Calcium, magnesium, potassium, sodium, chloride and phosphorus are the major dietary minerals involved in various biological functions and are commonly measured in the blood serum. Sufficient mineral intake is especially important for children due to their rapid growth. Currently, the genetic mechanisms influencing serum mineral levels are poorly understood, especially for children. We carried out a genome-wide association (GWA) study on 5,602 European-American children and 4,706 African-American children who had mineral measures available in their electronic medical records (EMR). While no locus met the criteria for genome-wide significant association, our results demonstrated a nominal association of total serum calcium levels with a missense variant in the calcium -sensing receptor (CASR) gene on 3q13 (rs1801725, P = 1.96 × 10(-3)) in the African-American pediatric cohort, a locus previously reported in Caucasians. We also confirmed the association result in our pediatric European-American cohort (P = 1.38 × 10(-4)). We further replicated two other loci associated with serum calcium levels in the European-American cohort (rs780094, GCKR, P = 4.26 × 10(-3); rs10491003, GATA3, P = 0.02). In addition, we replicated a previously reported locus on 1q21, demonstrating association of serum magnesium levels with MUC1 (rs4072037, P = 2.04 × 10(-6)). Moreover, in an extended gene-based association analysis we uncovered evidence for association of calcium levels with the previously reported gene locus DGKD in both European-American children and African-American children. Taken together, our results support a role for CASR and DGKD mediated calcium regulation in both African-American and European-American children, and corroborate the association of calcium levels with GCKR and GATA3, and the association of magnesium levels with MUC1 in the European-American children.

Direct reprogramming provides a fundamentally new approach for the generation of patient-specific cells. Here, by screening a pool of candidate transcription factors, we identify that a combination of the three factors, MITF, SOX10 and PAX3, directly converts mouse and human fibroblasts to functional melanocytes. Induced melanocytes (iMels) activate melanocyte-specific networks, express components of pigment production and delivery system and produce melanosomes. Human iMels properly integrate into the dermal-epidermal junction and produce and deliver melanin pigment to surrounding keratinocytes in a 3D organotypic skin reconstruct. Human iMels generate pigmented epidermis and hair follicles in skin reconstitution assays in vivo. The generation of iMels has important implications for studies of melanocyte lineage commitment, pigmentation disorders and cell replacement therapies.

Acute otitis media (AOM) is among the most common pediatric diseases, and the most frequent reason for antibiotic treatment in children. Risk of AOM is dependent on environmental and host factors, as well as a significant genetic component. We identify genome-wide significance at a locus on 6q25.3 (rs2932989, Pmeta=2.15 × 10-09), and show that the associated variants are correlated with the methylation status of the FNDC1 gene (cg05678571, P=1.43 × 10-06), and further show it is an eQTL for FNDC1 (P=9.3 × 10-05). The mouse homologue, Fndc1, is expressed in middle ear tissue and its expression is upregulated upon lipopolysaccharide treatment. In this first GWAS of AOM and the largest OM genetic study to date, we identify the first genome-wide significant locus associated with AOM.

To facilitate the clinical implementation of genomic medicine by next-generation sequencing, it will be critically important to obtain accurate and consistent variant calls on personal genomes. Multiple software tools for variant calling are available, but it is unclear how comparable these tools are or what their relative merits in real-world scenarios might be.

We performed risk assessment for Crohn's disease (CD) and ulcerative colitis (UC), the two common forms of inflammatory bowel disease (IBD), by using data from the International IBD Genetics Consortium's Immunochip project. This data set contains ~17,000 CD cases, ~13,000 UC cases, and ~22,000 controls from 15 European countries typed on the Immunochip. This custom chip provides a more comprehensive catalog of the most promising candidate variants by picking up the remaining common variants and certain rare variants that were missed in the first generation of GWAS. Given this unprecedented large sample size and wide variant spectrum, we employed the most recent machine-learning techniques to build optimal predictive models. Our final predictive models achieved areas under the curve (AUCs) of 0.86 and 0.83 for CD and UC, respectively, in an independent evaluation. To our knowledge, this is the best prediction performance ever reported for CD and UC to date.

