Comprehensive Molecular Characterization of Muscle-Invasive Bladder Cancer.

We report a comprehensive analysis of 412 muscle-invasive bladder cancers characterized by multiple TCGA analytical platforms. Fifty-eight genes were significantly mutated, and the overall mutational load was associated with APOBEC-signature mutagenesis. Clustering by mutation signature identified a high-mutation subset with 75% 5-year survival. mRNA expression clustering refined prior clustering analyses and identified a poor-survival "neuronal" subtype in which the majority of tumors lacked small cell or neuroendocrine histology. Clustering by mRNA, long non-coding RNA (lncRNA), and miRNA expression converged to identify subsets with differential epithelial-mesenchymal transition status, carcinoma in situ scores, histologic features, and survival. Our analyses identified 5 expression subtypes that may stratify response to different treatments.

A mesomelic skeletal dysplasia, Kantaputra-like, not related to HOXD cluster region, and with phenotypic gender differences.

Mesomelic skeletal dysplasia is a heterogeneous group of skeletal disorders that has grown since the molecular basis of these conditions is in the process of research and discovery. Here, we report a Brazilian family with eight affected members over three generations with a phenotype similar to mesomelic Kantaputra dysplasia. This family presents marked shortening of the upper limbs with hypotrophy of the lower limbs and clubfeet without synostosis. Array-based CNV analysis and exome sequencing of four family members failed to show any region or gene candidate. Interestingly, males were more severely affected than females in this family, suggesting that gender differences could play a role in the phenotypic expressivity of this condition.

A field guide to cultivating computational biology.

Evolving in sync with the computation revolution over the past 30 years, computational biology has emerged as a mature scientific field. While the field has made major contributions toward improving scientific knowledge and human health, individual computational biology practitioners at various institutions often languish in career development. As optimistic biologists passionate about the future of our field, we propose solutions for both eager and reluctant individual scientists, institutions, publishers, funding agencies, and educators to fully embrace computational biology. We believe that in order to pave the way for the next generation of discoveries, we need to improve recognition for computational biologists and better align pathways of career success with pathways of scientific progress. With 10 outlined steps, we call on all adjacent fields to move away from the traditional individual, single-discipline investigator research model and embrace multidisciplinary, data-driven, team science.

Multi-omics analysis suggests enhanced epileptogenesis in the Cornu Ammonis 3 of the pilocarpine model of mesial temporal lobe epilepsy.

Mesial temporal lobe epilepsy (MTLE) is a chronic neurological disorder characterized by the occurrence of seizures, and histopathological abnormalities in the mesial temporal lobe structures, mainly hippocampal sclerosis (HS). We used a multi-omics approach to determine the profile of transcript and protein expression in the dorsal and ventral hippocampal dentate gyrus (DG) and Cornu Ammonis 3 (CA3) in an animal model of MTLE induced by pilocarpine. We performed label-free proteomics and RNAseq from laser-microdissected tissue isolated from pilocarpine-induced Wistar rats. We divided the DG and CA3 into dorsal and ventral areas and analyzed them separately. We performed a data integration analysis and evaluated enriched signaling pathways, as well as the integrated networks generated based on the gene ontology processes. Our results indicate differences in the transcriptomic and proteomic profiles among the DG and the CA3 subfields of the hippocampus. Moreover, our data suggest that epileptogenesis is enhanced in the CA3 region when compared to the DG, with most abnormalities in transcript and protein levels occurring in the CA3. Furthermore, our results show that the epileptogenesis in the pilocarpine model involves predominantly abnormal regulation of excitatory neuronal mechanisms mediated by N-methyl D-aspartate (NMDA) receptors, changes in the serotonin signaling, and neuronal activity controlled by calcium/calmodulin-dependent protein kinase (CaMK) regulation and leucine-rich repeat kinase 2 (LRRK2)/WNT signaling pathways.

Molecular diagnosis in a cohort of 114 patients with rare skeletal dysplasias.

Molecular diagnosis is important to provide accurate genetic counseling of skeletal dysplasias (SD). Although next-generation sequencing (NGS) techniques are currently the preferred methods for analyzing these conditions, some of the published results have not shown a detection rate as high as it would be expected. The present study aimed to assess the diagnostic yield of targeted NGS combined with Sanger sequencing (SS) for low-coverage exons of genes of interest and exome sequencing (ES) in a series of patients with rare SD and use two patients as an example of our strategy. This study used two different in-house panels. Of 93 variants found in 88/114 (77%) patients, 57 are novel. The pathogenic variants found in the following genes: B3GALT6, PCYT1A, INPPL1, LIFR, of four patients were only detected by SS. In conclusion, the high diagnostic yield reached in the present study can be attributed to both a good selection of patients and the utilization of the SS for the insufficiently covered regions. Additionally, the two case reports-a patient with acrodysostosis related to PRKAR1A and another with ciliopathy associated with KIAA0753, add new and relevant clinical information to the current knowledge.

