Profiling the human protein-DNA interactome reveals ERK2 as a transcriptional repressor of interferon signaling.

Protein-DNA interactions (PDIs) mediate a broad range of functions essential for cellular differentiation, function, and survival. However, it is still a daunting task to comprehensively identify and profile sequence-specific PDIs in complex genomes. Here, we have used a combined bioinformatics and protein microarray-based strategy to systematically characterize the human protein-DNA interactome. We identified 17,718 PDIs between 460 DNA motifs predicted to regulate transcription and 4,191 human proteins of various functional classes. Among them, we recovered many known PDIs for transcription factors (TFs). We identified a large number of unanticipated PDIs for known TFs, as well as for previously uncharacterized TFs. We also found that over three hundred unconventional DNA-binding proteins (uDBPs)--which include RNA-binding proteins, mitochondrial proteins, and protein kinases--showed sequence-specific PDIs. One such uDBP, ERK2, acts as a transcriptional repressor for interferon gamma-induced genes, suggesting important biological roles for such proteins.

Great Lakes Runoff Intercomparison Project Phase 3: Lake Erie (GRIP-E).

Hydrologic model intercomparison studies help to evaluate the agility of models to simulate variables such as streamflow, evaporation, and soil moisture. This study is the third in a sequence of the Great Lakes Runoff Intercomparison Projects. The densely populated Lake Erie watershed studied here is an important international lake that has experienced recent flooding and shoreline erosion alongside excessive nutrient loads that have contributed to lake eutrophication. Understanding the sources and pathways of flows is critical to solve the complex issues facing this watershed. Seventeen hydrologic and land-surface models of different complexity are set up over this domain using the same meteorological forcings, and their simulated streamflows at 46 calibration and seven independent validation stations are compared. Results show that: (1) the good performance of Machine Learning models during calibration decreases significantly in validation due to the limited amount of training data; (2) models calibrated at individual stations perform equally well in validation; and (3) most distributed models calibrated over the entire domain have problems in simulating urban areas but outperform the other models in validation.

Whole-genome sequencing identifies a recurrent functional synonymous mutation in melanoma.

Synonymous mutations, which do not alter the protein sequence, have been shown to affect protein function [Sauna ZE, Kimchi-Sarfaty C (2011) Nat Rev Genet 12(10):683-691]. However, synonymous mutations are rarely investigated in the cancer genomics field. We used whole-genome and -exome sequencing to identify somatic mutations in 29 melanoma samples. Validation of one synonymous somatic mutation in BCL2L12 in 285 samples identified 12 cases that harbored the recurrent F17F mutation. This mutation led to increased BCL2L12 mRNA and protein levels because of differential targeting of WT and mutant BCL2L12 by hsa-miR-671-5p. Protein made from mutant BCL2L12 transcript bound p53, inhibited UV-induced apoptosis more efficiently than WT BCL2L12, and reduced endogenous p53 target gene transcription. This report shows selection of a recurrent somatic synonymous mutation in cancer. Our data indicate that silent alterations have a role to play in human cancer, emphasizing the importance of their investigation in future cancer genome studies.

Cancer proteomics: developments in technology, clinical use and commercialization.

In the last two decades, advances in genomic, transcriptomic and proteomic methods have enabled us to identify and classify cancers by their molecular profiles. Many anticipate that a molecular taxonomy of cancer will not only lead to more effective subtyping of cancers but also earlier diagnoses, more informative prognoses and more targeted treatments. This article reviews recent technological developments in the field of proteomics, recent discoveries in proteomic cancer biomarker research and trends in clinical use. Readers are also informed of examples of successful commercialization, and the future of proteomics in cancer diagnostics.

Mutational and functional analysis of the tumor-suppressor PTPRD in human melanoma.

Protein tyrosine phosphatases (PTPs) tightly regulate tyrosine phosphorylation essential for cell growth, adhesion, migration, and survival. We performed a mutational analysis of the PTP gene family in cutaneous metastatic melanoma and identified 23 phosphatase genes harboring somatic mutations. Among these, receptor-type tyrosine-protein phosphatase delta (PTPRD) was one of the most highly mutated genes, harboring 17 somatic mutations in 79 samples, a prevalence of 21.5%. Functional evaluation of six PTPRD mutations revealed enhanced anchorage-dependent and anchorage-independent growth. Interestingly, melanoma cells expressing mutant PTPRD were significantly more migratory than cells expressing wild-type PTPRD or vector alone, indicating a novel gain-of-function associated with mutant PTPRD. To understand the molecular mechanisms of PTPRD mutations, we searched for its binding partners by converting the active PTPRD enzyme into a "substrate trap" form. Using mass spectrometry and coimmunoprecipitation, we report desmoplakin, a desmosomal protein that is implicated in cell-cell adhesion, as a novel PTPRD substrate. Further analysis showed reduced phosphatase activity of mutant PTPRD against desmoplakin. Our findings identify an essential signaling cascade that is disrupted in melanoma. Moreover, because PTPRD is also mutated in glioblastomas and adenocarcinoma of the colon and lung, our data might be applicable to a large number of human cancers.

