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There is a need for new therapeutic targets with which to prevent Alzheimer’s disease (AD), a major contributor to aging-related cognitive decline. Here we report the construction and validation of a molecular network of the aging human frontal cortex. Using RNA sequence data from 478 individuals, we first build a molecular network using modules of coexpressed genes and then relate these modules to AD and its neuropathologic and cognitive endophenotypes. We confirm these associations in two independent AD datasets. We also illustrate the use of the network in prioritizing amyloid- and cognition-associated genes for in vitro validation in human neurons and astrocytes. These analyses based on unique cohorts enable us to resolve the role of distinct cortical modules that have a direct effect on the accumulation of AD pathology from those that have a direct effect on cognitive decline, exemplifying a network approach to complex diseases.

AbstractObjectiveTo determine if trimethoprim use for urinary tract infection (UTI) is associated with an increased risk of acute kidney injury, hyperkalaemia, or sudden death in the general population.DesignCohort study.SettingUK electronic primary care records from practices contributing to the Clinical Practice Research Datalink linked to the Hospital Episode Statistics database.ParticipantsAdults aged 65 and over with a prescription for trimethoprim, amoxicillin, cefalexin, ciprofloxacin, or nitrofurantoin prescribed up to three days after a primary care diagnosis of UTI between April 1997 and September 2015.Main outcome measuresThe outcomes were acute kidney injury, hyperkalaemia, and death within 14 days of a UTI treated with antibiotics.ResultsAmong a cohort of 1 191 905 patients aged 65 and over, 178 238 individuals were identified with at least one UTI treated with antibiotics, comprising a total of 422 514 episodes of UTIs treated with antibiotics. The odds of acute kidney injury in the 14 days following antibiotic initiation were higher following trimethoprim (adjusted odds ratio 1.72, 95% confidence interval 1.31 to 2.24) and ciprofloxacin (1.48, 1.03 to 2.13) compared with amoxicillin. The odds of hyperkalaemia in the 14 days following antibiotic initiation were only higher following trimethoprim (2.27, 1.49 to 3.45) compared with amoxicillin. However, the odds of death within the 14 days following antibiotic initiation were not higher with trimethoprim than with amoxicillin: in the whole population the adjusted odds ratio was 0.90 (95% confidence interval 0.76 to 1.07) while among users of renin-angiotensin system blockers the odds of death within 14 days of antibiotic initiation was 1.12 (0.80 to 1.57). The results suggest that, for 1000 UTIs treated with antibiotics among people 65 and over, treatment with trimethoprim instead of amoxicillin would result in one to two additional cases of hyperkalaemia and two admissions with acute kidney injury, regardless of renin-angiotensin system blockade. However, for people taking renin-angiotensin system blockers and spironolactone treatment with trimethoprim instead of amoxicillin there were 18 additional cases of hyperkalaemia and 11 admissions with acute kidney injury.ConclusionTrimethoprim is associated with a greater risk of acute kidney injury and hyperkalaemia compared with other antibiotics used to treat UTIs, but not a greater risk of death. The relative risk increase is similar across population groups, but the higher baseline risk among those taking renin-angiotensin system blockers and potassium-sparing diuretics translates into higher absolute risks of acute kidney injury and hyperkalaemia in these groups.

Indoor and outdoor number concentrations of fine particulate matter (PM2.5), black carbon (BC), carbon monoxide (CO), and nitrogen dioxide (NO2) were monitored continuously for two to seven days in 28 low-income homes in Denver, Colorado, during the 2016 and 2017 wildfire seasons. In the absence of indoor sources, all outdoor pollutant concentrations were higher than indoors except for CO. Results showed that long-range wildfire plumes elevated median indoor PM2.5 concentrations by up to 4.6 times higher than outdoors. BC, CO, and NO2 mass concentrations were higher indoors in homes closer to roadways compared to those further away. Four of the homes with mechanical ventilation systems had 18% higher indoor/outdoor (I/O) ratios of PM2.5 and 4% higher I/O ratios of BC compared to other homes. Homes with exhaust stove hoods had PM2.5 I/O ratios 49% less than the homes with recirculating hoods and 55% less than the homes with no stove hoods installed. Homes with windows open for more than 12 hours a day during sampling had indoor BC 2.4 times higher than homes with windows closed. This study provides evidence that long-range wildfire plumes, road proximity, and occupant behavior have a combined effect on indoor air quality in low-income homes.

