Mechanochemical treatment for degradation of ciprofloxacin (CIP) in solutions.

Ciprofloxacin (CIP) is a kind of widely used fluoroquinolone antibiotic, and the widespread presence of CIP in aquatic environment has become a serious issue. Mechanochemical treatment (MCT), as an effective approach to degrade persistent organic pollutants, has many advantages of low cost, simplicity, and being environmentally innocuous. However, little attention has been paid to employing MCT to treat effluents containing CIP. In this study, MCT was introduced to degrade CIP in aquatic solutions. A series of CIP degradation experiments were conducted by a planetary ball mill, and the influences of main parameters on CIP degradation efficiency were investigated. Furthermore, an optimum combination was selected through orthogonal experiments, and CIP degradation efficiency could reach as high as 99% in certain conditions. Besides, the biotoxicity of CIP solution was also studied. MCT exhibits satisfying performance for degrading CIP in solutions, which makes MCT a promising approach to CIP elimination and also encourages further applications in treating effluents containing other organic pollutants.

Group Lasso Regularized Deep Learning for Cancer Prognosis from Multi-Omics and Clinical Features.

Accurate prognosis of patients with cancer is important for the stratification of patients, the optimization of treatment strategies, and the design of clinical trials. Both clinical features and molecular data can be used for this purpose, for instance, to predict the survival of patients censored at specific time points. Multi-omics data, including genome-wide gene expression, methylation, protein expression, copy number alteration, and somatic mutation data, are becoming increasingly common in cancer studies. To harness the rich information in multi-omics data, we developed GDP (Group lass regularized Deep learning for cancer Prognosis), a computational tool for survival prediction using both clinical and multi-omics data. GDP integrated a deep learning framework and Cox proportional hazard model (CPH) together, and applied group lasso regularization to incorporate gene-level group prior knowledge into the model training process. We evaluated its performance in both simulated and real data from The Cancer Genome Atlas (TCGA) project. In simulated data, our results supported the importance of group prior information in the regularization of the model. Compared to the standard lasso regularization, we showed that group lasso achieved higher prediction accuracy when the group prior knowledge was provided. We also found that GDP performed better than CPH for complex survival data. Furthermore, analysis on real data demonstrated that GDP performed favorably against other methods in several cancers with large-scale omics data sets, such as glioblastoma multiforme, kidney renal clear cell carcinoma, and bladder urothelial carcinoma. In summary, we demonstrated that GDP is a powerful tool for prognosis of patients with cancer, especially when large-scale molecular features are available.

Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models.

Translating the vast data generated by genomic platforms into accurate predictions of clinical outcomes is a fundamental challenge in genomic medicine. Many prediction methods face limitations in learning from the high-dimensional profiles generated by these platforms, and rely on experts to hand-select a small number of features for training prediction models. In this paper, we demonstrate how deep learning and Bayesian optimization methods that have been remarkably successful in general high-dimensional prediction tasks can be adapted to the problem of predicting cancer outcomes. We perform an extensive comparison of Bayesian optimized deep survival models and other state of the art machine learning methods for survival analysis, and describe a framework for interpreting deep survival models using a risk backpropagation technique. Finally, we illustrate that deep survival models can successfully transfer information across diseases to improve prognostic accuracy. We provide an open-source software implementation of this framework called SurvivalNet that enables automatic training, evaluation and interpretation of deep survival models.

iCAGES: integrated CAncer GEnome Score for comprehensively prioritizing driver genes in personal cancer genomes.

Cancer results from the acquisition of somatic driver mutations. Several computational tools can predict driver genes from population-scale genomic data, but tools for analyzing personal cancer genomes are underdeveloped. Here we developed iCAGES, a novel statistical framework that infers driver variants by integrating contributions from coding, non-coding, and structural variants, identifies driver genes by combining genomic information and prior biological knowledge, then generates prioritized drug treatment. Analysis on The Cancer Genome Atlas (TCGA) data showed that iCAGES predicts whether patients respond to drug treatment (P = 0.006 by Fisher's exact test) and long-term survival (P = 0.003 from Cox regression). iCAGES is available at http://icages.wglab.org .

Long-read sequencing and de novo assembly of a Chinese genome.

Short-read sequencing has enabled the de novo assembly of several individual human genomes, but with inherent limitations in characterizing repeat elements. Here we sequence a Chinese individual HX1 by single-molecule real-time (SMRT) long-read sequencing, construct a physical map by NanoChannel arrays and generate a de novo assembly of 2.93 Gb (contig N50: 8.3 Mb, scaffold N50: 22.0 Mb, including 39.3 Mb N-bases), together with 206 Mb of alternative haplotypes. The assembly fully or partially fills 274 (28.4%) N-gaps in the reference genome GRCh38. Comparison to GRCh38 reveals 12.8 Mb of HX1-specific sequences, including 4.1 Mb that are not present in previously reported Asian genomes. Furthermore, long-read sequencing of the transcriptome reveals novel spliced genes that are not annotated in GENCODE and are missed by short-read RNA-Seq. Our results imply that improved characterization of genome functional variation may require the use of a range of genomic technologies on diverse human populations.

