Gene Selection Research Articles

Complete Mitogenomes of Xinjiang Hares and Their Selective Pressure Considerations.

Comparative analysis based on the mitogenomes of hares in Xinjiang, China, is limited. In this study, the complete mitochondrial genomes of seven hare samples including four hare species and their hybrids from different environments were sequenced, assembled, and annotated. Subsequently, we performed base content and bias analysis, tRNA analysis, phylogenetic analysis, and amino acid sequence analysis of the annotated genes to understand their characteristics and phylogenetic relationship. Their mitogenomes are circular molecules (from 16,691 to 17,598 bp) containing 13 protein-coding genes, two rRNA genes, 22 tRNA genes, and a control region, which are similar with other Lepus spp. worldwide. The relative synonymous codon usage analysis revealed that the adaptation of Lepus yarkandensis to its unique arid and hot environment might be associated with synthesizing amino acids like alanine, leucine, serine, arginine, and isoleucine and the terminator caused by the different usage of codons. Further, we utilized the MEME model and identified two positive selection genes (ND4, ND5) in Lepus tibetanus pamirensis and one (ND5) in L. yarkandensis that might be important to their adaptation to the plateau and dry and hot basin environments, respectively. Meanwhile, Lepus tolai lehmanni and Lepus timidus may have evolved different adaptive mechanisms for the same cold environment. This study explored the evolutionary dynamics of Xinjiang hares' mitochondrial genomes, providing significant support for future research into their adaptation mechanisms in extreme environments.

Stability assessment of housekeeping genes for qRT-PCR in Yersinia enterocolitica cultured at 22°C and 37°C.

Yersinia enterocolitica, a species within the genus Yersinia, thrives optimally at 22-25°C but can also grow at the mammalian core body temperature of 37°C. This dual temperature adaptability necessitates establishing both temperature conditions in research to examine the effects on various biological processes. In quantitative real-time PCR (qRT-PCR) assays, the selection of appropriate housekeeping genes is vital for data accuracy. Nevertheless, the lack of alternatives and information often leads to the default use of the 16S rRNA gene despite potential limitations. This investigation sourced 16 potential reference genes through a comprehensive review of the literature and transcriptome sequencing data analysis. We validated the expression stability of these genes via qRT-PCR across 12 Y. enterocolitica strains, representing the four prevalent serotypes O:3, O:5,27, O:8, and O:9, isolated from diarrheal patient stool samples. This approach aimed to minimize the impact of serotype heterogeneity. After acquiring Cq values, gene stability was evaluated using four established algorithms-ΔCq, geNorm, NormFinder, and BestKeeper-and subsequently synthesized into a consolidated ranking through the Robust Rank Aggregation (RRA) method. Our study suggests that the genes glnS, nuoB, glmS, gyrB, dnaK, and thrS maintain consistent expression across varying culture temperatures, supporting their candidacy as robust housekeeping genes. We advise against the exclusive use of 16S rRNA for this purpose. Should tradition prevail in its utilization, it must be employed with discernment, preferably alongside one or two of the housekeeping genes identified in this study as internal controls.IMPORTANCEIn our study, we focused on identifying stable reference genes for quantitative real-time PCR (qRT-PCR) experiments on Y. enterocolitica cultured at different temperatures (22°C and 37°C). After thoroughly evaluating 16 candidate genes, we identified six genes-glnS, nuoB, glmS, gyrB, dnaK, and thrS-as exhibiting stable expression across these temperature conditions, making them ideal reference genes for Y. enterocolitica studies. This discovery is crucial for ensuring the accuracy and reliability of qRT-PCR data, as the choice of appropriate reference genes is key to normalizing expression data and minimizing experimental variability. Importantly, our research extended beyond bioinformatics analysis by incorporating validation with clinical strains, bridging the gap between theoretical predictions and practical application. This approach not only underscores the robustness and reliability of our findings but also directly addresses the critical need for experimental validation in the field. By providing a set of validated, stably expressed reference genes, our work offers valuable guidance for designing experiments involving Y. enterocolitica, enhancing the reliability of research outcomes, and advancing our understanding of this significant pathogen.

Apoptosis-associated genetic mechanisms in the transition from rheumatoid arthritis to osteoporosis: A bioinformatics and functional analysis approach.

