BACKGROUND: A correct and stably expressing reference gene is prerequisite for successful quantitative real-time PCR (qRT-PCR). Investigating gene expression profiling during flower development could enhance our understanding of the molecular mechanisms of flower formation and fertility in Lycium.
RESULTS: In this study, 11 candidate reference genes in Lycium flower development were selected from transcriptome sequence data and evaluated with five traditional housekeeping genes from previous studies based on qRT-PCR amplification. Comparing the expression stability result of 16 candidate genes using GeNorm, NormFinder, BestKeeper, and Delta Ct algorithms, Lba04g01649 and Lba12g02820 were validated as the optimal reference genes for the flower development of Lycium.
CONCLUSIONS: The reference genes identified in this study would improve the accuracy of qRT-PCR quantification of target gene expression in Lycium flower development and facilitate future functional genomics studies on flower development. This research could lay the foundation for the study of the reproduction and development of the Lycium flower.

In Florida, angular leaf spot, caused by Xanthomonas fragariae, was the only known bacterial disease in strawberry, which is sporadic and affects the foliage and calyx. However, from the 2019-2020 to 2023-2024 Florida strawberry seasons, unusual bacterial-like symptoms were observed in commercial farms, with reports of up to 30 % disease incidence. Typical lesions were water-soaked and angular in early stages that later became necrotic with a circular-ellipsoidal purple halo, and consistently yielded colonies resembling Pseudomonas on culture media. Strains were pathogenic on strawberry, fluorescent, oxidase- and arginine-dihydrolase-negative, elicited a hypersensitive reaction on tobacco, and lacked pectolytic activity. Although phenotypic assays, such as fatty acid methyl profiles and Biolog protocols, placed the strains into the Pseudomonas group, there was a low similarity at the species level. Further analysis using 16S rRNA genes, housekeeping genes, and whole genome sequencing showed that the strains cluster into the Pseudomonas group but do not share more than 95 % average nucleotide identity compared to representative members. Therefore, the genomic and phenotypic analysis confirm that the strains causing bacterial spot in strawberry represent a new plant pathogenic bacterial species for which we propose the name Pseudomonas fragariae sp. nov. with 20-417T (17T=LMG 32456T=DSM 113340 T) as the type strain, in relation to Fragaria×ananassa, the plant species from which the pathogen was first isolated. Future work is needed to assess the epidemiology, cultivar susceptibility, chemical sensitivity, and disease management of this possible new emerging strawberry pathogen.

Identifying master epigenetic factors controlling proliferation and survival of cancer cells allows to discover new molecular targets exploitable to overcome resistance to current pharmacological regimens. In breast cancer (BC), resistance to endocrine therapy (ET) arises from aberrant Estrogen Receptor alpha (ERα) signaling caused by genetic and epigenetic events still mainly unknown. Targeting key upstream components of the ERα pathway provides a way to interfere with estrogen signaling in cancer cells independently from any other downstream event. By combining computational analysis of genome-wide \'drop-out\' screenings with siRNA-mediated gene knock-down (kd), we identified a set of essential genes in luminal-like, ERα + BC that includes BRPF1, encoding a bromodomain-containing protein belonging to a family of epigenetic readers that act as chromatin remodelers to control gene transcription. To gather mechanistic insights into the role of BRPF1 in BC and ERα signaling, we applied chromatin and transcriptome profiling, gene ablation and targeted pharmacological inhibition coupled to cellular and functional assays. Results indicate that BRPF1 associates with ERα onto BC cell chromatin and its blockade inhibits cell cycle progression, reduces cell proliferation and mediates transcriptome changes through the modulation of chromatin accessibility. This effect is elicited by a widespread inhibition of estrogen signaling, consequent to ERα gene silencing, in antiestrogen (AE) -sensitive and -resistant BC cells and pre-clinical patient-derived models (PDOs). Characterization of the functional interplay of BRPF1 with ERα reveals a new regulator of estrogen-responsive BC cell survival and suggests that this epigenetic factor is a potential new target for treatment of these tumors.

