A Systematically Improved High Quality Genome and Transcriptome of the Human Blood Fluke Schistosoma mansoni

Schistosomiasis is one of the most prevalent parasitic diseases, affecting millions of people in developing countries. Amongst the human-infective species, Schistosoma mansoni is also the most commonly used in the laboratory and here we present the systematic improvement of its draft genome. We used Sanger capillary and deep-coverage Illumina sequencing from clonal worms to upgrade the highly fragmented draft 380 Mb genome to one with only 885 scaffolds and more than 81% of the bases organised into chromosomes. We have also used transcriptome sequencing (RNA-seq) from four time points in the parasite's life cycle to refine gene predictions and profile their expression. More than 45% of predicted genes have been extensively modified and the total number has been reduced from 11,807 to 10,852. Using the new version of the genome, we identified trans-splicing events occurring in at least 11% of genes and identified clear cases where it is used to resolve polycistronic transcripts. We have produced a high-resolution map of temporal changes in expression for 9,535 genes, covering an unprecedented dynamic range for this organism. All of these data have been consolidated into a searchable format within the GeneDB (www.genedb.org) and SchistoDB (www.schistodb.net) databases. With further transcriptional profiling and genome sequencing increasingly accessible, the upgraded genome will form a fundamental dataset to underpin further advances in schistosome research

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Resolving the immune landscape of human prostate at a single-cell level in health and cancer.

The prostate gland produces prostatic fluid, high in zinc and citrate and essential for the maintenance of spermatozoa. Prostate cancer is a common condition with limited treatment efficacy in castration-resistant metastatic disease, including with immune checkpoint inhibitors. Using single-cell RNA-sequencing to perform an unbiased assessment of the cellular landscape of human prostate, we identify a subset of tumor-enriched androgen receptor-negative luminal epithelial cells with increased expression of cancer-associated genes. We also find a variety of innate and adaptive immune cells in normal prostate that were transcriptionally perturbed in prostate cancer. An exception is a prostate-specific, zinc transporter-expressing macrophage population (MAC-MT) that contributes to tissue zinc accumulation in homeostasis but shows enhanced inflammatory gene expression in tumors, including T cell-recruiting chemokines. Remarkably, enrichment of the MAC-MT signature in cancer biopsies is associated with improved disease-free survival, suggesting beneficial antitumor functions.Z.K.T. and M.R.C. are supported by a Medical Research Council Human Cell Atlas Research Grant (MR/S035842/1), K.W.L is supported by a Kidney Research UK Clinical Training Fellowship (TF_013_20171124), N.R. was supported by a Wellcome Fellowship (106809/Z/15/Z), M.R.C. is supported by a Versus Arthritis Cure Challenge Research Grant (21777), and an NIHR Research Professorship (RP-2017-08-ST2-002). AYW is supported by the Cancer Research UK Cambridge Centre (C9685/A25177) and NIHR Cambridge Biomedical Research Centre (BRC-1215-20014). Z.K.T. holds an honorary research fellow appointment with The University of Queensland Diamantina Institute. We are grateful for infrastructure support from the Cambridge NIHR Biomedical Campus and Cancer Research UK Cambridge Centre. The NIHR Cambridge Biomedical Research Centre (BRC) is a partnership between Cambridge University Hospitals NHS Foundation Trust and the University of Cambridge, funded by the National Institute for Health Research (NIHR)

Accurate detection of benign and malignant renal tumor subtypes with MethylBoostER: An epigenetic marker-driven learning framework.

Current gold standard diagnostic strategies are unable to accurately differentiate malignant from benign small renal masses preoperatively; consequently, 20% of patients undergo unnecessary surgery. Devising a more confident presurgical diagnosis is key to improving treatment decision-making. We therefore developed MethylBoostER, a machine learning model leveraging DNA methylation data from 1228 tissue samples, to classify pathological subtypes of renal tumors (benign oncocytoma, clear cell, papillary, and chromophobe RCC) and normal kidney. The prediction accuracy in the testing set was 0.960, with class-wise ROC AUCs >0.988 for all classes. External validation was performed on >500 samples from four independent datasets, achieving AUCs >0.89 for all classes and average accuracies of 0.824, 0.703, 0.875, and 0.894 for the four datasets. Furthermore, consistent classification of multiregion samples (N = 185) from the same patient demonstrates that methylation heterogeneity does not limit model applicability. Following further clinical studies, MethylBoostER could facilitate a more confident presurgical diagnosis to guide treatment decision-making in the future.Cancer Research UK Clinical Doctoral Fellowship. (S.H.R.) UK Medical Research Council doctoral training award. (I.N) UK Medical Research Council funding (MC UU 12022/10) (S.A.S, J.H) Isaac Newton Trust/Wellcome Trust ISSF/University of Cambridge grant (S.A.S, J.H) CRUK Fellowship award (A26718) (C.E.M.) The Mark Foundation for Cancer Research, the Cancer Research UK Cambridge Centre [C9685/A25177] and NIHR Cambridge Biomedical Research Centre (BRC- 1215-20014). (G.D.S., A.Y.W) The Human Research Tissue Bank at Addenbrooke’s Hospital is supported by the NIHR Cambridge Biomedical Research Centre. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care

Number of differentially expressed genes.

<p>Figures refer to those genes with significant differential expression (adjusted p-value<0.01). NB the v5.0 assembly contains 10,852 genes.</p

Detection of differentially expressed genes.

<p>The plot (left) shows the log fold change (y-axis) vs. log relative concentration (x-axis) for the cercariae – 3-hour schistosomula comparison. A total of 1,518 genes are differentially expressed between these two life cycles stages (adjusted p-value<0.01). On the right, example coverage plots for differentially and non-differentially expressed genes. Of particular interest, genes up regulated in the 3-hour schistosomula stage are enriched in G-protein coupled receptors and integrins, suggesting that signalling is a key process in this life-cycle transition.</p

Fate of gene models.

<p>The criteria for including genes into each category are described in the main text.</p>a<p>Version 4.0.</p>b<p>Version 5.0.</p

Characteristics of the old and improved Schistosoma mansoni genome assemblies.

a<p>Version 4.0 of the <i>S. mansoni</i> genome was the published draft genome <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001455#pntd.0001455-Berriman1" target="_blank">[3]</a>. Note the sequence in version v4.0 was identical to the previously released v3.1.</p>b<p>Version 5.0.</p>c<p>This figure refers to a “super-scaffold” of chromosome I sequence where 41 assembly-scaffolds are linked using genetic markers.</p>d<p>101,079,940 pairs of Illumina reads.</p>e<p>Reads mapped in their correct orientation and at a distance apart that corresponds to that predicted by the fragment library size.</p>f<p>Reads that mapped in their correct orientation but at a distance apart that did not match that predicted by the fragment library size.</p