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

Multilocus Pathogenic Variants Contribute to Intrafamilial Clinical Heterogeneity: A Retrospective Study of Sibling Pairs With Neurodevelopmental Disorders

BACKGROUND: Multilocus pathogenic variants (MPVs) are genetic changes that affect multiple gene loci or regions of the genome, collectively leading to multiple molecular diagnoses. MPVs may also contribute to intrafamilial phenotypic variability between affected individuals within a nuclear family. In this study, we aim to gain further insights into the influence of MPVs on a disease manifestation in individual research subjects and explore the complexities of the human genome within a familial context. METHODS: We conducted a systematic reanalysis of exome sequencing data and runs of homozygosity (ROH) regions of 47 sibling pairs previously diagnosed with various neurodevelopmental disorders (NDD). RESULTS: We found siblings with MPVs driven by long ROH regions in 8.5% of families (4/47). The patients with MPVs exhibited significantly higher F CONCLUSION: This study sheds light on the significance of considering MPVs in families with affected sibling pairs and the role of ROH as an adjuvant tool in explaining clinical variability within families. Identifying individuals carrying MPVs may have implications for disease management, identification of possible disease risks to different family members, genetic counseling and exploring personalized treatment approaches

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

The Genomics of Arthrogryposis, a Complex Trait: Candidate Genes and Further Evidence for Oligogenic Inheritance

Arthrogryposis is a clinical finding that is present either as a feature of a neuromuscular condition or as part of a systemic disease in over 400 Mendelian conditions. The underlying molecular etiology remains largely unknown because of genetic and phenotypic heterogeneity. We applied exome sequencing (ES) in a cohort of 89 families with the clinical sign of arthrogryposis. Additional molecular techniques including array comparative genomic hybridization (aCGH) and Droplet Digital PCR (ddPCR) were performed on individuals who were found to have pathogenic copy number variants (CNVs) and mosaicism, respectively. A molecular diagnosis was established in 65.2% (58/89) of families. Eleven out of 58 families (19.0%) showed evidence for potential involvement of pathogenic variation at more than one locus, probably driven by absence of heterozygosity (AOH) burden due to identity-by-descent (IBD). RYR3, MYOM2, ERGIC1, SPTBN4, and ABCA7 represent genes, identified in two or more families, for which mutations are probably causative for arthrogryposis. We also provide evidence for the involvement of CNVs in the etiology of arthrogryposis and for the idea that both mono-allelic and bi-allelic variants in the same gene cause either similar or distinct syndromes. We were able to identify the molecular etiology in nine out of 20 families who underwent reanalysis. In summary, our data from family-based ES further delineate the molecular etiology of arthrogryposis, yielded several candidate disease-associated genes, and provide evidence for mutational burden in a biological pathway or network. Our study also highlights the importance of reanalysis of individuals with unsolved diagnoses in conjunction with sequencing extended family members.National Human Genome Research Institute (NHGRI) [UM1 HG006542]; National Heart, Lung, and Blood Institute (NHLBI) [UM1 HG006542]; NHGRI [K08 HG008986]; National Institutes of Health - Brain Disorders and Development Training Grant [T32 NS043124-17]; Clinical Research Training Scholarship in Neuromuscular Disease; Tubitak project, Turkey [217S675]; Indian Council of Medical Research, New Delhi, India [5/13/58/2015/NCD-III]; [R35 NS105078]; [512848]This work was supported in part by R35 NS105078 and MDA#512848 to J.R.L. and a jointly funded National Human Genome Research Institute (NHGRI) and National Heart, Lung, and Blood Institute (NHLBI) grant to the Baylor-Hopkins Center for Mendelian Genomics (UM1 HG006542). J.E.P. is supported by NHGRI K08 HG008986. D.P. is supported by the National Institutes of Health - Brain Disorders and Development Training Grant (T32 NS043124-17) and a Clinical Research Training Scholarship in Neuromuscular Disease partnered by the American Brain Foundation (ABF) and Muscle Study Group (MSG). This study is partly funded by Tubitak project number 217S675, Turkey to N.E. and B.T.. This study is partly funded by Indian Council of Medical Research, New Delhi, India with File no.: No. 5/13/58/2015/NCD-III to A.S

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

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

HMZDupFinder: A Robust Computational Approach for Detecting Intragenic Homozygous Duplications From Exome Sequencing Data

Homozygous duplications contribute to genetic disease by altering gene dosage or disrupting gene regulation and can be more deleterious to organismal biology than heterozygous duplications. Intragenic exonic duplications can result in loss-of-function (LoF) or gain-of-function (GoF) alleles that when homozygosed, i.e. brought to homozygous state at a locus by identity by descent or state, could potentially result in autosomal recessive (AR) rare disease traits. However, the detection and functional interpretation of homozygous duplications from exome sequencing data remains a challenge. We developed a framework algorithm, HMZDupFinder, that is designed to detect exonic homozygous duplications from exome sequencing (ES) data. The HMZDupFinder algorithm can efficiently process large datasets and accurately identifies small intragenic duplications, including those associated with rare disease traits. HMZDupFinder called 965 homozygous duplications with three or less exons from 8,707 ES with a recall rate of 70.9% and a precision of 16.1%. We experimentally confirmed 8/10 rare homozygous duplications. Pathogenicity assessment of these copy number variant alleles allowed clinical genomics contextualization for three homozygous duplications alleles, including two affecting known OMIM disease genes EDAR (MIM# 224900), TNNT1(MIM# 605355), and one variant in a novel candidate disease gene: PAAF1