CRISPR-Cas9 screens in human cells and primary neurons identify modifiers of C9ORF72 dipeptide-repeat-protein toxicity.

Hexanucleotide-repeat expansions in the C9ORF72 gene are the most common cause of amyotrophic lateral sclerosis and frontotemporal dementia (c9ALS/FTD). The nucleotide-repeat expansions are translated into dipeptide-repeat (DPR) proteins, which are aggregation prone and may contribute to neurodegeneration. We used the CRISPR-Cas9 system to perform genome-wide gene-knockout screens for suppressors and enhancers of C9ORF72 DPR toxicity in human cells. We validated hits by performing secondary CRISPR-Cas9 screens in primary mouse neurons. We uncovered potent modifiers of DPR toxicity whose gene products function in nucleocytoplasmic transport, the endoplasmic reticulum (ER), proteasome, RNA-processing pathways, and chromatin modification. One modifier, TMX2, modulated the ER-stress signature elicited by C9ORF72 DPRs in neurons and improved survival of human induced motor neurons from patients with C9ORF72 ALS. Together, our results demonstrate the promise of CRISPR-Cas9 screens in defining mechanisms of neurodegenerative diseases

Identification of Human Papillomavirus Type 58 Lineages and the Distribution Worldwide

Background. Human papillomavirus type 58 (HPV-58) accounts for a much higher proportion of cervical cancers in East Asia than other types. A classification system of HPV-58, which is essential for molecular epidemiological study, is lacking. Methods and results. This study analyzed the sequences of 401 isolates collected from 15 countries and cities. The 268 unique concatenated E6-E7-E2-E5-L1-LCR sequences that comprised 57% of the whole HPV-58 genome showed 4 distinct clusters. L1 and LCR produced tree topologies that best resembled the concatenated sequences and thus are the most appropriate surrogate regions for lineage classification. Moreover, short fragments from L1 (nucleotides 6014–6539) and LCR (nucleotides 7257–7429 and 7540–52) were found to contain sequence signatures informative for lineage identification. Lineage A was the most prevalent lineage across all regions. Lineage C was more frequent in Africa than elsewhere, whereas lineage D was more prevalent in Africa than in Asia. Among lineage A variants, sublineage A2 dominated in Africa, the Americas, and Europe, but not in Asia. Sublineage A1, which represents the prototype that originated from a patient with cancer, was rare worldwide except in Asia. Conclusions. HPV-58 can be classified into 4 lineages that show some degree of ethnogeographic predilection in distribution. The evolutionary, epidemiological, and pathological characteristics of these lineages warrant further study

C-Terminal Region of EBNA-2 Determines the Superior Transforming Ability of Type 1 Epstein-Barr Virus by Enhanced Gene Regulation of LMP-1 and CXCR7

Type 1 Epstein-Barr virus (EBV) strains immortalize B lymphocytes in vitro much more efficiently than type 2 EBV, a difference previously mapped to the EBNA-2 locus. Here we demonstrate that the greater transforming activity of type 1 EBV correlates with a stronger and more rapid induction of the viral oncogene LMP-1 and the cell gene CXCR7 (which are both required for proliferation of EBV-LCLs) during infection of primary B cells with recombinant viruses. Surprisingly, although the major sequence differences between type 1 and type 2 EBNA-2 lie in N-terminal parts of the protein, the superior ability of type 1 EBNA-2 to induce proliferation of EBV-infected lymphoblasts is mostly determined by the C-terminus of EBNA-2. Substitution of the C-terminus of type 1 EBNA-2 into the type 2 protein is sufficient to confer a type 1 growth phenotype and type 1 expression levels of LMP-1 and CXCR7 in an EREB2.5 cell growth assay. Within this region, the RG, CR7 and TAD domains are the minimum type 1 sequences required. Sequencing the C-terminus of EBNA-2 from additional EBV isolates showed high sequence identity within type 1 isolates or within type 2 isolates, indicating that the functional differences mapped are typical of EBV type sequences. The results indicate that the C-terminus of EBNA-2 accounts for the greater ability of type 1 EBV to promote B cell proliferation, through mechanisms that include higher induction of genes (LMP-1 and CXCR7) required for proliferation and survival of EBV-LCLs

