Dick de Ridder

Insertional mutagenesis combined with acquired somatic mutations causes leukemogenesis following gene therapy of SCID-X1 patients

X-linked SCID (SCID-X1) is amenable to correction by gene therapy using conventional gammaretroviral vectors. Here, we describe the occurrence of clonal T cell acute lymphoblastic leukemia (T-ALL) promoted by insertional mutagenesis in a completed gene therapy trial of 10 SCID-X1 patients. Integration of the vector in an antisense orientation 35 kb upstream of the protooncogene LIM domain only 2 (LMO2) caused overexpression of LMO2 in the leukemic clone. However, leukemogenesis was likely precipitated by the acquisition of other genetic abnormalities unrelated to vector insertion, including a gain-of-function mutation in NOTCH1, deletion of the tumor suppressor gene locus cyclin-dependent kinase 2A (CDKN2A), and translocation of the TCR-beta region to the STIL-TAL1 locus. These findings highlight a general toxicity of endogenous gammaretroviral enhancer elements and also identify a combinatorial process during leukemic evolution that will be important for risk stratification and for future protocol design.

IMAGE PROCESSING WITH NEURAL NETWORKS–A REVIEW

Abstract We review more than 200 applications of neural networks in image processing and discuss the present and possible future role of neural networks, especially feed-forward neural networks, Kohonen feature maps and Hopfield neural networks. The various applications are categorised into a novel two-dimensional taxonomy for image processing algorithms. One dimension specifies the type of task performed by the algorithm: preprocessing, data reduction/feature extraction, segmentation, object recognition, image understanding and optimisation. The other dimension captures the abstraction level of the input data processed by the algorithm: pixel-level, local feature-level, structure-level, object-level, object-set-level and scene characterisation. Each of the six types of tasks poses specific constraints to a neural-based approach. These specific conditions are discussed in detail. A synthesis is made of unresolved problems related to the application of pattern recognition techniques in image processing and specifically to the application of neural networks. Finally, we present an outlook into the future application of neural networks and relate them to novel developments.

PanTools: representation, storage and exploration of pan-genomic data.

MOTIVATION Next-generation sequencing technology is generating a wealth of highly similar genome sequences for many species, paving the way for a transition from single-genome to pan-genome analyses. Accordingly, genomics research is going to switch from reference-centric to pan-genomic approaches. We define the pan-genome as a comprehensive representation of multiple annotated genomes, facilitating analyses on the similarity and divergence of the constituent genomes at the nucleotide, gene and genome structure level. Current pan-genomic approaches do not thoroughly address scalability, functionality and usability. RESULTS We introduce a generalized De Bruijn graph as a pan-genome representation, as well as an online algorithm to construct it. This representation is stored in a Neo4j graph database, which makes our approach scalable to large eukaryotic genomes. Besides the construction algorithm, our software package, called PanTools, currently provides functionality for annotating pan-genomes, adding sequences, grouping genes, retrieving gene sequences or genomic regions, reconstructing genomes and comparing and querying pan-genomes. We demonstrate the performance of the tool using datasets of 62 E. coli genomes, 93 yeast genomes and 19 Arabidopsis thaliana genomes. AVAILABILITY AND IMPLEMENTATION The Java implementation of PanTools is publicly available at http://www.bif.wur.nl CONTACT sandra.smit@wur.nl.

New insights on human T cell development by quantitative T cell receptor gene rearrangement studies and gene expression profiling

To gain more insight into initiation and regulation of T cell receptor (TCR) gene rearrangement during human T cell development, we analyzed TCR gene rearrangements by quantitative PCR analysis in nine consecutive T cell developmental stages, including CD34+ lin− cord blood cells as a reference. The same stages were used for gene expression profiling using DNA microarrays. We show that TCR loci rearrange in a highly ordered way (TCRD-TCRG-TCRB-TCRA) and that the initiating Dδ2-Dδ3 rearrangement occurs at the most immature CD34+CD38−CD1a− stage. TCRB rearrangement starts at the CD34+CD38+CD1a− stage and complete in-frame TCRB rearrangements were first detected in the immature single positive stage. TCRB rearrangement data together with the PTCRA (pTα) expression pattern show that human TCRβ-selection occurs at the CD34+CD38+CD1a+ stage. By combining the TCR rearrangement data with gene expression data, we identified candidate factors for the initiation/regulation of TCR recombination. Our data demonstrate that a number of key events occur earlier than assumed previously; therefore, human T cell development is much more similar to murine T cell development than reported before.