This study explores the mechanisms of glucocorticoid-induced osteoporosis (OP) and Rheumatoid arthritis (RA), focusing on apoptosis and its role in the progression from RA to OP. Using microarray data from the GEO database, differential gene expression analysis was conducted with the limma package, identifying significant genes in RA and OP. Weighted Gene Co-expression Network Analysis (WGCNA) further examined gene relationships with the disease status, identifying co-expression patterns. Key genes were pinpointed by intersecting differentially expressed genes from RA and OP datasets with WGCNA module genes. Functional enrichment analysis using the "clusterProfiler" package focused on Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways. Machine learning methods, including Lasso and Random Forest, refined the selection of key genes related to apoptosis. Immune infiltration analysis using CIBERSORT assessed immune cell differences between disease and normal samples. The study highlighted two crucial genes: ATXN2L and MMP14. These genes were identified through various analyses and found to be significantly associated with the progression of RA and OP. Gene Set Enrichment Analysis of ATXN2L and MMP14 revealed their involvement in specific biological processes and pathways. Correlation analysis between these key genes and immune cell infiltration showed significant associations. The ROC analysis evaluated the diagnostic performance of ATXN2L and MMP14, with miRNA regulatory networks related to these genes also predicted. In summary, this research provides valuable insights into the molecular mechanisms of RA and OP, emphasizing the importance of apoptosis and immune processes.

Genome-wide selection of potential target candidates for RNAi against Nilaparvata lugens

BackgroundNilaparvata lugens is one of the most destructive pests of rice. RNAi-based N. lugens control offers one alternative strategy to traditional chemical insecticides. However, selection of potential target for RNAi against N. lugens remains a major challenge. Only two target genes for nuclear transgenic N. lugens-resistant plants have been screened. Importantly, only one or few potential target genes against N. lugens were screened every time by knowledge of essential genes from model organisms in previous study.ResultsHere, in silico genome-wide selection of potential target genes against N. lugens through homology comparison was performed. Through genome synteny comparisons, about 3.5% of Drosophila melanogaster genome was found to have conserved genomic synteny with N. lugens genome. By using N. lugens proteins to search D. melanogaster homologs defining lethal or sterile phenotype, 358 N. lugens genes were first screened as putative target genes. Transgenic rice lines expressing dsRNA of randomly selected gene (NlRan or NlSRP54) from 358 putative target genes enhanced resistance to N. lugens. After expression check and safety check, 115 N. lugens genes were screened as potential target candidates.ConclusionThe combined efforts in this study firstly provide one in silico genome-wide homology-based screening approach for RNAi-based target genes against N. lugens, which not only offer one new opportunity to batch select potential target candidates in pests of interest, but also will facilitate the selection of RNAi target in many pest species by providing more than one hundred potential target candidates.

Genome Dynamics of Adaptation and Introgression in Neotropical Palms

ABSTRACTAimThe combined influences of demographic dynamics and gene flow on local adaptation in plants is still poorly understood. Here, we used a genome scan approach on three closely related Neotropical palms, Acrocomia aculeata, A. intumescens and A. totai, to identify the evolutionary processes generating shared and lineage‐specific patterns of differentiation and selection across the genomic landscape.LocationAmazonia, Atlantic Forest, Cerrado and Caatinga ecoregions of Brazil.TaxaArecaceae.MethodsWe used target sequence capture and analysis of climatic correlation, detection of selective sweeps, balancing selection, and spatial and non‐spatial models to identify signatures of natural selection and admixture. We also determined temporal dynamics in spatial distribution and demographic changes.ResultsWe found a higher number of lineage‐specific than shared adaptive sites (SNPs) and no evidence of selective sweeps in shared genes, suggesting lineage specific natural selection across species. Further, evidence of balancing selection in several genes was also identified in the three species. Niche‐based and coalescent models suggest that shifts in spatial range during the Quaternary caused overlapping distributions between species, leading to hybridisation between parapatric localities.Main ConclusionsInterspecific hybridisation may have spread both neutral and adaptive SNPs, which may explain the shared adaptive genes between species. Taken together, we show how genomic adaptation can occur despite introgression, through evolutionary processes that likely drive similar patterns of adaptation in other organisms.

Selection and validation of reference genes for quantitative expression analysis of regeneration-related genes in Cheilomenes sexmaculata by real-time qRT-PCR.

