Corrigendum to "Wound healing potential of Acacia catechu in streptozotocin-induced diabetic mice using in vivo and in silico approach" [J Tradit Compl Med 13 (5) (2023) 489-499].

[This corrects the article DOI: 10.1016/j.jtcme.2023.05.001.].

Real time changes in the expression of eicosanoid synthesizing enzymes during inflammation.

Immune responses during inflammation involve complex, well-coordinated lipid signaling pathways. Eicosanoids are a class of lipid signaling molecules derived from polyunsaturated fatty acids such as arachidonic acid and constitute a major network that controls inflammation and its subsequent resolution. Arachidonic acid is metabolized by enzymes in three different pathways to form a variety of lipid metabolites that can be either pro- or anti-inflammatory. Therefore, an understanding of the time-dependent gene expression, lipid metabolite profiles and cytokine profiles during the initial inflammatory response is necessary, as it will allow for the design of time-dependent therapeutics. Herein, we investigate the multi-level regulation of this process. After stimulating RAW 264.7 cells, a mouse-derived macrophage cell line commonly used to examine inflammatory responses, we examine the gene expression of 44 relevant lipid metabolizing enzymes from the different eicosanoid synthesizing classes. We also measure the formation of lipid metabolites and production of cytokines at selected time points. Results reveal a dynamic relationship between the time-course of inflammation dependent gene expression of the three eicosanoid synthesizing enzymes.

SPREd: a simulation-supervised neural network tool for gene regulatory network reconstruction.

Summary: Reconstruction of gene regulatory networks (GRNs) from expression data is a significant open problem. Common approaches train a machine learning (ML) model to predict a gene's expression using transcription factors' (TFs') expression as features and designate important features/TFs as regulators of the gene. Here, we present an entirely different paradigm, where GRN edges are directly predicted by the ML model. The new approach, named "SPREd," is a simulation-supervised neural network for GRN inference. Its inputs comprise expression relationships (e.g. correlation, mutual information) between the target gene and each TF and between pairs of TFs. The output includes binary labels indicating whether each TF regulates the target gene. We train the neural network model using synthetic expression data generated by a biophysics-inspired simulation model that incorporates linear as well as non-linear TF-gene relationships and diverse GRN configurations. We show SPREd to outperform state-of-the-art GRN reconstruction tools GENIE3, ENNET, PORTIA, and TIGRESS on synthetic datasets with high co-expression among TFs, similar to that seen in real data. A key advantage of the new approach is its robustness to relatively small numbers of conditions (columns) in the expression matrix, which is a common problem faced by existing methods. Finally, we evaluate SPREd on real data sets in yeast that represent gold-standard benchmarks of GRN reconstruction and show it to perform significantly better than or comparably to existing methods. In addition to its high accuracy and speed, SPREd marks a first step toward incorporating biophysics principles of gene regulation into ML-based approaches to GRN reconstruction. Availability and implementation: Data and code are available from https://github.com/iiiime/SPREd.

Consensus guideline for the diagnosis and management of pituitary adenomas in childhood and adolescence: Part 1, general recommendations.

Tumours of the anterior part of the pituitary gland represent just 1% of all childhood (aged <15 years) intracranial neoplasms, yet they can confer high morbidity and little evidence and guidance is in place for their management. Between 2014 and 2022, a multidisciplinary expert group systematically developed the first comprehensive clinical practice consensus guideline for children and young people under the age 19 years (hereafter referred to as CYP) presenting with a suspected pituitary adenoma to inform specialist care and improve health outcomes. Through robust literature searches and a Delphi consensus exercise with an international Delphi consensus panel of experts, the available scientific evidence and expert opinions were consolidated into 74 recommendations. Part 1 of this consensus guideline includes 17 pragmatic management recommendations related to clinical care, neuroimaging, visual assessment, histopathology, genetics, pituitary surgery and radiotherapy. While in many aspects the care for CYP is similar to that of adults, key differences exist, particularly in aetiology and presentation. CYP with suspected pituitary adenomas require careful clinical examination, appropriate hormonal work-up, dedicated pituitary imaging and visual assessment. Consideration should be given to the potential for syndromic disease and genetic assessment. Multidisciplinary discussion at both the local and national levels can be key for management. Surgery should be performed in specialist centres. The collection of outcome data on novel modalities of medical treatment, surgical intervention and radiotherapy is essential for optimal future treatment.