A discriminating messenger RNA signature for bipolar disorder formed by an aberrant expression of inflammatory genes in monocytes.

CONTEXT Mood disturbances are associated with an activated inflammatory response system. OBJECTIVE To identify a discriminating and coherent expression pattern of proinflammatory genes in monocytes of patients with bipolar disorder. DESIGN A quantitative polymerase chain reaction (Q-PCR) case-control gene expression study on purified monocytes of bipolar patients, the offspring of bipolar patients, and healthy control participants after having selected 22 discriminating inflammatory genes using whole genome analyses. SETTING Academic research setting in The Netherlands. PATIENTS Forty-two bipolar patients with 25 healthy controls, 54 offspring of a bipolar parent (13 had a mood disorder and 3 developed one during follow-up), and 70 healthy children underwent Q-PCR. MAIN OUTCOME MEASURE Inflammatory gene expression levels in monocytes. RESULTS We detected in the monocytes of bipolar patients a coherent mutually correlating set (signature) of 19 aberrantly expressed (P < .01) messenger RNAs of inflammatory (PDE4B, IL1B, IL6, TNF, TNFAIP3, PTGS2, and PTX3), trafficking (CCL2, CCL7, CCL20, CXCL2, CCR2, and CDC42), survival (BCL2A1 and EMP1), and mitogen-activated protein kinase pathway (MAPK6, DUSP2, NAB2, and ATF3) genes. Twenty-three of 42 bipolar patients (55%) had a positive signature test result vs 7 of 38 healthy controls (18%) (positive test result: positivity for PDE4B, ie, a messenger RNA 1 SD higher than the mean level found in healthy controls, plus 25% of the other genes with similar positive findings). Positive signature test results were also present in 11 of 13 offspring with a mood disorder (85%), 3 of 3 offspring developing a mood disorder (100%), and 17 of 38 euthymic offspring (45%) vs 13 of 70 healthy children (19%). Lithium carbonate and antipsychotic treatment downregulated the gene expression of most inflammatory genes. CONCLUSIONS The monocytes of a large proportion of bipolar patients and offspring of bipolar parents showed an inflammatory gene expression signature. This coherent set of genes opens new avenues for biomarker development with possibilities for disease prediction in individuals genetically at risk and for the subclassification of bipolar patients who could possibly benefit from anti-inflammatory treatment.

Shifts in growth strategies reflect tradeoffs in cellular economics.

The growth rate‐dependent regulation of cell size, ribosomal content, and metabolic efficiency follows a common pattern in unicellular organisms: with increasing growth rates, cell size and ribosomal content increase and a shift to energetically inefficient metabolism takes place. The latter two phenomena are also observed in fast growing tumour cells and cell lines. These patterns suggest a fundamental principle of design. In biology such designs can often be understood as the result of the optimization of fitness. Here we show that in basic models of self‐replicating systems these patterns are the consequence of maximizing the growth rate. Whereas most models of cellular growth consider a part of physiology, for instance only metabolism, the approach presented here integrates several subsystems to a complete self‐replicating system. Such models can yield fundamentally different optimal strategies. In particular, it is shown how the shift in metabolic efficiency originates from a tradeoff between investments in enzyme synthesis and metabolic yields for alternative catabolic pathways. The models elucidate how the optimization of growth by natural selection shapes growth strategies.