Regeneration is a fascinating phenomenon that has intrigued scientists for a long time. Cheilomenes sexmaculata (Fabricius), a zig-zag ladybird beetle, possesses a high capacity for limb regeneration. The molecular mechanics of the zig-zag ladybird beetle are under-explored. Current research trends are focused on uncovering functional genes associated with limb regeneration. Most of these investigations involve quantitative real-time PCR (qRT-PCR) for their rapid and accurate analysis of gene expression levels. Hence, a stable and suitable reference gene is required to normalize the gene expression data. In this study, five housekeeping genes were selected from the transcriptomics data (in-house unpublished data) of C. sexmaculata (Fabricius). The expression stabilities of the selected genes were evaluated under different time intervals post-amputation using geNorm, normFinder, and refFinder software. Actin was revealed to be the most stable housekeeping gene, along with elongation factor 2 and glyceraldehyde-3-phosphate dehydrogenase. A target gene named engrailed (an important segment-forming gene) was used to validate the selected reference genes. The expression levels were found to be consistent with the transcriptomics results. According to our study, actin, along with elongation factor 2 and glyceraldehyde-3-phosphate dehydrogenase, serve as the most stable reference genes and are suitable for regeneration-related research. This study is a groundbreaking effort to identify the most stable reference gene for limb regeneration in C. sexmaculata (Fabricius), and the findings can be applied to other related insect species.

Discovery of optimal cell type classification marker genes from single cell RNA sequencing data

BackgroundThe use of single cell/nucleus RNA sequencing (scRNA-seq) technologies that quantitively describe cell transcriptional phenotypes is revolutionizing our understanding of cell biology, leading to new insights in cell type identification, disease mechanisms, and drug development. The tremendous growth in scRNA-seq data has posed new challenges in efficiently characterizing data-driven cell types and identifying quantifiable marker genes for cell type classification. The use of machine learning and explainable artificial intelligence has emerged as an effective approach to study large-scale scRNA-seq data.MethodsNS-Forest is a random forest machine learning-based algorithm that aims to provide a scalable data-driven solution to identify minimum combinations of necessary and sufficient marker genes that capture cell type identity with maximum classification accuracy. Here, we describe the latest version, NS-Forest version 4.0 and its companion Python package (https://github.com/JCVenterInstitute/NSForest), with several enhancements to select marker gene combinations that exhibit highly selective expression patterns among closely related cell types and more efficiently perform marker gene selection for large-scale scRNA-seq data atlases with millions of cells.ResultsBy modularizing the final decision tree step, NS-Forest v4.0 can be used to compare the performance of user-defined marker genes with the NS-Forest computationally-derived marker genes based on the decision tree classifiers. To quantify how well the identified markers exhibit the desired pattern of being exclusively expressed at high levels within their target cell types, we introduce the On-Target Fraction metric that ranges from 0 to 1, with a metric of 1 assigned to markers that are only expressed within their target cell types and not in cells of any other cell types. NS-Forest v4.0 outperforms previous versions in simulation studies and on its ability to identify markers with higher On-Target Fraction values for closely related cell types in real data, and outperforms other marker gene selection approaches for cell type classification with significantly higher F-beta scores when applied to datasets from three human organs—brain, kidney, and lung.DiscussionFinally, we discuss potential use cases of the NS-Forest marker genes, including for designing spatial transcriptomics gene panels and semantic representation of cell types in biomedical ontologies, for the broad user community.

Protein aggregation in plant mitochondria lacking Lon1 inhibits translation and induces unfolded protein responses.

Loss of Lon1 led to stunted plant growth and accumulation of nuclear-encoded mitochondrial proteins including Lon1 substrates. However, an in-depth label-free proteomics quantification of mitochondrial proteins in lon1 revealed that the majority of mitochondrial-encoded proteins decreased in abundance. Additionally, we found that lon1 mutants contained protein aggregates in the mitochondrial that were enriched in metabolic enzymes, ribosomal subunits and PPR-containing proteins of the translation apparatus. These mutants exhibited reduced general mitochondrial translation as well as deficiencies in RNA splicing and editing. These findings support the role of Lon1 in maintaining a functional translational apparatus for mitochondrial-encoded gene translation. Transcriptome analysis of lon1 revealed a mitochondrial unfolded protein response reminiscent of the mitochondrial retrograde signalling dependent on the transcription factor ANAC017. Notably, lon1 mutants exhibited transiently elevated ethylene production, and the shortened hypocotyl observed in lon1 mutants during skotomorphogenesis was partially alleviated by ethylene inhibitors. Furthermore, the short root phenotype was partially ameliorated by introducing a mutation in the ethylene receptor ETR1. Interestingly, the upregulation of only a select few target genes was linked to ETR1-mediated ethylene signalling. Together this provides multiple steps in the link between loss of Lon1 and signalling responses to restore mitochondrial protein homoeostasis in plants.

