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DNA transposable elements and transposase-derived genes are present in most living organisms, including vertebrates, but their function is largely unknown. PiggyBac Transposable Element Derived 5 (PGBD5) is an evolutionarily conserved vertebrate DNA transposase-derived gene with retained nuclease activity in cells. Vertebrate brain development is known to be associated with prominent neuronal cell death and DNA breaks, but their causes and functions are not well understood. Here, we show that PGBD5 contributes to normal brain development in mice and humans, where its deficiency causes disorder of intellectual disability, movement and seizures. In mice, Pgbd5 is required for the developmental induction of post-mitotic DNA breaks and recurrent somatic genome rearrangements in neurons. Together, these studies nominate PGBD5 as the long-hypothesized neuronal DNA nuclease required for brain function in mammals.One-Sentence SummaryPiggyBac Transposable Element Derived 5 (PGBD5) is required for brain development in humans and mice by promoting somatic neuronal genome rearrangements.

Psychopathy; Fear conditioning; Children Psicopatía; Condicionamiento del miedo; Niños Psicopatia; Condicionament de la por; Nens Deficits in fear conditioning related to psychopathy have been widely studied in adults. However, evidence in children and adolescents is scarce and inconsistent. This research aimed to expand knowledge about fear conditioning in psychopathy and its dimensions in child and early adolescent clinical populations. Participants were 45 boys (outpatients) aged 6–14 years (M = 10.59, SD = 2.04). They were assessed with the parents’ and teachers’ versions of the Child Problematic Traits Inventory (CPTI). A fear conditioning paradigm (Neumann et al., in Biological Psychology, 79(3), 337–342, 2008) for children and adolescents was used. Conditioned stimuli (CS+ and CS-) were geometric shapes and the unconditioned stimulus (US) was an unpleasant sound of metal scraping on slate (83 dB). Difference scores (CS+ minus CS-) in skin conductance responses (SCR) and self-reported cognitive and affective measures were considered as indices of fear conditioning. Results showed that: a) deficits in fear conditioning were related to some psychopathy dimensions but not to psychopathy as a unitary construct; b) the Impulsivity-Need for Stimulation dimension was a predictor of impaired fear conditioning at a cognitive level; c) the interaction of Callous-Unemotional and Impulsivity-Need for Stimulation dimensions was a significant predictor of impaired electrodermal fear conditioning; d) by contrast, the Grandiose-Deceitful dimension, was marginally associated with a greater electrodermal fear conditioning. In conclusion, psychopathy dimensions and their interactions, but not psychopathy as a whole, predicted deficits in fear conditioning as measured by SCR and cognitive indices. These findings confirm the notion that psychopathic traits are associated with deficits in fear conditioning in child and adolescent clinical populations and provide support for a multidimensional approach to youth psychopathy. Open Access Funding provided by Universitat Autonoma de Barcelona. This research was financially supported by the Ministerio de Economía y Competitividad, Spanish Government (MINECO/FEDER, UE), reference: PSI2015-67441-R and by the AGAUR, Generalitat de Catalunya (2017 SGR1586). A.I. is recipient of a Ph.D. fellowship by the Secretaria d’Universitats i Recerca of Generalitat de Catalunya and European Social Fund (FI-DGR 2017).

We sought to provide evidence for a combined effect of two attentional mechanisms during associative learning. Participants’ eye movements were recorded as they predicted the outcomes following different pairs of cues. Across the trials of an initial stage, a relevant cue in each pair was consistently followed by one of two outcomes, while an irrelevant cue was equally followed by either of them. Thus, the relevant cue should have been associated with small relative prediction errors, compared to the irrelevant cue. In a later stage, each pair came to be followed by one outcome on a random half of the trials and by the other outcome on the remaining half, and thus there should have been a rise in the overall prediction error. Consistent with an attentional mechanism based on relative prediction error, an attentional advantage for the relevant cue was evident in the first stage. However, in accordance with a mechanism linked to overall prediction error, the attention paid to both types of cues increased at the beginning of the second stage. These results showed up in both dwell times and within-trial patterns of fixations, and they were predicted by a hybrid model of attention.

Neuroblastoma is a pediatric solid tumor characterized by strong clinical heterogeneity. Although certain complex genomic alterations, such as extrachromosomal DNA amplifications (ecDNA), have been recurrently detected in neuroblastomas, the mutational processes involved in their generation remain largely unclear. By examining the topography of complex rearrangements along with mutational signatures derived from all variant classes, we identify previously unrecognized co-occurring mutational footprints, which we termed mutational scenarios. We demonstrate that clinical neuroblastoma heterogeneity is linked to differences in the processes driving these mutational scenarios. Whereas high-risk MYCN-amplified neuroblastoma genomes were characterized by signs of replication slippage and stress, homologous recombination-associated signatures defined high-risk non-MYCN-amplified patients. Non-high-risk neuroblastomas, on the other hand, were marked by footprints of chromosome missegregation. This analysis provides a systematic perspective on the repertoire of mutational patterns that contribute to clinical neuroblastoma heterogeneity.

Most genome-wide association studies (GWAS) of complex traits are performed using models with additive allelic effects. Hundreds of loci associated with type 2 diabetes have been identified using this approach. Additive models, however, can miss loci with recessive effects, thereby leaving potentially important genes undiscovered. We conducted the largest GWAS meta-analysis using a recessive model for type 2 diabetes. Our discovery sample included 33,139 cases and 279,507 controls from seven European-ancestry cohorts including the UK Biobank. We identified 51 loci associated with type 2 diabetes, including five variants undetected by prior additive analyses. Two of the five had minor allele frequency less than 5% and were each associated with more than doubled risk in homozygous carriers. Using two additional cohorts, FinnGen and a Danish cohort, we replicated three of the variants, including one of the low-frequency variants, rs115018790, which had an odds ratio in homozygous carriers of 2.56 (95% CI 2.05-3.19, P=1´10-16) and a stronger effect in men than in women (interaction P=7´10-7). The signal was associated with multiple diabetes-related traits, with homozygous carriers showing a 10% decrease in LDL and a 20% increase in triglycerides, and colocalization analysis linked this signal to reduced expression of the nearby PELO gene. These results demonstrate that recessive models, when compared to GWAS using the additive approach, can identify novel loci, including large-effect variants with pathophysiological consequences relevant to type 2 diabetes.

Mantle cell lymphoma (MCL) is a mature B-cell neoplasm initially driven by CCND1 rearrangement with 2 molecular subtypes, conventional MCL (cMCL) and leukemic non-nodal MCL (nnMCL), that differ in their clinicobiological behavior. To identify the genetic and epigenetic alterations determining this diversity, we used whole-genome (n = 61) and exome (n = 21) sequencing (74% cMCL, 26% nnMCL) combined with transcriptome and DNA methylation profiles in the context of 5 MCL reference epigenomes. We identified that open and active chromatin at the major translocation cluster locus might facilitate the t(11;14)(q13;32), which modifies the 3-dimensional structure of the involved regions. This translocation is mainly acquired in precursor B cells mediated by recombination-activating genes in both MCL subtypes, whereas in 8% of cases the translocation occurs in mature B cells mediated by activation-induced cytidine deaminase. We identified novel recurrent MCL drivers, including CDKN1B, SAMHD1, BCOR, SYNE1, HNRNPH1, SMARCB1, and DAZAP1. Complex structural alterations emerge as a relevant early oncogenic mechanism in MCL, targeting key driver genes. Breakage-fusion-bridge cycles and translocations activated oncogenes (BMI1, MIR17HG, TERT, MYC, and MYCN), generating gene amplifications and remodeling regulatory regions. cMCL carried significant higher numbers of structural variants, copy number alterations, and driver changes than nnMCL, with exclusive alterations of ATM in cMCL, whereas TP53 and TERT alterations were slightly enriched in nnMCL. Several drivers had prognostic impact, but only TP53 and MYC aberrations added value independently of genomic complexity. An increasing genomic complexity, together with the presence of breakage-fusion-bridge cycles and high DNA methylation changes related to the proliferative cell history, defines patients with different clinical evolution.

The burden of several common diseases including obesity, diabetes, hypertension, asthma, and depression is increasing in most world populations. However, the mechanisms underlying the numerous epidemiological and genetic correlations among these disorders remain largely unknown. We investigated whether common polymorphic inversions underlie the shared genetic influence of these disorders. We performed an inversion association analysis including 21 inversions and 25 obesity-related traits on a total of 408,898 Europeans and validated the results in 67,299 independent individuals. Seven inversions were associated with multiple diseases while inversions at 8p23.1, 16p11.2, and 11q13.2 were strongly associated with the co-occurrence of obesity with other common diseases. Transcriptome analysis across numerous tissues revealed strong candidate genes for obesity-related traits. Analyses in human pancreatic islets indicated the potential mechanism of inversions in the susceptibility of diabetes by disrupting the cis-regulatory effect of SNPs from their target genes. Our data underscore the role of inversions as major genetic contributors to the joint susceptibility to common complex diseases., This research has received funding from Ministerio de Ciencia, Innovación y Universidades (MICIU), Agencia Estatal de Investigación (AEI), and Fondo Europeo de Desarrollo Regional, UE (RTI2018-100789-B-I00) also through the “Centro de Excelencia Severo Ochoa 2019-2023” Program (CEX2018-000806-S); and the Catalan Government (SGR2017/801 and #016FI_B 00272 to C.R.-A.) through the CERCA Program. J.G. is funded by the European Commission (H2020-ERC-2014-CoG-647900) and the MINECO/AEI/FEDER, EU (BFU2017-82937-P). The L.A.P.-J. lab was funded by the Spanish Ministry of Science and Innovation (ISCIII-FEDER P13/02481), the Catalan Department of Economy and Knowledge (SGR2014/1468, SGR2017/1974, and ICREA Acadèmia), and also acknowledges support from the Spanish Ministry of Economy and Competiveness“Programa de Excelencia María de Maeztu” (MDM-2014-0370). This research was conducted using the UK Biobank Resource under Application Number 43983. The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS.