Consensus guideline for the diagnosis and management of pituitary adenomas in childhood and adolescence: Part 2, specific diseases.

Pituitary adenomas are rare in children and young people under the age of 19 (hereafter referred to as CYP) but they pose some different diagnostic and management challenges in this age group than in adults. These rare neoplasms can disrupt maturational, visual, intellectual and developmental processes and, in CYP, they tend to have more occult presentation, aggressive behaviour and are more likely to have a genetic basis than in adults. Through standardized AGREE II methodology, literature review and Delphi consensus, a multidisciplinary expert group developed 74 pragmatic management recommendations aimed at optimizing care for CYP in the first-ever comprehensive consensus guideline to cover the care of CYP with pituitary adenoma. Part 2 of this consensus guideline details 57 recommendations for paediatric patients with prolactinomas, Cushing disease, growth hormone excess causing gigantism and acromegaly, clinically non-functioning adenomas, and the rare TSHomas. Compared with adult patients with pituitary adenomas, we highlight that, in the CYP group, there is a greater proportion of functioning tumours, including macroprolactinomas, greater likelihood of underlying genetic disease, more corticotrophinomas in boys aged under 10 years than in girls and difficulty of peri-pubertal diagnosis of growth hormone excess. Collaboration with pituitary specialists caring for adult patients, as part of commissioned and centralized multidisciplinary teams, is key for optimizing management, transition and lifelong care and facilitates the collection of health-related quality of survival outcomes of novel medical, surgical and radiotherapeutic treatments, which are currently largely missing.

Can ChatGPT outperform a neurosurgical trainee? A prospective comparative study.

PURPOSE: This study aimed to compare the performance of ChatGPT, a large language model (LLM), with human neurosurgical applicants in a neurosurgical national selection interview, to assess the potential of artificial intelligence (AI) and LLMs in healthcare and provide insights into their integration into the field. METHODS: In a prospective comparative study, a set of neurosurgical national selection-style interview questions were asked to eight human participants and ChatGPT in an online interview. All participants were doctors currently practicing in the UK who had applied for a neurosurgical National Training Number. Interviews were recorded, anonymised, and scored by three neurosurgical consultants with experience as interviewers for national selection. Answers provided by ChatGPT were used as a template for a virtual interview. Interview transcripts were subsequently scored by neurosurgical consultants using criteria utilised in real national selection interviews. Overall interview score and subdomain scores were compared between human participants and ChatGPT. RESULTS: For overall score, ChatGPT fell behind six human competitors and did not achieve a mean score higher than any individuals who achieved training positions. Several factors, including factual inaccuracies and deviations from expected structure and style may have contributed to ChatGPT's underperformance. CONCLUSIONS: LLMs such as ChatGPT have huge potential for integration in healthcare. However, this study emphasises the need for further development to address limitations and challenges. While LLMs have not surpassed human performance yet, collaboration between humans and AI systems holds promise for the future of healthcare.

Monoallelically expressed noncoding RNAs form nucleolar territories on NOR-containing chromosomes and regulate rRNA expression.

Out of the several hundred copies of rRNA genes arranged in the nucleolar organizing regions (NOR) of the five human acrocentric chromosomes, ~50% remain transcriptionally inactive. NOR-associated sequences and epigenetic modifications contribute to the differential expression of rRNAs. However, the mechanism(s) controlling the dosage of active versus inactive rRNA genes within each NOR in mammals is yet to be determined. We have discovered a family of ncRNAs, SNULs (Single NUcleolus Localized RNA), which form constrained sub-nucleolar territories on individual NORs and influence rRNA expression. Individual members of the SNULs monoallelically associate with specific NOR-containing chromosomes. SNULs share sequence similarity to pre-rRNA and localize in the sub-nucleolar compartment with pre-rRNA. Finally, SNULs control rRNA expression by influencing pre-rRNA sorting to the DFC compartment and pre-rRNA processing. Our study discovered a novel class of ncRNAs influencing rRNA expression by forming constrained nucleolar territories on individual NORs.

