Somatic hypermutation mechanisms during lymphomagenesis and transformation.

B cells undergoing physiologically programmed or aberrant genomic alterations provide an opportune system to study the causes and consequences of genome mutagenesis. Activated B cells in germinal centers express activation-induced cytidine deaminase (AID) to accomplish physiological somatic hypermutation (SHM) of their antibody-encoding genes. In attempting to diversify their immunoglobulin (Ig) heavy- and light-chain genes, several B-cell clones successfully optimize their antigen-binding affinities. However, SHM can sometimes occur at non-Ig loci, causing genetic alternations that lay the foundation for lymphomagenesis, particularly diffuse large B-cell lymphoma. Thus, SHM acts as a double-edged sword, bestowing superb humoral immunity at the potential risk of initiating disease. We refer to off-target, non-Ig AID mutations - that are often but not always associated with disease - as aberrant SHM (aSHM). A key challenge in understanding SHM and aSHM is determining how AID targets and mutates specific DNA sequences in the Ig loci to generate antibody diversity and non-Ig genes to initiate lymphomagenesis. Herein, we discuss some current advances regarding the regulation of AID's DNA mutagenesis activity in B cells.

Noncoding mutations cause super-enhancer retargeting resulting in protein synthesis dysregulation during B cell lymphoma progression.

Whole-genome sequencing of longitudinal tumor pairs representing transformation of follicular lymphoma to high-grade B cell lymphoma with MYC and BCL2 rearrangements (double-hit lymphoma) identified coding and noncoding genomic alterations acquired during lymphoma progression. Many of these transformation-associated alterations recurrently and focally occur at topologically associating domain resident regulatory DNA elements, including H3K4me3 promoter marks located within H3K27ac super-enhancer clusters in B cell non-Hodgkin lymphoma. One region found to undergo recurrent alteration upon transformation overlaps a super-enhancer affecting the expression of the PAX5/ZCCHC7 gene pair. ZCCHC7 encodes a subunit of the Trf4/5-Air1/2-Mtr4 polyadenylation-like complex and demonstrated copy number gain, chromosomal translocation and enhancer retargeting-mediated transcriptional upregulation upon lymphoma transformation. Consequently, lymphoma cells demonstrate nucleolar dysregulation via altered noncoding 5.8S ribosomal RNA processing. We find that a noncoding mutation acquired during lymphoma progression affects noncoding rRNA processing, thereby rewiring protein synthesis leading to oncogenic changes in the lymphoma proteome.

Efferocytosis induces macrophage proliferation to help resolve tissue injury.

Apoptotic cell clearance by macrophages (efferocytosis) promotes resolution signaling pathways, which can be triggered by molecules derived from the phagolysosomal degradation of apoptotic cells. We show here that nucleotides derived from the hydrolysis of apoptotic cell DNA by phagolysosomal DNase2a activate a DNA-PKcs-mTORC2/Rictor pathway that increases Myc to promote non-inflammatory macrophage proliferation. Efferocytosis-induced proliferation expands the pool of resolving macrophages in vitro and in mice, including zymosan-induced peritonitis, dexamethasone-induced thymocyte apoptosis, and atherosclerosis regression. In the dexamethasone-thymus model, hematopoietic Rictor deletion blocked efferocytosing macrophage proliferation, apoptotic cell clearance, and tissue resolution. In atherosclerosis regression, silencing macrophage Rictor or DNase2a blocked efferocyte proliferation, apoptotic cell clearance, and plaque stabilization. In view of previous work showing that other types of apoptotic cell cargo can promote resolution in individual efferocytosing macrophages, the findings here suggest that signaling-triggered apoptotic cell-derived nucleotides can amplify this benefit by increasing the number of these macrophages.

Characterising the long-term clinical outcomes of 1190 hospitalised patients with COVID-19 in New York City: a retrospective case series.

