Unemployment and sleep: evidence from the United States and Europe

Using data for over 2.5 million individuals in the United States over the period 2006–2019 from the Behavioral Risk Factor Surveillance System (BRFSS) survey series we show the unemployed suffer sleep disruption. The unemployed suffer more short and long sleep than the employed and are more likely to suffer from disturbed sleep. These are especially problematic for the long-term unemployed and for the jobless who say they are unable to work. Similar findings on unemployment and poor sleep quality are found in European data. Increases in the unemployment rate raise the incidence of short sleep and lower sleep durations.     

Assessment of Insulin Infusion Requirements in COVID-19-Infected Patients With Diabetic Ketoacidosis

Background/ObjectiveCoronavirus disease 2019 (COVID-19) is thought to contribute to diabetic ketoacidosis (DKA) and worse outcomes in patients with diabetes. This study compared the cumulative insulin dose required to achieve DKA resolution in the intensive care unit among patients with type 2 diabetes and COVID-19 infection versus without COVID-19 infection.MethodsThis retrospective cohort study evaluated 100 patients—50 patients with COVID-19 in cohort 1 and 50 patients without COVID-19 in cohort 2—treated with insulin infusions for DKA at a tertiary care teaching hospital. The primary outcome was to compare the cumulative insulin dose required to achieve DKA resolution in each cohort. The secondary outcomes included time to DKA resolution, mean insulin infusion rate, and mean weight-based cumulative insulin infusion dose required to achieve DKA resolution. All endpoints were adjusted for confounders.ResultsThe mean cumulative insulin dose was 190.3 units in cohort 1 versus 116.4 units in cohort 2 (P = .0038). Patients receiving steroids had a mean time to DKA resolution of 35.9 hours in cohort 1 versus 15.6 hours in cohort 2 (P = .0014). In cohort 1 versus cohort 2, the mean insulin infusion rate was 7.1 units/hour versus 5.3 units/hour (P = .0025), whereas the mean weight-based cumulative insulin infusion dose was 2.1 units/kg versus 1.5 units/kg (P = .0437), respectively.ConclusionCOVID-19-infected patients required a significantly larger cumulative insulin dose, longer time to DKA resolution, higher insulin infusion rate, and higher weight-based insulin infusion dose to achieve DKA resolution versus non–COVID-19-infected patients with type 2 diabetes.     

Identification of long chain specific aldehyde reductase and its use in enhanced fatty alcohol production in E. coli

Long chain fatty alcohols have wide application in chemical industries and transportation sector. There is no direct natural reservoir for long chain fatty alcohol production, thus many groups explored metabolic engineering approaches for its microbial production. Escherichia coli has been the major microbial platform for this effort, however, terminal endogenous enzyme responsible for converting fatty aldehydes of chain length C14-C18 to corresponding fatty alcohols is still been elusive. Through our in silico analysis we selected 35 endogenous enzymes of E. coli having potential of converting long chain fatty aldehydes to fatty alcohols and studied their role under in vivo condition. We found that deletion of ybbO gene, which encodes NADP⁺ dependent aldehyde reductase, led to >90% reduction in long chain fatty alcohol production. This feature was found to be strain transcending and reinstalling ybbO gene via plasmid retained the ability of mutant to produce long chain fatty alcohols. Enzyme kinetic study revealed that YbbO has wide substrate specificity ranging from C6 to C18 aldehyde, with maximum affinity and efficiency for C18 and C16 chain length aldehyde, respectively. Along with endogenous production of fatty aldehyde via optimized heterologous expression of cyanobaterial acyl-ACP reductase (AAR), YbbO overexpression resulted in 169 mg/L of long chain fatty alcohols. Further engineering involving modulation of fatty acid as well as of phospholipid biosynthesis pathway improved fatty alcohol production by 60%. Finally, the engineered strain produced 1989 mg/L of long chain fatty alcohol in bioreactor under fed-batch cultivation condition. Our study shows for the first time a predominant role of a single enzyme in production of long chain fatty alcohols from fatty aldehydes as well as of modulation of phospholipid pathway in increasing the fatty alcohol production.     

Antifreeze proteins in the grubby sculpin,Myoxocephalus aenaeus and the tomcod,Microgadus tomcod: comparisons of seasonal cycles

Synopsis Antifreeze protein levels in the plasma of the grubby sculpin, Myoxocephalus aenaeus and the tomcod, Microgadus tomcod of Long Island coastal waters start to increase by November in anticipation of midwinter freezing conditions. Peak levels of antifreeze, as measured by the difference in plasma melting and freezing points, were detected in January for both species. The thermal hysteresis values reached 0.459°C in sculpin and 0.51°C in tomcod. Antifreeze peptides and glycopeptides start to disappear when water temperatures begin to rise and are at insignificant levels by late spring. Aspects of the seasonal cycle and the level of antifreeze activity were compared in three sympatric species (sculpin, tomcod, flounder); in two closely related but ecologically distinct gadids (tomcod, Atlantic cod); and within the genus Myoxocephalus.     

