Cardiomyocyte hypertrophy plays a crucial role in heart failure development, potentially leading to sudden cardiac arrest and death. Previous studies suggest that micro-ribonucleic acids (miRNAs) show promise for the early diagnosis and treatment of cardiomyocyte hypertrophy.To investigate the miR-378 expression in the cardiomyocyte hypertrophy model, reverse transcription-polymerase chain reaction (RT-qPCR), Western blot, and immunofluorescence tests were conducted in angiotensin II (Ang II)-induced H9c2 cells and Ang II-induced mouse model of cardiomyocyte hypertrophy. The functional interaction between miR-378 and AKT2 was studied by dual-luciferase reporter, RNA pull-down, Western blot, and RT-qPCR assays.The results of RT-qPCR analysis showed the downregulated expression of miR-378 in both the cell and animal models of cardiomyocyte hypertrophy. It was observed that the introduction of the miR-378 mimic inhibited the hypertrophy of cardiomyocytes induced by Ang II. Furthermore, the co-transfection of AKT2 expression vector partially mitigated the negative impact of miR-378 overexpression on Ang II-induced cardiomyocytes. Molecular investigations provided evidence that miR-378 negatively regulated AKT2 expression by interacting with the 3\' untranslated region (UTR) of AKT2 mRNA.Decreased miR-378 expression and AKT2 activation are linked to Ang II-induced cardiomyocyte hypertrophy. Targeting miR-378/AKT2 axis offers therapeutic opportunity to alleviate cardiomyocyte hypertrophy.

BACKGROUND: Statistical epistasis, or \"gene-gene interaction\" in genetic association studies, means the nonadditive effects between the polymorphic sites on two different genes affecting the same phenotype. In the genetic association analysis of complex traits, nevertheless, the researchers haven\'t found enough clues of statistical epistasis so far.
METHODS: We developed a statistical model where the statistical epistasis was presented as an extra linkage disequilibrium between the polymorphic sites of different risk genes. The power of statistical test for identifying the gene-gene interaction was calculated and then compared in different hypothesis scenarios.
RESULTS: Our results show the statistical power increases with the increasing of interaction coefficient, relative risk, and linkage disequilibrium with genetic markers. However, the power of interaction discovery is much lower than that of regular single-site association test. When rigorous criteria were employed in statistical tests, the identification of gene-gene interaction became a very difficult task. Since the criterion of significance was given to be p-value ≤ 5.0 × 10-8, the same as that of many genome-wide association studies, there is little chance to identify the gene-gene interaction in all kind of circumstances.
CONCLUSIONS: The lack of epistasis tends to be an inevitable result caused by the statistical principles of methods in the genetic association studies and therefore is the inherent characteristic of the research itself.

The genes of Wnt/β-catenin pathway may have potential roles in fat accumulation of Non-traumatic osteonecrosis of the femoral head (ONFH), but the effects of their variants in the pathway on ONFH development have been remained unclear. To explore the potential roles of the variants in the development of ONFH, we completed the investigation of the paired interactions as well as their related biological functions of 17 variants of GSK3β, LRP5, and FRP4 genes etc. in the pathway. The genotyping of the 17 variants were finished by MASS ARRAY PLATFORM in a 560 ONFH case-control system. The association of variants interactions with ONFH risk and clinical traits was evaluated by logistic regression analysis etc. and bioinformatics technology. The results showed that the genotype, allele frequency, and genetic models of Gsk3β rs334558 (G/A), SFRP4 rs1052981 (A/G), and LRP5 rs312778 (T/C) were significantly associated with the increased and decreased ONFH risk and clinical traits, respectively (P < 0.001-0.0002). Particularly, the paired interactions of six variants as well as eight variants also showed statistically increased and decreased ONFH risk, bilateral hip lesions risk and stage IV risk of ONFH, respectively (P < 0.044-0.004). Our results not only at the first time simultaneously showed exact serum lipid disorder and abnormal platelet function of ONFH in the same study system with the 17 variants polymorphisms of Wnt/β-catenin pathway but also shed light on the variants closely intervening the lipid disorder and abnormal coagulation of ONFH.

