H1 Citrullination – An Atypical Modification Regulating Germline Fate in Arabidopsis

In flowering plants, the precursor of the germline is a spore mother cell (SMC), formed in dedicated tissues of the flower’s sexual organs. Sporogenesis enables a somatic-toreproductive cell fate transition. This transition is accompanied by large-scale reorganisation of chromatin structure and composition in Arabidopsis thaliana. This event is initiated by the eviction of somatic linker histones (H1) which are important for genome compaction and genome function, by stabilizing nucleosome arrays and chromatin fiber folding, and by influencing transcription and epigenetic regulation, respectively. While H1 eviction is known to be crucial for pluripotency establishment in the mammalian germline, its role in plant sporogenesis remains unknown. In my PhD work, I investigated the role and mechanisms of H1 eviction in Arabidopsis. Notably, I asked whether H1 eviction could be regulated by citrullination as this is the case in the mouse germline. The work explored the role of a putative citrullination site on H1.1 and the contribution of a putative plant citrullinase, an agmatine iminohydrolase (AIH), on H1 eviction. This work generated tools for conditional inhibition of H1.1 eviction which allowed to investigate the role of H1 eviction in sporogenesis, meiosis, gametogenesis, and seed development. Moreover, attempts to generate nanobodies to validate H1 citrullination in plants are described. My work demonstrated that mutations at putative citrullination residues on H1 and the downregulation of AIH impaired H1 eviction in SMCs. Consequently, this led to an increase of heterochromatin fraction and altered levels of H3K27me3. These findings strongly support a model where H1 is citrullinated in SMCs, leading to its destabilization from chromatin and enabling downstream degradation. Additionally, this process seems dispensable for meiosis but critical for gametogenesis and subsequently plant fertility. Collectively, this work revealed a mechanism and significance of H1 eviction for plant reproduction

Investigation of Tumor Suppressing Function of CACNA2D3 in Esophageal Squamous Cell Carcinoma.

Background: Deletion of 3p is one of the most frequent genetic alterations in esophageal squamous cell carcinoma (ESCC), suggesting the existence of one or more tumor suppressor genes (TSGs) within these regions. In this study, one TSG, CACNA2D3 at 3p21.1, was characterized. Methods: Expression of CACNA2D3 in ESCCs was tested by quantitative real-time PCR and tissue microarray. The mechanism of CACNA2D3 downregulation was investigated by methylation-specific polymerase chain reaction (MS-PCR). The tumor suppressive function of CACNA2D3 was characterized by both in vitro and in vivo tumorigenic assays, cell migration and invasion assays. Results: CACNA2D3 was frequently downregulated in ESCCs (24/48, 50%), which was significantly associated with promoter methylation and allele loss (P<0.05). Tissue microarray result showed that downregulation of CACNA2D3 was detected in (127/224, 56.7%) ESCCs, which was significantly associated with lymph node metastasis (P = 0.01), TNM staging (P = 0.003) and poor outcome of ESCC patients (P<0.05). Functional studies demonstrated that CACNA2D3 could inhibit tumorigenicity, cell motility and induce apoptosis. Mechanism study found that CACNA2D3 could arrest cell cycle at G1/S checkpoint by increasing expressions of p21 and p53 and decreasing expression of CDK2. In addition, CACNA2D3 could upregulate intracellular free cytosolic Ca2+ and subsequently induce apoptosis. Conclusion: CACNA2D3 is a novel TSG responsible to the 3p21 deletion event and plays a critical suppressing role in the development and progression of ESCC. © 2013 Li et al.link_to_OA_fulltex

Genetic Variants in WNT2B and BTRC Predict Melanoma Survival

Cutaneous melanoma (CM) is the most lethal skin cancer. The Wnt pathway has an impact on development, invasion and metastasis of CM, thus likely affecting CM prognosis. Using data from a published genome-wide association study (GWAS) from The University of Texas M.D. Anderson Cancer Center, we assessed the associations of 19,830 common single-nucleotide polymorphisms (SNPs) in 151 Wnt pathway autosomal genes with CM-specific survival (CMSS) and then validated significant SNPs in another GWAS from Harvard University. In the single-locus analysis, 1,855 SNPs were significantly associated with CMSS at P T and BTRC rs61873997 G>A) that showed a predictive role in CMSS, with an effect-allele-attributed hazards ratio [adjHR of 1.99 (95% confidence interval (CI) = 1.41-2.81, P = 8.10E-05) and 0.61 (0.46-0.80, 3.12E-04), respectively]. Collectively, these variants in the Wnt pathway genes may be biomarkers for outcomes of CM patients, if validated by larger studies

Genetic variants in the metzincin metallopeptidase family genes predict melanoma survival

