Search for: All records

Candidate bacterial phylum CSP1-3 has not been cultivated and is poorly understood. Here, we analyzed 112 CSP1-3 metagenome-assembled genomes and showed they are likely facultative anaerobes, with 3 of 5 families encoding autotrophy through the reductive glycine pathway (RGP), Wood–Ljungdahl pathway (WLP) or Calvin-Benson-Bassham (CBB), with hydrogen or sulfide as electron donors. Chemoautotrophic enrichments from hot spring sediments and fluorescence in situ hybridization revealed enrichment of six CSP1-3 genera, and both transcribed genes and DNA-stable isotope probing were consistent with proposed chemoautotrophic metabolisms. Ancestral state reconstructions showed that the ancestors of phylum CSP1-3 may have been acetogens that were autotrophic via the RGP, whereas the WLP and CBB were acquired by horizontal gene transfer. Our results reveal that CSP1-3 is a widely distributed phylum with the potential to contribute to the cycling of carbon, sulfur and nitrogen. The name Sysuimicrobiota phy. nov. is proposed.

 

Drought stress poses a substantial challenge to plant growth and agricultural productivity worldwide. Upon water depletion, plants activate an abscisic acid (ABA) signaling pathway, leading to stomatal closure to reduce water loss. The MYB family of transcription factors plays diverse roles in growth, development, stress responses, and biosynthesis, yet their involvement in stomatal regulation remains unclear. Here, we demonstrate that ABA significantly upregulates the expression of MYB41, MYB74, and MYB102, with MYB41 serving as a key regulator that induces the expression of both MYB74 and MYB102. Through luciferase assays, chromatin immunoprecipitation (ChIP) assays, and electrophoretic mobility shift assays (EMSA), we reveal that MYB41 engages in positive feedback regulation by binding to its own promoter, thus amplifying its transcription in Arabidopsis (Arabidopsis thaliana). Furthermore, our investigation showed that MYB41 recruits BRAHMA (BRM), the core ATPase subunit of the SWI/SNF complex, to the MYB41 promoter, facilitating the binding of HISTONE DEACETYLASE 6 (HDA6). This recruitment triggers epigenetic modifications, resulting in reduced MYB41 expression characterized by elevated H3K27me3 levels and concurrent decreases in H3ac, H3K27ac, and H3K14ac levels in wild-type plants compared to brm knockout mutant plants. Our genetic and molecular analyses show that ABA mediates autoregulation of the MYB41-BRM module, which intricately modulates stomatal movement in A. thaliana. This discovery sheds light on a drought response mechanism with the potential to greatly enhance agricultural productivity.

 

Over the past few months, the outbreak of Coronavirus disease (COVID-19) has been expanding over the world. A reliable and accurate dataset of the cases is vital for scientists to conduct related research and policy-makers to make better decisions. We collect the United States COVID-19 daily reported data from four open sources: the New York Times, the COVID-19 Data Repository by Johns Hopkins University, the COVID Tracking Project at the Atlantic, and the USAFacts, then compare the similarities and differences among them. To obtain reliable data for further analysis, we first examine the cyclical pattern and the following anomalies, which frequently occur in the reported cases: (1) the order dependencies violation, (2) the point or period anomalies, and (3) the issue of reporting delay. To address these detected issues, we propose the corresponding repairing methods and procedures if corrections are necessary. In addition, we integrate the COVID-19 reported cases with the county-level auxiliary information of the local features from official sources, such as health infrastructure, demographic, socioeconomic, and environmental information, which are also essential for understanding the spread of the virus. 

During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and government entities led by the Centers for Disease Control and Prevention and the US COVID-19 Forecast Hub (https://covid19forecasthub.org). We evaluated approximately 9.7 million forecasts of weekly state-level COVID-19 cases for predictions 1–4 weeks into the future submitted by 24 teams from August 2020 to December 2021. We assessed coverage of central prediction intervals and weighted interval scores (WIS), adjusting for missing forecasts relative to a baseline forecast, and used a Gaussian generalized estimating equation (GEE) model to evaluate differences in skill across epidemic phases that were defined by the effective reproduction number. Overall, we found high variation in skill across individual models, with ensemble-based forecasts outperforming other approaches. Forecast skill relative to the baseline was generally higher for larger jurisdictions (e.g., states compared to counties). Over time, forecasts generally performed worst in periods of rapid changes in reported cases (either in increasing or decreasing epidemic phases) with 95% prediction interval coverage dropping below 50% during the growth phases of the winter 2020, Delta, and Omicron waves. Ideally, case forecasts could serve as a leading indicator of changes in transmission dynamics. However, while most COVID-19 case forecasts outperformed a naïve baseline model, even the most accurate case forecasts were unreliable in key phases. Further research could improve forecasts of leading indicators, like COVID-19 cases, by leveraging additional real-time data, addressing performance across phases, improving the characterization of forecast confidence, and ensuring that forecasts were coherent across spatial scales. In the meantime, it is critical for forecast users to appreciate current limitations and use a broad set of indicators to inform pandemic-related decision making.