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Cui, Y.

Publications and source records attributed to Cui, Y..

At least 91 records · Page 5Linked to original sources

Photochemical processes drive thermal responses of dissolved organic matter in the dark ocean

How dissolved organic matter (DOM) responds to climate warming is critical for understanding its role in future ocean carbon cycling. Here, we use a highly resolved dataset of over 800 DOM samples covering the surface waters to the deep Atlantic, Southern, and Pacific oceans to examine the changes in DOM molecular composition in response to warming water temperatures, referred to as DOM thermal responses. Towards the deep waters, the strength (i.e., overall magnitude) and diversity (i.e., variation among molecules) of these thermal responses both decline. However, these responses show opposite trends with the concentration of more recalcitrant molecules, decreasing and increasing, respectively. These contrasting trends concur with the observation that, compared to the strength of thermal responses, their diversity is more strongly explained by photochemical processes of DOM. By projecting global ocean thermal responses from 1950 to 2020 using environmental temperature, salinity and radiation, we predict that increases in the diversity of thermal responses are unexpectedly largest at deeper depths (> 1,000 m). Such increases could elevate the recalcitrant deep-ocean carbon sink by approximately 10 Tg C yr-1, which accounts for > 5% of the carbon flux reaching and persisting in the deep ocean. Our findings highlight the role of photochemical processes in imprinting DOM thermal responses, offering new insights into the future capacity of the oceanic carbon sink under global climate change.

ecology↗

Structural and systems characterization of phosphorylation on metabolic enzymes identifies sex-specific metabolic reprogramming in obesity

Coordination of adaptive metabolism through cellular signaling networks and metabolic response is essential for balanced flow of energy and homeostasis. Post-translational modifications such as phosphorylation offer a rapid, efficient, and dynamic mechanism to regulate metabolic networks. Although numerous phosphorylation sites have been identified on metabolic enzymes, much remains unknown about their contribution to enzyme function and systemic metabolism. In this study, we stratify phosphorylation sites on metabolic enzymes based on their location with respect to functional and dimerization domains. Our analysis reveals that the majority of published phosphosites are on oxidoreductases, with particular enrichment of phosphotyrosine (pY) sites in proximity to binding domains for substrates, cofactors, active sites, or dimer interfaces. We identify phosphosites altered in obesity using a high fat diet (HFD) induced obesity model coupled to multiomics, and interrogate the functional impact of pY on hepatic metabolism. HFD induced dysregulation of redox homeostasis and reductive metabolism at the phosphoproteome and metabolome level in a sex-specific manner, which was reversed by supplementing with the antioxidant butylated hydroxyanisole (BHA). Partial least squares regression (PLSR) analysis identified pY sites that predict HFD or BHA induced changes of redox metabolites. We characterize predictive pY sites on glutathione S-transferase pi 1 (GSTP1), isocitrate dehydrogenase 1 (IDH1), and uridine monophosphate synthase (UMPS) using CRISPRi-rescue and stable isotope tracing. Our analysis revealed that sites on GSTP1 and UMPS inhibit enzyme activity while the pY site on IDH1 induces activity to promote reductive carboxylation. Overall, our approach provides insight into the convergence points where cellular signaling fine-tunes metabolism. Summary StatementBy employing a multi-disciplinary approach we stratify structural features of phosphorylation sites on metabolic enzymes, map the systems level changes induced by obesity, identify key pathways with sex specific phosphoproteomic responses, and validate the functional role of phosphorylation sites for select enzymes.

biochemistry↗

SPACE: Spatially variable gene clustering adjusting for cell type effect for improved spatial domain detection.

