Small molecule inhibitor of lipoteichoic acid synthesis is an antibiotic for Gram-positive bacteria

The current epidemic of infections caused by antibiotic-resistant Gram-positive bacteria requires the discovery of new drug targets and the development of new therapeutics. Lipoteichoic acid (LTA), a cell wall polymer of Gram-positive bacteria, consists of 1,3-polyglycerol-phosphate linked to glycolipid. LTA synthase (LtaS) polymerizes polyglycerol-phosphate from phosphatidylglycerol, a reaction that is essential for the growth of Gram-positive bacteria. We screened small molecule libraries for compounds inhibiting growth of Staphylococcus aureus but not of Gram-negative bacteria. Compound 1771 [2-oxo-2-(5-phenyl-1,3,4-oxadiazol-2-ylamino)ethyl 2-naphtho[2,1-b]furan-1-ylacetate] blocked phosphatidylglycerol binding to LtaS and inhibited LTA synthesis in S. aureus and in Escherichia coli expressing ltaS. Compound 1771 inhibited the growth of antibiotic-resistant Gram-positive bacteria and prolonged the survival of mice with lethal S. aureus challenge, validating LtaS as a target for the development of antibiotics

Genomic profiling for clinical decision making in myeloid neoplasms and acute leukemia

Myeloid neoplasms and acute leukemias derive from the clonal expansion of hematopoietic cells driven by somatic gene mutations. Although assessment of morphology plays a crucial role in the diagnostic evaluation of patients with these malignancies, genomic characterization has become increasingly important for accurate diagnosis, risk assessment, and therapeutic decision making. Conventional cytogenetics, a comprehensive and unbiased method for assessing chromosomal abnormalities, has been the mainstay of genomic testing over the past several decades and remains relevant today. However, more recent advances in sequencing technology have increased our ability to detect somatic mutations through the use of targeted gene panels, whole-exome sequencing, whole-genome sequencing, and whole-transcriptome sequencing or RNA sequencing. In patients with myeloid neoplasms, whole-genome sequencing represents a potential replacement for both conventional cytogenetic and sequencing approaches, providing rapid and accurate comprehensive genomic profiling. DNA sequencing methods are used not only for detecting somatically acquired gene mutations but also for identifying germline gene mutations associated with inherited predisposition to hematologic neoplasms. The 2022 International Consensus Classification of myeloid neoplasms and acute leukemias makes extensive use of genomic data. The aim of this report is to help physicians and laboratorians implement genomic testing for diagnosis, risk stratification, and clinical decision making and illustrates the potential of genomic profiling for enabling personalized medicine in patients with hematologic neoplasms

Single cell dissection of plasma cell heterogeneity in symptomatic and asymptomatic myeloma

Multiple myeloma, a plasma cell malignancy, is the second most common blood cancer. Despite extensive research, disease heterogeneity is poorly characterized, hampering efforts for early diagnosis and improved treatments. Here, we apply single cell RNA sequencing to study the heterogeneity of 40 individuals along the multiple myeloma progression spectrum, including 11 healthy controls, demonstrating high interindividual variability that can be explained by expression of known multiple myeloma drivers and additional putative factors. We identify extensive subclonal structures for 10 of 29 individuals with multiple myeloma. In asymptomatic individuals with early disease and in those with minimal residual disease post-treatment, we detect rare tumor plasma cells with molecular characteristics similar to those of active myeloma, with possible implications for personalized therapies. Single cell analysis of rare circulating tumor cells allows for accurate liquid biopsy and detection of malignant plasma cells, which reflect bone marrow disease. Our work establishes single cell RNA sequencing for dissecting blood malignancies and devising detailed molecular characterization of tumor cells in symptomatic and asymptomatic patients

GFI1 proteins regulate stem cell formation in the AGM

In vertebrates, the first haematopoietic stem cells (HSCs) with multi-lineage and long-term repopulating potential arise in the AGM (aorta-gonad-mesonephros) region. These HSCs are generated from a rare and transient subset of endothelial cells, called haemogenic endothelium (HE), through an endothelial-to-haematopoietic transition (EHT). Here, we establish the absolute requirement of the transcriptional repressors GFI1 and GFI1B (growth factor independence 1 and 1B) in this unique trans-differentiation process. We first demonstrate that Gfi1 expression specifically defines the rare population of HE that generates emerging HSCs. We further establish that in the absence of GFI1 proteins, HSCs and haematopoietic progenitor cells are not produced in the AGM, revealing the critical requirement for GFI1 proteins in intra-embryonic EHT. Finally, we demonstrate that GFI1 proteins recruit the chromatin-modifying protein LSD1, a member of the CoREST repressive complex, to epigenetically silence the endothelial program in HE and allow the emergence of blood cells.We thank the staff at the Advanced Imaging, animal facility, Molecular Biology Core facilities and Flow Cytometry of CRUK Manchester Institute for technical support and Michael Lie-A-Ling and Elli Marinopoulou for initiating the DamID-PIP bioinformatics project. We thank members of the Stem Cell Biology group, the Stem Cell Haematopoiesis groups and Martin Gering for valuable advice and critical reading of the manuscript. Work in our laboratory is supported by the Leukaemia and Lymphoma Research Foundation (LLR), Cancer Research UK (CRUK) and the Biotechnology and Biological Sciences Research Council (BBSRC). SC is the recipient of an MRC senior fellowship (MR/J009202/1).This is the author accepted manuscript. The final version is available from NPG via http://dx.doi.org/10.1038/ncb327

Clonal hematopoiesis is associated with risk of severe Covid-19.

Acquired somatic mutations in hematopoietic stem and progenitor cells (clonal hematopoiesis or CH) are associated with advanced age, increased risk of cardiovascular and malignant diseases, and decreased overall survival. These adverse sequelae may be mediated by altered inflammatory profiles observed in patients with CH. A pro-inflammatory immunologic profile is also associated with worse outcomes of certain infections, including SARS-CoV-2 and its associated disease Covid-19. Whether CH predisposes to severe Covid-19 or other infections is unknown. Among 525 individuals with Covid-19 from Memorial Sloan Kettering (MSK) and the Korean Clonal Hematopoiesis (KoCH) consortia, we show that CH is associated with severe Covid-19 outcomes (OR = 1.85, 95%=1.15-2.99, p = 0.01), in particular CH characterized by non-cancer driver mutations (OR = 2.01, 95% CI = 1.15-3.50, p = 0.01). We further explore the relationship between CH and risk of other infections in 14,211 solid tumor patients at MSK. CH is significantly associated with risk of Clostridium Difficile (HR = 2.01, 95% CI: 1.22-3.30, p = 6×10-3) and Streptococcus/Enterococcus infections (HR = 1.56, 95% CI = 1.15-2.13, p = 5×10-3). These findings suggest a relationship between CH and risk of severe infections that warrants further investigation

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts