Streamlined whole-genome genotyping through NGS-enhanced thermal asymmetric interlaced (TAIL)-PCR.

Whole-genome genotyping (WGG) stands as a pivotal element in genomic-assisted plant breeding. Nevertheless, sequencing-based approaches for WGG continue to be costly, primarily attributed to the high expenses associated with library preparation and the laborious protocol. During the prior development of Foreground and Background Integrated genotyping by sequencing (FBI-seq), we discovered that any sequence specific primer (SP) inherently possessed the capability to amplify a massive array of stable and reproducible non-specific PCR product across the genome. Here we further improved the FBI-seq by replacing the adapter ligated by Tn5 transposase with arbitrary degenerate (AD) primer. The protocol for the enhanced FBI-seq unexpectedly mirrors a simplified Thermal Asymmetric Interlaced (TAIL)-PCR, a technique widely employed for isolating flanking sequences. However, the improved TAIL-PCR maximizes the PTMA capabilities of both SP and AD primers. Additionally, leveraging next-generation sequencing enhances its ability to assay tens of thousands of genome-wide loci for any species. This cost-effective, user-friendly, and powerful WGG tool, TAIL-PCR by sequencing (TAIL-peq), holds great potential for widespread application in breeding programs, thereby facilitating genome-assisted crop improvement.

Application of AI-assisted MRI for the identification of surgical target areas in pediatric hip and periarticular infections.

OBJECTIVE: To develop an AI-assisted MRI model to identify surgical target areas in pediatric hip and periarticular infections. METHODS: A retrospective study was conducted on the pediatric patients with hip and periarticular infections who underwent Magnetic Resonance Imaging(MRI)examinations from January 2010 to January 2023 in three hospitals in China. A total of 7970 axial Short Tau Inversion Recovery (STIR) images were selected, and the corresponding regions of osteomyelitis (label 1) and abscess (label 2) were labeled using the Labelme software. The images were randomly divided into training group, validation group, and test group at a ratio of 7:2:1. A Mask R-CNN model was constructed and optimized, and the performance of identifying label 1 and label 2 was evaluated using receiver operating characteristic (ROC) curves. Calculation of the average time it took for the model and specialists to process an image in the test group. Comparison of the accuracy of the model in the interpretation of MRI images with four orthopaedic surgeons, with statistical significance set at P < 0.05. RESULTS: A total of 275 patients were enrolled, comprising 197 males and 78 females, with an average age of 7.10 ± 3.59 years, ranging from 0.00 to 14.00 years. The area under curve (AUC), accuracy, sensitivity, specificity, precision, and F1 score for the model to identify label 1 were 0.810, 0.976, 0.995, 0.969, 0.922, and 0.957, respectively. The AUC, accuracy, sensitivity, specificity, precision, and F1 score for the model to identify label 2 were 0.890, 0.957, 0.969, 0.915, 0.976, and 0.972, respectively. The model demonstrated a significant speed advantage, taking only 0.2 s to process an image compared to average 10 s required by the specialists. The model identified osteomyelitis with an accuracy of 0.976 and abscess with an accuracy of 0.957, both statistically better than the four orthopaedic surgeons, P < 0.05. CONCLUSION: The Mask R-CNN model is reliable for identifying surgical target areas in pediatric hip and periarticular infections, offering a more convenient and rapid option. It can assist unexperienced physicians in pre-treatment assessments, reducing the risk of missed and misdiagnosis.

Risk prediction models of depression in older adults with chronic diseases.

BACKGROUND: Detecting potential depression and identifying the critical predictors of depression among older adults with chronic diseases are essential for timely intervention and management of depression. Therefore, risk prediction models (RPMs) of depression in elderly people should be further explored. METHODS: A total of 3959 respondents aged 60 years or over from the wave four survey of the China Health and Retired Longitudinal Study (CHARLS) were included in this study. We used five machine learning (ML) algorithms and three data balancing techniques to construct RPMs of depression and calculated feature importance scores to determine which features are essential to depression. RESULTS: The prevalence of depression was 19.2 % among older Chinese adults with chronic diseases in the wave four survey. The random forest (RF) model was more accurate than the other models after balancing the data using the Synthetic Minority Oversampling Technique (SMOTE) algorithm, with an area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC) of 0.957 and 0.920, respectively, a balanced accuracy of 0.891 and a sensitivity of 0.875. Furthermore, we further identified several important predictors between male and female patients via constructed sex-stratified models. LIMITATIONS: Further research on the clinical impact studies of our models and external validation are needed. CONCLUSIONS: After several techniques were used to address class imbalance issues, most RPMs achieved satisfactory accuracy in predicting depression among elderly people with chronic diseases. RPMs may thus become valuable screening tools for both older individuals and healthcare practitioners to assess the risk of depression.

Revealing potential Rab proteins participate in regulation of secretory autophagy machinery.

Autophagy can be classified as degradative and secretory based on distinct functions. The small GTPase proteins Rab8a and Rab37 are responsible for secretory autophagy-mediated exocytosis of IL-1ß, insulin, and TIMP1 (tissue inhibitor of 54 metalloproteinase 1). Other Rab family members participating in secretory autophagy are poorly understood. Herein, we identified 26 overlapped Rab proteins in purified autophagosomes of mouse pancreatic ß-cell "Min-6" and human lung cancer cell "CL1-5-Q89L" with high secretory autophagy tendency by LC-MS/MS proteomics analysis. Six Rab proteins (Rab8a, Rab11b, Rab27a, Rab35, Rab37, and Rab7a) were detected in autophagosomes of four cell lines, associating them with autophagy-related vesicle trafficking. We used CL1-5-Q89L cell line model to evaluate the levels of Rab proteins colocalization with autophagy LC3 proteins and presence in purified autophagosomes. We found five Rab proteins (Rab8a, Rab11b, Rab27a, Rab35, and Rab37) are highly expressed in the autophagosome compared to the normal control by immunoblotting under active secretion conditions. However, only Rab8a, Rab35, and Rab37 showing high colocalization with LC3 protein by cofocal microscopy. Despite the discrepancy between the image and immunoblotting analysis, our data sustains the speculation that Rab8a, Rab11b, Rab27a, Rab35, and Rab37 are possibly associated with the secretory autophagy machinery. In contrast, Rab7a shows low colocalization with LC3 puncta and low level in the autophagosome, suggesting it regulates different vesicle trafficking machineries. Our findings open a new direction toward exploring the role of Rab proteins in secretory autophagy-related cargo exocytosis and identifying the cargoes and effectors regulated by specific Rab proteins.

Constructing eRNA-mediated gene regulatory networks to explore the genetic basis of muscle and fat-relevant traits in pigs.

BACKGROUND: Enhancer RNAs (eRNAs) play a crucial role in transcriptional regulation. While significant progress has been made in understanding epigenetic regulation mediated by eRNAs, research on the construction of eRNA-mediated gene regulatory networks (eGRN) and the identification of critical network components that influence complex traits is lacking. RESULTS: Here, employing the pig as a model, we conducted a comprehensive study using H3K27ac histone ChIP-seq and RNA-seq data to construct eRNA expression profiles from multiple tissues of two distinct pig breeds, namely Enshi Black (ES) and Duroc. In addition to revealing the regulatory landscape of eRNAs at the tissue level, we developed an innovative network construction and refinement method by integrating RNA-seq, ChIP-seq, genome-wide association study (GWAS) signals and enhancer-modulating effects of single nucleotide polymorphisms (SNPs) measured by self-transcribing active regulatory region sequencing (STARR-seq) experiments. Using this approach, we unraveled eGRN that significantly influence the growth and development of muscle and fat tissues, and identified several novel genes that affect adipocyte differentiation in a cell line model. CONCLUSIONS: Our work not only provides novel insights into the genetic basis of economic pig traits, but also offers a generalizable approach to elucidate the eRNA-mediated transcriptional regulation underlying a wide spectrum of complex traits for diverse organisms.

