Two subfamilies of murine retrotransposon ETn sequences

Early transposon (ETn) elements are 5.7-kb retrotransposons found in the murine genome. We have sequenced large portions of two ETn elements that have apparently transposed within the DNA of a murine myeloma cell line, P3.26Bu4. One of the transposed ETn elements has 5' and 3' long terminal repeats (LTRs) that are exact duplicates of each other and has a 6-bp target site duplication. These results suggest that this element, which inserted into an immunoglobulin [gamma]1 switch region, moved by a retrotransposition process. Our nucleotide sequences confirm that individual ETn elements are very similar to one another and lack open reading frames. However, the ETn sequences reported here and those previously described differ significantly near their 5' LTRs, including 200 bp of weak similarity and 240 bp of complete disparity. Southern hybridization analysis suggests that both subfamilies of ETn sequences are represented many times in the mouse genome. The possibility that the disparate sequences have a role in transposition by ETn elements is discussed.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/28722/1/0000543.pd

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Improving natural language information extraction from cancer pathology reports using transfer learning and zero-shot string similarity.

ObjectiveWe develop natural language processing (NLP) methods capable of accurately classifying tumor attributes from pathology reports given minimal labeled examples. Our hierarchical cancer to cancer transfer (HCTC) and zero-shot string similarity (ZSS) methods are designed to exploit shared information between cancers and auxiliary class features, respectively, to boost performance using enriched annotations which give both location-based information and document level labels for each pathology report.Materials and methodsOur data consists of 250 pathology reports each for kidney, colon, and lung cancer from 2002 to 2019 from a single institution (UCSF). For each report, we classified 5 attributes: procedure, tumor location, histology, grade, and presence of lymphovascular invasion. We develop novel NLP techniques involving transfer learning and string similarity trained on enriched annotations. We compare HCTC and ZSS methods to the state-of-the-art including conventional machine learning methods as well as deep learning methods.ResultsFor our HCTC method, we see an improvement of up to 0.1 micro-F1 score and 0.04 macro-F1 averaged across cancer and applicable attributes. For our ZSS method, we see an improvement of up to 0.26 micro-F1 and 0.23 macro-F1 averaged across cancer and applicable attributes. These comparisons are made after adjusting training data sizes to correct for the 20% increase in annotation time for enriched annotations compared to ordinary annotations.ConclusionsMethods based on transfer learning across cancers and augmenting information methods with string similarity priors can significantly reduce the amount of labeled data needed for accurate information extraction from pathology reports

Improving natural language information extraction from cancer pathology reports using transfer learning and zero-shot string similarity.

ObjectiveWe develop natural language processing (NLP) methods capable of accurately classifying tumor attributes from pathology reports given minimal labeled examples. Our hierarchical cancer to cancer transfer (HCTC) and zero-shot string similarity (ZSS) methods are designed to exploit shared information between cancers and auxiliary class features, respectively, to boost performance using enriched annotations which give both location-based information and document level labels for each pathology report.Materials and methodsOur data consists of 250 pathology reports each for kidney, colon, and lung cancer from 2002 to 2019 from a single institution (UCSF). For each report, we classified 5 attributes: procedure, tumor location, histology, grade, and presence of lymphovascular invasion. We develop novel NLP techniques involving transfer learning and string similarity trained on enriched annotations. We compare HCTC and ZSS methods to the state-of-the-art including conventional machine learning methods as well as deep learning methods.ResultsFor our HCTC method, we see an improvement of up to 0.1 micro-F1 score and 0.04 macro-F1 averaged across cancer and applicable attributes. For our ZSS method, we see an improvement of up to 0.26 micro-F1 and 0.23 macro-F1 averaged across cancer and applicable attributes. These comparisons are made after adjusting training data sizes to correct for the 20% increase in annotation time for enriched annotations compared to ordinary annotations.ConclusionsMethods based on transfer learning across cancers and augmenting information methods with string similarity priors can significantly reduce the amount of labeled data needed for accurate information extraction from pathology reports

'Life Strategy' by Ted Ramsay (image)

http://deepblue.lib.umich.edu/bitstream/2027.42/61014/1/2504.pd

Outcome in German and South African peripartum cardiomyopathy cohorts associates with medical therapy and fibrosis markers

Aims This study aims to compare the clinical course of peripartum cardiomyopathy (PPCM) cohorts from Germany (G‐PPCM) and South Africa (SA‐PPCM) with fibrosis‐related markers to get insights into novel pathomechanisms of PPCM. Methods and results G‐PPCM (n = 79) and SA‐PPCM (n = 72) patients and healthy pregnancy‐matched women from Germany (n = 56) and South Africa (n = 40) were enrolled. Circulating levels of procollagen type‐I (PINP) and type‐III (PIIINP) N‐terminal propeptides, soluble ST2, galectin‐3, and full‐length and cleaved osteopontin (OPN) were measured at diagnosis (baseline) and 6 months of follow‐up. Both cohorts received standard heart failure therapy while anticoagulation therapy was applied in 100% of G‐PPCM but only in 7% of SA‐PPCM patients. In G‐PPCM patients, baseline left ventricular ejection fraction (LVEF) was lower, and outcome was better (baseline LVEF, 24 ± 8%, full recovery: 52%, mortality: 0%) compared with SA‐PPCM patients (baseline LVEF: 30 ± 9%, full recovery: 32%, mortality: 11%; P < 0.05). At baseline, PINP/PIIINP ratio was lower in SA‐PPCM and higher in G‐PPCM compared with respective controls, whereas total OPN was elevated in both collectives. Cleaved OPN, which increases PIIINP levels, is generated by thrombin and was reduced in patients receiving anticoagulation therapy. High baseline galectin‐3, soluble ST2, and OPN levels were associated with poor outcome in all PPCM patients. Conclusions SA‐PPCM patients displayed a more profibrotic biomarker profile, which was associated with a less favourable outcome despite better cardiac function at baseline, compared with G‐PPCM patients. Use of bromocriptine and anticoagulation therapy in G‐PPCM may counteract fibrosis and may in part be responsible for their better outcome