Identification and characterization of bovine regulator of telomere length elongation helicase gene (RTEL): molecular cloning, expression distribution, splice variants and DNA methylation profile

BACKGROUND: The genetic basis of telomere length heterogeneity among mammalian species is still not well understood. Recently, a gene named regulator of telomere length elongation helicase (RTEL) was identified and predicted to be an essential participant in species-specific telomere length regulation in two murine species. To obtain broader insights into its structure and biological functions and to ascertain whether RTEL is also a candidate gene in the regulation of telomere length diversity in other mammalian species, data from other mammals may be helpful. RESULTS: Here we report the cDNA cloning, genomic structure, chromosomal location, alternative splicing pattern, expression distribution and DNA methylation profile of the bovine homolog of RTEL. The longest transcript of bovine RTEL is 4440 nt, encompassing 24.8 kb of genomic sequence that was mapped to chromosome 13q2.2. It encodes a conserved helicase-like protein containing seven characterized helicase motifs in the first 750 aa and a PIP box in the C-terminus. Four splice variants were identified within the transcripts in both the coding and 5'-untranslated regions; Western blot revealed that the most abundant splice variant SV-1 was translated to a truncated isoform of RTEL. The different 5'UTRs imply alternative transcription start sites in the promoter; Bovine RTEL was transcribed at the blastocyst stage, and expression levels were highest in adult testis, liver and ovary. DNA methylation analysis of tissues that differed significantly in expression level indicated that relatively low DNA methylation is associated with higher expression. CONCLUSION: In this study, we have identified and characterized a bovine RTEL homolog and obtained basic information about it, including gene structure, expression distribution, splice variants and profile of DNA methylation around two putative transcription start sites. These data may be helpful for further comparative and functional analysis of RTEL in mammals

The Short Text Matching Model Enhanced with Knowledge via Contrastive Learning

In recent years, short Text Matching tasks have been widely applied in the fields ofadvertising search and recommendation. The difficulty lies in the lack of semantic information and word ambiguity caused by the short length of the text. Previous works have introduced complement sentences or knowledge bases to provide additional feature information. However, these methods have not fully interacted between the original sentence and the complement sentence, and have not considered the noise issue that may arise from the introduction of external knowledge bases. Therefore, this paper proposes a short Text Matching model that combines contrastive learning and external knowledge. The model uses a generative model to generate corresponding complement sentences and uses the contrastive learning method to guide the model to obtain more semantically meaningful encoding of the original sentence. In addition, to avoid noise, we use keywords as the main semantics of the original sentence to retrieve corresponding knowledge words in the knowledge base, and construct a knowledge graph. The graph encoding model is used to integrate the knowledge base information into the model. Our designed model achieves state-of-the-art performance on two publicly available Chinese Text Matching datasets, demonstrating the effectiveness of our model.Comment: 11 pages,2 figure

Analysis of the expression pattern of the BCL11B gene and its relatives in patients with T-cell acute lymphoblastic leukemia

<p>Abstract</p> <p>Background</p> <p>In a human T-cell acute lymphoblastic leukemia (T-ALL) cell line (Molt-4), siRNA-mediated suppression of <it>BCL11B </it>expression was shown to inhibit proliferation and induce apoptosis, functions which may be related to genes involved in apoptosis (such as <it>TNFSF10 </it>and <it>BCL2L1</it>) and TGF-β pathways (such as <it>SPP1</it>and <it>CREBBP</it>).</p> <p>Methods</p> <p>The expression levels of the above mentioned genes and their correlation with the <it>BCL11B </it>gene were analyzed in patients with T-ALL using the TaqMan and SYBR Green I real-time polymerase chain reaction technique.</p> <p>Results</p> <p>Expression levels of <it>BCL11B, BCL2L1</it>, and <it>CREBBP </it>mRNA in T-ALL patients were significantly higher than those from healthy controls (<it>P <</it>0.05). In T-ALL patients, the <it>BCL11B </it>expression level was negatively correlated with the <it>BCL2L1 </it>expression level (<it>r</it>_s= -0.700; <it>P </it><it><</it>0.05), and positively correlated with the <it>SPP1 </it>expression level (<it>r</it>_s= 0.683; <it>P </it><it><</it>0.05). In healthy controls, the <it>BCL11B </it>expression level did not correlate with the <it>TNFSF10</it>, <it>BCL2L1</it>, <it>SPP1</it>, or <it>CREBBP </it>expression levels.</p> <p>Conclusions</p> <p>Over-expression of <it>BCL11B </it>might play a role in anti-apoptosis in T-ALL cells through up-regulation of its downstream genes <it>BCL2L1 </it>and <it>CREBBP</it>.</p