Therapeutic strategies for the treatment of metastatic melanoma show encouraging results in the clinic; however, not all patients respond equally and tumor resistance still poses a challenge. To identify novel therapeutic targets for melanoma, we screened a panel of structurally diverse organometallic inhibitors against human-derived normal and melanoma cells. We observed that a compound that targets PIM kinases (a family of Ser/Thr kinases) preferentially inhibited melanoma cell proliferation, invasion, and viability in adherent and three-dimensional (3D) melanoma models. Assessment of tumor tissue from melanoma patients showed that PIM kinases are expressed in pre- and post-treatment tumors, suggesting PIM kinases as promising targets in the clinic. Using knockdown studies, we showed that PIM1 contributes to melanoma cell proliferation and tumor growth in vivo; however, the presence of PIM2 and PIM3 could also influence the outcome. The inhibition of all PIM isoforms using SGI-1776 (a clinically-available PIM inhibitor) reduced melanoma proliferation and survival in preclinical models of melanoma. This was potentiated in the presence of the BRAF inhibitor PLX4720 and in the presence of PI3K inhibitors. Our findings suggest that PIM inhibitors provide promising additions to the targeted therapies available to melanoma patients.

Juvenile idiopathic arthritis (JIA) is the most common chronic rheumatic disease among children, the etiology of which involves a strong genetic component, but much of the underlying genetic determinants still remain unknown. Our aim was to identify novel genetic variants that predispose to JIA.

The rapid development of next generation sequencing (NGS) technology provides a new chance to extend the scale and resolution of genomic research. How to efficiently map millions of short reads to the reference genome and how to make accurate SNP calls are two major challenges in taking full advantage of NGS. In this article, we reviewed the current software tools for mapping and SNP calling, and evaluated their performance on samples from The Cancer Genome Atlas (TCGA) project. We found that BWA and Bowtie are better than the other alignment tools in comprehensive performance for Illumina platform, while NovoalignCS showed the best overall performance for SOLiD. Furthermore, we showed that next-generation sequencing platform has significantly lower coverage and poorer SNP-calling performance in the CpG islands, promoter and 5'-UTR regions of the genome. NGS experiments targeting for these regions should have higher sequencing depth than the normal genomic region.

Targeted BRAF inhibition (BRAFi) and combined BRAF and MEK inhibition (BRAFi and MEKi) therapies have markedly improved the clinical outcomes of patients with metastatic melanoma. Unfortunately, the efficacy of these treatments is often countered by the acquisition of drug resistance. Here we investigated the molecular mechanisms that underlie acquired resistance to BRAFi and to the combined therapy. Consistent with previous studies, we show that resistance to BRAFi is mediated by ERK pathway reactivation. Resistance to the combined therapy, however, is mediated by mechanisms independent of reactivation of ERK in many resistant cell lines and clinical samples. p21-activated kinases (PAKs) become activated in cells with acquired drug resistance and have a pivotal role in mediating resistance. Our screening, using a reverse-phase protein array, revealed distinct mechanisms by which PAKs mediate resistance to BRAFi and the combined therapy. In BRAFi-resistant cells, PAKs phosphorylate CRAF and MEK to reactivate ERK. In cells that are resistant to the combined therapy, PAKs regulate JNK and β-catenin phosphorylation and mTOR pathway activation, and inhibit apoptosis, thereby bypassing ERK. Together, our results provide insights into the molecular mechanisms underlying acquired drug resistance to current targeted therapies, and may help to direct novel drug development efforts to overcome acquired drug resistance.

Neuroblastoma is a highly complex and heterogeneous cancer in children. Acquired genomic alterations including MYCN amplification, 1p deletion and 11q deletion are important risk factors and biomarkers in neuroblastoma. Here, we performed a co-expression-based gene network analysis to study the intrinsic association between specific genomic changes and transcriptome organization. We identified multiple gene coexpression modules which are recurrent in two independent datasets and associated with functional pathways including nervous system development, cell cycle, immune system process and extracellular matrix/space. Our results also indicated that modules involved in nervous system development and cell cycle are highly associated with MYCN amplification and 1p deletion, while modules responding to immune system process are associated with MYCN amplification only. In summary, this integrated analysis provides novel insights into molecular heterogeneity and pathogenesis of neuroblastoma. This article is part of a Special Issue entitled: Accelerating Precision Medicine through Genetic and Genomic Big Data Analysis edited by Yudong Cai & Tao Huang.

