Evolutionary relationships between Rhynchosporium lolii sp. nov. and other Rhynchosporium species on grass.

Copyright: 2013 King et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are creditedThe fungal genus Rhynchosporium (causative agent of leaf blotch) contains several host-specialised species, including R. commune (colonising barley and brome-grass), R. agropyri (couch-grass), R. secalis (rye and triticale) and the more distantly related R. orthosporum (cocksfoot). This study used molecular fingerprinting, multilocus DNA sequence data, conidial morphology, host range tests and scanning electron microscopy to investigate the relationship between Rhynchosporium species on ryegrasses, both economically important forage grasses and common wild grasses in many cereal growing areas, and other plant species. Two different types of Rhynchosporium were found on ryegrasses in the UK. Firstly, there were isolates of R. commune that were pathogenic to both barley and Italian ryegrass. Secondly, there were isolates of a new species, here named R. lolii, that were pathogenic only to ryegrass species. R. lolii was most closely related to R. orthosporum, but exhibited clear molecular, morphological and host range differences. The species was estimated to have diverged from R. orthosporum ca. 5735 years before the present. The colonisation strategy of all of the different Rhynchosporium species involved extensive hyphal growth in the sub-cuticular regions of the leaves. Finally, new species-specific PCR diagnostic tests were developed that could distinguish between these five closely related Rhynchosporium species.Peer reviewedFinal Published versio

Engineering protein processing of the mammary gland to produce abundant hemophilia B therapy in milk

Both the low animal cell density of bioreactors and their ability to post-translationally process recombinant factor IX (rFIX) limit hemophilia B therapy to transgenic pigs to make rFIX in milk at about 3,000-fold higher output than provided by industrial bioreactors. However, this resulted in incomplete γ-carboxylation and propeptide cleavage where both processes are transmembrane mediated. We then bioengineered the co-expression of truncated, soluble human furin (rFurin) with pro-rFIX at a favorable enzyme to substrate ratio. This resulted in the complete conversion of pro-rFIX to rFIX while yielding a normal lactation. Importantly, these high levels of propeptide processing by soluble rFurin did not preempt γ-carboxylation in the ER and therefore was compartmentalized to the Trans-Golgi Network (TGN) and also to milk. The Golgi specific engineering demonstrated here segues the ER targeted enhancement of γ-carboxylation needed to biomanufacture coagulation proteins like rFIX using transgenic livestock

Engineering protein processing of the mammary gland to produce abundant hemophilia B therapy in milk

Both the low animal cell density of bioreactors and their ability to post-translationally process recombinant factor IX (rFIX) limit hemophilia B therapy to transgenic pigs to make rFIX in milk at about 3,000-fold higher output than provided by industrial bioreactors. However, this resulted in incomplete γ-carboxylation and propeptide cleavage where both processes are transmembrane mediated. We then bioengineered the co-expression of truncated, soluble human furin (rFurin) with pro-rFIX at a favorable enzyme to substrate ratio. This resulted in the complete conversion of pro-rFIX to rFIX while yielding a normal lactation. Importantly, these high levels of propeptide processing by soluble rFurin did not preempt γ-carboxylation in the ER and therefore was compartmentalized to the Trans-Golgi Network (TGN) and also to milk. The Golgi specific engineering demonstrated here segues the ER targeted enhancement of γ-carboxylation needed to biomanufacture coagulation proteins like rFIX using transgenic livestock

Discovery of an intermediate-luminosity red transient in M51 and its likely dust-obscured, infrared-variable progenitor

We present the discovery of an optical transient (OT) in Messier 51, designated M51 OT2019-1 (also ZTF19aadyppr, AT 2019abn, ATLAS19bzl), by the Zwicky Transient Facility (ZTF). The OT rose over 15 days to an observed luminosity of $M_r=-13$ (${\nu}L_{\nu}=9\times10^6~L_{\odot}$), in the luminosity gap between novae and typical supernovae (SNe). Spectra during the outburst show a red continuum, Balmer emission with a velocity width of $\approx400$ km s$^{-1}$, Ca II and [Ca II] emission, and absorption features characteristic of an F-type supergiant. The spectra and multiband light curves are similar to the so-called "SN impostors" and intermediate-luminosity red transients (ILRTs). We directly identify the likely progenitor in archival Spitzer Space Telescope imaging with a $4.5~\mu$m luminosity of $M_{[4.5]}\approx-12.2$ and a $[3.6]-[4.5]$ color redder than 0.74 mag, similar to those of the prototype ILRTs SN 2008S and NGC 300 OT2008-1. Intensive monitoring of M51 with Spitzer further reveals evidence for variability of the progenitor candidate at [4.5] in the years before the OT. The progenitor is not detected in pre-outburst Hubble Space Telescope optical and near-IR images. The optical colors during outburst combined with spectroscopic temperature constraints imply a higher reddening of $E(B-V)\approx0.7$ mag and higher intrinsic luminosity of $M_r\approx-14.9$ (${\nu}L_{\nu}=5.3\times10^7~L_{\odot}$) near peak than seen in previous ILRT candidates. Moreover, the extinction estimate is higher on the rise than on the plateau, suggestive of an extended phase of circumstellar dust destruction. These results, enabled by the early discovery of M51 OT2019-1 and extensive pre-outburst archival coverage, offer new clues about the debated origins of ILRTs and may challenge the hypothesis that they arise from the electron-capture induced collapse of extreme asymptotic giant branch stars.Comment: 21 pages, 5 figures, published in ApJ

