Metabolic Mapping Research Articles

Metabolic GWAS-based dissection of genetic basis underlying nutrient quality variation and domestication of cassava storage root

BackgroundMetabolites play critical roles in regulating nutritional qualities of plants, thereby influencing their consumption and human health. However, the genetic basis underlying the metabolite-based nutrient quality and domestication of root and tuber crops remain largely unknown.ResultsWe report a comprehensive study combining metabolic and phenotypic genome-wide association studies to dissect the genetic basis of metabolites in the storage root (SR) of cassava. We quantify 2,980 metabolic features in 299 cultivated cassava accessions. We detect 18,218 significant marker-metabolite associations via metabolic genome-wide association mapping and identify 12 candidate genes responsible for the levels of metabolites that are of potential nutritional importance. Me3GT, MeMYB4, and UGT85K4/UGT85K5, which are involved in flavone, anthocyanin, and cyanogenic glucoside metabolism, respectively, are functionally validated through in vitro enzyme assays and in vivo gene silencing analyses. We identify a cluster of cyanogenic glucoside biosynthesis genes, among which CYP79D1, CYP71E7b, and UGT85K5 are highly co-expressed and their allelic combination contributes to low linamarin content. We find MeMYB4 is responsible for variations in cyanidin 3-O-glucoside and delphinidin 3-O-rutinoside contents, thus controlling SR endothelium color. We find human selection affects quercetin 3-O-glucoside content and SR weight per plant. The candidate gene MeFLS1 is subject to selection during cassava domestication, leading to decreased quercetin 3-O-glucoside content and thus increased SR weight per plant.ConclusionsThese findings reveal the genetic basis of cassava SR metabolome variation, establish a linkage between metabolites and agronomic traits, and offer useful resources for genetically improving the nutrition of cassava and other root crops.

Increased glucose metabolism and impaired glutamate transport in human astrocytes are potential early triggers of abnormal extracellular glutamate accumulation in hiPSC-derived models of Alzheimer's disease.

Glutamate recycling between neurons and astrocytes is essential to maintain neurotransmitter homeostasis. Disturbances in glutamate homeostasis, resulting in excitotoxicity and neuronal death, have been described as a potential mechanism in Alzheimer's disease (AD) pathophysiology. However, glutamate neurotransmitter metabolism in different human brain cells, particularly astrocytes, has been poorly investigated at the early stages of AD. We sought to investigate glucose and glutamate metabolism in AD by employing human induced pluripotent stem cell (hiPSC)-derived astrocytes and neurons carrying mutations in the amyloid precursor protein (APP) or presenilin-1 (PSEN-1) gene as found in familial types of AD (fAD). Methods such as live-cell bioenergetics and metabolic mapping using [13C]-enriched substrates were used to examine metabolism in the early stages of AD. Our results revealed greater glycolysis and glucose oxidative metabolism in astrocytes and neurons with APP or PSEN-1 mutations, accompanied by an elevated glutamate synthesis compared to control WT cells. Astrocytes with APP or PSEN-1 mutations exhibited reduced expression of the excitatory amino acid transporter 2 (EAAT2), and glutamine uptake increased in mutated neurons, with enhanced glutamate release specifically in neurons with a PSEN-1 mutation. These results demonstrate a hypermetabolic phenotype in astrocytes with fAD mutations possibly linked to toxic glutamate accumulation. Our findings further identify metabolic imbalances that may occur in the early phases of AD pathophysiology.