A Genome-Wide Association Study of Diabetic Kidney Disease in Subjects With Type 2 Diabetes

dentification of sequence variants robustly associated with predisposition to diabetic kidney disease (DKD) has the potential to provide insights into the pathophysiological mechanisms responsible. We conducted a genome-wide association study (GWAS) of DKD in type 2 diabetes (T2D) using eight complementary dichotomous and quantitative DKD phenotypes: the principal dichotomous analysis involved 5,717 T2D subjects, 3,345 with DKD. Promising association signals were evaluated in up to 26,827 subjects with T2D (12,710 with DKD). A combined T1D+T2D GWAS was performed using complementary data available for subjects with T1D, which, with replication samples, involved up to 40,340 subjects with diabetes (18,582 with DKD). Analysis of specific DKD phenotypes identified a novel signal near GABRR1 (rs9942471, P = 4.5 x 10(-8)) associated with microalbuminuria in European T2D case subjects. However, no replication of this signal was observed in Asian subjects with T2D or in the equivalent T1D analysis. There was only limited support, in this substantially enlarged analysis, for association at previously reported DKD signals, except for those at UMOD and PRKAG2, both associated with estimated glomerular filtration rate. We conclude that, despite challenges in addressing phenotypic heterogeneity, access to increased sample sizes will continue to provide more robust inference regarding risk variant discovery for DKD.Peer reviewe

Global patient outcomes after elective surgery: prospective cohort study in 27 low-, middle- and high-income countries.

BACKGROUND: As global initiatives increase patient access to surgical treatments, there remains a need to understand the adverse effects of surgery and define appropriate levels of perioperative care. METHODS: We designed a prospective international 7-day cohort study of outcomes following elective adult inpatient surgery in 27 countries. The primary outcome was in-hospital complications. Secondary outcomes were death following a complication (failure to rescue) and death in hospital. Process measures were admission to critical care immediately after surgery or to treat a complication and duration of hospital stay. A single definition of critical care was used for all countries. RESULTS: A total of 474 hospitals in 19 high-, 7 middle- and 1 low-income country were included in the primary analysis. Data included 44 814 patients with a median hospital stay of 4 (range 2-7) days. A total of 7508 patients (16.8%) developed one or more postoperative complication and 207 died (0.5%). The overall mortality among patients who developed complications was 2.8%. Mortality following complications ranged from 2.4% for pulmonary embolism to 43.9% for cardiac arrest. A total of 4360 (9.7%) patients were admitted to a critical care unit as routine immediately after surgery, of whom 2198 (50.4%) developed a complication, with 105 (2.4%) deaths. A total of 1233 patients (16.4%) were admitted to a critical care unit to treat complications, with 119 (9.7%) deaths. Despite lower baseline risk, outcomes were similar in low- and middle-income compared with high-income countries. CONCLUSIONS: Poor patient outcomes are common after inpatient surgery. Global initiatives to increase access to surgical treatments should also address the need for safe perioperative care. STUDY REGISTRATION: ISRCTN5181700

Cancer Biomarker Discovery: The Entropic Hallmark

Background: It is a commonly accepted belief that cancer cells modify their transcriptional state during the progression of the disease. We propose that the progression of cancer cells towards malignant phenotypes can be efficiently tracked using high-throughput technologies that follow the gradual changes observed in the gene expression profiles by employing Shannon's mathematical theory of communication. Methods based on Information Theory can then quantify the divergence of cancer cells' transcriptional profiles from those of normally appearing cells of the originating tissues. The relevance of the proposed methods can be evaluated using microarray datasets available in the public domain but the method is in principle applicable to other high-throughput methods. Methodology/Principal Findings: Using melanoma and prostate cancer datasets we illustrate how it is possible to employ Shannon Entropy and the Jensen-Shannon divergence to trace the transcriptional changes progression of the disease. We establish how the variations of these two measures correlate with established biomarkers of cancer progression. The Information Theory measures allow us to identify novel biomarkers for both progressive and relatively more sudden transcriptional changes leading to malignant phenotypes. At the same time, the methodology was able to validate a large number of genes and processes that seem to be implicated in the progression of melanoma and prostate cancer. Conclusions/Significance: We thus present a quantitative guiding rule, a new unifying hallmark of cancer: the cancer cell's transcriptome changes lead to measurable observed transitions of Normalized Shannon Entropy values (as measured by high-throughput technologies). At the same time, tumor cells increment their divergence from the normal tissue profile increasing their disorder via creation of states that we might not directly measure. This unifying hallmark allows, via the the Jensen-Shannon divergence, to identify the arrow of time of the processes from the gene expression profiles, and helps to map the phenotypical and molecular hallmarks of specific cancer subtypes. The deep mathematical basis of the approach allows us to suggest that this principle is, hopefully, of general applicability for other diseases

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery