Widespread Proteome Remodeling and Aggregation in Aging C. elegans.

Aging has been associated with a progressive decline of proteostasis, but how this process affects proteome composition remains largely unexplored. Here, we profiled more than 5,000 proteins along the lifespan of the nematode C. elegans. We find that one-third of proteins change in abundance at least 2-fold during aging, resulting in a severe proteome imbalance. These changes are reduced in the long-lived daf-2 mutant but are enhanced in the short-lived daf-16 mutant. While ribosomal proteins decline and lose normal stoichiometry, proteasome complexes increase. Proteome imbalance is accompanied by widespread protein aggregation, with abundant proteins that exceed solubility contributing most to aggregate load. Notably, the properties by which proteins are selected for aggregation differ in the daf-2 mutant, and an increased formation of aggregates associated with small heat-shock proteins is observed. We suggest that sequestering proteins into chaperone-enriched aggregates is a protective strategy to slow proteostasis decline during nematode aging.

A perspective on genetic and polygenic risk scores-advances and limitations and overview of associated tools.

Polygenetic Risk Scores are used to evaluate an individual's vulnerability to developing specific diseases or conditions based on their genetic composition, by taking into account numerous genetic variations. This article provides an overview of the concept of Polygenic Risk Scores (PRS). We elucidate the historical advancements of PRS, their advantages and shortcomings in comparison with other predictive methods, and discuss their conceptual limitations in light of the complexity of biological systems. Furthermore, we provide a survey of published tools for computing PRS and associated resources. The various tools and software packages are categorized based on their technical utility for users or prospective developers. Understanding the array of available tools and their limitations is crucial for accurately assessing and predicting disease risks, facilitating early interventions, and guiding personalized healthcare decisions. Additionally, we also identify potential new avenues for future bioinformatic analyzes and advancements related to PRS.

Tyr-Asp inhibition of glyceraldehyde 3-phosphate dehydrogenase affects plant redox metabolism.

How organisms integrate metabolism with the external environment is a central question in biology. Here, we describe a novel regulatory small molecule, a proteogenic dipeptide Tyr-Asp, which improves plant tolerance to oxidative stress by directly interfering with glucose metabolism. Specifically, Tyr-Asp inhibits the activity of a key glycolytic enzyme, glyceraldehyde 3-phosphate dehydrogenase (GAPC), and redirects glucose toward pentose phosphate pathway (PPP) and NADPH production. In line with the metabolic data, Tyr-Asp supplementation improved the growth performance of both Arabidopsis and tobacco seedlings subjected to oxidative stress conditions. Moreover, inhibition of Arabidopsis phosphoenolpyruvate carboxykinase (PEPCK) activity by a group of branched-chain amino acid-containing dipeptides, but not by Tyr-Asp, points to a multisite regulation of glycolytic/gluconeogenic pathway by dipeptides. In summary, our results open the intriguing possibility that proteogenic dipeptides act as evolutionarily conserved small-molecule regulators at the nexus of stress, protein degradation, and metabolism.

Organization of pRF size along the AP axis of the hippocampus and adjacent medial temporal cortex is related to specialization for scenes versus faces.

The hippocampus is largely recognized for its integral contributions to memory processing. By contrast, its role in perceptual processing remains less clear. Hippocampal properties vary along the anterior-posterior (AP) axis. Based on past research suggesting a gradient in the scale of features processed along the AP extent of the hippocampus, the representations have been proposed to vary as a function of granularity along this axis. One way to quantify such granularity is with population receptive field (pRF) size measured during visual processing, which has so far received little attention. In this study, we compare the pRF sizes within the hippocampus to its activation for images of scenes versus faces. We also measure these functional properties in surrounding medial temporal lobe (MTL) structures. Consistent with past research, we find pRFs to be larger in the anterior than in the posterior hippocampus. Critically, our analysis of surrounding MTL regions, the perirhinal cortex, entorhinal cortex, and parahippocampal cortex shows a similar correlation between scene sensitivity and larger pRF size. These findings provide conclusive evidence for a tight relationship between the pRF size and the sensitivity to image content in the hippocampus and adjacent medial temporal cortex.

Neither Enhanced Nor Lost: The Unique Role of Attention in Children's Neural Representations.

