Clinical and functional characterization of germline PIK3CA variants in patients with PIK3CA-related overgrowth spectrum disorders.

Mosaic variants in the PIK3CA gene, encoding the catalytic subunit of phosphoinositide 3-kinase (PI3K), produce constitutive PI3K activation, which causes PIK3CA-related overgrowth spectrum disorders. To date, fewer than 20 patients have been described with germline alterations in PIK3CA. In this study, we describe three unrelated individuals with overgrowth and germline PIK3CA variants. These variants were discovered through whole-exome sequencing and confirmed as germline by testing multiple tissue types, when available. Functional analysis using Patient 1's fibroblast cell line and two previously reported patients' cell lines showed increased phosphorylation of AKT during cellular starvation revealing constitutive activation of the phosphoinositide-3-kinase/protein kinase B/mechanistic target of rapamycin (PI3K/AKT/mTOR) pathway. Alternatively, stimulation of the cells by fetal bovine serum produced a reduced response, indicating an activated status of the PI3K complex reducing the pathway response to further external stimulation. Additional studies utilizing Biolog Phenotype Microarray technology indicated reduced energy production when cells were exposed to growth factors stimulating the PI3K/AKT/mTOR pathway, confirming the trend observed in the AKT phosphorylation test after stimulation. Furthermore, treatment with inhibitors of the PI3K/AKT/mTOR pathway rescued the normal energy response in the patients' cells. Collectively, these data demonstrate that disease-causing germline PIK3CA variants have a functional consequence, similar to mosaic variants in the PI3K/AKT/mTOR pathway.

Enrichment of a subset of Neanderthal polymorphisms in autistic probands and siblings.

Homo sapiens and Neanderthals underwent hybridization during the Middle/Upper Paleolithic age, culminating in retention of small amounts of Neanderthal-derived DNA in the modern human genome. In the current study, we address the potential roles Neanderthal single nucleotide polymorphisms (SNP) may be playing in autism susceptibility in samples of black non-Hispanic, white Hispanic, and white non-Hispanic people using data from the Simons Foundation Powering Autism Research (SPARK), Genotype-Tissue Expression (GTEx), and 1000 Genomes (1000G) databases. We have discovered that rare variants are significantly enriched in autistic probands compared to race-matched controls. In addition, we have identified 25 rare and common SNPs that are significantly enriched in autism on different ethnic backgrounds, some of which show significant clinical associations. We have also identified other SNPs that share more specific genotype-phenotype correlations but which are not necessarily enriched in autism and yet may nevertheless play roles in comorbid phenotype expression (e.g., intellectual disability, epilepsy, and language regression). These results strongly suggest Neanderthal-derived DNA is playing a significant role in autism susceptibility across major populations in the United States.

Insights into gemcitabine resistance in pancreatic cancer: association with metabolic reprogramming and TP53 pathogenicity in patient derived xenografts.

BACKGROUND: With poor prognosis and high mortality, pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies. Standard of care therapies for PDAC have included gemcitabine for the past three decades, although resistance often develops within weeks of chemotherapy initiation through an array of possible mechanisms. METHODS: We reanalyzed publicly available RNA-seq gene expression profiles of 28 PDAC patient-derived xenograft (PDX) models before and after a 21-day gemcitabine treatment using our validated analysis pipeline to identify molecular markers of intrinsic and acquired resistance. RESULTS: Using normalized RNA-seq quantification measurements, we first identified oxidative phosphorylation and interferon alpha pathways as the two most enriched cancer hallmark gene sets in the baseline gene expression profile associated with intrinsic gemcitabine resistance and sensitivity, respectively. Furthermore, we discovered strong correlations between drug-induced expression changes in glycolysis and oxidative phosphorylation genes and response to gemcitabine, which suggests that these pathways may be associated with acquired gemcitabine resistance mechanisms. Thus, we developed prediction models using baseline gene expression profiles in those pathways and validated them in another dataset of 12 PDAC models from Novartis. We also developed prediction models based on drug-induced expression changes in genes from the Molecular Signatures Database (MSigDB)'s curated 50 cancer hallmark gene sets. Finally, pathogenic TP53 mutations correlated with treatment resistance. CONCLUSION: Our results demonstrate that concurrent upregulation of both glycolysis and oxidative phosphorylation pathways occurs in vivo in PDAC PDXs following gemcitabine treatment and that pathogenic TP53 status had association with gemcitabine resistance in these models. Our findings may elucidate the molecular basis for gemcitabine resistance and provide insights for effective drug combination in PDAC chemotherapy.

