A di-acetyl-decorated chromatin signature couples liquid condensation to suppress DNA end synapsis.

Appropriate DNA end synapsis, regulated by core components of the synaptic complex including KU70-KU80, LIG4, XRCC4, and XLF, is central to non-homologous end joining (NHEJ) repair of chromatinized DNA double-strand breaks (DSBs). However, it remains enigmatic whether chromatin modifications can influence the formation of NHEJ synaptic complex at DNA ends, and if so, how this is achieved. Here, we report that the mitotic deacetylase complex (MiDAC) serves as a key regulator of DNA end synapsis during NHEJ repair in mammalian cells. Mechanistically, MiDAC removes combinatorial acetyl marks on histone H2A (H2AK5acK9ac) around DSB-proximal chromatin, suppressing hyperaccumulation of bromodomain-containing protein BRD4 that would otherwise undergo liquid-liquid phase separation with KU80 and prevent the proper installation of LIG4-XRCC4-XLF onto DSB ends. This study provides mechanistic insight into the control of NHEJ synaptic complex assembly by a specific chromatin signature and highlights the critical role of H2A hypoacetylation in restraining unscheduled compartmentalization of DNA repair machinery.

A PARylation-phosphorylation cascade promotes TOPBP1 loading and RPA-RAD51 exchange in homologous recombination.

The efficiency of homologous recombination (HR) in the repair of DNA double-strand breaks (DSBs) is closely associated with genome stability and tumor response to chemotherapy. While many factors have been functionally characterized in HR, such as TOPBP1, their precise regulation remains unclear. Here, we report that TOPBP1 interacts with the RNA-binding protein HTATSF1 in a cell-cycle- and phosphorylation-dependent manner. Mechanistically, CK2 phosphorylates HTATSF1 to facilitate binding to TOPBP1, which promotes S-phase-specific TOPBP1 recruitment to damaged chromatin and subsequent RPA/RAD51-dependent HR, genome integrity, and cancer-cell viability. The localization of HTATSF1-TOPBP1 to DSBs is potentially independent of the transcription-coupled RNA-binding and processing capacity of HTATSF1 but rather relies on the recognition of poly(ADP-ribosyl)ated RPA by HTATSF1, which can be blunted with PARP inhibitors. Together, our study provides a mechanistic insight into TOPBP1 loading at HR-prone DSB sites via HTATSF1 and reveals how RPA-RAD51 exchange is tuned by a PARylation-phosphorylation cascade.

Brain-phenotype models fail for individuals who defy sample stereotypes.

Individual differences in brain functional organization track a range of traits, symptoms and behaviours1-12. So far, work modelling linear brain-phenotype relationships has assumed that a single such relationship generalizes across all individuals, but models do not work equally well in all participants13,14. A better understanding of in whom models fail and why is crucial to revealing robust, useful and unbiased brain-phenotype relationships. To this end, here we related brain activity to phenotype using predictive models-trained and tested on independent data to ensure generalizability15-and examined model failure. We applied this data-driven approach to a range of neurocognitive measures in a new, clinically and demographically heterogeneous dataset, with the results replicated in two independent, publicly available datasets16,17. Across all three datasets, we find that models reflect not unitary cognitive constructs, but rather neurocognitive scores intertwined with sociodemographic and clinical covariates; that is, models reflect stereotypical profiles, and fail when applied to individuals who defy them. Model failure is reliable, phenotype specific and generalizable across datasets. Together, these results highlight the pitfalls of a one-size-fits-all modelling approach and the effect of biased phenotypic measures18-20 on the interpretation and utility of resulting brain-phenotype models. We present a framework to address these issues so that such models may reveal the neural circuits that underlie specific phenotypes and ultimately identify individualized neural targets for clinical intervention.

CK2-HTATSF1-TOPBP1 signaling axis modulates tumor chemotherapy response.

