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Rare monogenic disorders often share molecular etiologies involved in the pathogenesis of common diseases. Congenital disorders of glycosylation (CDG) and deglycosylation (CDDG) are rare pediatric disorders with symptoms that range from mild to life threatening. A biological mechanism shared among CDG and CDDG as well as more common neurodegenerative diseases such as Alzheimer’s disease and amyotrophic lateral sclerosis, is endoplasmic reticulum (ER) stress. We developed isogenic human cellular models of two types of CDG and the only known CDDG to discover drugs that can alleviate ER stress. Systematic phenotyping confirmed ER stress and identified elevated autophagy among other phenotypes in each model. We screened 1049 compounds and scored their ability to correct aberrant morphology in each model using an agnostic cell-painting assay based on >300 cellular features. This primary screen identified multiple compounds able to correct morphological phenotypes. Independent validation shows they also correct cellular phenotypes and alleviate each of the ER stress markers identified in each model. Many of the active compounds are associated with microtubule dynamics, which points to new therapeutic opportunities for both rare and more common disorders presenting with ER stress, such as Alzheimer’s disease and amyotrophic lateral sclerosis.

Background MicroRNAs are a class of noncoding RNA molecules that co-regulate the expression of multiple genes via mRNA transcript degradation or translation inhibition. Since they often target entire pathways, they may be better drug targets than genes or proteins. MicroRNAs are known to be dysregulated in many tumours and associated with aggressive or poor prognosis phenotypes. Since they regulate mRNA in a tissue specific manner, their functional mRNA targets are poorly understood. In previous work, we developed a method to identify direct mRNA targets of microRNA using patient matched microRNA/mRNA expression data using an anti-correlation signature. This method, applied to clear cell Renal Cell Carcinoma (ccRCC), revealed many new regulatory pathways compromised in ccRCC. In the present paper, we apply this method to identify dysregulated microRNA/mRNA mechanisms in ovarian cancer using data from The Cancer Genome Atlas (TCGA). Methods TCGA Microarray data was normalized and samples whose class labels (tumour or normal) were ambiguous with respect to consensus ensemble K-Means clustering were removed. Significantly anti-correlated and correlated genes/microRNA differentially expressed between tumour and normal samples were identified. TargetScan was used to identify gene targets of microRNA. Results We identified novel microRNA/mRNA mechanisms in ovarian cancer. For example, the expression level of RAD51AP1 was found to be strongly anti-correlated with the expression of hsa-miR-140-3p, which was significantly down-regulated in the tumour samples. The anti-correlation signature was present separately in the tumour and normal samples, suggesting a direct causal dysregulation of RAD51AP1 by hsa-miR-140-3p in the ovary. Other pairs of potentially biological relevance include: hsa-miR-145/E2F3, hsa-miR-139-5p/TOP2A, and hsa-miR-133a/GCLC. We also identified sets of positively correlated microRNA/mRNA pairs that are most likely result from indirect regulatory mechanisms. Conclusions Our findings identify novel microRNA/mRNA relationships that can be verified experimentally. We identify both generic microRNA/mRNA regulation mechanisms in the ovary as well as specific microRNA/mRNA controls which are turned on or off in ovarian tumours. Our results suggest that the disease process uses specific mechanisms which may be significant for their utility as early detection biomarkers or in the development of microRNA therapies in treating ovarian cancers. The positively correlated microRNA/mRNA pairs suggest the existence of novel regulatory mechanisms that proceed via intermediate states (indirect regulation) in ovarian tumorigenesis.

Nonalcoholic fatty liver disease (NAFLD) is the most common global cause of chronic liver disease and remains under‐recognized within healthcare systems. Therapeutic interventions are rapidly advancing for its inflammatory phenotype, nonalcoholic steatohepatitis (NASH) at all stages of disease. Diagnosis codes alone fail to recognize and stratify at‐risk patients accurately. Our work aims to rapidly identify NAFLD patients within large electronic health record (EHR) databases for automated stratification and targeted intervention based on clinically relevant phenotypes. We present a rule‐based phenotyping algorithm for efficient identification of NAFLD patients developed using EHRs from 6.4 million patients at Columbia University Irving Medical Center (CUIMC) and validated at two independent healthcare centers. The algorithm uses the Observational Medical Outcomes Partnership (OMOP) Common Data Model and queries structured and unstructured data elements, including diagnosis codes, laboratory measurements, and radiology and pathology modalities. Our approach identified 16,006 CUIMC NAFLD patients, 10,753 (67%) previously unidentifiable by NAFLD diagnosis codes. Fibrosis scoring on patients without histology identified 943 subjects with scores indicative of advanced fibrosis (FIB‐4, APRI, NAFLD–FS). The algorithm was validated at two independent healthcare systems, University of Pennsylvania Health System (UPHS) and Vanderbilt Medical Center (VUMC), where 20,779 and 19,575 NAFLD patients were identified, respectively. Clinical chart review identified a high positive predictive value (PPV) across all healthcare systems: 91% at CUIMC, 75% at UPHS, and 85% at VUMC, and a sensitivity of 79.6%. Our rule‐based algorithm provides an accurate, automated approach for rapidly identifying, stratifying, and sub‐phenotyping NAFLD patients within a large EHR system.

