Systemic Infection and Limited Replication of SHIV Vaccine Virus in Brains of Macaques Inoculated Intracerebrally with Infectious Viral DNA

AbstractSHIV deleted in two accessory genes, ΔvpuΔnef SHIVPPC, functioned well as a vaccine against later challenge with highly pathogenic SHIVKU, and it was able to reach the brain after oral inoculation of live virus. In this study, the proviral genome cloned into a plasmid was inoculated as DNA intracerebrally and spread systemically. Few regions of the brain had detectable proviral DNA by real-time PCR. Two measures of virus replication, detection of viral mRNA expression and circular proviral DNA, were negative for those brain regions, with the exception of the infection site in the right parietal lobe, whereas lymphoid tissues were positive by both measures. Histopathological analyses of all the sampled brain and spinal cord regions did not reveal any abnormalities. Despite intracerebral inoculation of the viral DNA, the brain was not targeted for high levels of virus replication

The Large Dispersion and Scattering of FRB 20190520B Are Dominated by the Host Galaxy

The repeating fast radio burst FRB 20190520B is localized to a galaxy at z = 0.241, much closer than expected given its dispersion measure DM = 1205 ± 4 pc cm-3. Here we assess implications of the large DM and scattering observed from FRB 20190520B for the host galaxy's plasma properties. A sample of 75 bursts detected with the Five-hundred-meter Aperture Spherical radio Telescope shows scattering on two scales: a mean temporal delay τ(1.41 GHz) = 10.9 ± 1.5 ms, which is attributed to the host galaxy, and a mean scintillation bandwidth δν d(1.41 GHz) = 0.21 ± 0.01 MHz, which is attributed to the Milky Way. Balmer line measurements for the host imply an Hα emission measure (galaxy frame) EMs = 620 pc cm-6 × (T/104 K)0.9, implying DMHα of order the value inferred from the FRB DM budget, DMh=1121-138+89 pc cm-3 for plasma temperatures greater than the typical value 104 K. Combining τ and DMh yields a nominal constraint on the scattering amplification from the host galaxy FG=1.5-0.3+0.8(pc2km)-1/3, where F describes turbulent density fluctuations and G represents the geometric leverage to scattering that depends on the location of the scattering material. For a two-screen scattering geometry where τ arises from the host galaxy and Δν d from the Milky Way, the implied distance between the FRB source and dominant scattering material is ≤100 pc. The host galaxy scattering and DM contributions support a novel technique for estimating FRB redshifts using the τ-DM relation, and are consistent with previous findings that scattering of localized FRBs is largely dominated by plasma within host galaxies and the Milky Way

Variation in pre-PCR processing of FFPE samples leads to discrepancies in BRAF and EGFR mutation detection: a diagnostic RING trial.

Aims Mutation detection accuracy has been described extensively; however, it is surprising that pre-PCR processing of formalin-fixed paraffin-embedded (FFPE) samples has not been systematically assessed in clinical context. We designed a RING trial to (i) investigate pre-PCR variability, (ii) correlate pre-PCR variation with EGFR/BRAF mutation testing accuracy and (iii) investigate causes for observed variation. Methods 13 molecular pathology laboratories were recruited. 104 blinded FFPE curls including engineered FFPE curls, cell-negative FFPE curls and control FFPE tissue samples were distributed to participants for pre-PCR processing and mutation detection. Follow-up analysis was performed to assess sample purity, DNA integrity and DNA quantitation. Results Rate of mutation detection failure was 11.9%. Of these failures, 80% were attributed to pre-PCR error. Significant differences in DNA yields across all samples were seen using analysis of variance (p<0.0001), and yield variation from engineered samples was not significant (p=0.3782). Two laboratories failed DNA extraction from samples that may be attributed to operator error. DNA extraction protocols themselves were not found to contribute significant variation. 10/13 labs reported yields averaging 235.8ng (95% CI 90.7 to 380.9) from cell-negative samples, which was attributed to issues with spectrophotometry. DNA measurements using Qubit Fluorometry demonstrated a median fivefold overestimation of DNA quantity by Nanodrop Spectrophotometry. DNA integrity and PCR inhibition were factors not found to contribute significant variation. Conclusions In this study, we provide evidence demonstrating that variation in pre-PCR steps is prevalent and may detrimentally affect the patient's ability to receive critical therapy. We provide recommendations for preanalytical workflow optimisation that may reduce errors in down-stream sequencing and for next-generation sequencing library generation

A repeating fast radio burst associated with a persistent radio source

The dispersive sweep of fast radio bursts (FRBs) has been used to probe the ionized baryon content of the intergalactic medium1, which is assumed to dominate the total extragalactic dispersion. Although the host-galaxy contributions to the dispersion measure appear to be small for most FRBs2, in at least one case there is evidence for an extreme magneto-ionic local environment3,4 and a compact persistent radio source5. Here we report the detection and localization of the repeating FRB 20190520B, which is co-located with a compact, persistent radio source and associated with a dwarf host galaxy of high specific-star-formation rate at a redshift of 0.241 ± 0.001. The estimated host-galaxy dispersion measure of approximately 903−111+72 parsecs per cubic centimetre, which is nearly an order of magnitude higher than the average of FRB host galaxies2,6, far exceeds the dispersion-measure contribution of the intergalactic medium. Caution is thus warranted in inferring redshifts for FRBs without accurate host-galaxy identifications

Gene-SGAN: a method for discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering

Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN - a multi-view, weakly-supervised deep clustering method - which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer's disease and brain endophenotypes associated with hypertension, from MRI and SNP data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-driven neuroimaging phenotypes

New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk.

Levels of circulating glucose are tightly regulated. To identify new loci influencing glycemic traits, we performed meta-analyses of 21 genome-wide association studies informative for fasting glucose, fasting insulin and indices of beta-cell function (HOMA-B) and insulin resistance (HOMA-IR) in up to 46,186 nondiabetic participants. Follow-up of 25 loci in up to 76,558 additional subjects identified 16 loci associated with fasting glucose and HOMA-B and two loci associated with fasting insulin and HOMA-IR. These include nine loci newly associated with fasting glucose (in or near ADCY5, MADD, ADRA2A, CRY2, FADS1, GLIS3, SLC2A2, PROX1 and C2CD4B) and one influencing fasting insulin and HOMA-IR (near IGF1). We also demonstrated association of ADCY5, PROX1, GCK, GCKR and DGKB-TMEM195 with type 2 diabetes. Within these loci, likely biological candidate genes influence signal transduction, cell proliferation, development, glucose-sensing and circadian regulation. Our results demonstrate that genetic studies of glycemic traits can identify type 2 diabetes risk loci, as well as loci containing gene variants that are associated with a modest elevation in glucose levels but are not associated with overt diabetes