Biology subjects

Kong, R.

Publications and source records attributed to Kong, R..

At least 37 records · Page 2Linked to original sources

bioRxiv2023

Dissecting peri-implantation development using cultured human embryos and embryo-like assembloids

Studies of cultured embryos have provided insights into human peri-implantation development. However, detailed knowledge of peri-implantation lineage developments as well as underlying mechanisms remain obscure. Using 3D-cultured human embryos, herein we report a complete cell atlas of the early post-implantation lineages and decipher cellular composition and gene signatures of the epiblast and hypoblast derivatives. In addition, we develop an embryo-like assembloid (E-assembloid) by assembling naive hESCs and extraembryonic cells. Using human embryos and E-assembloids, we reveal that WNT, BMP and Nodal signaling synergistically, but functionally differently, orchestrate human peri-implantation lineage development. Specially, we dissect mechanisms underlying extraembryonic mesoderm and extraembryonic endoderm specifications. Finally, an improved E-assembloid is developed to recapitulate the epiblast and hypoblast developments and tissue architectures in the pre-gastrulation human embryo. Our findings provide insights of the human peri-implantation development, and the E-assembloid offers a useful model to disentangle cellular behaviors and signaling interactions that drive human embryogenesis.

developmental biology↗

bioRxiv2022

Homotopic local-global parcellation of the human cerebral cortex from resting-state functional connectivity

Resting-state fMRI is commonly used to derive brain parcellations, which are widely used for dimensionality reduction and interpreting human neuroscience studies. We previously developed a model that integrates local and global approaches for estimating areal-level cortical parcellations. The resulting local-global parcellations are often referred to as the Schaefer parcellations. However, the lack of homotopic correspondence between left and right Schaefer parcels has limited their use for brain lateralization studies. Here, we extend our previous model to derive homotopic areal-level parcellations. Using resting-fMRI and task-fMRI across diverse scanners, acquisition protocols, preprocessing and demographics, we show that the resulting homotopic parcellations are as homogeneous as the Schaefer parcellations, while being more homogeneous than five publicly available parcellations. Furthermore, weaker correlations between homotopic parcels are associated with greater lateralization in resting network organization, as well as lateralization in language and motor task activation. Finally, the homotopic parcellations agree with the boundaries of a number of cortical areas estimated from histology and visuotopic fMRI, while capturing sub-areal (e.g., somatotopic and visuotopic) features. Overall, these results suggest that the homotopic local- global parcellations represent neurobiologically meaningful subdivisions of the human cerebral cortex and will be a useful resource for future studies. Multi-resolution parcellations estimated from 1479 participants are publicly available (GITHUB_LINK).

neuroscience↗

bioRxiv2022

Comparison Between Gradients and Parcellations for Functional Connectivity Prediction of Behavior

Resting-state functional connectivity (RSFC) is widely used to predict behavioral measures. To predict behavioral measures, representing RSFC with parcellations and gradients are the two most popular approaches. Here, we compare parcellation and gradient approaches for RSFC-based prediction of a broad range of behavioral measures in the Human Connectome Project (HCP) and Adolescent Brain Cognitive Development (ABCD) datasets. Among the parcellation approaches, we consider group-average "hard" parcellations (Schaefer et al., 2018), individual-specific "hard" parcellations (Kong et al., 2021a), and an individual-specific "soft" parcellation (spatial independent component analysis with dual regression; Beckmann et al., 2009). For gradient approaches, we consider the well-known principal gradients (Margulies et al., 2016) and the local gradient approach that detects local RSFC changes (Laumann et al., 2015). Across two regression algorithms, individual-specific hard-parcellation performs the best in the HCP dataset, while the principal gradients, spatial independent component analysis and group-average "hard" parcellations exhibit similar performance. On the other hand, principal gradients and all parcellation approaches perform similarly in the ABCD dataset. Across both datasets, local gradients perform the worst. Finally, we find that the principal gradient approach requires at least 40 to 60 gradients to perform as well as parcellation approaches. While most principal gradient studies utilize a single gradient, our results suggest that incorporating higher order gradients can provide significant behaviorally relevant information. Future work will consider the inclusion of additional parcellation and gradient approaches for comparison.

