Epigenetic MRI: Noninvasive imaging of DNA methylation in the brain.

Both neuronal and genetic mechanisms regulate brain function. While there are excellent methods to study neuronal activity in vivo, there are no nondestructive methods to measure global gene expression in living brains. Here, we present a method, epigenetic MRI (eMRI), that overcomes this limitation via direct imaging of DNA methylation, a major gene-expression regulator. eMRI exploits the methionine metabolic pathways for DNA methylation to label genomic DNA through 13C-enriched diets. A 13C magnetic resonance spectroscopic imaging method then maps the spatial distribution of labeled DNA. We validated eMRI using pigs, whose brains have stronger similarity to humans in volume and anatomy than rodents, and confirmed efficient 13C-labeling of brain DNA. We also discovered strong regional differences in global DNA methylation. Just as functional MRI measurements of regional neuronal activity have had a transformational effect on neuroscience, we expect that the eMRI signal, both as a measure of regional epigenetic activity and as a possible surrogate for regional gene expression, will enable many new investigations of human brain function, behavior, and disease.

Accelerated 3D metabolite T1 mapping of the brain using variable-flip-angle SPICE.

PURPOSE: To develop a practical method to enable 3D T1 mapping of brain metabolites. THEORY AND METHODS: Due to the high dimensionality of the imaging problem underlying metabolite T1 mapping, measurement of metabolite T1 values has been currently limited to a single voxel or slice. This work achieved 3D metabolite T1 mapping by leveraging a recent ultrafast MRSI technique called SPICE (spectroscopic imaging by exploiting spatiospectral correlation). The Ernst-angle FID MRSI data acquisition used in SPICE was extended to variable flip angles, with variable-density sparse sampling for efficient encoding of metabolite T1 information. In data processing, a novel generalized series model was used to remove water and subcutaneous lipid signals; a low-rank tensor model with prelearned subspaces was used to reconstruct the variable-flip-angle metabolite signals jointly from the noisy data. RESULTS: The proposed method was evaluated using both phantom and healthy subject data. Phantom experimental results demonstrated that high-quality 3D metabolite T1 maps could be obtained and used for correction of T1 saturation effects. In vivo experimental results showed metabolite T1 maps with a large spatial coverage of 240 × 240 × 72 mm3 and good reproducibility coefficients (< 11%) in a 14.5-min scan. The metabolite T1 times obtained ranged from 0.99 to 1.44 s in gray matter and from 1.00 to 1.35 s in white matter. CONCLUSION: We successfully demonstrated the feasibility of 3D metabolite T1 mapping within a clinically acceptable scan time. The proposed method may prove useful for both T1 mapping of brain metabolites and correcting the T1-weighting effects in quantitative metabolic imaging.

Optimization of 3D dynamic speech MRI: Poisson-disc undersampling and locally higher-rank reconstruction through partial separability model with regional optimized temporal basis.

PURPOSE: To improve the spatiotemporal qualities of images and dynamics of speech MRI through an improved data sampling and image reconstruction approach. METHODS: For data acquisition, we used a Poisson-disc random under sampling scheme that reduced the undersampling coherence. For image reconstruction, we proposed a novel locally higher-rank partial separability model. This reconstruction model represented the oral and static regions using separate low-rank subspaces, therefore, preserving their distinct temporal signal characteristics. Regional optimized temporal basis was determined from the regional-optimized virtual coil approach. Overall, we achieved a better spatiotemporal image reconstruction quality with the potential of reducing total acquisition time by 50%. RESULTS: The proposed method was demonstrated through several 2-mm isotropic, 64 mm total thickness, dynamic acquisitions with 40 frames per second and compared to the previous approach using a global subspace model along with other k-space sampling patterns. Individual timeframe images and temporal profiles of speech samples were shown to illustrate the ability of the Poisson-disc under sampling pattern in reducing total acquisition time. Temporal information of sagittal and coronal directions was also shown to illustrate the effectiveness of the locally higher-rank operator and regional optimized temporal basis. To compare the reconstruction qualities of different regions, voxel-wise temporal SNR analysis were performed. CONCLUSION: Poisson-disc sampling combined with a locally higher-rank model and a regional-optimized temporal basis can drastically improve the spatiotemporal image quality and provide a 50% reduction in overall acquisition time.