Protective effect of chicken yolk antibody Y against Campylobacter jejuni induced diarrhea in cats.

Campylobacter jejuni (C. jejuni) is a common pathogen that often causes diarrhea, loss of appetite, and even enteritis in domestic cats, affecting their growth and development, especially in kittens under 6 months of age. Oral passive immunization with chicken yolk antibody Y has been proved effective for the treatment of gastrointestinal pathogen infections due to its high specificity. In this study, C. jejuni was isolated from diarrheal cat feces, and the specific egg yolk antibody Y against C. jejuni was demonstrated to effectively inhibit its proliferation in vitro experiments. To evaluate the effect of anti-C. jejuni IgY, the mouse C. jejuni infection model was established and it was found that IgY could alleviate C. jejuni-induced clinical symptoms. Consistent with these results, the reduction of pro-inflammatory factors and intestinal colonization by C. jejuni in the IgY-treated groups, especially in the high dose group. We then evaluated the protective effect of IgY on young Ragdoll cats infected with C. jejuni. This specific antibody reduced the rate of feline diarrhea, protected the growth of young cats, inhibited systemic inflammatory hyperactivation, and increased fecal short-chain fatty acid concentrations. Notably, IgY may have a protective role by changing intestinal amino acid metabolism and affecting C. jejuni chemotaxis. Collectively, specific IgY is a promising therapeutic strategy for C. jejuni-induced cat diarrhea.

Excess Salt Intake Activates IL-21-Dominant Autoimmune Diabetogenesis via a Salt-Regulated Ste20-Related Proline/Alanine-Rich Kinase in CD4 T Cells.

The fundamental mechanisms by which a diet affects susceptibility to or modifies autoimmune diseases are poorly understood. Excess dietary salt intake acts as a risk factor for autoimmune diseases; however, little information exists on the impact of salt intake on type 1 diabetes. To elucidate the potential effect of high salt intake on autoimmune diabetes, nonobese diabetic (NOD) mice were fed a high-salt diet (HSD) or a normal-salt diet (NSD) from 6 to 12 weeks of age and monitored for diabetes development. Our results revealed that the HSD accelerated diabetes progression with more severe insulitis in NOD mice in a CD4+ T-cell-autonomous manner when compared with the NSD group. Moreover, expression of IL-21 and SPAK in splenic CD4+ T cells from HSD-fed mice was significantly upregulated. Accordingly, we generated T-cell-specific SPAK knockout (CKO) NOD mice and demonstrated that SPAK deficiency in T cells significantly attenuated diabetes development in NOD mice by downregulating IL-21 expression in CD4+ T cells. Furthermore, HSD-triggered diabetes acceleration was abolished in HSD-fed SPAK CKO mice when compared with HSD-fed NOD mice, suggesting an essential role of SPAK in salt-exacerbated T-cell pathogenicity. Finally, pharmacological inhibition of SPAK activity using a specific SPAK inhibitor (closantel) in NOD mice ameliorated diabetogenesis, further illuminating the potential of a SPAK-targeting immunotherapeutic approach for autoimmune diabetes. Here, we illustrate that a substantial association between salt sensitivity and the functional impact of SPAK on T-cell pathogenicity is a central player linking high-salt-intake influences to immunopathophysiology of diabetogenesis in NOD mice.

Accurate detection and grading of pterygium through smartphone by a fusion training model.

BACKGROUND/AIMS: To improve the accuracy of pterygium screening and detection through smartphones, we established a fusion training model by blending a large number of slit-lamp image data with a small proportion of smartphone data. METHOD: Two datasets were used, a slit-lamp image dataset containing 20 987 images and a smartphone-based image dataset containing 1094 images. The RFRC (Faster RCNN based on ResNet101) model for the detection model. The SRU-Net (U-Net based on SE-ResNeXt50) for the segmentation models. The open-cv algorithm measured the width, length and area of pterygium in the cornea. RESULTS: The detection model (trained by slit-lamp images) obtained the mean accuracy of 95.24%. The fusion segmentation model (trained by smartphone and slit-lamp images) achieved a microaverage F1 score of 0.8981, sensitivity of 0.8709, specificity of 0.9668 and area under the curve (AUC) of 0.9295. Compared with the same group of patients' smartphone and slit-lamp images, the fusion model performance in smartphone-based images (F1 score of 0.9313, sensitivity of 0.9360, specificity of 0.9613, AUC of 0.9426, accuracy of 92.38%) is close to the model (trained by slit-lamp images) in slit-lamp images (F1 score of 0.9448, sensitivity of 0.9165, specificity of 0.9689, AUC of 0.9569 and accuracy of 94.29%). CONCLUSION: Our fusion model method got high pterygium detection and grading accuracy in insufficient smartphone data, and its performance is comparable to experienced ophthalmologists and works well in different smartphone brands.

Identification of peptides from honeybee gut symbionts as potential antimicrobial agents against Melissococcus plutonius.

Eusocial pollinators are crucial elements in global agriculture. The honeybees and bumblebees are associated with a simple yet host-restricted gut community, which protect the hosts against pathogen infections. Recent genome mining has led to the discovery of biosynthesis pathways of bioactive natural products mediating microbe-microbe interactions from the gut microbiota. Here, we investigate the diversity of biosynthetic gene clusters in the bee gut microbiota by analyzing 477 genomes from cultivated bacteria and metagenome-assembled genomes. We identify 744 biosynthetic gene clusters (BGCs) covering multiple chemical classes. While gene clusters for the post-translationally modified peptides are widely distributed in the bee guts, the distribution of the BGC classes varies significantly in different bee species among geographic locations, which is attributed to the strain-level variation of bee gut members in the chemical repertoire. Interestingly, we find that Gilliamella strains possessing a thiopeptide-like BGC show potent activity against the pathogenic Melissococcus plutonius. The spectrometry-guided genome mining reveals a RiPP-encoding BGC from Gilliamella with a 10 amino acid-long core peptide exhibiting antibacterial potentials. This study illustrates the widespread small-molecule-encoding BGCs in the bee gut symbionts and provides insights into the bacteria-derived natural products as potential antimicrobial agents against pathogenic infections.

An Analysis of a Transposable Element Expression Atlas during 27 Developmental Stages in Porcine Skeletal Muscle: Unveiling Molecular Insights into Pork Production Traits.

The development and growth of porcine skeletal muscle determine pork quality and yield. While genetic regulation of porcine skeletal muscle development has been extensively studied using various omics data, the role of transposable elements (TEs) in this context has been less explored. To bridge this gap, we constructed a comprehensive atlas of TE expression throughout the developmental stages of porcine skeletal muscle. This was achieved by integrating porcine TE genomic coordinates with whole-transcriptome RNA-Seq data from 27 developmental stages. We discovered that in pig skeletal muscle, active Tes are closely associated with active epigenomic marks, including low levels of DNA methylation, high levels of chromatin accessibility, and active histone modifications. Moreover, these TEs include 6074 self-expressed TEs that are significantly enriched in terms of muscle cell development and myofibril assembly. Using the TE expression data, we conducted a weighted gene co-expression network analysis (WGCNA) and identified a module that is significantly associated with muscle tissue development as well as genome-wide association studies (GWAS) of the signals of pig meat and carcass traits. Within this module, we constructed a TE-mediated gene regulatory network by adopting a unique multi-omics integration approach. This network highlighted several established candidate genes associated with muscle-relevant traits, including HES6, CHRNG, ACTC1, CHRND, MAMSTR, and PER2, as well as novel genes like ENSSSCG00000005518, ENSSSCG00000033601, and PIEZO2. These novel genes hold promise for regulating muscle-related traits in pigs. In summary, our research not only enhances the TE-centered dissection of the genetic basis underlying pork production traits, but also offers a general approach for constructing TE-mediated regulatory networks to study complex traits or diseases.