neuroscience↗

bioRxiv2022

Proportional intracranial volume correction differentially biases behavioral predictions across neuroanatomical features and populations

Individual differences in brain anatomy can be used to predict variability in cognitive function. Most studies to date have focused on broad population-level trends, but the extent to which the observed predictive features are shared across sexes and age groups remains to be established. While it is standard practice to account for intracranial volume (ICV) using proportion correction in both regional and whole-brain morphometric analyses, in the context of brain-behavior predictions the possible differential impact of ICV correction on anatomical features and subgroups within the population has yet to be systematically investigated. In this work, we evaluate the effect of proportional ICV correction on sex-independent and sex-specific predictive models of individual cognitive abilities across multiple anatomical properties (surface area, gray matter volume, and cortical thickness) in healthy young adults (Human Connectome Project; n=1013, 548 females) and typically developing children (Adolescent Brain Cognitive Development study; n=1823, 979 females). We demonstrate that ICV correction generally reduces predictive accuracies derived from surface area and gray matter volume, while increasing predictive accuracies based on cortical thickness in both adults and children. Furthermore, the extent to which predictive models generalize across sexes and age groups depends on ICV correction: models based on surface area and gray matter volume are more generalizable without ICV correction, while models based on cortical thickness are more generalizable with ICV correction. Finally, the observed neuroanatomical features predictive of cognitive abilities are unique across age groups regardless of ICV correction, but whether they are shared or unique across sexes (within age groups) depends on ICV correction. These findings highlight the importance of considering individual differences in ICV, and show that proportional ICV correction does not remove the effects of cranium volumes from anatomical measurements and can introduce ICV bias where previously there was none. ICV correction choices affect not just the strength of the relationships captured, but also the conclusions drawn regarding the neuroanatomical features that underlie those relationships.

neuroscience↗

bioRxiv2022

Comparison of individualized behavioral predictions across anatomical, diffusion and functional connectivity MRI

A fundamental goal across the neurosciences is the characterization of relationships linking brain anatomy, functioning, and behavior. Although various MRI modalities have been developed to probe these relationships, direct comparisons of their ability to predict behavior have been lacking. Here, we compared the ability of anatomical T1, diffusion and functional MRI (fMRI) to predict behavior at an individual level. Cortical thickness, area and volume were extracted from anatomical T1 images. Diffusion Tensor Imaging (DTI) and approximate Neurite Orientation Dispersion and Density Imaging (NODDI) models were fitted to the diffusion images. The resulting metrics were projected to the Tract-Based Spatial Statistics (TBSS) skeleton. We also ran probabilistic tractography for the diffusion images, from which we extracted the stream count, average stream length, and the average of each DTI and NODDI metric across tracts connecting each pair of brain regions. Functional connectivity (FC) was extracted from both task and resting-state fMRI. Individualized prediction of a wide range of behavioral measures were performed using kernel ridge regression, linear ridge regression and elastic net regression. Consistency of the results were investigated with the Human Connectome Project (HCP) and Adolescent Brain Cognitive Development (ABCD) datasets. In both datasets, FC-based models gave the best prediction performance, regardless of regression model or behavioral measure. This was especially true for the cognitive domain. Furthermore, all modalities were able to predict cognition better than other behavioral domains. Combining all modalities improved prediction of cognition, but not other behavioral domains. Finally, across all behaviors, combining resting and task FC yielded prediction performance similar to combining all modalities. Overall, our study suggests that in the case of healthy children and young adults, behaviorally-relevant information in T1 and diffusion features might reflect a subset of the variance captured by FC. HighlightsO_LIFC predicts behavior better than anatomical and diffusion features C_LIO_LICognition is predicted better than other behavioral domains regardless of modality C_LIO_LICombining resting & task FC improves prediction as much as combining all modalities C_LIO_LIFindings were replicated over 3 regression models and 2 datasets C_LI

neuroscience↗

bioRxiv2022

Transcriptional circuitry of NKX2-1 and SOX1 defines an unrecognized lineage subtype of small cell lung cancer