A defining feature of children's cognition is the especially slow development of their attention. Despite a rich behavioral literature characterizing the development of attention, little is known about how developing attentional abilities modulate neural representations in children. This information is critical to understanding how attentional development shapes the way children process information. One possibility is that attention might be less likely to shape neural representations in children as compared with adults. In particular, representations of attended items may be less likely to be enhanced relative to unattended items. To investigate this possibility, we measured brain activity using fMRI while children (seven to nine years; male and female) and adults (21-31 years; male and female) performed a one-back task in which they were directed to attend to either motion direction or an object in a display where both were present. We used multivoxel pattern analysis to compare decoding accuracy of attended and unattended information. Consistent with attentional enhancement, we found higher decoding accuracy for task-relevant information (i.e., objects in the object-attended condition) than for task-irrelevant information (i.e., motion in the object-attended condition) in adults' visual cortices. However, in children's visual cortices, both task-relevant and task-irrelevant information were decoded equally well. What is more, whole-brain analysis showed that the children represented task-irrelevant information more than adults in multiple regions across the brain, including the prefrontal cortex. These findings show that (1) attention does not modulate neural representations in the child visual cortex, and (2) developing brains can, and do, represent more information than mature brains.SIGNIFICANCE STATEMENT Children have been shown to struggle with maintaining their attention to specific information, and at the same time, can show better learning of "distractors." While these are critical properties of childhood, their underlying neural mechanisms are unknown. To fill in this critical knowledge gap, we explored how attention shapes what is represented in children's and adults' brains using fMRI while both were asked to focus on just one of two things (objects and motion). We found that unlike adults, who prioritize the information they were asked to focus on, children represent both what they were asked to prioritize and what they were asked to ignore. This shows that attention has a fundamentally different impact on children's neural representations.

The interplay of post-translational protein modifications in Arabidopsis leaves during photosynthesis induction.

Diurnal dark to light transition causes profound physiological changes in plant metabolism. These changes require distinct modes of regulation as a unique feature of photosynthetic lifestyle. The activities of several key metabolic enzymes are regulated by light-dependent post-translational modifications (PTM) and have been studied at depth at the level of individual proteins. In contrast, a global picture of the light-dependent PTMome dynamics is lacking, leaving the response of a large proportion of cellular function undefined. Here, we investigated the light-dependent metabolome and proteome changes in Arabidopsis rosettes in a time resolved manner to dissect their kinetic interplay, focusing on phosphorylation, lysine acetylation, and cysteine-based redox switches. Of over 24 000 PTM sites that were detected, more than 1700 were changed during the transition from dark to light. While the first changes, as measured 5 min after onset of illumination, occurred mainly in the chloroplasts, PTM changes at proteins in other compartments coincided with the full activation of the Calvin-Benson cycle and the synthesis of sugars at later timepoints. Our data reveal connections between metabolism and PTM-based regulation throughout the cell. The comprehensive multiome profiling analysis provides unique insight into the extent by which photosynthesis reprograms global cell function and adds a powerful resource for the dissection of diverse cellular processes in the context of photosynthetic function.

Updated Phylogeny and Protein Structure Predictions Revise the Hypothesis on the Origin of MADS-box Transcription Factors in Land Plants.

MADS-box transcription factors (TFs), among the first TFs extensively studied, exhibit a wide distribution across eukaryotes and play diverse functional roles. Varying by domain architecture, MADS-box TFs in land plants are categorized into Type I (M-type) and Type II (MIKC-type). Type I and II genes have been considered orthologous to the SRF and MEF2 genes in animals, respectively, presumably originating from a duplication before the divergence of eukaryotes. Here, we exploited the increasing availability of eukaryotic MADS-box sequences and reassessed their evolution. While supporting the ancient duplication giving rise to SRF- and MEF2-types, we found that Type I and II genes originated from the MEF2-type genes through another duplication in the most recent common ancestor (MRCA) of land plants. Protein structures predicted by AlphaFold2 and OmegaFold support our phylogenetic analyses, with plant Type I and II TFs resembling the MEF2-type structure, rather than SRFs. We hypothesize that the ancestral SRF-type TFs were lost in the MRCA of Archaeplastida (the kingdom Plantae sensu lato). The retained MEF2-type TFs acquired a Keratin-like domain and became MIKC-type before the divergence of Streptophyta. Subsequently in the MRCA of land plants, M-type TFs evolved from a duplicated MIKC-type precursor through loss of the Keratin-like domain, leading to the Type I clade. Both Type I and II TFs expanded and functionally differentiated in concert with the increasing complexity of land plant body architecture. The recruitment of these originally stress-responsive TFs into developmental programs, including those underlying reproduction, may have facilitated the adaptation to the terrestrial environment.