SNHG1 represses the anti-cancer roles of baicalein in cervical cancer through regulating miR-3127-5p/FZD4/Wnt/β-catenin signaling

As a flavonoid, baicalein exhibits remarkable anti-cancer roles in several cancers. However, the factors regulating the antitumorigenic roles of baicalein in cervical cancer remain undefined. Here, we revealed that long noncoding RNA SNHG1 is implicated in the tumor-suppressive roles of baicalein. Functional assays demonstrated that ectopic expression of SNHG1 attenuates the roles of baicalein in repressing cervical cancer cell viability, inducing apoptosis, and repressing migration. SNHG1 silencing promotes the tumor-suppressive roles of baicalein in cervical cancer cell viability, apoptosis, and migration. Xenograft assays showed that SNHG1 reverses the tumor-suppressive roles of baicalein in repressing cervical cancer growth in vivo. Mechanistic investigations revealed that SNHG1 directly binds miR-3127-5p and up-regulates FZD4, a target of miR-3127-5p. Via regulating miR-3127-5p/FZD4, SNHG1 activates Wnt/β-catenin signaling. Moreover, SNHG1 reverses the repressive role of baicalein on Wnt/β-catenin signaling. The effect of SNHG1 on the antitumorigenic process of baicalein was abolished by Wnt/β-catenin signaling inhibitor ICG-001. Together, our observations demonstrated that SNHG1 represses the tumor-suppressive roles of baicalein in cervical cancer through regulating miR-3127-5p/FZD4/Wnt/β-catenin axis, and suggested that targeting SNHG1 represents a potential strategy to enhance the tumor-suppressive roles of baicalein in cervical cancer.Impact statementBaicalein exhibits anti-cancer roles in several cancers. However, the factors influencing the antitumorigenic efficiencies of baicalein in CC remain largely unclear. Here, we provide convincing evidences that lncRNA SNHG1 attenuates the tumor-suppressive roles of baicalein in CC cell viability, apoptosis, migration, and CC tumor growth. This study further demonstrates that the influences of SNHG1 in the antitumorigenic process of baicalein are achieved through modulating the miR-3127-5p/FZD4Wnt/β-catenin axis. SNHG1 attenuates the repressive role of baicalein on Wnt/β-catenin. Therefore, SNHG1 is a novel modulator of the tumor-suppressive roles of baicalein and SNHG1 represents a therapeutic intervention target to reinforce the tumor-suppressive roles of baicalein in CC.     

Smart de novo Macromolecular Structure Modeling from Cryo-EM Maps

The study of macromolecular structures has expanded our understanding of the amazing cell machinery and such knowledge has changed how the pharmaceutical industry develops new vaccines in recent years. Traditionally, X-ray crystallography has been the main method for structure determination, however, cryogenic electron microscopy (cryo-EM) has increasingly become more popular due to recent advancements in hardware and software. The number of cryo-EM maps deposited in the EMDataResource (formerly EMDatabase) since 2002 has been dramatically increasing and it continues to do so. De novo macromolecular complex modeling is a labor-intensive process, therefore, it is highly desirable to develop software that can automate this process. Here we discuss our automated, data-driven, and artificial intelligence approaches including map processing, feature extraction, modeling building, and target identification. Recently, we have enabled DNA/RNA modeling in our deep learning-based prediction tool, DeepTracer. We have also developed DeepTracer-ID, a tool that can identify proteins solely based on the cryo-EM map. In this paper, we will present our accumulated experiences in developing deep learning-based methods surrounding macromolecule modeling applications.     

Recolonisation by diffusion can generate increasing rates of spread

Diffusion is one of the most frequently used assumptions to explain dispersal. Diffusion models and in particular reaction-diffusion equations usually lead to solutions moving at constant speeds, too slow compared to observations. As early as 1899, Reid had found that the rate of spread of tree species migrating to northern environments at the beginning of the Holocene was too fast to be explained by diffusive dispersal. Rapid spreading is generally explained using long distance dispersal events, modelled through integro-differential equations (IDEs) with exponentially unbounded (EU) kernels, i.e. decaying slower than any exponential. We show here that classical reaction-diffusion models of the Fisher-Kolmogorov-Petrovsky-Piskunov type can produce patterns of colonisation very similar to those of IDEs, if the initial population is EU at the beginning of the considered colonisation event. Many similarities between reaction-diffusion models with EU initial data and IDEs with EU kernels are found; in particular comparable accelerating rates of spread and flattening of the solutions. There was previously no systematic mathematical theory for such reaction-diffusion models with EU initial data. Yet, EU initial data can easily be understood as consequences of colonisation-retraction events and lead to fast spreading and accelerating rates of spread without the long distance hypothesis.     

Predicting novel disease mutations in the cardiac sodium channel

BackgroundVoltage-gated sodium channels Nav1.x mediate the rising phase of action potential in excitable cells. Variations in gene SCN5A, which encodes the hNav1.5 channel, are associated with arrhythmias and other heart diseases. About 1,400 SCN5A variants are listed in public databases, but for more than 30% of these the clinical significance is unknown and can currently only be derived by bioinformatics approaches.Methods and resultsWe used the ClinVar, SwissVar, Humsavar, gnomAD, and Ensembl databases to assemble a dataset of 1392 hNav1.5 variants (370 pathogenic variants, 602 benign variants and 420 variants of uncertain significance) as well as a dataset of 1766 damaging variants in 20 human sodium and calcium channel paralogs. Twelve in silico tools were tested for their ability to predict damaging mutations in hNav1.5. The best performing tool, MutPred, correctly predicted 93% of damaging variants in our hNav1.5 dataset. Among the 86 hNav1.5 variants for which electrophysiological data are also available, MutPred correctly predicted 82% of damaging variants. In the subset of 420 uncharacterized hNav1.5 variants MutPred predicted 196 new pathogenic variants. Among these, 74 variants are also annotated as damaging in at least one hNav1.5 paralog.ConclusionsUsing a combination of sequence-based bioinformatics techniques and paralogous annotation we have substantially expanded the knowledge on disease variants in the cardiac sodium channel and assigned a pathogenic status to a number of mutations that so far have been described as variants of uncertain significance. A list of reclassified hNav1.5 variants and their properties is provided.