p53 MAINTAINS HEPATIC CELL IDENTITY DURING LIVER REGENERATION

p53 MAINTAINS HEPATIC CELL IDENTITY DURING LIVER REGENERATION Zeynep Hande Coban Akdemir, B.S.,M.A. Advisory Professor: Michelle Craig Barton, Ph.D. p53 is a tumor suppressor that has been well studied in tumor-derived, cultured cells. However, its functions in normal proliferating cells and tissues are generally overlooked. We propose that p53 functions during the G1-S transition can be studied in normal, differentiated cells during surgery-induced liver regeneration. Two-thirds partial hepatectomy (PH) of mouse liver offers a unique model to compare p53 functions in regenerating versus sham (control) cells. My hypothesis is that intersection of global expression analyses (microarray and RNA sequencing) and profiling of p53 interactions with chromatin (ChIP sequencing) at the G1-S transition of normal cell cycle, corresponding to 24h post-PH in mice liver regeneration, will reveal p53 functions during cell cycle regulation in normal cells and during tissue regeneration. Combining chromatin immunoprecipitation with next generation sequencing technology (ChIP-Seq) allowed detection of genome-wide binding of p53 to target genes in liver. We found 5074 de novo p53 target genes, 92% of which participate in non-canonical p53 functions, mainly developmental processes. Integration of ChIP-Seq findings with global expression profiling (RNA-Seq) of both normal and p53-null liver allowed us to identify functional p53 target genes. Intriguingly, our data analysis revealed that a specific subset of p53-activated target genes is involved in liver-enriched functions such as lipid biosynthetic process, steroid metabolic process, circadian rhythm, and drug detoxification. These findings suggested that the loss of p53-chromatin interactions in regenerating liver may result in a decreased activity of differentiation-specific cellular processes and in attenuation of hepatic cell identity. Remarkably, p53 cooperates with the master regulator of hepatocyte differentiation, HNF4α, to induce 78% of these genes, including a number of liver-enriched transcription factors such as CCAAT/enhancer binding protein beta (CEBPβ), hepatocyte nuclear factor 6 alpha (HNF6α), hepatocyte nuclear factor 6 beta (HNF6β). Thus, p53 acts in concert with HNF4α to promote the maintenance of liver functions during the G1àS transition of the cell cycle of normal proliferating livers cells

Aenmd: Annotating Escape From Nonsense-Mediated Decay for Transcripts With Protein-Truncating Variants

DNA changes that cause premature termination codons (PTCs) represent a large fraction of clinically relevant pathogenic genomic variation. Typically, PTCs induce transcript degradation by nonsense-mediated mRNA decay (NMD) and render such changes loss-of-function alleles. However, certain PTC-containing transcripts escape NMD and can exert dominant-negative or gain-of-function (DN/GOF) effects. Therefore, systematic identification of human PTC-causing variants and their susceptibility to NMD contributes to the investigation of the role of DN/GOF alleles in human disease. Here we present aenmd, a software for annotating PTC-containing transcript-variant pairs for predicted escape from NMD. aenmd is user-friendly and self-contained. It offers functionality not currently available in other methods and is based on established and experimentally validated rules for NMD escape; the software is designed to work at scale, and to integrate seamlessly with existing analysis workflows. We applied aenmd to variants in the gnomAD, Clinvar, and GWAS catalog databases and report the prevalence of human PTC-causing variants in these databases, and the subset of these variants that could exert DN/GOF effects via NMD escape

Ai-Drugnet: a Network-Based Deep Learning Model For Drug Repurposing and Combination therapy in Neurological Disorders

Discovering effective therapies is difficult for neurological and developmental disorders in that disease progression is often associated with a complex and interactive mechanism. Over the past few decades, few drugs have been identified for treating Alzheimer\u27s disease (AD), especially for impacting the causes of cell death in AD. Although drug repurposing is gaining more success in developing therapeutic efficacy for complex diseases such as common cancer, the complications behind AD require further study. Here, we developed a novel prediction framework based on deep learning to identify potential repurposed drug therapies for AD, and more importantly, our framework is broadly applicable and may generalize to identifying potential drug combinations in other diseases. Our prediction framework is as follows: we first built a drug-target pair (DTP) network based on multiple drug features and target features, as well as the associations between DTP nodes where drug-target pairs are the DTP nodes and the associations between DTP nodes are represented as the edges in the AD disease network; furthermore, we incorporated the drug-target feature from the DTP network and the relationship information between drug-drug, target-target, drug-target within and outside of drug-target pairs, representing each drug-combination as a quartet to generate corresponding integrated features; finally, we developed an AI-based Drug discovery Network (AI-DrugNet), which exhibits robust predictive performance. The implementation of our network model help identify potential repurposed and combination drug options that may serve to treat AD and other diseases

