The genetic basis of psychological traits in infancy: Implications for understanding the causes of developmental psychopathology

This chapter reviews genetic studies that have aimed to identify genes influencing psychological traits in infancy (from birth to age 12 months), and considers how this research informs us about the causes of developmental psychopathology. Specifically, this chapter systematically reviews findings from studies that associated common genetic variants with individual variation in infants’ attention, temperament and behaviour, and attachment disorganisation. DRD4 and 5-HTTLPR genes were the most frequently studied candidate genes. Possibly the most coherent set of results relates to the L-DRD4 genotype, which is significantly associated with infant attention, temperament, and attachment style. Research in infant genetics has been strengthened by a careful focus on uniform age ranges within studies, by several longitudinal studies, and by exploration of gene-environment interactions between genes and maternal characteristics. However there is also considerable inconsistency in results in this field and possible reasons for this are discussed. The chapter outlines the main genetic methods that have been used and what new genetic approaches such as polygenic risk scoring could offer infant genetics. Recent findings suggest that some traits during infancy predict individual differences in developmental psychopathology in childhood. It is argued that infant genetic research has considerable potential for the identification of populations at risk for psychopathology in later life, and this remains an area open for future research.

CTCF modulates Estrogen Receptor function through specific chromatin and nuclear matrix interactions

Enhancer regions and transcription start sites of estrogen-target regulated genes are connected by means of Estrogen Receptor long-range chromatin interactions. Yet, the complete molecular mechanisms controlling the transcriptional output of engaged enhancers and subsequent activation of coding genes remain elusive. Here, we report that CTCF binding to enhancer RNAs is enriched when breast cancer cells are stimulated with estrogen. CTCF binding to enhancer regions results in modulation of estrogen-induced gene transcription by preventing Estrogen Receptor chromatin binding and by hindering the formation of additional enhancer-promoter ER looping. Furthermore, the depletion of CTCF facilitates the expression of target genes associated with cell division and increases the rate of breast cancer cell proliferation. We have also uncovered a genomic network connecting loci enriched in cell cycle regulator genes to nuclear lamina that mediates the CTCF function. The nuclear lamina and chromatin interactions are regulated by estrogen-ER. We have observed that the chromatin loops formed when cells are treated with estrogen establish contacts with the nuclear lamina. Once there, the portion of CTCF associated with the nuclear lamina interacts with enhancer regions, limiting the formation of ER loops and the induction of genes present in the loop. Collectively, our results reveal an important, unanticipated interplay between CTCF and nuclear lamina to control the transcription of ER target genes, which has great implications in the rate of growth of breast cancer cells.

An Immunogenetic Signature of Ongoing Antigen Interactions in Splenic Marginal Zone Lymphoma Expressing IGHV1-2*04 Receptors.

Purpose: Prompted by the extensive biases in the immunoglobulin (IG) gene repertoire of splenic marginal-zone lymphoma (SMZL), supporting antigen selection in SMZL ontogeny, we sought to investigate whether antigen involvement is also relevant post-transformation.

Experimental Design: We conducted a large-scale subcloning study of the IG rearrangements of 40 SMZL cases aimed at assessing intraclonal diversification (ID) due to ongoing somatic hypermutation (SHM).

Results: ID was identified in 17 of 21 (81%) rearrangements using the immunoglobulin heavy variable (IGHV)1-2*04 gene versus 8 of 19 (40%) rearrangements utilizing other IGHV genes (P= 0.001). ID was also evident in most analyzed IG light chain gene rearrangements, albeit was more limited compared with IG heavy chains. Identical sequence changes were shared by subclones from different patients utilizing the IGHV1-2*04 gene, confirming restricted ongoing SHM profiles. Non-IGHV1-2*04 cases displayed both a lower number of ongoing SHMs and a lack of shared mutations (per group of cases utilizing the same IGHV gene).

Conclusions: These findings support ongoing antigen involvement in a sizable portion of SMZL and further argue that IGHV1-2*04 SMZL may represent a distinct molecular subtype of the disease.

Comparison of module detection algorithms in protein networks and investigation of the biological meaning of predicted modules

Background
It is generally acknowledged that a functional understanding of a biological system can only be obtained by an understanding of the collective of molecular interactions in form of biological networks. Protein networks are one particular network type of special importance, because proteins form the functional base units of every biological cell. On a mesoscopic level of protein networks, modules are of significant importance because these building blocks may be the next elementary functional level above individual proteins allowing to gain insight into fundamental organizational principles of biological cells.
Results
In this paper, we provide a comparative analysis of five popular and four novel module detection algorithms. We study these module prediction methods for simulated benchmark networks as well as 10 biological protein interaction networks (PINs). A particular focus of our analysis is placed on the biological meaning of the predicted modules by utilizing the Gene Ontology (GO) database as gold standard for the definition of biological processes. Furthermore, we investigate the robustness of the results by perturbing the PINs simulating in this way our incomplete knowledge of protein networks.
Conclusions
Overall, our study reveals that there is a large heterogeneity among the different module prediction algorithms if one zooms-in the biological level of biological processes in the form of GO terms and all methods are severely affected by a slight perturbation of the networks. However, we also find pathways that are enriched in multiple modules, which could provide important information about the hierarchical organization of the system