The role of PPM1D mutations in de novo gliomagenesis has not been systematically explored. Here we analyze whole genome sequences of 170 pediatric high-grade gliomas and find that truncating mutations in PPM1D that increase the stability of its phosphatase are clonal driver events in 11% of Diffuse Midline Gliomas (DMGs) and are enriched in primary pontine tumors. Through the development of DMG mouse models, we show that PPM1D mutations potentiate gliomagenesis and that PPM1D phosphatase activity is required for in vivo oncogenesis. Finally, we apply integrative phosphoproteomic and functional genomics assays and find that oncogenic effects of PPM1D truncation converge on regulators of cell cycle, DNA damage response, and p53 pathways, revealing therapeutic vulnerabilities including MDM2 inhibition. PPM1D is a known mediator of p53 signalling, and has been linked to treatment resistance in glioma. In this work, the authors utilise genomics, proteomics, and mouse models to determine the role of PPM1D in the development of diffuse midline glioma.

Spheroid culture systems have allowed in vitro propagation of cells unable to grow in canonical cell culturing conditions, and may capture cellular contexts that model tumor growth better than current model systems. The insights gleaned from genome-wide clustered regularly interspaced short palindromic repeat (CRISPR) screening of thousands of cancer cell lines grown in conventional culture conditions illustrate the value of such CRISPR pooled screens. It is clear that similar genome-wide CRISPR screens of three-dimensional spheroid cultures will be important for future biological discovery. Here, we present a protocol for genome-wide CRISPR screening of three-dimensional neurospheres. While many in-depth protocols and discussions have been published for more typical cell lines, few detailed protocols are currently available in the literature for genome-wide screening in spheroidal cell lines. For those who want to screen such cell lines, and particularly neurospheres, we provide a step-by-step description of assay development tests to be performed before screening, as well as for the screen itself. We highlight considerations of variables that make these screens distinct from, or similar to, typical nonspheroid cell lines throughout. Finally, we illustrate typical outcomes of neurosphere genome-wide screens, and how neurosphere screens typically produce slightly more heterogeneous signal distributions than more canonical cancer cell lines. Completion of this entire protocol will take 8–12 weeks from the initial assay development tests to deconvolution of the sequencing data. The authors present a protocol for genome-wide clustered regularly interspaced short palindromic repeat screening of three-dimensional neurospheres.

Efforts to identify host determinants for malaria have been hindered by the absence of a nucleus in erythrocytes, which precludes genetic manipulation in the cell in which the parasite replicates. We used cultured red blood cells derived from hematopoietic stem cells to carry out a forward genetic screen for Plasmodium falciparum host determinants. We found that CD55 is an essential host factor for P. falciparum invasion. CD55-null erythrocytes were refractory to invasion by all isolates of P. falciparum because parasites failed to attach properly to the erythrocyte surface. Thus, CD55 is an attractive target for the development of malaria therapeutics. Hematopoietic stem cell-based forward genetic screens may be valuable for the identification of additional host determinants of malaria pathogenesis.