Recent advances in spatial transcriptomics have significantly deepened our understanding of biology. A primary focus has been identifying spatially variable genes (SVGs) which are crucial for downstream tasks like spatial domain detection. Traditional methods often use all or a set number of top SVGs for this purpose. However, in diverse datasets with many SVGs, this approach may not ensure accurate results. Instead, grouping SVGs by expression patterns and using all SVG groups in downstream analysis can improve accuracy. Furthermore, classifying SVGs in this manner is akin to identifying cell type marker genes, offering valuable biological insights. The challenge lies in accurately categorizing SVGs into relevant clusters, aggravated by the absence of prior knowledge regarding the number and spectrum of spatial gene patterns. Addressing this challenge, we propose SPACE, SPatially variable gene clustering Adjusting for Cell type Effect, a framework that classifies SVGs based on their spatial patterns by adjusting for confounding effects caused by shared cell types, to improve spatial domain detection. This method does not require prior knowledge of gene cluster numbers, spatial patterns, or cell type information. Our comprehensive simulations and real data analyses demonstrate that SPACE is an efficient and promising tool for spatial transcriptomics analysis. Key PointsO_LISPACE eliminates the need for prior knowledge about the number of gene clusters, known cell types, or the quantity of SVGs to identify clusters for downstream analysis. C_LIO_LISPACE offers a method to effectively leverage SVGs for low-dimensional embedding within each cluster to improve the accuracy of spatial domain detection. C_LIO_LIThe efficiency and utility of the SPACE algorithm have been validated across multiple datasets and simulations, demonstrating its effectiveness in producing meaningful and interpretable results. C_LI

genomics↗

SARM1 orthosteric base exchange inhibitors cause subinhibitory SARM1 activation

SARM1, an octameric NADase, is a key regulator of axon degeneration and an emerging target in small molecule drug discovery to treat a wide range of neurodegenerative diseases. Recently, a structurally diverse series of adduct-forming, orthosteric SARM1 inhibitors have been discovered. Here, we show that subinhibitory concentrations of these orthosteric inhibitors, under mildly SARM1 activating conditions, cause sustained SARM1 activation. This synergistic adverse effect leads to increased nicotinamide adenine dinucleotide (NAD) consumption, neurodegeneration and release of the biomarker neurofilament-light (NfL) in cultured cortical neurons. In two distinct animal models, we found that low-dose treatment with these orthosteric SARM1 inhibitors results in increased plasma NfL and adverse events when combined with cellular stress or injury conditions. This may present a critical liability for orthosteric SARM1 inhibitors in certain patient populations.

neuroscience↗

Identifying Differential Spatial Expression Patterns across Different Slices, Conditions and Developmental Stages with Interpretable Deep Learning

Spatially resolved transcriptomics technologies enable the mapping of multiplexed gene expression profiles within tissue contexts. To explore the gene spatial patterns in complex tissues, computational methods have been developed to identify spatially variable genes within single tissue slices. However, there is a lack of methods designed to identify genes with differential spatial expression patterns (DSEPs) across multiple slices or conditions, which becomes increasingly common in complex experimental designs. The challenges include the complexity of cross-slice gene expression and spatial information modeling, scalability issues in constructing large-scale cell graphs, and mixed factors of inter-slice heterogeneity. We propose DSEP gene identification as a new task and develop River, an interpretable deep learning-based method, to solve this task. River comprises a two-branch prediction model architecture and a post-hoc attribution method to prioritize DSEP genes that explain condition differences. Rivers special design for modeling spatial-informed gene expression makes it scalable to large-scale spatial omics datasets. We proposed strategies to decouple the spatial and non-spatial components of Rivers outcomes. We validated Rivers performance using simulated datasets and applied it to identify DSEP genes/proteins in diverse biological contexts, including embryo development, diabetes-induced alterations in spermatogenesis, and lupus-induced splenic changes. In a human triple-negative breast cancer dataset, River identified generalizable survival-related DSEPs, validated across unseen patient groups. River does not rely on specific data distribution assumptions and is compatible with various spatial omics data types, making it a versatile method for analyzing complex tissue architectures across multiple biological conditions.

bioinformatics↗

Sympathetic Nervous System Overactivation Induces Colonic Eosinophil-Associated Microinflammation and Contributes to the Pathogenesis of Irritable Bowel Syndrome

ObjectiveMucosal microinflammation is a characteristic clinical manifestation of irritable bowel syndrome (IBS), and its symptoms are often triggered by psychological stress. In the present study, we aimed to investigate the impact of early life stress-associated dysfunction of the sympathetic nervous system (SNS) on mucosal immune changes in the gastrointestinal tract (GI) and its contribution to IBS pathogenesis. DesignWe utilised a traditional animal model of IBS with maternal separation (MS) and evaluated colorectal hypersensitivity, immune alterations, and SNS activity in adult rats with MS. We conducted a series of experiments to manipulate peripheral SNS activity pharmacologically and chemogenetically to explore the interaction between SNS activity and GI events. ResultsThe MS-induced IBS model exhibited visceral hypersensitivity and eosinophilic infiltration in the colonic mucosa, along with SNS overactivation. Degeneration of the SNS using 6-OHDA neurotoxin decreased eosinophil infiltration and visceral hypersensitivity in the MS model. Notably, specific chemogenetic activation of the peripheral SNS induced eosinophil infiltration in the intestinal mucosa through the noradrenergic signalling-mediated release of eotaxin-1 from mesenchymal cells. ConclusionThis study highlights the critical role of SNS overactivation in eotaxin-1-driven eosinophil infiltration in the colon, leading to the development of visceral hypersensitivity in IBS. The results provide important insights into the mechanistic links among increased sympathetic activity, mucosal microinflammation, and visceral hypersensitivity in individuals with IBS, suggesting potential therapeutic approaches. What is already known on this topicO_LIA subgroup of patients with irritable bowel syndrome (IBS) presents with microinflammation in the gastrointestinal tract (GI). C_LIO_LIEarly life stress is recognised as a major risk factor for the development of IBS in adulthood. C_LIO_LIOveractivation of the sympathetic nervous system (SNS) is frequently associated with IBS. C_LI What this study addsO_LIMaternal separation (MS) stress induces eosinophil-associated microinflammation in the colonic mucosa of adult rats. C_LIO_LIInhibition of SNS activity suppresses eosinophil infiltration and mitigates visceral hypersensitivity in the MS model. C_LIO_LINoradrenergic signalling within the peripheral sympathetic activation stimulates mesenchymal cells to release eotaxin-1, leading to substantial eosinophil-predominant immune alterations in the colon. C_LI How this study might affect research, practice, or policyO_LITreatment with fibroblast-derived eotaxin-1 and targeting eosinophil-associated microinflammation could be a potential strategy to alleviate visceral pain in patients with IBS. C_LIO_LIThe chemogenomic method specifically manipulates peripheral SNS and provides a valuable tool for future research. C_LI

neuroscience↗

SOFisher: Reinforcement Learning-Guided Experiment Designs for Spatial Omics

Spatial omics technologies enable the precise detection of proteins and RNAs at high spatial resolution. Designing spatial omics experiments requires careful consideration of "what" targets to measure and "where" to position the field of views (FOVs). Current FOV sampling strategies often involve acquiring densely sampled FOVs and stitching them together, which is time-consuming, resource-intensive, and sometimes impossible. To optimize FOV sampling strategies, we developed SOFisher, a reinforcement learning-based framework that harnesses the knowledge gained from the sequence of previously sampled FOVs to guide the selection of the next FOV position, to improve the efficiency of capturing more regions of interest. We rigorously evaluated SOFishers performance using comprehensive simulations based on real spatial datasets, and our results clearly demonstrated that SOFisher consistently outperformed the conventional approach across various metrics. SOFishers robustness and generalizability were further validated through cross-domain generalization tests and its adaptability to varying FOV sizes. On a real Alzheimers Disease (AD) dataset, SOFisher successfully guided the selection of FOVs containing neurofibrillary tangles and amyloid-{beta} plaques in both single and dual target tissue landmark scenarios. Remarkably, SOFisher-guided experiment design of spatial single-omics on limited tissue areas yielded insights into AD-related cell states, subtypes, and gene programs previously obtained through extensive spatial multi-omics experiments. SOFisher has the potential to revolutionize the experiment design of spatial biology.

