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Modulation of Hippocampal-Prefrontal Circuitry During Spatial Working Memory
Spatial working memory (SWM) is an essential feature of goal-directed action. Locating a resource, a threat, or even oneself within a dynamic or unfamiliar environment requires a cached representation of relevant spatial features that must be continuously updated, preserved, and applied as needed to the execution of appropriate behaviors (Baddeley and Hitch 1974). SWM is disrupted in schizophrenia, as well as in multiple animal models of the disease. Patients with schizophrenia show impairment on tasks with both verbal and spatial working memory demands (Park and Holzman 1992, Conklin, Curtis et al. 2000) and exhibit abnormalities in neurophysiological signals that are
associated with normal cognitive performance. More specifically, convergent data
from diverse studies suggests that disruption of long-range functional connectivity
may underlie diverse cognitive and physiological symptoms of the schizophrenia.
It is therefore imperative that pathways of long-range functional connectivity that
support the cognitive processes impaired in schizophrenia be identified and
characterized, so that effective interventions can be targeted to the appropriate
neural structures and pathways.
Despite long-standing interest in the neurobiological underpinnings of
working memory, its multiple cognitive components, distributed anatomical
constituents, and distinct temporal phases have rendered its investigation elusive
(Logie 1995, Miyake and Shah 1999, Andrade 2001, de Zubicaray, McMahon et
al. 2001, Baddeley 2003, Klauer and Zhao 2004). Despite these challenges, an
extensive body of work supports the idea that the prefrontal cortex (PFC) plays a
central role in the successful execution of tasks requiring spatial working memory
(Curtis and D'Esposito 2004). Moreover, the joint contribution of medial prefrontal
cortex (mPFC) and hippocampus (HPC) supports successful spatial working
memory in rodents (Lee and Kesner 2003, Jones and Wilson 2005, Wang and
Cai 2006, Hyman, Zilli et al. 2010, Sigurdsson, Stark et al. 2010). It remains
unclear, however, which phase(s) of SWM (encoding, maintenance, and/or
retrieval) require the joint participation of HPC and mPFC, what behaviorally
relevant information is conveyed between the two structures, and by what
anatomical pathway(s) they interact.
Although HPC and mPFC share multiple second-degree anatomical
connections, including via striatum, amygdala, entorhinal cortex, and midline
thalamic nuclei, direct connectivity between the two structures is confined to a
unidirectional projection from the Ca1/subiculum of the ventral hippocampus
(vHPC) to prelimbic (PL) and infralimbic (IL) regions of the mPFC (Jay and Witter
1991, Hoover and Vertes 2007, Oh 2014).
Cells of both vHPC and mPFC exhibit location-specific firing that could
function to encode spatial cues critical to SWM (Jung, Wiener et al. 1994, Poucet,
Thinus-Blanc et al. 1994, Jung, Qin et al. 1998, Hok, Save et al. 2005, Kjelstrup,
Solstad et al. 2008, Burton, Hok et al. 2009, Royer, Sirota et al. 2010, Keinath,
Wang et al. 2014). Moreover, damage to the vHPC disrupts representations of
salient locations in mPFC (Burton, Hok et al. 2009), suggesting that the vHPCmPFC
projection may transmit SWM critical location information.