Using data‐driven approaches to reveal distinct neurocognitive profiles in posterior cortical atrophy

AbstractBackgroundPosterior cortical atrophy (PCA) is a clinically defined syndrome characterized by impairment in higher‐order visual processing due to neurodegeneration of posterior brain regions (occipito‐parietal and occipito‐temporal areas). Previous findings have reported broader cognitive impairment profiles, but their association with neural correlates remains unclear. In this project we aimed to use principal component analysis to explore the relationship between various cognitive dimensions and FDG‐PET hypometabolism in PCA patients.MethodWe included 59 patients with a clinical diagnosis of PCA (with supporting radiological evidence) from the Memory and Aging Center database at the University of California in San Francisco (Table 1). The sample consisted of 64.4% females, mean age was 63.0 ± 7.6, and mean MMSE was 20.7 ± 5.6 at the diagnostic visit. Participants underwent extensive neuropsychological assessment; these scores were subsequently entered into an orthogonally‐rotated principal component analysis. In a subset of patients with FDG‐PET (N = 24), associations between the emergent cognitive components and whole‐brain hypometabolism were examined to explore neural correlates of cognitive heterogeneity.ResultFive components explained 72% of overall performance variation in PCA patients: episodic memory, visuospatial function, working memory, verbal generation, and naming/depression (Figure 1). Labels were assigned base on the primary area of greatest cognitive dysfunction. Visualization of component scores suggested systematic performance variation within the PCA sample. The mean metabolism map showed, as expected, predominant hypometabolism in occipito‐parietal and occipito‐temporal regions (Figure 2A). Voxel‐wise correlations revealed that hypometabolism in distinct frontoparietal regions underpinned episodic memory and visuospatial (inferior/superior parietal lobes), working memory and verbal generation (superior/middle frontal gyrus) performance (Figure 2B).ConclusionPCA is a cognitively heterogeneous syndrome, where patients can show varying levels of cognitive deficits across episodic and working memory, executive functions, language and affect, over and above canonical visuospatial impairments.

Amniotic fluid metabolic fingerprinting contributes to shaping the unfavourable intrauterine environment in monochorionic diamniotic twins

Amniotic fluid (AF) is the primary intrauterine environment for fetal growth throughout gestation. Selective fetal growth restriction (sFGR) is an adverse complication characterized by unequal growth in twins with nearly identical genetic makeup. However, the influence of AF-mediated intrauterine environment on the development and progression of sFGR remains unexplored. High-throughput targeted metabolomics analysis (G350) was performed on AF samples collected from sFGR (n=18) and MCDA twins with birth weight concordance (MCDA-C, n=20) cases. Weighted correlation network analysis (WGCNA) was used to identify clinical features that may influence the metabolite composition in AF. Subsequently, partial least-squares discriminant analysis (PLS-DA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to compare the different types of sFGR and MCDA-C twins. Receiver operating characteristic (ROC) and multivariate ROC curves were utilized to explore potential AF markers in twins with sFGR. In our study, 182 metabolites were quantified in 76 AF samples. WGCNA indicated that the metabolite composition in late AF may not be influenced by gestational age. PLSDA demonstrated distinct variations between the metabolite profiles of AF in the sFGR and MCDA-C twins, with a significant emphasis on amino acids as the primary differential metabolite. The dissimilarities observed in sFGR twins were predominantly attributed to lipid metabolism-related metabolites. In particular, the KEGG enrichment metabolic pathway analysis revealed significant associations of both types of sFGR twins with central carbon metabolism in cancer. The multivariate ROC curves indicated that the combination of carnosine, sarcosine, l-alanine, beta-alanine, and alpha-n-phenylacetylglutamine significantly improved the AUC to 0.928. Notably, the ROC curves highlighted creatine (AUC:0.934) may be a potential biomarker for severe sFGR. The data presented in this study offer a comprehensive metabolic map of the AF in cases of sFGR, shedding light on potential biomarkers associated with fetal growth and development in MCDA twins.

Modeling Red Blood Cell Metabolism in the Omics Era.

Red blood cells (RBCs) are abundant (more than 80% of the total cells in the human body), yet relatively simple, as they lack nuclei and organelles, including mitochondria. Since the earliest days of biochemistry, the accessibility of blood and RBCs made them an ideal matrix for the characterization of metabolism. Because of this, investigations into RBC metabolism are of extreme relevance for research and diagnostic purposes in scientific and clinical endeavors. The relative simplicity of RBCs has made them an eligible model for the development of reconstruction maps of eukaryotic cell metabolism since the early days of systems biology. Computational models hold the potential to deepen knowledge of RBC metabolism, but also and foremost to predict in silico RBC metabolic behaviors in response to environmental stimuli. Here, we review now classic concepts on RBC metabolism, prior work in systems biology of unicellular organisms, and how this work paved the way for the development of reconstruction models of RBC metabolism. Translationally, we discuss how the fields of metabolomics and systems biology have generated evidence to advance our understanding of the RBC storage lesion, a process of decline in storage quality that impacts over a hundred million blood units transfused every year.

