eNPCs were differentiated into neurons using BrainPhys\u0026trade; Neuronal Medium (StemCell Technologies). NPCs were plated on poly-l-ornithine and laminin-coated plates at a density of 1.25 x 10\u003csup\u003e5\u003c/sup\u003e cells/cm\u0026sup2;. The medium was switched to BrainPhys\u0026trade; medium supplemented with N2, Brain-Derived Neurotrophic Factor (BDNF, 20 ng/mL), Glial-Derived Neurotrophic Factor (GDNF, 20 ng/mL), dibutyryl-cAMP (500 \u0026micro;M), and ascorbic acid (200 \u0026micro;M). Media changes were performed every 2\u0026ndash;3 days, and cells were maintained for 14\u0026ndash;20 days to promote neuronal maturation.\u003c/p\u003e\n\u003ch3\u003eImaging\u003c/h3\u003e\n\u003cp\u003eImmunocytochemistry was performed on 3 96-well PerkinElmer Phenoplates seeded with neurons. Neuronal induction was performed by seeding NPCs in the 3 plates with BrainPhys Complete Neuronal media (StemCell, cat# 05790). All plates were split between parent and patient cell lines, and further split into two seeding densities, 30k and 40k cells/well. Plates were fixed with 4% Paraformaldehyde (Thermo Scientific, cat# 043368-9M) for 15 minutes followed by 3x washes with DPBS (Gibco cat# 14190144) on days 1, 7 and 14. Cells were permeabilized with 0.25% TritonX-100 solution (Thermo Scientific, cat# 85111) diluted in DPBS for 15 minutes prior to blocking and staining. All plates were stained with DAPI and the proliferation marker Ki67 (Thermo Scientific, cat# MA5-14520). High Protein Blocking Buffer (eBioscience, Thermo Scientific cat# 00-4952-54) was added prior to primary antibody staining for 45 minutes, followed by primary antibody solution diluted (1:500) in blocking buffer and incubated overnight at 4\u0026deg;C. The following morning, the primary antibody solution was removed and 2 washes were performed with DPBS. Secondary antibody (Thermo Scientific) solution was diluted in Low Protein Blocking Buffer (eBioscience, Thermo Scientific cat# 00-4953-54, 1:2000 dilution) and incubated at room temperature for 1 hour protected from light. Two washes with DPBS were performed, followed by a brief incubation with DAPI (Thermo Scientific cat# D1306, 1:10,000 dilution) for 5 minutes at room temperature. A final DPBS wash was performed followed by a wash with sterile distilled water.\u003c/p\u003e \u003cp\u003ePlates were imaged using the PerkinElmer Opera Phenix confocal microscope at 20x magnification. Analysis was performed in PerkinElmer Harmony Image Analysis software via integration of manually trained cell masks to determine live/dead cell status through fluorescence intensity, nuclear shape, size and roundness. The program was trained on 5 distinct, randomly selected well sections. Live cell fraction (LCF) was then calculated by determining the proportion of live to dead nuclei. Ki67\u0026thinsp;+\u0026thinsp;nuclei were identified, quantified and compared to overall nuclei number for each timepoint.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSequencing\u003c/h2\u003e \u003cp\u003eRNA extraction was performed using Qiagen RNeasy Plus Mini Kit (Qiagen, Cat: 74134). RNA quality and purity was checked by testing 2uL of RNA on a High Sensitivity RNA screentape (Agilent Tapestation). RNA concentration was quantified using a Qubit 4 Fluorometer (Invitrogen). Bulk RNA libraries were prepared from 200ng of RNA input using Illumina Stranded Total RNA Prep with Ribo-Zero Plus kit. RNA library quality was checked by running 1uL of each library on a High Sensitivity D1000 screentape (Agilent Tapestation), and concentration was quantified by qPCR using a KAPA Universal Illumina library quantification kit (Roche). Samples were sequenced in batches of 8 or less, on a NextSeq 1000 using the standard loading protocol for a P2 200 cycle sequencing cartridge.