m6A RNA Modification Controls Cell Fate Transition in Mammalian Embryonic Stem Cells

N6-methyl-adenosine (m[superscript 6]A) is the most abundant modification on messenger RNAs and is linked to human diseases, but its functions in mammalian development are poorly understood. Here we reveal the evolutionary conservation and function of m[superscript 6]A by mapping the m[superscript 6]A methylome in mouse and human embryonic stem cells. Thousands of messenger and long noncoding RNAs show conserved m[superscript 6]A modification, including transcripts encoding core pluripotency transcription factors. m[superscript 6]A is enriched over 3′ untranslated regions at defined sequence motifs and marks unstable transcripts, including transcripts turned over upon differentiation. Genetic inactivation or depletion of mouse and human Mettl3, one of the m[superscript 6]A methylases, led to m[superscript 6]A erasure on select target genes, prolonged Nanog expression upon differentiation, and impaired ESC exit from self-renewal toward differentiation into several lineages in vitro and in vivo. Thus, m[superscript 6]A is a mark of transcriptome flexibility required for stem cells to differentiate to specific lineages

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Background: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. Methods: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. Findings: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. Interpretation: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic

Myc Localizes to Histone Locus Bodies during Replication in Drosophila

Myc is an important protein at the center of multiple pathways required for growth and proliferation in animals. The absence of Myc is lethal in flies and mice, and its over-production is a potent inducer of over-proliferation and cancer. Myc protein is localized to the nucleus where it executes its many functions, however the specific sub-nuclear localization of Myc has rarely been reported. The work we describe here began with an observation of unexpected, punctate spots of Myc protein in certain regions of Drosophila embryos. We investigated the identity of these puncta and demonstrate that Myc is co-localized with coilin, a marker for sub-nuclear organelles known as Cajal Bodies (CBs), in embryos, larvae and ovaries. Using antibodies specific for U7 snRNP component Lsm11, we show that the majority of Myc and coilin co-localization occurs in Histone Locus Bodies (HLBs), the sites of histone mRNA transcription and processing. Furthermore, Myc localizes to HLBs only during replication in mitotic and endocycling cells, suggesting that its role there relates to replication-dependent canonical histone gene transcription. These results provide evidence that sub-nuclear localization of Myc is cell-cycle dependent and potentially important for histone mRNA production and processing

MicroRNA miR-308 regulates dMyc through a negative feedback loop in Drosophila

Summary The abundance of Myc protein must be exquisitely controlled to avoid growth abnormalities caused by too much or too little Myc. An intriguing mode of regulation exists in which Myc protein itself leads to reduction in its abundance. We show here that dMyc binds to the miR-308 locus and increases its expression. Using our gain-of-function approach, we show that an increase in miR-308 causes a destabilization of dMyc mRNA and reduced dMyc protein levels. In vivo knockdown of miR-308 confirmed the regulation of dMyc levels in embryos. This regulatory loop is crucial for maintaining appropriate dMyc levels and normal development. Perturbation of the loop, either by elevated miR-308 or elevated dMyc, caused lethality. Combining elevated levels of both, therefore restoring balance between miR-308 and dMyc levels, resulted in lower apoptotic activity and suppression of lethality. These results reveal a sensitive feedback mechanism that is crucial to prevent the pathologies caused by abnormal levels of dMyc

Genome-Wide Maps of m6A circRNAs Identify Widespread and Cell-Type-Specific Methylation Patterns that Are Distinct from mRNAs

N6-methyladenosine (m6A) is the most abundant internal modification of mRNAs and is implicated in all aspects of post-transcriptional RNA metabolism. However, little is known about m6A modifications to circular (circ) RNAs. We developed a computational pipeline (AutoCirc) that, together with depletion of ribosomal RNA and m6A immunoprecipitation, defined thousands of m6A circRNAs with cell-type-specific expression. The presence of m6A circRNAs is corroborated by interaction between circRNAs and YTHDF1/YTHDF2, proteins that read m6A sites in mRNAs, and by reduced m6A levels upon depletion of METTL3, the m6A writer. Despite sharing m6A readers and writers, m6A circRNAs are frequently derived from exons that are not methylated in mRNAs, whereas mRNAs that are methylated on the same exons that compose m6A circRNAs exhibit less stability in a process regulated by YTHDF2. These results expand our understanding of the breadth of m6A modifications and uncover regulation of circRNAs through m6A modification

DIGIT Is a Conserved Long Noncoding RNA that Regulates GSC Expression to Control Definitive Endoderm Differentiation of Embryonic Stem Cells

Long noncoding RNAs (lncRNAs) exhibit diverse functions, including regulation of development. Here, we combine genome-wide mapping of SMAD3 occupancy with expression analysis to identify lncRNAs induced by activin signaling during endoderm differentiation of human embryonic stem cells (hESCs). We find that DIGIT is divergent to Goosecoid (GSC) and expressed during endoderm differentiation. Deletion of the SMAD3-occupied enhancer proximal to DIGIT inhibits DIGIT and GSC expression and definitive endoderm differentiation. Disruption of the gene encoding DIGIT and depletion of the DIGIT transcript reveal that DIGIT is required for definitive endoderm differentiation. In addition, we identify the mouse ortholog of DIGIT and show that it is expressed during development and promotes definitive endoderm differentiation of mouse ESCs. DIGIT regulates GSC in trans, and activation of endogenous GSC expression is sufficient to rescue definitive endoderm differentiation in DIGIT-deficient hESCs. Our study defines DIGIT as a conserved noncoding developmental regulator of definitive endoderm

lncRNA DIGIT and BRD3 protein form phase-separated condensates to regulate endoderm differentiation

© 2020, The Author(s), under exclusive licence to Springer Nature Limited. Cooperation between DNA, RNA and protein regulates gene expression and controls differentiation through interactions that connect regions of nucleic acids and protein domains and through the assembly of biomolecular condensates. Here, we report that endoderm differentiation is regulated by the interaction between the long non-coding RNA (lncRNA) DIGIT and the bromodomain and extraterminal domain protein BRD3. BRD3 forms phase-separated condensates of which the formation is promoted by DIGIT, occupies enhancers of endoderm transcription factors and is required for endoderm differentiation. BRD3 binds to histone H3 acetylated at lysine 18 (H3K18ac) in vitro and co-occupies the genome with H3K18ac. DIGIT is also enriched in regions of H3K18ac, and the depletion of DIGIT results in decreased recruitment of BRD3 to these regions. Our findings show that cooperation between DIGIT and BRD3 at regions of H3K18ac regulates the transcription factors that drive endoderm differentiation and suggest that protein–lncRNA phase-separated condensates have a broader role as regulators of transcription

Electro-Oxidation Method Applied for Activated Sludge Treatment: Experiment and Simulation Based on Supervised Machine Learning Methods

In the present research, an electro-oxidation method was applied to decrease the organic compounds and remove the available micro-organisms in activated sludge of the sewage. Within this method, low cost electrodes were used, including stainless steel, graphite, and Pb/PbO2, and the operating parameters (pH, current density, and operating time) were experimentally optimized. In order to determine sludge stabilization (removal of organic matters and microorganisms), the decrease of parameters like chemical oxygen demand, the increase of electroconductivity and the total dissolved solids, total coli form, and fecal coli form were investigated. Two machine learning techniques (artificial neural networks and support vector machines) were applied comparatively for prediction of the process efficiency. Accurate results were obtained by simulation, in agreement with experimental data