Simplified detection of genetic background admixture using artificial intelligence.

Admixture refers to the mixing of genetic ancestry from different populations. Admixture is important for genomic medicine because it can affect how an individual responds to certain medications, how they metabolize drugs, and susceptibility to certain diseases. For example, some genetic variants associated with drug metabolism and response may be more common in certain populations, and individuals with admixed ancestry may have a different frequency of these variants than individuals from the ancestral populations. Understanding the patterns of admixture in a population can also help researchers identify new genetic variants associated with diseases or traits and develop more personalized and targeted treatments. In this study, we compared and classified the known and self-reported genetic backgrounds from 1000 Genomes Project and admixed samples from GTEx projects using supervised, unsupervised and statistical classification methodologies. We developed a novel tool called Admix-AI that uses a one-dimensional convolutional neural network to understand and classify admixed genetic backgrounds using 213 DNA-marker based genetic background labels. Admix-AI can be used to discover admixed proportions in samples and ultimately aid personalized genomic medicine by identifying specific biomarker systems. We compared Admix-AI to the existing admixture categorization software and found our tool to be computationally faster with 2× speedup and streamlined usage. Admix-AI is available as open-source code under GPL version 3.0 license at https://github.com/rpauly/Admix-AI.

Sleep disturbances in Phelan-McDermid syndrome: Clinical and metabolic profiling of 56 individuals.

Phelan-McDermid Syndrome (PMS) is caused by deletions at chromosome 22q13.3 or pathogenic/likely pathogenic SHANK3 variants. The clinical presentation is extremely variable and includes global developmental delay/intellectual disability (ID), seizures, neonatal hypotonia, and sleep disturbances, among others. This study investigated the prevalence of sleep disturbances, and the genetic and metabolic features associated with them, in a cohort of 56 individuals with PMS. Sleep data were collected via standardized observer/caregiver questionnaires, while genetic data from array-CGH and sequencing of 9 candidate genes within the 22q13.3 region, and metabolic profiling utilized the Biolog Phenotype Mammalian MicroArray plates. Sleep disturbances were present in 64.3% of individuals with PMS, with the most common problem being waking during the night (39%). Sleep disturbances were more prevalent in individuals with a SHANK3 pathogenic variant (89%) compared to subjects with 22q13.3 deletions of any size (59.6%). Distinct metabolic profiles for individuals with PMS with and without sleep disturbances were also identified. These data are helpful information for recognizing and managing sleep disturbances in individuals with PMS, outlining the main candidate gene for this neurological manifestation, and highlighting potential biomarkers for early identification of at-risk subjects and molecular targets for novel treatment approaches.

Identification of condition-specific regulatory mechanisms in normal and cancerous human lung tissue.

BACKGROUND: Lung cancer is the leading cause of cancer death in both men and women. The most common lung cancer subtype is non-small cell lung carcinoma (NSCLC) comprising about 85% of all cases. NSCLC can be further divided into three subtypes: adenocarcinoma (LUAD), squamous cell carcinoma (LUSC), and large cell lung carcinoma. Specific genetic mutations and epigenetic aberrations play an important role in the developmental transition to a specific tumor subtype. The elucidation of normal lung versus lung tumor gene expression patterns and regulatory targets yields biomarker systems that discriminate lung phenotypes (i.e., biomarkers) and provide a foundation for the discovery of normal and aberrant gene regulatory mechanisms. RESULTS: We built condition-specific gene co-expression networks (csGCNs) for normal lung, LUAD, and LUSC conditions. Then, we integrated normal lung tissue-specific gene regulatory networks (tsGRNs) to elucidate control-target biomarker systems for normal and cancerous lung tissue. We characterized co-expressed gene edges, possibly under common regulatory control, for relevance in lung cancer. CONCLUSIONS: Our approach demonstrates the ability to elucidate csGCN:tsGRN merged biomarker systems based on gene expression correlation and regulation. The biomarker systems we describe can be used to classify and further describe lung specimens. Our approach is generalizable and can be used to discover and interpret complex gene expression patterns for any condition or species.

