The expression profile and tumorigenic mechanisms of CD97 (ADGRE5) in glioblastoma render it a targetable vulnerability.

Glioblastoma (GBM) is the most common and aggressive primary brain malignancy. Adhesion G protein-coupled receptors (aGPCRs) have attracted interest for their potential as treatment targets. Here, we show that CD97 (ADGRE5) is the most promising aGPCR target in GBM, by virtue of its de novo expression compared to healthy brain tissue. CD97 knockdown or knockout significantly reduces the tumor initiation capacity of patient-derived GBM cultures (PDGCs) in vitro and in vivo. We find that CD97 promotes glycolytic metabolism via the mitogen-activated protein kinase (MAPK) pathway, which depends on phosphorylation of its C terminus and recruitment of ß-arrestin. We also demonstrate that THY1/CD90 is a likely CD97 ligand in GBM. Lastly, we show that an anti-CD97 antibody-drug conjugate selectively kills tumor cells in vitro. Our studies identify CD97 as a regulator of tumor metabolism, elucidate mechanisms of receptor activation and signaling, and provide strong scientific rationale for developing biologics to target it therapeutically in GBM.

IL-1ß stimulates a novel, IKKα -dependent, NIK -independent activation of non-canonical NFκB signalling.

In this study, we examined the activation of non-canonical nuclear factor Kappa B (NFκB) signalling in U2OS cells, a cellular metastatic bone cancer model. Whilst Lymphotoxin α1ß2 (LTα1ß2) stimulated the expected slow, delayed, sustained activation of serine 866/870 p100 phosphorylation and increased cellular expression of p52 NFκB, we found that canonical agonists, Interleukin-1ß (IL-1ß) and also Tumour necrosis factor-α (TNFα) generated a rapid transient increase in pp100, which was maximal by 15-30 min. This rapid phosphorylation was also observed in other cells types, such as DU145 and HCAECs suggesting the phenomenon is universal. IKKα deletion using CRISPR/Cas9 revealed an IKKα-dependent mechanism for serine 866/870 and additionally serine 872 p100 phosphorylation for both IL-1ß and LTα1ß2. In contrast, knockdown of IKKß using siRNA or pharmacological inhibition of IKKß activity was without effect on p100 phosphorylation. Pre-incubation of cells with the NFκB inducing-kinase (NIK) inhibitor, CW15337, had no effect on IL-1ß induced phosphorylation of p100 however, the response to LTα1ß2 was virtually abolished. Surprisingly IL-1ß also stimulated p52 nuclear translocation as early as 60 min, this response and the concomitant p65 translocation was partially reduced by IKKα deletion. Furthermore, p52 nuclear translocation was unaffected by CW15337. In contrast, the response to LTα1ß2 was essentially abolished by both IKKα deletion and CW15337. Taken together, these finding reveal novel forms of NFκB non-canonical signalling stimulated by ligands that activate the canonical NFκB pathway strongly such as IL-1ß.

Fatty acid metabolism of Mycobacterium tuberculosis: A double-edged sword.

Unlike other heterotrophic bacteria, Mycobacterium tuberculosis (Mtb) can co-catabolize a range of carbon sources simultaneously. Evolution of Mtb within host nutrient environment allows Mtb to consume the host's fatty acids as a main carbon source during infection. The fatty acid-induced metabolic advantage greatly contributes to Mtb's pathogenicity and virulence. Thus, the identification of key enzymes involved in Mtb's fatty acid metabolism is urgently needed to aid new drug development. Two fatty acid metabolism enzymes, phosphoenolpyruvate carboxykinase (PEPCK) and isocitrate lyase (ICL) have been intensively studied as promising drug targets, but recently, Quinonez et al. (mBio, doi: 10.1128/mbio.03559-21) highlighted a link between the fatty acid-induced dormancy-like state and drug tolerance. Using metabolomics profiling of a PEPCK-deficient mutant, Quinonez et al. identified that over-accumulation of methylcitrate cycle (MCC) intermediates are phenotypically associated with enhanced drug tolerance against first- and second- line TB antibiotics. This finding was further corroborated by metabolomics and phenotypic characterization of Mtb mutants lacking either ICL or 2-methylcitrate dehydratase. Fatty acid metabolism induced drug-tolerance was also recapitulated in wildtype Mtb after treatment with authentic 2-methylisocitrate, an MCC intermediate. Together, the fatty acid-induced dormancy-like state and drug tolerance are attributed to dysregulated MCC activity.

