FADD and Caspase-8 Regulate Gut Homeostasis and Inflammation by Controlling MLKL- and GSDMD-Mediated Death of Intestinal Epithelial Cells.

Pathways controlling intestinal epithelial cell (IEC) death regulate gut immune homeostasis and contribute to the pathogenesis of inflammatory bowel diseases. Here, we show that caspase-8 and its adapter FADD act in IECs to regulate intestinal inflammation downstream of Z-DNA binding protein 1 (ZBP1)- and tumor necrosis factor receptor-1 (TNFR1)-mediated receptor interacting protein kinase 1 (RIPK1) and RIPK3 signaling. Mice with IEC-specific FADD or caspase-8 deficiency developed colitis dependent on mixed lineage kinase-like (MLKL)-mediated epithelial cell necroptosis. However, MLKL deficiency fully prevented ileitis caused by epithelial caspase-8 ablation, but only partially ameliorated ileitis in mice lacking FADD in IECs. Our genetic studies revealed that caspase-8 and gasdermin-D (GSDMD) were both required for the development of MLKL-independent ileitis in mice with epithelial FADD deficiency. Therefore, FADD prevents intestinal inflammation downstream of ZBP1 and TNFR1 by inhibiting both MLKL-induced necroptosis and caspase-8-GSDMD-dependent pyroptosis-like death of epithelial cells.

Nuclear export is a limiting factor in eukaryotic mRNA metabolism.

The eukaryotic mRNA life cycle includes transcription, nuclear mRNA export and degradation. To quantify all these processes simultaneously, we perform thiol-linked alkylation after metabolic labeling of RNA with 4-thiouridine (4sU), followed by sequencing of RNA (SLAM-seq) in the nuclear and cytosolic compartments of human cancer cells. We develop a model that reliably quantifies mRNA-specific synthesis, nuclear export, and nuclear and cytosolic degradation rates on a genome-wide scale. We find that nuclear degradation of polyadenylated mRNA is negligible and nuclear mRNA export is slow, while cytosolic mRNA degradation is comparatively fast. Consequently, an mRNA molecule generally spends most of its life in the nucleus. We also observe large differences in the nuclear export rates of different 3'UTR transcript isoforms. Furthermore, we identify genes whose expression is abruptly induced upon metabolic labeling. These transcripts are exported substantially faster than average mRNAs, suggesting the existence of alternative export pathways. Our results highlight nuclear mRNA export as a limiting factor in mRNA metabolism and gene regulation.

Reliable genotyping of recombinant genomes using a robust hidden Markov model.

Meiotic recombination is an essential mechanism during sexual reproduction and includes the exchange of chromosome segments between homologous chromosomes. New allelic combinations are transmitted to the new generation, introducing novel genetic variation in the offspring genomes. With the improvement of high-throughput whole-genome sequencing technologies, large numbers of recombinant individuals can now be sequenced with low sequencing depth at low costs, necessitating computational methods for reconstructing their haplotypes. The main challenge is the uncertainty in haplotype calling that arises from the low information content of a single genomic position. Straightforward sliding window-based approaches are difficult to tune and fail to place recombination breakpoints precisely. Hidden Markov model (HMM)-based approaches, on the other hand, tend to over-segment the genome. Here, we present RTIGER, an HMM-based model that exploits in a mathematically precise way the fact that true chromosome segments typically have a certain minimum length. We further separate the task of identifying the correct haplotype sequence from the accurate placement of haplotype borders, thereby maximizing the accuracy of border positions. By comparing segmentations based on simulated data with known underlying haplotypes, we highlight the reasons for RTIGER outperforming traditional segmentation approaches. We then analyze the meiotic recombination pattern of segregants of 2 Arabidopsis (Arabidopsis thaliana) accessions and a previously described hyper-recombining mutant. RTIGER is available as an R package with an efficient Julia implementation of the core algorithm.

Developmental HCN channelopathy results in decreased neural progenitor proliferation and microcephaly in mice.