Multi-Modal Analysis of human Hepatic Stellate Cells identifies novel therapeutic targets for Metabolic Dysfunction-Associated Steatotic Liver Disease

Background and aimsMetabolic dysfunction-associated steatotic liver disease (MASLD) ranges from Metabolic dysfunction-associated steatotic liver (MASL) to Metabolic dysfunction-associated steatohepatitis (MASH) with fibrosis. Activation of Hepatic Stellate Cells (HSCs) into fibrogenic myofibroblasts plays a critical role in the pathogenesis of MASH liver fibrosis. We compared transcriptome and chromatin accessibility of human HSCs from NORMAL, MASL, and MASH livers at single cell resolution. We aimed to identify genes that are upregulated in activated HSCs and to determine which of these genes are key in the pathogenesis of MASH fibrosis. Methods18 human livers were profiled using single-nucleus (sn)RNA-seq and snATAC-seq. High priority targets were identified, then tested in 2D human HSC cultures, 3D human liver spheroids, and HSC-specific gene knockout mice. ResultsMASH-enriched activated (A) HSC subclusters are the major source of extracellular matrix proteins. We identified a set of concurrently upregulated and more accessible core genes (GAS7, SPON1, SERPINE1, LTBP2, KLF9, EFEMP1) that drive activation of (A) HSC subclusters. Expression of these genes was regulated via crosstalk between lineage-specific (JUNB/AP1), cluster-specific (RUNX1/2) and signal-specific (FOXA1/2) transcription factors. The pathological relevance of the selected targets, such as SERPINE1 (PAI-1), was demonstrated using dsiRNA-based HSC-specific gene knockdown or pharmacological inhibition of PAI-1 in 3D human MASH liver spheroids, and HSC-specific Serpine1 knockout mice. ConclusionThis study identified novel gene targets and regulatory mechanisms underlying activation of MASH fibrogenic HSCs and demonstrated that genetic or pharmacological inhibition of select genes suppressed liver fibrosis. Impact and implicationsHere we present snRNA-seq and snATAC-seq analysis of human HSCs from NORMAL, MASL, and MASH livers. We identified additional subclusters that were not detected by previous studies and characterized the mechanism by which HSCs activate in the MASH livers, including the transcriptional machinery that activates HSCs into myofibroblasts. For the first time, we described the pathogenic role of activated HSC-derived PAI-1 (a product of SERPINE1 gene) in the development of MASH liver fibrosis. Targeting of RUNX1/2-SERPINE1 axis may provide a novel strategy for treatment of liver fibrosis in patients.

Comparative transcriptomic analyses of diploid and tetraploid citrus reveal how ploidy level influences salt stress tolerance.

Citrus is an important fruit crop for human health. The sensitivity of citrus trees to a wide range of abiotic stresses is a major challenge for their overall growth and productivity. Among these abiotic stresses, salinity results in a significant loss of global citrus yield. In order to find straightforward and sustainable solutions for the future and to ensure citrus productivity, it is of paramount importance to decipher the mechanisms responsible for salinity stress tolerance. Thisstudy aimed to investigate how ploidy levels influence salt stress tolerance in citrus by comparing the transcriptomic responses of diploid and tetraploid genotypes. In a previous article we investigated the physiological and biochemical response of four genotypes with different ploidy levels: diploid trifoliate orange (Poncirus trifoliata [L.] Raf.) (PO2x) and Cleopatra mandarin (Citrus reshni Hort. Ex Tan.) (CL2x) and their respective tetraploids (PO4x, CL4x). In this study, we useda multifactorial gene selection and gene clustering approach to finely dissect the influence of ploidy level on the salt stress response of each genotype. Following transcriptome sequencing, differentially expressed genes (DEGs) were identified in response to salt stress in leaves and roots of the different citrus genotypes. Gene expression profiles and functional characterization of genes involved in the response to salt stress, as a function of ploidy level and the interaction between stress response and ploidy level, have enabled us to highlight the mechanisms involved in the varieties tested. Saltstress induced overexpression of carbohydrate biosynthesis and cell wall remodelling- related genes specifically in CL4x Ploidy level enhanced oxidative stress response in PO and ion management capacity in both genotypes. Results further highlighted that under stress conditions, only the CL4x genotype up- regulated genes involved in sugar biosynthesis, transport management, cell wall remodelling, hormone signalling, enzyme regulation and antioxidant metabolism. These findings provide crucial insights that could inform breeding strategies for developing salt-tolerant citrus varieties.

