Expansion and Divergence of Argonaute Genes in the Oomycete Genus Phytophthora

Modulation of gene expression through RNA interference is well conserved in eukaryotes and is involved in many cellular processes. In the oomycete Phytophthora, research on the small RNA machinery and function has started to reveal potential roles in the pathogen, but much is still unknown. We examined Argonaute (AGO) homologs within oomycete genome sequences, especially among Phytophthora species, to gain a clearer understanding of the evolution of this well-conserved protein family. We identified AGO homologs across many representative oomycete and stramenopile species, and annotated representative homologs in P. sojae. Furthermore, we demonstrate variable transcript levels of all identified AGO homologs in comparison to previously identified Dicer-like (DCL) and RNA-dependent RNA polymerase (RDR) homologs. Our phylogenetic analysis further refines the relationship of the AGO homologs in oomycetes and identifies a conserved tandem duplication of AGO homologs in a subset of Phytophthora species

Phytophthora Have Distinct Endogenous Small RNA Populations That Include Short Interfering and microRNAs

In eukaryotes, RNA silencing pathways utilize 20-30-nucleotide small RNAs to regulate gene expression, specify and maintain chromatin structure, and repress viruses and mobile genetic elements. RNA silencing was likely present in the common ancestor of modern eukaryotes, but most research has focused on plant and animal RNA silencing systems. Phytophthora species belong to a phylogenetically distinct group of economically important plant pathogens that cause billions of dollars in yield losses annually as well as ecologically devastating outbreaks. We analyzed the small RNA-generating components of the genomes of P. infestans, P. sojae and P. ramorum using bioinformatics, genetic, phylogenetic and high-throughput sequencing-based methods. Each species produces two distinct populations of small RNAs that are predominantly 21- or 25-nucleotides long. The 25-nucleotide small RNAs were primarily derived from loci encoding transposable elements and we propose that these small RNAs define a pathway of short-interfering RNAs that silence repetitive genetic elements. The 21-nucleotide small RNAs were primarily derived from inverted repeats, including a novel microRNA family that is conserved among the three species, and several gene families, including Crinkler effectors and type III fibronectins. The Phytophthora microRNA is predicted to target a family of amino acid/auxin permeases, and we propose that 21-nucleotide small RNAs function at the post-transcriptional level. The functional significance of microRNA-guided regulation of amino acid/auxin permeases and the association of 21-nucleotide small RNAs with Crinkler effectors remains unclear, but this work provides a framework for testing the role of small RNAs in Phytophthora biology and pathogenesis in future work

Hierarchical Analysis of Diversity, Selfing, and Genetic Differentiation in Populations of the Oomycete Aphanomyces euteiches

<p>Relatively little is known about the population biology of the legume pathogen Aphanomyces euteiches. A. euteiches is a soilborne pathogen causing Aphanomyces root rot of several legumes, including alfalfa, bean, lentil, and pea. Our objectives were to assess the degree of diversity, selfing, and population differentiation in A. euteiches. We contrasted populations within and among two geographically separated fields with a history of pea production. Molecular genotyping relied on amplified fragment length polymorphism analysis. Samples of A. euteiches recovered from two fields in northeast Oregon and western Washington confirmed previous reports of moderately high genetic diversity in populations of A. euteiches at the regional scale, but revealed higher-than-expected genotypic diversity within individual soil samples. Populations of A. euteiches were significantly differentiated at the soil sample, field, and regional level. The population structure appears to be patterned by regular selfing via oospores, a mixed reproductive system including both asexual and sexual reproduction, with occasional migration of novel genotypes or outcrossing.</p

grunwaldlab/metacoder_documentation: First documentation release

This manual was released with the v0.1.2 version of MetacodeR. This repository is used to created the online content available at https://grunwaldlab.github.io/metacoder_documentation/

Resection of high frequency oscillations predicts seizure outcome in the individual patient

Abstract High frequency oscillations (HFOs) are recognized as biomarkers for epileptogenic brain tissue. A remaining challenge for epilepsy surgery is the prospective classification of tissue sampled by individual electrode contacts. We analysed long-term invasive recordings of 20 consecutive patients who subsequently underwent epilepsy surgery. HFOs were defined prospectively by a previously validated, automated algorithm in the ripple (80–250 Hz) and the fast ripple (FR, 250–500 Hz) frequency band. Contacts with the highest rate of ripples co-occurring with FR over several five-minute time intervals designated the HFO area. The HFO area was fully included in the resected area in all 13 patients who achieved seizure freedom (specificity 100%) and in 3 patients where seizures reoccurred (negative predictive value 81%). The HFO area was only partially resected in 4 patients suffering from recurrent seizures (positive predictive value 100%, sensitivity 57%). Thus, the resection of the prospectively defined HFO area proved to be highly specific and reproducible in 13/13 patients with seizure freedom, while it may have improved the outcome in 4/7 patients with recurrent seizures. We thus validated the clinical relevance of the HFO area in the individual patient with an automated procedure. This is a prerequisite before HFOs can guide surgical treatment in multicentre studies

