Occlusion enhanced pan-cancer classification via deep learning.

Quantitative measurement of RNA expression levels through RNA-Seq is an ideal replacement for conventional cancer diagnosis via microscope examination. Currently, cancer-related RNA-Seq studies focus on two aspects: classifying the status and tissue of origin of a sample and discovering marker genes. Existing studies typically identify marker genes by statistically comparing healthy and cancer samples. However, this approach overlooks marker genes with low expression level differences and may be influenced by experimental results. This paper introduces "GENESO," a novel framework for pan-cancer classification and marker gene discovery using the occlusion method in conjunction with deep learning. we first trained a baseline deep LSTM neural network capable of distinguishing the origins and statuses of samples utilizing RNA-Seq data. Then, we propose a novel marker gene discovery method called "Symmetrical Occlusion (SO)". It collaborates with the baseline LSTM network, mimicking the "gain of function" and "loss of function" of genes to evaluate their importance in pan-cancer classification quantitatively. By identifying the genes of utmost importance, we then isolate them to train new neural networks, resulting in higher-performance LSTM models that utilize only a reduced set of highly relevant genes. The baseline neural network achieves an impressive validation accuracy of 96.59% in pan-cancer classification. With the help of SO, the accuracy of the second network reaches 98.30%, while using 67% fewer genes. Notably, our method excels in identifying marker genes that are not differentially expressed. Moreover, we assessed the feasibility of our method using single-cell RNA-Seq data, employing known marker genes as a validation test.

GLDADec: marker-gene guided LDA modeling for bulk gene expression deconvolution.

Inferring cell type proportions from bulk transcriptome data is crucial in immunology and oncology. Here, we introduce guided LDA deconvolution (GLDADec), a bulk deconvolution method that guides topics using cell type-specific marker gene names to estimate topic distributions for each sample. Through benchmarking using blood-derived datasets, we demonstrate its high estimation performance and robustness. Moreover, we apply GLDADec to heterogeneous tissue bulk data and perform comprehensive cell type analysis in a data-driven manner. We show that GLDADec outperforms existing methods in estimation performance and evaluate its biological interpretability by examining enrichment of biological processes for topics. Finally, we apply GLDADec to The Cancer Genome Atlas tumor samples, enabling subtype stratification and survival analysis based on estimated cell type proportions, thus proving its practical utility in clinical settings. This approach, utilizing marker gene names as partial prior information, can be applied to various scenarios for bulk data deconvolution. GLDADec is available as an open-source Python package at https://github.com/mizuno-group/GLDADec.

Directly selecting cell-type marker genes for single-cell clustering analyses.

In single-cell RNA sequencing (scRNA-seq) studies, cell types and their marker genes are often identified by clustering and differentially expressed gene (DEG) analysis. A common practice is to select genes using surrogate criteria such as variance and deviance, then cluster them using selected genes and detect markers by DEG analysis assuming known cell types. The surrogate criteria can miss important genes or select unimportant genes, while DEG analysis has the selection-bias problem. We present Festem, a statistical method for the direct selection of cell-type markers for downstream clustering. Festem distinguishes marker genes with heterogeneous distribution across cells that are cluster informative. Simulation and scRNA-seq applications demonstrate that Festem can sensitively select markers with high precision and enables the identification of cell types often missed by other methods. In a large intrahepatic cholangiocarcinoma dataset, we identify diverse CD8+ T cell types and potential prognostic marker genes.

Bacterial biogeography of the Indian Ocean.

Historically, our understanding of bacterial ecology in the Indian Ocean has been limited to regional studies that place emphasis on community structure and function within oxygen minimum zones. Thus, bacterial community dynamics across the wider Indian Ocean are largely undescribed. As part of Bio-GO-SHIP, we sequenced the 16S rRNA gene from 465 samples collected on sections I07N and I09N. We found that (i) there were 23 distinct bioregions within the Indian Ocean, (ii) the southeastern gyre had the largest gradient in bacterial alpha-diversity, (iii) the Indian Ocean surface microbiome was primarily composed of a core set of taxa, and (iv) bioregions were characterized by transitions in physical and geochemical conditions. Overall, we showed that bacterial community structure spatially delineated the surface Indian Ocean and that these microbially-defined regions were reflective of subtle ocean physical and geochemical gradients. Therefore, incorporating metrics of in-situ microbial communities into marine ecological regions traditionally defined by remote sensing will improve our ability to delineate warm, oligotrophic regions.