Impact of potential biomarkers, SNRPE, COX7C, and RPS27, on idiopathic Parkinson's disease.

Parkinson's disease (PD) is a progressive neuro-degenerative disorder most common in older adults which is associated with impairments in movement and other body functions. Most PD cases are classified as idiopathic PD (IPD), meaning that the etiology remains unidentified. To identify key genes and molecular mechanisms to identify biomarkers applicable to IPD. We applied a bioinformatics approach using a gene expression in whole blood dataset to pinpoint differentially expressed genes (DEGs) and pathways involved in IPD. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of DEGs were subsequently performed. A protein-protein interaction (PPI) network was then constructed to select hub genes that may influence IPD. We further investigated the levels of differentially methylated regions (DMRs) and differentially expressed microRNA (DEMs) of whole blood of patients with IPD to validate hub genes. Additionally, we examined the hub gene expression patterns in the substantia nigra (STN) using single-cell RNA sequencing datasets. In total, we identified 124 DEGs in the blood samples of patients with IPD, with GO and KEGG analyses highlighting their significant enrichment. Analysis of PPI networks revealed three major clusters and hub genes: small nuclear ribonucleoprotein polypeptide E (SNRPE), cytochrome C oxidase subunit 7C (COX7C), and ribosomal protein S27 (RPS27). DMRs and DEMs analyses revealed hub gene regulation via epigenetic and RNA interference. In particular, SNRPE and RPS27 showed identically regulated gene expression in the STN. This study suggests that SNRPE, COX7C, and RPS27 in whole-blood samples derived from patients may be useful biomarkers for IPD.

Selective Cytotoxicity and Gene Expression Profiles in Response to Methanolic Extract of Phyllanthus amarus Extract against MCF-7 Breast Cancer Cells

Background Breast cancer, a malignant tumor, is the most common cancer type among women worldwide. Conventional chemotherapy treatments for breast cancer often cause adverse side effects in normal tissues and can lead to treatment failure. Plant-derived natural compounds offer an effective alternative with fewer side effects for cancer patients. Phyllanthus amarus is a medicinal plant widely distributed in China, India, and Brazil and reported to be used for the treatment of various types of cancer. Purpose This study was aimed to investigate the anticancer efficacy of methanolic extract of P. amarus against MCF-7 breast cancer cells. Methods We probed the anticancer activity in MCF-7 breast cancer cells by isolating the methanolic extract of P. amarus leaf extract (MEPA). The standard chemotherapy drug docetaxel is combined with MEPA to increase the drug efficacy. Moreover, the intracellular reactive oxygen species (ROS), alterations in mitochondrial membrane potential (MMP), nuclear condensation, and apoptotic activation were investigated in MCF-7 cells. Additionally, the customized PCR array comprising of cell cycle, apoptosis, mTOR, and JAK-STAT-related genes was studied during different MEPA treatments. Results In the MCF-7 breast cancer cells, the MEPA and docetaxel combination demonstrated strong cytotoxicity, with IC50 values of 45 and 2.6 µg/mL, respectively. MEPA and docetaxel together exhibited enhanced alterations in MMP and increased generation of ROS in MCF-7 cells. Furthermore, the combination of docetaxel with MEPA induces nuclear condensation and apoptotic cell death. During MEPA treatment, the gene expression profiles in MCF-7 cells were accessed. The JAK-STAT, mTOR signaling pathway, apoptosis, and cell growth-related genes were strongly affected by the dose-dependent administration of MEPA. Conclusion This study suggests that MEPA could be a useful alternative treatment for breast cancer. When combined with traditional chemotherapy, MEPA might offer better results with fewer side effects. More in vivo studies are needed to confirm MEPA’s effectiveness in animal models.

