Functional viromic screens uncover regulatory RNA elements.

The number of sequenced viral genomes has surged recently, presenting an opportunity to understand viral diversity and uncover unknown regulatory mechanisms. Here, we conducted a screening of 30,367 viral segments from 143 species representing 96 genera and 37 families. Using a library of viral segments in 3' UTR, we identified hundreds of elements impacting RNA abundance, translation, and nucleocytoplasmic distribution. To illustrate the power of this approach, we investigated K5, an element conserved in kobuviruses, and found its potent ability to enhance mRNA stability and translation in various contexts, including adeno-associated viral vectors and synthetic mRNAs. Moreover, we identified a previously uncharacterized protein, ZCCHC2, as a critical host factor for K5. ZCCHC2 recruits the terminal nucleotidyl transferase TENT4 to elongate poly(A) tails with mixed sequences, delaying deadenylation. This study provides a unique resource for virus and RNA research and highlights the potential of the virosphere for biological discoveries.

Time-resolved systems immunology reveals a late juncture linked to fatal COVID-19.

COVID-19 exhibits extensive patient-to-patient heterogeneity. To link immune response variation to disease severity and outcome over time, we longitudinally assessed circulating proteins as well as 188 surface protein markers, transcriptome, and T cell receptor sequence simultaneously in single peripheral immune cells from COVID-19 patients. Conditional-independence network analysis revealed primary correlates of disease severity, including gene expression signatures of apoptosis in plasmacytoid dendritic cells and attenuated inflammation but increased fatty acid metabolism in CD56dimCD16hi NK cells linked positively to circulating interleukin (IL)-15. CD8+ T cell activation was apparent without signs of exhaustion. Although cellular inflammation was depressed in severe patients early after hospitalization, it became elevated by days 17-23 post symptom onset, suggestive of a late wave of inflammatory responses. Furthermore, circulating protein trajectories at this time were divergent between and predictive of recovery versus fatal outcomes. Our findings stress the importance of timing in the analysis, clinical monitoring, and therapeutic intervention of COVID-19.

The Geometry of Abstraction in the Hippocampus and Prefrontal Cortex.

The curse of dimensionality plagues models of reinforcement learning and decision making. The process of abstraction solves this by constructing variables describing features shared by different instances, reducing dimensionality and enabling generalization in novel situations. Here, we characterized neural representations in monkeys performing a task described by different hidden and explicit variables. Abstraction was defined operationally using the generalization performance of neural decoders across task conditions not used for training, which requires a particular geometry of neural representations. Neural ensembles in prefrontal cortex, hippocampus, and simulated neural networks simultaneously represented multiple variables in a geometry reflecting abstraction but that still allowed a linear classifier to decode a large number of other variables (high shattering dimensionality). Furthermore, this geometry changed in relation to task events and performance. These findings elucidate how the brain and artificial systems represent variables in an abstract format while preserving the advantages conferred by high shattering dimensionality.

Therapeutic Targeting of MLL Degradation Pathways in MLL-Rearranged Leukemia.

Chromosomal translocations of the mixed-lineage leukemia (MLL) gene with various partner genes result in aggressive leukemia with dismal outcomes. Despite similar expression at the mRNA level from the wild-type and chimeric MLL alleles, the chimeric protein is more stable. We report that UBE2O functions in regulating the stability of wild-type MLL in response to interleukin-1 signaling. Targeting wild-type MLL degradation impedes MLL leukemia cell proliferation, and it downregulates a specific group of target genes of the MLL chimeras and their oncogenic cofactor, the super elongation complex. Pharmacologically inhibiting this pathway substantially delays progression, and it improves survival of murine leukemia through stabilizing wild-type MLL protein, which displaces the MLL chimera from some of its target genes and, therefore, relieves the cellular oncogenic addiction to MLL chimeras. Stabilization of MLL provides us with a paradigm in the development of therapies for aggressive MLL leukemia and perhaps for other cancers caused by translocations.