\u003c/p\u003e \u003cp\u003eDRUG-seq\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eNGN2-driven Neuronal Model\u003c/h3\u003e\n\u003cp\u003e10 cm dishes for NGN2-induced neural differentiation were coated with Matrigel according to the manufacturer's manual and incubated overnight at 37\u0026deg;C. On the day of induction, iPSCs were removed using Accutase (StemCell, Cat# 07920), centrifuged at 300 xg for 5 mins and resuspended in NGN2 Neural Induction Media containing 10 \u0026micro;M Y-27632 (StemCell, Cat# 72304). NGN2 Neural Induction Media was made using a base of Knockout DMEM/F12 (Gibco, Cat# 12660012) with the addition of 1x N2 Supplement-B (StemCell, Cat# 07156), 1x MEM Non-Essential Amino Acids (Gibco, Cat# 11140050), 1ug/mL Natural Mouse Laminin (1 mg/mL stock Life Technologies, Cat# 23017-015), 10 ng/mL NT-3 in sterile distilled water (StemCell, Cat# 78074), 10 ng/mL BDNF in sterile distilled water (StemCell, Cat# 78133) and 2 \u0026micro;g/mL doxycycline (stock concentration 20 mg/mL, Fisher Scientific, Cat# AAJ67043AD). Cells were counted using Trypan Blue (Invitrogen, Cat# T10282) on the Invitrogen Countess and plated at 5X10\u003csup\u003e6\u003c/sup\u003e cells/10 cm dish. Daily media changes were performed on days 2\u0026ndash;3 with NGN2 Neural Induction Medium.\u003c/p\u003e \u003cp\u003eOne day prior to replating cells for neuronal maturation, two 384-well PerkinElmer Pheno Plates were coated with 0.1mg/mL Poly-D-Lysine (Gibco, cat# A3890401) at 37\u0026deg;C overnight. The next morning, the plates were washed 3x with sterile water and dried under a BSC with the fan turned on. At least 2 hours prior to replating, 10 \u0026micro;g/mL Natural Mouse Laminin (Gibco, Cat# 23017015) diluted in DMEM/F12 (Gibco, Cat#11320033) was added to the plates and returned to a 37\u0026deg;C incubator.\u003c/p\u003e \u003cp\u003eOn day 4, cells were replated at 20000 cells/well in 384-well plates (PerkinElmer Phenoplates) for neuronal maturation in NGN2 Neuronal Medium containing 2 \u0026micro;g/mL doxycycline (Fisher Scientific, Cat# AAJ67043AD) and 5 \u0026micro;M of U-FdU. NGN2 Neuronal Medium was made using 1:1 DMEM/F12 (Gibco, Cat#11320033) and Neurobasal-A Medium (Gibco, Cat# 10888022) as the base with the addition of 1x MEM Non-Essential Amino Acids (Gibco, Cat# 11140050), 0.5x GlutaMAX Supplement (Gibco, Cat# 35050061), 1x N2 Supplement-B (StemCell, Cat# 07156), 1x B-27 Plus Supplement (Gibco, Cat# A3582801), 10 ng/mL NT-3 (StemCell, Cat# 78074), 10ng/mL BDNF (StemCell, Cat# 78133) and 1 \u0026micro;g/mL Natural Mouse Laminin (Gibco, Cat# 23017015). Half media changes were performed every 3\u0026ndash;4 days from Day 7-Day 15 using NGN2 Neuronal Medium.\u003c/p\u003e \u003cp\u003eNeurons were imaged using an Incucyte for quality control purposes prior to drug treatment using a basic confluence analysis program, followed by a Neurotrack program to identify cell body clusters and neurites.\u003c/p\u003e\n\u003ch3\u003eDrug Treatment\u003c/h3\u003e\n\u003cp\u003eNeurons were treated with a selection of FDA-approved small molecule compounds added at one-third decreasing dose for 10 doses: 10\u0026micro;M, 3.33\u0026micro;M, 1.11\u0026micro;M, 0.37\u0026micro;M, 0.12\u0026micro;M, 40nM, 14nM, 5nM, 2nM, and 0.5nM in quadruplicate. Additionally, a total of 16 wells were treated with DMSO as control on each 384-well plate. Compounds and DMSO controls were dispensed directly into the 384-well cell culture plates using an Echo 650 Liquid Handler (Beckman Coulter). Cells were held at 37\u0026deg;C for 72 hours following drug treatment. Following drug treatment, one of the two plates was used for RNA sequencing and the second was used for live cell imaging (See Supplementary Methods).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSequencing\u003c/h2\u003e \u003cp\u003eLibraries were prepared using a customized version of the DRUG-seq protocol \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Briefly, cell culture media was aspirated using a STARlet liquid handler (Hamilton) and lysis buffer was dispensed directly into each well with a Multidrop Combi Reagent Dispenser (ThermoFisher Scientific) and placed on a shaker for 15 minutes at 900 rpm. Cell lysate was transferred into 384-well PCR plates and reverse transcription (RT) mix was dispensed into each well. Using an Echo 650 Liquid Handler (Beckman Coulter), barcoded RT