Randomized Trial of Botulinum Toxin Type A in Hereditary Spastic Paraplegia - The SPASTOX Trial.

BACKGROUND: Hereditary spastic paraplegia presents spasticity as the main clinical manifestation, reducing gait quality and producing incapacity. Management with botulinum toxin type A (BoNT-A) is not well elucidated. The objective of the current study was to evaluate the efficacy and safety of BoNT-A in patients with hereditary spastic paraplegias. METHODS: This was a double-blind, randomized, placebo-controlled crossover trial. Each participant was randomly assigned to receive 1 injection session of either BoNT-A (100 IU/2 mL of Prosigne in each adductor magnus and each triceps surae) or saline 0.9% (2 mL). The primary outcome measure was change from baseline in maximal gait velocity, and secondary outcome measures included changes in gait at self-selected velocity, spasticity, muscle strength, Spastic Paraplegia Rating Scale, pain, fatigue, and subjective perception of improvement. We also looked at adverse events reported by the patients. RESULTS: We enrolled 55 patients, 36 of whom were men and 41 with the pure phenotype. Mean age was 43 ± 13.4 years (range, 19-72 years), mean age of onset waws 27 ± 13.1 years (range, <1 to 55 yars), and mean disease duration was 17 ± 12.7 years (range, 1-62 years). Compared with baseline, we did not find significant differences between groups in primary and secondary outcomes, except for reduction in adductor tone (P = 0.01). The adverse events were transient and tolerable, and their incidence did not significantly differ between treatments (P = 0.17). CONCLUSIONS: BoNT-A was safe in patients with hereditary spastic paraplegias and reduced the adductor tone, but it was not able to produce functional improvement considering the doses, injection protocol, measures, and instruments used. © 2021 International Parkinson and Movement Disorder Society.

Methodological differences can affect sequencing depth with a possible impact on the accuracy of genetic diagnosis.

For a better interpretation of variants, evidence-based databases, such as ClinVar, compile data on the presumed relationships between variants and phenotypes. In this study, we aimed to analyze the pattern of sequencing depth in variants from whole-exome sequencing data in the 1000 Genomes project phase 3, focusing on the variants present in the ClinVar database that were predicted to affect protein-coding regions. We demonstrate that the distribution of the sequencing depth varies across different sequencing centers (pair-wise comparison, p < 0.001). Most importantly, we found that the distribution pattern of sequencing depth is specific to each facility, making it possible to correctly assign 96.9% of the samples to their sequencing center. Thus, indicating the presence of a systematic bias, related to the methods used in the different facilities, which generates significant variations in breadth and depth in whole-exome sequencing data in clinically relevant regions. Our results show that methodological differences, leading to significant heterogeneity in sequencing depth, may potentially influence the accuracy of genetic diagnosis. Furthermore, our findings highlight how it is still challenging to integrate results from different sequencing centers, which may also have an impact on genomic research.

Dysregulation of NEUROG2 plays a key role in focal cortical dysplasia.

OBJECTIVE: Focal cortical dysplasias (FCDs) are an important cause of drug-resistant epilepsy. In this work, we aimed to investigate whether abnormal gene regulation, mediated by microRNA, could be involved in FCD type II. METHODS: We used total RNA from the brain tissue of 16 patients with FCD type II and 28 controls. MicroRNA expression was initially assessed by microarray. Quantitative polymerase chain reaction, in situ hybridization, luciferase reporter assays, and deep sequencing for genes in the mTOR pathway were performed to validate and further explore our initial study. RESULTS: hsa-let-7f (p = 0.039), hsa-miR-31 (p = 0.0078), and hsa-miR34a (p = 0.021) were downregulated in FCD type II, whereas a transcription factor involved in neuronal and glial fate specification, NEUROG2 (p < 0.05), was upregulated. We also found that the RND2 gene, a NEUROG2-target, is upregulated (p < 0.001). In vitro experiments showed that hsa-miR-34a downregulates NEUROG2 by binding to its 5'-untranslated region. Moreover, we observed strong nuclear expression of NEUROG2 in balloon cells and dysmorphic neurons and found that 28.5% of our patients presented brain somatic mutations in genes of the mTOR pathway. INTERPRETATION: Our findings suggest a new molecular mechanism, in which NEUROG2 has a pivotal and central role in the pathogenesis of FCD type II. In this way, we found that the downregulation of hsa-miR-34a leads to upregulation of NEUROG2, and consequently to overexpression of the RND2 gene. These findings indicate that a faulty coupling in neuronal differentiation and migration mechanisms may explain the presence of aberrant cells and complete dyslamination in FCD type II. Ann Neurol 2018;83:623-635.

