Changepoint problem with angular data using a measure of variation based on the intrinsic geometry of torus

In many temporally ordered data sets, it is observed that the parameters of the underlying distribution change abruptly at unknown times. The detection of such changepoints is important for many applications. While this problem has been studied substantially in the linear data setup, not much work has been done for angular data. In this article, we utilize the intrinsic geometry of a torus to introduce the notion of the `square of an angle' and use it to propose a new measure of variation, called the `curved variance', of an angular random variable. Using the above ideas, we propose new tests for the existence of changepoint(s) in the concentration, mean direction, and/or both of these. The limiting distributions of the test statistics are derived and their powers are obtained using extensive simulation. It is seen that the tests have better power than the corresponding existing tests. The proposed methods have been implemented on three real-life data sets revealing interesting insights. In particular, our method when used to detect simultaneous changes in mean direction and concentration for hourly wind direction measurements of the cyclonic storm `Amphan' identified changepoints that could be associated with important meteorological events

Learning Microbial Interaction Networks from Metagenomic Count Data

Abstract Many microbes associate with higher eukaryotes and impact their vitality. To engineer microbiomes for host benefit, we must understand the rules of community assembly and maintenance that, in large part, demand an understanding of the direct interactions among community members. Toward this end, we have developed a Poisson-multivariate normal hierarchical model to learn direct interactions from the count-based output of standard metagenomics sequencing experiments. Our model controls for confounding predictors at the Poisson layer and captures direct taxon–taxon interactions at the multivariate normal layer using an ℓ1 penalized precision matrix. We show in a synthetic experiment that our method handily outperforms state-of-the-art methods such as SparCC and the graphical lasso (glasso). In a real in planta perturbation experiment of a nine-member bacterial community, we show our model, but not SparCC or glasso, correctly resolves a direct interaction structure among three community members that ..

The molecular basis of host specialization in bean pathovars of Pseudomonas syringae

Biotrophic phytopathogens are typically limited to their adapted host range. In recent decades, investigations have teased apart the general molecular basis of intraspecific variation for innate immunity of plants, typically involving receptor proteins that enable perception of pathogen-associated molecular patterns or avirulence elicitors from the pathogen as triggers for defense induction. However, general consensus concerning evolutionary and molecular factors that alter host range across closely related phytopathogen isolates has been more elusive. Here, through genome comparisons and genetic manipulations, we investigate the underlying mechanisms that structure host range across closely related strains of Pseudomonas syringae isolated from different legume hosts. Although type III secretionindependent virulence factors are conserved across these three strains, we find that the presence of two genes encoding type III effectors (hopC1 and hopM1) and the absence of another (avrB2) potentially contribute to host range differences between pathovars glycinea and phaseolicola. These findings reinforce the idea that a complex genetic basis underlies host range evolution in plant pathogens. This complexity is present even in host–microbe interactions featuring relatively little divergence among both hosts and their adapted pathogens

Pseudomonas syringae Type III Effector HopBB1 Promotes Host Transcriptional Repressor Degradation to Regulate Phytohormone Responses and Virulence

Independently evolved pathogen effectors from three branches of life (ascomycete, eubacteria, and oomycete) converge onto the Arabidopsis TCP14 transcription factor to manipulate host defense. However, the mechanistic basis for defense control via TCP14 regulation is unknown. We demonstrate that TCP14 regulates the plant immune system by transcriptionally repressing a subset of the jasmonic acid (JA) hormone signaling outputs. A previously unstudied Pseudomonas syringae (Psy) type III effector, HopBB1, interacts with TCP14 and targets it to the SCFCOI1 degradation complex by connecting it to the JA signaling repressor JAZ3. Consequently, HopBB1 de-represses the TCP14-regulated subset of JA response genes and promotes pathogen virulence. Thus, HopBB1 fine-tunes host phytohormone crosstalk by precisely manipulating part of the JA regulon to avoid pleiotropic host responses while promoting pathogen proliferation

Phytochromes function as thermosensors in Arabidopsis

Plants are responsive to temperature, and can distinguish differences of 1ºC. In Arabidopsis, warmer temperature accelerates flowering and increases elongation growth hermomorphogenesis). The mechanisms of temperature perception are however largely unknown. We describe a major thermosensory role for the phytochromes (red light receptors) during the night. Phytochrome null plants display a constitutive warm temperature response, and consistent with this, we show in this background that the warm temperature transcriptome becomes de-repressed at low temperatures. We have discovered phytochrome B (phyB) directly associates with the promoters of key target genes in a temperature dependent manner. The rate of phyB inactivation is proportional to temperature in the dark, enabling phytochromes to function as thermal timers, integrating temperature information over the course of the night

Variable Suites of Non-effector Genes Are Co-regulated in the Type III Secretion Virulence Regulon across the Pseudomonas syringae Phylogeny

Pseudomonas syringae is a phylogenetically diverse species of Gram-negative bacterial plant pathogens responsible for crop diseases around the world. The HrpL sigma factor drives expression of the major P. syringae virulence regulon. HrpL controls expression of the genes encoding the structural and functional components of the type III secretion system (T3SS) and the type three secreted effector proteins (T3E) that are collectively essential for virulence. HrpL also regulates expression of an under-explored suite of non-type III effector genes (non-T3E), including toxin production systems and operons not previously associated with virulence. We implemented and refined genome-wide transcriptional analysis methods using cDNA-derived high-throughput sequencing (RNA-seq) data to characterize the HrpL regulon from six isolates of P. syringae spanning the diversity of the species. Our transcriptomes, mapped onto both complete and draft genomes, significantly extend earlier studies. We confirmed HrpL-regulation for a majority of previously defined T3E genes in these six strains. We identified two new T3E families from P. syringae pv. oryzae 1_6, a strain within the relatively underexplored phylogenetic Multi-Locus Sequence Typing (MLST) group IV. The HrpL regulons varied among strains in gene number and content across both their T3E and non-T3E gene suites. Strains within MLST group II consistently express the lowest number of HrpL-regulated genes. We identified events leading to recruitment into, and loss from, the HrpL regulon. These included gene gain and loss, and loss of HrpL regulation caused by group-specific cis element mutations in otherwise conserved genes. Novel non-T3E HrpL-regulated genes include an operon that we show is required for full virulence of P. syringae pv. phaseolicola 1448A on French bean. We highlight the power of integrating genomic, transcriptomic, and phylogenetic information to drive concise functional experimentation and to derive better insight into the evolution of virulence across an evolutionarily diverse pathogen species