Genetic variation in chromatin state across multiple tissues in Drosophila melanogaster.

We use ATAC-seq to examine chromatin accessibility for four different tissues in Drosophila melanogaster: adult female brain, ovaries, and both wing and eye-antennal imaginal discs from males. Each tissue is assayed in eight different inbred strain genetic backgrounds, seven associated with a reference quality genome assembly. We develop a method for the quantile normalization of ATAC-seq fragments and test for differences in coverage among genotypes, tissues, and their interaction at 44099 peaks throughout the euchromatic genome. For the strains with reference quality genome assemblies, we correct ATAC-seq profiles for read mis-mapping due to nearby polymorphic structural variants (SVs). Comparing coverage among genotypes without accounting for SVs results in a highly elevated rate (55%) of identifying false positive differences in chromatin state between genotypes. After SV correction, we identify 1050, 30383, and 4508 regions whose peak heights are polymorphic among genotypes, among tissues, or exhibit genotype-by-tissue interactions, respectively. Finally, we identify 3988 candidate causative variants that explain at least 80% of the variance in chromatin state at nearby ATAC-seq peaks.

Inhibitors of cullin-RING E3 ubiquitin ligase 4 with antitumor potential.

Cullin-RING (really intersting new gene) E3 ubiquitin ligases (CRLs) are the largest E3 family and direct numerous protein substrates for proteasomal degradation, thereby impacting a myriad of physiological and pathological processes including cancer. To date, there are no reported small-molecule inhibitors of the catalytic activity of CRLs. Here, we describe high-throughput screening and medicinal chemistry optimization efforts that led to the identification of two compounds, 33-11 and KH-4-43, which inhibit E3 CRL4 and exhibit antitumor potential. These compounds bind to CRL4's core catalytic complex, inhibit CRL4-mediated ubiquitination, and cause stabilization of CRL4's substrate CDT1 in cells. Treatment with 33-11 or KH-4-43 in a panel of 36 tumor cell lines revealed cytotoxicity. The antitumor activity was validated by the ability of the compounds to suppress the growth of human tumor xenografts in mice. Mechanistically, the compounds' cytotoxicity was linked to aberrant accumulation of CDT1 that is known to trigger apoptosis. Moreover, a subset of tumor cells was found to express cullin4 proteins at levels as much as 70-fold lower than those in other tumor lines. The low-cullin4-expressing tumor cells appeared to exhibit increased sensitivity to 33-11/KH-4-43, raising a provocative hypothesis for the role of low E3 abundance as a cancer vulnerability.

Feasibility of free-breathing 19 F MRI image acquisition to characterize ventilation defects in CF and healthy volunteers at wash-in.

PURPOSE: To explore the feasibility of measuring ventilation defect percentage (VDP) using 19 F MRI during free-breathing wash-in of fluorinated gas mixture with postacquisition denoising and to compare these results with those obtained through traditional Cartesian breath-hold acquisitions. METHODS: Eight adults with cystic fibrosis and 5 healthy volunteers completed a single MR session on a Siemens 3T Prisma. 1 H Ultrashort-TE MRI sequences were used for registration and masking, and ventilation images with 19 F MRI were obtained while the subjects breathed a normoxic mixture of 79% perfluoropropane and 21% oxygen (O2 ). 19 F MRI was performed during breath holds and while free breathing with one overlapping spiral scan at breath hold for VDP value comparison. The 19 F spiral data were denoised using a low-rank matrix recovery approach. RESULTS: VDP measured using 19 F VIBE and 19 F spiral images were highly correlated (r = 0.84) at 10 wash-in breaths. Second-breath VDPs were also highly correlated (r = 0.88). Denoising greatly increased SNR (pre-denoising spiral SNR, 2.46 ± 0.21; post-denoising spiral SNR, 33.91 ± 6.12; and breath-hold SNR, 17.52 ± 2.08). CONCLUSION: Free-breathing 19 F lung MRI VDP analysis was feasible and highly correlated with breath-hold measurements. Free-breathing methods are expected to increase patient comfort and extend ventilation MRI use to patients who are unable to perform breath holds, including younger subjects and those with more severe lung disease.

Optimal shrinkage denoising breaks the noise floor in high-resolution diffusion MRI.

