Unique Features of the Loblolly Pine (Pinus taeda L.) Megagenome Revealed Through Sequence Annotation

The largest genus in the conifer family Pinaceae is Pinus, with over 100 species. The size and complexity of their genomes (∼20–40 Gb, 2n = 24) have delayed the arrival of a well-annotated reference sequence. In this study, we present the annotation of the first whole-genome shotgun assembly of loblolly pine (Pinus taeda L.), which comprises 20.1 Gb of sequence. The MAKER-P annotation pipeline combined evidence-based alignments and ab initio predictions to generate 50,172 gene models, of which 15,653 are classified as high confidence. Clustering these gene models with 13 other plant species resulted in 20,646 gene families, of which 1554 are predicted to be unique to conifers. Among the conifer gene families, 159 are composed exclusively of loblolly pine members. The gene models for loblolly pine have the highest median and mean intron lengths of 24 fully sequenced plant genomes. Conifer genomes are full of repetitive DNA, with the most significant contributions from long-terminal-repeat retrotransposons. In depth analysis of the tandem and interspersed repetitive content yielded a combined estimate of 82%.     

It's All in Your Mind: Determining Germ Cell Fate by Neuronal IRE-1 in C. elegans

The C. elegans germline is pluripotent and mitotic, similar to self-renewing mammalian tissues. Apoptosis is triggered as part of the normal oogenesis program, and is increased in response to various stresses. Here, we examined the effect of endoplasmic reticulum (ER) stress on apoptosis in the C. elegans germline. We demonstrate that pharmacological or genetic induction of ER stress enhances germline apoptosis. This process is mediated by the ER stress response sensor IRE-1, but is independent of its canonical downstream target XBP-1. We further demonstrate that ire-1-dependent apoptosis in the germline requires both CEP-1/p53 and the same canonical apoptotic genes as DNA damage-induced germline apoptosis. Strikingly, we find that activation of ire-1, specifically in the ASI neurons, but not in germ cells, is sufficient to induce apoptosis in the germline. This implies that ER stress related germline apoptosis can be determined at the organism level, and is a result of active IRE-1 signaling in neurons. Altogether, our findings uncover ire-1 as a novel cell non-autonomous regulator of germ cell apoptosis, linking ER homeostasis in sensory neurons and germ cell fate.     

Bronchial epithelial cells are rendered insensitive to glucocorticoid transactivation by transforming growth factor-β1

Background

We have previously shown that transforming growth factor-beta (TGF-beta) impairs glucocorticoid (GC) function in pulmonary epithelial cell-lines. However, the signalling cascade leading to this impairment is unknown. In the present study, we provide the first evidence that TGF-beta impairs GC action in differentiated primary air-liquid interface (ALI) human bronchial epithelial cells (HBECs). Using the BEAS-2B bronchial epithelial cell line, we also present a systematic examination of the known pathways activated by TGF-beta, in order to ascertain the molecular mechanism through which TGF-beta impairs epithelial GC action.

Methods

GC transactivation was measured using a Glucocorticoid Response Element (GRE)–Secreted embryonic alkaline phosphatase (SEAP) reporter and measuring GC-inducible gene expression by qRT-PCR. GC transrepression was measured by examining GC regulation of pro-inflammatory mediators. TGF-beta signalling pathways were investigated using siRNA and small molecule kinase inhibitors. GRα level, phosphorylation and sub-cellular localisation were determined by western blotting, immunocytochemistry and localisation of GRα–Yellow Fluorescent Protein (YFP). Data are presented as the mean ± SEM for n independent experiments in cell lines, or for experiments on primary HBEC cells from n individual donors. All data were statistically analysed using GraphPad Prism 5.0 (Graphpad, San Diego, CA). In most cases, two-way analyses of variance (ANOVA) with Bonferroni post-hoc tests were used to analyse the data. In all cases, P <0.05 was considered to be statistically significant.

Results

TGF-beta impaired Glucocorticoid Response Element (GRE) activation and the GC induction of several anti-inflammatory genes, but did not broadly impair the regulation of pro-inflammatory gene expression in A549 and BEAS-2B cell lines. TGF-beta-impairment of GC transactivation was also observed in differentiated primary HBECs. The TGF-beta receptor (ALK5) inhibitor SB431541 fully prevented the GC transactivation impairment in the BEAS-2B cell line. However, neither inhibitors of the known downstream non-canonical signalling pathways, nor knocking down Smad4 by siRNA prevented the TGF-beta impairment of GC activity.

Conclusions

Our results indicate that TGF-beta profoundly impairs GC transactivation in bronchial epithelial cells through activating ALK5, but not through known non-canonical pathways, nor through Smad4-dependent signalling, suggesting that TGF-beta may impair GC action through a novel non-canonical signalling mechanism.     