primers were dispensed into each well. Plates were incubated at 42\u0026deg;C for 2 hours. Following RT, each well was pooled into a single tube and purified using DNA Clean \u0026amp; Concentrator-100 (Zymo Research) followed by concentration with Agencourt RNAClean XP beads (Beckman Coulter). Single-stranded DNA was removed by incubating cDNA with Exonuclease I (New England Biolabs) followed by pre-amplification of the cDNA with DRUG-seq PCR primers. cDNA was fragmented and tagged with Illumina sequencing adapters using Tagmentation DNA Enzyme (TDE1). A final PCR was performed to amplify the libraries and add P7, P5, and index sequences. Libraries were sequenced on a NovaSeq 6000 (Illumina).\u003c/p\u003e \u003cp\u003eWhole blood RNA-sequencing\u003c/p\u003e \u003cp\u003ePatient n-of-1 trial and data collection was performed in accordance with protocols reviewed and approved by the WGC Institutional Review Board (IRB) (WCG IRB protocol #2001-01). All data was collected with consent from the legal guardians of the patient.\u003c/p\u003e \u003cp\u003eBlood samples were collected from a 7 year old female patient diagnosed with 19q12 autism spectrum disorder caused by interruptions in q12 region of chromosome 19 affecting genes ZNF536 and TSHZ3. Whole blood samples were collected approximately 3 and 6 months after treatment started with Entrectinib along with a pre-treatment sample (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Blood samples were collected\u0026thinsp;\u0026lt;\u0026thinsp;24 hours after dosing.\u003c/p\u003e \u003cp\u003eBlood was collected in PAXgene tubes and shipped at room temperature to the sequencing facility (Genewiz, NJ) for processing within 24 hours for RNA sequencing. The RNA was extracted using the PAXgene RNA extraction kit. Barcoded libraries for RNA-seq were prepared with 100 ng of RNA using NEBNext Ultra II RNA Library Prep Kit (NEB, Ipswich, MA) for Illumina with QIAGEN QIAseq FastSelect -rRNA HMR Kit (QIAGEN, Germany) per manufacturer\u0026rsquo;s protocol. Paired-end sequencing (150 bp x 2) was performed on NovaSeq X Plus sequencers (Illumina, San Diego, CA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003eBulk RNA-seq\u003c/h2\u003e \u003cp\u003eBulk RNA-seq paired fastq files were aligned using Kallisto to the human GRCh38 assembly \u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. Differential expression analysis (DEA) was performed using the R Sleuth package to obtain fold change values by comparing a test group to a control group \u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. Pathway analysis was performed based on fold change values from Sleuth for a control and test comparison using Gene Set Enrichment Analysis (GSEA) using the R ssGSEA package \u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDRUG-seq\u003c/h2\u003e \u003cp\u003eThe fastq file for the compound treated 384-well plate was aligned and demultiplexed into wells using Kallisto and Bustools for fast quantification of transcripts \u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e,\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. Transcript-level counts were collapsed into gene-level counts for further analysis. Reads were mapped to the human GRCh38 assembly and transcript to gene mapping was performed using gtf version 107.\u003c/p\u003e \u003cp\u003eGene-level fold changes were calculated using pyDESeq2 for differential expression analysis (DEA) comparing wells treated with Entrectinib pairwise at each concentration to DMSO-vehicle control wells \u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e,\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. Prior to the DEA, each quadruplicate set of sample wells at each concentration and the 16 DMSO control wells were checked for quality. Wells with poor sequencing quality due to low sequencing depth (for example, due to cytotoxicity) were removed. Additionally, any DMSO wells that were dissimilar to the ensemble distribution were removed (see supplementary information methods). This sought to exclude any \u0026ldquo;bad\u0026rdquo; DMSO wells that deviated from the population and could artificially inflate the number of DE genes. Finally, a number of genes were also removed from the DEA analysis that were detected sparsely throughout the passing DMSO and compound-treated wells (see supplementary information methods).