Orchestrating high-throughput genomic analysis with Bioconductor.

Bioconductor is an open-source, open-development software project for the analysis and comprehension of high-throughput data in genomics and molecular biology. The project aims to enable interdisciplinary research, collaboration and rapid development of scientific software. Based on the statistical programming language R, Bioconductor comprises 934 interoperable packages contributed by a large, diverse community of scientists. Packages cover a range of bioinformatic and statistical applications. They undergo formal initial review and continuous automated testing. We present an overview for prospective users and contributors.

The challenges of delivering bioinformatics training in the analysis of high-throughput data.

High-throughput technologies are widely used in the field of functional genomics and used in an increasing number of applications. For many 'wet lab' scientists, the analysis of the large amount of data generated by such technologies is a major bottleneck that can only be overcome through very specialized training in advanced data analysis methodologies and the use of dedicated bioinformatics software tools. In this article, we wish to discuss the challenges related to delivering training in the analysis of high-throughput sequencing data and how we addressed these challenges in the hands-on training courses that we have developed at the European Bioinformatics Institute.

A multilevel model to address batch effects in copy number estimation using SNP arrays.

Submicroscopic changes in chromosomal DNA copy number dosage are common and have been implicated in many heritable diseases and cancers. Recent high-throughput technologies have a resolution that permits the detection of segmental changes in DNA copy number that span thousands of base pairs in the genome. Genomewide association studies (GWAS) may simultaneously screen for copy number phenotype and single nucleotide polymorphism (SNP) phenotype associations as part of the analytic strategy. However, genomewide array analyses are particularly susceptible to batch effects as the logistics of preparing DNA and processing thousands of arrays often involves multiple laboratories and technicians, or changes over calendar time to the reagents and laboratory equipment. Failure to adjust for batch effects can lead to incorrect inference and requires inefficient post hoc quality control procedures to exclude regions that are associated with batch. Our work extends previous model-based approaches for copy number estimation by explicitly modeling batch and using shrinkage to improve locus-specific estimates of copy number uncertainty. Key features of this approach include the use of biallelic genotype calls from experimental data to estimate batch-specific and locus-specific parameters of background and signal without the requirement of training data. We illustrate these ideas using a study of bipolar disease and a study of chromosome 21 trisomy. The former has batch effects that dominate much of the observed variation in the quantile-normalized intensities, while the latter illustrates the robustness of our approach to a data set in which approximately 27% of the samples have altered copy number. Locus-specific estimates of copy number can be plotted on the copy number scale to investigate mosaicism and guide the choice of appropriate downstream approaches for smoothing the copy number as a function of physical position. The software is open source and implemented in the R package crlmm at Bioconductor (http:www.bioconductor.org).

Performance assessment of copy number microarray platforms using a spike-in experiment.

MOTIVATION: Changes in the copy number of chromosomal DNA segments [copy number variants (CNVs)] have been implicated in human variation, heritable diseases and cancers. Microarray-based platforms are the current established technology of choice for studies reporting these discoveries and constitute the benchmark against which emergent sequence-based approaches will be evaluated. Research that depends on CNV analysis is rapidly increasing, and systematic platform assessments that distinguish strengths and weaknesses are needed to guide informed choice. RESULTS: We evaluated the sensitivity and specificity of six platforms, provided by four leading vendors, using a spike-in experiment. NimbleGen and Agilent platforms outperformed Illumina and Affymetrix in accuracy and precision of copy number dosage estimates. However, Illumina and Affymetrix algorithms that leverage single nucleotide polymorphism (SNP) information make up for this disadvantage and perform well at variant detection. Overall, the NimbleGen 2.1M platform outperformed others, but only with the use of an alternative data analysis pipeline to the one offered by the manufacturer. AVAILABILITY: The data is available from http://rafalab.jhsph.edu/cnvcomp/. CONTACT: pevsner@jhmi.edu; fspencer@jhmi.edu; rafa@jhu.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Plasma proteomic signature of major depressive episode in the elderly.

