Biology subjects

Blank, T.

Publications and source records attributed to Blank, T..

4 recordsLinked to original sources

bioRxiv2026

Deciphering underground decarboxylase activity towards Nε-modified lysine derivatives in enterobacteria.

Thermal food processing generates diverse compounds interacting with the gut microbiota. Despite their abundance, the microbial turnover of diet-borne N{varepsilon}-modified lysine derivatives remains largely unexplored. We demonstrate that the enterobacterial ornithine decarboxylase SpeC degrades the prevalent advanced glycation end product N{varepsilon}-carboxymethyllysine (CML) to carboxymethylcadaverine via an underground activity ([~]4 molecules/enzyme/min). This promiscuity extends to additional N{varepsilon}-modified lysine derivatives - namely fomylated (FmL), monomethylated (MML) and dimethylated (DML) lysine - yielding previously unknown biogenic amines (mono- and dimethylcadaverine, formylcadaverine). Functionally, SpeC enables Escherichia coli to utilize CML as a sole nitrogen source. In specific strains, this metabolism reinforces pH-stress responses, supporting survival under mild acidic conditions typical for the colon. Furthermore, SpeC orthologs are widespread across human gut genomes, correlating with geography, diet, and disease. Together, these findings suggest a potential diet-microbiome communication axis, linking the intake of modified dietary chemicals to microbial physiology and hypothesized host impacts.

microbiology↗

bioRxiv2025

Distinct and Combined Interferon-α/β-receptor-1 Loss in Neurons and Astrocytes Disrupt Brain Energy Metabolism and Drive Parkinsonian Dementia

Dysregulated interferon-alpha/beta-receptor 1 (IFNAR1) signaling was recently identified to contribute to the development of sporadic Parkinsons Disease (PD) into PD with Dementia (PDD). The molecular, cellular, and phenotypic impacts of brain IFNAR1 loss in aging have not been explored in vivo, which may reveal novel disease mechanisms and therapeutic targets. Here it is shown that baseline IFNAR1 expression varies in the major brain cell types, including neurons and astrocytes, and is differentially affected in PD and Lewy Body Dementia patients compared to unaffected controls. Neuron- and astrocyte-specific transcriptomic and proteomic alterations in Ifnar1-/- mice implicate mitochondrial defects and synergistic dysfunctional neurotransmission upon IFNAR1 loss, leading to glucose hypermetabolism measured by functional metabolic analysis. Consequently, Ifnar1-/- mice exhibited PDD-like pathogenesis, including dopaminergic cell loss in the substantia nigra, cortical neurodegeneration, Lewy-body-like inclusions, neuroinflammation, and progressive PDD-like behavior deficits. Brain cell-specific IFNAR1 loss examined in vivo revealed delayed but distinct development of PDD-like phenotypes, where neuropathology, motor, and cognitive behavior deficits were specifically recapitulated only in mice lacking neuronal IFNAR1, and behavior resembling neuropsychiatric abnormalities recapitulated only in mice lacking astrocytic IFNAR1. This work supports a crucial role of IFNAR1 in brain homeostasis and emphasizes a need for understanding neurodegenerative pathophysiology in cell-specific contexts. HighlightsO_LIIFNAR1 and related type-I IFN genes are differentially expressed among major brain cell types in Parkinsons Disease, Lewy Body Dementia, and unaffected controls C_LIO_LIEarly molecular alterations in Ifnar1-/- mice show lack of immunomodulation contributing to neuroinflammation, mitochondrial defects, and dysregulated energy metabolism C_LIO_LIIfnar1-/- mice develop a progressive Parkinsonian-like disease phenotype, including dopaminergic cell loss in substantia nigra, cortical neurodegeneration, phosphorylated (p)alpha-synuclein+ and pTau+ Lewy-body-like inclusions, neuroinflammation, and progressive motor, cognitive, and neuropsychiatric disturbance-like behavior deficits C_LIO_LINeuropathologies, motor, and cognitive deficits are recapitulated in mice lacking neuronal IFNAR1 (Syn1Cre;Ifnar1fl/fl) whereas neuropsychiatric abnormalities are recapitulated in mice lacking astrocytic IFNAR1 (GFAPCre;Ifnar1fl/fl) C_LI

neuroscience↗

bioRxiv2024

Brain resident macrophages regulate sleep, with repopulated ones being unable to reestablish the original sleep circuits

Sleep is a complex behavior regulated by various brain cell types. However, the roles of brain-resident macrophages, including microglia and CNS-associated macrophages (CAMs), particularly those derived postnatally, in sleep regulation remain poorly understood. Here, we investigated the effects of natural (embryo-derived) and repopulated (postnatally derived) brain-resident macrophages on the regulation of vigilance states. We found that depletion in embryonically-derived brain macrophages caused increased sleep in the active period, but reduced its quality, reflected in reduced power of brain sleep oscillations. This was observed both for the Non-REM and REM sleep stages. Subsequent repopulation by postnatal brain macrophages unexpectedly failed to reestablish normal sleep-wake patterns and additionally induced sleep fragmentation. Furthermore, brain macrophage depletion caused excitatory-inhibitory synaptic imbalance, which was resistant to repopulation, and led to increased inhibitory synapses. At the metabolite level, the distinct metabolite profile induced by brain macrophage depletion largely returned to normal after repopulation. Our findings suggest a so far largely unknown interaction between brain-resident macrophages and sleep and emphasizes striking functional differences between embryonic and postnatally-derived brain macrophages, paving the way to future exploration of the role of brain macrophages of different origin in sleep disorders and synaptic connectivity.

neuroscience↗

bioRxiv2020

Modeling cannabinoids from a large-scale sample of Cannabis sativa chemotypes

The accelerating legalization of Cannabis has opened the industry to using contemporary analytical techniques. The gene regulation and pharmacokinetics of dozens of cannabinoids remain poorly understood. Because retailers in many medical and recreational jurisdictions are required to report chemical concentrations of cannabinoids, commercial laboratories have growing chemotype datasets of diverse Cannabis cultivars. Using a data set of 17,600 cultivars tested by Steep Hill Inc., we apply machine learning techniques to interpolate missing chemotype observations and cluster cultivars together based on similarity. Our results show that cultivars cluster based on their chemotype, and that some imputation methods work better than others at grouping these cultivars based on chemotypic identity. However, due to the missing data for some of the cannabinoids their behavior could not be accurately predicted. These findings have implications for characterizing complex interactions in cannabinoid biosynthesis and improving phenotypical classification of Cannabis cultivars.

plant biology↗