\u003c/p\u003e \u003cp\u003ePathway analysis was performed using the fold change values from pyDESeq2 using gene set enrichment analysis (GSEA) \u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. To accommodate multiple concentrations a modified GSEA approach was utilized. Briefly, gene-level fold changes were ranked for a full concentration series for Entrectinib. An enrichment score was then calculated for each gene-concentration combination using published procedures for GSEA.\u003c/p\u003e \u003cp\u003eA combined p-value for fold changes and pathway enrichment scores was calculated using the Fisher test for the individual p-values across multiple timepoints and concentrations. False discovery rate corrected p-values (q-values) are reported where log2(Fold Change) values for individual genes are shown.\u003c/p\u003e \u003cp\u003eClinical evaluation\u003c/p\u003e \u003cp\u003eThe patient had two neuro-psychology evaluations, one on 6-18-2023 and another on 1-24-2025. The patient was ~\u0026thinsp;5 years 11 months at her first evaluation and 7 years 7 months during the second. Both evaluations were additionally performed at the same time of day to minimize any differences due to time of testing.\u003c/p\u003e \u003cp\u003eAt the evaluations, two tests were administered, the DAYC-2 (Developmental Assessment of Young Children, Second Edition) for cognitive functioning and caregiver reported Vineland Adaptive Behavior Scales for adaptive functioning. The DAYC-2 is a measure of developmental functioning in young children. Information is obtained through direct observation/assessment as well as caregiver report. It helps identify developmental delays, determine strengths, and track progress over time.\u003c/p\u003e \u003cp\u003eTo understand functional skills Vineland Adaptive Behavior Scales-3 was administered. This is a measure based on parental report and is a standardized measure of adaptive behavior\u0026ndash;the things that people do to function in their everyday lives.\u003c/p\u003e \u003cp\u003eIn addition to the aforementioned physician-performed evaluations, daily evaluations were also conducted by trained teaching staff at the patient\u0026rsquo;s school.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eWe would like to thank the patient\u0026rsquo;s family, physicians and caregivers for graciously being a part of the study in donating samples and sharing research findings. We would specifically like to thank Dr. David Clayton, M.D., at The Child and Family Counseling Group (VA, USA), for directing patient care. Finally we would like to thank philanthropic contributions for funding the research into rare diseases.\u003c/p\u003e\n\u003cp\u003eAuthor contributions\u003c/p\u003e\n\u003cp\u003eD. G., U.H., S.T., J.B., N.P., and C.M. contributed to study design. D.G., K.D., and N.P. analyzed the data. D.G., U.H., M.B., G.R.D.T., N.D., N.P., and K.D., wrote the manuscript. U.H., S.B., M.B., G.R.D.T., \u0026nbsp;and K.D., performed experiments. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003eData availability statement (mandatory)\u003c/p\u003e\n\u003cp\u003eAll associated data referenced in the manuscript can be found in the GEO database: accession GSE292758 for baseline characterization of neurons during differentiation, accession GSE292759 for DRUG-seq of wildtype neurons treated with Entrectinib and accession GSE292760 for data collected from patient blood post-treatment with Entrectinib.\u003c/p\u003e\n\u003cp\u003eAdditional Information (including a Competing Interests Statement)\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests. 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Moderated Estimation of Fold Change and Dispersion for RNA-Seq Data with DESeq2. \u003cem\u003eGenome Biol.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e (12), 550. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13059-014-0550\u0026ndash;8\u003c/span\u003e\u003cspan address=\"10.1186/s13059-014-0550\u0026ndash;8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Drug-discovery, high-throughput sequencing, 19q12, Entrectinib, neurodevelopmental, rare disease, disease signature, RNA-seq, DRUG-seq, transcriptomics, rare neurological, ZNF536, TSHZ3, autism, precision medicine, n-of-1 trial","lastPublishedDoi":"10.21203/rs.3.rs-6298254/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6298254/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDiscovering new and viable therapies for genetic diseases is a time consuming and cost intensive process. This is even more challenging for rare disorders that affect a small fraction of the population and do not incentivise research and development in the traditional drug discovery lifecycle. There is therefore a need for scalable and efficient solutions for discovering novel therapies for these rare disorders. In this study, we highlight how a high-throughput drug discovery engine was utilized to uncover drugs at scale that normalized the signature for a rare neurological neurodevelopmental disease, 19q12 autism spectrum disorder associated with deficiencies in ZNF536 and TSHZ3. We do this by uncovering and understanding the full 360\u003csup\u003eo\u003c/sup\u003e view of the biological impact of the drug in a relevant cell line via transcriptomics. This platform, in combination with a statistically viable n-of-1 trial, was able to quickly uncover and utilize a novel therapeutic potential of a previously approved drug for a patient living with from 19q12 autism spectrum disorder.\u003c/p\u003e","manuscriptTitle":"High-Throughput Drug Discovery for a Rare Neurological Disorder: Uncovering a Novel Therapeutic Opportunity for the 19q12 Autism Spectrum Disorder","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-13 01:20:29","doi":"10.21203/rs.3.rs-6298254/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-09T06:41:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-28T22:48:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"216658212070464316046450828763165204321","date":"2025-08-28T14:01:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-17T09:44:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"322510162317268954223333835596676123396","date":"2025-08-17T09:36:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"228905354917003982563934847051937513118","date":"2025-07-05T13:28:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-07T09:36:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"291228102049012177788125058536271890473","date":"2025-04-28T05:32:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"48268127320789092893804351066673848987","date":"2025-04-11T18:38:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-11T17:14:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-11T17:10:24+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-08T03:19:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-04T04:00:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-24T20:40:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"578e439f-35b8-4ed2-977f-0404fd0c0929","owner":[],"postedDate":"May 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":48189742,"name":"Biological sciences/Drug discovery/Biomarkers"},{"id":48189743,"name":"Biological sciences/Drug discovery/Drug screening/High throughput screening"}],"tags":[],"updatedAt":"2025-12-01T16:07:09+00:00","versionOfRecord":{"articleIdentity":"rs-6298254","link":"https://doi.org/10.1038/s41598-025-26015-y","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-11-25 15:56:52","publishedOnDateReadable":"November 25th, 2025"},"versionCreatedAt":"2025-05-13 01:20:29","video":"","vorDoi":"10.1038/s41598-025-26015-y","vorDoiUrl":"https://doi.org/10.1038/s41598-025-26015-y","workflowStages":[]},"version":"v1","identity":"rs-6298254","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6298254","identity":"rs-6298254","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}