Discovery of KT-413, a Targeted Protein Degrader of IRAK4 and IMiD Substrates Targeting MYD88 Mutant Diffuse Large B-Cell Lymphoma.

Developing therapies for the activated B-cell like (ABC) subtype of diffuse large B-cell lymphomas (DLBCL) remains an area of unmet medical need. A subset of ABC DLBCL tumors is driven by activating mutations in myeloid differentiation primary response protein 88 (MYD88), which lead to constitutive activation of interleukin-1 receptor associated kinase 4 (IRAK4) and cellular proliferation. IRAK4 signaling is driven by its catalytic and scaffolding functions, necessitating complete removal of this protein and its escape mechanisms for complete therapeutic suppression. Herein, we describe the identification and characterization of a dual-functioning molecule, KT-413 and show it efficiently degrades IRAK4 and the transcription factors Ikaros and Aiolos. KT-413 achieves concurrent degradation of these proteins by functioning as both a heterobifunctional degrader and a molecular glue. Based on the demonstrated activity and safety of KT-413 in preclinical studies, a phase 1 clinical trial in B-cell lymphomas, including MYD88 mutant ABC DLBCL, is currently underway.

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.

Experience transforms crossmodal object representations in the anterior temporal lobes.

Combining information from multiple senses is essential to object recognition, core to the ability to learn concepts, make new inferences, and generalize across distinct entities. Yet how the mind combines sensory input into coherent crossmodal representations - the crossmodal binding problem - remains poorly understood. Here, we applied multi-echo fMRI across a 4-day paradigm, in which participants learned three-dimensional crossmodal representations created from well-characterized unimodal visual shape and sound features. Our novel paradigm decoupled the learned crossmodal object representations from their baseline unimodal shapes and sounds, thus allowing us to track the emergence of crossmodal object representations as they were learned by healthy adults. Critically, we found that two anterior temporal lobe structures - temporal pole and perirhinal cortex - differentiated learned from non-learned crossmodal objects, even when controlling for the unimodal features that composed those objects. These results provide evidence for integrated crossmodal object representations in the anterior temporal lobes that were different from the representations for the unimodal features. Furthermore, we found that perirhinal cortex representations were by default biased toward visual shape, but this initial visual bias was attenuated by crossmodal learning. Thus, crossmodal learning transformed perirhinal representations such that they were no longer predominantly grounded in the visual modality, which may be a mechanism by which object concepts gain their abstraction.

Functional in vitro diversity of an intrinsically disordered plant protein during freeze-thawing is encoded by its structural plasticity.

Intrinsically disordered late embryogenesis abundant (LEA) proteins play a central role in the tolerance of plants and other organisms to dehydration brought upon, for example, by freezing temperatures, high salt concentration, drought or desiccation, and many LEA proteins have been found to stabilize dehydration-sensitive cellular structures. Their conformational ensembles are highly sensitive to the environment, allowing them to undergo conformational changes and adopt ordered secondary and quaternary structures and to participate in formation of membraneless organelles. In an interdisciplinary approach, we discovered how the functional diversity of the Arabidopsis thaliana LEA protein COR15A found in vitro is encoded in its structural repertoire, with the stabilization of membranes being achieved at the level of secondary structure and the stabilization of enzymes accomplished by the formation of oligomeric complexes. We provide molecular details on intra- and inter-monomeric helix-helix interactions, demonstrate how oligomerization is driven by an α-helical molecular recognition feature (α-MoRF) and provide a rationale that the formation of noncanonical, loosely packed, right-handed coiled-coils might be a recurring theme for homo- and hetero-oligomerization of LEA proteins.

Brief category learning distorts perceptual space for complex scenes.

