Tandem mass tag proteomic and untargeted metabolomic profiling reveals altered serum and CSF biochemical datasets in iron deficient monkeys

Multiomics: Functional Molecular Biomarkers of Micronutrients for Public Health Application

Adequate micronutrient intake and status are global public health goals. Vitamin and mineral deficiencies are widespread and known to impair health and survival across the life stages. However, knowledge of molecular effects, metabolic pathways, biological responses to variation in micronutrient nutriture, and abilities to assess populations for micronutrient deficiencies and their pathology remain lacking. Rapidly evolving methodological capabilities in genomics, epigenomics, proteomics, and metabolomics offer unparalleled opportunities for the nutrition research community to link micronutrient exposure to cellular health; discover new, arguably essential micronutrients of microbial origin; and integrate methods of molecular biology, epidemiology, and intervention trials to develop novel approaches to assess and prevent micronutrient deficiencies in populations. In this review article, we offer new terminology to specify nutritional application of multiomic approaches and encourage collaboration across the basic to public health sciences to advance micronutrient deficiency prevention.

Prognostic Performance of Hematological and Serum Iron and Metabolite Indices for Detection of Early Iron Deficiency Induced Metabolic Brain Dysfunction in Infant Rhesus Monkeys

BACKGROUND

The current pediatric practice of monitoring for infantile iron deficiency (ID) via hemoglobin (Hgb) screening at one y of age does not identify preanemic ID nor protect against later neurocognitive deficits.

OBJECTIVES

To identify biomarkers of iron-related metabolic alterations in the serum and brain and determine the sensitivity of conventional iron and heme indices for predicting risk of brain metabolic dysfunction using a nonhuman primate model of infantile ID.

METHODS

Simultaneous serum iron and RBC indices, and serum and cerebrospinal fluid (CSF) metabolomic profiles were determined in 20 rhesus infants, comparing iron sufficient (IS; N = 10) and ID (N = 10) infants at 2 and 4 mo of age.

RESULTS

Reticulocyte hemoglobin (RET-He) was lower at 2 wk in the ID group. Significant IS compared with ID differences in serum iron indices were present at 2 mo, but Hgb and RBC indices differed only at 4 mo (P < 0.05). Serum and CSF metabolomic profiles of the ID and IS groups differed at 2 and 4 mo (P < 0.05). Key metabolites, including homostachydrine and stachydrine (4-5-fold lower at 4 mo in ID group, P < 0.05), were altered in both serum and CSF. Iron indices and RET-He at 2 mo, but not Hgb or other RBC indices, were correlated with altered CSF metabolic profile at 4 mo and had comparable predictive accuracy (area under the receiver operating characteristic curve scores, 0.75-0.80).

CONCLUSIONS

Preanemic ID at 2 mo was associated with metabolic alterations in serum and CSF in infant monkeys. Among the RBC indices, only RET-He predicted the future risk of abnormal CSF metabolic profile with a predictive accuracy comparable to serum iron indices. The concordance of homostachydrine and stachydrine changes in serum and CSF indicates their potential use as early biomarkers of brain metabolic dysfunction in infantile ID.

Intranasal insulin treatment partially corrects the altered gene expression profile in the hippocampus of developing rats with perinatal iron deficiency

Perinatal iron deficiency (FeD) targets the hippocampus and leads to long-term cognitive deficits. Intranasal insulin administration improves cognitive deficits in adult humans with Alzheimer's disease and type 2 diabetes and could provide benefits in FeD-induced hippocampal dysfunction. To objective was to assess the effects of intranasal insulin administration intranasal insulin administration on the hippocampal transcriptome in a developing rat model of perinatal FeD. Perinatal FeD was induced using low-iron diet from gestational day 3 until postnatal day (P) 7, followed by an iron sufficient (FeS) diet through P21. Intranasal insulin was administered at a dose of 0.3 IU twice daily from P8 to P21. Hippocampi were removed on P21 from FeS control, FeD control, FeS insulin, and FeD insulin groups. Total RNA was isolated and profiled using next-generation sequencing. Gene expression profiles were characterized using custom workflows and expression patterns examined using ingenuity pathways analysis (n = 7-9 per group). Select RNAseq results were confirmed via qPCR. Transcriptomic profiling revealed that mitochondrial biogenesis and flux, oxidative phosphorylation, quantity of neurons, CREB signaling in neurons, and RICTOR-based mTOR signaling were disrupted with FeD and positively affected by intranasal insulin treatment with the most benefit observed in the FeD insulin group. Both perinatal FeD and intranasal insulin administration altered gene expression profile in the developing hippocampus. Intranasal insulin treatment reversed the adverse effects of FeD on many molecular pathways and could be explored as an adjunct therapy in perinatal FeD.

Prediction of Proteins in Cerebrospinal Fluid and Application to Glioma Biomarker Identification

Cerebrospinal fluid (CSF) proteins are very important because they can serve as biomarkers for central nervous system diseases. Although many CSF proteins have been identified with wet experiments, the identification of CSF proteins is still a challenge. In this paper, we propose a novel method to predict proteins in CSF based on protein features. A two-stage feature-selection method is employed to remove irrelevant features and redundant features. The deep neural network and bagging method are used to construct the model for the prediction of CSF proteins. The experiment results on the independent testing dataset demonstrate that our method performs better than other methods in the prediction of CSF proteins. Furthermore, our method is also applied to the identification of glioma biomarkers. A differentially expressed gene analysis is performed on the glioma data. After combining the analysis results with the prediction results of our model, the biomarkers of glioma are identified successfully.