Tooth colour of Hong Kong people - their satisfaction and preference

published_or_final_versio

Genome maps across 26 human populations reveal population-specific patterns of structural variation.

Large structural variants (SVs) in the human genome are difficult to detect and study by conventional sequencing technologies. With long-range genome analysis platforms, such as optical mapping, one can identify large SVs (>2 kb) across the genome in one experiment. Analyzing optical genome maps of 154 individuals from the 26 populations sequenced in the 1000 Genomes Project, we find that phylogenetic population patterns of large SVs are similar to those of single nucleotide variations in 86% of the human genome, while ~2% of the genome has high structural complexity. We are able to characterize SVs in many intractable regions of the genome, including segmental duplications and subtelomeric, pericentromeric, and acrocentric areas. In addition, we discover ~60 Mb of non-redundant genome content missing in the reference genome sequence assembly. Our results highlight the need for a comprehensive set of alternate haplotypes from different populations to represent SV patterns in the genome

Structural insight into SUMO chain recognition and manipulation by the ubiquitin ligase RNF4

The small ubiquitin-like modifier (SUMO) can form polymeric chains that are important signals in cellular processes such as meiosis, genome maintenance and stress response. The SUMO-targeted ubiquitin ligase RNF4 engages with SUMO chains on linked substrates and catalyses their ubiquitination, which targets substrates for proteasomal degradation. Here we use a segmental labelling approach combined with solution nuclear magnetic resonance (NMR) spectroscopy and biochemical characterization to reveal how RNF4 manipulates the conformation of the SUMO chain, thereby facilitating optimal delivery of the distal SUMO domain for ubiquitin transfer

The genome and sex-dependent responses to temperature in the common yellow butterfly, Eurema hecabe

This is the final version. Available on open access from BMC via the DOI in this recordAvailability of data and materials: The raw reads generated in this study have been deposited to the NCBI database under the BioProject accession PRJNA664668 [110]. The final chromosome assembly was submitted to NCBI Assembly under accession number JADANM000000000 in NCBI [111]. The genome annotation files were deposited in the Figshare [112].BACKGROUND: Lepidoptera (butterflies and moths) is one of the most geographically widespread insect orders in the world, and its species play important and diverse ecological and applied roles. Climate change is one of the biggest challenges to biodiversity this century, and lepidopterans are vulnerable to climate change. Temperature-dependent gene expression differences are of relevance under the ongoing climate crisis. However, little is known about how climate affects gene expression in lepidopterans and the ecological consequences of this, particularly with respect to genes with biased expression in one of the sexes. The common yellow butterfly, Eurema hecabe (Family Pieridae), is one of the most geographically widespread lepidopterans that can be found in Asia, Africa, and Australia. Nevertheless, what temperature-dependent effects there may be and whether the effects differ between the sexes remain largely unexplored. RESULTS: Here, we generated high-quality genomic resources for E. hecabe along with transcriptomes from eight developmental stages. Male and female butterflies were subjected to varying temperatures to assess sex-specific gene expression responses through mRNA and microRNA transcriptomics. We find that there are more temperature-dependent sex-biased genes in females than males, including genes that are involved in a range of biologically important functions, highlighting potential ecological impacts of increased temperatures. Further, by considering available butterfly data on sex-biased gene expression in a comparative genomic framework, we find that the pattern of sex-biased gene expression identified in E. hecabe is highly species-specific, rather than conserved across butterfly species, suggesting that sex-biased gene expression responses to climate change are complex in butterflies. CONCLUSIONS: Our study lays the foundation for further understanding of differential responses to environmental stress in a widespread lepidopteran model and demonstrates the potential complexity of sex-specific responses of lepidopterans to climate change.Hong Kong Research Grant Council Collaborative Research Fund CRFGeneral Research Fund GRFArea of ExcellenceChinese University of Hong KongBiotechnology and Biological Sciences Research Council (BBSRC

Impact of Splenectomy on Thrombocytopenia, Chemotherapy, and Survival in Patients with Unresectable Pancreatic Cancer

Patients with unresectable pancreatic cancer (PDAC) or endocrine tumors (PET) often develop splenic vein thrombosis, hypersplenism, and thrombocytopenia which limits the administration of chemotherapy. From 2001 to 2009, 15 patients with recurrent or unresectable PDAC or PET underwent splenectomy for hypersplenism and thrombocytopenia. The clinical variables of this group of patients were analyzed. The overall survival of patients with PDAC was compared to historical controls. Of the 15 total patients, 13 (87%) had PDAC and 2 (13%) had PET. All tumors were either locally advanced (n = 6, 40%) or metastatic (n = 9, 60%). The platelet counts significantly increased after splenectomy (p < 0.01). All patients were able to resume chemotherapy within a median of 11.5 days (range 6–27). The patients with PDAC had a median survival of 20 months (range 4–67) from the time of diagnosis and 10.6 months (range 0.6–39.8) from the time of splenectomy. Splenectomy for patients with unresectable PDAC or PET who developed hypersplenism and thrombocytopenia that limited the administration of chemotherapy, significantly increased platelet counts, and led to resumption of treatment in all patients. Patients with PDAC had better disease-specific survival as compared to historical controls

