Long noncoding RNA LIRIL2R modulates FOXP3 levels and suppressive function of human CD4+ regulatory T cells by regulating IL2RA.

Regulatory T cells (Tregs) are central in controlling immune responses, and dysregulation of their function can lead to autoimmune disorders or cancer. Despite extensive studies on Tregs, the basis of epigenetic regulation of human Treg development and function is incompletely understood. Long intergenic noncoding RNAs (lincRNA)s are important for shaping and maintaining the epigenetic landscape in different cell types. In this study, we identified a gene on the chromosome 6p25.3 locus, encoding a lincRNA, that was up-regulated during early differentiation of human Tregs. The lincRNA regulated the expression of interleukin-2 receptor alpha (IL2RA), and we named it the lincRNA regulator of IL2RA (LIRIL2R). Through transcriptomics, epigenomics, and proteomics analysis of LIRIL2R-deficient Tregs, coupled with global profiling of LIRIL2R binding sites using chromatin isolation by RNA purification, followed by sequencing, we identified IL2RA as a target of LIRIL2R. This nuclear lincRNA binds upstream of the IL2RA locus and regulates its epigenetic landscape and transcription. CRISPR-mediated deletion of the LIRIL2R-bound region at the IL2RA locus resulted in reduced IL2RA expression. Notably, LIRIL2R deficiency led to reduced expression of Treg-signature genes (e.g., FOXP3, CTLA4, and PDCD1), upregulation of genes associated with effector T cells (e.g., SATB1 and GATA3), and loss of Treg-mediated suppression.

Deep learning tools are top performers in long non-coding RNA prediction.

The increasing amount of transcriptomic data has brought to light vast numbers of potential novel RNA transcripts. Accurately distinguishing novel long non-coding RNAs (lncRNAs) from protein-coding messenger RNAs (mRNAs) has challenged bioinformatic tool developers. Most recently, tools implementing deep learning architectures have been developed for this task, with the potential of discovering sequence features and their interactions still not surfaced in current knowledge. We compared the performance of deep learning tools with other predictive tools that are currently used in lncRNA coding potential prediction. A total of 15 tools representing the variety of available methods were investigated. In addition to known annotated transcripts, we also evaluated the use of the tools in actual studies with real-life data. The robustness and scalability of the tools' performance was tested with varying sized test sets and test sets with different proportions of lncRNAs and mRNAs. In addition, the ease-of-use for each tested tool was scored. Deep learning tools were top performers in most metrics and labelled transcripts similarly with each other in the real-life dataset. However, the proportion of lncRNAs and mRNAs in the test sets affected the performance of all tools. Computational resources were utilized differently between the top-ranking tools, thus the nature of the study may affect the decision of choosing one well-performing tool over another. Nonetheless, the results suggest favouring the novel deep learning tools over other tools currently in broad use.

Host Adaptation in Legionellales Is 1.9 Ga, Coincident with Eukaryogenesis.

Bacteria adapting to living in a host cell caused the most salient events in the evolution of eukaryotes, namely the seminal fusion with an archaeon, and the emergence of both mitochondrion and chloroplast. A bacterial clade that may hold the key to understanding these events is the deep-branching gammaproteobacterial order Legionellales-containing among others Coxiella and Legionella-of which all known members grow inside eukaryotic cells. Here, by analyzing 35 novel Legionellales genomes mainly acquired through metagenomics, we show that this group is much more diverse than previously thought, and that key host-adaptation events took place very early in its evolution. Crucial virulence factors like the Type IVB secretion (Dot/Icm) system and two shared effector proteins were gained in the last Legionellales common ancestor (LLCA). Many metabolic gene families were lost in LLCA and its immediate descendants, including functions directly and indirectly related to molybdenum metabolism. On the other hand, genome sizes increased in the ancestors of the Legionella genus. We estimate that LLCA lived approximately 1.89 Ga, probably predating the last eukaryotic common ancestor by approximately 0.4-1.0 Gy. These elements strongly indicate that host adaptation arose only once in Legionellales, and that these bacteria were using advanced molecular machinery to exploit and manipulate host cells early in eukaryogenesis.

Discovery of a Novel CIP2A Variant (NOCIVA) with Clinical Relevance in Predicting TKI Resistance in Myeloid Leukemias.

PURPOSE: Cancerous inhibitor of protein phosphatase 2A (CIP2A) is an oncoprotein that inhibits the tumor suppressor PP2A-B56α. However, CIP2A mRNA variants remain uncharacterized. Here, we report the discovery of a CIP2A splicing variant, novel CIP2A variant (NOCIVA). EXPERIMENTAL DESIGN: Characterization of CIP2A variants was performed by both 3' and 5' rapid amplification of cDNA ends from cancer cells. The function of NOCIVA was assessed by structural and molecular biology approaches. Its clinical relevance was studied in an acute myeloid leukemia (AML) patient cohort and two independent chronic myeloid leukemia (CML) cohorts. RESULTS: NOCIVA contains CIP2A exons 1 to 13 fused to 349 nucleotides from CIP2A intron 13. Intriguingly, the first 39 nucleotides of the NOCIVA-specific sequence are in the coding frame with exon 13 of CIP2A and code for a 13-amino acid peptide tail nonhomologous to any known human protein sequence. Therefore, NOCIVA translates to a unique human protein. NOCIVA retains the capacity to bind to B56α, but, whereas CIP2A is predominantly a cytoplasmic protein, NOCIVA translocates to the nucleus. Indicative of prevalent alternative splicing from CIP2A to NOCIVA in myeloid malignancies, AML and CML patient samples overexpress NOCIVA, but not CIP2A mRNA. In AML, a high NOCIVA/CIP2A mRNA expression ratio is a marker for adverse overall survival. In CML, high NOCIVA expression is associated with inferior event-free survival among imatinib-treated patients, but not among patients treated with dasatinib or nilotinib. CONCLUSIONS: We discovered a novel variant of the oncoprotein CIP2A and its clinical relevance in predicting tyrosine kinase inhibitor therapy resistance in myeloid leukemias.