The spatial resolution attainable in diffusion magnetic resonance (MR) imaging is inherently limited by noise. The weaker signal associated with a smaller voxel size, especially at a high level of diffusion sensitization, is often buried under the noise floor owing to the non-Gaussian nature of the MR magnitude signal. Here, we show how the noise floor can be suppressed remarkably via optimal shrinkage of singular values associated with noise in complex-valued k-space data from multiple receiver channels. We explore and compare different low-rank signal matrix recovery strategies to utilize the inherently redundant information from multiple channels. In combination with background phase removal, the optimal strategy reduces the noise floor by 11 times. Our framework enables imaging with substantially improved resolution for precise characterization of tissue microstructure and white matter pathways without relying on expensive hardware upgrades and time-consuming acquisition repetitions, outperforming other related denoising methods.

A multimodal submillimeter MRI atlas of the human cerebellum.

The human cerebellum is engaged in a broad array of tasks related to motor coordination, cognition, language, attention, memory, and emotional regulation. A detailed cerebellar atlas can facilitate the investigation of the structural and functional organization of the cerebellum. However, existing cerebellar atlases are typically limited to a single imaging modality with insufficient characterization of tissue properties. Here, we introduce a multifaceted cerebellar atlas based on high-resolution multimodal MRI, facilitating the understanding of the neurodevelopment and neurodegeneration of the cerebellum based on cortical morphology, tissue microstructure, and intra-cerebellar and cerebello-cerebral connectivity.

Functional Hierarchy of the Human Neocortex from Cradle to Grave.

Recent evidence indicates that the organization of the human neocortex is underpinned by smooth spatial gradients of functional connectivity (FC). These gradients provide crucial insight into the relationship between the brain's topographic organization and the texture of human cognition. However, no studies to date have charted how intrinsic FC gradient architecture develops across the entire human lifespan. In this work, we model developmental trajectories of the three primary gradients of FC using a large, high-quality, and temporally-dense functional MRI dataset spanning from birth to 100 years of age. The gradient axes, denoted as sensorimotor-association (SA), visual-somatosensory (VS), and modulation-representation (MR), encode crucial hierarchical organizing principles of the brain in development and aging. By tracking their evolution throughout the human lifespan, we provide the first ever comprehensive low-dimensional normative reference of global FC hierarchical architecture. We observe significant age-related changes in global network features, with global markers of hierarchical organization increasing from birth to early adulthood and decreasing thereafter. During infancy and early childhood, FC organization is shaped by primary sensory processing, dense short-range connectivity, and immature association and control hierarchies. Functional differentiation of transmodal systems supported by long-range coupling drives a convergence toward adult-like FC organization during late childhood, while adolescence and early adulthood are marked by the expansion and refinement of SA and MR hierarchies. While gradient topographies remain stable during late adulthood and aging, we observe decreases in global gradient measures of FC differentiation and complexity from 30 to 100 years. Examining cortical microstructure gradients alongside our functional gradients, we observed that structure-function gradient coupling undergoes differential lifespan trajectories across multiple gradient axes.

Deep learning prediction of diffusion MRI data with microstructure-sensitive loss functions.

Deep learning prediction of diffusion MRI (DMRI) data relies on the utilization of effective loss functions. Existing losses typically measure the signal-wise differences between the predicted and target DMRI data without considering the quality of derived diffusion scalars that are eventually utilized for quantification of tissue microstructure. Here, we propose two novel loss functions, called microstructural loss and spherical variance loss, to explicitly consider the quality of both the predicted DMRI data and derived diffusion scalars. We apply these loss functions to the prediction of multi-shell data and enhancement of angular resolution. Evaluation based on infant and adult DMRI data indicates that both microstructural loss and spherical variance loss improve the quality of derived diffusion scalars.

Active pulmonary tuberculosis in a patient with secukinumab treatment.

Numerous investigations have documented active tuberculosis (TB) infection following biologic treatment. One of the most secure biologic medications for infections is secukinumab. Additionally, no cases of active TB while receiving secukinumab therapy were recorded. Secukinumab 150 mg per month has been administered for a 19-year-old man with spondyloarthritis since May 2020. A diagnosis of pulmonary TB was made when the patient complained of a moderate fever, a productive cough, and weight loss after 2 years. His fever and respiratory symptoms were relieved after 6 weeks of treatment by stopping secukinumab and utilizing 4 antibiotics: isoniazid, rifampicin, pyrazinamide, and ethambutol, while non-steroidal anti-inflammatory drugs reduced his joint and back discomfort. During biological therapy, even with secukinumab, annual screening for latent and active TB is crucial. We require additional study on secukinumab-treated patients with active TB in nations with high TB burdens, including Vietnam.