The combined analysis of haplotype panels with phenotype clinical cohorts is a common approach to explore the genetic architecture of human diseases. However, genetic studies are mainly based on single nucleotide variants (SNVs) and small insertions and deletions (indels). Here, we contribute to fill this gap by generating a dense haplotype map focused on the identification, characterization, and phasing of structural variants (SVs). By integrating multiple variant identification methods and Logistic Regression Models (LRMs), we present a catalogue of 35 431 441 variants, including 89 178 SVs (≥50 bp), 30 325 064 SNVs and 5 017 199 indels, across 785 Illumina high coverage (30x) whole-genomes from the Iberian GCAT Cohort, containing a median of 3.52M SNVs, 606 336 indels and 6393 SVs per individual. The haplotype panel is able to impute up to 14 360 728 SNVs/indels and 23 179 SVs, showing a 2.7-fold increase for SVs compared with available genetic variation panels. The value of this panel for SVs analysis is shown through an imputed rare Alu element located in a new locus associated with Mononeuritis of lower limb, a rare neuromuscular disease. This study represents the first deep characterization of genetic variation within the Iberian population and the first operational haplotype panel to systematically include the SVs into genome-wide genetic studies. GCAT|Genomes for Life, a cohort study of the Genomes of Catalonia, Fundació Institut Germans Trias i Pujol (IGTP); IGTP is part of the CERCA Program/Generalitat de Catalunya; GCAT is supported by Acción de Dinamización del ISCIII-MINECO; Ministry of Health of the Generalitat of Catalunya [ADE 10/00026]; Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR) [2017-SGR 529]; B.C. is supported by national grants [PI18/01512]; X.F. is supported by VEIS project [001-P-001647] (co-funded by European Regional Development Fund (ERDF), ‘A way to build Europe’); a full list of the investigators who contributed to the generation of the GCAT data is available from www.genomesforlife.com/; Severo Ochoa Program, awarded by the Spanish Government [SEV-2011-00067 and SEV2015-0493]; Spanish Ministry of Science [TIN2015-65316-P]; Innovation and by the Generalitat de Catalunya [2014-SGR-1051 to D.T.]; Agencia Estatal de Investigación (AEI, Spain) [BFU2016-77244-R and PID2019-107836RB-I00]; European Regional Development Fund (FEDER, EU) (to M.C.); Spanish Ministry of Science and Innovation [FPI BES-2016-0077344 to J.V.M.]; C.S. received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement [H2020-MSCA-COFUND-2016-754433]; this study made use of data generated by the UK10K Consortium from UK10K COHORT IMPUTATION [EGAS00001000713]; formal agreement with the Barcelona Supercomputing Center (BSC); this study made use of data generated by the Genome of the Netherlands’ project, which is funded by the Netherlands Organization for Scientific Research [184021007], allowing us to use the GoNL reference panel containing SVs, upon request (GoNL Data Access request 2019203); this study also used data generated by the Haplotype Reference Consortium (HRC) accessed through the European Genome-phenome Archive with the accession numbers EGAD00001002729; formal agreement of the Barcelona Supercomputing Center (BSC) with WTSI; this study made use of data generated by the 1000 Genomes (1000G), accessed through the FTP portal (http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/); this study used the GeneHancer-for-AnnotSV dump for GeneCards Suite Version 4.14, through a formal agreement between the BSC and the Weizmann Institute of Science. Funding for open access charge: GCAT|Genomes for Life, a cohort study of the Genomes of Catalonia, Fundació Institut Germans Trias i Pujol (IGTP); IGTP is part of the CERCA Program/Generalitat de Catalunya; GCAT is supported by Acción de Dinamización del ISCIII-MINECO; Ministry of Health of the Generalitat of Catalunya [ADE 10/00026]; Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR) [2017-SGR 529]; B.C. is supported by national grants [PI18/01512]; X.F. is supported by VEIS project [001-P-001647] (co-funded by European Regional Development Fund (ERDF), ‘A way to build Europe’); a full list of the investigators who contributed to the generation of the GCAT data is available from www.genomesforlife.com/; Severo Ochoa Program, awarded by the Spanish Government [SEV-2011-00067 and SEV2015-0493]; Spanish Ministry of Science [TIN2015-65316-P]; Innovation and by the Generalitat de Catalunya [2014-SGR-1051 to D.T.]; [Agencia Estatal de Investigación (AEI, Spain) [BFU2016-77244-R and PID2019-107836RB-I00]; European Regional Development Fund (FEDER, EU) (to M.C.); Spanish Ministry of Science and Innovation [FPI BES-2016-0077344 to J.V.M.]; C.S. received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement [H2020-MSCA-COFUND-2016-754433]; this study made use of data generated by the UK10K Consortium from UK10K COHORT IMPUTATION [EGAS00001000713]; formal agreement with the Barcelona Supercomputing Center (BSC); this study made use of data generated by the Genome of the Netherlands’ project, which is funded by the Netherlands Organization for Scientific Research [184021007], allowing us to use the GoNL reference panel containing SVs, upon request (GoNL Data Access request 2019203); this study also used data generated by the Haplotype Reference Consortium (HRC) accessed through the European Genome-phenome Archive with the accession numbers EGAD00001002729; formal agreement of the Barcelona Supercomputing Center (BSC) with WTSI; this study made use of data generated by the 1000 Genomes (1000G), accessed through the FTP portal (http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/); this study used the GeneHancer-for-AnnotSV dump for GeneCards Suite Version 4.14, through a formal agreement between the BSC and The Weizmann Institute of Science. "Article signat per 21 autors/es: Jordi Valls-Margarit, Iván Galván-Femenía, Daniel Matías-Sánchez, Natalia Blay, Montserrat Puiggròs, Anna Carreras, Cecilia Salvoro, Beatriz Cortés, Ramon Amela, Xavier Farre, Jon Lerga-Jaso, Marta Puig, Jose Francisco Sánchez-Herrero, Victor Moreno, Manuel Perucho, Lauro Sumoy, Lluís Armengol, Olivier Delaneau, Mario Cáceres, Rafael de Cid, David Torrents"

Genomic reference databases of residing populations are available in different countries and regions. Since they represent the whole genetic diversity of a geographical region, they have wide applications, from biomedical studies to forensic identifications. Uniparentally transmitted portions of the genome specifically are highly suitable for kinship analyses, mixed DNA cases and geographical ancestry inferences. We have sampled 808 individuals currently residing in Catalonia within the GCAT cohort, from which we have generated 808 high-quality whole mitochondrial DNA (mtDNA) genomes and 399 sequences of the male-specific part of the Y chromosome (MSY). We observe higher genetic diversity than in classical population genetics datasets. We test the robustness of whole sequences for unequivocal identifications, and we found that they have higher resolution than mitochondrial control region and Y chromosome short tandem repeats (Y-STRs), and that most of the variants they present are at low frequencies, increasing the discrimination capacity between individuals. These results confirm the forensic applicability of whole uniparental sequences and provide one of the largest high-quality reference datasets ever published., This work was supported by the Spanish Ministry of Economy and Competitiveness and Agencia Estatal de Investigación (grant numbers CGL2016–75389-P (MINEICO/FEDER, UE), PID2019–106485 GB-I00/AEI/10.13039/501100011033 (MINEICO), and “Unidad María de Maeztu” (CEX2018–000792-M) to FC and DC; and Agència de Gestió d’Ajuts Universitaris i de la Recerca (Generalitat de Catalunya, grant 2017SGR00702). Computing time at Barcelona Supercomputing Centre was granted by Red Española de Supercomputación (BCV-2019–3-00002). NF-P was supported by a FPU17/03501 fellowship. This study makes use of data generated by the GCAT=Genomes for Life. Cohort study of the Genomes of Catalonia, Fundacio IGTP with registration number PI-2018–03. IGTP is part of the CERCA Program / Generalitat de Catalunya. GCAT is supported by Acción de Dinamización del ISCIII-MINECO and the Ministry of Health of the Generalitat of Catalunya (ADE 10/00026) and; the Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR) (2017-SGR 529).