Interferon gamma constrains type 2 lymphocyte niche boundaries during mixed inflammation.

Allergic immunity is orchestrated by group 2 innate lymphoid cells (ILC2s) and type 2 helper T (Th2) cells prominently arrayed at epithelial- and microbial-rich barriers. However, ILC2s and Th2 cells are also present in fibroblast-rich niches within the adventitial layer of larger vessels and similar boundary structures in sterile deep tissues, and it remains unclear whether they undergo dynamic repositioning during immune perturbations. Here, we used thick-section quantitative imaging to show that allergic inflammation drives invasion of lung and liver non-adventitial parenchyma by ILC2s and Th2 cells. However, during concurrent type 1 and type 2 mixed inflammation, IFNγ from broadly distributed type 1 lymphocytes directly blocked both ILC2 parenchymal trafficking and subsequent cell survival. ILC2 and Th2 cell confinement to adventitia limited mortality by the type 1 pathogen Listeria monocytogenes. Our results suggest that the topography of tissue lymphocyte subsets is tightly regulated to promote appropriately timed and balanced immunity.

Arginine-Enriched Mixed-Charge Domains Provide Cohesion for Nuclear Speckle Condensation.

Low-complexity protein domains promote the formation of various biomolecular condensates. However, in many cases, the precise sequence features governing condensate formation and identity remain unclear. Here, we investigate the role of intrinsically disordered mixed-charge domains (MCDs) in nuclear speckle condensation. Proteins composed exclusively of arginine-aspartic acid dipeptide repeats undergo length-dependent condensation and speckle incorporation. Substituting arginine with lysine in synthetic and natural speckle-associated MCDs abolishes these activities, identifying a key role for multivalent contacts through arginine's guanidinium ion. MCDs can synergize with a speckle-associated RNA recognition motif to promote speckle specificity and residence. MCD behavior is tunable through net-charge: increasing negative charge abolishes condensation and speckle incorporation. Contrastingly, increasing positive charge through arginine leads to enhanced condensation, speckle enlargement, decreased splicing factor mobility, and defective mRNA export. Together, these results identify key sequence determinants of MCD-promoted speckle condensation and link the dynamic material properties of speckles with function in mRNA processing.

Nucleosome Turnover Regulates Histone Methylation Patterns over the Genome.

Recent studies have indicated that nucleosome turnover is rapid, occurring several times per cell cycle. To access the effect of nucleosome turnover on the epigenetic landscape, we investigated H3K79 methylation, which is produced by a single methyltransferase (Dot1l) with no known demethylase. Using chemical-induced proximity (CIP), we find that the valency of H3K79 methylation (mono-, di-, and tri-) is determined by nucleosome turnover rates. Furthermore, propagation of this mark is predicted by nucleosome turnover simulations over the genome and accounts for the asymmetric distribution of H3K79me toward the transcriptional unit. More broadly, a meta-analysis of other conserved histone modifications demonstrates that nucleosome turnover models predict both valency and chromosomal propagation of methylation marks. Based on data from worms, flies, and mice, we propose that the turnover of modified nucleosomes is a general means of propagation of epigenetic marks and a determinant of methylation valence.

The origins of unpredictability in life outcome prediction tasks.

Why are some life outcomes difficult to predict? We investigated this question through in-depth qualitative interviews with 40 families sampled from a multidecade longitudinal study. Our sampling and interviewing process was informed by the earlier efforts of hundreds of researchers to predict life outcomes for participants in this study. The qualitative evidence we uncovered in these interviews combined with a mathematical decomposition of prediction error led us to create a conceptual framework. Our specific evidence and our more general framework suggest that unpredictability should be expected in many life outcome prediction tasks, even in the presence of complex algorithms and large datasets. Our work provides a foundation for future empirical and theoretical work on unpredictability in human lives.

Reaction amplification with a gain: Triplet exciton-mediated quantum chain using mixed crystals with a tailor-made triplet sensitizer.