Unassigning bacterial species for microbiome studies.

The method of 16S rRNA marker gene sequencing has fueled microbiome research and continues to be relevant. A perceived weakness of the method is that taxonomic assignments are not possible to make at the rank of species. We show that by working to rule out bacterial or archaeal species membership, we can provide an answer that is more accurate and useful. The Unassigner software operates on 16S rRNA marker gene data and computes a rule-out probability for species membership using a beta-binomial distribution. We demonstrate that our approach is accurate based on full-genome comparisons. Our method is consistent with existing approaches and dramatically improves on them based on the percentage of reads it can associate with a species in a sample. The software is available at https://github.com/PennChopMicrobiomeProgram/unassigner.IMPORTANCEWhile existing methods do not provide reliable species-level assignments for 16S rRNA marker gene data, the Unassigner software solves this problem by ruling out species membership, allowing researchers to reason at the species level.

Holed up, but thriving: Impact of multitrophic cryoconite communities on glacier elemental cycles.

Cryoconite holes (water and sediment-filled depressions), found on glacier surfaces worldwide, serve as reservoirs of microbes, carbon, trace elements, and nutrients, transferring these components downstream via glacier hydrological networks. Through targeted amplicon sequencing of carbon and nitrogen cycling genes, coupled with functional inference-based methods, we explore the functional diversity of these mini-ecosystems within Antarctica and the Himalayas. These regions showcase distinct environmental gradients and experience varying rates of environmental change influenced by global climatic shifts. Analysis revealed a diverse array of photosynthetic microorganisms, including Stramenopiles, Cyanobacteria, Rhizobiales, Burkholderiales, and photosynthetic purple sulfur Proteobacteria. Functional inference highlighted the high potential for carbohydrate, amino acid, and lipid metabolism in the Himalayan region, where organic carbon concentrations surpassed those in Antarctica by up to 2 orders of magnitude. Nitrogen cycling processes, including fixation, nitrification, and denitrification, are evident, with Antarctic cryoconite exhibiting a pronounced capacity for nitrogen fixation, potentially compensating for the limited nitrate concentrations in this region. Processes associated with the respiration of elemental sulfur and inorganic sulfur compounds such as sulfate, sulfite, thiosulfate, and sulfide suggest the presence of a complete sulfur cycle. The Himalayan region exhibits a higher potential for sulfur cycling, likely due to the abundant sulfate ions and sulfur-bearing minerals in this region. The capability for complete iron cycling through iron oxidation and reduction reactions was also predicted. Methanogenic archaea that produce methane during organic matter decomposition and methanotrophic bacteria that utilize methane as carbon and energy sources co-exist in the cryoconite, suggesting that these niches support the complete cycling of methane. Additionally, the presence of various microfauna suggests the existence of a complex food web. Collectively, these results indicate that cryoconite holes are self-sustaining ecosystems that drive elemental cycles on glaciers and potentially control carbon, nitrogen, sulfur, and iron exports downstream.

Transcriptome analysis revealed potential mechanisms of channel catfish growth advantage over blue catfish in a tank culture environment.

