An Adaptation To Life In Acid Through A Novel Mevalonate Pathway.

Extreme acidophiles are capable of growth at pH values near zero. Sustaining life in acidic environments requires extensive adaptations of membranes, proton pumps, and DNA repair mechanisms. Here we describe an adaptation of a core biochemical pathway, the mevalonate pathway, in extreme acidophiles. Two previously known mevalonate pathways involve ATP dependent decarboxylation of either mevalonate 5-phosphate or mevalonate 5-pyrophosphate, in which a single enzyme carries out two essential steps: (1) phosphorylation of the mevalonate moiety at the 3-OH position and (2) subsequent decarboxylation. We now demonstrate that in extreme acidophiles, decarboxylation is carried out by two separate steps: previously identified enzymes generate mevalonate 3,5-bisphosphate and a new decarboxylase we describe here, mevalonate 3,5-bisphosphate decarboxylase, produces isopentenyl phosphate. Why use two enzymes in acidophiles when one enzyme provides both functionalities in all other organisms examined to date? We find that at low pH, the dual function enzyme, mevalonate 5-phosphate decarboxylase is unable to carry out the first phosphorylation step, yet retains its ability to perform decarboxylation. We therefore propose that extreme acidophiles had to replace the dual-purpose enzyme with two specialized enzymes to efficiently produce isoprenoids in extremely acidic environments

An exploding N-isocyanide reagent formally composed of anthracene, dinitrogen and a carbon atom

Targeted as an example of a compound composed of a carbon atom together with two stable neutral leaving groups, 7-isocyano-7-azadibenzonorbornadiene, CN[subscript 2]A (1, A = C[subscript 14]H[subscript 10] or anthracene) has been synthesized and spectroscopically and structurally characterized. The terminal C atom of 1 can be transferred: mesityl nitrile oxide reacts with 1 to produce carbon monoxide, likely via intermediacy of the N-isocyanate OCN[subscript 2]A. Reaction of 1 with [RuCl[subscript 2](CO)(PCy[subscript 3])[subscript 2]] leads to [RuCl2(CO)(1)(PCy3)2] which decomposes unselectively: in the product mixture, the carbide complex [RuCl[subscript 2[](C)(PCy[subscript 3])[subscript 2]] was detected. Upon heating in the solid state or in solution, 1 decomposes to A, N2 and cyanogen (C2N2) as substantiated using molecular beam mass spectrometry, IR and NMR spectroscopy techniques.Alexander von Humboldt Foundation (Feodor Lynen Postdoctoral Fellowship

Sulfur monoxide thermal release from an anthracene-based precursor, spectroscopic identification, and transfer reactivity

Sulfur monoxide (SO) is a highly reactive molecule and thus, eludes bulk isolation. We report here on synthesis and reactivity of a molecular precursor for SO generation, namely 7-sulfin-ylamino-7-azadibenzonorbornadiene (1). This compound has been shown to fragment readily driven by dinitrogen expulsion and anthracene formation on heating in the solid state and in solution, releasing SO at mild temperatures (<100 ◦C). The generated SO was detected in the gas phase by MS and rotational spectroscopy. In solution, 1 allows for SO transfer to organic molecules as well as transition metal complexes. Keywords: microwave spectroscopy; reactive intermediate; molecular precursor; astrochemistry; sulfur monoxid

A Molecular Precursor to Phosphaethyne and Its Application in Synthesis of the Aromatic 1,2,3,4-Phosphatriazolate Anion

Dibenzo-7-phosphanorbornadiene Ph[subscript 3]PC(H)PA (1, A = C[subscript 14]H[subscript 10], anthracene) is reported here as a molecular precursor to phosphaethyne (HC≡P), produced together with anthracene and triphenylphosphine. HCP generated by thermolysis of 1 has been observed by molecular beam mass spectrometry, laser-induced fluorescence, microwave spectroscopy, and nuclear magnetic resonance (NMR) spectroscopy. In toluene, fragmentation of 1 has been found to proceed with activation parameters of ΔH[superscript ⧧] = 25.5 kcal/mol and ΔS[superscript ⧧] = −2.43 eu and is accompanied by formation of an orange insoluble precipitate. Results from computational studies of the mechanism of HCP generation are in good agreement with experimental data. This high-temperature method of HCP generation has pointed to new reaction chemistry with azide anion to produce the 1,2,3,4-phosphatriazolate anion, HCPN3–, for which structural data have been obtained in a single-crystal X-ray diffraction study. Negative-ion photoelectron spectroscopy has shown the adiabatic detachment energy for this anion to be 3.555(10) eV. The aromaticity of HCPN[subscript 3]– has been assessed using nucleus-independent chemical shift, quantum theory of atoms in molecules, and natural bond orbital methods.National Science Foundation (U.S.) (NSF grant CHE-1362118)United States. Department of Energy (grant DE-FG0287ER13671)United States. National Aeronautics and Space Administration (NASA grant NNX13AE59G)Smithsonian Astrophysical Observatory (Harvard-Smithsonian Center for Astrophysics, CfA Postdoctoral Fellowship