Photochemical valence bond isomerization of a crystalline Dewar benzene (DB) diacid monoanion salt with an acetophenone-linked piperazinium cation that serves as an intramolecular triplet energy sensitizer (DB-AcPh-Pz) exhibits a quantum chain reaction with as many as 450 product molecules per photon absorbed (Φ ≈ 450). By contrast, isomorphous crystals of the DB diacid monosalt of an ethylbenzene-linked piperazinium (DB-EtPh-Pz) lacking a triplet sensitizer showed a less impressive quantum yield of ca. Φ ≈ 22. To establish the critical importance of a triplet excited state carrier in the adiabatic photochemical reaction we prepared mixed crystals with DB-AcPh-Pz as a dilute triplet sensitizer guest in crystals of DB-EtPh-Pz. As expected from their high structural similarities, solid solutions were easily formed with the triplet sensitizer salt in the range of 0.1 to 10%. Experiments carried out under conditions where light is absorbed by the triplet sensitizer-linked DB-AcPh-Pz can be used to initiate a triplet state adiabatic reaction from 3DB-AcPh-Pz to 3HB*-AcPh-Pz, which can serve as a chain carrier and transfer energy to an unreacted DB-EtPh-Pz where exciton delocalization in the crystalline solid solution can help carry out an efficient energy transfer and enable a quantum chain employing the photoproduct as a triplet chain carrier. Excitation of mixed crystals with as little as 0.1% triplet sensitizer resulted in an extraordinarily high quantum yield Φ ≈ 517.

Unraveling the molecular mechanism for enhanced gas adsorption in mixed-metal MOFs via solid-state NMR spectroscopy.

The incorporation of multiple metal ions in metal-organic frameworks (MOFs) through one-pot synthesis can induce unique properties originating from specific atomic-scale spatial apportionment, but the extraction of this crucial information poses challenges. Herein, nondestructive solid-state NMR spectroscopy was used to discern the atomic-scale metal apportionment in a series of bulk Mg1-xCox-MOF-74 samples via identification and quantification of eight distinct arrangements of Mg/Co ions labeled with a 13C-carboxylate, relative to Co content. Due to the structural characteristics of metal-oxygen chains, the number of metal permutations is infinite for Mg1-xCox-MOF-74, making the resolution of atomic-scale metal apportionment particularly challenging. The results were then employed in density functional theory calculations to unravel the molecular mechanism underlying the macroscopic adsorption properties of several industrially significant gases. It is found that the incorporation of weak adsorption sites (Mg2+ for CO and Co2+ for CO2 adsorption) into the MOF structure counterintuitively boosts the gas adsorption energy on strong sites (Co2+ for CO and Mg2+ for CO2 adsorption). Such effect is significant even for Co2+ remote from Mg2+ in the metal-oxygen chain, resulting in a greater enhancement of CO adsorption across a broad composition range, while the enhancement of CO2 adsorption is restricted to Mg2+ with adjacent Co2+. Dynamic breakthrough measurements unambiguously verified the trend in gas adsorption as a function of metal composition. This research thus illuminates the interplay between atomic-scale structures and macroscopic gas adsorption properties in mixed-metal MOFs and derived materials, paving the way for developing superior functional materials.

Joint models reveal genetic architecture of pubertal stage transitions and their association with BMI in admixed Chilean population.

