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Yuda, E.

Publications and source records attributed to Yuda, E..

2 recordsLinked to original sources

The minimal SUF system can substitute for the canonical iron-sulfur cluster biosynthesis systems by using inorganic sulfide as the sulfur source.

Biosynthesis of iron-sulfur (Fe-S) clusters is indispensable for living cells. Three biosynthesis systems termed NIF, ISC and SUF have been extensively characterized in both bacteria and eukarya. For these L-cysteine is the sulfur source. A bioinformatic survey suggested the presence of a minimal SUF system composed of only two components, SufB* (a putative ancestral form of SufB and SufD) and SufC, in anaerobic archaea and bacteria. Here, we report the successful complementation of an Escherichia coli mutant devoid of the usual ISC and SUF systems upon expression of the archaeal sufB*C genes. Strikingly, this heterologous complementation occurred under anaerobic conditions only when sulfide was supplemented to the culture media. Mutational analysis and structural predictions suggest that the archaeal SufB*C most likely forms a SufB*2C2 complex and serves as the scaffold for de novo Fe-S cluster assembly using the essential Cys and Glu residues conserved between SufB* and SufB, in conjunction with a His residue shared between SufB* and SufD. We also demonstrate artificial conversion of the SufB*2C2 structure to the SufBC2D type by introducing several mutations to the two copies of sufB*. Our study thus elucidates the molecular function of this minimal SUF system and suggests that it is the evolutionary prototype of the canonical SUF system.

microbiology↗

Quantitative detection of sleep apnea with wearable watch device

The spread of wearable watch devices with photoplethysmography (PPG) sensors has made it possible to use continuous pulse wave data during daily life. We examined if PPG pulse wave data can be used to detect sleep apnea, a common but underdiagnosed health problem associated with impaired quality of life and increased cardiovascular risk. In 41 patients undergoing diagnostic polysomnography (PSG) for sleep apnea, PPG was recorded simultaneously with a wearable watch device. The pulse interval data were analyzed by an automated algorithm called auto-correlated wave detection with adaptive threshold (ACAT) which was developed for electrocardiogram (ECG) to detect the cyclic variation of heart rate (CVHR), a characteristic heart rate pattern accompanying sleep apnea episodes. The median (IQR) apnea-hypopnea index (AHI) was 17.2 (4.4-28.4) and 22 (54%) subjects had AHI [&ge;]15. The hourly frequency of CVHR (Fcv) detected by the ACAT algorithm closely correlated with AHI (r = 0.81), while none of the time-domain, frequency-domain, or non-linear indices of pulse interval variability showed significant correlation. The Fcv was greater in subjects with AHI [&ge;]15 (19.6 {+/-} 12.3 /h) than in those with AHI <15 (6.4 {+/-} 4.6 /h), and was able to discriminate them with 82% sensitivity, 89% specificity, and 85% accuracy. The classification performance was comparable to that obtained when the ACAT algorithm was applied to ECG R-R intervals during the PSG. The analysis of wearable watch PPG by the ACAT algorithm could be used for the quantitative screening of sleep apnea.

bioengineering↗