Individuals with SATB2-associated syndrome with and without autism have a recognizable metabolic profile and distinctive cellular energy metabolism alterations.

SATB2-associated syndrome (SAS) is a multisystemic disorder characterized by developmental delay often with concurrent autistic tendencies. This study aimed to characterize cellular metabolic pathways and energy metabolism from cells derived from individuals with SAS. The cellular production of NADH (nicotinamide adenine dinucleotide, reduced form) as determined by the Phenotype Mammalian MicroArrays was measured in lymphoblastoid cell lines derived from 11 subjects with a molecularly confirmed diagnosis of SAS and compared to a control population of 50 age-matched typically developing individuals. All patients were evaluated clinically by a multidisciplinary team. Eleven individuals (five in a screening cohort and six in the validation cohort, mean age 6.1 years) were recruited to the study. All individuals had developmental delay and the diagnosis of autism was previously established in five of them. Key metabolic findings included reduced NADH production in the presence of phosphorylated carbohydrates (with corresponding increased production in the presence of alternative carbon-based energy sources), increased response to certain hormones (ß-estradiol in particular), and significantly reduced levels of NADH in wells containing tryptophan. The individual analysis revealed no particular differences among the SAS subjects based on molecular findings or phenotypic features. In conclusion, individuals with SAS have a common and recognizable metabolic profile. A lower capacity to utilize glucose as an energy substrate could be contributing to the neurodevelopment phenotype of SAS. The identified abnormalities offer previously unexplored insight into the potential pathophysiology of common SAS phenotypic features.

Neural Tube Defects and Associated Anomalies before and after Folic Acid Fortification.

OBJECTIVE: To examine the prevalence and types of neural tube defects and the types of anomalies co-occurring with neural tube defects in 6 years before fortification of cereal grain flour with folic acid (1992-1998) and 20 years after fortification (1999-2018) in South Carolina, a state with a historically high prevalence of these birth defects. STUDY DESIGN: The prevalence of neural tube defects was determined by active and passive surveillance methods in South Carolina since 1992. The types of neural tube defects and co-occurring malformations were determined by prenatal ultrasound and post-delivery examination. RESULTS: In the 6 prefortification years, 363 neural tube defects were identified among 279 163 live births and fetal deaths (1/769), 305 (84%) of which were isolated defects of the calvaria or spine. In the 20 fortification years, there were significant reductions in the prevalence and percentage of isolated defects: 938 neural tube defects were identified among 1 165 134 live births and fetal deaths (1/1242), 696 (74.2%) of which were isolated. The current prevalence of neural tube defects in South Carolina (0.56/1000 live births and fetal deaths) is comparable with that nationwide. CONCLUSIONS: The continued occurrence of neural tube defects, the majority of which are isolated, after folic acid fortification of cereal grain flours suggests that additional prevention measures are necessary to reduce further the prevalence of these serious defects of the brain and spine.

Constitutive activation of the PI3K-AKT pathway and cardiovascular abnormalities in an individual with Kosaki overgrowth syndrome.

Kosaki overgrowth syndrome is a recently described syndrome characterized by distinctive facial features, brain white matter lesions, and developmental delay. Germline activating heterozygous PDGFRB mutations have been reported in this condition. Systemic connective tissue-type findings have been described in some individuals. We describe a 19-year-old Caucasian female with a history of hydrocephalus, Dandy-Walker malformation, cervical spine arachnoid cyst, progressive scoliosis, and overgrowth. Her physical exam included distinctive craniofacial dysmorphism, as well as soft and hyperextensible skin. Cardiovascular imaging during adolescence revealed saccular aneurysms in both coronary artery systems and subtle tortuosity of the cervical vertebral arteries. Exome sequencing trio analysis identified a de novo previously reported pathogenic variant in PDGFRB, c.1696T>C (p.[Trp566Arg]). Further functional studies included platelet-derived growth factor cellular metabolic pathway activity that confirmed the variant causes a constitutive activation of the PI3K-AKT pathway. This is the first report to characterize the activating nature of this PDGFRB variant. We also highlight the connective tissue findings seen in Kosaki overgrowth syndrome and recommend baseline echocardiographic evaluation in all individuals with this condition with particular emphasis on coronary arteries.