The decisive role of electrostatic interactions in transport mode and phase segregation of lithium ions in LiFePO4.

Understanding the mechanism of slow lithium ion (Li+) transport kinetics in LiFePO4 is not only practically important for high power density batteries but also fundamentally significant as a prototypical ion-coupled electron transfer process. Substantial evidence has shown that the slow ion transport kinetics originates from the coupled transfer between electrons and ions and the phase segregation of Li+. Combining a model Hamiltonian analysis and DFT calculations, we reveal that electrostatic interactions play a decisive role in coupled charge transfer and Li+ segregation. The obtained potential energy surfaces prove that ion-electron coupled transfer is the optimal reaction pathway due to electrostatic attractions between Li+ and e- (Fe2+), while prohibitively large energy barriers are required for separate electron tunneling or ion hopping to overcome the electrostatic energy between the Li+-e- (Fe2+) pair. The model reveals that Li+-Li+ repulsive interaction in the [010] transport channels together with Li+-e- (Fe2+)-Li+ attractive interaction along the [100] direction cause the phase segregation of Li+. It explains why the thermodynamically stable phase interface between Li-rich and Li-poor phases in LiFePO4 is perpendicular to [010] channels.

Clinical value of chemiluminescence method for detection of antinuclear antibody profiles.

BACKGROUND: Antinuclear antibodies (ANAs) are crucial in diagnosing autoimmune diseases, mainly systemic lupus erythematosus (SLE). This study aimed to compare the performance of chemiluminescence assay (CLIA) and line immunoassay (LIA) in detecting ANAs in patients with autoimmune diseases, evaluate their diagnostic accuracy for SLE, and develop a novel diagnostic model using CLIA-detected antibodies for SLE. Specimens from patients with autoimmune diseases and physical examination specimens were collected to parallel detect specific antibodies. Individual antibodies' diagnostic performance and a model combining multiple antibodies were assessed. The findings provide valuable insights into improving the diagnosis of SLE through innovative approaches. AIM: To compare the performance of CLIA and LIA in detecting ANAs in patients with autoimmune diseases, assess their accuracy for SLE, and develop a novel diagnostic model using CLIA-detected antibodies for SLE. METHODS: Specimens have been obtained from 270 patients with clinically diagnosed autoimmune disorders, as well as 130 physical examination specimens. After that, parallel detection of anti-double-stranded DNA (dsDNA) antibody, anti-histone (Histone) antibody, anti-nucleosome (Nuc) antibody, anti-Smith (Sm) antibody, anti-ribosomal P protein (Rib-P) antibody, anti-sicca syndrome A (Ro60) antibody, anti-sicca syndrome A (Ro52) antibody, anti-sicca syndrome (SSB) antibody, anti-centromere protein B (Cenp-B) antibody, anti-DNA topoisomerase 1 (Scl-70) antibody, anti-histidyl tRNA synthetase (Jo-1) antibody, and anti-mitochondrial M2 (AMA-M2) antibody was performed using CLIA and LIA. The detection rates, compliance rates, and diagnostic performance for SLE were compared between the two methodologies, followed by developing a novel diagnostic model for SLE. RESULTS: CLIA and LIA exhibited essentially comparable detection rates for anti-dsDNA antibody, anti-Histone antibody, anti-Nuc antibody, anti-Sm antibody, anti-Rib-P antibody, anti-Ro60 antibody, anti-Ro52 antibody, anti-SSB antibody, anti-Cenp-B antibody, anti-DNAScl-70 antibody, anti-Jo-1 antibody and anti-AMA-M2 antibody (P > 0.05). The two methods displayed identical results for the detection of anti-dsDNA antibody, anti-Histone antibody, anti-Nuc antibody, anti-Sm antibody, anti-Ro60 antibody, anti-Ro52 antibody, anti-SSB antibody, anti-Cenp-B antibody, anti-Scl-70 antibody, and anti-AMA-M2 antibody (Kappa > 0.7, P < 0.05), but showed a moderate agreement for the detection of anti-Rib-P antibody and anti-Jo-1 antibody (Kappa = 0.671 and 0.665; P < 0.05). In addition, the diagnostic performance of these antibodies detected by both methods was similar for SLE. The diagnostic model's area under the curve values, sensitivity, and specificity, including an anti-dsDNA antibody and an anti-Ro60 antibody detected by CLIA, were 0.997, 0.962, and 0.978, respectively. These values were higher than the diagnostic performance of individual antibodies. CONCLUSION: CLIA and LIA demonstrated excellent overall consistency in detecting ANA profiles. A diagnostic model based on CLIA-detected antibodies can successfully contribute to developing a novel technique for detecting SLE.