Extrachromosomal circularization of DNA is an important genomic feature in cancer. The structure, composition and genome-wide frequency of extrachromosomal circular DNA, however, have not yet been extensively profiled. Here, we combined genomic and transcriptomic approaches to describe the landscape of extrachromosomal circular DNA in neuroblastoma, a tumor arising in childhood from primitive cells of the sympathetic nervous system. Our analysis identifies and characterizes a wide catalog of somatically acquired and undescribed extrachromosomal circular DNAs. Moreover, we find that extrachromosomal circular DNAs are an unanticipated major source of somatic rearrangements, contributing to oncogenic remodeling through chimeric circularization and reintegration of circular DNA into the linear genome. Cancer-causing lesions can emerge out of circle-derived rearrangements and are associated with adverse clinical outcome. It is highly probable that circle-derived rearrangements represent an ongoing mutagenic process. Thus, extrachromosomal circular DNAs represent a multi-hit mutagenic process, with important functional and clinical implications for the origins of genomic remodeling in cancer.

Recent work has unveiled a hidden microproteome composed by thousands of small proteins named microproteins: they are functional short proteins coded by genomic regions previously considered non-coding, and which had been completely ignored, mainly due to their small size ( Citation Format: Marion Martinez, Marta Hergueta, Pilar Ximénez de Embún, Ana Dueso, David Torrents, Teresa Macarulla, Javier Muñoz, Héctor Peinado, María Abad. Mining the secreted microproteome for novel regulators of PDAC progression [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr C074.

The combined analysis of haplotype panels with phenotype clinical cohorts is a common approach to explore the genetic architecture of human diseases. However, genetic studies are mainly based on single nucleotide variants (SNVs) and small insertions and deletions (indels). Here, we contribute to fill this gap by generating a dense haplotype map focused on the identification, characterization and phasing of structural variants (SVs). By integrating multiple variant identification methods and Logistic Regression models, we present a catalogue of 35,431,441 variants, including 89,178 SVs (≥50bp), 30,325,064 SNVs and 5,017,199 indels, across 785 Illumina high coverage (30X) whole-genomes from the Iberian GCAT Cohort, containing 3.52M SNVs, 606,336 indels and 6,393 SVs in median per individual. The haplotype panel is able to impute up to 14,360,728 SNVs/indels and 23,179 SVs, showing a 2.7-fold increase for SVs compared with available genetic variation panels. The value of this panel for SVs analysis is shown through an imputed rare Alu element located in a new locus associated with mononeuritis of lower limb, a rare neuromuscular disease. This study represents the first deep characterization of genetic variation within the Iberian population and the first operational haplotype panel to systematically include the SVs into genome-wide genetic studies.

ObjectiveMost genome-wide association studies (GWAS) of complex traits are performed using models with additive allelic effects. Hundreds of loci associated with type 2 diabetes have been identified using this approach. Additive models, however, can miss loci with recessive effects, thereby leaving potentially important genes undiscovered.Research Design and MethodsWe conducted the largest GWAS meta-analysis using a recessive model for type 2 diabetes. Our discovery sample included 33,139 cases and 279,507 controls from seven European-ancestry cohorts including the UK Biobank. We then used two additional cohorts, FinnGen and a Danish cohort, for replication. For the most significant recessive signal, we conducted a phenome-wide association study across hundreds of traits to make inferences about the pathophysiology underlying the increased risk seen in homozygous carriers.ResultsWe identified 51 loci associated with type 2 diabetes, including five variants with recessive effects undetected by prior additive analyses. Two of the five had minor allele frequency less than 5% and were each associated with more than doubled risk. We replicated three of the variants, including one of the low-frequency variants, rs115018790, which had an odds ratio in homozygous carriers of 2.56 (95% CI 2.05-3.19, P=1×10−16) and a stronger effect in men than in women (interaction P=7×10−7). Colocalization analysis linked this signal to reduced expression of the nearby PELO gene, and the signal was associated with multiple diabetes-related traits, with homozygous carriers showing a 10% decrease in LDL and a 20% increase in triglycerides.ConclusionsOur results demonstrate that recessive models, when compared to GWAS using the additive approach, can identify novel loci, including large-effect variants with pathophysiological consequences relevant to type 2 diabetes.

Background: For many years, a major question in the field of cancer genomics has been the identification of those variations that can have a functional role in cancer, and distinguish from the majority of genomic changes that have no functional consequences. This is particularly challenging when considering complex chromosomal rearrangements, which are often composed of multiple DNA breaks, resulting in difficulties to classify and interpret them functionally. Despite recent efforts towards the classification of structural variants (SVs), more robust statistical frames are needed to better classify these variants, and to isolate those that derive from specific molecular mechanisms. Results: We present a new statistical approach to analyze SVs patterns from 2392 real tumor samples from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium and to identify significant recurrence, which can inform of relevant mechanisms involved in the biology of tumors. The method is based on recursive KDE clustering of 152,926 SVs, graph mining techniques and statistical measures. The proposed methodology was able not only to identify complex patterns but also to prove them as not random occurrences. Furthermore, a new class of pattern that was not previously described has been identified. Conclusions: We developed a new and unbiased methodology for clustering SVs to search further for complex patterns by using a cost-efficient graph mining method. Followed by deep statistical analysis and applying randomization techniques, our proposed framework allows for discerning between stochastic chromosomal rearrangements and complex patterns that might have specific underlying mechanisms present in different cancer types.