Channel catfish (Ictalurus punctatus) and blue catfish (Ictalurus furcatus) are two economically important freshwater aquaculture species in the United States, with channel catfish contributing to nearly half of the country's aquaculture production. While differences in economic traits such as growth rate and disease resistance have been noted, the extent of transcriptomic variance across various tissues between these species remains largely unexplored. The hybridization of female channel catfish with male blue catfish has led to the development of superior hybrid catfish breeds that exhibit enhanced growth rates and improved disease resistance, which dominate more than half of the total US catfish production. While hybrid catfish have significant growth advantages in earthen ponds, channel catfish were reported to grow faster in tank culture environments. In this study, we confirmed channel fish's superiority in growth over blue catfish in 60-L tanks at 10.8 months of age (30.3 g and 11.6 g in this study, respectively; p < 0.001). In addition, we conducted RNA sequencing experiments and established transcriptomic resources for the heart, liver, intestine, mucus, and muscle of both species. The number of expressed genes varied across tissues, ranging from 5,036 in the muscle to over 20,000 in the mucus. Gene Ontology analysis has revealed the functional specificity of differentially expressed genes within their respective tissues, with significant pathway enrichment in metabolic pathways, immune activity, and stress responses. Noteworthy tissue-specific marker genes, including lrrc10, fabp2, myog, pth1a, hspa9, cyp21a2, agt, and ngtb, have been identified. This transcriptome resource is poised to support future investigations into the molecular mechanisms underlying environment-dependent heterosis and advance genetic breeding efforts of hybrid catfish.

Bioinformatics Analysis Reveals Prognostic Significance of the Macrophage Marker Gene Signature in Gastric Adenocarcinoma.

BACKGROUND: Gastric adenocarcinoma (GAC) is a malignant tumor with the highest incidence in the digestive system. Macrophages have been proven to play important roles in tumor microenvironment. METHODS: Herein, single-cell RNA sequencing (scRNA-seq) profiles from the Gene Expression Omnibus (GEO) and bulk RNA-seq data from the Cancer Genome Atlas (TCGA) database were utilized to construct a macrophage marker gene signature (MMGS) to predict the prognosis of GAC patients. Subsequently, a risk score model based on the MMGS was built to predict the prognosis of GAC patients; further, this was validated in the GEO cohort. The risk score categorized patients into the high- and low-risk groups. A nomogram model based on the risk score and clinic-pathological characteristics was developed. RESULTS: Seven genes, ABCA1, CTHRC1, GADD45B, NPC2, PLTP, PRSS23, and RNASE1, were included in the risk score model. Patients with a low-risk score showed a better prognosis. The MMGS had good sensitivity and specificity for predicting the prognosis inGAC patients. The risk score was an independent prognostic factor. The constructed nomogram exhibited favorable predictability and reliability for predicting GAC prognosis. CONCLUSION: In conclusion, the risk score model based on the seven MMGSs performed well in the predicting prognosis of GAC patients. Our study may provide new insights into clinical decision-making for the personalized treatment of patients with gastric cancer (GC).

[Transcriptome data mining combined with clinical and animal experiments for identification of syndrome element marker genes during entire course of steroid-induced osteonecrosis of femoral head].