The formation of categories is known to distort perceptual space: representations are pushed away from category boundaries and pulled toward categorical prototypes. This phenomenon has been studied with artificially constructed objects, whose feature dimensions are easily defined and manipulated. How such category-induced perceptual distortions arise for complex, real-world scenes, however, remains largely unknown due to the technical challenge of measuring and controlling scene features. We address this question by generating realistic scene images from a high-dimensional continuous space using generative adversarial networks and using the images as stimuli in a novel learning task. Participants learned to categorize the scene images along arbitrary category boundaries and later reconstructed the same scenes from memory. Systematic biases in reconstruction errors closely tracked each participant's subjective category boundaries. These findings suggest that the perception of global scene properties is warped to align with a newly learned category structure after only a brief learning experience.

The thylakoid proton antiporter KEA3 regulates photosynthesis in response to the chloroplast energy status.

Plant photosynthesis contains two functional modules, the light-driven reactions in the thylakoid membrane and the carbon-fixing reactions in the chloroplast stroma. In nature, light availability for photosynthesis often undergoes massive and rapid fluctuations. Efficient and productive use of such variable light supply requires an instant crosstalk and rapid synchronization of both functional modules. Here, we show that this communication involves the stromal exposed C-terminus of the thylakoid K+-exchange antiporter KEA3, which regulates the ΔpH across the thylakoid membrane and therefore pH-dependent photoprotection. By combining in silico, in vitro, and in vivo approaches, we demonstrate that the KEA3 C-terminus senses the energy state of the chloroplast in a pH-dependent manner and regulates transport activity in response. Together our data pinpoint a regulatory feedback loop by which the stromal energy state orchestrates light capture and photoprotection via multi-level regulation of KEA3.

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.

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.

Shape-Based Measures Improve Scene Categorization.

Converging evidence indicates that deep neural network models that are trained on large datasets are biased toward color and texture information. Humans, on the other hand, can easily recognize objects and scenes from images as well as from bounding contours. Mid-level vision is characterized by the recombination and organization of simple primary features into more complex ones by a set of so-called Gestalt grouping rules. While described qualitatively in the human literature, a computational implementation of these perceptual grouping rules is so far missing. In this article, we contribute a novel set of algorithms for the detection of contour-based cues in complex scenes. We use the medial axis transform (MAT) to locally score contours according to these grouping rules. We demonstrate the benefit of these cues for scene categorization in two ways: (i) Both human observers and CNN models categorize scenes most accurately when perceptual grouping information is emphasized. (ii) Weighting the contours with these measures boosts performance of a CNN model significantly compared to the use of unweighted contours. Our work suggests that, even though these measures are computed directly from contours in the image, current CNN models do not appear to extract or utilize these grouping cues.

Predictions of DNA mechanical properties at a genomic scale reveal potentially new functional roles of DNA flexibility.

Mechanical properties of DNA have been implied to influence many of its biological functions. Recently, a new high-throughput method, called loop-seq, which allows measuring the intrinsic bendability of DNA fragments, has been developed. Using loop-seq data, we created a deep learning model to explore the biological significance of local DNA flexibility in a range of different species from different kingdoms. Consistently, we observed a characteristic and largely dinucleotide-composition-driven change of local flexibility near transcription start sites. In the presence of a TATA-box, a pronounced peak of high flexibility can be observed. Furthermore, depending on the transcription factor investigated, flanking-sequence-dependent DNA flexibility was identified as a potential factor influencing DNA binding. Compared to randomized genomic sequences, depending on species and taxa, actual genomic sequences were observed both with increased and lowered flexibility. Furthermore, in Arabidopsis thaliana, mutation rates, both de novo and fixed, were found to be associated with relatively rigid sequence regions. Our study presents a range of significant correlations between characteristic DNA mechanical properties and genomic features, the significance of which with regard to detailed molecular relevance awaits further theoretical and experimental exploration.

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.

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.

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.

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.

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.

Tracing the emergence of the memorability benefit.

