Lorena Pantano

Bio: Lorena Pantano is an academic researcher from Harvard University. The author has contributed to research in topics: Small RNA & IsomiR. The author has an hindex of 21, co-authored 46 publications receiving 3620 citations. Previous affiliations of Lorena Pantano include Pompeu Fabra University & Catalan Institution for Research and Advanced Studies.

Genomic analyses identify molecular subtypes of pancreatic cancer

Abstract: Integrated genomic analysis of 456 pancreatic ductal adenocarcinomas identified 32 recurrently mutated genes that aggregate into 10 pathways: KRAS, TGF-β, WNT, NOTCH, ROBO/SLIT signalling, G1/S transition, SWI-SNF, chromatin modification, DNA repair and RNA processing. Expression analysis defined 4 subtypes: (1) squamous; (2) pancreatic progenitor; (3) immunogenic; and (4) aberrantly differentiated endocrine exocrine (ADEX) that correlate with histopathological characteristics. Squamous tumours are enriched for TP53 and KDM6A mutations, upregulation of the TP63∆N transcriptional network, hypermethylation of pancreatic endodermal cell-fate determining genes and have a poor prognosis. Pancreatic progenitor tumours preferentially express genes involved in early pancreatic development (FOXA2/3, PDX1 and MNX1). ADEX tumours displayed upregulation of genes that regulate networks involved in KRAS activation, exocrine (NR5A2 and RBPJL), and endocrine differentiation (NEUROD1 and NKX2-2). Immunogenic tumours contained upregulated immune networks including pathways involved in acquired immune suppression. These data infer differences in the molecular evolution of pancreatic cancer subtypes and identify opportunities for therapeutic development.

A myriad of miRNA variants in control and Huntington’s disease brain regions detected by massively parallel sequencing

Abstract: Huntington disease (HD) is a neurodegenerative disorder that predominantly affects neurons of the forebrain. We have applied the Illumina massively parallel sequencing to deeply analyze the small RNA populations of two different forebrain areas, the frontal cortex (FC) and the striatum (ST) of healthy individuals and individuals with HD. More than 80% of the small-RNAs were annotated as microRNAs (miRNAs) in all samples. Deep sequencing revealed length and sequence heterogeneity (IsomiRs) for the vast majority of miRNAs. Around 80-90% of the miRNAs presented modifications in the 3'-terminus mainly in the form of trimming and/or as nucleotide addition variants, while the 5'-terminus of the miRNAs was specially protected from changes. Expression profiling showed strong miRNA and isomiR expression deregulation in HD, most being common to both FC and ST. The analysis of the upstream regulatory regions in co-regulated miRNAs suggests a role for RE1-Silencing Transcription Factor (REST) and P53 in miRNAs downregulation in HD. The putative targets of deregulated miRNAs and seed-region IsomiRs strongly suggest that their altered expression contributes to the aberrant gene expression in HD. Our results show that miRNA variability is a ubiquitous phenomenon in the adult human brain, which may influence gene expression in physiological and pathological conditions.

SeqBuster, a bioinformatic tool for the processing and analysis of small RNAs datasets, reveals ubiquitous miRNA modifications in human embryonic cells.

Abstract: High-throughput sequencing technologies enable direct approaches to catalog and analyze snapshots of the total small RNA content of living cells. Characterization of high-throughput sequencing data requires bioinformatic tools offering a wide perspective of the small RNA transcriptome. Here we present SeqBuster, a highly versatile and reliable web-based toolkit to process and analyze large-scale small RNA datasets. The high flexibility of this tool is illustrated by the multiple choices offered in the pre-analysis for mapping purposes and in the different analysis modules for data manipulation. To overcome the storage capacity limitations of the web-based tool, SeqBuster offers a stand-alone version that permits the annotation against any custom database. SeqBuster integrates multiple analyses modules in a unique platform and constitutes the first bioinformatic tool offering a deep characterization of miRNA variants (isomiRs). The application of SeqBuster to small-RNA datasets of human embryonic stem cells revealed that most miRNAs present different types of isomiRs, some of them being associated to stem cell differentiation. The exhaustive description of the isomiRs provided by SeqBuster could help to identify miRNA-variants that are relevant in physiological and pathological processes. SeqBuster is available at http://estivill_lab.crg.es/seqbuster.

Genome assembly comparison identifies structural variants in the human genome.

Abstract: Numerous types of DNA variation exist, ranging from SNPs to larger structural alterations such as copy number variants (CNVs) and inversions. Alignment of DNA sequence from different sources has been used to identify SNPs1,2 and intermediate-sized variants (ISVs)3. However, only a small proportion of total heterogeneity is characterized, and little is known of the characteristics of most smaller-sized ( 1.5 million SNPs. Some differences were simple insertions and deletions, but in regions containing CNVs, segmental duplication and repetitive DNA, they were more complex. Our results uncover substantial undescribed variation in humans, highlighting the need for comprehensive annotation strategies to fully interpret genome scanning and personalized sequencing projects.

Integrative analysis of 111 reference human epigenomes

Abstract: The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.

Global variation in copy number in the human genome

Abstract: Copy number variation (CNV) of DNA sequences is functionally significant but has yet to be fully ascertained. We have constructed a first-generation CNV map of the human genome through the study of 270 individuals from four populations with ancestry in Europe, Africa or Asia (the HapMap collection). DNA from these individuals was screened for CNV using two complementary technologies: single-nucleotide polymorphism (SNP) genotyping arrays, and clone-based comparative genomic hybridization. A total of 1,447 copy number variable regions (CNVRs), which can encompass overlapping or adjacent gains or losses, covering 360 megabases (12% of the genome) were identified in these populations. These CNVRs contained hundreds of genes, disease loci, functional elements and segmental duplications. Notably, the CNVRs encompassed more nucleotide content per genome than SNPs, underscoring the importance of CNV in genetic diversity and evolution. The data obtained delineate linkage disequilibrium patterns for many CNVs, and reveal marked variation in copy number among populations. We also demonstrate the utility of this resource for genetic disease studies.

Integrative Genomics Viewer

Abstract: Rapid improvements in sequencing and array-based platforms are resulting in a flood of diverse genome-wide data, including data from exome and whole-genome sequencing, epigenetic surveys, expression profiling of coding and noncoding RNAs, single nucleotide polymorphism (SNP) and copy number profiling, and functional assays. Analysis of these large, diverse data sets holds the promise of a more comprehensive understanding of the genome and its relation to human disease. Experienced and knowledgeable human review is an essential component of this process, complementing computational approaches. This calls for efficient and intuitive visualization tools able to scale to very large data sets and to flexibly integrate multiple data types, including clinical data. However, the sheer volume and scope of data pose a significant challenge to the development of such tools.