Genomic imprints of unparalleled growth.

Chlorella ohadii was isolated from desert biological soil crusts, one of the harshest habitats on Earth, and is emerging as an exciting new green model for studying growth, photosynthesis and metabolism under a wide range of conditions. Here, we compared the genome of C. ohadii, the fastest growing alga on record, to that of other green algae, to reveal the genomic imprints empowering its unparalleled growth rate and resistance to various stressors, including extreme illumination. This included the genome of its close relative, but slower growing and photodamage sensitive, C. sorokiniana UTEX 1663. A larger number of ribosome-encoding genes, high intron abundance, increased codon bias and unique genes potentially involved in metabolic flexibility and resistance to photodamage are all consistent with the faster growth of C. ohadii. Some of these characteristics highlight general trends in Chlorophyta and Chlorella spp. evolution, and others open new broad avenues for mechanistic exploration of their relationship with growth. This work entails a unique case study for the genomic adaptations and costs of exceptionally fast growth and sheds light on the genomic signatures of fast growth in photosynthetic cells. It also provides an important resource for future studies leveraging the unique properties of C. ohadii for photosynthesis and stress response research alongside their utilization for synthetic biology and biotechnology aims.

Metabolome plasticity in 241 Arabidopsis thaliana accessions reveals evolutionary cold adaptation processes.

Acclimation and adaptation of metabolism to a changing environment are key processes for plant survival and reproductive success. In the present study, 241 natural accessions of Arabidopsis (Arabidopsis thaliana) were grown under two different temperature regimes, 16 °C and 6 °C, and growth parameters were recorded, together with metabolite profiles, to investigate the natural genome × environment effects on metabolome variation. The plasticity of metabolism, which was captured by metabolic distance measures, varied considerably between accessions. Both relative growth rates and metabolic distances were predictable by the underlying natural genetic variation of accessions. Applying machine learning methods, climatic variables of the original growth habitats were tested for their predictive power of natural metabolic variation among accessions. We found specifically habitat temperature during the first quarter of the year to be the best predictor of the plasticity of primary metabolism, indicating habitat temperature as the causal driver of evolutionary cold adaptation processes. Analyses of epigenome- and genome-wide associations revealed accession-specific differential DNA-methylation levels as potentially linked to the metabolome and identified FUMARASE2 as strongly associated with cold adaptation in Arabidopsis accessions. These findings were supported by calculations of the biochemical Jacobian matrix based on variance and covariance of metabolomics data, which revealed that growth under low temperatures most substantially affects the accession-specific plasticity of fumarate and sugar metabolism. Our findings indicate that the plasticity of metabolic regulation is predictable from the genome and epigenome and driven evolutionarily by Arabidopsis growth habitats.

Emergence of Novel RNA-Editing Sites by Changes in the Binding Affinity of a Conserved PPR Protein.

RNA editing converts cytidines to uridines in plant organellar transcripts. Editing typically restores codons for conserved amino acids. During evolution, specific C-to-U editing sites can be lost from some plant lineages by genomic C-to-T mutations. By contrast, the emergence of novel editing sites is less well documented. Editing sites are recognized by pentatricopeptide repeat (PPR) proteins with high specificity. RNA recognition by PPR proteins is partially predictable, but prediction is often inadequate for PPRs involved in RNA editing. Here we have characterized evolution and recognition of a recently gained editing site. We demonstrate that changes in the RNA recognition motifs that are not explainable with the current PPR code allow an ancient PPR protein, QED1, to uniquely target the ndhB-291 site in Brassicaceae. When expressed in tobacco, the Arabidopsis QED1 edits 33 high-confident off-target sites in chloroplasts and mitochondria causing a spectrum of mutant phenotypes. By manipulating the relative expression levels of QED1 and ndhB-291, we show that the target specificity of the PPR protein depends on the RNA:protein ratio. Finally, our data suggest that the low expression levels of PPR proteins are necessary to ensure the specificity of editing site selection and prevent deleterious off-target editing.