Primary intradural extraosseous Ewing's sarcoma of the L3 nerve root: illustrative case.

BACKGROUND: Ewing's sarcoma is an uncommon, aggressive malignancy that typically presents as an osseous lesion, most commonly in children and adolescents. Very rarely Ewing's sarcoma can present as an intradural extramedullary mass mimicking more common tumors. OBSERVATIONS: A 32-year-old female had a left L3 nerve root-associated lesion identified in the setting of recent-onset radiculopathy. Contrast-enhanced magnetic resonance imaging of the lumbar spine was favored to demonstrate a schwannoma or neurofibroma. Hemilaminectomy, facetectomy, and resection of the mass led to improved radiculopathy and a tissue diagnosis of Ewing's sarcoma. Immediate referral to medical oncology facilitated expeditious initiation of adjuvant chemotherapy and radiation. LESSONS: The differential diagnosis for newly identified nerve root-associated tumors should remain broad, including common benign pathologies and rare malignant entities. Tissue remains the gold standard for diagnosis, as preoperative imaging suggested a nerve sheath tumor. Malignant pathologies such as Ewing's sarcoma must be considered, especially in the setting of rapidly progressive symptoms or interval growth on serial imaging. Early diagnosis allows for the timely initiation of comprehensive oncological care. Long-term multidisciplinary follow-up is necessary for the surveillance of disease progression.

Alcohol for seizure induction in the epilepsy monitoring unit.

RATIONALE: Seizure induction techniques are used in the epilepsy monitoring unit (EMU) to increase diagnostic yield and reduce length of stay. There are insufficient data on the efficacy of alcohol as an induction technique. METHODS: We performed a retrospective cohort study using six years of EMU data at our institution. We compared cases who received alcohol for seizure induction to matched controls who did not. The groups were matched on the following variables: age, reason for admission, length of stay, number of antiseizure medications (ASM) at admission, whether ASMs were tapered during admission, and presence of interictal epileptiform discharges. We used both propensity score and exact matching strategies. We compared the likelihood of epileptic seizures and nonepileptic events in cases versus controls using Kaplan-Meier time-to-event analysis, as well as odds ratios for these outcomes occurring at any time during the admission. RESULTS: We analyzed 256 cases who received alcohol (median dose 2.5 standard drinks) and 256 propensity score-matched controls. Cases who received alcohol were no more likely than controls to have an epileptic seizure (X2(1) = 0.01, p = 0.93) or nonepileptic event (X2(1) = 2.1, p = 0.14) in the first 48 h after alcohol administration. For the admission overall, cases were no more likely to have an epileptic seizure (OR 0.89, 95 % CI 0.61-1.28, p = 0.58), nonepileptic event (OR 0.97, CI 0.62-1.53, p = 1.00), nor require rescue benzodiazepine (OR 0.63, CI 0.35-1.12, p = 0.15). Stratified analyses revealed no increased risk of epileptic seizure in any subgroups. Sensitivity analysis using exact matching showed that results were robust to matching strategy. CONCLUSIONS: Alcohol was not an effective induction technique in the EMU. This finding has implications for counseling patients with epilepsy about the risks of drinking alcohol in moderation in their daily lives.

A transcriptomic atlas of acute stress response to low pH in multiple Issatchenkia orientalis strains.