Some visual stimuli are consistently better remembered than others across individuals, due to variations in memorability (the stimulus-intrinsic property that determines ease of encoding into visual long-term memory (VLTM)). However, it remains unclear what cognitive processes give rise to this mnemonic benefit. One possibility is that this benefit is imbued within the capacity-limited bottleneck of VLTM encoding, namely visual working memory (VWM). More precisely, memorable stimuli may be preferentially encoded into VLTM because fewer cognitive resources are required to store them in VWM (efficiency hypothesis). Alternatively, memorable stimuli may be more competitive in obtaining cognitive resources than forgettable stimuli, leading to more successful storage in VWM (competitiveness hypothesis). Additionally, the memorability benefit might emerge post-VWM, specifically, if memorable stimuli are less prone to be forgotten (i.e., are "stickier") than forgettable stimuli after they pass through the encoding bottleneck (stickiness hypothesis). To test this, we conducted two experiments to examine how memorability benefits emerge by manipulating the stimulus memorability, set size, and degree of competition among stimuli as participants encoded them in the context of a working memory task. Subsequently, their memory for the encoded stimuli was tested in a VLTM task. In the VWM task, performance was better for memorable stimuli compared to forgettable stimuli, supporting the efficiency hypothesis. In addition, we found that when in direct competition, memorable stimuli were also better at attracting limited VWM resources than forgettable stimuli, supporting the competitiveness hypothesis. However, only the efficiency advantage translated to a performance benefit in VLTM. Lastly, we found that memorable stimuli were less likely to be forgotten after they passed through the encoding bottleneck imposed by VWM, supporting the "stickiness" hypothesis. Thus, our results demonstrate that the memorability benefit develops across multiple cognitive processes.

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.

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.

Specifics of Metabolite-Protein Interactions and Their Computational Analysis and Prediction.

Computational approaches to the characterization and prediction of compound-protein interactions have a long research history and are well established, driven primarily by the needs of drug development. While, in principle, many of the computational methods developed in the context of drug development can also be applied directly to the investigation of metabolite-protein interactions, the interactions of metabolites with proteins (enzymes) are characterized by a number of particularities that result from their natural evolutionary origin and their biological and biochemical roles, as well as from a different problem setting when investigating them. In this review, these special aspects will be highlighted and recent research on them and developed computational approaches presented, along with available resources. They concern, among others, binding promiscuity, allostery, the role of posttranslational modifications, molecular steering and crowding effects, and metabolic conversion rate predictions. Recent breakthroughs in the field of protein structure prediction and newly developed machine learning techniques are being discussed as a tremendous opportunity for developing a more detailed molecular understanding of metabolism.

Characterising and dissecting human perception of scene complexity.

Humans can effortlessly assess the complexity of the visual stimuli they encounter. However, our understanding of how we do this, and the relevant factors that result in our perception of scene complexity remain unclear; especially for the natural scenes in which we are constantly immersed. We introduce several new datasets to further understanding of human perception of scene complexity. Our first dataset (VISC-C) contains 800 scenes and 800 corresponding two-dimensional complexity annotations gathered from human observers, allowing exploration for how complexity perception varies across a scene. Our second dataset, (VISC-CI) consists of inverted scenes (reflection on the horizontal axis) with corresponding complexity maps, collected from human observers. Inverting images in this fashion is associated with destruction of semantic scene characteristics when viewed by humans, and hence allows analysis of the impact of semantics on perceptual complexity. We analysed perceptual complexity from both a single-score and a two-dimensional perspective, by evaluating a set of calculable and observable perceptual features based upon grounded psychological research (clutter, symmetry, entropy and openness). We considered these factors' relationship to complexity via hierarchical regressions analyses, tested the efficacy of various neural models against our datasets, and validated our perceptual features against a large and varied complexity dataset consisting of nearly 5000 images. Our results indicate that both global image properties and semantic features are important for complexity perception. We further verified this by combining identified perceptual features with the output of a neural network predictor capable of extracting semantics, and found that we could increase the amount of explained human variance in complexity beyond that of low-level measures alone. Finally, we dissect our best performing prediction network, determining that artificial neurons learn to extract both global image properties and semantic details from scenes for complexity prediction. Based on our experimental results, we propose the "dual information" framework of complexity perception, hypothesising that humans rely on both low-level image features and high-level semantic content to evaluate the complexity of images.