Changing What You Like: Modifying Contour Properties Shifts Aesthetic Valuations of Scenes.

To what extent do aesthetic experiences arise from the human ability to perceive and extract meaning from visual features? Ordinary scenes, such as a beach sunset, can elicit a sense of beauty in most observers. Although it appears that aesthetic responses can be shared among humans, little is known about the cognitive mechanisms that underlie this phenomenon. We developed a contour model of aesthetics that assigns values to visual properties in scenes, allowing us to predict aesthetic responses in adults from around the world. Through a series of experiments, we manipulate contours to increase or decrease aesthetic value while preserving scene semantic identity. Contour manipulations directly shift subjective aesthetic judgments. This provides the first experimental evidence for a causal relationship between contour properties and aesthetic valuation. Our findings support the notion that visual regularities underlie the human capacity to derive pleasure from visual information.

Memorability of line drawings of scenes: the role of contour properties.

Why are some images more likely to be remembered than others? Previous work focused on the influence of global, low-level visual features as well as image content on memorability. To better understand the role of local, shape-based contours, we here investigate the memorability of photographs and line drawings of scenes. We find that the memorability of photographs and line drawings of the same scenes is correlated. We quantitatively measure the role of contour properties and their spatial relationships for scene memorability using a Random Forest analysis. To determine whether this relationship is merely correlational or if manipulating these contour properties causes images to be remembered better or worse, we split each line drawing into two half-images, one with high and the other with low predicted memorability according to the trained Random Forest model. In a new memorability experiment, we find that the half-images predicted to be more memorable were indeed remembered better, confirming a causal role of shape-based contour features, and, in particular, T junctions in scene memorability. We performed a categorization experiment on half-images to test for differential access to scene content. We found that half-images predicted to be more memorable were categorized more accurately. However, categorization accuracy for individual images was not correlated with their memorability. These results demonstrate that we can measure the contributions of individual contour properties to scene memorability and verify their causal involvement with targeted image manipulations, thereby bridging the gap between low-level features and scene semantics in our understanding of memorability.

Anger is red, sadness is blue: Emotion depictions in abstract visual art by artists and non-artists.

Through the manipulation of color and form, visual abstract art is often used to convey feelings and emotions. Here, we explored how colors and lines are used to express basic emotions and whether non-artists express emotions through art in similar ways as trained artists. Both artists and non-artists created abstract color drawings and line drawings depicting six emotions (i.e., anger, disgust, fear, joy, sadness, and wonder). To test whether people represented basic emotions in similar ways, we computationally predicted the emotion of a given drawing by comparing it to a set of references created by averaging across all other participants' drawings within each emotion category. We found that prediction accuracy was higher for color drawings than line drawings and higher for color drawings by non-artists than by artists. In a behavioral experiment, we found that people (N = 242) could also accurately infer emotions, showing the same pattern of results as our computational predictions. Further computational analyses of the drawings revealed systematic use of certain colors and line features to depict each basic emotion (e.g., anger is generally redder and more densely drawn than other emotions, sadness is more blue and contains more vertical lines). Taken together, these results imply that abstract color and line drawings are able to convey certain emotions based on their visual features, which are also used by human observers to understand the intended emotional connotation of abstract artworks.

Neural Representations in the Prefrontal Cortex Are Task Dependent for Scene Attributes But Not for Scene Categories.