Genome-wide copy number variation study in anorectal malformations

Anorectal malformations (ARMs, congenital obstruction of the anal opening) are among the most common birth defects requiring surgical treatment (2-5/10 000 live-births) and carry significant chronic morbidity. ARMs present either as isolated or as part of the phenotypic spectrum of some chromosomal abnormalities or monogenic syndromes. The etiology is unknown. To assess the genetic contribution to ARMs, we investigated single-nucleotide polymorphisms and copy number variations (CNVs) at genome-wide scale. A total of 363 Han Chinese sporadic ARM patients and 4006 Han Chinese controls were included. Overall, we detected a 1.3-fold significant excess of rare CNVs in patients. Stratification of patients by presence/absence of other congenital anomalies showed that while syndromic ARM patients carried significantly longer rare duplications than controls (P = 0.049), non-syndromic patients were enriched with both rare deletions and duplications when compared with controls (P = 0.00031). Twelve chromosomal aberrations and 114 rare CNVs were observed in patients but not in 868 controls nor 11 943 healthy individuals from the Database of Genomic Variants. Importantly, these aberrations were observed in isolated ARM patients. Gene-based analysis revealed 79 genes interfered by CNVs in patients only. In particular, we identified a de novo DKK4 duplication. DKK4 is a member of the WNT signaling pathway which is involved in the development of the anorectal region. In mice, Wnt disruption results in ARMs. Our data suggest a role for rare CNVs not only in syndromic but also in isolated ARM patients and provide a list of plausible candidate genes for the disorder.postprin

Network Analysis of Differential Expression for the Identification of Disease-Causing Genes

Genetic studies (in particular linkage and association studies) identify chromosomal regions involved in a disease or phenotype of interest, but those regions often contain many candidate genes, only a few of which can be followed-up for biological validation. Recently, computational methods to identify (prioritize) the most promising candidates within a region have been proposed, but they are usually not applicable to cases where little is known about the phenotype (no or few confirmed disease genes, fragmentary understanding of the biological cascades involved). We seek to overcome this limitation by replacing knowledge about the biological process by experimental data on differential gene expression between affected and healthy individuals. Considering the problem from the perspective of a gene/protein network, we assess a candidate gene by considering the level of differential expression in its neighborhood under the assumption that strong candidates will tend to be surrounded by differentially expressed neighbors. We define a notion of soft neighborhood where each gene is given a contributing weight, which decreases with the distance from the candidate gene on the protein network. To account for multiple paths between genes, we define the distance using the Laplacian exponential diffusion kernel. We score candidates by aggregating the differential expression of neighbors weighted as a function of distance. Through a randomization procedure, we rank candidates by p-values. We illustrate our approach on four monogenic diseases and successfully prioritize the known disease causing genes

Investigating the validity of current network analysis on static conglomerate networks by protein network stratification

<p>Abstract</p> <p>Background</p> <p>A molecular network perspective forms the foundation of systems biology. A common practice in analyzing protein-protein interaction (PPI) networks is to perform network analysis on a conglomerate network that is an assembly of all available binary interactions in a given organism from diverse data sources. Recent studies on network dynamics suggested that this approach might have ignored the dynamic nature of context-dependent molecular systems.</p> <p>Results</p> <p>In this study, we employed a network stratification strategy to investigate the validity of the current network analysis on conglomerate PPI networks. Using the genome-scale tissue- and condition-specific proteomics data in <it>Arabidopsis thaliana</it>, we present here the first systematic investigation into this question. We stratified a conglomerate <it>A. thaliana </it>PPI network into three levels of context-dependent subnetworks. We then focused on three types of most commonly conducted network analyses, i.e., topological, functional and modular analyses, and compared the results from these network analyses on the conglomerate network and five stratified context-dependent subnetworks corresponding to specific tissues.</p> <p>Conclusions</p> <p>We found that the results based on the conglomerate PPI network are often significantly different from those of context-dependent subnetworks corresponding to specific tissues or conditions. This conclusion depends neither on relatively arbitrary cutoffs (such as those defining network hubs or bottlenecks), nor on specific network clustering algorithms for module extraction, nor on the possible high false positive rates of binary interactions in PPI networks. We also found that our conclusions are likely to be valid in human PPI networks. Furthermore, network stratification may help resolve many controversies in current research of systems biology.</p