Long-term participant retention and engagement patterns in an app and wearable-based multinational remote digital depression study

Recent growth in digital technologies has enabled the recruitment and monitoring of large and diverse populations in remote health studies. However, the generalizability of inference drawn from remotely collected health data could be severely impacted by uneven participant engagement and attrition over the course of the study. We report findings on long-term participant retention and engagement patterns in a large multinational observational digital study for depression containing active (surveys) and passive sensor data collected via Android smartphones, and Fitbit devices from 614 participants for up to 2 years. Majority of participants (67.6%) continued to remain engaged in the study after 43 weeks. Unsupervised clustering of participants' study apps and Fitbit usage data showed 3 distinct engagement subgroups for each data stream. We found: (i) the least engaged group had the highest depression severity (4 PHQ8 points higher) across all data streams; (ii) the least engaged group (completed 4 bi-weekly surveys) took significantly longer to respond to survey notifications (3.8 h more) and were 5 years younger compared to the most engaged group (completed 20 bi-weekly surveys); and (iii) a considerable proportion (44.6%) of the participants who stopped completing surveys after 8 weeks continued to share passive Fitbit data for significantly longer (average 42 weeks). Additionally, multivariate survival models showed participants' age, ownership and brand of smartphones, and recruitment sites to be associated with retention in the study. Together these findings could inform the design of future digital health studies to enable equitable and balanced data collection from diverse populations

Localization of Neuropeptide Gene Expression in Larvae of an Echinoderm, the Starfish Asterias rubens

This study was supported by a European Molecular Biology Organization fellowship awarded to TM (ASTF 168-2014), Russian Foundation for Basic Research (grant 15-04-04298 awarded to TM), BBSRC (grant BB/M001644/1), the Leverhulme Trust (grant RGP-2013-351), the China Scholarship Council and the Society for Experimental Biology

Repeat associated mechanisms of genome evolution and function revealed by the Mus caroli and Mus pahari genomes

Understanding the mechanisms driving lineage-specific evolution in both primates and rodents has been hindered by the lack of sister clades with a similar phylogenetic structure having high-quality genome assemblies. Here, we have created chromosome-level assemblies of the Mus caroli and Mus pahari genomes. Together with the Mus musculus and Rattus norvegicus genomes, this set of rodent genomes is similar in divergence times to the Hominidae (human-chimpanzee-gorilla-orangutan). By comparing the evolutionary dynamics between the Muridae and Hominidae, we identified punctate events of chromosome reshuffling that shaped the ancestral karyotype of Mus musculus and Mus caroli between 3 and 6 million yr ago, but that are absent in the Hominidae. Hominidae show between four- and sevenfold lower rates of nucleotide change and feature turnover in both neutral and functional sequences, suggesting an underlying coherence to the Muridae acceleration. Our system of matched, high-quality genome assemblies revealed how specific classes of repeats can play lineage-specific roles in related species. Recent LINE activity has remodeled protein-coding loci to a greater extent across the Muridae than the Hominidae, with functional consequences at the species level such as reproductive isolation. Furthermore, we charted a Muridae-specific retrotransposon expansion at unprecedented resolution, revealing how a single nucleotide mutation transformed a specific SINE element into an active CTCF binding site carrier specifically in Mus caroli, which resulted in thousands of novel, species-specific CTCF binding sites. Our results show that the comparison of matched phylogenetic sets of genomes will be an increasingly powerful strategy for understanding mammalian biology

Challenges in Using mHealth Data From Smartphones and Wearable Devices to Predict Depression Symptom Severity: Retrospective Analysis

A number of challenges exist for the analysis of mHealth data: maintaining participant engagement over extended time periods and therefore understanding what constitutes an acceptable threshold of missing data; distinguishing between the cross-sectional and longitudinal relationships for different features to determine their utility in tracking within-individual longitudinal variation or screening individuals at high risk; and understanding the heterogeneity with which depression manifests itself in behavioral patterns quantified by the passive features. From 479 participants with MDD, we extracted 21 features capturing mobility, sleep, and smartphone use. We investigated the impact of the number of days of available data on feature quality using the intraclass correlation coefficient and Bland-Altman analysis. We then examined the nature of the correlation between the 8-item Patient Health Questionnaire (PHQ-8) depression scale (measured every 14 days) and the features using the individual-mean correlation, repeated measures correlation, and linear mixed effects model. Furthermore, we stratified the participants based on their behavioral difference, quantified by the features, between periods of high (depression) and low (no depression) PHQ-8 scores using the Gaussian mixture model. We demonstrated that at least 8 (range 2-12) days were needed for reliable calculation of most of the features in the 14-day time window. We observed that features such as sleep onset time correlated better with PHQ-8 scores cross-sectionally than longitudinally, whereas features such as wakefulness after sleep onset correlated well with PHQ-8 longitudinally but worse cross-sectionally. Finally, we found that participants could be separated into 3 distinct clusters according to their behavioral difference between periods of depression and periods of no depression