IMPORTANCE: Issatchenkia orientalis is a promising industrial chassis to produce biofuels and bioproducts due to its high tolerance to multiple environmental stresses such as low pH, heat, and other chemicals otherwise toxic for the most widely used microbes. Yet, little is known about specific mechanisms of such tolerance in this organism, hindering our ability to engineer this species to produce valuable biochemicals. Here, we report a comprehensive study of the mechanisms of acidic tolerance in this species via transcriptome profiling across variable pH for 12 different strains with different phenotypes. We found multiple regulatory mechanisms involved in tolerance to low pH in different strains of I. orientalis, marking potential targets for future gene editing and perturbation experiments.

SEAMoD: A fully interpretable neural network for cis-regulatory analysis of differentially expressed genes.

A common way to investigate gene regulatory mechanisms is to identify differentially expressed genes using transcriptomics, find their candidate enhancers using epigenomics, and search for over-represented transcription factor (TF) motifs in these enhancers using bioinformatics tools. A related follow-up task is to model gene expression as a function of enhancer sequences and rank TF motifs by their contribution to such models, thus prioritizing among regulators. We present a new computational tool called SEAMoD that performs the above tasks of motif finding and sequence-to-expression modeling simultaneously. It trains a convolutional neural network model to relate enhancer sequences to differential expression in one or more biological conditions. The model uses TF motifs to interpret the sequences, learning these motifs and their relative importance to each biological condition from data. It also utilizes epigenomic information in the form of activity scores of putative enhancers and automatically searches for the most promising enhancer for each gene. Compared to existing neural network models of non-coding sequences, SEAMoD uses far fewer parameters, requires far less training data, and emphasizes biological interpretability. We used SEAMoD to understand regulatory mechanisms underlying the differentiation of neural stem cell (NSC) derived from mouse forebrain. We profiled gene expression and histone modifications in NSC and three differentiated cell types and used SEAMoD to model differential expression of nearly 12,000 genes with an accuracy of 81%, in the process identifying the Olig2, E2f family TFs, Foxo3, and Tcf4 as key transcriptional regulators of the differentiation process.

SPREd: A simulation-supervised neural network tool for gene regulatory network reconstruction.

Reconstruction of gene regulatory networks (GRNs) from expression data is a significant open problem. Common approaches train a machine learning (ML) model to predict a gene's expression using transcription factors' (TFs') expression as features and designate important features/TFs as regulators of the gene. Here, we present an entirely different paradigm, where GRN edges are directly predicted by the ML model. The new approach, named "SPREd" is a simulation-supervised neural network for GRN inference. Its inputs comprise expression relationships (e.g., correlation, mutual information) between the target gene and each TF and between pairs of TFs. The output includes binary labels indicating whether each TF regulates the target gene. We train the neural network model using synthetic expression data generated by a biophysics-inspired simulation model that incorporates linear as well as non-linear TF-gene relationships and diverse GRN configurations. We show SPREd to outperform state-of-the-art GRN reconstruction tools GENIE3, ENNET, PORTIA and TIGRESS on synthetic datasets with high co-expression among TFs, similar to that seen in real data. A key advantage of the new approach is its robustness to relatively small numbers of conditions (columns) in the expression matrix, which is a common problem faced by existing methods. Finally, we evaluate SPREd on real data sets in yeast that represent gold standard benchmarks of GRN reconstruction and show it to perform significantly better than or comparably to existing methods. In addition to its high accuracy and speed, SPREd marks a first step towards incorporating biophysics principles of gene regulation into ML-based approaches to GRN reconstruction.

High-resolution neural recordings improve the accuracy of speech decoding.

Patients suffering from debilitating neurodegenerative diseases often lose the ability to communicate, detrimentally affecting their quality of life. One solution to restore communication is to decode signals directly from the brain to enable neural speech prostheses. However, decoding has been limited by coarse neural recordings which inadequately capture the rich spatio-temporal structure of human brain signals. To resolve this limitation, we performed high-resolution, micro-electrocorticographic (µECoG) neural recordings during intra-operative speech production. We obtained neural signals with 57× higher spatial resolution and 48% higher signal-to-noise ratio compared to macro-ECoG and SEEG. This increased signal quality improved decoding by 35% compared to standard intracranial signals. Accurate decoding was dependent on the high-spatial resolution of the neural interface. Non-linear decoding models designed to utilize enhanced spatio-temporal neural information produced better results than linear techniques. We show that high-density µECoG can enable high-quality speech decoding for future neural speech prostheses.

Wound healing potential of Acacia catechu in streptozotocin-induced diabetic mice using in vivo and in silico approach.

Background and aim: Acacia catechu Wild. (Fabaceae) barks are traditionally used in the treatment of diabetes and wounds. Therefore, the objective of the present study was to evaluate the wound healing potential of the alcoholic extract of A. catechu (EAC) in streptozotocin-induced diabetic mice. Experimental procedures: EAC was first subjected to phytochemical estimations and standardization using (-) epicatechin as marker with the help of HPLC. Diabetes was induced in mice using streptozotocin and the wound healing potential of EAC was evaluated using excision and incision wound models on topical and oral treatment. Various biochemical parameters, in vivo antioxidants, cytokine profiling, VEGF, and histopathological examination were also performed. Further, molecular docking studies were performed using ligand (-) epicatechin on human inducible nitric oxide synthase. Results and conclusion: Phytochemically, EAC showed the presence of tannins, flavonoids, phenolic compounds, and saponins, while the content of (-) epicatechin was reported to be 7.81% w/w. The maximum healing of wounds (91.84 ± 1.10%) was observed in mice treated with a combination of both topical (10% gel) and oral (extract at 200 mg/kg) followed by topically and orally treated groups respectively after 14 days of treatment. These groups also showed significant restoration of altered biochemical parameters, antioxidant enzymes and cytokines. The molecular docking studies confirmed the role of (-) epicatechin in stabilizing the human inducible nitric oxide synthase with inhibitor showing binding energy of -8.31 kcal/mol. The present study confirmed the role of (-) epicatechin as a major marker in diabetic wound healing potential of A. catechu.

Influenza A virus infection disrupts oligodendrocyte homeostasis and alters the myelin lipidome in the adult mouse.

BACKGROUND: Recent data suggest that myelin may be altered by physiological events occurring outside of the central nervous system, which may cause changes to cognition and behavior. Similarly, peripheral infection by non-neurotropic viruses is also known to evoke changes to cognition and behavior. METHODS: Mice were inoculated with saline or influenza A virus. Bulk RNA-seq, lipidomics, RT-qPCR, flow cytometry, immunostaining, and western blots were used to determine the effect of infection on OL viability, protein expression and changes to the lipidome. To determine if microglia mediated infection-induced changes to OL homeostasis, mice were treated with GW2580, an inhibitor of microglia activation. Additionally, conditioned medium experiments using primary glial cell cultures were also used to test whether secreted factors from microglia could suppress OL gene expression. RESULTS: Transcriptomic and RT-qPCR analyses revealed temporal downregulation of OL-specific transcripts with concurrent upregulation of markers characteristic of cellular stress. OLs isolated from infected mice had reduced cellular expression of myelin proteins compared with those from saline-inoculated controls. In contrast, the expression of these proteins within myelin was not different between groups. Similarly, histological and immunoblotting analysis performed on various brain regions indicated that infection did not alter OL viability, but increased expression of a cellular stress marker. Shot-gun lipidomic analysis revealed that infection altered the lipid profile within the prefrontal cortex as well as in purified brain myelin and that these changes persisted after recovery from infection. Treatment with GW2580 during infection suppressed the expression of genes associated with glial activation and partially restored OL-specific transcripts to baseline levels. Finally, conditioned medium from activated microglia reduced OL-gene expression in primary OLs without altering their viability. CONCLUSIONS: These findings show that peripheral respiratory viral infection with IAV is capable of altering OL homeostasis and indicate that microglia activation is likely involved in the process.

BEDwARS: a robust Bayesian approach to bulk gene expression deconvolution with noisy reference signatures.