Early or late pubertal onset can lead to disease in adulthood, including cancer, obesity, type 2 diabetes, metabolic disorders, bone fractures, and psychopathologies. Thus, knowing the age at which puberty is attained is crucial as it can serve as a risk factor for future diseases. Pubertal development is divided into five stages of sexual maturation in boys and girls according to the standardized Tanner scale. We performed genome-wide association studies (GWAS) on the "Growth and Obesity Chilean Cohort Study" cohort composed of admixed children with mainly European and Native American ancestry. Using joint models that integrate time-to-event data with longitudinal trajectories of body mass index (BMI), we identified genetic variants associated with phenotypic transitions between pairs of Tanner stages. We identified $42$ novel significant associations, most of them in boys. The GWAS on Tanner $3\rightarrow 4$ transition in boys captured an association peak around the growth-related genes LARS2 and LIMD1 genes, the former of which causes ovarian dysfunction when mutated. The associated variants are expression and splicing Quantitative Trait Loci regulating gene expression and alternative splicing in multiple tissues. Further, higher individual Native American genetic ancestry proportions predicted a significantly earlier puberty onset in boys but not in girls. Finally, the joint models identified a longitudinal BMI parameter significantly associated with several Tanner stages' transitions, confirming the association of BMI with pubertal timing.

Tree-based QTL mapping with expected local genetic relatedness matrices.

Understanding the genetic basis of complex phenotypes is a central pursuit of genetics. Genome-wide association studies (GWASs) are a powerful way to find genetic loci associated with phenotypes. GWASs are widely and successfully used, but they face challenges related to the fact that variants are tested for association with a phenotype independently, whereas in reality variants at different sites are correlated because of their shared evolutionary history. One way to model this shared history is through the ancestral recombination graph (ARG), which encodes a series of local coalescent trees. Recent computational and methodological breakthroughs have made it feasible to estimate approximate ARGs from large-scale samples. Here, we explore the potential of an ARG-based approach to quantitative-trait locus (QTL) mapping, echoing existing variance-components approaches. We propose a framework that relies on the conditional expectation of a local genetic relatedness matrix (local eGRM) given the ARG. Simulations show that our method is especially beneficial for finding QTLs in the presence of allelic heterogeneity. By framing QTL mapping in terms of the estimated ARG, we can also facilitate the detection of QTLs in understudied populations. We use local eGRM to analyze two chromosomes containing known body size loci in a sample of Native Hawaiians. Our investigations can provide intuition about the benefits of using estimated ARGs in population- and statistical-genetic methods in general.

Scalable mixed model methods for set-based association studies on large-scale categorical data analysis and its application to exome-sequencing data in UK Biobank.

The ongoing release of large-scale sequencing data in the UK Biobank allows for the identification of associations between rare variants and complex traits. SAIGE-GENE+ is a valid approach to conducting set-based association tests for quantitative and binary traits. However, for ordinal categorical phenotypes, applying SAIGE-GENE+ with treating the trait as quantitative or binarizing the trait can cause inflated type I error rates or power loss. In this study, we propose a scalable and accurate method for rare-variant association tests, POLMM-GENE, in which we used a proportional odds logistic mixed model to characterize ordinal categorical phenotypes while adjusting for sample relatedness. POLMM-GENE fully utilizes the categorical nature of phenotypes and thus can well control type I error rates while remaining powerful. In the analyses of UK Biobank 450k whole-exome-sequencing data for five ordinal categorical traits, POLMM-GENE identified 54 gene-phenotype associations.

SpaNCMG: improving spatial domains identification of spatial transcriptomics using neighborhood-complementary mixed-view graph convolutional network.

The advancement of spatial transcriptomics (ST) technology contributes to a more profound comprehension of the spatial properties of gene expression within tissues. However, due to challenges of high dimensionality, pronounced noise and dynamic limitations in ST data, the integration of gene expression and spatial information to accurately identify spatial domains remains challenging. This paper proposes a SpaNCMG algorithm for the purpose of achieving precise spatial domain description and localization based on a neighborhood-complementary mixed-view graph convolutional network. The algorithm enables better adaptation to ST data at different resolutions by integrating the local information from KNN and the global structure from r-radius into a complementary neighborhood graph. It also introduces an attention mechanism to achieve adaptive fusion of different reconstructed expressions, and utilizes KPCA method for dimensionality reduction. The application of SpaNCMG on five datasets from four sequencing platforms demonstrates superior performance to eight existing advanced methods. Specifically, the algorithm achieved highest ARI accuracies of 0.63 and 0.52 on the datasets of the human dorsolateral prefrontal cortex and mouse somatosensory cortex, respectively. It accurately identified the spatial locations of marker genes in the mouse olfactory bulb tissue and inferred the biological functions of different regions. When handling larger datasets such as mouse embryos, the SpaNCMG not only identified the main tissue structures but also explored unlabeled domains. Overall, the good generalization ability and scalability of SpaNCMG make it an outstanding tool for understanding tissue structure and disease mechanisms. Our codes are available at https://github.com/ZhihaoSi/SpaNCMG.

