Brain-age prediction: Systematic evaluation of site effects, and sample age range and size.

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5-90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8-80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9-25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5-40 and 40-90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics.

Normative modelling of brain morphometry across the lifespan with CentileBrain: algorithm benchmarking and model optimisation.

The value of normative models in research and clinical practice relies on their robustness and a systematic comparison of different modelling algorithms and parameters; however, this has not been done to date. We aimed to identify the optimal approach for normative modelling of brain morphometric data through systematic empirical benchmarking, by quantifying the accuracy of different algorithms and identifying parameters that optimised model performance. We developed this framework with regional morphometric data from 37 407 healthy individuals (53% female and 47% male; aged 3-90 years) from 87 datasets from Europe, Australia, the USA, South Africa, and east Asia following a comparative evaluation of eight algorithms and multiple covariate combinations pertaining to image acquisition and quality, parcellation software versions, global neuroimaging measures, and longitudinal stability. The multivariate fractional polynomial regression (MFPR) emerged as the preferred algorithm, optimised with non-linear polynomials for age and linear effects of global measures as covariates. The MFPR models showed excellent accuracy across the lifespan and within distinct age-bins and longitudinal stability over a 2-year period. The performance of all MFPR models plateaued at sample sizes exceeding 3000 study participants. This model can inform about the biological and behavioural implications of deviations from typical age-related neuroanatomical changes and support future study designs. The model and scripts described here are freely available through CentileBrain.

The CYP80A and CYP80G Are Involved in the Biosynthesis of Benzylisoquinoline Alkaloids in the Sacred Lotus (Nelumbo nucifera).

Bisbenzylisoquinoline and aporphine alkaloids are the two main pharmacological compounds in the ancient sacred lotus (Nelumbo nucifera). The biosynthesis of bisbenzylisoquinoline and aporphine alkaloids has attracted extensive attention because bisbenzylisoquinoline alkaloids have been reported as potential therapeutic agents for COVID-19. Our study showed that NnCYP80A can catalyze C-O coupling in both (R)-N-methylcoclaurine and (S)-N-methylcoclaurine to produce bisbenzylisoquinoline alkaloids with three different linkages. In addition, NnCYP80G catalyzed C-C coupling in aporphine alkaloids with extensive substrate selectivity, specifically using (R)-N-methylcoclaurine, (S)-N-methylcoclaurine, coclaurine and reticuline as substrates, but the synthesis of C-ring alkaloids without hydroxyl groups in the lotus remains to be elucidated. The key residues of NnCYP80G were also studied using the 3D structure of the protein predicted using Alphafold 2, and six key amino acids (G39, G69, A211, P288, R425 and C427) were identified. The R425A mutation significantly decreased the catalysis of (R)-N-methylcoclaurine and coclaurine inactivation, which might play important role in the biosynthesis of alkaloids with new configurations.

Normative Modeling of Brain Morphometry Across the Lifespan Using CentileBrain: Algorithm Benchmarking and Model Optimization.

We present an empirically benchmarked framework for sex-specific normative modeling of brain morphometry that can inform about the biological and behavioral significance of deviations from typical age-related neuroanatomical changes and support future study designs. This framework was developed using regional morphometric data from 37,407 healthy individuals (53% female; aged 3-90 years) following a comparative evaluation of eight algorithms and multiple covariate combinations pertaining to image acquisition and quality, parcellation software versions, global neuroimaging measures, and longitudinal stability. The Multivariate Factorial Polynomial Regression (MFPR) emerged as the preferred algorithm optimized using nonlinear polynomials for age and linear effects of global measures as covariates. The MFPR models showed excellent accuracy across the lifespan and within distinct age-bins, and longitudinal stability over a 2-year period. The performance of all MFPR models plateaued at sample sizes exceeding 3,000 study participants. The model and scripts described here are freely available through CentileBrain (https://centilebrain.org/).