Construction of human activity-based phosphorylation networks.

The landscape of human phosphorylation networks has not been systematically explored, representing vast, unchartered territories within cellular signaling networks. Although a large number of in vivo phosphorylated residues have been identified by mass spectrometry (MS)-based approaches, assigning the upstream kinases to these residues requires biochemical analysis of kinase-substrate relationships (KSRs). Here, we developed a new strategy, called CEASAR, based on functional protein microarrays and bioinformatics to experimentally identify substrates for 289 unique kinases, resulting in 3656 high-quality KSRs. We then generated consensus phosphorylation motifs for each of the kinases and integrated this information, along with information about in vivo phosphorylation sites determined by MS, to construct a high-resolution map of phosphorylation networks that connects 230 kinases to 2591 in vivo phosphorylation sites in 652 substrates. The value of this data set is demonstrated through the discovery of a new role for PKA downstream of Btk (Bruton's tyrosine kinase) during B-cell receptor signaling. Overall, these studies provide global insights into kinase-mediated signaling pathways and promise to advance our understanding of cellular signaling processes in humans.

Organization for rare diseases India (ORDI) - addressing the challenges and opportunities for the Indian rare diseases' community.

In order to address the unmet needs and create opportunities that benefit patients with rare disease in India, a group of volunteers created a not-for-profit organization named Organization for Rare Diseases India (ORDI; www.ordindia.org). ORDI plans to represent the collective voice and advocate the needs of patients with rare diseases and other stakeholders in India. The ORDI team members come from diverse backgrounds such as genetics, molecular diagnostics, drug development, bioinformatics, communications, information technology, patient advocacy and public service. ORDI builds on the lessons learned from numerous similar organizations in the USA, European Union and disease-specific rare disease foundations in India. In this review, we provide a background on the landscape of rare diseases and the organizations that are active in this area globally and in India. We discuss the unique challenges in tackling rare diseases in India, and highlight the unmet needs of the key stakeholders of rare diseases. Finally, we define the vision, mission, goals and objectives of ORDI, identify the key developments in the health care context in India and welcome community feedback and comments on our approach.

Translational utility of next-generation sequencing.

The development of next-generation sequencing (NGS) technology has made DNA sequencing not only rapid and cost-effective, but also highly accurate and reproducible. The translational utility of genomic sequencing is clear, from understanding of human genetic variation and its association with disease risk and individual response to treatment, to the interpretation and translation of the data for clinical decision making. It will be a critical technology for disease characterization and monitoring in molecular pathology and is expected to become a central piece of routine healthcare management which will result in accurate and reliable reporting, a prerequisite for physicians to practice genomic medicine.

Computational and bioinformatics frameworks for next-generation whole exome and genome sequencing.

It has become increasingly apparent that one of the major hurdles in the genomic age will be the bioinformatics challenges of next-generation sequencing. We provide an overview of a general framework of bioinformatics analysis. For each of the three stages of (1) alignment, (2) variant calling, and (3) filtering and annotation, we describe the analysis required and survey the different software packages that are used. Furthermore, we discuss possible future developments as data sources grow and highlight opportunities for new bioinformatics tools to be developed.

Microglia-containing cerebral organoids derived from induced pluripotent stem cells for the study of neurological diseases.

Microglia play an important role in neuroinflammation and neurodegeneration. Here, we report an approach for generating microglia-containing cerebral organoids derived from human pluripotent stem cells involving the supplementation of growth factors (FGF, EGF, heparin) and 10% CO2 culture conditions. Using this platform, Western Pacific Amyotrophic Lateral Sclerosis and Parkinsonism-Dementia Complex (ALS-PDC) cerebral organoids were generated from patient-derived induced pluripotent stem cells (iPSCs). These ALS-PDC-affected organoids had more reactive astrocytes and M1 microglia, and had fewer M2 microglia than their unaffected counterparts, leading to impaired microglia-mediated phagocytosis. RNA-seq analysis of ALS-PDC and control organoids indicated that the most significant changes were microglia- and astrocyte-related genes (IFITM1/2, TGF-ß, and GFAP). The most significantly downregulated pathway was type I interferon signaling. Interferon-gamma supplementation increased IFITM expression, enhanced microglia-mediated phagocytosis, and reduced beta-amyloid accumulation in ALS-PDC-affected network. The results demonstrated the feasibility of using microglia-containing organoids for the study of neurodegenerative diseases.