Genome-Wide Detection of Copy Number Variations Associated with Miniature Features in Horses.

Copy number variations (CNVs) are crucial structural genomic variants affecting complex traits in humans and livestock animals. The current study was designed to conduct a comprehensive comparative copy number variation analysis among three breeds, Debao (DB), Baise (BS), and Warmblood (WB), with a specific focus on identifying genomic regions associated with miniature features in horses. Using whole-genome next-generation resequencing data, we identified 18,974 CNVs across 31 autosomes. Among the breeds, we found 4279 breed-specific CNV regions (CNVRs). Baise, Debao, and Warmblood displayed 2978, 986, and 895 distinct CNVRs, respectively, with 202 CNVRs shared across all three breeds. After removing duplicates, we obtained 1545 CNVRs from 26 horse genomes. Functional annotation reveals enrichment in biological functions, including antigen processing, cell metabolism, olfactory conduction, and nervous system development. Debao horses have 970 genes overlapping with CNVRs, possibly causing their small size and mountainous adaptations. We also found that the genes GHR, SOX9, and SOX11 may be responsible for the miniature features of the Debao horse by analyzing their overlapping CNVRs. Overall, this study offers valuable insights into the widespread presence of CNVs in the horse genome. The findings contribute to mapping horse CNVs and advance research on unique miniature traits observed in the Debao horse.

Long-term ozone exposure is negatively associated with estimated glomerular filtration rate in Chinese middle-aged and elderly adults.

Chronic kidney disease (CKD) is an inflammatory disease characterized by the deterioration of renal function, which imposes a significant burden on the healthcare system. In the recent decades, the ageing of the population and the increase of ozone pollution have accelerated. However, epidemiological associations between long-term ozone exposure and renal function in susceptible populations are understudied. In this study, we aimed to investigate the association of 1 y ozone exposure with renal function among the older adults in Xiamen City, China. We recruited 6024 eligible participants with a median age of 65.00 years, estimated their ozone exposure data, and collected questionnaires on demographic status and lifestyle factors as well as information on healthcare access. A generalized linear model was used to assess the association. An increase of 10 µg/m3 of 1 y ozone exposure was negatively associated with the estimated glomerular filtration rate (eGFR) [-3.12 (95% CI: -4.76, -1.48)]. The associations were stronger in men, non-smokers, and those with hypertension or T2DM. Clinical indicators of high-density lipoprotein, low-density lipoprotein, triglycerides, and total cholesterol were the main mediators to regulate the ozone-renal function association. Our results suggested that long-term ozone exposure is a potential risk factor for renal function in Chinese middle-aged and elderly adults.