Deep Genetic Connection Between Cancer and Developmental Disorders.

Cancer and developmental disorders (DDs) share dysregulated cellular processes such as proliferation and differentiation. There are well-known genes implicated in both in cancer and DDs. In this study, we aim to quantify this genetic connection using publicly available data. We found that among DD patients, germline damaging de novo variants are more enriched in cancer driver genes than non-drivers. We estimate that cancer driver genes comprise about a third of DD risk genes. Additionally, de novo likely-gene-disrupting variants are more enriched in tumor suppressors, and about 40% of implicated de novo damaging missense variants are located in cancer somatic mutation hotspots, indicating that many genes have a similar mode of action in cancer and DDs. Our results suggest that we can view tumors as natural laboratories for assessing the deleterious effects of mutations that are applicable to germline variants and identification of causal genes and variants in DDs.

Excess of rare variants in genes that are key epigenetic regulators of spermatogenesis in the patients with non-obstructive azoospermia.

Non-obstructive azoospermia (NOA), a severe form of male infertility, is often suspected to be linked to currently undefined genetic abnormalities. To explore the genetic basis of this condition, we successfully sequenced ~650 infertility-related genes in 757 NOA patients and 709 fertile males. We evaluated the contributions of rare variants to the etiology of NOA by identifying individual genes showing nominal associations and testing the genetic burden of a given biological process as a whole. We found a significant excess of rare, non-silent variants in genes that are key epigenetic regulators of spermatogenesis, such as BRWD1, DNMT1, DNMT3B, RNF17, UBR2, USP1 and USP26, in NOA patients (P = 5.5 × 10(-7)), corresponding to a carrier frequency of 22.5% of patients and 13.7% of controls (P = 1.4 × 10(-5)). An accumulation of low-frequency variants was also identified in additional epigenetic genes (BRDT and MTHFR). Our study suggested the potential associations of genetic defects in genes that are epigenetic regulators with spermatogenic failure in human.

Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies.

Accurate deleteriousness prediction for nonsynonymous variants is crucial for distinguishing pathogenic mutations from background polymorphisms in whole exome sequencing (WES) studies. Although many deleteriousness prediction methods have been developed, their prediction results are sometimes inconsistent with each other and their relative merits are still unclear in practical applications. To address these issues, we comprehensively evaluated the predictive performance of 18 current deleteriousness-scoring methods, including 11 function prediction scores (PolyPhen-2, SIFT, MutationTaster, Mutation Assessor, FATHMM, LRT, PANTHER, PhD-SNP, SNAP, SNPs&GO and MutPred), 3 conservation scores (GERP++, SiPhy and PhyloP) and 4 ensemble scores (CADD, PON-P, KGGSeq and CONDEL). We found that FATHMM and KGGSeq had the highest discriminative power among independent scores and ensemble scores, respectively. Moreover, to ensure unbiased performance evaluation of these prediction scores, we manually collected three distinct testing datasets, on which no current prediction scores were tuned. In addition, we developed two new ensemble scores that integrate nine independent scores and allele frequency. Our scores achieved the highest discriminative power compared with all the deleteriousness prediction scores tested and showed low false-positive prediction rate for benign yet rare nonsynonymous variants, which demonstrated the value of combining information from multiple orthologous approaches. Finally, to facilitate variant prioritization in WES studies, we have pre-computed our ensemble scores for 87 347 044 possible variants in the whole-exome and made them publicly available through the ANNOVAR software and the dbNSFP database.

Exome sequencing on malignant meningiomas identified mutations in neurofibromatosis type 2 (NF2) and meningioma 1 (MN1) genes.

BACKGROUND: Meningiomas are tumors originating from the membranous layers surrounding the central nervous system, and are generally regarded as "benign" tumors of the brain. Malignant meningiomas are rare and are typically associated with a higher risk of local tumor recurrence and a poorer prognosis (median survival time <2 years). Previous genome-wide association studies and exome sequencing studies have identified genes that play a role in susceptibility to meningiomas, but these studies did not focus specifically on malignant tumors. METHODS: We performed exome sequencing on five malignant meningiomas on the Illumina HiSeq2000 platform using Agilent SureSelect Human All Exon kits. We used wANNOVAR web server to annotate and prioritize variants, identified candidate genes with recurrent mutations, and validated selected mutations by Sanger sequencing. We next designed custom NimbleGen targeted region arrays on five candidate genes, and sequenced four additional malignant meningiomas. RESULTS: From exome sequencing data, we identified several frequently mutated genes including NF2, MN1, ARID1B, SEMA4D, and MUC2, with private mutations in tumors. We sequenced these genes in four additional samples and identified potential driver mutations in NF2 (neurofibromatosis type 2) and MN1 (meningioma 1). CONCLUSIONS: We confirmed that mutations in NF2 may play a role in progression of meningiomas, and nominated MN1 as a candidate gene for malignant transformation of meningiomas. Our sample size is limited by the extreme rarity of malignant meningiomas, but our study represents one of the first sequencing studies focusing on the malignant subtype.