Longitudinal Relationships Between Depressive Symptom Severity and Phone-Measured Mobility: Dynamic Structural Equation Modeling Study

Methods Results Discussion References Abbreviations Copyright Abstract Background: The mobility of an individual measured by phone-collected location data has been found to be associated with depression; however, the longitudinal relationships (the temporal direction of relationships) between depressive symptom severity and phone-measured mobility have yet to be fully explored. Objective: We aimed to explore the relationships and the direction of the relationships between depressive symptom severity and phone-measured mobility over time. Methods: Data used in this paper came from a major EU program, called the Remote Assessment of Disease and Relapse–Major Depressive Disorder, which was conducted in 3 European countries. Depressive symptom severity was measured with the 8-item Patient Health Questionnaire (PHQ-8) through mobile phones every 2 weeks. Participants’ location data were recorded by GPS and network sensors in mobile phones every 10 minutes, and 11 mobility features were extracted from location data for the 2 weeks prior to the PHQ-8 assessment. Dynamic structural equation modeling was used to explore the longitudinal relationships between depressive symptom severity and phone-measured mobility. Results: This study included 2341 PHQ-8 records and corresponding phone-collected location data from 290 participants (age: median 50.0 IQR 34.0, 59.0) years; of whom 215 (74.1%) were female, and 149 (51.4%) were employed. Significant negative correlations were found between depressive symptom severity and phone-measured mobility, and these correlations were more significant at the within-individual level than the between-individual level. For the direction of relationships over time, Homestay (time at home) (φ=0.09, P=.01), Location Entropy (time distribution on different locations) (φ=−0.04, P=.02), and Residential Location Count (reflecting traveling) (φ=0.05, P=.02) were significantly correlated with the subsequent changes in the PHQ-8 score, while changes in the PHQ-8 score significantly affected (φ=−0.07, P<.001) the subsequent periodicity of mobility. Conclusions: Several phone-derived mobility features have the potential to predict future depression, which may provide support for future clinical applications, relapse prevention, and remote mental health monitoring practices in real-world settings

Challenges in Using mHealth Data From Smartphones and Wearable Devices to Predict Depression Symptom Severity: Retrospective Analysis

BACKGROUND: Major depressive disorder (MDD) affects millions of people worldwide, but timely treatment is not often received owing in part to inaccurate subjective recall and variability in the symptom course. Objective and frequent MDD monitoring can improve subjective recall and help to guide treatment selection. Attempts have been made, with varying degrees of success, to explore the relationship between the measures of depression and passive digital phenotypes (features) extracted from smartphones and wearables devices to remotely and continuously monitor changes in symptomatology. However, a number of challenges exist for the analysis of these data. These include maintaining participant engagement over extended time periods and therefore understanding what constitutes an acceptable threshold of missing data; distinguishing between the cross-sectional and longitudinal relationships for different features to determine their utility in tracking within-individual longitudinal variation or screening individuals at high risk; and understanding the heterogeneity with which depression manifests itself in behavioral patterns quantified by the passive features. OBJECTIVE: We aimed to address these 3 challenges to inform future work in stratified analyses. METHODS: Using smartphone and wearable data collected from 479 participants with MDD, we extracted 21 features capturing mobility, sleep, and smartphone use. We investigated the impact of the number of days of available data on feature quality using the intraclass correlation coefficient and Bland-Altman analysis. We then examined the nature of the correlation between the 8-item Patient Health Questionnaire (PHQ-8) depression scale (measured every 14 days) and the features using the individual-mean correlation, repeated measures correlation, and linear mixed effects model. Furthermore, we stratified the participants based on their behavioral difference, quantified by the features, between periods of high (depression) and low (no depression) PHQ-8 scores using the Gaussian mixture model. RESULTS: We demonstrated that at least 8 (range 2-12) days were needed for reliable calculation of most of the features in the 14-day time window. We observed that features such as sleep onset time correlated better with PHQ-8 scores cross-sectionally than longitudinally, whereas features such as wakefulness after sleep onset correlated well with PHQ-8 longitudinally but worse cross-sectionally. Finally, we found that participants could be separated into 3 distinct clusters according to their behavioral difference between periods of depression and periods of no depression. CONCLUSIONS: This work contributes to our understanding of how these mobile health-derived features are associated with depression symptom severity to inform future work in stratified analyses