bioinformatics↗

Hepatic lipopolysaccharide binding protein partially uncouples inflammation from fibrosis in MAFLD

Non-alcoholic fatty liver disease (NAFLD), recently renamed metabolic-associated fatty liver disease (MAFLD), is the most common liver disease worldwide. The progression to fibrosis, occurring against a backdrop of hepatic steatosis and inflammation, critically determines liver-related morbidity and mortality. Inflammatory processes contribute to various stages of MAFLD and thought to instigate hepatic fibrosis. For this reason, targeting inflammation has been heavily nominated as a strategy to mitigate liver fibrosis. Lipopolysaccharide binding protein (LBP) is a secreted protein that plays an established role in innate immune responses. Here, using adoptive transfer studies and tissue-specific deletion models we show that hepatocytes are the dominant contributors to circulating LBP. In a murine model of MAFLD, hepatocyte-specific deletion of LBP restrained hepatic inflammation and improved liver function abnormalities, but not measures of fibrosis. Human studies, including genetic evidence, corroborate an important role for LBP in hepatic inflammation with minimal impact on fibrosis. Collectively, our data argues against the idea that targeting hepatic inflammation is a viable approach to reducing fibrosis.

physiology↗

TS-AI: A deep learning pipeline for multimodal subject-specific parcellation with task contrasts synthesis

Accurate mapping of brain functional subregions at an individual level is crucial. Task-based functional MRI (tfMRI) captures subject-specific activation patterns during various functions and behaviors, facilitating the individual localization of functionally distinct subregions. However, acquiring high-quality tfMRI is time-consuming and resource-intensive in both scientific and clinical settings. The present study proposes a two-stage network model, TS-AI, to individualize an atlas on cortical surfaces through the prediction of tfMRI data. TS-AI first synthesizes a battery of task contrast maps for each individual by leveraging tract-wise anatomical connectivity and resting-state networks. These synthesized maps, along with feature maps of tract-wise anatomical connectivity and resting-state networks, are then fed into an end-to-end deep neural network to individualize an atlas. TS-AI enables the synthesized task contrast maps to be used in individual parcellation without the acquisition of actual task fMRI scans. In addition, a novel feature consistency loss is designed to assign vertices with similar features to the same parcel, which increases individual specificity and mitigates overfitting risks caused by the absence of individual parcellation ground truth. The individualized parcellations were validated by assessing test-retest reliability, homogeneity, and cognitive behavior prediction using diverse reference atlases and datasets, demonstrating the superior performance and generalizability of TS-AI. Sensitivity analysis yielded insights into region-specific features influencing individual variation in functional regionalization. In addition, TS-AI identified accelerated shrinkage in the medial temporal and cingulate parcels during the progression of Alzheimers disease, suggesting its potential in clinical research and applications.

neuroscience↗

Sigh generation in preBötzinger Complex

We explored neural mechanisms underlying sighing. Photostimulation of parafacial (pF) neuromedin B (NMB) or gastrin releasing peptide (GRP), or preBotzinger Complex (preBotC) NMBR or GRPR neurons elicited ectopic sighs with latency inversely related to time from preceding endogenous sigh. Of particular note, ectopic sighs could be produced without involvement of these peptides or their receptors in preBotC. Moreover, chemogenetic or optogenetic activation of preBotC SST neurons induced sighing, even in the presence of NMBR and/or GRPR antagonists. We propose that an increase in the excitability of preBotC NMBR or GRPR neurons not requiring activation of their peptide receptors activates partially overlapping pathways to generate sighs, and that preBotC SST neurons are a downstream element in the sigh generation circuit that converts normal breaths into sighs.

neuroscience↗

Scalable Condition-relevant Cell Niche Analysis of Spatial Omics Data with Taichi