A research strategy combining transcriptome data mining and experimental verification was adopted to identify the marker genes characterizing the syndrome elements of phlegm, stasis, and deficiency in steroid-induced osteonecrosis of the femoral head(SONFH). Firstly, the common differentially expressed gene sets of SONFH with the syndromes of phlegm-stasis obstructing collaterals, vessel obstruction, and liver-kidney deficiency were obtained from the clinical transcriptomic analysis of a previous study. The differential expression trend analysis and functional gene mining were then employed to predict the candidate marker gene sets representing phlegm, stasis, and deficiency. The whole blood samples from SONFH patients, whole blood samples from SONFH rats, and affected femoral head tissue samples were collected for qPCR, which aimed to determine the expression levels of the candidate marker genes mentioned above. Furthermore, the receiver operating characteristic curve(ROC) was established to objectively evaluate the syndrome differentiation effectiveness of the candidate marker genes mentioned above. The transcriptome data analysis results showed that the candidate marker genes for phlegm was ELOVL fatty acid elongase 6(ELOVL6), and those for stasis were ankyrin 1(ANK1), glycophorin A/B(GYPA/B), and Rh-associated glycoprotein(RHAG). The candidate marker genes for deficiency were solute carrier family 2 member 1(SLC2A1) and stomatin(STOM). The qPCR results showed that compared with that in the non-SONFH group, ELOVL6 had the lowest expression level in the peripheral blood of the SONFH patients with the syndrome of phlegm-stasis obstructing collaterals(P<0.05). Compared with that in the normal control group, ELOVL6 had the lowest expression level in the peripheral blood and affected femoral head tissue of SONFH rats modeled for 4 weeks(P<0.01), and it showed better syndrome differentiation effectiveness of rats modeled for 4 weeks(AUC=0.850, P=0.006) than at other modeling time points(8, 12, 16, and 21 weeks, AUC of 0.689, 0.766, 0.588, and 0.662, respectively). Compared with that in the non-SONFH group, the expression levels of ANK1, GYPA, and RHAG were the lowest in the peripheral blood of SONFH patients with the vessel obstruction syndrome(P<0.05). The expression levels of the three genes were the lowest in the peripheral blood and affected femoral head tissue of SONFH rats modeled for 12 weeks(P<0.05, P<0.01), and their syndrome differentiation effectiveness in the rats modeled for 12 weeks(GYPA: AUC=0.861, P=0.012; ANK1: AUC=0.855, P=0.006; RHAG: AUC=0.854, P=0.009) was superior to that for 4, 8, 16, and 21 weeks(GYPA: AUC=0.646, 0.573, 0.691, and 0.617, respectively; ANK: AUC1=0.630, 0.658, 0.657, and 0.585, respectively; RHAG: AUC=0.592, 0.511, 0.515, and 0.536, respectively). Compared with the non-SONFH group, both SLC2A1 and STOM had the lowest expression levels in the peripheral blood of patients with the syndrome of liver and kidney deficiency(P<0.05). Compared with the normal control group, their expression levels were the lowest in the peripheral blood and affected femoral head tissue of SONFH rats modeled for 21 weeks(P<0.05, except STOM in the peripheral blood of rats). Moreover, the syndrome differentiation effectiveness of SLC2A1 in the rats modeled for 21 weeks(AUC=0.806, P=0.009) was superior to that for 4, 8, 12, and 16 weeks(AUC=0.520, 0.580, 0.741, 0.774, respectively), and STOM was meaningless in syndrome differentiation. In summary, the candidate marker gene for phlegm in SONFH is ELOVL6; the candidate marker genes for stasis are GYPA, RHAG, and ANK1; the candidate marker gene for deficiency is SLC2A1. The results help to reveal the biological connotations of phlegm, stasis, and deficiency in SONFH at the genetic level.

Analysis and Visualization of Single-Cell Sequencing Data with Scanpy and MetaCell: A Tutorial.

The emergence and development of single-cell RNA sequencing (scRNA-seq) techniques enable researchers to perform large-scale analysis of the transcriptomic profiling at cell-specific resolution. Unsupervised clustering of scRNA-seq data is central for most studies, which is essential to identify novel cell types and their gene expression logics. Although an increasing number of algorithms and tools are available for scRNA-seq analysis, a practical guide for users to navigate the landscape remains underrepresented. This chapter presents an overview of the scRNA-seq data analysis pipeline, quality control, batch effect correction, data standardization, cell clustering and visualization, cluster correlation analysis, and marker gene identification. Taking the two broadly used analysis packages, i.e., Scanpy and MetaCell, as examples, we provide a hands-on guideline and comparison regarding the best practices for the above essential analysis steps and data visualization. Additionally, we compare both packages and algorithms using a scRNA-seq dataset of the ctenophore Mnemiopsis leidyi, which is representative of one of the earliest animal lineages, critical to understanding the origin and evolution of animal novelties. This pipeline can also be helpful for analyses of other taxa, especially prebilaterian animals, where these tools are under development (e.g., placozoan and Porifera).

Light-Dependent High Ambient Temperature-Induced Senescence Assay Using Whole Seedlings.

High ambient temperature affects various plant developmental and physiological processes, including senescence. Here, we present a protocol for assaying light-dependent high ambient temperature-induced senescence using whole seedlings. The protocol covers all steps, from inducing senescence by darkness at high ambient temperature to determining the degree of senescence, and includes experimental tips and notes. The onset of senescence is established by quantifying the increased expression of senescence marker genes by quantitative real-time PCR (RT-qPCR). The degree of senescence is determined by measuring the loss of chlorophyll and the increase of ion leakage. This protocol can be adapted to study light-dependent high ambient temperature-induced senescence in other plant species by adjusting the temperature and duration of darkness.