RationaleThe current molecular classification of small cell lung cancer (SCLC) based on expression of four lineage transcription factors still leaves its major subtype SCLC-A as a heterogeneous group, necessitating more precise characterization of lineage subclasses. ObjectivesTo refine the current SCLC classification with epigenomic profiles and to identify features of the re-defined SCLC subtypes. MethodsWe performed unsupervised clustering of epigenomic profiles on 25 SCLC cell lines. Functional significance was evaluated by cell growth, apoptosis and xenograft using CRISPR-Cas9-mediated deletion. The specific cistromic profiles by ChIP-seq and its functional transcriptional partners using co-immunoprecipitation followed by mass spectrometry were determined. Rb1fl/flTrp53fl/fl and Rb1fl/flNkx2-1fl/fl mouse models were engineered to explore the function of Nkx2-1 in tumor initiation and differentiation. H3K27ac profiles were analyzed to reveal 6 human SCLC specimen and 20 mice tumors epigenomic landscapes. Measurements and Main ResultsWe identified an epigenomic subclusters of the major SCLC-A subtype, named SCLC-A and SCLC-A{sigma}. SCLC-A was characterized by the presence of a super-enhancer at the NKX2-1 locus, which was observed in human SCLC specimens and a murine SCLC model. We found NKX2-1, a dual lung and neural lineage factor, is uniquely relevant in SCLC-A. We further found maintenance of this neural identity in SCLC-A is mediated by collaborative transcriptional activity with another neuronal transcriptional factor SOX1. ConclusionsWe comprehensively describe an additional epigenomic heterogeneity of the major SCLC-A subtype, and define SCLC-A subtype by the core regulatory circuitry representing NKX2-1 and SOX1 super-enhancers and their functional collaborations to maintain neuronal linage state.

molecular biology↗

bioRxiv2022

The endoplasmic reticulum membrane protein complex (EMC) negatively regulates intestinal homeostasis through the Hippo signaling pathway

Disruption of tissue homeostasis often results in many diseases. Balanced control of stem cell proliferation and differentiation underlines tissue homeostasis. However, how endogenous factors influence the proliferation and differentiation of intestinal stem cells (ISCs) under physiological conditions remains not fully understood. Here, we find that the endoplasmic reticulum membrane protein complex (EMC) negatively regulates ISC proliferation in adult Drosophila midgut. Compromising EMC function in progenitors leads to excessive ISC proliferation and intestinal homeostasis disruption. Mechanistically, the EMC complex associates with and stabilizes Hippo (Hpo), the key component of the Hpo signaling pathway. In the absence of the EMC complex, Yki (Yorkie) is activated to promote ISC proliferation. Furthermore, the role of the EMC complex in stem cell proliferation control is evolutionarily conserved. Thus, our study uncovers the molecular mechanism of the EMC complex in controlling stem cell proliferation. Our results provide new insight into the underlying mechanisms of how stem cell proliferation is properly controlled under physiological conditions.

cell biology↗

bioRxiv2021

Anatomical and Functional Gradients Shape Dynamic Functional Connectivity in the Human Brain

Large-scale biophysical circuit models can provide mechanistic insights into the fundamental micro-scale and macro-scale properties of brain organization that shape complex patterns of spontaneous brain activity. By allowing local synaptic properties to vary across brain regions, recent large-scale circuit models have demonstrated better fit to empirical observations, such as inter-regional synchrony averaged over several minutes, i.e. static functional connectivity (FC). However, most previous models do not capture how inter-regional synchrony patterns vary over timescales of seconds, i.e., time-varying FC dynamics. Here we developed a spatially-heterogeneous large-scale dynamical circuit model that allowed for variation in local circuit properties across the human cortex. We showed that parameterizing local circuit properties with both anatomical and functional gradients was necessary for generating realistic static and dynamical properties of resting-state fMRI activity. Furthermore, empirical and simulated FC dynamics demonstrated remarkably similar sharp transitions in FC patterns, suggesting the existence of multiple attractors. We found that time-varying regional fMRI amplitude tracked multi-stability in FC dynamics. Causal manipulation of the large-scale circuit model suggested that sensory-motor regions were a driver of FC dynamics. Finally, the spatial distribution of sensory-motor drivers matched the principal gradient of gene expression that encompassed certain interneuron classes, suggesting that heterogeneity in excitation-inhibition balance might shape multi-stability in FC dynamics.

neuroscience↗

bioRxiv2021

Individual-Specific Areal-Level Parcellations Improve Functional Connectivity Prediction of Behavior

Resting-state functional MRI (rs-fMRI) allows estimation of individual-specific cortical parcellations. We have previously developed a multi-session hierarchical Bayesian model (MS-HBM) for estimating high-quality individual-specific network-level parcellations. Here, we extend the model to estimate individual-specific areal-level parcellations. While network-level parcellations comprise spatially distributed networks spanning the cortex, the consensus is that areal-level parcels should be spatially localized, i.e., should not span multiple lobes. There is disagreement about whether areal-level parcels should be strictly contiguous or comprise multiple non-contiguous components, therefore we considered three areal-level MS-HBM variants spanning these range of possibilities. Individual-specific MS-HBM parcellations estimated using 10min of data generalized better than other approaches using 150min of data to out-of-sample rs-fMRI and task-fMRI from the same individuals. Resting-state functional connectivity (RSFC) derived from MS-HBM parcellations also achieved the best behavioral prediction performance. Among the three MS-HBM variants, the strictly contiguous MS-HBM (cMS-HBM) exhibited the best resting-state homogeneity and most uniform within-parcel task activation. In terms of behavioral prediction, the gradient-infused MS-HBM (gMS-HBM) was numerically the best, but differences among MS-HBM variants were not statistically significant. Overall, these results suggest that areal-level MS-HBMs can capture behaviorally meaningful individual-specific parcellation features beyond group-level parcellations. Multi-resolution trained models and parcellations are publicly available (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Kong2022_ArealMSHBM).

neuroscience↗

bioRxiv2020

Virus-Like Particle Based-Vaccines Elicit Neutralizing Antibodies against the HIV-1 Fusion Peptide

Broadly neutralizing antibodies (bnAbs) isolated from HIV-infected individuals delineate vulnerable sites on the HIV envelope glycoprotein that are potential vaccine targets. A linear epitope at the N-terminal region of the HIV-1 fusion peptide (FP8) is the primary target of VRC34.01, a bnAb that neutralizes ~50% of primary HIV isolates. FP8 has attracted attention as a potential HIV vaccine target because it is a simple linear epitope. Here, we used platform technologies based on RNA bacteriophage virus-like particles (VLPs) to develop multivalent vaccines targeting the FP8 epitope. We produced recombinant MS2 VLPs displaying the FP8 peptide and we chemically conjugated synthetic FP8 peptides to Q{beta} VLPs. Both recombinant and conjugated FP8-VLPs induced high titers of FP8-specific antibodies in mice. A heterologous prime-boost-boost regimen employing the two FP8-VLP vaccines and native envelope trimer was the most effective approach for eliciting HIV-1 neutralizing antibodies. Given the potent immunogenicity of VLP-based vaccines, this vaccination strategy - inspired by bnAb-guided epitope mapping, VLP bioengineering, and optimal prime-boost immunization strategies - may be an effective strategy for eliciting bnAb responses against HIV.

immunology↗

bioRxiv2020

The Detailed Organization of the Human Cerebellum Estimated by Intrinsic Functional Connectivity Within the Individual

Distinct regions of the cerebellum connect to separate regions of the cerebral cortex forming a complex topography. While key properties of cerebellar organization have been revealed in group-averaged data, in-depth study of individuals provides an opportunity to discover functional-anatomical features that emerge at a higher spatial resolution. Here functional connectivity MRI was used to examine the cerebellum of two intensively-sampled individuals (each scanned across 31 MRI sessions). Connectivity to somatomotor cortex showed the expected crossed laterality and inversion of the body maps between the anterior and posterior lobes. A surprising discovery was connectivity to the primary visual cortex along the vermis with evidence for representation of the central field. Within the hemispheres, each individual displayed a hierarchical progression from the inverted anterior lobe somatomotor map through to higher-order association zones. The hierarchy ended near Crus I/II and then progressed in reverse order through to the upright somatomotor map in the posterior lobe. Evidence for a third set of networks was found in the most posterior extent of the cerebellum. Detailed analysis of the higher-order association networks around the Crus I/II apex revealed robust representations of two distinct networks linked to the default network, multiple networks linked to cognitive control, as well as a separate representation of a language network. While idiosyncratic spatial details emerged between subjects, each of these networks could be detected in both individuals, and small seed regions placed within the cerebellum recapitulated the full extent of the spatially-specific cerebral networks. The observation of multiple networks in juxtaposed regions at the Crus I/II apex confirms the importance of this zone to higher-order cognitive function and reveals new organizational details.

neuroscience↗

bioRxiv2020

The DHODH Inhibitor PTC299 Arrests SARS-CoV-2 Replication and Suppresses Induction of Inflammatory Cytokines

The coronavirus disease 2019 (COVID-19) pandemic has created an urgent need for therapeutics that inhibit the SARS-CoV-2 virus and suppress the fulminant inflammation characteristic of advanced illness. Here, we describe the anti-COVID-19 potential of PTC299, an orally available compound that is a potent inhibitor of dihydroorotate dehydrogenase (DHODH), the rate-limiting enzyme of the de novo pyrimidine biosynthesis pathway. In tissue culture, PTC299 manifests robust, dose-dependent, and DHODH-dependent inhibition of SARS CoV-2 replication (EC50 range, 2.0 to 31.6 nM) with a selectivity index >3,800. PTC299 also blocked replication of other RNA viruses, including Ebola virus. Consistent with known DHODH requirements for immunomodulatory cytokine production, PTC299 inhibited the production of interleukin (IL)-6, IL-17A (also called IL-17), IL-17F, and vascular endothelial growth factor (VEGF) in tissue culture models. The combination of anti-SARS-CoV-2 activity, cytokine inhibitory activity, and previously established favorable pharmacokinetic and human safety profiles render PTC299 a promising therapeutic for COVID-19.

immunology↗

bioRxiv2020

Heritability of individualized cortical network topography

Human cortex is patterned by a complex and interdigitated web of large-scale functional networks. Recent methodological breakthroughs reveal variation in the size, shape, and spatial topography of cortical networks across individuals. While spatial network organization emerges across development, is stable over time, and predictive of behavior, it is not yet clear to what extent genetic factors underlie inter-individual differences in network topography. Here, leveraging a novel non-linear multi-dimensional estimation of heritability, we provide evidence that individual variability in the size and topographic organization of cortical networks are under genetic control. Using twin and family data from the Human Connectome Project (n=1,023), we find increased variability and reduced heritability in the size of heteromodal association networks (h2: M=0.33, SD=0.071), relative to unimodal sensory/motor cortex (h2: M=0.44, SD=0.051). We then demonstrate that the spatial layout of cortical networks is influenced by genetics, using our multi-dimensional estimation of heritability (h2-multi; M=0.14, SD=0.015). However, topographic heritability did not differ between heteromodal and unimodal networks. Genetic factors had a regionally variable influence on brain organization, such that the heritability of network topography was greatest in prefrontal, precuneus, and posterior parietal cortex. Taken together, these data are consistent with relaxed genetic control of association cortices relative to primary sensory/motor regions, and have implications for understanding population-level variability in brain functioning, guiding both individualized prediction and the interpretation of analyses that integrate genetics and neuroimaging. SignificanceThe widespread use of population-average cortical parcellations has provided important insights into broad properties of human brain organization. However, the size, location, and spatial arrangement of regions comprising functional brain networks can vary substantially across individuals. Here, we demonstrate considerable heritability in both the size and spatial organization of individual-specific network topography across cortex. Genetic factors had a regionally variable influence on brain organization, such that heritability in network size, but not topography, was greater in unimodal relative to heteromodal cortices. These data suggest individual-specific network parcellations may provide an avenue to understand the genetic basis of variation in human cognition and behavior.

neuroscience↗

bioRxiv2020

Expression from DIF1-motif promoters of hetR and patS is dependent on HetZ and modulated by PatU3 during heterocyst differentiation

HetR and PatS/PatX-derived peptides are the activator and diffusible inhibitor for cell differentiation and patterning in heterocyst-forming cyanobacteria. HetR regulates target genes via HetR-recognition sites. However, some genes (such as patS/patX) upregulated at the early stage of heterocyst differentiation possess DIF1 (or DIF+) motif (TCCGGA) promoters rather than HetR-recognition sites; hetR possesses both regulatory elements. How HetR controls heterocyst-specific expression from DIF1 motif promoters remains to be answered. This study presents evidence that the expression from DIF1 motif promoters of hetR, patS and patX is more directly dependent on hetZ, a gene regulated by HetR. The HetR-binding site upstream of hetR is not required for its autoregulation. PatU3 (3 portion of PatU) that interacts with HetZ may modulate HetZ-dependent gene expression. These findings contribute to understanding of the mutual regulation of hetR, hetZ-patU and patS/patX in a large group of multicellular cyanobacteria.

microbiology↗

bioRxiv2020

Convergent molecular, cellular, and neural signatures of major depressive disorder

Major depressive disorder emerges from the complex interactions of biological systems that span across genes and molecules through cells, circuits, networks, and behavior. Establishing how neurobiological processes coalesce to contribute to the onset and maintenance of depression requires a multi-scale approach, encompassing measures of brain structure and function as well as genetic and cell-specific genomic data. Here, we examined anatomical (cortical thickness) and functional (functional variability, global brain connectivity) correlates of depression and negative affect across three population-imaging datasets: UK Biobank, Genome Superstruct Project, and ENIGMA (combined N[≥]23,723). Integrative analyses incorporated measures of cortical gene expression, post-mortem patient transcriptional data, depression GWAS, and single-cell transcription. Neuroimaging correlates of depression and negative affect were consistent across the three independent datasets. Linking ex-vivo gene downregulation with in-vivo neuroimaging, we found that genomic correlates of depression-linked neuroimaging phenotypes tracked gene downregulation in post-mortem cortical tissue samples of patients with depression. Integrated analysis of single-cell and Allen Human Brain Atlas expression data implicated somatostatin interneurons and astrocytes as consistent cell associates of depression, through both in-vivo imaging and ex-vivo cortical gene dysregulation. Providing converging evidence for these observations, GWAS derived polygenic risk for depression was enriched for genes expressed in interneurons, but not glia. Underscoring the translational potential of multi-scale approaches, the genomic correlates of depression-linked brain function and structure were enriched for known and novel disorder relevant molecular pathways. These findings bridge across levels to connect specific genes, cell classes, and biological pathways to in-vivo imaging correlates of depression. Key FindingsO_LIMajor depressive disorder and negative affect are associated with replicable profiles of cortical anatomy and function across independent population-level neuroimaging datasets (combined N[≥]23,723). C_LIO_LISomatostatin interneurons are consistent spatial transcriptional associates of in-vivo depression-linked imaging phenotypes. C_LIO_LIIntegrative single-cell gene expression analysis associate somatostatin interneurons and astrocytes with both in-vivo depression-linked imaging and ex-vivo gene downregulation in independent MDD cortical tissue samples. C_LIO_LITranscriptional correlates of in-vivo depression imaging phenotypes selectively capture gene downregulation in post-mortem tissue samples from patients with depression, but not other psychiatric disorders. C_LIO_LIIndicating that some cell classes are preferentially sensitive to inherited disease liability, genome-wide risk for depression is enriched among interneurons, but not glia. C_LIO_LIGene associates of depression-linked anatomy and function identify specific neurotransmitter systems, molecular signaling pathways, and receptors, suggesting possible targets for pharmaceutical intervention. C_LI

neuroscience↗

bioRxiv2019

Integrative genomic and epigenomic analyses identify a distinct role of c-Myc and L-Myc for lineage determination in small cell lung cancer

Comprehensive genomic analyses of small cell lung cancer (SCLC), the most aggressive form of lung cancer, have revealed near universal loss of tumor suppressors (RB1 and TP53) and frequent genomic amplification of all three MYC family members. The amplification of each Myc family member is mutually exclusive; hence it had been long suggested that they are functionally equivalent. However, their expression has more recently been associated with specific neuroendocrine markers and distinct histopathology. In this study, we explored a novel role of c-Myc and L-Myc as lineage determining factors contributing to SCLC molecular subtypes and histology. Integrated analyses of a gene regulatory network generated from mRNA expression of primary SCLC tumor and chromatin state profiling of SCLC cell lines showed that Myc family members impart distinct transcriptional programs associated with lineage state; wherein the L-Myc signature was enriched for neuronal pathways while the c-Myc signature was enriched for Notch signaling and epithelial-to-mesenchymal transition. We investigated the functional redundancy and distinction of c-Myc and L-Myc, and noted the insufficiency of L-Myc to induce lineage switch in contrast to the potential of c-Myc to induce trans-differentiation. c-Myc rewires the Myc-accessible landscape and activates neuron al repressor, Rest to mediate transition from ASCL1-SCLC to NeuroD1-SCLC characterized by distinct LCNEC-like histopathology. Collectively, our findings reveal a previously undescribed role of historically defined general oncogenes, c-Myc and L-Myc, for regulating lineage plasticity across molecular subtypes as well as histological subclasses.

cancer biology↗

bioRxiv2019

Time of day is associated with paradoxical reductions in global signal fluctuation and functional connectivity

1.The brain exhibits substantial diurnal variation in physiology and function but neuroscience studies rarely report or consider the effects of time of day. Here, we examined variation in resting-state fMRI in around 900 subjects scanned between 8am to 10pm on two different days. Multiple studies across animals and humans have demonstrated that the brains global signal amplitude (henceforth referred to as \"fluctuation\") increases with decreased arousal. Thus, in accord with known circadian variation in arousal, we hypothesised that global signal fluctuation would be lowest in the morning, increase in the mid-afternoon and dip in the early evening. Instead, we observed a cumulative decrease (22% between 9am to 9pm) in global signal fluctuation as the day progressed. To put the magnitude of this decrease in context, we note that task-evoked fMRI responses are typically in the order of 1% to 3%. Respiratory variation also decreased with time of day, although control analyses suggested that this did not account for the reduction in GS fluctuation. Finally, time of day was associated with marked decreases in resting state functional connectivity across the whole brain. The magnitude of decrease was significantly stronger than associations between functional connectivity and behaviour (e.g., fluid intelligence). These findings reveal unexpected effects of time of day on the resting human brain, which challenge the prevailing notion that the brains global signal reflects mostly arousal and physiological artefacts. We conclude by discussing potential mechanisms for the observed diurnal variation in resting brain activity and the importance of accounting for time of day in future studies.

neuroscience↗

bioRxiv2019

Cooperating lineage factors SOX2 and BRN2 govern the transcriptional program of the neural subtype of lung squamous cell carcinoma

Molecular characterization of lung squamous cell carcinoma (LUSC), a major subtype of lung cancer, has not sufficiently improved its non-stratified treatment strategies over decades. Accumulating evidence suggests that lineage-specific transcriptional regulators control differentiation states during cancer evolution, and underlie their distinct biological behaviors. In this study, by investigating the super-enhancer landscape of LUSC, we identified a previously undescribed neural subtype defined by Sox2 and a neural lineage factor Brn2, as well as the classical LUSC subtype defined by Sox2 and its classical squamous partner p63. Robust protein-protein interaction and genomic co-occupancy of Sox2 and Brn2, in place for p63 in the classical LUSC, indicated their transcriptional cooperation imparting this unique lineage state in the neural LUSC. Forced expression of p63 downregulated Brn2 in the neural LUSC cells and invoked the classical LUSC lineage with more squamous/epithelial features, which were accompanied by increased activities of ErbB/Akt and MAPK-ERK pathways suggesting differential dependency. Collectively, our data demonstrate heterogeneous cell lineage states of LUSC featured by Sox2 cooperation with Brn2 or p63, for which distinct therapeutic approaches may be warranted.

cancer biology↗