Partially Alternative Feeding with Fermented Distillers' Grains Modulates Gastrointestinal Flora and Metabolic Profile in Guanling Cattle.

Fermented distillers' grains (FDG) are commonly used to enhance the health and metabolic processes of livestock and poultry by regulating the composition and activity of the intestinal microbiota. Nevertheless, there is a scarcity of research on the effects of the FDG diet on the gastrointestinal microbiota and its metabolites in cattle. This study examines the impact of FDG dietary supplements on the gastrointestinal flora and metabolic profile of Guanling cattle. Eighteen cattle were randomly assigned to three treatment groups with six replicates per group. The treatments included a basal diet (BD), a 15% concentrate replaced by FDG (15% FDG) in the basal diet, and a 30% concentrate replaced by FDG (30% FDG) in the basal diet. Each group was fed for a duration of 60 days. At the conclusion of the experimental period, three cattle were randomly chosen from each group for slaughter and the microbial community structure and metabolic mapping of their abomasal and cecal contents were analyzed, utilizing 16S rDNA sequencing and LC-MS technology, respectively. At the phylum level, there was a significant increase in Bacteroidetes in both the abomasum and cecum for the 30%FDG group (p < 0.05). Additionally, there was a significant reduction in potential pathogenic bacteria such as Spirochetes and Proteobacteria for both the 15%FDG and 30%FDG groups (p < 0.05). At the genus level, there was a significant increase (p < 0.05) in Ruminococcaceae_UCG-010, Prevotellaceae_UCG-001, and Ruminococcaceae_UCG-005 fiber degradation bacteria. Non-target metabolomics analysis indicated that the FDG diet significantly impacted primary bile acid biosynthesis, bile secretion, choline metabolism in cancer, and other metabolic pathways (p < 0.05). There is a noteworthy correlation between the diverse bacterial genera and metabolites found in the abomasal and cecal contents of Guanling cattle, as demonstrated by correlation analysis. In conclusion, our findings suggest that partially substituting FDG for conventional feed leads to beneficial effects on both the structure of the gastrointestinal microbial community and the metabolism of its contents in Guanling cattle. These findings offer a scientific point of reference for the further use of FDG as a cattle feed resource.

The odor deterioration of tilapia (Oreochromis mossambicus) fillets during cold storage revealed by LC-MS based metabolomics and HS-SPME-GC-MS analysis.

Odor deterioration of tilapia during cold storage is unavoidable and affects flavor and quality severely. Odor is characterized by the abundance of volatile compounds and metabolites. In this study, headspace-solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) and metabolomic analysis were applied to explore the volatile compounds, differential metabolites, and metabolic pathways related to the odor deterioration of tilapia during cold storage. A total of 29 volatile compounds were detected to be associated with the odor deterioration of tilapia. And 485 differential metabolites were screened, of which 386 differential expressions were up-regulated and 99 differential expressions were down-regulated. Three major metabolic pathways including linoleic acid metabolism, alanine, aspartate, glutamate metabolism, and nucleotide metabolism were obtained. A potential metabolic network map was also proposed. This study contributes to revealing the metabolic mechanisms of odor deterioration in tilapia during cold storage and providing a theoretical reference for the regulation of flavor and quality.

The Warburg Effect Explained: Integration of Enhanced Glycolysis with Heterogeneous Mitochondria to Promote Cancer Cell Proliferation.