Depression is a complex and multifactorial disease, affecting about 6.5% of the elderly population in what is referred to as late-life depression (LLD). Despite its public health relevance, there is still limited information about the molecular mechanisms of LLD. We analyzed the blood plasma of 50 older adults, 19 with LLD and 31 controls, through untargeted mass spectrometry, and used systems biology tools to identify biochemical pathways and biological processes dysregulated in the disease. We found 96 differentially expressed proteins between LLD patients and control individuals. Using elastic-net regression, we generated a panel of 75 proteins that comprises a potential model for determining the molecular signature of LLD. We also showed that biological pathways related to vesicle-mediated transport and voltage-dependent calcium channels may be dysregulated in LLD. These data can help to build an understanding of the molecular basis of LLD, offering an integrated view of the biomolecular alterations that occur in this disorder. SIGNIFICANCE: Major depressive disorder in the elderly, called late-life depression (LLD), is a common and disabling disorder, with recent prevalence estimates of 6.5% in the general population. Despite the public health relevance, there is still limited information about the molecular mechanisms of LLD. The findings in this paper shed light on LLD heterogeneous biological mechanisms. We uncovered a potential novel biomolecular signature for LLD and biological pathways related to this condition which can be targets for the development of novel interventions for prevention, early diagnosis, and treatment of LLD.

Benchmarking the proteomic profile of animal models of mesial temporal epilepsy.

OBJECTIVES: We compared the proteomic signatures of the hippocampal lesion induced in three different animal models of mesial temporal lobe epilepsy with hippocampal sclerosis (MTLE+HS): the systemic pilocarpine model (PILO), the intracerebroventricular kainic acid model (KA), and the perforant pathway stimulation model (PPS). METHODS: We used shotgun proteomics to analyze the proteomes and find enriched biological pathways of the dorsal and ventral dentate gyrus (DG) isolated from the hippocampi of the three animal models. We also compared the proteomes obtained in the animal models to that from the DG of patients with pharmacoresistant MTLE+HS. RESULTS: We found that each animal model presents specific profiles of proteomic changes. The PILO model showed responses predominantly related to neuronal excitatory imbalance. The KA model revealed alterations mainly in synaptic activity. The PPS model displayed abnormalities in metabolism and oxidative stress. We also identified common biological pathways enriched in all three models, such as inflammation and immune response, which were also observed in tissue from patients. However, none of the models could recapitulate the profile of molecular changes observed in tissue from patients. SIGNIFICANCE: Our results indicate that each model has its own set of biological responses leading to epilepsy. Thus, it seems that only using a combination of the three models may one replicate more closely the mechanisms underlying MTLE+HS as seen in patients.

Comparing genotyping algorithms for Illumina's Infinium whole-genome SNP BeadChips.

BACKGROUND: Illumina's Infinium SNP BeadChips are extensively used in both small and large-scale genetic studies. A fundamental step in any analysis is the processing of raw allele A and allele B intensities from each SNP into genotype calls (AA, AB, BB). Various algorithms which make use of different statistical models are available for this task. We compare four methods (GenCall, Illuminus, GenoSNP and CRLMM) on data where the true genotypes are known in advance and data from a recently published genome-wide association study. RESULTS: In general, differences in accuracy are relatively small between the methods evaluated, although CRLMM and GenoSNP were found to consistently outperform GenCall. The performance of Illuminus is heavily dependent on sample size, with lower no call rates and improved accuracy as the number of samples available increases. For X chromosome SNPs, methods with sex-dependent models (Illuminus, CRLMM) perform better than methods which ignore gender information (GenCall, GenoSNP). We observe that CRLMM and GenoSNP are more accurate at calling SNPs with low minor allele frequency than GenCall or Illuminus. The sample quality metrics from each of the four methods were found to have a high level of agreement at flagging samples with unusual signal characteristics. CONCLUSIONS: CRLMM, GenoSNP and GenCall can be applied with confidence in studies of any size, as their performance was shown to be invariant to the number of samples available. Illuminus on the other hand requires a larger number of samples to achieve comparable levels of accuracy and its use in smaller studies (50 or fewer individuals) is not recommended.

Chromosome-wide mapping of DNA methylation patterns in normal and malignant prostate cells reveals pervasive methylation of gene-associated and conserved intergenic sequences.