Expansion of MHC-IIB Has Constrained the Evolution of MHC-IIA in Passerines.

The major histocompatibility complex (MHC) is central in adaptive immunity, with the highly polymorphic MHC genes encoding antigen-presenting molecules. Two MHC class II (MHC-II) loci, DA1 and DA2, predate the radiation of extant birds and persist throughout much of the avian phylogeny. Within each locus, the MHC-II molecules are encoded by A-genes (DAA) and B-genes (DAB), which are arranged in A-B dyads. However, in passerines (order Passeriformes), the DA2 locus has been lost, and the ancestral A-B dyad at the DA1 locus has been replaced by a putatively single A-gene (DAA1) and an array of highly polymorphic B-genes (DAB1). In this study, we genotyped the DAA1 gene of 15 passerine species and confirmed that passerines possess just one copy of DAA1. We then compared selection patterns in DAA1 between passerines and nonpasserines and found that exon 2, which encodes the antigen-presenting domain, has been subject to weaker positive selection and stronger negative selection in passerines compared with nonpasserines. Additional comparisons showed that the patterns of selection in the passerine DAA1 gene are unlikely to be related to the loss of the DA2 locus. Instead, our findings suggest that the expansion of DAB1 (MHC-IIB) has imposed an evolutionary constraint on the passerine DAA1 (MHC-IIA) gene. We speculate that this constraint may be the result of each DAA1 chain forming heterodimers with many different DAB1 chains.

Establishing the green algae Chlamydomonas incerta as a platform for recombinant protein production.

Chlamydomonas incerta, a genetically close relative of the model green alga Chlamydomonas reinhardtii, shows significant potential as a host for recombinant protein expression. Because of the close genetic relationship between C. incerta and C. reinhardtii, this species offers an additional reference point for advancing our understanding of photosynthetic organisms, and also provides a potential new candidate for biotechnological applications. This study investigates C. incerta's capacity to express three recombinant proteins: the fluorescent protein mCherry, the hemicellulose-degrading enzyme xylanase, and the plastic-degrading enzyme PHL7. We have also examined the capacity to target protein expression to various cellular compartments in this alga, including the cytosol, secretory pathway, cytoplasmic membrane, and cell wall. When compared directly with C. reinhardtii, C. incerta exhibited a distinct but notable capacity for recombinant protein production. Cellular transformation with a vector encoding mCherry revealed that C. incerta produced approximately 3.5 times higher fluorescence levels and a 3.7-fold increase in immunoblot intensity compared to C. reinhardtii. For xylanase expression and secretion, both C. incerta and C. reinhardtii showed similar secretion capacities and enzymatic activities, with comparable xylan degradation rates, highlighting the industrial applicability of xylanase expression in microalgae. Finally, C. incerta showed comparable PHL7 activity levels to C. reinhardtii, as demonstrated by the in vitro degradation of a polyester polyurethane suspension, Impranil® DLN. Finally, we also explored the potential of cellular fusion for the generation of genetic hybrids between C. incerta and C. reinhardtii as a means to enhance phenotypic diversity and augment genetic variation. We were able to generate genetic fusion that could exchange both the recombinant protein genes, as well as associated selectable marker genes into recombinant offspring. These findings emphasize C. incerta's potential as a robust platform for recombinant protein production, and as a powerful tool for gaining a better understanding of microalgal biology.

Whole genome resequencing analysis elucidate genetic features and signature of selection in binhu buffaloes

The world's buffaloes are primarily divided into two main categories: swamp buffaloes and riverine buffaloes. Binhu buffaloes (BB) are Chinese indigenous swamp buffaloes, characterized as strong body vigor, fit for durable work with docile nature. However, there is literature void regarding genomic architecture and selective sweeps analysis for this breed. Herein we utilized 20 newly whole genome sequences (WGS) of BB together with published WGS data of seventy-four buffaloes [Upper Yangtze (UY) buffaloes; n = 30, Middle-Lower Yangtze (MLY) buffaloes; (n = 30) and River buffaloes (RB); (n = 14)] to elucidate population structure, genetic diversity and selection characteristics of the BB. The results showed that the BB originated from swamp buffalos. The genetic diversity of the BB was lower than that of the RB and higher than that of the UY and the MLY buffaloes. In addition, employing five selective sweep detection methods numerous genes related to immunity (RELT, TP73, C5, CHMP1A, CDK10, ANKRD17), heat tolerance (DNAJB4, DNAJA1, HSF4, HELB, DNAJC28), growth (LYN, DYNC1I2, PLAG1, ADAMTSL3, CHKB, PDE1A, RXFP2), carcass and metabolism (SIRT6, LYPLA1, FADS1), nervous system (KIRREL3, AUTS2), and reproduction (SMG6, TSNAXIP1, CACNB2, ERCC3, RAD51, GDF9, CAMK4, KALRN, FANCA, SPIRE2, ATP2B1, AREG, EREG) were found to be under selection. Taken together, current investigation provides a genetic basis for the characteristics specific to the BB, such as high body strength; tolerance to roughage and easy to gain weight; docile nature; ability to endure labor; strong adaptability; and low fecundity and offers new ideas for the conservation, development and utilization of this breed.

Non-electrophilic NRF2 activators promote wound healing in human keratinocytes and diabetic mice and demonstrate selective downstream gene targeting

The transcription factor NRF2 plays an important role in many biological processes and is a promising therapeutic target for many disease states. NRF2 is highly expressed in the skin and is known to play a critical role in diabetic wound healing, a serious disease process for which treatment options are limited. However, many existing NRF2 activators display off-target effects due to their electrophilic mechanism, underscoring the need for alternative approaches. In this work, we investigated two recently described non-electrophilic NRF2 activators, ADJ-310 and PRL-295, and demonstrated their efficacy in vitro and in vivo in human keratinocytes and Leprdb/db diabetic mice. We also compared the downstream targets of PRL-295 to those of the widely used electrophilic NRF2 activator CDDO-Me by RNA sequencing. Both ADJ-310 and PRL-295 maintained human keratinocyte cell viability at increasing concentrations and maintained or improved cell proliferation over time. Both compounds also increased cell migration, improving in vitro wound closure. ADJ-310 and PRL-295 enhanced the oxidative stress response in vitro, and RNA-sequencing data showed that PRL-295 activated NRF2 with a narrower transcriptomic effect than CDDO-Me. In vivo, both ADJ-310 and PRL-295 improved wound healing in Leprdb/db diabetic mice and upregulated known downstream NRF2 target genes in treated tissue. These results highlight the non-electrophilic compounds ADJ-310 and PRL-295 as effective, innovative tools for investigating the function of NRF2. These compounds directly address the need for alternative NRF2 activators and offer a new approach to studying the role of NRF2 in human disease and its potential as a therapeutic across multiple disease states.

Reference Gene Selection for RT-qPCR Normalization in Toxoplasma gondii Exposed to Broxaldine.

Reverse transcription-quantitative real-time polymerase chain reaction (RT-qPCR) is widely used to accurately assess target gene expression. Evaluating gene expression requires the selection of appropriate reference genes. To identify reliable reference genes for Toxoplasma gondii (T. gondii) under varying concentrations of broxaldine (BRO), we employed the ΔCt method, BestKeeper, NormFinder, GeNorm, and the comprehensive web-based platform RefFinder to assess the expression stability of ten candidate reference genes in T. gondii. Herein, our findings reveal that the stability of these candidate reference genes is influenced by different experimental conditions. Under normal conditions, the most stable genes were TGME49_205470 and TGME49_226020. However, the most stable genes differed when BRO concentrations were at 1, 2, and 4 μg/mL. Across all samples, TGME49_247220 and TGME49_235930 were identified as the most stable reference genes. Moreover, we also confirmed the stability of TGME49_247220 and TGME49_235930 as reference genes through RT-qPCR assays. The present study provides a foundation for applying the RT-qPCR method to investigate target gene expression following BRO treatment in T. gondii.

PONYTA: prioritization of phenotype-related genes from mouse KO events using PU learning on a biological network.