Rapid Diffusion Magnetic Resonance Imaging Using Slice-Interleaved Encoding.

In this paper, we present a robust reconstruction scheme for diffusion MRI (dMRI) data acquired using slice-interleaved diffusion encoding (SIDE). When combined with SIDE undersampling and simultaneous multi-slice (SMS) imaging, our reconstruction strategy is capable of significantly reducing the amount of data that needs to be acquired, enabling high-speed diffusion imaging for pediatric, elderly, and claustrophobic individuals. In contrast to the conventional approach of acquiring a full diffusion-weighted (DW) volume per diffusion wavevector, SIDE acquires in each repetition time (TR) a volume that consists of interleaved slice groups, each group corresponding to a different diffusion wavevector. This strategy allows SIDE to rapidly acquire data covering a large number of wavevectors within a short period of time. The proposed reconstruction method uses a diffusion spectrum model and multi-dimensional total variation to recover full DW images from DW volumes that are slice-undersampled due to unacquired SIDE volumes. We formulate an inverse problem that can be solved efficiently using the alternating direction method of multipliers (ADMM). Experiment results demonstrate that DW images can be reconstructed with high fidelity even when the acquisition is accelerated by 25 folds.

Elevated expression of the colony-stimulating factor 1 (CSF1) induces prostatic intraepithelial neoplasia dependent of epithelial-Gp130.

Macrophages are increased in human benign prostatic hyperplasia and prostate cancer. We generate a Pb-Csf1 mouse model with prostate-specific overexpression of macrophage colony-stimulating factor (M-Csf/Csf1). Csf1 overexpression promotes immune cell infiltration into the prostate, modulates the macrophage polarity in a lobe-specific manner, and induces senescence and low-grade prostatic intraepithelial neoplasia (PIN). The Pb-Csf1 prostate luminal cells exhibit increased stem cell features and undergo an epithelial-to-mesenchymal transition. Human prostate cancer patients with high CSF-1 expression display similar transcriptional alterations with the Pb-Csf1 model. P53 knockout alleviates senescence but fails to progress PIN lesions. Ablating epithelial Gp130 but not Il1r1 substantially blocks PIN lesion formation. The androgen receptor (AR) is downregulated in Pb-Csf1 mice. ChIP-Seq analysis reveals altered AR binding in 2482 genes although there is no significant widespread change in global AR transcriptional activity. Collectively, our study demonstrates that increased macrophage infiltration causes PIN formation but fails to transform prostate cells.

Noise Mapping and Removal in Complex-Valued Multi-Channel MRI via Optimal Shrinkage of Singular Values.

In magnetic resonance imaging (MRI), noise is a limiting factor for higher spatial resolution and a major cause of prolonged scan time, owing to the need for repeated scans. Improving the signal-to-noise ratio is therefore key to faster and higher-resolution MRI. Here we propose a method for mapping and reducing noise in MRI by leveraging the inherent redundancy in complex-valued multi-channel MRI data. Our method leverages a provably optimal strategy for shrinking the singular values of a data matrix, allowing it to outperform state-of-the-art methods such as Marchenko-Pastur PCA in noise reduction. Our method reduces the noise floor in brain diffusion MRI by 5-fold and remarkably improves the contrast of spiral lung 19F MRI. Our framework is fast and does not require training and hyper-parameter tuning, therefore providing a convenient means for improving SNR in MRI.

Characterizing Intra-soma Diffusion with Spherical Mean Spectrum Imaging.

Most brain microstructure models are dedicated to the quantification of white matter microstructure, using for example sticks, cylinders, and zeppelins to model intra- and extra-axonal environments. Gray matter presents unique micro-architecture with cell bodies (somas) exhibiting diffusion characteristics that differ from axons in white matter. In this paper, we introduce a method to quantify soma microstructure, giving measures such as volume fraction, diffusivity, and kurtosis. Our method captures a spectrum of diffusion patterns and scales and does not rely on restrictive model assumptions. We show that our method yields unique and meaningful contrasts that are in agreement with histological data. We demonstrate its application in the mapping of the distinct spatial patterns of soma density in the cortex.

Estimating Tissue Microstructure with Undersampled Diffusion Data via Graph Convolutional Neural Networks.

Advanced diffusion models for tissue microstructure are widely employed to study brain disorders. However, these models usually require diffusion MRI (DMRI) data with densely sampled q-space, which is prohibitive in clinical settings. This problem can be resolved by using deep learning techniques, which learn the mapping between sparsely sampled q-space data and the high-quality diffusion microstructural indices estimated from densely sampled data. However, most existing methods simply view the input DMRI data as a vector without considering data structure in the q-space. In this paper, we propose to overcome this limitation by representing DMRI data using graphs and utilizing graph convolutional neural networks to estimate tissue microstructure. Our method makes full use of the q-space angular neighboring information to improve estimation accuracy. Experimental results based on data from the Baby Connectome Project demonstrate that our method outperforms state-of-the-art methods both qualitatively and quantitatively.

Fast Correction of Eddy-Current and Susceptibility-Induced Distortions Using Rotation-Invariant Contrasts.

Diffusion MRI (dMRI) is typically time consuming as it involves acquiring a series of 3D volumes, each associated with a wave-vector in q-space that determines the diffusion direction and strength. The acquisition time is further increased when "blip-up blip-down" scans are acquired with opposite phase encoding directions (PEDs) to facilitate distortion correction. In this work, we show that geometric distortions can be corrected without acquiring with opposite PEDs for each wave-vector, and hence the acquisition time can be halved. Our method uses complimentary rotation-invariant contrasts across shells of different diffusion weightings. Distortion-free structural T1-/T2-weighted MRI is used as reference for nonlinear registration in correcting the distortions. Signal dropout and pileup are corrected with the help of spherical harmonics. To demonstrate that our method is robust to changes in image appearance, we show that distortion correction with good structural alignment can be achieved within minutes for dMRI data of infants between 1 to 24 months of age.

Probing Tissue Microarchitecture of the Baby Brain via Spherical Mean Spectrum Imaging.

During the first years of life, the human brain undergoes dynamic spatially-heterogeneous changes, invo- lving differentiation of neuronal types, dendritic arbori- zation, axonal ingrowth, outgrowth and retraction, synaptogenesis, and myelination. To better quantify these changes, this article presents a method for probing tissue microarchitecture by characterizing water diffusion in a spectrum of length scales, factoring out the effects of intra-voxel orientation heterogeneity. Our method is based on the spherical means of the diffusion signal, computed over gradient directions for a set of diffusion weightings (i.e., b -values). We decompose the spherical mean profile at each voxel into a spherical mean spectrum (SMS), which essentially encodes the fractions of spin packets undergoing fine- to coarse-scale diffusion proce- sses, characterizing restricted and hindered diffusion stemming respectively from intra- and extra-cellular water compartments. From the SMS, multiple orientation distribution invariant indices can be computed, allowing for example the quantification of neurite density, microscopic fractional anisotropy ( µ FA), per-axon axial/radial diffusivity, and free/restricted isotropic diffusivity. We show that these indices can be computed for the developing brain for greater sensitivity and specificity to development related changes in tissue microstructure. Also, we demonstrate that our method, called spherical mean spectrum imaging (SMSI), is fast, accurate, and can overcome the biases associated with other state-of-the-art microstructure models.

Inhibition and Crystal Structure of the Human DHTKD1-Thiamin Diphosphate Complex.

DHTKD1 is the E1 component of the 2-oxoadipate dehydrogenase complex, which is an enzyme involved in the catabolism of (hydroxy-)lysine and tryptophan. Mutations in DHTKD1 have been associated with 2-aminoadipic and 2-oxoadipic aciduria, Charcot-Marie-Tooth disease type 2Q and eosinophilic esophagitis, but the pathophysiology of these clinically distinct disorders remains elusive. Here, we report the identification of adipoylphosphonic acid and tenatoprazole as DHTKD1 inhibitors using targeted and high throughput screening, respectively. We furthermore elucidate the DHTKD1 crystal structure with thiamin diphosphate bound at 2.25 Å. We also report the impact of 10 disease-associated missense mutations on DHTKD1. Whereas the majority of the DHTKD1 variants displayed impaired folding or reduced thermal stability in combination with absent or reduced enzyme activity, three variants showed no abnormalities. Our work provides chemical and structural tools for further understanding of the function of DHTKD1 and its role in several human pathologies.