Differential gene expression in bulk transcriptomics data can reflect change of transcript abundance within a cell type and/or change in the proportions of cell types. Expression deconvolution methods can help differentiate these scenarios. BEDwARS is a Bayesian deconvolution method designed to address differences between reference signatures of cell types and corresponding true signatures underlying bulk transcriptomic profiles. BEDwARS is more robust to noisy reference signatures and outperforms leading in-class methods for estimating cell type proportions and signatures. Application of BEDwARS to dihydropyridine dehydrogenase deficiency identified the possible involvement of ciliopathy and impaired translational control in the etiology of the disorder.

Analysing the impact of eriosematin E from Eriosema chinense Vogel. against different diarrhoeagenic pathovars of Escherichia coli using in silico and in vitro approach.

Since diarrhoea is reportedly the third largest cause of fatality among kids, therefore it is considered to be one of the major areas of concerns among developing nations. The main causative agents of diarrhoea include Escherichia coli, Vibrio cholera, and Shigella spp where E. coli shares the maximum contribution. The roots of the plant Eriosema chinense Vogel. (Fabaceae) are traditionally used by the native tribes of Meghalaya, India to treat diarrhoea. From previous reports, the plant and its marker eriosematin E have been reported to have antidiarrhoeal potential against pathogenic and nonpathogenic diarrhoea. Therefore, the objective of the current investigation was to use in silico studies to determine the efficacy of eriosematin E against different diarrhoeagenic strains of E. coli. Six different pathovars of E. coli i.e. enteropathogenic E. coli (EPEC), enterotoxigenic E. coli (ETEC), enterohaemorrhagic E. coli (EHEC), enteroaggregative E. coli (EAEC), uropathogenic E. coli (UPEC) and enteroinvasive E. coli (EIEC) were subjected to docking simulation studies utilizing Glide module of Schrodinger Maestro 2018-1 MM Share Version. Based on the obtained binding energy and balance between H-bonding, hydrophobic, and salt bridge interactions eriosematin E was found to be most effective against EPEC followed by EAEC and ETEC, while UPEC and EHEC were moderately affected. The molecular dynamics studies suggested a higher affinity of eriosematin E towards heat-labile enterotoxin b-pentamer from ETEC. The in vitro antibacterial studies against the universal strain S. aureus 12981 and E. coli 10418 revealed the effectiveness of eriosematin E showing MIC values of ≥256 µg/mL.Communicated by Ramaswamy H. Sarma.

The seizure severity score: a quantitative tool for comparing seizures and their response to therapy.

Objective.Epilepsy is a neurological disorder characterized by recurrent seizures which vary widely in severity, from clinically silent to prolonged convulsions. Measuring severity is crucial for guiding therapy, particularly when complete control is not possible. Seizure diaries, the current standard for guiding therapy, are insensitive to the duration of events or the propagation of seizure activity across the brain. We present a quantitative seizure severity score that incorporates electroencephalography (EEG) and clinical data and demonstrate how it can guide epilepsy therapies.Approach.We collected intracranial EEG and clinical semiology data from 54 epilepsy patients who had 256 seizures during invasive, in-hospital presurgical evaluation. We applied an absolute slope algorithm to EEG recordings to identify seizing channels. From this data, we developed a seizure severity score that combines seizure duration, spread, and semiology using non-negative matrix factorization. For validation, we assessed its correlation with independent measures of epilepsy burden: seizure types, epilepsy duration, a pharmacokinetic model of medication load, and response to epilepsy surgery. We investigated the association between the seizure severity score and preictal network features.Main results.The seizure severity score augmented clinical classification by objectively delineating seizure duration and spread from recordings in available electrodes. Lower preictal medication loads were associated with higher seizure severity scores (p= 0.018, 97.5% confidence interval = [-1.242, -0.116]) and lower pre-surgical severity was associated with better surgical outcome (p= 0.042). In 85% of patients with multiple seizure types, greater preictal change from baseline was associated with higher severity.Significance.We present a quantitative measure of seizure severity that includes EEG and clinical features, validated on gold standard in-patient recordings. We provide a framework for extending our tool's utility to ambulatory EEG devices, for linking it to seizure semiology measured by wearable sensors, and as a tool to advance data-driven epilepsy care.