Visualization and identification of benzylisoquinoline alkaloids in various nelumbo nucifera tissues.

Benzylisoquinoline alkaloids in lotus (Nelumbo nucifera) seed plumules and leaves exhibit significant tissue specificity for their pharmacological effects and potential nutritional properties. Herein, 46 benzylisoquinoline alkaloids were identified via UPLC-QTOF-HRMS, of which 9 were annotated as glycosylated monobenzylisoquinoline alkaloids concentrated in the seed plumules. The spatial distribution of targeted benzylisoquinoline alkaloids in leaves, seed plumules, and milky sap was determined via MALDI-MSI. Furthermore, 37 Nelumbo cultivars were investigated using targeted metabolomics to provide insights into functional tea development. While aporphine alkaloids comprised the main compounds present in lotus leaves, bisbenzylisoquinoline alkaloids were the main compounds in lotus plumules, where glycosylation primarily occurs. These findings can help understand the distribution of benzylisoquinoline alkaloids in lotus tissue and the directional breeding of varieties enriched with specific chemical functional groups for nutritional and pharmacological applications.

Collision Cross Section Prediction Based on Machine Learning.

Ion mobility-mass spectrometry (IM-MS) is a powerful separation technique providing an additional dimension of separation to support the enhanced separation and characterization of complex components from the tissue metabolome and medicinal herbs. The integration of machine learning (ML) with IM-MS can overcome the barrier to the lack of reference standards, promoting the creation of a large number of proprietary collision cross section (CCS) databases, which help to achieve the rapid, comprehensive, and accurate characterization of the contained chemical components. In this review, advances in CCS prediction using ML in the past 2 decades are summarized. The advantages of ion mobility-mass spectrometers and the commercially available ion mobility technologies with different principles (e.g., time dispersive, confinement and selective release, and space dispersive) are introduced and compared. The general procedures involved in CCS prediction based on ML (acquisition and optimization of the independent and dependent variables, model construction and evaluation, etc.) are highlighted. In addition, quantum chemistry, molecular dynamics, and CCS theoretical calculations are also described. Finally, the applications of CCS prediction in metabolomics, natural products, foods, and the other research fields are reflected.

Functional characterization and key residues engineering of a regiopromiscuity O-methyltransferase involved in benzylisoquinoline alkaloid biosynthesis in Nelumbo nucifera.

Lotus (Nelumbo nucifera), an ancient aquatic plant, possesses a unique pharmacological activity that is primarily contributed by benzylisoquinoline alkaloids (BIAs). However, only few genes and enzymes involved in BIA biosynthesis in N. nucifera have been isolated and characterized. In the present study we identified the regiopromiscuity of an O-methyltransferase, designated NnOMT6, isolated from N. nucifera; NnOMT6 was found to catalyze the methylation of monobenzylisoquinoline 6-O/7-O, aporphine skeleton 6-O, phenylpropanoid 3-O, and protoberberine 2-O. We further probed the key residues affecting NnOMT6 activity via molecular docking and molecular dynamics simulation. Verification using site-directed mutagenesis revealed that residues D316, N130, L135, N176A, D269, and E328 were critical for BIA O-methyltransferase activities; furthermore, N323A, a mutant of NnOMT6, demonstrated a substantial increase in catalytic efficiency for BIAs and a broader acceptor scope compared with wild-type NnOMT6. To the best of our knowledge, this is the first study to report the O-methyltransferase activity of an aporphine skeleton without benzyl moiety substitutions in N. nucifera. The study findings provide biocatalysts for the semisynthesis of related medical compounds and give insights into protein engineering to strengthen O-methyltransferase activity in plants.

Discovery of unreported ginkgolides of anti-PAF activity using characteristic ion and neutral loss recognition strategy in Ginkgo biloba L.