Expression and distribution of PPP2R5C gene in leukemia

<p>Abstract</p> <p>Background</p> <p>Recently, we clarified at the molecular level novel chromosomal translocation t(14;14)(q11;q32) in a case of Sézary syndrome, which caused a rearrangement from TRAJ7 to the <it>PPP2R5C </it>gene. <it>PPP2R5C </it>is one of the regulatory B subunits of protein phosphatase 2A (PP2A). It plays a crucial role in cell proliferation, differentiation, and transformation. To characterize the expression and distribution of five different transcript variants of the <it>PPP2R5C </it>gene in leukemia, we analyzed the expression level of <it>PPP2R5C </it>in peripheral blood mononuclear cells from 77 patients with <it>de novo </it>leukemia, 26 patients with leukemia in complete remission (CR), and 20 healthy individuals by real-time PCR and identified the different variants of <it>PPP2R5C </it>by RT-PCR.</p> <p>Findings</p> <p>Significantly higher expression of <it>PPP2R5C </it>was found in AML, CML, T-ALL, and B-CLL groups in comparison with healthy controls. High expression of <it>PPP2R5C </it>was detected in the B-ALL group; however, no significant difference was found compared with the healthy group. The expression level of <it>PPP2R5C </it>in the CML-CR group decreased significantly compared with that in the <it>de novo </it>CML group and was not significantly different from the level in the healthy group. By using different primer pairs that covered different exons, five transcript variants of <it>PPP2R5C </it>could be identified. All variants could be detected in healthy samples as well as in all the leukemia samples, and similar frequencies and distributions of <it>PPP2R5C </it>were indicated.</p> <p>Conclusions</p> <p>Overexpression of <it>PPP2R5C </it>in T-cell malignancy as well as in myeloid leukemia cells might relate to its proliferation and differentiation. Investigation of the effect of target inhibition of this gene might be beneficial to further characterization of molecular mechanisms and targeted therapy in leukemia.</p

Imputation of Missing Photometric Data and Photometric Redshift Estimation for CSST

Accurate photometric redshift (photo-$z$) estimation requires support from multi-band observational data. However, in the actual process of astronomical observations and data processing, some sources may have missing observational data in certain bands for various reasons. This could greatly affect the accuracy and reliability of photo-$z$ estimation for these sources, and even render some estimation methods unusable. The same situation may exist for the upcoming Chinese Space Station Telescope (CSST). In this study, we employ a deep learning method called Generative Adversarial Imputation Networks (GAIN) to impute the missing photometric data in CSST, aiming to reduce the impact of data missing on photo-$z$ estimation and improve estimation accuracy. Our results demonstrate that using the GAIN technique can effectively fill in the missing photometric data in CSST. Particularly, when the data missing rate is below 30\%, the imputation of photometric data exhibits high accuracy, with higher accuracy in the $g$, $r$, $i$, $z$, and $y$ bands compared to the $NUV$ and $u$ bands. After filling in the missing values, the quality of photo-$z$ estimation obtained by the widely used Easy and Accurate Zphot from Yale (EAZY) software is notably enhanced. Evaluation metrics for assessing the quality of photo-$z$ estimation, including the catastrophic outlier fraction ($f_{out}$), the normalized median absolute deviation ($\rm {\sigma_{NMAD}}$), and the bias of photometric redshift ($bias$), all show some degree of improvement. Our research will help maximize the utilization of observational data and provide a new method for handling sample missing values for applications that require complete photometry data to produce results