Homologous recombination (HR) plays a key role in maintaining genomic stability, and the efficiency of the HR system is closely associated with tumor response to chemotherapy. Our previous work reported that CK2 kinase phosphorylates HIV Tat-specific factor 1 (HTATSF1) Ser748 to facilitate HTATSF1 interaction with TOPBP1, which in turn, promotes RAD51 recruitment and HR repair. However, the clinical implication of the CK2-HTATSF1-TOPBP1 pathway in tumorigenesis and chemotherapeutic response remains to be elucidated. Here, we report that the CK2-HTATSF1-TOPBP1 axis is generally hyperactivated in multiple malignancies and renders breast tumors less responsive to chemotherapy. In contrast, deletion mutations of each gene in this axis, which also occur in breast and lung tumor samples, predict higher HR deficiency scores, and tumor cells bearing a loss-of-function mutation of HTATSF1 are vulnerable to poly(ADP-ribose) polymerase inhibitors or platinum drugs. Taken together, our study suggests that the integrity of the CK2-HTATSF1-TOPBP1 axis is closely linked to tumorigenesis and serves as an indicator of tumor HR status and modulates chemotherapy response.

Scalable batch-correction approach for integrating large-scale single-cell transcriptomes.

Integration of accumulative large-scale single-cell transcriptomes requires scalable batch-correction approaches. Here we propose Fugue, a simple and efficient batch-correction method that is scalable for integrating super large-scale single-cell transcriptomes from diverse sources. The core idea of the method is to encode batch information as trainable parameters and add it to single-cell expression profile; subsequently, a contrastive learning approach is used to learn feature representation of the additive expression profile. We demonstrate the scalability of Fugue by integrating all single cells obtained from the Human Cell Atlas. We benchmark Fugue against current state-of-the-art methods and show that Fugue consistently achieves improved performance in terms of data alignment and clustering preservation. Our study will facilitate the integration of single-cell transcriptomes at increasingly large scale.

A universal approach for integrating super large-scale single-cell transcriptomes by exploring gene rankings.

Advancement in single-cell RNA sequencing leads to exponential accumulation of single-cell expression data. However, there is still lack of tools that could integrate these unlimited accumulations of single-cell expression data. Here, we presented a universal approach iSEEEK for integrating super large-scale single-cell expression via exploring expression rankings of top-expressing genes. We developed iSEEEK with 11.9 million single cells. We demonstrated the efficiency of iSEEEK with canonical single-cell downstream tasks on five heterogenous datasets encompassing human and mouse samples. iSEEEK achieved good clustering performance benchmarked against well-annotated cell labels. In addition, iSEEEK could transfer its knowledge learned from large-scale expression data on new dataset that was not involved in its development. iSEEEK enables identification of gene-gene interaction networks that are characteristic of specific cell types. Our study presents a simple and yet effective method to integrate super large-scale single-cell transcriptomes and would facilitate translational single-cell research from bench to bedside.

Sex differences in default mode network connectivity in healthy aging adults.

Women show an increased lifetime risk of Alzheimer's disease (AD) compared with men. Characteristic brain connectivity changes, particularly within the default mode network (DMN), have been associated with both symptomatic and preclinical AD, but the impact of sex on DMN function throughout aging is poorly understood. We investigated sex differences in DMN connectivity over the lifespan in 595 cognitively healthy participants from the Human Connectome Project-Aging cohort. We used the intrinsic connectivity distribution (a robust voxel-based metric of functional connectivity) and a seed connectivity approach to determine sex differences within the DMN and between the DMN and whole brain. Compared with men, women demonstrated higher connectivity with age in posterior DMN nodes and lower connectivity in the medial prefrontal cortex. Differences were most prominent in the decades surrounding menopause. Seed-based analysis revealed higher connectivity in women from the posterior cingulate to angular gyrus, which correlated with neuropsychological measures of declarative memory, and hippocampus. Taken together, we show significant sex differences in DMN subnetworks over the lifespan, including patterns in aging women that resemble changes previously seen in preclinical AD. These findings highlight the importance of considering sex in neuroimaging studies of aging and neurodegeneration.

A generalizable connectome-based marker of in-scan sustained attention in neurodiverse youth.

Difficulty with attention is an important symptom in many conditions in psychiatry, including neurodiverse conditions such as autism. There is a need to better understand the neurobiological correlates of attention and leverage these findings in healthcare settings. Nevertheless, it remains unclear if it is possible to build dimensional predictive models of attentional state in a sample that includes participants with neurodiverse conditions. Here, we use 5 datasets to identify and validate functional connectome-based markers of attention. In dataset 1, we use connectome-based predictive modeling and observe successful prediction of performance on an in-scan sustained attention task in a sample of youth, including participants with a neurodiverse condition. The predictions are not driven by confounds, such as head motion. In dataset 2, we find that the attention network model defined in dataset 1 generalizes to predict in-scan attention in a separate sample of neurotypical participants performing the same attention task. In datasets 3-5, we use connectome-based identification and longitudinal scans to probe the stability of the attention network across months to years in individual participants. Our results help elucidate the brain correlates of attentional state in youth and support the further development of predictive dimensional models of other clinically relevant phenotypes.

