Unraveling the Genetic Etiology of Adult Antisocial Behavior: A Genome-Wide Association Study

Crime poses a major burden for society. The heterogeneous nature of criminal behavior makes it difficult to unravel its causes. Relatively little research has been conducted on the genetic influences of criminal behavior. The few twin and adoption studies that have been undertaken suggest that about half of the variance in antisocial behavior can be explained by genetic factors. In order to identify the specific common genetic variants underlying this behavior, we conduct the first genome-wide association study (GWAS) on adult antisocial behavior. Our sample comprised a community sample of 4816 individuals who had completed a self-report questionnaire. No genetic polymorphisms reached genome-wide significance for association with adult antisocial behavior. In addition, none of the traditional candidate genes can be confirmed in our study. While not genome-wide significant, the gene with the strongest association (p-value = 8.7×10⁻⁵) was DYRK1A, a gene previously related to abnormal brain development and mental retardation. Future studies should use larger, more homogeneous samples to disentangle the etiology of antisocial behavior. Biosocial criminological research allows a more empirically grounded understanding of criminal behavior, which could ultimately inform and improve current treatment strategies.     

Genomic regions associated with microdeletion/microduplication syndromes exhibit extreme diversity of structural variation

Segmental duplications (SDs) are a class of long, repetitive DNA elements whose paralogs share a high level of sequence similarity with each other. SDs mediate chromosomal rearrangements that lead to structural variation in the general population as well as genomic disorders associated with multiple congenital anomalies, including the 7q11.23 (Williams–Beuren Syndrome, WBS), 15q13.3, and 16p12.2 microdeletion syndromes. Population-level characterization of SDs has generally been lacking because most techniques used for analyzing these complex regions are both labor and cost intensive. In this study, we have used a high-throughput technique to genotype complex structural variation with a single molecule, long-range optical mapping approach. We characterized SDs and identified novel structural variants (SVs) at 7q11.23, 15q13.3, and 16p12.2 using optical mapping data from 154 phenotypically normal individuals from 26 populations comprising five super-populations. We detected several novel SVs for each locus, some of which had significantly different prevalence between populations. Additionally, we localized the microdeletion breakpoints to specific paralogous duplicons located within complex SDs in two patients with WBS, one patient with 15q13.3, and one patient with 16p12.2 microdeletion syndromes. The population-level data presented here highlights the extreme diversity of large and complex SVs within SD-containing regions. The approach we outline will greatly facilitate the investigation of the role of inter-SD structural variation as a driver of chromosomal rearrangements and genomic disorders.     

DNA methylation profiles provide a viable index for porcine pluripotent stem cells

Porcine induced pluripotent stem cells (iPSCs) provide useful information for translational research. The quality of iPSCs can be assessed by their ability to differentiate into various cell types after chimera formation. However, analysis of chimera formation in pigs is a labor‐intensive and costly process, necessitating a simple evaluation method for porcine iPSCs. Our previous study identified mouse embryonic stem cell (ESC)‐specific hypomethylated loci (EShypo‐T‐DMRs), and, in this study, 36 genes selected from these were used to evaluate porcine iPSC lines. Based on the methylation profiles of the 36 genes, the iPSC line, Porco Rosso‐4, was found closest to mouse pluripotent stem cells among 5 porcine iPSCs. Moreover, Porco Rosso‐4 more efficiently contributed to the inner cell mass (ICM) of blastocysts than the iPSC line showing the lowest reprogramming of the 36 genes (Porco Rosso‐622‐14), indicating that the DNA methylation profile correlates with efficiency of ICM contribution. Furthermore, factors known to enhance iPSC quality (serum‐free medium with PD0325901 and CHIR99021) improved the methylation status at the 36 genes. Thus, the DNA methylation profile of these 36 genes is a viable index for evaluation of porcine iPSCs. genesis 51:763–776. © 2013 Wiley Periodicals, Inc.     