Predicting the response to a drug for cancer disease patients based on genomic information is an important problem in modern clinical oncology. This problem occurs in part because many available drug sensitivity prediction algorithms do not consider better quality cancer cell lines and the adoption of new feature representations; both lead to the accurate prediction of drug responses. By predicting accurate drug responses to cancer, oncologists gain a more complete understanding of the effective treatments for each patient, which is a core goal in precision medicine.

The prevalence of Type II Diabetes (T2D) has been increasing and has become a disease of significant public health burden in Jordan. None of the previous genome-wide association studies (GWAS) have specifically investigated the Middle East populations. The Circassian and Chechen communities in Jordan represent unique populations that are genetically distinct from the Arab population and other populations in the Caucasus. Prevalence of T2D is very high in both the Circassian and Chechen communities in Jordan despite low obesity prevalence. We conducted GWAS on T2D in these two populations and further performed meta-analysis of the results. We identified a novel T2D locus at chr20p12.2 at genome-wide significance (rs6134031, P = 1.12 × 10-8) and we replicated the results in the Wellcome Trust Case Control Consortium (WTCCC) dataset. Another locus at chr12q24.31 is associated with T2D at suggestive significance level (top SNP rs4758690, P = 4.20 × 10-5) and it is a robust eQTL for the gene, MLXIP (P = 1.10 × 10-14), and is significantly associated with methylation level in MLXIP, the functions of which involves cellular glucose response. Therefore, in this first GWAS of T2D in Jordan subpopulations, we identified novel and unique susceptibility loci which may help inform the genetic underpinnings of T2D in other populations.

Humans and other mammals are colonized by microbial agents across the kingdom which can represent a unique microbiome pattern. Dysbiosis of the microbiome has been associated with pathology including cancer. We have identified a microbiome signature unique to ovarian cancers, one of the most lethal malignancies of the female reproductive system, primarily because of its asymptomatic nature during the early stages in development. We screened ovarian cancer samples along with matched, and non-matched control samples using our pan-pathogen array (PathoChip), combined with capture-next generation sequencing. The results show a distinct group of viral, bacterial, fungal and parasitic signatures of high significance in ovarian cases. Further analysis shows specific viral integration sites within the host genome of tumor samples, which may contribute to the carcinogenic process. The ovarian cancer microbiome signature provides insights for the development of targeted therapeutics against ovarian cancers.

Protein quality control is an important component of survival for all cells. The use of proteasome inhibitors for cancer therapy derives from the fact that tumor cells generally exhibit greater levels of proteotoxic stress than do normal cells, and thus cancer cells tend to be more sensitive to proteasome inhibition. However, this approach has been limited in some cases by toxicity to normal cells. Recently, the concept of inhibiting proteostasis in organelles for cancer therapy has been advanced, in part because it is predicted to have reduced toxicity for normal cells. Here we demonstrate that a fraction of the major stress-induced chaperone HSP70 (also called HSPA1A or HSP72, but hereafter HSP70) is abundantly present in mitochondria of tumor cells, but is expressed at quite low or undetectable levels in mitochondria of most normal tissues and non-tumor cell lines. We show that treatment of tumor cells with HSP70 inhibitors causes a marked change in mitochondrial protein quality control, loss of mitochondrial membrane potential, reduced oxygen consumption rate, and loss of ATP production. We identify several nuclear-encoded mitochondrial proteins, including polyadenylate binding protein-1 (PABPC1), which exhibit decreased abundance in mitochondria following treatment with HSP70 inhibitors. We also show that targeting HSP70 function leads to reduced levels of several mitochondrial-encoded RNA species that encode components of the electron transport chain. Our data indicate that small molecule inhibitors of HSP70 represent a new class of organelle proteostasis inhibitors that impair mitochondrial function in cancer cells, and therefore constitute novel therapeutics.

To date, most transcriptome studies of schizophrenia focus on the analysis of protein-coding genes. Long noncoding RNAs (lncRNAs) are emerging as key tissue-specific regulators of cellular and disease processes. The amygdala brain region has been implicated in the pathophysiology of schizophrenia. We performed unbiased whole transcriptome profiling of amygdala tissues from 22 schizophrenia patients and 24 non-psychiatric controls using RNA-seq. We reconstructed amygdala transcriptome and employed systems biology approaches to annotating the functional roles of lncRNAs. As a result, we identified 839 novel lncRNAs in amygdala. We found in amygdala lncRNAs are more subtype-specific than protein-coding genes. We identified functional modules associated with "synaptic transmission", "ribosome", and "immune responses" which were related to schizophrenia pathophysiology that involved lncRNAs. Integrative functional analyses associating individual lncRNAs with specific pathways and functions further show that amygdala lncRNAs are connected with all of these pathways. Our study presents the first systematic landscape of lncRNAs in amygdala tissue from schizophrenia cases.