The Warburg effect is the long-standing riddle of cancer biology. How does aerobic glycolysis, inefficient in producing ATP, confer a growth advantage to cancer cells? A new evaluation of a large set of literature findings covering the Warburg effect and its yeast counterpart, the Crabtree effect, led to an innovative working hypothesis presented here. It holds that enhanced glycolysis partially inactivates oxidative phosphorylation to induce functional rewiring of a set of TCA cycle enzymes to generate new non-canonical metabolic pathways that sustain faster growth rates. The hypothesis has been structured by constructing two metabolic maps, one for cancer metabolism and the other for the yeast Crabtree effect. New lines of investigation, suggested by these maps, are discussed as instrumental in leading toward a better understanding of cancer biology in order to allow the development of more efficient metabolism-targeted anticancer drugs.

An Integrated Molecular Networking and Docking Approach to Characterize the Metabolome of Helichrysum splendidum and Its Pharmaceutical Potentials.

South Africa is rich in diverse medicinal plants, and it is reported to have over 35% of the global Helichrysum species, many of which are utilized in traditional medicine. Various phytochemical studies have offered valuable insights into the chemistry of Helichrysum plants, hinting at bioactive components that define the medicinal properties of the plant. However, there are still knowledge gaps regarding the size and diversity of the Helichrysum chemical space. As such, continuous efforts are needed to comprehensively characterize the phytochemistry of Helichrysum, which will subsequently contribute to the discovery and exploration of Helichrysum-derived natural products for drug discovery. Thus, reported herein is a computational metabolomics work to comprehensively characterize the metabolic landscape of the medicinal herb Helichrysum splendidum, which is less studied. Metabolites were methanol-extracted and analyzed on a liquid chromatography-tandem mass spectrometry (LC-MS/MS) system. Spectral data were mined using molecular networking (MN) strategies. The results revealed that the metabolic map of H. splendidum is chemically diverse, with chemical superclasses that include organic polymers, benzenoids, lipid and lipid-like molecules, alkaloids, and derivatives, phenylpropanoids and polyketides. These results point to a vastly rich chemistry with potential bioactivities, and the latter was demonstrated through computationally assessing the binding of selected metabolites with CDK-2 and CCNB1 anti-cancer targets. Molecular docking results showed that flavonoids (luteolin, dihydroquercetin, and isorhamnetin) and terpenoids (tiliroside and silybin) interact strongly with the CDK-2 and CCNB1 targets. Thus, this work suggests that these flavonoid and terpenoid compounds from H. splendidum are potentially anti-cancer agents through their ability to interact with these proteins involved in cancer pathways and progression. As such, these actionable insights are a necessary step for further exploration and translational studies for H. splendidum-derived compounds for drug discovery.

Deuterium metabolic imaging for 3D mapping of glucose metabolism in humans with central nervous system lesionsat 3T.

To explore the potential of 3T deuterium metabolic imaging (DMI) using a birdcage 2 H radiofrequency (RF) coil in both healthy volunteers and patients with central nervous system (CNS) lesions. A modified gradient filter, home-built 2 H volume RF coil, and spherical k-space sampling were employed in a three-dimensional chemical shift imaging acquisition to obtain high-quality whole-brain metabolic images of 2 H-labeled water and glucose metabolic products. These images were acquired in a healthy volunteer and three subjects with CNS lesions of varying pathologies. Hardware and pulse sequence experiments were also conducted to improve the signal-to-noise ratio of DMI at 3T. The ability to quantify local glucose metabolism in correspondence to anatomical landmarks across patients with varying CNS lesions is demonstrated, and increased lactate is observed in one patient with the most active disease. DMI offers the potential to examine metabolic activity in human subjects with CNS lesions with DMI at 3T, promising for the potential of the future clinical translation of this metabolic imaging technique.

High-resolution proton metabolic mapping of the human brain at 7 T using free induction decay rosette spectroscopic imaging.