The liver plays a vital role in lipid synthesis and metabolism in poultry. To study the functional genes more effectively, it is essential to screen of reliable reference genes in the chicken liver, including females, males, embryos, as well as the Leghorn Male Hepatoma (LMH) cell line. Traditional reference gene screening involves selecting commonly used housekeeping genes (HKGs) for RT-qPCR experiments and using different algorithms to identify the most stable ones. However, this approach is limited in selecting the best reference gene from a small pool of HKGs. High-throughput sequencing technology may offer a solution to this limitation. This study aimed to identify the most consistently expressed genes by utilizing multiple published RNA-seq data of chicken liver and LMH cells. Subsequently, the stability of the newly identified reference genes was assessed in comparison to previously validated stable poultry liver expressed reference genes and the commonly employed HKGs using RT-qPCR. The findings indicated that there is a higher degree of similarity in stable expression genes between female and male liver (such as LSM14A and CDC40). In embryonic liver, the optimal new reference genes were SUDS3, TRIM33, and ERAL1. For LMH cells, the optimal new reference genes were ALDH9A1, UGGT1, and C21H1orf174. However, it is noteworthy that most HKGs did not exhibit stable expression across multiple samples, indicating potential instability under diverse conditions. Furthermore, RT-qPCR experiments proved that the stable expression genes identified from RNA-seq data outperformed commonly used HKGs and certain validated reference genes specific to poultry liver. Over all, this study successfully identified new stable reference genes in chicken liver and LMH cells using RNA-seq data, offering researchers a wider range of reference gene options for RT-qPCR in diverse situations.

Codon usage bias (CUB), the uneven usage of synonymous codons encoding the same amino acid, differs among genes within and across bacteria genomes. CUB is known to be influenced by gene expression and accordingly, CUB differs between the high-expression and low-expression genes in several bacteria. In this article, we have extended codon usage study considering gene essentiality as a feature. Using machine learning (ML) based approaches, we have analysed Relative Synonymous Codon Usage (RSCU) values between essential and non-essential genes in Escherichia coli and thirty-four other bacterial genomes whose gene essentiality features were available in public databases. We observed significant differences in codon usage patterns between essential and non-essential genes for majority of the bacterial genomes and accordingly, ML based classifiers achieved high area under curve (AUC) scores, with a minimum score of 70.0 across twenty-eight organisms. Further, importance of the codons towards classifying genes found to differ among the codons in each genome. Arg codon CGT and Gly codon GGT were observed to be the most preferred codons among essential genes in Escherichia coli. Interestingly, some of the codons like CGT, ATA, GGT and GGG observed to be contributing consistently towards classifying essential genes across thirty-five bacteria genomes studied. In other hand, codons TGY and CAY encoding amino acids Cys and His respectively were among the least contributing codons towards classification among all these bacteria. This study demonstrates the gene essentiality based differences in synonymous codon usage in bacteria genomes and presents a common codon usage pattern across bacteria.

Deep learning (DL) has shown potential to provide powerful representations of bulk RNA-seq data in cancer research. However, there is no consensus regarding the impact of design choices of DL approaches on the performance of the learned representation, including the model architecture, the training methodology and the various hyperparameters. To address this problem, we evaluate the performance of various design choices of DL representation learning methods using TCGA and DepMap pan-cancer datasets and assess their predictive power for survival and gene essentiality predictions. We demonstrate that baseline methods achieve comparable or superior performance compared to more complex models on survival predictions tasks. DL representation methods, however, are the most efficient to predict the gene essentiality of cell lines. We show that auto-encoders (AE) are consistently improved by techniques such as masking and multi-head training. Our results suggest that the impact of DL representations and of pretraining are highly task- and architecture-dependent, highlighting the need for adopting rigorous evaluation guidelines. These guidelines for robust evaluation are implemented in a pipeline made available to the research community.

The species Rhizobium indigoferae and Sinorhizobium kummerowiae were isolated from legume nodules and the 16S rRNA sequences of their respective type strains, CCBAU 71042T and CCBAU 71714T, were highly divergent from those of the other species of the genera Rhizobium and Sinorhizobium, respectively. However, the 16S rRNA gene sequences obtained for strains CCBAU 71042T and CCBAU 71714T several years after description, were different from the original ones, showing 100 % similarity to the type strains of Rhizobium leguminosarum and Sinorhizobium meliloti, respectively. Phylogenetic analyses of two housekeeping genes, recA and atpD, confirmed the high phylogenetic closeness of strains CCBAU 71042T and CCBAU 71714T to the respective type strains of R. leguminosarum and S. meliloti. In the present work, we compared the genomes of the type strains of R. indigoferae and S. kummerowiae available in several culture collections with those of the respective type strains of R. leguminosarum and S. meliloti, some of them obtained in this study. The calculated average nucleotide identity-blast and digital DNA-DNA hybridization values in both cases were higher than those recommended for species differentiation, supporting the proposal for the reclassification of the type strains of R. indigoferae and S. kummerowiae into the species R. leguminosarum and S. meliloti, respectively.

