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Elevated GGT to HDL ratio as a marker for the risk of NAFLD and liver fibrosis
A new-to-nature photosynthesis system enhances utilization of one-carbon substrates in Escherichia coli
Westermark’s Sign and Palla’s Sign in Pulmonary Embolism
Galactin-8 DNA methylation mediates macrophage autophagy through the MAPK/mTOR pathway to alleviate atherosclerosis
A recent large-scale intraspecific IR expansion and evolutionary dynamics of the plastome of Peucedanum japonicum
The predictive value of combined insulin resistance and β-cell secretion in Yemeni school-aged children for type 2 diabetes mellitus
Mitochondrial-cytochrome c oxidase II promotes glutaminolysis to sustain tumor cell survival upon glucose deprivation
AbstractGlucose deprivation, a hallmark of the tumor microenvironment, compels tumor cells to seek alternative energy sources for survival and growth. Here, we show that glucose deprivation upregulates the expression of mitochondrial-cytochrome c oxidase II (MT-CO2), a subunit essential for the respiratory chain complex IV, in facilitating glutaminolysis and sustaining tumor cell survival. Mechanistically, glucose deprivation activates Ras signaling to enhance MT-CO2 transcription and inhibits IGF2BP3, an RNA-binding protein, to stabilize MT-CO2 mRNA. Elevated MT-CO2 increases flavin adenosine dinucleotide (FAD) levels in activating lysine-specific demethylase 1 (LSD1) to epigenetically upregulate JUN transcription, consequently promoting glutaminase-1 (GLS1) and glutaminolysis for tumor cell survival. Furthermore, MT-CO2 is indispensable for oncogenic Ras-induced glutaminolysis and tumor growth, and elevated expression of MT-CO2 is associated with poor prognosis in lung cancer patients. Together, these findings reveal a role for MT-CO2 in adapting to metabolic stress and highlight MT-CO2 as a putative therapeutic target for Ras-driven cancers.
When Diversity Goals Meet Multiregional Trials
DNA promoter task-oriented dictionary mining and prediction model based on natural language technology
The usefulness of automated high frequency ultrasound image analysis in atopic dermatitis staging
Machine learning-based technique for gain prediction of mm-wave miniaturized 5G MIMO slotted antenna array with high isolation characteristics
Interfacial coupling effects in two-dimensional ordered arrays for microwave attenuation
NEJM at ESC — Asundexian versus Apixaban in Patients with Atrial Fibrillation
Biochemical properties and substrate specificity of GOB-38 in Elizabethkingia anophelis
Layer dependent thermal transport properties of one- to three-layer magnetic Fe:MoS2
Raiding nature’s genetic toolbox for UV-C resistance by functional metagenomics
AbstractAs we assess the habitability of other worlds, we are limited by being able to only study terrestrial life adapted to terrestrial conditions. The environments found on Earth, though tremendously diverse, do not approach the multitude of potentially habitable environments beyond Earth, and so limited terrestrial adaptive capabilities tell us little about the fundamental biochemical boundaries of life. One approach to this problem is to use experimental laboratory evolution to adapt microbes to these novel environmental conditions. This approach can be dramatically improved through functional metagenomics, large-scale introduction of foreign genetic material to screen for phenotypes in a new host organism. This takes advantage of Earth’s immense biological diversity with high-throughput screening for genetic tools that can facilitate adaptation. We address a key gap in functional metagenomics work by exploring the impact of the experimental parameters chosen for functional metagenomics libraries. Experimental design dictates both fragment size and copy number, and we show that both can have outsized effects on the resultant phenotypes in non-intuitive ways. These results highlight the potential of functional metagenomics for adapting life rapidly to challenging new environments, with important implications in both astrobiology and bioindustry, while also emphasizing the impacts of decisions in experimental design.
Universal, untargeted detection of bacteria in tissues using metabolomics workflows
Abstract Fast and reliable identification of bacteria directly in clinical samples is a critical factor in clinical microbiological diagnostics. Current approaches require time-consuming bacterial isolation and enrichment procedures, delaying stratified treatment. Here, we describe a biomarker-based strategy that utilises bacterial small molecular metabolites and lipids for direct detection of bacteria in complex samples using mass spectrometry (MS). A spectral metabolic library of 233 bacterial species is mined for markers showing specificity at different phylogenetic levels. Using a univariate statistical analysis method, we determine 359 so-called taxon-specific markers (TSMs). We apply these TSMs to the in situ detection of bacteria using healthy and cancerous gastrointestinal tissues as well as faecal samples. To demonstrate the MS method-agnostic nature, samples are analysed using spatial metabolomics and traditional bulk-based metabolomics approaches. In this work, TSMs are found in >90% of samples, suggesting the general applicability of this workflow to detect bacterial presence with standard MS-based analytical methods.