Magnetic resonance spectroscopic imaging (MRSI) provides information about the spatial distribution of metabolites in the brain. These metabolite maps can be valuable in diagnosing central nervous system pathology. However, MRSI generally suffers from a long acquisition time, poor spatial resolution, and a low metabolite signal-to-noise ratio (SNR). Ultrahigh field strengths (≥ 7 T) can benefit MRSI with an improved SNR and allow high-resolution metabolic mapping. Non-Cartesian spatial-spectral encoding techniques, such as rosette spectroscopic imaging, can efficiently sample spatial and temporal domains, which significantly reduces the imaging time and enables high-resolution metabolic mapping in a clinically relevant scan time. In the current study, high-resolution (in-plane resolution of 2 × 2 mm2 ) mapping of proton (1 H) metabolites in the human brain at 7 T, is demonstrated. Five healthy subjects participated in the study. Using a time-efficient rosette trajectory and short TR/TE free induction decay MRSI, high-resolution maps of 1 H metabolites were obtained in a clinically relevant imaging time (6 min). Suppression of the water signal was achieved with an optimized water suppression enhanced through T1 effects approach and lipid removal was performed using L2 -regularization in the postprocessing. Spatial distributions of N-acetyl-aspartate, total choline, creatine, N-acetyl-aspartyl glutamate, myo-inositol, and glutamate were generated with Cramer-Rao lower bounds of less than 20%.

Rabbit systemic glucose metabolism map by total-body dynamic PET/CT technology.

This study evaluated total-body glucose metabolism in a preclinical lab animal, the rabbit, by employing a dynamic glucose metabolic image obtained with total-body fluorine-18 fluorodeoxyglucose ( 18 F-FDG) PET/computed tomography (PET/CT). The dynamic total-body PET/CT system was used to obtain glucose metabolic imaging from 10 sedated body-matched rabbits. The standard uptake value (SUV) of 18 F-FDG was used to evaluate glucose metabolism. In addition, the correlation between glucose metabolism and sexes was assessed, as well as metabolic differences between left- and right sides. We found significant distribution heterogeneity of glucose in several organs across the entire body. There were no significant metabolic differences between sexes and between bilateral sides in the 10 rabbits. Thereafter, we assayed the major organ SUV changes by dynamic PET/CT of the major organs. The heart, liver, and urinary system showed more 18 F-FDG, whereas the skeletal muscle, brain, spinal cord, and lungs incorporated less 18 F-FDG. The phenotype of 18 F-FDG uptake was highly correlated with the physiological functions. The 18 F-FDG accumulation in urinary system were observed which could reflect the renal parenchyma glucose metabolism indirectly. However, the low 18 F-FDG uptake in the brain and spinal cord was due to sedation. The total-body glucose metabolic atlas depicted with 18 F-FDG dynamic PET/CT may be used as a reference for assessing pathological 18 F-FDG uptake. Furthermore, this study could be a reference for preclinical research involving abnormality of glucose metabolism.

Fucose as a nutrient ligand for Dikarya and a building block of early diverging lineages

Fucose is a deoxyhexose sugar present and studied in mammals. The process of fucosylation has been the primary focus in studies relating to fucose in animals due to the presence of fucose in Lewis antigens. Very few studies have reported its presence in Fungi, mostly in Mucoromycotina. The constitution of 25% and 12% of this sugar in the carbohydrates of cell wall in the respective Umbelopsis and Mucorales strains boosts the need to bridge the gap of knowledge on fucose metabolism across the fungal tree of life. In the absence of a network map involving fucose proteins, we carried out an in-silico approach to construct the fucose metabolic map in Fungi. We analyzed the taxonomic distribution of 85 protein families in Fungi including diverse early diverging fungal lineages. The expression of fucose-related protein-coding genes proteins was validated with the help of transcriptomic data originating from representatives of early diverging fungi. We found proteins involved in several metabolic activities apart from fucosylation such as synthesis, transport and binding. Most of the identified protein families are shared with Metazoa suggesting an ancestral origin in Opisthokonta. However, the overall complexity of fucose metabolism is greater in Metazoa than in Fungi. Massive gene loss has shaped the evolutionary history of these metabolic pathways, leading to a repeated reduction of these pathways in most yeast-forming lineages. Our results point to a distinctive mode of utilization of fucose among fungi belonging to Dikarya and the early diverging lineages. We speculate that, while Dikarya used fucose as a source of nutrients for metabolism, the early diverging group of fungi depended on fucose as a building block and signaling compound.Graphical abstract