Gene integrated set profile analysis: a context-based approach for inferring biological endpoints.

The identification of genes with specific patterns of change (e.g. down-regulated and methylated) as phenotype drivers or samples with similar profiles for a given gene set as drivers of clinical outcome, requires the integration of several genomic data types for which an 'integrate by intersection' (IBI) approach is often applied. In this approach, results from separate analyses of each data type are intersected, which has the limitation of a smaller intersection with more data types. We introduce a new method, GISPA (Gene Integrated Set Profile Analysis) for integrated genomic analysis and its variation, SISPA (Sample Integrated Set Profile Analysis) for defining respective genes and samples with the context of similar, a priori specified molecular profiles. With GISPA, the user defines a molecular profile that is compared among several classes and obtains ranked gene sets that satisfy the profile as drivers of each class. With SISPA, the user defines a gene set that satisfies a profile and obtains sample groups of profile activity. Our results from applying GISPA to human multiple myeloma (MM) cell lines contained genes of known profiles and importance, along with several novel targets, and their further SISPA application to MM coMMpass trial data showed clinical relevance.

Discovery of eQTL Alleles Associated with Autism Spectrum Disorder: A Case-Control Study.

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder characterized by challenges in social communication as well as repetitive or restrictive behaviors. Many genetic associations with ASD have been identified, but most associations occur in a fraction of the ASD population. Here, we searched for eQTL-associated DNA variants with significantly different allele distributions between ASD-affected and control. Thirty significant DNA variants associated with 174 tissue-specific eQTLs from ASD individuals in the SPARK project were identified. Several significant variants fell within brain-specific regulatory regions or had been associated with a significant change in gene expression in the brain. These eQTLs are a new class of biomarkers that could control the myriad of brain and non-brain phenotypic traits seen in ASD-affected individuals.

Identification of condition-specific biomarker systems in uterine cancer.

Uterine cancer is the fourth most common cancer among women, projected to affect 66,000 US women in 2021. Uterine cancer often arises in the inner lining of the uterus, known as the endometrium, but can present as several different types of cancer, including endometrioid cancer, serous adenocarcinoma, and uterine carcinosarcoma. Previous studies have analyzed the genetic changes between normal and cancerous uterine tissue to identify specific genes of interest, including TP53 and PTEN. Here we used Gaussian Mixture Models to build condition-specific gene coexpression networks for endometrial cancer, uterine carcinosarcoma, and normal uterine tissue. We then incorporated uterine regulatory edges and investigated potential coregulation relationships. These networks were further validated using differential expression analysis, functional enrichment, and a statistical analysis comparing the expression of transcription factors and their target genes across cancerous and normal uterine samples. These networks allow for a more comprehensive look into the biological networks and pathways affected in uterine cancer compared with previous singular gene analyses. We hope this study can be incorporated into existing knowledge surrounding the genetics of uterine cancer and soon become clinical biomarkers as a tool for better prognosis and treatment.

Circulating tumor DNA sequencing provides comprehensive mutation profiling for pediatric central nervous system tumors.

Molecular profiling of childhood CNS tumors is critical for diagnosis and clinical management, yet tissue access is restricted due to the sensitive tumor location. We developed a targeted deep sequencing platform to detect tumor driver mutations, copy number variations, and heterogeneity in the liquid biome. Here, we present the sensitivity, specificity, and clinical relevance of our minimally invasive platform for tumor mutation profiling in children diagnosed with CNS cancer.

New Strategies for Clinical Trials in Autism Spectrum Disorder.

BACKGROUND: Autism spectrum disorder (ASD) is a complex neurodevelopmental condition that poses several challenges in terms of clinical diagnosis and investigation of molecular etiology. The lack of knowledge on the pathogenic mechanisms underlying ASD has hampered the clinical trials that so far have tried to target ASD behavioral symptoms. In order to improve our understanding of the molecular abnormalities associated with ASD, a deeper and more extensive genetic profiling of targeted individuals with ASD was needed. METHODS: The recent availability of new and more powerful sequencing technologies (third-generation sequencing) has allowed to develop novel strategies for the characterization of comprehensive genetic profiles of individuals with ASD. In particular, this review will describe integrated approaches based on the combination of various omics technologies that will lead to a better stratification of targeted cohorts for the design of clinical trials in ASD. RESULTS: In order to analyze the big data collected by assays such as the whole genome, epigenome, transcriptome, and proteome, it is critical to develop an efficient computational infrastructure. Machine learning models are instrumental to identify non-linear relationships between the omics technologies and, therefore, establish a functional informative network among the different data sources. CONCLUSION: The potential advantage provided by these new integrated omics-based strategies is better characterization of the genetic background of ASD cohorts, to identify novel molecular targets for drug development, and ultimately offer a more personalized approach in the design of clinical trials for ASD.

Named Data Networking for Genomics Data Management and Integrated Workflows.

Advanced imaging and DNA sequencing technologies now enable the diverse biology community to routinely generate and analyze terabytes of high resolution biological data. The community is rapidly heading toward the petascale in single investigator laboratory settings. As evidence, the single NCBI SRA central DNA sequence repository contains over 45 petabytes of biological data. Given the geometric growth of this and other genomics repositories, an exabyte of mineable biological data is imminent. The challenges of effectively utilizing these datasets are enormous as they are not only large in the size but also stored in geographically distributed repositories in various repositories such as National Center for Biotechnology Information (NCBI), DNA Data Bank of Japan (DDBJ), European Bioinformatics Institute (EBI), and NASA's GeneLab. In this work, we first systematically point out the data-management challenges of the genomics community. We then introduce Named Data Networking (NDN), a novel but well-researched Internet architecture, is capable of solving these challenges at the network layer. NDN performs all operations such as forwarding requests to data sources, content discovery, access, and retrieval using content names (that are similar to traditional filenames or filepaths) and eliminates the need for a location layer (the IP address) for data management. Utilizing NDN for genomics workflows simplifies data discovery, speeds up data retrieval using in-network caching of popular datasets, and allows the community to create infrastructure that supports operations such as creating federation of content repositories, retrieval from multiple sources, remote data subsetting, and others. Named based operations also streamlines deployment and integration of workflows with various cloud platforms. Our contributions in this work are as follows 1) we enumerate the cyberinfrastructure challenges of the genomics community that NDN can alleviate, and 2) we describe our efforts in applying NDN for a contemporary genomics workflow (GEMmaker) and quantify the improvements. The preliminary evaluation shows a sixfold speed up in data insertion into the workflow. 3) As a pilot, we have used an NDN naming scheme (agreed upon by the community and discussed in Section 4) to publish data from broadly used data repositories including the NCBI SRA. We have loaded the NDN testbed with these pre-processed genomes that can be accessed over NDN and used by anyone interested in those datasets. Finally, we discuss our continued effort in integrating NDN with cloud computing platforms, such as the Pacific Research Platform (PRP). The reader should note that the goal of this paper is to introduce NDN to the genomics community and discuss NDN's properties that can benefit the genomics community. We do not present an extensive performance evaluation of NDN-we are working on extending and evaluating our pilot deployment and will present systematic results in a future work.

Position effects of 22q13 rearrangements on candidate genes in Phelan-McDermid syndrome.

Phelan-McDermid syndrome (PMS) is a multi-system disorder characterized by significant variability in clinical presentation. The genetic etiology is also variable with differing sizes of deletions in the chromosome 22q13 region and types of genetic abnormalities (e.g., terminal or interstitial deletions, translocations, ring chromosomes, or SHANK3 variants). Position effects have been shown to affect gene expression and function and play a role in the clinical presentation of various genetic conditions. This study employed a topologically associating domain (TAD) analysis approach to investigate position effects of chromosomal rearrangements on selected candidate genes mapped to 22q13 in 81 individuals with PMS. Data collected were correlated with clinical information from these individuals and with expression and metabolic profiles of lymphoblastoid cells from selected cases. The data confirmed TAD predictions for genes encompassed in the deletions and the clinical and molecular data indicated clear differences among individuals with different 22q13 deletion sizes. The results of the study indicate a positive correlation between deletion size and phenotype severity in PMS and provide evidence of the contribution of other genes to the clinical variability in this developmental disorder by reduced gene expression and altered metabolomics.

