Biology subjects

Valafar, H.

Publications and source records attributed to Valafar, H..

5 recordsLinked to original sources

bioRxiv2026

Pregnancy Impacts Maternal Sleep: Focus on Kynurenine Pathway Activation in Female Rats

Study Objectives: Poor maternal sleep is associated with adverse health outcomes for women and their offspring, and perturbations to tryptophan metabolism through the kynurenine pathway (KP) may contribute. Kynurenic acid (KYNA), a neuroactive KP metabolite and endogenous modulator of glutamatergic and cholinergic neurotransmission, is of particular interest because elevated KYNA has been linked to altered sleep-wake behavior and neurodevelopmental outcomes. Because sleep patterns and KP metabolism change during pregnancy, we tested whether pregnancy modifies the effects of kynurenine supplementation on sleep-wake architecture. Methods: To model enhanced KP metabolism, we used the embryonic kynurenine (EKyn) model, in which the maternal diet is supplemented with kynurenine, the initial KP metabolite, during the last week of gestation. Using a within-subject design, we evaluated sleep-wake parameters in pregnant and non-pregnant female rats and assessed KYNA in brain and saliva. Results: Pregnancy increased dark phase NREM and REM sleep in control-fed (ECon) dams, a pattern largely maintained in EKyn dams. However, near parturition, EKyn dams exhibited reduced light phase REM sleep that was not observed in controls. In non-pregnant females, kynurenine supplementation produced a stronger phenotype, consistently reducing light phase REM sleep across treatment days. During pregnancy, kynurenine-associated sleep effects were detectable only when compared to within-subject non-pregnant baseline. Salivary KYNA distinguished kynurenine-fed females from matched controls and correlated with brain KYNA levels. Conclusion: These findings show that pregnancy reshapes sleep-wake architecture and attenuates, but does not eliminate, kynurenine-associated REM sleep disruption, implicating KP metabolism and potentially elevated brain KYNA in maternal sleep regulation.

neuroscience↗

bioRxiv2026

Fragmented sleep during pregnancy induces inflammation and tryptophan-kynurenine pathway metabolism: Importance of elevated fetal brain kynurenic acid

Maternal sleep disturbance is an underrecognized risk factor for adverse offspring outcomes. Prolonged sleep disruption can elicit inflammation, an established risk factor for neuropsychiatric disorders in offspring. Sleep disruptions and inflammation elevate tryptophan degradation via the kynurenine pathway (KP), increasing kynurenic acid (KYNA), a metabolite that inhibits glutamatergic and cholinergic neurotransmission and may thereby affect neurodevelopment. Because KYNA is elevated in the brains of individuals with neurodevelopmental psychotic illnesses, we hypothesize that prenatal KYNA elevation may represent a mechanism link between disturbed maternal sleep, inflammation, and adverse offspring neurodevelopmental health. To test this hypothesis, we employed a novel maternal sleep fragmentation (SleepFrag) paradigm during the final week of gestation. We found that six days of SleepFrag increased maternal plasma inflammatory markers, placental KP metabolism, sex-specific placental inflammation, and fetal brain KP metabolism, including elevated KYNA, without altering KP metabolism in maternal plasma or brain. A parallel embryonic kynurenine (EKyn) model was tested to increase prenatal KP metabolism via a maternal kynurenine-supplemented diet. EKyn increased maternal plasma kynurenine and KYNA, and fetal brain KYNA, with a male-specific increase in fetal brain KYNA-to-kynurenine ratio, despite minimal effects on maternal sleep-wake architecture or inflammation. Together, these findings identify elevated fetal brain KYNA as a convergent outcome through which maternal sleep disruption, inflammation, and KP activation may influence sex-specific neurodevelopment. They further support the EKyn model as a translational tool for isolating consequences of increased prenatal KP metabolism. Protecting maternal sleep and stabilizing fetal brain KYNA levels may promote long-term offspring brain health.

neuroscience↗

bioRxiv2025

Novel Protein Structure Validation using PDBMine and Data Analytics Approaches

Protein structure prediction is essential for understanding biological functions and advancing drug development. Although experimental techniques like NMR, X-ray crystallography, and cryo-EM provide valuable insights, they are expensive and time-consuming, prompting reliance on computational approaches. AlphaFold2 revolutionized protein model predictions accuracy in 2020. However, limitations remain in the prediction of novel proteins, complex conformations, and mutations. To address these challenges, we leverage PDBMine software and machine learning for advanced data analytics. This approach detects and corrects structural inaccuracies, calculates fitness scores, and enhances model reliability, accelerating drug discovery and therapeutic breakthroughs by bridging gaps in current protein prediction capabilities.

bioinformatics↗

bioRxiv2022

Kynurenine aminotransferase II inhibition promotes sleep and rescues impairments induced by neurodevelopmental insult