The synthesis of the gonadotropin subunits is directed by pulsatile gonadotropin-releasing hormone (GnRH) from the hypothalamus, with the frequency of GnRH pulses governing the differential expression of the common alpha-subunit, luteinizing hormone beta-subunit (LHbeta) and follicle-stimulating hormone beta-subunit (FSHbeta). Three mitogen-activated protein kinases, (MAPKs), ERK1/2, JNK and p38, contribute uniquely and combinatorially to the expression of each of these subunit genes. In this study, using both experimental and computational methods, we found that dual specificity phosphatase regulation of the activity of the three MAPKs through negative feedback is required, and forms the basis for decoding the frequency of pulsatile GnRH. A fourth MAPK, ERK5, was shown also to be activated by GnRH. ERK5 was found to stimulate FSHbeta promoter activity and to increase FSHbeta mRNA levels, as well as enhancing its preference for low GnRH pulse frequencies. The latter is achieved through boosting the ultrasensitive behavior of FSHbeta gene expression by increasing the number of MAPK dependencies, and through modulating the feedforward effects of JNK activation on the GnRH receptor (GnRH-R). Our findings contribute to understanding the role of changing GnRH pulse-frequency in controlling transcription of the pituitary gonadotropins, which comprises a crucial aspect in regulating reproduction. Pulsatile stimuli and oscillating signals are integral to many biological processes, and elucidation of the mechanisms through which the pulsatility is decoded explains how the same stimulant can lead to various outcomes in a single cell.

The Li–Fraumeni syndrome (LFS) and its variant form (LFL) is a familial predisposition to multiple forms of childhood, adolescent, and adult cancers associated with germ-line mutation in the TP53 tumor suppressor gene. Individual disparities in tumor patterns are compounded by acceleration of cancer onset with successive generations. It has been suggested that this apparent anticipation pattern may result from germ-line genomic instability in TP53 mutation carriers, causing increased DNA copy-number variations (CNVs) with successive generations. To address the genetic basis of phenotypic disparities of LFS/LFL, we performed whole-genome sequencing (WGS) of 13 subjects from two generations of an LFS kindred. Neither de novo CNV nor significant difference in total CNV was detected in relation with successive generations or with age at cancer onset. These observations were consistent with an experimental mouse model system showing that trp53 deficiency in the germ line of father or mother did not increase CNV occurrence in the offspring. On the other hand, individual records on 1,771 TP53 mutation carriers from 294 pedigrees were compiled to assess genetic anticipation patterns (International Agency for Research on Cancer TP53 database). No strictly defined anticipation pattern was observed. Rather, in multigeneration families, cancer onset was delayed in older compared with recent generations. These observations support an alternative model for apparent anticipation in which rare variants from noncarrier parents may attenuate constitutive resistance to tumorigenesis in the offspring of TP53 mutation carriers with late cancer onset. Significance Germ-line mutation in the tumor suppressor TP53 causes Li–Fraumeni syndrome (LFS), a complex predisposition to multiple cancers. Types of cancers and ages at diagnosis vary among subjects and families, with apparent genetic anticipation: i.e., earlier cancer onset with successive generations. It has been proposed that anticipation is caused by accumulation of copy-number variations (CNV) in a context of TP53 haploinsufficiency. Using genome/exome sequencing, we found no evidence of increased rates of CNVs in two successive generations of TP53 mutation carriers and in successive generations of Trp53 -deficient mice. We propose a stochastic model called “genetic regression” to explain apparent anticipation in LFS, caused by segregation of rare SNP and de novo mutations rather than by cumulative DNA damage.

Motivation Next-generation sequencing (NGS) technologies have become much more efficient, allowing whole human genomes to be sequenced faster and cheaper than ever before. However, processing the raw sequence reads associated with NGS technologies requires care and sophistication in order to draw compelling inferences about phenotypic consequences of variation in human genomes. It has been shown that different approaches to variant calling from NGS data can lead to different conclusions. Ensuring appropriate accuracy and quality in variant calling can come at a computational cost. Results We describe our experience implementing and evaluating a group-based approach to calling variants on large numbers of whole human genomes. We explore the influence of many factors that may impact the accuracy and efficiency of group-based variant calling, including group size, the biogeographical backgrounds of the individuals who have been sequenced, and the computing environment used. We make efficient use of the Gordon supercomputer cluster at the San Diego Supercomputer Center by incorporating job-packing and parallelization considerations into our workflow while calling variants on 437 whole human genomes generated as part of large association study. Conclusions We ultimately find that our workflow resulted in high-quality variant calls in a computationally efficient manner. We argue that studies like ours should motivate further investigations combining hardware-oriented advances in computing systems with algorithmic developments to tackle emerging ‘big data’ problems in biomedical research brought on by the expansion of NGS technologies.