DeepIDA-GRU: a deep learning pipeline for integrative discriminant analysis of cross-sectional and longitudinal multiview data with applications to inflammatory bowel disease classification.

Biomedical research now commonly integrates diverse data types or views from the same individuals to better understand the pathobiology of complex diseases, but the challenge lies in meaningfully integrating these diverse views. Existing methods often require the same type of data from all views (cross-sectional data only or longitudinal data only) or do not consider any class outcome in the integration method, which presents limitations. To overcome these limitations, we have developed a pipeline that harnesses the power of statistical and deep learning methods to integrate cross-sectional and longitudinal data from multiple sources. In addition, it identifies key variables that contribute to the association between views and the separation between classes, providing deeper biological insights. This pipeline includes variable selection/ranking using linear and nonlinear methods, feature extraction using functional principal component analysis and Euler characteristics, and joint integration and classification using dense feed-forward networks for cross-sectional data and recurrent neural networks for longitudinal data. We applied this pipeline to cross-sectional and longitudinal multiomics data (metagenomics, transcriptomics and metabolomics) from an inflammatory bowel disease (IBD) study and identified microbial pathways, metabolites and genes that discriminate by IBD status, providing information on the etiology of IBD. We conducted simulations to compare the two feature extraction methods.

FastBiCmrMLM: a fast and powerful compressed variance component mixed logistic model for big genomic case-control genome-wide association study.

Large sample datasets have been regarded as the primary basis for innovative discoveries and the solution to missing heritability in genome-wide association studies. However, their computational complexity cannot consider all comprehensive effects and all polygenic backgrounds, which reduces the effectiveness of large datasets. To address these challenges, we included all effects and polygenic backgrounds in a mixed logistic model for binary traits and compressed four variance components into two. The compressed model combined three computational algorithms to develop an innovative method, called FastBiCmrMLM, for large data analysis. These algorithms were tailored to sample size, computational speed, and reduced memory requirements. To mine additional genes, linkage disequilibrium markers were replaced by bin-based haplotypes, which are analyzed by FastBiCmrMLM, named FastBiCmrMLM-Hap. Simulation studies highlighted the superiority of FastBiCmrMLM over GMMAT, SAIGE and fastGWA-GLMM in identifying dominant, small α (allele substitution effect), and rare variants. In the UK Biobank-scale dataset, we demonstrated that FastBiCmrMLM could detect variants as small as 0.03% and with α ≈ 0. In re-analyses of seven diseases in the WTCCC datasets, 29 candidate genes, with both functional and TWAS evidence, around 36 variants identified only by the new methods, strongly validated the new methods. These methods offer a new way to decipher the genetic architecture of binary traits and address the challenges outlined above.

Controlling swelling in mixed transport polymers through alkyl side-chain physical cross-linking.

Semiconducting conjugated polymers bearing glycol side chains can simultaneously transport both electronic and ionic charges with high charge mobilities, making them ideal electrode materials for a range of bioelectronic devices. However, heavily glycolated conjugated polymer films have been observed to swell irreversibly when subjected to an electrochemical bias in an aqueous electrolyte. The excessive swelling can lead to the degradation of their microstructure, and subsequently reduced device performance. An effective strategy to control polymer film swelling is to copolymerize glycolated repeat units with a fraction of monomers bearing alkyl side chains, although the microscopic mechanism that constrains swelling is unknown. Here we investigate, experimentally and computationally, a series of archetypal mixed transporting copolymers with varying ratios of glycolated and alkylated repeat units. Experimentally we observe that exchanging 10% of the glycol side chains for alkyl leads to significantly reduced film swelling and an increase in electrochemical stability. Through molecular dynamics simulation of the amorphous phase of the materials, we observe the formation of polymer networks mediated by alkyl side-chain interactions. When in the presence of water, the network becomes increasingly connected, counteracting the volumetric expansion of the polymer film.