Tissues are composed of heterogeneous cell niches, which can be investigated using spatial omics technologies. Large consortia have accumulated vast amounts of spatially resolved data, which typically assign slice-level condition labels without considering intra-slice heterogeneity, particularly differential cell niches that respond to certain perturbations. Here, we present Taichi, an efficient and scalable method for condition-relevant cell niche analysis that does not rely on pre-defined discrete spatial clustering. Taichi utilizes a scalable spatial co-embedding approach that effectively accounts for batch effects, incorporating advanced label refinement and graph heat diffusion techniques to explore condition-relevant cell niches across extensive multi-slice and multi-condition spatial omics datasets. Comprehensive benchmarks demonstrate Taichis ability to precisely identify condition-relevant niches under various levels of perturbations. We showcase Taichis effectiveness in accurately delineating major shifts in cell niches in a mouse model of diabetic kidney disease compared to a normal group, revealing disease-specific cell-cell interactions and spatial gene expression patterns. Furthermore, Taichi can identify key subtype-relevant niches between colorectal cancer patient groups with significantly different survival outcomes. Moreover, we demonstrate that Taichi can help discover more fine-grained clinical properties within the originally coarse-defined patient groups in large-scale tumor spatial atlases, reflecting intra-group heterogeneity obscured previously. Additionally, we combine Taichi and tensor decomposition to discover higher-order biomarkers relevant to the immunotherapy response of triple-negative breast cancer. Finally, we highlight Taichis speed and scalability by confirming its unique applicability in large-scale scenarios containing up to 16 million cells in [~] 12 minutes. Taichi provides a powerful tool for mining disease-relevant spatially resolved insights in the era of big data in spatial biology.

bioinformatics↗

Pandemic Vibrio parahaemolyticus wave succession by gene loss

While spontaneous mutation and gene acquisition are well-established drivers of pathogen adaptation, the role of gene loss remains underexplored. Here, we investigated the emergence and diversification of the pandemic clone (PC) of Vibrio parahaemolyticus through large-scale phylogenomic analysis of 8,684 global isolates. The PC rapidly acquired multiple marker genes and genomic islands, subsequently diverging into successive sublineages mediating independent waves of cross-country transmission, as also observed in V. cholerae. Wave succession in the last two decades was driven by loss of putrescine utilization (Puu) genes, conferring phenotypic advantages for environmental adaptation (enhanced biofilm formation) and human transmission (increased cell adhesion/intestinal colonization, reduced virulence), consistent with the virulence trade-off hypothesis. We identified Puu-gene loss in multiple bacterial genera, with effects on biofilm and adhesion replicated in V. cholerae and Escherichia coli, suggesting convergent evolution and universal phenotypic effects. Our results highlight the indispensable role of gene loss in bacterial pathogen adaptation.

microbiology↗

A safe insect-based Chikungunya fever vaccine affords rapid and durable protection in cynomolgus macaques

Chikungunya virus (CHIKV), which induces chikungunya fever and chronic arthralgia, is an emerging public health concern. Safe and efficient vaccination strategies are needed to prevent or mitigate virus-associated acute and chronic morbidities for preparation of future outbreaks. Eilat (EILV)/CHIKV, a chimeric alphavirus which contains the structural proteins of CHIKV and the non-structural proteins of EILV, does not replicate in vertebrate cells. The chimeric virus was previously reported to induce protective adaptive immunity in mice. Here, we assessed the capacity of the virus to induce quick and durable protection in cynomolgus macaques. EILV/CHIKV protected macaques from wild-type (WT) CHIKV infection one year after a single dose vaccination. Transcriptome and in vitro functional analyses reveal that the chimeric virus triggered toll-like receptor signaling and T cell, memory B cell and antibody responses in a dose-dependent manner. Notably, EILV/CHIKV preferentially induced more durable, robust, and broader repertoire of CHIKV-specific T cell responses, compared to a live attenuated CHIKV 181/25 vaccine strain. The insect-based chimeric virus did not cause skin hypersensitivity reactions in guinea pigs sensitized to mosquito bites. Furthermore, EILV/CHIKV induced strong neutralization antibodies and protected cynomolgus macaques from WT CHIKV infection within six days post vaccination. Transcriptome analysis also suggest that the chimeric virus induction of multiple innate immune pathways, including Toll-like receptor signaling, type I IFN and IL-12 signaling, antigen presenting cell activation, and NK receptor signaling. Our findings suggest that EILV/CHIKV is a safe, highly efficacious vaccine, and provides both rapid and long-lasting protection in cynomolgus macaques.

