Using Noise to Characterize Vision

Noise has been widely used to investigate the processing properties of various visual functions (e.g. detection, discrimination, attention, perceptual learning, averaging, crowding, face recognition), in various populations (e.g. older adults, amblyopes, migrainers, dyslexic children), using noise along various dimensions (e.g. pixel noise, orientation jitter, contrast jitter). The reason to use external noise is generally not to characterize visual processing in external noise per se, but rather to reveal how vision works in ordinary conditions when performance is limited by our intrinsic noise rather than externally added noise. For instance, reverse correlation aims at identifying the relevant information to perform a given task in noiseless conditions and measuring contrast thresholds in various noise levels can be used to understand the impact of intrinsic noise that limits sensitivity to noiseless stimuli. Why use noise? Since Fechner named it, psychophysics has always emphasized the systematic investigation of conditions that break vision. External noise raises threshold hugely and selectively. In hearing, Fletcher used noise in his famous critical-band experiments to reveal frequency-selective channels in hearing. Critical bands have been found in vision too. More generally, the big reliable effects of noise give important clues to how the system works. And simple models have been proposed to account for the effects of visual noise. As noise has been more widely used, questions have been raised about the simplifying assumptions that link the processing properties in noiseless conditions to measurements in external noise. For instance, it is usually assumed that the processing strategy (or mechanism) used to perform a task and its processing properties (e.g. filter tuning) are unaffected by the addition of external noise. Some have suggested that the processing properties could change with the addition of external noise (e.g. change in filter tuning or more lateral masking in noise), which would need to be considered before drawing conclusions about the processing properties in noiseless condition. Others have suggested that different processing properties (or mechanisms) could be solicited in low and high noise conditions, complicating the characterization of processing properties in noiseless condition based on processing properties identified in noise conditions. The current Research Topic probes further into what the effects of visual noise tell us about vision in ordinary conditions. Our Editorial gives an overview of the articles in this special issue

MSMS Peak Identification and its Applications

A peak detection algorithm for Tandem Mass Spectra is presented that scores a fragment using intensity and isotopic distribution. It classifies each fragment in a spectrum as noise or signal based on a maximum likelihood estimate derived from the distribution observed in a training set of 12,000 validated spectra. This is the largest such database known to the authors. We present three tools which apply this algorithm: the Quality Filter removes noisy spectra, Mod-Pro profiles modifications and amino acids in a sample and Spectrimilarity scores similarity of two spectra. Contact

A Metabolic Profiling Strategy for the Dissection of Plant Defense against Fungal Pathogens

<div><p>Here we present a metabolic profiling strategy employing direct infusion Orbitrap mass spectrometry (MS) and gas chromatography-mass spectrometry (GC/MS) for the monitoring of soybean's (<i>Glycine max</i> L.) global metabolism regulation in response to <i>Rhizoctonia solani</i> infection in a time-course. Key elements in the approach are the construction of a comprehensive metabolite library for soybean, which accelerates the steps of metabolite identification and biological interpretation of results, and bioinformatics tools for the visualization and analysis of its metabolome. The study of metabolic networks revealed that infection results in the mobilization of carbohydrates, disturbance of the amino acid pool, and activation of isoflavonoid, <i>α</i>-linolenate, and phenylpropanoid biosynthetic pathways of the plant. Components of these pathways include phytoalexins, coumarins, flavonoids, signaling molecules, and hormones, many of which exhibit antioxidant properties and bioactivity helping the plant to counterattack the pathogen's invasion. Unraveling the biochemical mechanism operating during soybean-<i>Rhizoctonia</i> interaction, in addition to its significance towards the understanding of the plant's metabolism regulation under biotic stress, provides valuable insights with potential for applications in biotechnology, crop breeding, and agrochemical and food industries.</p></div

A newly uncovered group of distantly related lysine methyltransferases preferentially interact with molecular chaperones to regulate their activity.

Methylation is a post-translational modification that can affect numerous features of proteins, notably cellular localization, turnover, activity, and molecular interactions. Recent genome-wide analyses have considerably extended the list of human genes encoding putative methyltransferases. Studies on protein methyltransferases have revealed that the regulatory function of methylation is not limited to epigenetics, with many non-histone substrates now being discovered. We present here our findings on a novel family of distantly related putative methyltransferases. Affinity purification coupled to mass spectrometry shows a marked preference for these proteins to associate with various chaperones. Based on the spectral data, we were able to identify methylation sites in substrates, notably trimethylation of K135 of KIN/Kin17, K561 of HSPA8/Hsc70 as well as corresponding lysine residues in other Hsp70 isoforms, and K315 of VCP/p97. All modification sites were subsequently confirmed in vitro. In the case of VCP, methylation by METTL21D was stimulated by the addition of the UBX cofactor ASPSCR1, which we show directly interacts with the methyltransferase. This stimulatory effect was lost when we used VCP mutants (R155H, R159G, and R191Q) known to cause Inclusion Body Myopathy with Paget's disease of bone and Fronto-temporal Dementia (IBMPFD) and/or familial Amyotrophic Lateral Sclerosis (ALS). Lysine 315 falls in proximity to the Walker B motif of VCP's first ATPase/D1 domain. Our results indicate that methylation of this site negatively impacts its ATPase activity. Overall, this report uncovers a new role for protein methylation as a regulatory pathway for molecular chaperones and defines a novel regulatory mechanism for the chaperone VCP, whose deregulation is causative of degenerative neuromuscular diseases