BACKGROUND: PRAME constitutes one of the largest multi-copy gene families in Eutherians, encoding cancer-testis antigens (CTAs) with leucine-rich repeats (LRR) domains, highly expressed in cancer cells and gametogenic germ cells. This study aims to elucidate genetic interactions between two members, Pramex1 and Pramel1, in the mouse Prame family during gametogenesis using a gene knockout approach.
RESULTS: Single-gene knockout (sKO) of either Pramex1 or Pramel1 resulted in approximately 7% of abnormal seminiferous tubules, characterized by a Sertoli-cell only (SCO) phenotype, impacting sperm count and fecundity significantly. Remarkably, sKO female mice displayed normal reproductive functions. In contrast, Pramex1/Pramel1 double knockout (dKO) mice exhibited reduced fecundity in both sexes. In dKO females, ovarian primary follicle count decreased by 50% compared to sKO and WT mice, correlating with a 50% fecundity decrease. This suggested compensatory roles during oogenesis in Pramex1 or Pramel1 sKO females. Conversely, dKO males showed an 18% frequency of SCO tubules, increased apoptotic germ cells, and decreased undifferentiated spermatogonia compared to sKO and WT testes. Western blot analysis with PRAMEX1- or PRAMEL1-specific antibodies on sKO testes revealed compensatory upregulation of each protein (30-50%) in response to the other gene\'s deletion. Double KO males exhibited more severe defects in sperm count and litter size, surpassing Pramex1 and Pramel1 sKO accumulative effects, indicating a synergistic enhancement interaction during spermatogenesis. Additional experiments administering trans-retinoic acid (RA) and its inhibitor (WIN18,446) in sKO, dKO, and WT mice suggested that PRAMEX1 and PRAMEL1 synergistically repress the RA signaling pathway during spermatogenesis.
CONCLUSIONS: Data from sKO and dKO mice unveil a synergistic interaction via the RA signaling pathway between Pramex1 and Pramel1 genes during gametogenesis. This discovery sets the stage for investigating interactions among other members within the Prame family, advancing our understanding of multi-copy gene families involved in germ cell formation and function.

Resistant starch (RS) can help prevent diabetes and decrease calorie intake and that from plants are the main source of mankind consumption. Rice is many people\'s staple food and that with higher RS will help health management. A significantly positive correlation exists between apparent amylose content (AAC) of rice and its RS content. In this study, 72 accessions with moderate or high AAC were selected to explore the regulatory mechanisms and physicochemical properties on different proceeding types of rice RS. RS in raw milled rice (RSm), hot cooked rice (RSc), and retrogradation rice (RSr) showed a wide variation and distinct controlling mechanisms. They were co-regulated by Waxy (Wx), soluble starch synthase (SS) IIb and SSI. Besides that, RSm was also regulated by SSIIa and SSIVb, RSc by granule-bound starch synthase (GBSS) II and RSr by GBSSII and Pullulanase (PUL). Moreover, Wx had significant interactions with SSIIa, SSI, SSIIb and SSIVb on RSm, but only the dominant interactions with SSIIb and SSI on RSc and RSr. Wx was the key factor for the formation of RS, especially the RSc and RSr. The genes had the highest expression at 17 days after flowering and were beneficial for RS formation. The longer the chain length of starch, the higher the RS3 content. RSc and RSr were likely to be contained in medium-size starch granules. The findings favor understanding the biosynthesis of different subtypes of RS.

The current concept of gene has been very useful during the 20th and 21st centuries. However, recent advances in molecular biology and bioinformatics, which have further diversified the functional and adaptive profile of genetic information and its integration with cell physiology and environmental response, have contributed to focusing on additional new gene properties besides the traditional definition. Considering the inherent complexity of gene expression, whose adaptive objective must be referred to the Tortoise-Hare model, in which two tendencies converge, one focused on rapid adaptation to achieve survival, and the other that prevents an over-adaptation effect. In this context, a revision of the gene concept must be made, which must include these new mechanisms and approaches. In this paper, we propose a new conception of the idea of a gene that moves from a static and defined version of hereditary information to a dynamic idea that preponderates gene interaction (circumscribed to that established between protein-protein, protein-nucleic acid, and nucleic acid-nucleic acid) and the selection it exerts, as the irreducible element that works in a coordinated way in a genomic regulatory network (GRN).

CONCLUSIONS: Transcriptomics and methylomics were used to identify the potential effects resulting from GM rice breeding stacks, which provided scientific data for the safety assessment strategy of stacked GM crops in China. Gene interaction is one of the main concerns for stacked genetically modified crop safety. With the development of technology, the combination of omics and bioinformatics has become a useful tool to evaluate the unintended effects of genetically modified crops. In this study, transcriptomics and methylomics were used as molecular profiling techniques to identify the potential effects of stack through breeding. Stacked transgenic rice En-12 × Ec-26 was used as material, which was obtained through hybridization using parents En-12 and Ec-26, in which the foreign protein can form functional EPSPS protein by intein-mediated trans-splitting. Differentially methylated region (DMR) analysis showed that the effect of stacking breeding on methylation was less than that of genetic transformation at the methylome level. Differentially expressed gene (DEG) analysis showed that the DEGs between En-12 × Ec-26 and its parents were far fewer than those between transgenic rice and Zhonghua 11 (ZH11), and no unintended new genes were found in En-12 × Ec-26. Statistical analysis of gene expression and methylation involved in shikimic acid metabolism showed that there was no difference in gene expression, although there were 16 and 10 DMR genes between En-12 × Ec-26 and its parents (En and Ec) in methylation, respectively. The results indicated that the effect of stacking breeding on gene expression and DNA methylation was less than the effect of genetic transformation. This study provides scientific data supporting safety assessments of stacked GM crops in China.