Balancing Nutrient Flows in an Urbanizing Landscape: Case Study of Yuen Long's Nutrient Metabolism

Due to rapid urbanization, nutrient-related issues, such as environmental pollution and waste of resources, are now significant in many cities. Urban metabolism is a multidisciplinary concept that focuses on figuring out the energy and various material flows in urban systems. To understand the holistic nutrient metabolism in the context of dynamic and complex town-scale urban systems, energy flow accounting in the discipline of urban metabolism and spatial nutrient flow mapping were applied as major research methods. This research shows the large accounted output nutrient waste and critically discusses the government measurement of decreasing the nutrient pollution from 1986 to 2020 in the case study area, Yuen Long Town, with management strategies proposal, expecting the nutrient can be reused with less output in the urban system after the design input.

Perspective on Quantitative Structure–Toxicity Relationship (QSTR) Models to Predict Hepatic Biotransformation of Xenobiotics

Biotransformation refers to the metabolic conversion of endogenous and xenobiotic chemicals into more hydrophilic substances. Xenobiotic biotransformation is accomplished by a restricted number of enzymes with broad substrate specificities. The biotransformation of xenobiotics is catalyzed by various enzyme systems that can be divided into four categories based on the reaction they catalyze. The primary concentration is in cytochrome P450, while the CYP enzymes responsible for xenobiotic biotransformation are located within the hepatic endoplasmic reticulum (microsomes). Cytochrome P450 (CYP450) enzymes are also present in extrahepatic tissues. Enzymes catalyzing biotransformation reactions often determine the intensity and duration of the action of drugs and play a key role in chemical toxicity and chemical tumorigenesis. The structure of a given biotransforming enzyme may differ among individuals, which can cause differences in the rates of xenobiotic biotransformation. The study of the molecular mechanisms underlying chemical liver injury is fundamental for preventing or devising new modalities of treatment for liver injury using chemicals. Active metabolites arise from the biotransformation of a parent drug compound using one or more xenobiotic-processing enzymes to generate metabolites with different pharmacological or toxicological properties. Understanding how exogenous chemicals (xenobiotics) are metabolized, distributed, and eliminated is critical to determining the impact of these compounds on human health. Computational tools such as Biotransformer have been developed to predict all the possible metabolites of xenobiotic and enzymatic profiles that are linked to the production of metabolites. The construction of xenobiotic metabolism maps can predict enzymes catalyzing metabolites capable of binding to DNA.

Metabonomics Analysis of Brain Stem Tissue in Rats with Primary Brain Stem Injury Caused Death.

To explore the potential biomarkers for the diagnosis of primary brain stem injury (PBSI) by using metabonomics method to observe the changes of metabolites in rats with PBSI caused death. PBSI, non-brain stem brain injury and decapitation rat models were established, and metabolic maps of brain stem were obtained by LC-MS metabonomics method and annotated to the HMDB database. Partial least square-discriminant analysis (PLS-DA) and random forest methods were used to screen potential biomarkers associated with PBSI diagnosis. Eighty-six potential metabolic markers associated with PBSI were screened by PLS-DA. They were modeled and predicted by random forest algorithm with an accuracy rate of 83.3%. The 818 metabolic markers annotated to HMDB database were used for random forest modeling and prediction, and the accuracy rate was 88.9%. According to the importance in the identification of cause of death, the most important metabolic markers that were significantly up-regulated in PBSI group were HMDB0038126 (genipinic acid, GA), HMDB0013272 (N-lauroylglycine), HMDB0005199 [(R)-salsolinol] and HMDB0013645 (N,N-dimethylsphingosine). GA, N-lauroylglycine, (R)-salsolinol and N,N-dimethylsphingosine are expected to be important metabolite indicators in the diagnosis of PBSI caused death, thus providing clues for forensic medicine practice.