Quantitative Proteomic Analysis in Metastatic Renal Cell Carcinoma Reveals a Unique Set of Proteins with Potential Prognostic Significance

Metastatic renal cell carcinoma (RCC) is one of the most treatment-resistant malignancies, and patients have a dismal prognosis, with a <10% five-year survival rate. The identification of markers that can predict the potential for metastases will have a great effect in improving patient outcomes. In this study, we used differential proteomics with isobaric tags for relative and absolute quantitation (iTRAQ) labeling and LC-MS/MS analysis to identify proteins that are differentially expressed in metastatic and primary RCC. We identified 1256 non-redundant proteins, and 456 of these were quantified. Further analysis identified 29 proteins that were differentially expressed (12 overexpressed and 17 underexpressed) in metastatic and primary RCC. Dysregulated protein expressions of profilin-1 (Pfn1), 14–3-3 zeta/delta (14–3-3ζ), and galectin-1 (Gal-1) were verified on two independent sets of tissues by means of Western blot and immunohistochemical analysis. Hierarchical clustering analysis showed that the protein expression profile specific for metastatic RCC can distinguish between aggressive and non-aggressive RCC. Pathway analysis showed that dysregulated proteins are involved in cellular processes related to tumor progression and metastasis. Furthermore, preliminary analysis using a small set of tumors showed that increased expression of Pfn1 is associated with poor outcome and is a potential prognostic marker in RCC. In addition, 14–3-3ζ and Gal-1 also showed higher expression in tumors with poor prognosis than in those with good prognosis. Dysregulated proteins in metastatic RCC represent potential prognostic markers for kidney cancer patients, and a greater understanding of their involved biological pathways can serve as the foundation of the development of novel targeted therapies for metastatic RCC.Renal cell carcinoma (RCC)¹ is the most common neoplasm of the adult kidney. Worldwide incidence and mortality rates of RCC are rising each decade (1). Seventy-five percent of kidney tumors are of the clear cell (ccRCC) subtype (2). Although modern imaging techniques for abdominal screening have led to increased incidental detection of renal tumors (3), unfortunately ∼25% to 30% of patients still have metastases at presentation.The prognosis with RCC is quite variable. The greatest risk of recurrence following nephrectomy is within the first 3 to 5 years (4). The ability to predict which tumors will metastasize would have a significant effect on patient outcomes, because the likelihood of a favorable response to treatment is greater when the metastatic burden is limited, and surgical resection of a single or limited number of metastases can result in longer survival (5). Furthermore, ∼3% of patients will develop a second primary renal tumor, either synchronous or metachronous. Currently, patient prognosis is assessed based on histological parameters and a multivariate analysis developed at Memorial Sloan Kettering (6), but neither is sufficiently accurate. A more accurate assessment of prognosis is urgently needed to better guide patient management.Although surgery can be curative for localized disease, many patients eventually relapse. Metastatic RCC is one of the most treatment-resistant malignancies, with chemotherapy and radiotherapy having limited effect. The five-year survival rate for metastatic RCC is ≤10% (7). Although there has been much progress in RCC treatment with the new era of antiangiogenic therapy, the majority of patients ultimately suffer a relapse and die from progression of the cancer. A more in-depth understanding of the pathogenesis of metastasis will be a cornerstone in the development of new targeted therapies. A number of prognostic markers have previously been identified based on comparative analysis of primary and metastatic tumors, including C-reactive protein, tetraspanin 7, hypoxia-inducible factor 1 α, phos-S6, U3 small nucleolar ribonucleoprotein protein, carbonic anhydrase IX, and microvascular density (8–14). However, no biomarker has yet had an established clinical role independent of stage (15). Differential protein expression between primary RCC and normal tissues was previously studied (16–18). Also, differential expression between primary and metastatic kidney disease has been investigated at the microRNA level (19, 20). Molecular analyses hold the promise of providing a better understanding of the pathogenesis of kidney cancer (21).In this study, we aimed to elucidate the pathogenesis of RCC metastasis through proteomic analysis and to identify potential prognostic markers for kidney cancer. We performed quantitative proteomic analysis using isobaric tags for relative and absolute quantitation (iTRAQ) labeling and LC-MS/MS to identify proteins that were dysregulated in metastatic RCC relative to primary RCC. Differential expressions of selected biologically interesting proteins—profilin-1 (Pfn1), 14–3-3 zeta/delta (14–3-3ζ), and galectin-1 (Gal-1)—were validated on two independent sets of tumors by means of western blot (WB) analysis and immunohistochemistry (IHC). Hierarchical clustering analysis showed that differential protein expression can distinguish between aggressive and non-aggressive tumors. In order to explore the role of these dysregulated proteins in tumor progression, we performed Gene Ontology (GO) and pathway analyses. In addition, we carried out a preliminary analysis to assess the potential of Pfn1, 14–3-3ζ, and Gal-1 as prognostic markers in RCC.