Bernett T.K. Lee1, Lailing Liew1, Jiahao Lim1, Jonathan K.L. Tan1, Tze Chuen Lee1, Pardha S. Veladandi1, Yun Ping Lim1, Hao Han1, Gunaretnam Rajagopal1 and N. Leigh Anderson2 1Bioinformatics Institute, 30 Biopolis Street, #07-01, Singapore 138671. 2The Plasma Proteome Insitute, P.O. Box: 53450, Washington DC, 20009-3450, U.S.A. AbstractCLUB (\"Candidate List of yoUr Biomarkers\") is a freely available, web-based resource designed to support Cancer biomarker research. It is targeted to provide a comprehensive list of candidate biomarkers for various cancers that have been reported by the research community. CLUB provides tools for comparison of marker candidates from different experimental platforms, with the ability to filter, search, query and explore, molecular interaction networks associated with cancer biomarkers from the published literature and from data uploaded by the community. This complex and ambitious project is implemented in phases. As a first step, we have compiled from the literature an initial set of differentially expressed human candidate cancer biomarkers. Each candidate is annotated with information from publicly available databases such as Gene Ontology, Swiss-Prot database, National Center for Biotechnology Information's reference sequences, Biomolecular Interaction Network Database and IntAct interaction. The user has the option to maintain private lists of biomarker candidates or share and export these for use by the community. Furthermore, users may customize and combine commonly used sets of selection procedures and apply them as a stored workflow using selected candidate lists. To enable an assessment by the user before taking a candidate biomarker to the experimental validation stage, the platform contains the functionality to identify pathways associated with cancer risk, staging, prognosis, outcome in cancer and other clinically associated phenotypes. The system is available at http://club.bii.a-star.edu.sg.

: The Gastric Cancer (Biomarkers) Knowledgebase (GCBKB) (http://biomarkers.bii.a-star.edu.sg/background/gastricCancerBiomarkersKb.php) is a curated and fully integrated knowledgebase that provides data relating to putative biomarkers that may be used in the diagnosis and prognosis of gastric cancer. It is freely available to all users. The data contained in the knowledgebase was derived from a large literature source and the putative biomarkers therein have been annotated with data from the public domain. The knowledgebase is maintained by a curation team who update the data from a defined source. As well as mining data from the literature, the knowledgebase will also be populated with unpublished experimental data from investigators working in the gastric cancer biomarker discovery field. Users can perform searches to identify potential markers defined by experiment type, tissue type and disease state. Search results may be saved, manipulated and retrieved at a later date. As far as the authors are aware this is the first open access database dedicated to the discovery and investigation of gastric cancer biomarkers.

The Gastric Cancer (Biomarkers) Knowledgebase (GCBKB) (http://biomarkers.bii.a-star.edu.sg/background/gastricCancerBiomarkersKb.php) is a curated and fully integrated knowledgebase that provides data relating to putative biomarkers that may be used in the diagnosis and prognosis of gastric cancer. It is freely available to all users. The data contained in the knowledgebase was derived from a large literature source and the putative biomarkers therein have been annotated with data from the public domain. The knowledgebase is maintained by a curation team who update the data from a defined source. As well as mining data from the literature, the knowledgebase will also be populated with unpublished experimental data from investigators working in the gastric cancer biomarker discovery field. Users can perform searches to identify potential markers defined by experiment type, tissue type and disease state. Search results may be saved, manipulated and retrieved at a later date. As far as the authors are aware this is the first open access database dedicated to the discovery and investigation of gastric cancer biomarkers.

CLUB (“Candidate List of yoUr Biomarkers”) is a freely available, web-based resource designed to support Cancer biomarker research. It is targeted to provide a comprehensive list of candidate biomarkers for various cancers that have been reported by the research community. CLUB provides tools for comparison of marker candidates from different experimental platforms, with the ability to filter, search, query and explore, molecular interaction networks associated with cancer biomarkers from the published literature and from data uploaded by the community. This complex and ambitious project is implemented in phases. As a first step, we have compiled from the literature an initial set of differentially expressed human candidate cancer biomarkers. Each candidate is annotated with information from publicly available databases such as Gene Ontology, Swiss-Prot database, National Center for Biotechnology Information’s reference sequences, Biomolecular Interaction Network Database and IntAct interaction. The user has the option to maintain private lists of biomarker candidates or share and export these for use by the community. Furthermore, users may customize and combine commonly used sets of selection procedures and apply them as a stored workflow using selected candidate lists. To enable an assessment by the user before taking a candidate biomarker to the experimental validation stage, the platform contains the functionality to identify pathways associated with cancer risk, staging, prognosis, outcome in cancer and other clinically associated phenotypes. The system is available at http://club.bii.a-star.edu.sg.