grunwaldlab/metacoder: metacoder 0.1.3

metacoder 0.1.3 Mostly minor improvements and bug fixes. Larger changes are waiting on the taxa package to be done, which will be the new home of the taxmap class and the associated dplyr-like manipulating functions like filter_taxa. Improvements Provided helpful error message when the evaluation nested too deeply: infinite recursion / options(expressions=)? occurs due to too many labels being printed. heat_tree: improved how the predicted bondries of text is calcuated, so text with any rotation, justification, or newlines influences margins correctly (i.e. does not get cut off). heat_tree: Can now save multiple file outputs in different formats at once Minor changes heat_tree now gives a warning if infinite values are given to it extract_taxonomy: There is now a warning message if class regex does not match (issue #123) heat_tree: Increased lengend text size and reduced number of labels extract_taxonomy: added batch_size option to help deal with invalid IDs better Added CITATION file Breaking changes The heat_tree option margin_size funcion now takes four values instead of 2. Bug fixes heat_tree: Fixed bug when color is set explicitly (e.g. "grey") instead of raw numbers and the legend is not removed. Now a mixure of raw numbers and color names can be used. Fixed bugs caused by dplyr version update Fixed bug in heat_tree that made values not in the input taxmap object not associate with the right taxa. See this post. extract_taxonomy: Fixed an error that occured when not all inputs could be classified and sequences were supplied Fixed bug in primersearch that cased the wrong primer sequence to be returned when primers match in the reverse direction Fixed a bug in parse_mothur_summary where "unclassified" had got changed to "untaxmap" during a search and replace Fixed outdated example code for extract_taxonomy Fixed a bug in mutate_taxa and mutate_obs that made replacing columns result in new columns with duplicate names

Supplementary Material for Frontiers Plant Genetics and Genomics 'Novel R tools for analysis of genome-wide population genetic data with emphasis on clonality'

<p>Authors</p> <p>Zhian N. Kamvar, Jonah C. Brooks, and Niklaus J. Grünwald</p

Human Intracranial High Frequency Oscillations (HFOs) Detected by Automatic Time-Frequency Analysis

Objectives High frequency oscillations (HFOs) have been proposed as a new biomarker for epileptogenic tissue. The exact characteristics of clinically relevant HFOs and their detection are still to be defined. Methods We propose a new method for HFO detection, which we have applied to six patient iEEGs. In a first stage, events of interest (EoIs) in the iEEG were defined by thresholds of energy and duration. To recognize HFOs among the EoIs, in a second stage the iEEG was Stockwell-transformed into the time-frequency domain, and the instantaneous power spectrum was parameterized. The parameters were optimized for HFO detection in patient 1 and tested in patients 2–5. Channels were ranked by HFO rate and those with rate above half maximum constituted the HFO area. The seizure onset zone (SOZ) served as gold standard. Results The detector distinguished HFOs from artifacts and other EEG activity such as interictal epileptiform spikes. Computation took few minutes. We found HFOs with relevant power at frequencies also below the 80–500 Hz band, which is conventionally associated with HFOs. The HFO area overlapped with the SOZ with good specificity > 90% for five patients and one patient was re-operated. The performance of the detector was compared to two well-known detectors. Conclusions Compared to methods detecting energy changes in filtered signals, our second stage - analysis in the time-frequency domain - discards spurious detections caused by artifacts or sharp epileptic activity and improves the detection of HFOs. The fast computation and reasonable accuracy hold promise for the diagnostic value of the detector.ISSN:1932-620

Human intracranial high frequency oscillations (HFOs) detected by automatic time-frequency analysis

OBJECTIVES: High frequency oscillations (HFOs) have been proposed as a new biomarker for epileptogenic tissue. The exact characteristics of clinically relevant HFOs and their detection are still to be defined. METHODS: We propose a new method for HFO detection, which we have applied to six patient iEEGs. In a first stage, events of interest (EoIs) in the iEEG were defined by thresholds of energy and duration. To recognize HFOs among the EoIs, in a second stage the iEEG was Stockwell-transformed into the time-frequency domain, and the instantaneous power spectrum was parameterized. The parameters were optimized for HFO detection in patient 1 and tested in patients 2-5. Channels were ranked by HFO rate and those with rate above half maximum constituted the HFO area. The seizure onset zone (SOZ) served as gold standard. RESULTS: The detector distinguished HFOs from artifacts and other EEG activity such as interictal epileptiform spikes. Computation took few minutes. We found HFOs with relevant power at frequencies also below the 80-500 Hz band, which is conventionally associated with HFOs. The HFO area overlapped with the SOZ with good specificity > 90% for five patients and one patient was re-operated. The performance of the detector was compared to two well-known detectors. CONCLUSIONS: Compared to methods detecting energy changes in filtered signals, our second stage - analysis in the time-frequency domain - discards spurious detections caused by artifacts or sharp epileptic activity and improves the detection of HFOs. The fast computation and reasonable accuracy hold promise for the diagnostic value of the detector