Longitudinal Harmonization for Improving Tractography in Baby Diffusion MRI.

The human brain develops very rapidly in the first years of life, resulting in significant changes in water diffusion anisotropy. Developmental changes pose significant challenges to longitudinally consistent white matter tractography. In this paper, we will introduce a method to harmonize infant diffusion MRI data longitudinally across time. Specifically, we harmonize diffusion MRI data collected at an earlier time point to data collected at a later time point. This will promote longitudinal consistency and allow sharpening of fiber orientation distribution functions (ODFs) based on information available at the later time point. For this purpose, we will introduce an approach that is based on the method of moments, which allows harmonization to be performed directly on the diffusion-attenuated signal without the need to fit any diffusion models to the data. Given two diffusion MRI datasets, our method harmonizes them voxel-wise using well-behaving mapping functions (i.e., monotonic, diffeomorphic, etc.), parameters of which are determined by matching the spherical moments (i.e., mean, variance, skewness, etc.) of signal measurements on each shell. The mapping functions we use is isotropic and does not introduce new orientations that are not already in the original data. Our analysis indicates that longitudinal harmonization sharpens ODFs and improves tractography in infant diffusion MRI.

Multi-Site Harmonization of Diffusion MRI Data via Method of Moments.

Diffusion MRI is a powerful tool for non-invasive probing of brain tissue microstructure. Recent multi-center efforts in the acquisition and analysis of diffusion MRI data significantly increase sample sizes and hence improve sensitivity and reliability in detecting subtle changes associated with development, aging, and diseases. However, discrepancies resulting from different scanner vendors, acquisition protocols, and image reconstruction algorithms can cause data incompatibility across imaging centers. In this paper, we introduce a model-free method that is based on the method of moments for the direct harmonization of diffusion MRI data to reduce site-specific variations. Our method directly harmonizes diffusion-attenuated signal without the need to fit any diffusion model. Moreover, our method allows the explicit definition of well-behaved mapping functions with properties such as invertibility, smoothness, and injectivity. We show that our method is effective in lowering the variations of diffusion scalars of traveling human phantoms scanned at different sites from 1%-3% to less than 0.9% for fractional anisotropy (FA) and mean diffusivity and from 1%-2.5% to 0.3%-1.2% for generalized FA. We also demonstrate its ability in preserving individual differences and in increasing across-site consistency in tractography and white matter connectivity.

Characterizing Non-Gaussian Diffusion in Heterogeneously Oriented Tissue Microenvironments.

Complex tissue microstructure involving various types of cells and their membranes can deviate the movement of water molecules from the typical Gaussian diffusion. This deviation can be quantified using excess kurtosis to characterize tissue structural complexity. However, true kurtosis measurements can be obscured by complex white matter configurations such as fiber crossing, bending, and branching, which are ubiquitous in the brain. In this paper, we extend diffusion kurtosis imaging (DKI) to allow characterization of diffusional kurtosis in microstructural environments that are oriented heterogeneously. Our method, called microscopic DKI (µDKI), fits a cylindrically symmetric kurtosis model to the spherical mean of the diffusion signal as a function of diffusion weighting. The spherical mean, computed for each b-shell, is invariant to the fiber orientation distribution and is a function of per-axon microstructural properties. Experimental results indicate that µDKI yields significantly higher consistency in quantifying microstructure than the conventional DKI in the presence of orientation heterogeneity.

Probing Brain Micro-architecture by Orientation Distribution Invariant Identification of Diffusion Compartments.

Precise quantification of brain tissue micro-architecture using diffusion MRI is hampered by the conflation of diffusion-attenuated signals from micro-environments that can be orientationally heterogeneous due to complex fiber configurations, such as crossing, fanning, and bending, and compartmentally heterogeneous due to variability in tissue organization. In this paper, we introduce a method, called Spherical Mean Spectrum Imaging (SMSI), for quantification of tissue microstructure. SMSI does not assume a fixed number of compartments, but characterizes the signal as a spectrum of fine- to coarse-scale diffusion processes. Using SMSI, multiple orientation distribution invariant indices can be computed, allowing for example the quantification of neurite density, microscopic fractional anisotropy (µFA), per-axon axial/radial diffusivity, and free/restricted isotropic diffusivity. We show that SMSI is fast, accurate, and can overcome biases in state-of-the-art microstructure models. We demonstrate its application in probing microstructural changes in the baby brain during the first two years of life.