Using a genome-scale, lentivirally delivered shRNA library, we performed massively parallel pooled shRNA screens in 216 cancer cell lines to identify genes that are required for cell proliferation and/or viability. Cell line dependencies on 11,000 genes were interrogated by 5 shRNAs per gene. The proliferation effect of each shRNA in each cell line was assessed by transducing a population of 11M cells with one shRNA-virus per cell and determining the relative enrichment or depletion of each of the 54,000 shRNAs after 16 population doublings using Next Generation Sequencing. All the cell lines were screened using standardized conditions to best assess differential genetic dependencies across cell lines. When combined with genomic characterization of these cell lines, this dataset facilitates the linkage of genetic dependencies with specific cellular contexts (e.g., gene mutations or cell lineage). To enable such comparisons, we developed and provided a bioinformatics tool to identify linear and nonlinear correlations between these features. Design Type(s) genotyping design • cell type comparison design • RNAi screening • loss-of-function screening by pooled shRNA Measurement Type(s) SNP interrogation genotyping • cell viability assay Technology Type(s) microfluidics platform • next generation sequencing Factor Type(s) Tumor Subtype • Growth Medium • Doubling Time • Study Personnel Sample Characteristic(s) Homo sapiens • A2780 cell • BJHTERT • C2BBe1 cell • COLO-783 • EFO-21 cell • GP2D cell • IGROV-1 cell • JHESOAD1 • KM12 • LN215 • LN319 • LN382 • NCI-H1792 cell • OAW42 cell • RMGI • SK-CO-1 cell • SLR24 • TCCSUP cell • THP-1 cell • TOV-21G cell • TT cell • 22RV1 cell • 697 cell • 786-O cell • A1207 • A172 cell • A-204 cell • A2058 cell • A549 cell • A673 cell • ACHN cell • AGS cell • AM38 • AML-193 cell • AsPC-1 cell • BT-20 cell • BT-474 cell • BT-549 cell • BxPC-3 cell • C32 cell • CADO-ES1 cell • CAKI-1 cell • CAL-120 cell • CAL-51 cell • CALU-1 cell • CAOV-3 cell • CAOV-4 cell • CAS-1 cell • CFPAC-1 • CH157MN • COLO205 • COLO-704 • COLO741 • COR-L23 cell • COV318 • COV362 • COV434 • COV504 • COV644 • DBTRG-05MG cell • DK-MG cell • DLD-1 cell • DU4475 cell • EFE-184 cell • EFM-19 cell • EFO-27 cell • EJM • EW8 • EWS502 • F36P • F5 cell • FU-OV-1 cell • GB1 • GCIY • GMS-10 cell • HCC1187 cell • HCC1395 cell • HCC1954 cell • HCC2218 cell • HCC2814 • HCC364 • HCC44 • HCC70 cell • HCC827 cell • HCC827GR5 • HCT116 • HCT116G9B • HEC-1-A cell • HEYA8 • HL-60 cell • HLF • HNT34 • HPAC cell • HPAF-II cell • HS683 • HS766T • HS944T • HT-1197 cell • HT-29 cell • HT55 cell • HUG1N • HUTU80 • IGR-39 cell • IOMMLEE • JHOC5 • JHOM1 • JHOS4 • JJN3 • K-562 cell • KALS1 • KASUMI-1 cell • KMS11 • KMS12BM • KMS20 • KMS26 • KMS34 • KNS60 • KNS81 • KP1NL • KP2 • KP4 • KURAMOCHI • KYSE-150 cell • KYSE-30 cell • KYSE-450 cell • KYSE-510 cell • L33 • L-363 cell • LAMA-84 cell • LK2 • LN-229 cell • LN235 • LN340 • LN428 • LN443 • LN464 • LNZ308 • LOVO cell • LP-1 cell • LS411N cell • LS513 cell • MCF7 cell • MDA-MB-453 cell • MIAPACA2 • MKN7 • MM1S • MOLM13 • MONO-MAC-1 cell • MONO-MAC-6 cell • MV-4-11 cell • NALM-6 cell • NB-4 cell • NCI-H1299 cell • NCI-H1437 cell • NCI-H1650 cell • NCI-H196 cell • NCI-H1975 cell • NCI-H2052 cell • NCI-H2122 cell • NCI-H2171 cell • NCI-H23 cell • NCI-H2452 cell • NCI-H441 cell • NCI-H508 cell • NCI-H524 cell • NCI-H660 cell • NCI-H661 cell • NCI-H716 cell • NCI-H82 cell • NCI-H838 cell • NCI-N87 cell • NIHOVCAR3 • NOMO1 • OCI-AML2 cell • OCIAML3 • OCI-AML5 cell • OE33 cell • OELE • OPM-2 cell • OV7 • OV-90 cell • OVCAR4 • OVCAR8 • OVISE • OVMANA • PANC0327 • PANC0813 • PANC1005 • PLB-985 cell • PSN1 cell • QGP1 • REH cell • RKN • RKO cell • RMUGS • RPE101 • RPE1A4D • RPE74 • RPMI8226 • RS411 • RT112 cell • SEM • SF126 • SF172 • SF295 • SF767 • SJSA-1 cell • SK-MEL-5 cell • SK-MM-2 cell • SKNO1 • SK-OV-3 cell • SKRC20 • SKRC31 • SLR20 • SLR21 • SLR23 • SLR25 • SLR26 • SNU1105 • SNU201 • SNU840 • SNU-C1 cell • SNU-C2A cell • SU8686 • SW 1417 cell • SW1783 • SW1990 • SW48 cell • SW480 cell • T98G cell • TC32 • TC71 • TE10 • TE15 cell • TE9 • TOV-112D cell • TYKNU • U178 • U251MG • U-343 Mga cell • U87MG • UOK101 • VCAP • YKG1 • ZR-75-30 cell Machine-accessible metadata file describing the reported data (ISA-Tab format)

