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Outward appearance and gross appearance of the reproductive organs of adult mice. 4.1N−/− 8-week-old male (A) and 6-week-old female (B) mice showed obvious growth retardation associated with decreased weight and size compared to 4.1N+/+ littermates. Bar graphs demonstrate that body weight of 4.1N-null mice was reduced compared with wild-type littermates both in males (A) and females (B) at 3–5 weeks (P < 0.001, n = 6) and 6–9 weeks (P < 0.001, n = 10). Macroscopic view of the testis and epididymis at 8 weeks of age from the control 4.1N+/+ littermates and 4.1N−/− mice showed that the male reproductive organs are dramatically smaller in the 4.1N−/− mice (A). Bottom panels are macroscopic views of the ovary and uterus at 20 weeks of age from the control 4.1N+/+ littermates (left) and 4.1N−/− mice showing these organs are much smaller in the 4.1N−/− mice.
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Protein 4.1N, a member of the protein 4.1 family, is highly expressed in the brain. But its function remains to be fully defined. Using 4.1N−/− mice, we explored the function of 4.1N in vivo. We show that 4.1N−/− mice were born at a significantly reduced Mendelian ratio and exhibited high mortality between 3 to 5 weeks of age. Live 4.1N−/− mice wer...
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Background:
The post-translational modifications of proteins control various physiological and pathological events in cells.
Objectives:
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Citations
Long non-coding RNAs (lncRNAs) play fundamental roles in cellular processes and pathologies, regulating gene expression at multiple levels. Despite being highly cell type-specific, their study at single-cell (sc) level has been challenging due to their less accurate annotation and low expression compared to protein-coding genes. To identify the important, albeit widely overlooked, highly specific lncRNAs from scRNA-seq data, here we develop a computational framework, ELATUS, based on the pseudoaligner Kallisto that enhances the detection of functional lncRNAs previously undetected and exhibits higher concordance with the ATAC-seq profiles in single-cell multiome data. Importantly, we then independently confirmed the expression patterns of cell type-specific lncRNAs exclusively detected with ELATUS and unveiled biologically important lncRNAs, such as AL121895.1, a previously undocumented cis-repressor lncRNA, whose role in proliferation of breast cancer cells was unnoticed by traditional methodologies. Our results emphasize the necessity for an alternative scRNA-seq workflow tailored to lncRNAs that sheds light on the multifaceted roles of lncRNAs.
Many different chicken breeds are found around the world, their features vary among them, and they are valuable resources. Currently, there is a huge lack of knowledge of the genetic determinants responsible for phenotypic and biochemical properties of these breeds of chickens. Understanding the underlying genetic mechanisms that explain across-breed variation can help breeders develop improved chicken breeds. The whole-genomes of 140 chickens from seven Shandong native breeds and 20 introduced recessive white chickens from China were re-sequenced. Comparative population genomics based on autosomal single nucleotide polymorphisms (SNPs) revealed geographically based clusters among the chickens. Through genome-wide scans for selective sweeps, we identified thyroid stimulating hormone receptor (TSHR, reproductive traits, circadian rhythm), erythrocyte membrane protein band 4.1 like 1 (EPB41L1, body size), and alkylglycerol monooxygenase (AGMO, aggressive behavior), as major candidate breed-specific determining genes in chickens. In addition, we used a machine learning classification model to predict chicken breeds based on the SNPs significantly associated with recourse characteristics, and the prediction accuracy was 92%, which can effectively achieve the breed identification of Laiwu Black chickens. We provide the first comprehensive genomic data of the Shandong indigenous chickens. Our analyses revealed phylogeographic patterns among the Shandong indigenous chickens and candidate genes that potentially contribute to breed-specific traits of the chickens. In addition, we developed a machine learning-based prediction model using SNP data to identify chicken breeds. The genetic basis of indigenous chicken breeds revealed in this study is useful to better understand the mechanisms underlying the resource characteristics of chicken.
Many different chicken breeds are found around the world, their features vary among them, and they are valuable resources. Currently, there is a huge lack of knowledge of the genetic determinants responsible for phenotypic and biochemical properties of these breeds of chickens. Understanding the underlying genetic mechanisms that explain across-breed variation can help breeders develop improved chicken breeds. The whole-genomes of 140 chickens from seven Shandong native breeds and 20 introduced recessive white chickens from China were re-sequenced. Comparative population genomics based on autosomal single nucleotide polymorphisms (SNPs) revealed geographically based clusters among the chickens. Through genome-wide scans for selective sweeps, we identified thyroid stimulating hormone receptor (TSHR, reproductive traits, circadian rhythm), erythrocyte membrane protein band 4.1 like 1 (EPB41L1,body size), and alkylglycerol monooxygenase (AGMO, aggressive behavior), as major candidate breed-specific determining genes in chickens. In addition, we used a machine learning classification model to predict chicken breeds based on the SNPs significantly associated with recourse characteristics, and the prediction accuracy was 92%, which can effectively achieve the breed identification of Laiwu Black chickens. We provide the first comprehensive genomic data of the Shandong indigenous chickens. Our analyses revealed phylogeographic patterns among the Shandong indigenous chickens and candidate genes that potentially contribute to breed-specific traits of the chickens. In addition, we developed a machine learning-based prediction model using SNP data to identify chicken breeds. The genetic basis of indigenous chicken breeds revealed in this study is useful to better understand the mechanisms underlying the resource characteristics of chicken.
Scaffolding protein 4.1N is a neuron-enriched 4.1 homologue. 4.1N contains three conserved domains, including the N-terminal 4.1-ezrin-radixin-moesin (FERM) domain, internal spectrin–actin–binding (SAB) domain, and C-terminal domain (CTD). Interspersed between the three domains are nonconserved domains, including U1, U2, and U3. The role of 4.1N was first reported in the nerve system. Then, extensive studies reported the role of 4.1N in cancers and other diseases. 4.1N performs numerous vital functions in signaling transduction by interacting, locating, supporting, and coordinating different partners and is involved in the molecular pathogenesis of various diseases. In this review, recent studies on the interactions between 4.1N and its contactors (including the α7AChr, IP3R1, GluR1/4, GluK1/2/3, mGluR8, KCC2, D2/3Rs, CASK, NuMA, PIKE, IP6K2, CAM 1/3, βII spectrin, flotillin-1, pp1, and 14-3-3) and the 4.1N-related biological functions in the nerve system and cancers are specifically and comprehensively discussed. This review provides critical detailed mechanistic insights into the role of 4.1N in disease relationships.