We therefore tested the role of vHPC-mPFC afferents in spatial working
memory using an a projection silencing approach that afforded anatomical and
temporal precision and found that the vHPC-mPFC direct input is necessary for
encoding, not maintenance or retrieval, of SWM-dependent cues. Combining this
approach with in vivo extracellular recordings of mPFC single units, we found that
location-selective firing in the mPFC during SWM is dependent on vHPC direct
input exclusively during the encoding phase of each trial. Finally, we found
evidence that the transmission of task-critical information in the vHPC-mPFC
pathway is mediated by the synchronizing of mPFC cells to gamma oscillations in
the vHPC. Together, these findings suggest a role for the vHPC-mPFC pathway
in the encoding of cues critical to SWM and may indicate a potential locus of
pathophysiological disruption underlying the cognitive impairments associated
with schiziphrenia
Integrated analysis of germline and somatic variants in ovarian cancer
We report the first large-scale exome-wide analysis of the combined germline-somatic landscape in ovarian cancer. Here we analyze germline and somatic alterations in 429 ovarian carcinoma cases and 557 controls. We identify 3,635 high confidence, rare truncation and 22,953 missense variants with predicted functional impact. We find germline truncation variants and large deletions across Fanconi pathway genes in 20% of cases. Enrichment of rare truncations is shown in BRCA1, BRCA2, and PALB2. Additionally, we observe germline truncation variants in genes not previously associated with ovarian cancer susceptibility (NF1, MAP3K4, CDKN2B, and MLL3). Evidence for loss of heterozygosity was found in 100% and 76% of cases with germline BRCA1 and BRCA2 truncations respectively. Germline-somatic interaction analysis combined with extensive bioinformatics annotation identifies 237 candidate functional germline truncation and missense variants, including 2 pathogenic BRCA1 and 1 TP53 deleterious variants. Finally, integrated analyses of germline and somatic variants identify significantly altered pathways, including the Fanconi, MAPK, and MLL pathways
Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context
Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts
Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas
Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN
Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas
This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing
molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin
Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images
Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images
of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL
maps are derived through computational staining using a convolutional neural network trained to
classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and
correlation with overall survival. TIL map structural patterns were grouped using standard
histopathological parameters. These patterns are enriched in particular T cell subpopulations
derived from molecular measures. TIL densities and spatial structure were differentially enriched
among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial
infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic
patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for
the TCGA image archives with insights into the tumor-immune microenvironment
Definition of a temporal distribution index for high temporal resolution precipitation data over Peninsular Spain and the Balearic Islands: the fractal dimension; and its synoptic implications
Precipitation on the Spanish mainland and in the Balearic archipelago exhibits a high degree of spatial and temporal variability, regardless of the temporal resolution of the data considered. The fractal dimension indicates the property of self-similarity, and in the case of this study, wherein it is applied to the temporal behaviour of rainfall at a fine (10-min) resolution from a total of 48 observatories, it provides insights into its more or less convective nature. The methodology of Jenkinson & Collison which automatically classifies synoptic situations at the surface, as well as an adaptation of this methodology at 500 hPa, was applied in order to gain insights into the synoptic implications of extreme values of the fractal dimension. The highest fractal dimension values in the study area were observed in places with precipitation that has a more random behaviour over time with generally high totals. Four different regions in which the atmospheric mechanisms giving rise to precipitation at the surface differ from the corresponding above-ground mechanisms have been identified in the study area based on the fractal dimension. In the north of the Iberian Peninsula, high fractal dimension values are linked to a lower frequency of anticyclonic situations, whereas the opposite occurs in the central region. In the Mediterranean, higher fractal dimension values are associated with a higher frequency of the anticyclonic type and a lower frequency of the advective type from the east. In the south, lower fractal dimension values indicate higher frequency with respect to the anticyclonic type from the east and lower frequency with respect to the cyclonic type
A comprehensive assessment of somatic mutation detection in cancer using whole-genome sequencing.