The role of biomarkers in the early detection of pancreatic cancer.

Pancreatic surveillance can detect early-stage pancreatic cancer and achieve long-term survival, but currently involves annual endoscopic ultrasound and MRI/MRCP, and is recommended only for individuals who meet familial/genetic risk criteria. To improve upon current approaches to pancreatic cancer early detection and to expand access, more accurate, inexpensive, and safe biomarkers are needed, but finding them has remained elusive. Newer approaches to early detection, such as using gene tests to personalize biomarker interpretation, and the increasing application of artificial intelligence approaches to integrate complex biomarker data, offer promise that clinically useful biomarkers for early pancreatic cancer detection are on the horizon.

Using a CA19-9 Tumor Marker Gene Test to Assess Outcome After Pancreatic Cancer Surgery.

BACKGROUND: Cancer antigen 19-9 (CA19-9) is widely used as a marker of pancreatic cancer tumor burden and response to therapy. Synthesis of CA19-9 and its circulating levels are determined by variants encoding the fucosyltransferases, FUT2 and FUT3. Individuals can be grouped into one of four functional FUT groups (FUT3-null, FUT-low, FUT-intermediate, FUT-high), each with its own CA19-9 reference range based on its predicted capacity to produce CA19-9. The authors hypothesized that a FUT variant-based CA19-9 tumor marker gene test could improve the prognostic performance of CA19-9. METHODS: Preoperative and pre-treatment CA19-9 levels were measured, and FUT variants were determined in 449 patients who underwent surgery for pancreatic ductal adenocarcinoma (PDAC) at Johns Hopkins Hospital between 2010 and 2020, including 270 patients who underwent neoadjuvant therapy. Factors associated with recurrence-free and overall survival were determined in Cox proportional hazards models. RESULTS: Higher preoperative CA19-9 levels were associated with recurrence and mortality for patients in the higher-FUT groups (FUT-intermediate, FUT-high for mortality, with adjustment for other prognostic factors; hazard ratio [HR], 1.34 and 1.58, respectively; P < 0.001), but not for those in the lower-FUT groups (FUT3-null, FUT-low). As a tumor marker, CA19-9 levels of 100 U/ml or lower after neoadjuvant therapy and normalization of CA19-9 based on FUT group were more sensitive but less specific predictors of evidence for a major pathologic response to therapy (little/no residual tumor) and of early recurrence (within 6 months). CONCLUSION: Among patients undergoing pancreatic cancer resection, a CA19-9 tumor marker gene test modestly improved the prognostic performance of CA19-9.

A classic screening marker does not affect antennal electrophysiology but strongly regulates reproductive behaviours in Bactrocera dorsalis.

The key phenotype white eye (white) has been used for decades to selectively remove females before release in sterile insect technique programs and as an effective screening marker in genetic engineering. Bactrocera dorsalis is a representative tephritid pest causing damage to more than 150 fruit crops. Yet, the function of white in important biological processes remains unclear in B. dorsalis. In this study, the impacts of the white gene on electrophysiology and reproductive behaviour in B. dorsalis were tested. The results indicated that knocking out Bdwhite disrupted eye pigmentation in adults, consistent with previous reports. Bdwhite did not affect the antennal electrophysiology response to 63 chemical components with various structures. However, reproductive behaviours in both males and females were significantly reduced in Bdwhite-/- . Both pre-copulatory and copulation behaviours were significantly reduced in Bdwhite-/- , and the effect was male-specific. Mutant females significantly delayed their oviposition towards γ-octalactone, and the peak of oviposition behaviour towards orange juice was lost. These results show that Bdwhite might not be an ideal screening marker in functional gene research aiming to identify molecular targets for behaviour-modifying chemicals. Instead, owing to its strong effect on B. dorsalis sexual behaviours, the downstream genes regulated by Bdwhite or the genes from white-linked areas could be alternate molecular targets that promote the development of better behavioural modifying chemical-based pest management techniques.

Microbial source tracking of fecal pollution to coral reef lagoons of Norfolk Island, Australia.