Alteration of microglial metabolism and inflammatory profile contributes to neurotoxicity in a hiPSC-derived microglia model of frontotemporal dementia 3

Frontotemporal dementia (FTD) is a common cause of early-onset dementia, with no current treatment options. FTD linked to chromosome 3 (FTD3) is a rare sub-form of the disease, caused by a point mutation in the Charged Multivesicular Body Protein 2B (CHMP2B). This mutation causes neuronal phenotypes, such as mitochondrial deficiencies, accompanied by metabolic changes and interrupted endosomal-lysosomal fusion. However, the contribution of glial cells to FTD3 pathogenesis has, until recently, been largely unexplored. Glial cells play an important role in most neurodegenerative disorders as drivers and facilitators of neuroinflammation. Microglia are at the center of current investigations as potential pro-inflammatory drivers. While gliosis has been observed in FTD3 patient brains, it has not yet been systematically analyzed. In the light of this, we investigated the role of microglia in FTD3 by implementing human induced pluripotent stem cells (hiPSC) with either a heterozygous or homozygous CHMP2B mutation, introduced into a healthy control hiPSC line via CRISPR-Cas9 precision gene editing. These hiPSC were differentiated into microglia to evaluate the pro-inflammatory profile and metabolic state. Moreover, hiPSC-derived neurons were cultured with conditioned microglia media to investigate disease specific interactions between the two cell populations. Interestingly, we identified two divergent inflammatory microglial phenotypes resulting from the underlying mutations: a severe pro-inflammatory profile in CHMP2B homozygous FTD3 microglia, and an “unresponsive” CHMP2B heterozygous FTD3 microglial state. These findings correlate with our observations of increased phagocytic activity in CHMP2B homozygous, and impaired protein degradation in CHMP2B heterozygous FTD3 microglia. Metabolic mapping confirmed these differences, revealing a metabolic reprogramming of the CHMP2B FTD3 microglia, displayed as a compensatory up-regulation of glutamine metabolism in the CHMP2B homozygous FTD3 microglia. Intriguingly, conditioned CHMP2B homozygous FTD3 microglia media caused neurotoxic effects, which was not evident for the heterozygous microglia. Strikingly, IFN-γ treatment initiated an immune boost of the CHMP2B heterozygous FTD3 microglia, and conditioned microglia media exposure promoted neural outgrowth. Our findings indicate that the microglial profile, activity, and behavior is highly dependent on the status of the CHMP2B mutation. Our results suggest that the heterozygous state of the mutation in FTD3 patients could potentially be exploited in form of immune-boosting intervention strategies to counteract neurodegeneration.

Proteomics-Guided Mining and Characterization of Epoxidase Involved in Camptothecin Biosynthesis from Camptotheca acuminata.

The detailed metabolic map for camptothecin (CPT) biosynthesis in Camptotheca acuminata has been proposed according to our combined omics results. However, the CYP450-mediated epoxidation step in CPT biosynthesis remains unexplored. A proteomics-guided approach was used to identify and annotate the proteins enriched during the vigorous CPT metabolism period in mature C. acuminata and seedlings. Comparative analyses revealed that the CPT and flavonoid biosyntheses were vigorous in stems and all of the samples except the leaves, respectively. The CYP71BE genes were screened based on their enrichment patterns at the transcriptomic-proteomic level and biochemically characterized in Saccharomyces cerevisiae WAT11. Four CYP71BE proteins exhibited in vitro isoliquiritigenin epoxidase activity. Additionally, CYP71BE206 showed epoxidase activity toward strictosamide, the critical precursor for CPT biosynthesis, both in vitro and in Nicotiana benthamiana. In planta functional verification suggested that CYP71BE206 is involved in CPT biosynthesis. Their catalytic conditions were optimized, and the enzymatic parameters were determined. This study provides valuable insight into the CYP71BE-mediated epoxidation step for CPT biosynthesis and offers evidence to verify that the newly characterized epoxidase (CYP71BE206) is simultaneously responsible for the biosynthesis of CPT and the flavonoid in this plant. An evolution event probably happened on ancestral CYP71BE, resulting in the neofunctionalization of CYP71BE206.

