Knockdown of EIF2AK2-OAS1 axis reduces ATP production inducing AMPK phosphorylation to inhibit the malignant behavior of gastric cancer cells.

Energy metabolism has always been a hot topic in cancer progression and targeted therapy, and exploring the role of genes in energy metabolic pathways in cancer cells has become key to address this issue. Eukaryotic translation initiation factor 2α kinase 2 (EIF2AK2) plays regulatory roles in cancer and disorders of energy metabolism. Indeed, the role of EIF2AK2 in energy metabolism has been underestimated. The aim of this study is to reveal the expression specificity of EIF2AK2 in gastric cancer (GC) progression and metastasis, and to demonstrate the role of EIF2AK2 in energy metabolism, cytoskeleton, proliferation, death and metastasis pathways in GC cells. Mechanistically, EIF2AK2 overexpression promoted cytoskeleton remodeling and ATP production, mediated cell proliferation and metastasis, upregulated OAS1 expression, decreases p-AMPK expression and inhibited apoptosis in GC cells. Conversely, knockdown of EIF2AK2 resulted in the opposite effect. However, overexpression of OAS1 mediated the upregulation of mitochondrial membrane potential and promoted ATP production and NAD+/NADH ratio, but knockdown of OAS1 inhibited the above effects. In addition, knockdown of OAS1 had no effect on EIF2AK2 expression, but inhibited AMPK and upregulated p-AMPK expression. In conclusion, our study identified EIF2AK2 and OAS1 as previously undescribed regulators of energy metabolism in GC cells. We hypothesized that EIF2AK2-OAS1 axis may regulate energy metabolism and inhibit cellular malignant behavior in cancer cells by affecting ATP production to induce AMPK phosphorylation, suggesting EIF2AK2 as a potential therapeutic target for cancer cell progression.

Probabilistic Contrastive Learning for Long-Tailed Visual Recognition.

Long-tailed distributions frequently emerge in real-world data, where a large number of minority categories contain a limited number of samples. Such imbalance issue considerably impairs the performance of standard supervised learning algorithms, which are mainly designed for balanced training sets. Recent investigations have revealed that supervised contrastive learning exhibits promising potential in alleviating the data imbalance. However, the performance of supervised contrastive learning is plagued by an inherent challenge: it necessitates sufficiently large batches of training data to construct contrastive pairs that cover all categories, yet this requirement is difficult to meet in the context of class-imbalanced data. To overcome this obstacle, we propose a novel probabilistic contrastive (ProCo) learning algorithm that estimates the data distribution of the samples from each class in the feature space, and samples contrastive pairs accordingly. In fact, estimating the distributions of all classes using features in a small batch, particularly for imbalanced data, is not feasible. Our key idea is to introduce a reasonable and simple assumption that the normalized features in contrastive learning follow a mixture of von Mises-Fisher (vMF) distributions on unit space, which brings two-fold benefits. First, the distribution parameters can be estimated using only the first sample moment, which can be efficiently computed in an online manner across different batches. Second, based on the estimated distribution, the vMF distribution allows us to sample an infinite number of contrastive pairs and derive a closed form of the expected contrastive loss for efficient optimization. Other than long-tailed problems, ProCo can be directly applied to semi-supervised learning by generating pseudo-labels for unlabeled data, which can subsequently be utilized to estimate the distribution of the samples inversely. Theoretically, we analyze the error bound of ProCo. Empirically, extensive experimental results on supervised/semi-supervised visual recognition and object detection tasks demonstrate that ProCo consistently outperforms existing methods across various datasets.

Interleukin-8-251A/T polymorphism and Helicobacter pylori infection influence risk for the development of gastric cardiac adenocarcinoma in a high-incidence area of China.

Polymorphisms in cytokine genes may contribute to increased susceptibility to different cancers. The aim of this paper is to investigate the association of IL-8-251A/T polymorphism and Helicobacter pylori (H. pylori) infection with the risk of developing gastric cardiac adenocarcinoma (GCA) in the south of Taihang Mountain, a high-incidence area of esophageal cancer in China. The IL-8-251 A/T polymorphism was genotyped in 519 cases of GCA and 504 healthy controls. The H. pylori infection in GCA patients and controls was detected by rapid urease test (RUT), histopathology or (14)C-urea breath test ((14)C-UBT). The results showed that family history of upper gastrointestinal cancer (UGIC) and H. pylori infection significantly increased the risk of developing GCA. The overall genotype and allelotype distributions of IL-8 promoter SNPs in GCA patients were significantly different from those in healthy controls. Compared with TT genotype, AA genotype significantly elevated the risk of developing GCA. The stratification analysis revealed that, compared with the TT genotype, the AA genotype significantly elevated the risk of developing GCA in both positive family history of UGIC and H. pylori infection subgroups. This study provides evidence to support a relationship of increased susceptibility to GCA in individuals of the south Taihang Mountain region with IL-8 251 AA genotype, especially for those individuals who have family history of UGIC or H. pylori infection.

The breeding of two polyploid rice lines with the characteristic of polyploid meiosis stability.

Polyploidization is a basic feature of plant evolution. Nearly all of the main food, cotton and oil crops are polyploid. When ploidy levels increase, yields double; this phenomenon suggested a new strategy of rice breeding that utilizes wide crosses and polyploidization dual advantages to breed super rice. Because low seed set rates in polyploid rice usually makes it difficult to breed, the selection of Ph-liked gene lines was emphasized. After progenies of indica-japonica were identified and selected, two polyploid lines, PMeS-1 and PMeS-2 with Polyploid Meiosis Stability (PMeS) genes were bred. The procedure included seven steps: selecting parents, crossing or multiple crossing, back-crossing, doubling chromosomes, identifying the polyploid, and choosing plants with high seed set rates that can breed themselves into stable lines. The characteristics of PMeS were determined by observing meiotic behaviors and by cross-identification of seed sets. PMeS-1 and PMeS-2, (japonica rice), have several characteristics different from other polyploid rice lines, including a higher rate of seed set (more than 65%, increasing to more than 70% in their F1 offspring); and stable meiotic behaviors (pairing with bivalents and quarivalents nearly without over-quarivalent in prophase, nearly without lagging chromosomes in metaphase and without micronuclei in anaphase and telophase). The latter was obviously different from control polyploid line Dure-4X, which displayed abnormal meiotic behaviors including a higher rate of multivalents, univalents and trivalents in prophase, lagging chromosomes in metaphase and micronuclei in anaphase and telophase. There were also three differences of the breeding method between PMeS lines and normal diploid lines: chromosomes doubling, polyploidism identifying and higher seed set testing. The selection of PMeS lines is the first step in polyploid rice breeding; their use will advance the progress of polyploid rice breeding, which will in turn offer a new way to breed super rice.