Comparative exome sequencing of metastatic lesions provides insights into the mutational progression of melanoma.

BACKGROUND: Metastasis is characterized by spreading of neoplastic cells to an organ other than where they originated and is the predominant cause of death among cancer patients. This holds true for melanoma, whose incidence is increasing more rapidly than any other cancer and once disseminated has few therapeutic options. Here we performed whole exome sequencing of two sets of matched normal and metastatic tumor DNAs. RESULTS: Using stringent criteria, we evaluated the similarities and differences between the lesions. We find that in both cases, 96% of the single nucleotide variants are shared between the two metastases indicating that clonal populations gave rise to the distant metastases. Analysis of copy number variation patterns of both metastatic sets revealed a trend similar to that seen with our single nucleotide variants. Analysis of pathway enrichment on tumor sets shows commonly mutated pathways enriched between individual sets of metastases and all metastases combined. CONCLUSIONS: These data provide a proof-of-concept suggesting that individual metastases may have sufficient similarity for successful targeting of driver mutations.

Analysis of the disintegrin-metalloproteinases family reveals ADAM29 and ADAM7 are often mutated in melanoma.

We performed a mutational analysis of the 19 disintegrin-metalloproteinases (ADAMs) genes in human cutaneous metastatic melanoma and identified eight to be somatically mutated in 79 samples, affecting 34% of the melanoma tumors analyzed. Functional analysis of the two frequently mutated ADAM genes, ADAM29 and ADAM7 demonstrated that the mutations affect adhesion of melanoma cells to specific extracellular matrix proteins and in some cases increase their migration ability. This suggests that mutated ADAM genes could play a role in melanoma progression.

Integrated analysis of homozygous deletions, focal amplifications, and sequence alterations in breast and colorectal cancers.

We have performed a genome-wide analysis of copy number changes in breast and colorectal tumors using approaches that can reliably detect homozygous deletions and amplifications. We found that the number of genes altered by major copy number changes, deletion of all copies or amplification to at least 12 copies per cell, averaged 17 per tumor. We have integrated these data with previous mutation analyses of the Reference Sequence genes in these same tumor types and have identified genes and cellular pathways affected by both copy number changes and point alterations. Pathways enriched for genetic alterations included those controlling cell adhesion, intracellular signaling, DNA topological change, and cell cycle control. These analyses provide an integrated view of copy number and sequencing alterations on a genome-wide scale and identify genes and pathways that could prove useful for cancer diagnosis and therapy.

Relating periodicity of nucleosome organization and gene regulation.

MOTIVATION: The relationship between nucleosome positioning and gene regulation is fundamental yet complex. Previous studies on genomic nucleosome positions have revealed a correlation between nucleosome occupancy on promoters and gene expression levels. Many of these studies focused on individual nucleosomes, especially those proximal to transcription start sites. To study the collective effect of multiple nucleosomes on the gene expression, we developed a mathematical approach based on autocorrelation to relate genomic nucleosome organization to gene regulation. RESULTS: We found that nucleosome organization in gene promoters can be well described by autocorrelation transformation. Some promoters show obvious periods in their nucleosome organization, while others have no clear periodicity. The genes with periodic nucleosome organization in promoters tend to be lower expressed than the genes without periodic nucleosome organization. These suggest that regular organization of nucleosomes plays a critical role in gene regulation. To quantitatively associate nucleosome organization and gene expression, we predicted gene expression solely based on nucleosome status and found that nucleosome status accounts for approximately 25% of the observed gene expression variability. Furthermore, we explored the underlying forces that maintain the periodicity in nucleosome organization, namely intrinsic (i.e. DNA sequence) and extrinsic forces (i.e. chromatin remodeling factors). We found that the extrinsic factors play a critical role in maintaining the periodic nucleosome organization.

Analysis of regulatory network topology reveals functionally distinct classes of microRNAs.

MicroRNAs (miRNAs) negatively regulate the expression of target genes at the post-transcriptional level. Little is known about the crosstalk between miRNAs and transcription factors (TFs). Here we provide data suggesting that the interaction patterns between TFs and miRNAs can influence the biological functions of miRNAs. From this global survey, we find that a regulated feedback loop, in which two TFs regulate each other and one miRNA regulates both of the factors, is the most significantly overrepresented network motif. Mathematical modeling shows that the miRNA in this motif stabilizes the feedback loop to resist environmental perturbation, providing one mechanism to explain the robustness of developmental programs that is contributed by miRNAs. Furthermore, on the basis of a network motif profile analysis, we demonstrate the existence of two classes of miRNAs with distinct network topological properties. The first class of miRNAs is regulated by a large number of TFs, whereas the second is regulated by only a few TFs. The differential expression level of the two classes of miRNAs in embryonic developmental stages versus adult tissues suggests that the two classes may have fundamentally different biological functions. Our results demonstrate that the TFs and miRNAs extensively interact with each other and the biological functions of miRNAs may be wired in the regulatory network topology.

Design and analysis issues in genome-wide somatic mutation studies of cancer.

The availability of the human genome sequence and progress in sequencing and bioinformatic technologies have enabled genome-wide investigation of somatic mutations in human cancers. This article briefly reviews challenges arising in the statistical analysis of mutational data of this kind. A first challenge is that of designing studies that efficiently allocate sequencing resources. We show that this can be addressed by two-stage designs and demonstrate via simulations that even relatively small studies can produce lists of candidate cancer genes that are highly informative for future research efforts. A second challenge is to distinguish mutated genes that are selected for by cancer (drivers) from mutated genes that have no role in the development of cancer and simply happened to mutate (passengers). We suggest that this question is best approached as a classification problem and discuss some of the difficulties of more traditional testing-based approaches. A third challenge is to identify biologic processes affected by the driver genes. This can be pursued by gene set analyses. These can reliably identify functional groups and pathways that are enriched for mutated genes even when the individual genes involved in those pathways or sets are not mutated at sufficient frequencies to provide conclusive evidence as drivers.

Protein microarrays for cancer diagnostics and therapy.

In this postgenomic age, cancer will be understood in intricate detail beyond the genomic level. New technologies are emerging that allow global proteomic level characterization, profiling and understanding. One of the most exciting emerging technologies of cancer proteomics is protein microarray. In this review, the different types of protein microarrays are discussed, including the methods, challenges and techniques of each type. Subsequently, the application of these specific methods to cancer diagnosis, prognosis and therapy will be overviewed, providing a general review of current methods, and proposing how protein arrays will help shape the future of oncoproteomics.

Is searching full text more effective than searching abstracts?

BACKGROUND: With the growing availability of full-text articles online, scientists and other consumers of the life sciences literature now have the ability to go beyond searching bibliographic records (title, abstract, metadata) to directly access full-text content. Motivated by this emerging trend, I posed the following question: is searching full text more effective than searching abstracts? This question is answered by comparing text retrieval algorithms on MEDLINE abstracts, full-text articles, and spans (paragraphs) within full-text articles using data from the TREC 2007 genomics track evaluation. Two retrieval models are examined: bm25 and the ranking algorithm implemented in the open-source Lucene search engine. RESULTS: Experiments show that treating an entire article as an indexing unit does not consistently yield higher effectiveness compared to abstract-only search. However, retrieval based on spans, or paragraphs-sized segments of full-text articles, consistently outperforms abstract-only search. Results suggest that highest overall effectiveness may be achieved by combining evidence from spans and full articles. CONCLUSION: Users searching full text are more likely to find relevant articles than searching only abstracts. This finding affirms the value of full text collections for text retrieval and provides a starting point for future work in exploring algorithms that take advantage of rapidly-growing digital archives. Experimental results also highlight the need to develop distributed text retrieval algorithms, since full-text articles are significantly longer than abstracts and may require the computational resources of multiple machines in a cluster. The MapReduce programming model provides a convenient framework for organizing such computations.

The genome and transcriptomes of the anti-tumor agent Clostridium novyi-NT.

Bacteriolytic anti-cancer therapies employ attenuated bacterial strains that selectively proliferate within tumors. Clostridium novyi-NT spores represent one of the most promising of these agents, as they generate potent anti-tumor effects in experimental animals. We have determined the 2.55-Mb genomic sequence of C. novyi-NT, identifying a new type of transposition and 139 genes that do not have homologs in other bacteria. The genomic sequence was used to facilitate the detection of transcripts expressed at various stages of the life cycle of this bacterium in vitro as well as in infections of tumors in vivo. Through this analysis, we found that C. novyi-NT spores contained mRNA and that the spore transcripts were distinct from those in vegetative forms of the bacterium.