Cancer therapy shapes the fitness landscape of clonal hematopoiesis.

Acquired mutations are pervasive across normal tissues. However, understanding of the processes that drive transformation of certain clones to cancer is limited. Here we study this phenomenon in the context of clonal hematopoiesis (CH) and the development of therapy-related myeloid neoplasms (tMNs). We find that mutations are selected differentially based on exposures. Mutations in ASXL1 are enriched in current or former smokers, whereas cancer therapy with radiation, platinum and topoisomerase II inhibitors preferentially selects for mutations in DNA damage response genes (TP53, PPM1D, CHEK2). Sequential sampling provides definitive evidence that DNA damage response clones outcompete other clones when exposed to certain therapies. Among cases in which CH was previously detected, the CH mutation was present at tMN diagnosis. We identify the molecular characteristics of CH that increase risk of tMN. The increasing implementation of clinical sequencing at diagnosis provides an opportunity to identify patients at risk of tMN for prevention strategies

Cancer therapy shapes the fitness landscape of clonal hematopoiesis.

Acquired mutations are pervasive across normal tissues. However, understanding of the processes that drive transformation of certain clones to cancer is limited. Here we study this phenomenon in the context of clonal hematopoiesis (CH) and the development of therapy-related myeloid neoplasms (tMNs). We find that mutations are selected differentially based on exposures. Mutations in ASXL1 are enriched in current or former smokers, whereas cancer therapy with radiation, platinum and topoisomerase II inhibitors preferentially selects for mutations in DNA damage response genes (TP53, PPM1D, CHEK2). Sequential sampling provides definitive evidence that DNA damage response clones outcompete other clones when exposed to certain therapies. Among cases in which CH was previously detected, the CH mutation was present at tMN diagnosis. We identify the molecular characteristics of CH that increase risk of tMN. The increasing implementation of clinical sequencing at diagnosis provides an opportunity to identify patients at risk of tMN for prevention strategies

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

Human Metapneumovirus Infection Induces Significant Changes in Small Noncoding RNA Expression in Airway Epithelial Cells

Small noncoding RNAs (sncRNAs), such as microRNAs (miRNA), virus-derived sncRNAs, and more recently identified tRNA-derived RNA fragments, are critical to posttranscriptional control of genes. Upon viral infection, host cells alter their sncRNA expression as a defense mechanism, while viruses can circumvent host defenses and promote their own propagation by affecting host cellular sncRNA expression or by expressing viral sncRNAs. Therefore, characterizing sncRNA profiles in response to viral infection is an important tool for understanding host–virus interaction, and for antiviral strategy development. Human metapneumovirus (hMPV), a recently identified pathogen, is a major cause of lower respiratory tract infections in infants and children. To investigate whether sncRNAs play a role in hMPV infection, we analyzed the changes in sncRNA profiles of airway epithelial cells in response to hMPV infection using ultrahigh-throughput sequencing. Of the cloned sncRNAs, miRNA was dominant in A549 cells, with the percentage of miRNA increasing in a time-dependent manner after the infection. In addition, several hMPV-derived sncRNAs and corresponding ribonucleases for their biogenesis were identified. hMPV M2-2 protein was revealed to be a key viral protein regulating miRNA expression. In summary, this study revealed several novel aspects of hMPV-mediated sncRNA expression, providing a new perspective on hMPV–host interactions

script

A Python script to construct FASTA files containing reconstructed genome sequences of evolved clones after DNA rearrangements

Scanning the Landscape of Genome Architecture of Non-O1 and Non-O139 <i>Vibrio cholerae</i> by Whole Genome Mapping Reveals Extensive Population Genetic Diversity

<div><p>Historically, cholera outbreaks have been linked to <i>V</i>. <i>cholerae</i> O1 serogroup strains or its derivatives of the O37 and O139 serogroups. A genomic study on the 2010 Haiti cholera outbreak strains highlighted the putative role of non O1/non-O139 <i>V</i>. <i>cholerae</i> in causing cholera and the lack of genomic sequences of such strains from around the world. Here we address these gaps by scanning a global collection of <i>V</i>. <i>cholerae</i> strains as a first step towards understanding the population genetic diversity and epidemic potential of non O1/non-O139 strains. Whole Genome Mapping (Optical Mapping) based bar coding produces a high resolution, ordered restriction map, depicting a complete view of the unique chromosomal architecture of an organism. To assess the genomic diversity of non-O1/non-O139 <i>V</i>. <i>cholerae</i>, we applied a Whole Genome Mapping strategy on a well-defined and geographically and temporally diverse strain collection, the Sakazaki serogroup type strains. Whole Genome Map data on 91 of the 206 serogroup type strains support the hypothesis that <i>V</i>. <i>cholerae</i> has an unprecedented genetic and genomic structural diversity. Interestingly, we discovered chromosomal fusions in two unusual strains that possess a single chromosome instead of the two chromosomes usually found in <i>V</i>. <i>cholerae</i>. We also found pervasive chromosomal rearrangements such as duplications and indels in many strains. The majority of <i>Vibrio</i> genome sequences currently in public databases are unfinished draft sequences. The Whole Genome Mapping approach presented here enables rapid screening of large strain collections to capture genomic complexities that would not have been otherwise revealed by unfinished draft genome sequencing and thus aids in assembling and finishing draft sequences of complex genomes. Furthermore, Whole Genome Mapping allows for prediction of novel <i>V</i>. <i>cholerae</i> non-O1/non-O139 strains that may have the potential to cause future cholera outbreaks.</p></div

Diverse patterns of genomic targeting by transcriptional regulators in Drosophila melanogaster

Annotation of regulatory elements and identification of the transcription-related factors (TRFs) targeting these elements are key steps in understanding how cells interpret their genetic blueprint and their environment during development, and how that process goes awry in the case of disease. One goal of the modENCODE (model organism ENCyclopedia of DNA Elements) Project is to survey a diverse sampling of TRFs, both DNA-binding and non-DNA-binding factors, to provide a framework for the subsequent study of the mechanisms by which transcriptional regulators target the genome. Here we provide an updated map of the Drosophila melanogaster regulatory genome based on the location of 84 TRFs at various stages of development. This regulatory map reveals a variety of genomic targeting patterns, including factors with strong preferences toward proximal promoter binding, factors that target intergenic and intronic DNA, and factors with distinct chromatin state preferences. The data also highlight the stringency of the Polycomb regulatory network, and show association of the Trithorax-like (Trl) protein with hotspots of DNA binding throughout development. Furthermore, the data identify more than 5800 instances in which TRFs target DNA regions with demonstrated enhancer activity. Regions of high TRF co-occupancy are more likely to be associated with open enhancers used across cell types, while lower TRF occupancy regions are associated with complex enhancers that are also regulated at the epigenetic level. Together these data serve as a resource for the research community in the continued effort to dissect transcriptional regulatory mechanisms directing Drosophila development

UPGMA method based dendrograms of Sakazaki serogroup strains.

<p>Whole Genome Mapping data based distance matrix with default parameters was used to generate the dendrograms. The three clusters (epidemic <i>V</i>. <i>cholerae</i> Classical and El Tor, one group of environmental isolates from rat, and <i>V</i>. <i>mimicus</i>) are indicated on the branches. The distribution of VPI and CTX on chromosome I in non-O1/non-O139 strains are indicated as well. The different colors of the highlighted strains indicate the following characteristics: No highlight- Clinical; Gray-environmental; Light green (serogroups O2-O4) source unknown; Yellow: epidemic O1 Classical and El Tor; Green: non-O1 epidemic strains; Pink: <i>V</i>. <i>mimicus</i>; Blue: One <i>V</i>. <i>mimicus</i> isolate (serogroup O115) that has the VPI cluster; Orange: Clinical and carry both VPI and CTX clusters; Light red (O77, O49, O80, O53): Clinical and carry VPI only. Scale: 0.2 = 20% dissimilarity.</p