Flexibility for fuelling reproduction in a pelagic ray (Mobula eregoodoo) suggested by bioenergetic modelling.

This study investigated the measurements of energy density and bioenergetic modelling for a pelagic ray, Mobula eregoodoo, to estimate its relative allocation to various bodily processes and especially reproduction. The data revealed M. eregoodoo uses up to 21.0% and 2.5% of its annual energy budget on growth and reproduction, respectively. During pregnancy, females depleted energy reserves in the liver, which, along with their biennial reproductive cycle, aligns with general theory that ectotherms are capital breeders and thus build energy reserves before reproduction. Nonetheless, the reduction in energy reserves did not account for all reproductive costs, and therefore, gravid females supplement reproductive costs through energy derived from the diet, according to an income-breeding strategy. These characteristics imply that M. eregoodoo exhibits some flexibility in fuelling reproduction depending on energy availability throughout the reproductive cycle, which may be prevalent in other elasmobranchs. The data represent the first estimates of both the metabolic costs of gestation in elasmobranchs and the relative cost of reproduction in rays. Energy costs and plasticity associated with highly variable reproductive strategies in elasmobranchs may influence long-term population viability under a rapidly changing environment.

The Role of Fatty Acid Metabolism in Drug Tolerance of Mycobacterium tuberculosis.

Mycobacterium tuberculosis can cocatabolize a range of carbon sources. Fatty acids are among the carbons available inside the host's macrophages. Here, we investigated the metabolic changes of the fatty acid-induced dormancy-like state of M. tuberculosis and its involvement in the acquisition of drug tolerance. We conducted metabolomics profiling using a phosphoenolpyruvate carboxykinase (PEPCK)-deficient M. tuberculosis strain in an acetate-induced dormancy-like state, highlighting an overaccumulation of methylcitrate cycle (MCC) intermediates that correlates with enhanced drug tolerance against isoniazid and bedaquiline. Further metabolomics analyses of two M. tuberculosis mutants, an ICL knockdown (KD) strain and PrpD knockout (KO) strain, each lacking an MCC enzyme-isocitrate lyase (ICL) and 2-methylcitrate dehydratase (PrpD), respectively-were conducted after treatment with antibiotics. The ICL KD strain, which lacks the last enzyme of the MCC, showed an overaccumulation of MCC intermediates and a high level of drug tolerance. The PrpD KO strain, however, failed to accumulate MCC intermediates as it lacks the second step of the MCC and showed only a minor level of drug tolerance compared to the ICL KD mutant and its parental strain (CDC1551). Notably, addition of authentic 2-methylisocitrate, an MCC intermediate, improved the M. tuberculosis drug tolerance against antibiotics even in glycerol medium. Furthermore, wild-type M. tuberculosis displayed levels of drug tolerance when cultured in acetate medium significantly greater than those in glycerol medium. Taken together, the fatty acid-induced dormancy-like state remodels the central carbon metabolism of M. tuberculosis that is functionally relevant to acquisition of M. tuberculosis drug tolerance. IMPORTANCE Understanding the mechanisms underlying M. tuberculosis adaptive strategies to achieve drug tolerance is crucial for the identification of new targets and the development of new drugs. Here, we show that acetate medium triggers a drug-tolerant state in M. tuberculosis when challenged with antituberculosis (anti-TB) drugs. This carbon-induced drug-tolerant state is linked to an accumulation of the methylcitrate cycle (MCC) intermediates, whose role was previously known as a detox pathway for propionate metabolism. Three mutant strains with mutations in gluconeogenesis and MCC were used to investigate the correlation between drug tolerance and the accumulation of MCC metabolites. We herein report a new role of the MCC used to provide a survival advantage to M. tuberculosis as a species against both anti-TB drugs upon specific carbon sources.

A general approach to explore prokaryotic protein glycosylation reveals the unique surface layer modulation of an anammox bacterium.

The enormous chemical diversity and strain variability of prokaryotic protein glycosylation makes their large-scale exploration exceptionally challenging. Therefore, despite the universal relevance of protein glycosylation across all domains of life, the understanding of their biological significance and the evolutionary forces shaping oligosaccharide structures remains highly limited. Here, we report on a newly established mass binning glycoproteomics approach that establishes the chemical identity of the carbohydrate components and performs untargeted exploration of prokaryotic oligosaccharides from large-scale proteomics data directly. We demonstrate our approach by exploring an enrichment culture of the globally relevant anaerobic ammonium-oxidizing bacterium Ca. Kuenenia stuttgartiensis. By doing so we resolve a remarkable array of oligosaccharides, which are produced by two seemingly unrelated biosynthetic routes, and which modify the same surface-layer protein simultaneously. More intriguingly, the investigated strain also accomplished modulation of highly specialized sugars, supposedly in response to its energy metabolism-the anaerobic oxidation of ammonium-which depends on the acquisition of substrates of opposite charges. Ultimately, we provide a systematic approach for the compositional exploration of prokaryotic protein glycosylation, and reveal a remarkable example for the evolution of complex oligosaccharides in bacteria.

Exposure to methylphenidate during peri-adolescence decouples the prefrontal cortex: a multimodal MRI study.

This study was designed to assess the effects of daily psychostimulant exposure during juvenility and peri-adolescence on brain morphology and functional connectivity using multimodal magnetic resonance imaging. We hypothesized that long-term exposure to methylphenidate would enhance connectivity with the prefrontal cortex. Male rats were given daily injections of either methylphenidate (n=10), dextroamphetamine (n=10) or saline vehicle (n=10) from postnatal day 21 to 42. They were imaged between postnatal day 43 and 48. Voxel-based morphometry, diffusion weighted imaging, and resting state functional connectivity were used to quantify brain structure and function. Images from each modality were registered and analyzed, using a 3D MRI rat atlas providing site-specific data over 171 different brain areas. Following imaging, rats were tested for cognitive function using novel object preference. Long-lasting psychostimulant treatment was associated with only a few significant changes in brain volume and measures of anisotropy compared to vehicle. Resting state functional connectivity imaging revealed decreased coupling between the prefrontal cortex, basal ganglia and sensory motor cortices. There were no significant differences between experimental groups for cognitive behavior. In this exploratory study, we showed that chronic psychostimulant treatment throughout juvenility and preadolescence has a minimal effect on brain volume and gray matter microarchitecture, but significantly uncouples the connectivity in the cerebral/basal ganglia circuitry.

Investigating the Chemolithoautotrophic and Formate Metabolism of Nitrospira moscoviensis by Constraint-Based Metabolic Modeling and 13C-Tracer Analysis.

Nitrite-oxidizing bacteria belonging to the genus Nitrospira mediate a key step in nitrification and play important roles in the biogeochemical nitrogen cycle and wastewater treatment. While these organisms have recently been shown to exhibit metabolic flexibility beyond their chemolithoautotrophic lifestyle, including the use of simple organic compounds to fuel their energy metabolism, the metabolic networks controlling their autotrophic and mixotrophic growth remain poorly understood. Here, we reconstructed a genome-scale metabolic model for Nitrospira moscoviensis (iNmo686) and used flux balance analysis to evaluate the metabolic networks controlling autotrophic and formatotrophic growth on nitrite and formate, respectively. Subsequently, proteomic analysis and [13C]bicarbonate and [13C]formate tracer experiments coupled to metabolomic analysis were performed to experimentally validate model predictions. Our findings corroborate that N. moscoviensis uses the reductive tricarboxylic acid cycle for CO2 fixation, and we also show that N. moscoviensis can indirectly use formate as a carbon source by oxidizing it first to CO2 followed by reassimilation, rather than direct incorporation via the reductive glycine pathway. Our study offers the first measurements of Nitrospira's in vivo central carbon metabolism and provides a quantitative tool that can be used for understanding and predicting their metabolic processes. IMPORTANCE Nitrospira spp. are globally abundant nitrifying bacteria in soil and aquatic ecosystems and in wastewater treatment plants, where they control the oxidation of nitrite to nitrate. Despite their critical contribution to nitrogen cycling across diverse environments, detailed understanding of their metabolic network and prediction of their function under different environmental conditions remains a major challenge. Here, we provide the first constraint-based metabolic model of Nitrospira moscoviensis representing the ubiquitous Nitrospira lineage II and subsequently validate this model using proteomics and 13C-tracers combined with intracellular metabolomic analysis. The resulting genome-scale model will serve as a knowledge base of Nitrospira metabolism and lays the foundation for quantitative systems biology studies of these globally important nitrite-oxidizing bacteria.

Novel diarylamides and diarylureas with N-substitution dependent activity against medulloblastoma.

