The Spatial Heterogeneity of Vegetation,Hydrology and Water Chemistry in a Peatland with Open-Water Pools

Ecosystems - Ombrotrophic bogs can comprise a mosaic of vegetation patches and open-water pools, with hydrological and biogeochemical connections between pools and the surrounding peat and...     

Predicting mean ribosome load for 5’UTR of any length using deep learning

The 5’ untranslated region plays a key role in regulating mRNA translation and consequently protein abundance. Therefore, accurate modeling of 5’UTR regulatory sequences shall provide insights into translational control mechanisms and help interpret genetic variants. Recently, a model was trained on a massively parallel reporter assay to predict mean ribosome load (MRL)—a proxy for translation rate—directly from 5’UTR sequence with a high degree of accuracy. However, this model is restricted to sequence lengths investigated in the reporter assay and therefore cannot be applied to the majority of human sequences without a substantial loss of information. Here, we introduced frame pooling, a novel neural network operation that enabled the development of an MRL prediction model for 5’UTRs of any length. Our model shows state-of-the-art performance on fixed length randomized sequences, while offering better generalization performance on longer sequences and on a variety of translation-related genome-wide datasets. Variant interpretation is demonstrated on a 5’UTR variant of the gene HBB associated with beta-thalassemia. Frame pooling could find applications in other bioinformatics predictive tasks. Moreover, our model, released open source, could help pinpoint pathogenic genetic variants.     

The repertoire of ICE in prokaryotes underscores the unity, diversity, and ubiquity of conjugation

Horizontal gene transfer shapes the genomes of prokaryotes by allowing rapid acquisition of novel adaptive functions. Conjugation allows the broadest range and the highest gene transfer input per transfer event. While conjugative plasmids have been studied for decades, the number and diversity of integrative conjugative elements (ICE) in prokaryotes remained unknown. We defined a large set of protein profiles of the conjugation machinery to scan over 1,000 genomes of prokaryotes. We found 682 putative conjugative systems among all major phylogenetic clades and showed that ICEs are the most abundant conjugative elements in prokaryotes. Nearly half of the genomes contain a type IV secretion system (T4SS), with larger genomes encoding more conjugative systems. Surprisingly, almost half of the chromosomal T4SS lack co-localized relaxases and, consequently, might be devoted to protein transport instead of conjugation. This class of elements is preponderant among small genomes, is less commonly associated with integrases, and is rarer in plasmids. ICEs and conjugative plasmids in proteobacteria have different preferences for each type of T4SS, but all types exist in both chromosomes and plasmids. Mobilizable elements outnumber self-conjugative elements in both ICEs and plasmids, which suggests an extensive use of T4SS in trans. Our evolutionary analysis indicates that switch of plasmids to and from ICEs were frequent and that extant elements began to differentiate only relatively recently. According to the present results, ICEs are the most abundant conjugative elements in practically all prokaryotic clades and might be far more frequently domesticated into non-conjugative protein transport systems than previously thought. While conjugative plasmids and ICEs have different means of genomic stabilization, their mechanisms of mobility by conjugation show strikingly conserved patterns, arguing for a unitary view of conjugation in shaping the genomes of prokaryotes by horizontal gene transfer.     

Characterization of the cell of origin for small cell lung cancer

Small cell lung carcinoma (SCLC) is a neuroendocrine subtype of lung cancer that affects more than 200,000 people worldwide every year with a very high mortality rate. Here, we used a mouse genetics approach to characterize the cell of origin for SCLC; in this mouse model, tumors are initiated by the deletion of the Rb and p53 tumor suppressor genes in the lung epithelium of adult mice. We found that mouse SCLCs often arise in the lung epithelium, where neuroendocrine cells are located, and that the majority of early lesions were composed of proliferating neuroendocrine cells. In addition, mice in which Rb and p53 are deleted in a variety of non-neuroendocrine lung epithelial cells did not develop SCLC. These data indicate that SCLC likely arises from neuroendocrine cells in the lung.Key words: Rb, p53, SCLC, cell of origin, cancer, lung, neuroendocrine     

Phylogeny of immunoglobulin heavy chain isotypes: structure of the constant region of Ambystoma mexicanum υ chain deduced from cDNA sequence

An RNA polymerase chain reaction strategy was used to amplify and clone a cDNA segment encoding for the complete constant part of the axolotl IgY heavy (C) chain. C is 433 amino acids long and organized into four domains (C1–C4); each has the typical internal disulfide bond and invariant tryptophane residues. Axolotl C is most closely related to Xenopus C (40% identical amino acid residues) and C1 shares 46.4% amino acid residues among these species. The presence of additional cysteines in C1 and C2 domains is consistent with an additional intra-domain S-S bond similar to that suggested for Xenopus C and C, and for the avian C and the human C. C4 ends with the Gly-Lys dipeptide characteristic of secreted mammalian C3, human C4, and avian and anuran C4, and contains the consensus [G/GT(AA)] nucleotide splice signal sequence for joining C4 to the transmembrane region. These results are consistent with the hypothesis of an ancestral structural relationship between amphibian, avian chains, and mammalian chains. However, these molecules have different biological properties: axolotl IgY is secretory Ig, anuran and avian IgY behave like mammalian IgG, and mammalian IgE is implicated in anaphylactic reactions.The nucleotide sequence data reported in this paper have been submitted to the EMBL/GenBank nucleotide sequence database and have been assigned the accession number X69492. Correspondence to: J. S. Fellah.