Fecal pollution contributes to global degradation of water quality and requires identification of the source(s) for predicting human health risk, tracking disease, and developing management strategies. While fecal indicator bacteria are commonly used to detect fecal pollution, they cannot identify sources. Novel approaches, such as microbial source tracking (MST), can be applied to evaluate the origin of fecal pollution. This study examined fecal pollution in the coral reef lagoons of Norfolk Island, Australia where reef health decline has been related to nutrient input. The primary objective of this study was to evaluate the host sensitivity and specificity of two human wastewater-associated marker genes (Bacteroides HF183 (HF183) and cross-assembly phage (crAssphage)) and four animal feces associated marker genes targeting avian, ruminant, dog, and pig (Helicobacter-associated GFD (GFD), Bacteroides BacR (BacR), Bacteroides DogBact (DogBact), and Bacteroides Pig-2-Bac (Pig-2-Bac)) in wastewater and animal fecal samples collected from Norfolk Island. The prevalence and concentrations of these marker genes along with enterococci genetic marker (ENT 23S rRNA) of general fecal pollution and human adenovirus (HAdV), which is considered predominantly a pathogen but also a human-wastewater associated marker gene, were determined in surface, ground, and marine water resources. A secondary objective of this study was to assess the sources and pathways of fecal pollution to a sensitive marine environment under rainfall events. HF183, crAssphage, HAdV, and BacR demonstrated absolute host sensitivity values of 1.00, while GFD and Pig-2-Bac had host sensitivity values of 0.60, and 0.20, respectively. Host specificity values were > 0.94 for all marker genes. Human and animal (avian, ruminant, dog) fecal sources were present in the coral reef lagoons and surface water whereas groundwater was polluted by human wastewater markers. This study provides understanding of fecal pollution in water resources on Norfolk Island, Australia after precipitation events. The results may aid in effective water quality management, mitigating potential adverse effects on both human and environmental health.

Removal of the selective marker gene in the fusion gene expression vector of plant anti-caries vaccine / 中国组织工程研究

BACKGROUND:In consideration of the food safety and ecological safety of transgenic plants,the retention of marker genes is the primary safety issue affecting transgenic plants. OBJECTIVE:Based on the principle of immune caries prevention,our research team successfully constructed the plant anti-caries vaccine fusion gene expression vector pCAMBIA-E8-APB-DOCK8 for these two caries causing virulence factors surface protein and glucosyltransferase,which provides a basis for the research and development of transgenic plant vaccine. METHODS:In this study,the selective marker genes Km and GUS in the plant caries vaccine fusion gene expression vector pCAMBIA-E8-APB-DOCK8 were removed by DNA recombination technology through a series of steps such as DNA fragment separation,connection,transformation,clone detection,and sequencing. RESULTS AND CONCLUSION:The efficiency of marker gene removal was 99%.This study has laid a good experimental foundation for the safe production of transgenic plant vaccine against dental caries,and also provided ideas for the construction of other plant vaccine vectors.

Chitooligosaccharide enhanced the efficacy of Bacillus amyloliquefaciens CAS02 for the control of tobacco black shank.

Introduction: Tobacco black shank is a devastating soil-borne disease caused by the oomycete Phytophthora nicotianae, severely hamper tobacco production worldwide. However, the synergistic effect of biocontrol bacteria and marine polysaccharides/oligosaccharides on tobacco black shank control was few documented. Methods: In this study, Bacillus amyloliquefaciens CAS02 (CAS02) and chitooligosaccharide (COS) were screened firstly, and their synergistic antagonistic effect against P. nicotianae and the underlying mechanism were investigated in vitro and in vivo. Results: In vitro experiments showed that, compared with the application of CAS02 or COS alone, co-application of CAS02 and COS significantly increased the inhibition rate against P. nicotianae by 11.67% and 63.31%, respectively. Furthermore, co-application of CAS02 and COS disrupted the structure of mycelia to a greater extent. The co-application of CAS02 and COS showed synergistic effect, with the relative control effect maintained above 60% during the 60-day pot experiment, significantly higher than that of application CAS02 or COS alone. The combined application of CAS02 and COS reduced the relative abundance of P. nicotianae in the rhizosphere soil and increased the relative abundance of bacterial taxa potentially involved in disease suppression, such as Nocardioides, Devosia and Bradyrhizobium. Meanwhile, CAS02 and COS synergistically activated salicylic acid (SA), ethylene (ET), and hypersensitive response (HR) defense signaling pathways in tobacco plants. Discussion: Our findings demonstrate that co-application of CAS02 and COS remarkably improve the relative control effect against tobacco black shank through multiple pathways and provide a promising strategy for the efficient green control of tobacco black shank.