Medulloblastoma - highly aggressive and heterogeneous tumours of the cerebellum - account for 15-20% of all childhood brain tumours, and are the most common high-grade childhood embryonal tumour of the central nervous system. Herein, potent in vitro anticancer activity against two established medulloblastoma cell lines of the sonic hedgehog subgroup, namely DAOY (p53 mutant) and ONS-76 (p53 wild type), has been achieved. A number of first-generation diarylamides and diarylureas were evaluated and activity is likely to be, in-part, conformation-dependent. The most active compound from this first-generation set of compounds, 1-naphthyl derivative 4b, was selected and a second-generation of compounds were optimised and tested for activity against the medulloblastoma cell lines. This process resulted in drug-like compounds with up to sixty times the activity (sub-micromolar) of the first-generation - thus providing potent new leads for further study.

Retooling Microbiome Engineering for a Sustainable Future.

Microbial communities (microbiomes) have been harnessed in biotechnology applications such as wastewater treatment and bioremediation for over a century. Traditionally, engineering approaches have focused on shaping the environment to steer microbiome function versus direct manipulation of the microbiome's metabolic network. While these selection-based approaches have proven to be invaluable for guiding bioprocess engineering, they do not enable the precise manipulation and control of microbiomes required for unlocking their full potential. Over the past 2 decades, systems biology has revolutionized our understanding of the metabolic networks driving microbiome processes, and more recently genetic engineering tools have started to emerge for nonmodel microorganisms and microbiomes. In this commentary, I discuss how systems biology approaches are being used to generate actionable understanding of microbiome functions in engineered ecosystems. I also highlight how integrating synthetic biology, automation, and machine learning can accelerate microbiome engineering to meet the sustainability challenges of the future.

U-Infuse: Democratization of Customizable Deep Learning for Object Detection.

Image data is one of the primary sources of ecological data used in biodiversity conservation and management worldwide. However, classifying and interpreting large numbers of images is time and resource expensive, particularly in the context of camera trapping. Deep learning models have been used to achieve this task but are often not suited to specific applications due to their inability to generalise to new environments and inconsistent performance. Models need to be developed for specific species cohorts and environments, but the technical skills required to achieve this are a key barrier to the accessibility of this technology to ecologists. Thus, there is a strong need to democratize access to deep learning technologies by providing an easy-to-use software application allowing non-technical users to train custom object detectors. U-Infuse addresses this issue by providing ecologists with the ability to train customised models using publicly available images and/or their own images without specific technical expertise. Auto-annotation and annotation editing functionalities minimize the constraints of manually annotating and pre-processing large numbers of images. U-Infuse is a free and open-source software solution that supports both multiclass and single class training and object detection, allowing ecologists to access deep learning technologies usually only available to computer scientists, on their own device, customised for their application, without sharing intellectual property or sensitive data. It provides ecological practitioners with the ability to (i) easily achieve object detection within a user-friendly GUI, generating a species distribution report, and other useful statistics, (ii) custom train deep learning models using publicly available and custom training data, (iii) achieve supervised auto-annotation of images for further training, with the benefit of editing annotations to ensure quality datasets. Broad adoption of U-Infuse by ecological practitioners will improve ecological image analysis and processing by allowing significantly more image data to be processed with minimal expenditure of time and resources, particularly for camera trap images. Ease of training and use of transfer learning means domain-specific models can be trained rapidly, and frequently updated without the need for computer science expertise, or data sharing, protecting intellectual property and privacy.

Experimentally Validated Reconstruction and Analysis of a Genome-Scale Metabolic Model of an Anaerobic Neocallimastigomycota Fungus.