IntroductionThere is a clear need for improved care quality and quality monitoring in aged care. Aged care providers collect an abundance of data, yet rarely are these data integrated and transformed in real-time into actionable information to support evidence-based care, nor are they shared with older people and informal caregivers. This protocol describes the co-design and testing of a dashboard in residential aged care facilities (nursing or care homes) and community-based aged care settings (formal care provided at home or in the community). The dashboard will comprise integrated data to provide an ‘at-a-glance’ overview of aged care clients, indicators to identify clients at risk of fall-related hospitalisations and poor quality of life, and evidence-based decision support to minimise these risks. Longer term plans for dashboard implementation and evaluation are also outlined.MethodsThis mixed-method study will involve (1) co-designing dashboard features with aged care staff, clients, informal caregivers and general practitioners (GPs), (2) integrating aged care data silos and developing risk models, and (3) testing dashboard prototypes with users. The dashboard features will be informed by direct observations of routine work, interviews, focus groups and co-design groups with users, and a community forum. Multivariable discrete time survival models will be used to develop risk indicators, using predictors from linked historical aged care and hospital data. Dashboard prototype testing will comprise interviews, focus groups and walk-through scenarios using a think-aloud approach with staff members, clients and informal caregivers, and a GP workshop.Ethics and disseminationThis study has received ethical approval from the New South Wales (NSW) Population & Health Services Research Ethics Committee and Macquarie University’s Human Research Ethics Committee. The research findings will be presented to the aged care provider who will share results with staff members, clients, residents and informal caregivers. Findings will be disseminated as peer-reviewed journal articles, policy briefs and conference presentations.

BET-bromodomain inhibition (BETi) has shown pre-clinical promise for MYC-amplified medulloblastoma. However, the mechanisms for its action, and ultimately for resistance, have not been fully defined. Here, using a combination of expression profiling, genome-scale CRISPR/Cas9-mediated loss of function and ORF/cDNA driven rescue screens, and cell-based models of spontaneous resistance, we identify bHLH/homeobox transcription factors and cell-cycle regulators as key genes mediating BETi’s response and resistance. Cells that acquire drug tolerance exhibit a more neuronally differentiated cell-state and expression of lineage-specific bHLH/homeobox transcription factors. However, they do not terminally differentiate, maintain expression of CCND2, and continue to cycle through S-phase. Moreover, CDK4/CDK6 inhibition delays acquisition of resistance. Therefore, our data provide insights about the mechanisms underlying BETi effects and the appearance of resistance and support the therapeutic use of combined cell-cycle inhibitors with BETi in MYC-amplified medulloblastoma. BET-bromodomain inhibitors could be used to treat medulloblastoma tumors with Myc amplifications. Here, the authors show that both the response and resistance to BET inhibitors in mice is mediated by bHLH/homeobox transcription factors.