Natural scenes deliver rich sensory information about the world. Decades of research has shown that the scene-selective network in the visual cortex represents various aspects of scenes. However, less is known about how such complex scene information is processed beyond the visual cortex, such as in the prefrontal cortex. It is also unknown how task context impacts the process of scene perception, modulating which scene content is represented in the brain. In this study, we investigate these questions using scene images from four natural scene categories, which also depict two types of scene attributes, temperature (warm or cold), and sound level (noisy or quiet). A group of healthy human subjects from both sexes participated in the present study using fMRI. In the study, participants viewed scene images under two different task conditions: temperature judgment and sound-level judgment. We analyzed how these scene attributes and categories are represented across the brain under these task conditions. Our findings show that scene attributes (temperature and sound level) are only represented in the brain when they are task relevant. However, scene categories are represented in the brain, in both the parahippocampal place area and the prefrontal cortex, regardless of task context. These findings suggest that the prefrontal cortex selectively represents scene content according to task demands, but this task selectivity depends on the types of scene content: task modulates neural representations of scene attributes but not of scene categories.SIGNIFICANCE STATEMENT Research has shown that visual scene information is processed in scene-selective regions in the occipital and temporal cortices. Here, we ask how scene content is processed and represented beyond the visual brain, in the prefrontal cortex (PFC). We show that both scene categories and scene attributes are represented in PFC, with interesting differences in task dependency: scene attributes are only represented in PFC when they are task relevant, but scene categories are represented in PFC regardless of the task context. Together, our work shows that scene information is processed beyond the visual cortex, and scene representation in PFC reflects how adaptively our minds extract relevant information from a scene.

Comparative transcriptomics analysis reveals a calcineurin B-like gene to positively regulate constitutive and acclimated freezing tolerance in potato.

Freezing stress is a major limiting factor in crop production. To increase frost-hardiness of crops via breeding, deciphering the genes conferring freezing-tolerance is vital. Potato cultivars (Solanum tuberosum) are generally freezing-sensitive, but some potato wild species are freezing-tolerant, including Solanum commersonii, Solanum malmeanum and Solanum acaule. However, the underlying molecular mechanisms conferring the freezing-tolerance to the wild species remain to be deciphered. In this study, five representative genotypes of the above-mentioned species with distinct freezing-tolerance were investigated. Comparative transcriptomics analysis showed that SaCBL1-like (calcineurin B-like protein) was upregulated substantially in all of the freezing-tolerant genotypes. Transgenic overexpression and known-down lines of SaCBL1-like were examined. SaCBL1-like was shown to confer freezing-tolerance without significantly impacting main agricultural traits. A functional mechanism analysis showed that SaCBL1-like increases the expression of the C-repeat binding factor-regulon as well as causes a prolonged higher expression of CBF1 after exposure to cold conditions. Furthermore, SaCBL1-like was found to only interact with SaCIPK3-1 (CBL-interacting protein kinase) among all apparent cold-responsive SaCIPKs. Our study identifies SaCBL1-like to play a vital role in conferring freezing tolerance in potato, which may provide a basis for a targeted potato breeding for frost-hardiness.

Determinants of correlated expression of transcription factors and their target genes.

While transcription factors (TFs) are known to regulate the expression of their target genes (TGs), only a weak correlation of expression between TFs and their TGs has generally been observed. As lack of correlation could be caused by additional layers of regulation, the overall correlation distribution may hide the presence of a subset of regulatory TF-TG pairs with tight expression coupling. Using reported regulatory pairs in the plant Arabidopsis thaliana along with comprehensive gene expression information and testing a wide array of molecular features, we aimed to discern the molecular determinants of high expression correlation of TFs and their TGs. TF-family assignment, stress-response process involvement, short genomic distances of the TF-binding sites to the transcription start site of their TGs, few required protein-protein-interaction connections to establish physical interactions between the TF and polymerase-II, unambiguous TF-binding motifs, increased numbers of miRNA target-sites in TF-mRNAs, and a young evolutionary age of TGs were found particularly indicative of high TF-TG correlation. The modulating roles of post-transcriptional, post-translational processes, and epigenetic factors have been characterized as well. Our study reveals that regulatory pairs with high expression coupling are associated with specific molecular determinants.

Chloroplast competition is controlled by lipid biosynthesis in evening primroses.

In most eukaryotes, organellar genomes are transmitted preferentially by the mother, but molecular mechanisms and evolutionary forces underlying this fundamental biological principle are far from understood. It is believed that biparental inheritance promotes competition between the cytoplasmic organelles and allows the spread of so-called selfish cytoplasmic elements. Those can be, for example, fast-replicating or aggressive chloroplasts (plastids) that are incompatible with the hybrid nuclear genome and therefore maladaptive. Here we show that the ability of plastids to compete against each other is a metabolic phenotype determined by extremely rapidly evolving genes in the plastid genome of the evening primrose Oenothera Repeats in the regulatory region of accD (the plastid-encoded subunit of the acetyl-CoA carboxylase, which catalyzes the first and rate-limiting step of lipid biosynthesis), as well as in ycf2 (a giant reading frame of still unknown function), are responsible for the differences in competitive behavior of plastid genotypes. Polymorphisms in these genes influence lipid synthesis and most likely profiles of the plastid envelope membrane. These in turn determine plastid division and/or turnover rates and hence competitiveness. This work uncovers cytoplasmic drive loci controlling the outcome of biparental chloroplast transmission. Here, they define the mode of chloroplast inheritance, as plastid competitiveness can result in uniparental inheritance (through elimination of the "weak" plastid) or biparental inheritance (when two similarly "strong" plastids are transmitted).