Anaerobic gut fungi in the phylum Neocallimastigomycota typically inhabit the digestive tracts of large mammalian herbivores, where they play an integral role in the decomposition of raw lignocellulose into its constitutive sugar monomers. However, quantitative tools to study their physiology are lacking, partially due to their complex and unresolved metabolism that includes the largely uncharacterized fungal hydrogenosome. Modern omics approaches combined with metabolic modeling can be used to establish an understanding of gut fungal metabolism and develop targeted engineering strategies to harness their degradation capabilities for lignocellulosic bioprocessing. Here, we introduce a high-quality genome of the anaerobic fungus Neocallimastix lanati from which we constructed the first genome-scale metabolic model of an anaerobic fungus. Relative to its size (200 Mbp, sequenced at 62× depth), it is the least fragmented publicly available gut fungal genome to date. Of the 1,788 lignocellulolytic enzymes annotated in the genome, 585 are associated with the fungal cellulosome, underscoring the powerful lignocellulolytic potential of N. lanati The genome-scale metabolic model captures the primary metabolism of N. lanati and accurately predicts experimentally validated substrate utilization requirements. Additionally, metabolic flux predictions are verified by 13C metabolic flux analysis, demonstrating that the model faithfully describes the underlying fungal metabolism. Furthermore, the model clarifies key aspects of the hydrogenosomal metabolism and can be used as a platform to quantitatively study these biotechnologically important yet poorly understood early-branching fungi.IMPORTANCE Recent genomic analyses have revealed that anaerobic gut fungi possess both the largest number and highest diversity of lignocellulolytic enzymes of all sequenced fungi, explaining their ability to decompose lignocellulosic substrates, e.g., agricultural waste, into fermentable sugars. Despite their potential, the development of engineering methods for these organisms has been slow due to their complex life cycle, understudied metabolism, and challenging anaerobic culture requirements. Currently, there is no framework that can be used to combine multi-omic data sets to understand their physiology. Here, we introduce a high-quality PacBio-sequenced genome of the anaerobic gut fungus Neocallimastix lanati Beyond identifying a trove of lignocellulolytic enzymes, we use this genome to construct the first genome-scale metabolic model of an anaerobic gut fungus. The model is experimentally validated and sheds light on unresolved metabolic features common to gut fungi. Model-guided analysis will pave the way for deepening our understanding of anaerobic gut fungi and provides a systematic framework to guide strain engineering efforts of these organisms for biotechnological use.

Machine learning for metabolic engineering: A review.

Machine learning provides researchers a unique opportunity to make metabolic engineering more predictable. In this review, we offer an introduction to this discipline in terms that are relatable to metabolic engineers, as well as providing in-depth illustrative examples leveraging omics data and improving production. We also include practical advice for the practitioner in terms of data management, algorithm libraries, computational resources, and important non-technical issues. A variety of applications ranging from pathway construction and optimization, to genetic editing optimization, cell factory testing, and production scale-up are discussed. Moreover, the promising relationship between machine learning and mechanistic models is thoroughly reviewed. Finally, the future perspectives and most promising directions for this combination of disciplines are examined.

Autotrophic and mixotrophic metabolism of an anammox bacterium revealed by in vivo 13C and 2H metabolic network mapping.

Anaerobic ammonium-oxidizing (anammox) bacteria mediate a key step in the biogeochemical nitrogen cycle and have been applied worldwide for the energy-efficient removal of nitrogen from wastewater. However, outside their core energy metabolism, little is known about the metabolic networks driving anammox bacterial anabolism and use of different carbon and energy substrates beyond genome-based predictions. Here, we experimentally resolved the central carbon metabolism of the anammox bacterium Candidatus 'Kuenenia stuttgartiensis' using time-series 13C and 2H isotope tracing, metabolomics, and isotopically nonstationary metabolic flux analysis. Our findings confirm predicted metabolic pathways used for CO2 fixation, central metabolism, and amino acid biosynthesis in K. stuttgartiensis, and reveal several instances where genomic predictions are not supported by in vivo metabolic fluxes. This includes the use of the oxidative branch of an incomplete tricarboxylic acid cycle for alpha-ketoglutarate biosynthesis, despite the genome not having an annotated citrate synthase. We also demonstrate that K. stuttgartiensis is able to directly assimilate extracellular formate via the Wood-Ljungdahl pathway instead of oxidizing it completely to CO2 followed by reassimilation. In contrast, our data suggest that K. stuttgartiensis is not capable of using acetate as a carbon or energy source in situ and that acetate oxidation occurred via the metabolic activity of a low-abundance microorganism in the bioreactor's side population. Together, these findings provide a foundation for understanding the carbon metabolism of anammox bacteria at a systems-level and will inform future studies aimed at elucidating factors governing their function and niche differentiation in natural and engineered ecosystems.

The reductive glycine pathway allows autotrophic growth of Desulfovibrio desulfuricans.