Integrative modeling of lncRNA-chromatin interaction maps reveals diverse mechanisms of nuclear retention.

BACKGROUND: Many long non-coding RNAs, known to be involved in transcriptional regulation, are enriched in the nucleus and interact with chromatin. However, their mechanisms of chromatin interaction and the served cellular functions are poorly understood. We sought to characterize the mechanisms of lncRNA nuclear retention by systematically mapping the sequence and chromatin features that distinguish lncRNA-interacting genomic segments. RESULTS: We found DNA 5-mer frequencies to be predictive of chromatin interactions for all lncRNAs, suggesting sequence-specificity as a global theme in the interactome. Sequence features representing protein-DNA and protein-RNA binding motifs revealed potential mechanisms for specific lncRNAs. Complementary to these global themes, transcription-related features and DNA-RNA triplex formation potential were noted to be highly predictive for two mutually exclusive sets of lncRNAs. DNA methylation was also noted to be a significant predictor, but only when combined with other epigenomic features. CONCLUSIONS: Taken together, our statistical findings suggest that a group of lncRNAs interacts with transcriptionally inactive chromatin through triplex formation, whereas another group interacts with transcriptionally active regions and is involved in DNA Damage Response (DDR) through formation of R-loops. Curiously, we observed a strong pattern of enrichment of 5-mers in four potentially interacting entities: lncRNA-bound DNA tiles, lncRNAs, miRNA seed sequences, and repeat elements. This finding points to a broad sequence-based network of interactions that may underlie regulation of fundamental cellular functions. Overall, this study reveals diverse sequence and chromatin features related to lncRNA-chromatin interactions, suggesting potential mechanisms of nuclear retention and regulatory function.

Sequence-to-expression approach to identify etiological non-coding DNA variations in P53 and cMYC-driven diseases.

Background and methods: Disease risk prediction based on DNA sequence and transcriptional profile can improve disease screening, prevention, and potential therapeutic approaches by revealing contributing genetic factors and altered regulatory networks. Despite identifying many disease-associated DNA variants through genome-wide association studies, distinguishing deleterious non-coding DNA variations remains poor for most common diseases. We previously reported that non-coding variations disrupting cis-overlapping motifs (CisOMs) of opposing transcription factors significantly affect enhancer activity. We designed in vitro experiments to uncover the significance of the co-occupancy and competitive binding and inhibition between P53 and cMYC on common target gene expression. Results: Analyzing publicly available ChIP-seq data for P53 and cMYC in human embryonic stem cells and mouse embryonic cells showed that ~ 344-366 genomic regions are co-occupied by P53 and cMYC. We identified, on average, two CisOMs per region, suggesting that co-occupancy is evolutionarily conserved in vertebrates. Our data showed that treating U2OS cells with doxorubicin increased P53 protein level while reducing cMYC level. In contrast, no change in protein levels was observed in Raji cells. ChIP-seq analysis illustrated that 16-922 genomic regions were co-occupied by P53 and cMYC before and after treatment, and substitutions of cMYC signals by P53 were detected after doxorubicin treatment in U2OS. Around 187 expressed genes near co-occupied regions were altered at mRNA level according to RNA-seq data. We utilized a computational motif-matching approach to determine that changes in predicted P53 binding affinity by DNA variations in CisOMs of co-occupied elements significantly correlate with alterations in reporter gene expression. We performed a similar analysis using SNPs mapped in CisOMs for P53 and cMYC from ChIP-seq data in U2OS and Raji, and expression of target genes from the GTEx portal. Conclusions: We found a significant correlation between change in motif-predicted cMYC binding affinity by SNPs in CisOMs and altered gene expression. Our study brings us closer to developing a generally applicable approach to filter etiological non-coding variations associated with P53 and cMYC-dependent diseases.