Broadband lens and graphene/silicon photodiode for wide spectral imaging.

We designed a broadband lens along with a graphene/silicon photodiode for wide spectral imaging ranging from ultraviolet to near-infrared wavelengths. By using five spherical glass lenses, the broadband lens, with the modulation transfer function of 0.38 at 100 lp/mm, corrects aberrations ranging from 340 to 1700 nm. Our design also includes a broadband graphene/silicon Schottky photodiode with the highest responsivity of 0.63 A/W ranging from ultraviolet to near-infrared. By using the proposed broadband lens and the broadband graphene/silicon photodiode, several single-pixel imaging designs in ultraviolet, visible, and near-infrared wavelengths are demonstrated. Experimental results show the advantages of integrating the lens with the photodiode and the potential to realize broadband imaging with a single set of lens and a detector.

Simultaneous high-resolution whole-brain MR spectroscopy and [18F]FDG PET for temporal lobe epilepsy.

PURPOSE: Precise lateralizing the epileptogenic zone in patients with drug-resistant mesial temporal lobe epilepsy (mTLE) remains challenging, particularly when routine MRI scans are inconclusive (MRI-negative). This study aimed to investigate the synergy of fast, high-resolution, whole-brain MRSI in conjunction with simultaneous [18F]FDG PET for the lateralization of mTLE. METHODS: Forty-eight drug-resistant mTLE patients (M/F 31/17, age 12-58) underwent MRSI and [18F]FDG PET on a hybrid PET/MR scanner. Lateralization of mTLE was evaluated by visual inspection and statistical classifiers of metabolic mappings against routine MRI. Additionally, this study explored how disease status influences the associations between altered N-acetyl aspartate (NAA) and FDG uptake using hierarchical moderated multiple regression. RESULTS: The high-resolution whole-brain MRSI data offers metabolite maps at comparable resolution to [18F]FDG PET. Visual examinations of combined MRSI and [18F]FDG PET showed an mTLE lateralization accuracy rate of 91.7% in a 48-patient cohort, surpassing routine MRI (52.1%). Notably, out of 23 MRI-negative mTLE, combined MRSI and [18F]FDG PET helped detect 19 cases. Logistical regression models combining hippocampal NAA level and FDG uptake improved lateralization performance (AUC=0.856), while further incorporating extrahippocampal regions such as amygdala, thalamus, and superior temporal gyrus increased the AUC to 0.939. Concurrent MRSI/PET revealed a moderating influence of disease duration and hippocampal atrophy on the association between hippocampal NAA and glucose uptake, providing significant new insights into the disease's trajectory. CONCLUSION: This paper reports the first metabolic imaging study using simultaneous high-resolution MRSI and [18F]FDG PET, which help visualize MRI-unidentifiable lesions and may thus advance diagnostic tools and management strategies for drug-resistant mTLE.

Identification and characterization of a novel hydroxylamine oxidase, DnfA, that catalyzes the oxidation of hydroxylamine to N2.

Nitrogen (N2) gas in the atmosphere is partially replenished by microbial denitrification of ammonia. Recent study has shown that Alcaligenes ammonioxydans oxidizes ammonia to dinitrogen via a process featuring the intermediate hydroxylamine, termed "Dirammox" (direct ammonia oxidation). However, the unique biochemistry of this process remains unknown. Here, we report an enzyme involved in Dirammox that catalyzes the conversion of hydroxylamine to N2. We tested previously annotated proteins involved in redox reactions, DnfA, DnfB, and DnfC, to determine their ability to catalyze the oxidation of ammonia or hydroxylamine. Our results showed that none of these proteins bound to ammonia or catalyzed its oxidation; however, we did find DnfA bound to hydroxylamine. Further experiments demonstrated that, in the presence of NADH and FAD, DnfA catalyzed the conversion of 15N-labeled hydroxylamine to 15N2. This conversion did not happen under oxygen (O2)-free conditions. Thus, we concluded that DnfA encodes a hydroxylamine oxidase. We demonstrate that DnfA is not homologous to any known hydroxylamine oxidoreductases and contains a diiron center, which was shown to be involved in catalysis via electron paramagnetic resonance experiments. Furthermore, enzyme kinetics of DnfA were assayed, revealing a Km of 92.9 ± 3.0 µM for hydroxylamine and a kcat of 0.028 ± 0.001 s-1. Finally, we show that DnfA was localized in the cytoplasm and periplasm as well as in tubular membrane invaginations in HO-1 cells. To the best of our knowledge, we conclude that DnfA is the first enzyme discovered that catalyzes oxidation of hydroxylamine to N2.