A dysbiotic microbiome can potentially contribute to the pathogenesis of many different diseases including cancer. Breast cancer is the second leading cause of cancer death in women. Thus, we investigated the diversity of the microbiome in the four major types of breast cancer: endocrine receptor (ER) positive, triple positive, Her2 positive and triple negative breast cancers. Using a whole genome and transcriptome amplification and a pan-pathogen microarray (PathoChip) strategy, we detected unique and common viral, bacterial, fungal and parasitic signatures for each of the breast cancer types. These were validated by PCR and Sanger sequencing. Hierarchical cluster analysis of the breast cancer samples, based on their detected microbial signatures, showed distinct patterns for the triple negative and triple positive samples, while the ER positive and Her2 positive samples shared similar microbial signatures. These signatures, unique or common to the different breast cancer types, provide a new line of investigation to gain further insights into prognosis, treatment strategies and clinical outcome, as well as better understanding of the role of the micro-organisms in the development and progression of breast cancer.

Most patients with advanced cancer eventually acquire resistance to targeted therapies, spurring extensive efforts to identify molecular events mediating therapy resistance. Many of these events involve synthetic rescue (SR) interactions, where the reduction in cancer cell viability caused by targeted gene inactivation is rescued by an adaptive alteration of another gene (the rescuer). Here, we perform a genome-wide in silico prediction of SR rescuer genes by analyzing tumor transcriptomics and survival data of 10,000 TCGA cancer patients. Predicted SR interactions are validated in new experimental screens. We show that SR interactions can successfully predict cancer patients' response and emerging resistance. Inhibiting predicted rescuer genes sensitizes resistant cancer cells to therapies synergistically, providing initial leads for developing combinatorial approaches to overcome resistance proactively. Finally, we show that the SR analysis of melanoma patients successfully identifies known mediators of resistance to immunotherapy and predicts novel rescuers.

Stomach adenocarcinoma (STAD), is one of the most lethal malignancies around the world. The aim of this study was to find the long noncoding RNAs (lncRNAs) acting as diagnostic and prognostic biomarker of STAD.

Clustering is a critical step in single cell-based studies. Most existing methods support unsupervised clustering without the a priori exploitation of any domain knowledge. When confronted by the high dimensionality and pervasive dropout events of scRNA-Seq data, purely unsupervised clustering methods may not produce biologically interpretable clusters, which complicates cell type assignment. In such cases, the only recourse is for the user to manually and repeatedly tweak clustering parameters until acceptable clusters are found. Consequently, the path to obtaining biologically meaningful clusters can be ad hoc and laborious. Here we report a principled clustering method named scDCC, that integrates domain knowledge into the clustering step. Experiments on various scRNA-seq datasets from thousands to tens of thousands of cells show that scDCC can significantly improve clustering performance, facilitating the interpretability of clusters and downstream analyses, such as cell type assignment.

With the advances in single-cell sequencing techniques, numerous analytical methods have been developed for delineating cell development. However, most are based on Euclidean space, which would distort the complex hierarchical structure of cell differentiation. Recently, methods acting on hyperbolic space have been proposed to visualize hierarchical structures in single-cell RNA-seq (scRNA-seq) data and have been proven to be superior to methods acting on Euclidean space. However, these methods have fundamental limitations and are not optimized for the highly sparse single-cell count data. To address these limitations, we propose scDHMap, a model-based deep learning approach to visualize the complex hierarchical structures of scRNA-seq data in low-dimensional hyperbolic space. The evaluations on extensive simulation and real experiments show that scDHMap outperforms existing dimensionality-reduction methods in various common analytical tasks as needed for scRNA-seq data, including revealing trajectory branches, batch correction, and denoising the count matrix with high dropout rates. In addition, we extend scDHMap to visualize single-cell ATAC-seq data.