As whole-genome sequencing for cancer genome analysis becomes a clinical tool, a full understanding of the variables affecting sequencing analysis output is required. Here using tumour-normal sample pairs from two different types of cancer, chronic lymphocytic leukaemia and medulloblastoma, we conduct a benchmarking exercise within the context of the International Cancer Genome Consortium. We compare sequencing methods, analysis pipelines and validation methods. We show that using PCR-free methods and increasing sequencing depth to ∼ 100 × shows benefits, as long as the tumour:control coverage ratio remains balanced. We observe widely varying mutation call rates and low concordance among analysis pipelines, reflecting the artefact-prone nature of the raw data and lack of standards for dealing with the artefacts. However, we show that, using the benchmark mutation set we have created, many issues are in fact easy to remedy and have an immediate positive impact on mutation detection accuracy.We thank the DKFZ Genomics and Proteomics Core Facility and the OICR Genome Technologies Platform for provision of sequencing services. Financial support was provided by the consortium projects READNA under grant agreement FP7 Health-F4-2008-201418, ESGI under grant agreement 262055, GEUVADIS under grant agreement 261123 of the European Commission Framework Programme 7, ICGC-CLL through the Spanish Ministry of Science and Innovation (MICINN), the Instituto de Salud Carlos III (ISCIII) and the Generalitat de Catalunya. Additional financial support was provided by the PedBrain Tumor Project contributing to the International Cancer Genome Consortium, funded by German Cancer Aid (109252) and by the German Federal Ministry of Education and Research (BMBF, grants #01KU1201A, MedSys #0315416C and NGFNplus #01GS0883; the Ontario Institute for Cancer Research to PCB and JDM through funding provided by the Government of Ontario, Ministry of Research and Innovation; Genome Canada; the Canada Foundation for Innovation and Prostate Cancer Canada with funding from the Movember Foundation (PCB). PCB was also supported by a Terry Fox Research Institute New Investigator Award, a CIHR New Investigator Award and a Genome Canada Large-Scale Applied Project Contract. The Synergie Lyon Cancer platform has received support from the French National Institute of Cancer (INCa) and from the ABS4NGS ANR project (ANR-11-BINF-0001-06). The ICGC RIKEN study was supported partially by RIKEN President’s Fund 2011, and the supercomputing resource for the RIKEN study was provided by the Human Genome Center, University of Tokyo. MDE, LB, AGL and CLA were supported by Cancer Research UK, the University of Cambridge and Hutchison-Whampoa Limited. SD is supported by the Torres Quevedo subprogram (MI CINN) under grant agreement PTQ-12-05391. EH is supported by the Research Council of Norway under grant agreements 221580 and 218241 and by the Norwegian Cancer Society under grant agreement 71220-PR-2006-0433. Very special thanks go to Jennifer Jennings for administrating the activity of the ICGC Verification Working Group and Anna Borrell for administrative support.This is the final version of the article. It first appeared from Nature Publishing Group via http://dx.doi.org/10.1038/ncomms1000
Integrated genomic characterization of pancreatic ductal adenocarcinoma
We performed integrated genomic, transcriptomic, and proteomic profiling of 150 pancreatic ductal adenocarcinoma (PDAC) specimens, including samples with characteristic low neoplastic cellularity. Deep whole-exome sequencing revealed recurrent somatic mutations in KRAS, TP53, CDKN2A, SMAD4, RNF43, ARID1A, TGFβR2, GNAS, RREB1, and PBRM1. KRAS wild-type tumors harbored alterations in other oncogenic drivers, including GNAS, BRAF, CTNNB1, and additional RAS pathway genes. A subset of tumors harbored multiple KRAS mutations, with some showing evidence of biallelic mutations. Protein profiling identified a favorable prognosis subset with low epithelial-mesenchymal transition and high MTOR pathway scores. Associations of non-coding RNAs with tumor-specific mRNA subtypes were also identified. Our integrated multi-platform analysis reveals a complex molecular landscape of PDAC and provides a roadmap for precision medicine
Pathogenicity and Impact of HLA Class I Alleles in Aplastic Anemia Patients of Different Ethnicities
Acquired aplastic anemia (AA) is caused by autoreactive T cell-mediated destruction of early hematopoietic cells. Somatic loss of human leukocyte antigen (HLA) class I alleles was identified as a mechanism of immune escape in surviving hematopoietic cells of some patients with AA. However, pathogenicity, structural characteristics, and clinical impact of specific HLA alleles in AA remain poorly understood. Here, we evaluated somatic HLA loss in 505 patients with AA from 2 multi-institutional cohorts. Using a combination of HLA mutation frequencies, peptide-binding structures, and association with AA in an independent cohort of 6,323 patients from the National Marrow Donor Program, we identified 19 AA risk alleles and 12 non-risk alleles and established a potentially novel AA HLA pathogenicity stratification. Our results define pathogenicity for the majority of common HLA-A/B alleles across diverse populations. Our study demonstrates that HLA alleles confer different risks of developing AA, but once AA develops, specific alleles are not associated with response to immunosuppression or transplant outcomes. However, higher pathogenicity alleles, particularly HLA-B*14:02, are associated with higher rates of clonal evolution in adult patients with AA. Our study provides insights into the immune pathogenesis of AA, opening the door to future autoantigen identification and improved understanding of clonal evolution in AA
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