Motifome comparison between modern human, Neanderthal and Denisovan.

BACKGROUND: The availability of the genomes of two archaic humans, Neanderthal and Denisovan, and that of modern humans provides researchers an opportunity to investigate genetic differences between these three subspecies on a genome-wide scale. Here we describe an algorithm that predicts statistically significant motifs based on the difference between a given motif's actual and expected distributions. The algorithm was previously applied to plants but was modified for this work. RESULTS: The result of applying the algorithm to the human, Neanderthal, and Denisovan genomes is a catalog of potential regulatory motifs in these three human subspecies. We examined the distributions of these motifs in genetic elements including human retroviruses, human accelerated regions, and human accelerated conserved noncoding sequences regions. Differences in these distributions could be the origin of differences in phenotype between the three subspecies. Twenty significant motifs common to all three genomes were found; thirty-three were found in endogenous retroviruses in Neanderthal and Denisovan. Ten of these motifs mapped to the 22 bp core of MiR-1304. The core of this genetic element regulates the ENAM and AMTN genes, which take part in odontogenesis and whose 3' UTRs contained significant motifs. The introns of 20 genes were found to contain a large number of significant motifs, which were also overrepresented in 49 human accelerated regions. These genes include NAV2, SorCS2, TRAPPC9, GRID1, PRDM16, CAMTA1, and ASIC which are all involved in neuroregulation. Further analysis of these genes using the GO database indicates that many are associated with neurodevelopment. Also, varying numbers of significant motifs were found to occur in regions of the Neanderthal and Denisovan genomes that are missing from the human genome, suggesting further functional differences between modern and archaic humans. CONCLUSION: Although Neanderthal and Denisovan are now extinct, detailed examination of elements from their genomes can shed light on possible phenotypic and cognitive differences between these two archaic human subspecies and modern humans. Genetic similarities and differences between these three subspecies and other fossil hominids would also be of interest.

Pluralistic and stochastic gene regulation: examples, models and consistent theory.

We present a theory of pluralistic and stochastic gene regulation. To bridge the gap between empirical studies and mathematical models, we integrate pre-existing observations with our meta-analyses of the ENCODE ChIP-Seq experiments. Earlier evidence includes fluctuations in levels, location, activity, and binding of transcription factors, variable DNA motifs, and bursts in gene expression. Stochastic regulation is also indicated by frequently subdued effects of knockout mutants of regulators, their evolutionary losses/gains and massive rewiring of regulatory sites. We report wide-spread pluralistic regulation in ≈800 000 tightly co-expressed pairs of diverse human genes. Typically, half of ≈50 observed regulators bind to both genes reproducibly, twice more than in independently expressed gene pairs. We also examine the largest set of co-expressed genes, which code for cytoplasmic ribosomal proteins. Numerous regulatory complexes are highly significant enriched in ribosomal genes compared to highly expressed non-ribosomal genes. We could not find any DNA-associated, strict sense master regulator. Despite major fluctuations in transcription factor binding, our machine learning model accurately predicted transcript levels using binding sites of 20+ regulators. Our pluralistic and stochastic theory is consistent with partially random binding patterns, redundancy, stochastic regulator binding, burst-like expression, degeneracy of binding motifs and massive regulatory rewiring during evolution.

Activation of the carbon concentrating mechanism by CO2 deprivation coincides with massive transcriptional restructuring in Chlamydomonas reinhardtii.

A CO(2)-concentrating mechanism (CCM) is essential for the growth of most eukaryotic algae under ambient (392 ppm) and very low (<100 ppm) CO(2) concentrations. In this study, we used replicated deep mRNA sequencing and regulatory network reconstruction to capture a remarkable scope of changes in gene expression that occurs when Chlamydomonas reinhardtii cells are shifted from high to very low levels of CO(2) (≤100 ppm). CCM induction 30 to 180 min post-CO(2) deprivation coincides with statistically significant changes in the expression of an astonishing 38% (5884) of the 15,501 nonoverlapping C. reinhardtii genes. Of these genes, 1088 genes were induced and 3828 genes were downregulated by a log(2) factor of 2. The latter indicate a global reduction in photosynthesis, protein synthesis, and energy-related biochemical pathways. The magnitude of transcriptional rearrangement and its major patterns are robust as analyzed by three different statistical methods. De novo DNA motif discovery revealed new putative binding sites for Myeloid oncogene family transcription factors potentially involved in activating low CO(2)-induced genes. The (CA)(n) repeat (9 ≤ n ≤ 25) is present in 29% of upregulated genes but almost absent from promoters of downregulated genes. These discoveries open many avenues for new research.

Analysis of immune-related genes during Nora virus infection of Drosophila melanogaster using next generation sequencing.

Drosophila melanogaster depends upon the innate immune system to regulate and combat viral infection. This is a complex, yet widely conserved process that involves a number of immune pathways and gene interactions. In addition, expression of genes involved in immunity are differentially regulated as the organism ages. This is particularly true for viruses that demonstrate chronic infection, as is seen with Nora virus. Nora virus is a persistent non-pathogenic virus that replicates in a horizontal manner in D. melanogaster. The genes involved in the regulation of the immune response to Nora virus infection are largely unknown. In addition, the temporal response of immune response genes as a result of infection has not been examined. In this study, D. melanogaster either infected with Nora virus or left uninfected were aged for 2, 10, 20 and 30 days. The RNA from these samples was analyzed by next generation sequencing (NGS) and the resulting immune-related genes evaluated by utilizing both the PANTHER and DAVID databases, as well as comparison to lists of immune related genes and FlyBase. The data demonstrate that Nora virus infected D. melanogaster exhibit an increase in immune related gene expression over time. In addition, at day 30, the data demonstrate that a persistent immune response may occur leading to an upregulation of specific immune response genes. These results demonstrate the utility of NGS in determining the potential immune system genes involved in Nora virus replication, chronic infection and involvement of antiviral pathways.

The National NeuroAIDS Tissue Consortium (NNTC) Database: an integrated database for HIV-related studies.

We herein present the National NeuroAIDS Tissue Consortium-Data Coordinating Center (NNTC-DCC) database, which is the only available database for neuroAIDS studies that contains data in an integrated, standardized form. This database has been created in conjunction with the NNTC, which provides human tissue and biofluid samples to individual researchers to conduct studies focused on neuroAIDS. The database contains experimental datasets from 1206 subjects for the following categories (which are further broken down into subcategories): gene expression, genotype, proteins, endo-exo-chemicals, morphometrics and other (miscellaneous) data. The database also contains a wide variety of downloadable data and metadata for 95 HIV-related studies covering 170 assays from 61 principal investigators. The data represent 76 tissue types, 25 measurement types, and 38 technology types, and reaches a total of 33,017,407 data points. We used the ISA platform to create the database and develop a searchable web interface for querying the data. A gene search tool is also available, which searches for NCBI GEO datasets associated with selected genes. The database is manually curated with many user-friendly features, and is cross-linked to the NCBI, HUGO and PubMed databases. A free registration is required for qualified users to access the database.