The development of the cerebral cortex relies on the controlled division of neural stem and progenitor cells. The requirement for precise spatiotemporal control of proliferation and cell fate places a high demand on the cell division machinery, and defective cell division can cause microcephaly and other brain malformations. Cell-extrinsic and -intrinsic factors govern the capacity of cortical progenitors to produce large numbers of neurons and glia within a short developmental time window. In particular, ion channels shape the intrinsic biophysical properties of precursor cells and neurons and control their membrane potential throughout the cell cycle. We found that hyperpolarization-activated cyclic nucleotide-gated cation (HCN) channel subunits are expressed in mouse, rat, and human neural progenitors. Loss of HCN channel function in rat neural stem cells impaired their proliferation by affecting the cell-cycle progression, causing G1 accumulation and dysregulation of genes associated with human microcephaly. Transgene-mediated, dominant-negative loss of HCN channel function in the embryonic mouse telencephalon resulted in pronounced microcephaly. Together, our findings suggest a role for HCN channel subunits as a part of a general mechanism influencing cortical development in mammals.

IGF1R expression by adult oligodendrocytes is not required in the steady-state but supports neuroinflammation.

In the central nervous system (CNS), insulin-like growth factor 1 (IGF-1) regulates myelination by oligodendrocyte (ODC) precursor cells and shows anti-apoptotic properties in neuronal cells in different in vitro and in vivo systems. Previous work also suggests that IGF-1 protects ODCs from cell death and enhances remyelination in models of toxin-induced and autoimmune demyelination. However, since evidence remains controversial, the therapeutic potential of IGF-1 in demyelinating CNS conditions is unclear. To finally shed light on the function of IGF1-signaling for ODCs, we deleted insulin-like growth factor 1 receptor (IGF1R) specifically in mature ODCs of the mouse. We found that ODC survival and myelin status were unaffected by the absence of IGF1R until 15 months of age, indicating that IGF-1 signaling does not play a major role in post-mitotic ODCs during homeostasis. Notably, the absence of IGF1R did neither affect ODC survival nor myelin status upon cuprizone intoxication or induction of experimental autoimmune encephalomyelitis (EAE), models for toxic and autoimmune demyelination, respectively. Surprisingly, however, the absence of IGF1R from ODCs protected against clinical neuroinflammation in the EAE model. Together, our data indicate that IGF-1 signaling is not required for the function and survival of mature ODCs in steady-state and disease.

Investigating non-inferiority or equivalence in time-to-event data under non-proportional hazards.

The classical approach to analyze time-to-event data, e.g. in clinical trials, is to fit Kaplan-Meier curves yielding the treatment effect as the hazard ratio between treatment groups. Afterwards, a log-rank test is commonly performed to investigate whether there is a difference in survival or, depending on additional covariates, a Cox proportional hazard model is used. However, in numerous trials these approaches fail due to the presence of non-proportional hazards, resulting in difficulties of interpreting the hazard ratio and a loss of power. When considering equivalence or non-inferiority trials, the commonly performed log-rank based tests are similarly affected by a violation of this assumption. Here we propose a parametric framework to assess equivalence or non-inferiority for survival data. We derive pointwise confidence bands for both, the hazard ratio and the difference of the survival curves. Further we propose a test procedure addressing non-inferiority and equivalence by directly comparing the survival functions at certain time points or over an entire range of time. Once the model's suitability is proven the method provides a noticeable power benefit, irrespectively of the shape of the hazard ratio. On the other hand, model selection should be carried out carefully as misspecification may cause type I error inflation in some situations. We investigate the robustness and demonstrate the advantages and disadvantages of the proposed methods by means of a simulation study. Finally, we demonstrate the validity of the methods by a clinical trial example.

Global analysis of eukaryotic mRNA degradation reveals Xrn1-dependent buffering of transcript levels.

The rates of mRNA synthesis and degradation determine cellular mRNA levels and can be monitored by comparative dynamic transcriptome analysis (cDTA) that uses nonperturbing metabolic RNA labeling. Here we present cDTA data for 46 yeast strains lacking genes involved in mRNA degradation and metabolism. In these strains, changes in mRNA degradation rates are generally compensated by changes in mRNA synthesis rates, resulting in a buffering of mRNA levels. We show that buffering of mRNA levels requires the RNA exonuclease Xrn1. The buffering is rapidly established when mRNA synthesis is impaired, but is delayed when mRNA degradation is impaired, apparently due to Xrn1-dependent transcription repressor induction. Cluster analysis of the data defines the general mRNA degradation machinery, reveals different substrate preferences for the two mRNA deadenylase complexes Ccr4-Not and Pan2-Pan3, and unveils an interwoven cellular mRNA surveillance network.