Gain-of-Function Variomics and Multi-Omics Network Biology For Precision Medicine

Traditionally, disease causal mutations were thought to disrupt gene function. However, it becomes more clear that many deleterious mutations could exhibit a gain-of-function (GOF) behavior. Systematic investigation of such mutations has been lacking and largely overlooked. Advances in next-generation sequencing have identified thousands of genomic variants that perturb the normal functions of proteins, further contributing to diverse phenotypic consequences in disease. Elucidating the functional pathways rewired by GOF mutations will be crucial for prioritizing disease-causing variants and their resultant therapeutic liabilities. In distinct cell types (with varying genotypes), precise signal transduction controls cell decision, including gene regulation and phenotypic output. When signal transduction goes awry due to GOF mutations, it would give rise to various disease types. Quantitative and molecular understanding of network perturbations by GOF mutations may provide explanations for \u27missing heritability in previous genome-wide association studies. We envision that it will be instrumental to push current paradigm toward a thorough functional and quantitative modeling of all GOF mutations and their mechanistic molecular events involved in disease development and progression. Many fundamental questions pertaining to genotype-phenotype relationships remain unresolved. For example, which GOF mutations are key for gene regulation and cellular decisions? What are the GOF mechanisms at various regulation levels? How do interaction networks undergo rewiring upon GOF mutations? Is it possible to leverage GOF mutations to reprogram signal transduction in cells, aiming to cure disease? to begin to address these questions, we will cover a wide range of topics regarding GOF disease mutations and their characterization by multi-omic networks. We highlight the fundamental function of GOF mutations and discuss the potential mechanistic effects in the context of signaling networks. We also discuss advances in bioinformatic and computational resources, which will dramatically help with studies on the functional and phenotypic consequences of GOF mutations

Ad-Syn-Net: Systematic Identification of alzheimer\u27s Disease-Associated Mutation and Co-Mutation Vulnerabilities Via Deep Learning

Alzheimer\u27s disease (AD) is one of the most challenging neurodegenerative diseases because of its complicated and progressive mechanisms, and multiple risk factors. Increasing research evidence demonstrates that genetics may be a key factor responsible for the occurrence of the disease. Although previous reports identified quite a few AD-associated genes, they were mostly limited owing to patient sample size and selection bias. There is a lack of comprehensive research aimed to identify AD-associated risk mutations systematically. to address this challenge, we hereby construct a large-scale AD mutation and co-mutation framework (\u27AD-Syn-Net\u27), and propose deep learning models named Deep-SMCI and Deep-CMCI configured with fully connected layers that are capable of predicting cognitive impairment of subjects effectively based on genetic mutation and co-mutation profiles. Next, we apply the customized frameworks to data sets to evaluate the importance scores of the mutations and identified mutation effectors and co-mutation combination vulnerabilities contributing to cognitive impairment. Furthermore, we evaluate the influence of mutation pairs on the network architecture to dissect the genetic organization of AD and identify novel co-mutations that could be responsible for dementia, laying a solid foundation for proposing future targeted therapy for AD precision medicine. Our deep learning model codes are available open access here: https://github.com/Pan-Bio/AD-mutation-effectors

NODAL Variants Are Associated With a Continuum of Laterality Defects From Simple D-Transposition of the Great Arteries to Heterotaxy

BACKGROUND: NODAL signaling plays a critical role in embryonic patterning and heart development in vertebrates. Genetic variants resulting in perturbations of the TGF-β/NODAL signaling pathway have reproducibly been shown to cause laterality defects in humans. To further explore this association and improve genetic diagnosis, the study aims to identify and characterize a broader range of NODAL variants in a large number of individuals with laterality defects. METHODS: We re-analyzed a cohort of 321 proband-only exomes of individuals with clinically diagnosed laterality congenital heart disease (CHD) using family-based, rare variant genomic analyses. To this cohort we added 12 affected subjects with known NODAL variants and CHD from institutional research and clinical cohorts to investigate an allelic series. For those with candidate contributory variants, variant allele confirmation and segregation analysis were studied by Sanger sequencing in available family members. Array comparative genomic hybridization and droplet digital PCR were utilized for copy number variants (CNV) validation and characterization. We performed Human Phenotype Ontology (HPO)-based quantitative phenotypic analyses to dissect allele-specific phenotypic differences. RESULTS: Missense, nonsense, splice site, indels, and/or structural variants of NODAL were identified as potential causes of heterotaxy and other laterality defects in 33 CHD cases. We describe a recurrent complex indel variant for which the nucleic acid secondary structure predictions implicate secondary structure mutagenesis as a possible mechanism for formation. We identified two CNV deletion alleles spanning NODAL in two unrelated CHD cases. Furthermore, 17 CHD individuals were found (16/17 with known Hispanic ancestry) to have the c.778G \u3e A:p.G260R NODAL missense variant which we propose reclassification from variant of uncertain significance (VUS) to likely pathogenic. Quantitative HPO-based analyses of the observed clinical phenotype for all cases with p.G260R variation, including heterozygous, homozygous, and compound heterozygous cases, reveal clustering of individuals with biallelic variation. This finding provides evidence for a genotypic-phenotypic correlation and an allele-specific gene dosage model. CONCLUSION: Our data further support a role for rare deleterious variants in NODAL as a cause for sporadic human laterality defects, expand the repertoire of observed anatomical complexity of potential cardiovascular anomalies, and implicate an allele specific gene dosage model