Vector integration is nonrandom and clustered and influences the fate of lymphopoiesis in SCID-X1 gene therapy

Recent reports have challenged the notion that retroviruses and retroviral vectors integrate randomly into the host genome. These reports pointed to a strong bias toward integration in and near gene coding regions and, for gammaretroviral vectors, around transcription start sites. Here, we report the results obtained from a large-scale mapping of 572 retroviral integration sites (RISs) isolated from cells of 9 patients with X-linked SCID (SCID-X1) treated with a retrovirus-based gene therapy protocol. Our data showed that two-thirds of insertions occurred in or very near to genes, of which more than half were highly expressed in CD34(+) progenitor cells. Strikingly, one-fourth of all integrations were clustered as common integration sites (CISs). The highly significant incidence of CISs in circulating T cells and the nature of their locations indicate that insertion in many gene loci has an influence on cell engraftment, survival, and proliferation. Beyond the observed cases of insertional mutagenesis in 3 patients, these data help to elucidate the relationship between vector insertion and long-term in vivo selection of transduced cells in human patients with SCID-X1.

Supervised locally linear embedding

Locally linear embedding (LLE) is a recently proposed method for unsupervised nonlinear dimensionality reduction. It has a number of attractive features: it does not require an iterative algorithm, and just a few parameters need to be set. Two extensions of LLE to supervised feature extraction were independently proposed by the authors of this paper. Here, both methods are unified in a common framework and applied to a number of benchmark data sets. Results show that they perform very well on high-dimensional data which exhibits a manifold structure.

Gammaretrovirus-mediated correction of SCID-X1 is associated with skewed vector integration site distribution in vivo

We treated 10 children with X-linked SCID (SCID-X1) using gammaretrovirus-mediated gene transfer. Those with sufficient follow-up were found to have recovered substantial immunity in the absence of any serious adverse events up to 5 years after treatment. To determine the influence of vector integration on lymphoid reconstitution, we compared retroviral integration sites (RISs) from peripheral blood CD3(+) T lymphocytes of 5 patients taken between 9 and 30 months after transplantation with transduced CD34(+) progenitor cells derived from 1 further patient and 1 healthy donor. Integration occurred preferentially in gene regions on either side of transcription start sites, was clustered, and correlated with the expression level in CD34(+) progenitors during transduction. In contrast to those in CD34(+) cells, RISs recovered from engrafted CD3(+) T cells were significantly overrepresented within or near genes encoding proteins with kinase or transferase activity or involved in phosphorus metabolism. Although gross patterns of gene expression were unchanged in transduced cells, the divergence of RIS target frequency between transduced progenitor cells and post-thymic T lymphocytes indicates that vector integration influences cell survival, engraftment, or proliferation.

De novo sequencing, assembly and analysis of the genome of the laboratory strain Saccharomyces cerevisiae CEN.PK113-7D, a model for modern industrial biotechnology.

Saccharomyces cerevisiae CEN.PK 113-7D is widely used for metabolic engineering and systems biology research in industry and academia. We sequenced, assembled, annotated and analyzed its genome. Single-nucleotide variations (SNV), insertions/deletions (indels) and differences in genome organization compared to the reference strain S. cerevisiae S288C were analyzed. In addition to a few large deletions and duplications, nearly 3000 indels were identified in the CEN.PK113-7D genome relative to S288C. These differences were overrepresented in genes whose functions are related to transcriptional regulation and chromatin remodelling. Some of these variations were caused by unstable tandem repeats, suggesting an innate evolvability of the corresponding genes. Besides a previously characterized mutation in adenylate cyclase, the CEN.PK113-7D genome sequence revealed a significant enrichment of non-synonymous mutations in genes encoding for components of the cAMP signalling pathway. Some phenotypic characteristics of the CEN.PK113-7D strains were explained by the presence of additional specific metabolic genes relative to S288C. In particular, the presence of the BIO1 and BIO6 genes correlated with a biotin prototrophy of CEN.PK113-7D. Furthermore, the copy number, chromosomal location and sequences of the MAL loci were resolved. The assembled sequence reveals that CEN.PK113-7D has a mosaic genome that combines characteristics of laboratory strains and wild-industrial strains.