Therapeutic vaccines based on dendritic cells offer a good approach to HIV-specific T-cell responses and partial control of the viral load after antiretroviral therapy interruption. The aim of the present study was to identify mRNA expression profiles and to assess the impact of the gut microbiome composition for predicting the viral load control after antiretroviral therapy interruption. We enrolled 29 patients to receive either placebo or a monocyte-derived dendritic cell vaccine. Patients with a decrease in their viral load of &gt, 0.5 log10 copies/mL by 12 weeks after antiretroviral therapy interruption were considered responders. In total, 66 genes were considered differentially expressed between responders and non-responders. Enrichment analysis revealed several upregulated pathways involved in the host defense response to a virus via the type I interferon signaling pathway. Regarding the gut microbiota, responders showed enriched levels of Bacteroidetes (p &lt, 0.005) and Verrucomicrobia (p = 0.017), while non-responders were enriched with Tenericutes (p = 0.049) and Actinobacteria (p &lt, 0.005). We also found important differences at the genus level. However, we did not discover any effect of the dendritic cell vaccine on the transcriptome or the gut microbiota. An alternative analysis did characterize that the microbiota from responders were associated with the metabolic production of short-chain fatty acids, which are key metabolites in the regulation of intestinal homeostasis. The evidence now consistently shows that short-chain fatty acid depletion occurs in HIV-infected individuals receiving antiretroviral treatment.

Introduction: Clonal evolution drives cancer development due to the emergence and/or selection of proliferatively advantageous subclones. Its understanding may facilitate the design of anticipation-based management strategies. Richter transformation (RT) is a paradigmatic tumor evolution in which chronic lymphocytic leukemia (CLL), an indolent neoplasia of mature B-cells, transforms into a high-grade lymphoma, usually diffuse large B-cell lymphoma (DLBCL), conferring a dismal prognosis. The evolutionary trajectories of RT and its driving (epi)genomic mechanisms remain largely unknown. Aims: To reconstruct the evolutionary history of RT and to reveal the molecular processes underlying this transformation. Methods: We characterized the whole genome (WGS), epigenome (DNA methylation, H3K27ac, ATAC-seq), and transcriptome (RNA-seq), combined with single-cell DNA and RNA sequencing analyses, of 19 CLL patients developing RT before (n=3) or after treatment with chemoimmunotherapy (n=6) and targeted therapies (BCR or BCL2 inhibitors, n=10). We analyzed 54 longitudinal samples covering up to 19 years of disease course. Results: Our WGS analyses uncovered that RT is characterized by a remarkable structural complexity. We also identified a novel treatment-independent RT-specific mutational process, which we named SBS-RT. The genetic driver landscape of RT is a compendium of alterations in genes involved in cell cycle, MYC, and NF-κB pathways, frequently targeted in single catastrophic events including chromothripsis and chromoplexy. The WGS-based phylogenic reconstruction and single-cell DNA/RNA-seq analyses identified a very early diversification of CLL leading to emergence of RT-cells carrying specific genetic drivers and transcriptomic profiles of RT already at CLL diagnosis. These small subclones were dormant for 6-19 years until rapid expansion associated with the clinical transformation. While the DNA methylome kept track of the cell of origin and proliferative history of RT cells, their chromatin configuration and transcriptional program converged into the overexpression of cell cycle regulators, Toll-like receptors, MYC, MTORC1, and OXPHOS related transcripts, as well as downregulation of BCR pathway. This phenotypic shift was related to de novo activation of key transcription factors. In vitro experiments confirmed that RT cells have a 4-fold higher oxygen consumption at routine respiration and electron transfer system capacity compared to CLL. The resistance of RT to BCR inhibition is consistent with its high OXPHOS and low BCR signaling, which mimics de novo DLBCL-OXPHOS insensitive to BCR inhibition. This OXPHOShigh-BCRlow transcriptional axis of RT can be exploited therapeutically. Conclusions: These findings demonstrate the early seeding of subclones driving advanced stages of cancer evolution and uncover therapeutic targets for the, once expanded, lethal Richter transformation. Citation Format: Ferran Nadeu, Romina Royo, Ramon Massoni-Badosa, Beatriz Garcia-Torre, Martí Duran-Ferrer, Kevin J. Dawson, Marta Kulis, Ander Diaz-Navarro, Neus Villamor, Juan L. Melero, Vicente Chapaprieta, Ana Dueso-Barroso, Julio Delgado, Riccardo Moia, Sara Ruiz-Gil, Domenica Marchese, Núria Verdaguer-Dot, Mónica Romo, Maria Rozman, Gerard Frigola, Alfredo Rivas-Delgado, Tycho Baumann, Miguel Alcoceba, Marcos González, Fina Climent, Pau Abrisqueta, Josep Castellví, Francesc Bosch, Marta Aymerich, Anna Enjuanes, Sílvia Ruiz-Gaspà, Armando López-Guillermo, Pedro Jares, Sílvia Beà, Dolors Colomer, Núria López-Bigas, Josep LlGelpí, David Torrents, Peter J. Campbell, Ivo Gut, Pablo M. Garcia-Roves, Davide Rossi, Gianluca Gaidano, Xose S. Puente, Holger Heyn, Francesco Maura, José I. Martín-Subero, Elías Campo. Early seeding of Richter transformation in chronic lymphocytic leukemia [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3795.

Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to genetic factors. Here, we identify 4 genomic loci with suggestive associations for SARS-CoV-2 susceptibility and 19 for COVID-19 disease severity. Four of these 23 loci likely have an ethnicity-specific component. Genome-wide association study (GWAS) signals in 11 loci colocalize with expression quantitative trait loci (eQTLs) associated with the expression of 20 genes in 62 tissues/cell types (range: 1:43 tissues/gene), including lung, brain, heart, muscle, and skin as well as the digestive system and immune system. We perform genetic fine mapping to compute 99% credible SNP sets, which identify 10 GWAS loci that have eight or fewer SNPs in the credible set, including three loci with one single likely causal SNP. Our study suggests that the diverse symptoms and disease severity of COVID-19 observed between individuals is associated with variants across the genome, affecting gene expression levels in a wide variety of tissue types., Graphical abstract, D’Antonio et al. characterize associations between GWAS signals for COVID-19 disease and eQTLs in 69 human tissues to identify causal variants and their underlying molecular mechanisms. They show that diverse symptoms and disease severity of COVID-19 are associated with variants affecting gene expression in a wide variety of tissues.

GWAS have identified more than 700 genetic signals associated with type 2 diabetes (T2D). To gain insight into the underlying molecular mechanisms, we created the Translational human pancreatic Islet Genotype tissue-Expression Resource (TIGER), aggregating >500 human islet RNA-seq and genotyping datasets. We imputed genotypes using 4 reference panels and meta-analyzed cohorts to improve coverage of expression quantitative trait loci (eQTL) and developed a method to combine allele-specific expression across samples (cASE). We identified >1 million islet eQTLs (56% novel), of which 53 colocalize with T2D signals (60% novel). Among them, a low-frequency allele that reduces T2D risk by half increases CCND2 expression. We identified 8 novel cASE colocalizations, among which an SLC30A8 T2D associated variant. We make all the data available through the open-access TIGER portal (http://tiger.bsc.es), which represents a comprehensive human islet genomic data resource to elucidate how genetic variation affects islet function and translate this into therapeutic insight and precision medicine for T2D.

People living-with HIV (PLHIV) presented less richness and diversity in the intestinal microbiota than non-infected individuals. Immunopathogenesis of HIV-1 infection has been extensively studied, however the influence of microbiota in those parameters that are key in the pathogenesis are less well characterized. The aim of the current study was to characterize the faecal microbiota composition in a cohort of PLHIV and its relationship with the clinical, metabolic, nutritional, inflammation, bacterial translocation and immunological markers. Baseline faecal samples from seventy-six HIV infected individuals with undetectable viral load (uVL) from a prospective, randomized feeding trial (NCT03846206) were collected and processed. The gut microbiota was determined by sequencing the V3-V4 region of the 16S rRNA gene. After analysing alpha diversity, women presented less diversity (Fisher alpha, P

Objective:To explore motor praxis in adults with Prader–Willi syndrome (PWS) in comparison with a control group of people with intellectual disability (ID) and to examine the relationship with brain structural measurements.Method:Thirty adult participants with PWS and 132 with ID of nongenetic etiology (matched by age, sex, and ID level) were assessed using a comprehensive evaluation of the praxis function, which included pantomime of tool use, imitation of meaningful and meaningless gestures, motor sequencing, and constructional praxis.Results:Results support specific praxis difficulties in PWS, with worse performance in the imitation of motor actions and better performance in constructional praxis than ID peers. Compared with both control groups, PWS showed increased gray matter volume in sensorimotor and subcortical regions. However, we found no obvious association between these alterations and praxis performance. Instead, praxis scores correlated with regional volume measures in distributed apparently normal brain areas.Conclusions:Our findings are consistent in showing significant impairment in gesture imitation abilities in PWS and, otherwise, further indicate that the visuospatial praxis domain is relatively preserved. Praxis disability in PWS was not associated with a specific, focal alteration of brain anatomy. Altered imitation gestures could, therefore, be a consequence of widespread brain dysfunction. However, the specific contribution of key brain structures (e.g., areas containing mirror neurons) should be more finely tested in future research.

Genome-wide association studies (GWAS) are not fully comprehensive, as current strategies typically test only the additive model, exclude the X chromosome, and use only one reference panel for genotype imputation. We implement an extensive GWAS strategy, GUIDANCE, which improves genotype imputation by using multiple reference panels and includes the analysis of the X chromosome and non-additive models to test for association. We apply this methodology to 62,281 subjects across 22 age-related diseases and identify 94 genome-wide associated loci, including 26 previously unreported. Moreover, we observe that 27.7% of the 94 loci are missed if we use standard imputation strategies with a single reference panel, such as HRC, and only test the additive model. Among the new findings, we identify three novel low-frequency recessive variants with odds ratios larger than 4, which need at least a three-fold larger sample size to be detected under the additive model. This study highlights the benefits of applying innovative strategies to better uncover the genetic architecture of complex diseases., Most genome-wide association studies assume an additive model, exclude the X chromosome, and use one reference panel. Here, the authors implement a strategy including non-additive models and find that the number of loci for age-related traits increases as compared to the additive model alone.

The identification and characterisation of genomic changes (variants) that can lead to human diseases is one of the central aims of biomedical research. The generation of catalogues of genetic variants that have an impact on specific diseases is the basis of Personalised Medicine, where diagnoses and treatment protocols are selected according to each patient’s profile. In this context, the study of complex diseases, such as Type 2 diabetes or cardiovascular alterations, is fundamental. However, these diseases result from the combination of multiple genetic and environmental factors, which makes the discovery of causal variants particularly challenging at a statistical and computational level. Genome-Wide Association Studies (GWAS), which are based on the statistical analysis of genetic variant frequencies across non-diseased and diseased individuals, have been successful in finding genetic variants that are associated to specific diseases or phenotypic traits. But GWAS methodology is limited when considering important genetic aspects of the disease and has not yet resulted in meaningful translation to clinical practice. This review presents an outlook on the study of the link between genetics and complex phenotypes. We first present an overview of the past and current statistical methods used in the field. Next, we discuss current practices and their main limitations. Finally, we describe the open challenges that remain and that might benefit greatly from further mathematical developments. L.A. was supported by grant BES-2017-081635. This publication is part of R&D and Innovation grant BES-2017-081635 funded by MCIN and by “FSE Investing in your future”I.M. was supported by grant FJCI-2017-31878. This publication is part of R&D and Innovation grant FJCI-2017-31878 funded by MCIN. C.S. received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement H2020-MSCA-COFUND-2016-754433.