Clustering of samples with a tree-shaped dependence structure, with an application to microscopic time lapse imaging.

MOTIVATION: Recent imaging technologies allow for high-throughput tracking of cells as they migrate, divide, express fluorescent markers and change their morphology. The interpretation of these data requires unbiased, efficient statistical methods that model the dynamics of cell phenotypes. RESULTS: We introduce treeHFM, a probabilistic model which generalizes the theory of hidden Markov models to tree structured data. While accounting for the entire genealogy of a cell, treeHFM categorizes cells according to their primary phenotypic features. It models all relevant events in a cell's life, including cell division, and thereby enables the analysis of event order and cell fate heterogeneity. Simulations show higher accuracy in predicting correct state labels when modeling the more complex, tree-shaped dependency of samples over standard HMM modeling. Applying treeHFM to time lapse images of hematopoietic progenitor cell differentiation, we demonstrate that progenitor cells undergo a well-ordered sequence of differentiation events. AVAILABILITY AND IMPLEMENTATION: The treeHFM is implemented in C++. We provide wrapper functions for the programming languages R (CRAN package, https://CRAN.R-project.org/package=treeHFM) and Matlab (available at Mathworks Central, http://se.mathworks.com/matlabcentral/fileexchange/57575-treehfml). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

GenoGAM: genome-wide generalized additive models for ChIP-Seq analysis.

MOTIVATION: Chromatin immunoprecipitation followed by deep sequencing (ChIP-Seq) is a widely used approach to study protein-DNA interactions. Often, the quantities of interest are the differential occupancies relative to controls, between genetic backgrounds, treatments, or combinations thereof. Current methods for differential occupancy of ChIP-Seq data rely however on binning or sliding window techniques, for which the choice of the window and bin sizes are subjective. RESULTS: Here, we present GenoGAM (Genome-wide Generalized Additive Model), which brings the well-established and flexible generalized additive models framework to genomic applications using a data parallelism strategy. We model ChIP-Seq read count frequencies as products of smooth functions along chromosomes. Smoothing parameters are objectively estimated from the data by cross-validation, eliminating ad hoc binning and windowing needed by current approaches. GenoGAM provides base-level and region-level significance testing for full factorial designs. Application to a ChIP-Seq dataset in yeast showed increased sensitivity over existing differential occupancy methods while controlling for type I error rate. By analyzing a set of DNA methylation data and illustrating an extension to a peak caller, we further demonstrate the potential of GenoGAM as a generic statistical modeling tool for genome-wide assays. AVAILABILITY AND IMPLEMENTATION: Software is available from Bioconductor: https://www.bioconductor.org/packages/release/bioc/html/GenoGAM.html . CONTACT: gagneur@in.tum.de. SUPPLEMENTARY INFORMATION: Supplementary information is available at Bioinformatics online.

Simultaneous characterization of sense and antisense genomic processes by the double-stranded hidden Markov model.

Hidden Markov models (HMMs) have been extensively used to dissect the genome into functionally distinct regions using data such as RNA expression or DNA binding measurements. It is a challenge to disentangle processes occurring on complementary strands of the same genomic region. We present the double-stranded HMM (dsHMM), a model for the strand-specific analysis of genomic processes. We applied dsHMM to yeast using strand specific transcription data, nucleosome data, and protein binding data for a set of 11 factors associated with the regulation of transcription.The resulting annotation recovers the mRNA transcription cycle (initiation, elongation, termination) while correctly predicting strand-specificity and directionality of the transcription process. We find that pre-initiation complex formation is an essentially undirected process, giving rise to a large number of bidirectional promoters and to pervasive antisense transcription. Notably, 12% of all transcriptionally active positions showed simultaneous activity on both strands. Furthermore, dsHMM reveals that antisense transcription is specifically suppressed by Nrd1, a yeast termination factor.