Synthetic molecular cage receptors for carbohydrate recognition
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The transcriptomic architecture of common cancers reflects synthetic lethal interactions
To maintain cell fitness, deleterious genetic alterations are buffered by compensatory changes in additional genes. In cancer, buffering processes could be targeted by synthetic lethality. However, despite the large-scale identification of synthetic lethal effects in preclinical models, evidence that these operate clinically is limited. This impedes the application of synthetic lethal approaches. By integrating molecular profiling data from >9,000 cancers with synthetic lethal screens, we show that transcriptomic buffering of tumor suppressor gene (TSG) loss by hyperexpression of synthetic lethal partners is a common phenomenon, extending to multiple TSGs and histotypes. Transcriptomic buffering is also notable in cancers that phenocopy TSG loss, such as BRCAness cancers, where expression of BRCA1/2 synthetic lethal genes correlates with clinical outcome. Synthetic lethal genes that exhibit transcriptomic buffering also represent more robust synthetic lethal effects. These observations have implications for understanding how tumor cells tolerate TSG loss, in part explain transcriptomic architectures in cancer and provide insight into target selection.
Central amygdala somatostatin neurons modulate stress-induced sleep-onset insomnia
Sleep-onset insomnia, characterized by difficulty falling asleep, is linked to increased health risks. Previous studies have shown that the central amygdala (CeA) plays a crucial role in stress regulation, with the somatostatin neurons in the CeA (CeASST+) involved in adaptive stress responses. However, the role of CeASST+ neurons in stress-induced sleep-onset insomnia remains unclear. In this study, we found that the activity of CeASST+ neurons is closely associated with stressful events using fiber photometry in mice. Acute optogenetic activation of CeASST+ neurons induced a rapid transition from non-rapid eye movement (NREM) sleep to wakefulness. Semi-chronic optogenetic and chemogenetic activation of CeASST+ neurons led to prolonged sleep-onset latency and increased wakefulness. Chemogenetic inhibition of these neurons ameliorated sleep-onset insomnia induced by stressful stimuli, but did not affect sleep-wake behavior under physiological conditions. Collectively, our results suggested that CeASST+ neurons are a key neural substrate for modulating stress-induced sleep-onset insomnia, without influencing physiological sleep. These findings highlight CeASST+ neurons as a promising target for treating stress-related sleep-onset insomnia in clinical practice.
Carbohydrates and carbohydrate degradation gene abundance and transcription in Atlantic waters of the Arctic
Carbohydrates are chemically and structurally diverse, represent a substantial fraction of marine organic matter and are key substrates for heterotrophic microbes. Studies on carbohydrate utilisation by marine microbes have been centred on phytoplankton blooms in temperate regions, while far less is known from high-latitude waters and during later seasonal stages. Here, we combine glycan microarrays and analytical chromatography with metagenomics and metatranscriptomics to show the spatial heterogeneity in glycan distribution and potential carbohydrate utilisation by microbes in Atlantic waters of the Arctic. The composition and abundance of monomers and glycan structures in POM varied with location and depth. Complex fucose-containing sulfated polysaccharides, known to accumulate in the ocean, were consistently detected, while the more labile β-1,3-glucan exhibited a patchy distribution. Through ‘omics analysis, we identify variations in the abundance and transcription of carbohydrate degradation-related genes across samples at the community and population level. The populations contributing the most to transcription were taxonomically related to those known as primary responders and key carbohydrate degraders in temperate ecosystems, such as NS4 Marine Group and Formosa. The unique transcription profiles for these populations suggest distinct substrate utilisation potentials, with predicted glycan targets corresponding to those structurally identified in POM from the same sampling sites. By combining cutting-edge technologies and protocols, we provide insights into the carbohydrate component of the carbon cycle in the Arctic during late summer and present a high-quality dataset that will be of great value for future comparative analyses.
Interracial contact shapes racial bias in the learning of person-knowledge
During impression formation, perceptual cues facilitate social categorization while person-knowledge can promote individuation and enhance person memory. Although there is extensive literature on the cross-race recognition deficit, observed when racial ingroup faces are recognized more than outgroup faces, it is unclear whether a similar deficit exists when recalling individuating information about outgroup members. To better understand how perceived race can bias person memory, the present study examined how self-identified White perceivers’ interracial contact impacts learning of perceptual cues and person-knowledge about perceived Black and White others over five sessions of training. While person-knowledge facilitated face recognition accuracy for low-contact perceivers, face recognition accuracy did not differ for high-contact perceivers based on person-knowledge availability. The results indicate a bias towards better recall of ingroup person knowledge, which decreased for high-contact perceivers across the five-day training but simultaneously increased for low-contact perceivers. Overall, the elimination of racial bias in recall of person-knowledge among high-contact perceivers amid a persistent cross-race deficit in face recognition suggests that contact may have a greater impact on the recall of person-knowledge than on face recognition.
Modelling and design of transcriptional enhancers
Transcriptional enhancers are the genomic elements that contain critical information for the regulation of gene expression. This information is encoded through precisely arranged transcription factor-binding sites. Genomic sequence-to-function models, computational models that take DNA sequences as input and predict gene regulatory features, have become essential for unravelling the complex combinatorial rules that govern cell-type-specific activities of enhancers. These models function as biological ‘oracles’, capable of accurately predicting the activity of novel DNA sequences. By leveraging these oracles, DNA sequences can be optimized towards designed synthetic enhancers with tailored cell-type-specific or cell-state-specific activities. In parallel, generative artificial intelligence is rapidly advancing in genomics and enhancer design. Synthetic enhancers hold great promise for a wide range of biomedical applications, from facilitating fundamental research to enabling gene therapies.
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