OBJECTIVE: To assess LRP5-/6 gene polymorphisms and its association with risk of abnormal bone mass (ABM) in postmenopausal women.
METHODS: The study recruited 166 patients with ABM (case group) and 106 patients with normal bone mass (control group) based on bone mineral density (BMD) results. Multi-factor dimensionality reduction (MDR) was used to analyze the interaction between the Low-density lipoprotein receptor-related protein 5 (LRP5) gene (rs41494349, rs2306862) and the Low-density lipoprotein receptor-related protein 6 (LRP6) gene (rs10743980, rs2302685) and the subjects\' clinical characteristics of age and menopausal years.
RESULTS: (1) Logistic regression analysis showed that the subjects with the CT or TT genotype at rs2306862 had a higher risk of ABM than those with the CC genotype (OR = 2.353, 95%CI = 1.039-6.186; OR = 2.434, 95%CI = 1.071, 5.531; P < 0.05). The subjects with the TC genotype at rs2302685 had a higher risk of ABM than those with the TT genotype (OR = 2.951, 95%CI = 1.030-8.457, P < 0.05). (2) When taking the three Single-nucleotide polymorphisms (SNPs) together, the accuracy was the highest with the cross-validation consistency of 10/10 (OR = 1.504, 95%CI:1.092-2.073, P < 0.05), indicating that the LRP5 rs41494349 and LRP6 rs10743980, rs2302685 were interactively associated with the risk of ABM. (3) Linkage disequilibrium (LD) results revealed that the LRP5 (rs41494349,rs2306862) were in strong LD (D\' > 0.9, r2 > 0.3). AC and AT haplotypes were significantly more frequently distributed in the ABM group than in the control group, indicating that subjects carrying the AC and AT haplotypes were associated with an increased risk of ABM (P < 0.01). (4) MDR showed that rs41494349 & rs2302685 & rs10743980 & age were the best model for ABM prediction. The risk of ABM in \"high-risk combination\" was 1.00 times that of \"low-risk combination\"(OR = 1.005, 95%CI: 1.002-1.008, P < 0.05). (5) MDR showed that there was no significant association between any of the SNPs and menopausal years and ABM susceptibility.
CONCLUSIONS: These findings indicate that LRP5-rs2306862 and LRP6-rs2302685 polymorphisms and gene-gene and gene-age interactions may increase the risk of ABM in postmenopausal women. There was no significant association between any of the SNPs and menopausal years and ABM susceptibility.

Gene pathway enrichment analysis is a widely used method to analyze whether a gene set is statistically enriched on certain biological pathway network. Current gene pathway enrichment methods commonly consider local importance of genes in pathways without considering the interactions between genes. In this paper, we propose a gene pathway enrichment method (GIGSEA) based on improved TF-IDF algorithm. This method employs gene interaction data to calculate the influence of genes based on the local importance in a pathway as well as the global specificity. Computational experiment result shows that, compared with traditional gene set enrichment analysis method, our proposed method in this paper can find more specific enriched pathways related to phenotype with higher efficiency.

Global genetic networks provide additional information for the analysis of human diseases, beyond the traditional analysis that focuses on single genes or local networks. The Gaussian graphical model (GGM) is widely applied to learn genetic networks because it defines an undirected graph decoding the conditional dependence between genes. Many algorithms based on the GGM have been proposed for learning genetic network structures. Because the number of gene variables is typically far more than the number of samples collected, and a real genetic network is typically sparse, the graphical lasso implementation of GGM becomes a popular tool for inferring the conditional interdependence among genes. However, graphical lasso, although showing good performance in low dimensional data sets, is computationally expensive and inefficient or even unable to work directly on genome-wide gene expression data sets. In this study, the method of Monte Carlo Gaussian graphical model (MCGGM) was proposed to learn global genetic networks of genes. This method uses a Monte Carlo approach to sample subnetworks from genome-wide gene expression data and graphical lasso to learn the structures of the subnetworks. The learned subnetworks are then integrated to approximate a global genetic network. The proposed method was evaluated with a relatively small real data set of RNA-seq expression levels. The results indicate the proposed method shows a strong ability of decoding the interactions with high conditional dependences among genes. The method was then applied to genome-wide data sets of RNA-seq expression levels. The gene interactions with high interdependence from the estimated global networks show that most of the predicted gene-gene interactions have been reported in the literatures playing important roles in different human cancers. Also, the results validate the ability and reliability of the proposed method to identify high conditional dependences among genes in large-scale data sets.