JSENSE-Pro: Joint sensitivity estimation and image reconstruction in parallel imaging using pre-learned subspaces of coil sensitivity functions.

PURPOSE: To improve calibrationless parallel imaging using pre-learned subspaces of coil sensitivity functions. THEORY AND METHODS: A subspace-based joint sensitivity estimation and image reconstruction method was developed for improved parallel imaging with no calibration data. Specifically, we proposed to use a probabilistic subspace model to capture prior information of the coil sensitivity functions from previous scans acquired using the same receiver system. Both the subspace basis and coefficient distributions were learned from a small set of training data. The learned subspace model was then incorporated into the regularized reconstruction formalism that includes a sparsity prior. The nonlinear optimization problem was solved using alternating minimization algorithm. Public fastMRI brain dataset was retrospectively undersampled by different schemes for performance evaluation of the proposed method. RESULTS: With no calibration data, the proposed method consistently produced the most accurate coil sensitivity estimation and highest quality image reconstructions at all acceleration factors tested in comparison with state-of-the-art methods including JSENSE, DeepSENSE, P-LORAKS, and Sparse BLIP. Our results are comparable to or even better than those from SparseSENSE, which used calibration data for sensitivity estimation. The work also demonstrated that the probabilistic subspace model learned from T2 w data can be generalized to aiding the reconstruction of FLAIR data acquired from the same receiver system. CONCLUSION: A subspace-based method named JSENSE-Pro has been proposed for accelerated parallel imaging without the acquisition of companion calibration data. The method is expected to further enhance the practical utility of parallel imaging, especially in applications where calibration data acquisition is not desirable or limited.

Enhancing linguistic research through 2-mm isotropic 3D dynamic speech MRI optimized by sparse temporal sampling and low-rank reconstruction.

PURPOSE: To enable a more comprehensive view of articulations during speech through near-isotropic 3D dynamic MRI with high spatiotemporal resolution and large vocal-tract coverage. METHODS: Using partial separability model-based low-rank reconstruction coupled with a sparse acquisition of both spatial and temporal models, we are able to achieve near-isotropic resolution 3D imaging with a high frame rate. The total acquisition time of the speech acquisition is shortened by introducing a sparse temporal sampling that interleaves one temporal navigator with four randomized phase and slice-encoded imaging samples. Memory and computation time are improved through compressing coils based on the region of interest for low-rank constrained reconstruction with an edge-preserving spatial penalty. RESULTS: The proposed method has been evaluated through experiments on several speech samples, including a standard reading passage. A near-isotropic 1.875 × 1.875 × 2 mm3 spatial resolution, 64-mm through-plane coverage, and a 35.6-fps temporal resolution are achieved. Investigations and analysis on specific speech samples support novel insights into nonsymmetric tongue movement, velum raising, and coarticulation events with adequate visualization of rapid articulatory movements. CONCLUSION: Three-dimensional dynamic images of the vocal tract structures during speech with high spatiotemporal resolution and axial coverage is capable of enhancing linguistic research, enabling visualization of soft tissue motions that are not possible with other modalities.

B1 mapping using pre-learned subspaces for quantitative brain imaging.