A Biallelic Frameshift Indel in PPP1R35 as a Cause of Primary Microcephaly

Protein phosphatase 1 regulatory subunit 35 (PPP1R35) encodes a centrosomal protein required for recruiting microtubule-binding elongation machinery. Several proteins in this centriole biogenesis pathway correspond to established primary microcephaly (MCPH) genes, and multiple model organism studies hypothesize PPP1R35 as a candidate MCPH gene. Here, using exome sequencing (ES) and family-based rare variant analyses, we report a homozygous, frameshifting indel deleting the canonical stop codon in the last exon of PPP1R35 [Chr7: c.753_*3delGGAAGCGTAGACCinsCG (p.Trp251Cysfs*22)]; the variant allele maps in a 3.7 Mb block of absence of heterozygosity (AOH) in a proband with severe MCPH (-4.3 SD at birth, -6.1 SD by 42 months), pachygyria, and global developmental delay from a consanguineous Turkish kindred. Droplet digital PCR (ddPCR) confirmed mutant mRNA expression in fibroblasts. In silico prediction of the translation of mutant PPP1R35 is expected to be elongated by 18 amino acids before encountering a downstream stop codon. This complex indel allele is absent in public databases (ClinVar, gnomAD, ARIC, 1000 genomes) and our in-house database of 14,000+ exomes including 1800+ Turkish exomes supporting predicted pathogenicity. Comprehensive literature searches for PPP1R35 variants yielded two probands affected with severe microcephaly (-15 SD and -12 SD) with the same homozygous indel from a single, consanguineous, Iranian family from a cohort of 404 predominantly Iranian families. The lack of heterozygous cases in two large cohorts representative of the genetic background of these two families decreased our suspicion of a founder allele and supports the contention of a recurrent mutation. We propose two potential secondary structure mutagenesis models for the origin of this variant allele mediated by hairpin formation between complementary GC rich segments flanking the stop codon via secondary structure mutagenesis

Whole-Exome Sequencing in Familial Parkinson Disease

IMPORTANCE: Parkinson disease (PD) is a progressive neurodegenerative disease for which susceptibility is linked to genetic and environmental risk factors. OBJECTIVE: To identify genetic variants contributing to disease risk in familial PD. DESIGN, SETTING, AND PARTICIPANTS: A 2-stage study design that included a discovery cohort of families with PD and a replication cohort of familial probands was used. In the discovery cohort, rare exonic variants that segregated in multiple affected individuals in a family and were predicted to be conserved or damaging were retained. Genes with retained variants were prioritized if expressed in the brain and located within PD-relevant pathways. Genes in which prioritized variants were observed in at least 4 families were selected as candidate genes for replication in the replication cohort. The setting was among individuals with familial PD enrolled from academic movement disorder specialty clinics across the United States. All participants had a family history of PD. MAIN OUTCOMES AND MEASURES: Identification of genes containing rare, likely deleterious, genetic variants in individuals with familial PD using a 2-stage exome sequencing study design. RESULTS: The 93 individuals from 32 families in the discovery cohort (49.5% [46 of 93] female) had a mean (SD) age at onset of 61.8 (10.0) years. The 49 individuals with familial PD in the replication cohort (32.6% [16 of 49] female) had a mean (SD) age at onset of 50.1 (15.7) years. Discovery cohort recruitment dates were 1999 to 2009, and replication cohort recruitment dates were 2003 to 2014. Data analysis dates were 2011 to 2015. Three genes containing a total of 13 rare and potentially damaging variants were prioritized in the discovery cohort. Two of these genes (TNK2 and TNR) also had rare variants that were predicted to be damaging in the replication cohort. All 9 variants identified in the 2 replicated genes in 12 families across the discovery and replication cohorts were confirmed via Sanger sequencing. CONCLUSIONS AND RELEVANCE: TNK2 and TNR harbored rare, likely deleterious, variants in individuals having familial PD, with similar findings in an independent cohort. To our knowledge, these genes have not been previously associated with PD, although they have been linked to critical neuronal functions. Further studies are required to confirm a potential role for these genes in the pathogenesis of PD