Ginkgolides are the most important bioactive components of Ginkgo biloba L, of which ginkgolide B has been successfully developed and marketed as a drug. The reported ginkgolides are very rare and exhibit a complex matrix due to the chemodiversity of Ginkgo biloba L. Herein, the global profile of characteristic ion and neutral loss recognition strategy were used for to discover eight undescribed ginkgolides, very rare cyclohexane ginkgolides R-V, ginkgolides D-F, and eight known ginkgolides. These ginkgolides were target isolated and identified using high-resolution mass spectrometry, nuclear magnetic resonance spectroscopy, and X-ray crystallography. The undescribed and known ginkgolides exhibited antiplatelet aggregation activities. In particular, compounds U and D had IC50 values of 2.20 ± 0.15 and 6.50 ± 0.87 µM, respectively. This study has enriched the known structural diversity of ginkgolides and extended the application of mass spectrometry to the global profiling of natural products present in Ginkgo biloba L. Moreover, it could help chemists rapidly discover unreported compounds from a complex matrix.

Identification and quantification of oligomeric proanthocyanidins, alkaloids, and flavonoids in lotus seeds: A potentially rich source of bioactive compounds.

Untargeted metabolomics was performed to study the profiles of 101 chemicals in lotus seeds using ultrahigh-performance liquid chromatography-photodiode array detection-high-resolution tandem mass spectrometry. Among them, 16 dimeric, 18 trimeric, and 4 tetrameric proanthocyanidins were theoretically identified based on the degree of polymerization, and the number of linkages and the presence of two dihydroflavonols and three glycosylated alkaloids were determined for the first time. The proanthocyanidin, flavonoid, amino acid, and total compound contents were quantified, revealing decreases in their levels during maturation as well as a polymerization process formation of polymers from monomers during seed maturation. Interestingly, glycosylated alkaloids were only detected in seed cotyledons being highest at green-brown stage, whereas proanthocyanidins were present at a concentration of 8,226.19 ± 249.96 µg/g (dry weight) in green-brown stage of seed coats. Our findings may provide insights into the utilization of lotus seeds as a functional food.

Qualitative analysis and differentiation of ginkgo cultivars based on UHPLC-QTOF-MS/MS with the characteristic ion and neutral loss strategy combined with chemometric methods.

The identification of chemical constituents can assist the discovery of active ingredients and can differentiate herbs with multiple cultivars. In this study, a diagnostic ion and neutral loss filtering strategy was developed for the qualitative analysis of ginkgo leaf. The strategy is based on an ultrahigh-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry. A large number of (110) of GL compounds were identified, including 8 potentially novel compounds and 42 previously unreported GL constituents. Moreover, 64 available compounds in 48 GL cultivars were analyzed via a combined multicomponent quantitative analysis and statistical analysis. The distribution of the 64 compounds among different cultivars was clarified in a principal component analysis and hot map visualization. Via a variable-importance-for-prediction score analysis, ten main differential compounds were found among the different cultivars. Collectively, these results indicated the usefulness of our approach in chemical profiling and discrimination of herbs with multiple botanical origins. This strategy can also help chemists rapidly identify novel compounds from a complex matrix.

Comprehensive Investigation of the Differences of the Roots of Wild and Cultivated Mirabilis himalaica (Edgew) Heim Based on Macroscopic and Microscopic Identification Using HPLC Fingerprint.

Mirabilishimalaica (Edgew) Heim (MH) is an important Tibetan medicine with demonstrated medicinal efficacy and promising developmental value. A previous study of MH was limited to vague morphological and microscopic descriptions, restricting its clinical application and further development as a medicine. The goal of this study was to comprehensively characterize wild and cultivated products of MH using macroscopic and microscopic identification using HPLC fingerprint. The results revealed that the cultivated and wild MH exhibited differences in macroscopic and microscopic characteristics and chemical components. This analysis can facilitate the establishment of a more comprehensive quality evaluation method for MH. These results provide the basis for clinical applications and the improvement of quality standards of MH as a step towards modernization of Tibetan medicine.