Naming the untouchable - environmental sequences and niche partitioning as taxonomical evidence in fungi.

Due to their submerged and cryptic lifestyle, the vast majority of fungal species are difficult to observe and describe morphologically, and many remain known to science only from sequences detected in environmental samples. The lack of practices to delimit and name most fungal species is a staggering limitation to communication and interpretation of ecology and evolution in kingdom Fungi. Here, we use environmental sequence data as taxonomical evidence and combine phylogenetic and ecological data to generate and test species hypotheses in the class Archaeorhizomycetes (Taphrinomycotina, Ascomycota). Based on environmental amplicon sequencing from a well-studied Swedish pine forest podzol soil, we generate 68 distinct species hypotheses of Archaeorhizomycetes, of which two correspond to the only described species in the class. Nine of the species hypotheses represent 78% of the sequenced Archaeorhizomycetes community, and are supported by long read data that form the backbone for delimiting species hypothesis based on phylogenetic branch lengths.Soil fungal communities are shaped by environmental filtering and competitive exclusion so that closely related species are less likely to co-occur in a niche if adaptive traits are evolutionarily conserved. In soil profiles, distinct vertical horizons represent a testable niche dimension, and we found significantly differential distribution across samples for a well-supported pair of sister species hypotheses. Based on the combination of phylogenetic and ecological evidence, we identify two novel species for which we provide molecular diagnostics and propose names. While environmental sequences cannot be automatically translated to species, they can be used to generate phylogenetically distinct species hypotheses that can be further tested using sequences as ecological evidence. We conclude that in the case of abundantly and frequently observed species, environmental sequences can support species recognition in the absences of physical specimens, while rare taxa remain uncaptured at our sampling and sequencing intensity.

Consequences of asymmetric competition between resident and invasive defoliators: a novel empirically based modelling approach.

Invasive species can have profound effects on a resident community via indirect interactions among community members. While long periodic cycles in population dynamics can make the experimental observation of the indirect effects difficult, modelling the possible effects on an evolutionary time scale may provide the much needed information on the potential threats of the invasive species on the ecosystem. Using empirical data from a recent invasion in northernmost Fennoscandia, we applied adaptive dynamics theory and modelled the long term consequences of the invasion by the winter moth into the resident community. Specifically, we investigated the outcome of the observed short-term asymmetric preferences of generalist predators and specialist parasitoids on the long term population dynamics of the invasive winter moth and resident autumnal moth sharing these natural enemies. Our results indicate that coexistence after the invasion is possible. However, the outcome of the indirect interaction on the population dynamics of the moth species was variable and the dynamics might not be persistent on an evolutionary time scale. In addition, the indirect interactions between the two moth species via shared natural enemies were able to cause asynchrony in the population cycles corresponding to field observations from previous sympatric outbreak areas. Therefore, the invasion may cause drastic changes in the resident community, for example by prolonging outbreak periods of birch-feeding moths, increasing the average population densities of the moths or, alternatively, leading to extinction of the resident moth species or to equilibrium densities of the two, formerly cyclic, herbivores.

Larval parasitism of the autumnal moth reduces feeding intensity on the mountain birch.

Plants respond to grazing by herbivorous insects by emitting a range of volatile organic compounds, which attract parasitoids to their insect hosts. However, a positive outcome for the host plant is a necessary precondition for making the attraction beneficial or even adaptive. Parasitoids benefit plants by killing herbivorous insects, thus reducing future herbivore pressure, but also by curtailing the feeding intensity of the still living, parasitised host. In this study, the effect of parasitism on food consumption of the 5th instar larvae of the autumnal moth (Epirrita autumnata) was examined under laboratory conditions. Daily food consumption, as well as the duration of the 5th instar, was measured for both parasitised and non-parasitised larvae. The results showed that parasitism by the solitary endoparasitoid Zele deceptor not only reduced leaf consumption significantly but also hastened the onset of pupation in autumnal moth larvae. On the basis of the results, an empirical model was derived to assess the affects on the scale of the whole tree. The model suggests that parasitoids might protect the tree from total defoliation at least at intermediate larval densities. Consequently, a potential for plant-parasitoid chemical signalling appears to exist, which seems to benefit the mountain birch (Betula pubescens ssp. czerepanovii) by reducing the overall intensity of herbivore defoliation due to parasitism by this hymenopteran parasitoid.