Scene wheels: Measuring perception and memory of real-world scenes with a continuous stimulus space.

Precisely characterizing mental representations of visual experiences requires careful control of experimental stimuli. Recent work leveraging such stimulus control has led to important insights; however, these findings are constrained to simple visual properties like color and line orientation. There remains a critical methodological barrier to characterizing perceptual and mnemonic representations of realistic visual experiences. Here, we introduce a novel method to systematically control visual properties of natural scene stimuli. Using generative adversarial networks (GANs), a state-of-the-art deep learning technique for creating highly realistic synthetic images, we generated scene wheels in which continuously changing visual properties smoothly transition between meaningful realistic scenes. To validate the efficacy of scene wheels, we conducted two behavioral experiments that assess perceptual and mnemonic representations attained from the scene wheels. In the perceptual validation experiment, we tested whether the continuous transition of scene images along the wheel is reflected in human perceptual similarity judgment. The perceived similarity of the scene images correspondingly decreased as distances between the images increase on the wheel. In the memory experiment, participants reconstructed to-be-remembered scenes from the scene wheels. Reconstruction errors for these scenes resemble error distributions observed in prior studies using simple stimulus properties. Importantly, perceptual similarity judgment and memory precision varied systematically with scene wheel radius. These findings suggest our novel approach offers a window into the mental representations of naturalistic visual experiences.

[Formula: see text]: ComplexOme-Structural Network Interpreter used to study spatial enrichment in metazoan ribosomes.

BACKGROUND: Upon environmental stimuli, ribosomes are surmised to undergo compositional rearrangements due to abundance changes among proteins assembled into the complex, leading to modulated structural and functional characteristics. Here, we present the ComplexOme-Structural Network Interpreter ([Formula: see text]), a computational method to allow testing whether ribosomal proteins (rProteins) that exhibit abundance changes under specific conditions are spatially confined to particular regions within the large ribosomal complex. RESULTS: [Formula: see text] translates experimentally determined structures into graphs, with nodes representing proteins and edges the spatial proximity between them. In its first implementation, [Formula: see text] considers rProteins and ignores rRNA and other objects. Spatial regions are defined using a random walk with restart methodology, followed by a procedure to obtain a minimum set of regions that cover all proteins in the complex. Structural coherence is achieved by applying weights to the edges reflecting the physical proximity between purportedly contacting proteins. The weighting probabilistically guides the random-walk path trajectory. Parameter tuning during region selection provides the option to tailor the method to specific biological questions by yielding regions of different sizes with minimum overlaps. In addition, other graph community detection algorithms may be used for the [Formula: see text] workflow, considering that they yield different sized, non-overlapping regions. All tested algorithms result in the same node kernels under equivalent regions. Based on the defined regions, available abundance change information of proteins is mapped onto the graph and subsequently tested for enrichment in any of the defined spatial regions. We applied [Formula: see text] to the cytosolic ribosome structures of Saccharomyces cerevisiae, Oryctolagus cuniculus, and Triticum aestivum using datasets with available quantitative protein abundance change information. We found that in yeast, substoichiometric rProteins depleted from translating polysomes are significantly constrained to a ribosomal region close to the tRNA entry and exit sites. CONCLUSIONS: [Formula: see text] offers a computational method to partition multi-protein complexes into structural regions and a statistical approach to test for spatial enrichments of any given subsets of proteins. [Formula: see text] is applicable to any multi-protein complex given appropriate structural and abundance-change data. [Formula: see text] is publicly available as a GitHub repository https://github.com/MSeidelFed/COSNet_i and can be installed using the python installer pip.