MFS Transporter Bdorwp Does Not Affect Antennal Electrophysiology but Regulates Reproductive Behaviors in Bactrocera dorsalis.

Developing behavioral modifying chemicals through molecular targets is a promising way to improve semiochemical-based technology for pest management. Identifying molecular targets that affect insect behavior largely relies on functional genetic techniques such as deletions, insertions, and substitutions. Selectable markers have thus been developed to increase the efficiency of screening for successful editing events. However, the effect of selectable markers on relevant phenotypic traits needs to be considered. In this study, we cloned the wp gene ofBactrocera dorsalis. Knocking out Bdorwp causes white pupae phenotypes. Reproductive behaviors in both males and females were strongly regulated by Bdorwp. Remarkably, Bdorwp did not affect the antennal electrophysiology response to 63 chemical components with various structures. It is recommended to indirectly apply Bdorwp as a selectable marker in functional gene research on behavioral modifying chemicals. Moreover, Bdorwp could also be a potential molecular target for developing new insecticides for tephritid species control.

Genomic diversity and taxonomic marker for Arcobacter species.

Arcobacter was recognized as an emerging enteropathogen and controversies regarding its classification persisted. This study aimed to reevaluate the taxonomy of Arcobacter utilizing the 16S rRNA gene, 23S rRNA gene, single-copy orthologous genes, as well as genomic indices such as Average Nucleotide Identity (ANI) and in silico DNA-DNA hybridization (isDDH). The taxonomy of this genus was reevaluated in this study using multiple indices with a dataset of 371 genomes comprising 34 known species and 14 potentially new species. Good discrimination could be achieved only in some species but not for the species with higher sequence similarity using the comparisons of the 16S rRNA gene and 23S rRNA gene sequences. A high-accuracy phylogenomic approach for Arcobacter was established using 84 single-copy orthologous genes obtained through various bioinformatics methods. One marker gene (gene711), which was found to possess the same distinguishing ability as ANI, isDDH, and single-copy orthologous methods, was identified as a reliable locus for inferring the phylogeny of the genus. The effective species classification was achieved by employing gene711 with a sequence similarity exceeding 96%, even for species like A. cloacae, A. lanthieri, and A. skirrowii, which exhibited ambiguous classification using ANI and isDDH. Additionally, excellent subspecies categorizing among A. cryaerophilus could be distinguished using gene711. In conclusion, this framework strategy had the potential advantage of developing rapid species identification, particularly for highly variable species, providing a novel insight into the behavior and characteristics of Arcobacter.

MAGNETO: Cell type marker panel generator from single-cell transcriptomic data.

Single-cell RNA sequencing experiments produce data useful to identify different cell types, including uncharacterized and rare ones. This enables us to study the specific functional roles of these cells in different microenvironments and contexts. After identifying a (novel) cell type of interest, it is essential to build succinct marker panels, composed of a few genes referring to cell surface proteins and clusters of differentiation molecules, able to discriminate the desired cells from the other cell populations. In this work, we propose a fully-automatic framework called MAGNETO, which can help construct optimal marker panels starting from a single-cell gene expression matrix and a cell type identity for each cell. MAGNETO builds effective marker panels solving a tailored bi-objective optimization problem, where the first objective regards the identification of the genes able to isolate a specific cell type, while the second conflicting objective concerns the minimization of the total number of genes included in the panel. Our results on three public datasets show that MAGNETO can identify marker panels that identify the cell populations of interest better than state-of-the-art approaches. Finally, by fine-tuning MAGNETO, our results demonstrate that it is possible to obtain marker panels with different specificity levels.