Transcriptome data from gene knock-out (KO) experiments in mice provide crucial insights into the intricate interactions between genotype and phenotype. Differentially expressed gene (DEG) analysis and network propagation (NP) are well-established methods for analysing transcriptome data. To determine genes related to phenotype changes from a KO experiment, we need to choose a cutoff value for the corresponding criterion based on the specific method. Using a rigorous cutoff value for DEG analysis and NP is likely to select mostly positive genes related to the phenotype, but many will be rejected as false negatives. On the other hand, using a loose cutoff value for either method is prone to include a number of genes that are not phenotype-related, which are false positives. Thus, the research problem at hand is how to deal with the trade-off between false negatives and false positives. We propose a novel framework called PONYTA for gene prioritization via positive-unlabeled (PU) learning on biological networks. Beginning with the selection of true phenotype-related genes using a rigorous cutoff value for DEG analysis and NP, we address the issue of handling false negatives by rescuing them through PU learning. Evaluations on transcriptome data from multiple studies show that our approach has superior gene prioritization ability compared to benchmark models. Therefore, PONYTA effectively prioritizes genes related to phenotypes derived from gene KO events and guides in vitro and in vivo gene KO experiments for increased efficiency. The source code of PONYTA is available at https://github.com/Jun-Hyeong-Kim/PONYTA.

Polymorphic Loci of Adaptively Significant Genes Selection for Determining Nucleotide Polymorphism of Pinus sylvestris L. Populations in the Urals

Background: Scots Pine is one of the main forest-forming species in boreal forests; it has great economic and ecological significance. This study aimed to develop and test primers for detecting nucleotide polymorphisms in genes that are promising for detecting adaptive genetic variability in populations of Pinus sylvestris in the Urals and adjacent territories. Objectives: The objects of the study were 13 populations of Scots Pine located in the Perm Territory, Chelyabinsk Region, and the Republic of Bashkortostan. Results: Sixteen pairs of primers to loci of potentially adaptively significant genes were developed, from which three pairs of primers were selected to detect the nucleotide diversity of the studied populations. The indicator of total haplotype diversity determined in the three studied loci varied from 0.620 (Pinus-12 locus) to 0.737 (Pinus-11 locus) and, on average, amounted to 0.662. The nucleotide diversity indicators in P. sylvestris in the study region were, on average, low (π = 0.004, θW = 0.013). Their highest values were found at the Pinus-12 locus (π = 0.005; θW = 0.032), and the lowest were found at the Pinus-15 locus (π = 0.003; θW = 0.002). This indicates that Pinus-15 is the most conserved of the three studied loci. In the three studied P. sylvestris loci associated with adaptation to environmental factors, 97 polymorphic positions were identified. The 13 populations of P. sylvestris are characterized by an average level of genetic diversity (Hd = 0.662; π = 0.004; θ = 0.013). Conclusions: The polymorphic loci of adaptively significant genes of P. sylvestris can help identify the adaptive potential of pine forests in conditions of increasing ambient temperatures.

Xist RNA binds select autosomal genes and depends on Repeat B to regulate their expression.

Xist, a pivotal player in X chromosome inactivation (XCI), has long been perceived as a cis-acting long noncoding RNA that binds exclusively to the inactive X chromosome (Xi). However, Xist's ability to diffuse under select circumstances has also been documented, leading us to suspect that Xist RNA may have targets and functions beyond the Xi. Here, using female mouse embryonic stem cells (ES) and mouse embryonic fibroblasts (MEF) as models, we demonstrate that Xist RNA indeed can localize beyond the Xi. However, its binding is limited to ~100 genes in cells undergoing XCI (ES cells) and in post-XCI cells (MEFs). The target genes are diverse in function but are unified by their active chromatin status. Xist binds discretely to promoters of target genes in neighborhoods relatively depleted for Polycomb marks, contrasting with the broad, Polycomb-enriched domains reported for human XIST RNA. We find that Xist binding is associated with down-modulation of autosomal gene expression. However, unlike on the Xi, Xist binding does not lead to full silencing and also does not spread beyond the target gene. Over-expressing Xist in transgenic ES cells similarly leads to autosomal gene suppression, while deleting Xist's Repeat B motif reduces autosomal binding and perturbs autosomal down-regulation. Furthermore, treating female ES cells with the Xist inhibitor, X1, leads to loss of autosomal suppression. Altogether, our findings reveal that Xist targets ~100 genes beyond the Xi, identify Repeat B as a crucial domain for its in-trans function in mice, and indicate that autosomal targeting can be disrupted by a small molecule inhibitor.