Between a chicken and a grape: estimating the number of human genes

Many people expected the question ''How many genes in the human genome?'' to be resolved with the publication of the genome sequence in 2001, but estimates continue to fluctuate.Ever since the discovery of the genetic code, scientists have been trying to catalog all the genes in the human genome. Over the years, the best estimate of the number of human genes has grown steadily smaller, but we still do not have an accurate count. Here we review the history of efforts to establish the human gene count and present the current best estimates.The first attempt to estimate the number of genes in the human genome appeared more than 45 years ago, while the genetic code was still being deciphered. Friedrich Vogel published his ''preliminary estimate'' in 1964 [1], based on the number of amino acids in the alpha- and beta-chains of hemoglobin (141 and 146, respectively). Knowing that three nucleotides corresponded to each amino acid, he extrapolated to compute the molecular weight of the DNA comprising these genes. He then made several assumptions in order to produce his estimate: that these proteins were typical in size (they are actually smaller than average); that nucleotide sequences were uninterrupted on the chromosomes (introns were discovered more than 10 years later [2,3]); and that the entire genome was protein coding. All these assumptions were reasonable at the time, but later discoveries would reveal that none of them was correct. Vogel then used the molecular weight of the human haploid chromosomes to correctly calculate the genome size as 3 × 10⁹ nucleotides, and dividing that by the size of a ''typical'' gene, came up with an estimate of 6.7 million genes.Even at the time, Vogel found this number ''disturbingly high'', but no one suspected in 1964 that most human genes were interrupted by multiple introns, nor did anyone know that vast regions of the human genome would turn out to contain seemingly meaningless repetitive sequences. Since Vogel''s initial attempt, many scientists have tried to estimate the number of genes in the human genome, using increasingly sophisticated molecular tools. Over the years, the number has gradually come down, in a process that has been humbling at times, as we realized that many other species - even plants - are predicted to have more genes than we do (Figure ​(Figure1).1). An estimate of 100,000 genes appeared in the 1990 joint National Institutes of Health (NIH)/Department of Energy (DOE) report on the Human Genome Project [4]; this was apparently based on a very rough (and incorrect) calculation that typical human genes are 30,000 bases long, and that genes cover the entire 3-gigabase genome.Open in a separate windowFigure 1Gene counts in a variety of species. Viruses, the simplest living entities, have only a handful of genes but are exquisitely well adapted to their environments. Bacteria such as Escherichia coli have a few thousand genes, and multicellular plants and animals have two to ten times more. Beyond these simple divisions, the number of genes in a species bears little relation to its size or to intuitive measures of complexity. The chicken and grape gene counts shown here are based on draft genomes [50,51] and may be revised substantially in the future.Many people, including many geneticists, expected that we would have a definitive gene count when the human genome was finally completed, and indeed one of the main surprises upon the initial publication of the human genome in February 2001 [5,6] was that the number had again dropped, quite precipitously. However, as we shall see, the publication of the human genome did not come anywhere close to producing a precise gene list or even a gene count, and in the years since the number has continued to fluctuate. As a result, even today''s best estimates still have a large amount of uncertainty associated with them.In order to count genes, we need to define what we mean by a ''gene'', a term whose meaning has changed dramatically over the past century. For our discussion, we will restrict the definition of gene to a region of the genome that is transcribed into messenger RNA and translated into one or more proteins. When multiple proteins are translated from the same region due to alternative mRNA splicing, we will consider this collection of alternative isoforms to be a single gene. In this respect, our definition of a gene is equivalent to what may also be called a chromosomal locus. We will exclude non-protein-coding RNA genes (such as microRNAs (miRNAs) and small nuclear RNAs (snRNAs)), in part because of the even greater uncertainty surrounding their numbers. In recent years, as a result of the dramatic breakthroughs in our understanding of RNA interference [7] and miRNAs [8], the number and variety of known RNA genes has grown dramatically, and we expect that it will be many more years before we have a clear picture of how many of these non-coding genes exist in the human genome.     

It is time to end the patenting of software

One of the most significant outcomes of genomics has been arapid increase in the rate that we as a community can generatedata on interesting biological systems. Rapid improvements intechnologies such as DNA microarrays and proteomics applicationshave produced a climate where the challenge is no longer collectinghigh quality data but rather managing and analyzing it. As wein the bioinformatics community have addressed this challenge,we have had to carefully consider the way in which the resultsof our intellectual efforts—the software tools that wedevelop—are made available to the wider research community.Increasingly, bioinformatics scientists are coming to call fordevelopment in an open source environment in which softwareis distributed with its underlying source code