Berberine ameliorate inflammation and apoptosis via modulating PI3K/AKT/NFκB and MAPK pathway on dry eye.

BACKGROUND: Dry eye disease (DED) is a multifactorial disease in ocular surface, and inflammation plays an etiological role. Berberine (BBR) has shown efficacy in treating inflammatory diseases. Yet, there was no adequate information related to the therapeutic effects of BBR for DED. PURPOSE: To detect the effects and explore the potential mechanisms of BBR on DED. STUDY DESIGN: In vitro, in vivo study and network pharmacology analysis were involved. METHOD: The human corneal epithelium cells viability was evaluated with different concentrations of BBR. Dry eye murine model was established by exposing to the desiccating stress, and Ciclosporin (CSA), BBR eye drops or vehicle were topical administration for 7 days. The phenol red cotton tests, Oregon-green-dextran staining and Periodic acid-Schiff staining were performed and evaluated the dry eye after treatment. Inflammation and apoptosis levels of ocular surface were quantified. The potential targets related to berberine and dry eye were collected from databases. The Protein-Protein interaction network analysis and GO & KEGG enrichment analysis were realized by STRING database, Metascape platform and Cytoscape software to find core targets and signaling pathways. The SchrÖdinger software was used to molecular docking and PyMOL software to visualization. Finally, the levels of PI3K/AKT/NFκB and MAPK pathways were detected. RESULT: The data revealed BBR could rescue impaired HCE under hyperosmotic conditions. In addition, BBR eye drops could ameliorate dry eye. And BBR eye drops suppressed the inflammatory factors and CD4+T cells infiltration in conjunctiva. Besides, BBR eye drops protected ocular surface by avoiding the severe apoptosis and decreasing the level of MMP-3 and MMP-9. 148 common targets intersection between BBR and dry eye were found via network pharmacology analysis. Core proteins and core pathways were identified through PPI and GO&KEGG enrichment analysis. Molecular docking displayed excellent binding between BBR and those core targets. Finally, in vivo study verified that BBR eye drops had a therapeutic effect in dry eye by inhibiting PI3K/AKT/NFκB and MAPK pathways. CONCLUSION: The research provided convincing evidence that BBR could be a candidate drug for dry eye.

DeepSATA: A Deep Learning-Based Sequence Analyzer Incorporating the Transcription Factor Binding Affinity to Dissect the Effects of Non-Coding Genetic Variants.

Utilizing large-scale epigenomics data, deep learning tools can predict the regulatory activity of genomic sequences, annotate non-coding genetic variants, and uncover mechanisms behind complex traits. However, these tools primarily rely on human or mouse data for training, limiting their performance when applied to other species. Furthermore, the limited exploration of many species, particularly in the case of livestock, has led to a scarcity of comprehensive and high-quality epigenetic data, posing challenges in developing reliable deep learning models for decoding their non-coding genomes. The cross-species prediction of the regulatory genome can be achieved by leveraging publicly available data from extensively studied organisms and making use of the conserved DNA binding preferences of transcription factors within the same tissue. In this study, we introduced DeepSATA, a novel deep learning-based sequence analyzer that incorporates the transcription factor binding affinity for the cross-species prediction of chromatin accessibility. By applying DeepSATA to analyze the genomes of pigs, chickens, cattle, humans, and mice, we demonstrated its ability to improve the prediction accuracy of chromatin accessibility and achieve reliable cross-species predictions in animals. Additionally, we showcased its effectiveness in analyzing pig genetic variants associated with economic traits and in increasing the accuracy of genomic predictions. Overall, our study presents a valuable tool to explore the epigenomic landscape of various species and pinpoint regulatory deoxyribonucleic acid (DNA) variants associated with complex traits.

Construction of Indole-Fused Seven- and Eight-Membered Azaheterocycles via a Tandem Pd/NBE-Catalyzed Decarbonylation and Dual C-H Activation Sequence.