A new test for autism spectrum disorder: Metabolic data from different cell types.

Experiments employing the Phenotype Mammalian Microarray (PM-M) technology were performed on lymphoblastoid cell lines (LCLs) from individuals with autism spectrum disorder (ASD) and age-matched controls. We used the custom-made PM-M plate designed to assess differential utilization of the amino acid tryptophan. Multiple parameters such as the sample size, incubation time, and cell concentration have been tested, leading to optimized protocols and minimized background noise by variable selection while controlling for false discoveries. The assay generated data based on the production of nicotinamide adenine dinucleotide (NADH) in the presence of different compounds containing tryptophan and showed clear differences between ASD and control samples.

Autistic Disorder: A 20 Year Chronicle.

The course of 187 individuals ages 3-21 years with Autistic Disorder was traced through a period of 20 years (enrollment: 1995-1998, follow up: 2014-2019). Specific genetic and environmental causes were identified in only a minority. Intellectual disability coexisted in 84%. Few became independent with 99% living at home with relatives, in disability group homes or in residential facilities. Seven individuals (3.7%) attained postsecondary education, two receiving baccalaureate degrees, two receiving associate degrees, and three receiving certificates from college disability programs. It may be anticipated that the long term outcome for individuals currently diagnosed with Autism Spectrum Disorder (ASD) will be substantially better than for individuals with Autistic Disorder in this cohort.

Abnormalities in the genes that encode Large Amino Acid Transporters increase the risk of Autism Spectrum Disorder.

BACKGROUND: Autism spectrum disorder (ASD) is a common neurodevelopmental disorder whose molecular mechanisms are largely unknown. Several studies have shown an association between ASD and abnormalities in the metabolism of amino acids, specifically tryptophan and branched-chain amino acids (BCAAs). METHODS: Ninety-seven patients with ASD were screened by Sanger sequencing the genes encoding the heavy (SLC3A2) and light subunits (SLC7A5 and SLC7A8) of the large amino acid transporters (LAT) 1 and 2. LAT1 and 2 are responsible for the transportation of tryptophan and BCAA across the blood-brain barrier and are expressed both in blood and brain. Functional studies were performed employing the Biolog Phenotype Microarray Mammalian (PM-M) technology to investigate the metabolic profiling in lymphoblastoid cell lines from 43 patients with ASD and 50 controls with particular focus on the amino acid substrates of LATs. RESULTS: We detected nine likely pathogenic variants in 11 of 97 patients (11.3%): three in SLC3A2, three in SLC7A5, and three in SLC7A8. Six variants of unknown significance were detected in eight patients, two of which also carrying a likely pathogenic variant. The functional studies showed a consistently reduced utilization of tryptophan, accompanied by evidence of reduced utilization of other large aromatic amino acids (LAAs), either alone or as part of a dipeptide. CONCLUSION: Coding variants in the LAT genes were detected in 17 of 97 patients with ASD (17.5%). Metabolic assays indicate that such abnormalities affect the utilization of certain amino acids, particularly tryptophan and other LAAs, with potential consequences on their transport across the blood barrier and their availability during brain development. Therefore, abnormalities in the LAT1 and two transporters are likely associated with an increased risk of developing ASD.

Genome-scale network model of metabolism and histone acetylation reveals metabolic dependencies of histone deacetylase inhibitors.

Histone acetylation plays a central role in gene regulation and is sensitive to the levels of metabolic intermediates. However, predicting the impact of metabolic alterations on acetylation in pathological conditions is a significant challenge. Here, we present a genome-scale network model that predicts the impact of nutritional environment and genetic alterations on histone acetylation. It identifies cell types that are sensitive to histone deacetylase inhibitors based on their metabolic state, and we validate metabolites that alter drug sensitivity. Our model provides a mechanistic framework for predicting how metabolic perturbations contribute to epigenetic changes and sensitivity to deacetylase inhibitors.