Color-coded fluctuation of the soybean's seedling metabolic network caused by <i>Rhizoctonia solani</i> infection at 24 h and 48 h post-inoculation, including portions of the amino acid biosynthesis, and the isoflavonoid and phenylpropanoid biosynthetic pathways, which were detected as important components of its defense mechanism.

<p>Genes in the reference pathway of KEGG for soybean that are hyperlinked to gene entries are displayed. Metabolite fluctuations are coded using a color code based on <i>P</i>-values (<i>P</i><0.05) performing the Student's t-test and means of scaled and centered PLS regression coefficients (CoeffCS) from five biological replications and a quality control sample per treatment. Dashed lines symbolize multi-step or not fully elucidated reactions and solid lines one-step reactions. [3PGA; 3-phosphoglycerate, isoflavone synthase 1; C3′H; coumaroylquinate (coumaroylshikimate) 3′-monooxygenase, EC: 1.14.13.36, NCBI-GeneID: 100811080, C4H; coumarate 4-hydroxylase, EC: 1.14.13.11, NCBI-GeneID: 100499623, CAD; cinnamyl-alcohol dehydrogenase, EC: 1.1.1.195, NCBI-GeneID: 100777558, CHI; chalconeisomerase (5 isozymes), F6P; fructose-6-phosphate, G6P; glucose-6-phosphate, β-Glu; glucosidase β-glucosidase, EC: 3.2.1.21, NCBI-GeneID: 100779642, HIDH; 2-hydroxyisoflavanone dehydratase, EC: 4.2.1.105, NCBI-GeneID: 547489, IF7GT; isoflavone 7′-O-glucosyltransferase, EC: 2.4.1.170, NCBI-GeneID: 100101902, IFS1; isoflavone synthase 1, EC: 5.4.99.-, NCBI-GeneID: 100037450, IOMT; isoflavone 7′-O-Methyltransferase (14 isozymes), PAL; phenylalanine ammonia-lyase, EC: 4.3.1.24, NCBI-Gene ID: 100787902, PEP; phosphoenolpyruvate].</p

Classification of soybean metabolites into chemical groups in response to <i>Rhizoctonia</i> infection.

<p>The total number of signatory metabolites at 24 h and 48 h post-inoculation (<i>A</i>), and the total number of signatory metabolites classified into increased and decreased in infected seedlings compared to controls at 24 h and 48 h post-inoculation (<i>B</i>), are displayed. For the chemical classification of metabolites information was retrieved from the database PubChem.</p

Proposed graphical model for the role of <i>Rhizoctonia solani</i> in activation of soybean defense mechanisms.

<p>[CM; cell membrane, CW; cell wall, G; Golgi body, M; mitochondrion, N; nucleus, P; plasmalemma, R; ribosomes, RER; rough endoplasmic reticulum, SER; soft endoplasmic reticulum, T; transporters, V; vacuole].</p

Fluctuations in the <i>α</i>-linolenate metabolism of soybean in response to <i>Rhizoctonia solani</i> at 24 h and 48 h post-inoculation coded using a color code based on <i>P</i>-values (<i>P</i><0.05) performing the Student's t-test or means of scaled and centered PLS regression coefficients (CoeffCS) from five biological replications and a quality control sample per treatment.

<p>Genes in the reference pathway of KEGG for soybean that are hyperlinked to gene entries are displayed. Dashed lines symbolize multi-step or not fully elucidated reactions and solid lines one-step reactions [13-HPOT; (9Z,11E,15Z)-(13S)-hydroperoxyoctadeca-9,11,15-trienoate, AOC; allene oxide cyclase, EC: 5.3.99.6, NCBI-GeneID:100800036, AOS; hydroperoxidedehydratase, EC: 4.2.1.92, NCBI-GeneID: 100037481, HPL; hydroperoxidelyase, EC: 4.1.2.-, NCBI-GeneID: 100784395, LOX; lipoxygenase, EC: 1.13.11.12, NCBI-GeneID: 100785480].</p

Classification of signatory metabolites of soybean response to <i>Rhizoctonia solani</i> invasion at 24 h and 48 h post-inoculation based on their participation in biosynthetic pathways, measured as instances, since a metabolite can be involved in more than one pathway.

<p>Classification of signatory metabolites of soybean response to <i>Rhizoctonia solani</i> invasion at 24 h and 48 h post-inoculation based on their participation in biosynthetic pathways, measured as instances, since a metabolite can be involved in more than one pathway.</p