Six CO2 fixation pathways are known to operate in photoautotrophic and chemoautotrophic microorganisms. Here, we describe chemolithoautotrophic growth of the sulphate-reducing bacterium Desulfovibrio desulfuricans (strain G11) with hydrogen and sulphate as energy substrates. Genomic, transcriptomic, proteomic and metabolomic analyses reveal that D. desulfuricans assimilates CO2 via the reductive glycine pathway, a seventh CO2 fixation pathway. In this pathway, CO2 is first reduced to formate, which is reduced and condensed with a second CO2 to generate glycine. Glycine is further reduced in D. desulfuricans by glycine reductase to acetyl-P, and then to acetyl-CoA, which is condensed with another CO2 to form pyruvate. Ammonia is involved in the operation of the pathway, which is reflected in the dependence of the autotrophic growth rate on the ammonia concentration. Our study demonstrates microbial autotrophic growth fully supported by this highly ATP-efficient CO2 fixation pathway.

Synthesis and crystal structures of 3-hy-droxy-2,4-dimethyl-2H-thio-phen-5-one and 3-hy-droxy-4-methyl-2H-thio-phen-5-one.

The structures of two hy-droxy-thio-phenone derivatives related to the anti-biotic thiol-actomycin are presented. These are the racemic 3-hy-droxy-2,4-dimethyl-2H-thio-phen-5-one, C6H8O2S, and 3-hy-droxy-4-methyl-2H-thio-phen-5-one, C5H6O2S. The main structural feature of both compounds is C(6) hydrogen-bonded chains formed between the OH and C=O groups. In achiral C5H6O2S, these chains propagate only by translation, corresponding to x + 1, y, z + 1. However, in contrast, for racemic C6H8O2S the hydrogen-bonded chains propagate through a -x + , y + , z operation, giving chains lying parallel to the crystallographic b-axis direction that are composed of alternate R and S enanti-omers. The crystals of 3-hydroxy-4-methyl-2H-thiophen-5-one were found to be twinned by a 180° rotation about the reciprocal 001 direction. In the final refinement the twin ratio refined to 0.568 (2):0.432 (2).

Evaluating blood-brain barrier permeability in a rat model of type 2 diabetes.

BACKGROUND: This is an exploratory study using a novel imaging modality, quantitative ultrashort time-to-echo, contrast enhanced (QUTE-CE) magnetic resonance imaging to evaluate the permeability of the blood-brain barrier in a rat model of type 2 diabetes with the presumption that small vessel disease is a contributing factor to neuropathology in diabetes. METHODS: The BBZDR/Wor rat, a model of type 2 diabetes, and age-matched controls were studied for changes in blood-brain barrier permeability. QUTE-CE, a quantitative vascular biomarker, generated angiographic images with over 500,000 voxels that were registered to a 3D MRI rat brain atlas providing site-specific information on blood-brain barrier permeability in 173 different brain areas. RESULTS: In this model of diabetes, without the support of insulin treatment, there was global capillary pathology with over 84% of the brain showing a significant increase in blood-brain barrier permeability over wild-type controls. Areas of the cerebellum and midbrain dopaminergic system were not significantly affected. CONCLUSION: Small vessel disease as assessed by permeability in the blood-brain barrier in type 2 diabetes is pervasive and includes much of the brain. The increase in blood-brain barrier permeability is a likely contributing factor to diabetic encephalopathy and dementia.

Using multimodal MRI to investigate alterations in brain structure and function in the BBZDR/Wor rat model of type 2 diabetes.

BACKGROUND: This is an exploratory study using multimodal magnetic resonance imaging (MRI) to interrogate the brain of rats with type 2 diabetes (T2DM) as compared to controls. It was hypothesized there would be changes in brain structure and function that reflected the human disorder, thus providing a model system by which to follow disease progression with noninvasive MRI. METHODS: The transgenic BBZDR/Wor rat, an animal model of T2MD, and age-matched controls were studied for changes in brain structure using voxel-based morphometry, alteration in white and gray matter microarchitecture using diffusion weighted imaging with indices of anisotropy, and functional coupling using resting-state BOLD functional connectivity. Images from each modality were registered to, and analyzed, using a 3D MRI rat atlas providing site-specific data on over 168 different brain areas. RESULTS: There was an overall reduction in brain volume focused primarily on the somatosensory cortex, cerebellum, and white matter tracts. The putative changes in white and gray matter microarchitecture were pervasive affecting much of the brain and not localized to any region. There was a general increase in connectivity in T2DM rats as compared to controls. The cerebellum presented with strong functional coupling to pons and brainstem in T2DM rats but negative connectivity to hippocampus. CONCLUSION: The neuroradiological measures collected in BBBKZ/Wor rats using multimodal imaging methods did not reflect those reported for T2DB patients in the clinic. The data would suggest the BBBKZ/Wor rat is not an appropriate imaging model for T2DM.