The Global Alliance for Genomics and Health (GA4GH) aims to accelerate biomedical advances by enabling the responsible sharing of clinical and genomic data through both harmonized data aggregation and federated approaches. The decreasing cost of genomic sequencing (along with other genome-wide molecular assays) and increasing evidence of its clinical utility will soon drive the generation of sequence data from tens of millions of humans, with increasing levels of diversity. In this perspective, we present the GA4GH strategies for addressing the major challenges of this data revolution. We describe the GA4GH organization, which is fueled by the development efforts of eight Work Streams and informed by the needs of 24 Driver Projects and other key stakeholders. We present the GA4GH suite of secure, interoperable technical standards and policy frameworks and review the current status of standards, their relevance to key domains of research and clinical care, and future plans of GA4GH. Broad international participation in building, adopting, and deploying GA4GH standards and frameworks will catalyze an unprecedented effort in data sharing that will be critical to advancing genomic medicine and ensuring that all populations can access its benefits., B.P.C. acknowledges funding from Abigail Wexner Research Institute at Nationwide Children’s Hospital; T.H. Nyrönen acknowledges funding from Academy of Finland grant #31996; A.M.-J., K.N., T.F.B., O.M.H., and Z.S. acknowledge funding from Australian Medical Research Future Fund; M.S. acknowledges funding from Biobank Japan; D. Bujold and S.J.M.J. acknowledge funding from Canada Foundation for Innovation; L.J.D. acknowledges funding from Canada Foundation for Innovation Cyber Infrastructure grant #34860; D. Bujold and G.B. acknowledge funding from CANARIE; L.J.D. acknowledges funding from CANARIE Research Data Management contract #RDM-090 (CHORD) and #RDM2-053 (ClinDIG); K.K.-L. acknowledges funding from CanSHARE; T.L.T. acknowledges funding from Chan Zuckerberg Initiative; T. Burdett acknowledges funding from Chan Zuckerberg Initiative grant #2017-171671; D. Bujold, G.B., and L.D.S. acknowledge funding from CIHR; L.J.D. acknowledges funding from CIHR grant #404896; M.J.S.B. acknowledges funding from CIHR grant #SBD-163124; M. Courtot and M. Linden acknowledge funding from CINECA project EU Horizon 2020 grant #825775; D. Bujold and G.B. acknowledge funding from Compute Canada; F.M.-G. acknowledges funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – NFDI 1/1 “GHGA – German Human Genome-Phenome Archive; R.M.H.-S. acknowledges funding from Duke-Margolis Center for Health Policy; S.B. and A.J.B. acknowledge funding from EJP-RD EU Horizon 2020 grant #825575; A. Niewielska, A.K., D.S., G.I.S., J.A.T., J.R., M.A.K., M. Baudis, M. Linden, S.B., S.S., T.H. Nyrönen, and T.M.K. acknowledge funding from ELIXIR; A. Niewielska acknowledges funding from EOSC-Life EU Horizon 2020 grant #824087; J.-P.H. acknowledges funding from ETH Domain Strategic Focal Area “Personalized Health and Related Technologies (PHRT)” grant #2017-201; F.M.-G. acknowledges funding from EUCANCan EU Horizon 2020 grant #825835; B.M.K., D. Bujold, G.B., L.D.S., M.J.S.B., N.S., S.E.W., and Y.J. acknowledge funding from Genome Canada; B.M.K., M.J.S.B., S.E.W., and Y.J. acknowledge funding from Genome Quebec; F.M.-G. acknowledges funding from German Human Genome-Phenome Archive; C. Voisin acknowledges funding from Google; A.J.B. acknowledges funding from Health Data Research UK Substantive Site Award; D.H. acknowledges funding from Howard Hughes Medical Institute; S.B. acknowledges funding from Instituto de Salud Carlos III; S.-S.K. and K.T. acknowledge funding from Japan Agency for Medical Research and Development (AMED); S. Ogishima acknowledges funding from Japan Agency for Medical Research and Development (AMED) grant #20kk0205014h0005; C.Y. and K. Kosaki acknowledge funding from Japan Agency for Medical Research and Development (AMED) grant #JP18kk0205012; GEM Japan acknowledges funding from Japan Agency for Medical Research and Development (AMED) grants #19kk0205014h0004, #20kk0205014h0005, #20kk0205013h0005, #20kk0205012h0005, #20km0405401h0003, and #19km0405001h0104; J.R. acknowledges funding from La Caixa Foundation under project #LCF/PR/GN13/50260009; R.R.F. acknowledges funding from Mayo Clinic Center for Individualized Medicine; Y.J. and S.E.W. acknowledge funding from Ministère de l’Économie et de l’Innovation du Québec for the Can-SHARE Connect Project; S.E.W. and S.O.M.D. acknowledge funding from Ministère de l’Économie et de l’Innovation du Québec for the Can-SHARE grant #141210; M.A.H., M.C.M.-T., J.O.J., H.E.P., and P.N.R. acknowledge funding from Monarch Initiative grant #R24OD011883 and Phenomics First NHGRI grant #1RM1HG010860; A.L.M. and E.B. acknowledge funding from MRC grant #MC_PC_19024; P.T. acknowledges funding from National University of Singapore and Agency for Science, Technology and Research; J.M.C. acknowledges funding from NHGRI; A.H.W. acknowledges funding from NHGRI awards K99HG010157, R00HG010157, and R35HG011949; A.M.-J., K.N., D.P.H., O.M.H., T.F.B., and Z.S. acknowledge funding from NHMRC grants #GNT1113531 and #GNT2000001; D.L.C. acknowledges funding from NHMRC Ideas grant #1188098; A.B.S. acknowledges funding from NHMRC Investigator Fellowship grant #APP177524; J.M.C. and L.D.S. acknowledge funding from NIH; A.A.P. acknowledges funding from NIH Anvil; A.V.S. acknowledges funding from NIH contract #HHSN268201800002I (TOPMed Informatics Research Center); S.U. acknowledges funding from NIH ENCODE grant #UM1HG009443; M.C.M.-T. and M.A.H. acknowledge funding from NIH grant #1U13CA221044; R.J.C. acknowledges funding from NIH grants #1U24HG010262 and #1U2COD023196; M.G. acknowledges funding from NIH grant #R00HG007940; J.B.A., S.L., P.G., E.B., H.L.R., and L.S. acknowledge funding from NIH grant #U24HG011025; K.P.E. acknowledges funding from NIH grant #U2C-RM-160010; J.A.E. acknowledges funding from NIH NCATS grant #U24TR002306; M.M. acknowledges funding from NIH NCI contract #HHSN261201400008c and ID/IQ Agreement #17X146 under contract #HHSN2612015000031 and #75N91019D00024; R.M.C.-D. acknowledges funding from NIH NCI grant #R01CA237118; M. Cline acknowledges funding from NIH NCI grant #U01CA242954; K.P.E. acknowledges funding from NIH NCI ITCR grant #1U24CA231877-01; O.L.G. acknowledges funding from NIH NCI ITCR grant #U24CA237719; R.L.G. acknowledges funding from NIH NCI task order #17X147F10 under contract #HHSN261200800001E; A.F.R. acknowledges funding from NIH NHGRI grant #RM1HG010461; N.M. and L.J.Z. acknowledge funding from NIH NHGRI grant #U24HG006941; R.R.F., T.H. Nelson, L.J.B., and H.L.R. acknowledge funding from NIH NHGRI grant #U41HG006834; B.J.W. acknowledges funding from NIH NHGRI grant #UM1HG009443A; M. Cline acknowledges funding from NIH NHLBI BioData Catalyst Fellowship grant #5118777; M.M. acknowledges funding from NIH NHLBI BioData Catalyst Program grant #1OT3HL142478-01; N.C.S. acknowledges funding from NIH NIGMS grant #R35-GM128636; M.C.M.-T., M.A.H., P.N.R., and R.R.F. acknowledge funding from NIH NLM contract #75N97019P00280; E.B. and A.L.M. acknowledge funding from NIHR; R.G. acknowledges funding from Project Ris3CAT VEIS; S.B. acknowledges funding from RD-Connect, Seventh Framework Program grant #305444; J.K. acknowledges funding from Robertson Foundation; S.B. and A.J.B. acknowledge funding from Solve-RD, EU Horizon 2020 grant #779257; T.S. and S. Oesterle acknowledge funding from Swiss Institute of Bioinformatics (SIB) and Swiss Personalized Health Network (SPHN), supported by the Swiss State Secretariat for Education, Research and Innovation SERI; S.J.M.J. acknowledges funding from Terry Fox Research Institute; A.E.H., M.P.B., M. Cupak, M.F., and J.F. acknowledge funding from the Digital Technology Supercluster; D.F.V. acknowledges funding from the Australian Medical Research Future Fund, as part of the Genomics Health Futures Mission grant #76749; M. Baudis acknowledges funding from the BioMedIT Network project of Swiss Institute of Bioinformatics (SIB) and Swiss Personalized Health Network (SPHN); B.M.K. acknowledges funding from the Canada Research Chair in Law and Medicine and CIHR grant #SBD-163124; D.S., G.I.S., M.A.K., S.B., S.S., and T.H. Nyrönen acknowledge funding from the EU Horizon 2020 Beyond 1 Million Genomes (B1MG) Project grant #951724; P.F., A.D.Y., F.C., H.S., I.U.L., D. Gupta, M. Courtot, S.E.H., T. Burdett, T.M.K., and S.F. acknowledge funding from the European Molecular Biology Laboratory; Y.J. and S.E.W. acknowledge funding from the Government of Canada; P.G. acknowledges funding from the Government of Canada through Genome Canada and the Ontario Genomics Institute (OGI-206); J.Z. acknowledges funding from the Government of Ontario; C.K.Y. acknowledges funding from the Government of Ontario, Canada Foundation for Innovation; C. Viner and M.M.H. acknowledge funding from the Natural Sciences and Engineering Research Council of Canada (grant #RGPIN-2015-03948 to M.M.H. and Alexander Graham Bell Canada Graduate Scholarship to C.V.); K.K.-L. acknowledges funding from the Program for Integrated Database of Clinical and Genomic Information; J.K. acknowledges funding from the Robertson Foundation; D.F.V. acknowledges funding from the Victorian State Government through the Operational Infrastructure Support (OIS) Program; A.M.L., R.N., and H.V.F. acknowledge funding from Wellcome (collaborative award); F.C., H.S., P.F., and S.E.H. acknowledge funding from Wellcome Trust grant #108749/Z/15/Z; A.D.Y., H.S., I.U.L., M. Courtot, H.E.P., P.F., and T.M.K. acknowledge funding from Wellcome Trust grant #201535/Z/16/Z; A.M., J.K.B., R.J.M., R.M.D., and T.M.K. acknowledge funding from Wellcome Trust grant #206194; E.B., P.F., P.G., and S.F. acknowledge funding from Wellcome Trust grant #220544/Z/20/Z; A. Hamosh acknowledges funding from NIH NHGRI grant U41HG006627 and U54HG006542; J.S.H. acknowledges funding from National Taiwan University #91F701-45C and #109T098-02; the work of K.W.R. was supported by the Intramural Research Program of the National Library of Medicine, NIH. For the purpose of open access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. H.V.F. acknowledges funding from Wellcome Grant 200990/A/16/Z ‘Designing, developing and delivering integrated foundations for genomic medicine'.