Radiomics predictive modeling from dual-time-point FDG PET Ki parametric maps: application to chemotherapy response in lymphoma

BackgroundTo investigate the use of dynamic radiomics features derived from dual-time-point (DTP-feature) [18F]FDG PET metabolic uptake rate Ki parametric maps to develop a predictive model for response to chemotherapy in lymphoma patients.MethodsWe analyzed 126 lesions from 45 lymphoma patients (responding n = 75 and non-responding n = 51) treated with chemotherapy from two different centers. Static and DTP radiomics features were extracted from baseline static PET images and DTP Ki parametric maps. Spearman’s rank correlations were calculated between static and DTP features to identify features with potential additional information. We first employed univariate analysis to determine correlations between individual features, and subsequently utilized multivariate analysis to derive predictive models utilizing DTP and static radiomics features before and after ComBat harmonization. For multivariate modeling, we utilized both the minimum redundancy maximum relevance feature selection technique and the XGBoost classifier. To evaluate our model, we partitioned the patient datasets into training/validation and testing sets using an 80/20% split. Different metrics for classification including area under the curve (AUC), sensitivity (SEN), specificity (SPE), and accuracy (ACC) were reported in test sets.ResultsVia Spearman’s rank correlations, there was negligible to moderate correlation between 32 out of 65 DTP features and some static features (ρ < 0.7); all the other 33 features showed high correlations (ρ ≥ 0.7). In univariate modeling, no significant difference between AUC of DTP and static features was observed. GLRLM_RLNU from static features demonstrated a strong correlation (AUC = 0.75, p value = 0.0001, q value = 0.0007) with therapy response. The most predictive DTP features were GLCM_Energy, GLCM_Entropy, and Uniformity, each with AUC = 0.73, p value = 0.0001, and q value < 0.0005. In multivariate analysis, the mean ranges of AUCs increased following harmonization. Use of harmonization plus combining DTP and static features was shown to provide significantly improved predictions (AUC = 0.97 ± 0.02, accuracy = 0.89 ± 0.05, sensitivity = 0.92 ± 0.09, and specificity = 0.88 ± 0.05). All models depicted significant performance in terms of AUC, ACC, SEN, and SPE (p < 0.05, Mann–Whitney test).ConclusionsOur results demonstrate significant value in harmonization of radiomics features as well as combining DTP and static radiomics models for predicting response to chemotherapy in lymphoma patients.

Anterior hypothalamic parvalbumin neurons are glutamatergic and promote escape behavior

The anterior hypothalamic area (AHA) is a critical structure for defensive responding. Here, we identified a cluster of parvalbumin-expressing neurons in the AHA (AHAPV) that are glutamatergic with fast-spiking properties and send axonal projections to the dorsal premammillary nucleus (PMD). Using invivo functional imaging, optogenetics, and behavioral assays, we determined the role of these AHAPV neurons in regulating behaviors essential for survival. We observed that AHAPV neuronal activity significantly increases when mice are exposed to a predator, and in a real-time place preference assay, we found that AHAPV neuron photoactivation is aversive. Moreover, activation of both AHAPV neurons and the AHAPV → PMD pathway triggers escape responding during a predator-looming test. Furthermore, escape responding is impaired after AHAPV neuron ablation, and anxiety-like behavior as measured by the open field and elevated plus maze assays does not seem to be affected by AHAPV neuron ablation. Finally, whole-brain metabolic mapping using positron emission tomography combined with AHAPV neuron photoactivation revealed discrete activation of downstream areas involved in arousal, affective, and defensive behaviors including the amygdala and the substantia nigra. Our results indicate that AHAPV neurons are a functional glutamatergic circuit element mediating defensive behaviors, thus expanding the identity of genetically defined neurons orchestrating fight-or-flight responses. Together, our work will serve as a foundation for understanding neuropsychiatric disorders triggered by escape such as post-traumatic stress disorder (PTSD).