BACKGROUND: DNA methylation has been linked to genome regulation and dysregulation in health and disease respectively, and methods for characterizing genomic DNA methylation patterns are rapidly emerging. We have developed/refined methods for enrichment of methylated genomic fragments using the methyl-binding domain of the human MBD2 protein (MBD2-MBD) followed by analysis with high-density tiling microarrays. This MBD-chip approach was used to characterize DNA methylation patterns across all non-repetitive sequences of human chromosomes 21 and 22 at high-resolution in normal and malignant prostate cells. RESULTS: Examining this data using computational methods that were designed specifically for DNA methylation tiling array data revealed widespread methylation of both gene promoter and non-promoter regions in cancer and normal cells. In addition to identifying several novel cancer hypermethylated 5' gene upstream regions that mediated epigenetic gene silencing, we also found several hypermethylated 3' gene downstream, intragenic and intergenic regions. The hypermethylated intragenic regions were highly enriched for overlap with intron-exon boundaries, suggesting a possible role in regulation of alternative transcriptional start sites, exon usage and/or splicing. The hypermethylated intergenic regions showed significant enrichment for conservation across vertebrate species. A sampling of these newly identified promoter (ADAMTS1 and SCARF2 genes) and non-promoter (downstream or within DSCR9, C21orf57 and HLCS genes) hypermethylated regions were effective in distinguishing malignant from normal prostate tissues and/or cell lines. CONCLUSIONS: Comparison of chromosome-wide DNA methylation patterns in normal and malignant prostate cells revealed significant methylation of gene-proximal and conserved intergenic sequences. Such analyses can be easily extended for genome-wide methylation analysis in health and disease.

A framework for oligonucleotide microarray preprocessing.

MOTIVATION: The availability of flexible open source software for the analysis of gene expression raw level data has greatly facilitated the development of widely used preprocessing methods for these technologies. However, the expansion of microarray applications has exposed the limitation of existing tools. RESULTS: We developed the oligo package to provide a more general solution that supports a wide range of applications. The package is based on the BioConductor principles of transparency, reproducibility and efficiency of development. It extends the existing tools and leverages existing code for visualization, accessing data and widely used preprocessing routines. The oligo package implements a unified paradigm for preprocessing data and interfaces with other BioConductor tools for downstream analysis. Our infrastructure is general and can be used by other BioConductor packages. AVAILABILITY: The oligo package is freely available through BioConductor, http://www.bioconductor.org.

Quantifying uncertainty in genotype calls.

MOTIVATION: Genome-wide association studies (GWAS) are used to discover genes underlying complex, heritable disorders for which less powerful study designs have failed in the past. The number of GWAS has skyrocketed recently with findings reported in top journals and the mainstream media. Microarrays are the genotype calling technology of choice in GWAS as they permit exploration of more than a million single nucleotide polymorphisms (SNPs) simultaneously. The starting point for the statistical analyses used by GWAS to determine association between loci and disease is making genotype calls (AA, AB or BB). However, the raw data, microarray probe intensities, are heavily processed before arriving at these calls. Various sophisticated statistical procedures have been proposed for transforming raw data into genotype calls. We find that variability in microarray output quality across different SNPs, different arrays and different sample batches have substantial influence on the accuracy of genotype calls made by existing algorithms. Failure to account for these sources of variability can adversely affect the quality of findings reported by the GWAS. RESULTS: We developed a method based on an enhanced version of the multi-level model used by CRLMM version 1. Two key differences are that we now account for variability across batches and improve the call-specific assessment of each call. The new model permits the development of quality metrics for SNPs, samples and batches of samples. Using three independent datasets, we demonstrate that the CRLMM version 2 outperforms CRLMM version 1 and the algorithm provided by Affymetrix, Birdseed. The main advantage of the new approach is that it enables the identification of low-quality SNPs, samples and batches. AVAILABILITY: Software implementing of the method described in this article is available as free and open source code in the crlmm R/BioConductor package. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Using the R Package crlmm for Genotyping and Copy Number Estimation.

Genotyping platforms such as Affymetrix can be used to assess genotype-phenotype as well as copy number-phenotype associations at millions of markers. While genotyping algorithms are largely concordant when assessed on HapMap samples, tools to assess copy number changes are more variable and often discordant. One explanation for the discordance is that copy number estimates are susceptible to systematic differences between groups of samples that were processed at different times or by different labs. Analysis algorithms that do not adjust for batch effects are prone to spurious measures of association. The R package crlmm implements a multilevel model that adjusts for batch effects and provides allele-specific estimates of copy number. This paper illustrates a workflow for the estimation of allele-specific copy number and integration of the marker-level estimates with complimentary Bioconductor software for inferring regions of copy number gain or loss. All analyses are performed in the statistical environment R.