PURPOSE: To develop a machine learning-based method for estimation of both transmitter and receiver B1 fields desired for correction of the B1 inhomogeneity effects in quantitative brain imaging. THEORY AND METHODS: A subspace model-based machine learning method was proposed for estimation of B1t and B1r fields. Probabilistic subspace models were used to capture scan-dependent variations in the B1 fields; the subspace basis and coefficient distributions were learned from pre-scanned training data. Estimation of the B1 fields for new experimental data was achieved by solving a linear optimization problem with prior distribution constraints. We evaluated the performance of the proposed method for B1 inhomogeneity correction in quantitative brain imaging scenarios, including T1 and proton density (PD) mapping from variable-flip-angle spoiled gradient-echo (SPGR) data as well as neurometabolic mapping from MRSI data, using phantom, healthy subject and brain tumor patient data. RESULTS: In both phantom and healthy subject data, the proposed method produced high-quality B1 maps. B1 correction on SPGR data using the estimated B1 maps produced significantly improved T1 and PD maps. In brain tumor patients, the proposed method produced more accurate and robust B1 estimation and correction results than conventional methods. The B1 maps were also applied to MRSI data from tumor patients and produced improved neurometabolite maps, with better separation between pathological and normal tissues. CONCLUSION: This work presents a novel method to estimate B1 variations using probabilistic subspace models and machine learning. The proposed method may make correction of B1 inhomogeneity effects more robust in practical applications.

Predicting the Onset of Ischemic Stroke With Fast High-Resolution 3D MR Spectroscopic Imaging.

BACKGROUND: Neurometabolite concentrations provide a direct index of infarction progression in stroke. However, their relationship with stroke onset time remains unclear. PURPOSE: To assess the temporal dynamics of N-acetylaspartate (NAA), creatine, choline, and lactate and estimate their value in predicting early (<6 hours) vs. late (6-24 hours) hyperacute stroke groups. STUDY TYPE: Cross-sectional cohort. POPULATION: A total of 73 ischemic stroke patients scanned at 1.8-302.5 hours after symptom onset, including 25 patients with follow-up scans. FIELD STRENGTH/SEQUENCE: A 3 T/magnetization-prepared rapid acquisition gradient echo sequence for anatomical imaging, diffusion-weighted imaging and fluid-attenuated inversion recovery imaging for lesion delineation, and 3D MR spectroscopic imaging (MRSI) for neurometabolic mapping. ASSESSMENT: Patients were divided into hyperacute (0-24 hours), acute (24 hours to 1 week), and subacute (1-2 weeks) groups, and into early (<6 hours) and late (6-24 hours) hyperacute groups. Bayesian logistic regression was used to compare classification performance between early and late hyperacute groups by using different combinations of neurometabolites as inputs. STATISTICAL TESTS: Linear mixed effects modeling was applied for group-wise comparisons between NAA, creatine, choline, and lactate. Pearson's correlation analysis was used for neurometabolites vs. time. P < 0.05 was considered statistically significant. RESULTS: Lesional NAA and creatine were significantly lower in subacute than in acute stroke. The main effects of time were shown on NAA (F = 14.321) and creatine (F = 12.261). NAA was significantly lower in late than early hyperacute patients, and was inversely related to time from symptom onset across both groups (r = -0.440). The decrease of NAA and increase of lactate were correlated with lesion volume (NAA: r = -0.472; lactate: r = 0.366) in hyperacute stroke. Discrimination was improved by combining NAA, creatine, and choline signals (area under the curve [AUC] = 0.90). DATA CONCLUSION: High-resolution 3D MRSI effectively assessed the neurometabolite changes and discriminated early and late hyperacute stroke lesions. EVIDENCE LEVEL: 1. TECHNICAL EFFICACY: Stage 2.

Design, synthesis and biological evaluation of cajanonic acid A analogues as potent PPAR γ antagonists.

Peroxisome proliferator-activated receptor γ (PPAR γ) antagonists are a key instrument of insulin sensitizers since they have the ability to sensitize insulin and can avoid adverse reactions caused by receptor agonist. In this paper, two series of 28 novel Cajanonic acid A (CAA) derivatives were designed and synthesized. The biological activity showed that a novel CAA derivative 9f was identified as a potential PPAR γ antagonist by medicinal chemistry efforts. The results in vitro displayed that compound 9f could improve the PPAR γ antagonist activity (96.2 % / 50.2 % decrease in PPAR γ transactivation at 10 µM / 1 µM, respectively). It also could improve the glucose consumption activity of insulin-resistant HepG2/3T3-L1 cell line (33.27 % / 72.61 % increase in glucose consumption). And in 3 T3-L1 adipocytes, it showed anti-adipogenesis activity (7.04 % increase in oil red staining). Further, in vivo study suggested that compound 9f could improve the oral glucose tolerance in db/db mice. Taken together, derivative 9f served as a promising candidate for anti-diabetic drug discovery and deserve further study.

The sulfonamide-resistance dihydropteroate synthase gene is crucial for efficient biodegradation of sulfamethoxazole by Paenarthrobacter species.

Sulfonamide antibiotics (SAs) are serious pollutants to ecosystems and environments. Previous studies showed that microbial degradation of SAs such as sulfamethoxazole (SMX) proceeds via a sad-encoded oxidative pathway, while the sulfonamide-resistant dihydropteroate synthase gene, sul, is responsible for SA resistance. However, the co-occurrence of sad and sul genes, as well as how the sul gene affects SMX degradation, was not explored. In this study, two SMX-degrading bacterial strains, SD-1 and SD-2, were cultivated from an SMX-degrading enrichment. Both strains were Paenarthrobacter species and were phylogenetically identical; however, they showed different SMX degradation activities. Specifically, strain SD-1 utilized SMX as the sole carbon and energy source for growth and was a highly efficient SMX degrader, while SD-2 did could not use SMX as a sole carbon or energy source and showed limited SMX degradation when an additional carbon source was supplied. Genome annotation, growth, enzymatic activity tests, and metabolite detection revealed that strains SD-1 and SD-2 shared a sad-encoded oxidative pathway for SMX degradation and a pathway of protocatechuate degradation. A new sulfonamide-resistant dihydropteroate synthase gene, sul918, was identified in strain SD-1, but not in SD-2. Moreover, the lack of sul918 resulted in low SMX degradation activity in strain SD-2. Genome data mining revealed the co-occurrence of sad and sul genes in efficient SMX-degrading Paenarthrobacter strains. We propose that the co-occurrence of sulfonamide-resistant dihydropteroate synthase and sad genes is crucial for efficient SMX biodegradation. KEY POINTS: • Two sulfamethoxazole-degrading strains with distinct degrading activity, Paenarthrobacter sp. SD-1 and Paenarthrobacter sp. SD-2, were isolated and identified. • Strains SD-1 and SD-2 shared a sad-encoded oxidative pathway for SMX degradation. • A new plasmid-borne SMX resistance gene (sul918) of strain SD-1 plays a crucial role in SMX degradation efficiency.

High-Dimensional MR Spatiospectral Imaging by Integrating Physics-Based Modeling and Data-Driven Machine Learning: Current progress and future directions.

Magnetic resonance spectroscopic imaging (MRSI) offers a unique molecular window into the physiological and pathological processes in the human body. However, the applications of MRSI have been limited by a number of long-standing technical challenges due to high dimensionality and low signal-to-noise ratio (SNR). Recent technological developments integrating physics-based modeling and data-driven machine learning that exploit unique physical and mathematical properties of MRSI signals have demonstrated impressive performance in addressing these challenges for rapid, high-resolution, quantitative MRSI. This paper provides a systematic review of these progresses in the context of MRSI physics and offers perspectives on promising future directions.

Preliminary Development of an MRI Atlas for Application to Cleft Care: Findings and Future Recommendations.

OBJECTIVE: To introduce a highly innovative imaging method to study the complex velopharyngeal (VP) system and introduce the potential future clinical applications of a VP atlas in cleft care. DESIGN: Four healthy adults participated in a 20-min dynamic magnetic resonance imaging scan that included a high-resolution T2-weighted turbo-spin-echo 3D structural scan and five custom dynamic speech imaging scans. Subjects repeated a variety of phrases when in the scanner as real-time audio was captured. SETTING: Multisite institution and clinical setting. PARTICIPANTS: Four adult subjects with normal anatomy were recruited for this study. MAIN OUTCOME: Establishment of 4-D atlas constructed from dynamic VP MRI data. RESULTS: Three-dimensional dynamic magnetic resonance imaging was successfully used to obtain high quality dynamic speech scans in an adult population. Scans were able to be re-sliced in various imaging planes. Subject-specific MR data were then reconstructed and time-aligned to create a velopharyngeal atlas representing the averaged physiological movements across the four subjects. CONCLUSIONS: The current preliminary study examined the feasibility of developing a VP atlas for potential clinical applications in cleft care. Our results indicate excellent potential for the development and use of a VP atlas for assessing VP physiology during speech.

DeepSENSE: Learning coil sensitivity functions for SENSE reconstruction using deep learning.

PURPOSE: To improve the estimation of coil sensitivity functions from limited auto-calibration signals (ACS) in SENSE-based reconstruction for brain imaging. METHODS: We propose to use deep learning to estimate coil sensitivity functions by leveraging information from previous scans obtained using the same RF receiver system. Specifically, deep convolutional neural networks were designed to learn an end-to-end mapping from the initial sensitivity to the high-resolution counterpart. Sensitivity alignment was further proposed to reduce the geometric variation caused by different subject positions and imaging FOVs. Cross-validation with a small set of datasets was performed to validate the learned neural network. Iterative SENSE reconstruction was adopted to evaluate the utility of the sensitivity functions from the proposed and conventional methods. RESULTS: The proposed method produced improved sensitivity estimates and SENSE reconstructions compared to the conventional methods in terms of aliasing and noise suppression with very limited ACS data. Cross-validation with a small set of data demonstrated the feasibility of learning coil sensitivity functions for brain imaging. The network learned on the spoiled GRE data can be applied to predict sensitivity functions for spin-echo and MPRAGE datasets. CONCLUSION: A deep learning-based method has been proposed for improving the estimation of coil sensitivity functions. Experimental results have demonstrated the feasibility and potential of the proposed method for improving SENSE-based reconstructions especially when the ACS data are limited.

T 2 ' mapping of the brain from water-unsuppressed 1 H-MRSI and turbo spin-echo data.

PURPOSE: To obtain high-quality T 2 ' $$ {\mathrm{T}}_2^{\prime } $$ maps of brain tissues from water-unsuppressed magnetic resonance spectroscopic imaging (MRSI) and turbo spin-echo (TSE) data. METHODS: T 2 ' $$ {\mathrm{T}}_2^{\prime } $$ mapping can be achieved using T 2 * $$ {\mathrm{T}}_2^{\ast } $$ mapping from water-unsuppressed MRSI data and T 2 $$ {\mathrm{T}}_2 $$ mapping from TSE data. However, T 2 * $$ {\mathrm{T}}_2^{\ast } $$ mapping often suffers from signal dephasing and distortions caused by B 0 $$ {\mathrm{B}}_0 $$ field inhomogeneity; T 2 $$ {\mathrm{T}}_2 $$ measurements may be biased due to system imperfections, especially for T 2 $$ {\mathrm{T}}_2 $$ -weighted image with small number of TEs. In this work, we corrected the B 0 $$ {\mathrm{B}}_0 $$ field inhomogeneity effect on T 2 * $$ {\mathrm{T}}_2^{\ast } $$ mapping using a subspace model-based method, incorporating pre-learned spectral basis functions of the water signals. T 2 $$ {\mathrm{T}}_2 $$ estimation bias was corrected using a TE-adjustment method, which modeled the deviation between measured and reference T 2 $$ {\mathrm{T}}_2 $$ decays as TE shifts. RESULTS: In vivo experiments were performed to evaluate the performance of the proposed method. High-quality T 2 * $$ {\mathrm{T}}_2^{\ast } $$ maps were obtained in the presence of large field inhomogeneity in the prefrontal cortex. Bias in T 2 $$ {\mathrm{T}}_2 $$ measurements obtained from TSE data was effectively reduced. Based on the T 2 * $$ {\mathrm{T}}_2^{\ast } $$ and T 2 $$ {\mathrm{T}}_2 $$ measurements produced by the proposed method, high-quality T 2 ' $$ {\mathrm{T}}_2^{\prime } $$ maps were obtained, along with neurometabolite maps, from MRSI and TSE data that were acquired in about 9 min. The results obtained from acute stroke and glioma patients demonstrated the feasibility of the proposed method in the clinical setting. CONCLUSIONS: High-quality T 2 ' $$ {\mathrm{T}}_2^{\prime } $$ maps can be obtained from water-unsuppressed 1 H-MRSI and TSE data using the proposed method. With further development, this method may lay a foundation for simultaneously imaging oxygenation and neurometabolic alterations of brain disorders.

