Despite the importance of soybean as a major crop, genome-wide variation and evolution of cultivated soybeans are largely unknown. Here, we catalogued genome variation in an annual soybean population by high-depth resequencing of 10 cultivated and 6 wild accessions and obtained 3.87 million high-quality single-nucleotide polymorphisms (SNPs) after excluding the sites with missing data in any accession. Nuclear genome phylogeny supported a single origin for the cultivated soybeans. We identified 10-fold longer linkage disequilibrium (LD) in the wild soybean relative to wild maize and rice. Despite the small population size, the long LD and large SNP data allowed us to identify 206 candidate domestication regions with significantly lower diversity in the cultivated, but not in the wild, soybeans. Some of the genes in these candidate regions were associated with soybean homologues of canonical domestication genes. However, several examples, which are likely specific to soybean or eudicot crop plants, were also observed. Consequently, the variation data identified in this study should be valuable for breeding and for identifying agronomically important genes in soybeans. However, the long LD of wild soybeans may hinder pinpointing causal gene(s) in the candidate regions.

DJ-1 is one of the causative genes for early onset familiar Parkinson’s disease (PD) and is also considered to influence the pathogenesis of sporadic PD. DJ-1 has various physiological functions which converge on controlling intracellular reactive oxygen species (ROS) levels. In RNA-sequencing analyses searching for novel anti-oxidant genes downstream of DJ-1, a gene encoding NADP+-dependent isocitrate dehydrogenase (IDH), which converts isocitrate into α-ketoglutarate, was detected. Loss of IDH induced hyper-sensitivity to oxidative stress accompanying age-dependent mitochondrial defects and dopaminergic (DA) neuron degeneration in Drosophila, indicating its critical roles in maintaining mitochondrial integrity and DA neuron survival. Further genetic analysis suggested that DJ-1 controls IDH gene expression through nuclear factor-E2-related factor2 (Nrf2). Using Drosophila and mammalian DA models, we found that IDH suppresses intracellular and mitochondrial ROS level and subsequent DA neuron loss downstream of DJ-1. Consistently, trimethyl isocitrate (TIC), a cell permeable isocitrate, protected mammalian DJ-1 null DA cells from oxidative stress in an IDH-dependent manner. These results suggest that isocitrate and its derivatives are novel treatments for PD associated with DJ-1 dysfunction.

The function of AarF domain-containing kinase 1 (ADCK1) has not been thoroughly revealed. Here we identified that ADCK1 utilizes YME1-like 1 ATPase (YME1L1) to control optic atrophy 1 (OPA1) and inner membrane mitochondrial protein (IMMT) in regulating mitochondrial dynamics and cristae structure. We firstly observed that a serious developmental impairment occurred in Drosophila ADCK1 ( dADCK1 ) deletion mutant, resulting in premature death before adulthood. By using temperature sensitive ubiquitously expression driver tub-Gal80 ts /tub-Gal4 or muscle-specific expression driver mhc-Gal4 , we observed severely defective locomotive activities and structural abnormality in the muscle along with increased mitochondrial fusion in the dADCK1 knockdown flies. Moreover, decreased mitochondrial membrane potential, ATP production and survival rate along with increased ROS and apoptosis in the flies further demonstrated that the structural abnormalities of mitochondria induced by dADCK1 knockdown led to their functional abnormalities. Consistent with the ADCK1 loss-of-function data in Drosophila , ADCK1 over-expression induced mitochondrial fission and clustering in addition to destruction of the cristae structure in Drosophila and mammalian cells. Interestingly, knockdown of YME1L1 rescued the phenotypes of ADCK1 over-expression. Furthermore, genetic epistasis from fly genetics and mammalian cell biology experiments led us to discover the interactions among IMMT, OPA1 and ADCK1. Collectively, these results established a mitochondrial signaling pathway composed of ADCK1, YME1L1, OPA1 and IMMT, which has essential roles in maintaining mitochondrial morphologies and functions in the muscle.

Background/Purpose The current study aimed to develop a prediction model using a multi‐marker panel as a diagnostic screening tool for pancreatic ductal adenocarcinoma. Methods Multi‐center cohort of 1991 blood samples were collected from January 2011 to September 2019, of which 609 were normal, 145 were other cancer (colorectal, thyroid, and breast cancer), 314 were pancreatic benign disease, and 923 were pancreatic ductal adenocarcinoma. The automated multi‐biomarker Enzyme‐Linked Immunosorbent Assay kit was developed using three potential biomarkers: LRG1, TTR, and CA 19‐9. Using a logistic regression model on a training data set, the predicted values for pancreatic ductal adenocarcinoma were obtained, and the result was classification into one of the three risk groups: low, intermediate, and high. The five covariates used to create the model were sex, age, and three biomarkers. Results Participants were categorized into four groups as normal (n = 609), other cancer (n = 145), pancreatic benign disease (n = 314), and pancreatic ductal adenocarcinoma (n = 923). The normal, other cancer, and pancreatic benign disease groups were clubbed into the non‐pancreatic ductal adenocarcinoma group (n = 1068). The positive and negative predictive value, sensitivity, and specificity were 94.12, 90.40, 93.81, and 90.86, respectively. Conclusions This study demonstrates a significant diagnostic performance of the multi‐marker panel in distinguishing pancreatic ductal adenocarcinoma from normal and benign pancreatic disease states, as well as patients with other cancers.

Purpose Diagnostic biomarkers of pancreatic ductal adenocarcinoma (PDAC) have been used for early detection to reduce its dismal survival rate. However, clinically feasible biomarkers are still rare. Therefore, in this study, we developed an automated multi-marker enzyme-linked immunosorbent assay (ELISA) kit using 3 biomarkers (leucine-rich alpha-2-glycoprotein [LRG1], transthyretin [TTR], and CA 19-9) that were previously discovered and proposed a diagnostic model for PDAC based on this kit for clinical usage. Methods Individual LRG1, TTR, and CA 19-9 panels were combined into a single automated ELISA panel and tested on 728 plasma samples, including PDAC (n = 381) and normal samples (n = 347). The consistency between individual panels of 3 biomarkers and the automated multi-panel ELISA kit were accessed by correlation. The diagnostic model was developed using logistic regression according to the automated ELISA kit to predict the risk of pancreatic cancer (high-, intermediate-, and low-risk groups). Results The Pearson correlation coefficient of predicted values between the triple-marker automated ELISA panel and the former individual ELISA was 0.865. The proposed model provided reliable prediction results with a positive predictive value of 92.05%, negative predictive value of 90.69%, specificity of 90.69%, and sensitivity of 92.05%, which all simultaneously exceed 90% cutoff value. Conclusion This diagnostic model based on the triple ELISA kit showed better diagnostic performance than previous markers for PDAC. In the future, it needs external validation to be used in the clinic.