Dysregulated sleep is commonly reported in individuals with neuropsychiatric disorders, including schizophrenia (SCZ) and bipolar disorder (BPD). Physiology and pathogenesis of these disorders points to aberrant metabolism, during neurodevelopment and adulthood, of tryptophan via the kynurenine pathway (KP). Kynurenic acid (KYNA), a neuroactive KP metabolite derived from its precursor kynurenine by kynurenine aminotransferase II (KAT II), is increased in the brains of individuals with SCZ and BPD. We hypothesize that elevated KYNA, an inhibitor of glutamatergic and cholinergic neurotransmission, contributes to sleep dysfunction. Employing the embryonic kynurenine (EKyn) paradigm to elevate KYNA in the fetal brain, we presently examined whether reducing KYNA in adulthood by pharmacologically inhibiting KAT II would improve sleep quality. Pregnant Wistar rats were fed either kynurenine (100 mg/day) (EKyn) or control wet mash (ECon) from embryonic day (ED) 15 to ED 22. In adulthood, male and female offspring were implanted with devices to record electroencephalogram (EEG) and electromyogram (EMG) telemetrically for continuous sleep-wake data acquisition. Each subject was treated with either vehicle or PF-04859989 (30 mg/kg, s.c.), an irreversible KAT II inhibitor, at zeitgeber time (ZT) 0 or ZT 12. KAT II inhibitor improved sleep architecture maintaining entrainment of the light-dark cycle; ZT 0 treatment with PF-04859989 induced transient improvements in rapid eye movement (REM) and non-REM (NREM) during the immediate light phase, while the impact of ZT 12 treatment was delayed until the subsequent light phase. PF-04859989 administration at ZT 0 enhanced NREM delta spectral power and reduced activity and body temperature. In conclusion, reducing de novo KYNA production alleviated sleep disturbances and increased sleep quality in EKyn, while also improving sleep outcomes in ECon offspring. Our findings place attention on KAT II inhibition as a novel mechanistic approach to treating disrupted sleep behavior with potential translational implications for patients with neurodevelopmental and neuropsychiatric disorders.

neuroscience↗

bioRxiv2020

REDCRAFT: A Computational Platform Using Residual Dipolar Coupling NMR Data for Determining Structures of Perdeuterated Proteins Without NOEs

Nuclear Magnetic Resonance (NMR) spectroscopy is one of the two primary experimental means of characterizing macromolecular structures, including protein structures. Structure determination by NMR spectroscopy has traditionally relied heavily on distance restraints derived from nuclear Overhauser effect (NOE) measurements. While structure determination of proteins from NOE-based restraints is well understood and broadly used, structure determination by NOEs imposes increasing quantity of data for analysis, increased cost of structure determination and is less available in the study of perdeuterated proteins. In the recent decade, Residual Dipolar Couplings (RDCs) have been investigated as an alternative source of data for structural elucidation of proteins by NMR. Several methods have been reported that utilize RDCs in addition to NOEs, and a few utilize RDC data alone. While these methods have individually demonstrated some successes, none of these methods have exposed the full potential of protein structure determination from RDCs. To date, structure determination of proteins from RDCs is limited to small proteins (less than 8.5 kDa) using RDC data from many alignment media (>3) that cannot be collected from larger proteins. Here we present the latest version of the REDCRAFT software package designed for structure determination of proteins from RDC data alone. We have demonstrated the success of REDCRAFT in structure determination of proteins ranging in size from 50 to 145 residues using experimentally collected data and large proteins (145 to 573 residues) using simulated RDC data that can be collected from perdeuterated proteins. Finally, we demonstrate the accuracy of structure determination of REDCRAFT from RDCs alone in application to the structurally novel PF.2048 protein. The RDC-based structure of PF.2048 exhibited 1.0 [A] of BB-RMSD with respect to the NOE-based structure by only using a small amount of backbone RDCs ([~]3 restraints per residue) compared to what is required by other approaches. Author SummaryResidual Dipolar Couplings have the potential to reduce the cost and the time needed to characterize protein structures. In addition, RDC data have been demonstrated to concurrently elucidate structure of proteins, perform assignment of resonances, and be used in characterization of the internal dynamics of proteins. Given all the advantages associated with the study of proteins from RDC data, based on the statistics provided by the Protein Databank (PDB), surprisingly the only 124 proteins (out of nearly 150,000 proteins) have utilized RDCs as part of their structure determination. Even a smaller subset of these proteins (approximately 7) have utilized RDCs as the primary source of data for structure determination. The impeding factor in the use of RDCs is the challenging computational and analytical aspects of this source of data. In this report, we demonstrate the success of the REDCRAFT software package in structure determination of proteins using RDC data that can be collected from small and large proteins in a routine fashion. REDCRAFT accomplishes the challenging task of structure determination from RDCs by introducing a unique search and optimization technique that is both robust and computationally tractable. Structure determination from routinely collectable RDC data using REDCRAFT can lead to faster and cheaper study of larger and more complex proteins by NMR spectroscopy in solution state.

bioinformatics↗