Herein, we report the transformation of aromatic acids to indole-fused seven- and eight-membered azaheterocycles. Two C-C bonds are formed via the cleavage of one C-C bond and two C-H bonds. The incorporation of indole moieties into bioactive pharmaceuticals and natural products to construct a medium-sized polyfused heterocycle demonstrates the synthetic utility of the protocol.

Elucidating cellular interactome of chikungunya virus identifies host dependency factors.

Chikungunya virus (CHIKV) is a re-emerging mosquito-transmitted RNA virus causing joint and muscle pain. To better understand how CHIKV rewires the host cell and usurps host cell functions, we generated a systematic CHIKV-human protein-protein interaction map and revealed several novel connections that will inform further mechanistic studies. One of these novel interactions, between the viral protein E1 and STIP1 homology and U-box containing protein 1 (STUB1), was found to mediate ubiquitination of E1 and degrade E1 through the proteasome. Capsid associated with G3BP1, G3BP2 and AAA+ â€‹ATPase valosin-containing protein (VCP). Furthermore, VCP inhibitors blocked CHIKV infection, suggesting VCP could serve as a therapeutic target. Further work is required to fully understand the functional consequences of these interactions. Given that CHIKV proteins are conserved across alphaviruses, many virus-host protein-protein interactions identified in this study might also exist in other alphaviruses. Construction of interactome of CHIKV provides the basis for further studying the function of alphavirus biology.

Springer: An R package for bi-level variable selection of high-dimensional longitudinal data.

In high-dimensional data analysis, the bi-level (or the sparse group) variable selection can simultaneously conduct penalization on the group level and within groups, which has been developed for continuous, binary, and survival responses in the literature. Zhou et al. (2022) (PMID: 35766061) has further extended it under the longitudinal response by proposing a quadratic inference function-based penalization method in gene-environment interaction studies. This study introduces "springer," an R package implementing the bi-level variable selection within the QIF framework developed in Zhou et al. (2022). In addition, R package "springer" has also implemented the generalized estimating equation-based sparse group penalization method. Alternative methods focusing only on the group level or individual level have also been provided by the package. In this study, we have systematically introduced the longitudinal penalization methods implemented in the "springer" package. We demonstrate the usage of the core and supporting functions, which is followed by the numerical examples and discussions. R package "springer" is available at https://cran.r-project.org/package=springer.

Formosanin C suppresses cancer cell proliferation and migration by impeding autophagy machinery.

Formosanin C (FC) is a natural compound extracted from Paris formosana Hayata with anticancer activity. FC induces both autophagy and apoptosis in human lung cancer cells. FC-induced depolarization of mitochondrial membrane potential (MMP) may trigger mitophagy. In this study, we clarified the effect of FC on autophagy, mitophagy, and the role of autophagy in FC-related cell death and motility. We found FC caused the continuous increase of LC3 II (representing autophagosomes) from 24 to 72 h without degradation after treatment of lung and colon cancer cells, indicating that FC blocks autophagic progression. In addition, we confirmed that FC also induces early stage autophagic activity. Altogether, FC is not only an inducer but also a blocker of autophagy progression. Moreover, FC increased MMP accompanied by overexpression of COX IV (mitochondria marker) and phosphorylated Parkin (p-Parkin, mitophagy marker) in lung cancer cells, but no colocalization of LC3 with COX IV or p-Parkin was detected under confocal microscopy. Moreover, FC could not block CCCP (mitophagy inducer)-induced mitophagy. These results imply that FC disrupts mitochondria dynamics in the treated cells, and the underlying mechanism deserves further exploration. Functional analysis reveals that FC suppresses cell proliferation and motility through apoptosis and EMT-related pathway, respectively. In conclusion, FC acts as an inducer as well as a blocker of autophagy that results in cancer cell apoptosis and decreased motility. Our findings shed the light on the development of combined therapy with FC and clinical anticancer drugs for cancer treatment.