OBJECTIVE: To characterise the long-term outcomes of patients with COVID-19 admitted to a large New York City medical centre at 3 and 6 months after hospitalisation and describe their healthcare usage, symptoms, morbidity and mortality. DESIGN: Retrospective cohort through manual chart review of the electronic medical record. SETTING: NewYork-Presbyterian/Columbia University Irving Medical Center, a quaternary care academic medical centre in New York City. PARTICIPANTS: The first 1190 consecutive patients with symptoms of COVID-19 who presented to the hospital for care between 1 March and 8 April 2020 and tested positive for SARS-CoV-2 on reverse transcriptase PCR assay. MAIN OUTCOME MEASURES: Type and frequency of follow-up encounters, self-reported symptoms, morbidity and mortality at 3 and 6 months after presentation, respectively; patient disposition information prior to admission, at discharge, and at 3 and 6 months after hospital presentation. RESULTS: Of the 1190 reviewed patients, 929 survived their initial hospitalisation and 261 died. Among survivors, 570 had follow-up encounters (488 at 3 months and 364 at 6 months). An additional 33 patients died in the follow-up period. In the first 3 months after admission, most encounters were telehealth visits (59%). Cardiopulmonary symptoms (35.7% and 28%), especially dyspnoea (22.1% and 15.9%), were the most common reported symptoms at 3-month and 6-month encounters, respectively. Additionally, a large number of patients reported generalised (26.4%) or neuropsychiatric (24.2%) symptoms 6 months after hospitalisation. Patients with severe COVID-19 were more likely to have reduced mobility, reduced independence or a new dialysis requirement in the 6 months after hospitalisation. CONCLUSIONS: Patients hospitalised with SARS-CoV-2 infection reported persistent symptoms up to 6 months after diagnosis. These results highlight the long-term morbidity of COVID-19 and its burden on patients and healthcare resources.

New software for automated cilia detection in cells (ACDC).

BACKGROUND: Primary cilia frequency and length are key metrics in studies of ciliogenesis and ciliopathies. Typically, quantitative cilia analysis is done manually, which is very time-consuming. While some open-source and commercial image analysis software applications can segment input data, they still require the user to optimize many parameters, suffer from user bias, and often lack rigorous performance quality assessment (e.g., false positives and false negatives). Further, optimal parameter combinations vary in detection accuracy depending on cilia reporter, cell type, and imaging modality. A good automated solution would analyze images quickly, robustly, and adaptably-across different experimental data sets-without significantly compromising the accuracy of manual analysis. METHODS: To solve this problem, we developed a new software for automated cilia detection in cells (ACDC). The software operates through four main steps: image importation, pre-processing, detection auto-optimization, and analysis. From a data set, a representative image with manually selected cilia (i.e., Ground Truth) is used for detection auto-optimization based on four parameters: signal-to-noise ratio, length, directional score, and intensity standard deviation. Millions of parameter combinations are automatically evaluated and optimized according to an accuracy 'F1' score, based on the amount of false positives and false negatives. Afterwards, the optimized parameter combination is used for automated detection and analysis of the entire data set. RESULTS: The ACDC software accurately and adaptably detected nuclei and primary cilia across different cell types (NIH3T3, RPE1), cilia reporters (AcTub, Smo-GFP, Arl13b), and image magnifications (60×, 40×). We found that false-positive and false-negative rates for Arl13b-stained cilia were 1-6%, yielding high F1 scores of 0.96-0.97 (max. = 1.00). The software detected significant differences in mean cilia length between control and cytochalasin D-treated cell populations and could monitor dynamic changes in cilia length from movie recordings. Automated analysis offered up to a 96-fold speed enhancement compared to manual analysis, requiring around 5 s/image, or nearly 18,000 cilia analyzed/hour. CONCLUSION: The ACDC software is a solution for robust automated analysis of microscopic images of ciliated cells. The software is extremely adaptable, accurate, and offers immense time-savings compared to traditional manual analysis.