Simultaneous mesoscopic and two-photon imaging of neuronal activity in cortical circuits.

Spontaneous and sensory-evoked activity propagates across varying spatial scales in the mammalian cortex, but technical challenges have limited conceptual links between the function of local neuronal circuits and brain-wide network dynamics. We present a method for simultaneous cellular-resolution two-photon calcium imaging of a local microcircuit and mesoscopic widefield calcium imaging of the entire cortical mantle in awake mice. Our multi-scale approach involves a microscope with an orthogonal axis design where the mesoscopic objective is oriented above the brain and the two-photon objective is oriented horizontally, with imaging performed through a microprism. We also introduce a viral transduction method for robust and widespread gene delivery in the mouse brain. These approaches allow us to identify the behavioral state-dependent functional connectivity of pyramidal neurons and vasoactive intestinal peptide-expressing interneurons with long-range cortical networks. Our imaging system provides a powerful strategy for investigating cortical architecture across a wide range of spatial scales.

Simultaneous cortex-wide fluorescence Ca2+ imaging and whole-brain fMRI.

Achieving a comprehensive understanding of brain function requires multiple imaging modalities with complementary strengths. We present an approach for concurrent widefield optical and functional magnetic resonance imaging. By merging these modalities, we can simultaneously acquire whole-brain blood-oxygen-level-dependent (BOLD) and whole-cortex calcium-sensitive fluorescent measures of brain activity. In a transgenic murine model, we show that calcium predicts the BOLD signal, using a model that optimizes a gamma-variant transfer function. We find consistent predictions across the cortex, which are best at low frequency (0.009-0.08 Hz). Furthermore, we show that the relationship between modality connectivity strengths varies by region. Our approach links cell-type-specific optical measurements of activity to the most widely used method for assessing human brain function.

Predicting depressed and elevated mood symptomatology in bipolar disorder using brain functional connectomes.

BACKGROUND: The study is aimed to identify brain functional connectomes predictive of depressed and elevated mood symptomatology in individuals with bipolar disorder (BD) using the machine learning approach Connectome-based Predictive Modeling (CPM). METHODS: Functional magnetic resonance imaging data were obtained from 81 adults with BD while they performed an emotion processing task. CPM with 5000 permutations of leave-one-out cross-validation was applied to identify functional connectomes predictive of depressed and elevated mood symptom scores on the Hamilton Depression and Young Mania rating scales. The predictive ability of the identified connectomes was tested in an independent sample of 43 adults with BD. RESULTS: CPM predicted the severity of depressed [concordance between actual and predicted values (r = 0.23, pperm (permutation test) = 0.031) and elevated (r = 0.27, pperm = 0.01) mood. Functional connectivity of left dorsolateral prefrontal cortex and supplementary motor area nodes, with inter- and intra-hemispheric connections to other anterior and posterior cortical, limbic, motor, and cerebellar regions, predicted depressed mood severity. Connectivity of left fusiform and right visual association area nodes with inter- and intra-hemispheric connections to the motor, insular, limbic, and posterior cortices predicted elevated mood severity. These networks were predictive of mood symptomatology in the independent sample (r ⩾ 0.45, p = 0.002). CONCLUSIONS: This study identified distributed functional connectomes predictive of depressed and elevated mood severity in BD. Connectomes subserving emotional, cognitive, and psychomotor control predicted depressed mood severity, while those subserving emotional and social perceptual functions predicted elevated mood severity. Identification of these connectome networks may help inform the development of targeted treatments for mood symptoms.

SMC5 Plays Independent Roles in Congenital Heart Disease and Neurodevelopmental Disability.