Generational differences in mental health trends in the twenty-first century.

Given the observed deterioration in mental health among Australians over the past decade, this study investigates to what extent this differs in people born in different decades-i.e., possible birth cohort differences in the mental health of Australians. Using 20 y of data from a large, nationally representative panel survey (N = 27,572), we find strong evidence that cohort effects are driving the increase in population-level mental ill-health. Deteriorating mental health is particularly pronounced among people born in the 1990s and seen to a lesser extent among the 1980s cohort. There is little evidence that mental health is worsening with age for people born prior to the 1980s. The findings from this study highlight that it is the poorer mental health of Millennials that is driving the apparent deterioration in population-level mental health. Understanding the context and changes in society that have differentially affected younger people may inform efforts to ameliorate this trend and prevent it continuing for emerging cohorts.

Strong enhancement of magnetic ordering temperature and structural/valence transitions in EuPd3S4 under high pressure.

We present a comprehensive study of the inhomogeneous mixed-valence compound, EuPd3S4, by electrical transport, X-ray diffraction, time-domain 151Eu synchrotron Mössbauer spectroscopy, and X-ray absorption spectroscopy measurements under high pressure. Electrical transport measurements show that the antiferromagnetic ordering temperature, TN, increases rapidly from 2.8 K at ambient pressure to 23.5 K at ~19 GPa and plateaus between ~19 and ~29 GPa after which no anomaly associated with TN is detected. A pressure-induced first-order structural transition from cubic to tetragonal is observed, with a rather broad coexistence region (~20 GPa to ~30 GPa) that corresponds to the TN plateau. Mössbauer spectroscopy measurements show a clear valence transition from approximately 50:50 Eu2+:Eu3+ to fully Eu3+ at ~28 GPa, consistent with the vanishing of the magnetic order at the same pressure. X-ray absorption data show a transition to a fully trivalent state at a similar pressure. Our results show that pressure first greatly enhances TN, most likely via enhanced hybridization between the Eu 4f states and the conduction band, and then, second, causes a structural phase transition that coincides with the conversion of the europium to a fully trivalent state.

Mixed Selectivity Coding of Content-Temporal Detail by Dorsomedial Posterior Parietal Neurons.

The dorsomedial posterior parietal cortex (dmPPC) is part of a higher-cognition network implicated in elaborate processes underpinning memory formation, recollection, episode reconstruction, and temporal information processing. Neural coding for complex episodic processing is however under-documented. Here, we recorded extracellular neural activities from three male rhesus macaques (Macaca mulatta) and revealed a set of neural codes of "neuroethogram" in the primate parietal cortex. Analyzing neural responses in macaque dmPPC to naturalistic videos, we discovered several groups of neurons that are sensitive to different categories of ethogram items, low-level sensory features, and saccadic eye movement. We also discovered that the processing of category and feature information by these neurons is sustained by the accumulation of temporal information over a long timescale of up to 30 s, corroborating its reported long temporal receptive windows. We performed an additional behavioral experiment with additional two male rhesus macaques and found that saccade-related activities could not account for the mixed neuronal responses elicited by the video stimuli. We further observed monkeys' scan paths and gaze consistency are modulated by video content. Taken altogether, these neural findings explain how dmPPC weaves fabrics of ongoing experiences together in real time. The high dimensionality of neural representations should motivate us to shift the focus of attention from pure selectivity neurons to mixed selectivity neurons, especially in increasingly complex naturalistic task designs.