Identification of genetic overlap and novel risk loci for attention-deficit/hyperactivity disorder and bipolar disorder

Differential diagnosis between childhood onset attention-deficit/hyperactivity disorder (ADHD) and bipolar disorder (BD) remains a challenge, mainly due to overlapping symptoms and high rates of comorbidity. Despite this, genetic correlation reported for these disorders is low and non-significant. Here we aimed to better characterize the genetic architecture of these disorders utilizing recent large genome-wide association studies (GWAS). We analyzed independent GWAS summary statistics for ADHD (19,099 cases and 34,194 controls) and BD (20,352 cases and 31,358 controls) applying the conditional/conjunctional false discovery rate (condFDR/conjFDR) statistical framework that increases the power to detect novel phenotype-specific and shared loci by leveraging the combined power of two GWAS. We observed cross-trait polygenic enrichment for ADHD conditioned on associations with BD, and vice versa. Leveraging this enrichment, we identified 19 novel ADHD risk loci and 40 novel BD risk loci at condFDR <0.05. Further, we identified five loci jointly associated with ADHD and BD (conjFDR < 0.05). Interestingly, these five loci show concordant directions of effect for ADHD and BD. These results highlight a shared underlying genetic risk for ADHD and BD which may help to explain the high comorbidity rates and difficulties in differentiating between ADHD and BD in the clinic. Improving our understanding of the underlying genetic architecture of these disorders may aid in the development of novel stratification tools to help reduce these diagnostic difficulties.acceptedVersio

Diagnostic Delay Is Associated with Complicated Disease and Growth Impairment in Paediatric Crohn\u27s Disease

Background: Paediatric data on the association between diagnostic delay and inflammatory bowel disease [IBD] complications are lacking. We aimed to determine the effect of diagnostic delay on stricturing/fistulising complications, surgery, and growth impairment in a large paediatric cohort, and to identify predictors of diagnostic delay. Methods: We conducted a national, prospective, multicentre IBD inception cohort study including 1399 children. Diagnostic delay was defined as time from symptom onset to diagnosis \u3e75th percentile. Multivariable proportional hazards [PH] regression was used to examine the association between diagnostic delay and stricturing/fistulising complications and surgery, and multivariable linear regression to examine the association between diagnostic delay and growth. Predictors of diagnostic delay were identified using Cox PH regression. Results: Overall (64% Crohn\u27s disease [CD]; 36% ulcerative colitis/IBD unclassified [UC/IBD-U]; 57% male]), median time to diagnosis was 4.2 (interquartile range [IQR] 2.0-9.2) months. For the overall cohort, diagnostic delay was \u3e9.2 months; in CD, \u3e10.8 months and in UC/IBD-U, \u3e6.6 months. In CD, diagnostic delay was associated with a 2.5-fold higher rate of strictures/internal fistulae (hazard ratio [HR] 2.53, 95% confidence interval [CI] 1.41-4.56). Every additional month of diagnostic delay was associated with a decrease in height-for-age z-score of 0.013 standard deviations [95% CI 0.005-0.021]. Associations persisted after adjusting for disease location and therapy. No independent association was observed between diagnostic delay and surgery in CD or UC/IBD-U. Diagnostic delay was more common in CD, particularly small bowel CD. Abdominal pain, including isolated abdominal pain in CD, was associated with diagnostic delay. Conclusions: Diagnostic delay represents a risk factor for stricturing/internal fistulising complications and growth impairment in paediatric CD

Hypergraph models of biological networks to identify genes critical to pathogenic viral response

Background: Representing biological networks as graphs is a powerful approach to reveal underlying patterns, signatures, and critical components from high-throughput biomolecular data. However, graphs do not natively capture the multi-way relationships present among genes and proteins in biological systems. Hypergraphs are generalizations of graphs that naturally model multi-way relationships and have shown promise in modeling systems such as protein complexes and metabolic reactions. In this paper we seek to understand how hypergraphs can more faithfully identify, and potentially predict, important genes based on complex relationships inferred from genomic expression data sets. Results: We compiled a novel data set of transcriptional host response to pathogenic viral infections and formulated relationships between genes as a hypergraph where hyperedges represent significantly perturbed genes, and vertices represent individual biological samples with specific experimental conditions. We find that hypergraph betweenness centrality is a superior method for identification of genes important to viral response when compared with graph centrality. Conclusions: Our results demonstrate the utility of using hypergraphs to represent complex biological systems and highlight central important responses in common to a variety of highly pathogenic viruses

How real-world data can facilitate the development of precision medicine treatment in psychiatry

Precision medicine has the ambition to improve treatment response and clinical outcomes through patient stratification and holds great potential for the treatment of mental disorders. However, several important factors are needed to transform current practice into a precision psychiatry framework. Most important are 1) the generation of accessible large real-world training and test data including genomic data integrated from multiple sources, 2) the development and validation of advanced analytical tools for stratification and prediction, and 3) the development of clinically useful management platforms for patient monitoring that can be integrated into health care systems in real-life settings. This narrative review summarizes strategies for obtaining the key elements-well-powered samples from large biobanks integrated with electronic health records and health registry data using novel artificial intelligence algorithms-to predict outcomes in severe mental disorders and translate these models into clinical management and treatment approaches. Key elements are massive mental health data and novel artificial intelligence algorithms. For the clinical translation of these strategies, we discuss a precision medicine platform for improved management of mental disorders. We use cases to illustrate how precision medicine interventions could be brought into psychiatry to improve the clinical outcomes of mental disorders