Up to 50% of patients with severe congenital heart disease (CHD) develop life-altering neurodevelopmental disability (NDD). It has been presumed that NDD arises in CHD cases because of hypoxia before, during, or after cardiac surgery. Recent studies detected an enrichment in de novo mutations in CHD and NDD, as well as significant overlap between CHD and NDD candidate genes. However, there is limited evidence demonstrating that genes causing CHD can produce NDD independent of hypoxia. A patient with hypoplastic left heart syndrome and gross motor delay presented with a de novo mutation in SMC5. Modeling mutation of smc5 in Xenopus tropicalis embryos resulted in reduced heart size, decreased brain length, and disrupted pax6 patterning. To evaluate the cardiac development, we induced the conditional knockout (cKO) of Smc5 in mouse cardiomyocytes, which led to the depletion of mature cardiomyocytes and abnormal contractility. To test a role for Smc5 specifically in the brain, we induced cKO in the mouse central nervous system, which resulted in decreased brain volume, and diminished connectivity between areas related to motor function but did not affect vascular or brain ventricular volume. We propose that genetic factors, rather than hypoxia alone, can contribute when NDD and CHD cases occur concurrently.

Functional Connectivity during Encoding Predicts Individual Differences in Long-Term Memory.

What is the neural basis of individual differences in the ability to hold information in long-term memory (LTM)? Here, we first characterize two whole-brain functional connectivity networks based on fMRI data acquired during an n-back task that robustly predict individual differences in two important forms of LTM, recognition and recollection. We then focus on the recognition memory model and contrast it with a working memory model. Although functional connectivity during the n-back task also predicts working memory performance and the two networks have some shared components, they are also largely distinct from each other: The recognition memory model performance remains robust when we control for working memory, and vice versa. Functional connectivity only within regions traditionally associated with LTM formation, such as the medial temporal lobe and those that show univariate subsequent memory effect, have little predictive power for both forms of LTM. Interestingly, the interactions between these regions and other brain regions play a more substantial role in predicting recollection memory than recognition memory. These results demonstrate that individual differences in LTM are dependent on the configuration of a whole-brain functional network including but not limited to regions associated with LTM during encoding and that such a network is separable from what supports the retention of information in working memory.

There is no single functional atlas even for a single individual: Functional parcel definitions change with task.

The goal of human brain mapping has long been to delineate the functional subunits in the brain and elucidate the functional role of each of these brain regions. Recent work has focused on whole-brain parcellation of functional Magnetic Resonance Imaging (fMRI) data to identify these subunits and create a functional atlas. Functional connectivity approaches to understand the brain at the network level require such an atlas to assess connections between parcels and extract network properties. While no single functional atlas has emerged as the dominant atlas to date, there remains an underlying assumption that such an atlas exists. Using fMRI data from a highly sampled subject as well as two independent replication data sets, we demonstrate that functional parcellations based on fMRI connectivity data reconfigure substantially and in a meaningful manner, according to brain state.

The Involvement of Mitochondrial Biogenesis in Selenium Reduced Hyperglycemia-Aggravated Cerebral Ischemia Injury.

Selenium has been shown to possess antioxidant and neuroprotective effects by modulating mitochondrial function and activating mitochondrial biogenesis. Our previous study has also suggested that selenium protected neurons against glutamate toxicity and hyperglycemia-induced damage by regulating mitochondrial fission and fusion. However, it is still not known whether the mitochondrial biogenesis is involved in selenium alleviating hyperglycemia-aggravated cerebral ischemia reperfusion (I/R) injury. The object of this study is to define whether selenium protects neurons against hyperglycemia-aggravated cerebral I/R injury by promoting mitochondrial biogenesis. In vitro oxygen deprivation plus high glucose model decreased cell viability, enhanced reactive oxygen species production, and meanwhile stimulated mitochondrial biogenesis signaling. Pretreated with selenium significantly decreased cell death and further activated the mitochondrial biogenesis signaling. In vivo 30 min of middle cerebral artery occlusion in the rats under hyperglycemic condition enhanced neurological deficits, enlarged infarct volume, exacerbated neuronal damage and oxidative stress compared with normoglycemic ischemic rats after 24 h reperfusion. Consistent to the in vitro results, selenium treatment alleviated ischemic damage in hyperglycemic ischemic animals. Furthermore, selenium reduced the structural changes of mitochondria caused by hyperglycemic ischemia and further promoted the mitochondrial biogenesis signaling. Selenium activates mitochondrial biogenesis signaling, protects mitochondrial structure integrity and ameliorates cerebral I/R injury in hyperglycemic rats.

The individual functional connectome is unique and stable over months to years.