microbiology↗

Circulating, cell-free methylated DNA indicates cellular sources of allograft injury after liver transplant

Post-transplant complications reduce allograft and recipient survival. Current approaches for detecting allograft injury non-invasively are limited and do not differentiate between cellular mechanisms. Here, we monitor cellular damages after liver transplants from cell-free DNA (cfDNA) fragments released from dying cells into the circulation. We analyzed 130 blood samples collected from 44 patients at different time points after transplant. Sequence-based methylation of cfDNA fragments were mapped to patterns established to identify cell types in different organs. For liver cell types DNA methylation patterns and multi-omic data integration show distinct enrichment in open chromatin and regulatory regions functionally important for the respective cell types. We find that multi-tissue cellular damages post-transplant recover in patients without allograft injury during the first post-operative week. However, sustained elevation of hepatocyte and biliary epithelial cfDNA beyond the first week indicates early-onset allograft injury. Further, cfDNA composition differentiates amongst causes of allograft injury indicating the potential for non-invasive monitoring and timely intervention.

molecular biology↗

Phospholipase C β4 promotes RANKL-dependent osteoclastogenesis by interacting with MKK3 and p38 MAPK

Phospholipase C beta (PLC{beta}) exerts diverse biological processes, including inflammatory responses and neurogenesis; however, its role in bone cell function is largely unknown. Among the PLC{beta} isoforms ({beta}1-{beta}4), we found that PLC{beta}4 was most highly upregulated during osteoclastogenesis. In this study, we used global knockout and osteoclast lineage-specific PLC{beta}4 conditional knockout (LysM-PLC{beta}4-/-) mice and demonstrated that PLC{beta}4 is a crucial regulator of receptor activator of nuclear factor {kappa}B ligand (RANKL)-induced osteoclast differentiation. Deletion of PLC{beta}4, both globally and in the osteoclast lineage, resulted in a significant reduction in osteoclast formation and the downregulation of osteoclast marker genes. Importantly, LysM-PLC{beta}4-/- male mice exhibited greater bone mass and a lower number of osteoclasts in vivo than their wild-type littermates, without altering osteoblast function. Mechanistically, we found that PLC{beta}4 forms a complex with p38 mitogen-activated protein kinase (MAPK) and MAPK kinase 3 (MKK3) in response to RANKL, thereby modulating p38 activation. An immunofluorescence assay further confirmed the colocalization of PLC{beta}4 with p38 after RANKL exposure. Moreover, p38 activation rescued the impaired osteoclast formation and restored the reduced p38 phosphorylation due to PLC{beta}4 deficiency. Thus, our findings reveal that PLC{beta}4 controls osteoclastogenesis via the RANKL-dependent MKK3-p38 MAPK pathway, and PLC{beta}4 may be a potential therapeutic candidate for bone diseases such as osteoporosis.

cell biology↗

DenoiseST: A dual-channel unsupervised deep learning-based denoising method to identify spatial domains and functionally variable genes in spatial transcriptomics

Spatial transcriptomics provides a unique opportunity for understanding cellular organization and function in a spatial context. However, spatial transcriptome exists the problem of dropout noise, exposing a major challenge for accurate downstream data analysis. Here, we proposed DenoiseST, a dual-channel unsupervised adaptive deep learning-based denoising method for data imputing, clustering, and identifying functionally variable genes in spatial transcriptomics. To leverage spatial information and gene expression profiles, we proposed a dual-channel joint learning strategy with graph convolutional networks to sufficiently explore both linear and nonlinear representation embeddings in an unsupervised manner, enhancing the discriminative information learning ability from the global perspectives of data distributions. In particular, DenoiseST enables the adaptively fitting of different gene distributions to the clustered domains and employs tissue-level spatial information to accurately identify functionally variable genes with different spatial resolutions, revealing their enrichment in corresponding gene pathways. Extensive validations on a total of 18 real spatial transcriptome datasets show that DenoiseST obtains excellent performance and results on brain tissue datasets indicate it outperforms the state-of-the-art methods when handling artificial dropout noise with a remarkable margin of [~]15%, demonstrating its effectiveness and robustness. Case study results demonstrate that when applied to identify biological structural regions on human breast cancer spatial transcriptomic datasets, DenoiseST successfully detected biologically significant immune-related structural regions, which are subsequently validated through Gene Ontology (GO), cell-cell communication, and survival analysis. In conclusion, we expect that DenoiseST is a novel and efficient method for spatial transcriptome analysis, offering unique insights into spatial organization and function.

bioinformatics↗

Macroscale brain states support the control of semantic cognition

Understanding how the human brain adapts to varying cognitive demands is crucial in neuroscience. Here, we examined how networks involved in controlled semantic retrieval reconfigure themselves to generate neurocognitive states appropriate to different task contexts. We parametrically varied the demands of two semantic tasks - global association and feature matching judgments - and contrasted these effects of cognitive control with those of non-semantic tasks. We then characterized these effects on the cortical surface and within a whole-brain state space, anchored by the top three dimensions of intrinsic connectivity. Our results revealed that demanding semantic association tasks elicited more activation in the anterior regions of the prefrontal and temporal cortex. In contrast, difficult semantic feature matching tasks produced more posterior activation, aligning closely with regions engaged during multiple demanding non-semantic tasks. In both semantic feature matching and non-semantic contexts, the difficulty effects were situated towards the controlled end of a dimension capturing functional separation between cognitive control and default mode regions. Conversely, in semantic association tasks, the difficulty effects elicited similar responses across both cognitive control and default mode networks. Furthermore, controlled association and non-semantic control were located towards the heteromodal end of a heteromodal- unimodal dimension, while semantic feature matching involved a brain state that was more visual and unimodal. These findings demonstrate that a variety of brain states underpin controlled cognition. Specifically, cognitive control regions interact with heteromodal semantic knowledge system to identify contextually relevant conceptual overlaps (e.g., associating DOG with BEACH), and separate from heteromodal memory regions for modality-specific conceptual overlaps (e.g., connecting DALMATIAN with BLACK AND WHITE).

neuroscience↗

MR-SPLIT: a novel method to address selection and weak instrument bias in one-sample Mendelian randomization studies

Mendelian Randomization (MR) is a widely embraced approach to assess causality in epidemiological studies. Two-stage least squares (2SLS) method is a predominant technique in MR analysis. However, it can lead to biased estimates when instrumental variables (IVs) are weak. Moreover, the issue of the winners curse could emerge when utilizing the same dataset for both IV selection and causal effect estimation, leading to biased estimates of causal effects and high false positives. Focusing on one-sample MR analysis, this paper introduces a novel method termed as Mendelian Randomization with adaptive Sample-sPLitting with cross-fitting InstrumenTs (MR-SPLIT), designed to address bias issues due to IV selection and weak IVs, under the 2SLS IV regression framework. We show that the MR-SPLIT estimator is more efficient than its counterpart cross-fitting MR (CFMR) estimator. Additionally, we introduce a multiple sample-splitting technique to enhance the robustness of the method. We conduct extensive simulation studies to compare the performance of our method with its counterparts. The results underscored its superiority in bias reduction, effective type I error control, and increased power. We further demonstrate its utility through the application of a real-world dataset. Our study underscores the importance of addressing bias issues due to IV selection in high dimensions and weak IVs in one-sample MR analyses and provides a robust solution to the challenge.

genetics↗