Metzincins are key molecules in the degradation of the extracellular matrix and play an important role in cellular processes such as cell migration, adhesion, and cell fusion of malignant tumors, including cutaneous melanoma (CM). We hypothesized that genetic variants of the metzincin metallopeptidase family genes would be associated with CM-specific survival (CMSS). To test this hypothesis, we first performed Cox proportional hazards regression analysis to evaluate the associations between genetic variants of 75 metzincin metallopeptidase family genes and CMSS using the dataset from the genome-wide association study (GWAS) from The University of Texas MD Anderson Cancer Center (MDACC) which included 858 non-Hispanic white patients with CM, and then validated using the dataset from the Harvard GWAS study which had 409 non-Hispanic white patients with invasive CM. Four independent SNPs (MMP16 rs10090371 C>A, ADAMTS3 rs788935 T>C, TLL2 rs10882807 T>C and MMP9 rs3918251 A>G) were identified as predictors of CMSS, with a variant-allele attributed hazards ratio (HR) of 1.73 (1.32-2.29, 9.68E-05), 1.46 (1.15-1.85, 0.002), 1.68 (1.31-2.14, 3.32E-05) and 0.67 (0.51-0.87, 0.003), respectively, in the meta-analysis of these two GWAS studies. Combined analysis of risk genotypes of these four SNPs revealed a decreased CMSS in a dose-response manner as the number of risk genotypes increased (Ptrend < 0.001). An improvement was observed in the prediction model (area under the curve [AUC] = 81.4% vs. 78.6%), when these risk genotypes were added to the model containing non-genotyping variables. Our findings suggest that these genetic variants may be promising prognostic biomarkers for CMSS

H-ensemble: An Information Theoretic Approach to Reliable Few-Shot Multi-Source-Free Transfer

Multi-source transfer learning is an effective solution to data scarcity by utilizing multiple source tasks for the learning of the target task. However, access to source data and model details is limited in the era of commercial models, giving rise to the setting of multi-source-free (MSF) transfer learning that aims to leverage source domain knowledge without such access. As a newly defined problem paradigm, MSF transfer learning remains largely underexplored and not clearly formulated. In this work, we adopt an information theoretic perspective on it and propose a framework named H-ensemble, which dynamically learns the optimal linear combination, or ensemble, of source models for the target task, using a generalization of maximal correlation regression. The ensemble weights are optimized by maximizing an information theoretic metric for transferability. Compared to previous works, H-ensemble is characterized by: 1) its adaptability to a novel and realistic MSF setting for few-shot target tasks, 2) theoretical reliability, 3) a lightweight structure easy to interpret and adapt. Our method is empirically validated by ablation studies, along with extensive comparative analysis with other task ensemble and transfer learning methods. We show that the H-ensemble can successfully learn the optimal task ensemble, as well as outperform prior arts.Comment: AAAI 202

Joint Coverage and Power Control in Highly Dynamic and Massive UAV Networks: An Aggregative Game-theoretic Learning Approach

Unmanned aerial vehicles (UAV) ad-hoc network is a significant contingency plan for communication after a natural disaster, such as typhoon and earthquake. To achieve efficient and rapid networks deployment, we employ noncooperative game theory and amended binary log-linear algorithm (BLLA) seeking for the Nash equilibrium which achieves the optimal network performance. We not only take channel overlap and power control into account but also consider coverage and the complexity of interference. However, extensive UAV game theoretical models show limitations in post-disaster scenarios which require large-scale UAV network deployments. Besides, the highly dynamic post-disaster scenarios cause strategies updating constraint and strategy-deciding error on UAV ad-hoc networks. To handle these problems, we employ aggregative game which could capture and cover those characteristics. Moreover, we propose a novel synchronous payoff-based binary log-linear learning algorithm (SPBLLA) to lessen information exchange and reduce time consumption. Ultimately, the experiments indicate that, under the same strategy-deciding error rate, SPBLLA's learning rate is manifestly faster than that of the revised BLLA. Hence, the new model and algorithm are more suitable and promising for large-scale highly dynamic scenarios

NIR Hyperspectral Imaging for Mapping of Moisture Content Distribution in Tea Buds during Dehydration

This work employed hyperspectral imaging technique to map the spatial distribution of moisture content (MC) in tea buds during dehydration. Hyperspectral images (874–1734 nm) of tea buds were acquired in six dehydrated periods (0, 3, 6, 9, 14 and 21 min) at 80°C. The spectral reflectance of tea buds were extracted from region of interests (ROIs) in the hyperspectral images. Competitive adaptive reweighted sampling (CARS) was used to select effective wavelengths (EWs) and ten representing the wavelengths were selected. The quantitative relationship between spectral reflectance and the measured MC values of tea buds was built using partial least square regression (PLSR) based on full spectra and EWs. The quantitative model established using EWs, which had a result of coefficient of correlation (RP) of 0.941 and root mean square error of prediction (RMSEP) of 5.31%, was considered as the optimal model for mapping MC distribution. The optimal model was finally applied to predict the MC of each pixel within of the tea bud sample and built the MC distribution maps by utilization of a developed image processing procedure. Results demonstrated that the hyperspectral imaging technique has the potential of mapping the MC spatial distribution in tea buds in dehydrated process