Functional connectomes computed from fMRI provide a means to characterize individual differences in the patterns of BOLD synchronization across regions of the entire brain. Using four resting-state fMRI datasets with a wide range of ages, we show that individual differences of the functional connectome are stable across 3 months to 1-2 years (and even detectable at above-chance levels across 3 years). Medial frontal and frontoparietal networks appear to be both unique and stable, resulting in high ID rates, as did a combination of these two networks. We conduct analyses demonstrating that these results are not driven by head motion. We also show that edges contributing the most to a successful ID tend to connect nodes in the frontal and parietal cortices, while edges contributing the least tend to connect cross-hemispheric homologs. Our results demonstrate that the functional connectome is stable across years and that high ID rates are not an idiosyncratic aspect of a specific dataset, but rather reflect stable individual differences in the functional connectivity of the brain.

Ten simple rules for predictive modeling of individual differences in neuroimaging.

Establishing brain-behavior associations that map brain organization to phenotypic measures and generalize to novel individuals remains a challenge in neuroimaging. Predictive modeling approaches that define and validate models with independent datasets offer a solution to this problem. While these methods can detect novel and generalizable brain-behavior associations, they can be daunting, which has limited their use by the wider connectivity community. Here, we offer practical advice and examples based on functional magnetic resonance imaging (fMRI) functional connectivity data for implementing these approaches. We hope these ten rules will increase the use of predictive models with neuroimaging data.

Dynamic-flip-angle ECG-gating with nuisance signal regression improves resting-state BOLD functional connectivity mapping by reducing cardiogenic noise.

PURPOSE: To investigate an ECG-gated dynamic-flip-angle BOLD sequence with improved robustness against cardiogenic noise in resting-state fMRI. METHODS: ECG-gating minimizes the cardiogenic noise but introduces T1 -dependent signal variation, which is minimized by combination of a dynamic-flip-angle technique and retrospective nuisance signal regression (NSR) using signals of white matter, CSF, and global average. The technique was studied with simulations in a wide range of T1 and B1 fields and phantom imaging with pre-programmed TR variations. Resting-state fMRI of 20 healthy subjects was acquired with non-gated BOLD (NG), ECG-gated constant-flip-angle BOLD (GCFA), ECG-gated BOLD with retrospective T1 -correction (GRC), and ECG-gated dynamic-flip-angle BOLD (GDFA), all processed by the same NSR method. GDFA was compared to alternative methods over temporal SNR (tSNR), seed-based connectivity, and whole-brain voxelwise connectivity based on intrinsic connectivity distribution (ICD). A previous large-cohort data set (N = 100) was used as a connectivity gold standard. RESULTS: Simulations and phantom imaging show substantial reduction of the T1 -dependent signal variation with GDFA alone, and further reduction with NSR. The resting-state study shows improved tSNR in the basal brain, comparing GDFA to NG, after both processed with NSR. Furthermore, GDFA significantly improved subcortical-subcortical and cortical-subcortical connectivity for several representative seeds and significantly improved ICD in the brainstem, thalamus, striatum, and prefrontal cortex, compared to the other 3 approaches. CONCLUSION: GDFA with NSR improves mapping of the resting-state functional connectivity of the basal-brain regions by reducing cardiogenic noise.

An exemplar-based approach to individualized parcellation reveals the need for sex specific functional networks.

Recent work with functional connectivity data has led to significant progress in understanding the functional organization of the brain. While the majority of the literature has focused on group-level parcellation approaches, there is ample evidence that the brain varies in both structure and function across individuals. In this work, we introduce a parcellation technique that incorporates delineation of functional networks both at the individual- and group-level. The proposed technique deploys the notion of "submodularity" to jointly parcellate the cerebral cortex while establishing an inclusive correspondence between the individualized functional networks. Using this parcellation technique, we successfully established a cross-validated predictive model that predicts individuals' sex, solely based on the parcellation schemes (i.e. the node-to-network assignment vectors). The sex prediction finding illustrates that individualized parcellation of functional networks can reveal subgroups in a population and suggests that the use of a global network parcellation may overlook fundamental differences in network organization. This is a particularly important point to consider in studies comparing patients versus controls or even patient subgroups. Network organization may differ between individuals and global configurations should not be assumed. This approach to the individualized study of functional organization in the brain has many implications for both neuroscience and clinical applications.