Genetic Foundations of Direct Ammonia Oxidation (Dirammox) to N2 and MocR-Like Transcriptional Regulator DnfR in Alcaligenes faecalis Strain JQ135.

Ammonia oxidation is an important process in both the natural nitrogen cycle and nitrogen removal from engineered ecosystems. Recently, a new ammonia oxidation pathway termed Dirammox (direct ammonia oxidation, NH3→NH2OH→N2) has been identified in Alcaligenes ammonioxydans. However, whether Dirammox is present in other microbes, as well as its genetic regulation, remains unknown. In this study, it was found that the metabolically versatile bacterium Alcaligenes faecalis strain JQ135 could efficiently convert ammonia into N2 via NH2OH under aerobic conditions. Genetic deletion and complementation results suggest that dnfABC is responsible for the ammonia oxidation to N2 in this strain. Strain JQ135 also employs aerobic denitrification, mainly producing N2O and trace amounts of N2, with nitrite as the sole nitrogen source. Deletion of the nirK and nosZ genes, which are essential for denitrification, did not impair the capability of JQ135 to oxidize ammonia to N2 (i.e., Dirammox is independent of denitrification). Furthermore, it was also demonstrated that pod (which encodes pyruvic oxime dioxygenase) was not involved in Dirammox and that AFA_16745 (which was previously annotated as ammonia monooxygenase and is widespread in heterotrophic bacteria) was not an ammonia monooxygenase. The MocR-family transcriptional regulator DnfR was characterized as an activator of the dnfABC operon with the binding motif 5'-TGGTCTGT-3' in the promoter region. A bioinformatic survey showed that homologs of dnf genes are widely distributed in heterotrophic bacteria. In conclusion, this work demonstrates that, besides A. ammonioxydans, Dirammox occurs in other bacteria and is regulated by the MocR-family transcriptional regulator DnfR. IMPORTANCE Microbial ammonia oxidation is a key and rate-limiting step of the nitrogen cycle. Three previously known ammonia oxidation pathways (i.e., nitrification, anaerobic ammonia oxidation [Anammox], and complete ammonia oxidation [Comammox]) are mediated by autotrophic microbes. However, the genetic foundations of ammonia oxidation by heterotrophic microorganisms have not been investigated in depth. Recently, a previously unknown pathway, termed direct ammonia oxidation to N2 (Dirammox), has been identified in the heterotrophic bacterium Alcaligenes ammonioxydans HO-1. This paper shows that, in the metabolically versatile bacterium Alcaligenes faecalis JQ135, the Dirammox pathway is mediated by dnf genes, which are independent of the denitrification pathway. A bioinformatic survey suggests that homologs of dnf genes are widely distributed in bacteria. These findings enhance the understanding of the molecular mechanisms of heterotrophic ammonia oxidation to N2.

Novel Alcaligenes ammonioxydans sp. nov. from wastewater treatment sludge oxidizes ammonia to N2 with a previously unknown pathway.

Heterotrophic nitrifiers are able to oxidize and remove ammonia from nitrogen-rich wastewaters but the genetic elements of heterotrophic ammonia oxidation are poorly understood. Here, we isolated and identified a novel heterotrophic nitrifier, Alcaligenes ammonioxydans sp. nov. strain HO-1, oxidizing ammonia to hydroxylamine and ending in the production of N2 gas. Genome analysis revealed that strain HO-1 encoded a complete denitrification pathway but lacks any genes coding for homologous to known ammonia monooxygenases or hydroxylamine oxidoreductases. Our results demonstrated strain HO-1 denitrified nitrite (not nitrate) to N2 and N2 O at anaerobic and aerobic conditions respectively. Further experiments demonstrated that inhibition of aerobic denitrification did not stop ammonia oxidation and N2 production. A gene cluster (dnfT1RT2ABCD) was cloned from strain HO-1 and enabled E. coli accumulated hydroxylamine. Sub-cloning showed that genetic cluster dnfAB or dnfABC already enabled E. coli cells to produce hydroxylamine and further to 15 N2 from (15 NH4 )2 SO4 . Transcriptome analysis revealed these three genes dnfA, dnfB and dnfC were significantly upregulated in response to ammonia stimulation. Taken together, we concluded that strain HO-1 has a novel dnf genetic cluster for ammonia oxidation and this dnf genetic cluster encoded a previously unknown pathway of direct ammonia oxidation (Dirammox) to N2 .

High-resolution sodium imaging using anatomical and sparsity constraints for denoising and recovery of novel features.

PURPOSE: To develop and evaluate a novel method for reconstruction of high-quality sodium MR images from noisy, limited k-space data. THEORY AND METHODS: A novel reconstruction method was developed for reconstruction of high-quality sodium images from noisy, limited k-space data. This method is based on a novel image model that contains a motion-compensated generalized series model and a sparse model. The motion-compensated generalized series model enables effective use of anatomical information from a proton image for denoising and resolution enhancement of sodium data, whereas the sparse model enables high-resolution reconstruction of sodium-dependent novel features. The underlying model estimation problems were solved efficiently using convex optimization algorithms. RESULTS: The proposed method has been evaluated using both simulation and experimental data obtained from phantoms, healthy human volunteers, and tumor patients. Results showed a substantial improvement in spatial resolution and SNR over state-of-the-art reconstruction methods, including compressed sensing and anatomically constrained reconstruction methods. Quantitative tissue sodium concentration maps were obtained from both healthy volunteers and brain tumor patients. These tissue sodium concentration maps showed improved lesion fidelity and allowed accurate interrogation of small targets. CONCLUSION: A new method has been developed to obtain high-resolution sodium images with good SNR at 3 T. The proposed method makes effective use of anatomical prior information for denoising, while using a sparse model synergistically to recover sodium-dependent novel features. Experimental results have been obtained to demonstrate the feasibility of achieving high-quality tissue sodium concentration maps and their potential for improved detection of spatially heterogeneous responses of tumor to treatment.

Improved estimation of myelin water fractions with learned parameter distributions.

PURPOSE: To improve estimation of myelin water fraction (MWF) in the brain from multi-echo gradient-echo imaging data. METHODS: A systematic sensitivity analysis was first conducted to characterize the conventional exponential models used for MWF estimation. A new estimation method was then proposed for improved estimation of MWF from practical gradient-echo imaging data. The proposed method uses an extended signal model that includes a finite impulse response filter to compensate for practical signal variations. This new model also enables the use of prelearned parameter distributions as well as low-rank signal structures to improve parameter estimation. The resulting parameter estimation problem was solved optimally in the Bayesian sense. RESULTS: Our sensitivity analysis results showed that the conventional exponential models were very sensitive to measurement noise and modeling errors. Our simulation and experimental results showed that our proposed method provided a substantial improvement in reliability, reproducibility, and robustness of MWF estimates over the conventional methods. Clinical results obtained from stroke patients indicated that the proposed method, with its improved capability, could reveal the loss of myelin in lesions, demonstrating its translational potentials. CONCLUSION: This paper addressed the problem of robust MWF estimation from gradient-echo imaging data. A new method was proposed to provide improved MWF estimation in the presence of significant noise and modeling errors. The performance of the proposed method has been evaluated using both simulated and experimental data, showing significantly improved robustness over the existing methods. The proposed method may prove useful for quantitative myelin imaging in clinical applications.