Bacterial species often consist of strains with variable gene content, collectively referred to as the pangenome. Variations in the genetic makeup of strains can alter bacterial physiology and fitness. To define biologically relevant genes of a genome, genome-wide transposon mutant libraries have been used to identify genes essential for survival or virulence in a given strain. Such phenotypic studies have been conducted in four different genotypes of the human pathogen Streptococcus pyogenes, yet challenges exist in comparing results across studies conducted in different genetic backgrounds and conditions. To advance genotype to phenotype inferences across different S. pyogenes strains, we built a pangenome database of 249 S. pyogenes reference genomes. We systematically re-analyzed publicly available transposon sequencing datasets from S. pyogenes using a transposon sequencing-specific analysis pipeline, Transit. Across four genetic backgrounds and nine phenotypic conditions, 355 genes were essential for survival, corresponding to ~24% of the core genome. Clusters of Orthologous Genes (COG) categories related to coenzyme and lipid transport and growth functions were overrepresented as essential. Finally, essential operons across S. pyogenes genotypes were defined, with an increased number of essential operons detected under in vivo conditions. This study provides an extendible database to which new studies can be added, and a searchable html-based resource to direct future investigations into S. pyogenes biology.IMPORTANCEStreptococcus pyogenes is a human-adapted pathogen occupying restricted ecological niches. Understanding the essentiality of genes across different strains and experimental conditions is important to direct research questions and efforts to prevent the large burden of disease caused by S. pyogenes. To this end we systematically reanalyzed transposon sequencing studies in S. pyogenes using transposon sequencing-specific methods, integrating them into an extendible meta-analysis framework. This provides a repository of gene essentiality in S. pyogenes which was used to highlight specific genes of interest and for the community to guide future phenotypic studies.

Over the years, comprehensive explorations of the model organisms Caenorhabditis elegans (elegant worm) and Drosophila melanogaster (vinegar fly) have contributed substantially to our understanding of complex biological processes and pathways in multicellular organisms generally. Extensive functional genomic-phenomic, genomic, transcriptomic, and proteomic data sets have enabled the discovery and characterisation of genes that are crucial for life, called \'essential genes\'. Recently, we investigated the feasibility of inferring essential genes from such data sets using advanced bioinformatics and showed that a machine learning (ML)-based workflow could be used to extract or engineer features from DNA, RNA, protein, and/or cellular data/information to underpin the reliable prediction of essential genes both within and between C. elegans and D. melanogaster. As these are two distantly related species within the Ecdysozoa, we proposed that this ML approach would be particularly well suited for species that are within the same phylum or evolutionary clade. In the present study, we cross-predicted essential genes within the phylum Nematoda (evolutionary clade V)-between C. elegans and the pathogenic parasitic nematode H. contortus-and then ranked and prioritised H. contortus proteins encoded by these genes as intervention (e.g., drug) target candidates. Using strong, validated predictors, we inferred essential genes of H. contortus that are involved predominantly in crucial biological processes/pathways including ribosome biogenesis, translation, RNA binding/processing, and signalling and which are highly transcribed in the germline, somatic gonad precursors, sex myoblasts, vulva cell precursors, various nerve cells, glia, or hypodermis. The findings indicate that this in silico workflow provides a promising avenue to identify and prioritise panels/groups of drug target candidates in parasitic nematodes for experimental validation in vitro and/or in vivo.

The proton pumping V-ATPase drives essential biological processes, such as acidification of intracellular organelles. Critically, the V-ATPase domains, V1 and VO, must assemble to produce a functional holoenzyme. V-ATPase dysfunction results in cancer, neurodegeneration, and diabetes, as well as systemic acidosis caused by reduced activity of proton-secreting kidney intercalated cells (ICs). However, little is known about the molecular regulation of V-ATPase in mammals. We identified a novel interactor of the mammalian V-ATPase, Drosophila melanogaster X chromosomal gene-like 1 (Dmxl1), aka Rabconnectin-3A. The yeast homologue of Dmxl1, Rav1p, is part of a complex that catalyzes the reversible assembly of the domains. We, therefore,hypothesized that Dmxl1 is a mammalian V-ATPase assembly factor. Here, we generated kidney IC-specific Dmxl1 knockout (KO) mice, which had high urine pH, like B1 V-ATPase KO mice, suggesting impaired V-ATPase function. Western blotting showed decreased B1 expression and B1 (V1) and a4 (VO) subunits were more intracellular and less colocalized in Dmxl1 KO ICs. In parallel, subcellular fractionation revealed less V1 associated B1 in the membrane fraction of KO cells relative to the cytosol. Furthermore, a proximity ligation assay performed using probes against B1 and a4 V-ATPase subunits also revealed decreased association. We propose that loss of Dmxl1 reduces V-ATPase holoenzyme assembly, thereby inhibiting proton pumping function. Dmxl1 may recruit the V1 domain to the membrane and facilitate assembly with the VO domain and in its absence V1 may be targeted for degradation. We conclude that Dmxl1 is a bona fide mammalian V-ATPase assembly factor.