Comprehensive annotation of the Parastagonospora nodorum reference genome using next-generation genomics, transcriptomics and proteogenomics

Parastagonospora nodorum, the causal agent of Septoria nodorum blotch (SNB), is an economically important pathogen of wheat (Triticum spp.), and a model for the study of necrotrophic pathology and genome evolution. The reference P. nodorum strain SN15 was the first Dothideomycete with a published genome sequence, and has been used as the basis for comparison within and between species. Here we present an updated reference genome assembly with corrections of SNP and indel errors in the underlying genome assembly from deep resequencing data as well as extensive manual annotation of gene models using transcriptomic and proteomic sources of evidence (https://github.com/robsyme/Parastagonospora_nodorum_SN15). The updated assembly and annotation includes 8,366 genes with modified protein sequence and 866 new genes. This study shows the benefits of using a wide variety of experimental methods allied to expert curation to generate a reliable set of gene models

Observing many researchers using the same data and hypothesis reveals a hidden universe of uncertainty

This study explores how researchers’ analytical choices affect the reliability of scientific findings. Most discussions of reliability problems in science focus on systematic biases. We broaden the lens to emphasize the idiosyncrasy of conscious and unconscious decisions that researchers make during data analysis. We coordinated 161 researchers in 73 research teams and observed their research decisions as they used the same data to independently test the same prominent social science hypothesis: that greater immigration reduces support for social policies among the public. In this typical case of social science research, research teams reported both widely diverging numerical findings and substantive conclusions despite identical start conditions. Researchers’ expertise, prior beliefs, and expectations barely predict the wide variation in research outcomes. More than 95% of the total variance in numerical results remains unexplained even after qualitative coding of all identifiable decisions in each team’s workflow. This reveals a universe of uncertainty that remains hidden when considering a single study in isolation. The idiosyncratic nature of how researchers’ results and conclusions varied is a previously underappreciated explanation for why many scientific hypotheses remain contested. These results call for greater epistemic humility and clarity in reporting scientific findings

A rapid method for profiling of volatile and semi-volatile phytohormones using methyl chloroformate derivatisation and GC–MS

Phytohormones are central components of complex signalling networks in plants. The interplay between these metabolites, which include abscisic acid (ABA), auxin (IAA), ethylene, jasmonic acid (JA) and salicylic acid (SA), regulate plant growth and development and modulate responses to biotic and abiotic stress. Few methods of phytohormone profiling can adequately quantify a large range of plant hormones simultaneously and without the requirement for laborious or highly specialised extraction protocols. Here we describe the development and validation of a phytohormone profiling protocol, based on methyl-chloroformate derivatisation of the plant metabolites and analysis by gas chromatography/mass spectrometry (GC–MS). We describe the analysis of 11 metabolites, either plant phytohormones or intermediates of phytohormone metabolism; ABA, azelaic acid, IAA, JA and SA, and the phytohormone precursors 1-aminocyclopropane 1-carboxylic acid, benzoic acid, cinnamic acid, 13-epi-12-oxophytodienoic acid (13-epi-OPDA), linoleic acid and linolenic acid, and validate the isolation from foliar tissue of the model legume Medicago truncatula. The preparation is insensitive to the presence of water, facilitating measurement of the volatile metabolites. Quantitation was linear over four orders of magnitude, and the limits of detection between two and 10 ng/mL for all measured metabolites using a single quadrupole GC–MS

Otoacoustic Emissions from Residual Oscillations of the Cochlear Basilar Membrane in a Human Ear Model

Sounds originating from within the inner ear, known as otoacoustic emissions (OAEs), are widely exploited in clinical practice but the mechanisms underlying their generation are not entirely clear. Here we present simulation results and theoretical considerations based on a hydrodynamic model of the human inner ear. Simulations show that, if the cochlear amplifier (CA) gain is a smooth function of position within the active cochlea, filtering performed by a middle ear with an irregular, i.e., nonsmooth, forward transfer function suffices to produce irregular and long-lasting residual oscillations of cochlear basilar membrane (BM) at selected frequencies. Feeding back to the middle ear through hydrodynamic coupling afforded by the cochlear fluid, these oscillations are detected as transient evoked OAEs in the ear canal. If, in addition, the CA gain profile is affected by irregularities, residual BM oscillations are even more irregular and tend to evolve towards self-sustaining oscillations at the loci of gain irregularities. Correspondingly, the spectrum of transient evoked OAEs exhibits sharp peaks. If both the CA gain and the middle-ear forward transfer function are smooth, residual BM oscillations have regular waveforms and extinguish rapidly. In this case no emissions are produced. Finally, and paradoxically albeit consistent with observations, simulating localized damage to the CA results in self-sustaining BM oscillations at the characteristic frequencies (CFs) of the sites adjacent to the damage region, accompanied by generation of spontaneous OAEs. Under these conditions, stimulus-frequency OAEs, with typical modulation patterns, are also observed for inputs near hearing threshold. This approach can be exploited to provide novel diagnostic tools and a better understanding of key phenomena relevant for hearing science